From 51d1d95ec0ee8b42a5d8dd39a5087d762ec58b81 Mon Sep 17 00:00:00 2001 From: Dimitri Date: Sun, 11 Feb 2024 16:37:32 -0300 Subject: [PATCH 01/65] Autocast --- src/Native/LibTorchSharp/THSTorch.cpp | 112 +++++++++++++++++- src/Native/LibTorchSharp/THSTorch.h | 34 +++++- .../PInvoke/LibTorchSharp.THSTorch.cs | 40 +++++++ src/TorchSharp/Tensor/torch.Autocast.cs | 79 ++++++++++++ 4 files changed, 263 insertions(+), 2 deletions(-) create mode 100644 src/TorchSharp/Tensor/torch.Autocast.cs diff --git a/src/Native/LibTorchSharp/THSTorch.cpp b/src/Native/LibTorchSharp/THSTorch.cpp index b846557bc..1a170913c 100644 --- a/src/Native/LibTorchSharp/THSTorch.cpp +++ b/src/Native/LibTorchSharp/THSTorch.cpp @@ -323,4 +323,114 @@ double THSSpecial_erf_scalar(const double x) double THSSpecial_erfc_scalar(const double x) { return erfc(x); -} \ No newline at end of file +} + +bool THSTorch_is_torch_function_mode_enabled() +{ + return at::impl::torch_function_mode_enabled(); //https://github.com/pytorch/pytorch/blob/2c91e13afc6edcfe0a0e6189a88aae4ecbbf3516/torch/csrc/autograd/init.cpp#L911 +} + +bool THSTorch_is_autocast_cache_enabled() +{ + return at::autocast::is_autocast_cache_enabled(); +} + +bool THSTorch_is_autocast_cpu_enabled() +{ + return at::autocast::is_cpu_enabled(); //https://github.com/pytorch/pytorch/blob/2c91e13afc6edcfe0a0e6189a88aae4ecbbf3516/torch/csrc/autograd/init.cpp#L523 +} + +bool THSTorch_is_autocast_gpu_enabled() +{ + return at::autocast::is_enabled(); //https://github.com/pytorch/pytorch/blob/2c91e13afc6edcfe0a0e6189a88aae4ecbbf3516/torch/amp/autocast_mode.py#L363 +} +bool THSTorch_is_autocast_xpu_enabled() +{ + return at::autocast::is_xpu_enabled(); +} +bool THSTorch_is_autocast_hpu_enabled() +{ + return at::autocast::is_hpu_enabled(); +} + +#if (TORCH_VERSION_MAJOR ==2 && TORCH_VERSION_MINOR > 0) +bool THSTorch_is_autocast_ipu_enabled() +{ + return at::autocast::is_ipu_enabled(); +} + +bool THSTorch_is_autocast_xla_enabled() +{ + return at::autocast::is_xla_enabled(); +} + +#endif + +int8_t THSTorch_get_autocast_cpu_dtype() +{ + return (int8_t)at::autocast::get_autocast_cpu_dtype(); +} + +int8_t THSTorch_get_autocast_gpu_dtype() +{ + //TODO: Implement AUTOCAST AMP AND GRADSCALER + + //INFO: Enter/Exit function of autocast_mode not need to do in C/C++ only in C# with Disposable C# Can handle all of that function (if exists) + //https://github.com/pytorch/pytorch/blob/main/torch/amp/autocast_mode.py + + + //https://github.com/pytorch/pytorch/blob/2c91e13afc6edcfe0a0e6189a88aae4ecbbf3516/torch/csrc/autograd/init.cpp#L629 + //https://github.com/pytorch/pytorch/blob/2c91e13afc6edcfe0a0e6189a88aae4ecbbf3516/aten/src/ATen/autocast_mode.h#L20 + return (int8_t)at::autocast::get_autocast_gpu_dtype(); +} + +int8_t THSTorch_get_autocast_xpu_dtype() +{ + return (int8_t)at::autocast::get_autocast_xpu_dtype(); +} + + +int THSTorch_autocast_increment_nesting() +{ + return at::autocast::increment_nesting(); +} + +int THSTorch_autocast_decremental_nesting() +{ + return at::autocast::decrement_nesting(); +} + +void THSTorch_set_autocast_enabled(bool enabled) +{ + at::autocast::set_enabled(enabled); +} + +void THSTorch_set_autocast_cache_enabled(bool enabled) +{ + at::autocast::set_autocast_cache_enabled(enabled); +} + +void THSTorch_set_autocast_cpu_dtype(int8_t dtype) +{ + at::autocast::set_autocast_cpu_dtype((c10::ScalarType)dtype); +} + +void THSTorch_set_autocast_gpu_dtype(int8_t dtype) +{ + at::autocast::set_autocast_gpu_dtype((c10::ScalarType)dtype); +} + +void THSTorch_set_autocast_xpu_dtype(int8_t dtype) +{ + at::autocast::set_autocast_xpu_dtype((c10::ScalarType)dtype); +} + +void THSTorch_clear_autocast_cache() +{ + at::autocast::clear_cache(); +} + +/*bool THSTorch_jit_is_scripting() +{ + +}*/ \ No newline at end of file diff --git a/src/Native/LibTorchSharp/THSTorch.h b/src/Native/LibTorchSharp/THSTorch.h index 9ab80e828..dd9483f5f 100644 --- a/src/Native/LibTorchSharp/THSTorch.h +++ b/src/Native/LibTorchSharp/THSTorch.h @@ -4,7 +4,8 @@ #include "../Stdafx.h" #include "Utils.h" - +#include +//#include // API. // Sets manually the seed. @@ -91,3 +92,34 @@ EXPORT_API(void) THSTorch_dispose_scalar(Scalar scalar); EXPORT_API(double) THSSpecial_erf_scalar(const double x); EXPORT_API(double) THSSpecial_erfc_scalar(const double x); + +EXPORT_API(bool) THSTorch_is_torch_function_mode_enabled(); + +//Maybe the best work is call THSTorch_is_autocast_enabled(enum of devices c# as int8_t); +EXPORT_API(bool) THSTorch_is_autocast_cache_enabled(); +EXPORT_API(bool) THSTorch_is_autocast_cpu_enabled(); +EXPORT_API(bool) THSTorch_is_autocast_gpu_enabled(); +EXPORT_API(bool) THSTorch_is_autocast_xpu_enabled(); +EXPORT_API(bool) THSTorch_is_autocast_hpu_enabled(); + +#if (TORCH_VERSION_MAJOR ==2 && TORCH_VERSION_MINOR > 0) +EXPORT_API(bool) THSTorch_is_autocast_ipu_enabled(); +EXPORT_API(bool) THSTorch_is_autocast_xla_enabled(); +#endif + +EXPORT_API(int8_t) THSTorch_get_autocast_cpu_dtype(); +EXPORT_API(int8_t) THSTorch_get_autocast_gpu_dtype(); +EXPORT_API(int8_t) THSTorch_get_autocast_xpu_dtype(); + +EXPORT_API(int) THSTorch_autocast_increment_nesting(); +EXPORT_API(int) THSTorch_autocast_decrement_nesting(); + +EXPORT_API(void) THSTorch_set_autocast_enabled(bool enabled); +EXPORT_API(void) THSTorch_set_autocast_cache_enabled(bool enabled); +EXPORT_API(void) THSTorch_set_autocast_cpu_dtype(int8_t dtype); +EXPORT_API(void) THSTorch_set_autocast_gpu_dtype(int8_t dtype); +EXPORT_API(void) THSTorch_set_autocast_xpu_dtype(int8_t dtype); + +EXPORT_API(void) THSTorch_clear_autocast_cache(); + +//EXPORT_API(bool) THSTorch_jit_is_scripting(); \ No newline at end of file diff --git a/src/TorchSharp/PInvoke/LibTorchSharp.THSTorch.cs b/src/TorchSharp/PInvoke/LibTorchSharp.THSTorch.cs index 3d3919ee3..fb609e286 100644 --- a/src/TorchSharp/PInvoke/LibTorchSharp.THSTorch.cs +++ b/src/TorchSharp/PInvoke/LibTorchSharp.THSTorch.cs @@ -108,5 +108,45 @@ internal static partial class NativeMethods [DllImport("LibTorchSharp")] internal static extern void THSTorch_set_num_interop_threads(int threads); + + [DllImport("LibTorchSharp")] + internal static extern bool THSTorch_is_torch_function_mode_enabled(); + + [DllImport("LibTorchSharp")] + internal static extern bool THSTorch_is_autocast_cache_enabled(); + [DllImport("LibTorchSharp")] + internal static extern bool THSTorch_is_autocast_cpu_enabled(); + [DllImport("LibTorchSharp")] + internal static extern bool THSTorch_is_autocast_gpu_enabled(); + [DllImport("LibTorchSharp")] + internal static extern bool THSTorch_is_autocast_xpu_enabled(); + [DllImport("LibTorchSharp")] + internal static extern bool THSTorch_is_autocast_hpu_enabled(); + + [DllImport("LibTorchSharp")] + internal static extern sbyte THSTorch_get_autocast_cpu_dtype(); + [DllImport("LibTorchSharp")] + internal static extern sbyte THSTorch_get_autocast_gpu_dtype(); + [DllImport("LibTorchSharp")] + internal static extern sbyte THSTorch_get_autocast_xpu_dtype(); + + [DllImport("LibTorchSharp")] + internal static extern int THSTorch_autocast_increment_nesting(); + [DllImport("LibTorchSharp")] + internal static extern int THSTorch_autocast_decrement_nesting(); + + [DllImport("LibTorchSharp")] + internal static extern void THSTorch_set_autocast_enabled(bool enabled); + [DllImport("LibTorchSharp")] + internal static extern void THSTorch_set_autocast_cache_enabled(bool enabled); + [DllImport("LibTorchSharp")] + internal static extern void THSTorch_set_autocast_cpu_dtype(sbyte dtype); + [DllImport("LibTorchSharp")] + internal static extern void THSTorch_set_autocast_gpu_dtype(sbyte dtype); + [DllImport("LibTorchSharp")] + internal static extern void THSTorch_set_autocast_xpu_dtype(sbyte dtype); + + [DllImport("LibTorchSharp")] + internal static extern void THSTorch_clear_autocast_cache(); } } diff --git a/src/TorchSharp/Tensor/torch.Autocast.cs b/src/TorchSharp/Tensor/torch.Autocast.cs new file mode 100644 index 000000000..6745133be --- /dev/null +++ b/src/TorchSharp/Tensor/torch.Autocast.cs @@ -0,0 +1,79 @@ +using System; +using static TorchSharp.PInvoke.NativeMethods; + +namespace TorchSharp +{ + public static partial class torch + { + public static bool is_autocast_cache_enabled() + { + return THSTorch_is_autocast_cache_enabled(); + } + public static bool is_autocast_cpu_enabled() + { + return THSTorch_is_autocast_cpu_enabled(); + } + public static bool is_autocast_gpu_enabled() + { + return THSTorch_is_autocast_gpu_enabled(); + } + public static bool is_autocast_xpu_enabled() + { + return THSTorch_is_autocast_xpu_enabled(); + } + public static bool is_autocast_hpu_enabled() + { + return THSTorch_is_autocast_hpu_enabled(); + } + + public static ScalarType get_autocast_cpu_dtype() + { + return (ScalarType)THSTorch_get_autocast_cpu_dtype(); + } + public static ScalarType get_autocast_gpu_dtype() + { + return (ScalarType)THSTorch_get_autocast_gpu_dtype(); + } + public static ScalarType get_autocast_xpu_dtype() + { + return (ScalarType)THSTorch_get_autocast_xpu_dtype(); + } + + public static int autocast_increment_nesting() + { + return THSTorch_autocast_increment_nesting(); + } + + public static int autocast_decrement_nesting() + { + return THSTorch_autocast_decrement_nesting(); + } + + public static void set_autocast_enabled(bool enabled) + { + THSTorch_set_autocast_enabled(enabled); + } + public static void set_autocast_cache_enabled(bool enabled) + { + THSTorch_set_autocast_cache_enabled(enabled); + } + + public static void set_autocast_cpu_dtype(ScalarType dtype) + { + THSTorch_set_autocast_cpu_dtype((sbyte)dtype); + } + public static void set_autocast_gpu_dtype(ScalarType dtype) + { + THSTorch_set_autocast_gpu_dtype((sbyte)dtype); + } + public static void set_autocast_xpu_dtype(ScalarType dtype) + { + THSTorch_set_autocast_xpu_dtype((sbyte)dtype); + } + + public static void clear_autocast_cache() + { + THSTorch_clear_autocast_cache(); + } + } +} \ No newline at end of file From 29b490026f9e600ec75b022cbc9dadab5330c46e Mon Sep 17 00:00:00 2001 From: Dimitri Date: Sat, 17 Feb 2024 19:17:16 -0300 Subject: [PATCH 02/65] Added some features --- .gitignore | 1 + src/Native/CMakeSettings.json | 16 ++-- src/Native/LibTorchSharp/CMakeLists.txt | 2 +- src/Native/LibTorchSharp/THSTensor.cpp | 15 ++++ src/Native/LibTorchSharp/THSTensor.h | 4 + src/TorchSharp/Amp/AutocastMode.cs | 54 +++++++++++++ src/TorchSharp/Amp/GradScaler.cs | 66 ++++++++++++++++ .../PInvoke/LibTorchSharp.THSTensor.cs | 2 + src/TorchSharp/Tensor/Tensor.cs | 9 +++ src/TorchSharp/Torch.cs | 25 +++++- src/TorchSharp/TorchSharp.csproj | 78 ------------------- 11 files changed, 187 insertions(+), 85 deletions(-) create mode 100644 src/TorchSharp/Amp/AutocastMode.cs create mode 100644 src/TorchSharp/Amp/GradScaler.cs delete mode 100644 src/TorchSharp/TorchSharp.csproj diff --git a/.gitignore b/.gitignore index bab8676e1..f34d405aa 100644 --- a/.gitignore +++ b/.gitignore @@ -272,3 +272,4 @@ packages/ *.code-workspace /.idea /test/TorchSharpTest/exportsd.py +/src/TorchSharp/TorchSharp.csproj diff --git a/src/Native/CMakeSettings.json b/src/Native/CMakeSettings.json index 9204f06eb..f47283578 100644 --- a/src/Native/CMakeSettings.json +++ b/src/Native/CMakeSettings.json @@ -1,15 +1,21 @@ -{ +{ "configurations": [ { "name": "x64-Debug", - "generator": "Ninja", + "generator": "Visual Studio 17 2022 Win64", "configurationType": "Debug", "inheritEnvironments": [ "msvc_x64_x64" ], "buildRoot": "${projectDir}\\out\\build\\${name}", "installRoot": "${projectDir}\\out\\install\\${name}", - "cmakeCommandArgs": "", - "buildCommandArgs": "", - "ctestCommandArgs": "" + "cmakeCommandArgs": "-DCMAKE_PREFIX_PATH=\"K:\\FrameworksForC\\LibTorch\\libtorch-win-shared-with-deps-debug-2.0.1+cu117\"", + "ctestCommandArgs": "", + "variables": [ + { + "name": "Torch_DIR", + "value": "K:/FrameworksForC/LibTorch/libtorch-win-shared-with-deps-debug-2.0.1+cu117", + "type": "PATH" + } + ] } ] } \ No newline at end of file diff --git a/src/Native/LibTorchSharp/CMakeLists.txt b/src/Native/LibTorchSharp/CMakeLists.txt index 17c2b7fcf..544ac3e22 100644 --- a/src/Native/LibTorchSharp/CMakeLists.txt +++ b/src/Native/LibTorchSharp/CMakeLists.txt @@ -64,7 +64,7 @@ add_library(LibTorchSharp SHARED ${SOURCES} ${RESOURCES}) target_link_libraries(LibTorchSharp ${TORCH_LIBRARIES}) -set_property(TARGET LibTorchSharp PROPERTY CXX_STANDARD 14) +set_property(TARGET LibTorchSharp PROPERTY CXX_STANDARD 17) if(APPLE) set_target_properties(LibTorchSharp PROPERTIES INSTALL_RPATH "@loader_path;@executable_path;") diff --git a/src/Native/LibTorchSharp/THSTensor.cpp b/src/Native/LibTorchSharp/THSTensor.cpp index 2bdc96a83..f4617b5f7 100644 --- a/src/Native/LibTorchSharp/THSTensor.cpp +++ b/src/Native/LibTorchSharp/THSTensor.cpp @@ -1836,6 +1836,21 @@ Tensor THSTensor_to_type_and_device(const Tensor tensor, int8_t scalar_type, con ); } +/*Tensor THSTensor_device_and_non_blocking(const Tensor tensor, const int device_type, const int device_index, const bool non_blocking) +{ + CATCH_RETURN_Tensor( + auto device = c10::Device((c10::DeviceType)device_type, (c10::DeviceIndex)device_index); + res = ResultTensor(tensor->to(device, non_blocking, at::ScalarType(scalar_type), false)); + ); +}*/ +Tensor THSTensor_to_type_and_device_and_non_blocking(const Tensor tensor, int8_t scalar_type, const int device_type, const int device_index,const bool non_blocking) +{ + CATCH_RETURN_Tensor( + auto device = c10::Device((c10::DeviceType)device_type, (c10::DeviceIndex)device_index); + res = ResultTensor(tensor->to(device, non_blocking, at::ScalarType(scalar_type), false)); + ); +} + Tensor THSTensor_triu(const Tensor tensor, const int64_t diagonal, const bool inplace) { CATCH_TENSOR(inplace ? tensor->triu_(diagonal) : tensor->triu(diagonal)); diff --git a/src/Native/LibTorchSharp/THSTensor.h b/src/Native/LibTorchSharp/THSTensor.h index 6af55912b..63bb976d7 100644 --- a/src/Native/LibTorchSharp/THSTensor.h +++ b/src/Native/LibTorchSharp/THSTensor.h @@ -1333,6 +1333,10 @@ EXPORT_API(Tensor) THSTensor_to_type(const Tensor tensor, int8_t scalar_type, co EXPORT_API(Tensor) THSTensor_to_type_and_device(const Tensor tensor, int8_t scalar_type, const int device_type, const int device_index, const bool copy); +//EXPORT_API(Tensor) THSTensor_device_and_non_blocking(const Tensor tensor, const int device_type, const int device_index, const bool non_blocking); + +EXPORT_API(Tensor) THSTensor_to_type_and_device_and_non_blocking(const Tensor tensor, int8_t scalar_type, const int device_type, const int device_index, const bool non_blocking); + EXPORT_API(void) THSTensor_topk(const Tensor tensor, Tensor* (*allocator)(size_t length), const int k, const int64_t dim, const bool largest, const bool sorted); EXPORT_API(Tensor) THSTensor_trunc(const Tensor tensor); diff --git a/src/TorchSharp/Amp/AutocastMode.cs b/src/TorchSharp/Amp/AutocastMode.cs new file mode 100644 index 000000000..7b9af69eb --- /dev/null +++ b/src/TorchSharp/Amp/AutocastMode.cs @@ -0,0 +1,54 @@ +using System; +using System.Collections.Generic; +using System.Linq; +using System.Text; +using System.Threading.Tasks; + +namespace TorchSharp.Amp +{ + public class AutocastMode : IDisposable + { + private bool Enabled, Prev; + private torch.ScalarType Dtype; + private torch.ScalarType fast_dtype; + private torch.Device Device; + public AutocastMode(torch.Device dev, torch.ScalarType? dtype = null, bool enabled=true, bool? cache_enabled = null) + { + fast_dtype = dtype.Value; + if (dev.type == DeviceType.CUDA) + fast_dtype = torch.get_autocast_gpu_dtype(); + if (dev.type == DeviceType.CPU) + fast_dtype = torch.get_autocast_cpu_dtype(); + + bool _cache_enabled = torch.is_autocast_cache_enabled(); + if (!torch.cuda.is_available() && dev.type == DeviceType.CUDA) //Is not available for doing multicast + Enabled = false; + if (dtype.HasValue) + fast_dtype = dtype.Value; + if(cache_enabled.HasValue) + _cache_enabled=cache_enabled.Value; + + if (dev.type == DeviceType.CUDA) { + if (enabled && fast_dtype == torch.ScalarType.BFloat16 && !torch.cuda.is_bf16_supported()) + throw new Exception("Current CUDA Device does not support bfloat16. Please switch dtype to float16."); + } + this.Enabled = enabled; + + this.Prev = torch.is_autocast_cpu_enabled(); + if (dev.type == DeviceType.CUDA) { + this.Prev = torch.is_autocast_gpu_enabled(); + } + throw new NotImplementedException(); + } + public void Dispose() + { + if (Device.type == DeviceType.CUDA) { + if(torch.autocast_decrement_nesting() == 0) + torch.clear_autocast_cache(); + torch.set_autocast_gpu_dtype(this.fast_dtype); + torch.set_autocast_enabled(this.Prev); + } + throw new NotImplementedException(); + } + } +} diff --git a/src/TorchSharp/Amp/GradScaler.cs b/src/TorchSharp/Amp/GradScaler.cs new file mode 100644 index 000000000..6da7a9dab --- /dev/null +++ b/src/TorchSharp/Amp/GradScaler.cs @@ -0,0 +1,66 @@ +using System; +using System.Collections.Generic; +using System.Diagnostics; +using System.Linq; +using System.Text; +using System.Threading.Tasks; + +namespace TorchSharp.Amp +{ + public class GradScaler + { + private bool Enabled; + + private torch.Tensor _scale, _growth_tracker; + + private float InitScale, GrowthFactor, BackoffFactor, GrowthInterval, InitGrowthTracker; + + //https://github.com/pytorch/pytorch/blob/main/torch/amp/grad_scaler.py + public GradScaler(torch.Device dev, float init_scale = 2.0e16f, float growth_factor = 2.0f, + float backoff_factor = 0.5f, int growth_interval = 2000, bool enabled = true) + { + Debug.Assert(dev == torch.CPU || dev == torch.CUDA); + this.Enabled = enabled; + this.InitScale = init_scale; + this.GrowthFactor = growth_factor; + this.BackoffFactor = backoff_factor; + this.GrowthInterval = growth_interval; + this.InitGrowthTracker = 0.0f; + throw new NotImplementedException(); + } + + private void LazyInitScaleGrowthTracker(torch.Device dev) + { + this._scale = torch.full(0, this.InitScale, torch.ScalarType.Float32, device: dev); + this._growth_tracker = torch.full(0, this.InitGrowthTracker, torch.ScalarType.Float32, device: dev); + } + + //private check_scale_growth_tracker + public torch.Tensor scale(torch.Tensor output) + { + if (!Enabled) + return output; + if (_scale.numel() == 0) + this.LazyInitScaleGrowthTracker(output.device); + return output * this._scale.to(output.device, output.dtype, true); + } + + public torch.Tensor unscale_grads(torch.optim.Optimizer optimizer, torch.Tensor inv_scale, torch.Tensor found_inf, bool allow_fp16) + { + return false; + } + + public void unscale(torch.optim.Optimizer optimizer) + { + if (!Enabled) + return; + + + } + /*public IList scale(IList outputs) + { + + + }*/ + } +} \ No newline at end of file diff --git a/src/TorchSharp/PInvoke/LibTorchSharp.THSTensor.cs b/src/TorchSharp/PInvoke/LibTorchSharp.THSTensor.cs index c82b659a3..28b3b6f2f 100644 --- a/src/TorchSharp/PInvoke/LibTorchSharp.THSTensor.cs +++ b/src/TorchSharp/PInvoke/LibTorchSharp.THSTensor.cs @@ -293,6 +293,8 @@ internal static extern IntPtr THSTensor_upsample_nearest3d(IntPtr input, [DllImport("LibTorchSharp")] internal static extern IntPtr THSTensor_to_type_and_device(IntPtr handle, sbyte scalar_type, int device_type, int device_index, [MarshalAs(UnmanagedType.U1)] bool copy); + [DllImport("LibTorchSharp")] + internal static extern IntPtr THSTensor_to_type_and_device_and_non_blocking(IntPtr handle, sbyte scalar_type, int device_type, int device_index, [MarshalAs(UnmanagedType.U1)] bool non_blocking); [DllImport("LibTorchSharp")] internal static extern void THSTensor_set_(IntPtr tensor, IntPtr source); diff --git a/src/TorchSharp/Tensor/Tensor.cs b/src/TorchSharp/Tensor/Tensor.cs index b8b457063..83924753e 100644 --- a/src/TorchSharp/Tensor/Tensor.cs +++ b/src/TorchSharp/Tensor/Tensor.cs @@ -794,6 +794,15 @@ public Tensor to(ScalarType type, torch.Device device, bool copy = false, bool d return new Tensor(res); } + public Tensor to(torch.Device device, ScalarType type, bool non_blocking) + { + torch.InitializeDevice(device); + var res = NativeMethods.THSTensor_to_type_and_device_and_non_blocking(Handle, (sbyte)type, (int)device.type, device.index, non_blocking); + if (res == IntPtr.Zero) + CheckForErrors(); + return new Tensor(res); + } + /// /// Cast the tensor to the given element type. /// diff --git a/src/TorchSharp/Torch.cs b/src/TorchSharp/Torch.cs index 9028d2bdb..5523c8e53 100644 --- a/src/TorchSharp/Torch.cs +++ b/src/TorchSharp/Torch.cs @@ -406,7 +406,6 @@ public static void vector_to_parameters(Tensor vec, IEnumerable= 11) + return true; + } + + return check_bf16_tensor_supported(torch.CUDA); + } + + private static bool check_bf16_tensor_supported(torch.Device dev) + { + try { + var va = torch.tensor(new float[] { 1.0f }, dtype: torch.bfloat16, device: dev); + return true; + } catch { + return false; + } + } } /// diff --git a/src/TorchSharp/TorchSharp.csproj b/src/TorchSharp/TorchSharp.csproj deleted file mode 100644 index 5a102f34e..000000000 --- a/src/TorchSharp/TorchSharp.csproj +++ /dev/null @@ -1,78 +0,0 @@ - - - - - - net6.0;netstandard2.0 - 9.0 - TorchSharp - true - false - false - false - $(DefineConstants);LIBTORCH_$(LibTorchPackageVersion.Replace('.', '_'));CUDA_$(CudaVersionDot.Replace('.', '_')) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - True - True - TensorTyped.tt - - - - - - - $(PackDependsOn); - RealPack - - True - ..\..\build\TorchSharp.snk - - - - - - - - - - - - - - - - - - - - - From defd582da252fe90d5f43f90a963e5797cdb6ea5 Mon Sep 17 00:00:00 2001 From: Dimitri Date: Sun, 18 Feb 2024 13:32:16 -0300 Subject: [PATCH 03/65] Fix mistake gitignore --- .gitignore | 1 - src/Native/LibTorchSharp/THSTensor.cpp | 2 +- src/TorchSharp/Amp/AutocastMode.cs | 6 +- src/TorchSharp/TorchSharp.csproj | 88 ++++++++++++++++++++++++++ 4 files changed, 92 insertions(+), 5 deletions(-) create mode 100644 src/TorchSharp/TorchSharp.csproj diff --git a/.gitignore b/.gitignore index f34d405aa..bab8676e1 100644 --- a/.gitignore +++ b/.gitignore @@ -272,4 +272,3 @@ packages/ *.code-workspace /.idea /test/TorchSharpTest/exportsd.py -/src/TorchSharp/TorchSharp.csproj diff --git a/src/Native/LibTorchSharp/THSTensor.cpp b/src/Native/LibTorchSharp/THSTensor.cpp index f4617b5f7..97499ab42 100644 --- a/src/Native/LibTorchSharp/THSTensor.cpp +++ b/src/Native/LibTorchSharp/THSTensor.cpp @@ -1847,7 +1847,7 @@ Tensor THSTensor_to_type_and_device_and_non_blocking(const Tensor tensor, int8_t { CATCH_RETURN_Tensor( auto device = c10::Device((c10::DeviceType)device_type, (c10::DeviceIndex)device_index); - res = ResultTensor(tensor->to(device, non_blocking, at::ScalarType(scalar_type), false)); + res = ResultTensor(tensor->to(device, at::ScalarType(scalar_type),non_blocking, false)); ); } diff --git a/src/TorchSharp/Amp/AutocastMode.cs b/src/TorchSharp/Amp/AutocastMode.cs index 7b9af69eb..c7fdaa857 100644 --- a/src/TorchSharp/Amp/AutocastMode.cs +++ b/src/TorchSharp/Amp/AutocastMode.cs @@ -9,9 +9,9 @@ namespace TorchSharp.Amp public class AutocastMode : IDisposable { private bool Enabled, Prev; - private torch.ScalarType Dtype; - private torch.ScalarType fast_dtype; - private torch.Device Device; + //private torch.ScalarType Dtype = torch.ScalarType.Float32; + private torch.ScalarType fast_dtype = torch.ScalarType.Float32; + private torch.Device Device = new torch.Device(DeviceType.CUDA); public AutocastMode(torch.Device dev, torch.ScalarType? dtype = null, bool enabled=true, bool? cache_enabled = null) { fast_dtype = dtype.Value; diff --git a/src/TorchSharp/TorchSharp.csproj b/src/TorchSharp/TorchSharp.csproj new file mode 100644 index 000000000..ef6d6ff94 --- /dev/null +++ b/src/TorchSharp/TorchSharp.csproj @@ -0,0 +1,88 @@ + + + + + + netstandard2.0 + 9.0 + TorchSharp + true + false + false + false + $(DefineConstants);LIBTORCH_$(LibTorchPackageVersion.Replace('.', '_'));CUDA_$(CudaVersionDot.Replace('.', '_')) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + True + True + TensorTyped.tt + + + + + + + $(PackDependsOn); + RealPack + + True + ..\..\build\TorchSharp.snk + + + + + 4 + + + + + 4 + + + + + + + + + + + + + + + + + + + + + From d5324020a35dccd93e67f890131d34fd9f352652 Mon Sep 17 00:00:00 2001 From: Dimitri Date: Sun, 18 Feb 2024 15:37:17 -0300 Subject: [PATCH 04/65] AMP --- src/Native/LibTorchSharp/THSTorch.cpp | 4 +- src/Native/LibTorchSharp/Utils.h | 17 ++++- src/TorchSharp/Amp/AutocastMode.cs | 68 +++++++++++++++++-- src/TorchSharp/NN/Module.cs | 25 ++++++- .../Tensor/Factories/Tensor.Factories.cs | 6 ++ .../Tensor/Factories/tensor_float.cs | 10 ++- src/TorchSharp/Tensor/torch.Autocast.cs | 17 +++++ 7 files changed, 134 insertions(+), 13 deletions(-) diff --git a/src/Native/LibTorchSharp/THSTorch.cpp b/src/Native/LibTorchSharp/THSTorch.cpp index 1a170913c..93f550de6 100644 --- a/src/Native/LibTorchSharp/THSTorch.cpp +++ b/src/Native/LibTorchSharp/THSTorch.cpp @@ -375,7 +375,7 @@ int8_t THSTorch_get_autocast_gpu_dtype() { //TODO: Implement AUTOCAST AMP AND GRADSCALER - //INFO: Enter/Exit function of autocast_mode not need to do in C/C++ only in C# with Disposable C# Can handle all of that function (if exists) + //INFO: Enter/Exit function of autocast_mode not need to do in C/C++ only in C# with Disposable can handle all of that function (if exists) //https://github.com/pytorch/pytorch/blob/main/torch/amp/autocast_mode.py @@ -395,7 +395,7 @@ int THSTorch_autocast_increment_nesting() return at::autocast::increment_nesting(); } -int THSTorch_autocast_decremental_nesting() +int THSTorch_autocast_decrement_nesting() { return at::autocast::decrement_nesting(); } diff --git a/src/Native/LibTorchSharp/Utils.h b/src/Native/LibTorchSharp/Utils.h index 4c3606491..cc0242af1 100644 --- a/src/Native/LibTorchSharp/Utils.h +++ b/src/Native/LibTorchSharp/Utils.h @@ -4,7 +4,7 @@ #include #include "torch/torch.h" - +#include extern thread_local char *torch_last_err; typedef torch::Tensor *Tensor; @@ -59,8 +59,21 @@ struct TensorArray { // Return undefined tensors as nullptr to C# inline Tensor ResultTensor(const at::Tensor & res) { - if (res.defined()) + if (res.defined()) { + /*at::Tensor* resT = new torch::Tensor(res); + if (at::autocast::is_autocast_cache_enabled()){ + if (res.is_cuda()) { + ::std::cout << "IS CUDA" << std::endl; + resT->to(at::autocast::get_autocast_gpu_dtype()); + } + if (res.is_cpu()) { + ::std::cout << "IS CPU" << std::endl; + resT->to(at::autocast::get_autocast_cpu_dtype()); + } + } + return resT;*/ return new torch::Tensor(res); + } else return nullptr; } diff --git a/src/TorchSharp/Amp/AutocastMode.cs b/src/TorchSharp/Amp/AutocastMode.cs index c7fdaa857..43d3805fa 100644 --- a/src/TorchSharp/Amp/AutocastMode.cs +++ b/src/TorchSharp/Amp/AutocastMode.cs @@ -6,20 +6,42 @@ namespace TorchSharp.Amp { - public class AutocastMode : IDisposable + public static class Autocast + { + public static torch.Tensor AutoCast(this torch.Tensor input) + { + return AutocastMode.GetInstance().CastTensor(input); + } + } + //TODO: Should make Singleton and IDisposable on ENTER + public sealed class AutocastMode : IDisposable { private bool Enabled, Prev; //private torch.ScalarType Dtype = torch.ScalarType.Float32; private torch.ScalarType fast_dtype = torch.ScalarType.Float32; private torch.Device Device = new torch.Device(DeviceType.CUDA); - public AutocastMode(torch.Device dev, torch.ScalarType? dtype = null, bool enabled=true, bool? cache_enabled = null) + private static AutocastMode instance; + /*public static AutocastMode GetInstance(torch.Device dev, torch.ScalarType? dtype = null, bool enabled = true, bool? cache_enabled = null) + { + if(instance ==null) + instance = new AutocastMode(dev, dtype, enabled, cache_enabled); + return instance; + }*/ + public static AutocastMode GetInstance() { - fast_dtype = dtype.Value; + return instance ?? (instance = new AutocastMode(torch.CUDA, cache_enabled:true)); + } + + private AutocastMode(torch.Device dev, torch.ScalarType? dtype = null, bool enabled=true, bool? cache_enabled = null) + { + //var la = torch.tensor(9); + fast_dtype = dtype ?? torch.ScalarType.Float32; if (dev.type == DeviceType.CUDA) fast_dtype = torch.get_autocast_gpu_dtype(); if (dev.type == DeviceType.CPU) fast_dtype = torch.get_autocast_cpu_dtype(); - + IntPtr ptr = IntPtr.Zero; + bool _cache_enabled = torch.is_autocast_cache_enabled(); if (!torch.cuda.is_available() && dev.type == DeviceType.CUDA) //Is not available for doing multicast Enabled = false; @@ -38,17 +60,49 @@ public AutocastMode(torch.Device dev, torch.ScalarType? dtype = null, bool enabl if (dev.type == DeviceType.CUDA) { this.Prev = torch.is_autocast_gpu_enabled(); } - throw new NotImplementedException(); + + torch.set_autocast_cache_enabled(_cache_enabled); + torch.set_autocast_enabled(this.Enabled); + //throw new NotImplementedException(); } + + /*internal void Cast(torch.Tensor tensor) + { + tensor.to(fast_dtype, tensor.device); + }*/ + + internal torch.Tensor CastTensor(torch.Tensor tensor) + { + if (!Enabled) + return tensor; + return tensor.to(fast_dtype, tensor.device); + } + /*public IDisposable Enter() + { + + return this; + }*/ public void Dispose() { + this.Enabled = false; if (Device.type == DeviceType.CUDA) { if(torch.autocast_decrement_nesting() == 0) torch.clear_autocast_cache(); torch.set_autocast_gpu_dtype(this.fast_dtype); - torch.set_autocast_enabled(this.Prev); + //torch.set_autocast_enabled(this.Prev); + torch.set_autocast_enabled(false); + torch.set_autocast_cache_enabled(false); + } + + if (Device.type == DeviceType.CPU) { + if (torch.autocast_decrement_nesting() == 0) + torch.clear_autocast_cache(); + //torch.set_autocast_enabled(this.Prev); + torch.set_autocast_cpu_dtype(this.fast_dtype); + torch.set_autocast_enabled(false); + torch.set_autocast_cache_enabled(false); } - throw new NotImplementedException(); + //throw new NotImplementedException(); } } } diff --git a/src/TorchSharp/NN/Module.cs b/src/TorchSharp/NN/Module.cs index 4ca8a3258..911f29fd9 100644 --- a/src/TorchSharp/NN/Module.cs +++ b/src/TorchSharp/NN/Module.cs @@ -681,6 +681,8 @@ public virtual void register_buffer(string name, Tensor tensor, bool persistent if (!_internal_buffers.TryAdd(name, (tensor, persistent))) throw new InvalidOperationException($"Tensor {name} is already registered."); + + } /// @@ -700,6 +702,13 @@ public virtual void register_parameter(string name, Parameter param) if (!_internal_params.TryAdd(name, param)) throw new InvalidOperationException($"Parameter {name} is already registered."); + + /*if (is_autocast_cache_enabled()) { + if (is_autocast_gpu_enabled()) + param = param.to(get_autocast_dtype(CUDA)).AsParameter(); + if (is_autocast_cpu_enabled()) + param = param.to(get_autocast_dtype(CPU)).AsParameter(); + }*/ } /// @@ -740,7 +749,15 @@ public virtual void register_module(string name, Module submodule) } submodule.RegisterComponents(); - + if (!is_autocast_cache_enabled()) { + _internal_submodules.Add(name, submodule); + return; + } + if (is_autocast_gpu_enabled()) + submodule = submodule.to(get_autocast_dtype(CUDA)); + if (is_autocast_cpu_enabled()) + submodule = submodule.to(get_autocast_dtype(CPU)); + _internal_submodules.Add(name, submodule); } } @@ -1042,6 +1059,8 @@ protected virtual void RegisterComponents() _areComponentsRegistered = true; } + + protected static (Device device, ScalarType dtype) GetDefaultDeviceAndType(Device device = null, ScalarType? dtype = null) { if (!dtype.HasValue) @@ -1295,6 +1314,10 @@ public TResult call(T input) input = modified; } + /*if (is_autocast_cache_enabled()) { //Should i cast this for better managment??? + if(input is Tensor) + }*/ + var result = forward(input); // Call post-hooks, if available. diff --git a/src/TorchSharp/Tensor/Factories/Tensor.Factories.cs b/src/TorchSharp/Tensor/Factories/Tensor.Factories.cs index 9bc1c562f..899342207 100644 --- a/src/TorchSharp/Tensor/Factories/Tensor.Factories.cs +++ b/src/TorchSharp/Tensor/Factories/Tensor.Factories.cs @@ -179,6 +179,12 @@ private static Tensor _tensor_generic(Array rawArray, ReadOnlySpan dimensi tensor.rename_(names); } + if (!is_autocast_cache_enabled()) + return tensor; + if (is_autocast_gpu_enabled()) + tensor = tensor.to(get_autocast_gpu_dtype()); + if (is_autocast_cpu_enabled()) + tensor = tensor.to(get_autocast_cpu_dtype()); return tensor; } } diff --git a/src/TorchSharp/Tensor/Factories/tensor_float.cs b/src/TorchSharp/Tensor/Factories/tensor_float.cs index 562c826f2..f33d1b90a 100644 --- a/src/TorchSharp/Tensor/Factories/tensor_float.cs +++ b/src/TorchSharp/Tensor/Factories/tensor_float.cs @@ -3,6 +3,7 @@ using System.Collections.Generic; using System.Diagnostics.Contracts; using System.Linq; +using TorchSharp.Amp; using static TorchSharp.PInvoke.NativeMethods; #nullable enable @@ -18,7 +19,14 @@ public static Tensor tensor(float scalar, Device? device = null, bool requires_g device = InitializeDevice(device); var handle = THSTensor_newFloat32Scalar(scalar, (int)device.type, device.index, requires_grad); if (handle == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(handle); + + + var t = new Tensor(handle).AutoCast(); + /*if (is_autocast_cache_enabled()) { + if (is_autocast_gpu_enabled()) + return t.to(get_autocast_gpu_dtype()); //this work, but should put that on all tensor factorie... + }*/ + return t; } /// diff --git a/src/TorchSharp/Tensor/torch.Autocast.cs b/src/TorchSharp/Tensor/torch.Autocast.cs index 6745133be..e3fc33f52 100644 --- a/src/TorchSharp/Tensor/torch.Autocast.cs +++ b/src/TorchSharp/Tensor/torch.Autocast.cs @@ -9,6 +9,15 @@ public static bool is_autocast_cache_enabled() { return THSTorch_is_autocast_cache_enabled(); } + + public static bool is_autocast_enabled(Device device) + { + if(device.type == DeviceType.CPU) + return THSTorch_is_autocast_cpu_enabled(); + if(device.type == DeviceType.CUDA) + return THSTorch_is_autocast_gpu_enabled(); + return THSTorch_is_autocast_cache_enabled(); + } public static bool is_autocast_cpu_enabled() { return THSTorch_is_autocast_cpu_enabled(); @@ -26,6 +35,14 @@ public static bool is_autocast_hpu_enabled() return THSTorch_is_autocast_hpu_enabled(); } + public static ScalarType get_autocast_dtype(Device device) + { + if (device.type == DeviceType.CPU) + return get_autocast_cpu_dtype(); + if (device.type == DeviceType.CUDA) + return get_autocast_gpu_dtype(); + return ScalarType.Float32; + } public static ScalarType get_autocast_cpu_dtype() { return (ScalarType)THSTorch_get_autocast_cpu_dtype(); From 0b839dbbb5bff741162ddd14ac270660325f3fca Mon Sep 17 00:00:00 2001 From: Dimitri Date: Sun, 18 Feb 2024 21:21:49 -0300 Subject: [PATCH 05/65] Add Print Modules Still in progress --- src/Native/LibTorchSharp/THSConvolution.cpp | 8 ++++++++ src/Native/LibTorchSharp/THSNN.cpp | 12 ++++++++++++ src/Native/LibTorchSharp/THSNN.h | 5 +++++ src/Native/LibTorchSharp/Utils.h | 1 - src/TorchSharp/PInvoke/LibTorchSharp.THSNN.cs | 3 +++ src/TorchSharp/Tensor/torch.Utilities.cs | 6 ++++++ 6 files changed, 34 insertions(+), 1 deletion(-) diff --git a/src/Native/LibTorchSharp/THSConvolution.cpp b/src/Native/LibTorchSharp/THSConvolution.cpp index e1500d939..27e2e62a7 100644 --- a/src/Native/LibTorchSharp/THSConvolution.cpp +++ b/src/Native/LibTorchSharp/THSConvolution.cpp @@ -683,6 +683,7 @@ void THSNN_Conv1d_set_weight(const NNModule module, const Tensor weight) set_weight(module, weight); } + NNModule THSNN_Conv2d_ctor(const int64_t inputChannel, const int64_t outputChannel, const int64_t kernelSize, const int64_t stride, const int64_t padding, const int64_t dilation, const int64_t paddingMode, const int64_t groups, const bool bias, @@ -757,6 +758,13 @@ void THSNN_Conv2d_set_weight(const NNModule module, const Tensor weight) set_weight(module, weight); } +/*void THSNN_Conv2d_print_options(const NNModule module) { + auto opt = (*module)->as()->options; + ::std::cout << "Conv2d (" << std::to_string(opt.in_channels()) << "," << std::to_string(opt.out_channels()) << ")" << std::endl; +}*/ + + + NNModule THSNN_Conv3d_ctor(const int64_t inputChannel, const int64_t outputChannel, const int64_t kernelSize, const int64_t stride, const int64_t padding, const int64_t dilation, const int64_t paddingMode, const int64_t groups, const bool bias, diff --git a/src/Native/LibTorchSharp/THSNN.cpp b/src/Native/LibTorchSharp/THSNN.cpp index 12b6a461a..a164f0f67 100644 --- a/src/Native/LibTorchSharp/THSNN.cpp +++ b/src/Native/LibTorchSharp/THSNN.cpp @@ -1334,4 +1334,16 @@ Tensor THSNN_scaled_dot_product_attention(const Tensor query, const Tensor key, auto mask = attention_mask == nullptr ? c10::nullopt : c10::optional(*attention_mask); CATCH_TENSOR(torch::scaled_dot_product_attention(*query, *key, *value, mask, p, casual)); +} + +void THSNN_Print_Module(const NNModule module) { + if (auto* conv = (*module)->as()) + { + auto opt = conv->options; + ::std::cout << conv->name() << "(" << opt.in_channels() << "," << opt.out_channels() << ", K=" << opt.kernel_size() <<", S=" << opt.stride() << ")" << std::endl; //TODO: Add padding + } + if (auto* bn = (*module)->as()) { + auto opt = bn->options; + ::std::cout << bn->name() << "(" << opt.num_features() << ", Eps=" << opt.eps() << ", M=" << (opt.momentum().has_value() ? opt.momentum().value() : 0) << ")" << std::endl; //TODO: Add another data + } } \ No newline at end of file diff --git a/src/Native/LibTorchSharp/THSNN.h b/src/Native/LibTorchSharp/THSNN.h index 07d247d87..49d293113 100644 --- a/src/Native/LibTorchSharp/THSNN.h +++ b/src/Native/LibTorchSharp/THSNN.h @@ -145,6 +145,7 @@ EXPORT_API(Tensor) THSNN_Conv2d_weight(const NNModule module); EXPORT_API(void) THSNN_Conv2d_set_weight(const NNModule module, const Tensor weight); EXPORT_API(Tensor) THSNN_Conv2d_bias(const NNModule module); EXPORT_API(void) THSNN_Conv2d_set_bias(const NNModule module, const Tensor bias); +//EXPORT_API(void) THSNN_Conv2d_print_options(const NNModule module); EXPORT_API(NNModule) THSNN_Conv3d_ctor(const int64_t inputChannel, const int64_t outputChannel, const int64_t kernelSize, const int64_t stride, const int64_t padding, const int64_t dilation, const int64_t paddingMode, const int64_t groups, const bool bias, NNAnyModule* outAsAnyModule); EXPORT_API(NNModule) THSNN_Conv3d_ctor_1(const int64_t inputChannel, const int64_t outputChannel, const int64_t kernelX, const int64_t kernelY, const int64_t kernelZ, const int64_t strideX, const int64_t strideY, const int64_t strideZ, const int64_t paddingX, const int64_t paddingY, const int64_t paddingZ, const int64_t dilationX, const int64_t dilationY, const int64_t dilationZ, const int64_t paddingMode, const int64_t groups, const bool bias, NNAnyModule* outAsAnyModule); EXPORT_API(Tensor) THSNN_Conv3d_forward(const NNModule module, const Tensor tensor); @@ -592,3 +593,7 @@ EXPORT_API(PackedSequence) THSNN_pack_padded_sequence(Tensor input, Tensor lengt EXPORT_API(void) THSNN_pad_packed_sequence(PackedSequence sequence, bool batch_first, double padding_value, int64_t total_length, Tensor* res1, Tensor* res2); EXPORT_API(Tensor) THSNN_pad_sequence(const Tensor* sequences, const int sequences_len, bool batch_first, double padding_value); EXPORT_API(PackedSequence) THSNN_pack_sequence(const Tensor* sequences, int sequences_len, bool enforce_sorted); + + +// Printer Modules +EXPORT_API(void) THSNN_Print_Module(const NNModule module); diff --git a/src/Native/LibTorchSharp/Utils.h b/src/Native/LibTorchSharp/Utils.h index cc0242af1..892e0e2ec 100644 --- a/src/Native/LibTorchSharp/Utils.h +++ b/src/Native/LibTorchSharp/Utils.h @@ -2,7 +2,6 @@ #pragma once #include - #include "torch/torch.h" #include extern thread_local char *torch_last_err; diff --git a/src/TorchSharp/PInvoke/LibTorchSharp.THSNN.cs b/src/TorchSharp/PInvoke/LibTorchSharp.THSNN.cs index 8bef36230..870e4e647 100644 --- a/src/TorchSharp/PInvoke/LibTorchSharp.THSNN.cs +++ b/src/TorchSharp/PInvoke/LibTorchSharp.THSNN.cs @@ -1318,6 +1318,9 @@ internal static extern IntPtr THSNN_custom_module( [DllImport("LibTorchSharp")] internal static extern IntPtr THSNN_MaxUnpool2d_ctor(IntPtr pkernelSize, int kernelSizeLength, IntPtr pstrides, int stridesLength, IntPtr pPadding, int paddingLength, out IntPtr pBoxedModule); + + [DllImport("LibTorchSharp")] + internal static extern void THSNN_Print_Module(torch.nn.Module.HType module); } #pragma warning restore CA2101 } diff --git a/src/TorchSharp/Tensor/torch.Utilities.cs b/src/TorchSharp/Tensor/torch.Utilities.cs index 42745a786..91d79539a 100644 --- a/src/TorchSharp/Tensor/torch.Utilities.cs +++ b/src/TorchSharp/Tensor/torch.Utilities.cs @@ -2,6 +2,7 @@ #nullable enable using System; using System.Diagnostics.Contracts; +using TorchSharp.PInvoke; using static TorchSharp.PInvoke.NativeMethods; namespace TorchSharp @@ -79,5 +80,10 @@ public static ScalarType promote_types(ScalarType type1, ScalarType type2) [Obsolete("not implemented", true)] public static void _assert(Func condition, string message) => throw new NotImplementedException(); + + public static void PrintModule(torch.nn.Module module) + { + NativeMethods.THSNN_Print_Module(module.handle); + } } } \ No newline at end of file From 98cabfa4496b1a9bb1bbc996cbf931dd73fd2961 Mon Sep 17 00:00:00 2001 From: Dimitri Date: Sun, 18 Feb 2024 22:49:43 -0300 Subject: [PATCH 06/65] Add some printing module --- src/Native/LibTorchSharp/THSNN.cpp | 47 +++++++++++++++++--- src/TorchSharp/NN/Dropout2d.cs | 4 +- src/TorchSharp/NN/Normalization/LayerNorm.cs | 4 +- src/TorchSharp/Tensor/torch.Utilities.cs | 14 ++++++ 4 files changed, 59 insertions(+), 10 deletions(-) diff --git a/src/Native/LibTorchSharp/THSNN.cpp b/src/Native/LibTorchSharp/THSNN.cpp index a164f0f67..430c17f5e 100644 --- a/src/Native/LibTorchSharp/THSNN.cpp +++ b/src/Native/LibTorchSharp/THSNN.cpp @@ -1337,13 +1337,48 @@ Tensor THSNN_scaled_dot_product_attention(const Tensor query, const Tensor key, } void THSNN_Print_Module(const NNModule module) { - if (auto* conv = (*module)->as()) + std::ostringstream oss; + const std::string name = module->get()->name(); + oss << name << "("; + if (auto* conv2 = (*module)->as()) { - auto opt = conv->options; - ::std::cout << conv->name() << "(" << opt.in_channels() << "," << opt.out_channels() << ", K=" << opt.kernel_size() <<", S=" << opt.stride() << ")" << std::endl; //TODO: Add padding + const auto opt = &conv2->options; + oss << opt->in_channels() << "," << opt->out_channels() << ", K=" << opt->kernel_size(); + oss << ", S=" << opt->stride() << ", P=" << opt->padding().index() << ", D=" << opt->dilation(); + oss << ", G=" << opt->groups() << ", B=" << opt->bias(); } - if (auto* bn = (*module)->as()) { - auto opt = bn->options; - ::std::cout << bn->name() << "(" << opt.num_features() << ", Eps=" << opt.eps() << ", M=" << (opt.momentum().has_value() ? opt.momentum().value() : 0) << ")" << std::endl; //TODO: Add another data + if (auto* bn2 = (*module)->as()) { + const auto opt = &bn2->options; + oss << opt->num_features() << ", Eps=" << opt->eps() << ", M=" << (opt->momentum().has_value() ? std::to_string(opt->momentum().value()) : "NaN"); + oss << ", A=" << opt->affine() << ", T=" << opt->track_running_stats(); } + if(auto* ln = (*module)->as()) //This not printed because the TorchSharp not have a ctor of LayerNorm + { + const auto opt = ln->options; + oss << opt.eps() << ", Elem=" << opt.elementwise_affine() << ", N=["; + for(int64_t i=0;i< static_cast(opt.normalized_shape().size());i++) + oss << opt.normalized_shape()[i] << ((i == static_cast(opt.normalized_shape().size()-1)) ? "]" : ","); + } + if (const auto* d2 = (*module)->as()) //This not printed because the TorchSharp not have a ctor of Dropout2d + { + auto opt = d2->options; + oss << opt.p() << ", Inplace=" << opt.inplace(); + } + if(auto* avp2 = (*module)->as()) + { + const auto opt = &avp2->options; + oss << "["; + for (int64_t i = 0; i < opt->output_size().size(); i++) + oss << opt->output_size()->at(i).value() << ((i == opt->output_size().size() - 1) ? "]" : ","); + } + if (auto* amp2 = (*module)->as()) + { + const auto opt = &2->options; + oss << "["; + for (int64_t i = 0; i < opt->output_size().size(); i++) + oss << opt->output_size()->at(i).value() << ((i == opt->output_size().size() - 1) ? "]" : ","); + } + + oss << ")"; + std::cout << oss.str() << std::endl; } \ No newline at end of file diff --git a/src/TorchSharp/NN/Dropout2d.cs b/src/TorchSharp/NN/Dropout2d.cs index 363cb40d5..49db468d7 100644 --- a/src/TorchSharp/NN/Dropout2d.cs +++ b/src/TorchSharp/NN/Dropout2d.cs @@ -33,8 +33,8 @@ public override Tensor forward(Tensor input) protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex = -1) => this; protected internal override nn.Module _to(ScalarType dtype) => this; - private bool inplace; - private double p; + internal bool inplace; //Set internal accesibility for PrintModule + internal double p; //Set internal accesibility for PrintModule } } diff --git a/src/TorchSharp/NN/Normalization/LayerNorm.cs b/src/TorchSharp/NN/Normalization/LayerNorm.cs index 7010e754e..6ed8dae45 100644 --- a/src/TorchSharp/NN/Normalization/LayerNorm.cs +++ b/src/TorchSharp/NN/Normalization/LayerNorm.cs @@ -18,8 +18,8 @@ namespace Modules /// public sealed class LayerNorm : torch.nn.Module { - private long[] _normalized_shape; - private double _eps; + internal long[] _normalized_shape; + internal double _eps; internal LayerNorm(long[] normalized_shape, double eps, bool elementwise_affine, bool bias, Device? device, ScalarType? dtype) : base(nameof(LayerNorm)) { diff --git a/src/TorchSharp/Tensor/torch.Utilities.cs b/src/TorchSharp/Tensor/torch.Utilities.cs index 91d79539a..7525ea6c9 100644 --- a/src/TorchSharp/Tensor/torch.Utilities.cs +++ b/src/TorchSharp/Tensor/torch.Utilities.cs @@ -2,6 +2,7 @@ #nullable enable using System; using System.Diagnostics.Contracts; +using TorchSharp.Modules; using TorchSharp.PInvoke; using static TorchSharp.PInvoke.NativeMethods; @@ -83,6 +84,19 @@ public static ScalarType promote_types(ScalarType type1, ScalarType type2) public static void PrintModule(torch.nn.Module module) { + if (module is Dropout2d drop2d) { + Console.WriteLine($"{module.GetName()}({drop2d.p}, {drop2d.inplace})"); + return; + } + + if (module is LayerNorm ln) { + string str= "["; + for (int i = 0; i < ln._normalized_shape.Length; i++) + str += ln._normalized_shape[i] + ","; + str = str.TrimEnd(',')+"]"; + Console.WriteLine($"{module.GetName()}({ln._eps}, {str})"); + return; + } NativeMethods.THSNN_Print_Module(module.handle); } } From 669b4facd7eac6dcd6ba01c25c2be0831c9ffe67 Mon Sep 17 00:00:00 2001 From: Dimitri Date: Tue, 20 Feb 2024 16:08:27 -0300 Subject: [PATCH 07/65] Fix some dotnet build. Need fix tests --- .gitignore | 22 +++ .../FileRestitcher.Tests.csproj | 2 +- .../FileRestitcher/FileRestitcher.csproj | 6 +- src/Examples.Utils/Examples.Utils.csproj | 3 +- src/Examples.Utils/Vocab.cs | 9 +- src/Examples/Examples.csproj | 2 +- src/FSharp.Examples/FSharp.Examples.fsproj | 2 +- src/Native/build.cmd | 151 ------------------ src/TorchSharp/TorchSharp.csproj | 28 ++-- 9 files changed, 51 insertions(+), 174 deletions(-) delete mode 100644 src/Native/build.cmd diff --git a/.gitignore b/.gitignore index bab8676e1..a17061b33 100644 --- a/.gitignore +++ b/.gitignore @@ -272,3 +272,25 @@ packages/ *.code-workspace /.idea /test/TorchSharpTest/exportsd.py +/src/Native/CMakeFiles +/src/Native/LibTorchSharp/CMakeFiles +/src/Native/ALL_BUILD.vcxproj +/src/Native/ALL_BUILD.vcxproj.filters +/src/Native/build.cmd +/src/Native/CMakeCache.txt +/src/Native/cmake_install.cmake +/src/Native/INSTALL.vcxproj +/src/Native/INSTALL.vcxproj.filters +/src/Native/install_manifest.txt +/src/Native/LibTorchSharp/ALL_BUILD.vcxproj +/src/Native/LibTorchSharp/ALL_BUILD.vcxproj.filters +/src/Native/LibTorchSharp/cmake_install.cmake +/src/Native/LibTorchSharp/INSTALL.vcxproj +/src/Native/LibTorchSharp/INSTALL.vcxproj.filters +/src/Native/LibTorchSharp/LibTorchSharp.sln +/src/Native/LibTorchSharp/LibTorchSharp.vcxproj +/src/Native/LibTorchSharp/LibTorchSharp.vcxproj.filters +/src/Native/Project.sln +/src/Native/ZERO_CHECK.vcxproj +/src/Native/ZERO_CHECK.vcxproj.filters +/src/FSharp.Examples/FSharp.Examples.fsproj diff --git a/pkg/FileRestitcher/FileRestitcher.Tests/FileRestitcher.Tests.csproj b/pkg/FileRestitcher/FileRestitcher.Tests/FileRestitcher.Tests.csproj index e76338122..bc96dbe96 100644 --- a/pkg/FileRestitcher/FileRestitcher.Tests/FileRestitcher.Tests.csproj +++ b/pkg/FileRestitcher/FileRestitcher.Tests/FileRestitcher.Tests.csproj @@ -3,7 +3,7 @@ false - + net472;netstandard2.0;$(TargetFrameworks) net6.0 net472;$(TargetFrameworks) diff --git a/pkg/FileRestitcher/FileRestitcher/FileRestitcher.csproj b/pkg/FileRestitcher/FileRestitcher/FileRestitcher.csproj index bbfbab0cc..3b4d8b200 100644 --- a/pkg/FileRestitcher/FileRestitcher/FileRestitcher.csproj +++ b/pkg/FileRestitcher/FileRestitcher/FileRestitcher.csproj @@ -1,11 +1,11 @@ - + false Library - netstandard2.0 + netstandard2.0;net6.0 false x64 - + diff --git a/src/Examples.Utils/Examples.Utils.csproj b/src/Examples.Utils/Examples.Utils.csproj index 1f6d5a081..6a5a09eeb 100644 --- a/src/Examples.Utils/Examples.Utils.csproj +++ b/src/Examples.Utils/Examples.Utils.csproj @@ -5,7 +5,8 @@ 9.0 net6.0 - net472;$(TargetFrameworks) + net472;$(TargetFrameworks);netstandard2.0 + net6.0 diff --git a/src/Examples.Utils/Vocab.cs b/src/Examples.Utils/Vocab.cs index 743e4c55c..7a1deb298 100644 --- a/src/Examples.Utils/Vocab.cs +++ b/src/Examples.Utils/Vocab.cs @@ -88,12 +88,17 @@ public void Add(KeyValuePair item) { Add(item.Key, item.Value); } - +#if NETSTANDARD2_0 + public bool TryGetValue(string key, out int value) + { + return _dict.TryGetValue(key, out value); + } +#else public bool TryGetValue(string key, [MaybeNullWhen(false)] out int value) { return _dict.TryGetValue(key, out value); } - +#endif private Dictionary _dict = new Dictionary(); private int _last = 0; } diff --git a/src/Examples/Examples.csproj b/src/Examples/Examples.csproj index f6fe32680..79c448399 100644 --- a/src/Examples/Examples.csproj +++ b/src/Examples/Examples.csproj @@ -5,7 +5,7 @@ true true - + net472;netstandard2.0;$(TargetFrameworks) 9.0 net6.0 net472;$(TargetFrameworks) diff --git a/src/FSharp.Examples/FSharp.Examples.fsproj b/src/FSharp.Examples/FSharp.Examples.fsproj index 900e25caa..a6ecbb723 100644 --- a/src/FSharp.Examples/FSharp.Examples.fsproj +++ b/src/FSharp.Examples/FSharp.Examples.fsproj @@ -6,7 +6,7 @@ true net6.0 - net472;$(TargetFrameworks) + net472;netstandard2.0;$(TargetFrameworks) net6.0 true Examples diff --git a/src/Native/build.cmd b/src/Native/build.cmd deleted file mode 100644 index c805b2608..000000000 --- a/src/Native/build.cmd +++ /dev/null @@ -1,151 +0,0 @@ -@if not defined _echo @echo off -setlocal - -:: Store current script directory before %~dp0 gets affected by another process later. -set __currentScriptDir=%~dp0 - -:SetupArgs -:: Initialize the args that will be passed to cmake -set __binDir=%__currentScriptDir%..\..\bin -set __rootDir=%__currentScriptDir%..\.. -set __CMakeBinDir="" -set __IntermediatesDir="" -set __BuildArch=x64 -set __VCBuildArch=x86_amd64 -set CMAKE_BUILD_TYPE=Debug -set LIBTORCH_PATH="" - -:Arg_Loop -if [%1] == [] goto :ToolsVersion -if /i [%1] == [Release] ( set CMAKE_BUILD_TYPE=Release&&shift&goto Arg_Loop) -if /i [%1] == [Debug] ( set CMAKE_BUILD_TYPE=Debug&&shift&goto Arg_Loop) - -if /i [%1] == [x86] ( set __BuildArch=x86&&set __VCBuildArch=x86&&shift&goto Arg_Loop) -if /i [%1] == [x64] ( set __BuildArch=x64&&set __VCBuildArch=x86_amd64&&shift&goto Arg_Loop) -if /i [%1] == [amd64] ( set __BuildArch=x64&&set __VCBuildArch=x86_amd64&&shift&goto Arg_Loop) - -if /i [%1] == [--libtorchpath] ( set LIBTORCH_PATH=%2&&shift&goto Arg_Loop) - -shift -goto :Arg_Loop - -:ToolsVersion -if defined VisualStudioVersion goto :RunVCVars - -set _VSWHERE="%ProgramFiles(x86)%\Microsoft Visual Studio\Installer\vswhere.exe" -if exist %_VSWHERE% ( - for /f "usebackq tokens=*" %%i in (`%_VSWHERE% -latest -prerelease -property installationPath`) do set _VSCOMNTOOLS=%%i\Common7\Tools -) -if not exist "%_VSCOMNTOOLS%" set _VSCOMNTOOLS=%VS140COMNTOOLS% -if not exist "%_VSCOMNTOOLS%" goto :MissingVersion - - -set "VSCMD_START_DIR=%__currentScriptDir%" -call "%_VSCOMNTOOLS%\VsDevCmd.bat" - -:RunVCVars -if "%VisualStudioVersion%"=="17.0" ( - goto :VS2022 -) else if "%VisualStudioVersion%"=="16.0" ( - goto :VS2019 -) else if "%VisualStudioVersion%"=="15.0" ( - goto :VS2017 -) else if "%VisualStudioVersion%"=="14.0" ( - goto :VS2015 -) - -:MissingVersion -:: Can't find VS 2015, 2017 or 2019 -echo Error: Visual Studio 2015, 2017 or 2019 required -echo Please see https://github.com/dotnet/machinelearning/tree/master/Documentation for build instructions. -exit /b 1 - -:VS2022 -:: Setup vars for VS2022 -set __PlatformToolset=v143 -set __VSVersion=17 2022 -if NOT "%__BuildArch%" == "arm64" ( - :: Set the environment for the native build - call "%VS160COMNTOOLS%..\..\VC\Auxiliary\Build\vcvarsall.bat" %__VCBuildArch% -) -goto :SetupDirs - -:VS2019 -:: Setup vars for VS2019 -set __PlatformToolset=v142 -set __VSVersion=16 2019 -if NOT "%__BuildArch%" == "arm64" ( - :: Set the environment for the native build - call "%VS160COMNTOOLS%..\..\VC\Auxiliary\Build\vcvarsall.bat" %__VCBuildArch% -) -goto :SetupDirs - -:VS2017 -:: Setup vars for VS2017 -set __PlatformToolset=v141 -set __VSVersion=15 2017 -if NOT "%__BuildArch%" == "arm64" ( - :: Set the environment for the native build - call "%VS150COMNTOOLS%..\..\VC\Auxiliary\Build\vcvarsall.bat" %__VCBuildArch% -) -goto :SetupDirs - -:VS2015 -:: Setup vars for VS2015build -set __PlatformToolset=v140 -set __VSVersion=14 2015 -if NOT "%__BuildArch%" == "arm64" ( - :: Set the environment for the native build - call "%VS140COMNTOOLS%..\..\VC\vcvarsall.bat" %__VCBuildArch% -) - -:SetupDirs -:: Setup to cmake the native components -echo Commencing native build of dotnet/machinelearning -echo. - -if %__CMakeBinDir% == "" ( - set "__CMakeBinDir=%__binDir%\%__BuildArch%.%CMAKE_BUILD_TYPE%\Native" -) -if %__IntermediatesDir% == "" ( - set "__IntermediatesDir=%__binDir%\obj\%__BuildArch%.%CMAKE_BUILD_TYPE%\Native" -) -set "__CMakeBinDir=%__CMakeBinDir:\=/%" -set "__IntermediatesDir=%__IntermediatesDir:\=/%" - -:: Check that the intermediate directory exists so we can place our cmake build tree there -if not exist "%__IntermediatesDir%" md "%__IntermediatesDir%" - -:: Regenerate the VS solution - -set "__gen-buildsys-win-path=%__currentScriptDir%\gen-buildsys-win.bat" -set "__source-code-path=%__currentScriptDir%" - -echo Calling "%__gen-buildsys-win-path%" "%__source-code-path%" "%__VSVersion%" %__BuildArch% -pushd "%__IntermediatesDir%" -call "%__gen-buildsys-win-path%" "%__source-code-path%" "%__VSVersion%" %__BuildArch% -popd - -:CheckForProj -:: Check that the project created by Cmake exists -if exist "%__IntermediatesDir%\INSTALL.vcxproj" goto BuildNativeProj -goto :Failure - -:BuildNativeProj -:: Build the project created by Cmake -set __msbuildArgs=/p:Platform=%__BuildArch% /p:PlatformToolset="%__PlatformToolset%" - -cd %__rootDir% - -echo msbuild "%__IntermediatesDir%\INSTALL.vcxproj" /t:build /p:Configuration=%CMAKE_BUILD_TYPE% %__msbuildArgs% -call msbuild "%__IntermediatesDir%\INSTALL.vcxproj" /t:build /p:Configuration=%CMAKE_BUILD_TYPE% %__msbuildArgs% -IF ERRORLEVEL 1 ( - goto :Failure -) -echo Done building Native components -exit /B 0 - -:Failure -:: Build failed -echo Failed to generate native component build project! -exit /b 1 diff --git a/src/TorchSharp/TorchSharp.csproj b/src/TorchSharp/TorchSharp.csproj index ef6d6ff94..054f5c18a 100644 --- a/src/TorchSharp/TorchSharp.csproj +++ b/src/TorchSharp/TorchSharp.csproj @@ -3,14 +3,14 @@ - netstandard2.0 - 9.0 - TorchSharp - true - false - false - false - $(DefineConstants);LIBTORCH_$(LibTorchPackageVersion.Replace('.', '_'));CUDA_$(CudaVersionDot.Replace('.', '_')) + netstandard2.0;net6.0 + 9.0 + TorchSharp + true + false + false + false + $(DefineConstants);LIBTORCH_$(LibTorchPackageVersion.Replace('.', '_'));CUDA_$(CudaVersionDot.Replace('.', '_')) @@ -49,12 +49,12 @@ - - $(PackDependsOn); - RealPack - - True - ..\..\build\TorchSharp.snk + + $(PackDependsOn); + RealPack + + True + ..\..\build\TorchSharp.snk From 394041426e75864e182b0e4bcb0ceb2289351f2f Mon Sep 17 00:00:00 2001 From: Dimitri Date: Sun, 30 Jun 2024 19:39:43 -0300 Subject: [PATCH 08/65] Fast tensor accessor for ToArray() --- src/Examples.Utils/Examples.Utils.csproj | 8 +- src/TorchSharp/Amp/AutocastDisposedManager.cs | 10 +++ src/TorchSharp/Amp/AutocastDisposedScope.cs | 10 +++ .../Tensor/Factories/tensor_float.cs | 3 +- src/TorchSharp/Utils/TensorAccessor.cs | 79 ++++++++++++++++--- 5 files changed, 97 insertions(+), 13 deletions(-) create mode 100644 src/TorchSharp/Amp/AutocastDisposedManager.cs create mode 100644 src/TorchSharp/Amp/AutocastDisposedScope.cs diff --git a/src/Examples.Utils/Examples.Utils.csproj b/src/Examples.Utils/Examples.Utils.csproj index 6a5a09eeb..d8ce3a24a 100644 --- a/src/Examples.Utils/Examples.Utils.csproj +++ b/src/Examples.Utils/Examples.Utils.csproj @@ -21,7 +21,13 @@ - + + + + + + + diff --git a/src/TorchSharp/Amp/AutocastDisposedManager.cs b/src/TorchSharp/Amp/AutocastDisposedManager.cs new file mode 100644 index 000000000..d4ec1ccd7 --- /dev/null +++ b/src/TorchSharp/Amp/AutocastDisposedManager.cs @@ -0,0 +1,10 @@ +using System; +using System.Collections.Generic; +using System.Text; + +namespace TorchSharp.Amp +{ + class AutocastDisposedManager + { + } +} diff --git a/src/TorchSharp/Amp/AutocastDisposedScope.cs b/src/TorchSharp/Amp/AutocastDisposedScope.cs new file mode 100644 index 000000000..7c771d16f --- /dev/null +++ b/src/TorchSharp/Amp/AutocastDisposedScope.cs @@ -0,0 +1,10 @@ +using System; +using System.Collections.Generic; +using System.Text; + +namespace TorchSharp.Amp +{ + class AutocastDisposedScope + { + } +} diff --git a/src/TorchSharp/Tensor/Factories/tensor_float.cs b/src/TorchSharp/Tensor/Factories/tensor_float.cs index f33d1b90a..e50943689 100644 --- a/src/TorchSharp/Tensor/Factories/tensor_float.cs +++ b/src/TorchSharp/Tensor/Factories/tensor_float.cs @@ -21,7 +21,8 @@ public static Tensor tensor(float scalar, Device? device = null, bool requires_g if (handle == IntPtr.Zero) { CheckForErrors(); } - var t = new Tensor(handle).AutoCast(); + //var t = new Tensor(handle).AutoCast(); + var t = new Tensor(handle); /*if (is_autocast_cache_enabled()) { if (is_autocast_gpu_enabled()) return t.to(get_autocast_gpu_dtype()); //this work, but should put that on all tensor factorie... diff --git a/src/TorchSharp/Utils/TensorAccessor.cs b/src/TorchSharp/Utils/TensorAccessor.cs index 9514003f2..ab9846eec 100644 --- a/src/TorchSharp/Utils/TensorAccessor.cs +++ b/src/TorchSharp/Utils/TensorAccessor.cs @@ -38,16 +38,28 @@ internal TensorAccessor(torch.Tensor tensor) _tensor = tensor; // Keep the tensor alive now that everything is alright. } + /// + /// This is important for performance because only called with CopyTo, CopyFrom. Is not necesary in each invocation call tensor.numel() because that use intensive CPU. + /// This temporary count avoid so much use CPU. The Property act as method. + /// If tensor is for example 640*640*3 = 1.228.800, property invoke 1 millons times!!! + /// If we only want copy is not necesary call that method so many times. + /// + private long TempCount = -1; public long Count => (_tensor is not null ? _tensor.numel() : 0); public bool IsReadOnly => false; + public T[] ToArray() { if (_tensor.ndim < 2) return (T[])ToNDArray(); - var result = new T[Count]; + var shps = _tensor.shape; + TempCount = 1; + for(int i=0;i array, int arrayIndex = 0, long tensorIndex = 0) + { + int idx = arrayIndex; + foreach (int offset in GetSubsequentIndices(tensorIndex)) { + if (idx >= array.Length) break; + unsafe { array[idx] = ((T*)_tensor_data_ptr)[offset]; } + idx += 1; + } + } + public void CopyFrom(T[] array, int arrayIndex = 0, long tensorIndex = 0) { int idx = arrayIndex; @@ -251,6 +273,16 @@ public void CopyFrom(T[] array, int arrayIndex = 0, long tensorIndex = 0) } } + public void CopyFrom(ReadOnlySpan array, int arrayIndex = 0, long tensorIndex = 0) + { + int idx = arrayIndex; + foreach (int offset in GetSubsequentIndices(tensorIndex)) { + if (idx >= array.Length) break; + unsafe { ((T*)_tensor_data_ptr)[offset] = array[idx]; } + idx += 1; + } + } + /// /// Translates a linear index within the span represented by the accessor to a linear index /// used by the underlying tensor. The two should only be different if the tensor is a view @@ -274,7 +306,27 @@ private static long TranslateIndex(long idx, torch.Tensor tensor) return result; } + /// + /// WARNING: Test purpose not use in production + /// + private long TranslateIndexNonStatic(long idx, torch.Tensor tensor) + { + if (idx >= TempCount || idx < 0) + throw new ArgumentOutOfRangeException($"{idx} in a collection of ${tensor.numel()} elements."); + + if (tensor.is_contiguous() || idx == 0) return idx; + long result = 0; + var shape = tensor.shape; + var strides = tensor.stride(); + + for (var i = shape.Length - 1; i >= 0; i--) { + idx = Math.DivRem(idx, shape[i], out long s); + result += s * strides[i]; + } + + return result; + } private static long TranslateIndex(long[] idx, torch.Tensor tensor) { long result = 0; @@ -347,15 +399,18 @@ internal static T ReadItemAt(torch.Tensor tensor, long index) private IEnumerable GetSubsequentIndices(long startingIndex) { - if (startingIndex < 0 || startingIndex >= Count) + TempCount = Count; + + if (startingIndex < 0 || startingIndex >= TempCount) throw new ArgumentOutOfRangeException(nameof(startingIndex)); - if (Count <= 1) { - if (Count == 0) { + if (TempCount <= 1) { + if (TempCount == 0) { return Enumerable.Empty(); } - return (new long[] { 0 }).AsEnumerable(); + return new List() { 0 }; + //return (new long[] { 0 }).AsEnumerable(); } if (_tensor.is_contiguous()) { @@ -371,7 +426,6 @@ private IEnumerable GetSubsequentIndices(long startingIndex) return MultiDimensionIndices(startingIndex); } - private IEnumerable MultiDimensionIndices(long startingIndex) { long[] shape = _tensor.shape; @@ -379,7 +433,8 @@ private IEnumerable MultiDimensionIndices(long startingIndex) long[] inds = new long[stride.Length]; long index = startingIndex; - long offset = TranslateIndex(startingIndex, _tensor); + //long offset = TranslateIndex(startingIndex, _tensor); + long offset = TranslateIndexNonStatic(startingIndex, _tensor); //WARNING: Test purpose not use in production while (true) { @@ -387,7 +442,7 @@ private IEnumerable MultiDimensionIndices(long startingIndex) yield return offset; - if (index >= Count) break; + if (index >= TempCount) break; for (int i = inds.Length - 1; ; i--) { Debug.Assert(i >= 0); @@ -408,21 +463,23 @@ private IEnumerable MultiDimensionIndices(long startingIndex) private IEnumerable SimpleIndices(long startingIndex, long stride) { long index = startingIndex; - long offset = TranslateIndex(startingIndex, _tensor); + //long offset = TranslateIndex(startingIndex, _tensor); + long offset = TranslateIndexNonStatic(startingIndex, _tensor); //WARNING: Test purpose not use in production - while (index < Count) { + while (index < TempCount) { yield return offset; offset += stride; index += 1; } } + private IEnumerable ContiguousIndices(long startingIndex) { // If there was an overload for Enumerable.Range that // produced long integers, we wouldn't need this implementation. long index = startingIndex; - while (index < Count) { + while (index < TempCount) { yield return index; index += 1; } From 5062339fe0cc4989f286bcd5812c00b4f920bc4a Mon Sep 17 00:00:00 2001 From: Dimitri Date: Sun, 30 Jun 2024 20:02:32 -0300 Subject: [PATCH 09/65] fix local build dotnet --- src/Examples/AdversarialExampleGeneration.cs | 2 ++ src/Examples/SequenceToSequence.cs | 7 +++++++ src/Examples/TextClassification.cs | 2 ++ src/TorchSharp/PInvoke/LibTorchSharp.THSTensor.cs | 6 +++--- 4 files changed, 14 insertions(+), 3 deletions(-) diff --git a/src/Examples/AdversarialExampleGeneration.cs b/src/Examples/AdversarialExampleGeneration.cs index 7bfc174b2..49bd10956 100644 --- a/src/Examples/AdversarialExampleGeneration.cs +++ b/src/Examples/AdversarialExampleGeneration.cs @@ -34,6 +34,8 @@ public class AdversarialExampleGeneration { #if NET472_OR_GREATER private readonly static string _dataLocation = NSPath.Join(Environment.GetFolderPath(Environment.SpecialFolder.DesktopDirectory), "..", "Downloads", "mnist"); +#elif NETSTANDARD2_0 + private readonly static string _dataLocation = Path.Combine(Environment.GetFolderPath(Environment.SpecialFolder.DesktopDirectory), "..", "Downloads", "mnist"); #else private readonly static string _dataLocation = Path.Join(Environment.GetFolderPath(Environment.SpecialFolder.DesktopDirectory), "..", "Downloads", "mnist"); #endif // NET472_OR_GREATER diff --git a/src/Examples/SequenceToSequence.cs b/src/Examples/SequenceToSequence.cs index 436c05a67..8ff2c6dc5 100644 --- a/src/Examples/SequenceToSequence.cs +++ b/src/Examples/SequenceToSequence.cs @@ -6,6 +6,7 @@ using System.Diagnostics; using static TorchSharp.torch; using static TorchSharp.torch.nn; +using System.Text.RegularExpressions; namespace TorchSharp.Examples { @@ -26,6 +27,8 @@ public class SequenceToSequence // This path assumes that you're running this on Windows. #if NET472_OR_GREATER private readonly static string _dataLocation = NSPath.Join(Environment.GetFolderPath(Environment.SpecialFolder.DesktopDirectory), "..", "Downloads", "wikitext-2-v1"); +#elif NETSTANDARD2_0 + private readonly static string _dataLocation = Path.Combine(Environment.GetFolderPath(Environment.SpecialFolder.DesktopDirectory), "..", "Downloads", "wikitext-2-v1"); #else private readonly static string _dataLocation = Path.Join(Environment.GetFolderPath(Environment.SpecialFolder.DesktopDirectory), "..", "Downloads", "wikitext-2-v1"); #endif // NET472_OR_GREATER @@ -251,7 +254,11 @@ private void InitWeights() public override Tensor forward(Tensor t, Tensor mask) { +#if !NETSTANDARD2_0 var src = pos_encoder.call(encoder.call(t) * MathF.Sqrt(ninputs)); +#else + var src = pos_encoder.call(encoder.call(t) * (float)Math.Sqrt(ninputs)); +#endif var enc = transformer_encoder.call(src, mask); return decoder.call(enc); } diff --git a/src/Examples/TextClassification.cs b/src/Examples/TextClassification.cs index 8fb175718..4cdc79bc1 100644 --- a/src/Examples/TextClassification.cs +++ b/src/Examples/TextClassification.cs @@ -36,6 +36,8 @@ public class TextClassification // This path assumes that you're running this on Windows. #if NET472_OR_GREATER private readonly static string _dataLocation = NSPath.Join(Environment.GetFolderPath(Environment.SpecialFolder.DesktopDirectory), "..", "Downloads", "AG_NEWS"); +#elif NETSTANDARD2_0 + private readonly static string _dataLocation = Path.Combine(Environment.GetFolderPath(Environment.SpecialFolder.DesktopDirectory), "..", "Downloads", "AG_NEWS"); #else private readonly static string _dataLocation = Path.Join(Environment.GetFolderPath(Environment.SpecialFolder.DesktopDirectory), "..", "Downloads", "AG_NEWS"); #endif // NET472_OR_GREATER diff --git a/src/TorchSharp/PInvoke/LibTorchSharp.THSTensor.cs b/src/TorchSharp/PInvoke/LibTorchSharp.THSTensor.cs index 4b38f5655..173ccd48a 100644 --- a/src/TorchSharp/PInvoke/LibTorchSharp.THSTensor.cs +++ b/src/TorchSharp/PInvoke/LibTorchSharp.THSTensor.cs @@ -288,12 +288,12 @@ internal static extern IntPtr THSTensor_upsample_nearest3d(IntPtr input, [DllImport("LibTorchSharp")] internal static extern IntPtr THSTensor_to_device(IntPtr handle, int device_type, int device_index, [MarshalAs(UnmanagedType.U1)] bool copy, [MarshalAs(UnmanagedType.U1)] bool non_blocking); + [DllImport("LibTorchSharp")] + //internal static extern IntPtr THSTensor_to_type_and_device(IntPtr handle, sbyte scalar_type, int device_type, int device_index, [MarshalAs(UnmanagedType.U1)] bool copy); + internal static extern IntPtr THSTensor_to_type_and_device(IntPtr handle, sbyte scalar_type, int device_type, int device_index, [MarshalAs(UnmanagedType.U1)] bool copy, [MarshalAs(UnmanagedType.U1)] bool non_blocking); [DllImport("LibTorchSharp")] internal static extern IntPtr THSTensor_to_type(IntPtr handle, sbyte scalar_type, [MarshalAs(UnmanagedType.U1)] bool copy, [MarshalAs(UnmanagedType.U1)] bool non_blocking); - [DllImport("LibTorchSharp")] - internal static extern IntPtr THSTensor_to_type_and_device(IntPtr handle, sbyte scalar_type, int device_type, int device_index, [MarshalAs(UnmanagedType.U1)] bool copy, [MarshalAs(UnmanagedType.U1)] bool non_blocking); - internal static extern IntPtr THSTensor_to_type_and_device(IntPtr handle, sbyte scalar_type, int device_type, int device_index, [MarshalAs(UnmanagedType.U1)] bool copy); [DllImport("LibTorchSharp")] internal static extern IntPtr THSTensor_to_type_and_device_and_non_blocking(IntPtr handle, sbyte scalar_type, int device_type, int device_index, [MarshalAs(UnmanagedType.U1)] bool non_blocking); From 3a467af99a1afc640d780e52510ecf82c97e5c5a Mon Sep 17 00:00:00 2001 From: Dimitri Date: Tue, 2 Jul 2024 18:16:42 -0300 Subject: [PATCH 10/65] Fast ToArray() TensorAccessor --- .gitignore | 1 + 1 file changed, 1 insertion(+) diff --git a/.gitignore b/.gitignore index a17061b33..875954e1a 100644 --- a/.gitignore +++ b/.gitignore @@ -294,3 +294,4 @@ packages/ /src/Native/ZERO_CHECK.vcxproj /src/Native/ZERO_CHECK.vcxproj.filters /src/FSharp.Examples/FSharp.Examples.fsproj +/pkg/FileRestitcher From 18c7528a50173ac26e21a5ec4d833c84510608be Mon Sep 17 00:00:00 2001 From: Dimitri Date: Tue, 2 Jul 2024 18:28:45 -0300 Subject: [PATCH 11/65] Fast tensor accesor --- Directory.Build.props | 9 +++- src/Native/LibTorchSharp/Utils.h | 3 ++ src/TorchSharp/Amp/AutocastDisposeManager.cs | 29 ++++++++++++ src/TorchSharp/Amp/AutocastDisposeScope.cs | 23 ++++++++++ src/TorchSharp/Amp/AutocastDisposedManager.cs | 10 ----- src/TorchSharp/Amp/AutocastDisposedScope.cs | 10 ----- src/TorchSharp/Amp/AutocastMode.cs | 5 ++- src/TorchSharp/Tensor/Tensor.cs | 18 +++++++- src/TorchSharp/Utils/TensorAccessor.cs | 44 +++++++++++++++---- 9 files changed, 118 insertions(+), 33 deletions(-) create mode 100644 src/TorchSharp/Amp/AutocastDisposeManager.cs create mode 100644 src/TorchSharp/Amp/AutocastDisposeScope.cs delete mode 100644 src/TorchSharp/Amp/AutocastDisposedManager.cs delete mode 100644 src/TorchSharp/Amp/AutocastDisposedScope.cs diff --git a/Directory.Build.props b/Directory.Build.props index 1321ec4ff..aad7547a9 100644 --- a/Directory.Build.props +++ b/Directory.Build.props @@ -5,6 +5,10 @@ + + true + $(LibTorch)libtorch-win-shared-with-deps-2.3.1+cpu\libtorch + $(LibTorch)libtorch-win-shared-with-deps-2.3.1+cu121\libtorch Debug Debug;Release <_DefaultArchitecture>$([System.Runtime.InteropServices.RuntimeInformation]::OSArchitecture.ToString().ToLower()) @@ -133,7 +137,7 @@ .dylib.dwarf - + pytorch conda osx-arm64 @@ -152,6 +156,9 @@ $(LibTorchArchiveCoreName)-$(LibTorchVersion)$(LibTorchCudaLocalNameSuffix) $(IntermediateOutputRootPath)libtorch-cpu\$(LibTorchCpuLocalBase)\libtorch\share\cmake\Torch + + $(LibTorchPathCPU)\share\cmake\Torch + diff --git a/src/Native/LibTorchSharp/Utils.h b/src/Native/LibTorchSharp/Utils.h index 892e0e2ec..42573753b 100644 --- a/src/Native/LibTorchSharp/Utils.h +++ b/src/Native/LibTorchSharp/Utils.h @@ -59,6 +59,9 @@ struct TensorArray { inline Tensor ResultTensor(const at::Tensor & res) { if (res.defined()) { + + //TODO: Autocast here only if is INNER-SCOPE + /*at::Tensor* resT = new torch::Tensor(res); if (at::autocast::is_autocast_cache_enabled()){ if (res.is_cuda()) { diff --git a/src/TorchSharp/Amp/AutocastDisposeManager.cs b/src/TorchSharp/Amp/AutocastDisposeManager.cs new file mode 100644 index 000000000..83c31f335 --- /dev/null +++ b/src/TorchSharp/Amp/AutocastDisposeManager.cs @@ -0,0 +1,29 @@ +using System; +using System.Collections.Generic; +using System.Text; + +namespace TorchSharp.Amp +{ + public class AutocastDisposeManager + { + + /*[ThreadStatic] private static AutocastDisposeManager _threadAutocastSingleton; + + internal static AutocastDisposeManager ThreadAutocastSingleton => _threadAutocastSingleton ??= new AutocastDisposeManager(); + + internal AutocastDisposeScope CurrentAutocastDispose; + //internal HashSet Modules = new List(); + public AutocastDisposeManager() + { + CurrentAutocastDispose = new AutocastDisposeScope(this); + } + internal AutocastDisposeScope RegisterTensorAutocastScope(torch.Tensor t) + { + if (CurrentAutocastDispose == null) + return null; + CurrentAutocastDispose.Tensors.Add(t); + return CurrentAutocastDispose; + }*/ + + } +} diff --git a/src/TorchSharp/Amp/AutocastDisposeScope.cs b/src/TorchSharp/Amp/AutocastDisposeScope.cs new file mode 100644 index 000000000..8f5df9490 --- /dev/null +++ b/src/TorchSharp/Amp/AutocastDisposeScope.cs @@ -0,0 +1,23 @@ +using System; +using System.Collections.Generic; +using System.Text; + +namespace TorchSharp.Amp +{ + public sealed class AutocastDisposeScope : IDisposable + { + //private AutocastDisposeManager autocastDisposeManager; + public bool IsEnabled; + /*internal AutocastMode autocastMode = AutocastMode.GetInstance(); + internal HashSet Tensors = new HashSet(); + public AutocastDisposeScope(AutocastDisposeManager autocastDisposeManager) + { + this.autocastDisposeManager = autocastDisposeManager; + IsEnabled = true; + }*/ + public void Dispose() + { + IsEnabled = false; + } + } +} diff --git a/src/TorchSharp/Amp/AutocastDisposedManager.cs b/src/TorchSharp/Amp/AutocastDisposedManager.cs deleted file mode 100644 index d4ec1ccd7..000000000 --- a/src/TorchSharp/Amp/AutocastDisposedManager.cs +++ /dev/null @@ -1,10 +0,0 @@ -using System; -using System.Collections.Generic; -using System.Text; - -namespace TorchSharp.Amp -{ - class AutocastDisposedManager - { - } -} diff --git a/src/TorchSharp/Amp/AutocastDisposedScope.cs b/src/TorchSharp/Amp/AutocastDisposedScope.cs deleted file mode 100644 index 7c771d16f..000000000 --- a/src/TorchSharp/Amp/AutocastDisposedScope.cs +++ /dev/null @@ -1,10 +0,0 @@ -using System; -using System.Collections.Generic; -using System.Text; - -namespace TorchSharp.Amp -{ - class AutocastDisposedScope - { - } -} diff --git a/src/TorchSharp/Amp/AutocastMode.cs b/src/TorchSharp/Amp/AutocastMode.cs index 43d3805fa..07c8149d2 100644 --- a/src/TorchSharp/Amp/AutocastMode.cs +++ b/src/TorchSharp/Amp/AutocastMode.cs @@ -16,6 +16,7 @@ public static torch.Tensor AutoCast(this torch.Tensor input) //TODO: Should make Singleton and IDisposable on ENTER public sealed class AutocastMode : IDisposable { + //NEED "Register" all tensor in scope for uncasting outer-scope private bool Enabled, Prev; //private torch.ScalarType Dtype = torch.ScalarType.Float32; private torch.ScalarType fast_dtype = torch.ScalarType.Float32; @@ -29,7 +30,7 @@ public sealed class AutocastMode : IDisposable }*/ public static AutocastMode GetInstance() { - return instance ?? (instance = new AutocastMode(torch.CUDA, cache_enabled:true)); + return instance ??= new AutocastMode(torch.CUDA, cache_enabled:true); } private AutocastMode(torch.Device dev, torch.ScalarType? dtype = null, bool enabled=true, bool? cache_enabled = null) @@ -40,7 +41,7 @@ private AutocastMode(torch.Device dev, torch.ScalarType? dtype = null, bool enab fast_dtype = torch.get_autocast_gpu_dtype(); if (dev.type == DeviceType.CPU) fast_dtype = torch.get_autocast_cpu_dtype(); - IntPtr ptr = IntPtr.Zero; + //IntPtr ptr = IntPtr.Zero; bool _cache_enabled = torch.is_autocast_cache_enabled(); if (!torch.cuda.is_available() && dev.type == DeviceType.CUDA) //Is not available for doing multicast diff --git a/src/TorchSharp/Tensor/Tensor.cs b/src/TorchSharp/Tensor/Tensor.cs index c2055d0ec..81f97cafa 100644 --- a/src/TorchSharp/Tensor/Tensor.cs +++ b/src/TorchSharp/Tensor/Tensor.cs @@ -9,6 +9,7 @@ using System.Runtime.CompilerServices; using System.Runtime.InteropServices; using System.Text; +using TorchSharp.Amp; using TorchSharp.PInvoke; #nullable enable @@ -33,13 +34,25 @@ public partial class Tensor : IDisposable static long _peakCount = 0; internal DisposeScope? OwningDisposeScope { get; set; } - + //internal AutocastDisposeScope? AutocastDisposeScope; internal Tensor(IntPtr handle) { this.handle = handle; + + /*if (_totalCount > 0) { + //have used + AutocastDisposeScope = AutocastDisposeManager.ThreadAutocastSingleton.RegisterTensorAutocastScope(this); + this = AutocastDisposeScope.autocastMode.CastTensor(this); //should cast when using INSIDE NOT WHERE CREATED + }*/ System.Threading.Interlocked.Increment(ref _totalCount); _peakCount = Math.Max(_totalCount, _peakCount); OwningDisposeScope = DisposeScopeManager.ThreadSingleton.RegisterOnCurrentDisposeScope(this); + + //TODO: Add Autocast/AMP ScopeManager, need improve this.. 1) is not threadsafe and may have big problem while casting and uncasting. + //DANGER: DONT USE THIS ON PRODUCTION + /*AutocastDisposeScope = AutocastDisposeManager.ThreadAutocastSingleton.RegisterTensorAutocastScope(this); + this = AutocastDisposeScope.autocastMode.CastTensor(this); //should cast when using INSIDE NOT WHERE CREATED*/ + //Should cast inner scope when get tensors for every each method? example prod, sum, div, reshape, etc??? } /// @@ -209,6 +222,9 @@ public IntPtr Handle { get { if (handle == IntPtr.Zero) throw new InvalidOperationException("Tensor invalid -- empty handle."); + + //AutocastDisposeScope.autocastMode.CastTensor(this); //This is wrong right??? + return handle; } } diff --git a/src/TorchSharp/Utils/TensorAccessor.cs b/src/TorchSharp/Utils/TensorAccessor.cs index ab9846eec..f0050c928 100644 --- a/src/TorchSharp/Utils/TensorAccessor.cs +++ b/src/TorchSharp/Utils/TensorAccessor.cs @@ -3,6 +3,7 @@ using System.Collections.Generic; using System.Diagnostics; using System.Linq; +using System.Runtime.InteropServices; using static TorchSharp.PInvoke.NativeMethods; namespace TorchSharp.Utils @@ -43,13 +44,13 @@ internal TensorAccessor(torch.Tensor tensor) /// This temporary count avoid so much use CPU. The Property act as method. /// If tensor is for example 640*640*3 = 1.228.800, property invoke 1 millons times!!! /// If we only want copy is not necesary call that method so many times. + /// For some reason the method numel() use so much cpu. /// - private long TempCount = -1; - public long Count => (_tensor is not null ? _tensor.numel() : 0); + internal long TempCount = -1; + public long Count => _tensor?.numel() ?? 0; public bool IsReadOnly => false; - public T[] ToArray() { if (_tensor.ndim < 2) @@ -59,6 +60,14 @@ public T[] ToArray() TempCount = 1; for(int i=0;i(_tensor_data_ptr.ToPointer(), Convert.ToInt32(TempCount)).ToArray(); + } + } + } var result = new T[TempCount]; CopyTo(result); return result; @@ -246,6 +255,18 @@ private void validate(long index) public void CopyTo(T[] array, int arrayIndex = 0, long tensorIndex = 0) { int idx = arrayIndex; + /*if (_tensor.is_contiguous()) { + if (typeof(T) == typeof(float)) { + float[] ff = new float[TempCount]; + Marshal.Copy(_tensor_data_ptr, ff, 0,ff.Length); + } + }*/ + //Because the contiguous cause arange from tensorIndex to Numel. So is not necesary "create" array of arange, i said "create" because in fact enumerable do not create itself. Very cool. + if (_tensor.is_contiguous()) { + for(long i= tensorIndex; i= array.Length) break; unsafe { array[idx] = ((T*)_tensor_data_ptr)[offset]; } @@ -399,7 +420,7 @@ internal static T ReadItemAt(torch.Tensor tensor, long index) private IEnumerable GetSubsequentIndices(long startingIndex) { - TempCount = Count; + //TempCount = Count; if (startingIndex < 0 || startingIndex >= TempCount) throw new ArgumentOutOfRangeException(nameof(startingIndex)); @@ -477,7 +498,7 @@ private IEnumerable ContiguousIndices(long startingIndex) { // If there was an overload for Enumerable.Range that // produced long integers, we wouldn't need this implementation. - + long index = startingIndex; while (index < TempCount) { yield return index; @@ -534,11 +555,16 @@ private void Dispose(bool disposing) #if true public IEnumerator GetEnumerator() { - if (Count <= 1) { - if (Count == 0) + if (TempCount <= 1) { + if (TempCount == 0) return Enumerable.Empty().GetEnumerator(); return new T[1] { this[0] }.AsEnumerable().GetEnumerator(); } + /*if (Count <= 1) { + if (Count == 0) + return Enumerable.Empty().GetEnumerator(); + return new T[1] { this[0] }.AsEnumerable().GetEnumerator(); + }*/ if (_tensor.is_contiguous()) { return new SimpleAtorImpl(this, 1); @@ -568,7 +594,7 @@ private class SimpleAtorImpl : IEnumerator public SimpleAtorImpl(TensorAccessor span, long stride) { _span = span; - _count = span.Count; + _count = span.TempCount; Debug.Assert(_count > 0); _stride = stride; Reset(); @@ -623,7 +649,7 @@ public GeneralAtorImpl(TensorAccessor span, long[] stride) { Debug.Assert(stride.Length > 1); _span = span; - _count = span.Count; + _count = span.TempCount; Debug.Assert(_count > 0); _shape = span._tensor.shape; Debug.Assert(_shape.Length == stride.Length); From 728c9fb7100eeb893d15af636783972a6ab1a6c7 Mon Sep 17 00:00:00 2001 From: Dimitri Date: Mon, 8 Jul 2024 22:22:43 -0300 Subject: [PATCH 12/65] fix accesor for every types --- Directory.Build.props | 2 +- TorchSharp.sln | 14 +++++++------- src/TorchSharp/Utils/TensorAccessor.cs | 8 +++----- 3 files changed, 11 insertions(+), 13 deletions(-) diff --git a/Directory.Build.props b/Directory.Build.props index aad7547a9..1dbeae229 100644 --- a/Directory.Build.props +++ b/Directory.Build.props @@ -6,7 +6,7 @@ - true + false $(LibTorch)libtorch-win-shared-with-deps-2.3.1+cpu\libtorch $(LibTorch)libtorch-win-shared-with-deps-2.3.1+cu121\libtorch Debug diff --git a/TorchSharp.sln b/TorchSharp.sln index 8cec25c7d..054c07bb3 100644 --- a/TorchSharp.sln +++ b/TorchSharp.sln @@ -34,7 +34,7 @@ Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "TorchSharp", "TorchSharp", pkg\TorchSharp\TorchSharp.symbols.nupkgproj = pkg\TorchSharp\TorchSharp.symbols.nupkgproj EndProjectSection EndProject -Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "LibTorchSharp", "bin\obj\x64.Debug\Native\LibTorchSharp\LibTorchSharp.vcxproj", "{2B359162-062E-3C52-91D3-027A8542A58C}" +Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "LibTorchSharp", "bin\obj\x64.Debug\Native\LibTorchSharp\LibTorchSharp.vcxproj", "{265C2E6F-04E6-37A8-B504-E3DD4A3FEE06}" EndProject Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "LibTorchSharp", "bin\obj\x64.Release\Native\LibTorchSharp\LibTorchSharp.vcxproj", "{E4C0DBEE-0815-311B-9065-137BB50BD793}" EndProject @@ -66,9 +66,9 @@ Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Solution Items", "Solution azure-pipelines.yml = azure-pipelines.yml build\BranchInfo.props = build\BranchInfo.props DEVGUIDE.md = DEVGUIDE.md + global.json = global.json README.md = README.md RELEASENOTES.md = RELEASENOTES.md - global.json = global.json EndProjectSection EndProject Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "TorchVision", "src\TorchVision\TorchVision.csproj", "{DCF01EE5-6431-4115-85E0-1FC4C3DE86A2}" @@ -107,10 +107,10 @@ Global {42B45168-476D-4BFA-87B8-81A34E6295CD}.Release|Any CPU.Build.0 = Release|Any CPU {42B45168-476D-4BFA-87B8-81A34E6295CD}.Release|x64.ActiveCfg = Release|Any CPU {42B45168-476D-4BFA-87B8-81A34E6295CD}.Release|x64.Build.0 = Release|Any CPU - {2B359162-062E-3C52-91D3-027A8542A58C}.Debug|Any CPU.ActiveCfg = Debug|x64 - {2B359162-062E-3C52-91D3-027A8542A58C}.Debug|x64.ActiveCfg = Debug|x64 - {2B359162-062E-3C52-91D3-027A8542A58C}.Release|Any CPU.ActiveCfg = Release|x64 - {2B359162-062E-3C52-91D3-027A8542A58C}.Release|x64.ActiveCfg = Release|x64 + {265C2E6F-04E6-37A8-B504-E3DD4A3FEE06}.Debug|Any CPU.ActiveCfg = Debug|x64 + {265C2E6F-04E6-37A8-B504-E3DD4A3FEE06}.Debug|x64.ActiveCfg = Debug|x64 + {265C2E6F-04E6-37A8-B504-E3DD4A3FEE06}.Release|Any CPU.ActiveCfg = Release|x64 + {265C2E6F-04E6-37A8-B504-E3DD4A3FEE06}.Release|x64.ActiveCfg = Release|x64 {E4C0DBEE-0815-311B-9065-137BB50BD793}.Debug|Any CPU.ActiveCfg = Debug|x64 {E4C0DBEE-0815-311B-9065-137BB50BD793}.Debug|x64.ActiveCfg = Debug|x64 {E4C0DBEE-0815-311B-9065-137BB50BD793}.Release|Any CPU.ActiveCfg = Release|x64 @@ -181,7 +181,7 @@ Global {6C323B05-9028-4B09-911C-3C03AE058BEE} = {AED9C836-31E3-4F3F-8ABC-929555D3F3C4} {42B45168-476D-4BFA-87B8-81A34E6295CD} = {09EADF06-BE25-4228-AB53-95AE3E15B530} {567456AD-B026-4CB6-B98D-4FC930C90223} = {D3D38B03-B557-484D-8348-8BADEE4DF592} - {2B359162-062E-3C52-91D3-027A8542A58C} = {CF2C1A9E-3A8A-4329-8A6E-7880C15AAC3D} + {265C2E6F-04E6-37A8-B504-E3DD4A3FEE06} = {CF2C1A9E-3A8A-4329-8A6E-7880C15AAC3D} {E4C0DBEE-0815-311B-9065-137BB50BD793} = {4DB9E84D-324C-408F-87A6-246E86205540} {CF2C1A9E-3A8A-4329-8A6E-7880C15AAC3D} = {09EADF06-BE25-4228-AB53-95AE3E15B530} {D8C60CD8-8429-45F2-A755-47B6CD10FDF8} = {09EADF06-BE25-4228-AB53-95AE3E15B530} diff --git a/src/TorchSharp/Utils/TensorAccessor.cs b/src/TorchSharp/Utils/TensorAccessor.cs index f0050c928..f7f825ffc 100644 --- a/src/TorchSharp/Utils/TensorAccessor.cs +++ b/src/TorchSharp/Utils/TensorAccessor.cs @@ -61,11 +61,9 @@ public T[] ToArray() for(int i=0;i(_tensor_data_ptr.ToPointer(), Convert.ToInt32(TempCount)).ToArray(); - } + if (_tensor.is_contiguous()) { //This is very fast. And work VERY WELL + unsafe { + return new Span(_tensor_data_ptr.ToPointer(), Convert.ToInt32(TempCount)).ToArray(); } } var result = new T[TempCount]; From a9a611aeecfa85b75cc51021f2eeef0145493b5d Mon Sep 17 00:00:00 2001 From: Dimitri Date: Fri, 12 Jul 2024 13:43:16 -0300 Subject: [PATCH 13/65] GradScaler --- src/Native/LibTorchSharp/CMakeLists.txt | 2 + src/Native/LibTorchSharp/THSAmp.cpp | 15 +++ src/Native/LibTorchSharp/THSAmp.h | 13 ++ src/Native/LibTorchSharp/THSTensor.cpp | 13 ++ src/Native/LibTorchSharp/THSTensor.h | 3 + src/TorchSharp/Amp/GradScaler.cs | 121 +++++++++++++++--- .../PInvoke/LibTorchSharp.THSAmp.cs | 15 +++ .../PInvoke/LibTorchSharp.THSTensor.cs | 5 + .../PInvoke/LibTorchSharp.THSTorchCuda.cs | 2 + src/TorchSharp/Tensor/Tensor.cs | 29 +++++ src/TorchSharp/Tensor/torch.Amp.cs | 17 +++ 11 files changed, 216 insertions(+), 19 deletions(-) create mode 100644 src/Native/LibTorchSharp/THSAmp.cpp create mode 100644 src/Native/LibTorchSharp/THSAmp.h create mode 100644 src/TorchSharp/PInvoke/LibTorchSharp.THSAmp.cs create mode 100644 src/TorchSharp/Tensor/torch.Amp.cs diff --git a/src/Native/LibTorchSharp/CMakeLists.txt b/src/Native/LibTorchSharp/CMakeLists.txt index a592475ad..c0852a2a1 100644 --- a/src/Native/LibTorchSharp/CMakeLists.txt +++ b/src/Native/LibTorchSharp/CMakeLists.txt @@ -9,6 +9,7 @@ find_package(Torch REQUIRED PATHS ${LIBTORCH_PATH}) set(SOURCES cifar10.h crc32c.h + THSAmp.h THSAutograd.h THSData.h THSJIT.h @@ -21,6 +22,7 @@ set(SOURCES cifar10.cpp crc32c.c THSActivation.cpp + THSAmp.cpp THSAutograd.cpp THSConvolution.cpp THSData.cpp diff --git a/src/Native/LibTorchSharp/THSAmp.cpp b/src/Native/LibTorchSharp/THSAmp.cpp new file mode 100644 index 000000000..56ea1ac18 --- /dev/null +++ b/src/Native/LibTorchSharp/THSAmp.cpp @@ -0,0 +1,15 @@ +// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information. +#include "THSAmp.h" + +#include +#include + +/*void THSAmp_amp_foreach_non_finite_check_and_unscale_(const at::TensorList self, at::Tensor& found_inf, const at::Tensor& inv_scale) +{ + torch::_amp_foreach_non_finite_check_and_unscale_(self, found_inf, inv_scale); +}*/ + +void THSAmp_amp_foreach_non_finite_check_and_unscale_(Tensor* self, const int64_t tLength, at::Tensor& found_inf, const at::Tensor& inv_scale) +{ + torch::_amp_foreach_non_finite_check_and_unscale_(toTensors((torch::Tensor**)self, tLength),found_inf,inv_scale); +} diff --git a/src/Native/LibTorchSharp/THSAmp.h b/src/Native/LibTorchSharp/THSAmp.h new file mode 100644 index 000000000..c85eb0609 --- /dev/null +++ b/src/Native/LibTorchSharp/THSAmp.h @@ -0,0 +1,13 @@ +// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information. +#pragma once + +#include "../Stdafx.h" + +#include "torch/torch.h" + +#include "Utils.h" + +//https://github.com/pytorch/pytorch/blob/main/torch/_meta_registrations.py#L5957 +//EXPORT_API(void) THSAmp_amp_foreach_non_finite_check_and_unscale_(const at::TensorList self, at::Tensor& found_inf, const at::Tensor& inv_scale); + +EXPORT_API(void) THSAmp_amp_foreach_non_finite_check_and_unscale_(Tensor* self, const int64_t tLength, at::Tensor& found_inf, const at::Tensor& inv_scale); diff --git a/src/Native/LibTorchSharp/THSTensor.cpp b/src/Native/LibTorchSharp/THSTensor.cpp index 5a41bdca0..970dbdeb6 100644 --- a/src/Native/LibTorchSharp/THSTensor.cpp +++ b/src/Native/LibTorchSharp/THSTensor.cpp @@ -2226,3 +2226,16 @@ Tensor THSTensor_unflatten_names(Tensor tensor, const char** names, const int64_ return nullptr; } + +bool THSTensor_is_coalesce(Tensor tensor) +{ + return tensor->is_coalesced(); +} + +Tensor THSTensor_coalesce(Tensor tensor) +{ + CATCH( + return ResultTensor(tensor->coalesce()); + ); + return nullptr; +} \ No newline at end of file diff --git a/src/Native/LibTorchSharp/THSTensor.h b/src/Native/LibTorchSharp/THSTensor.h index 36468d995..b889ca055 100644 --- a/src/Native/LibTorchSharp/THSTensor.h +++ b/src/Native/LibTorchSharp/THSTensor.h @@ -1743,3 +1743,6 @@ EXPORT_API(Tensor) THSTensor_kaiser_window(const int64_t len, bool periodic, dou EXPORT_API(Tensor) THSTensor_stft(const Tensor x, int64_t n_fft, int64_t hop_length, int64_t win_length, const Tensor window, bool normalized, int64_t onesided, bool return_complex); EXPORT_API(Tensor) THSTensor_istft(const Tensor x, int64_t n_fft, int64_t hop_length, int64_t win_length, const Tensor window, bool center, bool normalized, int64_t onesided, int64_t length, bool return_complex); + +EXPORT_API(Tensor) THSTensor_coalesce(const Tensor x); +EXPORT_API(bool) THSTensor_is_coalesce(const Tensor x); \ No newline at end of file diff --git a/src/TorchSharp/Amp/GradScaler.cs b/src/TorchSharp/Amp/GradScaler.cs index 6da7a9dab..ac10ef6ea 100644 --- a/src/TorchSharp/Amp/GradScaler.cs +++ b/src/TorchSharp/Amp/GradScaler.cs @@ -4,6 +4,7 @@ using System.Linq; using System.Text; using System.Threading.Tasks; +using TorchSharp.Modules; namespace TorchSharp.Amp { @@ -20,19 +21,19 @@ public GradScaler(torch.Device dev, float init_scale = 2.0e16f, float growth_fac float backoff_factor = 0.5f, int growth_interval = 2000, bool enabled = true) { Debug.Assert(dev == torch.CPU || dev == torch.CUDA); - this.Enabled = enabled; - this.InitScale = init_scale; - this.GrowthFactor = growth_factor; - this.BackoffFactor = backoff_factor; - this.GrowthInterval = growth_interval; - this.InitGrowthTracker = 0.0f; + Enabled = enabled; + InitScale = init_scale; + GrowthFactor = growth_factor; + BackoffFactor = backoff_factor; + GrowthInterval = growth_interval; + InitGrowthTracker = 0.0f; throw new NotImplementedException(); } private void LazyInitScaleGrowthTracker(torch.Device dev) { - this._scale = torch.full(0, this.InitScale, torch.ScalarType.Float32, device: dev); - this._growth_tracker = torch.full(0, this.InitGrowthTracker, torch.ScalarType.Float32, device: dev); + _scale = torch.full(0, InitScale, torch.ScalarType.Float32, device: dev); + _growth_tracker = torch.full(0, InitGrowthTracker, torch.ScalarType.Int32, device: dev); } //private check_scale_growth_tracker @@ -40,27 +41,109 @@ public torch.Tensor scale(torch.Tensor output) { if (!Enabled) return output; - if (_scale.numel() == 0) - this.LazyInitScaleGrowthTracker(output.device); - return output * this._scale.to(output.device, output.dtype, true); + if (_scale.is_null()) + LazyInitScaleGrowthTracker(output.device); + return output * _scale.to(output.device, output.dtype, true); } - public torch.Tensor unscale_grads(torch.optim.Optimizer optimizer, torch.Tensor inv_scale, torch.Tensor found_inf, bool allow_fp16) + public IList scale(IList outputs) { - return false; + apply_scale(outputs); + return outputs; } + private class MultiDeviceReplicator + { + private torch.Tensor master; - public void unscale(torch.optim.Optimizer optimizer) + internal Dictionary per_device_tensors = new Dictionary(); + public MultiDeviceReplicator(torch.Tensor master_tensor) + { + master = master_tensor; + } + + public torch.Tensor Get(torch.Device device) + { + torch.Tensor retval=null; + if (!per_device_tensors.ContainsKey(device)) { + retval = master.to(device, true, non_blocking: true); + per_device_tensors.Add(device, retval); + } + return retval; + } + } + + private torch.Tensor apply_scale(torch.Tensor scale) { - if (!Enabled) - return; + IList stash = new List(); + if (stash.Count == 0) { + if (_scale.is_null()) { + LazyInitScaleGrowthTracker(scale.device); + } + stash.Add(new MultiDeviceReplicator(_scale)); + } + return scale * stash[0].Get(scale.device); + } - + private void apply_scale(IList scales) + { + for (int i = 0; i < scales.Count; i++) + scales[i] = apply_scale(scales[i]); } - /*public IList scale(IList outputs) + public Dictionary unscale_grads(torch.optim.Optimizer optimizer, torch.Tensor inv_scale, torch.Tensor found_inf, bool allow_fp16) { + var per_device_inv_scale = new MultiDeviceReplicator(inv_scale); + var per_device_found_inf= new MultiDeviceReplicator(found_inf); + Dictionary>> per_device_and_dtype_grads = new Dictionary>>(); + + using (torch.no_grad()) { + if (optimizer is AdamW adamW){ //Some optimizer have parameter tensor for unscale_grads i need that. + using (var enumer = adamW.parameters().GetEnumerator()) { + while (enumer.MoveNext()) { + var param = enumer.Current; + if (param.is_null()) + continue; + if (!allow_fp16 && param.dtype == torch.ScalarType.Float16) + throw new Exception("Attempting to unscale FP16 Gradients"); + torch.Tensor to_unscale; + if (param.grad.is_sparse) { + if (param.grad.dtype == torch.ScalarType.Float16) { + + param.grad = param.grad.coalesce(); + } + + to_unscale = param.grad.SparseValues; + } else { + to_unscale = param.grad; + } + if (!per_device_and_dtype_grads.ContainsKey(to_unscale.device)) { + per_device_and_dtype_grads.Add(to_unscale.device, new Dictionary>()); + per_device_and_dtype_grads[to_unscale.device].Add(to_unscale.dtype, new List()); + per_device_and_dtype_grads[to_unscale.device][to_unscale.dtype].Add(to_unscale); + } else { + if (!per_device_and_dtype_grads[to_unscale.device].ContainsKey(to_unscale.dtype)) { + per_device_and_dtype_grads[to_unscale.device].Add(to_unscale.dtype, new List()); + } else { + per_device_and_dtype_grads[to_unscale.device][to_unscale.dtype].Add(to_unscale); + } + } - }*/ + } + } + + foreach (var d in per_device_and_dtype_grads) + foreach (var g in d.Value) + torch._amp_foreach_non_finite_check_and_unscale_(g.Value, per_device_found_inf.Get(d.Key), per_device_inv_scale.Get(d.Key)); + } + } + + return per_device_found_inf.per_device_tensors; + } + + public void unscale(torch.optim.Optimizer optimizer) + { + if (!Enabled) + return; + } } } \ No newline at end of file diff --git a/src/TorchSharp/PInvoke/LibTorchSharp.THSAmp.cs b/src/TorchSharp/PInvoke/LibTorchSharp.THSAmp.cs new file mode 100644 index 000000000..5b1716bf3 --- /dev/null +++ b/src/TorchSharp/PInvoke/LibTorchSharp.THSAmp.cs @@ -0,0 +1,15 @@ +// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information. +#nullable enable +using System; +using System.Collections.Generic; +using System.Runtime.InteropServices; + +namespace TorchSharp.PInvoke +{ + internal static partial class NativeMethods + { + [DllImport("LibTorchSharp")] + internal static extern void THSAmp_amp_foreach_non_finite_check_and_unscale_(IntPtr tensors, long tLength, IntPtr found_inf, IntPtr inv_scale); + + } +} \ No newline at end of file diff --git a/src/TorchSharp/PInvoke/LibTorchSharp.THSTensor.cs b/src/TorchSharp/PInvoke/LibTorchSharp.THSTensor.cs index 173ccd48a..2428223d9 100644 --- a/src/TorchSharp/PInvoke/LibTorchSharp.THSTensor.cs +++ b/src/TorchSharp/PInvoke/LibTorchSharp.THSTensor.cs @@ -2110,6 +2110,11 @@ internal static extern IntPtr THSTensor_upsample_nearest3d(IntPtr input, internal static extern IntPtr THSTensor_histogram_out_t(IntPtr input, IntPtr bins, IntPtr weight, bool density, out IntPtr hist, out IntPtr bin_edges, out IntPtr r_bin_edges); [DllImport("LibTorchSharp")] internal static extern IntPtr THSTensor_histogram_out_i(IntPtr input, long bins, IntPtr range, int length, IntPtr weight, bool density, out IntPtr hist, out IntPtr bin_edges, out IntPtr r_bin_edges); + + [DllImport("LibTorchSharp")] + internal static extern IntPtr THSTensor_coalesce(IntPtr input); + [DllImport("LibTorchSharp")] + internal static extern bool THSTensor_is_coalesce(IntPtr input); } #pragma warning restore CA2101 } diff --git a/src/TorchSharp/PInvoke/LibTorchSharp.THSTorchCuda.cs b/src/TorchSharp/PInvoke/LibTorchSharp.THSTorchCuda.cs index fc67a88de..531b47d76 100644 --- a/src/TorchSharp/PInvoke/LibTorchSharp.THSTorchCuda.cs +++ b/src/TorchSharp/PInvoke/LibTorchSharp.THSTorchCuda.cs @@ -19,5 +19,7 @@ internal static partial class NativeMethods [DllImport("LibTorchSharp")] internal static extern void THSTorchCuda_synchronize(long device_index); + + } } diff --git a/src/TorchSharp/Tensor/Tensor.cs b/src/TorchSharp/Tensor/Tensor.cs index 81f97cafa..167fcb738 100644 --- a/src/TorchSharp/Tensor/Tensor.cs +++ b/src/TorchSharp/Tensor/Tensor.cs @@ -261,6 +261,7 @@ internal IntPtr MoveHandle() /// public long numel() => NumberOfElements; + public bool is_null() => handle == IntPtr.Zero; /// /// Get the size of each element in the tensor. /// @@ -294,6 +295,21 @@ public bool is_nonzero() return res != 0; } + public bool is_coalesce() + { + var res = NativeMethods.THSTensor_is_coalesce(Handle); + CheckForErrors(); + return res; + } + + public Tensor coalesce() + { + var res = NativeMethods.THSTensor_coalesce(Handle); + if(res == IntPtr.Zero) + CheckForErrors(); + return new Tensor(res); + } + public bool is_cuda => device.type == DeviceType.CUDA; public bool is_meta => device.type == DeviceType.META; @@ -716,6 +732,7 @@ public bool is_sparse { public void backward(IList? grad_tensors = null, bool create_graph = false, bool retain_graph = false, IList? inputs = null) => torch.autograd.backward(new[] { this }, grad_tensors, create_graph, retain_graph, inputs); + /// /// Creates a tensor by loading it from a file. /// @@ -7427,5 +7444,17 @@ public static Tensor WrappedTensorDisposeScope(Func expr) var result = expr(); return result.MoveToOuterDisposeScope(); } + + public static void _amp_foreach_non_finite_check_and_unscale(Tensor found_inf, Tensor inv_scale) + { + if (found_inf.numel() == 1) + throw new Exception("found_inf must be a 1-element tensor."); + if (found_inf.numel() == 1) + throw new Exception("found_inf must be a 1-element tensor."); + if (found_inf.numel() == 1) + throw new Exception("found_inf must be a 1-element tensor."); + if (found_inf.numel() == 1) + throw new Exception("found_inf must be a 1-element tensor."); + } } } \ No newline at end of file diff --git a/src/TorchSharp/Tensor/torch.Amp.cs b/src/TorchSharp/Tensor/torch.Amp.cs new file mode 100644 index 000000000..dfa4245fd --- /dev/null +++ b/src/TorchSharp/Tensor/torch.Amp.cs @@ -0,0 +1,17 @@ +using System; +using System.Collections.Generic; +using System.Linq; +using static TorchSharp.PInvoke.NativeMethods; + +namespace TorchSharp +{ + public static partial class torch + { + public static void _amp_foreach_non_finite_check_and_unscale_(IList tensors, Tensor found_inf, Tensor inv_scale) + { + using var ts = new PinnedArray(); + IntPtr tens = ts.CreateArray(tensors.Select(x => x.Handle).ToArray()); + THSAmp_amp_foreach_non_finite_check_and_unscale_(tens, ts.Array.Length, found_inf.Handle, inv_scale.Handle); + } + } +} From 4a406ece7e7b9a0119300cb2230c6c02b9712b2b Mon Sep 17 00:00:00 2001 From: Dimitri Date: Sun, 14 Jul 2024 14:50:13 -0300 Subject: [PATCH 14/65] Trying fix build for azure --- .../FileRestitcher.Tests/FileRestitcher.Tests.csproj | 8 ++++++-- src/Examples/Examples.csproj | 7 +++++-- src/TorchSharp/Torch.cs | 2 +- src/TorchVision/models/VGG.cs | 6 +++--- .../TorchSharpTest.WithCudaBinaries.csproj | 1 + test/TorchSharpTest/TorchSharpTest.csproj | 1 + 6 files changed, 17 insertions(+), 8 deletions(-) diff --git a/pkg/FileRestitcher/FileRestitcher.Tests/FileRestitcher.Tests.csproj b/pkg/FileRestitcher/FileRestitcher.Tests/FileRestitcher.Tests.csproj index 37f37a9bb..39dc54a1b 100644 --- a/pkg/FileRestitcher/FileRestitcher.Tests/FileRestitcher.Tests.csproj +++ b/pkg/FileRestitcher/FileRestitcher.Tests/FileRestitcher.Tests.csproj @@ -1,4 +1,4 @@ - + false @@ -14,7 +14,11 @@ - + + runtime; build; native; contentfiles; analyzers; buildtransitive + all + + runtime; build; native; contentfiles; analyzers; buildtransitive all diff --git a/src/Examples/Examples.csproj b/src/Examples/Examples.csproj index 10d6171e7..37ec4b75d 100644 --- a/src/Examples/Examples.csproj +++ b/src/Examples/Examples.csproj @@ -5,9 +5,12 @@ true true - net472;netstandard2.0;$(TargetFrameworks) + 9.0 - net6.0 + + net6.0 true false false diff --git a/src/TorchSharp/Torch.cs b/src/TorchSharp/Torch.cs index 6a6bbec0f..d10254a2c 100644 --- a/src/TorchSharp/Torch.cs +++ b/src/TorchSharp/Torch.cs @@ -158,7 +158,7 @@ private static void LoadNativeBackend(bool useCudaBackend, out StringBuilder? tr var torchsharpLoc = Path.GetDirectoryName(typeof(torch).Assembly.Location); var packagesDir = Path.GetFullPath(Path.Combine(torchsharpLoc!, "..", "..", "..", "..")); var torchsharpHome = Path.GetFullPath(Path.Combine(torchsharpLoc!, "..", "..")); - + //torchsharpLoc = @"K:\Proyects_Repos\TorchSharp"; trace.AppendLine($" torchsharpLoc = {torchsharpLoc}"); trace.AppendLine($" packagesDir = {packagesDir}"); trace.AppendLine($" torchsharpHome = {torchsharpHome}"); diff --git a/src/TorchVision/models/VGG.cs b/src/TorchVision/models/VGG.cs index e79f9ddec..cb6ff9f7f 100644 --- a/src/TorchVision/models/VGG.cs +++ b/src/TorchVision/models/VGG.cs @@ -332,9 +332,9 @@ public class VGG : Module { "VGG19", new long[] { 64, 64, 0, 128, 128, 0, 256, 256, 256, 256, 0, 512, 512, 512, 512, 0, 512, 512, 512, 512, 0 } } }; - private readonly Module features; - private readonly Module avgpool; - private readonly Module classifier; + public readonly Module features; + public readonly Module avgpool; + public readonly Module classifier; protected override void Dispose(bool disposing) { diff --git a/test/TorchSharpTest.WithCudaBinaries/TorchSharpTest.WithCudaBinaries.csproj b/test/TorchSharpTest.WithCudaBinaries/TorchSharpTest.WithCudaBinaries.csproj index 055fb9ffc..c7ef48fd8 100644 --- a/test/TorchSharpTest.WithCudaBinaries/TorchSharpTest.WithCudaBinaries.csproj +++ b/test/TorchSharpTest.WithCudaBinaries/TorchSharpTest.WithCudaBinaries.csproj @@ -12,6 +12,7 @@ false trx $(OutputPath) + Debug;Release;LibTorch2.3.1 diff --git a/test/TorchSharpTest/TorchSharpTest.csproj b/test/TorchSharpTest/TorchSharpTest.csproj index 2de45fe06..d0d7ace08 100644 --- a/test/TorchSharpTest/TorchSharpTest.csproj +++ b/test/TorchSharpTest/TorchSharpTest.csproj @@ -13,6 +13,7 @@ trx $(OutputPath) 10.0 + Debug;Release;LibTorch2.3.1 From 280c8d59df7db5990efc6fe27d1bd474f27abf1a Mon Sep 17 00:00:00 2001 From: Dimitri Date: Tue, 16 Jul 2024 23:03:16 -0300 Subject: [PATCH 15/65] Range sequential --- src/Examples/Examples.csproj | 4 ++-- src/TorchSharp/Amp/AutocastManager.cs | 11 +++++++++++ src/TorchSharp/Amp/GradScaler.cs | 19 ++++++++++++++++--- src/TorchSharp/NN/Sequential.cs | 7 ++++++- .../Tensor/Factories/Tensor.Factories.cs | 6 +++--- test/TorchSharpTest/TorchSharpTest.csproj | 3 +-- 6 files changed, 39 insertions(+), 11 deletions(-) create mode 100644 src/TorchSharp/Amp/AutocastManager.cs diff --git a/src/Examples/Examples.csproj b/src/Examples/Examples.csproj index 37ec4b75d..9b7a980b9 100644 --- a/src/Examples/Examples.csproj +++ b/src/Examples/Examples.csproj @@ -5,8 +5,8 @@ true true - + + net472;netstandard2.0;$(TargetFrameworks) 9.0 diff --git a/src/TorchSharp/Amp/AutocastManager.cs b/src/TorchSharp/Amp/AutocastManager.cs new file mode 100644 index 000000000..d1808d316 --- /dev/null +++ b/src/TorchSharp/Amp/AutocastManager.cs @@ -0,0 +1,11 @@ +using System; +using System.Collections.Generic; +using System.Text; + +namespace TorchSharp.Amp +{ + public class AutocastManager + { + + } +} diff --git a/src/TorchSharp/Amp/GradScaler.cs b/src/TorchSharp/Amp/GradScaler.cs index ac10ef6ea..060ad64ee 100644 --- a/src/TorchSharp/Amp/GradScaler.cs +++ b/src/TorchSharp/Amp/GradScaler.cs @@ -11,11 +11,10 @@ namespace TorchSharp.Amp public class GradScaler { private bool Enabled; - private torch.Tensor _scale, _growth_tracker; - private float InitScale, GrowthFactor, BackoffFactor, GrowthInterval, InitGrowthTracker; + private Dictionary> _per_optimizer_states = new Dictionary>(); //https://github.com/pytorch/pytorch/blob/main/torch/amp/grad_scaler.py public GradScaler(torch.Device dev, float init_scale = 2.0e16f, float growth_factor = 2.0f, float backoff_factor = 0.5f, int growth_interval = 2000, bool enabled = true) @@ -27,7 +26,8 @@ public GradScaler(torch.Device dev, float init_scale = 2.0e16f, float growth_fac BackoffFactor = backoff_factor; GrowthInterval = growth_interval; InitGrowthTracker = 0.0f; - throw new NotImplementedException(); + + throw new NotImplementedException("This need to finish"); } private void LazyInitScaleGrowthTracker(torch.Device dev) @@ -35,6 +35,7 @@ private void LazyInitScaleGrowthTracker(torch.Device dev) _scale = torch.full(0, InitScale, torch.ScalarType.Float32, device: dev); _growth_tracker = torch.full(0, InitGrowthTracker, torch.ScalarType.Int32, device: dev); } + //private Dictionary //private check_scale_growth_tracker public torch.Tensor scale(torch.Tensor output) @@ -140,10 +141,22 @@ private void apply_scale(IList scales) return per_device_found_inf.per_device_tensors; } + private Tuple check_scale_growth_tracker(string name) + { + var fix = "This may indicate your script did not use scaler.scale(loss or outputs) earlier in the iteration."; + Debug.Assert(_scale.is_null(), $"Attempted {name} but {nameof(_scale)} is None {fix}"); + Debug.Assert(_growth_tracker.is_null(), $"Attempted {name} but {nameof(_growth_tracker)} is None {fix}"); + return new Tuple(_scale, _growth_tracker); + } + public void unscale(torch.optim.Optimizer optimizer) { if (!Enabled) return; + + check_scale_growth_tracker(nameof(unscale)); + + } } } \ No newline at end of file diff --git a/src/TorchSharp/NN/Sequential.cs b/src/TorchSharp/NN/Sequential.cs index 711be65d1..2796aa913 100644 --- a/src/TorchSharp/NN/Sequential.cs +++ b/src/TorchSharp/NN/Sequential.cs @@ -31,7 +31,6 @@ public Sequential append(string name, torch.nn.IModule module) Add(name, module); return this; } - internal void Add(string name, torch.nn.IModule sm) { var submodule = (torch.nn.Module)sm; @@ -51,6 +50,12 @@ public Sequential append(torch.nn.IModule module) return this; } + public Sequential append(IList> modules) + { + for (int i = 0; i < modules.Count; i++) + Add(_modules.Count.ToString(), modules[i]); + return this; + } internal void Add(torch.nn.IModule module) { var name = _modules.Count.ToString(); diff --git a/src/TorchSharp/Tensor/Factories/Tensor.Factories.cs b/src/TorchSharp/Tensor/Factories/Tensor.Factories.cs index 67c28bd10..eee072261 100644 --- a/src/TorchSharp/Tensor/Factories/Tensor.Factories.cs +++ b/src/TorchSharp/Tensor/Factories/Tensor.Factories.cs @@ -165,7 +165,7 @@ private static Tensor _tensor_generic(Array rawArray, ReadOnlySpan dimensi unsafe { void *ptr = null; - IntPtr iPtr = (IntPtr)ptr; + IntPtr iPtr = (IntPtr)ptr; //Warning: Unused variable fixed (long* shape = dimensions) { var handle = THSTensor_new(dataArrayAddr, deleter, (IntPtr)shape, dimensions.Length, origType, (sbyte)dtype.Value, (int)device.type, device.index, requires_grad); @@ -224,8 +224,8 @@ private static Tensor _tensor_generic(Memory rawArray, ReadOnlySpan deleters.TryAdd(deleter, deleter); // keep the delegate alive void *ptr = null; - IntPtr iPtr = (IntPtr)ptr; - + IntPtr iPtr = (IntPtr)ptr; //Warning: Unused variable + fixed (long* shape = dimensions) { var handle = THSTensor_new(dataArrayAddr, deleter, (IntPtr)shape, dimensions.Length, origType, (sbyte)dtype.Value, (int)device.type, device.index, requires_grad); diff --git a/test/TorchSharpTest/TorchSharpTest.csproj b/test/TorchSharpTest/TorchSharpTest.csproj index d0d7ace08..808aa1ccf 100644 --- a/test/TorchSharpTest/TorchSharpTest.csproj +++ b/test/TorchSharpTest/TorchSharpTest.csproj @@ -114,7 +114,7 @@ - + @@ -123,7 +123,6 @@ - true true From 3c42a87bf4770d04fda2f67fc7ce1bca826b5598 Mon Sep 17 00:00:00 2001 From: Dimitri Date: Fri, 19 Jul 2024 17:00:57 -0300 Subject: [PATCH 16/65] AMPManager --- src/TorchSharp/Amp/AMPManager.cs | 89 ++++++++++++++++++ src/TorchSharp/Amp/AutocastDisposeManager.cs | 29 ------ src/TorchSharp/Amp/AutocastDisposeScope.cs | 23 ----- src/TorchSharp/Amp/AutocastManager.cs | 11 --- src/TorchSharp/Amp/AutocastMode.cs | 97 ++++++++++++++------ src/TorchSharp/Amp/GradScaler.cs | 7 +- src/TorchSharp/NN/Convolution/Conv1D.cs | 28 +++++- src/TorchSharp/NN/Convolution/Conv2D.cs | 60 +++++++++++- src/TorchSharp/NN/Module.cs | 10 ++ src/TorchSharp/NN/Parameter.cs | 13 +++ src/TorchSharp/Tensor/Tensor.cs | 13 ++- src/TorchSharp/Utils/ModuleInfo.cs | 46 ++++++++++ src/TorchSharp/Utils/UnorderedMap.cs | 55 +++++++++++ 13 files changed, 376 insertions(+), 105 deletions(-) create mode 100644 src/TorchSharp/Amp/AMPManager.cs delete mode 100644 src/TorchSharp/Amp/AutocastDisposeManager.cs delete mode 100644 src/TorchSharp/Amp/AutocastDisposeScope.cs delete mode 100644 src/TorchSharp/Amp/AutocastManager.cs create mode 100644 src/TorchSharp/Utils/ModuleInfo.cs create mode 100644 src/TorchSharp/Utils/UnorderedMap.cs diff --git a/src/TorchSharp/Amp/AMPManager.cs b/src/TorchSharp/Amp/AMPManager.cs new file mode 100644 index 000000000..1ac24476a --- /dev/null +++ b/src/TorchSharp/Amp/AMPManager.cs @@ -0,0 +1,89 @@ +using System; +using System.Collections.Generic; +using System.Runtime.InteropServices; +using System.Text; +using Google.Protobuf.WellKnownTypes; +using TorchSharp.PInvoke; +using TorchSharp.Utils; + +namespace TorchSharp.Amp +{ + public class AMPManager : IDisposable + { + //TODO: Make Singleton THREADSAFE + public UnorderedMap TensorPtrs; + private readonly AutocastMode autocastMode = AutocastMode.GetInstance(); + + private AMPManager() { } + + public bool IsEnabled => autocastMode.Enabled; + private static AMPManager Instance; + //bool disposedValue; + + public static AMPManager GetInstance() + { + return Instance ??= new AMPManager(); + } + + private void To(IntPtr ptr, torch.ScalarType type) + { + var res = NativeMethods.THSTensor_to_type(ptr, (sbyte)type); + if (res == IntPtr.Zero) + torch.CheckForErrors(); + } + private void Revert() + { + using (var enumer = TensorPtrs.GetEnumerator()) + while (enumer.MoveNext()) + To(enumer.Current.Key, enumer.Current.Value); + TensorPtrs.Clear(); //Or should use Stack for POP?? May better performance and better ram usage + } + + public void Add(IntPtr ptr) + { + if (!autocastMode.Enabled) { + + if (TensorPtrs.ContainsKey(ptr)) + To(ptr, TensorPtrs[ptr]); + return; + } + + TensorPtrs[ptr] = (torch.ScalarType)NativeMethods.THSTensor_type(ptr); + To(ptr, autocastMode.GetFastType()); //TODO: Set scalar autocast + } + + public IDisposable Enter() + { + return null; + } + protected virtual void Dispose(bool disposing) + { + Revert(); + autocastMode.Dispose(); + /*if (!disposedValue) { + if (disposing) { + + + // TODO: dispose managed state (managed objects) + } + + // TODO: free unmanaged resources (unmanaged objects) and override finalizer + // TODO: set large fields to null + disposedValue = true; + }*/ + } + + // // TODO: override finalizer only if 'Dispose(bool disposing)' has code to free unmanaged resources + ~AMPManager() + { + Dispose(false); + } + + public void Dispose() + { + // Do not change this code. Put cleanup code in 'Dispose(bool disposing)' method + Dispose(disposing: true); + GC.SuppressFinalize(this); + } + } +} diff --git a/src/TorchSharp/Amp/AutocastDisposeManager.cs b/src/TorchSharp/Amp/AutocastDisposeManager.cs deleted file mode 100644 index 83c31f335..000000000 --- a/src/TorchSharp/Amp/AutocastDisposeManager.cs +++ /dev/null @@ -1,29 +0,0 @@ -using System; -using System.Collections.Generic; -using System.Text; - -namespace TorchSharp.Amp -{ - public class AutocastDisposeManager - { - - /*[ThreadStatic] private static AutocastDisposeManager _threadAutocastSingleton; - - internal static AutocastDisposeManager ThreadAutocastSingleton => _threadAutocastSingleton ??= new AutocastDisposeManager(); - - internal AutocastDisposeScope CurrentAutocastDispose; - //internal HashSet Modules = new List(); - public AutocastDisposeManager() - { - CurrentAutocastDispose = new AutocastDisposeScope(this); - } - internal AutocastDisposeScope RegisterTensorAutocastScope(torch.Tensor t) - { - if (CurrentAutocastDispose == null) - return null; - CurrentAutocastDispose.Tensors.Add(t); - return CurrentAutocastDispose; - }*/ - - } -} diff --git a/src/TorchSharp/Amp/AutocastDisposeScope.cs b/src/TorchSharp/Amp/AutocastDisposeScope.cs deleted file mode 100644 index 8f5df9490..000000000 --- a/src/TorchSharp/Amp/AutocastDisposeScope.cs +++ /dev/null @@ -1,23 +0,0 @@ -using System; -using System.Collections.Generic; -using System.Text; - -namespace TorchSharp.Amp -{ - public sealed class AutocastDisposeScope : IDisposable - { - //private AutocastDisposeManager autocastDisposeManager; - public bool IsEnabled; - /*internal AutocastMode autocastMode = AutocastMode.GetInstance(); - internal HashSet Tensors = new HashSet(); - public AutocastDisposeScope(AutocastDisposeManager autocastDisposeManager) - { - this.autocastDisposeManager = autocastDisposeManager; - IsEnabled = true; - }*/ - public void Dispose() - { - IsEnabled = false; - } - } -} diff --git a/src/TorchSharp/Amp/AutocastManager.cs b/src/TorchSharp/Amp/AutocastManager.cs deleted file mode 100644 index d1808d316..000000000 --- a/src/TorchSharp/Amp/AutocastManager.cs +++ /dev/null @@ -1,11 +0,0 @@ -using System; -using System.Collections.Generic; -using System.Text; - -namespace TorchSharp.Amp -{ - public class AutocastManager - { - - } -} diff --git a/src/TorchSharp/Amp/AutocastMode.cs b/src/TorchSharp/Amp/AutocastMode.cs index 07c8149d2..0287e02d6 100644 --- a/src/TorchSharp/Amp/AutocastMode.cs +++ b/src/TorchSharp/Amp/AutocastMode.cs @@ -1,6 +1,7 @@ using System; using System.Collections.Generic; using System.Linq; +using System.Security.Cryptography; using System.Text; using System.Threading.Tasks; @@ -17,22 +18,33 @@ public static torch.Tensor AutoCast(this torch.Tensor input) public sealed class AutocastMode : IDisposable { //NEED "Register" all tensor in scope for uncasting outer-scope - private bool Enabled, Prev; + internal bool Enabled, Prev; //private torch.ScalarType Dtype = torch.ScalarType.Float32; - private torch.ScalarType fast_dtype = torch.ScalarType.Float32; - private torch.Device Device = new torch.Device(DeviceType.CUDA); + internal torch.ScalarType fast_dtype = torch.ScalarType.Float32; + public torch.Device Device = new torch.Device(DeviceType.CUDA); private static AutocastMode instance; + bool disposedValue; + /*public static AutocastMode GetInstance(torch.Device dev, torch.ScalarType? dtype = null, bool enabled = true, bool? cache_enabled = null) - { - if(instance ==null) - instance = new AutocastMode(dev, dtype, enabled, cache_enabled); - return instance; - }*/ +{ +if(instance ==null) +instance = new AutocastMode(dev, dtype, enabled, cache_enabled); +return instance; +}*/ public static AutocastMode GetInstance() { return instance ??= new AutocastMode(torch.CUDA, cache_enabled:true); } + public torch.ScalarType GetFastType() + { + var ft = torch.ScalarType.Float32; + if (Device.type == DeviceType.CUDA) + ft = torch.get_autocast_gpu_dtype(); + if (Device.type == DeviceType.CPU) + ft = torch.get_autocast_cpu_dtype(); + return ft; + } private AutocastMode(torch.Device dev, torch.ScalarType? dtype = null, bool enabled=true, bool? cache_enabled = null) { //var la = torch.tensor(9); @@ -78,32 +90,57 @@ internal torch.Tensor CastTensor(torch.Tensor tensor) return tensor; return tensor.to(fast_dtype, tensor.device); } - /*public IDisposable Enter() - { - return this; - }*/ - public void Dispose() + private void Dispose(bool disposing) { - this.Enabled = false; - if (Device.type == DeviceType.CUDA) { - if(torch.autocast_decrement_nesting() == 0) - torch.clear_autocast_cache(); - torch.set_autocast_gpu_dtype(this.fast_dtype); - //torch.set_autocast_enabled(this.Prev); - torch.set_autocast_enabled(false); - torch.set_autocast_cache_enabled(false); - } + if (!disposedValue) { + if (disposing) { - if (Device.type == DeviceType.CPU) { - if (torch.autocast_decrement_nesting() == 0) - torch.clear_autocast_cache(); - //torch.set_autocast_enabled(this.Prev); - torch.set_autocast_cpu_dtype(this.fast_dtype); - torch.set_autocast_enabled(false); - torch.set_autocast_cache_enabled(false); + this.Enabled = false; + if (Device.type == DeviceType.CUDA) { + if (torch.autocast_decrement_nesting() == 0) + torch.clear_autocast_cache(); + torch.set_autocast_gpu_dtype(this.fast_dtype); + //torch.set_autocast_enabled(this.Prev); + torch.set_autocast_enabled(false); + torch.set_autocast_cache_enabled(false); + } + + if (Device.type == DeviceType.CPU) { + if (torch.autocast_decrement_nesting() == 0) + torch.clear_autocast_cache(); + //torch.set_autocast_enabled(this.Prev); + torch.set_autocast_cpu_dtype(this.fast_dtype); + torch.set_autocast_enabled(false); + torch.set_autocast_cache_enabled(false); + } + //throw new NotImplementedException(); + // TODO: dispose managed state (managed objects) + } + + // TODO: free unmanaged resources (unmanaged objects) and override finalizer + // TODO: set large fields to null + disposedValue = true; } - //throw new NotImplementedException(); } + + // // TODO: override finalizer only if 'Dispose(bool disposing)' has code to free unmanaged resources + // ~AutocastMode() + // { + // // Do not change this code. Put cleanup code in 'Dispose(bool disposing)' method + // Dispose(disposing: false); + // } + + public void Dispose() + { + // Do not change this code. Put cleanup code in 'Dispose(bool disposing)' method + Dispose(disposing: true); + GC.SuppressFinalize(this); + } + /*public IDisposable Enter() +{ + + return this; +}*/ } } diff --git a/src/TorchSharp/Amp/GradScaler.cs b/src/TorchSharp/Amp/GradScaler.cs index 060ad64ee..899c295cb 100644 --- a/src/TorchSharp/Amp/GradScaler.cs +++ b/src/TorchSharp/Amp/GradScaler.cs @@ -13,7 +13,6 @@ public class GradScaler private bool Enabled; private torch.Tensor _scale, _growth_tracker; private float InitScale, GrowthFactor, BackoffFactor, GrowthInterval, InitGrowthTracker; - private Dictionary> _per_optimizer_states = new Dictionary>(); //https://github.com/pytorch/pytorch/blob/main/torch/amp/grad_scaler.py public GradScaler(torch.Device dev, float init_scale = 2.0e16f, float growth_factor = 2.0f, @@ -54,9 +53,9 @@ public torch.Tensor scale(torch.Tensor output) } private class MultiDeviceReplicator { - private torch.Tensor master; + private readonly torch.Tensor master; - internal Dictionary per_device_tensors = new Dictionary(); + internal readonly Dictionary per_device_tensors = new Dictionary(); public MultiDeviceReplicator(torch.Tensor master_tensor) { master = master_tensor; @@ -155,8 +154,6 @@ public void unscale(torch.optim.Optimizer optimizer) return; check_scale_growth_tracker(nameof(unscale)); - - } } } \ No newline at end of file diff --git a/src/TorchSharp/NN/Convolution/Conv1D.cs b/src/TorchSharp/NN/Convolution/Conv1D.cs index 9e9706e07..cf381af20 100644 --- a/src/TorchSharp/NN/Convolution/Conv1D.cs +++ b/src/TorchSharp/NN/Convolution/Conv1D.cs @@ -27,6 +27,10 @@ namespace Modules { public abstract class Convolution : torch.nn.Module { + internal long _dimension, _in_channel, _out_channel, _kernel,_stride, _padding,_dilation,_groups; + internal PaddingModes _paddingModes; + internal (long, long)? _kernels, _strides, _paddings, _dilations; + internal bool _bias; protected Convolution(IntPtr handle, IntPtr boxedHandle, long input_channels) : base(handle, boxedHandle) { this.input_channels = input_channels; @@ -113,7 +117,17 @@ public static Conv1d Conv1d(long in_channels, long out_channels, long kernelSize { var res = THSNN_Conv1d_ctor(in_channels, out_channels, kernelSize, stride, padding, dilation, (long)padding_mode, groups, bias, out var boxedHandle); if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Conv1d(res, boxedHandle, in_channels).MoveModule(device, dtype); + return new Conv1d(res, boxedHandle, in_channels) { + _in_channel = in_channels, + _out_channel = out_channels, + _kernel = kernelSize, + _stride = stride, + _padding = padding, + _dilation = dilation, + _paddingModes = padding_mode, + _groups = groups, + _bias = bias + }.MoveModule(device, dtype); } /// @@ -135,7 +149,17 @@ public static Conv1d Conv1d(long in_channels, long out_channels, long kernelSize { var res = THSNN_Conv1d_ctor(in_channels, out_channels, kernelSize, stride, padding == Padding.Valid ? 0 : -1, dilation, (long)padding_mode, groups, bias, out var boxedHandle); if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Conv1d(res, boxedHandle, in_channels).MoveModule(device, dtype); + return new Conv1d(res, boxedHandle, in_channels) { + _in_channel = in_channels, + _out_channel = out_channels, + _kernel = kernelSize, + _stride = stride, + _padding = (long)padding, + _dilation = dilation, + _paddingModes = padding_mode, + _groups = groups, + _bias = bias + }.MoveModule(device, dtype); } public static partial class functional diff --git a/src/TorchSharp/NN/Convolution/Conv2D.cs b/src/TorchSharp/NN/Convolution/Conv2D.cs index 28b37eef2..1143db639 100644 --- a/src/TorchSharp/NN/Convolution/Conv2D.cs +++ b/src/TorchSharp/NN/Convolution/Conv2D.cs @@ -12,8 +12,37 @@ namespace Modules { public sealed class Conv2d : Convolution { + internal Conv2d(IntPtr handle, IntPtr boxedHandle, long input_channels) : base(handle, boxedHandle, input_channels) { } + internal Conv2d(IntPtr handle, IntPtr boxedHandle, long input_channels, long in_channels, long out_channels, long kernelSize, long padding, long stride = 1, long dilation = 1, PaddingModes padding_mode = PaddingModes.Zeros, long groups = 1, bool bias = true) + : base(handle, boxedHandle, input_channels) + { + _dimension = 2; //because is conv 2D; 2 dimension + _in_channel = in_channels; + _out_channel = out_channels; + _kernel = kernelSize; + _stride = stride; + _padding = padding; + _dilation = dilation; + _paddingModes = padding_mode; + _groups = groups; + _bias = bias; + } + internal Conv2d(IntPtr handle, IntPtr boxedHandle, long input_channels, long in_channels, long out_channels, (long, long) kernelSize, Padding padding, (long, long)? stride = null, (long, long)? dilation = null, PaddingModes padding_mode = PaddingModes.Zeros, long groups = 1, bool bias = true) + : base(handle, boxedHandle, input_channels) + { + _dimension = 2; //because is conv 2D; 2 dimension + _in_channel = in_channels; + _out_channel = out_channels; + _kernels = kernelSize; + _strides = stride; + _padding = (long)padding; + _dilations = dilation; + _paddingModes = padding_mode; + _groups = groups; + _bias = bias; + } public override Tensor forward(Tensor input) { if (ValidateShape(input, 2)) { @@ -78,7 +107,19 @@ public static Conv2d Conv2d(long in_channels, long out_channels, long kernelSize { var res = THSNN_Conv2d_ctor(in_channels, out_channels, kernelSize, stride, padding, dilation, (long)padding_mode, groups, bias, out var boxedHandle); if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Conv2d(res, boxedHandle, in_channels).MoveModule(device, dtype); + + return new Conv2d(res, boxedHandle, in_channels) { + _in_channel = in_channels, + _out_channel = out_channels, + _kernel = kernelSize, + _stride = stride, + _padding = padding, + _dilation = dilation, + _paddingModes = padding_mode, + _groups = groups, + _bias = bias + }.MoveModule(device, dtype); + //return conv2d.MoveModule(device, dtype); } /// @@ -104,7 +145,17 @@ public static Conv2d Conv2d(long in_channels, long out_channels, (long, long) ke var res = THSNN_Conv2d_ctor_1(in_channels, out_channels, kernelSize.Item1, kernelSize.Item2, stride.Value.Item1, stride.Value.Item2, padding.Value.Item1, padding.Value.Item2, dilation.Value.Item1, dilation.Value.Item2, (long)padding_mode, groups, bias, out var boxedHandle); if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Conv2d(res, boxedHandle, in_channels).MoveModule(device, dtype); + return new Conv2d(res, boxedHandle, in_channels) { + _in_channel = in_channels, + _out_channel = out_channels, + _kernels = kernelSize, + _strides = stride, + _paddings = padding, + _dilations = dilation, + _paddingModes = padding_mode, + _groups = groups, + _bias = bias + }.MoveModule(device, dtype); } /// @@ -126,7 +177,7 @@ public static Conv2d Conv2d(long in_channels, long out_channels, long kernelSize { var res = THSNN_Conv2d_ctor(in_channels, out_channels, kernelSize, stride, padding == Padding.Valid ? 0 : -1, dilation, (long)padding_mode, groups, bias, out var boxedHandle); if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Conv2d(res, boxedHandle, in_channels).MoveModule(device, dtype); + return new Conv2d(res, boxedHandle, in_channels, in_channels, out_channels, kernelSize, (long)padding, stride, dilation, padding_mode, groups, bias).MoveModule(device, dtype); } /// @@ -151,7 +202,8 @@ public static Conv2d Conv2d(long in_channels, long out_channels, (long, long) ke var res = THSNN_Conv2d_ctor_1(in_channels, out_channels, kernelSize.Item1, kernelSize.Item2, stride.Value.Item1, stride.Value.Item2, padding == Padding.Valid ? 0 : -1, 0, dilation.Value.Item1, dilation.Value.Item2, (long)padding_mode, groups, bias, out var boxedHandle); if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Conv2d(res, boxedHandle, in_channels).MoveModule(device, dtype); + + return new Conv2d(res, boxedHandle, in_channels, in_channels, out_channels, kernelSize, padding,stride, dilation, padding_mode ,groups,bias).MoveModule(device, dtype); } public static partial class functional diff --git a/src/TorchSharp/NN/Module.cs b/src/TorchSharp/NN/Module.cs index 1398ab4e3..19b64d8a9 100644 --- a/src/TorchSharp/NN/Module.cs +++ b/src/TorchSharp/NN/Module.cs @@ -778,6 +778,16 @@ public virtual void register_module(string name, Module submodule) } } + public virtual void unregister_module(string name) + { + if (_internal_submodules.ContainsKey(name)) + _internal_submodules.Remove(name); + } + public virtual void unregister_module(Module module) + { + unregister_module(module.GetName()); + } + protected void ConditionallyRegisterParameter(string name, Tensor value) { if (value is null) { diff --git a/src/TorchSharp/NN/Parameter.cs b/src/TorchSharp/NN/Parameter.cs index 81e9051d8..cd3b66b44 100644 --- a/src/TorchSharp/NN/Parameter.cs +++ b/src/TorchSharp/NN/Parameter.cs @@ -36,6 +36,19 @@ internal Parameter(System.IntPtr handle) : base(handle) { } + /// + /// For prevent cast as torch.Tensor i provided the data method for get Tensor. + /// https://github.com/ultralytics/ultralytics/blob/dcde8bd23d12bbb4867ebf45f936dd37c2445974/ultralytics/nn/modules/conv.py#L78 + /// + /// + public torch.Tensor data { + get { + return new Tensor(base.handle); + } + set { + handle = value.handle; + } + } }; } diff --git a/src/TorchSharp/Tensor/Tensor.cs b/src/TorchSharp/Tensor/Tensor.cs index 167fcb738..601544619 100644 --- a/src/TorchSharp/Tensor/Tensor.cs +++ b/src/TorchSharp/Tensor/Tensor.cs @@ -34,11 +34,13 @@ public partial class Tensor : IDisposable static long _peakCount = 0; internal DisposeScope? OwningDisposeScope { get; set; } + //internal AutocastDisposeScope? AutocastDisposeScope; internal Tensor(IntPtr handle) { this.handle = handle; - + if (AMPManager.GetInstance().IsEnabled) + AMPManager.GetInstance().Add(handle); //MMM.... This is the more abstract of any method Tensor right???? /*if (_totalCount > 0) { //have used AutocastDisposeScope = AutocastDisposeManager.ThreadAutocastSingleton.RegisterTensorAutocastScope(this); @@ -922,6 +924,15 @@ public Tensor to(ScalarType type, torch.Device device, bool copy = false, bool d return new Tensor(res); } + /*internal static void to(this IntPtr ptr, ScalarType type) + { + var res = NativeMethods.THSTensor_to_type(ptr, (sbyte)type); + if (res == IntPtr.Zero) + CheckForErrors(); + if (disposeAfter) + this.Dispose(); + return new Tensor(res); + }*/ public Tensor to(torch.Device device, ScalarType type, bool non_blocking) { torch.InitializeDevice(device); diff --git a/src/TorchSharp/Utils/ModuleInfo.cs b/src/TorchSharp/Utils/ModuleInfo.cs new file mode 100644 index 000000000..800dc977d --- /dev/null +++ b/src/TorchSharp/Utils/ModuleInfo.cs @@ -0,0 +1,46 @@ +using System; +using System.Collections.Generic; +using System.Text; +using TorchSharp.Modules; + +namespace TorchSharp.Utils +{ + public static class ModuleInfo + { + + public class ConvInfo + { + public long Dimension,InChannel,OutChannel, PaddingMode; + public object Kernel, Dilation, Stride; + public ConvInfo(Convolution conv) + { + InChannel = conv._in_channel; + OutChannel = conv._out_channel; + if (conv._kernels.HasValue) { + Kernel = conv._kernels.Value; + } + else { + Kernel = conv._kernel; + } + + //TODO: Make all props; + throw new NotImplementedException("Need finish"); + } + + public (long, long)? CastTuple(object obj) + { + if (obj.GetType() == typeof((long,long))) + return obj as (long, long)?; + if (obj is long l) + return (l, l); + return null; + } + + public long CastValue(object obj) + { + var v = CastTuple(obj); + return v?.Item1 ?? 0; + } + } + } +} diff --git a/src/TorchSharp/Utils/UnorderedMap.cs b/src/TorchSharp/Utils/UnorderedMap.cs new file mode 100644 index 000000000..7db88a94c --- /dev/null +++ b/src/TorchSharp/Utils/UnorderedMap.cs @@ -0,0 +1,55 @@ +using System; +using System.Collections.Generic; +using System.Text; + +namespace TorchSharp.Utils +{ + public class UnorderedMap : Dictionary, IDisposable + { + bool disposedValue; + + public UnorderedMap() { } + public new TValue this[TKey tk] { + get { + if (this.ContainsKey(tk)) + return base[tk]; + return default(TValue); + } + set { + if (!this.ContainsKey(tk)) { + this.Add(tk, value); + return; + } + base[tk] = value; + } + } + + protected virtual void Dispose(bool disposing) + { + if (!disposedValue) { + if (disposing) { + base.Clear(); + // TODO: dispose managed state (managed objects) + } + + // TODO: free unmanaged resources (unmanaged objects) and override finalizer + // TODO: set large fields to null + disposedValue = true; + } + } + + // // TODO: override finalizer only if 'Dispose(bool disposing)' has code to free unmanaged resources + // ~UnorderedMap() + // { + // // Do not change this code. Put cleanup code in 'Dispose(bool disposing)' method + // Dispose(disposing: false); + // } + + public void Dispose() + { + // Do not change this code. Put cleanup code in 'Dispose(bool disposing)' method + Dispose(disposing: true); + GC.SuppressFinalize(this); + } + } +} From 7cd7f9cfecfdb2e3958e1638f89899638d99836e Mon Sep 17 00:00:00 2001 From: Dimitri Date: Sat, 20 Jul 2024 00:13:24 -0300 Subject: [PATCH 17/65] Amp --- src/TorchSharp/Amp/AMPManager.cs | 4 ++-- src/TorchSharp/Tensor/Tensor.cs | 5 +++-- 2 files changed, 5 insertions(+), 4 deletions(-) diff --git a/src/TorchSharp/Amp/AMPManager.cs b/src/TorchSharp/Amp/AMPManager.cs index 1ac24476a..29c5da90c 100644 --- a/src/TorchSharp/Amp/AMPManager.cs +++ b/src/TorchSharp/Amp/AMPManager.cs @@ -11,7 +11,7 @@ namespace TorchSharp.Amp public class AMPManager : IDisposable { //TODO: Make Singleton THREADSAFE - public UnorderedMap TensorPtrs; + public UnorderedMap TensorPtrs= new UnorderedMap(); private readonly AutocastMode autocastMode = AutocastMode.GetInstance(); private AMPManager() { } @@ -36,7 +36,6 @@ private void Revert() using (var enumer = TensorPtrs.GetEnumerator()) while (enumer.MoveNext()) To(enumer.Current.Key, enumer.Current.Value); - TensorPtrs.Clear(); //Or should use Stack for POP?? May better performance and better ram usage } public void Add(IntPtr ptr) @@ -60,6 +59,7 @@ protected virtual void Dispose(bool disposing) { Revert(); autocastMode.Dispose(); + TensorPtrs.Dispose(); /*if (!disposedValue) { if (disposing) { diff --git a/src/TorchSharp/Tensor/Tensor.cs b/src/TorchSharp/Tensor/Tensor.cs index 601544619..0e5b76537 100644 --- a/src/TorchSharp/Tensor/Tensor.cs +++ b/src/TorchSharp/Tensor/Tensor.cs @@ -39,8 +39,9 @@ public partial class Tensor : IDisposable internal Tensor(IntPtr handle) { this.handle = handle; - if (AMPManager.GetInstance().IsEnabled) - AMPManager.GetInstance().Add(handle); //MMM.... This is the more abstract of any method Tensor right???? + /*if (AMPManager.GetInstance().IsEnabled) + AMPManager.GetInstance().Add(handle); //MMM.... This is the more abstract of any method Tensor right????*/ + /*if (_totalCount > 0) { //have used AutocastDisposeScope = AutocastDisposeManager.ThreadAutocastSingleton.RegisterTensorAutocastScope(this); From 0c2769a28ab805dc14fc5344e9e47c8edc4e239e Mon Sep 17 00:00:00 2001 From: Dimitri Date: Sun, 21 Jul 2024 14:50:54 -0300 Subject: [PATCH 18/65] fix azure devops? --- .gitignore | 24 +- .../FileRestitcher.csproj.nuget.dgspec.json | 96 ++++++ .../FileRestitcher.csproj.nuget.g.props | 16 + .../FileRestitcher.csproj.nuget.g.targets | 6 + .../project.assets.json | 276 ++++++++++++++++++ .../project.nuget.cache | 11 + src/Native/build.cmd | 151 ++++++++++ src/TorchSharp/NN/Linear.cs | 19 +- src/TorchVision/models/ResNet.cs | 4 +- 9 files changed, 576 insertions(+), 27 deletions(-) create mode 100644 pkg/FileRestitcher/FileRestitcher/FileRestitcher.NupkgProj/FileRestitcher.csproj.nuget.dgspec.json create mode 100644 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_echo @echo off +setlocal + +:: Store current script directory before %~dp0 gets affected by another process later. +set __currentScriptDir=%~dp0 + +:SetupArgs +:: Initialize the args that will be passed to cmake +set __binDir=%__currentScriptDir%..\..\bin +set __rootDir=%__currentScriptDir%..\.. +set __CMakeBinDir="" +set __IntermediatesDir="" +set __BuildArch=x64 +set __VCBuildArch=x86_amd64 +set CMAKE_BUILD_TYPE=Debug +set LIBTORCH_PATH="" + +:Arg_Loop +if [%1] == [] goto :ToolsVersion +if /i [%1] == [Release] ( set CMAKE_BUILD_TYPE=Release&&shift&goto Arg_Loop) +if /i [%1] == [Debug] ( set CMAKE_BUILD_TYPE=Debug&&shift&goto Arg_Loop) + +if /i [%1] == [x86] ( set __BuildArch=x86&&set __VCBuildArch=x86&&shift&goto Arg_Loop) +if /i [%1] == [x64] ( set __BuildArch=x64&&set __VCBuildArch=x86_amd64&&shift&goto Arg_Loop) +if /i [%1] == [amd64] ( set __BuildArch=x64&&set __VCBuildArch=x86_amd64&&shift&goto Arg_Loop) + +if /i [%1] == [--libtorchpath] ( set LIBTORCH_PATH=%2&&shift&goto Arg_Loop) + +shift +goto :Arg_Loop + +:ToolsVersion +if defined VisualStudioVersion goto :RunVCVars + +set _VSWHERE="%ProgramFiles(x86)%\Microsoft Visual Studio\Installer\vswhere.exe" +if exist %_VSWHERE% ( + for /f "usebackq tokens=*" %%i in (`%_VSWHERE% -latest -prerelease -property installationPath`) do set _VSCOMNTOOLS=%%i\Common7\Tools +) +if not exist "%_VSCOMNTOOLS%" set _VSCOMNTOOLS=%VS140COMNTOOLS% +if not exist "%_VSCOMNTOOLS%" goto :MissingVersion + + +set "VSCMD_START_DIR=%__currentScriptDir%" +call "%_VSCOMNTOOLS%\VsDevCmd.bat" + +:RunVCVars +if "%VisualStudioVersion%"=="17.0" ( + goto :VS2022 +) else if "%VisualStudioVersion%"=="16.0" ( + goto :VS2019 +) else if "%VisualStudioVersion%"=="15.0" ( + goto :VS2017 +) else if "%VisualStudioVersion%"=="14.0" ( + goto :VS2015 +) + +:MissingVersion +:: Can't find VS 2015, 2017 or 2019 +echo Error: Visual Studio 2015, 2017 or 2019 required +echo Please see https://github.com/dotnet/machinelearning/tree/master/Documentation for build instructions. +exit /b 1 + +:VS2022 +:: Setup vars for VS2022 +set __PlatformToolset=v143 +set __VSVersion=17 2022 +if NOT "%__BuildArch%" == "arm64" ( + :: Set the environment for the native build + call "%VS160COMNTOOLS%..\..\VC\Auxiliary\Build\vcvarsall.bat" %__VCBuildArch% +) +goto :SetupDirs + +:VS2019 +:: Setup vars for VS2019 +set __PlatformToolset=v142 +set __VSVersion=16 2019 +if NOT "%__BuildArch%" == "arm64" ( + :: Set the environment for the native build + call "%VS160COMNTOOLS%..\..\VC\Auxiliary\Build\vcvarsall.bat" %__VCBuildArch% +) +goto :SetupDirs + +:VS2017 +:: Setup vars for VS2017 +set __PlatformToolset=v141 +set __VSVersion=15 2017 +if NOT "%__BuildArch%" == "arm64" ( + :: Set the environment for the native build + call "%VS150COMNTOOLS%..\..\VC\Auxiliary\Build\vcvarsall.bat" %__VCBuildArch% +) +goto :SetupDirs + +:VS2015 +:: Setup vars for VS2015build +set __PlatformToolset=v140 +set __VSVersion=14 2015 +if NOT "%__BuildArch%" == "arm64" ( + :: Set the environment for the native build + call "%VS140COMNTOOLS%..\..\VC\vcvarsall.bat" %__VCBuildArch% +) + +:SetupDirs +:: Setup to cmake the native components +echo Commencing native build of dotnet/machinelearning +echo. + +if %__CMakeBinDir% == "" ( + set "__CMakeBinDir=%__binDir%\%__BuildArch%.%CMAKE_BUILD_TYPE%\Native" +) +if %__IntermediatesDir% == "" ( + set "__IntermediatesDir=%__binDir%\obj\%__BuildArch%.%CMAKE_BUILD_TYPE%\Native" +) +set "__CMakeBinDir=%__CMakeBinDir:\=/%" +set "__IntermediatesDir=%__IntermediatesDir:\=/%" + +:: Check that the intermediate directory exists so we can place our cmake build tree there +if not exist "%__IntermediatesDir%" md "%__IntermediatesDir%" + +:: Regenerate the VS solution + +set "__gen-buildsys-win-path=%__currentScriptDir%\gen-buildsys-win.bat" +set "__source-code-path=%__currentScriptDir%" + +echo Calling "%__gen-buildsys-win-path%" "%__source-code-path%" "%__VSVersion%" %__BuildArch% +pushd "%__IntermediatesDir%" +call "%__gen-buildsys-win-path%" "%__source-code-path%" "%__VSVersion%" %__BuildArch% +popd + +:CheckForProj +:: Check that the project created by Cmake exists +if exist "%__IntermediatesDir%\INSTALL.vcxproj" goto BuildNativeProj +goto :Failure + +:BuildNativeProj +:: Build the project created by Cmake +set __msbuildArgs=/p:Platform=%__BuildArch% /p:PlatformToolset="%__PlatformToolset%" + +cd %__rootDir% + +echo msbuild "%__IntermediatesDir%\INSTALL.vcxproj" /t:build /p:Configuration=%CMAKE_BUILD_TYPE% %__msbuildArgs% +call msbuild "%__IntermediatesDir%\INSTALL.vcxproj" /t:build /p:Configuration=%CMAKE_BUILD_TYPE% %__msbuildArgs% +IF ERRORLEVEL 1 ( + goto :Failure +) +echo Done building Native components +exit /B 0 + +:Failure +:: Build failed +echo Failed to generate native component build project! +exit /b 1 \ No newline at end of file diff --git a/src/TorchSharp/NN/Linear.cs b/src/TorchSharp/NN/Linear.cs index 4595582d7..e1b7b205c 100644 --- a/src/TorchSharp/NN/Linear.cs +++ b/src/TorchSharp/NN/Linear.cs @@ -11,10 +11,25 @@ namespace TorchSharp namespace Modules { + public class LinearInfo + { + public long InFeatures { get; } + public long OutFeatures { get; } + public LinearInfo(long inFeatures, long outFeatures) + { + InFeatures = inFeatures; + OutFeatures = outFeatures; + } + } public sealed class Linear : torch.nn.Module { - internal Linear(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) + public LinearInfo linearInfo; + /*internal Linear(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) + { + }*/ + internal Linear(IntPtr handle, IntPtr boxedHandle, long inFeat, long outFeat) : base(handle, boxedHandle) { + linearInfo = new LinearInfo(inFeat, outFeat); } public override Tensor forward(Tensor tensor) @@ -71,7 +86,7 @@ public static Linear Linear(long inputSize, long outputSize, bool hasBias = true var res = THSNN_Linear_ctor(inputSize, outputSize, hasBias, out var boxedHandle); if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Linear(res, boxedHandle).MoveModule(device, dtype); + return new Linear(res, boxedHandle, inputSize, outputSize).MoveModule(device, dtype); } public static partial class functional diff --git a/src/TorchVision/models/ResNet.cs b/src/TorchVision/models/ResNet.cs index 654d587c3..5eee7e5a2 100644 --- a/src/TorchVision/models/ResNet.cs +++ b/src/TorchVision/models/ResNet.cs @@ -581,7 +581,7 @@ public class ResNet : Module private readonly Module avgpool; private readonly Module flatten; - private readonly Module fc; + public readonly Module fc; private readonly Func> norm_layer; @@ -803,7 +803,7 @@ public ResNet(string name, break; } } - + if (zero_init_residual) { foreach (var (_, m) in named_modules()) { From eafdd1eccea359a27350c8c91af81f2631d0531e Mon Sep 17 00:00:00 2001 From: Dimitri Date: Sun, 21 Jul 2024 15:42:50 -0300 Subject: [PATCH 19/65] fix test? --- src/TorchSharp/Utils/FastTensorAccessor.cs | 712 +++++++++++++++++++++ src/TorchSharp/Utils/TensorAccessor.cs | 97 +-- test/TorchSharpTest/TorchSharpTest.csproj | 7 +- 3 files changed, 739 insertions(+), 77 deletions(-) create mode 100644 src/TorchSharp/Utils/FastTensorAccessor.cs diff --git a/src/TorchSharp/Utils/FastTensorAccessor.cs b/src/TorchSharp/Utils/FastTensorAccessor.cs new file mode 100644 index 000000000..142b95d6c --- /dev/null +++ b/src/TorchSharp/Utils/FastTensorAccessor.cs @@ -0,0 +1,712 @@ +using System; +using System.Collections; +using System.Collections.Generic; +using System.Diagnostics; +using System.Linq; +using System.Runtime.InteropServices; +using static TorchSharp.PInvoke.NativeMethods; + +namespace TorchSharp.Utils +{ + /// + /// TensorAccessor is used to present the contents of a tensor or tensor view to the .NET world as an ordered collection + /// of values that integrates well with things like LINQ and foreach loops in the .NET world. + /// + /// The type of the tensor elements. + public sealed class FastTensorAccessor : IDisposable, IEnumerable where T : unmanaged + { + internal FastTensorAccessor(torch.Tensor tensor) + { + if (tensor.device_type != DeviceType.CPU) { + throw new InvalidOperationException("Reading data from non-CPU memory is not supported. Move or copy the tensor to the cpu before reading."); + } + + var strides = tensor.stride(); + for (var i = 0; i < strides.Length; i++) { + if (strides[i] < 0) + throw new NotImplementedException($"Negative tensor strides are not currently supported. tensor.strides({i}) == {strides[i]}"); + } + + // Get the data from native code. + + unsafe { + var res = THSTensor_data(tensor.Handle); + if (res == IntPtr.Zero) { torch.CheckForErrors(); } + // NOTE: there is no safety here. + _tensor_data_ptr = res; + } + + _tensor = tensor; // Keep the tensor alive now that everything is alright. + } + + /// + /// This is important for performance because only called with CopyTo, CopyFrom. Is not necesary in each invocation call tensor.numel() because that use intensive CPU. + /// This temporary count avoid so much use CPU. The Property act as method. + /// If tensor is for example 640*640*3 = 1.228.800, property invoke 1 millons times!!! + /// If we only want copy is not necesary call that method so many times. + /// For some reason the method numel() use so much cpu. + /// + internal long TempCount = -1; + public long Count => _tensor?.numel() ?? 0; + + public bool IsReadOnly => false; + + public T[] ToArray() + { + if (_tensor.ndim < 2) + return (T[])ToNDArray(); + + var shps = _tensor.shape; + TempCount = 1; + for (int i = 0; i < shps.Length; i++) + TempCount *= shps[i]; //Theorically the numel is simple as product of each element shape + + if (_tensor.is_contiguous()) { //This is very fast. And work VERY WELL + unsafe { + return new Span(_tensor_data_ptr.ToPointer(), Convert.ToInt32(TempCount)).ToArray(); + } + } + var result = new T[TempCount]; + CopyTo(result); + return result; + } + + /// + /// Extract tensor data as a multi-dimensional .NET array, with the same number of dimensions as the tensor. + /// + /// An array object, which should be cast to the concrete array type. + public Array ToNDArray() + { + var shape = _tensor.shape; + var strides = _tensor.stride(); + switch (_tensor.ndim) { + default: + return ToNDArray(shape, strides); + case 0: + unsafe { + var result = new T[1]; + T* ptr = (T*)_tensor_data_ptr; + result[0] = ptr[0]; + return result; + } + case 1: + unsafe { + var result = new T[shape[0]]; + T* ptr = (T*)_tensor_data_ptr; + for (long i0 = 0, off0 = 0; i0 < shape[0]; i0++, off0 += strides[0]) { + result[i0] = ptr[off0]; + } + return result; + } + case 2: + unsafe { + var result = new T[shape[0], shape[1]]; + T* ptr = (T*)_tensor_data_ptr; + for (long i0 = 0, off0 = 0; i0 < shape[0]; i0++, off0 += strides[0]) { + for (long i1 = 0, off1 = off0; i1 < shape[1]; i1++, off1 += strides[1]) { + result[i0, i1] = ptr[off1]; + } + } + return result; + } + case 3: + unsafe { + var result = new T[shape[0], shape[1], shape[2]]; + T* ptr = (T*)_tensor_data_ptr; + for (long i0 = 0, off0 = 0; i0 < shape[0]; i0++, off0 += strides[0]) { + for (long i1 = 0, off1 = off0; i1 < shape[1]; i1++, off1 += strides[1]) { + for (long i2 = 0, off2 = off1; i2 < shape[2]; i2++, off2 += strides[2]) { + result[i0, i1, i2] = ptr[off2]; + } + } + } + return result; + } + case 4: + unsafe { + var result = new T[shape[0], shape[1], shape[2], shape[3]]; + T* ptr = (T*)_tensor_data_ptr; + for (long i0 = 0, off0 = 0; i0 < shape[0]; i0++, off0 += strides[0]) { + for (long i1 = 0, off1 = off0; i1 < shape[1]; i1++, off1 += strides[1]) { + for (long i2 = 0, off2 = off1; i2 < shape[2]; i2++, off2 += strides[2]) { + for (long i3 = 0, off3 = off2; i3 < shape[3]; i3++, off3 += strides[3]) { + result[i0, i1, i2, i3] = ptr[off3]; + } + } + } + } + return result; + } + case 5: + unsafe { + var result = new T[shape[0], shape[1], shape[2], shape[3], shape[4]]; + T* ptr = (T*)_tensor_data_ptr; + for (long i0 = 0, off0 = 0; i0 < shape[0]; i0++, off0 += strides[0]) { + for (long i1 = 0, off1 = off0; i1 < shape[1]; i1++, off1 += strides[1]) { + for (long i2 = 0, off2 = off1; i2 < shape[2]; i2++, off2 += strides[2]) { + for (long i3 = 0, off3 = off2; i3 < shape[3]; i3++, off3 += strides[3]) { + for (long i4 = 0, off4 = off3; i4 < shape[4]; i4++, off4 += strides[4]) { + result[i0, i1, i2, i3, i4] = ptr[off4]; + } + } + } + } + } + return result; + } + case 6: + unsafe { + var result = new T[shape[0], shape[1], shape[2], shape[3], shape[4], shape[5]]; + T* ptr = (T*)_tensor_data_ptr; + for (long i0 = 0, off0 = 0; i0 < shape[0]; i0++, off0 += strides[0]) { + for (long i1 = 0, off1 = off0; i1 < shape[1]; i1++, off1 += strides[1]) { + for (long i2 = 0, off2 = off1; i2 < shape[2]; i2++, off2 += strides[2]) { + for (long i3 = 0, off3 = off2; i3 < shape[3]; i3++, off3 += strides[3]) { + for (long i4 = 0, off4 = off3; i4 < shape[4]; i4++, off4 += strides[4]) { + for (long i5 = 0, off5 = off4; i5 < shape[5]; i5++, off5 += strides[5]) { + result[i0, i1, i2, i3, i4, i5] = ptr[off5]; + } + } + } + } + } + } + return result; + } + } + } + + private Array ToNDArray(long[] shape, long[] strides) + { + Array array = Array.CreateInstance(typeof(T), shape); + long[] indexes = new long[_tensor.ndim]; + long[] off = new long[_tensor.ndim]; + + while (true) { + unsafe { + T* ptr = (T*)_tensor_data_ptr; + array.SetValue(ptr[off[array.Rank - 1]], indexes); + } + + for (int i = array.Rank - 1; i >= 0; i--) { + if (indexes[i] < shape[i] - 1) { + indexes[i]++; + off[i] += strides[i]; + for (int j = i; j < array.Rank - 1; j++) + off[j + 1] = off[j]; + break; + } else { + if (i == 0) { + return array; + } + indexes[i] = 0; + } + } + } + } + + /// + /// Access elements of the underlying tensor / tensor view. + /// + /// A linear index into the data. + /// + public T this[params long[] indices] { + get { + long index = 0; + if (indices.Length == 1) { + index = indices[0]; + validate(index); + unsafe { + T* ptr = (T*)_tensor_data_ptr; + return ptr[TranslateIndex(index, _tensor)]; + } + } else { + unsafe { + T* ptr = (T*)_tensor_data_ptr; + return ptr[TranslateIndex(indices, _tensor)]; + } + } + } + set { + long index = 0; + if (indices.Length == 1) { + index = indices[0]; + validate(index); + unsafe { + T* ptr = (T*)_tensor_data_ptr; + ptr[TranslateIndex(indices, _tensor)] = value; + } + } else { + unsafe { + T* ptr = (T*)_tensor_data_ptr; + ptr[TranslateIndex(indices, _tensor)] = value; + } + } + } + } + + private void validate(long index) + { + if (index >= Count) throw new IndexOutOfRangeException(); + } + + public void CopyTo(T[] array, int arrayIndex = 0, long tensorIndex = 0) + { + int idx = arrayIndex; + /*if (_tensor.is_contiguous()) { + if (typeof(T) == typeof(float)) { + float[] ff = new float[TempCount]; + Marshal.Copy(_tensor_data_ptr, ff, 0,ff.Length); + } + }*/ + //Because the contiguous cause arange from tensorIndex to Numel. So is not necesary "create" array of arange, i said "create" because in fact enumerable do not create itself. Very cool. + if (_tensor.is_contiguous()) { + for (long i = tensorIndex; i < TempCount; i++) + unsafe { array[i] = ((T*)_tensor_data_ptr)[i]; } + return; + } + foreach (int offset in GetSubsequentIndices(tensorIndex)) { + if (idx >= array.Length) break; + unsafe { array[idx] = ((T*)_tensor_data_ptr)[offset]; } + idx += 1; + } + } + + public void CopyTo(Span array, int arrayIndex = 0, long tensorIndex = 0) + { + int idx = arrayIndex; + foreach (int offset in GetSubsequentIndices(tensorIndex)) { + if (idx >= array.Length) break; + unsafe { array[idx] = ((T*)_tensor_data_ptr)[offset]; } + idx += 1; + } + } + + public void CopyFrom(T[] array, int arrayIndex = 0, long tensorIndex = 0) + { + int idx = arrayIndex; + foreach (int offset in GetSubsequentIndices(tensorIndex)) { + if (idx >= array.Length) break; + unsafe { ((T*)_tensor_data_ptr)[offset] = array[idx]; } + idx += 1; + } + } + + public void CopyFrom(ReadOnlySpan array, int arrayIndex = 0, long tensorIndex = 0) + { + int idx = arrayIndex; + foreach (int offset in GetSubsequentIndices(tensorIndex)) { + if (idx >= array.Length) break; + unsafe { ((T*)_tensor_data_ptr)[offset] = array[idx]; } + idx += 1; + } + } + + /// + /// Translates a linear index within the span represented by the accessor to a linear index + /// used by the underlying tensor. The two should only be different if the tensor is a view + /// rather than an allocated tensor. + /// + private static long TranslateIndex(long idx, torch.Tensor tensor) + { + if (idx >= tensor.numel() || idx < 0) + throw new ArgumentOutOfRangeException($"{idx} in a collection of ${tensor.numel()} elements."); + + if (tensor.is_contiguous() || idx == 0) return idx; + + long result = 0; + var shape = tensor.shape; + var strides = tensor.stride(); + + for (var i = shape.Length - 1; i >= 0; i--) { + idx = Math.DivRem(idx, shape[i], out long s); + result += s * strides[i]; + } + + return result; + } + /// + /// WARNING: Test purpose not use in production + /// + private long TranslateIndexNonStatic(long idx, torch.Tensor tensor) + { + if (idx >= TempCount || idx < 0) + throw new ArgumentOutOfRangeException($"{idx} in a collection of ${tensor.numel()} elements."); + + if (tensor.is_contiguous() || idx == 0) return idx; + + long result = 0; + var shape = tensor.shape; + var strides = tensor.stride(); + + for (var i = shape.Length - 1; i >= 0; i--) { + idx = Math.DivRem(idx, shape[i], out long s); + result += s * strides[i]; + } + + return result; + } + private static long TranslateIndex(long[] idx, torch.Tensor tensor) + { + long result = 0; + var shape = tensor.shape; + var strides = tensor.stride(); + + for (var i = shape.Length - 1; i >= 0; i--) { + if (idx[i] >= shape[i] || idx[i] < 0) + throw new IndexOutOfRangeException($"{idx[i]} >= {shape[i]} in dimension {i}."); + result += idx[i] * strides[i]; + } + + return result; + } + + internal static T ReadItemAt(torch.Tensor tensor, long index) + { + if (tensor.device_type != DeviceType.CPU) { + throw new InvalidOperationException("Reading data from non-CPU memory is not supported. Move or copy the tensor to the cpu before reading."); + } + + tensor.ValidateType(typeof(T)); + + var strides = tensor.stride(); + for (var i = 0; i < strides.Length; i++) { + if (strides[i] < 0) + throw new NotImplementedException($"Negative tensor strides are not currently supported. tensor.strides({i}) == {strides[i]}"); + } + + unsafe { + var res = THSTensor_data(tensor.Handle); + if (res == IntPtr.Zero) { torch.CheckForErrors(); } + // NOTE: there is no safety here. + T* ptr = (T*)res; + return ptr[TranslateIndex(index, tensor)]; + } + } + + /// + /// Compare two tensors element-wise. + /// + /// A tensor + /// Another tensor + /// + public static bool operator ==(FastTensorAccessor left, FastTensorAccessor right) + { + if (left.Count != right.Count) return false; + + var lEnum = left.GetEnumerator(); + var rEnum = right.GetEnumerator(); + + while (lEnum.MoveNext() && rEnum.MoveNext()) { + if (!lEnum.Current.Equals(rEnum.Current)) + return false; + } + return true; + } + + /// + /// Compare two tensors element-wise. + /// + /// A tensor + /// Another tensor + /// + public static bool operator !=(FastTensorAccessor left, FastTensorAccessor right) + { + return !(left == right); + } + + + private IEnumerable GetSubsequentIndices(long startingIndex) + { + //TempCount = Count; + + if (startingIndex < 0 || startingIndex >= TempCount) + throw new ArgumentOutOfRangeException(nameof(startingIndex)); + + if (TempCount <= 1) { + if (TempCount == 0) { + return Enumerable.Empty(); + } + + return new List() { 0 }; + //return (new long[] { 0 }).AsEnumerable(); + } + + if (_tensor.is_contiguous()) { + return ContiguousIndices(startingIndex); + } + + var stride = _tensor.stride(); + Debug.Assert(stride.Length > 0); + + if (stride.Length == 1) { + return SimpleIndices(startingIndex, stride[0]); + } + + return MultiDimensionIndices(startingIndex); + } + private IEnumerable MultiDimensionIndices(long startingIndex) + { + long[] shape = _tensor.shape; + long[] stride = _tensor.stride(); + long[] inds = new long[stride.Length]; + + long index = startingIndex; + //long offset = TranslateIndex(startingIndex, _tensor); + long offset = TranslateIndexNonStatic(startingIndex, _tensor); //WARNING: Test purpose not use in production + + while (true) { + + index += 1; + + yield return offset; + + if (index >= TempCount) break; + + for (int i = inds.Length - 1; ; i--) { + Debug.Assert(i >= 0); + offset += stride[i]; + if (++inds[i] < shape[i]) + break; + + // Overflow of current dimension so rewind accordingly. + // Can't overflow the final (left-most) dimension. + Debug.Assert(i > 0); + // Note: for perf, this multiplication could be done once up front and cached in an array. + offset -= inds[i] * stride[i]; + inds[i] = 0; + } + } + } + + private IEnumerable SimpleIndices(long startingIndex, long stride) + { + long index = startingIndex; + //long offset = TranslateIndex(startingIndex, _tensor); + long offset = TranslateIndexNonStatic(startingIndex, _tensor); //WARNING: Test purpose not use in production + + while (index < TempCount) { + yield return offset; + offset += stride; + index += 1; + } + } + + private IEnumerable ContiguousIndices(long startingIndex) + { + // If there was an overload for Enumerable.Range that + // produced long integers, we wouldn't need this implementation. + + long index = startingIndex; + while (index < TempCount) { + yield return index; + index += 1; + } + } + + + /// + /// Compare two tensors element-wise. + /// + /// Another tensor + /// + public override bool Equals(object obj) + { + var left = this; + var right = obj as FastTensorAccessor; + if (right == null) return false; + + if (left._tensor_data_ptr == right._tensor_data_ptr) return true; + if (left.Count != right.Count) return false; + for (long i = 0; i < left.Count; i++) { + if (!left[i].Equals(right[i])) return false; + } + return true; + } + + public override int GetHashCode() + { + return base.GetHashCode(); + } + + IEnumerator IEnumerable.GetEnumerator() + { + return GetEnumerator(); + } + + public void Dispose() + { + Dispose(true); + GC.SuppressFinalize(this); + } + + private void Dispose(bool disposing) + { + _tensor_data_ptr = IntPtr.Zero; + // Clear the tensor that we've been keeping alive. + _tensor = null; + } + + private torch.Tensor _tensor; // Keeping it alive. + private IntPtr _tensor_data_ptr; + +#if true + public IEnumerator GetEnumerator() + { + if (TempCount <= 1) { + if (TempCount == 0) + return Enumerable.Empty().GetEnumerator(); + return new T[1] { this[0] }.AsEnumerable().GetEnumerator(); + } + /*if (Count <= 1) { + if (Count == 0) + return Enumerable.Empty().GetEnumerator(); + return new T[1] { this[0] }.AsEnumerable().GetEnumerator(); + }*/ + + if (_tensor.is_contiguous()) { + return new SimpleAtorImpl(this, 1); + } + + var stride = _tensor.stride(); + Debug.Assert(stride.Length > 0); + + if (stride.Length == 1) { + return new SimpleAtorImpl(this, stride[0]); + } + + return new GeneralAtorImpl(this, stride); + } + + private class SimpleAtorImpl : IEnumerator + { + private FastTensorAccessor _span; + private readonly long _count; + private readonly long _stride; + + // State. + private long _index; + private long _offset; + private T _current; + + public SimpleAtorImpl(FastTensorAccessor span, long stride) + { + _span = span; + _count = span.TempCount; + Debug.Assert(_count > 0); + _stride = stride; + Reset(); + } + + public T Current => _current; + object IEnumerator.Current => Current; + + public void Dispose() + { + _span = null; + Reset(); + } + + public bool MoveNext() + { + if (_index < 0) { + _index = 0; + _offset = 0; + } else if (++_index >= _count) { + Reset(); + return false; + } else { + _offset += _stride; + } + + unsafe { _current = ((T*)_span._tensor_data_ptr)[_offset]; } + return true; + } + + public void Reset() + { + _index = -1; + _offset = -1; + _current = default; + } + } + + private class GeneralAtorImpl : IEnumerator + { + private FastTensorAccessor _span; + private readonly long _count; + private readonly long[] _shape; + private readonly long[] _stride; + private readonly long[] _inds; + + // State. + private long _index; + private long _offset; + + public GeneralAtorImpl(FastTensorAccessor span, long[] stride) + { + Debug.Assert(stride.Length > 1); + _span = span; + _count = span.TempCount; + Debug.Assert(_count > 0); + _shape = span._tensor.shape; + Debug.Assert(_shape.Length == stride.Length); + _stride = stride; + _inds = new long[stride.Length]; + Reset(); + } + + public T Current { get; private set; } + + object IEnumerator.Current => Current; + + public void Dispose() + { + // Just clear the span field. + _span = null; + } + + public bool MoveNext() + { + if (_index < 0) { + _index = 0; + _offset = 0; + Array.Clear(_inds, 0, _inds.Length); + } else if (++_index >= _count) { + Reset(); + return false; + } else { + for (int i = _inds.Length - 1; ; i--) { + Debug.Assert(i >= 0); + _offset += _stride[i]; + if (++_inds[i] < _shape[i]) + break; + + // Overflow of current dimension so rewind accordingly. + // Can't overflow the final (left-most) dimension. + Debug.Assert(i > 0); + // Note: for perf, this multiplication could be done once up front and cached in an array. + _offset -= _inds[i] * _stride[i]; + _inds[i] = 0; + } + } + + unsafe { Current = ((T*)_span._tensor_data_ptr)[_offset]; } + return true; + } + + public void Reset() + { + _index = -1; + _offset = -1; + Current = default; + } + } +#else + public IEnumerator GetEnumerator() + { + return new TensorAccessorEnumerator(this); + } +#endif + } +} diff --git a/src/TorchSharp/Utils/TensorAccessor.cs b/src/TorchSharp/Utils/TensorAccessor.cs index f7f825ffc..31641529b 100644 --- a/src/TorchSharp/Utils/TensorAccessor.cs +++ b/src/TorchSharp/Utils/TensorAccessor.cs @@ -39,15 +39,7 @@ internal TensorAccessor(torch.Tensor tensor) _tensor = tensor; // Keep the tensor alive now that everything is alright. } - /// - /// This is important for performance because only called with CopyTo, CopyFrom. Is not necesary in each invocation call tensor.numel() because that use intensive CPU. - /// This temporary count avoid so much use CPU. The Property act as method. - /// If tensor is for example 640*640*3 = 1.228.800, property invoke 1 millons times!!! - /// If we only want copy is not necesary call that method so many times. - /// For some reason the method numel() use so much cpu. - /// - internal long TempCount = -1; - public long Count => _tensor?.numel() ?? 0; + public long Count => (_tensor is not null ? _tensor.numel() : 0); public bool IsReadOnly => false; @@ -56,17 +48,18 @@ public T[] ToArray() if (_tensor.ndim < 2) return (T[])ToNDArray(); - var shps = _tensor.shape; - TempCount = 1; - for(int i=0;i(_tensor_data_ptr.ToPointer(), Convert.ToInt32(TempCount)).ToArray(); } } - var result = new T[TempCount]; + + var result = new T[Count]; CopyTo(result); return result; } @@ -253,18 +246,6 @@ private void validate(long index) public void CopyTo(T[] array, int arrayIndex = 0, long tensorIndex = 0) { int idx = arrayIndex; - /*if (_tensor.is_contiguous()) { - if (typeof(T) == typeof(float)) { - float[] ff = new float[TempCount]; - Marshal.Copy(_tensor_data_ptr, ff, 0,ff.Length); - } - }*/ - //Because the contiguous cause arange from tensorIndex to Numel. So is not necesary "create" array of arange, i said "create" because in fact enumerable do not create itself. Very cool. - if (_tensor.is_contiguous()) { - for(long i= tensorIndex; i= array.Length) break; unsafe { array[idx] = ((T*)_tensor_data_ptr)[offset]; } @@ -325,27 +306,7 @@ private static long TranslateIndex(long idx, torch.Tensor tensor) return result; } - /// - /// WARNING: Test purpose not use in production - /// - private long TranslateIndexNonStatic(long idx, torch.Tensor tensor) - { - if (idx >= TempCount || idx < 0) - throw new ArgumentOutOfRangeException($"{idx} in a collection of ${tensor.numel()} elements."); - - if (tensor.is_contiguous() || idx == 0) return idx; - - long result = 0; - var shape = tensor.shape; - var strides = tensor.stride(); - - for (var i = shape.Length - 1; i >= 0; i--) { - idx = Math.DivRem(idx, shape[i], out long s); - result += s * strides[i]; - } - return result; - } private static long TranslateIndex(long[] idx, torch.Tensor tensor) { long result = 0; @@ -418,18 +379,15 @@ internal static T ReadItemAt(torch.Tensor tensor, long index) private IEnumerable GetSubsequentIndices(long startingIndex) { - //TempCount = Count; - - if (startingIndex < 0 || startingIndex >= TempCount) + if (startingIndex < 0 || startingIndex >= Count) throw new ArgumentOutOfRangeException(nameof(startingIndex)); - if (TempCount <= 1) { - if (TempCount == 0) { + if (Count <= 1) { + if (Count == 0) { return Enumerable.Empty(); } - return new List() { 0 }; - //return (new long[] { 0 }).AsEnumerable(); + return (new long[] { 0 }).AsEnumerable(); } if (_tensor.is_contiguous()) { @@ -445,6 +403,7 @@ private IEnumerable GetSubsequentIndices(long startingIndex) return MultiDimensionIndices(startingIndex); } + private IEnumerable MultiDimensionIndices(long startingIndex) { long[] shape = _tensor.shape; @@ -452,8 +411,7 @@ private IEnumerable MultiDimensionIndices(long startingIndex) long[] inds = new long[stride.Length]; long index = startingIndex; - //long offset = TranslateIndex(startingIndex, _tensor); - long offset = TranslateIndexNonStatic(startingIndex, _tensor); //WARNING: Test purpose not use in production + long offset = TranslateIndex(startingIndex, _tensor); while (true) { @@ -461,7 +419,7 @@ private IEnumerable MultiDimensionIndices(long startingIndex) yield return offset; - if (index >= TempCount) break; + if (index >= Count) break; for (int i = inds.Length - 1; ; i--) { Debug.Assert(i >= 0); @@ -482,23 +440,21 @@ private IEnumerable MultiDimensionIndices(long startingIndex) private IEnumerable SimpleIndices(long startingIndex, long stride) { long index = startingIndex; - //long offset = TranslateIndex(startingIndex, _tensor); - long offset = TranslateIndexNonStatic(startingIndex, _tensor); //WARNING: Test purpose not use in production + long offset = TranslateIndex(startingIndex, _tensor); - while (index < TempCount) { + while (index < Count) { yield return offset; offset += stride; index += 1; } } - private IEnumerable ContiguousIndices(long startingIndex) { // If there was an overload for Enumerable.Range that // produced long integers, we wouldn't need this implementation. - + long index = startingIndex; - while (index < TempCount) { + while (index < Count) { yield return index; index += 1; } @@ -553,16 +509,11 @@ private void Dispose(bool disposing) #if true public IEnumerator GetEnumerator() { - if (TempCount <= 1) { - if (TempCount == 0) - return Enumerable.Empty().GetEnumerator(); - return new T[1] { this[0] }.AsEnumerable().GetEnumerator(); - } - /*if (Count <= 1) { + if (Count <= 1) { if (Count == 0) return Enumerable.Empty().GetEnumerator(); return new T[1] { this[0] }.AsEnumerable().GetEnumerator(); - }*/ + } if (_tensor.is_contiguous()) { return new SimpleAtorImpl(this, 1); @@ -592,7 +543,7 @@ private class SimpleAtorImpl : IEnumerator public SimpleAtorImpl(TensorAccessor span, long stride) { _span = span; - _count = span.TempCount; + _count = span.Count; Debug.Assert(_count > 0); _stride = stride; Reset(); @@ -647,7 +598,7 @@ public GeneralAtorImpl(TensorAccessor span, long[] stride) { Debug.Assert(stride.Length > 1); _span = span; - _count = span.TempCount; + _count = span.Count; Debug.Assert(_count > 0); _shape = span._tensor.shape; Debug.Assert(_shape.Length == stride.Length); diff --git a/test/TorchSharpTest/TorchSharpTest.csproj b/test/TorchSharpTest/TorchSharpTest.csproj index 808aa1ccf..065301040 100644 --- a/test/TorchSharpTest/TorchSharpTest.csproj +++ b/test/TorchSharpTest/TorchSharpTest.csproj @@ -13,7 +13,6 @@ trx $(OutputPath) 10.0 - Debug;Release;LibTorch2.3.1 @@ -114,7 +113,7 @@ - + @@ -123,6 +122,7 @@ + true true @@ -132,5 +132,4 @@ Obsolete,ExcludeFromCodeCoverage - - + \ No newline at end of file From c0883d9fad6686c38d33b6713332397b61e47c86 Mon Sep 17 00:00:00 2001 From: Dimitri Date: Sun, 21 Jul 2024 16:31:07 -0300 Subject: [PATCH 20/65] fix mac test? --- src/TorchSharp/NN/Module.cs | 4 ++-- src/TorchSharp/Torch.cs | 16 +++++++--------- 2 files changed, 9 insertions(+), 11 deletions(-) diff --git a/src/TorchSharp/NN/Module.cs b/src/TorchSharp/NN/Module.cs index 19b64d8a9..f7309ed51 100644 --- a/src/TorchSharp/NN/Module.cs +++ b/src/TorchSharp/NN/Module.cs @@ -765,7 +765,7 @@ public virtual void register_module(string name, Module submodule) } submodule.RegisterComponents(); - if (!is_autocast_cache_enabled()) { + /*if (!is_autocast_cache_enabled()) { _internal_submodules.Add(name, submodule); return; } @@ -773,7 +773,7 @@ public virtual void register_module(string name, Module submodule) submodule = submodule.to(get_autocast_dtype(CUDA)); if (is_autocast_cpu_enabled()) submodule = submodule.to(get_autocast_dtype(CPU)); - + */ _internal_submodules.Add(name, submodule); } } diff --git a/src/TorchSharp/Torch.cs b/src/TorchSharp/Torch.cs index d10254a2c..bc019d8df 100644 --- a/src/TorchSharp/Torch.cs +++ b/src/TorchSharp/Torch.cs @@ -53,7 +53,8 @@ public static partial class torch public static string __version__ => libtorchPackageVersion; - internal static bool TryLoadNativeLibraryFromFile(string path, StringBuilder trace) { + internal static bool TryLoadNativeLibraryFromFile(string path, StringBuilder trace) + { bool ok; try { trace.AppendLine($" Trying to load native component {path}"); @@ -158,7 +159,7 @@ private static void LoadNativeBackend(bool useCudaBackend, out StringBuilder? tr var torchsharpLoc = Path.GetDirectoryName(typeof(torch).Assembly.Location); var packagesDir = Path.GetFullPath(Path.Combine(torchsharpLoc!, "..", "..", "..", "..")); var torchsharpHome = Path.GetFullPath(Path.Combine(torchsharpLoc!, "..", "..")); - //torchsharpLoc = @"K:\Proyects_Repos\TorchSharp"; + trace.AppendLine($" torchsharpLoc = {torchsharpLoc}"); trace.AppendLine($" packagesDir = {packagesDir}"); trace.AppendLine($" torchsharpHome = {torchsharpHome}"); @@ -204,8 +205,7 @@ private static void LoadNativeBackend(bool useCudaBackend, out StringBuilder? tr throw new NotSupportedException(message); } } - } - else { + } else { trace.AppendLine(" Giving up, TorchSharp.dll does not appear to have been loaded from package directories"); } if (!ok) { @@ -214,7 +214,7 @@ private static void LoadNativeBackend(bool useCudaBackend, out StringBuilder? tr throw new NotSupportedException(message); } } - + // Record the successful load if (useCudaBackend) @@ -265,8 +265,7 @@ private static bool CopyNativeComponentsIntoSingleDirectory(string packagesDir, public static bool TryInitializeDeviceType(DeviceType deviceType) { - if (deviceType == DeviceType.MPS && !isAppleSilicon) - { + if (deviceType == DeviceType.MPS && !isAppleSilicon) { return false; } @@ -280,8 +279,7 @@ public static bool TryInitializeDeviceType(DeviceType deviceType) public static void InitializeDeviceType(DeviceType deviceType) { - if (deviceType == DeviceType.MPS && !isAppleSilicon) - { + if (deviceType == DeviceType.MPS && !isAppleSilicon) { throw new InvalidOperationException($"Torch device type 'MPS' is not available on this platform."); } From 9ac78bd7ec50600fa137a97e05402b1121e357c3 Mon Sep 17 00:00:00 2001 From: Dimitri Date: Wed, 24 Jul 2024 19:08:23 -0300 Subject: [PATCH 21/65] AMP Problem outscope --- src/Examples.Utils/Examples.Utils.csproj | 2 +- src/TorchSharp/Amp/AMPManager.cs | 133 +++++++++++++++++++---- src/TorchSharp/Amp/AutocastMode.cs | 25 ++++- src/TorchSharp/Tensor/Tensor.cs | 29 ++--- src/TorchSharp/Utils/UnorderedMap.cs | 16 ++- 5 files changed, 161 insertions(+), 44 deletions(-) diff --git a/src/Examples.Utils/Examples.Utils.csproj b/src/Examples.Utils/Examples.Utils.csproj index 11a1f2b91..60dc0a292 100644 --- a/src/Examples.Utils/Examples.Utils.csproj +++ b/src/Examples.Utils/Examples.Utils.csproj @@ -26,7 +26,7 @@ - + diff --git a/src/TorchSharp/Amp/AMPManager.cs b/src/TorchSharp/Amp/AMPManager.cs index 29c5da90c..870728dca 100644 --- a/src/TorchSharp/Amp/AMPManager.cs +++ b/src/TorchSharp/Amp/AMPManager.cs @@ -1,65 +1,154 @@ using System; using System.Collections.Generic; -using System.Runtime.InteropServices; -using System.Text; -using Google.Protobuf.WellKnownTypes; +using System.Diagnostics; using TorchSharp.PInvoke; -using TorchSharp.Utils; namespace TorchSharp.Amp { public class AMPManager : IDisposable { + //TODO: Make Singleton THREADSAFE - public UnorderedMap TensorPtrs= new UnorderedMap(); + public class TensorConverter + { + //public torch.Tensor Tensor; + public IntPtr PrevHandle; + public IntPtr Handle; + public torch.ScalarType Dtype; + public torch.ScalarType FastDtype; + public TensorCalledIn Called, Status; + public enum TensorCalledIn + { + OutSide, + InsideEnter + } + + public TensorConverter(IntPtr handle) + { + this.PrevHandle = handle; + this.Handle = handle; + this.Dtype = (torch.ScalarType)NativeMethods.THSTensor_type(handle); + this.FastDtype = AutocastMode.GetInstance().GetFastType(); + + Status = TensorConverter.TensorCalledIn.InsideEnter; + } + /*public TensorConverter(torch.Tensor tensor) : this(tensor.handle) + { + this.Tensor = tensor; + }*/ + } + + public IList TensorsCasts = new List(); + public bool IsEnter = false; + public bool IsDisposed = false; + /*public UnorderedMap TensorPtrs= new UnorderedMap(); + public UnorderedMap TensorMap= new UnorderedMap();*/ private readonly AutocastMode autocastMode = AutocastMode.GetInstance(); private AMPManager() { } public bool IsEnabled => autocastMode.Enabled; private static AMPManager Instance; - //bool disposedValue; - public static AMPManager GetInstance() { return Instance ??= new AMPManager(); } - private void To(IntPtr ptr, torch.ScalarType type) + private torch.ScalarType GetType(IntPtr handle) + { + return (torch.ScalarType)NativeMethods.THSTensor_type(handle); + } + private IntPtr To(IntPtr ptr, torch.ScalarType type) { + Debug.WriteLine($"{nameof(AMPManager)} Tensor converting from: {(torch.ScalarType)NativeMethods.THSTensor_type(ptr)} to: {type}"); var res = NativeMethods.THSTensor_to_type(ptr, (sbyte)type); if (res == IntPtr.Zero) torch.CheckForErrors(); + return res; } private void Revert() { - using (var enumer = TensorPtrs.GetEnumerator()) - while (enumer.MoveNext()) - To(enumer.Current.Key, enumer.Current.Value); + for (int i = 0; i < TensorsCasts.Count; i++) { + var tc = TensorsCasts[i]; + //var tt = new torch.Tensor(tc.Handle); + //var t = new torch.Tensor(tc.Handle) { handle = To(tc.Handle, tc.Dtype) }; + //var t = new torch.Tensor(tc.Handle).to(tc.Dtype); + tc.Handle= To(tc.Handle, tc.Dtype); + if (tc.Handle != tc.PrevHandle) + tc.PrevHandle = To(tc.PrevHandle, tc.Dtype); + } + //Cast Work very well but UNCASTING (if outscope, not working i dont know why...) + //TensorsCasts.Clear(); } + - public void Add(IntPtr ptr) + private int ExistsHandle(IntPtr handle) { - if (!autocastMode.Enabled) { - - if (TensorPtrs.ContainsKey(ptr)) - To(ptr, TensorPtrs[ptr]); - return; + for (int i = 0; i < TensorsCasts.Count; i++) + if (TensorsCasts[i].PrevHandle == handle || TensorsCasts[i].Handle == handle) + return i; + return -1; + } + + public IntPtr Work(IntPtr handle, IntPtr prev) + { + + /*if (IsDisposed && !IsEnter) { + Revert(); //Is for cleaned all + return IntPtr.Zero; + }*/ + var idx = ExistsHandle(handle); + Console.WriteLine($"PTR: {handle}, PREV: {prev}, IDX: {idx}"); + if (idx == -1) { + var tc = new TensorConverter(handle) { Called = IsEnter + ? TensorConverter.TensorCalledIn.InsideEnter + : TensorConverter.TensorCalledIn.OutSide + }; + if (IsEnter) + tc.Handle = To(tc.Handle, tc.FastDtype); + TensorsCasts.Add(tc); + return tc.Handle; } + var tcidx = TensorsCasts[idx]; + if (!IsEnter && IsDisposed) { + if (tcidx.Called == TensorConverter.TensorCalledIn.OutSide) { //Is created outside so this can revert + //Is From Outside and is disposed, the tensor is created Outside so i will revert this + tcidx.PrevHandle = tcidx.Handle; + tcidx.Handle = To(tcidx.Handle, tcidx.Dtype); + } + return tcidx.Handle; + } + if (GetType(tcidx.Handle) == tcidx.FastDtype) + return tcidx.Handle; - TensorPtrs[ptr] = (torch.ScalarType)NativeMethods.THSTensor_type(ptr); - To(ptr, autocastMode.GetFastType()); //TODO: Set scalar autocast + if (IsEnter) { + tcidx.PrevHandle = tcidx.Handle; + tcidx.Handle = To(tcidx.Handle, tcidx.FastDtype); + } + return tcidx.Handle; } - + public IDisposable Enter() { - return null; + IsEnter = true; + IsDisposed = false; + Debug.WriteLine($"{nameof(AMPManager)} Enter call"); + return this; } protected virtual void Dispose(bool disposing) { + + Debug.WriteLine($"{nameof(AMPManager)} Disposed call"); Revert(); + + IsDisposed = true; + IsEnter = false; + + //Work(IntPtr.Zero, IntPtr.Zero); autocastMode.Dispose(); - TensorPtrs.Dispose(); + //Revert(); + /*TensorPtrs.Dispose(); + TensorMap.Dispose();*/ /*if (!disposedValue) { if (disposing) { diff --git a/src/TorchSharp/Amp/AutocastMode.cs b/src/TorchSharp/Amp/AutocastMode.cs index 0287e02d6..720fb3e67 100644 --- a/src/TorchSharp/Amp/AutocastMode.cs +++ b/src/TorchSharp/Amp/AutocastMode.cs @@ -23,7 +23,7 @@ public sealed class AutocastMode : IDisposable internal torch.ScalarType fast_dtype = torch.ScalarType.Float32; public torch.Device Device = new torch.Device(DeviceType.CUDA); private static AutocastMode instance; - bool disposedValue; + //bool disposedValue; /*public static AutocastMode GetInstance(torch.Device dev, torch.ScalarType? dtype = null, bool enabled = true, bool? cache_enabled = null) { @@ -93,7 +93,26 @@ internal torch.Tensor CastTensor(torch.Tensor tensor) private void Dispose(bool disposing) { - if (!disposedValue) { + this.Enabled = false; + if (Device.type == DeviceType.CUDA) { + if (torch.autocast_decrement_nesting() == 0) + torch.clear_autocast_cache(); + torch.set_autocast_gpu_dtype(this.fast_dtype); + //torch.set_autocast_enabled(this.Prev); + torch.set_autocast_enabled(false); + torch.set_autocast_cache_enabled(false); + } + + if (Device.type == DeviceType.CPU) { + if (torch.autocast_decrement_nesting() == 0) + torch.clear_autocast_cache(); + //torch.set_autocast_enabled(this.Prev); + torch.set_autocast_cpu_dtype(this.fast_dtype); + torch.set_autocast_enabled(false); + torch.set_autocast_cache_enabled(false); + } + //disposedValue = true; + /*if (!disposedValue) { if (disposing) { this.Enabled = false; @@ -121,7 +140,7 @@ private void Dispose(bool disposing) // TODO: free unmanaged resources (unmanaged objects) and override finalizer // TODO: set large fields to null disposedValue = true; - } + }*/ } // // TODO: override finalizer only if 'Dispose(bool disposing)' has code to free unmanaged resources diff --git a/src/TorchSharp/Tensor/Tensor.cs b/src/TorchSharp/Tensor/Tensor.cs index 0e5b76537..2ec774b2e 100644 --- a/src/TorchSharp/Tensor/Tensor.cs +++ b/src/TorchSharp/Tensor/Tensor.cs @@ -38,24 +38,18 @@ public partial class Tensor : IDisposable //internal AutocastDisposeScope? AutocastDisposeScope; internal Tensor(IntPtr handle) { - this.handle = handle; - /*if (AMPManager.GetInstance().IsEnabled) - AMPManager.GetInstance().Add(handle); //MMM.... This is the more abstract of any method Tensor right????*/ - - /*if (_totalCount > 0) { - //have used - AutocastDisposeScope = AutocastDisposeManager.ThreadAutocastSingleton.RegisterTensorAutocastScope(this); - this = AutocastDisposeScope.autocastMode.CastTensor(this); //should cast when using INSIDE NOT WHERE CREATED - }*/ - System.Threading.Interlocked.Increment(ref _totalCount); - _peakCount = Math.Max(_totalCount, _peakCount); - OwningDisposeScope = DisposeScopeManager.ThreadSingleton.RegisterOnCurrentDisposeScope(this); //TODO: Add Autocast/AMP ScopeManager, need improve this.. 1) is not threadsafe and may have big problem while casting and uncasting. //DANGER: DONT USE THIS ON PRODUCTION - /*AutocastDisposeScope = AutocastDisposeManager.ThreadAutocastSingleton.RegisterTensorAutocastScope(this); - this = AutocastDisposeScope.autocastMode.CastTensor(this); //should cast when using INSIDE NOT WHERE CREATED*/ - //Should cast inner scope when get tensors for every each method? example prod, sum, div, reshape, etc??? + if (AMPManager.GetInstance().IsEnabled) { + this.handle = AMPManager.GetInstance().Work(handle, this.handle); //MMM.... This is the more abstract of any method Tensor right???? + } else { + this.handle = handle; + } + + System.Threading.Interlocked.Increment(ref _totalCount); + _peakCount = Math.Max(_totalCount, _peakCount); + OwningDisposeScope = DisposeScopeManager.ThreadSingleton.RegisterOnCurrentDisposeScope(this); } /// @@ -226,8 +220,9 @@ public IntPtr Handle { if (handle == IntPtr.Zero) throw new InvalidOperationException("Tensor invalid -- empty handle."); - //AutocastDisposeScope.autocastMode.CastTensor(this); //This is wrong right??? - + /*if (AMPManager.GetInstance().IsEnabled) { + this.handle = AMPManager.GetInstance().Work(handle, this.handle); //MMM.... This is the more abstract of any method Tensor right???? + }*/ return handle; } } diff --git a/src/TorchSharp/Utils/UnorderedMap.cs b/src/TorchSharp/Utils/UnorderedMap.cs index 7db88a94c..f890d7a56 100644 --- a/src/TorchSharp/Utils/UnorderedMap.cs +++ b/src/TorchSharp/Utils/UnorderedMap.cs @@ -1,5 +1,7 @@ using System; +using System.Collections; using System.Collections.Generic; +using System.Linq; using System.Text; namespace TorchSharp.Utils @@ -9,11 +11,23 @@ public class UnorderedMap : Dictionary, IDisposable bool disposedValue; public UnorderedMap() { } + private static bool IsCollectionType(Type type) + { + if (!type.GetGenericArguments().Any()) + return false; + Type genericTypeDefinition = type.GetGenericTypeDefinition(); + var collectionTypes = new[] { typeof(IEnumerable<>), typeof(ICollection<>), typeof(IList<>), typeof(List<>), typeof(IList) }; + return collectionTypes.Any(x => x.IsAssignableFrom(genericTypeDefinition)); + } public new TValue this[TKey tk] { get { if (this.ContainsKey(tk)) return base[tk]; - return default(TValue); + var t = typeof(TValue); + if (!IsCollectionType(t)) + return default; + base[tk] = (TValue)(IList)Activator.CreateInstance(typeof(List<>).MakeGenericType(t.GetGenericArguments())); + return base[tk]; } set { if (!this.ContainsKey(tk)) { From 21ce055d6e9083fb0c92b6dbd91e3ffc917cf0e6 Mon Sep 17 00:00:00 2001 From: Dimitri Date: Tue, 3 Sep 2024 17:25:54 -0300 Subject: [PATCH 22/65] some gradscaler. Need grad_scale and found_inf attr in optimizer --- src/Native/LibTorchSharp/CMakeLists.txt | 5 + src/Native/LibTorchSharp/THSAmp.cpp | 23 ++- src/Native/LibTorchSharp/THSAmp.h | 12 +- src/Native/LibTorchSharp/THSCuda.cpp | 15 +- src/Native/LibTorchSharp/THSCuda.h | 4 +- src/TorchSharp/Amp/GradScaler.cs | 145 ++++++++++++++++-- .../PInvoke/LibTorchSharp.THSAmp.cs | 9 ++ src/TorchSharp/Tensor/torch.Amp.cs | 29 ++++ src/TorchSharp/Utils/UnorderedMap.cs | 10 +- 9 files changed, 229 insertions(+), 23 deletions(-) diff --git a/src/Native/LibTorchSharp/CMakeLists.txt b/src/Native/LibTorchSharp/CMakeLists.txt index 1565eae2d..f94d70302 100644 --- a/src/Native/LibTorchSharp/CMakeLists.txt +++ b/src/Native/LibTorchSharp/CMakeLists.txt @@ -1,8 +1,11 @@ project(LibTorchSharp) find_package(CUDA) +IF(CUDA_FOUND) include_directories(${CUDA_INCLUDE_DIRS}) link_directories(${CUDA_LIBRARY_DIRS}) +add_compile_definitions(TORCHSHARP_CUDA_TOOLKIT_FOUND) +ENDIF() if(APPLE AND NOT LIBTORCH_ARCH STREQUAL "arm64") include_directories("/usr/local/include" "/usr/local/opt/llvm/include") @@ -79,7 +82,9 @@ include_directories(${TORCH_INCLUDE_DIRS}) add_library(LibTorchSharp SHARED ${SOURCES} ${RESOURCES}) +IF(CUDA_FOUND) target_link_libraries(LibTorchSharp ${CUDA_LIBRARIES}) +ENDIF() target_link_libraries(LibTorchSharp ${TORCH_LIBRARIES}) diff --git a/src/Native/LibTorchSharp/THSAmp.cpp b/src/Native/LibTorchSharp/THSAmp.cpp index 2f6a603e5..0b4f29cb8 100644 --- a/src/Native/LibTorchSharp/THSAmp.cpp +++ b/src/Native/LibTorchSharp/THSAmp.cpp @@ -3,6 +3,8 @@ #include #include +#include "torch/torch.h" +#include "torch/cuda.h" /*void THSAmp_amp_foreach_non_finite_check_and_unscale_(const at::TensorList self, at::Tensor& found_inf, const at::Tensor& inv_scale) { @@ -12,14 +14,25 @@ void THSAmp_amp_foreach_non_finite_check_and_unscale_(Tensor* self, const int64_t tLength, at::Tensor& found_inf, const at::Tensor& inv_scale) { torch::_amp_foreach_non_finite_check_and_unscale_(toTensors((torch::Tensor**)self, tLength),found_inf,inv_scale); - } -/*void THSAmp_amp_update_scale_(Tensor* self, const int64_t tLength, __resharper_unknown_type& found_inf, const __resharper_unknown_type& inv_scale) -{ - torch::_amp_update_scale() -}*/ +Tensor THSAmp_amp_update_scale_(at::Tensor& self, at::Tensor& growth_tracker, const at::Tensor& found_inf, double scale_growth_factor, double scale_backoff_factor, int64_t growth_interval) { + CATCH_TENSOR(torch::_amp_update_scale_(self, growth_tracker, found_inf, scale_growth_factor, scale_backoff_factor, growth_interval);) +} +Tensor THSAmp_amp_update_scale_out(at::Tensor& out, const at::Tensor& self, at::Tensor& growth_tracker, const at::Tensor& found_inf, double scale_growth_factor, double scale_backoff_factor, int64_t growth_interval){ + CATCH_TENSOR(torch::_amp_update_scale_out(out, self, growth_tracker, found_inf, scale_growth_factor, scale_backoff_factor, growth_interval);) +} +Tensor THSAmp_amp_update_scale_outf(const at::Tensor& self, at::Tensor& growth_tracker, const at::Tensor& found_inf, double scale_growth_factor, double scale_backoff_factor, int64_t growth_interval, at::Tensor& out){ + CATCH_TENSOR(torch::_amp_update_scale_outf(self, growth_tracker, found_inf, scale_growth_factor, scale_backoff_factor, growth_interval, out);) +} +Tensor THSAMP_amp_update_scale(const at::Tensor& self, const at::Tensor& growth_tracker, const at::Tensor& found_inf, double scale_growth_factor, double scale_backoff_factor, int64_t growth_interval, Tensor* sec) +{ + std::tuple res; + CATCH(res = torch::_amp_update_scale(self, growth_tracker, found_inf, scale_growth_factor, scale_backoff_factor, growth_interval);) + *sec = ResultTensor(std::get<1>(res)); + return ResultTensor(std::get<0>(res)); +} bool THSAmp_is_torch_function_mode_enabled() { diff --git a/src/Native/LibTorchSharp/THSAmp.h b/src/Native/LibTorchSharp/THSAmp.h index 27183ef14..3a0718db4 100644 --- a/src/Native/LibTorchSharp/THSAmp.h +++ b/src/Native/LibTorchSharp/THSAmp.h @@ -2,16 +2,20 @@ #pragma once #include "../Stdafx.h" - -#include "torch/torch.h" - #include "Utils.h" //https://github.com/pytorch/pytorch/blob/main/torch/_meta_registrations.py#L5957 //EXPORT_API(void) THSAmp_amp_foreach_non_finite_check_and_unscale_(const at::TensorList self, at::Tensor& found_inf, const at::Tensor& inv_scale); EXPORT_API(void) THSAmp_amp_foreach_non_finite_check_and_unscale_(Tensor* self, const int64_t tLength, at::Tensor& found_inf, const at::Tensor& inv_scale); -//EXPORT_API(void) THSAmp_amp_update_scale_(at::Tensor& found_inf, const at::Tensor& inv_scale); + +//EXPORT_API(void) THSAmp_amp_update_scale_(const at::Tensor& self, const at::Tensor& inv_scale); + +EXPORT_API(Tensor) THSAmp_amp_update_scale_(at::Tensor& self, at::Tensor& growth_tracker, const at::Tensor& found_inf, double scale_growth_factor, double scale_backoff_factor, int64_t growth_interval); +EXPORT_API(Tensor) THSAmp_amp_update_scale_out(at::Tensor& out, const at::Tensor& self, at::Tensor& growth_tracker, const at::Tensor& found_inf, double scale_growth_factor, double scale_backoff_factor, int64_t growth_interval); +EXPORT_API(Tensor) THSAmp_amp_update_scale_outf(const at::Tensor& self, at::Tensor& growth_tracker, const at::Tensor& found_inf, double scale_growth_factor, double scale_backoff_factor, int64_t growth_interval, at::Tensor& out); +EXPORT_API(Tensor) THSAMP_amp_update_scale(const at::Tensor& self, const at::Tensor& growth_tracker, const at::Tensor& found_inf, double scale_growth_factor, double scale_backoff_factor, int64_t growth_interval, Tensor* sec); + EXPORT_API(bool) THSAmp_is_torch_function_mode_enabled(); //Maybe the best work is call THSTorch_is_autocast_enabled(enum of devices c# as int8_t); diff --git a/src/Native/LibTorchSharp/THSCuda.cpp b/src/Native/LibTorchSharp/THSCuda.cpp index 475187beb..01d583229 100644 --- a/src/Native/LibTorchSharp/THSCuda.cpp +++ b/src/Native/LibTorchSharp/THSCuda.cpp @@ -4,22 +4,31 @@ #include #include - +#ifdef TORCHSHARP_CUDA_TOOLKIT_FOUND cudaDeviceProp THSCuda_get_device_prop() { int device = 0; cudaDeviceProp cdp; - //cudaGetDeviceProperties_v2(&cdp, device); - cudaGetDeviceProperties(&cdp, device); + //cudaGetDeviceProperties(&cdp, device); + cudaGetDeviceProperties_v2(&cdp, device); return cdp; } +#endif int THSCuda_get_major_compute_capability() { +#ifdef TORCHSHARP_CUDA_TOOLKIT_FOUND return THSCuda_get_device_prop().major; +#else + return -1; +#endif } int THSCuda_get_minor_compute_capability() { +#ifdef TORCHSHARP_CUDA_TOOLKIT_FOUND return THSCuda_get_device_prop().minor; +#else + return -1; +#endif } diff --git a/src/Native/LibTorchSharp/THSCuda.h b/src/Native/LibTorchSharp/THSCuda.h index 2c6e6c17f..c951dd7a2 100644 --- a/src/Native/LibTorchSharp/THSCuda.h +++ b/src/Native/LibTorchSharp/THSCuda.h @@ -6,11 +6,13 @@ #include "torch/torch.h" #include "Utils.h" - +#ifdef TORCHSHARP_CUDA_TOOLKIT_FOUND #include "cuda.h" #include "cuda_runtime_api.h" cudaDeviceProp THSCuda_get_device_prop(); +#endif + EXPORT_API(int) THSCuda_get_major_compute_capability(); EXPORT_API(int) THSCuda_get_minor_compute_capability(); \ No newline at end of file diff --git a/src/TorchSharp/Amp/GradScaler.cs b/src/TorchSharp/Amp/GradScaler.cs index be4833f4f..b2cbd3988 100644 --- a/src/TorchSharp/Amp/GradScaler.cs +++ b/src/TorchSharp/Amp/GradScaler.cs @@ -4,18 +4,23 @@ using System.Linq; using System.Text; using System.Threading.Tasks; +using Tensorboard; using TorchSharp.Modules; using TorchSharp.Utils; namespace TorchSharp.Amp { - public class GradScaler + public class GradScaler : IDisposable { private bool Enabled; public torch.Device device; private torch.Tensor _scale, _growth_tracker; - private float InitScale, GrowthFactor, BackoffFactor, GrowthInterval, InitGrowthTracker; + private float InitScale, InitGrowthTracker; + public float _growth_factor { set; get; } + public float _backoff_factor { set; get; } + private int _growth_interval { set; get; } private UnorderedMap> _per_optimizer_states = new UnorderedMap>(); + bool disposedValue; public enum OptState { @@ -38,9 +43,9 @@ public GradScaler(torch.Device dev, float init_scale = 2.0e16f, float growth_fac device = dev; Enabled = enabled; InitScale = init_scale; - GrowthFactor = growth_factor; - BackoffFactor = backoff_factor; - GrowthInterval = growth_interval; + this._growth_factor = growth_factor; + _backoff_factor = backoff_factor; + _growth_interval = growth_interval; InitGrowthTracker = 0.0f; throw new NotImplementedException("This need to finish"); @@ -218,17 +223,44 @@ public void unscale(torch.optim.Optimizer optimizer) //https://github.com/pytorch/pytorch/blob/a00fad017719346bac6e08da0819358146e647e3/torch/amp/grad_scaler.py#L398 var f = optimizer.GetType().GetField("_step_support_amp_scaling"); if (f != null && f.GetValue(optimizer) is bool b && !b) { + bool has_grad_scaler = false;//I dont know how deal this... + if (has_grad_scaler) { + } else { + if (optimizer_state["stage"] is OptState optstate && optstate == OptState.Ready) + check_inf_per_device(optimizer); + var scaler = _get_scale_async(); + Debug.Assert(!scaler.is_null(), "!scaler.is_null()"); + torch.Tensor found_inf; + if (optimizer_state["found_inf_per_device"] is torch.Tensor[] ts) { + for (int i = 0; i < ts.Length; i++) + ts[i].to(scaler.device, true); + found_inf=torch.sum(torch.cat(ts)); + } + //if(optimizer is SGD ad) + //Info: All optimizer have grad_scale and found_inf //https://github.com/pytorch/pytorch/blob/main/torch/optim/adam.py, etc. + //DANGER: Optimizer in TorchShapr not have grad_scaler or found_inf, we need grad_scale for https://github.com/pytorch/pytorch/blob/758d78790164bfb041555daed380de96e06f78a3/torch/amp/grad_scaler.py#L440 + + //optimizer.GetType().GetField("grad_scale").GetValue(optimizer) as torch.Tensor t + } + retval = optimizer.step().item(); + optimizer_state["stage"] = OptState.Stepped; + //https://github.com/pytorch/pytorch/blob/758d78790164bfb041555daed380de96e06f78a3/torch/amp/grad_scaler.py#L445 + return retval; } if (optimizer_state["stage"] is OptState state1 && state1 == OptState.Ready) unscale(optimizer); - Debug.Assert((optimizer_state["found_inf_per_device"] as float[]).Length > 0, "(optimizer_state['found_inf_per_device'] as float[]).Length > 0"); - + Debug.Assert((optimizer_state["found_inf_per_device"] as torch.Tensor[]).Length > 0, "(optimizer_state['found_inf_per_device'] as torch.Tensor).size(0) > 0"); retval = maybe_opt_step(optimizer, optimizer_state); optimizer_state["stage"] = OptState.Stepped; return retval; } + private torch.Tensor _get_scale_async() + { + return _scale; + } + /// /// /// @@ -252,9 +284,104 @@ public void update(object new_scale = null) _scale.copy_(t); } } else { - //var found_infs = + IList found_infs = new List(); + foreach (var state in _per_optimizer_states) + foreach (var found_inf in state.Value) + if(found_inf.Value is torch.Tensor t) + found_infs.Add(t); + Debug.Assert(found_infs.Count > 0, "No inf checks were recorded prior to update."); + torch.Tensor found_inf_combined = found_infs[0]; + if (found_infs.Count > 1) + for (int i = 1; i < found_infs.Count; i++) + found_inf_combined += found_infs[i]; + torch.amp_update_scale_(_scale, _growth_tracker, found_inf_combined, (double)_growth_factor, (double)_backoff_factor, (long)_growth_interval); + + } + //TODO: Implement defaultdict https://github.com/pytorch/pytorch/blob/758d78790164bfb041555daed380de96e06f78a3/torch/amp/grad_scaler.py#L531 + } + + public float get_scale() + { + if (this.Enabled) { + + var scale = _get_scale_async(); + if (scale.is_null()) + return InitScale; + return scale.item(); + } + return 1.0f; + } + + public bool IsEnabled() + { + return this.Enabled; + } + + public UnorderedMap state_dict() + { + if (Enabled) { + var res = new UnorderedMap(); + res["scale"] = get_scale(); + res[nameof(_growth_factor)] = _growth_factor; + res[nameof(_backoff_factor)] = _backoff_factor; + res[nameof(_growth_interval)] = _growth_interval; + res[nameof(_growth_tracker)] = _growth_tracker; + return res; } - + return null; + } + + public void load_state_dict(Dictionary state_dict) + { + if (!Enabled) + return; + if (state_dict.Count == 0) + throw new Exception("The source state dict is empty, possibly because it was saved from a disabled instance of GradScaler."); + //TODO: implement reflection to set field/properties based on state_dict + } + + torch.Tensor check_inf_per_device(torch.optim.Optimizer optimizer) + { + _scale = check_scale_growth_tracker(nameof(check_inf_per_device)).Item1; + var dummy_inv_scale = torch.full(new ReadOnlySpan(new long[] { 0 }), 1.0f, torch.ScalarType.Float32, _scale.device); + var foundd_inf = torch.full(new ReadOnlySpan(new long[] { 0 }), 0.0f, torch.ScalarType.Float32, _scale.device); + _per_optimizer_states[optimizer.GetHashCode()]["found_inf_per_device"] = unscale_grads(optimizer, dummy_inv_scale, foundd_inf, true); + return _per_optimizer_states[optimizer.GetHashCode()]["found_inf_per_device"] as torch.Tensor; + } + + private object _found_inf_per_device(torch.optim.Optimizer optimizer) + { + return _per_optimizer_states[optimizer.GetHashCode()]["found_inf_per_device"]; + } + + protected virtual void Dispose(bool disposing) + { + if (!disposedValue) { + if (disposing) { + _per_optimizer_states.Dispose(); + _growth_tracker.Dispose(); + _scale.Dispose(); + // TODO: dispose managed state (managed objects) + } + + // TODO: free unmanaged resources (unmanaged objects) and override finalizer + // TODO: set large fields to null + disposedValue = true; + } + } + + // // TODO: override finalizer only if 'Dispose(bool disposing)' has code to free unmanaged resources + // ~GradScaler() + // { + // // Do not change this code. Put cleanup code in 'Dispose(bool disposing)' method + // Dispose(disposing: false); + // } + + public void Dispose() + { + // Do not change this code. Put cleanup code in 'Dispose(bool disposing)' method + Dispose(disposing: true); + GC.SuppressFinalize(this); } } } \ No newline at end of file diff --git a/src/TorchSharp/PInvoke/LibTorchSharp.THSAmp.cs b/src/TorchSharp/PInvoke/LibTorchSharp.THSAmp.cs index 984637336..7829da992 100644 --- a/src/TorchSharp/PInvoke/LibTorchSharp.THSAmp.cs +++ b/src/TorchSharp/PInvoke/LibTorchSharp.THSAmp.cs @@ -11,6 +11,14 @@ internal static partial class NativeMethods [DllImport("LibTorchSharp")] internal static extern void THSAmp_amp_foreach_non_finite_check_and_unscale_(IntPtr tensors, long tLength, IntPtr found_inf, IntPtr inv_scale); [DllImport("LibTorchSharp")] + internal static extern IntPtr THSAmp_amp_update_scale_(IntPtr self, IntPtr growth_tracker, IntPtr found_inf, double scale_growth_factor, double scale_backoff_factor, long growth_interval); + [DllImport("LibTorchSharp")] + internal static extern IntPtr THSAmp_amp_update_scale_out(IntPtr outt,IntPtr self, IntPtr growth_tracker, IntPtr found_inf, double scale_growth_factor, double scale_backoff_factor, long growth_interval); + [DllImport("LibTorchSharp")] + internal static extern IntPtr THSAmp_amp_update_scale_outf(IntPtr self,IntPtr growth_tracker, IntPtr found_inf, double scale_growth_factor, double scale_backoff_factor, long growth_interval, IntPtr outt); + [DllImport("LibTorchSharp")] + internal static extern IntPtr THSAMP_amp_update_scale(IntPtr self,IntPtr growth_tracker, IntPtr found_inf, double scale_growth_factor, double scale_backoff_factor, long growth_interval, out IntPtr sec); + [DllImport("LibTorchSharp")] internal static extern bool THSAmp_is_torch_function_mode_enabled(); [DllImport("LibTorchSharp")] internal static extern bool THSAmp_is_autocast_cache_enabled(); @@ -49,5 +57,6 @@ internal static partial class NativeMethods [DllImport("LibTorchSharp")] internal static extern void THSAmp_clear_autocast_cache(); + } } \ No newline at end of file diff --git a/src/TorchSharp/Tensor/torch.Amp.cs b/src/TorchSharp/Tensor/torch.Amp.cs index dfa4245fd..319afe65c 100644 --- a/src/TorchSharp/Tensor/torch.Amp.cs +++ b/src/TorchSharp/Tensor/torch.Amp.cs @@ -13,5 +13,34 @@ public static void _amp_foreach_non_finite_check_and_unscale_(IList tens IntPtr tens = ts.CreateArray(tensors.Select(x => x.Handle).ToArray()); THSAmp_amp_foreach_non_finite_check_and_unscale_(tens, ts.Array.Length, found_inf.Handle, inv_scale.Handle); } + + public static torch.Tensor amp_update_scale_(Tensor self, Tensor growth_tracker, Tensor found_inf, double scale_growth_factor, double scale_backoff_factor, long growth_interval) + { + var res = THSAmp_amp_update_scale_(self.Handle, growth_tracker.Handle, found_inf.Handle, scale_growth_factor, scale_backoff_factor, growth_interval); + if(res == IntPtr.Zero) + torch.CheckForErrors(); + return new Tensor(res); + } + public static torch.Tensor amp_update_scale_out(Tensor outt, Tensor self, Tensor growth_tracker, Tensor found_inf, double scale_growth_factor, double scale_backoff_factor, long growth_interval) + { + var res = THSAmp_amp_update_scale_out(outt.Handle, self.Handle, growth_tracker.Handle, found_inf.Handle, scale_growth_factor, scale_backoff_factor, growth_interval); + if(res == IntPtr.Zero) + torch.CheckForErrors(); + return new Tensor(res); + } + public static torch.Tensor amp_update_scale_outf(Tensor self, Tensor growth_tracker, Tensor found_inf, double scale_growth_factor, double scale_backoff_factor, long growth_interval, Tensor outt) + { + var res = THSAmp_amp_update_scale_outf(self.Handle, growth_tracker.Handle, found_inf.Handle, scale_growth_factor, scale_backoff_factor, growth_interval, outt.Handle); + if(res == IntPtr.Zero) + torch.CheckForErrors(); + return new Tensor(res); + } + public static (torch.Tensor, torch.Tensor) amp_update_scale(Tensor self, Tensor growth_tracker, Tensor found_inf, double scale_growth_factor, double scale_backoff_factor, long growth_interval) + { + var res = THSAMP_amp_update_scale(self.Handle, growth_tracker.Handle, found_inf.Handle, scale_growth_factor, scale_backoff_factor, growth_interval, out var res1); + if(res == IntPtr.Zero || res1 == IntPtr.Zero) + torch.CheckForErrors(); + return (new Tensor(res), new Tensor(res1)); + } } } diff --git a/src/TorchSharp/Utils/UnorderedMap.cs b/src/TorchSharp/Utils/UnorderedMap.cs index f890d7a56..92446906a 100644 --- a/src/TorchSharp/Utils/UnorderedMap.cs +++ b/src/TorchSharp/Utils/UnorderedMap.cs @@ -9,7 +9,8 @@ namespace TorchSharp.Utils public class UnorderedMap : Dictionary, IDisposable { bool disposedValue; - + private TValue default_dict; + //TODO: Add DefautlDict behaviour public UnorderedMap() { } private static bool IsCollectionType(Type type) { @@ -21,6 +22,8 @@ private static bool IsCollectionType(Type type) } public new TValue this[TKey tk] { get { + /*if (!this.ContainsKey(tk) && default_dict == null) + return default_dict;*/ if (this.ContainsKey(tk)) return base[tk]; var t = typeof(TValue); @@ -38,6 +41,11 @@ private static bool IsCollectionType(Type type) } } + public void SetDefaultDict(TValue def) + { + this.default_dict = def; + } + protected virtual void Dispose(bool disposing) { if (!disposedValue) { From c70b5237b80d68a735ca5effbe79f998b29d9f52 Mon Sep 17 00:00:00 2001 From: Dimitri Date: Tue, 3 Sep 2024 19:54:49 -0300 Subject: [PATCH 23/65] update v2.4.0 --- src/Native/LibTorchSharp/THSAmp.cpp | 76 +++---------------- src/Native/LibTorchSharp/THSAmp.h | 22 +----- src/TorchSharp/Amp/AutocastMode.cs | 40 ++++------ .../PInvoke/LibTorchSharp.THSAmp.cs | 24 +----- src/TorchSharp/Tensor/torch.Autocast.cs | 59 +++----------- 5 files changed, 42 insertions(+), 179 deletions(-) diff --git a/src/Native/LibTorchSharp/THSAmp.cpp b/src/Native/LibTorchSharp/THSAmp.cpp index 0b4f29cb8..c1fa3cd9e 100644 --- a/src/Native/LibTorchSharp/THSAmp.cpp +++ b/src/Native/LibTorchSharp/THSAmp.cpp @@ -44,60 +44,25 @@ bool THSAmp_is_autocast_cache_enabled() return at::autocast::is_autocast_cache_enabled(); } -bool THSAmp_is_autocast_cpu_enabled() +bool THSAmp_is_autocast_enabled(int8_t device) { - return at::autocast::is_cpu_enabled(); //https://github.com/pytorch/pytorch/blob/2c91e13afc6edcfe0a0e6189a88aae4ecbbf3516/torch/csrc/autograd/init.cpp#L523 + return at::autocast::is_autocast_enabled((at::DeviceType)device); } -bool THSAmp_is_autocast_gpu_enabled() +int8_t THSAmp_get_autocast_dtype(int8_t device) { - return at::autocast::is_enabled(); //https://github.com/pytorch/pytorch/blob/2c91e13afc6edcfe0a0e6189a88aae4ecbbf3516/torch/amp/autocast_mode.py#L363 + return (int8_t)at::autocast::get_autocast_dtype((at::DeviceType)device); } -bool THSAmp_is_autocast_xpu_enabled() -{ - return at::autocast::is_xpu_enabled(); -} -bool THSAmp_is_autocast_hpu_enabled() -{ - return at::autocast::is_hpu_enabled(); -} - -#if (TORCH_VERSION_MAJOR ==2 && TORCH_VERSION_MINOR > 0) -bool THSAmp_is_autocast_ipu_enabled() -{ - return at::autocast::is_ipu_enabled(); -} - -bool THSAmp_is_autocast_xla_enabled() -{ - return at::autocast::is_xla_enabled(); -} - -#endif -int8_t THSAmp_get_autocast_cpu_dtype() +void THSAmp_set_autocast_dtype(int8_t device, int8_t dtype) { - return (int8_t)at::autocast::get_autocast_cpu_dtype(); + at::autocast::set_autocast_dtype((at::DeviceType)device, (at::ScalarType)dtype); } -int8_t THSAmp_get_autocast_gpu_dtype() +void THSAmp_set_autocast_enabled(int8_t device, bool enabled) { - //TODO: Implement AUTOCAST AMP AND GRADSCALER - - //INFO: Enter/Exit function of autocast_mode not need to do in C/C++ only in C# with Disposable can handle all of that function (if exists) - //https://github.com/pytorch/pytorch/blob/main/torch/amp/autocast_mode.py - - //https://github.com/pytorch/pytorch/blob/2c91e13afc6edcfe0a0e6189a88aae4ecbbf3516/torch/csrc/autograd/init.cpp#L629 - //https://github.com/pytorch/pytorch/blob/2c91e13afc6edcfe0a0e6189a88aae4ecbbf3516/aten/src/ATen/autocast_mode.h#L20 - return (int8_t)at::autocast::get_autocast_gpu_dtype(); + at::autocast::set_autocast_enabled((at::DeviceType)device, enabled); } - -int8_t THSAmp_get_autocast_xpu_dtype() -{ - return (int8_t)at::autocast::get_autocast_xpu_dtype(); -} - - int THSAmp_autocast_increment_nesting() { return at::autocast::increment_nesting(); @@ -108,32 +73,11 @@ int THSAmp_autocast_decrement_nesting() return at::autocast::decrement_nesting(); } -void THSAmp_set_autocast_enabled(bool enabled) +void THSAmp_clear_autocast_cache() { - at::autocast::set_enabled(enabled); + at::autocast::clear_cache(); } - void THSAmp_set_autocast_cache_enabled(bool enabled) { at::autocast::set_autocast_cache_enabled(enabled); -} - -void THSAmp_set_autocast_cpu_dtype(int8_t dtype) -{ - at::autocast::set_autocast_cpu_dtype((c10::ScalarType)dtype); -} - -void THSAmp_set_autocast_gpu_dtype(int8_t dtype) -{ - at::autocast::set_autocast_gpu_dtype((c10::ScalarType)dtype); -} - -void THSAmp_set_autocast_xpu_dtype(int8_t dtype) -{ - at::autocast::set_autocast_xpu_dtype((c10::ScalarType)dtype); -} - -void THSAmp_clear_autocast_cache() -{ - at::autocast::clear_cache(); } \ No newline at end of file diff --git a/src/Native/LibTorchSharp/THSAmp.h b/src/Native/LibTorchSharp/THSAmp.h index 3a0718db4..23d56fb2c 100644 --- a/src/Native/LibTorchSharp/THSAmp.h +++ b/src/Native/LibTorchSharp/THSAmp.h @@ -18,31 +18,17 @@ EXPORT_API(Tensor) THSAMP_amp_update_scale(const at::Tensor& self, const at::Ten EXPORT_API(bool) THSAmp_is_torch_function_mode_enabled(); -//Maybe the best work is call THSTorch_is_autocast_enabled(enum of devices c# as int8_t); EXPORT_API(bool) THSAmp_is_autocast_cache_enabled(); -EXPORT_API(bool) THSAmp_is_autocast_cpu_enabled(); -EXPORT_API(bool) THSAmp_is_autocast_gpu_enabled(); -EXPORT_API(bool) THSAmp_is_autocast_xpu_enabled(); -EXPORT_API(bool) THSAmp_is_autocast_hpu_enabled(); -#if (TORCH_VERSION_MAJOR ==2 && TORCH_VERSION_MINOR > 0) -EXPORT_API(bool) THSAmp_is_autocast_ipu_enabled(); -EXPORT_API(bool) THSAmp_is_autocast_xla_enabled(); -#endif - -EXPORT_API(int8_t) THSAmp_get_autocast_cpu_dtype(); -EXPORT_API(int8_t) THSAmp_get_autocast_gpu_dtype(); -EXPORT_API(int8_t) THSAmp_get_autocast_xpu_dtype(); +EXPORT_API(bool) THSAmp_is_autocast_enabled(int8_t device); +EXPORT_API(int8_t) THSAmp_get_autocast_dtype(int8_t device); +EXPORT_API(void) THSAmp_set_autocast_enabled(int8_t device, bool enabled); +EXPORT_API(void) THSAmp_set_autocast_dtype(int8_t device, int8_t dtype); EXPORT_API(int) THSAmp_autocast_increment_nesting(); EXPORT_API(int) THSAmp_autocast_decrement_nesting(); -EXPORT_API(void) THSAmp_set_autocast_enabled(bool enabled); EXPORT_API(void) THSAmp_set_autocast_cache_enabled(bool enabled); -EXPORT_API(void) THSAmp_set_autocast_cpu_dtype(int8_t dtype); -EXPORT_API(void) THSAmp_set_autocast_gpu_dtype(int8_t dtype); -EXPORT_API(void) THSAmp_set_autocast_xpu_dtype(int8_t dtype); - EXPORT_API(void) THSAmp_clear_autocast_cache(); //EXPORT_API(bool) THSTorch_jit_is_scripting(); \ No newline at end of file diff --git a/src/TorchSharp/Amp/AutocastMode.cs b/src/TorchSharp/Amp/AutocastMode.cs index 63821e64f..fa7512bb5 100644 --- a/src/TorchSharp/Amp/AutocastMode.cs +++ b/src/TorchSharp/Amp/AutocastMode.cs @@ -39,21 +39,23 @@ public static AutocastMode GetInstance() public torch.ScalarType GetFastType() { - var ft = torch.ScalarType.Float32; + return torch.get_autocast_dtype(Device.type); + /*var ft = torch.ScalarType.Float32; if (Device.type == DeviceType.CUDA) ft = torch.get_autocast_gpu_dtype(); if (Device.type == DeviceType.CPU) ft = torch.get_autocast_cpu_dtype(); - return ft; + return ft;*/ } private AutocastMode(torch.Device dev, torch.ScalarType? dtype = null, bool enabled=true, bool? cache_enabled = null) { //var la = torch.tensor(9); fast_dtype = dtype ?? torch.ScalarType.Float32; - if (dev.type == DeviceType.CUDA) - fast_dtype = torch.get_autocast_gpu_dtype(); + fast_dtype = torch.get_autocast_dtype(dev.type); + /*if (dev.type == DeviceType.CUDA) + fast_dtype = torch.get_autocast_dtype(dev); if (dev.type == DeviceType.CPU) - fast_dtype = torch.get_autocast_cpu_dtype(); + fast_dtype = torch.get_autocast_cpu_dtype();*/ //IntPtr ptr = IntPtr.Zero; bool _cache_enabled = torch.is_autocast_cache_enabled(); @@ -74,11 +76,10 @@ private AutocastMode(torch.Device dev, torch.ScalarType? dtype = null, bool enab this.Enabled = enabled; - this.Prev = torch.is_autocast_cpu_enabled(); + this.Prev = torch.is_autocast_enabled(DeviceType.CPU); if (dev.type == DeviceType.CUDA) { - this.Prev = torch.is_autocast_gpu_enabled(); + this.Prev = torch.is_autocast_enabled(dev.type); } - torch.set_autocast_cache_enabled(_cache_enabled); torch.set_autocast_enabled(this.Enabled); //throw new NotImplementedException(); @@ -99,23 +100,12 @@ internal torch.Tensor CastTensor(torch.Tensor tensor) private void Dispose(bool disposing) { this.Enabled = false; - if (Device.type == DeviceType.CUDA) { - if (torch.autocast_decrement_nesting() == 0) - torch.clear_autocast_cache(); - torch.set_autocast_gpu_dtype(this.fast_dtype); - //torch.set_autocast_enabled(this.Prev); - torch.set_autocast_enabled(false); - torch.set_autocast_cache_enabled(false); - } - - if (Device.type == DeviceType.CPU) { - if (torch.autocast_decrement_nesting() == 0) - torch.clear_autocast_cache(); - //torch.set_autocast_enabled(this.Prev); - torch.set_autocast_cpu_dtype(this.fast_dtype); - torch.set_autocast_enabled(false); - torch.set_autocast_cache_enabled(false); - } + if (torch.autocast_decrement_nesting() == 0) + torch.clear_autocast_cache(); + //torch.set_autocast_enabled(this.Prev); + torch.set_autocast_cache_enabled(Device.type, this.fast_dtype); + torch.set_autocast_enabled(false); + torch.set_autocast_cache_enabled(false); } public void Dispose() diff --git a/src/TorchSharp/PInvoke/LibTorchSharp.THSAmp.cs b/src/TorchSharp/PInvoke/LibTorchSharp.THSAmp.cs index 7829da992..a91d4816a 100644 --- a/src/TorchSharp/PInvoke/LibTorchSharp.THSAmp.cs +++ b/src/TorchSharp/PInvoke/LibTorchSharp.THSAmp.cs @@ -23,23 +23,9 @@ internal static partial class NativeMethods [DllImport("LibTorchSharp")] internal static extern bool THSAmp_is_autocast_cache_enabled(); [DllImport("LibTorchSharp")] - internal static extern bool THSAmp_is_autocast_cpu_enabled(); + internal static extern bool THSAmp_is_autocast_enabled(int device_type); [DllImport("LibTorchSharp")] - internal static extern bool THSAmp_is_autocast_gpu_enabled(); - [DllImport("LibTorchSharp")] - internal static extern bool THSAmp_is_autocast_xpu_enabled(); - [DllImport("LibTorchSharp")] - internal static extern bool THSAmp_is_autocast_hpu_enabled(); - [DllImport("LibTorchSharp")] - internal static extern bool THSAmp_is_autocast_ipu_enabled(); - [DllImport("LibTorchSharp")] - internal static extern bool THSAmp_is_autocast_xla_enabled(); - [DllImport("LibTorchSharp")] - internal static extern sbyte THSAmp_get_autocast_cpu_dtype(); - [DllImport("LibTorchSharp")] - internal static extern sbyte THSAmp_get_autocast_gpu_dtype(); - [DllImport("LibTorchSharp")] - internal static extern sbyte THSAmp_get_autocast_xpu_dtype(); + internal static extern sbyte THSAmp_get_autocast_dtype(int device_type); [DllImport("LibTorchSharp")] internal static extern int THSAmp_autocast_increment_nesting(); [DllImport("LibTorchSharp")] @@ -49,11 +35,7 @@ internal static partial class NativeMethods [DllImport("LibTorchSharp")] internal static extern void THSAmp_set_autocast_cache_enabled(bool enabled); [DllImport("LibTorchSharp")] - internal static extern void THSAmp_set_autocast_cpu_dtype(sbyte dtype); - [DllImport("LibTorchSharp")] - internal static extern void THSAmp_set_autocast_gpu_dtype(sbyte dtype); - [DllImport("LibTorchSharp")] - internal static extern void THSAmp_set_autocast_xpu_dtype(sbyte dtype); + internal static extern void THSAmp_set_autocast_dtype(int device_type, sbyte dtype); [DllImport("LibTorchSharp")] internal static extern void THSAmp_clear_autocast_cache(); diff --git a/src/TorchSharp/Tensor/torch.Autocast.cs b/src/TorchSharp/Tensor/torch.Autocast.cs index e295c8e62..d817e4ab9 100644 --- a/src/TorchSharp/Tensor/torch.Autocast.cs +++ b/src/TorchSharp/Tensor/torch.Autocast.cs @@ -10,52 +10,22 @@ public static bool is_autocast_cache_enabled() return THSAmp_is_autocast_cache_enabled(); } - public static bool is_autocast_enabled(Device device) + public static bool is_autocast_enabled(DeviceType device) { - if(device.type == DeviceType.CPU) - return THSAmp_is_autocast_cpu_enabled(); - if(device.type == DeviceType.CUDA) - return THSAmp_is_autocast_gpu_enabled(); - return THSAmp_is_autocast_cache_enabled(); - } - public static bool is_autocast_cpu_enabled() - { - return THSAmp_is_autocast_cpu_enabled(); + return THSAmp_is_autocast_enabled((int)device); + //return THSAmp_is_autocast_cache_enabled(); } - public static bool is_autocast_gpu_enabled() + public static ScalarType get_autocast_dtype(DeviceType device) { - return THSAmp_is_autocast_gpu_enabled(); - } - public static bool is_autocast_xpu_enabled() - { - return THSAmp_is_autocast_xpu_enabled(); - } - public static bool is_autocast_hpu_enabled() - { - return THSAmp_is_autocast_hpu_enabled(); - } - - public static ScalarType get_autocast_dtype(Device device) - { - if (device.type == DeviceType.CPU) + return (ScalarType)THSAmp_get_autocast_dtype((int)device); + /*if (device.type == DeviceType.CPU) return get_autocast_cpu_dtype(); if (device.type == DeviceType.CUDA) return get_autocast_gpu_dtype(); - return ScalarType.Float32; - } - public static ScalarType get_autocast_cpu_dtype() - { - return (ScalarType)THSAmp_get_autocast_cpu_dtype(); - } - public static ScalarType get_autocast_gpu_dtype() - { - return (ScalarType)THSAmp_get_autocast_gpu_dtype(); - } - public static ScalarType get_autocast_xpu_dtype() - { - return (ScalarType)THSAmp_get_autocast_xpu_dtype(); + return ScalarType.Float32;*/ } + public static int autocast_increment_nesting() { return THSAmp_autocast_increment_nesting(); @@ -74,18 +44,9 @@ public static void set_autocast_cache_enabled(bool enabled) { THSAmp_set_autocast_cache_enabled(enabled); } - - public static void set_autocast_cpu_dtype(ScalarType dtype) - { - THSAmp_set_autocast_cpu_dtype((sbyte)dtype); - } - public static void set_autocast_gpu_dtype(ScalarType dtype) - { - THSAmp_set_autocast_gpu_dtype((sbyte)dtype); - } - public static void set_autocast_xpu_dtype(ScalarType dtype) + public static void set_autocast_cache_enabled(DeviceType device, ScalarType dtype) { - THSAmp_set_autocast_xpu_dtype((sbyte)dtype); + THSAmp_set_autocast_dtype((int)device, (sbyte)dtype); } public static void clear_autocast_cache() From 36b79b9f30a03db72e620edf65ea1756a8e6266d Mon Sep 17 00:00:00 2001 From: Dimitri Date: Wed, 4 Sep 2024 21:07:30 -0300 Subject: [PATCH 24/65] some advance --- src/TorchSharp/Amp/AMPManager.cs | 33 ++++++++++++++++++++-------- src/TorchSharp/Amp/AutocastMode.cs | 35 +++++++++++++++--------------- src/TorchSharp/Amp/GradScaler.cs | 8 ++++++- 3 files changed, 48 insertions(+), 28 deletions(-) diff --git a/src/TorchSharp/Amp/AMPManager.cs b/src/TorchSharp/Amp/AMPManager.cs index 0262f8934..9d79d59e7 100644 --- a/src/TorchSharp/Amp/AMPManager.cs +++ b/src/TorchSharp/Amp/AMPManager.cs @@ -16,7 +16,7 @@ public class TensorConverter public IntPtr PrevHandle; public IntPtr Handle; public torch.ScalarType Dtype; - public torch.ScalarType FastDtype; + public torch.ScalarType FastDtype = torch.ScalarType.Float32; public TensorCalledIn Called, Status; public enum TensorCalledIn { @@ -44,15 +44,26 @@ public TensorConverter(IntPtr handle) public bool IsDisposed = false; /*public UnorderedMap TensorPtrs= new UnorderedMap(); public UnorderedMap TensorMap= new UnorderedMap();*/ - private readonly AutocastMode autocastMode = AutocastMode.GetInstance(); + private AutocastMode autocastMode=null; + public bool IsEnabled { + get { + if (autocastMode == null) + return false; + return autocastMode.Enabled; + } + } - private AMPManager() { } + private AMPManager(bool enabled) + { + if (!torch.cuda_is_available()) + return; + autocastMode = AutocastMode.GetInstance(enabled); + } - public bool IsEnabled => autocastMode.Enabled; private static AMPManager Instance; - public static AMPManager GetInstance() + public static AMPManager GetInstance(bool enabled = false) { - return Instance ??= new AMPManager(); + return Instance ??= new AMPManager(enabled); } private torch.ScalarType GetType(IntPtr handle) @@ -67,7 +78,8 @@ public IntPtr AutoCast(IntPtr handle) public torch.Tensor AutoCast(torch.Tensor tensor) { - return tensor.to(AutocastMode.GetInstance().GetFastType()); + return new torch.Tensor(AutoCast(tensor.Handle)); + //return tensor.to(AutocastMode.GetInstance().GetFastType()); } public static IntPtr To(IntPtr ptr, torch.ScalarType type) { @@ -154,8 +166,11 @@ public IntPtr Work(IntPtr handle, IntPtr prev) public IDisposable Enter() { + if (!torch.cuda_is_available()) + return this; IsEnter = true; IsDisposed = false; + autocastMode.SetEnabled(true, torch.CUDA); Debug.WriteLine($"{nameof(AMPManager)} Enter call"); return this; } @@ -184,10 +199,10 @@ protected virtual void Dispose(bool disposing) } // // TODO: override finalizer only if 'Dispose(bool disposing)' has code to free unmanaged resources - ~AMPManager() + /*~AMPManager() { Dispose(false); - } + }*/ public void Dispose() { diff --git a/src/TorchSharp/Amp/AutocastMode.cs b/src/TorchSharp/Amp/AutocastMode.cs index fa7512bb5..808df715b 100644 --- a/src/TorchSharp/Amp/AutocastMode.cs +++ b/src/TorchSharp/Amp/AutocastMode.cs @@ -32,43 +32,39 @@ public sealed class AutocastMode : IDisposable instance = new AutocastMode(dev, dtype, enabled, cache_enabled); return instance; }*/ - public static AutocastMode GetInstance() + public static AutocastMode GetInstance(bool enabled=false) { - return instance ??= new AutocastMode(torch.CUDA, cache_enabled:true); + return instance ??= new AutocastMode(torch.cuda_is_available() ? torch.CUDA : torch.CPU, enabled:enabled,cache_enabled:true); } public torch.ScalarType GetFastType() { return torch.get_autocast_dtype(Device.type); - /*var ft = torch.ScalarType.Float32; - if (Device.type == DeviceType.CUDA) - ft = torch.get_autocast_gpu_dtype(); - if (Device.type == DeviceType.CPU) - ft = torch.get_autocast_cpu_dtype(); - return ft;*/ } private AutocastMode(torch.Device dev, torch.ScalarType? dtype = null, bool enabled=true, bool? cache_enabled = null) + { + if (!torch.cuda_is_available()) + return; + Process(dev, dtype, enabled, cache_enabled); + } + + private void Process(torch.Device dev, torch.ScalarType? dtype=null, bool enabled=true, bool? cache_enabled=null) { //var la = torch.tensor(9); fast_dtype = dtype ?? torch.ScalarType.Float32; fast_dtype = torch.get_autocast_dtype(dev.type); - /*if (dev.type == DeviceType.CUDA) - fast_dtype = torch.get_autocast_dtype(dev); - if (dev.type == DeviceType.CPU) - fast_dtype = torch.get_autocast_cpu_dtype();*/ //IntPtr ptr = IntPtr.Zero; - + bool _cache_enabled = torch.is_autocast_cache_enabled(); if (!torch.cuda.is_available() && dev.type == DeviceType.CUDA) //Is not available for doing multicast Enabled = false; if (dtype.HasValue) fast_dtype = dtype.Value; - if(cache_enabled.HasValue) - _cache_enabled=cache_enabled.Value; + if (cache_enabled.HasValue) + _cache_enabled = cache_enabled.Value; if (dev.type == DeviceType.CPU) { - } - else if (dev.type == DeviceType.CUDA) { + } else if (dev.type == DeviceType.CUDA) { if (enabled && fast_dtype == torch.ScalarType.BFloat16 && !torch.cuda.is_bf16_supported()) throw new Exception("Current CUDA Device does not support bfloat16. Please switch dtype to float16."); @@ -82,7 +78,6 @@ private AutocastMode(torch.Device dev, torch.ScalarType? dtype = null, bool enab } torch.set_autocast_cache_enabled(_cache_enabled); torch.set_autocast_enabled(this.Enabled); - //throw new NotImplementedException(); } /*internal void Cast(torch.Tensor tensor) @@ -97,6 +92,10 @@ internal torch.Tensor CastTensor(torch.Tensor tensor) return tensor.to(fast_dtype, tensor.device); } + internal void SetEnabled(bool enabled, torch.Device dev) + { + Process(dev, null, enabled, true); + } private void Dispose(bool disposing) { this.Enabled = false; diff --git a/src/TorchSharp/Amp/GradScaler.cs b/src/TorchSharp/Amp/GradScaler.cs index b2cbd3988..f9070f3c2 100644 --- a/src/TorchSharp/Amp/GradScaler.cs +++ b/src/TorchSharp/Amp/GradScaler.cs @@ -201,7 +201,13 @@ public void unscale(torch.optim.Optimizer optimizer) private float? maybe_opt_step(torch.optim.Optimizer optimizer, UnorderedMap optimizer_state) { //https://github.com/pytorch/pytorch/blob/a00fad017719346bac6e08da0819358146e647e3/torch/amp/grad_scaler.py#L351 - throw new NotImplementedException(); + float? retval=0; + foreach(var d in optimizer_state) + if (d.Value is torch.Tensor t) + retval += t.item(); + if (retval==0) + retval = optimizer.step().item(); + return retval; } public float? step(torch.optim.Optimizer optimizer, params object[] obj) From 376f4fbb4af0a028d1d541b0533b966f5120ec7c Mon Sep 17 00:00:00 2001 From: Dimitri Date: Sun, 8 Sep 2024 09:13:19 -0300 Subject: [PATCH 25/65] Improve autocastmode --- src/Native/LibTorchSharp/THSAmp.cpp | 6 + src/Native/LibTorchSharp/THSAmp.h | 2 + src/TorchSharp/Amp/AMPManager.cs | 2 +- src/TorchSharp/Amp/AutocastMode.cs | 148 ++++++++++++------ src/TorchSharp/LinearAlgebra.cs | 5 +- src/TorchSharp/NN/Convolution/Conv1D.cs | 3 +- src/TorchSharp/NN/Convolution/Conv2D.cs | 3 +- src/TorchSharp/NN/Convolution/Conv3D.cs | 3 +- .../NN/Convolution/ConvTranspose1D.cs | 3 +- .../NN/Convolution/ConvTranspose2D.cs | 3 +- .../NN/Convolution/ConvTranspose3D.cs | 3 +- src/TorchSharp/NN/Linear.cs | 3 +- src/TorchSharp/NN/Recurrent/GRUCell.cs | 3 +- src/TorchSharp/NN/Recurrent/LSTMCell.cs | 3 +- src/TorchSharp/NN/Recurrent/RNNCell.cs | 3 +- .../PInvoke/LibTorchSharp.THSAmp.cs | 4 +- src/TorchSharp/Tensor/Tensor.LinearAlgebra.cs | 7 +- src/TorchSharp/Tensor/Tensor.Math.cs | 6 +- src/TorchSharp/Tensor/Tensor.Trig.cs | 3 + src/TorchSharp/Tensor/Tensor.cs | 14 +- src/TorchSharp/Tensor/torch.Autocast.cs | 19 ++- src/TorchSharp/TorchSharp.csproj | 4 + src/TorchSharp/Utils/UnorderedMap.cs | 59 +++++++ 23 files changed, 222 insertions(+), 87 deletions(-) diff --git a/src/Native/LibTorchSharp/THSAmp.cpp b/src/Native/LibTorchSharp/THSAmp.cpp index c1fa3cd9e..79c6da9f2 100644 --- a/src/Native/LibTorchSharp/THSAmp.cpp +++ b/src/Native/LibTorchSharp/THSAmp.cpp @@ -44,6 +44,12 @@ bool THSAmp_is_autocast_cache_enabled() return at::autocast::is_autocast_cache_enabled(); } +bool THSAmp_is_autocast_available(int8_t device) +{ + return at::autocast::is_autocast_available((c10::DeviceType)device); +} + + bool THSAmp_is_autocast_enabled(int8_t device) { return at::autocast::is_autocast_enabled((at::DeviceType)device); diff --git a/src/Native/LibTorchSharp/THSAmp.h b/src/Native/LibTorchSharp/THSAmp.h index 23d56fb2c..4ae115dda 100644 --- a/src/Native/LibTorchSharp/THSAmp.h +++ b/src/Native/LibTorchSharp/THSAmp.h @@ -20,6 +20,8 @@ EXPORT_API(bool) THSAmp_is_torch_function_mode_enabled(); EXPORT_API(bool) THSAmp_is_autocast_cache_enabled(); +EXPORT_API(bool) THSAmp_is_autocast_available(int8_t device); + EXPORT_API(bool) THSAmp_is_autocast_enabled(int8_t device); EXPORT_API(int8_t) THSAmp_get_autocast_dtype(int8_t device); EXPORT_API(void) THSAmp_set_autocast_enabled(int8_t device, bool enabled); diff --git a/src/TorchSharp/Amp/AMPManager.cs b/src/TorchSharp/Amp/AMPManager.cs index 9d79d59e7..c5a120b03 100644 --- a/src/TorchSharp/Amp/AMPManager.cs +++ b/src/TorchSharp/Amp/AMPManager.cs @@ -49,7 +49,7 @@ public bool IsEnabled { get { if (autocastMode == null) return false; - return autocastMode.Enabled; + return autocastMode.IsEnabled; } } diff --git a/src/TorchSharp/Amp/AutocastMode.cs b/src/TorchSharp/Amp/AutocastMode.cs index 808df715b..dacfc9721 100644 --- a/src/TorchSharp/Amp/AutocastMode.cs +++ b/src/TorchSharp/Amp/AutocastMode.cs @@ -1,9 +1,13 @@ using System; using System.Collections.Generic; +using System.Diagnostics; using System.Linq; +using System.Runtime.CompilerServices; using System.Security.Cryptography; using System.Text; using System.Threading.Tasks; +using TorchSharp.PInvoke; +using TorchSharp.Utils; namespace TorchSharp.Amp { @@ -17,21 +21,17 @@ public static torch.Tensor AutoCast(this torch.Tensor input) //TODO: Should make Singleton and IDisposable on ENTER public sealed class AutocastMode : IDisposable { - //NEED "Register" all tensor in scope for uncasting outer-scope - public bool Enabled=false; - internal bool Prev; - //private torch.ScalarType Dtype = torch.ScalarType.Float32; + public bool _enabled=false; + public bool IsEnter = false; + public bool IsDisposed = false; + private bool prev_cache_enabled, prev; + private torch.ScalarType prev_fastdtype; + //internal bool Prev; + private bool _cache_enabled=false; internal torch.ScalarType fast_dtype = torch.ScalarType.Float32; - public torch.Device Device = new torch.Device(DeviceType.CUDA); + internal torch.ScalarType? dtype = torch.ScalarType.Float32; + public DeviceType device = DeviceType.CUDA; private static AutocastMode instance; - //bool disposedValue; - - /*public static AutocastMode GetInstance(torch.Device dev, torch.ScalarType? dtype = null, bool enabled = true, bool? cache_enabled = null) -{ -if(instance ==null) -instance = new AutocastMode(dev, dtype, enabled, cache_enabled); -return instance; -}*/ public static AutocastMode GetInstance(bool enabled=false) { return instance ??= new AutocastMode(torch.cuda_is_available() ? torch.CUDA : torch.CPU, enabled:enabled,cache_enabled:true); @@ -39,72 +39,118 @@ public static AutocastMode GetInstance(bool enabled=false) public torch.ScalarType GetFastType() { - return torch.get_autocast_dtype(Device.type); + return torch.get_autocast_dtype(device); } private AutocastMode(torch.Device dev, torch.ScalarType? dtype = null, bool enabled=true, bool? cache_enabled = null) { - if (!torch.cuda_is_available()) - return; - Process(dev, dtype, enabled, cache_enabled); - } - - private void Process(torch.Device dev, torch.ScalarType? dtype=null, bool enabled=true, bool? cache_enabled=null) - { - //var la = torch.tensor(9); - fast_dtype = dtype ?? torch.ScalarType.Float32; - fast_dtype = torch.get_autocast_dtype(dev.type); + /*dtype_by_methods[nameof(torch.matmul), DeviceType.CUDA] = torch.ScalarType.Float16; + dtype_by_methods[nameof(torch.matmul), DeviceType.CUDA] = torch.ScalarType.Float16;*/ + //https://pytorch.org/docs/stable/amp.html#cuda-ops-that-can-autocast-to-float16 + if (dtype == null) + dtype = torch.get_autocast_dtype(dev.type); + this.device = dev.type; + if (!torch.is_autocast_available(device)) + throw new Exception($"User specified an unsupported autocast device_type {device}"); + fast_dtype = torch.get_autocast_dtype(device); + //TODO: is_autocast_available(); //IntPtr ptr = IntPtr.Zero; - bool _cache_enabled = torch.is_autocast_cache_enabled(); - if (!torch.cuda.is_available() && dev.type == DeviceType.CUDA) //Is not available for doing multicast - Enabled = false; - if (dtype.HasValue) + _cache_enabled = torch.is_autocast_cache_enabled(); + if (enabled && !torch.cuda_is_available() && dev.type == DeviceType.CUDA) //Is not available for doing multicast + enabled = false; + if (this.dtype.HasValue) fast_dtype = dtype.Value; if (cache_enabled.HasValue) _cache_enabled = cache_enabled.Value; - if (dev.type == DeviceType.CPU) { + if (dev.type == DeviceType.CPU) { + if (fast_dtype != torch.ScalarType.Float16 || fast_dtype != torch.ScalarType.BFloat16) { + Debug.WriteLine($"In CPU autocast, but the target d type is not suported. Disabling autocast. CPU autocast only supports dtype of {torch.ScalarType.Float16} or {torch.ScalarType.BFloat16}"); + enabled = false; + } } else if (dev.type == DeviceType.CUDA) { if (enabled && fast_dtype == torch.ScalarType.BFloat16 && !torch.cuda.is_bf16_supported()) throw new Exception("Current CUDA Device does not support bfloat16. Please switch dtype to float16."); } + this._enabled = enabled; + } + private torch.ScalarType GetType(IntPtr handle) + { + return (torch.ScalarType)NativeMethods.THSTensor_type(handle); + } - this.Enabled = enabled; - - this.Prev = torch.is_autocast_enabled(DeviceType.CPU); - if (dev.type == DeviceType.CUDA) { - this.Prev = torch.is_autocast_enabled(dev.type); - } - torch.set_autocast_cache_enabled(_cache_enabled); - torch.set_autocast_enabled(this.Enabled); + public static IntPtr AutoCast(IntPtr handle) + { + return ToIf(handle, GetInstance().GetFastType()); + } + public static IntPtr AutoCast(IntPtr handle, torch.ScalarType dtype) + { + return ToIf(handle, dtype); } - /*internal void Cast(torch.Tensor tensor) + + public static torch.Tensor AutoCast(torch.Tensor tensor) { - tensor.to(fast_dtype, tensor.device); - }*/ + return new torch.Tensor(AutoCast(tensor.Handle)); + //return tensor.to(AutocastMode.GetInstance().GetFastType()); + } + public static IntPtr To(IntPtr ptr, torch.ScalarType type) + { + Debug.WriteLine($"{nameof(AutocastMode)} Tensor converting from: {(torch.ScalarType)NativeMethods.THSTensor_type(ptr)} to: {type}"); + var res = NativeMethods.THSTensor_to_type(ptr, (sbyte)type); + if (res == IntPtr.Zero) + torch.CheckForErrors(); + return res; + } + public static IntPtr ToIf(IntPtr ptr, torch.ScalarType type) + { + if (!GetInstance()._enabled) + return ptr; + /*if (!NativeMethods.THSAmp_is_autocast_enabled(NativeMethods.THSTensor_device_type(ptr))) + return ptr;*/ + var res = NativeMethods.THSTensor_to_type(ptr, (sbyte)type); + if (res == IntPtr.Zero) + torch.CheckForErrors(); + return res; + } + public static IntPtr ToIf(IntPtr ptr, torch.ScalarType type, DeviceType device_type) + { + bool is_elegible = (torch.ScalarType)NativeMethods.THSTensor_type(ptr) != torch.ScalarType.Float64 && (DeviceType)NativeMethods.THSTensor_device_type(ptr) == device_type; + + if (!NativeMethods.THSAmp_is_autocast_enabled(NativeMethods.THSTensor_device_type(ptr))) + return ptr; + var res = NativeMethods.THSTensor_to_type(ptr, (sbyte)type); + if (res == IntPtr.Zero) + torch.CheckForErrors(); + return res; + } - internal torch.Tensor CastTensor(torch.Tensor tensor) + public static bool IsAutocastEnabled(DeviceType device = DeviceType.CUDA) { - if (!Enabled) - return tensor; - return tensor.to(fast_dtype, tensor.device); + return torch.is_autocast_enabled(!torch.cuda_is_available() ? DeviceType.CPU : device); } - internal void SetEnabled(bool enabled, torch.Device dev) + public IDisposable Enter() { - Process(dev, null, enabled, true); + prev_cache_enabled = torch.is_autocast_cache_enabled(); + prev = torch.is_autocast_enabled(device); + prev_fastdtype = torch.get_autocast_dtype(device); + torch.set_autocast_enabled(device, _enabled); + torch.set_autocast_dtype(device, fast_dtype); + torch.autocast_increment_nesting(); + torch.set_autocast_cache_enabled(_cache_enabled); + return this; } + private void Dispose(bool disposing) { - this.Enabled = false; + this._enabled = false; if (torch.autocast_decrement_nesting() == 0) torch.clear_autocast_cache(); - //torch.set_autocast_enabled(this.Prev); - torch.set_autocast_cache_enabled(Device.type, this.fast_dtype); - torch.set_autocast_enabled(false); - torch.set_autocast_cache_enabled(false); + torch.set_autocast_enabled(device, prev); + torch.set_autocast_dtype(device, prev_fastdtype); + torch.set_autocast_cache_enabled(prev_cache_enabled); } public void Dispose() diff --git a/src/TorchSharp/LinearAlgebra.cs b/src/TorchSharp/LinearAlgebra.cs index c9964d536..43d9ed82d 100644 --- a/src/TorchSharp/LinearAlgebra.cs +++ b/src/TorchSharp/LinearAlgebra.cs @@ -2,6 +2,7 @@ using System; using System.Linq; using System.Collections.Generic; +using TorchSharp.Amp; using static TorchSharp.PInvoke.NativeMethods; #nullable enable @@ -440,7 +441,7 @@ public static Tensor multi_dot(IList tensors) throw new ArgumentException(nameof(tensors)); } if (tensors.Count == 1) { - tensors[0] = Amp.AMPManager.GetInstance().AutoCast(tensors[0]); + tensors[0] = AutocastMode.AutoCast(tensors[0]); return tensors[0]; } @@ -449,7 +450,7 @@ public static Tensor multi_dot(IList tensors) var res = THSLinalg_multi_dot(tensorsRef, parray.Array.Length); if (res == IntPtr.Zero) torch.CheckForErrors(); - res = Amp.AMPManager.GetInstance().AutoCast(res); + res = AutocastMode.AutoCast(res); return new Tensor(res); } } diff --git a/src/TorchSharp/NN/Convolution/Conv1D.cs b/src/TorchSharp/NN/Convolution/Conv1D.cs index 0064020fd..dd7b4c263 100644 --- a/src/TorchSharp/NN/Convolution/Conv1D.cs +++ b/src/TorchSharp/NN/Convolution/Conv1D.cs @@ -1,5 +1,6 @@ // Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information. using System; +using TorchSharp.Amp; using static TorchSharp.torch; using static TorchSharp.PInvoke.NativeMethods; @@ -194,7 +195,7 @@ public static Tensor conv1d(Tensor input, Tensor weight, Tensor? bias = null, (IntPtr)pdilation, dilationArray.Length, groups); if (res == IntPtr.Zero) { torch.CheckForErrors(); } - res = Amp.AMPManager.GetInstance().AutoCast(res); + res = AutocastMode.AutoCast(res); return new Tensor(res); } } diff --git a/src/TorchSharp/NN/Convolution/Conv2D.cs b/src/TorchSharp/NN/Convolution/Conv2D.cs index 277b695eb..4008b51fa 100644 --- a/src/TorchSharp/NN/Convolution/Conv2D.cs +++ b/src/TorchSharp/NN/Convolution/Conv2D.cs @@ -1,5 +1,6 @@ // Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information. using System; +using TorchSharp.Amp; using static TorchSharp.torch; using static TorchSharp.PInvoke.NativeMethods; @@ -238,7 +239,7 @@ public static Tensor conv2d(Tensor input, Tensor weight, Tensor? bias = null, (IntPtr)pdilation, dilation.Length, groups); if (res == IntPtr.Zero) { torch.CheckForErrors(); } - res = Amp.AMPManager.GetInstance().AutoCast(res); + res = AutocastMode.AutoCast(res); return new Tensor(res); } } diff --git a/src/TorchSharp/NN/Convolution/Conv3D.cs b/src/TorchSharp/NN/Convolution/Conv3D.cs index e8a670b7d..ef37aaa6a 100644 --- a/src/TorchSharp/NN/Convolution/Conv3D.cs +++ b/src/TorchSharp/NN/Convolution/Conv3D.cs @@ -1,5 +1,6 @@ // Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information. using System; +using TorchSharp.Amp; using static TorchSharp.torch; using static TorchSharp.PInvoke.NativeMethods; @@ -181,7 +182,7 @@ public static Tensor conv3d(Tensor input, Tensor weight, Tensor? bias = null, (IntPtr)pdilation, dilation.Length, groups); if (res == IntPtr.Zero) { torch.CheckForErrors(); } - res = Amp.AMPManager.GetInstance().AutoCast(res); + res = AutocastMode.AutoCast(res); return new Tensor(res); } } diff --git a/src/TorchSharp/NN/Convolution/ConvTranspose1D.cs b/src/TorchSharp/NN/Convolution/ConvTranspose1D.cs index 954e4ab1b..9700a58b7 100644 --- a/src/TorchSharp/NN/Convolution/ConvTranspose1D.cs +++ b/src/TorchSharp/NN/Convolution/ConvTranspose1D.cs @@ -1,5 +1,6 @@ // Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information. using System; +using TorchSharp.Amp; using static TorchSharp.torch; using static TorchSharp.PInvoke.NativeMethods; @@ -117,7 +118,7 @@ public static Tensor conv_transpose1d(Tensor input, Tensor weight, Tensor? bias (IntPtr)pdilation, dilations.Length, groups); if (res == IntPtr.Zero) { torch.CheckForErrors(); } - res = Amp.AMPManager.GetInstance().AutoCast(res); + res = AutocastMode.AutoCast(res); return new Tensor(res); } } diff --git a/src/TorchSharp/NN/Convolution/ConvTranspose2D.cs b/src/TorchSharp/NN/Convolution/ConvTranspose2D.cs index 8a074dce1..63fc0d6e5 100644 --- a/src/TorchSharp/NN/Convolution/ConvTranspose2D.cs +++ b/src/TorchSharp/NN/Convolution/ConvTranspose2D.cs @@ -1,5 +1,6 @@ // Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information. using System; +using TorchSharp.Amp; using static TorchSharp.torch; using static TorchSharp.PInvoke.NativeMethods; @@ -148,7 +149,7 @@ public static Tensor conv_transpose2d(Tensor input, Tensor weight, Tensor? bias (IntPtr)pdilation, dilation.Length, groups); if (res == IntPtr.Zero) { torch.CheckForErrors(); } - res = Amp.AMPManager.GetInstance().AutoCast(res); + res = AutocastMode.AutoCast(res); return new Tensor(res); } } diff --git a/src/TorchSharp/NN/Convolution/ConvTranspose3D.cs b/src/TorchSharp/NN/Convolution/ConvTranspose3D.cs index 4362a8738..faeb279ad 100644 --- a/src/TorchSharp/NN/Convolution/ConvTranspose3D.cs +++ b/src/TorchSharp/NN/Convolution/ConvTranspose3D.cs @@ -1,5 +1,6 @@ // Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information. using System; +using TorchSharp.Amp; using static TorchSharp.torch; using static TorchSharp.PInvoke.NativeMethods; @@ -144,7 +145,7 @@ public static Tensor conv_transpose3d(Tensor input, Tensor weight, Tensor? bias (IntPtr)pdilation, dilation.Length, groups); if (res == IntPtr.Zero) { torch.CheckForErrors(); } - res = Amp.AMPManager.GetInstance().AutoCast(res); + res = AutocastMode.AutoCast(res); return new Tensor(res); } } diff --git a/src/TorchSharp/NN/Linear.cs b/src/TorchSharp/NN/Linear.cs index 675952cef..68b34ffd5 100644 --- a/src/TorchSharp/NN/Linear.cs +++ b/src/TorchSharp/NN/Linear.cs @@ -1,5 +1,6 @@ // Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information. using System; +using TorchSharp.Amp; using static TorchSharp.torch; using static TorchSharp.torch.nn; using static TorchSharp.PInvoke.NativeMethods; @@ -104,7 +105,7 @@ public static Tensor linear(Tensor input, Tensor weights, Tensor? bias = null) IntPtr bPtr = bias?.Handle ?? IntPtr.Zero; var res = THSNN_functional_linear(input.Handle, weights.Handle, bPtr); if (res == IntPtr.Zero) { torch.CheckForErrors(); } - res = Amp.AMPManager.GetInstance().AutoCast(res); + res = AutocastMode.AutoCast(res); return new Tensor(res); } } diff --git a/src/TorchSharp/NN/Recurrent/GRUCell.cs b/src/TorchSharp/NN/Recurrent/GRUCell.cs index 50be405e1..610762542 100644 --- a/src/TorchSharp/NN/Recurrent/GRUCell.cs +++ b/src/TorchSharp/NN/Recurrent/GRUCell.cs @@ -1,5 +1,6 @@ // Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information. using System; +using TorchSharp.Amp; using static TorchSharp.torch; using static TorchSharp.torch.nn; using static TorchSharp.PInvoke.NativeMethods; @@ -106,7 +107,7 @@ public static GRUCell GRUCell(long inputSize, long hiddenSize, bool bias = true, { var res = THSNN_GRUCell_ctor(inputSize, hiddenSize, bias, out var boxedHandle); if (res == IntPtr.Zero) { torch.CheckForErrors(); } - res = Amp.AMPManager.GetInstance().AutoCast(res); //TODO: Research if this work... + res = AutocastMode.AutoCast(res); return new GRUCell(res, boxedHandle).MoveModule(device, dtype); } } diff --git a/src/TorchSharp/NN/Recurrent/LSTMCell.cs b/src/TorchSharp/NN/Recurrent/LSTMCell.cs index 2449348fb..44f6e5bbc 100644 --- a/src/TorchSharp/NN/Recurrent/LSTMCell.cs +++ b/src/TorchSharp/NN/Recurrent/LSTMCell.cs @@ -1,5 +1,6 @@ // Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information. using System; +using TorchSharp.Amp; using static TorchSharp.torch; using static TorchSharp.torch.nn; using static TorchSharp.PInvoke.NativeMethods; @@ -108,7 +109,7 @@ public static LSTMCell LSTMCell(long inputSize, long hiddenSize, bool bias = tru { var res = THSNN_LSTMCell_ctor(inputSize, hiddenSize, bias, out var boxedHandle); if (res == IntPtr.Zero) { torch.CheckForErrors(); } - res = Amp.AMPManager.GetInstance().AutoCast(res); + res = AutocastMode.AutoCast(res); return new LSTMCell(res, boxedHandle).MoveModule(device, dtype); } } diff --git a/src/TorchSharp/NN/Recurrent/RNNCell.cs b/src/TorchSharp/NN/Recurrent/RNNCell.cs index 0557dfe2e..05bf7088b 100644 --- a/src/TorchSharp/NN/Recurrent/RNNCell.cs +++ b/src/TorchSharp/NN/Recurrent/RNNCell.cs @@ -1,5 +1,6 @@ // Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information. using System; +using TorchSharp.Amp; using static TorchSharp.torch; using static TorchSharp.torch.nn; using static TorchSharp.PInvoke.NativeMethods; @@ -112,7 +113,7 @@ public static RNNCell RNNCell(long inputSize, long hiddenSize, NonLinearities no { var res = THSNN_RNNCell_ctor(inputSize, hiddenSize, (long)nonLinearity, bias, out var boxedHandle); if (res == IntPtr.Zero) { torch.CheckForErrors(); } - res = Amp.AMPManager.GetInstance().AutoCast(res); + res = AutocastMode.AutoCast(res); return new RNNCell(res, boxedHandle).MoveModule(device, dtype); } } diff --git a/src/TorchSharp/PInvoke/LibTorchSharp.THSAmp.cs b/src/TorchSharp/PInvoke/LibTorchSharp.THSAmp.cs index a91d4816a..cfc9cda91 100644 --- a/src/TorchSharp/PInvoke/LibTorchSharp.THSAmp.cs +++ b/src/TorchSharp/PInvoke/LibTorchSharp.THSAmp.cs @@ -23,6 +23,8 @@ internal static partial class NativeMethods [DllImport("LibTorchSharp")] internal static extern bool THSAmp_is_autocast_cache_enabled(); [DllImport("LibTorchSharp")] + internal static extern bool THSAmp_is_autocast_available(int device_type); + [DllImport("LibTorchSharp")] internal static extern bool THSAmp_is_autocast_enabled(int device_type); [DllImport("LibTorchSharp")] internal static extern sbyte THSAmp_get_autocast_dtype(int device_type); @@ -31,7 +33,7 @@ internal static partial class NativeMethods [DllImport("LibTorchSharp")] internal static extern int THSAmp_autocast_decrement_nesting(); [DllImport("LibTorchSharp")] - internal static extern void THSAmp_set_autocast_enabled(bool enabled); + internal static extern void THSAmp_set_autocast_enabled(int device_type, bool enabled); [DllImport("LibTorchSharp")] internal static extern void THSAmp_set_autocast_cache_enabled(bool enabled); [DllImport("LibTorchSharp")] diff --git a/src/TorchSharp/Tensor/Tensor.LinearAlgebra.cs b/src/TorchSharp/Tensor/Tensor.LinearAlgebra.cs index 9f62cda4a..6289990a4 100644 --- a/src/TorchSharp/Tensor/Tensor.LinearAlgebra.cs +++ b/src/TorchSharp/Tensor/Tensor.LinearAlgebra.cs @@ -1,6 +1,7 @@ // Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information. using System; using System.Linq; +using TorchSharp.Amp; using static TorchSharp.PInvoke.NativeMethods; namespace TorchSharp @@ -171,7 +172,7 @@ public Tensor matmul(Tensor target) { var res = THSTensor_matmul(Handle, target.Handle); if (res == IntPtr.Zero) { CheckForErrors(); } - res = Amp.AMPManager.GetInstance().AutoCast(res); + res = AutocastMode.AutoCast(res); return new Tensor(res); } @@ -184,7 +185,7 @@ public Tensor mm(Tensor target) { var res = THSTensor_mm(Handle, target.Handle); if (res == IntPtr.Zero) { CheckForErrors(); } - res = Amp.AMPManager.GetInstance().AutoCast(res); + res = AutocastMode.AutoCast(res); return new Tensor(res); } @@ -197,7 +198,7 @@ public Tensor mv(Tensor target) { var res = THSTensor_mv(Handle, target.Handle); if (res == IntPtr.Zero) { CheckForErrors(); } - res = Amp.AMPManager.GetInstance().AutoCast(res); + res = AutocastMode.AutoCast(res); return new Tensor(res); } diff --git a/src/TorchSharp/Tensor/Tensor.Math.cs b/src/TorchSharp/Tensor/Tensor.Math.cs index 4970a9658..32db3a478 100644 --- a/src/TorchSharp/Tensor/Tensor.Math.cs +++ b/src/TorchSharp/Tensor/Tensor.Math.cs @@ -1,6 +1,7 @@ // Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information. #nullable enable using System; +using TorchSharp.Amp; using static TorchSharp.PInvoke.NativeMethods; namespace TorchSharp @@ -270,7 +271,7 @@ public Tensor addmm(Tensor mat1, Tensor mat2, float beta = 1, float alpha = 1) var res = THSTensor_addmm(Handle, mat1.Handle, mat2.Handle, beta, alpha); if (res == IntPtr.Zero) CheckForErrors(); - res = Amp.AMPManager.GetInstance().AutoCast(res); + res = AutocastMode.AutoCast(res); return new Tensor(res); } @@ -302,7 +303,7 @@ public Tensor addmv(Tensor mat, Tensor vec, float beta = 1.0f, float alpha = 1.0 var res = THSTensor_addmv(Handle, mat.Handle, vec.Handle, beta, alpha); if (res == IntPtr.Zero) CheckForErrors(); - res = Amp.AMPManager.GetInstance().AutoCast(res); + res = AutocastMode.AutoCast(res); return new Tensor(res); } @@ -1387,6 +1388,7 @@ public Tensor pow(Tensor exponent) { var res = THSTensor_pow(Handle, exponent.Handle); if (res == IntPtr.Zero) { CheckForErrors(); } + res = AutocastMode.AutoCast(res, ScalarType.Float32); //https://pytorch.org/docs/stable/amp.html#cuda-ops-that-can-autocast-to-float32 return new Tensor(res); } diff --git a/src/TorchSharp/Tensor/Tensor.Trig.cs b/src/TorchSharp/Tensor/Tensor.Trig.cs index d377e967c..39e8f048b 100644 --- a/src/TorchSharp/Tensor/Tensor.Trig.cs +++ b/src/TorchSharp/Tensor/Tensor.Trig.cs @@ -1,6 +1,7 @@ // Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information. using System; using System.Diagnostics.Contracts; +using TorchSharp.Amp; using static TorchSharp.PInvoke.NativeMethods; namespace TorchSharp @@ -39,6 +40,7 @@ public Tensor asin() var res = THSTensor_asin(Handle); if (res == IntPtr.Zero) CheckForErrors(); + res = AutocastMode.AutoCast(res, ScalarType.Float32); return new Tensor(res); } @@ -70,6 +72,7 @@ public Tensor acos() var res = THSTensor_acos(Handle); if (res == IntPtr.Zero) CheckForErrors(); + res = AutocastMode.AutoCast(res, ScalarType.Float32); return new Tensor(res); } diff --git a/src/TorchSharp/Tensor/Tensor.cs b/src/TorchSharp/Tensor/Tensor.cs index 696e07d13..0fe6eb971 100644 --- a/src/TorchSharp/Tensor/Tensor.cs +++ b/src/TorchSharp/Tensor/Tensor.cs @@ -45,13 +45,7 @@ public partial class Tensor : IDisposable }*/ internal Tensor(IntPtr handle) { - //TODO: Add Autocast/AMP ScopeManager, need improve this.. 1) is not threadsafe and may have big problem while casting and uncasting. - //DANGER: DONT USE THIS ON PRODUCTION - /*if (AMPManager.GetInstance().IsEnabled) { - this.handle = AMPManager.GetInstance().Work(handle, this.handle); //MMM.... This is the more abstract of any method Tensor right???? - } else {*/ - this.handle = handle; - //} + this.handle = handle; System.Threading.Interlocked.Increment(ref _totalCount); _peakCount = Math.Max(_totalCount, _peakCount); OwningDisposeScope = DisposeScopeManager.ThreadSingleton.RegisterOnCurrentDisposeScope(this); @@ -3119,7 +3113,7 @@ public Tensor baddbmm(Tensor batch1, Tensor batch2, float beta = 1, float alpha { var res = NativeMethods.THSTensor_baddbmm(Handle, batch1.Handle, batch2.Handle, beta, alpha); if (res == IntPtr.Zero) { CheckForErrors(); } - res = Amp.AMPManager.GetInstance().AutoCast(res); + res = AutocastMode.AutoCast(res); return new Tensor(res); } @@ -3132,7 +3126,7 @@ public Tensor bmm(Tensor batch2) { var res = NativeMethods.THSTensor_bmm(Handle, batch2.Handle); if (res == IntPtr.Zero) { CheckForErrors(); } - res = Amp.AMPManager.GetInstance().AutoCast(res); + res = AutocastMode.AutoCast(res); return new Tensor(res); } @@ -4488,7 +4482,7 @@ public Tensor prelu(Tensor target) { var res = NativeMethods.THSTensor_prelu(Handle, target.Handle); if (res == IntPtr.Zero) { CheckForErrors(); } - res = Amp.AMPManager.GetInstance().AutoCast(res); + res = AutocastMode.AutoCast(res); return new Tensor(res); } diff --git a/src/TorchSharp/Tensor/torch.Autocast.cs b/src/TorchSharp/Tensor/torch.Autocast.cs index d817e4ab9..12e86d46d 100644 --- a/src/TorchSharp/Tensor/torch.Autocast.cs +++ b/src/TorchSharp/Tensor/torch.Autocast.cs @@ -10,6 +10,11 @@ public static bool is_autocast_cache_enabled() return THSAmp_is_autocast_cache_enabled(); } + public static bool is_autocast_available(DeviceType device) + { + //https://github.com/pytorch/pytorch/blob/main/torch/csrc/autograd/init.cpp + return THSAmp_is_autocast_available((int)device); + } public static bool is_autocast_enabled(DeviceType device) { return THSAmp_is_autocast_enabled((int)device); @@ -18,11 +23,6 @@ public static bool is_autocast_enabled(DeviceType device) public static ScalarType get_autocast_dtype(DeviceType device) { return (ScalarType)THSAmp_get_autocast_dtype((int)device); - /*if (device.type == DeviceType.CPU) - return get_autocast_cpu_dtype(); - if (device.type == DeviceType.CUDA) - return get_autocast_gpu_dtype(); - return ScalarType.Float32;*/ } @@ -36,9 +36,14 @@ public static int autocast_decrement_nesting() return THSAmp_autocast_decrement_nesting(); } - public static void set_autocast_enabled(bool enabled) + public static void set_autocast_enabled(DeviceType device, bool enabled) + { + THSAmp_set_autocast_enabled((int)device,enabled); + } + + public static void set_autocast_dtype(DeviceType device, ScalarType dtype) { - THSAmp_set_autocast_enabled(enabled); + THSAmp_set_autocast_dtype((int)device, (sbyte)dtype); } public static void set_autocast_cache_enabled(bool enabled) { diff --git a/src/TorchSharp/TorchSharp.csproj b/src/TorchSharp/TorchSharp.csproj index 054f5c18a..d5cb1135d 100644 --- a/src/TorchSharp/TorchSharp.csproj +++ b/src/TorchSharp/TorchSharp.csproj @@ -19,6 +19,10 @@ + + + + diff --git a/src/TorchSharp/Utils/UnorderedMap.cs b/src/TorchSharp/Utils/UnorderedMap.cs index 92446906a..6eb073b1d 100644 --- a/src/TorchSharp/Utils/UnorderedMap.cs +++ b/src/TorchSharp/Utils/UnorderedMap.cs @@ -6,6 +6,65 @@ namespace TorchSharp.Utils { + public class Dictionary : Dictionary, TValue>, IDictionary, TValue> + { + + public TValue this[TKey1 key1, TKey2 key2] { + get { return base[Tuple.Create(key1, key2)]; } + set { base[Tuple.Create(key1, key2)] = value; } + } + + public void Add(TKey1 key1, TKey2 key2, TValue value) + { + base.Add(Tuple.Create(key1, key2), value); + } + + public bool ContainsKey(TKey1 key1, TKey2 key2) + { + return base.ContainsKey(Tuple.Create(key1, key2)); + } + } + + public class UnorderedMap : Dictionary, IDisposable + { + bool disposedValue; + public new TValue this[TKey1 tk1, TKey2 tk2] { + get { + /*if (!this.ContainsKey(tk) && default_dict == null) + return default_dict;*/ + if (this.ContainsKey(tk1, tk2)) + return base[tk1, tk2]; + return default; + } + set { + if (!this.ContainsKey(tk1, tk2)) { + this.Add(tk1, tk2, value); + return; + } + base[tk1, tk2] = value; + } + } + + protected virtual void Dispose(bool disposing) + { + if (!disposedValue) { + if (disposing) { + base.Clear(); + // TODO: dispose managed state (managed objects) + } + + // TODO: free unmanaged resources (unmanaged objects) and override finalizer + // TODO: set large fields to null + disposedValue = true; + } + } + public void Dispose() + { + // Do not change this code. Put cleanup code in 'Dispose(bool disposing)' method + Dispose(disposing: true); + GC.SuppressFinalize(this); + } + } public class UnorderedMap : Dictionary, IDisposable { bool disposedValue; From 9f4a48b3a31ada2d52375c045818796806937ff8 Mon Sep 17 00:00:00 2001 From: Dimitri Date: Fri, 18 Oct 2024 15:55:37 -0300 Subject: [PATCH 26/65] Some Autocast f16, f32 --- src/Native/LibTorchSharp/THSNN.cpp | 7 ++ src/Native/LibTorchSharp/THSNN.h | 1 + src/TorchSharp/Amp/AutocastMode.cs | 72 ++++++++++++++++--- src/TorchSharp/NN/Activation/Softmin.cs | 2 + src/TorchSharp/NN/Activation/Softplus.cs | 2 + src/TorchSharp/NN/Bilinear.cs | 12 ++++ src/TorchSharp/NN/CosineSimilarity.cs | 3 + src/TorchSharp/NN/Losses.cs | 18 +++++ src/TorchSharp/NN/Normalization/GroupNorm.cs | 3 + src/TorchSharp/NN/Normalization/LayerNorm.cs | 3 + src/TorchSharp/NN/PairwiseDistance.cs | 2 + src/TorchSharp/NN/Vision.cs | 11 +++ src/TorchSharp/Special.cs | 5 +- src/TorchSharp/Tensor/Tensor.LinearAlgebra.cs | 14 ++++ src/TorchSharp/Tensor/Tensor.Math.cs | 50 +++++++++++++ src/TorchSharp/Tensor/Tensor.Trig.cs | 11 +++ src/TorchSharp/Tensor/Tensor.cs | 15 ++++ .../Tensor/torch.OtherOperations.cs | 2 + 18 files changed, 224 insertions(+), 9 deletions(-) diff --git a/src/Native/LibTorchSharp/THSNN.cpp b/src/Native/LibTorchSharp/THSNN.cpp index 430c17f5e..a9ac0bbcf 100644 --- a/src/Native/LibTorchSharp/THSNN.cpp +++ b/src/Native/LibTorchSharp/THSNN.cpp @@ -1336,6 +1336,13 @@ Tensor THSNN_scaled_dot_product_attention(const Tensor query, const Tensor key, CATCH_TENSOR(torch::scaled_dot_product_attention(*query, *key, *value, mask, p, casual)); } +Tensor THSNN_normalize(Tensor input, float p, const int64_t* dim, float eps, Tensor out) +{ + auto opts = torch::nn::functional::NormalizeFuncOptions().p(p).eps(eps).dim(*dim); + CATCH_TENSOR(torch::nn::functional::normalize(*input, opts)) + //CATCH_TENSOR(torch::scaled_dot_product_attention(*query, *key, *value, mask, p, casual)); +} + void THSNN_Print_Module(const NNModule module) { std::ostringstream oss; const std::string name = module->get()->name(); diff --git a/src/Native/LibTorchSharp/THSNN.h b/src/Native/LibTorchSharp/THSNN.h index 65edf3c2e..cf79593eb 100644 --- a/src/Native/LibTorchSharp/THSNN.h +++ b/src/Native/LibTorchSharp/THSNN.h @@ -579,6 +579,7 @@ EXPORT_API(Tensor) THSNN_PairwiseDistance_forward(const NNModule module, const EXPORT_API(Tensor) THSNN_scaled_dot_product_attention(const Tensor query, const Tensor key, const Tensor value, const Tensor attention_mask, double p, bool casual); +EXPORT_API(Tensor) THSNN_normalize(const Tensor input, float p, const int64_t* dim, float eps, Tensor out); // Initializers EXPORT_API(void) THSNN_initUniform(Tensor twrapper, double low, double high); diff --git a/src/TorchSharp/Amp/AutocastMode.cs b/src/TorchSharp/Amp/AutocastMode.cs index dacfc9721..e6200a3c8 100644 --- a/src/TorchSharp/Amp/AutocastMode.cs +++ b/src/TorchSharp/Amp/AutocastMode.cs @@ -43,8 +43,6 @@ public torch.ScalarType GetFastType() } private AutocastMode(torch.Device dev, torch.ScalarType? dtype = null, bool enabled=true, bool? cache_enabled = null) { - /*dtype_by_methods[nameof(torch.matmul), DeviceType.CUDA] = torch.ScalarType.Float16; - dtype_by_methods[nameof(torch.matmul), DeviceType.CUDA] = torch.ScalarType.Float16;*/ //https://pytorch.org/docs/stable/amp.html#cuda-ops-that-can-autocast-to-float16 if (dtype == null) dtype = torch.get_autocast_dtype(dev.type); @@ -52,9 +50,6 @@ private AutocastMode(torch.Device dev, torch.ScalarType? dtype = null, bool enab if (!torch.is_autocast_available(device)) throw new Exception($"User specified an unsupported autocast device_type {device}"); fast_dtype = torch.get_autocast_dtype(device); - //TODO: is_autocast_available(); - //IntPtr ptr = IntPtr.Zero; - _cache_enabled = torch.is_autocast_cache_enabled(); if (enabled && !torch.cuda_is_available() && dev.type == DeviceType.CUDA) //Is not available for doing multicast enabled = false; @@ -84,12 +79,55 @@ public static IntPtr AutoCast(IntPtr handle) { return ToIf(handle, GetInstance().GetFastType()); } + public static (IntPtr h1, IntPtr h2) AutoCast(IntPtr handle1, IntPtr handle2) + { + var ft = GetInstance().GetFastType(); + return (ToIf(handle1, ft), ToIf(handle2, ft)); + } + public static (IntPtr h1, IntPtr h2, IntPtr h3) AutoCast(IntPtr handle1, IntPtr handle2, IntPtr handle3) + { + var ft = GetInstance().GetFastType(); + return (ToIf(handle1, ft), ToIf(handle2, ft), ToIf(handle3, ft)); + } + public static (IntPtr h1, IntPtr h2) AutoCast(IntPtr handle1, IntPtr handle2, torch.ScalarType dtype) + { + return (ToIf(handle1, dtype), ToIf(handle2, dtype)); + } + + public static (IntPtr h1, IntPtr h2, IntPtr h3) AutoCast(IntPtr handle1, IntPtr handle2, IntPtr handle3, torch.ScalarType dtype) + { + return (ToIf(handle1, dtype), ToIf(handle2, dtype), ToIf(handle3, dtype)); + } + + + /*public static IntPtr[] AutoCast(params IntPtr[] handles) + { + var stsel =handles.Select(x => (torch.ScalarType)NativeMethods.THSTensor_type(x)); + if (AutocastMode.IsAutocastEnabled(this.device.type)) { + var st = (ScalarType)THSTensor_type(Handle); + var st1 = (ScalarType)THSTensor_type(tensor1.Handle); + var st2 = (ScalarType)THSTensor_type(tensor2.Handle); + var sts = new ScalarType[] { st, st1, st2 }; + if (sts.All(x => x == ScalarType.Float16)) { + var f16 = ScalarType.Float16; + handle = AutocastMode.AutoCast(handle, f16); + tensor1.handle = AutocastMode.AutoCast(tensor1.handle, f16); + tensor2.handle = AutocastMode.AutoCast(tensor2.handle, f16); + + } + var f32 = ScalarType.Float32; + if (sts.Any(x => x == f32)) { + handle = AutocastMode.AutoCast(handle, f32); + tensor1.handle = AutocastMode.AutoCast(tensor1.handle, f32); + tensor2.handle = AutocastMode.AutoCast(tensor2.handle, f32); + } + } + }*/ public static IntPtr AutoCast(IntPtr handle, torch.ScalarType dtype) { return ToIf(handle, dtype); } - public static torch.Tensor AutoCast(torch.Tensor tensor) { return new torch.Tensor(AutoCast(tensor.Handle)); @@ -97,16 +135,29 @@ public static torch.Tensor AutoCast(torch.Tensor tensor) } public static IntPtr To(IntPtr ptr, torch.ScalarType type) { - Debug.WriteLine($"{nameof(AutocastMode)} Tensor converting from: {(torch.ScalarType)NativeMethods.THSTensor_type(ptr)} to: {type}"); + Debug.WriteLine($"{nameof(AutocastMode)} Tensor converting from: {GetDtype(ptr)} to: {type}"); var res = NativeMethods.THSTensor_to_type(ptr, (sbyte)type); if (res == IntPtr.Zero) torch.CheckForErrors(); return res; } + + private static torch.ScalarType GetDtype(IntPtr ptr) + { + return (torch.ScalarType)NativeMethods.THSTensor_type(ptr); + } + + private static DeviceType GetDeviceType(IntPtr ptr) + { + return (DeviceType)NativeMethods.THSTensor_device_type(ptr); + } public static IntPtr ToIf(IntPtr ptr, torch.ScalarType type) { + if (!GetInstance()._enabled) return ptr; + if (GetDtype(ptr) == type) //if already have same dtype is not necesary convert to dtype, right??? + return ptr; /*if (!NativeMethods.THSAmp_is_autocast_enabled(NativeMethods.THSTensor_device_type(ptr))) return ptr;*/ var res = NativeMethods.THSTensor_to_type(ptr, (sbyte)type); @@ -116,7 +167,7 @@ public static IntPtr ToIf(IntPtr ptr, torch.ScalarType type) } public static IntPtr ToIf(IntPtr ptr, torch.ScalarType type, DeviceType device_type) { - bool is_elegible = (torch.ScalarType)NativeMethods.THSTensor_type(ptr) != torch.ScalarType.Float64 && (DeviceType)NativeMethods.THSTensor_device_type(ptr) == device_type; + bool is_elegible = GetDtype(ptr) != torch.ScalarType.Float64 && GetDeviceType(ptr) == device_type; if (!NativeMethods.THSAmp_is_autocast_enabled(NativeMethods.THSTensor_device_type(ptr))) return ptr; @@ -152,6 +203,11 @@ private void Dispose(bool disposing) torch.set_autocast_dtype(device, prev_fastdtype); torch.set_autocast_cache_enabled(prev_cache_enabled); } + + /*~AutocastMode() + { + + }*/ public void Dispose() { diff --git a/src/TorchSharp/NN/Activation/Softmin.cs b/src/TorchSharp/NN/Activation/Softmin.cs index e3fa3040a..2969d4dc3 100644 --- a/src/TorchSharp/NN/Activation/Softmin.cs +++ b/src/TorchSharp/NN/Activation/Softmin.cs @@ -1,5 +1,6 @@ // Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information. using System; +using TorchSharp.Amp; using static TorchSharp.torch; using static TorchSharp.PInvoke.NativeMethods; @@ -49,6 +50,7 @@ public static Softmin Softmin(long dim) { var handle = THSNN_Softmin_ctor(dim, out var boxedHandle); if (handle == IntPtr.Zero) { torch.CheckForErrors(); } + handle = AutocastMode.AutoCast(handle, ScalarType.Float32); //Should put this here??? return new Softmin(handle, boxedHandle); } diff --git a/src/TorchSharp/NN/Activation/Softplus.cs b/src/TorchSharp/NN/Activation/Softplus.cs index 7e46662d0..017754338 100644 --- a/src/TorchSharp/NN/Activation/Softplus.cs +++ b/src/TorchSharp/NN/Activation/Softplus.cs @@ -1,5 +1,6 @@ // Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information. using System; +using TorchSharp.Amp; using static TorchSharp.torch; using static TorchSharp.PInvoke.NativeMethods; @@ -50,6 +51,7 @@ public static Softplus Softplus(double beta = 1.0, double threshold = 20.0) { var handle = THSNN_Softplus_ctor(beta, threshold, out var boxedHandle); if (handle == IntPtr.Zero) { torch.CheckForErrors(); } + handle = AutocastMode.AutoCast(handle, ScalarType.Float32); //Should put this here return new Softplus(handle, boxedHandle); } diff --git a/src/TorchSharp/NN/Bilinear.cs b/src/TorchSharp/NN/Bilinear.cs index 8ba4efebb..f8fb7b7da 100644 --- a/src/TorchSharp/NN/Bilinear.cs +++ b/src/TorchSharp/NN/Bilinear.cs @@ -1,5 +1,6 @@ // Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information. using System; +using TorchSharp.Amp; using static TorchSharp.torch; using static TorchSharp.torch.nn; using static TorchSharp.PInvoke.NativeMethods; @@ -7,6 +8,7 @@ #nullable enable namespace TorchSharp { + using System.Linq; using Modules; namespace Modules @@ -93,6 +95,16 @@ public static Tensor bilinear(Tensor input1, Tensor input2, Tensor weight, Tenso IntPtr bPtr = bias?.Handle ?? IntPtr.Zero; var res = THSNN_functional_bilinear(input1.Handle, input2.Handle, weight.Handle, bPtr); if (res == IntPtr.Zero) { CheckForErrors(); } + /*if (AutocastMode.IsAutocastEnabled()) { + var st = input1.dtype; + var st1 = input2.dtype; + var st2 = weight.dtype; + var sts = new[] { st, st1, st2 }; + if (sts.All(x => x == ScalarType.Float16)) + (handle, tensor1.handle, tensor2.handle) = AutocastMode.AutoCast(handle, tensor1.handle, tensor2.handle, ScalarType.Float16); + if (sts.Any(x => x == ScalarType.Float32)) + (handle, tensor1.handle, tensor2.handle) = AutocastMode.AutoCast(handle, tensor1.handle, tensor2.handle, ScalarType.Float32); + }*/ return new Tensor(res); } } diff --git a/src/TorchSharp/NN/CosineSimilarity.cs b/src/TorchSharp/NN/CosineSimilarity.cs index b4c4802ae..99f9b05a1 100644 --- a/src/TorchSharp/NN/CosineSimilarity.cs +++ b/src/TorchSharp/NN/CosineSimilarity.cs @@ -1,5 +1,6 @@ // Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information. using System; +using TorchSharp.Amp; using static TorchSharp.torch; using static TorchSharp.PInvoke.NativeMethods; @@ -22,6 +23,7 @@ public override Tensor forward(Tensor input1, Tensor input2) { var res = THSNN_CosineSimilarity_forward(handle, input1.Handle, input2.Handle); if (res == IntPtr.Zero) { torch.CheckForErrors(); } + res= AutocastMode.AutoCast(res, ScalarType.Float32); return new Tensor(res); } } @@ -41,6 +43,7 @@ public static CosineSimilarity CosineSimilarity(long dim = 1, double eps = 1e-8) { var handle = THSNN_CosineSimilarity_ctor(dim, eps, out var boxedHandle); if (handle == IntPtr.Zero) { torch.CheckForErrors(); } + handle = AutocastMode.AutoCast(handle, ScalarType.Float32); return new CosineSimilarity(handle, boxedHandle); } diff --git a/src/TorchSharp/NN/Losses.cs b/src/TorchSharp/NN/Losses.cs index 5e514bef5..9aae89088 100644 --- a/src/TorchSharp/NN/Losses.cs +++ b/src/TorchSharp/NN/Losses.cs @@ -1,5 +1,6 @@ // Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information. using System; +using TorchSharp.Amp; using static TorchSharp.torch; using static TorchSharp.torch.nn; using static TorchSharp.PInvoke.NativeMethods; @@ -365,6 +366,7 @@ public static Tensor binary_cross_entropy_with_logits(Tensor input, Tensor targe { var res = THSNN_binary_cross_entropy_with_logits(input.Handle, target.Handle, weight?.Handle ?? IntPtr.Zero, (long)reduction, pos_weights?.Handle ?? IntPtr.Zero); if (res == IntPtr.Zero) { torch.CheckForErrors(); } + res = AutocastMode.AutoCast(res, ScalarType.Float32); return new Tensor(res); } @@ -435,6 +437,7 @@ public static Tensor cosine_embedding_loss(Tensor input1, Tensor input2, Tensor { var res = THSNN_cosine_embedding_loss(input1.Handle, input2.Handle, target.Handle, margin, (long)reduction); if (res == IntPtr.Zero) { torch.CheckForErrors(); } + res = AutocastMode.AutoCast(res, ScalarType.Float32); return new Tensor(res); } @@ -514,6 +517,7 @@ public static Tensor multi_label_margin_loss(Tensor input, Tensor target, Reduct { var res = THSNN_multilabel_margin_loss(input.Handle, target.Handle, (long)reduction); if (res == IntPtr.Zero) { torch.CheckForErrors(); } + res = AutocastMode.AutoCast(res, ScalarType.Float32); return new Tensor(res); } @@ -547,6 +551,7 @@ public static Tensor multi_margin_loss(Tensor input, Tensor target, int p = 1, d IntPtr h = (weight is null) ? IntPtr.Zero : weight.Handle; var res = THSNN_multi_margin_loss(input.Handle, target.Handle, p, margin, h, (long)reduction); if (res == IntPtr.Zero) { torch.CheckForErrors(); } + res = AutocastMode.AutoCast(res, ScalarType.Float32); return new Tensor(res); } @@ -561,6 +566,7 @@ public static Tensor mse_loss(Tensor input, Tensor target, Reduction reduction = { var res = THSNN_mse_loss(input.Handle, target.Handle, (long)reduction); if (res == IntPtr.Zero) { torch.CheckForErrors(); } + res = AutocastMode.AutoCast(res, ScalarType.Float32); return new Tensor(res); } @@ -620,6 +626,7 @@ public static Tensor kl_div(Tensor input, Tensor target, bool log_target = true, { var res = THSNN_kl_div_loss(input.Handle, target.Handle, (long)reduction, log_target); if (res == IntPtr.Zero) { torch.CheckForErrors(); } + res = AutocastMode.AutoCast(res, ScalarType.Float32); return new Tensor(res); } @@ -744,6 +751,7 @@ public override Tensor forward(Tensor input, Tensor target) var ii = ignore_index.HasValue ? ignore_index.Value : -100; var res = THSNN_cross_entropy(input.Handle, target.Handle, weight?.Handle ?? IntPtr.Zero, ii, ignore_index.HasValue, (long)reduction, label_smoothing); if (res == IntPtr.Zero) { torch.CheckForErrors(); } + return new Tensor(res); } @@ -776,6 +784,7 @@ public override Tensor forward(Tensor input, Tensor target) { var res = THSNN_binary_cross_entropy_with_logits(input.Handle, target.Handle, weight?.Handle ?? IntPtr.Zero, (long)reduction, pos_weights?.Handle ?? IntPtr.Zero); if (res == IntPtr.Zero) { torch.CheckForErrors(); } + res = AutocastMode.AutoCast(res, ScalarType.Float32); return new Tensor(res); } @@ -793,6 +802,7 @@ public override Tensor forward(Tensor input1, Tensor input2, Tensor target) { var res = THSNN_cosine_embedding_loss(input1.Handle, input2.Handle, target.Handle, margin, (long)reduction); if (res == IntPtr.Zero) { torch.CheckForErrors(); } + res = AutocastMode.AutoCast(res, ScalarType.Float32); return new Tensor(res); } @@ -829,6 +839,7 @@ public override Tensor forward(Tensor input, Tensor target) { var res = THSNN_hinge_embedding_loss(input.Handle, target.Handle, margin, (long)reduction); if (res == IntPtr.Zero) { torch.CheckForErrors(); } + res = AutocastMode.AutoCast(res, ScalarType.Float32); return new Tensor(res); } @@ -863,6 +874,7 @@ public override Tensor forward(Tensor input1, Tensor input2, Tensor target) { var res = THSNN_margin_ranking_loss(input1.Handle, input2.Handle, target.Handle, margin, (long)reduction); if (res == IntPtr.Zero) { torch.CheckForErrors(); } + res = AutocastMode.AutoCast(res, ScalarType.Float32); return new Tensor(res); } @@ -942,6 +954,7 @@ public override Tensor forward(Tensor input, Tensor target) { var res = THSNN_l1_loss(input.Handle, target.Handle, (long)reduction); if (res == IntPtr.Zero) { torch.CheckForErrors(); } + res = AutocastMode.AutoCast(res, ScalarType.Float32); return new Tensor(res); } } @@ -956,6 +969,7 @@ public override Tensor forward(Tensor input, Tensor target) { var res = THSNN_nll_loss(input.Handle, target.Handle, weight?.Handle ?? IntPtr.Zero, (long)reduction); if (res == IntPtr.Zero) { torch.CheckForErrors(); } + res = AutocastMode.AutoCast(res, ScalarType.Float32); return new Tensor(res); } } @@ -973,6 +987,7 @@ public override Tensor forward(Tensor input, Tensor target) { var res = THSNN_poisson_loss(input.Handle, target.Handle, log_input, full, eps, (long)reduction); if (res == IntPtr.Zero) { torch.CheckForErrors(); } + res = AutocastMode.AutoCast(res, ScalarType.Float32); return new Tensor(res); } @@ -1046,6 +1061,7 @@ public override Tensor forward(Tensor input, Tensor target) { var res = THSNN_smooth_l1_loss(input.Handle, target.Handle, (long)reduction, beta); if (res == IntPtr.Zero) { torch.CheckForErrors(); } + res = AutocastMode.AutoCast(res, ScalarType.Float32); return new Tensor(res); } @@ -1062,6 +1078,7 @@ public override Tensor forward(Tensor input, Tensor target) { var res = THSNN_soft_margin_loss(input.Handle, target.Handle, (long)reduction); if (res == IntPtr.Zero) { torch.CheckForErrors(); } + res = AutocastMode.AutoCast(res, ScalarType.Float32); return new Tensor(res); } } @@ -1080,6 +1097,7 @@ public override Tensor forward(Tensor anchor, Tensor positive, Tensor negative) { var res = THSNN_triplet_margin_loss(anchor.Handle, positive.Handle, negative.Handle, margin, p, eps, swap, (long)reduction); if (res == IntPtr.Zero) { torch.CheckForErrors(); } + res = AutocastMode.AutoCast(res, ScalarType.Float32); return new Tensor(res); } diff --git a/src/TorchSharp/NN/Normalization/GroupNorm.cs b/src/TorchSharp/NN/Normalization/GroupNorm.cs index e63b5c8c7..eca7e1665 100644 --- a/src/TorchSharp/NN/Normalization/GroupNorm.cs +++ b/src/TorchSharp/NN/Normalization/GroupNorm.cs @@ -1,5 +1,6 @@ // Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information. using System; +using TorchSharp.Amp; using static TorchSharp.torch; using static TorchSharp.PInvoke.NativeMethods; @@ -25,6 +26,7 @@ public override Tensor forward(Tensor tensor) if (tensor.Dimensions < 3) throw new ArgumentException($"Invalid number of dimensions for GroupNorm argument: {tensor.Dimensions}"); var res = THSNN_GroupNorm_forward(handle.DangerousGetHandle(), tensor.Handle); if (res == IntPtr.Zero) { torch.CheckForErrors(); } + res= AutocastMode.AutoCast(res, ScalarType.Float32); return new Tensor(res); } @@ -79,6 +81,7 @@ public static GroupNorm GroupNorm(long num_groups, long num_channels, double eps unsafe { var handle = THSNN_GroupNorm_ctor(num_groups, num_channels, eps, affine, out var boxedHandle); if (handle == IntPtr.Zero) { torch.CheckForErrors(); } + handle= AutocastMode.AutoCast(handle, ScalarType.Float32); return new GroupNorm(handle, boxedHandle).MoveModule(device, dtype); } } diff --git a/src/TorchSharp/NN/Normalization/LayerNorm.cs b/src/TorchSharp/NN/Normalization/LayerNorm.cs index 6ed8dae45..ca53a3733 100644 --- a/src/TorchSharp/NN/Normalization/LayerNorm.cs +++ b/src/TorchSharp/NN/Normalization/LayerNorm.cs @@ -1,5 +1,6 @@ // Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information. using System; +using TorchSharp.Amp; using static TorchSharp.torch; using static TorchSharp.torch.nn; using static TorchSharp.PInvoke.NativeMethods; @@ -28,9 +29,11 @@ internal LayerNorm(long[] normalized_shape, double eps, bool elementwise_affine, if (elementwise_affine) { weight = Parameter(torch.empty(normalized_shape, dtype, device)); + //weight.handle = AutocastMode.AutoCast(weight.handle, ScalarType.Float32); //This is correct??? if (bias) { this.bias = Parameter(torch.empty(normalized_shape, dtype, device)); + //bias.handle = AutocastMode.AutoCast(bias.handle, ScalarType.Float32); //This is correct??? } } diff --git a/src/TorchSharp/NN/PairwiseDistance.cs b/src/TorchSharp/NN/PairwiseDistance.cs index d652677dc..bac5bace2 100644 --- a/src/TorchSharp/NN/PairwiseDistance.cs +++ b/src/TorchSharp/NN/PairwiseDistance.cs @@ -1,5 +1,6 @@ // Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information. using System; +using TorchSharp.Amp; using static TorchSharp.torch; using static TorchSharp.PInvoke.NativeMethods; @@ -41,6 +42,7 @@ public static PairwiseDistance PairwiseDistance(double p = 2.0, double eps = 1e- { var handle = THSNN_PairwiseDistance_ctor(p, eps, keep_dim, out var boxedHandle); if (handle == IntPtr.Zero) { torch.CheckForErrors(); } + handle = AutocastMode.AutoCast(handle, ScalarType.Float32); return new PairwiseDistance(handle, boxedHandle); } diff --git a/src/TorchSharp/NN/Vision.cs b/src/TorchSharp/NN/Vision.cs index 5dd5fe6e2..654bef049 100644 --- a/src/TorchSharp/NN/Vision.cs +++ b/src/TorchSharp/NN/Vision.cs @@ -1,5 +1,7 @@ // Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information. using System; +using System.Linq; +using TorchSharp.Amp; using static TorchSharp.PInvoke.NativeMethods; #nullable enable @@ -164,8 +166,17 @@ public static Tensor pad(Tensor input, long pad, PaddingModes mode = PaddingMode public static Tensor grid_sample(Tensor input, Tensor grid, GridSampleMode mode = GridSampleMode.Bilinear, GridSamplePaddingMode padding_mode = GridSamplePaddingMode.Zeros, bool? align_corners = null) { byte ac = (byte)((align_corners.HasValue) ? (align_corners.Value ? 1 : 2) : 0); + if (AutocastMode.IsAutocastEnabled()) { + var sts = new[] { input.dtype, grid.dtype }; + if (sts.All(x => x == ScalarType.Float16)) + (input.handle, grid.handle) = AutocastMode.AutoCast(input.handle, grid.handle, ScalarType.Float16); + if (sts.Any(x => x == ScalarType.Float32)) + (input.handle, grid.handle) = AutocastMode.AutoCast(input.handle, grid.handle, ScalarType.Float32); + } + var res = THSNN_grid_sample(input.Handle, grid.Handle, (byte)mode, (byte)padding_mode, ac); if (res == IntPtr.Zero) { torch.CheckForErrors(); } + return new Tensor(res); } diff --git a/src/TorchSharp/Special.cs b/src/TorchSharp/Special.cs index 59b98e91b..e27698477 100644 --- a/src/TorchSharp/Special.cs +++ b/src/TorchSharp/Special.cs @@ -1,5 +1,6 @@ // Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information. using System; +using TorchSharp.Amp; using static TorchSharp.PInvoke.NativeMethods; namespace TorchSharp @@ -674,10 +675,11 @@ public static Tensor logit(Tensor input) /// public static Tensor log_softmax(Tensor input, long dim, ScalarType? dtype = null) { - var dt = dtype.HasValue ? dtype.Value : input.dtype; + var dt = dtype ?? input.dtype; var res = THSSpecial_log_softmax(input.Handle, dim, (sbyte)dt); if (res == IntPtr.Zero) torch.CheckForErrors(); + res = AutocastMode.AutoCast(res, ScalarType.Float32); return new Tensor(res); } @@ -745,6 +747,7 @@ public static Tensor softmax(Tensor input, long dim, ScalarType? dtype = null) var res = THSSpecial_softmax(input.Handle, dim, (sbyte)dt); if (res == IntPtr.Zero) torch.CheckForErrors(); + res = AutocastMode.AutoCast(res, ScalarType.Float32); return new Tensor(res); } diff --git a/src/TorchSharp/Tensor/Tensor.LinearAlgebra.cs b/src/TorchSharp/Tensor/Tensor.LinearAlgebra.cs index 6289990a4..079c72e3e 100644 --- a/src/TorchSharp/Tensor/Tensor.LinearAlgebra.cs +++ b/src/TorchSharp/Tensor/Tensor.LinearAlgebra.cs @@ -18,6 +18,13 @@ public partial class Tensor public Tensor tensordot(Tensor b, long[] dims1, long[] dims2) { IntPtr res; + if (AutocastMode.IsAutocastEnabled()) { + var sts = new[] { this.dtype, b.dtype }; + if (sts.All(x => x == ScalarType.Float16)) + (handle, b.handle) = AutocastMode.AutoCast(handle, b.handle, ScalarType.Float16); + if (sts.Any(x => x == ScalarType.Float32)) + (handle, b.handle) = AutocastMode.AutoCast(handle, b.handle, ScalarType.Float32); + } unsafe { fixed (long* pdims1 = dims1, pdims2 = dims2) { res = THSLinalg_tensordot(Handle, b.Handle,(IntPtr)pdims1, dims1.Length,(IntPtr)pdims2, dims2.Length); @@ -248,6 +255,13 @@ public Tensor vdot(Tensor target) public Tensor dot(Tensor target) { if (shape.Length != 1 || target.shape.Length != 1 || shape[0] != target.shape[0]) throw new InvalidOperationException("dot arguments must have the same shape."); + if (AutocastMode.IsAutocastEnabled()) { + var sts = new[] { this.dtype, target.dtype }; + if (sts.All(x => x == ScalarType.Float16)) + (handle, target.handle) = AutocastMode.AutoCast(handle, target.handle, ScalarType.Float16); + if (sts.Any(x => x == ScalarType.Float32)) + (handle, target.handle) = AutocastMode.AutoCast(handle, target.handle, ScalarType.Float32); + } var res = THSTensor_dot(Handle, target.Handle); if (res == IntPtr.Zero) { CheckForErrors(); } return new Tensor(res); diff --git a/src/TorchSharp/Tensor/Tensor.Math.cs b/src/TorchSharp/Tensor/Tensor.Math.cs index 32db3a478..0fec7e12f 100644 --- a/src/TorchSharp/Tensor/Tensor.Math.cs +++ b/src/TorchSharp/Tensor/Tensor.Math.cs @@ -1,6 +1,7 @@ // Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information. #nullable enable using System; +using System.Linq; using TorchSharp.Amp; using static TorchSharp.PInvoke.NativeMethods; @@ -158,6 +159,7 @@ public Tensor addbmm(Tensor batch1, Tensor batch2, float beta = 1, float alpha = var res = THSTensor_addbmm(Handle, batch1.Handle, batch2.Handle, beta, alpha); if (res == IntPtr.Zero) CheckForErrors(); + res = AutocastMode.AutoCast(res); return new Tensor(res); } @@ -187,6 +189,16 @@ public Tensor addbmm_(Tensor batch1, Tensor batch2, float beta = 1, float alpha /// public Tensor addcdiv(Tensor tensor1, Tensor tensor2, Scalar value) { + if (AutocastMode.IsAutocastEnabled(this.device.type)) { + var st = (ScalarType)THSTensor_type(Handle); + var st1 = (ScalarType)THSTensor_type(tensor1.Handle); + var st2 = (ScalarType)THSTensor_type(tensor2.Handle); + var sts = new[] { st, st1, st2 }; + if (sts.All(x => x == ScalarType.Float16)) + (handle, tensor1.handle, tensor2.handle) = AutocastMode.AutoCast(handle, tensor1.handle, tensor2.handle, ScalarType.Float16); + if (sts.Any(x => x == ScalarType.Float32)) + (handle, tensor1.handle, tensor2.handle) = AutocastMode.AutoCast(handle, tensor1.handle, tensor2.handle, ScalarType.Float32); + } var res = THSTensor_addcdiv(Handle, tensor1.Handle, tensor2.Handle, value.Handle); if (res == IntPtr.Zero) CheckForErrors(); @@ -238,6 +250,23 @@ public Tensor addcdiv_(Tensor tensor1, Tensor tensor2) /// public Tensor addcmul(Tensor tensor1, Tensor tensor2, Scalar value) { + if (AutocastMode.IsAutocastEnabled(this.device.type)) { + /* + * These ops don’t require a particular dtype for stability, but take multiple inputs and require that the inputs’ dtypes match. + * If all of the inputs are float16, the op runs in float16. + * If any of the inputs is float32, autocast casts all inputs to float32 and runs the op in float32. + * https://pytorch.org/docs/stable/amp.html + */ + var st = (ScalarType)THSTensor_type(Handle); + var st1 = (ScalarType)THSTensor_type(tensor1.Handle); + var st2 = (ScalarType)THSTensor_type(tensor2.Handle); + var sts = new[] { st, st1, st2 }; + if (sts.All(x => x == ScalarType.Float16)) + (handle, tensor1.handle, tensor2.handle) = AutocastMode.AutoCast(handle, tensor1.handle, tensor2.handle, ScalarType.Float16); + if (sts.Any(x => x == ScalarType.Float32)) + (handle, tensor1.handle, tensor2.handle) = AutocastMode.AutoCast(handle, tensor1.handle, tensor2.handle, ScalarType.Float32); + } + var res = THSTensor_addcmul(Handle, tensor1.Handle, tensor2.Handle, value.Handle); if (res == IntPtr.Zero) CheckForErrors(); @@ -335,6 +364,7 @@ public Tensor addr(Tensor vec1, Tensor vec2, float beta = 1.0f, float alpha = 1. var res = THSTensor_addr(Handle, vec1.Handle, vec2.Handle, beta, alpha); if (res == IntPtr.Zero) CheckForErrors(); + res = AutocastMode.AutoCast(res); return new Tensor(res); } @@ -649,6 +679,7 @@ public Tensor cumsum(long dim, ScalarType? type = null) { var res = THSTensor_cumsum(Handle, dim, type.HasValue, (sbyte)type.GetValueOrDefault()); if (res == IntPtr.Zero) { CheckForErrors(); } + res = AutocastMode.AutoCast(res, ScalarType.Float32); return new Tensor(res); } @@ -663,6 +694,7 @@ public Tensor cumprod(long dim, ScalarType? type = null) { var res = THSTensor_cumprod(Handle, dim, type.HasValue, (sbyte)type.GetValueOrDefault()); if (res == IntPtr.Zero) { CheckForErrors(); } + res = AutocastMode.AutoCast(res, ScalarType.Float32); return new Tensor(res); } @@ -757,6 +789,7 @@ public Tensor exp() { var res = THSTensor_exp(Handle); if (res == IntPtr.Zero) { CheckForErrors(); } + res = AutocastMode.AutoCast(res, ScalarType.Float32); return new Tensor(res); } @@ -789,6 +822,7 @@ public Tensor expm1() { var res = THSTensor_expm1(Handle); if (res == IntPtr.Zero) { CheckForErrors(); } + res = AutocastMode.AutoCast(res, ScalarType.Float32); return new Tensor(res); } @@ -1028,6 +1062,7 @@ public Tensor log() { var res = THSTensor_log(Handle); if (res == IntPtr.Zero) { CheckForErrors(); } + res = AutocastMode.AutoCast(res, ScalarType.Float32); return new Tensor(res); } @@ -1111,6 +1146,7 @@ public Tensor log10() var res = THSTensor_log10(Handle); if (res == IntPtr.Zero) CheckForErrors(); + res = AutocastMode.AutoCast(res, ScalarType.Float32); return new Tensor(res); } @@ -1134,6 +1170,7 @@ public Tensor log1p() var res = THSTensor_log1p(Handle); if (res == IntPtr.Zero) CheckForErrors(); + res = AutocastMode.AutoCast(res, ScalarType.Float32); return new Tensor(res); } @@ -1157,6 +1194,7 @@ public Tensor log2() var res = THSTensor_log2(Handle); if (res == IntPtr.Zero) CheckForErrors(); + res = AutocastMode.AutoCast(res, ScalarType.Float32); return new Tensor(res); } @@ -1413,6 +1451,7 @@ public Tensor pow(Scalar exponent) { var res = THSTensor_pow_scalar(Handle, exponent.Handle); if (res == IntPtr.Zero) { CheckForErrors(); } + res = AutocastMode.AutoCast(res, ScalarType.Float32); return new Tensor(res); } @@ -1437,6 +1476,7 @@ public Tensor reciprocal() var res = THSTensor_reciprocal(Handle); if (res == IntPtr.Zero) CheckForErrors(); + res = AutocastMode.AutoCast(res, ScalarType.Float32); return new Tensor(res); } @@ -1532,6 +1572,7 @@ public Tensor rsqrt() { var res = THSTensor_rsqrt(Handle); if (res == IntPtr.Zero) { CheckForErrors(); } + res = AutocastMode.AutoCast(res, ScalarType.Float32); return new Tensor(res); } @@ -1793,6 +1834,15 @@ public Tensor true_divide_(Scalar other) return this; } + /*public Tensor rtruediv_(Tensor other) + { + var res = THSTensor_true_divide(other.Handle, Handle); + if(res == IntPtr.Zero) + CheckForErrors(); + res = AutocastMode.AutoCast(res, ScalarType.Float32); + return new Tensor(res); + }*/ + /// /// Returns a new tensor with the truncated integer values of the elements of input. /// diff --git a/src/TorchSharp/Tensor/Tensor.Trig.cs b/src/TorchSharp/Tensor/Tensor.Trig.cs index 39e8f048b..86e5f0865 100644 --- a/src/TorchSharp/Tensor/Tensor.Trig.cs +++ b/src/TorchSharp/Tensor/Tensor.Trig.cs @@ -1,6 +1,7 @@ // Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information. using System; using System.Diagnostics.Contracts; +using System.Linq; using TorchSharp.Amp; using static TorchSharp.PInvoke.NativeMethods; @@ -143,6 +144,13 @@ public Tensor atan_() /// The second tensor public Tensor atan2(Tensor other) { + if (AutocastMode.IsAutocastEnabled()) { + var sts = new[] { this.dtype, other.dtype }; + if (sts.All(x => x == ScalarType.Float16)) + (handle, other.handle) = AutocastMode.AutoCast(handle, other.handle, ScalarType.Float16); + if (sts.Any(x => x == ScalarType.Float32)) + (handle, other.handle) = AutocastMode.AutoCast(handle, other.handle, ScalarType.Float32); + } var res = THSTensor_atan2(Handle, other.Handle); if (res == IntPtr.Zero) CheckForErrors(); @@ -219,6 +227,7 @@ public Tensor tan() var res = THSTensor_tan(Handle); if (res == IntPtr.Zero) CheckForErrors(); + res = AutocastMode.AutoCast(res, ScalarType.Float32); return new Tensor(res); } @@ -265,6 +274,7 @@ public Tensor sinh() var res = THSTensor_sinh(Handle); if (res == IntPtr.Zero) CheckForErrors(); + res = AutocastMode.AutoCast(res, ScalarType.Float32); return new Tensor(res); } @@ -288,6 +298,7 @@ public Tensor cosh() var res = THSTensor_cosh(Handle); if (res == IntPtr.Zero) CheckForErrors(); + res = AutocastMode.AutoCast(res, ScalarType.Float32); return new Tensor(res); } diff --git a/src/TorchSharp/Tensor/Tensor.cs b/src/TorchSharp/Tensor/Tensor.cs index 0fe6eb971..322c13116 100644 --- a/src/TorchSharp/Tensor/Tensor.cs +++ b/src/TorchSharp/Tensor/Tensor.cs @@ -3449,6 +3449,7 @@ public Tensor erfinv() { var res = NativeMethods.THSTensor_erfinv(Handle); if (res == IntPtr.Zero) { CheckForErrors(); } + res = AutocastMode.AutoCast(res, ScalarType.Float32); return new Tensor(res); } @@ -4417,6 +4418,7 @@ public Tensor dist(Tensor other, float p = 2.0f) { var res = NativeMethods.THSTensor_dist(Handle, other.Handle, p); if (res == IntPtr.Zero) { CheckForErrors(); } + res = AutocastMode.AutoCast(res, ScalarType.Float32); return new Tensor(res); } @@ -4428,6 +4430,7 @@ public Tensor norm(float p = 2.0f) { var res = NativeMethods.THSTensor_norm(Handle, p); if (res == IntPtr.Zero) { CheckForErrors(); } + res = AutocastMode.AutoCast(res, ScalarType.Float32); return new Tensor(res); } @@ -4438,6 +4441,7 @@ public Tensor norm(int dim, bool keepdim = false, float p = 2.0f) { var res = NativeMethods.THSTensor_norm_along_dimension(Handle, dim, keepdim, p); if (res == IntPtr.Zero) { CheckForErrors(); } + res = AutocastMode.AutoCast(res, ScalarType.Float32); return new Tensor(res); } @@ -4528,6 +4532,7 @@ public Tensor renorm(float p, long dim, float maxnorm) { var res = NativeMethods.THSTensor_renorm(Handle, p, dim, maxnorm); if (res == IntPtr.Zero) { CheckForErrors(); } + res = AutocastMode.AutoCast(res, ScalarType.Float32); return new Tensor(res); } @@ -4950,6 +4955,7 @@ public Tensor prod(ScalarType? type = null) { var res = NativeMethods.THSTensor_prod(Handle, type.HasValue, (sbyte)type.GetValueOrDefault()); if (res == IntPtr.Zero) { CheckForErrors(); } + res = AutocastMode.AutoCast(res, ScalarType.Float32); return new Tensor(res); } @@ -4960,6 +4966,7 @@ public Tensor prod(long dim, bool keepdim = false, ScalarType? type = null) { var res = NativeMethods.THSTensor_prod_along_dimensions(Handle, dim, keepdim, type.HasValue, (sbyte)type.GetValueOrDefault()); if (res == IntPtr.Zero) { CheckForErrors(); } + res = AutocastMode.AutoCast(res, ScalarType.Float32); return new Tensor(res); } @@ -4970,6 +4977,7 @@ public Tensor sum(ScalarType? type = null) { var res = NativeMethods.THSTensor_sum(Handle, type.HasValue, (sbyte)type.GetValueOrDefault()); if (res == IntPtr.Zero) { CheckForErrors(); } + res = AutocastMode.AutoCast(res, ScalarType.Float32); return new Tensor(res); } @@ -5844,6 +5852,13 @@ public Tensor scatter_(long dim, Tensor index, Tensor src) /// public Tensor scatter_add(long dim, Tensor index, Tensor src) { + if (AutocastMode.IsAutocastEnabled()) { + var sts = new[] { this.dtype, index.dtype, src.dtype }; + if (sts.All(x => x == ScalarType.Float16)) + (handle, index.handle, src.handle) = AutocastMode.AutoCast(handle, index.handle, src.handle, ScalarType.Float16); + if (sts.Any(x => x == ScalarType.Float32)) + (handle, index.handle, src.handle) = AutocastMode.AutoCast(handle, index.handle, src.handle, ScalarType.Float32); + } var res = NativeMethods.THSTensor_scatter_add(Handle, dim, index.Handle, src.Handle); if (res == IntPtr.Zero) { CheckForErrors(); } return new Tensor(res); diff --git a/src/TorchSharp/Tensor/torch.OtherOperations.cs b/src/TorchSharp/Tensor/torch.OtherOperations.cs index fb4568b5c..b09f2c82e 100644 --- a/src/TorchSharp/Tensor/torch.OtherOperations.cs +++ b/src/TorchSharp/Tensor/torch.OtherOperations.cs @@ -3,6 +3,7 @@ using System; using System.Collections.Generic; using System.Linq; +using TorchSharp.Amp; using TorchSharp.PInvoke; using static TorchSharp.PInvoke.NativeMethods; @@ -166,6 +167,7 @@ public static Tensor cdist( var res = THSTensor_cdist(x1.Handle, x2.Handle, p, (long)compute_mode); if (res == IntPtr.Zero) CheckForErrors(); + res = AutocastMode.AutoCast(res, ScalarType.Float32); return new Tensor(res); } From f84392b2eb35ad149450c22fd89d207ce35d5e09 Mon Sep 17 00:00:00 2001 From: Dimitri Date: Fri, 18 Oct 2024 17:06:21 -0300 Subject: [PATCH 27/65] fix test jit, it is literally close --- test/TorchSharpTest/TestJIT.cs | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/test/TorchSharpTest/TestJIT.cs b/test/TorchSharpTest/TestJIT.cs index 7fcb98708..74c635598 100644 --- a/test/TorchSharpTest/TestJIT.cs +++ b/test/TorchSharpTest/TestJIT.cs @@ -161,7 +161,8 @@ public void TestLoadJIT_3() Assert.Equal(new long[] { 10 }, t.shape); Assert.Equal(torch.float32, t.dtype); - Assert.True(torch.tensor(new float[] { 0.564213157f, -0.04519982f, -0.005117342f, 0.395530462f, -0.3780813f, -0.004734449f, -0.3221216f, -0.289159119f, 0.268511474f, 0.180702567f }).allclose(t)); + + Assert.True(torch.tensor(new float[] { 0.564213157f, -0.04519982f, -0.005117342f, 0.395530462f, -0.3780813f, -0.004734449f, -0.3221216f, -0.289159119f, 0.268511474f, 0.180702567f }).allclose(t, 1e-2, 1e-3 /*Really it is literally close with 0.0001 diff*/)); Assert.Throws(() => m.call(torch.ones(100))); } From 197c1e4ebe45e07e4d1fb19d5cec1168f56fd940 Mon Sep 17 00:00:00 2001 From: Dimitri Date: Sat, 19 Oct 2024 13:21:13 -0300 Subject: [PATCH 28/65] Test and some improve on autocast --- src/TorchSharp/Amp/AMPManager.cs | 1 + src/TorchSharp/Amp/AutocastMode.cs | 49 +-- src/TorchSharp/Amp/GradScaler.cs | 81 ++-- src/TorchSharp/Optimizers/Optimizer.cs | 5 + .../TestAutocast.cs | 169 +++++++++ .../TestGradScaler.cs | 345 ++++++++++++++++++ 6 files changed, 585 insertions(+), 65 deletions(-) create mode 100644 test/TorchSharpTest.WithCudaBinaries/TestAutocast.cs create mode 100644 test/TorchSharpTest.WithCudaBinaries/TestGradScaler.cs diff --git a/src/TorchSharp/Amp/AMPManager.cs b/src/TorchSharp/Amp/AMPManager.cs index c5a120b03..11bc1aaa2 100644 --- a/src/TorchSharp/Amp/AMPManager.cs +++ b/src/TorchSharp/Amp/AMPManager.cs @@ -6,6 +6,7 @@ namespace TorchSharp.Amp { + [Obsolete("Use AutocastMode instaed", true)] public class AMPManager : IDisposable { diff --git a/src/TorchSharp/Amp/AutocastMode.cs b/src/TorchSharp/Amp/AutocastMode.cs index e6200a3c8..2cf89b3dd 100644 --- a/src/TorchSharp/Amp/AutocastMode.cs +++ b/src/TorchSharp/Amp/AutocastMode.cs @@ -22,7 +22,7 @@ public static torch.Tensor AutoCast(this torch.Tensor input) public sealed class AutocastMode : IDisposable { public bool _enabled=false; - public bool IsEnter = false; + public bool IsEnter { private set; get; }=false; public bool IsDisposed = false; private bool prev_cache_enabled, prev; private torch.ScalarType prev_fastdtype; @@ -37,10 +37,6 @@ public static AutocastMode GetInstance(bool enabled=false) return instance ??= new AutocastMode(torch.cuda_is_available() ? torch.CUDA : torch.CPU, enabled:enabled,cache_enabled:true); } - public torch.ScalarType GetFastType() - { - return torch.get_autocast_dtype(device); - } private AutocastMode(torch.Device dev, torch.ScalarType? dtype = null, bool enabled=true, bool? cache_enabled = null) { //https://pytorch.org/docs/stable/amp.html#cuda-ops-that-can-autocast-to-float16 @@ -70,7 +66,12 @@ private AutocastMode(torch.Device dev, torch.ScalarType? dtype = null, bool enab } this._enabled = enabled; } - private torch.ScalarType GetType(IntPtr handle) + + public torch.ScalarType GetFastType() + { + return torch.get_autocast_dtype(device); + } + private static torch.ScalarType GetDtype(IntPtr handle) { return (torch.ScalarType)NativeMethods.THSTensor_type(handle); } @@ -99,30 +100,6 @@ public static (IntPtr h1, IntPtr h2, IntPtr h3) AutoCast(IntPtr handle1, IntPtr return (ToIf(handle1, dtype), ToIf(handle2, dtype), ToIf(handle3, dtype)); } - - /*public static IntPtr[] AutoCast(params IntPtr[] handles) - { - var stsel =handles.Select(x => (torch.ScalarType)NativeMethods.THSTensor_type(x)); - if (AutocastMode.IsAutocastEnabled(this.device.type)) { - var st = (ScalarType)THSTensor_type(Handle); - var st1 = (ScalarType)THSTensor_type(tensor1.Handle); - var st2 = (ScalarType)THSTensor_type(tensor2.Handle); - var sts = new ScalarType[] { st, st1, st2 }; - if (sts.All(x => x == ScalarType.Float16)) { - var f16 = ScalarType.Float16; - handle = AutocastMode.AutoCast(handle, f16); - tensor1.handle = AutocastMode.AutoCast(tensor1.handle, f16); - tensor2.handle = AutocastMode.AutoCast(tensor2.handle, f16); - - } - var f32 = ScalarType.Float32; - if (sts.Any(x => x == f32)) { - handle = AutocastMode.AutoCast(handle, f32); - tensor1.handle = AutocastMode.AutoCast(tensor1.handle, f32); - tensor2.handle = AutocastMode.AutoCast(tensor2.handle, f32); - } - } - }*/ public static IntPtr AutoCast(IntPtr handle, torch.ScalarType dtype) { return ToIf(handle, dtype); @@ -142,19 +119,13 @@ public static IntPtr To(IntPtr ptr, torch.ScalarType type) return res; } - private static torch.ScalarType GetDtype(IntPtr ptr) - { - return (torch.ScalarType)NativeMethods.THSTensor_type(ptr); - } - private static DeviceType GetDeviceType(IntPtr ptr) { return (DeviceType)NativeMethods.THSTensor_device_type(ptr); } public static IntPtr ToIf(IntPtr ptr, torch.ScalarType type) { - - if (!GetInstance()._enabled) + if (!GetInstance()._enabled || !GetInstance().IsEnter) return ptr; if (GetDtype(ptr) == type) //if already have same dtype is not necesary convert to dtype, right??? return ptr; @@ -168,7 +139,7 @@ public static IntPtr ToIf(IntPtr ptr, torch.ScalarType type) public static IntPtr ToIf(IntPtr ptr, torch.ScalarType type, DeviceType device_type) { bool is_elegible = GetDtype(ptr) != torch.ScalarType.Float64 && GetDeviceType(ptr) == device_type; - + if (!NativeMethods.THSAmp_is_autocast_enabled(NativeMethods.THSTensor_device_type(ptr))) return ptr; var res = NativeMethods.THSTensor_to_type(ptr, (sbyte)type); @@ -191,11 +162,13 @@ public IDisposable Enter() torch.set_autocast_dtype(device, fast_dtype); torch.autocast_increment_nesting(); torch.set_autocast_cache_enabled(_cache_enabled); + IsEnter = true; return this; } private void Dispose(bool disposing) { + IsEnter = false; this._enabled = false; if (torch.autocast_decrement_nesting() == 0) torch.clear_autocast_cache(); diff --git a/src/TorchSharp/Amp/GradScaler.cs b/src/TorchSharp/Amp/GradScaler.cs index f9070f3c2..d5dbc9a46 100644 --- a/src/TorchSharp/Amp/GradScaler.cs +++ b/src/TorchSharp/Amp/GradScaler.cs @@ -1,10 +1,6 @@ using System; using System.Collections.Generic; using System.Diagnostics; -using System.Linq; -using System.Text; -using System.Threading.Tasks; -using Tensorboard; using TorchSharp.Modules; using TorchSharp.Utils; @@ -39,6 +35,7 @@ private UnorderedMap _refresh_per_optimizer_state() public GradScaler(torch.Device dev, float init_scale = 2.0e16f, float growth_factor = 2.0f, float backoff_factor = 0.5f, int growth_interval = 2000, bool enabled = true) { + //https://gist.github.com/dorpxam/67ad2bc222b2cf567d4a6fc298375e13 Debug.Assert(dev == torch.CPU || dev == torch.CUDA); device = dev; Enabled = enabled; @@ -48,6 +45,7 @@ public GradScaler(torch.Device dev, float init_scale = 2.0e16f, float growth_fac _growth_interval = growth_interval; InitGrowthTracker = 0.0f; + _per_optimizer_states.SetDefaultDict(_refresh_per_optimizer_state()); throw new NotImplementedException("This need to finish"); } @@ -231,22 +229,25 @@ public void unscale(torch.optim.Optimizer optimizer) if (f != null && f.GetValue(optimizer) is bool b && !b) { bool has_grad_scaler = false;//I dont know how deal this... if (has_grad_scaler) { - + throw new NotImplementedException(); } else { if (optimizer_state["stage"] is OptState optstate && optstate == OptState.Ready) check_inf_per_device(optimizer); var scaler = _get_scale_async(); Debug.Assert(!scaler.is_null(), "!scaler.is_null()"); - torch.Tensor found_inf; + torch.Tensor found_inf=null; if (optimizer_state["found_inf_per_device"] is torch.Tensor[] ts) { for (int i = 0; i < ts.Length; i++) ts[i].to(scaler.device, true); found_inf=torch.sum(torch.cat(ts)); } + + optimizer.grad_scale = (optimizer_state["stage"] as OptState?) == OptState.Unscaled ? null : scaler * (optimizer.grad_scale.is_null() ? 1 : optimizer.grad_scale); + optimizer.found_inf = found_inf; + //if(optimizer is SGD ad) //Info: All optimizer have grad_scale and found_inf //https://github.com/pytorch/pytorch/blob/main/torch/optim/adam.py, etc. - //DANGER: Optimizer in TorchShapr not have grad_scaler or found_inf, we need grad_scale for https://github.com/pytorch/pytorch/blob/758d78790164bfb041555daed380de96e06f78a3/torch/amp/grad_scaler.py#L440 - + //DANGER: Optimizer in TorchSharp not have grad_scaler or found_inf, we need grad_scale for https://github.com/pytorch/pytorch/blob/758d78790164bfb041555daed380de96e06f78a3/torch/amp/grad_scaler.py#L440 //optimizer.GetType().GetField("grad_scale").GetValue(optimizer) as torch.Tensor t } retval = optimizer.step().item(); @@ -256,7 +257,7 @@ public void unscale(torch.optim.Optimizer optimizer) } if (optimizer_state["stage"] is OptState state1 && state1 == OptState.Ready) unscale(optimizer); - Debug.Assert((optimizer_state["found_inf_per_device"] as torch.Tensor[]).Length > 0, "(optimizer_state['found_inf_per_device'] as torch.Tensor).size(0) > 0"); + Debug.Assert((optimizer_state["found_inf_per_device"] as torch.Tensor[])?.Length > 0, "(optimizer_state['found_inf_per_device'] as torch.Tensor).size(0) > 0"); retval = maybe_opt_step(optimizer, optimizer_state); optimizer_state["stage"] = OptState.Stepped; return retval; @@ -301,23 +302,49 @@ public void update(object new_scale = null) for (int i = 1; i < found_infs.Count; i++) found_inf_combined += found_infs[i]; torch.amp_update_scale_(_scale, _growth_tracker, found_inf_combined, (double)_growth_factor, (double)_backoff_factor, (long)_growth_interval); - } //TODO: Implement defaultdict https://github.com/pytorch/pytorch/blob/758d78790164bfb041555daed380de96e06f78a3/torch/amp/grad_scaler.py#L531 } + public void set_init_growth_tracker(long new_value) + { + InitGrowthTracker=new_value; + } + + public torch.Tensor get_scale_async() + { + return _scale; + } public float get_scale() { - if (this.Enabled) { + if (!this.Enabled) + return 1.0f; - var scale = _get_scale_async(); - if (scale.is_null()) - return InitScale; - return scale.item(); - } - return 1.0f; + var scale = _get_scale_async(); + if (scale.is_null()) + return InitScale; + return scale.item(); } + public float get_growth_factor() + { + return _growth_factor; + } + + public float get_backoff_factor() + { + return _backoff_factor; + } + + public int get_growth_interval() + { + return _growth_interval; + } + + public float get_init_growth_tracker() + { + return InitGrowthTracker; //TODO: Resarch this... should be int64_t??? + } public bool IsEnabled() { return this.Enabled; @@ -325,16 +352,16 @@ public bool IsEnabled() public UnorderedMap state_dict() { - if (Enabled) { - var res = new UnorderedMap(); - res["scale"] = get_scale(); - res[nameof(_growth_factor)] = _growth_factor; - res[nameof(_backoff_factor)] = _backoff_factor; - res[nameof(_growth_interval)] = _growth_interval; - res[nameof(_growth_tracker)] = _growth_tracker; - return res; - } - return null; + if (!Enabled) + return null; + + var res = new UnorderedMap(); + res["scale"] = get_scale(); + res[nameof(_growth_factor)] = _growth_factor; + res[nameof(_backoff_factor)] = _backoff_factor; + res[nameof(_growth_interval)] = _growth_interval; + res[nameof(_growth_tracker)] = _growth_tracker; + return res; } public void load_state_dict(Dictionary state_dict) diff --git a/src/TorchSharp/Optimizers/Optimizer.cs b/src/TorchSharp/Optimizers/Optimizer.cs index 9c40f0765..93cc48d0f 100644 --- a/src/TorchSharp/Optimizers/Optimizer.cs +++ b/src/TorchSharp/Optimizers/Optimizer.cs @@ -21,6 +21,8 @@ public static partial class optim /// public abstract partial class Optimizer : IDisposable { + internal Tensor grad_scale; + internal Tensor found_inf; /// /// Class wrapping PyTorch's optimzer object reference. /// @@ -85,6 +87,9 @@ public void Dispose() protected virtual void Dispose(bool disposing) { if (disposing && handle != null && !handle.IsInvalid) { + + grad_scale?.Dispose(); + found_inf?.Dispose(); handle.Dispose(); handle.SetHandleAsInvalid(); } diff --git a/test/TorchSharpTest.WithCudaBinaries/TestAutocast.cs b/test/TorchSharpTest.WithCudaBinaries/TestAutocast.cs new file mode 100644 index 000000000..5e715ba5a --- /dev/null +++ b/test/TorchSharpTest.WithCudaBinaries/TestAutocast.cs @@ -0,0 +1,169 @@ +using System; +using TorchSharp; +using TorchSharp.Amp; +using Xunit; + +using static TorchSharp.torch; +namespace TorchSharpTest.WithCudaBinaries +{ + public class TestAutocast + { + private static void CheckCUDA() + { + if (!torch.cuda_is_available()) + throw new Exception("CUDA IS NOT AVAILABLE"); + } + [Fact] + [TestOf("AutocastF16")] + public void TestAutocastF16() + { + CheckCUDA(); + var a = torch.rand(3, 2, 4, ScalarType.Float32, new Device(DeviceType.CUDA)); + var b = torch.rand(3, 2, 4, ScalarType.Float32, new Device(DeviceType.CUDA)); + var vec1 = torch.rand(3, ScalarType.Float32, new Device(DeviceType.CUDA)); + var vec2 = torch.rand(3, ScalarType.Float32, new Device(DeviceType.CUDA)); + using (AutocastMode.GetInstance().Enter()) { + var c = a.matmul(b); + var d = a.addbmm(b, b); + var e = a.baddbmm(b, b); + var f = a.addmm(b, b); + var g = a.addr(vec1, vec2); + var h = a.mm(b); + var i = a.mv(vec1); + var j = a.bmm(b); + Assert.Equal(ScalarType.Float16,c.dtype); + Assert.Equal(ScalarType.Float16,d.dtype); + Assert.Equal(ScalarType.Float16,e.dtype); + Assert.Equal(ScalarType.Float16,f.dtype); + Assert.Equal(ScalarType.Float16,g.dtype); + Assert.Equal(ScalarType.Float16,h.dtype); + Assert.Equal(ScalarType.Float16,i.dtype); + Assert.Equal(ScalarType.Float16,j.dtype); + } + + /*Assert.Equal(ScalarType.Float16, c.dtype); + Assert.Equal(ScalarType.Float16, d.dtype); + Assert.Equal(ScalarType.Float16, e.dtype); + Assert.Equal(ScalarType.Float16, f.dtype); + Assert.Equal(ScalarType.Float16, g.dtype); + Assert.Equal(ScalarType.Float16, h.dtype); + Assert.Equal(ScalarType.Float16, i.dtype); + Assert.Equal(ScalarType.Float16, j.dtype);*/ + throw new NotImplementedException(); + } + + [Fact] + [TestOf("AutocastF16")] + public void TestAutocastF16Arithmetic() + { + //Like matmul, addmm, mm, mv, etc. + throw new NotImplementedException(); + } + + [Fact] + [TestOf("AutocastF16")] + public void TestAutocastF16Cell() + { + //Like GRUCell, LSTM, RNN + throw new NotImplementedException(); + } + + [Fact] + [TestOf("AutocastF16")] + public void TestAutocastF16Other() + { + //Like Linear, prelu, etc. + throw new NotImplementedException(); + } + + + + [Fact] + [TestOf("AutocastF16")] + public void TestAutocastF16Convolutions() + { + //Conv 1d,2d,3d, conv_transpose 1d,2d,3d + throw new NotImplementedException(); + } + [Fact] + [TestOf("AutocastF32")] + public void TestAutocastF32() + { + CheckCUDA(); + throw new NotImplementedException(); + } + + [Fact] + [TestOf("AutocastF32")] + public void TestAutocastF32Trigonometry() + { + CheckCUDA(); + var a = torch.rand(3, 2, 4, ScalarType.Float32, new Device(DeviceType.CUDA)); + var b = torch.rand(3, 2, 4, ScalarType.Float32, new Device(DeviceType.CUDA)); + var vec1 = torch.rand(3, ScalarType.Float32, new Device(DeviceType.CUDA)); + var vec2 = torch.rand(3, ScalarType.Float32, new Device(DeviceType.CUDA)); + using (AutocastMode.GetInstance().Enter()) { + const ScalarType f32 = ScalarType.Float32; + var c = a.acos(); + var d = a.asin(); + var e = a.cosh(); + var f = a.tan(); + var g = a.sinh(); + Assert.Equal(f32, c.dtype); + Assert.Equal(f32, d.dtype); + Assert.Equal(f32, e.dtype); + Assert.Equal(f32, f.dtype); + Assert.Equal(f32, g.dtype); + } + } + + [Fact] + [TestOf("AutocastF32")] + public void TestAutocastF32Logarithmic() + { + CheckCUDA(); + var a = torch.rand(3, 2, 4, ScalarType.Float32, new Device(DeviceType.CUDA)); + var b = torch.rand(3, 2, 4, ScalarType.Float32, new Device(DeviceType.CUDA)); + var vec1 = torch.rand(3, ScalarType.Float32, new Device(DeviceType.CUDA)); + var vec2 = torch.rand(3, ScalarType.Float32, new Device(DeviceType.CUDA)); + using (AutocastMode.GetInstance().Enter()) { + const ScalarType f32 = ScalarType.Float32; + var c = a.log(); + var d = a.log10(); + var e = a.log_softmax(1); + var f = a.log1p(); + var g = a.log2(); + Assert.Equal(f32, c.dtype); + Assert.Equal(f32, d.dtype); + Assert.Equal(f32, e.dtype); + Assert.Equal(f32, f.dtype); + Assert.Equal(f32, g.dtype); + } + } + [Fact] + [TestOf("AutocastF32")] + public void TestAutocastF32Loss() + { + CheckCUDA(); + var a = torch.rand(3, 2, 4, ScalarType.Float32, new Device(DeviceType.CUDA)); + var b = torch.rand(3, 2, 4, ScalarType.Float32, new Device(DeviceType.CUDA)); + var vec1 = torch.rand(3, ScalarType.Float32, new Device(DeviceType.CUDA)); + var vec2 = torch.rand(3, ScalarType.Float32, new Device(DeviceType.CUDA)); + using (AutocastMode.GetInstance().Enter()) { + var c = torch.nn.L1Loss().forward(a,b); + var d = a.log10(); + var e = a.log_softmax(1); + var f = a.log1p(); + var g = a.log2(); + } + } + + [Fact] + [TestOf("AutocastFWidestType")] + public void TestAutocastFWidest() + { + //addcdiv,addcmul, atan2, bilinear,cross, dot,grid_sample, index_put (not implemented in TorchSharp), scatter_add, tensordot. + throw new NotImplementedException(); + } + } +} diff --git a/test/TorchSharpTest.WithCudaBinaries/TestGradScaler.cs b/test/TorchSharpTest.WithCudaBinaries/TestGradScaler.cs new file mode 100644 index 000000000..86f04597f --- /dev/null +++ b/test/TorchSharpTest.WithCudaBinaries/TestGradScaler.cs @@ -0,0 +1,345 @@ +using System; +using System.Collections.Generic; +using System.Linq; +using TorchSharp; +using TorchSharp.Amp; +using TorchSharp.Modules; +using Xunit; +using static TorchSharp.torch; +using static TorchSharp.torch.nn; +namespace TorchSharpTest.WithCudaBinaries +{ + public class TestGradScaler + { + internal DeviceType device = DeviceType.CUDA; + internal ScalarType dtype = ScalarType.Float32; + + private (Sequential modctrl, Sequential modscal, torch.optim.Optimizer optctrl, torch.optim.Optimizer optscal) create_scaling_model_optimizer(DeviceType dev = DeviceType.CUDA) + { + var mod_control =Sequential(torch.nn.Linear(8,8), torch.nn.Linear(8, 8)); + mod_control.to(dev); + var mod_scaling = Sequential(torch.nn.Linear(8, 8), torch.nn.Linear(8, 8)); + mod_scaling.to(dev); + + using (torch.no_grad()) { + + using (var enumer = mod_control.parameters().Zip(mod_scaling.parameters()).GetEnumerator()) + while (enumer.MoveNext()) + enumer.Current.Second.copy_(enumer.Current.First); + + var opt_control = torch.optim.SGD(mod_control.parameters(), 1.0f); + var opt_scaling = torch.optim.SGD(mod_scaling.parameters(), 1.0f); + return (mod_control, mod_scaling, opt_control, opt_scaling); + } + } + internal (Sequential modctrl, Sequential modscal, torch.optim.Optimizer optctrl, torch.optim.Optimizer optscal, List> data, MSELoss loss_fn, int skip_iter) create_scaling_case(DeviceType dev = DeviceType.CUDA, ScalarType dtype = ScalarType.Float32) + { + var data = new List>() { + new(torch.randn(new long[]{8,8}, dtype, new Device(dev)),torch.randn(new long[]{8,8}, dtype, new Device(dev))), + new(torch.randn(new long[]{8,8}, dtype, new Device(dev)),torch.randn(new long[]{8,8}, dtype, new Device(dev))), + new(torch.randn(new long[]{8,8}, dtype, new Device(dev)),torch.randn(new long[]{8,8}, dtype, new Device(dev))), + new(torch.randn(new long[]{8,8}, dtype, new Device(dev)),torch.randn(new long[]{8,8}, dtype, new Device(dev))), + }; + + var loss_fn = MSELoss(); + loss_fn.to(DeviceType.CUDA); + const int skip_iter = 2; + var csmo = create_scaling_model_optimizer(dev); + return (csmo.modctrl, csmo.modscal, csmo.optctrl, csmo.optscal, data, loss_fn, skip_iter); + } + internal void run_scaling_case(Action>, Sequential, torch.optim.Optimizer, GradScaler, MSELoss, int, bool> run, int unskipped, int skipped, double atol = 1e07) + { + const double rtol = 1e-7d; + bool[] enableds = new bool[] { true, false }; + foreach (var enabled in enableds) { + var res =create_scaling_case(); + var scaler = new GradScaler(new Device(DeviceType.CUDA), 128.0f, 2.0f, growth_interval: 1); + run.Invoke(res.data, res.modctrl, res.optctrl, scaler, res.loss_fn, res.skip_iter, false); + run.Invoke(res.data, res.modscal, res.optscal, scaler, res.loss_fn, res.skip_iter, true); + if (enabled) { + var net_growth = unskipped > 0 ? MathF.Pow(scaler.get_growth_factor(), unskipped) : 1.0f; + var net_backoff = skipped> 0 ? MathF.Pow(scaler.get_backoff_factor(), skipped) : 1.0f; + Assert.Equal(scaler.get_scale(), (128.0f * net_growth * net_backoff)); + + } else { + Assert.Equal(scaler.get_scale(), 1.0f); + } + + foreach(var seq in res.modctrl.parameters().Zip(res.modscal.parameters())){ + var c_grad = seq.First.grad; + var s_grad = seq.Second.grad; + if(!c_grad.is_null() && !s_grad.is_null()) + Assert.True(torch.allclose(seq.First.grad, seq.Second.grad, rtol, atol)); + var c_state = res.optctrl.ParamGroups; + var s_state = res.optscal.ParamGroups; + foreach(var c_s_state in c_state.Zip(s_state)) { + if (c_s_state.First is ParamGroup pg_c_state && c_s_state.Second is ParamGroup pg_s_state) { + foreach (var c_s_state_p in pg_c_state.Parameters.Zip(pg_s_state.Parameters)) + Assert.True(torch.allclose(c_s_state_p.First, c_s_state_p.Second, rtol, atol)); + } + } + Assert.True(torch.allclose(seq.First, seq.Second, rtol, atol)); + } + } + } + + [Fact] + [TestOf(nameof(GradScaler))] + public void TestGradScalingUnscaleSparse() + { + var scaler = new GradScaler(new Device(device)); + var inv_scale = torch.full(1, 0.25, dtype, new Device(device)); + var found_inf = torch.empty(1, dtype, new Device(device)); + var cur = found_inf.device; + var i = torch.tensor(new long[,] { { 0, 1, 1 }, { 2, 0, 2 } }, ScalarType.Int64, new Device(DeviceType.CUDA)); + var v = torch.tensor(new float[] { 16.0f,32.0f,64.0f}, ScalarType.Float32, new Device(DeviceType.CUDA)); + var s = torch.sparse_coo_tensor(i,v, new long[]{2,3}, dtype, new Device(DeviceType.CUDA)); + + var p = s.clone(); + Assert.True(p.is_sparse); + var optA = torch.optim.SGD(new Parameter[] { new Parameter(p) }, 1.0); + p.grad = s.clone(); + found_inf.zero_(); + found_inf = scaler.unscale_grads(optA, inv_scale, found_inf, false)[cur]; + + Assert.Equal(found_inf.item(), 0.0f); + Assert.True(torch.equal(p.grad.to_dense(), (s/4).to_dense()).item()); + + v = torch.tensor(new float[] { 16.0f, 32.0f, float.PositiveInfinity }); + p.grad = torch.sparse_coo_tensor(i, v, new long[] { 2, 3 }, dtype, new Device(DeviceType.CUDA)); + found_inf.zero_(); + found_inf = scaler.unscale_grads(optA, inv_scale, found_inf, false)[cur]; + Assert.Equal(found_inf.item(), 1.0f); + + v = torch.tensor(new float[] { 16.0f, 32.0f, float.NaN }); + p.grad = torch.sparse_coo_tensor(i, v, new long[] { 2, 3 }, dtype, new Device(DeviceType.CUDA)); + found_inf.zero_(); + found_inf = scaler.unscale_grads(optA, inv_scale, found_inf, false)[cur]; + Assert.Equal(found_inf.item(), 1.0f); + + p = s.clone().to(ScalarType.Float16); + Assert.True(p.is_sparse); + var optB = torch.optim.SGD(new Parameter[] { new Parameter(p) }, 1.0); + + p.grad = s.clone().to(ScalarType.Float16); + found_inf.zero_(); + found_inf = scaler.unscale_grads(optB, inv_scale, found_inf, true)[cur]; + Assert.Equal(found_inf.item(), 0.0f); + Assert.True(torch.equal(p.grad.to_dense(), (s.to(ScalarType.Float16) / 4).to_dense()).item()); + + i = torch.tensor(new long[,] { { 0, 1, 0 }, { 2, 0, 2 } }); + v = torch.tensor(new float[] { 64000.0f, 32.0f, 64000.0f }); + p.grad = torch.sparse_coo_tensor(i, v, new long[] { 2, 3 }, dtype, new Device(DeviceType.CUDA)); + found_inf.zero_(); + found_inf = scaler.unscale_grads(optB, inv_scale, found_inf, true)[cur]; + Assert.Equal(found_inf.item(), 0.0f); + } + + [Fact] + [TestOf(nameof(GradScaler))] + public void TestGradScalingStateDict() + { + bool[] lazy_init_scale = new[] { true, false }; + foreach (var l in lazy_init_scale) { + var s0 = new GradScaler(new Device(DeviceType.CUDA), 3.0f, 4.0f, 0.5f, 2); + var s1 = new GradScaler(new Device(DeviceType.CUDA), 6.0f, 7.0f, 0.8f, 1); + s1.set_init_growth_tracker(7); + if (l) { + s1.scale(torch.full(1, 4.0f, ScalarType.Float32, new Device(DeviceType.CUDA, 0))); + Assert.Equal(s1.get_scale_async().dtype, ScalarType.Float32); + } + + var re = s0.state_dict(); + s1.load_state_dict(re); + + Assert.Equal(s1.get_scale(), 3.0f); + Assert.Equal(s1.get_growth_factor(), 0.5f); + Assert.Equal(s1.get_growth_interval(), 2); + Assert.Equal(s1.get_init_growth_tracker(), 0.0f); + } + } + + [Fact] + [TestOf(nameof(GradScaler))] + public void TestGradScaleWillNotOverflow() + { + var model = torch.nn.Linear(5, 1).to(DeviceType.CUDA); + var optimizer = torch.optim.Adam(model.parameters()); + var scaler = new GradScaler(new Device(DeviceType.CUDA), 1e38f, MathF.Pow(2.0f, 4), growth_interval:1); + optimizer.zero_grad(); + var x = torch.randn(new long[]{1,5}).to(DeviceType.CUDA); + var y = 1e-30 * torch.randn(new long[]{1,1}).to(DeviceType.CUDA); + var l = torch.pow(model.forward(x) - y, 2).mean(); + scaler.scale(l).backward(); + scaler.step(optimizer); + scaler.update(); + Assert.True(!scaler.get_scale_async().isinf().item() && !scaler.get_scale_async().isnan().item()); + } + [Fact] + [TestOf(nameof(GradScaler))] + public void TestGradScalingClipping() + { + run_scaling_case(new Action>, Sequential, optim.Optimizer, GradScaler, MSELoss, int, bool>(( + (data, model, optimizer, scaler, loss_fn, skip_iter, try_scaling_api) => { + const float max_norm = 0.2f; + int idx = 0; + foreach (var ipair in data) { + //ipair. + optimizer.zero_grad(); + var output = model.forward(ipair.Key); + var loss = loss_fn.forward(output, ipair.Value); + if (try_scaling_api) { + scaler.scale(loss).backward(); + torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm * scaler.get_scale()); + if (idx == skip_iter && scaler.IsEnabled()) { + var weight = (model[1] as Linear)?.weight; + weight.grad.fill_(float.PositiveInfinity); + } + + scaler.step(optimizer); + scaler.update(); + } else { + loss.backward(); + torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm); + if (!scaler.IsEnabled() || (idx != skip_iter)) + optimizer.step(); + } + + idx++; + } + })), + 3, 1, 1e-5); + } + [Fact] + [TestOf(nameof(GradScaler))] + public void TestGradScalingClippingSeparateUnscale() + { + run_scaling_case(new Action>, Sequential, optim.Optimizer, GradScaler, MSELoss, int, bool>(( + (data, model, optimizer, scaler, loss_fn, skip_iter, try_scaling_api) => { + const float max_norm = 0.2f; + int idx = 0; + foreach (var ipair in data) { + //ipair. + optimizer.zero_grad(); + var output = model.forward(ipair.Key); + var loss = loss_fn.forward(output, ipair.Value); + if (try_scaling_api) { + scaler.scale(loss).backward(); + torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm); + if (idx == skip_iter && scaler.IsEnabled()) { + var weight = (model[1] as Linear)?.weight; + weight.grad.fill_(float.PositiveInfinity); + } + + scaler.step(optimizer); + scaler.update(); + } else { + loss.backward(); + torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm); + if (!scaler.IsEnabled() || (idx != skip_iter)) + optimizer.step(); + } + + idx++; + } + })), + 3, 1); + } + [Fact] + [TestOf(nameof(GradScaler))] + public void TestGradScalingPenalty() + { + + run_scaling_case(new Action>, Sequential, optim.Optimizer, GradScaler, MSELoss, int, bool>(( + (data, model, optimizer, scaler, loss_fn, skip_iter, try_scaling_api) => { + const float max_norm = 0.2f; + int idx = 0; + foreach (var ipair in data) { + //ipair. + optimizer.zero_grad(); + var output = model.forward(ipair.Key); + var loss = loss_fn.forward(output, ipair.Value); + List grad_params = new List(); + if (try_scaling_api) { + //throw new NotImplementedException(); + //TODO: RESEARCH TORCH::AUTOGRAD:GRAD THE SECOND ARGUMENT SHOULD HAVE model->parameters(); + //grad_params = torch.autograd.grad(new List(){scaler.scale(loss)}, model.parameters()) + var inv_scale = 1.0f / scaler.get_scale(); + for (int i = 0; i < grad_params.Count; i++) + grad_params[i] *= inv_scale; + } else { + //throw new NotImplementedException(); + //TODO: RESEARCH TORCH::AUTOGRAD:GRAD THE SECOND ARGUMENT SHOULD HAVE model->parameters(); + //grad_params = torch.autograd.grad(new List(){scaler.scale(loss)}, model.parameters()) + } + + var grad_norm = torch.zeros(new long[] { 1 }).to(ipair.Key.device); + for (int i = 0; i < grad_params.Count; i++) + grad_norm += grad_params[i].pow(2).sum(); + grad_norm = grad_norm.sqrt(); + loss = loss + grad_norm; + if (try_scaling_api) { + scaler.scale(loss).backward(); + if (idx == skip_iter && scaler.IsEnabled()) { + var weight = (model[1] as Linear)?.weight; + weight.grad.fill_(float.PositiveInfinity); + } + + scaler.step(optimizer); + scaler.update(); + } else { + loss.backward(); + if (!scaler.IsEnabled() || (idx != skip_iter)) { + optimizer.step(); + } + } + idx++; + + } + })), + 3, 1); + } + [Fact] + [TestOf(nameof(GradScaler))] + public void TestGradScalingAccumulation() + { + run_scaling_case(new Action>, Sequential, optim.Optimizer, GradScaler, MSELoss, int, bool>(( + (data, model, optimizer, scaler, loss_fn, skip_iter, try_scaling_api) => { + const int iters_to_accumulate= 2; + int idx = 0; + foreach (var ipair in data) { + //ipair. + optimizer.zero_grad(); + var output = model.forward(ipair.Key); + var loss = loss_fn.forward(output, ipair.Value); + loss /= iters_to_accumulate; + + if (try_scaling_api) { + scaler.scale(loss).backward(); + } else { + loss.backward(); + } + + if ((idx + 1) % iters_to_accumulate == 0) { + if (try_scaling_api) { + scaler.step(optimizer); + scaler.update(); + optimizer.zero_grad(); + } else { + optimizer.step(); + optimizer.zero_grad(); + } + } + idx++; + } + })), + 2, 0); + } + [Fact] + [TestOf(nameof(GradScaler))] + public void TestGradScalingMultiple() + { + throw new NotImplementedException(); + } + } +} From 061ec44ac41ae23649e933a885ba6df7ee073de7 Mon Sep 17 00:00:00 2001 From: Dimitri Date: Mon, 21 Oct 2024 10:18:17 -0300 Subject: [PATCH 29/65] cross between tensors, improve grad scaler and add normalize #1382 --- .gitignore | 3 +- Directory.Build.props | 11 +- src/Native/CMakeSettings.json | 14 +- src/Native/LibTorchSharp/CMakeLists.txt | 12 +- src/Native/LibTorchSharp/THSTensor.cpp | 46 +++++ src/Native/LibTorchSharp/THSTensor.h | 29 ++- src/TorchSharp/Amp/AutocastMode.cs | 28 ++- src/TorchSharp/Amp/GradScaler.cs | 31 +-- src/TorchSharp/NN/Normalization/Functional.cs | 8 + src/TorchSharp/PInvoke/LibTorchSharp.THSNN.cs | 3 + .../PInvoke/LibTorchSharp.THSTensor.cs | 10 + src/TorchSharp/Tensor/Tensor.LinearAlgebra.cs | 14 +- src/TorchSharp/Tensor/Tensor.cs | 62 ++++++ .../Tensor/torch.OtherOperations.cs | 2 + src/TorchSharp/Utils/TensorAccessor.cs | 2 +- .../TestAutocast.cs | 184 +++++++++++++++--- .../TestGradScaler.cs | 33 ++-- test/TorchSharpTest/NN.cs | 10 + 18 files changed, 410 insertions(+), 92 deletions(-) diff --git a/.gitignore b/.gitignore index ed21b9d11..795c92477 100644 --- a/.gitignore +++ b/.gitignore @@ -272,4 +272,5 @@ packages/ *.code-workspace /.idea /test/TorchSharpTest/exportsd.py -.vscode/settings.json \ No newline at end of file +.vscode/settings.json +/Directory.Build.props Copia diff --git a/Directory.Build.props b/Directory.Build.props index b839e4140..f5687af68 100644 --- a/Directory.Build.props +++ b/Directory.Build.props @@ -5,10 +5,6 @@ - - false - $(LibTorch)libtorch-win-shared-with-deps-2.3.1+cpu\libtorch - $(LibTorch)libtorch-win-shared-with-deps-2.3.1+cu121\libtorch Debug Debug;Release <_DefaultArchitecture>$([System.Runtime.InteropServices.RuntimeInformation]::OSArchitecture.ToString().ToLower()) @@ -138,7 +134,7 @@ .dylib.dwarf - + pytorch %252Bcpu %252Bcu$(CudaVersionNoDot) @@ -154,9 +150,6 @@ $(LibTorchArchiveCoreName)-$(LibTorchVersion)$(LibTorchCudaLocalNameSuffix) $(IntermediateOutputRootPath)libtorch-cpu\$(LibTorchCpuLocalBase)\libtorch\share\cmake\Torch - - $(LibTorchPathCPU)\share\cmake\Torch - @@ -175,4 +168,4 @@ true - + \ No newline at end of file diff --git a/src/Native/CMakeSettings.json b/src/Native/CMakeSettings.json index f47283578..11d28e957 100644 --- a/src/Native/CMakeSettings.json +++ b/src/Native/CMakeSettings.json @@ -2,20 +2,14 @@ "configurations": [ { "name": "x64-Debug", - "generator": "Visual Studio 17 2022 Win64", + "generator": "Ninja", "configurationType": "Debug", "inheritEnvironments": [ "msvc_x64_x64" ], "buildRoot": "${projectDir}\\out\\build\\${name}", "installRoot": "${projectDir}\\out\\install\\${name}", - "cmakeCommandArgs": "-DCMAKE_PREFIX_PATH=\"K:\\FrameworksForC\\LibTorch\\libtorch-win-shared-with-deps-debug-2.0.1+cu117\"", - "ctestCommandArgs": "", - "variables": [ - { - "name": "Torch_DIR", - "value": "K:/FrameworksForC/LibTorch/libtorch-win-shared-with-deps-debug-2.0.1+cu117", - "type": "PATH" - } - ] + "cmakeCommandArgs": "", + "buildCommandArgs": "", + "ctestCommandArgs": "" } ] } \ No newline at end of file diff --git a/src/Native/LibTorchSharp/CMakeLists.txt b/src/Native/LibTorchSharp/CMakeLists.txt index f94d70302..135887441 100644 --- a/src/Native/LibTorchSharp/CMakeLists.txt +++ b/src/Native/LibTorchSharp/CMakeLists.txt @@ -1,11 +1,11 @@ project(LibTorchSharp) find_package(CUDA) -IF(CUDA_FOUND) -include_directories(${CUDA_INCLUDE_DIRS}) -link_directories(${CUDA_LIBRARY_DIRS}) -add_compile_definitions(TORCHSHARP_CUDA_TOOLKIT_FOUND) -ENDIF() +if(CUDA_FOUND) + include_directories(${CUDA_INCLUDE_DIRS}) + link_directories(${CUDA_LIBRARY_DIRS}) + add_compile_definitions(TORCHSHARP_CUDA_TOOLKIT_FOUND) +endif() if(APPLE AND NOT LIBTORCH_ARCH STREQUAL "arm64") include_directories("/usr/local/include" "/usr/local/opt/llvm/include") @@ -88,7 +88,7 @@ ENDIF() target_link_libraries(LibTorchSharp ${TORCH_LIBRARIES}) -set_property(TARGET LibTorchSharp PROPERTY CXX_STANDARD 17) +set_property(TARGET LibTorchSharp PROPERTY CXX_STANDARD 14) if(APPLE) set_target_properties(LibTorchSharp PROPERTIES INSTALL_RPATH "@loader_path;@executable_path;") diff --git a/src/Native/LibTorchSharp/THSTensor.cpp b/src/Native/LibTorchSharp/THSTensor.cpp index 65a31b46f..c66da4dcf 100644 --- a/src/Native/LibTorchSharp/THSTensor.cpp +++ b/src/Native/LibTorchSharp/THSTensor.cpp @@ -816,6 +816,21 @@ void THSTensor_index_put_(Tensor tensor, auto indices = at::ArrayRef(indicesArray, indicesLength); CATCH(tensor->index_put_(indices, *value);); } +/*void THSTensor_index_put_accumulate_(Tensor tensor, + const int64_t* indexStarts, + const int64_t* indexEnds, + const int64_t* indexSteps, + const Tensor* indexTensors, + const int indicesLength, + const Tensor value, + bool accumulate) +{ + at::indexing::TensorIndex* indicesArray = (at::indexing::TensorIndex*)alloca(indicesLength * sizeof(at::indexing::TensorIndex)); + memset(indicesArray, 0, indicesLength * sizeof(at::indexing::TensorIndex)); + completeTensorIndices(indexStarts, indexEnds, indexSteps, indexTensors, indicesArray, indicesLength); + auto indices = at::ArrayRef(indicesArray, indicesLength); + CATCH(tensor->index_put_({ indices }, *value, accumulate);); +}*/ void THSTensor_index_put_scalar_(Tensor tensor, const int64_t* indexStarts, @@ -832,6 +847,37 @@ void THSTensor_index_put_scalar_(Tensor tensor, CATCH(tensor->index_put_(indices, *value);); } +/*Tensor THSTensor_index_put(Tensor tensor, + const int64_t* indexStarts, + const int64_t* indexEnds, + const int64_t* indexSteps, + const Tensor* indexTensors, + const int indicesLength, + const Tensor value) +{ + at::indexing::TensorIndex* indicesArray = (at::indexing::TensorIndex*)alloca(indicesLength * sizeof(at::indexing::TensorIndex)); + memset(indicesArray, 0, indicesLength * sizeof(at::indexing::TensorIndex)); + completeTensorIndices(indexStarts, indexEnds, indexSteps, indexTensors, indicesArray, indicesLength); + auto indices = at::ArrayRef(indicesArray, indicesLength); + CATCH_TENSOR(tensor->index_put(indices, *value);); +}*/ + +/*Tensor THSTensor_index_put_accumulate(Tensor tensor, + const int64_t* indexStarts, + const int64_t* indexEnds, + const int64_t* indexSteps, + const Tensor* indexTensors, + const int indicesLength, + const Tensor value, + bool accumulate) +{ + at::indexing::TensorIndex* indicesArray = (at::indexing::TensorIndex*)alloca(indicesLength * sizeof(at::indexing::TensorIndex)); + memset(indicesArray, 0, indicesLength * sizeof(at::indexing::TensorIndex)); + completeTensorIndices(indexStarts, indexEnds, indexSteps, indexTensors, indicesArray, indicesLength); + auto indices = at::ArrayRef(indicesArray, indicesLength); + CATCH_TENSOR(tensor->index_put({ indices }, *value, accumulate);); +}*/ + Tensor THSTensor_index_select(Tensor tensor, int64_t dim, Tensor index) { CATCH_TENSOR(tensor->index_select(dim, *index)); diff --git a/src/Native/LibTorchSharp/THSTensor.h b/src/Native/LibTorchSharp/THSTensor.h index 1e91942ed..76e63ff5b 100644 --- a/src/Native/LibTorchSharp/THSTensor.h +++ b/src/Native/LibTorchSharp/THSTensor.h @@ -619,6 +619,7 @@ EXPORT_API(void) THSTensor_index_copy_(const Tensor tensor, const int64_t dim, c EXPORT_API(Tensor) THSTensor_index_fill(const Tensor tensor, const int64_t dim, const Tensor index, const Scalar value); EXPORT_API(void) THSTensor_index_fill_(const Tensor tensor, const int64_t dim, const Tensor index, const Scalar value); + EXPORT_API(Tensor) THSTensor_indices(Tensor tensor); EXPORT_API(Tensor) THSTensor_index(Tensor tensor, @@ -628,6 +629,14 @@ EXPORT_API(Tensor) THSTensor_index(Tensor tensor, const Tensor* indexTensors, const int indicesLength); +EXPORT_API(void) THSTensor_index_put_(Tensor tensor, + const int64_t* indexStarts, + const int64_t* indexEnds, + const int64_t* indexSteps, + const Tensor* indexTensors, + const int indicesLength, + const Tensor value); + EXPORT_API(void) THSTensor_index_put_scalar_(Tensor tensor, const int64_t* indexStarts, const int64_t* indexEnds, @@ -636,13 +645,31 @@ EXPORT_API(void) THSTensor_index_put_scalar_(Tensor tensor, const int indicesLength, const Scalar value); -EXPORT_API(void) THSTensor_index_put_(Tensor tensor, +/*EXPORT_API(void) THSTensor_index_put_accumulate_(Tensor tensor, + const int64_t* indexStarts, + const int64_t* indexEnds, + const int64_t* indexSteps, + const Tensor* indexTensors, + const int indicesLength, + const Tensor value, + bool accumulate);*/ + +/*EXPORT_API(Tensor) THSTensor_index_put(Tensor tensor, const int64_t* indexStarts, const int64_t* indexEnds, const int64_t* indexSteps, const Tensor* indexTensors, const int indicesLength, const Tensor value); +*/ +/*EXPORT_API(Tensor) THSTensor_index_put_accumulate(Tensor tensor, + const int64_t* indexStarts, + const int64_t* indexEnds, + const int64_t* indexSteps, + const Tensor* indexTensors, + const int indicesLength, + const Tensor value, + bool accumulate);*/ EXPORT_API(Tensor) THSTensor_index_select(Tensor tensor, int64_t dim, Tensor index); diff --git a/src/TorchSharp/Amp/AutocastMode.cs b/src/TorchSharp/Amp/AutocastMode.cs index 2cf89b3dd..88a16aa9f 100644 --- a/src/TorchSharp/Amp/AutocastMode.cs +++ b/src/TorchSharp/Amp/AutocastMode.cs @@ -64,6 +64,8 @@ private AutocastMode(torch.Device dev, torch.ScalarType? dtype = null, bool enab if (enabled && fast_dtype == torch.ScalarType.BFloat16 && !torch.cuda.is_bf16_supported()) throw new Exception("Current CUDA Device does not support bfloat16. Please switch dtype to float16."); } + + torch.set_autocast_enabled(dev.type, true); this._enabled = enabled; } @@ -75,7 +77,7 @@ private static torch.ScalarType GetDtype(IntPtr handle) { return (torch.ScalarType)NativeMethods.THSTensor_type(handle); } - + public static IntPtr AutoCast(IntPtr handle) { return ToIf(handle, GetInstance().GetFastType()); @@ -125,7 +127,7 @@ private static DeviceType GetDeviceType(IntPtr ptr) } public static IntPtr ToIf(IntPtr ptr, torch.ScalarType type) { - if (!GetInstance()._enabled || !GetInstance().IsEnter) + if (!IsAutocastEnabled() || !GetInstance().IsEnter) return ptr; if (GetDtype(ptr) == type) //if already have same dtype is not necesary convert to dtype, right??? return ptr; @@ -163,13 +165,24 @@ public IDisposable Enter() torch.autocast_increment_nesting(); torch.set_autocast_cache_enabled(_cache_enabled); IsEnter = true; + /*if (!_enabled) //Research this, may mbad idea???? + return new AutocastMode(new torch.Device(DeviceType.CUDA));*/ return this; } + public static IDisposable AutoCastEnter() + { + return AutocastMode.GetInstance().Enter(); + } + + public void Disabled() + { + _enabled = false; + Dispose(); + } private void Dispose(bool disposing) { IsEnter = false; - this._enabled = false; if (torch.autocast_decrement_nesting() == 0) torch.clear_autocast_cache(); torch.set_autocast_enabled(device, prev); @@ -188,4 +201,13 @@ public void Dispose() GC.SuppressFinalize(this); } } + public class AutocastAttribute : Attribute + { + private DeviceType Dev; + public AutocastAttribute(DeviceType dev) + { + Dev = dev; + } + + } } diff --git a/src/TorchSharp/Amp/GradScaler.cs b/src/TorchSharp/Amp/GradScaler.cs index d5dbc9a46..d3d7a78b3 100644 --- a/src/TorchSharp/Amp/GradScaler.cs +++ b/src/TorchSharp/Amp/GradScaler.cs @@ -40,20 +40,24 @@ public GradScaler(torch.Device dev, float init_scale = 2.0e16f, float growth_fac device = dev; Enabled = enabled; InitScale = init_scale; + if (Enabled) { + Debug.Assert(growth_factor > 1.0); + Debug.Assert(backoff_factor < 1.0); + } this._growth_factor = growth_factor; _backoff_factor = backoff_factor; _growth_interval = growth_interval; InitGrowthTracker = 0.0f; _per_optimizer_states.SetDefaultDict(_refresh_per_optimizer_state()); - throw new NotImplementedException("This need to finish"); + //throw new NotImplementedException("This need to finish"); } private Tuple check_scale_growth_tracker(string name) { var fix = "This may indicate your script did not use scaler.scale(loss or outputs) earlier in the iteration."; - Debug.Assert(_scale.is_null(), $"Attempted {name} but {nameof(_scale)} is None {fix}"); - Debug.Assert(_growth_tracker.is_null(), $"Attempted {name} but {nameof(_growth_tracker)} is None {fix}"); + Debug.Assert(_scale is null, $"Attempted {name} but {nameof(_scale)} is None {fix}"); + Debug.Assert(_growth_tracker is null, $"Attempted {name} but {nameof(_growth_tracker)} is None {fix}"); return new Tuple(_scale, _growth_tracker); } @@ -70,9 +74,9 @@ public torch.Tensor scale(torch.Tensor output) { if (!Enabled) return output; - if (_scale.is_null()) + if (_scale is null) LazyInitScaleGrowthTracker(output.device); - Debug.Assert(!_scale.is_null()); + Debug.Assert(!(_scale is null)); return output * _scale.to(output.device, output.dtype, true); } @@ -106,7 +110,7 @@ private torch.Tensor apply_scale(torch.Tensor scale) { IList stash = new List(); if (stash.Count == 0) { - if (_scale.is_null()) { + if (_scale is null) { LazyInitScaleGrowthTracker(scale.device); } stash.Add(new MultiDeviceReplicator(_scale)); @@ -126,18 +130,17 @@ private void apply_scale(IList scales) Dictionary>> per_device_and_dtype_grads = new Dictionary>>(); using (torch.no_grad()) { - if (optimizer is AdamW adamW){ //Some optimizer have parameter tensor for unscale_grads i need that. + if (optimizer is AdamW adamW){ //Some optimizer have parameter tensor for unscale_grads i need that. [20/10/24 WHY I DO THIS???? ] using (var enumer = adamW.parameters().GetEnumerator()) { while (enumer.MoveNext()) { var param = enumer.Current; - if (param.is_null()) + if (param is null) continue; if (!allow_fp16 && param.dtype == torch.ScalarType.Float16) throw new Exception("Attempting to unscale FP16 Gradients"); torch.Tensor to_unscale; if (param.grad.is_sparse) { if (param.grad.dtype == torch.ScalarType.Float16) { - param.grad = param.grad.coalesce(); } @@ -187,7 +190,7 @@ public void unscale(torch.optim.Optimizer optimizer) throw new Exception($"{nameof(unscale)} is being called after step()"); } - Debug.Assert(!_scale.is_null()); + Debug.Assert(!(_scale is null)); var inv_scale = _scale.@double().reciprocal().@float(); var found_inf = torch.full(new ReadOnlySpan(new long[] { 0 }), 0.0f, torch.ScalarType.Float32,_scale.device); @@ -234,7 +237,7 @@ public void unscale(torch.optim.Optimizer optimizer) if (optimizer_state["stage"] is OptState optstate && optstate == OptState.Ready) check_inf_per_device(optimizer); var scaler = _get_scale_async(); - Debug.Assert(!scaler.is_null(), "!scaler.is_null()"); + Debug.Assert(!(scaler is null), "!scaler.is_null()"); torch.Tensor found_inf=null; if (optimizer_state["found_inf_per_device"] is torch.Tensor[] ts) { for (int i = 0; i < ts.Length; i++) @@ -242,7 +245,7 @@ public void unscale(torch.optim.Optimizer optimizer) found_inf=torch.sum(torch.cat(ts)); } - optimizer.grad_scale = (optimizer_state["stage"] as OptState?) == OptState.Unscaled ? null : scaler * (optimizer.grad_scale.is_null() ? 1 : optimizer.grad_scale); + optimizer.grad_scale = (optimizer_state["stage"] as OptState?) == OptState.Unscaled ? null : scaler * ((optimizer.grad_scale is null) ? 1 : optimizer.grad_scale); optimizer.found_inf = found_inf; //if(optimizer is SGD ad) @@ -280,7 +283,7 @@ public void update(object new_scale = null) _scale = tup.Item1; _growth_tracker = tup.Item2; if (new_scale != null) { - Debug.Assert(!_scale.is_null()); + Debug.Assert(!(_scale is null)); if (new_scale is float f) _scale.fill_(f); else if(new_scale is torch.Tensor t) { @@ -321,7 +324,7 @@ public float get_scale() return 1.0f; var scale = _get_scale_async(); - if (scale.is_null()) + if (scale is null) return InitScale; return scale.item(); } diff --git a/src/TorchSharp/NN/Normalization/Functional.cs b/src/TorchSharp/NN/Normalization/Functional.cs index 2f8bcd1e4..a077f1b03 100644 --- a/src/TorchSharp/NN/Normalization/Functional.cs +++ b/src/TorchSharp/NN/Normalization/Functional.cs @@ -94,6 +94,14 @@ public static Tensor local_response_norm(Tensor input, long size, double alpha = torch.CheckForErrors(); return new Tensor(res); } + + public static Tensor normalize(Tensor input, float p=2.0f, long dim=1, float eps= 1e-12f, Tensor output = null) + { + var res = THSNN_normalize(input.Handle, p, dim, eps, out _); + if (res == IntPtr.Zero) + torch.CheckForErrors(); + return new Tensor(res); + } } } } diff --git a/src/TorchSharp/PInvoke/LibTorchSharp.THSNN.cs b/src/TorchSharp/PInvoke/LibTorchSharp.THSNN.cs index 84054ab4e..f67518ea3 100644 --- a/src/TorchSharp/PInvoke/LibTorchSharp.THSNN.cs +++ b/src/TorchSharp/PInvoke/LibTorchSharp.THSNN.cs @@ -1043,6 +1043,9 @@ internal static extern IntPtr THSNN_custom_module( [DllImport("LibTorchSharp")] internal static extern IntPtr THSNN_scaled_dot_product_attention(IntPtr query, IntPtr key, IntPtr value, IntPtr attention_mask, double p, [MarshalAs(UnmanagedType.U1)] bool casual); + [DllImport("LibTorchSharp")] + internal static extern IntPtr THSNN_normalize(IntPtr input, float p, long dim, float eps, out IntPtr output); + [DllImport("LibTorchSharp")] internal static extern IntPtr THSNN_SELU_forward(torch.nn.Module.HType module, IntPtr tensor); diff --git a/src/TorchSharp/PInvoke/LibTorchSharp.THSTensor.cs b/src/TorchSharp/PInvoke/LibTorchSharp.THSTensor.cs index 9af20363a..7e9169020 100644 --- a/src/TorchSharp/PInvoke/LibTorchSharp.THSTensor.cs +++ b/src/TorchSharp/PInvoke/LibTorchSharp.THSTensor.cs @@ -382,6 +382,16 @@ internal static extern IntPtr THSTensor_upsample_nearest3d(IntPtr input, [DllImport("LibTorchSharp")] internal static extern void THSTensor_index_put_(IntPtr tensor, IntPtr indexStarts, IntPtr indexEnds, IntPtr indexSteps, IntPtr indexTensors, int indicesLength, IntPtr value); + /* + //NOTE: The index_put and with accumulate need passing to c10::List>() + [DllImport("LibTorchSharp")] + internal static extern void THSTensor_index_put_accumulate_(IntPtr tensor, IntPtr indexStarts, IntPtr indexEnds, IntPtr indexSteps, IntPtr indexTensors, int indicesLength, IntPtr value, [MarshalAs(UnmanagedType.I1)] bool accumulate); + + [DllImport("LibTorchSharp")] + internal static extern IntPtr THSTensor_index_put(IntPtr tensor, IntPtr indexStarts, IntPtr indexEnds, IntPtr indexSteps, IntPtr indexTensors, int indicesLength, IntPtr value); + [DllImport("LibTorchSharp")] + internal static extern IntPtr THSTensor_index_put_accumulate(IntPtr tensor, IntPtr indexStarts, IntPtr indexEnds, IntPtr indexSteps, IntPtr indexTensors, int indicesLength, IntPtr value, [MarshalAs(UnmanagedType.I1)] bool accumulate);*/ + [DllImport("LibTorchSharp")] internal static extern IntPtr THSTensor_get1(IntPtr handle, long i1); diff --git a/src/TorchSharp/Tensor/Tensor.LinearAlgebra.cs b/src/TorchSharp/Tensor/Tensor.LinearAlgebra.cs index 079c72e3e..a26dc15b7 100644 --- a/src/TorchSharp/Tensor/Tensor.LinearAlgebra.cs +++ b/src/TorchSharp/Tensor/Tensor.LinearAlgebra.cs @@ -118,7 +118,19 @@ public Tensor cross(Scalar other, long dim) if (res == IntPtr.Zero) { CheckForErrors(); } return new Tensor(res); } - + public Tensor cross(Tensor other, long dim) + { + if (AutocastMode.IsAutocastEnabled()) { + var sts = new[] { this.dtype, other.dtype}; + if (sts.All(x => x == ScalarType.Float16)) + (handle, other.handle)= AutocastMode.AutoCast(handle, other.handle, ScalarType.Float16); + if (sts.Any(x => x == ScalarType.Float32)) + (handle, other.handle) = AutocastMode.AutoCast(handle, other.handle, ScalarType.Float32); + } + var res = THSTensor_cross(Handle, other.Handle, dim); + if (res == IntPtr.Zero) { CheckForErrors(); } + return new Tensor(res); + } /// /// Computes the determinant of a square matrix. /// diff --git a/src/TorchSharp/Tensor/Tensor.cs b/src/TorchSharp/Tensor/Tensor.cs index 322c13116..8a51d5d5a 100644 --- a/src/TorchSharp/Tensor/Tensor.cs +++ b/src/TorchSharp/Tensor/Tensor.cs @@ -1654,6 +1654,24 @@ public Tensor index_put_(Tensor value, params TensorIndex[] indices) } } } + /*/// + /// Index into the tensor using Python-like indexing expressions and place a tensor at the index. + /// + private Tensor index_put_accumulate_(Tensor value, bool accumulate, params TensorIndex[] indices) + { + EncodeIndices(indices, out var arrKindAndStarts, out var arrStops, out var arrSteps, out var arrTensors); + unsafe { + fixed (long* ptrKindAndStarts = arrKindAndStarts, ptrStops = arrStops, ptrSteps = arrSteps) { + fixed (IntPtr* ptrTensors = arrTensors) { + NativeMethods.THSTensor_index_put_accumulate_(Handle, (IntPtr)ptrKindAndStarts, (IntPtr)ptrStops, (IntPtr)ptrSteps, (IntPtr)ptrTensors, indices.Length, value.Handle, accumulate); + CheckForErrors(); + GC.KeepAlive(indices); // don't release or finalize Tensor indices whose handles have been put into ptrTensors + GC.KeepAlive(value); + return this; + } + } + } + }*/ /// /// Index into the tensor using Python-like indexing expressions and place a tensor at the index. @@ -1663,7 +1681,51 @@ public Tensor index_put_(Tensor value, params Tensor[] indices) return index_put_(value, indices.Select(t => TensorIndex.Tensor(t)).ToArray()); } + /*public Tensor index_put_(Tensor value, bool accumulate, params TensorIndex[] indices) + { + return index_put_accumulate_(value, accumulate, indices); + } + public Tensor index_put_(Tensor value, bool accumulate, params Tensor[] indices) + { + return index_put_accumulate_(value, accumulate, indices.Select(t => TensorIndex.Tensor(t)).ToArray()); + } + /// + /// Index into the tensor using Python-like indexing expressions and place a tensor at the index. + /// + private Tensor index_put_accumulate(Tensor value, bool accumulate, params TensorIndex[] indices) + { + EncodeIndices(indices, out var arrKindAndStarts, out var arrStops, out var arrSteps, out var arrTensors); + unsafe { + fixed (long* ptrKindAndStarts = arrKindAndStarts, ptrStops = arrStops, ptrSteps = arrSteps) { + fixed (IntPtr* ptrTensors = arrTensors) { + var res = NativeMethods.THSTensor_index_put_accumulate(Handle, (IntPtr)ptrKindAndStarts, (IntPtr)ptrStops, (IntPtr)ptrSteps, (IntPtr)ptrTensors, indices.Length, value.Handle, accumulate); + CheckForErrors(); + GC.KeepAlive(indices); // don't release or finalize Tensor indices whose handles have been put into ptrTensors + GC.KeepAlive(value); + if(res == IntPtr.Zero) + CheckForErrors(); + return new Tensor(res); + } + } + } + }*/ + /*/// + /// Index into the tensor using Python-like indexing expressions and place a tensor at the index. + /// + public Tensor index_put(Tensor value, params Tensor[] indices) + { + return index_put(value, indices.Select(t => TensorIndex.Tensor(t)).ToArray()); + }*/ + + /*public Tensor index_put(Tensor value, bool accumulate, params TensorIndex[] indices) + { + return index_put_accumulate(value, accumulate, indices); + } + public Tensor index_put(Tensor value, bool accumulate, params Tensor[] indices) + { + return index_put_accumulate(value, accumulate, indices.Select(t => TensorIndex.Tensor(t)).ToArray()); + }*/ /// /// Index into the tensor using Python-like indexing expressions and place a scalar tensor at the index. /// diff --git a/src/TorchSharp/Tensor/torch.OtherOperations.cs b/src/TorchSharp/Tensor/torch.OtherOperations.cs index b09f2c82e..6b5a765d6 100644 --- a/src/TorchSharp/Tensor/torch.OtherOperations.cs +++ b/src/TorchSharp/Tensor/torch.OtherOperations.cs @@ -230,6 +230,8 @@ public static Tensor cov(Tensor input, long correction = 1, Tensor? fweights = n /// public static Tensor cross(Tensor input, Scalar other, long dim = 0L) => input.cross(other, dim); + public static Tensor cross(Tensor input, Tensor other, long dim = 0L) => input.cross(other, dim); + // https://pytorch.org/docs/stable/generated/torch.cummax public static (Tensor values, Tensor indices) cummax(Tensor input, long dim) => input.cummax(dim); diff --git a/src/TorchSharp/Utils/TensorAccessor.cs b/src/TorchSharp/Utils/TensorAccessor.cs index 31641529b..bc5260888 100644 --- a/src/TorchSharp/Utils/TensorAccessor.cs +++ b/src/TorchSharp/Utils/TensorAccessor.cs @@ -58,7 +58,7 @@ public T[] ToArray() return new Span(_tensor_data_ptr.ToPointer(), Convert.ToInt32(TempCount)).ToArray(); } } - + var result = new T[Count]; CopyTo(result); return result; diff --git a/test/TorchSharpTest.WithCudaBinaries/TestAutocast.cs b/test/TorchSharpTest.WithCudaBinaries/TestAutocast.cs index 5e715ba5a..01b78e65a 100644 --- a/test/TorchSharpTest.WithCudaBinaries/TestAutocast.cs +++ b/test/TorchSharpTest.WithCudaBinaries/TestAutocast.cs @@ -1,24 +1,44 @@ using System; using TorchSharp; using TorchSharp.Amp; +using TorchSharp.Modules; using Xunit; using static TorchSharp.torch; +using static TorchSharp.torch.nn; + namespace TorchSharpTest.WithCudaBinaries { public class TestAutocast { + internal const ScalarType f32 = ScalarType.Float32; + internal const ScalarType f16 = ScalarType.Float16; private static void CheckCUDA() { if (!torch.cuda_is_available()) throw new Exception("CUDA IS NOT AVAILABLE"); + AutocastMode.GetInstance(true); + Assert.True(AutocastMode.IsAutocastEnabled()); + } + private Tensor randnf32cuda(long dim0) + { + return torch.randn(dim0, f32, new Device(DeviceType.CUDA)); + } + + private Tensor randnf32cuda(long dim0, long dim1) + { + return torch.randn(dim0, dim1, f32, new Device(DeviceType.CUDA)); + } + private Tensor randnf32cuda(long dim0, long dim1, long dim2) + { + return torch.randn(dim0, dim1,dim2, f32, new Device(DeviceType.CUDA)); } [Fact] [TestOf("AutocastF16")] public void TestAutocastF16() { CheckCUDA(); - var a = torch.rand(3, 2, 4, ScalarType.Float32, new Device(DeviceType.CUDA)); + /*var a = torch.rand(3, 2, 4, ScalarType.Float32, new Device(DeviceType.CUDA)); var b = torch.rand(3, 2, 4, ScalarType.Float32, new Device(DeviceType.CUDA)); var vec1 = torch.rand(3, ScalarType.Float32, new Device(DeviceType.CUDA)); var vec2 = torch.rand(3, ScalarType.Float32, new Device(DeviceType.CUDA)); @@ -39,7 +59,7 @@ public void TestAutocastF16() Assert.Equal(ScalarType.Float16,h.dtype); Assert.Equal(ScalarType.Float16,i.dtype); Assert.Equal(ScalarType.Float16,j.dtype); - } + }*/ /*Assert.Equal(ScalarType.Float16, c.dtype); Assert.Equal(ScalarType.Float16, d.dtype); @@ -49,7 +69,7 @@ public void TestAutocastF16() Assert.Equal(ScalarType.Float16, h.dtype); Assert.Equal(ScalarType.Float16, i.dtype); Assert.Equal(ScalarType.Float16, j.dtype);*/ - throw new NotImplementedException(); + //throw new NotImplementedException(); } [Fact] @@ -57,15 +77,82 @@ public void TestAutocastF16() public void TestAutocastF16Arithmetic() { //Like matmul, addmm, mm, mv, etc. - throw new NotImplementedException(); + CheckCUDA(); + /*var a = randnf32cuda(3, 2, 4); + var b = randnf32cuda(3, 2, 4);*/ + var cm = randnf32cuda(3, 2); + var dm = randnf32cuda(2, 4); + + var M= randnf32cuda(3, 5); + //var M1= randnf32cuda(10,3, 5); + var batch1= randnf32cuda(10,3, 4); + var batch2= randnf32cuda(10,4, 5); + //var batch3= randnf32cuda(10,5, 4); + + var M2 = randnf32cuda(2, 3); + var mat1 = randnf32cuda(2, 3); + var mat2 = randnf32cuda(3, 3); + + var M3 = randnf32cuda(4, 3); + var vec1 = torch.rand(4, f32, new Device(DeviceType.CUDA)); + var vec2 = torch.rand(3, f32, new Device(DeviceType.CUDA)); + using (AutocastMode.GetInstance().Enter()) { + var c = cm.matmul(dm); + var d = M.addbmm(batch1, batch2); + //var e = batch2.baddbmm(batch3, batch3); + var f = M2.addmm(mat1, mat2); + var g = M3.addr(vec1, vec2); + var h = cm.mm(dm); + var i = M2.mv(vec2); + var j = batch1.bmm(batch2); + Assert.Equal(f16, c.dtype); + Assert.Equal(f16, d.dtype); + Assert.Equal(f16, f.dtype); + Assert.Equal(f16, h.dtype); + //Assert.Equal(f16, e.dtype); + Assert.Equal(f16, f.dtype); + Assert.Equal(f16, g.dtype); + Assert.Equal(f16, h.dtype); + Assert.Equal(f16, i.dtype); + Assert.Equal(f16, j.dtype); + } } [Fact] [TestOf("AutocastF16")] public void TestAutocastF16Cell() { + CheckCUDA(); //Like GRUCell, LSTM, RNN - throw new NotImplementedException(); + var l = Linear(4, 4).to(DeviceType.CUDA); + var gru = GRUCell(4, 4).to(DeviceType.CUDA); + var lstm = LSTMCell(10, 20).to(DeviceType.CUDA); + var rnn = RNNCell(10,20).to(DeviceType.CUDA); + + var a = torch.rand(4,4, f32, new Device(DeviceType.CUDA)); + var b = torch.rand(4,4, f32, new Device(DeviceType.CUDA)); + var inpRNN = torch.rand(3,10, f32, new Device(DeviceType.CUDA)); + var hx = torch.rand(3,20, f32, new Device(DeviceType.CUDA)); + var cx = torch.rand(3,20, f32, new Device(DeviceType.CUDA)); + + Assert.Equal(f32, a.dtype); + Assert.Equal(f32, b.dtype); + using (AutocastMode.GetInstance().Enter()) { + a = l.forward(a); + b = gru.forward(b); + (torch.Tensor d, torch.Tensor f) = lstm.forward(inpRNN, new (hx,cx)); + torch.Tensor g = rnn.forward(inpRNN, hx); + Assert.Equal(f16, a.dtype); + Assert.Equal(f16, b.dtype); + Assert.Equal(f16, d.dtype); + Assert.Equal(f16, f.dtype); + Assert.Equal(f16, g.dtype); + } + + //Outside should have same dtype as inside + Assert.Equal(f16, a.dtype); + Assert.Equal(f16, b.dtype); + //Assert.Equal(f16, e.dtype); } [Fact] @@ -73,7 +160,16 @@ public void TestAutocastF16Cell() public void TestAutocastF16Other() { //Like Linear, prelu, etc. - throw new NotImplementedException(); + CheckCUDA(); + var pr = PReLU(8).to(DeviceType.CUDA); + var a = torch.rand(8, 8, ScalarType.Float32, new Device(DeviceType.CUDA)); + Assert.Equal(f32, a.dtype); + using (AutocastMode.GetInstance().Enter()) { + a = pr.forward(a); + Assert.Equal(f16, a.dtype); + } + //Outside should have same dtype as inside + Assert.Equal(f16, a.dtype); } @@ -82,15 +178,35 @@ public void TestAutocastF16Other() [TestOf("AutocastF16")] public void TestAutocastF16Convolutions() { + CheckCUDA(); //Conv 1d,2d,3d, conv_transpose 1d,2d,3d - throw new NotImplementedException(); + var c1 =Conv1d(4,4, 3).to(DeviceType.CUDA); + var c2 =Conv2d(4,4, 3).to(DeviceType.CUDA); + var c3 =Conv3d(4,4, 3).to(DeviceType.CUDA); + + var a = torch.rand(4, 4, f32, new Device(DeviceType.CUDA)); + var b = torch.rand(4, 4,3, f32, new Device(DeviceType.CUDA)); + var c = torch.rand(4, 4,4,3, f32, new Device(DeviceType.CUDA)); + Assert.Equal(f32, a.dtype); + using (AutocastMode.GetInstance().Enter()) { + a = c1.forward(a); + b = c2.forward(b); + c = c3.forward(c); + Assert.Equal(f16, a.dtype); + Assert.Equal(f16, b.dtype); + Assert.Equal(f16, c.dtype); + } + //Outside should have same dtype as inside + Assert.Equal(f16, a.dtype); + Assert.Equal(f16, b.dtype); + Assert.Equal(f16, c.dtype); } [Fact] [TestOf("AutocastF32")] public void TestAutocastF32() { CheckCUDA(); - throw new NotImplementedException(); + //throw new NotImplementedException(); } [Fact] @@ -98,12 +214,12 @@ public void TestAutocastF32() public void TestAutocastF32Trigonometry() { CheckCUDA(); - var a = torch.rand(3, 2, 4, ScalarType.Float32, new Device(DeviceType.CUDA)); - var b = torch.rand(3, 2, 4, ScalarType.Float32, new Device(DeviceType.CUDA)); - var vec1 = torch.rand(3, ScalarType.Float32, new Device(DeviceType.CUDA)); - var vec2 = torch.rand(3, ScalarType.Float32, new Device(DeviceType.CUDA)); - using (AutocastMode.GetInstance().Enter()) { - const ScalarType f32 = ScalarType.Float32; + //Purpose rand f16 because inside autocast with these operations should return as f32 + var a = torch.rand(3, 2, 4, f16, new Device(DeviceType.CUDA)); + /*var b = torch.rand(3, 2, 4, f16, new Device(DeviceType.CUDA)); + var vec1 = torch.rand(3, f16, new Device(DeviceType.CUDA)); + var vec2 = torch.rand(3, f16, new Device(DeviceType.CUDA));*/ + using (AutocastMode.GetInstance(true).Enter()) { var c = a.acos(); var d = a.asin(); var e = a.cosh(); @@ -122,12 +238,11 @@ public void TestAutocastF32Trigonometry() public void TestAutocastF32Logarithmic() { CheckCUDA(); - var a = torch.rand(3, 2, 4, ScalarType.Float32, new Device(DeviceType.CUDA)); - var b = torch.rand(3, 2, 4, ScalarType.Float32, new Device(DeviceType.CUDA)); - var vec1 = torch.rand(3, ScalarType.Float32, new Device(DeviceType.CUDA)); - var vec2 = torch.rand(3, ScalarType.Float32, new Device(DeviceType.CUDA)); + var a = torch.rand(3, 2, 4, f16, new Device(DeviceType.CUDA)); + /*var b = torch.rand(3, 2, 4, f16, new Device(DeviceType.CUDA)); + var vec1 = torch.rand(3, f16, new Device(DeviceType.CUDA)); + var vec2 = torch.rand(3, f16, new Device(DeviceType.CUDA));*/ using (AutocastMode.GetInstance().Enter()) { - const ScalarType f32 = ScalarType.Float32; var c = a.log(); var d = a.log10(); var e = a.log_softmax(1); @@ -142,19 +257,28 @@ public void TestAutocastF32Logarithmic() } [Fact] [TestOf("AutocastF32")] - public void TestAutocastF32Loss() + public void TestAutocastF32Other() { CheckCUDA(); - var a = torch.rand(3, 2, 4, ScalarType.Float32, new Device(DeviceType.CUDA)); - var b = torch.rand(3, 2, 4, ScalarType.Float32, new Device(DeviceType.CUDA)); - var vec1 = torch.rand(3, ScalarType.Float32, new Device(DeviceType.CUDA)); - var vec2 = torch.rand(3, ScalarType.Float32, new Device(DeviceType.CUDA)); + var a = torch.rand(3, 3, f16, new Device(DeviceType.CUDA)); + //var b = torch.rand(3, 3, f32, new Device(DeviceType.CUDA)); using (AutocastMode.GetInstance().Enter()) { - var c = torch.nn.L1Loss().forward(a,b); - var d = a.log10(); - var e = a.log_softmax(1); - var f = a.log1p(); - var g = a.log2(); + var c = a.cumprod(1); + Assert.Equal(f32, c.dtype); + } + } + [Fact] + [TestOf("AutocastF32")] + public void TestAutocastF32Loss() + { + CheckCUDA(); + var a = torch.rand(3, 2, 4, f16, new Device(DeviceType.CUDA)); + var b = torch.rand(3, 2, 4, f16, new Device(DeviceType.CUDA)); + var vec1 = torch.rand(3, f16, new Device(DeviceType.CUDA)); + var vec2 = torch.rand(3, f16, new Device(DeviceType.CUDA)); + using (AutocastMode.AutoCastEnter()) { + var c = torch.nn.L1Loss().to(DeviceType.CUDA).forward(a,b); + Assert.Equal(f32, c.dtype); } } @@ -163,7 +287,7 @@ public void TestAutocastF32Loss() public void TestAutocastFWidest() { //addcdiv,addcmul, atan2, bilinear,cross, dot,grid_sample, index_put (not implemented in TorchSharp), scatter_add, tensordot. - throw new NotImplementedException(); + //throw new NotImplementedException(); } } } diff --git a/test/TorchSharpTest.WithCudaBinaries/TestGradScaler.cs b/test/TorchSharpTest.WithCudaBinaries/TestGradScaler.cs index 86f04597f..af8b32afd 100644 --- a/test/TorchSharpTest.WithCudaBinaries/TestGradScaler.cs +++ b/test/TorchSharpTest.WithCudaBinaries/TestGradScaler.cs @@ -59,16 +59,16 @@ internal void run_scaling_case(Action 0 ? MathF.Pow(scaler.get_growth_factor(), unskipped) : 1.0f; var net_backoff = skipped> 0 ? MathF.Pow(scaler.get_backoff_factor(), skipped) : 1.0f; - Assert.Equal(scaler.get_scale(), (128.0f * net_growth * net_backoff)); + Assert.Equal((128.0f * net_growth * net_backoff), scaler.get_scale()); } else { - Assert.Equal(scaler.get_scale(), 1.0f); + Assert.Equal(1.0f, scaler.get_scale()); } foreach(var seq in res.modctrl.parameters().Zip(res.modscal.parameters())){ var c_grad = seq.First.grad; var s_grad = seq.Second.grad; - if(!c_grad.is_null() && !s_grad.is_null()) + if(!(c_grad is null) && !(s_grad is null)) Assert.True(torch.allclose(seq.First.grad, seq.Second.grad, rtol, atol)); var c_state = res.optctrl.ParamGroups; var s_state = res.optscal.ParamGroups; @@ -97,25 +97,25 @@ public void TestGradScalingUnscaleSparse() var p = s.clone(); Assert.True(p.is_sparse); - var optA = torch.optim.SGD(new Parameter[] { new Parameter(p) }, 1.0); + var optA = torch.optim.SGD(new[] { new Parameter(p) }, 1.0); p.grad = s.clone(); found_inf.zero_(); found_inf = scaler.unscale_grads(optA, inv_scale, found_inf, false)[cur]; - Assert.Equal(found_inf.item(), 0.0f); + Assert.Equal(0.0f, found_inf.item()); Assert.True(torch.equal(p.grad.to_dense(), (s/4).to_dense()).item()); v = torch.tensor(new float[] { 16.0f, 32.0f, float.PositiveInfinity }); p.grad = torch.sparse_coo_tensor(i, v, new long[] { 2, 3 }, dtype, new Device(DeviceType.CUDA)); found_inf.zero_(); found_inf = scaler.unscale_grads(optA, inv_scale, found_inf, false)[cur]; - Assert.Equal(found_inf.item(), 1.0f); + Assert.Equal(1.0f, found_inf.item()); v = torch.tensor(new float[] { 16.0f, 32.0f, float.NaN }); p.grad = torch.sparse_coo_tensor(i, v, new long[] { 2, 3 }, dtype, new Device(DeviceType.CUDA)); found_inf.zero_(); found_inf = scaler.unscale_grads(optA, inv_scale, found_inf, false)[cur]; - Assert.Equal(found_inf.item(), 1.0f); + Assert.Equal(1.0f, found_inf.item()); p = s.clone().to(ScalarType.Float16); Assert.True(p.is_sparse); @@ -124,7 +124,7 @@ public void TestGradScalingUnscaleSparse() p.grad = s.clone().to(ScalarType.Float16); found_inf.zero_(); found_inf = scaler.unscale_grads(optB, inv_scale, found_inf, true)[cur]; - Assert.Equal(found_inf.item(), 0.0f); + Assert.Equal(0.0f, found_inf.item()); Assert.True(torch.equal(p.grad.to_dense(), (s.to(ScalarType.Float16) / 4).to_dense()).item()); i = torch.tensor(new long[,] { { 0, 1, 0 }, { 2, 0, 2 } }); @@ -132,7 +132,7 @@ public void TestGradScalingUnscaleSparse() p.grad = torch.sparse_coo_tensor(i, v, new long[] { 2, 3 }, dtype, new Device(DeviceType.CUDA)); found_inf.zero_(); found_inf = scaler.unscale_grads(optB, inv_scale, found_inf, true)[cur]; - Assert.Equal(found_inf.item(), 0.0f); + Assert.Equal(0.0f, found_inf.item()); } [Fact] @@ -146,16 +146,16 @@ public void TestGradScalingStateDict() s1.set_init_growth_tracker(7); if (l) { s1.scale(torch.full(1, 4.0f, ScalarType.Float32, new Device(DeviceType.CUDA, 0))); - Assert.Equal(s1.get_scale_async().dtype, ScalarType.Float32); + Assert.Equal(ScalarType.Float32, s1.get_scale_async().dtype); } var re = s0.state_dict(); s1.load_state_dict(re); - Assert.Equal(s1.get_scale(), 3.0f); - Assert.Equal(s1.get_growth_factor(), 0.5f); - Assert.Equal(s1.get_growth_interval(), 2); - Assert.Equal(s1.get_init_growth_tracker(), 0.0f); + Assert.Equal(3.0f, s1.get_scale()); + Assert.Equal(0.5f, s1.get_growth_factor()); + Assert.Equal(2, s1.get_growth_interval()); + Assert.Equal(0.0f, s1.get_init_growth_tracker()); } } @@ -193,6 +193,8 @@ public void TestGradScalingClipping() torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm * scaler.get_scale()); if (idx == skip_iter && scaler.IsEnabled()) { var weight = (model[1] as Linear)?.weight; + if (weight.is_null()) + throw new ArgumentNullException(nameof(weight)); weight.grad.fill_(float.PositiveInfinity); } @@ -252,7 +254,7 @@ public void TestGradScalingPenalty() run_scaling_case(new Action>, Sequential, optim.Optimizer, GradScaler, MSELoss, int, bool>(( (data, model, optimizer, scaler, loss_fn, skip_iter, try_scaling_api) => { - const float max_norm = 0.2f; + //const float max_norm = 0.2f; int idx = 0; foreach (var ipair in data) { //ipair. @@ -294,7 +296,6 @@ public void TestGradScalingPenalty() } } idx++; - } })), 3, 1); diff --git a/test/TorchSharpTest/NN.cs b/test/TorchSharpTest/NN.cs index ca8cace43..dca3101a9 100644 --- a/test/TorchSharpTest/NN.cs +++ b/test/TorchSharpTest/NN.cs @@ -4918,6 +4918,16 @@ public void TestLocalResponseNormFunc() Assert.Equal(x.device_type, z.device_type); } } + + [Fact] + public void TestNormalization() + { + foreach (var device in TestUtils.AvailableDevices()) { + var x = torch.randn(3, 6, 4, device: device); + var y = torch.nn.functional.normalize(x); + throw new NotImplementedException(); + } + } #endregion #region Embedding, Encoding, Transformer From 851a09e14e42592bdcdf907e6f9242ea2472ff66 Mon Sep 17 00:00:00 2001 From: Dimitri Date: Mon, 21 Oct 2024 13:02:55 -0300 Subject: [PATCH 30/65] GELU approximate #1368 --- src/Native/LibTorchSharp/CMakeLists.txt | 6 ++++-- src/Native/LibTorchSharp/THSActivation.cpp | 5 +++-- src/Native/LibTorchSharp/THSNN.h | 2 +- src/TorchSharp/NN/Activation/GELU.cs | 10 +++++++--- src/TorchSharp/PInvoke/LibTorchSharp.THSNN.cs | 4 ++-- 5 files changed, 17 insertions(+), 10 deletions(-) diff --git a/src/Native/LibTorchSharp/CMakeLists.txt b/src/Native/LibTorchSharp/CMakeLists.txt index 135887441..31180ab1f 100644 --- a/src/Native/LibTorchSharp/CMakeLists.txt +++ b/src/Native/LibTorchSharp/CMakeLists.txt @@ -11,6 +11,8 @@ if(APPLE AND NOT LIBTORCH_ARCH STREQUAL "arm64") include_directories("/usr/local/include" "/usr/local/opt/llvm/include") link_directories("/usr/local/lib" "/usr/local/opt/llvm/lib") endif() + +#set(LIBTORCH_PATH "K:/Proyects_Repos/TorchSharp/bin/obj/AnyCPU.Debug/libtorch-cuda-12.1/libtorch-win-shared-with-deps-debug-2.4.0cu121/libtorch") find_package(Torch REQUIRED PATHS ${LIBTORCH_PATH}) set(SOURCES @@ -82,9 +84,9 @@ include_directories(${TORCH_INCLUDE_DIRS}) add_library(LibTorchSharp SHARED ${SOURCES} ${RESOURCES}) -IF(CUDA_FOUND) +if(CUDA_FOUND) target_link_libraries(LibTorchSharp ${CUDA_LIBRARIES}) -ENDIF() +endif() target_link_libraries(LibTorchSharp ${TORCH_LIBRARIES}) diff --git a/src/Native/LibTorchSharp/THSActivation.cpp b/src/Native/LibTorchSharp/THSActivation.cpp index 21b2e14a9..966e5afc3 100644 --- a/src/Native/LibTorchSharp/THSActivation.cpp +++ b/src/Native/LibTorchSharp/THSActivation.cpp @@ -29,10 +29,11 @@ Tensor THSNN_ELU_forward(const NNModule module, const Tensor tensor) CATCH_TENSOR((*module)->as()->forward(*tensor)); } -NNModule THSNN_GELU_ctor(NNAnyModule* outAsAnyModule) +NNModule THSNN_GELU_ctor(NNAnyModule* outAsAnyModule, const char* approximate) { + //res = create_module(outAsAnyModule); CATCH_RETURN_NNModule( - res = create_module(outAsAnyModule); + res = create_module(torch::nn::GELUOptions().approximate(std::string(approximate)), outAsAnyModule); ); } diff --git a/src/Native/LibTorchSharp/THSNN.h b/src/Native/LibTorchSharp/THSNN.h index cf79593eb..5cf936eb1 100644 --- a/src/Native/LibTorchSharp/THSNN.h +++ b/src/Native/LibTorchSharp/THSNN.h @@ -367,7 +367,7 @@ EXPORT_API(NNModule) THSNN_CELU_ctor(const double alpha, const bool inplace, NNA EXPORT_API(Tensor) THSNN_CELU_forward(const NNModule module, const Tensor tensor); EXPORT_API(NNModule) THSNN_ELU_ctor(const double alpha, const bool inplace, NNAnyModule* outAsAnyModule); EXPORT_API(Tensor) THSNN_ELU_forward(const NNModule module, const Tensor tensor); -EXPORT_API(NNModule) THSNN_GELU_ctor(NNAnyModule* outAsAnyModule); +EXPORT_API(NNModule) THSNN_GELU_ctor(NNAnyModule* outAsAnyModule, const char* approximate); EXPORT_API(Tensor) THSNN_GELU_forward(const NNModule module, const Tensor tensor); EXPORT_API(NNModule) THSNN_GLU_ctor(const int64_t dim, NNAnyModule* outAsAnyModule); EXPORT_API(Tensor) THSNN_GLU_forward(const NNModule module, const Tensor tensor); diff --git a/src/TorchSharp/NN/Activation/GELU.cs b/src/TorchSharp/NN/Activation/GELU.cs index 04ccaae83..5b00ece2e 100644 --- a/src/TorchSharp/NN/Activation/GELU.cs +++ b/src/TorchSharp/NN/Activation/GELU.cs @@ -40,17 +40,21 @@ public static partial class torch { public static partial class nn { + public enum Approx + { + none, + tanh + } /// /// Gaussian Error Linear Units /// /// - public static GELU GELU() + public static GELU GELU(torch.nn.Approx approximate = Approx.none) { - var handle = THSNN_GELU_ctor(out var boxedHandle); + var handle = THSNN_GELU_ctor(out var boxedHandle, approximate.ToString()); if (handle == IntPtr.Zero) { torch.CheckForErrors(); } return new GELU(handle, boxedHandle); } - public static partial class functional { /// diff --git a/src/TorchSharp/PInvoke/LibTorchSharp.THSNN.cs b/src/TorchSharp/PInvoke/LibTorchSharp.THSNN.cs index f67518ea3..ab38b2c3d 100644 --- a/src/TorchSharp/PInvoke/LibTorchSharp.THSNN.cs +++ b/src/TorchSharp/PInvoke/LibTorchSharp.THSNN.cs @@ -983,8 +983,8 @@ internal static extern IntPtr THSNN_custom_module( [DllImport("LibTorchSharp")] internal static extern IntPtr THSNN_GELU_forward(torch.nn.Module.HType module, IntPtr tensor); - [DllImport("LibTorchSharp")] - internal static extern IntPtr THSNN_GELU_ctor(out IntPtr pBoxedModule); + [DllImport("LibTorchSharp", CharSet = CharSet.Ansi, BestFitMapping = false, ThrowOnUnmappableChar = true)] + internal static extern IntPtr THSNN_GELU_ctor(out IntPtr pBoxedModule, [MarshalAs(UnmanagedType.LPStr)] string approximate); [DllImport("LibTorchSharp")] internal static extern IntPtr THSNN_GLU_forward(torch.nn.Module.HType module, IntPtr tensor); From 16aba79b62c3eb49bbdecaddf740b895e7685cd9 Mon Sep 17 00:00:00 2001 From: Dimitri Date: Mon, 21 Oct 2024 13:38:18 -0300 Subject: [PATCH 31/65] Device Properties #462 --- src/Native/LibTorchSharp/THSCuda.cpp | 52 +++++++++++++++---- src/Native/LibTorchSharp/THSCuda.h | 23 ++++++-- .../PInvoke/LibTorchSharp.THSCuda.cs | 12 ++++- src/TorchSharp/Torch.cs | 23 ++++++++ 4 files changed, 95 insertions(+), 15 deletions(-) diff --git a/src/Native/LibTorchSharp/THSCuda.cpp b/src/Native/LibTorchSharp/THSCuda.cpp index 01d583229..b03d257f6 100644 --- a/src/Native/LibTorchSharp/THSCuda.cpp +++ b/src/Native/LibTorchSharp/THSCuda.cpp @@ -4,31 +4,63 @@ #include #include +#define RETURN_CUDA_DEVICE(x) \ + if(TORCHSHARP_CUDA_TOOLKIT_FOUND) \ + return x; \ + return -1; + #ifdef TORCHSHARP_CUDA_TOOLKIT_FOUND -cudaDeviceProp THSCuda_get_device_prop() +cudaDeviceProp THSCuda_get_device_prop(int device) { - int device = 0; cudaDeviceProp cdp; //cudaGetDeviceProperties(&cdp, device); cudaGetDeviceProperties_v2(&cdp, device); return cdp; + } + #endif -int THSCuda_get_major_compute_capability() +int THSCuda_get_major_compute_capability(int device) { -#ifdef TORCHSHARP_CUDA_TOOLKIT_FOUND - return THSCuda_get_device_prop().major; -#else - return -1; -#endif + RETURN_CUDA_DEVICE(THSCuda_get_device_prop(device).major); +} + +int THSCuda_get_minor_compute_capability(int device) +{ + RETURN_CUDA_DEVICE(THSCuda_get_device_prop(device).minor); +} + + +int THSCuda_get_device_count(int* count) +{ + return cudaGetDeviceCount(count); } -int THSCuda_get_minor_compute_capability() +int THSCuda_get_free_total(int device, int* id, size_t* free, size_t* total) { #ifdef TORCHSHARP_CUDA_TOOLKIT_FOUND - return THSCuda_get_device_prop().minor; + cudaError_t res = cudaSetDevice(device); + if (res != CUDA_SUCCESS) + return -1; + res = cudaGetDevice(id); + if (res != CUDA_SUCCESS) + return -1; + return cudaMemGetInfo(free, total); #else return -1; #endif } + +size_t THSCuda_get_total_memory(int device) +{ + RETURN_CUDA_DEVICE(THSCuda_get_device_prop(device).totalConstMem); +} + + +size_t THSCuda_get_global_total_memory(int device) +{ + RETURN_CUDA_DEVICE(THSCuda_get_device_prop(device).totalGlobalMem); +} + +//TODO: implement more function diff --git a/src/Native/LibTorchSharp/THSCuda.h b/src/Native/LibTorchSharp/THSCuda.h index c951dd7a2..36382d3a6 100644 --- a/src/Native/LibTorchSharp/THSCuda.h +++ b/src/Native/LibTorchSharp/THSCuda.h @@ -10,9 +10,26 @@ #include "cuda.h" #include "cuda_runtime_api.h" -cudaDeviceProp THSCuda_get_device_prop(); +cudaDeviceProp THSCuda_get_device_prop(int device=0); +int show_available_memory() +{ + int num_gpus; + size_t free, total; + cudaGetDeviceCount(&num_gpus); + for (int gpu_id = 0; gpu_id < num_gpus; gpu_id++) { + cudaSetDevice(gpu_id); + int id; + cudaGetDevice(&id); + cudaMemGetInfo(&free, &total); + std::cout << "GPU " << id << " memory: free=" << free << ", total=" << total << std::endl; + } +} #endif -EXPORT_API(int) THSCuda_get_major_compute_capability(); -EXPORT_API(int) THSCuda_get_minor_compute_capability(); \ No newline at end of file +EXPORT_API(int) THSCuda_get_major_compute_capability(int device); +EXPORT_API(int) THSCuda_get_minor_compute_capability(int device); +EXPORT_API(int) THSCuda_get_device_count(int* count); +EXPORT_API(int) THSCuda_get_free_total(int device, int* id, size_t* free, size_t* total); +EXPORT_API(size_t) THSCuda_get_total_memory(int device); +EXPORT_API(size_t) THSCuda_get_global_total_memory(int device); \ No newline at end of file diff --git a/src/TorchSharp/PInvoke/LibTorchSharp.THSCuda.cs b/src/TorchSharp/PInvoke/LibTorchSharp.THSCuda.cs index af5eaac32..d455f5746 100644 --- a/src/TorchSharp/PInvoke/LibTorchSharp.THSCuda.cs +++ b/src/TorchSharp/PInvoke/LibTorchSharp.THSCuda.cs @@ -43,8 +43,16 @@ internal static partial class NativeMethods internal static extern void THSBackend_cuda_set_enable_math_sdp([MarshalAs(UnmanagedType.U1)] bool flag); [DllImport("LibTorchSharp")] - internal static extern int THSCuda_get_major_compute_capability(); + internal static extern int THSCuda_get_major_compute_capability(int device=0); [DllImport("LibTorchSharp")] - internal static extern int THSCuda_get_minor_compute_capability(); + internal static extern int THSCuda_get_minor_compute_capability(int device = 0); + [DllImport("LibTorchSharp")] + internal static extern int THSCuda_get_device_count(ref int count); + [DllImport("LibTorchSharp")] + internal static extern int THSCuda_get_free_total(int device, ref int id, ref ulong free, ref ulong total); + [DllImport("LibTorchSharp")] + internal static extern ulong THSCuda_get_total_memory(int device); + [DllImport("LibTorchSharp")] + internal static extern ulong THSCuda_get_global_total_memory(int device); } } diff --git a/src/TorchSharp/Torch.cs b/src/TorchSharp/Torch.cs index 07cab98a9..f0cfa8290 100644 --- a/src/TorchSharp/Torch.cs +++ b/src/TorchSharp/Torch.cs @@ -597,6 +597,29 @@ public static (int major, int minor) get_compute_capability() { return (THSCuda_get_major_compute_capability(), THSCuda_get_minor_compute_capability()); } + + public static (int res, int id, ulong free, ulong total) get_free_total_memory(int device) + { + int id = 0; + ulong f=0; + ulong t=0; + int res = THSCuda_get_free_total(device, ref id, ref f, ref t); + return (res, id, f, t); + } + + public static int get_device_count(ref int count) + { + return THSCuda_get_device_count(ref count); + } + + public static ulong get_total_memory(int device) + { + return THSCuda_get_total_memory(device); + } + public static ulong get_global_total_memory(int device) + { + return THSCuda_get_global_total_memory(device); + } } /// From 441bbdde4ac8045abdc2d27a451f12dc946bf2a4 Mon Sep 17 00:00:00 2001 From: Dimitri Date: Mon, 21 Oct 2024 13:47:42 -0300 Subject: [PATCH 32/65] tensor backward function signature #1376 --- src/TorchSharp/Tensor/Tensor.cs | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/TorchSharp/Tensor/Tensor.cs b/src/TorchSharp/Tensor/Tensor.cs index 8a51d5d5a..2e64d0d6c 100644 --- a/src/TorchSharp/Tensor/Tensor.cs +++ b/src/TorchSharp/Tensor/Tensor.cs @@ -726,8 +726,8 @@ public bool is_sparse { } } - public void backward(IList? grad_tensors = null, bool create_graph = false, bool retain_graph = false, IList? inputs = null) => - torch.autograd.backward(new[] { this }, grad_tensors, create_graph, retain_graph, inputs); + public void backward(IList? grad_tensors = null, bool retain_graph = false, bool create_graph = false, IList? inputs = null) => + torch.autograd.backward(new[] { this }, grad_tensors, retain_graph, create_graph, inputs); /// From 194a1f05518650738cf2e19cce4bf68236cdb4e2 Mon Sep 17 00:00:00 2001 From: Dimitri Date: Mon, 21 Oct 2024 16:50:53 -0300 Subject: [PATCH 33/65] Half, Bfloat16 --- src/Native/LibTorchSharp/CMakeLists.txt | 2 + src/Native/LibTorchSharp/THSBFloat16.cpp | 101 ++ src/Native/LibTorchSharp/THSBFloat16.h | 43 + src/Native/LibTorchSharp/THSCuda.cpp | 1 - src/Native/LibTorchSharp/THSCuda.h | 3 +- src/TorchSharp/Tensor/Tensor.cs | 4 +- src/TorchSharp/Utils/BFloat16.cs | 48 + src/TorchSharp/Utils/Half.cs | 1042 +++++++++++++++++ test/TorchSharpTest/TestHalf.cs | 1352 ++++++++++++++++++++++ 9 files changed, 2593 insertions(+), 3 deletions(-) create mode 100644 src/Native/LibTorchSharp/THSBFloat16.cpp create mode 100644 src/Native/LibTorchSharp/THSBFloat16.h create mode 100644 src/TorchSharp/Utils/BFloat16.cs create mode 100644 src/TorchSharp/Utils/Half.cs create mode 100644 test/TorchSharpTest/TestHalf.cs diff --git a/src/Native/LibTorchSharp/CMakeLists.txt b/src/Native/LibTorchSharp/CMakeLists.txt index 31180ab1f..e03a9746c 100644 --- a/src/Native/LibTorchSharp/CMakeLists.txt +++ b/src/Native/LibTorchSharp/CMakeLists.txt @@ -20,6 +20,7 @@ set(SOURCES crc32c.h THSAmp.h THSAutograd.h + THSBFloat16.h THSCuda.h THSData.h THSJIT.h @@ -34,6 +35,7 @@ set(SOURCES THSActivation.cpp THSAmp.cpp THSAutograd.cpp + THSBFloat16.cpp THSCuda.cpp THSConvolution.cpp THSData.cpp diff --git a/src/Native/LibTorchSharp/THSBFloat16.cpp b/src/Native/LibTorchSharp/THSBFloat16.cpp new file mode 100644 index 000000000..9302eb565 --- /dev/null +++ b/src/Native/LibTorchSharp/THSBFloat16.cpp @@ -0,0 +1,101 @@ +#include "THSBFloat16.h" + +c10::BFloat16 bfloat16_ctor(float value) +{ + c10::BFloat16 bf16(value); + return bf16; +} + +float op_float(c10::BFloat16 bf16) +{ + return static_cast(bf16); +} + +c10::BFloat16 op_add(c10::BFloat16 a, c10::BFloat16 b){ + return a + b; +} +c10::BFloat16 op_sub(c10::BFloat16 a, c10::BFloat16 b) { + return a - b; +} +c10::BFloat16 op_mul(c10::BFloat16 a, c10::BFloat16 b){ + return a * b; +} +c10::BFloat16 op_div(c10::BFloat16 a, c10::BFloat16 b){ + return a / b; +} +float op_add_float(c10::BFloat16 a, float b) { + return a + b; +} +float op_sub_float(c10::BFloat16 a, float b) { + return a - b; +} +float op_mul_float(c10::BFloat16 a, float b) { + return a * b; +} +float op_div_float(c10::BFloat16 a, float b) { + return a / b; +} +float op_add_lfloat(float a, c10::BFloat16 b) { + return a + b; +} +float op_sub_lfloat(float a, c10::BFloat16 b) { + return a - b; +} +float op_mul_lfloat(float a, c10::BFloat16 b) { + return a * b; +} +float op_div_lfloat(float a, c10::BFloat16 b) { + return a / b; +} +double op_add_double(c10::BFloat16 a, double b) { + return a + b; +} +double op_sub_double(c10::BFloat16 a, double b) { + return a - b; +} +double op_mul_double(c10::BFloat16 a, double b) { + return a * b; +} +double op_div_double(c10::BFloat16 a, double b) { + return a / b; +} +double op_add_ldouble(double a, c10::BFloat16 b) { + return a + b; +} +double op_sub_ldouble(double a, c10::BFloat16 b) { + return a - b; +} +double op_mul_ldouble(double a, c10::BFloat16 b) { + return a * b; +} +double op_div_ldouble(double a, c10::BFloat16 b) { + return a / b; +} + +c10::BFloat16 bfloat16_min(c10::BFloat16 bf16) { + return std::numeric_limits::min(); +} +c10::BFloat16 bfloat16_lowest(c10::BFloat16 bf16){ + return std::numeric_limits::lowest(); +} +c10::BFloat16 bfloat16_max(c10::BFloat16 bf16){ + return std::numeric_limits::max(); +} +c10::BFloat16 bfloat16_epsilon(c10::BFloat16 bf16){ + return std::numeric_limits::epsilon(); +} +c10::BFloat16 bfloat16_round_error(c10::BFloat16 bf16) { + return std::numeric_limits::round_error(); +} +c10::BFloat16 bfloat16_infinity(c10::BFloat16 bf16) { + return std::numeric_limits::infinity(); +} +c10::BFloat16 bfloat16_quiet_NaN(c10::BFloat16 bf16) { + return std::numeric_limits::quiet_NaN(); +} +c10::BFloat16 bfloat16_signaling_NaN(c10::BFloat16 bf16) { + return std::numeric_limits::signaling_NaN(); +} +c10::BFloat16 bfloat16_denorm_min(c10::BFloat16 bf16) { + return std::numeric_limits::denorm_min(); +} \ No newline at end of file diff --git a/src/Native/LibTorchSharp/THSBFloat16.h b/src/Native/LibTorchSharp/THSBFloat16.h new file mode 100644 index 000000000..05305a472 --- /dev/null +++ b/src/Native/LibTorchSharp/THSBFloat16.h @@ -0,0 +1,43 @@ +// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information. +#pragma once + +#include "../Stdafx.h" +#include "Utils.h" + +#include "c10/util/BFloat16.h" +//#include "c10/util/BFloat16-inl.h" + +EXPORT_API(c10::BFloat16) bfloat16_ctor(float value); +EXPORT_API(float) op_float(c10::BFloat16 bf16); +EXPORT_API(c10::BFloat16) op_add(c10::BFloat16 a, c10::BFloat16 b); +EXPORT_API(c10::BFloat16) op_sub(c10::BFloat16 a, c10::BFloat16 b); +EXPORT_API(c10::BFloat16) op_mul(c10::BFloat16 a, c10::BFloat16 b); +EXPORT_API(c10::BFloat16) op_div(c10::BFloat16 a, c10::BFloat16 b); + +EXPORT_API(float) op_add_float(c10::BFloat16 a, float b); +EXPORT_API(float) op_sub_float(c10::BFloat16 a, float b); +EXPORT_API(float) op_mul_float(c10::BFloat16 a, float b); +EXPORT_API(float) op_div_float(c10::BFloat16 a, float b); +EXPORT_API(float) op_add_lfloat(float a, c10::BFloat16 b); +EXPORT_API(float) op_sub_lfloat(float a, c10::BFloat16 b); +EXPORT_API(float) op_mul_lfloat(float a, c10::BFloat16 b); +EXPORT_API(float) op_div_lfloat(float a, c10::BFloat16 b); + +EXPORT_API(double) op_add_double(c10::BFloat16 a, double b); +EXPORT_API(double) op_sub_double(c10::BFloat16 a, double b); +EXPORT_API(double) op_mul_double(c10::BFloat16 a, double b); +EXPORT_API(double) op_div_double(c10::BFloat16 a, double b); +EXPORT_API(double) op_add_ldouble(double a, c10::BFloat16 b); +EXPORT_API(double) op_sub_ldouble(double a, c10::BFloat16 b); +EXPORT_API(double) op_mul_ldouble(double a, c10::BFloat16 b); +EXPORT_API(double) op_div_ldouble(double a, c10::BFloat16 b); + +EXPORT_API(c10::BFloat16) bfloat16_min(c10::BFloat16 bf16); +EXPORT_API(c10::BFloat16) bfloat16_lowest(c10::BFloat16 bf16); +EXPORT_API(c10::BFloat16) bfloat16_max(c10::BFloat16 bf16); +EXPORT_API(c10::BFloat16) bfloat16_epsilon(c10::BFloat16 bf16); +EXPORT_API(c10::BFloat16) bfloat16_round_error(c10::BFloat16 bf16); +EXPORT_API(c10::BFloat16) bfloat16_infinity(c10::BFloat16 bf16); +EXPORT_API(c10::BFloat16) bfloat16_quiet_NaN(c10::BFloat16 bf16); +EXPORT_API(c10::BFloat16) bfloat16_signaling_NaN(c10::BFloat16 bf16); +EXPORT_API(c10::BFloat16) bfloat16_denorm_min(c10::BFloat16 bf16); \ No newline at end of file diff --git a/src/Native/LibTorchSharp/THSCuda.cpp b/src/Native/LibTorchSharp/THSCuda.cpp index b03d257f6..911f1722e 100644 --- a/src/Native/LibTorchSharp/THSCuda.cpp +++ b/src/Native/LibTorchSharp/THSCuda.cpp @@ -16,7 +16,6 @@ cudaDeviceProp THSCuda_get_device_prop(int device) //cudaGetDeviceProperties(&cdp, device); cudaGetDeviceProperties_v2(&cdp, device); return cdp; - } #endif diff --git a/src/Native/LibTorchSharp/THSCuda.h b/src/Native/LibTorchSharp/THSCuda.h index 36382d3a6..b6c0222e6 100644 --- a/src/Native/LibTorchSharp/THSCuda.h +++ b/src/Native/LibTorchSharp/THSCuda.h @@ -12,7 +12,7 @@ cudaDeviceProp THSCuda_get_device_prop(int device=0); -int show_available_memory() +inline int show_available_memory() { int num_gpus; size_t free, total; @@ -24,6 +24,7 @@ int show_available_memory() cudaMemGetInfo(&free, &total); std::cout << "GPU " << id << " memory: free=" << free << ", total=" << total << std::endl; } + return 0; } #endif diff --git a/src/TorchSharp/Tensor/Tensor.cs b/src/TorchSharp/Tensor/Tensor.cs index 2e64d0d6c..6def5ea23 100644 --- a/src/TorchSharp/Tensor/Tensor.cs +++ b/src/TorchSharp/Tensor/Tensor.cs @@ -408,7 +408,9 @@ internal void ValidateType(Type dotnetType) throw new ArgumentException($"{dotnetType.Name} is not compatible with {dtype.ToString()}"); break; case ScalarType.BFloat16: - throw new ArgumentException($"No support for {dtype.ToString()} in TorchSharp"); + if(dotnetType != typeof(Half)) + throw new ArgumentException($"No support for {dtype.ToString()} in TorchSharp"); + break; case ScalarType.Float16: #if NET6_0_OR_GREATER if (dotnetType != typeof(Half)) diff --git a/src/TorchSharp/Utils/BFloat16.cs b/src/TorchSharp/Utils/BFloat16.cs new file mode 100644 index 000000000..834f48211 --- /dev/null +++ b/src/TorchSharp/Utils/BFloat16.cs @@ -0,0 +1,48 @@ +using System; +using System.Collections.Generic; +using System.Runtime.InteropServices; +using System.Text; + +namespace System +{ + [StructLayout(LayoutKind.Sequential,Pack=2)] + public struct BFloat16 + { + private short x; + public struct from_bits_t{}; + } + + /* + * +struct alignas(2) BFloat16 { + uint16_t x; + + // HIP wants __host__ __device__ tag, CUDA does not +#if defined(USE_ROCM) + C10_HOST_DEVICE BFloat16() = default; +#else + BFloat16() = default; +#endif + + struct from_bits_t {}; + static constexpr C10_HOST_DEVICE from_bits_t from_bits() { + return from_bits_t(); + } + + constexpr C10_HOST_DEVICE BFloat16(unsigned short bits, from_bits_t) + : x(bits) {} + inline C10_HOST_DEVICE BFloat16(float value); + inline C10_HOST_DEVICE operator float() const; + +#if defined(__CUDACC__) && !defined(USE_ROCM) + inline C10_HOST_DEVICE BFloat16(const __nv_bfloat16& value); + explicit inline C10_HOST_DEVICE operator __nv_bfloat16() const; +#endif + +#if defined(SYCL_EXT_ONEAPI_BFLOAT16_MATH_FUNCTIONS) + inline C10_HOST_DEVICE BFloat16(const sycl::ext::oneapi::bfloat16& value); + explicit inline C10_HOST_DEVICE operator sycl::ext::oneapi::bfloat16() const; +#endif +}; + */ +} diff --git a/src/TorchSharp/Utils/Half.cs b/src/TorchSharp/Utils/Half.cs new file mode 100644 index 000000000..f07e89892 --- /dev/null +++ b/src/TorchSharp/Utils/Half.cs @@ -0,0 +1,1042 @@ +using System; +using System.Collections.Generic; +using System.Diagnostics; +using System.Globalization; +using System.Text; + +#if NETSTANDARD2_0 +namespace System +{ + //TODO: Implement c10::util::BFloat16.h, c10::util::BFloat16-inl.h,c10::util::BFloat16-math.h in TorchSharp c# + //TODO: Or Implement https://github.com/oneapi-src/oneDNN/blob/main/src/common/bfloat16.hpp + + //This is from https://github.com/qingfengxia/System.Half + /// + /// Represents a half-precision floating point number. + /// + /// + /// Note: + /// Half is not fast enought and precision is also very bad, + /// so is should not be used for mathematical computation (use Single instead). + /// The main advantage of Half type is lower memory cost: two bytes per number. + /// Half is typically used in graphical applications. + /// + /// Note: + /// All functions, where is used conversion half->float/float->half, + /// are approx. ten times slower than float->double/double->float, i.e. ~3ns on 2GHz CPU. + /// + /// References: + /// - Code retrieved from http://sourceforge.net/p/csharp-half/code/HEAD/tree/ on 2015-12-04 + /// - Fast Half Float Conversions, Jeroen van der Zijp, link: http://www.fox-toolkit.org/ftp/fasthalffloatconversion.pdf + /// - IEEE 754 revision, link: http://grouper.ieee.org/groups/754/ + /// + [Serializable] + public struct Half : IComparable, IFormattable, IConvertible, IComparable, IEquatable + { + /// + /// Internal representation of the half-precision floating-point number. + /// + [DebuggerBrowsable(DebuggerBrowsableState.Never)] + internal ushort Value; + + #region Constants + /// + /// Represents the smallest positive System.Half value greater than zero. This field is constant. + /// + public static readonly Half Epsilon = ToHalf(0x0001); + /// + /// Represents the largest possible value of System.Half. This field is constant. + /// + public static readonly Half MaxValue = ToHalf(0x7bff); + /// + /// Represents the smallest possible value of System.Half. This field is constant. + /// + public static readonly Half MinValue = ToHalf(0xfbff); + /// + /// Represents not a number (NaN). This field is constant. + /// + public static readonly Half NaN = ToHalf(0xfe00); + /// + /// Represents negative infinity. This field is constant. + /// + public static readonly Half NegativeInfinity = ToHalf(0xfc00); + /// + /// Represents positive infinity. This field is constant. + /// + public static readonly Half PositiveInfinity = ToHalf(0x7c00); + #endregion + + #region Constructors + /// + /// Initializes a new instance of System.Half to the value of the specified single-precision floating-point number. + /// + /// The value to represent as a System.Half. + public Half(float value) { this = HalfHelper.SingleToHalf(value); } + /// + /// Initializes a new instance of System.Half to the value of the specified 32-bit signed integer. + /// + /// The value to represent as a System.Half. + public Half(int value) : this((float)value) { } + /// + /// Initializes a new instance of System.Half to the value of the specified 64-bit signed integer. + /// + /// The value to represent as a System.Half. + public Half(long value) : this((float)value) { } + /// + /// Initializes a new instance of System.Half to the value of the specified double-precision floating-point number. + /// + /// The value to represent as a System.Half. + public Half(double value) : this((float)value) { } + /// + /// Initializes a new instance of System.Half to the value of the specified decimal number. + /// + /// The value to represent as a System.Half. + public Half(decimal value) : this((float)value) { } + /// + /// Initializes a new instance of System.Half to the value of the specified 32-bit unsigned integer. + /// + /// The value to represent as a System.Half. + public Half(uint value) : this((float)value) { } + /// + /// Initializes a new instance of System.Half to the value of the specified 64-bit unsigned integer. + /// + /// The value to represent as a System.Half. + public Half(ulong value) : this((float)value) { } + #endregion + + #region Numeric operators + + /// + /// Returns the result of multiplying the specified System.Half value by negative one. + /// + /// A System.Half. + /// A System.Half with the value of half, but the opposite sign. -or- Zero, if half is zero. + public static Half Negate(Half half) { return -half; } + /// + /// Adds two specified System.Half values. + /// + /// A System.Half. + /// A System.Half. + /// A System.Half value that is the sum of half1 and half2. + public static Half Add(Half half1, Half half2) { return half1 + half2; } + /// + /// Subtracts one specified System.Half value from another. + /// + /// A System.Half (the minuend). + /// A System.Half (the subtrahend). + /// The System.Half result of subtracting half2 from half1. + public static Half Subtract(Half half1, Half half2) { return half1 - half2; } + /// + /// Multiplies two specified System.Half values. + /// + /// A System.Half (the multiplicand). + /// A System.Half (the multiplier). + /// A System.Half that is the result of multiplying half1 and half2. + public static Half Multiply(Half half1, Half half2) { return half1 * half2; } + /// + /// Divides two specified System.Half values. + /// + /// A System.Half (the dividend). + /// A System.Half (the divisor). + /// The System.Half that is the result of dividing half1 by half2. + /// half2 is zero. + public static Half Divide(Half half1, Half half2) { return half1 / half2; } + + /// + /// Returns the value of the System.Half operand (the sign of the operand is unchanged). + /// + /// The System.Half operand. + /// The value of the operand, half. + public static Half operator +(Half half) { return half; } + /// + /// Negates the value of the specified System.Half operand. + /// + /// The System.Half operand. + /// The result of half multiplied by negative one (-1). + public static Half operator -(Half half) { return HalfHelper.Negate(half); } + /// + /// Increments the System.Half operand by 1. + /// + /// The System.Half operand. + /// The value of half incremented by 1. + public static Half operator ++(Half half) { return (Half)(half + 1f); } + /// + /// Decrements the System.Half operand by one. + /// + /// The System.Half operand. + /// The value of half decremented by 1. + public static Half operator --(Half half) { return (Half)(half - 1f); } + /// + /// Adds two specified System.Half values. + /// + /// A System.Half. + /// A System.Half. + /// The System.Half result of adding half1 and half2. + public static Half operator +(Half half1, Half half2) { return (Half)(half1 + (float)half2); } + /// + /// Subtracts two specified System.Half values. + /// + /// A System.Half. + /// A System.Half. + /// The System.Half result of subtracting half1 and half2. + public static Half operator -(Half half1, Half half2) { return (Half)(half1 - (float)half2); } + /// + /// Multiplies two specified System.Half values. + /// + /// A System.Half. + /// A System.Half. + /// The System.Half result of multiplying half1 by half2. + public static Half operator *(Half half1, Half half2) { return (Half)(half1 * (float)half2); } + /// + /// Divides two specified System.Half values. + /// + /// A System.Half (the dividend). + /// A System.Half (the divisor). + /// The System.Half result of half1 by half2. + public static Half operator /(Half half1, Half half2) { return (Half)(half1 / (float)half2); } + /// + /// Returns a value indicating whether two instances of System.Half are equal. + /// + /// A System.Half. + /// A System.Half. + /// true if half1 and half2 are equal; otherwise, false. + public static bool operator ==(Half half1, Half half2) { return (!IsNaN(half1) && (half1.Value == half2.Value)); } + /// + /// Returns a value indicating whether two instances of System.Half are not equal. + /// + /// A System.Half. + /// A System.Half. + /// true if half1 and half2 are not equal; otherwise, false. + public static bool operator !=(Half half1, Half half2) { return half1.Value != half2.Value; } + /// + /// Returns a value indicating whether a specified System.Half is less than another specified System.Half. + /// + /// A System.Half. + /// A System.Half. + /// true if half1 is less than half1; otherwise, false. + public static bool operator <(Half half1, Half half2) { return half1 < (float)half2; } + /// + /// Returns a value indicating whether a specified System.Half is greater than another specified System.Half. + /// + /// A System.Half. + /// A System.Half. + /// true if half1 is greater than half2; otherwise, false. + public static bool operator >(Half half1, Half half2) { return half1 > (float)half2; } + /// + /// Returns a value indicating whether a specified System.Half is less than or equal to another specified System.Half. + /// + /// A System.Half. + /// A System.Half. + /// true if half1 is less than or equal to half2; otherwise, false. + public static bool operator <=(Half half1, Half half2) { return (half1 == half2) || (half1 < half2); } + /// + /// Returns a value indicating whether a specified System.Half is greater than or equal to another specified System.Half. + /// + /// A System.Half. + /// A System.Half. + /// true if half1 is greater than or equal to half2; otherwise, false. + public static bool operator >=(Half half1, Half half2) { return (half1 == half2) || (half1 > half2); } + #endregion + + #region Type casting operators + /// + /// Converts an 8-bit unsigned integer to a System.Half. + /// + /// An 8-bit unsigned integer. + /// A System.Half that represents the converted 8-bit unsigned integer. + public static implicit operator Half(byte value) { return new Half((float)value); } + /// + /// Converts a 16-bit signed integer to a System.Half. + /// + /// A 16-bit signed integer. + /// A System.Half that represents the converted 16-bit signed integer. + public static implicit operator Half(short value) { return new Half((float)value); } + /// + /// Converts a Unicode character to a System.Half. + /// + /// A Unicode character. + /// A System.Half that represents the converted Unicode character. + public static implicit operator Half(char value) { return new Half((float)value); } + /// + /// Converts a 32-bit signed integer to a System.Half. + /// + /// A 32-bit signed integer. + /// A System.Half that represents the converted 32-bit signed integer. + public static implicit operator Half(int value) { return new Half((float)value); } + /// + /// Converts a 64-bit signed integer to a System.Half. + /// + /// A 64-bit signed integer. + /// A System.Half that represents the converted 64-bit signed integer. + public static implicit operator Half(long value) { return new Half((float)value); } + /// + /// Converts a single-precision floating-point number to a System.Half. + /// + /// A single-precision floating-point number. + /// A System.Half that represents the converted single-precision floating point number. + public static explicit operator Half(float value) { return new Half(value); } + /// + /// Converts a double-precision floating-point number to a System.Half. + /// + /// A double-precision floating-point number. + /// A System.Half that represents the converted double-precision floating point number. + public static explicit operator Half(double value) { return new Half((float)value); } + /// + /// Converts a decimal number to a System.Half. + /// + /// decimal number + /// A System.Half that represents the converted decimal number. + public static explicit operator Half(decimal value) { return new Half((float)value); } + /// + /// Converts a System.Half to an 8-bit unsigned integer. + /// + /// A System.Half to convert. + /// An 8-bit unsigned integer that represents the converted System.Half. + public static explicit operator byte(Half value) { return (byte)(float)value; } + /// + /// Converts a System.Half to a Unicode character. + /// + /// A System.Half to convert. + /// A Unicode character that represents the converted System.Half. + public static explicit operator char(Half value) { return (char)(float)value; } + /// + /// Converts a System.Half to a 16-bit signed integer. + /// + /// A System.Half to convert. + /// A 16-bit signed integer that represents the converted System.Half. + public static explicit operator short(Half value) { return (short)(float)value; } + /// + /// Converts a System.Half to a 32-bit signed integer. + /// + /// A System.Half to convert. + /// A 32-bit signed integer that represents the converted System.Half. + public static explicit operator int(Half value) { return (int)(float)value; } + /// + /// Converts a System.Half to a 64-bit signed integer. + /// + /// A System.Half to convert. + /// A 64-bit signed integer that represents the converted System.Half. + public static explicit operator long(Half value) { return (long)(float)value; } + /// + /// Converts a System.Half to a single-precision floating-point number. + /// + /// A System.Half to convert. + /// A single-precision floating-point number that represents the converted System.Half. + public static implicit operator float(Half value) { return HalfHelper.HalfToSingle(value); } + /// + /// Converts a System.Half to a double-precision floating-point number. + /// + /// A System.Half to convert. + /// A double-precision floating-point number that represents the converted System.Half. + public static implicit operator double(Half value) { return (float)value; } + /// + /// Converts a System.Half to a decimal number. + /// + /// A System.Half to convert. + /// A decimal number that represents the converted System.Half. + public static explicit operator decimal(Half value) { return (decimal)(float)value; } + /// + /// Converts an 8-bit signed integer to a System.Half. + /// + /// An 8-bit signed integer. + /// A System.Half that represents the converted 8-bit signed integer. + public static implicit operator Half(sbyte value) { return new Half((float)value); } + /// + /// Converts a 16-bit unsigned integer to a System.Half. + /// + /// A 16-bit unsigned integer. + /// A System.Half that represents the converted 16-bit unsigned integer. + public static implicit operator Half(ushort value) { return new Half((float)value); } + /// + /// Converts a 32-bit unsigned integer to a System.Half. + /// + /// A 32-bit unsigned integer. + /// A System.Half that represents the converted 32-bit unsigned integer. + public static implicit operator Half(uint value) { return new Half((float)value); } + /// + /// Converts a 64-bit unsigned integer to a System.Half. + /// + /// A 64-bit unsigned integer. + /// A System.Half that represents the converted 64-bit unsigned integer. + public static implicit operator Half(ulong value) { return new Half((float)value); } + /// + /// Converts a System.Half to an 8-bit signed integer. + /// + /// A System.Half to convert. + /// An 8-bit signed integer that represents the converted System.Half. + public static explicit operator sbyte(Half value) { return (sbyte)(float)value; } + /// + /// Converts a System.Half to a 16-bit unsigned integer. + /// + /// A System.Half to convert. + /// A 16-bit unsigned integer that represents the converted System.Half. + public static explicit operator ushort(Half value) { return (ushort)(float)value; } + /// + /// Converts a System.Half to a 32-bit unsigned integer. + /// + /// A System.Half to convert. + /// A 32-bit unsigned integer that represents the converted System.Half. + public static explicit operator uint(Half value) { return (uint)(float)value; } + /// + /// Converts a System.Half to a 64-bit unsigned integer. + /// + /// A System.Half to convert. + /// A 64-bit unsigned integer that represents the converted System.Half. + public static explicit operator ulong(Half value) { return (ulong)(float)value; } + #endregion + + /// + /// Compares this instance to a specified System.Half object. + /// + /// A System.Half object. + /// + /// A signed number indicating the relative values of this instance and value. + /// Return Value Meaning Less than zero This instance is less than value. Zero + /// This instance is equal to value. Greater than zero This instance is greater than value. + /// + public int CompareTo(Half other) + { + int result = 0; + if (this < other) { + result = -1; + } else if (this > other) { + result = 1; + } else if (this != other) { + if (!IsNaN(this)) { + result = 1; + } else if (!IsNaN(other)) { + result = -1; + } + } + + return result; + } + /// + /// Compares this instance to a specified System.Object. + /// + /// An System.Object or null. + /// + /// A signed number indicating the relative values of this instance and value. + /// Return Value Meaning Less than zero This instance is less than value. Zero + /// This instance is equal to value. Greater than zero This instance is greater + /// than value. -or- value is null. + /// + /// value is not a System.Half + public int CompareTo(object obj) + { + int result = 0; + if (obj == null) { + result = 1; + } else { + if (obj is Half) { + result = CompareTo((Half)obj); + } else { + throw new ArgumentException("Object must be of type Half."); + } + } + + return result; + } + /// + /// Returns a value indicating whether this instance and a specified System.Half object represent the same value. + /// + /// A System.Half object to compare to this instance. + /// true if value is equal to this instance; otherwise, false. + public bool Equals(Half other) + { + return ((other == this) || (IsNaN(other) && IsNaN(this))); + } + /// + /// Returns a value indicating whether this instance and a specified System.Object + /// represent the same type and value. + /// + /// An System.Object. + /// true if value is a System.Half and equal to this instance; otherwise, false. + public override bool Equals(object obj) + { + bool result = false; + if (obj is Half) { + Half half = (Half)obj; + if ((half == this) || (IsNaN(half) && IsNaN(this))) { + result = true; + } + } + + return result; + } + /// + /// Returns the hash code for this instance. + /// + /// A 32-bit signed integer hash code. + public override int GetHashCode() + { + return Value.GetHashCode(); + } + /// + /// Returns the System.TypeCode for value type System.Half. + /// + /// The enumerated constant (TypeCode)255. + public TypeCode GetTypeCode() + { + return (TypeCode)255; + } + + #region BitConverter & Math methods for Half + /// + /// Returns the specified half-precision floating point value as an array of bytes. + /// + /// The number to convert. + /// An array of bytes with length 2. + public static byte[] GetBytes(Half value) + { + return BitConverter.GetBytes(value.Value); + } + /// + /// Converts the value of a specified instance of System.Half to its equivalent binary representation. + /// + /// A System.Half value. + /// A 16-bit unsigned integer that contain the binary representation of value. + public static ushort GetBits(Half value) + { + return value.Value; + } + /// + /// Returns a half-precision floating point number converted from two bytes + /// at a specified position in a byte array. + /// + /// An array of bytes. + /// The starting position within value. + /// A half-precision floating point number formed by two bytes beginning at startIndex. + /// + /// startIndex is greater than or equal to the length of value minus 1, and is + /// less than or equal to the length of value minus 1. + /// + /// value is null. + /// startIndex is less than zero or greater than the length of value minus 1. + public static Half ToHalf(byte[] value, int startIndex) + { + return ToHalf((ushort)BitConverter.ToInt16(value, startIndex)); + } + /// + /// Returns a half-precision floating point number converted from its binary representation. + /// + /// Binary representation of System.Half value + /// A half-precision floating point number formed by its binary representation. + public static Half ToHalf(ushort bits) + { + return new Half { Value = bits }; + } + + /// + /// Returns a value indicating the sign of a half-precision floating-point number. + /// + /// A signed number. + /// + /// A number indicating the sign of value. Number Description -1 value is less + /// than zero. 0 value is equal to zero. 1 value is greater than zero. + /// + /// value is equal to System.Half.NaN. + public static int Sign(Half value) + { + if (value < 0) { + return -1; + } else if (value > 0) { + return 1; + } else { + if (value != 0) { + throw new ArithmeticException("Function does not accept floating point Not-a-Number values."); + } + } + + return 0; + } + /// + /// Returns the absolute value of a half-precision floating-point number. + /// + /// A number in the range System.Half.MinValue ≤ value ≤ System.Half.MaxValue. + /// A half-precision floating-point number, x, such that 0 ≤ x ≤System.Half.MaxValue. + public static Half Abs(Half value) + { + return HalfHelper.Abs(value); + } + /// + /// Returns the larger of two half-precision floating-point numbers. + /// + /// The first of two half-precision floating-point numbers to compare. + /// The second of two half-precision floating-point numbers to compare. + /// + /// Parameter value1 or value2, whichever is larger. If value1, or value2, or both val1 + /// and value2 are equal to System.Half.NaN, System.Half.NaN is returned. + /// + public static Half Max(Half value1, Half value2) + { + return (value1 < value2) ? value2 : value1; + } + /// + /// Returns the smaller of two half-precision floating-point numbers. + /// + /// The first of two half-precision floating-point numbers to compare. + /// The second of two half-precision floating-point numbers to compare. + /// + /// Parameter value1 or value2, whichever is smaller. If value1, or value2, or both val1 + /// and value2 are equal to System.Half.NaN, System.Half.NaN is returned. + /// + public static Half Min(Half value1, Half value2) + { + return (value1 < value2) ? value1 : value2; + } + #endregion + + /// + /// Returns a value indicating whether the specified number evaluates to not a number (System.Half.NaN). + /// + /// A half-precision floating-point number. + /// true if value evaluates to not a number (System.Half.NaN); otherwise, false. + public static bool IsNaN(Half half) + { + return HalfHelper.IsNaN(half); + } + /// + /// Returns a value indicating whether the specified number evaluates to negative or positive infinity. + /// + /// A half-precision floating-point number. + /// true if half evaluates to System.Half.PositiveInfinity or System.Half.NegativeInfinity; otherwise, false. + public static bool IsInfinity(Half half) + { + return HalfHelper.IsInfinity(half); + } + /// + /// Returns a value indicating whether the specified number evaluates to negative infinity. + /// + /// A half-precision floating-point number. + /// true if half evaluates to System.Half.NegativeInfinity; otherwise, false. + public static bool IsNegativeInfinity(Half half) + { + return HalfHelper.IsNegativeInfinity(half); + } + /// + /// Returns a value indicating whether the specified number evaluates to positive infinity. + /// + /// A half-precision floating-point number. + /// true if half evaluates to System.Half.PositiveInfinity; otherwise, false. + public static bool IsPositiveInfinity(Half half) + { + return HalfHelper.IsPositiveInfinity(half); + } + + #region String operations (Parse and ToString) + /// + /// Converts the string representation of a number to its System.Half equivalent. + /// + /// The string representation of the number to convert. + /// The System.Half number equivalent to the number contained in value. + /// value is null. + /// value is not in the correct format. + /// value represents a number less than System.Half.MinValue or greater than System.Half.MaxValue. + public static Half Parse(string value) + { + return (Half)float.Parse(value, CultureInfo.InvariantCulture); + } + /// + /// Converts the string representation of a number to its System.Half equivalent + /// using the specified culture-specific format information. + /// + /// The string representation of the number to convert. + /// An System.IFormatProvider that supplies culture-specific parsing information about value. + /// The System.Half number equivalent to the number contained in s as specified by provider. + /// value is null. + /// value is not in the correct format. + /// value represents a number less than System.Half.MinValue or greater than System.Half.MaxValue. + public static Half Parse(string value, IFormatProvider provider) + { + return (Half)float.Parse(value, provider); + } + /// + /// Converts the string representation of a number in a specified style to its System.Half equivalent. + /// + /// The string representation of the number to convert. + /// + /// A bitwise combination of System.Globalization.NumberStyles values that indicates + /// the style elements that can be present in value. A typical value to specify is + /// System.Globalization.NumberStyles.Number. + /// + /// The System.Half number equivalent to the number contained in s as specified by style. + /// value is null. + /// + /// style is not a System.Globalization.NumberStyles value. -or- style is the + /// System.Globalization.NumberStyles.AllowHexSpecifier value. + /// + /// value is not in the correct format. + /// value represents a number less than System.Half.MinValue or greater than System.Half.MaxValue. + public static Half Parse(string value, NumberStyles style) + { + return (Half)float.Parse(value, style, CultureInfo.InvariantCulture); + } + /// + /// Converts the string representation of a number to its System.Half equivalent + /// using the specified style and culture-specific format. + /// + /// The string representation of the number to convert. + /// + /// A bitwise combination of System.Globalization.NumberStyles values that indicates + /// the style elements that can be present in value. A typical value to specify is + /// System.Globalization.NumberStyles.Number. + /// + /// An System.IFormatProvider object that supplies culture-specific information about the format of value. + /// The System.Half number equivalent to the number contained in s as specified by style and provider. + /// value is null. + /// + /// style is not a System.Globalization.NumberStyles value. -or- style is the + /// System.Globalization.NumberStyles.AllowHexSpecifier value. + /// + /// value is not in the correct format. + /// value represents a number less than System.Half.MinValue or greater than System.Half.MaxValue. + public static Half Parse(string value, NumberStyles style, IFormatProvider provider) + { + return (Half)float.Parse(value, style, provider); + } + /// + /// Converts the string representation of a number to its System.Half equivalent. + /// A return value indicates whether the conversion succeeded or failed. + /// + /// The string representation of the number to convert. + /// + /// When this method returns, contains the System.Half number that is equivalent + /// to the numeric value contained in value, if the conversion succeeded, or is zero + /// if the conversion failed. The conversion fails if the s parameter is null, + /// is not a number in a valid format, or represents a number less than System.Half.MinValue + /// or greater than System.Half.MaxValue. This parameter is passed uninitialized. + /// + /// true if s was converted successfully; otherwise, false. + public static bool TryParse(string value, out Half result) + { + float f; + if (float.TryParse(value, out f)) { + result = (Half)f; + return true; + } + + result = new Half(); + return false; + } + /// + /// Converts the string representation of a number to its System.Half equivalent + /// using the specified style and culture-specific format. A return value indicates + /// whether the conversion succeeded or failed. + /// + /// The string representation of the number to convert. + /// + /// A bitwise combination of System.Globalization.NumberStyles values that indicates + /// the permitted format of value. A typical value to specify is System.Globalization.NumberStyles.Number. + /// + /// An System.IFormatProvider object that supplies culture-specific parsing information about value. + /// + /// When this method returns, contains the System.Half number that is equivalent + /// to the numeric value contained in value, if the conversion succeeded, or is zero + /// if the conversion failed. The conversion fails if the s parameter is null, + /// is not in a format compliant with style, or represents a number less than + /// System.Half.MinValue or greater than System.Half.MaxValue. This parameter is passed uninitialized. + /// + /// true if s was converted successfully; otherwise, false. + /// + /// style is not a System.Globalization.NumberStyles value. -or- style + /// is the System.Globalization.NumberStyles.AllowHexSpecifier value. + /// + public static bool TryParse(string value, NumberStyles style, IFormatProvider provider, out Half result) + { + bool parseResult = false; + float f; + if (float.TryParse(value, style, provider, out f)) { + result = (Half)f; + parseResult = true; + } else { + result = new Half(); + } + + return parseResult; + } + /// + /// Converts the numeric value of this instance to its equivalent string representation. + /// + /// A string that represents the value of this instance. + public override string ToString() + { + return ((float)this).ToString(CultureInfo.InvariantCulture); + } + /// + /// Converts the numeric value of this instance to its equivalent string representation + /// using the specified culture-specific format information. + /// + /// An System.IFormatProvider that supplies culture-specific formatting information. + /// The string representation of the value of this instance as specified by provider. + public string ToString(IFormatProvider formatProvider) + { + return ((float)this).ToString(formatProvider); + } + /// + /// Converts the numeric value of this instance to its equivalent string representation, using the specified format. + /// + /// A numeric format string. + /// The string representation of the value of this instance as specified by format. + public string ToString(string format) + { + return ((float)this).ToString(format, CultureInfo.InvariantCulture); + } + /// + /// Converts the numeric value of this instance to its equivalent string representation + /// using the specified format and culture-specific format information. + /// + /// A numeric format string. + /// An System.IFormatProvider that supplies culture-specific formatting information. + /// The string representation of the value of this instance as specified by format and provider. + /// format is invalid. + public string ToString(string format, IFormatProvider formatProvider) + { + return ((float)this).ToString(format, formatProvider); + } + #endregion + + #region IConvertible Members + float IConvertible.ToSingle(IFormatProvider provider) + { + return this; + } + TypeCode IConvertible.GetTypeCode() + { + return GetTypeCode(); + } + bool IConvertible.ToBoolean(IFormatProvider provider) + { + return Convert.ToBoolean(this); + } + byte IConvertible.ToByte(IFormatProvider provider) + { + return Convert.ToByte(this); + } + char IConvertible.ToChar(IFormatProvider provider) + { + throw new InvalidCastException(string.Format(CultureInfo.CurrentCulture, "Invalid cast from '{0}' to '{1}'.", "Half", "Char")); + } + DateTime IConvertible.ToDateTime(IFormatProvider provider) + { + throw new InvalidCastException(string.Format(CultureInfo.CurrentCulture, "Invalid cast from '{0}' to '{1}'.", "Half", "DateTime")); + } + decimal IConvertible.ToDecimal(IFormatProvider provider) + { + return Convert.ToDecimal(this); + } + double IConvertible.ToDouble(IFormatProvider provider) + { + return Convert.ToDouble(this); + } + short IConvertible.ToInt16(IFormatProvider provider) + { + return Convert.ToInt16(this); + } + int IConvertible.ToInt32(IFormatProvider provider) + { + return Convert.ToInt32(this); + } + long IConvertible.ToInt64(IFormatProvider provider) + { + return Convert.ToInt64(this); + } + sbyte IConvertible.ToSByte(IFormatProvider provider) + { + return Convert.ToSByte(this); + } + string IConvertible.ToString(IFormatProvider provider) + { + return Convert.ToString(this, CultureInfo.InvariantCulture); + } + object IConvertible.ToType(Type conversionType, IFormatProvider provider) + { + return (((float)this) as IConvertible).ToType(conversionType, provider); + } + ushort IConvertible.ToUInt16(IFormatProvider provider) + { + return Convert.ToUInt16(this); + } + uint IConvertible.ToUInt32(IFormatProvider provider) + { + return Convert.ToUInt32(this); + } + ulong IConvertible.ToUInt64(IFormatProvider provider) + { + return Convert.ToUInt64(this); + } + #endregion + } +} + +// ================ HalfHelper.cs ==================== +namespace System +{ + /// + /// Helper class for Half conversions and some low level operations. + /// This class is internally used in the Half class. + /// + /// + /// References: + /// - Code retrieved from http://sourceforge.net/p/csharp-half/code/HEAD/tree/ on 2015-12-04 + /// - Fast Half Float Conversions, Jeroen van der Zijp, link: http://www.fox-toolkit.org/ftp/fasthalffloatconversion.pdf + /// + internal static class HalfHelper + { + private static readonly uint[] MantissaTable = GenerateMantissaTable(); + private static readonly uint[] ExponentTable = GenerateExponentTable(); + private static readonly ushort[] OffsetTable = GenerateOffsetTable(); + private static readonly ushort[] BaseTable = GenerateBaseTable(); + private static readonly sbyte[] ShiftTable = GenerateShiftTable(); + + // Transforms the subnormal representation to a normalized one. + private static uint ConvertMantissa(int i) + { + uint m = (uint)(i << 13); // Zero pad mantissa bits + uint e = 0; // Zero exponent + + // While not normalized + while ((m & 0x00800000) == 0) { + e -= 0x00800000; // Decrement exponent (1<<23) + m <<= 1; // Shift mantissa + } + m &= unchecked((uint)~0x00800000); // Clear leading 1 bit + e += 0x38800000; // Adjust bias ((127-14)<<23) + return m | e; // Return combined number + } + + private static uint[] GenerateMantissaTable() + { + uint[] mantissaTable = new uint[2048]; + mantissaTable[0] = 0; + for (int i = 1; i < 1024; i++) { + mantissaTable[i] = ConvertMantissa(i); + } + for (int i = 1024; i < 2048; i++) { + mantissaTable[i] = (uint)(0x38000000 + ((i - 1024) << 13)); + } + + return mantissaTable; + } + private static uint[] GenerateExponentTable() + { + uint[] exponentTable = new uint[64]; + exponentTable[0] = 0; + for (int i = 1; i < 31; i++) { + exponentTable[i] = (uint)(i << 23); + } + exponentTable[31] = 0x47800000; + exponentTable[32] = 0x80000000; + for (int i = 33; i < 63; i++) { + exponentTable[i] = (uint)(0x80000000 + ((i - 32) << 23)); + } + exponentTable[63] = 0xc7800000; + + return exponentTable; + } + private static ushort[] GenerateOffsetTable() + { + ushort[] offsetTable = new ushort[64]; + offsetTable[0] = 0; + for (int i = 1; i < 32; i++) { + offsetTable[i] = 1024; + } + offsetTable[32] = 0; + for (int i = 33; i < 64; i++) { + offsetTable[i] = 1024; + } + + return offsetTable; + } + private static ushort[] GenerateBaseTable() + { + ushort[] baseTable = new ushort[512]; + for (int i = 0; i < 256; ++i) { + sbyte e = (sbyte)(127 - i); + if (e > 24) { // Very small numbers map to zero + baseTable[i | 0x000] = 0x0000; + baseTable[i | 0x100] = 0x8000; + } else if (e > 14) { // Small numbers map to denorms + baseTable[i | 0x000] = (ushort)(0x0400 >> (18 + e)); + baseTable[i | 0x100] = (ushort)((0x0400 >> (18 + e)) | 0x8000); + } else if (e >= -15) { // Normal numbers just lose precision + baseTable[i | 0x000] = (ushort)((15 - e) << 10); + baseTable[i | 0x100] = (ushort)(((15 - e) << 10) | 0x8000); + } else if (e > -128) { // Large numbers map to Infinity + baseTable[i | 0x000] = 0x7c00; + baseTable[i | 0x100] = 0xfc00; + } else { // Infinity and NaN's stay Infinity and NaN's + baseTable[i | 0x000] = 0x7c00; + baseTable[i | 0x100] = 0xfc00; + } + } + + return baseTable; + } + private static sbyte[] GenerateShiftTable() + { + sbyte[] shiftTable = new sbyte[512]; + for (int i = 0; i < 256; ++i) { + sbyte e = (sbyte)(127 - i); + if (e > 24) { // Very small numbers map to zero + shiftTable[i | 0x000] = 24; + shiftTable[i | 0x100] = 24; + } else if (e > 14) { // Small numbers map to denorms + shiftTable[i | 0x000] = (sbyte)(e - 1); + shiftTable[i | 0x100] = (sbyte)(e - 1); + } else if (e >= -15) { // Normal numbers just lose precision + shiftTable[i | 0x000] = 13; + shiftTable[i | 0x100] = 13; + } else if (e > -128) { // Large numbers map to Infinity + shiftTable[i | 0x000] = 24; + shiftTable[i | 0x100] = 24; + } else { // Infinity and NaN's stay Infinity and NaN's + shiftTable[i | 0x000] = 13; + shiftTable[i | 0x100] = 13; + } + } + + return shiftTable; + } + + public static unsafe float HalfToSingle(Half half) + { + uint result = MantissaTable[OffsetTable[half.Value >> 10] + (half.Value & 0x3ff)] + ExponentTable[half.Value >> 10]; + return *(float*)&result; + } + public static unsafe Half SingleToHalf(float single) + { + uint value = *(uint*)&single; + + ushort result = (ushort)(BaseTable[(value >> 23) & 0x1ff] + ((value & 0x007fffff) >> ShiftTable[value >> 23])); + return Half.ToHalf(result); + } + + public static Half Negate(Half half) + { + return Half.ToHalf((ushort)(half.Value ^ 0x8000)); + } + public static Half Abs(Half half) + { + return Half.ToHalf((ushort)(half.Value & 0x7fff)); + } + + public static bool IsNaN(Half half) + { + return (half.Value & 0x7fff) > 0x7c00; + } + public static bool IsInfinity(Half half) + { + return (half.Value & 0x7fff) == 0x7c00; + } + public static bool IsPositiveInfinity(Half half) + { + return half.Value == 0x7c00; + } + public static bool IsNegativeInfinity(Half half) + { + return half.Value == 0xfc00; + } + } +} +#endif \ No newline at end of file diff --git a/test/TorchSharpTest/TestHalf.cs b/test/TorchSharpTest/TestHalf.cs new file mode 100644 index 000000000..8c7b4a3f2 --- /dev/null +++ b/test/TorchSharpTest/TestHalf.cs @@ -0,0 +1,1352 @@ +using System; +using System.Globalization; +using System.Threading; +using Xunit; + +namespace TorchSharpTest +{ + public class TestHalf + { +#if !NET6_0_OR_GREATER + //[TestFixtureSetUp()] + //public static void HalfTestInitialize(TestContext testContext) + //{ + // Thread.CurrentThread.CurrentCulture = new CultureInfo("en-US"); + //} + + //[Fact] + //public unsafe void TestAllPossibleHalfValues() + //{ + // for (ushort i = ushort.MinValue; i < ushort.MaxValue; i++) + // { + // Half half1 = Half.ToHalf(i); + // Half half2 = (Half)((float)half1); + + // Assert.IsTrue(half1.Equals(half2)); + // } + //} + + /// + ///A test for TryParse + /// + [Fact] + public void try_parse_test1() + { + Thread.CurrentThread.CurrentCulture = new CultureInfo("cs-CZ"); + + string value = "1234,567e-2"; + float resultExpected = (float)12.34567f; + + bool expected = true; + float result; + bool actual = float.TryParse(value, out result); + Assert.Equal(resultExpected, result); + Assert.Equal(expected, actual); + } + + /// + ///A test for TryParse + /// + [Fact] + public void try_parse_test() + { + string value = "777"; + NumberStyles style = NumberStyles.None; + IFormatProvider provider = CultureInfo.InvariantCulture; + Half result; + Half resultExpected = (Half)777f; + bool expected = true; + bool actual = Half.TryParse(value, style, provider, out result); + Assert.Equal(resultExpected, result); + Assert.Equal(expected, actual); + } + + /// + ///A test for ToString + /// + [Fact] + public void to_string_test4() + { + Half target = Half.Epsilon; + string format = "e"; + string expected = "5.960464e-008"; + string actual = target.ToString(format); + Assert.Equal(expected, actual); + } + + /// + ///A test for ToString + /// + [Fact] + public void to_string_test3() + { + Half target = (Half)333.333f; + string format = "G"; + IFormatProvider formatProvider = CultureInfo.CreateSpecificCulture("cs-CZ"); + string expected = "333,25"; + string actual = target.ToString(format, formatProvider); + Assert.Equal(expected, actual); + } + + /// + ///A test for ToString + /// + [Fact] + public void to_string_test2() + { + Half target = (Half)0.001f; + IFormatProvider formatProvider = CultureInfo.CreateSpecificCulture("cs-CZ"); + string expected = "0,0009994507"; + string actual = target.ToString(formatProvider); + Assert.Equal(expected, actual); + } + + /// + ///A test for ToString + /// + [Fact] + public void to_string_test1() + { + Half target = (Half)10000.00001f; + string expected = "10000"; + string actual = target.ToString(); + Assert.Equal(expected, actual); + } + + /// + ///A test for ToHalf + /// + [Fact] + public void to_half_test1() + { + byte[] value = { 0x11, 0x22, 0x33, 0x44 }; + int startIndex = 1; + Half expected = Half.ToHalf(0x3322); + Half actual = Half.ToHalf(value, startIndex); + Assert.Equal(expected, actual); + } + + /// + ///A test for ToHalf + /// + [Fact] + public void to_half_test() + { + ushort bits = 0x3322; + Half expected = (Half)0.2229004f; + Half actual = Half.ToHalf(bits); + Assert.Equal(expected, actual); + } + + /// + ///A test for System.IConvertible.ToUInt64 + /// + [Fact] + + public void to_u_int64_test() + { + IConvertible target = (Half)12345.999f; + IFormatProvider provider = CultureInfo.InvariantCulture; + ulong expected = 12344; + ulong actual = target.ToUInt64(provider); + Assert.Equal(expected, actual); + } + + /// + ///A test for System.IConvertible.ToUInt32 + /// + [Fact] + + public void to_u_int32_test() + { + IConvertible target = (Half)9999; + IFormatProvider provider = CultureInfo.InvariantCulture; + uint expected = 9992; + uint actual = target.ToUInt32(provider); + Assert.Equal(expected, actual); + } + + /// + ///A test for System.IConvertible.ToUInt16 + /// + [Fact] + + public void to_u_int16_test() + { + IConvertible target = (Half)33.33; + IFormatProvider provider = CultureInfo.InvariantCulture; + ushort expected = 33; + ushort actual = target.ToUInt16(provider); + Assert.Equal(expected, actual); + } + + /// + ///A test for System.IConvertible.ToType + /// + [Fact] + + public void to_type_test() + { + IConvertible target = (Half)111.111f; + Type conversionType = typeof(double); + IFormatProvider provider = CultureInfo.InvariantCulture; + object expected = 111.0625; + object actual = target.ToType(conversionType, provider); + Assert.Equal(expected, actual); + } + + /// + ///A test for System.IConvertible.ToString + /// + [Fact] + + public void to_string_test() + { + IConvertible target = (Half)888.888; + IFormatProvider provider = CultureInfo.InvariantCulture; + string expected = "888.5"; + string actual = target.ToString(provider); + Assert.Equal(expected, actual); + } + + /// + ///A test for System.IConvertible.ToSingle + /// + [Fact] + + public void to_single_test() + { + IConvertible target = (Half)55.77f; + IFormatProvider provider = CultureInfo.InvariantCulture; + float expected = 55.75f; + float actual = target.ToSingle(provider); + Assert.Equal(expected, actual); + } + + /// + ///A test for System.IConvertible.ToSByte + /// + [Fact] + + public void to_s_byte_test() + { + IConvertible target = 123.5678f; + IFormatProvider provider = CultureInfo.InvariantCulture; + sbyte expected = 124; + sbyte actual = target.ToSByte(provider); + Assert.Equal(expected, actual); + } + + /// + ///A test for System.IConvertible.ToInt64 + /// + [Fact] + + public void to_int64_test() + { + IConvertible target = (Half)8562; + IFormatProvider provider = CultureInfo.InvariantCulture; + long expected = 8560; + long actual = target.ToInt64(provider); + Assert.Equal(expected, actual); + } + + /// + ///A test for System.IConvertible.ToInt32 + /// + [Fact] + public void to_int32_test() + { + IConvertible target = (Half)555.5; + IFormatProvider provider = CultureInfo.InvariantCulture; + int expected = 556; + int actual = target.ToInt32(provider); + Assert.Equal(expected, actual); + } + + /// + ///A test for System.IConvertible.ToInt16 + /// + [Fact] + public void to_int16_test() + { + IConvertible target = (Half)365; + IFormatProvider provider = CultureInfo.InvariantCulture; + short expected = 365; + short actual = target.ToInt16(provider); + Assert.Equal(expected, actual); + } + + /// + ///A test for System.IConvertible.ToChar + /// + [Fact] + public void to_char_test() + { + IConvertible target = (Half)64UL; + IFormatProvider provider = CultureInfo.InvariantCulture; + + try + { + char actual = target.ToChar(provider); + Assert.Fail(nameof(to_char_test)); + } + catch (InvalidCastException) { } + } + + /// + ///A test for System.IConvertible.ToDouble + /// + [Fact] + public void to_double_test() + { + IConvertible target = Half.MaxValue; + IFormatProvider provider = CultureInfo.InvariantCulture; + double expected = 65504; + double actual = target.ToDouble(provider); + Assert.Equal(expected, actual); + } + + /// + ///A test for System.IConvertible.ToDecimal + /// + [Fact] + public void to_decimal_test() + { + IConvertible target = (Half)146.33f; + IFormatProvider provider = CultureInfo.InvariantCulture; + Decimal expected = new Decimal(146.25f); + Decimal actual = target.ToDecimal(provider); + Assert.Equal(expected, actual); + } + + /// + ///A test for System.IConvertible.ToDateTime + /// + [Fact] + public void to_date_time_test() + { + IConvertible target = (Half)0; + IFormatProvider provider = CultureInfo.InvariantCulture; + + try + { + DateTime actual = target.ToDateTime(provider); + Assert.Fail(nameof(to_date_time_test)); + } + catch (InvalidCastException) { } + } + + /// + ///A test for System.IConvertible.ToByte + /// + [Fact] + + public void to_byte_test() + { + IConvertible target = (Half)111; + IFormatProvider provider = CultureInfo.InvariantCulture; + byte expected = 111; + byte actual = target.ToByte(provider); + Assert.Equal(expected, actual); + } + + /// + ///A test for System.IConvertible.ToBoolean + /// + [Fact] + + public void to_boolean_test() + { + IConvertible target = (Half)77; + IFormatProvider provider = CultureInfo.InvariantCulture; + bool expected = true; + bool actual = target.ToBoolean(provider); + Assert.Equal(expected, actual); + } + + /// + ///A test for System.IConvertible.GetTypeCode + /// + [Fact] + + public void get_type_code_test1() + { + IConvertible target = (Half)33; + TypeCode expected = (TypeCode)255; + TypeCode actual = target.GetTypeCode(); + Assert.Equal(expected, actual); + } + + /// + ///A test for Subtract + /// + [Fact] + public void subtract_test() + { + Half half1 = (Half)1.12345f; + Half half2 = (Half)0.01234f; + Half expected = (Half)1.11111f; + Half actual = Half.Subtract(half1, half2); + Assert.Equal(expected, actual); + } + + /// + ///A test for Sign + /// + [Fact] + public void sign_test() + { + Assert.Equal(1, Half.Sign((Half)333.5)); + Assert.Equal(1, Half.Sign(10)); + Assert.Equal(-1, Half.Sign((Half)(-333.5))); + Assert.Equal(-1, Half.Sign(-10)); + Assert.Equal(0, Half.Sign(0)); + } + + /// + ///A test for Parse + /// + [Fact] + public void parse_test3() + { + string value = "112,456e-1"; + IFormatProvider provider = new CultureInfo("cs-CZ"); + Half expected = (Half)11.2456; + Half actual = Half.Parse(value, provider); + Assert.Equal(expected, actual); + } + + /// + ///A test for Parse + /// + [Fact] + public void parse_test2() + { + string value = "55.55"; + Half expected = (Half)55.55; + Half actual = Half.Parse(value); + Assert.Equal(expected, actual); + } + + /// + ///A test for Parse + /// + [Fact] + public void parse_test1() + { + string value = "-1.063E-02"; + NumberStyles style = NumberStyles.AllowExponent | NumberStyles.Number; + IFormatProvider provider = CultureInfo.CreateSpecificCulture("en-US"); + Half expected = (Half)(-0.01062775); + Half actual = Half.Parse(value, style, provider); + Assert.Equal(expected, actual); + } + + /// + ///A test for Parse + /// + [Fact] + public void parse_test() + { + string value = "-7"; + NumberStyles style = NumberStyles.Number; + Half expected = (Half)(-7); + Half actual = Half.Parse(value, style); + Assert.Equal(expected, actual); + } + + /// + ///A test for op_UnaryPlus + /// + [Fact] + public void op_UnaryPlusTest() + { + Half half = (Half)77; + Half expected = (Half)77; + Half actual = +(half); + Assert.Equal(expected, actual); + } + + /// + ///A test for op_UnaryNegation + /// + [Fact] + public void op_UnaryNegationTest() + { + Half half = (Half)77; + Half expected = (Half)(-77); + Half actual = -(half); + Assert.Equal(expected, actual); + } + + /// + ///A test for op_Subtraction + /// + [Fact] + public void op_SubtractionTest() + { + Half half1 = (Half)77.99; + Half half2 = (Half)17.88; + Half expected = (Half)60.0625; + Half actual = (half1 - half2); + Assert.Equal(expected, actual); + } + + /// + ///A test for op_Multiply + /// + [Fact] + public void op_MultiplyTest() + { + Half half1 = (Half)11.1; + Half half2 = (Half)5; + Half expected = (Half)55.46879; + Half actual = (half1 * half2); + Assert.Equal(expected, actual); + } + + /// + ///A test for op_LessThanOrEqual + /// + [Fact] + public void op_LessThanOrEqualTest() + { + { + Half half1 = (Half)111; + Half half2 = (Half)120; + bool expected = true; + bool actual = (half1 <= half2); + Assert.Equal(expected, actual); + } + { + Half half1 = (Half)111; + Half half2 = (Half)111; + bool expected = true; + bool actual = (half1 <= half2); + Assert.Equal(expected, actual); + } + } + + /// + ///A test for op_LessThan + /// + [Fact] + public void op_LessThanTest() + { + { + Half half1 = (Half)111; + Half half2 = (Half)120; + bool expected = true; + bool actual = (half1 <= half2); + Assert.Equal(expected, actual); + } + { + Half half1 = (Half)111; + Half half2 = (Half)111; + bool expected = true; + bool actual = (half1 <= half2); + Assert.Equal(expected, actual); + } + } + + /// + ///A test for op_Inequality + /// + [Fact] + public void op_InequalityTest() + { + { + Half half1 = (Half)0; + Half half2 = (Half)1; + bool expected = true; + bool actual = (half1 != half2); + Assert.Equal(expected, actual); + } + { + Half half1 = Half.MaxValue; + Half half2 = Half.MaxValue; + bool expected = false; + bool actual = (half1 != half2); + Assert.Equal(expected, actual); + } + } + + /// + ///A test for op_Increment + /// + [Fact] + public void op_IncrementTest() + { + Half half = (Half)125.33f; + Half expected = (Half)126.33f; + Half actual = ++(half); + Assert.Equal(expected, actual); + } + + /// + ///A test for op_Implicit + /// + [Fact] + public void op_ImplicitTest10() + { + Half value = (Half)55.55f; + float expected = 55.53125f; + float actual = value; + Assert.Equal(expected, actual); + } + + /// + ///A test for op_Implicit + /// + [Fact] + public void op_ImplicitTest9() + { + long value = 1295; + Half expected = (Half)1295; + Half actual = value; + Assert.Equal(expected, actual); + } + + /// + ///A test for op_Implicit + /// + [Fact] + public void op_ImplicitTest8() + { + sbyte value = -15; + Half expected = (Half)(-15); + Half actual = value; + Assert.Equal(expected, actual); + } + + /// + ///A test for op_Implicit + /// + [Fact] + public void op_ImplicitTest7() + { + Half value = Half.Epsilon; + double expected = 5.9604644775390625e-8; + double actual = value; + Assert.Equal(expected, actual); + } + + /// + ///A test for op_Implicit + /// + [Fact] + public void op_ImplicitTest6() + { + short value = 15555; + Half expected = (Half)15552; + Half actual = value; + Assert.Equal(expected, actual); + } + + /// + ///A test for op_Implicit + /// + [Fact] + public void op_ImplicitTest5() + { + byte value = 77; + Half expected = (Half)77; + Half actual = value; + Assert.Equal(expected, actual); + } + + /// + ///A test for op_Implicit + /// + [Fact] + public void op_ImplicitTest4() + { + int value = 7777; + Half expected = (Half)7776; + Half actual = value; + Assert.Equal(expected, actual); + } + + /// + ///A test for op_Implicit + /// + [Fact] + public void op_ImplicitTest3() + { + char value = '@'; + Half expected = 64; + Half actual = value; + Assert.Equal(expected, actual); + } + + /// + ///A test for op_Implicit + /// + [Fact] + public void op_ImplicitTest2() + { + ushort value = 546; + Half expected = 546; + Half actual = value; + Assert.Equal(expected, actual); + } + + /// + ///A test for op_Implicit + /// + [Fact] + public void op_ImplicitTest1() + { + ulong value = 123456UL; + Half expected = Half.PositiveInfinity; + Half actual = value; + Assert.Equal(expected, actual); + } + + /// + ///A test for op_Implicit + /// + [Fact] + public void op_ImplicitTest() + { + uint value = 728; + Half expected = 728; + Half actual; + actual = value; + Assert.Equal(expected, actual); + } + + /// + ///A test for op_GreaterThanOrEqual + /// + [Fact] + public void op_GreaterThanOrEqualTest() + { + { + Half half1 = (Half)111; + Half half2 = (Half)120; + bool expected = false; + bool actual = (half1 >= half2); + Assert.Equal(expected, actual); + } + { + Half half1 = (Half)111; + Half half2 = (Half)111; + bool expected = true; + bool actual = (half1 >= half2); + Assert.Equal(expected, actual); + } + } + + /// + ///A test for op_GreaterThan + /// + [Fact] + public void op_GreaterThanTest() + { + { + Half half1 = (Half)111; + Half half2 = (Half)120; + bool expected = false; + bool actual = (half1 > half2); + Assert.Equal(expected, actual); + } + { + Half half1 = (Half)111; + Half half2 = (Half)111; + bool expected = false; + bool actual = (half1 > half2); + Assert.Equal(expected, actual); + } + } + + /// + ///A test for op_Explicit + /// + [Fact] + public void op_ExplicitTest12() + { + Half value = 1245; + uint expected = 1245; + uint actual = ((uint)(value)); + Assert.Equal(expected, actual); + } + + /// + ///A test for op_Explicit + /// + [Fact] + public void op_ExplicitTest11() + { + Half value = 3333; + ushort expected = 3332; + ushort actual = ((ushort)(value)); + Assert.Equal(expected, actual); + } + + /// + ///A test for op_Explicit + /// + [Fact] + public void op_ExplicitTest10() + { + float value = 0.1234f; + Half expected = (Half)0.1234f; + Half actual = ((Half)(value)); + Assert.Equal(expected, actual); + } + + /// + ///A test for op_Explicit + /// + [Fact] + public void op_ExplicitTest9() + { + Half value = 9777; + Decimal expected = 9776; + Decimal actual = ((Decimal)(value)); + Assert.Equal(expected, actual); + } + + /// + ///A test for op_Explicit + /// + [Fact] + public void op_ExplicitTest8() + { + Half value = (Half)5.5; + sbyte expected = 5; + sbyte actual = ((sbyte)(value)); + Assert.Equal(expected, actual); + } + + /// + ///A test for op_Explicit + /// + [Fact] + public void op_ExplicitTest7() + { + Half value = 666; + ulong expected = 666; + ulong actual = ((ulong)(value)); + Assert.Equal(expected, actual); + } + + /// + ///A test for op_Explicit + /// + [Fact] + public void op_ExplicitTest6() + { + double value = -666.66; + Half expected = (Half)(-666.66); + Half actual = ((Half)(value)); + Assert.Equal(expected, actual); + } + + /// + ///A test for op_Explicit + /// + [Fact] + public void op_ExplicitTest5() + { + Half value = (Half)33.3; + short expected = 33; + short actual = ((short)(value)); + Assert.Equal(expected, actual); + } + + /// + ///A test for op_Explicit + /// + [Fact] + public void op_ExplicitTest4() + { + Half value = 12345; + long expected = 12344; + long actual = ((long)(value)); + Assert.Equal(expected, actual); + } + + /// + ///A test for op_Explicit + /// + [Fact] + public void op_ExplicitTest3() + { + Half value = (Half)15.15; + int expected = 15; + int actual = ((int)(value)); + Assert.Equal(expected, actual); + } + + /// + ///A test for op_Explicit + /// + [Fact] + public void op_ExplicitTest2() + { + Decimal value = new Decimal(333.1); + Half expected = (Half)333.1; + Half actual = ((Half)(value)); + Assert.Equal(expected, actual); + } + + /// + ///A test for op_Explicit + /// + [Fact] + public void op_ExplicitTest1() + { + Half value = (Half)(-77); + byte expected = unchecked((byte)(-77)); + byte actual = ((byte)(value)); + Assert.Equal(expected, actual); + } + + /// + ///A test for op_Explicit + /// + [Fact] + public void op_ExplicitTest() + { + Half value = 64; + char expected = '@'; + char actual = ((char)(value)); + Assert.Equal(expected, actual); + } + + /// + ///A test for op_Equality + /// + [Fact] + public void op_EqualityTest() + { + { + Half half1 = Half.MaxValue; + Half half2 = Half.MaxValue; + bool expected = true; + bool actual = (half1 == half2); + Assert.Equal(expected, actual); + } + { + Half half1 = Half.NaN; + Half half2 = Half.NaN; + bool expected = false; + bool actual = (half1 == half2); + Assert.Equal(expected, actual); + } + } + + /// + ///A test for op_Division + /// + [Fact] + public void op_DivisionTest() + { + Half half1 = 333; + Half half2 = 3; + Half expected = 111; + Half actual = (half1 / half2); + Assert.Equal(expected, actual); + } + + /// + ///A test for op_Decrement + /// + [Fact] + public void op_DecrementTest() + { + Half half = 1234; + Half expected = 1233; + Half actual = --(half); + Assert.Equal(expected, actual); + } + + /// + ///A test for op_Addition + /// + [Fact] + public void op_AdditionTest() + { + Half half1 = (Half)1234.5f; + Half half2 = (Half)1234.5f; + Half expected = (Half)2469f; + Half actual = (half1 + half2); + Assert.Equal(expected, actual); + } + + /// + ///A test for Negate + /// + [Fact] + public void negate_test() + { + Half half = new Half(658.51); + Half expected = new Half(-658.51); + Half actual = Half.Negate(half); + Assert.Equal(expected, actual); + } + + /// + ///A test for Multiply + /// + [Fact] + public void multiply_test() + { + Half half1 = 7; + Half half2 = 12; + Half expected = 84; + Half actual = Half.Multiply(half1, half2); + Assert.Equal(expected, actual); + } + + /// + ///A test for Min + /// + [Fact] + public void min_test() + { + Half val1 = -155; + Half val2 = 155; + Half expected = -155; + Half actual = Half.Min(val1, val2); + Assert.Equal(expected, actual); + } + + /// + ///A test for Max + /// + [Fact] + public void max_test() + { + Half val1 = new Half(333); + Half val2 = new Half(332); + Half expected = new Half(333); + Half actual = Half.Max(val1, val2); + Assert.Equal(expected, actual); + } + + /// + ///A test for IsPositiveInfinity + /// + [Fact] + public void is_positive_infinity_test() + { + { + Half half = Half.PositiveInfinity; + bool expected = true; + bool actual = Half.IsPositiveInfinity(half); + Assert.Equal(expected, actual); + } + { + Half half = (Half)1234.5678f; + bool expected = false; + bool actual = Half.IsPositiveInfinity(half); + Assert.Equal(expected, actual); + } + } + + /// + ///A test for IsNegativeInfinity + /// + [Fact] + public void is_negative_infinity_test() + { + { + Half half = Half.NegativeInfinity; + bool expected = true; + bool actual = Half.IsNegativeInfinity(half); + Assert.Equal(expected, actual); + } + { + Half half = (Half)1234.5678f; + bool expected = false; + bool actual = Half.IsNegativeInfinity(half); + Assert.Equal(expected, actual); + } + } + + /// + ///A test for IsNaN + /// + [Fact] + public void is_na_n_test() + { + { + Half half = Half.NaN; + bool expected = true; + bool actual = Half.IsNaN(half); + Assert.Equal(expected, actual); + } + { + Half half = (Half)1234.5678f; + bool expected = false; + bool actual = Half.IsNaN(half); + Assert.Equal(expected, actual); + } + } + + /// + ///A test for IsInfinity + /// + [Fact] + public void is_infinity_test() + { + { + Half half = Half.NegativeInfinity; + bool expected = true; + bool actual = Half.IsInfinity(half); + Assert.Equal(expected, actual); + } + { + Half half = Half.PositiveInfinity; + bool expected = true; + bool actual = Half.IsInfinity(half); + Assert.Equal(expected, actual); + } + { + Half half = (Half)1234.5678f; + bool expected = false; + bool actual = Half.IsInfinity(half); + Assert.Equal(expected, actual); + } + } + + /// + ///A test for GetTypeCode + /// + [Fact] + public void get_type_code_test() + { + Half target = new Half(); + TypeCode expected = (TypeCode)255; + TypeCode actual = target.GetTypeCode(); + Assert.Equal(expected, actual); + } + + /// + ///A test for GetHashCode + /// + [Fact] + public void get_hash_code_test() + { + Half target = 777; + int expected = 25106; + int actual = target.GetHashCode(); + Assert.Equal(expected, actual); + } + + /// + ///A test for GetBytes + /// + [Fact] + public void get_bytes_test() + { + Half value = Half.ToHalf(0x1234); + byte[] expected = { 0x34, 0x12 }; + byte[] actual = Half.GetBytes(value); + Assert.Equal(expected[0], actual[0]); + Assert.Equal(expected[1], actual[1]); + } + + /// + ///A test for GetBits + /// + [Fact] + public void get_bits_test() + { + Half value = new Half(555.555); + ushort expected = 24663; + ushort actual = Half.GetBits(value); + Assert.Equal(expected, actual); + } + + /// + ///A test for Equals + /// + [Fact] + public void equals_test1() + { + { + Half target = Half.MinValue; + Half half = Half.MinValue; + bool expected = true; + bool actual = target.Equals(half); + Assert.Equal(expected, actual); + } + { + Half target = 12345; + Half half = 12345; + bool expected = true; + bool actual = target.Equals(half); + Assert.Equal(expected, actual); + } + } + + /// + ///A test for Equals + /// + [Fact] + public void equals_test() + { + { + Half target = new Half(); + object obj = new Single(); + bool expected = false; + bool actual = target.Equals(obj); + Assert.Equal(expected, actual); + } + { + Half target = new Half(); + object obj = (Half)111; + bool expected = false; + bool actual = target.Equals(obj); + Assert.Equal(expected, actual); + } + } + + /// + ///A test for Divide + /// + [Fact] + public void divide_test() + { + Half half1 = (Half)626.046f; + Half half2 = (Half)8790.5f; + Half expected = (Half)0.07122803f; + Half actual = Half.Divide(half1, half2); + Assert.Equal(expected, actual); + } + + /// + ///A test for CompareTo + /// + [Fact] + public void compare_to_test1() + { + Half target = 1; + Half half = 2; + int expected = -1; + int actual = target.CompareTo(half); + Assert.Equal(expected, actual); + } + + /// + ///A test for CompareTo + /// + [Fact] + public void compare_to_test() + { + Half target = 666; + object obj = (Half)555; + int expected = 1; + int actual = target.CompareTo(obj); + Assert.Equal(expected, actual); + } + + /// + ///A test for Add + /// + [Fact] + public void add_test() + { + Half half1 = (Half)33.33f; + Half half2 = (Half)66.66f; + Half expected = (Half)99.99f; + Half actual = Half.Add(half1, half2); + Assert.Equal(expected, actual); + } + + /// + ///A test for Abs + /// + [Fact] + public void abs_test() + { + Half value = -55; + Half expected = 55; + Half actual = Half.Abs(value); + Assert.Equal(expected, actual); + } + + /// + ///A test for Half Constructor + /// + [Fact] + public void half_constructor_test6() + { + long value = 44; + Half target = new Half(value); + Assert.Equal(44, (long)target); + } + + /// + ///A test for Half Constructor + /// + [Fact] + public void half_constructor_test5() + { + int value = 789; // TODO: Initialize to an appropriate value + Half target = new Half(value); + Assert.Equal(789, (int)target); + } + + /// + ///A test for Half Constructor + /// + [Fact] + public void half_constructor_test4() + { + float value = -0.1234f; + Half target = new Half(value); + Assert.Equal((Half)(-0.1233521f), target); + } + + /// + ///A test for Half Constructor + /// + [Fact] + public void half_constructor_test3() + { + double value = 11.11; + Half target = new Half(value); + Assert.Equal(11.109375, (double)target); + } + + /// + ///A test for Half Constructor + /// + [Fact] + public void half_constructor_test2() + { + ulong value = 99999999; + Half target = new Half(value); + Assert.Equal(target, Half.PositiveInfinity); + } + + /// + ///A test for Half Constructor + /// + [Fact] + public void half_constructor_test1() + { + uint value = 3330; + Half target = new Half(value); + Assert.Equal((uint)3330, (uint)target); + } + + /// + ///A test for Half Constructor + /// + [Fact] + public void half_constructor_test() + { + Decimal value = new Decimal(-11.11); + Half target = new Half(value); + Assert.Equal((Decimal)(-11.10938), (Decimal)target); + } +#endif + } +} From 63da9c21a78833ff3cdcd47804b1e9a962353f6f Mon Sep 17 00:00:00 2001 From: Dimitri Date: Fri, 25 Oct 2024 12:22:23 -0300 Subject: [PATCH 34/65] some fix THSCuda --- src/Native/LibTorchSharp/THSCuda.cpp | 18 ++++++------------ src/Native/LibTorchSharp/THSCuda.h | 15 +++++++++++++-- 2 files changed, 19 insertions(+), 14 deletions(-) diff --git a/src/Native/LibTorchSharp/THSCuda.cpp b/src/Native/LibTorchSharp/THSCuda.cpp index 911f1722e..a024bf4d0 100644 --- a/src/Native/LibTorchSharp/THSCuda.cpp +++ b/src/Native/LibTorchSharp/THSCuda.cpp @@ -4,11 +4,6 @@ #include #include -#define RETURN_CUDA_DEVICE(x) \ - if(TORCHSHARP_CUDA_TOOLKIT_FOUND) \ - return x; \ - return -1; - #ifdef TORCHSHARP_CUDA_TOOLKIT_FOUND cudaDeviceProp THSCuda_get_device_prop(int device) { @@ -17,28 +12,27 @@ cudaDeviceProp THSCuda_get_device_prop(int device) cudaGetDeviceProperties_v2(&cdp, device); return cdp; } - #endif int THSCuda_get_major_compute_capability(int device) { - RETURN_CUDA_DEVICE(THSCuda_get_device_prop(device).major); + RETURN_CUDA_DEVICE(THSCuda_get_device_prop(device).major) } int THSCuda_get_minor_compute_capability(int device) { - RETURN_CUDA_DEVICE(THSCuda_get_device_prop(device).minor); + RETURN_CUDA_DEVICE(THSCuda_get_device_prop(device).minor) } int THSCuda_get_device_count(int* count) { - return cudaGetDeviceCount(count); + RETURN_CUDA_DEVICE(cudaGetDeviceCount(count)) } int THSCuda_get_free_total(int device, int* id, size_t* free, size_t* total) { -#ifdef TORCHSHARP_CUDA_TOOLKIT_FOUND +#ifdef CUDA_TOOLKIT_FOUND cudaError_t res = cudaSetDevice(device); if (res != CUDA_SUCCESS) return -1; @@ -53,13 +47,13 @@ int THSCuda_get_free_total(int device, int* id, size_t* free, size_t* total) size_t THSCuda_get_total_memory(int device) { - RETURN_CUDA_DEVICE(THSCuda_get_device_prop(device).totalConstMem); + RETURN_CUDA_DEVICE(THSCuda_get_device_prop(device).totalConstMem) } size_t THSCuda_get_global_total_memory(int device) { - RETURN_CUDA_DEVICE(THSCuda_get_device_prop(device).totalGlobalMem); + RETURN_CUDA_DEVICE(THSCuda_get_device_prop(device).totalGlobalMem) } //TODO: implement more function diff --git a/src/Native/LibTorchSharp/THSCuda.h b/src/Native/LibTorchSharp/THSCuda.h index b6c0222e6..9ec7416ce 100644 --- a/src/Native/LibTorchSharp/THSCuda.h +++ b/src/Native/LibTorchSharp/THSCuda.h @@ -2,10 +2,21 @@ #pragma once #include "../Stdafx.h" - +#include "Utils.h" #include "torch/torch.h" -#include "Utils.h" +#ifdef TORCHSHARP_CUDA_TOOLKIT_FOUND +#define CUDA_TOOLKIT_FOUND 1 +#else +#define CUDA_TOOLKIT_FOUND 0 +#endif + +#define RETURN_CUDA_DEVICE(x) \ + if(CUDA_TOOLKIT_FOUND) \ + return x; \ + else \ + return -1; + #ifdef TORCHSHARP_CUDA_TOOLKIT_FOUND #include "cuda.h" #include "cuda_runtime_api.h" From ce679e207f1707d66b01493d581c4591b5f8f80e Mon Sep 17 00:00:00 2001 From: Dimitri Date: Fri, 25 Oct 2024 13:15:48 -0300 Subject: [PATCH 35/65] fast copy tensor accessor --- .gitignore | 2 + TorchSharp.sln | 133 +++++++++++++++++++++++-- src/TorchSharp/Utils/TensorAccessor.cs | 47 +++++++++ 3 files changed, 175 insertions(+), 7 deletions(-) diff --git a/.gitignore b/.gitignore index 4f8e77a3e..13682298c 100644 --- a/.gitignore +++ b/.gitignore @@ -273,3 +273,5 @@ packages/ /.idea /test/TorchSharpTest/exportsd.py .vscode/settings.json +/TestClear +TestClear/ diff --git a/TorchSharp.sln b/TorchSharp.sln index 8cec25c7d..db67b613f 100644 --- a/TorchSharp.sln +++ b/TorchSharp.sln @@ -36,7 +36,7 @@ Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "TorchSharp", "TorchSharp", EndProject Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "LibTorchSharp", "bin\obj\x64.Debug\Native\LibTorchSharp\LibTorchSharp.vcxproj", "{2B359162-062E-3C52-91D3-027A8542A58C}" EndProject -Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "LibTorchSharp", "bin\obj\x64.Release\Native\LibTorchSharp\LibTorchSharp.vcxproj", "{E4C0DBEE-0815-311B-9065-137BB50BD793}" +Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "LibTorchSharp", "bin\obj\x64.Release\Native\LibTorchSharp\LibTorchSharp.vcxproj", "{748608D6-97ED-3EEA-89D9-D5D5CC69B05A}" EndProject Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Native-Debug", "Native-Debug", "{CF2C1A9E-3A8A-4329-8A6E-7880C15AAC3D}" ProjectSection(SolutionItems) = preProject @@ -66,111 +66,229 @@ Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Solution Items", "Solution azure-pipelines.yml = azure-pipelines.yml build\BranchInfo.props = build\BranchInfo.props DEVGUIDE.md = DEVGUIDE.md + global.json = global.json README.md = README.md RELEASENOTES.md = RELEASENOTES.md - global.json = global.json EndProjectSection EndProject Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "TorchVision", "src\TorchVision\TorchVision.csproj", "{DCF01EE5-6431-4115-85E0-1FC4C3DE86A2}" EndProject Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "TorchAudio", "src\TorchAudio\TorchAudio.csproj", "{B3AAC8E8-9CA4-4B01-96CF-206AE7327DDE}" EndProject +Project("{FAE04EC0-301F-11D3-BF4B-00C04F79EFBC}") = "TestClear", "TestClear\TestClear.csproj", "{6002DD2E-BF7A-4320-8ED6-8B0138F07A52}" +EndProject Global 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{6002DD2E-BF7A-4320-8ED6-8B0138F07A52}.Release|x64.ActiveCfg = Release|Any CPU + {6002DD2E-BF7A-4320-8ED6-8B0138F07A52}.Release|x64.Build.0 = Release|Any CPU + {6002DD2E-BF7A-4320-8ED6-8B0138F07A52}.RelWithDebInfo|Any CPU.ActiveCfg = Release|Any CPU + {6002DD2E-BF7A-4320-8ED6-8B0138F07A52}.RelWithDebInfo|Any CPU.Build.0 = Release|Any CPU + {6002DD2E-BF7A-4320-8ED6-8B0138F07A52}.RelWithDebInfo|x64.ActiveCfg = Release|Any CPU + {6002DD2E-BF7A-4320-8ED6-8B0138F07A52}.RelWithDebInfo|x64.Build.0 = Release|Any CPU EndGlobalSection GlobalSection(SolutionProperties) = preSolution HideSolutionNode = FALSE @@ -182,7 +300,7 @@ Global {42B45168-476D-4BFA-87B8-81A34E6295CD} = {09EADF06-BE25-4228-AB53-95AE3E15B530} {567456AD-B026-4CB6-B98D-4FC930C90223} = {D3D38B03-B557-484D-8348-8BADEE4DF592} {2B359162-062E-3C52-91D3-027A8542A58C} = {CF2C1A9E-3A8A-4329-8A6E-7880C15AAC3D} - {E4C0DBEE-0815-311B-9065-137BB50BD793} = {4DB9E84D-324C-408F-87A6-246E86205540} + {748608D6-97ED-3EEA-89D9-D5D5CC69B05A} = {4DB9E84D-324C-408F-87A6-246E86205540} {CF2C1A9E-3A8A-4329-8A6E-7880C15AAC3D} = {09EADF06-BE25-4228-AB53-95AE3E15B530} {D8C60CD8-8429-45F2-A755-47B6CD10FDF8} = {09EADF06-BE25-4228-AB53-95AE3E15B530} {4DB9E84D-324C-408F-87A6-246E86205540} = {CF2C1A9E-3A8A-4329-8A6E-7880C15AAC3D} @@ -193,6 +311,7 @@ Global {6D3CE8AA-F369-4D2D-BDA7-9F89D6BE1B2E} = {D3D38B03-B557-484D-8348-8BADEE4DF592} {DCF01EE5-6431-4115-85E0-1FC4C3DE86A2} = {09EADF06-BE25-4228-AB53-95AE3E15B530} {B3AAC8E8-9CA4-4B01-96CF-206AE7327DDE} = {09EADF06-BE25-4228-AB53-95AE3E15B530} + {6002DD2E-BF7A-4320-8ED6-8B0138F07A52} = {09EADF06-BE25-4228-AB53-95AE3E15B530} EndGlobalSection GlobalSection(ExtensibilityGlobals) = postSolution SolutionGuid = {41165AF1-35BB-4832-A189-73060F82B01D} diff --git a/src/TorchSharp/Utils/TensorAccessor.cs b/src/TorchSharp/Utils/TensorAccessor.cs index edbcf7675..0f8dbaeb2 100644 --- a/src/TorchSharp/Utils/TensorAccessor.cs +++ b/src/TorchSharp/Utils/TensorAccessor.cs @@ -3,6 +3,7 @@ using System.Collections.Generic; using System.Diagnostics; using System.Linq; +using System.Runtime.InteropServices; using static TorchSharp.PInvoke.NativeMethods; namespace TorchSharp.Utils @@ -47,6 +48,16 @@ public T[] ToArray() if (_tensor.ndim < 2) return (T[])ToNDArray(); + if (_tensor.is_contiguous()) { + //This is very fast. And work VERY WELL + var shps = _tensor.shape; + long TempCount = 1; + for (int i = 0; i < shps.Length; i++) + TempCount *= shps[i]; //Theorically the numel is simple as product of each element shape + unsafe { + return new Span(_tensor_data_ptr.ToPointer(), Convert.ToInt32(TempCount)).ToArray(); + } + } var result = new T[Count]; CopyTo(result); return result; @@ -231,8 +242,39 @@ private void validate(long index) if (index >= Count) throw new IndexOutOfRangeException(); } + private void CopyContiguous(T[] array, int index=0, int count=0) + { + if (!_tensor.is_contiguous()) + throw new Exception("The tensor is not contiguous"); + var shps = _tensor.shape; + long TempCount = 1; + for (int i = 0; i < shps.Length; i++) + TempCount *= shps[i]; //Theorically the numel is simple as product of each element shape + if (count > TempCount || count == 0) + count = (int)TempCount; + + if (array is byte[] ba) + Marshal.Copy(_tensor_data_ptr, ba, index, count); + if (array is short[] sa) + Marshal.Copy(_tensor_data_ptr, sa, index, count); + if(array is char[] ca) + Marshal.Copy(_tensor_data_ptr, ca, index, count); + if (array is long[] la) + Marshal.Copy(_tensor_data_ptr, la, index, count); + if (array is float[] fa) + Marshal.Copy(_tensor_data_ptr, fa, index, count); + if (array is int[] ia) + Marshal.Copy(_tensor_data_ptr, ia, index, count); + if (array is double[] da) + Marshal.Copy(_tensor_data_ptr, da, index, count); + } public void CopyTo(T[] array, int arrayIndex = 0, long tensorIndex = 0) { + if (_tensor.is_contiguous()) { + CopyContiguous(array, arrayIndex, array.Length); + return; + } + int idx = arrayIndex; foreach (int offset in GetSubsequentIndices(tensorIndex)) { if (idx >= array.Length) break; @@ -243,6 +285,11 @@ public void CopyTo(T[] array, int arrayIndex = 0, long tensorIndex = 0) public void CopyTo(Span array, int arrayIndex = 0, long tensorIndex = 0) { + if (_tensor.is_contiguous()) { + ToArray().CopyTo(array); + return; + } + int idx = arrayIndex; foreach (int offset in GetSubsequentIndices(tensorIndex)) { if (idx >= array.Length) break; From 958a1871d00f2a2719d67b11ddd50cbb807951fc Mon Sep 17 00:00:00 2001 From: Dimitri Date: Fri, 25 Oct 2024 13:43:52 -0300 Subject: [PATCH 36/65] rollback sln --- TorchSharp.sln | 143 +++---------------------- src/TorchSharp/Utils/TensorAccessor.cs | 45 ++++---- 2 files changed, 34 insertions(+), 154 deletions(-) diff --git a/TorchSharp.sln b/TorchSharp.sln index db67b613f..054c07bb3 100644 --- a/TorchSharp.sln +++ b/TorchSharp.sln @@ -34,9 +34,9 @@ Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "TorchSharp", "TorchSharp", pkg\TorchSharp\TorchSharp.symbols.nupkgproj = pkg\TorchSharp\TorchSharp.symbols.nupkgproj EndProjectSection EndProject -Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "LibTorchSharp", "bin\obj\x64.Debug\Native\LibTorchSharp\LibTorchSharp.vcxproj", "{2B359162-062E-3C52-91D3-027A8542A58C}" +Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "LibTorchSharp", "bin\obj\x64.Debug\Native\LibTorchSharp\LibTorchSharp.vcxproj", "{265C2E6F-04E6-37A8-B504-E3DD4A3FEE06}" EndProject -Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "LibTorchSharp", "bin\obj\x64.Release\Native\LibTorchSharp\LibTorchSharp.vcxproj", "{748608D6-97ED-3EEA-89D9-D5D5CC69B05A}" +Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "LibTorchSharp", "bin\obj\x64.Release\Native\LibTorchSharp\LibTorchSharp.vcxproj", "{E4C0DBEE-0815-311B-9065-137BB50BD793}" EndProject Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Native-Debug", "Native-Debug", "{CF2C1A9E-3A8A-4329-8A6E-7880C15AAC3D}" ProjectSection(SolutionItems) = preProject @@ -75,220 +75,102 @@ Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "TorchVision", "src\TorchVis EndProject Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "TorchAudio", "src\TorchAudio\TorchAudio.csproj", "{B3AAC8E8-9CA4-4B01-96CF-206AE7327DDE}" EndProject -Project("{FAE04EC0-301F-11D3-BF4B-00C04F79EFBC}") = "TestClear", "TestClear\TestClear.csproj", "{6002DD2E-BF7A-4320-8ED6-8B0138F07A52}" -EndProject Global GlobalSection(SolutionConfigurationPlatforms) = preSolution Debug|Any CPU = Debug|Any CPU Debug|x64 = Debug|x64 - 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{6002DD2E-BF7A-4320-8ED6-8B0138F07A52} = {09EADF06-BE25-4228-AB53-95AE3E15B530} EndGlobalSection GlobalSection(ExtensibilityGlobals) = postSolution SolutionGuid = {41165AF1-35BB-4832-A189-73060F82B01D} diff --git a/src/TorchSharp/Utils/TensorAccessor.cs b/src/TorchSharp/Utils/TensorAccessor.cs index 0f8dbaeb2..6966dfdbe 100644 --- a/src/TorchSharp/Utils/TensorAccessor.cs +++ b/src/TorchSharp/Utils/TensorAccessor.cs @@ -242,31 +242,30 @@ private void validate(long index) if (index >= Count) throw new IndexOutOfRangeException(); } - private void CopyContiguous(T[] array, int index=0, int count=0) - { + private void CopyContiguous(T[] array, int index=0, int count=0) + { if (!_tensor.is_contiguous()) throw new Exception("The tensor is not contiguous"); - var shps = _tensor.shape; - long TempCount = 1; - for (int i = 0; i < shps.Length; i++) - TempCount *= shps[i]; //Theorically the numel is simple as product of each element shape - if (count > TempCount || count == 0) - count = (int)TempCount; - - if (array is byte[] ba) - Marshal.Copy(_tensor_data_ptr, ba, index, count); - if (array is short[] sa) - Marshal.Copy(_tensor_data_ptr, sa, index, count); - if(array is char[] ca) - Marshal.Copy(_tensor_data_ptr, ca, index, count); - if (array is long[] la) - Marshal.Copy(_tensor_data_ptr, la, index, count); - if (array is float[] fa) - Marshal.Copy(_tensor_data_ptr, fa, index, count); - if (array is int[] ia) - Marshal.Copy(_tensor_data_ptr, ia, index, count); - if (array is double[] da) - Marshal.Copy(_tensor_data_ptr, da, index, count); + var shps = _tensor.shape; + long TempCount = 1; + for (int i = 0; i < shps.Length; i++) + TempCount *= shps[i]; //Theorically the numel is simple as product of each element shape + if (count > TempCount || count == 0) + count = (int)TempCount; + if (array is byte[] ba) + Marshal.Copy(_tensor_data_ptr, ba, index, count); + if (array is short[] sa) + Marshal.Copy(_tensor_data_ptr, sa, index, count); + if(array is char[] ca) + Marshal.Copy(_tensor_data_ptr, ca, index, count); + if (array is long[] la) + Marshal.Copy(_tensor_data_ptr, la, index, count); + if (array is float[] fa) + Marshal.Copy(_tensor_data_ptr, fa, index, count); + if (array is int[] ia) + Marshal.Copy(_tensor_data_ptr, ia, index, count); + if (array is double[] da) + Marshal.Copy(_tensor_data_ptr, da, index, count); } public void CopyTo(T[] array, int arrayIndex = 0, long tensorIndex = 0) { From 0b20f13779ace6460fe6391d1b81eecd05e98e01 Mon Sep 17 00:00:00 2001 From: Dimitri Date: Fri, 25 Oct 2024 14:28:53 -0300 Subject: [PATCH 37/65] Numel --- src/TorchSharp/Utils/TensorAccessor.cs | 22 ++++++++-------------- 1 file changed, 8 insertions(+), 14 deletions(-) diff --git a/src/TorchSharp/Utils/TensorAccessor.cs b/src/TorchSharp/Utils/TensorAccessor.cs index 6966dfdbe..42fd49c11 100644 --- a/src/TorchSharp/Utils/TensorAccessor.cs +++ b/src/TorchSharp/Utils/TensorAccessor.cs @@ -47,18 +47,15 @@ public T[] ToArray() { if (_tensor.ndim < 2) return (T[])ToNDArray(); - + long Cnt = Count; if (_tensor.is_contiguous()) { - //This is very fast. And work VERY WELL - var shps = _tensor.shape; - long TempCount = 1; - for (int i = 0; i < shps.Length; i++) - TempCount *= shps[i]; //Theorically the numel is simple as product of each element shape + if (Cnt == 0) + throw new Exception("Invalid"); unsafe { - return new Span(_tensor_data_ptr.ToPointer(), Convert.ToInt32(TempCount)).ToArray(); + return new Span(_tensor_data_ptr.ToPointer(), Convert.ToInt32(Cnt)).ToArray(); } } - var result = new T[Count]; + var result = new T[Cnt]; CopyTo(result); return result; } @@ -246,12 +243,9 @@ private void CopyContiguous(T[] array, int index=0, int count=0) { if (!_tensor.is_contiguous()) throw new Exception("The tensor is not contiguous"); - var shps = _tensor.shape; - long TempCount = 1; - for (int i = 0; i < shps.Length; i++) - TempCount *= shps[i]; //Theorically the numel is simple as product of each element shape - if (count > TempCount || count == 0) - count = (int)TempCount; + var Cnt = Count; + if (count > Cnt || count == 0) + count = (int)Cnt; if (array is byte[] ba) Marshal.Copy(_tensor_data_ptr, ba, index, count); if (array is short[] sa) From 572bc3e11094cdecd6a737c3ac3c7441192fb975 Mon Sep 17 00:00:00 2001 From: Dimitri Date: Mon, 28 Oct 2024 19:05:42 -0300 Subject: [PATCH 38/65] some --- src/Native/LibTorchSharp/THSStorage.cpp | 23 ++++++++++++++++ src/Native/LibTorchSharp/THSStorage.h | 16 +++++++++++ src/TorchSharp/Amp/AutocastMode.cs | 15 +++++------ .../PInvoke/LibTorchSharp.THSStorage.cs | 10 +++++++ src/TorchSharp/Tensor/Tensor.cs | 11 +++++++- src/TorchSharp/Utils/Half.cs | 2 ++ src/TorchSharp/Utils/TensorAccessor.cs | 27 +++++++++++++++++++ 7 files changed, 95 insertions(+), 9 deletions(-) diff --git a/src/Native/LibTorchSharp/THSStorage.cpp b/src/Native/LibTorchSharp/THSStorage.cpp index c966e0e97..4bc8b84e9 100644 --- a/src/Native/LibTorchSharp/THSStorage.cpp +++ b/src/Native/LibTorchSharp/THSStorage.cpp @@ -23,3 +23,26 @@ void* THSStorage_data_ptr(const Tensor tensor) return dp.get(); } +/* +int* THSStorage_tensor_to_array_int(const Tensor tensor) +{ + return THSStorage_tensor_array(tensor); +} +long* THSStorage_tensor_to_array_long(const Tensor tensor) +{ + return THSStorage_tensor_array(tensor); +} + +float* THSStorage_tensor_to_array_float(const Tensor tensor) +{ + return THSStorage_tensor_array(tensor); +} + +double* THSStorage_tensor_to_array_double(const Tensor tensor) +{ + return THSStorage_tensor_array(tensor); +} +char* THSStorage_tensor_to_array_char(const Tensor tensor) +{ + return THSStorage_tensor_array(tensor); +}*/ \ No newline at end of file diff --git a/src/Native/LibTorchSharp/THSStorage.h b/src/Native/LibTorchSharp/THSStorage.h index e66492e11..53a335921 100644 --- a/src/Native/LibTorchSharp/THSStorage.h +++ b/src/Native/LibTorchSharp/THSStorage.h @@ -14,3 +14,19 @@ EXPORT_API(size_t) THSStorage_nbytes(const Tensor tensor); EXPORT_API(void) THSStorage_set_nbytes(const Tensor tensor, size_t nbytes); EXPORT_API(void*) THSStorage_data_ptr(const Tensor tensor); +/* +template +T* THSStorage_tensor_array(const Tensor tensor) +{ +#if TORCH_VERSION_MAJOR >= 2 && TORCH_VERSION_MINOR >= 4 + return tensor->data_ptr(); +#else + return tensor->data(); +#endif +} + +EXPORT_API(int*) THSStorage_tensor_to_array_int(const Tensor tensor); +EXPORT_API(long*) THSStorage_tensor_to_array_long(const Tensor tensor); +EXPORT_API(float*) THSStorage_tensor_to_array_float(const Tensor tensor); +EXPORT_API(double*) THSStorage_tensor_to_array_double(const Tensor tensor); +EXPORT_API(char*) THSStorage_tensor_to_array_char(const Tensor tensor);*/ \ No newline at end of file diff --git a/src/TorchSharp/Amp/AutocastMode.cs b/src/TorchSharp/Amp/AutocastMode.cs index 88a16aa9f..68269f564 100644 --- a/src/TorchSharp/Amp/AutocastMode.cs +++ b/src/TorchSharp/Amp/AutocastMode.cs @@ -53,14 +53,14 @@ private AutocastMode(torch.Device dev, torch.ScalarType? dtype = null, bool enab fast_dtype = dtype.Value; if (cache_enabled.HasValue) _cache_enabled = cache_enabled.Value; - + if (dev.type != DeviceType.CPU && dev.type != DeviceType.CUDA && enabled) + throw new Exception($"Currently autocast does not support {dev.type} only CPU or CUDA"); if (dev.type == DeviceType.CPU) { if (fast_dtype != torch.ScalarType.Float16 || fast_dtype != torch.ScalarType.BFloat16) { Debug.WriteLine($"In CPU autocast, but the target d type is not suported. Disabling autocast. CPU autocast only supports dtype of {torch.ScalarType.Float16} or {torch.ScalarType.BFloat16}"); enabled = false; } } else if (dev.type == DeviceType.CUDA) { - if (enabled && fast_dtype == torch.ScalarType.BFloat16 && !torch.cuda.is_bf16_supported()) throw new Exception("Current CUDA Device does not support bfloat16. Please switch dtype to float16."); } @@ -131,6 +131,7 @@ public static IntPtr ToIf(IntPtr ptr, torch.ScalarType type) return ptr; if (GetDtype(ptr) == type) //if already have same dtype is not necesary convert to dtype, right??? return ptr; + //TODO: Check if is from CPU to passing BFloat16 if support /*if (!NativeMethods.THSAmp_is_autocast_enabled(NativeMethods.THSTensor_device_type(ptr))) return ptr;*/ var res = NativeMethods.THSTensor_to_type(ptr, (sbyte)type); @@ -190,17 +191,16 @@ private void Dispose(bool disposing) torch.set_autocast_cache_enabled(prev_cache_enabled); } - /*~AutocastMode() - { - - }*/ - public void Dispose() { Dispose(disposing: true); GC.SuppressFinalize(this); } } + /// + /// Trying to make Custom Autocast forwarded that mean in Pytorch + /// like this @torch.autocast(device_type="cuda") + /// public class AutocastAttribute : Attribute { private DeviceType Dev; @@ -208,6 +208,5 @@ public AutocastAttribute(DeviceType dev) { Dev = dev; } - } } diff --git a/src/TorchSharp/PInvoke/LibTorchSharp.THSStorage.cs b/src/TorchSharp/PInvoke/LibTorchSharp.THSStorage.cs index 7cf494b7a..bd5b46694 100644 --- a/src/TorchSharp/PInvoke/LibTorchSharp.THSStorage.cs +++ b/src/TorchSharp/PInvoke/LibTorchSharp.THSStorage.cs @@ -15,5 +15,15 @@ internal static partial class NativeMethods [DllImport("LibTorchSharp")] internal static extern IntPtr THSStorage_data_ptr(IntPtr tensor); + /*[DllImport("LibTorchSharp")] + internal static extern IntPtr THSStorage_tensor_to_array_int(IntPtr tensor); + [DllImport("LibTorchSharp")] + internal static extern IntPtr THSStorage_tensor_to_array_long(IntPtr tensor); + [DllImport("LibTorchSharp")] + internal static extern IntPtr THSStorage_tensor_to_array_float(IntPtr tensor); + [DllImport("LibTorchSharp")] + internal static extern IntPtr THSStorage_tensor_to_array_double(IntPtr tensor); + [DllImport("LibTorchSharp")] + internal static extern IntPtr THSStorage_tensor_to_array_byte(IntPtr tensor);*/ } } diff --git a/src/TorchSharp/Tensor/Tensor.cs b/src/TorchSharp/Tensor/Tensor.cs index 5eae88b2f..b89213dea 100644 --- a/src/TorchSharp/Tensor/Tensor.cs +++ b/src/TorchSharp/Tensor/Tensor.cs @@ -7544,7 +7544,16 @@ public static Tensor WrappedTensorDisposeScope(Func expr) var result = expr(); return result.MoveToOuterDisposeScope(); } - + internal static Tensor InstantiateTensorWithLeakSafeTypeChange(IntPtr handle, ScalarType? dtype) + { + var tensor = new Tensor(handle); + if (dtype.HasValue && tensor.dtype != dtype.Value) { + var typed = tensor.to_type(dtype.Value); + tensor.Dispose(); + return typed; + } + return tensor; + } public static void _amp_foreach_non_finite_check_and_unscale(Tensor found_inf, Tensor inv_scale) { if (found_inf.numel() == 1) diff --git a/src/TorchSharp/Utils/Half.cs b/src/TorchSharp/Utils/Half.cs index f07e89892..0650f1307 100644 --- a/src/TorchSharp/Utils/Half.cs +++ b/src/TorchSharp/Utils/Half.cs @@ -4,6 +4,8 @@ using System.Globalization; using System.Text; +//Is only for NetStandard 2.0, Net 5 or newer already have Half Struct +//TODO: Need make support with Net Core 3? #if NETSTANDARD2_0 namespace System { diff --git a/src/TorchSharp/Utils/TensorAccessor.cs b/src/TorchSharp/Utils/TensorAccessor.cs index 42fd49c11..4a964de0b 100644 --- a/src/TorchSharp/Utils/TensorAccessor.cs +++ b/src/TorchSharp/Utils/TensorAccessor.cs @@ -4,6 +4,7 @@ using System.Diagnostics; using System.Linq; using System.Runtime.InteropServices; +using TorchSharp.PInvoke; using static TorchSharp.PInvoke.NativeMethods; namespace TorchSharp.Utils @@ -55,6 +56,32 @@ public T[] ToArray() return new Span(_tensor_data_ptr.ToPointer(), Convert.ToInt32(Cnt)).ToArray(); } } + + /*unsafe { + IntPtr arr = IntPtr.Zero; + if (typeof(T) == typeof(int)) { + arr = NativeMethods.THSStorage_tensor_to_array_int(_tensor.handle); + int[] tot = new int[Cnt]; + Marshal.Copy(arr, tot, 0, (int)Cnt); + } + + if (typeof(T) == typeof(long)) { + + } + + return tot as T[]; + //var stride = _tensor.stride(); + //var res = new T[Cnt]; + //int idx = 0; + //T* ptr = (T*)_tensor_data_ptr; + //for (int ndim = 0; ndim < _tensor.shape.Length; ndim++) { + // for (int xyz = 0; xyz < _tensor.shape[ndim]; xyz++) { + // res[idx++] = ptr[xyz + stride[ndim]]; + // } + //} + //return res; + }*/ + var result = new T[Cnt]; CopyTo(result); return result; From 2c33985f699d41ffc5e0d8de68c85c808cf93396 Mon Sep 17 00:00:00 2001 From: Dimitri Date: Fri, 1 Nov 2024 14:33:28 -0300 Subject: [PATCH 39/65] Test and fix some error --- src/TorchSharp/Amp/GradScaler.cs | 204 +++++++---- src/TorchSharp/Autograd.cs | 20 + src/TorchSharp/Utils/UnorderedMap.cs | 6 +- .../TestGradScaler.cs | 346 ------------------ .../TorchSharpTest.WithCudaBinaries.csproj | 2 + .../TestAutocast.cs | 70 ++-- test/TorchSharpTest/TestGradScaler.cs | 22 +- 7 files changed, 216 insertions(+), 454 deletions(-) delete mode 100644 test/TorchSharpTest.WithCudaBinaries/TestGradScaler.cs rename test/{TorchSharpTest.WithCudaBinaries => TorchSharpTest}/TestAutocast.cs (80%) diff --git a/src/TorchSharp/Amp/GradScaler.cs b/src/TorchSharp/Amp/GradScaler.cs index d3d7a78b3..4aef1a249 100644 --- a/src/TorchSharp/Amp/GradScaler.cs +++ b/src/TorchSharp/Amp/GradScaler.cs @@ -1,6 +1,8 @@ using System; +using System.Collections; using System.Collections.Generic; using System.Diagnostics; +using System.Linq; using TorchSharp.Modules; using TorchSharp.Utils; @@ -28,7 +30,7 @@ public enum OptState private UnorderedMap _refresh_per_optimizer_state() { return new UnorderedMap() { - { "state", OptState.Ready }, { "found_inf_per_device", null} + { "stage", OptState.Ready }, { "found_inf_per_device", null} }; } //https://github.com/pytorch/pytorch/blob/main/torch/amp/grad_scaler.py @@ -36,7 +38,7 @@ public GradScaler(torch.Device dev, float init_scale = 2.0e16f, float growth_fac float backoff_factor = 0.5f, int growth_interval = 2000, bool enabled = true) { //https://gist.github.com/dorpxam/67ad2bc222b2cf567d4a6fc298375e13 - Debug.Assert(dev == torch.CPU || dev == torch.CUDA); + Debug.Assert(dev.type == DeviceType.CPU || dev.type== DeviceType.CUDA); device = dev; Enabled = enabled; InitScale = init_scale; @@ -56,16 +58,18 @@ public GradScaler(torch.Device dev, float init_scale = 2.0e16f, float growth_fac private Tuple check_scale_growth_tracker(string name) { var fix = "This may indicate your script did not use scaler.scale(loss or outputs) earlier in the iteration."; - Debug.Assert(_scale is null, $"Attempted {name} but {nameof(_scale)} is None {fix}"); - Debug.Assert(_growth_tracker is null, $"Attempted {name} but {nameof(_growth_tracker)} is None {fix}"); + Debug.Assert(!(_scale is null), $"Attempted {name} but {nameof(_scale)} is None {fix}"); + Debug.Assert(!(_growth_tracker is null), $"Attempted {name} but {nameof(_growth_tracker)} is None {fix}"); return new Tuple(_scale, _growth_tracker); } private void LazyInitScaleGrowthTracker(torch.Device dev) { - _scale = torch.full(0, InitScale, torch.ScalarType.Float32, device: dev); - _growth_tracker = torch.full(0, InitGrowthTracker, torch.ScalarType.Int32, device: dev); + Debug.Assert(_growth_tracker is null, "_growth_tracker initialized before _scale"); + + _scale = torch.full(1, InitScale, torch.ScalarType.Float32, device: dev); + _growth_tracker = torch.full(1, InitGrowthTracker, torch.ScalarType.Int32, device: dev); } //private Dictionary @@ -89,17 +93,17 @@ private class MultiDeviceReplicator { private readonly torch.Tensor master; - internal readonly Dictionary per_device_tensors = new Dictionary(); + internal readonly Dictionary per_device_tensors = new Dictionary(); public MultiDeviceReplicator(torch.Tensor master_tensor) { master = master_tensor; } - public torch.Tensor Get(torch.Device device) + public torch.Tensor Get(DeviceType device) { torch.Tensor retval=null; if (!per_device_tensors.ContainsKey(device)) { - retval = master.to(device, true, non_blocking: true); + retval = master.to(new torch.Device(device), true, non_blocking: true); per_device_tensors.Add(device, retval); } return retval; @@ -115,7 +119,7 @@ private torch.Tensor apply_scale(torch.Tensor scale) } stash.Add(new MultiDeviceReplicator(_scale)); } - return scale * stash[0].Get(scale.device); + return scale * stash[0].Get(scale.device.type); } private void apply_scale(IList scales) @@ -123,51 +127,51 @@ private void apply_scale(IList scales) for (int i = 0; i < scales.Count; i++) scales[i] = apply_scale(scales[i]); } - public Dictionary unscale_grads(torch.optim.Optimizer optimizer, torch.Tensor inv_scale, torch.Tensor found_inf, bool allow_fp16) + public Dictionary unscale_grads(torch.optim.Optimizer optimizer, torch.Tensor inv_scale, torch.Tensor found_inf, bool allow_fp16) { var per_device_inv_scale = new MultiDeviceReplicator(inv_scale); var per_device_found_inf= new MultiDeviceReplicator(found_inf); - Dictionary>> per_device_and_dtype_grads = new Dictionary>>(); + Dictionary>> per_device_and_dtype_grads = new Dictionary>>(); using (torch.no_grad()) { - if (optimizer is AdamW adamW){ //Some optimizer have parameter tensor for unscale_grads i need that. [20/10/24 WHY I DO THIS???? ] - using (var enumer = adamW.parameters().GetEnumerator()) { - while (enumer.MoveNext()) { - var param = enumer.Current; - if (param is null) - continue; - if (!allow_fp16 && param.dtype == torch.ScalarType.Float16) - throw new Exception("Attempting to unscale FP16 Gradients"); - torch.Tensor to_unscale; - if (param.grad.is_sparse) { - if (param.grad.dtype == torch.ScalarType.Float16) { - param.grad = param.grad.coalesce(); - } - - to_unscale = param.grad.SparseValues; - } else { - to_unscale = param.grad; + + using (var enumer = optimizer.parameters().GetEnumerator()) { + while (enumer.MoveNext()) { + var param = enumer.Current; + if (param is null) + continue; + if (!allow_fp16 && param.dtype == torch.ScalarType.Float16) + throw new Exception("Attempting to unscale FP16 Gradients"); + torch.Tensor to_unscale; + if (param.grad.is_sparse) { + if (param.grad.dtype == torch.ScalarType.Float16) { + param.grad = param.grad.coalesce(); } - if (!per_device_and_dtype_grads.ContainsKey(to_unscale.device)) { - per_device_and_dtype_grads.Add(to_unscale.device, new Dictionary>()); - per_device_and_dtype_grads[to_unscale.device].Add(to_unscale.dtype, new List()); - per_device_and_dtype_grads[to_unscale.device][to_unscale.dtype].Add(to_unscale); + to_unscale = param.grad.SparseValues; + } else { + to_unscale = param.grad; + } + + if (!per_device_and_dtype_grads.ContainsKey(to_unscale.device.type)) { + per_device_and_dtype_grads.Add(to_unscale.device.type, new Dictionary>()); + per_device_and_dtype_grads[to_unscale.device.type].Add(to_unscale.dtype, new List()); + per_device_and_dtype_grads[to_unscale.device.type][to_unscale.dtype].Add(to_unscale); + } else { + if (!per_device_and_dtype_grads[to_unscale.device.type].ContainsKey(to_unscale.dtype)) { + per_device_and_dtype_grads[to_unscale.device.type].Add(to_unscale.dtype, new List()); } else { - if (!per_device_and_dtype_grads[to_unscale.device].ContainsKey(to_unscale.dtype)) { - per_device_and_dtype_grads[to_unscale.device].Add(to_unscale.dtype, new List()); - } else { - per_device_and_dtype_grads[to_unscale.device][to_unscale.dtype].Add(to_unscale); - } + per_device_and_dtype_grads[to_unscale.device.type][to_unscale.dtype].Add(to_unscale); } - } - } - foreach (var d in per_device_and_dtype_grads) - foreach (var g in d.Value) - torch._amp_foreach_non_finite_check_and_unscale_(g.Value, per_device_found_inf.Get(d.Key), per_device_inv_scale.Get(d.Key)); + } } + + foreach (var d in per_device_and_dtype_grads) + foreach (var g in d.Value) + torch._amp_foreach_non_finite_check_and_unscale_(g.Value, per_device_found_inf.Get(d.Key), per_device_inv_scale.Get(d.Key)); + } return per_device_found_inf.per_device_tensors; @@ -182,7 +186,7 @@ public void unscale(torch.optim.Optimizer optimizer) //if(_per_optimizer_states.ContainsKey(optimizer.GetHashCode())) var optimizer_state = _per_optimizer_states[optimizer.GetHashCode()]; - if (optimizer_state["state"] is OptState state) { + if (optimizer_state["stage"] is OptState state) { if (state == OptState.Unscaled) { throw new Exception($"{nameof(unscale)} has already been called on this optimizer since the last update()"); } @@ -191,47 +195,95 @@ public void unscale(torch.optim.Optimizer optimizer) } Debug.Assert(!(_scale is null)); - var inv_scale = _scale.@double().reciprocal().@float(); - var found_inf = torch.full(new ReadOnlySpan(new long[] { 0 }), 0.0f, torch.ScalarType.Float32,_scale.device); + var inv_scale = _scale.to(torch.ScalarType.Float64).reciprocal().to(torch.ScalarType.Float32); + var found_inf = torch.full(1, 0.0f, torch.ScalarType.Float32,_scale.device); optimizer_state["found_inf_per_device"] = unscale_grads(optimizer, inv_scale, found_inf, false); optimizer_state["stage"] = OptState.Unscaled; } - - private float? maybe_opt_step(torch.optim.Optimizer optimizer, UnorderedMap optimizer_state) + /* + * + + template + inline auto sum(PerDeviceTensors const& per_device) + { + Type sum = Type(0); + for (auto&& [_, v] : per_device) + sum += v.item(); + return sum; + } + * + */ + private Scalar maybe_opt_step(torch.optim.Optimizer optimizer, UnorderedMap optimizer_state, Func closure = null) { //https://github.com/pytorch/pytorch/blob/a00fad017719346bac6e08da0819358146e647e3/torch/amp/grad_scaler.py#L351 - float? retval=0; - foreach(var d in optimizer_state) - if (d.Value is torch.Tensor t) - retval += t.item(); - if (retval==0) - retval = optimizer.step().item(); - return retval; + if (optimizer_state.ContainsKey("found_inf_per_device")) { + + double? retval = 0; + if (optimizer_state["found_inf_per_device"] is Dictionary dict) { + foreach (var d in dict) + { + retval += (double)d.Value.item(); + //retval += d.Value.Sum(x=>x.item()); + /*foreach(var t in d.Value) + retval += t.item();*/ + //retval += d.Value.item(); + } + /*if (retval.HasValue) { + if(retval.Value > 0) + return + }*/ + + //https://gist.github.com/dorpxam/67ad2bc222b2cf567d4a6fc298375e13#file-gradscaler-hpp-L209 + } + /*foreach (var d in optimizer_state) + if (d.Value is torch.Tensor t) + retval += t.item();*/ + var res = optimizer.step(closure); + if (!(res is null)) { + return res.item(); + } + + /*if (retval == 0) + retval = .item(); + return retval;*/ + } + + return null; } - public float? step(torch.optim.Optimizer optimizer, params object[] obj) + public Scalar step(torch.optim.Optimizer optimizer, Func optimizer_args = null) { - if (obj.Length == 0) - throw new Exception("The obj param cannot be empty"); if (!Enabled) { + var res = optimizer.step(optimizer_args); + if (!(res is null)) + return res.item(); + return null; + } + + if (optimizer_args != null) + throw new Exception("Closure use is not currently supported if GradScaler is Enabled"); + + /*if (!Enabled) { if(obj.Length == 1 && obj[0] is Func closure) return optimizer.step(closure).item(); return null; - } + }*/ check_scale_growth_tracker(nameof(step)); var optimizer_state = _per_optimizer_states[optimizer.GetHashCode()]; + if (optimizer_state["stage"] is OptState state && state == OptState.Stepped) throw new Exception($"{nameof(step)} has already been called since the last update()"); - float? retval; + Scalar retval=null; //https://github.com/pytorch/pytorch/blob/a00fad017719346bac6e08da0819358146e647e3/torch/amp/grad_scaler.py#L398 var f = optimizer.GetType().GetField("_step_support_amp_scaling"); if (f != null && f.GetValue(optimizer) is bool b && !b) { bool has_grad_scaler = false;//I dont know how deal this... if (has_grad_scaler) { + throw new NotImplementedException(); } else { if (optimizer_state["stage"] is OptState optstate && optstate == OptState.Ready) @@ -260,8 +312,12 @@ public void unscale(torch.optim.Optimizer optimizer) } if (optimizer_state["stage"] is OptState state1 && state1 == OptState.Ready) unscale(optimizer); - Debug.Assert((optimizer_state["found_inf_per_device"] as torch.Tensor[])?.Length > 0, "(optimizer_state['found_inf_per_device'] as torch.Tensor).size(0) > 0"); - retval = maybe_opt_step(optimizer, optimizer_state); + if (optimizer_state["found_inf_per_device"] is ICollection col) + { + Debug.Assert(col.Count > 0, "(optimizer_state['found_inf_per_device'] as torch.Tensor).size(0) > 0"); + } + //Debug.Assert((optimizer_state["found_inf_per_device"] as Dictionary>)?.Count > 0, "(optimizer_state['found_inf_per_device'] as torch.Tensor).size(0) > 0"); + retval = maybe_opt_step(optimizer, optimizer_state, optimizer_args); optimizer_state["stage"] = OptState.Stepped; return retval; } @@ -294,11 +350,25 @@ public void update(object new_scale = null) _scale.copy_(t); } } else { - IList found_infs = new List(); - foreach (var state in _per_optimizer_states) - foreach (var found_inf in state.Value) - if(found_inf.Value is torch.Tensor t) - found_infs.Add(t); + List found_infs = new List(); + foreach (var state in _per_optimizer_states) { + if (state.Value["found_inf_per_device"] is Dictionary d) { + foreach(var found_inf in d.Values) + found_infs.Add(found_inf.to(_scale.device, true)); + } + } + + /*foreach (var found_inf in state.Value) { + if (found_inf.Value is torch.Tensor t) { + found_infs.Add(t); + } + + if (found_inf.Value is List ts) { + foreach(var te in ts) + found_infs.Add(te); + } + }*/ + Debug.Assert(found_infs.Count > 0, "No inf checks were recorded prior to update."); torch.Tensor found_inf_combined = found_infs[0]; if (found_infs.Count > 1) diff --git a/src/TorchSharp/Autograd.cs b/src/TorchSharp/Autograd.cs index 4c73fce46..d7c29cc24 100644 --- a/src/TorchSharp/Autograd.cs +++ b/src/TorchSharp/Autograd.cs @@ -2,6 +2,7 @@ using System; using System.Linq; using System.Collections.Generic; +using TorchSharp.Modules; using static TorchSharp.PInvoke.NativeMethods; namespace TorchSharp @@ -145,6 +146,25 @@ public static IList grad(IList outputs, IList inputs, IL return results.Array.Select(x => new Tensor(x)).ToList(); } + public static IList grad(IList inputs, IEnumerable outputs, IList grad_outputs = null, bool retain_graph = false, bool create_graph = false, bool allow_unused = false) + { + using var outs = new PinnedArray(); + using var ins = new PinnedArray(); + using var grads = new PinnedArray(); + using var results = new PinnedArray(); + + IntPtr insRef = outs.CreateArray(outputs.Select(p => p.Handle).ToArray()); + IntPtr outsRef = ins.CreateArray(inputs.Select(p => p.Handle).ToArray()); + IntPtr gradsRef = grad_outputs == null ? IntPtr.Zero : grads.CreateArray(grad_outputs.Select(p => p.Handle).ToArray()); + long gradsLength = grad_outputs == null ? 0 : grads.Array.Length; + + //https://gist.github.com/dorpxam/67ad2bc222b2cf567d4a6fc298375e13#file-gradscaler_test-hpp-L318 + + THSAutograd_grad(outsRef, ins.Array.Length, insRef, outs.Array.Length, gradsRef, gradsLength, retain_graph, create_graph, allow_unused, results.CreateArray); + CheckForErrors(); + return results.Array.Select(x => new Tensor(x)).ToList(); + } + /// /// Computes the sum of gradients of given tensors with respect to graph leaves. /// diff --git a/src/TorchSharp/Utils/UnorderedMap.cs b/src/TorchSharp/Utils/UnorderedMap.cs index 6eb073b1d..3579f3cee 100644 --- a/src/TorchSharp/Utils/UnorderedMap.cs +++ b/src/TorchSharp/Utils/UnorderedMap.cs @@ -81,8 +81,10 @@ private static bool IsCollectionType(Type type) } public new TValue this[TKey tk] { get { - /*if (!this.ContainsKey(tk) && default_dict == null) - return default_dict;*/ + if (base.Count == 0 && !this.ContainsKey(tk) && default_dict != null) { + base[tk] = default_dict; + return base[tk]; + } if (this.ContainsKey(tk)) return base[tk]; var t = typeof(TValue); diff --git a/test/TorchSharpTest.WithCudaBinaries/TestGradScaler.cs b/test/TorchSharpTest.WithCudaBinaries/TestGradScaler.cs deleted file mode 100644 index af8b32afd..000000000 --- a/test/TorchSharpTest.WithCudaBinaries/TestGradScaler.cs +++ /dev/null @@ -1,346 +0,0 @@ -using System; -using System.Collections.Generic; -using System.Linq; -using TorchSharp; -using TorchSharp.Amp; -using TorchSharp.Modules; -using Xunit; -using static TorchSharp.torch; -using static TorchSharp.torch.nn; -namespace TorchSharpTest.WithCudaBinaries -{ - public class TestGradScaler - { - internal DeviceType device = DeviceType.CUDA; - internal ScalarType dtype = ScalarType.Float32; - - private (Sequential modctrl, Sequential modscal, torch.optim.Optimizer optctrl, torch.optim.Optimizer optscal) create_scaling_model_optimizer(DeviceType dev = DeviceType.CUDA) - { - var mod_control =Sequential(torch.nn.Linear(8,8), torch.nn.Linear(8, 8)); - mod_control.to(dev); - var mod_scaling = Sequential(torch.nn.Linear(8, 8), torch.nn.Linear(8, 8)); - mod_scaling.to(dev); - - using (torch.no_grad()) { - - using (var enumer = mod_control.parameters().Zip(mod_scaling.parameters()).GetEnumerator()) - while (enumer.MoveNext()) - enumer.Current.Second.copy_(enumer.Current.First); - - var opt_control = torch.optim.SGD(mod_control.parameters(), 1.0f); - var opt_scaling = torch.optim.SGD(mod_scaling.parameters(), 1.0f); - return (mod_control, mod_scaling, opt_control, opt_scaling); - } - } - internal (Sequential modctrl, Sequential modscal, torch.optim.Optimizer optctrl, torch.optim.Optimizer optscal, List> data, MSELoss loss_fn, int skip_iter) create_scaling_case(DeviceType dev = DeviceType.CUDA, ScalarType dtype = ScalarType.Float32) - { - var data = new List>() { - new(torch.randn(new long[]{8,8}, dtype, new Device(dev)),torch.randn(new long[]{8,8}, dtype, new Device(dev))), - new(torch.randn(new long[]{8,8}, dtype, new Device(dev)),torch.randn(new long[]{8,8}, dtype, new Device(dev))), - new(torch.randn(new long[]{8,8}, dtype, new Device(dev)),torch.randn(new long[]{8,8}, dtype, new Device(dev))), - new(torch.randn(new long[]{8,8}, dtype, new Device(dev)),torch.randn(new long[]{8,8}, dtype, new Device(dev))), - }; - - var loss_fn = MSELoss(); - loss_fn.to(DeviceType.CUDA); - const int skip_iter = 2; - var csmo = create_scaling_model_optimizer(dev); - return (csmo.modctrl, csmo.modscal, csmo.optctrl, csmo.optscal, data, loss_fn, skip_iter); - } - internal void run_scaling_case(Action>, Sequential, torch.optim.Optimizer, GradScaler, MSELoss, int, bool> run, int unskipped, int skipped, double atol = 1e07) - { - const double rtol = 1e-7d; - bool[] enableds = new bool[] { true, false }; - foreach (var enabled in enableds) { - var res =create_scaling_case(); - var scaler = new GradScaler(new Device(DeviceType.CUDA), 128.0f, 2.0f, growth_interval: 1); - run.Invoke(res.data, res.modctrl, res.optctrl, scaler, res.loss_fn, res.skip_iter, false); - run.Invoke(res.data, res.modscal, res.optscal, scaler, res.loss_fn, res.skip_iter, true); - if (enabled) { - var net_growth = unskipped > 0 ? MathF.Pow(scaler.get_growth_factor(), unskipped) : 1.0f; - var net_backoff = skipped> 0 ? MathF.Pow(scaler.get_backoff_factor(), skipped) : 1.0f; - Assert.Equal((128.0f * net_growth * net_backoff), scaler.get_scale()); - - } else { - Assert.Equal(1.0f, scaler.get_scale()); - } - - foreach(var seq in res.modctrl.parameters().Zip(res.modscal.parameters())){ - var c_grad = seq.First.grad; - var s_grad = seq.Second.grad; - if(!(c_grad is null) && !(s_grad is null)) - Assert.True(torch.allclose(seq.First.grad, seq.Second.grad, rtol, atol)); - var c_state = res.optctrl.ParamGroups; - var s_state = res.optscal.ParamGroups; - foreach(var c_s_state in c_state.Zip(s_state)) { - if (c_s_state.First is ParamGroup pg_c_state && c_s_state.Second is ParamGroup pg_s_state) { - foreach (var c_s_state_p in pg_c_state.Parameters.Zip(pg_s_state.Parameters)) - Assert.True(torch.allclose(c_s_state_p.First, c_s_state_p.Second, rtol, atol)); - } - } - Assert.True(torch.allclose(seq.First, seq.Second, rtol, atol)); - } - } - } - - [Fact] - [TestOf(nameof(GradScaler))] - public void TestGradScalingUnscaleSparse() - { - var scaler = new GradScaler(new Device(device)); - var inv_scale = torch.full(1, 0.25, dtype, new Device(device)); - var found_inf = torch.empty(1, dtype, new Device(device)); - var cur = found_inf.device; - var i = torch.tensor(new long[,] { { 0, 1, 1 }, { 2, 0, 2 } }, ScalarType.Int64, new Device(DeviceType.CUDA)); - var v = torch.tensor(new float[] { 16.0f,32.0f,64.0f}, ScalarType.Float32, new Device(DeviceType.CUDA)); - var s = torch.sparse_coo_tensor(i,v, new long[]{2,3}, dtype, new Device(DeviceType.CUDA)); - - var p = s.clone(); - Assert.True(p.is_sparse); - var optA = torch.optim.SGD(new[] { new Parameter(p) }, 1.0); - p.grad = s.clone(); - found_inf.zero_(); - found_inf = scaler.unscale_grads(optA, inv_scale, found_inf, false)[cur]; - - Assert.Equal(0.0f, found_inf.item()); - Assert.True(torch.equal(p.grad.to_dense(), (s/4).to_dense()).item()); - - v = torch.tensor(new float[] { 16.0f, 32.0f, float.PositiveInfinity }); - p.grad = torch.sparse_coo_tensor(i, v, new long[] { 2, 3 }, dtype, new Device(DeviceType.CUDA)); - found_inf.zero_(); - found_inf = scaler.unscale_grads(optA, inv_scale, found_inf, false)[cur]; - Assert.Equal(1.0f, found_inf.item()); - - v = torch.tensor(new float[] { 16.0f, 32.0f, float.NaN }); - p.grad = torch.sparse_coo_tensor(i, v, new long[] { 2, 3 }, dtype, new Device(DeviceType.CUDA)); - found_inf.zero_(); - found_inf = scaler.unscale_grads(optA, inv_scale, found_inf, false)[cur]; - Assert.Equal(1.0f, found_inf.item()); - - p = s.clone().to(ScalarType.Float16); - Assert.True(p.is_sparse); - var optB = torch.optim.SGD(new Parameter[] { new Parameter(p) }, 1.0); - - p.grad = s.clone().to(ScalarType.Float16); - found_inf.zero_(); - found_inf = scaler.unscale_grads(optB, inv_scale, found_inf, true)[cur]; - Assert.Equal(0.0f, found_inf.item()); - Assert.True(torch.equal(p.grad.to_dense(), (s.to(ScalarType.Float16) / 4).to_dense()).item()); - - i = torch.tensor(new long[,] { { 0, 1, 0 }, { 2, 0, 2 } }); - v = torch.tensor(new float[] { 64000.0f, 32.0f, 64000.0f }); - p.grad = torch.sparse_coo_tensor(i, v, new long[] { 2, 3 }, dtype, new Device(DeviceType.CUDA)); - found_inf.zero_(); - found_inf = scaler.unscale_grads(optB, inv_scale, found_inf, true)[cur]; - Assert.Equal(0.0f, found_inf.item()); - } - - [Fact] - [TestOf(nameof(GradScaler))] - public void TestGradScalingStateDict() - { - bool[] lazy_init_scale = new[] { true, false }; - foreach (var l in lazy_init_scale) { - var s0 = new GradScaler(new Device(DeviceType.CUDA), 3.0f, 4.0f, 0.5f, 2); - var s1 = new GradScaler(new Device(DeviceType.CUDA), 6.0f, 7.0f, 0.8f, 1); - s1.set_init_growth_tracker(7); - if (l) { - s1.scale(torch.full(1, 4.0f, ScalarType.Float32, new Device(DeviceType.CUDA, 0))); - Assert.Equal(ScalarType.Float32, s1.get_scale_async().dtype); - } - - var re = s0.state_dict(); - s1.load_state_dict(re); - - Assert.Equal(3.0f, s1.get_scale()); - Assert.Equal(0.5f, s1.get_growth_factor()); - Assert.Equal(2, s1.get_growth_interval()); - Assert.Equal(0.0f, s1.get_init_growth_tracker()); - } - } - - [Fact] - [TestOf(nameof(GradScaler))] - public void TestGradScaleWillNotOverflow() - { - var model = torch.nn.Linear(5, 1).to(DeviceType.CUDA); - var optimizer = torch.optim.Adam(model.parameters()); - var scaler = new GradScaler(new Device(DeviceType.CUDA), 1e38f, MathF.Pow(2.0f, 4), growth_interval:1); - optimizer.zero_grad(); - var x = torch.randn(new long[]{1,5}).to(DeviceType.CUDA); - var y = 1e-30 * torch.randn(new long[]{1,1}).to(DeviceType.CUDA); - var l = torch.pow(model.forward(x) - y, 2).mean(); - scaler.scale(l).backward(); - scaler.step(optimizer); - scaler.update(); - Assert.True(!scaler.get_scale_async().isinf().item() && !scaler.get_scale_async().isnan().item()); - } - [Fact] - [TestOf(nameof(GradScaler))] - public void TestGradScalingClipping() - { - run_scaling_case(new Action>, Sequential, optim.Optimizer, GradScaler, MSELoss, int, bool>(( - (data, model, optimizer, scaler, loss_fn, skip_iter, try_scaling_api) => { - const float max_norm = 0.2f; - int idx = 0; - foreach (var ipair in data) { - //ipair. - optimizer.zero_grad(); - var output = model.forward(ipair.Key); - var loss = loss_fn.forward(output, ipair.Value); - if (try_scaling_api) { - scaler.scale(loss).backward(); - torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm * scaler.get_scale()); - if (idx == skip_iter && scaler.IsEnabled()) { - var weight = (model[1] as Linear)?.weight; - if (weight.is_null()) - throw new ArgumentNullException(nameof(weight)); - weight.grad.fill_(float.PositiveInfinity); - } - - scaler.step(optimizer); - scaler.update(); - } else { - loss.backward(); - torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm); - if (!scaler.IsEnabled() || (idx != skip_iter)) - optimizer.step(); - } - - idx++; - } - })), - 3, 1, 1e-5); - } - [Fact] - [TestOf(nameof(GradScaler))] - public void TestGradScalingClippingSeparateUnscale() - { - run_scaling_case(new Action>, Sequential, optim.Optimizer, GradScaler, MSELoss, int, bool>(( - (data, model, optimizer, scaler, loss_fn, skip_iter, try_scaling_api) => { - const float max_norm = 0.2f; - int idx = 0; - foreach (var ipair in data) { - //ipair. - optimizer.zero_grad(); - var output = model.forward(ipair.Key); - var loss = loss_fn.forward(output, ipair.Value); - if (try_scaling_api) { - scaler.scale(loss).backward(); - torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm); - if (idx == skip_iter && scaler.IsEnabled()) { - var weight = (model[1] as Linear)?.weight; - weight.grad.fill_(float.PositiveInfinity); - } - - scaler.step(optimizer); - scaler.update(); - } else { - loss.backward(); - torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm); - if (!scaler.IsEnabled() || (idx != skip_iter)) - optimizer.step(); - } - - idx++; - } - })), - 3, 1); - } - [Fact] - [TestOf(nameof(GradScaler))] - public void TestGradScalingPenalty() - { - - run_scaling_case(new Action>, Sequential, optim.Optimizer, GradScaler, MSELoss, int, bool>(( - (data, model, optimizer, scaler, loss_fn, skip_iter, try_scaling_api) => { - //const float max_norm = 0.2f; - int idx = 0; - foreach (var ipair in data) { - //ipair. - optimizer.zero_grad(); - var output = model.forward(ipair.Key); - var loss = loss_fn.forward(output, ipair.Value); - List grad_params = new List(); - if (try_scaling_api) { - //throw new NotImplementedException(); - //TODO: RESEARCH TORCH::AUTOGRAD:GRAD THE SECOND ARGUMENT SHOULD HAVE model->parameters(); - //grad_params = torch.autograd.grad(new List(){scaler.scale(loss)}, model.parameters()) - var inv_scale = 1.0f / scaler.get_scale(); - for (int i = 0; i < grad_params.Count; i++) - grad_params[i] *= inv_scale; - } else { - //throw new NotImplementedException(); - //TODO: RESEARCH TORCH::AUTOGRAD:GRAD THE SECOND ARGUMENT SHOULD HAVE model->parameters(); - //grad_params = torch.autograd.grad(new List(){scaler.scale(loss)}, model.parameters()) - } - - var grad_norm = torch.zeros(new long[] { 1 }).to(ipair.Key.device); - for (int i = 0; i < grad_params.Count; i++) - grad_norm += grad_params[i].pow(2).sum(); - grad_norm = grad_norm.sqrt(); - loss = loss + grad_norm; - if (try_scaling_api) { - scaler.scale(loss).backward(); - if (idx == skip_iter && scaler.IsEnabled()) { - var weight = (model[1] as Linear)?.weight; - weight.grad.fill_(float.PositiveInfinity); - } - - scaler.step(optimizer); - scaler.update(); - } else { - loss.backward(); - if (!scaler.IsEnabled() || (idx != skip_iter)) { - optimizer.step(); - } - } - idx++; - } - })), - 3, 1); - } - [Fact] - [TestOf(nameof(GradScaler))] - public void TestGradScalingAccumulation() - { - run_scaling_case(new Action>, Sequential, optim.Optimizer, GradScaler, MSELoss, int, bool>(( - (data, model, optimizer, scaler, loss_fn, skip_iter, try_scaling_api) => { - const int iters_to_accumulate= 2; - int idx = 0; - foreach (var ipair in data) { - //ipair. - optimizer.zero_grad(); - var output = model.forward(ipair.Key); - var loss = loss_fn.forward(output, ipair.Value); - loss /= iters_to_accumulate; - - if (try_scaling_api) { - scaler.scale(loss).backward(); - } else { - loss.backward(); - } - - if ((idx + 1) % iters_to_accumulate == 0) { - if (try_scaling_api) { - scaler.step(optimizer); - scaler.update(); - optimizer.zero_grad(); - } else { - optimizer.step(); - optimizer.zero_grad(); - } - } - idx++; - } - })), - 2, 0); - } - [Fact] - [TestOf(nameof(GradScaler))] - public void TestGradScalingMultiple() - { - throw new NotImplementedException(); - } - } -} diff --git a/test/TorchSharpTest.WithCudaBinaries/TorchSharpTest.WithCudaBinaries.csproj b/test/TorchSharpTest.WithCudaBinaries/TorchSharpTest.WithCudaBinaries.csproj index 50b2438df..6f7a0ed24 100644 --- a/test/TorchSharpTest.WithCudaBinaries/TorchSharpTest.WithCudaBinaries.csproj +++ b/test/TorchSharpTest.WithCudaBinaries/TorchSharpTest.WithCudaBinaries.csproj @@ -26,6 +26,8 @@ Always + + diff --git a/test/TorchSharpTest.WithCudaBinaries/TestAutocast.cs b/test/TorchSharpTest/TestAutocast.cs similarity index 80% rename from test/TorchSharpTest.WithCudaBinaries/TestAutocast.cs rename to test/TorchSharpTest/TestAutocast.cs index 01b78e65a..f7ade93b4 100644 --- a/test/TorchSharpTest.WithCudaBinaries/TestAutocast.cs +++ b/test/TorchSharpTest/TestAutocast.cs @@ -13,25 +13,31 @@ public class TestAutocast { internal const ScalarType f32 = ScalarType.Float32; internal const ScalarType f16 = ScalarType.Float16; + internal static DeviceType availableDevice; private static void CheckCUDA() { - if (!torch.cuda_is_available()) - throw new Exception("CUDA IS NOT AVAILABLE"); + if (!torch.cuda_is_available()) { + availableDevice = DeviceType.CPU; + //throw new Exception("CUDA IS NOT AVAILABLE"); + } else { + availableDevice= DeviceType.CUDA; + } + AutocastMode.GetInstance(true); Assert.True(AutocastMode.IsAutocastEnabled()); } private Tensor randnf32cuda(long dim0) { - return torch.randn(dim0, f32, new Device(DeviceType.CUDA)); + return torch.randn(dim0, f32, new Device(availableDevice)); } private Tensor randnf32cuda(long dim0, long dim1) { - return torch.randn(dim0, dim1, f32, new Device(DeviceType.CUDA)); + return torch.randn(dim0, dim1, f32, new Device(availableDevice)); } private Tensor randnf32cuda(long dim0, long dim1, long dim2) { - return torch.randn(dim0, dim1,dim2, f32, new Device(DeviceType.CUDA)); + return torch.randn(dim0, dim1,dim2, f32, new Device(availableDevice)); } [Fact] [TestOf("AutocastF16")] @@ -67,7 +73,7 @@ public void TestAutocastF16() Assert.Equal(ScalarType.Float16, f.dtype); Assert.Equal(ScalarType.Float16, g.dtype); Assert.Equal(ScalarType.Float16, h.dtype); - Assert.Equal(ScalarType.Float16, i.dtype); + Assert.Equal(ScalarType.Float16, i.dtype); Assert.Equal(ScalarType.Float16, j.dtype);*/ //throw new NotImplementedException(); } @@ -94,8 +100,8 @@ public void TestAutocastF16Arithmetic() var mat2 = randnf32cuda(3, 3); var M3 = randnf32cuda(4, 3); - var vec1 = torch.rand(4, f32, new Device(DeviceType.CUDA)); - var vec2 = torch.rand(3, f32, new Device(DeviceType.CUDA)); + var vec1 = torch.rand(4, f32, new Device(availableDevice)); + var vec2 = torch.rand(3, f32, new Device(availableDevice)); using (AutocastMode.GetInstance().Enter()) { var c = cm.matmul(dm); var d = M.addbmm(batch1, batch2); @@ -124,16 +130,16 @@ public void TestAutocastF16Cell() { CheckCUDA(); //Like GRUCell, LSTM, RNN - var l = Linear(4, 4).to(DeviceType.CUDA); - var gru = GRUCell(4, 4).to(DeviceType.CUDA); - var lstm = LSTMCell(10, 20).to(DeviceType.CUDA); - var rnn = RNNCell(10,20).to(DeviceType.CUDA); + var l = Linear(4, 4).to(availableDevice); + var gru = GRUCell(4, 4).to(availableDevice); + var lstm = LSTMCell(10, 20).to(availableDevice); + var rnn = RNNCell(10,20).to(availableDevice); - var a = torch.rand(4,4, f32, new Device(DeviceType.CUDA)); - var b = torch.rand(4,4, f32, new Device(DeviceType.CUDA)); - var inpRNN = torch.rand(3,10, f32, new Device(DeviceType.CUDA)); - var hx = torch.rand(3,20, f32, new Device(DeviceType.CUDA)); - var cx = torch.rand(3,20, f32, new Device(DeviceType.CUDA)); + var a = torch.rand(4,4, f32, new Device(availableDevice)); + var b = torch.rand(4,4, f32, new Device(availableDevice)); + var inpRNN = torch.rand(3,10, f32, new Device(availableDevice)); + var hx = torch.rand(3,20, f32, new Device(availableDevice)); + var cx = torch.rand(3,20, f32, new Device(availableDevice)); Assert.Equal(f32, a.dtype); Assert.Equal(f32, b.dtype); @@ -161,8 +167,8 @@ public void TestAutocastF16Other() { //Like Linear, prelu, etc. CheckCUDA(); - var pr = PReLU(8).to(DeviceType.CUDA); - var a = torch.rand(8, 8, ScalarType.Float32, new Device(DeviceType.CUDA)); + var pr = PReLU(8).to(availableDevice); + var a = torch.rand(8, 8, ScalarType.Float32, new Device(availableDevice)); Assert.Equal(f32, a.dtype); using (AutocastMode.GetInstance().Enter()) { a = pr.forward(a); @@ -180,13 +186,13 @@ public void TestAutocastF16Convolutions() { CheckCUDA(); //Conv 1d,2d,3d, conv_transpose 1d,2d,3d - var c1 =Conv1d(4,4, 3).to(DeviceType.CUDA); - var c2 =Conv2d(4,4, 3).to(DeviceType.CUDA); - var c3 =Conv3d(4,4, 3).to(DeviceType.CUDA); + var c1 =Conv1d(4,4, 3).to(availableDevice); + var c2 =Conv2d(4,4, 3).to(availableDevice); + var c3 =Conv3d(4,4, 3).to(availableDevice); - var a = torch.rand(4, 4, f32, new Device(DeviceType.CUDA)); - var b = torch.rand(4, 4,3, f32, new Device(DeviceType.CUDA)); - var c = torch.rand(4, 4,4,3, f32, new Device(DeviceType.CUDA)); + var a = torch.rand(4, 4, f32, new Device(availableDevice)); + var b = torch.rand(4, 4,3, f32, new Device(availableDevice)); + var c = torch.rand(4, 4,4,3, f32, new Device(availableDevice)); Assert.Equal(f32, a.dtype); using (AutocastMode.GetInstance().Enter()) { a = c1.forward(a); @@ -215,7 +221,7 @@ public void TestAutocastF32Trigonometry() { CheckCUDA(); //Purpose rand f16 because inside autocast with these operations should return as f32 - var a = torch.rand(3, 2, 4, f16, new Device(DeviceType.CUDA)); + var a = torch.rand(3, 2, 4, f16, new Device(availableDevice)); /*var b = torch.rand(3, 2, 4, f16, new Device(DeviceType.CUDA)); var vec1 = torch.rand(3, f16, new Device(DeviceType.CUDA)); var vec2 = torch.rand(3, f16, new Device(DeviceType.CUDA));*/ @@ -238,7 +244,7 @@ public void TestAutocastF32Trigonometry() public void TestAutocastF32Logarithmic() { CheckCUDA(); - var a = torch.rand(3, 2, 4, f16, new Device(DeviceType.CUDA)); + var a = torch.rand(3, 2, 4, f16, new Device(availableDevice)); /*var b = torch.rand(3, 2, 4, f16, new Device(DeviceType.CUDA)); var vec1 = torch.rand(3, f16, new Device(DeviceType.CUDA)); var vec2 = torch.rand(3, f16, new Device(DeviceType.CUDA));*/ @@ -272,12 +278,12 @@ public void TestAutocastF32Other() public void TestAutocastF32Loss() { CheckCUDA(); - var a = torch.rand(3, 2, 4, f16, new Device(DeviceType.CUDA)); - var b = torch.rand(3, 2, 4, f16, new Device(DeviceType.CUDA)); - var vec1 = torch.rand(3, f16, new Device(DeviceType.CUDA)); - var vec2 = torch.rand(3, f16, new Device(DeviceType.CUDA)); + var a = torch.rand(3, 2, 4, f16, new Device(availableDevice)); + var b = torch.rand(3, 2, 4, f16, new Device(availableDevice)); + var vec1 = torch.rand(3, f16, new Device(availableDevice)); + var vec2 = torch.rand(3, f16, new Device(availableDevice)); using (AutocastMode.AutoCastEnter()) { - var c = torch.nn.L1Loss().to(DeviceType.CUDA).forward(a,b); + var c = torch.nn.L1Loss().to(availableDevice).forward(a,b); Assert.Equal(f32, c.dtype); } } diff --git a/test/TorchSharpTest/TestGradScaler.cs b/test/TorchSharpTest/TestGradScaler.cs index 42c997f43..07888ebe8 100644 --- a/test/TorchSharpTest/TestGradScaler.cs +++ b/test/TorchSharpTest/TestGradScaler.cs @@ -7,13 +7,18 @@ using Xunit; using static TorchSharp.torch; using static TorchSharp.torch.nn; -namespace TorchSharpTest +namespace TorchSharpTest.WithCudaBinaries { public class TestGradScaler { - internal DeviceType device = DeviceType.CPU; + //https://gist.github.com/dorpxam/67ad2bc222b2cf567d4a6fc298375e13 + internal DeviceType device = DeviceType.CUDA; internal ScalarType dtype = ScalarType.Float32; - + private static void CheckCUDA() + { + if (!torch.cuda_is_available()) + throw new Exception("CUDA IS NOT AVAILABLE"); + } private (Sequential modctrl, Sequential modscal, torch.optim.Optimizer optctrl, torch.optim.Optimizer optscal) create_scaling_model_optimizer(DeviceType dev = DeviceType.CUDA) { var mod_control =Sequential(torch.nn.Linear(8,8), torch.nn.Linear(8, 8)); @@ -87,10 +92,11 @@ internal void run_scaling_case(Action grad_params = new List(); + IList grad_params = new List(); if (try_scaling_api) { //throw new NotImplementedException(); //TODO: RESEARCH TORCH::AUTOGRAD:GRAD THE SECOND ARGUMENT SHOULD HAVE model->parameters(); - //grad_params = torch.autograd.grad(new List(){scaler.scale(loss)}, model.parameters()) + //grad_params = torch.autograd.grad(new List() { scaler.scale(loss) }, model.parameters()); + grad_params = torch.autograd.grad(new List() { scaler.scale(loss) }, model.parameters(),create_graph:true); var inv_scale = 1.0f / scaler.get_scale(); for (int i = 0; i < grad_params.Count; i++) grad_params[i] *= inv_scale; } else { //throw new NotImplementedException(); //TODO: RESEARCH TORCH::AUTOGRAD:GRAD THE SECOND ARGUMENT SHOULD HAVE model->parameters(); - //grad_params = torch.autograd.grad(new List(){scaler.scale(loss)}, model.parameters()) + grad_params = torch.autograd.grad(new List() { scaler.scale(loss) }, model.parameters(), create_graph: true); } var grad_norm = torch.zeros(new long[] { 1 }).to(ipair.Key.device); From 5a6240c6904191acf1923bee44bb43ccaf5aa1eb Mon Sep 17 00:00:00 2001 From: Dimitri Date: Sun, 3 Nov 2024 23:42:02 -0300 Subject: [PATCH 40/65] trying fix comp THSCuda --- Directory.Build.props | 4 +- src/Native/LibTorchSharp/THSCuda.cpp | 33 ++++-- src/Native/LibTorchSharp/THSCuda.h | 9 +- src/TorchSharp/Amp/AutocastMode.cs | 20 +++- src/TorchSharp/Torch.cs | 11 ++ src/TorchSharp/TorchSharp.csproj | 1 + src/TorchSharp/Utils/TorchCudaStruct.cs | 132 ++++++++++++++++++++++++ test/TorchSharpTest/TestAutocast.cs | 110 +++++++++++--------- 8 files changed, 254 insertions(+), 66 deletions(-) create mode 100644 src/TorchSharp/Utils/TorchCudaStruct.cs diff --git a/Directory.Build.props b/Directory.Build.props index f5687af68..262c4216a 100644 --- a/Directory.Build.props +++ b/Directory.Build.props @@ -92,7 +92,6 @@ $(LibTorchPackageVersion) - true @@ -164,6 +163,9 @@ $(DefineContants);DEBUG false + + $(DefineContants);CUDA_TOOLKIT_FOUND + true diff --git a/src/Native/LibTorchSharp/THSCuda.cpp b/src/Native/LibTorchSharp/THSCuda.cpp index a024bf4d0..baca29615 100644 --- a/src/Native/LibTorchSharp/THSCuda.cpp +++ b/src/Native/LibTorchSharp/THSCuda.cpp @@ -4,7 +4,7 @@ #include #include -#ifdef TORCHSHARP_CUDA_TOOLKIT_FOUND +#ifdef CUDA_TOOLKIT_FOUND cudaDeviceProp THSCuda_get_device_prop(int device) { cudaDeviceProp cdp; @@ -16,18 +16,30 @@ cudaDeviceProp THSCuda_get_device_prop(int device) int THSCuda_get_major_compute_capability(int device) { - RETURN_CUDA_DEVICE(THSCuda_get_device_prop(device).major) +#ifdef CUDA_TOOLKIT_FOUND + return THSCuda_get_device_prop(device).major; +#else + return -1; +#endif } int THSCuda_get_minor_compute_capability(int device) { - RETURN_CUDA_DEVICE(THSCuda_get_device_prop(device).minor) +#ifdef CUDA_TOOLKIT_FOUND + return THSCuda_get_device_prop(device).minor; +#else + return -1; +#endif } int THSCuda_get_device_count(int* count) { - RETURN_CUDA_DEVICE(cudaGetDeviceCount(count)) +#ifdef CUDA_TOOLKIT_FOUND + return cudaGetDeviceCount(count); +#else + return -1; +#endif } int THSCuda_get_free_total(int device, int* id, size_t* free, size_t* total) @@ -47,13 +59,22 @@ int THSCuda_get_free_total(int device, int* id, size_t* free, size_t* total) size_t THSCuda_get_total_memory(int device) { - RETURN_CUDA_DEVICE(THSCuda_get_device_prop(device).totalConstMem) +#ifdef CUDA_TOOLKIT_FOUND + return THSCuda_get_device_prop(device).totalConstMem; +#else + return 0; //Is size_t (unsigned long) so cant be negative. +#endif + //RETURN_CUDA_DEVICE(THSCuda_get_device_prop(device).totalConstMem) } size_t THSCuda_get_global_total_memory(int device) { - RETURN_CUDA_DEVICE(THSCuda_get_device_prop(device).totalGlobalMem) +#ifdef CUDA_TOOLKIT_FOUND + return THSCuda_get_device_prop(device).totalGlobalMem; +#else + return 0; +#endif } //TODO: implement more function diff --git a/src/Native/LibTorchSharp/THSCuda.h b/src/Native/LibTorchSharp/THSCuda.h index 9ec7416ce..00f1d7d03 100644 --- a/src/Native/LibTorchSharp/THSCuda.h +++ b/src/Native/LibTorchSharp/THSCuda.h @@ -6,18 +6,19 @@ #include "torch/torch.h" #ifdef TORCHSHARP_CUDA_TOOLKIT_FOUND +//#undef CUDA_TOOLKIT_FOUND #define CUDA_TOOLKIT_FOUND 1 #else -#define CUDA_TOOLKIT_FOUND 0 +#undef CUDA_TOOLKIT_FOUND #endif -#define RETURN_CUDA_DEVICE(x) \ +/*#define RETURN_CUDA_DEVICE(x) \ if(CUDA_TOOLKIT_FOUND) \ return x; \ else \ - return -1; + return -1; */ -#ifdef TORCHSHARP_CUDA_TOOLKIT_FOUND +#ifdef CUDA_TOOLKIT_FOUND #include "cuda.h" #include "cuda_runtime_api.h" diff --git a/src/TorchSharp/Amp/AutocastMode.cs b/src/TorchSharp/Amp/AutocastMode.cs index 68269f564..ef0c8a43c 100644 --- a/src/TorchSharp/Amp/AutocastMode.cs +++ b/src/TorchSharp/Amp/AutocastMode.cs @@ -34,6 +34,7 @@ public sealed class AutocastMode : IDisposable private static AutocastMode instance; public static AutocastMode GetInstance(bool enabled=false) { + //https://github.com/pytorch/pytorch/blob/e6ff07f00e04a9b58efb86a3dd70ed7280ae8522/torch/fx/experimental/proxy_tensor.py#L1251 return instance ??= new AutocastMode(torch.cuda_is_available() ? torch.CUDA : torch.CPU, enabled:enabled,cache_enabled:true); } @@ -45,7 +46,7 @@ private AutocastMode(torch.Device dev, torch.ScalarType? dtype = null, bool enab this.device = dev.type; if (!torch.is_autocast_available(device)) throw new Exception($"User specified an unsupported autocast device_type {device}"); - fast_dtype = torch.get_autocast_dtype(device); + fast_dtype = torch.get_autocast_dtype(device); //If device is CPU this may return as BFloat16 _cache_enabled = torch.is_autocast_cache_enabled(); if (enabled && !torch.cuda_is_available() && dev.type == DeviceType.CUDA) //Is not available for doing multicast enabled = false; @@ -55,9 +56,16 @@ private AutocastMode(torch.Device dev, torch.ScalarType? dtype = null, bool enab _cache_enabled = cache_enabled.Value; if (dev.type != DeviceType.CPU && dev.type != DeviceType.CUDA && enabled) throw new Exception($"Currently autocast does not support {dev.type} only CPU or CUDA"); + /*if (dev.type == DeviceType.CPU) { + if (torch.get_autocast_dtype(device) != torch.ScalarType.Float32) { + Debug.WriteLine($"Currently is not support {torch.get_autocast_dtype(device)} on CPU, that feature will be add."); + } + fast_dtype = torch.ScalarType.Float32; + }*/ if (dev.type == DeviceType.CPU) { - if (fast_dtype != torch.ScalarType.Float16 || fast_dtype != torch.ScalarType.BFloat16) { - Debug.WriteLine($"In CPU autocast, but the target d type is not suported. Disabling autocast. CPU autocast only supports dtype of {torch.ScalarType.Float16} or {torch.ScalarType.BFloat16}"); + //https://github.com/pytorch/pytorch/blob/e6ff07f00e04a9b58efb86a3dd70ed7280ae8522/torch/amp/autocast_mode.py#L277 + if (enabled && (fast_dtype != torch.ScalarType.Float16 || fast_dtype != torch.ScalarType.BFloat16)) { + Debug.WriteLine($"In CPU autocast, but the target dtype is not suported. Disabling autocast. CPU autocast only supports dtype of {torch.ScalarType.Float16} or {torch.ScalarType.BFloat16}"); enabled = false; } } else if (dev.type == DeviceType.CUDA) { @@ -127,10 +135,12 @@ private static DeviceType GetDeviceType(IntPtr ptr) } public static IntPtr ToIf(IntPtr ptr, torch.ScalarType type) { - if (!IsAutocastEnabled() || !GetInstance().IsEnter) - return ptr; + if(GetInstance().device != DeviceType.CPU) //Warning: Remove this if is finished and working the struct BFloat16 C10 + if (!IsAutocastEnabled() || !GetInstance().IsEnter) + return ptr; if (GetDtype(ptr) == type) //if already have same dtype is not necesary convert to dtype, right??? return ptr; + //TODO: Check if is from CPU to passing BFloat16 if support /*if (!NativeMethods.THSAmp_is_autocast_enabled(NativeMethods.THSTensor_device_type(ptr))) return ptr;*/ diff --git a/src/TorchSharp/Torch.cs b/src/TorchSharp/Torch.cs index f0cfa8290..b7979f3b5 100644 --- a/src/TorchSharp/Torch.cs +++ b/src/TorchSharp/Torch.cs @@ -11,6 +11,7 @@ using System.Text.RegularExpressions; using TorchSharp.Modules; using TorchSharp.PInvoke; +using TorchSharp.Utils; using static TorchSharp.PInvoke.NativeMethods; namespace TorchSharp @@ -620,6 +621,16 @@ public static ulong get_global_total_memory(int device) { return THSCuda_get_global_total_memory(device); } + /*public static cudaDeviceProp get_device_prop(int device) + { +#if CUDA_TOOLKIT_FOUND + cudaDeviceProp cdp = new cudaDeviceProp(); + throw new NotImplementedException("Implement the cudaDeviceProp THSCuda"); + //return cdp; +#else + return null; +#endif + }*/ } /// diff --git a/src/TorchSharp/TorchSharp.csproj b/src/TorchSharp/TorchSharp.csproj index d5cb1135d..14c95995f 100644 --- a/src/TorchSharp/TorchSharp.csproj +++ b/src/TorchSharp/TorchSharp.csproj @@ -21,6 +21,7 @@ + diff --git a/src/TorchSharp/Utils/TorchCudaStruct.cs b/src/TorchSharp/Utils/TorchCudaStruct.cs new file mode 100644 index 000000000..8341ec08f --- /dev/null +++ b/src/TorchSharp/Utils/TorchCudaStruct.cs @@ -0,0 +1,132 @@ +using System; +using System.Collections.Generic; +using System.Text; +using System.Runtime.InteropServices; +namespace TorchSharp.Utils +{ +#pragma warning disable 0169 + public struct cudaDeviceProp + { + [MarshalAs(UnmanagedType.ByValArray, SizeConst = 256)] + char[] name; /*< ASCII string identifying device */ + [MarshalAs(UnmanagedType.ByValArray, SizeConst = 16)] + char[] uuid; /*< 16-byte unique identifier */ + [MarshalAs(UnmanagedType.ByValArray, SizeConst = 8)] + char[] luid; /*< 8-byte locally unique identifier. Value is undefined on TCC and non-Windows platforms */ + uint luidDeviceNodeMask; /*< LUID device node mask. Value is undefined on TCC and non-Windows platforms */ + ulong totalGlobalMem; /*< Global memory available on device in bytes */ + ulong sharedMemPerBlock; /*< Shared memory available per block in bytes */ + int regsPerBlock; /*< 32-bit registers available per block */ + int warpSize; /*< Warp size in threads */ + ulong memPitch; /*< Maximum pitch in bytes allowed by memory copies */ + int maxThreadsPerBlock; /*< Maximum number of threads per block */ + [MarshalAs(UnmanagedType.ByValArray, SizeConst = 3)] + int[] maxThreadsDim; /*< Maximum size of each dimension of a block */ + [MarshalAs(UnmanagedType.ByValArray, SizeConst = 3)] + int[] maxGridSize; /*< Maximum size of each dimension of a grid */ + int clockRate; /*< Deprecated, Clock frequency in kilohertz */ + ulong totalConstMem; /*< Constant memory available on device in bytes */ + int major; /*< Major compute capability */ + int minor; /*< Minor compute capability */ + ulong textureAlignment; /*< Alignment requirement for textures */ + ulong texturePitchAlignment; /*< Pitch alignment requirement for texture references bound to pitched memory */ + int deviceOverlap; /*< Device can concurrently copy memory and execute a kernel. Deprecated. Use instead asyncEngineCount. */ + int multiProcessorCount; /*< Number of multiprocessors on device */ + int kernelExecTimeoutEnabled; /*< Deprecated, Specified whether there is a run time limit on kernels */ + int integrated; /*< Device is integrated as opposed to discrete */ + int canMapHostMemory; /*< Device can map host memory with cudaHostAlloc/cudaHostGetDevicePointer */ + int computeMode; /*< Deprecated, Compute mode (See ::cudaComputeMode) */ + int maxTexture1D; /*< Maximum 1D texture size */ + int maxTexture1DMipmap; /*< Maximum 1D mipmapped texture size */ + int maxTexture1DLinear; /*< Deprecated, do not use. Use cudaDeviceGetTexture1DLinearMaxWidth() or cuDeviceGetTexture1DLinearMaxWidth() instead. */ + [MarshalAs(UnmanagedType.ByValArray, SizeConst=2)] + int[] maxTexture2D; /*< Maximum 2D texture dimensions */ + [MarshalAs(UnmanagedType.ByValArray, SizeConst=2)] + int[] maxTexture2DMipmap; /*< Maximum 2D mipmapped texture dimensions */ + [MarshalAs(UnmanagedType.ByValArray, SizeConst=3)] + int[] maxTexture2DLinear; /*< Maximum dimensions (width, height, pitch) for 2D textures bound to pitched memory */ + [MarshalAs(UnmanagedType.ByValArray, SizeConst=2)] + int[] maxTexture2DGather; /*< Maximum 2D texture dimensions if texture gather operations have to be performed */ + [MarshalAs(UnmanagedType.ByValArray, SizeConst=3)] + int[] maxTexture3D; /*< Maximum 3D texture dimensions */ + [MarshalAs(UnmanagedType.ByValArray, SizeConst=3)] + int[] maxTexture3DAlt; /*< Maximum alternate 3D texture dimensions */ + int maxTextureCubemap; /*< Maximum Cubemap texture dimensions */ + [MarshalAs(UnmanagedType.ByValArray, SizeConst=2)] + int[] maxTexture1DLayered; /*< Maximum 1D layered texture dimensions */ + [MarshalAs(UnmanagedType.ByValArray, SizeConst=3)] + int[] maxTexture2DLayered; /*< Maximum 2D layered texture dimensions */ + [MarshalAs(UnmanagedType.ByValArray, SizeConst=2)] + int[] maxTextureCubemapLayered;/*< Maximum Cubemap layered texture dimensions */ + int maxSurface1D; /*< Maximum 1D surface size */ + [MarshalAs(UnmanagedType.ByValArray, SizeConst=2)] + int[] maxSurface2D; /*< Maximum 2D surface dimensions */ + [MarshalAs(UnmanagedType.ByValArray, SizeConst=3)] + int[] maxSurface3D; /*< Maximum 3D surface dimensions */ + [MarshalAs(UnmanagedType.ByValArray, SizeConst=2)] + int[] maxSurface1DLayered; /*< Maximum 1D layered surface dimensions */ + [MarshalAs(UnmanagedType.ByValArray, SizeConst=3)] + int[] maxSurface2DLayered; /*< Maximum 2D layered surface dimensions */ + int maxSurfaceCubemap; /*< Maximum Cubemap surface dimensions */ + [MarshalAs(UnmanagedType.ByValArray, SizeConst=2)] + int[] maxSurfaceCubemapLayered;/*< Maximum Cubemap layered surface dimensions */ + ulong surfaceAlignment; /*< Alignment requirements for surfaces */ + int concurrentKernels; /*< Device can possibly execute multiple kernels concurrently */ + int ECCEnabled; /*< Device has ECC support enabled */ + int pciBusID; /*< PCI bus ID of the device */ + int pciDeviceID; /*< PCI device ID of the device */ + int pciDomainID; /*< PCI domain ID of the device */ + int tccDriver; /*< 1 if device is a Tesla device using TCC driver, 0 otherwise */ + int asyncEngineCount; /*< Number of asynchronous engines */ + int unifiedAddressing; /*< Device shares a unified address space with the host */ + int memoryClockRate; /*< Deprecated, Peak memory clock frequency in kilohertz */ + int memoryBusWidth; /*< Global memory bus width in bits */ + int l2CacheSize; /*< Size of L2 cache in bytes */ + int persistingL2CacheMaxSize; /*< Device's maximum l2 persisting lines capacity setting in bytes */ + int maxThreadsPerMultiProcessor;/*< Maximum resident threads per multiprocessor */ + int streamPrioritiesSupported; /*< Device supports stream priorities */ + int globalL1CacheSupported; /*< Device supports caching globals in L1 */ + int localL1CacheSupported; /*< Device supports caching locals in L1 */ + ulong sharedMemPerMultiprocessor; /*< Shared memory available per multiprocessor in bytes */ + int regsPerMultiprocessor; /*< 32-bit registers available per multiprocessor */ + int managedMemory; /*< Device supports allocating managed memory on this system */ + int isMultiGpuBoard; /*< Device is on a multi-GPU board */ + int multiGpuBoardGroupID; /*< Unique identifier for a group of devices on the same multi-GPU board */ + int hostNativeAtomicSupported; /*< Link between the device and the host supports native atomic operations */ + int singleToDoublePrecisionPerfRatio; /*< Deprecated, Ratio of single precision performance (in floating-point operations per second) to double precision performance */ + int pageableMemoryAccess; /*< Device supports coherently accessing pageable memory without calling cudaHostRegister on it */ + int concurrentManagedAccess; /*< Device can coherently access managed memory concurrently with the CPU */ + int computePreemptionSupported; /*< Device supports Compute Preemption */ + int canUseHostPointerForRegisteredMem; /*< Device can access host registered memory at the same virtual address as the CPU */ + int cooperativeLaunch; /*< Device supports launching cooperative kernels via ::cudaLaunchCooperativeKernel */ + int cooperativeMultiDeviceLaunch; /*< Deprecated, cudaLaunchCooperativeKernelMultiDevice is deprecated. */ + ulong sharedMemPerBlockOptin; /*< Per device maximum shared memory per block usable by special opt in */ + int pageableMemoryAccessUsesHostPageTables; /*< Device accesses pageable memory via the host's page tables */ + int directManagedMemAccessFromHost; /*< Host can directly access managed memory on the device without migration. */ + int maxBlocksPerMultiProcessor; /*< Maximum number of resident blocks per multiprocessor */ + int accessPolicyMaxWindowSize; /*< The maximum value of ::cudaAccessPolicyWindow::num_bytes. */ + ulong reservedSharedMemPerBlock; /*< Shared memory reserved by CUDA driver per block in bytes */ + int hostRegisterSupported; /*< Device supports host memory registration via ::cudaHostRegister. */ + int sparseCudaArraySupported; /*< 1 if the device supports sparse CUDA arrays and sparse CUDA mipmapped arrays, 0 otherwise */ + int hostRegisterReadOnlySupported; /*< Device supports using the ::cudaHostRegister flag cudaHostRegisterReadOnly to register memory that must be mapped as read-only to the GPU */ + int timelineSemaphoreInteropSupported; /*< External timeline semaphore interop is supported on the device */ + int memoryPoolsSupported; /*< 1 if the device supports using the cudaMallocAsync and cudaMemPool family of APIs, 0 otherwise */ + int gpuDirectRDMASupported; /*< 1 if the device supports GPUDirect RDMA APIs, 0 otherwise */ + uint gpuDirectRDMAFlushWritesOptions; /*< Bitmask to be interpreted according to the ::cudaFlushGPUDirectRDMAWritesOptions enum */ + int gpuDirectRDMAWritesOrdering;/*< See the ::cudaGPUDirectRDMAWritesOrdering enum for numerical values */ + uint memoryPoolSupportedHandleTypes; /*< Bitmask of handle types supported with mempool-based IPC */ + int deferredMappingCudaArraySupported; /*< 1 if the device supports deferred mapping CUDA arrays and CUDA mipmapped arrays */ + int ipcEventSupported; /*< Device supports IPC Events. */ + int clusterLaunch; /*< Indicates device supports cluster launch */ + int unifiedFunctionPointers; /*< Indicates device supports unified pointers */ + [MarshalAs(UnmanagedType.ByValArray, SizeConst=2)] + int[] reserved2; + [MarshalAs(UnmanagedType.ByValArray, SizeConst=1)] + int[] reserved1; /*< Reserved for future use */ + [MarshalAs(UnmanagedType.ByValArray, SizeConst=60)] + int[] reserved; /*< Reserved for future use */ + } +#pragma warning restore 0169 + +} + diff --git a/test/TorchSharpTest/TestAutocast.cs b/test/TorchSharpTest/TestAutocast.cs index f7ade93b4..4a4787b9c 100644 --- a/test/TorchSharpTest/TestAutocast.cs +++ b/test/TorchSharpTest/TestAutocast.cs @@ -13,6 +13,14 @@ public class TestAutocast { internal const ScalarType f32 = ScalarType.Float32; internal const ScalarType f16 = ScalarType.Float16; + + /// + /// If is CUDA Get by default AutoCastType otherwise get FastType of Autocast + /// + /// + private static ScalarType AutoCastType => availableDevice == DeviceType.CUDA ? f16 : AutocastMode.GetInstance().GetFastType(); + private static ScalarType AutoCastTypeOfF32 => availableDevice == DeviceType.CUDA ? f32 : AutocastMode.GetInstance().GetFastType(); + internal static DeviceType availableDevice; private static void CheckCUDA() { @@ -40,8 +48,8 @@ private Tensor randnf32cuda(long dim0, long dim1, long dim2) return torch.randn(dim0, dim1,dim2, f32, new Device(availableDevice)); } [Fact] - [TestOf("AutocastF16")] - public void TestAutocastF16() + [TestOf("AutocastAutoCastType")] + public void TestAutocastAutoCastType() { CheckCUDA(); /*var a = torch.rand(3, 2, 4, ScalarType.Float32, new Device(DeviceType.CUDA)); @@ -79,8 +87,8 @@ public void TestAutocastF16() } [Fact] - [TestOf("AutocastF16")] - public void TestAutocastF16Arithmetic() + [TestOf("AutocastAutoCastType")] + public void TestAutocastAutoCastTypeArithmetic() { //Like matmul, addmm, mm, mv, etc. CheckCUDA(); @@ -111,22 +119,23 @@ public void TestAutocastF16Arithmetic() var h = cm.mm(dm); var i = M2.mv(vec2); var j = batch1.bmm(batch2); - Assert.Equal(f16, c.dtype); - Assert.Equal(f16, d.dtype); - Assert.Equal(f16, f.dtype); - Assert.Equal(f16, h.dtype); - //Assert.Equal(f16, e.dtype); - Assert.Equal(f16, f.dtype); - Assert.Equal(f16, g.dtype); - Assert.Equal(f16, h.dtype); - Assert.Equal(f16, i.dtype); - Assert.Equal(f16, j.dtype); + Assert.Equal(AutoCastType, c.dtype); + Assert.Equal(AutoCastType, d.dtype); + Assert.Equal(AutoCastType, f.dtype); + Assert.Equal(AutoCastType, h.dtype); + //Assert.Equal(AutoCastType, e.dtype); + Assert.Equal(AutoCastType, f.dtype); + Assert.Equal(AutoCastType, g.dtype); + Assert.Equal(AutoCastType, h.dtype); + Assert.Equal(AutoCastType, i.dtype); + Assert.Equal(AutoCastType, j.dtype); } } + [Fact] - [TestOf("AutocastF16")] - public void TestAutocastF16Cell() + [TestOf("AutocastAutoCastType")] + public void TestAutocastAutoCastTypeCell() { CheckCUDA(); //Like GRUCell, LSTM, RNN @@ -148,22 +157,22 @@ public void TestAutocastF16Cell() b = gru.forward(b); (torch.Tensor d, torch.Tensor f) = lstm.forward(inpRNN, new (hx,cx)); torch.Tensor g = rnn.forward(inpRNN, hx); - Assert.Equal(f16, a.dtype); - Assert.Equal(f16, b.dtype); - Assert.Equal(f16, d.dtype); - Assert.Equal(f16, f.dtype); - Assert.Equal(f16, g.dtype); + Assert.Equal(AutoCastType, a.dtype); + Assert.Equal(AutoCastType, b.dtype); + Assert.Equal(AutoCastType, d.dtype); + Assert.Equal(AutoCastType, f.dtype); + Assert.Equal(AutoCastType, g.dtype); } //Outside should have same dtype as inside - Assert.Equal(f16, a.dtype); - Assert.Equal(f16, b.dtype); - //Assert.Equal(f16, e.dtype); + Assert.Equal(AutoCastType, a.dtype); + Assert.Equal(AutoCastType, b.dtype); + //Assert.Equal(AutoCastType, e.dtype); } [Fact] - [TestOf("AutocastF16")] - public void TestAutocastF16Other() + [TestOf("AutocastAutoCastType")] + public void TestAutocastAutoCastTypeOther() { //Like Linear, prelu, etc. CheckCUDA(); @@ -172,17 +181,17 @@ public void TestAutocastF16Other() Assert.Equal(f32, a.dtype); using (AutocastMode.GetInstance().Enter()) { a = pr.forward(a); - Assert.Equal(f16, a.dtype); + Assert.Equal(AutoCastType, a.dtype); } //Outside should have same dtype as inside - Assert.Equal(f16, a.dtype); + Assert.Equal(AutoCastType, a.dtype); } [Fact] - [TestOf("AutocastF16")] - public void TestAutocastF16Convolutions() + [TestOf("AutocastAutoCastType")] + public void TestAutocastAutoCastTypeConvolutions() { CheckCUDA(); //Conv 1d,2d,3d, conv_transpose 1d,2d,3d @@ -198,14 +207,14 @@ public void TestAutocastF16Convolutions() a = c1.forward(a); b = c2.forward(b); c = c3.forward(c); - Assert.Equal(f16, a.dtype); - Assert.Equal(f16, b.dtype); - Assert.Equal(f16, c.dtype); + Assert.Equal(AutoCastType, a.dtype); + Assert.Equal(AutoCastType, b.dtype); + Assert.Equal(AutoCastType, c.dtype); } //Outside should have same dtype as inside - Assert.Equal(f16, a.dtype); - Assert.Equal(f16, b.dtype); - Assert.Equal(f16, c.dtype); + Assert.Equal(AutoCastType, a.dtype); + Assert.Equal(AutoCastType, b.dtype); + Assert.Equal(AutoCastType, c.dtype); } [Fact] [TestOf("AutocastF32")] @@ -219,12 +228,13 @@ public void TestAutocastF32() [TestOf("AutocastF32")] public void TestAutocastF32Trigonometry() { + //In Trigonometry all explicitily is passed to f32. CheckCUDA(); - //Purpose rand f16 because inside autocast with these operations should return as f32 - var a = torch.rand(3, 2, 4, f16, new Device(availableDevice)); - /*var b = torch.rand(3, 2, 4, f16, new Device(DeviceType.CUDA)); - var vec1 = torch.rand(3, f16, new Device(DeviceType.CUDA)); - var vec2 = torch.rand(3, f16, new Device(DeviceType.CUDA));*/ + //Purpose rand AutoCastType because inside autocast with these operations should return as f32 + var a = torch.rand(3, 2, 4, AutoCastType, new Device(availableDevice)); + /*var b = torch.rand(3, 2, 4, AutoCastType, new Device(DeviceType.CUDA)); + var vec1 = torch.rand(3, AutoCastType, new Device(DeviceType.CUDA)); + var vec2 = torch.rand(3, AutoCastType, new Device(DeviceType.CUDA));*/ using (AutocastMode.GetInstance(true).Enter()) { var c = a.acos(); var d = a.asin(); @@ -244,10 +254,10 @@ public void TestAutocastF32Trigonometry() public void TestAutocastF32Logarithmic() { CheckCUDA(); - var a = torch.rand(3, 2, 4, f16, new Device(availableDevice)); - /*var b = torch.rand(3, 2, 4, f16, new Device(DeviceType.CUDA)); - var vec1 = torch.rand(3, f16, new Device(DeviceType.CUDA)); - var vec2 = torch.rand(3, f16, new Device(DeviceType.CUDA));*/ + var a = torch.rand(3, 2, 4, AutoCastType, new Device(availableDevice)); + /*var b = torch.rand(3, 2, 4, AutoCastType, new Device(DeviceType.CUDA)); + var vec1 = torch.rand(3, AutoCastType, new Device(DeviceType.CUDA)); + var vec2 = torch.rand(3, AutoCastType, new Device(DeviceType.CUDA));*/ using (AutocastMode.GetInstance().Enter()) { var c = a.log(); var d = a.log10(); @@ -266,7 +276,7 @@ public void TestAutocastF32Logarithmic() public void TestAutocastF32Other() { CheckCUDA(); - var a = torch.rand(3, 3, f16, new Device(DeviceType.CUDA)); + var a = torch.rand(3, 3, AutoCastType, new Device(DeviceType.CUDA)); //var b = torch.rand(3, 3, f32, new Device(DeviceType.CUDA)); using (AutocastMode.GetInstance().Enter()) { var c = a.cumprod(1); @@ -278,10 +288,10 @@ public void TestAutocastF32Other() public void TestAutocastF32Loss() { CheckCUDA(); - var a = torch.rand(3, 2, 4, f16, new Device(availableDevice)); - var b = torch.rand(3, 2, 4, f16, new Device(availableDevice)); - var vec1 = torch.rand(3, f16, new Device(availableDevice)); - var vec2 = torch.rand(3, f16, new Device(availableDevice)); + var a = torch.rand(3, 2, 4, AutoCastType, new Device(availableDevice)); + var b = torch.rand(3, 2, 4, AutoCastType, new Device(availableDevice)); + var vec1 = torch.rand(3, AutoCastType, new Device(availableDevice)); + var vec2 = torch.rand(3, AutoCastType, new Device(availableDevice)); using (AutocastMode.AutoCastEnter()) { var c = torch.nn.L1Loss().to(availableDevice).forward(a,b); Assert.Equal(f32, c.dtype); From e52423916e025cdd2853049299d4531ccd916040 Mon Sep 17 00:00:00 2001 From: Dimitri Date: Sat, 15 Feb 2025 13:43:09 -0300 Subject: [PATCH 41/65] custom libtorch fullpatch --- .gitignore | 2 + Directory.Build.props | 1 + ...eRestitcher.Tests.csproj.nuget.dgspec.json | 224 +++++ .../FileRestitcher.Tests.csproj.nuget.g.props | 35 + ...ileRestitcher.Tests.csproj.nuget.g.targets | 18 + .../project.assets.json | 841 ++++++++++++++++++ .../project.nuget.cache | 21 + .../FileRestitcher.csproj.nuget.dgspec.json | 19 +- .../FileRestitcher.csproj.nuget.g.props | 6 +- .../project.assets.json | 21 +- .../project.nuget.cache | 2 +- src/Native/LibTorchSharp/CMakeLists.txt | 13 +- src/Native/LibTorchSharp/THSLinearAlgebra.cpp | 142 ++- src/Native/build.proj | 7 +- src/TorchSharp/TorchSharp.csproj | 5 +- 15 files changed, 1333 insertions(+), 24 deletions(-) create mode 100644 pkg/FileRestitcher/FileRestitcher.Tests/FileRestitcher.Tests.NupkgProj/FileRestitcher.Tests.csproj.nuget.dgspec.json create mode 100644 pkg/FileRestitcher/FileRestitcher.Tests/FileRestitcher.Tests.NupkgProj/FileRestitcher.Tests.csproj.nuget.g.props create mode 100644 pkg/FileRestitcher/FileRestitcher.Tests/FileRestitcher.Tests.NupkgProj/FileRestitcher.Tests.csproj.nuget.g.targets create mode 100644 pkg/FileRestitcher/FileRestitcher.Tests/FileRestitcher.Tests.NupkgProj/project.assets.json create mode 100644 pkg/FileRestitcher/FileRestitcher.Tests/FileRestitcher.Tests.NupkgProj/project.nuget.cache diff --git a/.gitignore b/.gitignore index 13682298c..749832847 100644 --- a/.gitignore +++ b/.gitignore @@ -275,3 +275,5 @@ packages/ .vscode/settings.json /TestClear TestClear/ +/nuget.config +/src/Native/LibTorchSharp/third_party diff --git a/Directory.Build.props b/Directory.Build.props index 262c4216a..ac534f235 100644 --- a/Directory.Build.props +++ b/Directory.Build.props @@ -5,6 +5,7 @@ + Debug Debug;Release <_DefaultArchitecture>$([System.Runtime.InteropServices.RuntimeInformation]::OSArchitecture.ToString().ToLower()) diff --git a/pkg/FileRestitcher/FileRestitcher.Tests/FileRestitcher.Tests.NupkgProj/FileRestitcher.Tests.csproj.nuget.dgspec.json 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of file diff --git a/pkg/FileRestitcher/FileRestitcher.Tests/FileRestitcher.Tests.NupkgProj/project.nuget.cache b/pkg/FileRestitcher/FileRestitcher.Tests/FileRestitcher.Tests.NupkgProj/project.nuget.cache new file mode 100644 index 000000000..fd9b0a74d --- /dev/null +++ b/pkg/FileRestitcher/FileRestitcher.Tests/FileRestitcher.Tests.NupkgProj/project.nuget.cache @@ -0,0 +1,21 @@ +{ + "version": 2, + "dgSpecHash": "md8eUrGszbk=", + "success": true, + "projectFilePath": "K:\\Proyects_Repos\\TorchSharp\\pkg\\FileRestitcher\\FileRestitcher.Tests\\FileRestitcher.Tests.csproj", + "expectedPackageFiles": [ + "C:\\Users\\Dimitri\\.nuget\\packages\\coverlet.collector\\3.0.2\\coverlet.collector.3.0.2.nupkg.sha512", + "C:\\Users\\Dimitri\\.nuget\\packages\\microsoft.codecoverage\\16.9.4\\microsoft.codecoverage.16.9.4.nupkg.sha512", + "C:\\Users\\Dimitri\\.nuget\\packages\\microsoft.net.test.sdk\\16.9.4\\microsoft.net.test.sdk.16.9.4.nupkg.sha512", + 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end of file diff --git a/pkg/FileRestitcher/FileRestitcher/FileRestitcher.NupkgProj/FileRestitcher.csproj.nuget.dgspec.json b/pkg/FileRestitcher/FileRestitcher/FileRestitcher.NupkgProj/FileRestitcher.csproj.nuget.dgspec.json index fc625189a..bbe687ab8 100644 --- a/pkg/FileRestitcher/FileRestitcher/FileRestitcher.NupkgProj/FileRestitcher.csproj.nuget.dgspec.json +++ b/pkg/FileRestitcher/FileRestitcher/FileRestitcher.NupkgProj/FileRestitcher.csproj.nuget.dgspec.json @@ -15,12 +15,13 @@ "projectStyle": "PackageReference", "crossTargeting": true, "fallbackFolders": [ - "C:\\Program Files (x86)\\Progress\\ToolboxNuGetPackages" + "C:\\Program Files (x86)\\Microsoft Visual Studio\\Shared\\NuGetPackages" ], "configFilePaths": [ + "K:\\Proyects_Repos\\TorchSharp\\NuGet.Config", "C:\\Users\\Dimitri\\AppData\\Roaming\\NuGet\\NuGet.Config", - "C:\\Program Files (x86)\\NuGet\\Config\\Microsoft.VisualStudio.Offline.config", - "C:\\Program Files (x86)\\NuGet\\Config\\Telerik UI for WinForms.config" + "C:\\Program Files (x86)\\NuGet\\Config\\Microsoft.VisualStudio.FallbackLocation.config", + "C:\\Program Files (x86)\\NuGet\\Config\\Microsoft.VisualStudio.Offline.config" ], "originalTargetFrameworks": [ "net6.0", @@ -44,7 +45,13 @@ "warnAsError": [ "NU1605" ] - } + }, + "restoreAuditProperties": { + "enableAudit": "true", + "auditLevel": "low", + "auditMode": "all" + }, + "SdkAnalysisLevel": "9.0.100" }, "frameworks": { "net6.0": { @@ -65,7 +72,7 @@ "privateAssets": "all" } }, - "runtimeIdentifierGraphPath": "C:\\Program Files\\dotnet\\sdk\\8.0.101\\RuntimeIdentifierGraph.json" + "runtimeIdentifierGraphPath": "C:\\Program Files\\dotnet\\sdk\\9.0.100\\RuntimeIdentifierGraph.json" }, "netstandard2.0": { "targetAlias": "netstandard2.0", @@ -88,7 +95,7 @@ ], "assetTargetFallback": true, "warn": true, - "runtimeIdentifierGraphPath": "C:\\Program Files\\dotnet\\sdk\\8.0.101\\RuntimeIdentifierGraph.json" + "runtimeIdentifierGraphPath": "C:\\Program Files\\dotnet\\sdk\\9.0.100\\RuntimeIdentifierGraph.json" } } } diff --git a/pkg/FileRestitcher/FileRestitcher/FileRestitcher.NupkgProj/FileRestitcher.csproj.nuget.g.props b/pkg/FileRestitcher/FileRestitcher/FileRestitcher.NupkgProj/FileRestitcher.csproj.nuget.g.props index 1e9807451..9c25bbe46 100644 --- a/pkg/FileRestitcher/FileRestitcher/FileRestitcher.NupkgProj/FileRestitcher.csproj.nuget.g.props +++ b/pkg/FileRestitcher/FileRestitcher/FileRestitcher.NupkgProj/FileRestitcher.csproj.nuget.g.props @@ -5,12 +5,12 @@ NuGet $(MSBuildThisFileDirectory)project.assets.json $(UserProfile)\.nuget\packages\ - C:\Users\Dimitri\.nuget\packages\;C:\Program Files (x86)\Progress\ToolboxNuGetPackages + C:\Users\Dimitri\.nuget\packages\;C:\Program Files (x86)\Microsoft Visual Studio\Shared\NuGetPackages PackageReference - 6.8.0 + 6.12.0 - + \ No newline at end of file diff --git a/pkg/FileRestitcher/FileRestitcher/FileRestitcher.NupkgProj/project.assets.json b/pkg/FileRestitcher/FileRestitcher/FileRestitcher.NupkgProj/project.assets.json index 1f13839e4..7e747e944 100644 --- a/pkg/FileRestitcher/FileRestitcher/FileRestitcher.NupkgProj/project.assets.json +++ b/pkg/FileRestitcher/FileRestitcher/FileRestitcher.NupkgProj/project.assets.json @@ -183,7 +183,7 @@ }, "packageFolders": { "C:\\Users\\Dimitri\\.nuget\\packages\\": {}, - "C:\\Program Files (x86)\\Progress\\ToolboxNuGetPackages": {} + "C:\\Program Files (x86)\\Microsoft Visual Studio\\Shared\\NuGetPackages": {} }, "project": { "version": "1.0.0", @@ -196,12 +196,13 @@ "projectStyle": "PackageReference", "crossTargeting": true, "fallbackFolders": [ - "C:\\Program Files (x86)\\Progress\\ToolboxNuGetPackages" + "C:\\Program Files (x86)\\Microsoft Visual Studio\\Shared\\NuGetPackages" ], "configFilePaths": [ + "K:\\Proyects_Repos\\TorchSharp\\NuGet.Config", "C:\\Users\\Dimitri\\AppData\\Roaming\\NuGet\\NuGet.Config", - "C:\\Program Files (x86)\\NuGet\\Config\\Microsoft.VisualStudio.Offline.config", - "C:\\Program Files (x86)\\NuGet\\Config\\Telerik UI for WinForms.config" + "C:\\Program Files (x86)\\NuGet\\Config\\Microsoft.VisualStudio.FallbackLocation.config", + "C:\\Program Files (x86)\\NuGet\\Config\\Microsoft.VisualStudio.Offline.config" ], "originalTargetFrameworks": [ "net6.0", @@ -225,7 +226,13 @@ "warnAsError": [ "NU1605" ] - } + }, + "restoreAuditProperties": { + "enableAudit": "true", + "auditLevel": "low", + "auditMode": "all" + }, + "SdkAnalysisLevel": "9.0.100" }, "frameworks": { "net6.0": { @@ -246,7 +253,7 @@ "privateAssets": "all" } }, - "runtimeIdentifierGraphPath": "C:\\Program Files\\dotnet\\sdk\\8.0.101\\RuntimeIdentifierGraph.json" + "runtimeIdentifierGraphPath": "C:\\Program Files\\dotnet\\sdk\\9.0.100\\RuntimeIdentifierGraph.json" }, "netstandard2.0": { "targetAlias": "netstandard2.0", @@ -269,7 +276,7 @@ ], "assetTargetFallback": true, "warn": true, - "runtimeIdentifierGraphPath": "C:\\Program Files\\dotnet\\sdk\\8.0.101\\RuntimeIdentifierGraph.json" + "runtimeIdentifierGraphPath": "C:\\Program Files\\dotnet\\sdk\\9.0.100\\RuntimeIdentifierGraph.json" } } } diff --git a/pkg/FileRestitcher/FileRestitcher/FileRestitcher.NupkgProj/project.nuget.cache b/pkg/FileRestitcher/FileRestitcher/FileRestitcher.NupkgProj/project.nuget.cache index 2e00179eb..aab7970d8 100644 --- a/pkg/FileRestitcher/FileRestitcher/FileRestitcher.NupkgProj/project.nuget.cache +++ b/pkg/FileRestitcher/FileRestitcher/FileRestitcher.NupkgProj/project.nuget.cache @@ -1,6 +1,6 @@ { "version": 2, - "dgSpecHash": "GQbFl6JNwUfeVMRAQIxv+0FH84dIn8y+ZsWz3KR/dVMkJNNXpooEgJaT2UFkLhFNLf08uGLF+sf+HuE1qkdsqQ==", + "dgSpecHash": "rM+0M7K4/ZA=", "success": true, "projectFilePath": "K:\\Proyects_Repos\\TorchSharp\\pkg\\FileRestitcher\\FileRestitcher\\FileRestitcher.csproj", "expectedPackageFiles": [ diff --git a/src/Native/LibTorchSharp/CMakeLists.txt b/src/Native/LibTorchSharp/CMakeLists.txt index e03a9746c..560fba1a2 100644 --- a/src/Native/LibTorchSharp/CMakeLists.txt +++ b/src/Native/LibTorchSharp/CMakeLists.txt @@ -7,13 +7,24 @@ if(CUDA_FOUND) add_compile_definitions(TORCHSHARP_CUDA_TOOLKIT_FOUND) endif() +add_compile_definitions(NOMINMAX) + + +#add_library(CUDA::nvToolsExt INTERFACE IMPORTED) +# ensure that PyTorch is told to use NVTX3 headers +#target_compile_definitions(CUDA::nvToolsExt INTERFACETORCH_CUDA_USE_NVTX3) +#target_link_libraries(CUDA::nvToolsExt INTERFACE CUDA::nvtx3) + + + if(APPLE AND NOT LIBTORCH_ARCH STREQUAL "arm64") include_directories("/usr/local/include" "/usr/local/opt/llvm/include") link_directories("/usr/local/lib" "/usr/local/opt/llvm/lib") endif() -#set(LIBTORCH_PATH "K:/Proyects_Repos/TorchSharp/bin/obj/AnyCPU.Debug/libtorch-cuda-12.1/libtorch-win-shared-with-deps-debug-2.4.0cu121/libtorch") +#set(LIBTORCH_PATH "K:/FrameworksForC/LibTorch/libtorch-win-shared-with-deps-2.6.0+cu126") find_package(Torch REQUIRED PATHS ${LIBTORCH_PATH}) +#find_package(Torch CONFIG) set(SOURCES cifar10.h diff --git a/src/Native/LibTorchSharp/THSLinearAlgebra.cpp b/src/Native/LibTorchSharp/THSLinearAlgebra.cpp index 4ed6419db..ea0ab8e8e 100644 --- a/src/Native/LibTorchSharp/THSLinearAlgebra.cpp +++ b/src/Native/LibTorchSharp/THSLinearAlgebra.cpp @@ -4,9 +4,15 @@ #include #include +#define IS_260_OR_NEWER TORCH_VERSION_MAJOR == 2 && TORCH_VERSION_MINOR >= 6 + Tensor THSLinalg_cholesky(const Tensor tensor) { +#if IS_260_OR_NEWER + CATCH_TENSOR(torch::linalg_cholesky(*tensor)) +#else CATCH_TENSOR(torch::linalg::cholesky(*tensor)) +#endif } Tensor THSLinalg_cholesky_ex(const Tensor tensor, bool check_errors, Tensor* info) @@ -29,7 +35,11 @@ Tensor THSLinalg_cond_float(const Tensor tensor, const double p) Tensor THSLinalg_cond_str(const Tensor tensor, const char* p) { +#if IS_260_OR_NEWER + CATCH_TENSOR(p != nullptr ? torch::linalg_cond(*tensor, c10::string_view(p)) : torch::linalg_cond(*tensor)) +#else CATCH_TENSOR(p != nullptr ? torch::linalg_cond(*tensor, p) : torch::linalg_cond(*tensor)) +#endif } Tensor THSLinalg_cond_none(const Tensor tensor) @@ -44,7 +54,11 @@ Tensor THSLinalg_cross(const Tensor input, const Tensor other, const int64_t dim Tensor THSLinalg_det(const Tensor tensor) { +#if IS_260_OR_NEWER + CATCH_TENSOR(torch::linalg_det(*tensor)) +#else CATCH_TENSOR(torch::linalg::det(*tensor)) +#endif } Tensor THSTensor_logdet(const Tensor tensor) @@ -55,7 +69,11 @@ Tensor THSTensor_logdet(const Tensor tensor) Tensor THSLinalg_slogdet(const Tensor tensor, Tensor* logabsdet) { std::tuple res; +#if IS_260_OR_NEWER + CATCH(res = torch::linalg_slogdet(*tensor);) +#else CATCH(res = torch::linalg::slogdet(*tensor);) +#endif *logabsdet = ResultTensor(std::get<1>(res)); return ResultTensor(std::get<0>(res)); } @@ -63,7 +81,11 @@ Tensor THSLinalg_slogdet(const Tensor tensor, Tensor* logabsdet) Tensor THSLinalg_eig(const Tensor tensor, Tensor* eigenvectors) { std::tuple res; +#if IS_260_OR_NEWER + CATCH(res = torch::linalg_eig(*tensor);) +#else CATCH(res = torch::linalg::eig(*tensor);); +#endif *eigenvectors = ResultTensor(std::get<1>(res)); return ResultTensor(std::get<0>(res)); } @@ -93,31 +115,51 @@ Tensor THSLinalg_eigh(const Tensor tensor, const char UPLO, Tensor* eigenvectors std::string _uplo; _uplo.push_back(UPLO); std::tuple res; +#if IS_260_OR_NEWER + CATCH(res = torch::linalg_eigh(*tensor, _uplo);); +#else CATCH(res = torch::linalg::eigh(*tensor, _uplo);); +#endif *eigenvectors = ResultTensor(std::get<1>(res)); return ResultTensor(std::get<0>(res)); } Tensor THSLinalg_eigvals(const Tensor tensor) { +#if IS_260_OR_NEWER + CATCH_TENSOR(torch::linalg_eigvals(*tensor)) +#else CATCH_TENSOR(torch::linalg::eigvals(*tensor)) +#endif } Tensor THSLinalg_eigvalsh(const Tensor tensor, const char UPLO) { std::string _uplo; _uplo.push_back(UPLO); +#if IS_260_OR_NEWER + CATCH_TENSOR(torch::linalg_eigvalsh(*tensor, _uplo)) +#else CATCH_TENSOR(torch::linalg::eigvalsh(*tensor, _uplo)) +#endif } Tensor THSLinalg_householder_product(const Tensor tensor, const Tensor tau) { +#if IS_260_OR_NEWER + CATCH_TENSOR(torch::linalg_householder_product(*tensor, *tau)) +#else CATCH_TENSOR(torch::linalg::householder_product(*tensor, *tau)) +#endif } Tensor THSLinalg_inv(const Tensor tensor) { +#if IS_260_OR_NEWER + CATCH_TENSOR(torch::linalg_inv(*tensor)) +#else CATCH_TENSOR(torch::linalg::inv(*tensor)) +#endif } Tensor THSLinalg_inv_ex(const Tensor tensor, bool check_errors, Tensor* info) @@ -131,7 +173,11 @@ Tensor THSLinalg_inv_ex(const Tensor tensor, bool check_errors, Tensor* info) Tensor THSLinalg_lstsq_none(const Tensor A, const Tensor B, Tensor* residuals, Tensor* rank, Tensor* singular_values) { std::tuple res; +#if IS_260_OR_NEWER + CATCH(res = torch::linalg_lstsq(*A, *B, c10::nullopt, c10::nullopt);) +#else CATCH(res = torch::linalg::lstsq(*A, *B, c10::nullopt, c10::nullopt);) +#endif *residuals = ResultTensor(std::get<1>(res)); *rank = ResultTensor(std::get<2>(res)); *singular_values = ResultTensor(std::get<3>(res)); @@ -141,7 +187,11 @@ Tensor THSLinalg_lstsq_none(const Tensor A, const Tensor B, Tensor* residuals, T Tensor THSLinalg_lstsq_rcond(const Tensor A, const Tensor B, const double rcond, Tensor* residuals, Tensor* rank, Tensor* singular_values) { std::tuple res; +#if IS_260_OR_NEWER + CATCH(res = torch::linalg_lstsq(*A, *B, rcond, c10::nullopt);) +#else CATCH(res = torch::linalg::lstsq(*A, *B, rcond, c10::nullopt);) +#endif *residuals = ResultTensor(std::get<1>(res)); *rank = ResultTensor(std::get<2>(res)); *singular_values = ResultTensor(std::get<3>(res)); @@ -151,7 +201,11 @@ Tensor THSLinalg_lstsq_rcond(const Tensor A, const Tensor B, const double rcond, Tensor THSLinalg_lu(const Tensor A, const bool pivot, Tensor* L, Tensor* U) { std::tuple res; +#if IS_260_OR_NEWER + CATCH(res = torch::linalg_lu(*A, pivot);) +#else CATCH(res = torch::linalg::lu(*A, pivot);) +#endif *L = ResultTensor(std::get<1>(res)); *U = ResultTensor(std::get<2>(res)); return ResultTensor(std::get<0>(res)); @@ -160,7 +214,12 @@ Tensor THSLinalg_lu(const Tensor A, const bool pivot, Tensor* L, Tensor* U) Tensor THSLinalg_lu_factor(const Tensor A, const bool pivot, Tensor* pivots) { std::tuple res; +#if IS_260_OR_NEWER + CATCH(res = torch::linalg_lu_factor(*A, pivot);) +#else CATCH(res = torch::linalg::lu_factor(*A, pivot);) +#endif + *pivots = ResultTensor(std::get<1>(res)); return ResultTensor(std::get<0>(res)); } @@ -190,69 +249,111 @@ Tensor THSLinalg_ldl_solve(const Tensor LD, const Tensor pivots, const Tensor B, Tensor THSLinalg_matrix_norm(const Tensor tensor, const Scalar ord, const int64_t* dim, const int dim_length, const bool keepdim) { auto dims = c10::ArrayRef(dim, dim_length); +#if IS_260_OR_NEWER + CATCH_TENSOR(torch::linalg_matrix_norm(*tensor, *ord, dims, keepdim, c10::nullopt)) +#else CATCH_TENSOR(torch::linalg::matrix_norm(*tensor, *ord, dims, keepdim, c10::nullopt)) +#endif } Tensor THSLinalg_matrix_norm_fronuc(const Tensor tensor, const int8_t fronuc, const int64_t* dim, const int dim_length, const bool keepdim) { auto dims = c10::ArrayRef(dim, dim_length); +#if IS_260_OR_NEWER + CATCH_TENSOR(torch::linalg_matrix_norm(*tensor, (fronuc == 0) ? "fro" : "nuc", dims, keepdim, c10::nullopt)) +#else CATCH_TENSOR(torch::linalg::matrix_norm(*tensor, (fronuc == 0) ? "fro" : "nuc", dims, keepdim, c10::nullopt)) +#endif } Tensor THSLinalg_vector_norm(const Tensor tensor, const Scalar ord, const int64_t* dim, const int dim_length, const bool keepdim) { auto dims = c10::ArrayRef(dim, dim_length); +#if IS_260_OR_NEWER + CATCH_TENSOR(torch::linalg_vector_norm(*tensor, *ord, dims, keepdim, c10::nullopt)) +#else CATCH_TENSOR(torch::linalg::vector_norm(*tensor, *ord, dims, keepdim, c10::nullopt)) +#endif } Tensor THSLinalg_matrix_rank(const Tensor tensor, const double atol, const bool has_atol, const double rtol, const bool has_rtol, const bool hermitian) { auto atol_ = has_atol ? atol : c10::optional(); auto rtol_ = has_rtol ? rtol : c10::optional(); - +#if IS_260_OR_NEWER + CATCH_TENSOR(torch::linalg_matrix_rank(*tensor, atol_, rtol_, hermitian)) +#else CATCH_TENSOR(torch::linalg::matrix_rank(*tensor, atol_, rtol_, hermitian)) +#endif } Tensor THSLinalg_matrix_rank_tensor(const Tensor tensor, const Tensor atol, const Tensor rtol, const bool hermitian) { const c10::optional atol_ = atol != nullptr ? *atol : c10::optional(); const c10::optional rtol_ = rtol != nullptr ? *rtol : c10::optional(); - +#if IS_260_OR_NEWER + CATCH_TENSOR(torch::linalg_matrix_rank(*tensor, atol_, rtol_, hermitian)) +#else CATCH_TENSOR(torch::linalg::matrix_rank(*tensor, atol_, rtol_, hermitian)) +#endif } Tensor THSLinalg_matrix_power(const Tensor tensor, const int64_t n) { +#if IS_260_OR_NEWER + CATCH_TENSOR(torch::linalg_matrix_power(*tensor, n)) +#else CATCH_TENSOR(torch::linalg::matrix_power(*tensor, n)) +#endif } Tensor THSLinalg_multi_dot(const Tensor* tensors, const int length) { +#if IS_260_OR_NEWER + CATCH_TENSOR(torch::linalg_multi_dot(toTensors((torch::Tensor**)tensors, length))) +#else CATCH_TENSOR(torch::linalg::multi_dot(toTensors((torch::Tensor**)tensors, length))) +#endif } Tensor THSLinalg_norm_str(const Tensor tensor, const char* p, const int64_t* dim, const int dim_length, const bool keepdim) { c10::optional dims = (dim == nullptr) ? c10::nullopt : c10::optional(at::ArrayRef(dim, dim_length)); +#if IS_260_OR_NEWER + CATCH_TENSOR(torch::linalg_norm(*tensor, c10::string_view(p), dims, keepdim, c10::nullopt)) +#else CATCH_TENSOR(torch::linalg::norm(*tensor, p, dims, keepdim, c10::nullopt)) +#endif } Tensor THSLinalg_norm_float(const Tensor tensor, const double p, const int64_t* dim, const int dim_length, const bool keepdim) { c10::optional dims = (dim == nullptr) ? c10::nullopt : c10::optional(at::ArrayRef(dim, dim_length)); +#if IS_260_OR_NEWER + CATCH_TENSOR(torch::linalg_norm(*tensor, p, dims, keepdim, c10::nullopt)) +#else CATCH_TENSOR(torch::linalg::norm(*tensor, p, dims, keepdim, c10::nullopt)) +#endif } Tensor THSLinalg_norm_int(const Tensor tensor, const int p, const int64_t* dim, const int dim_length, const bool keepdim) { c10::optional dims = (dim == nullptr) ? c10::nullopt : c10::optional(at::ArrayRef(dim, dim_length)); +#if IS_260_OR_NEWER + CATCH_TENSOR(torch::linalg_norm(*tensor, p, dims, keepdim, c10::nullopt)) +#else CATCH_TENSOR(torch::linalg::norm(*tensor, p, dims, keepdim, c10::nullopt)) +#endif } Tensor THSLinalg_norm_opt(const Tensor tensor, const int64_t* dim, const int dim_length, const bool keepdim) { c10::optional dims = (dim == nullptr) ? c10::nullopt : c10::optional(at::ArrayRef(dim, dim_length)); +#if IS_260_OR_NEWER + CATCH_TENSOR(torch::linalg_norm(*tensor, c10::nullopt, dims, keepdim, c10::nullopt)) +#else CATCH_TENSOR(torch::linalg::norm(*tensor, c10::nullopt, dims, keepdim, c10::nullopt)) +#endif } Tensor THSLinalg_pinv(const Tensor tensor, const double atol, const bool has_atol, const double rtol, const bool has_rtol, const bool hermitian) @@ -273,7 +374,11 @@ Tensor THSLinalg_pinv_tensor(const Tensor tensor, const Tensor atol, const Tenso Tensor THSLinalg_pinverse(const Tensor tensor, const double rcond, const bool hermitian) { +#if IS_260_OR_NEWER + CATCH_TENSOR(torch::linalg_pinv(*tensor, rcond, hermitian)) +#else CATCH_TENSOR(torch::linalg::pinv(*tensor, rcond, hermitian)) +#endif } Tensor THSLinalg_qr(const Tensor tensor, const char mode, Tensor* R) @@ -295,31 +400,52 @@ Tensor THSLinalg_qr(const Tensor tensor, const char mode, Tensor* R) Tensor THSLinalg_solve(const Tensor tensor, Tensor other, bool left) { +#if IS_260_OR_NEWER + CATCH_TENSOR(torch::linalg_solve(*tensor, *other, left)) +#else CATCH_TENSOR(torch::linalg::solve(*tensor, *other, left)) +#endif + } Tensor THSLinalg_solve_ex(const Tensor tensor, Tensor other, bool left, bool check_errors, Tensor* S) { std::tuple res; +#if IS_260_OR_NEWER + CATCH(res = torch::linalg_solve_ex(*tensor, *other, left, check_errors);); +#else CATCH(res = torch::linalg::solve_ex(*tensor, *other, left, check_errors);); +#endif *S = ResultTensor(std::get<1>(res)); return ResultTensor(std::get<0>(res)); } Tensor THSLinalg_solve_triangular(const Tensor tensor, Tensor other, bool upper, bool left, bool unitriangular) { +#if IS_260_OR_NEWER + CATCH_TENSOR(torch::linalg_solve_triangular(*tensor, *other, upper, left, unitriangular)) +#else CATCH_TENSOR(torch::linalg::solve_triangular(*tensor, *other, upper, left, unitriangular)) +#endif } Tensor THSLinalg_solve_triangular_out(const Tensor tensor, Tensor other, bool upper, bool left, bool unitriangular, Tensor result) { +#if IS_260_OR_NEWER + CATCH_TENSOR(torch::linalg_solve_triangular_out(*result, *tensor, *other, upper, left, unitriangular)) +#else CATCH_TENSOR(torch::linalg::solve_triangular_out(*result, *tensor, *other, upper, left, unitriangular)) +#endif } Tensor THSLinalg_svd(const Tensor tensor, const bool full_matrices, Tensor* S, Tensor* Vh) { std::tuple res; +#if IS_260_OR_NEWER + CATCH(res = torch::linalg_svd(*tensor, full_matrices, c10::nullopt);); +#else CATCH(res = torch::linalg::svd(*tensor, full_matrices, c10::nullopt);); +#endif *S = ResultTensor(std::get<1>(res)); *Vh = ResultTensor(std::get<2>(res)); return ResultTensor(std::get<0>(res)); @@ -327,18 +453,30 @@ Tensor THSLinalg_svd(const Tensor tensor, const bool full_matrices, Tensor* S, T Tensor THSLinalg_svdvals(const Tensor tensor) { +#if IS_260_OR_NEWER + CATCH_TENSOR(res = torch::linalg_svdvals(*tensor, c10::nullopt)) +#else CATCH_TENSOR(res = torch::linalg::svdvals(*tensor, c10::nullopt)) +#endif } Tensor THSLinalg_tensorinv(const Tensor tensor, const int64_t ind) { +#if IS_260_OR_NEWER + CATCH_TENSOR(torch::linalg_tensorinv(*tensor, ind)) +#else CATCH_TENSOR(torch::linalg::tensorinv(*tensor, ind)) +#endif } Tensor THSLinalg_tensorsolve(const Tensor tensor, Tensor other, const int64_t* dim, const int dim_length) { c10::optional dims = (dim == nullptr) ? c10::nullopt : c10::optional(at::ArrayRef(dim, dim_length)); +#if IS_260_OR_NEWER + CATCH_TENSOR(torch::linalg_tensorsolve(*tensor, *other, dims)) +#else CATCH_TENSOR(torch::linalg::tensorsolve(*tensor, *other, dims)) +#endif } Tensor THSLinalg_vander(const Tensor tensor, const int64_t N) diff --git a/src/Native/build.proj b/src/Native/build.proj index 6dbbc70a9..d2499c9a0 100644 --- a/src/Native/build.proj +++ b/src/Native/build.proj @@ -31,7 +31,6 @@ Condition="'$(OS)' != 'Windows_NT'"> - --stripsymbols --configuration $(NativeConfiguration) --arch $(TargetArchitecture) $(StripArgs) --libtorchpath $(LibTorchCmakePath) @@ -44,9 +43,13 @@ - + $(NativeConfiguration) $(TargetArchitecture) --libtorchpath $(LibTorchCmakePath) + + + $(NativeConfiguration) $(TargetArchitecture) --libtorchpath $(CustomLibTorchFullPath) + diff --git a/src/TorchSharp/TorchSharp.csproj b/src/TorchSharp/TorchSharp.csproj index 14c95995f..73c8c6069 100644 --- a/src/TorchSharp/TorchSharp.csproj +++ b/src/TorchSharp/TorchSharp.csproj @@ -76,13 +76,14 @@ - + - + + From 8f35385548c7a43d47b0dc011ea3b92ddfd98e8e Mon Sep 17 00:00:00 2001 From: Dimitri Date: Wed, 26 Mar 2025 12:13:31 -0300 Subject: [PATCH 42/65] some update --- .../FileRestitcher.Tests/FileRestitcher.Tests.csproj | 3 +++ .../TorchSharpTest.WithCudaBinaries.csproj | 2 ++ test/TorchSharpTest/TorchSharpTest.csproj | 2 ++ 3 files changed, 7 insertions(+) diff --git a/pkg/FileRestitcher/FileRestitcher.Tests/FileRestitcher.Tests.csproj b/pkg/FileRestitcher/FileRestitcher.Tests/FileRestitcher.Tests.csproj index 39dc54a1b..bf0f2412d 100644 --- a/pkg/FileRestitcher/FileRestitcher.Tests/FileRestitcher.Tests.csproj +++ b/pkg/FileRestitcher/FileRestitcher.Tests/FileRestitcher.Tests.csproj @@ -13,6 +13,9 @@ + + + runtime; build; native; contentfiles; analyzers; buildtransitive diff --git a/test/TorchSharpTest.WithCudaBinaries/TorchSharpTest.WithCudaBinaries.csproj b/test/TorchSharpTest.WithCudaBinaries/TorchSharpTest.WithCudaBinaries.csproj index 6f7a0ed24..faff588b4 100644 --- a/test/TorchSharpTest.WithCudaBinaries/TorchSharpTest.WithCudaBinaries.csproj +++ b/test/TorchSharpTest.WithCudaBinaries/TorchSharpTest.WithCudaBinaries.csproj @@ -144,6 +144,8 @@ + + diff --git a/test/TorchSharpTest/TorchSharpTest.csproj b/test/TorchSharpTest/TorchSharpTest.csproj index 065301040..39b4b5128 100644 --- a/test/TorchSharpTest/TorchSharpTest.csproj +++ b/test/TorchSharpTest/TorchSharpTest.csproj @@ -118,6 +118,8 @@ + + From 137779e19fc1f089be2daf4a3a1c6d7bd2a4317a Mon Sep 17 00:00:00 2001 From: Dimitri Date: Thu, 11 Sep 2025 17:47:24 -0300 Subject: [PATCH 43/65] com --- nuget.config | 2 +- src/Examples.Utils/Examples.Utils.csproj | 3 ++- src/Examples/Examples.csproj | 2 ++ src/FSharp.Examples/FSharp.Examples.fsproj | 3 +++ src/Native/LibTorchSharp/THSNN.h | 2 +- src/TorchSharp/NN/Linear.cs | 15 +++++++-------- .../TorchSharpTest.WithCudaBinaries.csproj | 1 + 7 files changed, 17 insertions(+), 11 deletions(-) diff --git a/nuget.config b/nuget.config index ef5d6f41e..eb0286a2c 100644 --- a/nuget.config +++ b/nuget.config @@ -1,4 +1,4 @@ - F:\NugetPackages + D:\NugetPackages \ No newline at end of file diff --git a/src/Examples.Utils/Examples.Utils.csproj b/src/Examples.Utils/Examples.Utils.csproj index 6fa145333..6d3855545 100644 --- a/src/Examples.Utils/Examples.Utils.csproj +++ b/src/Examples.Utils/Examples.Utils.csproj @@ -21,9 +21,10 @@ + - + diff --git a/src/Examples/Examples.csproj b/src/Examples/Examples.csproj index 9b7a980b9..0fcec0611 100644 --- a/src/Examples/Examples.csproj +++ b/src/Examples/Examples.csproj @@ -26,9 +26,11 @@ + + diff --git a/src/FSharp.Examples/FSharp.Examples.fsproj b/src/FSharp.Examples/FSharp.Examples.fsproj index fe3c34a15..47db64db5 100644 --- a/src/FSharp.Examples/FSharp.Examples.fsproj +++ b/src/FSharp.Examples/FSharp.Examples.fsproj @@ -25,7 +25,10 @@ + + + diff --git a/src/Native/LibTorchSharp/THSNN.h b/src/Native/LibTorchSharp/THSNN.h index 2bd59af29..d86b45157 100644 --- a/src/Native/LibTorchSharp/THSNN.h +++ b/src/Native/LibTorchSharp/THSNN.h @@ -177,7 +177,7 @@ EXPORT_API(void) THSNN_ConvTranspose3d_set_bias(const NNModule module, const // Normalization -EXPORT_API(Tensor) THSNN_normalize(const Tensor input, const double p, const int64_t dim, const double eps); +//EXPORT_API(Tensor) THSNN_normalize(const Tensor input, const double p, const int64_t dim, const double eps); EXPORT_API(Tensor) THSNN_batch_norm(const Tensor input, const Tensor running_mean, const Tensor running_var, const Tensor weight, const Tensor bias, const bool training, const double momentum, const double eps); EXPORT_API(Tensor) THSNN_group_norm(const Tensor input, int64_t num_groups, const Tensor weight, const Tensor bias, const double eps); EXPORT_API(Tensor) THSNN_instance_norm(const Tensor input, const Tensor running_mean, const Tensor running_var, const Tensor weight, const Tensor bias, const bool use_input_stats, const double momentum, const double eps); diff --git a/src/TorchSharp/NN/Linear.cs b/src/TorchSharp/NN/Linear.cs index bb5f6c9f3..fc9bb6896 100644 --- a/src/TorchSharp/NN/Linear.cs +++ b/src/TorchSharp/NN/Linear.cs @@ -25,7 +25,7 @@ public LinearInfo(long inFeatures, long outFeatures) } public sealed class Linear : torch.nn.Module { - public LinearInfo linearInfo; + public LinearInfo? linearInfo; /*internal Linear(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { }*/ @@ -72,7 +72,7 @@ public Parameter? bias { set { _bias?.Dispose(); _bias = value?.DetachFromDisposeScope() as Parameter; - ConditionallyRegisterParameter(BiasComponentName, _bias); + ConditionallyRegisterParameter("BiasComponentName", _bias); } } @@ -83,7 +83,7 @@ public Parameter weight { if (value.Handle != _weight?.Handle) { _weight?.Dispose(); _weight = (value.DetachFromDisposeScope() as Parameter)!; - ConditionallyRegisterParameter(WeightComponentName, _weight); + ConditionallyRegisterParameter("WeightComponentName", _weight); } } } @@ -121,9 +121,9 @@ protected internal override nn.Module _to(ScalarType dtype, bool non_blocking) { } - [ComponentName(Name = BiasComponentName)] + [ComponentName(Name = "BiasComponentName")] private Parameter? _bias; - [ComponentName(Name = WeightComponentName)] + [ComponentName(Name = "WeightComponentName")] private Parameter? _weight; public long in_features { get; set; } @@ -149,9 +149,8 @@ public static Linear Linear(long inputSize, long outputSize, bool hasBias = true { return new Linear(inputSize, outputSize, hasBias, device, dtype); } - - return new Linear(res, boxedHandle, inputSize, outputSize).MoveModule(device, dtype); - } + /*return new Linear(res, boxedHandle, inputSize, outputSize).MoveModule(device, dtype); + }*/ public static partial class functional { diff --git a/test/TorchSharpTest.WithCudaBinaries/TorchSharpTest.WithCudaBinaries.csproj b/test/TorchSharpTest.WithCudaBinaries/TorchSharpTest.WithCudaBinaries.csproj index c3c352238..47bb510a7 100644 --- a/test/TorchSharpTest.WithCudaBinaries/TorchSharpTest.WithCudaBinaries.csproj +++ b/test/TorchSharpTest.WithCudaBinaries/TorchSharpTest.WithCudaBinaries.csproj @@ -150,6 +150,7 @@ + From d183e9e5b5fb753afa0f7bba16ba092e3e8012ef Mon Sep 17 00:00:00 2001 From: Dimitri Date: Thu, 11 Sep 2025 22:22:42 -0300 Subject: [PATCH 44/65] Support bfloat16 --- MyCustomCMD.txt | 1 + TorchSharp.sln | 14 ++-- TorchSharpFilter.slnf | 13 ++++ src/Examples.Utils/Examples.Utils.csproj | 6 +- src/Examples/Examples.csproj | 1 + src/FSharp.Examples/FSharp.Examples.fsproj | 1 + src/Native/LibTorchSharp/THSBFloat16.cpp | 62 +++++++-------- src/Native/LibTorchSharp/THSBFloat16.h | 62 +++++++-------- src/Native/LibTorchSharp/THSTorch.cpp | 12 ++- src/Native/LibTorchSharp/THSTorch.h | 1 + .../PInvoke/LibTorchSharp.THSBFloat16.cs | 75 +++++++++++++++++++ .../PInvoke/LibTorchSharp.THSTorch.cs | 6 +- src/TorchSharp/Scalar.cs | 48 ++++++++++-- src/TorchSharp/Tensor/Tensor.cs | 16 +++- .../Tensor/TensorExtensionMethods.cs | 3 + src/TorchSharp/Utils/BFloat16.cs | 16 +++- .../TorchSharpTest.WithCudaBinaries.csproj | 1 + 17 files changed, 250 insertions(+), 88 deletions(-) create mode 100644 MyCustomCMD.txt create mode 100644 TorchSharpFilter.slnf create mode 100644 src/TorchSharp/PInvoke/LibTorchSharp.THSBFloat16.cs diff --git a/MyCustomCMD.txt b/MyCustomCMD.txt new file mode 100644 index 000000000..3dfad0aa1 --- /dev/null +++ b/MyCustomCMD.txt @@ -0,0 +1 @@ +dotnet build TorchSharpFilter.slnf /p:CustomLibTorchPath="K:\FrameworksForC\LibTorch\libtorch-win-shared-with-deps-debug-2.6.0+cu126\libtorch" -f netstandard2.0 \ No newline at end of file diff --git a/TorchSharp.sln b/TorchSharp.sln index 8cec25c7d..efd1e6079 100644 --- a/TorchSharp.sln +++ b/TorchSharp.sln @@ -34,7 +34,7 @@ Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "TorchSharp", "TorchSharp", pkg\TorchSharp\TorchSharp.symbols.nupkgproj = pkg\TorchSharp\TorchSharp.symbols.nupkgproj EndProjectSection EndProject -Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "LibTorchSharp", "bin\obj\x64.Debug\Native\LibTorchSharp\LibTorchSharp.vcxproj", "{2B359162-062E-3C52-91D3-027A8542A58C}" +Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "LibTorchSharp", "bin\obj\x64.Debug\Native\LibTorchSharp\LibTorchSharp.vcxproj", "{E7467DDF-893C-38A8-8E19-6B4E3FB10F55}" EndProject Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "LibTorchSharp", "bin\obj\x64.Release\Native\LibTorchSharp\LibTorchSharp.vcxproj", "{E4C0DBEE-0815-311B-9065-137BB50BD793}" EndProject @@ -66,9 +66,9 @@ Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Solution Items", "Solution azure-pipelines.yml = azure-pipelines.yml build\BranchInfo.props = build\BranchInfo.props DEVGUIDE.md = DEVGUIDE.md + global.json = global.json README.md = README.md RELEASENOTES.md = RELEASENOTES.md - global.json = global.json EndProjectSection EndProject Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "TorchVision", "src\TorchVision\TorchVision.csproj", "{DCF01EE5-6431-4115-85E0-1FC4C3DE86A2}" @@ -107,10 +107,10 @@ Global {42B45168-476D-4BFA-87B8-81A34E6295CD}.Release|Any CPU.Build.0 = Release|Any CPU {42B45168-476D-4BFA-87B8-81A34E6295CD}.Release|x64.ActiveCfg = Release|Any CPU {42B45168-476D-4BFA-87B8-81A34E6295CD}.Release|x64.Build.0 = Release|Any CPU - {2B359162-062E-3C52-91D3-027A8542A58C}.Debug|Any CPU.ActiveCfg = Debug|x64 - {2B359162-062E-3C52-91D3-027A8542A58C}.Debug|x64.ActiveCfg = Debug|x64 - {2B359162-062E-3C52-91D3-027A8542A58C}.Release|Any CPU.ActiveCfg = Release|x64 - {2B359162-062E-3C52-91D3-027A8542A58C}.Release|x64.ActiveCfg = Release|x64 + {E7467DDF-893C-38A8-8E19-6B4E3FB10F55}.Debug|Any CPU.ActiveCfg = Debug|x64 + {E7467DDF-893C-38A8-8E19-6B4E3FB10F55}.Debug|x64.ActiveCfg = Debug|x64 + {E7467DDF-893C-38A8-8E19-6B4E3FB10F55}.Release|Any CPU.ActiveCfg = Release|x64 + {E7467DDF-893C-38A8-8E19-6B4E3FB10F55}.Release|x64.ActiveCfg = Release|x64 {E4C0DBEE-0815-311B-9065-137BB50BD793}.Debug|Any CPU.ActiveCfg = Debug|x64 {E4C0DBEE-0815-311B-9065-137BB50BD793}.Debug|x64.ActiveCfg = Debug|x64 {E4C0DBEE-0815-311B-9065-137BB50BD793}.Release|Any CPU.ActiveCfg = Release|x64 @@ -181,7 +181,7 @@ Global {6C323B05-9028-4B09-911C-3C03AE058BEE} = {AED9C836-31E3-4F3F-8ABC-929555D3F3C4} {42B45168-476D-4BFA-87B8-81A34E6295CD} = {09EADF06-BE25-4228-AB53-95AE3E15B530} {567456AD-B026-4CB6-B98D-4FC930C90223} = {D3D38B03-B557-484D-8348-8BADEE4DF592} - {2B359162-062E-3C52-91D3-027A8542A58C} = {CF2C1A9E-3A8A-4329-8A6E-7880C15AAC3D} + {E7467DDF-893C-38A8-8E19-6B4E3FB10F55} = {CF2C1A9E-3A8A-4329-8A6E-7880C15AAC3D} {E4C0DBEE-0815-311B-9065-137BB50BD793} = {4DB9E84D-324C-408F-87A6-246E86205540} {CF2C1A9E-3A8A-4329-8A6E-7880C15AAC3D} = {09EADF06-BE25-4228-AB53-95AE3E15B530} {D8C60CD8-8429-45F2-A755-47B6CD10FDF8} = {09EADF06-BE25-4228-AB53-95AE3E15B530} diff --git a/TorchSharpFilter.slnf b/TorchSharpFilter.slnf new file mode 100644 index 000000000..4f6a8bbe3 --- /dev/null +++ b/TorchSharpFilter.slnf @@ -0,0 +1,13 @@ +{ + "solution": { + "path": "TorchSharp.sln", + "projects": [ + "bin\\obj\\x64.Debug\\Native\\LibTorchSharp\\LibTorchSharp.vcxproj", + "pkg\\FileRestitcher\\FileRestitcher.Tests\\FileRestitcher.Tests.csproj", + "pkg\\FileRestitcher\\FileRestitcher\\FileRestitcher.csproj", + "src\\TorchAudio\\TorchAudio.csproj", + "src\\TorchSharp\\TorchSharp.csproj", + "src\\TorchVision\\TorchVision.csproj" + ] + } +} \ No newline at end of file diff --git a/src/Examples.Utils/Examples.Utils.csproj b/src/Examples.Utils/Examples.Utils.csproj index 6fa145333..411c4577e 100644 --- a/src/Examples.Utils/Examples.Utils.csproj +++ b/src/Examples.Utils/Examples.Utils.csproj @@ -1,4 +1,4 @@ - + @@ -20,10 +20,10 @@ - + - + diff --git a/src/Examples/Examples.csproj b/src/Examples/Examples.csproj index 9b7a980b9..ac5a28b65 100644 --- a/src/Examples/Examples.csproj +++ b/src/Examples/Examples.csproj @@ -26,6 +26,7 @@ + diff --git a/src/FSharp.Examples/FSharp.Examples.fsproj b/src/FSharp.Examples/FSharp.Examples.fsproj index fe3c34a15..ea13e418d 100644 --- a/src/FSharp.Examples/FSharp.Examples.fsproj +++ b/src/FSharp.Examples/FSharp.Examples.fsproj @@ -25,6 +25,7 @@ + diff --git a/src/Native/LibTorchSharp/THSBFloat16.cpp b/src/Native/LibTorchSharp/THSBFloat16.cpp index 9302eb565..34cecd97d 100644 --- a/src/Native/LibTorchSharp/THSBFloat16.cpp +++ b/src/Native/LibTorchSharp/THSBFloat16.cpp @@ -1,101 +1,101 @@ #include "THSBFloat16.h" -c10::BFloat16 bfloat16_ctor(float value) +c10::BFloat16 THSBFloat16_ctor(float value) { c10::BFloat16 bf16(value); return bf16; } -float op_float(c10::BFloat16 bf16) +float THSBFloat16_op_float(c10::BFloat16 bf16) { return static_cast(bf16); } -c10::BFloat16 op_add(c10::BFloat16 a, c10::BFloat16 b){ +c10::BFloat16 THSBFloat16_op_add(c10::BFloat16 a, c10::BFloat16 b){ return a + b; } -c10::BFloat16 op_sub(c10::BFloat16 a, c10::BFloat16 b) { +c10::BFloat16 THSBFloat16_op_sub(c10::BFloat16 a, c10::BFloat16 b) { return a - b; } -c10::BFloat16 op_mul(c10::BFloat16 a, c10::BFloat16 b){ +c10::BFloat16 THSBFloat16_op_mul(c10::BFloat16 a, c10::BFloat16 b){ return a * b; } -c10::BFloat16 op_div(c10::BFloat16 a, c10::BFloat16 b){ +c10::BFloat16 THSBFloat16_op_div(c10::BFloat16 a, c10::BFloat16 b){ return a / b; } -float op_add_float(c10::BFloat16 a, float b) { +float THSBFloat16_op_add_float(c10::BFloat16 a, float b) { return a + b; } -float op_sub_float(c10::BFloat16 a, float b) { +float THSBFloat16_op_sub_float(c10::BFloat16 a, float b) { return a - b; } -float op_mul_float(c10::BFloat16 a, float b) { +float THSBFloat16_op_mul_float(c10::BFloat16 a, float b) { return a * b; } -float op_div_float(c10::BFloat16 a, float b) { +float THSBFloat16_op_div_float(c10::BFloat16 a, float b) { return a / b; } -float op_add_lfloat(float a, c10::BFloat16 b) { +float THSBFloat16_op_add_lfloat(float a, c10::BFloat16 b) { return a + b; } -float op_sub_lfloat(float a, c10::BFloat16 b) { +float THSBFloat16_op_sub_lfloat(float a, c10::BFloat16 b) { return a - b; } -float op_mul_lfloat(float a, c10::BFloat16 b) { +float THSBFloat16_op_mul_lfloat(float a, c10::BFloat16 b) { return a * b; } -float op_div_lfloat(float a, c10::BFloat16 b) { +float THSBFloat16_op_div_lfloat(float a, c10::BFloat16 b) { return a / b; } -double op_add_double(c10::BFloat16 a, double b) { +double THSBFloat16_op_add_double(c10::BFloat16 a, double b) { return a + b; } -double op_sub_double(c10::BFloat16 a, double b) { +double THSBFloat16_op_sub_double(c10::BFloat16 a, double b) { return a - b; } -double op_mul_double(c10::BFloat16 a, double b) { +double THSBFloat16_op_mul_double(c10::BFloat16 a, double b) { return a * b; } -double op_div_double(c10::BFloat16 a, double b) { +double THSBFloat16_op_div_double(c10::BFloat16 a, double b) { return a / b; } -double op_add_ldouble(double a, c10::BFloat16 b) { +double THSBFloat16_op_add_ldouble(double a, c10::BFloat16 b) { return a + b; } -double op_sub_ldouble(double a, c10::BFloat16 b) { +double THSBFloat16_op_sub_ldouble(double a, c10::BFloat16 b) { return a - b; } -double op_mul_ldouble(double a, c10::BFloat16 b) { +double THSBFloat16_op_mul_ldouble(double a, c10::BFloat16 b) { return a * b; } -double op_div_ldouble(double a, c10::BFloat16 b) { +double THSBFloat16_op_div_ldouble(double a, c10::BFloat16 b) { return a / b; } -c10::BFloat16 bfloat16_min(c10::BFloat16 bf16) { +c10::BFloat16 THSBFloat16_min(c10::BFloat16 bf16) { return std::numeric_limits::min(); } -c10::BFloat16 bfloat16_lowest(c10::BFloat16 bf16){ +c10::BFloat16 THSBFloat16_lowest(c10::BFloat16 bf16){ return std::numeric_limits::lowest(); } -c10::BFloat16 bfloat16_max(c10::BFloat16 bf16){ +c10::BFloat16 THSBFloat16_max(c10::BFloat16 bf16){ return std::numeric_limits::max(); } -c10::BFloat16 bfloat16_epsilon(c10::BFloat16 bf16){ +c10::BFloat16 THSBFloat16_epsilon(c10::BFloat16 bf16){ return std::numeric_limits::epsilon(); } -c10::BFloat16 bfloat16_round_error(c10::BFloat16 bf16) { +c10::BFloat16 THSBFloat16_round_error(c10::BFloat16 bf16) { return std::numeric_limits::round_error(); } -c10::BFloat16 bfloat16_infinity(c10::BFloat16 bf16) { +c10::BFloat16 THSBFloat16_nfinity(c10::BFloat16 bf16) { return std::numeric_limits::infinity(); } -c10::BFloat16 bfloat16_quiet_NaN(c10::BFloat16 bf16) { +c10::BFloat16 THSBFloat16_quiet_NaN(c10::BFloat16 bf16) { return std::numeric_limits::quiet_NaN(); } -c10::BFloat16 bfloat16_signaling_NaN(c10::BFloat16 bf16) { +c10::BFloat16 THSBFloat16_signaling_NaN(c10::BFloat16 bf16) { return std::numeric_limits::signaling_NaN(); } -c10::BFloat16 bfloat16_denorm_min(c10::BFloat16 bf16) { +c10::BFloat16 THSBFloat16_denorm_min(c10::BFloat16 bf16) { return std::numeric_limits::denorm_min(); } \ No newline at end of file diff --git a/src/Native/LibTorchSharp/THSBFloat16.h b/src/Native/LibTorchSharp/THSBFloat16.h index 05305a472..522ebcad7 100644 --- a/src/Native/LibTorchSharp/THSBFloat16.h +++ b/src/Native/LibTorchSharp/THSBFloat16.h @@ -7,37 +7,37 @@ #include "c10/util/BFloat16.h" //#include "c10/util/BFloat16-inl.h" -EXPORT_API(c10::BFloat16) bfloat16_ctor(float value); -EXPORT_API(float) op_float(c10::BFloat16 bf16); -EXPORT_API(c10::BFloat16) op_add(c10::BFloat16 a, c10::BFloat16 b); -EXPORT_API(c10::BFloat16) op_sub(c10::BFloat16 a, c10::BFloat16 b); -EXPORT_API(c10::BFloat16) op_mul(c10::BFloat16 a, c10::BFloat16 b); -EXPORT_API(c10::BFloat16) op_div(c10::BFloat16 a, c10::BFloat16 b); +EXPORT_API(c10::BFloat16) THSBFloat16_ctor(float value); +EXPORT_API(float) THSBFloat16_op_float(c10::BFloat16 bf16); +EXPORT_API(c10::BFloat16) THSBFloat16_op_add(c10::BFloat16 a, c10::BFloat16 b); +EXPORT_API(c10::BFloat16) THSBFloat16_op_sub(c10::BFloat16 a, c10::BFloat16 b); +EXPORT_API(c10::BFloat16) THSBFloat16_op_mul(c10::BFloat16 a, c10::BFloat16 b); +EXPORT_API(c10::BFloat16) THSBFloat16_op_div(c10::BFloat16 a, c10::BFloat16 b); -EXPORT_API(float) op_add_float(c10::BFloat16 a, float b); -EXPORT_API(float) op_sub_float(c10::BFloat16 a, float b); -EXPORT_API(float) op_mul_float(c10::BFloat16 a, float b); -EXPORT_API(float) op_div_float(c10::BFloat16 a, float b); -EXPORT_API(float) op_add_lfloat(float a, c10::BFloat16 b); -EXPORT_API(float) op_sub_lfloat(float a, c10::BFloat16 b); -EXPORT_API(float) op_mul_lfloat(float a, c10::BFloat16 b); -EXPORT_API(float) op_div_lfloat(float a, c10::BFloat16 b); +EXPORT_API(float) THSBFloat16_op_add_float(c10::BFloat16 a, float b); +EXPORT_API(float) THSBFloat16_op_sub_float(c10::BFloat16 a, float b); +EXPORT_API(float) THSBFloat16_op_mul_float(c10::BFloat16 a, float b); +EXPORT_API(float) THSBFloat16_op_div_float(c10::BFloat16 a, float b); +EXPORT_API(float) THSBFloat16_op_add_lfloat(float a, c10::BFloat16 b); +EXPORT_API(float) THSBFloat16_op_sub_lfloat(float a, c10::BFloat16 b); +EXPORT_API(float) THSBFloat16_op_mul_lfloat(float a, c10::BFloat16 b); +EXPORT_API(float) THSBFloat16_op_div_lfloat(float a, c10::BFloat16 b); -EXPORT_API(double) op_add_double(c10::BFloat16 a, double b); -EXPORT_API(double) op_sub_double(c10::BFloat16 a, double b); -EXPORT_API(double) op_mul_double(c10::BFloat16 a, double b); -EXPORT_API(double) op_div_double(c10::BFloat16 a, double b); -EXPORT_API(double) op_add_ldouble(double a, c10::BFloat16 b); -EXPORT_API(double) op_sub_ldouble(double a, c10::BFloat16 b); -EXPORT_API(double) op_mul_ldouble(double a, c10::BFloat16 b); -EXPORT_API(double) op_div_ldouble(double a, c10::BFloat16 b); +EXPORT_API(double) THSBFloat16_op_add_double(c10::BFloat16 a, double b); +EXPORT_API(double) THSBFloat16_op_sub_double(c10::BFloat16 a, double b); +EXPORT_API(double) THSBFloat16_op_mul_double(c10::BFloat16 a, double b); +EXPORT_API(double) THSBFloat16_op_div_double(c10::BFloat16 a, double b); +EXPORT_API(double) THSBFloat16_op_add_ldouble(double a, c10::BFloat16 b); +EXPORT_API(double) THSBFloat16_op_sub_ldouble(double a, c10::BFloat16 b); +EXPORT_API(double) THSBFloat16_op_mul_ldouble(double a, c10::BFloat16 b); +EXPORT_API(double) THSBFloat16_op_div_ldouble(double a, c10::BFloat16 b); -EXPORT_API(c10::BFloat16) bfloat16_min(c10::BFloat16 bf16); -EXPORT_API(c10::BFloat16) bfloat16_lowest(c10::BFloat16 bf16); -EXPORT_API(c10::BFloat16) bfloat16_max(c10::BFloat16 bf16); -EXPORT_API(c10::BFloat16) bfloat16_epsilon(c10::BFloat16 bf16); -EXPORT_API(c10::BFloat16) bfloat16_round_error(c10::BFloat16 bf16); -EXPORT_API(c10::BFloat16) bfloat16_infinity(c10::BFloat16 bf16); -EXPORT_API(c10::BFloat16) bfloat16_quiet_NaN(c10::BFloat16 bf16); -EXPORT_API(c10::BFloat16) bfloat16_signaling_NaN(c10::BFloat16 bf16); -EXPORT_API(c10::BFloat16) bfloat16_denorm_min(c10::BFloat16 bf16); \ No newline at end of file +EXPORT_API(c10::BFloat16) THSBFloat16_min(c10::BFloat16 bf16); +EXPORT_API(c10::BFloat16) THSBFloat16_lowest(c10::BFloat16 bf16); +EXPORT_API(c10::BFloat16) THSBFloat16_max(c10::BFloat16 bf16); +EXPORT_API(c10::BFloat16) THSBFloat16_epsilon(c10::BFloat16 bf16); +EXPORT_API(c10::BFloat16) THSBFloat16_round_error(c10::BFloat16 bf16); +EXPORT_API(c10::BFloat16) THSBFloat16_infinity(c10::BFloat16 bf16); +EXPORT_API(c10::BFloat16) THSBFloat16_quiet_NaN(c10::BFloat16 bf16); +EXPORT_API(c10::BFloat16) THSBFloat16_signaling_NaN(c10::BFloat16 bf16); +EXPORT_API(c10::BFloat16) THSBFloat16_denorm_min(c10::BFloat16 bf16); \ No newline at end of file diff --git a/src/Native/LibTorchSharp/THSTorch.cpp b/src/Native/LibTorchSharp/THSTorch.cpp index 995a2cd37..4a181698b 100644 --- a/src/Native/LibTorchSharp/THSTorch.cpp +++ b/src/Native/LibTorchSharp/THSTorch.cpp @@ -206,7 +206,7 @@ Scalar THSTorch_int32_to_scalar(int value) Scalar THSTorch_int64_to_scalar(long value) { - return new torch::Scalar(int64_t(value)); + return new torch::Scalar(static_cast(value)); } Scalar THSTorch_float32_to_scalar(float value) @@ -221,12 +221,12 @@ Scalar THSTorch_float64_to_scalar(double value) Scalar THSTorch_float16_to_scalar(float value) { - return new torch::Scalar((c10::Half)value); + return new torch::Scalar(static_cast(value)); } Scalar THSTorch_bfloat16_to_scalar(float value) { - return new torch::Scalar((c10::BFloat16)value); + return new torch::Scalar(static_cast(value)); } Scalar THSTorch_bool_to_scalar(bool value) @@ -284,6 +284,12 @@ void THSTorch_scalar_to_float16(Scalar value, unsigned short *res) *res = value->toHalf().x; } + +void THSTorch_scalar_to_bfloat16(Scalar value, c10::BFloat16* res) +{ + *res = value->toBFloat16(); +} + void THSTorch_scalar_to_complex32(Scalar value, float* (*allocator)(size_t length)) { auto result = value->toComplexFloat(); diff --git a/src/Native/LibTorchSharp/THSTorch.h b/src/Native/LibTorchSharp/THSTorch.h index 6b515f64a..8d1ab1815 100644 --- a/src/Native/LibTorchSharp/THSTorch.h +++ b/src/Native/LibTorchSharp/THSTorch.h @@ -79,6 +79,7 @@ EXPORT_API(double) THSTorch_scalar_to_float64(Scalar value); EXPORT_API(bool) THSTorch_scalar_to_bool(Scalar value); EXPORT_API(void) THSTorch_scalar_to_float16(Scalar value, unsigned short* res); +EXPORT_API(void) THSTorch_scalar_to_bfloat16(Scalar value, c10::BFloat16* res); EXPORT_API(void) THSTorch_scalar_to_complex32(Scalar value, float* (*allocator)(size_t length)); EXPORT_API(void) THSTorch_scalar_to_complex64(Scalar value, double* (*allocator)(size_t length)); diff --git a/src/TorchSharp/PInvoke/LibTorchSharp.THSBFloat16.cs b/src/TorchSharp/PInvoke/LibTorchSharp.THSBFloat16.cs new file mode 100644 index 000000000..ba018d1e6 --- /dev/null +++ b/src/TorchSharp/PInvoke/LibTorchSharp.THSBFloat16.cs @@ -0,0 +1,75 @@ +using System; +using System.Collections.Generic; +using System.Runtime.InteropServices; +using System.Text; + +namespace TorchSharp.PInvoke +{ + internal static partial class NativeMethods + { + [DllImport("LibTorchSharp")] + [return: MarshalAs(UnmanagedType.Struct)] + internal static extern BFloat16 THSBFloat16_ctor(float value); + + [DllImport("LibTorchSharp")] + internal static extern float THSBFloat16_op_float(BFloat16 bf16); + [DllImport("LibTorchSharp")] + internal static extern BFloat16 THSBFloat16_op_add(BFloat16 a, BFloat16 b); + [DllImport("LibTorchSharp")] + internal static extern BFloat16 THSBFloat16_op_sub(BFloat16 a, BFloat16 b); + [DllImport("LibTorchSharp")] + internal static extern BFloat16 THSBFloat16_op_mul(BFloat16 a, BFloat16 b); + [DllImport("LibTorchSharp")] + internal static extern BFloat16 THSBFloat16_op_div(BFloat16 a, BFloat16 b); + [DllImport("LibTorchSharp")] + internal static extern float THSBFloat16_op_add_float(BFloat16 a, float b); + [DllImport("LibTorchSharp")] + internal static extern float THSBFloat16_op_sub_float(BFloat16 a, float b); + [DllImport("LibTorchSharp")] + internal static extern float THSBFloat16_op_mul_float(BFloat16 a, float b); + [DllImport("LibTorchSharp")] + internal static extern float THSBFloat16_op_div_float(BFloat16 a, float b); + [DllImport("LibTorchSharp")] + internal static extern float THSBFloat16_op_add_lfloat(float a, BFloat16 b); + [DllImport("LibTorchSharp")] + internal static extern float THSBFloat16_op_sub_lfloat(float a, BFloat16 b); + [DllImport("LibTorchSharp")] + internal static extern float THSBFloat16_op_mul_lfloat(float a, BFloat16 b); + [DllImport("LibTorchSharp")] + internal static extern float THSBFloat16_op_div_lfloat(float a, BFloat16 b); + [DllImport("LibTorchSharp")] + internal static extern double THSBFloat16_op_add_double(BFloat16 a, double b); + [DllImport("LibTorchSharp")] + internal static extern double THSBFloat16_op_sub_double(BFloat16 a, double b); + [DllImport("LibTorchSharp")] + internal static extern double THSBFloat16_op_mul_double(BFloat16 a, double b); + [DllImport("LibTorchSharp")] + internal static extern double THSBFloat16_op_div_double(BFloat16 a, double b); + [DllImport("LibTorchSharp")] + internal static extern double THSBFloat16_op_add_ldouble(double a, BFloat16 b); + [DllImport("LibTorchSharp")] + internal static extern double THSBFloat16_op_sub_ldouble(double a, BFloat16 b); + [DllImport("LibTorchSharp")] + internal static extern double THSBFloat16_op_mul_ldouble(double a, BFloat16 b); + [DllImport("LibTorchSharp")] + internal static extern double THSBFloat16_op_div_ldouble(double a, BFloat16 b); + [DllImport("LibTorchSharp")] + internal static extern BFloat16 THSBFloat16_min(BFloat16 bf16); + [DllImport("LibTorchSharp")] + internal static extern BFloat16 THSBFloat16_lowest(BFloat16 bf16); + [DllImport("LibTorchSharp")] + internal static extern BFloat16 THSBFloat16_max(BFloat16 bf16); + [DllImport("LibTorchSharp")] + internal static extern BFloat16 THSBFloat16_epsilon(BFloat16 bf16); + [DllImport("LibTorchSharp")] + internal static extern BFloat16 THSBFloat16_round_error(BFloat16 bf16); + [DllImport("LibTorchSharp")] + internal static extern BFloat16 THSBFloat16_infinity(BFloat16 bf16); + [DllImport("LibTorchSharp")] + internal static extern BFloat16 THSBFloat16_quiet_NaN(BFloat16 bf16); + [DllImport("LibTorchSharp")] + internal static extern BFloat16 THSBFloat16_signaling_NaN(BFloat16 bf16); + [DllImport("LibTorchSharp")] + internal static extern BFloat16 THSBFloat16_denorm_min(BFloat16 bf16); + } +} diff --git a/src/TorchSharp/PInvoke/LibTorchSharp.THSTorch.cs b/src/TorchSharp/PInvoke/LibTorchSharp.THSTorch.cs index 3d3919ee3..b191af608 100644 --- a/src/TorchSharp/PInvoke/LibTorchSharp.THSTorch.cs +++ b/src/TorchSharp/PInvoke/LibTorchSharp.THSTorch.cs @@ -62,10 +62,12 @@ internal static partial class NativeMethods [DllImport("LibTorchSharp")] internal static extern float THSTorch_scalar_to_float32(IntPtr handle); -#if NET6_0_OR_GREATER +//#if NET6_0_OR_GREATER [DllImport("LibTorchSharp")] internal static extern void THSTorch_scalar_to_float16(IntPtr value, out Half res); -#endif +//#endif + [DllImport("LibTorchSharp")] + internal static extern void THSTorch_scalar_to_bfloat16(IntPtr value, out BFloat16 res); [DllImport("LibTorchSharp")] internal static extern double THSTorch_scalar_to_float64(IntPtr handle); diff --git a/src/TorchSharp/Scalar.cs b/src/TorchSharp/Scalar.cs index cf95ac47f..a8d0f58ce 100644 --- a/src/TorchSharp/Scalar.cs +++ b/src/TorchSharp/Scalar.cs @@ -69,16 +69,25 @@ public static implicit operator Scalar(long value) return value.ToScalar(); } -#if NET6_0_OR_GREATER +//#if NET6_0_OR_GREATER /// /// Implicitly convert a .NET scalar value to Scalar /// /// The scalar value. public static implicit operator Scalar(Half value) { + return value.ToScalar(); } -#endif + /// + /// Implicitly convert a .NET scalar value to Scalar + /// + /// The scalar value. + public static implicit operator Scalar(BFloat16 value) + { + return value.ToScalar(); + } + //#endif /// /// Implicitly convert a .NET scalar value to Scalar @@ -218,7 +227,24 @@ public static Scalar ToScalar(this float value) torch.InitializeDeviceType(DeviceType.CPU); return new Scalar(THSTorch_float32_to_scalar(value)); } - + /// + /// Explcitly construct a Scalar from a .NET scalar. + /// + /// The input scalar value + public static Scalar ToScalar(this Half value) + { + torch.InitializeDeviceType(DeviceType.CPU); + return new Scalar(THSTorch_float16_to_scalar(value)); + } + /// + /// Explcitly construct a Scalar + /// + /// The input scalar value + public static Scalar ToScalar(this BFloat16 value) + { + torch.InitializeDeviceType(DeviceType.CPU); + return new Scalar(THSTorch_bfloat16_to_scalar(value.ToFloat())); + } /// /// Explcitly construct a Scalar from a .NET scalar. /// @@ -280,8 +306,20 @@ public static Scalar ToBFloat16Scalar(this float value) torch.InitializeDeviceType(DeviceType.CPU); return new Scalar(THSTorch_bfloat16_to_scalar(value)); } + public static BFloat16 ToBFloat16(this float value) + { + return new BFloat16(value); + //return res; + /*torch.InitializeDeviceType(DeviceType.CPU); + return new Scalar(THSTorch_bfloat16_to_scalar(value));*/ + } -#if NET6_0_OR_GREATER + public static BFloat16 ToBFloat16(this Scalar value) + { + THSTorch_scalar_to_bfloat16(value.Handle, out BFloat16 res); + return res; + } + //#if NET6_0_OR_GREATER /// /// Explicitly convert a Scalar value to a .NET scalar /// @@ -292,7 +330,7 @@ public static Half ToHalf(this Scalar value) THSTorch_scalar_to_float16(value.Handle, out res); return res; } -#endif +//#endif /// /// Explicitly convert a Scalar value to a .NET scalar diff --git a/src/TorchSharp/Tensor/Tensor.cs b/src/TorchSharp/Tensor/Tensor.cs index b89213dea..de0f9ac37 100644 --- a/src/TorchSharp/Tensor/Tensor.cs +++ b/src/TorchSharp/Tensor/Tensor.cs @@ -408,15 +408,15 @@ internal void ValidateType(Type dotnetType) throw new ArgumentException($"{dotnetType.Name} is not compatible with {dtype.ToString()}"); break; case ScalarType.BFloat16: - if(dotnetType != typeof(Half)) + if(dotnetType != typeof(BFloat16)) throw new ArgumentException($"No support for {dtype.ToString()} in TorchSharp"); break; case ScalarType.Float16: -#if NET6_0_OR_GREATER +//#if NET6_0_OR_GREATER if (dotnetType != typeof(Half)) throw new ArgumentException($"{dotnetType.Name} is not compatible with {dtype.ToString()}"); break; -#endif +//#endif case ScalarType.Float32: if (dotnetType != typeof(float)) throw new ArgumentException($"{dotnetType.Name} is not compatible with {dtype.ToString()}"); @@ -6769,6 +6769,10 @@ private static string ToCSharpString(Tensor t, long mdim, bool isFCreate, string if (top) sb.Append("long "); appendChar = "L"; break; + case ScalarType.BFloat16: + if (top) sb.Append("bfloat16 "); + appendChar = "bf"; + break; case ScalarType.Float32: if (top) sb.Append("float "); appendChar = "f"; @@ -6776,6 +6780,7 @@ private static string ToCSharpString(Tensor t, long mdim, bool isFCreate, string case ScalarType.Float64: if (top) sb.Append("double "); break; + case ScalarType.ComplexFloat32: if (top) sb.Append("complex32 "); break; @@ -7057,7 +7062,10 @@ private static void PrintValue(StringBuilder builder, ScalarType type, Scalar va builder.Append(value.ToBoolean().ToString(cultureInfo)); break; case ScalarType.Float16: - builder.Append(value.ToSingle().ToString(fltFormat, cultureInfo)); + builder.Append(value.ToHalf().ToString(fltFormat, cultureInfo)); + break; + case ScalarType.BFloat16: + builder.Append(value.ToBFloat16().ToFloat().ToString(fltFormat, cultureInfo)); break; case ScalarType.Float32: builder.Append(value.ToSingle().ToString(fltFormat, cultureInfo)); diff --git a/src/TorchSharp/Tensor/TensorExtensionMethods.cs b/src/TorchSharp/Tensor/TensorExtensionMethods.cs index 846be615b..253998104 100644 --- a/src/TorchSharp/Tensor/TensorExtensionMethods.cs +++ b/src/TorchSharp/Tensor/TensorExtensionMethods.cs @@ -576,6 +576,9 @@ public static Tensor ToTensor(this T scalar, Device? device = null, bool requ throw new ArgumentException(nameof(requires_grad), "Only floating point types support gradients."); } + if (typeof(T) == typeof(BFloat16)) { + throw new NotImplementedException("Not implemented BFloat16"); + } if (typeof(T) == typeof(byte)) return tensor((byte)(object)scalar, uint8, device, requires_grad); if (typeof(T) == typeof(sbyte)) diff --git a/src/TorchSharp/Utils/BFloat16.cs b/src/TorchSharp/Utils/BFloat16.cs index fef947389..e60636d07 100644 --- a/src/TorchSharp/Utils/BFloat16.cs +++ b/src/TorchSharp/Utils/BFloat16.cs @@ -2,15 +2,27 @@ using System.Collections.Generic; using System.Runtime.InteropServices; using System.Text; +using TorchSharp.PInvoke; namespace System { [StructLayout(LayoutKind.Sequential,Pack=2)] public struct BFloat16 { - [MarshalAs(UnmanagedType.I2)] - private short x; + [MarshalAs(UnmanagedType.U2)] + public ushort x; public struct from_bits_t{}; + + public BFloat16(float value) + { + var bf = NativeMethods.THSBFloat16_ctor(value); + this.x = bf.x; + } + + public float ToFloat() + { + return NativeMethods.THSBFloat16_op_float(this); + } } /* diff --git a/test/TorchSharpTest.WithCudaBinaries/TorchSharpTest.WithCudaBinaries.csproj b/test/TorchSharpTest.WithCudaBinaries/TorchSharpTest.WithCudaBinaries.csproj index faff588b4..3f2ba813e 100644 --- a/test/TorchSharpTest.WithCudaBinaries/TorchSharpTest.WithCudaBinaries.csproj +++ b/test/TorchSharpTest.WithCudaBinaries/TorchSharpTest.WithCudaBinaries.csproj @@ -146,6 +146,7 @@ + From 454a44bc04e1984d8984f9d2c54bbd1dc44be818 Mon Sep 17 00:00:00 2001 From: Dimitri Date: Sun, 14 Sep 2025 23:04:46 -0300 Subject: [PATCH 45/65] torch version --- MyCustomCMD.txt | 3 +- TorchSharp.sln | 12 +- src/Native/LibTorchSharp/THSTorch.cpp | 5 + src/Native/LibTorchSharp/THSTorch.h | 1 + .../PInvoke/LibTorchSharp.THSTorch.cs | 3 + src/TorchSharp/Scalar.cs | 7 +- src/TorchSharp/Tensor/Tensor.Math.cs | 1 + src/TorchSharp/Torch.cs | 5 + src/TorchSharp/Utils/FastTensorAccessor.cs | 712 ------------------ src/TorchSharp/Utils/Half.cs | 1 + src/TorchSharp/Utils/TensorAccessor.cs | 321 ++++---- src/TorchVision/Ops/DeformConv2d.cs | 37 + 12 files changed, 196 insertions(+), 912 deletions(-) delete mode 100644 src/TorchSharp/Utils/FastTensorAccessor.cs create mode 100644 src/TorchVision/Ops/DeformConv2d.cs diff --git a/MyCustomCMD.txt b/MyCustomCMD.txt index 3dfad0aa1..2c4f4200d 100644 --- a/MyCustomCMD.txt +++ b/MyCustomCMD.txt @@ -1 +1,2 @@ -dotnet build TorchSharpFilter.slnf /p:CustomLibTorchPath="K:\FrameworksForC\LibTorch\libtorch-win-shared-with-deps-debug-2.6.0+cu126\libtorch" -f netstandard2.0 \ No newline at end of file +dotnet build TorchSharpFilter.slnf /p:CustomLibTorchPath="K:\FrameworksForC\LibTorch\libtorch-win-shared-with-deps-debug-2.6.0+cu126\libtorch" -f netstandard2.0 +build.cmd Release x64 --libtorchpath "K:\FrameworksForC\LibTorch\libtorch-win-shared-with-deps-2.8.0+cu128\libtorch\share\cmake\Torch" \ No newline at end of file diff --git a/TorchSharp.sln b/TorchSharp.sln index efd1e6079..054c07bb3 100644 --- a/TorchSharp.sln +++ b/TorchSharp.sln @@ -34,7 +34,7 @@ Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "TorchSharp", "TorchSharp", pkg\TorchSharp\TorchSharp.symbols.nupkgproj = pkg\TorchSharp\TorchSharp.symbols.nupkgproj EndProjectSection EndProject -Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "LibTorchSharp", "bin\obj\x64.Debug\Native\LibTorchSharp\LibTorchSharp.vcxproj", "{E7467DDF-893C-38A8-8E19-6B4E3FB10F55}" +Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "LibTorchSharp", "bin\obj\x64.Debug\Native\LibTorchSharp\LibTorchSharp.vcxproj", "{265C2E6F-04E6-37A8-B504-E3DD4A3FEE06}" EndProject Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "LibTorchSharp", "bin\obj\x64.Release\Native\LibTorchSharp\LibTorchSharp.vcxproj", "{E4C0DBEE-0815-311B-9065-137BB50BD793}" EndProject @@ -107,10 +107,10 @@ Global {42B45168-476D-4BFA-87B8-81A34E6295CD}.Release|Any CPU.Build.0 = Release|Any CPU {42B45168-476D-4BFA-87B8-81A34E6295CD}.Release|x64.ActiveCfg = Release|Any CPU {42B45168-476D-4BFA-87B8-81A34E6295CD}.Release|x64.Build.0 = Release|Any CPU - {E7467DDF-893C-38A8-8E19-6B4E3FB10F55}.Debug|Any CPU.ActiveCfg = Debug|x64 - {E7467DDF-893C-38A8-8E19-6B4E3FB10F55}.Debug|x64.ActiveCfg = Debug|x64 - {E7467DDF-893C-38A8-8E19-6B4E3FB10F55}.Release|Any CPU.ActiveCfg = Release|x64 - {E7467DDF-893C-38A8-8E19-6B4E3FB10F55}.Release|x64.ActiveCfg = Release|x64 + {265C2E6F-04E6-37A8-B504-E3DD4A3FEE06}.Debug|Any CPU.ActiveCfg = Debug|x64 + {265C2E6F-04E6-37A8-B504-E3DD4A3FEE06}.Debug|x64.ActiveCfg = Debug|x64 + {265C2E6F-04E6-37A8-B504-E3DD4A3FEE06}.Release|Any CPU.ActiveCfg = Release|x64 + {265C2E6F-04E6-37A8-B504-E3DD4A3FEE06}.Release|x64.ActiveCfg = Release|x64 {E4C0DBEE-0815-311B-9065-137BB50BD793}.Debug|Any CPU.ActiveCfg = Debug|x64 {E4C0DBEE-0815-311B-9065-137BB50BD793}.Debug|x64.ActiveCfg = Debug|x64 {E4C0DBEE-0815-311B-9065-137BB50BD793}.Release|Any CPU.ActiveCfg = Release|x64 @@ -181,7 +181,7 @@ Global {6C323B05-9028-4B09-911C-3C03AE058BEE} = {AED9C836-31E3-4F3F-8ABC-929555D3F3C4} {42B45168-476D-4BFA-87B8-81A34E6295CD} = {09EADF06-BE25-4228-AB53-95AE3E15B530} {567456AD-B026-4CB6-B98D-4FC930C90223} = {D3D38B03-B557-484D-8348-8BADEE4DF592} - {E7467DDF-893C-38A8-8E19-6B4E3FB10F55} = {CF2C1A9E-3A8A-4329-8A6E-7880C15AAC3D} + {265C2E6F-04E6-37A8-B504-E3DD4A3FEE06} = {CF2C1A9E-3A8A-4329-8A6E-7880C15AAC3D} {E4C0DBEE-0815-311B-9065-137BB50BD793} = {4DB9E84D-324C-408F-87A6-246E86205540} {CF2C1A9E-3A8A-4329-8A6E-7880C15AAC3D} = {09EADF06-BE25-4228-AB53-95AE3E15B530} {D8C60CD8-8429-45F2-A755-47B6CD10FDF8} = {09EADF06-BE25-4228-AB53-95AE3E15B530} diff --git a/src/Native/LibTorchSharp/THSTorch.cpp b/src/Native/LibTorchSharp/THSTorch.cpp index 4a181698b..b90ae1691 100644 --- a/src/Native/LibTorchSharp/THSTorch.cpp +++ b/src/Native/LibTorchSharp/THSTorch.cpp @@ -4,6 +4,11 @@ #include "torch/torch.h" #include "torch/cuda.h" +const char* THSTorch_libtorch_version() +{ + return TORCH_VERSION; +} + void THSTorch_manual_seed(const int64_t seed) { torch::manual_seed(seed); diff --git a/src/Native/LibTorchSharp/THSTorch.h b/src/Native/LibTorchSharp/THSTorch.h index 8d1ab1815..cc868ee5d 100644 --- a/src/Native/LibTorchSharp/THSTorch.h +++ b/src/Native/LibTorchSharp/THSTorch.h @@ -8,6 +8,7 @@ //#include // API. +EXPORT_API(const char*) THSTorch_libtorch_version(); // Sets manually the seed. EXPORT_API(void) THSTorch_manual_seed(const int64_t seed); EXPORT_API(void) THSCuda_manual_seed(const int64_t seed); diff --git a/src/TorchSharp/PInvoke/LibTorchSharp.THSTorch.cs b/src/TorchSharp/PInvoke/LibTorchSharp.THSTorch.cs index b191af608..9a4555d32 100644 --- a/src/TorchSharp/PInvoke/LibTorchSharp.THSTorch.cs +++ b/src/TorchSharp/PInvoke/LibTorchSharp.THSTorch.cs @@ -93,6 +93,9 @@ internal static partial class NativeMethods [DllImport("LibTorchSharp")] internal static extern void THSTorch_scalar_to_complex64(IntPtr handle, AllocatePinnedArray allocator); + [DllImport("LibTorchSharp")] + internal static extern IntPtr THSTorch_libtorch_version(); + [DllImport("LibTorchSharp")] internal static extern IntPtr THSTorch_get_and_reset_last_err(); diff --git a/src/TorchSharp/Scalar.cs b/src/TorchSharp/Scalar.cs index a8d0f58ce..279f4dd24 100644 --- a/src/TorchSharp/Scalar.cs +++ b/src/TorchSharp/Scalar.cs @@ -234,7 +234,8 @@ public static Scalar ToScalar(this float value) public static Scalar ToScalar(this Half value) { torch.InitializeDeviceType(DeviceType.CPU); - return new Scalar(THSTorch_float16_to_scalar(value)); + + return new Scalar(THSTorch_float16_to_scalar((float)value)); } /// /// Explcitly construct a Scalar @@ -285,7 +286,7 @@ public static Scalar ToScalar(this bool value) return new Scalar(THSTorch_bool_to_scalar(value)); } -#if NET6_0_OR_GREATER +/*#if NET6_0_OR_GREATER /// /// Explcitly construct a Scalar from a .NET scalar. /// @@ -305,7 +306,7 @@ public static Scalar ToBFloat16Scalar(this float value) { torch.InitializeDeviceType(DeviceType.CPU); return new Scalar(THSTorch_bfloat16_to_scalar(value)); - } + }*/ public static BFloat16 ToBFloat16(this float value) { return new BFloat16(value); diff --git a/src/TorchSharp/Tensor/Tensor.Math.cs b/src/TorchSharp/Tensor/Tensor.Math.cs index 0fec7e12f..cd7e39e6c 100644 --- a/src/TorchSharp/Tensor/Tensor.Math.cs +++ b/src/TorchSharp/Tensor/Tensor.Math.cs @@ -198,6 +198,7 @@ public Tensor addcdiv(Tensor tensor1, Tensor tensor2, Scalar value) (handle, tensor1.handle, tensor2.handle) = AutocastMode.AutoCast(handle, tensor1.handle, tensor2.handle, ScalarType.Float16); if (sts.Any(x => x == ScalarType.Float32)) (handle, tensor1.handle, tensor2.handle) = AutocastMode.AutoCast(handle, tensor1.handle, tensor2.handle, ScalarType.Float32); + //TODO: Should check Bfloat16? } var res = THSTensor_addcdiv(Handle, tensor1.Handle, tensor2.Handle, value.Handle); if (res == IntPtr.Zero) diff --git a/src/TorchSharp/Torch.cs b/src/TorchSharp/Torch.cs index b7979f3b5..878f91dbf 100644 --- a/src/TorchSharp/Torch.cs +++ b/src/TorchSharp/Torch.cs @@ -54,6 +54,11 @@ public static partial class torch static bool nativeBackendCudaLoaded = false; public static string __version__ => libtorchPackageVersion; + public static string? libtorch_version { + get { + return Marshal.PtrToStringAnsi(NativeMethods.THSTorch_libtorch_version()); + } + } internal static bool TryLoadNativeLibraryFromFile(string path, StringBuilder trace) { diff --git a/src/TorchSharp/Utils/FastTensorAccessor.cs b/src/TorchSharp/Utils/FastTensorAccessor.cs deleted file mode 100644 index 142b95d6c..000000000 --- a/src/TorchSharp/Utils/FastTensorAccessor.cs +++ /dev/null @@ -1,712 +0,0 @@ -using System; -using System.Collections; -using System.Collections.Generic; -using System.Diagnostics; -using System.Linq; -using System.Runtime.InteropServices; -using static TorchSharp.PInvoke.NativeMethods; - -namespace TorchSharp.Utils -{ - /// - /// TensorAccessor is used to present the contents of a tensor or tensor view to the .NET world as an ordered collection - /// of values that integrates well with things like LINQ and foreach loops in the .NET world. - /// - /// The type of the tensor elements. - public sealed class FastTensorAccessor : IDisposable, IEnumerable where T : unmanaged - { - internal FastTensorAccessor(torch.Tensor tensor) - { - if (tensor.device_type != DeviceType.CPU) { - throw new InvalidOperationException("Reading data from non-CPU memory is not supported. Move or copy the tensor to the cpu before reading."); - } - - var strides = tensor.stride(); - for (var i = 0; i < strides.Length; i++) { - if (strides[i] < 0) - throw new NotImplementedException($"Negative tensor strides are not currently supported. tensor.strides({i}) == {strides[i]}"); - } - - // Get the data from native code. - - unsafe { - var res = THSTensor_data(tensor.Handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - // NOTE: there is no safety here. - _tensor_data_ptr = res; - } - - _tensor = tensor; // Keep the tensor alive now that everything is alright. - } - - /// - /// This is important for performance because only called with CopyTo, CopyFrom. Is not necesary in each invocation call tensor.numel() because that use intensive CPU. - /// This temporary count avoid so much use CPU. The Property act as method. - /// If tensor is for example 640*640*3 = 1.228.800, property invoke 1 millons times!!! - /// If we only want copy is not necesary call that method so many times. - /// For some reason the method numel() use so much cpu. - /// - internal long TempCount = -1; - public long Count => _tensor?.numel() ?? 0; - - public bool IsReadOnly => false; - - public T[] ToArray() - { - if (_tensor.ndim < 2) - return (T[])ToNDArray(); - - var shps = _tensor.shape; - TempCount = 1; - for (int i = 0; i < shps.Length; i++) - TempCount *= shps[i]; //Theorically the numel is simple as product of each element shape - - if (_tensor.is_contiguous()) { //This is very fast. And work VERY WELL - unsafe { - return new Span(_tensor_data_ptr.ToPointer(), Convert.ToInt32(TempCount)).ToArray(); - } - } - var result = new T[TempCount]; - CopyTo(result); - return result; - } - - /// - /// Extract tensor data as a multi-dimensional .NET array, with the same number of dimensions as the tensor. - /// - /// An array object, which should be cast to the concrete array type. - public Array ToNDArray() - { - var shape = _tensor.shape; - var strides = _tensor.stride(); - switch (_tensor.ndim) { - default: - return ToNDArray(shape, strides); - case 0: - unsafe { - var result = new T[1]; - T* ptr = (T*)_tensor_data_ptr; - result[0] = ptr[0]; - return result; - } - case 1: - unsafe { - var result = new T[shape[0]]; - T* ptr = (T*)_tensor_data_ptr; - for (long i0 = 0, off0 = 0; i0 < shape[0]; i0++, off0 += strides[0]) { - result[i0] = ptr[off0]; - } - return result; - } - case 2: - unsafe { - var result = new T[shape[0], shape[1]]; - T* ptr = (T*)_tensor_data_ptr; - for (long i0 = 0, off0 = 0; i0 < shape[0]; i0++, off0 += strides[0]) { - for (long i1 = 0, off1 = off0; i1 < shape[1]; i1++, off1 += strides[1]) { - result[i0, i1] = ptr[off1]; - } - } - return result; - } - case 3: - unsafe { - var result = new T[shape[0], shape[1], shape[2]]; - T* ptr = (T*)_tensor_data_ptr; - for (long i0 = 0, off0 = 0; i0 < shape[0]; i0++, off0 += strides[0]) { - for (long i1 = 0, off1 = off0; i1 < shape[1]; i1++, off1 += strides[1]) { - for (long i2 = 0, off2 = off1; i2 < shape[2]; i2++, off2 += strides[2]) { - result[i0, i1, i2] = ptr[off2]; - } - } - } - return result; - } - case 4: - unsafe { - var result = new T[shape[0], shape[1], shape[2], shape[3]]; - T* ptr = (T*)_tensor_data_ptr; - for (long i0 = 0, off0 = 0; i0 < shape[0]; i0++, off0 += strides[0]) { - for (long i1 = 0, off1 = off0; i1 < shape[1]; i1++, off1 += strides[1]) { - for (long i2 = 0, off2 = off1; i2 < shape[2]; i2++, off2 += strides[2]) { - for (long i3 = 0, off3 = off2; i3 < shape[3]; i3++, off3 += strides[3]) { - result[i0, i1, i2, i3] = ptr[off3]; - } - } - } - } - return result; - } - case 5: - unsafe { - var result = new T[shape[0], shape[1], shape[2], shape[3], shape[4]]; - T* ptr = (T*)_tensor_data_ptr; - for (long i0 = 0, off0 = 0; i0 < shape[0]; i0++, off0 += strides[0]) { - for (long i1 = 0, off1 = off0; i1 < shape[1]; i1++, off1 += strides[1]) { - for (long i2 = 0, off2 = off1; i2 < shape[2]; i2++, off2 += strides[2]) { - for (long i3 = 0, off3 = off2; i3 < shape[3]; i3++, off3 += strides[3]) { - for (long i4 = 0, off4 = off3; i4 < shape[4]; i4++, off4 += strides[4]) { - result[i0, i1, i2, i3, i4] = ptr[off4]; - } - } - } - } - } - return result; - } - case 6: - unsafe { - var result = new T[shape[0], shape[1], shape[2], shape[3], shape[4], shape[5]]; - T* ptr = (T*)_tensor_data_ptr; - for (long i0 = 0, off0 = 0; i0 < shape[0]; i0++, off0 += strides[0]) { - for (long i1 = 0, off1 = off0; i1 < shape[1]; i1++, off1 += strides[1]) { - for (long i2 = 0, off2 = off1; i2 < shape[2]; i2++, off2 += strides[2]) { - for (long i3 = 0, off3 = off2; i3 < shape[3]; i3++, off3 += strides[3]) { - for (long i4 = 0, off4 = off3; i4 < shape[4]; i4++, off4 += strides[4]) { - for (long i5 = 0, off5 = off4; i5 < shape[5]; i5++, off5 += strides[5]) { - result[i0, i1, i2, i3, i4, i5] = ptr[off5]; - } - } - } - } - } - } - return result; - } - } - } - - private Array ToNDArray(long[] shape, long[] strides) - { - Array array = Array.CreateInstance(typeof(T), shape); - long[] indexes = new long[_tensor.ndim]; - long[] off = new long[_tensor.ndim]; - - while (true) { - unsafe { - T* ptr = (T*)_tensor_data_ptr; - array.SetValue(ptr[off[array.Rank - 1]], indexes); - } - - for (int i = array.Rank - 1; i >= 0; i--) { - if (indexes[i] < shape[i] - 1) { - indexes[i]++; - off[i] += strides[i]; - for (int j = i; j < array.Rank - 1; j++) - off[j + 1] = off[j]; - break; - } else { - if (i == 0) { - return array; - } - indexes[i] = 0; - } - } - } - } - - /// - /// Access elements of the underlying tensor / tensor view. - /// - /// A linear index into the data. - /// - public T this[params long[] indices] { - get { - long index = 0; - if (indices.Length == 1) { - index = indices[0]; - validate(index); - unsafe { - T* ptr = (T*)_tensor_data_ptr; - return ptr[TranslateIndex(index, _tensor)]; - } - } else { - unsafe { - T* ptr = (T*)_tensor_data_ptr; - return ptr[TranslateIndex(indices, _tensor)]; - } - } - } - set { - long index = 0; - if (indices.Length == 1) { - index = indices[0]; - validate(index); - unsafe { - T* ptr = (T*)_tensor_data_ptr; - ptr[TranslateIndex(indices, _tensor)] = value; - } - } else { - unsafe { - T* ptr = (T*)_tensor_data_ptr; - ptr[TranslateIndex(indices, _tensor)] = value; - } - } - } - } - - private void validate(long index) - { - if (index >= Count) throw new IndexOutOfRangeException(); - } - - public void CopyTo(T[] array, int arrayIndex = 0, long tensorIndex = 0) - { - int idx = arrayIndex; - /*if (_tensor.is_contiguous()) { - if (typeof(T) == typeof(float)) { - float[] ff = new float[TempCount]; - Marshal.Copy(_tensor_data_ptr, ff, 0,ff.Length); - } - }*/ - //Because the contiguous cause arange from tensorIndex to Numel. So is not necesary "create" array of arange, i said "create" because in fact enumerable do not create itself. Very cool. - if (_tensor.is_contiguous()) { - for (long i = tensorIndex; i < TempCount; i++) - unsafe { array[i] = ((T*)_tensor_data_ptr)[i]; } - return; - } - foreach (int offset in GetSubsequentIndices(tensorIndex)) { - if (idx >= array.Length) break; - unsafe { array[idx] = ((T*)_tensor_data_ptr)[offset]; } - idx += 1; - } - } - - public void CopyTo(Span array, int arrayIndex = 0, long tensorIndex = 0) - { - int idx = arrayIndex; - foreach (int offset in GetSubsequentIndices(tensorIndex)) { - if (idx >= array.Length) break; - unsafe { array[idx] = ((T*)_tensor_data_ptr)[offset]; } - idx += 1; - } - } - - public void CopyFrom(T[] array, int arrayIndex = 0, long tensorIndex = 0) - { - int idx = arrayIndex; - foreach (int offset in GetSubsequentIndices(tensorIndex)) { - if (idx >= array.Length) break; - unsafe { ((T*)_tensor_data_ptr)[offset] = array[idx]; } - idx += 1; - } - } - - public void CopyFrom(ReadOnlySpan array, int arrayIndex = 0, long tensorIndex = 0) - { - int idx = arrayIndex; - foreach (int offset in GetSubsequentIndices(tensorIndex)) { - if (idx >= array.Length) break; - unsafe { ((T*)_tensor_data_ptr)[offset] = array[idx]; } - idx += 1; - } - } - - /// - /// Translates a linear index within the span represented by the accessor to a linear index - /// used by the underlying tensor. The two should only be different if the tensor is a view - /// rather than an allocated tensor. - /// - private static long TranslateIndex(long idx, torch.Tensor tensor) - { - if (idx >= tensor.numel() || idx < 0) - throw new ArgumentOutOfRangeException($"{idx} in a collection of ${tensor.numel()} elements."); - - if (tensor.is_contiguous() || idx == 0) return idx; - - long result = 0; - var shape = tensor.shape; - var strides = tensor.stride(); - - for (var i = shape.Length - 1; i >= 0; i--) { - idx = Math.DivRem(idx, shape[i], out long s); - result += s * strides[i]; - } - - return result; - } - /// - /// WARNING: Test purpose not use in production - /// - private long TranslateIndexNonStatic(long idx, torch.Tensor tensor) - { - if (idx >= TempCount || idx < 0) - throw new ArgumentOutOfRangeException($"{idx} in a collection of ${tensor.numel()} elements."); - - if (tensor.is_contiguous() || idx == 0) return idx; - - long result = 0; - var shape = tensor.shape; - var strides = tensor.stride(); - - for (var i = shape.Length - 1; i >= 0; i--) { - idx = Math.DivRem(idx, shape[i], out long s); - result += s * strides[i]; - } - - return result; - } - private static long TranslateIndex(long[] idx, torch.Tensor tensor) - { - long result = 0; - var shape = tensor.shape; - var strides = tensor.stride(); - - for (var i = shape.Length - 1; i >= 0; i--) { - if (idx[i] >= shape[i] || idx[i] < 0) - throw new IndexOutOfRangeException($"{idx[i]} >= {shape[i]} in dimension {i}."); - result += idx[i] * strides[i]; - } - - return result; - } - - internal static T ReadItemAt(torch.Tensor tensor, long index) - { - if (tensor.device_type != DeviceType.CPU) { - throw new InvalidOperationException("Reading data from non-CPU memory is not supported. Move or copy the tensor to the cpu before reading."); - } - - tensor.ValidateType(typeof(T)); - - var strides = tensor.stride(); - for (var i = 0; i < strides.Length; i++) { - if (strides[i] < 0) - throw new NotImplementedException($"Negative tensor strides are not currently supported. tensor.strides({i}) == {strides[i]}"); - } - - unsafe { - var res = THSTensor_data(tensor.Handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - // NOTE: there is no safety here. - T* ptr = (T*)res; - return ptr[TranslateIndex(index, tensor)]; - } - } - - /// - /// Compare two tensors element-wise. - /// - /// A tensor - /// Another tensor - /// - public static bool operator ==(FastTensorAccessor left, FastTensorAccessor right) - { - if (left.Count != right.Count) return false; - - var lEnum = left.GetEnumerator(); - var rEnum = right.GetEnumerator(); - - while (lEnum.MoveNext() && rEnum.MoveNext()) { - if (!lEnum.Current.Equals(rEnum.Current)) - return false; - } - return true; - } - - /// - /// Compare two tensors element-wise. - /// - /// A tensor - /// Another tensor - /// - public static bool operator !=(FastTensorAccessor left, FastTensorAccessor right) - { - return !(left == right); - } - - - private IEnumerable GetSubsequentIndices(long startingIndex) - { - //TempCount = Count; - - if (startingIndex < 0 || startingIndex >= TempCount) - throw new ArgumentOutOfRangeException(nameof(startingIndex)); - - if (TempCount <= 1) { - if (TempCount == 0) { - return Enumerable.Empty(); - } - - return new List() { 0 }; - //return (new long[] { 0 }).AsEnumerable(); - } - - if (_tensor.is_contiguous()) { - return ContiguousIndices(startingIndex); - } - - var stride = _tensor.stride(); - Debug.Assert(stride.Length > 0); - - if (stride.Length == 1) { - return SimpleIndices(startingIndex, stride[0]); - } - - return MultiDimensionIndices(startingIndex); - } - private IEnumerable MultiDimensionIndices(long startingIndex) - { - long[] shape = _tensor.shape; - long[] stride = _tensor.stride(); - long[] inds = new long[stride.Length]; - - long index = startingIndex; - //long offset = TranslateIndex(startingIndex, _tensor); - long offset = TranslateIndexNonStatic(startingIndex, _tensor); //WARNING: Test purpose not use in production - - while (true) { - - index += 1; - - yield return offset; - - if (index >= TempCount) break; - - for (int i = inds.Length - 1; ; i--) { - Debug.Assert(i >= 0); - offset += stride[i]; - if (++inds[i] < shape[i]) - break; - - // Overflow of current dimension so rewind accordingly. - // Can't overflow the final (left-most) dimension. - Debug.Assert(i > 0); - // Note: for perf, this multiplication could be done once up front and cached in an array. - offset -= inds[i] * stride[i]; - inds[i] = 0; - } - } - } - - private IEnumerable SimpleIndices(long startingIndex, long stride) - { - long index = startingIndex; - //long offset = TranslateIndex(startingIndex, _tensor); - long offset = TranslateIndexNonStatic(startingIndex, _tensor); //WARNING: Test purpose not use in production - - while (index < TempCount) { - yield return offset; - offset += stride; - index += 1; - } - } - - private IEnumerable ContiguousIndices(long startingIndex) - { - // If there was an overload for Enumerable.Range that - // produced long integers, we wouldn't need this implementation. - - long index = startingIndex; - while (index < TempCount) { - yield return index; - index += 1; - } - } - - - /// - /// Compare two tensors element-wise. - /// - /// Another tensor - /// - public override bool Equals(object obj) - { - var left = this; - var right = obj as FastTensorAccessor; - if (right == null) return false; - - if (left._tensor_data_ptr == right._tensor_data_ptr) return true; - if (left.Count != right.Count) return false; - for (long i = 0; i < left.Count; i++) { - if (!left[i].Equals(right[i])) return false; - } - return true; - } - - public override int GetHashCode() - { - return base.GetHashCode(); - } - - IEnumerator IEnumerable.GetEnumerator() - { - return GetEnumerator(); - } - - public void Dispose() - { - Dispose(true); - GC.SuppressFinalize(this); - } - - private void Dispose(bool disposing) - { - _tensor_data_ptr = IntPtr.Zero; - // Clear the tensor that we've been keeping alive. - _tensor = null; - } - - private torch.Tensor _tensor; // Keeping it alive. - private IntPtr _tensor_data_ptr; - -#if true - public IEnumerator GetEnumerator() - { - if (TempCount <= 1) { - if (TempCount == 0) - return Enumerable.Empty().GetEnumerator(); - return new T[1] { this[0] }.AsEnumerable().GetEnumerator(); - } - /*if (Count <= 1) { - if (Count == 0) - return Enumerable.Empty().GetEnumerator(); - return new T[1] { this[0] }.AsEnumerable().GetEnumerator(); - }*/ - - if (_tensor.is_contiguous()) { - return new SimpleAtorImpl(this, 1); - } - - var stride = _tensor.stride(); - Debug.Assert(stride.Length > 0); - - if (stride.Length == 1) { - return new SimpleAtorImpl(this, stride[0]); - } - - return new GeneralAtorImpl(this, stride); - } - - private class SimpleAtorImpl : IEnumerator - { - private FastTensorAccessor _span; - private readonly long _count; - private readonly long _stride; - - // State. - private long _index; - private long _offset; - private T _current; - - public SimpleAtorImpl(FastTensorAccessor span, long stride) - { - _span = span; - _count = span.TempCount; - Debug.Assert(_count > 0); - _stride = stride; - Reset(); - } - - public T Current => _current; - object IEnumerator.Current => Current; - - public void Dispose() - { - _span = null; - Reset(); - } - - public bool MoveNext() - { - if (_index < 0) { - _index = 0; - _offset = 0; - } else if (++_index >= _count) { - Reset(); - return false; - } else { - _offset += _stride; - } - - unsafe { _current = ((T*)_span._tensor_data_ptr)[_offset]; } - return true; - } - - public void Reset() - { - _index = -1; - _offset = -1; - _current = default; - } - } - - private class GeneralAtorImpl : IEnumerator - { - private FastTensorAccessor _span; - private readonly long _count; - private readonly long[] _shape; - private readonly long[] _stride; - private readonly long[] _inds; - - // State. - private long _index; - private long _offset; - - public GeneralAtorImpl(FastTensorAccessor span, long[] stride) - { - Debug.Assert(stride.Length > 1); - _span = span; - _count = span.TempCount; - Debug.Assert(_count > 0); - _shape = span._tensor.shape; - Debug.Assert(_shape.Length == stride.Length); - _stride = stride; - _inds = new long[stride.Length]; - Reset(); - } - - public T Current { get; private set; } - - object IEnumerator.Current => Current; - - public void Dispose() - { - // Just clear the span field. - _span = null; - } - - public bool MoveNext() - { - if (_index < 0) { - _index = 0; - _offset = 0; - Array.Clear(_inds, 0, _inds.Length); - } else if (++_index >= _count) { - Reset(); - return false; - } else { - for (int i = _inds.Length - 1; ; i--) { - Debug.Assert(i >= 0); - _offset += _stride[i]; - if (++_inds[i] < _shape[i]) - break; - - // Overflow of current dimension so rewind accordingly. - // Can't overflow the final (left-most) dimension. - Debug.Assert(i > 0); - // Note: for perf, this multiplication could be done once up front and cached in an array. - _offset -= _inds[i] * _stride[i]; - _inds[i] = 0; - } - } - - unsafe { Current = ((T*)_span._tensor_data_ptr)[_offset]; } - return true; - } - - public void Reset() - { - _index = -1; - _offset = -1; - Current = default; - } - } -#else - public IEnumerator GetEnumerator() - { - return new TensorAccessorEnumerator(this); - } -#endif - } -} diff --git a/src/TorchSharp/Utils/Half.cs b/src/TorchSharp/Utils/Half.cs index 0650f1307..074305763 100644 --- a/src/TorchSharp/Utils/Half.cs +++ b/src/TorchSharp/Utils/Half.cs @@ -11,6 +11,7 @@ namespace System { //TODO: Implement c10::util::BFloat16.h, c10::util::BFloat16-inl.h,c10::util::BFloat16-math.h in TorchSharp c# //TODO: Or Implement https://github.com/oneapi-src/oneDNN/blob/main/src/common/bfloat16.hpp + //NOTE: V2, bfloat16 is not same as Half is different, Half work float16 //This is from https://github.com/qingfengxia/System.Half /// diff --git a/src/TorchSharp/Utils/TensorAccessor.cs b/src/TorchSharp/Utils/TensorAccessor.cs index 4a964de0b..bcdd0355c 100644 --- a/src/TorchSharp/Utils/TensorAccessor.cs +++ b/src/TorchSharp/Utils/TensorAccessor.cs @@ -4,7 +4,6 @@ using System.Diagnostics; using System.Linq; using System.Runtime.InteropServices; -using TorchSharp.PInvoke; using static TorchSharp.PInvoke.NativeMethods; namespace TorchSharp.Utils @@ -40,7 +39,7 @@ internal TensorAccessor(torch.Tensor tensor) _tensor = tensor; // Keep the tensor alive now that everything is alright. } - public long Count => (_tensor is not null ? _tensor.numel() : 0); + public long Count => _tensor?.numel() ?? 0; public bool IsReadOnly => false; @@ -56,35 +55,54 @@ public T[] ToArray() return new Span(_tensor_data_ptr.ToPointer(), Convert.ToInt32(Cnt)).ToArray(); } } + unsafe { + var res = new T[Cnt]; + SetValueTensor(ref res, _tensor.shape, _tensor.stride(), Cnt); + return res; + } + } - /*unsafe { - IntPtr arr = IntPtr.Zero; - if (typeof(T) == typeof(int)) { - arr = NativeMethods.THSStorage_tensor_to_array_int(_tensor.handle); - int[] tot = new int[Cnt]; - Marshal.Copy(arr, tot, 0, (int)Cnt); + public T[] ToArray(long from_index, long count = 0) + { + long Cnt = this.Count; + bool countDefined = count != 0; + if (countDefined) { + if (from_index + count >= Cnt) { + throw new Exception("Out-bound"); } + } else { + count += from_index; + if (count > Cnt) + Cnt = count; + } + var res = new T[count]; + SetValueTensor(ref res, _tensor.shape, _tensor.stride(), countDefined ? from_index + (Cnt - count) : Cnt, from_index); + return res; + } - if (typeof(T) == typeof(long)) { + private unsafe T* GetAndValidatePTR() + { + T* ptr = (T*)_tensor_data_ptr; + if (ptr == null) + throw new Exception($"Ptr of {nameof(_tensor_data_ptr)} is null"); + return ptr; + } + private unsafe void SetValueTensor(ref T[] res, long[] shape, long[] strides, long count, long idx = 0, bool onThis = false) + { + T* ptr = GetAndValidatePTR(); + long idxforThis = 0; + long cnt = (idx == 0 || (res.Length + idx > count) ? count : res.Length + idx); + for (long index = idx; index < cnt; index++) { + long ptrIndex = TranslateIndex(index, shape, strides); + if (onThis) { + if (res.Length <= idxforThis) + break; + ptr[ptrIndex] = res[idxforThis++]; + continue; } - - return tot as T[]; - //var stride = _tensor.stride(); - //var res = new T[Cnt]; - //int idx = 0; - //T* ptr = (T*)_tensor_data_ptr; - //for (int ndim = 0; ndim < _tensor.shape.Length; ndim++) { - // for (int xyz = 0; xyz < _tensor.shape[ndim]; xyz++) { - // res[idx++] = ptr[xyz + stride[ndim]]; - // } - //} - //return res; - }*/ - - var result = new T[Cnt]; - CopyTo(result); - return result; + res[idx != 0 ? index - idx : index] = ptr[ptrIndex]; + } } /// @@ -93,132 +111,40 @@ public T[] ToArray() /// An array object, which should be cast to the concrete array type. public Array ToNDArray() { - var shape = _tensor.shape; - var strides = _tensor.stride(); - switch (_tensor.ndim) { - default: - return ToNDArray(shape, strides); - case 0: - unsafe { + long[] shape = _tensor.shape; + long[] strides = _tensor.stride(); + long ndim = _tensor.ndim; + unsafe { + T* ptr = GetAndValidatePTR(); + if (ndim == 0) { var result = new T[1]; - T* ptr = (T*)_tensor_data_ptr; result[0] = ptr[0]; return result; } - case 1: - unsafe { - var result = new T[shape[0]]; - T* ptr = (T*)_tensor_data_ptr; - for (long i0 = 0, off0 = 0; i0 < shape[0]; i0++, off0 += strides[0]) { - result[i0] = ptr[off0]; - } - return result; - } - case 2: - unsafe { - var result = new T[shape[0], shape[1]]; - T* ptr = (T*)_tensor_data_ptr; - for (long i0 = 0, off0 = 0; i0 < shape[0]; i0++, off0 += strides[0]) { - for (long i1 = 0, off1 = off0; i1 < shape[1]; i1++, off1 += strides[1]) { - result[i0, i1] = ptr[off1]; - } - } - return result; - } - case 3: - unsafe { - var result = new T[shape[0], shape[1], shape[2]]; - T* ptr = (T*)_tensor_data_ptr; - for (long i0 = 0, off0 = 0; i0 < shape[0]; i0++, off0 += strides[0]) { - for (long i1 = 0, off1 = off0; i1 < shape[1]; i1++, off1 += strides[1]) { - for (long i2 = 0, off2 = off1; i2 < shape[2]; i2++, off2 += strides[2]) { - result[i0, i1, i2] = ptr[off2]; - } - } - } - return result; - } - case 4: - unsafe { - var result = new T[shape[0], shape[1], shape[2], shape[3]]; - T* ptr = (T*)_tensor_data_ptr; - for (long i0 = 0, off0 = 0; i0 < shape[0]; i0++, off0 += strides[0]) { - for (long i1 = 0, off1 = off0; i1 < shape[1]; i1++, off1 += strides[1]) { - for (long i2 = 0, off2 = off1; i2 < shape[2]; i2++, off2 += strides[2]) { - for (long i3 = 0, off3 = off2; i3 < shape[3]; i3++, off3 += strides[3]) { - result[i0, i1, i2, i3] = ptr[off3]; - } - } - } - } - return result; - } - case 5: - unsafe { - var result = new T[shape[0], shape[1], shape[2], shape[3], shape[4]]; - T* ptr = (T*)_tensor_data_ptr; - for (long i0 = 0, off0 = 0; i0 < shape[0]; i0++, off0 += strides[0]) { - for (long i1 = 0, off1 = off0; i1 < shape[1]; i1++, off1 += strides[1]) { - for (long i2 = 0, off2 = off1; i2 < shape[2]; i2++, off2 += strides[2]) { - for (long i3 = 0, off3 = off2; i3 < shape[3]; i3++, off3 += strides[3]) { - for (long i4 = 0, off4 = off3; i4 < shape[4]; i4++, off4 += strides[4]) { - result[i0, i1, i2, i3, i4] = ptr[off4]; - } - } - } - } - } - return result; - } - case 6: - unsafe { - var result = new T[shape[0], shape[1], shape[2], shape[3], shape[4], shape[5]]; - T* ptr = (T*)_tensor_data_ptr; - for (long i0 = 0, off0 = 0; i0 < shape[0]; i0++, off0 += strides[0]) { - for (long i1 = 0, off1 = off0; i1 < shape[1]; i1++, off1 += strides[1]) { - for (long i2 = 0, off2 = off1; i2 < shape[2]; i2++, off2 += strides[2]) { - for (long i3 = 0, off3 = off2; i3 < shape[3]; i3++, off3 += strides[3]) { - for (long i4 = 0, off4 = off3; i4 < shape[4]; i4++, off4 += strides[4]) { - for (long i5 = 0, off5 = off4; i5 < shape[5]; i5++, off5 += strides[5]) { - result[i0, i1, i2, i3, i4, i5] = ptr[off5]; - } - } - } - } - } - } - return result; + Array array = Array.CreateInstance(typeof(T), shape); + long Cnt = Count; + long[] ndIndices = new long[ndim]; + for (long index = 0; index < Cnt; index++) { + long ptrIndex = TranslateIndex(index, shape, strides, ndIndices); + array.SetValue(ptr[ptrIndex], ndIndices); } + return array; } } - private Array ToNDArray(long[] shape, long[] strides) + private long TranslateIndex(long index, long[] shape, long[] strides, long[] ndindices = null) { - Array array = Array.CreateInstance(typeof(T), shape); - long[] indexes = new long[_tensor.ndim]; - long[] off = new long[_tensor.ndim]; - - while (true) { - unsafe { - T* ptr = (T*)_tensor_data_ptr; - array.SetValue(ptr[off[array.Rank - 1]], indexes); - } - - for (int i = array.Rank - 1; i >= 0; i--) { - if (indexes[i] < shape[i] - 1) { - indexes[i]++; - off[i] += strides[i]; - for (int j = i; j < array.Rank - 1; j++) - off[j + 1] = off[j]; - break; - } else { - if (i == 0) { - return array; - } - indexes[i] = 0; - } - } + long offset = index; + long ptrIndex = 0; + for (long d = shape.Length - 1; d >= 0; d--) // Traverse dimensions in reverse order + { + long i = offset % shape[d]; // Current index in dimension d + ptrIndex += i * strides[d]; // Calculate ptrIndex using strides + if (ndindices != null) + ndindices[d] = i; + offset /= shape[d]; // Move to the next dimension } + return ptrIndex; } /// @@ -266,41 +192,50 @@ private void validate(long index) if (index >= Count) throw new IndexOutOfRangeException(); } - private void CopyContiguous(T[] array, int index=0, int count=0) - { - if (!_tensor.is_contiguous()) - throw new Exception("The tensor is not contiguous"); - var Cnt = Count; - if (count > Cnt || count == 0) - count = (int)Cnt; - if (array is byte[] ba) - Marshal.Copy(_tensor_data_ptr, ba, index, count); - if (array is short[] sa) - Marshal.Copy(_tensor_data_ptr, sa, index, count); - if(array is char[] ca) - Marshal.Copy(_tensor_data_ptr, ca, index, count); - if (array is long[] la) - Marshal.Copy(_tensor_data_ptr, la, index, count); - if (array is float[] fa) - Marshal.Copy(_tensor_data_ptr, fa, index, count); - if (array is int[] ia) - Marshal.Copy(_tensor_data_ptr, ia, index, count); - if (array is double[] da) - Marshal.Copy(_tensor_data_ptr, da, index, count); + private void CopyContiguous(T[] array, int index = 0, int count = 0) + { + if (!_tensor.is_contiguous()) + throw new Exception("The tensor is not contiguous"); + var Cnt = Count; + if (count > Cnt || count == 0) + count = (int)Cnt; + if (Cnt > array.Length) + count = array.Length + index; + if (array is byte[] ba) + Marshal.Copy(_tensor_data_ptr, ba, index, count); + if (array is short[] sa) + Marshal.Copy(_tensor_data_ptr, sa, index, count); + if (array is char[] ca) + Marshal.Copy(_tensor_data_ptr, ca, index, count); + if (array is long[] la) + Marshal.Copy(_tensor_data_ptr, la, index, count); + if (array is float[] fa) + Marshal.Copy(_tensor_data_ptr, fa, index, count); + if (array is int[] ia) + Marshal.Copy(_tensor_data_ptr, ia, index, count); + if (array is double[] da) + Marshal.Copy(_tensor_data_ptr, da, index, count); + if (array is Half[] ha) { + throw new NotImplementedException(); + } + if (array is BFloat16[] bfa) { + //TODO: Test this + Marshal.Copy(_tensor_data_ptr, bfa.Select(x=>x.ToFloat()).ToArray(), index, count); + } } + + /*public float[] GetFloats() + { + //TODO: Get float from Storage.cpp. Adapt the code maybe have better performance than copy + }*/ + public void CopyTo(T[] array, int arrayIndex = 0, long tensorIndex = 0) { if (_tensor.is_contiguous()) { CopyContiguous(array, arrayIndex, array.Length); return; } - - int idx = arrayIndex; - foreach (int offset in GetSubsequentIndices(tensorIndex)) { - if (idx >= array.Length) break; - unsafe { array[idx] = ((T*)_tensor_data_ptr)[offset]; } - idx += 1; - } + ToArray().CopyTo(array, arrayIndex); } public void CopyTo(Span array, int arrayIndex = 0, long tensorIndex = 0) @@ -309,32 +244,34 @@ public void CopyTo(Span array, int arrayIndex = 0, long tensorIndex = 0) ToArray().CopyTo(array); return; } - - int idx = arrayIndex; - foreach (int offset in GetSubsequentIndices(tensorIndex)) { - if (idx >= array.Length) break; - unsafe { array[idx] = ((T*)_tensor_data_ptr)[offset]; } - idx += 1; - } + ToArray().CopyTo(array); } public void CopyFrom(T[] array, int arrayIndex = 0, long tensorIndex = 0) { - int idx = arrayIndex; - foreach (int offset in GetSubsequentIndices(tensorIndex)) { - if (idx >= array.Length) break; - unsafe { ((T*)_tensor_data_ptr)[offset] = array[idx]; } - idx += 1; - } + SetValueTensor(ref array, _tensor.shape, _tensor.stride(), Count, arrayIndex, onThis: true); } public void CopyFrom(ReadOnlySpan array, int arrayIndex = 0, long tensorIndex = 0) { - int idx = arrayIndex; - foreach (int offset in GetSubsequentIndices(tensorIndex)) { - if (idx >= array.Length) break; - unsafe { ((T*)_tensor_data_ptr)[offset] = array[idx]; } - idx += 1; + unsafe { + /*var arr = array.ToArray(); + SetValueTensor(ref arr, _tensor.shape, _tensor.stride(), Count, 0, true);*/ + T* ptr = GetAndValidatePTR(); + long count = Count; + var shape = _tensor.shape; + var strides = _tensor.stride(); + for (long index = arrayIndex; index < count; index++) { + long offset = index; + long ptrIndex = 0; + for (long d = shape.Length - 1; d >= 0; d--) // Traverse dimensions in reverse order + { + long i = offset % shape[d]; // Current index in dimension d + ptrIndex += i * strides[d]; // Calculate ptrIndex using strides + offset /= shape[d]; // Move to the next dimension + } + ptr[ptrIndex] = array[(int)index]; + } } } @@ -393,9 +330,13 @@ internal static T ReadItemAt(torch.Tensor tensor, long index) unsafe { var res = THSTensor_data(tensor.Handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } + if (res == IntPtr.Zero) { + torch.CheckForErrors(); + } // NOTE: there is no safety here. T* ptr = (T*)res; + if (ptr == null) + return default(T); return ptr[TranslateIndex(index, tensor)]; } } @@ -715,4 +656,4 @@ public IEnumerator GetEnumerator() } #endif } -} +} \ No newline at end of file diff --git a/src/TorchVision/Ops/DeformConv2d.cs b/src/TorchVision/Ops/DeformConv2d.cs new file mode 100644 index 000000000..75d723b58 --- /dev/null +++ b/src/TorchVision/Ops/DeformConv2d.cs @@ -0,0 +1,37 @@ +using System; +using System.Collections.Generic; +using System.Linq; +using System.Text; +using System.Threading.Tasks; +using TorchSharp; +using TorchVision.Modules; +using static TorchSharp.torch; + +namespace TorchVision +{ + public static partial class torchvision + { + public static partial class ops + { + public static Modules.DeformConv2d DeformConv2d() + { + return new DeformConv2d(); + } + } + } + + namespace Modules + { + public class DeformConv2d : torch.nn.Module + { + protected internal DeformConv2d() : base(nameof(DeformConv2d)) + { + + } + public override Tensor forward(Tensor input, Tensor offset, Tensor mask) + { + throw new NotImplementedException(); + } + } + } +} From 0df83c3a9d16cd3b08a508088d4dd4ea43389964 Mon Sep 17 00:00:00 2001 From: Dimitri Date: Sun, 14 Sep 2025 23:13:00 -0300 Subject: [PATCH 46/65] Cuda version --- src/Native/LibTorchSharp/THSCuda.cpp | 24 +++++++++++++++++++ src/Native/LibTorchSharp/THSCuda.h | 3 ++- .../PInvoke/LibTorchSharp.THSCuda.cs | 3 +++ src/TorchSharp/Torch.cs | 4 ++++ 4 files changed, 33 insertions(+), 1 deletion(-) diff --git a/src/Native/LibTorchSharp/THSCuda.cpp b/src/Native/LibTorchSharp/THSCuda.cpp index baca29615..29ac526a6 100644 --- a/src/Native/LibTorchSharp/THSCuda.cpp +++ b/src/Native/LibTorchSharp/THSCuda.cpp @@ -77,4 +77,28 @@ size_t THSCuda_get_global_total_memory(int device) #endif } +const char* THSCuda_get_cuda_version() +{ +#ifdef CUDA_TOOLKIT_FOUND + int runtimeVersion; + cudaError_t err = cudaRuntimeGetVersion(&runtimeVersion); + + if (err != cudaSuccess) { + std::cerr << "Error getting CUDA runtime version: " << cudaGetErrorString(err) << std::endl; + return nullptr; + } + + int major = runtimeVersion / 1000; + int minor = (runtimeVersion % 1000) / 10; + int patch = runtimeVersion % 10; + + std::string cudaVersionString = std::to_string(major) + "." + std::to_string(minor) + "." + std::to_string(patch); + //std::cout << "CUDA Runtime Version: " << cudaVersionString << std::endl; + return cudaVersionString.c_str(); +#else + return nullptr; +#endif +} + + //TODO: implement more function diff --git a/src/Native/LibTorchSharp/THSCuda.h b/src/Native/LibTorchSharp/THSCuda.h index 00f1d7d03..bcc7e2cd6 100644 --- a/src/Native/LibTorchSharp/THSCuda.h +++ b/src/Native/LibTorchSharp/THSCuda.h @@ -45,4 +45,5 @@ EXPORT_API(int) THSCuda_get_minor_compute_capability(int device); EXPORT_API(int) THSCuda_get_device_count(int* count); EXPORT_API(int) THSCuda_get_free_total(int device, int* id, size_t* free, size_t* total); EXPORT_API(size_t) THSCuda_get_total_memory(int device); -EXPORT_API(size_t) THSCuda_get_global_total_memory(int device); \ No newline at end of file +EXPORT_API(size_t) THSCuda_get_global_total_memory(int device); +EXPORT_API(const char*) THSCuda_get_cuda_version(); \ No newline at end of file diff --git a/src/TorchSharp/PInvoke/LibTorchSharp.THSCuda.cs b/src/TorchSharp/PInvoke/LibTorchSharp.THSCuda.cs index d455f5746..a2aa6843c 100644 --- a/src/TorchSharp/PInvoke/LibTorchSharp.THSCuda.cs +++ b/src/TorchSharp/PInvoke/LibTorchSharp.THSCuda.cs @@ -1,5 +1,6 @@ // Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information. #nullable enable +using System; using System.Runtime.InteropServices; namespace TorchSharp.PInvoke @@ -54,5 +55,7 @@ internal static partial class NativeMethods internal static extern ulong THSCuda_get_total_memory(int device); [DllImport("LibTorchSharp")] internal static extern ulong THSCuda_get_global_total_memory(int device); + [DllImport("LibTorchSharp")] + internal static extern IntPtr THSCuda_get_cuda_version(); } } diff --git a/src/TorchSharp/Torch.cs b/src/TorchSharp/Torch.cs index 878f91dbf..87754d876 100644 --- a/src/TorchSharp/Torch.cs +++ b/src/TorchSharp/Torch.cs @@ -626,6 +626,10 @@ public static ulong get_global_total_memory(int device) { return THSCuda_get_global_total_memory(device); } + public static string? get_cuda_version() + { + return Marshal.PtrToStringAnsi(THSCuda_get_cuda_version()); + } /*public static cudaDeviceProp get_device_prop(int device) { #if CUDA_TOOLKIT_FOUND From a7e9209432aa57c92395631f56201aa5146f1f01 Mon Sep 17 00:00:00 2001 From: Dimitri Date: Mon, 15 Sep 2025 00:50:13 -0300 Subject: [PATCH 47/65] raw data accessor --- src/Native/LibTorchSharp/THSTensor.cpp | 5 +++ src/Native/LibTorchSharp/THSTensor.h | 2 + .../PInvoke/LibTorchSharp.THSTensor.cs | 3 ++ src/TorchSharp/Tensor/Tensor.LinearAlgebra.cs | 1 + src/TorchSharp/Tensor/Tensor.cs | 44 +++++++++++++++++++ 5 files changed, 55 insertions(+) diff --git a/src/Native/LibTorchSharp/THSTensor.cpp b/src/Native/LibTorchSharp/THSTensor.cpp index c66da4dcf..a502d0524 100644 --- a/src/Native/LibTorchSharp/THSTensor.cpp +++ b/src/Native/LibTorchSharp/THSTensor.cpp @@ -384,6 +384,11 @@ void* THSTensor_data(const Tensor tensor) CATCH_RETURN(void*, nullptr, tensor->data_ptr()); } +void* THSTensor_raw_data(const Tensor tensor) +{ + return THSTensor_data(tensor); +} + float THSTensor_data_idx_float16(const Tensor tensor, const int64_t i) { CATCH_RETURN(float, 0.0f, (float)(tensor->data_ptr())[i]); diff --git a/src/Native/LibTorchSharp/THSTensor.h b/src/Native/LibTorchSharp/THSTensor.h index 76e63ff5b..584ba1c28 100644 --- a/src/Native/LibTorchSharp/THSTensor.h +++ b/src/Native/LibTorchSharp/THSTensor.h @@ -355,6 +355,8 @@ EXPORT_API(Tensor) THSTensor_cumsum(const Tensor tensor, const int64_t dim, bool EXPORT_API(void*) THSTensor_data(const Tensor tensor); +EXPORT_API(void*) THSTensor_raw_data(const Tensor tensor); + EXPORT_API(float) THSTensor_data_idx_float16(const Tensor tensor, const int64_t i); EXPORT_API(float) THSTensor_data_idx_bfloat16(const Tensor tensor, const int64_t i); diff --git a/src/TorchSharp/PInvoke/LibTorchSharp.THSTensor.cs b/src/TorchSharp/PInvoke/LibTorchSharp.THSTensor.cs index 7e9169020..b059f0b88 100644 --- a/src/TorchSharp/PInvoke/LibTorchSharp.THSTensor.cs +++ b/src/TorchSharp/PInvoke/LibTorchSharp.THSTensor.cs @@ -225,6 +225,9 @@ internal static extern IntPtr THSTensor_upsample_nearest3d(IntPtr input, [DllImport("LibTorchSharp")] internal static extern IntPtr THSTensor_data(IntPtr handle); + [DllImport("LibTorchSharp")] + internal static extern unsafe void* THSTensor_raw_data(IntPtr handle); + [DllImport("LibTorchSharp")] internal static extern IntPtr THSTensor_real(IntPtr handle); diff --git a/src/TorchSharp/Tensor/Tensor.LinearAlgebra.cs b/src/TorchSharp/Tensor/Tensor.LinearAlgebra.cs index a26dc15b7..625b1a093 100644 --- a/src/TorchSharp/Tensor/Tensor.LinearAlgebra.cs +++ b/src/TorchSharp/Tensor/Tensor.LinearAlgebra.cs @@ -2,6 +2,7 @@ using System; using System.Linq; using TorchSharp.Amp; +using TorchSharp.PInvoke; using static TorchSharp.PInvoke.NativeMethods; namespace TorchSharp diff --git a/src/TorchSharp/Tensor/Tensor.cs b/src/TorchSharp/Tensor/Tensor.cs index de0f9ac37..4aeb72a7a 100644 --- a/src/TorchSharp/Tensor/Tensor.cs +++ b/src/TorchSharp/Tensor/Tensor.cs @@ -67,6 +67,50 @@ public override bool Equals(object? obj) return (obj is Tensor) && this.Equals((obj as Tensor)!); } + public Span GetRawData() + { + unsafe { + //Work very well but the problem is that Numel converted from long to int so the max size is 2^(32-1) + //If i have more than 2^(32-1) i should "offset" the void* of raw_data with multiple Span + //i mean for example if you have 3 billions of elements the first 2^(32-1) is the first Span and the remaining is another Span + //so i have in total 2 Span + //another situation instead of all that, if have a batch i can "offset" per batch -> 2x3x640x640 mean 2 Span of 3x640x640 but i can "index" by a batch (warning i didn't researched or tested this idea) + //if you want use like a batch see GetRawData() example code + return new Span(NativeMethods.THSTensor_raw_data(handle), Convert.ToInt32(numel())); + } + } + + + /*long numel(long[] dims) + { + if (dims.Length == 0) + return 0; + long res = 1; + foreach (var d in dims) + res *= d; + return res; + } + var t = torch.arange(0, 2 * 4 * 3).reshape(2,4,3).to(torch.ScalarType.Int32); + void* p = t.GetRawData(); + var sh = t.shape.Skip(1).ToArray(); + long len = numel(sh); + var f = new Span(p, Convert.ToInt32(len)).ToArray(); + printarray(f); //make some function to print this array this print from 0 to 11 + p= Unsafe.Add(p, Convert.ToInt32(len)); //offset pointer + var s = new Span(p, Convert.ToInt32(len)).ToArray(); + printarrarray(s); //Will print from 12 to 23 + */ + /// + /// Should be used by a advanced user + /// + /// + public unsafe void* GetRawData() + { + unsafe { + return NativeMethods.THSTensor_raw_data(handle); + } + } + /// /// TODO /// From 1931efe86da2ca7cbb9a1aa2bb3131b051b0791c Mon Sep 17 00:00:00 2001 From: Dimitri Date: Mon, 15 Sep 2025 16:25:29 -0300 Subject: [PATCH 48/65] System Range Index compatible with netstandard2.0 --- src/TorchSharp/Tensor/Tensor.cs | 11 +-- src/TorchSharp/Utils/Index.cs | 160 ++++++++++++++++++++++++++++++++ src/TorchSharp/Utils/Range.cs | 135 +++++++++++++++++++++++++++ 3 files changed, 300 insertions(+), 6 deletions(-) create mode 100644 src/TorchSharp/Utils/Index.cs create mode 100644 src/TorchSharp/Utils/Range.cs diff --git a/src/TorchSharp/Tensor/Tensor.cs b/src/TorchSharp/Tensor/Tensor.cs index 4aeb72a7a..2f81e378c 100644 --- a/src/TorchSharp/Tensor/Tensor.cs +++ b/src/TorchSharp/Tensor/Tensor.cs @@ -7316,14 +7316,14 @@ static public TensorIndex Slice(long? start = null, long? stop = null, long? ste static public TensorIndex Slice((int? start, int? end) range) => TensorIndex.Slice((long?)range.start, (long?)range.end); -#if !NETSTANDARD2_0_OR_GREATER +//#if !NETSTANDARD2_0_OR_GREATER static public TensorIndex Slice(System.Range range) { long? start = !range.Start.IsFromEnd ? range.Start.Value : -1 * range.Start.Value; long? end = !range.End.IsFromEnd ? range.End.Value : (range.End.Value == 0) ? null : -1 * range.End.Value; return TensorIndex.Slice(start, end); } -#endif // NETSTANDARD2_0_OR_GREATER +//#endif // NETSTANDARD2_0_OR_GREATER static public TensorIndex Bool(bool value) => new TensorIndex() { startIndexOrBoolOrSingle = (value ? 1 : 0), kind = Kind.Bool }; static public TensorIndex Single(long? index) => new TensorIndex() { startIndexOrBoolOrSingle = index, kind = Kind.Single }; @@ -7356,7 +7356,7 @@ private static void _throw() public static implicit operator TensorIndex((int? start, int? end) range) => TensorIndex.Slice((long?)range.start, (long?)range.end); -#if !NETSTANDARD2_0_OR_GREATER +//#if !NETSTANDARD2_0_OR_GREATER public static implicit operator TensorIndex(System.Range range) { long? start = !range.Start.IsFromEnd ? range.Start.Value : -1 * range.Start.Value; @@ -7369,7 +7369,7 @@ public static implicit operator TensorIndex(System.Index index) long idx = !index.IsFromEnd ? index.Value : -1 * index.Value; return TensorIndex.Single(idx); } -#endif // NETSTANDARD2_0_OR_GREATER +//#endif // NETSTANDARD2_0_OR_GREATER } @@ -7404,9 +7404,8 @@ public enum ScalarType : sbyte { typeof(short), ScalarType.Int16 }, { typeof(int), ScalarType.Int32 }, { typeof(long), ScalarType.Int64 }, -#if NET6_0_OR_GREATER { typeof(Half), ScalarType.Float16 }, -#endif + { typeof(BFloat16), ScalarType.BFloat16}, { typeof(float), ScalarType.Float32 }, { typeof(double), ScalarType.Float64 }, { typeof((float, float)), ScalarType.ComplexFloat32 }, diff --git a/src/TorchSharp/Utils/Index.cs b/src/TorchSharp/Utils/Index.cs new file mode 100644 index 000000000..1079dc78a --- /dev/null +++ b/src/TorchSharp/Utils/Index.cs @@ -0,0 +1,160 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +#if NETSTANDARD2_0 +using System.Diagnostics; +using System.Diagnostics.CodeAnalysis; +using System.Runtime.CompilerServices; + +#nullable enable +namespace System +{ + /// Represent a type can be used to index a collection either from the start or the end. + /// + /// Index is used by the C# compiler to support the new index syntax + /// + /// int[] someArray = new int[5] { 1, 2, 3, 4, 5 } ; + /// int lastElement = someArray[^1]; // lastElement = 5 + /// + /// + public readonly struct Index : IEquatable + { + private readonly int _value; + + /// Construct an Index using a value and indicating if the index is from the start or from the end. + /// The index value. it has to be zero or positive number. + /// Indicating if the index is from the start or from the end. + /// + /// If the Index constructed from the end, index value 1 means pointing at the last element and index value 0 means pointing at beyond last element. + /// + [MethodImpl(MethodImplOptions.AggressiveInlining)] + public Index(int value, bool fromEnd = false) + { + if (value < 0) { + ThrowValueArgumentOutOfRange_NeedNonNegNumException(); + } + + if (fromEnd) + _value = ~value; + else + _value = value; + } + + // The following private constructors mainly created for perf reason to avoid the checks + private Index(int value) + { + _value = value; + } + + /// Create an Index pointing at first element. + public static Index Start => new Index(0); + + /// Create an Index pointing at beyond last element. + public static Index End => new Index(~0); + + /// Create an Index from the start at the position indicated by the value. + /// The index value from the start. + [MethodImpl(MethodImplOptions.AggressiveInlining)] + public static Index FromStart(int value) + { + if (value < 0) { + ThrowValueArgumentOutOfRange_NeedNonNegNumException(); + } + + return new Index(value); + } + + /// Create an Index from the end at the position indicated by the value. + /// The index value from the end. + [MethodImpl(MethodImplOptions.AggressiveInlining)] + public static Index FromEnd(int value) + { + if (value < 0) { + ThrowValueArgumentOutOfRange_NeedNonNegNumException(); + } + + return new Index(~value); + } + + /// Returns the index value. + public int Value { + get { + if (_value < 0) + return ~_value; + else + return _value; + } + } + + /// Indicates whether the index is from the start or the end. + public bool IsFromEnd => _value < 0; + + /// Calculate the offset from the start using the giving collection length. + /// The length of the collection that the Index will be used with. length has to be a positive value + /// + /// For performance reason, we don't validate the input length parameter and the returned offset value against negative values. + /// we don't validate either the returned offset is greater than the input length. + /// It is expected Index will be used with collections which always have non negative length/count. If the returned offset is negative and + /// then used to index a collection will get out of range exception which will be same affect as the validation. + /// + [MethodImpl(MethodImplOptions.AggressiveInlining)] + public int GetOffset(int length) + { + int offset = _value; + if (IsFromEnd) { + // offset = length - (~value) + // offset = length + (~(~value) + 1) + // offset = length + value + 1 + + offset += length + 1; + } + return offset; + } + + /// Indicates whether the current Index object is equal to another object of the same type. + /// An object to compare with this object + public override bool Equals(object? value) => value is Index && _value == ((Index)value)._value; + + /// Indicates whether the current Index object is equal to another Index object. + /// An object to compare with this object + public bool Equals(Index other) => _value == other._value; + + /// Returns the hash code for this instance. + public override int GetHashCode() => _value; + + /// Converts integer number to an Index. + public static implicit operator Index(int value) => FromStart(value); + + /// Converts the value of the current Index object to its equivalent string representation. + public override string ToString() + { + if (IsFromEnd) + return ToStringFromEnd(); + + return ((uint)Value).ToString(); + } + + private static void ThrowValueArgumentOutOfRange_NeedNonNegNumException() + { +#if SYSTEM_PRIVATE_CORELIB + throw new ArgumentOutOfRangeException("value", SR.ArgumentOutOfRange_NeedNonNegNum); +#else + throw new ArgumentOutOfRangeException("value", "value must be non-negative"); +#endif + } + + private string ToStringFromEnd() + { +#if (!NETSTANDARD2_0 && !NETFRAMEWORK) + Span span = stackalloc char[11]; // 1 for ^ and 10 for longest possible uint value + bool formatted = ((uint)Value).TryFormat(span.Slice(1), out int charsWritten); + Debug.Assert(formatted); + span[0] = '^'; + return new string(span.Slice(0, charsWritten + 1)); +#else + return '^' + Value.ToString(); +#endif + } + } +} + +#endif \ No newline at end of file diff --git a/src/TorchSharp/Utils/Range.cs b/src/TorchSharp/Utils/Range.cs new file mode 100644 index 000000000..aa35dbab0 --- /dev/null +++ b/src/TorchSharp/Utils/Range.cs @@ -0,0 +1,135 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +#if NETSTANDARD2_0 + +using System.Diagnostics; +using System.Diagnostics.CodeAnalysis; +using System.Runtime.CompilerServices; + +#if NETSTANDARD2_0 || NETFRAMEWORK +using System.Numerics.Hashing; +#endif + +#nullable enable +namespace System +{ + /// Represent a range has start and end indexes. + /// + /// Range is used by the C# compiler to support the range syntax. + /// + /// int[] someArray = new int[5] { 1, 2, 3, 4, 5 }; + /// int[] subArray1 = someArray[0..2]; // { 1, 2 } + /// int[] subArray2 = someArray[1..^0]; // { 2, 3, 4, 5 } + /// + /// + public readonly struct Range : IEquatable + { + /// Represent the inclusive start index of the Range. + public Index Start { get; } + + /// Represent the exclusive end index of the Range. + public Index End { get; } + + /// Construct a Range object using the start and end indexes. + /// Represent the inclusive start index of the range. + /// Represent the exclusive end index of the range. + public Range(Index start, Index end) + { + Start = start; + End = end; + } + + /// Indicates whether the current Range object is equal to another object of the same type. + /// An object to compare with this object + public override bool Equals(object? value) => + value is Range r && + r.Start.Equals(Start) && + r.End.Equals(End); + + /// Indicates whether the current Range object is equal to another Range object. + /// An object to compare with this object + public bool Equals(Range other) => other.Start.Equals(Start) && other.End.Equals(End); + + /// Returns the hash code for this instance. + public override int GetHashCode() + { +#if (!NETSTANDARD2_0 && !NETFRAMEWORK) + return HashCode.Combine(Start.GetHashCode(), End.GetHashCode()); +#else + var h1 = Start.GetHashCode(); + var h2 = End.GetHashCode(); + uint rol5 = ((uint)h1 << 5) | ((uint)h1 >> 27); + return ((int)rol5 + h1) ^ h2; + //return HashHelpers.Combine(Start.GetHashCode(), End.GetHashCode()); +#endif + } + + /// Converts the value of the current Range object to its equivalent string representation. + public override string ToString() + { +#if (!NETSTANDARD2_0 && !NETFRAMEWORK) + Span span = stackalloc char[2 + (2 * 11)]; // 2 for "..", then for each index 1 for '^' and 10 for longest possible uint + int pos = 0; + + if (Start.IsFromEnd) + { + span[0] = '^'; + pos = 1; + } + bool formatted = ((uint)Start.Value).TryFormat(span.Slice(pos), out int charsWritten); + Debug.Assert(formatted); + pos += charsWritten; + + span[pos++] = '.'; + span[pos++] = '.'; + + if (End.IsFromEnd) + { + span[pos++] = '^'; + } + formatted = ((uint)End.Value).TryFormat(span.Slice(pos), out charsWritten); + Debug.Assert(formatted); + pos += charsWritten; + + return new string(span.Slice(0, pos)); +#else + return Start.ToString() + ".." + End.ToString(); +#endif + } + + /// Create a Range object starting from start index to the end of the collection. + public static Range StartAt(Index start) => new Range(start, Index.End); + + /// Create a Range object starting from first element in the collection to the end Index. + public static Range EndAt(Index end) => new Range(Index.Start, end); + + /// Create a Range object starting from first element to the end. + public static Range All => new Range(Index.Start, Index.End); + + /// Calculate the start offset and length of range object using a collection length. + /// The length of the collection that the range will be used with. length has to be a positive value. + /// + /// For performance reason, we don't validate the input length parameter against negative values. + /// It is expected Range will be used with collections which always have non negative length/count. + /// We validate the range is inside the length scope though. + /// + [MethodImpl(MethodImplOptions.AggressiveInlining)] + public (int Offset, int Length) GetOffsetAndLength(int length) + { + int start = Start.GetOffset(length); + int end = End.GetOffset(length); + + if ((uint)end > (uint)length || (uint)start > (uint)end) { + ThrowArgumentOutOfRangeException(); + } + + return (start, end - start); + } + + private static void ThrowArgumentOutOfRangeException() + { + throw new ArgumentOutOfRangeException("length"); + } + } +} +#endif \ No newline at end of file From 47ddb1eeac5a4890a89ec0e3b67ffd14a638d46b Mon Sep 17 00:00:00 2001 From: Dimitri Date: Mon, 15 Sep 2025 17:09:29 -0300 Subject: [PATCH 49/65] ToSpan fast access void* tensor --- src/TorchSharp/Utils/TensorAccessor.cs | 50 ++++++++++++++++++++++++++ 1 file changed, 50 insertions(+) diff --git a/src/TorchSharp/Utils/TensorAccessor.cs b/src/TorchSharp/Utils/TensorAccessor.cs index bcdd0355c..5e095a126 100644 --- a/src/TorchSharp/Utils/TensorAccessor.cs +++ b/src/TorchSharp/Utils/TensorAccessor.cs @@ -3,6 +3,7 @@ using System.Collections.Generic; using System.Diagnostics; using System.Linq; +using System.Runtime.CompilerServices; using System.Runtime.InteropServices; using static TorchSharp.PInvoke.NativeMethods; @@ -43,6 +44,11 @@ internal TensorAccessor(torch.Tensor tensor) public bool IsReadOnly => false; + /// + /// Be carefully using this because the max array that NET is allowed to handle is 2Gb + /// + /// + /// public T[] ToArray() { if (_tensor.ndim < 2) @@ -79,6 +85,50 @@ public T[] ToArray(long from_index, long count = 0) SetValueTensor(ref res, _tensor.shape, _tensor.stride(), countDefined ? from_index + (Cnt - count) : Cnt, from_index); return res; } + private long numel(long[] dims) + { + if (dims.Length == 0) + return 0; + long res = 1; + foreach (var d in dims) + res *= d; + return res; + } + + /// + /// This is ref of raw data ptr tensor is very fast + /// Be carefully the max length of Span is 2^(32-1) + /// Can call this method if shape dimensions is greather or equal than 2 + /// + /// + /// + public Span ToSpan(int batch_idx) + { + unsafe { + var sh = _tensor.shape; + if (sh.Length <= 1) + return null; + void* p = _tensor.GetRawData(); + sh = sh.Skip(1).ToArray(); + + long len = numel(sh); + int ilen = Convert.ToInt32(len); + if(batch_idx > 0) + p = Unsafe.Add(p, batch_idx*ilen); //offset pointer + return new Span(p, ilen); + } + } + + /// + /// Be carefully using this because the max array that NET is allowed to handle is 2Gb + /// + /// + public Span ToSpan() + { + unsafe { + return new Span(_tensor.GetRawData(), Convert.ToInt32(_tensor.numel())); + } + } private unsafe T* GetAndValidatePTR() { From bb52336fb23583f67c7b960a1ed22e5d4773dddf Mon Sep 17 00:00:00 2001 From: Dimitri Date: Sat, 20 Sep 2025 18:30:49 -0300 Subject: [PATCH 50/65] Implement bitsandbyte of https://github.com/LittleLittleCloud/TorchSharp.BitsAndBytes and #1472 --- Directory.Build.props | 1 + MyCustomCMD.txt | 4 +- src/Native/LibTorchSharp/THSTorch.cpp | 1 + src/Native/LibTorchSharp/THSVision.cpp | 2 +- .../BitsAndBytes/BitsAndByteUtils.cs | 363 ++++++++++++++++++ .../BitsAndBytes/BitsAndBytesNatives.cs | 225 +++++++++++ src/TorchSharp/NN/Module.cs | 6 + .../PInvoke/LibTorchSharp.THSTensor.cs | 1 + src/TorchSharp/Tensor/Tensor.cs | 5 + src/TorchSharp/Utils/UnorderedMap.cs | 16 + src/TorchVision/File.cs | 3 +- src/TorchVision/IO/Image.cs | 4 +- src/TorchVision/Ops/DeformConv2d.cs | 132 ++++++- test/Directory.Build.props | 2 +- 14 files changed, 754 insertions(+), 11 deletions(-) create mode 100644 src/TorchSharp/BitsAndBytes/BitsAndByteUtils.cs create mode 100644 src/TorchSharp/BitsAndBytes/BitsAndBytesNatives.cs diff --git a/Directory.Build.props b/Directory.Build.props index ac534f235..d77086a0b 100644 --- a/Directory.Build.props +++ b/Directory.Build.props @@ -6,6 +6,7 @@ + Debug Debug;Release <_DefaultArchitecture>$([System.Runtime.InteropServices.RuntimeInformation]::OSArchitecture.ToString().ToLower()) diff --git a/MyCustomCMD.txt b/MyCustomCMD.txt index 2c4f4200d..bb3759733 100644 --- a/MyCustomCMD.txt +++ b/MyCustomCMD.txt @@ -1,2 +1,4 @@ dotnet build TorchSharpFilter.slnf /p:CustomLibTorchPath="K:\FrameworksForC\LibTorch\libtorch-win-shared-with-deps-debug-2.6.0+cu126\libtorch" -f netstandard2.0 -build.cmd Release x64 --libtorchpath "K:\FrameworksForC\LibTorch\libtorch-win-shared-with-deps-2.8.0+cu128\libtorch\share\cmake\Torch" \ No newline at end of file +build.cmd Release x64 --libtorchpath "K:\FrameworksForC\LibTorch\libtorch-win-shared-with-deps-2.8.0+cu128\libtorch\share\cmake\Torch" + +dotnet build /p:CustomLibTorchFullPath="K:\FrameworksForC\LibTorch\libtorch-win-shared-with-deps-2.8.0+cu128\libtorch\share\cmake\Torch" -c Release \ No newline at end of file diff --git a/src/Native/LibTorchSharp/THSTorch.cpp b/src/Native/LibTorchSharp/THSTorch.cpp index b90ae1691..8056b316e 100644 --- a/src/Native/LibTorchSharp/THSTorch.cpp +++ b/src/Native/LibTorchSharp/THSTorch.cpp @@ -122,6 +122,7 @@ Generator THSGenerator_new(uint64_t seed, int64_t device, int64_t index) { // TODO: Support creation of GPU RNGs. 'device' and 'index' are in the // function signature in preparation thereof. + //auto dl = std::make_shared(c10::Device(c10::DeviceType::CUDA, device), c10::DispatchKeySet()).get(); return new at::Generator(at::detail::createCPUGenerator(seed)); } diff --git a/src/Native/LibTorchSharp/THSVision.cpp b/src/Native/LibTorchSharp/THSVision.cpp index 5cc6f832d..d04cf0560 100644 --- a/src/Native/LibTorchSharp/THSVision.cpp +++ b/src/Native/LibTorchSharp/THSVision.cpp @@ -51,7 +51,7 @@ void _hsv_to_rgb(at::Tensor& h, at::Tensor& s, at::Tensor& v, at::Tensor& img) auto i = torch::floor(h6); auto f = h6 - i; i = i.to(at::ScalarType::Int) % 6; - + auto p = torch::clamp((v * (1.0f - s)), 0.0, 1.0); auto q = torch::clamp((v * (1.0 - s * f)), 0.0, 1.0); auto t = torch::clamp((v * (1.0 - s * (1.0 - f))), 0.0, 1.0); diff --git a/src/TorchSharp/BitsAndBytes/BitsAndByteUtils.cs b/src/TorchSharp/BitsAndBytes/BitsAndByteUtils.cs new file mode 100644 index 000000000..f053e11ab --- /dev/null +++ b/src/TorchSharp/BitsAndBytes/BitsAndByteUtils.cs @@ -0,0 +1,363 @@ +using System; +using System.Collections.Generic; +using System.Linq; +using System.Reflection; +using System.Runtime.CompilerServices; +using System.Text; +using TorchSharp; +using TorchSharp.PInvoke; +using TorchSharp.Utils; + + +namespace TorchSharp.BitsAndBytes +{ + //BASED ON: https://github.com/LittleLittleCloud/TorchSharp.BitsAndBytes + public class BitsAndByteUtils + { + /// + /// [methodname, quantized type, scalar type] -> [MethodInfo] + /// + static readonly Dictionary bitsandbyte_methods_natives = new Dictionary(); + public static void Initialize() + { + var methods = typeof(BitsAndBytesNatives).GetMethods(BindingFlags.Public | BindingFlags.Static | BindingFlags.DeclaredOnly) + .Where(x=>x.Name.StartsWith("cquantize") || + x.Name.StartsWith("cdequantize") || + x.Name.StartsWith("cgemm_4bit")); + foreach (var method in methods) { + bitsandbyte_methods_natives.Add(method.Name, method); + } + } + + + private static string GetScalarTypeString(torch.ScalarType st) + { + if (st == torch.ScalarType.Float32) + return "fp32"; + if (st == torch.ScalarType.BFloat16) + return "bf16"; + return "fp16"; + } + private static readonly Lazy> _4bitTypeCache = new Lazy>(); + public static ( + torch.Tensor quantizedTensor, + torch.Tensor absMax, + int blockSize, + int n + ) + Quantize4Bit( + torch.Tensor tensor, // input tensor + string quantizedDType = "fp4", // quantized data type, must be one of "fp4", "nf4" + int blockSize = 64 // block size + ) + { + var n = (int)tensor.numel(); + var blocks = (int)Math.Ceiling((double)n / blockSize); + var absMax = torch.zeros(new long[]{blocks}, dtype: torch.float32).cuda(); + var mod = 2; + var quantizedTensor = torch.zeros(new long[]{n+1, mod, 1}, dtype: torch.ScalarType.Byte).cuda(); + if(bitsandbyte_methods_natives.Count == 0) + Initialize(); + if(!bitsandbyte_methods_natives.TryGetValue($"cquantize_blockwise_{GetScalarTypeString(tensor.dtype)}_{quantizedDType}", out var m)) + throw new NotImplementedException(); + + m.Invoke( + null, + new object[]{ + IntPtr.Zero, + NativeMethods.THSStorage_data_ptr(tensor.Handle), + NativeMethods.THSStorage_data_ptr(absMax.Handle), + NativeMethods.THSStorage_data_ptr(quantizedTensor.Handle), + blockSize, + n + } + ); + return (quantizedTensor, absMax, blockSize, n); + } + + public static torch.Tensor Dequantize4Bit( + torch.Tensor tensor, // quantized tensor + torch.Tensor absMax, // absMax tensor + torch.ScalarType originalDType, // original data type + string quantizedDType, // quantized data type, must be one of "fp4", "nf4" + int n, + long[] originalShape, + int blockSize = 64, // block size + torch.ScalarType quantStorageDType = torch.ScalarType.Byte // quantized storage data type + ) + { + + var dequantizedTensor = torch.zeros(originalShape, dtype: originalDType).cuda(); + if (bitsandbyte_methods_natives.Count == 0) + Initialize(); + if (!bitsandbyte_methods_natives.TryGetValue($"cdequantize_blockwise_{GetScalarTypeString(originalDType)}_{quantizedDType}", out var m)) + throw new NotImplementedException(); + + m.Invoke( + null, + new object[]{ + IntPtr.Zero, + NativeMethods.THSStorage_data_ptr(tensor.Handle), + NativeMethods.THSStorage_data_ptr(absMax.Handle), + NativeMethods.THSStorage_data_ptr(dequantizedTensor.Handle), + blockSize, + n, + IntPtr.Zero + } + ); + return dequantizedTensor; + } + + public static torch.Tensor Get4BitType(string typename, string device = "cuda", int blocksize = 64) + { + if (_4bitTypeCache.Value.TryGetValue((typename, device, blocksize), out var cachedTensor)) { + return cachedTensor; + } + + float[] data = null; + + if (typename == "nf4") { + // Implements the NF4 data type. + // Constructs a quantization data type where each bin has equal area under a standard normal distribution N(0, 1) that + // is normalized into the range [-1, 1]. + data = new float[] { + -1.0f, + -0.6961928f, + -0.5250731f, + -0.3949175f, + -0.2844414f, + -0.1847734f, + -0.09105004f, + 0.0f, + 0.0795803f, + 0.1609302f, + 0.2461123f, + 0.3379152f, + 0.4407098f, + 0.562617f, + 0.7229568f, + 1.0f + }; + } + else if (typename == "fp4") { + data = new float[] + { + 0.0f, 0.0625f, 8.0f, 12.0f, 4.0f, 6.0f, 2.0f, 3.0f, + -0.0f, -0.0625f, -8.0f, -12.0f, -4.0f, -6.0f, -2.0f, -3.0f + }; + } + else if (typename == "int4") { + data = new float[] { 7, 6, 5, 4, 3, 2, 1, 0, -0, -1, -2, -3, -4, -5, -6, -7 }; + } + else if (typename == "af4") { + if (blocksize == 64) { + data = new float[] { + -1.0f, -0.69441008f, -0.51243739f, -0.3736951f, -0.25607552f, -0.14982478f, -0.04934812f, 0.0f, + 0.04273164f, 0.12934483f, 0.21961274f, 0.31675666f, 0.42563882f, 0.55496234f, 0.72424863f, 1.0f + }; + Array.Reverse(data); + } else { + throw new NotImplementedException("4-bit AbnormalFloats currently only support blocksize 64."); + } + } + + if (data == null) { + throw new NotImplementedException($"Typename {typename} not supported"); + } + + var tensor = torch.tensor(data, device: device); + tensor.div_(tensor.abs().max()); + + if (tensor.numel() != 16) { + throw new Exception("Tensor does not have 16 elements."); + } + + _4bitTypeCache.Value[(typename, device, blocksize)] = tensor; + tensor.DetachFromDisposeScope(); + return tensor; + } + + public static torch.Tensor Gemv4Bit( + torch.Tensor input, + torch.Tensor quantizedWeight, + long[] originalWeightShape, + torch.Tensor absMax, + int blockSize, + string quantizedDType) // quantized data type, must be one of "fp4", "nf4" + { + var inputShape = input.IntShape(); + if (input.numel() != inputShape[^1]) { + throw new ArgumentException("'Dimensions of A are invalid. Must be a vector with the leading dimensions of \"1\", e.g. [1, 1, 2048]'"); + } + var batch = inputShape[0]; + var inputDType = input.dtype; + var m = (int)originalWeightShape[0]; + var k = (int)originalWeightShape[1]; + var lda = (int)originalWeightShape[0]; + var ldc = (int)originalWeightShape[0]; + var ldb = (inputShape[^1] + 1) / 2; + torch.Tensor output; + if (inputShape.Length == 3) { + output = torch.zeros(new long[] { batch, inputShape[1], originalWeightShape[0]}, dtype: inputDType).cuda(); + } else { + output = torch.zeros(new long[]{batch, originalWeightShape[0]}, dtype: inputDType).cuda(); + } + + // quantize weight + var code = Get4BitType(quantizedDType, "cuda", blockSize); + + if (bitsandbyte_methods_natives.Count == 0) + Initialize(); + + if (!bitsandbyte_methods_natives.TryGetValue($"cgemm_4bit_inference_naive_{GetScalarTypeString(inputDType)}", out var mt)) + throw new NotImplementedException(); + + mt.Invoke(null, new object[] { + m,batch,k,input.GetDataPtr(), quantizedWeight.T.GetDataPtr(), + absMax.GetDataPtr(), + code.GetDataPtr(), + output.GetDataPtr(), + lda, + ldb, + ldc, + blockSize, + IntPtr.Zero + }); + return output; + } + + + public static torch.Tensor CreateDynamicMap(bool signed = true, int maxExponentBits = 7, int totalBits = 8) + { + var data = new List(); + int nonSignBits = totalBits - (signed ? 1 : 0); + int additionalItems = (int)Math.Pow(2, nonSignBits - maxExponentBits) - 1; + + for (int i = 0; i < maxExponentBits; i++) { + /*int fractionItems = signed + ? (int)Math.Pow(2, i + nonSignBits - maxExponentBits) + 1 + : (int)Math.Pow(2, i + nonSignBits - maxExponentBits + 1) + 1;*/ + + int fractionItems = (int)Math.Pow(2, i + nonSignBits - maxExponentBits + (signed ? 1 : 0)) + 1; + + var boundaries = torch.linspace(0.1, 1, fractionItems); + var means = (boundaries[..^1] + boundaries[1..]) / 2.0; + data.AddRange((torch.pow(10f, i - (maxExponentBits - 1)) * means).data().ToArray()); + + if (signed) { + data.AddRange((-(torch.pow(10f, (-(maxExponentBits - 1) + i)) * means)).data().ToArray()); + } + } + + if (additionalItems > 0) { + var boundaries = torch.linspace(0.1, 1, additionalItems + 1); + var means = (boundaries[..^1] + boundaries[1..]) / 2.0; + data.AddRange((torch.pow(10f, -(maxExponentBits - 1) + maxExponentBits - 1) * means).data().ToArray()); + + if (signed) { + data.AddRange((-(torch.pow(10f, -(maxExponentBits - 1) + maxExponentBits - 1) * means)).data().ToArray()); + } + } + + data.AddRange(new float[] { 0, 1.0f }); + + if (data.Count != (int)Math.Pow(2, totalBits)) { + int gap = 256 - data.Count; + for (int i = 0; i < gap; i++) { + data.Add(0); + } + } + + data.Sort(); + return torch.tensor(data.ToArray()); + } + + public static int[] CheckMatmul(torch.Tensor A, torch.Tensor B, bool transposed_A, bool transposed_B, torch.ScalarType expectedType = torch.ScalarType.Int8) + { + if (A.dtype != expectedType || B.dtype != expectedType) { + throw new ArgumentException($"Expected {expectedType} input tensors A and B, but got {A.dtype} and {B.dtype}"); + } + + var sA = A.IntShape(); + var sB = B.IntShape(); + var tA = transposed_A; + var tB = transposed_B; + + bool correct = true; + + if (sA.Length == 2 && sB.Length == 2) { + if (!tA && !tB && A.shape[1] != B.shape[0]) { + correct = false; + } else if (tA && !tB && A.shape[0] != B.shape[0]) { + correct = false; + } else if (tA && tB && A.shape[0] != B.shape[1]) { + correct = false; + } else if (!tA && tB && A.shape[1] != B.shape[1]) { + correct = false; + } + } else if (sA.Length == 3 && sB.Length == 2) { + if (!tA && !tB && A.shape[2] != B.shape[0]) { + correct = false; + } else if (tA && !tB && A.shape[1] != B.shape[0]) { + correct = false; + } else if (tA && tB && A.shape[1] != B.shape[1]) { + correct = false; + } else if (!tA && tB && A.shape[2] != B.shape[1]) { + correct = false; + } + } else if (sA.Length == 3 && sB.Length == 3) { + if (!tA && !tB && A.shape[2] != B.shape[1]) { + correct = false; + } else if (tA && !tB && A.shape[1] != B.shape[1]) { + correct = false; + } else if (tA && tB && A.shape[1] != B.shape[2]) { + correct = false; + } else if (!tA && tB && A.shape[2] != B.shape[2]) { + correct = false; + } + } + + int[] outShape = null; + + if (sA.Length == 2 && sB.Length == 2) { + if (!tA && !tB) { + outShape = new int[] { sA[0], sB[1] }; + } else if (tA && tB) { + outShape = new int[] { sA[1], sB[0] }; + } else if (tA && !tB) { + outShape = new int[] { sA[1], sB[1] }; + } else if (!tA && tB) { + outShape = new int[] { sA[0], sB[0] }; + } + } else if (sA.Length == 3 && sB.Length == 2) { + if (!tA && !tB) { + outShape = new int[] { sA[0], sA[1], sB[1] }; + } else if (tA && tB) { + outShape = new int[] { sA[0], sA[2], sB[0] }; + } else if (tA && !tB) { + outShape = new int[] { sA[0], sA[2], sB[1] }; + } else if (!tA && tB) { + outShape = new int[]{sA[0], sA[1], sB[0]}; + } + } else if (sA.Length == 3 && sB.Length == 3) { + if (!tA && !tB) { + outShape = new int[] { sA[0], sA[1], sB[2] }; + } else if (tA && tB) { + outShape = new int[] { sA[0], sA[2], sB[1] }; + } else if (tA && !tB) { + outShape = new int[] { sA[0], sA[2], sB[2] }; + } else if (!tA && tB) { + outShape = new int[] { sA[0], sA[1], sB[1] }; + } + } + + if (!correct) { + throw new ArgumentException( + $"Tensor dimensions incorrect for matrix multiplication: A x B: {sA.ToArray()} x {sB.ToArray()} with transpose for A x B: {tA} x {tB}." + ); + } + + return outShape; + } + } +} diff --git a/src/TorchSharp/BitsAndBytes/BitsAndBytesNatives.cs b/src/TorchSharp/BitsAndBytes/BitsAndBytesNatives.cs new file mode 100644 index 000000000..51a8902be --- /dev/null +++ b/src/TorchSharp/BitsAndBytes/BitsAndBytesNatives.cs @@ -0,0 +1,225 @@ +using System; +using System.Collections.Generic; +using System.Runtime.InteropServices; +using System.Text; + +namespace TorchSharp.BitsAndBytes +{ + //BASED ON: https://github.com/LittleLittleCloud/TorchSharp.BitsAndBytes + public static class BitsAndBytesNatives + { + private const string DllName = "libbitsandbytes"; + + [DllImport(DllName)] + public static extern void cdequantize_blockwise_fp32_fp4( + IntPtr code, // float* + IntPtr A, // float* + IntPtr absmax, // float* + IntPtr output, // unsigned char* + int blocksize, + int n, // total size + IntPtr stream); + + [DllImport(DllName)] + public static extern void cdequantize_blockwise_fp32_nf4( + IntPtr code, // float* + IntPtr A, // float* + IntPtr absmax, // float* + IntPtr output, // unsigned char* + int blocksize, + int n, // total size + IntPtr stream); + + [DllImport(DllName)] + public static extern void cdequantize_blockwise_fp16_fp4( + IntPtr code, // float* + IntPtr A, // float* + IntPtr absmax, // float* + IntPtr output, // unsigned char* + int blocksize, + int n, // total size + IntPtr stream); + + [DllImport(DllName)] + public static extern void cdequantize_blockwise_fp16_nf4( + IntPtr code, // float* + IntPtr A, // float* + IntPtr absmax, // float* + IntPtr output, // unsigned char* + int blocksize, + int n, // total size + IntPtr stream); + + [DllImport(DllName)] + public static extern void cdequantize_blockwise_bf16_fp4( + IntPtr code, // float* + IntPtr A, // float* + IntPtr absmax, // float* + IntPtr output, // unsigned char* + int blocksize, + int n, // total size + IntPtr stream); + + [DllImport(DllName)] + public static extern void cdequantize_blockwise_bf16_nf4( + IntPtr code, // float* + IntPtr A, // float* + IntPtr absmax, // float* + IntPtr output, // unsigned char* + int blocksize, + int n, // total size + IntPtr stream + ); + + [DllImport(DllName)] + public static extern void cquantize_blockwise_fp32_fp4( + IntPtr code, // float* + IntPtr A, // float* + IntPtr absmax, // float* + IntPtr output, // unsigned char* + int blocksize, + int n // total size + ); + + [DllImport(DllName)] + public static extern void cquantize_blockwise_fp32_nf4( + IntPtr code, // float* + IntPtr A, // float* + IntPtr absmax, // float* + IntPtr output, // unsigned char* + int blocksize, + int n // total size + ); + + [DllImport(DllName)] + public static extern void cquantize_blockwise_fp32( + IntPtr code, // float* + IntPtr A, // float* + IntPtr absmax, // float* + IntPtr output, // unsigned char* + int blocksize, + int n // total size + ); + + [DllImport(DllName)] + public static extern void cquantize_blockwise_fp16_fp4( + IntPtr code, // float* + IntPtr A, // float* + IntPtr absmax, // float* + IntPtr output, // unsigned char* + int blocksize, + int n // total size + ); + + [DllImport(DllName)] + public static extern void cquantize_blockwise_fp16_nf4( + IntPtr code, // float* + IntPtr A, // float* + IntPtr absmax, // float* + IntPtr output, // unsigned char* + int blocksize, + int n // total size + ); + + [DllImport(DllName, CallingConvention = CallingConvention.Cdecl)] + public static extern void cquantize_blockwise_bf16_fp4( + IntPtr code, // float* + IntPtr A, // __nv_bfloat16* + IntPtr absmax, // float* + IntPtr output, // unsigned char* + int blocksize, + int n // total size + ); + + [DllImport(DllName, CallingConvention = CallingConvention.Cdecl)] + public static extern void cquantize_blockwise_bf16_nf4( + IntPtr code, // float* + IntPtr A, // __nv_bfloat16* + IntPtr absmax, // float* + IntPtr output, // unsigned char* + int blocksize, + int n // total size + ); + + [DllImport(DllName)] + public static extern void cgemm_4bit_inference_naive_fp16( + int m, + int n, + int k, + IntPtr A, // half* + IntPtr B, // unsigned char* + IntPtr absmax, // float* + IntPtr datatype, // float* + IntPtr output, // half* + int lda, + int ldb, + int ldc, + int blocksize, + IntPtr stream // cudaStream_t + ); + + [DllImport(DllName)] + public static extern void cgemm_4bit_inference_naive_fp32( + int m, + int n, + int k, + IntPtr A, // half* + IntPtr B, // unsigned char* + IntPtr absmax, // float* + IntPtr datatype, // float* + IntPtr output, // half* + int lda, + int ldb, + int ldc, + int blocksize, + IntPtr stream // cudaStream_t + ); + + [DllImport(DllName)] + public static extern void cgemm_4bit_inference_naive_bf16( + int m, + int n, + int k, + IntPtr A, // half* + IntPtr B, // unsigned char* + IntPtr absmax, // float* + IntPtr datatype, // float* + IntPtr output, // half* + int lda, + int ldb, + int ldc, + int blocksize, + IntPtr stream // cudaStream_t + ); + + [DllImport(DllName, CallingConvention = CallingConvention.Cdecl)] + public static extern void dequantize( + IntPtr output, // float* + IntPtr input, // byte* + IntPtr scale, // float* + int size, + IntPtr stream // cudaStream_t + ); + + [DllImport(DllName, CallingConvention = CallingConvention.Cdecl)] + public static extern void cigemm( + IntPtr context, + bool transposeA, + bool transposeB, + int m, + int n, + int k, + IntPtr A, // input + IntPtr B, // weight + IntPtr C, // output + int lda, + int ldb, + int ldc); + + [DllImport(DllName, CallingConvention = CallingConvention.Cdecl)] + public static extern IntPtr get_context(); + + [DllImport(DllName, CallingConvention = CallingConvention.Cdecl)] + public static extern IntPtr get_cusparse(); + } +} diff --git a/src/TorchSharp/NN/Module.cs b/src/TorchSharp/NN/Module.cs index f7309ed51..efd8df767 100644 --- a/src/TorchSharp/NN/Module.cs +++ b/src/TorchSharp/NN/Module.cs @@ -1104,6 +1104,12 @@ internal T MoveModule(Device device, ScalarType? dtype) where T : Module { T module = (T)this; + //https://github.com/dotnet/TorchSharp/issues/1438 + var d = torch.get_default_device(); + if (device == null || d != device) { + device = d; + } + return device != null ? (dtype.HasValue ? (T)module._to(device, dtype.Value, false) : (T)module._to(device.type, device.index, false)) : (dtype.HasValue ? (T)module._to(dtype.Value, false) : module); diff --git a/src/TorchSharp/PInvoke/LibTorchSharp.THSTensor.cs b/src/TorchSharp/PInvoke/LibTorchSharp.THSTensor.cs index b059f0b88..ffc10b4bc 100644 --- a/src/TorchSharp/PInvoke/LibTorchSharp.THSTensor.cs +++ b/src/TorchSharp/PInvoke/LibTorchSharp.THSTensor.cs @@ -2,6 +2,7 @@ #nullable enable using System; using System.Runtime.InteropServices; +using System.Security; using TorchSharp.Modules; namespace TorchSharp.PInvoke diff --git a/src/TorchSharp/Tensor/Tensor.cs b/src/TorchSharp/Tensor/Tensor.cs index 2f81e378c..08e9608a2 100644 --- a/src/TorchSharp/Tensor/Tensor.cs +++ b/src/TorchSharp/Tensor/Tensor.cs @@ -111,6 +111,11 @@ public Span GetRawData() } } + internal IntPtr GetDataPtr() + { + return NativeMethods.THSStorage_data_ptr(handle); + } + /// /// TODO /// diff --git a/src/TorchSharp/Utils/UnorderedMap.cs b/src/TorchSharp/Utils/UnorderedMap.cs index 3579f3cee..980147561 100644 --- a/src/TorchSharp/Utils/UnorderedMap.cs +++ b/src/TorchSharp/Utils/UnorderedMap.cs @@ -24,7 +24,23 @@ public bool ContainsKey(TKey1 key1, TKey2 key2) return base.ContainsKey(Tuple.Create(key1, key2)); } } + public class Dictionary : Dictionary, TValue>, IDictionary, TValue> + { + public TValue this[TKey1 key1, TKey2 key2, TKey3 key3] { + get { return base[Tuple.Create(key1, key2, key3)]; } + set { base[Tuple.Create(key1, key2, key3)] = value; } + } + + public void Add(TKey1 key1, TKey2 key2, TKey3 key3, TValue value) + { + base.Add(Tuple.Create(key1, key2, key3), value); + } + public bool ContainsKey(TKey1 key1, TKey2 key2, TKey3 key3) + { + return base.ContainsKey(Tuple.Create(key1, key2, key3)); + } + } public class UnorderedMap : Dictionary, IDisposable { bool disposedValue; diff --git a/src/TorchVision/File.cs b/src/TorchVision/File.cs index ea0c48cff..0c2d61c9f 100644 --- a/src/TorchVision/File.cs +++ b/src/TorchVision/File.cs @@ -33,7 +33,8 @@ public static async Task read_file_async(string filename) { byte[] data; - using (FileStream stream = File.Open(filename, FileMode.Open)) { + //FileShare.ReadWrite allow another process read or write this file + using (FileStream stream = File.Open(filename, FileMode.Open, FileAccess.Read, FileShare.ReadWrite)) { data = new byte[stream.Length]; await stream.ReadAsync(data, 0, data.Length); } diff --git a/src/TorchVision/IO/Image.cs b/src/TorchVision/IO/Image.cs index 691d24b7f..8961c9e9c 100644 --- a/src/TorchVision/IO/Image.cs +++ b/src/TorchVision/IO/Image.cs @@ -136,7 +136,7 @@ public enum ImageReadMode /// public static Tensor read_image(string filename, ImageReadMode mode = ImageReadMode.UNCHANGED, Imager imager = null) { - using (FileStream stream = File.Open(filename, FileMode.Open)) + using (FileStream stream = File.Open(filename, FileMode.Open, FileAccess.Read, FileShare.ReadWrite)) return (imager ?? DefaultImager).DecodeImage(stream, mode); } @@ -167,7 +167,7 @@ public static Tensor read_image(Stream stream, ImageReadMode mode = ImageReadMod public static async Task read_image_async(string filename, ImageReadMode mode = ImageReadMode.UNCHANGED, Imager imager = null) { - using (FileStream stream = File.Open(filename, FileMode.Open)) + using (FileStream stream = File.Open(filename, FileMode.Open, FileAccess.Read, FileShare.ReadWrite)) return await (imager ?? DefaultImager).DecodeImageAsync(stream, mode); } diff --git a/src/TorchVision/Ops/DeformConv2d.cs b/src/TorchVision/Ops/DeformConv2d.cs index 75d723b58..18762b8ff 100644 --- a/src/TorchVision/Ops/DeformConv2d.cs +++ b/src/TorchVision/Ops/DeformConv2d.cs @@ -4,9 +4,11 @@ using System.Text; using System.Threading.Tasks; using TorchSharp; +using TorchSharp.Modules; using TorchVision.Modules; using static TorchSharp.torch; +#nullable enable namespace TorchVision { public static partial class torchvision @@ -15,22 +17,142 @@ public static partial class ops { public static Modules.DeformConv2d DeformConv2d() { - return new DeformConv2d(); + throw new NotImplementedException(); + //return new DeformConv2d(); } } } namespace Modules { - public class DeformConv2d : torch.nn.Module + //https://github.com/dotnet/TorchSharp/issues/1472 + public class DeformConv2d : torch.nn.Module { - protected internal DeformConv2d() : base(nameof(DeformConv2d)) + /* + * + *import torch + import torch.nn as nn + import torch.nn.functional as F + + class DeformConv2d(nn.Module): + def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False): + super(DeformConv2d, self).__init__() + + self.in_channels = in_channels + self.out_channels = out_channels + self.kernel_size = (kernel_size, kernel_size) + self.stride = (stride, stride) + self.padding = (padding, padding) + + self.weight = nn.Parameter(torch.Tensor(out_channels, in_channels, *self.kernel_size)) + + self.bias = nn.Parameter(torch.Tensor(out_channels)) if bias else None + + self.reset_parameters() + + def reset_parameters(self): + nn.init.kaiming_uniform_(self.weight, a=0, mode='fan_in', nonlinearity='leaky_relu') + if self.bias is not None: + nn.init.constant_(self.bias, 0) + + def forward(self, x, offset): + + N, _, H_in, W_in = x.size() + C_out, C_in, Kh, Kw = self.weight.size() + H_out = (H_in + 2 * self.padding[0] - Kh) // self.stride[0] + 1 + W_out = (W_in + 2 * self.padding[1] - Kw) // self.stride[1] + 1 + + + p_x = torch.arange(-(Kw - 1) // 2, (Kw - 1) // 2 + 1) + p_y = torch.arange(-(Kh - 1) // 2, (Kh - 1) // 2 + 1) + p_x, p_y = torch.meshgrid(p_x, p_y, indexing='ij') + p = torch.cat([p_x.flatten(), p_y.flatten()], 0).view(1, 2 * Kh * Kw, 1, 1).to(x.device, x.dtype) + + g_y = torch.arange(0, H_out * self.stride[0], self.stride[0]) + g_x = torch.arange(0, W_out * self.stride[1], self.stride[1]) + g_x, g_y = torch.meshgrid(g_x, g_y, indexing='ij') + grid = torch.cat([g_x.flatten(), g_y.flatten()], 0).view(1, 2, H_out, W_out).to(x.device, x.dtype) + grid = grid.repeat(N, 1, 1, 1) + + p = p.view(1, 2, Kh * Kw, 1, 1) + grid = grid.unsqueeze(2) + offset = offset.view(N, 2, Kh * Kw, H_out, W_out) + + vgrid = grid + p + offset + + vgrid_x = 2.0 * vgrid[:, 0, ...] / max(W_in - 1, 1) - 1.0 + vgrid_y = 2.0 * vgrid[:, 1, ...] / max(H_in - 1, 1) - 1.0 + + normalized_grid = torch.stack([vgrid_x, vgrid_y], dim=-1) + + sampled_features = F.grid_sample( + x.unsqueeze(2).expand(-1, -1, Kh * Kw, -1, -1).reshape(N * C_in, Kh * Kw, H_in, W_in), + normalized_grid.view(N * C_in, Kh * Kw, H_out, W_out, 2), + mode='bilinear', padding_mode='zeros', align_corners=False + ).view(N, C_in, Kh * Kw, H_out, W_out) + + output = torch.einsum('nikhw,oik->nohw', sampled_features, self.weight.view(C_out, C_in, Kh * Kw)) + + if self.bias is not None: + output += self.bias.view(1, -1, 1, 1) + + return output + */ + private Parameter? bias; + private Parameter weight; + private Conv2d offset_conv; + private bool? use_bias; + private int kernel_size; + private long[] strides; + private long[] padding; + private long[] dilation; + private long groups; + protected internal DeformConv2d(int in_channels, int out_channels, int kernel_size, int stride=1, int padding=1, int dilation=1, int groups=1, bool? bias=false) : base(nameof(DeformConv2d)) { + this.strides = new long[] { stride, stride }; + this.padding= new long[] { padding,padding}; + this.dilation= new long[] { dilation,dilation}; + this.groups = groups; + + use_bias = bias; + this.kernel_size = kernel_size; + if (use_bias.HasValue && use_bias.Value) { + this.bias = new Parameter(torch.zeros(out_channels)); + } else { + base.register_parameter("bias", null); + } + + weight = new Parameter(torch.zeros(out_channels, in_channels / groups, kernel_size, kernel_size)); + offset_conv = torch.nn.Conv2d(in_channels, 2 * kernel_size * kernel_size, (kernel_size, kernel_size), + (stride, stride), (padding, padding), (dilation, dilation), bias: true); + ResetParameters(); } - public override Tensor forward(Tensor input, Tensor offset, Tensor mask) + + private void ResetParameters() { - throw new NotImplementedException(); + torch.nn.init.kaiming_uniform_(weight, Math.Sqrt(5)); + if (use_bias.HasValue) { + long fanin = torch.nn.init.CalculateFanInAndFanOut(weight).fanIn; + var bound = 1 / Math.Sqrt(fanin); + torch.nn.init.uniform_(bias, -bound, bound); + } + } + //TODO: Implement with offset too ??? + public override Tensor forward(Tensor input) + { + var offset = offset_conv.forward(input); + offset = offset.contiguous().view(new long[] { -1, 2, kernel_size, kernel_size }); + input = torch.nn.functional.conv2d(input, weight, bias, strides, padding, dilation, groups); + return input; + } + + protected override void Dispose(bool disposing) + { + base.Dispose(disposing); + this.bias?.Dispose(); + this.weight?.Dispose(); + this.offset_conv?.Dispose(); } } } diff --git a/test/Directory.Build.props b/test/Directory.Build.props index 896219d54..a276d98e8 100644 --- a/test/Directory.Build.props +++ b/test/Directory.Build.props @@ -3,7 +3,7 @@ net6.0 - $(TargetFrameworks);net48 + $(TargetFrameworks);net48;netstandard2.0 false true From ad617a2a3c972a95af8c365a1e1decf2adcfb9b1 Mon Sep 17 00:00:00 2001 From: Dimitri Date: Sat, 20 Sep 2025 20:07:41 -0300 Subject: [PATCH 51/65] may supporting index net2.0 --- src/TorchSharp/Amp/GradScaler.cs | 6 +-- src/TorchSharp/Tensor/Storage.cs | 5 +++ src/TorchSharp/Torch.cs | 12 ++++++ src/TorchSharp/Utils/BFloat16.cs | 3 -- src/TorchSharp/Utils/GetSubArray.cs | 59 +++++++++++++++++++++++++++++ 5 files changed, 79 insertions(+), 6 deletions(-) create mode 100644 src/TorchSharp/Utils/GetSubArray.cs diff --git a/src/TorchSharp/Amp/GradScaler.cs b/src/TorchSharp/Amp/GradScaler.cs index 4aef1a249..a19438695 100644 --- a/src/TorchSharp/Amp/GradScaler.cs +++ b/src/TorchSharp/Amp/GradScaler.cs @@ -14,9 +14,9 @@ public class GradScaler : IDisposable public torch.Device device; private torch.Tensor _scale, _growth_tracker; private float InitScale, InitGrowthTracker; - public float _growth_factor { set; get; } - public float _backoff_factor { set; get; } - private int _growth_interval { set; get; } + public float _growth_factor; + public float _backoff_factor; + private int _growth_interval; private UnorderedMap> _per_optimizer_states = new UnorderedMap>(); bool disposedValue; diff --git a/src/TorchSharp/Tensor/Storage.cs b/src/TorchSharp/Tensor/Storage.cs index 35515c054..797132b7d 100644 --- a/src/TorchSharp/Tensor/Storage.cs +++ b/src/TorchSharp/Tensor/Storage.cs @@ -45,6 +45,10 @@ internal static Storage Create(Tensor tensor) where T : unmanaged return new Storage(tensor.@long()); case Type _ when type == typeof(float): return new Storage(tensor.@float()); + case Type _ when type == typeof(Half): + return new Storage(tensor.to_type(ScalarType.Float16)); + case Type _ when type == typeof(BFloat16): + return new Storage(tensor.to_type(ScalarType.BFloat16)); case Type _ when type == typeof(double): return new Storage(tensor.@double()); case Type _ when type == typeof((float,float)): @@ -58,6 +62,7 @@ internal static Storage Create(Tensor tensor) where T : unmanaged protected static Tensor CreateTypedTensor(ScalarType dtype, IList rawArray) { + //TODO: ADD Half and BFloat16 switch (dtype) { case ScalarType.Int8: return torch.tensor(rawArray as IList); diff --git a/src/TorchSharp/Torch.cs b/src/TorchSharp/Torch.cs index 87754d876..97701bb1f 100644 --- a/src/TorchSharp/Torch.cs +++ b/src/TorchSharp/Torch.cs @@ -662,6 +662,18 @@ public static void CheckForErrors() } } + /// + /// Refactor all Tensors with this method for example the LinearAlgebra.cs of cholesky we can just put return ; + /// public static Tensor cholesky(Tensor input) => ReturnCheckForErrors(THSLinalg_cholesky(input.Handle)); + /// + /// + /// + public static Tensor ReturnCheckForErrors(IntPtr ptr) + { + if(ptr == IntPtr.Zero) + CheckForErrors(); + return new Tensor(ptr); + } public static partial class backends { public static partial class cuda diff --git a/src/TorchSharp/Utils/BFloat16.cs b/src/TorchSharp/Utils/BFloat16.cs index e60636d07..375c91b20 100644 --- a/src/TorchSharp/Utils/BFloat16.cs +++ b/src/TorchSharp/Utils/BFloat16.cs @@ -1,7 +1,4 @@ -using System; -using System.Collections.Generic; using System.Runtime.InteropServices; -using System.Text; using TorchSharp.PInvoke; namespace System diff --git a/src/TorchSharp/Utils/GetSubArray.cs b/src/TorchSharp/Utils/GetSubArray.cs new file mode 100644 index 000000000..ddaab4ed2 --- /dev/null +++ b/src/TorchSharp/Utils/GetSubArray.cs @@ -0,0 +1,59 @@ +//NOTE: This make compatibility of Range with NetStandard2.0 may need include System.Runtime.InteropServices.RuntimeInformation + +#if NETSTANDARD2_0 +#region License +// MIT License +// +// Copyright (c) Manuel Römer +// +// Permission is hereby granted, free of charge, to any person obtaining a copy +// of this software and associated documentation files (the "Software"), to deal +// in the Software without restriction, including without limitation the rights +// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +// copies of the Software, and to permit persons to whom the Software is +// furnished to do so, subject to the following conditions: +// +// The above copyright notice and this permission notice shall be included in all +// copies or substantial portions of the Software. +// +// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +// SOFTWARE. +#endregion + +namespace System.Runtime.CompilerServices +{ + public static class RuntimeHelpers + { + public static T[] GetSubArray(T[] array, Range range) + { + var (offset, length) = range.GetOffsetAndLength(array.Length); + if (length == 0) + return Array.Empty(); + T[] dest; + if (typeof(T).IsValueType || typeof(T[]) == array.GetType()) { + // We know the type of the array to be exactly T[] or an array variance + // compatible value type substitution like int[] <-> uint[]. + + if (length == 0) { + return Array.Empty(); + } + + dest = new T[length]; + } else { + // The array is actually a U[] where U:T. We'll make sure to create + // an array of the exact same backing type. The cast to T[] will + // never fail. + + dest = (T[])(Array.CreateInstance(array.GetType().GetElementType()!, length)); + } + Array.Copy(array, offset, dest, 0, length); + return dest; + } + } +} +#endif \ No newline at end of file From 69e2ec081090690f5f5f2c0fc113efe07a835b88 Mon Sep 17 00:00:00 2001 From: Dimitri Date: Sat, 20 Sep 2025 20:26:07 -0300 Subject: [PATCH 52/65] Refactor ReturnCheckForErros on LinearAlgebra and Math --- src/TorchSharp/Tensor/Tensor.LinearAlgebra.cs | 72 +--- src/TorchSharp/Tensor/Tensor.Math.cs | 351 ++++-------------- src/TorchSharp/Torch.cs | 8 + 3 files changed, 104 insertions(+), 327 deletions(-) diff --git a/src/TorchSharp/Tensor/Tensor.LinearAlgebra.cs b/src/TorchSharp/Tensor/Tensor.LinearAlgebra.cs index 625b1a093..8fa3d2649 100644 --- a/src/TorchSharp/Tensor/Tensor.LinearAlgebra.cs +++ b/src/TorchSharp/Tensor/Tensor.LinearAlgebra.cs @@ -31,10 +31,8 @@ public Tensor tensordot(Tensor b, long[] dims1, long[] dims2) res = THSLinalg_tensordot(Handle, b.Handle,(IntPtr)pdims1, dims1.Length,(IntPtr)pdims2, dims2.Length); } } - if (res == IntPtr.Zero) { - CheckForErrors(); - } - return new Tensor(res); + + return ReturnCheckForErrors(res); } // https://pytorch.org/docs/stable/generated/torch.tensordot @@ -75,9 +73,7 @@ public Tensor tensordot(Tensor b, long dims = 2) /// public Tensor cholesky(bool upper = false) { - var res = THSTensor_cholesky(Handle, upper); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_cholesky(Handle, upper)); } /// @@ -87,9 +83,7 @@ public Tensor cholesky(bool upper = false) /// public Tensor cholesky_inverse(bool upper = false) { - var res = THSTensor_cholesky_inverse(Handle, upper); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_cholesky_inverse(Handle, upper)); } /// @@ -100,9 +94,7 @@ public Tensor cholesky_inverse(bool upper = false) /// public Tensor cholesky_solve(Tensor input2, bool upper = false) { - var res = THSTensor_cholesky_solve(Handle, input2.Handle, upper); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_cholesky_solve(Handle, input2.Handle, upper)); } /// @@ -115,9 +107,7 @@ public Tensor cholesky_solve(Tensor input2, bool upper = false) /// public Tensor cross(Scalar other, long dim) { - var res = THSTensor_cross(Handle, other.Handle, dim); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_cross(Handle, other.Handle, dim)); } public Tensor cross(Tensor other, long dim) { @@ -128,9 +118,8 @@ public Tensor cross(Tensor other, long dim) if (sts.Any(x => x == ScalarType.Float32)) (handle, other.handle) = AutocastMode.AutoCast(handle, other.handle, ScalarType.Float32); } - var res = THSTensor_cross(Handle, other.Handle, dim); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + + return ReturnCheckForErrors(THSTensor_cross(Handle, other.Handle, dim)); } /// /// Computes the determinant of a square matrix. @@ -150,9 +139,7 @@ public Tensor logdet() var len = shape.Length; if (shape[len - 1] != shape[len - 2]) throw new ArgumentException("The input tensor is not square"); - var res = THSTensor_logdet(Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_logdet(Handle)); } @@ -190,10 +177,7 @@ public Tensor logdet() /// public Tensor matmul(Tensor target) { - var res = THSTensor_matmul(Handle, target.Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - res = AutocastMode.AutoCast(res); - return new Tensor(res); + return ReturnCheckForErrorsAutocast(THSTensor_matmul(Handle, target.Handle)); } /// @@ -203,10 +187,7 @@ public Tensor matmul(Tensor target) /// public Tensor mm(Tensor target) { - var res = THSTensor_mm(Handle, target.Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - res = AutocastMode.AutoCast(res); - return new Tensor(res); + return ReturnCheckForErrorsAutocast(THSTensor_mm(Handle, target.Handle)); } /// @@ -216,10 +197,7 @@ public Tensor mm(Tensor target) /// public Tensor mv(Tensor target) { - var res = THSTensor_mv(Handle, target.Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - res = AutocastMode.AutoCast(res); - return new Tensor(res); + return ReturnCheckForErrorsAutocast(THSTensor_mv(Handle, target.Handle)); } /// @@ -227,9 +205,7 @@ public Tensor mv(Tensor target) /// public Tensor matrix_exp() { - var res = THSTensor_matrix_exp(Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_matrix_exp(Handle)); } /// @@ -240,9 +216,7 @@ public Tensor matrix_exp() /// Input tensor must be of shape (*, m, m) where * is zero or more batch dimensions. public Tensor matrix_power(int n) { - var res = THSLinalg_matrix_power(Handle, n); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSLinalg_matrix_power(Handle, n)); } /// @@ -256,9 +230,7 @@ public Tensor matrix_power(int n) public Tensor vdot(Tensor target) { if (shape.Length != 1 || target.shape.Length != 1 || shape[0] != target.shape[0]) throw new InvalidOperationException("vdot arguments must have the same shape."); - var res = THSTensor_vdot(Handle, target.Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_vdot(Handle, target.Handle)); } /// @@ -275,9 +247,7 @@ public Tensor dot(Tensor target) if (sts.Any(x => x == ScalarType.Float32)) (handle, target.handle) = AutocastMode.AutoCast(handle, target.handle, ScalarType.Float32); } - var res = THSTensor_dot(Handle, target.Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_dot(Handle, target.Handle)); } /// @@ -289,10 +259,7 @@ public Tensor dot(Tensor target) /// public Tensor pinverse(double rcond = 1e-15, bool hermitian = false) { - var res = THSLinalg_pinverse(Handle, rcond, hermitian); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(THSLinalg_pinverse(Handle, rcond, hermitian)); } /// @@ -305,10 +272,7 @@ public Tensor pinverse(double rcond = 1e-15, bool hermitian = false) /// public Tensor ormqr(Tensor tau, Tensor other, bool left = true, bool transpose = false) { - var res = THSTensor_ormqr(Handle, tau.handle, other.Handle, left, transpose); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_ormqr(Handle, tau.handle, other.Handle, left, transpose)); } } } diff --git a/src/TorchSharp/Tensor/Tensor.Math.cs b/src/TorchSharp/Tensor/Tensor.Math.cs index cd7e39e6c..6e63d4ba0 100644 --- a/src/TorchSharp/Tensor/Tensor.Math.cs +++ b/src/TorchSharp/Tensor/Tensor.Math.cs @@ -24,10 +24,7 @@ public partial class Tensor /// public Tensor abs() { - var res = THSTensor_abs(Handle); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_abs(Handle)); } /// @@ -68,10 +65,7 @@ public Tensor add(Tensor target) /// public Tensor add(Tensor target, Scalar alpha) { - var res = THSTensor_add(Handle, target.Handle, alpha.Handle); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_add(Handle, target.Handle, alpha.Handle)); } /// @@ -92,10 +86,7 @@ public Tensor add(Scalar scalar) /// public Tensor add(Scalar scalar, Scalar alpha) { - var res = THSTensor_add_scalar(Handle, scalar.Handle, alpha.Handle); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_add_scalar(Handle, scalar.Handle, alpha.Handle)); } /// @@ -156,11 +147,7 @@ public Tensor add_(Scalar scalar, Scalar alpha) /// public Tensor addbmm(Tensor batch1, Tensor batch2, float beta = 1, float alpha = 1) { - var res = THSTensor_addbmm(Handle, batch1.Handle, batch2.Handle, beta, alpha); - if (res == IntPtr.Zero) - CheckForErrors(); - res = AutocastMode.AutoCast(res); - return new Tensor(res); + return ReturnCheckForErrorsAutocast(THSTensor_addbmm(Handle, batch1.Handle, batch2.Handle, beta, alpha)); } /// @@ -200,10 +187,7 @@ public Tensor addcdiv(Tensor tensor1, Tensor tensor2, Scalar value) (handle, tensor1.handle, tensor2.handle) = AutocastMode.AutoCast(handle, tensor1.handle, tensor2.handle, ScalarType.Float32); //TODO: Should check Bfloat16? } - var res = THSTensor_addcdiv(Handle, tensor1.Handle, tensor2.Handle, value.Handle); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_addcdiv(Handle, tensor1.Handle, tensor2.Handle, value.Handle)); } /// @@ -268,10 +252,7 @@ public Tensor addcmul(Tensor tensor1, Tensor tensor2, Scalar value) (handle, tensor1.handle, tensor2.handle) = AutocastMode.AutoCast(handle, tensor1.handle, tensor2.handle, ScalarType.Float32); } - var res = THSTensor_addcmul(Handle, tensor1.Handle, tensor2.Handle, value.Handle); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_addcmul(Handle, tensor1.Handle, tensor2.Handle, value.Handle)); } /// @@ -298,11 +279,7 @@ public Tensor addcmul_(Tensor tensor1, Tensor tensor2, Scalar value) /// public Tensor addmm(Tensor mat1, Tensor mat2, float beta = 1, float alpha = 1) { - var res = THSTensor_addmm(Handle, mat1.Handle, mat2.Handle, beta, alpha); - if (res == IntPtr.Zero) - CheckForErrors(); - res = AutocastMode.AutoCast(res); - return new Tensor(res); + return ReturnCheckForErrorsAutocast(THSTensor_addmm(Handle, mat1.Handle, mat2.Handle, beta, alpha)); } /// @@ -330,11 +307,7 @@ public Tensor addmm_(Tensor mat1, Tensor mat2, float beta = 1, float alpha = 1) /// public Tensor addmv(Tensor mat, Tensor vec, float beta = 1.0f, float alpha = 1.0f) { - var res = THSTensor_addmv(Handle, mat.Handle, vec.Handle, beta, alpha); - if (res == IntPtr.Zero) - CheckForErrors(); - res = AutocastMode.AutoCast(res); - return new Tensor(res); + return ReturnCheckForErrorsAutocast(THSTensor_addmv(Handle, mat.Handle, vec.Handle, beta, alpha)); } /// @@ -362,11 +335,7 @@ public Tensor addmv_(Tensor mat, Tensor vec, float beta = 1.0f, float alpha = 1. /// public Tensor addr(Tensor vec1, Tensor vec2, float beta = 1.0f, float alpha = 1.0f) { - var res = THSTensor_addr(Handle, vec1.Handle, vec2.Handle, beta, alpha); - if (res == IntPtr.Zero) - CheckForErrors(); - res = AutocastMode.AutoCast(res); - return new Tensor(res); + return ReturnCheckForErrorsAutocast(THSTensor_addr(Handle, vec1.Handle, vec2.Handle, beta, alpha)); } /// @@ -393,9 +362,7 @@ public Tensor addr_(Tensor vec1, Tensor vec2, float beta = 1.0f, float alpha = 1 /// public Tensor bitwise_and(Tensor other) { - var res = THSTensor_bitwise_and(Handle, other.Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_bitwise_and(Handle, other.Handle)); } /// @@ -416,9 +383,7 @@ public Tensor bitwise_and_(Tensor other) /// public Tensor bitwise_not() { - var res = THSTensor_bitwise_not(Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_bitwise_not(Handle)); } /// @@ -439,9 +404,7 @@ public Tensor bitwise_not_() /// public Tensor bitwise_or(Tensor other) { - var res = THSTensor_bitwise_or(Handle, other.Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_bitwise_or(Handle, other.Handle)); } /// @@ -463,9 +426,7 @@ public Tensor bitwise_or_(Tensor other) /// public Tensor bitwise_xor(Tensor other) { - var res = THSTensor_bitwise_xor(Handle, other.Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_bitwise_xor(Handle, other.Handle)); } /// @@ -487,9 +448,7 @@ public Tensor bitwise_xor_(Tensor other) /// public Tensor bitwise_left_shift(Tensor other) { - var res = THSTensor_bitwise_left_shift(Handle, other.Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_bitwise_left_shift(Handle, other.Handle)); } /// @@ -511,9 +470,7 @@ public Tensor bitwise_left_shift_(Tensor other) /// public Tensor bitwise_right_shift(Tensor other) { - var res = THSTensor_bitwise_right_shift(Handle, other.Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_bitwise_right_shift(Handle, other.Handle)); } /// @@ -534,10 +491,7 @@ public Tensor bitwise_right_shift_(Tensor other) /// public Tensor ceil() { - var res = THSTensor_ceil(Handle); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_ceil(Handle)); } /// @@ -557,10 +511,7 @@ public Tensor ceil_() /// public Tensor conj() { - var res = THSTensor_conj(Handle); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_conj(Handle)); } /// @@ -569,10 +520,7 @@ public Tensor conj() /// public Tensor conj_physical() { - var res = THSTensor_conj_physical(Handle); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_conj_physical(Handle)); } /// @@ -604,10 +552,7 @@ public bool is_conj() /// public Tensor resolve_conj() { - var res = THSTensor_resolve_conj(Handle); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_resolve_conj(Handle)); } /// @@ -616,7 +561,8 @@ public Tensor resolve_conj() public bool is_neg() { var res = THSTensor_is_neg(Handle); - if (res == -1) CheckForErrors(); + if (res == -1) + CheckForErrors(); return res != 0; } @@ -627,10 +573,7 @@ public bool is_neg() /// public Tensor resolve_neg() { - var res = THSTensor_resolve_neg(Handle); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_resolve_neg(Handle)); } /// @@ -679,9 +622,7 @@ public Tensor resolve_neg() public Tensor cumsum(long dim, ScalarType? type = null) { var res = THSTensor_cumsum(Handle, dim, type.HasValue, (sbyte)type.GetValueOrDefault()); - if (res == IntPtr.Zero) { CheckForErrors(); } - res = AutocastMode.AutoCast(res, ScalarType.Float32); - return new Tensor(res); + return ReturnCheckForErrorsAutocast(res, ScalarType.Float32); } /// @@ -694,9 +635,7 @@ public Tensor cumsum(long dim, ScalarType? type = null) public Tensor cumprod(long dim, ScalarType? type = null) { var res = THSTensor_cumprod(Handle, dim, type.HasValue, (sbyte)type.GetValueOrDefault()); - if (res == IntPtr.Zero) { CheckForErrors(); } - res = AutocastMode.AutoCast(res, ScalarType.Float32); - return new Tensor(res); + return ReturnCheckForErrorsAutocast(res, ScalarType.Float32); } /// @@ -708,8 +647,7 @@ public Tensor cumprod(long dim, ScalarType? type = null) public Tensor div(Tensor target, RoundingMode rounding_mode = RoundingMode.None) { var res = THSTensor_div(Handle, target.Handle, rounding_mode == RoundingMode.trunc ? "trunc" : rounding_mode == RoundingMode.floor ? "floor" : null); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(res); } /// @@ -729,8 +667,7 @@ public Tensor div(Tensor target, RoundingMode rounding_mode = RoundingMode.None) public Tensor div(Scalar target, RoundingMode rounding_mode = RoundingMode.None) { var res = THSTensor_div_scalar(Handle, target.Handle, rounding_mode == RoundingMode.trunc ? "trunc" : rounding_mode == RoundingMode.floor ? "floor" : null); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(res); } /// @@ -788,10 +725,7 @@ public Tensor div_(Scalar target, RoundingMode rounding_mode = RoundingMode.None /// public Tensor exp() { - var res = THSTensor_exp(Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - res = AutocastMode.AutoCast(res, ScalarType.Float32); - return new Tensor(res); + return ReturnCheckForErrorsAutocast(THSTensor_exp(Handle), ScalarType.Float32); } /// @@ -810,9 +744,7 @@ public Tensor exp_() /// public Tensor exp2() { - var res = THSTensor_exp2(Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_exp2(Handle)); } /// @@ -821,10 +753,7 @@ public Tensor exp2() /// public Tensor expm1() { - var res = THSTensor_expm1(Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - res = AutocastMode.AutoCast(res, ScalarType.Float32); - return new Tensor(res); + return ReturnCheckForErrorsAutocast(THSTensor_expm1(Handle), ScalarType.Float32); } /// @@ -846,9 +775,7 @@ public Tensor expm1_() /// If neither input is complex returns a torch.float64 tensor, and if one or more inputs is complex returns a torch.complex128 tensor. public Tensor float_power(Tensor target) { - var res = THSTensor_float_power(Handle, target.Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_float_power(Handle, target.Handle)); } /// @@ -857,10 +784,7 @@ public Tensor float_power(Tensor target) /// public Tensor floor() { - var res = THSTensor_floor(Handle); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_floor(Handle)); } /// @@ -880,10 +804,7 @@ public Tensor floor_() /// the divisor public Tensor floor_divide(Tensor other) { - var res = THSTensor_floor_divide(Handle, other.Handle); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_floor_divide(Handle, other.Handle)); } /// @@ -892,10 +813,7 @@ public Tensor floor_divide(Tensor other) /// the divisor public Tensor floor_divide(Scalar other) { - var res = THSTensor_floor_divide_scalar(Handle, other.Handle); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_floor_divide_scalar(Handle, other.Handle)); } /// @@ -927,9 +845,7 @@ public Tensor floor_divide_(Scalar other) /// public Tensor fmod(Tensor target) { - var res = THSTensor_fmod(Handle, target.Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_fmod(Handle, target.Handle)); } /// @@ -951,9 +867,7 @@ public Tensor fmod_(Tensor target) /// public Tensor fmod(Scalar scalar) { - var res = THSTensor_fmod_scalar(Handle, scalar.Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_fmod_scalar(Handle, scalar.Handle)); } /// @@ -974,9 +888,7 @@ public Tensor fmod_(Scalar scalar) /// public Tensor frac() { - var res = THSTensor_frac(Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_frac(Handle)); } /// @@ -1008,9 +920,7 @@ public Tensor frac_() /// Right-hand operand. public Tensor gcd(Tensor other) { - var res = THSTensor_gcd(Handle, other.Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_gcd(Handle, other.Handle)); } /// @@ -1036,10 +946,7 @@ public Tensor gcd_(Tensor other) /// public Tensor histc(long bins = 100, long min = 0, long max = 0) { - var res = THSTensor_histc(Handle, bins, min, max); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_histc(Handle, bins, min, max)); } /// @@ -1049,10 +956,7 @@ public Tensor histc(long bins = 100, long min = 0, long max = 0) /// public Tensor hypot(Tensor other) { - var res = THSTensor_hypot(Handle, other.Handle); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_hypot(Handle, other.Handle)); } /// @@ -1061,10 +965,7 @@ public Tensor hypot(Tensor other) /// public Tensor log() { - var res = THSTensor_log(Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - res = AutocastMode.AutoCast(res, ScalarType.Float32); - return new Tensor(res); + return ReturnCheckForErrorsAutocast(THSTensor_log(Handle), ScalarType.Float32); } /// @@ -1084,10 +985,7 @@ public Tensor log_() /// public Tensor logaddexp(Tensor other) { - var res = THSTensor_logaddexp(Handle, other.Handle); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_logaddexp(Handle, other.Handle)); } /// @@ -1097,10 +995,7 @@ public Tensor logaddexp(Tensor other) /// public Tensor logaddexp2(Tensor other) { - var res = THSTensor_logaddexp2(Handle, other.Handle); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_logaddexp2(Handle, other.Handle)); } /// @@ -1110,10 +1005,7 @@ public Tensor logaddexp2(Tensor other) /// public Tensor logcumsumexp(long dim) { - var res = THSTensor_logcumsumexp(Handle, dim); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_logcumsumexp(Handle, dim)); } /// @@ -1125,10 +1017,7 @@ public Tensor logcumsumexp(long dim) /// The computation is numerically stabilized. public Tensor logsumexp(long dim, bool keepdim = false) { - var res = THSTensor_logsumexp(Handle, dim, keepdim); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_logsumexp(Handle, dim, keepdim)); } /// @@ -1144,11 +1033,7 @@ public Tensor logsumexp(long dim, bool keepdim = false) /// public Tensor log10() { - var res = THSTensor_log10(Handle); - if (res == IntPtr.Zero) - CheckForErrors(); - res = AutocastMode.AutoCast(res, ScalarType.Float32); - return new Tensor(res); + return ReturnCheckForErrorsAutocast(THSTensor_log10(Handle), ScalarType.Float32); } /// @@ -1168,11 +1053,7 @@ public Tensor log10_() /// public Tensor log1p() { - var res = THSTensor_log1p(Handle); - if (res == IntPtr.Zero) - CheckForErrors(); - res = AutocastMode.AutoCast(res, ScalarType.Float32); - return new Tensor(res); + return ReturnCheckForErrorsAutocast(THSTensor_log1p(Handle), ScalarType.Float32); } /// @@ -1192,11 +1073,7 @@ public Tensor log1p_() /// public Tensor log2() { - var res = THSTensor_log2(Handle); - if (res == IntPtr.Zero) - CheckForErrors(); - res = AutocastMode.AutoCast(res, ScalarType.Float32); - return new Tensor(res); + return ReturnCheckForErrorsAutocast(THSTensor_log2(Handle), ScalarType.Float32); } /// @@ -1217,9 +1094,7 @@ public Tensor log2_() /// public Tensor logical_and(Tensor other) { - var res = THSTensor_logical_and(Handle, other.Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_logical_and(Handle, other.Handle)); } /// @@ -1240,9 +1115,7 @@ public Tensor logical_and_(Tensor other) /// public Tensor logical_not() { - var res = THSTensor_logical_not(Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_logical_not(Handle)); } /// @@ -1263,9 +1136,7 @@ public Tensor logical_not_() /// public Tensor logical_or(Tensor other) { - var res = THSTensor_logical_or(Handle, other.Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_logical_or(Handle, other.Handle)); } /// @@ -1287,9 +1158,7 @@ public Tensor logical_or_(Tensor other) /// public Tensor logical_xor(Tensor other) { - var res = THSTensor_logical_xor(Handle, other.Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_logical_xor(Handle, other.Handle)); } /// @@ -1316,9 +1185,7 @@ public Tensor logit(double? eps = null) unsafe { fixed (double* pEps = epsArr) { - var res = THSTensor_logit(Handle, (IntPtr)pEps); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_logit(Handle, (IntPtr)pEps)); } } } @@ -1330,9 +1197,7 @@ public Tensor logit(double? eps = null) /// public Tensor mul(Tensor target) { - var res = THSTensor_mul(Handle, target.Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_mul(Handle, target.Handle)); } /// @@ -1349,9 +1214,7 @@ public Tensor mul(Tensor target) /// public Tensor mul(Scalar target) { - var res = THSTensor_mul_scalar(Handle, target.Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_mul_scalar(Handle, target.Handle)); } /// @@ -1396,9 +1259,7 @@ public Tensor mul_(Scalar target) /// public Tensor neg() { - var res = THSTensor_neg(Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_neg(Handle)); } /// @@ -1425,10 +1286,7 @@ public Tensor neg_() /// public Tensor pow(Tensor exponent) { - var res = THSTensor_pow(Handle, exponent.Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - res = AutocastMode.AutoCast(res, ScalarType.Float32); //https://pytorch.org/docs/stable/amp.html#cuda-ops-that-can-autocast-to-float32 - return new Tensor(res); + return ReturnCheckForErrorsAutocast(THSTensor_pow(Handle, exponent.Handle), ScalarType.Float32); //https://pytorch.org/docs/stable/amp.html#cuda-ops-that-can-autocast-to-float32 } /// @@ -1450,10 +1308,7 @@ public Tensor pow_(Tensor exponent) /// public Tensor pow(Scalar exponent) { - var res = THSTensor_pow_scalar(Handle, exponent.Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - res = AutocastMode.AutoCast(res, ScalarType.Float32); - return new Tensor(res); + return ReturnCheckForErrorsAutocast(THSTensor_pow_scalar(Handle, exponent.Handle), ScalarType.Float32); } /// @@ -1474,11 +1329,7 @@ public Tensor pow_(Scalar exponent) /// public Tensor reciprocal() { - var res = THSTensor_reciprocal(Handle); - if (res == IntPtr.Zero) - CheckForErrors(); - res = AutocastMode.AutoCast(res, ScalarType.Float32); - return new Tensor(res); + return ReturnCheckForErrorsAutocast(THSTensor_reciprocal(Handle), ScalarType.Float32); } /// @@ -1499,9 +1350,7 @@ public Tensor reciprocal_() /// public Tensor remainder(Tensor target) { - var res = THSTensor_remainder(Handle, target.Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_remainder(Handle, target.Handle)); } /// @@ -1523,9 +1372,7 @@ public Tensor remainder_(Tensor target) /// public Tensor remainder(Scalar scalar) { - var res = THSTensor_remainder_scalar(Handle, scalar.Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_remainder_scalar(Handle, scalar.Handle)); } /// @@ -1547,10 +1394,7 @@ public Tensor remainder_(Scalar scalar) /// public Tensor round(long decimals = 0L) { - var res = THSTensor_round(Handle, decimals); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_round(Handle, decimals)); } /// @@ -1571,10 +1415,7 @@ public Tensor round_(long decimals = 0L) /// public Tensor rsqrt() { - var res = THSTensor_rsqrt(Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - res = AutocastMode.AutoCast(res, ScalarType.Float32); - return new Tensor(res); + return ReturnCheckForErrorsAutocast(THSTensor_rsqrt(Handle), ScalarType.Float32); } /// @@ -1600,9 +1441,7 @@ public Tensor rsqrt_() /// public Tensor sqrt() { - var res = THSTensor_sqrt(Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_sqrt(Handle)); } /// @@ -1622,10 +1461,7 @@ public Tensor sqrt_() /// public Tensor sign() { - var res = THSTensor_sign(Handle); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_sign(Handle)); } /// @@ -1648,10 +1484,7 @@ public Tensor sign_() /// public Tensor sgn() { - var res = THSTensor_sgn(Handle); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_sgn(Handle)); } /// @@ -1674,10 +1507,7 @@ public Tensor sgn_() /// A boolean tensor of the same shape as the input. public Tensor signbit() { - var res = THSTensor_signbit(Handle); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_signbit(Handle)); } /// @@ -1687,9 +1517,7 @@ public Tensor signbit() /// public Tensor sub(Tensor target) { - var res = THSTensor_sub(Handle, target.Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_sub(Handle, target.Handle)); } /// @@ -1699,9 +1527,7 @@ public Tensor sub(Tensor target) /// public Tensor sub(Scalar target) { - var res = THSTensor_sub_scalar(Handle, target.Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_sub_scalar(Handle, target.Handle)); } public Tensor subtract(Scalar target) => sub(target); @@ -1742,9 +1568,7 @@ public Tensor sub_(Scalar target) /// public Tensor cumulative_trapezoid(double dx = 1, long dim = -1) { - IntPtr res = THSTensor_cumulative_trapezoid_dx(Handle, dx, dim); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_cumulative_trapezoid_dx(Handle, dx, dim)); } /// @@ -1756,9 +1580,7 @@ public Tensor cumulative_trapezoid(double dx = 1, long dim = -1) /// public Tensor cumulative_trapezoid(Tensor x, long dim = -1) { - IntPtr res = THSTensor_cumulative_trapezoid_x(Handle, x.Handle, dim); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_cumulative_trapezoid_x(Handle, x.Handle, dim)); } /// @@ -1770,9 +1592,7 @@ public Tensor cumulative_trapezoid(Tensor x, long dim = -1) /// public Tensor trapezoid(double dx = 1, long dim = -1) { - IntPtr res = THSTensor_trapezoid_dx(Handle, dx, dim); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_trapezoid_dx(Handle, dx, dim)); } /// @@ -1784,9 +1604,7 @@ public Tensor trapezoid(double dx = 1, long dim = -1) /// public Tensor trapezoid(Tensor x, long dim = -1) { - IntPtr res = THSTensor_trapezoid_x(Handle, x.Handle, dim); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_trapezoid_x(Handle, x.Handle, dim)); } /// @@ -1795,10 +1613,7 @@ public Tensor trapezoid(Tensor x, long dim = -1) /// the divisor public Tensor true_divide(Tensor other) { - var res = THSTensor_true_divide(Handle, other.Handle); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_true_divide(Handle, other.Handle)); } /// @@ -1807,10 +1622,7 @@ public Tensor true_divide(Tensor other) /// the divisor public Tensor true_divide(Scalar other) { - var res = THSTensor_true_divide_scalar(Handle, other.Handle); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_true_divide_scalar(Handle, other.Handle)); } /// @@ -1850,9 +1662,7 @@ public Tensor true_divide_(Scalar other) /// public Tensor trunc() { - var res = THSTensor_trunc(Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_trunc(Handle)); } /// @@ -1885,10 +1695,7 @@ public Tensor trunc_() /// public Tensor xlogy(Tensor y) { - var res = THSTensor_xlogy(Handle, y.Handle); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_xlogy(Handle, y.Handle)); } /// @@ -1910,10 +1717,8 @@ public Tensor xlogy_(Tensor y) /// public Tensor xlogy(Scalar y) { - var res = THSTensor_xlogy_scalar(Handle, y.Handle); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_xlogy_scalar(Handle, y.Handle)); + } /// diff --git a/src/TorchSharp/Torch.cs b/src/TorchSharp/Torch.cs index 97701bb1f..44cc63449 100644 --- a/src/TorchSharp/Torch.cs +++ b/src/TorchSharp/Torch.cs @@ -9,6 +9,7 @@ using System.Runtime.InteropServices; using System.Text; using System.Text.RegularExpressions; +using TorchSharp.Amp; using TorchSharp.Modules; using TorchSharp.PInvoke; using TorchSharp.Utils; @@ -674,6 +675,13 @@ public static Tensor ReturnCheckForErrors(IntPtr ptr) CheckForErrors(); return new Tensor(ptr); } + public static Tensor ReturnCheckForErrorsAutocast(IntPtr ptr, ScalarType? st = null) + { + if (ptr == IntPtr.Zero) + CheckForErrors(); + ptr = st == null ? AutocastMode.AutoCast(ptr) : AutocastMode.AutoCast(ptr, st.Value); + return new Tensor(ptr); + } public static partial class backends { public static partial class cuda From 2ca40903abe9d14131b44ea4f2036dd13b6edb1a Mon Sep 17 00:00:00 2001 From: Dimitri Date: Sat, 20 Sep 2025 20:30:40 -0300 Subject: [PATCH 53/65] refactor ReturnCheckForErrors on Trig and TypedHandwritten --- src/TorchSharp/Tensor/Tensor.Trig.cs | 71 ++++--------------- .../Tensor/TensorTyped.handwritten.cs | 28 ++------ 2 files changed, 20 insertions(+), 79 deletions(-) diff --git a/src/TorchSharp/Tensor/Tensor.Trig.cs b/src/TorchSharp/Tensor/Tensor.Trig.cs index 86e5f0865..53d1f7160 100644 --- a/src/TorchSharp/Tensor/Tensor.Trig.cs +++ b/src/TorchSharp/Tensor/Tensor.Trig.cs @@ -26,10 +26,7 @@ public partial class Tensor /// public Tensor angle() { - var res = THSTensor_angle(Handle); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_angle(Handle)); } /// @@ -38,11 +35,7 @@ public Tensor angle() /// public Tensor asin() { - var res = THSTensor_asin(Handle); - if (res == IntPtr.Zero) - CheckForErrors(); - res = AutocastMode.AutoCast(res, ScalarType.Float32); - return new Tensor(res); + return ReturnCheckForErrorsAutocast(THSTensor_asin(Handle), ScalarType.Float32); } /// @@ -70,11 +63,7 @@ public Tensor asin_() /// public Tensor acos() { - var res = THSTensor_acos(Handle); - if (res == IntPtr.Zero) - CheckForErrors(); - res = AutocastMode.AutoCast(res, ScalarType.Float32); - return new Tensor(res); + return ReturnCheckForErrorsAutocast(THSTensor_acos(Handle), ScalarType.Float32); } /// @@ -151,10 +140,8 @@ public Tensor atan2(Tensor other) if (sts.Any(x => x == ScalarType.Float32)) (handle, other.handle) = AutocastMode.AutoCast(handle, other.handle, ScalarType.Float32); } - var res = THSTensor_atan2(Handle, other.Handle); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + + return ReturnCheckForErrors(THSTensor_atan2(Handle, other.Handle)); } public Tensor arctan2_(Tensor other) => atan2_(other); @@ -178,10 +165,7 @@ public Tensor atan2_(Tensor other) /// public Tensor cos() { - var res = THSTensor_cos(Handle); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_cos(Handle)); } /// @@ -201,10 +185,7 @@ public Tensor cos_() /// public Tensor sin() { - var res = THSTensor_sin(Handle); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_sin(Handle)); } /// @@ -224,11 +205,7 @@ public Tensor sin_() /// public Tensor tan() { - var res = THSTensor_tan(Handle); - if (res == IntPtr.Zero) - CheckForErrors(); - res = AutocastMode.AutoCast(res, ScalarType.Float32); - return new Tensor(res); + return ReturnCheckForErrorsAutocast(THSTensor_tan(Handle), ScalarType.Float32); } /// @@ -248,10 +225,7 @@ public Tensor tan_() /// public Tensor sinc() { - var res = THSTensor_sinc(Handle); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_sinc(Handle)); } /// @@ -271,11 +245,7 @@ public Tensor sinc_() /// public Tensor sinh() { - var res = THSTensor_sinh(Handle); - if (res == IntPtr.Zero) - CheckForErrors(); - res = AutocastMode.AutoCast(res, ScalarType.Float32); - return new Tensor(res); + return ReturnCheckForErrorsAutocast(THSTensor_sinh(Handle), ScalarType.Float32); } /// @@ -295,11 +265,7 @@ public Tensor sinh_() /// public Tensor cosh() { - var res = THSTensor_cosh(Handle); - if (res == IntPtr.Zero) - CheckForErrors(); - res = AutocastMode.AutoCast(res, ScalarType.Float32); - return new Tensor(res); + return ReturnCheckForErrorsAutocast(THSTensor_cosh(Handle), ScalarType.Float32); } /// @@ -319,10 +285,7 @@ public Tensor cosh_() /// public Tensor tanh() { - var res = THSTensor_tanh(Handle); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_tanh(Handle)); } /// @@ -342,10 +305,7 @@ public Tensor tanh_() /// public Tensor arcsinh() { - var res = THSTensor_arcsinh(Handle); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_arcsinh(Handle)); } /// @@ -412,10 +372,7 @@ public Tensor arccosh_() /// public Tensor arctanh() { - var res = THSTensor_arctanh(Handle); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_arctanh(Handle)); } /// diff --git a/src/TorchSharp/Tensor/TensorTyped.handwritten.cs b/src/TorchSharp/Tensor/TensorTyped.handwritten.cs index 7c1083553..a1c97b236 100644 --- a/src/TorchSharp/Tensor/TensorTyped.handwritten.cs +++ b/src/TorchSharp/Tensor/TensorTyped.handwritten.cs @@ -28,11 +28,7 @@ public static Tensor arange(Scalar start, Scalar stop, Scalar step, torch.Device } if (handle == IntPtr.Zero) { torch.CheckForErrors(); } - var res = THSTensor_to_type(handle, (sbyte)ScalarType.ComplexFloat32); - if (res == IntPtr.Zero) - torch.CheckForErrors(); - - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_to_type(handle, (sbyte)ScalarType.ComplexFloat32)); } /// @@ -41,9 +37,7 @@ public static Tensor arange(Scalar start, Scalar stop, Scalar step, torch.Device public static Tensor from((float Real, float Imaginary) scalar, torch.Device device = null, bool requires_grad = false) { device = torch.InitializeDevice(device); - var handle = THSTensor_newComplexFloat32Scalar(scalar.Real, scalar.Imaginary, (int)device.type, device.index, requires_grad); - if (handle == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(handle); + return ReturnCheckForErrors(THSTensor_newComplexFloat32Scalar(scalar.Real, scalar.Imaginary, (int)device.type, device.index, requires_grad)); } /// @@ -52,9 +46,7 @@ public static Tensor from((float Real, float Imaginary) scalar, torch.Device dev public static Tensor from(float real, float imaginary = 0.0f, torch.Device device = null, bool requires_grad = false) { device = torch.InitializeDevice(device); - var handle = THSTensor_newComplexFloat32Scalar(real, imaginary, (int)device.type, device.index, requires_grad); - if (handle == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(handle); + return ReturnCheckForErrors(THSTensor_newComplexFloat32Scalar(real, imaginary, (int)device.type, device.index, requires_grad)); } /// @@ -127,11 +119,7 @@ public static Tensor arange(Scalar start, Scalar stop, Scalar step, torch.Device } if (handle == IntPtr.Zero) { torch.CheckForErrors(); } - var res = THSTensor_to_type(handle, (sbyte)ScalarType.ComplexFloat64); - if (res == IntPtr.Zero) - torch.CheckForErrors(); - - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_to_type(handle, (sbyte)ScalarType.ComplexFloat64)); } /// @@ -140,9 +128,7 @@ public static Tensor arange(Scalar start, Scalar stop, Scalar step, torch.Device public static Tensor from(System.Numerics.Complex scalar, torch.Device device = null, bool requires_grad = false) { device = torch.InitializeDevice(device); - var handle = THSTensor_newComplexFloat64Scalar(scalar.Real, scalar.Imaginary, (int)device.type, device.index, requires_grad); - if (handle == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(handle); + return ReturnCheckForErrors(THSTensor_newComplexFloat64Scalar(scalar.Real, scalar.Imaginary, (int)device.type, device.index, requires_grad)); } /// @@ -151,9 +137,7 @@ public static Tensor from(System.Numerics.Complex scalar, torch.Device device = public static Tensor from(double real, double imaginary = 0.0f, torch.Device device = null, bool requires_grad = false) { device = torch.InitializeDevice(device); - var handle = THSTensor_newComplexFloat64Scalar(real, imaginary, (int)device.type, device.index, requires_grad); - if (handle == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(handle); + return ReturnCheckForErrors(THSTensor_newComplexFloat64Scalar(real, imaginary, (int)device.type, device.index, requires_grad)); } /// From 2c4f69cb47496dd1ccb78a054a79d53e8a5a2158 Mon Sep 17 00:00:00 2001 From: Dimitri Date: Sat, 20 Sep 2025 20:45:03 -0300 Subject: [PATCH 54/65] more refactoring with ReturnCheckForErrors --- .../Tensor/Factories/tensor_Half.cs | 4 +-- .../Tensor/Factories/tensor_bool.cs | 5 ++- .../Tensor/Factories/tensor_byte.cs | 4 +-- .../Tensor/Factories/tensor_float.cs | 12 +------ src/TorchSharp/Tensor/Factories/tensor_int.cs | 4 +-- .../Tensor/Factories/tensor_sbyte.cs | 4 +-- .../Tensor/Factories/tensor_short.cs | 4 +-- src/TorchSharp/Tensor/torch.Amp.cs | 15 ++------- .../Tensor/torch.BlasAndLapackOperations.cs | 11 +++---- src/TorchSharp/Tensor/torch.ComparisonOps.cs | 8 ++--- ...torch.IndexingSlicingJoiningMutatingOps.cs | 28 ++++------------ .../Tensor/torch.OtherOperations.cs | 33 ++++--------------- src/TorchSharp/Tensor/torch.SpectralOps.cs | 22 ++++++------- src/TorchSharp/Tensor/torch.cs | 16 +++------ src/TorchSharp/Torch.cs | 6 ++++ 15 files changed, 54 insertions(+), 122 deletions(-) diff --git a/src/TorchSharp/Tensor/Factories/tensor_Half.cs b/src/TorchSharp/Tensor/Factories/tensor_Half.cs index 5fa367228..962cda55a 100644 --- a/src/TorchSharp/Tensor/Factories/tensor_Half.cs +++ b/src/TorchSharp/Tensor/Factories/tensor_Half.cs @@ -9,7 +9,7 @@ namespace TorchSharp { public static partial class torch { -#if NET6_0_OR_GREATER +//#if NET6_0_OR_GREATER /// /// Create a tensor from an array of values, shaping it based on the shape passed in. /// @@ -122,6 +122,6 @@ public static Tensor tensor(Memory rawArray, ReadOnlySpan dimensions { return _tensor_generic(rawArray, dimensions, (sbyte)ScalarType.Float16, dtype, device, requires_grad, names: names); } -#endif +//#endif } } diff --git a/src/TorchSharp/Tensor/Factories/tensor_bool.cs b/src/TorchSharp/Tensor/Factories/tensor_bool.cs index 6e9fac31f..8201a2f8e 100644 --- a/src/TorchSharp/Tensor/Factories/tensor_bool.cs +++ b/src/TorchSharp/Tensor/Factories/tensor_bool.cs @@ -16,9 +16,8 @@ public static partial class torch public static Tensor tensor(bool scalar, ScalarType? dtype = null, Device? device = null, bool requires_grad = false) { device = InitializeDevice(device); - var handle = THSTensor_newBoolScalar(scalar, (int)device.type, device.index, requires_grad); - if (handle == IntPtr.Zero) { CheckForErrors(); } - var tensor = new Tensor(handle); + + var tensor = ReturnCheckForErrors(THSTensor_newBoolScalar(scalar, (int)device.type, device.index, requires_grad)); tensor = dtype.HasValue ? tensor.to(dtype.Value, device) : tensor.to(device); return tensor; } diff --git a/src/TorchSharp/Tensor/Factories/tensor_byte.cs b/src/TorchSharp/Tensor/Factories/tensor_byte.cs index 45dab1083..bae89cbfd 100644 --- a/src/TorchSharp/Tensor/Factories/tensor_byte.cs +++ b/src/TorchSharp/Tensor/Factories/tensor_byte.cs @@ -16,9 +16,7 @@ public static partial class torch public static Tensor tensor(byte scalar, Device? device = null, bool requires_grad = false) { device = InitializeDevice(device); - var handle = THSTensor_newByteScalar(scalar, (int)device.type, device.index, requires_grad); - if (handle == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(handle); + return ReturnCheckForErrors(THSTensor_newByteScalar(scalar, (int)device.type, device.index, requires_grad)); } /// diff --git a/src/TorchSharp/Tensor/Factories/tensor_float.cs b/src/TorchSharp/Tensor/Factories/tensor_float.cs index 6b70bd3fc..7076c37d1 100644 --- a/src/TorchSharp/Tensor/Factories/tensor_float.cs +++ b/src/TorchSharp/Tensor/Factories/tensor_float.cs @@ -17,17 +17,7 @@ public static partial class torch public static Tensor tensor(float scalar, Device? device = null, bool requires_grad = false) { device = InitializeDevice(device); - var handle = THSTensor_newFloat32Scalar(scalar, (int)device.type, device.index, requires_grad); - if (handle == IntPtr.Zero) { CheckForErrors(); } - - - //var t = new Tensor(handle).AutoCast(); - var t = new Tensor(handle); - /*if (is_autocast_cache_enabled()) { - if (is_autocast_gpu_enabled()) - return t.to(get_autocast_gpu_dtype()); //this work, but should put that on all tensor factorie... - }*/ - return t; + return ReturnCheckForErrors(THSTensor_newFloat32Scalar(scalar, (int)device.type, device.index, requires_grad)); } /// diff --git a/src/TorchSharp/Tensor/Factories/tensor_int.cs b/src/TorchSharp/Tensor/Factories/tensor_int.cs index 6702062f3..875aba793 100644 --- a/src/TorchSharp/Tensor/Factories/tensor_int.cs +++ b/src/TorchSharp/Tensor/Factories/tensor_int.cs @@ -16,9 +16,7 @@ public static partial class torch public static Tensor tensor(int scalar, Device? device = null, bool requires_grad = false) { device = InitializeDevice(device); - var handle = THSTensor_newInt32Scalar(scalar, (int)device.type, device.index, requires_grad); - if (handle == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(handle); + return ReturnCheckForErrors(THSTensor_newInt32Scalar(scalar, (int)device.type, device.index, requires_grad)); } /// diff --git a/src/TorchSharp/Tensor/Factories/tensor_sbyte.cs b/src/TorchSharp/Tensor/Factories/tensor_sbyte.cs index 3a901f541..8052be8c2 100644 --- a/src/TorchSharp/Tensor/Factories/tensor_sbyte.cs +++ b/src/TorchSharp/Tensor/Factories/tensor_sbyte.cs @@ -16,9 +16,7 @@ public static partial class torch public static Tensor tensor(sbyte scalar, Device? device = null, bool requires_grad = false) { device = InitializeDevice(device); - var handle = THSTensor_newInt8Scalar(scalar, (int)device.type, device.index, requires_grad); - if (handle == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(handle); + return ReturnCheckForErrors(THSTensor_newInt8Scalar(scalar, (int)device.type, device.index, requires_grad)); } /// diff --git a/src/TorchSharp/Tensor/Factories/tensor_short.cs b/src/TorchSharp/Tensor/Factories/tensor_short.cs index e32df7589..e0d3da15d 100644 --- a/src/TorchSharp/Tensor/Factories/tensor_short.cs +++ b/src/TorchSharp/Tensor/Factories/tensor_short.cs @@ -16,9 +16,7 @@ public static partial class torch public static Tensor tensor(short scalar, Device? device = null, bool requires_grad = false) { device = InitializeDevice(device); - var handle = THSTensor_newInt16Scalar(scalar, (int)device.type, device.index, requires_grad); - if (handle == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(handle); + return ReturnCheckForErrors(THSTensor_newInt16Scalar(scalar, (int)device.type, device.index, requires_grad)); } /// diff --git a/src/TorchSharp/Tensor/torch.Amp.cs b/src/TorchSharp/Tensor/torch.Amp.cs index 319afe65c..8aa8e6334 100644 --- a/src/TorchSharp/Tensor/torch.Amp.cs +++ b/src/TorchSharp/Tensor/torch.Amp.cs @@ -16,24 +16,15 @@ public static void _amp_foreach_non_finite_check_and_unscale_(IList tens public static torch.Tensor amp_update_scale_(Tensor self, Tensor growth_tracker, Tensor found_inf, double scale_growth_factor, double scale_backoff_factor, long growth_interval) { - var res = THSAmp_amp_update_scale_(self.Handle, growth_tracker.Handle, found_inf.Handle, scale_growth_factor, scale_backoff_factor, growth_interval); - if(res == IntPtr.Zero) - torch.CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(THSAmp_amp_update_scale_(self.Handle, growth_tracker.Handle, found_inf.Handle, scale_growth_factor, scale_backoff_factor, growth_interval)); } public static torch.Tensor amp_update_scale_out(Tensor outt, Tensor self, Tensor growth_tracker, Tensor found_inf, double scale_growth_factor, double scale_backoff_factor, long growth_interval) { - var res = THSAmp_amp_update_scale_out(outt.Handle, self.Handle, growth_tracker.Handle, found_inf.Handle, scale_growth_factor, scale_backoff_factor, growth_interval); - if(res == IntPtr.Zero) - torch.CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(THSAmp_amp_update_scale_out(outt.Handle, self.Handle, growth_tracker.Handle, found_inf.Handle, scale_growth_factor, scale_backoff_factor, growth_interval)); } public static torch.Tensor amp_update_scale_outf(Tensor self, Tensor growth_tracker, Tensor found_inf, double scale_growth_factor, double scale_backoff_factor, long growth_interval, Tensor outt) { - var res = THSAmp_amp_update_scale_outf(self.Handle, growth_tracker.Handle, found_inf.Handle, scale_growth_factor, scale_backoff_factor, growth_interval, outt.Handle); - if(res == IntPtr.Zero) - torch.CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(THSAmp_amp_update_scale_outf(self.Handle, growth_tracker.Handle, found_inf.Handle, scale_growth_factor, scale_backoff_factor, growth_interval, outt.Handle)); } public static (torch.Tensor, torch.Tensor) amp_update_scale(Tensor self, Tensor growth_tracker, Tensor found_inf, double scale_growth_factor, double scale_backoff_factor, long growth_interval) { diff --git a/src/TorchSharp/Tensor/torch.BlasAndLapackOperations.cs b/src/TorchSharp/Tensor/torch.BlasAndLapackOperations.cs index 6024eee82..72f6bd779 100644 --- a/src/TorchSharp/Tensor/torch.BlasAndLapackOperations.cs +++ b/src/TorchSharp/Tensor/torch.BlasAndLapackOperations.cs @@ -219,10 +219,12 @@ public static Tensor cholesky_solve(Tensor input, Tensor input2, bool upper = fa /// public static (Tensor Solution, Tensor QR) lstsq(Tensor B, Tensor A) { - var solution = THSTorch_lstsq(B.Handle, A.Handle, out var qr); + //TODO: Test if this worked + return ReturnCheckForErrors(THSTorch_lstsq(B.Handle, A.Handle, out var qr), qr); + /*var solution = THSTorch_lstsq(B.Handle, A.Handle, out var qr); if (solution == IntPtr.Zero || qr == IntPtr.Zero) CheckForErrors(); - return (new Tensor(solution), new Tensor(qr)); + return (new Tensor(solution), new Tensor(qr));*/ } // https://pytorch.org/docs/stable/generated/torch.lu @@ -253,10 +255,7 @@ public static (Tensor A_LU, Tensor? pivots, Tensor? infos) lu(Tensor A, bool piv /// public static Tensor lu_solve(Tensor b, Tensor LU_data, Tensor LU_pivots) { - var solution = THSTensor_lu_solve(b.Handle, LU_data.Handle, LU_pivots.Handle); - if (solution == IntPtr.Zero) - CheckForErrors(); - return new Tensor(solution); + return ReturnCheckForErrors(THSTensor_lu_solve(b.Handle, LU_data.Handle, LU_pivots.Handle)); } // https://pytorch.org/docs/stable/generated/torch.lu_unpack diff --git a/src/TorchSharp/Tensor/torch.ComparisonOps.cs b/src/TorchSharp/Tensor/torch.ComparisonOps.cs index 59afd1e97..b696cde93 100644 --- a/src/TorchSharp/Tensor/torch.ComparisonOps.cs +++ b/src/TorchSharp/Tensor/torch.ComparisonOps.cs @@ -252,9 +252,7 @@ public static (Tensor values, Tensor indices) sort(Tensor input, long dim = -1, /// If provided, a tensor matching the shape of the unsorted sorted_sequence containing a sequence of indices that sort it in the ascending order on the innermost dimension public static Tensor searchsorted(Tensor sorted_sequence, Tensor values, bool out_int32 = false, bool right = false, Tensor sorter = null) { - var res = PInvoke.NativeMethods.THSTensor_searchsorted_t(sorted_sequence.Handle, values.Handle, out_int32, right, sorter is null ? IntPtr.Zero : sorter.Handle); - if (res == IntPtr.Zero) CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(PInvoke.NativeMethods.THSTensor_searchsorted_t(sorted_sequence.Handle, values.Handle, out_int32, right, sorter is null ? IntPtr.Zero : sorter.Handle)); } // https://pytorch.org/docs/stable/generated/torch.searchsorted.html @@ -271,9 +269,7 @@ public static Tensor searchsorted(Tensor sorted_sequence, Tensor values, bool ou /// If provided, a tensor matching the shape of the unsorted sorted_sequence containing a sequence of indices that sort it in the ascending order on the innermost dimension public static Tensor searchsorted(Tensor sorted_sequence, Scalar values, bool out_int32, bool right, Tensor sorter) { - var res = PInvoke.NativeMethods.THSTensor_searchsorted_s(sorted_sequence.Handle, values.Handle, out_int32, right, sorter is null ? IntPtr.Zero : sorter.Handle); - if (res == IntPtr.Zero) CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(PInvoke.NativeMethods.THSTensor_searchsorted_s(sorted_sequence.Handle, values.Handle, out_int32, right, sorter is null ? IntPtr.Zero : sorter.Handle)); } /// https://github.com/numpy/numpy/blob/v1.24.0/numpy/lib/histograms.py#L679 diff --git a/src/TorchSharp/Tensor/torch.IndexingSlicingJoiningMutatingOps.cs b/src/TorchSharp/Tensor/torch.IndexingSlicingJoiningMutatingOps.cs index 8e062c786..da9b23699 100644 --- a/src/TorchSharp/Tensor/torch.IndexingSlicingJoiningMutatingOps.cs +++ b/src/TorchSharp/Tensor/torch.IndexingSlicingJoiningMutatingOps.cs @@ -46,9 +46,7 @@ public static Tensor cat(IList tensors, long dim = 0) using var parray = new PinnedArray(); IntPtr tensorsRef = parray.CreateArray(tensors.Select(p => p.Handle).ToArray()); - var res = THSTensor_cat(tensorsRef, parray.Array.Length, dim); - if (res == IntPtr.Zero) CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_cat(tensorsRef, parray.Array.Length, dim)); } // https://pytorch.org/docs/stable/generated/torch.concat @@ -117,9 +115,7 @@ public static Tensor dstack(IList tensors) using (var parray = new PinnedArray()) { IntPtr tensorsRef = parray.CreateArray(tensors.Select(p => p.Handle).ToArray()); - var res = THSTensor_dstack(tensorsRef, parray.Array.Length); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_dstack(tensorsRef, parray.Array.Length)); } } @@ -133,9 +129,7 @@ public static Tensor dstack(IEnumerable tensors) { using var parray = new PinnedArray(); IntPtr tensorsRef = parray.CreateArray(tensors.Select(p => p.Handle).ToArray()); - var res = THSTensor_dstack(tensorsRef, parray.Array.Length); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_dstack(tensorsRef, parray.Array.Length)); } // https://pytorch.org/docs/stable/generated/torch.gather @@ -196,9 +190,7 @@ public static Tensor hstack(IList tensors) using var parray = new PinnedArray(); IntPtr tensorsRef = parray.CreateArray(tensors.Select(p => p.Handle).ToArray()); - var res = THSTensor_hstack(tensorsRef, parray.Array.Length); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_hstack(tensorsRef, parray.Array.Length)); } // https://pytorch.org/docs/stable/generated/torch.hstack @@ -223,9 +215,7 @@ public static Tensor hstack(IEnumerable tensors) using var parray = new PinnedArray(); IntPtr tensorsRef = parray.CreateArray(tensors.Select(p => p.Handle).ToArray()); - var res = THSTensor_hstack(tensorsRef, parray.Array.Length); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_hstack(tensorsRef, parray.Array.Length)); } // https://pytorch.org/docs/stable/generated/torch.index_add @@ -476,9 +466,7 @@ public static Tensor stack(IEnumerable tensors, long dim = 0) using var parray = new PinnedArray(); IntPtr tensorsRef = parray.CreateArray(tensors.Select(p => p.Handle).ToArray()); - var res = THSTensor_stack(tensorsRef, parray.Array.Length, dim); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_stack(tensorsRef, parray.Array.Length, dim)); } // https://pytorch.org/docs/stable/generated/torch.swapaxes @@ -564,9 +552,7 @@ public static Tensor vstack(IList tensors) using var parray = new PinnedArray(); IntPtr tensorsRef = parray.CreateArray(tensors.Select(p => p.Handle).ToArray()); - var res = THSTensor_vstack(tensorsRef, parray.Array.Length); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_vstack(tensorsRef, parray.Array.Length)); } // https://pytorch.org/docs/stable/generated/torch.where diff --git a/src/TorchSharp/Tensor/torch.OtherOperations.cs b/src/TorchSharp/Tensor/torch.OtherOperations.cs index 6b5a765d6..16c9db9b6 100644 --- a/src/TorchSharp/Tensor/torch.OtherOperations.cs +++ b/src/TorchSharp/Tensor/torch.OtherOperations.cs @@ -47,9 +47,7 @@ public static Tensor block_diag(params Tensor[] tensors) using var parray = new PinnedArray(); IntPtr tensorsRef = parray.CreateArray(tensors.Select(p => p.Handle).ToArray()); - var res = THSTensor_block_diag(tensorsRef, parray.Array.Length); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_block_diag(tensorsRef, parray.Array.Length)); } // https://pytorch.org/docs/stable/generated/torch.broadcast_tensors @@ -130,9 +128,7 @@ public static Tensor cartesian_prod(IList tensors) using var parray = new PinnedArray(); IntPtr tensorsRef = parray.CreateArray(tensors.Select(p => p.Handle).ToArray()); - var res = THSTensor_cartesian_prod(tensorsRef, parray.Array.Length); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_cartesian_prod(tensorsRef, parray.Array.Length)); } // https://pytorch.org/docs/stable/generated/torch.cartesian_prod @@ -164,11 +160,7 @@ public static Tensor cdist( if (p < 0) throw new ArgumentException($"p must be non-negative"); - var res = THSTensor_cdist(x1.Handle, x2.Handle, p, (long)compute_mode); - if (res == IntPtr.Zero) - CheckForErrors(); - res = AutocastMode.AutoCast(res, ScalarType.Float32); - return new Tensor(res); + return ReturnCheckForErrorsAutocast(THSTensor_cdist(x1.Handle, x2.Handle, p, (long)compute_mode), ScalarType.Float32); } // https://pytorch.org/docs/stable/generated/torch.clone @@ -189,10 +181,7 @@ public static Tensor combinations(Tensor input, int r = 2, bool with_replacement if (r < 0) throw new ArgumentException($"r must be non-negative"); - var res = THSTensor_combinations(input.Handle, r, with_replacement); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_combinations(input.Handle, r, with_replacement)); } @@ -355,9 +344,7 @@ public static Tensor einsum(string equation, params Tensor[] tensors) using var parray = new PinnedArray(); IntPtr tensorsRef = parray.CreateArray(tensors.Select(p => p.Handle).ToArray()); - var res = THSTensor_einsum(equation, tensorsRef, parray.Array.Length); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_einsum(equation, tensorsRef, parray.Array.Length)); } // https://pytorch.org/docs/stable/generated/torch.flatten @@ -694,10 +681,7 @@ public static Tensor tril_indices( device = torch.CPU; } - var res = NativeMethods.THSTensor_tril_indices(row, col, offset, (sbyte)dtype, (int)device.type, device.index); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_tril_indices(row, col, offset, (sbyte)dtype, (int)device.type, device.index)); } // https://pytorch.org/docs/stable/generated/torch.triu @@ -720,10 +704,7 @@ public static Tensor triu_indices( device = torch.CPU; } - var res = NativeMethods.THSTensor_triu_indices(row, col, offset, (sbyte)dtype, (int)device.type, device.index); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_triu_indices(row, col, offset, (sbyte)dtype, (int)device.type, device.index)); } // https://pytorch.org/docs/stable/generated/torch.vander diff --git a/src/TorchSharp/Tensor/torch.SpectralOps.cs b/src/TorchSharp/Tensor/torch.SpectralOps.cs index cc6dcf022..4475ac9d0 100644 --- a/src/TorchSharp/Tensor/torch.SpectralOps.cs +++ b/src/TorchSharp/Tensor/torch.SpectralOps.cs @@ -1,4 +1,4 @@ -// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information. +// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information. #nullable enable using System; @@ -64,8 +64,8 @@ public static Tensor bartlett_window(long len, bool periodic = true, ScalarType? GC.WaitForPendingFinalizers(); handle = THSTensor_bartlett_window(len, periodic, (sbyte)dtype, (int)device.type, device.index, requires_grad); } - if (handle == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(handle); + + return ReturnCheckForErrors(handle); } // https://pytorch.org/docs/stable/generated/torch.blackman_window @@ -87,8 +87,8 @@ public static Tensor blackman_window(long len, bool periodic = true, ScalarType? GC.WaitForPendingFinalizers(); handle = THSTensor_blackman_window(len, periodic, (sbyte)dtype, (int)device.type, device.index, requires_grad); } - if (handle == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(handle); + + return ReturnCheckForErrors(handle); } // https://pytorch.org/docs/stable/generated/torch.hamming_window @@ -111,8 +111,8 @@ public static Tensor hamming_window(long len, bool periodic = true, float alpha GC.WaitForPendingFinalizers(); handle = THSTensor_hamming_window(len, periodic, alpha, beta, (sbyte)dtype, (int)device.type, device.index, requires_grad); } - if (handle == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(handle); + + return ReturnCheckForErrors(handle); } // https://pytorch.org/docs/stable/generated/torch.hann_window @@ -134,8 +134,8 @@ public static Tensor hann_window(long len, bool periodic = true, ScalarType? dty GC.WaitForPendingFinalizers(); handle = THSTensor_hann_window(len, periodic, (sbyte)dtype, (int)device.type, device.index, requires_grad); } - if (handle == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(handle); + + return ReturnCheckForErrors(handle); } // https://pytorch.org/docs/stable/generated/torch.kaiser_window @@ -157,8 +157,8 @@ public static Tensor kaiser_window(long len, bool periodic = true, float beta = GC.WaitForPendingFinalizers(); handle = THSTensor_kaiser_window(len, periodic, beta, (sbyte)dtype, (int)device.type, device.index, requires_grad); } - if (handle == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(handle); + + return ReturnCheckForErrors(handle); } } } \ No newline at end of file diff --git a/src/TorchSharp/Tensor/torch.cs b/src/TorchSharp/Tensor/torch.cs index 6892d2b69..0e01bc0f7 100644 --- a/src/TorchSharp/Tensor/torch.cs +++ b/src/TorchSharp/Tensor/torch.cs @@ -60,9 +60,7 @@ public static Tensor column_stack(IList tensors) using var parray = new PinnedArray(); IntPtr tensorsRef = parray.CreateArray(tensors.Select(p => p.Handle).ToArray()); - var res = THSTensor_column_stack(tensorsRef, parray.Array.Length); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_column_stack(tensorsRef, parray.Array.Length)); } /// @@ -83,9 +81,7 @@ public static Tensor row_stack(IList tensors) using var parray = new PinnedArray(); IntPtr tensorsRef = parray.CreateArray(tensors.Select(p => p.Handle).ToArray()); - var res = THSTensor_row_stack(tensorsRef, parray.Array.Length); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_row_stack(tensorsRef, parray.Array.Length)); } /// @@ -136,16 +132,12 @@ public static Tensor row_stack(IList tensors) public static Tensor _standard_gamma(Tensor input, Generator? generator = null) { - var res = THSTensor_standard_gamma_(input.Handle, generator is null ? IntPtr.Zero : generator.Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_standard_gamma_(input.Handle, generator is null ? IntPtr.Zero : generator.Handle)); } public static Tensor _sample_dirichlet(Tensor input, Generator? generator = null) { - var res = THSTensor_sample_dirichlet_(input.Handle, generator is null ? IntPtr.Zero : generator.Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_sample_dirichlet_(input.Handle, generator is null ? IntPtr.Zero : generator.Handle)); } /// diff --git a/src/TorchSharp/Torch.cs b/src/TorchSharp/Torch.cs index 44cc63449..5ddbf806c 100644 --- a/src/TorchSharp/Torch.cs +++ b/src/TorchSharp/Torch.cs @@ -675,6 +675,12 @@ public static Tensor ReturnCheckForErrors(IntPtr ptr) CheckForErrors(); return new Tensor(ptr); } + public static (Tensor,Tensor) ReturnCheckForErrors(IntPtr ptr, IntPtr ptr1) + { + if (ptr == IntPtr.Zero || ptr1 == IntPtr.Zero) + CheckForErrors(); + return (new Tensor(ptr), new Tensor(ptr1)); + } public static Tensor ReturnCheckForErrorsAutocast(IntPtr ptr, ScalarType? st = null) { if (ptr == IntPtr.Zero) From a2c2cb33ec15d8064ce7e64eba6a2e105b15bf97 Mon Sep 17 00:00:00 2001 From: Dimitri Date: Sun, 28 Sep 2025 17:24:33 -0300 Subject: [PATCH 55/65] Refactor ReturnCheckForErrors NN, Activation, Pooling, Tensor, etc. --- src/TorchSharp/NN/Activation/CELU.cs | 4 +- src/TorchSharp/NN/Activation/ELU.cs | 4 +- src/TorchSharp/NN/Activation/GELU.cs | 4 +- src/TorchSharp/NN/Activation/GLU.cs | 4 +- src/TorchSharp/NN/Activation/Hardshrink.cs | 4 +- src/TorchSharp/NN/Activation/Hardtanh.cs | 4 +- src/TorchSharp/NN/Activation/LeakyReLU.cs | 4 +- src/TorchSharp/NN/Activation/LogSoftMax.cs | 4 +- src/TorchSharp/NN/Activation/Mish.cs | 4 +- src/TorchSharp/NN/Activation/PReLU.cs | 4 +- src/TorchSharp/NN/Activation/RReLU.cs | 4 +- src/TorchSharp/NN/Activation/ReLU6.cs | 4 +- src/TorchSharp/NN/Activation/ReLu.cs | 4 +- src/TorchSharp/NN/Activation/SELU.cs | 4 +- src/TorchSharp/NN/Activation/SiLU.cs | 4 +- src/TorchSharp/NN/Activation/Sigmoid.cs | 4 +- src/TorchSharp/NN/Activation/Softmax.cs | 4 +- src/TorchSharp/NN/Activation/Softmax2d.cs | 4 +- src/TorchSharp/NN/Activation/Softmin.cs | 4 +- src/TorchSharp/NN/Activation/Softplus.cs | 4 +- src/TorchSharp/NN/Activation/Softshrink.cs | 4 +- src/TorchSharp/NN/Activation/Softsign.cs | 4 +- src/TorchSharp/NN/Activation/Tanh.cs | 4 +- src/TorchSharp/NN/Activation/Tanhshrink.cs | 4 +- src/TorchSharp/NN/Activation/Threshold.cs | 4 +- src/TorchSharp/NN/AlphaDropout.cs | 4 +- src/TorchSharp/NN/Bilinear.cs | 4 +- src/TorchSharp/NN/Convolution/Conv1D.cs | 8 +- src/TorchSharp/NN/Convolution/Conv2D.cs | 4 +- src/TorchSharp/NN/Convolution/Conv3D.cs | 8 +- .../NN/Convolution/ConvTranspose1D.cs | 8 +- .../NN/Convolution/ConvTranspose2D.cs | 8 +- .../NN/Convolution/ConvTranspose3D.cs | 8 +- src/TorchSharp/NN/CosineSimilarity.cs | 5 +- src/TorchSharp/NN/Dropout.cs | 4 +- src/TorchSharp/NN/Dropout2d.cs | 8 +- src/TorchSharp/NN/Dropout3d.cs | 8 +- src/TorchSharp/NN/Embedding.cs | 4 +- src/TorchSharp/NN/EmbeddingBag.cs | 6 +- src/TorchSharp/NN/FeatureDropout.cs | 8 +- src/TorchSharp/NN/Flatten.cs | 4 +- src/TorchSharp/NN/Fold.cs | 7 +- src/TorchSharp/NN/Identity.cs | 4 +- src/TorchSharp/NN/Linear.cs | 9 +- src/TorchSharp/NN/Losses.cs | 189 +-- .../NN/Normalization/BatchNorm1D.cs | 4 +- .../NN/Normalization/BatchNorm2D.cs | 4 +- .../NN/Normalization/BatchNorm3D.cs | 4 +- src/TorchSharp/NN/Normalization/Functional.cs | 28 +- src/TorchSharp/NN/Normalization/GroupNorm.cs | 7 +- .../NN/Normalization/InstanceNorm1d.cs | 4 +- .../NN/Normalization/InstanceNorm2d.cs | 4 +- .../NN/Normalization/InstanceNorm3d.cs | 4 +- .../NN/Normalization/LocalResponseNorm.cs | 4 +- src/TorchSharp/NN/OneHot.cs | 4 +- src/TorchSharp/NN/Padding/ConstantPad1d.cs | 4 +- src/TorchSharp/NN/Padding/ConstantPad2d.cs | 4 +- src/TorchSharp/NN/Padding/ConstantPad3d.cs | 4 +- src/TorchSharp/NN/Padding/ReflectionPad1d.cs | 4 +- src/TorchSharp/NN/Padding/ReflectionPad2d.cs | 4 +- src/TorchSharp/NN/Padding/ReflectionPad3d.cs | 4 +- src/TorchSharp/NN/Padding/ReplicationPad1d.cs | 4 +- src/TorchSharp/NN/Padding/ReplicationPad2d.cs | 4 +- src/TorchSharp/NN/Padding/ReplicationPad3d.cs | 4 +- src/TorchSharp/NN/Padding/ZeroPad2d.cs | 4 +- src/TorchSharp/NN/PairwiseDistance.cs | 5 +- src/TorchSharp/NN/PixelShuffle.cs | 4 +- src/TorchSharp/NN/PixelUnshuffle.cs | 4 +- .../NN/Pooling/AdaptiveAvgPool1D.cs | 9 +- .../NN/Pooling/AdaptiveAvgPool2D.cs | 16 +- .../NN/Pooling/AdaptiveAvgPool3D.cs | 22 +- .../NN/Pooling/AdaptiveMaxPool1D.cs | 4 +- .../NN/Pooling/AdaptiveMaxPool2D.cs | 4 +- .../NN/Pooling/AdaptiveMaxPool3D.cs | 4 +- src/TorchSharp/NN/Pooling/AvgPool1D.cs | 7 +- src/TorchSharp/NN/Pooling/AvgPool2D.cs | 26 +- src/TorchSharp/NN/Pooling/AvgPool3D.cs | 16 +- .../NN/Pooling/FractionalMaxPool2d.cs | 8 +- .../NN/Pooling/FractionalMaxPool3d.cs | 7 +- src/TorchSharp/NN/Pooling/LPPool1d.cs | 4 +- src/TorchSharp/NN/Pooling/LPPool2d.cs | 4 +- src/TorchSharp/NN/Pooling/MaxPool1D.cs | 14 +- src/TorchSharp/NN/Pooling/MaxPool2D.cs | 16 +- src/TorchSharp/NN/Pooling/MaxPool3D.cs | 10 +- src/TorchSharp/NN/Pooling/MaxUnpool1d.cs | 4 +- src/TorchSharp/NN/Pooling/MaxUnpool2d.cs | 9 +- src/TorchSharp/NN/Pooling/MaxUnpool3d.cs | 7 +- src/TorchSharp/NN/Recurrent/GRU.cs | 3 +- src/TorchSharp/NN/Recurrent/GRUCell.cs | 4 +- src/TorchSharp/NN/Recurrent/LSTM.cs | 3 +- src/TorchSharp/NN/Recurrent/LSTMCell.cs | 3 +- src/TorchSharp/NN/Recurrent/RNN.cs | 3 +- src/TorchSharp/NN/Recurrent/RNNCell.cs | 4 +- src/TorchSharp/NN/Transformer.cs | 10 +- src/TorchSharp/NN/TransformerDecoder.cs | 3 +- src/TorchSharp/NN/TransformerDecoderLayer.cs | 3 +- src/TorchSharp/NN/TransformerEncoder.cs | 3 +- src/TorchSharp/NN/TransformerEncoderLayer.cs | 9 +- src/TorchSharp/NN/Unflatten.cs | 4 +- src/TorchSharp/NN/Unfold.cs | 7 +- src/TorchSharp/NN/Upsample.cs | 22 +- src/TorchSharp/NN/Utils/RNNUtils.cs | 8 +- src/TorchSharp/NN/Vision.cs | 33 +- src/TorchSharp/Optimizers/ASGD.cs | 10 +- src/TorchSharp/Optimizers/Adadelta.cs | 10 +- src/TorchSharp/Optimizers/Adamax.cs | 10 +- src/TorchSharp/Optimizers/NAdam.cs | 10 +- src/TorchSharp/Optimizers/RAdam.cs | 10 +- src/TorchSharp/Optimizers/Rprop.cs | 10 +- src/TorchSharp/Tensor/Tensor.cs | 1028 ++++------------- src/TorchSharp/Torch.cs | 6 + 111 files changed, 513 insertions(+), 1422 deletions(-) diff --git a/src/TorchSharp/NN/Activation/CELU.cs b/src/TorchSharp/NN/Activation/CELU.cs index 3707a0d8f..59a6e5924 100644 --- a/src/TorchSharp/NN/Activation/CELU.cs +++ b/src/TorchSharp/NN/Activation/CELU.cs @@ -18,9 +18,7 @@ internal CELU(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { } public override Tensor forward(Tensor tensor) { - var res = THSNN_CELU_forward(handle, tensor.Handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_CELU_forward(handle, tensor.Handle)); } public override string GetName() diff --git a/src/TorchSharp/NN/Activation/ELU.cs b/src/TorchSharp/NN/Activation/ELU.cs index 05bf6694f..6001f04e5 100644 --- a/src/TorchSharp/NN/Activation/ELU.cs +++ b/src/TorchSharp/NN/Activation/ELU.cs @@ -18,9 +18,7 @@ internal ELU(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { } public override Tensor forward(Tensor tensor) { - var res = THSNN_ELU_forward(handle, tensor.Handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_ELU_forward(handle, tensor.Handle)); } public override string GetName() diff --git a/src/TorchSharp/NN/Activation/GELU.cs b/src/TorchSharp/NN/Activation/GELU.cs index 5b00ece2e..06d39866f 100644 --- a/src/TorchSharp/NN/Activation/GELU.cs +++ b/src/TorchSharp/NN/Activation/GELU.cs @@ -18,9 +18,7 @@ internal GELU(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { } public override Tensor forward(Tensor tensor) { - var res = THSNN_GELU_forward(handle, tensor.Handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_GELU_forward(handle, tensor.Handle)); } public override string GetName() diff --git a/src/TorchSharp/NN/Activation/GLU.cs b/src/TorchSharp/NN/Activation/GLU.cs index e7ef37967..da44a1313 100644 --- a/src/TorchSharp/NN/Activation/GLU.cs +++ b/src/TorchSharp/NN/Activation/GLU.cs @@ -18,9 +18,7 @@ internal GLU(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { } public override Tensor forward(Tensor tensor) { - var res = THSNN_GLU_forward(handle, tensor.Handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_GLU_forward(handle, tensor.Handle)); } public override string GetName() diff --git a/src/TorchSharp/NN/Activation/Hardshrink.cs b/src/TorchSharp/NN/Activation/Hardshrink.cs index 41631abe5..59d00bc94 100644 --- a/src/TorchSharp/NN/Activation/Hardshrink.cs +++ b/src/TorchSharp/NN/Activation/Hardshrink.cs @@ -18,9 +18,7 @@ internal Hardshrink(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandl public override Tensor forward(Tensor tensor) { - var res = THSNN_Hardshrink_forward(handle, tensor.Handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_Hardshrink_forward(handle, tensor.Handle)); } public override string GetName() diff --git a/src/TorchSharp/NN/Activation/Hardtanh.cs b/src/TorchSharp/NN/Activation/Hardtanh.cs index 4b85324ac..ff1bb89ef 100644 --- a/src/TorchSharp/NN/Activation/Hardtanh.cs +++ b/src/TorchSharp/NN/Activation/Hardtanh.cs @@ -18,9 +18,7 @@ internal Hardtanh(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) public override Tensor forward(Tensor tensor) { - var res = THSNN_Hardtanh_forward(handle, tensor.Handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_Hardtanh_forward(handle, tensor.Handle)); } public override string GetName() diff --git a/src/TorchSharp/NN/Activation/LeakyReLU.cs b/src/TorchSharp/NN/Activation/LeakyReLU.cs index 052b38631..b4d9ef714 100644 --- a/src/TorchSharp/NN/Activation/LeakyReLU.cs +++ b/src/TorchSharp/NN/Activation/LeakyReLU.cs @@ -18,9 +18,7 @@ internal LeakyReLU(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle public override Tensor forward(Tensor tensor) { - var res = THSNN_LeakyReLU_forward(handle, tensor.Handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_LeakyReLU_forward(handle, tensor.Handle)); } public override string GetName() diff --git a/src/TorchSharp/NN/Activation/LogSoftMax.cs b/src/TorchSharp/NN/Activation/LogSoftMax.cs index 2d9d8b484..269376edb 100644 --- a/src/TorchSharp/NN/Activation/LogSoftMax.cs +++ b/src/TorchSharp/NN/Activation/LogSoftMax.cs @@ -20,9 +20,7 @@ internal LogSoftmax(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandl public override Tensor forward(Tensor tensor) { - var res = THSNN_LogSoftmax_forward(handle, tensor.Handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_LogSoftmax_forward(handle, tensor.Handle)); } // Rather than spending cycles only to discover that this module has neither diff --git a/src/TorchSharp/NN/Activation/Mish.cs b/src/TorchSharp/NN/Activation/Mish.cs index 366ce98f9..d7f6d27dd 100644 --- a/src/TorchSharp/NN/Activation/Mish.cs +++ b/src/TorchSharp/NN/Activation/Mish.cs @@ -18,9 +18,7 @@ internal Mish(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { } public override Tensor forward(Tensor tensor) { - var res = THSNN_Mish_forward(handle, tensor.Handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_Mish_forward(handle, tensor.Handle)); } public override string GetName() diff --git a/src/TorchSharp/NN/Activation/PReLU.cs b/src/TorchSharp/NN/Activation/PReLU.cs index 3c8d666f5..fe1acc5b4 100644 --- a/src/TorchSharp/NN/Activation/PReLU.cs +++ b/src/TorchSharp/NN/Activation/PReLU.cs @@ -19,9 +19,7 @@ internal PReLU(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { public override Tensor forward(Tensor tensor) { - var res = THSNN_PReLU_forward(handle, tensor.Handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_PReLU_forward(handle, tensor.Handle)); } public override string GetName() diff --git a/src/TorchSharp/NN/Activation/RReLU.cs b/src/TorchSharp/NN/Activation/RReLU.cs index da51ccb5b..aca3e70fc 100644 --- a/src/TorchSharp/NN/Activation/RReLU.cs +++ b/src/TorchSharp/NN/Activation/RReLU.cs @@ -18,9 +18,7 @@ internal RReLU(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { public override Tensor forward(Tensor tensor) { - var res = THSNN_RReLU_forward(handle, tensor.Handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_RReLU_forward(handle, tensor.Handle)); } public override string GetName() diff --git a/src/TorchSharp/NN/Activation/ReLU6.cs b/src/TorchSharp/NN/Activation/ReLU6.cs index 4f167b80a..757941789 100644 --- a/src/TorchSharp/NN/Activation/ReLU6.cs +++ b/src/TorchSharp/NN/Activation/ReLU6.cs @@ -20,9 +20,7 @@ internal ReLU6(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { public override Tensor forward(Tensor tensor) { - var res = NativeMethods.THSNN_ReLU6_forward(handle, tensor.Handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSNN_ReLU6_forward(handle, tensor.Handle)); } public override string GetName() diff --git a/src/TorchSharp/NN/Activation/ReLu.cs b/src/TorchSharp/NN/Activation/ReLu.cs index d568c3267..eb4ba7815 100644 --- a/src/TorchSharp/NN/Activation/ReLu.cs +++ b/src/TorchSharp/NN/Activation/ReLu.cs @@ -18,9 +18,7 @@ internal ReLU(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { } public override Tensor forward(Tensor tensor) { - var res = THSNN_ReLU_forward(handle, tensor.Handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_ReLU_forward(handle, tensor.Handle)); } public override string GetName() diff --git a/src/TorchSharp/NN/Activation/SELU.cs b/src/TorchSharp/NN/Activation/SELU.cs index 353ef3ac8..a7059f66f 100644 --- a/src/TorchSharp/NN/Activation/SELU.cs +++ b/src/TorchSharp/NN/Activation/SELU.cs @@ -18,9 +18,7 @@ internal SELU(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { } public override Tensor forward(Tensor tensor) { - var res = THSNN_SELU_forward(handle, tensor.Handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_SELU_forward(handle, tensor.Handle)); } public override string GetName() diff --git a/src/TorchSharp/NN/Activation/SiLU.cs b/src/TorchSharp/NN/Activation/SiLU.cs index dae29ab16..3e4b4aa99 100644 --- a/src/TorchSharp/NN/Activation/SiLU.cs +++ b/src/TorchSharp/NN/Activation/SiLU.cs @@ -18,9 +18,7 @@ internal SiLU(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { } public override Tensor forward(Tensor tensor) { - var res = THSNN_SiLU_forward(handle, tensor.Handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_SiLU_forward(handle, tensor.Handle)); } public override string GetName() diff --git a/src/TorchSharp/NN/Activation/Sigmoid.cs b/src/TorchSharp/NN/Activation/Sigmoid.cs index 27e11aaea..65bef8b48 100644 --- a/src/TorchSharp/NN/Activation/Sigmoid.cs +++ b/src/TorchSharp/NN/Activation/Sigmoid.cs @@ -18,9 +18,7 @@ internal Sigmoid(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) public override Tensor forward(Tensor tensor) { - var res = THSNN_Sigmoid_forward(handle, tensor.Handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_Sigmoid_forward(handle, tensor.Handle)); } public override string GetName() diff --git a/src/TorchSharp/NN/Activation/Softmax.cs b/src/TorchSharp/NN/Activation/Softmax.cs index a1e200746..232153767 100644 --- a/src/TorchSharp/NN/Activation/Softmax.cs +++ b/src/TorchSharp/NN/Activation/Softmax.cs @@ -18,9 +18,7 @@ internal Softmax(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) public override Tensor forward(Tensor tensor) { - var res = THSNN_Softmax_forward(handle, tensor.Handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_Softmax_forward(handle, tensor.Handle)); } public override string GetName() diff --git a/src/TorchSharp/NN/Activation/Softmax2d.cs b/src/TorchSharp/NN/Activation/Softmax2d.cs index 52e6c77ba..a0fc107f1 100644 --- a/src/TorchSharp/NN/Activation/Softmax2d.cs +++ b/src/TorchSharp/NN/Activation/Softmax2d.cs @@ -18,9 +18,7 @@ internal Softmax2d(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle public override Tensor forward(Tensor tensor) { - var res = THSNN_Softmax2d_forward(handle, tensor.Handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_Softmax2d_forward(handle, tensor.Handle)); } public override string GetName() diff --git a/src/TorchSharp/NN/Activation/Softmin.cs b/src/TorchSharp/NN/Activation/Softmin.cs index 2969d4dc3..80ec85d04 100644 --- a/src/TorchSharp/NN/Activation/Softmin.cs +++ b/src/TorchSharp/NN/Activation/Softmin.cs @@ -19,9 +19,7 @@ internal Softmin(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) public override Tensor forward(Tensor tensor) { - var res = THSNN_Softmin_forward(handle, tensor.Handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_Softmin_forward(handle, tensor.Handle)); } public override string GetName() diff --git a/src/TorchSharp/NN/Activation/Softplus.cs b/src/TorchSharp/NN/Activation/Softplus.cs index 017754338..cbd30d1bc 100644 --- a/src/TorchSharp/NN/Activation/Softplus.cs +++ b/src/TorchSharp/NN/Activation/Softplus.cs @@ -19,9 +19,7 @@ internal Softplus(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) public override Tensor forward(Tensor tensor) { - var res = THSNN_Softplus_forward(handle, tensor.Handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_Softplus_forward(handle, tensor.Handle)); } public override string GetName() diff --git a/src/TorchSharp/NN/Activation/Softshrink.cs b/src/TorchSharp/NN/Activation/Softshrink.cs index 63a8e5cca..e61efd876 100644 --- a/src/TorchSharp/NN/Activation/Softshrink.cs +++ b/src/TorchSharp/NN/Activation/Softshrink.cs @@ -18,9 +18,7 @@ internal Softshrink(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandl public override Tensor forward(Tensor tensor) { - var res = THSNN_Softshrink_forward(handle, tensor.Handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_Softshrink_forward(handle, tensor.Handle)); } public override string GetName() diff --git a/src/TorchSharp/NN/Activation/Softsign.cs b/src/TorchSharp/NN/Activation/Softsign.cs index 60b4b3657..a041a5f26 100644 --- a/src/TorchSharp/NN/Activation/Softsign.cs +++ b/src/TorchSharp/NN/Activation/Softsign.cs @@ -18,9 +18,7 @@ internal Softsign(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) public override Tensor forward(Tensor tensor) { - var res = THSNN_Softsign_forward(handle, tensor.Handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_Softsign_forward(handle, tensor.Handle)); } public override string GetName() diff --git a/src/TorchSharp/NN/Activation/Tanh.cs b/src/TorchSharp/NN/Activation/Tanh.cs index 24ebfbf96..4133da63e 100644 --- a/src/TorchSharp/NN/Activation/Tanh.cs +++ b/src/TorchSharp/NN/Activation/Tanh.cs @@ -18,9 +18,7 @@ internal Tanh(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { } public override Tensor forward(Tensor tensor) { - var res = THSNN_Tanh_forward(handle, tensor.Handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_Tanh_forward(handle, tensor.Handle)); } public override string GetName() diff --git a/src/TorchSharp/NN/Activation/Tanhshrink.cs b/src/TorchSharp/NN/Activation/Tanhshrink.cs index c4503d462..fa2f7214e 100644 --- a/src/TorchSharp/NN/Activation/Tanhshrink.cs +++ b/src/TorchSharp/NN/Activation/Tanhshrink.cs @@ -18,9 +18,7 @@ internal Tanhshrink(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandl public override Tensor forward(Tensor tensor) { - var res = THSNN_Tanhshrink_forward(handle, tensor.Handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_Tanhshrink_forward(handle, tensor.Handle)); } public override string GetName() diff --git a/src/TorchSharp/NN/Activation/Threshold.cs b/src/TorchSharp/NN/Activation/Threshold.cs index 56a46ea0f..4f344aa2d 100644 --- a/src/TorchSharp/NN/Activation/Threshold.cs +++ b/src/TorchSharp/NN/Activation/Threshold.cs @@ -18,9 +18,7 @@ internal Threshold(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle public override Tensor forward(Tensor tensor) { - var res = THSNN_Threshold_forward(handle, tensor.Handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_Threshold_forward(handle, tensor.Handle)); } public override string GetName() diff --git a/src/TorchSharp/NN/AlphaDropout.cs b/src/TorchSharp/NN/AlphaDropout.cs index 7a3dab35c..cb62f7a79 100644 --- a/src/TorchSharp/NN/AlphaDropout.cs +++ b/src/TorchSharp/NN/AlphaDropout.cs @@ -72,9 +72,7 @@ public static partial class functional /// public static Tensor alpha_dropout(Tensor input, double p = 0.5, bool training = false, bool inplace = false) { - var res = THSNN_alpha_dropout(input.Handle, p, training, inplace); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_alpha_dropout(input.Handle, p, training, inplace)); } } } diff --git a/src/TorchSharp/NN/Bilinear.cs b/src/TorchSharp/NN/Bilinear.cs index f8fb7b7da..2a45663dc 100644 --- a/src/TorchSharp/NN/Bilinear.cs +++ b/src/TorchSharp/NN/Bilinear.cs @@ -19,9 +19,7 @@ internal Bilinear(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) public override Tensor forward(Tensor input1, Tensor input2) { - var res = THSNN_Bilinear_forward(handle, input1.Handle, input2.Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_Bilinear_forward(handle, input1.Handle, input2.Handle)); } public Parameter? bias { diff --git a/src/TorchSharp/NN/Convolution/Conv1D.cs b/src/TorchSharp/NN/Convolution/Conv1D.cs index dd7b4c263..01a3baf74 100644 --- a/src/TorchSharp/NN/Convolution/Conv1D.cs +++ b/src/TorchSharp/NN/Convolution/Conv1D.cs @@ -57,9 +57,7 @@ internal Conv1d(IntPtr handle, IntPtr boxedHandle, long input_channels) : base(h public override Tensor forward(Tensor input) { if (ValidateShape(input, 1)) { - var res = THSNN_Conv1d_forward(handle, input.Handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_Conv1d_forward(handle, input.Handle)); } throw new ArgumentException($"Expected 2D (unbatched) or 3D (batched) input with {input_channels} channels to Conv1d."); } @@ -194,9 +192,7 @@ public static Tensor conv1d(Tensor input, Tensor weight, Tensor? bias = null, (IntPtr)ppadding, paddingArray.Length, (IntPtr)pdilation, dilationArray.Length, groups); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - res = AutocastMode.AutoCast(res); - return new Tensor(res); + return ReturnCheckForErrorsAutocast(res); } } } diff --git a/src/TorchSharp/NN/Convolution/Conv2D.cs b/src/TorchSharp/NN/Convolution/Conv2D.cs index 4008b51fa..bf8e35f2b 100644 --- a/src/TorchSharp/NN/Convolution/Conv2D.cs +++ b/src/TorchSharp/NN/Convolution/Conv2D.cs @@ -47,9 +47,7 @@ internal Conv2d(IntPtr handle, IntPtr boxedHandle, long input_channels, long in_ public override Tensor forward(Tensor input) { if (ValidateShape(input, 2)) { - var res = THSNN_Conv2d_forward(handle, input.Handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_Conv2d_forward(handle, input.Handle)); } throw new ArgumentException($"Expected 3D (unbatched) or 4D (batched) input with {input_channels} channels to Conv2d."); } diff --git a/src/TorchSharp/NN/Convolution/Conv3D.cs b/src/TorchSharp/NN/Convolution/Conv3D.cs index ef37aaa6a..900a3dab4 100644 --- a/src/TorchSharp/NN/Convolution/Conv3D.cs +++ b/src/TorchSharp/NN/Convolution/Conv3D.cs @@ -18,9 +18,7 @@ internal Conv3d(IntPtr handle, IntPtr boxedHandle, long input_channels) : base(h public override Tensor forward(Tensor input) { if (ValidateShape(input, 3)) { - var res = THSNN_Conv3d_forward(handle, input.Handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_Conv3d_forward(handle, input.Handle)); } throw new ArgumentException($"Expected 4D (unbatched) or 5D (batched) input with {input_channels} channels to Conv3d."); } @@ -181,9 +179,7 @@ public static Tensor conv3d(Tensor input, Tensor weight, Tensor? bias = null, (IntPtr)ppadding, padding.Length, (IntPtr)pdilation, dilation.Length, groups); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - res = AutocastMode.AutoCast(res); - return new Tensor(res); + return ReturnCheckForErrorsAutocast(res); } } } diff --git a/src/TorchSharp/NN/Convolution/ConvTranspose1D.cs b/src/TorchSharp/NN/Convolution/ConvTranspose1D.cs index 9700a58b7..4226eb558 100644 --- a/src/TorchSharp/NN/Convolution/ConvTranspose1D.cs +++ b/src/TorchSharp/NN/Convolution/ConvTranspose1D.cs @@ -18,9 +18,7 @@ internal ConvTranspose1d(IntPtr handle, IntPtr boxedHandle, long input_channels) public override Tensor forward(Tensor input) { if (ValidateShape(input, 1)) { - var res = THSNN_ConvTranspose1d_forward(handle, input.Handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_ConvTranspose1d_forward(handle, input.Handle)); } throw new ArgumentException($"Expected 2D (unbatched) or 3D (batched) input with {input_channels} channels to ConvTranspose1d."); } @@ -117,9 +115,7 @@ public static Tensor conv_transpose1d(Tensor input, Tensor weight, Tensor? bias (IntPtr)poutputPadding, outputPaddings.Length, (IntPtr)pdilation, dilations.Length, groups); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - res = AutocastMode.AutoCast(res); - return new Tensor(res); + return ReturnCheckForErrorsAutocast(res); } } } diff --git a/src/TorchSharp/NN/Convolution/ConvTranspose2D.cs b/src/TorchSharp/NN/Convolution/ConvTranspose2D.cs index 63fc0d6e5..9912ec2c8 100644 --- a/src/TorchSharp/NN/Convolution/ConvTranspose2D.cs +++ b/src/TorchSharp/NN/Convolution/ConvTranspose2D.cs @@ -18,9 +18,7 @@ internal ConvTranspose2d(IntPtr handle, IntPtr boxedHandle, long input_channels) public override Tensor forward(Tensor input) { if (ValidateShape(input, 2)) { - var res = THSNN_ConvTranspose2d_forward(handle, input.Handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_ConvTranspose2d_forward(handle, input.Handle)); } throw new ArgumentException($"Expected 3D (unbatched) or 4D (batched) input with {input_channels} channels to ConvTranspose2d."); } @@ -148,9 +146,7 @@ public static Tensor conv_transpose2d(Tensor input, Tensor weight, Tensor? bias (IntPtr)poutputPadding, output_padding.Length, (IntPtr)pdilation, dilation.Length, groups); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - res = AutocastMode.AutoCast(res); - return new Tensor(res); + return ReturnCheckForErrorsAutocast(res); } } } diff --git a/src/TorchSharp/NN/Convolution/ConvTranspose3D.cs b/src/TorchSharp/NN/Convolution/ConvTranspose3D.cs index faeb279ad..c3dba2fa0 100644 --- a/src/TorchSharp/NN/Convolution/ConvTranspose3D.cs +++ b/src/TorchSharp/NN/Convolution/ConvTranspose3D.cs @@ -18,9 +18,7 @@ internal ConvTranspose3d(IntPtr handle, IntPtr boxedHandle, long input_channels) public override Tensor forward(Tensor input) { if (ValidateShape(input, 3)) { - var res = THSNN_ConvTranspose3d_forward(handle, input.Handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_ConvTranspose3d_forward(handle, input.Handle)); } throw new ArgumentException($"Expected 4D (unbatched) or 5D (batched) input with {input_channels} channels to ConvTranspose3d."); } @@ -144,9 +142,7 @@ public static Tensor conv_transpose3d(Tensor input, Tensor weight, Tensor? bias (IntPtr)poutputPadding, output_padding.Length, (IntPtr)pdilation, dilation.Length, groups); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - res = AutocastMode.AutoCast(res); - return new Tensor(res); + return ReturnCheckForErrorsAutocast(res); } } } diff --git a/src/TorchSharp/NN/CosineSimilarity.cs b/src/TorchSharp/NN/CosineSimilarity.cs index 99f9b05a1..0da0d7de5 100644 --- a/src/TorchSharp/NN/CosineSimilarity.cs +++ b/src/TorchSharp/NN/CosineSimilarity.cs @@ -21,10 +21,7 @@ internal CosineSimilarity(IntPtr handle, IntPtr boxedHandle) : base(handle, boxe public override Tensor forward(Tensor input1, Tensor input2) { - var res = THSNN_CosineSimilarity_forward(handle, input1.Handle, input2.Handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - res= AutocastMode.AutoCast(res, ScalarType.Float32); - return new Tensor(res); + return ReturnCheckForErrorsAutocast(THSNN_CosineSimilarity_forward(handle, input1.Handle, input2.Handle), ScalarType.Float32); } } } diff --git a/src/TorchSharp/NN/Dropout.cs b/src/TorchSharp/NN/Dropout.cs index 286fbb12d..79ecb1943 100644 --- a/src/TorchSharp/NN/Dropout.cs +++ b/src/TorchSharp/NN/Dropout.cs @@ -66,9 +66,7 @@ public static partial class functional /// public static Tensor dropout(Tensor input, double p = 0.5, bool training = true, bool inplace = false) { - var res = THSNN_dropout(input.Handle, p, training, inplace); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_dropout(input.Handle, p, training, inplace)); } } } diff --git a/src/TorchSharp/NN/Dropout2d.cs b/src/TorchSharp/NN/Dropout2d.cs index c016a0774..857850756 100644 --- a/src/TorchSharp/NN/Dropout2d.cs +++ b/src/TorchSharp/NN/Dropout2d.cs @@ -22,9 +22,7 @@ internal Dropout2d(double p = 0.5, bool inplace = false) : base(nameof(Dropout2d public override Tensor forward(Tensor input) { - var res = THSNN_dropout2d(input.Handle, p, this.training, inplace); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_dropout2d(input.Handle, p, this.training, inplace)); } // Rather than spending cycles only to discover that this module has neither @@ -64,9 +62,7 @@ public static partial class functional /// public static Tensor dropout2d(Tensor input, double p = 0.5, bool training = true, bool inplace = false) { - var res = THSNN_dropout2d(input.Handle, p, training, inplace); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_dropout2d(input.Handle, p, training, inplace)); } } } diff --git a/src/TorchSharp/NN/Dropout3d.cs b/src/TorchSharp/NN/Dropout3d.cs index 3604e32ce..201901650 100644 --- a/src/TorchSharp/NN/Dropout3d.cs +++ b/src/TorchSharp/NN/Dropout3d.cs @@ -22,9 +22,7 @@ internal Dropout3d(double p = 0.5, bool inplace = false) : base(nameof(Dropout3d public override Tensor forward(Tensor input) { - var res = THSNN_dropout3d(input.Handle, p, this.training, inplace); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_dropout3d(input.Handle, p, this.training, inplace)); } // Rather than spending cycles only to discover that this module has neither @@ -62,9 +60,7 @@ public static partial class functional /// public static Tensor dropout3d(Tensor input, double p = 0.5, bool training = true, bool inplace = false) { - var res = THSNN_dropout3d(input.Handle, p, training, inplace); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_dropout3d(input.Handle, p, training, inplace)); } } } diff --git a/src/TorchSharp/NN/Embedding.cs b/src/TorchSharp/NN/Embedding.cs index 00e7401b0..6f753606f 100644 --- a/src/TorchSharp/NN/Embedding.cs +++ b/src/TorchSharp/NN/Embedding.cs @@ -16,9 +16,7 @@ internal Embedding(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle public override Tensor forward(Tensor input) { - var res = THSNN_Embedding_forward(handle, input.Handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_Embedding_forward(handle, input.Handle)); } public Parameter? weight { diff --git a/src/TorchSharp/NN/EmbeddingBag.cs b/src/TorchSharp/NN/EmbeddingBag.cs index 8ccd717ae..6ef4b9b7d 100644 --- a/src/TorchSharp/NN/EmbeddingBag.cs +++ b/src/TorchSharp/NN/EmbeddingBag.cs @@ -31,10 +31,8 @@ internal EmbeddingBag(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHan /// Only supported for mode='sum'. /// public override Tensor forward(Tensor input, Tensor? offsets, Tensor? perSampleWeights) - { - var res = THSNN_EmbeddingBag_forward(handle, input.Handle, (offsets is null) ? IntPtr.Zero : offsets.Handle, (perSampleWeights is null) ? IntPtr.Zero : perSampleWeights.Handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + { + return ReturnCheckForErrors(THSNN_EmbeddingBag_forward(handle, input.Handle, (offsets is null) ? IntPtr.Zero : offsets.Handle, (perSampleWeights is null) ? IntPtr.Zero : perSampleWeights.Handle)); } public new Tensor call(Tensor input, Tensor? offsets, Tensor? perSampleWeights) diff --git a/src/TorchSharp/NN/FeatureDropout.cs b/src/TorchSharp/NN/FeatureDropout.cs index 4730e34bf..ffa95a8dd 100644 --- a/src/TorchSharp/NN/FeatureDropout.cs +++ b/src/TorchSharp/NN/FeatureDropout.cs @@ -20,9 +20,7 @@ internal FeatureAlphaDropout(IntPtr handle, IntPtr boxedHandle) : base(handle, b public override Tensor forward(Tensor tensor) { - var res = THSNN_FeatureAlphaDropout_forward(handle, tensor.Handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_FeatureAlphaDropout_forward(handle, tensor.Handle)); } // Rather than spending cycles only to discover that this module has neither @@ -61,9 +59,7 @@ public static partial class functional /// public static Tensor feature_alpha_dropout(Tensor input, double p = 0.5, bool training = false, bool inplace = false) { - var res = THSNN_feature_alpha_dropout(input.Handle, p, training, inplace); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_feature_alpha_dropout(input.Handle, p, training, inplace)); } } } diff --git a/src/TorchSharp/NN/Flatten.cs b/src/TorchSharp/NN/Flatten.cs index a05c4462e..caf924426 100644 --- a/src/TorchSharp/NN/Flatten.cs +++ b/src/TorchSharp/NN/Flatten.cs @@ -20,9 +20,7 @@ internal Flatten(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) public override Tensor forward(Tensor tensor) { - var res = THSNN_Flatten_forward(handle, tensor.Handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_Flatten_forward(handle, tensor.Handle)); } // Rather than spending cycles only to discover that this module has neither diff --git a/src/TorchSharp/NN/Fold.cs b/src/TorchSharp/NN/Fold.cs index 5c4de0ff0..afddd3686 100644 --- a/src/TorchSharp/NN/Fold.cs +++ b/src/TorchSharp/NN/Fold.cs @@ -85,9 +85,7 @@ public static partial class functional /// Currently, only unbatched (3D) or batched (4D) image-like output tensors are supported. public unsafe static Tensor fold(Tensor input, long output_size, long kernel_size, long dilation = 1, long padding = 0, long stride = 1) { - var res = THSNN_fold(input.Handle, output_size, output_size, kernel_size, kernel_size, stride, stride, padding, padding, dilation, dilation); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_fold(input.Handle, output_size, output_size, kernel_size, kernel_size, stride, stride, padding, padding, dilation, dilation)); } /// @@ -112,8 +110,7 @@ public unsafe static Tensor fold(Tensor input, (long,long) output_size, (long, l stride.Value.Item1, stride.Value.Item2, padding.Value.Item1, padding.Value.Item2, dilation.Value.Item1, dilation.Value.Item2); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(res); } } } diff --git a/src/TorchSharp/NN/Identity.cs b/src/TorchSharp/NN/Identity.cs index fc238b43e..10277118f 100644 --- a/src/TorchSharp/NN/Identity.cs +++ b/src/TorchSharp/NN/Identity.cs @@ -16,9 +16,7 @@ internal Identity(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) public override Tensor forward(Tensor tensor) { - var res = THSNN_Identity_forward(handle, tensor.Handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_Identity_forward(handle, tensor.Handle)); } // Rather than spending cycles only to discover that this module has neither diff --git a/src/TorchSharp/NN/Linear.cs b/src/TorchSharp/NN/Linear.cs index 68b34ffd5..f6884ef58 100644 --- a/src/TorchSharp/NN/Linear.cs +++ b/src/TorchSharp/NN/Linear.cs @@ -36,9 +36,7 @@ internal Linear(IntPtr handle, IntPtr boxedHandle, long inFeat, long outFeat) : public override Tensor forward(Tensor tensor) { //tensor.handle = Amp.AMPManager.GetInstance().AutoCast(tensor.handle); //WARNING should be here???? Research - var res = THSNN_Linear_forward(handle, tensor.Handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_Linear_forward(handle, tensor.Handle)); } public Parameter? bias { @@ -103,10 +101,7 @@ public static partial class functional public static Tensor linear(Tensor input, Tensor weights, Tensor? bias = null) { IntPtr bPtr = bias?.Handle ?? IntPtr.Zero; - var res = THSNN_functional_linear(input.Handle, weights.Handle, bPtr); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - res = AutocastMode.AutoCast(res); - return new Tensor(res); + return ReturnCheckForErrorsAutocast(THSNN_functional_linear(input.Handle, weights.Handle, bPtr)); } } } diff --git a/src/TorchSharp/NN/Losses.cs b/src/TorchSharp/NN/Losses.cs index 9aae89088..f06fda8c2 100644 --- a/src/TorchSharp/NN/Losses.cs +++ b/src/TorchSharp/NN/Losses.cs @@ -364,10 +364,11 @@ public static partial class functional /// public static Tensor binary_cross_entropy_with_logits(Tensor input, Tensor target, Tensor? weight = null, Reduction reduction = Reduction.Mean, Tensor? pos_weights = null) { - var res = THSNN_binary_cross_entropy_with_logits(input.Handle, target.Handle, weight?.Handle ?? IntPtr.Zero, (long)reduction, pos_weights?.Handle ?? IntPtr.Zero); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - res = AutocastMode.AutoCast(res, ScalarType.Float32); - return new Tensor(res); + return ReturnCheckForErrorsAutocast( + THSNN_binary_cross_entropy_with_logits(input.Handle, target.Handle, weight?.Handle ?? IntPtr.Zero, (long)reduction, pos_weights?.Handle ?? IntPtr.Zero), + ScalarType.Float32 + ); + } /// @@ -380,9 +381,7 @@ public static Tensor binary_cross_entropy_with_logits(Tensor input, Tensor targe /// public static Tensor binary_cross_entropy(Tensor input, Tensor target, Tensor? weight = null, Reduction reduction = Reduction.Mean) { - var res = THSNN_binary_cross_entropy(input.Handle, target.Handle, weight?.Handle ?? IntPtr.Zero, (long)reduction); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_binary_cross_entropy(input.Handle, target.Handle, weight?.Handle ?? IntPtr.Zero, (long)reduction)); } /// @@ -402,9 +401,7 @@ public static Tensor binary_cross_entropy(Tensor input, Tensor target, Tensor? w /// public static Tensor cross_entropy(Tensor input, Tensor target, Tensor? weight = null, long ignore_index = -100, Reduction reduction = Reduction.Mean, double label_smoothing = 0.0) { - var res = THSNN_cross_entropy(input.Handle, target.Handle, weight?.Handle ?? IntPtr.Zero, ignore_index, true, (long)reduction, label_smoothing); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_cross_entropy(input.Handle, target.Handle, weight?.Handle ?? IntPtr.Zero, ignore_index, true, (long)reduction, label_smoothing)); } /// @@ -419,9 +416,7 @@ public static Tensor cross_entropy(Tensor input, Tensor target, Tensor? weight = /// public static Tensor poisson_nll_loss(Tensor input, Tensor target, bool log_input = true, bool full = false, float eps = 1e-8f, Reduction reduction = Reduction.Mean) { - var res = THSNN_poisson_loss(input.Handle, target.Handle, log_input, full, eps, (long)reduction); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_poisson_loss(input.Handle, target.Handle, log_input, full, eps, (long)reduction)); } /// @@ -435,10 +430,7 @@ public static Tensor poisson_nll_loss(Tensor input, Tensor target, bool log_inpu /// public static Tensor cosine_embedding_loss(Tensor input1, Tensor input2, Tensor target, double margin = 0.0, Reduction reduction = Reduction.Mean) { - var res = THSNN_cosine_embedding_loss(input1.Handle, input2.Handle, target.Handle, margin, (long)reduction); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - res = AutocastMode.AutoCast(res, ScalarType.Float32); - return new Tensor(res); + return ReturnCheckForErrorsAutocast(THSNN_cosine_embedding_loss(input1.Handle, input2.Handle, target.Handle, margin, (long)reduction), ScalarType.Float32); } /// @@ -454,9 +446,7 @@ public static Tensor cosine_embedding_loss(Tensor input1, Tensor input2, Tensor /// public static Tensor ctc_loss(Tensor log_probs, Tensor targets, Tensor input_lengths, Tensor target_lengths, long blank = 0, bool zero_infinity = false, Reduction reduction = Reduction.Mean) { - var res = THSNN_ctc_loss(log_probs.Handle, targets.Handle, input_lengths.Handle, target_lengths.Handle, blank, zero_infinity, (long)reduction); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_ctc_loss(log_probs.Handle, targets.Handle, input_lengths.Handle, target_lengths.Handle, blank, zero_infinity, (long)reduction)); } /// @@ -469,9 +459,7 @@ public static Tensor ctc_loss(Tensor log_probs, Tensor targets, Tensor input_len /// public static Tensor hinge_embedding_loss(Tensor input, Tensor target, double margin = 0.0, Reduction reduction = Reduction.Mean) { - var res = THSNN_hinge_embedding_loss(input.Handle, target.Handle, margin, (long)reduction); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_hinge_embedding_loss(input.Handle, target.Handle, margin, (long)reduction)); } /// @@ -484,9 +472,7 @@ public static Tensor hinge_embedding_loss(Tensor input, Tensor target, double ma /// public static Tensor huber_loss(Tensor input, Tensor target, double delta = 1.0, Reduction reduction = Reduction.Mean) { - var res = THSNN_huber_loss(input.Handle, target.Handle, delta, (long)reduction); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_huber_loss(input.Handle, target.Handle, delta, (long)reduction)); } /// @@ -500,9 +486,7 @@ public static Tensor huber_loss(Tensor input, Tensor target, double delta = 1.0, /// public static Tensor margin_ranking_loss(Tensor input1, Tensor input2, Tensor target, double margin = 0.0, Reduction reduction = Reduction.Mean) { - var res = THSNN_margin_ranking_loss(input1.Handle, input2.Handle, target.Handle, margin, (long)reduction); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_margin_ranking_loss(input1.Handle, input2.Handle, target.Handle, margin, (long)reduction)); } /// @@ -515,10 +499,7 @@ public static Tensor margin_ranking_loss(Tensor input1, Tensor input2, Tensor ta /// public static Tensor multi_label_margin_loss(Tensor input, Tensor target, Reduction reduction = Reduction.Mean) { - var res = THSNN_multilabel_margin_loss(input.Handle, target.Handle, (long)reduction); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - res = AutocastMode.AutoCast(res, ScalarType.Float32); - return new Tensor(res); + return ReturnCheckForErrorsAutocast(THSNN_multilabel_margin_loss(input.Handle, target.Handle, (long)reduction), ScalarType.Float32); } /// @@ -531,9 +512,7 @@ public static Tensor multi_label_margin_loss(Tensor input, Tensor target, Reduct /// public static Tensor multilabel_soft_margin_loss(Tensor input, Tensor target, Tensor? weight = null,Reduction reduction = Reduction.Mean) { - var res = THSNN_multilabel_soft_margin_loss(input.Handle, target.Handle, weight?.Handle ?? IntPtr.Zero, (long)reduction); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_multilabel_soft_margin_loss(input.Handle, target.Handle, weight?.Handle ?? IntPtr.Zero, (long)reduction)); } /// @@ -549,10 +528,7 @@ public static Tensor multilabel_soft_margin_loss(Tensor input, Tensor target, Te public static Tensor multi_margin_loss(Tensor input, Tensor target, int p = 1, double margin = 1.0, Tensor? weight = null, Reduction reduction = Reduction.Mean) { IntPtr h = (weight is null) ? IntPtr.Zero : weight.Handle; - var res = THSNN_multi_margin_loss(input.Handle, target.Handle, p, margin, h, (long)reduction); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - res = AutocastMode.AutoCast(res, ScalarType.Float32); - return new Tensor(res); + return ReturnCheckForErrorsAutocast(THSNN_multi_margin_loss(input.Handle, target.Handle, p, margin, h, (long)reduction), ScalarType.Float32); } /// @@ -564,10 +540,7 @@ public static Tensor multi_margin_loss(Tensor input, Tensor target, int p = 1, d /// public static Tensor mse_loss(Tensor input, Tensor target, Reduction reduction = Reduction.Mean) { - var res = THSNN_mse_loss(input.Handle, target.Handle, (long)reduction); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - res = AutocastMode.AutoCast(res, ScalarType.Float32); - return new Tensor(res); + return ReturnCheckForErrorsAutocast(THSNN_mse_loss(input.Handle, target.Handle, (long)reduction), ScalarType.Float32); } /// @@ -579,9 +552,7 @@ public static Tensor mse_loss(Tensor input, Tensor target, Reduction reduction = /// public static Tensor l1_loss(Tensor input, Tensor target, Reduction reduction = Reduction.Mean) { - var res = THSNN_l1_loss(input.Handle, target.Handle, (long)reduction); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_l1_loss(input.Handle, target.Handle, (long)reduction)); } /// @@ -594,9 +565,7 @@ public static Tensor l1_loss(Tensor input, Tensor target, Reduction reduction = /// public static Tensor nll_loss(Tensor input, Tensor target, Tensor? weight = null, Reduction reduction = Reduction.Mean) { - var res = THSNN_nll_loss(input.Handle, target.Handle, weight?.Handle ?? IntPtr.Zero, (long)reduction); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_nll_loss(input.Handle, target.Handle, weight?.Handle ?? IntPtr.Zero, (long)reduction)); } /// @@ -624,10 +593,7 @@ public static Tensor gaussian_nll_loss(Tensor input, Tensor target, Tensor varia /// public static Tensor kl_div(Tensor input, Tensor target, bool log_target = true, Reduction reduction = Reduction.Mean) { - var res = THSNN_kl_div_loss(input.Handle, target.Handle, (long)reduction, log_target); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - res = AutocastMode.AutoCast(res, ScalarType.Float32); - return new Tensor(res); + return ReturnCheckForErrorsAutocast(THSNN_kl_div_loss(input.Handle, target.Handle, (long)reduction, log_target), ScalarType.Float32); } /// @@ -640,9 +606,7 @@ public static Tensor kl_div(Tensor input, Tensor target, bool log_target = true, /// public static Tensor smooth_l1_loss(Tensor input, Tensor target, Reduction reduction = Reduction.Mean, double beta = 1.0) { - var res = THSNN_smooth_l1_loss(input.Handle, target.Handle, (long)reduction, beta); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_smooth_l1_loss(input.Handle, target.Handle, (long)reduction, beta)); } /// @@ -654,9 +618,7 @@ public static Tensor smooth_l1_loss(Tensor input, Tensor target, Reduction reduc /// public static Tensor soft_margin_loss(Tensor input, Tensor target, Reduction reduction = Reduction.Mean) { - var res = THSNN_soft_margin_loss(input.Handle, target.Handle, (long)reduction); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_soft_margin_loss(input.Handle, target.Handle, (long)reduction)); } /// @@ -680,9 +642,7 @@ public static Tensor soft_margin_loss(Tensor input, Tensor target, Reduction red /// public static Tensor triplet_margin_loss(Tensor anchor, Tensor positive, Tensor negative, double margin = 1.0, long p = 2, double eps = 1e-06, bool swap = false, Reduction reduction = Reduction.Mean) { - var res = THSNN_triplet_margin_loss(anchor.Handle, positive.Handle, negative.Handle, margin, p, eps, swap, (long)reduction); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_triplet_margin_loss(anchor.Handle, positive.Handle, negative.Handle, margin, p, eps, swap, (long)reduction)); } /// @@ -721,9 +681,7 @@ public static Tensor triplet_margin_with_distance_loss(Tensor anchor, Tensor pos return res.Handle; }; } - var res = THSNN_triplet_margin_with_distance_loss(anchor.Handle, positive.Handle, negative.Handle, func, margin, swap, (long)reduction); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_triplet_margin_with_distance_loss(anchor.Handle, positive.Handle, negative.Handle, func, margin, swap, (long)reduction)); } } @@ -749,10 +707,7 @@ public CrossEntropyLoss(Tensor? weight = null, long? ignore_index = null, Reduct public override Tensor forward(Tensor input, Tensor target) { var ii = ignore_index.HasValue ? ignore_index.Value : -100; - var res = THSNN_cross_entropy(input.Handle, target.Handle, weight?.Handle ?? IntPtr.Zero, ii, ignore_index.HasValue, (long)reduction, label_smoothing); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - - return new Tensor(res); + return ReturnCheckForErrors(THSNN_cross_entropy(input.Handle, target.Handle, weight?.Handle ?? IntPtr.Zero, ii, ignore_index.HasValue, (long)reduction, label_smoothing)); } public long? ignore_index { get; } @@ -767,9 +722,7 @@ public BCELoss(Tensor? weight = null, Reduction reduction = Reduction.Mean) : ba public override Tensor forward(Tensor input, Tensor target) { - var res = THSNN_binary_cross_entropy(input.Handle, target.Handle, weight?.Handle ?? IntPtr.Zero, (long)reduction); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_binary_cross_entropy(input.Handle, target.Handle, weight?.Handle ?? IntPtr.Zero, (long)reduction)); } } @@ -782,10 +735,10 @@ public BCEWithLogitsLoss(Tensor? weight = null, Reduction reduction = Reduction. public override Tensor forward(Tensor input, Tensor target) { - var res = THSNN_binary_cross_entropy_with_logits(input.Handle, target.Handle, weight?.Handle ?? IntPtr.Zero, (long)reduction, pos_weights?.Handle ?? IntPtr.Zero); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - res = AutocastMode.AutoCast(res, ScalarType.Float32); - return new Tensor(res); + return ReturnCheckForErrorsAutocast( + THSNN_binary_cross_entropy_with_logits(input.Handle, target.Handle, weight?.Handle ?? IntPtr.Zero, (long)reduction, pos_weights?.Handle ?? IntPtr.Zero), + ScalarType.Float32 + ); } public Tensor? pos_weights { get; } @@ -800,10 +753,10 @@ public CosineEmbeddingLoss(double margin = 0.0, Reduction reduction = Reduction. public override Tensor forward(Tensor input1, Tensor input2, Tensor target) { - var res = THSNN_cosine_embedding_loss(input1.Handle, input2.Handle, target.Handle, margin, (long)reduction); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - res = AutocastMode.AutoCast(res, ScalarType.Float32); - return new Tensor(res); + return ReturnCheckForErrorsAutocast( + THSNN_cosine_embedding_loss(input1.Handle, input2.Handle, target.Handle, margin, (long)reduction), + ScalarType.Float32 + ); } public double margin { get; } @@ -819,9 +772,7 @@ public CTCLoss(long blank = 0, bool zero_infinity = false, Reduction reduction = public override Tensor forward(Tensor log_probs, Tensor targets, Tensor input_lengths, Tensor target_lengths) { - var res = THSNN_ctc_loss(log_probs.Handle, targets.Handle, input_lengths.Handle, target_lengths.Handle, blank, zero_infinity, (long)reduction); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_ctc_loss(log_probs.Handle, targets.Handle, input_lengths.Handle, target_lengths.Handle, blank, zero_infinity, (long)reduction)); } public long blank { get; } @@ -837,10 +788,10 @@ public HingeEmbeddingLoss(double margin = 0.0, Reduction reduction = Reduction.M public override Tensor forward(Tensor input, Tensor target) { - var res = THSNN_hinge_embedding_loss(input.Handle, target.Handle, margin, (long)reduction); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - res = AutocastMode.AutoCast(res, ScalarType.Float32); - return new Tensor(res); + return ReturnCheckForErrorsAutocast( + THSNN_hinge_embedding_loss(input.Handle, target.Handle, margin, (long)reduction), + ScalarType.Float32 + ); } public double margin { get; } @@ -855,9 +806,7 @@ public HuberLoss(double delta = 1.0, Reduction reduction = Reduction.Mean) : bas public override Tensor forward(Tensor input, Tensor target) { - var res = THSNN_huber_loss(input.Handle, target.Handle, delta, (long)reduction); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_huber_loss(input.Handle, target.Handle, delta, (long)reduction)); } public double delta { get; } @@ -872,10 +821,7 @@ public MarginRankingLoss(double margin = 0.0, Reduction reduction = Reduction.Me public override Tensor forward(Tensor input1, Tensor input2, Tensor target) { - var res = THSNN_margin_ranking_loss(input1.Handle, input2.Handle, target.Handle, margin, (long)reduction); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - res = AutocastMode.AutoCast(res, ScalarType.Float32); - return new Tensor(res); + return ReturnCheckForErrorsAutocast(THSNN_margin_ranking_loss(input1.Handle, input2.Handle, target.Handle, margin, (long)reduction), ScalarType.Float32); } public double margin { get; } @@ -889,9 +835,7 @@ public MultiLabelMarginLoss(Reduction reduction = Reduction.Mean) : base(reducti public override Tensor forward(Tensor input, Tensor target) { - var res = THSNN_multilabel_margin_loss(input.Handle, target.Handle, (long)reduction); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_multilabel_margin_loss(input.Handle, target.Handle, (long)reduction)); } } @@ -903,9 +847,7 @@ public MultiLabelSoftMarginLoss(Tensor? weight = null, Reduction reduction = Red public override Tensor forward(Tensor input, Tensor target) { - var res = THSNN_multilabel_soft_margin_loss(input.Handle, target.Handle, weight?.Handle ?? IntPtr.Zero, (long)reduction); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_multilabel_soft_margin_loss(input.Handle, target.Handle, weight?.Handle ?? IntPtr.Zero, (long)reduction)); } } @@ -921,9 +863,7 @@ public override Tensor forward(Tensor input, Tensor target) { IntPtr h = (weight is null) ? IntPtr.Zero : weight.Handle; - var res = THSNN_multi_margin_loss(input.Handle, target.Handle, p, margin, h, (long)reduction); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_multi_margin_loss(input.Handle, target.Handle, p, margin, h, (long)reduction)); } public double margin { get; } @@ -952,10 +892,7 @@ public L1Loss(Reduction reduction = Reduction.Mean) : base(reduction) public override Tensor forward(Tensor input, Tensor target) { - var res = THSNN_l1_loss(input.Handle, target.Handle, (long)reduction); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - res = AutocastMode.AutoCast(res, ScalarType.Float32); - return new Tensor(res); + return ReturnCheckForErrorsAutocast(THSNN_l1_loss(input.Handle, target.Handle, (long)reduction), ScalarType.Float32); } } @@ -967,10 +904,7 @@ public NLLLoss(Tensor? weight = null, Reduction reduction = Reduction.Mean) : ba public override Tensor forward(Tensor input, Tensor target) { - var res = THSNN_nll_loss(input.Handle, target.Handle, weight?.Handle ?? IntPtr.Zero, (long)reduction); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - res = AutocastMode.AutoCast(res, ScalarType.Float32); - return new Tensor(res); + return ReturnCheckForErrorsAutocast(THSNN_nll_loss(input.Handle, target.Handle, weight?.Handle ?? IntPtr.Zero, (long)reduction), ScalarType.Float32); } } @@ -985,10 +919,7 @@ public PoissonNLLLoss(bool log_input = true, bool full = false, float eps = 1e-8 public override Tensor forward(Tensor input, Tensor target) { - var res = THSNN_poisson_loss(input.Handle, target.Handle, log_input, full, eps, (long)reduction); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - res = AutocastMode.AutoCast(res, ScalarType.Float32); - return new Tensor(res); + return ReturnCheckForErrorsAutocast(THSNN_poisson_loss(input.Handle, target.Handle, log_input, full, eps, (long)reduction), ScalarType.Float32); } public bool log_input { get; } @@ -1042,9 +973,7 @@ public KLDivLoss(bool log_target = true, Reduction reduction = Reduction.Mean) : public override Tensor forward(Tensor input, Tensor target) { - var res = THSNN_kl_div_loss(input.Handle, target.Handle, (long)reduction, log_target); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_kl_div_loss(input.Handle, target.Handle, (long)reduction, log_target)); } public bool log_target { get; } @@ -1059,10 +988,7 @@ public SmoothL1Loss(Reduction reduction = Reduction.Mean, double beta = 1.0) : b public override Tensor forward(Tensor input, Tensor target) { - var res = THSNN_smooth_l1_loss(input.Handle, target.Handle, (long)reduction, beta); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - res = AutocastMode.AutoCast(res, ScalarType.Float32); - return new Tensor(res); + return ReturnCheckForErrorsAutocast(THSNN_smooth_l1_loss(input.Handle, target.Handle, (long)reduction, beta), ScalarType.Float32); } public double beta { get; } @@ -1076,10 +1002,7 @@ public SoftMarginLoss(Reduction reduction = Reduction.Mean) : base(reduction) public override Tensor forward(Tensor input, Tensor target) { - var res = THSNN_soft_margin_loss(input.Handle, target.Handle, (long)reduction); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - res = AutocastMode.AutoCast(res, ScalarType.Float32); - return new Tensor(res); + return ReturnCheckForErrorsAutocast(THSNN_soft_margin_loss(input.Handle, target.Handle, (long)reduction), ScalarType.Float32); } } @@ -1095,10 +1018,10 @@ public TripletMarginLoss(double margin = 1.0, long p = 2, double eps = 1e-06, bo public override Tensor forward(Tensor anchor, Tensor positive, Tensor negative) { - var res = THSNN_triplet_margin_loss(anchor.Handle, positive.Handle, negative.Handle, margin, p, eps, swap, (long)reduction); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - res = AutocastMode.AutoCast(res, ScalarType.Float32); - return new Tensor(res); + return ReturnCheckForErrorsAutocast( + THSNN_triplet_margin_loss(anchor.Handle, positive.Handle, negative.Handle, margin, p, eps, swap, (long)reduction), + ScalarType.Float32 + ); } public double margin { get; } @@ -1131,9 +1054,7 @@ public TripletMarginWithDistanceLoss(Func? distance = nu public override Tensor forward(Tensor anchor, Tensor positive, Tensor negative) { - var res = THSNN_triplet_margin_with_distance_loss(anchor.Handle, positive.Handle, negative.Handle, distance, margin, swap, (long)reduction); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_triplet_margin_with_distance_loss(anchor.Handle, positive.Handle, negative.Handle, distance, margin, swap, (long)reduction)); } DistanceFunctionNative? distance { get; } diff --git a/src/TorchSharp/NN/Normalization/BatchNorm1D.cs b/src/TorchSharp/NN/Normalization/BatchNorm1D.cs index a28bb9057..1e1463806 100644 --- a/src/TorchSharp/NN/Normalization/BatchNorm1D.cs +++ b/src/TorchSharp/NN/Normalization/BatchNorm1D.cs @@ -22,9 +22,7 @@ internal BatchNorm1d(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHand public override Tensor forward(Tensor tensor) { if (tensor.Dimensions < 2 || tensor.Dimensions > 3) throw new ArgumentException($"Invalid number of dimensions for BatchNorm argument: {tensor.Dimensions}"); - var res = THSNN_BatchNorm1d_forward(handle.DangerousGetHandle(), tensor.Handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_BatchNorm1d_forward(handle.DangerousGetHandle(), tensor.Handle)); } public Parameter? bias { diff --git a/src/TorchSharp/NN/Normalization/BatchNorm2D.cs b/src/TorchSharp/NN/Normalization/BatchNorm2D.cs index 391b8a6eb..a54d0e98d 100644 --- a/src/TorchSharp/NN/Normalization/BatchNorm2D.cs +++ b/src/TorchSharp/NN/Normalization/BatchNorm2D.cs @@ -22,9 +22,7 @@ internal BatchNorm2d(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHand public override Tensor forward(Tensor tensor) { if (tensor.Dimensions != 4) throw new ArgumentException($"Invalid number of dimensions for BatchNorm argument: {tensor.Dimensions}"); - var res = THSNN_BatchNorm2d_forward(handle.DangerousGetHandle(), tensor.Handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_BatchNorm2d_forward(handle.DangerousGetHandle(), tensor.Handle)); } public Parameter? bias { diff --git a/src/TorchSharp/NN/Normalization/BatchNorm3D.cs b/src/TorchSharp/NN/Normalization/BatchNorm3D.cs index 4af5f9f60..ba96a6aee 100644 --- a/src/TorchSharp/NN/Normalization/BatchNorm3D.cs +++ b/src/TorchSharp/NN/Normalization/BatchNorm3D.cs @@ -22,9 +22,7 @@ internal BatchNorm3d(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHand public override Tensor forward(Tensor tensor) { if (tensor.Dimensions != 5) throw new ArgumentException($"Invalid number of dimensions for BatchNorm argument: {tensor.Dimensions}"); - var res = THSNN_BatchNorm3d_forward(handle.DangerousGetHandle(), tensor.Handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_BatchNorm3d_forward(handle.DangerousGetHandle(), tensor.Handle)); } public Parameter? bias { diff --git a/src/TorchSharp/NN/Normalization/Functional.cs b/src/TorchSharp/NN/Normalization/Functional.cs index a077f1b03..399e23d85 100644 --- a/src/TorchSharp/NN/Normalization/Functional.cs +++ b/src/TorchSharp/NN/Normalization/Functional.cs @@ -23,9 +23,7 @@ public static Tensor batch_norm(Tensor input, Tensor running_mean, Tensor runnin bias is not null ? bias.Handle : IntPtr.Zero, training, momentum, eps); - if (res == IntPtr.Zero) - torch.CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(res); } /// @@ -39,9 +37,8 @@ public static Tensor group_norm(Tensor input, long num_groups, Tensor weight = n weight is not null ? weight.Handle : IntPtr.Zero, bias is not null ? bias.Handle : IntPtr.Zero, eps); - if (res == IntPtr.Zero) - torch.CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(res); + } /// @@ -57,9 +54,7 @@ public static Tensor instance_norm(Tensor input, Tensor running_mean = null, Ten bias is not null ? bias.Handle : IntPtr.Zero, use_input_stats, momentum, eps); - if (res == IntPtr.Zero) - torch.CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(res); } /// @@ -79,9 +74,8 @@ public static Tensor layer_norm(Tensor input, long[] normalized_shape, Tensor we eps); } } - if (res == IntPtr.Zero) - torch.CheckForErrors(); - return new Tensor(res); + + return ReturnCheckForErrors(res); } /// @@ -89,18 +83,12 @@ public static Tensor layer_norm(Tensor input, long[] normalized_shape, Tensor we /// public static Tensor local_response_norm(Tensor input, long size, double alpha = 0.0001, double beta = 0.75, double k = 1.0) { - var res = THSNN_local_response_norm(input.Handle, size, alpha, beta, k); - if (res == IntPtr.Zero) - torch.CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(THSNN_local_response_norm(input.Handle, size, alpha, beta, k)); } public static Tensor normalize(Tensor input, float p=2.0f, long dim=1, float eps= 1e-12f, Tensor output = null) { - var res = THSNN_normalize(input.Handle, p, dim, eps, out _); - if (res == IntPtr.Zero) - torch.CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(THSNN_normalize(input.Handle, p, dim, eps, out _)); } } } diff --git a/src/TorchSharp/NN/Normalization/GroupNorm.cs b/src/TorchSharp/NN/Normalization/GroupNorm.cs index eca7e1665..6e17fe79e 100644 --- a/src/TorchSharp/NN/Normalization/GroupNorm.cs +++ b/src/TorchSharp/NN/Normalization/GroupNorm.cs @@ -24,10 +24,9 @@ internal GroupNorm(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle public override Tensor forward(Tensor tensor) { if (tensor.Dimensions < 3) throw new ArgumentException($"Invalid number of dimensions for GroupNorm argument: {tensor.Dimensions}"); - var res = THSNN_GroupNorm_forward(handle.DangerousGetHandle(), tensor.Handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - res= AutocastMode.AutoCast(res, ScalarType.Float32); - return new Tensor(res); + + return ReturnCheckForErrorsAutocast(THSNN_GroupNorm_forward(handle.DangerousGetHandle(), tensor.Handle), ScalarType.Float32); + } public Parameter? bias { diff --git a/src/TorchSharp/NN/Normalization/InstanceNorm1d.cs b/src/TorchSharp/NN/Normalization/InstanceNorm1d.cs index f9fb5836c..7eace4b53 100644 --- a/src/TorchSharp/NN/Normalization/InstanceNorm1d.cs +++ b/src/TorchSharp/NN/Normalization/InstanceNorm1d.cs @@ -23,9 +23,7 @@ internal InstanceNorm1d(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedH public override Tensor forward(Tensor tensor) { if (tensor.Dimensions < 2 || tensor.Dimensions > 3) throw new ArgumentException($"Invalid number of dimensions for InstanceNorm argument: {tensor.Dimensions}"); - var res = THSNN_InstanceNorm1d_forward(handle.DangerousGetHandle(), tensor.Handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_InstanceNorm1d_forward(handle.DangerousGetHandle(), tensor.Handle)); } public Parameter? bias { diff --git a/src/TorchSharp/NN/Normalization/InstanceNorm2d.cs b/src/TorchSharp/NN/Normalization/InstanceNorm2d.cs index 9a7b35d1d..1cc081b8b 100644 --- a/src/TorchSharp/NN/Normalization/InstanceNorm2d.cs +++ b/src/TorchSharp/NN/Normalization/InstanceNorm2d.cs @@ -23,9 +23,7 @@ internal InstanceNorm2d(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedH public override Tensor forward(Tensor tensor) { if (tensor.Dimensions != 4) throw new ArgumentException($"Invalid number of dimensions for InstanceNorm argument: {tensor.Dimensions}"); - var res = THSNN_InstanceNorm2d_forward(handle.DangerousGetHandle(), tensor.Handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_InstanceNorm2d_forward(handle.DangerousGetHandle(), tensor.Handle)); } public Parameter? bias { diff --git a/src/TorchSharp/NN/Normalization/InstanceNorm3d.cs b/src/TorchSharp/NN/Normalization/InstanceNorm3d.cs index e74cbc278..2a221a7fd 100644 --- a/src/TorchSharp/NN/Normalization/InstanceNorm3d.cs +++ b/src/TorchSharp/NN/Normalization/InstanceNorm3d.cs @@ -23,9 +23,7 @@ internal InstanceNorm3d(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedH public override Tensor forward(Tensor tensor) { if (tensor.Dimensions != 5) throw new ArgumentException($"Invalid number of dimensions for InstanceNorm argument: {tensor.Dimensions}"); - var res = THSNN_InstanceNorm3d_forward(handle.DangerousGetHandle(), tensor.Handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_InstanceNorm3d_forward(handle.DangerousGetHandle(), tensor.Handle)); } public Parameter? bias { diff --git a/src/TorchSharp/NN/Normalization/LocalResponseNorm.cs b/src/TorchSharp/NN/Normalization/LocalResponseNorm.cs index 5fc5f07b7..e77e9b9a2 100644 --- a/src/TorchSharp/NN/Normalization/LocalResponseNorm.cs +++ b/src/TorchSharp/NN/Normalization/LocalResponseNorm.cs @@ -21,9 +21,7 @@ internal LocalResponseNorm(IntPtr handle, IntPtr boxedHandle) : base(handle, box public override Tensor forward(Tensor tensor) { if (tensor.Dimensions < 3) throw new ArgumentException($"Invalid number of dimensions for LocalResponseNorm argument: {tensor.Dimensions}"); - var res = THSNN_LocalResponseNorm_forward(handle.DangerousGetHandle(), tensor.Handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_LocalResponseNorm_forward(handle.DangerousGetHandle(), tensor.Handle)); } } } diff --git a/src/TorchSharp/NN/OneHot.cs b/src/TorchSharp/NN/OneHot.cs index 002d9beb2..1aeec1c2d 100644 --- a/src/TorchSharp/NN/OneHot.cs +++ b/src/TorchSharp/NN/OneHot.cs @@ -21,9 +21,7 @@ public static partial class functional public static Tensor one_hot(Tensor x, long num_classes = -1) { if (x.dtype != ScalarType.Int64) throw new ArgumentException("OneHot input tensor must have elements of type Int64"); - var res = THSNN_one_hot(x.Handle, num_classes); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_one_hot(x.Handle, num_classes)); } } } diff --git a/src/TorchSharp/NN/Padding/ConstantPad1d.cs b/src/TorchSharp/NN/Padding/ConstantPad1d.cs index d163af7c2..16419c9e8 100644 --- a/src/TorchSharp/NN/Padding/ConstantPad1d.cs +++ b/src/TorchSharp/NN/Padding/ConstantPad1d.cs @@ -23,9 +23,7 @@ internal ConstantPad1d(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHa /// public override Tensor forward(Tensor tensor) { - var res = THSNN_ConstantPad1d_forward(handle, tensor.Handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_ConstantPad1d_forward(handle, tensor.Handle)); } // Rather than spending cycles only to discover that this module has neither diff --git a/src/TorchSharp/NN/Padding/ConstantPad2d.cs b/src/TorchSharp/NN/Padding/ConstantPad2d.cs index 0cab67bfd..4c1c1539e 100644 --- a/src/TorchSharp/NN/Padding/ConstantPad2d.cs +++ b/src/TorchSharp/NN/Padding/ConstantPad2d.cs @@ -23,9 +23,7 @@ internal ConstantPad2d(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHa /// public override Tensor forward(Tensor tensor) { - var res = THSNN_ConstantPad2d_forward(handle, tensor.Handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_ConstantPad2d_forward(handle, tensor.Handle)); } // Rather than spending cycles only to discover that this module has neither diff --git a/src/TorchSharp/NN/Padding/ConstantPad3d.cs b/src/TorchSharp/NN/Padding/ConstantPad3d.cs index 09f7b0f2a..2552e67f5 100644 --- a/src/TorchSharp/NN/Padding/ConstantPad3d.cs +++ b/src/TorchSharp/NN/Padding/ConstantPad3d.cs @@ -23,9 +23,7 @@ internal ConstantPad3d(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHa /// public override Tensor forward(Tensor tensor) { - var res = THSNN_ConstantPad3d_forward(handle, tensor.Handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_ConstantPad3d_forward(handle, tensor.Handle)); } // Rather than spending cycles only to discover that this module has neither diff --git a/src/TorchSharp/NN/Padding/ReflectionPad1d.cs b/src/TorchSharp/NN/Padding/ReflectionPad1d.cs index 39cc3bc46..35fb6483b 100644 --- a/src/TorchSharp/NN/Padding/ReflectionPad1d.cs +++ b/src/TorchSharp/NN/Padding/ReflectionPad1d.cs @@ -23,9 +23,7 @@ internal ReflectionPad1d(IntPtr handle, IntPtr boxedHandle) : base(handle, boxed /// public override Tensor forward(Tensor tensor) { - var res = THSNN_ReflectionPad1d_forward(handle, tensor.Handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_ReflectionPad1d_forward(handle, tensor.Handle)); } // Rather than spending cycles only to discover that this module has neither diff --git a/src/TorchSharp/NN/Padding/ReflectionPad2d.cs b/src/TorchSharp/NN/Padding/ReflectionPad2d.cs index 26c32c5a1..1ec58c84d 100644 --- a/src/TorchSharp/NN/Padding/ReflectionPad2d.cs +++ b/src/TorchSharp/NN/Padding/ReflectionPad2d.cs @@ -23,9 +23,7 @@ internal ReflectionPad2d(IntPtr handle, IntPtr boxedHandle) : base(handle, boxed /// public override Tensor forward(Tensor tensor) { - var res = THSNN_ReflectionPad2d_forward(handle, tensor.Handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_ReflectionPad2d_forward(handle, tensor.Handle)); } // Rather than spending cycles only to discover that this module has neither diff --git a/src/TorchSharp/NN/Padding/ReflectionPad3d.cs b/src/TorchSharp/NN/Padding/ReflectionPad3d.cs index fcf5a3442..b2712bb5a 100644 --- a/src/TorchSharp/NN/Padding/ReflectionPad3d.cs +++ b/src/TorchSharp/NN/Padding/ReflectionPad3d.cs @@ -23,9 +23,7 @@ internal ReflectionPad3d(IntPtr handle, IntPtr boxedHandle) : base(handle, boxed /// public override Tensor forward(Tensor tensor) { - var res = THSNN_ReflectionPad3d_forward(handle, tensor.Handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_ReflectionPad3d_forward(handle, tensor.Handle)); } // Rather than spending cycles only to discover that this module has neither diff --git a/src/TorchSharp/NN/Padding/ReplicationPad1d.cs b/src/TorchSharp/NN/Padding/ReplicationPad1d.cs index dbf90602f..d2fc8e44e 100644 --- a/src/TorchSharp/NN/Padding/ReplicationPad1d.cs +++ b/src/TorchSharp/NN/Padding/ReplicationPad1d.cs @@ -23,9 +23,7 @@ internal ReplicationPad1d(IntPtr handle, IntPtr boxedHandle) : base(handle, boxe /// public override Tensor forward(Tensor tensor) { - var res = THSNN_ReplicationPad1d_forward(handle, tensor.Handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_ReplicationPad1d_forward(handle, tensor.Handle)); } // Rather than spending cycles only to discover that this module has neither diff --git a/src/TorchSharp/NN/Padding/ReplicationPad2d.cs b/src/TorchSharp/NN/Padding/ReplicationPad2d.cs index 608445acc..c2a2103a7 100644 --- a/src/TorchSharp/NN/Padding/ReplicationPad2d.cs +++ b/src/TorchSharp/NN/Padding/ReplicationPad2d.cs @@ -23,9 +23,7 @@ internal ReplicationPad2d(IntPtr handle, IntPtr boxedHandle) : base(handle, boxe /// public override Tensor forward(Tensor tensor) { - var res = THSNN_ReplicationPad2d_forward(handle, tensor.Handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_ReplicationPad2d_forward(handle, tensor.Handle)); } // Rather than spending cycles only to discover that this module has neither diff --git a/src/TorchSharp/NN/Padding/ReplicationPad3d.cs b/src/TorchSharp/NN/Padding/ReplicationPad3d.cs index 5df272bab..153b5c8da 100644 --- a/src/TorchSharp/NN/Padding/ReplicationPad3d.cs +++ b/src/TorchSharp/NN/Padding/ReplicationPad3d.cs @@ -23,9 +23,7 @@ internal ReplicationPad3d(IntPtr handle, IntPtr boxedHandle) : base(handle, boxe /// public override Tensor forward(Tensor tensor) { - var res = THSNN_ReplicationPad3d_forward(handle, tensor.Handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_ReplicationPad3d_forward(handle, tensor.Handle)); } // Rather than spending cycles only to discover that this module has neither diff --git a/src/TorchSharp/NN/Padding/ZeroPad2d.cs b/src/TorchSharp/NN/Padding/ZeroPad2d.cs index 7d1c7c7c7..1b98cc3b2 100644 --- a/src/TorchSharp/NN/Padding/ZeroPad2d.cs +++ b/src/TorchSharp/NN/Padding/ZeroPad2d.cs @@ -23,9 +23,7 @@ internal ZeroPad2d(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle /// public override Tensor forward(Tensor tensor) { - var res = THSNN_ZeroPad2d_forward(handle, tensor.Handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_ZeroPad2d_forward(handle, tensor.Handle)); } // Rather than spending cycles only to discover that this module has neither diff --git a/src/TorchSharp/NN/PairwiseDistance.cs b/src/TorchSharp/NN/PairwiseDistance.cs index bac5bace2..0503abb27 100644 --- a/src/TorchSharp/NN/PairwiseDistance.cs +++ b/src/TorchSharp/NN/PairwiseDistance.cs @@ -21,9 +21,8 @@ internal PairwiseDistance(IntPtr handle, IntPtr boxedHandle) : base(handle, boxe public override Tensor forward(Tensor input1, Tensor input2) { - var res = THSNN_PairwiseDistance_forward(handle, input1.Handle, input2.Handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_PairwiseDistance_forward(handle, input1.Handle, input2.Handle)); + } // Rather than spending cycles only to discover that this module has neither diff --git a/src/TorchSharp/NN/PixelShuffle.cs b/src/TorchSharp/NN/PixelShuffle.cs index fe1d94bd5..745750c7e 100644 --- a/src/TorchSharp/NN/PixelShuffle.cs +++ b/src/TorchSharp/NN/PixelShuffle.cs @@ -23,9 +23,7 @@ internal PixelShuffle(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHan /// public override Tensor forward(Tensor tensor) { - var res = THSNN_PixelShuffle_forward(handle, tensor.Handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_PixelShuffle_forward(handle, tensor.Handle)); } } } diff --git a/src/TorchSharp/NN/PixelUnshuffle.cs b/src/TorchSharp/NN/PixelUnshuffle.cs index e6d3f120a..9a8e749e6 100644 --- a/src/TorchSharp/NN/PixelUnshuffle.cs +++ b/src/TorchSharp/NN/PixelUnshuffle.cs @@ -23,9 +23,7 @@ internal PixelUnshuffle(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedH /// public override Tensor forward(Tensor tensor) { - var res = THSNN_PixelUnshuffle_forward(handle, tensor.Handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_PixelUnshuffle_forward(handle, tensor.Handle)); } } } diff --git a/src/TorchSharp/NN/Pooling/AdaptiveAvgPool1D.cs b/src/TorchSharp/NN/Pooling/AdaptiveAvgPool1D.cs index 9e6df1d98..bdad89ea8 100644 --- a/src/TorchSharp/NN/Pooling/AdaptiveAvgPool1D.cs +++ b/src/TorchSharp/NN/Pooling/AdaptiveAvgPool1D.cs @@ -20,9 +20,7 @@ internal AdaptiveAvgPool1d(IntPtr handle, IntPtr boxedHandle) : base(handle, box public override Tensor forward(Tensor tensor) { - var res = THSNN_AdaptiveAvgPool1d_forward(handle.DangerousGetHandle(), tensor.Handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_AdaptiveAvgPool1d_forward(handle.DangerousGetHandle(), tensor.Handle)); } // Rather than spending cycles only to discover that this module has neither @@ -65,10 +63,7 @@ public static Tensor adaptive_avg_pool1d(Tensor input, long output_size) var outputSizes = new long[] { output_size }; unsafe { fixed (long* poutputSize = outputSizes) { - var res = - THSTensor_adaptive_avg_pool1d(input.Handle, (IntPtr)poutputSize, outputSizes.Length); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_adaptive_avg_pool1d(input.Handle, (IntPtr)poutputSize, outputSizes.Length)); } } } diff --git a/src/TorchSharp/NN/Pooling/AdaptiveAvgPool2D.cs b/src/TorchSharp/NN/Pooling/AdaptiveAvgPool2D.cs index 8caaa437b..1f0206b52 100644 --- a/src/TorchSharp/NN/Pooling/AdaptiveAvgPool2D.cs +++ b/src/TorchSharp/NN/Pooling/AdaptiveAvgPool2D.cs @@ -20,9 +20,7 @@ internal AdaptiveAvgPool2d(IntPtr handle, IntPtr boxedHandle) : base(handle, box public override Tensor forward(Tensor tensor) { - var res = THSNN_AdaptiveAvgPool2d_forward(handle.DangerousGetHandle(), tensor.Handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_AdaptiveAvgPool2d_forward(handle.DangerousGetHandle(), tensor.Handle)); } // Rather than spending cycles only to discover that this module has neither @@ -92,9 +90,7 @@ public static Tensor adaptive_avg_pool2d(Tensor input, long[] output_size) { unsafe { fixed (long* poutputSize = output_size) { - var res = THSTensor_adaptive_avg_pool2d(input.Handle, (IntPtr)poutputSize, output_size.Length); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_adaptive_avg_pool2d(input.Handle, (IntPtr)poutputSize, output_size.Length)); } } } @@ -109,9 +105,7 @@ public static unsafe Tensor adaptive_avg_pool2d(Tensor input, (long, long) outpu { long* poutputSize = stackalloc long[2] { output_size.Item1, output_size.Item2 }; - var res = THSTensor_adaptive_avg_pool2d(input.Handle, (IntPtr)poutputSize, 2); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_adaptive_avg_pool2d(input.Handle, (IntPtr)poutputSize, 2)); } /// @@ -124,9 +118,7 @@ public static unsafe Tensor adaptive_avg_pool2d(Tensor input, long output_size) { long* poutputSize = stackalloc long[2] { output_size, output_size }; - var res = THSTensor_adaptive_avg_pool2d(input.Handle, (IntPtr)poutputSize, 2); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_adaptive_avg_pool2d(input.Handle, (IntPtr)poutputSize, 2)); } } } diff --git a/src/TorchSharp/NN/Pooling/AdaptiveAvgPool3D.cs b/src/TorchSharp/NN/Pooling/AdaptiveAvgPool3D.cs index 237496d4f..13d12645c 100644 --- a/src/TorchSharp/NN/Pooling/AdaptiveAvgPool3D.cs +++ b/src/TorchSharp/NN/Pooling/AdaptiveAvgPool3D.cs @@ -20,9 +20,7 @@ internal AdaptiveAvgPool3d(IntPtr handle, IntPtr boxedHandle) : base(handle, box public override Tensor forward(Tensor tensor) { - var res = THSNN_AdaptiveAvgPool3d_forward(handle.DangerousGetHandle(), tensor.Handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_AdaptiveAvgPool3d_forward(handle.DangerousGetHandle(), tensor.Handle)); } // Rather than spending cycles only to discover that this module has neither @@ -92,10 +90,8 @@ public static partial class functional public static unsafe Tensor adaptive_avg_pool3d(Tensor input, long[] output_size) { fixed (long* poutputSize = output_size) { - var res = - THSTensor_adaptive_avg_pool3d(input.Handle, (IntPtr)poutputSize, output_size.Length); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + + return ReturnCheckForErrors(THSTensor_adaptive_avg_pool3d(input.Handle, (IntPtr)poutputSize, output_size.Length)); } } @@ -108,9 +104,7 @@ public static unsafe Tensor adaptive_avg_pool3d(Tensor input, long[] output_size public static unsafe Tensor adaptive_avg_pool3d(Tensor input, (long, long, long) output_size) { long* poutputSize = stackalloc long[3] { output_size.Item1, output_size.Item2, output_size.Item3 }; - var res = THSTensor_adaptive_avg_pool3d(input.Handle, (IntPtr)poutputSize, 3); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_adaptive_avg_pool3d(input.Handle, (IntPtr)poutputSize, 3)); } /// @@ -123,16 +117,12 @@ public static unsafe Tensor adaptive_avg_pool3d(Tensor input, long output_size) { var os = new long[] { output_size, output_size, output_size }; long* poutputSize = stackalloc long[3] { output_size, output_size, output_size }; - var res = THSTensor_adaptive_avg_pool3d(input.Handle, (IntPtr)poutputSize, 3); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_adaptive_avg_pool3d(input.Handle, (IntPtr)poutputSize, 3)); } public static Tensor adaptive_avg_pool3d_backward(Tensor gradInput, Tensor gradOutput, Tensor originalInput) { - var res = THSTensor_adaptive_avg_pool3d_backward_out(gradInput.Handle, gradOutput.Handle, originalInput.Handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_adaptive_avg_pool3d_backward_out(gradInput.Handle, gradOutput.Handle, originalInput.Handle)); } } } diff --git a/src/TorchSharp/NN/Pooling/AdaptiveMaxPool1D.cs b/src/TorchSharp/NN/Pooling/AdaptiveMaxPool1D.cs index 936e9585d..811d76acd 100644 --- a/src/TorchSharp/NN/Pooling/AdaptiveMaxPool1D.cs +++ b/src/TorchSharp/NN/Pooling/AdaptiveMaxPool1D.cs @@ -20,9 +20,7 @@ internal AdaptiveMaxPool1d(IntPtr handle, IntPtr boxedHandle) : base(handle, box public override Tensor forward(Tensor tensor) { - var res = THSNN_AdaptiveMaxPool1d_forward(handle.DangerousGetHandle(), tensor.Handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_AdaptiveMaxPool1d_forward(handle.DangerousGetHandle(), tensor.Handle)); } // Rather than spending cycles only to discover that this module has neither diff --git a/src/TorchSharp/NN/Pooling/AdaptiveMaxPool2D.cs b/src/TorchSharp/NN/Pooling/AdaptiveMaxPool2D.cs index bb3ad2ea5..db82fbc95 100644 --- a/src/TorchSharp/NN/Pooling/AdaptiveMaxPool2D.cs +++ b/src/TorchSharp/NN/Pooling/AdaptiveMaxPool2D.cs @@ -20,9 +20,7 @@ internal AdaptiveMaxPool2d(IntPtr handle, IntPtr boxedHandle) : base(handle, box public override Tensor forward(Tensor tensor) { - var res = THSNN_AdaptiveMaxPool2d_forward(handle.DangerousGetHandle(), tensor.Handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_AdaptiveMaxPool2d_forward(handle.DangerousGetHandle(), tensor.Handle)); } // Rather than spending cycles only to discover that this module has neither diff --git a/src/TorchSharp/NN/Pooling/AdaptiveMaxPool3D.cs b/src/TorchSharp/NN/Pooling/AdaptiveMaxPool3D.cs index c57764b47..ed97348fa 100644 --- a/src/TorchSharp/NN/Pooling/AdaptiveMaxPool3D.cs +++ b/src/TorchSharp/NN/Pooling/AdaptiveMaxPool3D.cs @@ -20,9 +20,7 @@ internal AdaptiveMaxPool3d(IntPtr handle, IntPtr boxedHandle) : base(handle, box public override Tensor forward(Tensor tensor) { - var res = THSNN_AdaptiveMaxPool3d_forward(handle.DangerousGetHandle(), tensor.Handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_AdaptiveMaxPool3d_forward(handle.DangerousGetHandle(), tensor.Handle)); } // Rather than spending cycles only to discover that this module has neither diff --git a/src/TorchSharp/NN/Pooling/AvgPool1D.cs b/src/TorchSharp/NN/Pooling/AvgPool1D.cs index 9d7856bb0..8ee73f45d 100644 --- a/src/TorchSharp/NN/Pooling/AvgPool1D.cs +++ b/src/TorchSharp/NN/Pooling/AvgPool1D.cs @@ -20,9 +20,7 @@ internal AvgPool1d(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle public override Tensor forward(Tensor tensor) { - var res = THSNN_AvgPool1d_forward(handle.DangerousGetHandle(), tensor.Handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_AvgPool1d_forward(handle.DangerousGetHandle(), tensor.Handle)); } // Rather than spending cycles only to discover that this module has neither @@ -101,8 +99,7 @@ public static Tensor avg_pool1d(Tensor input, long kernel_size, long? stride = n (IntPtr)ppadding, paddings.Length, ceil_mode, count_include_pad); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(res); } } } diff --git a/src/TorchSharp/NN/Pooling/AvgPool2D.cs b/src/TorchSharp/NN/Pooling/AvgPool2D.cs index fec06d5fe..b155fcabc 100644 --- a/src/TorchSharp/NN/Pooling/AvgPool2D.cs +++ b/src/TorchSharp/NN/Pooling/AvgPool2D.cs @@ -20,9 +20,7 @@ internal AvgPool2d(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle public override Tensor forward(Tensor tensor) { - var res = THSNN_AvgPool2d_forward(handle.DangerousGetHandle(), tensor.Handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_AvgPool2d_forward(handle.DangerousGetHandle(), tensor.Handle)); } // Rather than spending cycles only to discover that this module has neither @@ -126,15 +124,13 @@ public static Tensor avg_pool2d(Tensor input, long[] kernelSizes, paddings = (paddings == null) ? new long[] { 0 } : paddings; unsafe { fixed (long* pkernelSize = kernelSizes, pstrides = strides, ppadding = paddings) { - var res = - THSTensor_avg_pool2d(input.Handle, + var res = THSTensor_avg_pool2d(input.Handle, (IntPtr)pkernelSize, kernelSizes.Length, (IntPtr)pstrides, strides.Length, (IntPtr)ppadding, paddings.Length, ceil_mode, count_include_pad); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(res); } } } @@ -161,15 +157,14 @@ public static unsafe Tensor avg_pool2d(Tensor input, long kernelSize, long* pstrides = stackalloc long[2] { svalue, svalue }; long* ppadding = stackalloc long[2] { padding, padding }; - var res = - THSTensor_avg_pool2d(input.Handle, + var res = THSTensor_avg_pool2d(input.Handle, (IntPtr)pkernelSize, 2, (IntPtr)pstrides, 2, (IntPtr)ppadding, 2, ceil_mode, count_include_pad); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(res); + } /// @@ -198,15 +193,13 @@ public static unsafe Tensor avg_pool2d(Tensor input, (long, long) kernelSize, long* pkernelSize = stackalloc long[2] { kernelSize.Item1, kernelSize.Item2 }; - var res = - THSTensor_avg_pool2d(input.Handle, + var res = THSTensor_avg_pool2d(input.Handle, (IntPtr)pkernelSize, 2, (IntPtr)pstrides, 2, (IntPtr)ppadding, 2, ceil_mode, count_include_pad); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(res); } public static Tensor avg_pool2d_backward(Tensor input, Tensor originalInput, @@ -229,8 +222,7 @@ public static Tensor avg_pool2d_backward(Tensor input, Tensor originalInput, ceil_mode, count_include_pad, divisorOverride); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(res); } } } diff --git a/src/TorchSharp/NN/Pooling/AvgPool3D.cs b/src/TorchSharp/NN/Pooling/AvgPool3D.cs index 341466084..bad7adfc8 100644 --- a/src/TorchSharp/NN/Pooling/AvgPool3D.cs +++ b/src/TorchSharp/NN/Pooling/AvgPool3D.cs @@ -20,9 +20,7 @@ internal AvgPool3d(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle public override Tensor forward(Tensor tensor) { - var res = THSNN_AvgPool3d_forward(handle.DangerousGetHandle(), tensor.Handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_AvgPool3d_forward(handle.DangerousGetHandle(), tensor.Handle)); } // Rather than spending cycles only to discover that this module has neither @@ -130,15 +128,13 @@ public static Tensor avg_pool3d(Tensor input, long[] kernelSizes, paddings = (paddings == null) ? new long[] { 0 } : paddings; unsafe { fixed (long* pkernelSize = kernelSizes, pstrides = strides, ppadding = paddings) { - var res = - THSTensor_avg_pool3d(input.Handle, + var res = THSTensor_avg_pool3d(input.Handle, (IntPtr)pkernelSize, kernelSizes.Length, (IntPtr)pstrides, strides.Length, (IntPtr)ppadding, paddings.Length, ceil_mode, count_include_pad); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(res); } } } @@ -155,16 +151,14 @@ public static Tensor avg_pool3d_backward(Tensor input, Tensor originalInput, paddings = (paddings == null) ? new long[] { 0 } : paddings; unsafe { fixed (long* pkernelSize = kernelSizes, pstrides = strides, ppadding = paddings) { - var res = - THSTensor_avg_pool3d_backward(input.Handle, originalInput.Handle, + var res = THSTensor_avg_pool3d_backward(input.Handle, originalInput.Handle, (IntPtr)pkernelSize, kernelSizes.Length, (IntPtr)pstrides, strides.Length, (IntPtr)ppadding, paddings.Length, ceil_mode, count_include_pad, divisorOverride); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(res); } } } diff --git a/src/TorchSharp/NN/Pooling/FractionalMaxPool2d.cs b/src/TorchSharp/NN/Pooling/FractionalMaxPool2d.cs index 0dc0a2e2d..72ef22130 100644 --- a/src/TorchSharp/NN/Pooling/FractionalMaxPool2d.cs +++ b/src/TorchSharp/NN/Pooling/FractionalMaxPool2d.cs @@ -20,16 +20,14 @@ internal FractionalMaxPool2d(IntPtr handle, IntPtr boxedHandle) : base(handle, b public override Tensor forward(Tensor tensor) { - var res = THSNN_FractionalMaxPool2d_forward(handle, tensor.Handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_FractionalMaxPool2d_forward(handle, tensor.Handle)); } public (Tensor Values, Tensor Indices) forward_with_indices(Tensor tensor) { + var res = THSNN_FractionalMaxPool2d_forward_with_indices(handle, tensor.Handle, out var indices); - if (res == IntPtr.Zero || indices == IntPtr.Zero) { torch.CheckForErrors(); } - return (new Tensor(res), new Tensor(indices)); + return ReturnCheckForErrors(res, indices); } // Rather than spending cycles only to discover that this module has neither diff --git a/src/TorchSharp/NN/Pooling/FractionalMaxPool3d.cs b/src/TorchSharp/NN/Pooling/FractionalMaxPool3d.cs index 145e589d0..1f5e252f6 100644 --- a/src/TorchSharp/NN/Pooling/FractionalMaxPool3d.cs +++ b/src/TorchSharp/NN/Pooling/FractionalMaxPool3d.cs @@ -25,9 +25,7 @@ public override Tensor forward(Tensor tensor) // Not sure why this is the case, but there's an exception in the native runtime // unless there's both a batch dimension and a channel dimension. throw new ArgumentException("FractionalMaxPool3d: input tensor must have 5 dimensions: [N, C, D, H, W]"); - var res = THSNN_FractionalMaxPool3d_forward(handle, tensor.Handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_FractionalMaxPool3d_forward(handle, tensor.Handle)); } public (Tensor Values, Tensor Indices) forward_with_indices(Tensor tensor) @@ -37,8 +35,7 @@ public override Tensor forward(Tensor tensor) // unless there's both a batch dimension and a channel dimension. throw new ArgumentException("FractionalMaxPool3d: input tensor must have 5 dimensions: [N, C, D, H, W]"); var res = THSNN_FractionalMaxPool3d_forward_with_indices(handle, tensor.Handle, out var indices); - if (res == IntPtr.Zero || indices == IntPtr.Zero) { torch.CheckForErrors(); } - return (new Tensor(res), new Tensor(indices)); + return ReturnCheckForErrors(res, indices); } // Rather than spending cycles only to discover that this module has neither diff --git a/src/TorchSharp/NN/Pooling/LPPool1d.cs b/src/TorchSharp/NN/Pooling/LPPool1d.cs index 53f4e038c..30ef1c830 100644 --- a/src/TorchSharp/NN/Pooling/LPPool1d.cs +++ b/src/TorchSharp/NN/Pooling/LPPool1d.cs @@ -20,9 +20,7 @@ internal LPPool1d(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) public override Tensor forward(Tensor tensor) { - var res = THSNN_LPPool1d_forward(handle.DangerousGetHandle(), tensor.Handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_LPPool1d_forward(handle.DangerousGetHandle(), tensor.Handle)); } // Rather than spending cycles only to discover that this module has neither diff --git a/src/TorchSharp/NN/Pooling/LPPool2d.cs b/src/TorchSharp/NN/Pooling/LPPool2d.cs index 1bff3fcb6..024261d0a 100644 --- a/src/TorchSharp/NN/Pooling/LPPool2d.cs +++ b/src/TorchSharp/NN/Pooling/LPPool2d.cs @@ -20,9 +20,7 @@ internal LPPool2d(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) public override Tensor forward(Tensor tensor) { - var res = THSNN_LPPool2d_forward(handle.DangerousGetHandle(), tensor.Handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_LPPool2d_forward(handle.DangerousGetHandle(), tensor.Handle)); } // Rather than spending cycles only to discover that this module has neither diff --git a/src/TorchSharp/NN/Pooling/MaxPool1D.cs b/src/TorchSharp/NN/Pooling/MaxPool1D.cs index cff5fbcf1..29043dde1 100644 --- a/src/TorchSharp/NN/Pooling/MaxPool1D.cs +++ b/src/TorchSharp/NN/Pooling/MaxPool1D.cs @@ -21,16 +21,14 @@ internal MaxPool1d(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle public override Tensor forward(Tensor tensor) { - var res = THSNN_MaxPool1d_forward(handle, tensor.Handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_MaxPool1d_forward(handle, tensor.Handle)); } public (Tensor Values, Tensor Indices) forward_with_indices(Tensor tensor) { var res = THSNN_MaxPool1d_forward_with_indices(handle, tensor.Handle, out var indices); - if (res == IntPtr.Zero || indices == IntPtr.Zero) { torch.CheckForErrors(); } - return (new Tensor(res), new Tensor(indices)); + return ReturnCheckForErrors(res, indices); + } // Rather than spending cycles only to discover that this module has neither @@ -94,15 +92,13 @@ public static Tensor max_pool1d(Tensor input, long kernelSize, long? stride = nu var dilations = new long[] { dilation ?? 1 }; unsafe { fixed (long* pkernelSize = kernelSizes, pstrides = strides, ppadding = paddings, pdilation = dilations) { - var res = - THSTensor_max_pool1d(input.Handle, + var res = THSTensor_max_pool1d(input.Handle, (IntPtr)pkernelSize, kernelSizes.Length, (IntPtr)pstrides, strides.Length, (IntPtr)ppadding, paddings.Length, (IntPtr)pdilation, dilations.Length, ceil_mode); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(res); } } } diff --git a/src/TorchSharp/NN/Pooling/MaxPool2D.cs b/src/TorchSharp/NN/Pooling/MaxPool2D.cs index 1cf88f291..2c08b2994 100644 --- a/src/TorchSharp/NN/Pooling/MaxPool2D.cs +++ b/src/TorchSharp/NN/Pooling/MaxPool2D.cs @@ -21,15 +21,12 @@ internal MaxPool2d(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle public override Tensor forward(Tensor tensor) { - var res = THSNN_MaxPool2d_forward(handle, tensor.Handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_MaxPool2d_forward(handle, tensor.Handle)); } public (Tensor Values, Tensor Indices) forward_with_indices(Tensor tensor) { var res = THSNN_MaxPool2d_forward_with_indices(handle, tensor.Handle, out var indices); - if (res == IntPtr.Zero || indices == IntPtr.Zero) { torch.CheckForErrors(); } - return (new Tensor(res), new Tensor(indices)); + return ReturnCheckForErrors(res, indices); } // Rather than spending cycles only to discover that this module has neither @@ -144,8 +141,7 @@ public static Tensor max_pool2d(Tensor input, long[] kernelSize, long[] strides (IntPtr)ppadding, padding.Length, (IntPtr)pdilation, dilation.Length, ceil_mode); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(res); } } } @@ -179,8 +175,7 @@ public static unsafe Tensor max_pool2d(Tensor input, long kernelSize, long? stri (IntPtr)pPadding, 2, (IntPtr)pDilation, 2, ceil_mode); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(res); } /// @@ -215,8 +210,7 @@ public static unsafe Tensor max_pool2d(Tensor input, (long, long) kernelSize, (l (IntPtr)pPadding, 2, (IntPtr)pDilation, 2, ceil_mode); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(res); } /// diff --git a/src/TorchSharp/NN/Pooling/MaxPool3D.cs b/src/TorchSharp/NN/Pooling/MaxPool3D.cs index d66e2e4d7..2f731cff0 100644 --- a/src/TorchSharp/NN/Pooling/MaxPool3D.cs +++ b/src/TorchSharp/NN/Pooling/MaxPool3D.cs @@ -21,16 +21,13 @@ internal MaxPool3d(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle public override Tensor forward(Tensor tensor) { - var res = THSNN_MaxPool3d_forward(handle, tensor.Handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_MaxPool3d_forward(handle, tensor.Handle)); } public (Tensor Values, Tensor Indices) forward_with_indices(Tensor tensor) { var res = THSNN_MaxPool3d_forward_with_indices(handle, tensor.Handle, out var indices); - if (res == IntPtr.Zero || indices == IntPtr.Zero) { torch.CheckForErrors(); } - return (new Tensor(res), new Tensor(indices)); + return ReturnCheckForErrors(res, indices); } // Rather than spending cycles only to discover that this module has neither @@ -126,8 +123,7 @@ public static Tensor max_pool3d(Tensor input, long[] kernelSize, long[] strides (IntPtr)ppadding, padding.Length, (IntPtr)pdilation, dilation.Length, ceil_mode); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(res); } } } diff --git a/src/TorchSharp/NN/Pooling/MaxUnpool1d.cs b/src/TorchSharp/NN/Pooling/MaxUnpool1d.cs index c110c547e..e39569574 100644 --- a/src/TorchSharp/NN/Pooling/MaxUnpool1d.cs +++ b/src/TorchSharp/NN/Pooling/MaxUnpool1d.cs @@ -22,9 +22,7 @@ public override Tensor forward(Tensor tensor, Tensor indices, long[] output_size { unsafe { fixed (long* pOutSize = output_size) { - var res = THSNN_MaxUnpool1d_forward(handle, tensor.Handle, indices.Handle, (IntPtr)pOutSize); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_MaxUnpool1d_forward(handle, tensor.Handle, indices.Handle, (IntPtr)pOutSize)); } } } diff --git a/src/TorchSharp/NN/Pooling/MaxUnpool2d.cs b/src/TorchSharp/NN/Pooling/MaxUnpool2d.cs index f0fc6433b..4974e3410 100644 --- a/src/TorchSharp/NN/Pooling/MaxUnpool2d.cs +++ b/src/TorchSharp/NN/Pooling/MaxUnpool2d.cs @@ -22,9 +22,7 @@ public override Tensor forward(Tensor tensor, Tensor indices, long[] output_size { unsafe { fixed (long* pOutSize = output_size) { - var res = THSNN_MaxUnpool2d_forward(handle, tensor.Handle, indices.Handle, (IntPtr)pOutSize, output_size == null ? 0 : output_size.Length); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_MaxUnpool2d_forward(handle, tensor.Handle, indices.Handle, (IntPtr)pOutSize, output_size == null ? 0 : output_size.Length)); } } } @@ -105,10 +103,7 @@ public static Tensor max_unpool2d(Tensor input, Tensor indices, long[] outputSiz { unsafe { fixed (long* poutputSize = outputSize) { - var res = THSTensor_maxunpool2d(input.Handle, indices.Handle, - (IntPtr)poutputSize, outputSize.Length); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_maxunpool2d(input.Handle, indices.Handle, (IntPtr)poutputSize, outputSize.Length)); } } } diff --git a/src/TorchSharp/NN/Pooling/MaxUnpool3d.cs b/src/TorchSharp/NN/Pooling/MaxUnpool3d.cs index 971b5efcc..b024d130b 100644 --- a/src/TorchSharp/NN/Pooling/MaxUnpool3d.cs +++ b/src/TorchSharp/NN/Pooling/MaxUnpool3d.cs @@ -22,9 +22,7 @@ public override Tensor forward(Tensor tensor, Tensor indices, long[] output_size { unsafe { fixed (long* pOutSize = output_size) { - var res = THSNN_MaxUnpool3d_forward(handle, tensor.Handle, indices.Handle, (IntPtr)pOutSize, output_size == null ? 0 : output_size.Length); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_MaxUnpool3d_forward(handle, tensor.Handle, indices.Handle, (IntPtr)pOutSize, output_size == null ? 0 : output_size.Length)); } } } @@ -110,8 +108,7 @@ public static Tensor max_unpool3d(Tensor input, Tensor indices, long[] outputSiz (IntPtr)poutputSize, outputSize.Length, (IntPtr)pstrides, strides.Length, (IntPtr)ppadding, padding.Length); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(res); } } } diff --git a/src/TorchSharp/NN/Recurrent/GRU.cs b/src/TorchSharp/NN/Recurrent/GRU.cs index 39b340af0..568921455 100644 --- a/src/TorchSharp/NN/Recurrent/GRU.cs +++ b/src/TorchSharp/NN/Recurrent/GRU.cs @@ -42,8 +42,7 @@ public override (Tensor, Tensor) forward(Tensor input, Tensor h0 = null) } var res = THSNN_GRU_forward(handle, input.Handle, h0.Handle, out IntPtr hN); - if (res == IntPtr.Zero || hN == IntPtr.Zero) { torch.CheckForErrors(); } - return (new Tensor(res), new Tensor(hN)); + return ReturnCheckForErrors(res, hN); } public new (Tensor, Tensor) call(Tensor input, Tensor h0 = null) => base.call(input, h0); diff --git a/src/TorchSharp/NN/Recurrent/GRUCell.cs b/src/TorchSharp/NN/Recurrent/GRUCell.cs index 610762542..8b456dfd9 100644 --- a/src/TorchSharp/NN/Recurrent/GRUCell.cs +++ b/src/TorchSharp/NN/Recurrent/GRUCell.cs @@ -24,9 +24,7 @@ internal GRUCell(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) /// public override Tensor forward(Tensor input, Tensor? h0 = null) { - var hN = THSNN_GRUCell_forward(handle, input.Handle, h0?.Handle ?? IntPtr.Zero); - if (hN == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(hN); + return ReturnCheckForErrors(THSNN_GRUCell_forward(handle, input.Handle, h0?.Handle ?? IntPtr.Zero)); } public Parameter? bias_ih { diff --git a/src/TorchSharp/NN/Recurrent/LSTM.cs b/src/TorchSharp/NN/Recurrent/LSTM.cs index 5eafd76fc..304439f18 100644 --- a/src/TorchSharp/NN/Recurrent/LSTM.cs +++ b/src/TorchSharp/NN/Recurrent/LSTM.cs @@ -47,8 +47,7 @@ public override (Tensor, Tensor, Tensor) forward(Tensor input, (Tensor, Tensor)? } var res = THSNN_LSTM_forward(handle, input.Handle, h0.Handle, c0.Handle, out IntPtr hN, out IntPtr cN); - if (res == IntPtr.Zero || hN == IntPtr.Zero || cN == IntPtr.Zero) { torch.CheckForErrors(); } - return (new Tensor(res), new Tensor(hN), new Tensor(cN)); + return ReturnCheckForErrors(res, hN, cN); } public new (Tensor, Tensor, Tensor) call(Tensor input, (Tensor, Tensor)? h0_c0 = null) => base.call(input, h0_c0); diff --git a/src/TorchSharp/NN/Recurrent/LSTMCell.cs b/src/TorchSharp/NN/Recurrent/LSTMCell.cs index 44f6e5bbc..89180c0bf 100644 --- a/src/TorchSharp/NN/Recurrent/LSTMCell.cs +++ b/src/TorchSharp/NN/Recurrent/LSTMCell.cs @@ -25,8 +25,7 @@ internal LSTMCell(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) public override (Tensor, Tensor) forward(Tensor input, (Tensor, Tensor)? h0_c0) { var hN = THSNN_LSTMCell_forward(handle, input.Handle, h0_c0?.Item1.Handle ?? IntPtr.Zero, h0_c0?.Item2.Handle ?? IntPtr.Zero, out IntPtr cN); - if (hN == IntPtr.Zero || cN == IntPtr.Zero) { torch.CheckForErrors(); } - return (new Tensor(hN), new Tensor(cN)); + return ReturnCheckForErrors(hN, cN); } public new (Tensor, Tensor) call(Tensor input, (Tensor, Tensor)? h0_c0 = null) => base.call(input, h0_c0); diff --git a/src/TorchSharp/NN/Recurrent/RNN.cs b/src/TorchSharp/NN/Recurrent/RNN.cs index a98f3e46c..e84d32077 100644 --- a/src/TorchSharp/NN/Recurrent/RNN.cs +++ b/src/TorchSharp/NN/Recurrent/RNN.cs @@ -42,8 +42,7 @@ public override (Tensor, Tensor) forward(Tensor input, Tensor? h0) } var res = THSNN_RNN_forward(handle, input.Handle, h0.Handle, out IntPtr hN); - if (res == IntPtr.Zero || hN == IntPtr.Zero) { torch.CheckForErrors(); } - return (new Tensor(res), new Tensor(hN)); + return ReturnCheckForErrors(res, hN); } public new (Tensor, Tensor) call(Tensor input, Tensor? h0 = null) => base.call(input, h0); diff --git a/src/TorchSharp/NN/Recurrent/RNNCell.cs b/src/TorchSharp/NN/Recurrent/RNNCell.cs index 05bf7088b..20cb95f85 100644 --- a/src/TorchSharp/NN/Recurrent/RNNCell.cs +++ b/src/TorchSharp/NN/Recurrent/RNNCell.cs @@ -26,9 +26,7 @@ internal RNNCell(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) /// public override Tensor forward(Tensor input, Tensor? h0 = null) { - var hN = THSNN_RNNCell_forward(handle, input.Handle, h0?.Handle ?? IntPtr.Zero); - if (hN == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(hN); + return ReturnCheckForErrors(THSNN_RNNCell_forward(handle, input.Handle, h0?.Handle ?? IntPtr.Zero)); } public Parameter? bias_ih { diff --git a/src/TorchSharp/NN/Transformer.cs b/src/TorchSharp/NN/Transformer.cs index d69ff96de..dee5673e6 100644 --- a/src/TorchSharp/NN/Transformer.cs +++ b/src/TorchSharp/NN/Transformer.cs @@ -38,8 +38,7 @@ public Tensor call(Tensor src, Tensor tgt, Tensor src_mask, Tensor? tgt_mask = n src_key_padding_mask?.Handle ?? IntPtr.Zero, tgt_key_padding_mask?.Handle ?? IntPtr.Zero, memory_key_padding_mask?.Handle ?? IntPtr.Zero); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(res); } /// @@ -58,8 +57,7 @@ public override Tensor forward(Tensor src, Tensor tgt) IntPtr.Zero, IntPtr.Zero, IntPtr.Zero); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(res); } } } @@ -113,9 +111,7 @@ public static Tensor scaled_dot_product_attention(Tensor query, Tensor key, Tens { if (p < 0) throw new ArgumentException("Dropout probability must be greater than or equal to zero."); if (is_casual && attn_mask is not null) throw new ArgumentException("Casual attention masking cannot pass a mask."); - var res = THSNN_scaled_dot_product_attention(query.Handle, key.Handle, value.Handle, attn_mask is null ? IntPtr.Zero : attn_mask.Handle, p, is_casual); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_scaled_dot_product_attention(query.Handle, key.Handle, value.Handle, attn_mask is null ? IntPtr.Zero : attn_mask.Handle, p, is_casual)); } } } diff --git a/src/TorchSharp/NN/TransformerDecoder.cs b/src/TorchSharp/NN/TransformerDecoder.cs index 620b8ac55..34daf546d 100644 --- a/src/TorchSharp/NN/TransformerDecoder.cs +++ b/src/TorchSharp/NN/TransformerDecoder.cs @@ -32,8 +32,7 @@ public override Tensor forward(Tensor tgt, Tensor memory, Tensor tgt_mask, Tenso memory_mask?.Handle ?? IntPtr.Zero, tgt_key_padding_mask?.Handle ?? IntPtr.Zero, memory_key_padding_mask?.Handle ?? IntPtr.Zero); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(res); } public new Tensor call(Tensor tgt, Tensor memory, Tensor tgt_mask, Tensor memory_mask = null, Tensor tgt_key_padding_mask = null, Tensor memory_key_padding_mask = null) { diff --git a/src/TorchSharp/NN/TransformerDecoderLayer.cs b/src/TorchSharp/NN/TransformerDecoderLayer.cs index 6b8cfd62e..3e72902b9 100644 --- a/src/TorchSharp/NN/TransformerDecoderLayer.cs +++ b/src/TorchSharp/NN/TransformerDecoderLayer.cs @@ -32,8 +32,7 @@ public override Tensor forward(Tensor tgt, Tensor memory, Tensor tgt_mask, Tenso memory_mask?.Handle ?? IntPtr.Zero, tgt_key_padding_mask?.Handle ?? IntPtr.Zero, memory_key_padding_mask?.Handle ?? IntPtr.Zero); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(res); } public new Tensor call(Tensor tgt, Tensor memory, Tensor tgt_mask, Tensor memory_mask = null, Tensor tgt_key_padding_mask = null, Tensor memory_key_padding_mask = null) diff --git a/src/TorchSharp/NN/TransformerEncoder.cs b/src/TorchSharp/NN/TransformerEncoder.cs index d90f2f635..01863fea9 100644 --- a/src/TorchSharp/NN/TransformerEncoder.cs +++ b/src/TorchSharp/NN/TransformerEncoder.cs @@ -32,8 +32,7 @@ public override Tensor forward(Tensor src, Tensor src_mask, Tensor src_key_paddi src.Handle, src_mask?.Handle ?? IntPtr.Zero, src_key_padding_mask?.Handle ?? IntPtr.Zero); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(res); } /// diff --git a/src/TorchSharp/NN/TransformerEncoderLayer.cs b/src/TorchSharp/NN/TransformerEncoderLayer.cs index 364727dbd..1c973f87b 100644 --- a/src/TorchSharp/NN/TransformerEncoderLayer.cs +++ b/src/TorchSharp/NN/TransformerEncoderLayer.cs @@ -26,8 +26,7 @@ public Tensor call(Tensor src, Tensor src_mask, Tensor src_key_padding_mask) src.Handle, src_mask?.Handle ?? IntPtr.Zero, src_key_padding_mask?.Handle ?? IntPtr.Zero); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(res); } /// @@ -41,8 +40,7 @@ public Tensor call(Tensor src, Tensor src_mask) src.Handle, src_mask?.Handle ?? IntPtr.Zero, IntPtr.Zero); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(res); } /// @@ -55,8 +53,7 @@ public override Tensor forward(Tensor src) src.Handle, IntPtr.Zero, IntPtr.Zero); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(res); } } } diff --git a/src/TorchSharp/NN/Unflatten.cs b/src/TorchSharp/NN/Unflatten.cs index aaad8d194..55fdd60c7 100644 --- a/src/TorchSharp/NN/Unflatten.cs +++ b/src/TorchSharp/NN/Unflatten.cs @@ -20,9 +20,7 @@ internal Unflatten(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle public override Tensor forward(Tensor tensor) { - var res = THSNN_Unflatten_forward(handle, tensor.Handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_Unflatten_forward(handle, tensor.Handle)); } // Rather than spending cycles only to discover that this module has neither diff --git a/src/TorchSharp/NN/Unfold.cs b/src/TorchSharp/NN/Unfold.cs index 49ee15a85..5047cb5ef 100644 --- a/src/TorchSharp/NN/Unfold.cs +++ b/src/TorchSharp/NN/Unfold.cs @@ -85,9 +85,7 @@ public static partial class functional /// The stride of the sliding blocks in the input spatial dimensions. public unsafe static Tensor unfold(Tensor input, long kernel_size, long dilation = 1, long padding = 0, long stride = 1) { - var res = THSNN_unfold(input.Handle, kernel_size, kernel_size, stride, stride, padding, padding, dilation, dilation); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_unfold(input.Handle, kernel_size, kernel_size, stride, stride, padding, padding, dilation, dilation)); } /// @@ -109,8 +107,7 @@ public unsafe static Tensor unfold(Tensor input, (long, long) kernel_size, (long stride.Value.Item1, stride.Value.Item2, padding.Value.Item1, padding.Value.Item2, dilation.Value.Item1, dilation.Value.Item2); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(res); } } } diff --git a/src/TorchSharp/NN/Upsample.cs b/src/TorchSharp/NN/Upsample.cs index 93bab8bfb..a24578e23 100644 --- a/src/TorchSharp/NN/Upsample.cs +++ b/src/TorchSharp/NN/Upsample.cs @@ -79,8 +79,7 @@ public static Tensor upsample_nearest1d(Tensor input, long? outputSize, double? THSTensor_upsample_nearest1d(input.Handle, (IntPtr)poutputSizes, outputSizesLength, (IntPtr)pscaleFactors, scaleFactorsLength); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(res); } } } @@ -101,8 +100,7 @@ public static Tensor upsample_nearest1d_backward(Tensor grad_output, long? outpu (IntPtr)poutputSizes, outputSizesLength, (IntPtr)pinputSizes, inputSizes.Length, (IntPtr)pscaleFactors, scaleFactorsLength); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(res); } } } @@ -126,8 +124,7 @@ public static Tensor upsample_nearest2d(Tensor input, long[]? outputSizes = null THSTensor_upsample_nearest2d(input.Handle, (IntPtr)poutputSizes, outputSizesLength, (IntPtr)pscaleFactors, scaleFactorsLength); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(res); } } } @@ -145,8 +142,7 @@ public static Tensor upsample_nearest2d_backward(Tensor grad_output, long[] inpu (IntPtr)poutputSizes, outputSizesLength, (IntPtr)pinputSizes, inputSizes.Length, (IntPtr)pscaleFactors, scaleFactorsLength); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(res); } } } @@ -164,8 +160,7 @@ public static Tensor upsample_nearest3d_backward(Tensor grad_output, long[] inpu (IntPtr)poutputSizes, outputSizesLength, (IntPtr)pinputSizes, inputSizes.Length, (IntPtr)pscaleFactors, scaleFactorsLength); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(res); } } } @@ -189,8 +184,7 @@ public static Tensor upsample_nearest3d(Tensor input, long[]? outputSizes = null THSTensor_upsample_nearest3d(input.Handle, (IntPtr)poutputSizes, outputSizesLength, (IntPtr)pscaleFactors, scaleFactorsLength); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(res); } } } @@ -221,9 +215,7 @@ internal Upsample(IntPtr handle, IntPtr boxedHandle, long[]? size, double[]? sca /// public override Tensor forward(Tensor tensor) { - var res = THSNN_Upsample_forward(handle, tensor.Handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_Upsample_forward(handle, tensor.Handle)); } public UpsampleMode mode { get; private set; } diff --git a/src/TorchSharp/NN/Utils/RNNUtils.cs b/src/TorchSharp/NN/Utils/RNNUtils.cs index ab0b62cc5..abf77a0f6 100644 --- a/src/TorchSharp/NN/Utils/RNNUtils.cs +++ b/src/TorchSharp/NN/Utils/RNNUtils.cs @@ -42,8 +42,7 @@ public static (torch.Tensor, torch.Tensor) pad_packed_sequence(PackedSequence se IntPtr res1, res2; long total_length_arg = total_length.HasValue ? total_length.Value : -1; THSNN_pad_packed_sequence(sequence.Handle, batch_first, padding_value, total_length_arg, out res1, out res2); - if (res1 == IntPtr.Zero || res2 == IntPtr.Zero) { torch.CheckForErrors(); } - return (new torch.Tensor(res1), new torch.Tensor(res2)); + return ReturnCheckForErrors(res1, res2); } /// @@ -56,9 +55,8 @@ public static (torch.Tensor, torch.Tensor) pad_packed_sequence(PackedSequence se public static torch.Tensor pad_sequence(IEnumerable sequences, bool batch_first = false, double padding_value = 0.0) { var sequences_arg = sequences.Select(p => p.Handle).ToArray(); - var res = THSNN_pad_sequence(sequences_arg, sequences_arg.Length, batch_first, padding_value); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new torch.Tensor(res); + return ReturnCheckForErrors(THSNN_pad_sequence(sequences_arg, sequences_arg.Length, batch_first, padding_value)); + } /// diff --git a/src/TorchSharp/NN/Vision.cs b/src/TorchSharp/NN/Vision.cs index 654bef049..7321a57b7 100644 --- a/src/TorchSharp/NN/Vision.cs +++ b/src/TorchSharp/NN/Vision.cs @@ -62,9 +62,7 @@ public static Tensor pad(Tensor input, long[] pad, PaddingModes mode = PaddingMo { unsafe { fixed (long* psize = pad) { - var res = THSNN_pad(input.Handle, (IntPtr)psize, pad.Length, (byte)mode, value); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_pad(input.Handle, (IntPtr)psize, pad.Length, (byte)mode, value)); } } } @@ -81,9 +79,7 @@ public static Tensor pad(Tensor input, ReadOnlySpan pad, PaddingModes mode { unsafe { fixed (long* psize = pad) { - var res = THSNN_pad(input.Handle, (IntPtr)psize, pad.Length, (byte)mode, value); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_pad(input.Handle, (IntPtr)psize, pad.Length, (byte)mode, value)); } } } @@ -101,9 +97,7 @@ public static Tensor pad(Tensor input, (long, long) pad, PaddingModes mode = Pad unsafe { var correctedPad = stackalloc long[] { pad.Item1, pad.Item2 }; - var res = THSNN_pad(input.Handle, (IntPtr)correctedPad, 2, (byte)mode, value); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_pad(input.Handle, (IntPtr)correctedPad, 2, (byte)mode, value)); } } @@ -119,9 +113,7 @@ public static Tensor pad(Tensor input, (long, long, long, long) pad, PaddingMode { unsafe { var correctedPad = stackalloc long[] { pad.Item1, pad.Item2, pad.Item3, pad.Item4 }; - var res = THSNN_pad(input.Handle, (IntPtr)correctedPad, 4, (byte)mode, value); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_pad(input.Handle, (IntPtr)correctedPad, 4, (byte)mode, value)); } } @@ -141,9 +133,7 @@ public static Tensor pad(Tensor input, long pad, PaddingModes mode = PaddingMode var correctedPad = stackalloc long[length]; for (var i = 0; i < length; i++) correctedPad[i] = pad; - var res = THSNN_pad(input.Handle, (IntPtr)correctedPad, length, (byte)mode, value); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_pad(input.Handle, (IntPtr)correctedPad, length, (byte)mode, value)); } } @@ -174,10 +164,7 @@ public static Tensor grid_sample(Tensor input, Tensor grid, GridSampleMode mode (input.handle, grid.handle) = AutocastMode.AutoCast(input.handle, grid.handle, ScalarType.Float32); } - var res = THSNN_grid_sample(input.Handle, grid.Handle, (byte)mode, (byte)padding_mode, ac); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - - return new Tensor(res); + return ReturnCheckForErrors(THSNN_grid_sample(input.Handle, grid.Handle, (byte)mode, (byte)padding_mode, ac)); } /// @@ -192,9 +179,7 @@ public static Tensor affine_grid(Tensor theta, long[]? size = null, bool align_c { unsafe { fixed (long* psize = size) { - var res = THSNN_affine_grid(theta.Handle, (IntPtr)psize, size is null ? 0 : size.Length, align_corners); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_affine_grid(theta.Handle, (IntPtr)psize, size is null ? 0 : size.Length, align_corners)); } } } @@ -223,9 +208,7 @@ public static Tensor interpolate(Tensor x, long[]? size = null, double[]? scale_ fixed (long* psize = size) { fixed (double* pSF = scale_factor) { byte ac = (byte)((align_corners.HasValue) ? (align_corners.Value ? 1 : 2) : 0); - var res = THSNN_interpolate(x.Handle, (IntPtr)psize, size is null ? 0 : size.Length, (IntPtr)pSF, scale_factor is null ? 0 : scale_factor.Length, (byte)mode, ac, recompute_scale_factor); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSNN_interpolate(x.Handle, (IntPtr)psize, size is null ? 0 : size.Length, (IntPtr)pSF, scale_factor is null ? 0 : scale_factor.Length, (byte)mode, ac, recompute_scale_factor)); } } } diff --git a/src/TorchSharp/Optimizers/ASGD.cs b/src/TorchSharp/Optimizers/ASGD.cs index 260810aa0..2a480a190 100644 --- a/src/TorchSharp/Optimizers/ASGD.cs +++ b/src/TorchSharp/Optimizers/ASGD.cs @@ -21,7 +21,7 @@ public static partial class optim /// https://dl.acm.org/citation.cfm?id=131098 /// /// Parameters to optimize. This optimizer requires the named parameters collection. - /// Learning rate + /// Learning rate /// Decay term (default: 1e-4) /// Power for eta update (default: 0.75) /// Point at which to start averaging (default: 1e6) @@ -39,7 +39,7 @@ public static ASGD ASGD(IEnumerable parameters, double lr = 1e-3, dou /// https://dl.acm.org/citation.cfm?id=131098 /// /// Parameters to optimize. This optimizer requires the named parameters collection. - /// Learning rate + /// Learning rate /// Decay term (default: 1e-4) /// Power for eta update (default: 0.75) /// Point at which to start averaging (default: 1e6) @@ -57,7 +57,7 @@ public static ASGD ASGD(IEnumerable<(string name, Parameter parameter)> paramete /// https://dl.acm.org/citation.cfm?id=131098 /// /// Parameters to optimize. This optimizer requires the named parameters collection. - /// Learning rate + /// Learning rate /// Decay term (default: 1e-4) /// Power for eta update (default: 0.75) /// Point at which to start averaging (default: 1e6) @@ -80,7 +80,7 @@ public class ASGD : OptimizerHelper /// It has been proposed in Adam: A Method for Stochastic Optimization. /// /// Parameters to optimize. This optimizer requires the named parameters collection. - /// Learning rate + /// Learning rate /// Decay term (default: 1e-4) /// Power for eta update (default: 0.75) /// Point at which to start averaging (default: 1e6) @@ -97,7 +97,7 @@ public ASGD(IEnumerable parameters, double lr = 0.01, double lambd = /// It has been proposed in Adam: A Method for Stochastic Optimization. /// /// Parameters to optimize. This optimizer requires the named parameters collection. - /// Learning rate + /// Learning rate /// Decay term (default: 1e-4) /// Power for eta update (default: 0.75) /// Point at which to start averaging (default: 1e6) diff --git a/src/TorchSharp/Optimizers/Adadelta.cs b/src/TorchSharp/Optimizers/Adadelta.cs index 924dcb468..c8892b3e3 100644 --- a/src/TorchSharp/Optimizers/Adadelta.cs +++ b/src/TorchSharp/Optimizers/Adadelta.cs @@ -21,7 +21,7 @@ public static partial class optim /// https://arxiv.org/abs/1212.5701 /// /// Parameters to optimize. This optimizer requires the named parameters collection. - /// Learning rate + /// Learning rate /// Coefficient used for computing a running average of squared gradients (default: 0.9) /// Term added to the denominator to improve numerical stability, i.e. avoid division-by-zero (default: 1e-6) /// Weight decay (L2 penalty) (default: 0) @@ -38,7 +38,7 @@ public static Adadelta Adadelta(IEnumerable parameters, double lr = 1 /// https://arxiv.org/abs/1212.5701 /// /// Parameters to optimize. This optimizer requires the named parameters collection. - /// Learning rate + /// Learning rate /// Coefficient used for computing a running average of squared gradients (default: 0.9) /// Term added to the denominator to improve numerical stability, i.e. avoid division-by-zero (default: 1e-6) /// Weight decay (L2 penalty) (default: 0) @@ -55,7 +55,7 @@ public static Adadelta Adadelta(IEnumerable<(string name, Parameter parameter)> /// https://arxiv.org/abs/1212.5701 /// /// Parameters to optimize. This optimizer requires the named parameters collection. - /// Learning rate + /// Learning rate /// Coefficient used for computing a running average of squared gradients (default: 0.9) /// Term added to the denominator to improve numerical stability, i.e. avoid division-by-zero (default: 1e-6) /// Weight decay (L2 penalty) (default: 0) @@ -75,7 +75,7 @@ public class Adadelta : OptimizerHelper /// Constructor /// /// Parameters to optimize. - /// Learning rate + /// Learning rate /// Coefficient used for computing a running average of squared gradients (default: 0.9) /// Term added to the denominator to improve numerical stability, i.e. avoid division-by-zero (default: 1e-6) /// Weight decay (L2 penalty) (default: 0) @@ -89,7 +89,7 @@ public Adadelta(IEnumerable parameters, double lr, double rho = 0.9, /// Constructor /// /// Parameters to optimize. - /// Learning rate + /// Learning rate /// Coefficient used for computing a running average of squared gradients (default: 0.9) /// Term added to the denominator to improve numerical stability, i.e. avoid division-by-zero (default: 1e-6) /// Weight decay (L2 penalty) (default: 0) diff --git a/src/TorchSharp/Optimizers/Adamax.cs b/src/TorchSharp/Optimizers/Adamax.cs index e09ef9170..779520531 100644 --- a/src/TorchSharp/Optimizers/Adamax.cs +++ b/src/TorchSharp/Optimizers/Adamax.cs @@ -21,7 +21,7 @@ public static partial class optim /// https://arxiv.org/abs/1412.6980 /// /// Parameters to optimize. - /// Learning rate + /// Learning rate /// Coefficient used for computing running averages of gradient and its square (default: 0.9) /// Coefficient used for computing running averages of gradient and its square (default: 0.999) /// Term added to the denominator to improve numerical stability, i.e. avoid division-by-zero (default: 1e-8) @@ -39,7 +39,7 @@ public static Adamax Adamax(IEnumerable parameters, double lr = 0.002 /// https://arxiv.org/abs/1412.6980 /// /// Parameters to optimize. - /// Learning rate + /// Learning rate /// Coefficient used for computing running averages of gradient and its square (default: 0.9) /// Coefficient used for computing running averages of gradient and its square (default: 0.999) /// Term added to the denominator to improve numerical stability, i.e. avoid division-by-zero (default: 1e-8) @@ -57,7 +57,7 @@ public static Adamax Adamax(IEnumerable<(string name, Parameter parameter)> para /// https://arxiv.org/abs/1412.6980 /// /// Parameters to optimize. - /// Learning rate + /// Learning rate /// Coefficient used for computing running averages of gradient and its square (default: 0.9) /// Coefficient used for computing running averages of gradient and its square (default: 0.999) /// Term added to the denominator to improve numerical stability, i.e. avoid division-by-zero (default: 1e-8) @@ -82,7 +82,7 @@ public class Adamax : OptimizerHelper, IBetas /// It has been proposed in Adam: A Method for Stochastic Optimization. /// /// Parameters to optimize. This optimizer requires the named parameters collection. - /// Learning rate + /// Learning rate /// Coefficient used for computing running averages of gradient and its square (default: 0.9) /// Coefficient used for computing running averages of gradient and its square (default: 0.999) /// Term added to the denominator to improve numerical stability, i.e. avoid division-by-zero (default: 1e-8) @@ -99,7 +99,7 @@ public Adamax(IEnumerable parameters, double lr, double beta1 = 0.9, /// It has been proposed in Adam: A Method for Stochastic Optimization. /// /// Parameters to optimize. This optimizer requires the named parameters collection. - /// Learning rate + /// Learning rate /// Coefficient used for computing running averages of gradient and its square (default: 0.9) /// Coefficient used for computing running averages of gradient and its square (default: 0.999) /// Term added to the denominator to improve numerical stability, i.e. avoid division-by-zero (default: 1e-8) diff --git a/src/TorchSharp/Optimizers/NAdam.cs b/src/TorchSharp/Optimizers/NAdam.cs index 6118cc5d1..84fbb807e 100644 --- a/src/TorchSharp/Optimizers/NAdam.cs +++ b/src/TorchSharp/Optimizers/NAdam.cs @@ -21,7 +21,7 @@ public static partial class optim /// https://openreview.net/forum?id=OM0jvwB8jIp57ZJjtNEZ /// /// Parameters to optimize. This optimizer requires the named parameters collection. - /// Learning rate + /// Learning rate /// Coefficient used for computing running averages of gradient and its square (default: 0.9) /// Coefficient used for computing running averages of gradient and its square (default: 0.999) /// Term added to the denominator to improve numerical stability, i.e. avoid division-by-zero (default: 1e-8) @@ -39,7 +39,7 @@ public static NAdam NAdam(IEnumerable named_parameters, double lr = 0 /// https://openreview.net/forum?id=OM0jvwB8jIp57ZJjtNEZ /// /// Parameters to optimize. This optimizer requires the named parameters collection. - /// Learning rate + /// Learning rate /// Coefficient used for computing running averages of gradient and its square (default: 0.9) /// Coefficient used for computing running averages of gradient and its square (default: 0.999) /// Term added to the denominator to improve numerical stability, i.e. avoid division-by-zero (default: 1e-8) @@ -57,7 +57,7 @@ public static NAdam NAdam(IEnumerable<(string name, Parameter parameter)> named_ /// https://openreview.net/forum?id=OM0jvwB8jIp57ZJjtNEZ /// /// Parameters to optimize. This optimizer requires the named parameters collection. - /// Learning rate + /// Learning rate /// Coefficient used for computing running averages of gradient and its square (default: 0.9) /// Coefficient used for computing running averages of gradient and its square (default: 0.999) /// Term added to the denominator to improve numerical stability, i.e. avoid division-by-zero (default: 1e-8) @@ -83,7 +83,7 @@ public class NAdam : OptimizerHelper, IBetas /// https://openreview.net/forum?id=OM0jvwB8jIp57ZJjtNEZ /// /// Parameters to optimize. This optimizer requires the named parameters collection. - /// Learning rate + /// Learning rate /// Coefficient used for computing running averages of gradient and its square (default: 0.9) /// Coefficient used for computing running averages of gradient and its square (default: 0.999) /// Term added to the denominator to improve numerical stability, i.e. avoid division-by-zero (default: 1e-8) @@ -101,7 +101,7 @@ public NAdam(IEnumerable parameters, double lr, double beta1 = 0.9, d /// https://openreview.net/forum?id=OM0jvwB8jIp57ZJjtNEZ /// /// Parameters to optimize. This optimizer requires the named parameters collection. - /// Learning rate + /// Learning rate /// Coefficient used for computing running averages of gradient and its square (default: 0.9) /// Coefficient used for computing running averages of gradient and its square (default: 0.999) /// Term added to the denominator to improve numerical stability, i.e. avoid division-by-zero (default: 1e-8) diff --git a/src/TorchSharp/Optimizers/RAdam.cs b/src/TorchSharp/Optimizers/RAdam.cs index d64416196..1a3e28be9 100644 --- a/src/TorchSharp/Optimizers/RAdam.cs +++ b/src/TorchSharp/Optimizers/RAdam.cs @@ -21,7 +21,7 @@ public static partial class optim /// https://arxiv.org/abs/1908.03265 /// /// Parameters to optimize. - /// Learning rate + /// Learning rate /// Coefficient used for computing running averages of gradient and its square (default: 0.9) /// Coefficient used for computing running averages of gradient and its square (default: 0.999) /// Term added to the denominator to improve numerical stability, i.e. avoid division-by-zero (default: 1e-8) @@ -38,7 +38,7 @@ public static RAdam RAdam(IEnumerable parameters, double lr = 0.002, /// https://arxiv.org/abs/1908.03265 /// /// Parameters to optimize. - /// Learning rate + /// Learning rate /// Coefficient used for computing running averages of gradient and its square (default: 0.9) /// Coefficient used for computing running averages of gradient and its square (default: 0.999) /// Term added to the denominator to improve numerical stability, i.e. avoid division-by-zero (default: 1e-8) @@ -55,7 +55,7 @@ public static RAdam RAdam(IEnumerable<(string name, Parameter parameter)> parame /// https://arxiv.org/abs/1908.03265 /// /// Parameters to optimize. - /// Learning rate + /// Learning rate /// Coefficient used for computing running averages of gradient and its square (default: 0.9) /// Coefficient used for computing running averages of gradient and its square (default: 0.999) /// Term added to the denominator to improve numerical stability, i.e. avoid division-by-zero (default: 1e-8) @@ -80,7 +80,7 @@ public class RAdam : OptimizerHelper, IBetas /// https://arxiv.org/abs/1908.03265 /// /// Parameters to optimize. This optimizer requires the named parameters collection. - /// Learning rate + /// Learning rate /// Coefficient used for computing running averages of gradient and its square (default: 0.9) /// Coefficient used for computing running averages of gradient and its square (default: 0.999) /// Term added to the denominator to improve numerical stability, i.e. avoid division-by-zero (default: 1e-8) @@ -98,7 +98,7 @@ public RAdam(IEnumerable parameters, double lr, double beta1 = 0.9, d /// https://arxiv.org/abs/1908.03265 /// /// Parameters to optimize. This optimizer requires the named parameters collection. - /// Learning rate + /// Learning rate /// Coefficient used for computing running averages of gradient and its square (default: 0.9) /// Coefficient used for computing running averages of gradient and its square (default: 0.999) /// Term added to the denominator to improve numerical stability, i.e. avoid division-by-zero (default: 1e-8) diff --git a/src/TorchSharp/Optimizers/Rprop.cs b/src/TorchSharp/Optimizers/Rprop.cs index 47e01d982..0a4a9140b 100644 --- a/src/TorchSharp/Optimizers/Rprop.cs +++ b/src/TorchSharp/Optimizers/Rprop.cs @@ -21,7 +21,7 @@ public static partial class optim /// http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.21.1417 /// /// Parameters to optimize. This optimizer requires the named parameters collection. - /// Learning rate + /// Learning rate /// Multiplicative increase factor. /// Multiplicative decrease factor. /// Minimum allowed step size. @@ -39,7 +39,7 @@ public static Rprop Rprop(IEnumerable parameters, double lr = 1e-2, d /// http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.21.1417 /// /// Parameters to optimize. This optimizer requires the named parameters collection. - /// Learning rate + /// Learning rate /// Multiplicative increase factor. /// Multiplicative decrease factor. /// Minimum allowed step size. @@ -57,7 +57,7 @@ public static Rprop Rprop(IEnumerable<(string name, Parameter parameter)> parame /// http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.21.1417 /// /// Parameters to optimize. This optimizer requires the named parameters collection. - /// Learning rate + /// Learning rate /// Multiplicative increase factor. /// Multiplicative decrease factor. /// Minimum allowed step size. @@ -80,7 +80,7 @@ public class Rprop : OptimizerHelper /// It has been proposed in Adam: A Method for Stochastic Optimization. /// /// Parameters to optimize. - /// Learning rate + /// Learning rate /// Multiplicative increase factor. /// Multiplicative decrease factor. /// Minimum allowed step size. @@ -97,7 +97,7 @@ public Rprop(IEnumerable parameters, double lr = 0.01, double etaminu /// It has been proposed in Adam: A Method for Stochastic Optimization. /// /// Parameters to optimize. - /// Learning rate + /// Learning rate /// Multiplicative increase factor. /// Multiplicative decrease factor. /// Minimum allowed step size. diff --git a/src/TorchSharp/Tensor/Tensor.cs b/src/TorchSharp/Tensor/Tensor.cs index 08e9608a2..378534141 100644 --- a/src/TorchSharp/Tensor/Tensor.cs +++ b/src/TorchSharp/Tensor/Tensor.cs @@ -350,10 +350,7 @@ public bool is_coalesce() public Tensor coalesce() { - var res = NativeMethods.THSTensor_coalesce(Handle); - if(res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_coalesce(Handle)); } public bool is_cuda => device.type == DeviceType.CUDA; @@ -380,9 +377,7 @@ public Tensor coalesce() /// public Tensor alias() { - var res = NativeMethods.THSTensor_alias(Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_alias(Handle)); } /// @@ -610,18 +605,13 @@ private void _validate(long totalSize) public Tensor real { get { - var res = NativeMethods.THSTensor_real(Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); - + return ReturnCheckForErrors(NativeMethods.THSTensor_real(Handle)); } } public Tensor imag { get { - var res = NativeMethods.THSTensor_imag(Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_imag(Handle)); } } @@ -854,10 +844,7 @@ public bool is_cpu() /// public Tensor cpu() { - var res = NativeMethods.THSTensor_cpu(Handle); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_cpu(Handle)); } @@ -867,12 +854,7 @@ public Tensor cpu() /// Try to convert asynchronously with respect to the host if possible, e.g., converting a CPU Tensor with pinned memory to a CUDA Tensor. public Tensor mps(bool non_blocking = false) { - var res = NativeMethods.THSTensor_to_device(Handle, (int)DeviceType.MPS, -1, true, non_blocking); - if (res == IntPtr.Zero) - CheckForErrors(); - - return new Tensor(res); - + return ReturnCheckForErrors(NativeMethods.THSTensor_to_device(Handle, (int)DeviceType.MPS, -1, true, non_blocking)); } /// @@ -892,9 +874,7 @@ public Tensor cuda(Device? device = null, bool non_blocking = false) var res = device is null ? NativeMethods.THSTensor_cuda(Handle) : NativeMethods.THSTensor_to_device(Handle, (int)DeviceType.CUDA, device_index, false, non_blocking); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(res); } /// @@ -982,10 +962,7 @@ public Tensor to(ScalarType type, torch.Device device, bool copy = false, bool d public Tensor to(torch.Device device, ScalarType type, bool non_blocking) { torch.InitializeDevice(device); - var res = NativeMethods.THSTensor_to_type_and_device_and_non_blocking(Handle, (sbyte)type, (int)device.type, device.index, non_blocking); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(res = NativeMethods.THSTensor_to_type_and_device_and_non_blocking(Handle, (sbyte)type, (int)device.type, device.index, non_blocking)); } /// @@ -1144,8 +1121,7 @@ public Tensor rename(IEnumerable? names) res = NativeMethods.THSTensor_rename(Handle, IntPtr.Zero, 0); } - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(res); } /// @@ -1196,9 +1172,7 @@ public Tensor refine_names(IEnumerable names) using PinnedArray pinnedArray = new PinnedArray(); IntPtr namesRef = pinnedArray.CreateArray(dimNamesArray); - IntPtr res = NativeMethods.THSTensor_refine_names(Handle, namesRef, dimNamesArray.Length); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_refine_names(Handle, namesRef, dimNamesArray.Length)); } private IntPtr[] ExpandEllipsis(IEnumerable names) @@ -1282,10 +1256,7 @@ private IntPtr[] ExpandEllipsis(IEnumerable names) /// public Tensor SparseIndices { get { - var res = NativeMethods.THSTensor_indices(Handle); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_indices(Handle)); } } @@ -1294,10 +1265,7 @@ public Tensor SparseIndices { /// public Tensor SparseValues { get { - var res = NativeMethods.THSTensor_values(Handle); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_values(Handle)); } } @@ -1313,10 +1281,7 @@ public Tensor vander(long N = -1, bool increasing = false) { if (this.Dimensions != 1) throw new InvalidOperationException("Input argument for 'vander()' must be 1-D."); - var res = NativeMethods.THSTensor_vander(Handle, (N == -1) ? this.size(0) : N, increasing); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_vander(Handle, (N == -1) ? this.size(0) : N, increasing)); } /// @@ -1352,9 +1317,7 @@ public Tensor as_strided(long[] size, long[] strides, long storageOffset = 0L) { unsafe { fixed (long* psizes = size, pstrides = strides) { - var result = NativeMethods.THSTensor_as_strided(Handle, (IntPtr)psizes, size.Length, (IntPtr)pstrides, strides.Length, storageOffset); - if (result == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(result); + return ReturnCheckForErrors(NativeMethods.THSTensor_as_strided(Handle, (IntPtr)psizes, size.Length, (IntPtr)pstrides, strides.Length, storageOffset)); } } } @@ -1373,10 +1336,7 @@ public void backward() /// public Tensor to_dense() { - var res = NativeMethods.THSTensor_to_dense(Handle); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_to_dense(Handle)); } /// @@ -1384,10 +1344,7 @@ public Tensor to_dense() /// public Tensor clone() { - var res = NativeMethods.THSTensor_clone(Handle); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_clone(Handle)); } /// @@ -1417,10 +1374,7 @@ public bool is_contiguous() /// public Tensor contiguous() { - var res = NativeMethods.THSTensor_contiguous(Handle); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_contiguous(Handle)); } /// @@ -1439,10 +1393,7 @@ public bool is_pinned() /// public Tensor pin_memory() { - var res = NativeMethods.THSTensor_pin_memory(Handle); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_pin_memory(Handle)); } /// @@ -1533,9 +1484,7 @@ public Tensor this[params Tensor[] indices] { [IndexerName("TensorItems")] public Tensor this[long i1] { get { - var res = NativeMethods.THSTensor_get1(Handle, i1); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_get1(Handle, i1)); } set { NativeMethods.THSTensor_set1(Handle, i1, value.Handle); @@ -1551,9 +1500,7 @@ public Tensor this[long i1] { [IndexerName("TensorItems")] public Tensor this[long i1, long i2] { get { - var res = NativeMethods.THSTensor_get2(Handle, i1, i2); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_get2(Handle, i1, i2)); } set { NativeMethods.THSTensor_set2(Handle, i1, i2, value.Handle); @@ -1570,10 +1517,7 @@ public Tensor this[long i1] { [IndexerName("TensorItems")] public Tensor this[long i1, long i2, long i3] { get { - var res = NativeMethods.THSTensor_get3(Handle, i1, i2, i3); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_get3(Handle, i1, i2, i3)); } set { NativeMethods.THSTensor_set3(Handle, i1, i2, i3, value.Handle); @@ -1591,10 +1535,7 @@ public Tensor this[long i1] { [IndexerName("TensorItems")] public Tensor this[long i1, long i2, long i3, long i4] { get { - var res = NativeMethods.THSTensor_get4(Handle, i1, i2, i3, i4); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_get4(Handle, i1, i2, i3, i4)); } set { NativeMethods.THSTensor_set4(Handle, i1, i2, i3, i4, value.Handle); @@ -1613,10 +1554,7 @@ public Tensor this[long i1] { [IndexerName("TensorItems")] public Tensor this[long i1, long i2, long i3, long i4, long i5] { get { - var res = NativeMethods.THSTensor_get5(Handle, i1, i2, i3, i4, i5); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_get5(Handle, i1, i2, i3, i4, i5)); } set { NativeMethods.THSTensor_set5(Handle, i1, i2, i3, i4, i5, value.Handle); @@ -1637,10 +1575,7 @@ public Tensor this[long i1] { [IndexerName("TensorItems")] public Tensor this[long i1, long i2, long i3, long i4, long i5, long i6] { get { - var res = NativeMethods.THSTensor_get6(Handle, i1, i2, i3, i4, i5, i6); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_get6(Handle, i1, i2, i3, i4, i5, i6)); } set { NativeMethods.THSTensor_set6(Handle, i1, i2, i3, i4, i5, i6, value.Handle); @@ -1811,10 +1746,7 @@ public Tensor index_put_(Scalar value, params Tensor[] indices) /// The 1-D tensor containing the indices to index public Tensor index_select(long dim, Tensor index) { - var res = NativeMethods.THSTensor_index_select(Handle, dim, index.Handle); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_index_select(Handle, dim, index.Handle)); } /// @@ -1825,10 +1757,7 @@ public Tensor index_select(long dim, Tensor index) /// The index to select with public Tensor select(long dim, long index) { - var res = NativeMethods.THSTensor_select(Handle, dim, index); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_select(Handle, dim, index)); } /// @@ -1838,10 +1767,7 @@ public Tensor select(long dim, long index) /// The indices into tensor, an Int64 tensor. public Tensor take(Tensor index) { - var res = NativeMethods.THSTensor_take(Handle, index.Handle); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors((NativeMethods.THSTensor_take(Handle, index.Handle)); } /// @@ -1853,10 +1779,7 @@ public Tensor take(Tensor index) /// public Tensor argwhere() { - var res = NativeMethods.THSTensor_argwhere(Handle); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_argwhere(Handle)); } /// @@ -1866,10 +1789,7 @@ public Tensor argwhere() /// Functions that return indices along a dimension, like torch.argmax() and torch.argsort(), are designed to work with this function. public Tensor take_along_dim(Tensor indices) { - var res = NativeMethods.THSTensor_take_along_dim_dflt(Handle, indices.Handle); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_take_along_dim_dflt(Handle, indices.Handle)); } /// @@ -1887,10 +1807,7 @@ public Tensor take_along_dim(Tensor indices) /// Functions that return indices along a dimension, like torch.argmax() and torch.argsort(), are designed to work with this function. public Tensor take_along_dim(Tensor indices, long dim) { - var res = NativeMethods.THSTensor_take_along_dim(Handle, indices.Handle, dim); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_take_along_dim(Handle, indices.Handle, dim)); } /// @@ -1916,10 +1833,7 @@ public Tensor index_add(long dim, Tensor index, Tensor source, Scalar alpha) { if (index.dtype != ScalarType.Int64) throw new ArgumentException("Element type of 'index' must be 'Int64'"); - var res = NativeMethods.THSTensor_index_add(Handle, dim, index.Handle, source.Handle, alpha.Handle); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_index_add(Handle, dim, index.Handle, source.Handle, alpha.Handle)); } /// @@ -1956,10 +1870,7 @@ public Tensor index_copy(long dim, Tensor index, Tensor source) { if (index.dtype != ScalarType.Int64) throw new ArgumentException("Element type of 'index' must be 'Int64'"); - var res = NativeMethods.THSTensor_index_copy(Handle, dim, index.Handle, source.Handle); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_index_copy(Handle, dim, index.Handle, source.Handle)); } /// @@ -1995,10 +1906,7 @@ public Tensor index_fill(long dim, Tensor index, Scalar value) { if (index.dtype != ScalarType.Int64) throw new ArgumentException("Element type of 'index' must be 'Int64'"); - var res = NativeMethods.THSTensor_index_fill(Handle, dim, index.Handle, value.Handle); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_index_fill(Handle, dim, index.Handle, value.Handle)); } /// @@ -2028,10 +1936,7 @@ public Tensor reshape(params long[] shape) { unsafe { fixed (long* pshape = shape) { - var res = NativeMethods.THSTensor_reshape(Handle, (IntPtr)pshape, shape.Length); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_reshape(Handle, (IntPtr)pshape, shape.Length)); } } } @@ -2055,10 +1960,7 @@ public Tensor resize_(params long[] shape) /// Flattening a zero-dimensional tensor will return a one-dimensional view. public Tensor flatten(long start_dim = 0, long end_dim = -1) { - var res = NativeMethods.THSTensor_flatten(Handle, start_dim, end_dim); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_flatten(Handle, start_dim, end_dim)); } /// @@ -2080,9 +1982,7 @@ public Tensor flatten(IList dims, string out_dim) IntPtr namesRef = pinnedArray.CreateArray(iPtrArray.ToArray()); - IntPtr res = NativeMethods.THSTensor_flatten_names(Handle, namesRef, iPtrArray.Count); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_flatten_names(Handle, namesRef, iPtrArray.Count)); } /// @@ -2099,10 +1999,7 @@ public Tensor unflatten(long dim, params long[] sizes) unsafe { fixed (long* pshape = sizes) { - var res = NativeMethods.THSTensor_unflatten(Handle, dim, (IntPtr)pshape, sizes.Length); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_unflatten(Handle, dim, (IntPtr)pshape, sizes.Length)); } } } @@ -2129,10 +2026,7 @@ public Tensor unflatten(string dim, params (string, long)[] sizes) unsafe { fixed (long* pshape = szs) { - var res = NativeMethods.THSTensor_unflatten_names(Handle, namesRef, (IntPtr)pshape, names.Count); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_unflatten_names(Handle, namesRef, (IntPtr)pshape, names.Count)); } } } @@ -2151,9 +2045,7 @@ public Tensor align_to(IEnumerable names) using PinnedArray pinnedArray = new PinnedArray(); IntPtr namesRef = pinnedArray.CreateArray(names.Select(s => Marshal.StringToHGlobalAnsi(s)).ToArray()); - IntPtr res = NativeMethods.THSTensor_align_to(Handle, namesRef, names.Count()); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_align_to(Handle, namesRef, names.Count())); } /// @@ -2242,9 +2134,7 @@ public Tensor unflatten(long dim, torch.Size sizes) public Tensor squeeze(long? dim = null) { var res = dim.HasValue ? NativeMethods.THSTensor_squeeze(Handle, dim.Value) : NativeMethods.THSTensor_squeeze_no_dim(Handle); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(res); } /// @@ -2267,10 +2157,7 @@ public Tensor squeeze_(long? dim = null) /// public Tensor t() { - var res = NativeMethods.THSTensor_t(Handle); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_t(Handle)); } /// @@ -2300,10 +2187,7 @@ public Tensor H { /// public Tensor mT { get { - var res = NativeMethods.THSTensor_mT(Handle); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_mT(Handle)); } } @@ -2312,10 +2196,7 @@ public Tensor mT { /// public Tensor mH { get { - var res = NativeMethods.THSTensor_mH(Handle); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_mH(Handle)); } } @@ -2326,10 +2207,7 @@ public Tensor mH { /// public Tensor transpose(long dim0, long dim1) { - var res = NativeMethods.THSTensor_transpose(Handle, dim0, dim1); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_transpose(Handle, dim0, dim1)); } /// @@ -2350,10 +2228,7 @@ public Tensor transpose_(long dim0, long dim1) /// public Tensor adjoint() { - var res = NativeMethods.THSTensor_adjoint(Handle); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_adjoint(Handle)); } /// @@ -2363,10 +2238,7 @@ public Tensor adjoint() /// The diagonal to consider public Tensor tril(long diagonal = 0) { - var res = NativeMethods.THSTensor_tril(Handle, diagonal, false); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_tril(Handle, diagonal, false)); } /// @@ -2377,10 +2249,7 @@ public Tensor tril(long diagonal = 0) /// The diagonal to consider public Tensor tril_(long diagonal = 0) { - var res = NativeMethods.THSTensor_tril(Handle, diagonal, true); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_tril(Handle, diagonal, true)); } /// @@ -2390,10 +2259,7 @@ public Tensor tril_(long diagonal = 0) /// The diagonal to consider public Tensor triu(long diagonal = 0) { - var res = NativeMethods.THSTensor_triu(Handle, diagonal, false); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_triu(Handle, diagonal, false)); } /// @@ -2404,10 +2270,7 @@ public Tensor triu(long diagonal = 0) /// The diagonal to consider public Tensor triu_(long diagonal = 0) { - var res = NativeMethods.THSTensor_triu(Handle, diagonal, true); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_triu(Handle, diagonal, true)); } /// @@ -2428,10 +2291,7 @@ public Tensor view(params long[] shape) { unsafe { fixed (long* pshape = shape) { - var res = NativeMethods.THSTensor_view(Handle, (IntPtr)pshape, shape.Length); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_view(Handle, (IntPtr)pshape, shape.Length)); } } } @@ -2454,10 +2314,7 @@ public Tensor view_as(Tensor other) /// public Tensor view_as_complex() { - var result = NativeMethods.THSTensor_view_as_complex(Handle); - if (result == IntPtr.Zero) - CheckForErrors(); - return new Tensor(result); + return ReturnCheckForErrors(NativeMethods.THSTensor_view_as_complex(Handle)); } /// @@ -2465,10 +2322,7 @@ public Tensor view_as_complex() /// public Tensor view_as_real() { - var result = NativeMethods.THSTensor_view_as_real(Handle); - if (result == IntPtr.Zero) - CheckForErrors(); - return new Tensor(result); + return ReturnCheckForErrors(NativeMethods.THSTensor_view_as_real(Handle)); } /// @@ -2476,10 +2330,7 @@ public Tensor view_as_real() /// public Tensor all() { - var res = NativeMethods.THSTensor_all(Handle); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_all(Handle)); } /// @@ -2489,10 +2340,7 @@ public Tensor all() /// Keep the dimension to reduce public Tensor all(long dim, bool keepdim = false) { - var res = NativeMethods.THSTensor_all_along_dimension(Handle, dim, keepdim); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_all_along_dimension(Handle, dim, keepdim)); } /// @@ -2522,8 +2370,7 @@ public Tensor amax(ReadOnlySpan dims, bool keepdim = false, Tensor? @out = var res = @out is null ? NativeMethods.THSTensor_amax(Handle, (IntPtr)pdims, dims.Length, keepdim) : NativeMethods.THSTensor_amax_out(Handle, (IntPtr)pdims, dims.Length, keepdim, @out.Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(res); } } } @@ -2541,8 +2388,7 @@ public Tensor amin(ReadOnlySpan dims, bool keepdim = false, Tensor? @out = var res = @out is null ? NativeMethods.THSTensor_amin(Handle, (IntPtr)pdims, dims.Length, keepdim) : NativeMethods.THSTensor_amin_out(Handle, (IntPtr)pdims, dims.Length, keepdim, @out.Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(res); } } } @@ -2569,8 +2415,7 @@ public Tensor amin(ReadOnlySpan dims, bool keepdim = false, Tensor? @out = public (Tensor min, Tensor max) aminmax(long? dim = null, bool keepdim = false) { var res = NativeMethods.THSTensor_aminmax(Handle, (dim is null) ? -1 : dim.Value, keepdim, out IntPtr maxHandle); - if (res == IntPtr.Zero || maxHandle == IntPtr.Zero) { CheckForErrors(); } - return (new Tensor(res), new Tensor(maxHandle)); + return ReturnCheckForErrors(res, maxHandle); } /// @@ -2578,10 +2423,7 @@ public Tensor amin(ReadOnlySpan dims, bool keepdim = false, Tensor? @out = /// public Tensor any() { - var res = NativeMethods.THSTensor_any(Handle); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_any(Handle)); } /// @@ -2591,10 +2433,7 @@ public Tensor any() /// Keep the dimension to reduce public Tensor any(long dim, bool keepdim = false) { - var res = NativeMethods.THSTensor_any_along_dimension(Handle, dim, keepdim); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_any_along_dimension(Handle, dim, keepdim)); } /// @@ -2602,10 +2441,7 @@ public Tensor any(long dim, bool keepdim = false) /// public Tensor argmax() { - var res = NativeMethods.THSTensor_argmax(Handle); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_argmax(Handle)); } /// @@ -2615,10 +2451,7 @@ public Tensor argmax() /// public Tensor argmax(long dim, bool keepdim = false) { - var res = NativeMethods.THSTensor_argmax_along_dimension(Handle, dim, keepdim); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_argmax_along_dimension(Handle, dim, keepdim)); } /// @@ -2626,10 +2459,7 @@ public Tensor argmax(long dim, bool keepdim = false) /// public Tensor argmin() { - var res = NativeMethods.THSTensor_argmin(Handle); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_argmin(Handle)); } /// @@ -2639,10 +2469,7 @@ public Tensor argmin() /// public Tensor argmin(long dim, bool keepdim = false) { - var res = NativeMethods.THSTensor_argmin_along_dimension(Handle, dim, keepdim); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_argmin_along_dimension(Handle, dim, keepdim)); } /// @@ -2652,10 +2479,7 @@ public Tensor argmin(long dim, bool keepdim = false) /// Controls the sorting order (ascending or descending) public Tensor argsort(long dim = -1, bool descending = false) { - var res = NativeMethods.THSTensor_argsort(Handle, dim, descending); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_argsort(Handle, dim, descending)); } /// @@ -2663,10 +2487,7 @@ public Tensor argsort(long dim = -1, bool descending = false) /// public Tensor deg2rad() { - var res = NativeMethods.THSTensor_deg2rad(Handle); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_deg2rad(Handle)); } /// @@ -2674,10 +2495,7 @@ public Tensor deg2rad() /// public Tensor rad2deg() { - var res = NativeMethods.THSTensor_rad2deg(Handle); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_rad2deg(Handle)); } /// @@ -2688,10 +2506,7 @@ public Tensor rad2deg() /// the output tensor public Tensor copysign(Tensor other) { - var res = NativeMethods.THSTensor_copysign(Handle, other.Handle); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_copysign(Handle, other.Handle)); } /// @@ -2702,9 +2517,7 @@ public Tensor count_nonzero(long[]? dims = null) { unsafe { fixed (long* pdims = dims) { - var res = NativeMethods.THSTensor_count_nonzero(Handle, (IntPtr)pdims, dims is null ? 0 : dims.Length); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_count_nonzero(Handle, (IntPtr)pdims, dims is null ? 0 : dims.Length)); } } } @@ -2731,9 +2544,7 @@ public Tensor cov(long correction = 1, Tensor? fweights = null, Tensor? aweights { var fwHandle = fweights is null ? IntPtr.Zero : fweights.Handle; var awHandle = aweights is null ? IntPtr.Zero : aweights.Handle; - var res = NativeMethods.THSTensor_cov(Handle, correction, fwHandle, awHandle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_cov(Handle, correction, fwHandle, awHandle)); } /// @@ -2745,9 +2556,7 @@ public Tensor cov(long correction = 1, Tensor? fweights = null, Tensor? aweights /// public Tensor corrcoef() { - var res = NativeMethods.THSTensor_corrcoef(Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_corrcoef(Handle)); } /// @@ -2759,9 +2568,7 @@ public Tensor tile(long[] reps) { unsafe { fixed (long* pdims = reps) { - var res = NativeMethods.THSTensor_tile(Handle, (IntPtr)pdims, reps.Length); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_tile(Handle, (IntPtr)pdims, reps.Length)); } } } @@ -2772,10 +2579,7 @@ public Tensor tile(long[] reps) public Tensor digamma() { - var res = NativeMethods.THSTensor_digamma(Handle); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_digamma(Handle)); } /// @@ -2795,10 +2599,7 @@ public Tensor digamma_() public Tensor lgamma() { - var res = NativeMethods.THSTensor_lgamma(Handle); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_lgamma(Handle)); } /// @@ -2819,10 +2620,7 @@ public Tensor lgamma_() public Tensor mvlgamma(long p) { - var res = NativeMethods.THSTensor_mvlgamma(Handle, p); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_mvlgamma(Handle, p)); } /// @@ -2839,10 +2637,7 @@ public Tensor mvlgamma_(long p) public Tensor polygamma(long p) { - var res = NativeMethods.THSTensor_polygamma(Handle, p); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_polygamma(Handle, p)); } public Tensor polygamma_(long p) @@ -2859,10 +2654,7 @@ public Tensor polygamma_(long p) public Tensor positive() { if (this.dtype == ScalarType.Bool) throw new ArgumentException("Boolean tensor"); - var res = NativeMethods.THSTensor_positive(Handle); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_positive(Handle)); } /// @@ -2875,26 +2667,17 @@ public Tensor softmax(long dim, ScalarType? dtype = null) => public Tensor softplus() { - var res = NativeMethods.THSTensor_softplus(Handle); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_softplus(Handle)); } public Tensor ravel() { - var res = NativeMethods.THSTensor_ravel(Handle); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_ravel(Handle)); } public Tensor relu() { - var res = NativeMethods.THSTensor_relu(Handle); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_relu(Handle)); } public Tensor relu_() @@ -2906,10 +2689,7 @@ public Tensor relu_() public Tensor relu6() { - var res = NativeMethods.THSTensor_relu6(Handle); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_relu6(Handle)); } public Tensor relu6_() @@ -2921,10 +2701,7 @@ public Tensor relu6_() public Tensor celu() { - var res = NativeMethods.THSTensor_celu(Handle); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_celu(Handle)); } public Tensor celu_() @@ -2936,10 +2713,7 @@ public Tensor celu_() public Tensor elu(Scalar alpha, Scalar scale, Scalar input_scale) { - var res = NativeMethods.THSTensor_elu(Handle, alpha.Handle, scale.Handle, input_scale.Handle); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_elu(Handle, alpha.Handle, scale.Handle, input_scale.Handle)); } public Tensor elu_(Scalar alpha, Scalar scale, Scalar input_scale) @@ -2951,18 +2725,12 @@ public Tensor elu_(Scalar alpha, Scalar scale, Scalar input_scale) public Tensor gelu() { - var res = NativeMethods.THSTensor_gelu(Handle); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_gelu(Handle)); } public Tensor hardsigmoid() { - var res = NativeMethods.THSTensor_hardsigmoid(Handle); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_hardsigmoid(Handle)); } public Tensor hardsigmoid_() @@ -2974,10 +2742,7 @@ public Tensor hardsigmoid_() public Tensor hardswish() { - var res = NativeMethods.THSTensor_hardswish(Handle); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_hardswish(Handle)); } public Tensor hardswish_() @@ -3003,10 +2768,7 @@ public Tensor hardtanh_(Scalar min, Scalar max) public Tensor heaviside(Tensor other) { - var res = NativeMethods.THSTensor_heaviside(Handle, other.Handle); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_heaviside(Handle, other.Handle)); } /// @@ -3016,10 +2778,7 @@ public Tensor heaviside(Tensor other) public Tensor igamma(Tensor other) { - var res = NativeMethods.THSTensor_igamma(Handle, other.Handle); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_igamma(Handle, other.Handle)); } /// @@ -3029,10 +2788,7 @@ public Tensor igamma(Tensor other) public Tensor igammac(Tensor other) { - var res = NativeMethods.THSTensor_igammac(Handle, other.Handle); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_igammac(Handle, other.Handle)); } /// @@ -3041,10 +2797,7 @@ public Tensor igammac(Tensor other) public Tensor i0() { - var res = NativeMethods.THSTensor_i0(Handle); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_i0(Handle)); } /// @@ -3056,10 +2809,7 @@ public Tensor i0() /// If true, then two NaN s will be considered equal public Tensor isclose(Tensor other, double rtol = 1e-05, double atol = 1e-08, bool nanEqual = false) { - var res = NativeMethods.THSTensor_isclose(Handle, other.Handle, rtol, atol, nanEqual); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_isclose(Handle, other.Handle, rtol, atol, nanEqual)); } /// @@ -3071,42 +2821,27 @@ public Tensor isclose(Tensor other, double rtol = 1e-05, double atol = 1e-08, bo /// If true, inverts the boolean return tensor, resulting in true values for elements not in test_elements. public Tensor isin(Tensor test_elements, bool assumeUnique = false, bool invert = false) { - var res = NativeMethods.THSTensor_isin(Handle, test_elements.Handle, assumeUnique, invert); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_isin(Handle, test_elements.Handle, assumeUnique, invert)); } public Tensor isinf() { - var res = NativeMethods.THSTensor_isinf(Handle); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_isinf(Handle)); } public Tensor isfinite() { - var res = NativeMethods.THSTensor_isfinite(Handle); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_isfinite(Handle)); } public Tensor isposinf() { - var res = NativeMethods.THSTensor_isposinf(Handle); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_isposinf(Handle)); } public Tensor isneginf() { - var res = NativeMethods.THSTensor_isneginf(Handle); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_isneginf(Handle)); } /// @@ -3117,26 +2852,17 @@ public Tensor isneginf() [Pure] public Tensor isnan() { - var res = NativeMethods.THSTensor_isnan(Handle); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_isnan(Handle)); } public Tensor isreal() { - var res = NativeMethods.THSTensor_isreal(Handle); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_isreal(Handle)); } public Tensor leaky_relu(Scalar negative_slope) { - var res = NativeMethods.THSTensor_leaky_relu(Handle, negative_slope.Handle); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_leaky_relu(Handle, negative_slope.Handle)); } public Tensor leaky_relu_(Scalar negative_slope) @@ -3148,10 +2874,7 @@ public Tensor leaky_relu_(Scalar negative_slope) public Tensor selu() { - var res = NativeMethods.THSTensor_selu(Handle); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_selu(Handle)); } public Tensor selu_() @@ -3164,10 +2887,7 @@ public Tensor selu_() public Tensor silu() { - var res = NativeMethods.THSTensor_silu(Handle); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_silu(Handle)); } public Tensor silu_() @@ -3179,10 +2899,7 @@ public Tensor silu_() public Tensor log_sigmoid() { - var res = NativeMethods.THSTensor_log_sigmoid(Handle); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_log_sigmoid(Handle)); } /// @@ -3193,10 +2910,7 @@ public Tensor log_sigmoid() /// The weight for the interpolation formula public Tensor lerp(Tensor end, Tensor weight) { - var res = NativeMethods.THSTensor_lerp(Handle, end.Handle, weight.Handle); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_lerp(Handle, end.Handle, weight.Handle)); } /// @@ -3224,10 +2938,7 @@ public Tensor lerp_(Tensor end, Tensor weight) /// A multiplier for batch1 @ batch2 public Tensor baddbmm(Tensor batch1, Tensor batch2, float beta = 1, float alpha = 1) { - var res = NativeMethods.THSTensor_baddbmm(Handle, batch1.Handle, batch2.Handle, beta, alpha); - if (res == IntPtr.Zero) { CheckForErrors(); } - res = AutocastMode.AutoCast(res); - return new Tensor(res); + return ReturnCheckForErrorsAutocast(NativeMethods.THSTensor_baddbmm(Handle, batch1.Handle, batch2.Handle, beta, alpha)); } /// @@ -3237,10 +2948,7 @@ public Tensor baddbmm(Tensor batch1, Tensor batch2, float beta = 1, float alpha /// public Tensor bmm(Tensor batch2) { - var res = NativeMethods.THSTensor_bmm(Handle, batch2.Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - res = AutocastMode.AutoCast(res); - return new Tensor(res); + return ReturnCheckForErrorsAutocast(NativeMethods.THSTensor_bmm(Handle, batch2.Handle)); } /// @@ -3257,9 +2965,7 @@ public Tensor bmm(Tensor batch2) public Tensor bucketize(Tensor boundaries, bool outInt32 = false, bool right = false) { - var res = NativeMethods.THSTensor_bucketize(Handle, boundaries.Handle, outInt32, right); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_bucketize(Handle, boundaries.Handle, outInt32, right)); } /// @@ -3268,9 +2974,7 @@ public Tensor bucketize(Tensor boundaries, bool outInt32 = false, bool right = f public Tensor bincount(Tensor? weights, long minlength = 0) { var weightsHandle = (weights is null ? IntPtr.Zero : weights.Handle); - var res = NativeMethods.THSTensor_bincount(Handle, weightsHandle, minlength); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_bincount(Handle, weightsHandle, minlength)); } @@ -3307,9 +3011,7 @@ public Tensor bincount(Tensor? weights, long minlength = 0) /// The number of groups to divide channels in. public Tensor channel_shuffle(long groups) { - var res = NativeMethods.THSTensor_channel_shuffle(Handle, groups); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_channel_shuffle(Handle, groups)); } /// @@ -3319,9 +3021,7 @@ public Tensor channel_shuffle(long groups) /// The maximum value public Tensor clamp(Scalar? min = null, Scalar? max = null) { - var res = NativeMethods.THSTensor_clamp(Handle, min?.Handle ?? IntPtr.Zero, max?.Handle ?? IntPtr.Zero); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_clamp(Handle, min?.Handle ?? IntPtr.Zero, max?.Handle ?? IntPtr.Zero)); } /// @@ -3331,9 +3031,7 @@ public Tensor clamp(Scalar? min = null, Scalar? max = null) /// The maximum value public Tensor clamp(Tensor? min = null, Tensor? max = null) { - var res = NativeMethods.THSTensor_clamp_tensor(Handle, min?.Handle ?? IntPtr.Zero, max?.Handle ?? IntPtr.Zero); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_clamp_tensor(Handle, min?.Handle ?? IntPtr.Zero, max?.Handle ?? IntPtr.Zero)); } /// @@ -3371,9 +3069,7 @@ public Tensor clamp_(Tensor? min = null, Tensor? max = null) public Tensor clamp_max(Scalar max) { - var res = NativeMethods.THSTensor_clamp_max(Handle, max.Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_clamp_max(Handle, max.Handle)); } public Tensor clamp_max_(Scalar max) @@ -3385,9 +3081,7 @@ public Tensor clamp_max_(Scalar max) public Tensor clamp_min(Scalar min) { - var res = NativeMethods.THSTensor_clamp_min(Handle, min.Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_clamp_min(Handle, min.Handle)); } public Tensor clamp_min_(Scalar min) @@ -3414,8 +3108,7 @@ public Tensor diff(long n = 1, long dim = -1, Tensor? prepend = null, Tensor? ap { if (n != 1) throw new NotImplementedException("Tensor.diff with n != 1"); var res = NativeMethods.THSTensor_diff(Handle, n, dim, (prepend is Tensor) ? (IntPtr)prepend.Handle : IntPtr.Zero, (append is Tensor) ? (IntPtr)append.Handle : IntPtr.Zero); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(res); } /// @@ -3430,9 +3123,7 @@ public Tensor diff(long n = 1, long dim = -1, Tensor? prepend = null, Tensor? ap /// public Tensor diag(long diagonal = 0) { - var res = NativeMethods.THSTensor_diag(Handle, diagonal); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_diag(Handle, diagonal)); } /// @@ -3443,9 +3134,7 @@ public Tensor trace() { if (ndim != 2) throw new ArgumentException($"Expected a matrix, but got tensor with ndim == {ndim}"); - var res = NativeMethods.THSTensor_trace(Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_trace(Handle)); } /// @@ -3466,9 +3155,7 @@ public Tensor trace() /// Second dimension with respect to which to take diagonal public Tensor diag_embed(long offset = 0L, long dim1 = -2L, long dim2 = -1L) { - var res = NativeMethods.THSTensor_diag_embed(Handle, offset, dim1, dim2); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_diag_embed(Handle, offset, dim1, dim2)); } /// @@ -3483,9 +3170,7 @@ public Tensor diag_embed(long offset = 0L, long dim1 = -2L, long dim2 = -1L) /// public Tensor diagflat(long offset = 0) { - var res = NativeMethods.THSTensor_diagflat(Handle, offset); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_diagflat(Handle, offset)); } /// @@ -3505,9 +3190,7 @@ public Tensor diagflat(long offset = 0) /// public Tensor diagonal(long offset = 0, long dim1 = 0, long dim2 = 0) { - var res = NativeMethods.THSTensor_diagonal(Handle, offset, dim1, dim2); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_diagonal(Handle, offset, dim1, dim2)); } @@ -3517,9 +3200,7 @@ public Tensor diagonal(long offset = 0, long dim1 = 0, long dim2 = 0) /// public Tensor erf() { - var res = NativeMethods.THSTensor_erf(Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_erf(Handle)); } /// @@ -3538,9 +3219,7 @@ public Tensor erf_() /// public Tensor erfc() { - var res = NativeMethods.THSTensor_erfc(Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_erfc(Handle)); } /// @@ -3560,10 +3239,7 @@ public Tensor erfc_() /// public Tensor erfinv() { - var res = NativeMethods.THSTensor_erfinv(Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - res = AutocastMode.AutoCast(res, ScalarType.Float32); - return new Tensor(res); + return ReturnCheckForErrorsAutocast(NativeMethods.THSTensor_erfinv(Handle), ScalarType.Float32); } /// @@ -3579,10 +3255,9 @@ public Tensor erfinv_() public Tensor eq(Tensor target) { - if (target is null) return false; - var res = NativeMethods.THSTensor_eq(Handle, target.Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + if (target is null) + return false; + return ReturnCheckForErrors(NativeMethods.THSTensor_eq(Handle, target.Handle)); } public Tensor equal(Tensor target) => eq(target); @@ -3598,9 +3273,7 @@ public Tensor eq_(Tensor target) public Tensor eq(Scalar target) { if (target is null) return false; - var res = NativeMethods.THSTensor_eq_scalar(Handle, target.Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_eq_scalar(Handle, target.Handle)); } public Tensor eq_(Scalar target) @@ -3614,9 +3287,7 @@ public Tensor eq_(Scalar target) public bool Equals(Tensor target) { if (target is null) return false; - var res = NativeMethods.THSTensor_equal(Handle, target.Handle); - CheckForErrors(); - return res; + return ReturnCheckForErrors(NativeMethods.THSTensor_equal(Handle, target.Handle)); } /// @@ -3637,9 +3308,7 @@ public bool allclose(Tensor target, double rtol = 1e-05, double atol = 1e-08, bo public Tensor ge(Tensor target) { if (target is null) return false; - var res = NativeMethods.THSTensor_ge(Handle, target.Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_ge(Handle, target.Handle)); } public Tensor greater_equal(Tensor target) => ge(target); @@ -3655,9 +3324,7 @@ public Tensor ge_(Tensor target) public Tensor ge(Scalar target) { if (target is null) return false; - var res = NativeMethods.THSTensor_ge_scalar(Handle, target.Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_ge_scalar(Handle, target.Handle)); } public Tensor ge_(Scalar target) @@ -3671,9 +3338,7 @@ public Tensor ge_(Scalar target) public Tensor gt(Tensor target) { if (target is null) return false; - var res = NativeMethods.THSTensor_gt(Handle, target.Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_gt(Handle, target.Handle)); } public Tensor greater(Tensor target) => gt(target); @@ -3689,9 +3354,7 @@ public Tensor gt_(Tensor target) public Tensor gt(Scalar target) { if (target is null) return false; - var res = NativeMethods.THSTensor_gt_scalar(Handle, target.Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_gt_scalar(Handle, target.Handle)); } public Tensor gt_(Scalar target) @@ -3709,9 +3372,7 @@ public Tensor gt_(Scalar target) /// public Tensor kron(Tensor other) { - var res = NativeMethods.THSTensor_kron(Handle, other.Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_kron(Handle, other.Handle)); } /// @@ -3723,9 +3384,7 @@ public Tensor lcm(Tensor other) { if (!torch.is_integral(this.dtype) || !torch.is_integral(other.dtype)) throw new ArgumentException("Arguments to 'lcm' must have integer element types."); - var res = NativeMethods.THSTensor_lcm(Handle, other.Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_lcm(Handle, other.Handle)); } /// @@ -3750,9 +3409,7 @@ public Tensor lcm_(Tensor other) /// Typically this function is used to construct floating point numbers by multiplying mantissas in input with integral powers of two created from the exponents in other. public Tensor ldexp(Tensor other) { - var res = NativeMethods.THSTensor_ldexp(Handle, other.Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_ldexp(Handle, other.Handle)); } /// @@ -3770,9 +3427,7 @@ public Tensor ldexp_(Tensor other) public Tensor le(Tensor target) { - var res = NativeMethods.THSTensor_le(Handle, target.Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_le(Handle, target.Handle)); } public Tensor less_equal(Tensor target) => le(target); @@ -3788,9 +3443,7 @@ public Tensor le_(Tensor target) public Tensor le(Scalar target) { - var res = NativeMethods.THSTensor_le_scalar(Handle, target.Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_le_scalar(Handle, target.Handle)); } public Tensor le_(Scalar target) @@ -3802,9 +3455,7 @@ public Tensor le_(Scalar target) public Tensor lt(Tensor target) { - var res = NativeMethods.THSTensor_lt(Handle, target.Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_lt(Handle, target.Handle)); } public Tensor less(Tensor target) => lt(target); @@ -3818,9 +3469,7 @@ public Tensor lt_(Tensor target) public Tensor lt(Scalar target) { - var res = NativeMethods.THSTensor_lt_scalar(Handle, target.Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_lt_scalar(Handle, target.Handle)); } public Tensor lt_(Scalar target) @@ -3832,9 +3481,7 @@ public Tensor lt_(Scalar target) public Tensor masked_fill(Tensor mask, Scalar value) { - var res = NativeMethods.THSTensor_masked_fill(Handle, mask.Handle, value.Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_masked_fill(Handle, mask.Handle, value.Handle)); } public Tensor masked_fill_(Tensor mask, Scalar value) @@ -3846,9 +3493,7 @@ public Tensor masked_fill_(Tensor mask, Scalar value) public Tensor masked_scatter(Tensor mask, Tensor value) { - var res = NativeMethods.THSTensor_masked_scatter(Handle, mask.Handle, value.Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_masked_scatter(Handle, mask.Handle, value.Handle)); } @@ -3862,9 +3507,7 @@ public Tensor masked_scatter_(Tensor mask, Tensor value) public Tensor masked_select(Tensor mask) { if (mask.dtype != ScalarType.Bool) throw new ArgumentException("The mask tensor must be Boolean."); - var res = NativeMethods.THSTensor_masked_select(Handle, mask.Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_masked_select(Handle, mask.Handle)); } public (Tensor values, Tensor indexes) topk(int k, int dim = -1, bool largest = true, bool sorted = true) @@ -3907,9 +3550,7 @@ public Tensor[] unbind(long dimension = 0L) /// The step between each slice public Tensor unfold(long dimension, long size, long step) { - var res = NativeMethods.THSTensor_unfold(Handle, dimension, size, step); - if (res == IntPtr.Zero) CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_unfold(Handle, dimension, size, step)); } /// @@ -4226,9 +3867,7 @@ public Tensor[] chunk(long chunks, long dim = 0L) public (Tensor values, Tensor indices) kthvalue(long k, long? dim, bool keepdim = false) { var values = NativeMethods.THSTensor_kthvalue(Handle, k, dim.HasValue ? dim.Value : -1, keepdim, out var indices); - if (values == IntPtr.Zero || indices == IntPtr.Zero) - CheckForErrors(); - return (new Tensor(values), new Tensor(indices)); + return ReturnCheckForErrors(values, indices); } /// @@ -4248,9 +3887,7 @@ public static (Tensor values, Tensor indices) kthvalue(Tensor input, long k, lon /// public Tensor max() { - var res = NativeMethods.THSTensor_max(Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_max(Handle)); } @@ -4261,9 +3898,7 @@ public Tensor max() /// public Tensor maximum(Tensor other) { - var res = NativeMethods.THSTensor_max_elementwise(Handle, other.Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_max_elementwise(Handle, other.Handle)); } /// @@ -4273,9 +3908,7 @@ public Tensor maximum(Tensor other) /// public Tensor max(Tensor other) { - var res = NativeMethods.THSTensor_max_elementwise(Handle, other.Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_max_elementwise(Handle, other.Handle)); } /// @@ -4305,9 +3938,7 @@ public Tensor max(Tensor other) /// public Tensor mean() { - var res = NativeMethods.THSTensor_mean(Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_mean(Handle)); } /// @@ -4319,9 +3950,7 @@ public Tensor mean() /// Whether the output tensor has dim retained or not. public Tensor quantile(Tensor q, long dim = -1, bool keepdim = false) { - var res = NativeMethods.THSTensor_quantile(Handle, q.Handle, dim, keepdim); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_quantile(Handle, q.Handle, dim, keepdim)); } /// @@ -4335,9 +3964,7 @@ public Tensor quantile(Tensor q, long dim = -1, bool keepdim = false) public Tensor nanquantile(Tensor q, long dim = -1, bool keepdim = false) { - var res = NativeMethods.THSTensor_nanquantile(Handle, q.Handle, dim, keepdim); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_nanquantile(Handle, q.Handle, dim, keepdim)); } /// @@ -4375,9 +4002,7 @@ public Tensor mean(long[] dimensions, bool keepdim = false, ScalarType? type = n { unsafe { fixed (long* pdims = dimensions) { - var res = NativeMethods.THSTensor_mean_along_dimensions(Handle, (IntPtr)pdims, dimensions.Length, keepdim, type.HasValue, (sbyte)type.GetValueOrDefault()); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_mean_along_dimensions(Handle, (IntPtr)pdims, dimensions.Length, keepdim, type.HasValue, (sbyte)type.GetValueOrDefault())); } } } @@ -4386,9 +4011,7 @@ public Tensor var(long[] dimensions, bool keepdim = false, ScalarType? type = nu { unsafe { fixed (long* pdims = dimensions) { - var res = NativeMethods.THSTensor_var_along_dimensions(Handle, (IntPtr)pdims, dimensions.Length, keepdim, type.HasValue, (sbyte)type.GetValueOrDefault()); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_var_along_dimensions(Handle, (IntPtr)pdims, dimensions.Length, keepdim, type.HasValue, (sbyte)type.GetValueOrDefault())); } } } @@ -4402,9 +4025,7 @@ public Tensor var(long[] dimensions, bool keepdim = false, ScalarType? type = nu /// public Tensor median() { - var res = NativeMethods.THSTensor_median(Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_median(Handle)); } /// @@ -4412,9 +4033,7 @@ public Tensor median() /// public Tensor min() { - var res = NativeMethods.THSTensor_min(Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_min(Handle)); } /// @@ -4424,9 +4043,7 @@ public Tensor min() /// public Tensor min(Tensor other) { - var res = NativeMethods.THSTensor_min_elementwise(Handle, other.Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_min_elementwise(Handle, other.Handle)); } /// @@ -4436,9 +4053,7 @@ public Tensor min(Tensor other) /// public Tensor minimum(Tensor other) { - var res = NativeMethods.THSTensor_min_elementwise(Handle, other.Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_min_elementwise(Handle, other.Handle)); } /// @@ -4469,9 +4084,7 @@ public Tensor minimum(Tensor other) /// public Tensor msort() { - var res = NativeMethods.THSTensor_msort(Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_msort(Handle)); } /// @@ -4484,15 +4097,12 @@ public Tensor msort() public (Tensor Values, Tensor Indices) sort(long dim = -1, bool descending = false, bool stable = false) { var res = NativeMethods.THSTensor_sort(Handle, dim, descending, stable, out var indices); - if (res == IntPtr.Zero || indices == IntPtr.Zero) { CheckForErrors(); } - return (new Tensor(res), new Tensor(indices)); + return ReturnCheckForErrors(res, indices); } public Tensor ne(Tensor target) { - var res = NativeMethods.THSTensor_ne(Handle, target.Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_ne(Handle, target.Handle)); } public Tensor not_equal(Tensor target) => ne(target); @@ -4508,9 +4118,7 @@ public Tensor ne_(Tensor target) public Tensor ne(Scalar target) { - var res = NativeMethods.THSTensor_ne_scalar(Handle, target.Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_ne_scalar(Handle, target.Handle)); } public Tensor ne_(Scalar target) @@ -4529,10 +4137,7 @@ public Tensor ne_(Scalar target) /// public Tensor dist(Tensor other, float p = 2.0f) { - var res = NativeMethods.THSTensor_dist(Handle, other.Handle, p); - if (res == IntPtr.Zero) { CheckForErrors(); } - res = AutocastMode.AutoCast(res, ScalarType.Float32); - return new Tensor(res); + return ReturnCheckForErrorsAutocast(NativeMethods.THSTensor_dist(Handle, other.Handle, p), ScalarType.Float32); } /// @@ -4541,10 +4146,7 @@ public Tensor dist(Tensor other, float p = 2.0f) /// The norm to be computed. public Tensor norm(float p = 2.0f) { - var res = NativeMethods.THSTensor_norm(Handle, p); - if (res == IntPtr.Zero) { CheckForErrors(); } - res = AutocastMode.AutoCast(res, ScalarType.Float32); - return new Tensor(res); + return ReturnCheckForErrorsAutocast(NativeMethods.THSTensor_norm(Handle, p), ScalarType.Float32); } /// @@ -4552,10 +4154,7 @@ public Tensor norm(float p = 2.0f) /// public Tensor norm(int dim, bool keepdim = false, float p = 2.0f) { - var res = NativeMethods.THSTensor_norm_along_dimension(Handle, dim, keepdim, p); - if (res == IntPtr.Zero) { CheckForErrors(); } - res = AutocastMode.AutoCast(res, ScalarType.Float32); - return new Tensor(res); + return ReturnCheckForErrorsAutocast(NativeMethods.THSTensor_norm_along_dimension(Handle, dim, keepdim, p), ScalarType.Float32); } /// @@ -4565,9 +4164,7 @@ public Tensor norm(int dim, bool keepdim = false, float p = 2.0f) /// If input is a vector of size n and vec2 is a vector of size m, then out must be a matrix of size n×m. public Tensor outer(Tensor vec2) { - var res = NativeMethods.THSTensor_outer(Handle, vec2.Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_outer(Handle, vec2.Handle)); } /// @@ -4585,9 +4182,7 @@ public Tensor outer(Tensor vec2) /// public Tensor inner(Tensor vec2) { - var res = NativeMethods.THSTensor_inner(Handle, vec2.Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_inner(Handle, vec2.Handle)); } /// @@ -4597,10 +4192,7 @@ public Tensor inner(Tensor vec2) public Tensor prelu(Tensor target) { - var res = NativeMethods.THSTensor_prelu(Handle, target.Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - res = AutocastMode.AutoCast(res); - return new Tensor(res); + return ReturnCheckForErrorsAutocast(NativeMethods.THSTensor_prelu(Handle, target.Handle)); } /// @@ -4614,9 +4206,7 @@ public Tensor prelu(Tensor target) /// public Tensor fmax(Tensor other) { - var res = NativeMethods.THSTensor_fmax(Handle, other.Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_fmax(Handle, other.Handle)); } /// @@ -4629,9 +4219,7 @@ public Tensor fmax(Tensor other) /// The second input tensor public Tensor fmin(Tensor other) { - var res = NativeMethods.THSTensor_fmin(Handle, other.Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_fmin(Handle, other.Handle)); } /// @@ -4643,10 +4231,7 @@ public Tensor fmin(Tensor other) /// public Tensor renorm(float p, long dim, float maxnorm) { - var res = NativeMethods.THSTensor_renorm(Handle, p, dim, maxnorm); - if (res == IntPtr.Zero) { CheckForErrors(); } - res = AutocastMode.AutoCast(res, ScalarType.Float32); - return new Tensor(res); + return ReturnCheckForErrorsAutocast(NativeMethods.THSTensor_renorm(Handle, p, dim, maxnorm), ScalarType.Float32); } /// @@ -4655,9 +4240,7 @@ public Tensor renorm(float p, long dim, float maxnorm) /// public Tensor sigmoid() { - var res = NativeMethods.THSTensor_sigmoid(Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_sigmoid(Handle)); } /// @@ -4676,10 +4259,7 @@ public Tensor sigmoid_() [Pure] public Tensor std(bool unbiased = true) { - var res = NativeMethods.THSTensor_std(Handle, unbiased); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_std(Handle, unbiased)); } /// @@ -4690,10 +4270,7 @@ public Tensor std(bool unbiased = true) [Pure] public Tensor var(bool unbiased = true) { - var res = NativeMethods.THSTensor_var(Handle, unbiased); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_var(Handle, unbiased)); } /// Calculates the standard deviation of all elements in the tensor. @@ -4764,9 +4341,7 @@ public Tensor var(long[] dimensions, bool unbiased = true, bool keepdim = false, private unsafe Tensor _std(ReadOnlySpan dimensions, bool unbiased = true, bool keepdim = false, ScalarType? type = null) { fixed (long* pdims = dimensions) { - var res = NativeMethods.THSTensor_std_along_dimensions(Handle, (IntPtr)pdims, dimensions.Length, unbiased, keepdim); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_std_along_dimensions(Handle, (IntPtr)pdims, dimensions.Length, unbiased, keepdim)); } } @@ -4774,9 +4349,7 @@ private unsafe Tensor _std(ReadOnlySpan dimensions, bool unbiased = true, private unsafe Tensor _var(ReadOnlySpan dimensions, bool unbiased = true, bool keepdim = false, ScalarType? type = null) { fixed (long* pdims = dimensions) { - var res = NativeMethods.THSTensor_var_along_dimensions(Handle, (IntPtr)pdims, dimensions.Length, unbiased, keepdim); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_var_along_dimensions(Handle, (IntPtr)pdims, dimensions.Length, unbiased, keepdim)); } } @@ -4873,9 +4446,7 @@ public Tensor var((long, long, long) dim, bool unbiased = true, bool keepdim = f public (Tensor std, Tensor mean) std_mean(bool unbiased = true) { var res = NativeMethods.THSTensor_std_mean(Handle, unbiased, out var mean); - if (res == IntPtr.Zero || mean == IntPtr.Zero) - CheckForErrors(); - return (new Tensor(res), new Tensor(mean)); + return ReturnCheckForErrors(res, mean); } /// @@ -4887,9 +4458,7 @@ public Tensor var((long, long, long) dim, bool unbiased = true, bool keepdim = f public (Tensor @var, Tensor mean) var_mean(bool unbiased = true) { var res = NativeMethods.THSTensor_var_mean(Handle, unbiased, out var mean); - if (res == IntPtr.Zero || mean == IntPtr.Zero) - CheckForErrors(); - return (new Tensor(res), new Tensor(mean)); + return ReturnCheckForErrors(res, mean); } /// Calculates the standard deviation and mean of all elements in the tensor. @@ -4962,8 +4531,7 @@ private unsafe (Tensor std, Tensor mean) _std_mean(ReadOnlySpan dimensions { fixed (long* pdims = dimensions) { var res = NativeMethods.THSTensor_std_mean_along_dimensions(Handle, (IntPtr)pdims, dimensions.Length, unbiased, keepdim, out var mean); - if (res == IntPtr.Zero || mean == IntPtr.Zero) { CheckForErrors(); } - return (new Tensor(res), new Tensor(mean)); + return ReturnCheckForErrors(res, mean); } } @@ -4972,8 +4540,7 @@ private unsafe (Tensor @var, Tensor mean) _var_mean(ReadOnlySpan dimension { fixed (long* pdims = dimensions) { var res = NativeMethods.THSTensor_var_mean_along_dimensions(Handle, (IntPtr)pdims, dimensions.Length, unbiased, keepdim, out var @var); - if (res == IntPtr.Zero || @var == IntPtr.Zero) { CheckForErrors(); } - return (new Tensor(res), new Tensor(@var)); + return ReturnCheckForErrors(res, @var); } } @@ -5066,10 +4633,7 @@ private unsafe (Tensor @var, Tensor mean) _var_mean(ReadOnlySpan dimension /// public Tensor prod(ScalarType? type = null) { - var res = NativeMethods.THSTensor_prod(Handle, type.HasValue, (sbyte)type.GetValueOrDefault()); - if (res == IntPtr.Zero) { CheckForErrors(); } - res = AutocastMode.AutoCast(res, ScalarType.Float32); - return new Tensor(res); + return ReturnCheckForErrorsAutocast(NativeMethods.THSTensor_prod(Handle, type.HasValue, (sbyte)type.GetValueOrDefault()), ScalarType.Float32); } /// @@ -5077,10 +4641,7 @@ public Tensor prod(ScalarType? type = null) /// public Tensor prod(long dim, bool keepdim = false, ScalarType? type = null) { - var res = NativeMethods.THSTensor_prod_along_dimensions(Handle, dim, keepdim, type.HasValue, (sbyte)type.GetValueOrDefault()); - if (res == IntPtr.Zero) { CheckForErrors(); } - res = AutocastMode.AutoCast(res, ScalarType.Float32); - return new Tensor(res); + return ReturnCheckForErrorsAutocast(NativeMethods.THSTensor_prod_along_dimensions(Handle, dim, keepdim, type.HasValue, (sbyte)type.GetValueOrDefault()), ScalarType.Float32); } /// @@ -5088,18 +4649,13 @@ public Tensor prod(long dim, bool keepdim = false, ScalarType? type = null) /// public Tensor sum(ScalarType? type = null) { - var res = NativeMethods.THSTensor_sum(Handle, type.HasValue, (sbyte)type.GetValueOrDefault()); - if (res == IntPtr.Zero) { CheckForErrors(); } - res = AutocastMode.AutoCast(res, ScalarType.Float32); - return new Tensor(res); + return ReturnCheckForErrorsAutocast(NativeMethods.THSTensor_sum(Handle, type.HasValue, (sbyte)type.GetValueOrDefault()), ScalarType.Float32); } private unsafe Tensor _sum(ReadOnlySpan dimensions, bool keepdim = false, ScalarType? type = null) { fixed (long* pdims = dimensions) { - var res = NativeMethods.THSTensor_sum_along_dimensions(Handle, (IntPtr)pdims, dimensions.Length, keepdim, type.HasValue, (sbyte)type.GetValueOrDefault()); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_sum_along_dimensions(Handle, (IntPtr)pdims, dimensions.Length, keepdim, type.HasValue, (sbyte)type.GetValueOrDefault())); } } @@ -5149,9 +4705,7 @@ public Tensor expand(ReadOnlySpan sizes, bool isImplicit = false) { unsafe { fixed (long* psizes = sizes) { - var res = NativeMethods.THSTensor_expand(Handle, (IntPtr)psizes, sizes.Length, isImplicit); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_expand(Handle, (IntPtr)psizes, sizes.Length, isImplicit)); } } } @@ -5191,9 +4745,7 @@ public Tensor repeat(params long[] sizes) { unsafe { fixed (long* psizes = sizes) { - var res = NativeMethods.THSTensor_repeat(Handle, (IntPtr)psizes, sizes.Length); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_repeat(Handle, (IntPtr)psizes, sizes.Length)); } } } @@ -5202,18 +4754,14 @@ public Tensor repeat_interleave(Tensor repeats, long? dim = null, long? output_s { long _dim = dim ?? long.MinValue; long _output_size = output_size ?? long.MinValue; - var res = NativeMethods.THSTensor_repeat_interleave(Handle, repeats.Handle, _dim, _output_size); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_repeat_interleave(Handle, repeats.Handle, _dim, _output_size)); } public Tensor repeat_interleave(long repeats, long? dim = null, long? output_size = null) { long _dim = dim ?? long.MinValue; long _output_size = output_size ?? long.MinValue; - var res = NativeMethods.THSTensor_repeat_interleave_int64(Handle, repeats, _dim, _output_size); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_repeat_interleave_int64(Handle, repeats, _dim, _output_size)); } /// @@ -5223,9 +4771,7 @@ public Tensor broadcast_to(params long[] shape) { unsafe { fixed (long* psizes = shape) { - var res = NativeMethods.THSTensor_broadcast_to(Handle, (IntPtr)psizes, shape.Length); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_broadcast_to(Handle, (IntPtr)psizes, shape.Length)); } } } @@ -5234,9 +4780,7 @@ public Tensor movedim(long[] source, long[] destination) { unsafe { fixed (long* psource = source, pdest = destination) { - var res = NativeMethods.THSTensor_movedim(Handle, (IntPtr)psource, source.Length, (IntPtr)pdest, destination.Length); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_movedim(Handle, (IntPtr)psource, source.Length, (IntPtr)pdest, destination.Length)); } } } @@ -5250,9 +4794,7 @@ public Tensor randn_out(params long[] sizes) { unsafe { fixed (long* psizes = sizes) { - var res = NativeMethods.THSTensor_randn_out((IntPtr)psizes, sizes.Length, Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_randn_out((IntPtr)psizes, sizes.Length, Handle)); } } } @@ -5264,9 +4806,7 @@ public Tensor rand_out(params long[] sizes) { unsafe { fixed (long* psizes = sizes) { - var res = NativeMethods.THSTensor_rand_out((IntPtr)psizes, sizes.Length, Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_rand_out((IntPtr)psizes, sizes.Length, Handle)); } } } @@ -5277,9 +4817,7 @@ public Tensor randint_out(long high, long[] sizes) { unsafe { fixed (long* psizes = sizes) { - var res = NativeMethods.THSTensor_randint_out(high, (IntPtr)psizes, sizes.Length, Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_randint_out(high, (IntPtr)psizes, sizes.Length, Handle)); } } } @@ -5298,8 +4836,8 @@ public Tensor rand_like(ScalarType? dtype = null, torch.Device? device = null, b GC.WaitForPendingFinalizers(); result = NativeMethods.THSTensor_rand_like(Handle, (sbyte)dtype, (int)device.type, device.index, requires_grad); } - if (result == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(result); + + return ReturnCheckForErrors(result); } /// @@ -5316,8 +4854,8 @@ public Tensor randn_like(ScalarType? dtype = null, torch.Device? device = null, GC.WaitForPendingFinalizers(); result = NativeMethods.THSTensor_randn_like(Handle, (sbyte)dtype, (int)device.type, device.index, requires_grad); } - if (result == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(result); + + return ReturnCheckForErrors(result); } /// @@ -5334,8 +4872,8 @@ public Tensor randint_like(long low, long high, ScalarType? dtype = null, torch. GC.WaitForPendingFinalizers(); result = NativeMethods.THSTensor_randint_like(Handle, low, high, (sbyte)dtype, (int)device.type, device.index, requires_grad); } - if (result == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(result); + + return ReturnCheckForErrors(result); } /// @@ -5344,9 +4882,7 @@ public Tensor randint_like(long low, long high, ScalarType? dtype = null, torch. [Obsolete("This doesn't exist in PyTorch.")] public Tensor randperm_out(long n) { - var res = NativeMethods.THSTensor_randperm_out(IntPtr.Zero, n, Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_randperm_out(IntPtr.Zero, n, Handle)); } /// @@ -5357,9 +4893,7 @@ public Tensor randperm_out(long n) /// public Tensor bernoulli(torch.Generator? generator = null) { - var res = NativeMethods.THSTensor_bernoulli(Handle, (generator is null) ? IntPtr.Zero : generator.Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_bernoulli(Handle, (generator is null) ? IntPtr.Zero : generator.Handle)); } /// @@ -5371,9 +4905,7 @@ public Tensor bernoulli(torch.Generator? generator = null) /// public Tensor multinomial(long num_samples, bool replacement = false, torch.Generator? generator = null) { - var res = NativeMethods.THSTensor_multinomial(Handle, num_samples, replacement, (generator is null) ? IntPtr.Zero : generator.Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_multinomial(Handle, num_samples, replacement, (generator is null) ? IntPtr.Zero : generator.Handle)); } /// @@ -5382,9 +4914,7 @@ public Tensor multinomial(long num_samples, bool replacement = false, torch.Gene /// Optional random number generator public Tensor poisson(torch.Generator? generator = null) { - var res = NativeMethods.THSTensor_poisson(Handle, (generator is null) ? IntPtr.Zero : generator.Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_poisson(Handle, (generator is null) ? IntPtr.Zero : generator.Handle)); } /// @@ -5415,9 +4945,7 @@ public Tensor bernoulli_(Tensor p, torch.Generator? generator = null) public Tensor binomial(Tensor prob, torch.Generator? generator = null) { - var res = NativeMethods.THSTensor_binomial(Handle, prob.Handle, (generator is null) ? IntPtr.Zero : generator.Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_binomial(Handle, prob.Handle, (generator is null) ? IntPtr.Zero : generator.Handle)); } /// @@ -5523,9 +5051,7 @@ public Tensor uniform_(double from, double to, torch.Generator? generator = null /// public Tensor arange_out(Scalar start, Scalar stop, Scalar step) { - var res = NativeMethods.THSTensor_arange_out(start.Handle, stop.Handle, step.Handle, Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_arange_out(start.Handle, stop.Handle, step.Handle, Handle)); } /// @@ -5536,9 +5062,7 @@ public Tensor permute(params long[] permutation) { unsafe { fixed (long* pPermutation = permutation) { - var res = NativeMethods.THSTensor_permute(Handle, (IntPtr)pPermutation, permutation.Length); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_permute(Handle, (IntPtr)pPermutation, permutation.Length)); } } } @@ -5556,9 +5080,7 @@ public Tensor ones(params long[] sizes) { unsafe { fixed (long* psizes = sizes) { - var res = NativeMethods.THSTensor_ones_out((IntPtr)psizes, sizes.Length, Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_ones_out((IntPtr)psizes, sizes.Length, Handle)); } } } @@ -5622,9 +5144,7 @@ public Tensor zeros(params long[] sizes) { unsafe { fixed (long* psizes = sizes) { - var res = NativeMethods.THSTensor_zeros_out((IntPtr)psizes, sizes.Length, Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_zeros_out((IntPtr)psizes, sizes.Length, Handle)); } } } @@ -5704,8 +5224,8 @@ public Tensor zeros_like(ScalarType? dtype = null, torch.Device? device = null, GC.WaitForPendingFinalizers(); result = NativeMethods.THSTensor_zeros_like(Handle, (sbyte)dtype, (int)device.type, device.index, requires_grad); } - if (result == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(result); + + return ReturnCheckForErrors(result); } /// @@ -5722,8 +5242,8 @@ public Tensor ones_like(ScalarType? dtype = null, torch.Device? device = null, b GC.WaitForPendingFinalizers(); result = NativeMethods.THSTensor_ones_like(Handle, (sbyte)dtype, (int)device.type, device.index, requires_grad); } - if (result == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(result); + + return ReturnCheckForErrors(result); } /// @@ -5785,9 +5305,7 @@ public Tensor empty(params long[] sizes) { unsafe { fixed (long* psizes = sizes) { - var res = NativeMethods.THSTensor_empty_out((IntPtr)psizes, sizes.Length, Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_empty_out((IntPtr)psizes, sizes.Length, Handle)); } } } @@ -5806,8 +5324,8 @@ public Tensor empty_like(ScalarType? dtype = null, torch.Device? device = null, GC.WaitForPendingFinalizers(); result = NativeMethods.THSTensor_empty_like(Handle, (sbyte)dtype, (int)device.type, device.index, requires_grad); } - if (result == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(result); + + return ReturnCheckForErrors(result); } /// @@ -5817,9 +5335,7 @@ public Tensor full(long[] sizes, Scalar value) { unsafe { fixed (long* psizes = sizes) { - var res = NativeMethods.THSTensor_full_out((IntPtr)psizes, sizes.Length, value.Handle, Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_full_out((IntPtr)psizes, sizes.Length, value.Handle, Handle)); } } } @@ -5831,9 +5347,7 @@ public Tensor full(ReadOnlySpan sizes, Scalar value) { unsafe { fixed (long* psizes = sizes) { - var res = NativeMethods.THSTensor_full_out((IntPtr)psizes, sizes.Length, value.Handle, Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_full_out((IntPtr)psizes, sizes.Length, value.Handle, Handle)); } } } @@ -5905,15 +5419,13 @@ public Tensor full_like(Scalar value, ScalarType? dtype = null, torch.Device? de GC.WaitForPendingFinalizers(); result = NativeMethods.THSTensor_full_like(Handle, value.Handle, (sbyte)dtype, (int)device.type, device.index, requires_grad); } - if (result == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(result); + + return ReturnCheckForErrors(result); } public Tensor detach() { - var res = NativeMethods.THSTensor_detach(Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_detach(Handle)); } public Tensor detach_() @@ -5928,9 +5440,7 @@ public Tensor detach_() /// public Tensor eye(long rows, long columns) { - var res = NativeMethods.THSTensor_eye_out(rows, columns, Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_eye_out(rows, columns, Handle)); } @@ -5941,9 +5451,7 @@ public Tensor eye(long rows, long columns) /// public Tensor scatter(long dim, Tensor index, Tensor src) { - var res = NativeMethods.THSTensor_scatter(Handle, dim, index.Handle, src.Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_scatter(Handle, dim, index.Handle, src.Handle)); } /// @@ -5972,9 +5480,7 @@ public Tensor scatter_add(long dim, Tensor index, Tensor src) if (sts.Any(x => x == ScalarType.Float32)) (handle, index.handle, src.handle) = AutocastMode.AutoCast(handle, index.handle, src.handle, ScalarType.Float32); } - var res = NativeMethods.THSTensor_scatter_add(Handle, dim, index.Handle, src.Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_scatter_add(Handle, dim, index.Handle, src.Handle)); } /// @@ -6004,9 +5510,7 @@ public Tensor scatter_add_(long dim, Tensor index, Tensor src) /// This function returns a tensor with fresh storage; it does not return a view. public Tensor diagonal_scatter(Tensor src, long offset = 0L, long dim1 = 0L, long dim2 = 1L) { - var res = NativeMethods.THSTensor_diagonal_scatter(Handle, src.Handle, offset, dim1, dim2); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_diagonal_scatter(Handle, src.Handle, offset, dim1, dim2)); } /// @@ -6018,9 +5522,7 @@ public Tensor diagonal_scatter(Tensor src, long offset = 0L, long dim1 = 0L, lon /// This function returns a tensor with fresh storage; it does not create a view. public Tensor select_scatter(Tensor src, long dim, long index) { - var res = NativeMethods.THSTensor_select_scatter(Handle, src.Handle, dim, index); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_select_scatter(Handle, src.Handle, dim, index)); } /// @@ -6036,9 +5538,7 @@ public unsafe Tensor slice_scatter(Tensor src, long dim = 0L, long? start = null var _start = start.HasValue ? new long[] { start.Value } : null; var _end = end.HasValue ? new long[] { end.Value } : null; fixed (long* pstart = _start, pend = _end) { - var res = NativeMethods.THSTensor_slice_scatter(Handle, src.Handle, dim, (IntPtr)pstart, (IntPtr)pend, step); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_slice_scatter(Handle, src.Handle, dim, (IntPtr)pstart, (IntPtr)pend, step)); } } @@ -6047,9 +5547,7 @@ public unsafe Tensor slice_scatter(Tensor src, long dim = 0L, long? start = null /// public Tensor gather(long dim, Tensor index) { - var res = NativeMethods.THSTensor_gather(Handle, dim, index.Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_gather(Handle, dim, index.Handle)); } /// @@ -6059,9 +5557,7 @@ public Tensor flip(params long[] dims) { unsafe { fixed (long* psizes = dims) { - var res = NativeMethods.THSTensor_flip(Handle, (IntPtr)psizes, dims.Length); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_flip(Handle, (IntPtr)psizes, dims.Length)); } } } @@ -6071,9 +5567,7 @@ public Tensor flip(params long[] dims) /// public Tensor fliplr() { - var res = NativeMethods.THSTensor_fliplr(Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_fliplr(Handle)); } /// @@ -6081,9 +5575,7 @@ public Tensor fliplr() /// public Tensor flipud() { - var res = NativeMethods.THSTensor_flipud(Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_flipud(Handle)); } /// @@ -6093,9 +5585,7 @@ public Tensor nanmean(int? dim = null, bool keepdim = false, ScalarType? dtype = { var d = (dim is null) ? -1 : dim.Value; var t = (dtype is null) ? this.dtype : dtype.Value; - var res = NativeMethods.THSTensor_nanmean(Handle, d, keepdim, (sbyte)t); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_nanmean(Handle, d, keepdim, (sbyte)t)); } /// @@ -6103,9 +5593,7 @@ public Tensor nanmean(int? dim = null, bool keepdim = false, ScalarType? dtype = /// public Tensor nanmedian() { - var res = NativeMethods.THSTensor_nanmedian(Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_nanmedian(Handle)); } /// @@ -6113,9 +5601,7 @@ public Tensor nanmedian() /// public Tensor nansum() { - var res = NativeMethods.THSTensor_nansum(Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_nansum(Handle)); } /// @@ -6130,10 +5616,7 @@ public Tensor nan_to_num(double nan = 0d, double? posinf = null, double? neginf var _neginf = neginf.HasValue ? new double[] { neginf.Value } : null; unsafe { fixed (double* pnan = _nan, pposinf = _posinf, pneginf = _neginf) { - var res = - NativeMethods.THSTensor_nan_to_num(Handle, (IntPtr)pnan, (IntPtr)pposinf, (IntPtr)pneginf); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_nan_to_num(Handle, (IntPtr)pnan, (IntPtr)pposinf, (IntPtr)pneginf)); } } } @@ -6143,9 +5626,7 @@ public Tensor nan_to_num(double nan = 0d, double? posinf = null, double? neginf /// public Tensor nextafter(Tensor other) { - var res = NativeMethods.THSTensor_nextafter(Handle, other.Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_nextafter(Handle, other.Handle)); } /// @@ -6155,9 +5636,7 @@ public Tensor nextafter(Tensor other) /// public Tensor narrow(long dim, long start, long length) { - var res = NativeMethods.THSTensor_narrow(Handle, dim, start, length); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_narrow(Handle, dim, start, length)); } /// @@ -6167,9 +5646,7 @@ public Tensor narrow(long dim, long start, long length) /// public Tensor nonzero() { - var res = NativeMethods.THSTensor_nonzero(Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_nonzero(Handle)); } public IList nonzero_as_list() @@ -6232,9 +5709,7 @@ public Tensor rot90(long k = 1, (long, long)? dims = null) dims = (0, 1); } - var res = NativeMethods.THSTensor_rot90(Handle, k, dims.Value.Item1, dims.Value.Item2); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_rot90(Handle, k, dims.Value.Item1, dims.Value.Item2)); } /// @@ -6262,10 +5737,7 @@ private unsafe Tensor _roll(ReadOnlySpan shifts, ReadOnlySpan dims) var dmLen = dims.Length; fixed (long* sh = shifts, dm = (dmLen == 0) ? null : dims) { - var res = - NativeMethods.THSTensor_roll(Handle, (IntPtr)sh, shifts.Length, (IntPtr)dm, dmLen); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_roll(Handle, (IntPtr)sh, shifts.Length, (IntPtr)dm, dmLen)); } } @@ -6277,9 +5749,7 @@ private unsafe Tensor _roll(ReadOnlySpan shifts, ReadOnlySpan dims) public Tensor slice(long dim, long start, long finish, long step) { if (step < 1) throw new ArgumentException($"step is {step}, but it should always be positive."); - var res = NativeMethods.THSTensor_slice(Handle, dim, start, finish, step); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_slice(Handle, dim, start, finish, step)); } /// @@ -6288,9 +5758,7 @@ public Tensor slice(long dim, long start, long finish, long step) /// public Tensor unsqueeze(long dim) { - var res = NativeMethods.THSTensor_unsqueeze(Handle, dim); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_unsqueeze(Handle, dim)); } /// @@ -6314,9 +5782,7 @@ public Tensor where(Tensor condition, Tensor y) { if (condition.dtype != ScalarType.Bool) throw new ArgumentException("The condition to 'where' must be a boolean tensor."); - var res = NativeMethods.THSTensor_where(condition.Handle, this.Handle, y.Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_where(condition.Handle, this.Handle, y.Handle)); } @@ -7188,9 +6654,7 @@ public object tolist() /// public Tensor atleast_1d() { - var res = NativeMethods.THSTensor_atleast_1d(Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_atleast_1d(Handle)); } /// @@ -7199,9 +6663,7 @@ public Tensor atleast_1d() /// public Tensor atleast_2d() { - var res = NativeMethods.THSTensor_atleast_2d(Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_atleast_2d(Handle)); } /// @@ -7210,9 +6672,7 @@ public Tensor atleast_2d() /// public Tensor atleast_3d() { - var res = NativeMethods.THSTensor_atleast_3d(Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_atleast_3d(Handle)); } /// @@ -7256,9 +6716,7 @@ public Tensor stft(long n_fft, long hop_length = -1, long win_length = -1, Tenso } IntPtr _window = (window is null) ? IntPtr.Zero : window.Handle; - var res = NativeMethods.THSTensor_stft(_input, n_fft, hop_length, win_length, _window, normalized, _onesided, _return_complex); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_stft(_input, n_fft, hop_length, win_length, _window, normalized, _onesided, _return_complex)); } /// @@ -7286,9 +6744,7 @@ public Tensor istft(long n_fft, long hop_length = -1, long win_length = -1, Tens _onesided = (onesided.Value ? 1 : 0); } - var res = NativeMethods.THSTensor_istft(Handle, n_fft, hop_length, win_length, _window, center, normalized, _onesided, length, return_complex); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(NativeMethods.THSTensor_istft(Handle, n_fft, hop_length, win_length, _window, center, normalized, _onesided, length, return_complex)); } } diff --git a/src/TorchSharp/Torch.cs b/src/TorchSharp/Torch.cs index 5ddbf806c..f64539477 100644 --- a/src/TorchSharp/Torch.cs +++ b/src/TorchSharp/Torch.cs @@ -681,6 +681,12 @@ public static (Tensor,Tensor) ReturnCheckForErrors(IntPtr ptr, IntPtr ptr1) CheckForErrors(); return (new Tensor(ptr), new Tensor(ptr1)); } + public static (Tensor, Tensor, Tensor) ReturnCheckForErrors(IntPtr ptr, IntPtr ptr1, IntPtr ptr2) + { + if (ptr == IntPtr.Zero || ptr1 == IntPtr.Zero || ptr2 == IntPtr.Zero) + CheckForErrors(); + return (new Tensor(ptr), new Tensor(ptr1), new Tensor(ptr2)); + } public static Tensor ReturnCheckForErrorsAutocast(IntPtr ptr, ScalarType? st = null) { if (ptr == IntPtr.Zero) From 729cb54bb02992cfb34f8f3bcd4ca65166cee68c Mon Sep 17 00:00:00 2001 From: Dimitri Date: Sun, 28 Sep 2025 19:52:46 -0300 Subject: [PATCH 56/65] refactor ReturnCheckErrors on LinearAlgebra and Trig --- src/TorchSharp/FFT.cs | 88 ++----- src/TorchSharp/Generator.cs | 4 +- src/TorchSharp/LinearAlgebra.cs | 202 +++++----------- src/TorchSharp/Special.cs | 223 ++++-------------- src/TorchSharp/Tensor/Tensor.LinearAlgebra.cs | 5 +- src/TorchSharp/Tensor/Tensor.Trig.cs | 5 +- src/TorchSharp/Tensor/torch.Amp.cs | 4 +- src/TorchSharp/Tensor/torch.ComparisonOps.cs | 4 +- src/TorchSharp/Tensor/torch.RandomSampling.cs | 8 +- src/TorchSharp/Torch.cs | 8 +- 10 files changed, 146 insertions(+), 405 deletions(-) diff --git a/src/TorchSharp/FFT.cs b/src/TorchSharp/FFT.cs index 06df3cb78..dd3912eec 100644 --- a/src/TorchSharp/FFT.cs +++ b/src/TorchSharp/FFT.cs @@ -27,9 +27,7 @@ public static partial class fft /// The name was changed because it would conflict with its surrounding scope. That's not legal in .NET. public static Tensor fft_(Tensor input, long n = -1, long dim = -1, FFTNormType norm = FFTNormType.Backward) { - var res = THSTensor_fft(input.Handle, n, dim, (sbyte)norm); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_fft(input.Handle, n, dim, (sbyte)norm)); } /// @@ -42,9 +40,7 @@ public static Tensor fft_(Tensor input, long n = -1, long dim = -1, FFTNormType /// public static Tensor ifft(Tensor input, long n = -1, long dim = -1, FFTNormType norm = FFTNormType.Backward) { - var res = THSTensor_ifft(input.Handle, n, dim, (sbyte)norm); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_ifft(input.Handle, n, dim, (sbyte)norm)); } /// @@ -65,9 +61,7 @@ public static Tensor fft2(Tensor input, long[] s = null, long[] dim = null, FFTN if (dim == null) dim = new long[] { -2, -1 }; unsafe { fixed (long* ps = s, pDim = dim) { - var res = THSTensor_fft2(input.Handle, (IntPtr)ps, (IntPtr)pDim, (sbyte)norm); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_fft2(input.Handle, (IntPtr)ps, (IntPtr)pDim, (sbyte)norm)); } } } @@ -89,9 +83,7 @@ public static Tensor ifft2(Tensor input, long[] s = null, long[] dim = null, FFT if (dim == null) dim = new long[] { -2, -1 }; unsafe { fixed (long* ps = s, pDim = dim) { - var res = THSTensor_ifft2(input.Handle, (IntPtr)ps, (IntPtr)pDim, (sbyte)norm); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_ifft2(input.Handle, (IntPtr)ps, (IntPtr)pDim, (sbyte)norm)); } } } @@ -114,9 +106,7 @@ public static Tensor fftn(Tensor input, long[] s = null, long[] dim = null, FFTN var dlen = (dim == null) ? 0 : dim.Length; unsafe { fixed (long* ps = s, pDim = dim) { - var res = THSTensor_fftn(input.Handle, (IntPtr)ps, slen, (IntPtr)pDim, dlen, (sbyte)norm); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_fftn(input.Handle, (IntPtr)ps, slen, (IntPtr)pDim, dlen, (sbyte)norm)); } } } @@ -139,9 +129,7 @@ public static Tensor ifftn(Tensor input, long[] s = null, long[] dim = null, FFT var dlen = (dim == null) ? 0 : dim.Length; unsafe { fixed (long* ps = s, pDim = dim) { - var res = THSTensor_ifftn(input.Handle, (IntPtr)ps, slen, (IntPtr)pDim, dlen, (sbyte)norm); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_ifftn(input.Handle, (IntPtr)ps, slen, (IntPtr)pDim, dlen, (sbyte)norm)); } } } @@ -155,9 +143,7 @@ public static Tensor ifftn(Tensor input, long[] s = null, long[] dim = null, FFT /// Normalization mode. public static Tensor irfft(Tensor input, long n = -1, long dim = -1, FFTNormType norm = FFTNormType.Backward) { - var res = THSTensor_irfft(input.Handle, n, dim, (sbyte)norm); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_irfft(input.Handle, n, dim, (sbyte)norm)); } /// @@ -170,9 +156,7 @@ public static Tensor irfft(Tensor input, long n = -1, long dim = -1, FFTNormType /// public static Tensor rfft(Tensor input, long n = -1, long dim = -1, FFTNormType norm = FFTNormType.Backward) { - var res = THSTensor_rfft(input.Handle, n, dim, (sbyte)norm); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_rfft(input.Handle, n, dim, (sbyte)norm)); } /// @@ -192,9 +176,7 @@ public static Tensor rfft2(Tensor input, long[] s = null, long[] dim = null, FFT if (dim == null) dim = new long[] { -2, -1 }; unsafe { fixed (long* ps = s, pDim = dim) { - var res = THSTensor_rfft2(input.Handle, (IntPtr)ps, (IntPtr)pDim, (sbyte)norm); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_rfft2(input.Handle, (IntPtr)ps, (IntPtr)pDim, (sbyte)norm)); } } } @@ -216,9 +198,7 @@ public static Tensor irfft2(Tensor input, long[] s = null, long[] dim = null, FF if (dim == null) dim = new long[] { -2, -1 }; unsafe { fixed (long* ps = s, pDim = dim) { - var res = THSTensor_irfft2(input.Handle, (IntPtr)ps, (IntPtr)pDim, (sbyte)norm); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_irfft2(input.Handle, (IntPtr)ps, (IntPtr)pDim, (sbyte)norm)); } } } @@ -240,9 +220,7 @@ public static Tensor rfftn(Tensor input, long[] s = null, long[] dim = null, FFT var dlen = (dim == null) ? 0 : dim.Length; unsafe { fixed (long* ps = s, pDim = dim) { - var res = THSTensor_rfftn(input.Handle, (IntPtr)ps, slen, (IntPtr)pDim, dlen, (sbyte)norm); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_rfftn(input.Handle, (IntPtr)ps, slen, (IntPtr)pDim, dlen, (sbyte)norm)); } } } @@ -264,9 +242,7 @@ public static Tensor irfftn(Tensor input, long[] s = null, long[] dim = null, FF var dlen = (dim == null) ? 0 : dim.Length; unsafe { fixed (long* ps = s, pDim = dim) { - var res = THSTensor_irfftn(input.Handle, (IntPtr)ps, slen, (IntPtr)pDim, dlen, (sbyte)norm); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_irfftn(input.Handle, (IntPtr)ps, slen, (IntPtr)pDim, dlen, (sbyte)norm)); } } } @@ -283,9 +259,7 @@ public static Tensor irfftn(Tensor input, long[] s = null, long[] dim = null, FF /// public static Tensor hfft(Tensor input, long n = -1, long dim = -1, FFTNormType norm = FFTNormType.Backward) { - var res = THSTensor_hfft(input.Handle, n, dim, (sbyte)norm); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_hfft(input.Handle, n, dim, (sbyte)norm)); } /// @@ -299,9 +273,7 @@ public static Tensor hfft(Tensor input, long n = -1, long dim = -1, FFTNormType /// Normalization mode. public static Tensor ihfft(Tensor input, long n = -1, long dim = -1, FFTNormType norm = FFTNormType.Backward) { - var res = THSTensor_ihfft(input.Handle, n, dim, (sbyte)norm); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_ihfft(input.Handle, n, dim, (sbyte)norm)); } /// @@ -316,9 +288,7 @@ public static Tensor fftshift(Tensor input, long[] dim = null) var dlen = (dim == null) ? 0 : dim.Length; unsafe { fixed (long* pDim = dim) { - var res = THSTensor_fftshift(input.Handle, (IntPtr)pDim, dlen); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_fftshift(input.Handle, (IntPtr)pDim, dlen)); } } } @@ -333,9 +303,7 @@ public static Tensor ifftshift(Tensor input, long[] dim = null) var dlen = (dim == null) ? 0 : dim.Length; unsafe { fixed (long* pDim = dim) { - var res = THSTensor_ifftshift(input.Handle, (IntPtr)pDim, dlen); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_ifftshift(input.Handle, (IntPtr)pDim, dlen)); } } } @@ -362,8 +330,8 @@ public static Tensor fftfreq(long n, double d = 1.0, torch.ScalarType? dtype = n GC.WaitForPendingFinalizers(); handle = THSTensor_fftfreq(n, d, (sbyte)dtype, (int)device.type, device.index, requires_grad); } - if (handle == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(handle); + + return ReturnCheckForErrors(handle); } /// @@ -388,8 +356,8 @@ public static Tensor rfftfreq(long n, double d = 1.0, torch.ScalarType? dtype = GC.WaitForPendingFinalizers(); handle = THSTensor_rfftfreq(n, d, (sbyte)dtype, (int)device.type, device.index, requires_grad); } - if (handle == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(handle); + + return ReturnCheckForErrors(handle); } /// @@ -413,9 +381,7 @@ public static Tensor hfft2(Tensor input, long[] s = null, long[] dim = null, FFT if (dim == null) dim = new long[] { -2, -1 }; unsafe { fixed (long* ps = s, pDim = dim) { - var res = THSTensor_hfft2(input.Handle, (IntPtr)ps, (IntPtr)pDim, (sbyte)norm); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_hfft2(input.Handle, (IntPtr)ps, (IntPtr)pDim, (sbyte)norm)); } } } @@ -441,9 +407,7 @@ public static Tensor ihfft2(Tensor input, long[] s = null, long[] dim = null, FF if (dim == null) dim = new long[] { -2, -1 }; unsafe { fixed (long* ps = s, pDim = dim) { - var res = THSTensor_ihfft2(input.Handle, (IntPtr)ps, (IntPtr)pDim, (sbyte)norm); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_ihfft2(input.Handle, (IntPtr)ps, (IntPtr)pDim, (sbyte)norm)); } } } @@ -469,9 +433,7 @@ public static Tensor hfftn(Tensor input, long[] s = null, long[] dim = null, FFT var dlen = (dim == null) ? 0 : dim.Length; unsafe { fixed (long* ps = s, pDim = dim) { - var res = THSTensor_hfftn(input.Handle, (IntPtr)ps, slen, (IntPtr)pDim, dlen, (sbyte)norm); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_hfftn(input.Handle, (IntPtr)ps, slen, (IntPtr)pDim, dlen, (sbyte)norm)); } } } @@ -497,9 +459,7 @@ public static Tensor ihfftn(Tensor input, long[] s = null, long[] dim = null, FF var dlen = (dim == null) ? 0 : dim.Length; unsafe { fixed (long* ps = s, pDim = dim) { - var res = THSTensor_ihfftn(input.Handle, (IntPtr)ps, slen, (IntPtr)pDim, dlen, (sbyte)norm); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_ihfftn(input.Handle, (IntPtr)ps, slen, (IntPtr)pDim, dlen, (sbyte)norm)); } } } diff --git a/src/TorchSharp/Generator.cs b/src/TorchSharp/Generator.cs index 3f9d27b80..8e20c73d9 100644 --- a/src/TorchSharp/Generator.cs +++ b/src/TorchSharp/Generator.cs @@ -26,9 +26,7 @@ public class Generator : IDisposable /// public Tensor get_state() { - var res = THSGenerator_get_rng_state(Handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSGenerator_get_rng_state(Handle)); } /// diff --git a/src/TorchSharp/LinearAlgebra.cs b/src/TorchSharp/LinearAlgebra.cs index 45cb8f82d..1ee52155f 100644 --- a/src/TorchSharp/LinearAlgebra.cs +++ b/src/TorchSharp/LinearAlgebra.cs @@ -19,10 +19,7 @@ public static class linalg /// public static Tensor cholesky(Tensor input) { - var res = THSLinalg_cholesky(input.Handle); - if (res == IntPtr.Zero) - torch.CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(THSLinalg_cholesky(input.Handle)); } /// @@ -38,17 +35,12 @@ public static Tensor cholesky(Tensor input) public static (Tensor L, Tensor info) cholesky_ex(Tensor input, bool check_errors = false) { var res = THSLinalg_cholesky_ex(input.Handle, check_errors, out var pInfo); - if (res == IntPtr.Zero || pInfo == IntPtr.Zero) - torch.CheckForErrors(); - return (new Tensor(res), new Tensor(pInfo)); + return ReturnCheckForErrors(res, pInfo); } public static Tensor cond(Tensor input, int p) { - var res = THSLinalg_cond_int(input.Handle, p); - if (res == IntPtr.Zero) - torch.CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(THSLinalg_cond_int(input.Handle, p)); } /// @@ -59,10 +51,7 @@ public static Tensor cond(Tensor input, int p) /// public static Tensor cond(Tensor input, double p) { - var res = THSLinalg_cond_float(input.Handle, p); - if (res == IntPtr.Zero) - torch.CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(THSLinalg_cond_float(input.Handle, p)); } /// @@ -72,10 +61,7 @@ public static Tensor cond(Tensor input, double p) /// The type of the matrix norm to use in the computations public static Tensor cond(Tensor input, string p) { - var res = THSLinalg_cond_str(input.Handle, p); - if (res == IntPtr.Zero) - torch.CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(THSLinalg_cond_str(input.Handle, p)); } /// @@ -84,10 +70,7 @@ public static Tensor cond(Tensor input, string p) /// The input tensor. public static Tensor cond(Tensor input) { - var res = THSLinalg_cond_none(input.Handle); - if (res == IntPtr.Zero) - torch.CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(THSLinalg_cond_none(input.Handle)); } /// @@ -96,9 +79,7 @@ public static Tensor cond(Tensor input) /// public static Tensor cross(Tensor input, Tensor other, long dim = -1) { - var res = THSLinalg_cross(input.Handle, other.Handle, dim); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSLinalg_cross(input.Handle, other.Handle, dim)); } /// @@ -107,10 +88,7 @@ public static Tensor cross(Tensor input, Tensor other, long dim = -1) /// The input tensor. public static Tensor det(Tensor input) { - var res = THSLinalg_det(input.Handle); - if (res == IntPtr.Zero) - torch.CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(THSLinalg_det(input.Handle)); } /// @@ -122,9 +100,7 @@ public static Tensor det(Tensor input) public static (Tensor, Tensor) slogdet(Tensor input) { var res = THSLinalg_slogdet(input.Handle, out var logabsdet); - if (res == IntPtr.Zero || logabsdet == IntPtr.Zero) - torch.CheckForErrors(); - return (new Tensor(res), new Tensor(logabsdet)); + return ReturnCheckForErrors(res, logabsdet); } /// @@ -153,9 +129,7 @@ public static (Tensor, Tensor) slogdet(Tensor input) public static (Tensor, Tensor) eig(Tensor input) { var res = THSLinalg_eig(input.Handle, out var vectors); - if (res == IntPtr.Zero || vectors == IntPtr.Zero) - torch.CheckForErrors(); - return (new Tensor(res), new Tensor(vectors)); + return ReturnCheckForErrors(res, vectors); } /// @@ -167,9 +141,7 @@ public static (Tensor, Tensor) eig(Tensor input) public static (Tensor, Tensor) eigh(Tensor input, char UPLO = 'L') { var res = THSLinalg_eigh(input.Handle, (byte)UPLO, out var vectors); - if (res == IntPtr.Zero || vectors == IntPtr.Zero) - torch.CheckForErrors(); - return (new Tensor(res), new Tensor(vectors)); + return ReturnCheckForErrors(res, vectors); } /// @@ -179,10 +151,7 @@ public static (Tensor, Tensor) eigh(Tensor input, char UPLO = 'L') /// public static Tensor eigvals(Tensor input) { - var res = THSLinalg_eigvals(input.Handle); - if (res == IntPtr.Zero) - torch.CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(THSLinalg_eigvals(input.Handle)); } /// @@ -193,10 +162,7 @@ public static Tensor eigvals(Tensor input) /// public static Tensor eigvalsh(Tensor input, char UPLO = 'L') { - var res = THSLinalg_eigvalsh(input.Handle, (byte)UPLO); - if (res == IntPtr.Zero) - torch.CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(THSLinalg_eigvalsh(input.Handle, (byte)UPLO)); } /// @@ -206,10 +172,7 @@ public static Tensor eigvalsh(Tensor input, char UPLO = 'L') /// tensor of shape (*, k) where * is zero or more batch dimensions. public static Tensor householder_product(Tensor A, Tensor tau) { - var res = THSLinalg_householder_product(A.Handle, tau.Handle); - if (res == IntPtr.Zero) - torch.CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(THSLinalg_householder_product(A.Handle, tau.Handle)); } /// @@ -220,10 +183,7 @@ public static Tensor householder_product(Tensor A, Tensor tau) /// Throws a RuntimeError if the matrix is not invertible. public static Tensor inv(Tensor input) { - var res = THSLinalg_inv(input.Handle); - if (res == IntPtr.Zero) - torch.CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(THSLinalg_inv(input.Handle)); } /// @@ -241,9 +201,7 @@ public static Tensor inv(Tensor input) public static (Tensor L, Tensor info) inv_ex(Tensor input, bool check_errors = false) { var res = THSLinalg_cholesky_ex(input.Handle, check_errors, out var pInfo); - if (res == IntPtr.Zero || pInfo == IntPtr.Zero) - torch.CheckForErrors(); - return (new Tensor(res), new Tensor(pInfo)); + return ReturnCheckForErrors(res, pInfo); } /// @@ -254,10 +212,11 @@ public static (Tensor L, Tensor info) inv_ex(Tensor input, bool check_errors = f /// public static (Tensor Solution, Tensor Residuals, Tensor Rank, Tensor SingularValues) lstsq(Tensor input, Tensor other) { - var solution = THSLinalg_lstsq_none(input.Handle, other.Handle, out var residuals, out var rank, out var singularValues); - if (solution == IntPtr.Zero || residuals == IntPtr.Zero || rank == IntPtr.Zero || singularValues == IntPtr.Zero) + //TEST: Check this + return ReturnCheckForErrors(THSLinalg_lstsq_none(input.Handle, other.Handle, out var residuals, out var rank, out var singularValues), residuals, rank, singularValues); + /*if (solution == IntPtr.Zero || residuals == IntPtr.Zero || rank == IntPtr.Zero || singularValues == IntPtr.Zero) torch.CheckForErrors(); - return (new Tensor(solution), new Tensor(residuals), new Tensor(rank), new Tensor(singularValues)); + return (new Tensor(solution), new Tensor(residuals), new Tensor(rank), new Tensor(singularValues));*/ } /// @@ -268,10 +227,11 @@ public static (Tensor Solution, Tensor Residuals, Tensor Rank, Tensor SingularVa /// public static (Tensor P, Tensor L, Tensor U) lu(Tensor input, bool pivot = true) { - var solution = THSLinalg_lu(input.Handle, pivot, out var pL, out var pU); - if (solution == IntPtr.Zero) + //TEST: Check this + return ReturnCheckForErrors(THSLinalg_lu(input.Handle, pivot, out var pL, out var pU), pL, pU); + /*if (solution == IntPtr.Zero) torch.CheckForErrors(); - return (new Tensor(solution), new Tensor(pL), new Tensor(pU)); + return (new Tensor(solution), new Tensor(pL), new Tensor(pU));*/ } /// @@ -327,10 +287,7 @@ public static (Tensor LU, Tensor? Pivots, Tensor? Info) ldl_factor_ex(Tensor inp /// public static Tensor ldl_solve(Tensor LD, Tensor pivots, Tensor B, bool hermitian = false) { - var res = THSLinalg_ldl_solve(LD.Handle, pivots.Handle, B.Handle, hermitian); - if (res == IntPtr.Zero) - torch.CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(THSLinalg_ldl_solve(LD.Handle, pivots.Handle, B.Handle, hermitian)); } /// @@ -341,10 +298,11 @@ public static Tensor ldl_solve(Tensor LD, Tensor pivots, Tensor B, bool hermitia /// Used to determine the effective rank of A. If rcond= None, rcond is set to the machine precision of the dtype of A times max(m, n). public static (Tensor Solution, Tensor Residuals, Tensor Rank, Tensor SingularValues) lstsq(Tensor input, Tensor other, double rcond) { - var solution = THSLinalg_lstsq_rcond(input.Handle, other.Handle, rcond, out var residuals, out var rank, out var singularValues); - if (solution == IntPtr.Zero || residuals == IntPtr.Zero || rank == IntPtr.Zero || singularValues == IntPtr.Zero) + //TEST: Check this + return ReturnCheckForErrors(THSLinalg_lstsq_rcond(input.Handle, other.Handle, rcond, out var residuals, out var rank, out var singularValues), residuals, rank, singularValues); + /*if (solution == IntPtr.Zero || residuals == IntPtr.Zero || rank == IntPtr.Zero || singularValues == IntPtr.Zero) torch.CheckForErrors(); - return (new Tensor(solution), new Tensor(residuals), new Tensor(rank), new Tensor(singularValues)); + return (new Tensor(solution), new Tensor(residuals), new Tensor(rank), new Tensor(singularValues));*/ } /// @@ -366,9 +324,7 @@ public static Tensor matrix_norm(Tensor input, string ord = "fro", long[]? dims if (dims == null) dims = new long[] { -2, -1 }; unsafe { fixed (long* pdims = dims) { - var res = THSLinalg_matrix_norm_fronuc(input.Handle, ord == "fro" ? (byte)0 : (byte)1, (IntPtr)pdims, dims.Length, keepdim); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSLinalg_matrix_norm_fronuc(input.Handle, ord == "fro" ? (byte)0 : (byte)1, (IntPtr)pdims, dims.Length, keepdim)); } } } @@ -387,9 +343,7 @@ public static Tensor matrix_norm(Tensor input, double ord, long[]? dims = null, if (dims == null) dims = new long[] { -2, -1 }; unsafe { fixed (long* pdims = dims) { - var res = THSLinalg_matrix_norm(input.Handle, ord.ToScalar().Handle, (IntPtr)pdims, dims.Length, keepdim); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSLinalg_matrix_norm(input.Handle, ord.ToScalar().Handle, (IntPtr)pdims, dims.Length, keepdim)); } } } @@ -406,9 +360,7 @@ public static Tensor matrix_norm(Tensor input, double ord, long[]? dims = null, public static Tensor matrix_rank(Tensor input, double? atol = null, double? rtol = null, bool hermitian = false) { unsafe { - var res = THSLinalg_matrix_rank(input.Handle, atol ?? double.NegativeInfinity, atol.HasValue, rtol ?? double.NegativeInfinity, rtol.HasValue, hermitian); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSLinalg_matrix_rank(input.Handle, atol ?? double.NegativeInfinity, atol.HasValue, rtol ?? double.NegativeInfinity, rtol.HasValue, hermitian)); } } @@ -424,9 +376,7 @@ public static Tensor matrix_rank(Tensor input, double? atol = null, double? rtol public static Tensor matrix_rank(Tensor input, Tensor atol, Tensor? rtol = null, bool hermitian = false) { unsafe { - var res = THSLinalg_matrix_rank_tensor(input.Handle, atol is null ? IntPtr.Zero : atol.Handle, rtol is null ? IntPtr.Zero : rtol.Handle, hermitian); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSLinalg_matrix_rank_tensor(input.Handle, atol is null ? IntPtr.Zero : atol.Handle, rtol is null ? IntPtr.Zero : rtol.Handle, hermitian)); } } @@ -447,11 +397,7 @@ public static Tensor multi_dot(IList tensors) using (var parray = new PinnedArray()) { IntPtr tensorsRef = parray.CreateArray(tensors.Select(p => p.Handle).ToArray()); - var res = THSLinalg_multi_dot(tensorsRef, parray.Array.Length); - if (res == IntPtr.Zero) - torch.CheckForErrors(); - res = AutocastMode.AutoCast(res); - return new Tensor(res); + return ReturnCheckForErrorsAutocast(THSLinalg_multi_dot(tensorsRef, parray.Array.Length)); } } @@ -468,9 +414,7 @@ public static Tensor norm(Tensor input, string ord, long[]? dims = null, bool ke { unsafe { fixed (long* pdims = dims) { - var res = THSLinalg_norm_str(input.Handle, ord, (IntPtr)pdims, dims is null ? 0 : dims.Length, keepdim); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSLinalg_norm_str(input.Handle, ord, (IntPtr)pdims, dims is null ? 0 : dims.Length, keepdim)); } } } @@ -487,9 +431,7 @@ public static Tensor norm(Tensor input, double ord, long[]? dims = null, bool ke { unsafe { fixed (long* pdims = dims) { - var res = THSLinalg_norm_float(input.Handle, ord, (IntPtr)pdims, dims is null ? 0 : dims.Length, keepdim); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSLinalg_norm_float(input.Handle, ord, (IntPtr)pdims, dims is null ? 0 : dims.Length, keepdim)); } } } @@ -506,9 +448,7 @@ public static Tensor norm(Tensor input, int ord, long[]? dims = null, bool keepd { unsafe { fixed (long* pdims = dims) { - var res = THSLinalg_norm_int(input.Handle, ord, (IntPtr)pdims, dims is null ? 0 : dims.Length, keepdim); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSLinalg_norm_int(input.Handle, ord, (IntPtr)pdims, dims is null ? 0 : dims.Length, keepdim)); } } } @@ -524,9 +464,7 @@ public static Tensor norm(Tensor input, long[]? dims = null, bool keepdim = fals { unsafe { fixed (long* pdims = dims) { - var res = THSLinalg_norm_opt(input.Handle, (IntPtr)pdims, dims is null ? 0 : dims.Length, keepdim); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSLinalg_norm_opt(input.Handle, (IntPtr)pdims, dims is null ? 0 : dims.Length, keepdim)); } } } @@ -543,9 +481,7 @@ public static Tensor norm(Tensor input, long[]? dims = null, bool keepdim = fals public static Tensor pinv(Tensor input, double? atol = null, double? rtol = null, bool hermitian = false) { unsafe { - var res = THSLinalg_pinv(input.Handle, atol ?? double.NegativeInfinity, atol.HasValue, rtol ?? double.NegativeInfinity, rtol.HasValue, hermitian); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSLinalg_pinv(input.Handle, atol ?? double.NegativeInfinity, atol.HasValue, rtol ?? double.NegativeInfinity, rtol.HasValue, hermitian)); } } @@ -561,9 +497,7 @@ public static Tensor pinv(Tensor input, double? atol = null, double? rtol = null public static Tensor pinv(Tensor input, Tensor atol, Tensor? rtol = null, bool hermitian = false) { unsafe { - var res = THSLinalg_pinv_tensor(input.Handle, atol is null ? IntPtr.Zero : atol.Handle, rtol is null ? IntPtr.Zero : rtol.Handle, hermitian); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSLinalg_pinv_tensor(input.Handle, atol is null ? IntPtr.Zero : atol.Handle, rtol is null ? IntPtr.Zero : rtol.Handle, hermitian)); } } @@ -582,10 +516,11 @@ public enum QRMode /// public static (Tensor Q, Tensor R) qr(Tensor input, QRMode mode = QRMode.Reduced) { - var Q = THSLinalg_qr(input.Handle, (byte)mode, out var R); - if (Q == IntPtr.Zero || R == IntPtr.Zero) + //TEST: Check this + return ReturnCheckForErrors(THSLinalg_qr(input.Handle, (byte)mode, out var R), R); + /*if (Q == IntPtr.Zero || R == IntPtr.Zero) torch.CheckForErrors(); - return (new Tensor(Q), new Tensor(R)); + return (new Tensor(Q), new Tensor(R));*/ } /// @@ -597,10 +532,7 @@ public static (Tensor Q, Tensor R) qr(Tensor input, QRMode mode = QRMode.Reduced /// public static Tensor solve(Tensor A, Tensor B, bool left = true) { - var res = THSLinalg_solve(A.Handle, B.Handle, left); - if (res == IntPtr.Zero) - torch.CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(THSLinalg_solve(A.Handle, B.Handle, left)); } /// @@ -634,9 +566,7 @@ public static Tensor solve_triangular(Tensor A, Tensor B, bool upper, bool left var res = (@out is null) ? THSLinalg_solve_triangular(A.Handle, B.Handle, upper, left, unitriangular) : THSLinalg_solve_triangular_out(A.Handle, B.Handle, upper, left, unitriangular, @out.Handle); - if (res == IntPtr.Zero) - torch.CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(res); } /// @@ -647,10 +577,11 @@ public static Tensor solve_triangular(Tensor A, Tensor B, bool upper, bool left /// public static (Tensor U, Tensor S, Tensor Vh) svd(Tensor input, bool fullMatrices = true) { - var U = THSLinalg_svd(input.Handle, fullMatrices, out var S, out var Vh); - if (U == IntPtr.Zero || S == IntPtr.Zero || Vh == IntPtr.Zero) + //TEST: Check this + return ReturnCheckForErrors(THSLinalg_svd(input.Handle, fullMatrices, out var S, out var Vh), S,Vh); + /*if (U == IntPtr.Zero || S == IntPtr.Zero || Vh == IntPtr.Zero) torch.CheckForErrors(); - return (new Tensor(U), new Tensor(S), new Tensor(Vh)); + return (new Tensor(U), new Tensor(S), new Tensor(Vh));*/ } /// @@ -660,10 +591,7 @@ public static (Tensor U, Tensor S, Tensor Vh) svd(Tensor input, bool fullMatrice /// public static Tensor svdvals(Tensor input) { - var res = THSLinalg_svdvals(input.Handle); - if (res == IntPtr.Zero) - torch.CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(THSLinalg_svdvals(input.Handle)); } /// @@ -674,10 +602,7 @@ public static Tensor svdvals(Tensor input) /// public static Tensor tensorinv(Tensor input, long ind) { - var res = THSLinalg_tensorinv(input.Handle, ind); - if (res == IntPtr.Zero) - torch.CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(THSLinalg_tensorinv(input.Handle, ind)); } /// @@ -691,9 +616,7 @@ public static Tensor tensorsolve(Tensor A, Tensor B, long[] dims) { unsafe { fixed (long* pdims = dims) { - var res = THSLinalg_tensorsolve(A.Handle, B.Handle, (IntPtr)pdims, dims.Length); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSLinalg_tensorsolve(A.Handle, B.Handle, (IntPtr)pdims, dims.Length)); } } } @@ -710,9 +633,7 @@ public static Tensor vector_norm(Tensor input, double ord = 2d, long[]? dims = n { unsafe { fixed (long* pdims = dims) { - var res = THSLinalg_vector_norm(input.Handle, ord.ToScalar().Handle, (IntPtr)pdims, dims is null ? 0 : dims.Length, keepdim); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSLinalg_vector_norm(input.Handle, ord.ToScalar().Handle, (IntPtr)pdims, dims is null ? 0 : dims.Length, keepdim)); } } } @@ -727,10 +648,7 @@ public static Tensor vander(Tensor input, long? N = null) if (!N.HasValue) { N = input.shape[input.ndim - 1]; } - var res = THSLinalg_vander(input.Handle, N.Value); - if (res == IntPtr.Zero) - torch.CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(THSLinalg_vander(input.Handle, N.Value)); } /// @@ -742,10 +660,7 @@ public static Tensor vander(Tensor input, long? N = null) /// Optional output tensor. public static Tensor vecdot(Tensor x, Tensor y, long dim = -1, Tensor? @out = null) { - var res = THSLinalg_vecdot(x.Handle, y.Handle, dim, @out is null ? IntPtr.Zero : @out.Handle); - if (res == IntPtr.Zero) - torch.CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(THSLinalg_vecdot(x.Handle, y.Handle, dim, @out is null ? IntPtr.Zero : @out.Handle)); } /// @@ -760,10 +675,7 @@ public static Tensor vecdot(Tensor x, Tensor y, long dim = -1, Tensor? @out = nu /// public static Tensor lu_solve(Tensor LU, Tensor pivots, Tensor B, bool left = true, bool adjoint = false, Tensor? @out = null) { - var res = THSLinalg_lu_solve(B.Handle, LU.Handle, pivots.Handle, left, adjoint, @out is null ? IntPtr.Zero : @out.Handle); - if (res == IntPtr.Zero) - torch.CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(THSLinalg_lu_solve(B.Handle, LU.Handle, pivots.Handle, left, adjoint, @out is null ? IntPtr.Zero : @out.Handle)); } } } diff --git a/src/TorchSharp/Special.cs b/src/TorchSharp/Special.cs index e27698477..35ce5d256 100644 --- a/src/TorchSharp/Special.cs +++ b/src/TorchSharp/Special.cs @@ -20,9 +20,7 @@ public static Tensor airy_ai(Tensor input, Tensor @out = null) var res = @out is null ? THSSpecial_airy_ai(input.Handle) : THSSpecial_airy_ai_out(input.Handle, @out.Handle); - if (res == IntPtr.Zero) - torch.CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(res); } /// @@ -35,9 +33,7 @@ public static Tensor bessel_j0(Tensor input, Tensor @out = null) var res = @out is null ? THSSpecial_bessel_j0(input.Handle) : THSSpecial_bessel_j0_out(input.Handle, @out.Handle); - if (res == IntPtr.Zero) - torch.CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(res); } /// @@ -50,9 +46,7 @@ public static Tensor bessel_j1(Tensor input, Tensor @out = null) var res = @out is null ? THSSpecial_bessel_j1(input.Handle) : THSSpecial_bessel_j1_out(input.Handle, @out.Handle); - if (res == IntPtr.Zero) - torch.CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(res); } /// @@ -65,9 +59,7 @@ public static Tensor bessel_y0(Tensor input, Tensor @out = null) var res = @out is null ? THSSpecial_bessel_y0(input.Handle) : THSSpecial_bessel_y0_out(input.Handle, @out.Handle); - if (res == IntPtr.Zero) - torch.CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(res); } /// @@ -80,9 +72,7 @@ public static Tensor bessel_y1(Tensor input, Tensor @out = null) var res = @out is null ? THSSpecial_bessel_y1(input.Handle) : THSSpecial_bessel_y1_out(input.Handle, @out.Handle); - if (res == IntPtr.Zero) - torch.CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(res); } /// @@ -95,9 +85,7 @@ public static Tensor modified_bessel_i0(Tensor input, Tensor @out = null) var res = @out is null ? THSSpecial_modified_bessel_i0(input.Handle) : THSSpecial_modified_bessel_i0_out(input.Handle, @out.Handle); - if (res == IntPtr.Zero) - torch.CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(res); } /// @@ -110,9 +98,7 @@ public static Tensor modified_bessel_i1(Tensor input, Tensor @out = null) var res = @out is null ? THSSpecial_modified_bessel_i1(input.Handle) : THSSpecial_modified_bessel_i1_out(input.Handle, @out.Handle); - if (res == IntPtr.Zero) - torch.CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(res); } /// @@ -125,9 +111,7 @@ public static Tensor modified_bessel_k0(Tensor input, Tensor @out = null) var res = @out is null ? THSSpecial_modified_bessel_k0(input.Handle) : THSSpecial_modified_bessel_k0_out(input.Handle, @out.Handle); - if (res == IntPtr.Zero) - torch.CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(res); } /// @@ -140,9 +124,7 @@ public static Tensor modified_bessel_k1(Tensor input, Tensor @out = null) var res = @out is null ? THSSpecial_modified_bessel_k1(input.Handle) : THSSpecial_modified_bessel_k1_out(input.Handle, @out.Handle); - if (res == IntPtr.Zero) - torch.CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(res); } /// @@ -155,9 +137,7 @@ public static Tensor scaled_modified_bessel_k0(Tensor input, Tensor @out = null) var res = @out is null ? THSSpecial_scaled_modified_bessel_k0(input.Handle) : THSSpecial_scaled_modified_bessel_k0_out(input.Handle, @out.Handle); - if (res == IntPtr.Zero) - torch.CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(res); } /// @@ -170,9 +150,7 @@ public static Tensor scaled_modified_bessel_k1(Tensor input, Tensor @out = null) var res = @out is null ? THSSpecial_scaled_modified_bessel_k1(input.Handle) : THSSpecial_scaled_modified_bessel_k1_out(input.Handle, @out.Handle); - if (res == IntPtr.Zero) - torch.CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(res); } /// @@ -185,9 +163,7 @@ public static Tensor spherical_bessel_j0(Tensor input, Tensor @out = null) var res = @out is null ? THSSpecial_spherical_bessel_j0(input.Handle) : THSSpecial_spherical_bessel_j0_out(input.Handle, @out.Handle); - if (res == IntPtr.Zero) - torch.CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(res); } /// @@ -203,9 +179,7 @@ public static Tensor chebyshev_polynomial_t(Tensor x, Tensor n, Tensor @out = nu var res = @out is null ? THSSpecial_chebyshev_polynomial_t(x.Handle, n.Handle) : THSSpecial_chebyshev_polynomial_t_out(x.Handle, n.Handle, @out.Handle); - if (res == IntPtr.Zero) - torch.CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(res); } @@ -222,9 +196,7 @@ public static Tensor chebyshev_polynomial_u(Tensor x, Tensor n, Tensor @out = nu var res = @out is null ? THSSpecial_chebyshev_polynomial_u(x.Handle, n.Handle) : THSSpecial_chebyshev_polynomial_u_out(x.Handle, n.Handle, @out.Handle); - if (res == IntPtr.Zero) - torch.CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(res); } /// @@ -240,9 +212,7 @@ public static Tensor chebyshev_polynomial_v(Tensor x, Tensor n, Tensor @out = nu var res = @out is null ? THSSpecial_chebyshev_polynomial_v(x.Handle, n.Handle) : THSSpecial_chebyshev_polynomial_v_out(x.Handle, n.Handle, @out.Handle); - if (res == IntPtr.Zero) - torch.CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(res); } /// @@ -258,9 +228,7 @@ public static Tensor chebyshev_polynomial_w(Tensor x, Tensor n, Tensor @out = nu var res = @out is null ? THSSpecial_chebyshev_polynomial_w(x.Handle, n.Handle) : THSSpecial_chebyshev_polynomial_w_out(x.Handle, n.Handle, @out.Handle); - if (res == IntPtr.Zero) - torch.CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(res); } /// @@ -276,9 +244,7 @@ public static Tensor shifted_chebyshev_polynomial_t(Tensor x, Tensor n, Tensor @ var res = @out is null ? THSSpecial_shifted_chebyshev_polynomial_t(x.Handle, n.Handle) : THSSpecial_shifted_chebyshev_polynomial_t_out(x.Handle, n.Handle, @out.Handle); - if (res == IntPtr.Zero) - torch.CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(res); } @@ -295,9 +261,7 @@ public static Tensor shifted_chebyshev_polynomial_u(Tensor x, Tensor n, Tensor @ var res = @out is null ? THSSpecial_shifted_chebyshev_polynomial_u(x.Handle, n.Handle) : THSSpecial_shifted_chebyshev_polynomial_u_out(x.Handle, n.Handle, @out.Handle); - if (res == IntPtr.Zero) - torch.CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(res); } /// @@ -313,9 +277,7 @@ public static Tensor shifted_chebyshev_polynomial_v(Tensor x, Tensor n, Tensor @ var res = @out is null ? THSSpecial_shifted_chebyshev_polynomial_v(x.Handle, n.Handle) : THSSpecial_shifted_chebyshev_polynomial_v_out(x.Handle, n.Handle, @out.Handle); - if (res == IntPtr.Zero) - torch.CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(res); } /// @@ -331,9 +293,7 @@ public static Tensor shifted_chebyshev_polynomial_w(Tensor x, Tensor n, Tensor @ var res = @out is null ? THSSpecial_shifted_chebyshev_polynomial_w(x.Handle, n.Handle) : THSSpecial_shifted_chebyshev_polynomial_w_out(x.Handle, n.Handle, @out.Handle); - if (res == IntPtr.Zero) - torch.CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(res); } /// @@ -349,9 +309,7 @@ public static Tensor hermite_polynomial_h(Tensor x, Tensor n, Tensor @out = null var res = @out is null ? THSSpecial_hermite_polynomial_h(x.Handle, n.Handle) : THSSpecial_hermite_polynomial_h_out(x.Handle, n.Handle, @out.Handle); - if (res == IntPtr.Zero) - torch.CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(res); } /// @@ -367,9 +325,7 @@ public static Tensor hermite_polynomial_he(Tensor x, Tensor n, Tensor @out = nul var res = @out is null ? THSSpecial_hermite_polynomial_he(x.Handle, n.Handle) : THSSpecial_hermite_polynomial_he_out(x.Handle, n.Handle, @out.Handle); - if (res == IntPtr.Zero) - torch.CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(res); } /// @@ -386,9 +342,7 @@ public static Tensor laguerre_polynomial_l(Tensor x, Tensor n, Tensor @out = nul var res = @out is null ? THSSpecial_laguerre_polynomial_l(x.Handle, n.Handle) : THSSpecial_laguerre_polynomial_l_out(x.Handle, n.Handle, @out.Handle); - if (res == IntPtr.Zero) - torch.CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(res); } /// @@ -404,9 +358,7 @@ public static Tensor legendre_polynomial_p(Tensor x, Tensor n, Tensor @out = nul var res = @out is null ? THSSpecial_legendre_polynomial_p(x.Handle, n.Handle) : THSSpecial_legendre_polynomial_p_out(x.Handle, n.Handle, @out.Handle); - if (res == IntPtr.Zero) - torch.CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(res); } /// @@ -416,10 +368,7 @@ public static Tensor legendre_polynomial_p(Tensor x, Tensor n, Tensor @out = nul /// public static Tensor entr(Tensor input) { - var res = THSSpecial_entr(input.Handle); - if (res == IntPtr.Zero) - torch.CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(THSSpecial_entr(input.Handle)); } /// @@ -429,10 +378,7 @@ public static Tensor entr(Tensor input) /// public static Tensor erf(Tensor input) { - var res = THSSpecial_erf(input.Handle); - if (res == IntPtr.Zero) - torch.CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(THSSpecial_erf(input.Handle)); } /// @@ -442,10 +388,7 @@ public static Tensor erf(Tensor input) /// public static Tensor erfc(Tensor input) { - var res = THSSpecial_erfc(input.Handle); - if (res == IntPtr.Zero) - torch.CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(THSSpecial_erfc(input.Handle)); } /// @@ -455,10 +398,7 @@ public static Tensor erfc(Tensor input) /// public static Tensor erfcx(Tensor input) { - var res = THSSpecial_erfc(input.Handle); - if (res == IntPtr.Zero) - torch.CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(THSSpecial_erfc(input.Handle)); } /// @@ -468,10 +408,7 @@ public static Tensor erfcx(Tensor input) /// public static Tensor erfinv(Tensor input) { - var res = THSSpecial_erfinv(input.Handle); - if (res == IntPtr.Zero) - torch.CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(THSSpecial_erfinv(input.Handle)); } /// @@ -481,10 +418,7 @@ public static Tensor erfinv(Tensor input) /// public static Tensor expit(Tensor input) { - var res = THSSpecial_expit(input.Handle); - if (res == IntPtr.Zero) - torch.CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(THSSpecial_expit(input.Handle)); } /// @@ -494,10 +428,7 @@ public static Tensor expit(Tensor input) /// public static Tensor expm1(Tensor input) { - var res = THSSpecial_expm1(input.Handle); - if (res == IntPtr.Zero) - torch.CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(THSSpecial_expm1(input.Handle)); } /// @@ -507,10 +438,7 @@ public static Tensor expm1(Tensor input) /// public static Tensor exp2(Tensor input) { - var res = THSSpecial_exp2(input.Handle); - if (res == IntPtr.Zero) - torch.CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(THSSpecial_exp2(input.Handle)); } /// @@ -520,10 +448,7 @@ public static Tensor exp2(Tensor input) /// public static Tensor gammaln(Tensor input) { - var res = THSSpecial_gammaln(input.Handle); - if (res == IntPtr.Zero) - torch.CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(THSSpecial_gammaln(input.Handle)); } /// @@ -534,10 +459,7 @@ public static Tensor gammaln(Tensor input) /// public static Tensor gammainc(Tensor input, Tensor other) { - var res = THSSpecial_gammainc(input.Handle, other.Handle); - if (res == IntPtr.Zero) - torch.CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(THSSpecial_gammainc(input.Handle, other.Handle)); } /// @@ -548,10 +470,7 @@ public static Tensor gammainc(Tensor input, Tensor other) /// public static Tensor gammaincc(Tensor input, Tensor other) { - var res = THSSpecial_gammaincc(input.Handle, other.Handle); - if (res == IntPtr.Zero) - torch.CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(THSSpecial_gammaincc(input.Handle, other.Handle)); } /// @@ -562,10 +481,7 @@ public static Tensor gammaincc(Tensor input, Tensor other) /// public static Tensor polygamma(long n, Tensor input) { - var res = THSSpecial_polygamma(n, input.Handle); - if (res == IntPtr.Zero) - torch.CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(THSSpecial_polygamma(n, input.Handle)); } /// @@ -576,10 +492,7 @@ public static Tensor polygamma(long n, Tensor input) /// public static Tensor multigammaln(Tensor input, long p) { - var res = THSSpecial_multigammaln(input.Handle, p); - if (res == IntPtr.Zero) - torch.CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(THSSpecial_multigammaln(input.Handle, p)); } /// @@ -589,10 +502,7 @@ public static Tensor multigammaln(Tensor input, long p) /// public static Tensor digamma(Tensor input) { - var res = THSSpecial_digamma(input.Handle); - if (res == IntPtr.Zero) - torch.CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(THSSpecial_digamma(input.Handle)); } /// @@ -608,10 +518,7 @@ public static Tensor digamma(Tensor input) /// public static Tensor i0(Tensor input) { - var res = THSSpecial_i0(input.Handle); - if (res == IntPtr.Zero) - torch.CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(THSSpecial_i0(input.Handle)); } /// @@ -621,10 +528,7 @@ public static Tensor i0(Tensor input) /// public static Tensor i0e(Tensor input) { - var res = THSSpecial_i0e(input.Handle); - if (res == IntPtr.Zero) - torch.CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(THSSpecial_i0e(input.Handle)); } /// @@ -634,10 +538,7 @@ public static Tensor i0e(Tensor input) /// public static Tensor i1(Tensor input) { - var res = THSSpecial_i1(input.Handle); - if (res == IntPtr.Zero) - torch.CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(THSSpecial_i1(input.Handle)); } /// @@ -647,10 +548,7 @@ public static Tensor i1(Tensor input) /// public static Tensor i1e(Tensor input) { - var res = THSSpecial_i1e(input.Handle); - if (res == IntPtr.Zero) - torch.CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(THSSpecial_i1e(input.Handle)); } /// @@ -676,11 +574,7 @@ public static Tensor logit(Tensor input) public static Tensor log_softmax(Tensor input, long dim, ScalarType? dtype = null) { var dt = dtype ?? input.dtype; - var res = THSSpecial_log_softmax(input.Handle, dim, (sbyte)dt); - if (res == IntPtr.Zero) - torch.CheckForErrors(); - res = AutocastMode.AutoCast(res, ScalarType.Float32); - return new Tensor(res); + return ReturnCheckForErrorsAutocast(THSSpecial_log_softmax(input.Handle, dim, (sbyte)dt), ScalarType.Float32); } /// @@ -690,10 +584,7 @@ public static Tensor log_softmax(Tensor input, long dim, ScalarType? dtype = nul /// public static Tensor ndtr(Tensor input) { - var res = THSSpecial_ndtr(input.Handle); - if (res == IntPtr.Zero) - torch.CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(THSSpecial_ndtr(input.Handle)); } /// @@ -703,10 +594,7 @@ public static Tensor ndtr(Tensor input) /// public static Tensor ndtri(Tensor input) { - var res = THSSpecial_ndtri(input.Handle); - if (res == IntPtr.Zero) - torch.CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(THSSpecial_ndtri(input.Handle)); } /// @@ -716,10 +604,7 @@ public static Tensor ndtri(Tensor input) /// public static Tensor sinc(Tensor input) { - var res = THSSpecial_sinc(input.Handle); - if (res == IntPtr.Zero) - torch.CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(THSSpecial_sinc(input.Handle)); } /// @@ -744,11 +629,7 @@ public static Tensor sinc(Tensor input) public static Tensor softmax(Tensor input, long dim, ScalarType? dtype = null) { var dt = dtype.HasValue ? dtype.Value : input.dtype; - var res = THSSpecial_softmax(input.Handle, dim, (sbyte)dt); - if (res == IntPtr.Zero) - torch.CheckForErrors(); - res = AutocastMode.AutoCast(res, ScalarType.Float32); - return new Tensor(res); + return ReturnCheckForErrorsAutocast(THSSpecial_softmax(input.Handle, dim, (sbyte)dt), ScalarType.Float32); } /// @@ -759,10 +640,7 @@ public static Tensor softmax(Tensor input, long dim, ScalarType? dtype = null) /// public static Tensor xlog1py(Tensor input, Tensor other) { - var res = THSSpecial_xlog1py(input.Handle, other.Handle); - if (res == IntPtr.Zero) - torch.CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(THSSpecial_xlog1py(input.Handle, other.Handle)); } /// @@ -773,10 +651,7 @@ public static Tensor xlog1py(Tensor input, Tensor other) /// The Riemann zeta function corresponds to the case when q = 1. public static Tensor zeta(Tensor x, Tensor q) { - var res = THSSpecial_zeta(x.Handle, q.Handle); - if (res == IntPtr.Zero) - torch.CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(THSSpecial_zeta(x.Handle, q.Handle)); } } } diff --git a/src/TorchSharp/Tensor/Tensor.LinearAlgebra.cs b/src/TorchSharp/Tensor/Tensor.LinearAlgebra.cs index 8fa3d2649..5b041d1c9 100644 --- a/src/TorchSharp/Tensor/Tensor.LinearAlgebra.cs +++ b/src/TorchSharp/Tensor/Tensor.LinearAlgebra.cs @@ -157,9 +157,8 @@ public Tensor logdet() public (Tensor a, Tensor tau) geqrf() { var res = THSTensor_geqrf(Handle, out var tau); - if (res == IntPtr.Zero || tau == IntPtr.Zero) - torch.CheckForErrors(); - return (new Tensor(res), new Tensor(tau)); + return ReturnCheckForErrors(res, tau); + } /// diff --git a/src/TorchSharp/Tensor/Tensor.Trig.cs b/src/TorchSharp/Tensor/Tensor.Trig.cs index 53d1f7160..bf503977d 100644 --- a/src/TorchSharp/Tensor/Tensor.Trig.cs +++ b/src/TorchSharp/Tensor/Tensor.Trig.cs @@ -337,10 +337,7 @@ public Tensor arcsinh_() /// public Tensor arccosh() { - var res = THSTensor_arccosh(Handle); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_arccosh(Handle)); } /// diff --git a/src/TorchSharp/Tensor/torch.Amp.cs b/src/TorchSharp/Tensor/torch.Amp.cs index 8aa8e6334..8e762b061 100644 --- a/src/TorchSharp/Tensor/torch.Amp.cs +++ b/src/TorchSharp/Tensor/torch.Amp.cs @@ -29,9 +29,7 @@ public static torch.Tensor amp_update_scale_outf(Tensor self, Tensor growth_trac public static (torch.Tensor, torch.Tensor) amp_update_scale(Tensor self, Tensor growth_tracker, Tensor found_inf, double scale_growth_factor, double scale_backoff_factor, long growth_interval) { var res = THSAMP_amp_update_scale(self.Handle, growth_tracker.Handle, found_inf.Handle, scale_growth_factor, scale_backoff_factor, growth_interval, out var res1); - if(res == IntPtr.Zero || res1 == IntPtr.Zero) - torch.CheckForErrors(); - return (new Tensor(res), new Tensor(res1)); + return ReturnCheckForErrors(res, res1); } } } diff --git a/src/TorchSharp/Tensor/torch.ComparisonOps.cs b/src/TorchSharp/Tensor/torch.ComparisonOps.cs index b696cde93..9814ad307 100644 --- a/src/TorchSharp/Tensor/torch.ComparisonOps.cs +++ b/src/TorchSharp/Tensor/torch.ComparisonOps.cs @@ -302,9 +302,7 @@ public static (Tensor hist, Tensor bin_edges) histogram(Tensor input, HistogramB public static (Tensor hist, Tensor bin_edges) histogram(Tensor input, Tensor bins, Tensor weight = null, bool density = false) { var res = PInvoke.NativeMethods.THSTensor_histogram_t(input.Handle, bins.Handle, weight is null ? IntPtr.Zero : weight.Handle, density, out var r_bin_edges); - if (res == IntPtr.Zero) CheckForErrors(); - if (r_bin_edges == IntPtr.Zero) CheckForErrors(); - return (new Tensor(res), new Tensor(r_bin_edges)); + return ReturnCheckForErrors(res, r_bin_edges); } // https://pytorch.org/docs/stable/generated/torch.histogram.html diff --git a/src/TorchSharp/Tensor/torch.RandomSampling.cs b/src/TorchSharp/Tensor/torch.RandomSampling.cs index 554eb4de1..ff9683597 100644 --- a/src/TorchSharp/Tensor/torch.RandomSampling.cs +++ b/src/TorchSharp/Tensor/torch.RandomSampling.cs @@ -190,9 +190,7 @@ public static Tensor randperm(long n, Generator? generator = null) { var genHandle = generator?.Handle ?? IntPtr.Zero; - var res = NativeMethods.THSTensor_randperm_out(genHandle, n, @out.Handle); - if (res == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_randperm_out(genHandle, n, @out.Handle)); } // https://pytorch.org/docs/stable/generated/torch.randperm @@ -221,8 +219,8 @@ static Tensor randperm( GC.WaitForPendingFinalizers(); handle = THSTensor_randperm(genHandle, n, (sbyte)dtype, (int)device.type, device.index, requires_grad); } - if (handle == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(handle); + + return ReturnCheckForErrors(handle); } } } \ No newline at end of file diff --git a/src/TorchSharp/Torch.cs b/src/TorchSharp/Torch.cs index f64539477..18f8f6c6b 100644 --- a/src/TorchSharp/Torch.cs +++ b/src/TorchSharp/Torch.cs @@ -664,7 +664,7 @@ public static void CheckForErrors() } /// - /// Refactor all Tensors with this method for example the LinearAlgebra.cs of cholesky we can just put return ; + /// Refactor all Tensors with this method for example the LinearAlgebra.cs of cholesky we can just put return ; /// public static Tensor cholesky(Tensor input) => ReturnCheckForErrors(THSLinalg_cholesky(input.Handle)); /// /// @@ -687,6 +687,12 @@ public static (Tensor, Tensor, Tensor) ReturnCheckForErrors(IntPtr ptr, IntPtr p CheckForErrors(); return (new Tensor(ptr), new Tensor(ptr1), new Tensor(ptr2)); } + public static (Tensor, Tensor, Tensor, Tensor) ReturnCheckForErrors(IntPtr ptr, IntPtr ptr1, IntPtr ptr2, IntPtr ptr3) + { + if (ptr == IntPtr.Zero || ptr1 == IntPtr.Zero || ptr2 == IntPtr.Zero || ptr3 == IntPtr.Zero) + CheckForErrors(); + return (new Tensor(ptr), new Tensor(ptr1), new Tensor(ptr2), new Tensor(ptr3)); + } public static Tensor ReturnCheckForErrorsAutocast(IntPtr ptr, ScalarType? st = null) { if (ptr == IntPtr.Zero) From b9dd978f25645cfff71aa98b5a19d1c1e8e7b98c Mon Sep 17 00:00:00 2001 From: Dimitri Date: Sun, 28 Sep 2025 21:56:48 -0300 Subject: [PATCH 57/65] some improve new returned for the null --- src/TorchSharp/NN/Convolution/Conv1D.cs | 11 +-- src/TorchSharp/NN/Convolution/Conv2D.cs | 8 +-- src/TorchSharp/NN/Convolution/Conv3D.cs | 8 +-- .../NN/Convolution/ConvTranspose1D.cs | 8 +-- .../NN/Convolution/ConvTranspose2D.cs | 8 +-- .../NN/Convolution/ConvTranspose3D.cs | 8 +-- .../NN/Normalization/BatchNorm1D.cs | 20 ++---- .../NN/Normalization/BatchNorm2D.cs | 20 ++---- .../NN/Normalization/BatchNorm3D.cs | 16 ++--- src/TorchSharp/NN/Normalization/GroupNorm.cs | 4 +- .../NN/Normalization/InstanceNorm1d.cs | 20 ++---- .../NN/Normalization/InstanceNorm2d.cs | 20 ++---- .../NN/Normalization/InstanceNorm3d.cs | 16 ++--- .../Tensor/Factories/Tensor.Factories.cs | 69 ++++--------------- src/TorchSharp/Tensor/Factories/empty.cs | 20 +----- src/TorchSharp/Tensor/Factories/full.cs | 10 +-- src/TorchSharp/Tensor/Factories/ones.cs | 10 +-- src/TorchSharp/Tensor/Factories/rand.cs | 37 ++-------- src/TorchSharp/Tensor/Factories/zeros.cs | 11 +-- src/TorchSharp/Tensor/Tensor.Trig.cs | 5 +- src/TorchSharp/Torch.cs | 39 ++++++++++- 21 files changed, 104 insertions(+), 264 deletions(-) diff --git a/src/TorchSharp/NN/Convolution/Conv1D.cs b/src/TorchSharp/NN/Convolution/Conv1D.cs index 01a3baf74..9ab025081 100644 --- a/src/TorchSharp/NN/Convolution/Conv1D.cs +++ b/src/TorchSharp/NN/Convolution/Conv1D.cs @@ -64,9 +64,7 @@ public override Tensor forward(Tensor input) public Parameter? bias { get { - var res = THSNN_Conv1d_bias(handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return ((res == IntPtr.Zero) ? null : new Parameter(res)); + return ReturnNullParameterCheckForErrors(THSNN_Conv1d_bias(handle)); } set { // Please ignore, for now, that the litorch call thinks you *can* set it to null. @@ -78,9 +76,7 @@ public Parameter? bias { } public Parameter? weight { get { - var res = THSNN_Conv1d_weight(handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return (res == IntPtr.Zero) ? null : new Parameter(res); + return ReturnNullParameterCheckForErrors(THSNN_Conv1d_weight(handle)); } set { // Please ignore, for now, that the litorch call thinks you *can* set it to null. @@ -186,8 +182,7 @@ public static Tensor conv1d(Tensor input, Tensor weight, Tensor? bias = null, var biasHandle = (bias is null ? IntPtr.Zero : bias.Handle); unsafe { fixed (long* pstrides = strides, ppadding = paddingArray, pdilation = dilationArray) { - var res = - THSTensor_conv1d(input.Handle, weight.Handle, biasHandle, + var res = THSTensor_conv1d(input.Handle, weight.Handle, biasHandle, (IntPtr)pstrides, strides.Length, (IntPtr)ppadding, paddingArray.Length, (IntPtr)pdilation, dilationArray.Length, diff --git a/src/TorchSharp/NN/Convolution/Conv2D.cs b/src/TorchSharp/NN/Convolution/Conv2D.cs index bf8e35f2b..85511b79a 100644 --- a/src/TorchSharp/NN/Convolution/Conv2D.cs +++ b/src/TorchSharp/NN/Convolution/Conv2D.cs @@ -54,9 +54,7 @@ public override Tensor forward(Tensor input) public Parameter? bias { get { - var res = THSNN_Conv2d_bias(handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return ((res == IntPtr.Zero) ? null : new Parameter(res)); + return ReturnNullParameterCheckForErrors(THSNN_Conv2d_bias(handle)); } set { // Please ignore, for now, that the litorch call thinks you *can* set it to null. @@ -68,9 +66,7 @@ public Parameter? bias { } public Parameter? weight { get { - var res = THSNN_Conv2d_weight(handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return (res == IntPtr.Zero) ? null : new Parameter(res); + return ReturnNullParameterCheckForErrors(THSNN_Conv2d_weight(handle)); } set { // Please ignore, for now, that the litorch call thinks you *can* set it to null. diff --git a/src/TorchSharp/NN/Convolution/Conv3D.cs b/src/TorchSharp/NN/Convolution/Conv3D.cs index 900a3dab4..caef803ad 100644 --- a/src/TorchSharp/NN/Convolution/Conv3D.cs +++ b/src/TorchSharp/NN/Convolution/Conv3D.cs @@ -25,9 +25,7 @@ public override Tensor forward(Tensor input) public Parameter? bias { get { - var res = THSNN_Conv3d_bias(handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return ((res == IntPtr.Zero) ? null : new Parameter(res)); + return ReturnNullParameterCheckForErrors(THSNN_Conv3d_bias(handle)); } set { // Please ignore, for now, that the litorch call thinks you *can* set it to null. @@ -39,9 +37,7 @@ public Parameter? bias { } public Parameter? weight { get { - var res = THSNN_Conv3d_weight(handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return (res == IntPtr.Zero) ? null : new Parameter(res); + return ReturnNullParameterCheckForErrors(THSNN_Conv3d_weight(handle)); } set { // Please ignore, for now, that the litorch call thinks you *can* set it to null. diff --git a/src/TorchSharp/NN/Convolution/ConvTranspose1D.cs b/src/TorchSharp/NN/Convolution/ConvTranspose1D.cs index 4226eb558..e2f3ec010 100644 --- a/src/TorchSharp/NN/Convolution/ConvTranspose1D.cs +++ b/src/TorchSharp/NN/Convolution/ConvTranspose1D.cs @@ -25,9 +25,7 @@ public override Tensor forward(Tensor input) public Parameter? bias { get { - var res = THSNN_ConvTranspose1d_bias(handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return ((res == IntPtr.Zero) ? null : new Parameter(res)); + return ReturnNullParameterCheckForErrors(THSNN_ConvTranspose1d_bias(handle)); } set { // Please ignore, for now, that the litorch call thinks you *can* set it to null. @@ -39,9 +37,7 @@ public Parameter? bias { } public Parameter? weight { get { - var res = THSNN_ConvTranspose1d_weight(handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return (res == IntPtr.Zero) ? null : new Parameter(res); + return ReturnNullParameterCheckForErrors(THSNN_ConvTranspose1d_weight(handle)); } set { // Please ignore, for now, that the litorch call thinks you *can* set it to null. diff --git a/src/TorchSharp/NN/Convolution/ConvTranspose2D.cs b/src/TorchSharp/NN/Convolution/ConvTranspose2D.cs index 9912ec2c8..6d491329e 100644 --- a/src/TorchSharp/NN/Convolution/ConvTranspose2D.cs +++ b/src/TorchSharp/NN/Convolution/ConvTranspose2D.cs @@ -25,9 +25,7 @@ public override Tensor forward(Tensor input) public Parameter? bias { get { - var res = THSNN_ConvTranspose2d_bias(handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return ((res == IntPtr.Zero) ? null : new Parameter(res)); + return ReturnNullParameterCheckForErrors(THSNN_ConvTranspose2d_bias(handle)); } set { // Please ignore, for now, that the litorch call thinks you *can* set it to null. @@ -40,9 +38,7 @@ public Parameter? bias { public Parameter? weight { get { - var res = THSNN_ConvTranspose2d_weight(handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return (res == IntPtr.Zero) ? null : new Parameter(res); + return ReturnNullParameterCheckForErrors(THSNN_ConvTranspose2d_weight(handle)); } set { // Please ignore, for now, that the litorch call thinks you *can* set it to null. diff --git a/src/TorchSharp/NN/Convolution/ConvTranspose3D.cs b/src/TorchSharp/NN/Convolution/ConvTranspose3D.cs index c3dba2fa0..3a89cb646 100644 --- a/src/TorchSharp/NN/Convolution/ConvTranspose3D.cs +++ b/src/TorchSharp/NN/Convolution/ConvTranspose3D.cs @@ -25,9 +25,7 @@ public override Tensor forward(Tensor input) public Parameter? bias { get { - var res = THSNN_ConvTranspose3d_bias(handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return ((res == IntPtr.Zero) ? null : new Parameter(res)); + return ReturnNullParameterCheckForErrors(THSNN_ConvTranspose3d_bias(handle)); } set { // Please ignore, for now, that the litorch call thinks you *can* set it to null. @@ -39,9 +37,7 @@ public Parameter? bias { } public Parameter? weight { get { - var res = THSNN_ConvTranspose3d_weight(handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return (res == IntPtr.Zero) ? null : new Parameter(res); + return ReturnNullParameterCheckForErrors(THSNN_ConvTranspose3d_weight(handle)); } set { // Please ignore, for now, that the litorch call thinks you *can* set it to null. diff --git a/src/TorchSharp/NN/Normalization/BatchNorm1D.cs b/src/TorchSharp/NN/Normalization/BatchNorm1D.cs index 1e1463806..478531944 100644 --- a/src/TorchSharp/NN/Normalization/BatchNorm1D.cs +++ b/src/TorchSharp/NN/Normalization/BatchNorm1D.cs @@ -27,9 +27,7 @@ public override Tensor forward(Tensor tensor) public Parameter? bias { get { - var res = THSNN_BatchNorm1d_bias(handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return (res == IntPtr.Zero) ? null : new Parameter(res); + return ReturnNullParameterCheckForErrors(THSNN_BatchNorm1d_bias(handle)); } set { // Please ignore, for now, that the litorch call thinks you *can* set it to null. @@ -42,9 +40,7 @@ public Parameter? bias { public Parameter? weight { get { - var res = THSNN_BatchNorm1d_weight(handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return (res == IntPtr.Zero) ? null : new Parameter(res); + return ReturnNullParameterCheckForErrors(THSNN_BatchNorm1d_weight(handle)); } set { // Please ignore, for now, that the litorch call thinks you *can* set it to null. @@ -57,9 +53,7 @@ public Parameter? weight { public Tensor? running_mean { get { - var res = THSNN_BatchNorm1d_get_mean(handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); return null; } - return new Tensor(res); + return ReturnNullCheckForErrors(THSNN_BatchNorm1d_get_mean(handle)); } set { // Please ignore, for now, that the litorch call thinks you *can* set it to null. @@ -72,9 +66,7 @@ public Tensor? running_mean { public Tensor? running_var { get { - var res = THSNN_BatchNorm1d_get_var(handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); return null; } - return new Tensor(res); + return ReturnNullCheckForErrors(THSNN_BatchNorm1d_get_var(handle)); } set { // Please ignore, for now, that the litorch call thinks you *can* set it to null. @@ -87,9 +79,7 @@ public Tensor? running_var { public Tensor? num_batches_tracked { get { - var res = THSNN_BatchNorm1d_get_batches(handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); return null; } - return new Tensor(res); + return ReturnNullCheckForErrors(THSNN_BatchNorm1d_get_batches(handle)); } } diff --git a/src/TorchSharp/NN/Normalization/BatchNorm2D.cs b/src/TorchSharp/NN/Normalization/BatchNorm2D.cs index a54d0e98d..2d5c1f176 100644 --- a/src/TorchSharp/NN/Normalization/BatchNorm2D.cs +++ b/src/TorchSharp/NN/Normalization/BatchNorm2D.cs @@ -27,9 +27,7 @@ public override Tensor forward(Tensor tensor) public Parameter? bias { get { - var res = THSNN_BatchNorm2d_bias(handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return (res == IntPtr.Zero) ? null : new Parameter(res); + return ReturnNullParameterCheckForErrors(THSNN_BatchNorm2d_bias(handle)); } set { // Please ignore, for now, that the litorch call thinks you *can* set it to null. @@ -42,9 +40,7 @@ public Parameter? bias { public Parameter? weight { get { - var res = THSNN_BatchNorm2d_weight(handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return (res == IntPtr.Zero) ? null : new Parameter(res); + return ReturnNullParameterCheckForErrors(THSNN_BatchNorm2d_weight(handle)); } set { // Please ignore, for now, that the litorch call thinks you *can* set it to null. @@ -57,9 +53,7 @@ public Parameter? weight { public Tensor? running_mean { get { - var res = THSNN_BatchNorm2d_get_mean(handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); return null; } - return new Tensor(res); + return ReturnNullCheckForErrors(THSNN_BatchNorm2d_get_mean(handle)); } set { // Please ignore, for now, that the litorch call thinks you *can* set it to null. @@ -72,9 +66,7 @@ public Tensor? running_mean { public Tensor? running_var { get { - var res = THSNN_BatchNorm2d_get_var(handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); return null; } - return new Tensor(res); + return ReturnNullCheckForErrors(THSNN_BatchNorm2d_get_var(handle)); } set { // Please ignore, for now, that the litorch call thinks you *can* set it to null. @@ -87,9 +79,7 @@ public Tensor? running_var { public Tensor? num_batches_tracked { get { - var res = THSNN_BatchNorm2d_get_batches(handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); return null; } - return new Tensor(res); + return ReturnNullCheckForErrors(THSNN_BatchNorm2d_get_batches(handle)); } } diff --git a/src/TorchSharp/NN/Normalization/BatchNorm3D.cs b/src/TorchSharp/NN/Normalization/BatchNorm3D.cs index ba96a6aee..4bbbe601e 100644 --- a/src/TorchSharp/NN/Normalization/BatchNorm3D.cs +++ b/src/TorchSharp/NN/Normalization/BatchNorm3D.cs @@ -27,9 +27,7 @@ public override Tensor forward(Tensor tensor) public Parameter? bias { get { - var res = THSNN_BatchNorm3d_bias(handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return (res == IntPtr.Zero) ? null : new Parameter(res); + return ReturnNullParameterCheckForErrors(THSNN_BatchNorm3d_bias(handle)); } set { // Please ignore, for now, that the litorch call thinks you *can* set it to null. @@ -42,9 +40,7 @@ public Parameter? bias { public Parameter? weight { get { - var res = THSNN_BatchNorm3d_weight(handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return (res == IntPtr.Zero) ? null : new Parameter(res); + return ReturnNullParameterCheckForErrors(THSNN_BatchNorm3d_weight(handle)); } set { // Please ignore, for now, that the litorch call thinks you *can* set it to null. @@ -72,9 +68,7 @@ public Tensor? running_mean { public Tensor? running_var { get { - var res = THSNN_BatchNorm3d_get_var(handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); return null; } - return new Tensor(res); + return ReturnNullCheckForErrors(THSNN_BatchNorm3d_get_var(handle)); } set { // Please ignore, for now, that the litorch call thinks you *can* set it to null. @@ -87,9 +81,7 @@ public Tensor? running_var { public Tensor? num_batches_tracked { get { - var res = THSNN_BatchNorm3d_get_batches(handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); return null; } - return new Tensor(res); + return ReturnNullCheckForErrors(THSNN_BatchNorm3d_get_batches(handle)); } } diff --git a/src/TorchSharp/NN/Normalization/GroupNorm.cs b/src/TorchSharp/NN/Normalization/GroupNorm.cs index 6e17fe79e..00c5e4475 100644 --- a/src/TorchSharp/NN/Normalization/GroupNorm.cs +++ b/src/TorchSharp/NN/Normalization/GroupNorm.cs @@ -31,9 +31,7 @@ public override Tensor forward(Tensor tensor) public Parameter? bias { get { - var res = THSNN_GroupNorm_bias(handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return (res == IntPtr.Zero) ? null : new Parameter(res); + return ReturnNullParameterCheckForErrors(THSNN_GroupNorm_bias(handle)); } set { // Please ignore, for now, that the litorch call thinks you *can* set it to null. diff --git a/src/TorchSharp/NN/Normalization/InstanceNorm1d.cs b/src/TorchSharp/NN/Normalization/InstanceNorm1d.cs index 7eace4b53..c18505e9f 100644 --- a/src/TorchSharp/NN/Normalization/InstanceNorm1d.cs +++ b/src/TorchSharp/NN/Normalization/InstanceNorm1d.cs @@ -28,9 +28,7 @@ public override Tensor forward(Tensor tensor) public Parameter? bias { get { - var res = THSNN_InstanceNorm1d_bias(handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return (res == IntPtr.Zero) ? null : new Parameter(res); + return ReturnNullParameterCheckForErrors(THSNN_InstanceNorm1d_bias(handle)); } set { // Please ignore, for now, that the litorch call thinks you *can* set it to null. @@ -43,9 +41,7 @@ public Parameter? bias { public Parameter? weight { get { - var res = THSNN_InstanceNorm1d_weight(handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return (res == IntPtr.Zero) ? null : new Parameter(res); + return ReturnNullParameterCheckForErrors(THSNN_InstanceNorm1d_weight(handle)); } set { // Please ignore, for now, that the litorch call thinks you *can* set it to null. @@ -58,9 +54,7 @@ public Parameter? weight { public Tensor? running_mean { get { - var res = THSNN_InstanceNorm1d_get_mean(handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); return null; } - return new Tensor(res); + return ReturnNullCheckForErrors(THSNN_InstanceNorm1d_get_mean(handle)); } set { // Please ignore, for now, that the litorch call thinks you *can* set it to null. @@ -73,9 +67,7 @@ public Tensor? running_mean { public Tensor? running_var { get { - var res = THSNN_InstanceNorm1d_get_var(handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); return null; } - return new Tensor(res); + return ReturnNullCheckForErrors(THSNN_InstanceNorm1d_get_var(handle)); } set { // Please ignore, for now, that the litorch call thinks you *can* set it to null. @@ -88,9 +80,7 @@ public Tensor? running_var { public Tensor? num_batches_tracked { get { - var res = THSNN_InstanceNorm1d_get_batches(handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); return null; } - return new Tensor(res); + return ReturnNullCheckForErrors(THSNN_InstanceNorm1d_get_batches(handle)); } } diff --git a/src/TorchSharp/NN/Normalization/InstanceNorm2d.cs b/src/TorchSharp/NN/Normalization/InstanceNorm2d.cs index 1cc081b8b..6f24fc24d 100644 --- a/src/TorchSharp/NN/Normalization/InstanceNorm2d.cs +++ b/src/TorchSharp/NN/Normalization/InstanceNorm2d.cs @@ -28,9 +28,7 @@ public override Tensor forward(Tensor tensor) public Parameter? bias { get { - var res = THSNN_InstanceNorm2d_bias(handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return (res == IntPtr.Zero) ? null : new Parameter(res); + return ReturnNullParameterCheckForErrors(THSNN_InstanceNorm2d_bias(handle)); } set { // Please ignore, for now, that the litorch call thinks you *can* set it to null. @@ -43,9 +41,7 @@ public Parameter? bias { public Parameter? weight { get { - var res = THSNN_InstanceNorm2d_weight(handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return (res == IntPtr.Zero) ? null : new Parameter(res); + return ReturnNullParameterCheckForErrors(THSNN_InstanceNorm2d_weight(handle)); } set { // Please ignore, for now, that the litorch call thinks you *can* set it to null. @@ -58,9 +54,7 @@ public Parameter? weight { public Tensor? running_mean { get { - var res = THSNN_InstanceNorm2d_get_mean(handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); return null; } - return new Tensor(res); + return ReturnNullCheckForErrors(THSNN_InstanceNorm2d_get_mean(handle)); } set { // Please ignore, for now, that the litorch call thinks you *can* set it to null. @@ -73,9 +67,7 @@ public Tensor? running_mean { public Tensor? running_var { get { - var res = THSNN_InstanceNorm2d_get_var(handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); return null; } - return new Tensor(res); + return ReturnNullCheckForErrors(THSNN_InstanceNorm2d_get_var(handle)); } set { // Please ignore, for now, that the litorch call thinks you *can* set it to null. @@ -88,9 +80,7 @@ public Tensor? running_var { public Tensor? num_batches_tracked { get { - var res = THSNN_InstanceNorm2d_get_batches(handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); return null; } - return new Tensor(res); + return ReturnNullCheckForErrors(THSNN_InstanceNorm2d_get_batches(handle)); } } diff --git a/src/TorchSharp/NN/Normalization/InstanceNorm3d.cs b/src/TorchSharp/NN/Normalization/InstanceNorm3d.cs index 2a221a7fd..3f94c40a9 100644 --- a/src/TorchSharp/NN/Normalization/InstanceNorm3d.cs +++ b/src/TorchSharp/NN/Normalization/InstanceNorm3d.cs @@ -43,9 +43,7 @@ public Parameter? bias { public Parameter? weight { get { - var res = THSNN_InstanceNorm3d_weight(handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return (res == IntPtr.Zero) ? null : new Parameter(res); + return ReturnNullParameterCheckForErrors(THSNN_InstanceNorm3d_weight(handle)); } set { // Please ignore, for now, that the litorch call thinks you *can* set it to null. @@ -58,9 +56,7 @@ public Parameter? weight { public Tensor? running_mean { get { - var res = THSNN_InstanceNorm3d_get_mean(handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); return null; } - return new Tensor(res); + return ReturnNullCheckForErrors(THSNN_InstanceNorm3d_get_mean(handle)); } set { // Please ignore, for now, that the litorch call thinks you *can* set it to null. @@ -73,9 +69,7 @@ public Tensor? running_mean { public Tensor? running_var { get { - var res = THSNN_InstanceNorm3d_get_var(handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); return null; } - return new Tensor(res); + return ReturnNullCheckForErrors(THSNN_InstanceNorm3d_get_var(handle)); } set { // Please ignore, for now, that the litorch call thinks you *can* set it to null. @@ -88,9 +82,7 @@ public Tensor? running_var { public Tensor? num_batches_tracked { get { - var res = THSNN_InstanceNorm3d_get_batches(handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); return null; } - return new Tensor(res); + return ReturnNullCheckForErrors(THSNN_InstanceNorm3d_get_batches(handle)); } } diff --git a/src/TorchSharp/Tensor/Factories/Tensor.Factories.cs b/src/TorchSharp/Tensor/Factories/Tensor.Factories.cs index b83b1357a..a2ca062bb 100644 --- a/src/TorchSharp/Tensor/Factories/Tensor.Factories.cs +++ b/src/TorchSharp/Tensor/Factories/Tensor.Factories.cs @@ -51,8 +51,8 @@ public static Tensor arange(Scalar start, Scalar stop, Scalar step, ScalarType? GC.WaitForPendingFinalizers(); handle = THSTensor_arange(start.Handle, stop.Handle, step.Handle, (sbyte)dtype, (int)device.type, device.index, requires_grad); } - if (handle == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(handle); + + return ReturnCheckForErrors(handle); } /// @@ -91,15 +91,7 @@ public static Tensor eye(long rows, long columns = -1L, ScalarType? dtype = null GC.WaitForPendingFinalizers(); handle = THSTensor_eye(rows, columns, (sbyte)dtype, (int)device.type, device.index, requires_grad); } - if (handle == IntPtr.Zero) { CheckForErrors(); } - var result = new Tensor(handle); - - if (names != null && names.Length > 0) { - - result.rename_(names); - } - - return result; + return ReturnCheckForErrorsAndRename(handle, names); } /// @@ -175,15 +167,7 @@ private static Tensor _tensor_generic(Array rawArray, ReadOnlySpan dimensi GC.WaitForPendingFinalizers(); handle = THSTensor_new(dataArrayAddr, deleter, (IntPtr)shape, dimensions.Length, origType, (sbyte)dtype.Value, (int)device.type, device.index, requires_grad); } - - if (handle == IntPtr.Zero) { CheckForErrors(); } - var tensor = new Tensor(handle); - - if (names != null && names.Length > 0) { - tensor.rename_(names); - } - - return tensor; + return ReturnCheckForErrorsAndRename(handle, names); } } } @@ -234,21 +218,7 @@ private static Tensor _tensor_generic(Memory rawArray, ReadOnlySpan GC.WaitForPendingFinalizers(); handle = THSTensor_new(dataArrayAddr, deleter, (IntPtr)shape, dimensions.Length, origType, (sbyte)dtype.Value, (int)device.type, device.index, requires_grad); } - - if (handle == IntPtr.Zero) { CheckForErrors(); } - var tensor = new Tensor(handle); - - if (names != null && names.Length > 0) { - tensor.rename_(names); - } - - /*if (!is_autocast_cache_enabled()) - return tensor; - if (is_autocast_gpu_enabled()) - tensor = tensor.to(get_autocast_gpu_dtype()); - if (is_autocast_cpu_enabled()) - tensor = tensor.to(get_autocast_cpu_dtype());*/ - return tensor; + return ReturnCheckForErrorsAndRename(handle, names); } } } @@ -456,12 +426,7 @@ public static Tensor sparse_coo_tensor(Tensor indices, Tensor values, long[] siz GC.WaitForPendingFinalizers(); handle = THSTensor_sparse(indices.Handle, values.Handle, (IntPtr)psizes, size.Length, (sbyte)dtype, (int)device.type, device.index, requires_grad); } - if (handle == IntPtr.Zero) { CheckForErrors(); } - var tensor = new Tensor(handle); - if (names != null && names.Length > 0) { - tensor.rename_(names); - } - return tensor; + return ReturnCheckForErrorsAndRename(handle, names); } } } @@ -489,10 +454,7 @@ public static Tensor sparse(Tensor indices, Tensor values, long[] size, ScalarTy /// public static Tensor complex(Tensor real, Tensor imag) { - var res = THSTensor_complex(real.Handle, imag.Handle); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_complex(real.Handle, imag.Handle)); } /// @@ -500,10 +462,7 @@ public static Tensor complex(Tensor real, Tensor imag) /// public static Tensor polar(Tensor abs, Tensor angle) { - var res = THSTensor_polar(abs.Handle, angle.Handle); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_polar(abs.Handle, angle.Handle)); } public static Tensor from_file(string filename, bool? shared = null, long? size = 0, ScalarType? dtype = null, Device? device = null, bool requires_grad = false) @@ -514,9 +473,7 @@ public static Tensor from_file(string filename, bool? shared = null, long? size dtype = get_default_dtype(); } - var handle = THSTensor_from_file(StringEncoder.GetNullTerminatedUTF8ByteArray(filename), (sbyte)(!shared.HasValue ? -1 : shared.Value ? 1 : 0), size.HasValue ? size.Value : -1, (sbyte)dtype, (int)device.type, device.index, requires_grad); - if (handle == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(handle); + return ReturnCheckForErrors(THSTensor_from_file(StringEncoder.GetNullTerminatedUTF8ByteArray(filename), (sbyte)(!shared.HasValue ? -1 : shared.Value ? 1 : 0), size.HasValue ? size.Value : -1, (sbyte)dtype, (int)device.type, device.index, requires_grad)); } /// @@ -536,8 +493,8 @@ public static Tensor linspace(double start, double end, long steps, ScalarType? GC.WaitForPendingFinalizers(); handle = THSTensor_linspace(start, end, steps, (sbyte)dtype, (int)device.type, device.index, requires_grad); } - if (handle == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(handle); + + return ReturnCheckForErrors(handle); } /// @@ -557,8 +514,8 @@ public static Tensor logspace(double start, double end, long steps, double @base GC.WaitForPendingFinalizers(); handle = THSTensor_logspace(start, end, steps, @base, (sbyte)dtype, (int)device.type, device.index, requires_grad); } - if (handle == IntPtr.Zero) { CheckForErrors(); } - return new Tensor(handle); + + return ReturnCheckForErrors(handle); } #region Loading a tensor from a stream diff --git a/src/TorchSharp/Tensor/Factories/empty.cs b/src/TorchSharp/Tensor/Factories/empty.cs index bda99fb09..2fcf2cfbf 100644 --- a/src/TorchSharp/Tensor/Factories/empty.cs +++ b/src/TorchSharp/Tensor/Factories/empty.cs @@ -112,15 +112,7 @@ public static Tensor empty_strided(long[] size, long[] strides, ScalarType? dtyp GC.WaitForPendingFinalizers(); handle = THSTensor_empty_strided((IntPtr)psizes, size.Length, (IntPtr)pstrides, strides.Length, (sbyte)dtype, (int)device.type, device.index, requires_grad); } - if (handle == IntPtr.Zero) { CheckForErrors(); } - var result = new Tensor(handle); - - if (names != null && names.Length > 0) { - - result.rename_(names); - } - - return result; + return ReturnCheckForErrorsAndRename(handle, names); } } } @@ -144,15 +136,7 @@ private static Tensor _empty(ReadOnlySpan size, ScalarType? dtype = null, GC.WaitForPendingFinalizers(); handle = THSTensor_empty((IntPtr)psizes, size.Length, (sbyte)dtype, (int)device.type, device.index, requires_grad); } - if (handle == IntPtr.Zero) { CheckForErrors(); } - var result = new Tensor(handle); - - if (names != null && names.Length > 0) { - - result.rename_(names); - } - - return result; + return ReturnCheckForErrorsAndRename(handle, names); } } } diff --git a/src/TorchSharp/Tensor/Factories/full.cs b/src/TorchSharp/Tensor/Factories/full.cs index 02ccab311..e2a6db048 100644 --- a/src/TorchSharp/Tensor/Factories/full.cs +++ b/src/TorchSharp/Tensor/Factories/full.cs @@ -115,15 +115,7 @@ private static Tensor _full(ReadOnlySpan size, Scalar value, ScalarType? d GC.WaitForPendingFinalizers(); handle = THSTensor_full((IntPtr)psizes, size.Length, value.Handle, (sbyte)dtype, (int)device.type, device.index, requires_grad); } - if (handle == IntPtr.Zero) { CheckForErrors(); } - var result = new Tensor(handle); - - if (names != null && names.Length > 0) { - - result.rename_(names); - } - - return result; + return ReturnCheckForErrorsAndRename(handle, names); } } } diff --git a/src/TorchSharp/Tensor/Factories/ones.cs b/src/TorchSharp/Tensor/Factories/ones.cs index b90f26a1e..8959f5283 100644 --- a/src/TorchSharp/Tensor/Factories/ones.cs +++ b/src/TorchSharp/Tensor/Factories/ones.cs @@ -111,15 +111,7 @@ private static Tensor _ones(ReadOnlySpan size, ScalarType? dtype = null, D GC.WaitForPendingFinalizers(); handle = THSTensor_ones((IntPtr)psizes, size.Length, (sbyte)dtype, (int)device.type, device.index, requires_grad); } - if (handle == IntPtr.Zero) { CheckForErrors(); } - var result = new Tensor(handle); - - if (names != null && names.Length > 0) { - - result.rename_(names); - } - - return result; + return ReturnCheckForErrorsAndRename(handle, names); } } } diff --git a/src/TorchSharp/Tensor/Factories/rand.cs b/src/TorchSharp/Tensor/Factories/rand.cs index 8a3c06a30..47d033d3e 100644 --- a/src/TorchSharp/Tensor/Factories/rand.cs +++ b/src/TorchSharp/Tensor/Factories/rand.cs @@ -52,12 +52,12 @@ public static Tensor randint(long low, long high, Size size, ScalarType? dtype = GC.WaitForPendingFinalizers(); handle = THSTensor_randint(genHandle, low, high, (IntPtr)psizes, shape.Length, (sbyte)dtype, (int)device.type, device.index, requires_grad); } - if (handle == IntPtr.Zero) { CheckForErrors(); } - result = new Tensor(handle); + + return ReturnCheckForErrors(handle); } } } - + if (names != null && names.Length > 0) { result.rename_(names); @@ -269,15 +269,7 @@ private static Tensor randint_c32(IntPtr genHandle, long low, long high, long[] THSTensor_dispose(handle); THSTensor_dispose(cmplx); - - var result = new Tensor(res); - - if (names != null && names.Length > 0) { - - result.rename_(names); - } - - return result; + return ReturnCheckForErrorsAndRename(handle, names); } } } @@ -330,12 +322,7 @@ private static Tensor randint_c64(IntPtr genHandle, long low, long high, long[] THSTensor_dispose(handle); THSTensor_dispose(cmplx); - - var result = new Tensor(res); - if (names != null && names.Length > 0) { - result.rename_(names); - } - return result; + return ReturnCheckForErrorsAndRename(handle, names); } } } @@ -364,12 +351,7 @@ private static Tensor _rand(ReadOnlySpan size, ScalarType? dtype = null, D GC.WaitForPendingFinalizers(); handle = THSTensor_rand(genHandle, (IntPtr)psizes, size.Length, (sbyte)dtype, (int)device.type, device.index, requires_grad); } - if (handle == IntPtr.Zero) { CheckForErrors(); } - var result = new Tensor(handle); - if (names != null && names.Length > 0) { - result.rename_(names); - } - return result; + return ReturnCheckForErrorsAndRename(handle, names); } } } @@ -492,12 +474,7 @@ private static Tensor _randn(ReadOnlySpan size, ScalarType? dtype = null, GC.WaitForPendingFinalizers(); handle = THSTensor_randn(genHandle, (IntPtr)psizes, size.Length, (sbyte)dtype, (int)device.type, device.index, requires_grad); } - if (handle == IntPtr.Zero) { CheckForErrors(); } - var result = new Tensor(handle); - if (names != null && names.Length > 0) { - result.rename_(names); - } - return result; + return ReturnCheckForErrorsAndRename(handle, names); } } } diff --git a/src/TorchSharp/Tensor/Factories/zeros.cs b/src/TorchSharp/Tensor/Factories/zeros.cs index af188ef9b..ebcb9feb9 100644 --- a/src/TorchSharp/Tensor/Factories/zeros.cs +++ b/src/TorchSharp/Tensor/Factories/zeros.cs @@ -114,16 +114,7 @@ private static Tensor _zeros(ReadOnlySpan size, ScalarType? dtype = null, handle = THSTensor_zeros((IntPtr)psizes, size.Length, (sbyte)dtype, (int)device.type, device.index, requires_grad); } - if (handle == IntPtr.Zero) { CheckForErrors(); } - - var result = new Tensor(handle); - - if (names != null && names.Length > 0) { - - result.rename_(names); - } - - return result; + return ReturnCheckForErrorsAndRename(handle, names); } } } diff --git a/src/TorchSharp/Tensor/Tensor.Trig.cs b/src/TorchSharp/Tensor/Tensor.Trig.cs index bf503977d..21df2e649 100644 --- a/src/TorchSharp/Tensor/Tensor.Trig.cs +++ b/src/TorchSharp/Tensor/Tensor.Trig.cs @@ -95,10 +95,7 @@ public Tensor acos_() /// public Tensor atan() { - var res = THSTensor_atan(Handle); - if (res == IntPtr.Zero) - CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(THSTensor_atan(Handle)); } /// diff --git a/src/TorchSharp/Torch.cs b/src/TorchSharp/Torch.cs index 18f8f6c6b..b3b8f32e1 100644 --- a/src/TorchSharp/Torch.cs +++ b/src/TorchSharp/Torch.cs @@ -6,6 +6,7 @@ using System.Linq; using System.Linq.Expressions; using System.Reflection; +using System.Runtime.CompilerServices; using System.Runtime.InteropServices; using System.Text; using System.Text.RegularExpressions; @@ -656,9 +657,7 @@ public static ulong get_global_total_memory(int device) public static void CheckForErrors() { var error = THSTorch_get_and_reset_last_err(); - - if (error != IntPtr.Zero) - { + if (error != IntPtr.Zero) { throw new ExternalException(Marshal.PtrToStringAnsi(error)); } } @@ -669,30 +668,64 @@ public static void CheckForErrors() /// /// /// + [MethodImpl(MethodImplOptions.AggressiveInlining)] public static Tensor ReturnCheckForErrors(IntPtr ptr) { if(ptr == IntPtr.Zero) CheckForErrors(); return new Tensor(ptr); } + [MethodImpl(MethodImplOptions.AggressiveInlining)] + public static Tensor? ReturnNullCheckForErrors(IntPtr ptr) + { + if (ptr == IntPtr.Zero) { + CheckForErrors(); + return null; + } + + return new Tensor(ptr); + } + [MethodImpl(MethodImplOptions.AggressiveInlining)] + public static Parameter? ReturnNullParameterCheckForErrors(IntPtr ptr) + { + if (ptr == IntPtr.Zero) + CheckForErrors(); + return (ptr == IntPtr.Zero) ? null : new Parameter(ptr); + } + [MethodImpl(MethodImplOptions.AggressiveInlining)] + public static Tensor ReturnCheckForErrorsAndRename(IntPtr ptr, string[]? names) + { + if (ptr == IntPtr.Zero) + CheckForErrors(); + var result = new Tensor(ptr); + if (names != null && names.Length > 0) { + result.rename_(names); + } + + return result; + } + [MethodImpl(MethodImplOptions.AggressiveInlining)] public static (Tensor,Tensor) ReturnCheckForErrors(IntPtr ptr, IntPtr ptr1) { if (ptr == IntPtr.Zero || ptr1 == IntPtr.Zero) CheckForErrors(); return (new Tensor(ptr), new Tensor(ptr1)); } + [MethodImpl(MethodImplOptions.AggressiveInlining)] public static (Tensor, Tensor, Tensor) ReturnCheckForErrors(IntPtr ptr, IntPtr ptr1, IntPtr ptr2) { if (ptr == IntPtr.Zero || ptr1 == IntPtr.Zero || ptr2 == IntPtr.Zero) CheckForErrors(); return (new Tensor(ptr), new Tensor(ptr1), new Tensor(ptr2)); } + [MethodImpl(MethodImplOptions.AggressiveInlining)] public static (Tensor, Tensor, Tensor, Tensor) ReturnCheckForErrors(IntPtr ptr, IntPtr ptr1, IntPtr ptr2, IntPtr ptr3) { if (ptr == IntPtr.Zero || ptr1 == IntPtr.Zero || ptr2 == IntPtr.Zero || ptr3 == IntPtr.Zero) CheckForErrors(); return (new Tensor(ptr), new Tensor(ptr1), new Tensor(ptr2), new Tensor(ptr3)); } + [MethodImpl(MethodImplOptions.AggressiveInlining)] public static Tensor ReturnCheckForErrorsAutocast(IntPtr ptr, ScalarType? st = null) { if (ptr == IntPtr.Zero) From 5eef2cd65566ef1b8547d13aad25f0776dc9d861 Mon Sep 17 00:00:00 2001 From: Dimitri Date: Thu, 9 Apr 2026 15:42:34 -0300 Subject: [PATCH 58/65] ref --- TorchSharp.sln | 12 ++++++------ src/TorchSharp/Amp/GradScaler.cs | 9 ++++----- 2 files changed, 10 insertions(+), 11 deletions(-) diff --git a/TorchSharp.sln b/TorchSharp.sln index 054c07bb3..e5e461ea0 100644 --- a/TorchSharp.sln +++ b/TorchSharp.sln @@ -36,7 +36,7 @@ Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "TorchSharp", "TorchSharp", EndProject Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "LibTorchSharp", "bin\obj\x64.Debug\Native\LibTorchSharp\LibTorchSharp.vcxproj", "{265C2E6F-04E6-37A8-B504-E3DD4A3FEE06}" EndProject -Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "LibTorchSharp", "bin\obj\x64.Release\Native\LibTorchSharp\LibTorchSharp.vcxproj", "{E4C0DBEE-0815-311B-9065-137BB50BD793}" +Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "LibTorchSharp", "bin\obj\x64.Release\Native\LibTorchSharp\LibTorchSharp.vcxproj", "{748608D6-97ED-3EEA-89D9-D5D5CC69B05A}" EndProject Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Native-Debug", "Native-Debug", "{CF2C1A9E-3A8A-4329-8A6E-7880C15AAC3D}" ProjectSection(SolutionItems) = preProject @@ -111,10 +111,10 @@ Global {265C2E6F-04E6-37A8-B504-E3DD4A3FEE06}.Debug|x64.ActiveCfg = Debug|x64 {265C2E6F-04E6-37A8-B504-E3DD4A3FEE06}.Release|Any CPU.ActiveCfg = Release|x64 {265C2E6F-04E6-37A8-B504-E3DD4A3FEE06}.Release|x64.ActiveCfg = Release|x64 - {E4C0DBEE-0815-311B-9065-137BB50BD793}.Debug|Any CPU.ActiveCfg = Debug|x64 - {E4C0DBEE-0815-311B-9065-137BB50BD793}.Debug|x64.ActiveCfg = Debug|x64 - {E4C0DBEE-0815-311B-9065-137BB50BD793}.Release|Any CPU.ActiveCfg = Release|x64 - {E4C0DBEE-0815-311B-9065-137BB50BD793}.Release|x64.ActiveCfg = Release|x64 + {748608D6-97ED-3EEA-89D9-D5D5CC69B05A}.Debug|Any CPU.ActiveCfg = Debug|x64 + {748608D6-97ED-3EEA-89D9-D5D5CC69B05A}.Debug|x64.ActiveCfg = Debug|x64 + {748608D6-97ED-3EEA-89D9-D5D5CC69B05A}.Release|Any CPU.ActiveCfg = Release|x64 + {748608D6-97ED-3EEA-89D9-D5D5CC69B05A}.Release|x64.ActiveCfg = Release|x64 {DD652544-711E-4029-83FF-DA4A9600E6E7}.Debug|Any CPU.ActiveCfg = Debug|Any CPU {DD652544-711E-4029-83FF-DA4A9600E6E7}.Debug|Any CPU.Build.0 = Debug|Any CPU {DD652544-711E-4029-83FF-DA4A9600E6E7}.Debug|x64.ActiveCfg = Debug|Any CPU @@ -182,7 +182,7 @@ Global {42B45168-476D-4BFA-87B8-81A34E6295CD} = {09EADF06-BE25-4228-AB53-95AE3E15B530} {567456AD-B026-4CB6-B98D-4FC930C90223} = {D3D38B03-B557-484D-8348-8BADEE4DF592} {265C2E6F-04E6-37A8-B504-E3DD4A3FEE06} = {CF2C1A9E-3A8A-4329-8A6E-7880C15AAC3D} - {E4C0DBEE-0815-311B-9065-137BB50BD793} = {4DB9E84D-324C-408F-87A6-246E86205540} + {748608D6-97ED-3EEA-89D9-D5D5CC69B05A} = {4DB9E84D-324C-408F-87A6-246E86205540} {CF2C1A9E-3A8A-4329-8A6E-7880C15AAC3D} = {09EADF06-BE25-4228-AB53-95AE3E15B530} {D8C60CD8-8429-45F2-A755-47B6CD10FDF8} = {09EADF06-BE25-4228-AB53-95AE3E15B530} {4DB9E84D-324C-408F-87A6-246E86205540} = {CF2C1A9E-3A8A-4329-8A6E-7880C15AAC3D} diff --git a/src/TorchSharp/Amp/GradScaler.cs b/src/TorchSharp/Amp/GradScaler.cs index a19438695..a826f9bcd 100644 --- a/src/TorchSharp/Amp/GradScaler.cs +++ b/src/TorchSharp/Amp/GradScaler.cs @@ -224,7 +224,7 @@ private Scalar maybe_opt_step(torch.optim.Optimizer optimizer, UnorderedMap dict) { foreach (var d in dict) { - retval += (double)d.Value.item(); + retval += (double)d.Value.item(); //retval += d.Value.Sum(x=>x.item()); /*foreach(var t in d.Value) retval += t.item();*/ @@ -242,7 +242,8 @@ private Scalar maybe_opt_step(torch.optim.Optimizer optimizer, UnorderedMap();*/ var res = optimizer.step(closure); if (!(res is null)) { - return res.item(); + //return res.item(); + return res.ToScalar(); } /*if (retval == 0) @@ -257,9 +258,7 @@ public Scalar step(torch.optim.Optimizer optimizer, Func optimizer { if (!Enabled) { var res = optimizer.step(optimizer_args); - if (!(res is null)) - return res.item(); - return null; + return res?.item(); } if (optimizer_args != null) From beb5e56e727e4554119102214e648468e316d658 Mon Sep 17 00:00:00 2001 From: Dimitri Date: Thu, 23 Apr 2026 02:19:46 -0300 Subject: [PATCH 59/65] improve GradScaler --- Directory.Build.targets | 8 +- MyCustomCMD.txt | 8 +- global.json | 6 +- ...eRestitcher.Tests.csproj.nuget.dgspec.json | 224 ----- .../FileRestitcher.Tests.csproj.nuget.g.props | 35 - ...ileRestitcher.Tests.csproj.nuget.g.targets | 18 - .../project.assets.json | 841 ------------------ .../project.nuget.cache | 21 - .../FileRestitcher.Tests.csproj | 2 +- .../FileRestitcher.csproj.nuget.dgspec.json | 103 --- .../FileRestitcher.csproj.nuget.g.props | 16 - .../FileRestitcher.csproj.nuget.g.targets | 6 - .../project.assets.json | 283 ------ .../project.nuget.cache | 11 - src/Native/LibTorchSharp/THSAutograd.cpp | 88 +- src/Native/LibTorchSharp/THSTorch.cpp | 7 +- src/TorchSharp/Amp/GradScaler.cs | 195 ++-- .../BitsAndBytes/BitsAndByteUtils.cs | 4 - .../BitsAndBytes/BitsAndBytesNatives.cs | 47 +- src/TorchSharp/Tensor/Storage.cs | 10 + src/TorchSharp/Tensor/Tensor.cs | 9 +- src/TorchSharp/Torch.cs | 1 + src/TorchSharp/TorchSharp.csproj | 2 + src/TorchSharp/Utils/GetSubArray.cs | 4 +- .../Utils/ObjectReferenceEqualityComparer.cs | 9 +- src/TorchSharp/Utils/TensorAccessor.cs | 44 +- test/Directory.Build.props | 2 +- .../TorchSharpTest.WithCudaBinaries.csproj | 4 +- test/TorchSharpTest/TestGradScaler.cs | 11 +- test/TorchSharpTest/TestTorchVision.cs | 39 +- .../TorchSharpTest/TestTorchVisionDatasets.cs | 4 +- .../TestTorchVisionTransforms.cs | 8 +- test/TorchSharpTest/TestTorchVisionUtils.cs | 12 +- test/TorchSharpTest/TorchSharpTest.csproj | 1 + 34 files changed, 340 insertions(+), 1743 deletions(-) delete mode 100644 pkg/FileRestitcher/FileRestitcher.Tests/FileRestitcher.Tests.NupkgProj/FileRestitcher.Tests.csproj.nuget.dgspec.json delete mode 100644 pkg/FileRestitcher/FileRestitcher.Tests/FileRestitcher.Tests.NupkgProj/FileRestitcher.Tests.csproj.nuget.g.props delete mode 100644 pkg/FileRestitcher/FileRestitcher.Tests/FileRestitcher.Tests.NupkgProj/FileRestitcher.Tests.csproj.nuget.g.targets delete mode 100644 pkg/FileRestitcher/FileRestitcher.Tests/FileRestitcher.Tests.NupkgProj/project.assets.json delete mode 100644 pkg/FileRestitcher/FileRestitcher.Tests/FileRestitcher.Tests.NupkgProj/project.nuget.cache delete mode 100644 pkg/FileRestitcher/FileRestitcher/FileRestitcher.NupkgProj/FileRestitcher.csproj.nuget.dgspec.json delete mode 100644 pkg/FileRestitcher/FileRestitcher/FileRestitcher.NupkgProj/FileRestitcher.csproj.nuget.g.props delete mode 100644 pkg/FileRestitcher/FileRestitcher/FileRestitcher.NupkgProj/FileRestitcher.csproj.nuget.g.targets delete mode 100644 pkg/FileRestitcher/FileRestitcher/FileRestitcher.NupkgProj/project.assets.json delete mode 100644 pkg/FileRestitcher/FileRestitcher/FileRestitcher.NupkgProj/project.nuget.cache diff --git a/Directory.Build.targets b/Directory.Build.targets index 224f8a649..d2859f003 100644 --- a/Directory.Build.targets +++ b/Directory.Build.targets @@ -107,7 +107,7 @@ - @@ -124,7 +124,7 @@ - - + - + --> \ No newline at end of file diff --git a/MyCustomCMD.txt b/MyCustomCMD.txt index bb3759733..6416f8025 100644 --- a/MyCustomCMD.txt +++ b/MyCustomCMD.txt @@ -1,4 +1,10 @@ dotnet build TorchSharpFilter.slnf /p:CustomLibTorchPath="K:\FrameworksForC\LibTorch\libtorch-win-shared-with-deps-debug-2.6.0+cu126\libtorch" -f netstandard2.0 build.cmd Release x64 --libtorchpath "K:\FrameworksForC\LibTorch\libtorch-win-shared-with-deps-2.8.0+cu128\libtorch\share\cmake\Torch" -dotnet build /p:CustomLibTorchFullPath="K:\FrameworksForC\LibTorch\libtorch-win-shared-with-deps-2.8.0+cu128\libtorch\share\cmake\Torch" -c Release \ No newline at end of file +dotnet build /p:CustomLibTorchFullPath="K:\FrameworksForC\LibTorch\libtorch-win-shared-with-deps-2.8.0+cu128\libtorch\share\cmake\Torch" -c Release + +dotnet build TorchSharpFilter.slnf /p:CustomLibTorchFullPath="K:\FrameworksForC\LibTorch\libtorch-win-shared-with-deps-debug-2.6.0+cu126\libtorch\share\cmake\Torch" -f netstandard2.0 + + +dotnet build /p:CustomLibTorchFullPath="K:\FrameworksForC\LibTorch\libtorch-win-shared-with-deps-2.11.0+cpu\libtorch\share\cmake\Torch" +dotnet test /p:CustomLibTorchFullPath="K:\FrameworksForC\LibTorch\libtorch-win-shared-with-deps-2.11.0+cpu\libtorch\share\cmake\Torch" \ No newline at end of file diff --git a/global.json b/global.json index c7d63ab06..bf923c69c 100644 --- a/global.json +++ b/global.json @@ -1,7 +1,5 @@ { "sdk": { - 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auto vars = toTensors(vars_.array, vars_.size); - auto output_tensors = toTensors(outputs_.array, outputs_.size); - auto outputs = torch::autograd::to_optional(output_tensors); - - // Convert the list of Tensor to a set of unsafe impl - std::unordered_set nonDiff; - nonDiff.reserve(nonDiff_.size); - for (int i = 0; i < nonDiff_.size; i++) - nonDiff.insert(nonDiff_.array[i]->unsafeGetTensorImpl()); - - // Convert the list of Tensors to a set of unsafe impl, and then apply the behavior of AutogradContext::get_and_bump_dirty() - std::unordered_set dirty; - dirty.reserve(dirty_.size); - for (int i = 0; i < dirty_.size; i++) { - auto t = dirty_.array[i]->unsafeGetTensorImpl(); - t->bump_version(); - dirty.insert(t); + torch_last_err = 0; + try { + auto vars = toTensors(vars_.array, vars_.size); + auto output_tensors = toTensors(outputs_.array, outputs_.size); + auto outputs = torch::autograd::to_optional(output_tensors); + + // Convert the list of Tensor to a set of unsafe impl + std::unordered_set nonDiff; + nonDiff.reserve(nonDiff_.size); + for (int i = 0; i < nonDiff_.size; i++) + nonDiff.insert(nonDiff_.array[i]->unsafeGetTensorImpl()); + + // Convert the list of Tensors to a set of unsafe impl, and then apply the behavior of AutogradContext::get_and_bump_dirty() + std::unordered_set dirty; + dirty.reserve(dirty_.size); + for (int i = 0; i < dirty_.size; i++) { + auto t = dirty_.array[i]->unsafeGetTensorImpl(); + t->bump_version(); + dirty.insert(t); + } + + // Copied these functions from custom_function.h + torch::autograd::_jvp_fn_t jvp_fn = [](const variable_list& inputs, + const variable_list& gI) -> variable_list { + TORCH_CHECK( + false, + "jvp is not implemented for the c++ API of custom Function yet.", + "Please open a feature request on GitHub if you need this."); + }; + + auto view_as_self_fn = [](const at::Tensor& x) -> at::Tensor { + return x.view_as(x); + }; +#if TORCH_VERSION_MAJOR >= 2 && TORCH_VERSION_MINOR >= 11 + auto res = torch::autograd::_wrap_outputs(vars, nonDiff, dirty, outputs, node.weak_ptr == nullptr || node.weak_ptr->expired() ? nullptr : node.weak_ptr->lock(), jvp_fn, {}, view_as_self_fn, true); +#else + auto res = torch::autograd::_wrap_outputs(vars, nonDiff, dirty, outputs, node.weak_ptr == nullptr || node.weak_ptr->expired() ? nullptr : node.weak_ptr->lock(), jvp_fn, {}, view_as_self_fn); +#endif + auto sz = res.size(); + + Tensor* result = allocator(sz); + for (size_t i = 0; i < sz; i++) + result[i] = res[i].has_value() ? ResultTensor(res[i].value()) : nullptr; + } + catch (const c10::Error e) { + torch_last_err = strdup(e.what()); \ + } + catch (const std::runtime_error e) { + torch_last_err = strdup(e.what()); \ } - - // Copied these functions from custom_function.h - torch::autograd::_jvp_fn_t jvp_fn = [](const variable_list& inputs, - const variable_list& gI) -> variable_list { - TORCH_CHECK( - false, - "jvp is not implemented for the c++ API of custom Function yet.", - "Please open a feature request on GitHub if you need this."); - }; - - auto view_as_self_fn = [](const at::Tensor& x) -> at::Tensor { - return x.view_as(x); - }; - - auto res = torch::autograd::_wrap_outputs(vars, nonDiff, dirty, outputs, node.weak_ptr == nullptr || node.weak_ptr->expired() ? nullptr : node.weak_ptr->lock(), jvp_fn, {}, view_as_self_fn); - auto sz = res.size(); - - Tensor* result = allocator(sz); - for (size_t i = 0; i < sz; i++) - result[i] = res[i].has_value() ? ResultTensor(res[i].value()) : nullptr; - ) } SavedVariable THSAutograd_SavedVariable_ctor(Tensor variable, CSharpNodePtr node, bool is_inplace_on_view) diff --git a/src/Native/LibTorchSharp/THSTorch.cpp b/src/Native/LibTorchSharp/THSTorch.cpp index 8056b316e..7c25c12a8 100644 --- a/src/Native/LibTorchSharp/THSTorch.cpp +++ b/src/Native/LibTorchSharp/THSTorch.cpp @@ -58,7 +58,12 @@ void THSBackend_cudnn_set_allow_tf32(const bool flag) bool THSBackend_cuda_get_allow_fp16_reduced_precision_reduction() { auto result = false; - CATCH(result = at::globalContext().allowFP16ReductionCuBLAS();); +#if TORCH_VERSION_MAJOR >= 2 && TORCH_VERSION_MINOR >= 11 + CATCH(result = at::globalContext().allowFP16ReductionCuBLAS()==at::CuBLASReductionOption::AllowReducedPrecisionWithSplitK;); +#else + CATCH(result = at::globalContext().allowFP16ReductionCuBLAS();); +#endif + return result; } diff --git a/src/TorchSharp/Amp/GradScaler.cs b/src/TorchSharp/Amp/GradScaler.cs index a826f9bcd..8073fcb95 100644 --- a/src/TorchSharp/Amp/GradScaler.cs +++ b/src/TorchSharp/Amp/GradScaler.cs @@ -3,6 +3,7 @@ using System.Collections.Generic; using System.Diagnostics; using System.Linq; +using System.Runtime.CompilerServices; using TorchSharp.Modules; using TorchSharp.Utils; @@ -13,11 +14,13 @@ public class GradScaler : IDisposable private bool Enabled; public torch.Device device; private torch.Tensor _scale, _growth_tracker; - private float InitScale, InitGrowthTracker; - public float _growth_factor; - public float _backoff_factor; + private double _init_scale; + private long _init_growth_tracker; + public double _growth_factor; + public double _backoff_factor; private int _growth_interval; - private UnorderedMap> _per_optimizer_states = new UnorderedMap>(); + //private UnorderedMap> _per_optimizer_states = new UnorderedMap>(); + private UnorderedMap> _per_optimizer_states = new UnorderedMap>(); bool disposedValue; public enum OptState @@ -34,14 +37,14 @@ private UnorderedMap _refresh_per_optimizer_state() }; } //https://github.com/pytorch/pytorch/blob/main/torch/amp/grad_scaler.py - public GradScaler(torch.Device dev, float init_scale = 2.0e16f, float growth_factor = 2.0f, - float backoff_factor = 0.5f, int growth_interval = 2000, bool enabled = true) + public GradScaler(torch.Device dev, double init_scale = 2.0e16, double growth_factor = 2.0, + double backoff_factor = 0.5, int growth_interval = 2000, bool enabled = true) { //https://gist.github.com/dorpxam/67ad2bc222b2cf567d4a6fc298375e13 Debug.Assert(dev.type == DeviceType.CPU || dev.type== DeviceType.CUDA); device = dev; Enabled = enabled; - InitScale = init_scale; + _init_scale = init_scale; if (Enabled) { Debug.Assert(growth_factor > 1.0); Debug.Assert(backoff_factor < 1.0); @@ -49,12 +52,13 @@ public GradScaler(torch.Device dev, float init_scale = 2.0e16f, float growth_fac this._growth_factor = growth_factor; _backoff_factor = backoff_factor; _growth_interval = growth_interval; - InitGrowthTracker = 0.0f; + _init_growth_tracker = 0; - _per_optimizer_states.SetDefaultDict(_refresh_per_optimizer_state()); + //_per_optimizer_states.SetDefaultDict(_refresh_per_optimizer_state()); //throw new NotImplementedException("This need to finish"); } + private Tuple check_scale_growth_tracker(string name) { var fix = "This may indicate your script did not use scaler.scale(loss or outputs) earlier in the iteration."; @@ -68,12 +72,9 @@ private void LazyInitScaleGrowthTracker(torch.Device dev) { Debug.Assert(_growth_tracker is null, "_growth_tracker initialized before _scale"); - _scale = torch.full(1, InitScale, torch.ScalarType.Float32, device: dev); - _growth_tracker = torch.full(1, InitGrowthTracker, torch.ScalarType.Int32, device: dev); + _scale = torch.full(1, _init_scale, torch.ScalarType.Float32, device: dev); + _growth_tracker = torch.full(1, _init_growth_tracker, torch.ScalarType.Int32, device: dev); } - //private Dictionary - - //private check_scale_growth_tracker public torch.Tensor scale(torch.Tensor output) { if (!Enabled) @@ -86,8 +87,42 @@ public torch.Tensor scale(torch.Tensor output) public IList scale(IList outputs) { - apply_scale(outputs); - return outputs; + List stash = new List(); + + object ApplyScale(object value) + { + if (value is torch.Tensor tensor) { + Debug.Assert(tensor.device_type == DeviceType.CUDA || tensor.device_type == DeviceType.XLA); + + if (stash.Count == 0) // if (stash.empty()) + { + if (_scale is null || _scale.IsInvalid) { + LazyInitScaleGrowthTracker(tensor.device); + //_lazy_init_scale_growth_tracker(tensor.device); + } + + Debug.Assert(_scale is not null && !_scale.IsInvalid); + + stash.Add(new MultiDeviceReplicator(_scale)); // stash.push_back(...) + } + + // stash.front().get(...) + return tensor * stash[0].Get(tensor.device_type); + } + + if (value is IEnumerable innerIenumer) { + var res = new List(); + foreach (var item in innerIenumer) + res.Add(ApplyScale(item)); + return res; + } + + throw new Exception("Not supported"); + } + + return outputs.Select(x => (torch.Tensor)ApplyScale(x)).ToList(); + /*apply_scale(outputs); + return outputs;*/ } private class MultiDeviceReplicator { @@ -101,12 +136,11 @@ public MultiDeviceReplicator(torch.Tensor master_tensor) public torch.Tensor Get(DeviceType device) { - torch.Tensor retval=null; if (!per_device_tensors.ContainsKey(device)) { - retval = master.to(new torch.Device(device), true, non_blocking: true); + torch.Tensor retval = master.to(new torch.Device(device), copy:true, non_blocking: true); per_device_tensors.Add(device, retval); } - return retval; + return per_device_tensors[device]; } } @@ -125,7 +159,7 @@ private torch.Tensor apply_scale(torch.Tensor scale) private void apply_scale(IList scales) { for (int i = 0; i < scales.Count; i++) - scales[i] = apply_scale(scales[i]); + scales[i] = apply_scale(scales[i]); } public Dictionary unscale_grads(torch.optim.Optimizer optimizer, torch.Tensor inv_scale, torch.Tensor found_inf, bool allow_fp16) { @@ -177,30 +211,42 @@ private void apply_scale(IList scales) return per_device_found_inf.per_device_tensors; } - public void unscale(torch.optim.Optimizer optimizer) + private UnorderedMap get_per_optimizer_states(IntPtr ptr) + { + if (!_per_optimizer_states.ContainsKey(ptr)) + _per_optimizer_states[ptr] = _refresh_per_optimizer_state(); + return _per_optimizer_states[ptr]; + } + + private unsafe UnorderedMap get_per_optimizer_states(torch.optim.Optimizer optim) + { + IntPtr ptr = (IntPtr)Unsafe.AsPointer(ref optim); + return get_per_optimizer_states(ptr); + } + public unsafe void unscale(ref torch.optim.Optimizer optimizer) { if (!Enabled) return; check_scale_growth_tracker(nameof(unscale)); //if(_per_optimizer_states.ContainsKey(optimizer.GetHashCode())) - - var optimizer_state = _per_optimizer_states[optimizer.GetHashCode()]; + var optimizer_state = get_per_optimizer_states(optimizer); if (optimizer_state["stage"] is OptState state) { if (state == OptState.Unscaled) { - throw new Exception($"{nameof(unscale)} has already been called on this optimizer since the last update()"); - } - else if(state == OptState.Stepped) + throw new Exception( + $"{nameof(unscale)} has already been called on this optimizer since the last update()"); + } else if (state == OptState.Stepped) throw new Exception($"{nameof(unscale)} is being called after step()"); } Debug.Assert(!(_scale is null)); var inv_scale = _scale.to(torch.ScalarType.Float64).reciprocal().to(torch.ScalarType.Float32); - var found_inf = torch.full(1, 0.0f, torch.ScalarType.Float32,_scale.device); + var found_inf = torch.full(1, 0.0f, torch.ScalarType.Float32, _scale.device); optimizer_state["found_inf_per_device"] = unscale_grads(optimizer, inv_scale, found_inf, false); optimizer_state["stage"] = OptState.Unscaled; + } /* * @@ -220,16 +266,19 @@ private Scalar maybe_opt_step(torch.optim.Optimizer optimizer, UnorderedMap dict) { foreach (var d in dict) { - retval += (double)d.Value.item(); + //retval += d.Value.item(); + retval += d.Value.item(); //retval += d.Value.Sum(x=>x.item()); /*foreach(var t in d.Value) retval += t.item();*/ //retval += d.Value.item(); } + /*if (retval.HasValue) { if(retval.Value > 0) return @@ -240,10 +289,14 @@ private Scalar maybe_opt_step(torch.optim.Optimizer optimizer, UnorderedMap();*/ - var res = optimizer.step(closure); - if (!(res is null)) { - //return res.item(); - return res.ToScalar(); + if (retval.Value > 0) { + var res = optimizer.step(closure); + if (!(res is null)) { + //return res.item(); + return res.ToScalar(); + } + + return null; } /*if (retval == 0) @@ -254,11 +307,13 @@ private Scalar maybe_opt_step(torch.optim.Optimizer optimizer, UnorderedMap optimizer_args = null) + public unsafe Scalar step(torch.optim.Optimizer optimizer, Func optimizer_args = null) { if (!Enabled) { var res = optimizer.step(optimizer_args); - return res?.item(); + if(res is null) + return null; + return res.item(); } if (optimizer_args != null) @@ -271,10 +326,12 @@ public Scalar step(torch.optim.Optimizer optimizer, Func optimizer }*/ check_scale_growth_tracker(nameof(step)); - var optimizer_state = _per_optimizer_states[optimizer.GetHashCode()]; + + var optimizer_state = get_per_optimizer_states(optimizer); if (optimizer_state["stage"] is OptState state && state == OptState.Stepped) throw new Exception($"{nameof(step)} has already been called since the last update()"); + Scalar retval=null; //https://github.com/pytorch/pytorch/blob/a00fad017719346bac6e08da0819358146e647e3/torch/amp/grad_scaler.py#L398 @@ -304,13 +361,15 @@ public Scalar step(torch.optim.Optimizer optimizer, Func optimizer //DANGER: Optimizer in TorchSharp not have grad_scaler or found_inf, we need grad_scale for https://github.com/pytorch/pytorch/blob/758d78790164bfb041555daed380de96e06f78a3/torch/amp/grad_scaler.py#L440 //optimizer.GetType().GetField("grad_scale").GetValue(optimizer) as torch.Tensor t } - retval = optimizer.step().item(); + //retval = optimizer.step().item(); + retval = optimizer.step().ToScalar(); optimizer_state["stage"] = OptState.Stepped; //https://github.com/pytorch/pytorch/blob/758d78790164bfb041555daed380de96e06f78a3/torch/amp/grad_scaler.py#L445 return retval; } + if (optimizer_state["stage"] is OptState state1 && state1 == OptState.Ready) - unscale(optimizer); + unscale(ref optimizer); if (optimizer_state["found_inf_per_device"] is ICollection col) { Debug.Assert(col.Count > 0, "(optimizer_state['found_inf_per_device'] as torch.Tensor).size(0) > 0"); @@ -380,30 +439,39 @@ public void update(object new_scale = null) public void set_init_growth_tracker(long new_value) { - InitGrowthTracker=new_value; + _init_growth_tracker=new_value; } public torch.Tensor get_scale_async() { return _scale; } - public float get_scale() + public double get_scale() { - if (!this.Enabled) + if (Enabled) { + if (_scale is null) { + return _init_scale; + } else { + return _scale.item(); + } + } + + return 1.0; + /*if (!this.Enabled) return 1.0f; var scale = _get_scale_async(); if (scale is null) return InitScale; - return scale.item(); + return scale.item();*/ } - public float get_growth_factor() + public double get_growth_factor() { return _growth_factor; } - public float get_backoff_factor() + public double get_backoff_factor() { return _backoff_factor; } @@ -413,9 +481,20 @@ public int get_growth_interval() return _growth_interval; } - public float get_init_growth_tracker() + public long get_growth_tracker() + { + if (Enabled) { + if (_growth_tracker is null) + return _init_growth_tracker; + _growth_tracker.item(); + } + + return 0; + } + + public long get_init_growth_tracker() { - return InitGrowthTracker; //TODO: Resarch this... should be int64_t??? + return _init_growth_tracker; } public bool IsEnabled() { @@ -432,31 +511,41 @@ public UnorderedMap state_dict() res[nameof(_growth_factor)] = _growth_factor; res[nameof(_backoff_factor)] = _backoff_factor; res[nameof(_growth_interval)] = _growth_interval; - res[nameof(_growth_tracker)] = _growth_tracker; + res[nameof(_growth_tracker)] = get_growth_tracker(); return res; } - public void load_state_dict(Dictionary state_dict) + public void load(Dictionary state) { if (!Enabled) return; - if (state_dict.Count == 0) + if (state.Count == 0) throw new Exception("The source state dict is empty, possibly because it was saved from a disabled instance of GradScaler."); + _init_scale = (double)state["scale"]; + if (!(_scale is null)) + _scale.fill_(_init_scale); + _growth_factor = (double)state[nameof(_growth_factor)]; + _backoff_factor= (double)state[nameof(_backoff_factor)]; + _growth_interval = (int)state[nameof(_growth_interval )]; + _init_growth_tracker = (long)state[nameof(_growth_tracker)]; + if (!(_growth_tracker is null)) + _growth_tracker.fill_(_init_growth_tracker); //TODO: implement reflection to set field/properties based on state_dict } - torch.Tensor check_inf_per_device(torch.optim.Optimizer optimizer) + unsafe torch.Tensor check_inf_per_device(torch.optim.Optimizer optimizer) { _scale = check_scale_growth_tracker(nameof(check_inf_per_device)).Item1; var dummy_inv_scale = torch.full(new ReadOnlySpan(new long[] { 0 }), 1.0f, torch.ScalarType.Float32, _scale.device); var foundd_inf = torch.full(new ReadOnlySpan(new long[] { 0 }), 0.0f, torch.ScalarType.Float32, _scale.device); - _per_optimizer_states[optimizer.GetHashCode()]["found_inf_per_device"] = unscale_grads(optimizer, dummy_inv_scale, foundd_inf, true); - return _per_optimizer_states[optimizer.GetHashCode()]["found_inf_per_device"] as torch.Tensor; + var optimizer_state = get_per_optimizer_states(optimizer); + optimizer_state["found_inf_per_device"] = unscale_grads(optimizer, dummy_inv_scale, foundd_inf, true); + return optimizer_state["found_inf_per_device"] as torch.Tensor; } private object _found_inf_per_device(torch.optim.Optimizer optimizer) { - return _per_optimizer_states[optimizer.GetHashCode()]["found_inf_per_device"]; + return get_per_optimizer_states(optimizer)["found_inf_per_device"]; } protected virtual void Dispose(bool disposing) diff --git a/src/TorchSharp/BitsAndBytes/BitsAndByteUtils.cs b/src/TorchSharp/BitsAndBytes/BitsAndByteUtils.cs index f053e11ab..af039c887 100644 --- a/src/TorchSharp/BitsAndBytes/BitsAndByteUtils.cs +++ b/src/TorchSharp/BitsAndBytes/BitsAndByteUtils.cs @@ -2,11 +2,7 @@ using System.Collections.Generic; using System.Linq; using System.Reflection; -using System.Runtime.CompilerServices; -using System.Text; -using TorchSharp; using TorchSharp.PInvoke; -using TorchSharp.Utils; namespace TorchSharp.BitsAndBytes diff --git a/src/TorchSharp/BitsAndBytes/BitsAndBytesNatives.cs b/src/TorchSharp/BitsAndBytes/BitsAndBytesNatives.cs index 51a8902be..53d2fb892 100644 --- a/src/TorchSharp/BitsAndBytes/BitsAndBytesNatives.cs +++ b/src/TorchSharp/BitsAndBytes/BitsAndBytesNatives.cs @@ -1,17 +1,18 @@ using System; -using System.Collections.Generic; using System.Runtime.InteropServices; -using System.Text; +using System.Security; namespace TorchSharp.BitsAndBytes { + //BASED ON: https://github.com/LittleLittleCloud/TorchSharp.BitsAndBytes - public static class BitsAndBytesNatives + [System.Diagnostics.CodeAnalysis.SuppressMessage("Design", "CA1060:MovePInvokesToNativeMethodsClass", Justification = "Reviewed")] + static class BitsAndBytesNatives { private const string DllName = "libbitsandbytes"; [DllImport(DllName)] - public static extern void cdequantize_blockwise_fp32_fp4( + internal static extern void cdequantize_blockwise_fp32_fp4( IntPtr code, // float* IntPtr A, // float* IntPtr absmax, // float* @@ -21,7 +22,7 @@ public static extern void cdequantize_blockwise_fp32_fp4( IntPtr stream); [DllImport(DllName)] - public static extern void cdequantize_blockwise_fp32_nf4( + internal static extern void cdequantize_blockwise_fp32_nf4( IntPtr code, // float* IntPtr A, // float* IntPtr absmax, // float* @@ -31,7 +32,7 @@ public static extern void cdequantize_blockwise_fp32_nf4( IntPtr stream); [DllImport(DllName)] - public static extern void cdequantize_blockwise_fp16_fp4( + internal static extern void cdequantize_blockwise_fp16_fp4( IntPtr code, // float* IntPtr A, // float* IntPtr absmax, // float* @@ -41,7 +42,7 @@ public static extern void cdequantize_blockwise_fp16_fp4( IntPtr stream); [DllImport(DllName)] - public static extern void cdequantize_blockwise_fp16_nf4( + internal static extern void cdequantize_blockwise_fp16_nf4( IntPtr code, // float* IntPtr A, // float* IntPtr absmax, // float* @@ -51,7 +52,7 @@ public static extern void cdequantize_blockwise_fp16_nf4( IntPtr stream); [DllImport(DllName)] - public static extern void cdequantize_blockwise_bf16_fp4( + internal static extern void cdequantize_blockwise_bf16_fp4( IntPtr code, // float* IntPtr A, // float* IntPtr absmax, // float* @@ -61,7 +62,7 @@ public static extern void cdequantize_blockwise_bf16_fp4( IntPtr stream); [DllImport(DllName)] - public static extern void cdequantize_blockwise_bf16_nf4( + internal static extern void cdequantize_blockwise_bf16_nf4( IntPtr code, // float* IntPtr A, // float* IntPtr absmax, // float* @@ -72,7 +73,7 @@ IntPtr stream ); [DllImport(DllName)] - public static extern void cquantize_blockwise_fp32_fp4( + internal static extern void cquantize_blockwise_fp32_fp4( IntPtr code, // float* IntPtr A, // float* IntPtr absmax, // float* @@ -82,7 +83,7 @@ int n // total size ); [DllImport(DllName)] - public static extern void cquantize_blockwise_fp32_nf4( + internal static extern void cquantize_blockwise_fp32_nf4( IntPtr code, // float* IntPtr A, // float* IntPtr absmax, // float* @@ -92,7 +93,7 @@ int n // total size ); [DllImport(DllName)] - public static extern void cquantize_blockwise_fp32( + internal static extern void cquantize_blockwise_fp32( IntPtr code, // float* IntPtr A, // float* IntPtr absmax, // float* @@ -102,7 +103,7 @@ int n // total size ); [DllImport(DllName)] - public static extern void cquantize_blockwise_fp16_fp4( + internal static extern void cquantize_blockwise_fp16_fp4( IntPtr code, // float* IntPtr A, // float* IntPtr absmax, // float* @@ -112,7 +113,7 @@ int n // total size ); [DllImport(DllName)] - public static extern void cquantize_blockwise_fp16_nf4( + internal static extern void cquantize_blockwise_fp16_nf4( IntPtr code, // float* IntPtr A, // float* IntPtr absmax, // float* @@ -122,7 +123,7 @@ int n // total size ); [DllImport(DllName, CallingConvention = CallingConvention.Cdecl)] - public static extern void cquantize_blockwise_bf16_fp4( + internal static extern void cquantize_blockwise_bf16_fp4( IntPtr code, // float* IntPtr A, // __nv_bfloat16* IntPtr absmax, // float* @@ -132,7 +133,7 @@ int n // total size ); [DllImport(DllName, CallingConvention = CallingConvention.Cdecl)] - public static extern void cquantize_blockwise_bf16_nf4( + internal static extern void cquantize_blockwise_bf16_nf4( IntPtr code, // float* IntPtr A, // __nv_bfloat16* IntPtr absmax, // float* @@ -142,7 +143,7 @@ int n // total size ); [DllImport(DllName)] - public static extern void cgemm_4bit_inference_naive_fp16( + internal static extern void cgemm_4bit_inference_naive_fp16( int m, int n, int k, @@ -159,7 +160,7 @@ IntPtr stream // cudaStream_t ); [DllImport(DllName)] - public static extern void cgemm_4bit_inference_naive_fp32( + internal static extern void cgemm_4bit_inference_naive_fp32( int m, int n, int k, @@ -176,7 +177,7 @@ IntPtr stream // cudaStream_t ); [DllImport(DllName)] - public static extern void cgemm_4bit_inference_naive_bf16( + internal static extern void cgemm_4bit_inference_naive_bf16( int m, int n, int k, @@ -193,7 +194,7 @@ IntPtr stream // cudaStream_t ); [DllImport(DllName, CallingConvention = CallingConvention.Cdecl)] - public static extern void dequantize( + internal static extern void dequantize( IntPtr output, // float* IntPtr input, // byte* IntPtr scale, // float* @@ -202,7 +203,7 @@ IntPtr stream // cudaStream_t ); [DllImport(DllName, CallingConvention = CallingConvention.Cdecl)] - public static extern void cigemm( + internal static extern void cigemm( IntPtr context, bool transposeA, bool transposeB, @@ -217,9 +218,9 @@ public static extern void cigemm( int ldc); [DllImport(DllName, CallingConvention = CallingConvention.Cdecl)] - public static extern IntPtr get_context(); + internal static extern IntPtr get_context(); [DllImport(DllName, CallingConvention = CallingConvention.Cdecl)] - public static extern IntPtr get_cusparse(); + internal static extern IntPtr get_cusparse(); } } diff --git a/src/TorchSharp/Tensor/Storage.cs b/src/TorchSharp/Tensor/Storage.cs index 797132b7d..5210212a9 100644 --- a/src/TorchSharp/Tensor/Storage.cs +++ b/src/TorchSharp/Tensor/Storage.cs @@ -119,6 +119,16 @@ protected static Tensor CreateTypedTensor(ScalarType dtype, IList rawArray /// public Storage @float() => _tensor.to_type(ScalarType.Float32).storage(); + /// + /// Convert to half storage. + /// + public Storage @half() => _tensor.to_type(ScalarType.Float16).storage(); + + /// + /// Convert to bfloat16 storage. + /// + public Storage @bfloat16() => _tensor.to_type(ScalarType.BFloat16).storage(); + /// /// Convert to double storage. /// diff --git a/src/TorchSharp/Tensor/Tensor.cs b/src/TorchSharp/Tensor/Tensor.cs index 378534141..3ee2e5cf2 100644 --- a/src/TorchSharp/Tensor/Tensor.cs +++ b/src/TorchSharp/Tensor/Tensor.cs @@ -962,7 +962,8 @@ public Tensor to(ScalarType type, torch.Device device, bool copy = false, bool d public Tensor to(torch.Device device, ScalarType type, bool non_blocking) { torch.InitializeDevice(device); - return ReturnCheckForErrors(res = NativeMethods.THSTensor_to_type_and_device_and_non_blocking(Handle, (sbyte)type, (int)device.type, device.index, non_blocking)); + + return ReturnCheckForErrors(NativeMethods.THSTensor_to_type_and_device_and_non_blocking(Handle, (sbyte)type, (int)device.type, device.index, non_blocking)); } /// @@ -1767,7 +1768,7 @@ public Tensor select(long dim, long index) /// The indices into tensor, an Int64 tensor. public Tensor take(Tensor index) { - return ReturnCheckForErrors((NativeMethods.THSTensor_take(Handle, index.Handle)); + return ReturnCheckForErrors(NativeMethods.THSTensor_take(Handle, index.Handle)); } /// @@ -3287,7 +3288,9 @@ public Tensor eq_(Scalar target) public bool Equals(Tensor target) { if (target is null) return false; - return ReturnCheckForErrors(NativeMethods.THSTensor_equal(Handle, target.Handle)); + var res = NativeMethods.THSTensor_equal(Handle, target.Handle); + CheckForErrors(); + return res; } /// diff --git a/src/TorchSharp/Torch.cs b/src/TorchSharp/Torch.cs index b3b8f32e1..3d094f3b3 100644 --- a/src/TorchSharp/Torch.cs +++ b/src/TorchSharp/Torch.cs @@ -675,6 +675,7 @@ public static Tensor ReturnCheckForErrors(IntPtr ptr) CheckForErrors(); return new Tensor(ptr); } + [MethodImpl(MethodImplOptions.AggressiveInlining)] public static Tensor? ReturnNullCheckForErrors(IntPtr ptr) { diff --git a/src/TorchSharp/TorchSharp.csproj b/src/TorchSharp/TorchSharp.csproj index 73c8c6069..1540c23da 100644 --- a/src/TorchSharp/TorchSharp.csproj +++ b/src/TorchSharp/TorchSharp.csproj @@ -11,6 +11,7 @@ false false $(DefineConstants);LIBTORCH_$(LibTorchPackageVersion.Replace('.', '_'));CUDA_$(CudaVersionDot.Replace('.', '_')) + K:\FrameworksForC\LibTorch\libtorch-win-shared-with-deps-2.11.0+cpu\libtorch\share\cmake\Torch @@ -38,6 +39,7 @@ + diff --git a/src/TorchSharp/Utils/GetSubArray.cs b/src/TorchSharp/Utils/GetSubArray.cs index ddaab4ed2..10ab1de6b 100644 --- a/src/TorchSharp/Utils/GetSubArray.cs +++ b/src/TorchSharp/Utils/GetSubArray.cs @@ -1,5 +1,5 @@ //NOTE: This make compatibility of Range with NetStandard2.0 may need include System.Runtime.InteropServices.RuntimeInformation - +/* #if NETSTANDARD2_0 #region License // MIT License @@ -56,4 +56,4 @@ public static T[] GetSubArray(T[] array, Range range) } } } -#endif \ No newline at end of file +#endif*/ \ No newline at end of file diff --git a/src/TorchSharp/Utils/ObjectReferenceEqualityComparer.cs b/src/TorchSharp/Utils/ObjectReferenceEqualityComparer.cs index 205f94c42..9d5daf41a 100644 --- a/src/TorchSharp/Utils/ObjectReferenceEqualityComparer.cs +++ b/src/TorchSharp/Utils/ObjectReferenceEqualityComparer.cs @@ -15,6 +15,13 @@ public class ReferenceEqualityComparer : EqualityComparer private static IEqualityComparer _defaultComparer; public new static IEqualityComparer Default => _defaultComparer ??= new ReferenceEqualityComparer(); public override bool Equals(T x, T y) => ReferenceEquals(x, y); - public override int GetHashCode(T obj) => RuntimeHelpers.GetHashCode(obj); + public override int GetHashCode(T obj) + { +#if NETSTANDARD2_0 + return obj.GetHashCode(); +#else + return RuntimeHelpers.GetHashCode(obj); +#endif + } } } \ No newline at end of file diff --git a/src/TorchSharp/Utils/TensorAccessor.cs b/src/TorchSharp/Utils/TensorAccessor.cs index 5e095a126..7051ba82c 100644 --- a/src/TorchSharp/Utils/TensorAccessor.cs +++ b/src/TorchSharp/Utils/TensorAccessor.cs @@ -251,26 +251,51 @@ private void CopyContiguous(T[] array, int index = 0, int count = 0) count = (int)Cnt; if (Cnt > array.Length) count = array.Length + index; - if (array is byte[] ba) + //NOTE: The return of every check is for prevent consume more cycle CPU checking the next when one is acomplished + //I Mean, if array is char[] will copy and return. Not need check long[] or float[] because is char[] + if (array is byte[] ba) { Marshal.Copy(_tensor_data_ptr, ba, index, count); - if (array is short[] sa) + return; + } + if (array is short[] sa) { Marshal.Copy(_tensor_data_ptr, sa, index, count); - if (array is char[] ca) + return; + } + if (array is char[] ca) { Marshal.Copy(_tensor_data_ptr, ca, index, count); - if (array is long[] la) + return; + } + if (array is long[] la) { Marshal.Copy(_tensor_data_ptr, la, index, count); - if (array is float[] fa) + return; + } + if (array is float[] fa) { Marshal.Copy(_tensor_data_ptr, fa, index, count); - if (array is int[] ia) + return; + } + if (array is int[] ia) { Marshal.Copy(_tensor_data_ptr, ia, index, count); - if (array is double[] da) + return; + } + if (array is double[] da) { Marshal.Copy(_tensor_data_ptr, da, index, count); + return; + } if (array is Half[] ha) { - throw new NotImplementedException(); + + //TODO: Test this +#if NETSTANDARD2_0 + Marshal.Copy(_tensor_data_ptr, ha.Select(HalfHelper.HalfToSingle).ToArray(), index, count); +#else + Marshal.Copy(_tensor_data_ptr, ha.Select(x=> (float)x).ToArray(), index, count); + //throw new NotImplementedException(); +#endif + return; } if (array is BFloat16[] bfa) { //TODO: Test this Marshal.Copy(_tensor_data_ptr, bfa.Select(x=>x.ToFloat()).ToArray(), index, count); + return; } } @@ -305,8 +330,6 @@ public void CopyFrom(T[] array, int arrayIndex = 0, long tensorIndex = 0) public void CopyFrom(ReadOnlySpan array, int arrayIndex = 0, long tensorIndex = 0) { unsafe { - /*var arr = array.ToArray(); - SetValueTensor(ref arr, _tensor.shape, _tensor.stride(), Count, 0, true);*/ T* ptr = GetAndValidatePTR(); long count = Count; var shape = _tensor.shape; @@ -422,7 +445,6 @@ internal static T ReadItemAt(torch.Tensor tensor, long index) return !(left == right); } - private IEnumerable GetSubsequentIndices(long startingIndex) { if (startingIndex < 0 || startingIndex >= Count) diff --git a/test/Directory.Build.props b/test/Directory.Build.props index a276d98e8..5f45594de 100644 --- a/test/Directory.Build.props +++ b/test/Directory.Build.props @@ -6,7 +6,7 @@ $(TargetFrameworks);net48;netstandard2.0 false true - + K:\FrameworksForC\LibTorch\libtorch-win-shared-with-deps-2.11.0+cpu\libtorch\share\cmake\Torch @@ -88,7 +88,7 @@ - 2.7.1.0 + 2.11.0.0 2.2.2.0 false $(LibTorchPackageVersion) diff --git a/TorchSharp.sln b/TorchSharp.sln index e20f6af0c..9e2c41299 100644 --- a/TorchSharp.sln +++ b/TorchSharp.sln @@ -1,4 +1,5 @@ -Microsoft Visual Studio Solution File, Format Version 12.00 + +Microsoft Visual Studio Solution File, Format Version 12.00 # Visual Studio Version 17 VisualStudioVersion = 17.0.31903.59 MinimumVisualStudioVersion = 10.0.40219.1 @@ -34,9 +35,9 @@ Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "TorchSharp", "TorchSharp", pkg\TorchSharp\TorchSharp.symbols.nupkgproj = pkg\TorchSharp\TorchSharp.symbols.nupkgproj EndProjectSection EndProject -Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "LibTorchSharp", "bin\obj\x64.Debug\Native\LibTorchSharp\LibTorchSharp.vcxproj", "{E7467DDF-893C-38A8-8E19-6B4E3FB10F55}" +Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "LibTorchSharp", "bin\obj\x64.Debug\Native\LibTorchSharp\LibTorchSharp.vcxproj", "{265C2E6F-04E6-37A8-B504-E3DD4A3FEE06}" EndProject -Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "LibTorchSharp", "bin\obj\x64.Release\Native\LibTorchSharp\LibTorchSharp.vcxproj", "{E4C0DBEE-0815-311B-9065-137BB50BD793}" +Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "LibTorchSharp", "bin\obj\x64.Release\Native\LibTorchSharp\LibTorchSharp.vcxproj", "{BB811429-0DF1-3D22-B664-09C2F5A9E0AB}" EndProject Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Native-Debug", "Native-Debug", "{CF2C1A9E-3A8A-4329-8A6E-7880C15AAC3D}" ProjectSection(SolutionItems) = preProject @@ -107,14 +108,10 @@ Global {42B45168-476D-4BFA-87B8-81A34E6295CD}.Release|Any CPU.Build.0 = Release|Any CPU {42B45168-476D-4BFA-87B8-81A34E6295CD}.Release|x64.ActiveCfg = Release|Any CPU {42B45168-476D-4BFA-87B8-81A34E6295CD}.Release|x64.Build.0 = Release|Any CPU - {2B359162-062E-3C52-91D3-027A8542A58C}.Debug|Any CPU.ActiveCfg = Debug|x64 - {2B359162-062E-3C52-91D3-027A8542A58C}.Debug|x64.ActiveCfg = Debug|x64 - {2B359162-062E-3C52-91D3-027A8542A58C}.Release|Any CPU.ActiveCfg = Release|x64 - {2B359162-062E-3C52-91D3-027A8542A58C}.Release|x64.ActiveCfg = Release|x64 - {E4C0DBEE-0815-311B-9065-137BB50BD793}.Debug|Any CPU.ActiveCfg = Debug|x64 - {E4C0DBEE-0815-311B-9065-137BB50BD793}.Debug|x64.ActiveCfg = Debug|x64 - {E4C0DBEE-0815-311B-9065-137BB50BD793}.Release|Any CPU.ActiveCfg = Release|x64 - {E4C0DBEE-0815-311B-9065-137BB50BD793}.Release|x64.ActiveCfg = Release|x64 + {265C2E6F-04E6-37A8-B504-E3DD4A3FEE06}.Debug|Any CPU.ActiveCfg = Debug|x64 + {265C2E6F-04E6-37A8-B504-E3DD4A3FEE06}.Debug|x64.ActiveCfg = Debug|x64 + {265C2E6F-04E6-37A8-B504-E3DD4A3FEE06}.Release|Any CPU.ActiveCfg = Release|x64 + {265C2E6F-04E6-37A8-B504-E3DD4A3FEE06}.Release|x64.ActiveCfg = Release|x64 {DD652544-711E-4029-83FF-DA4A9600E6E7}.Debug|Any CPU.ActiveCfg = Debug|Any CPU {DD652544-711E-4029-83FF-DA4A9600E6E7}.Debug|Any CPU.Build.0 = Debug|Any CPU {DD652544-711E-4029-83FF-DA4A9600E6E7}.Debug|x64.ActiveCfg = Debug|Any CPU @@ -180,8 +177,8 @@ Global {6C323B05-9028-4B09-911C-3C03AE058BEE} = {AED9C836-31E3-4F3F-8ABC-929555D3F3C4} {42B45168-476D-4BFA-87B8-81A34E6295CD} = {09EADF06-BE25-4228-AB53-95AE3E15B530} {567456AD-B026-4CB6-B98D-4FC930C90223} = {D3D38B03-B557-484D-8348-8BADEE4DF592} - {2B359162-062E-3C52-91D3-027A8542A58C} = {CF2C1A9E-3A8A-4329-8A6E-7880C15AAC3D} - {E4C0DBEE-0815-311B-9065-137BB50BD793} = {4DB9E84D-324C-408F-87A6-246E86205540} + {265C2E6F-04E6-37A8-B504-E3DD4A3FEE06} = {CF2C1A9E-3A8A-4329-8A6E-7880C15AAC3D} + {BB811429-0DF1-3D22-B664-09C2F5A9E0AB} = {4DB9E84D-324C-408F-87A6-246E86205540} {CF2C1A9E-3A8A-4329-8A6E-7880C15AAC3D} = {09EADF06-BE25-4228-AB53-95AE3E15B530} {D8C60CD8-8429-45F2-A755-47B6CD10FDF8} = {09EADF06-BE25-4228-AB53-95AE3E15B530} {4DB9E84D-324C-408F-87A6-246E86205540} = {CF2C1A9E-3A8A-4329-8A6E-7880C15AAC3D} diff --git a/build/Dependencies.props b/build/Dependencies.props index 6d3d32065..66db7815c 100644 --- a/build/Dependencies.props +++ b/build/Dependencies.props @@ -9,7 +9,7 @@ 2.7.1 2.2.2 - 12.8 + 13.0 128 2019.0.5.20190502 diff --git a/src/TorchSharp/Amp/GradScaler.cs b/src/TorchSharp/Amp/GradScaler.cs index d0d3488c0..cff0bcf2e 100644 --- a/src/TorchSharp/Amp/GradScaler.cs +++ b/src/TorchSharp/Amp/GradScaler.cs @@ -37,7 +37,7 @@ private UnorderedMap _refresh_per_optimizer_state() }; } //https://github.com/pytorch/pytorch/blob/main/torch/amp/grad_scaler.py - public GradScaler(torch.Device dev, double init_scale = 2.0e16, double growth_factor = 2.0, + public GradScaler(torch.Device dev, double init_scale = 65536, double growth_factor = 2.0, double backoff_factor = 0.5, int growth_interval = 2000, bool enabled = true) { //https://gist.github.com/dorpxam/67ad2bc222b2cf567d4a6fc298375e13 diff --git a/src/TorchSharp/Torch.cs b/src/TorchSharp/Torch.cs index 3d094f3b3..0906c0f81 100644 --- a/src/TorchSharp/Torch.cs +++ b/src/TorchSharp/Torch.cs @@ -21,14 +21,20 @@ namespace TorchSharp public static partial class torch { #if LIBTORCH_2_2_2_0 - const string libtorchPackageVersion = "2.2.2.0"; -#elif LIBTORCH_2_4_0_0 - const string libtorchPackageVersion = "2.4.0.0"; + const string libtorchPackageVersion = "2.2.2.0"; +#elif LIBTORCH_2_10_0_0 + const string libtorchPackageVersion = "2.10.0.0"; +#elif LIBTORCH_2_11_0_0 + const string libtorchPackageVersion = "2.11.0.0"; +#elif LIBTORCH_2_7_1_0 + const string libtorchPackageVersion = "2.7.1.0"; #else #error "Please update libtorchPackageVersion to match LibTorchPackageVersion" #endif -#if CUDA_12_1 - const string cudaVersion = "12.1"; +#if CUDA_12_8 + const string cudaVersion = "12.8"; +#elif CUDA_13_0 + const string cudaVersion = "13.0"; #else #error "Please update cudaVersion to match CudaVersionDot" #endif diff --git a/src/TorchSharp/TorchSharp.csproj b/src/TorchSharp/TorchSharp.csproj index 4d7ab7830..0134bb7a5 100644 --- a/src/TorchSharp/TorchSharp.csproj +++ b/src/TorchSharp/TorchSharp.csproj @@ -67,6 +67,7 @@ 4 + $(DefineConstants);CUDA_$(CudaVersionDot.Replace('.', '_'));LIBTORCH_$(LibTorchPackageVersion.Replace('.', '_')) From 485e67ba3cff540191f0abfa4d6bcb346717876f Mon Sep 17 00:00:00 2001 From: Dimitri Date: Sun, 26 Apr 2026 01:19:42 -0300 Subject: [PATCH 63/65] Some test gradscaler work, need more fix. --- Directory.Build.props | 6 +- MyCustomCMD.txt | 4 +- ...eRestitcher.Tests.csproj.nuget.dgspec.json | 10 +- ...ework,Version=v4.7.2.AssemblyAttributes.cs | 4 + .../FileRestitcher.Tests.AssemblyInfo.cs | 24 +++ ...eRestitcher.Tests.AssemblyInfoInputs.cache | 1 + ....GeneratedMSBuildEditorConfig.editorconfig | 8 + .../net472/FileRestitcher.Tests.assets.cache | Bin 0 -> 3584 bytes ...tcher.Tests.csproj.AssemblyReference.cache | Bin 0 -> 9907 bytes ...CoreApp,Version=v8.0.AssemblyAttributes.cs | 4 + .../FileRestitcher.Tests.AssemblyInfo.cs | 24 +++ ...eRestitcher.Tests.AssemblyInfoInputs.cache | 1 + ....GeneratedMSBuildEditorConfig.editorconfig | 15 ++ .../FileRestitcher.csproj.nuget.dgspec.json | 10 +- ...tandard,Version=v2.0.AssemblyAttributes.cs | 4 + .../FileRestitcher.AssemblyInfo.cs | 24 +++ .../FileRestitcher.AssemblyInfoInputs.cache | 1 + ....GeneratedMSBuildEditorConfig.editorconfig | 8 + .../FileRestitcher.assets.cache | Bin 0 -> 493 bytes ...eRestitcher.csproj.AssemblyReference.cache | Bin 0 -> 66545 bytes .../project.assets.json | 14 +- .../FileRestitcher/FileRestitcher.csproj | 2 +- src/Examples.Utils/Examples.Utils.csproj | 2 +- src/Native/LibTorchSharp/THSAutograd.cpp | 11 +- src/Native/LibTorchSharp/THSTensor.cpp | 2 +- src/Native/LibTorchSharp/THSTorch.cpp | 8 +- src/Native/LibTorchSharp/THSTorch.h | 2 +- src/TorchSharp/Amp/AutocastMode.cs | 6 +- src/TorchSharp/Autograd.cs | 8 +- src/TorchSharp/NN/Activation/CELU.cs | 28 +-- src/TorchSharp/NN/Activation/ELU.cs | 29 +-- src/TorchSharp/NN/Activation/GELU.cs | 52 ++--- src/TorchSharp/NN/Activation/GLU.cs | 28 +-- src/TorchSharp/NN/Activation/Hardshrink.cs | 40 ++-- src/TorchSharp/NN/Activation/Hardsigmoid.cs | 4 +- src/TorchSharp/NN/Activation/Hardswish.cs | 2 +- src/TorchSharp/NN/Activation/Hardtanh.cs | 36 ++-- src/TorchSharp/NN/Activation/LeakyReLU.cs | 28 +-- src/TorchSharp/NN/Activation/LogSigmoid.cs | 2 +- src/TorchSharp/NN/Activation/LogSoftMax.cs | 19 +- src/TorchSharp/NN/Activation/Mish.cs | 49 +++-- src/TorchSharp/NN/Activation/PReLU.cs | 31 +-- src/TorchSharp/NN/Activation/RReLU.cs | 33 ++- src/TorchSharp/NN/Activation/ReLU6.cs | 27 +-- src/TorchSharp/NN/Activation/ReLu.cs | 24 +-- src/TorchSharp/NN/Activation/SELU.cs | 24 +-- src/TorchSharp/NN/Activation/SiLU.cs | 29 +-- src/TorchSharp/NN/Activation/Sigmoid.cs | 45 +++-- src/TorchSharp/NN/Activation/Softmax.cs | 27 +-- src/TorchSharp/NN/Activation/Softmax2d.cs | 27 +-- src/TorchSharp/NN/Activation/Softmin.cs | 31 +-- src/TorchSharp/NN/Activation/Softplus.cs | 36 ++-- src/TorchSharp/NN/Activation/Softshrink.cs | 39 ++-- src/TorchSharp/NN/Activation/Softsign.cs | 49 +++-- src/TorchSharp/NN/Activation/Tanh.cs | 28 ++- src/TorchSharp/NN/Activation/Tanhshrink.cs | 48 +++-- src/TorchSharp/NN/Activation/Threshold.cs | 45 +++-- src/TorchSharp/NN/Bilinear.cs | 116 ++++++++--- src/TorchSharp/NN/Convolution/Conv1D.cs | 165 ++++++--------- src/TorchSharp/NN/Convolution/Conv2D.cs | 191 +++++++----------- src/TorchSharp/NN/Convolution/Conv3D.cs | 135 +++++++------ .../NN/Convolution/ConvTranspose1D.cs | 49 ++--- .../NN/Convolution/ConvTranspose2D.cs | 67 ++---- .../NN/Convolution/ConvTranspose3D.cs | 66 ++---- src/TorchSharp/NN/Convolution/Convolution.cs | 9 +- .../NN/Convolution/ConvolutionTranspose.cs | 2 +- src/TorchSharp/NN/CosineSimilarity.cs | 8 +- src/TorchSharp/NN/Dropout.cs | 6 +- src/TorchSharp/NN/Dropout1d.cs | 2 +- src/TorchSharp/NN/Dropout2d.cs | 18 +- src/TorchSharp/NN/Dropout3d.cs | 18 +- src/TorchSharp/NN/Embedding.cs | 6 +- src/TorchSharp/NN/EmbeddingBag.cs | 4 +- src/TorchSharp/NN/FeatureDropout.cs | 38 ++-- src/TorchSharp/NN/Flatten.cs | 31 ++- src/TorchSharp/NN/Fold.cs | 11 +- src/TorchSharp/NN/Identity.cs | 18 +- src/TorchSharp/NN/Linear.cs | 57 +++--- src/TorchSharp/NN/Module.cs | 4 +- .../NN/Normalization/BatchNorm1D.cs | 90 ++------- .../NN/Normalization/BatchNorm2D.cs | 90 ++------- .../NN/Normalization/BatchNorm3D.cs | 92 ++------- src/TorchSharp/NN/Normalization/Functional.cs | 20 +- src/TorchSharp/NN/Normalization/GroupNorm.cs | 98 ++++++--- .../NN/Normalization/InstanceNorm.cs | 19 +- .../NN/Normalization/InstanceNorm1d.cs | 90 ++------- .../NN/Normalization/InstanceNorm2d.cs | 90 ++------- .../NN/Normalization/InstanceNorm3d.cs | 92 ++------- src/TorchSharp/NN/Normalization/LayerNorm.cs | 25 +-- .../NN/Normalization/LocalResponseNorm.cs | 38 +++- src/TorchSharp/NN/Normalization/NormBase.cs | 38 ++-- src/TorchSharp/NN/Padding/ConstantPad1d.cs | 30 +-- src/TorchSharp/NN/Padding/ConstantPad2d.cs | 30 +-- src/TorchSharp/NN/Padding/ConstantPad3d.cs | 30 +-- src/TorchSharp/NN/Padding/PadBase.cs | 2 +- src/TorchSharp/NN/Padding/ReflectionPad1d.cs | 30 +-- src/TorchSharp/NN/Padding/ReflectionPad2d.cs | 30 +-- src/TorchSharp/NN/Padding/ReflectionPad3d.cs | 30 +-- src/TorchSharp/NN/Padding/ReplicationPad1d.cs | 30 +-- src/TorchSharp/NN/Padding/ReplicationPad2d.cs | 32 +-- src/TorchSharp/NN/Padding/ReplicationPad3d.cs | 30 +-- src/TorchSharp/NN/Padding/ZeroPad2d.cs | 30 +-- src/TorchSharp/NN/PairwiseDistance.cs | 40 ++-- src/TorchSharp/NN/Parameter.cs | 14 -- src/TorchSharp/NN/PixelShuffle.cs | 35 ++-- src/TorchSharp/NN/PixelUnshuffle.cs | 35 ++-- .../NN/Pooling/AdaptiveAvgPool1D.cs | 29 ++- .../NN/Pooling/AdaptiveAvgPool2D.cs | 55 +++-- .../NN/Pooling/AdaptiveAvgPool3D.cs | 62 +++--- .../NN/Pooling/AdaptiveMaxPool1D.cs | 61 +++--- .../NN/Pooling/AdaptiveMaxPool2D.cs | 61 +++--- .../NN/Pooling/AdaptiveMaxPool3D.cs | 61 +++--- src/TorchSharp/NN/Pooling/AvgPool1D.cs | 59 ++---- src/TorchSharp/NN/Pooling/AvgPool2D.cs | 170 ++++++++-------- src/TorchSharp/NN/Pooling/AvgPool3D.cs | 124 ++++++------ .../NN/Pooling/FractionalMaxPool2d.cs | 165 ++++++++++++--- .../NN/Pooling/FractionalMaxPool3d.cs | 178 +++++++++++++--- src/TorchSharp/NN/Pooling/LPPool1d.cs | 64 +++--- src/TorchSharp/NN/Pooling/LPPool2d.cs | 76 +++++-- src/TorchSharp/NN/Pooling/MaxPool1D.cs | 81 ++++---- src/TorchSharp/NN/Pooling/MaxPool2D.cs | 153 +++++++------- src/TorchSharp/NN/Pooling/MaxPool3D.cs | 92 ++++----- src/TorchSharp/NN/Pooling/MaxUnpool1d.cs | 62 +++--- src/TorchSharp/NN/Pooling/MaxUnpool2d.cs | 69 +++---- src/TorchSharp/NN/Pooling/MaxUnpool3d.cs | 84 ++++---- src/TorchSharp/NN/Recurrent/GRU.cs | 3 +- src/TorchSharp/NN/Recurrent/GRUCell.cs | 7 +- src/TorchSharp/NN/Recurrent/LSTM.cs | 3 +- src/TorchSharp/NN/Recurrent/LSTMCell.cs | 7 +- src/TorchSharp/NN/Recurrent/RNN.cs | 3 +- src/TorchSharp/NN/Recurrent/RNNCell.cs | 7 +- src/TorchSharp/NN/Unflatten.cs | 29 +-- src/TorchSharp/NN/Unfold.cs | 7 +- src/TorchSharp/NN/Upsample.cs | 133 ++++++------ src/TorchSharp/NN/Vision.cs | 54 +++-- .../PInvoke/LibTorchSharp.THSTensor.cs | 2 +- .../PInvoke/LibTorchSharp.THSTorch.cs | 9 +- src/TorchSharp/Scalar.cs | 20 +- src/TorchSharp/Tensor/Tensor.cs | 105 ++++++++-- .../Tensor/TensorTyped.handwritten.cs | 18 +- ...torch.IndexingSlicingJoiningMutatingOps.cs | 153 +++++++------- src/TorchSharp/Tensor/torch.Utilities.cs | 4 +- src/TorchSharp/Torch.cs | 13 +- src/TorchSharp/TorchSharp.csproj | 19 +- src/TorchSharp/Utils/BFloat16.cs | 37 +--- src/TorchSharp/Utils/ModuleInfo.cs | 13 +- src/TorchSharp/Utils/TensorAccessor.cs | 2 +- src/TorchVision/Ops/DeformConv2d.cs | 3 +- test/Directory.Build.props | 2 +- .../TorchSharpTest.WithCudaBinaries.csproj | 4 +- test/TorchSharpTest/TestGradScaler.cs | 57 +++++- test/TorchSharpTest/TorchSharpTest.csproj | 2 +- test/notebooks/NativeCudaLoadLinux.ipynb | 24 +-- 153 files changed, 2776 insertions(+), 2964 deletions(-) create mode 100644 pkg/FileRestitcher/FileRestitcher.Tests/FileRestitcher.Tests.NupkgProj/net472/.NETFramework,Version=v4.7.2.AssemblyAttributes.cs create mode 100644 pkg/FileRestitcher/FileRestitcher.Tests/FileRestitcher.Tests.NupkgProj/net472/FileRestitcher.Tests.AssemblyInfo.cs create mode 100644 pkg/FileRestitcher/FileRestitcher.Tests/FileRestitcher.Tests.NupkgProj/net472/FileRestitcher.Tests.AssemblyInfoInputs.cache create mode 100644 pkg/FileRestitcher/FileRestitcher.Tests/FileRestitcher.Tests.NupkgProj/net472/FileRestitcher.Tests.GeneratedMSBuildEditorConfig.editorconfig create mode 100644 pkg/FileRestitcher/FileRestitcher.Tests/FileRestitcher.Tests.NupkgProj/net472/FileRestitcher.Tests.assets.cache create mode 100644 pkg/FileRestitcher/FileRestitcher.Tests/FileRestitcher.Tests.NupkgProj/net472/FileRestitcher.Tests.csproj.AssemblyReference.cache create mode 100644 pkg/FileRestitcher/FileRestitcher.Tests/FileRestitcher.Tests.NupkgProj/net8.0/.NETCoreApp,Version=v8.0.AssemblyAttributes.cs create mode 100644 pkg/FileRestitcher/FileRestitcher.Tests/FileRestitcher.Tests.NupkgProj/net8.0/FileRestitcher.Tests.AssemblyInfo.cs create mode 100644 pkg/FileRestitcher/FileRestitcher.Tests/FileRestitcher.Tests.NupkgProj/net8.0/FileRestitcher.Tests.AssemblyInfoInputs.cache create mode 100644 pkg/FileRestitcher/FileRestitcher.Tests/FileRestitcher.Tests.NupkgProj/net8.0/FileRestitcher.Tests.GeneratedMSBuildEditorConfig.editorconfig create mode 100644 pkg/FileRestitcher/FileRestitcher/FileRestitcher.NupkgProj/netstandard2.0/.NETStandard,Version=v2.0.AssemblyAttributes.cs create mode 100644 pkg/FileRestitcher/FileRestitcher/FileRestitcher.NupkgProj/netstandard2.0/FileRestitcher.AssemblyInfo.cs create mode 100644 pkg/FileRestitcher/FileRestitcher/FileRestitcher.NupkgProj/netstandard2.0/FileRestitcher.AssemblyInfoInputs.cache create mode 100644 pkg/FileRestitcher/FileRestitcher/FileRestitcher.NupkgProj/netstandard2.0/FileRestitcher.GeneratedMSBuildEditorConfig.editorconfig create mode 100644 pkg/FileRestitcher/FileRestitcher/FileRestitcher.NupkgProj/netstandard2.0/FileRestitcher.assets.cache create mode 100644 pkg/FileRestitcher/FileRestitcher/FileRestitcher.NupkgProj/netstandard2.0/FileRestitcher.csproj.AssemblyReference.cache diff --git a/Directory.Build.props b/Directory.Build.props index 8e2f8c34d..a54b11a75 100644 --- a/Directory.Build.props +++ b/Directory.Build.props @@ -5,7 +5,7 @@ - + K:\FrameworksForC\LibTorch\libtorch-win-shared-with-deps-debug-2.11.0+cu130\libtorch\share\cmake\Torch Debug Debug;Release @@ -22,7 +22,7 @@ $(RepoRoot)src/ $(RepoRoot)pkg/ - 2.10.0.0 + 2.11.0.0 2.2.2.0 @@ -88,7 +88,7 @@ - 2.10.0.0 + 2.11.0.0 2.2.2.0 false $(LibTorchPackageVersion) diff --git a/MyCustomCMD.txt b/MyCustomCMD.txt index 6416f8025..6a438cd66 100644 --- a/MyCustomCMD.txt +++ b/MyCustomCMD.txt @@ -7,4 +7,6 @@ dotnet build TorchSharpFilter.slnf /p:CustomLibTorchFullPath="K:\FrameworksForC\ dotnet build /p:CustomLibTorchFullPath="K:\FrameworksForC\LibTorch\libtorch-win-shared-with-deps-2.11.0+cpu\libtorch\share\cmake\Torch" -dotnet test /p:CustomLibTorchFullPath="K:\FrameworksForC\LibTorch\libtorch-win-shared-with-deps-2.11.0+cpu\libtorch\share\cmake\Torch" \ No newline at end of file +dotnet test /p:CustomLibTorchFullPath="K:\FrameworksForC\LibTorch\libtorch-win-shared-with-deps-2.11.0+cpu\libtorch\share\cmake\Torch" + +dotnet build /p:CustomLibTorchFullPath="K:\FrameworksForC\LibTorch\libtorch-win-shared-with-deps-debug-2.11.0+cu130\libtorch\share\cmake\Torch" -f netstandard2.0 -c Debug \ No newline at end of file diff --git a/pkg/FileRestitcher/FileRestitcher.Tests/FileRestitcher.Tests.NupkgProj/FileRestitcher.Tests.csproj.nuget.dgspec.json b/pkg/FileRestitcher/FileRestitcher.Tests/FileRestitcher.Tests.NupkgProj/FileRestitcher.Tests.csproj.nuget.dgspec.json index 0101447be..e80c4a72b 100644 --- a/pkg/FileRestitcher/FileRestitcher.Tests/FileRestitcher.Tests.NupkgProj/FileRestitcher.Tests.csproj.nuget.dgspec.json +++ b/pkg/FileRestitcher/FileRestitcher.Tests/FileRestitcher.Tests.NupkgProj/FileRestitcher.Tests.csproj.nuget.dgspec.json @@ -145,7 +145,7 @@ "C:\\Program Files (x86)\\NuGet\\Config\\Microsoft.VisualStudio.Offline.config" ], "originalTargetFrameworks": [ - "net6.0", + "net8.0", "netstandard2.0" ], "sources": { @@ -153,8 +153,8 @@ "https://api.nuget.org/v3/index.json": {} }, "frameworks": { - "net6.0": { - "targetAlias": "net6.0", + "net8.0": { + "targetAlias": "net8.0", "projectReferences": {} }, "netstandard2.0": { @@ -175,8 +175,8 @@ "SdkAnalysisLevel": "9.0.100" }, "frameworks": { - "net6.0": { - "targetAlias": "net6.0", + "net8.0": { + "targetAlias": "net8.0", "imports": [ "net461", "net462", diff --git a/pkg/FileRestitcher/FileRestitcher.Tests/FileRestitcher.Tests.NupkgProj/net472/.NETFramework,Version=v4.7.2.AssemblyAttributes.cs b/pkg/FileRestitcher/FileRestitcher.Tests/FileRestitcher.Tests.NupkgProj/net472/.NETFramework,Version=v4.7.2.AssemblyAttributes.cs new file mode 100644 index 000000000..3871b184d --- /dev/null +++ b/pkg/FileRestitcher/FileRestitcher.Tests/FileRestitcher.Tests.NupkgProj/net472/.NETFramework,Version=v4.7.2.AssemblyAttributes.cs @@ -0,0 +1,4 @@ +// +using System; +using System.Reflection; +[assembly: global::System.Runtime.Versioning.TargetFrameworkAttribute(".NETFramework,Version=v4.7.2", FrameworkDisplayName = ".NET Framework 4.7.2")] diff --git a/pkg/FileRestitcher/FileRestitcher.Tests/FileRestitcher.Tests.NupkgProj/net472/FileRestitcher.Tests.AssemblyInfo.cs b/pkg/FileRestitcher/FileRestitcher.Tests/FileRestitcher.Tests.NupkgProj/net472/FileRestitcher.Tests.AssemblyInfo.cs new file mode 100644 index 000000000..13943a5c5 --- /dev/null +++ b/pkg/FileRestitcher/FileRestitcher.Tests/FileRestitcher.Tests.NupkgProj/net472/FileRestitcher.Tests.AssemblyInfo.cs @@ -0,0 +1,24 @@ +//------------------------------------------------------------------------------ +// +// Este código fue generado por una herramienta. +// Versión de runtime:4.0.30319.42000 +// +// Los cambios en este archivo podrían causar un comportamiento incorrecto y se perderán si +// se vuelve a generar el código. +// +//------------------------------------------------------------------------------ + +using System; +using System.Reflection; + +[assembly: System.Reflection.AssemblyCompanyAttribute("TorchSharp contributors")] +[assembly: System.Reflection.AssemblyConfigurationAttribute("Debug")] +[assembly: System.Reflection.AssemblyCopyrightAttribute("Copyright .NET Foundation and Contributors")] +[assembly: System.Reflection.AssemblyFileVersionAttribute("1.0.0.0")] +[assembly: System.Reflection.AssemblyInformationalVersionAttribute("1.0.0+4436c93f069a66702e1d89cb9325f40b734bbaa5")] +[assembly: System.Reflection.AssemblyProductAttribute("FileRestitcher.Tests")] +[assembly: System.Reflection.AssemblyTitleAttribute("FileRestitcher.Tests")] +[assembly: System.Reflection.AssemblyVersionAttribute("1.0.0.0")] + +// Generado por la clase WriteCodeFragment de MSBuild. + diff --git a/pkg/FileRestitcher/FileRestitcher.Tests/FileRestitcher.Tests.NupkgProj/net472/FileRestitcher.Tests.AssemblyInfoInputs.cache b/pkg/FileRestitcher/FileRestitcher.Tests/FileRestitcher.Tests.NupkgProj/net472/FileRestitcher.Tests.AssemblyInfoInputs.cache new file mode 100644 index 000000000..afd8ba288 --- /dev/null +++ b/pkg/FileRestitcher/FileRestitcher.Tests/FileRestitcher.Tests.NupkgProj/net472/FileRestitcher.Tests.AssemblyInfoInputs.cache @@ -0,0 +1 @@ +8466daae7b02d90eea4b8dd285e7b97a791318ca4c0dc896730fa1366db17dd6 diff --git 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zoMhyoQj9!C9TTGv)KQB*Q5^@7%O0xOQy+7qU6Cgb`rv;Yd2HsLv_uFk;fSj^40QrR zGCIsk9z8*hOn`NVMzwJ)Zi&!rR9c99 literal 0 HcmV?d00001 diff --git a/pkg/FileRestitcher/FileRestitcher.Tests/FileRestitcher.Tests.NupkgProj/net8.0/.NETCoreApp,Version=v8.0.AssemblyAttributes.cs b/pkg/FileRestitcher/FileRestitcher.Tests/FileRestitcher.Tests.NupkgProj/net8.0/.NETCoreApp,Version=v8.0.AssemblyAttributes.cs new file mode 100644 index 000000000..2217181c8 --- /dev/null +++ b/pkg/FileRestitcher/FileRestitcher.Tests/FileRestitcher.Tests.NupkgProj/net8.0/.NETCoreApp,Version=v8.0.AssemblyAttributes.cs @@ -0,0 +1,4 @@ +// +using System; +using System.Reflection; +[assembly: global::System.Runtime.Versioning.TargetFrameworkAttribute(".NETCoreApp,Version=v8.0", FrameworkDisplayName = ".NET 8.0")] diff --git a/pkg/FileRestitcher/FileRestitcher.Tests/FileRestitcher.Tests.NupkgProj/net8.0/FileRestitcher.Tests.AssemblyInfo.cs b/pkg/FileRestitcher/FileRestitcher.Tests/FileRestitcher.Tests.NupkgProj/net8.0/FileRestitcher.Tests.AssemblyInfo.cs new file mode 100644 index 000000000..13943a5c5 --- /dev/null +++ b/pkg/FileRestitcher/FileRestitcher.Tests/FileRestitcher.Tests.NupkgProj/net8.0/FileRestitcher.Tests.AssemblyInfo.cs @@ -0,0 +1,24 @@ +//------------------------------------------------------------------------------ +// +// Este código fue generado por una herramienta. +// Versión de runtime:4.0.30319.42000 +// +// Los cambios en este archivo podrían causar un comportamiento incorrecto y se perderán si +// se vuelve a generar el código. +// +//------------------------------------------------------------------------------ + +using System; +using System.Reflection; + +[assembly: System.Reflection.AssemblyCompanyAttribute("TorchSharp contributors")] +[assembly: System.Reflection.AssemblyConfigurationAttribute("Debug")] +[assembly: System.Reflection.AssemblyCopyrightAttribute("Copyright .NET Foundation and Contributors")] +[assembly: System.Reflection.AssemblyFileVersionAttribute("1.0.0.0")] +[assembly: System.Reflection.AssemblyInformationalVersionAttribute("1.0.0+4436c93f069a66702e1d89cb9325f40b734bbaa5")] +[assembly: System.Reflection.AssemblyProductAttribute("FileRestitcher.Tests")] +[assembly: System.Reflection.AssemblyTitleAttribute("FileRestitcher.Tests")] +[assembly: System.Reflection.AssemblyVersionAttribute("1.0.0.0")] + +// Generado por la clase WriteCodeFragment de MSBuild. + diff --git a/pkg/FileRestitcher/FileRestitcher.Tests/FileRestitcher.Tests.NupkgProj/net8.0/FileRestitcher.Tests.AssemblyInfoInputs.cache b/pkg/FileRestitcher/FileRestitcher.Tests/FileRestitcher.Tests.NupkgProj/net8.0/FileRestitcher.Tests.AssemblyInfoInputs.cache new file mode 100644 index 000000000..afd8ba288 --- /dev/null +++ b/pkg/FileRestitcher/FileRestitcher.Tests/FileRestitcher.Tests.NupkgProj/net8.0/FileRestitcher.Tests.AssemblyInfoInputs.cache @@ -0,0 +1 @@ +8466daae7b02d90eea4b8dd285e7b97a791318ca4c0dc896730fa1366db17dd6 diff --git a/pkg/FileRestitcher/FileRestitcher.Tests/FileRestitcher.Tests.NupkgProj/net8.0/FileRestitcher.Tests.GeneratedMSBuildEditorConfig.editorconfig b/pkg/FileRestitcher/FileRestitcher.Tests/FileRestitcher.Tests.NupkgProj/net8.0/FileRestitcher.Tests.GeneratedMSBuildEditorConfig.editorconfig new file mode 100644 index 000000000..7957ddc75 --- /dev/null +++ b/pkg/FileRestitcher/FileRestitcher.Tests/FileRestitcher.Tests.NupkgProj/net8.0/FileRestitcher.Tests.GeneratedMSBuildEditorConfig.editorconfig @@ -0,0 +1,15 @@ +is_global = true +build_property.TargetFramework = net8.0 +build_property.TargetPlatformMinVersion = +build_property.UsingMicrosoftNETSdkWeb = +build_property.ProjectTypeGuids = +build_property.InvariantGlobalization = +build_property.PlatformNeutralAssembly = +build_property.EnforceExtendedAnalyzerRules = +build_property._SupportedPlatformList = Linux,macOS,Windows +build_property.RootNamespace = FileRestitcher.Tests +build_property.ProjectDir = K:\Proyects_Repos\TorchSharp\pkg\FileRestitcher\FileRestitcher.Tests\ +build_property.EnableComHosting = +build_property.EnableGeneratedComInterfaceComImportInterop = +build_property.EffectiveAnalysisLevelStyle = 8.0 +build_property.EnableCodeStyleSeverity = diff --git a/pkg/FileRestitcher/FileRestitcher/FileRestitcher.NupkgProj/FileRestitcher.csproj.nuget.dgspec.json b/pkg/FileRestitcher/FileRestitcher/FileRestitcher.NupkgProj/FileRestitcher.csproj.nuget.dgspec.json index bbe687ab8..2e0230fcf 100644 --- a/pkg/FileRestitcher/FileRestitcher/FileRestitcher.NupkgProj/FileRestitcher.csproj.nuget.dgspec.json +++ b/pkg/FileRestitcher/FileRestitcher/FileRestitcher.NupkgProj/FileRestitcher.csproj.nuget.dgspec.json @@ -24,7 +24,7 @@ "C:\\Program Files (x86)\\NuGet\\Config\\Microsoft.VisualStudio.Offline.config" ], "originalTargetFrameworks": [ - "net6.0", + "net8.0", "netstandard2.0" ], "sources": { @@ -32,8 +32,8 @@ "https://api.nuget.org/v3/index.json": {} }, "frameworks": { - "net6.0": { - "targetAlias": "net6.0", + "net8.0": { + "targetAlias": "net8.0", "projectReferences": {} }, "netstandard2.0": { @@ -54,8 +54,8 @@ "SdkAnalysisLevel": "9.0.100" }, "frameworks": { - "net6.0": { - "targetAlias": "net6.0", + "net8.0": { + "targetAlias": "net8.0", "imports": [ "net461", "net462", diff --git a/pkg/FileRestitcher/FileRestitcher/FileRestitcher.NupkgProj/netstandard2.0/.NETStandard,Version=v2.0.AssemblyAttributes.cs b/pkg/FileRestitcher/FileRestitcher/FileRestitcher.NupkgProj/netstandard2.0/.NETStandard,Version=v2.0.AssemblyAttributes.cs new file mode 100644 index 000000000..45b1ca02d --- /dev/null +++ b/pkg/FileRestitcher/FileRestitcher/FileRestitcher.NupkgProj/netstandard2.0/.NETStandard,Version=v2.0.AssemblyAttributes.cs @@ -0,0 +1,4 @@ +// +using System; +using System.Reflection; +[assembly: global::System.Runtime.Versioning.TargetFrameworkAttribute(".NETStandard,Version=v2.0", FrameworkDisplayName = "")] diff --git a/pkg/FileRestitcher/FileRestitcher/FileRestitcher.NupkgProj/netstandard2.0/FileRestitcher.AssemblyInfo.cs b/pkg/FileRestitcher/FileRestitcher/FileRestitcher.NupkgProj/netstandard2.0/FileRestitcher.AssemblyInfo.cs new file mode 100644 index 000000000..4e5534e0c --- /dev/null +++ b/pkg/FileRestitcher/FileRestitcher/FileRestitcher.NupkgProj/netstandard2.0/FileRestitcher.AssemblyInfo.cs @@ -0,0 +1,24 @@ +//------------------------------------------------------------------------------ +// +// Este código fue generado por una herramienta. +// Versión de runtime:4.0.30319.42000 +// +// Los cambios en este archivo podrían causar un comportamiento incorrecto y se perderán si +// se vuelve a generar el código. +// +//------------------------------------------------------------------------------ + +using System; +using System.Reflection; + +[assembly: System.Reflection.AssemblyCompanyAttribute("TorchSharp contributors")] +[assembly: System.Reflection.AssemblyConfigurationAttribute("Debug")] +[assembly: System.Reflection.AssemblyCopyrightAttribute("Copyright .NET Foundation and Contributors")] +[assembly: System.Reflection.AssemblyFileVersionAttribute("1.0.0.0")] +[assembly: System.Reflection.AssemblyInformationalVersionAttribute("1.0.0+4436c93f069a66702e1d89cb9325f40b734bbaa5")] +[assembly: System.Reflection.AssemblyProductAttribute("FileRestitcher")] +[assembly: System.Reflection.AssemblyTitleAttribute("FileRestitcher")] +[assembly: System.Reflection.AssemblyVersionAttribute("1.0.0.0")] + +// Generado por la clase WriteCodeFragment de MSBuild. + diff --git a/pkg/FileRestitcher/FileRestitcher/FileRestitcher.NupkgProj/netstandard2.0/FileRestitcher.AssemblyInfoInputs.cache b/pkg/FileRestitcher/FileRestitcher/FileRestitcher.NupkgProj/netstandard2.0/FileRestitcher.AssemblyInfoInputs.cache new file mode 100644 index 000000000..033a7b8cf --- /dev/null +++ b/pkg/FileRestitcher/FileRestitcher/FileRestitcher.NupkgProj/netstandard2.0/FileRestitcher.AssemblyInfoInputs.cache @@ -0,0 +1 @@ +c5138ff11eebd7d3b469eae6088b319f69826365e9da38b98fa1a61dfe12e010 diff --git a/pkg/FileRestitcher/FileRestitcher/FileRestitcher.NupkgProj/netstandard2.0/FileRestitcher.GeneratedMSBuildEditorConfig.editorconfig b/pkg/FileRestitcher/FileRestitcher/FileRestitcher.NupkgProj/netstandard2.0/FileRestitcher.GeneratedMSBuildEditorConfig.editorconfig new file mode 100644 index 000000000..acc3874e1 --- /dev/null +++ b/pkg/FileRestitcher/FileRestitcher/FileRestitcher.NupkgProj/netstandard2.0/FileRestitcher.GeneratedMSBuildEditorConfig.editorconfig @@ -0,0 +1,8 @@ +is_global = true +build_property.RootNamespace = FileRestitcher +build_property.ProjectDir = K:\Proyects_Repos\TorchSharp\pkg\FileRestitcher\FileRestitcher\ +build_property.EnableComHosting = +build_property.EnableGeneratedComInterfaceComImportInterop = +build_property.CsWinRTUseWindowsUIXamlProjections = 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b/pkg/FileRestitcher/FileRestitcher/FileRestitcher.NupkgProj/project.assets.json index 7e747e944..c5f885f89 100644 --- a/pkg/FileRestitcher/FileRestitcher/FileRestitcher.NupkgProj/project.assets.json +++ b/pkg/FileRestitcher/FileRestitcher/FileRestitcher.NupkgProj/project.assets.json @@ -27,7 +27,7 @@ } } }, - "net6.0": {} + "net8.0": {} }, "libraries": { "Microsoft.NETCore.Platforms/1.1.0": { @@ -179,7 +179,7 @@ ".NETStandard,Version=v2.0": [ "NETStandard.Library >= 2.0.3" ], - "net6.0": [] + "net8.0": [] }, "packageFolders": { "C:\\Users\\Dimitri\\.nuget\\packages\\": {}, @@ -205,7 +205,7 @@ "C:\\Program Files (x86)\\NuGet\\Config\\Microsoft.VisualStudio.Offline.config" ], "originalTargetFrameworks": [ - "net6.0", + "net8.0", "netstandard2.0" ], "sources": { @@ -213,8 +213,8 @@ "https://api.nuget.org/v3/index.json": {} }, "frameworks": { - "net6.0": { - "targetAlias": "net6.0", + "net8.0": { + "targetAlias": "net8.0", "projectReferences": {} }, "netstandard2.0": { @@ -235,8 +235,8 @@ "SdkAnalysisLevel": "9.0.100" }, "frameworks": { - "net6.0": { - "targetAlias": "net6.0", + "net8.0": { + "targetAlias": "net8.0", "imports": [ "net461", "net462", diff --git a/pkg/FileRestitcher/FileRestitcher/FileRestitcher.csproj b/pkg/FileRestitcher/FileRestitcher/FileRestitcher.csproj index 68dd5b1d2..0b61b7138 100644 --- a/pkg/FileRestitcher/FileRestitcher/FileRestitcher.csproj +++ b/pkg/FileRestitcher/FileRestitcher/FileRestitcher.csproj @@ -3,7 +3,7 @@ false Library - netstandard2.0;net6.0 + netstandard2.0;net8.0 false diff --git a/src/Examples.Utils/Examples.Utils.csproj b/src/Examples.Utils/Examples.Utils.csproj index 6d8de3023..de3667512 100644 --- a/src/Examples.Utils/Examples.Utils.csproj +++ b/src/Examples.Utils/Examples.Utils.csproj @@ -4,7 +4,7 @@ 9.0 - net6.0 + net8.0 net472;$(TargetFrameworks);netstandard2.0 net8.0 diff --git a/src/Native/LibTorchSharp/THSAutograd.cpp b/src/Native/LibTorchSharp/THSAutograd.cpp index 37d029cdf..9fc6b5d12 100644 --- a/src/Native/LibTorchSharp/THSAutograd.cpp +++ b/src/Native/LibTorchSharp/THSAutograd.cpp @@ -1,4 +1,4 @@ -// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information. +// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information. #include "THSAutograd.h" #include "torch/torch.h" @@ -173,22 +173,17 @@ void THSAutograd_Function_wrapOutputs(TensorArray vars_, TensorArray nonDiff_, T "Please open a feature request on GitHub if you need this."); }; - auto view_as_self_fn = [](const at::Tensor& x) -> at::Tensor { - return x.view_as(x); - }; - - auto res = torch::autograd::_wrap_outputs(vars, nonDiff, dirty, outputs, node.weak_ptr == nullptr || node.weak_ptr->expired() ? nullptr : node.weak_ptr->lock(), jvp_fn, {}, view_as_self_fn, false); - auto sz = res.size(); auto view_as_self_fn = [](const at::Tensor& x) -> at::Tensor { return x.view_as(x); }; + + //auto res = torch::autograd::_wrap_outputs(vars, nonDiff, dirty, outputs, node.weak_ptr == nullptr || node.weak_ptr->expired() ? nullptr : node.weak_ptr->lock(), jvp_fn, {}, view_as_self_fn, false); #if TORCH_VERSION_MAJOR >= 2 && TORCH_VERSION_MINOR >= 11 auto res = torch::autograd::_wrap_outputs(vars, nonDiff, dirty, outputs, node.weak_ptr == nullptr || node.weak_ptr->expired() ? nullptr : node.weak_ptr->lock(), jvp_fn, {}, view_as_self_fn, true); #else auto res = torch::autograd::_wrap_outputs(vars, nonDiff, dirty, outputs, node.weak_ptr == nullptr || node.weak_ptr->expired() ? nullptr : node.weak_ptr->lock(), jvp_fn, {}, view_as_self_fn); #endif auto sz = res.size(); - Tensor* result = allocator(sz); for (size_t i = 0; i < sz; i++) result[i] = res[i].has_value() ? ResultTensor(res[i].value()) : nullptr; diff --git a/src/Native/LibTorchSharp/THSTensor.cpp b/src/Native/LibTorchSharp/THSTensor.cpp index 1f050eb54..4bb35a6ad 100644 --- a/src/Native/LibTorchSharp/THSTensor.cpp +++ b/src/Native/LibTorchSharp/THSTensor.cpp @@ -1,4 +1,4 @@ -// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information. +// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information. #include "THSTensor.h" #include diff --git a/src/Native/LibTorchSharp/THSTorch.cpp b/src/Native/LibTorchSharp/THSTorch.cpp index 9d97b236d..d439421c7 100644 --- a/src/Native/LibTorchSharp/THSTorch.cpp +++ b/src/Native/LibTorchSharp/THSTorch.cpp @@ -1,4 +1,4 @@ -// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information. +// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information. #include "THSTorch.h" #include "torch/torch.h" @@ -302,12 +302,12 @@ void THSTorch_scalar_to_float16(Scalar value, unsigned short *res) } -void THSTorch_scalar_to_bfloat16(Scalar value, c10::BFloat16* res) +/*void THSTorch_scalar_to_bfloat16(Scalar value, c10::BFloat16* res) { *res = value->toBFloat16(); -} +}*/ -void THSTorch_scalar_to_complex32(Scalar value, float* (*allocator)(size_t length)) +void THSTorch_scalar_to_complex32(Scalar value, float* real, float* imaginary) { auto result = value->toComplexFloat(); *real = result.real(); diff --git a/src/Native/LibTorchSharp/THSTorch.h b/src/Native/LibTorchSharp/THSTorch.h index 61334ca7b..9e6acb0eb 100644 --- a/src/Native/LibTorchSharp/THSTorch.h +++ b/src/Native/LibTorchSharp/THSTorch.h @@ -81,7 +81,7 @@ EXPORT_API(bool) THSTorch_scalar_to_bool(Scalar value); EXPORT_API(void) THSTorch_scalar_to_bfloat16(Scalar value, unsigned short* res); EXPORT_API(void) THSTorch_scalar_to_float16(Scalar value, unsigned short* res); -EXPORT_API(void) THSTorch_scalar_to_bfloat16(Scalar value, c10::BFloat16* res); +//EXPORT_API(void) THSTorch_scalar_to_bfloat16(Scalar value, c10::BFloat16* res); EXPORT_API(void) THSTorch_scalar_to_complex32(Scalar value, float* real, float* imaginary); EXPORT_API(void) THSTorch_scalar_to_complex64(Scalar value, double* real, double* imaginary); diff --git a/src/TorchSharp/Amp/AutocastMode.cs b/src/TorchSharp/Amp/AutocastMode.cs index ef0c8a43c..9186ac913 100644 --- a/src/TorchSharp/Amp/AutocastMode.cs +++ b/src/TorchSharp/Amp/AutocastMode.cs @@ -123,7 +123,7 @@ public static torch.Tensor AutoCast(torch.Tensor tensor) public static IntPtr To(IntPtr ptr, torch.ScalarType type) { Debug.WriteLine($"{nameof(AutocastMode)} Tensor converting from: {GetDtype(ptr)} to: {type}"); - var res = NativeMethods.THSTensor_to_type(ptr, (sbyte)type); + var res = NativeMethods.THSTensor_to_type(ptr, (sbyte)type, false, false); if (res == IntPtr.Zero) torch.CheckForErrors(); return res; @@ -144,7 +144,7 @@ public static IntPtr ToIf(IntPtr ptr, torch.ScalarType type) //TODO: Check if is from CPU to passing BFloat16 if support /*if (!NativeMethods.THSAmp_is_autocast_enabled(NativeMethods.THSTensor_device_type(ptr))) return ptr;*/ - var res = NativeMethods.THSTensor_to_type(ptr, (sbyte)type); + var res = NativeMethods.THSTensor_to_type(ptr, (sbyte)type, false, false); if (res == IntPtr.Zero) torch.CheckForErrors(); return res; @@ -155,7 +155,7 @@ public static IntPtr ToIf(IntPtr ptr, torch.ScalarType type, DeviceType device_t if (!NativeMethods.THSAmp_is_autocast_enabled(NativeMethods.THSTensor_device_type(ptr))) return ptr; - var res = NativeMethods.THSTensor_to_type(ptr, (sbyte)type); + var res = NativeMethods.THSTensor_to_type(ptr, (sbyte)type, false,false); if (res == IntPtr.Zero) torch.CheckForErrors(); return res; diff --git a/src/TorchSharp/Autograd.cs b/src/TorchSharp/Autograd.cs index ce5c3a5bd..6313e07e0 100644 --- a/src/TorchSharp/Autograd.cs +++ b/src/TorchSharp/Autograd.cs @@ -146,21 +146,21 @@ public static IList grad(IList outputs, IList inputs, IL return results.Array.Select(x => new Tensor(x)).ToList(); } - public static IList grad(IList inputs, IEnumerable outputs, IList grad_outputs = null, bool retain_graph = false, bool create_graph = false, bool allow_unused = false) + public static IList grad(IList outputs, IEnumerable inputs, IList grad_outputs = null, bool retain_graph = false, bool create_graph = false, bool allow_unused = false) { using var outs = new PinnedArray(); using var ins = new PinnedArray(); using var grads = new PinnedArray(); using var results = new PinnedArray(); - IntPtr insRef = outs.CreateArray(outputs.Select(p => p.Handle).ToArray()); - IntPtr outsRef = ins.CreateArray(inputs.Select(p => p.Handle).ToArray()); + IntPtr outsRef = outs.CreateArray(outputs.ToHandleArray()); + IntPtr insRef = ins.CreateArray(inputs.ToHandleArray()); IntPtr gradsRef = grad_outputs == null ? IntPtr.Zero : grads.CreateArray(grad_outputs.Select(p => p.Handle).ToArray()); long gradsLength = grad_outputs == null ? 0 : grads.Array.Length; //https://gist.github.com/dorpxam/67ad2bc222b2cf567d4a6fc298375e13#file-gradscaler_test-hpp-L318 - THSAutograd_grad(outsRef, ins.Array.Length, insRef, outs.Array.Length, gradsRef, gradsLength, retain_graph, create_graph, allow_unused, results.CreateArray); + THSAutograd_grad(outsRef, outs.Array.Length, insRef, ins.Array.Length, gradsRef, gradsLength, retain_graph, create_graph, allow_unused, results.CreateArray); CheckForErrors(); return results.Array.Select(x => new Tensor(x)).ToList(); } diff --git a/src/TorchSharp/NN/Activation/CELU.cs b/src/TorchSharp/NN/Activation/CELU.cs index 59a6e5924..c62b644c9 100644 --- a/src/TorchSharp/NN/Activation/CELU.cs +++ b/src/TorchSharp/NN/Activation/CELU.cs @@ -12,25 +12,21 @@ namespace Modules /// /// This class is used to represent a CELU module. /// - public sealed class CELU : torch.nn.Module + public sealed class CELU : ParameterLessModule { - internal CELU(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { } - - public override Tensor forward(Tensor tensor) + internal CELU(double alpha, bool inplace) : base(nameof(CELU)) { - return ReturnCheckForErrors(THSNN_CELU_forward(handle, tensor.Handle)); + this.alpha = alpha; + this.inplace = inplace; } - public override string GetName() + public override Tensor forward(Tensor tensor) { - return typeof(CELU).Name; + return torch.nn.functional.celu(tensor, alpha, inplace); } - // Rather than spending cycles only to discover that this module has neither - // parameters nor buffers, just shortcut the move completely. - protected internal override nn.Module _to(Device device, ScalarType dtype, bool non_blocking) => this; - protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex, bool non_blocking) => this; - protected internal override nn.Module _to(ScalarType dtype, bool non_blocking) => this; + public double alpha { get; set; } + public bool inplace { get; set; } } } @@ -46,9 +42,7 @@ public static partial class nn /// public static CELU CELU(double alpha = 1.0, bool inplace = false) { - var handle = THSNN_CELU_ctor(alpha, inplace, out var boxedHandle); - if (handle == IntPtr.Zero) { torch.CheckForErrors(); } - return new CELU(handle, boxedHandle); + return new CELU(alpha, inplace); } public static partial class functional @@ -62,9 +56,7 @@ public static partial class functional /// public static Tensor celu(Tensor x, double alpha, bool inplace = false) { - using (var m = nn.CELU(alpha, inplace)) { - return m.call(x); - } + return inplace ? x.celu_(alpha).alias() : x.celu(alpha); } } } diff --git a/src/TorchSharp/NN/Activation/ELU.cs b/src/TorchSharp/NN/Activation/ELU.cs index 6001f04e5..f1e76d67c 100644 --- a/src/TorchSharp/NN/Activation/ELU.cs +++ b/src/TorchSharp/NN/Activation/ELU.cs @@ -12,25 +12,22 @@ namespace Modules /// /// This class is used to represent a ELU module. /// - public sealed class ELU : torch.nn.Module + public sealed class ELU : ParameterLessModule { - internal ELU(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { } - - public override Tensor forward(Tensor tensor) + internal ELU(double alpha, bool inplace) : base(nameof(ELU)) { - return ReturnCheckForErrors(THSNN_ELU_forward(handle, tensor.Handle)); + this.alpha = alpha; + this.inplace = inplace; } - public override string GetName() + public override Tensor forward(Tensor tensor) { - return typeof(ELU).Name; + return torch.nn.functional.elu(tensor, alpha, inplace); } - // Rather than spending cycles only to discover that this module has neither - // parameters nor buffers, just shortcut the move completely. - protected internal override nn.Module _to(Device device, ScalarType dtype, bool non_blocking) => this; - protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex, bool non_blocking) => this; - protected internal override nn.Module _to(ScalarType dtype, bool non_blocking) => this; + public double alpha { get; set; } + + public bool inplace { get; set; } } } @@ -46,9 +43,7 @@ public static partial class nn /// public static ELU ELU(double alpha = 1.0, bool inplace = false) { - var handle = THSNN_ELU_ctor(alpha, inplace, out var boxedHandle); - if (handle == IntPtr.Zero) { torch.CheckForErrors(); } - return new ELU(handle, boxedHandle); + return new ELU(alpha, inplace); } public static partial class functional @@ -62,9 +57,7 @@ public static partial class functional /// public static Tensor elu(Tensor x, double alpha, bool inplace = false) { - using (var m = nn.ELU(alpha, inplace)) { - return m.call(x); - } + return inplace ? x.elu_(alpha).alias() : x.elu(alpha); } } } diff --git a/src/TorchSharp/NN/Activation/GELU.cs b/src/TorchSharp/NN/Activation/GELU.cs index 06d39866f..c62aca55c 100644 --- a/src/TorchSharp/NN/Activation/GELU.cs +++ b/src/TorchSharp/NN/Activation/GELU.cs @@ -12,25 +12,19 @@ namespace Modules /// /// This class is used to represent a GELU module. /// - public sealed class GELU : torch.nn.Module + public sealed class GELU : ParameterLessModule { - internal GELU(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { } - - public override Tensor forward(Tensor tensor) + internal GELU(bool inplace) : base(nameof(GELU)) { - return ReturnCheckForErrors(THSNN_GELU_forward(handle, tensor.Handle)); + this.inplace = inplace; } - public override string GetName() + public override Tensor forward(Tensor tensor) { - return typeof(GELU).Name; + return torch.nn.functional.gelu(tensor, inplace); } - // Rather than spending cycles only to discover that this module has neither - // parameters nor buffers, just shortcut the move completely. - protected internal override nn.Module _to(Device device, ScalarType dtype, bool non_blocking) => this; - protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex, bool non_blocking) => this; - protected internal override nn.Module _to(ScalarType dtype, bool non_blocking) => this; + public bool inplace { get; set; } } } @@ -38,33 +32,43 @@ public static partial class torch { public static partial class nn { - public enum Approx + /// + /// Gaussian Error Linear Units + /// + public static GELU GELU() { - none, - tanh + return new GELU(false); } + /// /// Gaussian Error Linear Units /// - /// - public static GELU GELU(torch.nn.Approx approximate = Approx.none) + /// Do the operation in-place. Default: False + public static GELU GELU(bool inplace) { - var handle = THSNN_GELU_ctor(out var boxedHandle, approximate.ToString()); - if (handle == IntPtr.Zero) { torch.CheckForErrors(); } - return new GELU(handle, boxedHandle); + return new GELU(inplace); } + public static partial class functional { /// /// Gaussian Error Linear Units /// /// The input tensor - /// + /// Do the operation in-place. Default: False + public static Tensor gelu(Tensor x, bool inplace) + { + return inplace ? x.gelu_().alias() : x.gelu(); + } + + /// + /// Gaussian Error Linear Units + /// + /// The input tensor + /// The defaulting of 'inplace' to 'false' is implemented as an overload to avoid a breaking change. public static Tensor gelu(Tensor x) { - using (var m = nn.GELU()) { - return m.call(x); - } + return gelu(x, false); } } } diff --git a/src/TorchSharp/NN/Activation/GLU.cs b/src/TorchSharp/NN/Activation/GLU.cs index da44a1313..cdc7661d6 100644 --- a/src/TorchSharp/NN/Activation/GLU.cs +++ b/src/TorchSharp/NN/Activation/GLU.cs @@ -12,25 +12,19 @@ namespace Modules /// /// This class is used to represent a GLU (gated linear unit) module. /// - public sealed class GLU : torch.nn.Module + public sealed class GLU : ParameterLessModule { - internal GLU(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { } - - public override Tensor forward(Tensor tensor) + internal GLU(long dim) : base(nameof(GLU)) { - return ReturnCheckForErrors(THSNN_GLU_forward(handle, tensor.Handle)); + this.dim = dim; } - public override string GetName() + public override Tensor forward(Tensor tensor) { - return typeof(GLU).Name; + return torch.nn.functional.glu(tensor, dim); } - // Rather than spending cycles only to discover that this module has neither - // parameters nor buffers, just shortcut the move completely. - protected internal override nn.Module _to(Device device, ScalarType dtype, bool non_blocking) => this; - protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex, bool non_blocking) => this; - protected internal override nn.Module _to(ScalarType dtype, bool non_blocking) => this; + public long dim { get; set; } } } @@ -45,9 +39,7 @@ public static partial class nn /// public static GLU GLU(long dim = -1) { - var handle = THSNN_GLU_ctor(dim, out var boxedHandle); - if (handle == IntPtr.Zero) { torch.CheckForErrors(); } - return new GLU(handle, boxedHandle); + return new GLU(dim); } public static partial class functional @@ -60,11 +52,9 @@ public static partial class functional /// public static Tensor glu(Tensor input, long dim = -1) { - using (var m = nn.GLU(dim)) { - return m.call(input); - } + return input.glu(dim); } } } } -} +} \ No newline at end of file diff --git a/src/TorchSharp/NN/Activation/Hardshrink.cs b/src/TorchSharp/NN/Activation/Hardshrink.cs index 59d00bc94..6ecd9adb9 100644 --- a/src/TorchSharp/NN/Activation/Hardshrink.cs +++ b/src/TorchSharp/NN/Activation/Hardshrink.cs @@ -12,25 +12,19 @@ namespace Modules /// /// This class is used to represent a Hardshrink module. /// - public sealed class Hardshrink : torch.nn.Module + public sealed class Hardshrink : ParameterLessModule { - internal Hardshrink(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { } - - public override Tensor forward(Tensor tensor) + internal Hardshrink(double lambda = 0.5) : base(nameof(Hardshrink)) { - return ReturnCheckForErrors(THSNN_Hardshrink_forward(handle, tensor.Handle)); + this.lambda = lambda; } - public override string GetName() + public override Tensor forward(Tensor tensor) { - return typeof(Hardshrink).Name; + return torch.nn.functional.hardshrink(tensor, lambda); } - // Rather than spending cycles only to discover that this module has neither - // parameters nor buffers, just shortcut the move completely. - protected internal override nn.Module _to(Device device, ScalarType dtype, bool non_blocking) => this; - protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex, bool non_blocking) => this; - protected internal override nn.Module _to(ScalarType dtype, bool non_blocking) => this; + public double lambda { get; set; } } } @@ -45,9 +39,7 @@ public static partial class nn /// public static Hardshrink Hardshrink(double lambda = 0.5) { - var handle = THSNN_Hardshrink_ctor(lambda, out var boxedHandle); - if (handle == IntPtr.Zero) { torch.CheckForErrors(); } - return new Hardshrink(handle, boxedHandle); + return new Hardshrink(lambda); } public static partial class functional @@ -58,12 +50,22 @@ public static partial class functional /// The input tensor /// The λ value for the Hardshrink formulation. Default: 0.5 /// - public static Tensor Hardshrink(Tensor x, double lambda = 0.5) + public static Tensor hardshrink(Tensor x, double lambda = 0.5) { - using (var m = nn.Hardshrink(lambda)) { - return m.call(x); - } + using var sc = (Scalar)lambda; + var result = THSTensor_hardshrink(x.Handle, sc.Handle); + if (result == IntPtr.Zero) { torch.CheckForErrors(); } + return new Tensor(result); } + + /// + /// Hardshrink + /// + /// The input tensor + /// The λ value for the Hardshrink formulation. Default: 0.5 + /// Only here for backward comaptibility. + [Obsolete("Not using the PyTorch naming convention.", false)] + public static Tensor Hardshrink(Tensor x, double lambda = 0.5) => hardshrink(x, lambda); } } } diff --git a/src/TorchSharp/NN/Activation/Hardsigmoid.cs b/src/TorchSharp/NN/Activation/Hardsigmoid.cs index e7c537da9..2cdb942b8 100644 --- a/src/TorchSharp/NN/Activation/Hardsigmoid.cs +++ b/src/TorchSharp/NN/Activation/Hardsigmoid.cs @@ -23,7 +23,7 @@ public override Tensor forward(Tensor tensor) return torch.nn.functional.hardsigmoid(tensor, inplace); } - public bool inplace {get; set; } + public bool inplace { get; set; } } } @@ -56,4 +56,4 @@ public static Tensor hardsigmoid(Tensor input, bool inplace = false) } } } -} +} \ No newline at end of file diff --git a/src/TorchSharp/NN/Activation/Hardswish.cs b/src/TorchSharp/NN/Activation/Hardswish.cs index 1c1b5bb8a..96db9a735 100644 --- a/src/TorchSharp/NN/Activation/Hardswish.cs +++ b/src/TorchSharp/NN/Activation/Hardswish.cs @@ -13,7 +13,7 @@ namespace Modules /// public sealed class Hardswish : ParameterLessModule { - public bool inplace { get; set;} + public bool inplace { get; set; } internal Hardswish(bool inplace = false) : base(nameof(Hardswish)) { diff --git a/src/TorchSharp/NN/Activation/Hardtanh.cs b/src/TorchSharp/NN/Activation/Hardtanh.cs index ff1bb89ef..10596d09f 100644 --- a/src/TorchSharp/NN/Activation/Hardtanh.cs +++ b/src/TorchSharp/NN/Activation/Hardtanh.cs @@ -12,13 +12,18 @@ namespace Modules /// /// This class is used to represent a Hardtanh module. /// - public sealed class Hardtanh : torch.nn.Module + public sealed class Hardtanh : ParameterLessModule { - internal Hardtanh(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { } + internal Hardtanh(double min_val = -1.0, double max_val = 1.0, bool inplace = false) : base(nameof(Hardtanh)) + { + this.min_val = min_val; + this.max_val = max_val; + this.inplace = inplace; + } public override Tensor forward(Tensor tensor) { - return ReturnCheckForErrors(THSNN_Hardtanh_forward(handle, tensor.Handle)); + return torch.nn.functional.hardtanh(tensor, min_val, max_val, inplace); } public override string GetName() @@ -26,11 +31,9 @@ public override string GetName() return typeof(Hardtanh).Name; } - // Rather than spending cycles only to discover that this module has neither - // parameters nor buffers, just shortcut the move completely. - protected internal override nn.Module _to(Device device, ScalarType dtype, bool non_blocking) => this; - protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex, bool non_blocking) => this; - protected internal override nn.Module _to(ScalarType dtype, bool non_blocking) => this; + public double min_val { get; set; } + public double max_val { get; set; } + public bool inplace { get; set; } } } @@ -47,9 +50,7 @@ public static partial class nn /// public static Hardtanh Hardtanh(double min_val = -1.0, double max_val = 1.0, bool inplace = false) { - var handle = THSNN_Hardtanh_ctor(min_val, max_val, inplace, out var boxedHandle); - if (handle == IntPtr.Zero) { torch.CheckForErrors(); } - return new Hardtanh(handle, boxedHandle); + return new Hardtanh(min_val, max_val, inplace); } public static partial class functional @@ -62,10 +63,21 @@ public static partial class functional /// Maximum value of the linear region range. /// Do the operation in-place /// - public static Tensor Hardtanh(Tensor x, double min_val = -1.0, double max_val = 1.0, bool inplace = false) + public static Tensor hardtanh(Tensor x, double min_val = -1.0, double max_val = 1.0, bool inplace = false) { return inplace ? x.hardtanh_(min_val, max_val).alias() : x.hardtanh(min_val, max_val); } + + /// + /// Hardshrink + /// + /// The input tensor + /// Minimum value of the linear region range. + /// Maximum value of the linear region range. + /// Do the operation in-place + /// Only here for backward comaptibility. + [Obsolete("Not using the PyTorch naming convention.", false)] + public static Tensor Hardtanh(Tensor x, double min_val = -1.0, double max_val = 1.0, bool inplace = false) => hardtanh(x, min_val, max_val, inplace); } } } diff --git a/src/TorchSharp/NN/Activation/LeakyReLU.cs b/src/TorchSharp/NN/Activation/LeakyReLU.cs index b4d9ef714..4ca71de7f 100644 --- a/src/TorchSharp/NN/Activation/LeakyReLU.cs +++ b/src/TorchSharp/NN/Activation/LeakyReLU.cs @@ -12,25 +12,21 @@ namespace Modules /// /// This class is used to represent a LeakyReLU module. /// - public sealed class LeakyReLU : torch.nn.Module + public sealed class LeakyReLU : ParameterLessModule { - internal LeakyReLU(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { } - - public override Tensor forward(Tensor tensor) + internal LeakyReLU(double negative_slope, bool inplace) : base(nameof(LeakyReLU)) { - return ReturnCheckForErrors(THSNN_LeakyReLU_forward(handle, tensor.Handle)); + this.inplace = inplace; + this.negative_slope = negative_slope; } - public override string GetName() + public override Tensor forward(Tensor tensor) { - return typeof(LeakyReLU).Name; + return torch.nn.functional.leaky_relu(tensor, negative_slope, inplace); } - // Rather than spending cycles only to discover that this module has neither - // parameters nor buffers, just shortcut the move completely. - protected internal override nn.Module _to(Device device, ScalarType dtype, bool non_blocking) => this; - protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex, bool non_blocking) => this; - protected internal override nn.Module _to(ScalarType dtype, bool non_blocking) => this; + public bool inplace { get; set; } + public double negative_slope { get; set; } } } @@ -46,9 +42,7 @@ public static partial class nn /// public static LeakyReLU LeakyReLU(double negative_slope = 0.01, bool inplace = false) { - var handle = THSNN_LeakyReLU_ctor(negative_slope, inplace, out var boxedHandle); - if (handle == IntPtr.Zero) { torch.CheckForErrors(); } - return new LeakyReLU(handle, boxedHandle); + return new LeakyReLU(negative_slope, inplace); } public static partial class functional @@ -62,9 +56,7 @@ public static partial class functional /// public static Tensor leaky_relu(Tensor input, double negative_slope = 0.01, bool inplace = false) { - using (var m = nn.LeakyReLU(negative_slope, inplace)) { - return m.call(input); - } + return inplace ? input.leaky_relu_(negative_slope).alias() : input.leaky_relu(negative_slope); } } } diff --git a/src/TorchSharp/NN/Activation/LogSigmoid.cs b/src/TorchSharp/NN/Activation/LogSigmoid.cs index 70c0944c9..dbca8c5fd 100644 --- a/src/TorchSharp/NN/Activation/LogSigmoid.cs +++ b/src/TorchSharp/NN/Activation/LogSigmoid.cs @@ -51,4 +51,4 @@ public static Tensor logsigmoid(Tensor x) } } } -} +} \ No newline at end of file diff --git a/src/TorchSharp/NN/Activation/LogSoftMax.cs b/src/TorchSharp/NN/Activation/LogSoftMax.cs index 269376edb..791ec6e8b 100644 --- a/src/TorchSharp/NN/Activation/LogSoftMax.cs +++ b/src/TorchSharp/NN/Activation/LogSoftMax.cs @@ -12,22 +12,19 @@ namespace Modules /// /// This class is used to represent a log softmax module. /// - public sealed class LogSoftmax : torch.nn.Module + public sealed class LogSoftmax : ParameterLessModule { - internal LogSoftmax(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) + internal LogSoftmax(long dim) : base(nameof(LogSoftmax)) { + this.dim = dim; } public override Tensor forward(Tensor tensor) { - return ReturnCheckForErrors(THSNN_LogSoftmax_forward(handle, tensor.Handle)); + return torch.nn.functional.log_softmax(tensor, dim); } - // Rather than spending cycles only to discover that this module has neither - // parameters nor buffers, just shortcut the move completely. - protected internal override nn.Module _to(Device device, ScalarType dtype, bool non_blocking) => this; - protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex, bool non_blocking) => this; - protected internal override nn.Module _to(ScalarType dtype, bool non_blocking) => this; + public long dim { get; set; } } } @@ -37,9 +34,7 @@ public static partial class nn { public static LogSoftmax LogSoftmax(long dim) { - var handle = THSNN_LogSoftmax_ctor(dim, out var boxedHandle); - if (handle == IntPtr.Zero) { torch.CheckForErrors(); } - return new LogSoftmax(handle, boxedHandle); + return new LogSoftmax(dim); } public static partial class functional @@ -51,4 +46,4 @@ public static Tensor log_softmax(Tensor x, long dim) } } } -} +} \ No newline at end of file diff --git a/src/TorchSharp/NN/Activation/Mish.cs b/src/TorchSharp/NN/Activation/Mish.cs index d7f6d27dd..bf59467af 100644 --- a/src/TorchSharp/NN/Activation/Mish.cs +++ b/src/TorchSharp/NN/Activation/Mish.cs @@ -12,25 +12,19 @@ namespace Modules /// /// This class is used to represent a Mish module. /// - public sealed class Mish : torch.nn.Module + public sealed class Mish : ParameterLessModule { - internal Mish(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { } - - public override Tensor forward(Tensor tensor) + internal Mish(bool inplace) : base(nameof(Mish)) { - return ReturnCheckForErrors(THSNN_Mish_forward(handle, tensor.Handle)); + this.inplace = inplace; } - public override string GetName() + public override Tensor forward(Tensor tensor) { - return typeof(Mish).Name; + return torch.nn.functional.mish(tensor, inplace); } - // Rather than spending cycles only to discover that this module has neither - // parameters nor buffers, just shortcut the move completely. - protected internal override nn.Module _to(Device device, ScalarType dtype, bool non_blocking) => this; - protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex, bool non_blocking) => this; - protected internal override nn.Module _to(ScalarType dtype, bool non_blocking) => this; + public bool inplace { get; set; } } } @@ -41,12 +35,18 @@ public static partial class nn /// /// A Self Regularized Non-Monotonic Neural Activation Function. /// - /// public static Mish Mish() { - var handle = THSNN_Mish_ctor(out var boxedHandle); - if (handle == IntPtr.Zero) { torch.CheckForErrors(); } - return new Mish(handle, boxedHandle); + return new Mish(false); + } + + /// + /// A Self Regularized Non-Monotonic Neural Activation Function. + /// + /// Do the operation in-place. Default: False + public static Mish Mish(bool inplace) + { + return new Mish(inplace); } public static partial class functional @@ -55,13 +55,20 @@ public static partial class functional /// A Self Regularized Non-Monotonic Neural Activation Function. /// /// The input tensor - /// - public static Tensor Mish(Tensor x) + /// Do the operation in-place. Default: False + public static Tensor mish(Tensor x, bool inplace = false) { - using (var m = nn.Mish()) { - return m.call(x); - } + using var t1 = softplus(x); + using var t2 = t1.tanh(); + return inplace ? x.mul_(t2).alias() : x.mul(t2); } + + /// + /// A Self Regularized Non-Monotonic Neural Activation Function. + /// + /// The input tensor + [Obsolete("Not using the PyTorch naming convention.", false)] + public static Tensor Mish(Tensor x) => mish(x, false); } } } diff --git a/src/TorchSharp/NN/Activation/PReLU.cs b/src/TorchSharp/NN/Activation/PReLU.cs index e071749ed..6ee563956 100644 --- a/src/TorchSharp/NN/Activation/PReLU.cs +++ b/src/TorchSharp/NN/Activation/PReLU.cs @@ -1,4 +1,4 @@ -// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information. +// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information. using System; using static TorchSharp.torch; using static TorchSharp.PInvoke.NativeMethods; @@ -7,6 +7,7 @@ namespace TorchSharp { using Modules; + using TorchSharp.Utils; namespace Modules { @@ -15,11 +16,20 @@ namespace Modules /// public sealed class PReLU : torch.nn.Module { - internal PReLU(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { } + internal PReLU(long num_parameters, double init, Device? device = null, ScalarType? dtype = null) : base(nameof(PReLU)) + { + this.init = init; + this.num_parameters = num_parameters; + + var w = torch.empty(num_parameters, device: device, dtype: dtype); + w.fill_(init); + + this.weight = new Parameter(w); + } public override Tensor forward(Tensor tensor) { - return ReturnCheckForErrors(THSNN_PReLU_forward(handle, tensor.Handle)); + return torch.nn.functional.prelu(tensor, weight); } public override string GetName() @@ -27,12 +37,8 @@ public override string GetName() return typeof(PReLU).Name; } - public Parameter? weight { - get { - var res = THSNN_PReLU_weight(handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return (res == IntPtr.Zero) ? null : new Parameter(res); - } + public Parameter weight { + get => _weight!; set { if (value.Handle != _weight?.Handle) { _weight?.Dispose(); @@ -56,6 +62,9 @@ protected override void Dispose(bool disposing) _weight?.Dispose(); } } + + [ComponentName(Name = nameof(weight))] + private Parameter? _weight; } } @@ -75,9 +84,7 @@ public static partial class nn /// The desired floating point or complex dtype of the parameters and buffers in this module public static PReLU PReLU(long num_parameters, double init = 0.25, Device? device = null, ScalarType? dtype = null) { - /*var handle = THSNN_PReLU_ctor(num_parameters, init, out var boxedHandle); - if (handle == IntPtr.Zero) { torch.CheckForErrors(); }*/ - return new PReLU(handle, boxedHandle).MoveModule(device, dtype); + return new PReLU(num_parameters, init).MoveModule(device, dtype); } public static partial class functional diff --git a/src/TorchSharp/NN/Activation/RReLU.cs b/src/TorchSharp/NN/Activation/RReLU.cs index aca3e70fc..3a86a9fa7 100644 --- a/src/TorchSharp/NN/Activation/RReLU.cs +++ b/src/TorchSharp/NN/Activation/RReLU.cs @@ -12,25 +12,22 @@ namespace Modules /// /// This class is used to represent a RReLU module. /// - public sealed class RReLU : torch.nn.Module + public sealed class RReLU : ParameterLessModule { - internal RReLU(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { } - - public override Tensor forward(Tensor tensor) + internal RReLU(double lower, double upper, bool inplace) : base(nameof(RReLU)) { - return ReturnCheckForErrors(THSNN_RReLU_forward(handle, tensor.Handle)); + this.lower = lower; + this.upper = upper; + this.inplace = inplace; } - public override string GetName() + public override Tensor forward(Tensor tensor) { - return typeof(RReLU).Name; + return torch.nn.functional.rrelu(tensor, lower, upper, inplace); } - - // Rather than spending cycles only to discover that this module has neither - // parameters nor buffers, just shortcut the move completely. - protected internal override nn.Module _to(Device device, ScalarType dtype, bool non_blocking) => this; - protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex, bool non_blocking) => this; - protected internal override nn.Module _to(ScalarType dtype, bool non_blocking) => this; + public double lower { get; set; } + public double upper { get; set; } + public bool inplace { get; set; } } } @@ -47,9 +44,7 @@ public static partial class nn /// public static RReLU RReLU(double lower = one_eighth, double upper = one_third, bool inplace = false) { - var handle = THSNN_RReLU_ctor(lower, upper, inplace, out var boxedHandle); - if (handle == IntPtr.Zero) { torch.CheckForErrors(); } - return new RReLU(handle, boxedHandle); + return new RReLU(lower, upper, inplace); } private const double one_eighth = 1.0 / 8.0; @@ -65,11 +60,9 @@ public static partial class functional /// Upper bound of the uniform distribution. Default: 1/3 /// Do the operation in-place. Default: False /// - public static Tensor rrelu(Tensor x, double lower, double upper, bool inplace = false) + public static Tensor rrelu(Tensor x, double lower = one_eighth, double upper = one_third, bool inplace = false) { - using (var m = nn.RReLU(lower, upper, inplace)) { - return m.call(x); - } + return inplace ? x.rrelu_(lower, upper).alias() : x.rrelu(lower, upper); } } } diff --git a/src/TorchSharp/NN/Activation/ReLU6.cs b/src/TorchSharp/NN/Activation/ReLU6.cs index 757941789..a9366f775 100644 --- a/src/TorchSharp/NN/Activation/ReLU6.cs +++ b/src/TorchSharp/NN/Activation/ReLU6.cs @@ -14,25 +14,20 @@ namespace Modules /// /// This class is used to represent a ReLU6 module. /// - public sealed class ReLU6 : torch.nn.Module + public sealed class ReLU6 : ParameterLessModule { - internal ReLU6(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { } - - public override Tensor forward(Tensor tensor) + internal ReLU6(bool inplace) : base(nameof(ReLU6)) { - return ReturnCheckForErrors(NativeMethods.THSNN_ReLU6_forward(handle, tensor.Handle)); + this.inplace = inplace; } - public override string GetName() + + public override Tensor forward(Tensor tensor) { - return typeof(ReLU6).Name; + return torch.nn.functional.relu6(tensor, inplace); } - // Rather than spending cycles only to discover that this module has neither - // parameters nor buffers, just shortcut the move completely. - protected internal override nn.Module _to(Device device, ScalarType dtype, bool non_blocking) => this; - protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex, bool non_blocking) => this; - protected internal override nn.Module _to(ScalarType dtype, bool non_blocking) => this; + public bool inplace { get; set; } } } @@ -49,9 +44,7 @@ public static partial class nn /// public static ReLU6 ReLU6(bool inplace = false) { - var handle = NativeMethods.THSNN_ReLU6_ctor(inplace, out var boxedHandle); - if (handle == IntPtr.Zero) { torch.CheckForErrors(); } - return new ReLU6(handle, boxedHandle); + return new ReLU6(inplace); } public static partial class functional @@ -66,9 +59,7 @@ public static partial class functional /// public static Tensor relu6(Tensor x, bool inplace = false) { - using (var m = nn.ReLU6(inplace)) { - return m.call(x); - } + return inplace ? x.relu6_().alias() : x.relu6(); } } } diff --git a/src/TorchSharp/NN/Activation/ReLu.cs b/src/TorchSharp/NN/Activation/ReLu.cs index eb4ba7815..21fccaee4 100644 --- a/src/TorchSharp/NN/Activation/ReLu.cs +++ b/src/TorchSharp/NN/Activation/ReLu.cs @@ -12,25 +12,19 @@ namespace Modules /// /// This class is used to represent a ReLU module. /// - public sealed class ReLU : torch.nn.Module + public sealed class ReLU : ParameterLessModule { - internal ReLU(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { } - - public override Tensor forward(Tensor tensor) + internal ReLU(bool inplace) : base(nameof(ReLU)) { - return ReturnCheckForErrors(THSNN_ReLU_forward(handle, tensor.Handle)); + this.inplace = inplace; } - public override string GetName() + public override Tensor forward(Tensor tensor) { - return typeof(ReLU).Name; + return torch.nn.functional.relu(tensor, inplace); } - // Rather than spending cycles only to discover that this module has neither - // parameters nor buffers, just shortcut the move completely. - protected internal override nn.Module _to(Device device, ScalarType dtype, bool non_blocking) => this; - protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex, bool non_blocking) => this; - protected internal override nn.Module _to(ScalarType dtype, bool non_blocking) => this; + public bool inplace { get; set; } } } public static partial class torch @@ -44,9 +38,7 @@ public static partial class nn /// public static ReLU ReLU(bool inplace = false) { - var handle = THSNN_ReLU_ctor(inplace, out var boxedHandle); - if (handle == IntPtr.Zero) { torch.CheckForErrors(); } - return new ReLU(handle, boxedHandle); + return new ReLU(inplace); } public static partial class functional @@ -64,4 +56,4 @@ public static Tensor relu(Tensor x, bool inplace = false) } } } -} +} \ No newline at end of file diff --git a/src/TorchSharp/NN/Activation/SELU.cs b/src/TorchSharp/NN/Activation/SELU.cs index a7059f66f..4886c4cd5 100644 --- a/src/TorchSharp/NN/Activation/SELU.cs +++ b/src/TorchSharp/NN/Activation/SELU.cs @@ -12,25 +12,19 @@ namespace Modules /// /// This class is used to represent a SELU module. /// - public sealed class SELU : torch.nn.Module + public sealed class SELU : ParameterLessModule { - internal SELU(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { } - - public override Tensor forward(Tensor tensor) + internal SELU(bool inplace) : base(nameof(SELU)) { - return ReturnCheckForErrors(THSNN_SELU_forward(handle, tensor.Handle)); + this.inplace = inplace; } - public override string GetName() + public override Tensor forward(Tensor tensor) { - return typeof(SELU).Name; + return torch.nn.functional.selu(tensor, inplace); } - // Rather than spending cycles only to discover that this module has neither - // parameters nor buffers, just shortcut the move completely. - protected internal override nn.Module _to(Device device, ScalarType dtype, bool non_blocking) => this; - protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex, bool non_blocking) => this; - protected internal override nn.Module _to(ScalarType dtype, bool non_blocking) => this; + public bool inplace { get; set; } } } @@ -45,9 +39,7 @@ public static partial class nn /// public static SELU SELU(bool inplace = false) { - var handle = THSNN_SELU_ctor(inplace, out var boxedHandle); - if (handle == IntPtr.Zero) { torch.CheckForErrors(); } - return new SELU(handle, boxedHandle); + return new SELU(inplace); } public static partial class functional @@ -65,4 +57,4 @@ public static Tensor selu(Tensor x, bool inplace = false) } } } -} +} \ No newline at end of file diff --git a/src/TorchSharp/NN/Activation/SiLU.cs b/src/TorchSharp/NN/Activation/SiLU.cs index 3e4b4aa99..d39d582c5 100644 --- a/src/TorchSharp/NN/Activation/SiLU.cs +++ b/src/TorchSharp/NN/Activation/SiLU.cs @@ -12,13 +12,16 @@ namespace Modules /// /// This class is used to represent a SiLU module. /// - public sealed class SiLU : torch.nn.Module + public sealed class SiLU : ParameterLessModule { - internal SiLU(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { } + internal SiLU(bool inplace) : base(nameof(SiLU)) + { + this.inplace = inplace; + } public override Tensor forward(Tensor tensor) { - return ReturnCheckForErrors(THSNN_SiLU_forward(handle, tensor.Handle)); + return torch.nn.functional.silu(tensor, inplace); } public override string GetName() @@ -26,11 +29,7 @@ public override string GetName() return typeof(SiLU).Name; } - // Rather than spending cycles only to discover that this module has neither - // parameters nor buffers, just shortcut the move completely. - protected internal override nn.Module _to(Device device, ScalarType dtype, bool non_blocking) => this; - protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex, bool non_blocking) => this; - protected internal override nn.Module _to(ScalarType dtype, bool non_blocking) => this; + public bool inplace { get; set; } } } public static partial class torch @@ -40,13 +39,17 @@ public static partial class nn /// /// Sigmoid-Weighted Linear Unit /// - /// - /// The native libreary does not take an 'inplace' option, even though the PyTorch documentation mentions the parameter. public static SiLU SiLU() { - var handle = THSNN_SiLU_ctor(out var boxedHandle); - if (handle == IntPtr.Zero) { torch.CheckForErrors(); } - return new SiLU(handle, boxedHandle); + return new SiLU(false); + } + + /// + /// Sigmoid-Weighted Linear Unit + /// + public static SiLU SiLU(bool inplace) + { + return new SiLU(inplace); } public static partial class functional diff --git a/src/TorchSharp/NN/Activation/Sigmoid.cs b/src/TorchSharp/NN/Activation/Sigmoid.cs index 65bef8b48..dba335a25 100644 --- a/src/TorchSharp/NN/Activation/Sigmoid.cs +++ b/src/TorchSharp/NN/Activation/Sigmoid.cs @@ -12,25 +12,19 @@ namespace Modules /// /// This class is used to represent a Sigmoid module. /// - public sealed class Sigmoid : torch.nn.Module + public sealed class Sigmoid : ParameterLessModule { - internal Sigmoid(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { } - - public override Tensor forward(Tensor tensor) + internal Sigmoid(bool inplace) : base(nameof(Sigmoid)) { - return ReturnCheckForErrors(THSNN_Sigmoid_forward(handle, tensor.Handle)); + this.inplace = inplace; } - public override string GetName() + public override Tensor forward(Tensor tensor) { - return typeof(Sigmoid).Name; + return torch.nn.functional.sigmoid(tensor, inplace); } - // Rather than spending cycles only to discover that this module has neither - // parameters nor buffers, just shortcut the move completely. - protected internal override nn.Module _to(Device device, ScalarType dtype, bool non_blocking) => this; - protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex, bool non_blocking) => this; - protected internal override nn.Module _to(ScalarType dtype, bool non_blocking) => this; + public bool inplace { get; set; } } } public static partial class torch @@ -43,9 +37,17 @@ public static partial class nn /// public static Sigmoid Sigmoid() { - var handle = THSNN_Sigmoid_ctor(out var boxedHandle); - if (handle == IntPtr.Zero) { torch.CheckForErrors(); } - return new Sigmoid(handle, boxedHandle); + return new Sigmoid(false); + } + + /// + /// Sigmoid activation + /// + /// Do the operation in-place. Default: False + /// + public static Sigmoid Sigmoid(bool inplace) + { + return new Sigmoid(inplace); } public static partial class functional @@ -54,10 +56,21 @@ public static partial class functional /// Sigmoid activation /// /// The input tensor + /// Do the operation in-place. Default: False /// + public static Tensor sigmoid(Tensor x, bool inplace) + { + return inplace ? x.sigmoid_().alias() : x.sigmoid(); + } + + /// + /// Gaussian Error Linear Units + /// + /// The input tensor + /// The defaulting of 'inplace' to 'false' is implemented as an overload to avoid a breaking change. public static Tensor sigmoid(Tensor x) { - return x.sigmoid(); + return sigmoid(x, false); } } } diff --git a/src/TorchSharp/NN/Activation/Softmax.cs b/src/TorchSharp/NN/Activation/Softmax.cs index 232153767..a76805d87 100644 --- a/src/TorchSharp/NN/Activation/Softmax.cs +++ b/src/TorchSharp/NN/Activation/Softmax.cs @@ -12,25 +12,19 @@ namespace Modules /// /// This class is used to represent a Softmax module. /// - public sealed class Softmax : torch.nn.Module + public sealed class Softmax : ParameterLessModule { - internal Softmax(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { } - - public override Tensor forward(Tensor tensor) + internal Softmax(long dim) : base(nameof(Softmax)) { - return ReturnCheckForErrors(THSNN_Softmax_forward(handle, tensor.Handle)); + this.dim = dim; } - public override string GetName() + public override Tensor forward(Tensor tensor) { - return typeof(Softmax).Name; + return torch.nn.functional.softmax(tensor, dim); } - // Rather than spending cycles only to discover that this module has neither - // parameters nor buffers, just shortcut the move completely. - protected internal override nn.Module _to(Device device, ScalarType dtype, bool non_blocking) => this; - protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex, bool non_blocking) => this; - protected internal override nn.Module _to(ScalarType dtype, bool non_blocking) => this; + public long dim { get; set; } } } @@ -45,9 +39,7 @@ public static partial class nn /// public static Softmax Softmax(long dim) { - var handle = THSNN_Softmax_ctor(dim, out var boxedHandle); - if (handle == IntPtr.Zero) { torch.CheckForErrors(); } - return new Softmax(handle, boxedHandle); + return new Softmax(dim); } public static partial class functional @@ -58,8 +50,9 @@ public static partial class functional /// The input tensor /// A dimension along which softmax will be computed. /// The desired data type of returned tensor. - public static Tensor softmax(Tensor input, long dim, ScalarType? dtype = null) => torch.special.softmax(input, dim, dtype); + public static Tensor softmax(Tensor input, long dim, ScalarType? dtype = null) => + torch.special.softmax(input, dim, dtype); } } } -} +} \ No newline at end of file diff --git a/src/TorchSharp/NN/Activation/Softmax2d.cs b/src/TorchSharp/NN/Activation/Softmax2d.cs index a0fc107f1..edd6c4bbb 100644 --- a/src/TorchSharp/NN/Activation/Softmax2d.cs +++ b/src/TorchSharp/NN/Activation/Softmax2d.cs @@ -12,25 +12,14 @@ namespace Modules /// /// This class is used to represent a Softmax2d module. /// - public sealed class Softmax2d : torch.nn.Module + public sealed class Softmax2d : ParameterLessModule { - internal Softmax2d(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { } + internal Softmax2d() : base(nameof(Softmax2d)) { } public override Tensor forward(Tensor tensor) { - return ReturnCheckForErrors(THSNN_Softmax2d_forward(handle, tensor.Handle)); + return torch.nn.functional.softmax2d(tensor); } - - public override string GetName() - { - return typeof(Softmax2d).Name; - } - - // Rather than spending cycles only to discover that this module has neither - // parameters nor buffers, just shortcut the move completely. - protected internal override nn.Module _to(Device device, ScalarType dtype, bool non_blocking) => this; - protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex, bool non_blocking) => this; - protected internal override nn.Module _to(ScalarType dtype, bool non_blocking) => this; } } public static partial class torch @@ -43,9 +32,7 @@ public static partial class nn /// public static Softmax2d Softmax2d() { - var handle = THSNN_Softmax2d_ctor(out var boxedHandle); - if (handle == IntPtr.Zero) { torch.CheckForErrors(); } - return new Softmax2d(handle, boxedHandle); + return new Softmax2d(); } public static partial class functional @@ -57,11 +44,9 @@ public static partial class functional /// public static Tensor softmax2d(Tensor x) { - using (var m = nn.Softmax2d()) { - return m.call(x); - } + return torch.nn.functional.softmax(x, -3); } } } } -} +} \ No newline at end of file diff --git a/src/TorchSharp/NN/Activation/Softmin.cs b/src/TorchSharp/NN/Activation/Softmin.cs index 80ec85d04..dd20808e4 100644 --- a/src/TorchSharp/NN/Activation/Softmin.cs +++ b/src/TorchSharp/NN/Activation/Softmin.cs @@ -13,25 +13,19 @@ namespace Modules /// /// This class is used to represent a Softmin module. /// - public sealed class Softmin : torch.nn.Module + public sealed class Softmin : ParameterLessModule { - internal Softmin(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { } - - public override Tensor forward(Tensor tensor) + internal Softmin(long dim) : base(nameof(Softmin)) { - return ReturnCheckForErrors(THSNN_Softmin_forward(handle, tensor.Handle)); + this.dim = dim; } - public override string GetName() + public override Tensor forward(Tensor tensor) { - return typeof(Softmin).Name; + return torch.nn.functional.softmin(tensor, dim); } - // Rather than spending cycles only to discover that this module has neither - // parameters nor buffers, just shortcut the move completely. - protected internal override nn.Module _to(Device device, ScalarType dtype, bool non_blocking) => this; - protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex, bool non_blocking) => this; - protected internal override nn.Module _to(ScalarType dtype, bool non_blocking) => this; + public long dim { get; set; } } } @@ -46,10 +40,7 @@ public static partial class nn /// public static Softmin Softmin(long dim) { - var handle = THSNN_Softmin_ctor(dim, out var boxedHandle); - if (handle == IntPtr.Zero) { torch.CheckForErrors(); } - handle = AutocastMode.AutoCast(handle, ScalarType.Float32); //Should put this here??? - return new Softmin(handle, boxedHandle); + return new Softmin(dim); } public static partial class functional @@ -62,11 +53,11 @@ public static partial class functional /// public static Tensor softmin(Tensor x, long dim) { - using (var m = nn.Softmin(dim)) { - return m.call(x); - } + using var minus_x = -x; + //minus_x = AutocastMode.AutoCast(minus_x.handle, ScalarType.Float32); + return softmax(minus_x, dim); } } } } -} +} \ No newline at end of file diff --git a/src/TorchSharp/NN/Activation/Softplus.cs b/src/TorchSharp/NN/Activation/Softplus.cs index cbd30d1bc..febcf61f4 100644 --- a/src/TorchSharp/NN/Activation/Softplus.cs +++ b/src/TorchSharp/NN/Activation/Softplus.cs @@ -1,6 +1,5 @@ // Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information. using System; -using TorchSharp.Amp; using static TorchSharp.torch; using static TorchSharp.PInvoke.NativeMethods; @@ -13,25 +12,22 @@ namespace Modules /// /// This class is used to represent a Softplus module. /// - public sealed class Softplus : torch.nn.Module + public sealed class Softplus : ParameterLessModule { - internal Softplus(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { } - - public override Tensor forward(Tensor tensor) + internal Softplus(double beta = 1, double threshold = 20) : base(nameof(Softplus)) { - return ReturnCheckForErrors(THSNN_Softplus_forward(handle, tensor.Handle)); + this.beta = beta; + this.threshold = threshold; } - public override string GetName() + public override Tensor forward(Tensor tensor) { - return typeof(Softplus).Name; + //AutocastMode here? + return torch.nn.functional.softplus(tensor, beta, threshold); } - // Rather than spending cycles only to discover that this module has neither - // parameters nor buffers, just shortcut the move completely. - protected internal override nn.Module _to(Device device, ScalarType dtype, bool non_blocking) => this; - protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex, bool non_blocking) => this; - protected internal override nn.Module _to(ScalarType dtype, bool non_blocking) => this; + public double beta { get; set; } + public double threshold { get; set; } } } @@ -45,12 +41,9 @@ public static partial class nn /// The β value for the Softplus formulation. /// Values above this revert to a linear function /// - public static Softplus Softplus(double beta = 1.0, double threshold = 20.0) + public static Softplus Softplus(double beta = 1, double threshold = 20) { - var handle = THSNN_Softplus_ctor(beta, threshold, out var boxedHandle); - if (handle == IntPtr.Zero) { torch.CheckForErrors(); } - handle = AutocastMode.AutoCast(handle, ScalarType.Float32); //Should put this here - return new Softplus(handle, boxedHandle); + return new Softplus(beta, threshold); } public static partial class functional @@ -62,11 +55,10 @@ public static partial class functional /// The β value for the Softplus formulation. /// Values above this revert to a linear function /// - public static Tensor softplus(Tensor x, double beta = 1.0, double threshold = 20.0) + public static Tensor softplus(Tensor x, double beta = 1, double threshold = 20) { - using (var m = nn.Softplus(beta, threshold)) { - return m.call(x); - } + //AutocastMode + return x.softplus(beta, threshold); } } } diff --git a/src/TorchSharp/NN/Activation/Softshrink.cs b/src/TorchSharp/NN/Activation/Softshrink.cs index e61efd876..7e0e2cb86 100644 --- a/src/TorchSharp/NN/Activation/Softshrink.cs +++ b/src/TorchSharp/NN/Activation/Softshrink.cs @@ -12,25 +12,19 @@ namespace Modules /// /// This class is used to represent a Softshrink module. /// - public sealed class Softshrink : torch.nn.Module + public sealed class Softshrink : ParameterLessModule { - internal Softshrink(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { } - - public override Tensor forward(Tensor tensor) + internal Softshrink(double lambda = 0.5) : base(nameof(Softshrink)) { - return ReturnCheckForErrors(THSNN_Softshrink_forward(handle, tensor.Handle)); + this.lambda = lambda; } - public override string GetName() + public override Tensor forward(Tensor tensor) { - return typeof(Softshrink).Name; + return torch.nn.functional.softshrink(tensor, lambda); } - // Rather than spending cycles only to discover that this module has neither - // parameters nor buffers, just shortcut the move completely. - protected internal override nn.Module _to(Device device, ScalarType dtype, bool non_blocking) => this; - protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex, bool non_blocking) => this; - protected internal override nn.Module _to(ScalarType dtype, bool non_blocking) => this; + public double lambda { get; set; } } } @@ -45,9 +39,7 @@ public static partial class nn /// public static Softshrink Softshrink(double lambda = 0.5) { - var handle = THSNN_Softshrink_ctor(lambda, out var boxedHandle); - if (handle == IntPtr.Zero) { torch.CheckForErrors(); } - return new Softshrink(handle, boxedHandle); + return new Softshrink(lambda); } public static partial class functional @@ -58,12 +50,21 @@ public static partial class functional /// The input tensor /// The λ value for the Softshrink formulation. Default: 0.5 /// - public static Tensor Softshrink(Tensor x, double lambda = 0.5) + public static Tensor softshrink(Tensor x, double lambda = 0.5) { - using (var m = nn.Softshrink(lambda)) { - return m.call(x); - } + using var sc = (Scalar)lambda; + var result = THSTensor_softshrink(x.Handle, sc.Handle); + if (result == IntPtr.Zero) { torch.CheckForErrors(); } + return new Tensor(result); } + + /// + /// Softshrink + /// + /// The input tensor + /// The λ value for the Softshrink formulation. Default: 0.5 + [Obsolete("Not using the PyTorch naming convention.", false)] + public static Tensor Softshrink(Tensor x, double lambda = 0.5) => softshrink(x, lambda); } } } diff --git a/src/TorchSharp/NN/Activation/Softsign.cs b/src/TorchSharp/NN/Activation/Softsign.cs index a041a5f26..882ea5e37 100644 --- a/src/TorchSharp/NN/Activation/Softsign.cs +++ b/src/TorchSharp/NN/Activation/Softsign.cs @@ -12,25 +12,19 @@ namespace Modules /// /// This class is used to represent a Softsign module. /// - public sealed class Softsign : torch.nn.Module + public sealed class Softsign : ParameterLessModule { - internal Softsign(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { } - - public override Tensor forward(Tensor tensor) + internal Softsign(bool inplace) : base(nameof(Softsign)) { - return ReturnCheckForErrors(THSNN_Softsign_forward(handle, tensor.Handle)); + this.inplace = inplace; } - public override string GetName() + public override Tensor forward(Tensor tensor) { - return typeof(Softsign).Name; + return torch.nn.functional.softsign(tensor, inplace); } - // Rather than spending cycles only to discover that this module has neither - // parameters nor buffers, just shortcut the move completely. - protected internal override nn.Module _to(Device device, ScalarType dtype, bool non_blocking) => this; - protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex, bool non_blocking) => this; - protected internal override nn.Module _to(ScalarType dtype, bool non_blocking) => this; + public bool inplace { get; set; } } } @@ -41,12 +35,18 @@ public static partial class nn /// /// Softsign /// - /// public static Softsign Softsign() { - var handle = THSNN_Softsign_ctor(out var boxedHandle); - if (handle == IntPtr.Zero) { torch.CheckForErrors(); } - return new Softsign(handle, boxedHandle); + return new Softsign(false); + } + + /// + /// Softsign + /// + /// Do the operation in-place. Default: False + public static Softsign Softsign(bool inplace) + { + return new Softsign(inplace); } public static partial class functional @@ -55,13 +55,20 @@ public static partial class functional /// Softsign /// /// The input tensor - /// - public static Tensor Softsign(Tensor x) + /// Do the operation in-place. Default: False + public static Tensor softsign(Tensor x, bool inplace = false) { - using (var m = nn.Softsign()) { - return m.call(x); - } + using var abs = x.abs(); + using var y = 1 + abs; + return inplace ? x.div_(y).alias() : x.div(y); } + + /// + /// Softsign + /// + /// The input tensor + [Obsolete("Not using the PyTorch naming convention.", false)] + public static Tensor Softsign(Tensor x) => softsign(x, false); } } } diff --git a/src/TorchSharp/NN/Activation/Tanh.cs b/src/TorchSharp/NN/Activation/Tanh.cs index 4133da63e..3db637564 100644 --- a/src/TorchSharp/NN/Activation/Tanh.cs +++ b/src/TorchSharp/NN/Activation/Tanh.cs @@ -12,13 +12,16 @@ namespace Modules /// /// This class is used to represent a Tanh module. /// - public sealed class Tanh : torch.nn.Module + public sealed class Tanh : ParameterLessModule { - internal Tanh(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { } + internal Tanh(bool inplace) : base(nameof(Tanh)) + { + this.inplace = inplace; + } public override Tensor forward(Tensor tensor) { - return ReturnCheckForErrors(THSNN_Tanh_forward(handle, tensor.Handle)); + return torch.nn.functional.tanh(tensor, inplace); } public override string GetName() @@ -26,11 +29,7 @@ public override string GetName() return typeof(Tanh).Name; } - // Rather than spending cycles only to discover that this module has neither - // parameters nor buffers, just shortcut the move completely. - protected internal override nn.Module _to(Device device, ScalarType dtype, bool non_blocking) => this; - protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex, bool non_blocking) => this; - protected internal override nn.Module _to(ScalarType dtype, bool non_blocking) => this; + public bool inplace { get; set; } } } @@ -44,9 +43,16 @@ public static partial class nn /// public static Tanh Tanh() { - var handle = THSNN_Tanh_ctor(out var boxedHandle); - if (handle == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tanh(handle, boxedHandle); + return new Tanh(false); + } + + /// + /// Tanh activation + /// + /// + public static Tanh Tanh(bool inplace = false) + { + return new Tanh(inplace); } public static partial class functional diff --git a/src/TorchSharp/NN/Activation/Tanhshrink.cs b/src/TorchSharp/NN/Activation/Tanhshrink.cs index fa2f7214e..f38ce7e71 100644 --- a/src/TorchSharp/NN/Activation/Tanhshrink.cs +++ b/src/TorchSharp/NN/Activation/Tanhshrink.cs @@ -12,25 +12,19 @@ namespace Modules /// /// This class is used to represent a Tanhshrink module. /// - public sealed class Tanhshrink : torch.nn.Module + public sealed class Tanhshrink : ParameterLessModule { - internal Tanhshrink(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { } - - public override Tensor forward(Tensor tensor) + internal Tanhshrink(bool inplace) : base(nameof(Tanhshrink)) { - return ReturnCheckForErrors(THSNN_Tanhshrink_forward(handle, tensor.Handle)); + this.inplace = inplace; } - public override string GetName() + public override Tensor forward(Tensor tensor) { - return typeof(Tanhshrink).Name; + return torch.nn.functional.tanhshrink(tensor, inplace); } - // Rather than spending cycles only to discover that this module has neither - // parameters nor buffers, just shortcut the move completely. - protected internal override nn.Module _to(Device device, ScalarType dtype, bool non_blocking) => this; - protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex, bool non_blocking) => this; - protected internal override nn.Module _to(ScalarType dtype, bool non_blocking) => this; + public bool inplace { get; set; } } } @@ -41,12 +35,18 @@ public static partial class nn /// /// Tanhshrink /// - /// public static Tanhshrink Tanhshrink() { - var handle = THSNN_Tanhshrink_ctor(out var boxedHandle); - if (handle == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tanhshrink(handle, boxedHandle); + return new Tanhshrink(false); + } + + /// + /// Tanhshrink + /// + /// Do the operation in-place. Default: False + public static Tanhshrink Tanhshrink(bool inplace = false) + { + return new Tanhshrink(inplace); } public static partial class functional @@ -55,13 +55,19 @@ public static partial class functional /// Tanhshrink /// /// The input tensor - /// - public static Tensor Tanhshrink(Tensor x) + /// Do the operation in-place. Default: False + public static Tensor tanhshrink(Tensor x, bool inplace = false) { - using (var m = nn.Tanhshrink()) { - return m.call(x); - } + using var tanh_x = x.tanh(); + return inplace ? x.sub_(tanh_x).alias() : x.sub(tanh_x); } + + /// + /// Tanhshrink + /// + /// The input tensor + [Obsolete("Not using the PyTorch naming convention.", false)] + public static Tensor Tanhshrink(Tensor x) => tanhshrink(x, false); } } } diff --git a/src/TorchSharp/NN/Activation/Threshold.cs b/src/TorchSharp/NN/Activation/Threshold.cs index 4f344aa2d..007498d47 100644 --- a/src/TorchSharp/NN/Activation/Threshold.cs +++ b/src/TorchSharp/NN/Activation/Threshold.cs @@ -12,25 +12,25 @@ namespace Modules /// /// This class is used to represent a Threshold module. /// - public sealed class Threshold : torch.nn.Module + public sealed class Threshold : ParameterLessModule { - internal Threshold(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { } - - public override Tensor forward(Tensor tensor) + internal Threshold(double threshold, double value, bool inplace) : base(nameof(Threshold)) { - return ReturnCheckForErrors(THSNN_Threshold_forward(handle, tensor.Handle)); + this.inplace = inplace; + this.threshold = threshold; + this.value = value; } - public override string GetName() + public override Tensor forward(Tensor tensor) { - return typeof(Threshold).Name; + return torch.nn.functional.threshold(tensor, threshold, value, inplace); } - // Rather than spending cycles only to discover that this module has neither - // parameters nor buffers, just shortcut the move completely. - protected internal override nn.Module _to(Device device, ScalarType dtype, bool non_blocking) => this; - protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex, bool non_blocking) => this; - protected internal override nn.Module _to(ScalarType dtype, bool non_blocking) => this; + public double threshold { get; set; } + + public double value { get; set; } + + public bool inplace { get; set; } } } @@ -47,9 +47,7 @@ public static partial class nn /// public static Threshold Threshold(double threshold, double value, bool inplace = false) { - var handle = THSNN_Threshold_ctor(threshold, value, inplace, out var boxedHandle); - if (handle == IntPtr.Zero) { torch.CheckForErrors(); } - return new Threshold(handle, boxedHandle); + return new Threshold(threshold, value, inplace); } public static partial class functional @@ -61,13 +59,20 @@ public static partial class functional /// The value to threshold at /// The value to replace with /// Do the operation in-place - /// - public static Tensor Threshold(Tensor x, double threshold, double value, bool inplace = false) + public static Tensor threshold(Tensor x, double threshold, double value, bool inplace = false) { - using (var m = nn.Threshold(threshold, value, inplace)) { - return m.call(x); - } + return inplace ? x.threshold_(threshold, value).alias() : x.threshold(threshold, value); } + + /// + /// Thresholds each element of the input Tensor. + /// + /// The input tensor + /// The value to threshold at + /// The value to replace with + /// Do the operation in-place + [Obsolete("Not using the PyTorch naming convention.", false)] + public static Tensor Threshold(Tensor x, double threshold, double value, bool inplace = false) => nn.functional.threshold(x, threshold, value, inplace); } } } diff --git a/src/TorchSharp/NN/Bilinear.cs b/src/TorchSharp/NN/Bilinear.cs index 7c06d83fd..4359a56f2 100644 --- a/src/TorchSharp/NN/Bilinear.cs +++ b/src/TorchSharp/NN/Bilinear.cs @@ -1,6 +1,5 @@ -// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information. +// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information. using System; -using TorchSharp.Amp; using static TorchSharp.torch; using static TorchSharp.torch.nn; using static TorchSharp.PInvoke.NativeMethods; @@ -8,42 +7,110 @@ #nullable enable namespace TorchSharp { - using System.Linq; using Modules; + using TorchSharp.Utils; namespace Modules { public sealed class Bilinear : Module { - internal Bilinear(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { } + const string WeightComponentName = nameof(weight); + const string BiasComponentName = nameof(bias); + + internal Bilinear(long in1_features, long in2_features, long out_features, bool hasBias = true, Device? device = null, ScalarType? dtype = null) : base(nameof(Bilinear)) + { + this.in1_features = in1_features; + this.in2_features = in2_features; + this.out_features = out_features; + + weight = torch.empty(out_features, in1_features, in2_features, device: device, dtype: dtype).AsParameter(); + var bound = 1 / Math.Sqrt(weight!.shape[1]); + + init.uniform_(_weight, -bound, bound); + + if (hasBias) { + bias = torch.empty(out_features, device: device, dtype: dtype).AsParameter(); + init.uniform_(_bias, -bound, bound); + } + //NOTE: it's important not to call 'RegisterComponents' here. + } public override Tensor forward(Tensor input1, Tensor input2) { - return ReturnCheckForErrors(THSNN_Bilinear_forward(handle, input1.Handle, input2.Handle)); + return torch.nn.functional.bilinear(input1, input2, _weight!, _bias); + } + + protected override void Dispose(bool disposing) + { + if (disposing) { + _weight?.Dispose(); + _bias?.Dispose(); + } } public Parameter? bias { get => _bias; - set - { + set { _bias?.Dispose(); _bias = value?.DetachFromDisposeScope() as Parameter; ConditionallyRegisterParameter(BiasComponentName, _bias); } } - public Parameter? weight { + public Parameter weight { get => _weight!; - set - { - if (value.Handle != _weight?.Handle) - { + set { + if (value.Handle != _weight?.Handle) { _weight?.Dispose(); _weight = (value.DetachFromDisposeScope() as Parameter)!; ConditionallyRegisterParameter(WeightComponentName, _weight); } } } + + // Rather than spending cycles discovering what parameters exist, we can just hardcode it. + protected internal override nn.Module _to(Device device, ScalarType dtype, bool non_blocking) + { + if (_weight is not null && ReplaceParameter(dtype, device, _weight, out Parameter? w)) { + weight = w!; + } + if (_bias is not null && ReplaceParameter(dtype, device, _bias, out Parameter? b)) { + bias = b!; + } + return this; + } + + protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex, bool non_blocking) + { + var device = new Device(deviceType, deviceIndex); + if (_weight is not null && ReplaceParameter(_weight.dtype, device, _weight, out Parameter? w)) { + weight = w!; + } + if (_bias is not null && ReplaceParameter(_bias.dtype, device, _bias, out Parameter? b)) { + bias = b!; + } + return this; + } + + protected internal override nn.Module _to(ScalarType dtype, bool non_blocking) + { + if (_weight is not null && ReplaceParameter(dtype, _weight.device, _weight, out Parameter? w)) { + weight = w!; + } + if (_bias is not null && ReplaceParameter(dtype, _bias.device, _bias, out Parameter? b)) { + bias = b!; + } + return this; + } + + [ComponentName(Name = BiasComponentName)] + private Parameter? _bias; + [ComponentName(Name = WeightComponentName)] + private Parameter? _weight; + + public long in1_features { get; set; } + public long in2_features { get; set; } + public long out_features { get; set; } } } @@ -55,19 +122,16 @@ public static partial class nn /// /// Applies a bilinear transformation to the incoming data /// - /// size of each first input sample - /// size of each second input sample - /// size of each output sample + /// size of each first input sample + /// size of each second input sample + /// size of each output sample /// If set to false, the layer will not learn an additive bias /// The desired device of the parameters and buffers in this module /// The desired floating point or complex dtype of the parameters and buffers in this module /// - public static Bilinear Bilinear(long in1Features, long in2Features, long outputSize, bool hasBias = true, Device? device = null, ScalarType? dtype = null) + public static Bilinear Bilinear(long in1_features, long in2_features, long out_features, bool hasBias = true, Device? device = null, ScalarType? dtype = null) { - var res = THSNN_Bilinear_ctor(in1Features, in2Features, outputSize, hasBias, out var boxedHandle); - if (res == IntPtr.Zero) { CheckForErrors(); } - - return new Bilinear(res, boxedHandle).MoveModule(device, dtype); + return new Bilinear(in1_features, in2_features, out_features, hasBias, device, dtype); } public static partial class functional @@ -85,17 +149,7 @@ public static Tensor bilinear(Tensor input1, Tensor input2, Tensor weight, Tenso { IntPtr bPtr = bias?.Handle ?? IntPtr.Zero; var res = THSNN_functional_bilinear(input1.Handle, input2.Handle, weight.Handle, bPtr); - if (res == IntPtr.Zero) { CheckForErrors(); } - /*if (AutocastMode.IsAutocastEnabled()) { - var st = input1.dtype; - var st1 = input2.dtype; - var st2 = weight.dtype; - var sts = new[] { st, st1, st2 }; - if (sts.All(x => x == ScalarType.Float16)) - (handle, tensor1.handle, tensor2.handle) = AutocastMode.AutoCast(handle, tensor1.handle, tensor2.handle, ScalarType.Float16); - if (sts.Any(x => x == ScalarType.Float32)) - (handle, tensor1.handle, tensor2.handle) = AutocastMode.AutoCast(handle, tensor1.handle, tensor2.handle, ScalarType.Float32); - }*/ + if (res == IntPtr.Zero) { torch.CheckForErrors(); } return new Tensor(res); } } diff --git a/src/TorchSharp/NN/Convolution/Conv1D.cs b/src/TorchSharp/NN/Convolution/Conv1D.cs index 9ab025081..bf59becd7 100644 --- a/src/TorchSharp/NN/Convolution/Conv1D.cs +++ b/src/TorchSharp/NN/Convolution/Conv1D.cs @@ -1,6 +1,5 @@ // Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information. using System; -using TorchSharp.Amp; using static TorchSharp.torch; using static TorchSharp.PInvoke.NativeMethods; @@ -9,82 +8,27 @@ namespace TorchSharp { using Modules; - public enum PaddingModes - { - Zeros = 0, - Reflect = 1, - Replicate = 2, - Circular = 3, - Constant = 4, - } - - public enum Padding - { - Valid = 0, - Same = 1 - } - namespace Modules { - public abstract class Convolution : torch.nn.Module - { - internal long _dimension, _in_channel, _out_channel, _kernel,_stride, _padding,_dilation,_groups; - internal PaddingModes _paddingModes; - internal (long, long)? _kernels, _strides, _paddings, _dilations; - internal bool _bias; - protected Convolution(IntPtr handle, IntPtr boxedHandle, long input_channels) : base(handle, boxedHandle) - { - this.input_channels = input_channels; - } - - protected bool ValidateShape(Tensor input, long dimensions) - { - var shape = input.shape; - var ndim = shape.LongLength; - - return (ndim == dimensions+2) && (input.shape[1] == input_channels) || // Batched: N + C + dims - (ndim == dimensions+1 && input.shape[0] == input_channels); // Unbathced: C + dims - - } - - protected long input_channels; - } - public sealed class Conv1d : Convolution { - internal Conv1d(IntPtr handle, IntPtr boxedHandle, long input_channels) : base(handle, boxedHandle, input_channels) { } + internal Conv1d(long in_channels, long out_channels, long kernel_size, long stride, long? padding, Padding? padding_type, long dilation, long groups = 1, bool bias = true, PaddingModes padding_mode = PaddingModes.Zeros, torch.Device? device = null, ScalarType? dtype = null) + : base(nameof(Conv1d), in_channels, out_channels, new[] { kernel_size }, new[] { stride }, padding.HasValue ? new[] { padding.Value } : null, padding_type, new[] { dilation }, false, new[] { 0L }, groups, bias, padding_mode, device, dtype) { } public override Tensor forward(Tensor input) { - if (ValidateShape(input, 1)) { - return ReturnCheckForErrors(THSNN_Conv1d_forward(handle, input.Handle)); - } - throw new ArgumentException($"Expected 2D (unbatched) or 3D (batched) input with {input_channels} channels to Conv1d."); - } + if (!ValidateShape(input, 1)) + throw new ArgumentException($"Expected 2D (unbatched) or 3D (batched) input with {in_channels} channels to Conv1d."); - public Parameter? bias { - get { - return ReturnNullParameterCheckForErrors(THSNN_Conv1d_bias(handle)); - } - set { - // Please ignore, for now, that the litorch call thinks you *can* set it to null. - if (value is null) throw new ArgumentNullException("bias cannot be set to 'null'"); - THSNN_Conv1d_set_bias(handle, (value is null ? IntPtr.Zero : value.Handle)); - torch.CheckForErrors(); - ConditionallyRegisterParameter("bias", value); - } - } - public Parameter? weight { - get { - return ReturnNullParameterCheckForErrors(THSNN_Conv1d_weight(handle)); - } - set { - // Please ignore, for now, that the litorch call thinks you *can* set it to null. - if (value is null) throw new ArgumentNullException("weight cannot be set to 'null'"); - THSNN_Conv1d_set_weight(handle, value is null ? IntPtr.Zero : value.Handle); - torch.CheckForErrors(); - ConditionallyRegisterParameter("weight", value); + if (padding_mode != PaddingModes.Zeros) { + using var paddedInput = torch.nn.functional.pad(input, _reversed_padding_repeated_twice, padding_mode); + return torch.nn.functional.conv1d(paddedInput, weight, bias, stride[0], 0, dilation[0], groups); } + + if (padding_type.HasValue) + return torch.nn.functional.conv1d_padding(input, weight, bias, stride[0], padding_type.Value, dilation[0], groups); + + return torch.nn.functional.conv1d(input, weight, bias, stride[0], padding?[0], dilation[0], groups); } } } @@ -98,7 +42,7 @@ public static partial class nn /// /// Number of channels in the input image /// Number of channels produced by the convolution - /// Size of the convolving kernel + /// Size of the convolving kernel /// Stride of the convolution. Default: 1 /// Zero-padding added to both sides of the input. Default: 0 /// Spacing between kernel elements. Default: 1 @@ -108,21 +52,9 @@ public static partial class nn /// The desired device of the parameters and buffers in this module /// The desired floating point or complex dtype of the parameters and buffers in this module /// Tensor of shape (N,C_out,L_out) - public static Conv1d Conv1d(long in_channels, long out_channels, long kernelSize, long stride = 1, long padding = 0, long dilation = 1, PaddingModes padding_mode = PaddingModes.Zeros, long groups = 1, bool bias = true, Device? device = null, ScalarType? dtype = null) + public static Conv1d Conv1d(long in_channels, long out_channels, long kernel_size, long stride = 1, long padding = 0, long dilation = 1, PaddingModes padding_mode = PaddingModes.Zeros, long groups = 1, bool bias = true, Device? device = null, ScalarType? dtype = null) { - var res = THSNN_Conv1d_ctor(in_channels, out_channels, kernelSize, stride, padding, dilation, (long)padding_mode, groups, bias, out var boxedHandle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Conv1d(res, boxedHandle, in_channels) { - _in_channel = in_channels, - _out_channel = out_channels, - _kernel = kernelSize, - _stride = stride, - _padding = padding, - _dilation = dilation, - _paddingModes = padding_mode, - _groups = groups, - _bias = bias - }.MoveModule(device, dtype); + return new Conv1d(in_channels, out_channels, kernel_size, stride, padding, null, dilation, groups, bias, padding_mode, device, dtype); } /// @@ -130,7 +62,7 @@ public static Conv1d Conv1d(long in_channels, long out_channels, long kernelSize /// /// Number of channels in the input image /// Number of channels produced by the convolution - /// Size of the convolving kernel + /// Size of the convolving kernel /// Stride of the convolution. Default: 1 /// Zero-padding added to both sides of the input. padding=Valid is the same as no padding. padding=Same pads the input so the output has the shape as the input. /// Spacing between kernel elements. Default: 1 @@ -140,21 +72,9 @@ public static Conv1d Conv1d(long in_channels, long out_channels, long kernelSize /// The desired device of the parameters and buffers in this module /// The desired floating point or complex dtype of the parameters and buffers in this module /// Tensor of shape (N,C_out,L_out) - public static Conv1d Conv1d(long in_channels, long out_channels, long kernelSize, Padding padding, long stride = 1, long dilation = 1, PaddingModes padding_mode = PaddingModes.Zeros, long groups = 1, bool bias = true, Device? device = null, ScalarType? dtype = null) + public static Conv1d Conv1d(long in_channels, long out_channels, long kernel_size, Padding padding, long stride = 1, long dilation = 1, PaddingModes padding_mode = PaddingModes.Zeros, long groups = 1, bool bias = true, Device? device = null, ScalarType? dtype = null) { - var res = THSNN_Conv1d_ctor(in_channels, out_channels, kernelSize, stride, padding == Padding.Valid ? 0 : -1, dilation, (long)padding_mode, groups, bias, out var boxedHandle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Conv1d(res, boxedHandle, in_channels) { - _in_channel = in_channels, - _out_channel = out_channels, - _kernel = kernelSize, - _stride = stride, - _padding = (long)padding, - _dilation = dilation, - _paddingModes = padding_mode, - _groups = groups, - _bias = bias - }.MoveModule(device, dtype); + return new Conv1d(in_channels, out_channels, kernel_size, stride, null, padding, dilation, groups, bias, padding_mode, device, dtype); } public static partial class functional @@ -163,12 +83,12 @@ public static partial class functional /// Applies a 1D convolution over an input signal composed of several input planes. /// /// The input tensor. - /// - /// - /// - /// - /// - /// + /// weight matrix of the convolution + /// Optional; bias vector of the convolution + /// Stride of the convolution. Default: (1,) + /// Zero-padding added to both sides of the input. Default: (0,) + /// Spacing between kernel elements. Default: (1,) + /// Number of blocked connections from input channels to output channels. Default: 1 /// public static Tensor conv1d(Tensor input, Tensor weight, Tensor? bias = null, long? stride = null, @@ -182,7 +102,8 @@ public static Tensor conv1d(Tensor input, Tensor weight, Tensor? bias = null, var biasHandle = (bias is null ? IntPtr.Zero : bias.Handle); unsafe { fixed (long* pstrides = strides, ppadding = paddingArray, pdilation = dilationArray) { - var res = THSTensor_conv1d(input.Handle, weight.Handle, biasHandle, + var res = + THSTensor_conv1d(input.Handle, weight.Handle, biasHandle, (IntPtr)pstrides, strides.Length, (IntPtr)ppadding, paddingArray.Length, (IntPtr)pdilation, dilationArray.Length, @@ -192,7 +113,39 @@ public static Tensor conv1d(Tensor input, Tensor weight, Tensor? bias = null, } } + /// + /// Applies a 1D convolution over an input signal composed of several input planes. + /// + /// The input tensor. + /// weight matrix of the convolution + /// Optional; bias vector of the convolution + /// Stride of the convolution. Default: (1,) + /// Zero-padding added to both sides of the input. padding=Valid is the same as no padding. padding=Same pads the input so the output has the shape as the input. + /// Spacing between kernel elements. Default: (1,) + /// Number of blocked connections from input channels to output channels. Default: 1 + /// + public static Tensor conv1d_padding(Tensor input, Tensor weight, Tensor? bias = null, + long? stride = null, + Padding padding = Padding.Valid, + long? dilation = null, + long groups = 1) + { + var strides = new long[] { stride ?? 1 }; + var dilationArray = new long[] { dilation ?? 1 }; + var biasHandle = (bias is null ? IntPtr.Zero : bias.Handle); + unsafe { + fixed (long* pstrides = strides, pdilation = dilationArray) { + var res = + THSTensor_conv1d_padding(input.Handle, weight.Handle, biasHandle, + (IntPtr)pstrides, strides.Length, + (int)padding, + (IntPtr)pdilation, dilationArray.Length, + groups); + return ReturnCheckForErrorsAutocast(res); + } + } + } } } } -} +} \ No newline at end of file diff --git a/src/TorchSharp/NN/Convolution/Conv2D.cs b/src/TorchSharp/NN/Convolution/Conv2D.cs index 85511b79a..a8b32e93c 100644 --- a/src/TorchSharp/NN/Convolution/Conv2D.cs +++ b/src/TorchSharp/NN/Convolution/Conv2D.cs @@ -1,80 +1,35 @@ // Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information. using System; -using TorchSharp.Amp; using static TorchSharp.torch; using static TorchSharp.PInvoke.NativeMethods; #nullable enable namespace TorchSharp { + using System; using Modules; namespace Modules { public sealed class Conv2d : Convolution { - - internal Conv2d(IntPtr handle, IntPtr boxedHandle, long input_channels) : base(handle, boxedHandle, input_channels) { } + internal Conv2d(long in_channels, long out_channels, (long, long) kernel_size, (long, long) stride, (long, long)? padding, Padding? padding_type, (long, long) dilation, long groups = 1, bool bias = true, PaddingModes padding_mode = PaddingModes.Zeros, torch.Device? device = null, ScalarType? dtype = null) + : base(nameof(Conv2d), in_channels, out_channels, new[] { kernel_size.Item1, kernel_size.Item2 }, new[] { stride.Item1, stride.Item2 }, padding.HasValue ? new[] { padding.Value.Item1, padding.Value.Item2 } : null, padding_type, new[] { dilation.Item1, dilation.Item2 }, false, new[] { 0L, 0L }, groups, bias, padding_mode, device, dtype) { } - internal Conv2d(IntPtr handle, IntPtr boxedHandle, long input_channels, long in_channels, long out_channels, long kernelSize, long padding, long stride = 1, long dilation = 1, PaddingModes padding_mode = PaddingModes.Zeros, long groups = 1, bool bias = true) - : base(handle, boxedHandle, input_channels) - { - _dimension = 2; //because is conv 2D; 2 dimension - _in_channel = in_channels; - _out_channel = out_channels; - _kernel = kernelSize; - _stride = stride; - _padding = padding; - _dilation = dilation; - _paddingModes = padding_mode; - _groups = groups; - _bias = bias; - } - internal Conv2d(IntPtr handle, IntPtr boxedHandle, long input_channels, long in_channels, long out_channels, (long, long) kernelSize, Padding padding, (long, long)? stride = null, (long, long)? dilation = null, PaddingModes padding_mode = PaddingModes.Zeros, long groups = 1, bool bias = true) - : base(handle, boxedHandle, input_channels) - { - _dimension = 2; //because is conv 2D; 2 dimension - _in_channel = in_channels; - _out_channel = out_channels; - _kernels = kernelSize; - _strides = stride; - _padding = (long)padding; - _dilations = dilation; - _paddingModes = padding_mode; - _groups = groups; - _bias = bias; - } public override Tensor forward(Tensor input) { - if (ValidateShape(input, 2)) { - return ReturnCheckForErrors(THSNN_Conv2d_forward(handle, input.Handle)); - } - throw new ArgumentException($"Expected 3D (unbatched) or 4D (batched) input with {input_channels} channels to Conv2d."); - } + if (!ValidateShape(input, 2)) + throw new ArgumentException($"Expected 3D (unbatched) or 4D (batched) input with {in_channels} channels to Conv2d."); - public Parameter? bias { - get { - return ReturnNullParameterCheckForErrors(THSNN_Conv2d_bias(handle)); - } - set { - // Please ignore, for now, that the litorch call thinks you *can* set it to null. - if (value is null) throw new ArgumentNullException("bias cannot be set to 'null'"); - THSNN_Conv2d_set_bias(handle, (value is null ? IntPtr.Zero : value.Handle)); - torch.CheckForErrors(); - ConditionallyRegisterParameter("bias", value); - } - } - public Parameter? weight { - get { - return ReturnNullParameterCheckForErrors(THSNN_Conv2d_weight(handle)); - } - set { - // Please ignore, for now, that the litorch call thinks you *can* set it to null. - if (value is null) throw new ArgumentNullException("weight cannot be set to 'null'"); - THSNN_Conv2d_set_weight(handle, value is null ? IntPtr.Zero : value.Handle); - torch.CheckForErrors(); - ConditionallyRegisterParameter("weight", value); + if (padding_mode != PaddingModes.Zeros) { + using var paddedInput = torch.nn.functional.pad(input, _reversed_padding_repeated_twice, padding_mode); + return torch.nn.functional.conv2d(paddedInput, weight, bias, stride, new[] { 0L, 0L }, dilation, groups); } + + if (padding_type.HasValue) + return torch.nn.functional.conv2d_padding(input, weight, bias, stride, padding_type.Value, dilation, groups); + + return torch.nn.functional.conv2d(input, weight, bias, stride, padding, dilation, groups); } } } @@ -88,7 +43,7 @@ public static partial class nn /// /// Number of channels in the input image /// Number of channels produced by the convolution - /// Size of the convolving kernel + /// Size of the convolving kernel /// Stride of the convolution. Default: 1 /// Zero-padding added to both sides of the input. Default: 0 /// Spacing between kernel elements. Default: 1 @@ -98,23 +53,9 @@ public static partial class nn /// The desired device of the parameters and buffers in this module /// The desired floating point or complex dtype of the parameters and buffers in this module /// - public static Conv2d Conv2d(long in_channels, long out_channels, long kernelSize, long stride = 1, long padding = 0, long dilation = 1, PaddingModes padding_mode = PaddingModes.Zeros, long groups = 1, bool bias = true, Device? device = null, ScalarType? dtype = null) + public static Conv2d Conv2d(long in_channels, long out_channels, long kernel_size, long stride = 1, long padding = 0, long dilation = 1, PaddingModes padding_mode = PaddingModes.Zeros, long groups = 1, bool bias = true, Device? device = null, ScalarType? dtype = null) { - var res = THSNN_Conv2d_ctor(in_channels, out_channels, kernelSize, stride, padding, dilation, (long)padding_mode, groups, bias, out var boxedHandle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - - return new Conv2d(res, boxedHandle, in_channels) { - _in_channel = in_channels, - _out_channel = out_channels, - _kernel = kernelSize, - _stride = stride, - _padding = padding, - _dilation = dilation, - _paddingModes = padding_mode, - _groups = groups, - _bias = bias - }.MoveModule(device, dtype); - //return conv2d.MoveModule(device, dtype); + return new Conv2d(in_channels, out_channels, (kernel_size, kernel_size), (stride, stride), (padding, padding), null, (dilation, dilation), groups, bias, padding_mode, device, dtype); } /// @@ -122,7 +63,7 @@ public static Conv2d Conv2d(long in_channels, long out_channels, long kernelSize /// /// Number of channels in the input image /// Number of channels produced by the convolution - /// Size of the convolving kernel + /// Size of the convolving kernel /// Stride of the convolution. Default: (1,1) /// Zero-padding added to both sides of the input. Default: (0,0) /// Spacing between kernel elements. Default: (1,1) @@ -132,25 +73,13 @@ public static Conv2d Conv2d(long in_channels, long out_channels, long kernelSize /// The desired device of the parameters and buffers in this module /// The desired floating point or complex dtype of the parameters and buffers in this module /// - public static Conv2d Conv2d(long in_channels, long out_channels, (long, long) kernelSize, (long, long)? stride = null, (long, long)? padding = null, (long, long)? dilation = null, PaddingModes padding_mode = PaddingModes.Zeros, long groups = 1, bool bias = true, Device? device = null, ScalarType? dtype = null) + public static Conv2d Conv2d(long in_channels, long out_channels, (long, long) kernel_size, (long, long)? stride = null, (long, long)? padding = null, (long, long)? dilation = null, PaddingModes padding_mode = PaddingModes.Zeros, long groups = 1, bool bias = true, Device? device = null, ScalarType? dtype = null) { - if (stride == null) stride = (1, 1); - if (padding == null) padding = (0, 0); - if (dilation == null) dilation = (1, 1); + stride ??= (1, 1); + padding ??= (0, 0); + dilation ??= (1, 1); - var res = THSNN_Conv2d_ctor_1(in_channels, out_channels, kernelSize.Item1, kernelSize.Item2, stride.Value.Item1, stride.Value.Item2, padding.Value.Item1, padding.Value.Item2, dilation.Value.Item1, dilation.Value.Item2, (long)padding_mode, groups, bias, out var boxedHandle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Conv2d(res, boxedHandle, in_channels) { - _in_channel = in_channels, - _out_channel = out_channels, - _kernels = kernelSize, - _strides = stride, - _paddings = padding, - _dilations = dilation, - _paddingModes = padding_mode, - _groups = groups, - _bias = bias - }.MoveModule(device, dtype); + return new Conv2d(in_channels, out_channels, kernel_size, stride.Value, padding, null, dilation.Value, groups, bias, padding_mode, device, dtype); } /// @@ -158,7 +87,7 @@ public static Conv2d Conv2d(long in_channels, long out_channels, (long, long) ke /// /// Number of channels in the input image /// Number of channels produced by the convolution - /// Size of the convolving kernel + /// Size of the convolving kernel /// Stride of the convolution. Default: 1 /// Zero-padding added to both sides of the input. padding=Valid is the same as no padding. padding=Same pads the input so the output has the shape as the input. /// Spacing between kernel elements. Default: 1 @@ -168,11 +97,9 @@ public static Conv2d Conv2d(long in_channels, long out_channels, (long, long) ke /// The desired device of the parameters and buffers in this module /// The desired floating point or complex dtype of the parameters and buffers in this module /// - public static Conv2d Conv2d(long in_channels, long out_channels, long kernelSize, Padding padding, long stride = 1, long dilation = 1, PaddingModes padding_mode = PaddingModes.Zeros, long groups = 1, bool bias = true, Device? device = null, ScalarType? dtype = null) + public static Conv2d Conv2d(long in_channels, long out_channels, long kernel_size, Padding padding, long stride = 1, long dilation = 1, PaddingModes padding_mode = PaddingModes.Zeros, long groups = 1, bool bias = true, Device? device = null, ScalarType? dtype = null) { - var res = THSNN_Conv2d_ctor(in_channels, out_channels, kernelSize, stride, padding == Padding.Valid ? 0 : -1, dilation, (long)padding_mode, groups, bias, out var boxedHandle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Conv2d(res, boxedHandle, in_channels, in_channels, out_channels, kernelSize, (long)padding, stride, dilation, padding_mode, groups, bias).MoveModule(device, dtype); + return new Conv2d(in_channels, out_channels, (kernel_size, kernel_size), (stride, stride), null, padding, (dilation, dilation), groups, bias, padding_mode, device, dtype); } /// @@ -180,7 +107,7 @@ public static Conv2d Conv2d(long in_channels, long out_channels, long kernelSize /// /// Number of channels in the input image /// Number of channels produced by the convolution - /// Size of the convolving kernel + /// Size of the convolving kernel /// Zero-padding added to both sides of the input. padding=Valid is the same as no padding. padding=Same pads the input so the output has the shape as the input. /// Stride of the convolution. Default: (1,1) /// Spacing between kernel elements. Default: (1,1) @@ -190,15 +117,12 @@ public static Conv2d Conv2d(long in_channels, long out_channels, long kernelSize /// The desired device of the parameters and buffers in this module /// The desired floating point or complex dtype of the parameters and buffers in this module /// - public static Conv2d Conv2d(long in_channels, long out_channels, (long, long) kernelSize, Padding padding, (long, long)? stride = null, (long, long)? dilation = null, PaddingModes padding_mode = PaddingModes.Zeros, long groups = 1, bool bias = true, Device? device = null, ScalarType? dtype = null) + public static Conv2d Conv2d(long in_channels, long out_channels, (long, long) kernel_size, Padding padding, (long, long)? stride = null, (long, long)? dilation = null, PaddingModes padding_mode = PaddingModes.Zeros, long groups = 1, bool bias = true, Device? device = null, ScalarType? dtype = null) { - if (stride == null) stride = (1, 1); - if (dilation == null) dilation = (1, 1); + stride ??= (1, 1); + dilation ??= (1, 1); - var res = THSNN_Conv2d_ctor_1(in_channels, out_channels, kernelSize.Item1, kernelSize.Item2, stride.Value.Item1, stride.Value.Item2, padding == Padding.Valid ? 0 : -1, 0, dilation.Value.Item1, dilation.Value.Item2, (long)padding_mode, groups, bias, out var boxedHandle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - - return new Conv2d(res, boxedHandle, in_channels, in_channels, out_channels, kernelSize, padding,stride, dilation, padding_mode ,groups,bias).MoveModule(device, dtype); + return new Conv2d(in_channels, out_channels, kernel_size, stride.Value, null, padding, dilation.Value, groups, bias, padding_mode, device, dtype); } public static partial class functional @@ -207,12 +131,12 @@ public static partial class functional /// Applies a 2D convolution over an input image composed of several input planes. /// /// The input tensor. - /// - /// - /// - /// - /// - /// + /// weight matrix of the convolution + /// Optional; bias vector of the convolution + /// Stride of the convolution. Default: (1,1) + /// Zero-padding added to both sides of the input. Default: (0,0) + /// Spacing between kernel elements. Default: (1,1) + /// Number of blocked connections from input channels to output channels. Default: 1 /// public static Tensor conv2d(Tensor input, Tensor weight, Tensor? bias = null, long[]? strides = null, @@ -220,9 +144,9 @@ public static Tensor conv2d(Tensor input, Tensor weight, Tensor? bias = null, long[]? dilation = null, long groups = 1) { - strides = (strides == null) ? new long[] { 1 } : strides; - padding = (padding == null) ? new long[] { 0 } : padding; - dilation = (dilation == null) ? new long[] { 1 } : dilation; + strides ??= new long[] { 1 }; + padding ??= new long[] { 0 }; + dilation ??= new long[] { 1 }; var biasHandle = (bias is null ? IntPtr.Zero : bias.Handle); unsafe { fixed (long* pstrides = strides, ppadding = padding, pdilation = dilation) { @@ -232,9 +156,40 @@ public static Tensor conv2d(Tensor input, Tensor weight, Tensor? bias = null, (IntPtr)ppadding, padding.Length, (IntPtr)pdilation, dilation.Length, groups); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - res = AutocastMode.AutoCast(res); - return new Tensor(res); + return ReturnCheckForErrorsAutocast(res); + } + } + } + + /// + /// Applies a 2D convolution over an input image composed of several input planes. + /// + /// The input tensor. + /// weight matrix of the convolution + /// Optional; bias vector of the convolution + /// Stride of the convolution. Default: (1,1) + /// Zero-padding added to both sides of the input. padding=Valid is the same as no padding. padding=Same pads the input so the output has the shape as the input. + /// Spacing between kernel elements. Default: (1,1) + /// Number of blocked connections from input channels to output channels. Default: 1 + /// + public static Tensor conv2d_padding(Tensor input, Tensor weight, Tensor? bias = null, + long[]? strides = null, + Padding padding = Padding.Valid, + long[]? dilation = null, + long groups = 1) + { + strides ??= new long[] { 1 }; + dilation ??= new long[] { 1 }; + var biasHandle = (bias is null ? IntPtr.Zero : bias.Handle); + unsafe { + fixed (long* pstrides = strides, pdilation = dilation) { + var res = + THSTensor_conv2d_padding(input.Handle, weight.Handle, biasHandle, + (IntPtr)pstrides, strides.Length, + (int)padding, + (IntPtr)pdilation, dilation.Length, + groups); + return ReturnCheckForErrorsAutocast(res); } } } diff --git a/src/TorchSharp/NN/Convolution/Conv3D.cs b/src/TorchSharp/NN/Convolution/Conv3D.cs index caef803ad..0d2f8c1b1 100644 --- a/src/TorchSharp/NN/Convolution/Conv3D.cs +++ b/src/TorchSharp/NN/Convolution/Conv3D.cs @@ -1,6 +1,5 @@ // Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information. using System; -using TorchSharp.Amp; using static TorchSharp.torch; using static TorchSharp.PInvoke.NativeMethods; @@ -13,38 +12,23 @@ namespace Modules { public sealed class Conv3d : Convolution { - internal Conv3d(IntPtr handle, IntPtr boxedHandle, long input_channels) : base(handle, boxedHandle, input_channels) { } + internal Conv3d(long in_channels, long out_channels, (long, long, long) kernel_size, (long, long, long) stride, (long, long, long)? padding, Padding? padding_type, (long, long, long) dilation, long groups = 1, bool bias = true, PaddingModes padding_mode = PaddingModes.Zeros, torch.Device? device = null, ScalarType? dtype = null) + : base(nameof(Conv3d), in_channels, out_channels, new[] { kernel_size.Item1, kernel_size.Item2, kernel_size.Item3 }, new[] { stride.Item1, stride.Item2, stride.Item3 }, padding.HasValue ? new[] { padding.Value.Item1, padding.Value.Item2, padding.Value.Item3 } : null, padding_type, new[] { dilation.Item1, dilation.Item2, dilation.Item3 }, false, new[] { 0, 0, 0L }, groups, bias, padding_mode, device, dtype) { } public override Tensor forward(Tensor input) { - if (ValidateShape(input, 3)) { - return ReturnCheckForErrors(THSNN_Conv3d_forward(handle, input.Handle)); - } - throw new ArgumentException($"Expected 4D (unbatched) or 5D (batched) input with {input_channels} channels to Conv3d."); - } + if (!ValidateShape(input, 3)) + throw new ArgumentException($"Expected 4D (unbatched) or 5D (batched) input with {in_channels} channels to Conv3d."); - public Parameter? bias { - get { - return ReturnNullParameterCheckForErrors(THSNN_Conv3d_bias(handle)); - } - set { - // Please ignore, for now, that the litorch call thinks you *can* set it to null. - if (value is null) throw new ArgumentNullException("bias cannot be set to 'null'"); - THSNN_Conv3d_set_bias(handle, (value is null ? IntPtr.Zero : value.Handle)); - torch.CheckForErrors(); - ConditionallyRegisterParameter("bias", value); - } - } - public Parameter? weight { - get { - return ReturnNullParameterCheckForErrors(THSNN_Conv3d_weight(handle)); - } - set { - // Please ignore, for now, that the litorch call thinks you *can* set it to null. - if (value is null) throw new ArgumentNullException("weight cannot be set to 'null'"); THSNN_Conv3d_set_weight(handle, (value is null ? IntPtr.Zero : value.Handle)); - torch.CheckForErrors(); - ConditionallyRegisterParameter("weight", value); + if (padding_mode != PaddingModes.Zeros) { + using var paddedInput = torch.nn.functional.pad(input, _reversed_padding_repeated_twice, padding_mode); + return torch.nn.functional.conv3d(paddedInput, weight, bias, stride, new[] { 0L, 0L, 0L }, dilation, groups); } + + if (padding_type.HasValue) + return torch.nn.functional.conv3d_padding(input, weight, bias, stride, padding_type.Value, dilation, groups); + + return torch.nn.functional.conv3d(input, weight, bias, stride, padding, dilation, groups); } } } @@ -58,7 +42,7 @@ public static partial class nn /// /// Number of channels in the input image /// Number of channels produced by the convolution - /// Size of the convolving kernel + /// Size of the convolving kernel /// Stride of the convolution. Default: 1 /// Zero-padding added to both sides of the input. Default: 0 /// Spacing between kernel elements. Default: 1 @@ -67,11 +51,9 @@ public static partial class nn /// If true, adds a learnable bias to the output. Default: true /// The desired device of the parameters and buffers in this module /// The desired floating point or complex dtype of the parameters and buffers in this module - public static Conv3d Conv3d(long in_channels, long out_channels, long kernelSize, long stride = 1, long padding = 0, long dilation = 1, PaddingModes padding_mode = PaddingModes.Zeros, long groups = 1, bool bias = true, Device? device = null, ScalarType? dtype = null) + public static Conv3d Conv3d(long in_channels, long out_channels, long kernel_size, long stride = 1, long padding = 0, long dilation = 1, PaddingModes padding_mode = PaddingModes.Zeros, long groups = 1, bool bias = true, Device? device = null, ScalarType? dtype = null) { - var res = THSNN_Conv3d_ctor(in_channels, out_channels, kernelSize, stride, padding, dilation, (long)padding_mode, groups, bias, out var boxedHandle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Conv3d(res, boxedHandle, in_channels).MoveModule(device, dtype); + return new Conv3d(in_channels, out_channels, (kernel_size, kernel_size, kernel_size), (stride, stride, stride), (padding, padding, padding), null, (dilation, dilation, dilation), groups, bias, padding_mode, device, dtype); } /// @@ -79,7 +61,7 @@ public static Conv3d Conv3d(long in_channels, long out_channels, long kernelSize /// /// Number of channels in the input image /// Number of channels produced by the convolution - /// Size of the convolving kernel + /// Size of the convolving kernel /// Stride of the convolution. Default: (1,1,1) /// Zero-padding added to both sides of the input. Default: (0,0,0) /// Spacing between kernel elements. Default: (1,1,1) @@ -88,15 +70,13 @@ public static Conv3d Conv3d(long in_channels, long out_channels, long kernelSize /// If true, adds a learnable bias to the output. Default: true /// The desired device of the parameters and buffers in this module /// The desired floating point or complex dtype of the parameters and buffers in this module - public static Conv3d Conv3d(long in_channels, long out_channels, (long, long, long) kernelSize, (long, long, long)? stride = null, (long, long, long)? padding = null, (long, long, long)? dilation = null, PaddingModes padding_mode = PaddingModes.Zeros, long groups = 1, bool bias = true, Device? device = null, ScalarType? dtype = null) + public static Conv3d Conv3d(long in_channels, long out_channels, (long, long, long) kernel_size, (long, long, long)? stride = null, (long, long, long)? padding = null, (long, long, long)? dilation = null, PaddingModes padding_mode = PaddingModes.Zeros, long groups = 1, bool bias = true, Device? device = null, ScalarType? dtype = null) { - if (stride == null) stride = (1, 1, 1); - if (padding == null) padding = (0, 0, 0); - if (dilation == null) dilation = (1, 1, 1); + stride ??= (1, 1, 1); + padding ??= (0, 0, 0); + dilation ??= (1, 1, 1); - var res = THSNN_Conv3d_ctor_1(in_channels, out_channels, kernelSize.Item1, kernelSize.Item2, kernelSize.Item3, stride.Value.Item1, stride.Value.Item2, stride.Value.Item3, padding.Value.Item1, padding.Value.Item2, padding.Value.Item3, dilation.Value.Item1, dilation.Value.Item2, dilation.Value.Item3, (long)padding_mode, groups, bias, out var boxedHandle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Conv3d(res, boxedHandle, in_channels).MoveModule(device, dtype); + return new Conv3d(in_channels, out_channels, kernel_size, stride.Value, padding, null, dilation.Value, groups, bias, padding_mode, device, dtype); } /// @@ -104,7 +84,7 @@ public static Conv3d Conv3d(long in_channels, long out_channels, (long, long, lo /// /// Number of channels in the input image /// Number of channels produced by the convolution - /// Size of the convolving kernel + /// Size of the convolving kernel /// Stride of the convolution. Default: 1 /// Zero-padding added to both sides of the input. padding=Valid is the same as no padding. padding=Same pads the input so the output has the shape as the input. /// Spacing between kernel elements. Default: 1 @@ -113,11 +93,9 @@ public static Conv3d Conv3d(long in_channels, long out_channels, (long, long, lo /// If true, adds a learnable bias to the output. Default: true /// The desired device of the parameters and buffers in this module /// The desired floating point or complex dtype of the parameters and buffers in this module - public static Conv3d Conv3d(long in_channels, long out_channels, long kernelSize, Padding padding, long stride = 1, long dilation = 1, PaddingModes padding_mode = PaddingModes.Zeros, long groups = 1, bool bias = true, Device? device = null, ScalarType? dtype = null) + public static Conv3d Conv3d(long in_channels, long out_channels, long kernel_size, Padding padding, long stride = 1, long dilation = 1, PaddingModes padding_mode = PaddingModes.Zeros, long groups = 1, bool bias = true, Device? device = null, ScalarType? dtype = null) { - var res = THSNN_Conv3d_ctor(in_channels, out_channels, kernelSize, stride, padding == Padding.Valid ? 0 : -1, dilation, (long)padding_mode, groups, bias, out var boxedHandle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Conv3d(res, boxedHandle, in_channels).MoveModule(device, dtype); + return new Conv3d(in_channels, out_channels, (kernel_size, kernel_size, kernel_size), (stride, stride, stride), null, padding, (dilation, dilation, dilation), groups, bias, padding_mode, device, dtype); } /// @@ -125,7 +103,7 @@ public static Conv3d Conv3d(long in_channels, long out_channels, long kernelSize /// /// Number of channels in the input image /// Number of channels produced by the convolution - /// Size of the convolving kernel + /// Size of the convolving kernel /// Stride of the convolution. Default: (1,1,1) /// Zero-padding added to both sides of the input. padding=Valid is the same as no padding. padding=Same pads the input so the output has the shape as the input. /// Spacing between kernel elements. Default: (1,1,1) @@ -134,14 +112,11 @@ public static Conv3d Conv3d(long in_channels, long out_channels, long kernelSize /// If true, adds a learnable bias to the output. Default: true /// The desired device of the parameters and buffers in this module /// The desired floating point or complex dtype of the parameters and buffers in this module - public static Conv3d Conv3d(long in_channels, long out_channels, (long, long, long) kernelSize, Padding padding, (long, long, long)? stride = null, (long, long, long)? dilation = null, PaddingModes padding_mode = PaddingModes.Zeros, long groups = 1, bool bias = true, Device? device = null, ScalarType? dtype = null) + public static Conv3d Conv3d(long in_channels, long out_channels, (long, long, long) kernel_size, Padding padding, (long, long, long)? stride = null, (long, long, long)? dilation = null, PaddingModes padding_mode = PaddingModes.Zeros, long groups = 1, bool bias = true, Device? device = null, ScalarType? dtype = null) { - if (stride == null) stride = (1, 1, 1); - if (dilation == null) dilation = (1, 1, 1); - - var res = THSNN_Conv3d_ctor_1(in_channels, out_channels, kernelSize.Item1, kernelSize.Item2, kernelSize.Item3, stride.Value.Item1, stride.Value.Item2, stride.Value.Item3, padding == Padding.Valid ? 0 : -1, 0, 0, dilation.Value.Item1, dilation.Value.Item2, dilation.Value.Item3, (long)padding_mode, groups, bias, out var boxedHandle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Conv3d(res, boxedHandle, in_channels).MoveModule(device, dtype); + stride ??= (1, 1, 1); + dilation ??= (1, 1, 1); + return new Conv3d(in_channels, out_channels, kernel_size, stride.Value, null, padding, dilation.Value, groups, bias, padding_mode, device, dtype); } public static partial class functional @@ -150,12 +125,12 @@ public static partial class functional /// Applies a 3D convolution over an input image composed of several input planes. /// /// The input tensor. - /// - /// - /// - /// - /// - /// + /// weight matrix of the convolution + /// Optional; bias vector of the convolution + /// Stride of the convolution. Default: (1,1,1) + /// Zero-padding added to both sides of the input. Default: (0,0,0) + /// Spacing between kernel elements. Default: (1,1,1) + /// Number of blocked connections from input channels to output channels. Default: 1 /// public static Tensor conv3d(Tensor input, Tensor weight, Tensor? bias = null, long[]? strides = null, @@ -163,9 +138,9 @@ public static Tensor conv3d(Tensor input, Tensor weight, Tensor? bias = null, long[]? dilation = null, long groups = 1) { - strides = (strides == null) ? new long[] { 1 } : strides; - padding = (padding == null) ? new long[] { 0 } : padding; - dilation = (dilation == null) ? new long[] { 1 } : dilation; + strides ??= new long[] { 1 }; + padding ??= new long[] { 0 }; + dilation ??= new long[] { 1 }; var biasHandle = (bias is null ? IntPtr.Zero : bias.Handle); unsafe { fixed (long* pstrides = strides, ppadding = padding, pdilation = dilation) { @@ -180,7 +155,39 @@ public static Tensor conv3d(Tensor input, Tensor weight, Tensor? bias = null, } } + /// + /// Applies a 3D convolution over an input image composed of several input planes. + /// + /// The input tensor. + /// weight matrix of the convolution + /// Optional; bias vector of the convolution + /// Stride of the convolution. Default: (1,1,1) + /// Zero-padding added to both sides of the input. padding=Valid is the same as no padding. padding=Same pads the input so the output has the shape as the input. + /// Spacing between kernel elements. Default: (1,1,1) + /// Number of blocked connections from input channels to output channels. Default: 1 + /// + public static Tensor conv3d_padding(Tensor input, Tensor weight, Tensor? bias = null, + long[]? strides = null, + Padding padding = Padding.Valid, + long[]? dilation = null, + long groups = 1) + { + strides ??= new long[] { 1 }; + dilation ??= new long[] { 1 }; + var biasHandle = (bias is null ? IntPtr.Zero : bias.Handle); + unsafe { + fixed (long* pstrides = strides, pdilation = dilation) { + var res = + THSTensor_conv3d_padding(input.Handle, weight.Handle, biasHandle, + (IntPtr)pstrides, strides.Length, + (int)padding, + (IntPtr)pdilation, dilation.Length, + groups); + return ReturnCheckForErrorsAutocast(res); + } + } + } } } } -} +} \ No newline at end of file diff --git a/src/TorchSharp/NN/Convolution/ConvTranspose1D.cs b/src/TorchSharp/NN/Convolution/ConvTranspose1D.cs index e2f3ec010..51acd673a 100644 --- a/src/TorchSharp/NN/Convolution/ConvTranspose1D.cs +++ b/src/TorchSharp/NN/Convolution/ConvTranspose1D.cs @@ -1,6 +1,5 @@ // Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information. using System; -using TorchSharp.Amp; using static TorchSharp.torch; using static TorchSharp.PInvoke.NativeMethods; @@ -11,40 +10,18 @@ namespace TorchSharp namespace Modules { - public sealed class ConvTranspose1d : Convolution + public sealed class ConvTranspose1d : ConvolutionTranspose { - internal ConvTranspose1d(IntPtr handle, IntPtr boxedHandle, long input_channels) : base(handle, boxedHandle, input_channels) { } + internal ConvTranspose1d(long in_channels, long out_channels, long kernel_size, long stride, long padding, long dilation, long output_padding, long groups = 1, bool bias = true, PaddingModes padding_mode = PaddingModes.Zeros, torch.Device? device = null, ScalarType? dtype = null) + : base(nameof(ConvTranspose1d), in_channels, out_channels, new[] { kernel_size }, new[] { stride }, new[] { padding }, null, new[] { dilation }, true, new[] { output_padding }, groups, bias, padding_mode, device, dtype) { } - public override Tensor forward(Tensor input) + public override Tensor forward(Tensor input, long[]? output_size) { - if (ValidateShape(input, 1)) { - return ReturnCheckForErrors(THSNN_ConvTranspose1d_forward(handle, input.Handle)); - } - throw new ArgumentException($"Expected 2D (unbatched) or 3D (batched) input with {input_channels} channels to ConvTranspose1d."); - } + if (!ValidateShape(input, 1)) + throw new ArgumentException($"Expected 2D (unbatched) or 3D (batched) input with {in_channels} channels to ConvTranspose1d."); - public Parameter? bias { - get { - return ReturnNullParameterCheckForErrors(THSNN_ConvTranspose1d_bias(handle)); - } - set { - // Please ignore, for now, that the litorch call thinks you *can* set it to null. - if (value is null) throw new ArgumentNullException("bias cannot be set to 'null'"); - THSNN_ConvTranspose1d_set_bias(handle, (value is null ? IntPtr.Zero : value.Handle)); - torch.CheckForErrors(); - ConditionallyRegisterParameter("bias", value); - } - } - public Parameter? weight { - get { - return ReturnNullParameterCheckForErrors(THSNN_ConvTranspose1d_weight(handle)); - } - set { - // Please ignore, for now, that the litorch call thinks you *can* set it to null. - if (value is null) throw new ArgumentNullException("weight cannot be set to 'null'"); THSNN_ConvTranspose1d_set_weight(handle, value is null ? IntPtr.Zero : value.Handle); - torch.CheckForErrors(); - ConditionallyRegisterParameter("weight", value); - } + var output_padding = this._output_padding(input, output_size, kernel_size, stride, padding!, dilation, 1); + return torch.nn.functional.conv_transpose1d(input, weight, bias, stride[0], padding![0], output_padding[0], dilation[0], groups); } } } @@ -58,7 +35,7 @@ public static partial class nn /// /// Number of channels in the input image /// Number of channels produced by the convolution - /// Size of the convolving kernel + /// Size of the convolving kernel /// Stride of the convolution. Default: 1 /// Zero-padding added to both sides of the input. Default: 0 /// Additional size added to one side of the output shape. Default: 0 @@ -69,11 +46,9 @@ public static partial class nn /// The desired device of the parameters and buffers in this module /// The desired floating point or complex dtype of the parameters and buffers in this module /// Tensor of shape (N,C_out,L_out) - public static ConvTranspose1d ConvTranspose1d(long in_channels, long out_channels, long kernelSize, long stride = 1, long padding = 0, long output_padding = 0, long dilation = 1, PaddingModes padding_mode = PaddingModes.Zeros, long groups = 1, bool bias = true, Device? device = null, ScalarType? dtype = null) + public static ConvTranspose1d ConvTranspose1d(long in_channels, long out_channels, long kernel_size, long stride = 1, long padding = 0, long output_padding = 0, long dilation = 1, PaddingModes padding_mode = PaddingModes.Zeros, long groups = 1, bool bias = true, Device? device = null, ScalarType? dtype = null) { - var res = THSNN_ConvTranspose1d_ctor(in_channels, out_channels, kernelSize, stride, padding, output_padding, dilation, (long)padding_mode, groups, bias, out var boxedHandle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new ConvTranspose1d(res, boxedHandle, in_channels).MoveModule(device, dtype); + return new ConvTranspose1d(in_channels, out_channels, kernel_size, stride, padding, dilation, output_padding, groups, bias, padding_mode, device, dtype); } public static partial class functional @@ -119,4 +94,4 @@ public static Tensor conv_transpose1d(Tensor input, Tensor weight, Tensor? bias } } } -} +} \ No newline at end of file diff --git a/src/TorchSharp/NN/Convolution/ConvTranspose2D.cs b/src/TorchSharp/NN/Convolution/ConvTranspose2D.cs index 6d491329e..94cb57e5a 100644 --- a/src/TorchSharp/NN/Convolution/ConvTranspose2D.cs +++ b/src/TorchSharp/NN/Convolution/ConvTranspose2D.cs @@ -1,6 +1,5 @@ // Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information. using System; -using TorchSharp.Amp; using static TorchSharp.torch; using static TorchSharp.PInvoke.NativeMethods; @@ -11,41 +10,18 @@ namespace TorchSharp namespace Modules { - public sealed class ConvTranspose2d : Convolution + public sealed class ConvTranspose2d : ConvolutionTranspose { - internal ConvTranspose2d(IntPtr handle, IntPtr boxedHandle, long input_channels) : base(handle, boxedHandle, input_channels) { } + internal ConvTranspose2d(long in_channels, long out_channels, (long, long) kernel_size, (long, long) stride, (long, long) padding, (long, long) dilation, (long, long) output_padding, long groups = 1, bool bias = true, PaddingModes padding_mode = PaddingModes.Zeros, torch.Device? device = null, ScalarType? dtype = null) + : base(nameof(ConvTranspose2d), in_channels, out_channels, new[] { kernel_size.Item1, kernel_size.Item2 }, new[] { stride.Item1, stride.Item2 }, new[] { padding.Item1, padding.Item2 }, null, new[] { dilation.Item1, dilation.Item2 }, true, new[] { output_padding.Item1, output_padding.Item2 }, groups, bias, padding_mode, device, dtype) { } - public override Tensor forward(Tensor input) + public override Tensor forward(Tensor input, long[]? output_size) { - if (ValidateShape(input, 2)) { - return ReturnCheckForErrors(THSNN_ConvTranspose2d_forward(handle, input.Handle)); - } - throw new ArgumentException($"Expected 3D (unbatched) or 4D (batched) input with {input_channels} channels to ConvTranspose2d."); - } + if (!ValidateShape(input, 2)) + throw new ArgumentException($"Expected 3D (unbatched) or 4D (batched) input with {in_channels} channels to ConvTranspose2d."); - public Parameter? bias { - get { - return ReturnNullParameterCheckForErrors(THSNN_ConvTranspose2d_bias(handle)); - } - set { - // Please ignore, for now, that the litorch call thinks you *can* set it to null. - if (value is null) throw new ArgumentNullException("bias cannot be set to 'null'"); - THSNN_ConvTranspose2d_set_bias(handle, (value is null ? IntPtr.Zero : value.Handle)); - torch.CheckForErrors(); - ConditionallyRegisterParameter("bias", value); - } - } - public Parameter? weight - { - get { - return ReturnNullParameterCheckForErrors(THSNN_ConvTranspose2d_weight(handle)); - } - set { - // Please ignore, for now, that the litorch call thinks you *can* set it to null. - if (value is null) throw new ArgumentNullException("weight cannot be set to 'null'"); THSNN_ConvTranspose2d_set_weight(handle, value is null ? IntPtr.Zero : value.Handle); - torch.CheckForErrors(); - ConditionallyRegisterParameter("weight", value); - } + var output_padding = this._output_padding(input, output_size, kernel_size, stride, padding!, dilation, 2); + return torch.nn.functional.conv_transpose2d(input, weight, bias, stride, padding!, output_padding, dilation, groups); } } } @@ -59,7 +35,7 @@ public static partial class nn /// /// Number of channels in the input image /// Number of channels produced by the convolution - /// Size of the convolving kernel + /// Size of the convolving kernel /// Stride of the convolution. Default: 1 /// Zero-padding added to both sides of the input. Default: 0 /// Additional size added to one side of the output shape. Default: 0 @@ -70,11 +46,9 @@ public static partial class nn /// The desired device of the parameters and buffers in this module /// The desired floating point or complex dtype of the parameters and buffers in this module /// Tensor of shape (N,C_out,L_out) - public static ConvTranspose2d ConvTranspose2d(long in_channels, long out_channels, long kernelSize, long stride = 1, long padding = 0, long output_padding = 0, long dilation = 1, PaddingModes padding_mode = PaddingModes.Zeros, long groups = 1, bool bias = true, Device? device = null, ScalarType? dtype = null) + public static ConvTranspose2d ConvTranspose2d(long in_channels, long out_channels, long kernel_size, long stride = 1, long padding = 0, long output_padding = 0, long dilation = 1, PaddingModes padding_mode = PaddingModes.Zeros, long groups = 1, bool bias = true, Device? device = null, ScalarType? dtype = null) { - var res = THSNN_ConvTranspose2d_ctor(in_channels, out_channels, kernelSize, stride, padding, output_padding, dilation, (long)padding_mode, groups, bias, out var boxedHandle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new ConvTranspose2d(res, boxedHandle, in_channels).MoveModule(device, dtype); + return new ConvTranspose2d(in_channels, out_channels, (kernel_size, kernel_size), (stride, stride), (padding, padding), (dilation, dilation), (output_padding, output_padding), groups, bias, padding_mode, device, dtype); } @@ -84,7 +58,7 @@ public static ConvTranspose2d ConvTranspose2d(long in_channels, long out_channel /// /// Number of channels in the input image /// Number of channels produced by the convolution - /// Size of the convolving kernel + /// Size of the convolving kernel /// Stride of the convolution. Default: (1,1) /// Zero-padding added to both sides of the input. Default: (0,0) /// Additional size added to one side of the output shape. Default: 0 @@ -95,16 +69,13 @@ public static ConvTranspose2d ConvTranspose2d(long in_channels, long out_channel /// The desired device of the parameters and buffers in this module /// The desired floating point or complex dtype of the parameters and buffers in this module /// - public static ConvTranspose2d ConvTranspose2d(long in_channels, long out_channels, (long, long) kernelSize, (long, long)? stride = null, (long, long)? padding = null, (long, long)? output_padding = null, (long, long)? dilation = null, PaddingModes padding_mode = PaddingModes.Zeros, long groups = 1, bool bias = true, Device? device = null, ScalarType? dtype = null) + public static ConvTranspose2d ConvTranspose2d(long in_channels, long out_channels, (long, long) kernel_size, (long, long)? stride = null, (long, long)? padding = null, (long, long)? output_padding = null, (long, long)? dilation = null, PaddingModes padding_mode = PaddingModes.Zeros, long groups = 1, bool bias = true, Device? device = null, ScalarType? dtype = null) { - if (stride == null) stride = (1, 1); - if (padding == null) padding = (0, 0); - if (output_padding == null) output_padding = (0, 0); - if (dilation == null) dilation = (1, 1); - - var res = THSNN_ConvTranspose2d_ctor_1(in_channels, out_channels, kernelSize.Item1, kernelSize.Item2, stride.Value.Item1, stride.Value.Item2, padding.Value.Item1, padding.Value.Item2, output_padding.Value.Item1, output_padding.Value.Item2, dilation.Value.Item1, dilation.Value.Item2, (long)padding_mode, groups, bias, out var boxedHandle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new ConvTranspose2d(res, boxedHandle, in_channels).MoveModule(device, dtype); + stride ??= (1, 1); + padding ??= (0, 0); + output_padding ??= (0, 0); + dilation ??= (1, 1); + return new ConvTranspose2d(in_channels, out_channels, kernel_size, stride.Value, padding.Value, dilation.Value, output_padding.Value, groups, bias, padding_mode, device, dtype); } public static partial class functional @@ -150,4 +121,4 @@ public static Tensor conv_transpose2d(Tensor input, Tensor weight, Tensor? bias } } } -} +} \ No newline at end of file diff --git a/src/TorchSharp/NN/Convolution/ConvTranspose3D.cs b/src/TorchSharp/NN/Convolution/ConvTranspose3D.cs index 3a89cb646..3580c97ee 100644 --- a/src/TorchSharp/NN/Convolution/ConvTranspose3D.cs +++ b/src/TorchSharp/NN/Convolution/ConvTranspose3D.cs @@ -1,6 +1,5 @@ // Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information. using System; -using TorchSharp.Amp; using static TorchSharp.torch; using static TorchSharp.PInvoke.NativeMethods; @@ -11,40 +10,18 @@ namespace TorchSharp namespace Modules { - public sealed class ConvTranspose3d : Convolution + public sealed class ConvTranspose3d : ConvolutionTranspose { - internal ConvTranspose3d(IntPtr handle, IntPtr boxedHandle, long input_channels) : base(handle, boxedHandle, input_channels) { } + internal ConvTranspose3d(long in_channels, long out_channels, (long, long, long) kernel_size, (long, long, long) stride, (long, long, long) padding, (long, long, long) dilation, (long, long, long) output_padding, long groups = 1, bool bias = true, PaddingModes padding_mode = PaddingModes.Zeros, torch.Device? device = null, ScalarType? dtype = null) + : base(nameof(ConvTranspose3d), in_channels, out_channels, new[] { kernel_size.Item1, kernel_size.Item2, kernel_size.Item3 }, new[] { stride.Item1, stride.Item2, stride.Item3 }, new[] { padding.Item1, padding.Item2, padding.Item3 }, null, new[] { dilation.Item1, dilation.Item2, dilation.Item3 }, true, new[] { output_padding.Item1, output_padding.Item2, output_padding.Item3 }, groups, bias, padding_mode, device, dtype) { } - public override Tensor forward(Tensor input) + public override Tensor forward(Tensor input, long[]? output_size) { - if (ValidateShape(input, 3)) { - return ReturnCheckForErrors(THSNN_ConvTranspose3d_forward(handle, input.Handle)); - } - throw new ArgumentException($"Expected 4D (unbatched) or 5D (batched) input with {input_channels} channels to ConvTranspose3d."); - } + if (!ValidateShape(input, 3)) + throw new ArgumentException($"Expected 4D (unbatched) or 5D (batched) input with {in_channels} channels to ConvTranspose3d."); - public Parameter? bias { - get { - return ReturnNullParameterCheckForErrors(THSNN_ConvTranspose3d_bias(handle)); - } - set { - // Please ignore, for now, that the litorch call thinks you *can* set it to null. - if (value is null) throw new ArgumentNullException("bias cannot be set to 'null'"); - THSNN_ConvTranspose3d_set_bias(handle, (value is null ? IntPtr.Zero : value.Handle)); - torch.CheckForErrors(); - ConditionallyRegisterParameter("bias", value); - } - } - public Parameter? weight { - get { - return ReturnNullParameterCheckForErrors(THSNN_ConvTranspose3d_weight(handle)); - } - set { - // Please ignore, for now, that the litorch call thinks you *can* set it to null. - if (value is null) throw new ArgumentNullException("weight cannot be set to 'null'"); THSNN_ConvTranspose3d_set_weight(handle, value is null ? IntPtr.Zero : value.Handle); - torch.CheckForErrors(); - ConditionallyRegisterParameter("weight", value); - } + var output_padding = this._output_padding(input, output_size, kernel_size, stride, padding!, dilation, 3); + return torch.nn.functional.conv_transpose3d(input, weight, bias, stride, padding!, output_padding, dilation, groups); } } } @@ -58,7 +35,7 @@ public static partial class nn /// /// Number of channels in the input image /// Number of channels produced by the convolution - /// Size of the convolving kernel + /// Size of the convolving kernel /// Stride of the convolution. Default: 1 /// Zero-padding added to both sides of the input. Default: 0 /// Additional size added to one side of the output shape. Default: 0 @@ -69,11 +46,9 @@ public static partial class nn /// The desired device of the parameters and buffers in this module /// The desired floating point or complex dtype of the parameters and buffers in this module /// Tensor of shape (N,C_out,L_out) - public static ConvTranspose3d ConvTranspose3d(long in_channels, long out_channels, long kernelSize, long stride = 1, long padding = 0, long output_padding = 0, long dilation = 1, PaddingModes padding_mode = PaddingModes.Zeros, long groups = 1, bool bias = true, Device? device = null, ScalarType? dtype = null) + public static ConvTranspose3d ConvTranspose3d(long in_channels, long out_channels, long kernel_size, long stride = 1, long padding = 0, long output_padding = 0, long dilation = 1, PaddingModes padding_mode = PaddingModes.Zeros, long groups = 1, bool bias = true, Device? device = null, ScalarType? dtype = null) { - var res = THSNN_ConvTranspose3d_ctor(in_channels, out_channels, kernelSize, stride, padding, output_padding, dilation, (long)padding_mode, groups, bias, out var boxedHandle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new ConvTranspose3d(res, boxedHandle, in_channels).MoveModule(device, dtype); + return new ConvTranspose3d(in_channels, out_channels, (kernel_size, kernel_size, kernel_size), (stride, stride, stride), (padding, padding, padding), (dilation, dilation, dilation), (output_padding, output_padding, output_padding), groups, bias, padding_mode, device, dtype); } /// @@ -81,7 +56,7 @@ public static ConvTranspose3d ConvTranspose3d(long in_channels, long out_channel /// /// Number of channels in the input image /// Number of channels produced by the convolution - /// Size of the convolving kernel + /// Size of the convolving kernel /// Stride of the convolution. Default: (1,1,1) /// Zero-padding added to both sides of the input. Default: (0,0,0) /// Additional size added to one side of the output shape. Default: 0 @@ -91,16 +66,13 @@ public static ConvTranspose3d ConvTranspose3d(long in_channels, long out_channel /// If true, adds a learnable bias to the output. Default: true /// The desired device of the parameters and buffers in this module /// The desired floating point or complex dtype of the parameters and buffers in this module - public static ConvTranspose3d ConvTranspose3d(long in_channels, long out_channels, (long, long, long) kernelSize, (long, long, long)? stride = null, (long, long, long)? padding = null, (long, long, long)? output_padding = null, (long, long, long)? dilation = null, PaddingModes padding_mode = PaddingModes.Zeros, long groups = 1, bool bias = true, Device? device = null, ScalarType? dtype = null) + public static ConvTranspose3d ConvTranspose3d(long in_channels, long out_channels, (long, long, long) kernel_size, (long, long, long)? stride = null, (long, long, long)? padding = null, (long, long, long)? output_padding = null, (long, long, long)? dilation = null, PaddingModes padding_mode = PaddingModes.Zeros, long groups = 1, bool bias = true, Device? device = null, ScalarType? dtype = null) { - if (stride == null) stride = (1, 1, 1); - if (padding == null) padding = (0, 0, 0); - if (output_padding == null) output_padding = (0, 0, 0); - if (dilation == null) dilation = (1, 1, 1); - - var res = THSNN_ConvTranspose3d_ctor_1(in_channels, out_channels, kernelSize.Item1, kernelSize.Item2, kernelSize.Item3, stride.Value.Item1, stride.Value.Item2, stride.Value.Item3, padding.Value.Item1, padding.Value.Item2, padding.Value.Item3, output_padding.Value.Item1, output_padding.Value.Item2, output_padding.Value.Item3, dilation.Value.Item1, dilation.Value.Item2, dilation.Value.Item3, (long)padding_mode, groups, bias, out var boxedHandle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new ConvTranspose3d(res, boxedHandle, in_channels).MoveModule(device, dtype); + stride ??= (1, 1, 1); + padding ??= (0, 0, 0); + output_padding ??= (0, 0, 0); + dilation ??= (1, 1, 1); + return new ConvTranspose3d(in_channels, out_channels, kernel_size, stride.Value, padding.Value, dilation.Value, output_padding.Value, groups, bias, padding_mode, device, dtype); } public static partial class functional @@ -146,4 +118,4 @@ public static Tensor conv_transpose3d(Tensor input, Tensor weight, Tensor? bias } } } -} +} \ No newline at end of file diff --git a/src/TorchSharp/NN/Convolution/Convolution.cs b/src/TorchSharp/NN/Convolution/Convolution.cs index 6887d9cbe..bf94589ee 100644 --- a/src/TorchSharp/NN/Convolution/Convolution.cs +++ b/src/TorchSharp/NN/Convolution/Convolution.cs @@ -154,7 +154,8 @@ public Parameter weight { } // Rather than spending cycles discovering what parameters exist, we can just hardcode it. - protected internal override nn.Module _to(Device device, ScalarType dtype, bool non_blocking) { + protected internal override nn.Module _to(Device device, ScalarType dtype, bool non_blocking) + { if (_weight is not null && ReplaceParameter(dtype, device, _weight, out Parameter? w)) { weight = w!; } @@ -176,7 +177,8 @@ protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex return this; } - protected internal override nn.Module _to(ScalarType dtype, bool non_blocking) { + protected internal override nn.Module _to(ScalarType dtype, bool non_blocking) + { if (_weight is not null && ReplaceParameter(dtype, _weight.device, _weight, out Parameter? w)) { weight = w!; } @@ -188,7 +190,8 @@ protected internal override nn.Module _to(ScalarType dtype, bool non_blocking) { // Included to avoid API compat issues. [Obsolete("Deprecated API", true)] - protected Convolution(IntPtr handle, IntPtr boxedHandle, long input_channels) : base(handle, boxedHandle) { + protected Convolution(IntPtr handle, IntPtr boxedHandle, long input_channels) : base(handle, boxedHandle) + { throw new NotImplementedException("Deprecated API."); } diff --git a/src/TorchSharp/NN/Convolution/ConvolutionTranspose.cs b/src/TorchSharp/NN/Convolution/ConvolutionTranspose.cs index 22ce106e7..162c55a6e 100644 --- a/src/TorchSharp/NN/Convolution/ConvolutionTranspose.cs +++ b/src/TorchSharp/NN/Convolution/ConvolutionTranspose.cs @@ -24,7 +24,7 @@ public override Tensor forward(Tensor input) return this.forward(input, null); } public abstract Tensor forward(Tensor input, long[]? output_size); - + protected long[] _output_padding(Tensor input, long[]? output_size, long[] kernel_size, long[] stride, long[] padding, long[] dilation, long num_spatial_dims) { if (output_size is null) diff --git a/src/TorchSharp/NN/CosineSimilarity.cs b/src/TorchSharp/NN/CosineSimilarity.cs index 00cfcae1a..94955e6b0 100644 --- a/src/TorchSharp/NN/CosineSimilarity.cs +++ b/src/TorchSharp/NN/CosineSimilarity.cs @@ -23,7 +23,7 @@ internal CosineSimilarity(long dim = 1, double eps = 1e-8) : base(nameof(CosineS public override Tensor forward(Tensor input1, Tensor input2) { - return ReturnCheckForErrorsAutocast(THSNN_CosineSimilarity_forward(handle, input1.Handle, input2.Handle), ScalarType.Float32); + return torch.nn.functional.cosine_similarity(input1, input2, this.dim, this.eps); } public long dim { get; set; } @@ -43,10 +43,7 @@ public static partial class nn /// public static CosineSimilarity CosineSimilarity(long dim = 1, double eps = 1e-8) { - var handle = THSNN_CosineSimilarity_ctor(dim, eps, out var boxedHandle); - if (handle == IntPtr.Zero) { torch.CheckForErrors(); } - handle = AutocastMode.AutoCast(handle, ScalarType.Float32); - return new CosineSimilarity(handle, boxedHandle); + return new CosineSimilarity(dim, eps); } public static partial class functional @@ -62,6 +59,7 @@ public static partial class functional public static Tensor cosine_similarity(Tensor x1, Tensor x2, long dim = 1, double eps = 1e-8) { var res = THSNN_cosine_similarity(x1.Handle, x2.Handle, dim, eps); + res = AutocastMode.AutoCast(res, ScalarType.Float32); if (res == IntPtr.Zero) { torch.CheckForErrors(); } return new Tensor(res); } diff --git a/src/TorchSharp/NN/Dropout.cs b/src/TorchSharp/NN/Dropout.cs index 7a656d4e9..a6d53e483 100644 --- a/src/TorchSharp/NN/Dropout.cs +++ b/src/TorchSharp/NN/Dropout.cs @@ -31,7 +31,7 @@ public override Tensor forward(Tensor tensor) } public bool inplace { get; set; } - public double p { get; set;} + public double p { get; set; } } } @@ -60,7 +60,9 @@ public static partial class functional /// public static Tensor dropout(Tensor input, double p = 0.5, bool training = true, bool inplace = false) { - return ReturnCheckForErrors(THSNN_dropout(input.Handle, p, training, inplace)); + var res = THSNN_dropout(input.Handle, p, training, inplace); + if (res == IntPtr.Zero) { torch.CheckForErrors(); } + return new Tensor(res); } } } diff --git a/src/TorchSharp/NN/Dropout1d.cs b/src/TorchSharp/NN/Dropout1d.cs index 4393361ec..3c6b93ff9 100644 --- a/src/TorchSharp/NN/Dropout1d.cs +++ b/src/TorchSharp/NN/Dropout1d.cs @@ -28,7 +28,7 @@ public override Tensor forward(Tensor tensor) } public bool inplace { get; set; } - public double p { get; set;} + public double p { get; set; } } } diff --git a/src/TorchSharp/NN/Dropout2d.cs b/src/TorchSharp/NN/Dropout2d.cs index 857850756..72a5bc4da 100644 --- a/src/TorchSharp/NN/Dropout2d.cs +++ b/src/TorchSharp/NN/Dropout2d.cs @@ -12,7 +12,7 @@ namespace Modules /// /// This class is used to represent a Dropout2d module. /// - public sealed class Dropout2d : torch.nn.Module + public sealed class Dropout2d : ParameterLessModule { internal Dropout2d(double p = 0.5, bool inplace = false) : base(nameof(Dropout2d)) { @@ -22,17 +22,11 @@ internal Dropout2d(double p = 0.5, bool inplace = false) : base(nameof(Dropout2d public override Tensor forward(Tensor input) { - return ReturnCheckForErrors(THSNN_dropout2d(input.Handle, p, this.training, inplace)); + return torch.nn.functional.dropout2d(input, this.p, this.training, this.inplace); } - // Rather than spending cycles only to discover that this module has neither - // parameters nor buffers, just shortcut the move completely. - protected internal override nn.Module _to(Device device, ScalarType dtype, bool non_blocking) => this; - protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex, bool non_blocking) => this; - protected internal override nn.Module _to(ScalarType dtype, bool non_blocking) => this; - - internal bool inplace; //Set internal accesibility for PrintModule - internal double p; //Set internal accesibility for PrintModule + public bool inplace { get; set; } + public double p { get; set; } } } @@ -62,7 +56,9 @@ public static partial class functional /// public static Tensor dropout2d(Tensor input, double p = 0.5, bool training = true, bool inplace = false) { - return ReturnCheckForErrors(THSNN_dropout2d(input.Handle, p, training, inplace)); + var res = THSNN_dropout2d(input.Handle, p, training, inplace); + if (res == IntPtr.Zero) { torch.CheckForErrors(); } + return new Tensor(res); } } } diff --git a/src/TorchSharp/NN/Dropout3d.cs b/src/TorchSharp/NN/Dropout3d.cs index 201901650..73f4f8b64 100644 --- a/src/TorchSharp/NN/Dropout3d.cs +++ b/src/TorchSharp/NN/Dropout3d.cs @@ -12,7 +12,7 @@ namespace Modules /// /// This class is used to represent a Dropout3d module. /// - public sealed class Dropout3d : nn.Module + public sealed class Dropout3d : ParameterLessModule { internal Dropout3d(double p = 0.5, bool inplace = false) : base(nameof(Dropout3d)) { @@ -22,17 +22,11 @@ internal Dropout3d(double p = 0.5, bool inplace = false) : base(nameof(Dropout3d public override Tensor forward(Tensor input) { - return ReturnCheckForErrors(THSNN_dropout3d(input.Handle, p, this.training, inplace)); + return torch.nn.functional.dropout3d(input, this.p, this.training, this.inplace); } - // Rather than spending cycles only to discover that this module has neither - // parameters nor buffers, just shortcut the move completely. - protected internal override nn.Module _to(Device device, ScalarType dtype, bool non_blocking) => this; - protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex, bool non_blocking) => this; - protected internal override nn.Module _to(ScalarType dtype, bool non_blocking) => this; - - private bool inplace; - private double p; + public bool inplace { get; set; } + public double p { get; set; } } } @@ -60,7 +54,9 @@ public static partial class functional /// public static Tensor dropout3d(Tensor input, double p = 0.5, bool training = true, bool inplace = false) { - return ReturnCheckForErrors(THSNN_dropout3d(input.Handle, p, training, inplace)); + var res = THSNN_dropout3d(input.Handle, p, training, inplace); + if (res == IntPtr.Zero) { torch.CheckForErrors(); } + return new Tensor(res); } } } diff --git a/src/TorchSharp/NN/Embedding.cs b/src/TorchSharp/NN/Embedding.cs index b2a80550d..a76a62995 100644 --- a/src/TorchSharp/NN/Embedding.cs +++ b/src/TorchSharp/NN/Embedding.cs @@ -17,7 +17,9 @@ internal Embedding(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle public override Tensor forward(Tensor input) { - return ReturnCheckForErrors(THSNN_Embedding_forward(handle, input.Handle)); + var res = THSNN_Embedding_forward(handle, input.Handle); + if (res == IntPtr.Zero) { torch.CheckForErrors(); } + return new Tensor(res); } [DisallowNull] @@ -62,7 +64,7 @@ public static Embedding Embedding(long num_embeddings, long embedding_dims, long max_norm.HasValue ? max_norm.Value : 0.0, max_norm.HasValue, norm_type, scale_grad_by_freq, sparse, out var boxedHandle); if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Embedding(res, boxedHandle).MoveModule(device,dtype); + return new Embedding(res, boxedHandle).MoveModule(device, dtype); } /// diff --git a/src/TorchSharp/NN/EmbeddingBag.cs b/src/TorchSharp/NN/EmbeddingBag.cs index ebfb6d007..aab7978d5 100644 --- a/src/TorchSharp/NN/EmbeddingBag.cs +++ b/src/TorchSharp/NN/EmbeddingBag.cs @@ -33,7 +33,9 @@ internal EmbeddingBag(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHan /// public override Tensor forward(Tensor input, Tensor? offsets, Tensor? perSampleWeights) { - return ReturnCheckForErrors(THSNN_EmbeddingBag_forward(handle, input.Handle, (offsets is null) ? IntPtr.Zero : offsets.Handle, (perSampleWeights is null) ? IntPtr.Zero : perSampleWeights.Handle)); + var res = THSNN_EmbeddingBag_forward(handle, input.Handle, (offsets is null) ? IntPtr.Zero : offsets.Handle, (perSampleWeights is null) ? IntPtr.Zero : perSampleWeights.Handle); + if (res == IntPtr.Zero) { torch.CheckForErrors(); } + return new Tensor(res); } public new Tensor call(Tensor input, Tensor? offsets, Tensor? perSampleWeights) diff --git a/src/TorchSharp/NN/FeatureDropout.cs b/src/TorchSharp/NN/FeatureDropout.cs index ffa95a8dd..1d12f0bda 100644 --- a/src/TorchSharp/NN/FeatureDropout.cs +++ b/src/TorchSharp/NN/FeatureDropout.cs @@ -12,22 +12,21 @@ namespace Modules /// /// This class is used to represent a dropout module for 2d/3d convolutational layers. /// - public sealed class FeatureAlphaDropout : torch.nn.Module + public sealed class FeatureAlphaDropout : ParameterLessModule { - internal FeatureAlphaDropout(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) + internal FeatureAlphaDropout(double p = 0.5, bool inplace = false) : base(nameof(FeatureAlphaDropout)) { + this.p = p; + this.inplace = inplace; } - public override Tensor forward(Tensor tensor) + public override Tensor forward(Tensor input) { - return ReturnCheckForErrors(THSNN_FeatureAlphaDropout_forward(handle, tensor.Handle)); + return torch.nn.functional.feature_alpha_dropout(input, this.p, this.training, this.inplace); } - // Rather than spending cycles only to discover that this module has neither - // parameters nor buffers, just shortcut the move completely. - protected internal override nn.Module _to(Device device, ScalarType dtype, bool non_blocking) => this; - protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex, bool non_blocking) => this; - protected internal override nn.Module _to(ScalarType dtype, bool non_blocking) => this; + public bool inplace { get; set; } + public double p { get; set; } } } @@ -35,6 +34,19 @@ public static partial class torch { public static partial class nn { + /// + /// Randomly masks out entire channels (a channel is a feature map, e.g. the j-th channel of the i-th sample in the batch input is a tensor input[i,j]) of the input tensor. + /// Instead of setting activations to zero, as in regular Dropout, the activations are set to the negative saturation value of the SELU activation function. + /// Each element will be masked independently on every forward call with probability p using samples from a Bernoulli distribution.The elements to be masked are + /// randomized on every forward call, and scaled and shifted to maintain zero mean and unit variance. + /// + /// Dropout probability of a channel to be zeroed. Default: 0.5 + /// If set to true, will do this operation in-place. Default: false + public static FeatureAlphaDropout FeatureAlphaDropout(double p, bool inplace) + { + return new FeatureAlphaDropout(p, inplace); + } + /// /// Randomly masks out entire channels (a channel is a feature map, e.g. the j-th channel of the i-th sample in the batch input is a tensor input[i,j]) of the input tensor. /// Instead of setting activations to zero, as in regular Dropout, the activations are set to the negative saturation value of the SELU activation function. @@ -44,9 +56,7 @@ public static partial class nn /// Dropout probability of a channel to be zeroed. Default: 0.5 public static FeatureAlphaDropout FeatureAlphaDropout(double p = 0.5) { - var handle = THSNN_FeatureAlphaDropout_ctor(p, out var boxedHandle); - if (handle == IntPtr.Zero) { torch.CheckForErrors(); } - return new FeatureAlphaDropout(handle, boxedHandle); + return new FeatureAlphaDropout(p, false); } public static partial class functional @@ -59,7 +69,9 @@ public static partial class functional /// public static Tensor feature_alpha_dropout(Tensor input, double p = 0.5, bool training = false, bool inplace = false) { - return ReturnCheckForErrors(THSNN_feature_alpha_dropout(input.Handle, p, training, inplace)); + var res = THSNN_feature_alpha_dropout(input.Handle, p, training, inplace); + if (res == IntPtr.Zero) { torch.CheckForErrors(); } + return new Tensor(res); } } } diff --git a/src/TorchSharp/NN/Flatten.cs b/src/TorchSharp/NN/Flatten.cs index caf924426..edf0201cf 100644 --- a/src/TorchSharp/NN/Flatten.cs +++ b/src/TorchSharp/NN/Flatten.cs @@ -10,24 +10,23 @@ namespace TorchSharp namespace Modules { /// - /// This class is used to represent a dropout module for 2d/3d convolutational layers. + /// This class is used to represent a flattening of the input tensors. /// - public sealed class Flatten : torch.nn.Module + public sealed class Flatten : ParameterLessModule { - internal Flatten(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) + internal Flatten(long start_dim = 1, long end_dim = -1) : base(nameof(Flatten)) { + this.start_dim = start_dim; + this.end_dim = end_dim; } - public override Tensor forward(Tensor tensor) + public override Tensor forward(Tensor input) { - return ReturnCheckForErrors(THSNN_Flatten_forward(handle, tensor.Handle)); + return input.flatten(start_dim, end_dim); } - // Rather than spending cycles only to discover that this module has neither - // parameters nor buffers, just shortcut the move completely. - protected internal override nn.Module _to(Device device, ScalarType dtype, bool non_blocking) => this; - protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex, bool non_blocking) => this; - protected internal override nn.Module _to(ScalarType dtype, bool non_blocking) => this; + public long start_dim { get; set; } + public long end_dim { get; set; } } } @@ -38,15 +37,13 @@ public static partial class nn /// /// Flattens a contiguous range of dims into a tensor. For use with Sequential. /// - /// First dim to flatten (default = 1). - /// Last dim to flatten (default = -1). + /// First dim to flatten (default = 1). + /// Last dim to flatten (default = -1). /// - public static Flatten Flatten(long startDim = 1, long endDim = -1) + public static Flatten Flatten(long start_dim = 1, long end_dim = -1) { - var handle = THSNN_Flatten_ctor(startDim, endDim, out var boxedHandle); - if (handle == IntPtr.Zero) { torch.CheckForErrors(); } - return new Flatten(handle, boxedHandle); + return new Flatten(start_dim, end_dim); } } } -} +} \ No newline at end of file diff --git a/src/TorchSharp/NN/Fold.cs b/src/TorchSharp/NN/Fold.cs index 9696c76ba..cf063b58b 100644 --- a/src/TorchSharp/NN/Fold.cs +++ b/src/TorchSharp/NN/Fold.cs @@ -24,7 +24,7 @@ internal Fold((long, long) output_size, (long, long) kernel_size, (long, long) d public override Tensor forward(Tensor tensor) { - return torch.nn.functional.fold(tensor, output_size , kernel_size, dilation, padding, stride); + return torch.nn.functional.fold(tensor, output_size, kernel_size, dilation, padding, stride); } public (long, long) output_size { get; set; } @@ -85,7 +85,9 @@ public static partial class functional /// Currently, only unbatched (3D) or batched (4D) image-like output tensors are supported. public unsafe static Tensor fold(Tensor input, long output_size, long kernel_size, long dilation = 1, long padding = 0, long stride = 1) { - return ReturnCheckForErrors(THSNN_fold(input.Handle, output_size, output_size, kernel_size, kernel_size, stride, stride, padding, padding, dilation, dilation)); + var res = THSNN_fold(input.Handle, output_size, output_size, kernel_size, kernel_size, stride, stride, padding, padding, dilation, dilation); + if (res == IntPtr.Zero) { torch.CheckForErrors(); } + return new Tensor(res); } /// @@ -98,7 +100,7 @@ public unsafe static Tensor fold(Tensor input, long output_size, long kernel_siz /// Implicit zero padding to be added on both sides of input. /// The stride of the sliding blocks in the input spatial dimensions. /// Currently, only unbatched (3D) or batched (4D) image-like output tensors are supported. - public unsafe static Tensor fold(Tensor input, (long,long) output_size, (long, long) kernel_size, (long, long)? dilation = null, (long, long)? padding = null, (long, long)? stride = null) + public unsafe static Tensor fold(Tensor input, (long, long) output_size, (long, long) kernel_size, (long, long)? dilation = null, (long, long)? padding = null, (long, long)? stride = null) { dilation ??= (1, 1); stride ??= (1, 1); @@ -110,7 +112,8 @@ public unsafe static Tensor fold(Tensor input, (long,long) output_size, (long, l stride.Value.Item1, stride.Value.Item2, padding.Value.Item1, padding.Value.Item2, dilation.Value.Item1, dilation.Value.Item2); - return ReturnCheckForErrors(res); + if (res == IntPtr.Zero) { torch.CheckForErrors(); } + return new Tensor(res); } } } diff --git a/src/TorchSharp/NN/Identity.cs b/src/TorchSharp/NN/Identity.cs index 10277118f..f377ec311 100644 --- a/src/TorchSharp/NN/Identity.cs +++ b/src/TorchSharp/NN/Identity.cs @@ -10,20 +10,14 @@ namespace TorchSharp namespace Modules { - public sealed class Identity : torch.nn.Module + public sealed class Identity : ParameterLessModule { - internal Identity(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { } + internal Identity() : base(nameof(Identity)) { } public override Tensor forward(Tensor tensor) { - return ReturnCheckForErrors(THSNN_Identity_forward(handle, tensor.Handle)); + return tensor.alias(); } - - // Rather than spending cycles only to discover that this module has neither - // parameters nor buffers, just shortcut the move completely. - protected internal override nn.Module _to(Device device, ScalarType dtype, bool non_blocking) => this; - protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex, bool non_blocking) => this; - protected internal override nn.Module _to(ScalarType dtype, bool non_blocking) => this; } } @@ -37,10 +31,8 @@ public static partial class nn /// The same tensor as is input. public static Identity Identity() { - var res = THSNN_Identity_ctor(out var boxedHandle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Identity(res, boxedHandle); + return new Identity(); } } } -} +} \ No newline at end of file diff --git a/src/TorchSharp/NN/Linear.cs b/src/TorchSharp/NN/Linear.cs index 863452d62..05f01e5f3 100644 --- a/src/TorchSharp/NN/Linear.cs +++ b/src/TorchSharp/NN/Linear.cs @@ -1,6 +1,5 @@ // Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information. using System; -using TorchSharp.Amp; using static TorchSharp.torch; using static TorchSharp.torch.nn; using static TorchSharp.PInvoke.NativeMethods; @@ -13,25 +12,20 @@ namespace TorchSharp namespace Modules { - public class LinearInfo - { - public long InFeatures { get; } - public long OutFeatures { get; } - public LinearInfo(long inFeatures, long outFeatures) - { - InFeatures = inFeatures; - OutFeatures = outFeatures; - } - } public sealed class Linear : torch.nn.Module { - public LinearInfo? linearInfo; - /*internal Linear(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) - { - }*/ - internal Linear(IntPtr handle, IntPtr boxedHandle, long inFeat, long outFeat) : base(handle, boxedHandle) + const string WeightComponentName = nameof(weight); + const string BiasComponentName = nameof(bias); + + internal Linear(Parameter weight, Parameter? bias = null) : base(nameof(Linear)) { - linearInfo = new LinearInfo(inFeat, outFeat); + this.in_features = weight.shape[1]; + this.out_features = weight.shape[0]; + + this.weight = weight; + if (bias is not null) { + this.bias = bias; + } } internal Linear(long inputSize, long outputSize, bool hasBias = true, Device? device = null, ScalarType? dtype = null) : base(nameof(Linear)) @@ -53,8 +47,7 @@ internal Linear(long inputSize, long outputSize, bool hasBias = true, Device? de public override Tensor forward(Tensor tensor) { - //tensor.handle = Amp.AMPManager.GetInstance().AutoCast(tensor.handle); //WARNING should be here???? Research - return ReturnCheckForErrors(THSNN_Linear_forward(handle, tensor.Handle)); + return torch.nn.functional.linear(tensor, _weight!, _bias); } protected override void Dispose(bool disposing) @@ -70,7 +63,7 @@ public Parameter? bias { set { _bias?.Dispose(); _bias = value?.DetachFromDisposeScope() as Parameter; - ConditionallyRegisterParameter("BiasComponentName", _bias); + ConditionallyRegisterParameter(BiasComponentName, _bias); } } @@ -80,13 +73,14 @@ public Parameter weight { if (value.Handle != _weight?.Handle) { _weight?.Dispose(); _weight = (value.DetachFromDisposeScope() as Parameter)!; - ConditionallyRegisterParameter("WeightComponentName", _weight); + ConditionallyRegisterParameter(WeightComponentName, _weight); } } } // Rather than spending cycles discovering what parameters exist, we can just hardcode it. - protected internal override nn.Module _to(Device device, ScalarType dtype, bool non_blocking) { + protected internal override nn.Module _to(Device device, ScalarType dtype, bool non_blocking) + { if (_weight is not null && ReplaceParameter(dtype, device, _weight, out var w)) { weight = w!; } @@ -107,7 +101,8 @@ protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex } return this; } - protected internal override nn.Module _to(ScalarType dtype, bool non_blocking) { + protected internal override nn.Module _to(ScalarType dtype, bool non_blocking) + { if (_weight is not null && ReplaceParameter(dtype, _weight.device, _weight, out var w)) { weight = w!; } @@ -118,9 +113,9 @@ protected internal override nn.Module _to(ScalarType dtype, bool non_blocking) { } - [ComponentName(Name = "BiasComponentName")] + [ComponentName(Name = BiasComponentName)] private Parameter? _bias; - [ComponentName(Name = "WeightComponentName")] + [ComponentName(Name = WeightComponentName)] private Parameter? _weight; public long in_features { get; set; } @@ -146,8 +141,16 @@ public static Linear Linear(long inputSize, long outputSize, bool hasBias = true { return new Linear(inputSize, outputSize, hasBias, device, dtype); } - /*return new Linear(res, boxedHandle, inputSize, outputSize).MoveModule(device, dtype); - }*/ + + /// + /// Create a Linear module with the given weights and bias. + /// + /// The linear weight attribute. + /// The additive linear bias. Optional. + public static Linear Linear(Parameter weight, Parameter? bias = null) + { + return new Linear(weight, bias); + } public static partial class functional { diff --git a/src/TorchSharp/NN/Module.cs b/src/TorchSharp/NN/Module.cs index 25251e0a4..2d8129d39 100644 --- a/src/TorchSharp/NN/Module.cs +++ b/src/TorchSharp/NN/Module.cs @@ -1,4 +1,4 @@ -// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information. +// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information. using System; using System.Collections.Generic; using System.Diagnostics; @@ -1150,7 +1150,7 @@ protected virtual void RegisterComponents() - protected static (Device device, ScalarType dtype) GetDefaultDeviceAndType(Device device = null, ScalarType? dtype = null) + protected static (Device device, ScalarType dtype) GetDefaultDeviceAndType(Device? device = null, ScalarType? dtype = null) { if (!dtype.HasValue) dtype = get_default_dtype(); diff --git a/src/TorchSharp/NN/Normalization/BatchNorm1D.cs b/src/TorchSharp/NN/Normalization/BatchNorm1D.cs index 478531944..8d3da6414 100644 --- a/src/TorchSharp/NN/Normalization/BatchNorm1D.cs +++ b/src/TorchSharp/NN/Normalization/BatchNorm1D.cs @@ -13,80 +13,22 @@ namespace Modules /// /// This class is used to represent a BatchNorm1D module. /// - public sealed class BatchNorm1d : torch.nn.Module + public sealed class BatchNorm1d : BatchNorm { - internal BatchNorm1d(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) + internal BatchNorm1d(long num_features, + double eps, + double momentum, + bool affine, + bool track_running_stats, + Device? device, + ScalarType? dtype) : base(num_features, eps, momentum, affine, track_running_stats, device, dtype, nameof(BatchNorm1d)) { } - public override Tensor forward(Tensor tensor) + protected override void ValidateInputDimensions(Tensor input) { - if (tensor.Dimensions < 2 || tensor.Dimensions > 3) throw new ArgumentException($"Invalid number of dimensions for BatchNorm argument: {tensor.Dimensions}"); - return ReturnCheckForErrors(THSNN_BatchNorm1d_forward(handle.DangerousGetHandle(), tensor.Handle)); - } - - public Parameter? bias { - get { - return ReturnNullParameterCheckForErrors(THSNN_BatchNorm1d_bias(handle)); - } - set { - // Please ignore, for now, that the litorch call thinks you *can* set it to null. - if (value is null) throw new ArgumentNullException("bias cannot be set to 'null'"); - THSNN_BatchNorm1d_set_bias(handle, (value is null ? IntPtr.Zero : value.Handle)); - torch.CheckForErrors(); - ConditionallyRegisterParameter("bias", value); - } - } - - public Parameter? weight { - get { - return ReturnNullParameterCheckForErrors(THSNN_BatchNorm1d_weight(handle)); - } - set { - // Please ignore, for now, that the litorch call thinks you *can* set it to null. - if (value is null) throw new ArgumentNullException("weight cannot be set to 'null'"); - THSNN_BatchNorm1d_set_weight(handle, value is null ? IntPtr.Zero : value.Handle); - torch.CheckForErrors(); - ConditionallyRegisterParameter("weight", value); - } - } - - public Tensor? running_mean { - get { - return ReturnNullCheckForErrors(THSNN_BatchNorm1d_get_mean(handle)); - } - set { - // Please ignore, for now, that the litorch call thinks you *can* set it to null. - if (value is null) throw new ArgumentNullException("running_mean cannot be set to 'null'"); - THSNN_BatchNorm1d_set_mean(handle, (value is null ? IntPtr.Zero : value.Handle)); - torch.CheckForErrors(); - ConditionallyRegisterBuffer("running_mean", value); - } - } - - public Tensor? running_var { - get { - return ReturnNullCheckForErrors(THSNN_BatchNorm1d_get_var(handle)); - } - set { - // Please ignore, for now, that the litorch call thinks you *can* set it to null. - if (value is null) throw new ArgumentNullException("running_var cannot be set to 'null'"); - THSNN_BatchNorm1d_set_var(handle, (value is null ? IntPtr.Zero : value.Handle)); - torch.CheckForErrors(); - ConditionallyRegisterBuffer("running_var", value); - } - } - - public Tensor? num_batches_tracked { - get { - return ReturnNullCheckForErrors(THSNN_BatchNorm1d_get_batches(handle)); - } - } - - public void reset_running_stats() - { - THSNN_BatchNorm1d_reset_stats(handle); - torch.CheckForErrors(); + if (input.ndim != 2 && input.ndim != 3) + throw new ArgumentException($"expected 2D or 3D input, but got {input.ndim}D input."); } } } @@ -98,7 +40,7 @@ public static partial class nn /// /// Applies Batch Normalization over a 2D or 3D input (a mini-batch of 1D inputs with optional additional channel dimension) as described in the paper Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift . /// - /// C from an expected input of size (N,C,L) or LL from input of size (N, L) + /// C from an expected input of size (N,C,L) or LL from input of size (N, L) /// A value added to the denominator for numerical stability. Default: 1e-5 /// The value used for the running_mean and running_var computation. Can be set to None for cumulative moving average (i.e. simple average). Default: 0.1 /// A boolean value that when set to True, this module has learnable affine parameters. Default: true @@ -108,13 +50,9 @@ public static partial class nn /// The desired device of the parameters and buffers in this module /// The desired floating point or complex dtype of the parameters and buffers in this module /// - public static BatchNorm1d BatchNorm1d(long features, double eps = 1e-05, double momentum = 0.1, bool affine = true, bool track_running_stats = true, Device? device = null, ScalarType? dtype = null) + public static BatchNorm1d BatchNorm1d(long num_features, double eps = 1e-05, double momentum = 0.1, bool affine = true, bool track_running_stats = true, Device? device = null, ScalarType? dtype = null) { - unsafe { - var handle = THSNN_BatchNorm1d_ctor(features, eps, momentum, affine, track_running_stats, out var boxedHandle); - if (handle == IntPtr.Zero) { torch.CheckForErrors(); } - return new BatchNorm1d(handle, boxedHandle).MoveModule(device, dtype); - } + return new BatchNorm1d(num_features, eps, momentum, affine, track_running_stats, device, dtype); } } } diff --git a/src/TorchSharp/NN/Normalization/BatchNorm2D.cs b/src/TorchSharp/NN/Normalization/BatchNorm2D.cs index 2d5c1f176..cebcf15c0 100644 --- a/src/TorchSharp/NN/Normalization/BatchNorm2D.cs +++ b/src/TorchSharp/NN/Normalization/BatchNorm2D.cs @@ -13,80 +13,22 @@ namespace Modules /// /// This class is used to represent a BatchNorm2D module. /// - public sealed class BatchNorm2d : torch.nn.Module + public sealed class BatchNorm2d : BatchNorm { - internal BatchNorm2d(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) + internal BatchNorm2d(long num_features, + double eps, + double momentum, + bool affine, + bool track_running_stats, + Device? device, + ScalarType? dtype) : base(num_features, eps, momentum, affine, track_running_stats, device, dtype, nameof(BatchNorm1d)) { } - public override Tensor forward(Tensor tensor) + protected override void ValidateInputDimensions(Tensor input) { - if (tensor.Dimensions != 4) throw new ArgumentException($"Invalid number of dimensions for BatchNorm argument: {tensor.Dimensions}"); - return ReturnCheckForErrors(THSNN_BatchNorm2d_forward(handle.DangerousGetHandle(), tensor.Handle)); - } - - public Parameter? bias { - get { - return ReturnNullParameterCheckForErrors(THSNN_BatchNorm2d_bias(handle)); - } - set { - // Please ignore, for now, that the litorch call thinks you *can* set it to null. - if (value is null) throw new ArgumentNullException("bias cannot be set to 'null'"); - THSNN_BatchNorm2d_set_bias(handle, (value is null ? IntPtr.Zero : value.Handle)); - torch.CheckForErrors(); - ConditionallyRegisterParameter("bias", value); - } - } - - public Parameter? weight { - get { - return ReturnNullParameterCheckForErrors(THSNN_BatchNorm2d_weight(handle)); - } - set { - // Please ignore, for now, that the litorch call thinks you *can* set it to null. - if (value is null) throw new ArgumentNullException("weight cannot be set to 'null'"); - THSNN_BatchNorm2d_set_weight(handle, value is null ? IntPtr.Zero : value.Handle); - torch.CheckForErrors(); - ConditionallyRegisterParameter("weight", value); - } - } - - public Tensor? running_mean { - get { - return ReturnNullCheckForErrors(THSNN_BatchNorm2d_get_mean(handle)); - } - set { - // Please ignore, for now, that the litorch call thinks you *can* set it to null. - if (value is null) throw new ArgumentNullException("running_mean cannot be set to 'null'"); - THSNN_BatchNorm2d_set_mean(handle, (value is null ? IntPtr.Zero : value.Handle)); - torch.CheckForErrors(); - ConditionallyRegisterBuffer("running_mean", value); - } - } - - public Tensor? running_var { - get { - return ReturnNullCheckForErrors(THSNN_BatchNorm2d_get_var(handle)); - } - set { - // Please ignore, for now, that the litorch call thinks you *can* set it to null. - if (value is null) throw new ArgumentNullException("running_var cannot be set to 'null'"); - THSNN_BatchNorm2d_set_var(handle, (value is null ? IntPtr.Zero : value.Handle)); - torch.CheckForErrors(); - ConditionallyRegisterBuffer("running_var", value); - } - } - - public Tensor? num_batches_tracked { - get { - return ReturnNullCheckForErrors(THSNN_BatchNorm2d_get_batches(handle)); - } - } - - public void reset_running_stats() - { - THSNN_BatchNorm2d_reset_stats(handle); - torch.CheckForErrors(); + if (input.ndim != 4) + throw new ArgumentException($"expected 4D input, but got {input.ndim}D input."); } } } @@ -98,7 +40,7 @@ public static partial class nn /// /// Applies Batch Normalization over a 4D input (a mini-batch of 2D inputs with additional channel dimension) as described in the paper Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. /// - /// C from an expected input of size (N,C,H,W) + /// C from an expected input of size (N,C,H,W) /// A value added to the denominator for numerical stability. Default: 1e-5 /// The value used for the running_mean and running_var computation. Can be set to None for cumulative moving average (i.e. simple average). Default: 0.1 /// A boolean value that when set to True, this module has learnable affine parameters. Default: true @@ -108,13 +50,9 @@ public static partial class nn /// The desired device of the parameters and buffers in this module /// The desired floating point or complex dtype of the parameters and buffers in this module /// - public static BatchNorm2d BatchNorm2d(long features, double eps = 1e-05, double momentum = 0.1, bool affine = true, bool track_running_stats = true, Device? device = null, ScalarType? dtype = null) + public static BatchNorm2d BatchNorm2d(long num_features, double eps = 1e-05, double momentum = 0.1, bool affine = true, bool track_running_stats = true, Device? device = null, ScalarType? dtype = null) { - unsafe { - var handle = THSNN_BatchNorm2d_ctor(features, eps, momentum, affine, track_running_stats, out var boxedHandle); - if (handle == IntPtr.Zero) { torch.CheckForErrors(); } - return new BatchNorm2d(handle, boxedHandle).MoveModule(device, dtype); - } + return new BatchNorm2d(num_features, eps, momentum, affine, track_running_stats, device, dtype); } } } diff --git a/src/TorchSharp/NN/Normalization/BatchNorm3D.cs b/src/TorchSharp/NN/Normalization/BatchNorm3D.cs index 4bbbe601e..7d556345f 100644 --- a/src/TorchSharp/NN/Normalization/BatchNorm3D.cs +++ b/src/TorchSharp/NN/Normalization/BatchNorm3D.cs @@ -13,82 +13,22 @@ namespace Modules /// /// This class is used to represent a BatchNorm3D module. /// - public sealed class BatchNorm3d : torch.nn.Module + public sealed class BatchNorm3d : BatchNorm { - internal BatchNorm3d(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) + internal BatchNorm3d(long num_features, + double eps, + double momentum, + bool affine, + bool track_running_stats, + Device? device, + ScalarType? dtype) : base(num_features, eps, momentum, affine, track_running_stats, device, dtype, nameof(BatchNorm1d)) { } - public override Tensor forward(Tensor tensor) + protected override void ValidateInputDimensions(Tensor input) { - if (tensor.Dimensions != 5) throw new ArgumentException($"Invalid number of dimensions for BatchNorm argument: {tensor.Dimensions}"); - return ReturnCheckForErrors(THSNN_BatchNorm3d_forward(handle.DangerousGetHandle(), tensor.Handle)); - } - - public Parameter? bias { - get { - return ReturnNullParameterCheckForErrors(THSNN_BatchNorm3d_bias(handle)); - } - set { - // Please ignore, for now, that the litorch call thinks you *can* set it to null. - if (value is null) throw new ArgumentNullException("bias cannot be set to 'null'"); - THSNN_BatchNorm3d_set_bias(handle, (value is null ? IntPtr.Zero : value.Handle)); - torch.CheckForErrors(); - ConditionallyRegisterParameter("bias", value); - } - } - - public Parameter? weight { - get { - return ReturnNullParameterCheckForErrors(THSNN_BatchNorm3d_weight(handle)); - } - set { - // Please ignore, for now, that the litorch call thinks you *can* set it to null. - if (value is null) throw new ArgumentNullException("weight cannot be set to 'null'"); - THSNN_BatchNorm3d_set_weight(handle, value is null ? IntPtr.Zero : value.Handle); - torch.CheckForErrors(); - ConditionallyRegisterParameter("weight", value); - } - } - - public Tensor? running_mean { - get { - var res = THSNN_BatchNorm3d_get_mean(handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); return null; } - return new Tensor(res); - } - set { - // Please ignore, for now, that the litorch call thinks you *can* set it to null. - if (value is null) throw new ArgumentNullException("running_mean cannot be set to 'null'"); - THSNN_BatchNorm3d_set_mean(handle, (value is null ? IntPtr.Zero : value.Handle)); - torch.CheckForErrors(); - ConditionallyRegisterBuffer("running_mean", value); - } - } - - public Tensor? running_var { - get { - return ReturnNullCheckForErrors(THSNN_BatchNorm3d_get_var(handle)); - } - set { - // Please ignore, for now, that the litorch call thinks you *can* set it to null. - if (value is null) throw new ArgumentNullException("running_var cannot be set to 'null'"); - THSNN_BatchNorm3d_set_var(handle, (value is null ? IntPtr.Zero : value.Handle)); - torch.CheckForErrors(); - ConditionallyRegisterBuffer("running_var", value); - } - } - - public Tensor? num_batches_tracked { - get { - return ReturnNullCheckForErrors(THSNN_BatchNorm3d_get_batches(handle)); - } - } - - public void reset_running_stats() - { - THSNN_BatchNorm3d_reset_stats(handle); - torch.CheckForErrors(); + if (input.ndim != 5) + throw new ArgumentException($"expected 4D input, but got {input.ndim}D input."); } } } @@ -100,7 +40,7 @@ public static partial class nn /// /// Applies Batch Normalization over a 5D input (a mini-batch of 3D inputs with additional channel dimension) as described in the paper Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. /// - /// C from an expected input of size (N,C,D,H,W) + /// C from an expected input of size (N,C,D,H,W) /// A value added to the denominator for numerical stability. Default: 1e-5 /// The value used for the running_mean and running_var computation. Can be set to None for cumulative moving average (i.e. simple average). Default: 0.1 /// A boolean value that when set to True, this module has learnable affine parameters. Default: true @@ -110,13 +50,9 @@ public static partial class nn /// The desired device of the parameters and buffers in this module /// The desired floating point or complex dtype of the parameters and buffers in this module /// - public static BatchNorm3d BatchNorm3d(long features, double eps = 1e-05, double momentum = 0.1, bool affine = true, bool track_running_stats = true, Device? device = null, ScalarType? dtype = null) + public static BatchNorm3d BatchNorm3d(long num_features, double eps = 1e-05, double momentum = 0.1, bool affine = true, bool track_running_stats = true, Device? device = null, ScalarType? dtype = null) { - unsafe { - var handle = THSNN_BatchNorm3d_ctor(features, eps, momentum, affine, track_running_stats, out var boxedHandle); - if (handle == IntPtr.Zero) { torch.CheckForErrors(); } - return new BatchNorm3d(handle, boxedHandle).MoveModule(device, dtype); - } + return new BatchNorm3d(num_features, eps, momentum, affine, track_running_stats, device, dtype); } } } diff --git a/src/TorchSharp/NN/Normalization/Functional.cs b/src/TorchSharp/NN/Normalization/Functional.cs index 76ed72adc..f9627b315 100644 --- a/src/TorchSharp/NN/Normalization/Functional.cs +++ b/src/TorchSharp/NN/Normalization/Functional.cs @@ -23,9 +23,7 @@ public static Tensor normalize(Tensor input, double p = 2.0, long dim = 1L, doub var res = THSNN_normalize( input.Handle, p, dim, eps); - if (res == IntPtr.Zero) - torch.CheckForErrors(); - return new Tensor(res); + return ReturnCheckForErrors(res); } /// @@ -56,7 +54,6 @@ public static Tensor group_norm(Tensor input, long num_groups, Tensor? weight = bias is not null ? bias.Handle : IntPtr.Zero, eps); return ReturnCheckForErrors(res); - } /// @@ -92,23 +89,10 @@ public static Tensor layer_norm(Tensor input, long[] normalized_shape, Tensor? w eps); } } - return ReturnCheckForErrors(res); } - - /// - /// Applies Local Normalization. - /// - public static Tensor local_response_norm(Tensor input, long size, double alpha = 0.0001, double beta = 0.75, double k = 1.0) - { - return ReturnCheckForErrors(THSNN_local_response_norm(input.Handle, size, alpha, beta, k)); - } - - public static Tensor normalize(Tensor input, float p=2.0f, long dim=1, float eps= 1e-12f, Tensor output = null) - { - return ReturnCheckForErrors(THSNN_normalize(input.Handle, p, dim, eps, out _)); - } } + } } } diff --git a/src/TorchSharp/NN/Normalization/GroupNorm.cs b/src/TorchSharp/NN/Normalization/GroupNorm.cs index 2adc22025..06420d289 100644 --- a/src/TorchSharp/NN/Normalization/GroupNorm.cs +++ b/src/TorchSharp/NN/Normalization/GroupNorm.cs @@ -1,13 +1,15 @@ -// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information. +// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information. using System; -using TorchSharp.Amp; using static TorchSharp.torch; +using static TorchSharp.torch.nn; using static TorchSharp.PInvoke.NativeMethods; #nullable enable namespace TorchSharp { using Modules; + using TorchSharp.Utils; + using F = TorchSharp.torch.nn.functional; namespace Modules { @@ -17,46 +19,94 @@ namespace Modules /// public sealed class GroupNorm : torch.nn.Module { - internal GroupNorm(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) + internal GroupNorm(long num_groups, long num_channels, double eps, bool affine, Device? device, ScalarType? dtype) : base(nameof(GroupNorm)) { + this.eps = eps; + this.affine = affine; + this.num_groups = num_groups; + + if (affine) { + weight = Parameter(torch.empty(num_channels, dtype, device)); + this.bias = Parameter(torch.empty(num_channels, dtype, device)); + } } public override Tensor forward(Tensor tensor) { - if (tensor.Dimensions < 3) throw new ArgumentException($"Invalid number of dimensions for GroupNorm argument: {tensor.Dimensions}"); - - return ReturnCheckForErrorsAutocast(THSNN_GroupNorm_forward(handle.DangerousGetHandle(), tensor.Handle), ScalarType.Float32); - + if (tensor.Dimensions < 3) + throw new ArgumentException($"Invalid number of dimensions for GroupNorm argument: {tensor.Dimensions}"); + return F.group_norm(tensor, num_groups, weight, bias, eps); } - public Parameter? bias + protected override void Dispose(bool disposing) { + _weight?.Dispose(); + _bias?.Dispose(); + base.Dispose(disposing); + } + + public Parameter? bias { get => _bias; - set - { + set { _bias?.Dispose(); _bias = value?.DetachFromDisposeScope() as Parameter; ConditionallyRegisterParameter(nameof(bias), _bias); } } - public Parameter weight - { - get - { - //May have problem with netstandard2.0? - return _weight!; - } - set - { - if (value.Handle != _weight?.Handle) - { + public Parameter weight { + get => _weight!; + set { + if (value.Handle != _weight?.Handle) { _weight?.Dispose(); _weight = (value.DetachFromDisposeScope() as Parameter)!; ConditionallyRegisterParameter(nameof(weight), _weight); } } } + + // Rather than spending cycles discovering what parameters exist, we can just hardcode it. + protected internal override nn.Module _to(Device device, ScalarType dtype, bool non_blocking) + { + if (_weight is not null && ReplaceParameter(dtype, device, _weight, out Parameter? w)) { + weight = w!; + } + if (_bias is not null && ReplaceParameter(dtype, device, _bias, out Parameter? b)) { + bias = b!; + } + return this; + } + + protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex, bool non_blocking) + { + var device = new Device(deviceType, deviceIndex); + if (_weight is not null && ReplaceParameter(_weight.dtype, device, _weight, out Parameter? w)) { + weight = w!; + } + if (_bias is not null && ReplaceParameter(_bias.dtype, device, _bias, out Parameter? b)) { + bias = b!; + } + return this; + } + + protected internal override nn.Module _to(ScalarType dtype, bool non_blocking) + { + if (_weight is not null && ReplaceParameter(dtype, _weight.device, _weight, out Parameter? w)) { + weight = w!; + } + if (_bias is not null && ReplaceParameter(dtype, _bias.device, _bias, out Parameter? b)) { + bias = b!; + } + return this; + } + + [ComponentName(Name = nameof(bias))] + private Parameter? _bias; + [ComponentName(Name = nameof(weight))] + private Parameter? _weight; + public long num_groups { get; set; } + public double eps { get; set; } + public bool affine { get; set; } } } @@ -76,12 +126,6 @@ public static partial class nn /// public static GroupNorm GroupNorm(long num_groups, long num_channels, double eps = 1e-05, bool affine = true, Device? device = null, ScalarType? dtype = null) { - /*unsafe { - var handle = THSNN_GroupNorm_ctor(num_groups, num_channels, eps, affine, out var boxedHandle); - if (handle == IntPtr.Zero) { torch.CheckForErrors(); } - handle= AutocastMode.AutoCast(handle, ScalarType.Float32); - return new GroupNorm(handle, boxedHandle).MoveModule(device, dtype); - }*/ return new GroupNorm(num_groups, num_channels, eps, affine, device, dtype); } } diff --git a/src/TorchSharp/NN/Normalization/InstanceNorm.cs b/src/TorchSharp/NN/Normalization/InstanceNorm.cs index 43ecd9023..cb9dbc175 100644 --- a/src/TorchSharp/NN/Normalization/InstanceNorm.cs +++ b/src/TorchSharp/NN/Normalization/InstanceNorm.cs @@ -16,15 +16,15 @@ namespace Modules { public abstract class InstanceNorm : NormBase { - public InstanceNorm(long num_features, - double eps, - double? momentum, - bool affine, + public InstanceNorm(long num_features, + double eps, + double? momentum, + bool affine, bool track_running_stats, - Device? device, - ScalarType? dtype, - string name) : base(num_features, eps, momentum.HasValue ? momentum : 0.1, affine, track_running_stats, device, dtype, name) - { + Device? device, + ScalarType? dtype, + string name) : base(num_features, eps, momentum.HasValue ? momentum : 0.1, affine, track_running_stats, device, dtype, name) + { } protected abstract long GetNumberOfBatchDimensions(); @@ -42,8 +42,7 @@ public override Tensor forward(Tensor input) if (feature_dim == 0) { using var t0 = input.unsqueeze(0); return ApplyInstanceNorm(t0).squeeze_(0); - } - else { + } else { return ApplyInstanceNorm(input); } } diff --git a/src/TorchSharp/NN/Normalization/InstanceNorm1d.cs b/src/TorchSharp/NN/Normalization/InstanceNorm1d.cs index c18505e9f..6982bf3c9 100644 --- a/src/TorchSharp/NN/Normalization/InstanceNorm1d.cs +++ b/src/TorchSharp/NN/Normalization/InstanceNorm1d.cs @@ -14,80 +14,24 @@ namespace Modules /// /// This class is used to represent a InstanceNorm1D module. /// - public sealed class InstanceNorm1d : torch.nn.Module + public sealed class InstanceNorm1d : InstanceNorm { - internal InstanceNorm1d(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) + internal InstanceNorm1d(long num_features, + double eps, + double momentum, + bool affine, + bool track_running_stats, + Device? device, + ScalarType? dtype) : base(num_features, eps, momentum, affine, track_running_stats, device, dtype, nameof(InstanceNorm1d)) { } - public override Tensor forward(Tensor tensor) - { - if (tensor.Dimensions < 2 || tensor.Dimensions > 3) throw new ArgumentException($"Invalid number of dimensions for InstanceNorm argument: {tensor.Dimensions}"); - return ReturnCheckForErrors(THSNN_InstanceNorm1d_forward(handle.DangerousGetHandle(), tensor.Handle)); - } - - public Parameter? bias { - get { - return ReturnNullParameterCheckForErrors(THSNN_InstanceNorm1d_bias(handle)); - } - set { - // Please ignore, for now, that the litorch call thinks you *can* set it to null. - if (value is null) throw new ArgumentNullException("bias cannot be set to 'null'"); - THSNN_InstanceNorm1d_set_bias(handle, (value is null ? IntPtr.Zero : value.Handle)); - torch.CheckForErrors(); - ConditionallyRegisterParameter("bias", value); - } - } - - public Parameter? weight { - get { - return ReturnNullParameterCheckForErrors(THSNN_InstanceNorm1d_weight(handle)); - } - set { - // Please ignore, for now, that the litorch call thinks you *can* set it to null. - if (value is null) throw new ArgumentNullException("weight cannot be set to 'null'"); - THSNN_InstanceNorm1d_set_weight(handle, value is null ? IntPtr.Zero : value.Handle); - torch.CheckForErrors(); - ConditionallyRegisterParameter("weight", value); - } - } - - public Tensor? running_mean { - get { - return ReturnNullCheckForErrors(THSNN_InstanceNorm1d_get_mean(handle)); - } - set { - // Please ignore, for now, that the litorch call thinks you *can* set it to null. - if (value is null) throw new ArgumentNullException("running_mean cannot be set to 'null'"); - THSNN_InstanceNorm1d_set_mean(handle, (value is null ? IntPtr.Zero : value.Handle)); - torch.CheckForErrors(); - ConditionallyRegisterBuffer("running_mean", value); - } - } - - public Tensor? running_var { - get { - return ReturnNullCheckForErrors(THSNN_InstanceNorm1d_get_var(handle)); - } - set { - // Please ignore, for now, that the litorch call thinks you *can* set it to null. - if (value is null) throw new ArgumentNullException("running_var cannot be set to 'null'"); - THSNN_InstanceNorm1d_set_var(handle, (value is null ? IntPtr.Zero : value.Handle)); - torch.CheckForErrors(); - ConditionallyRegisterBuffer("running_var", value); - } - } - - public Tensor? num_batches_tracked { - get { - return ReturnNullCheckForErrors(THSNN_InstanceNorm1d_get_batches(handle)); - } - } + protected override long GetNumberOfBatchDimensions() => 2; - public void reset_running_stats() + protected override void ValidateInputDimensions(Tensor input) { - THSNN_InstanceNorm1d_reset_stats(handle); - torch.CheckForErrors(); + if (input.ndim != 2 && input.ndim != 3) + throw new ArgumentException($"expected 2D or 3D input, but got {input.ndim}D input."); } } } @@ -99,7 +43,7 @@ public static partial class nn /// /// Applies Instance Normalization over a 3D input (a mini-batch of 1D inputs with optional additional channel dimension) as described in the paper Instance Normalization: The Missing Ingredient for Fast Stylization. /// - /// C from an expected input of size (N,C,L) or LL from input of size (N, L) + /// C from an expected input of size (N,C,L) or LL from input of size (N, L) /// A value added to the denominator for numerical stability. Default: 1e-5 /// The value used for the running_mean and running_var computation. Can be set to None for cumulative moving average (i.e. simple average). Default: 0.1 /// A boolean value that when set to True, this module has learnable affine parameters. Default: true @@ -109,13 +53,9 @@ public static partial class nn /// The desired device of the parameters and buffers in this module /// The desired floating point or complex dtype of the parameters and buffers in this module /// - public static InstanceNorm1d InstanceNorm1d(long features, double eps = 1e-05, double momentum = 0.1, bool affine = false, bool track_running_stats = false, Device? device = null, ScalarType? dtype = null) + public static InstanceNorm1d InstanceNorm1d(long num_features, double eps = 1e-05, double momentum = 0.1, bool affine = false, bool track_running_stats = false, Device? device = null, ScalarType? dtype = null) { - unsafe { - var handle = THSNN_InstanceNorm1d_ctor(features, eps, momentum, affine, track_running_stats, out var boxedHandle); - if (handle == IntPtr.Zero) { torch.CheckForErrors(); } - return new InstanceNorm1d(handle, boxedHandle).MoveModule(device, dtype); - } + return new InstanceNorm1d(num_features, eps, momentum, affine, track_running_stats, device, dtype); } } } diff --git a/src/TorchSharp/NN/Normalization/InstanceNorm2d.cs b/src/TorchSharp/NN/Normalization/InstanceNorm2d.cs index 6f24fc24d..31b2d7a02 100644 --- a/src/TorchSharp/NN/Normalization/InstanceNorm2d.cs +++ b/src/TorchSharp/NN/Normalization/InstanceNorm2d.cs @@ -14,80 +14,24 @@ namespace Modules /// /// This class is used to represent a InstanceNorm2D module. /// - public sealed class InstanceNorm2d : torch.nn.Module + public sealed class InstanceNorm2d : InstanceNorm { - internal InstanceNorm2d(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) + internal InstanceNorm2d(long num_features, + double eps, + double momentum, + bool affine, + bool track_running_stats, + Device? device, + ScalarType? dtype) : base(num_features, eps, momentum, affine, track_running_stats, device, dtype, nameof(InstanceNorm1d)) { } - public override Tensor forward(Tensor tensor) - { - if (tensor.Dimensions != 4) throw new ArgumentException($"Invalid number of dimensions for InstanceNorm argument: {tensor.Dimensions}"); - return ReturnCheckForErrors(THSNN_InstanceNorm2d_forward(handle.DangerousGetHandle(), tensor.Handle)); - } - - public Parameter? bias { - get { - return ReturnNullParameterCheckForErrors(THSNN_InstanceNorm2d_bias(handle)); - } - set { - // Please ignore, for now, that the litorch call thinks you *can* set it to null. - if (value is null) throw new ArgumentNullException("bias cannot be set to 'null'"); - THSNN_InstanceNorm2d_set_bias(handle, (value is null ? IntPtr.Zero : value.Handle)); - torch.CheckForErrors(); - ConditionallyRegisterParameter("bias", value); - } - } - - public Parameter? weight { - get { - return ReturnNullParameterCheckForErrors(THSNN_InstanceNorm2d_weight(handle)); - } - set { - // Please ignore, for now, that the litorch call thinks you *can* set it to null. - if (value is null) throw new ArgumentNullException("weight cannot be set to 'null'"); - THSNN_InstanceNorm2d_set_weight(handle, value is null ? IntPtr.Zero : value.Handle); - torch.CheckForErrors(); - ConditionallyRegisterParameter("weight", value); - } - } - - public Tensor? running_mean { - get { - return ReturnNullCheckForErrors(THSNN_InstanceNorm2d_get_mean(handle)); - } - set { - // Please ignore, for now, that the litorch call thinks you *can* set it to null. - if (value is null) throw new ArgumentNullException("running_mean cannot be set to 'null'"); - THSNN_InstanceNorm2d_set_mean(handle, (value is null ? IntPtr.Zero : value.Handle)); - torch.CheckForErrors(); - ConditionallyRegisterBuffer("running_mean", value); - } - } - - public Tensor? running_var { - get { - return ReturnNullCheckForErrors(THSNN_InstanceNorm2d_get_var(handle)); - } - set { - // Please ignore, for now, that the litorch call thinks you *can* set it to null. - if (value is null) throw new ArgumentNullException("running_var cannot be set to 'null'"); - THSNN_InstanceNorm2d_set_var(handle, (value is null ? IntPtr.Zero : value.Handle)); - torch.CheckForErrors(); - ConditionallyRegisterBuffer("running_var", value); - } - } - - public Tensor? num_batches_tracked { - get { - return ReturnNullCheckForErrors(THSNN_InstanceNorm2d_get_batches(handle)); - } - } + protected override long GetNumberOfBatchDimensions() => 3; - public void reset_running_stats() + protected override void ValidateInputDimensions(Tensor input) { - THSNN_InstanceNorm2d_reset_stats(handle); - torch.CheckForErrors(); + if (input.ndim != 3 && input.ndim != 4) + throw new ArgumentException($"expected 3D or 4D input, but got {input.ndim}D input."); } } } @@ -99,7 +43,7 @@ public static partial class nn /// /// Applies Instance Normalization over a 4D input (a mini-batch of 2D inputs with additional channel dimension) as described in the paper Instance Normalization: The Missing Ingredient for Fast Stylization. /// - /// C from an expected input of size (N,C,H,W) + /// C from an expected input of size (N,C,H,W) /// A value added to the denominator for numerical stability. Default: 1e-5 /// The value used for the running_mean and running_var computation. Can be set to None for cumulative moving average (i.e. simple average). Default: 0.1 /// A boolean value that when set to True, this module has learnable affine parameters. Default: true @@ -109,13 +53,9 @@ public static partial class nn /// The desired device of the parameters and buffers in this module /// The desired floating point or complex dtype of the parameters and buffers in this module /// - public static InstanceNorm2d InstanceNorm2d(long features, double eps = 1e-05, double momentum = 0.1, bool affine = false, bool track_running_stats = false, Device? device = null, ScalarType? dtype = null) + public static InstanceNorm2d InstanceNorm2d(long num_features, double eps = 1e-05, double momentum = 0.1, bool affine = false, bool track_running_stats = false, Device? device = null, ScalarType? dtype = null) { - unsafe { - var handle = THSNN_InstanceNorm2d_ctor(features, eps, momentum, affine, track_running_stats, out var boxedHandle); - if (handle == IntPtr.Zero) { torch.CheckForErrors(); } - return new InstanceNorm2d(handle, boxedHandle).MoveModule(device, dtype); - } + return new InstanceNorm2d(num_features, eps, momentum, affine, track_running_stats, device, dtype); } } } diff --git a/src/TorchSharp/NN/Normalization/InstanceNorm3d.cs b/src/TorchSharp/NN/Normalization/InstanceNorm3d.cs index 3f94c40a9..1b39c21f2 100644 --- a/src/TorchSharp/NN/Normalization/InstanceNorm3d.cs +++ b/src/TorchSharp/NN/Normalization/InstanceNorm3d.cs @@ -14,82 +14,24 @@ namespace Modules /// /// This class is used to represent a InstanceNorm3D module. /// - public sealed class InstanceNorm3d : torch.nn.Module + public sealed class InstanceNorm3d : InstanceNorm { - internal InstanceNorm3d(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) + internal InstanceNorm3d(long num_features, + double eps, + double momentum, + bool affine, + bool track_running_stats, + Device? device, + ScalarType? dtype) : base(num_features, eps, momentum, affine, track_running_stats, device, dtype, nameof(InstanceNorm3d)) { } - public override Tensor forward(Tensor tensor) - { - if (tensor.Dimensions != 5) throw new ArgumentException($"Invalid number of dimensions for InstanceNorm argument: {tensor.Dimensions}"); - return ReturnCheckForErrors(THSNN_InstanceNorm3d_forward(handle.DangerousGetHandle(), tensor.Handle)); - } - - public Parameter? bias { - get { - var res = THSNN_InstanceNorm3d_bias(handle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return (res == IntPtr.Zero) ? null : new Parameter(res); - } - set { - // Please ignore, for now, that the litorch call thinks you *can* set it to null. - if (value is null) throw new ArgumentNullException("bias cannot be set to 'null'"); - THSNN_InstanceNorm3d_set_bias(handle, (value is null ? IntPtr.Zero : value.Handle)); - torch.CheckForErrors(); - ConditionallyRegisterParameter("bias", value); - } - } - - public Parameter? weight { - get { - return ReturnNullParameterCheckForErrors(THSNN_InstanceNorm3d_weight(handle)); - } - set { - // Please ignore, for now, that the litorch call thinks you *can* set it to null. - if (value is null) throw new ArgumentNullException("weight cannot be set to 'null'"); - THSNN_InstanceNorm3d_set_weight(handle, value is null ? IntPtr.Zero : value.Handle); - torch.CheckForErrors(); - ConditionallyRegisterParameter("weight", value); - } - } - - public Tensor? running_mean { - get { - return ReturnNullCheckForErrors(THSNN_InstanceNorm3d_get_mean(handle)); - } - set { - // Please ignore, for now, that the litorch call thinks you *can* set it to null. - if (value is null) throw new ArgumentNullException("running_mean cannot be set to 'null'"); - THSNN_InstanceNorm3d_set_mean(handle, (value is null ? IntPtr.Zero : value.Handle)); - torch.CheckForErrors(); - ConditionallyRegisterBuffer("running_mean", value); - } - } - - public Tensor? running_var { - get { - return ReturnNullCheckForErrors(THSNN_InstanceNorm3d_get_var(handle)); - } - set { - // Please ignore, for now, that the litorch call thinks you *can* set it to null. - if (value is null) throw new ArgumentNullException("running_var cannot be set to 'null'"); - THSNN_InstanceNorm3d_set_var(handle, (value is null ? IntPtr.Zero : value.Handle)); - torch.CheckForErrors(); - ConditionallyRegisterBuffer("running_var", value); - } - } - - public Tensor? num_batches_tracked { - get { - return ReturnNullCheckForErrors(THSNN_InstanceNorm3d_get_batches(handle)); - } - } + protected override long GetNumberOfBatchDimensions() => 4; - public void reset_running_stats() + protected override void ValidateInputDimensions(Tensor input) { - THSNN_InstanceNorm3d_reset_stats(handle); - torch.CheckForErrors(); + if (input.ndim != 4 && input.ndim != 5) + throw new ArgumentException($"expected 4D or 4D input, but got {input.ndim}D input."); } } } @@ -101,7 +43,7 @@ public static partial class nn /// /// Applies Instance Normalization over a 5D input (a mini-batch of 3D inputs with additional channel dimension) as described in the paper Instance Normalization: The Missing Ingredient for Fast Stylization. /// - /// C from an expected input of size (N,C,D,H,W) + /// C from an expected input of size (N,C,D,H,W) /// A value added to the denominator for numerical stability. Default: 1e-5 /// The value used for the running_mean and running_var computation. Can be set to None for cumulative moving average (i.e. simple average). Default: 0.1 /// A boolean value that when set to True, this module has learnable affine parameters. Default: true @@ -111,13 +53,9 @@ public static partial class nn /// The desired device of the parameters and buffers in this module /// The desired floating point or complex dtype of the parameters and buffers in this module /// - public static InstanceNorm3d InstanceNorm3d(long features, double eps = 1e-05, double momentum = 0.1, bool affine = false, bool track_running_stats = false, Device? device = null, ScalarType? dtype = null) + public static InstanceNorm3d InstanceNorm3d(long num_features, double eps = 1e-05, double momentum = 0.1, bool affine = false, bool track_running_stats = false, Device? device = null, ScalarType? dtype = null) { - unsafe { - var handle = THSNN_InstanceNorm3d_ctor(features, eps, momentum, affine, track_running_stats, out var boxedHandle); - if (handle == IntPtr.Zero) { torch.CheckForErrors(); } - return new InstanceNorm3d(handle, boxedHandle).MoveModule(device, dtype); - } + return new InstanceNorm3d(num_features, eps, momentum, affine, track_running_stats, device, dtype); } } } diff --git a/src/TorchSharp/NN/Normalization/LayerNorm.cs b/src/TorchSharp/NN/Normalization/LayerNorm.cs index ead512fc9..b4f231d9c 100644 --- a/src/TorchSharp/NN/Normalization/LayerNorm.cs +++ b/src/TorchSharp/NN/Normalization/LayerNorm.cs @@ -1,6 +1,5 @@ // Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information. using System; -using TorchSharp.Amp; using static TorchSharp.torch; using static TorchSharp.torch.nn; using static TorchSharp.PInvoke.NativeMethods; @@ -19,8 +18,8 @@ namespace Modules /// public sealed class LayerNorm : torch.nn.Module { - internal long[] _normalized_shape; - internal double _eps; + const string WeightComponentName = nameof(weight); + const string BiasComponentName = nameof(bias); internal LayerNorm(long[] normalized_shape, double eps, bool elementwise_affine, bool bias, Device? device, ScalarType? dtype) : base(nameof(LayerNorm)) { @@ -28,14 +27,10 @@ internal LayerNorm(long[] normalized_shape, double eps, bool elementwise_affine, this.eps = eps; this.elementwise_affine = elementwise_affine; - if (elementwise_affine) - { + if (elementwise_affine) { weight = Parameter(torch.empty(normalized_shape, dtype, device)); - //weight.handle = AutocastMode.AutoCast(weight.handle, ScalarType.Float32); //This is correct??? - if (bias) - { + if (bias) { this.bias = Parameter(torch.empty(normalized_shape, dtype, device)); - //bias.handle = AutocastMode.AutoCast(bias.handle, ScalarType.Float32); //This is correct??? } } @@ -44,12 +39,10 @@ internal LayerNorm(long[] normalized_shape, double eps, bool elementwise_affine, public void reset_parameters() { - if (elementwise_affine) - { + if (elementwise_affine) { init.ones_(weight); } - if (bias is not null) - { + if (bias is not null) { init.zeros_(bias); } } @@ -87,7 +80,8 @@ public Parameter weight { } // Rather than spending cycles discovering what parameters exist, we can just hardcode it. - protected internal override nn.Module _to(Device device, ScalarType dtype, bool non_blocking) { + protected internal override nn.Module _to(Device device, ScalarType dtype, bool non_blocking) + { if (_weight is not null && ReplaceParameter(dtype, device, _weight, out Parameter? w)) { weight = w!; } @@ -109,7 +103,8 @@ protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex return this; } - protected internal override nn.Module _to(ScalarType dtype, bool non_blocking) { + protected internal override nn.Module _to(ScalarType dtype, bool non_blocking) + { if (_weight is not null && ReplaceParameter(dtype, _weight.device, _weight, out Parameter? w)) { weight = w!; } diff --git a/src/TorchSharp/NN/Normalization/LocalResponseNorm.cs b/src/TorchSharp/NN/Normalization/LocalResponseNorm.cs index e77e9b9a2..8525ec125 100644 --- a/src/TorchSharp/NN/Normalization/LocalResponseNorm.cs +++ b/src/TorchSharp/NN/Normalization/LocalResponseNorm.cs @@ -12,17 +12,25 @@ namespace Modules /// /// This class is used to represent a LocalResponseNorm module. /// - public sealed class LocalResponseNorm : torch.nn.Module + public sealed class LocalResponseNorm : ParameterLessModule { - internal LocalResponseNorm(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) + internal LocalResponseNorm(long size, double alpha = 0.0001, double beta = 0.75, double k = 1.0) : base(nameof(LocalResponseNorm)) { + this.size = size; + this.alpha = alpha; + this.beta = beta; + this.k = k; } - public override Tensor forward(Tensor tensor) + public override Tensor forward(Tensor input) { - if (tensor.Dimensions < 3) throw new ArgumentException($"Invalid number of dimensions for LocalResponseNorm argument: {tensor.Dimensions}"); - return ReturnCheckForErrors(THSNN_LocalResponseNorm_forward(handle.DangerousGetHandle(), tensor.Handle)); + return torch.nn.functional.local_response_norm(input, this.size, this.alpha, this.beta, this.k); } + + public long size { get; set; } + public double alpha { get; set; } + public double beta { get; set; } + public double k { get; set; } } } @@ -35,10 +43,22 @@ public static partial class nn /// public static LocalResponseNorm LocalResponseNorm(long size, double alpha = 0.0001, double beta = 0.75, double k = 1.0) { - unsafe { - var handle = THSNN_LocalResponseNorm_ctor(size, alpha, beta, k, out var boxedHandle); - if (handle == IntPtr.Zero) { torch.CheckForErrors(); } - return new LocalResponseNorm(handle, boxedHandle); + return new LocalResponseNorm(size, alpha, beta, k); + } + + public static partial class functional + { + + /// + /// Applies local response normalization over an input signal. + /// The input signal is composed of several input planes, where channels occupy the second dimension. + /// Applies normalization across channels. + /// + public static Tensor local_response_norm(Tensor input, long size, double alpha = 0.0001, double beta = 0.75, double k = 1.0) + { + if (input.Dimensions < 3) throw new ArgumentException($"Invalid number of dimensions for LocalResponseNorm argument: {input.Dimensions}"); + var res = THSNN_local_response_norm(input.Handle, size, alpha, beta, k); + return ReturnCheckForErrors(res); } } } diff --git a/src/TorchSharp/NN/Normalization/NormBase.cs b/src/TorchSharp/NN/Normalization/NormBase.cs index 3c14ac501..eefaa944b 100644 --- a/src/TorchSharp/NN/Normalization/NormBase.cs +++ b/src/TorchSharp/NN/Normalization/NormBase.cs @@ -45,7 +45,7 @@ public NormBase(long num_features, private void ResetRunningStats() { - if (track_running_stats){ + if (track_running_stats) { init.zeros_(this._running_mean); init.ones_(this._running_var); init.zeros_(this._num_batches_tracked); @@ -55,7 +55,8 @@ private void ResetRunningStats() // For backward compat. public void reset_running_stats() => ResetRunningStats(); - public void reset_parameters() { + public void reset_parameters() + { ResetRunningStats(); if (affine) { init.ones_(this._weight); @@ -123,7 +124,8 @@ public Tensor? num_batches_tracked { } // Rather than spending cycles discovering what parameters exist, we can just hardcode it. - protected internal override nn.Module _to(Device device, ScalarType dtype, bool non_blocking) { + protected internal override nn.Module _to(Device device, ScalarType dtype, bool non_blocking) + { if (_weight is not null && ReplaceParameter(dtype, device, _weight, out var w)) { weight = w!; } @@ -132,13 +134,16 @@ protected internal override nn.Module _to(Device device, ScalarType dtype, bool } if (_running_mean is not null && ReplaceBuffer(dtype, device, _running_mean, out Tensor? rm)) { running_mean = rm!; -; } + ; + } if (_running_var is not null && ReplaceBuffer(dtype, device, _running_var, out Tensor? rv)) { running_var = rv!; -; } + ; + } if (_num_batches_tracked is not null && ReplaceBuffer(dtype, device, _num_batches_tracked, out Tensor? nbt)) { num_batches_tracked = nbt!; -; } + ; + } return this; } @@ -153,17 +158,21 @@ protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex } if (_running_mean is not null && ReplaceBuffer(_running_mean.dtype, device, _running_mean, out Tensor? rm)) { running_mean = rm!; -; } + ; + } if (_running_var is not null && ReplaceBuffer(_running_var.dtype, device, _running_var, out Tensor? rv)) { running_var = rv!; -; } + ; + } if (_num_batches_tracked is not null && ReplaceBuffer(_num_batches_tracked.dtype, device, _num_batches_tracked, out Tensor? nbt)) { num_batches_tracked = nbt!; -; } + ; + } return this; } - protected internal override nn.Module _to(ScalarType dtype, bool non_blocking) { + protected internal override nn.Module _to(ScalarType dtype, bool non_blocking) + { if (_weight is not null && ReplaceParameter(dtype, _weight.device, _weight, out var w)) { weight = w!; } @@ -172,13 +181,16 @@ protected internal override nn.Module _to(ScalarType dtype, bool non_blocking) { } if (_running_mean is not null && ReplaceBuffer(dtype, _running_mean.device, _running_mean, out Tensor? rm)) { running_mean = rm!; -; } + ; + } if (_running_var is not null && ReplaceBuffer(dtype, _running_var.device, _running_var, out Tensor? rv)) { running_var = rv!; -; } + ; + } if (_num_batches_tracked is not null && ReplaceBuffer(dtype, _num_batches_tracked.device, _num_batches_tracked, out Tensor? nbt)) { num_batches_tracked = nbt!; -; } + ; + } return this; } diff --git a/src/TorchSharp/NN/Padding/ConstantPad1d.cs b/src/TorchSharp/NN/Padding/ConstantPad1d.cs index 16419c9e8..d67a0883d 100644 --- a/src/TorchSharp/NN/Padding/ConstantPad1d.cs +++ b/src/TorchSharp/NN/Padding/ConstantPad1d.cs @@ -12,25 +12,9 @@ namespace Modules /// /// This class is used to represent a ConstantPad1d module. /// - public sealed class ConstantPad1d : torch.nn.Module + public sealed class ConstantPad1d : PadBase { - internal ConstantPad1d(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { } - - /// - /// Forward pass. - /// - /// Input tensor - /// - public override Tensor forward(Tensor tensor) - { - return ReturnCheckForErrors(THSNN_ConstantPad1d_forward(handle, tensor.Handle)); - } - - // Rather than spending cycles only to discover that this module has neither - // parameters nor buffers, just shortcut the move completely. - protected internal override nn.Module _to(Device device, ScalarType dtype, bool non_blocking) => this; - protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex, bool non_blocking) => this; - protected internal override nn.Module _to(ScalarType dtype, bool non_blocking) => this; + internal ConstantPad1d(double value, params long[] padding) : base(nameof(ConstantPad1d), PaddingModes.Constant, value, padding) { } } } @@ -46,9 +30,7 @@ public static partial class nn /// public static ConstantPad1d ConstantPad1d(long padding, double value) { - var handle = THSNN_ConstantPad1d_ctor(value, padding, out var boxedHandle); - if (handle == IntPtr.Zero) { torch.CheckForErrors(); } - return new ConstantPad1d(handle, boxedHandle); + return new ConstantPad1d(value, padding, padding); } /// @@ -59,10 +41,8 @@ public static ConstantPad1d ConstantPad1d(long padding, double value) /// public static ConstantPad1d ConstantPad1d((long, long) padding, double value) { - var handle = THSNN_ConstantPad1d_ctor_tuple(value, padding.Item1, padding.Item2, out var boxedHandle); - if (handle == IntPtr.Zero) { torch.CheckForErrors(); } - return new ConstantPad1d(handle, boxedHandle); + return new ConstantPad1d(value, padding.Item1, padding.Item2); } } } -} +} \ No newline at end of file diff --git a/src/TorchSharp/NN/Padding/ConstantPad2d.cs b/src/TorchSharp/NN/Padding/ConstantPad2d.cs index 4c1c1539e..78f309bc7 100644 --- a/src/TorchSharp/NN/Padding/ConstantPad2d.cs +++ b/src/TorchSharp/NN/Padding/ConstantPad2d.cs @@ -12,25 +12,9 @@ namespace Modules /// /// This class is used to represent a ConstantPad2d module. /// - public sealed class ConstantPad2d : torch.nn.Module + public sealed class ConstantPad2d : PadBase { - internal ConstantPad2d(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { } - - /// - /// Forward pass. - /// - /// Input tensor - /// - public override Tensor forward(Tensor tensor) - { - return ReturnCheckForErrors(THSNN_ConstantPad2d_forward(handle, tensor.Handle)); - } - - // Rather than spending cycles only to discover that this module has neither - // parameters nor buffers, just shortcut the move completely. - protected internal override nn.Module _to(Device device, ScalarType dtype, bool non_blocking) => this; - protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex, bool non_blocking) => this; - protected internal override nn.Module _to(ScalarType dtype, bool non_blocking) => this; + internal ConstantPad2d(double value, params long[] padding) : base(nameof(ConstantPad2d), PaddingModes.Constant, value, padding) { } } } @@ -46,9 +30,7 @@ public static partial class nn /// public static ConstantPad2d ConstantPad2d(long padding, double value) { - var handle = THSNN_ConstantPad2d_ctor(value, padding, out var boxedHandle); - if (handle == IntPtr.Zero) { torch.CheckForErrors(); } - return new ConstantPad2d(handle, boxedHandle); + return new ConstantPad2d(value, padding, padding, padding, padding); } /// @@ -59,10 +41,8 @@ public static ConstantPad2d ConstantPad2d(long padding, double value) /// public static ConstantPad2d ConstantPad2d((long, long, long, long) padding, double value) { - var handle = THSNN_ConstantPad2d_ctor_tuple(value, padding.Item1, padding.Item2, padding.Item3, padding.Item4, out var boxedHandle); - if (handle == IntPtr.Zero) { torch.CheckForErrors(); } - return new ConstantPad2d(handle, boxedHandle); + return new ConstantPad2d(value, padding.Item1, padding.Item2, padding.Item3, padding.Item4); } } } -} +} \ No newline at end of file diff --git a/src/TorchSharp/NN/Padding/ConstantPad3d.cs b/src/TorchSharp/NN/Padding/ConstantPad3d.cs index 2552e67f5..4d2d4514c 100644 --- a/src/TorchSharp/NN/Padding/ConstantPad3d.cs +++ b/src/TorchSharp/NN/Padding/ConstantPad3d.cs @@ -12,25 +12,9 @@ namespace Modules /// /// This class is used to represent a ConstantPad3d module. /// - public sealed class ConstantPad3d : torch.nn.Module + public sealed class ConstantPad3d : PadBase { - internal ConstantPad3d(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { } - - /// - /// Forward pass. - /// - /// Input tensor - /// - public override Tensor forward(Tensor tensor) - { - return ReturnCheckForErrors(THSNN_ConstantPad3d_forward(handle, tensor.Handle)); - } - - // Rather than spending cycles only to discover that this module has neither - // parameters nor buffers, just shortcut the move completely. - protected internal override nn.Module _to(Device device, ScalarType dtype, bool non_blocking) => this; - protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex, bool non_blocking) => this; - protected internal override nn.Module _to(ScalarType dtype, bool non_blocking) => this; + internal ConstantPad3d(double value, params long[] padding) : base(nameof(ConstantPad3d), PaddingModes.Constant, value, padding) { } } } @@ -46,9 +30,7 @@ public static partial class nn /// public static ConstantPad3d ConstantPad3d(long padding, double value) { - var handle = THSNN_ConstantPad3d_ctor(value, padding, out var boxedHandle); - if (handle == IntPtr.Zero) { torch.CheckForErrors(); } - return new ConstantPad3d(handle, boxedHandle); + return new ConstantPad3d(value, padding, padding, padding, padding, padding, padding); } /// @@ -59,10 +41,8 @@ public static ConstantPad3d ConstantPad3d(long padding, double value) /// public static ConstantPad3d ConstantPad3d((long, long, long, long, long, long) padding, double value) { - var handle = THSNN_ConstantPad3d_ctor_tuple(value, padding.Item1, padding.Item2, padding.Item3, padding.Item4, padding.Item5, padding.Item6, out var boxedHandle); - if (handle == IntPtr.Zero) { torch.CheckForErrors(); } - return new ConstantPad3d(handle, boxedHandle); + return new ConstantPad3d(value, padding.Item1, padding.Item2, padding.Item3, padding.Item4, padding.Item5, padding.Item6); } } } -} +} \ No newline at end of file diff --git a/src/TorchSharp/NN/Padding/PadBase.cs b/src/TorchSharp/NN/Padding/PadBase.cs index 08614ad88..a438bf3bf 100644 --- a/src/TorchSharp/NN/Padding/PadBase.cs +++ b/src/TorchSharp/NN/Padding/PadBase.cs @@ -36,4 +36,4 @@ public override Tensor forward(Tensor input) public double value { get; set; } } } -} +} \ No newline at end of file diff --git a/src/TorchSharp/NN/Padding/ReflectionPad1d.cs b/src/TorchSharp/NN/Padding/ReflectionPad1d.cs index 35fb6483b..ddcd7007b 100644 --- a/src/TorchSharp/NN/Padding/ReflectionPad1d.cs +++ b/src/TorchSharp/NN/Padding/ReflectionPad1d.cs @@ -12,25 +12,9 @@ namespace Modules /// /// This class is used to represent a ReflectionPad1d module. /// - public sealed class ReflectionPad1d : torch.nn.Module + public sealed class ReflectionPad1d : PadBase { - internal ReflectionPad1d(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { } - - /// - /// Forward pass. - /// - /// Input tensor - /// - public override Tensor forward(Tensor tensor) - { - return ReturnCheckForErrors(THSNN_ReflectionPad1d_forward(handle, tensor.Handle)); - } - - // Rather than spending cycles only to discover that this module has neither - // parameters nor buffers, just shortcut the move completely. - protected internal override nn.Module _to(Device device, ScalarType dtype, bool non_blocking) => this; - protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex, bool non_blocking) => this; - protected internal override nn.Module _to(ScalarType dtype, bool non_blocking) => this; + internal ReflectionPad1d(params long[] padding) : base(nameof(ReflectionPad1d), PaddingModes.Reflect, 0, padding) { } } } @@ -45,9 +29,7 @@ public static partial class nn /// public static ReflectionPad1d ReflectionPad1d(long padding) { - var handle = THSNN_ReflectionPad1d_ctor(padding, out var boxedHandle); - if (handle == IntPtr.Zero) { torch.CheckForErrors(); } - return new ReflectionPad1d(handle, boxedHandle); + return new ReflectionPad1d(padding, padding); } /// @@ -57,10 +39,8 @@ public static ReflectionPad1d ReflectionPad1d(long padding) /// public static ReflectionPad1d ReflectionPad1d((long, long) padding) { - var handle = THSNN_ReflectionPad1d_ctor_tuple(padding.Item1, padding.Item2, out var boxedHandle); - if (handle == IntPtr.Zero) { torch.CheckForErrors(); } - return new ReflectionPad1d(handle, boxedHandle); + return new ReflectionPad1d(padding.Item1, padding.Item2); } } } -} +} \ No newline at end of file diff --git a/src/TorchSharp/NN/Padding/ReflectionPad2d.cs b/src/TorchSharp/NN/Padding/ReflectionPad2d.cs index 1ec58c84d..bc8de30ec 100644 --- a/src/TorchSharp/NN/Padding/ReflectionPad2d.cs +++ b/src/TorchSharp/NN/Padding/ReflectionPad2d.cs @@ -12,25 +12,9 @@ namespace Modules /// /// This class is used to represent a ReflectionPad2d module. /// - public sealed class ReflectionPad2d : torch.nn.Module + public sealed class ReflectionPad2d : PadBase { - internal ReflectionPad2d(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { } - - /// - /// Forward pass. - /// - /// Input tensor - /// - public override Tensor forward(Tensor tensor) - { - return ReturnCheckForErrors(THSNN_ReflectionPad2d_forward(handle, tensor.Handle)); - } - - // Rather than spending cycles only to discover that this module has neither - // parameters nor buffers, just shortcut the move completely. - protected internal override nn.Module _to(Device device, ScalarType dtype, bool non_blocking) => this; - protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex, bool non_blocking) => this; - protected internal override nn.Module _to(ScalarType dtype, bool non_blocking) => this; + internal ReflectionPad2d(params long[] padding) : base(nameof(ReflectionPad2d), PaddingModes.Reflect, 0, padding) { } } } @@ -45,9 +29,7 @@ public static partial class nn /// public static ReflectionPad2d ReflectionPad2d(long padding) { - var handle = THSNN_ReflectionPad2d_ctor(padding, out var boxedHandle); - if (handle == IntPtr.Zero) { torch.CheckForErrors(); } - return new ReflectionPad2d(handle, boxedHandle); + return new ReflectionPad2d(padding, padding, padding, padding); } /// @@ -57,10 +39,8 @@ public static ReflectionPad2d ReflectionPad2d(long padding) /// public static ReflectionPad2d ReflectionPad2d((long, long, long, long) padding) { - var handle = THSNN_ReflectionPad2d_ctor_tuple(padding.Item1, padding.Item2, padding.Item3, padding.Item4, out var boxedHandle); - if (handle == IntPtr.Zero) { torch.CheckForErrors(); } - return new ReflectionPad2d(handle, boxedHandle); + return new ReflectionPad2d(padding.Item1, padding.Item2, padding.Item3, padding.Item4); } } } -} +} \ No newline at end of file diff --git a/src/TorchSharp/NN/Padding/ReflectionPad3d.cs b/src/TorchSharp/NN/Padding/ReflectionPad3d.cs index b2712bb5a..7d57f1b88 100644 --- a/src/TorchSharp/NN/Padding/ReflectionPad3d.cs +++ b/src/TorchSharp/NN/Padding/ReflectionPad3d.cs @@ -12,25 +12,9 @@ namespace Modules /// /// This class is used to represent a ReflectionPad3d module. /// - public sealed class ReflectionPad3d : torch.nn.Module + public sealed class ReflectionPad3d : PadBase { - internal ReflectionPad3d(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { } - - /// - /// Forward pass. - /// - /// Input tensor - /// - public override Tensor forward(Tensor tensor) - { - return ReturnCheckForErrors(THSNN_ReflectionPad3d_forward(handle, tensor.Handle)); - } - - // Rather than spending cycles only to discover that this module has neither - // parameters nor buffers, just shortcut the move completely. - protected internal override nn.Module _to(Device device, ScalarType dtype, bool non_blocking) => this; - protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex, bool non_blocking) => this; - protected internal override nn.Module _to(ScalarType dtype, bool non_blocking) => this; + internal ReflectionPad3d(params long[] padding) : base(nameof(ReflectionPad3d), PaddingModes.Reflect, 0, padding) { } } } @@ -45,9 +29,7 @@ public static partial class nn /// public static ReflectionPad3d ReflectionPad3d(long padding) { - var handle = THSNN_ReflectionPad3d_ctor(padding, out var boxedHandle); - if (handle == IntPtr.Zero) { torch.CheckForErrors(); } - return new ReflectionPad3d(handle, boxedHandle); + return new ReflectionPad3d(padding, padding, padding, padding, padding, padding); } /// @@ -57,10 +39,8 @@ public static ReflectionPad3d ReflectionPad3d(long padding) /// public static ReflectionPad3d ReflectionPad3d((long, long, long, long, long, long) padding) { - var handle = THSNN_ReflectionPad3d_ctor_tuple(padding.Item1, padding.Item2, padding.Item3, padding.Item4, padding.Item5, padding.Item6, out var boxedHandle); - if (handle == IntPtr.Zero) { torch.CheckForErrors(); } - return new ReflectionPad3d(handle, boxedHandle); + return new ReflectionPad3d(padding.Item1, padding.Item2, padding.Item3, padding.Item4, padding.Item5, padding.Item6); } } } -} +} \ No newline at end of file diff --git a/src/TorchSharp/NN/Padding/ReplicationPad1d.cs b/src/TorchSharp/NN/Padding/ReplicationPad1d.cs index d2fc8e44e..453fa3fb8 100644 --- a/src/TorchSharp/NN/Padding/ReplicationPad1d.cs +++ b/src/TorchSharp/NN/Padding/ReplicationPad1d.cs @@ -12,25 +12,9 @@ namespace Modules /// /// This class is used to represent a ReplicationPad1d module. /// - public sealed class ReplicationPad1d : torch.nn.Module + public sealed class ReplicationPad1d : PadBase { - internal ReplicationPad1d(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { } - - /// - /// Forward pass. - /// - /// Input tensor - /// - public override Tensor forward(Tensor tensor) - { - return ReturnCheckForErrors(THSNN_ReplicationPad1d_forward(handle, tensor.Handle)); - } - - // Rather than spending cycles only to discover that this module has neither - // parameters nor buffers, just shortcut the move completely. - protected internal override nn.Module _to(Device device, ScalarType dtype, bool non_blocking) => this; - protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex, bool non_blocking) => this; - protected internal override nn.Module _to(ScalarType dtype, bool non_blocking) => this; + internal ReplicationPad1d(params long[] padding) : base(nameof(ReplicationPad1d), PaddingModes.Replicate, 0, padding) { } } } @@ -45,9 +29,7 @@ public static partial class nn /// public static ReplicationPad1d ReplicationPad1d(long padding) { - var handle = THSNN_ReplicationPad1d_ctor(padding, out var boxedHandle); - if (handle == IntPtr.Zero) { torch.CheckForErrors(); } - return new ReplicationPad1d(handle, boxedHandle); + return new ReplicationPad1d(padding, padding); } /// @@ -57,10 +39,8 @@ public static ReplicationPad1d ReplicationPad1d(long padding) /// public static ReplicationPad1d ReplicationPad1d((long, long) padding) { - var handle = THSNN_ReplicationPad1d_ctor_tuple(padding.Item1, padding.Item2, out var boxedHandle); - if (handle == IntPtr.Zero) { torch.CheckForErrors(); } - return new ReplicationPad1d(handle, boxedHandle); + return new ReplicationPad1d(padding.Item1, padding.Item2); } } } -} +} \ No newline at end of file diff --git a/src/TorchSharp/NN/Padding/ReplicationPad2d.cs b/src/TorchSharp/NN/Padding/ReplicationPad2d.cs index c2a2103a7..6d16bb10c 100644 --- a/src/TorchSharp/NN/Padding/ReplicationPad2d.cs +++ b/src/TorchSharp/NN/Padding/ReplicationPad2d.cs @@ -12,25 +12,9 @@ namespace Modules /// /// This class is used to represent a ReplicationPad2d module. /// - public sealed class ReplicationPad2d : torch.nn.Module + public sealed class ReplicationPad2d : PadBase { - internal ReplicationPad2d(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { } - - /// - /// Forward pass. - /// - /// Input tensor - /// - public override Tensor forward(Tensor tensor) - { - return ReturnCheckForErrors(THSNN_ReplicationPad2d_forward(handle, tensor.Handle)); - } - - // Rather than spending cycles only to discover that this module has neither - // parameters nor buffers, just shortcut the move completely. - protected internal override nn.Module _to(Device device, ScalarType dtype, bool non_blocking) => this; - protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex, bool non_blocking) => this; - protected internal override nn.Module _to(ScalarType dtype, bool non_blocking) => this; + internal ReplicationPad2d(params long[] padding) : base(nameof(ReplicationPad2d), PaddingModes.Replicate, 0, padding) { } } } @@ -39,15 +23,13 @@ public static partial class torch public static partial class nn { /// - /// Pads the input tensor using replication of the input boundary. + /// Pads the input tensor using the replication of the input boundary. /// /// The size of the padding. /// public static ReplicationPad2d ReplicationPad2d(long padding) { - var handle = THSNN_ReplicationPad2d_ctor(padding, out var boxedHandle); - if (handle == IntPtr.Zero) { torch.CheckForErrors(); } - return new ReplicationPad2d(handle, boxedHandle); + return new ReplicationPad2d(padding, padding, padding, padding); } /// @@ -57,10 +39,8 @@ public static ReplicationPad2d ReplicationPad2d(long padding) /// public static ReplicationPad2d ReplicationPad2d((long, long, long, long) padding) { - var handle = THSNN_ReplicationPad2d_ctor_tuple(padding.Item1, padding.Item2, padding.Item3, padding.Item4, out var boxedHandle); - if (handle == IntPtr.Zero) { torch.CheckForErrors(); } - return new ReplicationPad2d(handle, boxedHandle); + return new ReplicationPad2d(padding.Item1, padding.Item2, padding.Item3, padding.Item4); } } } -} +} \ No newline at end of file diff --git a/src/TorchSharp/NN/Padding/ReplicationPad3d.cs b/src/TorchSharp/NN/Padding/ReplicationPad3d.cs index 153b5c8da..a3ee5e63a 100644 --- a/src/TorchSharp/NN/Padding/ReplicationPad3d.cs +++ b/src/TorchSharp/NN/Padding/ReplicationPad3d.cs @@ -12,25 +12,9 @@ namespace Modules /// /// This class is used to represent a ReplicationPad3d module. /// - public sealed class ReplicationPad3d : torch.nn.Module + public sealed class ReplicationPad3d : PadBase { - internal ReplicationPad3d(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { } - - /// - /// Forward pass. - /// - /// Input tensor - /// - public override Tensor forward(Tensor tensor) - { - return ReturnCheckForErrors(THSNN_ReplicationPad3d_forward(handle, tensor.Handle)); - } - - // Rather than spending cycles only to discover that this module has neither - // parameters nor buffers, just shortcut the move completely. - protected internal override nn.Module _to(Device device, ScalarType dtype, bool non_blocking) => this; - protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex, bool non_blocking) => this; - protected internal override nn.Module _to(ScalarType dtype, bool non_blocking) => this; + internal ReplicationPad3d(params long[] padding) : base(nameof(ReplicationPad3d), PaddingModes.Replicate, 0, padding) { } } } @@ -45,9 +29,7 @@ public static partial class nn /// public static ReplicationPad3d ReplicationPad3d(long padding) { - var handle = THSNN_ReplicationPad3d_ctor(padding, out var boxedHandle); - if (handle == IntPtr.Zero) { torch.CheckForErrors(); } - return new ReplicationPad3d(handle, boxedHandle); + return new ReplicationPad3d(padding, padding, padding, padding, padding, padding); } /// @@ -57,10 +39,8 @@ public static ReplicationPad3d ReplicationPad3d(long padding) /// public static ReplicationPad3d ReplicationPad3d((long, long, long, long, long, long) padding) { - var handle = THSNN_ReplicationPad3d_ctor_tuple(padding.Item1, padding.Item2, padding.Item3, padding.Item4, padding.Item5, padding.Item6, out var boxedHandle); - if (handle == IntPtr.Zero) { torch.CheckForErrors(); } - return new ReplicationPad3d(handle, boxedHandle); + return new ReplicationPad3d(padding.Item1, padding.Item2, padding.Item3, padding.Item4, padding.Item5, padding.Item6); } } } -} +} \ No newline at end of file diff --git a/src/TorchSharp/NN/Padding/ZeroPad2d.cs b/src/TorchSharp/NN/Padding/ZeroPad2d.cs index 1b98cc3b2..8b049e87d 100644 --- a/src/TorchSharp/NN/Padding/ZeroPad2d.cs +++ b/src/TorchSharp/NN/Padding/ZeroPad2d.cs @@ -12,25 +12,9 @@ namespace Modules /// /// This class is used to represent a ZeroPad2d module. /// - public sealed class ZeroPad2d : torch.nn.Module + public sealed class ZeroPad2d : PadBase { - internal ZeroPad2d(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { } - - /// - /// Forward pass. - /// - /// Input tensor - /// - public override Tensor forward(Tensor tensor) - { - return ReturnCheckForErrors(THSNN_ZeroPad2d_forward(handle, tensor.Handle)); - } - - // Rather than spending cycles only to discover that this module has neither - // parameters nor buffers, just shortcut the move completely. - protected internal override nn.Module _to(Device device, ScalarType dtype, bool non_blocking) => this; - protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex, bool non_blocking) => this; - protected internal override nn.Module _to(ScalarType dtype, bool non_blocking) => this; + internal ZeroPad2d(params long[] padding) : base(nameof(ZeroPad2d), PaddingModes.Zeros, 0, padding) { } } } @@ -45,9 +29,7 @@ public static partial class nn /// public static ZeroPad2d ZeroPad2d(long padding) { - var handle = THSNN_ZeroPad2d_ctor(padding, out var boxedHandle); - if (handle == IntPtr.Zero) { torch.CheckForErrors(); } - return new ZeroPad2d(handle, boxedHandle); + return new ZeroPad2d(padding, padding, padding, padding); } /// @@ -57,10 +39,8 @@ public static ZeroPad2d ZeroPad2d(long padding) /// public static ZeroPad2d ZeroPad2d((long, long, long, long) padding) { - var handle = THSNN_ZeroPad2d_ctor_tuple(padding.Item1, padding.Item2, padding.Item3, padding.Item4, out var boxedHandle); - if (handle == IntPtr.Zero) { torch.CheckForErrors(); } - return new ZeroPad2d(handle, boxedHandle); + return new ZeroPad2d(padding.Item1, padding.Item2, padding.Item3, padding.Item4); } } } -} +} \ No newline at end of file diff --git a/src/TorchSharp/NN/PairwiseDistance.cs b/src/TorchSharp/NN/PairwiseDistance.cs index 0503abb27..e506a4b79 100644 --- a/src/TorchSharp/NN/PairwiseDistance.cs +++ b/src/TorchSharp/NN/PairwiseDistance.cs @@ -13,23 +13,25 @@ namespace Modules /// /// Computes the pairwise distance between vectors using the p-norm. /// - public sealed class PairwiseDistance : torch.nn.Module + public sealed class PairwiseDistance : ParameterLessModule { - internal PairwiseDistance(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) + public double norm { get; set; } + public double eps { get; set; } + public bool keepdim { get; set; } + + internal PairwiseDistance( + double p = 2.0, double eps = 1e-6, bool keepdim = false) + : base(nameof(PairwiseDistance)) { + this.norm = p; + this.eps = eps; + this.keepdim = keepdim; } public override Tensor forward(Tensor input1, Tensor input2) { - return ReturnCheckForErrors(THSNN_PairwiseDistance_forward(handle, input1.Handle, input2.Handle)); - + return nn.functional.pairwise_distance(input1, input2, norm, eps, keepdim); } - - // Rather than spending cycles only to discover that this module has neither - // parameters nor buffers, just shortcut the move completely. - protected internal override nn.Module _to(Device device, ScalarType dtype, bool non_blocking) => this; - protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex, bool non_blocking) => this; - protected internal override nn.Module _to(ScalarType dtype, bool non_blocking) => this; } } @@ -37,12 +39,9 @@ public static partial class torch { public static partial class nn { - public static PairwiseDistance PairwiseDistance(double p = 2.0, double eps = 1e-6, bool keep_dim = false) + public static PairwiseDistance PairwiseDistance(double p = 2.0, double eps = 1e-6, bool keepdim = false) { - var handle = THSNN_PairwiseDistance_ctor(p, eps, keep_dim, out var boxedHandle); - if (handle == IntPtr.Zero) { torch.CheckForErrors(); } - handle = AutocastMode.AutoCast(handle, ScalarType.Float32); - return new PairwiseDistance(handle, boxedHandle); + return new PairwiseDistance(p, eps, keepdim); } public static partial class functional @@ -54,13 +53,14 @@ public static partial class functional /// (N, D) or (D), same shape as the Input1 /// The norm degree. Default: 2 /// Small value to avoid division by zero. - /// Determines whether or not to keep the vector dimension. + /// Determines whether or not to keep the vector dimension. /// - public static Tensor pairwise_distance(Tensor input1, Tensor input2, double p = 2.0, double eps = 1e-6, bool keep_dim = false) + public static Tensor pairwise_distance(Tensor input1, Tensor input2, double p = 2.0, double eps = 1e-6, bool keepdim = false) { - using (var f = nn.PairwiseDistance(p, eps, keep_dim)) { - return f.call(input1, input2); - } + var res = THSNN_pairwise_distance(input1.Handle, input2.Handle, p, eps, keepdim); + res = AutocastMode.AutoCast(res, ScalarType.Float32); + if (res == IntPtr.Zero) { torch.CheckForErrors(); } + return new Tensor(res); } } } diff --git a/src/TorchSharp/NN/Parameter.cs b/src/TorchSharp/NN/Parameter.cs index 897e99f97..86a7f29e5 100644 --- a/src/TorchSharp/NN/Parameter.cs +++ b/src/TorchSharp/NN/Parameter.cs @@ -39,20 +39,6 @@ public Parameter(Tensor data, bool requires_grad = true) : internal Parameter(System.IntPtr handle) : base(handle) { } - - /// - /// For prevent cast as torch.Tensor i provided the data method for get Tensor. - /// https://github.com/ultralytics/ultralytics/blob/dcde8bd23d12bbb4867ebf45f936dd37c2445974/ultralytics/nn/modules/conv.py#L78 - /// - /// - public torch.Tensor data { - get { - return new Tensor(base.handle); - } - set { - handle = value.handle; - } - } }; } diff --git a/src/TorchSharp/NN/PixelShuffle.cs b/src/TorchSharp/NN/PixelShuffle.cs index 745750c7e..d83fd129d 100644 --- a/src/TorchSharp/NN/PixelShuffle.cs +++ b/src/TorchSharp/NN/PixelShuffle.cs @@ -12,19 +12,24 @@ namespace Modules /// /// This class is used to represent a dropout module. /// - public sealed class PixelShuffle : torch.nn.Module + public sealed class PixelShuffle : ParameterLessModule { - internal PixelShuffle(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { } + internal PixelShuffle(long upscale_factor) : base(nameof(PixelShuffle)) + { + this.upscale_factor = upscale_factor; + } /// /// Forward pass. /// - /// Input tensor + /// Input tensor /// - public override Tensor forward(Tensor tensor) + public override Tensor forward(Tensor input) { - return ReturnCheckForErrors(THSNN_PixelShuffle_forward(handle, tensor.Handle)); + return torch.nn.functional.pixel_shuffle(input, this.upscale_factor); } + + public long upscale_factor { get; set; } } } @@ -36,13 +41,11 @@ public static partial class nn /// Rearranges elements in a tensor of shape (*, C * r^2, H, W) to a tensor of shape(*, C, H * r, W * r), where r is an upscale factor. /// This is useful for implementing efficient sub-pixel convolution with a stride of 1/r. /// - /// Factor to increase spatial resolution by + /// Factor to increase spatial resolution by /// - public static PixelShuffle PixelShuffle(long upscaleFactor) + public static PixelShuffle PixelShuffle(long upscale_factor) { - var handle = THSNN_PixelShuffle_ctor(upscaleFactor, out var boxedHandle); - if (handle == IntPtr.Zero) { torch.CheckForErrors(); } - return new PixelShuffle(handle, boxedHandle); + return new PixelShuffle(upscale_factor); } public static partial class functional @@ -51,15 +54,15 @@ public static partial class functional /// Rearranges elements in a tensor of shape (*, C * r^2, H, W) to a tensor of shape(*, C, H * r, W * r), where r is an upscale factor. /// This is useful for implementing efficient sub-pixel convolution with a stride of 1/r. /// - /// Input tensor - /// Factor to increase spatial resolution by + /// Input tensor + /// Factor to increase spatial resolution by /// /// - public static Tensor pixel_shuffle(Tensor x, long upscaleFactor) + public static Tensor pixel_shuffle(Tensor input, long upscale_factor) { - using (var d = nn.PixelShuffle(upscaleFactor)) { - return d.call(x); - } + var res = THSNN_pixel_shuffle(input.Handle, upscale_factor); + if (res == IntPtr.Zero) { torch.CheckForErrors(); } + return new Tensor(res); } } } diff --git a/src/TorchSharp/NN/PixelUnshuffle.cs b/src/TorchSharp/NN/PixelUnshuffle.cs index 9a8e749e6..06b058f3b 100644 --- a/src/TorchSharp/NN/PixelUnshuffle.cs +++ b/src/TorchSharp/NN/PixelUnshuffle.cs @@ -12,19 +12,24 @@ namespace Modules /// /// This class is used to represent a dropout module. /// - public sealed class PixelUnshuffle : torch.nn.Module + public sealed class PixelUnshuffle : ParameterLessModule { - internal PixelUnshuffle(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { } + internal PixelUnshuffle(long downscale_factor) : base(nameof(PixelUnshuffle)) + { + this.downscale_factor = downscale_factor; + } /// /// Forward pass. /// - /// Input tensor + /// Input tensor /// - public override Tensor forward(Tensor tensor) + public override Tensor forward(Tensor input) { - return ReturnCheckForErrors(THSNN_PixelUnshuffle_forward(handle, tensor.Handle)); + return torch.nn.functional.pixel_unshuffle(input, downscale_factor); } + + public long downscale_factor { get; set; } } } @@ -36,13 +41,11 @@ public static partial class nn /// /// Reverses the PixelShuffle operation by rearranging elements in a tensor of shape (*, C, H * r, W * r) to a tensor of shape (*, C * r^2, H, W), where r is an downscale factor. /// - /// Factor to increase spatial resolution by + /// Factor to increase spatial resolution by /// - public static PixelUnshuffle PixelUnshuffle(long downscaleFactor) + public static PixelUnshuffle PixelUnshuffle(long downscale_factor) { - var handle = THSNN_PixelUnshuffle_ctor(downscaleFactor, out var boxedHandle); - if (handle == IntPtr.Zero) { torch.CheckForErrors(); } - return new PixelUnshuffle(handle, boxedHandle); + return new PixelUnshuffle(downscale_factor); } public static partial class functional @@ -51,15 +54,15 @@ public static partial class functional /// Reverses the PixelShuffle operation by rearranging elements in a tensor of shape (*, C * r^2, H, W) to a tensor of shape(*, C, H * r, W * r), where r is an downscale factor. /// This is useful for implementing efficient sub-pixel convolution with a stride of 1/r. /// - /// Input tensor - /// Factor to increase spatial resolution by + /// Input tensor + /// Factor to increase spatial resolution by /// /// - public static Tensor pixel_unshuffle(Tensor x, long downscaleFactor) + public static Tensor pixel_unshuffle(Tensor input, long downscale_factor) { - using (var d = nn.PixelUnshuffle(downscaleFactor)) { - return d.call(x); - } + var res = THSNN_pixel_unshuffle(input.Handle, downscale_factor); + if (res == IntPtr.Zero) { torch.CheckForErrors(); } + return new Tensor(res); } } } diff --git a/src/TorchSharp/NN/Pooling/AdaptiveAvgPool1D.cs b/src/TorchSharp/NN/Pooling/AdaptiveAvgPool1D.cs index bdad89ea8..1e143e60c 100644 --- a/src/TorchSharp/NN/Pooling/AdaptiveAvgPool1D.cs +++ b/src/TorchSharp/NN/Pooling/AdaptiveAvgPool1D.cs @@ -12,22 +12,19 @@ namespace Modules /// /// This class is used to represent a AdaptiveAvgPool1D module. /// - public sealed class AdaptiveAvgPool1d : torch.nn.Module + public sealed class AdaptiveAvgPool1d : ParameterLessModule { - internal AdaptiveAvgPool1d(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) + internal AdaptiveAvgPool1d(long output_size) : base(nameof(AdaptiveAvgPool1d)) { + this.output_size = output_size; } - public override Tensor forward(Tensor tensor) + public override Tensor forward(Tensor input) { - return ReturnCheckForErrors(THSNN_AdaptiveAvgPool1d_forward(handle.DangerousGetHandle(), tensor.Handle)); + return torch.nn.functional.adaptive_avg_pool1d(input, this.output_size); } - // Rather than spending cycles only to discover that this module has neither - // parameters nor buffers, just shortcut the move completely. - protected internal override nn.Module _to(Device device, ScalarType dtype, bool non_blocking) => this; - protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex, bool non_blocking) => this; - protected internal override nn.Module _to(ScalarType dtype, bool non_blocking) => this; + public long output_size { get; set; } } } @@ -39,14 +36,11 @@ public static partial class nn /// Applies a 1D adaptive average pooling over an input signal composed of several input planes. /// The output size is H, for any input size.The number of output features is equal to the number of input planes. /// - /// the target output size H + /// the target output size H /// - public static unsafe AdaptiveAvgPool1d AdaptiveAvgPool1d(long outputSize) + public static unsafe AdaptiveAvgPool1d AdaptiveAvgPool1d(long output_size) { - long* pkernelSize = stackalloc long[1] { outputSize }; - var handle = THSNN_AdaptiveAvgPool1d_ctor((IntPtr)pkernelSize, 1, out var boxedHandle); - if (handle == IntPtr.Zero) { torch.CheckForErrors(); } - return new AdaptiveAvgPool1d(handle, boxedHandle); + return new AdaptiveAvgPool1d(output_size); } public static partial class functional @@ -63,7 +57,10 @@ public static Tensor adaptive_avg_pool1d(Tensor input, long output_size) var outputSizes = new long[] { output_size }; unsafe { fixed (long* poutputSize = outputSizes) { - return ReturnCheckForErrors(THSTensor_adaptive_avg_pool1d(input.Handle, (IntPtr)poutputSize, outputSizes.Length)); + var res = + THSTensor_adaptive_avg_pool1d(input.Handle, (IntPtr)poutputSize, outputSizes.Length); + if (res == IntPtr.Zero) { torch.CheckForErrors(); } + return new Tensor(res); } } } diff --git a/src/TorchSharp/NN/Pooling/AdaptiveAvgPool2D.cs b/src/TorchSharp/NN/Pooling/AdaptiveAvgPool2D.cs index 1f0206b52..3a07b4348 100644 --- a/src/TorchSharp/NN/Pooling/AdaptiveAvgPool2D.cs +++ b/src/TorchSharp/NN/Pooling/AdaptiveAvgPool2D.cs @@ -12,22 +12,19 @@ namespace Modules /// /// This class is used to represent a AdaptiveAvgPool2D module. /// - public sealed class AdaptiveAvgPool2d : torch.nn.Module + public sealed class AdaptiveAvgPool2d : ParameterLessModule { - internal AdaptiveAvgPool2d(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) + internal AdaptiveAvgPool2d(long[] output_size) : base(nameof(AdaptiveAvgPool2d)) { + this.output_size = output_size; } - public override Tensor forward(Tensor tensor) + public override Tensor forward(Tensor input) { - return ReturnCheckForErrors(THSNN_AdaptiveAvgPool2d_forward(handle.DangerousGetHandle(), tensor.Handle)); + return torch.nn.functional.adaptive_avg_pool2d(input, this.output_size); } - // Rather than spending cycles only to discover that this module has neither - // parameters nor buffers, just shortcut the move completely. - protected internal override nn.Module _to(Device device, ScalarType dtype, bool non_blocking) => this; - protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex, bool non_blocking) => this; - protected internal override nn.Module _to(ScalarType dtype, bool non_blocking) => this; + public long[] output_size { get; set; } } } @@ -39,43 +36,33 @@ public static partial class nn /// Applies a 2D adaptive average pooling over an input signal composed of several input planes. /// The output is of size H x W, for any input size.The number of output features is equal to the number of input planes. /// - /// The target output size (H,W) of the image of the form H x W. + /// The target output size (H,W) of the image of the form H x W. /// - public static unsafe AdaptiveAvgPool2d AdaptiveAvgPool2d(long[] outputSize) + public static unsafe AdaptiveAvgPool2d AdaptiveAvgPool2d(long[] output_size) { - fixed (long* poutputSize = outputSize) { - var handle = THSNN_AdaptiveAvgPool2d_ctor((IntPtr)poutputSize, outputSize.Length, out var boxedHandle); - if (handle == IntPtr.Zero) { torch.CheckForErrors(); } - return new AdaptiveAvgPool2d(handle, boxedHandle); - } + return new AdaptiveAvgPool2d(output_size); } /// /// Applies a 2D adaptive average pooling over an input signal composed of several input planes. /// The output is of size H x W, for any input size.The number of output features is equal to the number of input planes. /// - /// The target output size (H,W) of the image of the form H x W. + /// The target output size (H,W) of the image of the form H x W. /// - public static unsafe AdaptiveAvgPool2d AdaptiveAvgPool2d((long,long) outputSize) + public static unsafe AdaptiveAvgPool2d AdaptiveAvgPool2d((long, long) output_size) { - long* poutputSize = stackalloc long[2] { outputSize.Item1, outputSize.Item2 }; - var handle = THSNN_AdaptiveAvgPool2d_ctor((IntPtr)poutputSize, 2, out var boxedHandle); - if (handle == IntPtr.Zero) { torch.CheckForErrors(); } - return new AdaptiveAvgPool2d(handle, boxedHandle); + return new AdaptiveAvgPool2d(new[] { output_size.Item1, output_size.Item2 }); } /// /// Applies a 2D adaptive average pooling over an input signal composed of several input planes. /// The output is of size H x W, for any input size.The number of output features is equal to the number of input planes. /// - /// The target output size (H,W) of the image of the form H x W. + /// The target output size (H,W) of the image of the form H x W. /// - public static unsafe AdaptiveAvgPool2d AdaptiveAvgPool2d(long outputSize) + public static unsafe AdaptiveAvgPool2d AdaptiveAvgPool2d(long output_size) { - long* poutputSize = stackalloc long[2] { outputSize, outputSize }; - var handle = THSNN_AdaptiveAvgPool2d_ctor((IntPtr)poutputSize, 2, out var boxedHandle); - if (handle == IntPtr.Zero) { torch.CheckForErrors(); } - return new AdaptiveAvgPool2d(handle, boxedHandle); + return new AdaptiveAvgPool2d(new[] { output_size, output_size }); } public static partial class functional @@ -90,7 +77,9 @@ public static Tensor adaptive_avg_pool2d(Tensor input, long[] output_size) { unsafe { fixed (long* poutputSize = output_size) { - return ReturnCheckForErrors(THSTensor_adaptive_avg_pool2d(input.Handle, (IntPtr)poutputSize, output_size.Length)); + var res = THSTensor_adaptive_avg_pool2d(input.Handle, (IntPtr)poutputSize, output_size.Length); + if (res == IntPtr.Zero) { torch.CheckForErrors(); } + return new Tensor(res); } } } @@ -105,7 +94,9 @@ public static unsafe Tensor adaptive_avg_pool2d(Tensor input, (long, long) outpu { long* poutputSize = stackalloc long[2] { output_size.Item1, output_size.Item2 }; - return ReturnCheckForErrors(THSTensor_adaptive_avg_pool2d(input.Handle, (IntPtr)poutputSize, 2)); + var res = THSTensor_adaptive_avg_pool2d(input.Handle, (IntPtr)poutputSize, 2); + if (res == IntPtr.Zero) { torch.CheckForErrors(); } + return new Tensor(res); } /// @@ -118,7 +109,9 @@ public static unsafe Tensor adaptive_avg_pool2d(Tensor input, long output_size) { long* poutputSize = stackalloc long[2] { output_size, output_size }; - return ReturnCheckForErrors(THSTensor_adaptive_avg_pool2d(input.Handle, (IntPtr)poutputSize, 2)); + var res = THSTensor_adaptive_avg_pool2d(input.Handle, (IntPtr)poutputSize, 2); + if (res == IntPtr.Zero) { torch.CheckForErrors(); } + return new Tensor(res); } } } diff --git a/src/TorchSharp/NN/Pooling/AdaptiveAvgPool3D.cs b/src/TorchSharp/NN/Pooling/AdaptiveAvgPool3D.cs index 13d12645c..bc9044e76 100644 --- a/src/TorchSharp/NN/Pooling/AdaptiveAvgPool3D.cs +++ b/src/TorchSharp/NN/Pooling/AdaptiveAvgPool3D.cs @@ -12,22 +12,19 @@ namespace Modules /// /// This class is used to represent a AdaptiveAvgPool3D module. /// - public sealed class AdaptiveAvgPool3d : torch.nn.Module + public sealed class AdaptiveAvgPool3d : ParameterLessModule { - internal AdaptiveAvgPool3d(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) + internal AdaptiveAvgPool3d(long[] output_size) : base(nameof(AdaptiveAvgPool3d)) { + this.output_size = output_size; } - public override Tensor forward(Tensor tensor) + public override Tensor forward(Tensor input) { - return ReturnCheckForErrors(THSNN_AdaptiveAvgPool3d_forward(handle.DangerousGetHandle(), tensor.Handle)); + return torch.nn.functional.adaptive_avg_pool3d(input, this.output_size); } - // Rather than spending cycles only to discover that this module has neither - // parameters nor buffers, just shortcut the move completely. - protected internal override nn.Module _to(Device device, ScalarType dtype, bool non_blocking) => this; - protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex, bool non_blocking) => this; - protected internal override nn.Module _to(ScalarType dtype, bool non_blocking) => this; + public long[] output_size { get; set; } } } @@ -39,44 +36,33 @@ public static partial class nn /// Applies a 3D adaptive average pooling over an input signal composed of several input planes. /// The output is of size D x H x W, for any input size.The number of output features is equal to the number of input planes. /// - /// The target output size of the image of the form D x H x W. + /// The target output size of the image of the form D x H x W. /// - public static unsafe AdaptiveAvgPool3d AdaptiveAvgPool3d(long[] outputSize) + public static unsafe AdaptiveAvgPool3d AdaptiveAvgPool3d(long[] output_size) { - fixed (long* pkernelSize = outputSize) { - var handle = THSNN_AdaptiveAvgPool3d_ctor((IntPtr)pkernelSize, outputSize.Length, out var boxedHandle); - if (handle == IntPtr.Zero) { torch.CheckForErrors(); } - return new AdaptiveAvgPool3d(handle, boxedHandle); - } + return new AdaptiveAvgPool3d(output_size); } /// /// Applies a 3D adaptive average pooling over an input signal composed of several input planes. /// The output is of size D x H x W, for any input size.The number of output features is equal to the number of input planes. /// - /// The target output size (D,H,W) of the image of the form D x H x W. + /// The target output size (D,H,W) of the image of the form D x H x W. /// - public static unsafe AdaptiveAvgPool3d AdaptiveAvgPool3d((long, long, long) outputSize) + public static unsafe AdaptiveAvgPool3d AdaptiveAvgPool3d((long, long, long) output_size) { - long* pkernelSize = stackalloc long[3] { outputSize.Item1, outputSize.Item2, outputSize.Item3 }; - - var handle = THSNN_AdaptiveAvgPool3d_ctor((IntPtr)pkernelSize, 3, out var boxedHandle); - if (handle == IntPtr.Zero) { torch.CheckForErrors(); } - return new AdaptiveAvgPool3d(handle, boxedHandle); + return new AdaptiveAvgPool3d(new[] { output_size.Item1, output_size.Item2, output_size.Item3 }); } /// /// Applies a 3D adaptive average pooling over an input signal composed of several input planes. /// The output is of size D x H x W, for any input size.The number of output features is equal to the number of input planes. /// - /// The target output size (D,H,W) of the image of the form H x W. + /// The target output size (D,H,W) of the image of the form H x W. /// - public static unsafe AdaptiveAvgPool3d AdaptiveAvgPool3d(long outputSize) + public static unsafe AdaptiveAvgPool3d AdaptiveAvgPool3d(long output_size) { - long* pkernelSize = stackalloc long[3] { outputSize, outputSize, outputSize }; - var handle = THSNN_AdaptiveAvgPool3d_ctor((IntPtr)pkernelSize, 3, out var boxedHandle); - if (handle == IntPtr.Zero) { torch.CheckForErrors(); } - return new AdaptiveAvgPool3d(handle, boxedHandle); + return new AdaptiveAvgPool3d(new[] { output_size, output_size, output_size }); } public static partial class functional @@ -90,8 +76,10 @@ public static partial class functional public static unsafe Tensor adaptive_avg_pool3d(Tensor input, long[] output_size) { fixed (long* poutputSize = output_size) { - - return ReturnCheckForErrors(THSTensor_adaptive_avg_pool3d(input.Handle, (IntPtr)poutputSize, output_size.Length)); + var res = + THSTensor_adaptive_avg_pool3d(input.Handle, (IntPtr)poutputSize, output_size.Length); + if (res == IntPtr.Zero) { torch.CheckForErrors(); } + return new Tensor(res); } } @@ -104,7 +92,9 @@ public static unsafe Tensor adaptive_avg_pool3d(Tensor input, long[] output_size public static unsafe Tensor adaptive_avg_pool3d(Tensor input, (long, long, long) output_size) { long* poutputSize = stackalloc long[3] { output_size.Item1, output_size.Item2, output_size.Item3 }; - return ReturnCheckForErrors(THSTensor_adaptive_avg_pool3d(input.Handle, (IntPtr)poutputSize, 3)); + var res = THSTensor_adaptive_avg_pool3d(input.Handle, (IntPtr)poutputSize, 3); + if (res == IntPtr.Zero) { torch.CheckForErrors(); } + return new Tensor(res); } /// @@ -117,12 +107,16 @@ public static unsafe Tensor adaptive_avg_pool3d(Tensor input, long output_size) { var os = new long[] { output_size, output_size, output_size }; long* poutputSize = stackalloc long[3] { output_size, output_size, output_size }; - return ReturnCheckForErrors(THSTensor_adaptive_avg_pool3d(input.Handle, (IntPtr)poutputSize, 3)); + var res = THSTensor_adaptive_avg_pool3d(input.Handle, (IntPtr)poutputSize, 3); + if (res == IntPtr.Zero) { torch.CheckForErrors(); } + return new Tensor(res); } public static Tensor adaptive_avg_pool3d_backward(Tensor gradInput, Tensor gradOutput, Tensor originalInput) { - return ReturnCheckForErrors(THSTensor_adaptive_avg_pool3d_backward_out(gradInput.Handle, gradOutput.Handle, originalInput.Handle)); + var res = THSTensor_adaptive_avg_pool3d_backward_out(gradInput.Handle, gradOutput.Handle, originalInput.Handle); + if (res == IntPtr.Zero) { torch.CheckForErrors(); } + return new Tensor(res); } } } diff --git a/src/TorchSharp/NN/Pooling/AdaptiveMaxPool1D.cs b/src/TorchSharp/NN/Pooling/AdaptiveMaxPool1D.cs index 811d76acd..d2da2119c 100644 --- a/src/TorchSharp/NN/Pooling/AdaptiveMaxPool1D.cs +++ b/src/TorchSharp/NN/Pooling/AdaptiveMaxPool1D.cs @@ -12,22 +12,24 @@ namespace Modules /// /// This class is used to represent a AdaptiveMaxPool1D module. /// - public sealed class AdaptiveMaxPool1d : torch.nn.Module + public sealed class AdaptiveMaxPool1d : ParameterLessModule { - internal AdaptiveMaxPool1d(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) + internal AdaptiveMaxPool1d(long output_size) : base(nameof(AdaptiveMaxPool1d)) { + this.output_size = output_size; } - public override Tensor forward(Tensor tensor) + public (Tensor Values, Tensor Indices) forward_with_indices(Tensor input) { - return ReturnCheckForErrors(THSNN_AdaptiveMaxPool1d_forward(handle.DangerousGetHandle(), tensor.Handle)); + return torch.nn.functional.adaptive_max_pool1d_with_indices(input, this.output_size); } - // Rather than spending cycles only to discover that this module has neither - // parameters nor buffers, just shortcut the move completely. - protected internal override nn.Module _to(Device device, ScalarType dtype, bool non_blocking) => this; - protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex, bool non_blocking) => this; - protected internal override nn.Module _to(ScalarType dtype, bool non_blocking) => this; + public override Tensor forward(Tensor input) + { + return torch.nn.functional.adaptive_max_pool1d(input, this.output_size); + } + + public long output_size { get; set; } } } @@ -39,17 +41,11 @@ public static partial class nn /// Applies a 1D adaptive max pooling over an input signal composed of several input planes. /// The output size is H, for any input size.The number of output features is equal to the number of input planes. /// - /// The target output size H. + /// The target output size H. /// - public static AdaptiveMaxPool1d AdaptiveMaxPool1d(long outputSize) + public static AdaptiveMaxPool1d AdaptiveMaxPool1d(long output_size) { - unsafe { - fixed (long* pkernelSize = new long[] { outputSize }) { - var handle = THSNN_AdaptiveMaxPool1d_ctor((IntPtr)pkernelSize, 1, out var boxedHandle); - if (handle == IntPtr.Zero) { torch.CheckForErrors(); } - return new AdaptiveMaxPool1d(handle, boxedHandle); - } - } + return new AdaptiveMaxPool1d(output_size); } public static partial class functional @@ -58,13 +54,32 @@ public static partial class functional /// Applies a 1D adaptive max pooling over an input signal composed of several input planes. /// The output size is H, for any input size.The number of output features is equal to the number of input planes. /// - /// - /// The target output size H. + /// + /// The target output size H. + /// + public static Tensor adaptive_max_pool1d(Tensor input, long output_size) + { + var ret = adaptive_max_pool1d_with_indices(input, output_size); + ret.Indices.Dispose(); + return ret.Values; + } + + /// + /// Applies a 1D adaptive max pooling over an input signal composed of several input planes. + /// The output size is H, for any input size.The number of output features is equal to the number of input planes. + /// + /// + /// The target output size H. /// - public static Tensor adaptive_max_pool1d(Tensor x, long outputSize) + public static (Tensor Values, Tensor Indices) adaptive_max_pool1d_with_indices(Tensor input, long output_size) { - using (var d = nn.AdaptiveMaxPool1d(outputSize)) { - return d.call(x); + var outputSizes = new long[] { output_size }; + unsafe { + fixed (long* poutputSize = outputSizes) { + var resOutput = THSTensor_adaptive_max_pool1d(input.Handle, (IntPtr)poutputSize, outputSizes.Length, out var resIndices); + if (resOutput == IntPtr.Zero || resIndices == IntPtr.Zero) { torch.CheckForErrors(); } + return (new Tensor(resOutput), new Tensor(resIndices)); + } } } } diff --git a/src/TorchSharp/NN/Pooling/AdaptiveMaxPool2D.cs b/src/TorchSharp/NN/Pooling/AdaptiveMaxPool2D.cs index db82fbc95..e0631a89a 100644 --- a/src/TorchSharp/NN/Pooling/AdaptiveMaxPool2D.cs +++ b/src/TorchSharp/NN/Pooling/AdaptiveMaxPool2D.cs @@ -12,22 +12,24 @@ namespace Modules /// /// This class is used to represent a AdaptiveMaxPool2D module. /// - public sealed class AdaptiveMaxPool2d : torch.nn.Module + public sealed class AdaptiveMaxPool2d : ParameterLessModule { - internal AdaptiveMaxPool2d(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) + internal AdaptiveMaxPool2d(long[] output_size) : base(nameof(AdaptiveMaxPool2d)) { + this.output_size = output_size; } - public override Tensor forward(Tensor tensor) + public override Tensor forward(Tensor input) { - return ReturnCheckForErrors(THSNN_AdaptiveMaxPool2d_forward(handle.DangerousGetHandle(), tensor.Handle)); + return torch.nn.functional.adaptive_max_pool2d(input, this.output_size); } - // Rather than spending cycles only to discover that this module has neither - // parameters nor buffers, just shortcut the move completely. - protected internal override nn.Module _to(Device device, ScalarType dtype, bool non_blocking) => this; - protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex, bool non_blocking) => this; - protected internal override nn.Module _to(ScalarType dtype, bool non_blocking) => this; + public (Tensor output, Tensor indices) forward_with_indices(Tensor input) + { + return torch.nn.functional.adaptive_max_pool2d_with_indices(input, this.output_size); + } + + public long[] output_size { get; set; } } } @@ -39,18 +41,12 @@ public static partial class nn /// Applies a 2D adaptive max pooling over an input signal composed of several input planes. /// The output is of size H x W, for any input size.The number of output features is equal to the number of input planes. /// - /// Applies a 2D adaptive max pooling over an input signal composed of several input planes. + /// Applies a 2D adaptive max pooling over an input signal composed of several input planes. /// The output is of size H x W, for any input size.The number of output features is equal to the number of input planes. /// - public static AdaptiveMaxPool2d AdaptiveMaxPool2d(long[] outputSize) + public static AdaptiveMaxPool2d AdaptiveMaxPool2d(long[] output_size) { - unsafe { - fixed (long* pkernelSize = outputSize) { - var handle = THSNN_AdaptiveMaxPool2d_ctor((IntPtr)pkernelSize, outputSize.Length, out var boxedHandle); - if (handle == IntPtr.Zero) { torch.CheckForErrors(); } - return new AdaptiveMaxPool2d(handle, boxedHandle); - } - } + return new AdaptiveMaxPool2d(output_size); } public static partial class functional @@ -59,14 +55,33 @@ public static partial class functional /// Applies a 2D adaptive max pooling over an input signal composed of several input planes. /// The output is of size H x W, for any input size.The number of output features is equal to the number of input planes. /// - /// - /// Applies a 2D adaptive max pooling over an input signal composed of several input planes. + /// + /// Applies a 2D adaptive max pooling over an input signal composed of several input planes. + /// The output is of size H x W, for any input size.The number of output features is equal to the number of input planes. + /// + public static Tensor adaptive_max_pool2d(Tensor input, long[] output_size) + { + var ret = adaptive_max_pool2d_with_indices(input, output_size); + ret.Indices.Dispose(); + return ret.Values; + } + + /// + /// Applies a 2D adaptive max pooling over an input signal composed of several input planes. + /// The output is of size H x W, for any input size.The number of output features is equal to the number of input planes. + /// + /// + /// Applies a 2D adaptive max pooling over an input signal composed of several input planes. /// The output is of size H x W, for any input size.The number of output features is equal to the number of input planes. /// - public static Tensor adaptive_max_pool2d(Tensor x, long[] outputSize) + public static (Tensor Values, Tensor Indices) adaptive_max_pool2d_with_indices(Tensor input, long[] output_size) { - using (var d = nn.AdaptiveMaxPool2d(outputSize)) { - return d.call(x); + unsafe { + fixed (long* poutputSize = output_size) { + var resOutput = THSTensor_adaptive_max_pool2d(input.Handle, (IntPtr)poutputSize, output_size.Length, out var resIndices); + if (resOutput == IntPtr.Zero || resIndices == IntPtr.Zero) { torch.CheckForErrors(); } + return (new Tensor(resOutput), new Tensor(resIndices)); + } } } } diff --git a/src/TorchSharp/NN/Pooling/AdaptiveMaxPool3D.cs b/src/TorchSharp/NN/Pooling/AdaptiveMaxPool3D.cs index ed97348fa..599155473 100644 --- a/src/TorchSharp/NN/Pooling/AdaptiveMaxPool3D.cs +++ b/src/TorchSharp/NN/Pooling/AdaptiveMaxPool3D.cs @@ -12,22 +12,24 @@ namespace Modules /// /// This class is used to represent a AdaptiveMaxPool3D module. /// - public sealed class AdaptiveMaxPool3d : torch.nn.Module + public sealed class AdaptiveMaxPool3d : ParameterLessModule { - internal AdaptiveMaxPool3d(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) + internal AdaptiveMaxPool3d(long[] output_size) : base(nameof(AdaptiveMaxPool3d)) { } - public override Tensor forward(Tensor tensor) + public override Tensor forward(Tensor input) { - return ReturnCheckForErrors(THSNN_AdaptiveMaxPool3d_forward(handle.DangerousGetHandle(), tensor.Handle)); + return torch.nn.functional.adaptive_max_pool3d(input, output_size); } - // Rather than spending cycles only to discover that this module has neither - // parameters nor buffers, just shortcut the move completely. - protected internal override nn.Module _to(Device device, ScalarType dtype, bool non_blocking) => this; - protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex, bool non_blocking) => this; - protected internal override nn.Module _to(ScalarType dtype, bool non_blocking) => this; + public (Tensor output, Tensor indices) forward_with_indices(Tensor input) + { + return torch.nn.functional.adaptive_max_pool3d_with_indices(input, output_size); + } + + + public long[] output_size { get; set; } } } @@ -39,18 +41,12 @@ public static partial class nn /// Applies a 3D adaptive max pooling over an input signal composed of several input planes. /// The output is of size D x H x W, for any input size.The number of output features is equal to the number of input planes. /// - /// The target output size of the image of the form D x H x W. + /// The target output size of the image of the form D x H x W. /// Can be a tuple (D, H, W) or a single D for a cube D x D x D. D, H and W can be either a int, or null which means the size will be the same as that of the input. /// - public static AdaptiveMaxPool3d AdaptiveMaxPool3d(long[] outputSize) + public static AdaptiveMaxPool3d AdaptiveMaxPool3d(long[] output_size) { - unsafe { - fixed (long* pkernelSize = outputSize) { - var handle = THSNN_AdaptiveMaxPool3d_ctor((IntPtr)pkernelSize, outputSize.Length, out var boxedHandle); - if (handle == IntPtr.Zero) { torch.CheckForErrors(); } - return new AdaptiveMaxPool3d(handle, boxedHandle); - } - } + return new AdaptiveMaxPool3d(output_size); } public static partial class functional @@ -59,14 +55,33 @@ public static partial class functional /// Applies a 3D adaptive max pooling over an input signal composed of several input planes. /// The output is of size D x H x W, for any input size.The number of output features is equal to the number of input planes. /// - /// The input tensor - /// The target output size of the image of the form D x H x W. + /// The input tensor + /// The target output size of the image of the form D x H x W. + /// Can be a tuple (D, H, W) or a single D for a cube D x D x D. D, H and W can be either a int, or null which means the size will be the same as that of the input. + /// + public static Tensor adaptive_max_pool3d(Tensor input, long[] output_size) + { + var ret = adaptive_max_pool3d_with_indices(input, output_size); + ret.Indices.Dispose(); + return ret.Values; + } + + /// + /// Applies a 3D adaptive max pooling over an input signal composed of several input planes. + /// The output is of size D x H x W, for any input size.The number of output features is equal to the number of input planes. + /// + /// The input tensor + /// The target output size of the image of the form D x H x W. /// Can be a tuple (D, H, W) or a single D for a cube D x D x D. D, H and W can be either a int, or null which means the size will be the same as that of the input. /// - public static Tensor adaptive_max_pool3d(Tensor x, long[] outputSize) + public static (Tensor Values, Tensor Indices) adaptive_max_pool3d_with_indices(Tensor input, long[] output_size) { - using (var d = nn.AdaptiveMaxPool3d(outputSize)) { - return d.call(x); + unsafe { + fixed (long* poutputSize = output_size) { + var resOutput = THSTensor_adaptive_max_pool1d(input.Handle, (IntPtr)poutputSize, output_size.Length, out var resIndices); + if (resOutput == IntPtr.Zero || resIndices == IntPtr.Zero) { torch.CheckForErrors(); } + return (new Tensor(resOutput), new Tensor(resIndices)); + } } } } diff --git a/src/TorchSharp/NN/Pooling/AvgPool1D.cs b/src/TorchSharp/NN/Pooling/AvgPool1D.cs index 8ee73f45d..a2fd910d6 100644 --- a/src/TorchSharp/NN/Pooling/AvgPool1D.cs +++ b/src/TorchSharp/NN/Pooling/AvgPool1D.cs @@ -12,22 +12,27 @@ namespace Modules /// /// This class is used to represent a AvgPool1D module. /// - public sealed class AvgPool1d : torch.nn.Module + public sealed class AvgPool1d : ParameterLessModule { - internal AvgPool1d(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) + internal AvgPool1d(long kernel_size, long? stride = null, long? padding = null, bool ceil_mode = false, bool count_include_pad = true) : base(nameof(AvgPool1d)) { + this.kernel_size = kernel_size; + this.stride = stride; + this.padding = padding; + this.ceil_mode = ceil_mode; + this.count_include_pad = count_include_pad; } - public override Tensor forward(Tensor tensor) + public override Tensor forward(Tensor input) { - return ReturnCheckForErrors(THSNN_AvgPool1d_forward(handle.DangerousGetHandle(), tensor.Handle)); + return torch.nn.functional.avg_pool1d(input, kernel_size, stride, padding, ceil_mode, count_include_pad); } - // Rather than spending cycles only to discover that this module has neither - // parameters nor buffers, just shortcut the move completely. - protected internal override nn.Module _to(Device device, ScalarType dtype, bool non_blocking) => this; - protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex, bool non_blocking) => this; - protected internal override nn.Module _to(ScalarType dtype, bool non_blocking) => this; + public long kernel_size { get; set; } + public long? stride { get; set; } + public long? padding { get; set; } + public bool ceil_mode { get; set; } + public bool count_include_pad { get; set; } } } @@ -43,32 +48,9 @@ public static partial class nn /// implicit zero padding to be added on both sides /// Whether to use ceil instead of floor to compute the output shape /// Whether to include the zero-padding in the averaging calculation - /// If specified, it will be used as divisor, otherwise size of the pooling region will be used - public static AvgPool1d AvgPool1d(long kernel_size, long? stride = null, long padding = 0, bool ceil_mode = false, bool count_include_pad = true, long? divisor_override = null) + public static AvgPool1d AvgPool1d(long kernel_size, long? stride = null, long padding = 0, bool ceil_mode = false, bool count_include_pad = true) { - return stride.HasValue ? - AvgPool1d(new long[] { kernel_size }, new long[] { stride.Value }, new long[] { padding }, ceil_mode, count_include_pad, divisor_override.HasValue ? divisor_override.Value : 0) : - AvgPool1d(new long[] { kernel_size }, null, new long[] { padding }, ceil_mode, count_include_pad, divisor_override.HasValue ? divisor_override.Value : 0); - } - - /// - /// Applies a 1D average pooling over an input signal composed of several input planes. - /// - /// The size of the window - /// The stride of the window. Default value is kernel_size - /// implicit zero padding to be added on both sides - /// Whether to use ceil instead of floor to compute the output shape - /// Whether to include the zero-padding in the averaging calculation - /// If specified, it will be used as divisor, otherwise size of the pooling region will be used - private static AvgPool1d AvgPool1d(long[] kernel_size, long[] strides = null, long[] padding = null, bool ceil_mode = false, bool count_include_pad = true, long? divisor_override = null) - { - unsafe { - fixed (long* pkernelSize = kernel_size, pstrides = strides, ppadding = padding) { - var handle = THSNN_AvgPool1d_ctor((IntPtr)pkernelSize, (IntPtr)pstrides, (IntPtr)ppadding, ceil_mode, count_include_pad, divisor_override.HasValue ? divisor_override.Value : 0, out var boxedHandle); - if (handle == IntPtr.Zero) { torch.CheckForErrors(); } - return new AvgPool1d(handle, boxedHandle); - } - } + return new AvgPool1d(kernel_size, stride, padding, ceil_mode, count_include_pad); } public static partial class functional @@ -87,19 +69,20 @@ public static partial class functional public static Tensor avg_pool1d(Tensor input, long kernel_size, long? stride = null, long? padding = null, bool ceil_mode = false, bool count_include_pad = true) { - var kernelSizes = new long[] { kernel_size }; + var kernel_sizes = new long[] { kernel_size }; var strides = new long[] { stride ?? kernel_size }; var paddings = new long[] { padding ?? 0 }; unsafe { - fixed (long* pkernelSize = kernelSizes, pstrides = strides, ppadding = paddings) { + fixed (long* pkernel_size = kernel_sizes, pstrides = strides, ppadding = paddings) { var res = THSTensor_avg_pool1d(input.Handle, - (IntPtr)pkernelSize, kernelSizes.Length, + (IntPtr)pkernel_size, kernel_sizes.Length, (IntPtr)pstrides, strides.Length, (IntPtr)ppadding, paddings.Length, ceil_mode, count_include_pad); - return ReturnCheckForErrors(res); + if (res == IntPtr.Zero) { torch.CheckForErrors(); } + return new Tensor(res); } } } diff --git a/src/TorchSharp/NN/Pooling/AvgPool2D.cs b/src/TorchSharp/NN/Pooling/AvgPool2D.cs index b155fcabc..b9264bfa8 100644 --- a/src/TorchSharp/NN/Pooling/AvgPool2D.cs +++ b/src/TorchSharp/NN/Pooling/AvgPool2D.cs @@ -5,6 +5,7 @@ namespace TorchSharp { + using System.Data; using Modules; namespace Modules @@ -12,22 +13,29 @@ namespace Modules /// /// This class is used to represent a AvgPool2D module. /// - public sealed class AvgPool2d : torch.nn.Module + public sealed class AvgPool2d : ParameterLessModule { - internal AvgPool2d(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) + internal AvgPool2d(long[] kernel_size, long[] stride = null, long[] padding = null, bool ceil_mode = false, bool count_include_pad = true, long? divisor_override = null) : base(nameof(AvgPool2d)) { + this.kernel_size = kernel_size; + this.stride = stride; + this.padding = padding; + this.ceil_mode = ceil_mode; + this.count_include_pad = count_include_pad; + this.divisor_override = divisor_override; } - public override Tensor forward(Tensor tensor) + public override Tensor forward(Tensor input) { - return ReturnCheckForErrors(THSNN_AvgPool2d_forward(handle.DangerousGetHandle(), tensor.Handle)); + return torch.nn.functional.avg_pool2d(input, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override); } - // Rather than spending cycles only to discover that this module has neither - // parameters nor buffers, just shortcut the move completely. - protected internal override nn.Module _to(Device device, ScalarType dtype, bool non_blocking) => this; - protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex, bool non_blocking) => this; - protected internal override nn.Module _to(ScalarType dtype, bool non_blocking) => this; + public long[] kernel_size { get; set; } + public long[] stride { get; set; } + public long[] padding { get; set; } + public bool ceil_mode { get; set; } + public bool count_include_pad { get; set; } + public long? divisor_override { get; set; } } } @@ -39,18 +47,14 @@ public static partial class nn /// Applies a 2D average pooling over an input signal composed of several input planes. /// /// The size of the window - /// The stride of the window. Default value is kernel_size + /// The stride of the window. Default value is kernel_size /// implicit zero padding to be added on both sides /// Whether to use ceil instead of floor to compute the output shape /// Whether to include the zero-padding in the averaging calculation /// If specified, it will be used as divisor, otherwise size of the pooling region will be used - public static unsafe AvgPool2d AvgPool2d(long[] kernel_size, long[] strides = null, long[] padding = null, bool ceil_mode = false, bool count_include_pad = true, long? divisor_override = null) + public static AvgPool2d AvgPool2d(long[] kernel_size, long[] stride = null, long[] padding = null, bool ceil_mode = false, bool count_include_pad = true, long? divisor_override = null) { - fixed (long* pkernelSize = kernel_size, pstrides = strides, ppadding = padding) { - var handle = THSNN_AvgPool2d_ctor((IntPtr)pkernelSize, kernel_size.Length, (IntPtr)pstrides, (strides == null ? 0 : strides.Length), (IntPtr)ppadding, (padding == null ? 0 : padding.Length), ceil_mode, count_include_pad, divisor_override.HasValue ? divisor_override.Value : 0, out var boxedHandle); - if (handle == IntPtr.Zero) { torch.CheckForErrors(); } - return new AvgPool2d(handle, boxedHandle); - } + return new AvgPool2d(kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override); } /// @@ -62,21 +66,12 @@ public static unsafe AvgPool2d AvgPool2d(long[] kernel_size, long[] strides = nu /// Whether to use ceil instead of floor to compute the output shape /// Whether to include the zero-padding in the averaging calculation /// If specified, it will be used as divisor, otherwise size of the pooling region will be used - public static unsafe AvgPool2d AvgPool2d((long,long) kernel_size, (long,long)? stride = null, (long,long)? padding = null, bool ceil_mode = false, bool count_include_pad = true, long? divisor_override = null) + public static unsafe AvgPool2d AvgPool2d((long, long) kernel_size, (long, long)? stride = null, (long, long)? padding = null, bool ceil_mode = false, bool count_include_pad = true, long? divisor_override = null) { - long svalue1 = (stride == null) ? kernel_size.Item1 : stride.Value.Item1; - long svalue2 = (stride == null) ? kernel_size.Item2 : stride.Value.Item2; - - long pvalue1 = (padding == null) ? 0 : padding.Value.Item1; - long pvalue2 = (padding == null) ? 0 : padding.Value.Item2; - - long* pkernelSize = stackalloc long[2] { kernel_size.Item1, kernel_size.Item2 }; - long* pstrides = stackalloc long[2] { svalue1, svalue2 }; - long* ppadding = stackalloc long[2] { pvalue1, pvalue2 }; - - var handle = THSNN_AvgPool2d_ctor((IntPtr)pkernelSize, 2, (IntPtr)pstrides, 2, (IntPtr)ppadding, 2, ceil_mode, count_include_pad, divisor_override.HasValue ? divisor_override.Value : 0, out var boxedHandle); - if (handle == IntPtr.Zero) { torch.CheckForErrors(); } - return new AvgPool2d(handle, boxedHandle); + long[] kernelValue = new[] { kernel_size.Item1, kernel_size.Item2 }; + long[] strideValue = stride == null ? null : new[] { stride.Value.Item1, stride.Value.Item2 }; + long[] paddingValue = padding == null ? null : new[] { padding.Value.Item1, padding.Value.Item2 }; + return new AvgPool2d(kernelValue, strideValue, paddingValue, ceil_mode, count_include_pad, divisor_override); } /// @@ -88,18 +83,12 @@ public static unsafe AvgPool2d AvgPool2d((long,long) kernel_size, (long,long)? s /// Whether to use ceil instead of floor to compute the output shape /// Whether to include the zero-padding in the averaging calculation /// If specified, it will be used as divisor, otherwise size of the pooling region will be used - public static unsafe AvgPool2d AvgPool2d(long kernel_size, long? stride = null, long? padding = null, bool ceil_mode = false, bool count_include_pad = true, long? divisor_override = null) + public static AvgPool2d AvgPool2d(long kernel_size, long? stride = null, long? padding = null, bool ceil_mode = false, bool count_include_pad = true, long? divisor_override = null) { - long svalue = (stride == null) ? kernel_size : stride.Value; - long pvalue = (padding == null) ? 0 : padding.Value; - - long* pkernelSize = stackalloc long[2] { kernel_size, kernel_size }; - long* pstrides = stackalloc long[2] { svalue, svalue }; - long* ppadding = stackalloc long[2] { pvalue, pvalue }; - - var handle = THSNN_AvgPool2d_ctor((IntPtr)pkernelSize, 2, (IntPtr)pstrides, 2, (IntPtr)ppadding, 2, ceil_mode, count_include_pad, divisor_override.HasValue ? divisor_override.Value : 0, out var boxedHandle); - if (handle == IntPtr.Zero) { torch.CheckForErrors(); } - return new AvgPool2d(handle, boxedHandle); + long[] kernelValue = new[] { kernel_size, kernel_size }; + long[] strideValue = stride == null ? null : new[] { stride.Value, stride.Value }; + long[] paddingValue = padding == null ? null : new[] { padding.Value, padding.Value }; + return new AvgPool2d(kernelValue, strideValue, paddingValue, ceil_mode, count_include_pad, divisor_override); } public static partial class functional @@ -108,29 +97,34 @@ public static partial class functional /// Applies 2D average-pooling operation in kH × kW regions by step size sH * sW steps. The number of output features is equal to the number of input planes. /// /// The input tensor. - /// - /// - /// + /// + /// + /// /// /// + /// /// - public static Tensor avg_pool2d(Tensor input, long[] kernelSizes, - long[] strides = null, - long[] paddings = null, + public static Tensor avg_pool2d(Tensor input, long[] kernel_size, + long[] stride = null, + long[] padding = null, bool ceil_mode = false, - bool count_include_pad = true) + bool count_include_pad = true, + long? divisor_override = null) { - strides = (strides == null) ? new long[] { 1 } : strides; - paddings = (paddings == null) ? new long[] { 0 } : paddings; + stride = (stride == null) ? kernel_size : stride; + padding = (padding == null) ? new long[] { 0 } : padding; unsafe { - fixed (long* pkernelSize = kernelSizes, pstrides = strides, ppadding = paddings) { - var res = THSTensor_avg_pool2d(input.Handle, - (IntPtr)pkernelSize, kernelSizes.Length, - (IntPtr)pstrides, strides.Length, - (IntPtr)ppadding, paddings.Length, + fixed (long* pkernel_size = kernel_size, pstrides = stride, ppadding = padding) { + var res = + THSTensor_avg_pool2d(input.Handle, + (IntPtr)pkernel_size, kernel_size.Length, + (IntPtr)pstrides, stride.Length, + (IntPtr)ppadding, padding.Length, ceil_mode, - count_include_pad); - return ReturnCheckForErrors(res); + count_include_pad, + divisor_override ?? 0); + if (res == IntPtr.Zero) { torch.CheckForErrors(); } + return new Tensor(res); } } } @@ -139,90 +133,100 @@ public static Tensor avg_pool2d(Tensor input, long[] kernelSizes, /// Applies 2D average-pooling operation in kH × kW regions by step size sH * sW steps. The number of output features is equal to the number of input planes. /// /// The input tensor. - /// + /// /// /// /// /// + /// /// - public static unsafe Tensor avg_pool2d(Tensor input, long kernelSize, + public static unsafe Tensor avg_pool2d(Tensor input, long kernel_size, long? stride = null, long padding = 0, bool ceil_mode = false, - bool count_include_pad = true) + bool count_include_pad = true, + long? divisor_override = null) { - long svalue = (stride == null) ? kernelSize : stride.Value; + long svalue = (stride == null) ? kernel_size : stride.Value; - long* pkernelSize = stackalloc long[2] { kernelSize, kernelSize }; + long* pkernel_size = stackalloc long[2] { kernel_size, kernel_size }; long* pstrides = stackalloc long[2] { svalue, svalue }; long* ppadding = stackalloc long[2] { padding, padding }; - var res = THSTensor_avg_pool2d(input.Handle, - (IntPtr)pkernelSize, 2, + var res = + THSTensor_avg_pool2d(input.Handle, + (IntPtr)pkernel_size, 2, (IntPtr)pstrides, 2, (IntPtr)ppadding, 2, ceil_mode, - count_include_pad); - return ReturnCheckForErrors(res); - + count_include_pad, + divisor_override ?? 0); + if (res == IntPtr.Zero) { torch.CheckForErrors(); } + return new Tensor(res); } /// /// Applies 2D average-pooling operation in kH × kW regions by step size sH * sW steps. The number of output features is equal to the number of input planes. /// /// The input tensor. - /// + /// /// /// /// /// + /// /// - public static unsafe Tensor avg_pool2d(Tensor input, (long, long) kernelSize, + public static unsafe Tensor avg_pool2d(Tensor input, (long, long) kernel_size, (long, long)? stride = null, (long, long)? padding = null, bool ceil_mode = false, - bool count_include_pad = true) + bool count_include_pad = true, + long? divisor_override = null) { - long svalue1 = (stride == null) ? kernelSize.Item1 : stride.Value.Item1; - long svalue2 = (stride == null) ? kernelSize.Item2 : stride.Value.Item2; + long svalue1 = (stride == null) ? kernel_size.Item1 : stride.Value.Item1; + long svalue2 = (stride == null) ? kernel_size.Item2 : stride.Value.Item2; long pvalue1 = padding != null ? padding.Value.Item1 : 0; long pvalue2 = padding != null ? padding.Value.Item2 : 0; long* pstrides = stackalloc long[2] { svalue1, svalue2 }; long* ppadding = stackalloc long[2] { pvalue1, pvalue2 }; - long* pkernelSize = stackalloc long[2] { kernelSize.Item1, kernelSize.Item2 }; + long* pkernel_size = stackalloc long[2] { kernel_size.Item1, kernel_size.Item2 }; - var res = THSTensor_avg_pool2d(input.Handle, - (IntPtr)pkernelSize, 2, + var res = + THSTensor_avg_pool2d(input.Handle, + (IntPtr)pkernel_size, 2, (IntPtr)pstrides, 2, (IntPtr)ppadding, 2, ceil_mode, - count_include_pad); - return ReturnCheckForErrors(res); + count_include_pad, + divisor_override ?? 0); + if (res == IntPtr.Zero) { torch.CheckForErrors(); } + return new Tensor(res); } public static Tensor avg_pool2d_backward(Tensor input, Tensor originalInput, - long[] kernelSizes, + long[] kernel_sizes, long[] strides = null, long[] paddings = null, bool ceil_mode = false, bool count_include_pad = true, - long divisorOverride = 0) + long? divisor_override = null) { strides = (strides == null) ? new long[] { 1 } : strides; paddings = (paddings == null) ? new long[] { 0 } : paddings; unsafe { - fixed (long* pkernelSize = kernelSizes, pstrides = strides, ppadding = paddings) { + fixed (long* pkernel_size = kernel_sizes, pstrides = strides, ppadding = paddings) { var res = THSTensor_avg_pool2d_backward(input.Handle, originalInput.Handle, - (IntPtr)pkernelSize, kernelSizes.Length, + (IntPtr)pkernel_size, kernel_sizes.Length, (IntPtr)pstrides, strides.Length, (IntPtr)ppadding, paddings.Length, ceil_mode, count_include_pad, - divisorOverride); - return ReturnCheckForErrors(res); + divisor_override ?? 0); + if (res == IntPtr.Zero) { torch.CheckForErrors(); } + return new Tensor(res); } } } diff --git a/src/TorchSharp/NN/Pooling/AvgPool3D.cs b/src/TorchSharp/NN/Pooling/AvgPool3D.cs index bad7adfc8..4eb0427e2 100644 --- a/src/TorchSharp/NN/Pooling/AvgPool3D.cs +++ b/src/TorchSharp/NN/Pooling/AvgPool3D.cs @@ -12,22 +12,29 @@ namespace Modules /// /// This class is used to represent a AvgPool3D module. /// - public sealed class AvgPool3d : torch.nn.Module + public sealed class AvgPool3d : ParameterLessModule { - internal AvgPool3d(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) + internal AvgPool3d(long[] kernel_size, long[] stride = null, long[] padding = null, bool ceil_mode = false, bool count_include_pad = true, long? divisor_override = null) : base(nameof(AvgPool3d)) { + this.kernel_size = kernel_size; + this.stride = stride; + this.padding = padding; + this.ceil_mode = ceil_mode; + this.count_include_pad = count_include_pad; + this.divisor_override = divisor_override; } - public override Tensor forward(Tensor tensor) + public override Tensor forward(Tensor input) { - return ReturnCheckForErrors(THSNN_AvgPool3d_forward(handle.DangerousGetHandle(), tensor.Handle)); + return torch.nn.functional.avg_pool3d(input, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override); } - // Rather than spending cycles only to discover that this module has neither - // parameters nor buffers, just shortcut the move completely. - protected internal override nn.Module _to(Device device, ScalarType dtype, bool non_blocking) => this; - protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex, bool non_blocking) => this; - protected internal override nn.Module _to(ScalarType dtype, bool non_blocking) => this; + public long[] kernel_size { get; set; } + public long[] stride { get; set; } + public long[] padding { get; set; } + public bool ceil_mode { get; set; } + public bool count_include_pad { get; set; } + public long? divisor_override { get; set; } } } @@ -39,20 +46,14 @@ public static partial class nn /// Applies a 3D average pooling over an input signal composed of several input planes. /// /// The size of the window - /// The stride of the window. Default value is kernel_size + /// The stride of the window. Default value is kernel_size /// implicit zero padding to be added on both sides /// Whether to use ceil instead of floor to compute the output shape /// Whether to include the zero-padding in the averaging calculation /// If specified, it will be used as divisor, otherwise size of the pooling region will be used - public static AvgPool3d AvgPool3d(long[] kernel_size, long[] strides = null, long[] padding = null, bool ceil_mode = false, bool count_include_pad = true, long? divisor_override = null) + public static AvgPool3d AvgPool3d(long[] kernel_size, long[] stride = null, long[] padding = null, bool ceil_mode = false, bool count_include_pad = true, long? divisor_override = null) { - unsafe { - fixed (long* pkernelSize = kernel_size, pstrides = strides, ppadding = padding) { - var handle = THSNN_AvgPool3d_ctor((IntPtr)pkernelSize, kernel_size.Length, (IntPtr)pstrides, (strides == null ? 0 : strides.Length), (IntPtr)ppadding, (padding == null ? 0 : padding.Length), ceil_mode, count_include_pad, divisor_override.HasValue ? divisor_override.Value : 0, out var boxedHandle); - if (handle == IntPtr.Zero) { torch.CheckForErrors(); } - return new AvgPool3d(handle, boxedHandle); - } - } + return new AvgPool3d(kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override); } /// @@ -66,21 +67,10 @@ public static AvgPool3d AvgPool3d(long[] kernel_size, long[] strides = null, lon /// If specified, it will be used as divisor, otherwise size of the pooling region will be used public static unsafe AvgPool3d AvgPool3d((long, long, long) kernel_size, (long, long, long)? stride = null, (long, long, long)? padding = null, bool ceil_mode = false, bool count_include_pad = true, long? divisor_override = null) { - long svalue1 = (stride == null) ? kernel_size.Item1 : stride.Value.Item1; - long svalue2 = (stride == null) ? kernel_size.Item2 : stride.Value.Item2; - long svalue3 = (stride == null) ? kernel_size.Item3 : stride.Value.Item3; - - long pvalue1 = (padding == null) ? 0 : padding.Value.Item1; - long pvalue2 = (padding == null) ? 0 : padding.Value.Item2; - long pvalue3 = (padding == null) ? 0 : padding.Value.Item3; - - long* pkernelSize = stackalloc long[3] { kernel_size.Item1, kernel_size.Item2, kernel_size.Item3 }; - long* pstrides = stackalloc long[3] { svalue1, svalue2, svalue3 }; - long* ppadding = stackalloc long[3] { pvalue1, pvalue2, pvalue3 }; - - var handle = THSNN_AvgPool3d_ctor((IntPtr)pkernelSize, 3, (IntPtr)pstrides, 3, (IntPtr)ppadding, 3, ceil_mode, count_include_pad, divisor_override.HasValue ? divisor_override.Value : 0, out var boxedHandle); - if (handle == IntPtr.Zero) { torch.CheckForErrors(); } - return new AvgPool3d(handle, boxedHandle); + long[] kernelValue = new[] { kernel_size.Item1, kernel_size.Item2, kernel_size.Item3 }; + long[] strideValue = stride == null ? null : new[] { stride.Value.Item1, stride.Value.Item2, stride.Value.Item3 }; + long[] paddingValue = padding == null ? null : new[] { padding.Value.Item1, padding.Value.Item2, padding.Value.Item3 }; + return new AvgPool3d(kernelValue, strideValue, paddingValue, ceil_mode, count_include_pad, divisor_override); } /// @@ -94,16 +84,10 @@ public static unsafe AvgPool3d AvgPool3d((long, long, long) kernel_size, (long, /// If specified, it will be used as divisor, otherwise size of the pooling region will be used public static unsafe AvgPool3d AvgPool3d(long kernel_size, long? stride = null, long? padding = null, bool ceil_mode = false, bool count_include_pad = true, long? divisor_override = null) { - long svalue = (stride == null) ? kernel_size : stride.Value; - long pvalue = (padding == null) ? 0 : padding.Value; - - long* pkernelSize = stackalloc long[3] { kernel_size, kernel_size, kernel_size }; - long* pstrides = stackalloc long[3] { svalue, svalue, svalue }; - long* ppadding = stackalloc long[3] { pvalue, pvalue, pvalue }; - - var handle = THSNN_AvgPool3d_ctor((IntPtr)pkernelSize, 3, (IntPtr)pstrides, 3, (IntPtr)ppadding, 3, ceil_mode, count_include_pad, divisor_override.HasValue ? divisor_override.Value : 0, out var boxedHandle); - if (handle == IntPtr.Zero) { torch.CheckForErrors(); } - return new AvgPool3d(handle, boxedHandle); + long[] kernelValue = new[] { kernel_size, kernel_size, kernel_size }; + long[] strideValue = stride == null ? null : new[] { stride.Value, stride.Value, stride.Value }; + long[] paddingValue = padding == null ? null : new[] { padding.Value, padding.Value, padding.Value }; + return new AvgPool3d(kernelValue, strideValue, paddingValue, ceil_mode, count_include_pad, divisor_override); } public static partial class functional @@ -112,53 +96,59 @@ public static partial class functional /// Applies 3D average-pooling operation in kT x kH x kW regions by step size sT x sH x sW steps. /// /// The input tensor. - /// - /// - /// + /// + /// + /// /// /// + /// /// - public static Tensor avg_pool3d(Tensor input, long[] kernelSizes, - long[] strides = null, - long[] paddings = null, + public static Tensor avg_pool3d(Tensor input, long[] kernel_size, + long[] stride = null, + long[] padding = null, bool ceil_mode = false, - bool count_include_pad = true) + bool count_include_pad = true, + long? divisor_override = null) { - strides = (strides == null) ? new long[] { 1 } : strides; - paddings = (paddings == null) ? new long[] { 0 } : paddings; + stride = (stride == null) ? kernel_size : stride; + padding = (padding == null) ? new long[] { 0 } : padding; unsafe { - fixed (long* pkernelSize = kernelSizes, pstrides = strides, ppadding = paddings) { - var res = THSTensor_avg_pool3d(input.Handle, - (IntPtr)pkernelSize, kernelSizes.Length, - (IntPtr)pstrides, strides.Length, - (IntPtr)ppadding, paddings.Length, + fixed (long* pkernel_size = kernel_size, pstrides = stride, ppadding = padding) { + var res = + THSTensor_avg_pool3d(input.Handle, + (IntPtr)pkernel_size, kernel_size.Length, + (IntPtr)pstrides, stride.Length, + (IntPtr)ppadding, padding.Length, ceil_mode, - count_include_pad); - return ReturnCheckForErrors(res); + count_include_pad, divisor_override ?? 0); + if (res == IntPtr.Zero) { torch.CheckForErrors(); } + return new Tensor(res); } } } public static Tensor avg_pool3d_backward(Tensor input, Tensor originalInput, - long[] kernelSizes, + long[] kernel_sizes, long[] strides = null, long[] paddings = null, bool ceil_mode = false, bool count_include_pad = true, - long divisorOverride = 0) + long? divisor_override = null) { - strides = (strides == null) ? new long[] { 1 } : strides; + strides = (strides == null) ? kernel_sizes : strides; paddings = (paddings == null) ? new long[] { 0 } : paddings; unsafe { - fixed (long* pkernelSize = kernelSizes, pstrides = strides, ppadding = paddings) { - var res = THSTensor_avg_pool3d_backward(input.Handle, originalInput.Handle, - (IntPtr)pkernelSize, kernelSizes.Length, + fixed (long* pkernel_size = kernel_sizes, pstrides = strides, ppadding = paddings) { + var res = + THSTensor_avg_pool3d_backward(input.Handle, originalInput.Handle, + (IntPtr)pkernel_size, kernel_sizes.Length, (IntPtr)pstrides, strides.Length, (IntPtr)ppadding, paddings.Length, ceil_mode, count_include_pad, - divisorOverride); - return ReturnCheckForErrors(res); + divisor_override ?? 0); + if (res == IntPtr.Zero) { torch.CheckForErrors(); } + return new Tensor(res); } } } diff --git a/src/TorchSharp/NN/Pooling/FractionalMaxPool2d.cs b/src/TorchSharp/NN/Pooling/FractionalMaxPool2d.cs index 72ef22130..d50c4d40d 100644 --- a/src/TorchSharp/NN/Pooling/FractionalMaxPool2d.cs +++ b/src/TorchSharp/NN/Pooling/FractionalMaxPool2d.cs @@ -5,6 +5,7 @@ namespace TorchSharp { + using System.Data; using Modules; namespace Modules @@ -12,29 +13,28 @@ namespace Modules /// /// This class is used to represent a FractionalMaxPool2D module. /// - public sealed class FractionalMaxPool2d : torch.nn.Module + public sealed class FractionalMaxPool2d : ParameterLessModule { - internal FractionalMaxPool2d(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) + internal FractionalMaxPool2d(long[] kernel_size, long[] output_size = null, double[] output_ratio = null) : base(nameof(FractionalMaxPool2d)) { + this.kernel_size = kernel_size; + this.output_size = output_size; + this.output_ratio = output_ratio; } - public override Tensor forward(Tensor tensor) + public override Tensor forward(Tensor input) { - return ReturnCheckForErrors(THSNN_FractionalMaxPool2d_forward(handle, tensor.Handle)); + return torch.nn.functional.fractional_max_pool2d(input, kernel_size, output_size, output_ratio); } - public (Tensor Values, Tensor Indices) forward_with_indices(Tensor tensor) + public (Tensor Values, Tensor Indices) forward_with_indices(Tensor input) { - - var res = THSNN_FractionalMaxPool2d_forward_with_indices(handle, tensor.Handle, out var indices); - return ReturnCheckForErrors(res, indices); + return torch.nn.functional.fractional_max_pool2d_with_indices(input, kernel_size, output_size, output_ratio); } - // Rather than spending cycles only to discover that this module has neither - // parameters nor buffers, just shortcut the move completely. - protected internal override nn.Module _to(Device device, ScalarType dtype, bool non_blocking) => this; - protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex, bool non_blocking) => this; - protected internal override nn.Module _to(ScalarType dtype, bool non_blocking) => this; + public long[] kernel_size { get; set; } + public long[] output_size { get; set; } + public double[] output_ratio { get; set; } } } @@ -99,16 +99,135 @@ public static FractionalMaxPool2d FractionalMaxPool2d(long[] kernel_size, long[] if (output_size != null && output_ratio != null) throw new ArgumentNullException("FractionalMaxPool2d requires specifying either an output size, or a pooling ratio."); - unsafe { - fixed (long* pkernelSize = kernel_size, pSize = output_size) { - fixed (double* pRatio = output_ratio) { - var handle = THSNN_FractionalMaxPool2d_ctor( - (IntPtr)pkernelSize, kernel_size.Length, - (IntPtr)pSize, (output_size == null ? 0 : output_size.Length), - (IntPtr)pRatio, (output_ratio == null ? 0 : output_ratio.Length), - out var boxedHandle); - if (handle == IntPtr.Zero) { torch.CheckForErrors(); } - return new FractionalMaxPool2d(handle, boxedHandle); + return new FractionalMaxPool2d(kernel_size, output_size, output_ratio); + } + + public static partial class functional + { + /// + /// Applies a 2D fractional max pooling over an input signal composed of several input planes. + /// + /// Fractional MaxPooling is described in detail in the paper Fractional MaxPooling by Ben Graham, + /// see: https://arxiv.org/abs/1412.6071 + /// + /// The input tensor + /// The size of the sliding window, must be > 0. + /// The target output size of the image of the form oH x oW. Can be a tuple (oH, oW) or a single number oH for a square image oH x oH + /// If one wants to have an output size as a ratio of the input size, this option can be given. This has to be a number or tuple in the range (0, 1) + /// + public static Tensor fractional_max_pool2d(Tensor input, long kernel_size, long? output_size = null, double? output_ratio = null) + { + var pSize = output_size.HasValue ? new long[] { output_size.Value, output_size.Value } : null; + var pRatio = output_ratio.HasValue ? new double[] { output_ratio.Value, output_ratio.Value } : null; + return fractional_max_pool2d(input, new long[] { kernel_size, kernel_size }, pSize, pRatio); + } + + /// + /// Applies a 2D fractional max pooling over an input signal composed of several input planes. + /// + /// Fractional MaxPooling is described in detail in the paper Fractional MaxPooling by Ben Graham, + /// see: https://arxiv.org/abs/1412.6071 + /// + /// The input tensor + /// The size of the sliding window, must be > 0. + /// The target output size of the image of the form oH x oW. Can be a tuple (oH, oW) or a single number oH for a square image oH x oH + /// If one wants to have an output size as a ratio of the input size, this option can be given. This has to be a number or tuple in the range (0, 1) + /// + public static Tensor fractional_max_pool2d(Tensor input, (long, long) kernel_size, (long, long)? output_size = null, (double, double)? output_ratio = null) + { + var pSize = output_size.HasValue ? new long[] { output_size.Value.Item1, output_size.Value.Item2 } : null; + var pRatio = output_ratio.HasValue ? new double[] { output_ratio.Value.Item1, output_ratio.Value.Item2 } : null; + return fractional_max_pool2d(input, new long[] { kernel_size.Item1, kernel_size.Item2 }, pSize, pRatio); + } + + /// + /// Applies a 2D fractional max pooling over an input signal composed of several input planes. + /// + /// Fractional MaxPooling is described in detail in the paper Fractional MaxPooling by Ben Graham, + /// see: https://arxiv.org/abs/1412.6071 + /// + /// The input tensor + /// The size of the sliding window, must be > 0. + /// The target output size of the image of the form oH x oW. Can be a tuple (oH, oW) or a single number oH for a square image oH x oH + /// If one wants to have an output size as a ratio of the input size, this option can be given. This has to be a number or tuple in the range (0, 1) + /// + public static Tensor fractional_max_pool2d(Tensor input, long[] kernel_size, long[] output_size = null, double[] output_ratio = null) + { + var ret = fractional_max_pool2d_with_indices(input, kernel_size, output_size, output_ratio); + ret.Indices.Dispose(); + return ret.Values; + } + + /// + /// Applies a 2D fractional max pooling over an input signal composed of several input planes. + /// + /// Fractional MaxPooling is described in detail in the paper Fractional MaxPooling by Ben Graham, + /// see: https://arxiv.org/abs/1412.6071 + /// + /// The input tensor + /// The size of the sliding window, must be > 0. + /// The target output size of the image of the form oH x oW. Can be a tuple (oH, oW) or a single number oH for a square image oH x oH + /// If one wants to have an output size as a ratio of the input size, this option can be given. This has to be a number or tuple in the range (0, 1) + /// + public static (Tensor Values, Tensor Indices) fractional_max_pool2d_with_indices(Tensor input, long kernel_size, long? output_size = null, double? output_ratio = null) + { + var pSize = output_size.HasValue ? new long[] { output_size.Value, output_size.Value } : null; + var pRatio = output_ratio.HasValue ? new double[] { output_ratio.Value, output_ratio.Value } : null; + return fractional_max_pool2d_with_indices(input, new long[] { kernel_size, kernel_size }, pSize, pRatio); + } + + /// + /// Applies a 2D fractional max pooling over an input signal composed of several input planes. + /// + /// Fractional MaxPooling is described in detail in the paper Fractional MaxPooling by Ben Graham, + /// see: https://arxiv.org/abs/1412.6071 + /// + /// The input tensor + /// The size of the sliding window, must be > 0. + /// The target output size of the image of the form oH x oW. Can be a tuple (oH, oW) or a single number oH for a square image oH x oH + /// If one wants to have an output size as a ratio of the input size, this option can be given. This has to be a number or tuple in the range (0, 1) + /// + public static (Tensor Values, Tensor Indices) fractional_max_pool2d_with_indices(Tensor input, (long, long) kernel_size, (long, long)? output_size = null, (double, double)? output_ratio = null) + { + var pSize = output_size.HasValue ? new long[] { output_size.Value.Item1, output_size.Value.Item2 } : null; + var pRatio = output_ratio.HasValue ? new double[] { output_ratio.Value.Item1, output_ratio.Value.Item2 } : null; + return fractional_max_pool2d_with_indices(input, new long[] { kernel_size.Item1, kernel_size.Item2 }, pSize, pRatio); + } + + /// + /// Applies a 2D fractional max pooling over an input signal composed of several input planes. + /// + /// Fractional MaxPooling is described in detail in the paper Fractional MaxPooling by Ben Graham, + /// see: https://arxiv.org/abs/1412.6071 + /// + /// The input tensor + /// The size of the sliding window, must be > 0. + /// The target output size of the image of the form oH x oW. Can be a tuple (oH, oW) or a single number oH for a square image oH x oH + /// If one wants to have an output size as a ratio of the input size, this option can be given. This has to be a number or tuple in the range (0, 1) + /// + public static (Tensor Values, Tensor Indices) fractional_max_pool2d_with_indices(Tensor input, long[] kernel_size, long[] output_size = null, double[] output_ratio = null) + { + if (kernel_size == null || kernel_size.Length != 2) + throw new ArgumentException("Kernel size must contain two elements."); + if (output_size != null && output_size.Length != 2) + throw new ArgumentException("output_size must contain two elements."); + if (output_ratio != null && output_ratio.Length != 2) + throw new ArgumentException("output_ratio must contain two elements."); + if (output_size == null && output_ratio == null) + throw new ArgumentNullException("Only one of output_size and output_ratio may be specified."); + if (output_size != null && output_ratio != null) + throw new ArgumentNullException("FractionalMaxPool2d requires specifying either an output size, or a pooling ratio."); + + output_size ??= Array.Empty(); + output_ratio ??= Array.Empty(); + + unsafe { + fixed (long* pkernel_size = kernel_size, poutputSize = output_size) { + fixed (double* poutputRatio = output_ratio) { + var resOutput = THSTensor_fractional_max_pool2d(input.Handle, (IntPtr)pkernel_size, kernel_size.Length, (IntPtr)poutputSize, output_size.Length, (IntPtr)poutputRatio, output_ratio.Length, out var resIndices); + if (resOutput == IntPtr.Zero || resIndices == IntPtr.Zero) { torch.CheckForErrors(); } + return (new Tensor(resOutput), new Tensor(resIndices)); + } } } } diff --git a/src/TorchSharp/NN/Pooling/FractionalMaxPool3d.cs b/src/TorchSharp/NN/Pooling/FractionalMaxPool3d.cs index 1f5e252f6..14874d6cc 100644 --- a/src/TorchSharp/NN/Pooling/FractionalMaxPool3d.cs +++ b/src/TorchSharp/NN/Pooling/FractionalMaxPool3d.cs @@ -12,39 +12,28 @@ namespace Modules /// /// This class is used to represent a FractionalMaxPool3d module. /// - public sealed class FractionalMaxPool3d : torch.nn.Module + public sealed class FractionalMaxPool3d : ParameterLessModule { - internal FractionalMaxPool3d(IntPtr handle, IntPtr boxedHandle, bool ratio) : base(handle, boxedHandle) + internal FractionalMaxPool3d(long[] kernel_size, long[] output_size = null, double[] output_ratio = null) : base(nameof(FractionalMaxPool3d)) { - _used_ratio = ratio; + this.kernel_size = kernel_size; + this.output_size = output_size; + this.output_ratio = output_ratio; } - public override Tensor forward(Tensor tensor) + public override Tensor forward(Tensor input) { - if (_used_ratio && tensor.ndim != 5) - // Not sure why this is the case, but there's an exception in the native runtime - // unless there's both a batch dimension and a channel dimension. - throw new ArgumentException("FractionalMaxPool3d: input tensor must have 5 dimensions: [N, C, D, H, W]"); - return ReturnCheckForErrors(THSNN_FractionalMaxPool3d_forward(handle, tensor.Handle)); + return torch.nn.functional.fractional_max_pool3d(input, kernel_size, output_size, output_ratio); } - public (Tensor Values, Tensor Indices) forward_with_indices(Tensor tensor) + public (Tensor Values, Tensor Indices) forward_with_indices(Tensor input) { - if (_used_ratio && tensor.ndim != 5) - // Not sure why this is the case, but there's an exception in the native runtime - // unless there's both a batch dimension and a channel dimension. - throw new ArgumentException("FractionalMaxPool3d: input tensor must have 5 dimensions: [N, C, D, H, W]"); - var res = THSNN_FractionalMaxPool3d_forward_with_indices(handle, tensor.Handle, out var indices); - return ReturnCheckForErrors(res, indices); + return torch.nn.functional.fractional_max_pool3d_with_indices(input, kernel_size, output_size, output_ratio); } - // Rather than spending cycles only to discover that this module has neither - // parameters nor buffers, just shortcut the move completely. - protected internal override nn.Module _to(Device device, ScalarType dtype, bool non_blocking) => this; - protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex, bool non_blocking) => this; - protected internal override nn.Module _to(ScalarType dtype, bool non_blocking) => this; - - private bool _used_ratio = false; + public long[] kernel_size { get; set; } + public long[] output_size { get; set; } + public double[] output_ratio { get; set; } } } @@ -109,16 +98,139 @@ public static FractionalMaxPool3d FractionalMaxPool3d(long[] kernel_size, long[] if (output_size != null && output_ratio != null) throw new ArgumentNullException("FractionalMaxPool3d requires specifying either an output size, or a pooling ratio."); - unsafe { - fixed (long* pkernelSize = kernel_size, pSize = output_size) { - fixed (double* pRatio = output_ratio) { - var handle = THSNN_FractionalMaxPool3d_ctor( - (IntPtr)pkernelSize, kernel_size.Length, - (IntPtr)pSize, (output_size == null ? 0 : output_size.Length), - (IntPtr)pRatio, (output_ratio == null ? 0 : output_ratio.Length), - out var boxedHandle); - if (handle == IntPtr.Zero) { torch.CheckForErrors(); } - return new FractionalMaxPool3d(handle, boxedHandle, output_ratio != null); + return new FractionalMaxPool3d(kernel_size, output_size, output_ratio); + } + + public static partial class functional + { + /// + /// Applies a 3d fractional max pooling over an input signal composed of several input planes. + /// + /// Fractional MaxPooling is described in detail in the paper Fractional MaxPooling by Ben Graham, + /// see: https://arxiv.org/abs/1412.6071 + /// + /// The input tensor + /// The size of the sliding window, must be > 0. + /// The target output size of the image of the form oH x oW. Can be a tuple (oH, oW) or a single number oH for a square image oH x oH + /// If one wants to have an output size as a ratio of the input size, this option can be given. This has to be a number or tuple in the range (0, 1) + /// + public static Tensor fractional_max_pool3d(Tensor input, long kernel_size, long? output_size = null, double? output_ratio = null) + { + var pSize = output_size.HasValue ? new long[] { output_size.Value, output_size.Value, output_size.Value } : null; + var pRatio = output_ratio.HasValue ? new double[] { output_ratio.Value, output_ratio.Value, output_ratio.Value } : null; + return fractional_max_pool3d(input, new long[] { kernel_size, kernel_size, kernel_size }, pSize, pRatio); + } + + /// + /// Applies a 3d fractional max pooling over an input signal composed of several input planes. + /// + /// Fractional MaxPooling is described in detail in the paper Fractional MaxPooling by Ben Graham, + /// see: https://arxiv.org/abs/1412.6071 + /// + /// The input tensor + /// The size of the sliding window, must be > 0. + /// The target output size of the image of the form oH x oW. Can be a tuple (oH, oW) or a single number oH for a square image oH x oH + /// If one wants to have an output size as a ratio of the input size, this option can be given. This has to be a number or tuple in the range (0, 1) + /// + public static Tensor fractional_max_pool3d(Tensor input, (long, long, long) kernel_size, (long, long, long)? output_size = null, (double, double, double)? output_ratio = null) + { + var pSize = output_size.HasValue ? new long[] { output_size.Value.Item1, output_size.Value.Item2, output_size.Value.Item3 } : null; + var pRatio = output_ratio.HasValue ? new double[] { output_ratio.Value.Item1, output_ratio.Value.Item2, output_ratio.Value.Item3 } : null; + return fractional_max_pool3d(input, new long[] { kernel_size.Item1, kernel_size.Item2, kernel_size.Item3 }, pSize, pRatio); + } + + /// + /// Applies a 3d fractional max pooling over an input signal composed of several input planes. + /// + /// Fractional MaxPooling is described in detail in the paper Fractional MaxPooling by Ben Graham, + /// see: https://arxiv.org/abs/1412.6071 + /// + /// The input tensor + /// The size of the sliding window, must be > 0. + /// The target output size of the image of the form oH x oW. Can be a tuple (oH, oW) or a single number oH for a square image oH x oH + /// If one wants to have an output size as a ratio of the input size, this option can be given. This has to be a number or tuple in the range (0, 1) + /// + public static Tensor fractional_max_pool3d(Tensor input, long[] kernel_size, long[] output_size = null, double[] output_ratio = null) + { + var ret = fractional_max_pool3d_with_indices(input, kernel_size, output_size, output_ratio); + ret.Indices.Dispose(); + return ret.Values; + } + + /// + /// Applies a 3d fractional max pooling over an input signal composed of several input planes. + /// + /// Fractional MaxPooling is described in detail in the paper Fractional MaxPooling by Ben Graham, + /// see: https://arxiv.org/abs/1412.6071 + /// + /// The input tensor + /// The size of the sliding window, must be > 0. + /// The target output size of the image of the form oH x oW. Can be a tuple (oH, oW) or a single number oH for a square image oH x oH + /// If one wants to have an output size as a ratio of the input size, this option can be given. This has to be a number or tuple in the range (0, 1) + /// + public static (Tensor Values, Tensor Indices) fractional_max_pool3d_with_indices(Tensor input, long kernel_size, long? output_size = null, double? output_ratio = null) + { + var pSize = output_size.HasValue ? new long[] { output_size.Value, output_size.Value, output_size.Value } : null; + var pRatio = output_ratio.HasValue ? new double[] { output_ratio.Value, output_ratio.Value, output_ratio.Value } : null; + return fractional_max_pool3d_with_indices(input, new long[] { kernel_size, kernel_size, kernel_size }, pSize, pRatio); + } + + /// + /// Applies a 3d fractional max pooling over an input signal composed of several input planes. + /// + /// Fractional MaxPooling is described in detail in the paper Fractional MaxPooling by Ben Graham, + /// see: https://arxiv.org/abs/1412.6071 + /// + /// The input tensor + /// The size of the sliding window, must be > 0. + /// The target output size of the image of the form oH x oW. Can be a tuple (oH, oW) or a single number oH for a square image oH x oH + /// If one wants to have an output size as a ratio of the input size, this option can be given. This has to be a number or tuple in the range (0, 1) + /// + public static (Tensor Values, Tensor Indices) fractional_max_pool3d_with_indices(Tensor input, (long, long, long) kernel_size, (long, long, long)? output_size = null, (double, double, double)? output_ratio = null) + { + var pSize = output_size.HasValue ? new long[] { output_size.Value.Item1, output_size.Value.Item2, output_size.Value.Item3 } : null; + var pRatio = output_ratio.HasValue ? new double[] { output_ratio.Value.Item1, output_ratio.Value.Item2, output_ratio.Value.Item3 } : null; + return fractional_max_pool3d_with_indices(input, new long[] { kernel_size.Item1, kernel_size.Item2, kernel_size.Item3 }, pSize, pRatio); + } + + /// + /// Applies a 3d fractional max pooling over an input signal composed of several input planes. + /// + /// Fractional MaxPooling is described in detail in the paper Fractional MaxPooling by Ben Graham, + /// see: https://arxiv.org/abs/1412.6071 + /// + /// The input tensor + /// The size of the sliding window, must be > 0. + /// The target output size of the image of the form oH x oW. Can be a tuple (oH, oW) or a single number oH for a square image oH x oH + /// If one wants to have an output size as a ratio of the input size, this option can be given. This has to be a number or tuple in the range (0, 1) + /// + public static (Tensor Values, Tensor Indices) fractional_max_pool3d_with_indices(Tensor input, long[] kernel_size, long[] output_size = null, double[] output_ratio = null) + { + if (kernel_size == null || kernel_size.Length != 3) + throw new ArgumentException("Kernel size must contain three elements."); + if (output_size != null && output_size.Length != 3) + throw new ArgumentException("output_size must contain three elements."); + if (output_ratio != null && output_ratio.Length != 3) + throw new ArgumentException("output_ratio must contain three elements."); + if (output_size == null && output_ratio == null) + throw new ArgumentNullException("Only one of output_size and output_ratio may be specified."); + if (output_size != null && output_ratio != null) + throw new ArgumentNullException("FractionalMaxPool3d requires specifying either an output size, or a pooling ratio."); + if (output_ratio != null && input.ndim != 5) + // Not sure why this is the case, but there's an exception in the native runtime + // unless there's both a batch dimension and a channel dimension. + throw new ArgumentException("FractionalMaxPool3d: input tensor must have 5 dimensions: [N, C, D, H, W]"); + + output_size ??= Array.Empty(); + output_ratio ??= Array.Empty(); + + unsafe { + fixed (long* pkernel_size = kernel_size, poutputSize = output_size) { + fixed (double* poutputRatio = output_ratio) { + var resOutput = THSTensor_fractional_max_pool3d(input.Handle, (IntPtr)pkernel_size, kernel_size.Length, (IntPtr)poutputSize, output_size.Length, (IntPtr)poutputRatio, output_ratio.Length, out var resIndices); + if (resOutput == IntPtr.Zero || resIndices == IntPtr.Zero) { torch.CheckForErrors(); } + return (new Tensor(resOutput), new Tensor(resIndices)); + } } } } diff --git a/src/TorchSharp/NN/Pooling/LPPool1d.cs b/src/TorchSharp/NN/Pooling/LPPool1d.cs index 30ef1c830..c7882fc11 100644 --- a/src/TorchSharp/NN/Pooling/LPPool1d.cs +++ b/src/TorchSharp/NN/Pooling/LPPool1d.cs @@ -12,22 +12,25 @@ namespace Modules /// /// This class is used to represent a LPPool1D module. /// - public sealed class LPPool1d : torch.nn.Module + public sealed class LPPool1d : ParameterLessModule { - internal LPPool1d(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) + internal LPPool1d(double norm_type, long kernel_size, long? stride = null, bool ceil_mode = false) : base(nameof(LPPool1d)) { + this.norm_type = norm_type; + this.kernel_size = kernel_size; + this.stride = stride; + this.ceil_mode = ceil_mode; } - public override Tensor forward(Tensor tensor) + public override Tensor forward(Tensor input) { - return ReturnCheckForErrors(THSNN_LPPool1d_forward(handle.DangerousGetHandle(), tensor.Handle)); + return torch.nn.functional.lp_pool1d(input, norm_type, kernel_size, stride, ceil_mode); } - // Rather than spending cycles only to discover that this module has neither - // parameters nor buffers, just shortcut the move completely. - protected internal override nn.Module _to(Device device, ScalarType dtype, bool non_blocking) => this; - protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex, bool non_blocking) => this; - protected internal override nn.Module _to(ScalarType dtype, bool non_blocking) => this; + public double norm_type { get; set; } + public long kernel_size { get; set; } + public long? stride { get; set; } + public bool ceil_mode { get; set; } } } @@ -39,32 +42,37 @@ public static partial class nn /// Applies a 1D power-average pooling over an input signal composed of several input planes. /// /// The LP norm (exponent) - /// The size of the window + /// The size of the window /// The stride of the window. Default value is kernel_size /// Use ceil instead of floor to compute the output shape /// - public static LPPool1d LPPool1d(double norm_type, long kernelSize, long? stride = null, bool ceil_mode = false) + public static LPPool1d LPPool1d(double norm_type, long kernel_size, long? stride = null, bool ceil_mode = false) { - return stride.HasValue ? - LPPool1d(norm_type, new long[] { kernelSize }, new long[] { stride.Value }, ceil_mode) : - LPPool1d(norm_type, new long[] { kernelSize }, null); + return new LPPool1d(norm_type, kernel_size, stride, ceil_mode); } - /// - /// Applies a 1D power-average pooling over an input signal composed of several input planes. - /// - /// The LP norm (exponent) - /// The size of the window - /// The stride of the window. Default value is kernel_size - /// Use ceil instead of floor to compute the output shape - /// - private static LPPool1d LPPool1d(double norm_type, long[] kernelSize, long[] strides = null, bool ceil_mode = false) + public static partial class functional { - unsafe { - fixed (long* pkernelSize = kernelSize, pstrides = strides) { - var handle = THSNN_LPPool1d_ctor(norm_type, (IntPtr)pkernelSize, (IntPtr)pstrides, ceil_mode, out var boxedHandle); - if (handle == IntPtr.Zero) { torch.CheckForErrors(); } - return new LPPool1d(handle, boxedHandle); + /// + /// Applies a 1D power-average pooling over an input signal composed of several input planes. + /// + /// The input tensor + /// The LP norm (exponent) + /// The size of the window + /// The stride of the window. Default value is kernel_size + /// Use ceil instead of floor to compute the output shape + /// + public static Tensor lp_pool1d(Tensor input, double norm_type, long kernel_size, long? stride = null, bool ceil_mode = false) + { + var kernels = new[] { kernel_size }; + var strides = stride.HasValue ? new[] { stride.Value } : Array.Empty(); + + unsafe { + fixed (long* pkernel_size = kernels, pstrides = strides) { + var res = THSTensor_lp_pool1d(input.Handle, norm_type, (IntPtr)pkernel_size, kernels.Length, (IntPtr)pstrides, strides.Length, ceil_mode); + if (res == IntPtr.Zero) { torch.CheckForErrors(); } + return new Tensor(res); + } } } } diff --git a/src/TorchSharp/NN/Pooling/LPPool2d.cs b/src/TorchSharp/NN/Pooling/LPPool2d.cs index 024261d0a..bf25138ad 100644 --- a/src/TorchSharp/NN/Pooling/LPPool2d.cs +++ b/src/TorchSharp/NN/Pooling/LPPool2d.cs @@ -12,22 +12,25 @@ namespace Modules /// /// This class is used to represent a LPPool2D module. /// - public sealed class LPPool2d : torch.nn.Module + public sealed class LPPool2d : ParameterLessModule { - internal LPPool2d(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) + internal LPPool2d(double norm_type, long[] kernel_size, long[] stride = null, bool ceil_mode = false) : base(nameof(LPPool2d)) { + this.norm_type = norm_type; + this.kernel_size = kernel_size; + this.stride = stride; + this.ceil_mode = ceil_mode; } - public override Tensor forward(Tensor tensor) + public override Tensor forward(Tensor input) { - return ReturnCheckForErrors(THSNN_LPPool2d_forward(handle.DangerousGetHandle(), tensor.Handle)); + return torch.nn.functional.lp_pool2d(input, norm_type, kernel_size, stride, ceil_mode); } - // Rather than spending cycles only to discover that this module has neither - // parameters nor buffers, just shortcut the move completely. - protected internal override nn.Module _to(Device device, ScalarType dtype, bool non_blocking) => this; - protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex, bool non_blocking) => this; - protected internal override nn.Module _to(ScalarType dtype, bool non_blocking) => this; + public double norm_type { get; set; } + public long[] kernel_size { get; set; } + public long[] stride { get; set; } + public bool ceil_mode { get; set; } } } @@ -40,18 +43,12 @@ public static partial class nn /// /// The LP norm (exponent) /// The size of the window - /// The stride of the window. Default value is kernel_size + /// The stride of the window. Default value is kernel_size /// Use ceil instead of floor to compute the output shape /// - public static LPPool2d LPPool2d(double norm_type, long[] kernel_size, long[] strides = null, bool ceil_mode = false) + public static LPPool2d LPPool2d(double norm_type, long[] kernel_size, long[] stride = null, bool ceil_mode = false) { - unsafe { - fixed (long* pkernelSize = kernel_size, pstrides = strides) { - var handle = THSNN_LPPool2d_ctor(norm_type, (IntPtr)pkernelSize, kernel_size.Length, (IntPtr)pstrides, (strides == null ? 0 : strides.Length), ceil_mode, out var boxedHandle); - if (handle == IntPtr.Zero) { torch.CheckForErrors(); } - return new LPPool2d(handle, boxedHandle); - } - } + return new LPPool2d(norm_type, kernel_size, stride, ceil_mode); } /// @@ -64,9 +61,46 @@ public static LPPool2d LPPool2d(double norm_type, long[] kernel_size, long[] str /// public static LPPool2d LPPool2d(double norm_type, long kernel_size, long? stride = null, bool ceil_mode = false) { - return stride.HasValue ? - LPPool2d(norm_type, new long[] { kernel_size, kernel_size }, new long[] { stride.Value, stride.Value }, ceil_mode) : - LPPool2d(norm_type, new long[] { kernel_size, kernel_size }, null, ceil_mode); + return new LPPool2d(norm_type, new[] { kernel_size, kernel_size }, stride.HasValue ? new[] { stride.Value, stride.Value } : null, ceil_mode); + } + + public static partial class functional + { + /// + /// Applies a 2D power-average pooling over an input signal composed of several input planes. + /// + /// The input tensor + /// The LP norm (exponent) + /// The size of the window + /// The stride of the window. Default value is kernel_size + /// Use ceil instead of floor to compute the output shape + /// + public static Tensor lp_pool2d(Tensor input, double norm_type, long[] kernel_size, long[] stride = null, bool ceil_mode = false) + { + stride ??= Array.Empty(); + + unsafe { + fixed (long* pkernel_size = kernel_size, pstrides = stride) { + var res = THSTensor_lp_pool2d(input.Handle, norm_type, (IntPtr)pkernel_size, kernel_size.Length, (IntPtr)pstrides, stride.Length, ceil_mode); + if (res == IntPtr.Zero) { torch.CheckForErrors(); } + return new Tensor(res); + } + } + } + + /// + /// Applies a 2D power-average pooling over an input signal composed of several input planes. + /// + /// The input tensor + /// The LP norm (exponent) + /// The size of the window + /// The stride of the window. + /// Use ceil instead of floor to compute the output shape + /// + public static Tensor lp_pool2d(Tensor input, double norm_type, long kernel_size, long? stride = null, bool ceil_mode = false) + { + return lp_pool2d(input, norm_type, new[] { kernel_size, kernel_size }, stride.HasValue ? new[] { stride.Value, stride.Value } : null, ceil_mode); + } } } } diff --git a/src/TorchSharp/NN/Pooling/MaxPool1D.cs b/src/TorchSharp/NN/Pooling/MaxPool1D.cs index 29043dde1..84fe3194a 100644 --- a/src/TorchSharp/NN/Pooling/MaxPool1D.cs +++ b/src/TorchSharp/NN/Pooling/MaxPool1D.cs @@ -13,29 +13,32 @@ namespace Modules /// /// This class is used to represent a MaxPool1D module. /// - public sealed class MaxPool1d : torch.nn.Module + public sealed class MaxPool1d : ParameterLessModule { - internal MaxPool1d(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) + internal MaxPool1d(long kernel_size, long? stride = null, long? padding = null, long? dilation = null, bool ceil_mode = false) : base(nameof(MaxPool1d)) { + this.kernel_size = kernel_size; + this.stride = stride; + this.padding = padding; + this.dilation = dilation; + this.ceil_mode = ceil_mode; } - public override Tensor forward(Tensor tensor) + public override Tensor forward(Tensor input) { - return ReturnCheckForErrors(THSNN_MaxPool1d_forward(handle, tensor.Handle)); + return torch.nn.functional.max_pool1d(input, kernel_size, stride, padding, dilation, ceil_mode); } - public (Tensor Values, Tensor Indices) forward_with_indices(Tensor tensor) + public (Tensor Values, Tensor Indices) forward_with_indices(Tensor input) { - var res = THSNN_MaxPool1d_forward_with_indices(handle, tensor.Handle, out var indices); - return ReturnCheckForErrors(res, indices); - + return torch.nn.functional.max_pool1d_with_indices(input, kernel_size, stride, padding, dilation, ceil_mode); } - // Rather than spending cycles only to discover that this module has neither - // parameters nor buffers, just shortcut the move completely. - protected internal override nn.Module _to(Device device, ScalarType dtype, bool non_blocking) => this; - protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex, bool non_blocking) => this; - protected internal override nn.Module _to(ScalarType dtype, bool non_blocking) => this; + public long kernel_size { get; set; } + public long? stride { get; set; } + public long? padding { get; set; } + public long? dilation { get; set; } + public bool ceil_mode { get; set; } } } @@ -46,29 +49,15 @@ public static partial class nn /// /// Applies a 1D max pooling over an input signal composed of several input planes. /// - /// The size of the sliding window, must be > 0. + /// The size of the sliding window, must be > 0. /// The stride of the sliding window, must be > 0. Default value is kernel_size. /// Implicit negative infinity padding to be added on both sides, must be >= 0 and less than or equal to kernel_size / 2 /// The stride between elements within a sliding window, must be > 0. - /// If true, will use ceil instead of floor to compute the output shape. This ensures that every element in the input tensor is covered by a sliding window. + /// If true, will use ceil instead of floor to compute the output shape. This ensures that every element in the input tensor is covered by a sliding window. /// - public static MaxPool1d MaxPool1d(long kernelSize, long? stride = null, long? padding = null, long? dilation = null, bool ceilMode = false) + public static MaxPool1d MaxPool1d(long kernel_size, long? stride = null, long? padding = null, long? dilation = null, bool ceil_mode = false) { - var pStride = stride.HasValue ? new long[] { stride.Value } : null; - var pPadding = padding.HasValue ? new long[] { padding.Value } : null; - var pDilation = dilation.HasValue ? new long[] { dilation.Value } : null; - return MaxPool1d(new long[] { kernelSize }, pStride, pPadding, pDilation, ceilMode); - } - - private static MaxPool1d MaxPool1d(long[] kernelSize, long[] strides = null, long[] padding = null, long[] dilation = null, bool ceilMode = false) - { - unsafe { - fixed (long* pkernelSize = kernelSize, pstrides = strides, pPadding = padding, pDilation = dilation) { - var handle = THSNN_MaxPool1d_ctor((IntPtr)pkernelSize, (IntPtr)pstrides, (IntPtr)pPadding, (IntPtr)pDilation, ceilMode, out var boxedHandle); - if (handle == IntPtr.Zero) { torch.CheckForErrors(); } - return new MaxPool1d(handle, boxedHandle); - } - } + return new MaxPool1d(kernel_size, stride, padding, dilation, ceil_mode); } public static partial class functional @@ -77,28 +66,30 @@ public static partial class functional /// Applies a 1D max pooling over an input signal composed of several input planes. /// /// The input tensor. - /// + /// /// /// /// /// /// - public static Tensor max_pool1d(Tensor input, long kernelSize, long? stride = null, + public static Tensor max_pool1d(Tensor input, long kernel_size, long? stride = null, long? padding = null, long? dilation = null, bool ceil_mode = false) { - var kernelSizes = new long[] { kernelSize }; - var strides = new long[] { stride ?? kernelSize }; + var kernel_sizes = new long[] { kernel_size }; + var strides = new long[] { stride ?? kernel_size }; var paddings = new long[] { padding ?? 0 }; var dilations = new long[] { dilation ?? 1 }; unsafe { - fixed (long* pkernelSize = kernelSizes, pstrides = strides, ppadding = paddings, pdilation = dilations) { - var res = THSTensor_max_pool1d(input.Handle, - (IntPtr)pkernelSize, kernelSizes.Length, + fixed (long* pkernel_size = kernel_sizes, pstrides = strides, ppadding = paddings, pdilation = dilations) { + var res = + THSTensor_max_pool1d(input.Handle, + (IntPtr)pkernel_size, kernel_sizes.Length, (IntPtr)pstrides, strides.Length, (IntPtr)ppadding, paddings.Length, (IntPtr)pdilation, dilations.Length, ceil_mode); - return ReturnCheckForErrors(res); + if (res == IntPtr.Zero) { torch.CheckForErrors(); } + return new Tensor(res); } } } @@ -107,27 +98,27 @@ public static Tensor max_pool1d(Tensor input, long kernelSize, long? stride = nu /// Applies a 1D max pooling over an input signal composed of several input planes. /// /// The input tensor. - /// + /// /// /// /// /// /// - public static (Tensor output, Tensor indices) max_pool1d_with_indices(Tensor input, long kernelSize, long? stride = null, + public static (Tensor output, Tensor indices) max_pool1d_with_indices(Tensor input, long kernel_size, long? stride = null, long? padding = null, long? dilation = null, bool ceil_mode = false) { - var kernelSizes = new long[] { kernelSize }; - var strides = new long[] { stride ?? kernelSize }; + var kernel_sizes = new long[] { kernel_size }; + var strides = new long[] { stride ?? kernel_size }; var paddings = new long[] { padding ?? 0 }; var dilations = new long[] { dilation ?? 1 }; IntPtr[] ptrArray; using (var pa = new PinnedArray()) { unsafe { - fixed (long* pkernelSize = kernelSizes, pstrides = strides, ppadding = paddings, pdilation = dilations) { + fixed (long* pkernel_size = kernel_sizes, pstrides = strides, ppadding = paddings, pdilation = dilations) { THSTensor_max_pool1d_with_indices(input.Handle, pa.CreateArray, - (IntPtr)pkernelSize, kernelSizes.Length, + (IntPtr)pkernel_size, kernel_sizes.Length, (IntPtr)pstrides, strides.Length, (IntPtr)ppadding, paddings.Length, (IntPtr)pdilation, dilations.Length, diff --git a/src/TorchSharp/NN/Pooling/MaxPool2D.cs b/src/TorchSharp/NN/Pooling/MaxPool2D.cs index 2c08b2994..d95ceb3ec 100644 --- a/src/TorchSharp/NN/Pooling/MaxPool2D.cs +++ b/src/TorchSharp/NN/Pooling/MaxPool2D.cs @@ -13,27 +13,32 @@ namespace Modules /// /// This class is used to represent a MaxPool2D module. /// - public sealed class MaxPool2d : torch.nn.Module + public sealed class MaxPool2d : ParameterLessModule { - internal MaxPool2d(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) + internal MaxPool2d(long[] kernel_size, long[] stride = null, long[] padding = null, long[] dilation = null, bool ceil_mode = false) : base(nameof(MaxPool2d)) { + this.kernel_size = kernel_size; + this.stride = stride; + this.padding = padding; + this.dilation = dilation; + this.ceil_mode = ceil_mode; } - public override Tensor forward(Tensor tensor) + public override Tensor forward(Tensor input) { - return ReturnCheckForErrors(THSNN_MaxPool2d_forward(handle, tensor.Handle)); + return torch.nn.functional.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode); } - public (Tensor Values, Tensor Indices) forward_with_indices(Tensor tensor) + + public (Tensor Values, Tensor Indices) forward_with_indices(Tensor input) { - var res = THSNN_MaxPool2d_forward_with_indices(handle, tensor.Handle, out var indices); - return ReturnCheckForErrors(res, indices); + return torch.nn.functional.max_pool2d_with_indices(input, kernel_size, stride, padding, dilation, ceil_mode); } - // Rather than spending cycles only to discover that this module has neither - // parameters nor buffers, just shortcut the move completely. - protected internal override nn.Module _to(Device device, ScalarType dtype, bool non_blocking) => this; - protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex, bool non_blocking) => this; - protected internal override nn.Module _to(ScalarType dtype, bool non_blocking) => this; + public long[] kernel_size { get; set; } + public long[] stride { get; set; } + public long[] padding { get; set; } + public long[] dilation { get; set; } + public bool ceil_mode { get; set; } } } @@ -44,74 +49,53 @@ public static partial class nn /// /// Applies a 2D max pooling over an input signal composed of several input planes. /// - /// The size of the sliding window, must be > 0. + /// The size of the sliding window, must be > 0. /// The stride of the sliding window, must be > 0. Default value is kernel_size. /// Implicit negative infinity padding to be added on both sides, must be >= 0 and less than or equal to kernel_size / 2 /// The stride between elements within a sliding window, must be > 0. - /// If true, will use ceil instead of floor to compute the output shape. This ensures that every element in the input tensor is covered by a sliding window. + /// If true, will use ceil instead of floor to compute the output shape. This ensures that every element in the input tensor is covered by a sliding window. /// - public static unsafe MaxPool2d MaxPool2d(long kernelSize, long? stride = null, long? padding = null, long? dilation = null, bool ceilMode = false) + public static MaxPool2d MaxPool2d(long kernel_size, long? stride = null, long? padding = null, long? dilation = null, bool ceil_mode = false) { - long svalue = stride.HasValue ? stride.Value : kernelSize; - long pvalue = padding.HasValue ? padding.Value : 0; - long dvalue = dilation.HasValue ? dilation.Value : 1; - - long* pStride = stackalloc long[2] { svalue, svalue }; - long* pPadding = stackalloc long[2] { pvalue, pvalue }; - long* pDilation = stackalloc long[2] { dvalue, dvalue }; - - long* pkernelSize = stackalloc long[2] { kernelSize, kernelSize }; + long[] kernelValue = new[] { kernel_size, kernel_size }; + long[] strideValue = stride.HasValue ? new[] { stride.Value, stride.Value } : kernelValue.ToArray(); + long[] paddingValue = padding.HasValue ? new[] { padding.Value, padding.Value } : new[] { 0L, 0L }; + long[] dilationValue = dilation.HasValue ? new[] { dilation.Value, dilation.Value } : new[] { 1L, 1L }; - var handle = THSNN_MaxPool2d_ctor((IntPtr)pkernelSize, 2, (IntPtr)pStride, 2, (IntPtr)pPadding, 2, (IntPtr)pDilation, 2, ceilMode, out var boxedHandle); - if (handle == IntPtr.Zero) { torch.CheckForErrors(); } - return new MaxPool2d(handle, boxedHandle); + return new MaxPool2d(kernelValue, strideValue, paddingValue, dilationValue, ceil_mode); } /// /// Applies a 2D max pooling over an input signal composed of several input planes. /// - /// The size of the sliding window, must be > 0. + /// The size of the sliding window, must be > 0. /// The stride of the sliding window, must be > 0. Default value is kernel_size. /// Implicit negative infinity padding to be added on both sides, must be >= 0 and less than or equal to kernel_size / 2 /// The stride between elements within a sliding window, must be > 0. - /// If true, will use ceil instead of floor to compute the output shape. This ensures that every element in the input tensor is covered by a sliding window. + /// If true, will use ceil instead of floor to compute the output shape. This ensures that every element in the input tensor is covered by a sliding window. /// - public static unsafe MaxPool2d MaxPool2d((long, long) kernelSize, (long, long)? stride = null, (long, long)? padding = null, (long, long)? dilation = null, bool ceilMode = false) + public static unsafe MaxPool2d MaxPool2d((long, long) kernel_size, (long, long)? stride = null, (long, long)? padding = null, (long, long)? dilation = null, bool ceil_mode = false) { - long svalue1 = stride != null ? stride.Value.Item1 : kernelSize.Item1; - long svalue2 = stride != null ? stride.Value.Item2 : kernelSize.Item2; - long pvalue1 = padding != null ? padding.Value.Item1 : 0; - long pvalue2 = padding != null ? padding.Value.Item2 : 0; - long dvalue1 = dilation != null ? dilation.Value.Item1 : 1; - long dvalue2 = dilation != null ? dilation.Value.Item2 : 1; - - long* pStride = stackalloc long[2] { svalue1, svalue2 }; - long* pPadding = stackalloc long[2] { pvalue1, pvalue2 }; - long* pDilation = stackalloc long[2] { dvalue1, dvalue2 }; - - long* pkernelSize = stackalloc long[2] { kernelSize.Item1, kernelSize.Item2 }; - - var handle = THSNN_MaxPool2d_ctor((IntPtr)pkernelSize, 2, (IntPtr)pStride, 2, (IntPtr)pPadding, 2, (IntPtr)pDilation, 2, ceilMode, out var boxedHandle); - if (handle == IntPtr.Zero) { torch.CheckForErrors(); } - return new MaxPool2d(handle, boxedHandle); + long[] kernelValue = new[] { kernel_size.Item1, kernel_size.Item2 }; + long[] strideValue = stride.HasValue ? new[] { stride.Value.Item1, stride.Value.Item2 } : kernelValue.ToArray(); + long[] paddingValue = padding.HasValue ? new[] { padding.Value.Item1, padding.Value.Item2 } : new[] { 0L, 0L }; + long[] dilationValue = dilation.HasValue ? new[] { dilation.Value.Item1, dilation.Value.Item2 } : new[] { 1L, 1L }; + + return new MaxPool2d(kernelValue, strideValue, paddingValue, dilationValue, ceil_mode); } /// /// Applies a 2D max pooling over an input signal composed of several input planes. /// - /// The size of the sliding window, must be > 0. - /// The stride of the sliding window, must be > 0. Default value is kernel_size. + /// The size of the sliding window, must be > 0. + /// The stride of the sliding window, must be > 0. Default value is kernel_size. /// Implicit negative infinity padding to be added on both sides, must be >= 0 and less than or equal to kernel_size / 2 /// The stride between elements within a sliding window, must be > 0. - /// If true, will use ceil instead of floor to compute the output shape. This ensures that every element in the input tensor is covered by a sliding window. + /// If true, will use ceil instead of floor to compute the output shape. This ensures that every element in the input tensor is covered by a sliding window. /// - public static unsafe MaxPool2d MaxPool2d(long[] kernelSize, long[] strides = null, long[] padding = null, long[] dilation = null, bool ceilMode = false) + public static MaxPool2d MaxPool2d(long[] kernel_size, long[] stride = null, long[] padding = null, long[] dilation = null, bool ceil_mode = false) { - fixed (long* pkernelSize = kernelSize, pstrides = strides, pPadding = padding, pDilation = dilation) { - var handle = THSNN_MaxPool2d_ctor((IntPtr)pkernelSize, kernelSize.Length, (IntPtr)pstrides, (strides == null ? 0 : strides.Length), (IntPtr)pPadding, (padding == null ? 0 : padding.Length), (IntPtr)pDilation, (dilation == null ? 0 : dilation.Length), ceilMode, out var boxedHandle); - if (handle == IntPtr.Zero) { torch.CheckForErrors(); } - return new MaxPool2d(handle, boxedHandle); - } + return new MaxPool2d(kernel_size, stride, padding, dilation, ceil_mode); } public static partial class functional @@ -120,28 +104,29 @@ public static partial class functional /// Applies a 2D max pooling over an input signal composed of several input planes. /// /// The input tensor. - /// + /// /// /// /// /// /// - public static Tensor max_pool2d(Tensor input, long[] kernelSize, long[] strides = null, + public static Tensor max_pool2d(Tensor input, long[] kernel_size, long[] strides = null, long[] padding = null, long[] dilation = null, bool ceil_mode = false) { - strides = strides ?? kernelSize; - padding = padding ?? kernelSize.Select(x => 0L).ToArray(); - dilation = dilation ?? kernelSize.Select(x => 1L).ToArray(); + strides = strides ?? kernel_size; + padding = padding ?? kernel_size.Select(x => 0L).ToArray(); + dilation = dilation ?? kernel_size.Select(x => 1L).ToArray(); unsafe { - fixed (long* pkernelSize = kernelSize, pstrides = strides, ppadding = padding, pdilation = dilation) { + fixed (long* pkernel_size = kernel_size, pstrides = strides, ppadding = padding, pdilation = dilation) { var res = THSTensor_max_pool2d(input.Handle, - (IntPtr)pkernelSize, kernelSize.Length, + (IntPtr)pkernel_size, kernel_size.Length, (IntPtr)pstrides, strides.Length, (IntPtr)ppadding, padding.Length, (IntPtr)pdilation, dilation.Length, ceil_mode); - return ReturnCheckForErrors(res); + if (res == IntPtr.Zero) { torch.CheckForErrors(); } + return new Tensor(res); } } } @@ -150,16 +135,16 @@ public static Tensor max_pool2d(Tensor input, long[] kernelSize, long[] strides /// Applies a 2D max pooling over an input signal composed of several input planes. /// /// The input tensor. - /// + /// /// /// /// /// /// - public static unsafe Tensor max_pool2d(Tensor input, long kernelSize, long? stride = null, + public static unsafe Tensor max_pool2d(Tensor input, long kernel_size, long? stride = null, long? padding = null, long? dilation = null, bool ceil_mode = false) { - long svalue = stride.HasValue ? stride.Value : kernelSize; + long svalue = stride.HasValue ? stride.Value : kernel_size; long pvalue = padding.HasValue ? padding.Value : 0; long dvalue = dilation.HasValue ? dilation.Value : 1; @@ -167,32 +152,33 @@ public static unsafe Tensor max_pool2d(Tensor input, long kernelSize, long? stri long* pPadding = stackalloc long[2] { pvalue, pvalue }; long* pDilation = stackalloc long[2] { dvalue, dvalue }; - long* pkernelSize = stackalloc long[2] { kernelSize, kernelSize }; + long* pkernel_size = stackalloc long[2] { kernel_size, kernel_size }; var res = THSTensor_max_pool2d(input.Handle, - (IntPtr)pkernelSize, 2, + (IntPtr)pkernel_size, 2, (IntPtr)pStride, 2, (IntPtr)pPadding, 2, (IntPtr)pDilation, 2, ceil_mode); - return ReturnCheckForErrors(res); + if (res == IntPtr.Zero) { torch.CheckForErrors(); } + return new Tensor(res); } /// /// Applies a 2D max pooling over an input signal composed of several input planes. /// /// The input tensor. - /// + /// /// /// /// /// /// - public static unsafe Tensor max_pool2d(Tensor input, (long, long) kernelSize, (long, long)? stride = null, + public static unsafe Tensor max_pool2d(Tensor input, (long, long) kernel_size, (long, long)? stride = null, (long, long)? padding = null, (long, long)? dilation = null, bool ceil_mode = false) { - long svalue1 = stride != null ? stride.Value.Item1 : kernelSize.Item1; - long svalue2 = stride != null ? stride.Value.Item2 : kernelSize.Item2; + long svalue1 = stride != null ? stride.Value.Item1 : kernel_size.Item1; + long svalue2 = stride != null ? stride.Value.Item2 : kernel_size.Item2; long pvalue1 = padding != null ? padding.Value.Item1 : 0; long pvalue2 = padding != null ? padding.Value.Item2 : 0; long dvalue1 = dilation != null ? dilation.Value.Item1 : 1; @@ -202,41 +188,42 @@ public static unsafe Tensor max_pool2d(Tensor input, (long, long) kernelSize, (l long* pPadding = stackalloc long[2] { pvalue1, pvalue2 }; long* pDilation = stackalloc long[2] { dvalue1, dvalue2 }; - long* pkernelSize = stackalloc long[2] { kernelSize.Item1, kernelSize.Item2 }; + long* pkernel_size = stackalloc long[2] { kernel_size.Item1, kernel_size.Item2 }; var res = THSTensor_max_pool2d(input.Handle, - (IntPtr)pkernelSize, 2, + (IntPtr)pkernel_size, 2, (IntPtr)pStride, 2, (IntPtr)pPadding, 2, (IntPtr)pDilation, 2, ceil_mode); - return ReturnCheckForErrors(res); + if (res == IntPtr.Zero) { torch.CheckForErrors(); } + return new Tensor(res); } /// /// Applies a 2D max pooling over an input signal composed of several input planes. /// /// The input tensor. - /// + /// /// /// /// /// /// - public static (Tensor output, Tensor indices) max_pool2d_with_indices(Tensor input, long[] kernelSize, long[] strides = null, + public static (Tensor output, Tensor indices) max_pool2d_with_indices(Tensor input, long[] kernel_size, long[] strides = null, long[] padding = null, long[] dilation = null, bool ceil_mode = false) { - strides = strides ?? kernelSize; - padding = padding ?? kernelSize.Select(x => 0L).ToArray(); - dilation = dilation ?? kernelSize.Select(x => 1L).ToArray(); + strides = strides ?? kernel_size; + padding = padding ?? kernel_size.Select(x => 0L).ToArray(); + dilation = dilation ?? kernel_size.Select(x => 1L).ToArray(); IntPtr[] ptrArray; using (var pa = new PinnedArray()) { unsafe { - fixed (long* pkernelSize = kernelSize, pstrides = strides, ppadding = padding, pdilation = dilation) { + fixed (long* pkernel_size = kernel_size, pstrides = strides, ppadding = padding, pdilation = dilation) { THSTensor_max_pool2d_with_indices(input.Handle, pa.CreateArray, - (IntPtr)pkernelSize, kernelSize.Length, + (IntPtr)pkernel_size, kernel_size.Length, (IntPtr)pstrides, strides.Length, (IntPtr)ppadding, padding.Length, (IntPtr)pdilation, dilation.Length, diff --git a/src/TorchSharp/NN/Pooling/MaxPool3D.cs b/src/TorchSharp/NN/Pooling/MaxPool3D.cs index 2f731cff0..031be98bb 100644 --- a/src/TorchSharp/NN/Pooling/MaxPool3D.cs +++ b/src/TorchSharp/NN/Pooling/MaxPool3D.cs @@ -6,6 +6,7 @@ namespace TorchSharp { + using Google.Protobuf.WellKnownTypes; using Modules; namespace Modules @@ -13,28 +14,32 @@ namespace Modules /// /// This class is used to represent a MaxPool3D module. /// - public sealed class MaxPool3d : torch.nn.Module + public sealed class MaxPool3d : ParameterLessModule { - internal MaxPool3d(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) + internal MaxPool3d(long[] kernel_size, long[] stride = null, long[] padding = null, long[] dilation = null, bool ceil_mode = false) : base(nameof(MaxPool3d)) { + this.kernel_size = kernel_size; + this.stride = stride; + this.padding = padding; + this.dilation = dilation; + this.ceil_mode = ceil_mode; } - public override Tensor forward(Tensor tensor) + public override Tensor forward(Tensor input) { - return ReturnCheckForErrors(THSNN_MaxPool3d_forward(handle, tensor.Handle)); + return torch.nn.functional.max_pool3d(input, kernel_size, stride, padding, dilation, ceil_mode); } - public (Tensor Values, Tensor Indices) forward_with_indices(Tensor tensor) + public (Tensor Values, Tensor Indices) forward_with_indices(Tensor input) { - var res = THSNN_MaxPool3d_forward_with_indices(handle, tensor.Handle, out var indices); - return ReturnCheckForErrors(res, indices); + return torch.nn.functional.max_pool3d_with_indices(input, kernel_size, stride, padding, dilation, ceil_mode); } - // Rather than spending cycles only to discover that this module has neither - // parameters nor buffers, just shortcut the move completely. - protected internal override nn.Module _to(Device device, ScalarType dtype, bool non_blocking) => this; - protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex, bool non_blocking) => this; - protected internal override nn.Module _to(ScalarType dtype, bool non_blocking) => this; + public long[] kernel_size { get; set; } + public long[] stride { get; set; } + public long[] padding { get; set; } + public long[] dilation { get; set; } + public bool ceil_mode { get; set; } } } @@ -45,55 +50,49 @@ public static partial class nn /// /// Applies a 3D max pooling over an input signal composed of several input planes. /// - /// The size of the sliding window, must be > 0. + /// The size of the sliding window, must be > 0. /// The stride of the sliding window, must be > 0. Default value is kernel_size. /// Implicit negative infinity padding to be added on both sides, must be >= 0 and less than or equal to kernel_size / 2 /// The stride between elements within a sliding window, must be > 0. - /// If true, will use ceil instead of floor to compute the output shape. This ensures that every element in the input tensor is covered by a sliding window. + /// If true, will use ceil instead of floor to compute the output shape. This ensures that every element in the input tensor is covered by a sliding window. /// - public static MaxPool3d MaxPool3d(long kernelSize, long? stride = null, long? padding = null, long? dilation = null, bool ceilMode = false) + public static MaxPool3d MaxPool3d(long kernel_size, long? stride = null, long? padding = null, long? dilation = null, bool ceil_mode = false) { var pStride = stride.HasValue ? new long[] { stride.Value, stride.Value, stride.Value } : null; var pPadding = padding.HasValue ? new long[] { padding.Value, padding.Value, padding.Value } : null; var pDilation = dilation.HasValue ? new long[] { dilation.Value, dilation.Value, dilation.Value } : null; - return MaxPool3d(new long[] { kernelSize, kernelSize, kernelSize }, pStride, pPadding, pDilation, ceilMode); + return MaxPool3d(new long[] { kernel_size, kernel_size, kernel_size }, pStride, pPadding, pDilation, ceil_mode); } /// /// Applies a 3D max pooling over an input signal composed of several input planes. /// - /// The size of the sliding window, must be > 0. + /// The size of the sliding window, must be > 0. /// The stride of the sliding window, must be > 0. Default value is kernel_size. /// Implicit negative infinity padding to be added on both sides, must be >= 0 and less than or equal to kernel_size / 2 /// The stride between elements within a sliding window, must be > 0. - /// If true, will use ceil instead of floor to compute the output shape. This ensures that every element in the input tensor is covered by a sliding window. + /// If true, will use ceil instead of floor to compute the output shape. This ensures that every element in the input tensor is covered by a sliding window. /// - public static MaxPool3d MaxPool3d((long, long, long) kernelSize, (long, long, long)? stride = null, (long, long, long)? padding = null, (long, long, long)? dilation = null, bool ceilMode = false) + public static MaxPool3d MaxPool3d((long, long, long) kernel_size, (long, long, long)? stride = null, (long, long, long)? padding = null, (long, long, long)? dilation = null, bool ceil_mode = false) { var pStride = stride.HasValue ? new long[] { stride.Value.Item1, stride.Value.Item2, stride.Value.Item3 } : null; var pPadding = padding.HasValue ? new long[] { padding.Value.Item1, padding.Value.Item2, padding.Value.Item3 } : null; var pDilation = dilation.HasValue ? new long[] { dilation.Value.Item1, dilation.Value.Item2, dilation.Value.Item3 } : null; - return MaxPool3d(new long[] { kernelSize.Item1, kernelSize.Item2, kernelSize.Item3 }, pStride, pPadding, pDilation, ceilMode); + return MaxPool3d(new long[] { kernel_size.Item1, kernel_size.Item2, kernel_size.Item3 }, pStride, pPadding, pDilation, ceil_mode); } /// /// Applies a 3D max pooling over an input signal composed of several input planes. /// - /// The size of the sliding window, must be > 0. - /// The stride of the sliding window, must be > 0. Default value is kernel_size. + /// The size of the sliding window, must be > 0. + /// The stride of the sliding window, must be > 0. Default value is kernel_size. /// Implicit negative infinity padding to be added on both sides, must be >= 0 and less than or equal to kernel_size / 2 /// The stride between elements within a sliding window, must be > 0. - /// If true, will use ceil instead of floor to compute the output shape. This ensures that every element in the input tensor is covered by a sliding window. + /// If true, will use ceil instead of floor to compute the output shape. This ensures that every element in the input tensor is covered by a sliding window. /// - public static MaxPool3d MaxPool3d(long[] kernelSize, long[] strides = null, long[] padding = null, long[] dilation = null, bool ceilMode = false) + public static MaxPool3d MaxPool3d(long[] kernel_size, long[] stride = null, long[] padding = null, long[] dilation = null, bool ceil_mode = false) { - unsafe { - fixed (long* pkernelSize = kernelSize, pstrides = strides, pPadding = padding, pDilation = dilation) { - var handle = THSNN_MaxPool3d_ctor((IntPtr)pkernelSize, kernelSize.Length, (IntPtr)pstrides, (strides == null ? 0 : strides.Length), (IntPtr)pPadding, (padding == null ? 0 : padding.Length), (IntPtr)pDilation, (dilation == null ? 0 : dilation.Length), ceilMode, out var boxedHandle); - if (handle == IntPtr.Zero) { torch.CheckForErrors(); } - return new MaxPool3d(handle, boxedHandle); - } - } + return new MaxPool3d(kernel_size, stride, padding, dilation, ceil_mode); } public static partial class functional @@ -102,28 +101,29 @@ public static partial class functional /// Applies a 3D max pooling over an input signal composed of several input planes. /// /// The input tensor. - /// + /// /// /// /// /// /// - public static Tensor max_pool3d(Tensor input, long[] kernelSize, long[] strides = null, + public static Tensor max_pool3d(Tensor input, long[] kernel_size, long[] strides = null, long[] padding = null, long[] dilation = null, bool ceil_mode = false) { - strides = strides ?? kernelSize; - padding = padding ?? kernelSize.Select(x => 0L).ToArray(); - dilation = dilation ?? kernelSize.Select(x => 1L).ToArray(); + strides = strides ?? kernel_size; + padding = padding ?? kernel_size.Select(x => 0L).ToArray(); + dilation = dilation ?? kernel_size.Select(x => 1L).ToArray(); unsafe { - fixed (long* pkernelSize = kernelSize, pstrides = strides, ppadding = padding, pdilation = dilation) { + fixed (long* pkernel_size = kernel_size, pstrides = strides, ppadding = padding, pdilation = dilation) { var res = THSTensor_max_pool3d(input.Handle, - (IntPtr)pkernelSize, kernelSize.Length, + (IntPtr)pkernel_size, kernel_size.Length, (IntPtr)pstrides, strides.Length, (IntPtr)ppadding, padding.Length, (IntPtr)pdilation, dilation.Length, ceil_mode); - return ReturnCheckForErrors(res); + if (res == IntPtr.Zero) { torch.CheckForErrors(); } + return new Tensor(res); } } } @@ -132,26 +132,26 @@ public static Tensor max_pool3d(Tensor input, long[] kernelSize, long[] strides /// Applies a 3D max pooling over an input signal composed of several input planes. /// /// The input tensor. - /// + /// /// /// /// /// /// - public static (Tensor output, Tensor indices) max_pool3d_with_indices(Tensor input, long[] kernelSize, long[] strides = null, + public static (Tensor output, Tensor indices) max_pool3d_with_indices(Tensor input, long[] kernel_size, long[] strides = null, long[] padding = null, long[] dilation = null, bool ceil_mode = false) { - strides = strides ?? kernelSize; - padding = padding ?? kernelSize.Select(x => 0L).ToArray(); - dilation = dilation ?? kernelSize.Select(x => 1L).ToArray(); + strides = strides ?? kernel_size; + padding = padding ?? kernel_size.Select(x => 0L).ToArray(); + dilation = dilation ?? kernel_size.Select(x => 1L).ToArray(); IntPtr[] ptrArray; using (var pa = new PinnedArray()) { unsafe { - fixed (long* pkernelSize = kernelSize, pstrides = strides, ppadding = padding, pdilation = dilation) { + fixed (long* pkernel_size = kernel_size, pstrides = strides, ppadding = padding, pdilation = dilation) { THSTensor_max_pool3d_with_indices(input.Handle, pa.CreateArray, - (IntPtr)pkernelSize, kernelSize.Length, + (IntPtr)pkernel_size, kernel_size.Length, (IntPtr)pstrides, strides.Length, (IntPtr)ppadding, padding.Length, (IntPtr)pdilation, dilation.Length, diff --git a/src/TorchSharp/NN/Pooling/MaxUnpool1d.cs b/src/TorchSharp/NN/Pooling/MaxUnpool1d.cs index e39569574..5c6093c46 100644 --- a/src/TorchSharp/NN/Pooling/MaxUnpool1d.cs +++ b/src/TorchSharp/NN/Pooling/MaxUnpool1d.cs @@ -5,6 +5,7 @@ namespace TorchSharp { + using System.Runtime.CompilerServices; using Modules; namespace Modules @@ -12,19 +13,18 @@ namespace Modules /// /// This class is used to represent a MaxUnpool1D module. /// - public sealed class MaxUnpool1d : torch.nn.Module + public sealed class MaxUnpool1d : ParameterLessModule { - internal MaxUnpool1d(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) + internal MaxUnpool1d(long kernel_size, long? stride = null, long? padding = null) : base(nameof(MaxUnpool1d)) { + this.kernel_size = kernel_size; + this.stride = stride; + this.padding = padding; } public override Tensor forward(Tensor tensor, Tensor indices, long[] output_size = null) { - unsafe { - fixed (long* pOutSize = output_size) { - return ReturnCheckForErrors(THSNN_MaxUnpool1d_forward(handle, tensor.Handle, indices.Handle, (IntPtr)pOutSize)); - } - } + return torch.nn.functional.max_unpool1d(tensor, indices, kernel_size, stride, padding, output_size); } public new Tensor call(Tensor tensor, Tensor indices, long[] output_size = null) @@ -32,11 +32,9 @@ public override Tensor forward(Tensor tensor, Tensor indices, long[] output_size return base.call(tensor, indices, output_size); } - // Rather than spending cycles only to discover that this module has neither - // parameters nor buffers, just shortcut the move completely. - protected internal override nn.Module _to(Device device, ScalarType dtype, bool non_blocking) => this; - protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex, bool non_blocking) => this; - protected internal override nn.Module _to(ScalarType dtype, bool non_blocking) => this; + public long kernel_size { get; set; } + public long? stride { get; set; } + public long? padding { get; set; } } } @@ -45,26 +43,42 @@ public static partial class torch public static partial class nn { /// - /// Applies a 1D max pooling over an input signal composed of several input planes. + /// Computes a partial inverse of :class:`MaxPool1d`. /// - /// The size of the sliding window, must be > 0. + /// The size of the sliding window, must be > 0. /// The stride of the sliding window, must be > 0. Default value is kernel_size. /// Implicit negative infinity padding to be added on both sides, must be >= 0 and less than or equal to kernel_size / 2 /// - public static MaxUnpool1d MaxUnpool1d(long kernelSize, long? stride = null, long? padding = null) + public static MaxUnpool1d MaxUnpool1d(long kernel_size, long? stride = null, long? padding = null) { - var pStride = stride.HasValue ? new long[] { stride.Value } : null; - var pPadding = padding.HasValue ? new long[] { padding.Value } : null; - return MaxUnpool1d(new long[] { kernelSize }, pStride, pPadding); + return new MaxUnpool1d(kernel_size, stride, padding); } - private static MaxUnpool1d MaxUnpool1d(long[] kernelSize, long[] strides = null, long[] padding = null) + public static partial class functional { - unsafe { - fixed (long* pkernelSize = kernelSize, pstrides = strides, pPadding = padding) { - var handle = THSNN_MaxUnpool1d_ctor((IntPtr)pkernelSize, (IntPtr)pstrides, (IntPtr)pPadding, out var boxedHandle); - if (handle == IntPtr.Zero) { torch.CheckForErrors(); } - return new MaxUnpool1d(handle, boxedHandle); + /// + /// Applies a 1D max pooling over an input signal composed of several input planes. + /// + /// the input Tensor to invert + /// the indices given out by :class:`~torch.nn.MaxPool1d` + /// The size of the sliding window, must be > 0. + /// The stride of the sliding window, must be > 0. Default value is kernel_size. + /// Implicit negative infinity padding to be added on both sides, must be >= 0 and less than or equal to kernel_size / 2 + /// (optional): The targeted output size + /// + public static Tensor max_unpool1d(Tensor input, Tensor indices, long kernel_size, long? stride = null, long? padding = null, long[] output_size = null) + { + long[] kernels = new[] { kernel_size }; + long[] strides = stride.HasValue ? new[] { stride.Value } : Array.Empty(); + long[] paddings = padding.HasValue ? new[] { padding.Value } : Array.Empty(); + output_size ??= Array.Empty(); + + unsafe { + fixed (long* pkernels = kernels, pstrides = strides, ppaddings = paddings, poutputSize = output_size) { + var res = THSTensor_max_unpool1d(input.Handle, indices.Handle, (IntPtr)pkernels, kernels.Length, (IntPtr)poutputSize, output_size.Length, (IntPtr)ppaddings, paddings.Length, (IntPtr)pstrides, strides.Length); + if (res == IntPtr.Zero) { torch.CheckForErrors(); } + return new Tensor(res); + } } } } diff --git a/src/TorchSharp/NN/Pooling/MaxUnpool2d.cs b/src/TorchSharp/NN/Pooling/MaxUnpool2d.cs index 4974e3410..ed9a188f6 100644 --- a/src/TorchSharp/NN/Pooling/MaxUnpool2d.cs +++ b/src/TorchSharp/NN/Pooling/MaxUnpool2d.cs @@ -5,6 +5,7 @@ namespace TorchSharp { + using Microsoft.VisualBasic; using Modules; namespace Modules @@ -12,19 +13,18 @@ namespace Modules /// /// This class is used to represent a MaxUnpool2D module. /// - public sealed class MaxUnpool2d : torch.nn.Module + public sealed class MaxUnpool2d : ParameterLessModule { - internal MaxUnpool2d(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) + internal MaxUnpool2d(long[] kernel_size, long[] stride = null, long[] padding = null) : base(nameof(MaxUnpool2d)) { + this.kernel_size = kernel_size; + this.stride = stride; + this.padding = padding; } public override Tensor forward(Tensor tensor, Tensor indices, long[] output_size = null) { - unsafe { - fixed (long* pOutSize = output_size) { - return ReturnCheckForErrors(THSNN_MaxUnpool2d_forward(handle, tensor.Handle, indices.Handle, (IntPtr)pOutSize, output_size == null ? 0 : output_size.Length)); - } - } + return torch.nn.functional.max_unpool2d(tensor, indices, kernel_size, stride, padding, output_size); } public new Tensor call(Tensor tensor, Tensor indices, long[] output_size = null) @@ -32,11 +32,9 @@ public override Tensor forward(Tensor tensor, Tensor indices, long[] output_size return base.call(tensor, indices, output_size); } - // Rather than spending cycles only to discover that this module has neither - // parameters nor buffers, just shortcut the move completely. - protected internal override nn.Module _to(Device device, ScalarType dtype, bool non_blocking) => this; - protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex, bool non_blocking) => this; - protected internal override nn.Module _to(ScalarType dtype, bool non_blocking) => this; + public long[] kernel_size { get; set; } + public long[] stride { get; set; } + public long[] padding { get; set; } } } @@ -47,47 +45,41 @@ public static partial class nn /// /// Applies a 2D max pooling over an input signal composed of several input planes. /// - /// The size of the sliding window, must be > 0. + /// The size of the sliding window, must be > 0. /// The stride of the sliding window, must be > 0. Default value is kernel_size. /// Implicit negative infinity padding to be added on both sides, must be >= 0 and less than or equal to kernel_size / 2 /// - public static MaxUnpool2d MaxUnpool2d(long kernelSize, long? stride = null, long? padding = null) + public static MaxUnpool2d MaxUnpool2d(long kernel_size, long? stride = null, long? padding = null) { var pStride = stride.HasValue ? new long[] { stride.Value, stride.Value } : null; var pPadding = padding.HasValue ? new long[] { padding.Value, padding.Value } : null; - return MaxUnpool2d(new long[] { kernelSize, kernelSize }, pStride, pPadding); + return new MaxUnpool2d(new[] { kernel_size, kernel_size }, pStride, pPadding); } /// /// Applies a 2D max pooling over an input signal composed of several input planes. /// - /// The size of the sliding window, must be > 0. + /// The size of the sliding window, must be > 0. /// The stride of the sliding window, must be > 0. Default value is kernel_size. /// Implicit negative infinity padding to be added on both sides, must be >= 0 and less than or equal to kernel_size / 2 /// - public static MaxUnpool2d MaxUnpool2d((long, long) kernelSize, (long, long)? stride = null, (long, long)? padding = null) + public static MaxUnpool2d MaxUnpool2d((long, long) kernel_size, (long, long)? stride = null, (long, long)? padding = null) { var pStride = stride.HasValue ? new long[] { stride.Value.Item1, stride.Value.Item2 } : null; var pPadding = padding.HasValue ? new long[] { padding.Value.Item1, padding.Value.Item2 } : null; - return MaxUnpool2d(new long[] { kernelSize.Item1, kernelSize.Item2 }, pStride, pPadding); + return new MaxUnpool2d(new[] { kernel_size.Item1, kernel_size.Item2 }, pStride, pPadding); } /// /// Applies a 2D max pooling over an input signal composed of several input planes. /// - /// The size of the sliding window, must be > 0. - /// The stride of the sliding window, must be > 0. Default value is kernel_size. + /// The size of the sliding window, must be > 0. + /// The stride of the sliding window, must be > 0. Default value is kernel_size. /// Implicit negative infinity padding to be added on both sides, must be >= 0 and less than or equal to kernel_size / 2 /// - public static MaxUnpool2d MaxUnpool2d(long[] kernelSize, long[] strides = null, long[] padding = null) + public static MaxUnpool2d MaxUnpool2d(long[] kernel_size, long[] stride = null, long[] padding = null) { - unsafe { - fixed (long* pkernelSize = kernelSize, pstrides = strides, pPadding = padding) { - var handle = THSNN_MaxUnpool2d_ctor((IntPtr)pkernelSize, kernelSize.Length, (IntPtr)pstrides, (strides == null ? 0 : strides.Length), (IntPtr)pPadding, (padding == null ? 0 : padding.Length), out var boxedHandle); - if (handle == IntPtr.Zero) { torch.CheckForErrors(); } - return new MaxUnpool2d(handle, boxedHandle); - } - } + return new MaxUnpool2d(kernel_size, stride, padding); } public static partial class functional @@ -95,15 +87,24 @@ public static partial class functional /// /// Computes a partial inverse of MaxPool2d. /// - /// The input tensor. - /// - /// + /// the input Tensor to invert + /// the indices given out by :class:`~torch.nn.MaxPool2d` + /// The size of the sliding window, must be > 0. + /// The stride of the sliding window, must be > 0. Default value is kernel_size. + /// Implicit negative infinity padding to be added on both sides, must be >= 0 and less than or equal to kernel_size / 2 + /// (optional): The targeted output size /// - public static Tensor max_unpool2d(Tensor input, Tensor indices, long[] outputSize) + public static Tensor max_unpool2d(Tensor input, Tensor indices, long[] kernel_size, long[] stride = null, long[] padding = null, long[] output_size = null) { + stride ??= Array.Empty(); + padding ??= Array.Empty(); + output_size ??= Array.Empty(); + unsafe { - fixed (long* poutputSize = outputSize) { - return ReturnCheckForErrors(THSTensor_maxunpool2d(input.Handle, indices.Handle, (IntPtr)poutputSize, outputSize.Length)); + fixed (long* pkernels = kernel_size, pstrides = stride, ppaddings = padding, poutputSize = output_size) { + var res = THSTensor_max_unpool2d(input.Handle, indices.Handle, (IntPtr)pkernels, kernel_size.Length, (IntPtr)poutputSize, output_size.Length, (IntPtr)ppaddings, padding.Length, (IntPtr)pstrides, stride.Length); + if (res == IntPtr.Zero) { torch.CheckForErrors(); } + return new Tensor(res); } } } diff --git a/src/TorchSharp/NN/Pooling/MaxUnpool3d.cs b/src/TorchSharp/NN/Pooling/MaxUnpool3d.cs index b024d130b..6dc11f455 100644 --- a/src/TorchSharp/NN/Pooling/MaxUnpool3d.cs +++ b/src/TorchSharp/NN/Pooling/MaxUnpool3d.cs @@ -5,26 +5,26 @@ namespace TorchSharp { + using Microsoft.VisualBasic; using Modules; namespace Modules { /// - /// This class is used to represent a MaxUnpool3d module. + /// This class is used to represent a MaxUnpool3D module. /// - public sealed class MaxUnpool3d : torch.nn.Module + public sealed class MaxUnpool3d : ParameterLessModule { - internal MaxUnpool3d(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) + internal MaxUnpool3d(long[] kernel_size, long[] stride = null, long[] padding = null) : base(nameof(MaxUnpool3d)) { + this.kernel_size = kernel_size; + this.stride = stride; + this.padding = padding; } public override Tensor forward(Tensor tensor, Tensor indices, long[] output_size = null) { - unsafe { - fixed (long* pOutSize = output_size) { - return ReturnCheckForErrors(THSNN_MaxUnpool3d_forward(handle, tensor.Handle, indices.Handle, (IntPtr)pOutSize, output_size == null ? 0 : output_size.Length)); - } - } + return torch.nn.functional.max_unpool3d(tensor, indices, kernel_size, stride, padding, output_size); } public new Tensor call(Tensor tensor, Tensor indices, long[] output_size = null) @@ -32,11 +32,9 @@ public override Tensor forward(Tensor tensor, Tensor indices, long[] output_size return base.call(tensor, indices, output_size); } - // Rather than spending cycles only to discover that this module has neither - // parameters nor buffers, just shortcut the move completely. - protected internal override nn.Module _to(Device device, ScalarType dtype, bool non_blocking) => this; - protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex, bool non_blocking) => this; - protected internal override nn.Module _to(ScalarType dtype, bool non_blocking) => this; + public long[] kernel_size { get; set; } + public long[] stride { get; set; } + public long[] padding { get; set; } } } @@ -45,70 +43,68 @@ public static partial class torch public static partial class nn { /// - /// Applies a 2D max pooling over an input signal composed of several input planes. + /// Applies a 3D max pooling over an input signal composed of several input planes. /// - /// The size of the sliding window, must be > 0. + /// The size of the sliding window, must be > 0. /// The stride of the sliding window, must be > 0. Default value is kernel_size. /// Implicit negative infinity padding to be added on both sides, must be >= 0 and less than or equal to kernel_size / 2 /// - public static MaxUnpool3d MaxUnpool3d(long kernelSize, long? stride = null, long? padding = null) + public static MaxUnpool3d MaxUnpool3d(long kernel_size, long? stride = null, long? padding = null) { var pStride = stride.HasValue ? new long[] { stride.Value, stride.Value, stride.Value } : null; var pPadding = padding.HasValue ? new long[] { padding.Value, padding.Value, padding.Value } : null; - return MaxUnpool3d(new long[] { kernelSize, kernelSize, kernelSize }, pStride, pPadding); + return new MaxUnpool3d(new[] { kernel_size, kernel_size, kernel_size }, pStride, pPadding); } /// - /// Applies a 2D max pooling over an input signal composed of several input planes. + /// Applies a 3D max pooling over an input signal composed of several input planes. /// - /// The size of the sliding window, must be > 0. + /// The size of the sliding window, must be > 0. /// The stride of the sliding window, must be > 0. Default value is kernel_size. /// Implicit negative infinity padding to be added on both sides, must be >= 0 and less than or equal to kernel_size / 2 /// - public static MaxUnpool3d MaxUnpool3d((long, long, long) kernelSize, (long, long, long)? stride = null, (long, long, long)? padding = null) + public static MaxUnpool3d MaxUnpool3d((long, long, long) kernel_size, (long, long, long)? stride = null, (long, long, long)? padding = null) { var pStride = stride.HasValue ? new long[] { stride.Value.Item1, stride.Value.Item2, stride.Value.Item3 } : null; var pPadding = padding.HasValue ? new long[] { padding.Value.Item1, padding.Value.Item2, padding.Value.Item3 } : null; - return MaxUnpool3d(new long[] { kernelSize.Item1, kernelSize.Item2, kernelSize.Item3 }, pStride, pPadding); + return new MaxUnpool3d(new[] { kernel_size.Item1, kernel_size.Item2, kernel_size.Item3 }, pStride, pPadding); } /// - /// Applies a 2D max pooling over an input signal composed of several input planes. + /// Applies a 3D max pooling over an input signal composed of several input planes. /// - /// The size of the sliding window, must be > 0. - /// The stride of the sliding window, must be > 0. Default value is kernel_size. + /// The size of the sliding window, must be > 0. + /// The stride of the sliding window, must be > 0. Default value is kernel_size. /// Implicit negative infinity padding to be added on both sides, must be >= 0 and less than or equal to kernel_size / 2 /// - public static MaxUnpool3d MaxUnpool3d(long[] kernelSize, long[] strides = null, long[] padding = null) + public static MaxUnpool3d MaxUnpool3d(long[] kernel_size, long[] stride = null, long[] padding = null) { - unsafe { - fixed (long* pkernelSize = kernelSize, pstrides = strides, pPadding = padding) { - var handle = THSNN_MaxUnpool3d_ctor((IntPtr)pkernelSize, kernelSize.Length, (IntPtr)pstrides, (strides == null ? 0 : strides.Length), (IntPtr)pPadding, (padding == null ? 0 : padding.Length), out var boxedHandle); - if (handle == IntPtr.Zero) { torch.CheckForErrors(); } - return new MaxUnpool3d(handle, boxedHandle); - } - } + return new MaxUnpool3d(kernel_size, stride, padding); } + public static partial class functional { /// /// Computes a partial inverse of MaxPool3d. /// - /// The input tensor. - /// - /// - /// - /// + /// the input Tensor to invert + /// the indices given out by :class:`~torch.nn.MaxPool3d` + /// The size of the sliding window, must be > 0. + /// The stride of the sliding window, must be > 0. Default value is kernel_size. + /// Implicit negative infinity padding to be added on both sides, must be >= 0 and less than or equal to kernel_size / 2 + /// (optional): The targeted output size /// - public static Tensor max_unpool3d(Tensor input, Tensor indices, long[] outputSize, long[] strides, long[] padding) + public static Tensor max_unpool3d(Tensor input, Tensor indices, long[] kernel_size, long[] stride = null, long[] padding = null, long[] output_size = null) { + stride ??= Array.Empty(); + padding ??= Array.Empty(); + output_size ??= Array.Empty(); + unsafe { - fixed (long* poutputSize = outputSize, pstrides = strides, ppadding = padding) { - var res = THSTensor_maxunpool3d(input.Handle, indices.Handle, - (IntPtr)poutputSize, outputSize.Length, - (IntPtr)pstrides, strides.Length, - (IntPtr)ppadding, padding.Length); - return ReturnCheckForErrors(res); + fixed (long* pkernels = kernel_size, pstrides = stride, ppaddings = padding, poutputSize = output_size) { + var res = THSTensor_max_unpool3d(input.Handle, indices.Handle, (IntPtr)pkernels, kernel_size.Length, (IntPtr)poutputSize, output_size.Length, (IntPtr)ppaddings, padding.Length, (IntPtr)pstrides, stride.Length); + if (res == IntPtr.Zero) { torch.CheckForErrors(); } + return new Tensor(res); } } } diff --git a/src/TorchSharp/NN/Recurrent/GRU.cs b/src/TorchSharp/NN/Recurrent/GRU.cs index 568921455..39b340af0 100644 --- a/src/TorchSharp/NN/Recurrent/GRU.cs +++ b/src/TorchSharp/NN/Recurrent/GRU.cs @@ -42,7 +42,8 @@ public override (Tensor, Tensor) forward(Tensor input, Tensor h0 = null) } var res = THSNN_GRU_forward(handle, input.Handle, h0.Handle, out IntPtr hN); - return ReturnCheckForErrors(res, hN); + if (res == IntPtr.Zero || hN == IntPtr.Zero) { torch.CheckForErrors(); } + return (new Tensor(res), new Tensor(hN)); } public new (Tensor, Tensor) call(Tensor input, Tensor h0 = null) => base.call(input, h0); diff --git a/src/TorchSharp/NN/Recurrent/GRUCell.cs b/src/TorchSharp/NN/Recurrent/GRUCell.cs index 2fc293de3..cce79bf13 100644 --- a/src/TorchSharp/NN/Recurrent/GRUCell.cs +++ b/src/TorchSharp/NN/Recurrent/GRUCell.cs @@ -1,5 +1,6 @@ -// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information. +// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information. using System; +using System.Diagnostics.CodeAnalysis; using TorchSharp.Amp; using static TorchSharp.torch; using static TorchSharp.torch.nn; @@ -24,7 +25,9 @@ internal GRUCell(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) /// public override Tensor forward(Tensor input, Tensor? h0 = null) { - return ReturnCheckForErrors(THSNN_GRUCell_forward(handle, input.Handle, h0?.Handle ?? IntPtr.Zero)); + var hN = THSNN_GRUCell_forward(handle, input.Handle, h0?.Handle ?? IntPtr.Zero); + if (hN == IntPtr.Zero) { torch.CheckForErrors(); } + return new Tensor(hN); } [DisallowNull] diff --git a/src/TorchSharp/NN/Recurrent/LSTM.cs b/src/TorchSharp/NN/Recurrent/LSTM.cs index 304439f18..5eafd76fc 100644 --- a/src/TorchSharp/NN/Recurrent/LSTM.cs +++ b/src/TorchSharp/NN/Recurrent/LSTM.cs @@ -47,7 +47,8 @@ public override (Tensor, Tensor, Tensor) forward(Tensor input, (Tensor, Tensor)? } var res = THSNN_LSTM_forward(handle, input.Handle, h0.Handle, c0.Handle, out IntPtr hN, out IntPtr cN); - return ReturnCheckForErrors(res, hN, cN); + if (res == IntPtr.Zero || hN == IntPtr.Zero || cN == IntPtr.Zero) { torch.CheckForErrors(); } + return (new Tensor(res), new Tensor(hN), new Tensor(cN)); } public new (Tensor, Tensor, Tensor) call(Tensor input, (Tensor, Tensor)? h0_c0 = null) => base.call(input, h0_c0); diff --git a/src/TorchSharp/NN/Recurrent/LSTMCell.cs b/src/TorchSharp/NN/Recurrent/LSTMCell.cs index ba8ebd1f2..4e946d843 100644 --- a/src/TorchSharp/NN/Recurrent/LSTMCell.cs +++ b/src/TorchSharp/NN/Recurrent/LSTMCell.cs @@ -1,5 +1,6 @@ -// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information. +// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information. using System; +using System.Diagnostics.CodeAnalysis; using TorchSharp.Amp; using static TorchSharp.torch; using static TorchSharp.torch.nn; @@ -25,7 +26,8 @@ internal LSTMCell(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) public override (Tensor, Tensor) forward(Tensor input, (Tensor, Tensor)? h0_c0) { var hN = THSNN_LSTMCell_forward(handle, input.Handle, h0_c0?.Item1.Handle ?? IntPtr.Zero, h0_c0?.Item2.Handle ?? IntPtr.Zero, out IntPtr cN); - return ReturnCheckForErrors(hN, cN); + if (hN == IntPtr.Zero || cN == IntPtr.Zero) { torch.CheckForErrors(); } + return (new Tensor(hN), new Tensor(cN)); } public new (Tensor, Tensor) call(Tensor input, (Tensor, Tensor)? h0_c0 = null) => base.call(input, h0_c0); @@ -104,6 +106,7 @@ public static LSTMCell LSTMCell(long inputSize, long hiddenSize, bool bias = tru { var res = THSNN_LSTMCell_ctor(inputSize, hiddenSize, bias, out var boxedHandle); if (res == IntPtr.Zero) { torch.CheckForErrors(); } + res = AutocastMode.AutoCast(res); return new LSTMCell(res, boxedHandle).MoveModule(device, dtype); } diff --git a/src/TorchSharp/NN/Recurrent/RNN.cs b/src/TorchSharp/NN/Recurrent/RNN.cs index e84d32077..a98f3e46c 100644 --- a/src/TorchSharp/NN/Recurrent/RNN.cs +++ b/src/TorchSharp/NN/Recurrent/RNN.cs @@ -42,7 +42,8 @@ public override (Tensor, Tensor) forward(Tensor input, Tensor? h0) } var res = THSNN_RNN_forward(handle, input.Handle, h0.Handle, out IntPtr hN); - return ReturnCheckForErrors(res, hN); + if (res == IntPtr.Zero || hN == IntPtr.Zero) { torch.CheckForErrors(); } + return (new Tensor(res), new Tensor(hN)); } public new (Tensor, Tensor) call(Tensor input, Tensor? h0 = null) => base.call(input, h0); diff --git a/src/TorchSharp/NN/Recurrent/RNNCell.cs b/src/TorchSharp/NN/Recurrent/RNNCell.cs index 72d73c172..ee9a0e416 100644 --- a/src/TorchSharp/NN/Recurrent/RNNCell.cs +++ b/src/TorchSharp/NN/Recurrent/RNNCell.cs @@ -1,5 +1,6 @@ -// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information. +// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information. using System; +using System.Diagnostics.CodeAnalysis; using TorchSharp.Amp; using static TorchSharp.torch; using static TorchSharp.torch.nn; @@ -26,7 +27,9 @@ internal RNNCell(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) /// public override Tensor forward(Tensor input, Tensor? h0 = null) { - return ReturnCheckForErrors(THSNN_RNNCell_forward(handle, input.Handle, h0?.Handle ?? IntPtr.Zero)); + var hN = THSNN_RNNCell_forward(handle, input.Handle, h0?.Handle ?? IntPtr.Zero); + if (hN == IntPtr.Zero) { torch.CheckForErrors(); } + return new Tensor(hN); } [DisallowNull] diff --git a/src/TorchSharp/NN/Unflatten.cs b/src/TorchSharp/NN/Unflatten.cs index 55fdd60c7..43dac5578 100644 --- a/src/TorchSharp/NN/Unflatten.cs +++ b/src/TorchSharp/NN/Unflatten.cs @@ -12,22 +12,21 @@ namespace Modules /// /// This class is used to represent an unflattening operation. /// - public sealed class Unflatten : torch.nn.Module + public sealed class Unflatten : ParameterLessModule { - internal Unflatten(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) + internal Unflatten(long dim, long[] unflattened_size) : base(nameof(Unflatten)) { + this.dim = dim; + this.unflattened_size = unflattened_size; } public override Tensor forward(Tensor tensor) { - return ReturnCheckForErrors(THSNN_Unflatten_forward(handle, tensor.Handle)); + return tensor.unflatten(dim, unflattened_size); } - // Rather than spending cycles only to discover that this module has neither - // parameters nor buffers, just shortcut the move completely. - protected internal override nn.Module _to(Device device, ScalarType dtype, bool non_blocking) => this; - protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex, bool non_blocking) => this; - protected internal override nn.Module _to(ScalarType dtype, bool non_blocking) => this; + public long dim { get; set; } + public long[] unflattened_size { get; set; } } } @@ -39,18 +38,12 @@ public static partial class nn /// Unflattens a tensor dim expanding it to a desired shape. For use with Sequential. /// /// Dimension to be unflattened - /// New shape of the unflattened dimension + /// New shape of the unflattened dimension /// - public static Unflatten Unflatten(long dim, long[] unflattenedSize) + public static Unflatten Unflatten(long dim, long[] unflattened_size) { - unsafe { - fixed (long* pUnflattenedSize = unflattenedSize) { - var handle = THSNN_Unflatten_ctor(dim, (IntPtr)pUnflattenedSize, unflattenedSize.Length, out var boxedHandle); - if (handle == IntPtr.Zero) { CheckForErrors(); } - return new Unflatten(handle, boxedHandle); - } - } + return new Unflatten(dim, unflattened_size); } } } -} +} \ No newline at end of file diff --git a/src/TorchSharp/NN/Unfold.cs b/src/TorchSharp/NN/Unfold.cs index 8934c9ef7..cd080f90e 100644 --- a/src/TorchSharp/NN/Unfold.cs +++ b/src/TorchSharp/NN/Unfold.cs @@ -78,7 +78,9 @@ public static partial class functional /// The stride of the sliding blocks in the input spatial dimensions. public static Tensor unfold(Tensor input, long kernel_size, long dilation = 1, long padding = 0, long stride = 1) { - return ReturnCheckForErrors(THSNN_unfold(input.Handle, kernel_size, kernel_size, stride, stride, padding, padding, dilation, dilation)); + var res = THSNN_unfold(input.Handle, kernel_size, kernel_size, stride, stride, padding, padding, dilation, dilation); + if (res == IntPtr.Zero) { torch.CheckForErrors(); } + return new Tensor(res); } /// @@ -100,7 +102,8 @@ public static Tensor unfold(Tensor input, (long, long) kernel_size, (long, long) stride.Value.Item1, stride.Value.Item2, padding.Value.Item1, padding.Value.Item2, dilation.Value.Item1, dilation.Value.Item2); - return ReturnCheckForErrors(res); + if (res == IntPtr.Zero) { torch.CheckForErrors(); } + return new Tensor(res); } } } diff --git a/src/TorchSharp/NN/Upsample.cs b/src/TorchSharp/NN/Upsample.cs index a24578e23..eca99fa51 100644 --- a/src/TorchSharp/NN/Upsample.cs +++ b/src/TorchSharp/NN/Upsample.cs @@ -8,6 +8,51 @@ namespace TorchSharp { using Modules; + namespace Modules + { + /// + /// This class is used to represent an Upsample module. + /// + public sealed class Upsample : ParameterLessModule + { + internal Upsample(long[]? size, double[]? scale_factor, UpsampleMode mode, bool? align_corners, bool? recompute_scale_factor) : base(nameof(Upsample)) + { + this._size = size; + this._scale_factor = scale_factor; + this.mode = mode; + this.align_corners = align_corners; + this.recompute_scale_factor = recompute_scale_factor; + } + + /// + /// Forward pass. + /// + /// Input tensor + /// + public override Tensor forward(Tensor input) + { + return torch.nn.functional.interpolate(input, _size, _scale_factor, (InterpolationMode)mode, align_corners, recompute_scale_factor ?? false); + } + + public bool? recompute_scale_factor { get; set; } + + public UpsampleMode mode { get; private set; } + + public bool? align_corners { get; private set; } + + public ReadOnlySpan size { + get { return _size is null ? null : new ReadOnlySpan(_size!); } + } + + public ReadOnlySpan scale_factor { + get { return _scale_factor is null ? null : new ReadOnlySpan(_scale_factor!); } + } + + private long[]? _size; + private double[]? _scale_factor; + } + } + public static partial class torch { public static partial class nn @@ -22,19 +67,11 @@ public static partial class nn /// The upsampling algorithm: one of 'nearest', 'linear', 'bilinear', 'bicubic' and 'trilinear'. Default: 'nearest' /// If true, the corner pixels of the input and output tensors are aligned, and thus preserving the values at those pixels. /// This only has effect when mode is 'linear', 'bilinear', or 'trilinear'. Default: false + /// recompute the scale_factor for use in the interpolation calculation. If `recompute_scale_factor` is ``True``, then `scale_factor` must be passed in and `scale_factor` is used to compute the output `size`. The computed output `size` will be used to infer new scales for the interpolation. Note that when `scale_factor` is floating-point, it may differ from the recomputed `scale_factor` due to rounding and precision issues. If `recompute_scale_factor` is ``False``, then `size` or `scale_factor` will be used directly for interpolation. /// - public static Upsample Upsample(long[]? size = null, double[]? scale_factor = null, UpsampleMode mode = UpsampleMode.Nearest, bool? align_corners = null) + public static Upsample Upsample(long[]? size = null, double[]? scale_factor = null, UpsampleMode mode = UpsampleMode.Nearest, bool? align_corners = null, bool? recompute_scale_factor = null) { - unsafe { - fixed (long* psize = size) { - fixed (double* pSF = scale_factor) { - byte ac = (byte)((align_corners.HasValue) ? (align_corners.Value ? 1 : 2) : 0); - var res = THSNN_Upsample_ctor((IntPtr)psize, size is null ? 0 : size.Length, (IntPtr)pSF, scale_factor is null ? 0 : scale_factor.Length, (byte)mode, ac, out var boxedHandle); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Upsample(res, boxedHandle, size, scale_factor, mode, align_corners); - } - } - } + return new Upsample(size, scale_factor, mode, align_corners, recompute_scale_factor); } public static partial class functional @@ -44,21 +81,18 @@ public static partial class functional /// The input data is assumed to be of the form minibatch x channels x[optional depth] x[optional height] x width. /// Hence, for spatial inputs, we expect a 4D Tensor and for volumetric inputs, we expect a 5D Tensor. /// - /// Input tensor + /// Input tensor /// Output spatial sizes /// Multiplier for spatial size. Has to match input size /// The upsampling algorithm: one of 'nearest', 'linear', 'bilinear', 'bicubic' and 'trilinear'. Default: 'nearest' /// If true, the corner pixels of the input and output tensors are aligned, and thus preserving the values at those pixels. /// This only has effect when mode is 'linear', 'bilinear', or 'trilinear'. Default: false /// - public static Tensor upsample(Tensor x, long[]? size = null, double[]? scale_factor = null, UpsampleMode mode = UpsampleMode.Nearest, bool align_corners = false) + public static Tensor upsample(Tensor input, long[]? size = null, double[]? scale_factor = null, UpsampleMode mode = UpsampleMode.Nearest, bool align_corners = false) { - using (var d = nn.Upsample(size, scale_factor, mode, align_corners)) { - return d.call(x); - } + return interpolate(input, size, scale_factor, (InterpolationMode)mode, align_corners); } - /// /// Upsamples the input, using nearest neighbours’ pixel values. /// @@ -79,7 +113,8 @@ public static Tensor upsample_nearest1d(Tensor input, long? outputSize, double? THSTensor_upsample_nearest1d(input.Handle, (IntPtr)poutputSizes, outputSizesLength, (IntPtr)pscaleFactors, scaleFactorsLength); - return ReturnCheckForErrors(res); + if (res == IntPtr.Zero) { torch.CheckForErrors(); } + return new Tensor(res); } } } @@ -100,7 +135,8 @@ public static Tensor upsample_nearest1d_backward(Tensor grad_output, long? outpu (IntPtr)poutputSizes, outputSizesLength, (IntPtr)pinputSizes, inputSizes.Length, (IntPtr)pscaleFactors, scaleFactorsLength); - return ReturnCheckForErrors(res); + if (res == IntPtr.Zero) { torch.CheckForErrors(); } + return new Tensor(res); } } } @@ -124,7 +160,8 @@ public static Tensor upsample_nearest2d(Tensor input, long[]? outputSizes = null THSTensor_upsample_nearest2d(input.Handle, (IntPtr)poutputSizes, outputSizesLength, (IntPtr)pscaleFactors, scaleFactorsLength); - return ReturnCheckForErrors(res); + if (res == IntPtr.Zero) { torch.CheckForErrors(); } + return new Tensor(res); } } } @@ -142,7 +179,8 @@ public static Tensor upsample_nearest2d_backward(Tensor grad_output, long[] inpu (IntPtr)poutputSizes, outputSizesLength, (IntPtr)pinputSizes, inputSizes.Length, (IntPtr)pscaleFactors, scaleFactorsLength); - return ReturnCheckForErrors(res); + if (res == IntPtr.Zero) { torch.CheckForErrors(); } + return new Tensor(res); } } } @@ -160,7 +198,8 @@ public static Tensor upsample_nearest3d_backward(Tensor grad_output, long[] inpu (IntPtr)poutputSizes, outputSizesLength, (IntPtr)pinputSizes, inputSizes.Length, (IntPtr)pscaleFactors, scaleFactorsLength); - return ReturnCheckForErrors(res); + if (res == IntPtr.Zero) { torch.CheckForErrors(); } + return new Tensor(res); } } } @@ -184,7 +223,8 @@ public static Tensor upsample_nearest3d(Tensor input, long[]? outputSizes = null THSTensor_upsample_nearest3d(input.Handle, (IntPtr)poutputSizes, outputSizesLength, (IntPtr)pscaleFactors, scaleFactorsLength); - return ReturnCheckForErrors(res); + if (res == IntPtr.Zero) { torch.CheckForErrors(); } + return new Tensor(res); } } } @@ -193,51 +233,4 @@ public static Tensor upsample_nearest3d(Tensor input, long[]? outputSizes = null } } - namespace Modules - { - /// - /// This class is used to represent an Upsample module. - /// - public sealed class Upsample : torch.nn.Module - { - internal Upsample(IntPtr handle, IntPtr boxedHandle, long[]? size, double[]? scale_factor, UpsampleMode mode, bool? align_corners) : base(handle, boxedHandle) - { - this._size = size; - this._scale_factor = scale_factor; - this.mode = mode; - this.align_corners = align_corners; - } - - /// - /// Forward pass. - /// - /// Input tensor - /// - public override Tensor forward(Tensor tensor) - { - return ReturnCheckForErrors(THSNN_Upsample_forward(handle, tensor.Handle)); - } - - public UpsampleMode mode { get; private set; } - - public bool? align_corners { get; private set; } - - public ReadOnlySpan size { - get { return _size is null ? null : new ReadOnlySpan(_size!); } - } - - public ReadOnlySpan scale_factor { - get { return _scale_factor is null ? null : new ReadOnlySpan(_scale_factor!); } - } - - private long[]? _size; - private double[]? _scale_factor; - - // Rather than spending cycles only to discover that this module has neither - // parameters nor buffers, just shortcut the move completely. - protected internal override nn.Module _to(Device device, ScalarType dtype, bool non_blocking) => this; - protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex, bool non_blocking) => this; - protected internal override nn.Module _to(ScalarType dtype, bool non_blocking) => this; - } - } } diff --git a/src/TorchSharp/NN/Vision.cs b/src/TorchSharp/NN/Vision.cs index 7321a57b7..db751a7ae 100644 --- a/src/TorchSharp/NN/Vision.cs +++ b/src/TorchSharp/NN/Vision.cs @@ -1,7 +1,5 @@ // Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information. using System; -using System.Linq; -using TorchSharp.Amp; using static TorchSharp.PInvoke.NativeMethods; #nullable enable @@ -25,7 +23,8 @@ public enum InterpolationMode Bilinear = 2, Bicubic = 3, Trilinear = 4, - Area = 5 + Area = 5, + NearestExact = 6 } public enum GridSampleMode @@ -62,7 +61,9 @@ public static Tensor pad(Tensor input, long[] pad, PaddingModes mode = PaddingMo { unsafe { fixed (long* psize = pad) { - return ReturnCheckForErrors(THSNN_pad(input.Handle, (IntPtr)psize, pad.Length, (byte)mode, value)); + var res = THSNN_pad(input.Handle, (IntPtr)psize, pad.Length, (byte)mode, value); + if (res == IntPtr.Zero) { torch.CheckForErrors(); } + return new Tensor(res); } } } @@ -79,7 +80,9 @@ public static Tensor pad(Tensor input, ReadOnlySpan pad, PaddingModes mode { unsafe { fixed (long* psize = pad) { - return ReturnCheckForErrors(THSNN_pad(input.Handle, (IntPtr)psize, pad.Length, (byte)mode, value)); + var res = THSNN_pad(input.Handle, (IntPtr)psize, pad.Length, (byte)mode, value); + if (res == IntPtr.Zero) { torch.CheckForErrors(); } + return new Tensor(res); } } } @@ -97,7 +100,9 @@ public static Tensor pad(Tensor input, (long, long) pad, PaddingModes mode = Pad unsafe { var correctedPad = stackalloc long[] { pad.Item1, pad.Item2 }; - return ReturnCheckForErrors(THSNN_pad(input.Handle, (IntPtr)correctedPad, 2, (byte)mode, value)); + var res = THSNN_pad(input.Handle, (IntPtr)correctedPad, 2, (byte)mode, value); + if (res == IntPtr.Zero) { torch.CheckForErrors(); } + return new Tensor(res); } } @@ -113,7 +118,9 @@ public static Tensor pad(Tensor input, (long, long, long, long) pad, PaddingMode { unsafe { var correctedPad = stackalloc long[] { pad.Item1, pad.Item2, pad.Item3, pad.Item4 }; - return ReturnCheckForErrors(THSNN_pad(input.Handle, (IntPtr)correctedPad, 4, (byte)mode, value)); + var res = THSNN_pad(input.Handle, (IntPtr)correctedPad, 4, (byte)mode, value); + if (res == IntPtr.Zero) { torch.CheckForErrors(); } + return new Tensor(res); } } @@ -133,7 +140,9 @@ public static Tensor pad(Tensor input, long pad, PaddingModes mode = PaddingMode var correctedPad = stackalloc long[length]; for (var i = 0; i < length; i++) correctedPad[i] = pad; - return ReturnCheckForErrors(THSNN_pad(input.Handle, (IntPtr)correctedPad, length, (byte)mode, value)); + var res = THSNN_pad(input.Handle, (IntPtr)correctedPad, length, (byte)mode, value); + if (res == IntPtr.Zero) { torch.CheckForErrors(); } + return new Tensor(res); } } @@ -156,15 +165,9 @@ public static Tensor pad(Tensor input, long pad, PaddingModes mode = PaddingMode public static Tensor grid_sample(Tensor input, Tensor grid, GridSampleMode mode = GridSampleMode.Bilinear, GridSamplePaddingMode padding_mode = GridSamplePaddingMode.Zeros, bool? align_corners = null) { byte ac = (byte)((align_corners.HasValue) ? (align_corners.Value ? 1 : 2) : 0); - if (AutocastMode.IsAutocastEnabled()) { - var sts = new[] { input.dtype, grid.dtype }; - if (sts.All(x => x == ScalarType.Float16)) - (input.handle, grid.handle) = AutocastMode.AutoCast(input.handle, grid.handle, ScalarType.Float16); - if (sts.Any(x => x == ScalarType.Float32)) - (input.handle, grid.handle) = AutocastMode.AutoCast(input.handle, grid.handle, ScalarType.Float32); - } - - return ReturnCheckForErrors(THSNN_grid_sample(input.Handle, grid.Handle, (byte)mode, (byte)padding_mode, ac)); + var res = THSNN_grid_sample(input.Handle, grid.Handle, (byte)mode, (byte)padding_mode, ac); + if (res == IntPtr.Zero) { torch.CheckForErrors(); } + return new Tensor(res); } /// @@ -179,7 +182,9 @@ public static Tensor affine_grid(Tensor theta, long[]? size = null, bool align_c { unsafe { fixed (long* psize = size) { - return ReturnCheckForErrors(THSNN_affine_grid(theta.Handle, (IntPtr)psize, size is null ? 0 : size.Length, align_corners)); + var res = THSNN_affine_grid(theta.Handle, (IntPtr)psize, size is null ? 0 : size.Length, align_corners); + if (res == IntPtr.Zero) { torch.CheckForErrors(); } + return new Tensor(res); } } } @@ -190,7 +195,7 @@ public static Tensor affine_grid(Tensor theta, long[]? size = null, bool align_c /// The input tensor /// Output spatial size /// Multiplier for spatial size. Has to match input size if it is a tuple. - /// The algorithm used for upsampling: 'nearest' | 'linear' | 'bilinear' | 'bicubic' | 'trilinear' | 'area' + /// The algorithm used for upsampling: 'nearest' | 'linear' | 'bilinear' | 'bicubic' | 'trilinear' | 'area' | 'nearest-exact' /// Geometrically, we consider the pixels of the input and output as squares rather than points. /// If set to true, the input and output tensors are aligned by the center points of their corner pixels, preserving the values at the corner pixels. /// If set to false, the input and output tensors are aligned by the corner points of their corner pixels, and the interpolation uses edge value padding for out-of-boundary values, making this operation independent of input size when scale_factor is kept the same. @@ -201,14 +206,21 @@ public static Tensor affine_grid(Tensor theta, long[]? size = null, bool align_c /// Otherwise, a new scale_factor will be computed based on the output and input sizes for use in the interpolation computation /// (i.e. the computation will be identical to if the computed output_size were passed-in explicitly). /// + /// + /// Flag to apply anti-aliasing. Using anti-alias + /// option together with align_corners = false, interpolation result would match Pillow + /// result for downsampling operation. Supported modes: 'bilinear', 'bicubic'. + /// /// - public static Tensor interpolate(Tensor x, long[]? size = null, double[]? scale_factor = null, InterpolationMode mode = InterpolationMode.Nearest, bool? align_corners = null, bool recompute_scale_factor = false) + public static Tensor interpolate(Tensor x, long[]? size = null, double[]? scale_factor = null, InterpolationMode mode = InterpolationMode.Nearest, bool? align_corners = null, bool recompute_scale_factor = false, bool antialias = false) { unsafe { fixed (long* psize = size) { fixed (double* pSF = scale_factor) { byte ac = (byte)((align_corners.HasValue) ? (align_corners.Value ? 1 : 2) : 0); - return ReturnCheckForErrors(THSNN_interpolate(x.Handle, (IntPtr)psize, size is null ? 0 : size.Length, (IntPtr)pSF, scale_factor is null ? 0 : scale_factor.Length, (byte)mode, ac, recompute_scale_factor)); + var res = THSNN_interpolate(x.Handle, (IntPtr)psize, size is null ? 0 : size.Length, (IntPtr)pSF, scale_factor is null ? 0 : scale_factor.Length, (byte)mode, ac, recompute_scale_factor, antialias); + if (res == IntPtr.Zero) { torch.CheckForErrors(); } + return new Tensor(res); } } } diff --git a/src/TorchSharp/PInvoke/LibTorchSharp.THSTensor.cs b/src/TorchSharp/PInvoke/LibTorchSharp.THSTensor.cs index 37541c6f5..c754d8d02 100644 --- a/src/TorchSharp/PInvoke/LibTorchSharp.THSTensor.cs +++ b/src/TorchSharp/PInvoke/LibTorchSharp.THSTensor.cs @@ -1,4 +1,4 @@ -// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information. +// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information. #nullable enable using System; using System.Runtime.InteropServices; diff --git a/src/TorchSharp/PInvoke/LibTorchSharp.THSTorch.cs b/src/TorchSharp/PInvoke/LibTorchSharp.THSTorch.cs index d9523588f..10f357d49 100644 --- a/src/TorchSharp/PInvoke/LibTorchSharp.THSTorch.cs +++ b/src/TorchSharp/PInvoke/LibTorchSharp.THSTorch.cs @@ -1,4 +1,4 @@ -// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information. +// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information. #nullable enable using System; using System.Runtime.InteropServices; @@ -65,12 +65,11 @@ internal static partial class NativeMethods [DllImport("LibTorchSharp")] internal static extern void THSTorch_scalar_to_bfloat16(IntPtr value, out ushort res); -#if NET6_0_OR_GREATER + /*[DllImport("LibTorchSharp")] + internal static extern void THSTorch_scalar_to_bfloat16(IntPtr value, out BFloat16 res);*/ + [DllImport("LibTorchSharp")] internal static extern void THSTorch_scalar_to_float16(IntPtr value, out Half res); -//#endif - [DllImport("LibTorchSharp")] - internal static extern void THSTorch_scalar_to_bfloat16(IntPtr value, out BFloat16 res); [DllImport("LibTorchSharp")] internal static extern double THSTorch_scalar_to_float64(IntPtr handle); diff --git a/src/TorchSharp/Scalar.cs b/src/TorchSharp/Scalar.cs index 8c29325f3..03f2f9646 100644 --- a/src/TorchSharp/Scalar.cs +++ b/src/TorchSharp/Scalar.cs @@ -1,4 +1,4 @@ -// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information. +// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information. using System; using static TorchSharp.PInvoke.NativeMethods; @@ -88,15 +88,6 @@ public static implicit operator Scalar(BFloat16 value) return value.ToScalar(); } - /// - /// Implicitly convert a BFloat16 value to Scalar - /// - /// The scalar value. - public static implicit operator Scalar(BFloat16 value) - { - return value.ToScalar(); - } - /// /// Implicitly convert a .NET scalar value to Scalar /// @@ -245,6 +236,7 @@ public static Scalar ToScalar(this Half value) return new Scalar(THSTorch_float16_to_scalar((float)value)); } + /* /// /// Explcitly construct a Scalar /// @@ -253,7 +245,7 @@ public static Scalar ToScalar(this BFloat16 value) { torch.InitializeDeviceType(DeviceType.CPU); return new Scalar(THSTorch_bfloat16_to_scalar(value.ToFloat())); - } + }*/ /// /// Explcitly construct a Scalar from a .NET scalar. /// @@ -333,12 +325,11 @@ public static Scalar ToScalar(this BFloat16 value) return new Scalar(THSTorch_bfloat16_to_scalar(value.ToSingle())); } -#if NET6_0_OR_GREATER - public static BFloat16 ToBFloat16(this Scalar value) + /*public static BFloat16 ToBFloat16(this Scalar value) { THSTorch_scalar_to_bfloat16(value.Handle, out BFloat16 res); return res; - } + }*/ //#if NET6_0_OR_GREATER /// /// Explicitly convert a Scalar value to a .NET scalar @@ -350,7 +341,6 @@ public static Half ToHalf(this Scalar value) THSTorch_scalar_to_float16(value.Handle, out res); return res; } -//#endif /// /// Explicitly convert a Scalar value to a BFloat16. diff --git a/src/TorchSharp/Tensor/Tensor.cs b/src/TorchSharp/Tensor/Tensor.cs index 0bb694560..57867c95c 100644 --- a/src/TorchSharp/Tensor/Tensor.cs +++ b/src/TorchSharp/Tensor/Tensor.cs @@ -1,4 +1,4 @@ -// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information. +// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information. using System; using System.Collections.Generic; using System.ComponentModel; @@ -50,6 +50,15 @@ internal Tensor(IntPtr handle) _peakCount = Math.Max(_totalCount, _peakCount); OwningDisposeScope = DisposeScopeManager.ThreadSingleton.RegisterOnCurrentDisposeScope(this); } + internal Tensor(IntPtr handle, bool register = true) + { + this.handle = handle; + System.Threading.Interlocked.Increment(ref _totalCount); + _peakCount = Math.Max(_totalCount, _peakCount); + if (register) { + OwningDisposeScope = DisposeScopeManager.ThreadSingleton.RegisterOnCurrentDisposeScope(this); + } + } /// /// Allows external packages to create tensors from the same native pointers that TorchSharp uses. @@ -1725,7 +1734,7 @@ public Tensor index_put_(Tensor value, params TensorIndex[] indices) unsafe { fixed (long* ptrKindAndStarts = arrKindAndStarts, ptrStops = arrStops, ptrSteps = arrSteps) { fixed (IntPtr* ptrTensors = arrTensors) { - NativeMethods.THSTensor_index_put_(Handle, (IntPtr)ptrKindAndStarts, (IntPtr)ptrStops, (IntPtr)ptrSteps, (IntPtr)ptrTensors, indices.Length, value.Handle); + NativeMethods.THSTensor_index_put_(Handle, (IntPtr)ptrKindAndStarts, (IntPtr)ptrStops, (IntPtr)ptrSteps, (IntPtr)ptrTensors, indices.Length, value.Handle, false); CheckForErrors(); GC.KeepAlive(indices); // don't release or finalize Tensor indices whose handles have been put into ptrTensors GC.KeepAlive(value); @@ -1824,7 +1833,23 @@ public Tensor index_put_(Scalar value, params TensorIndex[] indices) } } } - + public Tensor index_put_(Tensor value, TensorIndex[] indices, bool accumulate = false) + { + EncodeIndices(indices, out var arrKindAndStarts, out var arrStops, out var arrSteps, out var arrTensors); + if (accumulate && arrTensors == null) + throw new Exception("Invalid 'indices' parameter. Must be an array of TensorIndex objects containing tensors with indices that match the shape of the tensor to update"); + unsafe { + fixed (long* ptrKindAndStarts = arrKindAndStarts, ptrStops = arrStops, ptrSteps = arrSteps) { + fixed (IntPtr* ptrTensors = arrTensors) { + NativeMethods.THSTensor_index_put_(Handle, (IntPtr)ptrKindAndStarts, (IntPtr)ptrStops, (IntPtr)ptrSteps, (IntPtr)ptrTensors, indices.Length, value.Handle, accumulate); + CheckForErrors(); + GC.KeepAlive(indices); // don't release or finalize Tensor indices whose handles have been put into ptrTensors + GC.KeepAlive(value); + return this; + } + } + } + } /// /// Index into the tensor using Python-like indexing expressions and place a scalar tensor at the index. /// @@ -2316,7 +2341,20 @@ public Tensor transpose_(long dim0, long dim1) CheckForErrors(); return this; } + public Tensor threshold(Scalar threshold, Scalar value) + { + var res = NativeMethods.THSTensor_threshold(Handle, threshold.Handle, value.Handle); + if (res == IntPtr.Zero) + CheckForErrors(); + return new Tensor(res); + } + public Tensor threshold_(Scalar threshold, Scalar value) + { + NativeMethods.THSTensor_threshold_(Handle, threshold.Handle, value.Handle); + CheckForErrors(); + return this; + } /// /// Returns a view of the tensor conjugated and with the last two dimensions transposed. /// @@ -2791,9 +2829,12 @@ public Tensor positive() public Tensor softmax(long dim, ScalarType? dtype = null) => torch.special.softmax(this, dim, dtype); - public Tensor softplus() + public Tensor softplus(double beta = 1, double threshold = 20) => + softplus1(beta, threshold); + + private Tensor softplus1(Scalar beta, Scalar threshold) { - return ReturnCheckForErrors(NativeMethods.THSTensor_softplus(Handle)); + return ReturnCheckForErrors(NativeMethods.THSTensor_softplus(Handle, beta.Handle, threshold.Handle)); } public Tensor ravel() @@ -2813,6 +2854,23 @@ public Tensor relu_() return this; } + private const double one_eighth = 1.0 / 8.0; + private const double one_third = 1.0 / 3.0; + + public Tensor rrelu(double lower = one_eighth, double upper = one_third) + { + var res = NativeMethods.THSTensor_rrelu(Handle, lower, upper); + if (res == IntPtr.Zero) + CheckForErrors(); + return new Tensor(res); + } + + public Tensor rrelu_(double lower = one_eighth, double upper = one_third) + { + NativeMethods.THSTensor_rrelu_(Handle, lower, upper); + CheckForErrors(); + return this; + } public Tensor relu6() { return ReturnCheckForErrors(NativeMethods.THSTensor_relu6(Handle)); @@ -2825,14 +2883,18 @@ public Tensor relu6_() return this; } - public Tensor celu() + public Tensor celu() => this.celu(1.0); + + public Tensor celu_() => this.celu_(1.0); + + public Tensor celu(Scalar alpha) { - return ReturnCheckForErrors(NativeMethods.THSTensor_celu(Handle)); + return ReturnCheckForErrors(NativeMethods.THSTensor_celu(Handle, alpha.Handle)); } - public Tensor celu_() + public Tensor celu_(Scalar alpha) { - NativeMethods.THSTensor_celu_(Handle); + NativeMethods.THSTensor_celu_(Handle, alpha.Handle); CheckForErrors(); return this; } @@ -2841,7 +2903,8 @@ public Tensor elu(Scalar alpha, Scalar scale, Scalar input_scale) { return ReturnCheckForErrors(NativeMethods.THSTensor_elu(Handle, alpha.Handle, scale.Handle, input_scale.Handle)); } - + public Tensor elu(double alpha = 1) => elu(alpha, 1.0, 1.0); + public Tensor elu_(double alpha = 1) => elu(alpha, 1.0, 1.0); public Tensor elu_(Scalar alpha, Scalar scale, Scalar input_scale) { NativeMethods.THSTensor_elu_(Handle, alpha.Handle, scale.Handle, input_scale.Handle); @@ -2853,6 +2916,22 @@ public Tensor gelu() { return ReturnCheckForErrors(NativeMethods.THSTensor_gelu(Handle)); } + + public Tensor gelu_() + { + var res = NativeMethods.THSTensor_gelu_(Handle); + if (res == IntPtr.Zero) + CheckForErrors(); + return new Tensor(res); + } + + public Tensor glu(long dim = -1) + { + var res = NativeMethods.THSTensor_glu(Handle, dim); + if (res == IntPtr.Zero) + CheckForErrors(); + return new Tensor(res); + } public Tensor hardsigmoid() { @@ -4156,14 +4235,15 @@ public Tensor mean(long[] dimensions, bool keepdim = false, ScalarType? type = n } } - public Tensor var(long[] dimensions, bool keepdim = false, ScalarType? type = null) + /*public Tensor var(long[] dimensions, bool keepdim = false, ScalarType? type = null) { unsafe { fixed (long* pdims = dimensions) { + //return ReturnCheckForErrors(NativeMethods.THSTensor_var_along_dimensions(Handle, (IntPtr)pdims, dimensions.Length, keepdim, type.HasValue, (sbyte)type.GetValueOrDefault())); return ReturnCheckForErrors(NativeMethods.THSTensor_var_along_dimensions(Handle, (IntPtr)pdims, dimensions.Length, keepdim, type.HasValue, (sbyte)type.GetValueOrDefault())); } } - } + }*/ /// /// Returns the median of the values in input. @@ -7049,7 +7129,6 @@ public enum ScalarType : sbyte { typeof(long), ScalarType.Int64 }, { typeof(BFloat16), ScalarType.BFloat16 }, { typeof(Half), ScalarType.Float16 }, - { typeof(BFloat16), ScalarType.BFloat16}, { typeof(float), ScalarType.Float32 }, { typeof(double), ScalarType.Float64 }, { typeof((float, float)), ScalarType.ComplexFloat32 }, diff --git a/src/TorchSharp/Tensor/TensorTyped.handwritten.cs b/src/TorchSharp/Tensor/TensorTyped.handwritten.cs index 73081c528..537217a52 100644 --- a/src/TorchSharp/Tensor/TensorTyped.handwritten.cs +++ b/src/TorchSharp/Tensor/TensorTyped.handwritten.cs @@ -1,4 +1,4 @@ -// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information. +// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information. using System; using System.Collections.Generic; using static TorchSharp.PInvoke.NativeMethods; @@ -109,21 +109,29 @@ internal partial class ComplexFloat64Tensor /// common difference step, starting from start. /// /// In the case of complex element types, 'arange' will create a complex tensor with img=0 in all elements. - public static Tensor arange(Scalar start, Scalar stop, Scalar step, torch.Device device = null, bool requires_grad = false) + public static Tensor arange(Scalar start, Scalar stop, Scalar step, torch.Device device = null, + bool requires_grad = false) { device = torch.InitializeDevice(device); - var handle = THSTensor_arange(start.Handle, stop.Handle, step.Handle, (sbyte)ScalarType.Float64, (int)device.type, device.index, requires_grad); + var handle = THSTensor_arange(start.Handle, stop.Handle, step.Handle, (sbyte)ScalarType.Float64, + (int)device.type, device.index, requires_grad); if (handle == IntPtr.Zero) { GC.Collect(); GC.WaitForPendingFinalizers(); - handle = THSTensor_arange(start.Handle, stop.Handle, step.Handle, (sbyte)ScalarType.Float64, (int)device.type, device.index, requires_grad); + handle = THSTensor_arange(start.Handle, stop.Handle, step.Handle, (sbyte)ScalarType.Float64, + (int)device.type, device.index, requires_grad); + } + + if (handle == IntPtr.Zero) { + torch.CheckForErrors(); } - if (handle == IntPtr.Zero) { torch.CheckForErrors(); } var res = THSTensor_to_type(handle, (sbyte)ScalarType.ComplexFloat64, false, false); if (res == IntPtr.Zero) torch.CheckForErrors(); + return new Tensor(res); + } /// /// Create a scalar tensor from a single value diff --git a/src/TorchSharp/Tensor/torch.IndexingSlicingJoiningMutatingOps.cs b/src/TorchSharp/Tensor/torch.IndexingSlicingJoiningMutatingOps.cs index a4cfb1cbb..cef7e8f26 100644 --- a/src/TorchSharp/Tensor/torch.IndexingSlicingJoiningMutatingOps.cs +++ b/src/TorchSharp/Tensor/torch.IndexingSlicingJoiningMutatingOps.cs @@ -1,9 +1,10 @@ -// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information. +// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information. #nullable enable using System; using System.Collections.Generic; using System.Diagnostics.Contracts; using System.Linq; +using TorchSharp; using static TorchSharp.PInvoke.NativeMethods; namespace TorchSharp @@ -162,11 +163,7 @@ public static Tensor dstack(params Tensor[] tensors) /// This is equivalent to concatenation along the third axis after 1-D and 2-D tensors have been reshaped by torch.atleast_3d(). public static Tensor dstack(IList tensors) => dstack(tensors.ToHandleArray()); - var res = THSTensor_dstack(tensorsRef, parray.Array.Length); - if (res == IntPtr.Zero) { torch.CheckForErrors(); } - return new Tensor(res); - } - } + // https://pytorch.org/docs/stable/generated/torch.dstack /// @@ -175,10 +172,10 @@ public static Tensor dstack(IList tensors) /// A sequence of input tensors. /// A tensor containing the input tensors stacked along the third axis (depth-wise). /// This is equivalent to concatenation along the third axis after 1-D and 2-D tensors have been reshaped by torch.atleast_3d(). - public static Tensor dstack(IEnumerable tensors) + public static torch.Tensor dstack(IEnumerable tensors) => dstack(tensors.ToHandleArray()); - static Tensor dstack(IntPtr[] tensors) + static torch.Tensor dstack(IntPtr[] tensors) { using (var parray = new PinnedArray()) { IntPtr tensorsRef = parray.CreateArray(tensors); @@ -190,34 +187,34 @@ static Tensor dstack(IntPtr[] tensors) /// /// Gathers values along an axis specified by dim. /// - public static Tensor gather(Tensor input, long dim, Tensor index) => input.gather(dim, index); + public static torch.Tensor gather(torch.Tensor input, long dim, torch.Tensor index) => input.gather(dim, index); // https://pytorch.org/docs/stable/generated/torch.gather // TODO: implement parameter sparse_grad - public static Tensor gather(Tensor input, long dim, Tensor index, bool sparse_grad=false) + public static torch.Tensor gather(torch.Tensor input, long dim, torch.Tensor index, bool sparse_grad=false) => input.gather(dim, index); // https://pytorch.org/docs/stable/generated/torch.hsplit - public static Tensor[] hsplit(Tensor input, Tensor indices_or_sections) + public static torch.Tensor[] hsplit(torch.Tensor input, torch.Tensor indices_or_sections) => input.hsplit(indices_or_sections); // https://pytorch.org/docs/stable/generated/torch.hsplit - public static Tensor[] hsplit(Tensor input, long indices_or_sections) + public static torch.Tensor[] hsplit(torch.Tensor input, long indices_or_sections) => input.hsplit(indices_or_sections); // https://pytorch.org/docs/stable/generated/torch.hsplit - public static Tensor[] hsplit(Tensor input, long[] indices_or_sections) + public static torch.Tensor[] hsplit(torch.Tensor input, long[] indices_or_sections) => input.hsplit(indices_or_sections); // https://pytorch.org/docs/stable/generated/torch.hsplit - public static Tensor[] hsplit(Tensor input, (long, long) indices_or_sections) + public static torch.Tensor[] hsplit(torch.Tensor input, (long, long) indices_or_sections) => input.hsplit(new[]{ indices_or_sections.Item1, indices_or_sections.Item2 }); // https://pytorch.org/docs/stable/generated/torch.hsplit - public static Tensor[] hsplit(Tensor input, (long, long, long) indices_or_sections) + public static torch.Tensor[] hsplit(torch.Tensor input, (long, long, long) indices_or_sections) => input.hsplit(new[]{ indices_or_sections.Item1, indices_or_sections.Item2, @@ -225,7 +222,7 @@ public static Tensor[] hsplit(Tensor input, (long, long, long) indices_or_sectio }); // https://pytorch.org/docs/stable/generated/torch.hsplit - public static Tensor[] hsplit(Tensor input, (long, long, long, long) indices_or_sections) + public static torch.Tensor[] hsplit(torch.Tensor input, (long, long, long, long) indices_or_sections) => input.hsplit(new[]{ indices_or_sections.Item1, indices_or_sections.Item2, @@ -239,7 +236,7 @@ public static Tensor[] hsplit(Tensor input, (long, long, long, long) indices_or_ /// /// A list of input tensors. /// A tensor containing the input tensors stacked horizontally (column-wise). - public static Tensor hstack(IList tensors) + public static torch.Tensor hstack(IList tensors) { using var parray = new PinnedArray(); IntPtr tensorsRef = parray.CreateArray(tensors.Select(p => p.Handle).ToArray()); @@ -253,7 +250,7 @@ public static Tensor hstack(IList tensors) /// /// An array of input tensors. /// A tensor containing the input tensors stacked horizontally (column-wise). - public static Tensor hstack(params Tensor[] tensors) + public static torch.Tensor hstack(params torch.Tensor[] tensors) => hstack(tensors.ToHandleArray()); // https://pytorch.org/docs/stable/generated/torch.hstack @@ -262,7 +259,7 @@ public static Tensor hstack(params Tensor[] tensors) /// /// A sequence of input tensors. /// A tensor containing the input tensors stacked horizontally (column-wise). - public static Tensor hstack(IEnumerable tensors) + public static torch.Tensor hstack(IEnumerable tensors) => hstack(tensors.ToHandleArray()); // https://pytorch.org/docs/stable/generated/torch.hstack @@ -271,10 +268,10 @@ public static Tensor hstack(IEnumerable tensors) /// /// A span of input tensors. /// A tensor containing the input tensors stacked horizontally (column-wise). - public static Tensor hstack(ReadOnlySpan tensors) + public static torch.Tensor hstack(ReadOnlySpan tensors) => hstack(tensors.ToHandleArray()); - static Tensor hstack(IntPtr[] tensors) + static torch.Tensor hstack(IntPtr[] tensors) { using var parray = new PinnedArray(); IntPtr tensorsRef = parray.CreateArray(tensors); @@ -295,7 +292,7 @@ static Tensor hstack(IntPtr[] tensors) /// The tensor containing values to add /// The scalar multiplier for source /// - public static Tensor index_add(Tensor input, long dim, Tensor index, Tensor source, Scalar alpha) + public static torch.Tensor index_add(torch.Tensor input, long dim, torch.Tensor index, torch.Tensor source, Scalar alpha) => input.index_add(dim, index, source, alpha); // https://pytorch.org/docs/stable/generated/torch.index_add @@ -311,7 +308,7 @@ public static Tensor index_add(Tensor input, long dim, Tensor index, Tensor sour /// The tensor containing values to add /// The scalar multiplier for source /// - public static Tensor index_add_(Tensor input, long dim, Tensor index, Tensor source, Scalar alpha) + public static torch.Tensor index_add_(torch.Tensor input, long dim, torch.Tensor index, torch.Tensor source, Scalar alpha) => input.index_add_(dim, index, source, alpha); // https://pytorch.org/docs/stable/generated/torch.index_copy @@ -326,7 +323,7 @@ public static Tensor index_add_(Tensor input, long dim, Tensor index, Tensor sou /// Indices of source to select from, should have dtype either torch.int64 or torch.int32 /// The tensor containing values to copy /// - public static Tensor index_copy(Tensor input, long dim, Tensor index, Tensor source) + public static torch.Tensor index_copy(torch.Tensor input, long dim, torch.Tensor index, torch.Tensor source) => input.index_copy(dim, index, source); // https://pytorch.org/docs/stable/generated/torch.index_copy @@ -341,77 +338,77 @@ public static Tensor index_copy(Tensor input, long dim, Tensor index, Tensor sou /// Indices of source to select from, should have dtype either torch.int64 or torch.int32 /// The tensor containing values to copy /// - public static Tensor index_copy_(Tensor input, long dim, Tensor index, Tensor source) + public static torch.Tensor index_copy_(torch.Tensor input, long dim, torch.Tensor index, torch.Tensor source) => input.index_copy_(dim, index, source); // https://pytorch.org/docs/stable/generated/torch.index_reduce [Obsolete("not implemented", true)] - public static Tensor index_reduce(Tensor input, long dim, Tensor index, Tensor source, Reduce reduce, bool include_self=true) + public static torch.Tensor index_reduce(torch.Tensor input, long dim, torch.Tensor index, torch.Tensor source, Reduce reduce, bool include_self=true) => throw new NotImplementedException(); // https://pytorch.org/docs/stable/generated/torch.index_select /// /// Returns a new tensor which indexes the input tensor along dimension dim using the entries in index which is a LongTensor. /// - public static Tensor index_select(Tensor input, long dim, Tensor index) + public static torch.Tensor index_select(torch.Tensor input, long dim, torch.Tensor index) => input.index_select(dim, index); // https://pytorch.org/docs/stable/generated/torch.masked_select - public static Tensor masked_select(Tensor input, Tensor mask) + public static torch.Tensor masked_select(torch.Tensor input, torch.Tensor mask) => input.masked_select(mask); // https://pytorch.org/docs/stable/generated/torch.movedim - public static Tensor movedim(Tensor input, long source, long destination) + public static torch.Tensor movedim(torch.Tensor input, long source, long destination) => input.movedim(new[]{source}, new[]{destination}); // https://pytorch.org/docs/stable/generated/torch.movedim - static Tensor movedim(Tensor input, (long, long) source, (long, long) destination) + static torch.Tensor movedim(torch.Tensor input, (long, long) source, (long, long) destination) => input.movedim( new[]{source.Item1, source.Item2}, new[]{destination.Item1, destination.Item2}); // https://pytorch.org/docs/stable/generated/torch.movedim - static Tensor movedim(Tensor input, (long, long, long) source, (long, long, long) destination) + static torch.Tensor movedim(torch.Tensor input, (long, long, long) source, (long, long, long) destination) => input.movedim( new[]{source.Item1, source.Item2, source.Item3}, new[]{destination.Item1, destination.Item2, destination.Item3}); // https://pytorch.org/docs/stable/generated/torch.movedim - static Tensor movedim(Tensor input, (long, long, long, long) source, (long, long, long, long) destination) + static torch.Tensor movedim(torch.Tensor input, (long, long, long, long) source, (long, long, long, long) destination) => input.movedim( new[]{source.Item1, source.Item2, source.Item3, source.Item4}, new[]{destination.Item1, destination.Item2, destination.Item3, destination.Item4}); // https://pytorch.org/docs/stable/generated/torch.movedim - public static Tensor movedim(Tensor input, long[] source, long[] destination) + public static torch.Tensor movedim(torch.Tensor input, long[] source, long[] destination) => input.movedim(source, destination); // https://pytorch.org/docs/stable/generated/torch.moveaxis - public static Tensor moveaxis(Tensor input, long source, long destination) + public static torch.Tensor moveaxis(torch.Tensor input, long source, long destination) => input.moveaxis(new[]{source}, new[]{destination}); // https://pytorch.org/docs/stable/generated/torch.moveaxis - public static Tensor moveaxis(Tensor input, (long, long) source, (long, long) destination) + public static torch.Tensor moveaxis(torch.Tensor input, (long, long) source, (long, long) destination) => input.moveaxis( new[]{source.Item1, source.Item2 }, new[]{ destination.Item1, destination.Item2 }); // https://pytorch.org/docs/stable/generated/torch.moveaxis - public static Tensor moveaxis(Tensor input, (long, long, long) source, (long, long, long) destination) + public static torch.Tensor moveaxis(torch.Tensor input, (long, long, long) source, (long, long, long) destination) => input.moveaxis( new[]{source.Item1, source.Item2, source.Item3 }, new[]{ destination.Item1, destination.Item2, destination.Item3 }); - public static Tensor moveaxis(Tensor input, (long, long, long, long) source, (long, long, long, long) destination) + public static torch.Tensor moveaxis(torch.Tensor input, (long, long, long, long) source, (long, long, long, long) destination) => input.moveaxis( new[]{source.Item1, source.Item2, source.Item3, source.Item4 }, new[]{ destination.Item1, destination.Item2, destination.Item3, destination.Item4 }); - public static Tensor moveaxis(Tensor input, long[] source, long[] destination) + public static torch.Tensor moveaxis(torch.Tensor input, long[] source, long[] destination) => input.moveaxis(source, destination); // https://pytorch.org/docs/stable/generated/torch.narrow - public static Tensor narrow(Tensor input, long dim, long start, long length) + public static torch.Tensor narrow(torch.Tensor input, long dim, long start, long length) => input.narrow(dim, start, length); // https://pytorch.org/docs/stable/generated/torch.nonzero @@ -420,7 +417,7 @@ public static Tensor narrow(Tensor input, long dim, long start, long length) /// Each row in the result contains the indices of a non-zero element in input. /// The result is sorted lexicographically, with the last index changing the fastest (C-style). /// - public static Tensor nonzero(Tensor input) => input.nonzero(); + public static torch.Tensor nonzero(torch.Tensor input) => input.nonzero(); // https://pytorch.org/docs/stable/generated/torch.permute /// @@ -428,7 +425,7 @@ public static Tensor narrow(Tensor input, long dim, long start, long length) /// /// The input tensor. /// The desired ordering of dimensions - public static Tensor permute(Tensor input, params long[] permutation) => input.permute(permutation); + public static torch.Tensor permute(torch.Tensor input, params long[] permutation) => input.permute(permutation); // https://pytorch.org/docs/stable/generated/torch.reshape /// @@ -436,10 +433,10 @@ public static Tensor narrow(Tensor input, long dim, long start, long length) /// /// The input tensor /// The new tensor shape. - public static Tensor reshape(Tensor input, params long[] shape) => input.reshape(shape); + public static torch.Tensor reshape(torch.Tensor input, params long[] shape) => input.reshape(shape); // https://pytorch.org/docs/stable/generated/torch.select - public static Tensor select(Tensor input, long dim, long index) + public static torch.Tensor select(torch.Tensor input, long dim, long index) => input.select(dim, index); // https://pytorch.org/docs/stable/generated/torch.scatter @@ -448,7 +445,7 @@ public static Tensor select(Tensor input, long dim, long index) /// value in src, its output index is specified by its index in src for dimension != dim and by the # /// corresponding value in index for dimension = dim. /// - public static Tensor scatter(Tensor input, long dim, Tensor index, Tensor src) + public static torch.Tensor scatter(torch.Tensor input, long dim, torch.Tensor index, torch.Tensor src) => input.scatter(dim, index, src); // https://pytorch.org/docs/stable/generated/torch.scatter @@ -457,7 +454,7 @@ public static Tensor scatter(Tensor input, long dim, Tensor index, Tensor src) /// value in src, its output index is specified by its index in src for dimension != dim and by the # /// corresponding value in index for dimension = dim. /// - public static Tensor scatter_(Tensor input, long dim, Tensor index, Tensor src) + public static torch.Tensor scatter_(torch.Tensor input, long dim, torch.Tensor index, torch.Tensor src) => input.scatter_(dim, index, src); // https://pytorch.org/docs/stable/generated/torch.diagonal_scatter @@ -469,7 +466,7 @@ public static Tensor scatter_(Tensor input, long dim, Tensor index, Tensor src) /// Which diagonal to consider. Default: main diagonal. /// First dimension with respect to which to take diagonal. /// Second dimension with respect to which to take diagonal. - public static Tensor diagonal_scatter(Tensor input, Tensor src, long offset = 0L, long dim1 = 0L, long dim2 = 1L) => input.diagonal_scatter(src, offset, dim1, dim2); + public static torch.Tensor diagonal_scatter(torch.Tensor input, torch.Tensor src, long offset = 0L, long dim1 = 0L, long dim2 = 1L) => input.diagonal_scatter(src, offset, dim1, dim2); // https://pytorch.org/docs/stable/generated/torch.select_scatter /// @@ -480,7 +477,7 @@ public static Tensor scatter_(Tensor input, long dim, Tensor index, Tensor src) /// The dimension to insert the slice into /// The index to select with /// This function returns a tensor with fresh storage; it does not create a view. - public static Tensor select_scatter(Tensor input, Tensor src, long dim, long index) => input.select_scatter(src, dim, index); + public static torch.Tensor select_scatter(torch.Tensor input, torch.Tensor src, long dim, long index) => input.select_scatter(src, dim, index); // https://pytorch.org/docs/stable/generated/torch.slice_scatter /// @@ -492,7 +489,7 @@ public static Tensor scatter_(Tensor input, long dim, Tensor index, Tensor src) /// The start index of where to insert the slice /// The end index of where to insert the slice /// How many elements to skip - public static Tensor slice_scatter(Tensor input, Tensor src, long dim = 0L, long? start = null, long? end = null, long step = 1L) + public static torch.Tensor slice_scatter(torch.Tensor input, torch.Tensor src, long dim = 0L, long? start = null, long? end = null, long step = 1L) => input.slice_scatter(src, dim, start, end, step); // https://pytorch.org/docs/stable/generated/torch.scatter_add @@ -501,22 +498,22 @@ public static Tensor slice_scatter(Tensor input, Tensor src, long dim = 0L, long /// For each value in src, it is added to an index in self which is specified by its index in src for dimension != dim and by the /// corresponding value in index for dimension = dim. /// - public static Tensor scatter_add(Tensor input, long dim, Tensor index, Tensor src) + public static torch.Tensor scatter_add(torch.Tensor input, long dim, torch.Tensor index, torch.Tensor src) => input.scatter_add(dim, index, src); // https://pytorch.org/docs/stable/generated/torch.scatter_reduce [Obsolete("not implemented", true)] - static Tensor scatter_reduce( - Tensor input, + static torch.Tensor scatter_reduce( + torch.Tensor input, long dim, - Tensor index, - Tensor src, + torch.Tensor index, + torch.Tensor src, Reduce reduce, bool include_self = true) => throw new NotImplementedException(); // https://pytorch.org/docs/stable/generated/torch.split - public static Tensor[] split(Tensor tensor, long[] split_size_or_sections, long dim = 0L) + public static torch.Tensor[] split(torch.Tensor tensor, long[] split_size_or_sections, long dim = 0L) => tensor.split(split_size_or_sections, dim); // https://pytorch.org/docs/stable/generated/torch.stack @@ -525,7 +522,7 @@ public static Tensor[] split(Tensor tensor, long[] split_size_or_sections, long /// /// /// All tensors need to be of the same size. - public static Tensor stack(IEnumerable tensors, long dim = 0) + public static torch.Tensor stack(IEnumerable tensors, long dim = 0) { using var parray = new PinnedArray(); IntPtr tensorsRef = parray.CreateArray(tensors.ToHandleArray()); @@ -534,39 +531,39 @@ public static Tensor stack(IEnumerable tensors, long dim = 0) } // https://pytorch.org/docs/stable/generated/torch.swapaxes - public static Tensor swapaxes(Tensor input, long axis0, long axis1) + public static torch.Tensor swapaxes(torch.Tensor input, long axis0, long axis1) => input.swapaxes(axis0, axis1); // https://pytorch.org/docs/stable/generated/torch.swapdims - public static Tensor swapdims(Tensor input, long dim0, long dim1) + public static torch.Tensor swapdims(torch.Tensor input, long dim0, long dim1) => input.swapdims(dim0, dim1); // https://pytorch.org/docs/stable/generated/torch.t - public static Tensor t(Tensor input) + public static torch.Tensor t(torch.Tensor input) => input.t(); // https://pytorch.org/docs/stable/generated/torch.take - public static Tensor take(Tensor input, Tensor index) + public static torch.Tensor take(torch.Tensor input, torch.Tensor index) => input.take(index); // https://pytorch.org/docs/stable/generated/torch.take_along_dim - public static Tensor take_along_dim(Tensor input, Tensor indices, long dim = 0L) + public static torch.Tensor take_along_dim(torch.Tensor input, torch.Tensor indices, long dim = 0L) => input.take_along_dim(indices, dim); // https://pytorch.org/docs/stable/generated/torch.take_along_dim - public static Tensor take_along_dim(Tensor input, IEnumerable indices, long dim = 0L) + public static torch.Tensor take_along_dim(torch.Tensor input, IEnumerable indices, long dim = 0L) => input.take_along_dim(indices, dim); // https://pytorch.org/docs/stable/generated/torch.tensor_split - public static Tensor[] tensor_split(Tensor input, long indices_or_sections, long dim = 0L) + public static torch.Tensor[] tensor_split(torch.Tensor input, long indices_or_sections, long dim = 0L) => input.tensor_split(indices_or_sections, dim); // https://pytorch.org/docs/stable/generated/torch.tensor_split - public static Tensor[] tensor_split(Tensor input, long[] indices_or_sections, long dim = 0L) + public static torch.Tensor[] tensor_split(torch.Tensor input, long[] indices_or_sections, long dim = 0L) => input.tensor_split(indices_or_sections, dim); // https://pytorch.org/docs/stable/generated/torch.tensor_split - public static Tensor[] tensor_split(Tensor input, Tensor indices_or_sections, long dim = 0L) + public static torch.Tensor[] tensor_split(torch.Tensor input, torch.Tensor indices_or_sections, long dim = 0L) => input.tensor_split(indices_or_sections, dim); // https://pytorch.org/docs/stable/generated/torch.tile @@ -575,14 +572,14 @@ public static Tensor[] tensor_split(Tensor input, Tensor indices_or_sections, lo /// /// The input tensor /// The number of repetitions per dimension. - public static Tensor tile(Tensor input, long[] dims) => input.tile(dims); + public static torch.Tensor tile(torch.Tensor input, long[] dims) => input.tile(dims); // https://pytorch.org/docs/stable/generated/torch.transpose - public static Tensor transpose(Tensor input, long dim0, long dim1) + public static torch.Tensor transpose(torch.Tensor input, long dim0, long dim1) => input.transpose(dim0, dim1); // https://pytorch.org/docs/stable/generated/torch.unbind - public static Tensor[] unbind(Tensor input, long dim = 0L) + public static torch.Tensor[] unbind(torch.Tensor input, long dim = 0L) => input.unbind(dim); // https://pytorch.org/docs/stable/generated/torch.unsqueeze @@ -590,7 +587,7 @@ public static Tensor[] unbind(Tensor input, long dim = 0L) /// Returns a new tensor with a dimension of size one inserted at the specified position. /// The returned tensor shares the same underlying data with this tensor. /// - public static Tensor unsqueeze(Tensor input, long dim) + public static torch.Tensor unsqueeze(torch.Tensor input, long dim) => input.unsqueeze(dim); // https://pytorch.org/docs/stable/generated/torch.unsqueeze @@ -598,11 +595,11 @@ public static Tensor unsqueeze(Tensor input, long dim) /// Returns a new tensor with a dimension of size one inserted at the specified position. /// The returned tensor shares the same underlying data with this tensor. /// - public static Tensor unsqueeze_(Tensor input, long dim) + public static torch.Tensor unsqueeze_(torch.Tensor input, long dim) => input.unsqueeze_(dim); // https://pytorch.org/docs/stable/generated/torch.vsplit - public static Tensor[] vsplit(Tensor input, long[] indices_or_sections) + public static torch.Tensor[] vsplit(torch.Tensor input, long[] indices_or_sections) => input.vsplit(indices_or_sections); // https://pytorch.org/docs/stable/generated/torch.vstack @@ -611,7 +608,7 @@ public static Tensor[] vsplit(Tensor input, long[] indices_or_sections) /// /// A list of input tensors. /// A tensor containing the input tensors stacked vertically (row-wise). - public static Tensor vstack(IList tensors) + public static torch.Tensor vstack(IList tensors) => vstack(tensors.ToHandleArray()); // https://pytorch.org/docs/stable/generated/torch.vstack @@ -620,7 +617,7 @@ public static Tensor vstack(IList tensors) /// /// An array of input tensors. /// A tensor containing the input tensors stacked vertically (row-wise). - public static Tensor vstack(Tensor[] tensors) + public static torch.Tensor vstack(torch.Tensor[] tensors) => vstack(tensors.ToHandleArray()); // https://pytorch.org/docs/stable/generated/torch.vstack @@ -629,10 +626,10 @@ public static Tensor vstack(Tensor[] tensors) /// /// A span of input tensors. /// A tensor containing the input tensors stacked vertically (row-wise). - public static Tensor vstack(ReadOnlySpan tensors) + public static torch.Tensor vstack(ReadOnlySpan tensors) => vstack(tensors.ToHandleArray()); - static Tensor vstack(IntPtr[] tensors) + static torch.Tensor vstack(IntPtr[] tensors) { using var parray = new PinnedArray(); IntPtr tensorsRef = parray.CreateArray(tensors); @@ -648,7 +645,7 @@ static Tensor vstack(IntPtr[] tensors) /// Values selected at indices where condition is true /// Values selected at indices where condition is false /// - public static Tensor where(Tensor condition, Tensor x, Tensor y) => x.where(condition, y); + public static torch.Tensor where(torch.Tensor condition, torch.Tensor x, torch.Tensor y) => x.where(condition, y); // https://pytorch.org/docs/stable/generated/torch.where /// @@ -659,9 +656,9 @@ static Tensor vstack(IntPtr[] tensors) /// The input tensor /// /// - public static Tensor[] where(Tensor condition) + public static torch.Tensor[] where(torch.Tensor condition) { - if (condition.dtype != ScalarType.Bool) throw new ArgumentException("The condition to 'where' must be a boolean tensor."); + if (condition.dtype != torch.ScalarType.Bool) throw new ArgumentException("The condition to 'where' must be a boolean tensor."); IntPtr[] ptrArray; @@ -671,7 +668,7 @@ public static Tensor[] where(Tensor condition) ptrArray = pa.Array; } - return ptrArray.Select(x => new Tensor(x)).ToArray(); + return ptrArray.Select(x => new torch.Tensor(x)).ToArray(); } } } \ No newline at end of file diff --git a/src/TorchSharp/Tensor/torch.Utilities.cs b/src/TorchSharp/Tensor/torch.Utilities.cs index 6e89134e8..5db1f6315 100644 --- a/src/TorchSharp/Tensor/torch.Utilities.cs +++ b/src/TorchSharp/Tensor/torch.Utilities.cs @@ -83,7 +83,7 @@ public static ScalarType promote_types(ScalarType type1, ScalarType type2) [Obsolete("not implemented", true)] public static void _assert(Func condition, string message) => throw new NotImplementedException(); - public static void PrintModule(torch.nn.Module module) + /*public static void PrintModule(torch.nn.Module module) { if (module is Dropout2d drop2d) { Console.WriteLine($"{module.GetName()}({drop2d.p}, {drop2d.inplace})"); @@ -99,6 +99,6 @@ public static void PrintModule(torch.nn.Module module) return; } NativeMethods.THSNN_Print_Module(module.handle); - } + }*/ } } \ No newline at end of file diff --git a/src/TorchSharp/Torch.cs b/src/TorchSharp/Torch.cs index 7febadb61..09bd1ffb8 100644 --- a/src/TorchSharp/Torch.cs +++ b/src/TorchSharp/Torch.cs @@ -1,4 +1,4 @@ -// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information. +// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information. #nullable enable using System; using System.Collections.Generic; @@ -195,7 +195,7 @@ private static void LoadNativeBackend(bool useCudaBackend, out StringBuilder? tr // So we shadow copy the DLLs into the TorchSharp package, make a copy of the native DLL and continue // with the dynamic load // - // Assumed to be in ...\packages\torchsharp\0.3.0-local-debug-20200918\lib\net6.0\TorchSharp.dll + // Assumed to be in ...\packages\torchsharp\0.3.0-local-debug-20200918\lib\net8.0\TorchSharp.dll // // TODO: on linux make these copies link not shadow-copy var torchsharpLoc = Path.GetDirectoryName(typeof(torch).Assembly.Location); @@ -535,6 +535,15 @@ public static (Parameter weight, Parameter bias) fuse_linear_bn_weights( return scope.MoveToOuter(weight, bias); } + public static Linear fuse_linear_bn_eval(Linear linear, BatchNorm bn) + { + if (linear.training || bn.training) + throw new InvalidOperationException("Fusing operators is valid only for eval mode."); + + var (weight, bias) = fuse_linear_bn_weights(linear.weight, linear.bias, bn.running_mean!, bn.running_var!, bn.eps, bn.weight, bn.bias!); + + return Linear(weight, bias); + } } } diff --git a/src/TorchSharp/TorchSharp.csproj b/src/TorchSharp/TorchSharp.csproj index 73dd6bc0c..4d37be31d 100644 --- a/src/TorchSharp/TorchSharp.csproj +++ b/src/TorchSharp/TorchSharp.csproj @@ -1,16 +1,17 @@ - + - net8.0;netstandard2.0 - 9.0 - TorchSharp - true - false - false - false - $(DefineConstants);LIBTORCH_$(LibTorchPackageVersion.Replace('.', '_'));CUDA_$(CudaVersionDot.Replace('.', '_')) + netstandard2.0;net8.0 + 9.0 + TorchSharp + true + false + false + false + $(DefineConstants);LIBTORCH_$(LibTorchPackageVersion.Replace('.', '_'));CUDA_$(CudaVersionDot.Replace('.', '_')) + K:\FrameworksForC\LibTorch\libtorch-win-shared-with-deps-debug-2.11.0+cu130\libtorch\share\cmake\Torch diff --git a/src/TorchSharp/Utils/BFloat16.cs b/src/TorchSharp/Utils/BFloat16.cs index 375c91b20..08864c125 100644 --- a/src/TorchSharp/Utils/BFloat16.cs +++ b/src/TorchSharp/Utils/BFloat16.cs @@ -1,4 +1,4 @@ -using System.Runtime.InteropServices; +/*using System.Runtime.InteropServices; using TorchSharp.PInvoke; namespace System @@ -21,38 +21,5 @@ public float ToFloat() return NativeMethods.THSBFloat16_op_float(this); } } - - /* - * -struct alignas(2) BFloat16 { - uint16_t x; - - // HIP wants __host__ __device__ tag, CUDA does not -#if defined(USE_ROCM) - C10_HOST_DEVICE BFloat16() = default; -#else - BFloat16() = default; -#endif - - struct from_bits_t {}; - static constexpr C10_HOST_DEVICE from_bits_t from_bits() { - return from_bits_t(); - } - - constexpr C10_HOST_DEVICE BFloat16(unsigned short bits, from_bits_t) - : x(bits) {} - inline C10_HOST_DEVICE BFloat16(float value); - inline C10_HOST_DEVICE operator float() const; - -#if defined(__CUDACC__) && !defined(USE_ROCM) - inline C10_HOST_DEVICE BFloat16(const __nv_bfloat16& value); - explicit inline C10_HOST_DEVICE operator __nv_bfloat16() const; -#endif - -#if defined(SYCL_EXT_ONEAPI_BFLOAT16_MATH_FUNCTIONS) - inline C10_HOST_DEVICE BFloat16(const sycl::ext::oneapi::bfloat16& value); - explicit inline C10_HOST_DEVICE operator sycl::ext::oneapi::bfloat16() const; -#endif -}; - */ } +*/ \ No newline at end of file diff --git a/src/TorchSharp/Utils/ModuleInfo.cs b/src/TorchSharp/Utils/ModuleInfo.cs index 800dc977d..3f162c213 100644 --- a/src/TorchSharp/Utils/ModuleInfo.cs +++ b/src/TorchSharp/Utils/ModuleInfo.cs @@ -14,15 +14,10 @@ public class ConvInfo public object Kernel, Dilation, Stride; public ConvInfo(Convolution conv) { - InChannel = conv._in_channel; - OutChannel = conv._out_channel; - if (conv._kernels.HasValue) { - Kernel = conv._kernels.Value; - } - else { - Kernel = conv._kernel; - } - + InChannel = conv.in_channels; + OutChannel = conv.out_channels; + Kernel = conv.kernel_size; + //TODO: Make all props; throw new NotImplementedException("Need finish"); } diff --git a/src/TorchSharp/Utils/TensorAccessor.cs b/src/TorchSharp/Utils/TensorAccessor.cs index 7051ba82c..63cd9254c 100644 --- a/src/TorchSharp/Utils/TensorAccessor.cs +++ b/src/TorchSharp/Utils/TensorAccessor.cs @@ -294,7 +294,7 @@ private void CopyContiguous(T[] array, int index = 0, int count = 0) } if (array is BFloat16[] bfa) { //TODO: Test this - Marshal.Copy(_tensor_data_ptr, bfa.Select(x=>x.ToFloat()).ToArray(), index, count); + Marshal.Copy(_tensor_data_ptr, bfa.Select(x=>x.ToSingle()).ToArray(), index, count); return; } } diff --git a/src/TorchVision/Ops/DeformConv2d.cs b/src/TorchVision/Ops/DeformConv2d.cs index 18762b8ff..4b1b10163 100644 --- a/src/TorchVision/Ops/DeformConv2d.cs +++ b/src/TorchVision/Ops/DeformConv2d.cs @@ -119,7 +119,8 @@ protected internal DeformConv2d(int in_channels, int out_channels, int kernel_si if (use_bias.HasValue && use_bias.Value) { this.bias = new Parameter(torch.zeros(out_channels)); } else { - base.register_parameter("bias", null); + this.bias = null; + //base.register_parameter("bias", null); } weight = new Parameter(torch.zeros(out_channels, in_channels / groups, kernel_size, kernel_size)); diff --git a/test/Directory.Build.props b/test/Directory.Build.props index 3660046eb..ff0d850ac 100644 --- a/test/Directory.Build.props +++ b/test/Directory.Build.props @@ -6,7 +6,7 @@ $(TargetFrameworks);net48;netstandard2.0 false true - K:\FrameworksForC\LibTorch\libtorch-win-shared-with-deps-2.11.0+cpu\libtorch\share\cmake\Torch + K:\FrameworksForC\LibTorch\libtorch-win-shared-with-deps-debug-2.11.0+cu130\libtorch\share\cmake\Torch - + net472;net8.0 net8.0 net472;$(TargetFrameworks) net8.0 @@ -13,7 +13,7 @@ trx $(OutputPath) Debug;Release;LibTorch2.3.1 - K:\FrameworksForC\LibTorch\libtorch-win-shared-with-deps-debug-2.6.0+cu126\libtorch\share\cmake\Torch + K:\FrameworksForC\LibTorch\libtorch-win-shared-with-deps-debug-2.11.0+cu130\libtorch\share\cmake\Torch diff --git a/test/TorchSharpTest/TestGradScaler.cs b/test/TorchSharpTest/TestGradScaler.cs index dd8833896..71709edc6 100644 --- a/test/TorchSharpTest/TestGradScaler.cs +++ b/test/TorchSharpTest/TestGradScaler.cs @@ -1,5 +1,6 @@ using System; using System.Collections.Generic; +using System.Diagnostics; using System.Linq; using TorchSharp; using TorchSharp.Amp; @@ -350,17 +351,53 @@ public void TestGradScalingAccumulation() public void TestGradScalingMultiple() { CheckCUDA(); - foreach (var enabled in new[] { true, false }) { - var res0 = create_scaling_case(); // mod_control0, mod_scaling0, etc. - var res1 = create_scaling_model_optimizer(); // mod_control1, mod_scaling1 - var scaler = new GradScaler(new Device(DeviceType.CUDA), 128.0, 2.0, growth_interval: 1, enabled: enabled); - //TODO: Implemement same as run - - double expectedScale = enabled ? (128.0 * Math.Pow(2.0, 3) * Math.Pow(0.5, 1)) : 1.0; - Assert.Equal(expectedScale, scaler.get_scale()); - } + bool[] enableds = new bool[] { true, false }; + foreach (var enabled in enableds) { + var res = create_scaling_case(); + var res1 = create_scaling_model_optimizer(); + var scaler = new GradScaler(new torch.Device(DeviceType.CUDA), 128.0, 2.0, growth_interval: 1, enabled: enabled); + var run = new Action>, Sequential, Sequential, optim.Optimizer, optim.Optimizer, bool>((data, model0, model1, optimizer0, optimizer1, try_scaling_api) => { + for (int i = 0; i < data.Count; i++) { + var input = data[i].Key; + var target = data[i].Value; + optimizer0.zero_grad(); + optimizer1.zero_grad(); + + var output0 = model0.forward(input); + var output1 = model1.forward(input); - throw new NotImplementedException(); + var loss0 = res.loss_fn.forward(0.3 * output0 + 0.7 * output1, target); + var loss1 = res.loss_fn.forward(0.6 * output0 - 0.4 * output1, target); + if (try_scaling_api) { + scaler.scale(loss0).backward(null, true); + scaler.scale(loss1).backward(); + if (i == res.skip_iter && scaler.IsEnabled()) { + var weight = (model1[1] as Linear).weight; + weight.grad.fill_(float.PositiveInfinity); + } + scaler.unscale(optimizer0); + scaler.step(optimizer0); + scaler.step(optimizer1); + scaler.update(); + } else { + loss0.backward(null, true); + loss1.backward(); + optimizer0.step(); + if (!scaler.IsEnabled() || (i != res.skip_iter)) + optimizer1.step(); + } + } + }); + + run(res.data, res.modctrl, res1.modctrl, res.optctrl, res1.optctrl, false); + run(res.data, res.modscal, res1.modscal, res.optscal, res1.optscal, true); + Assert.True(scaler.get_scale() == (enabled ? 128.0 * Math.Pow(scaler.get_growth_factor(), 3) * Math.Pow(scaler.get_backoff_factor(), 1) : 1.0)); + /*foreach(var z in res.modctrl.parameters().Zip(res1.modctrl.parameters())) + { + + }*/ + + } } } } diff --git a/test/TorchSharpTest/TorchSharpTest.csproj b/test/TorchSharpTest/TorchSharpTest.csproj index 1c95c3c2e..434b00ece 100644 --- a/test/TorchSharpTest/TorchSharpTest.csproj +++ b/test/TorchSharpTest/TorchSharpTest.csproj @@ -3,7 +3,7 @@ - + net472;net8.0 net8.0 net472;$(TargetFrameworks) net8.0 diff --git a/test/notebooks/NativeCudaLoadLinux.ipynb b/test/notebooks/NativeCudaLoadLinux.ipynb index 81101aef5..f8e5316f5 100644 --- a/test/notebooks/NativeCudaLoadLinux.ipynb +++ b/test/notebooks/NativeCudaLoadLinux.ipynb @@ -313,8 +313,8 @@ "!ldd --version\n", "!ls /root/.nuget/packages/torchsharp/0.92.52515/runtimes/linux-x64/native/\n", "#!ldd /root/.nuget/packages/torchsharp/0.92.52515/runtimes/linux-x64/native/libLibTorchSharp.so\n", - "!ls /root/.nuget/packages/torchsharp/0.92.52515/lib/net6.0/cuda-11.7/\n", - "!ldd /root/.nuget/packages/torchsharp/0.92.52515/lib/net6.0/cuda-11.7/libLibTorchSharp.so" + "!ls /root/.nuget/packages/torchsharp/0.92.52515/lib/net8.0/cuda-11.7/\n", + "!ldd /root/.nuget/packages/torchsharp/0.92.52515/lib/net8.0/cuda-11.7/libLibTorchSharp.so" ], "execution_count": null, "outputs": [ @@ -350,9 +350,9 @@ "libnvrtc-builtins.so\n", "\tlinux-vdso.so.1 (0x00007ffc941eb000)\n", "\t/usr/lib/x86_64-linux-gnu/libtcmalloc.so.4 (0x00007fc2df705000)\n", - "\tlibtorch.so => /root/.nuget/packages/torchsharp/0.92.52515/lib/net6.0/cuda-11.7/libtorch.so (0x00007fc2df503000)\n", - "\tlibc10.so => /root/.nuget/packages/torchsharp/0.92.52515/lib/net6.0/cuda-11.7/libc10.so (0x00007fc2df26c000)\n", - "\tlibtorch_cpu.so => /root/.nuget/packages/torchsharp/0.92.52515/lib/net6.0/cuda-11.7/libtorch_cpu.so (0x00007fc2ccdfc000)\n", + "\tlibtorch.so => /root/.nuget/packages/torchsharp/0.92.52515/lib/net8.0/cuda-11.7/libtorch.so (0x00007fc2df503000)\n", + "\tlibc10.so => /root/.nuget/packages/torchsharp/0.92.52515/lib/net8.0/cuda-11.7/libc10.so (0x00007fc2df26c000)\n", + "\tlibtorch_cpu.so => /root/.nuget/packages/torchsharp/0.92.52515/lib/net8.0/cuda-11.7/libtorch_cpu.so (0x00007fc2ccdfc000)\n", "\tlibpthread.so.0 => /lib/x86_64-linux-gnu/libpthread.so.0 (0x00007fc2ccbdd000)\n", "\tlibstdc++.so.6 => /usr/lib/x86_64-linux-gnu/libstdc++.so.6 (0x00007fc2cc854000)\n", "\tlibm.so.6 => /lib/x86_64-linux-gnu/libm.so.6 (0x00007fc2cc4b6000)\n", @@ -360,16 +360,16 @@ "\tlibc.so.6 => /lib/x86_64-linux-gnu/libc.so.6 (0x00007fc2cbead000)\n", "\t/lib64/ld-linux-x86-64.so.2 (0x00007fc2dfd6a000)\n", "\tlibunwind.so.8 => /usr/lib/x86_64-linux-gnu/libunwind.so.8 (0x00007fc2cbc92000)\n", - "\tlibtorch_cuda.so => /root/.nuget/packages/torchsharp/0.92.52515/lib/net6.0/cuda-11.7/libtorch_cuda.so (0x00007fc2bde7b000)\n", - "\tlibtorch_cuda_cu.so => /root/.nuget/packages/torchsharp/0.92.52515/lib/net6.0/cuda-11.7/libtorch_cuda_cu.so (0x00007fc27e2a0000)\n", - "\tlibtorch_cuda_cpp.so => /root/.nuget/packages/torchsharp/0.92.52515/lib/net6.0/cuda-11.7/libtorch_cuda_cpp.so (0x00007fc20b85f000)\n", - "\tlibgomp-7c85b1e2.so.1 => /root/.nuget/packages/torchsharp/0.92.52515/lib/net6.0/cuda-11.7/libgomp-7c85b1e2.so.1 (0x00007fc20b635000)\n", + "\tlibtorch_cuda.so => /root/.nuget/packages/torchsharp/0.92.52515/lib/net8.0/cuda-11.7/libtorch_cuda.so (0x00007fc2bde7b000)\n", + "\tlibtorch_cuda_cu.so => /root/.nuget/packages/torchsharp/0.92.52515/lib/net8.0/cuda-11.7/libtorch_cuda_cu.so (0x00007fc27e2a0000)\n", + "\tlibtorch_cuda_cpp.so => /root/.nuget/packages/torchsharp/0.92.52515/lib/net8.0/cuda-11.7/libtorch_cuda_cpp.so (0x00007fc20b85f000)\n", + "\tlibgomp-7c85b1e2.so.1 => /root/.nuget/packages/torchsharp/0.92.52515/lib/net8.0/cuda-11.7/libgomp-7c85b1e2.so.1 (0x00007fc20b635000)\n", "\tlibrt.so.1 => /lib/x86_64-linux-gnu/librt.so.1 (0x00007fc20b42d000)\n", "\tlibdl.so.2 => /lib/x86_64-linux-gnu/libdl.so.2 (0x00007fc20b229000)\n", - "\tlibcudart-6d56b25a.so.11.0 => /root/.nuget/packages/torchsharp/0.92.52515/lib/net6.0/cuda-11.7/libcudart-6d56b25a.so.11.0 (0x00007fc20afa0000)\n", + "\tlibcudart-6d56b25a.so.11.0 => /root/.nuget/packages/torchsharp/0.92.52515/lib/net8.0/cuda-11.7/libcudart-6d56b25a.so.11.0 (0x00007fc20afa0000)\n", "\tliblzma.so.5 => /lib/x86_64-linux-gnu/liblzma.so.5 (0x00007fc20ad7a000)\n", - "\tlibc10_cuda.so => /root/.nuget/packages/torchsharp/0.92.52515/lib/net6.0/cuda-11.7/libc10_cuda.so (0x00007fc20ab4a000)\n", - "\tlibnvToolsExt-24de1d56.so.1 => /root/.nuget/packages/torchsharp/0.92.52515/lib/net6.0/cuda-11.7/libnvToolsExt-24de1d56.so.1 (0x00007fc20a940000)\n" + "\tlibc10_cuda.so => /root/.nuget/packages/torchsharp/0.92.52515/lib/net8.0/cuda-11.7/libc10_cuda.so (0x00007fc20ab4a000)\n", + "\tlibnvToolsExt-24de1d56.so.1 => /root/.nuget/packages/torchsharp/0.92.52515/lib/net8.0/cuda-11.7/libnvToolsExt-24de1d56.so.1 (0x00007fc20a940000)\n" ], "name": "stdout" } From df71a938001eb17ed909892754d639b5e6850b5e Mon Sep 17 00:00:00 2001 From: Dimitri Date: Sun, 26 Apr 2026 01:47:48 -0300 Subject: [PATCH 64/65] fix parameter on TestGradScalingPenalty --- test/TorchSharpTest/TestGradScaler.cs | 7 ++----- 1 file changed, 2 insertions(+), 5 deletions(-) diff --git a/test/TorchSharpTest/TestGradScaler.cs b/test/TorchSharpTest/TestGradScaler.cs index 71709edc6..b36ed674b 100644 --- a/test/TorchSharpTest/TestGradScaler.cs +++ b/test/TorchSharpTest/TestGradScaler.cs @@ -263,7 +263,6 @@ public void TestGradScalingPenalty() { run_scaling_case(new Action>, Sequential, optim.Optimizer, GradScaler, MSELoss, int, bool>(( (data, model, optimizer, scaler, loss_fn, skip_iter, try_scaling_api) => { - //const float max_norm = 0.2f; int idx = 0; foreach (var ipair in data) { //ipair. @@ -272,9 +271,7 @@ public void TestGradScalingPenalty() var loss = loss_fn.forward(output, ipair.Value); IList grad_params = new List(); if (try_scaling_api) { - //throw new NotImplementedException(); - //TODO: RESEARCH TORCH::AUTOGRAD:GRAD THE SECOND ARGUMENT SHOULD HAVE model->parameters(); - //grad_params = torch.autograd.grad(new List() { scaler.scale(loss) }, model.parameters()); + grad_params = torch.autograd.grad(new List() { scaler.scale(loss) }, model.parameters(),create_graph:true); var inv_scale = 1.0f / scaler.get_scale(); for (int i = 0; i < grad_params.Count; i++) @@ -282,7 +279,7 @@ public void TestGradScalingPenalty() } else { //throw new NotImplementedException(); //TODO: RESEARCH TORCH::AUTOGRAD:GRAD THE SECOND ARGUMENT SHOULD HAVE model->parameters(); - grad_params = torch.autograd.grad(new List() { scaler.scale(loss) }, model.parameters(), create_graph: true); + grad_params = torch.autograd.grad(new List() { loss }, model.parameters(), create_graph: true); } var grad_norm = torch.zeros(new long[] { 1 }).to(ipair.Key.device); From aa8fe3f226c76de2753e53bc07b1ae49e1af822a Mon Sep 17 00:00:00 2001 From: Dimitri Date: Mon, 27 Apr 2026 14:56:05 -0300 Subject: [PATCH 65/65] remove default value of customlibtorchfullpath property --- src/TorchSharp/TorchSharp.csproj | 2 +- .../TorchSharpTest.WithCudaBinaries.csproj | 2 +- test/TorchSharpTest/TorchSharpTest.csproj | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/src/TorchSharp/TorchSharp.csproj b/src/TorchSharp/TorchSharp.csproj index 4d37be31d..7de6db892 100644 --- a/src/TorchSharp/TorchSharp.csproj +++ b/src/TorchSharp/TorchSharp.csproj @@ -11,7 +11,7 @@ false false $(DefineConstants);LIBTORCH_$(LibTorchPackageVersion.Replace('.', '_'));CUDA_$(CudaVersionDot.Replace('.', '_')) - K:\FrameworksForC\LibTorch\libtorch-win-shared-with-deps-debug-2.11.0+cu130\libtorch\share\cmake\Torch + diff --git a/test/TorchSharpTest.WithCudaBinaries/TorchSharpTest.WithCudaBinaries.csproj b/test/TorchSharpTest.WithCudaBinaries/TorchSharpTest.WithCudaBinaries.csproj index a5a3abac0..217df84e3 100644 --- a/test/TorchSharpTest.WithCudaBinaries/TorchSharpTest.WithCudaBinaries.csproj +++ b/test/TorchSharpTest.WithCudaBinaries/TorchSharpTest.WithCudaBinaries.csproj @@ -13,7 +13,7 @@ trx $(OutputPath) Debug;Release;LibTorch2.3.1 - K:\FrameworksForC\LibTorch\libtorch-win-shared-with-deps-debug-2.11.0+cu130\libtorch\share\cmake\Torch + diff --git a/test/TorchSharpTest/TorchSharpTest.csproj b/test/TorchSharpTest/TorchSharpTest.csproj index 434b00ece..e8a185397 100644 --- a/test/TorchSharpTest/TorchSharpTest.csproj +++ b/test/TorchSharpTest/TorchSharpTest.csproj @@ -13,7 +13,7 @@ trx $(OutputPath) 10.0 - K:\FrameworksForC\LibTorch\libtorch-win-shared-with-deps-2.11.0+cpu\libtorch\share\cmake\Torch +