[Draft] Redmule platform#67
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Minimal port of RedMulE-platform code from the user's redmule_platform
branch (which had accumulated unrelated CCT_Optim merges) onto a clean
devel base.
What landed:
- New target Deeploy/Targets/Redmule/ (Platform, Engine, Deployer,
Bindings, Parsers, Tiler, Templates, TileConstraints,
TopologyOptimizationPasses).
- FP32 RedMulE matmul kernel TargetLibraries/PULPOpen/src/Matmul_fp32_Redmule.c
- Test runner DeeployTest/testRunner_tiled_siracusa_w_redmule.py plus
Float test fixtures (testFloat{Matmul,MatmulLarge,MatmulLarge256,2DConvolution,2dConvLarge,GEMM,GEMMtransB}).
- Wiring in platformMapping.py, top-level CMakeLists.txt,
DeeployTest/CMakeLists.txt, TargetLibraries/PULPOpen/CMakeLists.txt.
- Makefile: GVSOC_COMMIT_HASH points at runwangdl/gvsoc fork 35d00d1
(carries the light_redmule vendored copy + Siracusa cluster wiring).
Fixes / portings required for devel compatibility:
- Deeploy/Targets/PULPOpen/Templates/FloatGemmTemplate.py: define
float32_tPtr locally (unresolved import left on devel).
- Deeploy/Targets/Redmule/TopologyOptimizationPasses/Passes.py: switch
from the retired _permuteLastTwoDims / _appendTransposeNode helpers
to upstream's _appendTranspose.
- Add empty __init__.py to Targets/{Chimera,Redmule,SoftHier}.
What intentionally did NOT land:
- CCT_Optim-era edits to PULPOpen Templates (Add/Conv/GELU/Layernorm/
MatMul/MaxPool/Relu/Softmax), Generic Layers.py computeOps, CCT test
suites, parallel/unroll rewrites.
- Buggy -march=rv32imc inside meson-build-script-rv32imf.txt.
- Hard-to-merge edits to DeeployTest/Platforms/Siracusa/src/deeploytest.c.
- The old-style .github/workflows/TestRunnerTiledSiracusaWithRedmule.yml;
new-style ci-platform-siracusa-redmule-tiled.yml TBD.
Verified end-to-end: testFloatMatmul on GVSoC (runwangdl/gvsoc@35d00d1,
pulp submodule @ 371772c) passes with 'Errors: 0 out of 256'.
The Tests/ directory layout on devel was reorganized into Kernels/, Models/, Others/ subdirectories. Drop the flat-path Float test inputs ported from redmule_platform; they'll be re-added under the new structure in a follow-up.
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Mirrors the neureka-tiled pattern:
- DeeployTest/test_siracusa_redmule_tiled_config.py with empty
L2_{SINGLE,DOUBLE}BUFFER_KERNELS dicts (to be populated once Float
kernel test fixtures land under Tests/Kernels/Float/).
- conftest.py: register 'siracusa_redmule_tiled' pytest marker.
- test_platforms.py: two parametrized test functions (L2 single- and
double-buffer) for the redmule platform.
- .github/workflows/_runner-siracusa-redmule-tiled.yml: reusable runner
mirroring _runner-siracusa-neureka-tiled.yml.
- .github/workflows/ci-platform-siracusa-redmule-tiled.yml: top-level
trigger, defaults to ghcr.io/runwangdl/deeploy:redmule Docker image.
With empty configs the tests collect and skip cleanly (pytest 'got
empty parameter set'). No wmem variants since RedMulE does not use
Neureka weight memory.
- yapf / isort / autoflake / trailing-whitespace across the Redmule Python target and platformMapping wiring. - clang-format over TargetLibraries/PULPOpen/src/Matmul_fp32_Redmule.c. - Add SPDX/license header to Matmul_fp32_Redmule.c (reuse hook).
The GAP9 CI uses ghcr.io/pulp-platform/deeploy-gap9:devel, which is only pullable with pulp-platform org credentials. On a fork the job fails at 'Initialize containers'. Add github.repository_owner guard so forks skip the jobs cleanly.
The docs workflow publishes to gh-pages, which on a fork races with external pushes and lacks origin remote setup. Gate on github.repository_owner == 'pulp-platform' so only upstream publishes.
Point the redmule tiled CI config at existing upstream FP32 kernel test fixtures under Tests/Kernels/FP32/GEMM (Regular, TransB). Both single-buffer and double-buffer variants verified locally end-to-end on GVSoC (Errors: 0 / 256, runtime ~4k cycles).
Without this fallback _select-env.yml resolves to the upstream pulp-platform/deeploy:devel image, which ships a GVSoC build that does not include the light_redmule model — the redmule test runner then hangs. Point the default at the fork's custom image so push events get the correct GVSoC build.
ghcr.io/runwangdl/deeploy:redmule is a private package; add credentials block using the workflow's GITHUB_TOKEN so the runner container step can pull it.
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runwangdl
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The Siracusa+RedMulE training CI on 1782a88 got past Python codegen but failed at link time: ld.lld: error: undefined symbol: Conv2d_Im2Col_fp32_fp32_fp32_HWC_8_Redmule >>> referenced by TrainingNetwork.c:5386 in _node_1_tokenizer_..._Conv_cluster_fork The original RedMulE PR (pulp-platform/Deeploy#67) shipped only the matmul kernel TargetLibraries/PULPOpen/src/Matmul_fp32_Redmule.c. The ConvTemplate references a `Conv2d_Im2Col_..._8_Redmule` kernel that has no corresponding source in the tree, and 67b754b already deleted the testFloat2DConvolution / testFloat2dConvLarge fixtures that would have exercised the Redmule Conv path. So the Conv binding has always been load-bearing only for non-test models like CCT_train, and on those it breaks the link. Two coupled changes route Conv through the existing PULPClusterEngine (which has a working PULP_Conv2d_Im2Col_fp32_fp32_fp32_HWC): - Drop 'Conv' from RedmuleMapping. Without it Conv falls through to the second engine in RedmulePlatform's engine list (PULPCluster). - Drop RedMuleAdjustWeightMemoryLayoutPass from the lowering passes. That pass transposed Conv weights from [F,H,W,Cin] to [H,W,Cin,F] for the RedMulE accelerator's expected layout; once Conv is on the PULPCluster engine, PULP expects [F,H,W,Cin] and the pre-applied transpose makes Tiling produce out-of-bounds tile rectangles (locally repro'd: AssertionError "Rectangle offset should be zero when the dimensions are the same. Received rectangle HyperRectangle(offset=(3, 0, 0, 0), dims=(3, 3, 3, 32))" in TilingCodegen.minimizeRectangle). Both are clearly marked in-source as "restore when the RedMulE Conv kernel lands." Locally validated end-to-end: - testMVPTraining.py -> exit 0 (TrainingNetwork.c emits PULP_Conv2d_Im2Col_fp32_fp32_fp32_HWC for the tokenizer Conv). - testMVPOptimizer.py -> exit 0. Matmul / Gemm continue to bind to RedMulE as before. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
runwangdl
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Implements the kernel symbol that Deeploy/Targets/Redmule/Templates/ConvTemplate.py has been pointing at since the original pulp-platform/Deeploy#67 port -- it was a declared- but-never-defined dangling reference, which is why 78a05d4 had to unmap Conv from RedmuleMapping and route it through PULPCluster. - TargetLibraries/PULPOpen/src/Conv2d_Im2Col_fp32_Redmule.c All 8 cluster cores cooperatively build the [N_out, P*Q*C] im2col matrix in the hoisted L1 transient buffer (contiguous slices of output positions, zero-pad when h_in/w_in fall outside the input). Core 0 then triggers a single RedMulE GEMM [N_out, K] @ [K, F] -> [N_out, F] via MatMul_*_Redmule / Gemm_*_Redmule from Matmul_fp32_Redmule.c. When has_bias is true the [F] bias is broadcast in-place into pOut and Gemm runs with y_addr = z_addr = pOut (same pattern the existing MatMul kernel already uses for its Y=Z=pDstY zero-init). - Conv.h declares the new symbol. - ConvTemplate.py: * forwards ${bias} and ${has_bias} (PULPFPConv2DParser already populates them) -- the previous template silently dropped bias. * sizes the im2col transient buffer to the full per-tile H_out * W_out * (C*P*Q) footprint instead of the prior 8-row scratch; one big GEMM amortises RedMulE's MMIO setup cost. - Engine.RedmuleMapping restores 'Conv': ConvLayer([Conv2DRedmuleMapper]). - Deployer.py restores RedMuleAdjustWeightMemoryLayoutPass -- it permutes Conv weights from [F,P,Q,C] to [P,Q,C,F] = flat [P*Q*C, F], exactly the right operand the im2col GEMM consumes. Both Conv and the layout pass were disabled together in 78a05d4 (PULPCluster fallback expects [F,P,Q,C]); both come back together now. Locally validated: testMVPTraining.py + testMVPOptimizer.py both exit 0 on Models/Training/CCT/cct_train @ Siracusa_w_redmule; generated TrainingNetwork.c now emits Conv2d_Im2Col_fp32_fp32_fp32_HWC_8_Redmule for the tokenizer Conv (was PULP_Conv2d_Im2Col_*_HWC). GVSoC numerical tolerance still has to be checked on CI -- this is a new kernel, not a wrapper around an existing one, and the broadcasted- bias path was never exercised before. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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Redmule Platform
(Rebased on Picolib imf PR and CCT optim PR)
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develcommit and pointing todevel.CHANGELOG.mdfile has been updated.