Added missing activation checkpointing for DeepseekV4 model

src/maxtext/configs/base.yml

@@ -356,6 +356,7 @@ moe_mlpwo: 'remat'
 query_proj: 'remat'
 key_proj: 'remat'
 value_proj: 'remat'
+kv_proj: 'remat'
 qkv_proj: 'remat'
 out_proj: 'remat'
 query_wa_proj: 'remat'
@@ -1133,7 +1134,7 @@ enable_prefix_caching: false
 prefix_caching_hbm_byte: 10_000_000_000 # 10 GB
 prefix_caching_dram_byte: 100_000_000_000 # 100 GB
 
-# This is a temporary flag that will be removed soon after the fix lands in TE
+# This is a temporary flag that will be removed soon after the fix lands in Transformer Engine
 enable_padding_causal_mask: true
 
 # Llama4-specific

src/maxtext/configs/types.py

@@ -518,7 +518,10 @@ class ModelArchitecture(BaseModel):
       True,
       description="Whether to apply scale on query and key normalizations (default True).",
   )
-  v_norm_with_scale: bool = Field(True, description="Whether to apply scale on value normalization (default True).")
+  v_norm_with_scale: bool = Field(
+      True,
+      description="Whether to apply scale on value normalization (default True).",
+  )
 
 
 class MTP(BaseModel):
@@ -624,7 +627,7 @@ class Attention(BaseModel):
       ),
   )
   ragged_block_size: int = Field(256, description="Block size for ragged attention.")
-  enable_padding_causal_mask: bool = Field(True, description="Temporary flag for TE padding.")
+  enable_padding_causal_mask: bool = Field(True, description="Temporary flag for Transformer Engine padding.")
   use_tokamax_splash: bool = Field(False, description="Whether to use tokamax splash attention.")
   use_jax_splash: bool = Field(False, description="Whether to use jax splash attention.")
   force_q_layout: bool = Field(False, description="Force the Q layout")
@@ -656,7 +659,10 @@ class CompressedAttention(BaseModel):
 
   o_lora_rank: NonNegativeInt = Field(0, description="Output LoRA rank for Compressed Attention.")
   o_groups: NonNegativeInt = Field(0, description="Output groups for Compressed Attention.")
-  compress_ratios: list[int] = Field(default_factory=list, description="Per-layer compression ratios (0, 4, 128, etc).")
+  compress_ratios: list[int] = Field(
+      default_factory=list,
+      description="Per-layer compression ratios (0, 4, 128, etc).",
+  )
   compressed_rope_max_timescale: int = Field(
       160000, description="If positive, used for Compressed Sparse/Heavy Attention."
   )
@@ -757,14 +763,18 @@ class MoEGeneral(BaseModel):
   num_experts: PositiveInt = Field(1, description="The total number of experts in each MoE layer.")
   num_experts_per_tok: PositiveInt = Field(1, description="The number of experts to route each token to.")
   capacity_factor: float = Field(-1.0, description="Expert capacity factor. If < 0, no token dropping.")
-  ragged_buffer_factor: float = Field(-1.0, description="Ragged buffer factor. If < 0, ragged buffer is worst case size.")
+  ragged_buffer_factor: float = Field(
+      -1.0,
+      description="Ragged buffer factor. If < 0, ragged buffer is worst case size.",
+  )
   moe_expert_input_dim: int = Field(
       -1,
       description="Dimension of tokens entering the MoE layer. If < 0, defaults to emb_dim.",
   )
   base_moe_mlp_dim: int = Field(-1, description="Intermediate dimension at MoE layer.")
   padded_base_moe_mlp_dim: Optional[int] = Field(
-      None, description="Padded intermediate dimension at MoE layer for efficient GMM_v2 kernel execution."
+      None,
+      description="Padded intermediate dimension at MoE layer for efficient GMM_v2 kernel execution.",
   )
   load_balance_loss_weight: NonNegativeFloat = Field(0.0, description="Weight for the load balancing auxiliary loss.")
   use_custom_sort_vjp: bool = Field(
@@ -783,7 +793,8 @@ class MoEGeneral(BaseModel):
       ),
   )
   use_ragged_sort: bool = Field(
-      False, description="Whether to use ragged kernel for sorting, improve performance when EP is enabled."
+      False,
+      description="Whether to use ragged kernel for sorting, improve performance when EP is enabled.",
   )
   use_gather_mosaic_kernel: bool = Field(
       False,
@@ -919,7 +930,8 @@ class DeepSeekMoE(BaseModel):
   )
   n_routing_groups: int = Field(-1, description="Number of groups for routing, disabled by default.")
   first_num_hash_layers: int = Field(
-      0, description="Number of hash routing layers, used in DeepSeek V4 (0 means disabled)."
+      0,
+      description="Number of hash routing layers, used in DeepSeek V4 (0 means disabled).",
   )
   topk_routing_group: int = Field(-1, description="Number of top groups to route inputs to.")
   use_batch_split_schedule: bool = Field(
@@ -993,7 +1005,8 @@ class HardwareAndMesh(BaseModel):
   )
   custom_mesh: str = Field("", description="Available options: ['hybrid_ring_64x4', 'hybrid_ring_32x8']")
   custom_mesh_and_rule: CustomRule = Field(
-      CustomRule.DEFAULT, description="Customized mesh and logical rules for granularity."
+      CustomRule.DEFAULT,
+      description="Customized mesh and logical rules for granularity.",
   )
   allow_split_physical_axes: bool = Field(False, description="Allow splitting physical axes for device mesh creation.")
   enable_nnx: bool = Field(True, description="Whether to use NNX for model definition.")
@@ -1002,7 +1015,8 @@ class HardwareAndMesh(BaseModel):
   pure_nnx_decoder: bool = Field(True, description="Whether to enable pure NNX decoder.")
   pure_nnx: bool = Field(True, description="Whether to enable pure NNX mode.")
   remove_size_one_mesh_axis_from_type: bool = Field(
-      True, description="Whether to remove size one mesh axis from type through jax.config."
+      True,
+      description="Whether to remove size one mesh axis from type through jax.config.",
   )
 
 
@@ -1028,7 +1042,10 @@ class LayoutAndSharding(BaseModel):
       "with auto sharding, megablox kernel, and EP / FSDP parallelisms.",
   )
   shard_optimizer_over_data: bool = Field(False, description="Enable ZeRO-1 optimizer sharding over the data axis.")
-  internal_compile: bool = Field(False, description="Use internal_compile to bypass open-source topology mappings.")
+  internal_compile: bool = Field(
+      False,
+      description="Use internal_compile to bypass open-source topology mappings.",
+  )
   internal_compile_num_devices: int = Field(-1, description="Number of devices when using internal_compile.")
   compile_xla_flags: str = Field("", description="Compiler options for compilation only.")
 
@@ -1073,7 +1090,8 @@ class PipelineParallelism(BaseModel):
   """Configuration for pipeline parallelism."""
 
   pipeline_fsdp_ag_per_repeat: bool = Field(
-      False, description="Enable weight prefetching for circular pipeline parallelism."
+      False,
+      description="Enable weight prefetching for circular pipeline parallelism.",
   )
   num_layers_per_pipeline_stage: int = Field(1, description="Number of layers to place on each pipeline stage.")
   num_pipeline_repeats: int = Field(
@@ -1139,6 +1157,7 @@ class RematAndOffload(BaseModel):
   query_proj: RematLocation = Field(RematLocation.REMAT, description="Remat policy for the query projection.")
   key_proj: RematLocation = Field(RematLocation.REMAT, description="Remat policy for the key projection.")
   value_proj: RematLocation = Field(RematLocation.REMAT, description="Remat policy for the value projection.")
+  kv_proj: RematLocation = Field(RematLocation.REMAT, description="Remat policy for the unified KV projection.")
   query_wa_proj: RematLocation = Field(
       RematLocation.REMAT,
       description="Remat policy for the MLA query weighted attention projection.",
@@ -1321,7 +1340,10 @@ class OlmoGrainDataset(BaseModel):
   ``data_shuffle_seed``); only OLMo-specific fields are listed here.
   """
 
-  olmo_index_path: PathStr = Field("", description="Path or gs:// URI to the JSON index from build_olmo_npy_index.py.")
+  olmo_index_path: PathStr = Field(
+      "",
+      description="Path or gs:// URI to the JSON index from build_olmo_npy_index.py.",
+  )
   olmo_path_remap_from: PathStr = Field(
       "",
       description="If set, rewrite index file paths starting with this prefix to olmo_path_remap_to.",
@@ -1428,19 +1450,24 @@ class Distillation(BaseModel):
   distill_layer_indices: None | list = Field(None, description="Feature indices for feature loss.")
   distill_alpha_end: Optional[float] = Field(None, description="Target alpha at end of training. None keeps alpha fixed.")
   distill_alpha_schedule: Literal["constant", "linear", "cosine"] = Field(
-      "constant", description="Schedule type for alpha annealing ('constant', 'linear', or 'cosine')."
+      "constant",
+      description="Schedule type for alpha annealing ('constant', 'linear', or 'cosine').",
   )
   distill_temperature_end: Optional[float] = Field(
-      None, description="Target temperature at end of training. None keeps temperature fixed."
+      None,
+      description="Target temperature at end of training. None keeps temperature fixed.",
   )
   distill_temperature_schedule: Literal["constant", "linear", "cosine"] = Field(
-      "constant", description="Schedule type for temperature annealing ('constant', 'linear', or 'cosine')."
+      "constant",
+      description="Schedule type for temperature annealing ('constant', 'linear', or 'cosine').",
   )
   distill_beta_end: Optional[float] = Field(
-      None, description="Target beta_feature at end of training. None keeps beta fixed."
+      None,
+      description="Target beta_feature at end of training. None keeps beta fixed.",
   )
   distill_beta_schedule: Literal["constant", "linear", "cosine"] = Field(
-      "constant", description="Schedule type for beta annealing ('constant', 'linear', or 'cosine')."
+      "constant",
+      description="Schedule type for beta annealing ('constant', 'linear', or 'cosine').",
   )
 
   # --- Learn to init related parameters --
@@ -1463,11 +1490,13 @@ class Distillation(BaseModel):
   )
 
   attn_module_name: Optional[str] = Field(
-      None, description="Attention nnx module attribute name to augment with LTI logic"
+      None,
+      description="Attention nnx module attribute name to augment with LTI logic",
   )
 
   lti_layer_indices: Optional[list[int]] = Field(
-      None, description="List of layer indices to apply LTI modifications. If None, applied to all layers."
+      None,
+      description="List of layer indices to apply LTI modifications. If None, applied to all layers.",
   )
   # ---------------------------------------
 
@@ -1532,11 +1561,13 @@ class DilocoParams(BaseModel):
   diloco_outer_lr: float = Field(0.3, description="learning rate for outer optimizer.")
   diloco_outer_momentum: float = Field(0.9, description="momentum for outer optimizer.")
   dcn_bandwidth_limit: str = Field(
-      "", description="Programmatic DCN egress bandwidth limit (e.g., '28gbit'). Empty means no limit."
+      "",
+      description="Programmatic DCN egress bandwidth limit (e.g., '28gbit'). Empty means no limit.",
   )
   dcn_bandwidth_burst: str = Field("10mb", description="Burst size for Token Bucket Filter (TBF) traffic shaping.")
   dcn_bandwidth_latency: str = Field(
-      "50ms", description="Latency threshold for Token Bucket Filter (TBF) traffic shaping."
+      "50ms",
+      description="Latency threshold for Token Bucket Filter (TBF) traffic shaping.",
   )
   dcn_bandwidth_interface: str = Field("eth0", description="Network interface to apply bandwidth limits on.")
 
@@ -1829,7 +1860,8 @@ class Profiling(BaseModel):
   tpu_num_chips_to_profile_per_task: int = Field(1, description="Specifies the number of TPU chips to profile per task.")
   tpu_num_sparse_cores_to_trace: int = Field(2, description="Specifies the number of TPU chips to profile per task.")
   tpu_num_sparse_core_tiles_to_trace: int = Field(
-      1, description="Specifies the number of tiles within each sparse core to trace on the TPU."
+      1,
+      description="Specifies the number of tiles within each sparse core to trace on the TPU.",
   )
   xprof_tpu_power_trace_level: XProfTPUPowerTraceMode = Field(
       XProfTPUPowerTraceMode.POWER_TRACE_NONE,
@@ -2770,7 +2802,11 @@ def validate_and_set_hlo_dump_defaults():
       )
     for param_name, schedule, end_value in [
         ("distill_alpha", self.distill_alpha_schedule, self.distill_alpha_end),
-        ("distill_temperature", self.distill_temperature_schedule, self.distill_temperature_end),
+        (
+            "distill_temperature",
+            self.distill_temperature_schedule,
+            self.distill_temperature_end,
+        ),
         ("distill_beta", self.distill_beta_schedule, self.distill_beta_end),
     ]:
       if schedule != "constant" and end_value is None:
@@ -2827,7 +2863,10 @@ def get_num_target_devices():
 
     # Check for AQT deprecation warning
     if self.quantization and not self.use_qwix_quantization:
-      if self.quantization not in ("fp8", "nanoo_fp8") and not self.quantization.startswith("te_"):
+      if self.quantization not in (
+          "fp8",
+          "nanoo_fp8",
+      ) and not self.quantization.startswith("te_"):
         logger.warning(
             "WARNING: AQT quantization is deprecated and will be removed in a future release. "
             "Please migrate to Qwix by setting use_qwix_quantization=True."
@@ -2935,6 +2974,7 @@ def calculate_global_batch_sizes(per_device_batch_size, expansion_factor, num_de
           "query_proj",
           "key_proj",
           "value_proj",
+          "kv_proj",
           "query_wa_proj",
           "kv_wa_proj",
           "mla_kv",