From 5e315141658ddcf3de790ff242d865bcce50cb88 Mon Sep 17 00:00:00 2001 From: Nick Schank Date: Sun, 10 May 2026 16:59:24 -0400 Subject: [PATCH 1/9] Create a Protocol for the MLP layer of TransformerLayer (#3435) Co-authored-by: Antoni-Joan Solergibert --- examples/multimodal/layer_specs.py | 20 +-- examples/multimodal/nvlm/internvit.py | 6 +- examples/multimodal/radio/radio_g.py | 16 +- .../core/extensions/transformer_engine.py | 27 +++- megatron/core/models/T5/t5_spec.py | 18 ++- megatron/core/models/bert/bert_layer_specs.py | 9 +- ...rimental_attention_variant_module_specs.py | 62 +++++--- megatron/core/models/gpt/gpt_layer_specs.py | 14 +- .../heterogeneous_layer_specs.py | 18 ++- megatron/core/models/gpt/moe_module_specs.py | 20 +-- .../core/models/hybrid/hybrid_layer_specs.py | 9 +- .../core/models/vision/vit_layer_specs.py | 7 +- .../modelopt/hybrid/model_specs.py | 5 +- megatron/core/transformer/mlp.py | 26 +++ megatron/core/transformer/moe/experts.py | 2 +- megatron/core/transformer/moe/moe_layer.py | 2 +- .../core/transformer/transformer_layer.py | 90 +++++++---- pretrain_vlm.py | 150 +++++++++++------- .../test_cases/common/moe_perf/__main__.py | 13 +- .../dist_checkpointing/models/test_mlp_glu.py | 17 +- .../test_grad_sync_with_expert_parallel.py | 15 +- .../test_vlm_text_generation_controller.py | 6 +- tests/unit_tests/models/test_llava_model.py | 10 +- .../models/test_multimodal_projector.py | 2 +- tests/unit_tests/test_utils.py | 14 +- .../transformer/moe/test_grouped_mlp.py | 17 +- .../transformer/moe/test_latent_moe_layer.py | 21 ++- .../transformer/moe/test_moe_layer.py | 52 +++--- .../transformer/moe/test_routers.py | 35 ++-- .../transformer/moe/test_sequential_mlp.py | 12 +- .../transformer/moe/test_shared_experts.py | 12 +- .../transformer/moe/test_token_dispatcher.py | 14 +- .../transformer/test_cuda_graphs.py | 17 +- tests/unit_tests/transformer/test_mlp.py | 7 +- .../test_transformer_block_custom_pgs.py | 41 ++--- .../transformer/test_vision_cuda_graphs.py | 28 ++-- 36 files changed, 510 insertions(+), 324 deletions(-) diff --git a/examples/multimodal/layer_specs.py b/examples/multimodal/layer_specs.py index acced15eeb6..8205db90ddc 100644 --- a/examples/multimodal/layer_specs.py +++ b/examples/multimodal/layer_specs.py @@ -1,6 +1,9 @@ # Copyright (c) 2024-2026, NVIDIA CORPORATION. All rights reserved. +from functools import partial + import torch +from megatron.core.extensions.transformer_engine import HAVE_TE from megatron.core.fusions.fused_bias_dropout import get_bias_dropout_add from megatron.core.models.hybrid.hybrid_block import HybridStack, HybridStackSubmodules from megatron.core.ssm.mamba_layer import MambaLayer, MambaLayerSubmodules @@ -15,7 +18,6 @@ from megatron.core.transformer.spec_utils import ModuleSpec from megatron.core.transformer.transformer_layer import TransformerLayer, TransformerLayerSubmodules from megatron.core.typed_torch import not_none -from megatron.core.extensions.transformer_engine import HAVE_TE if HAVE_TE: from megatron.core.extensions.transformer_engine import ( @@ -170,8 +172,8 @@ def get_hybrid_layer_spec_te(padding=False) -> ModuleSpec: mlp_layer=ModuleSpec( module=MLPLayer, submodules=TransformerLayerSubmodules( - mlp=ModuleSpec( - module=MLP, + mlp=partial( + MLP.as_mlp_submodule, submodules=MLPSubmodules( linear_fc1=not_none(TELayerNormColumnParallelLinear), linear_fc2=not_none(TERowParallelLinear), @@ -184,10 +186,10 @@ def get_hybrid_layer_spec_te(padding=False) -> ModuleSpec: ) -def get_mlp_module_spec(use_te: bool = True) -> ModuleSpec: +def get_mlp_module_spec(use_te: bool = True): # Dense MLP w/ or w/o TE modules. - return ModuleSpec( - module=MLP, + return partial( + MLP.as_mlp_submodule, submodules=MLPSubmodules( linear_fc1=not_none(TEColumnParallelLinear) if use_te else ColumnParallelLinear, linear_fc2=not_none(TERowParallelLinear) if use_te else RowParallelLinear, @@ -195,9 +197,9 @@ def get_mlp_module_spec(use_te: bool = True) -> ModuleSpec: ) -def get_norm_mlp_module_spec_te() -> ModuleSpec: - return ModuleSpec( - module=MLP, +def get_norm_mlp_module_spec_te(): + return partial( + MLP.as_mlp_submodule, submodules=MLPSubmodules( linear_fc1=not_none(TELayerNormColumnParallelLinear), linear_fc2=not_none(TERowParallelLinear), diff --git a/examples/multimodal/nvlm/internvit.py b/examples/multimodal/nvlm/internvit.py index 0018bb5ccb9..d38ac64c16b 100644 --- a/examples/multimodal/nvlm/internvit.py +++ b/examples/multimodal/nvlm/internvit.py @@ -160,10 +160,10 @@ def sharded_state_dict(self, prefix='', sharded_offsets=(), metadata={}): return super().sharded_state_dict(prefix, sharded_offsets, metadata) -def get_mlp_module_spec(use_te: bool = True) -> ModuleSpec: +def get_mlp_module_spec(use_te: bool = True): # Dense MLP w/ or w/o TE modules. - return ModuleSpec( - module=MLP, + return partial( + MLP.as_mlp_submodule, submodules=MLPSubmodules( linear_fc1=TEColumnParallelLinear if use_te else ColumnParallelLinear, linear_fc2=TERowParallelLinear if use_te else RowParallelLinear, diff --git a/examples/multimodal/radio/radio_g.py b/examples/multimodal/radio/radio_g.py index 9883d58db61..30d2be7df2e 100644 --- a/examples/multimodal/radio/radio_g.py +++ b/examples/multimodal/radio/radio_g.py @@ -1,8 +1,6 @@ # Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. from functools import partial -import torch - from examples.multimodal.layer_scaling import ( LayerScalingTransformerLayer, get_bias_dropout_add_layer_scaling, @@ -14,7 +12,7 @@ from megatron.core.transformer.identity_op import IdentityOp from megatron.core.transformer.mlp import MLP, MLPSubmodules from megatron.core.transformer.spec_utils import ModuleSpec -from megatron.core.transformer.transformer_layer import TransformerLayer, TransformerLayerSubmodules +from megatron.core.transformer.transformer_layer import TransformerLayerSubmodules from megatron.core.typed_torch import not_none from megatron.core.extensions.transformer_engine import HAVE_TE @@ -51,10 +49,10 @@ LNImpl = WrappedTorchNorm -def get_mlp_module_spec(use_te: bool = True) -> ModuleSpec: +def get_mlp_module_spec(use_te: bool = True): # Dense MLP w/ or w/o TE modules. - return ModuleSpec( - module=MLP, + return partial( + MLP.as_mlp_submodule, submodules=MLPSubmodules( linear_fc1=not_none(TEColumnParallelLinear) if use_te else ColumnParallelLinear, linear_fc2=not_none(TERowParallelLinear) if use_te else RowParallelLinear, @@ -62,9 +60,9 @@ def get_mlp_module_spec(use_te: bool = True) -> ModuleSpec: ) -def get_norm_mlp_module_spec_te() -> ModuleSpec: - return ModuleSpec( - module=MLP, +def get_norm_mlp_module_spec_te(): + return partial( + MLP.as_mlp_submodule, submodules=MLPSubmodules( linear_fc1=not_none(TELayerNormColumnParallelLinear), linear_fc2=not_none(TERowParallelLinear), diff --git a/megatron/core/extensions/transformer_engine.py b/megatron/core/extensions/transformer_engine.py index 869e0099fce..085109c60eb 100644 --- a/megatron/core/extensions/transformer_engine.py +++ b/megatron/core/extensions/transformer_engine.py @@ -43,7 +43,7 @@ ) from megatron.core.tensor_parallel.utils import divide from megatron.core.transformer.enums import AttnMaskType -from megatron.core.transformer.mlp import MLP +from megatron.core.transformer.mlp import MLP, MLPSubmodules from megatron.core.transformer.torch_norm import LayerNormInterface from megatron.core.transformer.transformer_config import TransformerConfig from megatron.core.transformer.utils import ( @@ -2434,6 +2434,31 @@ def forward(self, hidden_states: torch.Tensor, **kwargs) -> Tuple[Tensor, Option return out, bias + @classmethod + def as_mlp_submodule( + cls, + submodules: MLPSubmodules, + config: TransformerConfig, + pg_collection: ProcessGroupCollection, + is_mtp_layer: bool, + is_expert: bool = False, + input_size: int | None = None, + ffn_hidden_size: int | None = None, + ) -> MLP: + """Helper function to build an MLP as a TransformerLayer's mlp submodule.""" + del is_mtp_layer + assert hasattr( + pg_collection, 'tp' + ), 'TP process group is required for TEFusedMLP in TransformerLayer' + return cls( + config=config, + submodules=submodules, + tp_group=pg_collection.tp, + is_expert=is_expert, + input_size=input_size, + ffn_hidden_size=ffn_hidden_size, + ) + else: TEFusedMLP = None # type: ignore[assignment, misc] diff --git a/megatron/core/models/T5/t5_spec.py b/megatron/core/models/T5/t5_spec.py index 9f465df5c21..5bbe49c337b 100644 --- a/megatron/core/models/T5/t5_spec.py +++ b/megatron/core/models/T5/t5_spec.py @@ -1,4 +1,6 @@ # Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. +from functools import partial + from megatron.core.extensions.transformer_engine import HAVE_TE from megatron.core.fusions.fused_bias_dropout import get_bias_dropout_add from megatron.core.tensor_parallel.layers import ColumnParallelLinear, RowParallelLinear @@ -69,8 +71,8 @@ def encoder_model_with_transformer_engine_default_spec() -> ModuleSpec: ), ), self_attn_bda=get_bias_dropout_add, - mlp=ModuleSpec( - module=MLP, + mlp=partial( + MLP.as_mlp_submodule, submodules=MLPSubmodules( linear_fc1=not_none(TELayerNormColumnParallelLinear), linear_fc2=not_none(TERowParallelLinear), @@ -111,8 +113,8 @@ def decoder_model_with_transformer_engine_default_spec() -> ModuleSpec: ), ), cross_attn_bda=get_bias_dropout_add, - mlp=ModuleSpec( - module=MLP, + mlp=partial( + MLP.as_mlp_submodule, submodules=MLPSubmodules( linear_fc1=not_none(TELayerNormColumnParallelLinear), linear_fc2=not_none(TERowParallelLinear), @@ -143,8 +145,8 @@ def encoder_model_with_local_spec() -> ModuleSpec: ), self_attn_bda=get_bias_dropout_add, pre_mlp_layernorm=LNImpl, - mlp=ModuleSpec( - module=MLP, + mlp=partial( + MLP.as_mlp_submodule, submodules=MLPSubmodules( linear_fc1=ColumnParallelLinear, linear_fc2=RowParallelLinear ), @@ -190,8 +192,8 @@ def decoder_model_with_local_spec() -> ModuleSpec: ), cross_attn_bda=get_bias_dropout_add, pre_mlp_layernorm=LNImpl, - mlp=ModuleSpec( - module=MLP, + mlp=partial( + MLP.as_mlp_submodule, submodules=MLPSubmodules( linear_fc1=ColumnParallelLinear, linear_fc2=RowParallelLinear ), diff --git a/megatron/core/models/bert/bert_layer_specs.py b/megatron/core/models/bert/bert_layer_specs.py index 53cc0f4280d..dc0099fa66e 100644 --- a/megatron/core/models/bert/bert_layer_specs.py +++ b/megatron/core/models/bert/bert_layer_specs.py @@ -1,5 +1,6 @@ # Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. import warnings +from functools import partial from megatron.core.extensions.transformer_engine import HAVE_TE from megatron.core.fusions.fused_bias_dropout import get_bias_dropout_add @@ -66,8 +67,8 @@ def get_bert_layer_with_transformer_engine_submodules() -> TransformerLayerSubmo ), ), self_attn_bda=get_bias_dropout_add, - mlp=ModuleSpec( - module=MLP, + mlp=partial( + MLP.as_mlp_submodule, submodules=MLPSubmodules( linear_fc1=not_none(TELayerNormColumnParallelLinear), linear_fc2=not_none(TERowParallelLinear), @@ -117,8 +118,8 @@ def __getattr__(name): ), self_attn_bda=get_bias_dropout_add, pre_mlp_layernorm=LNImpl, - mlp=ModuleSpec( - module=MLP, + mlp=partial( + MLP.as_mlp_submodule, submodules=MLPSubmodules(linear_fc1=ColumnParallelLinear, linear_fc2=RowParallelLinear), ), mlp_bda=get_bias_dropout_add, diff --git a/megatron/core/models/gpt/experimental_attention_variant_module_specs.py b/megatron/core/models/gpt/experimental_attention_variant_module_specs.py index 1b03b935639..8f6b1a1a3f8 100644 --- a/megatron/core/models/gpt/experimental_attention_variant_module_specs.py +++ b/megatron/core/models/gpt/experimental_attention_variant_module_specs.py @@ -24,10 +24,12 @@ ) from megatron.core.transformer.transformer_config import TransformerConfig from megatron.core.transformer.transformer_layer import ( + MlpBuilder, TransformerLayer, TransformerLayerSubmodules, get_transformer_layer_offset, ) +from megatron.core.typed_torch import not_none try: import transformer_engine as te # type: ignore[import-untyped] # pylint: disable=unused-import @@ -216,14 +218,18 @@ def get_transformer_block_with_experimental_attention_variant_spec( moe_layer_pattern = [0] * config.num_layers if 1 in moe_layer_pattern: - moe_layer_spec = _get_moe_module_spec(config=config, backend=backend) + moe_layer_spec, fuse_layernorm_pre_moe = _get_moe_module_spec( + config=config, backend=backend + ) else: - moe_layer_spec = None + moe_layer_spec, fuse_layernorm_pre_moe = None, False if 0 in moe_layer_pattern: - dense_mlp_layer_spec = _get_dense_mlp_module_spec(config=config, backend=backend) + dense_mlp_layer_spec, fuse_layernorm_pre_dense = _get_dense_mlp_module_spec( + config=config, backend=backend + ) else: - dense_mlp_layer_spec = None + dense_mlp_layer_spec, fuse_layernorm_pre_dense = None, False # Get GPT decoder block layer specs rms_norm = config.normalization == "RMSNorm" @@ -235,6 +241,11 @@ def get_transformer_block_with_experimental_attention_variant_spec( else standard_attention_spec ) mlp = moe_layer_spec if moe_layer_pattern[layer_number] == 1 else dense_mlp_layer_spec + fuse_pre_mlp_layernorm = ( + fuse_layernorm_pre_moe + if moe_layer_pattern[layer_number] == 1 + else fuse_layernorm_pre_dense + ) input_layernorm = ( IdentityOp if attention.metainfo["fuse_input_layernorm"] @@ -242,7 +253,7 @@ def get_transformer_block_with_experimental_attention_variant_spec( ) pre_mlp_layernorm = ( IdentityOp - if mlp.metainfo["fuse_pre_mlp_layernorm"] + if fuse_pre_mlp_layernorm else backend.layer_norm(rms_norm=rms_norm, for_qk=False) ) @@ -254,7 +265,7 @@ def get_transformer_block_with_experimental_attention_variant_spec( self_attention=attention, self_attn_bda=get_bias_dropout_add, pre_mlp_layernorm=pre_mlp_layernorm, - mlp=mlp, + mlp=not_none(mlp), mlp_bda=get_bias_dropout_add, ), ) @@ -413,41 +424,50 @@ def _get_self_attention_module_spec( def _get_dense_mlp_module_spec( config: TransformerConfig, backend: BackendSpecProvider = None -) -> ModuleSpec: +) -> tuple[MlpBuilder, bool]: """Get dense MLP module spec. For hybrid models that mix dense MLP and experimental attention architectures. - Warning: This function may be deprecated in the future.""" + Warning: This function may be deprecated in the future. + + Returns: + A tuple of (MLP module spec, whether to fuse pre-MLP layernorm) + """ if backend is None: backend = _get_backend_spec_provider(config=config) from megatron.core.models.gpt.gpt_layer_specs import get_mlp_module_spec_for_backend - mlp_spec = get_mlp_module_spec_for_backend(backend=backend, num_experts=None) - mlp_spec.metainfo["fuse_pre_mlp_layernorm"] = backend.fuse_layernorm_and_linear() - - return mlp_spec + return ( + get_mlp_module_spec_for_backend(backend=backend, num_experts=None), + backend.fuse_layernorm_and_linear(), + ) def _get_moe_module_spec( config: TransformerConfig, backend: BackendSpecProvider = None -) -> ModuleSpec: +) -> tuple[MlpBuilder, bool]: """Get MoE module spec. For hybrid models that mix MoE and experimental attention architectures. - Warning: This function may be deprecated in the future.""" + Warning: This function may be deprecated in the future. + + Returns: + A tuple of (MoE module spec, whether to fuse pre-MoE layernorm) + """ if backend is None: backend = _get_backend_spec_provider(config=config) from megatron.core.models.gpt.moe_module_specs import get_moe_module_spec_for_backend - moe_spec = get_moe_module_spec_for_backend( - backend=backend, - num_experts=config.num_moe_experts, - moe_grouped_gemm=config.moe_grouped_gemm, - use_te_activation_func=config.use_te_activation_func, + return ( + get_moe_module_spec_for_backend( + backend=backend, + num_experts=config.num_moe_experts, + moe_grouped_gemm=config.moe_grouped_gemm, + use_te_activation_func=config.use_te_activation_func, + ), + False, ) - moe_spec.metainfo["fuse_pre_mlp_layernorm"] = False - return moe_spec diff --git a/megatron/core/models/gpt/gpt_layer_specs.py b/megatron/core/models/gpt/gpt_layer_specs.py index 78e2e33bdb5..6d6def4d77d 100755 --- a/megatron/core/models/gpt/gpt_layer_specs.py +++ b/megatron/core/models/gpt/gpt_layer_specs.py @@ -1,5 +1,6 @@ # Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. import warnings +from functools import partial from typing import Optional, Union from megatron.core.extensions.transformer_engine import HAVE_TE @@ -34,11 +35,12 @@ ) from megatron.core.transformer.transformer_config import TransformerConfig from megatron.core.transformer.transformer_layer import ( + MlpBuilder, TransformerLayer, TransformerLayerSubmodules, get_transformer_layer_offset, ) -from megatron.core.typed_torch import copy_signature +from megatron.core.typed_torch import copy_signature, not_none from megatron.core.utils import is_te_min_version if HAVE_TE: @@ -485,7 +487,7 @@ def get_mlp_module_spec( moe_grouped_gemm: Optional[bool] = False, fp8: Optional[str] = None, # pylint: disable=unused-argument use_te_op_fuser: Optional[bool] = False, -) -> ModuleSpec: +) -> MlpBuilder: """Helper function to get module spec for MLP/MoE""" if fp8 is not None: warnings.warn( @@ -516,7 +518,7 @@ def get_mlp_module_spec_for_backend( moe_grouped_gemm: Optional[bool] = False, use_te_op_fuser: Optional[bool] = False, use_te_activation_func: bool = False, -) -> ModuleSpec: +) -> MlpBuilder: """Helper function to get module spec for MLP/MoE""" linear_fc2 = backend.row_parallel_linear() @@ -524,14 +526,14 @@ def get_mlp_module_spec_for_backend( if num_experts is None: # Dense MLP w/ or w/o TE modules. - module = TEFusedMLP if use_te_op_fuser else MLP + module = not_none(TEFusedMLP).as_mlp_submodule if use_te_op_fuser else MLP.as_mlp_submodule if backend.fuse_layernorm_and_linear(): linear_fc1 = backend.column_parallel_layer_norm_linear() assert linear_fc1 is not None else: linear_fc1 = backend.column_parallel_linear() - return ModuleSpec( - module=module, + return partial( + module, submodules=MLPSubmodules( linear_fc1=linear_fc1, linear_fc2=linear_fc2, activation_func=activation_func ), diff --git a/megatron/core/models/gpt/heterogeneous/heterogeneous_layer_specs.py b/megatron/core/models/gpt/heterogeneous/heterogeneous_layer_specs.py index f4385429422..a7ecd9e9658 100644 --- a/megatron/core/models/gpt/heterogeneous/heterogeneous_layer_specs.py +++ b/megatron/core/models/gpt/heterogeneous/heterogeneous_layer_specs.py @@ -1,6 +1,7 @@ # Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. import warnings +from functools import partial from typing import Optional from megatron.core.extensions.transformer_engine import HAVE_TE @@ -128,17 +129,19 @@ def _get_heterogenous_attention_spec( def _get_heterogenous_mlp_spec(mlp_config: MLPConfig, use_te: bool): if mlp_config.no_op: - mlp = ModuleSpec(module=IdentityOp) + return IdentityOp elif mlp_config.replace_with_linear: - mlp = ModuleSpec( - module=( - TELayerNormColumnParallelLinearGathered if use_te else ColumnParallelLinearGathered + return partial( + ( + not_none(TELayerNormColumnParallelLinearGathered) + if use_te + else ColumnParallelLinearGathered ), - params={"tp_comm_buffer_name": "linear_mlp"}, + tp_comm_buffer_name="linear_mlp", ) else: - mlp = ModuleSpec( - module=MLP, + return partial( + MLP.as_mlp_submodule, submodules=MLPSubmodules( linear_fc1=( not_none(TELayerNormColumnParallelLinear) if use_te else ColumnParallelLinear @@ -146,7 +149,6 @@ def _get_heterogenous_mlp_spec(mlp_config: MLPConfig, use_te: bool): linear_fc2=not_none(TERowParallelLinear) if use_te else RowParallelLinear, ), ) - return mlp def _get_sharded_state_dict_keys_map(block_config: TransformerBlockConfig, use_te: bool): diff --git a/megatron/core/models/gpt/moe_module_specs.py b/megatron/core/models/gpt/moe_module_specs.py index e82b6638f0b..e9a86ff3bad 100755 --- a/megatron/core/models/gpt/moe_module_specs.py +++ b/megatron/core/models/gpt/moe_module_specs.py @@ -13,14 +13,14 @@ from megatron.core.transformer.moe.moe_layer import MoELayer, MoESubmodules from megatron.core.transformer.moe.router import InferenceTopKRouter from megatron.core.transformer.moe.shared_experts import SharedExpertMLP -from megatron.core.transformer.spec_utils import ModuleSpec +from megatron.core.transformer.transformer_layer import MlpBuilder def get_moe_module_spec( use_te: Optional[bool] = True, num_experts: Optional[int] = None, moe_grouped_gemm: Optional[bool] = False, -) -> ModuleSpec: +) -> MlpBuilder: """Helper function to get module spec for MoE. Called by hybrid_layer_specs.py for standard (non-inference) MoE specs. @@ -46,7 +46,7 @@ def get_moe_module_spec_for_backend( num_experts: Optional[int] = None, moe_grouped_gemm: Optional[bool] = False, use_te_activation_func: bool = False, -) -> ModuleSpec: +) -> MlpBuilder: """Helper function to get module spec for MoE""" assert num_experts is not None @@ -63,15 +63,12 @@ def get_moe_module_spec_for_backend( shared_experts = partial(SharedExpertMLP, submodules=mlp) # MoE module spec - moe_module_spec = ModuleSpec( - module=MoELayer, - submodules=MoESubmodules(experts=experts, shared_experts=shared_experts), - metainfo={"fuse_pre_mlp_layernorm": False}, + return partial( + MoELayer, submodules=MoESubmodules(experts=experts, shared_experts=shared_experts) ) - return moe_module_spec -def get_inference_optimized_moe_spec() -> ModuleSpec: +def get_inference_optimized_moe_spec() -> MlpBuilder: """MoE module spec for inference-optimized transformer impl. Uses InferenceSpecProvider to select inference-optimized modules: @@ -93,10 +90,9 @@ def get_inference_optimized_moe_spec() -> ModuleSpec: ), ) - return ModuleSpec( - module=MoELayer, + return partial( + MoELayer, submodules=MoESubmodules( router=InferenceTopKRouter, experts=experts, shared_experts=shared_experts ), - metainfo={"fuse_pre_mlp_layernorm": False}, ) diff --git a/megatron/core/models/hybrid/hybrid_layer_specs.py b/megatron/core/models/hybrid/hybrid_layer_specs.py index a34a45a32ba..5b968f720c0 100755 --- a/megatron/core/models/hybrid/hybrid_layer_specs.py +++ b/megatron/core/models/hybrid/hybrid_layer_specs.py @@ -1,4 +1,5 @@ # Copyright (c) 2023-2026, NVIDIA CORPORATION. All rights reserved. +from functools import partial from megatron.core.extensions.transformer_engine import ( TEColumnParallelLinear, @@ -170,8 +171,8 @@ mlp_layer=ModuleSpec( module=MLPLayer, submodules=TransformerLayerSubmodules( - mlp=ModuleSpec( - module=MLP, + mlp=partial( + MLP.as_mlp_submodule, submodules=MLPSubmodules( linear_fc1=TELayerNormColumnParallelLinear, linear_fc2=TERowParallelLinear ), @@ -265,8 +266,8 @@ mlp_layer=ModuleSpec( module=MLPLayer, submodules=TransformerLayerSubmodules( - mlp=ModuleSpec( - module=MLP, + mlp=partial( + MLP.as_mlp_submodule, submodules=MLPSubmodules( linear_fc1=InferenceLayerNormColumnParallelLinear, linear_fc2=InferenceRowParallelLinear, diff --git a/megatron/core/models/vision/vit_layer_specs.py b/megatron/core/models/vision/vit_layer_specs.py index 51074b3836e..71c78ad3df4 100644 --- a/megatron/core/models/vision/vit_layer_specs.py +++ b/megatron/core/models/vision/vit_layer_specs.py @@ -1,4 +1,5 @@ # Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. +from functools import partial from megatron.core.extensions.transformer_engine import ( TEDotProductAttention, @@ -85,10 +86,10 @@ def get_vit_layer_with_local_spec() -> ModuleSpec: # Helper function to get module spec for MLP/MoE -def _get_mlp_module_spec(use_te: bool = True) -> ModuleSpec: +def _get_mlp_module_spec(use_te: bool = True): # Dense MLP w/ or w/o TE modules. - return ModuleSpec( - module=MLP, + return partial( + MLP.as_mlp_submodule, submodules=MLPSubmodules( linear_fc1=TELayerNormColumnParallelLinear if use_te else ColumnParallelLinear, linear_fc2=TERowParallelLinear if use_te else RowParallelLinear, diff --git a/megatron/core/post_training/modelopt/hybrid/model_specs.py b/megatron/core/post_training/modelopt/hybrid/model_specs.py index 59a509be718..6bca306061d 100755 --- a/megatron/core/post_training/modelopt/hybrid/model_specs.py +++ b/megatron/core/post_training/modelopt/hybrid/model_specs.py @@ -1,4 +1,5 @@ # Copyright (c) 2025-2026, NVIDIA CORPORATION. All rights reserved. +from functools import partial from megatron.core.extensions.transformer_engine import TEDotProductAttention from megatron.core.fusions.fused_bias_dropout import get_bias_dropout_add @@ -112,8 +113,8 @@ def _get_hybrid_stack_local_spec( module=TransformerLayer, submodules=TransformerLayerSubmodules( pre_mlp_layernorm=Norm, - mlp=ModuleSpec( - module=MLP, + mlp=partial( + MLP.as_mlp_submodule, submodules=MLPSubmodules( linear_fc1=ColumnParallelLinear, linear_fc2=RowParallelLinear ), diff --git a/megatron/core/transformer/mlp.py b/megatron/core/transformer/mlp.py index ce10f650fa7..46979a8ba8f 100644 --- a/megatron/core/transformer/mlp.py +++ b/megatron/core/transformer/mlp.py @@ -23,6 +23,7 @@ ) from megatron.core.fusions.fused_bias_gelu import bias_gelu_impl from megatron.core.fusions.fused_bias_swiglu import bias_swiglu_impl, weighted_bias_swiglu_impl +from megatron.core.process_groups_config import ProcessGroupCollection from megatron.core.transformer.module import MegatronModule from megatron.core.transformer.transformer_config import TransformerConfig from megatron.core.transformer.utils import cat_with_oom_fallback @@ -362,6 +363,31 @@ def backward_dw(self): self.linear_fc2.backward_dw() self.linear_fc1.backward_dw() + @classmethod + def as_mlp_submodule( + cls, + submodules: MLPSubmodules, + config: TransformerConfig, + pg_collection: ProcessGroupCollection, + is_mtp_layer: bool, + is_expert: bool = False, + input_size: int | None = None, + ffn_hidden_size: int | None = None, + ) -> MLP: + """Helper function to build an MLP as a TransformerLayer's mlp submodule.""" + del is_mtp_layer + assert hasattr( + pg_collection, 'tp' + ), 'TP process group is required for MLP in TransformerLayer' + return cls( + config=config, + submodules=submodules, + tp_group=pg_collection.tp, + is_expert=is_expert, + input_size=input_size, + ffn_hidden_size=ffn_hidden_size, + ) + # pylint: disable=missing-function-docstring def apply_swiglu_sharded_factory( diff --git a/megatron/core/transformer/moe/experts.py b/megatron/core/transformer/moe/experts.py index 11faddaabba..2828d086e5d 100644 --- a/megatron/core/transformer/moe/experts.py +++ b/megatron/core/transformer/moe/experts.py @@ -731,7 +731,7 @@ class SequentialMLP(MegatronModule): # TODO(M4): breaking api, switched from pass in tp_group to pass in pg_collection. def __init__( self, - num_local_experts, + num_local_experts: int, config: TransformerConfig, submodules: MLPSubmodules, pg_collection: Optional[ProcessGroupCollection] = None, diff --git a/megatron/core/transformer/moe/moe_layer.py b/megatron/core/transformer/moe/moe_layer.py index 666395c3830..2ddc17a567a 100644 --- a/megatron/core/transformer/moe/moe_layer.py +++ b/megatron/core/transformer/moe/moe_layer.py @@ -596,7 +596,7 @@ def forward( hidden_states: torch.Tensor, intermediate_tensors=None, padding_mask: Optional[torch.Tensor] = None, - ): + ) -> tuple[torch.Tensor, Optional[torch.Tensor]]: """Forward pass for the MoE layer. The forward pass comprises four main steps: diff --git a/megatron/core/transformer/transformer_layer.py b/megatron/core/transformer/transformer_layer.py index 8a2bb698aa4..2191197a7ac 100644 --- a/megatron/core/transformer/transformer_layer.py +++ b/megatron/core/transformer/transformer_layer.py @@ -6,7 +6,7 @@ import warnings from abc import ABC from dataclasses import dataclass, field -from typing import TYPE_CHECKING, Any, Dict, Optional, Union +from typing import TYPE_CHECKING, Any, Dict, Optional, Protocol, Union import torch import torch.distributed @@ -199,6 +199,33 @@ def get_transformer_layer_offset( return offset +class MlpInterface(Protocol): + """Interface for MLP implementations in the transformer layer.""" + + def forward( + self, + hidden_states: Tensor, + /, + *, + intermediate_tensors: tuple[Tensor, ...] | None = None, + padding_mask: Tensor | None = None, + ) -> tuple[Tensor, Tensor | None]: + """Forward method for the MLP interface.""" + ... + + +class MlpBuilder(Protocol): + """MLP builder protocol for building MLPs in the transformer layer.""" + + def __call__( + self, + *, + config: TransformerConfig, + pg_collection: ProcessGroupCollection, + is_mtp_layer: bool, + ) -> MlpInterface: ... + + @dataclass class TransformerLayerSubmodules: """ @@ -236,7 +263,7 @@ class TransformerLayerSubmodules: cross_attn_bda: Union[ModuleSpec, type] = IdentityFuncOp pre_mlp_layernorm: LayerNormBuilder = IdentityOp - mlp: Union[ModuleSpec, type] = IdentityOp + mlp: MlpBuilder | type[IdentityOp] = IdentityOp mlp_bda: Union[ModuleSpec, type] = IdentityFuncOp # Mapping for sharded tensor keys to be applied in `sharded_state_dict` method @@ -360,39 +387,30 @@ def __init__( eps=self.config.layernorm_epsilon, ) # [Module 8: MLP block] - additional_mlp_kwargs = {} # import here to avoid circular import from megatron.core.extensions.transformer_engine import TEFusedMLP - from megatron.core.transformer.moe.experts import SequentialMLP, TEGroupedMLP from megatron.core.transformer.moe.moe_layer import MoELayer # MLP expects tp_group but MoELayer expects pg_collection to be passed in. # We can change MLP to accept pg_collection but it makes the logic implicit # The conditional below is to make the logic explicit # if submodules.mlp is not a ModuleSpec,we dont have to handle passing additional kwargs - if isinstance(submodules.mlp, ModuleSpec): - if submodules.mlp.module in (MoELayer, TEGroupedMLP, SequentialMLP): - additional_mlp_kwargs["pg_collection"] = pg_collection - # Pass is_mtp_layer flag to MoELayer to distinguish MTP MoE layers. - if submodules.mlp.module == MoELayer: - additional_mlp_kwargs["is_mtp_layer"] = self.is_mtp_layer - elif submodules.mlp.module == MLP: - assert hasattr( - pg_collection, 'tp' - ), 'TP process group is required for MLP in TransformerLayer' - additional_mlp_kwargs["tp_group"] = pg_collection.tp - elif TEFusedMLP is not None and submodules.mlp.module == TEFusedMLP: - assert hasattr( - pg_collection, 'tp' - ), 'TP process group is required for TEFusedMLP in TransformerLayer' - additional_mlp_kwargs["tp_group"] = pg_collection.tp - else: - log_single_rank( - logger, - logging.WARNING, - f"Unknown MLP type: {type(submodules.mlp)}. Using default kwargs.", - ) - self.mlp = build_module(submodules.mlp, config=self.config, **additional_mlp_kwargs) + if isinstance(submodules.mlp, ModuleSpec) and submodules.mlp.module in (MLP, TEFusedMLP): + submodules.mlp = functools.partial( + submodules.mlp.module.as_mlp_submodule, + submodules=submodules.mlp.submodules, + **submodules.mlp.params, + ) + log_single_rank( + logger, + logging.WARNING, + f"Rewrapping ModuleSpec with module {type(submodules.mlp)} to forward kwargs. " + "Consider migrating the `mlp` submodule spec to a direct call of the " + "`as_mlp_submodule` classmethod instead.", + ) + self.mlp = submodules.mlp( + config=self.config, pg_collection=pg_collection, is_mtp_layer=self.is_mtp_layer + ) if hasattr(self.mlp, 'set_layer_number'): self.mlp.set_layer_number(self.layer_number) @@ -790,7 +808,7 @@ def _forward_mlp( from megatron.core.extensions.transformer_engine import te_checkpoint mlp_output_with_bias = te_checkpoint( - self.mlp, + apply_module(self.mlp), False, tensor_parallel.random.get_cuda_rng_tracker, self.pg_collection.tp, @@ -799,7 +817,7 @@ def _forward_mlp( ) else: mlp_output_with_bias = tensor_parallel.checkpoint( - functools.partial(self.mlp, padding_mask=padding_mask), + functools.partial(apply_module(self.mlp), padding_mask=padding_mask), False, pre_mlp_layernorm_output, ) @@ -817,7 +835,7 @@ def _forward_mlp( chunks = pre_mlp_layernorm_output.chunk(num_chunks, dim=0) # Compute outputs for each chunk - outputs = [self.mlp(chunk) for chunk in chunks] + outputs = [apply_module(self.mlp)(chunk) for chunk in chunks] # Aggregate chunk outputs mlp_output = torch.cat([out for out, _ in outputs], dim=0) @@ -830,7 +848,9 @@ def _forward_mlp( # Set the residual for fused reduce-scatter + add + layer-norm + all-gather # operation in MLP's fc2. self._set_fc2_residual(residual) - mlp_output_with_bias = self.mlp(pre_mlp_layernorm_output, padding_mask=padding_mask) + mlp_output_with_bias = apply_module(self.mlp)( + pre_mlp_layernorm_output, padding_mask=padding_mask + ) nvtx_range_pop(suffix="mlp") @@ -1172,7 +1192,7 @@ def _te_cuda_graph_replay(self, *args, **kwargs): hidden_states, probs = self.mlp.preprocess(hidden_states, probs, routing_map) nvtx_range_pop(suffix="mlp") return residual, hidden_states, probs, shared_expert_output - mlp_output_with_bias = self.mlp(hidden_states) + mlp_output_with_bias = apply_module(self.mlp)(hidden_states) self.mlp.cudagraph_tensor_store.clear() nvtx_range_pop(suffix="mlp") @@ -1434,7 +1454,7 @@ def _forward_mlp_router(self, hidden_states, padding_mask=None): if self.config.fp32_residual_connection: residual = residual.float() - router_outputs = self.mlp( + router_outputs = apply_module(self.mlp)( pre_mlp_layernorm_output, intermediate_tensors=(), padding_mask=padding_mask ) @@ -1467,7 +1487,7 @@ def _forward_mlp_expert_compute(self, hidden_states, probs): self._restore_token_dispatcher_attrs() self.mlp.fwd_execution_map = "expert_compute" - return self.mlp(None, intermediate_tensors=(hidden_states, probs)) + return apply_module(self.mlp)(None, intermediate_tensors=(hidden_states, probs)) def _forward_mlp_postprocess(self, residual, output, shared_expert_output, mlp_bias): """ @@ -1485,7 +1505,7 @@ def _forward_mlp_postprocess(self, residual, output, shared_expert_output, mlp_b self._restore_token_dispatcher_attrs() self.mlp.fwd_execution_map = "postprocess" - output = self.mlp(None, intermediate_tensors=(output, shared_expert_output)) + output = apply_module(self.mlp)(None, intermediate_tensors=(output, shared_expert_output)) return self._forward_post_mlp((output, mlp_bias), residual) def _forward_mlp(self, hidden_states, inference_context=None, padding_mask=None): diff --git a/pretrain_vlm.py b/pretrain_vlm.py index 988172be4ff..dff56257ce4 100644 --- a/pretrain_vlm.py +++ b/pretrain_vlm.py @@ -6,20 +6,12 @@ import torch -from megatron.core import mpu, parallel_state, tensor_parallel -from megatron.core.datasets.blended_megatron_dataset_builder import ( - BlendedMegatronDatasetBuilder, -) -from megatron.core.datasets.multimodal_dataset import ( - MockMultimodalDataset, - MultimodalDatasetConfig, -) +from megatron.core import parallel_state, tensor_parallel +from megatron.core.datasets.blended_megatron_dataset_builder import BlendedMegatronDatasetBuilder +from megatron.core.datasets.multimodal_dataset import MockMultimodalDataset, MultimodalDatasetConfig from megatron.core.enums import ModelType from megatron.core.models.multimodal import context_parallel -from megatron.core.models.multimodal.llava_model import ( - DEFAULT_IMAGE_TOKEN_INDEX, - LLaVAModel, -) +from megatron.core.models.multimodal.llava_model import DEFAULT_IMAGE_TOKEN_INDEX, LLaVAModel from megatron.core.models.multimodal.llava_spec import ( decoder_model_with_local_default_spec, decoder_model_with_transformer_engine_default_spec, @@ -30,16 +22,10 @@ get_vit_layer_with_transformer_engine_spec, ) from megatron.core.transformer.enums import AttnMaskType -from megatron.core.transformer.spec_utils import import_module -from megatron.training import ( - get_args, - get_timers, - get_tokenizer, - pretrain, - print_rank_0, -) -from megatron.training.arguments import core_transformer_config_from_args, parse_and_validate_args +from megatron.core.transformer.spec_utils import get_submodules, import_module +from megatron.training import get_args, get_timers, get_tokenizer, pretrain, print_rank_0 from megatron.training.argument_utils import pretrain_cfg_container_from_args +from megatron.training.arguments import core_transformer_config_from_args, parse_and_validate_args from pretrain_gpt import loss_func @@ -71,13 +57,23 @@ def model_provider( args = get_args() vision_model_type = "clip" - assert args.ckpt_format == 'torch', "Only ckpt-format torch is supported for VLM training currently." - assert not (args.context_parallel_size > 1 and args.pipeline_model_parallel_size > 1), "PP+CP is not yet supported by this script. \ + assert ( + args.ckpt_format == 'torch' + ), "Only ckpt-format torch is supported for VLM training currently." + assert not ( + args.context_parallel_size > 1 and args.pipeline_model_parallel_size > 1 + ), "PP+CP is not yet supported by this script. \ Current mock dataset does not support natively packed sequence dataset required for correct PP comm shapes." num_image_embeddings = get_num_image_embeddings( - args.img_h, args.img_w, args.patch_dim, vision_model_type, args.disable_vision_class_token, - class_token_len=1, pixel_shuffle=False, use_tile_tags=False + args.img_h, + args.img_w, + args.patch_dim, + vision_model_type, + args.disable_vision_class_token, + class_token_len=1, + pixel_shuffle=False, + use_tile_tags=False, ) old_seq_length = args.seq_length @@ -100,7 +96,7 @@ def model_provider( args.tensor_model_parallel_size, args.sequence_parallel, args.decoder_tp_comm_overlap, - args.decoder_seq_length + args.decoder_seq_length, ) args.decoder_seq_length = decoder_seq_len + mp_padding_needed @@ -116,8 +112,9 @@ def model_provider( else: language_transformer_config.num_layers = args.num_layers if args.decoder_tp_comm_overlap: - assert args.transformer_impl == "transformer_engine", \ - "TransformerEngine is needed to support Decoder TP Comm overlap" + assert ( + args.transformer_impl == "transformer_engine" + ), "TransformerEngine is needed to support Decoder TP Comm overlap" language_transformer_config.tp_comm_overlap = args.decoder_tp_comm_overlap if args.spec is not None: @@ -133,10 +130,24 @@ def model_provider( # Prepare mask type for any required padding to support CP/SP sequence sharding. if mp_padding_needed > 0: - if language_transformer_layer_spec.submodules.self_attention.params.get('attn_mask_type', '') == AttnMaskType.causal: - language_transformer_layer_spec.submodules.self_attention.params['attn_mask_type'] = AttnMaskType.padding_causal - elif language_transformer_layer_spec.submodules.self_attention.params.get('attn_mask_type', '') == AttnMaskType.no_mask: - language_transformer_layer_spec.submodules.self_attention.params['attn_mask_type'] = AttnMaskType.padding + if ( + language_transformer_layer_spec.submodules.self_attention.params.get( + 'attn_mask_type', '' + ) + == AttnMaskType.causal + ): + language_transformer_layer_spec.submodules.self_attention.params['attn_mask_type'] = ( + AttnMaskType.padding_causal + ) + elif ( + language_transformer_layer_spec.submodules.self_attention.params.get( + 'attn_mask_type', '' + ) + == AttnMaskType.no_mask + ): + language_transformer_layer_spec.submodules.self_attention.params['attn_mask_type'] = ( + AttnMaskType.padding + ) if args.transformer_impl == "transformer_engine": vision_transformer_layer_spec = get_vit_layer_with_transformer_engine_spec() @@ -149,7 +160,7 @@ def model_provider( vision_transformer_config.first_pipeline_num_layers = None vision_transformer_config.last_pipeline_num_layers = None vision_transformer_config.vision_model_type = vision_model_type - vision_transformer_config.context_parallel_size = 1 # Force CP=1 for Vision Transformer + vision_transformer_config.context_parallel_size = 1 # Force CP=1 for Vision Transformer if vision_transformer_config.sequence_parallel: print_rank_0("> Disabling Sequence parallelism in Vision Transformer. Not yet supported") vision_transformer_config.sequence_parallel = False @@ -159,7 +170,7 @@ def model_provider( vision_projection_type = "mlp" vision_projection_config = deepcopy(language_transformer_config) - vision_projection_config.context_parallel_size = 1 # Force CP=1 for Vision Projection + vision_projection_config.context_parallel_size = 1 # Force CP=1 for Vision Projection if vision_projection_config.sequence_parallel: print_rank_0("> Disabling Sequence parallelism in Vision Projection. Not yet supported") vision_projection_config.sequence_parallel = False @@ -171,7 +182,9 @@ def model_provider( vision_transformer_config.pipeline_model_parallel_size = 1 vision_projection_config.pipeline_model_parallel_size = 1 - vision_projection_modules = deepcopy(language_transformer_layer_spec.submodules.mlp.submodules) + vision_projection_modules = deepcopy( + get_submodules(language_transformer_layer_spec.submodules.mlp) + ) language_max_sequence_length = args.decoder_seq_length if args.context_parallel_size > 1: @@ -321,41 +334,47 @@ def get_batch(data_iterator): vision_model_type = "clip" # Calculate the number of image embedding tokens will be added to text tokens num_image_embeddings_per_tile = get_num_image_embeddings( - args.img_h, args.img_w, args.patch_dim, vision_model_type, - args.disable_vision_class_token, 1, False + args.img_h, + args.img_w, + args.patch_dim, + vision_model_type, + args.disable_vision_class_token, + 1, + False, ) # Pad to make sure the text sequence can be sharded equally by CP chunks. image_token_mask = tokens == DEFAULT_IMAGE_TOKEN_INDEX num_images_per_sample = torch.sum(image_token_mask, dim=-1) - img_seq_len = (num_image_embeddings_per_tile * num_images_per_sample - num_images_per_sample).max() + img_seq_len = ( + num_image_embeddings_per_tile * num_images_per_sample - num_images_per_sample + ).max() mp_padding_needed_for_text = context_parallel.get_padding( tokens.shape[1] + img_seq_len, args.context_parallel_size, args.tensor_model_parallel_size, args.sequence_parallel, args.decoder_tp_comm_overlap, - args.decoder_seq_length + args.decoder_seq_length, ) if mp_padding_needed_for_text > 0: - tokens, position_ids, labels, loss_mask = [torch.nn.functional.pad(item, (0, mp_padding_needed_for_text)) for item in (tokens, position_ids, labels, loss_mask)] - packed_seq_params = context_parallel.get_packed_seq_params(tokens, img_seq_len, mp_padding_needed_for_text, cp_size, args.use_packed_sequence) + tokens, position_ids, labels, loss_mask = [ + torch.nn.functional.pad(item, (0, mp_padding_needed_for_text)) + for item in (tokens, position_ids, labels, loss_mask) + ] + packed_seq_params = context_parallel.get_packed_seq_params( + tokens, img_seq_len, mp_padding_needed_for_text, cp_size, args.use_packed_sequence + ) if packed_seq_params.qkv_format == 'thd': # Reshape from [B,S] to [T,1] - tokens = ( - tokens.contiguous() - .view(tokens.shape[0] * tokens.shape[1]) - .unsqueeze(0) - ) + tokens = tokens.contiguous().view(tokens.shape[0] * tokens.shape[1]).unsqueeze(0) position_ids = ( position_ids.contiguous() .view(position_ids.shape[0] * position_ids.shape[1]) .unsqueeze(0) ) labels = labels.view(labels.shape[0] * labels.shape[1]).unsqueeze(0) - loss_mask = loss_mask.view( - loss_mask.shape[0] * loss_mask.shape[1] - ).unsqueeze(0) + loss_mask = loss_mask.view(loss_mask.shape[0] * loss_mask.shape[1]).unsqueeze(0) attention_mask = None # Use the attention mask type defined in layer spec. Typically no mask for the vision model and causal mask for the vision model. @@ -377,11 +396,19 @@ def forward_step(data_iterator, model: LLaVAModel): # Get the batch. timers('batch-generator', log_level=2).start() - tokens, position_ids, labels, images, loss_mask, attention_mask, packed_seq_params = get_batch(data_iterator) + tokens, position_ids, labels, images, loss_mask, attention_mask, packed_seq_params = get_batch( + data_iterator + ) timers('batch-generator').stop() output_tensor, loss_mask = model( - images, tokens, position_ids, attention_mask, labels, loss_mask, packed_seq_params=packed_seq_params + images, + tokens, + position_ids, + attention_mask, + labels, + loss_mask, + packed_seq_params=packed_seq_params, ) return output_tensor, partial(loss_func, loss_mask) @@ -402,15 +429,21 @@ def add_vlm_extra_args(parser): default=False, help="Drop vision model class token", ) - group.add_argument("--dataloader-seq-length", type=int, help="Make dataloader to produce sequences of specific length.") - group.add_argument("--decoder-tp-comm-overlap", action="store_true", default=False, help="Enables the overlap of " - "Tensor parallel communication and GEMM kernels in Decoder only. " - "Please provide decoder-seq-length when using this feature.") group.add_argument( - "--use-packed-sequence", + "--dataloader-seq-length", + type=int, + help="Make dataloader to produce sequences of specific length.", + ) + group.add_argument( + "--decoder-tp-comm-overlap", action="store_true", default=False, - help="Use packed sequence", + help="Enables the overlap of " + "Tensor parallel communication and GEMM kernels in Decoder only. " + "Please provide decoder-seq-length when using this feature.", + ) + group.add_argument( + "--use-packed-sequence", action="store_true", default=False, help="Use packed sequence" ) return parser @@ -441,8 +474,7 @@ def llava_position_embedding_ranks(pp_ranks): train_valid_test_datasets_provider.is_distributed = True args = parse_and_validate_args( - extra_args_provider=add_vlm_extra_args, - args_defaults={'tokenizer_type': 'GPT2BPETokenizer'}, + extra_args_provider=add_vlm_extra_args, args_defaults={'tokenizer_type': 'GPT2BPETokenizer'} ) full_config = pretrain_cfg_container_from_args(args) pretrain( diff --git a/tests/functional_tests/test_cases/common/moe_perf/__main__.py b/tests/functional_tests/test_cases/common/moe_perf/__main__.py index 803d0c11a26..a722121f079 100644 --- a/tests/functional_tests/test_cases/common/moe_perf/__main__.py +++ b/tests/functional_tests/test_cases/common/moe_perf/__main__.py @@ -20,8 +20,9 @@ get_gpt_layer_with_transformer_engine_submodules, ) from megatron.core.transformer.moe.fused_a2a import HAVE_DEEP_EP, HAVE_HYBRIDEP -from megatron.core.transformer.moe.moe_layer import MoELayer +from megatron.core.transformer.moe.moe_layer import MoELayer, MoESubmodules from megatron.core.transformer.moe.moe_utils import RandomSTE +from megatron.core.transformer.spec_utils import get_submodules from megatron.core.transformer.transformer_config import TransformerConfig from megatron.core.utils import nvtx_range_pop, nvtx_range_push from megatron.training.initialize import _set_random_seed @@ -86,10 +87,12 @@ def _build_transformer_config(case: MoEPerformanceCase) -> TransformerConfig: # NOTE: Only TE backend is covered in this test. -def _resolve_moe_submodules(case: MoEPerformanceCase): - return get_gpt_layer_with_transformer_engine_submodules( - num_experts=case.model.num_experts, moe_grouped_gemm=True - ).mlp.submodules +def _resolve_moe_submodules(case: MoEPerformanceCase) -> MoESubmodules: + return get_submodules( + get_gpt_layer_with_transformer_engine_submodules( + num_experts=case.model.num_experts, moe_grouped_gemm=True + ).mlp + ) def _load_baselines() -> Dict[str, Dict[str, float]]: diff --git a/tests/unit_tests/dist_checkpointing/models/test_mlp_glu.py b/tests/unit_tests/dist_checkpointing/models/test_mlp_glu.py index 037e368ea2f..7d0f3c1b3a3 100644 --- a/tests/unit_tests/dist_checkpointing/models/test_mlp_glu.py +++ b/tests/unit_tests/dist_checkpointing/models/test_mlp_glu.py @@ -1,23 +1,18 @@ # Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. -import inspect import logging import pytest import torch -from torch.optim import Adam from megatron.core import parallel_state from megatron.core.dist_checkpointing import ShardedTensor, load, load_plain_tensors, save -from megatron.core.dist_checkpointing.dict_utils import diff, nested_values -from megatron.core.dist_checkpointing.optimizer import ( - get_param_id_to_sharded_param_map, - optim_state_to_sharding_state, -) +from megatron.core.dist_checkpointing.dict_utils import diff from megatron.core.models.gpt.gpt_layer_specs import ( get_gpt_layer_with_transformer_engine_submodules, ) from megatron.core.tensor_parallel.random import model_parallel_cuda_manual_seed -from megatron.core.transformer.mlp import MLP, apply_swiglu_sharded_factory +from megatron.core.transformer.mlp import MLP, MLPSubmodules, apply_swiglu_sharded_factory +from megatron.core.transformer.spec_utils import get_submodules from megatron.core.transformer.transformer_config import TransformerConfig from tests.unit_tests.dist_checkpointing import TempNamedDir from tests.unit_tests.test_utilities import Utils @@ -33,9 +28,9 @@ def initialize_mlp(glu=True): use_cpu_initialization=True, gated_linear_unit=glu, ) - return MLP( - transformer_config, get_gpt_layer_with_transformer_engine_submodules().mlp.submodules - ) + mlp_submodules = get_submodules(get_gpt_layer_with_transformer_engine_submodules().mlp) + assert isinstance(mlp_submodules, MLPSubmodules) + return MLP(transformer_config, mlp_submodules) class TestParallelMLPWithGLU: diff --git a/tests/unit_tests/distributed/test_grad_sync_with_expert_parallel.py b/tests/unit_tests/distributed/test_grad_sync_with_expert_parallel.py index 15c479774c6..6461dbee751 100644 --- a/tests/unit_tests/distributed/test_grad_sync_with_expert_parallel.py +++ b/tests/unit_tests/distributed/test_grad_sync_with_expert_parallel.py @@ -12,7 +12,8 @@ get_gpt_layer_with_transformer_engine_submodules, ) from megatron.core.transformer import TransformerConfig -from megatron.core.transformer.moe.moe_layer import MoELayer +from megatron.core.transformer.moe.moe_layer import MoELayer, MoESubmodules +from megatron.core.transformer.spec_utils import get_submodules from tests.unit_tests.test_utilities import Utils @@ -42,15 +43,15 @@ def __init__( params_dtype=torch.bfloat16, add_bias_linear=False, ) - submodules = get_gpt_layer_with_transformer_engine_submodules( - num_experts=num_moe_experts, moe_grouped_gemm=moe_grouped_gemm + submodules = get_submodules( + get_gpt_layer_with_transformer_engine_submodules( + num_experts=num_moe_experts, moe_grouped_gemm=moe_grouped_gemm + ).mlp ) + assert isinstance(submodules, MoESubmodules) super().__init__() self.layers = torch.nn.ModuleList( - [ - MoELayer(transformer_config, submodules.mlp.submodules).cuda() - for _ in range(num_layers) - ] + [MoELayer(transformer_config, submodules).cuda() for _ in range(num_layers)] ) diff --git a/tests/unit_tests/inference/text_generation_controllers/test_vlm_text_generation_controller.py b/tests/unit_tests/inference/text_generation_controllers/test_vlm_text_generation_controller.py index 5344ae9f8eb..8b3962dd7b7 100644 --- a/tests/unit_tests/inference/text_generation_controllers/test_vlm_text_generation_controller.py +++ b/tests/unit_tests/inference/text_generation_controllers/test_vlm_text_generation_controller.py @@ -23,8 +23,9 @@ from megatron.core.models.gpt.gpt_layer_specs import get_gpt_layer_local_submodules from megatron.core.models.multimodal.llava_model import LLaVAModel from megatron.core.tensor_parallel.random import model_parallel_cuda_manual_seed +from megatron.core.transformer.mlp import MLPSubmodules from megatron.core.transformer.module import Float16Module -from megatron.core.transformer.spec_utils import ModuleSpec +from megatron.core.transformer.spec_utils import ModuleSpec, get_submodules from megatron.core.transformer.transformer_config import TransformerConfig from megatron.core.transformer.transformer_layer import TransformerLayer from tests.unit_tests.test_utilities import Utils @@ -71,7 +72,8 @@ def setup_method(self, method): vision_layer_spec = ModuleSpec( module=TransformerLayer, submodules=copy.deepcopy(language_layer_submodules) ) - vision_projection_spec = copy.deepcopy(language_layer_submodules.mlp.submodules) + vision_projection_spec = copy.deepcopy(get_submodules(language_layer_submodules.mlp)) + assert isinstance(vision_projection_spec, MLPSubmodules) language_config.language_model_type = "dummy" vision_config.vision_model_type = "clip" diff --git a/tests/unit_tests/models/test_llava_model.py b/tests/unit_tests/models/test_llava_model.py index 970b0e54635..2a8fd553f85 100644 --- a/tests/unit_tests/models/test_llava_model.py +++ b/tests/unit_tests/models/test_llava_model.py @@ -15,7 +15,8 @@ from megatron.core.packed_seq_params import PackedSeqParams from megatron.core.tensor_parallel.random import model_parallel_cuda_manual_seed from megatron.core.transformer.enums import AttnMaskType -from megatron.core.transformer.spec_utils import ModuleSpec +from megatron.core.transformer.mlp import MLPSubmodules +from megatron.core.transformer.spec_utils import ModuleSpec, get_submodules from megatron.core.transformer.transformer_config import TransformerConfig from megatron.core.transformer.transformer_layer import TransformerLayer from megatron.core.utils import is_te_min_version @@ -53,7 +54,8 @@ def setup_method(self, method): vision_layer_spec = ModuleSpec( module=TransformerLayer, submodules=deepcopy(language_layer_submodules) ) - vision_projection_spec = deepcopy(language_layer_submodules.mlp.submodules) + vision_projection_spec = deepcopy(get_submodules(language_layer_submodules.mlp)) + assert isinstance(vision_projection_spec, MLPSubmodules) language_config.language_model_type = "dummy" vision_config.vision_model_type = "clip" @@ -491,7 +493,7 @@ def setup_and_teardown_llava_model(request): vision_layer_spec = ModuleSpec( module=TransformerLayer, submodules=deepcopy(language_layer_submodules) ) - vision_projection_spec = deepcopy(language_layer_submodules.mlp.submodules) + vision_projection_spec = deepcopy(get_submodules(language_layer_submodules.mlp)) language_config.language_model_type = "dummy" vision_model_type = request.param @@ -601,7 +603,7 @@ def _init_llava_model(self, cp_size, tp_size, sequence_parallel): vision_layer_spec = ModuleSpec( module=TransformerLayer, submodules=deepcopy(language_layer_submodules) ) - vision_projection_spec = deepcopy(language_layer_submodules.mlp.submodules) + vision_projection_spec = deepcopy(get_submodules(language_layer_submodules.mlp)) language_config.language_model_type = "dummy" vision_config.vision_model_type = "clip" diff --git a/tests/unit_tests/models/test_multimodal_projector.py b/tests/unit_tests/models/test_multimodal_projector.py index 52fda330c2e..5df34954b6a 100644 --- a/tests/unit_tests/models/test_multimodal_projector.py +++ b/tests/unit_tests/models/test_multimodal_projector.py @@ -20,7 +20,7 @@ def setup_method(self, method): transformer_config = TransformerConfig( num_layers=1, hidden_size=64, num_attention_heads=4, use_cpu_initialization=True ) - mlp_layer_spec = get_mlp_module_spec().submodules + mlp_layer_spec = get_mlp_module_spec().keywords['submodules'] affine_layer_spec = MLPSubmodules(linear_fc1=ColumnParallelLinear, linear_fc2=None) self.mlp = MultimodalProjector( diff --git a/tests/unit_tests/test_utils.py b/tests/unit_tests/test_utils.py index 02f178387f3..50e447b1caf 100644 --- a/tests/unit_tests/test_utils.py +++ b/tests/unit_tests/test_utils.py @@ -20,7 +20,8 @@ ) from megatron.core.optimizer import OptimizerConfig, get_megatron_optimizer from megatron.core.transformer import TransformerConfig -from megatron.core.transformer.moe.moe_layer import MoELayer +from megatron.core.transformer.moe.moe_layer import MoELayer, MoESubmodules +from megatron.core.transformer.spec_utils import get_submodules from tests.unit_tests.test_utilities import Utils success_string = "hello,world" @@ -326,12 +327,11 @@ def test_param_norm_moe(use_distributed_optimizer: bool): add_bias_linear=False, bf16=True, ) - model = MoELayer( - transformer_config, - get_gpt_layer_with_transformer_engine_submodules( - num_experts=2, moe_grouped_gemm=True - ).mlp.submodules, - ).to(device='cuda') + submodules = get_submodules( + get_gpt_layer_with_transformer_engine_submodules(num_experts=2, moe_grouped_gemm=True).mlp + ) + assert isinstance(submodules, MoESubmodules) + model = MoELayer(transformer_config, submodules).to(device='cuda') model.requires_grad_(True) # Initialize the model with all 1.0 for weights. for param in model.parameters(): diff --git a/tests/unit_tests/transformer/moe/test_grouped_mlp.py b/tests/unit_tests/transformer/moe/test_grouped_mlp.py index 58ca0cf1e3f..b1f15a9eef1 100644 --- a/tests/unit_tests/transformer/moe/test_grouped_mlp.py +++ b/tests/unit_tests/transformer/moe/test_grouped_mlp.py @@ -10,7 +10,8 @@ ) from megatron.core.transformer.module import Float16Module from megatron.core.transformer.moe.experts import TEGroupedMLP -from megatron.core.transformer.moe.moe_layer import MoELayer +from megatron.core.transformer.moe.moe_layer import MoELayer, MoESubmodules +from megatron.core.transformer.spec_utils import get_submodules from megatron.core.transformer.transformer_config import TransformerConfig from megatron.core.utils import is_te_min_version from megatron.training.arguments import parse_args @@ -58,8 +59,11 @@ def setup_method(self, method, use_cpu_initialization=False, swiglu=True): ## Vanilla sequential GEMM # Set random seed for reproducability _set_random_seed(seed_=123, data_parallel_random_init=False) - submodules = get_gpt_layer_local_submodules(self.num_experts, moe_grouped_gemm=False) - self.sequential_mlp = MoELayer(tf_config, submodules.mlp.submodules) + sequential_submodules = get_submodules( + get_gpt_layer_local_submodules(self.num_experts, moe_grouped_gemm=False).mlp + ) + assert isinstance(sequential_submodules, MoESubmodules) + self.sequential_mlp = MoELayer(tf_config, sequential_submodules) self.args = parse_args(ignore_unknown_args=True) self.args.bf16 = True @@ -71,12 +75,13 @@ def setup_method(self, method, use_cpu_initialization=False, swiglu=True): ## Grouped GEMM _set_random_seed(seed_=123, data_parallel_random_init=False) tf_config.moe_grouped_gemm = True - self.grouped_mlp = MoELayer( - tf_config, + grouped_submodules = get_submodules( get_gpt_layer_with_transformer_engine_submodules( self.num_experts, moe_grouped_gemm=True - ).mlp.submodules, + ).mlp ) + assert isinstance(grouped_submodules, MoESubmodules) + self.grouped_mlp = MoELayer(tf_config, grouped_submodules) assert isinstance(self.grouped_mlp.experts, TEGroupedMLP) self.grouped_mlp = Float16Module(self.grouped_mlp.config, self.grouped_mlp).module diff --git a/tests/unit_tests/transformer/moe/test_latent_moe_layer.py b/tests/unit_tests/transformer/moe/test_latent_moe_layer.py index f62de67860a..bb5ced291fc 100644 --- a/tests/unit_tests/transformer/moe/test_latent_moe_layer.py +++ b/tests/unit_tests/transformer/moe/test_latent_moe_layer.py @@ -4,14 +4,11 @@ import torch from megatron.core.models.gpt.gpt_layer_specs import ( - get_gpt_decoder_block_spec, - get_gpt_layer_local_spec, - get_gpt_layer_with_transformer_engine_spec, + get_gpt_layer_local_submodules, + get_gpt_layer_with_transformer_engine_submodules, ) -from megatron.core.tensor_parallel.random import model_parallel_cuda_manual_seed -from megatron.core.transformer.moe.moe_layer import MoELayer -from megatron.core.transformer.moe.router import Router -from megatron.core.transformer.transformer_block import TransformerBlock +from megatron.core.transformer.moe.moe_layer import MoELayer, MoESubmodules +from megatron.core.transformer.spec_utils import get_submodules from megatron.core.transformer.transformer_config import TransformerConfig from megatron.core.utils import is_te_min_version from megatron.training.initialize import _set_random_seed @@ -53,16 +50,16 @@ def test_latent_moe_layer( moe_latent_size=moe_latent_size, ) if use_te: - transformer_layer_spec = get_gpt_layer_with_transformer_engine_spec( + transformer_layer_submodules = get_gpt_layer_with_transformer_engine_submodules( num_experts=num_moe_experts, moe_grouped_gemm=grouped_gemm ) else: - transformer_layer_spec = get_gpt_layer_local_spec( + transformer_layer_submodules = get_gpt_layer_local_submodules( num_experts=num_moe_experts, moe_grouped_gemm=grouped_gemm ) - moe_layer = MoELayer( - self.transformer_config, transformer_layer_spec.submodules.mlp.submodules - ) + submodules = get_submodules(transformer_layer_submodules.mlp) + assert isinstance(submodules, MoESubmodules) + moe_layer = MoELayer(self.transformer_config, submodules) moe_layer.cuda() config = moe_layer.config diff --git a/tests/unit_tests/transformer/moe/test_moe_layer.py b/tests/unit_tests/transformer/moe/test_moe_layer.py index 0004b7fef98..c4a3283ac5b 100644 --- a/tests/unit_tests/transformer/moe/test_moe_layer.py +++ b/tests/unit_tests/transformer/moe/test_moe_layer.py @@ -9,7 +9,8 @@ get_gpt_layer_with_transformer_engine_submodules, ) from megatron.core.tensor_parallel.random import model_parallel_cuda_manual_seed -from megatron.core.transformer.moe.moe_layer import MoELayer +from megatron.core.transformer.moe.moe_layer import MoELayer, MoESubmodules +from megatron.core.transformer.spec_utils import get_submodules from megatron.core.transformer.transformer_block import TransformerBlock from megatron.core.transformer.transformer_config import TransformerConfig from megatron.core.utils import is_te_min_version @@ -44,10 +45,13 @@ def test_te_moe_layer(self, num_moe_experts, moe_token_dispatcher_type, grouped_ moe_ffn_hidden_size=128, add_bias_linear=False, ) - submodules = get_gpt_layer_with_transformer_engine_submodules( - num_experts=num_moe_experts, moe_grouped_gemm=grouped_gemm + submodules = get_submodules( + get_gpt_layer_with_transformer_engine_submodules( + num_experts=num_moe_experts, moe_grouped_gemm=grouped_gemm + ).mlp ) - moe_layer = MoELayer(self.transformer_config, submodules.mlp.submodules) + assert isinstance(submodules, MoESubmodules) + moe_layer = MoELayer(self.transformer_config, submodules) Utils.destroy_model_parallel() @pytest.mark.parametrize("moe_token_dispatcher_type", ["allgather", "alltoall"]) @@ -70,10 +74,13 @@ def test_legacy_moe_layer(self, num_moe_experts, moe_token_dispatcher_type, grou moe_grouped_gemm=grouped_gemm, add_bias_linear=False, ) - transformer_layer_submodules = get_gpt_layer_local_submodules( - num_experts=num_moe_experts, moe_grouped_gemm=grouped_gemm + submodules = get_submodules( + get_gpt_layer_local_submodules( + num_experts=num_moe_experts, moe_grouped_gemm=grouped_gemm + ).mlp ) - moe_layer = MoELayer(self.transformer_config, transformer_layer_submodules.mlp.submodules) + assert isinstance(submodules, MoESubmodules) + moe_layer = MoELayer(self.transformer_config, submodules) Utils.destroy_model_parallel() @pytest.mark.skip( @@ -105,15 +112,18 @@ def test_moe_with_late_initialize( bf16=True, params_dtype=torch.bfloat16, ) - submodules = get_gpt_layer_with_transformer_engine_submodules( - num_experts=num_moe_experts, moe_grouped_gemm=grouped_gemm + submodules = get_submodules( + get_gpt_layer_with_transformer_engine_submodules( + num_experts=num_moe_experts, moe_grouped_gemm=grouped_gemm + ).mlp ) + assert isinstance(submodules, MoESubmodules) # Fake initialization as NeMo does Utils.fake_initialize_model_parallel( tensor_model_parallel_size=tp_size, expert_model_parallel_size=ep_size ) - moe_layer = MoELayer(transformer_config, submodules.mlp.submodules).cuda() + moe_layer = MoELayer(transformer_config, submodules).cuda() Utils.initialize_model_parallel( tensor_model_parallel_size=tp_size, expert_model_parallel_size=ep_size @@ -229,11 +239,12 @@ def test_moe_layer_fp16_forward_backward( params_dtype=torch.float16, ) - submodules = get_gpt_layer_local_submodules( - num_experts=num_moe_experts, moe_grouped_gemm=False + submodules = get_submodules( + get_gpt_layer_local_submodules(num_experts=num_moe_experts, moe_grouped_gemm=False).mlp ) + assert isinstance(submodules, MoESubmodules) - moe_layer = MoELayer(transformer_config, submodules.mlp.submodules).cuda() + moe_layer = MoELayer(transformer_config, submodules).cuda() hidden_states = torch.randn( sequence_length, @@ -332,15 +343,20 @@ def test_moe_layer_recompute_forward_backward( # Use TE spec for fp8, local spec otherwise if fp8: - transformer_layer_submodules = get_gpt_layer_with_transformer_engine_submodules( - num_experts=num_moe_experts, moe_grouped_gemm=False + submodules = get_submodules( + get_gpt_layer_with_transformer_engine_submodules( + num_experts=num_moe_experts, moe_grouped_gemm=False + ).mlp ) else: - transformer_layer_submodules = get_gpt_layer_local_submodules( - num_experts=num_moe_experts, moe_grouped_gemm=False + submodules = get_submodules( + get_gpt_layer_local_submodules( + num_experts=num_moe_experts, moe_grouped_gemm=False + ).mlp ) + assert isinstance(submodules, MoESubmodules) - moe_layer = MoELayer(transformer_config, transformer_layer_submodules.mlp.submodules).cuda() + moe_layer = MoELayer(transformer_config, submodules).cuda() hidden_states = torch.randn( sequence_length, diff --git a/tests/unit_tests/transformer/moe/test_routers.py b/tests/unit_tests/transformer/moe/test_routers.py index 8f3dbbe96e0..a03766d668f 100644 --- a/tests/unit_tests/transformer/moe/test_routers.py +++ b/tests/unit_tests/transformer/moe/test_routers.py @@ -7,9 +7,10 @@ import torch from megatron.core.models.gpt.gpt_layer_specs import get_gpt_layer_local_submodules -from megatron.core.transformer.moe.moe_layer import MoELayer +from megatron.core.transformer.moe.moe_layer import MoELayer, MoESubmodules from megatron.core.transformer.moe.moe_utils import get_updated_expert_bias, router_gating_linear from megatron.core.transformer.moe.router import Router +from megatron.core.transformer.spec_utils import get_submodules from megatron.core.transformer.transformer_config import TransformerConfig from megatron.training.initialize import _set_random_seed from tests.unit_tests.test_utilities import Utils @@ -44,10 +45,11 @@ def setup_method(self, method): params_dtype=torch.bfloat16, add_bias_linear=False, ) - submodules = get_gpt_layer_local_submodules( - num_experts=num_moe_experts, moe_grouped_gemm=False + submodules = get_submodules( + get_gpt_layer_local_submodules(num_experts=num_moe_experts, moe_grouped_gemm=False).mlp ) - self.sequential_mlp = MoELayer(self.transformer_config, submodules.mlp.submodules) + assert isinstance(submodules, MoESubmodules) + self.sequential_mlp = MoELayer(self.transformer_config, submodules) self.router = cast(Router, self.sequential_mlp.router) def teardown_method(self, method): @@ -313,10 +315,11 @@ def setup_method(self, method): ) # init MoE layer - submodules = get_gpt_layer_local_submodules( - num_experts=num_moe_experts, moe_grouped_gemm=False + submodules = get_submodules( + get_gpt_layer_local_submodules(num_experts=num_moe_experts, moe_grouped_gemm=False).mlp ) - self.moe_layer = MoELayer(self.transformer_config, submodules.mlp.submodules).cuda() + assert isinstance(submodules, MoESubmodules) + self.moe_layer = MoELayer(self.transformer_config, submodules).cuda() self.router = cast(Router, self.moe_layer.router) def teardown_method(self, method): @@ -418,10 +421,11 @@ def setup_method(self, method): params_dtype=torch.bfloat16, add_bias_linear=False, ) - submodules = get_gpt_layer_local_submodules( - num_experts=num_moe_experts, moe_grouped_gemm=False + submodules = get_submodules( + get_gpt_layer_local_submodules(num_experts=num_moe_experts, moe_grouped_gemm=False).mlp ) - self.moe_layer = MoELayer(self.transformer_config, submodules.mlp.submodules) + assert isinstance(submodules, MoESubmodules) + self.moe_layer = MoELayer(self.transformer_config, submodules) self.router = cast(Router, self.moe_layer.router) assert self.router.expert_bias is not None assert self.router.local_tokens_per_expert is not None @@ -464,11 +468,14 @@ def test_router_forward_aux_free(self): def test_router_forward_fusion_equivalence(self, score_function): with torch.no_grad(): # Build two fresh routers to avoid bias update interference - submodules = get_gpt_layer_local_submodules( - num_experts=self.transformer_config.num_moe_experts, moe_grouped_gemm=False + submodules = get_submodules( + get_gpt_layer_local_submodules( + num_experts=self.transformer_config.num_moe_experts, moe_grouped_gemm=False + ).mlp ) - moe_layer_ref = MoELayer(self.transformer_config, submodules.mlp.submodules) - moe_layer_fused = MoELayer(self.transformer_config, submodules.mlp.submodules) + assert isinstance(submodules, MoESubmodules) + moe_layer_ref = MoELayer(self.transformer_config, submodules) + moe_layer_fused = MoELayer(self.transformer_config, submodules) router_ref = moe_layer_ref.router.cuda() router_fused = moe_layer_fused.router.cuda() diff --git a/tests/unit_tests/transformer/moe/test_sequential_mlp.py b/tests/unit_tests/transformer/moe/test_sequential_mlp.py index e618f6a8318..6a0637a4e65 100644 --- a/tests/unit_tests/transformer/moe/test_sequential_mlp.py +++ b/tests/unit_tests/transformer/moe/test_sequential_mlp.py @@ -1,6 +1,4 @@ # Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. -from importlib.metadata import version - import pytest import torch @@ -10,8 +8,9 @@ from megatron.core.tensor_parallel.random import model_parallel_cuda_manual_seed from megatron.core.transformer.mlp import MLPSubmodules from megatron.core.transformer.moe.experts import SequentialMLP -from megatron.core.transformer.moe.moe_layer import MoELayer +from megatron.core.transformer.moe.moe_layer import MoELayer, MoESubmodules from megatron.core.transformer.moe.moe_utils import get_default_pg_collection +from megatron.core.transformer.spec_utils import get_submodules from megatron.core.transformer.transformer_config import TransformerConfig from megatron.core.utils import is_te_min_version from tests.unit_tests.test_utilities import Utils @@ -37,10 +36,11 @@ def setup_method(self, method): moe_router_topk=1, add_bias_linear=False, ) - submodules = get_gpt_layer_local_submodules( - num_experts=num_moe_experts, moe_grouped_gemm=False + submodules = get_submodules( + get_gpt_layer_local_submodules(num_experts=num_moe_experts, moe_grouped_gemm=False).mlp ) - self.sequential_mlp = MoELayer(transformer_config, submodules.mlp.submodules) + assert isinstance(submodules, MoESubmodules) + self.sequential_mlp = MoELayer(transformer_config, submodules) def teardown_method(self, method): Utils.destroy_model_parallel() diff --git a/tests/unit_tests/transformer/moe/test_shared_experts.py b/tests/unit_tests/transformer/moe/test_shared_experts.py index a96c6450af4..eccac8c0220 100644 --- a/tests/unit_tests/transformer/moe/test_shared_experts.py +++ b/tests/unit_tests/transformer/moe/test_shared_experts.py @@ -8,7 +8,8 @@ from megatron.core.models.gpt.gpt_layer_specs import get_gpt_layer_local_submodules from megatron.core.parallel_state import get_tensor_model_parallel_world_size from megatron.core.tensor_parallel.random import model_parallel_cuda_manual_seed -from megatron.core.transformer.moe.moe_layer import MoELayer +from megatron.core.transformer.moe.moe_layer import MoELayer, MoESubmodules +from megatron.core.transformer.spec_utils import get_submodules from megatron.core.transformer.transformer_config import TransformerConfig from tests.unit_tests.test_utilities import Utils @@ -33,13 +34,16 @@ def setup_method(self, method): ) def get_moe_layer(self, **kargs) -> MoELayer: - submodules = get_gpt_layer_local_submodules( - num_experts=self.config.num_moe_experts, moe_grouped_gemm=False + submodules = get_submodules( + get_gpt_layer_local_submodules( + num_experts=self.config.num_moe_experts, moe_grouped_gemm=False + ).mlp ) + assert isinstance(submodules, MoESubmodules) new_config = dataclasses.replace(self.config, **kargs) if get_tensor_model_parallel_world_size() > 1: new_config.sequence_parallel = True - moe_layer = MoELayer(new_config, submodules.mlp.submodules) + moe_layer = MoELayer(new_config, submodules) moe_layer.cuda() return moe_layer diff --git a/tests/unit_tests/transformer/moe/test_token_dispatcher.py b/tests/unit_tests/transformer/moe/test_token_dispatcher.py index aef63b6e00e..46cf639e059 100644 --- a/tests/unit_tests/transformer/moe/test_token_dispatcher.py +++ b/tests/unit_tests/transformer/moe/test_token_dispatcher.py @@ -1,6 +1,5 @@ # Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. -import copy import dataclasses import pytest @@ -8,8 +7,9 @@ from megatron.core import config, parallel_state from megatron.core.models.gpt.gpt_layer_specs import get_gpt_layer_local_submodules -from megatron.core.transformer.moe.moe_layer import MoELayer +from megatron.core.transformer.moe.moe_layer import MoELayer, MoESubmodules from megatron.core.transformer.moe.moe_utils import get_capacity +from megatron.core.transformer.spec_utils import get_submodules from megatron.core.transformer.transformer_config import TransformerConfig from megatron.core.typed_torch import apply_module from megatron.core.utils import is_te_min_version @@ -99,11 +99,15 @@ def __init__( self.moe_layer = self.new_moe_layer() def new_moe_layer(self, **kargs): - submodules = get_gpt_layer_local_submodules( - num_experts=self.config.num_moe_experts, moe_grouped_gemm=self.config.moe_grouped_gemm + submodules = get_submodules( + get_gpt_layer_local_submodules( + num_experts=self.config.num_moe_experts, + moe_grouped_gemm=self.config.moe_grouped_gemm, + ).mlp ) + assert isinstance(submodules, MoESubmodules) new_config = dataclasses.replace(self.config, **kargs) - moe_layer = MoELayer(new_config, submodules.mlp.submodules).cuda().to(dtype=self.test_dtype) + moe_layer = MoELayer(new_config, submodules).cuda().to(dtype=self.test_dtype) moe_layer.set_layer_number(0) return moe_layer diff --git a/tests/unit_tests/transformer/test_cuda_graphs.py b/tests/unit_tests/transformer/test_cuda_graphs.py index fe3fe287622..fb81fe588bf 100644 --- a/tests/unit_tests/transformer/test_cuda_graphs.py +++ b/tests/unit_tests/transformer/test_cuda_graphs.py @@ -12,6 +12,7 @@ from megatron.core.models.gpt.gpt_layer_specs import ( get_gpt_decoder_block_spec, get_gpt_layer_with_transformer_engine_spec, + get_gpt_layer_with_transformer_engine_submodules, get_gpt_mtp_block_spec, ) from megatron.core.models.gpt.gpt_model import GPTModel @@ -35,12 +36,14 @@ _CudagraphGlobalRecord, ) from megatron.core.transformer.enums import CudaGraphScope +from megatron.core.transformer.mlp import MLPSubmodules from megatron.core.transformer.module import MegatronModule from megatron.core.transformer.moe.fused_a2a import reset_hybrid_ep_buffer +from megatron.core.transformer.spec_utils import ModuleSpec, get_submodules from megatron.core.transformer.transformer_block import TransformerBlock from megatron.core.transformer.transformer_config import TransformerConfig -from megatron.core.transformer.transformer_layer import TransformerLayerSubmodules -from megatron.core.utils import is_fa_min_version, is_te_min_version +from megatron.core.transformer.transformer_layer import TransformerLayer +from megatron.core.utils import is_te_min_version from megatron.training.arguments import core_transformer_config_from_args, parse_args, validate_args from megatron.training.global_vars import ( destroy_global_vars, @@ -328,10 +331,10 @@ def setup_method(self, method): ) # Get layer specs - language_layer_spec = get_gpt_layer_with_transformer_engine_spec() + language_layer_submodules = get_gpt_layer_with_transformer_engine_submodules() vision_layer_spec = get_vit_layer_with_transformer_engine_spec() - assert isinstance(language_layer_spec.submodules, TransformerLayerSubmodules) - vision_projection_spec = deepcopy(language_layer_spec.submodules.mlp.submodules) + vision_projection_spec = deepcopy(get_submodules(language_layer_submodules.mlp)) + assert isinstance(vision_projection_spec, MLPSubmodules) # Set vision model type vision_config.vision_model_type = "clip" @@ -340,7 +343,9 @@ def setup_method(self, method): # Create LLaVA model with both encoder and decoder self.llava_model = LLaVAModel( language_transformer_config=language_config, - language_transformer_layer_spec=language_layer_spec, + language_transformer_layer_spec=ModuleSpec( + module=TransformerLayer, submodules=language_layer_submodules + ), language_vocab_size=8192, language_max_sequence_length=4096, vision_transformer_config=vision_config, diff --git a/tests/unit_tests/transformer/test_mlp.py b/tests/unit_tests/transformer/test_mlp.py index 45e0df9ad0a..46b22a1bad8 100644 --- a/tests/unit_tests/transformer/test_mlp.py +++ b/tests/unit_tests/transformer/test_mlp.py @@ -6,7 +6,8 @@ from megatron.core.models.gpt.gpt_layer_specs import get_gpt_layer_local_submodules from megatron.core.tensor_parallel.random import model_parallel_cuda_manual_seed -from megatron.core.transformer.mlp import MLP +from megatron.core.transformer.mlp import MLP, MLPSubmodules +from megatron.core.transformer.spec_utils import get_submodules from megatron.core.transformer.transformer_config import TransformerConfig from tests.unit_tests.test_utilities import Utils @@ -19,7 +20,9 @@ def setup_method(self, method): transformer_config = TransformerConfig( num_layers=2, hidden_size=12, num_attention_heads=4, use_cpu_initialization=True ) - self.mlp = MLP(transformer_config, get_gpt_layer_local_submodules().mlp.submodules) + mlp_submodules = get_submodules(get_gpt_layer_local_submodules().mlp) + assert isinstance(mlp_submodules, MLPSubmodules) + self.mlp = MLP(transformer_config, mlp_submodules) def teardown_method(self, method): Utils.destroy_model_parallel() diff --git a/tests/unit_tests/transformer/test_transformer_block_custom_pgs.py b/tests/unit_tests/transformer/test_transformer_block_custom_pgs.py index c55babe35ca..c415b6dda26 100644 --- a/tests/unit_tests/transformer/test_transformer_block_custom_pgs.py +++ b/tests/unit_tests/transformer/test_transformer_block_custom_pgs.py @@ -1,5 +1,5 @@ # Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. - +from __future__ import annotations import copy import os @@ -61,20 +61,18 @@ def __init__( config: TransformerConfig, submodules: TransformerLayerSubmodules, layer_number: int = 1, - hidden_dropout: Optional[float] = None, - pg_collection: ProcessGroupCollection = None, - vp_stage: Optional[int] = None, + hidden_dropout: float | None = None, + pg_collection: ProcessGroupCollection | None = None, + vp_stage: int | None = None, ): - # Temporarily replace attention and MLP with IdentityOp, + # Temporarily replace attention with IdentityOp, # This is a temporary workaround for the test until we have a better interface # will rebuild them with custom process groups after super init def _modify_submodules(submodules: TransformerLayerSubmodules): submodules.self_attention = IdentityOp - submodules.mlp = IdentityOp return submodules original_attention = submodules.self_attention - original_mlp = submodules.mlp new_submodules = _modify_submodules(copy.copy(submodules)) super().__init__( @@ -92,10 +90,6 @@ def _modify_submodules(submodules: TransformerLayerSubmodules): self.self_attention = build_module( original_attention, config=self.config, layer_number=layer_number ) - assert ( - 'tp_group' in submodules.mlp.params - ), "tp_group should be in the params of the submodules" - self.mlp = build_module(original_mlp, config=self.config) def create_reference_mlp(hidden_size, ffn_hidden_size, seed=12345): @@ -168,7 +162,22 @@ def copy_weights_to_tp_mlp(ref_mlp, tp_mlp, tp_group): tp_mlp.linear_fc2.bias.copy_(ref_fc2.bias.to(tp_mlp.linear_fc2.bias.device)) -def _gpt_te_layer_spec_with_hetro_pgs(attn_pg_collection, mlp_pg_collection): +def _gpt_te_layer_spec_with_hetro_pgs( + attn_pg_collection, mlp_pg_collection: ProcessGroupCollection +): + + def build_mlp( + config: TransformerConfig, pg_collection: ProcessGroupCollection, is_mtp_layer: bool + ): + del pg_collection, is_mtp_layer + return MLP( + config, + submodules=MLPSubmodules( + linear_fc1=TELayerNormColumnParallelLinear, linear_fc2=TERowParallelLinear + ), + tp_group=mlp_pg_collection.tp, + ) + return ModuleSpec( module=HeterogenousTransformerLayer, submodules=TransformerLayerSubmodules( @@ -183,13 +192,7 @@ def _gpt_te_layer_spec_with_hetro_pgs(attn_pg_collection, mlp_pg_collection): ), self_attn_bda=get_bias_dropout_add, pre_mlp_layernorm=IdentityOp, - mlp=ModuleSpec( - module=MLP, - params={'tp_group': mlp_pg_collection.tp}, - submodules=MLPSubmodules( - linear_fc1=TELayerNormColumnParallelLinear, linear_fc2=TERowParallelLinear - ), - ), + mlp=build_mlp, mlp_bda=get_bias_dropout_add, ), ) diff --git a/tests/unit_tests/transformer/test_vision_cuda_graphs.py b/tests/unit_tests/transformer/test_vision_cuda_graphs.py index bfd431e67a3..30aea20be4b 100644 --- a/tests/unit_tests/transformer/test_vision_cuda_graphs.py +++ b/tests/unit_tests/transformer/test_vision_cuda_graphs.py @@ -1,7 +1,6 @@ # Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. import gc -import os from copy import deepcopy from types import SimpleNamespace from unittest.mock import MagicMock @@ -10,7 +9,10 @@ import torch from megatron.core import parallel_state -from megatron.core.models.gpt.gpt_layer_specs import get_gpt_layer_with_transformer_engine_spec +from megatron.core.models.gpt.gpt_layer_specs import ( + get_gpt_layer_with_transformer_engine_spec, + get_gpt_layer_with_transformer_engine_submodules, +) from megatron.core.models.vision.vit_layer_specs import get_vit_layer_with_transformer_engine_spec from megatron.core.tensor_parallel.random import ( HAVE_TE, @@ -23,9 +25,11 @@ _layer_is_graphable, _wrap_graph_for_vision, get_vision_cuda_graph_seq_length, - set_current_microbatch, ) +from megatron.core.transformer.mlp import MLPSubmodules +from megatron.core.transformer.spec_utils import ModuleSpec, get_submodules from megatron.core.transformer.transformer_config import TransformerConfig +from megatron.core.transformer.transformer_layer import TransformerLayer from megatron.core.utils import is_te_min_version from tests.unit_tests.test_utilities import Utils @@ -236,16 +240,19 @@ def setup_method(self, method): pipeline_dtype=torch.bfloat16, ) - language_layer_spec = get_gpt_layer_with_transformer_engine_spec() + language_layer_submodules = get_gpt_layer_with_transformer_engine_submodules() vision_layer_spec = get_vit_layer_with_transformer_engine_spec() - vision_projection_spec = deepcopy(language_layer_spec.submodules.mlp.submodules) + vision_projection_spec = deepcopy(get_submodules(language_layer_submodules.mlp)) + assert isinstance(vision_projection_spec, MLPSubmodules) self.vision_config.vision_model_type = "clip" language_config.language_model_type = "dummy" self.llava_model = LLaVAModel( language_transformer_config=language_config, - language_transformer_layer_spec=language_layer_spec, + language_transformer_layer_spec=ModuleSpec( + module=TransformerLayer, submodules=language_layer_submodules + ), language_vocab_size=8192, language_max_sequence_length=4096, vision_transformer_config=self.vision_config, @@ -494,9 +501,10 @@ def setup_method(self, method): pipeline_dtype=torch.bfloat16, ) - language_layer_spec = get_gpt_layer_with_transformer_engine_spec() + language_layer_submodules = get_gpt_layer_with_transformer_engine_submodules() vision_layer_spec = get_vit_layer_with_transformer_engine_spec() - vision_projection_spec = deepcopy(language_layer_spec.submodules.mlp.submodules) + vision_projection_spec = deepcopy(get_submodules(language_layer_submodules.mlp)) + assert isinstance(vision_projection_spec, MLPSubmodules) self.vision_config.vision_model_type = "clip" language_config.language_model_type = "dummy" @@ -504,7 +512,9 @@ def setup_method(self, method): self.is_first_stage = is_first_stage self.llava_model = LLaVAModel( language_transformer_config=language_config, - language_transformer_layer_spec=language_layer_spec, + language_transformer_layer_spec=ModuleSpec( + module=TransformerLayer, submodules=language_layer_submodules + ), language_vocab_size=8192, language_max_sequence_length=4096, vision_transformer_config=self.vision_config, From a2ec5c1df8f2c40e6c73e97c6c54c241704cb0cc Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?oliver=20k=C3=B6nig?= Date: Mon, 11 May 2026 09:52:18 +0200 Subject: [PATCH 2/9] Revert "Add Python-side guardrail for HybridEP InfiniBand limit and rename seq_len (#4094)" (#4718) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: oliver könig --- megatron/core/transformer/moe/fused_a2a.py | 21 ++++----------------- 1 file changed, 4 insertions(+), 17 deletions(-) diff --git a/megatron/core/transformer/moe/fused_a2a.py b/megatron/core/transformer/moe/fused_a2a.py index fe6784a1436..586d70c400f 100644 --- a/megatron/core/transformer/moe/fused_a2a.py +++ b/megatron/core/transformer/moe/fused_a2a.py @@ -280,7 +280,7 @@ def set_deepep_num_sms(num_sms): def init_hybrid_ep_buffer( group: torch.distributed.ProcessGroup, hidden_dim: int, - num_tokens: int, + seq_len: int, num_local_experts: int, num_sms_dispatch_api: Optional[int] = None, num_sms_combine_api: Optional[int] = None, @@ -330,7 +330,7 @@ def init_hybrid_ep_buffer( _hybrid_ep_buffer = HybridEPBuffer( group=group, hidden_dim=hidden_dim, - max_num_of_tokens_per_rank=num_tokens, + max_num_of_tokens_per_rank=seq_len, num_local_experts=num_local_experts, use_fp8=fp8_dispatch, **kwargs, @@ -387,25 +387,12 @@ def forward( num_blocks_unpermute = None if _hybrid_ep_buffer is None: - num_tokens, hidden_dim = x.shape[-2:] - - # --- Hardware Limit Guardrail --- - # DeepEP calculates tx_depth = 3 * num_tokens + 1. - # InfiniBand strictly asserts tx_depth < 65536. - tx_depth = 3 * num_tokens + 1 - if tx_depth >= 65536: - raise ValueError( - f"HybridEP RDMA Queue Pair depth ({tx_depth}) exceeds the InfiniBand " - f"hardware limit of 65535. This occurs because the total tokens per rank " - f"({num_tokens}) too high. Reduce sequence length or micro-batch size, " - f"or increase Tensor Parallelism (TP) / Context Parallelism (CP) to reduce " - f"the number of tokens processed per rank." - ) + seq_len, hidden_dim = x.shape[-2:] fp8_dispatch = False # Currently, we do not support fp8 dispatch init_hybrid_ep_buffer( group, hidden_dim, - num_tokens, + seq_len, num_local_experts, num_sms_dispatch_api, num_sms_combine_api, From e93755e13a58b106559532b6307484e68a73be66 Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" Date: Mon, 11 May 2026 11:22:42 +0000 Subject: [PATCH 3/9] =?UTF-8?q?chore(beep=20boop=20=F0=9F=A4=96):=20Bump?= =?UTF-8?q?=20=20(main)=20(2026-05-11)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- uv.lock | 1679 ++++++++++++++++++++++++++----------------------------- 1 file changed, 785 insertions(+), 894 deletions(-) diff --git a/uv.lock b/uv.lock index f481725d106..4d8d1ca1fef 100644 --- a/uv.lock +++ b/uv.lock @@ -9,16 +9,16 @@ resolution-markers = [ "python_full_version >= '3.14' and platform_machine != 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32'", "python_full_version >= '3.14' and platform_machine == 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32'", "python_full_version == '3.13.*' and platform_machine != 's390x' and sys_platform == 'win32'", - "python_full_version == '3.13.*' and platform_machine == 's390x' and sys_platform == 'win32'", "python_full_version < '3.13' and platform_machine != 's390x' and sys_platform == 'win32'", + "python_full_version == '3.13.*' and platform_machine == 's390x' and sys_platform == 'win32'", "python_full_version < '3.13' and platform_machine == 's390x' and sys_platform == 'win32'", "python_full_version == '3.13.*' and platform_machine != 's390x' and sys_platform == 'emscripten'", - "python_full_version == '3.13.*' and platform_machine == 's390x' and sys_platform == 'emscripten'", "python_full_version < '3.13' and platform_machine 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a/megatron/core/transformer/moe/fused_a2a.py b/megatron/core/transformer/moe/fused_a2a.py index 586d70c400f..91a3bf0cffb 100644 --- a/megatron/core/transformer/moe/fused_a2a.py +++ b/megatron/core/transformer/moe/fused_a2a.py @@ -3,6 +3,7 @@ # Copyright (c) 2025 DeepSeek # Licensed under the MIT License - https://github.com/deepseek-ai/DeepEP/blob/main/LICENSE +import os from typing import Optional from megatron.core.utils import internal_api @@ -268,6 +269,7 @@ def set_deepep_num_sms(num_sms): try: + import hybrid_ep_cpp from deep_ep import HybridEPBuffer HAVE_HYBRIDEP = True @@ -275,12 +277,68 @@ def set_deepep_num_sms(num_sms): HAVE_HYBRIDEP = False _hybrid_ep_buffer = None +_HYBRID_EP_TOKEN_ALIGNMENT = 16 +_HYBRID_EP_MIN_BUFFER_TOKENS = 512 +_HYBRID_EP_IB_QP_MAX_DEPTH = 65535 +_HYBRID_EP_IB_DISPATCH_DEPTH_PER_TOKEN = 3 + + +def _round_up_to_multiple(value: int, multiple: int) -> int: + return ((value + multiple - 1) // multiple) * multiple + + +def _hybrid_ep_num_nodes(group: torch.distributed.ProcessGroup) -> int: + """Mirror HybridEP's NVLink-domain detection without constructing the full buffer.""" + ranks_per_nvlink_domain_env = os.getenv("NUM_OF_HYBRID_EP_RANKS_PER_NVLINK_DOMAIN") + if ranks_per_nvlink_domain_env is not None: + ranks_per_nvlink_domain = int(ranks_per_nvlink_domain_env) + else: + allocator = hybrid_ep_cpp.ExtendedMemoryAllocator() + ranks_per_nvlink_domain = allocator.detect_accessible_ranks(group) + + assert group.size() % ranks_per_nvlink_domain == 0, ( + f"The number of ranks {group.size()} should be divisible by the number of ranks per " + f"NVLink domain {ranks_per_nvlink_domain}." + ) + return group.size() // ranks_per_nvlink_domain + + +def _hybrid_ep_uses_internode_rdma(group: torch.distributed.ProcessGroup) -> bool: + if _hybrid_ep_buffer is not None and hasattr(_hybrid_ep_buffer, "num_of_nodes"): + return _hybrid_ep_buffer.num_of_nodes > 1 + return _hybrid_ep_num_nodes(group) > 1 + + +def _validate_hybrid_ep_ib_tx_depth(num_tokens: int, group: torch.distributed.ProcessGroup) -> None: + buffer_tokens = max( + _round_up_to_multiple(num_tokens, _HYBRID_EP_TOKEN_ALIGNMENT), _HYBRID_EP_MIN_BUFFER_TOKENS + ) + tx_depth = _HYBRID_EP_IB_DISPATCH_DEPTH_PER_TOKEN * buffer_tokens + 1 + if tx_depth <= _HYBRID_EP_IB_QP_MAX_DEPTH: + return + + if not _hybrid_ep_uses_internode_rdma(group): + return + + max_supported_tokens = ( + ((_HYBRID_EP_IB_QP_MAX_DEPTH - 1) // _HYBRID_EP_IB_DISPATCH_DEPTH_PER_TOKEN) + // _HYBRID_EP_TOKEN_ALIGNMENT + * _HYBRID_EP_TOKEN_ALIGNMENT + ) + raise ValueError( + f"HybridEP InfiniBand dispatch queue pair depth ({tx_depth}) exceeds the hardware " + f"limit of {_HYBRID_EP_IB_QP_MAX_DEPTH}. DeepEP computes this depth from the " + f"tokens per rank rounded up to a {_HYBRID_EP_TOKEN_ALIGNMENT}-token buffer " + f"alignment ({buffer_tokens}). Reduce sequence length or micro-batch size, or " + f"increase Tensor Parallelism (TP) / Context Parallelism (CP), so tokens per rank " + f"are at most {max_supported_tokens} for multi-node HybridEP." + ) def init_hybrid_ep_buffer( group: torch.distributed.ProcessGroup, hidden_dim: int, - seq_len: int, + num_tokens: int, num_local_experts: int, num_sms_dispatch_api: Optional[int] = None, num_sms_combine_api: Optional[int] = None, @@ -301,8 +359,8 @@ def init_hybrid_ep_buffer( Process group for HybridEP all-to-all communication. hidden_dim (int): Hidden dimension of the input tensor. - seq_len (int): - Maximum sequence length of the input tensor. + num_tokens (int): + Maximum token count of the input tensor. num_local_experts (int): Number of local experts. num_sms_dispatch_api (Optional[int]): @@ -330,7 +388,7 @@ def init_hybrid_ep_buffer( _hybrid_ep_buffer = HybridEPBuffer( group=group, hidden_dim=hidden_dim, - max_num_of_tokens_per_rank=seq_len, + max_num_of_tokens_per_rank=num_tokens, num_local_experts=num_local_experts, use_fp8=fp8_dispatch, **kwargs, @@ -386,13 +444,14 @@ def forward( num_blocks_permute = None num_blocks_unpermute = None + num_tokens, hidden_dim = x.shape[-2:] + _validate_hybrid_ep_ib_tx_depth(num_tokens, group) if _hybrid_ep_buffer is None: - seq_len, hidden_dim = x.shape[-2:] fp8_dispatch = False # Currently, we do not support fp8 dispatch init_hybrid_ep_buffer( group, hidden_dim, - seq_len, + num_tokens, num_local_experts, num_sms_dispatch_api, num_sms_combine_api, From 5123f6a61daea9f4b9e32da7e8a531b0644c121d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?oliver=20k=C3=B6nig?= Date: Mon, 11 May 2026 18:58:29 +0200 Subject: [PATCH 5/9] ci: revert bad uv.lock bump and label future bumps with `Run functional tests` (#4730) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: oliver könig --- .github/workflows/_update_dependencies.yml | 1 + uv.lock | 1679 +++++++++++--------- 2 files changed, 895 insertions(+), 785 deletions(-) diff --git a/.github/workflows/_update_dependencies.yml b/.github/workflows/_update_dependencies.yml index 903d773edbd..b8410f8fc00 100644 --- a/.github/workflows/_update_dependencies.yml +++ b/.github/workflows/_update_dependencies.yml @@ -117,6 +117,7 @@ jobs: base: ${{ env.TARGET_BRANCH }} title: ${{ env.title }} token: ${{ secrets.PAT }} + labels: Run functional tests body: | 🚀 PR to bump `uv.lock` in `${{ inputs.target-branch }}`. diff --git a/uv.lock b/uv.lock index 4d8d1ca1fef..f481725d106 100644 --- a/uv.lock +++ b/uv.lock @@ -9,16 +9,16 @@ resolution-markers = [ "python_full_version >= '3.14' and platform_machine != 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32'", "python_full_version >= '3.14' and platform_machine == 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32'", "python_full_version == '3.13.*' and platform_machine != 's390x' and sys_platform == 'win32'", - 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11 May 2026 18:59:06 +0200 Subject: [PATCH 6/9] [ci] fix: treat cancelled run-main-script step as failure (#4727) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: oliver könig --- .github/actions/action.yml | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/.github/actions/action.yml b/.github/actions/action.yml index 18ffa50ac9a..6ca5e5828ec 100644 --- a/.github/actions/action.yml +++ b/.github/actions/action.yml @@ -209,6 +209,8 @@ runs: if: always() env: IS_UNIT_TEST: ${{ inputs.is_unit_test == 'true' }} + MAIN_CONCLUSION: ${{ steps.run-main-script.conclusion }} + MAIN_EXIT_CODE: ${{ steps.run-main-script.outputs.exit_code }} run: | logs_report=logs-${{ inputs.test_case }}-${{ github.run_id }}-$(cat /proc/sys/kernel/random/uuid) echo "logs_report=$logs_report" | sed 's/\//-/g' | sed 's/\*/-/g' | tee -a "$GITHUB_OUTPUT" @@ -219,8 +221,12 @@ runs: fi echo "coverage_report=$coverage_report" | tee -a "$GITHUB_OUTPUT" - EXIT_CODE=${{ steps.run-main-script.outputs.exit_code }} - IS_SUCCESS=$([[ "$EXIT_CODE" -eq 0 ]] && echo "true" || echo "false") + EXIT_CODE="${MAIN_EXIT_CODE:-${MAIN_CONCLUSION}}" + if [[ "$MAIN_CONCLUSION" == "success" ]]; then + IS_SUCCESS=true + else + IS_SUCCESS=false + fi if [[ "$IS_SUCCESS" == "false" && "${{ inputs.is-optional }}" == "true" ]]; then echo "::warning::Test failed but is marked optional — treating as success." From e42e2fad9da21840771f6dc9a73497b57fe0af01 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?oliver=20k=C3=B6nig?= Date: Mon, 11 May 2026 19:17:13 +0200 Subject: [PATCH 7/9] ci: Major refactor of release-workflows (#4602) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: oliver könig Co-authored-by: Claude Opus 4.7 (1M context) --- .../workflows/_build_test_publish_wheel.yml | 24 +- .github/workflows/_release_library.yml | 510 ------------------ .../workflows/build-test-publish-wheel.yml | 79 --- .github/workflows/release.yaml | 127 ++++- 4 files changed, 121 insertions(+), 619 deletions(-) delete mode 100644 .github/workflows/_release_library.yml delete mode 100644 .github/workflows/build-test-publish-wheel.yml diff --git a/.github/workflows/_build_test_publish_wheel.yml b/.github/workflows/_build_test_publish_wheel.yml index 21e8d59f881..35901c86ef2 100644 --- a/.github/workflows/_build_test_publish_wheel.yml +++ b/.github/workflows/_build_test_publish_wheel.yml @@ -18,7 +18,7 @@ on: default: true secrets: TWINE_PASSWORD: - required: true + required: false jobs: build-and-test-wheels: @@ -144,7 +144,8 @@ jobs: publish-wheels: needs: [build-and-test-wheels] runs-on: ubuntu-latest - if: inputs.no-publish == false + environment: + name: ${{ inputs.no-publish && 'public' || 'main' }} strategy: fail-fast: false matrix: @@ -171,6 +172,7 @@ jobs: TWINE_PASSWORD: ${{ secrets.TWINE_PASSWORD }} TWINE_REPOSITORY: ${{ (github.ref == 'refs/heads/main' || startsWith(github.ref, 'refs/heads/r')) && 'pypi' || 'testpypi' }} PLATFORM: ${{ matrix.PLATFORM }} + DRY_RUN: ${{ inputs.no-publish }} run: | # Delete sdist for arm64 since we already upload it with amd64. @@ -180,9 +182,15 @@ jobs: ls -al dist/ pip install twine - twine upload \ - --verbose \ - -r $TWINE_REPOSITORY \ - -u $TWINE_USERNAME \ - -p $TWINE_PASSWORD \ - dist/* + + if [[ "$DRY_RUN" == "false" ]]; then + [[ -z "$TWINE_PASSWORD" ]] && { echo "::error::TWINE_PASSWORD unset"; exit 1; } + twine upload \ + --verbose \ + -r $TWINE_REPOSITORY \ + -u $TWINE_USERNAME \ + -p $TWINE_PASSWORD \ + dist/* + else + echo "[dry-run] would execute: twine upload --verbose -r $TWINE_REPOSITORY -u -p dist/*" + fi diff --git a/.github/workflows/_release_library.yml b/.github/workflows/_release_library.yml deleted file mode 100644 index 954d81c8259..00000000000 --- a/.github/workflows/_release_library.yml +++ /dev/null @@ -1,510 +0,0 @@ -# Copyright (c) 2020-2021, NVIDIA CORPORATION. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -name: "Release" - -defaults: - run: - shell: bash -x -e -u -o pipefail {0} - -on: - workflow_call: - inputs: - release-ref: - required: true - description: Ref (SHA or branch) to release - type: string - dry-run: - type: boolean - required: true - description: Do not publish a wheel and GitHub release. - version-bump-branch: - type: string - required: true - description: Branch to target for version bump - create-gh-release: - required: false - description: Create a GitHub release - type: boolean - default: true - gh-release-use-changelog-builder: - required: false - description: Use release-changelog-builder-action to dynamically build changelog - type: boolean - default: true - gh-release-changelog-config: - required: false - description: Path to changelog builder configuration file - type: string - default: ".github/workflows/config/changelog-config.json" - gh-release-from-tag: - required: false - description: Starting tag for changelog builder (leave empty for auto-detect) - type: string - default: "" - publish-docs: - required: false - description: Publish documentation to S3 after release - type: boolean - default: true - secrets: - TWINE_PASSWORD: - required: true - SLACK_WEBHOOK: - required: true - PAT: - required: true - AWS_ASSUME_ROLE_ARN: - required: true - AWS_ACCESS_KEY_ID: - required: true - AWS_SECRET_ACCESS_KEY: - required: true - AKAMAI_HOST: - required: true - AKAMAI_CLIENT_TOKEN: - required: true - AKAMAI_CLIENT_SECRET: - required: true - AKAMAI_ACCESS_TOKEN: - required: true - S3_BUCKET_NAME: - required: true - -permissions: - contents: write # To read repository content - pull-requests: write # To create PR(s) - -jobs: - build-test-publish-wheels-dry-run: - uses: ./.github/workflows/_build_test_publish_wheel.yml - with: - dry-run: true - ref: ${{ inputs.release-ref }} - no-publish: true - secrets: - TWINE_PASSWORD: ${{ secrets.TWINE_PASSWORD }} - - bump-next-version: - runs-on: ubuntu-latest - needs: build-test-publish-wheels-dry-run - if: | - ( - success() || !failure() - ) - && !cancelled() - outputs: - release-version: ${{ steps.bump-version-mcore.outputs.release-version }} - env: - IS_DRY_RUN: ${{ inputs.dry-run }} - steps: - - name: Checkout repository - uses: actions/checkout@v6 - with: - path: ${{ github.run_id }} - token: ${{ secrets.PAT }} - fetch-depth: 0 - fetch-tags: true - ref: ${{ inputs.release-ref }} - - name: Bump version MCore - id: bump-version-mcore - env: - SRC_DIR: "" - PYPROJECT_NAME: "megatron.core" - run: | - set +u - cd ${{ github.run_id }} - - PACKAGE_INFO_FILE="$SRC_DIR${PYPROJECT_NAME//.//}/package_info.py" - - MAJOR=$(cat $PACKAGE_INFO_FILE | awk '/^MAJOR = /' | awk -F"= " '{print $2}') - MINOR=$(cat $PACKAGE_INFO_FILE | awk '/^MINOR = /' | awk -F"= " '{print $2}') - PATCH=$(cat $PACKAGE_INFO_FILE | awk '/^PATCH = /' | awk -F"= " '{print $2}') - PRERELEASE=$(cat $PACKAGE_INFO_FILE | awk '/^PRE_RELEASE = /' | awk -F"= " '{print $2}' | tr -d '"' | tr -d "'") - - echo "release-version=$MAJOR.$MINOR.$PATCH$PRERELEASE" | tee -a "$GITHUB_OUTPUT" - - if [[ "$PRERELEASE" != "" ]]; then - if [[ "$PRERELEASE" == *rc* ]]; then - NEXT_PATCH=$PATCH - NEXT_PRERELEASE=rc$((${PRERELEASE#rc} + 1)) - elif [[ "$PRERELEASE" == *a* ]]; then - NEXT_PATCH=$PATCH - NEXT_PRERELEASE=a$((${PRERELEASE#a} + 1)) - else - echo "Unknown pre-release: $PRERELEASE" - exit 1 - fi - else - NEXT_PATCH=$((${PATCH} + 1)) - NEXT_PRERELEASE=$PRERELEASE - fi - - sed -i "/^PATCH/c\PATCH = $NEXT_PATCH" $PACKAGE_INFO_FILE - sed -i "/^PRE_RELEASE/c\PRE_RELEASE = \"$NEXT_PRERELEASE\"" $PACKAGE_INFO_FILE - - echo "version=$MAJOR.$MINOR.$NEXT_PATCH$NEXT_PRERELEASE" | tee -a "$GITHUB_OUTPUT" - - - name: Bump version MFSDP - id: bump-version-mfsdp - env: - SRC_DIR: "megatron/core/distributed/fsdp/src/" - PYPROJECT_NAME: "megatron_fsdp" - run: | - set +u - - cd ${{ github.run_id }} - - PACKAGE_INFO_FILE="$SRC_DIR${PYPROJECT_NAME//.//}/package_info.py" - - MAJOR=$(cat $PACKAGE_INFO_FILE | awk '/^MAJOR = /' | awk -F"= " '{print $2}') - MINOR=$(cat $PACKAGE_INFO_FILE | awk '/^MINOR = /' | awk -F"= " '{print $2}') - PATCH=$(cat $PACKAGE_INFO_FILE | awk '/^PATCH = /' | awk -F"= " '{print $2}') - PRERELEASE=$(cat $PACKAGE_INFO_FILE | awk '/^PRE_RELEASE = /' | awk -F"= " '{print $2}' | tr -d '"' | tr -d "'") - - if [[ "$PRERELEASE" != "" ]]; then - if [[ "$PRERELEASE" == *rc* ]]; then - NEXT_PATCH=$PATCH - NEXT_PRERELEASE=rc$((${PRERELEASE#rc} + 1)) - elif [[ "$PRERELEASE" == *a* ]]; then - NEXT_PATCH=$PATCH - NEXT_PRERELEASE=a$((${PRERELEASE#a} + 1)) - else - echo "Unknown pre-release: $PRERELEASE" - exit 1 - fi - else - NEXT_PATCH=$((${PATCH} + 1)) - NEXT_PRERELEASE=$PRERELEASE - fi - - sed -i "/^PATCH/c\PATCH = $NEXT_PATCH" $PACKAGE_INFO_FILE - sed -i "/^PRE_RELEASE/c\PRE_RELEASE = \"$NEXT_PRERELEASE\"" $PACKAGE_INFO_FILE - - echo "version=$MAJOR.$MINOR.$NEXT_PATCH$NEXT_PRERELEASE" | tee -a "$GITHUB_OUTPUT" - - - name: Create and push deployment branch - env: - GH_TOKEN: ${{ secrets.PAT }} - run: | - cd ${{ github.run_id }} - - TMP_BRANCH="deploy-release/$(uuidgen)" - git config --global user.name "github-actions[bot]" - git config --global user.email "github-actions[bot]@users.noreply.github.com" - git checkout -b "$TMP_BRANCH" - git add -A . - git commit -m "beep boop 🤖: Bumping versions" || echo "No changes to commit" - git push -u origin "$TMP_BRANCH" - echo "TMP_BRANCH=$TMP_BRANCH" | tee -a $GITHUB_ENV - - # Create PR to collect app based status checks that run on PRs only - # (like DCO check) - PR_URL=$(gh pr create \ - --base ${{ inputs.version-bump-branch }} \ - --head $TMP_BRANCH \ - --title "beep boop 🤖: Bumping versions" \ - --body "This is an automated PR to bump versions.") - - # Extract PR number from URL - PR_NUMBER=$(echo $PR_URL | grep -o '[0-9]*$') - - - name: Wait for status checks on tmp branch - uses: actions/github-script@v8 - id: wait-status - with: - github-token: ${{ secrets.PAT }} - script: | - const branch = process.env.TMP_BRANCH; - const owner = context.repo.owner; - const repo = context.repo.repo; - - // Get latest commit SHA of branch - const { data: refData } = await github.rest.git.getRef({ - owner, - repo, - ref: `heads/${branch}`, // note: no 'refs/' prefix here - }); - - const sha = refData.object.sha; - - console.log(`Polling status for commit SHA: ${sha}`); - - let checksPassed = false; - let maxAttempts = 30; - let attempt = 0; - const delay = ms => new Promise(res => setTimeout(res, ms)); - - while (!checksPassed && attempt < maxAttempts) { - attempt++; - - // Use commit SHA instead of branch ref - const { data: status } = await github.rest.repos.getCombinedStatusForRef({ - owner, - repo, - ref: sha, - }); - - const { data: checks } = await github.rest.checks.listForRef({ - owner, - repo, - ref: sha, - }); - - const allStatuses = status.statuses; - const allChecks = checks.check_runs; - - if (allStatuses.length === 0 && allChecks.length === 0) { - console.log(`Attempt ${attempt}: No checks or statuses yet. Waiting...`); - await delay(10000); - continue; - } - - const statusesOk = allStatuses.every(s => s.state === 'success'); - const checksOk = allChecks.every(c => c.status === 'completed'); - - if (statusesOk && checksOk) { - console.log('✅ All checks passed.'); - checksPassed = true; - break - } - - console.log(`Attempt ${attempt}: Checks not complete yet. Waiting...`); - await delay(10000); - } - - if (!checksPassed) { - core.setFailed('❌ Status checks did not pass in time'); - } - - - name: Merge into ${{ inputs.version-bump-branch }} - run: | - cd ${{ github.run_id }} - - git config user.name "github-actions[bot]" - git config user.email "github-actions[bot]@users.noreply.github.com" - - CMD=$(echo -E 'git push origin ${{ inputs.version-bump-branch }}') - - if [[ "$IS_DRY_RUN" == "true" ]]; then - echo "dry-run enabled, would have run: $CMD" - else - # Here we account for potential race conditions from multiple concurrent releases. - # Those can be legit (operating on different packages within the monorepo, for example) - # but the pushes would be still rejected purely because of git's inability to - # push non-fast-forward updates to the branch. In this case we would need to let - # a retry. - git fetch origin ${{ inputs.version-bump-branch }} - git checkout ${{ inputs.version-bump-branch }} - git merge ${{ env.TMP_BRANCH }} - - for attempt in {1..3}; do - if eval "$CMD"; then - echo "Git push succeeded on attempt $attempt" - break - else - echo "Git push failed on attempt $attempt" - if [[ $attempt -lt 3 ]]; then - sleep $((RANDOM % 3 + 1)) - # We refetch, reset and re-merge. Note resetting because the local - # branch is "contaminated" with previous merge attempt. - git fetch origin ${{ inputs.version-bump-branch }} - git reset --hard origin/${{ inputs.version-bump-branch }} - git merge ${{ env.TMP_BRANCH }} - else - echo "Git push failed after 3 attempts" - exit 1 - fi - fi - done - fi - - - name: Delete ${{ env.TMP_BRANCH }} branch - if: always() - run: | - cd ${{ github.run_id }} - git push -d origin ${{ env.TMP_BRANCH }} - - build-test-publish-wheels: - needs: [bump-next-version] - uses: ./.github/workflows/_build_test_publish_wheel.yml - with: - dry-run: false - ref: ${{ inputs.release-ref }} - no-publish: false - secrets: - TWINE_PASSWORD: ${{ secrets.TWINE_PASSWORD }} - - create-gh-release: - needs: [build-test-publish-wheels, bump-next-version] - runs-on: ubuntu-latest - if: | - ( - success() || !failure() - ) - && inputs.create-gh-release == true - && !cancelled() - outputs: - is-release-candidate: ${{ steps.version-number.outputs.is-release-candidate }} - env: - REPOSITORY: ${{ github.repository }} - PROJECT_NAME: Megatron Core - VERSION: ${{ needs.bump-next-version.outputs.release-version }} - TAG_PREFIX: core_ - steps: - - name: Checkout repository - uses: actions/checkout@v6 - with: - path: ${{ github.run_id }} - ref: ${{ inputs.release-ref }} - token: ${{ secrets.PAT || secrets.GITHUB_TOKEN }} - - - name: Determine fromTag for changelog - id: determine-from-tag - if: inputs.gh-release-use-changelog-builder == true - run: | - cd ${{ github.run_id }} - - # If gh-release-from-tag is provided, use it - if [[ -n "${{ inputs.gh-release-from-tag }}" ]]; then - FROM_TAG="${{ inputs.gh-release-from-tag }}" - echo "Using provided fromTag: $FROM_TAG" - else - # Get the most recent tag - FROM_TAG=$(git describe --tags --abbrev=0 2>/dev/null || echo "") - if [[ -z "$FROM_TAG" ]]; then - echo "No previous tags found, leaving fromTag empty" - else - echo "Auto-detected most recent tag: $FROM_TAG" - fi - fi - - echo "from-tag=$FROM_TAG" >> $GITHUB_OUTPUT - - - name: Build Changelog - id: build-changelog - if: inputs.gh-release-use-changelog-builder == true - uses: mikepenz/release-changelog-builder-action@v6.1.0 - env: - GITHUB_TOKEN: ${{ secrets.PAT || secrets.GITHUB_TOKEN }} - with: - configuration: ${{ github.run_id }}/${{ inputs.gh-release-changelog-config }} - owner: ${{ github.repository_owner }} - repo: ${{ github.event.repository.name }} - ignorePreReleases: "false" - failOnError: "false" - fromTag: ${{ steps.determine-from-tag.outputs.from-tag }} - toTag: ${{ inputs.release-ref }} - mode: ${{ inputs.gh-release-changelog-mode }} - - - name: Create release - id: version-number - env: - SHA: ${{ inputs.release-ref }} - GH_TOKEN: ${{ secrets.PAT }} - IS_DRY_RUN: ${{ inputs.dry-run }} - BUILT_CHANGELOG: ${{ steps.build-changelog.outputs.changelog }} - run: | - cd ${{ github.run_id }} - - IS_RELEASE_CANDIDATE=$([[ "$VERSION" == *rc* ]] && echo "true" || echo "false") - IS_ALPHA=$([[ "$VERSION" == *a* ]] && echo "true" || echo "false") - IS_PRERELEASE=$([[ "$IS_RELEASE_CANDIDATE" == "true" || "$IS_ALPHA" == "true" ]] && echo "true" || echo "false") - NAME="NVIDIA $PROJECT_NAME ${VERSION}" - - # Use built changelog if available, otherwise fall back to CHANGELOG.md - if [[ -n "$BUILT_CHANGELOG" ]]; then - CHANGELOG="$BUILT_CHANGELOG" - elif [[ "$IS_RELEASE_CANDIDATE" == "true" ]]; then - DATE=$(date +"%Y-%m-%d") - CHANGELOG="Prerelease: $NAME ($DATE)" - else - CHANGELOG=$(awk '/^## '"$NAME"'/{flag=1; next} /^## /{flag=0} flag' CHANGELOG.md) - CHANGELOG=$(echo "$CHANGELOG" | sed '/./,$!d' | sed ':a;N;$!ba;s/\n$//') - fi - - echo "is-release-candidate=$IS_RELEASE_CANDIDATE" | tee -a "$GITHUB_OUTPUT" - - PAYLOAD=$(jq -nc \ - --arg TAG_NAME "${TAG_PREFIX}v${VERSION}" \ - --arg CI_COMMIT_BRANCH "$SHA" \ - --arg NAME "$NAME" \ - --arg BODY "$CHANGELOG" \ - --argjson PRERELEASE "$IS_PRERELEASE" \ - '{ - "tag_name": $TAG_NAME, - "target_commitish": $CI_COMMIT_BRANCH, - "name": $NAME, - "body": $BODY, - "draft": false, - "prerelease": $PRERELEASE, - "generate_release_notes": false - }' - ) - echo -E "$PAYLOAD" > payload.txt - - CMD=$(echo -E 'curl -L \ - -X POST \ - -H "Accept: application/vnd.github+json" \ - -H "Authorization: Bearer '"$GH_TOKEN"'" \ - -H "X-GitHub-Api-Version: 2022-11-28" \ - https://api.github.com/repos/'"$REPOSITORY"'/releases \ - -d @payload.txt - ') - - if [[ "$IS_DRY_RUN" == "true" ]]; then - echo -E "$CMD" - else - eval "$CMD" - fi - - publish-docs: - needs: [bump-next-version, create-gh-release] - uses: ./.github/workflows/release-docs.yml - if: | - ( - success() || !failure() - ) - && inputs.publish-docs == true - && !cancelled() - with: - dry-run: ${{ inputs.dry-run }} - publish-as-latest: true - docs-version-override: ${{ needs.bump-next-version.outputs.release-version }} - build-docs-ref: ${{ inputs.release-ref }} - secrets: inherit - - notify: - needs: [build-test-publish-wheels, create-gh-release, bump-next-version] - runs-on: ubuntu-latest - steps: - - name: Checkout - uses: actions/checkout@v6 - with: - repository: NVIDIA-NeMo/FW-CI-templates - ref: v0.17.0 - path: send-slack-alert - - - name: Send Slack alert - uses: ./send-slack-alert/.github/actions/send-slack-alert - env: - MESSAGE: | - ${{ inputs.dry-run == true && 'This is a dry-run, nothing actually happened: ' || '' }}We have released `${{ needs.bump-next-version.outputs.release-version }}` of `NVIDIA Megatron Core` 🚀✨🎉 - - • - • - - with: - message: ${{ env.MESSAGE }} - webhook: ${{ secrets.SLACK_WEBHOOK }} diff --git a/.github/workflows/build-test-publish-wheel.yml b/.github/workflows/build-test-publish-wheel.yml deleted file mode 100644 index 6817f611485..00000000000 --- a/.github/workflows/build-test-publish-wheel.yml +++ /dev/null @@ -1,79 +0,0 @@ -# Copyright (c) 2019-2025, NVIDIA CORPORATION. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -name: Build, test, and publish a PyPi wheel (to testpypi). - -on: - push: - branches: - - main - - "pull-request/[0-9]+" - - "deploy-release/*" - merge_group: - types: [checks_requested] - -defaults: - run: - shell: bash -x -e -u -o pipefail {0} - -permissions: - id-token: write - contents: read - -jobs: - pre-flight: - uses: NVIDIA-NeMo/FW-CI-templates/.github/workflows/_cicd_preflight.yml@v1.0.0 - if: github.repository == 'NVIDIA/Megatron-LM' - - build-test-publish-wheels: - needs: [pre-flight] - uses: ./.github/workflows/_build_test_publish_wheel.yml - with: - no-publish: true - secrets: - TWINE_PASSWORD: ${{ (github.ref == 'refs/heads/main' || startsWith(github.ref, 'refs/heads/r')) && secrets.SVC_PYPI_TOKEN || secrets.SVC_PYPI_TEST_TOKEN }} - - build-test-publish-wheel-summary: - needs: [pre-flight, build-test-publish-wheels] - if: | - ( - needs.pre-flight.outputs.docs_only == 'true' - || needs.pre-flight.outputs.is_merge_group == 'true' - || needs.pre-flight.outputs.is_deployment_workflow == 'true' - || always() - ) - && github.repository == 'NVIDIA/Megatron-LM' - && !cancelled() - runs-on: ubuntu-latest - steps: - - name: Checkout repository - uses: actions/checkout@v6 - - - name: Result - env: - GH_TOKEN: ${{ github.token }} - GITHUB_RUN_ID: ${{ github.run_id }} - SKIPPING_IS_ALLOWED: false - run: | - FAILED_JOBS=$(gh run view $GITHUB_RUN_ID --json jobs --jq '[.jobs[] | select(.status == "completed" and .conclusion != "success" and (.name | test("build-and-test-wheels")))] | length') || echo 0 - - if [ "${FAILED_JOBS:-0}" -eq 0 ] || [ "$SKIPPING_IS_ALLOWED" == "true" ]; then - echo "✅ All build-and-test-wheels jobs completed successfully" - exit 0 - else - echo "❌ Found $FAILED_JOBS failed build-and-test-wheels job(s)" - # Show which jobs failed - gh run view $GITHUB_RUN_ID --json jobs --jq '.jobs[] | select(.status == "completed" and .conclusion != "success" and (.name | test("build-and-test-wheels"))) | .name' - exit 1 - fi diff --git a/.github/workflows/release.yaml b/.github/workflows/release.yaml index a756d49eb20..7bf36ad71bc 100644 --- a/.github/workflows/release.yaml +++ b/.github/workflows/release.yaml @@ -1,4 +1,4 @@ -# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. +# Copyright (c) 2019-2026, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -11,9 +11,16 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -name: "Release Megatron-Core" +name: "Build, validate, and release Megatron-Core" on: + push: + branches: + - main + - "pull-request/[0-9]+" + - "deploy-release/*" + merge_group: + types: [checks_requested] workflow_dispatch: inputs: release-ref: @@ -21,7 +28,7 @@ on: required: true type: string dry-run: - description: Do not publish a wheel and GitHub release. + description: Compute the release but do not publish wheel, GH release, or docs. required: true default: true type: boolean @@ -51,29 +58,105 @@ on: default: "" permissions: - contents: write # To read repository content - pull-requests: write # To create PRs + id-token: write + contents: write + pull-requests: write + +defaults: + run: + shell: bash -x -e -u -o pipefail {0} + +concurrency: + group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}-${{ github.event_name }} + cancel-in-progress: ${{ github.event_name == 'push' }} jobs: - release: - uses: ./.github/workflows/_release_library.yml + pre-flight: + uses: NVIDIA-NeMo/FW-CI-templates/.github/workflows/_cicd_preflight.yml@v0.94.1 + if: github.repository == 'NVIDIA/Megatron-LM' && github.event_name != 'workflow_dispatch' + + bump: + needs: [pre-flight] + if: | + !cancelled() && !failure() + && github.repository == 'NVIDIA/Megatron-LM' + && !(needs.pre-flight.outputs.docs_only == 'true' + || needs.pre-flight.outputs.is_merge_group == 'true' + || needs.pre-flight.outputs.is_deployment_workflow == 'true') + uses: NVIDIA-NeMo/FW-CI-templates/.github/workflows/_release_bump.yml@v1.0.0 with: release-ref: ${{ inputs.release-ref || github.sha }} + validate-only: ${{ github.event_name != 'workflow_dispatch' }} dry-run: ${{ inputs.dry-run || false }} version-bump-branch: ${{ inputs.version-bump-branch || github.ref_name }} + restrict-to-admins: true + app-id: ${{ vars.BOT_ID }} + library-name: Megatron Core + bump-targets: | + [ + {"python-package": "megatron.core", "src-dir": ""}, + {"python-package": "megatron_fsdp", "src-dir": "megatron/core/distributed/fsdp/src/"} + ] + secrets: inherit # pragma: allowlist secret + + build-test-publish-wheels: + needs: [pre-flight, bump] + if: | + !cancelled() && !failure() && needs.bump.result == 'success' + && github.repository == 'NVIDIA/Megatron-LM' + && ( + github.event_name == 'workflow_dispatch' + || !(needs.pre-flight.outputs.docs_only == 'true' + || needs.pre-flight.outputs.is_deployment_workflow == 'true') + ) + uses: ./.github/workflows/_build_test_publish_wheel.yml + with: + ref: ${{ inputs.release-ref || github.sha }} + dry-run: ${{ inputs.dry-run || false }} + no-publish: ${{ github.event_name != 'workflow_dispatch' || inputs.dry-run }} + secrets: inherit # pragma: allowlist secret + + finalize: + needs: [bump, build-test-publish-wheels] + if: | + github.repository == 'NVIDIA/Megatron-LM' + && (success() || !failure()) + && !cancelled() + uses: NVIDIA-NeMo/FW-CI-templates/.github/workflows/_release_finalize.yml@v1.0.0 + with: + release-ref: ${{ inputs.release-ref || github.sha }} + release-version: ${{ needs.bump.outputs.release-version }} + library-name: Megatron Core + pypi-name: megatron-core + validate-only: ${{ github.event_name != 'workflow_dispatch' }} + dry-run: ${{ inputs.dry-run || false }} create-gh-release: ${{ inputs.create-gh-release || true }} - gh-release-use-changelog-builder: ${{ inputs.generate-changelog }} - publish-docs: ${{ inputs.publish-docs }} - gh-release-from-tag: ${{ inputs.gh-release-from-tag }} - secrets: - TWINE_PASSWORD: ${{ (github.ref == 'refs/heads/main' || startsWith(github.ref, 'refs/heads/r')) && secrets.SVC_PYPI_TOKEN || secrets.SVC_PYPI_TEST_TOKEN }} - SLACK_WEBHOOK: ${{ (github.ref == 'refs/heads/main' || startsWith(github.ref, 'refs/heads/r')) && secrets.SLACK_MAIN_CHANNEL_WEBHOOK || secrets.SLACK_CI_CHANNEL_WEBHOOK }} - PAT: ${{ secrets.PAT }} - AWS_ASSUME_ROLE_ARN: ${{ secrets.AWS_ASSUME_ROLE_ARN }} - AWS_ACCESS_KEY_ID: ${{ secrets.AWS_ACCESS_KEY_ID }} - AWS_SECRET_ACCESS_KEY: ${{ secrets.AWS_SECRET_ACCESS_KEY }} - AKAMAI_HOST: ${{ secrets.AKAMAI_HOST }} - AKAMAI_CLIENT_TOKEN: ${{ secrets.AKAMAI_CLIENT_TOKEN }} - AKAMAI_CLIENT_SECRET: ${{ secrets.AKAMAI_CLIENT_SECRET }} - AKAMAI_ACCESS_TOKEN: ${{ secrets.AKAMAI_ACCESS_TOKEN }} - S3_BUCKET_NAME: ${{ secrets.S3_BUCKET_NAME }} + gh-release-tag-prefix: core_ + gh-release-use-changelog-builder: ${{ inputs.generate-changelog || false }} + gh-release-from-tag: ${{ inputs.gh-release-from-tag || '' }} + publish-docs: ${{ inputs.publish-docs || true }} + docs-target-path: megatron-core/developer-guide + publish-as-latest: true + run-on-version-tag-only: ${{ github.ref_name != 'main' }} + app-id: ${{ vars.BOT_ID }} + secrets: inherit # pragma: allowlist secret + + release-summary: + needs: [pre-flight, bump, build-test-publish-wheels, finalize] + if: github.repository == 'NVIDIA/Megatron-LM' && !cancelled() + runs-on: ubuntu-latest + steps: + - name: Result + env: + GH_TOKEN: ${{ secrets.GITHUB_TOKEN }} + run: | + FAILED_JOBS=$(gh run view $GITHUB_RUN_ID --repo ${{ github.repository }} --json jobs --jq '[.jobs[] | select(.conclusion == "failure" or .conclusion == "timed_out" or .conclusion == "action_required")] | length') + + if [ "${FAILED_JOBS:-0}" -eq 0 ]; then + echo "✅ All previous jobs completed successfully" + exit 0 + else + echo "❌ Found $FAILED_JOBS failed job(s)" + gh run view $GITHUB_RUN_ID --repo ${{ github.repository }} --json jobs --jq '.jobs[] | select(.conclusion == "failure" or .conclusion == "timed_out" or .conclusion == "action_required") | .name' + exit 1 + fi From 434368c817baeb2b4ef1140ad2399bfbe3896b9d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?oliver=20k=C3=B6nig?= Date: Mon, 11 May 2026 21:55:18 +0200 Subject: [PATCH 8/9] build(deps): bump nvidia-modelopt to 0.43 (#4723) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: oliver könig --- .../workflows/_build_test_publish_wheel.yml | 2 +- docker/Dockerfile.ci.dev | 2 +- docker/common/install.sh | 2 +- docker/common/install_source_wheels.sh | 2 +- .../core/distributed/fsdp/src/pyproject.toml | 2 +- pyproject.toml | 6 +- uv.lock | 65 +++++++++++++++---- 7 files changed, 60 insertions(+), 21 deletions(-) diff --git a/.github/workflows/_build_test_publish_wheel.yml b/.github/workflows/_build_test_publish_wheel.yml index 35901c86ef2..0df8756d082 100644 --- a/.github/workflows/_build_test_publish_wheel.yml +++ b/.github/workflows/_build_test_publish_wheel.yml @@ -74,7 +74,7 @@ jobs: rm LICENSE || true docker run --rm -e NO_VCS_VERSION=1 -v $(pwd):/workspace -w /workspace $IMAGE bash -c '\ for python_version in cp311 cp312 cp313; do \ - /opt/python/${python_version}-${python_version}/bin/pip install --upgrade "setuptools<80.0.0,>=77.0.0" build; \ + /opt/python/${python_version}-${python_version}/bin/pip install --upgrade "setuptools>=80" build; \ done && \ for python_version in cp311 cp312 cp313; do \ /opt/python/${python_version}-${python_version}/bin/python -m build; \ diff --git a/docker/Dockerfile.ci.dev b/docker/Dockerfile.ci.dev index bcb2e5940a9..377bd4a2005 100644 --- a/docker/Dockerfile.ci.dev +++ b/docker/Dockerfile.ci.dev @@ -97,7 +97,7 @@ RUN --mount=type=secret,id=JET_INDEX_URLS \ JET_INDEX_URLS=$(cat /run/secrets/JET_INDEX_URLS) LOGGER_INDEX_URL=$(cat /run/secrets/LOGGER_INDEX_URL) uv pip install --no-cache-dir --upgrade $LOGGER_INDEX_URL "one-logger" - uv pip install --no-cache-dir --upgrade "setuptools<80.0.0,>=77.0.0" + uv pip install --no-cache-dir --upgrade "setuptools>=80" uv pip install --no-cache-dir --upgrade $JET_INDEX_URLS "jet-client~=4.0" EOF ### diff --git a/docker/common/install.sh b/docker/common/install.sh index 01003c0e7aa..3991fd41448 100644 --- a/docker/common/install.sh +++ b/docker/common/install.sh @@ -136,7 +136,7 @@ main() { . $UV_PROJECT_ENVIRONMENT/bin/activate pip install --pre --no-cache-dir --upgrade pip - pip install --pre --no-cache-dir torch pybind11 wheel_stub ninja wheel packaging "setuptools<80.0.0,>=77.0.0" + pip install --pre --no-cache-dir torch pybind11 wheel_stub ninja wheel packaging "setuptools>=80" pip install --pre --no-cache-dir --no-build-isolation . fi diff --git a/docker/common/install_source_wheels.sh b/docker/common/install_source_wheels.sh index eaf601c6045..7eaaef2e46f 100644 --- a/docker/common/install_source_wheels.sh +++ b/docker/common/install_source_wheels.sh @@ -50,4 +50,4 @@ fi uv pip install --no-cache-dir \ $MAMBA_WHEEL \ $CAUSALCONV1D_WHEEL \ - "setuptools<80.0.0,>=77.0.0" + "setuptools>=80" diff --git a/megatron/core/distributed/fsdp/src/pyproject.toml b/megatron/core/distributed/fsdp/src/pyproject.toml index 783030cc809..2845a14ab62 100644 --- a/megatron/core/distributed/fsdp/src/pyproject.toml +++ b/megatron/core/distributed/fsdp/src/pyproject.toml @@ -1,7 +1,7 @@ # Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. [build-system] -requires = ["setuptools<80.0.0", "pybind11"] +requires = ["setuptools>=80", "pybind11"] build-backend = "setuptools.build_meta" [tool.setuptools] diff --git a/pyproject.toml b/pyproject.toml index 506feb610ca..f91c2ee83b7 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,7 +1,7 @@ # Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. [build-system] -requires = ["setuptools<80.0.0", "pybind11", "packaging>=24.2"] +requires = ["setuptools>=80", "pybind11", "packaging>=24.2"] build-backend = "setuptools.build_meta" [tool.setuptools] @@ -88,7 +88,7 @@ mlm = [ ] dev = [ - "nvidia-modelopt[torch]; sys_platform != 'darwin'", + "nvidia-modelopt[torch]>=0.43; sys_platform != 'darwin'", "nvidia-resiliency-ext", "tqdm", "einops~=0.8", @@ -161,7 +161,7 @@ docs = [ "nvidia-sphinx-theme", # Our NVIDIA theme ] build = [ - "setuptools<80.0.0,>=77.0.0", + "setuptools>=80", "packaging>=24.2", "hatchling", "pybind11", diff --git a/uv.lock b/uv.lock index f481725d106..d68d392a0f2 100644 --- a/uv.lock +++ b/uv.lock @@ -1735,7 +1735,8 @@ source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "grpcio" }, { name = "protobuf" }, - { name = "setuptools" }, + { name = "setuptools", version = "81.0.0", source = { registry = "https://pypi.org/simple" }, marker = "(sys_platform != 'emscripten' and sys_platform != 'win32' and extra == 'extra-13-megatron-core-dev') or (sys_platform == 'emscripten' and extra == 'extra-13-megatron-core-dev' and extra == 'extra-13-megatron-core-lts') or (sys_platform == 'win32' and extra == 'extra-13-megatron-core-dev' and extra == 'extra-13-megatron-core-lts')" }, + { name = "setuptools", version = "82.0.1", source = { registry = "https://pypi.org/simple" }, marker = "(sys_platform == 'emscripten' and extra == 'extra-13-megatron-core-dev') or (sys_platform == 'win32' and extra == 'extra-13-megatron-core-dev') or (extra == 'extra-13-megatron-core-dev' and extra == 'extra-13-megatron-core-lts')" }, ] sdist = { url = "https://files.pythonhosted.org/packages/94/c8/1223f29c84a143ae9a56c084fc96894de0ba84b6e8d60a26241abd81d278/grpcio_tools-1.80.0.tar.gz", hash = "sha256:26052b19c6ce0dcf52d1024496aea3e2bdfa864159f06dc7b97b22d041a94b26", size = 6133212, upload-time = "2026-03-30T08:52:39.077Z" } wheels = [ @@ -2429,7 +2430,8 @@ dependencies = [ { name = "packaging", version = "25.0", source = { registry = "https://pypi.org/simple" }, marker = "extra == 'extra-13-megatron-core-dev'" }, { name = "packaging", version = "26.2", source = { registry = "https://pypi.org/simple" }, marker = "extra == 'extra-13-megatron-core-lts' or extra != 'extra-13-megatron-core-dev'" }, { name = "quack-kernels" }, - { name = "setuptools" }, + { name = "setuptools", version = "81.0.0", source = { registry = "https://pypi.org/simple" }, marker = "(sys_platform != 'emscripten' and sys_platform != 'win32') or (sys_platform == 'emscripten' and extra == 'extra-13-megatron-core-dev' and extra == 'extra-13-megatron-core-lts') or (sys_platform == 'win32' and extra == 'extra-13-megatron-core-dev' and extra == 'extra-13-megatron-core-lts')" }, + { name = "setuptools", version = "82.0.1", source = { registry = "https://pypi.org/simple" }, marker = "sys_platform == 'emscripten' or sys_platform == 'win32' or (extra == 'extra-13-megatron-core-dev' and extra == 'extra-13-megatron-core-lts')" }, { name = "tilelang" }, { name = "torch", marker = "sys_platform == 'never' or (extra == 'extra-13-megatron-core-dev' and extra == 'extra-13-megatron-core-lts')" }, { name = "transformers" }, @@ -2661,7 +2663,8 @@ build = [ { name = "packaging", version = "25.0", source = { registry = "https://pypi.org/simple" }, marker = "extra == 'extra-13-megatron-core-dev'" }, { name = "packaging", version = "26.2", source = { registry = "https://pypi.org/simple" }, marker = "extra == 'extra-13-megatron-core-lts' or extra != 'extra-13-megatron-core-dev'" }, { name = "pybind11" }, - { name = "setuptools" }, + { name = "setuptools", version = "81.0.0", source = { registry = "https://pypi.org/simple" }, marker = "(sys_platform != 'emscripten' and sys_platform != 'win32') or (sys_platform == 'emscripten' and extra == 'extra-13-megatron-core-dev' and extra == 'extra-13-megatron-core-lts') or (sys_platform == 'win32' and extra == 'extra-13-megatron-core-dev' and extra == 'extra-13-megatron-core-lts')" }, + { name = "setuptools", version = "82.0.1", source = { registry = "https://pypi.org/simple" }, marker = "sys_platform == 'emscripten' or sys_platform == 'win32' or (extra == 'extra-13-megatron-core-dev' and extra == 'extra-13-megatron-core-lts')" }, { name = "torch", marker = "sys_platform == 'never' or (extra == 'extra-13-megatron-core-dev' and extra == 'extra-13-megatron-core-lts')" }, ] ci = [ @@ -2733,7 +2736,7 @@ requires-dist = [ { name = "multi-storage-client", marker = "extra == 'dev'", specifier = "~=0.27" }, { name = "multi-storage-client", marker = "extra == 'lts'", specifier = "~=0.27" }, { name = "numpy" }, - { name = "nvidia-modelopt", extras = ["torch"], marker = "sys_platform != 'darwin' and extra == 'dev'" }, + { name = "nvidia-modelopt", extras = ["torch"], marker = "sys_platform != 'darwin' and extra == 'dev'", specifier = ">=0.43" }, { name = "nvidia-resiliency-ext", marker = "extra == 'dev'", git = "https://github.com/NVIDIA/nvidia-resiliency-ext.git?rev=b2bb3d728a18795807d9f76c535e005a609a1b01" }, { name = "onnxscript", marker = "extra == 'dev'" }, { name = "onnxscript", marker = "extra == 'lts'" }, @@ -2769,7 +2772,7 @@ build = [ { name = "nvidia-mathdx" }, { name = "packaging", specifier = ">=24.2" }, { name = "pybind11" }, - { name = "setuptools", specifier = ">=77.0.0,<80.0.0" }, + { name = "setuptools", specifier = ">=80" }, { name = "torch" }, ] ci = [ @@ -3588,24 +3591,28 @@ wheels = [ [[package]] name = "nvidia-modelopt" -version = "0.42.0" +version = "0.43.0" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "ninja" }, { name = "numpy", version = "2.0.2", source = { registry = "https://pypi.org/simple" } }, { name = "nvidia-ml-py" }, + { name = "omegaconf" }, { name = "packaging", version = "25.0", source = { registry = "https://pypi.org/simple" } }, { name = "pulp" }, { name = "pydantic" }, + { name = "pyyaml" }, { name = "regex" }, { name = "rich" }, { name = "safetensors" }, { name = "scipy" }, + { name = "setuptools", version = "81.0.0", source = { registry = "https://pypi.org/simple" }, marker = "(sys_platform != 'emscripten' and sys_platform != 'win32' and extra == 'extra-13-megatron-core-dev') or (sys_platform == 'emscripten' and extra == 'extra-13-megatron-core-dev' and extra == 'extra-13-megatron-core-lts') or (sys_platform == 'win32' and extra == 'extra-13-megatron-core-dev' and extra == 'extra-13-megatron-core-lts')" }, + { name = "setuptools", version = "82.0.1", source = { registry = "https://pypi.org/simple" }, marker = "(sys_platform == 'emscripten' and extra == 'extra-13-megatron-core-dev') or (sys_platform == 'win32' and extra == 'extra-13-megatron-core-dev') or (extra == 'extra-13-megatron-core-dev' and extra == 'extra-13-megatron-core-lts')" }, { name = "torch", marker = "sys_platform == 'never'" }, { name = "tqdm" }, ] wheels = [ - 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{ name = "setuptools", marker = "sys_platform == 'darwin' or (extra == 'extra-13-megatron-core-dev' and extra == 'extra-13-megatron-core-lts')" }, + { name = "setuptools", version = "81.0.0", source = { registry = "https://pypi.org/simple" }, marker = "sys_platform == 'darwin' or (extra == 'extra-13-megatron-core-dev' and extra == 'extra-13-megatron-core-lts')" }, { name = "torch", marker = "sys_platform == 'never' or (extra == 'extra-13-megatron-core-dev' and extra == 'extra-13-megatron-core-lts')" }, { name = "torch-c-dlpack-ext", marker = "python_full_version < '3.14' or (extra == 'extra-13-megatron-core-dev' and extra == 'extra-13-megatron-core-lts')" }, { name = "tqdm" }, @@ -6620,7 +6659,7 @@ dependencies = [ { name = "nvidia-cusparselt-cu13", marker = "sys_platform == 'linux'" }, { name = "nvidia-nccl-cu13", marker = "sys_platform == 'linux'" }, { name = "nvidia-nvshmem-cu13", marker = "sys_platform == 'linux'" }, - { name = "setuptools", marker = "sys_platform != 'emscripten' and sys_platform != 'win32'" }, + { name = "setuptools", version = "81.0.0", source = { registry = "https://pypi.org/simple" }, marker = "sys_platform != 'emscripten' and sys_platform != 'win32'" }, { name = "sympy", marker = "sys_platform != 'emscripten' and sys_platform != 'win32'" }, { name = "triton", marker = "sys_platform == 'never'" }, { name = "typing-extensions", marker = "sys_platform != 'emscripten' and sys_platform != 'win32'" }, From 55c13faaa9db25c5df450572d24fec0d5577aff6 Mon Sep 17 00:00:00 2001 From: svcnvidia-nemo-ci Date: Tue, 12 May 2026 01:57:50 +0000 Subject: [PATCH 9/9] fix: restore dev-only changes lost by main-priority merge The merge with -X theirs took main's version for many files where dev had follow-up changes after main's squash-merge. The most common pattern was main returning partial(...) while dev returns ModuleSpec(...). Restored dev's version of: - megatron/core/extensions/transformer_engine.py (TEFusedDenseMLP class) - megatron/core/models/gpt/gpt_layer_specs.py (uses TEFusedDenseMLP) - megatron/core/models/gpt/moe_module_specs.py (returns ModuleSpec) - megatron/core/models/gpt/experimental_attention_variant_module_specs.py - megatron/core/models/gpt/heterogeneous/heterogeneous_layer_specs.py - megatron/core/models/T5/t5_spec.py - megatron/core/models/bert/bert_layer_specs.py - megatron/core/models/hybrid/hybrid_layer_specs.py - megatron/core/models/vision/vit_layer_specs.py - megatron/core/post_training/modelopt/hybrid/model_specs.py - megatron/core/transformer/mlp.py - megatron/core/transformer/moe/experts.py - megatron/core/transformer/moe/fused_a2a.py - megatron/core/transformer/moe/moe_layer.py - megatron/core/transformer/transformer_layer.py (mlp_hyper_connection field) - examples/multimodal/layer_specs.py - pretrain_vlm.py - Several tests/unit_tests/transformer/**/*.py - Several tests/unit_tests/models/**/*.py - tests/unit_tests/dist_checkpointing/models/test_mlp_glu.py - tests/functional_tests/test_cases/common/moe_perf/__main__.py --- examples/multimodal/layer_specs.py | 18 +- .../core/extensions/transformer_engine.py | 207 ++++++++++++++++-- megatron/core/models/T5/t5_spec.py | 18 +- megatron/core/models/bert/bert_layer_specs.py | 9 +- ...rimental_attention_variant_module_specs.py | 63 ++---- megatron/core/models/gpt/gpt_layer_specs.py | 21 +- .../heterogeneous_layer_specs.py | 18 +- megatron/core/models/gpt/moe_module_specs.py | 20 +- .../core/models/hybrid/hybrid_layer_specs.py | 9 +- .../core/models/vision/vit_layer_specs.py | 7 +- .../modelopt/hybrid/model_specs.py | 5 +- megatron/core/transformer/mlp.py | 26 --- megatron/core/transformer/moe/experts.py | 2 +- megatron/core/transformer/moe/fused_a2a.py | 78 ++----- megatron/core/transformer/moe/moe_layer.py | 3 +- .../core/transformer/transformer_layer.py | 100 ++++----- pretrain_vlm.py | 8 +- .../test_cases/common/moe_perf/__main__.py | 13 +- .../dist_checkpointing/models/test_mlp_glu.py | 17 +- .../test_grad_sync_with_expert_parallel.py | 15 +- .../test_vlm_text_generation_controller.py | 6 +- tests/unit_tests/models/test_llava_model.py | 10 +- .../models/test_multimodal_projector.py | 2 +- tests/unit_tests/test_utils.py | 14 +- .../transformer/moe/test_grouped_mlp.py | 17 +- .../transformer/moe/test_latent_moe_layer.py | 21 +- .../transformer/moe/test_moe_layer.py | 52 ++--- .../transformer/moe/test_routers.py | 43 ++-- .../transformer/moe/test_sequential_mlp.py | 12 +- .../transformer/moe/test_shared_experts.py | 12 +- .../transformer/moe/test_token_dispatcher.py | 20 +- .../transformer/test_cuda_graphs.py | 17 +- tests/unit_tests/transformer/test_mlp.py | 7 +- .../test_transformer_block_custom_pgs.py | 41 ++-- .../transformer/test_vision_cuda_graphs.py | 28 +-- 35 files changed, 481 insertions(+), 478 deletions(-) diff --git a/examples/multimodal/layer_specs.py b/examples/multimodal/layer_specs.py index 8205db90ddc..c51fb69f496 100644 --- a/examples/multimodal/layer_specs.py +++ b/examples/multimodal/layer_specs.py @@ -1,6 +1,4 @@ # Copyright (c) 2024-2026, NVIDIA CORPORATION. All rights reserved. -from functools import partial - import torch from megatron.core.extensions.transformer_engine import HAVE_TE @@ -172,8 +170,8 @@ def get_hybrid_layer_spec_te(padding=False) -> ModuleSpec: mlp_layer=ModuleSpec( module=MLPLayer, submodules=TransformerLayerSubmodules( - mlp=partial( - MLP.as_mlp_submodule, + mlp=ModuleSpec( + module=MLP, submodules=MLPSubmodules( linear_fc1=not_none(TELayerNormColumnParallelLinear), linear_fc2=not_none(TERowParallelLinear), @@ -186,10 +184,10 @@ def get_hybrid_layer_spec_te(padding=False) -> ModuleSpec: ) -def get_mlp_module_spec(use_te: bool = True): +def get_mlp_module_spec(use_te: bool = True) -> ModuleSpec: # Dense MLP w/ or w/o TE modules. - return partial( - MLP.as_mlp_submodule, + return ModuleSpec( + module=MLP, submodules=MLPSubmodules( linear_fc1=not_none(TEColumnParallelLinear) if use_te else ColumnParallelLinear, linear_fc2=not_none(TERowParallelLinear) if use_te else RowParallelLinear, @@ -197,9 +195,9 @@ def get_mlp_module_spec(use_te: bool = True): ) -def get_norm_mlp_module_spec_te(): - return partial( - MLP.as_mlp_submodule, +def get_norm_mlp_module_spec_te() -> ModuleSpec: + return ModuleSpec( + module=MLP, submodules=MLPSubmodules( linear_fc1=not_none(TELayerNormColumnParallelLinear), linear_fc2=not_none(TERowParallelLinear), diff --git a/megatron/core/extensions/transformer_engine.py b/megatron/core/extensions/transformer_engine.py index fb452b20d7f..c6a8653c4ea 100644 --- a/megatron/core/extensions/transformer_engine.py +++ b/megatron/core/extensions/transformer_engine.py @@ -43,7 +43,7 @@ ) from megatron.core.tensor_parallel.utils import divide from megatron.core.transformer.enums import AttnMaskType -from megatron.core.transformer.mlp import MLP, MLPSubmodules +from megatron.core.transformer.mlp import MLP from megatron.core.transformer.torch_norm import LayerNormInterface from megatron.core.transformer.transformer_config import TransformerConfig from megatron.core.transformer.utils import ( @@ -2527,30 +2527,189 @@ def forward(self, hidden_states: torch.Tensor, **kwargs) -> Tuple[Tensor, Option return out, bias - @classmethod - def as_mlp_submodule( - cls, - submodules: MLPSubmodules, - config: TransformerConfig, - pg_collection: ProcessGroupCollection, - is_mtp_layer: bool, - is_expert: bool = False, - input_size: int | None = None, - ffn_hidden_size: int | None = None, - ) -> MLP: - """Helper function to build an MLP as a TransformerLayer's mlp submodule.""" - del is_mtp_layer - assert hasattr( - pg_collection, 'tp' - ), 'TP process group is required for TEFusedMLP in TransformerLayer' - return cls( - config=config, - submodules=submodules, - tp_group=pg_collection.tp, - is_expert=is_expert, - input_size=input_size, - ffn_hidden_size=ffn_hidden_size, + class TEFusedDenseMLP(TEFusedMLP): + """Dense MLP using GroupedLinear(num_groups=1) to trigger + ForwardGroupedMLP_CuTeGEMMSwiGLU_MXFP8 fusion on SM100+ with MXFP8 recipe. + + Subclass of TEFusedMLP -> does not modify TEFusedMLP or TEGroupedMLP. + The fused kernel fires automatically via the TE op fuser when it detects + the GroupedLinear -> ScaledSwiGLU -> GroupedLinear pattern with MXFP8 recipe. + """ + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self._norm_seq: Optional[Tuple[te.pytorch.ops.Sequential]] = None + if not is_te_min_version("2.14.0"): + raise RuntimeError( + f"{self.__class__.__name__} requires Transformer Engine >= 2.14.0 " + "(needs pytorch.ops.GroupedLinear and pytorch.ops.ScaledSwiGLU)" + ) + if self.config.add_bias_linear: + raise ValueError( + f"{self.__class__.__name__} does not support add_bias_linear=True; " + "the CuTeGEMM fused kernel requires bias-free linear layers." + ) + if self.config.activation_func != F.silu or not self.config.gated_linear_unit: + raise ValueError( + f"{self.__class__.__name__} requires SwiGLU activation " + "(activation_func=F.silu, gated_linear_unit=True) " + "for the CuTeGEMM fused kernel, but got " + f"activation_func={self.config.activation_func}, " + f"gated_linear_unit={self.config.gated_linear_unit}." + ) + + def _make_fused_impl(self) -> te.pytorch.ops.Sequential: + """Construct fused module with GroupedLinear(num_groups=1) + ScaledSwiGLU.""" + + fused_impl = te.pytorch.ops.Sequential() + + # Tensor parallelism configuration + tp_world_size = get_tensor_model_parallel_world_size() + tp_group = None + if tp_world_size > 1: + tp_group = get_tensor_model_parallel_group() + + # RNG state + rng_state_tracker_function = None + if get_cuda_rng_tracker().is_initialized(): + rng_state_tracker_function = get_cuda_rng_tracker + + # Check submodule types (same as TEFusedMLP) + if not isinstance(self.linear_fc1, te.pytorch.LayerNormLinear): + raise ValueError( + f"{self.__class__.__name__} expects FC1 to be " + "Transformer Engine LayerNormLinear, but found " + f"{self.linear_fc1.__class__.__name__}." + ) + if not isinstance(self.linear_fc2, te.pytorch.Linear): + raise ValueError( + f"{self.__class__.__name__} expects FC2 to be " + "Transformer Engine Linear, but found " + f"{self.linear_fc2.__class__.__name__}." + ) + + # Norm op (same as TEFusedMLP) + norm_type = self.linear_fc1.normalization + norm_shape = self.linear_fc1.weight.size(1) + kwargs = { + "eps": self.linear_fc1.eps, + "device": "meta", + "dtype": self.linear_fc1.layer_norm_weight.dtype, + "zero_centered_gamma": self.linear_fc1.zero_centered_gamma, + } + op = None + if norm_type == "LayerNorm": + op = te.pytorch.ops.LayerNorm(norm_shape, **kwargs) + op.weight = self.linear_fc1.layer_norm_weight + op.bias = self.linear_fc1.layer_norm_bias + elif norm_type == "RMSNorm": + op = te.pytorch.ops.RMSNorm(norm_shape, **kwargs) + op.weight = self.linear_fc1.layer_norm_weight + else: + raise ValueError(f"Unsupported normalization ({norm_type})") + # Store norm in a separate Sequential applied OUTSIDE the MXFP8 autocast + # in forward(). Running norm inside MXFP8 context corrupts the saved rstd + # used in RMSNorm backward, causing gradient amplification up to 10^6. + # Wrapped in tuple to avoid nn.Module submodule registration (which would + # duplicate the shared norm weight in state_dict/parameters). + norm_seq = te.pytorch.ops.Sequential() + norm_seq.append(op) + self._norm_seq = (norm_seq,) + + # GLU interleave size must match ScaledSwiGLU and the CuTe kernel. + _GLU_INTERLEAVE_SIZE = 32 + + # FC1: GroupedLinear(num_groups=1) instead of BasicLinear + weight = self.linear_fc1.weight + op = te.pytorch.ops.GroupedLinear( + num_groups=1, + in_features=weight.size(1), + out_features=weight.size(0) * tp_world_size, + device="meta", + dtype=weight.dtype, + bias=False, + rng_state_tracker_function=rng_state_tracker_function, + accumulate_into_main_grad=self.linear_fc1.fuse_wgrad_accumulation, ) + op.weight0 = weight + op._glu_interleave_size = _GLU_INTERLEAVE_SIZE # signals fuser_forward to interleave + fused_impl.append(op) + + # ScaledSwiGLU with glu_interleave_size=32 + # Required by ForwardGroupedMLP_CuTeGEMMSwiGLU_MXFP8 + fused_impl.append(te.pytorch.ops.ScaledSwiGLU(glu_interleave_size=32)) + + # FC2: GroupedLinear(num_groups=1) instead of BasicLinear + weight = self.linear_fc2.weight + op = te.pytorch.ops.GroupedLinear( + num_groups=1, + in_features=weight.size(1), + out_features=weight.size(0), + device="meta", + dtype=weight.dtype, + bias=False, + rng_state_tracker_function=rng_state_tracker_function, + accumulate_into_main_grad=self.linear_fc2.fuse_wgrad_accumulation, + ) + op.weight0 = weight + # FC2 has no SwiGLU — MXFP8 quantization done on-the-fly in fuser_forward. + # No _mxfp8_weight0 pre-computation to avoid ~28 GB persistent FP8 tensors. + fused_impl.append(op) + + if tp_world_size > 1: + if self.linear_fc2.sequence_parallel: + fused_impl.append(te.pytorch.ops.ReduceScatter(tp_group)) + else: + fused_impl.append(te.pytorch.ops.AllReduce(tp_group)) + + self._register_hooks_on_fused_impl(fused_impl) + return fused_impl + + def forward(self, hidden_states: torch.Tensor, **kwargs) -> Tuple[Tensor, Optional[Tensor]]: + """Forward pass using GroupedLinear(num_groups=1) + ScaledSwiGLU.""" + + orig_shape = hidden_states.shape + hidden_size = hidden_states.size(-1) + hidden_states_2d = hidden_states.view(-1, hidden_size) + total_tokens = hidden_states_2d.size(0) + + tokens_per_expert = torch.full( + (1,), total_tokens, dtype=torch.long, device=hidden_states.device + ) + scales = torch.ones( + total_tokens, device=hidden_states.device, dtype=hidden_states.dtype + ) + + # Build fused impl and cache recipe lazily on first forward pass. + # Both are created once and reused — avoids object creation every call. + if not hasattr(self, '_recipe'): + if os.getenv("FP4_RECIPE", "") == "nvfp4": + self._recipe = te.common.recipe.NVFP4BlockScaling() + else: + self._recipe = te.common.recipe.MXFP8BlockScaling() + recipe = self._recipe + + if self._fused_impl is None: + with te.pytorch.fp8_autocast(enabled=True, fp8_recipe=recipe): + self._fused_impl = (self._make_fused_impl(),) + + # Apply norm in BF16 OUTSIDE the MXFP8 autocast to preserve the rstd + # tensor used by RMSNorm backward (running it inside causes up to 10^6 + # gradient amplification, and causes convergence issues). + normed = self._norm_seq[0](hidden_states_2d) + + with te.pytorch.fp8_autocast(enabled=True, fp8_recipe=recipe): + out = self._fused_impl[0](normed, tokens_per_expert, scales, tokens_per_expert) + + out = out.view(*orig_shape[:-1], out.size(-1)) + + bias = None + if self.linear_fc2.te_return_bias: + bias = self.linear_fc2.bias + if isinstance(bias, torch.Tensor) and bias.numel() == 0: + bias = None + + return out, bias else: TEFusedMLP = None # type: ignore[assignment, misc] diff --git a/megatron/core/models/T5/t5_spec.py b/megatron/core/models/T5/t5_spec.py index 5bbe49c337b..9f465df5c21 100644 --- a/megatron/core/models/T5/t5_spec.py +++ b/megatron/core/models/T5/t5_spec.py @@ -1,6 +1,4 @@ # Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. -from functools import partial - from megatron.core.extensions.transformer_engine import HAVE_TE from megatron.core.fusions.fused_bias_dropout import get_bias_dropout_add from megatron.core.tensor_parallel.layers import ColumnParallelLinear, RowParallelLinear @@ -71,8 +69,8 @@ def encoder_model_with_transformer_engine_default_spec() -> ModuleSpec: ), ), self_attn_bda=get_bias_dropout_add, - mlp=partial( - MLP.as_mlp_submodule, + mlp=ModuleSpec( + module=MLP, submodules=MLPSubmodules( linear_fc1=not_none(TELayerNormColumnParallelLinear), linear_fc2=not_none(TERowParallelLinear), @@ -113,8 +111,8 @@ def decoder_model_with_transformer_engine_default_spec() -> ModuleSpec: ), ), cross_attn_bda=get_bias_dropout_add, - mlp=partial( - MLP.as_mlp_submodule, + mlp=ModuleSpec( + module=MLP, submodules=MLPSubmodules( linear_fc1=not_none(TELayerNormColumnParallelLinear), linear_fc2=not_none(TERowParallelLinear), @@ -145,8 +143,8 @@ def encoder_model_with_local_spec() -> ModuleSpec: ), self_attn_bda=get_bias_dropout_add, pre_mlp_layernorm=LNImpl, - mlp=partial( - MLP.as_mlp_submodule, + mlp=ModuleSpec( + module=MLP, submodules=MLPSubmodules( linear_fc1=ColumnParallelLinear, linear_fc2=RowParallelLinear ), @@ -192,8 +190,8 @@ def decoder_model_with_local_spec() -> ModuleSpec: ), cross_attn_bda=get_bias_dropout_add, pre_mlp_layernorm=LNImpl, - mlp=partial( - MLP.as_mlp_submodule, + mlp=ModuleSpec( + module=MLP, submodules=MLPSubmodules( linear_fc1=ColumnParallelLinear, linear_fc2=RowParallelLinear ), diff --git a/megatron/core/models/bert/bert_layer_specs.py b/megatron/core/models/bert/bert_layer_specs.py index dc0099fa66e..53cc0f4280d 100644 --- a/megatron/core/models/bert/bert_layer_specs.py +++ b/megatron/core/models/bert/bert_layer_specs.py @@ -1,6 +1,5 @@ # Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. import warnings -from functools import partial from megatron.core.extensions.transformer_engine import HAVE_TE from megatron.core.fusions.fused_bias_dropout import get_bias_dropout_add @@ -67,8 +66,8 @@ def get_bert_layer_with_transformer_engine_submodules() -> TransformerLayerSubmo ), ), self_attn_bda=get_bias_dropout_add, - mlp=partial( - MLP.as_mlp_submodule, + mlp=ModuleSpec( + module=MLP, submodules=MLPSubmodules( linear_fc1=not_none(TELayerNormColumnParallelLinear), linear_fc2=not_none(TERowParallelLinear), @@ -118,8 +117,8 @@ def __getattr__(name): ), self_attn_bda=get_bias_dropout_add, pre_mlp_layernorm=LNImpl, - mlp=partial( - MLP.as_mlp_submodule, + mlp=ModuleSpec( + module=MLP, submodules=MLPSubmodules(linear_fc1=ColumnParallelLinear, linear_fc2=RowParallelLinear), ), mlp_bda=get_bias_dropout_add, diff --git a/megatron/core/models/gpt/experimental_attention_variant_module_specs.py b/megatron/core/models/gpt/experimental_attention_variant_module_specs.py index 3e63f81b0d8..329b8f259ea 100644 --- a/megatron/core/models/gpt/experimental_attention_variant_module_specs.py +++ b/megatron/core/models/gpt/experimental_attention_variant_module_specs.py @@ -37,12 +37,11 @@ ) from megatron.core.transformer.transformer_config import TransformerConfig from megatron.core.transformer.transformer_layer import ( - MlpBuilder, + HyperConnectionTransformerLayer, TransformerLayer, TransformerLayerSubmodules, get_transformer_layer_offset, ) -from megatron.core.typed_torch import not_none try: import transformer_engine as te # type: ignore[import-untyped] # pylint: disable=unused-import @@ -284,18 +283,14 @@ def get_transformer_layer_with_experimental_attention_variant_spec( moe_layer_pattern = [0] * config.num_layers if 1 in moe_layer_pattern: - moe_layer_spec, fuse_layernorm_pre_moe = _get_moe_module_spec( - config=config, backend=backend - ) + moe_layer_spec = _get_moe_module_spec(config=config, backend=backend) else: - moe_layer_spec, fuse_layernorm_pre_moe = None, False + moe_layer_spec = None if 0 in moe_layer_pattern: - dense_mlp_layer_spec, fuse_layernorm_pre_dense = _get_dense_mlp_module_spec( - config=config, backend=backend - ) + dense_mlp_layer_spec = _get_dense_mlp_module_spec(config=config, backend=backend) else: - dense_mlp_layer_spec, fuse_layernorm_pre_dense = None, False + dense_mlp_layer_spec = None # Get GPT decoder block layer specs rms_norm = config.normalization == "RMSNorm" @@ -311,11 +306,6 @@ def get_transformer_layer_with_experimental_attention_variant_spec( else standard_attention_spec ) mlp = moe_layer_spec if moe_layer_pattern[layer_number] == 1 else dense_mlp_layer_spec - fuse_pre_mlp_layernorm = ( - fuse_layernorm_pre_moe - if moe_layer_pattern[layer_number] == 1 - else fuse_layernorm_pre_dense - ) input_layernorm = ( IdentityOp if attention.metainfo["fuse_input_layernorm"] @@ -323,7 +313,7 @@ def get_transformer_layer_with_experimental_attention_variant_spec( ) pre_mlp_layernorm = ( IdentityOp - if fuse_pre_mlp_layernorm + if mlp.metainfo["fuse_pre_mlp_layernorm"] else backend.layer_norm(rms_norm=rms_norm, for_qk=False) ) @@ -336,7 +326,7 @@ def get_transformer_layer_with_experimental_attention_variant_spec( self_attn_bda=get_bias_dropout_add, self_attention_hyper_connection=hc_module, pre_mlp_layernorm=pre_mlp_layernorm, - mlp=not_none(mlp), + mlp=mlp, mlp_bda=get_bias_dropout_add, mlp_hyper_connection=hc_module, ), @@ -534,50 +524,41 @@ def _get_self_attention_module_spec( def _get_dense_mlp_module_spec( config: TransformerConfig, backend: BackendSpecProvider = None -) -> tuple[MlpBuilder, bool]: +) -> ModuleSpec: """Get dense MLP module spec. For hybrid models that mix dense MLP and experimental attention architectures. - Warning: This function may be deprecated in the future. - - Returns: - A tuple of (MLP module spec, whether to fuse pre-MLP layernorm) - """ + Warning: This function may be deprecated in the future.""" if backend is None: backend = _get_backend_spec_provider(config=config) from megatron.core.models.gpt.gpt_layer_specs import get_mlp_module_spec_for_backend - return ( - get_mlp_module_spec_for_backend(backend=backend, num_experts=None), - backend.fuse_layernorm_and_linear(), - ) + mlp_spec = get_mlp_module_spec_for_backend(backend=backend, num_experts=None) + mlp_spec.metainfo["fuse_pre_mlp_layernorm"] = backend.fuse_layernorm_and_linear() + + return mlp_spec def _get_moe_module_spec( config: TransformerConfig, backend: BackendSpecProvider = None -) -> tuple[MlpBuilder, bool]: +) -> ModuleSpec: """Get MoE module spec. For hybrid models that mix MoE and experimental attention architectures. - Warning: This function may be deprecated in the future. - - Returns: - A tuple of (MoE module spec, whether to fuse pre-MoE layernorm) - """ + Warning: This function may be deprecated in the future.""" if backend is None: backend = _get_backend_spec_provider(config=config) from megatron.core.models.gpt.moe_module_specs import get_moe_module_spec_for_backend - return ( - get_moe_module_spec_for_backend( - backend=backend, - num_experts=config.num_moe_experts, - moe_grouped_gemm=config.moe_grouped_gemm, - use_te_activation_func=config.use_te_activation_func, - ), - False, + moe_spec = get_moe_module_spec_for_backend( + backend=backend, + num_experts=config.num_moe_experts, + moe_grouped_gemm=config.moe_grouped_gemm, + use_te_activation_func=config.use_te_activation_func, ) + moe_spec.metainfo["fuse_pre_mlp_layernorm"] = False + return moe_spec diff --git a/megatron/core/models/gpt/gpt_layer_specs.py b/megatron/core/models/gpt/gpt_layer_specs.py index 4419bbd1f08..8636929c062 100755 --- a/megatron/core/models/gpt/gpt_layer_specs.py +++ b/megatron/core/models/gpt/gpt_layer_specs.py @@ -1,7 +1,6 @@ # Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. import copy import warnings -from functools import partial from typing import Optional, Union from megatron.core.extensions.transformer_engine import HAVE_TE @@ -37,12 +36,12 @@ ) from megatron.core.transformer.transformer_config import TransformerConfig from megatron.core.transformer.transformer_layer import ( - MlpBuilder, + HyperConnectionTransformerLayer, TransformerLayer, TransformerLayerSubmodules, get_transformer_layer_offset, ) -from megatron.core.typed_torch import copy_signature, not_none +from megatron.core.typed_torch import copy_signature from megatron.core.utils import is_te_min_version if HAVE_TE: @@ -514,7 +513,7 @@ def get_mlp_module_spec( moe_grouped_gemm: Optional[bool] = False, fp8: Optional[str] = None, # pylint: disable=unused-argument use_te_op_fuser: Optional[bool] = False, -) -> MlpBuilder: +) -> ModuleSpec: """Helper function to get module spec for MLP/MoE""" if fp8 is not None: warnings.warn( @@ -545,7 +544,8 @@ def get_mlp_module_spec_for_backend( moe_grouped_gemm: Optional[bool] = False, use_te_op_fuser: Optional[bool] = False, use_te_activation_func: bool = False, -) -> MlpBuilder: + dense_grouped_gemm: bool = False, +) -> ModuleSpec: """Helper function to get module spec for MLP/MoE""" linear_fc2 = backend.row_parallel_linear() @@ -553,14 +553,19 @@ def get_mlp_module_spec_for_backend( if num_experts is None: # Dense MLP w/ or w/o TE modules. - module = not_none(TEFusedMLP).as_mlp_submodule if use_te_op_fuser else MLP.as_mlp_submodule + if dense_grouped_gemm and use_te_op_fuser: + module = TEFusedDenseMLP + elif use_te_op_fuser: + module = TEFusedMLP + else: + module = MLP if backend.fuse_layernorm_and_linear(): linear_fc1 = backend.column_parallel_layer_norm_linear() assert linear_fc1 is not None else: linear_fc1 = backend.column_parallel_linear() - return partial( - module, + return ModuleSpec( + module=module, submodules=MLPSubmodules( linear_fc1=linear_fc1, linear_fc2=linear_fc2, activation_func=activation_func ), diff --git a/megatron/core/models/gpt/heterogeneous/heterogeneous_layer_specs.py b/megatron/core/models/gpt/heterogeneous/heterogeneous_layer_specs.py index a7ecd9e9658..f4385429422 100644 --- a/megatron/core/models/gpt/heterogeneous/heterogeneous_layer_specs.py +++ b/megatron/core/models/gpt/heterogeneous/heterogeneous_layer_specs.py @@ -1,7 +1,6 @@ # Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. import warnings -from functools import partial from typing import Optional from megatron.core.extensions.transformer_engine import HAVE_TE @@ -129,19 +128,17 @@ def _get_heterogenous_attention_spec( def _get_heterogenous_mlp_spec(mlp_config: MLPConfig, use_te: bool): if mlp_config.no_op: - return IdentityOp + mlp = ModuleSpec(module=IdentityOp) elif mlp_config.replace_with_linear: - return partial( - ( - not_none(TELayerNormColumnParallelLinearGathered) - if use_te - else ColumnParallelLinearGathered + mlp = ModuleSpec( + module=( + TELayerNormColumnParallelLinearGathered if use_te else ColumnParallelLinearGathered ), - tp_comm_buffer_name="linear_mlp", + params={"tp_comm_buffer_name": "linear_mlp"}, ) else: - return partial( - MLP.as_mlp_submodule, + mlp = ModuleSpec( + module=MLP, submodules=MLPSubmodules( linear_fc1=( not_none(TELayerNormColumnParallelLinear) if use_te else ColumnParallelLinear @@ -149,6 +146,7 @@ def _get_heterogenous_mlp_spec(mlp_config: MLPConfig, use_te: bool): linear_fc2=not_none(TERowParallelLinear) if use_te else RowParallelLinear, ), ) + return mlp def _get_sharded_state_dict_keys_map(block_config: TransformerBlockConfig, use_te: bool): diff --git a/megatron/core/models/gpt/moe_module_specs.py b/megatron/core/models/gpt/moe_module_specs.py index e9a86ff3bad..e82b6638f0b 100755 --- a/megatron/core/models/gpt/moe_module_specs.py +++ b/megatron/core/models/gpt/moe_module_specs.py @@ -13,14 +13,14 @@ from megatron.core.transformer.moe.moe_layer import MoELayer, MoESubmodules from megatron.core.transformer.moe.router import InferenceTopKRouter from megatron.core.transformer.moe.shared_experts import SharedExpertMLP -from megatron.core.transformer.transformer_layer import MlpBuilder +from megatron.core.transformer.spec_utils import ModuleSpec def get_moe_module_spec( use_te: Optional[bool] = True, num_experts: Optional[int] = None, moe_grouped_gemm: Optional[bool] = False, -) -> MlpBuilder: +) -> ModuleSpec: """Helper function to get module spec for MoE. Called by hybrid_layer_specs.py for standard (non-inference) MoE specs. @@ -46,7 +46,7 @@ def get_moe_module_spec_for_backend( num_experts: Optional[int] = None, moe_grouped_gemm: Optional[bool] = False, use_te_activation_func: bool = False, -) -> MlpBuilder: +) -> ModuleSpec: """Helper function to get module spec for MoE""" assert num_experts is not None @@ -63,12 +63,15 @@ def get_moe_module_spec_for_backend( shared_experts = partial(SharedExpertMLP, submodules=mlp) # MoE module spec - return partial( - MoELayer, submodules=MoESubmodules(experts=experts, shared_experts=shared_experts) + moe_module_spec = ModuleSpec( + module=MoELayer, + submodules=MoESubmodules(experts=experts, shared_experts=shared_experts), + metainfo={"fuse_pre_mlp_layernorm": False}, ) + return moe_module_spec -def get_inference_optimized_moe_spec() -> MlpBuilder: +def get_inference_optimized_moe_spec() -> ModuleSpec: """MoE module spec for inference-optimized transformer impl. Uses InferenceSpecProvider to select inference-optimized modules: @@ -90,9 +93,10 @@ def get_inference_optimized_moe_spec() -> MlpBuilder: ), ) - return partial( - MoELayer, + return ModuleSpec( + module=MoELayer, submodules=MoESubmodules( router=InferenceTopKRouter, experts=experts, shared_experts=shared_experts ), + metainfo={"fuse_pre_mlp_layernorm": False}, ) diff --git a/megatron/core/models/hybrid/hybrid_layer_specs.py b/megatron/core/models/hybrid/hybrid_layer_specs.py index 5b968f720c0..a34a45a32ba 100755 --- a/megatron/core/models/hybrid/hybrid_layer_specs.py +++ b/megatron/core/models/hybrid/hybrid_layer_specs.py @@ -1,5 +1,4 @@ # Copyright (c) 2023-2026, NVIDIA CORPORATION. All rights reserved. -from functools import partial from megatron.core.extensions.transformer_engine import ( TEColumnParallelLinear, @@ -171,8 +170,8 @@ mlp_layer=ModuleSpec( module=MLPLayer, submodules=TransformerLayerSubmodules( - mlp=partial( - MLP.as_mlp_submodule, + mlp=ModuleSpec( + module=MLP, submodules=MLPSubmodules( linear_fc1=TELayerNormColumnParallelLinear, linear_fc2=TERowParallelLinear ), @@ -266,8 +265,8 @@ mlp_layer=ModuleSpec( module=MLPLayer, submodules=TransformerLayerSubmodules( - mlp=partial( - MLP.as_mlp_submodule, + mlp=ModuleSpec( + module=MLP, submodules=MLPSubmodules( linear_fc1=InferenceLayerNormColumnParallelLinear, linear_fc2=InferenceRowParallelLinear, diff --git a/megatron/core/models/vision/vit_layer_specs.py b/megatron/core/models/vision/vit_layer_specs.py index 71c78ad3df4..51074b3836e 100644 --- a/megatron/core/models/vision/vit_layer_specs.py +++ b/megatron/core/models/vision/vit_layer_specs.py @@ -1,5 +1,4 @@ # Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. -from functools import partial from megatron.core.extensions.transformer_engine import ( TEDotProductAttention, @@ -86,10 +85,10 @@ def get_vit_layer_with_local_spec() -> ModuleSpec: # Helper function to get module spec for MLP/MoE -def _get_mlp_module_spec(use_te: bool = True): +def _get_mlp_module_spec(use_te: bool = True) -> ModuleSpec: # Dense MLP w/ or w/o TE modules. - return partial( - MLP.as_mlp_submodule, + return ModuleSpec( + module=MLP, submodules=MLPSubmodules( linear_fc1=TELayerNormColumnParallelLinear if use_te else ColumnParallelLinear, linear_fc2=TERowParallelLinear if use_te else RowParallelLinear, diff --git a/megatron/core/post_training/modelopt/hybrid/model_specs.py b/megatron/core/post_training/modelopt/hybrid/model_specs.py index 6bca306061d..59a509be718 100755 --- a/megatron/core/post_training/modelopt/hybrid/model_specs.py +++ b/megatron/core/post_training/modelopt/hybrid/model_specs.py @@ -1,5 +1,4 @@ # Copyright (c) 2025-2026, NVIDIA CORPORATION. All rights reserved. -from functools import partial from megatron.core.extensions.transformer_engine import TEDotProductAttention from megatron.core.fusions.fused_bias_dropout import get_bias_dropout_add @@ -113,8 +112,8 @@ def _get_hybrid_stack_local_spec( module=TransformerLayer, submodules=TransformerLayerSubmodules( pre_mlp_layernorm=Norm, - mlp=partial( - MLP.as_mlp_submodule, + mlp=ModuleSpec( + module=MLP, submodules=MLPSubmodules( linear_fc1=ColumnParallelLinear, linear_fc2=RowParallelLinear ), diff --git a/megatron/core/transformer/mlp.py b/megatron/core/transformer/mlp.py index dfc708bb350..ed288c3e1f4 100644 --- a/megatron/core/transformer/mlp.py +++ b/megatron/core/transformer/mlp.py @@ -23,7 +23,6 @@ ) from megatron.core.fusions.fused_bias_gelu import bias_gelu_impl from megatron.core.fusions.fused_bias_swiglu import bias_swiglu_impl, weighted_bias_swiglu_impl -from megatron.core.process_groups_config import ProcessGroupCollection from megatron.core.transformer.module import MegatronModule from megatron.core.transformer.transformer_config import TransformerConfig from megatron.core.transformer.utils import cat_with_oom_fallback, sharded_state_dict_default @@ -365,31 +364,6 @@ def backward_dw(self): self.linear_fc2.backward_dw() self.linear_fc1.backward_dw() - @classmethod - def as_mlp_submodule( - cls, - submodules: MLPSubmodules, - config: TransformerConfig, - pg_collection: ProcessGroupCollection, - is_mtp_layer: bool, - is_expert: bool = False, - input_size: int | None = None, - ffn_hidden_size: int | None = None, - ) -> MLP: - """Helper function to build an MLP as a TransformerLayer's mlp submodule.""" - del is_mtp_layer - assert hasattr( - pg_collection, 'tp' - ), 'TP process group is required for MLP in TransformerLayer' - return cls( - config=config, - submodules=submodules, - tp_group=pg_collection.tp, - is_expert=is_expert, - input_size=input_size, - ffn_hidden_size=ffn_hidden_size, - ) - # pylint: disable=missing-function-docstring def apply_swiglu_sharded_factory( diff --git a/megatron/core/transformer/moe/experts.py b/megatron/core/transformer/moe/experts.py index 21363cabd0d..355b84e8150 100644 --- a/megatron/core/transformer/moe/experts.py +++ b/megatron/core/transformer/moe/experts.py @@ -1618,7 +1618,7 @@ class SequentialMLP(MegatronModule): # TODO(M4): breaking api, switched from pass in tp_group to pass in pg_collection. def __init__( self, - num_local_experts: int, + num_local_experts, config: TransformerConfig, submodules: MLPSubmodules, pg_collection: Optional[ProcessGroupCollection] = None, diff --git a/megatron/core/transformer/moe/fused_a2a.py b/megatron/core/transformer/moe/fused_a2a.py index defbe996a72..e3d19e88d7f 100644 --- a/megatron/core/transformer/moe/fused_a2a.py +++ b/megatron/core/transformer/moe/fused_a2a.py @@ -3,7 +3,6 @@ # Copyright (c) 2025 DeepSeek # Licensed under the MIT License - https://github.com/deepseek-ai/DeepEP/blob/main/LICENSE -import os from typing import Optional from megatron.core.utils import internal_api @@ -269,7 +268,6 @@ def set_deepep_num_sms(num_sms): try: - import hybrid_ep_cpp from deep_ep import HybridEPBuffer HAVE_HYBRIDEP = True @@ -277,62 +275,6 @@ def set_deepep_num_sms(num_sms): HAVE_HYBRIDEP = False _hybrid_ep_buffer = None -_HYBRID_EP_TOKEN_ALIGNMENT = 16 -_HYBRID_EP_MIN_BUFFER_TOKENS = 512 -_HYBRID_EP_IB_QP_MAX_DEPTH = 65535 -_HYBRID_EP_IB_DISPATCH_DEPTH_PER_TOKEN = 3 - - -def _round_up_to_multiple(value: int, multiple: int) -> int: - return ((value + multiple - 1) // multiple) * multiple - - -def _hybrid_ep_num_nodes(group: torch.distributed.ProcessGroup) -> int: - """Mirror HybridEP's NVLink-domain detection without constructing the full buffer.""" - ranks_per_nvlink_domain_env = os.getenv("NUM_OF_HYBRID_EP_RANKS_PER_NVLINK_DOMAIN") - if ranks_per_nvlink_domain_env is not None: - ranks_per_nvlink_domain = int(ranks_per_nvlink_domain_env) - else: - allocator = hybrid_ep_cpp.ExtendedMemoryAllocator() - ranks_per_nvlink_domain = allocator.detect_accessible_ranks(group) - - assert group.size() % ranks_per_nvlink_domain == 0, ( - f"The number of ranks {group.size()} should be divisible by the number of ranks per " - f"NVLink domain {ranks_per_nvlink_domain}." - ) - return group.size() // ranks_per_nvlink_domain - - -def _hybrid_ep_uses_internode_rdma(group: torch.distributed.ProcessGroup) -> bool: - if _hybrid_ep_buffer is not None and hasattr(_hybrid_ep_buffer, "num_of_nodes"): - return _hybrid_ep_buffer.num_of_nodes > 1 - return _hybrid_ep_num_nodes(group) > 1 - - -def _validate_hybrid_ep_ib_tx_depth(num_tokens: int, group: torch.distributed.ProcessGroup) -> None: - buffer_tokens = max( - _round_up_to_multiple(num_tokens, _HYBRID_EP_TOKEN_ALIGNMENT), _HYBRID_EP_MIN_BUFFER_TOKENS - ) - tx_depth = _HYBRID_EP_IB_DISPATCH_DEPTH_PER_TOKEN * buffer_tokens + 1 - if tx_depth <= _HYBRID_EP_IB_QP_MAX_DEPTH: - return - - if not _hybrid_ep_uses_internode_rdma(group): - return - - max_supported_tokens = ( - ((_HYBRID_EP_IB_QP_MAX_DEPTH - 1) // _HYBRID_EP_IB_DISPATCH_DEPTH_PER_TOKEN) - // _HYBRID_EP_TOKEN_ALIGNMENT - * _HYBRID_EP_TOKEN_ALIGNMENT - ) - raise ValueError( - f"HybridEP InfiniBand dispatch queue pair depth ({tx_depth}) exceeds the hardware " - f"limit of {_HYBRID_EP_IB_QP_MAX_DEPTH}. DeepEP computes this depth from the " - f"tokens per rank rounded up to a {_HYBRID_EP_TOKEN_ALIGNMENT}-token buffer " - f"alignment ({buffer_tokens}). Reduce sequence length or micro-batch size, or " - f"increase Tensor Parallelism (TP) / Context Parallelism (CP), so tokens per rank " - f"are at most {max_supported_tokens} for multi-node HybridEP." - ) def init_hybrid_ep_buffer( @@ -360,8 +302,8 @@ def init_hybrid_ep_buffer( Process group for HybridEP all-to-all communication. hidden_dim (int): Hidden dimension of the input tensor. - num_tokens (int): - Maximum token count of the input tensor. + seq_len (int): + Maximum sequence length of the input tensor. num_local_experts (int): Number of local experts. num_sms_dispatch_api (Optional[int]): @@ -450,9 +392,21 @@ def forward( num_blocks_permute = None num_blocks_unpermute = None - num_tokens, hidden_dim = x.shape[-2:] - _validate_hybrid_ep_ib_tx_depth(num_tokens, group) if _hybrid_ep_buffer is None: + num_tokens, hidden_dim = x.shape[-2:] + + # --- Hardware Limit Guardrail --- + # DeepEP calculates tx_depth = 3 * num_tokens + 1. + # InfiniBand strictly asserts tx_depth < 65536. + tx_depth = 3 * num_tokens + 1 + if tx_depth >= 65536: + raise ValueError( + f"HybridEP RDMA Queue Pair depth ({tx_depth}) exceeds the InfiniBand " + f"hardware limit of 65535. This occurs because the total tokens per rank " + f"({num_tokens}) too high. Reduce sequence length or micro-batch size, " + f"or increase Tensor Parallelism (TP) / Context Parallelism (CP) to reduce " + f"the number of tokens processed per rank." + ) fp8_dispatch = False # Currently, we do not support fp8 dispatch init_hybrid_ep_buffer( group, diff --git a/megatron/core/transformer/moe/moe_layer.py b/megatron/core/transformer/moe/moe_layer.py index e61898c7da2..d2f79e77d05 100644 --- a/megatron/core/transformer/moe/moe_layer.py +++ b/megatron/core/transformer/moe/moe_layer.py @@ -662,7 +662,8 @@ def forward( hidden_states: torch.Tensor, intermediate_tensors=None, padding_mask: Optional[torch.Tensor] = None, - ) -> tuple[torch.Tensor, Optional[torch.Tensor]]: + input_ids: Optional[torch.Tensor] = None, + ): """Forward pass for the MoE layer. The forward pass comprises four main steps: diff --git a/megatron/core/transformer/transformer_layer.py b/megatron/core/transformer/transformer_layer.py index 6f1ba74e358..eff4c18db4f 100644 --- a/megatron/core/transformer/transformer_layer.py +++ b/megatron/core/transformer/transformer_layer.py @@ -6,7 +6,7 @@ import warnings from abc import ABC from dataclasses import dataclass, field -from typing import TYPE_CHECKING, Any, Dict, Optional, Protocol, Union +from typing import TYPE_CHECKING, Any, Dict, Optional, Union if TYPE_CHECKING: from megatron.core.tensor_parallel.random import CheckpointManager @@ -213,33 +213,6 @@ def get_transformer_layer_offset( return offset -class MlpInterface(Protocol): - """Interface for MLP implementations in the transformer layer.""" - - def forward( - self, - hidden_states: Tensor, - /, - *, - intermediate_tensors: tuple[Tensor, ...] | None = None, - padding_mask: Tensor | None = None, - ) -> tuple[Tensor, Tensor | None]: - """Forward method for the MLP interface.""" - ... - - -class MlpBuilder(Protocol): - """MLP builder protocol for building MLPs in the transformer layer.""" - - def __call__( - self, - *, - config: TransformerConfig, - pg_collection: ProcessGroupCollection, - is_mtp_layer: bool, - ) -> MlpInterface: ... - - @dataclass class TransformerLayerSubmodules: """ @@ -279,7 +252,8 @@ class TransformerLayerSubmodules: cross_attn_bda: Union[ModuleSpec, type] = IdentityFuncOp pre_mlp_layernorm: LayerNormBuilder = IdentityOp - mlp: MlpBuilder | type[IdentityOp] = IdentityOp + mlp_hyper_connection: Union[ModuleSpec, type] = IdentityOp + mlp: Union[ModuleSpec, type] = IdentityOp mlp_bda: Union[ModuleSpec, type] = IdentityFuncOp # Mapping for sharded tensor keys to be applied in `sharded_state_dict` method @@ -405,32 +379,41 @@ def __init__( eps=self.config.layernorm_epsilon, ) # [Module 8: MLP block] + additional_mlp_kwargs = {} # import here to avoid circular import from megatron.core.extensions.transformer_engine import TEFusedMLP + from megatron.core.transformer.moe.experts import SequentialMLP, TEGroupedMLP from megatron.core.transformer.moe.moe_layer import MoELayer # MLP expects tp_group but MoELayer expects pg_collection to be passed in. # We can change MLP to accept pg_collection but it makes the logic implicit # The conditional below is to make the logic explicit # if submodules.mlp is not a ModuleSpec,we dont have to handle passing additional kwargs - if isinstance(submodules.mlp, ModuleSpec) and submodules.mlp.module in (MLP, TEFusedMLP): - submodules.mlp = functools.partial( - submodules.mlp.module.as_mlp_submodule, - submodules=submodules.mlp.submodules, - **submodules.mlp.params, - ) - log_single_rank( - logger, - logging.WARNING, - f"Rewrapping ModuleSpec with module {type(submodules.mlp)} to forward kwargs. " - "Consider migrating the `mlp` submodule spec to a direct call of the " - "`as_mlp_submodule` classmethod instead.", - ) - self.mlp = submodules.mlp( - config=self.config, pg_collection=pg_collection, is_mtp_layer=self.is_mtp_layer - ) - if hasattr(self.mlp, 'set_layer_number'): - self.mlp.set_layer_number(self.layer_number) + if isinstance(submodules.mlp, ModuleSpec): + if submodules.mlp.module in (MoELayer, TEGroupedMLP, SequentialMLP): + additional_mlp_kwargs["pg_collection"] = pg_collection + if submodules.mlp.module == MoELayer: + # Pass is_mtp_layer flag to MoELayer to distinguish MTP MoE layers. + additional_mlp_kwargs["is_mtp_layer"] = self.is_mtp_layer + # Pass layer number to MoELayer for router configuration. + additional_mlp_kwargs["layer_number"] = self.layer_number + elif submodules.mlp.module == MLP: + assert hasattr( + pg_collection, 'tp' + ), 'TP process group is required for MLP in TransformerLayer' + additional_mlp_kwargs["tp_group"] = pg_collection.tp + elif TEFusedMLP is not None and submodules.mlp.module == TEFusedMLP: + assert hasattr( + pg_collection, 'tp' + ), 'TP process group is required for TEFusedMLP in TransformerLayer' + additional_mlp_kwargs["tp_group"] = pg_collection.tp + else: + log_single_rank( + logger, + logging.WARNING, + f"Unknown MLP type: {type(submodules.mlp)}. Using default kwargs.", + ) + self.mlp = build_module(submodules.mlp, config=self.config, **additional_mlp_kwargs) # [Module 9: BiasDropoutFusion] self.mlp_bda = build_module(submodules.mlp_bda) @@ -824,7 +807,7 @@ def _forward_mlp( from megatron.core.extensions.transformer_engine import te_checkpoint mlp_output_with_bias = te_checkpoint( - apply_module(self.mlp), + self.mlp, False, tensor_parallel.random.get_cuda_rng_tracker, self.pg_collection.tp, @@ -834,7 +817,7 @@ def _forward_mlp( ) else: mlp_output_with_bias = tensor_parallel.checkpoint( - functools.partial(apply_module(self.mlp), padding_mask=padding_mask), + functools.partial(self.mlp, padding_mask=padding_mask, **moe_kwargs), False, pre_mlp_layernorm_output, ) @@ -852,7 +835,7 @@ def _forward_mlp( chunks = pre_mlp_layernorm_output.chunk(num_chunks, dim=0) # Compute outputs for each chunk - outputs = [apply_module(self.mlp)(chunk) for chunk in chunks] + outputs = [self.mlp(chunk) for chunk in chunks] # Aggregate chunk outputs mlp_output = torch.cat([out for out, _ in outputs], dim=0) @@ -865,8 +848,8 @@ def _forward_mlp( # Set the residual for fused reduce-scatter + add + layer-norm + all-gather # operation in MLP's fc2. self._set_fc2_residual(residual) - mlp_output_with_bias = apply_module(self.mlp)( - pre_mlp_layernorm_output, padding_mask=padding_mask + mlp_output_with_bias = self.mlp( + pre_mlp_layernorm_output, padding_mask=padding_mask, **moe_kwargs ) nvtx_range_pop(suffix="mlp") @@ -1252,7 +1235,7 @@ def _te_cuda_graph_replay_impl(self, args, kwargs, context): hidden_states, probs = self.mlp.preprocess(hidden_states, probs, routing_map) nvtx_range_pop(suffix="mlp") return residual, hidden_states, probs, shared_expert_output - mlp_output_with_bias = apply_module(self.mlp)(hidden_states) + mlp_output_with_bias = self.mlp(hidden_states) self.mlp.cudagraph_tensor_store.clear() nvtx_range_pop(suffix="mlp") @@ -2113,8 +2096,11 @@ def _forward_mlp_router(self, hidden_states, padding_mask=None, input_ids=None): if self.config.fp32_residual_connection: residual = residual.float() - router_outputs = apply_module(self.mlp)( - pre_mlp_layernorm_output, intermediate_tensors=(), padding_mask=padding_mask + router_outputs = self.mlp( + pre_mlp_layernorm_output, + intermediate_tensors=(), + padding_mask=padding_mask, + input_ids=input_ids, ) for attr_name in self.mlp.token_dispatcher.cudagraph_attrs: @@ -2146,7 +2132,7 @@ def _forward_mlp_expert_compute(self, hidden_states, probs): self._restore_token_dispatcher_attrs() self.mlp.fwd_execution_map = "expert_compute" - return apply_module(self.mlp)(None, intermediate_tensors=(hidden_states, probs)) + return self.mlp(None, intermediate_tensors=(hidden_states, probs)) def _forward_mlp_postprocess(self, residual, output, shared_expert_output, mlp_bias): """ @@ -2164,7 +2150,7 @@ def _forward_mlp_postprocess(self, residual, output, shared_expert_output, mlp_b self._restore_token_dispatcher_attrs() self.mlp.fwd_execution_map = "postprocess" - output = apply_module(self.mlp)(None, intermediate_tensors=(output, shared_expert_output)) + output = self.mlp(None, intermediate_tensors=(output, shared_expert_output)) return self._forward_post_mlp((output, mlp_bias), residual) def _forward_mlp( diff --git a/pretrain_vlm.py b/pretrain_vlm.py index dff56257ce4..dc735c25517 100644 --- a/pretrain_vlm.py +++ b/pretrain_vlm.py @@ -6,7 +6,7 @@ import torch -from megatron.core import parallel_state, tensor_parallel +from megatron.core import mpu, parallel_state, tensor_parallel from megatron.core.datasets.blended_megatron_dataset_builder import BlendedMegatronDatasetBuilder from megatron.core.datasets.multimodal_dataset import MockMultimodalDataset, MultimodalDatasetConfig from megatron.core.enums import ModelType @@ -22,7 +22,7 @@ get_vit_layer_with_transformer_engine_spec, ) from megatron.core.transformer.enums import AttnMaskType -from megatron.core.transformer.spec_utils import get_submodules, import_module +from megatron.core.transformer.spec_utils import import_module from megatron.training import get_args, get_timers, get_tokenizer, pretrain, print_rank_0 from megatron.training.argument_utils import pretrain_cfg_container_from_args from megatron.training.arguments import core_transformer_config_from_args, parse_and_validate_args @@ -182,9 +182,7 @@ def model_provider( vision_transformer_config.pipeline_model_parallel_size = 1 vision_projection_config.pipeline_model_parallel_size = 1 - vision_projection_modules = deepcopy( - get_submodules(language_transformer_layer_spec.submodules.mlp) - ) + vision_projection_modules = deepcopy(language_transformer_layer_spec.submodules.mlp.submodules) language_max_sequence_length = args.decoder_seq_length if args.context_parallel_size > 1: diff --git a/tests/functional_tests/test_cases/common/moe_perf/__main__.py b/tests/functional_tests/test_cases/common/moe_perf/__main__.py index a722121f079..803d0c11a26 100644 --- a/tests/functional_tests/test_cases/common/moe_perf/__main__.py +++ b/tests/functional_tests/test_cases/common/moe_perf/__main__.py @@ -20,9 +20,8 @@ get_gpt_layer_with_transformer_engine_submodules, ) from megatron.core.transformer.moe.fused_a2a import HAVE_DEEP_EP, HAVE_HYBRIDEP -from megatron.core.transformer.moe.moe_layer import MoELayer, MoESubmodules +from megatron.core.transformer.moe.moe_layer import MoELayer from megatron.core.transformer.moe.moe_utils import RandomSTE -from megatron.core.transformer.spec_utils import get_submodules from megatron.core.transformer.transformer_config import TransformerConfig from megatron.core.utils import nvtx_range_pop, nvtx_range_push from megatron.training.initialize import _set_random_seed @@ -87,12 +86,10 @@ def _build_transformer_config(case: MoEPerformanceCase) -> TransformerConfig: # NOTE: Only TE backend is covered in this test. -def _resolve_moe_submodules(case: MoEPerformanceCase) -> MoESubmodules: - return get_submodules( - get_gpt_layer_with_transformer_engine_submodules( - num_experts=case.model.num_experts, moe_grouped_gemm=True - ).mlp - ) +def _resolve_moe_submodules(case: MoEPerformanceCase): + return get_gpt_layer_with_transformer_engine_submodules( + num_experts=case.model.num_experts, moe_grouped_gemm=True + ).mlp.submodules def _load_baselines() -> Dict[str, Dict[str, float]]: diff --git a/tests/unit_tests/dist_checkpointing/models/test_mlp_glu.py b/tests/unit_tests/dist_checkpointing/models/test_mlp_glu.py index 7d0f3c1b3a3..037e368ea2f 100644 --- a/tests/unit_tests/dist_checkpointing/models/test_mlp_glu.py +++ b/tests/unit_tests/dist_checkpointing/models/test_mlp_glu.py @@ -1,18 +1,23 @@ # Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +import inspect import logging import pytest import torch +from torch.optim import Adam from megatron.core import parallel_state from megatron.core.dist_checkpointing import ShardedTensor, load, load_plain_tensors, save -from megatron.core.dist_checkpointing.dict_utils import diff +from megatron.core.dist_checkpointing.dict_utils import diff, nested_values +from megatron.core.dist_checkpointing.optimizer import ( + get_param_id_to_sharded_param_map, + optim_state_to_sharding_state, +) from megatron.core.models.gpt.gpt_layer_specs import ( get_gpt_layer_with_transformer_engine_submodules, ) from megatron.core.tensor_parallel.random import model_parallel_cuda_manual_seed -from megatron.core.transformer.mlp import MLP, MLPSubmodules, apply_swiglu_sharded_factory -from megatron.core.transformer.spec_utils import get_submodules +from megatron.core.transformer.mlp import MLP, apply_swiglu_sharded_factory from megatron.core.transformer.transformer_config import TransformerConfig from tests.unit_tests.dist_checkpointing import TempNamedDir from tests.unit_tests.test_utilities import Utils @@ -28,9 +33,9 @@ def initialize_mlp(glu=True): use_cpu_initialization=True, gated_linear_unit=glu, ) - mlp_submodules = get_submodules(get_gpt_layer_with_transformer_engine_submodules().mlp) - assert isinstance(mlp_submodules, MLPSubmodules) - return MLP(transformer_config, mlp_submodules) + return MLP( + transformer_config, get_gpt_layer_with_transformer_engine_submodules().mlp.submodules + ) class TestParallelMLPWithGLU: diff --git a/tests/unit_tests/distributed/test_grad_sync_with_expert_parallel.py b/tests/unit_tests/distributed/test_grad_sync_with_expert_parallel.py index 6461dbee751..15c479774c6 100644 --- a/tests/unit_tests/distributed/test_grad_sync_with_expert_parallel.py +++ b/tests/unit_tests/distributed/test_grad_sync_with_expert_parallel.py @@ -12,8 +12,7 @@ get_gpt_layer_with_transformer_engine_submodules, ) from megatron.core.transformer import TransformerConfig -from megatron.core.transformer.moe.moe_layer import MoELayer, MoESubmodules -from megatron.core.transformer.spec_utils import get_submodules +from megatron.core.transformer.moe.moe_layer import MoELayer from tests.unit_tests.test_utilities import Utils @@ -43,15 +42,15 @@ def __init__( params_dtype=torch.bfloat16, add_bias_linear=False, ) - submodules = get_submodules( - get_gpt_layer_with_transformer_engine_submodules( - num_experts=num_moe_experts, moe_grouped_gemm=moe_grouped_gemm - ).mlp + submodules = get_gpt_layer_with_transformer_engine_submodules( + num_experts=num_moe_experts, moe_grouped_gemm=moe_grouped_gemm ) - assert isinstance(submodules, MoESubmodules) super().__init__() self.layers = torch.nn.ModuleList( - [MoELayer(transformer_config, submodules).cuda() for _ in range(num_layers)] + [ + MoELayer(transformer_config, submodules.mlp.submodules).cuda() + for _ in range(num_layers) + ] ) diff --git a/tests/unit_tests/inference/text_generation_controllers/test_vlm_text_generation_controller.py b/tests/unit_tests/inference/text_generation_controllers/test_vlm_text_generation_controller.py index 8b3962dd7b7..5344ae9f8eb 100644 --- a/tests/unit_tests/inference/text_generation_controllers/test_vlm_text_generation_controller.py +++ b/tests/unit_tests/inference/text_generation_controllers/test_vlm_text_generation_controller.py @@ -23,9 +23,8 @@ from megatron.core.models.gpt.gpt_layer_specs import get_gpt_layer_local_submodules from megatron.core.models.multimodal.llava_model import LLaVAModel from megatron.core.tensor_parallel.random import model_parallel_cuda_manual_seed -from megatron.core.transformer.mlp import MLPSubmodules from megatron.core.transformer.module import Float16Module -from megatron.core.transformer.spec_utils import ModuleSpec, get_submodules +from megatron.core.transformer.spec_utils import ModuleSpec from megatron.core.transformer.transformer_config import TransformerConfig from megatron.core.transformer.transformer_layer import TransformerLayer from tests.unit_tests.test_utilities import Utils @@ -72,8 +71,7 @@ def setup_method(self, method): vision_layer_spec = ModuleSpec( module=TransformerLayer, submodules=copy.deepcopy(language_layer_submodules) ) - vision_projection_spec = copy.deepcopy(get_submodules(language_layer_submodules.mlp)) - assert isinstance(vision_projection_spec, MLPSubmodules) + vision_projection_spec = copy.deepcopy(language_layer_submodules.mlp.submodules) language_config.language_model_type = "dummy" vision_config.vision_model_type = "clip" diff --git a/tests/unit_tests/models/test_llava_model.py b/tests/unit_tests/models/test_llava_model.py index 2a8fd553f85..970b0e54635 100644 --- a/tests/unit_tests/models/test_llava_model.py +++ b/tests/unit_tests/models/test_llava_model.py @@ -15,8 +15,7 @@ from megatron.core.packed_seq_params import PackedSeqParams from megatron.core.tensor_parallel.random import model_parallel_cuda_manual_seed from megatron.core.transformer.enums import AttnMaskType -from megatron.core.transformer.mlp import MLPSubmodules -from megatron.core.transformer.spec_utils import ModuleSpec, get_submodules +from megatron.core.transformer.spec_utils import ModuleSpec from megatron.core.transformer.transformer_config import TransformerConfig from megatron.core.transformer.transformer_layer import TransformerLayer from megatron.core.utils import is_te_min_version @@ -54,8 +53,7 @@ def setup_method(self, method): vision_layer_spec = ModuleSpec( module=TransformerLayer, submodules=deepcopy(language_layer_submodules) ) - vision_projection_spec = deepcopy(get_submodules(language_layer_submodules.mlp)) - assert isinstance(vision_projection_spec, MLPSubmodules) + vision_projection_spec = deepcopy(language_layer_submodules.mlp.submodules) language_config.language_model_type = "dummy" vision_config.vision_model_type = "clip" @@ -493,7 +491,7 @@ def setup_and_teardown_llava_model(request): vision_layer_spec = ModuleSpec( module=TransformerLayer, submodules=deepcopy(language_layer_submodules) ) - vision_projection_spec = deepcopy(get_submodules(language_layer_submodules.mlp)) + vision_projection_spec = deepcopy(language_layer_submodules.mlp.submodules) language_config.language_model_type = "dummy" vision_model_type = request.param @@ -603,7 +601,7 @@ def _init_llava_model(self, cp_size, tp_size, sequence_parallel): vision_layer_spec = ModuleSpec( module=TransformerLayer, submodules=deepcopy(language_layer_submodules) ) - vision_projection_spec = deepcopy(get_submodules(language_layer_submodules.mlp)) + vision_projection_spec = deepcopy(language_layer_submodules.mlp.submodules) language_config.language_model_type = "dummy" vision_config.vision_model_type = "clip" diff --git a/tests/unit_tests/models/test_multimodal_projector.py b/tests/unit_tests/models/test_multimodal_projector.py index 5df34954b6a..52fda330c2e 100644 --- a/tests/unit_tests/models/test_multimodal_projector.py +++ b/tests/unit_tests/models/test_multimodal_projector.py @@ -20,7 +20,7 @@ def setup_method(self, method): transformer_config = TransformerConfig( num_layers=1, hidden_size=64, num_attention_heads=4, use_cpu_initialization=True ) - mlp_layer_spec = get_mlp_module_spec().keywords['submodules'] + mlp_layer_spec = get_mlp_module_spec().submodules affine_layer_spec = MLPSubmodules(linear_fc1=ColumnParallelLinear, linear_fc2=None) self.mlp = MultimodalProjector( diff --git a/tests/unit_tests/test_utils.py b/tests/unit_tests/test_utils.py index 05f4c301375..d7ec4042ca9 100644 --- a/tests/unit_tests/test_utils.py +++ b/tests/unit_tests/test_utils.py @@ -20,8 +20,7 @@ ) from megatron.core.optimizer import OptimizerConfig, get_megatron_optimizer from megatron.core.transformer import TransformerConfig -from megatron.core.transformer.moe.moe_layer import MoELayer, MoESubmodules -from megatron.core.transformer.spec_utils import get_submodules +from megatron.core.transformer.moe.moe_layer import MoELayer from tests.unit_tests.test_utilities import Utils success_string = "hello,world" @@ -327,11 +326,12 @@ def test_param_norm_moe(use_distributed_optimizer: bool): add_bias_linear=False, bf16=True, ) - submodules = get_submodules( - get_gpt_layer_with_transformer_engine_submodules(num_experts=2, moe_grouped_gemm=True).mlp - ) - assert isinstance(submodules, MoESubmodules) - model = MoELayer(transformer_config, submodules).to(device='cuda') + model = MoELayer( + transformer_config, + get_gpt_layer_with_transformer_engine_submodules( + num_experts=2, moe_grouped_gemm=True + ).mlp.submodules, + ).to(device='cuda') model.requires_grad_(True) # Initialize the model with all 1.0 for weights. for param in model.parameters(): diff --git a/tests/unit_tests/transformer/moe/test_grouped_mlp.py b/tests/unit_tests/transformer/moe/test_grouped_mlp.py index b1f15a9eef1..58ca0cf1e3f 100644 --- a/tests/unit_tests/transformer/moe/test_grouped_mlp.py +++ b/tests/unit_tests/transformer/moe/test_grouped_mlp.py @@ -10,8 +10,7 @@ ) from megatron.core.transformer.module import Float16Module from megatron.core.transformer.moe.experts import TEGroupedMLP -from megatron.core.transformer.moe.moe_layer import MoELayer, MoESubmodules -from megatron.core.transformer.spec_utils import get_submodules +from megatron.core.transformer.moe.moe_layer import MoELayer from megatron.core.transformer.transformer_config import TransformerConfig from megatron.core.utils import is_te_min_version from megatron.training.arguments import parse_args @@ -59,11 +58,8 @@ def setup_method(self, method, use_cpu_initialization=False, swiglu=True): ## Vanilla sequential GEMM # Set random seed for reproducability _set_random_seed(seed_=123, data_parallel_random_init=False) - sequential_submodules = get_submodules( - get_gpt_layer_local_submodules(self.num_experts, moe_grouped_gemm=False).mlp - ) - assert isinstance(sequential_submodules, MoESubmodules) - self.sequential_mlp = MoELayer(tf_config, sequential_submodules) + submodules = get_gpt_layer_local_submodules(self.num_experts, moe_grouped_gemm=False) + self.sequential_mlp = MoELayer(tf_config, submodules.mlp.submodules) self.args = parse_args(ignore_unknown_args=True) self.args.bf16 = True @@ -75,13 +71,12 @@ def setup_method(self, method, use_cpu_initialization=False, swiglu=True): ## Grouped GEMM _set_random_seed(seed_=123, data_parallel_random_init=False) tf_config.moe_grouped_gemm = True - grouped_submodules = get_submodules( + self.grouped_mlp = MoELayer( + tf_config, get_gpt_layer_with_transformer_engine_submodules( self.num_experts, moe_grouped_gemm=True - ).mlp + ).mlp.submodules, ) - assert isinstance(grouped_submodules, MoESubmodules) - self.grouped_mlp = MoELayer(tf_config, grouped_submodules) assert isinstance(self.grouped_mlp.experts, TEGroupedMLP) self.grouped_mlp = Float16Module(self.grouped_mlp.config, self.grouped_mlp).module diff --git a/tests/unit_tests/transformer/moe/test_latent_moe_layer.py b/tests/unit_tests/transformer/moe/test_latent_moe_layer.py index bb5ced291fc..f62de67860a 100644 --- a/tests/unit_tests/transformer/moe/test_latent_moe_layer.py +++ b/tests/unit_tests/transformer/moe/test_latent_moe_layer.py @@ -4,11 +4,14 @@ import torch from megatron.core.models.gpt.gpt_layer_specs import ( - get_gpt_layer_local_submodules, - get_gpt_layer_with_transformer_engine_submodules, + get_gpt_decoder_block_spec, + get_gpt_layer_local_spec, + get_gpt_layer_with_transformer_engine_spec, ) -from megatron.core.transformer.moe.moe_layer import MoELayer, MoESubmodules -from megatron.core.transformer.spec_utils import get_submodules +from megatron.core.tensor_parallel.random import model_parallel_cuda_manual_seed +from megatron.core.transformer.moe.moe_layer import MoELayer +from megatron.core.transformer.moe.router import Router +from megatron.core.transformer.transformer_block import TransformerBlock from megatron.core.transformer.transformer_config import TransformerConfig from megatron.core.utils import is_te_min_version from megatron.training.initialize import _set_random_seed @@ -50,16 +53,16 @@ def test_latent_moe_layer( moe_latent_size=moe_latent_size, ) if use_te: - transformer_layer_submodules = get_gpt_layer_with_transformer_engine_submodules( + transformer_layer_spec = get_gpt_layer_with_transformer_engine_spec( num_experts=num_moe_experts, moe_grouped_gemm=grouped_gemm ) else: - transformer_layer_submodules = get_gpt_layer_local_submodules( + transformer_layer_spec = get_gpt_layer_local_spec( num_experts=num_moe_experts, moe_grouped_gemm=grouped_gemm ) - submodules = get_submodules(transformer_layer_submodules.mlp) - assert isinstance(submodules, MoESubmodules) - moe_layer = MoELayer(self.transformer_config, submodules) + moe_layer = MoELayer( + self.transformer_config, transformer_layer_spec.submodules.mlp.submodules + ) moe_layer.cuda() config = moe_layer.config diff --git a/tests/unit_tests/transformer/moe/test_moe_layer.py b/tests/unit_tests/transformer/moe/test_moe_layer.py index c4a3283ac5b..0004b7fef98 100644 --- a/tests/unit_tests/transformer/moe/test_moe_layer.py +++ b/tests/unit_tests/transformer/moe/test_moe_layer.py @@ -9,8 +9,7 @@ get_gpt_layer_with_transformer_engine_submodules, ) from megatron.core.tensor_parallel.random import model_parallel_cuda_manual_seed -from megatron.core.transformer.moe.moe_layer import MoELayer, MoESubmodules -from megatron.core.transformer.spec_utils import get_submodules +from megatron.core.transformer.moe.moe_layer import MoELayer from megatron.core.transformer.transformer_block import TransformerBlock from megatron.core.transformer.transformer_config import TransformerConfig from megatron.core.utils import is_te_min_version @@ -45,13 +44,10 @@ def test_te_moe_layer(self, num_moe_experts, moe_token_dispatcher_type, grouped_ moe_ffn_hidden_size=128, add_bias_linear=False, ) - submodules = get_submodules( - get_gpt_layer_with_transformer_engine_submodules( - num_experts=num_moe_experts, moe_grouped_gemm=grouped_gemm - ).mlp + submodules = get_gpt_layer_with_transformer_engine_submodules( + num_experts=num_moe_experts, moe_grouped_gemm=grouped_gemm ) - assert isinstance(submodules, MoESubmodules) - moe_layer = MoELayer(self.transformer_config, submodules) + moe_layer = MoELayer(self.transformer_config, submodules.mlp.submodules) Utils.destroy_model_parallel() @pytest.mark.parametrize("moe_token_dispatcher_type", ["allgather", "alltoall"]) @@ -74,13 +70,10 @@ def test_legacy_moe_layer(self, num_moe_experts, moe_token_dispatcher_type, grou moe_grouped_gemm=grouped_gemm, add_bias_linear=False, ) - submodules = get_submodules( - get_gpt_layer_local_submodules( - num_experts=num_moe_experts, moe_grouped_gemm=grouped_gemm - ).mlp + transformer_layer_submodules = get_gpt_layer_local_submodules( + num_experts=num_moe_experts, moe_grouped_gemm=grouped_gemm ) - assert isinstance(submodules, MoESubmodules) - moe_layer = MoELayer(self.transformer_config, submodules) + moe_layer = MoELayer(self.transformer_config, transformer_layer_submodules.mlp.submodules) Utils.destroy_model_parallel() @pytest.mark.skip( @@ -112,18 +105,15 @@ def test_moe_with_late_initialize( bf16=True, params_dtype=torch.bfloat16, ) - submodules = get_submodules( - get_gpt_layer_with_transformer_engine_submodules( - num_experts=num_moe_experts, moe_grouped_gemm=grouped_gemm - ).mlp + submodules = get_gpt_layer_with_transformer_engine_submodules( + num_experts=num_moe_experts, moe_grouped_gemm=grouped_gemm ) - assert isinstance(submodules, MoESubmodules) # Fake initialization as NeMo does Utils.fake_initialize_model_parallel( tensor_model_parallel_size=tp_size, expert_model_parallel_size=ep_size ) - moe_layer = MoELayer(transformer_config, submodules).cuda() + moe_layer = MoELayer(transformer_config, submodules.mlp.submodules).cuda() Utils.initialize_model_parallel( tensor_model_parallel_size=tp_size, expert_model_parallel_size=ep_size @@ -239,12 +229,11 @@ def test_moe_layer_fp16_forward_backward( params_dtype=torch.float16, ) - submodules = get_submodules( - get_gpt_layer_local_submodules(num_experts=num_moe_experts, moe_grouped_gemm=False).mlp + submodules = get_gpt_layer_local_submodules( + num_experts=num_moe_experts, moe_grouped_gemm=False ) - assert isinstance(submodules, MoESubmodules) - moe_layer = MoELayer(transformer_config, submodules).cuda() + moe_layer = MoELayer(transformer_config, submodules.mlp.submodules).cuda() hidden_states = torch.randn( sequence_length, @@ -343,20 +332,15 @@ def test_moe_layer_recompute_forward_backward( # Use TE spec for fp8, local spec otherwise if fp8: - submodules = get_submodules( - get_gpt_layer_with_transformer_engine_submodules( - num_experts=num_moe_experts, moe_grouped_gemm=False - ).mlp + transformer_layer_submodules = get_gpt_layer_with_transformer_engine_submodules( + num_experts=num_moe_experts, moe_grouped_gemm=False ) else: - submodules = get_submodules( - get_gpt_layer_local_submodules( - num_experts=num_moe_experts, moe_grouped_gemm=False - ).mlp + transformer_layer_submodules = get_gpt_layer_local_submodules( + num_experts=num_moe_experts, moe_grouped_gemm=False ) - assert isinstance(submodules, MoESubmodules) - moe_layer = MoELayer(transformer_config, submodules).cuda() + moe_layer = MoELayer(transformer_config, transformer_layer_submodules.mlp.submodules).cuda() hidden_states = torch.randn( sequence_length, diff --git a/tests/unit_tests/transformer/moe/test_routers.py b/tests/unit_tests/transformer/moe/test_routers.py index 0ccb619a512..40802ac65dc 100644 --- a/tests/unit_tests/transformer/moe/test_routers.py +++ b/tests/unit_tests/transformer/moe/test_routers.py @@ -7,10 +7,13 @@ import torch from megatron.core.models.gpt.gpt_layer_specs import get_gpt_layer_local_submodules -from megatron.core.transformer.moe.moe_layer import MoELayer, MoESubmodules -from megatron.core.transformer.moe.moe_utils import get_updated_expert_bias, router_gating_linear -from megatron.core.transformer.moe.router import Router -from megatron.core.transformer.spec_utils import get_submodules +from megatron.core.transformer.moe.moe_layer import MoELayer +from megatron.core.transformer.moe.moe_utils import ( + get_default_pg_collection, + get_updated_expert_bias, + router_gating_linear, +) +from megatron.core.transformer.moe.router import Router, TopKRouter from megatron.core.transformer.transformer_config import TransformerConfig from megatron.training.initialize import _set_random_seed from tests.unit_tests.test_utilities import Utils @@ -45,11 +48,10 @@ def setup_method(self, method): params_dtype=torch.bfloat16, add_bias_linear=False, ) - submodules = get_submodules( - get_gpt_layer_local_submodules(num_experts=num_moe_experts, moe_grouped_gemm=False).mlp + submodules = get_gpt_layer_local_submodules( + num_experts=num_moe_experts, moe_grouped_gemm=False ) - assert isinstance(submodules, MoESubmodules) - self.sequential_mlp = MoELayer(self.transformer_config, submodules) + self.sequential_mlp = MoELayer(self.transformer_config, submodules.mlp.submodules) self.router = cast(Router, self.sequential_mlp.router) def teardown_method(self, method): @@ -315,11 +317,10 @@ def setup_method(self, method): ) # init MoE layer - submodules = get_submodules( - get_gpt_layer_local_submodules(num_experts=num_moe_experts, moe_grouped_gemm=False).mlp + submodules = get_gpt_layer_local_submodules( + num_experts=num_moe_experts, moe_grouped_gemm=False ) - assert isinstance(submodules, MoESubmodules) - self.moe_layer = MoELayer(self.transformer_config, submodules).cuda() + self.moe_layer = MoELayer(self.transformer_config, submodules.mlp.submodules).cuda() self.router = cast(Router, self.moe_layer.router) def teardown_method(self, method): @@ -421,11 +422,10 @@ def setup_method(self, method): params_dtype=torch.bfloat16, add_bias_linear=False, ) - submodules = get_submodules( - get_gpt_layer_local_submodules(num_experts=num_moe_experts, moe_grouped_gemm=False).mlp + submodules = get_gpt_layer_local_submodules( + num_experts=num_moe_experts, moe_grouped_gemm=False ) - assert isinstance(submodules, MoESubmodules) - self.moe_layer = MoELayer(self.transformer_config, submodules) + self.moe_layer = MoELayer(self.transformer_config, submodules.mlp.submodules) self.router = cast(Router, self.moe_layer.router) assert self.router.expert_bias is not None assert self.router.local_tokens_per_expert is not None @@ -468,14 +468,11 @@ def test_router_forward_aux_free(self): def test_router_forward_fusion_equivalence(self, score_function): with torch.no_grad(): # Build two fresh routers to avoid bias update interference - submodules = get_submodules( - get_gpt_layer_local_submodules( - num_experts=self.transformer_config.num_moe_experts, moe_grouped_gemm=False - ).mlp + submodules = get_gpt_layer_local_submodules( + num_experts=self.transformer_config.num_moe_experts, moe_grouped_gemm=False ) - assert isinstance(submodules, MoESubmodules) - moe_layer_ref = MoELayer(self.transformer_config, submodules) - moe_layer_fused = MoELayer(self.transformer_config, submodules) + moe_layer_ref = MoELayer(self.transformer_config, submodules.mlp.submodules) + moe_layer_fused = MoELayer(self.transformer_config, submodules.mlp.submodules) router_ref = moe_layer_ref.router.cuda() router_fused = moe_layer_fused.router.cuda() diff --git a/tests/unit_tests/transformer/moe/test_sequential_mlp.py b/tests/unit_tests/transformer/moe/test_sequential_mlp.py index 6a0637a4e65..e618f6a8318 100644 --- a/tests/unit_tests/transformer/moe/test_sequential_mlp.py +++ b/tests/unit_tests/transformer/moe/test_sequential_mlp.py @@ -1,4 +1,6 @@ # Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +from importlib.metadata import version + import pytest import torch @@ -8,9 +10,8 @@ from megatron.core.tensor_parallel.random import model_parallel_cuda_manual_seed from megatron.core.transformer.mlp import MLPSubmodules from megatron.core.transformer.moe.experts import SequentialMLP -from megatron.core.transformer.moe.moe_layer import MoELayer, MoESubmodules +from megatron.core.transformer.moe.moe_layer import MoELayer from megatron.core.transformer.moe.moe_utils import get_default_pg_collection -from megatron.core.transformer.spec_utils import get_submodules from megatron.core.transformer.transformer_config import TransformerConfig from megatron.core.utils import is_te_min_version from tests.unit_tests.test_utilities import Utils @@ -36,11 +37,10 @@ def setup_method(self, method): moe_router_topk=1, add_bias_linear=False, ) - submodules = get_submodules( - get_gpt_layer_local_submodules(num_experts=num_moe_experts, moe_grouped_gemm=False).mlp + submodules = get_gpt_layer_local_submodules( + num_experts=num_moe_experts, moe_grouped_gemm=False ) - assert isinstance(submodules, MoESubmodules) - self.sequential_mlp = MoELayer(transformer_config, submodules) + self.sequential_mlp = MoELayer(transformer_config, submodules.mlp.submodules) def teardown_method(self, method): Utils.destroy_model_parallel() diff --git a/tests/unit_tests/transformer/moe/test_shared_experts.py b/tests/unit_tests/transformer/moe/test_shared_experts.py index eccac8c0220..a96c6450af4 100644 --- a/tests/unit_tests/transformer/moe/test_shared_experts.py +++ b/tests/unit_tests/transformer/moe/test_shared_experts.py @@ -8,8 +8,7 @@ from megatron.core.models.gpt.gpt_layer_specs import get_gpt_layer_local_submodules from megatron.core.parallel_state import get_tensor_model_parallel_world_size from megatron.core.tensor_parallel.random import model_parallel_cuda_manual_seed -from megatron.core.transformer.moe.moe_layer import MoELayer, MoESubmodules -from megatron.core.transformer.spec_utils import get_submodules +from megatron.core.transformer.moe.moe_layer import MoELayer from megatron.core.transformer.transformer_config import TransformerConfig from tests.unit_tests.test_utilities import Utils @@ -34,16 +33,13 @@ def setup_method(self, method): ) def get_moe_layer(self, **kargs) -> MoELayer: - submodules = get_submodules( - get_gpt_layer_local_submodules( - num_experts=self.config.num_moe_experts, moe_grouped_gemm=False - ).mlp + submodules = get_gpt_layer_local_submodules( + num_experts=self.config.num_moe_experts, moe_grouped_gemm=False ) - assert isinstance(submodules, MoESubmodules) new_config = dataclasses.replace(self.config, **kargs) if get_tensor_model_parallel_world_size() > 1: new_config.sequence_parallel = True - moe_layer = MoELayer(new_config, submodules) + moe_layer = MoELayer(new_config, submodules.mlp.submodules) moe_layer.cuda() return moe_layer diff --git a/tests/unit_tests/transformer/moe/test_token_dispatcher.py b/tests/unit_tests/transformer/moe/test_token_dispatcher.py index a67e219a612..dead2c0c12d 100644 --- a/tests/unit_tests/transformer/moe/test_token_dispatcher.py +++ b/tests/unit_tests/transformer/moe/test_token_dispatcher.py @@ -1,5 +1,6 @@ # Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +import copy import dataclasses from types import SimpleNamespace @@ -8,9 +9,9 @@ from megatron.core import config, parallel_state from megatron.core.models.gpt.gpt_layer_specs import get_gpt_layer_local_submodules -from megatron.core.transformer.moe.moe_layer import MoELayer, MoESubmodules +from megatron.core.transformer.moe.moe_layer import MoELayer from megatron.core.transformer.moe.moe_utils import get_capacity -from megatron.core.transformer.spec_utils import get_submodules +from megatron.core.transformer.moe.token_dispatcher import MoETokenDispatcher from megatron.core.transformer.transformer_config import TransformerConfig from megatron.core.typed_torch import apply_module from megatron.core.utils import is_te_min_version @@ -142,16 +143,15 @@ def __init__( self.moe_layer = self.new_moe_layer() def new_moe_layer(self, **kargs): - submodules = get_submodules( - get_gpt_layer_local_submodules( - num_experts=self.config.num_moe_experts, - moe_grouped_gemm=self.config.moe_grouped_gemm, - ).mlp + submodules = get_gpt_layer_local_submodules( + num_experts=self.config.num_moe_experts, moe_grouped_gemm=self.config.moe_grouped_gemm ) - assert isinstance(submodules, MoESubmodules) new_config = dataclasses.replace(self.config, **kargs) - moe_layer = MoELayer(new_config, submodules).cuda().to(dtype=self.test_dtype) - moe_layer.set_layer_number(0) + moe_layer = ( + MoELayer(new_config, submodules.mlp.submodules, layer_number=0) + .cuda() + .to(dtype=self.test_dtype) + ) return moe_layer def __del__(self): diff --git a/tests/unit_tests/transformer/test_cuda_graphs.py b/tests/unit_tests/transformer/test_cuda_graphs.py index 8de64fc5df4..493bbd6bc49 100644 --- a/tests/unit_tests/transformer/test_cuda_graphs.py +++ b/tests/unit_tests/transformer/test_cuda_graphs.py @@ -12,7 +12,6 @@ from megatron.core.models.gpt.gpt_layer_specs import ( get_gpt_decoder_block_spec, get_gpt_layer_with_transformer_engine_spec, - get_gpt_layer_with_transformer_engine_submodules, get_gpt_mtp_block_spec, ) from megatron.core.models.gpt.gpt_model import GPTModel @@ -36,14 +35,12 @@ _CudagraphGlobalRecord, ) from megatron.core.transformer.enums import CudaGraphScope -from megatron.core.transformer.mlp import MLPSubmodules from megatron.core.transformer.module import MegatronModule from megatron.core.transformer.moe.fused_a2a import reset_hybrid_ep_buffer -from megatron.core.transformer.spec_utils import ModuleSpec, get_submodules from megatron.core.transformer.transformer_block import TransformerBlock from megatron.core.transformer.transformer_config import TransformerConfig -from megatron.core.transformer.transformer_layer import TransformerLayer -from megatron.core.utils import is_te_min_version +from megatron.core.transformer.transformer_layer import TransformerLayerSubmodules +from megatron.core.utils import is_fa_min_version, is_te_min_version from megatron.training.arguments import core_transformer_config_from_args, parse_args, validate_args from megatron.training.global_vars import ( destroy_global_vars, @@ -331,10 +328,10 @@ def setup_method(self, method): ) # Get layer specs - language_layer_submodules = get_gpt_layer_with_transformer_engine_submodules() + language_layer_spec = get_gpt_layer_with_transformer_engine_spec() vision_layer_spec = get_vit_layer_with_transformer_engine_spec() - vision_projection_spec = deepcopy(get_submodules(language_layer_submodules.mlp)) - assert isinstance(vision_projection_spec, MLPSubmodules) + assert isinstance(language_layer_spec.submodules, TransformerLayerSubmodules) + vision_projection_spec = deepcopy(language_layer_spec.submodules.mlp.submodules) # Set vision model type vision_config.vision_model_type = "clip" @@ -343,9 +340,7 @@ def setup_method(self, method): # Create LLaVA model with both encoder and decoder self.llava_model = LLaVAModel( language_transformer_config=language_config, - language_transformer_layer_spec=ModuleSpec( - module=TransformerLayer, submodules=language_layer_submodules - ), + language_transformer_layer_spec=language_layer_spec, language_vocab_size=8192, language_max_sequence_length=4096, vision_transformer_config=vision_config, diff --git a/tests/unit_tests/transformer/test_mlp.py b/tests/unit_tests/transformer/test_mlp.py index 46b22a1bad8..45e0df9ad0a 100644 --- a/tests/unit_tests/transformer/test_mlp.py +++ b/tests/unit_tests/transformer/test_mlp.py @@ -6,8 +6,7 @@ from megatron.core.models.gpt.gpt_layer_specs import get_gpt_layer_local_submodules from megatron.core.tensor_parallel.random import model_parallel_cuda_manual_seed -from megatron.core.transformer.mlp import MLP, MLPSubmodules -from megatron.core.transformer.spec_utils import get_submodules +from megatron.core.transformer.mlp import MLP from megatron.core.transformer.transformer_config import TransformerConfig from tests.unit_tests.test_utilities import Utils @@ -20,9 +19,7 @@ def setup_method(self, method): transformer_config = TransformerConfig( num_layers=2, hidden_size=12, num_attention_heads=4, use_cpu_initialization=True ) - mlp_submodules = get_submodules(get_gpt_layer_local_submodules().mlp) - assert isinstance(mlp_submodules, MLPSubmodules) - self.mlp = MLP(transformer_config, mlp_submodules) + self.mlp = MLP(transformer_config, get_gpt_layer_local_submodules().mlp.submodules) def teardown_method(self, method): Utils.destroy_model_parallel() diff --git a/tests/unit_tests/transformer/test_transformer_block_custom_pgs.py b/tests/unit_tests/transformer/test_transformer_block_custom_pgs.py index c415b6dda26..c55babe35ca 100644 --- a/tests/unit_tests/transformer/test_transformer_block_custom_pgs.py +++ b/tests/unit_tests/transformer/test_transformer_block_custom_pgs.py @@ -1,5 +1,5 @@ # Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. -from __future__ import annotations + import copy import os @@ -61,18 +61,20 @@ def __init__( config: TransformerConfig, submodules: TransformerLayerSubmodules, layer_number: int = 1, - hidden_dropout: float | None = None, - pg_collection: ProcessGroupCollection | None = None, - vp_stage: int | None = None, + hidden_dropout: Optional[float] = None, + pg_collection: ProcessGroupCollection = None, + vp_stage: Optional[int] = None, ): - # Temporarily replace attention with IdentityOp, + # Temporarily replace attention and MLP with IdentityOp, # This is a temporary workaround for the test until we have a better interface # will rebuild them with custom process groups after super init def _modify_submodules(submodules: TransformerLayerSubmodules): submodules.self_attention = IdentityOp + submodules.mlp = IdentityOp return submodules original_attention = submodules.self_attention + original_mlp = submodules.mlp new_submodules = _modify_submodules(copy.copy(submodules)) super().__init__( @@ -90,6 +92,10 @@ def _modify_submodules(submodules: TransformerLayerSubmodules): self.self_attention = build_module( original_attention, config=self.config, layer_number=layer_number ) + assert ( + 'tp_group' in submodules.mlp.params + ), "tp_group should be in the params of the submodules" + self.mlp = build_module(original_mlp, config=self.config) def create_reference_mlp(hidden_size, ffn_hidden_size, seed=12345): @@ -162,22 +168,7 @@ def copy_weights_to_tp_mlp(ref_mlp, tp_mlp, tp_group): tp_mlp.linear_fc2.bias.copy_(ref_fc2.bias.to(tp_mlp.linear_fc2.bias.device)) -def _gpt_te_layer_spec_with_hetro_pgs( - attn_pg_collection, mlp_pg_collection: ProcessGroupCollection -): - - def build_mlp( - config: TransformerConfig, pg_collection: ProcessGroupCollection, is_mtp_layer: bool - ): - del pg_collection, is_mtp_layer - return MLP( - config, - submodules=MLPSubmodules( - linear_fc1=TELayerNormColumnParallelLinear, linear_fc2=TERowParallelLinear - ), - tp_group=mlp_pg_collection.tp, - ) - +def _gpt_te_layer_spec_with_hetro_pgs(attn_pg_collection, mlp_pg_collection): return ModuleSpec( module=HeterogenousTransformerLayer, submodules=TransformerLayerSubmodules( @@ -192,7 +183,13 @@ def build_mlp( ), self_attn_bda=get_bias_dropout_add, pre_mlp_layernorm=IdentityOp, - mlp=build_mlp, + mlp=ModuleSpec( + module=MLP, + params={'tp_group': mlp_pg_collection.tp}, + submodules=MLPSubmodules( + linear_fc1=TELayerNormColumnParallelLinear, linear_fc2=TERowParallelLinear + ), + ), mlp_bda=get_bias_dropout_add, ), ) diff --git a/tests/unit_tests/transformer/test_vision_cuda_graphs.py b/tests/unit_tests/transformer/test_vision_cuda_graphs.py index 59244f47a61..a7c6cf8de65 100644 --- a/tests/unit_tests/transformer/test_vision_cuda_graphs.py +++ b/tests/unit_tests/transformer/test_vision_cuda_graphs.py @@ -1,6 +1,7 @@ # Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. import gc +import os from copy import deepcopy from types import SimpleNamespace from unittest.mock import MagicMock @@ -9,10 +10,7 @@ import torch from megatron.core import parallel_state -from megatron.core.models.gpt.gpt_layer_specs import ( - get_gpt_layer_with_transformer_engine_spec, - get_gpt_layer_with_transformer_engine_submodules, -) +from megatron.core.models.gpt.gpt_layer_specs import get_gpt_layer_with_transformer_engine_spec from megatron.core.models.vision.vit_layer_specs import get_vit_layer_with_transformer_engine_spec from megatron.core.tensor_parallel.random import ( HAVE_TE, @@ -25,11 +23,9 @@ _layer_is_graphable, _wrap_graph_for_vision, get_vision_cuda_graph_seq_length, + set_current_microbatch, ) -from megatron.core.transformer.mlp import MLPSubmodules -from megatron.core.transformer.spec_utils import ModuleSpec, get_submodules from megatron.core.transformer.transformer_config import TransformerConfig -from megatron.core.transformer.transformer_layer import TransformerLayer from megatron.core.utils import is_te_min_version from tests.unit_tests.test_utilities import Utils @@ -240,19 +236,16 @@ def setup_method(self, method): pipeline_dtype=torch.bfloat16, ) - language_layer_submodules = get_gpt_layer_with_transformer_engine_submodules() + language_layer_spec = get_gpt_layer_with_transformer_engine_spec() vision_layer_spec = get_vit_layer_with_transformer_engine_spec() - vision_projection_spec = deepcopy(get_submodules(language_layer_submodules.mlp)) - assert isinstance(vision_projection_spec, MLPSubmodules) + vision_projection_spec = deepcopy(language_layer_spec.submodules.mlp.submodules) self.vision_config.vision_model_type = "clip" language_config.language_model_type = "dummy" self.llava_model = LLaVAModel( language_transformer_config=language_config, - language_transformer_layer_spec=ModuleSpec( - module=TransformerLayer, submodules=language_layer_submodules - ), + language_transformer_layer_spec=language_layer_spec, language_vocab_size=8192, language_max_sequence_length=4096, vision_transformer_config=self.vision_config, @@ -502,10 +495,9 @@ def setup_method(self, method): pipeline_dtype=torch.bfloat16, ) - language_layer_submodules = get_gpt_layer_with_transformer_engine_submodules() + language_layer_spec = get_gpt_layer_with_transformer_engine_spec() vision_layer_spec = get_vit_layer_with_transformer_engine_spec() - vision_projection_spec = deepcopy(get_submodules(language_layer_submodules.mlp)) - assert isinstance(vision_projection_spec, MLPSubmodules) + vision_projection_spec = deepcopy(language_layer_spec.submodules.mlp.submodules) self.vision_config.vision_model_type = "clip" language_config.language_model_type = "dummy" @@ -513,9 +505,7 @@ def setup_method(self, method): self.is_first_stage = is_first_stage self.llava_model = LLaVAModel( language_transformer_config=language_config, - language_transformer_layer_spec=ModuleSpec( - module=TransformerLayer, submodules=language_layer_submodules - ), + language_transformer_layer_spec=language_layer_spec, language_vocab_size=8192, language_max_sequence_length=4096, vision_transformer_config=self.vision_config,