From 5e315141658ddcf3de790ff242d865bcce50cb88 Mon Sep 17 00:00:00 2001 From: Nick Schank Date: Sun, 10 May 2026 16:59:24 -0400 Subject: [PATCH 001/346] 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 002/346] 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 003/346] =?UTF-8?q?chore(beep=20boop=20=F0=9F=A4=96):=20Bu?= =?UTF-8?q?mp=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 005/346] 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 006/346] [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 007/346] 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 008/346] 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" }, - 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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 74687fe09384b7755c3027a13aec03cb56e4e4d6 Mon Sep 17 00:00:00 2001 From: Shivanjan Chakravorty Date: Tue, 12 May 2026 01:40:25 +0800 Subject: [PATCH 009/346] fix(fsdp): recognize legacy GDN TP metadata (#4664) Signed-off-by: Shivanjan Chakravorty Co-authored-by: Cory Ye <44509866+cspades@users.noreply.github.com> --- .../fsdp/src/megatron_fsdp/utils.py | 4 ++ .../test_fsdp_dtensor_checkpoint.py | 38 +++++++++++++++++++ 2 files changed, 42 insertions(+) diff --git a/megatron/core/distributed/fsdp/src/megatron_fsdp/utils.py b/megatron/core/distributed/fsdp/src/megatron_fsdp/utils.py index fe87a880390..9c9773a7123 100644 --- a/megatron/core/distributed/fsdp/src/megatron_fsdp/utils.py +++ b/megatron/core/distributed/fsdp/src/megatron_fsdp/utils.py @@ -822,6 +822,10 @@ def get_mcore_tensor_parallel_partition_dim(param: torch.Tensor) -> Optional[int return 0 elif param._tensor_parallel_mode == "row": return 1 + if getattr(param, "tensor_model_parallel", False): + partition_dim = getattr(param, "partition_dim", None) + if partition_dim is not None and partition_dim >= 0: + return int(partition_dim) return None diff --git a/tests/unit_tests/transformer/test_fsdp_dtensor_checkpoint.py b/tests/unit_tests/transformer/test_fsdp_dtensor_checkpoint.py index 20da61c90ef..e591d7cd2d8 100644 --- a/tests/unit_tests/transformer/test_fsdp_dtensor_checkpoint.py +++ b/tests/unit_tests/transformer/test_fsdp_dtensor_checkpoint.py @@ -33,6 +33,15 @@ import pytest import torch +from megatron.core.distributed.fsdp.src.megatron_fsdp.utils import ( + get_mcore_tensor_parallel_partition_dim, + is_mcore_tensor_model_parallel, + is_mcore_tensor_parallel_duplicated, +) +from megatron.core.tensor_parallel.layers import ( + copy_tensor_model_parallel_attributes, + set_tensor_model_parallel_attributes, +) from megatron.core.transformer.fsdp_dtensor_checkpoint import ( flatten_state_dict, get_expert_index_from_key, @@ -515,6 +524,35 @@ def test_tp_affects_split_sizes(self): assert r1[0][2] == 128 and r2[0][2] == 64 +class TestGDNFSDPTensorParallelMetadata: + """Regression coverage for GDN conv1d FSDP checkpoint splitting. + + GDN conv1d parameters are manually annotated with the legacy Megatron + tensor-parallel attrs. The GDN FSDP splitter copies those attrs to meta + tensors before calling make_fsdp_dtensor, so the FSDP utility must still + recognize them as tensor-parallel. + """ + + def test_legacy_tp_attrs_are_recognized_after_copy(self): + source = torch.nn.Parameter(torch.empty(8, 1, 4)) + set_tensor_model_parallel_attributes(source, True, 0, 1) + + meta = torch.empty(4, 1, 4, device="meta") + copy_tensor_model_parallel_attributes(meta, source) + + assert get_mcore_tensor_parallel_partition_dim(meta) == 0 + assert is_mcore_tensor_model_parallel(meta) + assert not is_mcore_tensor_parallel_duplicated(meta) + + def test_replicated_legacy_attrs_are_not_tp_sharded(self): + source = torch.nn.Parameter(torch.empty(8)) + set_tensor_model_parallel_attributes(source, False, -1, 1) + + assert get_mcore_tensor_parallel_partition_dim(source) is None + assert not is_mcore_tensor_model_parallel(source) + assert is_mcore_tensor_parallel_duplicated(source) + + # ============================================================================ # Test GDN state dict key generation after splitting # ============================================================================ From 9718f7d787ef4a737032311b99824c52a3c95f9d Mon Sep 17 00:00:00 2001 From: Maanu Grover Date: Mon, 11 May 2026 15:09:16 -0700 Subject: [PATCH 010/346] Fixes for Nemotron3 Super release test config (#4544) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: Maanu Grover Signed-off-by: oliver könig Co-authored-by: oliver könig --- .../golden_values_dev_dgx_gb200.json | 28658 ++++++++++++++++ .../model_config.yaml | 29 +- .../golden_values_dev_dgx_gb200.json | 28658 ++++++++++++++++ .../model_config.yaml | 161 + 4 files changed, 57489 insertions(+), 17 deletions(-) create mode 100644 tests/functional_tests/test_cases/nemotron/nemotron3_super_release_gb200/golden_values_dev_dgx_gb200.json rename tests/functional_tests/test_cases/nemotron/{nemotron3_super_release_g200 => nemotron3_super_release_gb200}/model_config.yaml (85%) create mode 100644 tests/functional_tests/test_cases/nemotron/nemotron3_super_release_gb200_sm/golden_values_dev_dgx_gb200.json create mode 100644 tests/functional_tests/test_cases/nemotron/nemotron3_super_release_gb200_sm/model_config.yaml diff --git a/tests/functional_tests/test_cases/nemotron/nemotron3_super_release_gb200/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/nemotron/nemotron3_super_release_gb200/golden_values_dev_dgx_gb200.json new file mode 100644 index 00000000000..d6fbbdd9b26 --- /dev/null +++ b/tests/functional_tests/test_cases/nemotron/nemotron3_super_release_gb200/golden_values_dev_dgx_gb200.json @@ -0,0 +1,28658 @@ +{ + "lm loss": { + "start_step": 1, + "end_step": 23840, + "step_interval": 5, + "values": { + "1": 10.78729, + "5": 10.78738, + "10": 10.77796, + "15": 10.68195, + "20": 10.4819, + "25": 9.75975, + 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diff --git a/tests/functional_tests/test_cases/nemotron/nemotron3_super_release_g200/model_config.yaml b/tests/functional_tests/test_cases/nemotron/nemotron3_super_release_gb200/model_config.yaml similarity index 85% rename from tests/functional_tests/test_cases/nemotron/nemotron3_super_release_g200/model_config.yaml rename to tests/functional_tests/test_cases/nemotron/nemotron3_super_release_gb200/model_config.yaml index 9c5f1807c2d..6fa15c22b5d 100644 --- a/tests/functional_tests/test_cases/nemotron/nemotron3_super_release_g200/model_config.yaml +++ b/tests/functional_tests/test_cases/nemotron/nemotron3_super_release_gb200/model_config.yaml @@ -3,7 +3,7 @@ ENV_VARS: NVTE_BWD_LAYERNORM_SM_MARGIN: 16 TORCHINDUCTOR_WORKER_START: fork QUANTIZATION_TYPE_DEBUG: 1 - PYTORCH_CUDA_ALLOC_CONF: expandable_segments:True + # PYTORCH_CUDA_ALLOC_CONF: expandable_segments:True NUM_OF_HYBRID_EP_RANKS_PER_NVLINK_DOMAIN: 64 USE_MNNVL: 1 TEST_TYPE: "release" @@ -25,24 +25,24 @@ MODEL_ARGS: # Training args --micro-batch-size: 1 - --global-batch-size: 3072 + --global-batch-size: 512 --train-samples: 12207031 --cross-entropy-loss-fusion: true --cross-entropy-fusion-impl: native --attention-backend: flash --enable-cuda-graph: true - --cuda-graph-scope: mamba attn moe_router + --cuda-graph-scope: "[mamba attn]" --te-rng-tracker: true --manual-gc: true --manual-gc-interval: 10 --no-create-attention-mask-in-dataloader: true --num-workers: 1 - --exit-interval: 51000 + --exit-interval: 23840 --override-opt_param-scheduler: true # Network size args --use-mcore-models: true - --spec: megatron.core.models.hybrid.hybrid_layer_specs hybrid_stack_spec + --spec: "[megatron.core.models.hybrid.hybrid_layer_specs hybrid_stack_spec]" --is-hybrid-model: true --mamba-num-heads: 128 --num-layers: 88 @@ -87,10 +87,8 @@ MODEL_ARGS: --moe-permute-fusion: true --moe-latent-size: 1024 --moe-shared-expert-intermediate-size: 5376 - --moe-shared-expert-compute-before-router: true # MTP args - --mtp-spec: megatron.core.models.hybrid.hybrid_layer_specs hybrid_stack_spec --mtp-num-layers: 2 --mtp-hybrid-override-pattern: \"*E\" --calculate-per-token-loss: true @@ -98,10 +96,7 @@ MODEL_ARGS: # Mixed precision / quantization args --bf16: true - --keep-mtp-spec-in-bf16: true - --keep-mamba-stack-attention-linear-in-bf16: true - --keep-mamba-out-proj-in-mxfp8: true - --keep-moe-latent-projections-in-bf16: true + --te-precision-config-file: /mnt/artifacts/model/nemotron3_super_release_gb200/te_quant.cfg --first-last-layers-bf16: true --num-layers-at-start-in-bf16: 0 --num-layers-at-end-in-bf16: 14 @@ -134,15 +129,15 @@ MODEL_ARGS: --ckpt-assume-constant-structure: true --async-save: true --use-persistent-ckpt-worker: true - --save-interval: 1000 - --save-retain-interval: 5000 + --save-interval: 100 + --save-retain-interval: 2000 # Validation args - --eval-interval: 1000 + --eval-interval: 100 --eval-iters: 14 # Logging args - --log-interval: 100 + --log-interval: 10 --log-params-norm: true --log-num-zeros-in-grad: true --log-timers-to-tensorboard: true @@ -150,10 +145,10 @@ MODEL_ARGS: --log-throughput: true --log-progress: true --log-energy: true - --log-memory-interval: 500 + --log-memory-interval: 50 --logging-level: 20 --timing-log-option: minmax - --check-weight-hash-across-dp-replicas-interval: 20000 + --check-weight-hash-across-dp-replicas-interval: 2000 --tensorboard-dir: ${TENSORBOARD_PATH} --wandb-project: megatron-core-release-runs --wandb-entity: adlr diff --git a/tests/functional_tests/test_cases/nemotron/nemotron3_super_release_gb200_sm/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/nemotron/nemotron3_super_release_gb200_sm/golden_values_dev_dgx_gb200.json new file mode 100644 index 00000000000..d6fbbdd9b26 --- /dev/null +++ b/tests/functional_tests/test_cases/nemotron/nemotron3_super_release_gb200_sm/golden_values_dev_dgx_gb200.json @@ -0,0 +1,28658 @@ +{ + "lm loss": { + "start_step": 1, + "end_step": 23840, + "step_interval": 5, + 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14.84946, + "23815": "nan", + "23820": 13.50638, + "23825": "nan", + "23830": 13.58337, + "23835": "nan", + "23840": 13.57023 + } + } +} \ No newline at end of file diff --git a/tests/functional_tests/test_cases/nemotron/nemotron3_super_release_gb200_sm/model_config.yaml b/tests/functional_tests/test_cases/nemotron/nemotron3_super_release_gb200_sm/model_config.yaml new file mode 100644 index 00000000000..5cbc331f2c1 --- /dev/null +++ b/tests/functional_tests/test_cases/nemotron/nemotron3_super_release_gb200_sm/model_config.yaml @@ -0,0 +1,161 @@ +ENV_VARS: + NVTE_FWD_LAYERNORM_SM_MARGIN: 16 + NVTE_BWD_LAYERNORM_SM_MARGIN: 16 + TORCHINDUCTOR_WORKER_START: fork + QUANTIZATION_TYPE_DEBUG: 1 + # PYTORCH_CUDA_ALLOC_CONF: expandable_segments:True + NUM_OF_HYBRID_EP_RANKS_PER_NVLINK_DOMAIN: 64 + USE_MNNVL: 1 +TEST_TYPE: "release" +MODEL_ARGS: + # Distributed args + --tensor-model-parallel-size: 2 + --pipeline-model-parallel-size: 1 + --expert-model-parallel-size: 64 + --expert-tensor-parallel-size: 1 + --use-distributed-optimizer: true + --overlap-grad-reduce: true + --overlap-param-gather: true + --sequence-parallel: true + --ddp-num-buckets: 10 + --ddp-pad-buckets-for-high-nccl-busbw: true + --high-priority-stream-groups: ep + --distributed-timeout-minutes: 10 + --disable-gloo-process-groups: true + + # Training args + --micro-batch-size: 1 + --global-batch-size: 512 + --train-samples: 12207031 + --cross-entropy-loss-fusion: true + --cross-entropy-fusion-impl: native + --attention-backend: flash + --enable-cuda-graph: true + --cuda-graph-scope: "[mamba attn]" + --te-rng-tracker: true + --manual-gc: true + --manual-gc-interval: 10 + --no-create-attention-mask-in-dataloader: true + --num-workers: 1 + --exit-interval: 4768 + --override-opt_param-scheduler: true + + # Network size args + --use-mcore-models: true + --spec: "[megatron.core.models.hybrid.hybrid_layer_specs hybrid_stack_spec]" + --is-hybrid-model: true + --mamba-num-heads: 128 + --num-layers: 88 + --hidden-size: 4096 + --ffn-hidden-size: 2688 + --num-attention-heads: 32 + --group-query-attention: true + --num-query-groups: 2 + --kv-channels: 128 + --hybrid-override-pattern: MEMEMEM*EMEMEMEM*EMEMEMEM*EMEMEMEMEM*EMEMEMEMEM*EMEMEMEMEM*EMEMEMEMEM*EMEMEMEM*EMEMEMEME + --position-embedding-type: none + --normalization: RMSNorm + --untie-embeddings-and-output-weights: true + --init-method-std: 0.014 + --disable-bias-linear: true + --squared-relu: true + --use-fused-weighted-squared-relu: true + + # Data args + --seq-length: 8192 + --max-position-embeddings: 8192 + --data-path: ${DATA_BLEND} + --data-cache-path: ${DATA_CACHE_PATH} + --tiktoken-pattern: v2 + --tokenizer-type: ${TOKENIZER_TYPE} + --tokenizer-model: ${TOKENIZER_MODEL_PATH} + --no-mmap-bin-files: true + + # MoE args + --num-experts: 512 + --moe-router-topk: 22 + --moe-router-topk-scaling-factor: 5.0 + --moe-router-score-function: sigmoid + --moe-router-enable-expert-bias: true + --moe-router-dtype: fp32 + --moe-router-load-balancing-type: seq_aux_loss + --moe-aux-loss-coeff: 1e-4 + --moe-token-dispatcher-type: flex + --moe-flex-dispatcher-backend: hybridep + --moe-hybridep-num-sms: 32 + --moe-grouped-gemm: true + --moe-permute-fusion: true + --moe-latent-size: 1024 + --moe-shared-expert-intermediate-size: 5376 + + # MTP args + --mtp-num-layers: 2 + --mtp-hybrid-override-pattern: \"*E\" + --calculate-per-token-loss: true + --mtp-loss-scaling-factor: 0.3 + + # Mixed precision / quantization args + --bf16: true + --te-precision-config-file: /mnt/artifacts/model/nemotron3_super_release_gb200/te_quant.cfg + --first-last-layers-bf16: true + --num-layers-at-start-in-bf16: 0 + --num-layers-at-end-in-bf16: 14 + --fp4-format: e2m1 + --fp4-recipe: nvfp4 + + # Regularization args + --attention-dropout: 0.0 + --hidden-dropout: 0.0 + --clip-grad: 1.0 + --weight-decay: 0.1 + + # Learning rate args + --lr: 4.5e-4 + --min-lr: 4.5e-6 + --lr-decay-style: WSD + --lr-warmup-samples: 24414063 + --lr-decay-samples: 3048706055 + --lr-wsd-decay-style: minus_sqrt + --lr-wsd-decay-samples: 610351563 + --adam-beta1: 0.9 + --adam-beta2: 0.95 + + # Checkpointing args + --save: ${CHECKPOINT_SAVE_PATH} + --load: ${CHECKPOINT_LOAD_PATH} + --ckpt-format: torch_dist + --ckpt-fully-parallel-save: true + --ckpt-fully-parallel-load: true + --ckpt-assume-constant-structure: true + --async-save: true + --use-persistent-ckpt-worker: true + --save-interval: 100 + --save-retain-interval: 2000 + + # Validation args + --eval-interval: 100 + --eval-iters: 14 + + # Logging args + --log-interval: 10 + --log-params-norm: true + --log-num-zeros-in-grad: true + --log-timers-to-tensorboard: true + --log-memory-to-tensorboard: true + --log-throughput: true + --log-progress: true + --log-energy: true + --log-memory-interval: 50 + --logging-level: 20 + --timing-log-option: minmax + --check-weight-hash-across-dp-replicas-interval: 2000 + --tensorboard-dir: ${TENSORBOARD_PATH} + --wandb-project: megatron-core-release-runs + --wandb-entity: adlr + --wandb-exp-name: ${WANDB_EXPERIMENT} + --wandb-save-dir: ${WANDB_SAVE_PATH} +METRICS: + - "iteration-time" + - "lm loss" + - "mem-allocated-bytes" + - "mem-max-allocated-bytes" From 97f3bce8801e4f1e6dbe1395feb5c8eccf27b43b Mon Sep 17 00:00:00 2001 From: Shivanjan Chakravorty Date: Tue, 12 May 2026 06:10:13 +0800 Subject: [PATCH 011/346] feat(gpt): add output postprocess hook (#4686) Signed-off-by: Shivanjan Chakravorty --- .../common/model_chunk_schedule_plan.py | 11 +- .../core/models/gpt/fine_grained_callables.py | 2 + megatron/core/models/gpt/gpt_model.py | 41 ++++++- tests/unit_tests/models/test_gpt_model.py | 116 ++++++++++++++++++ 4 files changed, 168 insertions(+), 2 deletions(-) diff --git a/megatron/core/models/common/model_chunk_schedule_plan.py b/megatron/core/models/common/model_chunk_schedule_plan.py index 9032d337e00..72b12591451 100644 --- a/megatron/core/models/common/model_chunk_schedule_plan.py +++ b/megatron/core/models/common/model_chunk_schedule_plan.py @@ -1,7 +1,7 @@ # Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. from contextlib import nullcontext -from typing import Optional +from typing import Any, Callable, Optional import torch from torch import Tensor @@ -282,6 +282,9 @@ def __init__( runtime_gather_output: Optional[bool] = None, loss_mask: Optional[Tensor] = None, padding_mask=None, + *, + output_processor: Optional[Callable[..., Tensor]] = None, + output_processor_context: Optional[Any] = None, ): """Initialize the schedule plan of all Transformer layers' sub-modules. @@ -299,6 +302,10 @@ def __init__( extra_block_kwargs: Additional keyword arguments for blocks. runtime_gather_output: Whether to gather output at runtime. loss_mask (torch.Tensor): Used to mask out some portions of the loss + output_processor (Callable): Custom postprocess hook to run instead of the + default logits/loss path. + output_processor_context (Any): User-defined context object forwarded to + `output_processor`. Returns: The model chunk schedule plan. @@ -324,6 +331,8 @@ def __init__( self._model_chunk_state.padding_mask = padding_mask self._model_chunk_state.extra_block_kwargs = extra_block_kwargs self._model_chunk_state.runtime_gather_output = runtime_gather_output + self._model_chunk_state.output_processor = output_processor + self._model_chunk_state.output_processor_context = output_processor_context self._model_chunk_state.model = model self._model_chunk_state.context = None self._model_chunk_state.context_mask = None diff --git a/megatron/core/models/gpt/fine_grained_callables.py b/megatron/core/models/gpt/fine_grained_callables.py index fa2a2ec4934..acfa7a1e8a8 100644 --- a/megatron/core/models/gpt/fine_grained_callables.py +++ b/megatron/core/models/gpt/fine_grained_callables.py @@ -228,6 +228,8 @@ def forward_impl(self, hidden_states): sequence_len_offset=self.chunk_state.sequence_len_offset, runtime_gather_output=self.chunk_state.runtime_gather_output, extra_block_kwargs=self.chunk_state.extra_block_kwargs, + output_processor=self.chunk_state.output_processor, + output_processor_context=self.chunk_state.output_processor_context, ) # For now, 1f1b only supports fp16 module diff --git a/megatron/core/models/gpt/gpt_model.py b/megatron/core/models/gpt/gpt_model.py index cd1ff4df87d..da8a0e2fdfd 100644 --- a/megatron/core/models/gpt/gpt_model.py +++ b/megatron/core/models/gpt/gpt_model.py @@ -1,7 +1,7 @@ # Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. from collections import OrderedDict -from typing import Dict, Literal, Optional +from typing import Any, Callable, Dict, Literal, Optional import torch from torch import Tensor @@ -491,6 +491,8 @@ def forward( inference_params: Optional[BaseInferenceContext] = None, loss_mask: Optional[Tensor] = None, padding_mask: Optional[Tensor] = None, + output_processor: Optional[Callable[..., Tensor]] = None, + output_processor_context: Optional[Any] = None, ) -> Tensor: """Forward function of the GPT Model This function passes the input tensors through the embedding layer, and then the decoder and finally into the post @@ -504,6 +506,10 @@ def forward( padding_mask (Tensor, optional): Padding mask for MoE routing. Shape [bsz, seq_length]. True = padding (exclude), False = valid (include). Only used for MoE layers to exclude padding tokens from routing computations. + output_processor (Callable, optional): Custom postprocess hook that receives + decoder hidden states and output-layer helpers, then returns the model output. + output_processor_context (Any, optional): User-defined context object forwarded to + `output_processor`. """ if self.config.fine_grained_activation_offloading: self.preprocess_for_fine_grained_offloading() @@ -563,6 +569,8 @@ def forward( runtime_gather_output=runtime_gather_output, extra_block_kwargs=extra_block_kwargs, inference_context=inference_context, + output_processor=output_processor, + output_processor_context=output_processor_context, ) def _postprocess( @@ -584,6 +592,8 @@ def _postprocess( runtime_gather_output=None, extra_block_kwargs=None, inference_context=None, + output_processor=None, + output_processor_context=None, ): """Postprocesses decoder hidden states to generate logits or compute loss. @@ -650,6 +660,26 @@ def _postprocess( ) sequence_parallel_override = False + if output_processor is not None: + return output_processor( + hidden_states=hidden_states, + output_layer=self.output_layer, + output_weight=output_weight, + labels=labels, + loss_mask=loss_mask, + input_ids=input_ids, + position_ids=position_ids, + attention_mask=attention_mask, + decoder_input=decoder_input, + inference_context=inference_context, + packed_seq_params=packed_seq_params, + runtime_gather_output=runtime_gather_output, + context=output_processor_context, + compute_language_model_loss=self.compute_language_model_loss, + scale_logits=self._scale_logits, + config=self.config, + ) + if in_inference_mode and inference_context.config.materialize_only_last_token_logits: if inference_context.is_static_batching(): hidden_states = hidden_states[-1:, :, :] @@ -719,6 +749,9 @@ def build_schedule_plan( inference_params: Optional[BaseInferenceContext] = None, loss_mask: Optional[Tensor] = None, padding_mask: Optional[Tensor] = None, + *, + output_processor: Optional[Callable[..., Tensor]] = None, + output_processor_context: Optional[Any] = None, ): """Builds a computation schedule plan for the model. @@ -745,6 +778,10 @@ def build_schedule_plan( Parameters for inference. Defaults to None. loss_mask (Optional[Tensor], optional): Loss mask. Defaults to None. padding_mask (Optional[Tensor], optional): Padding mask. Defaults to None. + output_processor (Callable, optional): Custom postprocess hook to run in the + schedule-plan postprocess node instead of the default logits/loss path. + output_processor_context (Any, optional): User-defined context object forwarded to + `output_processor`. Returns: TransformerModelChunkSchedulePlan: The model chunk schedule plan. @@ -767,6 +804,8 @@ def build_schedule_plan( runtime_gather_output, loss_mask, padding_mask, + output_processor=output_processor, + output_processor_context=output_processor_context, ) def sharded_state_dict( diff --git a/tests/unit_tests/models/test_gpt_model.py b/tests/unit_tests/models/test_gpt_model.py index 336ff0552b0..d1352be0c8e 100644 --- a/tests/unit_tests/models/test_gpt_model.py +++ b/tests/unit_tests/models/test_gpt_model.py @@ -7,6 +7,7 @@ import pytest import torch +import torch.nn.functional as F from packaging import version from pytest import approx from transformer_engine.pytorch.fp8 import check_fp8_support @@ -112,6 +113,121 @@ def test_post_process_forward(self): assert logits.shape[1] == sequence_length assert logits.shape[2] == self.gpt_model.vocab_size + @pytest.mark.internal + def test_output_processor_forward(self): + config: TransformerConfig = self.gpt_model.config + sequence_length = self.gpt_model.max_sequence_length + micro_batch_size = 2 + + self.gpt_model.cuda() + self.gpt_model.eval() + + data = list(range(sequence_length)) + input_ids = torch.tensor(data, dtype=torch.int64).repeat((micro_batch_size, 1)).cuda() + labels = (input_ids + 1) % self.gpt_model.vocab_size + position_ids = torch.tensor(data, dtype=torch.int64).repeat((micro_batch_size, 1)).cuda() + attention_mask = torch.ones( + (micro_batch_size, 1, sequence_length, sequence_length), dtype=bool + ).cuda() + + context = {"selected_token_positions": torch.tensor([0, 2], device="cuda")} + seen = {} + + def output_processor(**kwargs): + seen.update(kwargs) + logits, _ = kwargs["output_layer"]( + kwargs["hidden_states"], + weight=kwargs["output_weight"], + runtime_gather_output=kwargs["runtime_gather_output"], + ) + logits = kwargs["scale_logits"](logits).transpose(0, 1).contiguous().float() + token_logprobs = F.log_softmax(logits, dim=-1).gather( + dim=-1, index=kwargs["labels"].unsqueeze(-1) + ) + token_logprobs = token_logprobs.squeeze(-1) + return token_logprobs.index_select(1, kwargs["context"]["selected_token_positions"]) + + with torch.no_grad(): + logits = self.gpt_model.forward( + input_ids=input_ids, position_ids=position_ids, attention_mask=attention_mask + ).float() + expected = F.log_softmax(logits, dim=-1).gather(dim=-1, index=labels.unsqueeze(-1)) + expected = expected.squeeze(-1).index_select(1, context["selected_token_positions"]) + + output = self.gpt_model.forward( + input_ids=input_ids, + position_ids=position_ids, + attention_mask=attention_mask, + labels=labels, + output_processor=output_processor, + output_processor_context=context, + ) + + assert torch.allclose(output, expected) + assert seen["context"] is context + assert seen["output_layer"] is self.gpt_model.output_layer + assert seen["output_weight"] is None + assert seen["labels"] is labels + assert seen["runtime_gather_output"] is None + assert seen["config"] is config + assert seen["compute_language_model_loss"].__self__ is self.gpt_model + assert seen["scale_logits"].__self__ is self.gpt_model + + @pytest.mark.internal + def test_build_schedule_plan_threads_output_processor(self): + sequence_length = self.gpt_model.max_sequence_length + micro_batch_size = 2 + + self.gpt_model.cuda() + + data = list(range(sequence_length)) + input_ids = torch.tensor(data, dtype=torch.int64).repeat((micro_batch_size, 1)).cuda() + labels = torch.tensor(data, dtype=torch.int64).repeat((micro_batch_size, 1)).cuda() + position_ids = torch.tensor(data, dtype=torch.int64).repeat((micro_batch_size, 1)).cuda() + attention_mask = torch.ones( + (micro_batch_size, 1, sequence_length, sequence_length), dtype=bool + ).cuda() + + context = {"loss_mask": torch.ones_like(labels)} + + seen = {} + + def output_processor(**kwargs): + seen.update(kwargs) + return kwargs["hidden_states"].sum() + + schedule_plan = self.gpt_model.build_schedule_plan( + input_ids=input_ids, + position_ids=position_ids, + attention_mask=attention_mask, + labels=labels, + output_processor=output_processor, + output_processor_context=context, + ) + + assert schedule_plan.state.output_processor is output_processor + assert schedule_plan.state.output_processor_context is context + + schedule_plan.state.decoder_input = None + schedule_plan.state.rotary_pos_emb = None + schedule_plan.state.rotary_pos_cos = None + schedule_plan.state.rotary_pos_sin = None + schedule_plan.state.sequence_len_offset = None + + hidden_states = torch.randn( + sequence_length, + micro_batch_size, + self.gpt_model.config.hidden_size, + device="cuda", + requires_grad=True, + ) + output = schedule_plan.post_process.forward(hidden_states) + + assert output.item() == pytest.approx(hidden_states.sum().item()) + assert seen["context"] is context + assert seen["labels"] is labels + assert seen["output_layer"] is self.gpt_model.output_layer + def test_get_mlp_module_spec_interface(): # Get the function signature From f744215afc195ae853f3466c99dbbee6e10c7501 Mon Sep 17 00:00:00 2001 From: Ajay Date: Mon, 11 May 2026 15:39:37 -0700 Subject: [PATCH 012/346] Add bump-base-image skill and update golden value comparison (#4733) Signed-off-by: Ajay Balasa Co-authored-by: Cursor --- skills/bump-base-image/SKILL.md | 150 ++++++++++++++++++ skills/update-golden-values/SKILL.md | 137 ++++++++-------- .../compare_golden_values_kl.py | 136 +++++----------- 3 files changed, 265 insertions(+), 158 deletions(-) create mode 100644 skills/bump-base-image/SKILL.md diff --git a/skills/bump-base-image/SKILL.md b/skills/bump-base-image/SKILL.md new file mode 100644 index 00000000000..a12955e2eca --- /dev/null +++ b/skills/bump-base-image/SKILL.md @@ -0,0 +1,150 @@ +--- +name: bump-base-image +description: Bump the NVIDIA PyTorch base image (`nvcr.io/nvidia/pytorch:-py3`) used by Megatron-LM CI. Covers the two pin sites (GitHub CI in `docker/.ngc_version.dev` and GitLab CI in `.gitlab/stages/01.build.yml`), the post-bump CI loop (re-run functional tests, refresh golden values, mark broken tests), and the gotchas that bit PRs #4611 and #4688. +when_to_use: User wants to upgrade the PyTorch container (e.g. "bump base image to 26.04"); CI is failing after a previous bump because the GitLab pin was missed; functional tests are failing with `lm loss` / `num-zeros` / `iteration-time` drift right after a container bump; a functional test hangs, times out, or OOMs after a bump; the user mentions `.ngc_version.dev`, `nvcr.io/nvidia/pytorch`, "container base image", or "Update Docker image version". +--- + +# Bump the PyTorch base image + +End-to-end workflow for moving Megatron-LM's CI to a newer `nvcr.io/nvidia/pytorch:-py3` container. The most common failure mode is forgetting that **GitHub CI and GitLab CI have separate pins** — a bump that only touches the former lands green, then breaks GitLab CI on `main` and forces an immediate follow-up PR. Always update both in the same PR. + +## Inputs to gather from the user + +1. **Target tag**, e.g. `26.04-py3`. NVIDIA NGC PyTorch containers are released as `nvcr.io/nvidia/pytorch:YY.MM-py3`. +2. **Scope** — usually `dev` only. The `lts` pin (`docker/.ngc_version.lts`, plus the `IMAGE_TYPE: lts` rows in GitLab) is bumped on a different cadence; only touch it if the user explicitly asks. +3. **Workflow run ID** (optional but typical) — after the first CI run, the user will provide a GitHub Actions run ID for golden-value refresh. + +## Workflow + +``` +- [ ] Step 1: Update the GitHub CI pin (docker/.ngc_version.dev) +- [ ] Step 2: Update the GitLab CI pin (.gitlab/stages/01.build.yml) +- [ ] Step 3: Open the PR with the `Run functional tests` label +- [ ] Step 4: Re-run failing tests via `/ok to test ` +- [ ] Step 5: For golden-value drift → refresh with the `update-golden-values` skill +- [ ] Step 6: For hangs / real regressions → mark tests `mr-broken` and file tracking issues +- [ ] Step 7: Verify both pins are in sync before merging +``` + +### Step 1 — GitHub CI pin + +`docker/.ngc_version.dev` is a single-line file consumed by `docker/Dockerfile.ci.dev` (via `FROM_IMAGE_NAME=$(cat docker/.ngc_version.dev)`). Overwrite it: + +```bash +echo 'nvcr.io/nvidia/pytorch:-py3' > docker/.ngc_version.dev +``` + +The file has no trailing newline historically; preserving or adding one is fine — the build args treat the value as `$(cat ...)`. Do **not** touch `docker/.ngc_version.lts` unless bumping LTS too. + +### Step 2 — GitLab CI pin + +GitLab CI does **not** read `docker/.ngc_version.dev`. It hardcodes `BASE_IMAGE` in a `parallel: matrix:` block. Update the two `IMAGE_TYPE: dev` rows (one per platform): + +```yaml +# .gitlab/stages/01.build.yml — under test:pre_build_image -> parallel.matrix +- IMAGE: CI_MCORE_DEV_IMAGE + FILE: Dockerfile.ci.dev + IMAGE_TYPE: dev + BASE_IMAGE: nvcr.io/nvidia/pytorch:-py3 # amd64 row + PLATFORM: amd64 +- IMAGE: CI_MCORE_DEV_IMAGE + FILE: Dockerfile.ci.dev + IMAGE_TYPE: dev + BASE_IMAGE: nvcr.io/nvidia/pytorch:-py3 # arm64 row + PLATFORM: arm64 +``` + +Leave the `IMAGE_TYPE: lts` rows alone. Quick sanity check before commit: + +```bash +rg -n '^\s*BASE_IMAGE: nvcr\.io/nvidia/pytorch:' .gitlab/stages/01.build.yml +# expect: lts pin × 2 unchanged, dev pin × 2 == new tag +``` + +### Step 3 — Open the PR + +- Title convention: `chore: Update Docker image version to -py3` (see #4611). +- **Apply the `Run functional tests` label** before the first push. This unlocks the full functional matrix on the PR; without it the bump only runs the standard GH PR checks and you'll miss the drift. +- Push as draft first if you're still iterating; the bot will auto-draft otherwise. + +### Step 4 — Re-running CI on a new commit + +For PRs from forks (the typical contributor case), each new commit needs an explicit `/ok to test ` PR comment to authorize NVIDIA runners (see the `copy-pr-bot` flow in #4611). One comment per commit. If `copy-pr-bot` reports "had a problem deploying to test", just push another commit (or re-issue the comment after the next push); the deploy is per-commit, not per-comment. + +### Step 5 — Golden-value drift + +Container bumps shift CUDA / cuBLAS / cuDNN / kernel autotuning, which moves `lm loss`, `num-zeros`, `iteration-time`, and `mem-*` metrics on a large fraction of functional tests. This is **expected** and is not a correctness regression — refresh the golden values rather than chasing each test. + +Hand off to the `update-golden-values` skill with: + +- `--source github` +- `--pipeline-id ` from the failing CI run +- `--only-failing` (refresh just the trajectories that drifted) + +PR #4611 refreshed **78 golden-value files** across `dev_dgx_h100` and `dev_dgx_gb200` for GPT / MoE / MIMO / hybrid suites in a single pass via this exact flow. The per-metric relative-difference summary the skill produces is the recommended PR description blurb — reviewers expect to see it. + +### Step 6 — Real regressions: mark broken, don't block the bump + +A small number of tests will genuinely break (hangs, OOM, real numerical regressions). Don't gate the base-image bump on fixing them — that conflates two changes. Instead: + +1. **File a GitHub issue** describing the failure mode and linking the failing CI run. +2. **Flip the test's scope to the `-broken` variant** in the recipe YAML under `tests/test_utils/recipes//`, with an inline comment that references the issue. Pattern: + + ```yaml + - test_case: [hybrid_dynamic_inference_tp1_ep8_nanov3_chunked_prefill] + products: + - environment: [dev] + # Broken: hangs on repeat iter 3, exceeds 1h job limit — see issue #. + scope: [mr-broken, mr-github-broken] # was: [mr, mr-github] + platforms: [dgx_h100] + ``` + + Scope mapping (replace, don't append): + + | Before | After | + | ------------- | -------------------- | + | `mr` | `mr-broken` | + | `mr-github` | `mr-github-broken` | + | `nightly` | `nightly-broken` | + + The recipe still runs in the `-broken` scope, but failures stop blocking PR merges. + +### Step 7 — Sync check before merging + +The single biggest failure mode of this workflow is shipping #4611 without #4688. Before you ask for the merge, confirm both pins resolve to the same tag: + +```bash +echo -n "ngc_version.dev: " && cat docker/.ngc_version.dev +echo +echo "gitlab dev rows:" +rg -n '^\s*BASE_IMAGE: nvcr\.io/nvidia/pytorch:' .gitlab/stages/01.build.yml \ + | rg -B1 'IMAGE_TYPE: dev' \ + | rg 'BASE_IMAGE' +``` + +All three lines should show `nvcr.io/nvidia/pytorch:-py3`. If they don't, fix it before merge — otherwise GitLab CI keeps building on the old container and the next person hits the same trap. + +## File-touch cheat sheet + +| Path | Edit | +| --------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------- | +| `docker/.ngc_version.dev` | Overwrite with new `nvcr.io/nvidia/pytorch:-py3` | +| `.gitlab/stages/01.build.yml` | Update both `IMAGE_TYPE: dev` `BASE_IMAGE:` rows (amd64 + arm64) | +| `tests/functional_tests/test_cases/**/golden_values_dev_dgx_{h100,gb200}.json` | Refresh via the `update-golden-values` skill | +| `tests/test_utils/recipes//.yaml` | Flip drifting / hanging cases to `mr-broken` / `mr-github-broken` with an issue link | +| `docker/.ngc_version.lts`, `.gitlab/stages/01.build.yml` `IMAGE_TYPE: lts` rows | **Skip unless explicitly bumping LTS.** LTS has its own release cadence. | + +## Gotchas + +- **GitHub vs GitLab pins are independent.** `docker/.ngc_version.dev` only drives GitHub CI's local container build via `Dockerfile.ci.dev`. GitLab CI has its own hardcoded `BASE_IMAGE:` matrix in `.gitlab/stages/01.build.yml`. PR #4688 existed solely because #4611 forgot the second one — don't repeat this. +- **Don't bump LTS along with dev.** The `IMAGE_TYPE: lts` rows and `docker/.ngc_version.lts` are stability-pinned for the `container::lts` label path. Bump them in a dedicated PR with its own LTS validation. +- **Don't fix golden-value drift by hand.** Use `tests/test_utils/python_scripts/download_golden_values.py` via the `update-golden-values` skill. Hand-editing the JSONs invites diff noise and relative-difference regressions on subsequent bumps. +- **`mr-broken` is a real scope, not a comment marker.** It keeps the recipe wired into the matrix (so it stays discoverable and runnable on demand) without gating merges. Don't delete the test case from the recipe. +- **`/ok to test` is per-commit.** A new force-push or fixup commit needs a fresh `/ok to test ` comment to re-trigger NVIDIA-runner CI on a fork PR. +- **Don't merge until the GitLab pin matches.** Use the Step 7 grep before requesting review. + +## Related skills + +- [update-golden-values](../update-golden-values/SKILL.md) — call this as soon as the first post-bump CI run finishes and you have a workflow run ID with failing golden checks. Produces the per-metric relative-difference summary you paste into the PR description. +- [build-and-dependency](../build-and-dependency/SKILL.md) — for verifying the new image builds locally before opening the PR (`docker build --target main --build-arg FROM_IMAGE_NAME=$(cat docker/.ngc_version.dev) ...`). +- [cicd](../cicd/SKILL.md) — for the PR scope-label semantics (`Run functional tests`, `complexity::*`) and the `copy-pr-bot` flow. diff --git a/skills/update-golden-values/SKILL.md b/skills/update-golden-values/SKILL.md index 71e3d1399a7..97172156244 100644 --- a/skills/update-golden-values/SKILL.md +++ b/skills/update-golden-values/SKILL.md @@ -1,17 +1,17 @@ --- name: update-golden-values -description: Refresh golden values from a GitHub Actions workflow run (failing-only or all jobs), score the change with KL divergence, and produce a PR-ready summary. Use when the user asks to update goldens for a CI run, refresh golden values from a workflow ID, or generate a golden-value diff summary for a PR description. -when_to_use: User provides a GitHub Actions workflow run ID and asks to refresh golden values; user asks to update goldens for "failing tests only" or "all tests"; user asks for a KL-divergence summary of the golden-value diff; user wants a PR description blurb after running download_golden_values.py. +description: Refresh golden values from a GitHub Actions workflow run (failing-only or all jobs), score the change with average normalized relative differences, and produce a PR-ready summary. Use when the user asks to update goldens for a CI run, refresh golden values from a workflow ID, or generate a golden-value diff summary for a PR description. +when_to_use: User provides a GitHub Actions workflow run ID and asks to refresh golden values; user asks to update goldens for "failing tests only" or "all tests"; user asks for a per-metric relative-difference summary of the golden-value diff; user wants a PR description blurb after running download_golden_values.py. --- -# Update golden values + KL summary +# Update golden values + relative-diff summary -End-to-end workflow for refreshing golden values from a GitHub Actions workflow run, validating the update with KL divergence, and writing a PR-ready summary. +End-to-end workflow for refreshing golden values from a GitHub Actions workflow run, scoring the update with a per-metric average normalized relative difference, and writing a PR-ready summary. The skill orchestrates two scripts that already live in the repo: - `tests/test_utils/python_scripts/download_golden_values.py` — pulls artifacts from a workflow run and overwrites `tests/functional_tests/test_cases/**/golden_values_*.json`. -- `tests/test_utils/python_scripts/compare_golden_values_kl.py` — diffs the working-tree goldens against `git HEAD` and reports per-metric KL. +- `tests/test_utils/python_scripts/compare_golden_values_kl.py` — diffs the working-tree goldens against `git HEAD` and reports per-metric `avg_rel_diff = mean((old − new) / old)`. (Filename keeps the legacy `_kl` suffix; the script no longer computes KL divergence.) ## Inputs to gather from the user @@ -29,7 +29,7 @@ The skill orchestrates two scripts that already live in the repo: - [ ] Step 1: Set up env (token + venv with deps) - [ ] Step 2: Reset prior golden-value edits - [ ] Step 3: Download goldens (scope = only-failing | all) -- [ ] Step 4: Run KL comparison + capture CSV +- [ ] Step 4: Run relative-diff comparison + capture CSV - [ ] Step 5: Produce summary blurb ``` @@ -46,7 +46,7 @@ python3 -m venv /tmp/gv_venv /tmp/gv_venv/bin/pip install --quiet click python-gitlab requests ``` -Reuse `/tmp/gv_venv` if it already exists. The KL script only depends on `click` (also in the venv). +Reuse `/tmp/gv_venv` if it already exists. The comparison script only depends on `click` (also in the venv). ### Step 2 — Reset prior edits (only if user re-runs) @@ -83,42 +83,50 @@ INFO:__main__:Total tests with golden values: INFO:__main__:Total golden values found: ``` -### Step 4 — KL comparison +### Step 4 — Relative-diff comparison ```bash /tmp/gv_venv/bin/python tests/test_utils/python_scripts/compare_golden_values_kl.py \ - --top 20 --csv /tmp/kl_summary.csv + --top 20 --csv /tmp/reldiff_summary.csv ``` -The CSV holds one row per `(file, metric)` with columns: -`file, metric, n_steps, KL(old||new), KL(new||old), sym_KL, max|d|, mean|d|, mean rel|d|`. +The CSV holds one row per `(file, metric)` with four columns: + +`file, metric, n_steps, avg_rel_diff` + +- `n_steps` — count of shared steps that contributed (steps where `|old| < 1e-12` are skipped to avoid div-by-zero; NaN/inf are dropped). +- `avg_rel_diff` — `mean((old − new) / old)`. **Signed**: positive = the new run is smaller than the old run at the typical step (e.g. loss decreased), negative = larger. Then derive aggregates from the CSV (do this in Python; do not paste raw CSV into the summary): ```python import csv, collections -rows = list(csv.DictReader(open('/tmp/kl_summary.csv'))) +rows = list(csv.DictReader(open('/tmp/reldiff_summary.csv'))) for r in rows: - for k in ('KL(old||new)','KL(new||old)','sym_KL','max|d|','mean|d|','mean rel|d|'): - r[k] = float(r[k]) + r['n_steps'] = int(r['n_steps']) + r['avg_rel_diff'] = float(r['avg_rel_diff']) + r['abs'] = abs(r['avg_rel_diff']) by_metric = collections.defaultdict(list) for r in rows: - by_metric[r['metric']].append(r['sym_KL']) - -# headline numbers per metric -for m, syms in sorted(by_metric.items()): - syms.sort() - print(m, len(syms), 'median', syms[len(syms)//2], 'max', syms[-1]) - -# bucket counts across all rows -buckets = [('==0',lambda x:x==0), ('(0,1e-9)',lambda x:0=1e-1',lambda x:x>=1e-1)] -syms_all = [r['sym_KL'] for r in rows] + by_metric[r['metric']].append(r['abs']) + +# headline numbers per metric (using |avg_rel_diff|) +for m, vs in sorted(by_metric.items()): + vs.sort() + print(m, len(vs), 'median', vs[len(vs)//2], 'max', vs[-1]) + +# bucket counts across all rows, on |avg_rel_diff| +buckets = [('==0', lambda x: x == 0), + ('(0,1e-6)', lambda x: 0 < x < 1e-6), + ('[1e-6,1e-4)', lambda x: 1e-6 <= x < 1e-4), + ('[1e-4,1e-3)', lambda x: 1e-4 <= x < 1e-3), + ('[1e-3,1e-2)', lambda x: 1e-3 <= x < 1e-2), + ('[1e-2,1e-1)', lambda x: 1e-2 <= x < 1e-1), + ('>=1e-1', lambda x: x >= 1e-1)] +abs_all = [r['abs'] for r in rows] for label, pred in buckets: - print(label, sum(1 for s in syms_all if pred(s))) + print(label, sum(1 for v in abs_all if pred(v))) ``` ### Step 5 — Summary blurb @@ -142,59 +150,60 @@ Match the `download_golden_values.py` command in the bullet list to the scope us - Re-ran `tests/test_utils/python_scripts/download_golden_values.py --source github --pipeline-id <--only-failing if scope=only-failing>`. - Updated ** golden-value files** under `tests/functional_tests/test_cases/`. -### KL divergence summary +### Relative-difference summary -Comparison covers files × metrics = ** `(file, metric)` pairs**. +Comparison covers files × metrics = ** `(file, metric)` pairs**. Per row: `avg_rel_diff = mean((old − new) / old)` over shared steps. -**Per-metric headline numbers** +**Per-metric headline numbers** (over `|avg_rel_diff|`) -| metric | n | median sym_KL | max sym_KL | -| ------------------------- | --: | ------------: | ---------: | -| `lm loss` | <…> | <…> | <…> | -| `num-zeros` | <…> | <…> | <…> | -| `iteration-time` | <…> | <…> | <…> | -| `mem-allocated-bytes` | <…> | <…> | <…> | -| `mem-max-allocated-bytes` | <…> | <…> | <…> | +| metric | n | median \|avg_rel_diff\| | max \|avg_rel_diff\| | +| ------------------------- | --: | -----------------------: | -------------------: | +| `lm loss` | <…> | <…> | <…> | +| `num-zeros` | <…> | <…> | <…> | +| `iteration-time` | <…> | <…> | <…> | +| `mem-allocated-bytes` | <…> | <…> | <…> | +| `mem-max-allocated-bytes` | <…> | <…> | <…> | -**Distribution of symmetric KL across all rows** +**Distribution of `|avg_rel_diff|` across all rows** -| sym_KL bucket | count | -| -------------- | ----: | -| `== 0` | <…> | -| `(0, 1e-9)` | <…> | -| `[1e-9, 1e-6)` | <…> | -| `[1e-6, 1e-3)` | <…> | -| `[1e-3, 1e-2)` | <…> | -| `[1e-2, 1e-1)` | <…> | -| `>= 1e-1` | <…> | +| \|avg_rel_diff\| bucket | count | +| ----------------------- | ----: | +| `== 0` | <…> | +| `(0, 1e-6)` | <…> | +| `[1e-6, 1e-4)` | <…> | +| `[1e-4, 1e-3)` | <…> | +| `[1e-3, 1e-2)` | <…> | +| `[1e-2, 1e-1)` | <…> | +| `>= 1e-1` | <…> | **Interpretation** (apply only the bullets that match the data) -- `lm loss` max sym_KL / median — loss trajectories match old goldens to numerical noise. -- `mem-*` metrics are flat in KL even when raw-byte `max|d|` is large (constant offset). -- `iteration-time` divergences are warmup/scheduler noise, not a correctness signal. +- `lm loss` max `|avg_rel_diff|` / median — loss trajectories match old goldens to numerical noise (sub-1e-4 is within run-to-run variance). +- `mem-*` metrics typically sit at `== 0` or `(0, 1e-6)`; flag any row that lands above `[1e-4, 1e-3)`. +- `iteration-time` movement is dominated by warmup/scheduler noise; signed avg near zero means the run was simply jitterier, not slower or faster on average. - `num-zeros` shifts cluster on ``; within historical run-to-run variance. ```` -## Reading the KL columns +## Reading the columns + +| column | meaning | +| -------------- | ------------------------------------------------------------------------------------------------ | +| `n_steps` | shared step indices used in the average (NaN/inf and steps with `\|old\| < 1e-12` are dropped). | +| `avg_rel_diff` | `mean((old − new) / old)` over `n_steps`. Signed: positive = new < old, negative = new > old. | -| column | meaning | -| --- | --- | -| `n_steps` | shared step indices after dropping NaN/inf | -| `KL(old\|\|new)` / `KL(new\|\|old)` | KL divergence in nats, both directions (asymmetric) | -| `sym_KL` | `KL(old\|\|new) + KL(new\|\|old)`; primary ranking column | -| `max\|d\|`, `mean\|d\|` | step-wise absolute diffs in raw metric units | -| `mean rel\|d\|` | average of `\|old − new\| / max(\|old\|, ε)`; scale-free | +When sorting / filtering, the script ranks by `|avg_rel_diff|`. Keep the sign in the printed table so reviewers can see direction. Triage rules of thumb: -- `lm loss` / `num-zeros` rows with `sym_KL` ≲ 1e-6 are run-to-run noise. -- `iteration-time` divergences are usually warmup/scheduler noise, not correctness. -- Focus reviewer attention on `lm loss` and `num-zeros` rows with `sym_KL` ≥ ~1e-3, and check `mean rel|d|` for an intuitive magnitude. +- `lm loss` / `num-zeros` rows with `|avg_rel_diff|` ≲ 1e-4 are run-to-run noise. +- `iteration-time` divergences are usually warmup/scheduler noise; a small signed mean near zero says the run was jitterier, not systematically faster or slower. +- Focus reviewer attention on `lm loss` and `num-zeros` rows with `|avg_rel_diff|` ≥ ~1e-3. ## Notes & gotchas - The download script's `_fetch_and_filter_artifacts` honors `--only-failing` only on the GitHub path. The Gitlab path applies it per-job inside `download_from_gitlab`. -- A brand-new golden file (no `git HEAD` baseline) is silently skipped by the KL script with a warning. Subtract these from the file count when reporting "files with baseline". -- Some artifacts have a literal string `"nan"` in step 1 of `iteration-time`; the KL script filters those out, so divergences for that metric still come through. Don't flag `iteration-time` as a correctness problem unless something else also moved. +- A brand-new golden file (no `git HEAD` baseline) is silently skipped by the comparison script with a warning. Subtract these from the file count when reporting "files with baseline". +- Steps where `|old|` is below `1e-12` are excluded from the average — division blows up there (think `num-zeros` step 0 on a dense model, or `mem-*` before allocation). If every shared step is excluded for a metric, that `(file, metric)` row is omitted entirely. +- Some artifacts have a literal string `"nan"` in step 1 of `iteration-time`; the comparison script filters those out, so other steps for that metric still contribute. Don't flag `iteration-time` as a correctness problem unless something else also moved. +- The script's filename is `compare_golden_values_kl.py` for legacy reasons; it no longer computes KL divergence. The function and CSV column names reflect what it actually does (`avg_rel_diff`). - Never commit `GITHUB_TOKEN`, `RO_API_TOKEN`, or any value derived from `gh auth token`. If the user wants you to commit, only stage golden-value files and the optional CSV — not the env or the venv. diff --git a/tests/test_utils/python_scripts/compare_golden_values_kl.py b/tests/test_utils/python_scripts/compare_golden_values_kl.py index 6320748c5cd..2b31037d217 100644 --- a/tests/test_utils/python_scripts/compare_golden_values_kl.py +++ b/tests/test_utils/python_scripts/compare_golden_values_kl.py @@ -1,7 +1,7 @@ # Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. #!/usr/bin/env python3 -"""Compare old vs. new golden-value JSONs using KL divergence. +"""Compare old vs. new golden-value JSONs by average normalized relative difference. The golden-value JSON files produced by `download_golden_values.py` look like: @@ -13,25 +13,20 @@ "num-zeros": { ... } } -For each metric we treat the per-step value series as a discrete distribution -(after restricting to steps present in both files and applying a strictly -positive shift) and compute KL divergence plus a couple of summary stats. +For each (file, metric) we compute a single number: -The printed table and the CSV both emit the same 6 columns, each answering -a distinct question: + avg_rel_diff = mean( (old_v - new_v) / old_v ) over shared steps - * file / metric -- which test, which metric - * sym_KL -- did the distribution shape change, and by how - much? - * median(old)/median(new) -- typical value before/after (matches what the - framework's median-based check uses, e.g. for - iteration-time) - * max rel|d| -- worst-step relative shift; catches single bad - steps that approximate-equality checks reject +Steps where `|old_v|` is below a small epsilon are skipped to avoid a +division blow-up. The result is **signed**: a positive value means the +new run is smaller than the old run at the typical step (e.g. loss went +down); negative means it went up. The magnitude `|avg_rel_diff|` is the +average fractional shift, so it's directly comparable across metrics of +wildly different absolute scales. By default the script compares the working-tree version of each modified -golden-value file against its `git show HEAD:` version, so the typical -flow is: +golden-value file against its `git show HEAD:` version, so the +typical flow is: # after running download_golden_values.py, before committing python tests/test_utils/python_scripts/compare_golden_values_kl.py @@ -45,10 +40,10 @@ python .../compare_golden_values_kl.py --old old.json --new new.json # write a CSV summary - python .../compare_golden_values_kl.py --csv kl_summary.csv + python .../compare_golden_values_kl.py --csv summary.csv - # only print rows whose symmetric KL exceeds a threshold - python .../compare_golden_values_kl.py --threshold 1e-4 + # only print rows whose |avg_rel_diff| exceeds a threshold + python .../compare_golden_values_kl.py --threshold 1e-3 """ from __future__ import annotations @@ -73,6 +68,10 @@ ).stdout.strip() ) +# Steps where |old| is below this are skipped — (old - new) / old is not +# meaningful when old ≈ 0 (e.g. num-zeros on dense models, mem-* at step 0). +ZERO_EPS = 1e-12 + # --------------------------------------------------------------------------- # Loading @@ -138,39 +137,6 @@ def list_modified_golden_files() -> list[pathlib.Path]: return sorted(set(paths)) -# --------------------------------------------------------------------------- -# Math -# --------------------------------------------------------------------------- - - -def _normalize(values: list[float]) -> list[float]: - """Shift to be strictly positive and normalize so they sum to 1. - - Loss-style trajectories are not probability distributions, so we treat the - series as positive masses. Shifting by `min - eps` keeps the relative - differences between adjacent steps well-defined while making KL finite. - """ - eps = 1e-12 - m = min(values) - shift = 0.0 if m > eps else (-m + eps) - shifted = [v + shift for v in values] - s = sum(shifted) - if s <= 0: - return [1.0 / len(shifted)] * len(shifted) - return [v / s for v in shifted] - - -def kl_divergence(p: list[float], q: list[float]) -> float: - """KL(P || Q) in nats. Assumes p, q are probability vectors of equal length.""" - eps = 1e-12 - out = 0.0 - for pi, qi in zip(p, q): - if pi <= 0: - continue - out += pi * math.log(pi / max(qi, eps)) - return out - - # --------------------------------------------------------------------------- # Per-metric comparison # --------------------------------------------------------------------------- @@ -180,17 +146,8 @@ def kl_divergence(p: list[float], q: list[float]) -> float: class MetricStats: file: str metric: str - sym_kl: float - median_old: float - median_new: float - max_rel_diff: float - - -def _median(xs: list[float]) -> float: - n = len(xs) - s = sorted(xs) - mid = n // 2 - return s[mid] if n % 2 == 1 else 0.5 * (s[mid - 1] + s[mid]) + n_steps: int # number of shared steps that contributed (after skipping old≈0) + avg_rel_diff: float # mean( (old - new) / old ) def _extract_series(metric_block: dict) -> dict[int, float]: @@ -213,26 +170,23 @@ def compare_metric( old_series = _extract_series(old_block) new_series = _extract_series(new_block) shared = sorted(set(old_series) & set(new_series)) - if len(shared) < 2: - return None - old_vals = [old_series[s] for s in shared] - new_vals = [new_series[s] for s in shared] - - p = _normalize(old_vals) - q = _normalize(new_vals) - - rel_diffs = [abs(a - b) / max(abs(a), 1e-12) for a, b in zip(old_vals, new_vals)] + rel_diffs: list[float] = [] + for s in shared: + old_v = old_series[s] + new_v = new_series[s] + if abs(old_v) < ZERO_EPS: + continue + rel_diffs.append((old_v - new_v) / old_v) - sym_kl = kl_divergence(p, q) + kl_divergence(q, p) + if not rel_diffs: + return None return MetricStats( file=file_label, metric=metric_name, - sym_kl=sym_kl, - median_old=_median(old_vals), - median_new=_median(new_vals), - max_rel_diff=max(rel_diffs), + n_steps=len(rel_diffs), + avg_rel_diff=sum(rel_diffs) / len(rel_diffs), ) @@ -250,18 +204,12 @@ def compare_files(file_label: str, old_doc: dict, new_doc: dict) -> list[MetricS # Output # --------------------------------------------------------------------------- -# Six columns, each answers a distinct question: -# file / metric -- which test, which metric -# sym_KL -- did the distribution shape change, and by how much? -# median(old)/median(new) -- typical value before/after (matches the -# framework's median-based check, e.g. -# iteration-time) -# max rel|d| -- worst-step relative shift; catches single bad -# steps that approximate-equality checks reject -_HEADERS = ["file", "metric", "sym_KL", "median(old)", "median(new)", "max rel|d|"] +_HEADERS = ["file", "metric", "n_steps", "avg_rel_diff"] def _fmt(x: float) -> str: + if isinstance(x, int): + return str(x) if x == 0: return "0" if abs(x) < 1e-4 or abs(x) >= 1e4: @@ -270,7 +218,7 @@ def _fmt(x: float) -> str: def _row(r: MetricStats) -> list: - return [r.file, r.metric, r.sym_kl, r.median_old, r.median_new, r.max_rel_diff] + return [r.file, r.metric, r.n_steps, r.avg_rel_diff] def print_table(rows: Iterable[MetricStats]) -> None: @@ -330,13 +278,13 @@ def write_csv(rows: Iterable[MetricStats], path: pathlib.Path) -> None: type=float, default=0.0, show_default=True, - help="Only print metrics with sym_KL >= threshold.", + help="Only print metrics whose |avg_rel_diff| >= threshold.", ) @click.option( "--top", type=int, default=0, - help="If >0, also print the top-N metrics by sym_KL across all files.", + help="If >0, also print the top-N metrics by |avg_rel_diff| across all files.", ) @click.option( "--csv", @@ -353,7 +301,7 @@ def main( top: int, csv_path: pathlib.Path | None, ): - """Compare old vs new golden-value JSONs using KL divergence.""" + """Compare old vs new golden-value JSONs by average normalized relative difference.""" logging.basicConfig(level=logging.INFO, format="%(message)s") all_rows: list[MetricStats] = [] @@ -390,15 +338,15 @@ def main( label = str(p.relative_to(REPO_ROOT)) all_rows.extend(compare_files(label, old_doc, new_doc)) - rows = [r for r in all_rows if r.sym_kl >= threshold] - rows.sort(key=lambda r: r.sym_kl, reverse=True) + rows = [r for r in all_rows if abs(r.avg_rel_diff) >= threshold] + rows.sort(key=lambda r: abs(r.avg_rel_diff), reverse=True) print_table(rows) if top > 0: print() - print(f"Top {min(top, len(all_rows))} metrics by symmetric KL:") - top_rows = sorted(all_rows, key=lambda r: r.sym_kl, reverse=True)[:top] + print(f"Top {min(top, len(all_rows))} metrics by |avg_rel_diff|:") + top_rows = sorted(all_rows, key=lambda r: abs(r.avg_rel_diff), reverse=True)[:top] print_table(top_rows) if csv_path is not None: From 6486d52c1bdc73f589641dcc9ed23438c279840e Mon Sep 17 00:00:00 2001 From: Maanu Grover Date: Mon, 11 May 2026 16:27:52 -0700 Subject: [PATCH 013/346] Guard omegaconf imports (#4685) Signed-off-by: Maanu Grover --- megatron/training/config/container.py | 31 ++++++++++++++----- megatron/training/config/instantiate_utils.py | 14 +++++++-- pyproject.toml | 2 ++ .../training/config/test_container_base.py | 4 +-- uv.lock | 4 +++ 5 files changed, 43 insertions(+), 12 deletions(-) diff --git a/megatron/training/config/container.py b/megatron/training/config/container.py index 505e0527bf7..2e1ba5a652c 100644 --- a/megatron/training/config/container.py +++ b/megatron/training/config/container.py @@ -1,18 +1,31 @@ # Copyright (c) 2026, NVIDIA CORPORATION. All rights reserved. import copy import os -from dataclasses import dataclass, field, fields as dataclass_fields, is_dataclass +from dataclasses import dataclass, field +from dataclasses import fields as dataclass_fields +from dataclasses import is_dataclass from typing import Any, Type, TypeVar + import yaml -from omegaconf import OmegaConf -from megatron.training.config.common_config import RNGConfig, DistributedInitConfig, ProfilingConfig -from megatron.training.config.training_config import TokenizerConfig, TrainingConfig, ValidationConfig, SchedulerConfig, LoggerConfig, CheckpointConfig -from megatron.core.optimizer import OptimizerConfig -from megatron.core.msc_utils import MultiStorageClientFeature + from megatron.core.distributed.distributed_data_parallel_config import DistributedDataParallelConfig -from megatron.training.config.resilience_config import RerunStateMachineConfig, StragglerDetectionConfig -from megatron.training.config.utils import sanitize_dataclass_config +from megatron.core.msc_utils import MultiStorageClientFeature +from megatron.core.optimizer import OptimizerConfig +from megatron.training.config.common_config import DistributedInitConfig, ProfilingConfig, RNGConfig from megatron.training.config.instantiate_utils import InstantiationMode, instantiate +from megatron.training.config.resilience_config import ( + RerunStateMachineConfig, + StragglerDetectionConfig, +) +from megatron.training.config.training_config import ( + CheckpointConfig, + LoggerConfig, + SchedulerConfig, + TokenizerConfig, + TrainingConfig, + ValidationConfig, +) +from megatron.training.config.utils import sanitize_dataclass_config from megatron.training.config.yaml_utils import safe_yaml_representers T = TypeVar("T", bound="ConfigContainerBase") @@ -80,6 +93,8 @@ def from_yaml(cls: Type[T], yaml_path: str, mode: InstantiationMode = Instantiat Returns: A new instance of this class initialized with the YAML file values """ + from omegaconf import OmegaConf + if MultiStorageClientFeature.is_enabled(): msc = MultiStorageClientFeature.import_package() yaml_path_exists = msc.os.path.exists(yaml_path) diff --git a/megatron/training/config/instantiate_utils.py b/megatron/training/config/instantiate_utils.py index 362d8574690..3039b514c5b 100644 --- a/megatron/training/config/instantiate_utils.py +++ b/megatron/training/config/instantiate_utils.py @@ -7,8 +7,13 @@ from textwrap import dedent from typing import Any, Callable, Sequence -from omegaconf import OmegaConf -from omegaconf._utils import is_structured_config +try: + from omegaconf import OmegaConf + from omegaconf._utils import is_structured_config + + HAVE_OMEGACONF = True +except ImportError: + HAVE_OMEGACONF = False class InstantiationException(Exception): @@ -158,6 +163,11 @@ def instantiate( or instantiation fails in STRICT mode. TypeError: If the _partial_ flag is not a boolean. """ + if not HAVE_OMEGACONF: + raise ImportError( + "omegaconf is required for config instantiation. " + "Install via `pip install omegaconf`." + ) # Return None if config is None if config is None: diff --git a/pyproject.toml b/pyproject.toml index f91c2ee83b7..a7bc48ec9a3 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -75,6 +75,7 @@ training = [ "wandb", "transformers", "accelerate", + "omegaconf", ] ### 'mlm' group is deprecated. please use 'training' instead ### @@ -85,6 +86,7 @@ mlm = [ "wandb", "transformers", "accelerate", + "omegaconf", ] dev = [ diff --git a/tests/unit_tests/training/config/test_container_base.py b/tests/unit_tests/training/config/test_container_base.py index 2b87c69679f..3dbec27856a 100644 --- a/tests/unit_tests/training/config/test_container_base.py +++ b/tests/unit_tests/training/config/test_container_base.py @@ -203,7 +203,7 @@ def test_from_yaml_file_not_found(self): TestConfigContainer.from_yaml("non_existent_file.yaml") @patch("megatron.training.config.container.MultiStorageClientFeature.is_enabled") - @patch("megatron.training.config.container.OmegaConf") + @patch("omegaconf.OmegaConf") @patch("builtins.open", new_callable=mock_open) @patch("os.path.exists") def test_from_yaml_success(self, mock_exists, mock_file, mock_omegaconf, mock_msc): @@ -251,7 +251,7 @@ def test_from_yaml_with_mode(self, mock_exists, mock_msc): with patch("builtins.open", mock_open()): with patch("yaml.safe_load", return_value={}): - with patch("megatron.training.config.container.OmegaConf") as mock_omegaconf: + with patch("omegaconf.OmegaConf") as mock_omegaconf: # Mock OmegaConf methods to return expected values mock_conf = MagicMock() mock_omegaconf.create.return_value = mock_conf diff --git a/uv.lock b/uv.lock index d68d392a0f2..890fbb29034 100644 --- a/uv.lock +++ b/uv.lock @@ -2634,6 +2634,7 @@ lts = [ mlm = [ { name = "accelerate" }, { name = "flask-restful" }, + { name = "omegaconf" }, { name = "sentencepiece" }, { name = "tiktoken" }, { name = "transformers" }, @@ -2649,6 +2650,7 @@ te = [ training = [ { name = "accelerate" }, { name = "flask-restful" }, + { name = "omegaconf" }, { name = "sentencepiece" }, { name = "tiktoken" }, { name = "transformers" }, @@ -2738,6 +2740,8 @@ requires-dist = [ { name = "numpy" }, { 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 = "omegaconf", marker = "extra == 'mlm'" }, + { name = "omegaconf", marker = "extra == 'training'" }, { name = "onnxscript", marker = "extra == 'dev'" }, { name = "onnxscript", marker = "extra == 'lts'" }, { name = "openai", extras = ["aiohttp"], marker = "extra == 'dev'" }, From 7d24b28a291727d597a9542b879a77d929317023 Mon Sep 17 00:00:00 2001 From: Ajay Date: Mon, 11 May 2026 22:10:19 -0700 Subject: [PATCH 014/346] Fix a regression introduced by #4625 for nightly runs (#4734) --- .../python_scripts/recipe_parser.py | 23 +++++++++++++++++-- 1 file changed, 21 insertions(+), 2 deletions(-) diff --git a/tests/test_utils/python_scripts/recipe_parser.py b/tests/test_utils/python_scripts/recipe_parser.py index ec86654095c..627e47ea91a 100644 --- a/tests/test_utils/python_scripts/recipe_parser.py +++ b/tests/test_utils/python_scripts/recipe_parser.py @@ -38,6 +38,18 @@ } +def _resolve_scope_alias(scope_value: str) -> str: + """Resolve a legacy scope value to its L-tier alias (or return it unchanged). + + Applied both to recipe rows when flattening and to the `--scope` filter + input, so callers can pass either the legacy name (e.g. `nightly`) or the + new L-tier name (e.g. `L2`) and hit the same recipe rows. + """ + if scope_value in LEGACY_SCOPE_ALIASES: + return LEGACY_SCOPE_ALIASES[scope_value][0] + return scope_value + + def _apply_scope_alias(scope_value: str, explicit_cadence: Optional[List[str]]) -> tuple: """Resolve a legacy scope value to (new_scope, cadence). @@ -206,11 +218,18 @@ def filter_by_test_case(workload_manifests: List[dotdict], test_case: str) -> Op def filter_by_scope(workload_manifests: List[dotdict], scope: str) -> List[dotdict]: - """Returns all workload with matching scope.""" + """Returns all workload with matching scope. + + The filter input is run through the same legacy-scope alias as recipe + rows, so callers passing the legacy name (e.g. `--scope nightly`, + `--scope mr-github`) match recipes that have already been rewritten to + the new L-tier vocabulary (e.g. `scope: [L2]`, `scope: [L1]`). + """ + resolved_scope = _resolve_scope_alias(scope) workload_manifests = list( workload_manifest for workload_manifest in workload_manifests - if workload_manifest.spec["scope"] == scope + if workload_manifest.spec["scope"] == resolved_scope ) if len(workload_manifests) == 0: From 6853b4793e684f19b3927a1ef4b27267dbe7eb08 Mon Sep 17 00:00:00 2001 From: Chen Cui Date: Tue, 12 May 2026 04:14:21 -0700 Subject: [PATCH 015/346] Add LLaVA audio (sound) model support (#4402) Signed-off-by: Chen Cui Co-authored-by: Tuomas Rintamaki Co-authored-by: Tyler Poon Co-authored-by: Collin McCarthy Co-authored-by: Matthieu Le Co-authored-by: Piotr Zelasko Co-authored-by: Ehsan Hosseini Asl Co-authored-by: Claude Sonnet 4.6 --- .../models/huggingface/fastconformer_model.py | 102 ++++ megatron/core/models/huggingface/module.py | 25 + .../models/multimodal/context_parallel.py | 425 ++++++++++++++ .../core/models/multimodal/llava_model.py | 396 +++++++++++-- megatron/core/models/vision/radio.py | 464 +++++++++++++-- .../models/test_fastconformer_model.py | 368 ++++++++++++ tests/unit_tests/models/test_llava_sound.py | 379 ++++++++++++ .../test_multimodal_context_parallel.py | 489 ++++++++++++++++ tests/unit_tests/models/test_radio_model.py | 539 ++++++++++++++++++ 9 files changed, 3098 insertions(+), 89 deletions(-) create mode 100644 megatron/core/models/huggingface/fastconformer_model.py create mode 100644 tests/unit_tests/models/test_fastconformer_model.py create mode 100644 tests/unit_tests/models/test_llava_sound.py create mode 100644 tests/unit_tests/models/test_multimodal_context_parallel.py diff --git a/megatron/core/models/huggingface/fastconformer_model.py b/megatron/core/models/huggingface/fastconformer_model.py new file mode 100644 index 00000000000..25265871240 --- /dev/null +++ b/megatron/core/models/huggingface/fastconformer_model.py @@ -0,0 +1,102 @@ +# Copyright (c) 2026, NVIDIA CORPORATION. All rights reserved. +import torch + +from megatron.core.models.huggingface import HuggingFaceModule + +# NeMo model loading is slow, so cache the (preprocessor, encoder) tuple per +# `sound_model_type`. Keying by model id avoids returning a stale cached encoder +# when the same process constructs more than one Parakeet variant. +_NEMO_SOUND_MODEL_CACHE: dict[str, tuple] = {} + + +def get_nemo_sound_model(sound_model_type): + """Load (and cache) a NeMo ASR encoder + preprocessor for the given ``nemo://`` model id.""" + if sound_model_type not in _NEMO_SOUND_MODEL_CACHE: + import nemo.collections.asr as nemo_asr + + asr_model = nemo_asr.models.ASRModel.from_pretrained( + model_name=sound_model_type.split("nemo://")[1] + ) + # Avoid hangs from an unnecessary max-seq-len NCCL sync in some edge cases. + asr_model.encoder.sync_max_audio_length = False + for layer in asr_model.encoder.layers: + layer.self_attn.use_pytorch_sdpa = True + _NEMO_SOUND_MODEL_CACHE[sound_model_type] = (asr_model.preprocessor, asr_model.encoder) + return _NEMO_SOUND_MODEL_CACHE[sound_model_type] + + +class ParakeetHuggingFaceModel(HuggingFaceModule): + """Wrapper for Parakeet sound encoders. + + Supports two backends, selected by ``config.sound_model_type`` prefix: + + - ``nemo://`` loads a NeMo ASR encoder + preprocessor. + - ``hf://`` loads the upstream Hugging Face FastConformer model + via ``transformers.AutoModel`` / ``AutoFeatureExtractor``. + """ + + def __init__(self, config): + super().__init__(config) + + self.use_nemo = config.sound_model_type.startswith("nemo://") + if self.use_nemo: + self.feature_extractor, self.model = get_nemo_sound_model(config.sound_model_type) + + for module in self.model.modules(): + if module.__class__.__name__.lower() == "dropout": + module.p = config.hidden_dropout + + if config.recompute_granularity is not None: + from torch.distributed.algorithms._checkpoint.checkpoint_wrapper import ( + checkpoint_wrapper, + ) + + self.model = checkpoint_wrapper(self.model) + elif config.sound_model_type.startswith("hf://"): + from transformers import AutoFeatureExtractor, AutoModel + + sound_model_type = config.sound_model_type.split("hf://")[1] + self.feature_extractor = AutoFeatureExtractor.from_pretrained(sound_model_type) + self.model = AutoModel.from_pretrained(sound_model_type) + + if config.recompute_granularity is not None: + self.model.gradient_checkpointing_enable() + else: + raise ValueError(f"Unknown sound model type: {config.sound_model_type}") + + def _model_dtype(self) -> torch.dtype: + """Return the dtype of the encoder's first parameter (defaults to bf16).""" + for param in self.model.parameters(): + return param.dtype + return torch.bfloat16 + + def _sampling_rate(self) -> int: + """Return the sampling rate the feature extractor expects (default 16 kHz).""" + return int(getattr(self.feature_extractor, "sampling_rate", 16000)) + + def forward(self, *args, **kwargs): + """Forward pass returning (hidden_states, lengths). + + Args: + args[0]: Sound clips tensor. + args[1]: Sound length tensor (used by NeMo backend; ignored for HF). + """ + if self.use_nemo: + features = self.feature_extractor(input_signal=args[0], length=args[1]) + y = self.model(audio_signal=features[0], length=features[1]) + # NeMo encoder returns [B, H, T]; LLaVA expects [B, T, H]. + return y[0].permute(0, 2, 1), y[1] + else: + # HF feature extractor expects audio as the first arg only, + # not (audio, length) as in NeMo. + sound_clips = args[0] + features = self.feature_extractor( + sound_clips, + **kwargs, + return_tensors="pt", + sampling_rate=self._sampling_rate(), + return_attention_mask=True, + ) + y = self.model(features.input_features.to(self._model_dtype()), features.attention_mask) + lengths = features.attention_mask.sum(dim=-1).to(y.last_hidden_state.device) + return y.last_hidden_state, lengths diff --git a/megatron/core/models/huggingface/module.py b/megatron/core/models/huggingface/module.py index 5c78fc96708..2d874c7513b 100644 --- a/megatron/core/models/huggingface/module.py +++ b/megatron/core/models/huggingface/module.py @@ -68,6 +68,27 @@ def get_hf_model_type(model_path): "please install it with `pip install transformers`" ) + # Parakeet is a special case: its model id may be `nemo://...`, which + # AutoConfig cannot resolve, so detect it from the prefix. Require the + # `nemo://` or `hf://` scheme so unrelated local paths that happen to + # contain "parakeet" (e.g. a user directory) don't get misrouted. + lowered = model_path.lower() + if lowered.startswith(("nemo://", "hf://")): + model_id = lowered.split("://", 1)[1] + # Match a path segment whose name begins with "parakeet" (e.g. + # `nvidia/parakeet-tdt-0.6b-v2`). Substring-anywhere matches like + # `myparakeet-clone` are intentionally rejected. + if any(seg.startswith("parakeet") for seg in model_id.split("/")): + return "parakeet" + # Any other `nemo://` model can't be resolved by AutoConfig below; + # raise a clear error rather than letting `split("hf://")[1]` raise + # an IndexError with no context. + if lowered.startswith("nemo://"): + raise NotImplementedError( + f"nemo:// scheme is currently only supported for parakeet models, " + f"got {model_path}" + ) + hf_config = AutoConfig.from_pretrained(model_path.split("hf://")[1]) model_type = hf_config.architectures[0].lower() @@ -91,6 +112,10 @@ def build_hf_model(config, model_path): from megatron.core.models.huggingface.clip_model import SiglipHuggingFaceModel model = SiglipHuggingFaceModel(config) + elif "parakeet" in model_type: + from megatron.core.models.huggingface.fastconformer_model import ParakeetHuggingFaceModel + + model = ParakeetHuggingFaceModel(config) else: raise NotImplementedError(f"unsupported huggingface model {config.hf_config}") diff --git a/megatron/core/models/multimodal/context_parallel.py b/megatron/core/models/multimodal/context_parallel.py index 6a3cb8bdf48..ceee2d0af7d 100644 --- a/megatron/core/models/multimodal/context_parallel.py +++ b/megatron/core/models/multimodal/context_parallel.py @@ -1,9 +1,16 @@ # Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. """Multimodal Sequence Parallel (SP) and Context Parallel (CP) functionality.""" +import math + import torch from megatron.core.packed_seq_params import PackedSeqParams +from megatron.core.parallel_state import ( + get_context_parallel_group, + get_context_parallel_rank, + get_context_parallel_world_size, +) def get_padding( @@ -109,3 +116,421 @@ def get_packed_seq_params(tokens, img_seq_len, padding_needed, cp_size, use_pack ) return packed_seq_params + + +def split_to_context_parallel_ranks(global_t, pad_value=0): + """Split the tensor global_t into context parallel world size parts. + + Args: + global_t: [batch, ...] + pad_value: Value to pad the last rank with. + + Returns: + local_t: [samples_per_rank, ...]. samples_per_rank is the # of samples per CP rank. + global_pad: Total padding to have equal samples_per_rank across context parallel ranks. + """ + cp_size = get_context_parallel_world_size() + cp_rank = get_context_parallel_rank() + + samples_per_rank = (global_t.shape[0] + cp_size - 1) // cp_size + local_t = global_t[cp_rank * samples_per_rank : (cp_rank + 1) * samples_per_rank] + global_pad = samples_per_rank * cp_size - global_t.shape[0] + + if local_t.shape[0] < samples_per_rank: + local_pad = samples_per_rank - local_t.shape[0] + zeros = torch.full( + (local_pad, *local_t.shape[1:]), pad_value, device=local_t.device, dtype=local_t.dtype + ) + local_t = torch.cat([local_t, zeros], dim=0) + + return local_t, global_pad + + +def _gather_along_second_dim(local_t): + group = get_context_parallel_group() + cp_size = get_context_parallel_world_size() + if cp_size == 1: + return local_t + + tensor_list = [ + torch.empty(local_t.shape, device=local_t.device, dtype=local_t.dtype) + for _ in range(cp_size) + ] + torch.distributed.all_gather(tensor_list, local_t, group=group) + return torch.cat(tensor_list, dim=1) + + +def _reduce_scatter_along_second_dim(global_t): + cp_size = get_context_parallel_world_size() + if cp_size == 1: + return global_t + + assert global_t.shape[1] % cp_size == 0 + samples_per_rank = global_t.shape[1] // cp_size + + tensor_list = [ + global_t[:, cp_rank * samples_per_rank : (cp_rank + 1) * samples_per_rank] + for cp_rank in range(cp_size) + ] + + local_t = torch.zeros( + global_t.shape[0], + samples_per_rank, + *global_t.shape[2:], + device=global_t.device, + dtype=global_t.dtype, + ) + + torch.distributed.reduce_scatter(local_t, tensor_list, group=get_context_parallel_group()) + return local_t + + +class GatherFromContextParallelRanks(torch.autograd.Function): + """Gather the input from context parallel ranks.""" + + @staticmethod + def symbolic(graph, input_): + """Symbolic forward used during ``torch.jit`` tracing.""" + return _gather_along_second_dim(input_) + + @staticmethod + def forward(ctx, input_): + """All-gather ``input_`` along its second dimension across CP ranks.""" + return _gather_along_second_dim(input_) + + @staticmethod + def backward(ctx, grad_output): + """Reduce-scatter the gradient along the second dimension.""" + return _reduce_scatter_along_second_dim(grad_output) + + +def gather_from_context_parallel_ranks(local_t, global_pad): + """Gather ``local_t`` across CP ranks, removing ``global_pad`` trailing pad tokens.""" + global_t = GatherFromContextParallelRanks.apply(local_t) + if global_pad > 0: + global_t = global_t[:, :-global_pad] + return global_t + + +def gather_from_context_parallel_ranks_dynamic_res(local_t, num_padded_imgs=0): + """Gather dynamic-resolution tensors (variable seq per rank) from CP ranks.""" + cp_size = get_context_parallel_world_size() + shape = torch.as_tensor(local_t.shape, device=local_t.device) + shapes = [torch.empty_like(shape) for _ in range(cp_size)] + + torch.distributed.all_gather(shapes, shape, group=get_context_parallel_group()) + + inputs = [local_t] * cp_size + outputs = [torch.empty(*s, dtype=local_t.dtype, device=local_t.device) for s in shapes] + torch.distributed.nn.functional.all_to_all(outputs, inputs, group=get_context_parallel_group()) + + if num_padded_imgs > 0: + outputs = outputs[:-num_padded_imgs] + + return torch.cat(outputs, dim=0) + + +def _compute_tubelet_aware_split_points(num_frames, temporal_patch_size, cp_size, total_frames): + """Compute frame-space split points that respect tubelet boundaries within videos. + + Returns ``cp_size + 1`` split points in **frame** indices (not tubelet indices), + since callers slice per-frame ``cu_seqlens`` and ``imgs_sizes`` with these bounds. + Splits land on either media boundaries or tubelet boundaries inside a media so + that no rank receives a partial tubelet. + """ + T = temporal_patch_size + target_per_rank = total_frames / cp_size + + media_boundaries = [0] + for nf in num_frames: + media_boundaries.append(media_boundaries[-1] + nf) + boundary_set = set(media_boundaries) + + split_points = [0] + for rank in range(1, cp_size): + target_split = int(rank * target_per_rank) + + # If the target lands exactly on a media boundary, split there cleanly + # without forcing a cut into the next media. + if target_split in boundary_set: + split_point = target_split + else: + media_idx = 0 + for i, boundary in enumerate(media_boundaries[1:], 1): + if boundary > target_split: + media_idx = i - 1 + break + else: + media_idx = len(num_frames) - 1 + + media_start = media_boundaries[media_idx] + media_end = media_boundaries[media_idx + 1] + nf = num_frames[media_idx] + num_tubelets = math.ceil(nf / T) + + if num_tubelets <= 1: + if target_split - media_start < media_end - target_split: + split_point = media_start + else: + split_point = media_end + else: + offset_in_media = target_split - media_start + tubelet_idx = round(offset_in_media / T) + tubelet_idx = max(1, min(tubelet_idx, num_tubelets - 1)) + split_point = media_start + tubelet_idx * T + split_point = min(split_point, media_end) + + split_point = max(split_point, split_points[-1]) + split_points.append(split_point) + + split_points.append(total_frames) + return split_points + + +def _split_num_frames(num_frames, lb, ub): + """Return per-media frame counts clipped to the frame range ``[lb, ub)``. + + ``lb`` and ``ub`` are frame indices (the same coordinate system used by + :func:`_compute_tubelet_aware_split_points` and the per-frame ``seqlens`` + array in :func:`split_to_context_parallel_ranks_dynamic_res`). The returned + list has one entry per media that contributes at least one frame to the + range, with the value being the number of frames of that media in the + range. + """ + new_num_frames = [] + frame_idx = 0 + for nf in num_frames: + media_start = frame_idx + media_end = frame_idx + nf + overlap_start = max(media_start, lb) + overlap_end = min(media_end, ub) + if overlap_start < overlap_end: + new_num_frames.append(overlap_end - overlap_start) + frame_idx = media_end + return new_num_frames + + +def split_to_context_parallel_ranks_dynamic_res( + global_t, + global_imgs_sizes, + global_packed_seq_params, + *, + patch_dim, + fp8_enabled=False, + fp8_recipe=None, + num_frames=None, + temporal_patch_size=1, +): + """Split patched vision input across CP ranks. + + ``global_packed_seq_params`` provides per-image seqlens; the split respects them + so each rank owns an integer number of images. When ``temporal_patch_size > 1``, + splits also respect tubelet boundaries and ``num_frames`` is required. + + Args: + global_t: ``[1, total_patches, C * patch_dim * patch_dim]`` patched tokens + (pre-embedder). The last dim must equal ``3 * patch_dim * patch_dim``. + global_imgs_sizes: ``[num_imgs, 2]`` per-image (H, W) in pixels. + global_packed_seq_params: ``PackedSeqParams`` with per-image ``cu_seqlens_q``. + patch_dim: Patch size of the vision backbone (e.g. 14 for SigLIP, 16 for + many ViTs). Required because dummy padding tensors are sized in patch + units and the default would silently mismatch some backbones. + fp8_enabled: If True, pad each rank's local sequence to the FP8 multiple + (16 by default; 32 for ``mxfp8``). + fp8_recipe: Forwarded to :func:`get_padding` so the FP8 padding multiple + matches the active recipe. + num_frames: Per-media frame count, required when ``temporal_patch_size > 1``. + temporal_patch_size: Tubelet size for temporal compression. + + Returns: + (local_t, local_imgs_sizes, local_packed_seq_params, has_padding, + num_padded_ranks, local_num_frames) + """ + cp_size = get_context_parallel_world_size() + cp_rank = get_context_parallel_rank() + + use_tubelet_aware_split = temporal_patch_size > 1 + if use_tubelet_aware_split: + assert num_frames is not None, ( + f"num_frames must be provided when using temporal compression " + f"(temporal_patch_size={temporal_patch_size})" + ) + num_frames_list = num_frames.tolist() if hasattr(num_frames, "tolist") else list(num_frames) + + cu_seqlens = global_packed_seq_params.cu_seqlens_q + + num_imgs = len(global_imgs_sizes) + if use_tubelet_aware_split: + T = temporal_patch_size + total_tubelets = sum(math.ceil(nf / T) for nf in num_frames_list) + num_padded_imgs = max(0, cp_size - total_tubelets) + else: + num_padded_imgs = max(0, cp_size - num_imgs) + + # This function operates on pre-embedder patches, so the hidden dim is + # exactly ``3 * patch_dim * patch_dim``. Both the dummy padding image and + # the FP8 right-pad tensor below assume this layout. + expected_hidden = 3 * patch_dim * patch_dim + assert int(global_t.shape[2]) == expected_hidden, ( + f"split_to_context_parallel_ranks_dynamic_res expects pre-embedder patches " + f"with hidden dim 3*patch_dim*patch_dim={expected_hidden}, got " + f"{int(global_t.shape[2])} (patch_dim={patch_dim})." + ) + + dummy_img_size = torch.tensor( + [[patch_dim, patch_dim]], device=global_imgs_sizes.device, dtype=global_imgs_sizes.dtype + ) + hidden_dim = expected_hidden + dummy_seqlen = 1 + dummy_img = torch.zeros( + [1, dummy_seqlen, hidden_dim], device=global_t.device, dtype=global_t.dtype + ) + + def _add_dummies(n, global_t, global_imgs_sizes, cu_seqlens, num_frames_list): + seqlens = cu_seqlens[1:] - cu_seqlens[:-1] + for _ in range(n): + global_imgs_sizes = torch.cat([global_imgs_sizes, dummy_img_size], dim=0) + global_t = torch.cat([global_t, dummy_img], dim=1) + seqlens = torch.cat( + [seqlens, torch.tensor([dummy_seqlen], device=seqlens.device, dtype=seqlens.dtype)] + ) + if use_tubelet_aware_split: + num_frames_list = num_frames_list + [1] * n + cu_seqlens = torch.cat( + [ + torch.tensor([0], device=cu_seqlens.device, dtype=cu_seqlens.dtype), + torch.cumsum(seqlens, dim=0), + ] + ) + return global_t, global_imgs_sizes, cu_seqlens, num_frames_list + + if num_padded_imgs > 0: + global_t, global_imgs_sizes, cu_seqlens, num_frames_list = _add_dummies( + num_padded_imgs, + global_t, + global_imgs_sizes, + cu_seqlens, + num_frames_list if use_tubelet_aware_split else None, + ) + + seqlens = cu_seqlens[1:] - cu_seqlens[:-1] + total_frames = len(global_imgs_sizes) + num_padded_ranks = num_padded_imgs + + if use_tubelet_aware_split: + for _retry in range(cp_size): + total_frames = len(global_imgs_sizes) + split_points = _compute_tubelet_aware_split_points( + num_frames_list, temporal_patch_size, cp_size, total_frames + ) + num_empty = sum(1 for k in range(cp_size) if split_points[k] == split_points[k + 1]) + if num_empty == 0: + break + global_t, global_imgs_sizes, cu_seqlens, num_frames_list = _add_dummies( + num_empty, global_t, global_imgs_sizes, cu_seqlens, num_frames_list + ) + num_padded_imgs += num_empty + seqlens = cu_seqlens[1:] - cu_seqlens[:-1] + + original_total_frames = total_frames - num_padded_imgs + if num_padded_imgs > 0 and original_total_frames not in split_points: + for k in range(cp_size): + if split_points[k] < original_total_frames < split_points[k + 1]: + split_points[k + 1] = original_total_frames + break + + num_padded_ranks = 0 + if num_padded_imgs > 0: + for i in range(cp_size - 1, -1, -1): + if split_points[i] >= original_total_frames: + num_padded_ranks += 1 + else: + break + + lb = split_points[cp_rank] + ub = split_points[cp_rank + 1] + local_num_frames = _split_num_frames(num_frames_list, lb, ub) + else: + seq_per_rank = total_frames // cp_size + lb = cp_rank * seq_per_rank + # The last rank absorbs the remainder so the union of [lb, ub) ranges + # exactly covers the [0, total_frames) image set. + ub = (cp_rank + 1) * seq_per_rank if cp_rank < cp_size - 1 else total_frames + local_num_frames = None + + seqlens_local = torch.cat([torch.tensor([0], device=seqlens.device), seqlens[lb:ub]]) + cu_seqlens_local = torch.cumsum(seqlens_local, dim=0).to(torch.int32) + + final_seqlen = cu_seqlens_local[-1] + + pad_img = None + if fp8_enabled: + padding_needed = get_padding( + final_seqlen, 1, 1, False, fp8_enabled=True, fp8_recipe=fp8_recipe + ) + if padding_needed > 0: + pad_img = torch.zeros( + [1, padding_needed, patch_dim * patch_dim * 3], + device=global_t.device, + dtype=global_t.dtype, + ) + cu_seqlens_local = torch.cat( + [ + cu_seqlens_local, + torch.tensor( + [final_seqlen + padding_needed], + device=cu_seqlens_local.device, + dtype=cu_seqlens_local.dtype, + ), + ] + ) + + has_padding = pad_img is not None + + local_packed_seq_params = PackedSeqParams( + qkv_format="thd", + cu_seqlens_q=cu_seqlens_local, + cu_seqlens_kv=cu_seqlens_local, + cu_seqlens_q_padded=None, + cu_seqlens_kv_padded=None, + ) + max_seqlen_local = max(seqlens_local).to(torch.int32) + local_packed_seq_params.max_seqlen_q = max_seqlen_local + local_packed_seq_params.max_seqlen_kv = max_seqlen_local + + local_imgs_sizes = global_imgs_sizes[lb:ub] + if has_padding: + local_imgs_sizes = torch.cat( + [ + local_imgs_sizes, + torch.tensor( + [[patch_dim, patch_dim * padding_needed]], + device=local_imgs_sizes.device, + dtype=local_imgs_sizes.dtype, + ), + ] + ) + + offset = torch.cumsum(seqlens[:lb], dim=0)[-1] if lb > 0 else 0 + + if not has_padding: + local_t = global_t[:, offset + cu_seqlens_local[0] : offset + cu_seqlens_local[-1]] + else: + local_t = torch.cat( + [global_t[:, offset + cu_seqlens_local[0] : offset + cu_seqlens_local[-2]], pad_img], + dim=1, + ) + + if local_num_frames is not None: + local_num_frames = torch.tensor( + local_num_frames, dtype=torch.int32, device=global_imgs_sizes.device + ) + + return ( + local_t, + local_imgs_sizes, + local_packed_seq_params, + has_padding, + num_padded_ranks, + local_num_frames, + ) diff --git a/megatron/core/models/multimodal/llava_model.py b/megatron/core/models/multimodal/llava_model.py index a2e0eed68b2..a73c0f9eed3 100644 --- a/megatron/core/models/multimodal/llava_model.py +++ b/megatron/core/models/multimodal/llava_model.py @@ -12,6 +12,10 @@ from megatron.core.inference.contexts import BaseInferenceContext from megatron.core.models.gpt import GPTModel from megatron.core.models.hybrid.hybrid_model import HybridModel +from megatron.core.models.multimodal.context_parallel import ( + gather_from_context_parallel_ranks_dynamic_res, + split_to_context_parallel_ranks_dynamic_res, +) from megatron.core.models.vision.clip_vit_model import CLIPViTModel, get_num_image_embeddings from megatron.core.models.vision.multimodal_projector import MultimodalProjector from megatron.core.models.vision.radio import RADIOViTModel @@ -43,8 +47,10 @@ IGNORE_INDEX = -100 # ID for labels that should be ignored. # Image token index can be tokenizer dependent so the default value does not work in all cases. DEFAULT_IMAGE_TOKEN_INDEX = -200 +DEFAULT_SOUND_TOKEN_INDEX = -300 IMAGE_TOKEN = "" VIDEO_TOKEN = "