diff --git a/.github/oncall_schedule.json b/.github/oncall_schedule.json index 709775c567e..eea6acdef57 100644 --- a/.github/oncall_schedule.json +++ b/.github/oncall_schedule.json @@ -1,8 +1,4 @@ [ - { - "user": "guihong-nv", - "date": "2026-06-10" - }, { "user": "Phlip79", "date": "2026-06-17" @@ -46,5 +42,9 @@ { "user": "ilml", "date": "2026-08-26" + }, + { + "user": "janEbert", + "date": "2026-09-02" } ] diff --git a/AGENTS.md b/AGENTS.md index d8448db8232..e747867f8b0 100644 --- a/AGENTS.md +++ b/AGENTS.md @@ -24,6 +24,7 @@ skill keyword — infer it from the artifact you read. - All PRs must be created as **drafts**. Use `gh pr create --draft` or the GitHub UI draft option. - Never push branches directly to `https://github.com/NVIDIA/Megatron-LM`. You must push your branch to a personal fork (e.g. `https://github.com//Megatron-LM`), then open a PR from the fork's branch against `NVIDIA/Megatron-LM`. +- Commit PR changes with both `-s` and `-S`: `-s` adds the required `Signed-off-by` trailer, and `-S` signs the commit so copy-pr-bot and `/ok to test` can verify the pushed commit without manually specifying the SHA. - Read @docs/developer/contribute.md for the full contribution policy, including code style, commit message conventions, and issue guidelines. ### Code Quality diff --git a/docker/lts/requirements.txt b/docker/lts/requirements.txt index 60b97be5abe..17889e7d401 100644 --- a/docker/lts/requirements.txt +++ b/docker/lts/requirements.txt @@ -21,3 +21,4 @@ fastapi==0.136.3 # was: fastapi~=0.50 (forces compat with datasets==4.8.5 emerging_optimizers @ git+https://github.com/NVIDIA-NeMo/Emerging-Optimizers.git@v0.2.0 nvidia-resiliency-ext==0.6.0 +zstandard==0.25.0 diff --git a/docs/user-guide/features/fine_grained_activation_offloading.md b/docs/user-guide/features/fine_grained_activation_offloading.md index da4ffcde652..f83645d7ec4 100644 --- a/docs/user-guide/features/fine_grained_activation_offloading.md +++ b/docs/user-guide/features/fine_grained_activation_offloading.md @@ -13,7 +13,7 @@ Contributed in collaboration with RedNote. Memory is often the limiting factor for very large sparse MoE models such as DeepSeek-V3 and Qwen3-235B. Fine-grained recomputation lowers activation memory at the cost of extra compute. Offloading can use host-device bandwidth so that reload overlaps compute and keeps overhead small in many setups. Fine-grained activation offloading moves activations at module granularity so you can tune how much activation memory leaves the device and adjust training throughput. -Supported offloading modules are `"attn_norm"`, `"core_attn"`, `"attn_proj"`, `"mlp_norm"`, `"expert_fc1"`, and `"moe_act"`. They can be combined with fine-grained recomputation to free almost all activations for a transformer layer on the device. +Supported offloading modules are `"attn_norm"`, `"qkv_linear"`, `"core_attn"`, `"attn_proj"`, `"mlp_norm"`, `"expert_fc1"`, `"moe_act"`, and `"fused_group_mlp"`. They can be combined with fine-grained recomputation to free almost all activations for a transformer layer on the device. `fused_group_mlp` requires `--use-transformer-engine-op-fuser` and offloads the whole fused grouped MLP, so it cannot be combined with `expert_fc1` or `moe_act`. ## Features @@ -33,7 +33,7 @@ Supported offloading modules are `"attn_norm"`, `"core_attn"`, `"attn_proj"`, `" --fine-grained-activation-offloading # Modules whose inputs are offloaded (refer to your training script for list or delimiter syntax). -# Choices: "attn_norm", "core_attn", "attn_proj", "mlp_norm", "expert_fc1", "moe_act". +# Choices: "attn_norm", "qkv_linear", "core_attn", "attn_proj", "mlp_norm", "expert_fc1", "moe_act", "fused_group_mlp". --offload-modules expert_fc1 ``` diff --git a/docs/user-guide/features/paged_stash.md b/docs/user-guide/features/paged_stash.md index 4b7d807ace2..b5b97144905 100644 --- a/docs/user-guide/features/paged_stash.md +++ b/docs/user-guide/features/paged_stash.md @@ -21,7 +21,7 @@ Whenever `moe_expert_rank_capacity_factor` is set, a **runner** wraps forward-ba ## Prerequisites -HybridEP + TE fused grouped experts are required whenever `moe_expert_rank_capacity_factor` is set. With `moe_paged_stash` enabled: capacity factor must be set; no `cpu_offloading`; `offload_modules` must not include `expert_fc1` or `moe_act`. The runner is active whenever capacity factor is set (even without `--moe-paged-stash`) for over-budget reruns; stash overflow is checked only when paged stashing is on. +HybridEP + TE fused grouped experts are required whenever `moe_expert_rank_capacity_factor` is set. With `moe_paged_stash` enabled: capacity factor must be set; no `cpu_offloading`; `offload_modules` must not include `expert_fc1`, `moe_act`, or `fused_group_mlp`. The runner is active whenever capacity factor is set (even without `--moe-paged-stash`) for over-budget reruns; stash overflow is checked only when paged stashing is on. ## Configuration diff --git a/examples/mimo/training/runtime.py b/examples/mimo/training/runtime.py new file mode 100644 index 00000000000..ee7119c1501 --- /dev/null +++ b/examples/mimo/training/runtime.py @@ -0,0 +1,154 @@ +# Copyright (c) 2026, NVIDIA CORPORATION. All rights reserved. + +"""Per-rank runtime setup (RNG seeding, freezing, DDP wrapping) for hetero MIMO training.""" + +from __future__ import annotations + +import argparse + +import torch + +from examples.mimo.training.topology import HeteroTopology +from megatron.core.distributed import DistributedDataParallel, DistributedDataParallelConfig +from megatron.core.models.mimo.config.role import MIMO_LANGUAGE_MODULE_KEY +from megatron.core.models.mimo.model.base import MimoModel +from megatron.core.process_groups_config import ProcessGroupCollection +from megatron.core.tensor_parallel.random import model_parallel_cuda_manual_seed +from megatron.core.transformer.module import Float16Module +from megatron.core.utils import get_pg_rank, get_pg_size +from megatron.training.training import resolve_ddp_bucket_size, wrap_model_chunks_with_ddp +from megatron.training.utils import print_rank_0 + + +class _EncoderFloat16Module(Float16Module): + """Float16Module that keeps encoder outputs in model precision for the bridge.""" + + def forward(self, *inputs, fp32_output=False, **kwargs): # noqa: D102 + return super().forward(*inputs, fp32_output=fp32_output, **kwargs) + + +def configure_module_rng( + args: argparse.Namespace, pg_collection: ProcessGroupCollection, role_seed_offset: int +) -> None: + """Seed the CUDA RNG tracker for one module role from its tp/pp coordinates plus the offset. + + The seed is shared across a module's DP/CP replicas but distinct across PP stages and roles, + so disjoint modules (and stages) get independent RNG state. Caller invokes once per active + module on this rank. + """ + for _required in ("pp", "tp", "ep", "expt_tp"): + assert ( + getattr(pg_collection, _required, None) is not None + ), f"pg_collection passed to configure_module_rng must define {_required}" + pp_rank = get_pg_rank(pg_collection.pp) + tp_rank = get_pg_rank(pg_collection.tp) + ep_rank = get_pg_rank(pg_collection.ep) + expt_tp_rank = get_pg_rank(pg_collection.expt_tp) + seed = args.seed + role_seed_offset + (100 * pp_rank) + torch.manual_seed(seed) + model_parallel_cuda_manual_seed( + seed, tp_rank=tp_rank, ep_rank=ep_rank, etp_rank=expt_tp_rank, force_reset_rng=True + ) + + +def _freeze_modality_submodule(submodule: torch.nn.Module, args: argparse.Namespace) -> None: + """Freeze the encoder backbone (--freeze-vit) and/or projector (--freeze-projection).""" + if getattr(args, "freeze_vit", False): + submodule.encoders.requires_grad_(False) + if getattr(args, "freeze_projection", False): + submodule.input_projections.requires_grad_(False) + submodule.output_projections.requires_grad_(False) + + +def _module_config(module: torch.nn.Module): + """Return the module's own config, else the first descendant config (e.g. an encoder).""" + config = getattr(module, "config", None) + if config is not None: + return config + for child in module.modules(): + config = getattr(child, "config", None) + if config is not None: + return config + raise ValueError("Cannot resolve a config for DDP wrapping from module") + + +def _maybe_float16_wrap(module: torch.nn.Module, config, is_encoder: bool) -> torch.nn.Module: + """Wrap a submodule in Float16Module when fp16/bf16 is enabled; encoders keep bf16 outputs.""" + if not (getattr(config, "fp16", False) or getattr(config, "bf16", False)): + return module + cls = _EncoderFloat16Module if is_encoder else Float16Module + return cls(config, module) + + +def wrap_active_modules_with_ddp( + args: argparse.Namespace, mimo_model: MimoModel, topology: HeteroTopology +) -> None: + """Freeze (per --freeze-* flags), Float16Module-wrap, and DDP-wrap each active module.""" + pad_buckets = getattr(args, "ddp_pad_buckets_for_high_nccl_busbw", False) + grad_reduce_in_fp32 = getattr(args, "accumulate_allreduce_grads_in_fp32", True) + + ddp_stream = torch.cuda.Stream() + ddp_stream.wait_stream(torch.cuda.current_stream()) + with torch.cuda.stream(ddp_stream): + if mimo_model.language_model is not None: + if getattr(args, "freeze_lm", False): + mimo_model.language_model.requires_grad_(False) + overlap = getattr(args, "overlap_grad_reduce", False) + ddp_config = DistributedDataParallelConfig( + overlap_grad_reduce=overlap, + overlap_param_gather=getattr(args, "overlap_param_gather", False), + num_buckets=getattr(args, "ddp_num_buckets", None), + bucket_size=getattr(args, "ddp_bucket_size", None), + pad_buckets_for_high_nccl_busbw=pad_buckets, + use_distributed_optimizer=True, + grad_reduce_in_fp32=grad_reduce_in_fp32, + ) + # Resolve the absolute bucket size on the real config, as get_model does. + ddp_config.bucket_size = resolve_ddp_bucket_size( + ddp_config, + topology.module_pgs[MIMO_LANGUAGE_MODULE_KEY].dp_cp, + overlap, + sum(p.numel() for p in mimo_model.language_model.parameters()), + ) + lm_config = _module_config(mimo_model.language_model) + lm_module = _maybe_float16_wrap(mimo_model.language_model, lm_config, is_encoder=False) + print_rank_0("wrapping language model in DDP") + mimo_model.language_model = wrap_model_chunks_with_ddp( + [lm_module], + lm_config, + ddp_config, + DP=DistributedDataParallel, + pg_collection=topology.module_pgs[MIMO_LANGUAGE_MODULE_KEY], + )[0] + + for name, submodule in mimo_model.modality_submodules.items(): + if submodule is None or name not in topology.module_pgs: + continue + _freeze_modality_submodule(submodule, args) + ddp_config = DistributedDataParallelConfig( + overlap_grad_reduce=False, + overlap_param_gather=False, + num_buckets=getattr(args, "ddp_num_buckets", None), + bucket_size=getattr(args, "ddp_bucket_size", None), + pad_buckets_for_high_nccl_busbw=pad_buckets, + use_distributed_optimizer=True, + grad_reduce_in_fp32=grad_reduce_in_fp32, + ) + # Encoders keep overlap off; resolve_ddp_bucket_size returns None there. + ddp_config.bucket_size = resolve_ddp_bucket_size( + ddp_config, + topology.module_pgs[name].dp_cp, + False, + sum(p.numel() for p in submodule.parameters()), + ) + enc_config = _module_config(submodule) + enc_module = _maybe_float16_wrap(submodule, enc_config, is_encoder=True) + print_rank_0(f"wrapping modality submodule {name!r} in DDP") + mimo_model.modality_submodules[name] = wrap_model_chunks_with_ddp( + [enc_module], + enc_config, + ddp_config, + DP=DistributedDataParallel, + pg_collection=topology.module_pgs[name], + )[0] + torch.cuda.current_stream().wait_stream(ddp_stream) diff --git a/megatron/core/distributed/finalize_model_grads.py b/megatron/core/distributed/finalize_model_grads.py index baa214249cf..e494bbde2b6 100644 --- a/megatron/core/distributed/finalize_model_grads.py +++ b/megatron/core/distributed/finalize_model_grads.py @@ -353,6 +353,7 @@ def _update_router_expert_bias( hasattr(module, 'expert_bias') and module.training and module.expert_bias is not None + and not getattr(module, 'frozen_expert_bias', False) ): tokens_per_expert_list.append(module.local_tokens_per_expert) expert_bias_list.append(module.expert_bias) diff --git a/.github/workflows/sync-skills.yml b/megatron/core/distributed/fsdp/src/megatron_fsdp/experimental/__init__.py similarity index 68% rename from .github/workflows/sync-skills.yml rename to megatron/core/distributed/fsdp/src/megatron_fsdp/experimental/__init__.py index 75b8c20dca0..1bd55b7d995 100644 --- a/.github/workflows/sync-skills.yml +++ b/megatron/core/distributed/fsdp/src/megatron_fsdp/experimental/__init__.py @@ -11,19 +11,10 @@ # 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: Sync skills → agent dirs -on: - workflow_dispatch: - push: - branches: - - main - paths: - - "skills/**" - - "AGENTS.md" +"""Experimental Megatron-FSDP implementation.""" -jobs: - sync: - uses: NVIDIA-NeMo/FW-CI-templates/.github/workflows/_sync_skills.yml@v0.91.0 - secrets: - PAT: ${{ secrets.PAT }} +from .dbuffer import DBuffer +from .placement import Flat, Partial, Placement, Replicate + +__all__ = ["DBuffer", "Flat", "Partial", "Placement", "Replicate"] diff --git a/megatron/core/distributed/fsdp/src/megatron_fsdp/experimental/dbuffer.py b/megatron/core/distributed/fsdp/src/megatron_fsdp/experimental/dbuffer.py new file mode 100644 index 00000000000..51f52451089 --- /dev/null +++ b/megatron/core/distributed/fsdp/src/megatron_fsdp/experimental/dbuffer.py @@ -0,0 +1,454 @@ +# Copyright (c) 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. +# 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. + +"""Distributed tensor buffers for Megatron-FSDP.""" + +import dataclasses +from collections.abc import Iterable + +import torch +import torch.distributed as dist +import torch.distributed.tensor as dist_tensor +from torch.distributed import DeviceMesh +from torch.distributed.tensor import DTensor + +from .layout import GlobalLayout, Shape, non_leading_numel +from .placement import Flat, Partial, Placement, Replicate + + +@dataclasses.dataclass(frozen=True) +class _OwnedRange: + numel: int + tensor_relative_offset: int + buffer_relative_offset: int + + +def _validate_mesh_axis(mesh: DeviceMesh, axis: int) -> None: + if not isinstance(axis, int) or isinstance(axis, bool): + raise TypeError(f"Mesh axis must be an int, got {type(axis).__name__}.") + if axis < 0 or axis >= mesh.ndim: + raise ValueError(f"Mesh axis {axis} is out of bounds for mesh ndim {mesh.ndim}.") + + +def _validate_placements(placements: Iterable[Placement]) -> None: + seen_flat = False + for placement in placements: + if not isinstance(placement, (Replicate, Partial, Flat)): + raise TypeError(f"Unsupported DBuffer placement: {placement!r}.") + if isinstance(placement, Flat): + seen_flat = True + elif seen_flat: + raise ValueError( + "Flat placements must be a suffix of the placement list so each " + "local buffer is a contiguous global-buffer range." + ) + + +class DBuffer: + """A distributed buffer holding a group of logical tensors. + + DBuffer is analogous to DTensor, but manages a group of logical tensors in + one local storage tensor. It stores enough metadata to return per-tensor + views, redistribute the buffer across mesh axes, and materialize per-tensor + DTensors for optimizer state or distributed checkpointing. + """ + + # DBuffer owns only the data-parallel sub-mesh. Higher-level callers, such as + # ParameterGroup, should extend returned DTensors with tensor-parallel mesh axes + # because TP sharding metadata lives on nn.Parameter in MCore/TransformerEngine. + mesh: DeviceMesh + placements: tuple[Placement, ...] + layout: GlobalLayout + offset: int + local_buffer: torch.Tensor + + def __init__( + self, + mesh: DeviceMesh, + placements: Iterable[Placement], + tensor_shapes: Iterable[Shape], + dtype: torch.dtype, + device: torch.device | str, + ) -> None: + """Create a DBuffer and allocate its local buffer. + + Args: + mesh: Device mesh whose dimensions correspond to ``placements``. + placements: Per-mesh-axis DBuffer placements. + tensor_shapes: Global shapes for each logical tensor in this buffer. + dtype: Dtype for the local buffer. + device: Device for the local buffer. + """ + placements = tuple(placements) + if len(placements) != mesh.ndim: + raise ValueError( + f"Expected {mesh.ndim} placements for device mesh, got {len(placements)}." + ) + _validate_placements(placements) + + self.mesh = mesh + self.placements = placements + + tensor_shapes = tuple(torch.Size(shape) for shape in tensor_shapes) + self.layout = GlobalLayout.build(tensor_shapes, dp_size=self.mesh.size()) + + self.offset, local_numel = self.layout.get_local_range(self.mesh, self.placements) + self.local_buffer = torch.empty(local_numel, dtype=dtype, device=device) + + @property + def dtype(self) -> torch.dtype: + """Dtype of the local buffer.""" + return self.local_buffer.dtype + + @property + def device(self) -> torch.device: + """Device of the local buffer.""" + return self.local_buffer.device + + def _get_owned_range(self, tensor_index: int) -> _OwnedRange | None: + """Return this buffer's owned range for logical tensor ``tensor_index``.""" + tensor_start = self.layout.tensor_to_offset[tensor_index] + tensor_end = tensor_start + self.layout.tensor_shapes[tensor_index].numel() + buffer_start = self.offset + buffer_end = self.offset + self.local_buffer.numel() + + overlap_start = max(tensor_start, buffer_start) + overlap_end = min(tensor_end, buffer_end) + if overlap_start >= overlap_end: + return None + + return _OwnedRange( + numel=overlap_end - overlap_start, + tensor_relative_offset=overlap_start - tensor_start, + buffer_relative_offset=overlap_start - buffer_start, + ) + + @classmethod + def from_local( + cls, + local_buffer: torch.Tensor, + mesh: DeviceMesh, + placements: Iterable[Placement], + tensor_shapes: Iterable[Shape], + ) -> "DBuffer": + """Create a DBuffer from an existing local buffer. + + Args: + local_buffer: Contiguous local tensor storage for this rank. DBuffer + uses it directly in collectives such as all-gather and + reduce-scatter, which are efficient with contiguous tensors. + mesh: Device mesh whose dimensions correspond to ``placements``. + placements: Per-mesh-axis DBuffer placements. + tensor_shapes: Global shapes for each logical tensor in this buffer. + + Returns: + A DBuffer that reuses ``local_buffer`` without allocating storage. + """ + placements = tuple(placements) + if len(placements) != mesh.ndim: + raise ValueError( + f"Expected {mesh.ndim} placements for device mesh, got {len(placements)}." + ) + _validate_placements(placements) + if local_buffer.dim() != 1: + raise ValueError("local_buffer must be a flat 1D tensor.") + if not local_buffer.is_contiguous(): + raise ValueError("local_buffer must be contiguous for collective operations.") + + tensor_shapes = tuple(torch.Size(shape) for shape in tensor_shapes) + layout = GlobalLayout.build(tensor_shapes, dp_size=mesh.size()) + offset, local_numel = layout.get_local_range(mesh, placements) + if local_buffer.numel() != local_numel: + raise ValueError( + f"Expected local_buffer with {local_numel} elements, got " + f"{local_buffer.numel()}." + ) + + buffer = cls.__new__(cls) + buffer.mesh = mesh + buffer.placements = placements + buffer.layout = layout + buffer.offset = offset + buffer.local_buffer = local_buffer + return buffer + + @classmethod + def distribute_tensors( + cls, tensors: Iterable[torch.Tensor], mesh: DeviceMesh, placements: Iterable[Placement] + ) -> "DBuffer": + """Distribute full local tensor values into a DBuffer. + + Args: + tensors: Full tensor values available on this rank. + mesh: Device mesh whose dimensions correspond to ``placements``. + placements: Per-mesh-axis DBuffer placements. + + Returns: + A DBuffer whose local storage matches ``placements``. + """ + tensors = tuple(tensor.detach().contiguous() for tensor in tensors) + if not tensors: + raise ValueError("DBuffer.distribute_tensors() requires at least one tensor.") + + dtype = tensors[0].dtype + for tensor in tensors: + if tensor.dtype != dtype: + raise ValueError("All tensors in a DBuffer must have the same dtype.") + + tensor_shapes = tuple(tensor.shape for tensor in tensors) + buffer = cls( + mesh=mesh, + placements=placements, + tensor_shapes=tensor_shapes, + dtype=dtype, + device=mesh.device_type, + ) + # Only logical tensor ranges are initialized. Padding and layout gaps are not + # observable through get_local_tensor() and can remain unspecified. + for index, tensor in enumerate(tensors): + owned_range = buffer._get_owned_range(index) + if owned_range is None: + continue + + source_slice = tensor.view(-1).narrow( + 0, owned_range.tensor_relative_offset, owned_range.numel + ) + buffer.local_buffer.narrow( + 0, owned_range.buffer_relative_offset, owned_range.numel + ).copy_(source_slice) + return buffer + + def _create_or_validate_out( + self, placements: Iterable[Placement], out: "DBuffer | None" + ) -> "DBuffer": + placements = tuple(placements) + if out is None: + return DBuffer( + mesh=self.mesh, + placements=placements, + tensor_shapes=self.layout.tensor_shapes, + dtype=self.dtype, + device=self.device, + ) + + if out.mesh != self.mesh: + raise ValueError(f"Expected out mesh {self.mesh!r}, got {out.mesh!r}.") + if out.placements != placements: + raise ValueError(f"Expected out placements {placements!r}, got {out.placements!r}.") + if out.layout != self.layout: + raise ValueError(f"Expected out layout {self.layout!r}, got {out.layout!r}.") + if out.dtype != self.dtype: + raise ValueError(f"Expected out dtype {self.dtype}, got {out.dtype}.") + if out.device != self.device: + raise ValueError(f"Expected out device {self.device}, got {out.device}.") + return out + + def redistribute( + self, new_placements: Iterable[Placement], *, out: "DBuffer | None" = None + ) -> "DBuffer": + """Redistribute this buffer to ``new_placements``. + + This dispatcher supports the one-axis transitions: + Flat -> Replicate, Partial -> Replicate, Partial -> Flat, and + Replicate -> Flat. Other placement changes are intentionally unsupported. + """ + new_placements = tuple(new_placements) + if len(new_placements) != self.mesh.ndim: + raise ValueError( + f"Expected {self.mesh.ndim} placements for device mesh, got " + f"{len(new_placements)}." + ) + _validate_placements(new_placements) + + changed_axis: int | None = None + for axis, (old_placement, new_placement) in enumerate( + zip(self.placements, new_placements, strict=True) + ): + if old_placement == new_placement: + continue + if changed_axis is not None: + raise NotImplementedError( + "redistribute() currently supports one placement change, " + f"got changed axes {changed_axis} and {axis}." + ) + changed_axis = axis + + if changed_axis is None: + if out is None: + return self + out = self._create_or_validate_out(new_placements, out) + out.local_buffer.copy_(self.local_buffer) + return out + + axis = changed_axis + old_placement = self.placements[axis] + new_placement = new_placements[axis] + if isinstance(old_placement, Flat) and isinstance(new_placement, Replicate): + return self.allgather(axis, out=out) + if isinstance(old_placement, Partial) and isinstance(new_placement, Replicate): + return self.allreduce(axis, out=out) + if isinstance(old_placement, Partial) and isinstance(new_placement, Flat): + return self.reduce_scatter(axis, new_placement, out=out) + if isinstance(old_placement, Replicate) and isinstance(new_placement, Flat): + return self.scatter(axis, new_placement, out=out) + raise NotImplementedError( + "Unsupported DBuffer placement transition on axis " + f"{axis}: {old_placement!r} -> {new_placement!r}." + ) + + def allgather(self, mesh_axis: int, *, out: "DBuffer | None" = None) -> "DBuffer": + """All-gather a sharded axis into Replicate placement.""" + _validate_mesh_axis(self.mesh, mesh_axis) + if not isinstance(self.placements[mesh_axis], Flat): + raise ValueError( + f"allgather() currently requires Flat placement on axis {mesh_axis!r}." + ) + + placements = list(self.placements) + placements[mesh_axis] = Replicate() + _validate_placements(placements) + out = self._create_or_validate_out(placements, out) + dist.all_gather_into_tensor( + output_tensor=out.local_buffer, + input_tensor=self.local_buffer, + group=self.mesh.get_group(mesh_axis), + ) + return out + + def allreduce(self, mesh_axis: int, *, out: "DBuffer | None" = None) -> "DBuffer": + """All-reduce a Partial axis into Replicate placement.""" + _validate_mesh_axis(self.mesh, mesh_axis) + axis = mesh_axis + partial_placement = self.placements[axis] + if not isinstance(partial_placement, Partial): + raise ValueError(f"allreduce() requires Partial placement on axis {mesh_axis!r}.") + + placements = list(self.placements) + placements[axis] = Replicate() + out = self._create_or_validate_out(placements, out) + out.local_buffer.copy_(self.local_buffer) + dist.all_reduce( + out.local_buffer, op=partial_placement.reduce_op, group=self.mesh.get_group(axis) + ) + return out + + def reduce_scatter( + self, mesh_axis: int, new_placement: Placement, *, out: "DBuffer | None" = None + ) -> "DBuffer": + """Reduce-scatter a Partial axis into ``new_placement``.""" + _validate_mesh_axis(self.mesh, mesh_axis) + axis = mesh_axis + if not isinstance(new_placement, Flat): + raise NotImplementedError("DBuffer currently supports reduce_scatter() to Flat only.") + partial_placement = self.placements[axis] + if not isinstance(partial_placement, Partial): + raise ValueError(f"reduce_scatter() requires Partial placement on axis {mesh_axis!r}.") + + placements = list(self.placements) + placements[axis] = new_placement + _validate_placements(placements) + out = self._create_or_validate_out(placements, out) + dist.reduce_scatter_tensor( + output=out.local_buffer, + input=self.local_buffer, + op=partial_placement.reduce_op, + group=self.mesh.get_group(axis), + ) + return out + + def scatter( + self, mesh_axis: int, new_placement: Placement, *, out: "DBuffer | None" = None + ) -> "DBuffer": + """Locally chunk a Replicate axis into ``new_placement``.""" + _validate_mesh_axis(self.mesh, mesh_axis) + axis = mesh_axis + if not isinstance(new_placement, Flat): + raise NotImplementedError("DBuffer currently supports scatter() to Flat only.") + if not isinstance(self.placements[axis], Replicate): + raise ValueError(f"scatter() requires Replicate placement on axis {mesh_axis!r}.") + + placements = list(self.placements) + placements[axis] = new_placement + _validate_placements(placements) + + if out is None: + destination_offset, destination_numel = self.layout.get_local_range( + self.mesh, placements + ) + else: + out = self._create_or_validate_out(placements, out) + destination_offset = out.offset + destination_numel = out.local_buffer.numel() + + local_buffer_offset = destination_offset - self.offset + if ( + local_buffer_offset < 0 + or local_buffer_offset + destination_numel > self.local_buffer.numel() + ): + raise RuntimeError("scatter() destination is not contained in the source local buffer.") + local_slice = self.local_buffer.narrow(0, local_buffer_offset, destination_numel) + if out is None: + return DBuffer.from_local(local_slice, self.mesh, placements, self.layout.tensor_shapes) + + out.local_buffer.copy_(local_slice) + return out + + def get_local_tensor(self, index: int) -> torch.Tensor: + """Return this rank's local view for logical tensor ``index``. + + Flat placements shard dim 0, so the returned view preserves all + non-leading dimensions and only changes the leading dimension. + """ + shape = self.layout.tensor_shapes[index] + owned_range = self._get_owned_range(index) + + row_size = non_leading_numel(shape) + if owned_range is None: + empty_shape = torch.Size((0, *shape[1:])) + return torch.empty(empty_shape, dtype=self.dtype, device=self.device) + + if owned_range.tensor_relative_offset % row_size != 0 or owned_range.numel % row_size != 0: + raise RuntimeError( + f"Local tensor shard for tensor {index} does not preserve dim-0 boundaries." + ) + local_shape = torch.Size((owned_range.numel // row_size, *shape[1:])) + return self.local_buffer.narrow( + 0, owned_range.buffer_relative_offset, owned_range.numel + ).view(local_shape) + + def get_dtensor(self, index: int) -> DTensor: + """Return logical tensor ``index`` as a DTensor.""" + torch_placements = [] + for placement in self.placements: + if isinstance(placement, Replicate): + torch_placements.append(dist_tensor.Replicate()) + elif isinstance(placement, Flat): + torch_placements.append(dist_tensor.Shard(0)) + elif isinstance(placement, Partial): + raise ValueError("Partial DBuffer placements cannot be represented as DTensor.") + else: + raise TypeError(f"Unsupported placement for DTensor conversion: {placement!r}.") + + local_tensor = self.get_local_tensor(index) + tensor_shape = self.layout.tensor_shapes[index] + # DBuffer uses contiguous flat storage, and Flat only shards dim 0, so + # the local view's stride matches the logical global tensor stride. + return DTensor.from_local( + local_tensor=local_tensor, + device_mesh=self.mesh, + placements=tuple(torch_placements), + run_check=False, + shape=tensor_shape, + stride=local_tensor.stride(), + ) diff --git a/megatron/core/distributed/fsdp/src/megatron_fsdp/experimental/layout.py b/megatron/core/distributed/fsdp/src/megatron_fsdp/experimental/layout.py new file mode 100644 index 00000000000..11070495e4d --- /dev/null +++ b/megatron/core/distributed/fsdp/src/megatron_fsdp/experimental/layout.py @@ -0,0 +1,251 @@ +# Copyright (c) 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. +# 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. + +"""Global tensor layout metadata for DBuffer.""" + +import dataclasses +import math +from collections.abc import Iterable +from typing import TypeAlias + +import torch +from torch.distributed import DeviceMesh + +from .placement import Flat, Placement + +Shape: TypeAlias = torch.Size | Iterable[int] + + +@dataclasses.dataclass(frozen=True) +class GlobalLayout: + """Global tensor layout in element coordinates.""" + + tensor_shapes: tuple[torch.Size, ...] + tensor_to_offset: tuple[int, ...] + size: int + + @classmethod + def build(cls, shapes: Iterable[Shape], dp_size: int) -> "GlobalLayout": + """Compute global tensor element offsets and padded size. + + This is a DBuffer-specific reimplementation of + ``param_and_grad_buffer.build_data_parallel_buffer_index``. It keeps only + the global offset construction and final padding so each rank-local shard + size is a multiple of ``chunk_size``; DBuffer derives rank-local slices + later through DTensor placements. + + The computed layout is compatible with Flat, TensorAtomic, and BlockAtomic, + even though the latter two are not implemented. + + ``chunk_size`` is the least common multiple of each tensor's row size + (``shape[1:].numel()``). For example, with shapes P0=(2, 6), P1=(4, 4), + P2=(4, 4), P3=(1, 2), P4=(1, 6), ``chunk_size = LCM(6, 4, 4, 2, 6) = 12`` + and a 5-rank DP layout has equal-size rank shards: + + ``` + rank 0 [ 0, 12): | P0 row 0 | P0 row 1 | + rank 1 [12, 24): | P1 row 0 | P1 row 1 | P1 row 2 | + rank 2 [24, 36): | P1 row 3 | P3 | gap | P2 row 0 | + rank 3 [36, 48): | P2 row 1 | P2 row 2 | P2 row 3 | + rank 4 [48, 60): | P4 | pad | + ``` + + The diagram uses four character columns per element; segment widths are + proportional. Every chunk boundary is aligned to each tensor's row size, + so each DP shard owns full rows even when fragments fill regular-tensor + padding gaps. + + Args: + shapes: Logical tensor shapes in tensor-id order. + dp_size: Data-parallel shard count for this global layout. + + Returns: + Global layout with row-aligned tensor offsets and a total size padded + to a multiple of ``chunk_size * dp_size``, so every rank-local shard + length is a multiple of ``chunk_size``. + """ + if dp_size <= 0: + raise ValueError(f"DP size must be positive, got {dp_size}.") + + tensor_shapes = tuple(torch.Size(shape) for shape in shapes) + chunk_size = 1 + for shape in tensor_shapes: + row_size = non_leading_numel(shape) + if row_size <= 0: + raise ValueError( + f"Cannot compute a layout for zero-sized non-leading dims: {shape}." + ) + chunk_size = math.lcm(chunk_size, row_size) + + # chunk_size is the packing unit. Since every tensor row size divides it, + # DP shard boundaries that are multiples of chunk_size avoid splitting dim-0 rows. + UNASSIGNED_OFFSET = -1 + tensor_to_offset: list[int] = [UNASSIGNED_OFFSET] * len(tensor_shapes) + fragment_items = [] + regular_items = [] + for tensor_id, shape in enumerate(tensor_shapes): + if shape.numel() < chunk_size: + fragment_items.append((tensor_id, shape)) + else: + regular_items.append((tensor_id, shape)) + + # Regular tensors anchor the layout. Fragments are held back to fill padding + # gaps left by regular tensors whose sizes are not exact multiples of chunk_size. + fragment_items.sort(key=lambda id_shape: id_shape[1].numel(), reverse=True) + + next_offset = 0 + while regular_items: + tensor_id, shape = regular_items.pop(0) + tensor_numel = shape.numel() + tensor_to_offset[tensor_id] = next_offset + + if tensor_numel % chunk_size == 0: + next_offset += tensor_numel + continue + + gap_offset = next_offset + tensor_numel + next_offset += _pad_to_multiple(tensor_numel, chunk_size) + fragment_gap_end = next_offset + remainder = tensor_numel % chunk_size + + # Try to pair this non-divisible regular tensor with a conjugate regular + # tensor whose remainder fits in the same chunk_size interval. The + # conjugate starts in the gap and then continues with full chunk_size + # intervals after this one. + conjugate_item = None + for candidate_item in regular_items[:]: + _, candidate_shape = candidate_item + candidate_numel = candidate_shape.numel() + candidate_remainder = candidate_numel % chunk_size + if candidate_remainder == 0: + continue + if remainder + candidate_remainder <= chunk_size: + conjugate_item = candidate_item + regular_items.remove(candidate_item) + break + + if conjugate_item is not None: + conjugate_id, conjugate_shape = conjugate_item + conjugate_numel = conjugate_shape.numel() + conjugate_remainder = conjugate_numel % chunk_size + conjugate_offset = next_offset - conjugate_remainder + tensor_to_offset[conjugate_id] = conjugate_offset + fragment_gap_end = conjugate_offset + next_offset += (conjugate_numel // chunk_size) * chunk_size + + # Fill any remaining gap with fragments, keeping each fragment aligned to + # its own row size so dim-0 rows remain contiguous within DP shards. + for fragment in fragment_items[:]: + frag_id, frag_shape = fragment + frag_numel = frag_shape.numel() + aligned_gap_offset = _pad_to_multiple(gap_offset, non_leading_numel(frag_shape)) + if aligned_gap_offset + frag_numel > fragment_gap_end: + continue + tensor_to_offset[frag_id] = aligned_gap_offset + gap_offset = aligned_gap_offset + frag_numel + fragment_items.remove(fragment) + + # Fragments that did not fit into regular-tensor gaps are appended at the tail. + for frag_id, frag_shape in fragment_items: + next_offset = _pad_to_multiple(next_offset, non_leading_numel(frag_shape)) + tensor_to_offset[frag_id] = next_offset + next_offset += frag_shape.numel() + + return cls( + tensor_shapes=tensor_shapes, + tensor_to_offset=tuple(tensor_to_offset), + size=_pad_to_multiple(next_offset, chunk_size * dp_size), + ) + + def __post_init__(self) -> None: + """Validate tensor offsets are row-aligned, in bounds, and non-overlapping.""" + + @dataclasses.dataclass(frozen=True) + class TensorRange: + """Contiguous global element range occupied by one logical tensor.""" + + start: int + end: int + tensor_id: int + + if self.size < 0: + raise AssertionError(f"Global layout size {self.size} is negative.") + if len(self.tensor_shapes) != len(self.tensor_to_offset): + raise AssertionError( + "Global layout has mismatched tensor shapes and offsets: " + f"{len(self.tensor_shapes)} shapes and {len(self.tensor_to_offset)} offsets." + ) + + tensor_ranges: list[TensorRange] = [] + for tensor_id, (shape, start) in enumerate( + zip(self.tensor_shapes, self.tensor_to_offset, strict=True) + ): + if start < 0: + raise AssertionError(f"Tensor {tensor_id} offset {start} is negative.") + + row_size = non_leading_numel(shape) + if row_size <= 0: + raise AssertionError(f"Tensor {tensor_id} has invalid row size {row_size}.") + if start % row_size != 0: + raise AssertionError( + f"Tensor {tensor_id} offset {start} is not aligned to row size {row_size}." + ) + + end = start + shape.numel() + if end > self.size: + raise AssertionError( + f"Tensor {tensor_id} range [{start}, {end}) exceeds " + f"layout size {self.size}." + ) + tensor_ranges.append(TensorRange(start, end, tensor_id)) + + previous_range: TensorRange | None = None + for current_range in sorted(tensor_ranges, key=lambda tensor_range: tensor_range.start): + if previous_range is not None and current_range.start < previous_range.end: + raise AssertionError( + "Global layout tensors overlap: " + f"tensor {previous_range.tensor_id} " + f"[{previous_range.start}, {previous_range.end}) and " + f"tensor {current_range.tensor_id} " + f"[{current_range.start}, {current_range.end})." + ) + previous_range = current_range + + def get_local_range(self, mesh: DeviceMesh, placements: Iterable[Placement]) -> tuple[int, int]: + """Return this rank's local element offset and length for ``placements``.""" + offset = 0 + numel = self.size + for axis, placement in reversed(tuple(enumerate(placements))): + if not isinstance(placement, Flat): + continue + axis_size = mesh.size(axis) + if numel % axis_size != 0: + raise ValueError( + f"Local range size {numel} is not divisible by Flat axis size {axis_size}." + ) + shard_size = numel // axis_size + offset += mesh.get_local_rank(axis) * shard_size + numel = shard_size + return offset, numel + + +def non_leading_numel(shape: torch.Size) -> int: + """Return the number of elements after dim 0 for a non-scalar shape.""" + if len(shape) == 0: + raise ValueError(f"DBuffer layout does not support 0D tensor shapes: {shape}.") + return shape[1:].numel() + + +def _pad_to_multiple(value: int, multiple: int) -> int: + return ((value + multiple - 1) // multiple) * multiple diff --git a/megatron/core/distributed/fsdp/src/megatron_fsdp/experimental/placement.py b/megatron/core/distributed/fsdp/src/megatron_fsdp/experimental/placement.py new file mode 100644 index 00000000000..1b561c9634d --- /dev/null +++ b/megatron/core/distributed/fsdp/src/megatron_fsdp/experimental/placement.py @@ -0,0 +1,55 @@ +# Copyright (c) 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. +# 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. + +"""DBuffer placement definitions. + +These placement concepts are borrowed from PyTorch DTensor placements: +``Replicate`` and ``Partial`` mirror DTensor's placements. ``Flat`` is the +only sharded DBuffer placement implemented so far; it stores dim-0 shards in a +flattened local buffer. + +============= ============= ==================== +Source Destination DBuffer operation +============= ============= ==================== +sharded ``Replicate`` ``allgather()`` +``Partial`` sharded ``reduce_scatter()`` +``Partial`` ``Replicate`` ``allreduce()`` +``Replicate`` sharded ``scatter()`` (local) +============= ============= ==================== +""" + +import dataclasses + +import torch.distributed as dist + + +class Placement: + """Base class for DBuffer placements.""" + + +@dataclasses.dataclass(frozen=True) +class Replicate(Placement): + """Replicated local buffer placement.""" + + +@dataclasses.dataclass(frozen=True) +class Partial(Placement): + """Unreduced replicated local buffer placement.""" + + reduce_op: dist.ReduceOp.RedOpType = dist.ReduceOp.SUM + + +@dataclasses.dataclass(frozen=True) +class Flat(Placement): + """Flat per-unit dim-0 sharded local buffer placement.""" diff --git a/megatron/core/extensions/transformer_engine.py b/megatron/core/extensions/transformer_engine.py index d31a5c6d7ae..64bce710993 100644 --- a/megatron/core/extensions/transformer_engine.py +++ b/megatron/core/extensions/transformer_engine.py @@ -1869,6 +1869,14 @@ def forward( # Update Q K outside of TE Attention API core_attn_out, batch_max_attention_logits = core_attn_out + # The max attention logit is only used as a statistic for qk-clip + # and logging, so it never needs gradients. Detach it from the + # autograd graph, otherwise accumulating it into + # current_max_attn_logits keeps every batch's attention forward + # graph alive and leaks memory (most visibly when only + # log_max_attention_logit is set and clip_qk() never resets it). + batch_max_attention_logits = batch_max_attention_logits.detach() + # Update QK_Clip balancing eta if self.current_max_attn_logits is None: self.current_max_attn_logits = batch_max_attention_logits diff --git a/megatron/core/inference/inference_request.py b/megatron/core/inference/inference_request.py index d6e7c67a959..f9fd60f1033 100644 --- a/megatron/core/inference/inference_request.py +++ b/megatron/core/inference/inference_request.py @@ -373,6 +373,9 @@ class DynamicInferenceRequest(InferenceRequest): routing_indices: Optional[np.ndarray] = None finished_chunk_token_count: int = 0 stop_word_ids: Optional[List[List[int]]] = None # Tokenized stop words (populated internally) + # Consecutive steps this request has been deferred by CG-aware admission gating. + # Reset to 0 on successful admission. Used only for starvation logging. + cg_wait_iters: int = 0 # Prefix caching fields block_size_tokens: Optional[int] = None # Block size for hash computation diff --git a/megatron/core/inference/shards.py b/megatron/core/inference/shards.py new file mode 100644 index 00000000000..8d73e0ad56b --- /dev/null +++ b/megatron/core/inference/shards.py @@ -0,0 +1,248 @@ +# Copyright (c) 2026, NVIDIA CORPORATION. All rights reserved. + +"""Framework-agnostic primitives for heterogeneous inference sharding: build a +``ProcessGroupCollection`` per shard, each over a contiguous rank window at its +own parallelism.""" + +from dataclasses import dataclass +from typing import List, Optional, Sequence, Union + +import torch.distributed as dist + +from megatron.core import mpu +from megatron.core.hyper_comm_grid import HyperCommGrid +from megatron.core.inference.shards_spec import InferenceShardSpec, normalize_shard_specs +from megatron.core.process_groups_config import ProcessGroupCollection + + +def build_inference_pg_collection( + world_size: int, + tp_size: Optional[int] = None, + pp_size: Optional[int] = None, + cp_size: Optional[int] = None, + ep_size: Optional[int] = None, + expt_tp_size: Optional[int] = None, + use_tp_pp_dp_mapping: bool = False, + rank_offset: int = 0, +) -> ProcessGroupCollection: + """Build a ProcessGroupCollection for one inference model. + + Uses two HyperCommGrids matching mpu: + - decoder_grid for dense/attention layers (tp, cp, dp, pp) + - expert_grid for MoE expert layers (expt_tp, ep, expt_dp, pp) + + Args: + world_size: Number of ranks in this inference window. + tp_size: Tensor model parallel size. Defaults to training's TP size. + pp_size: Pipeline parallel size. Defaults to training's PP size. + cp_size: Context parallel size. Defaults to training's CP size. + ep_size: Expert parallel size. Defaults to training's EP size. + expt_tp_size: Expert tensor parallel size. Defaults to training's + expert TP size. + use_tp_pp_dp_mapping: If True, use 'tp-pp-dp' order; otherwise + 'tp-dp-pp'. + rank_offset: Starting global rank of the window. Use ``0`` for + collocated inference (shares ranks with training); use a non-zero + offset for non-collocated setups where inference ranks are disjoint + from training ranks. + + Returns: + ProcessGroupCollection configured for the inference model. On ranks + outside the ``[rank_offset, rank_offset + world_size)`` window every + process-group field is a non-member sentinel returned by + :func:`torch.distributed.new_subgroups_by_enumeration` — callers should + not use that instance; see + :func:`build_inference_pg_collections_for_shards` for the right way to + filter. + """ + if tp_size is None: + tp_size = mpu.get_tensor_model_parallel_world_size() + if cp_size is None: + cp_size = mpu.get_context_parallel_world_size() + if pp_size is None: + pp_size = mpu.get_pipeline_model_parallel_world_size() + if ep_size is None: + ep_size = mpu.get_expert_model_parallel_world_size() + if expt_tp_size is None: + expt_tp_size = mpu.get_expert_tensor_parallel_world_size() + + # Dense layer DP size (world = tp * cp * dp * pp) + dp_size = world_size // (tp_size * cp_size * pp_size) + assert dp_size >= 1 and (tp_size * cp_size * dp_size * pp_size) == world_size, ( + f"World size ({world_size}) must be divisible by tp*cp*pp " + f"({tp_size * cp_size * pp_size})" + ) + + # Expert DP size (world = expt_tp * ep * expt_dp * pp) + expt_dp_size = world_size // (expt_tp_size * ep_size * pp_size) + assert expt_dp_size >= 1 and (expt_tp_size * ep_size * expt_dp_size * pp_size) == world_size, ( + f"World size ({world_size}) must be divisible by expt_tp*ep*pp " + f"({expt_tp_size * ep_size * pp_size})" + ) + + rank = dist.get_rank() + + if use_tp_pp_dp_mapping: + decoder_grid = HyperCommGrid( + [tp_size, cp_size, pp_size, dp_size], ["tp", "cp", "pp", "dp"], rank_offset=rank_offset + ) + else: + decoder_grid = HyperCommGrid( + [tp_size, cp_size, dp_size, pp_size], ["tp", "cp", "dp", "pp"], rank_offset=rank_offset + ) + + tp_group = decoder_grid.create_pg("tp") + cp_group = decoder_grid.create_pg("cp") + pp_group = decoder_grid.create_pg("pp") + dp_group = decoder_grid.create_pg("dp") + mp_group = decoder_grid.create_pg(["tp", "pp"]) + tp_cp_group = decoder_grid.create_pg(["tp", "cp"]) + dp_cp_group = decoder_grid.create_pg(["cp", "dp"]) + tp_dp_cp_group = decoder_grid.create_pg(["tp", "cp", "dp"]) + + if use_tp_pp_dp_mapping: + expert_grid = HyperCommGrid( + [expt_tp_size, ep_size, pp_size, expt_dp_size], + ["tp", "ep", "pp", "dp"], + rank_offset=rank_offset, + ) + else: + expert_grid = HyperCommGrid( + [expt_tp_size, ep_size, expt_dp_size, pp_size], + ["tp", "ep", "dp", "pp"], + rank_offset=rank_offset, + ) + + decoder_pp_enum = decoder_grid.get_rank_enum("pp") + expert_pp_enum = expert_grid.get_rank_enum("pp") + assert decoder_pp_enum == expert_pp_enum, ( + f"PP groups must match between decoder and expert grids. " + f"Decoder: {decoder_pp_enum}, Expert: {expert_pp_enum}" + ) + + ep_group = expert_grid.create_pg("ep") + expt_tp_group = expert_grid.create_pg("tp") + expt_dp_group = expert_grid.create_pg("dp") + tp_ep_group = expert_grid.create_pg(["tp", "ep"]) + tp_ep_pp_group = expert_grid.create_pg(["tp", "ep", "pp"]) + + embd_group = None + pos_embd_group = None + pp_rank_enum = decoder_grid.get_rank_enum("pp") + for pp_ranks in pp_rank_enum: + if len(pp_ranks) == 1: + embd_ranks = [pp_ranks[0]] + else: + embd_ranks = [pp_ranks[0], pp_ranks[-1]] + group = dist.new_group(ranks=embd_ranks) + if rank in embd_ranks: + embd_group = group + pos_embd_ranks = [pp_ranks[0]] + group = dist.new_group(ranks=pos_embd_ranks) + if rank in pos_embd_ranks: + pos_embd_group = group + + return ProcessGroupCollection( + tp=tp_group, + cp=cp_group, + pp=pp_group, + ep=ep_group, + embd=embd_group, + pos_embd=pos_embd_group, + dp=dp_group, + tp_cp=tp_cp_group, + mp=mp_group, + expt_tp=expt_tp_group, + expt_dp=expt_dp_group, + tp_ep=tp_ep_group, + tp_ep_pp=tp_ep_pp_group, + dp_cp=dp_cp_group, + tp_dp_cp=tp_dp_cp_group, + ) + + +@dataclass +class InferenceShard: + """One shard in a multi-shard inference layout: its identity, its rank + window, and this rank's process groups for it. + + Attributes: + spec: This shard's :class:`~megatron.core.inference.shards_spec.InferenceShardSpec` + (``tp``/``pp``/``ep``/``expt_tp``/``dp`` and optional + ``role`` = ``prefill``/``decode``). + rank_offset: First global rank belonging to this shard. + world_size: Number of ranks in this shard (tp*pp*dp). + pg_collection: The shard's ProcessGroupCollection if the current rank + is a member of this shard, else ``None`` -- the ``is not None`` check + is how a rank finds its own shard. Every rank still participates in + the collective process-group creation for every shard + (``dist.new_group`` is world-collective); only members get a usable + handle. + """ + + spec: InferenceShardSpec + rank_offset: int + world_size: int + pg_collection: Optional[ProcessGroupCollection] + + +def build_inference_pg_collections_for_shards( + total_world_size: int, + shards: Union[str, Sequence[InferenceShardSpec], Sequence[dict]], + use_tp_pp_dp_mapping: bool = False, +) -> List[InferenceShard]: + """Build one ProcessGroupCollection per heterogeneous inference shard. + + Partitions global ranks into contiguous non-overlapping windows, one per + shard. Shard ``i`` owns ranks + ``[offset_i, offset_i + tp_i*pp_i*dp_i)``. + + Every rank must call this function so the collective ``dist.new_group`` + calls inside :func:`build_inference_pg_collection` succeed for every shard. + The returned ``pg_collection`` is populated only on ranks belonging to + that shard; others see ``None``. + + Args: + total_world_size: Full world size across training + all inference + shards. + shards: Shard layout in any form ``normalize_shard_specs`` accepts -- a + spec string, a list of :class:`InferenceShardSpec`, or a list of + raw dicts. Normalized internally to validated specs. + use_tp_pp_dp_mapping: Passed through to ``build_inference_pg_collection``. + + Returns: + List of :class:`InferenceShard`, one per input spec. + """ + specs = normalize_shard_specs(shards, total_world_size) + rank = dist.get_rank() + results: List[InferenceShard] = [] + offset = 0 + for i, spec in enumerate(specs): + tp, pp, ep, expt_tp, dp = spec.tp, spec.pp, spec.ep, spec.expt_tp, spec.dp + shard_world = tp * pp * dp + assert offset + shard_world <= total_world_size, ( + f"Shard {i} ({spec}) runs out of ranks: needs " + f"[{offset}, {offset + shard_world}), total_world_size={total_world_size}." + ) + pgc = build_inference_pg_collection( + world_size=shard_world, + tp_size=tp, + pp_size=pp, + # Inference shards don't context-parallelize; the spec validates cp == 1. + cp_size=spec.cp, + ep_size=ep, + expt_tp_size=expt_tp, + use_tp_pp_dp_mapping=use_tp_pp_dp_mapping, + rank_offset=offset, + ) + in_shard = offset <= rank < offset + shard_world + results.append( + InferenceShard( + spec=spec, + rank_offset=offset, + world_size=shard_world, + pg_collection=pgc if in_shard else None, + ) + ) + offset += shard_world + return results diff --git a/megatron/core/inference/shards_spec.py b/megatron/core/inference/shards_spec.py new file mode 100644 index 00000000000..6b2bd06ac10 --- /dev/null +++ b/megatron/core/inference/shards_spec.py @@ -0,0 +1,177 @@ +# Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. + +"""Parser and typed representation (:class:`InferenceShardSpec`) for the +``--inference-shards`` shard-layout string.""" + +from dataclasses import dataclass +from typing import List, Optional, Sequence, Union + +VALID_INT_KEYS = ("tp", "pp", "ep", "expt_tp", "dp", "cp") +VALID_ROLES = ("prefill", "decode") +VALID_KEYS = (*VALID_INT_KEYS, "role") + + +@dataclass(frozen=True) +class InferenceShardSpec: + """One inference shard's parallelism -- the canonical shard-layout type. + + Frozen and self-validating: ``expt_tp`` defaults to ``tp`` (resolved at + construction), the expert decomposition is checked to tile within the shard, + and ``role`` (``"prefill"`` / ``"decode"`` for disaggregation, else ``None``) + is validated. A list of these, produced by :func:`normalize_shard_specs`, is + what every inference-shard consumer (the PG builder, the disaggregation + setup) operates on. + """ + + tp: int = 1 + pp: int = 1 + ep: int = 1 + dp: int = 1 + cp: int = 1 + expt_tp: Optional[int] = None + role: Optional[str] = None + + def __post_init__(self): + if self.role is not None and self.role not in VALID_ROLES: + raise ValueError(f"role must be one of {VALID_ROLES} or None; got {self.role!r}") + # cp is accepted in the spec for forward-compatibility, but inference + # shards don't context-parallelize (the KV reshard models layer x head, + # not the sequence dim), so reject anything but cp=1 with a clear error. + if self.cp != 1: + raise ValueError( + f"context parallelism is not supported for inference shards; cp must be 1, " + f"got cp={self.cp}" + ) + # Resolve expt_tp's default (tp) on this frozen instance. + if self.expt_tp is None: + object.__setattr__(self, "expt_tp", self.tp) + # Expert decomposition must tile cleanly within the shard. + if self.world_size % (self.expt_tp * self.ep * self.pp) != 0: + raise ValueError( + f"shard {self} has tp*pp*dp={self.world_size} but expt_tp*ep*pp=" + f"{self.expt_tp * self.ep * self.pp} does not divide it; " + f"choose compatible sizes." + ) + + @property + def world_size(self) -> int: + """Number of ranks this shard occupies (``tp * pp * dp``).""" + return self.tp * self.pp * self.dp + + def to_dict(self) -> dict: + """Plain-dict form (e.g. for serialization or external consumers).""" + d = {"tp": self.tp, "pp": self.pp, "ep": self.ep, "dp": self.dp, "expt_tp": self.expt_tp} + if self.role is not None: + d["role"] = self.role + return d + + +def normalize_shard_specs( + shards: Union[str, Sequence["InferenceShardSpec"], Sequence[dict]], world_size: int +) -> List["InferenceShardSpec"]: + """Coerce the public shard-spec input (a spec string, a list of + :class:`InferenceShardSpec`, or a list of raw dicts) into the validated + list of :class:`InferenceShardSpec` the shard builders consume.""" + if isinstance(shards, str): + return parse_inference_shards_spec(shards, world_size) + out: List[InferenceShardSpec] = [ + s if isinstance(s, InferenceShardSpec) else InferenceShardSpec(**dict(s)) for s in shards + ] + return _finalize_and_validate(out, world_size) + + +def spec_declares_disaggregation(spec_str: str) -> bool: + """Whether a shard spec tags any shard with a ``role=`` (prefill/decode). + + A role tag is what marks the layout as a prefill->decode handoff rather + than plain multi-shard / data-parallel inference. Cheap and world_size- + free, so it can be checked at arg-validation time; full parsing + + validation is :func:`parse_inference_shards_spec`. + """ + if not spec_str: + return False + return any( + kv.strip().startswith("role=") + for shard in spec_str.replace("+", ";").split(";") + for kv in shard.split(",") + ) + + +def parse_inference_shards_spec(spec_str: str, world_size: int) -> List[dict]: + """Parse + validate the ``--inference-shards`` string. + + Args: + spec_str: Raw CLI value, e.g. ``"tp=2,dp=1+tp=1,dp=2"`` or with + disaggregation roles ``"tp=2,role=prefill+tp=1,role=decode"``. + world_size: Total number of ranks. Specs must partition it + exactly (no idle ranks; see note below). + + Returns: + List of :class:`InferenceShardSpec`, one per shard. Order matches the + input (left-to-right corresponds to ascending ``rank_offset``). + + Raises: + AssertionError: on syntax errors, unknown keys, or a rank-count + mismatch with ``world_size``. Idle ranks are rejected to keep the + partition explicit — any world-collective consumer must be able to + enumerate every rank's shard membership from the parsed list alone. + ValueError: on an expert-grid mismatch within a shard (raised by + :class:`InferenceShardSpec`). + """ + parsed: List[InferenceShardSpec] = [] + # ``+`` is convenient from shell recipes where ``;`` would otherwise + # be treated as a command terminator. Normalize before splitting. + shards_raw = spec_str.replace("+", ";") + for shard_str in shards_raw.split(";"): + shard_str = shard_str.strip() + if not shard_str: + continue + kwargs: dict = {} + for kv in shard_str.split(","): + kv = kv.strip() + if not kv: + continue + if "=" not in kv: + raise AssertionError( + f"Bad --inference-shards spec entry {kv!r}: expected key=value." + ) + k, v = kv.split("=", 1) + k = k.strip() + v = v.strip() + if k not in VALID_KEYS: + raise AssertionError( + f"Unknown key {k!r} in --inference-shards " + f"(allowed: {','.join(VALID_KEYS)})." + ) + if k == "role": + role = v.lower() + assert role in VALID_ROLES, ( + f"Unknown role {v!r} in --inference-shards " + f"(allowed: {','.join(VALID_ROLES)})." + ) + kwargs[k] = role + else: + kwargs[k] = int(v) + parsed.append(InferenceShardSpec(**kwargs)) + + return _finalize_and_validate(parsed, world_size) + + +def _finalize_and_validate( + specs: List["InferenceShardSpec"], world_size: int +) -> List["InferenceShardSpec"]: + """Assert the shards partition the world exactly. + + Shared by the string parser and the object path (:func:`normalize_shard_specs`). + Per-shard defaults and expert-grid validation live in + :class:`InferenceShardSpec`; this only enforces the cross-shard total. Idle + ranks are rejected so any world-collective consumer can enumerate every + rank's shard membership from the list alone. + """ + assert specs, "--inference-shards was empty." + total_ranks = sum(s.world_size for s in specs) + assert total_ranks == world_size, ( + f"--inference-shards consumes {total_ranks} ranks but world size is " + f"{world_size}; specs must partition the full world." + ) + return specs diff --git a/megatron/core/inference/unified_memory.py b/megatron/core/inference/unified_memory.py index 53d8d862b92..613fb246f10 100644 --- a/megatron/core/inference/unified_memory.py +++ b/megatron/core/inference/unified_memory.py @@ -277,6 +277,16 @@ def create_unified_mempool() -> "MemPool": + details ) else: + # torch.cuda.MemPool can't coexist with expandable_segments; bail so the + # caller falls back to non-UVM allocation. + alloc_conf = os.environ.get("PYTORCH_CUDA_ALLOC_CONF", "") + if "expandable_segments:true" in alloc_conf.lower(): + raise UnifiedMemoryUnsupportedError( + "UVM mempool is incompatible with the expandable-segments allocator " + "(PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True, which " + "torch.cuda.MemPool does not support). Unset expandable_segments to " + "use UVM." + ) return MemPool(allocator=_alloc) diff --git a/megatron/core/model_parallel_config.py b/megatron/core/model_parallel_config.py index ba2f4715fc7..5c2786285b1 100644 --- a/megatron/core/model_parallel_config.py +++ b/megatron/core/model_parallel_config.py @@ -179,6 +179,11 @@ class ModelParallelConfig: None, no function is called on the loss. """ + moe_grad_scale_func: Optional[Callable] = None + """If using loss scaling for MoE auxiliary losses, this function should return the + scale tensor for MoE aux loss. If None, falls back to grad_scale_func. + """ + mtp_grad_scale_func: Optional[Callable] = None """If using loss scaling for MTP (Multi-Token Prediction), this function should return the scalar or size-1 scale value for MTP loss. The value is converted to the output tensor diff --git a/megatron/core/models/common/embeddings/rope_utils.py b/megatron/core/models/common/embeddings/rope_utils.py index ad790e5c5ad..ed9893f2223 100644 --- a/megatron/core/models/common/embeddings/rope_utils.py +++ b/megatron/core/models/common/embeddings/rope_utils.py @@ -331,6 +331,8 @@ def apply_rotary_pos_emb( # Keep for backward compatibility. Will deprecate in the future. if cp_group is None: cp_group = parallel_state.get_context_parallel_group() + if mla_rotary_interleaved is None: + mla_rotary_interleaved = config.multi_latent_attention if config.apply_rope_fusion: if cu_seqlens is None: diff --git a/megatron/core/models/mimo/model/base.py b/megatron/core/models/mimo/model/base.py index 334a7883d92..e52e9ab8258 100644 --- a/megatron/core/models/mimo/model/base.py +++ b/megatron/core/models/mimo/model/base.py @@ -278,6 +278,19 @@ def set_input_tensor(self, input_tensor): if self.language_model is not None and hasattr(self.language_model, 'set_input_tensor'): self.language_model.set_input_tensor(input_tensor) + def _active_submodules(self): + """Yield this rank's present submodules.""" + if self.language_model is not None: + yield self.language_model + for submodule in self.modality_submodules.values(): + if submodule is not None: + yield submodule + + def zero_grad_buffer(self): + """Zero each active submodule's DDP grad buffer.""" + for module in self._active_submodules(): + module.zero_grad_buffer() + def get_text_embeddings( self, input_ids: torch.Tensor, position_ids: torch.Tensor, special_token_ids: Dict[str, int] ) -> torch.Tensor: diff --git a/megatron/core/models/vision/radio.py b/megatron/core/models/vision/radio.py index 1886b352ce0..d621640cab4 100644 --- a/megatron/core/models/vision/radio.py +++ b/megatron/core/models/vision/radio.py @@ -173,6 +173,7 @@ def __init__( gather_output=True, disable_grad_reduce=True, init_method=lambda tensor: torch.nn.init.normal_(tensor, mean=0.0, std=1.0), + tp_group=pg_collection.tp if pg_collection is not None else None, ) self.video_embedder = ColumnParallelLinear( input_size=3 * self.temporal_patch_dim * self.patch_dim * self.patch_dim, @@ -182,6 +183,7 @@ def __init__( gather_output=True, disable_grad_reduce=True, init_method=lambda tensor: torch.nn.init.normal_(tensor, mean=0.0, std=1.0), + tp_group=pg_collection.tp if pg_collection is not None else None, ) else: self.embedder = ColumnParallelLinear( @@ -192,6 +194,7 @@ def __init__( gather_output=True, disable_grad_reduce=True, init_method=lambda tensor: torch.nn.init.normal_(tensor, mean=0.0, std=1.0), + tp_group=pg_collection.tp if pg_collection is not None else None, ) transformer_config.sequence_parallel = orig_sequence_parallel diff --git a/megatron/core/optimizer/__init__.py b/megatron/core/optimizer/__init__.py index 4bcd7b8c57b..27b675d1b8d 100644 --- a/megatron/core/optimizer/__init__.py +++ b/megatron/core/optimizer/__init__.py @@ -1002,6 +1002,17 @@ def get_megatron_optimizer( Instance of MegatronOptimizer. """ + # A MimoModel routes to the heterogeneous per-module optimizer builder. + from megatron.core.models.mimo.model.base import MimoModel + + if isinstance(model_chunks[0], MimoModel): + from megatron.core.models.mimo.optimizer import get_mimo_optimizer + + assert ( + len(model_chunks) == 1 + ), "MimoModel does not support virtual pipeline parallelism (multiple model chunks)" + return get_mimo_optimizer(model_chunks[0], config) + # None → apply standard defaults. To extend defaults with custom overrides, # start from get_standard_config_overrides(config) and merge yours in. if config_overrides is None: diff --git a/megatron/core/optimizer/distrib_optimizer.py b/megatron/core/optimizer/distrib_optimizer.py index fdea36c59e2..0b6fb468c22 100644 --- a/megatron/core/optimizer/distrib_optimizer.py +++ b/megatron/core/optimizer/distrib_optimizer.py @@ -2918,6 +2918,47 @@ def _normalize_state_dict_for_grouped_params(state_dict_flat, model_chunk): for i, tensor in enumerate(split_tensors): state_dict_flat[f"{key_prefix}{indexed_suffixes[i]}"] = tensor + @staticmethod + def _synthesize_state_dict_params_for_model(state_dict_flat, model_chunk): + """Let modules materialize runtime params before optimizer state-dict matching. + + This covers modules whose runtime parameter is assembled from multiple checkpoint + tensors. Normal model loading can handle this in _load_from_state_dict(), but + reload_model_params(state_dict=...) matches optimizer master params directly by name. + """ + for module_name, module in model_chunk.named_modules(): + synthesize = getattr(module, '_synthesize_fused_qkv_down_weight', None) + key_suffixes = getattr(module, '_synthetic_state_dict_key_suffixes', None) + if not callable(synthesize) or not callable(key_suffixes): + continue + + # Optional compatibility hook contract: + # - key_suffixes() returns source checkpoint keys under this module, used only + # to infer checkpoint prefixes despite wrapper-added path segments. + # - synthesize(state_dict_flat, module_prefix) mutates the flat checkpoint dict + # in place, materializing runtime parameter names from checkpoint tensors. + for inner_key in key_suffixes(): + for module_prefix in DistributedOptimizer._state_dict_module_prefixes( + state_dict_flat, module_name, inner_key + ): + synthesize(state_dict_flat, module_prefix) + + @staticmethod + def _state_dict_module_prefixes(state_dict_flat, module_name, inner_key): + """Find checkpoint prefixes for a module by suffix-matching one inner key.""" + module_parts = module_name.split(".") if module_name else [] + for start_idx in range(len(module_parts) + 1): + module_suffix = ".".join(module_parts[start_idx:]) + key_suffix = f"{module_suffix}.{inner_key}" if module_suffix else inner_key + prefixes = { + state_key[: len(state_key) - len(inner_key)] + for state_key in state_dict_flat + if state_key.endswith(key_suffix) + } + if prefixes: + return prefixes + return set() + def _build_model_param_to_state_dict_param_map(self, state_dict): """Create a map from model params to tensors in state_dict based on their names.""" state_dict_list = [] @@ -2940,6 +2981,7 @@ def _build_model_param_to_state_dict_param_map(self, state_dict): model_param_to_state_dict_param_map = {} for chunk_idx, model_chunk in enumerate(self.model_chunks): self._normalize_state_dict_for_grouped_params(state_dict_list[chunk_idx], model_chunk) + self._synthesize_state_dict_params_for_model(state_dict_list[chunk_idx], model_chunk) names_in_state_dict = set(state_dict_list[chunk_idx].keys()) for name, model_param in model_chunk.named_parameters(): while name.startswith("module."): diff --git a/megatron/core/optimizer/qk_clip.py b/megatron/core/optimizer/qk_clip.py index 284a39daa22..e1c9133261a 100644 --- a/megatron/core/optimizer/qk_clip.py +++ b/megatron/core/optimizer/qk_clip.py @@ -40,5 +40,13 @@ def clip_qk(model, log_max_only=False) -> float: ) if not log_max_only: transformer_layer.self_attention.clip_qk() + else: + # When qk-clip is disabled, clip_qk() is not called and + # would otherwise never reset current_max_attn_logits. + # Reset it here so the logged value reflects the current + # step and stale references are not retained. + transformer_layer.self_attention.core_attention.current_max_attn_logits = ( + None + ) return log_max_attention_logit diff --git a/megatron/core/pipeline_parallel/fine_grained_activation_offload.py b/megatron/core/pipeline_parallel/fine_grained_activation_offload.py index 1901a3c07da..81ebc546bcc 100644 --- a/megatron/core/pipeline_parallel/fine_grained_activation_offload.py +++ b/megatron/core/pipeline_parallel/fine_grained_activation_offload.py @@ -1,4 +1,4 @@ -# Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. from collections import defaultdict, deque from contextlib import nullcontext @@ -25,6 +25,22 @@ def debug_rank(message): print(message) +def _te_do_not_offload(tensor): + """Return whether TE marked a tensor-like object as non-offloadable.""" + if getattr(tensor, "_TE_do_not_offload", False): + return True + if not hasattr(tensor, "get_data_tensors"): + return False + try: + data_tensors = tensor.get_data_tensors() + except Exception: # pragma: no cover - best effort for third-party tensor wrappers + return False + return any( + data_tensor is not None and getattr(data_tensor, "_TE_do_not_offload", False) + for data_tensor in data_tensors + ) + + def print_offload_summary_table(total_offload_bytes: Dict[str, int]): """ Print an ASCII table summarizing offload bytes across all ranks. @@ -344,9 +360,9 @@ def __init__(self, name): self.total_offload_bytes = 0 self.total_tensor_count = 0 # Using memory pool is for the compatibility with cuda graph. - # Shapes of tensors for expert_fc1 and moe_act are not known in advance, + # Shapes of tensors for MoE activation offload groups are not known in advance, # so we do not use CPU pool for them. - if name == "expert_fc1" or name == "moe_act": + if name in ("expert_fc1", "moe_act", "fused_group_mlp"): self.use_cpu_pool = False else: self.use_cpu_pool = True @@ -747,6 +763,7 @@ def mark_not_offload(self, tensor: torch.Tensor): """Mark the current forward chunk as not offloadable.""" if tensor is not None: tensor._do_not_offload = True + tensor.offloading_activation = False def __enter__(self): """Enter context manager to enable activation offloading hooks.""" @@ -790,7 +807,7 @@ def on_get_saved_tensor(self, saved_state: Any) -> torch.Tensor: Hook called when autograd retrieves a saved tensor during backward pass. Returns the actual tensor (potentially reloading from CPU). """ - debug_rank(f"----on_get_saved_tensor {saved_state}") + debug_rank("----on_get_saved_tensor") return self.cur_backward_chunk().tensor_pop(saved_state) @@ -913,8 +930,9 @@ def find_next_group(self, name=None): assert name is not None, "Name is required" return self.find_group_with_name(self.offload_groups, name, self._offloaded_group_index) - def tensor_push(self, tensor): - """Push tensor to the offload handler.""" + @staticmethod + def _can_manage_tensor_for_offload(tensor): + """Return whether the tensor can be managed by activation offload hooks.""" torch_stray_tensor = isinstance( tensor, ( @@ -922,7 +940,16 @@ def tensor_push(self, tensor): torch._subclasses.functional_tensor.FunctionalTensor, ), ) - assert not torch_stray_tensor, "Stray tensor should not be offloaded" + return ( + not isinstance(tensor, torch.nn.Parameter) + and not torch_stray_tensor + and tensor.device.type == "cuda" + ) + + def tensor_push(self, tensor): + """Push tensor to the offload handler.""" + if not self._can_manage_tensor_for_offload(tensor): + return tensor # Assign unique tag based on group index and position within group tensor_tag = (self._offloaded_group_index, self._tensor_count_current_group) @@ -933,6 +960,9 @@ def tensor_push(self, tensor): def tensor_pop(self, tensor_tag): """Pop tensor from the offload handler.""" + if isinstance(tensor_tag, torch.Tensor): + debug_rank(f"--------tensor_pop passthrough tensor {tensor_tag.shape}") + return tensor_tag debug_rank(f"--------tensor_pop {tensor_tag}") group_id, idx = tensor_tag tensor = self.offload_groups[group_id - 1].pop_tensor(tensor_tag) @@ -944,13 +974,19 @@ def tensor_pop(self, tensor_tag): def tensor_need_offloading_checker(self, tensor): """Check if the tensor needs to be offloaded.""" - debug_rank("tensor_need_offloading_checker") + debug_rank( + f"tensor_need_offloading_checker {getattr(tensor, 'offloading_activation', None)}" + ) + if not self._can_manage_tensor_for_offload(tensor): + return False + if _te_do_not_offload(tensor): + return False if tensor.numel() < self.min_offloaded_tensor_size: return False # Respect tensor's offload preference if specified - if getattr(tensor, "_TE_do_not_offload", False) or getattr( - tensor, "_do_not_offload", False - ): + if hasattr(tensor, "offloading_activation") and not tensor.offloading_activation: + return False + if getattr(tensor, "_do_not_offload", False): return False return True diff --git a/megatron/core/resharding/planner.py b/megatron/core/resharding/planner.py index f0e38004d0e..1eda91e914b 100644 --- a/megatron/core/resharding/planner.py +++ b/megatron/core/resharding/planner.py @@ -404,6 +404,17 @@ def _extract_metadata(module, rank_offset): dst_rank_params = dst_param_metadata_by_rank.get(dst_rank, {}) for resolved_name, dst_metadata in dst_rank_params.items(): src_meta_list = src_param_metadata.get(resolved_name) + if not src_meta_list and resolved_name.endswith("output_layer.weight"): + # Tied embeddings: the source shares the output projection with + # the input embedding, so it has no separate output_layer.weight. + # A pp>1 destination materializes one (embedding and output land + # on different stages), e.g. pp=1 (tied) -> pp=2. Source it from + # the embedding weight (same shape + vocab/TP shard); that tensor + # then feeds both the destination embedding and output_layer. + for emb_name in ("embedding.word_embeddings.weight", "word_embeddings.weight"): + src_meta_list = src_param_metadata.get(emb_name) + if src_meta_list: + break if not src_meta_list: raise RuntimeError( f"Destination parameter '{resolved_name}' on rank {dst_rank} " diff --git a/megatron/core/ssm/mamba_mixer.py b/megatron/core/ssm/mamba_mixer.py index e185455846d..6c91fd6d75d 100644 --- a/megatron/core/ssm/mamba_mixer.py +++ b/megatron/core/ssm/mamba_mixer.py @@ -5,6 +5,7 @@ # This source code is licensed under the Apache license found in the # LICENSE file in the root directory of this source tree. +import inspect import logging import math from dataclasses import dataclass, replace @@ -82,6 +83,12 @@ RMSNormGated = MagicMock() HAVE_MAMBA_SSM = False +MAMBA_HAS_STATE_DTYPE = ( + HAVE_MAMBA_SSM + and ("state_dtype" in inspect.signature(mamba_split_conv1d_scan_combined).parameters) + and ("state_dtype" in inspect.signature(mamba_chunk_scan_combined).parameters) +) + try: from einops import rearrange, repeat @@ -208,6 +215,14 @@ def __init__( assert pg_collection is not None, "pg_collection must be provided for MambaMixer" self.pg_collection = pg_collection self.use_mem_eff_path = self.config.use_mamba_mem_eff_path + self.mamba_training_ssm_states_dtype = ( + config.mamba_training_ssm_states_dtype or config.params_dtype + ) + if config.mamba_training_ssm_states_dtype is not None and not MAMBA_HAS_STATE_DTYPE: + raise RuntimeError( + "mamba_training_ssm_states_dtype is set, but the installed mamba_ssm does " + "not accept the `state_dtype` argument. Upgrade mamba_ssm or unset the option." + ) self.d_state = self.config.mamba_state_dim self.headdim = self.config.mamba_head_dim self.ngroups = self.config.mamba_num_groups @@ -738,6 +753,9 @@ def _ssm_training( assert sequence_packing_available, reason_for_no_sequence_packing seq_idx = packed_seq_params.seq_idx + state_dtype_kwarg = ( + {"state_dtype": self.mamba_training_ssm_states_dtype} if MAMBA_HAS_STATE_DTYPE else {} + ) y = mamba_split_conv1d_scan_combined( zxBCdt, rearrange(self.cp.get_conv1d_weight(), "d 1 w -> d w"), @@ -755,6 +773,7 @@ def _ssm_training( ngroups=self.cp.ngroups_local_tpcp, norm_before_gate=self.norm_before_gate, seq_idx=seq_idx, + **state_dtype_kwarg, ) y = rearrange(y, "b l d -> l b d").contiguous() @@ -1031,6 +1050,11 @@ def _ssm_prefill( else: # Non-dynamic-batching path (static batching) initial_ssm_state = None + state_dtype_kwarg = ( + {"state_dtype": self.mamba_training_ssm_states_dtype} + if MAMBA_HAS_STATE_DTYPE + else {} + ) y = mamba_chunk_scan_combined( x, dt, @@ -1048,6 +1072,7 @@ def _ssm_prefill( dt_softplus=True, return_final_states=ssm_state is not None, initial_states=initial_ssm_state, + **state_dtype_kwarg, ) if ssm_state is not None: diff --git a/megatron/core/transformer/moe/README.md b/megatron/core/transformer/moe/README.md index 2f731fcb6f0..57a657d0a61 100644 --- a/megatron/core/transformer/moe/README.md +++ b/megatron/core/transformer/moe/README.md @@ -386,7 +386,7 @@ Unlike recomputation (which trades compute for memory), offloading trades **GPU- **Usage** ```bash --fine-grained-activation-offloading ---offload-modules expert_fc1 moe_act # Choices: attn_norm, core_attn, attn_proj, mlp_norm, expert_fc1, moe_act +--offload-modules expert_fc1 moe_act # Choices: attn_norm, qkv_linear, core_attn, attn_proj, mlp_norm, expert_fc1, moe_act, fused_group_mlp ``` For more details, see `docs/user-guide/features/fine_grained_activation_offloading.md` diff --git a/megatron/core/transformer/moe/experts.py b/megatron/core/transformer/moe/experts.py index 70ca30ac515..0504d3ccd39 100644 --- a/megatron/core/transformer/moe/experts.py +++ b/megatron/core/transformer/moe/experts.py @@ -250,6 +250,11 @@ def __init__( and "moe_act" in self.config.offload_modules ) + self.offload_fused_group_mlp = ( + self.config.fine_grained_activation_offloading + and "fused_group_mlp" in self.config.offload_modules + ) + self.activation_recompute = ( self.config.recompute_granularity == 'selective' and "moe_act" in self.config.recompute_modules @@ -655,14 +660,28 @@ def _fused_forward( ) else: stash_context = nullcontext() - with stash_context: - # Call fused impl - output = ops( - permuted_local_hidden_states, - tokens_per_expert, # FC1 - permuted_probs, # Scaled SwiGLU - tokens_per_expert, # FC2 + fine_grained_activation_offloading = getattr(self, "offload_fused_group_mlp", False) + offload_name = "fused_group_mlp" + fused_group_mlp_manager = off_interface( + fine_grained_activation_offloading, permuted_local_hidden_states, offload_name + ) + with fused_group_mlp_manager as permuted_local_hidden_states: + forced_released_tensors = ( + [permuted_local_hidden_states] if fine_grained_activation_offloading else [] ) + with stash_context: + # Call fused impl + output = ops( + permuted_local_hidden_states, + tokens_per_expert, # FC1 + permuted_probs, # Scaled activation + tokens_per_expert, # FC2 + ) + output = fused_group_mlp_manager.group_offload( + output, + forced_released_tensors=forced_released_tensors, + delay_offload=self.config.delay_offload_until_cuda_graph, + ) # Remove padding if needed if unpadded_tokens_per_expert is not None: output = self.quantization_unpadding(output, unpadded_tokens_per_expert) diff --git a/megatron/core/transformer/moe/router.py b/megatron/core/transformer/moe/router.py index 548a136d543..a4d6c97c70c 100644 --- a/megatron/core/transformer/moe/router.py +++ b/megatron/core/transformer/moe/router.py @@ -193,6 +193,7 @@ def __init__( self.score_function = self.config.moe_router_score_function self.input_jitter = None self.mtp_layer_number: Optional[int] = None + self.frozen_expert_bias = False if self.config.moe_n_hash_layers > 0: assert layer_number is not None, "layer_number is required for the hash-based router." diff --git a/megatron/core/transformer/moe/token_dispatcher_inference.py b/megatron/core/transformer/moe/token_dispatcher_inference.py index e85115528b8..081497f734c 100644 --- a/megatron/core/transformer/moe/token_dispatcher_inference.py +++ b/megatron/core/transformer/moe/token_dispatcher_inference.py @@ -80,6 +80,16 @@ def _valid_tokens(cls) -> torch.Tensor: def _get_host_valid_tokens_estimate(cls) -> Optional[int]: return cls._host_valid_tokens_estimate + @classmethod + def allocate_valid_tokens_tensor(cls) -> None: + """Allocate the per-step valid-tokens scalar shared across all dispatcher subclasses. + + Called at model init from the dynamic context to ensure the buffer receives a valid pointer. + Must run outside CUDA graph capture so the stable address is available during replay. + """ + device = torch.cuda.current_device() + cls._valid_tokens_tensor = torch.zeros(1, dtype=torch.int32, device=device) + def update_metadata(self, local_tokens: int) -> None: """Per-step metadata refresh fired from the first instance's token_dispatch. @@ -178,6 +188,8 @@ def token_dispatch(self, hidden_states, probs): Also updates self.routing_map to [total_tokens, topk]. """ if self.ep_size == 1: + if self._runs_metadata_sync: + InferenceAllGatherDispatcherBase._valid_tokens_tensor.fill_(hidden_states.shape[0]) return hidden_states, probs if self._runs_metadata_sync: @@ -505,6 +517,8 @@ def token_dispatch(self, hidden_states, probs): Also updates self.routing_map to [global_max, topk] int64. """ if self.ep_size == 1: + if self._runs_metadata_sync: + InferenceAllGatherDispatcherBase._valid_tokens_tensor.fill_(hidden_states.shape[0]) return hidden_states, probs if self._runs_metadata_sync: diff --git a/megatron/core/transformer/transformer_config.py b/megatron/core/transformer/transformer_config.py index e1ef8fb344f..1a44adcb0d7 100644 --- a/megatron/core/transformer/transformer_config.py +++ b/megatron/core/transformer/transformer_config.py @@ -1250,6 +1250,10 @@ class TransformerConfig(ModelParallelConfig): """The number of heads used in Mamba layers. If None, the number of heads will be hidden_size * expand // mamba_head_dim.""" + mamba_training_ssm_states_dtype: Optional[torch.dtype] = None + """dtype of the materialized inter-chunk SSM states in Mamba training forwards and backwards. + None causes the states to follow the activation dtype.""" + use_mamba_mem_eff_path: bool = field( default=True, metadata={"argparse_meta": {"arg_names": ["--disable-mamba-mem-eff-path"]}} ) @@ -1294,7 +1298,7 @@ class TransformerConfig(ModelParallelConfig): offload_modules: Optional[list[str]] = field(default_factory=list) """The submodules to offload its input. choices: "attn_norm", "qkv_linear", "core_attn", "attn_proj", - "mlp_norm", "expert_fc1", "moe_act". + "mlp_norm", "expert_fc1", "moe_act", "fused_group_mlp". "attn_norm": offload the input of the normalization in the attention part. "qkv_linear": offload the input of the qkv linear part. "core_attn": offload the input of the core attention part. @@ -1302,6 +1306,7 @@ class TransformerConfig(ModelParallelConfig): "mlp_norm": offload the input of the normalization in the mlp part. "expert_fc1": offload the input of the expert fc1 part. "moe_act": offload the input of the moe act part. + "fused_group_mlp": offload the input of the whole fused grouped MLP. """ min_offloaded_tensor_size: int = 1024 * 1024 """The minimum size of the tensor to be offloaded.""" @@ -2029,6 +2034,7 @@ def __post_init__(self): "core_attn", "attn_proj", "expert_fc1", + "fused_group_mlp", "moe_act", "attn_norm", "mlp_norm", @@ -2067,16 +2073,28 @@ def __post_init__(self): assert ( self.fine_grained_offloading_max_inflight_offloads >= 0 ), "fine_grained_offloading_max_inflight_offloads must be non-negative when set." + if "fused_group_mlp" in self.offload_modules: + if not self.use_transformer_engine_op_fuser: + raise ValueError("fused_group_mlp requires use_transformer_engine_op_fuser.") + moe_partial_offload = {"expert_fc1", "moe_act"} & set(self.offload_modules) + if moe_partial_offload: + raise ValueError( + "fused_group_mlp offloads the whole fused grouped MLP and cannot be " + f"combined with expert_fc1 or moe_act. Remove: {moe_partial_offload}" + ) if self.moe_paged_stash: assert not self.cpu_offloading, "moe_paged_stash cannot be enabled with cpu_offloading." assert self.moe_expert_rank_capacity_factor is not None, ( "moe_paged_stash requires moe_expert_rank_capacity_factor to be set; " "there is no need to use paged stashing without it." ) - moe_offload_conflict = {"expert_fc1", "moe_act"} & set(self.offload_modules) + moe_offload_conflict = {"expert_fc1", "moe_act", "fused_group_mlp"} & set( + self.offload_modules + ) assert not moe_offload_conflict, ( "When moe_paged_stash is enabled, offload_modules must not include " - f"expert_fc1 or moe_act (paged stash covers those activations). " + f"expert_fc1, moe_act, or fused_group_mlp " + f"(paged stash covers those activations). " f"Remove: {moe_offload_conflict}" ) @@ -2784,9 +2802,22 @@ def _scope_to_str(s): ) if self.fine_grained_activation_offloading: - assert self.cuda_graph_impl in ("transformer_engine", "full_iteration"), ( + offload_modules = set(self.offload_modules or []) + local_partial_moe_offload = ( + self.cuda_graph_impl == "local" + and bool(offload_modules) + and offload_modules <= {"expert_fc1", "moe_act", "fused_group_mlp"} + and CudaGraphModule.moe not in self.cuda_graph_modules + ) + assert ( + self.cuda_graph_impl in ("transformer_engine", "full_iteration") + or local_partial_moe_offload + ), ( "fine-grained activation offloading is only supported with " - "transformer_engine or full_iteration CUDA graph implementation." + "transformer_engine CUDA graph implementation or local CUDA graph " + "implementation with full_iteration scope. Local partial CUDA graphs " + "are supported only for expert_fc1, moe_act, or fused_group_mlp " + "offload when the full MoE module is not captured." ) assert ( CudaGraphModule.moe not in self.cuda_graph_modules diff --git a/megatron/rl/inference/megatron.py b/megatron/rl/inference/megatron.py index dab92a42236..27a38300b2b 100644 --- a/megatron/rl/inference/megatron.py +++ b/megatron/rl/inference/megatron.py @@ -99,6 +99,10 @@ async def launch(cls, model: GPTModel, **kwargs): "WARNING: Tokenizer has no BOS token so prompt will not have BOS token", ) + # RL needs log probs, but not prompt log probs. + args.return_log_probs = True + args.skip_prompt_log_probs = True + inference_engine: DynamicInferenceEngine = get_dynamic_inference_engine(model=model) dp_addr = await inference_engine.start_listening_to_data_parallel_coordinator( inference_coordinator_port=41521, launch_inference_coordinator=True diff --git a/megatron/rl/rl_profiling.py b/megatron/rl/rl_profiling.py new file mode 100644 index 00000000000..a00a9212378 --- /dev/null +++ b/megatron/rl/rl_profiling.py @@ -0,0 +1,902 @@ +# Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. + +""" +RL Profiling Infrastructure + +Provides structured logging of timer data for analysis across runs and iterations. +Outputs: + - JSONL file: One JSON object per iteration with full timer breakdown + - CSV summary: Aggregated stats (mean, min, max, p50, p95, p99) at run end + - WandB/TensorBoard: Timer metrics logged as scalars for visualization + +Usage: + from megatron.rl.rl_profiling import get_rl_profiler, log_iteration_profile + + # At each iteration (after timers.log) + log_iteration_profile(iteration, timers) + + # At run end + get_rl_profiler().export_summary() +""" + +import csv +import json +import logging +import os +import statistics +from collections import defaultdict +from dataclasses import dataclass, field +from datetime import datetime +from pathlib import Path +from typing import Any, Dict, List, Optional, Tuple + +import torch +import torch.distributed as dist + +try: + from megatron.rl.rl_utils import get_rl_runtime_state + + has_rl_runtime_state = True +except ImportError: + has_rl_runtime_state = False + +logger = logging.getLogger(__name__) + + +# RL timer names split into loggable (added to timers_to_log in training.py) and +# profiler-only (already logged by base Megatron, or fine-grained profiler detail). +# Import RL_LOGGABLE_TIMER_NAMES in training.py instead of hardcoding the list. +RL_LOGGABLE_TIMER_NAMES = [ + # Top-level RL phases + "rl/rollout-collection", + "rl/prepare-data-for-update", + # Rollout collection breakdown + "rl/inference-setup", + "rl/collect-rollouts", + "rl/sync-rollouts", + "rl/suspend-engine", + # Optimizer offload/restore + "rl/offload-optimizer-before-inference", + "rl/restore-optimizer-after-inference", + "rl/offload-kv-cache-after-inference", + "rl/restore-kv-cache-before-inference", + # Fine-grained offload/restore breakdown + "rl/restore/grad-buffers", + "rl/restore/optimizer-state", + "rl/restore/wait-for-transfers", + "rl/offload/grad-buffers", + "rl/offload/optimizer-state", + # Weight prefetching + "rl/prefetch-weights-to-gpu", + "rl/prefetch-weights-to-cpu", + # Data preparation + "rl/compute-group-stats", + "rl/prepare-advantages", + "rl/prepare-trajectories", + "rl/get-ltor-masks", + "rl/create-dataloader", + "rl/sequence-packing", + "rl/align-inference-logprobs", + "rl/log-wandb-tb", + "rl/pack-sequences", + "rl/regather-trajectories", + # Logprobs computation + "rl/compute-logprobs", + "rl/compute-old-logprobs", + "rl/compute-ref-logprobs", + "rl/get-logprobs", + "rl/forward-pass", + "rl/log-softmax", + # Inference / cuda graphs + "rl/build-cuda-graphs", + "rl/wait-for-decode-only", +] + +RL_NONLOGGABLE_TIMER_NAMES = [ + # Already logged by base Megatron (not added to timers_to_log) + "forward-backward", + "optimizer", + # Profiler-only detail + "rl/pack-logprobs", + "rl/train/forward", + "rl/train/grpo-loss", + # Megatron training internals + "embedding-grads-all-reduce", + "all-grads-sync", + "params-all-gather", + "optimizer-copy-to-main-grad", + "optimizer-inner-step", + "optimizer-copy-main-to-model-params", +] + +RL_TIMER_NAMES = RL_LOGGABLE_TIMER_NAMES + RL_NONLOGGABLE_TIMER_NAMES + +# Define timer hierarchy for de-duplication analysis +TIMER_HIERARCHY = { + "rl/rollout-collection": [ + "rl/inference-setup", + "rl/collect-rollouts", + "rl/sync-rollouts", + "rl/suspend-engine", + "rl/offload-optimizer-before-inference", + "rl/restore-optimizer-after-inference", + "rl/prefetch-weights-to-gpu", + "rl/prefetch-weights-to-cpu", + "rl/restore-kv-cache-before-inference", + "rl/offload-kv-cache-after-inference", + ], + "rl/prepare-data-for-update": [ + "rl/compute-group-stats", + "rl/prepare-advantages", + "rl/prepare-trajectories", + "rl/get-ltor-masks", + "rl/create-dataloader", + "rl/sequence-packing", + "rl/pack-logprobs", + "rl/align-inference-logprobs", + "rl/log-wandb-tb", + "rl/compute-logprobs", + ], + "rl/compute-logprobs": ["rl/compute-old-logprobs", "rl/compute-ref-logprobs"], + "rl/get-logprobs": ["rl/forward-pass", "rl/log-softmax"], + "optimizer": [ + "optimizer-copy-to-main-grad", + "optimizer-inner-step", + "optimizer-copy-main-to-model-params", + ], +} + + +@dataclass +class IterationProfile: + """Profile data for a single iteration.""" + + iteration: int + timestamp: str + elapsed_time_ms: float # Total iteration time + timers: Dict[str, Tuple[float, float]] # timer_name -> (min_ms, max_ms) + throughput_tflops: Optional[float] = None + global_batch_size: Optional[int] = None + tokens_per_sec: Optional[float] = None + tokens_per_sec_per_gpu: Optional[float] = None + # Sequence packing metrics + compute_tokens_per_sec: Optional[float] = None + compute_tokens_per_sec_per_gpu: Optional[float] = None + actual_tokens_per_sec: Optional[float] = None + actual_tokens_per_sec_per_gpu: Optional[float] = None + packing_efficiency: Optional[float] = None + + # Computed metrics + load_imbalance: Dict[str, float] = field(default_factory=dict) # timer -> max/min ratio + # Rank0 times (for detailed analysis - rank0 is typically the coordinator) + rank0_timers: Dict[str, float] = field(default_factory=dict) # timer_name -> rank0_ms + + def to_dict(self) -> Dict[str, Any]: + """Convert to dictionary for JSON serialization.""" + result = { + "iteration": self.iteration, + "timestamp": self.timestamp, + "elapsed_time_ms": self.elapsed_time_ms, + "throughput_tflops": self.throughput_tflops, + "global_batch_size": self.global_batch_size, + "tokens_per_sec": self.tokens_per_sec, + "tokens_per_sec_per_gpu": self.tokens_per_sec_per_gpu, + "compute_tokens_per_sec": self.compute_tokens_per_sec, + "compute_tokens_per_sec_per_gpu": self.compute_tokens_per_sec_per_gpu, + "actual_tokens_per_sec": self.actual_tokens_per_sec, + "actual_tokens_per_sec_per_gpu": self.actual_tokens_per_sec_per_gpu, + "packing_efficiency": self.packing_efficiency, + } + + # Flatten timer data + for name, (min_t, max_t) in self.timers.items(): + safe_name = name.replace("/", "_").replace("-", "_") + result[f"timer_{safe_name}_min_ms"] = min_t + result[f"timer_{safe_name}_max_ms"] = max_t + + # Add rank0 timer values + for name, rank0_t in self.rank0_timers.items(): + safe_name = name.replace("/", "_").replace("-", "_") + result[f"timer_{safe_name}_rank0_ms"] = rank0_t + + # Add load imbalance metrics + for name, ratio in self.load_imbalance.items(): + safe_name = name.replace("/", "_").replace("-", "_") + result[f"imbalance_{safe_name}"] = ratio + + return result + + +@dataclass +class RunSummary: + """Aggregated statistics across all iterations in a run.""" + + run_id: str + start_time: str + end_time: str + num_iterations: int + world_size: int + + # Per-timer aggregated stats + timer_stats: Dict[str, Dict[str, float]] = field(default_factory=dict) + # Format: timer_name -> {"mean", "min", "max", "std", "p50", "p95", "p99"} + + +class RLProfiler: + """ + Profiling infrastructure for RL training. + + Collects timer data at each iteration and provides: + - Per-iteration JSONL logging + - End-of-run CSV summary + - Integration with WandB/TensorBoard + """ + + def __init__( + self, + output_dir: Optional[str] = None, + run_id: Optional[str] = None, + enabled: bool = True, + log_to_wandb: bool = True, + log_to_tensorboard: bool = True, + timer_names: Optional[List[str]] = None, + ): + """ + Initialize the RL Profiler. + + Args: + output_dir: Directory to write profiling data. If None, uses LANGRL_LOG_DIR or ./profiles + run_id: Unique identifier for this run. If None, generates from timestamp + enabled: Whether profiling is enabled + log_to_wandb: Whether to log timer metrics to WandB + log_to_tensorboard: Whether to log timer metrics to TensorBoard + timer_names: List of timer names to track. If None, uses RL_TIMER_NAMES + """ + self.enabled = enabled + self.log_to_wandb = log_to_wandb + self.log_to_tensorboard = log_to_tensorboard + self.timer_names = timer_names or RL_TIMER_NAMES + + # Determine output directory + if output_dir: + self.output_dir = Path(output_dir) + else: + env_dir = os.environ.get("LANGRL_LOG_DIR") + if env_dir: + self.output_dir = Path(env_dir) / "profiles" + else: + self.output_dir = Path("./profiles") + + # Generate run ID + if run_id: + self.run_id = run_id + else: + self.run_id = datetime.now().strftime("%Y%m%d_%H%M%S") + + self.start_time = datetime.now().isoformat() + + # Storage for iteration data + self.iteration_profiles: List[IterationProfile] = [] + self._timer_history: Dict[str, List[float]] = defaultdict(list) # For stats + + # File handles (lazy init) + self._jsonl_file = None + self._initialized = False + + def _ensure_initialized(self): + """Lazy initialization of output files (only on rank 0).""" + if self._initialized: + return + + # Only rank 0 writes files + rank = dist.get_rank() if dist.is_initialized() else 0 + if rank != 0: + self._initialized = True + return + + # Create output directory + self.output_dir.mkdir(parents=True, exist_ok=True) + + # Open JSONL file for streaming writes + jsonl_path = self.output_dir / f"profile_{self.run_id}.jsonl" + self._jsonl_file = open(jsonl_path, "w") + + logger.info(f"[RLProfiler] Writing profiles to {jsonl_path}") + self._initialized = True + + def log_iteration( + self, + iteration: int, + timers, # Timers object from megatron + elapsed_time_ms: float, + throughput_tflops: Optional[float] = None, + global_batch_size: Optional[int] = None, + extra_metrics: Optional[Dict[str, float]] = None, + wandb_writer=None, + tb_writer=None, + ): + """ + Log profiling data for a single iteration. + + Token throughput metrics (tokens_per_sec, actual_tokens_per_sec, + packing_efficiency) are read from RLRuntimeState, which is populated + by the observability metrics code in training_log(). + + Args: + iteration: Current training iteration + timers: Megatron Timers object + elapsed_time_ms: Total iteration time in milliseconds + throughput_tflops: TFLOPS throughput + global_batch_size: Global batch size + extra_metrics: Additional metrics to log + wandb_writer: WandB writer for metric logging + tb_writer: TensorBoard writer for metric logging + """ + if not self.enabled: + return + + self._ensure_initialized() + + # Read token throughput metrics from RLRuntimeState (populated by + # observability code in training_log, runs before this call). + tokens_per_sec = None + tokens_per_sec_per_gpu = None + compute_tokens_per_sec = None + compute_tokens_per_sec_per_gpu = None + actual_tokens_per_sec = None + actual_tokens_per_sec_per_gpu = None + packing_efficiency = None + if has_rl_runtime_state: + rs = get_rl_runtime_state() + tokens_per_sec = getattr(rs, 'tokens_per_sec', None) + compute_tokens_per_sec = getattr(rs, 'compute_tokens_per_sec', None) + actual_tokens_per_sec = getattr(rs, 'actual_tokens_per_sec', None) + packing_efficiency = getattr(rs, 'packing_efficiency', None) + world_size = getattr(rs, 'world_size', None) + if world_size: + if tokens_per_sec is not None: + tokens_per_sec_per_gpu = tokens_per_sec / world_size + if compute_tokens_per_sec is not None: + compute_tokens_per_sec_per_gpu = compute_tokens_per_sec / world_size + if actual_tokens_per_sec is not None: + actual_tokens_per_sec_per_gpu = actual_tokens_per_sec / world_size + + # Collect timer data (min, max across ranks) and rank0 times + timer_data, rank0_timers = self._collect_timer_data(timers) + + # Warn if no timer data was collected (might indicate timing issue) + if not timer_data: + logger.warning( + f"[RLProfiler] No timer data collected for iteration {iteration}. " + "Timers may have been reset before profiling." + ) + + # Compute load imbalance metrics + load_imbalance = {} + for name, (min_t, max_t) in timer_data.items(): + if min_t > 0: + load_imbalance[name] = max_t / min_t + + # Create profile + profile = IterationProfile( + iteration=iteration, + timestamp=datetime.now().isoformat(), + elapsed_time_ms=elapsed_time_ms, + timers=timer_data, + throughput_tflops=throughput_tflops, + global_batch_size=global_batch_size, + tokens_per_sec=tokens_per_sec, + tokens_per_sec_per_gpu=tokens_per_sec_per_gpu, + compute_tokens_per_sec=compute_tokens_per_sec, + compute_tokens_per_sec_per_gpu=compute_tokens_per_sec_per_gpu, + actual_tokens_per_sec=actual_tokens_per_sec, + actual_tokens_per_sec_per_gpu=actual_tokens_per_sec_per_gpu, + packing_efficiency=packing_efficiency, + load_imbalance=load_imbalance, + rank0_timers=rank0_timers, + ) + + self.iteration_profiles.append(profile) + + # Track history for summary stats + for name, (_, max_t) in timer_data.items(): + self._timer_history[name].append(max_t) + + # Write to JSONL (rank 0 only) + rank = dist.get_rank() if dist.is_initialized() else 0 + if rank == 0 and self._jsonl_file: + self._jsonl_file.write(json.dumps(profile.to_dict()) + "\n") + self._jsonl_file.flush() + + # Log to WandB/TensorBoard + if self.log_to_wandb and wandb_writer: + self._log_to_wandb(profile, wandb_writer, iteration, extra_metrics) + if self.log_to_tensorboard and tb_writer: + self._log_to_tensorboard(profile, tb_writer, iteration, extra_metrics) + + def _collect_timer_data( + self, timers + ) -> Tuple[Dict[str, Tuple[float, float]], Dict[str, float]]: + """Collect min/max timer data across ranks and rank0 times. + + Returns: + Tuple of: + - Dict mapping timer_name -> (min_ms, max_ms) across all ranks + - Dict mapping timer_name -> rank0_ms (rank0's local time) + """ + # Get the full per-rank timing data (calls all_gather internally) + rank_name_to_time = timers._get_elapsed_time_all_ranks( + names=self.timer_names, + reset=False, # Don't reset - let the main logging code handle that + barrier=False, + ) + + if rank_name_to_time is None: + return {}, {} + + # Convert from seconds to milliseconds + normalizer = 1000.0 # seconds -> ms + + # Extract min/max and rank0 times + name_to_min_max = {} + rank0_timers = {} + + for i, name in enumerate(self.timer_names): + # Get times for all ranks for this timer + rank_times = rank_name_to_time[:, i].tolist() + # Filter out zeros (timers that weren't active) + active_times = [t for t in rank_times if t > 0.0] + + if active_times: + min_time = min(active_times) * normalizer + max_time = max(active_times) * normalizer + name_to_min_max[name] = (min_time, max_time) + + # Get rank0's time specifically (index 0) + rank0_time = rank_times[0] * normalizer if rank_times else 0.0 + if rank0_time > 0: + rank0_timers[name] = rank0_time + + return name_to_min_max, rank0_timers + + def _log_to_wandb( + self, + profile: IterationProfile, + wandb_writer, + iteration: int, + extra_metrics: Optional[Dict[str, float]] = None, + ): + """Log timer metrics to WandB.""" + metrics = {} + + # Log max times for each timer (most relevant for optimization) + for name, (min_t, max_t) in profile.timers.items(): + metrics[f"profile/{name}_max_ms"] = max_t + metrics[f"profile/{name}_min_ms"] = min_t + + # Log imbalance for timers with significant spread + if name in profile.load_imbalance and profile.load_imbalance[name] > 1.1: + metrics[f"profile/{name}_imbalance"] = profile.load_imbalance[name] + + # Log aggregated phase times + phase_times = self._compute_phase_times(profile) + for phase, time_ms in phase_times.items(): + metrics[f"profile/phase_{phase}_ms"] = time_ms + + if extra_metrics: + metrics.update(extra_metrics) + + wandb_writer.log(metrics, step=iteration) + + def _log_to_tensorboard( + self, + profile: IterationProfile, + tb_writer, + iteration: int, + extra_metrics: Optional[Dict[str, float]] = None, + ): + """Log timer metrics to TensorBoard.""" + # Log max times for each timer + for name, (min_t, max_t) in profile.timers.items(): + tb_writer.add_scalar(f"profile/{name}_max_ms", max_t, iteration) + + # Log phase times + phase_times = self._compute_phase_times(profile) + for phase, time_ms in phase_times.items(): + tb_writer.add_scalar(f"profile/phase_{phase}_ms", time_ms, iteration) + + if extra_metrics: + for key, value in extra_metrics.items(): + tb_writer.add_scalar(key, value, iteration) + + def _compute_phase_times(self, profile: IterationProfile) -> Dict[str, float]: + """Compute high-level phase times from detailed timers.""" + timers = profile.timers + phases = {} + + # Helper to get max time safely + def get_max(name: str) -> float: + return timers.get(name, (0, 0))[1] + + # Rollout generation (actual generation, not container) + phases["rollout_generation"] = get_max("rl/collect-rollouts") + + # Optimizer memory management + phases["optimizer_offload"] = get_max("rl/offload-optimizer-before-inference") + phases["optimizer_restore"] = get_max("rl/restore-optimizer-after-inference") + phases["optimizer_memory_mgmt"] = phases["optimizer_offload"] + phases["optimizer_restore"] + + # Logprobs computation + phases["logprobs_old"] = get_max("rl/compute-old-logprobs") + phases["logprobs_ref"] = get_max("rl/compute-ref-logprobs") + phases["logprobs_total"] = phases["logprobs_old"] + phases["logprobs_ref"] + + # Training + phases["training"] = get_max("forward-backward") + + # Wait/sync time (proxy for load imbalance) + phases["sync_wait"] = get_max("rl/suspend-engine") + get_max("rl/sync-rollouts") + + return phases + + def export_summary(self, output_path: Optional[str] = None) -> Optional[str]: + """ + Export aggregated summary statistics to CSV. + + Args: + output_path: Path for CSV output. If None, uses default in output_dir. + + Returns: + Path to the exported CSV file, or None if no data. + """ + if not self.enabled or not self._timer_history: + return None + + rank = dist.get_rank() if dist.is_initialized() else 0 + if rank != 0: + return None + + self._ensure_initialized() + + # Compute statistics for each timer + stats = {} + for name, values in self._timer_history.items(): + if not values: + continue + sorted_values = sorted(values) + n = len(sorted_values) + stats[name] = { + "mean": statistics.mean(values), + "std": statistics.stdev(values) if n > 1 else 0, + "min": min(values), + "max": max(values), + "p50": sorted_values[int(n * 0.50)], + "p95": sorted_values[int(n * 0.95)] if n >= 20 else sorted_values[-1], + "p99": sorted_values[int(n * 0.99)] if n >= 100 else sorted_values[-1], + "count": n, + } + + # Write CSV + if output_path: + csv_path = Path(output_path) + else: + csv_path = self.output_dir / f"summary_{self.run_id}.csv" + + with open(csv_path, "w", newline="") as f: + writer = csv.writer(f) + writer.writerow( + [ + "timer_name", + "mean_ms", + "std_ms", + "min_ms", + "max_ms", + "p50_ms", + "p95_ms", + "p99_ms", + "count", + ] + ) + for name in self.timer_names: + if name in stats: + s = stats[name] + writer.writerow( + [ + name, + f"{s['mean']:.2f}", + f"{s['std']:.2f}", + f"{s['min']:.2f}", + f"{s['max']:.2f}", + f"{s['p50']:.2f}", + f"{s['p95']:.2f}", + f"{s['p99']:.2f}", + s['count'], + ] + ) + + logger.info(f"[RLProfiler] Exported summary to {csv_path}") + return str(csv_path) + + def print_summary(self): + """Print a summary of timer statistics to stdout.""" + if not self.enabled or not self._timer_history: + return + + rank = dist.get_rank() if dist.is_initialized() else 0 + if rank != 0: + return + + print("\n" + "=" * 80) + print("RL PROFILING SUMMARY") + print("=" * 80) + print(f"Run ID: {self.run_id}") + print(f"Iterations: {len(self.iteration_profiles)}") + print("-" * 80) + print(f"{'Timer Name':<50} {'Mean':>8} {'P95':>8} {'Max':>8}") + print("-" * 80) + + for name in self.timer_names: + if name not in self._timer_history or not self._timer_history[name]: + continue + values = self._timer_history[name] + sorted_values = sorted(values) + n = len(sorted_values) + mean = statistics.mean(values) + p95 = sorted_values[int(n * 0.95)] if n >= 20 else sorted_values[-1] + max_v = max(values) + print(f"{name:<50} {mean:>7.1f}ms {p95:>7.1f}ms {max_v:>7.1f}ms") + + print("=" * 80 + "\n") + + def close(self): + """Close file handles and export final summary.""" + if self._jsonl_file: + self._jsonl_file.close() + self._jsonl_file = None + self.export_summary() + self.print_summary() + + +# Global profiler instance +_RL_PROFILER: Optional[RLProfiler] = None + + +def get_rl_profiler() -> Optional[RLProfiler]: + """Get the global RL profiler instance.""" + return _RL_PROFILER + + +def initialize_rl_profiler( + output_dir: Optional[str] = None, run_id: Optional[str] = None, enabled: bool = True, **kwargs +) -> RLProfiler: + """ + Initialize the global RL profiler. + + Should be called once at training start. + """ + global _RL_PROFILER + _RL_PROFILER = RLProfiler(output_dir=output_dir, run_id=run_id, enabled=enabled, **kwargs) + return _RL_PROFILER + + +def log_iteration_profile( + iteration: int, + timers, + elapsed_time_ms: float, + throughput_tflops: Optional[float] = None, + global_batch_size: Optional[int] = None, + extra_metrics: Optional[Dict[str, float]] = None, + wandb_writer=None, + tb_writer=None, +): + """ + Convenience function to log iteration profile using global profiler. + + Token throughput metrics are read from RLRuntimeState automatically. + """ + profiler = get_rl_profiler() + if profiler: + profiler.log_iteration( + iteration=iteration, + timers=timers, + elapsed_time_ms=elapsed_time_ms, + throughput_tflops=throughput_tflops, + global_batch_size=global_batch_size, + extra_metrics=extra_metrics, + wandb_writer=wandb_writer, + tb_writer=tb_writer, + ) + + +def shutdown_rl_profiler(): + """Shutdown the global RL profiler and export final data.""" + global _RL_PROFILER + if _RL_PROFILER: + _RL_PROFILER.close() + _RL_PROFILER = None + + +# ============================================================================ +# Analysis utilities for cross-run comparison +# ============================================================================ + + +def load_profile_jsonl(path: str) -> List[Dict[str, Any]]: + """Load profile data from a JSONL file.""" + profiles = [] + with open(path, "r") as f: + for line in f: + if line.strip(): + profiles.append(json.loads(line)) + return profiles + + +def load_summary_csv(path: str) -> Dict[str, Dict[str, float]]: + """Load summary statistics from a CSV file.""" + stats = {} + with open(path, "r") as f: + reader = csv.DictReader(f) + for row in reader: + timer_name = row["timer_name"] + stats[timer_name] = { + "mean_ms": float(row["mean_ms"]), + "std_ms": float(row["std_ms"]), + "min_ms": float(row["min_ms"]), + "max_ms": float(row["max_ms"]), + "p50_ms": float(row["p50_ms"]), + "p95_ms": float(row["p95_ms"]), + "p99_ms": float(row["p99_ms"]), + "count": int(row["count"]), + } + return stats + + +def compare_runs( + run_paths: List[str], run_names: Optional[List[str]] = None, output_path: Optional[str] = None +) -> str: + """ + Compare profiling data across multiple runs. + + Args: + run_paths: List of paths to summary CSV files + run_names: Optional names for each run (defaults to filenames) + output_path: Optional path to write comparison CSV + + Returns: + Formatted comparison string + """ + if run_names is None: + run_names = [Path(p).stem for p in run_paths] + + # Load all summaries + summaries = [] + for path in run_paths: + summaries.append(load_summary_csv(path)) + + # Get all timer names across all runs + all_timers = set() + for summary in summaries: + all_timers.update(summary.keys()) + + # Build comparison table + lines = [] + header = f"{'Timer':<50}" + for name in run_names: + header += f" {name:>12}" + lines.append(header) + lines.append("-" * len(header)) + + for timer in sorted(all_timers): + line = f"{timer:<50}" + for summary in summaries: + if timer in summary: + line += f" {summary[timer]['mean_ms']:>11.1f}ms" + else: + line += f" {'N/A':>12}" + lines.append(line) + + result = "\n".join(lines) + + if output_path: + with open(output_path, "w") as f: + f.write(result) + + return result + + +def analyze_bottlenecks(profile_path: str, top_n: int = 10) -> str: + """ + Analyze a profile to identify top bottlenecks. + + Args: + profile_path: Path to summary CSV file + top_n: Number of top timers to report + + Returns: + Formatted analysis string + """ + stats = load_summary_csv(profile_path) + + # Sort by mean time (descending) + sorted_timers = sorted(stats.items(), key=lambda x: x[1]["mean_ms"], reverse=True) + + lines = [] + lines.append("=" * 70) + lines.append("BOTTLENECK ANALYSIS") + lines.append("=" * 70) + lines.append(f"{'Rank':<6} {'Timer':<45} {'Mean':>8} {'P95':>8}") + lines.append("-" * 70) + + for i, (timer, s) in enumerate(sorted_timers[:top_n], 1): + lines.append(f"{i:<6} {timer:<45} {s['mean_ms']:>7.1f}ms {s['p95_ms']:>7.1f}ms") + + # Compute phase breakdown + lines.append("") + lines.append("PHASE BREAKDOWN:") + lines.append("-" * 70) + + phases = { + "Rollout Generation": ["rl/collect-rollouts"], + "Optimizer Memory Mgmt": [ + "rl/offload-optimizer-before-inference", + "rl/restore-optimizer-after-inference", + ], + "Logprobs Computation": ["rl/compute-old-logprobs", "rl/compute-ref-logprobs"], + "Training": ["forward-backward"], + "Sync/Wait": ["rl/suspend-engine", "rl/sync-rollouts"], + } + + for phase_name, timer_list in phases.items(): + total = sum(stats.get(t, {}).get("mean_ms", 0) for t in timer_list) + if total > 0: + lines.append(f" {phase_name:<40} {total:>7.1f}ms") + + lines.append("=" * 70) + + return "\n".join(lines) + + +# ============================================================================ +# CLI for analysis (can be run as: python -m megatron.rl.rl_profiling ...) +# ============================================================================ + + +def main(): + """Command-line interface for profile analysis.""" + import argparse + + parser = argparse.ArgumentParser(description="RL Profiling Analysis") + subparsers = parser.add_subparsers(dest="command", help="Available commands") + + # analyze command + analyze_parser = subparsers.add_parser("analyze", help="Analyze a single run") + analyze_parser.add_argument("profile", help="Path to summary CSV file") + analyze_parser.add_argument("--top", type=int, default=10, help="Top N bottlenecks") + + # compare command + compare_parser = subparsers.add_parser("compare", help="Compare multiple runs") + compare_parser.add_argument("profiles", nargs="+", help="Paths to summary CSV files") + compare_parser.add_argument("--names", nargs="+", help="Names for each run") + compare_parser.add_argument("--output", help="Output file path") + + # list command + list_parser = subparsers.add_parser("list", help="List iterations in JSONL file") + list_parser.add_argument("profile", help="Path to JSONL profile file") + list_parser.add_argument("--last", type=int, default=10, help="Show last N iterations") + + args = parser.parse_args() + + if args.command == "analyze": + print(analyze_bottlenecks(args.profile, args.top)) + elif args.command == "compare": + print(compare_runs(args.profiles, args.names, args.output)) + elif args.command == "list": + profiles = load_profile_jsonl(args.profile) + for p in profiles[-args.last :]: + print(f"Iteration {p['iteration']}: {p['elapsed_time_ms']:.1f}ms") + else: + parser.print_help() + + +if __name__ == "__main__": + main() diff --git a/megatron/training/arguments.py b/megatron/training/arguments.py index ad8bdb2a498..5125c5abc92 100644 --- a/megatron/training/arguments.py +++ b/megatron/training/arguments.py @@ -91,6 +91,7 @@ def add_megatron_arguments(parser: argparse.ArgumentParser): parser = _add_kitchen_quantization_arguments(parser) parser = _add_sft_args(parser) parser = _add_varlen_dataset_args(parser) + parser = _add_logits_distillation_args(parser) parser = _add_fault_injector_args(parser) @@ -1186,6 +1187,7 @@ def validate_args(args, defaults={}): args.exp_avg_sq_dtype = map_dtype(args.exp_avg_sq_dtype) args.mamba_inference_conv_states_dtype = map_dtype(args.mamba_inference_conv_states_dtype) args.mamba_inference_ssm_states_dtype = map_dtype(args.mamba_inference_ssm_states_dtype) + args.mamba_training_ssm_states_dtype = map_dtype(args.mamba_training_ssm_states_dtype) args.megatron_fsdp_main_params_dtype = map_dtype(args.megatron_fsdp_main_params_dtype) args.megatron_fsdp_main_grads_dtype = map_dtype(args.megatron_fsdp_main_grads_dtype) @@ -2646,6 +2648,19 @@ def _add_inference_args(parser): 'Pause/unpause take effect as soon as the signal is delivered to a rank. ' 'Only safe when EP coordination is not required (e.g. ep_world_size == 1).', ) + group.add_argument( + '--inference-shards', + type=str, + default=None, + metavar='SPEC', + help='Partition the world into independent inference models, each with ' + 'its own parallelism, e.g. "tp=2,role=prefill+tp=1,dp=2,role=decode". ' + 'Shards are separated by "+" or ";"; per-shard keys are ' + 'tp,pp,ep,expt_tp,dp (each defaults to 1) and must partition the full ' + 'world. Tagging shards role=prefill|decode enables disaggregated ' + 'inference (prefill hands KV to the decode pool); a dp>1 decode shard ' + 'is several independent decode instances.', + ) group.add_argument( '--inference-cuda-graph-all-prefills', action='store_true', @@ -2674,6 +2689,7 @@ def _add_network_size_args(parser): "timers", "finalize_model_grads_func", "grad_scale_func", + "moe_grad_scale_func", "mtp_grad_scale_func", "no_sync_func", "grad_sync_func", @@ -2760,6 +2776,7 @@ def _add_network_size_args(parser): "bias_dropout_fusion", "apply_rope_fusion", "apply_dsa_kernel_fusion", + "mamba_training_ssm_states_dtype", ] transformer_factory = ArgumentGroupFactory(TransformerConfig, exclude=exclude) transformer_group = transformer_factory.build_group(parser, "transformer configuration") @@ -3528,6 +3545,18 @@ def _add_rl_args(parser): default=False, help='Skip BOS token at the beginning of the sequences. Default is False.', ) + group.add_argument( + '--rl-profile', + action='store_true', + default=False, + help='Enable RL profiling to collect detailed timer data (JSONL + CSV).', + ) + group.add_argument( + '--rl-profile-dir', + type=str, + default=None, + help='Directory to write RL profiling data. Defaults to {save}/profiles.', + ) group.add_argument( '--rl-inference-parsers', nargs='*', @@ -3793,6 +3822,9 @@ def _add_learning_rate_args(parser): help='Minimum value for learning rate for the input and output layer. The scheduler' 'clip values below this threshold', ) + group.add_argument( + '--freeze-all-layers', action='store_true', help='Freeze all layers of the model.' + ) return parser @@ -3914,6 +3946,13 @@ def _add_mixed_precision_args(parser): action='store_true', help='If True, reuse the grad buffer for MXFP8 parameter all-gather.', ) + group.add_argument( + '--mamba-training-ssm-states-dtype', + type=str, + choices=['fp32', 'bf16'], + default=None, + help='Dtype of the materialized inter-chunk SSM states in Mamba training', + ) return parser @@ -5152,6 +5191,47 @@ def _add_varlen_dataset_args(parser): return parser +def _add_logits_distillation_args(parser): + group = parser.add_argument_group(title='Logits Distillation') + + group.add_argument('--logits-save-top-k', type=int, default=None, + help='Number of top logits to save.') + group.add_argument('--logits-save-top-p', type=float, default=None, + help='Top-P (nucleus) threshold applied after top-K ' + 'selection when saving logits. Only the smallest ' + 'set of entries whose cumulative probability mass ' + 'reaches this threshold is kept. Must be in (0, 1].') + group.add_argument('--logits-save-top-p-min-k', type=int, default=1, + help='Minimum number of entries kept per token when ' + 'top-P masking is active, regardless of ' + 'cumulative mass. Default: 1.') + group.add_argument('--logits-save-dir', type=str, default=None, + help='Directory to save logits.') + group.add_argument('--logits-save-dtype', type=str, default='fp16', + choices=['fp16', 'bf16', 'fp32'], + help='Dtype for on-disk top-K log-probabilities.') + group.add_argument('--logits-load-dir', type=str, default=None, + help='Directory to load logits.') + group.add_argument('--logits-load-decode-threads', type=int, default=4, + help='Number of decode threads for cached-logits zstd ' + 'decompression and torch.load processing.') + group.add_argument('--logits-load-prefetch-factor', type=int, default=3, + help='PyTorch DataLoader prefetch factor for decoded ' + 'cached-logits iterations. (Non-MSC only)') + group.add_argument('--logits-load-msc-prefetch-depth', type=int, default=2, + help='For MSC/object-storage logits tar shards, number ' + 'of whole tar shards to prefetch into the MSC ' + 'cache ahead of sequential tar consumption.') + group.add_argument('--logits-load-kd-loss-alpha', type=float, default=1.0, + help='KD loss alpha for loading logits. Total loss is calculated as ' + 'alpha * kd_loss + (1 - alpha) * lm_loss.') + group.add_argument('--logits-load-ignore-errors', action='store_true', + default=False, + help='When set, KD loss errors are logged as warnings and ' + 'training falls back to LM-only loss instead of crashing.') + return parser + + def _add_fault_injector_args(parser): from megatron.training.config import FaultInjectorConfig diff --git a/megatron/training/distillation/__init__.py b/megatron/training/distillation/__init__.py new file mode 100644 index 00000000000..18aeadb72d9 --- /dev/null +++ b/megatron/training/distillation/__init__.py @@ -0,0 +1,6 @@ +# Copyright (c) 2026, NVIDIA CORPORATION. All rights reserved. + +from megatron.training.distillation.cached_logits_loss import LossFuncCallable, StudentLogitsCapture +from megatron.training.distillation.logits_saver import LogitsSaverHooks, get_logits_saver + +__all__ = ["LossFuncCallable", "LogitsSaverHooks", "StudentLogitsCapture", "get_logits_saver"] diff --git a/megatron/training/distillation/cached_logits_loss.py b/megatron/training/distillation/cached_logits_loss.py new file mode 100644 index 00000000000..13aeeecb1a5 --- /dev/null +++ b/megatron/training/distillation/cached_logits_loss.py @@ -0,0 +1,867 @@ +# Copyright (c) 2026, NVIDIA CORPORATION. All rights reserved. + +""" +Offline knowledge distillation loss using cached teacher top-K log-probabilities. + +This module provides a loss function that loads pre-computed teacher top-K +log-probabilities from disk (as saved by ``LogitsSaverHooks`` in ``logits_saver.py``) +and computes forward KL divergence against live student logits. + +Design highlights +----------------- +* **Sparse KL** – each rank uses only the teacher top-K positions that fall in + its local vocab shard, avoiding a full-vocab-sized dense teacher tensor. +* **TP-aware student normalisation** – the student log-probabilities are + normalised across tensor-parallel vocab shards before gathering the sparse + teacher top-K positions. +* **Custom tar streaming + DataLoader** – a :class:`torch.utils.data.DataLoader` + with ``pin_memory=True`` streams batched tar shards so that disk I/O overlaps + with GPU compute, and the CPU→GPU copy can be issued with + ``non_blocking=True``. +* **Own-rank loading only** – each rank loads only cp-dp shards matching its + parallelism coordinates (no cross-rank file I/O beyond DP resharding). +* **Multi-threaded decode pipeline** – the tar ``IterableDataset`` runs + inside a single DataLoader worker (to preserve shard iteration order) and + parallelises the CPU-bound zstd decompression + ``torch.load`` + index + unpacking across an internal ``ThreadPoolExecutor``. This removes the + single-thread bottleneck that dominates per-iteration loader cost when each + saved tar contains a large multi-microbatch blob. + +Assumptions +----------- +* The student run uses the **same random seed** and data pipeline as the + teacher run that produced the cached log-probs, so the microbatch ordering + matches. +* **Same CP layout** (``cp_rank``) as the teacher run. The DP size may differ: + both upscaling (saved < current) and downscaling (saved > current) are + supported provided one evenly divides the other. +* **Same micro-batch size** as the teacher run. +* Teacher log-probs were saved by ``LogitsSaverHooks`` (see ``logits_saver.py``). + +Usage example +------------- +:: + + from megatron.training.distillation import LossFuncCallable + + # Instantiate once at the start of training + loss_func = LossFuncCallable( + logprobs_dir="/data/teacher_logprobs", + kd_loss_alpha=0.5, + ) + + # Pass as the loss_func to Megatron's training loop: + # loss, num_tokens, report = loss_func(loss_mask, output_tensor, model) +""" + +import concurrent.futures +import logging +from collections import deque +from typing import Any, Iterator, List, Optional, Tuple + +import torch +import torch.distributed as dist +import torch.distributed.nn as dist_nn +import torch.utils.data + +from megatron.core import parallel_state +from megatron.core.models.common.language_module.language_module import LanguageModule +from megatron.training.distillation.utils_logits import ( + BATCHED_TAR_RE, + CACHED_LOGITS_LOGPROB_SENTINEL, + LogprobsTarEntry, + TarShardPrefetcher, + batched_tar_prefix, + compute_dataset_hash, + decode_logprobs_payload, + detect_saved_dp_size, + get_current_iteration, + is_remote_storage_path, + iter_logprobs_tar_entries, + sorted_batched_tars, + storage_basename, + storage_glob_with_caching, +) +from megatron.training.utils import print_rank_0 + +logger = logging.getLogger(__name__) + +# Module-level reference so the training loss can consume logits captured +# from the model's output layer without requiring model-specific attributes. +_ACTIVE_STUDENT_LOGITS_CAPTURE: Optional["StudentLogitsCapture"] = None + + +def get_student_logits_capture() -> Optional["StudentLogitsCapture"]: + """Return the active :class:`StudentLogitsCapture` instance, or *None*.""" + return _ACTIVE_STUDENT_LOGITS_CAPTURE + + +class StudentLogitsCapture: + """Forward-hook helper that keeps the latest differentiable student logits.""" + + def __init__(self): + self._logits: Optional[torch.Tensor] = None + self._hook_handles: List[Any] = [] + + def attach_hooks(self, model: LanguageModule) -> None: + """Attach forward hooks to the model's output layer.""" + handle = model.output_layer.register_forward_hook(self._capture_logits) + self._hook_handles.append(handle) + + global _ACTIVE_STUDENT_LOGITS_CAPTURE + _ACTIVE_STUDENT_LOGITS_CAPTURE = self + + def _capture_logits(self, module: torch.nn.Module, input: Any, output: Any) -> None: + if not module.training: + return + # NOTE: Assumes main head runs after MTP layers, overwriting this value prior to pop(). + self._logits = output[0] if isinstance(output, tuple) else output + + def pop(self) -> torch.Tensor: + """Return captured logits and clear the reference for the next forward.""" + if self._logits is None: + raise RuntimeError( + "No student logits were captured for cached-logits KD. " + "Attach StudentLogitsCapture to the model output layer during setup." + ) + logits = self._logits + self._logits = None + return logits + + def remove_hooks(self) -> None: + """Remove all registered hooks and clear captured state.""" + for handle in self._hook_handles: + handle.remove() + self._hook_handles.clear() + self._logits = None + + +def _compute_dp_remapping( + logprobs_dir: str, dp_rank: int, dp_size: int +) -> Tuple[List[int], int, int, int]: + """Compute the DP rank remapping when loading data saved with a different DP size. + + Scans *logprobs_dir* via :func:`detect_saved_dp_size` to infer the + saved DP world size. When no batched tars are found, returns the + identity mapping ``([dp_rank], 0, 1, dp_size)``. + + Data is distributed across DP ranks in a round-robin (wrapping) + fashion: with *dp_size_saved* ranks and *G* global microbatches, + saved rank *d* holds microbatch indices ``d, d + dp_size_saved, + d + 2·dp_size_saved, …``. + + **Upscaling** (``dp_size_saved < dp_size``): each saved rank's data + is shared by ``dp_size // dp_size_saved`` current ranks that stride + through its microbatches. + + **Downscaling** (``dp_size_saved > dp_size``): each current rank + loads from ``dp_size_saved // dp_size`` saved files and interleaves + their microbatches to reconstruct its share of the global ordering. + + Returns: + ``(source_dp_ranks, sub_rank, dp_ratio, dp_size_saved)`` where + + - *source_dp_ranks* is the list of saved-file DP ranks to read. + Length 1 for identity or upscaling, length > 1 for downscaling. + - *sub_rank* selects the strided microbatch slice within each + source file (non-zero only when upscaling). + - *dp_ratio* is ``dp_size / dp_size_saved``: > 1 when upscaling + (used as the stride in microbatch slicing), < 1 when downscaling + (informational only — the multi-source interleave path is used + instead), and 1 for the identity case. + - *dp_size_saved* is the DP world size used when the data was + written. + """ + dp_size_saved = detect_saved_dp_size(logprobs_dir) + + if dp_size_saved is None: + return [dp_rank], 0, 1, dp_size + if dp_size_saved == dp_size: + return [dp_rank], 0, 1, dp_size_saved + + if dp_size_saved < dp_size: + # Upscaling: multiple current ranks share one saved file + if dp_size % dp_size_saved != 0: + raise ValueError( + f"Current DP size ({dp_size}) is not an exact multiple of " + f"saved DP size ({dp_size_saved})." + ) + dp_ratio = dp_size // dp_size_saved + mapped_dp_rank = dp_rank % dp_size_saved + sub_rank = dp_rank // dp_size_saved + return [mapped_dp_rank], sub_rank, dp_ratio, dp_size_saved + + # Downscaling: each current rank loads from multiple saved files + if dp_size_saved % dp_size != 0: + raise ValueError( + f"Saved DP size ({dp_size_saved}) is not an exact multiple of " + f"current DP size ({dp_size})." + ) + num_sources = dp_size_saved // dp_size + source_dp_ranks = [dp_rank + i * dp_size for i in range(num_sources)] + dp_ratio = dp_size / dp_size_saved # e.g. 0.5 when halving DP + return source_dp_ranks, 0, dp_ratio, dp_size_saved + + +# --------------------------------------------------------------------------- +# Dataset – streaming batched tar shards +# --------------------------------------------------------------------------- + + +class TeacherTarDataset(torch.utils.data.IterableDataset): + """Streaming dataset that reads teacher log-probs from batched tar shards. + + Supports a single on-disk layout: ``cp{C}_dp{D}__{B}.tar``. Shards are + saved by TP rank 0 with the full top-K; every TP rank loads the same + cp-dp tar. + + **DP resharding** is supported in both directions: + + * **Upscaling** (saved DP < current DP) — multiple current ranks share + one saved file and stride through its microbatches. + * **Downscaling** (saved DP > current DP) — each current rank loads + from multiple saved files and interleaves their microbatches to + reconstruct its share of the global microbatch ordering. + + **Shard discovery**: after known shards are exhausted, discovery refreshes + the directory listing so a concurrent writer can publish more data. Remote + refreshes are issued by rank 0 and broadcast to avoid object-store list + storms. + + Yields ``(values_list, indices_list)``. + """ + + def __init__( + self, + logprobs_dir: str, + cp_rank: int, + dp_rank: int, + dp_size: int, + start_iteration: int = 0, + decode_threads: int = 1, + decode_lookahead: Optional[int] = None, + msc_prefetch_depth: int = 2, + ): + self.logprobs_dir = logprobs_dir + self.cp_rank = cp_rank + self.dp_rank = dp_rank + self.start_iteration = start_iteration + + self._remote_logprobs = is_remote_storage_path(logprobs_dir) + self._msc_prefetch_depth = max(0, msc_prefetch_depth) + + # Per-tar dataset-identity verification: each tar stream validates its + # leading _meta.json before yielding any payload data. + self._expected_hash, _ = compute_dataset_hash() + + self._decode_threads = max(1, decode_threads) + if decode_lookahead is None: + decode_lookahead = max(2 * self._decode_threads, 4) + self._decode_lookahead = max(1, decode_lookahead) + if self._decode_threads > 1: + print_rank_0( + f"Teacher logits decode: using {self._decode_threads} decode threads " + f"(lookahead={self._decode_lookahead}) in the logits loader worker" + ) + if self._remote_logprobs and self._msc_prefetch_depth > 0: + print_rank_0( + f"Teacher logits remote tar prefetch: {self._msc_prefetch_depth} " "shard(s) ahead" + ) + + # DP remapping: detect saved DP size and compute mapping + self._source_dp_ranks, self._sub_rank, self._dp_ratio, dp_size_saved = ( + _compute_dp_remapping(logprobs_dir, dp_rank, dp_size) + ) + if len(self._source_dp_ranks) > 1: + print_rank_0( + f"DP downscaling: dp_rank {dp_rank} loads from saved dp_ranks " + f"{self._source_dp_ranks} (saved_dp_size {dp_size_saved}, " + f"current_dp_size {dp_size})" + ) + elif self._dp_ratio > 1: + print_rank_0( + f"DP upscaling: dp_rank {dp_rank} -> mapped_dp_rank " + f"{self._source_dp_ranks[0]} (sub_rank {self._sub_rank}, " + f"saved_dp_size {dp_size_saved}, current_dp_size {dp_size})" + ) + + def _discover_shards(self, already_processed: set, dp_rank: int) -> list: + """Glob for new cp-dp batched tar shards. + + Args: + already_processed: Set of URLs already processed (to skip). + dp_rank: Saved DP rank to discover shards for. + """ + prefix = batched_tar_prefix(self.cp_rank, dp_rank) + all_urls = sorted_batched_tars( + storage_glob_with_caching(self.logprobs_dir, f"{prefix}*.tar", cached=False) + ) + new_urls = [] + for url in all_urls: + if url in already_processed: + continue + m = BATCHED_TAR_RE.match(storage_basename(url)) + if m and int(m.group("iter")) >= self.start_iteration: + new_urls.append(url) + return new_urls + + def _slice_microbatches( + self, values_list: List[torch.Tensor], indices_list: List[torch.Tensor] + ) -> Tuple[List[torch.Tensor], List[torch.Tensor]]: + """Return only this rank's strided microbatch slice when DP ratio > 1.""" + if self._dp_ratio <= 1: + return values_list, indices_list + + num_mb = len(values_list) + if num_mb % self._dp_ratio != 0: + raise ValueError( + f"Saved microbatch count ({num_mb}) is not divisible by " + f"DP ratio ({self._dp_ratio}). Cannot evenly split " + f"microbatches across remapped DP ranks." + ) + return ( + values_list[self._sub_rank :: self._dp_ratio], + indices_list[self._sub_rank :: self._dp_ratio], + ) + + @staticmethod + def _interleave_microbatches( + all_values: List[List[torch.Tensor]], all_indices: List[List[torch.Tensor]] + ) -> Tuple[List[torch.Tensor], List[torch.Tensor]]: + """Interleave microbatches from multiple source dp_ranks (downscaling). + + Given *N* sources each with *M* microbatches, produces a single + list of *N·M* microbatches ordered so that the round-robin global + microbatch assignment is reconstructed: + ``src0_mb0, src1_mb0, …, srcN_mb0, src0_mb1, src1_mb1, …`` + """ + num_sources = len(all_values) + num_mb = len(all_values[0]) + merged_values: List[torch.Tensor] = [] + merged_indices: List[torch.Tensor] = [] + for mb_idx in range(num_mb): + for src_idx in range(num_sources): + merged_values.append(all_values[src_idx][mb_idx]) + merged_indices.append(all_indices[src_idx][mb_idx]) + return merged_values, merged_indices + + # ------------------------------------------------------------------ + # Shard discovery + # ------------------------------------------------------------------ + + def _not_found_error(self) -> FileNotFoundError: + """Build a descriptive FileNotFoundError for missing shards.""" + dp_ranks = self._source_dp_ranks + if len(dp_ranks) == 1: + dp = dp_ranks[0] + return FileNotFoundError( + f"No batched tar shards for " + f"cp{self.cp_rank}_dp{dp} " + f"found at or after iteration {self.start_iteration} " + f"in '{self.logprobs_dir}'" + ) + return FileNotFoundError( + f"No batched tar shards for source dp_ranks " + f"{dp_ranks} (cp{self.cp_rank}) " + f"found at or after iteration {self.start_iteration} " + f"in '{self.logprobs_dir}'" + ) + + def _shard_groups( + self, processed: set, prefetcher: TarShardPrefetcher + ) -> Iterator[Tuple[str, ...]]: + """Yield source-DP shard URL groups as new shards are discovered. + + Each yielded tuple has one URL per source DP rank. Handles dynamic + re-discovery: when all current groups are exhausted, re-globs for new + shards and yields the next batch. Raises :class:`FileNotFoundError` if + no shards exist at all; returns normally when no *new* shards appear. + """ + while True: + urls_per_src = [ + self._discover_shards(processed, src_dp) for src_dp in self._source_dp_ranks + ] + if not all(urls_per_src): + if not processed: + raise self._not_found_error() + return + groups = list(zip(*urls_per_src)) + for group in prefetcher.iter_prefetched(groups): + processed.update(group) + yield group + + # ------------------------------------------------------------------ + # Tar decode and DP resharding helpers + # ------------------------------------------------------------------ + + def _decode_entry( + self, entry: LogprobsTarEntry + ) -> Tuple[int, List[torch.Tensor], List[torch.Tensor]]: + """Decode one raw tar payload into logical iteration tensors.""" + values_list, indices_list = decode_logprobs_payload(entry.data) + return entry.iteration, values_list, indices_list + + def _iter_entries_parallel( + self, pool: concurrent.futures.ThreadPoolExecutor, entries: Iterator[LogprobsTarEntry] + ) -> Iterator[Tuple[int, List[torch.Tensor], List[torch.Tensor]]]: + """Yield decoded entries using a decode thread pool. + + The main thread streams raw tar members one at a time and submits the + CPU-heavy zstd + ``torch.load`` + index unpacking work to *pool*. + Results are yielded in tar order via a FIFO of futures. + """ + pending: "deque[concurrent.futures.Future]" = deque() + exhausted = False + + def submit_next() -> None: + nonlocal exhausted + if exhausted: + return + try: + entry = next(entries) + except StopIteration: + exhausted = True + return + pending.append(pool.submit(self._decode_entry, entry)) + + for _ in range(self._decode_lookahead): + submit_next() + if exhausted: + break + + while pending: + fut = pending.popleft() + decoded = fut.result() + submit_next() + yield decoded + + def _iter_decoded_entries( + self, pool: Optional[concurrent.futures.ThreadPoolExecutor], url: str + ) -> Iterator[Tuple[int, List[torch.Tensor], List[torch.Tensor]]]: + """Yield decoded iteration payloads from one tar URL.""" + entries = iter_logprobs_tar_entries( + url, start_iteration=self.start_iteration, expected_hash=self._expected_hash + ) + if pool is None: + for entry in entries: + yield self._decode_entry(entry) + else: + yield from self._iter_entries_parallel(pool, entries) + + def _interleave_decoded_group( + self, decoded_group: Tuple[Tuple[int, List[torch.Tensor], List[torch.Tensor]], ...] + ) -> Tuple[List[torch.Tensor], List[torch.Tensor]]: + """Interleave matching iterations from multiple source DP ranks.""" + all_values: List[List[torch.Tensor]] = [] + all_indices: List[List[torch.Tensor]] = [] + ref_iteration: Optional[int] = None + for decoded in decoded_group: + iteration, vals, inds = decoded + if ref_iteration is None: + ref_iteration = iteration + elif iteration != ref_iteration: + raise RuntimeError( + f"Iteration mismatch across source dp_ranks during " + f"downscaled DP loading: expected iteration " + f"{ref_iteration} but got {iteration}." + ) + all_values.append(vals) + all_indices.append(inds) + return self._interleave_microbatches(all_values, all_indices) + + def _iter_single_source_group( + self, pool: Optional[concurrent.futures.ThreadPoolExecutor], url: str + ) -> Iterator[Tuple[List[torch.Tensor], List[torch.Tensor]]]: + """Yield DP-sliced microbatches from one source DP tar stream.""" + for _, values_list, indices_list in self._iter_decoded_entries(pool, url): + yield self._slice_microbatches(values_list, indices_list) + + def _iter_downscaled_group( + self, pool: Optional[concurrent.futures.ThreadPoolExecutor], urls: Tuple[str, ...] + ) -> Iterator[Tuple[List[torch.Tensor], List[torch.Tensor]]]: + """Yield interleaved microbatches from source DP tar streams in lockstep.""" + decoded_iters = [self._iter_decoded_entries(pool, url) for url in urls] + for decoded_group in zip(*decoded_iters): + yield self._interleave_decoded_group(decoded_group) + + def _iter_group( + self, pool: Optional[concurrent.futures.ThreadPoolExecutor], group: Tuple[str, ...] + ) -> Iterator[Tuple[List[torch.Tensor], List[torch.Tensor]]]: + """Yield logical training iterations from one shard group.""" + if len(self._source_dp_ranks) > 1: + yield from self._iter_downscaled_group(pool, group) + else: + yield from self._iter_single_source_group(pool, group[0]) + + # ------------------------------------------------------------------ + # Main iteration entry point + # ------------------------------------------------------------------ + + def __iter__(self): + processed: set = set() + with TarShardPrefetcher( + enabled=self._remote_logprobs, + depth=self._msc_prefetch_depth, + max_workers=max(1, self._msc_prefetch_depth * len(self._source_dp_ranks)), + ) as prefetcher: + if self._decode_threads == 1: + for group in self._shard_groups(processed, prefetcher): + yield from self._iter_group(None, group) + else: + with concurrent.futures.ThreadPoolExecutor( + max_workers=self._decode_threads, thread_name_prefix="teacher-decode" + ) as pool: + for group in self._shard_groups(processed, prefetcher): + yield from self._iter_group(pool, group) + + +# --------------------------------------------------------------------------- +# Top-K KL divergence +# --------------------------------------------------------------------------- + + +def topk_kl_div( + student_logits: torch.Tensor, + teacher_topk_logprobs: torch.Tensor, + teacher_topk_indices: torch.Tensor, + tp_size: int, + tp_rank: int, + tp_group: dist.ProcessGroup, + add_ghost_token: bool = False, +) -> torch.Tensor: + """Compute the KL divergence between student and teacher's top-K log-probs.""" + student_logits = student_logits.float() + teacher_topk_logprobs = teacher_topk_logprobs.float() + + # ---- TP-aware student softmax / log-softmax ---- + logits_max, _ = student_logits.max(dim=-1, keepdim=True) + if tp_size > 1: + dist.all_reduce(logits_max, op=dist.ReduceOp.MAX, group=tp_group) + student_logits -= logits_max.detach() # purely for numerical stability + + sum_exp = student_logits.exp().sum(dim=-1, keepdim=True) + if tp_size > 1: + sum_exp = dist_nn.functional.all_reduce(sum_exp, op=dist.ReduceOp.SUM, group=tp_group) + student_logprobs = student_logits - sum_exp.log() + + # ---- Gather student log-probs at teacher's top-K positions (local shard only) ---- + local_vocab_size = student_logits.size(-1) + offset = local_vocab_size * tp_rank + mask = ( + (teacher_topk_indices >= offset) + & (teacher_topk_indices < offset + local_vocab_size) + & (teacher_topk_logprobs != CACHED_LOGITS_LOGPROB_SENTINEL) + ) + # Clamp out-of-range indices to avoid index errors; their contributions are zeroed by the mask + teacher_local_indices = (teacher_topk_indices - offset).clamp(0, local_vocab_size - 1) + # In TP > 1, may contain duplicate values due to clamping above, so they are masked downstream + student_topk_logprobs = torch.gather(student_logprobs, -1, teacher_local_indices) + + # ---- Add a "ghost" token containing sum of non-top-K probabilities to both student and teacher ---- + if add_ghost_token: + eps = 1e-8 + student_topk_logprobs_exp = ( + student_topk_logprobs.exp() * mask + ) # don't sum duplicate indices if any + student_topk_exp_sum = student_topk_logprobs_exp.sum(dim=-1, keepdim=True) + if tp_size > 1: + student_topk_exp_sum = dist_nn.functional.all_reduce( + student_topk_exp_sum, op=dist.ReduceOp.SUM, group=tp_group + ) + student_residual = torch.log((1.0 - student_topk_exp_sum).clamp(min=eps)) + teacher_residual = torch.log( + (1.0 - teacher_topk_logprobs.exp().sum(dim=-1, keepdim=True)).clamp(min=eps) + ) + student_topk_logprobs = torch.cat([student_topk_logprobs, student_residual], dim=-1) + teacher_topk_logprobs = torch.cat([teacher_topk_logprobs, teacher_residual], dim=-1) + mask = torch.cat([mask, mask.new_full((*mask.shape[:-1], 1), float(tp_rank == 0))], dim=-1) + + # ---- Sparse KL divergence (summed over top-K dimension) ---- + kl_div = teacher_topk_logprobs.exp() * (teacher_topk_logprobs - student_topk_logprobs) + kl_loss = torch.sum(mask * kl_div, dim=-1) + + return kl_loss.transpose(0, 1).contiguous() # [S, B] -> [B, S] + + +# --------------------------------------------------------------------------- +# KD dataloading + loss class +# --------------------------------------------------------------------------- + + +class CachedLogitsKDLoss: + """Offline knowledge-distillation loss backed by cached teacher top-K log-probs. + + For each microbatch the loss function: + + 1. Retrieves (or prefetches via the DataLoader) this rank's cp-dp tar shard + containing the teacher's full top-K log-probabilities and global vocab + indices. + 2. Computes the student's globally-normalised log-probabilities via a + TP-aware softmax, gathers them at the teacher's top-K positions, and + returns the **forward KL divergence** + ``KL(teacher ‖ student) = Σ_k p_T(k)·[log p_T(k) − log p_S(k)]`` + averaged over all token positions. + + A :class:`torch.utils.data.DataLoader` with ``pin_memory=True`` streams + batched tar shards from disk so that I/O overlaps with GPU compute. + Because the tensors arrive in pinned host memory, the subsequent + ``tensor.to(device, non_blocking=True)`` call can overlap the DMA transfer + with ongoing GPU kernels. + + A custom tar reader sequentially streams ``cp{C}_dp{D}__{B}.tar`` shards + through the same storage layer used by the writer. + + The DataLoader is initialised lazily on the first ``__call__`` because the + starting iteration is not known at construction time. + + Args: + logprobs_dir: Root directory containing log-probs data written by + :class:`LogitsSaverHooks`. + decode_threads: Number of decode threads inside the single DataLoader + worker used to parallelise zstd decompression and ``torch.load`` + across in-flight samples. + prefetch_factor: How many decoded iterations the PyTorch DataLoader + worker pre-loads ahead. + msc_prefetch_depth: For remote MSC tar shards, how many whole + shard objects to materialize into the MSC cache ahead of + sequential tar consumption. + """ + + def __init__( + self, + logprobs_dir: str, + decode_threads: int = 1, + prefetch_factor: int = 2, + msc_prefetch_depth: int = 2, + ): + self.logprobs_dir = logprobs_dir + self._decode_threads = decode_threads + self._prefetch_factor = prefetch_factor + self._msc_prefetch_depth = msc_prefetch_depth + + # ---- parallel-state bookkeeping ---- + self.tp_rank = parallel_state.get_tensor_model_parallel_rank() + self.tp_size = parallel_state.get_tensor_model_parallel_world_size() + self.tp_group = parallel_state.get_tensor_model_parallel_group() + self.cp_rank = parallel_state.get_context_parallel_rank() + self.dp_rank = parallel_state.get_data_parallel_rank() + self.dp_size = parallel_state.get_data_parallel_world_size() + + # ---- DataLoader (lazy-initialised on first call) ---- + self._dataloader_iter: Optional[Iterator] = None + + # ---- iteration / microbatch tracking ---- + self._current_iteration: Optional[int] = None + self._microbatch_counter: int = 0 + + # ---- current iteration's teacher data (pinned CPU tensors) ---- + self._current_values: Optional[List[torch.Tensor]] = None + self._current_indices: Optional[List[torch.Tensor]] = None + + def _init_dataloader(self, start_iteration: int) -> None: + """Create the DataLoader starting from *start_iteration*.""" + # TeacherTarDataset yields a single ordered stream, so multiple DataLoader + # workers would split shards and interleave results non-deterministically. + # Cap at 1 DataLoader worker; intra-worker parallelism is obtained via + # the ThreadPoolExecutor inside TeacherTarDataset. + dataset = TeacherTarDataset( + self.logprobs_dir, + self.cp_rank, + self.dp_rank, + self.dp_size, + start_iteration=start_iteration, + decode_threads=self._decode_threads, + msc_prefetch_depth=self._msc_prefetch_depth, + ) + # Remote shard discovery uses rank-0 collectives, so it must run in the + # main training process rather than a DataLoader worker. + dataloader_workers = 0 if is_remote_storage_path(self.logprobs_dir) else 1 + loader_kwargs = dict( + dataset=dataset, + batch_size=None, + collate_fn=lambda x: x, + pin_memory=True, + num_workers=dataloader_workers, + ) + if dataloader_workers > 0: + loader_kwargs["prefetch_factor"] = self._prefetch_factor + loader_kwargs["persistent_workers"] = True + loader = torch.utils.data.DataLoader(**loader_kwargs) + self._dataloader_iter = iter(loader) + + def _advance_iteration(self) -> None: + """Fetch the next iteration's data from the DataLoader.""" + assert self._dataloader_iter is not None + try: + values_list, indices_list = next(self._dataloader_iter) + except StopIteration as e: + raise StopIteration( + f"No more teacher log-prob data available in " + f"{self.logprobs_dir}. The DataLoader has been exhausted." + ) from e + self._current_values = values_list + self._current_indices = indices_list + self._microbatch_counter = 0 + + def __call__( + self, + student_logits: torch.Tensor, + iteration: Optional[int] = None, + microbatch_idx: Optional[int] = None, + ) -> torch.Tensor: + """Compute KL-divergence loss for one microbatch. + + Args: + student_logits: ``(seq_len, batch, local_vocab_size)`` – raw + (pre-softmax) student logits from this TP rank's vocab shard. + iteration: Training iteration. Defaults to ``args.curr_iteration``. + microbatch_idx: Microbatch index within the current iteration. + Defaults to an auto-incremented counter that resets on each + new iteration. + + Returns: + Tensor of shape ``(batch, seq_len)`` – per-token forward KL + divergence (unreduced). + """ + # ---- resolve iteration ---- + if iteration is None: + iteration = get_current_iteration() + + # ---- lazy DataLoader init ---- + if self._dataloader_iter is None: + self._init_dataloader(iteration) + + # ---- detect iteration change → pull next prefetched item ---- + if self._current_iteration != iteration: + self._advance_iteration() + self._current_iteration = iteration + + # ---- resolve microbatch index ---- + if microbatch_idx is None: + microbatch_idx = self._microbatch_counter + self._microbatch_counter += 1 + + if microbatch_idx >= len(self._current_values): + raise IndexError( + f"Microbatch index {microbatch_idx} out of range: only " + f"{len(self._current_values)} microbatches were saved for " + f"iteration {iteration}." + ) + + # ---- move teacher data to GPU (non-blocking: tensors are pinned) ---- + teacher_values = self._current_values[microbatch_idx].to( + student_logits.device, non_blocking=True + ) + teacher_indices = self._current_indices[microbatch_idx].to( + student_logits.device, non_blocking=True + ) + + logger.debug( + "[TP%s-CP%s-DP%s]: Iter_%s Batch_%s", + self.tp_rank, + self.cp_rank, + self.dp_rank, + iteration, + microbatch_idx, + ) + + # ---- compute loss ---- + return topk_kl_div( + student_logits, + teacher_values, + teacher_indices, + self.tp_size, + self.tp_rank, + self.tp_group, + add_ghost_token=True, + ) + + +# --------------------------------------------------------------------------- +# Main callable wrapper to pass to Megatron LM training loop +# --------------------------------------------------------------------------- + + +class LossFuncCallable: + def __init__( + self, + logprobs_dir: str, + decode_threads: int = 1, + prefetch_factor: int = 2, + kd_loss_alpha: float = 0.5, + ignore_errors: bool = False, + msc_prefetch_depth: int = 2, + ): + self.logprobs_dir = logprobs_dir + self.decode_threads = decode_threads + self.prefetch_factor = prefetch_factor + self.msc_prefetch_depth = msc_prefetch_depth + self.kd_func = None + self.alpha = kd_loss_alpha + self.ignore_errors = ignore_errors + + @staticmethod + def _mask_loss(output_tensor, loss_mask): + """Apply mask to the unreduced loss tensor.""" + losses = output_tensor.view(-1).float() + loss_mask = loss_mask.reshape(-1).float() + return torch.sum(losses * loss_mask) + + def __call__(self, loss_mask: torch.Tensor, output_tensor: torch.Tensor, model: LanguageModule): + """Loss function wrapper for compatibility with Megatron LM training loop. + + Args: + loss_mask (Tensor): Used to mask out some portions of the loss + output_tensor (Tensor): The tensor with the losses + model (LanguageModule): The model (can be wrapped) + """ + if self.kd_func is None: + # Construct here so parallel_state is initialized by now + self.kd_func = CachedLogitsKDLoss( + logprobs_dir=self.logprobs_dir, + decode_threads=self.decode_threads, + prefetch_factor=self.prefetch_factor, + msc_prefetch_depth=self.msc_prefetch_depth, + ) + + # LM loss + loss_lm = self._mask_loss(output_tensor, loss_mask) + num_tokens = loss_mask.sum().clone().detach().to(torch.int) + report = {'lm loss': torch.cat([loss_lm.clone().detach().view(1), num_tokens.view(1)])} + + # Eval case + if not model.training: + # During evaluation, return only the LM loss, as eval logits are not stored to disk + return loss_lm, num_tokens, report + + # KD loss + try: + logits_capture = get_student_logits_capture() + if logits_capture is None: + raise RuntimeError( + "Cached-logits KD requires StudentLogitsCapture to be attached " + "to the model output layer during setup." + ) + logits = logits_capture.pop() + + loss_kd = self.kd_func(logits) + loss_kd = self._mask_loss(loss_kd, loss_mask) + # Requires extra TP reduction + dist.all_reduce(loss_kd, group=parallel_state.get_tensor_model_parallel_group()) + except Exception as e: + if not self.ignore_errors: + raise + # Don't fail the entire training process if KD loss fails + logger.warning( + f">>>>>> KD LOSS FAILED — falling back to LM loss. {type(e).__name__}: {e} <<<<<<" + ) + return loss_lm, num_tokens, report + + report["logits distillation loss"] = torch.cat( + [loss_kd.clone().detach().view(1), num_tokens.view(1)] + ) + + loss_total = (1 - self.alpha) * loss_lm + self.alpha * loss_kd + report["total loss"] = torch.cat([loss_total.clone().detach().view(1), num_tokens.view(1)]) + + return loss_total, num_tokens, report diff --git a/megatron/training/distillation/logits_saver.py b/megatron/training/distillation/logits_saver.py new file mode 100644 index 00000000000..bb7762320b3 --- /dev/null +++ b/megatron/training/distillation/logits_saver.py @@ -0,0 +1,576 @@ +# Copyright (c) 2026, NVIDIA CORPORATION. All rights reserved. + +""" +Utilities for saving top-K log-probabilities during model training with minimal overhead. + +This module provides hook-based utilities to capture and persist top-K log-probabilities +during training while respecting tensor parallelism and minimizing communication, +computation, I/O, and disk space overhead. + +The forward hook accumulates raw logits across microbatches and, once all microbatches +have been collected, converts them to log-probs and writes them to disk in a single I/O call. + +For efficient global top-K log-prob computation across TP ranks: +- Local top-K is computed on fp32 logits first to avoid materializing + a full vocab-sized log-softmax tensor +- The log-softmax denominator (log-sum-exp) is computed TP-aware and applied + only to the top-K values +- Local candidates are gathered to TP rank 0 for global top-K selection +- TP rank 0 saves the full top-K log-probabilities (unsharded) + +Index storage optimization: +- Global vocab requires 17 bits, so we store lower 16 bits as uint16 +- The 17th bit is stored separately as torch.bool (same shape as indices) +- To reconstruct: global_index = (bit_17 << 16) | low_16_bits +""" + +import io +import json +import logging +import os +import tarfile +from collections import OrderedDict +from types import MethodType +from typing import Any, Dict, List, Optional, Tuple + +import torch +import torch.distributed as dist +import zstandard + +from megatron.core import parallel_state +from megatron.core.models.common.language_module.language_module import LanguageModule +from megatron.core.msc_utils import MultiStorageClientFeature +from megatron.core.num_microbatches_calculator import get_num_microbatches +from megatron.training import get_args, get_tensorboard_writer +from megatron.training.distillation.utils_logits import ( + CACHED_LOGITS_INDEX_SENTINEL, + CACHED_LOGITS_LOGPROB_SENTINEL, + LOGPROBS_TAR_MEMBER_SUFFIX, + META_TAR_MEMBER, + batched_tar_filename, + compute_dataset_hash, + get_current_iteration, + is_remote_storage_path, + open_logit_file, + pack_indices, + storage_makedirs, + storage_move, +) +from megatron.training.utils import print_rank_0 + +logger = logging.getLogger(__name__) + +# Module-level reference so checkpoint/shutdown code can call flush(). +_ACTIVE_LOGITS_SAVER: Optional["LogitsSaverHooks"] = None + + +def get_logits_saver() -> Optional["LogitsSaverHooks"]: + """Return the active :class:`LogitsSaverHooks` instance, or *None*.""" + return _ACTIVE_LOGITS_SAVER + + +_MAX_VOCAB_SIZE = 2**17 # 131072 - maximum supported vocab size + + +class LogitsSaverHooks: + """ + Hook-based utility to save top-K log-probabilities during training with minimal overhead. + + This class provides a forward hook to attach to the module that outputs logits. + The hook accumulates logits across microbatches and saves all log-probs to disk + in a single I/O call once all microbatches have been collected. + + Top-K selection is performed on raw logits first, then log-probabilities + are computed only for the selected positions using the log-softmax + denominator (log-sum-exp). Because log-softmax is monotonically + increasing, the top-K positions are identical to those of the raw logits. + This avoids materializing a full vocab-sized log-softmax tensor. + + The implementation efficiently handles tensor parallelism by: + - Computing local top-K on fp32 logits to reduce memory and communication + - Concatenating values and indices before all_gather + - Computing global top-K from gathered results + - Having TP rank 0 save the full top-K for each CP/DP rank + + When ``p`` is set, a top-P (nucleus) mask is applied after global top-K + selection. The K dimension is truncated to the maximum per-token nucleus + size (reducing the amount of data written to disk), and tokens whose + nucleus is smaller than that maximum have their trailing entries masked + with cached-logit value/index sentinels. These sentinels are + compatible with :func:`topk_kl_div` in ``cached_logits_loss.py`` because + the value sentinel makes the masked teacher probability effectively zero. + At least ``min_k`` entries are always kept per token. + + Indices are stored efficiently: uint16 for lower 16 bits + separate high bit tensor. + CP and DP ranks are stored in the tar filename for flexibility with multi-digit values. + + Iteration data is accumulated in memory and flushed as a tar archive at + checkpoint time via the async checkpoint queue. Call :meth:`flush` at + shutdown to write any remaining buffered data synchronously. + + Args: + save_dir: Directory to save log-prob files + k: Number of top log-probabilities to save globally + p: Optional top-P (nucleus) threshold applied after global top-K + selection. When set, only the smallest set of leading entries + per token whose cumulative probability mass reaches ``p`` is + kept; remaining entries are masked with cached-logit value/index + sentinels. + min_k: Minimum number of entries kept per token when top-P masking + is active, regardless of cumulative mass. Defaults to 1. + save_dtype: String name of the dtype for top-K log-probabilities on + disk. One of ``'fp16'``, ``'bf16'``, or ``'fp32'``. + """ + + _DTYPE_MAP = {'fp16': torch.float16, 'bf16': torch.bfloat16, 'fp32': torch.float32} + + def __init__( + self, + save_dir: str, + k: int, + *, + p: Optional[float] = None, + min_k: int = 1, + save_dtype: str = 'fp16', + ): + assert k > 0, "Number of top log-probabilities to save must be positive" + assert save_dir is not None, "Save directory must be provided" + if save_dtype not in self._DTYPE_MAP: + raise ValueError( + f"save_dtype must be one of {list(self._DTYPE_MAP)}, got '{save_dtype}'" + ) + self._save_dtype = self._DTYPE_MAP[save_dtype] + + if p is not None and not (0.0 < p <= 1.0): + raise ValueError(f"p must be in (0, 1] or None, got {p}") + if min_k < 1: + raise ValueError(f"min_k must be >= 1, got {min_k}") + + self.save_dir = save_dir + self.k = k + self.p = p + self.min_k = min_k + + # Parallel state info + self.tp_rank = parallel_state.get_tensor_model_parallel_rank() + self.tp_size = parallel_state.get_tensor_model_parallel_world_size() + self.tp_group = parallel_state.get_tensor_model_parallel_group() + self.cp_rank = parallel_state.get_context_parallel_rank() + self.dp_rank = parallel_state.get_data_parallel_rank() + self._tp_dst_rank_global = parallel_state.get_tensor_model_parallel_src_rank() + + # Track number of MTP outputs to ignore + args = get_args() + self._mtp_num_layers = args.mtp_num_layers or 0 + self._curr_mtp_passes = 0 + + # Dataset-identity hash + serialised metadata, written as the + # first member of every batched tar so the student loader can + # verify alignment per-tar without an extra tarfile.open. + self.dataset_hash, self._dataset_identifiers = compute_dataset_hash() + self.metadata_dict: Dict[str, Any] = { + "hash": self.dataset_hash, + "identifiers": self._dataset_identifiers, + "saver": {"k": self.k, "p": self.p, "min_k": self.min_k, "save_dtype": save_dtype}, + } + self._meta_bytes: bytes = json.dumps( + self.metadata_dict, sort_keys=False, separators=(',', ':') + ).encode("utf-8") + + # Hook states – store already-processed top-K results (not full logits) + self._accumulated_results: List[Tuple[torch.Tensor, torch.Tensor, torch.Tensor]] = [] + self._hook_handles: List[Any] = [] + self._loss_overrides: List[Tuple[torch.nn.Module, Any]] = [] + + # Batched tar state — accumulates until checkpoint time. + self._pending_writes: OrderedDict[int, bytes] = OrderedDict() + + # Top-P logging: per-microbatch kept counts (populated by _apply_topp_truncation) + self._topp_kept_counts: List[float] = [] + + # Create save directory if needed + storage_makedirs(self.save_dir, exist_ok=True) + + # Register as the active saver so checkpoint code can flush + global _ACTIVE_LOGITS_SAVER + _ACTIVE_LOGITS_SAVER = self + + def _forward_hook( + self, module: torch.nn.Module, input: Any, output: Tuple[torch.Tensor, ...] + ) -> None: + """Capture top-K log-probs for one output-layer forward. + + This hook should be registered on the module that outputs logits. + Each microbatch is processed immediately to extract only the small + top-K values and indices, avoiding storage of full vocab-sized logits + across microbatches. + """ + if not module.training: + return + + if self._curr_mtp_passes == self._mtp_num_layers: + # Main output logits come after MTP logits. + logits = output[0] if isinstance(output, tuple) else output + + with torch.no_grad(): + result = self._process_single_microbatch(logits) + if result is not None: + self._accumulated_results.append(result) + + if len(self._accumulated_results) == get_num_microbatches(): + self._save_accumulated_log_probs() + self._accumulated_results.clear() + + self._curr_mtp_passes = 0 + else: + self._curr_mtp_passes += 1 + + def attach_hooks(self, model: LanguageModule) -> None: + """Attach logits-saving hooks and skip expensive LM loss computation. + + Args: + model: Model to instrument. + """ + fwd_handle = model.output_layer.register_forward_hook(self._forward_hook) + self._hook_handles.append(fwd_handle) + self._override_language_model_loss(model) + + def _override_language_model_loss(self, model: LanguageModule) -> None: + """Replace LM loss with a zero-valued tensor that preserves gradient edges.""" + + def _compute_zero_language_model_loss(_self, _labels, logits): + return (logits * 0).sum(dim=-1).transpose(0, 1).contiguous() + + original_loss = model.compute_language_model_loss + self._loss_overrides.append((model, original_loss)) + model.compute_language_model_loss = MethodType(_compute_zero_language_model_loss, model) + + def remove_hooks(self) -> None: + """Remove all registered hooks.""" + for handle in self._hook_handles: + handle.remove() + self._hook_handles.clear() + for model_chunk, original_loss in self._loss_overrides: + model_chunk.compute_language_model_loss = original_loss + self._loss_overrides.clear() + + def _save_accumulated_log_probs(self) -> None: + """Move accumulated top-K results to CPU and save to disk. + + By this point each microbatch has already been processed in the + forward hook, so this method only transfers the small top-K tensors + to CPU and writes them out in a single I/O call. + """ + if self.tp_rank != 0: + return + if not self._accumulated_results: + logger.warning("No accumulated log-probs to save") + return + + all_values = [] + all_indices_low = [] + all_high_bits = [] + + for values, indices_low, high_bit in self._accumulated_results: + all_values.append(values.cpu()) + all_indices_low.append(indices_low.cpu()) + all_high_bits.append(high_bit.cpu()) + + self._buffer_iteration(all_values, all_indices_low, all_high_bits) + + if self._topp_kept_counts: + # Log the average number of top-P kept tokens per microbatch to tensorboard + avg_kept = sum(self._topp_kept_counts) / len(self._topp_kept_counts) + self._topp_kept_counts.clear() + if (writer := get_tensorboard_writer()) is not None: + writer.add_scalar('avg-logprobs-kept', avg_kept, get_current_iteration()) + + def _process_single_microbatch( + self, logits: torch.Tensor + ) -> Optional[Tuple[torch.Tensor, torch.Tensor, torch.Tensor]]: + """Process a single logits tensor and return top-K log-probability data. + + Selects local top-K positions on raw logits first, then computes + the global log-sum-exp denominator using ``torch.logsumexp`` for + the local shard (leveraging PyTorch's fused CUDA kernel) combined + across TP ranks via a numerically stable log-space reduction. + This avoids materializing a full vocab-sized log-softmax tensor. + + All TP ranks participate in the collective operations (all-reduce for + the log-sum-exp combination, gather for top-K candidates), but only + TP rank 0 performs the final global top-K and returns the result. + Other ranks return ``None``. + + Args: + logits: Tensor of shape (seq_len, batch, local_vocab_size) + + Returns: + Tuple of (log_prob_values, indices_low, high_bit) on TP rank 0, + ``None`` on other TP ranks. + """ + local_vocab_size = logits.shape[-1] + global_vocab_size = local_vocab_size * self.tp_size + + assert ( + global_vocab_size <= _MAX_VOCAB_SIZE + ), f"Global vocab size {global_vocab_size} exceeds maximum supported {_MAX_VOCAB_SIZE} (17 bits)" + + effective_k = min(self.k, global_vocab_size) + local_k = min(effective_k, local_vocab_size) + + # Convert to fp32 to lessen precision issues + logits = logits.float() + + local_logit_vals, local_indices = torch.topk(logits, local_k, dim=-1) + + # Local log-sum-exp via PyTorch's fused CUDA kernel + local_lse = torch.logsumexp(logits, dim=-1, keepdim=True) + + if self.tp_size > 1: + # Combine local log-sum-exp values across TP ranks using + # the standard numerically stable log-space reduction: + # global_lse = max_lse + log(sum_i(exp(lse_i - max_lse))) + max_lse = local_lse.clone() + dist.all_reduce(max_lse, op=dist.ReduceOp.MAX, group=self.tp_group) + sum_exp_lse = torch.exp(local_lse - max_lse) + dist.all_reduce(sum_exp_lse, op=dist.ReduceOp.SUM, group=self.tp_group) + global_lse = max_lse + torch.log(sum_exp_lse) + else: + global_lse = local_lse + + local_logprob_vals = local_logit_vals - global_lse + + if self.tp_size > 1: + result = self._compute_global_topk( + local_logit_vals, local_logprob_vals, local_indices, effective_k, local_vocab_size + ) + if result is None: + return None + global_values, global_indices = result + else: + global_values, global_indices = local_logprob_vals, local_indices + + if self.p is not None: + global_values, global_indices = self._apply_topp_truncation( + global_values, global_indices + ) + + global_values = global_values.to(self._save_dtype) + indices_low, high_bit = pack_indices(global_indices) + + return global_values, indices_low, high_bit + + def _compute_global_topk( + self, + local_logit_vals: torch.Tensor, + local_logprob_vals: torch.Tensor, + local_indices: torch.Tensor, + k: int, + local_vocab_size: int, + ) -> Optional[Tuple[torch.Tensor, torch.Tensor]]: + """Gather local top-K candidates to rank 0 and compute global top-K. + + The ranking is performed on fp32 *logit* values for numerical + precision; the returned log-prob values are still fp32 here and + are only cast to the on-disk ``save_dtype`` by the caller. + + Only TP rank 0 receives the gathered data and performs the final + top-K selection. Other ranks return ``None`` after participating + in the ``gather`` collective. + + Args: + local_logit_vals: Local top-K fp32 logit values (seq, batch, local_k) + local_logprob_vals: Local top-K fp32 log-prob values at the + same positions (seq, batch, local_k) + local_indices: Local top-K indices in [0, local_vocab_size) + k: Target number of top elements + local_vocab_size: Size of the local vocab shard (vocab_size / tp_size) + + Returns: + Tuple of (global_logprob_values, global_indices) on TP rank 0, + ``None`` on other TP ranks. + """ + vocab_offset = self.tp_rank * local_vocab_size + global_indices = local_indices + vocab_offset + + # Pack logits, log-probs, and indices into a single tensor. + # fp32 can exactly represent integers up to 2^24, sufficient for + # 17-bit vocab indices. Shape: (seq, batch, local_k, 3) + combined = torch.stack( + [local_logit_vals, local_logprob_vals.float(), global_indices.float()], dim=-1 + ) + + if self.tp_rank == 0: + gather_list = [torch.empty_like(combined) for _ in range(self.tp_size)] + else: + gather_list = None + dist.gather(combined, gather_list, dst=self._tp_dst_rank_global, group=self.tp_group) + + if self.tp_rank != 0: + return None + + # (seq, batch, tp_size * local_k, 3) + gathered = torch.cat(gather_list, dim=-2) + + gathered_logits = gathered[..., 0] + gathered_logprobs = gathered[..., 1].to(local_logprob_vals.dtype) + gathered_indices = gathered[..., 2].to(local_indices.dtype) + + _, topk_positions = torch.topk(gathered_logits, k, dim=-1) + + topk_logprobs = torch.gather(gathered_logprobs, -1, topk_positions) + topk_global_indices = torch.gather(gathered_indices, -1, topk_positions) + + return topk_logprobs, topk_global_indices + + def _apply_topp_truncation( + self, values: torch.Tensor, indices: torch.Tensor + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Apply top-P (nucleus) mask to already-sorted top-K log-probs. + + Keeps the smallest set of leading entries per token whose + cumulative probability mass is at least ``self.p``, with a floor + of ``self.min_k`` kept entries. + + Because the number of surviving entries varies per token, the K + dimension is truncated to the *maximum* kept count across all + tokens in the microbatch. Tokens whose individual nucleus is + smaller than that maximum have their trailing entries masked with + cached-logit value/index sentinels. These sentinels are compatible + with :func:`topk_kl_div` because the value sentinel makes masked + teacher probability effectively zero. + + Args: + values: Top-K log-prob values, sorted descending along the + last dimension. Shape ``(seq, batch, K)``. + indices: Corresponding global vocab indices (int64). + + Returns: + Tuple of ``(values, indices)`` with K dimension truncated to + the maximum per-token nucleus size, and trailing out-of-nucleus + entries masked with sentinels. + """ + probs = values.float().exp() + cumprobs = probs.cumsum(dim=-1) + # Standard nucleus rule: keep entry i iff the cumulative mass + # *before* it is < p. This guarantees we include the entry + # that crosses the threshold and always keeps top-1. + keep_mask = (cumprobs - probs) < self.p + + k = values.size(-1) + min_keep = min(self.min_k, k) + arange = torch.arange(k, device=values.device) + keep_mask = keep_mask | (arange < min_keep) + + kept_per_token = keep_mask.sum(dim=-1) + # For logging purposes + self._topp_kept_counts.append(kept_per_token.float().mean().item()) + + # Truncate to reduce storage + max_kept = int(kept_per_token.max().item()) + values = values[..., :max_kept] + indices = indices[..., :max_kept] + keep_mask = keep_mask[..., :max_kept] + + # Mask out-of-nucleus entries + values = torch.where(keep_mask, values, CACHED_LOGITS_LOGPROB_SENTINEL) + indices = torch.where(keep_mask, indices, CACHED_LOGITS_INDEX_SENTINEL) + # The index sentinel does not survive pack_indices()/unpack_indices(); + # topk_kl_div() also masks by the value sentinel to compensate. + + return values, indices + + def _buffer_iteration( + self, + values_list: List[torch.Tensor], + indices_low_list: List[torch.Tensor], + bit_17_list: List[torch.Tensor], + ) -> None: + """Serialize microbatch data and buffer for async flush at checkpoint time. + + File format: serialized dict with: + - values: list of tensors of log-probabilities (one per microbatch) + in ``self._save_dtype`` (default ``torch.float16``) + - indices_low: list of uint16 tensors (lower 16 bits of vocab indices) + - bit_17: list of bool tensors (17th bit, same shape as indices_low) + """ + # Serialize all tensors together + buffer = io.BytesIO() + torch.save( + {'values': values_list, 'indices_low': indices_low_list, 'bit_17': bit_17_list}, buffer + ) + data = buffer.getvalue() + iteration = get_current_iteration() + self._pending_writes[iteration] = data + + # ------------------------------------------------------------------ + # Flush helpers + # ------------------------------------------------------------------ + + def take_pending_data(self) -> Tuple[str, "OrderedDict[int, bytes]", bytes, bool]: + """Take ownership of buffered data for async flush at checkpoint time. + + Returns: + Tuple of (tar_path, writes, meta_bytes, msc_enabled). If there is + no pending data (e.g. non-TP-rank-0), ``writes`` will be an empty + OrderedDict and ``tar_path`` will be an empty string. + """ + # NOTE: We need to re-enabled MSC in the async saving process, so we pass this flag. + msc_enabled = MultiStorageClientFeature.is_enabled() + + if not self._pending_writes: + return ("", OrderedDict(), self._meta_bytes, msc_enabled) + + writes = self._pending_writes + self._pending_writes = OrderedDict() + + last_iter = max(writes.keys()) + tar_filename = batched_tar_filename(self.cp_rank, self.dp_rank, last_iter) + tar_path = os.path.join(self.save_dir, tar_filename) + print_rank_0(f"Handing off {len(writes)} logit iterations for async flush") + return (tar_path, writes, self._meta_bytes, msc_enabled) + + @staticmethod + def _write_batched_tar( + tar_path: str, + writes: "OrderedDict[int, bytes]", + meta_bytes: bytes, + msc_enabled: bool = False, + ) -> None: + """Write a tar archive containing multiple iterations. + + Called in the async checkpoint background process. Early-returns + when there is nothing to write (non-TP-rank-0 ranks). + + The dataset-identity metadata is written as the **first** member + (named :data:`META_TAR_MEMBER`) so the student-side tar reader + sees it before any payload members and can + verify alignment before any iteration data is decoded. + + Each subsequent member is named ``{iteration}.pt.zst`` so that + the student-side tar reader can stream iterations by member name. + """ + if not writes: + return + if msc_enabled: + # NOTE: MSC is not enabled in the async saving process by default. + MultiStorageClientFeature.enable() + + storage_makedirs(os.path.dirname(tar_path), exist_ok=True) + write_path = tar_path if is_remote_storage_path(tar_path) else f"{tar_path}.tmp" + compressor = zstandard.ZstdCompressor(level=3) + + with open_logit_file(write_path, "wb") as stream: + with tarfile.open(fileobj=stream, mode="w") as tar: + info = tarfile.TarInfo(name=META_TAR_MEMBER) + info.size = len(meta_bytes) + tar.addfile(info, io.BytesIO(meta_bytes)) + + for iteration, data in writes.items(): + data = compressor.compress(data) + member_name = f"{iteration}{LOGPROBS_TAR_MEMBER_SUFFIX}" + info = tarfile.TarInfo(name=member_name) + info.size = len(data) + tar.addfile(info, io.BytesIO(data)) + if write_path != tar_path: + storage_move(write_path, tar_path) diff --git a/megatron/training/distillation/utils_logits.py b/megatron/training/distillation/utils_logits.py new file mode 100644 index 00000000000..5b08488b9fe --- /dev/null +++ b/megatron/training/distillation/utils_logits.py @@ -0,0 +1,440 @@ +# Copyright (c) 2026, NVIDIA CORPORATION. All rights reserved. + +"""Storage helpers for cached-logit tar shards. + +This module keeps object-storage and tar-format plumbing out of the cached +logits writer/reader code paths. The helpers are intentionally small and +focused on the current batched tar layout. +""" + +import concurrent.futures +import fnmatch +import glob +import hashlib +import io +import json +import logging +import os +import re +import tarfile +import time +from collections import OrderedDict +from typing import Any, Dict, Iterable, Iterator, List, NamedTuple, Optional, Sequence, Tuple + +import torch +import torch.distributed as dist +import zstandard +from torch.utils.data import get_worker_info + +from megatron.core.msc_utils import MultiStorageClientFeature +from megatron.training import get_args +from megatron.training.utils import get_blend_and_blend_per_split + +logger = logging.getLogger(__name__) + +MSC_PREFIX = "msc://" + +# Matches batched tar filenames (``cp{C}_dp{D}__{I}.tar``). Named groups: +# cp – CP rank +# dp – DP rank +# iter – trailing iteration number *I* +BATCHED_TAR_RE = re.compile(r"^cp(?P\d+)_dp(?P\d+)__(?P\d+)\.tar$") + +# Name of the metadata member written as the first entry of every batched +# tar. Contains the dataset-identity hash and the fields that produced it. +META_TAR_MEMBER = "_meta.json" + +LOGPROBS_TAR_MEMBER_SUFFIX = ".pt.zst" + +# Matches compressed iteration payload members inside a batched tar archive. +LOGPROBS_TAR_MEMBER_RE = re.compile(rf"^(?P\d+){re.escape(LOGPROBS_TAR_MEMBER_SUFFIX)}$") + +CACHED_LOGITS_LOGPROB_SENTINEL = -1e3 +CACHED_LOGITS_INDEX_SENTINEL = -1 + +# Cache to not have to re-run the glob operation for the same pattern, which is expensive for MSC. +_STORAGE_GLOB_CACHE: dict[str, List[str]] = {} + + +def is_msc_path(path: str) -> bool: + """Return whether *path* is an MSC URL.""" + return str(path).startswith(MSC_PREFIX) + + +def is_remote_storage_path(path: str) -> bool: + """Return whether *path* needs object-storage handling.""" + return is_msc_path(path) + + +def _require_msc(): + """Return the MSC package or raise a Megatron-style feature error.""" + return MultiStorageClientFeature.import_package() + + +def _msc_if_needed(path: str): + if is_msc_path(path): + return _require_msc() + if MultiStorageClientFeature.is_enabled(): + return MultiStorageClientFeature.import_package() + return None + + +def storage_basename(path: str) -> str: + """Return the final path component for local paths and MSC URLs.""" + return os.path.basename(str(path).rstrip("/")) + + +def storage_makedirs(path: str, exist_ok: bool = True) -> None: + """Create a local or MSC directory/prefix.""" + if not path: + return + msc = _msc_if_needed(path) + if msc is not None: + msc.os.makedirs(path, exist_ok=exist_ok) + else: + os.makedirs(path, exist_ok=exist_ok) + + +def storage_move(src: str, dst: str) -> None: + """Atomically publish a local temporary file.""" + if is_msc_path(src) or is_msc_path(dst): + raise ValueError("storage_move is local-only; write MSC objects directly") + + os.replace(src, dst) + + +def storage_glob(pattern: str) -> List[str]: + """Return paths matching *pattern* for local files or MSC URLs.""" + if is_msc_path(pattern): + msc = _require_msc() + return list(msc.glob(pattern)) + + return glob.glob(pattern) + + +def _storage_glob_rank0(pattern: str, cached: bool = False) -> List[str]: + """Run ``storage_glob`` on rank 0 and broadcast the result.""" + if cached: + if (paths := _STORAGE_GLOB_CACHE.get(pattern)) is not None: + return paths + + if dist.is_available() and dist.is_initialized() and dist.get_world_size() > 1: + payload = [storage_glob(pattern) if dist.get_rank() == 0 else None] + dist.broadcast_object_list(payload, src=0) + paths = payload[0] + else: + paths = storage_glob(pattern) + + paths = list(paths or []) + _STORAGE_GLOB_CACHE[pattern] = paths + return paths + + +def storage_glob_with_caching(root: str, name_pattern: str, cached: bool = True) -> List[str]: + """Glob under *root* and filter the result by basename.""" + if not is_remote_storage_path(root): + return storage_glob(os.path.join(root, name_pattern)) + if get_worker_info() is not None: + raise RuntimeError( + "Remote cached-logits shard listing must run in the main " + "training process, not a DataLoader worker." + ) + listing = _storage_glob_rank0(os.path.join(root, "*.tar"), cached=cached) + + return [ + str(path) for path in listing if fnmatch.fnmatch(storage_basename(str(path)), name_pattern) + ] + + +def get_current_iteration() -> int: + """Return the current training iteration from ``get_args()``.""" + args = get_args() + iteration = getattr(args, 'curr_iteration', None) + if iteration is None: + iteration = getattr(args, 'iteration') + return iteration + + +def _blend_identifiers(args: Any) -> Dict[str, Any]: + """Build a path-agnostic representation of the training data blend.""" + blend, blend_per_split = get_blend_and_blend_per_split(args) + + def _normalise(blend_tuple) -> Optional[List[List[Any]]]: + if blend_tuple is None: + return None + prefixes, weights = blend_tuple + if weights is None: + weights = [1.0] * len(prefixes) + return [ + [float(w), os.path.splitext(os.path.basename(str(p)))[0]] + for w, p in zip(weights, prefixes) + ] + + if blend is not None: + return {"kind": "blend", "blend": _normalise(blend)} + if blend_per_split is not None: + # Index 0 is the train split (see ``megatron.core.datasets.utils.Split``). + return {"kind": "blend_per_split", "train": _normalise(blend_per_split[0])} + return {"kind": "mock", "mock": True} + + +def compute_dataset_hash() -> Tuple[str, Dict[str, Any]]: + """Compute the dataset-identity hash for the current training run. + + The fields included are exactly those that determine the global sample + stream itself: ``seed``, ``sequence_length``, ``train_samples`` (with a + fall-back to ``train_iters * global_batch_size``), and the data ``blend``. + """ + args = get_args() + train_samples = getattr(args, 'train_samples', None) + if train_samples is None: + train_iters = getattr(args, 'train_iters', None) + global_batch_size = getattr(args, 'global_batch_size', None) + if train_iters is not None and global_batch_size is not None: + train_samples = int(train_iters) * int(global_batch_size) + + identifiers = OrderedDict() + identifiers["seed"] = getattr(args, 'seed', None) + identifiers["sequence_length"] = getattr(args, 'seq_length', None) + identifiers["train_samples"] = train_samples + identifiers["blend"] = _blend_identifiers(args) + + description = json.dumps(identifiers, sort_keys=False, separators=(',', ':')) + md5_hex = hashlib.md5(description.encode("utf-8"), usedforsecurity=False).hexdigest() + return md5_hex, dict(identifiers) + + +def batched_tar_filename(cp_rank: int, dp_rank: int, last_iter: int) -> str: + """Return the filename for a cp-dp batched tar shard.""" + return f"cp{cp_rank}_dp{dp_rank}__{last_iter}.tar" + + +def batched_tar_prefix(cp_rank: int, dp_rank: int) -> str: + """Return the glob prefix for cp-dp batched tar shards.""" + return f"cp{cp_rank}_dp{dp_rank}__" + + +def sorted_batched_tars(paths: List[str]) -> List[str]: + """Sort batched tar paths by their iteration number (numeric, ascending).""" + keyed = [] + for path in paths: + if match := BATCHED_TAR_RE.match(storage_basename(path)): + keyed.append((int(match.group("iter")), path)) + keyed.sort() + return [path for _, path in keyed] + + +def pack_indices(indices: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: + """Split 17-bit global indices into uint16 lower bits + bool 17th bit.""" + low_bits = (indices & 0xFFFF).to(torch.uint16) + bit_17 = (indices >> 16).to(torch.bool) + return low_bits, bit_17 + + +def unpack_indices(low_bits: torch.Tensor, bit_17: torch.Tensor) -> torch.Tensor: + """Reconstruct indices from uint16 lower bits + bool 17th bit.""" + return (bit_17.long() << 16) | low_bits.long() + + +class LogprobsTarEntry(NamedTuple): + """Raw cached-logits payload read from one batched tar member.""" + + iteration: int + data: bytes + + +def open_logit_file(path: str, mode: str = "rb", **kwargs): + """Open a local or MSC file, filtering MSC-only kwargs for builtin open.""" + msc = _msc_if_needed(path) + if msc is not None: + return msc.open(path, mode, **kwargs) + + msc_open_kwargs = { + "attributes", + "check_source_version", + "disable_read_cache", + "memory_load_limit", + "prefetch_file", + } + local_kwargs = {k: v for k, v in kwargs.items() if k not in msc_open_kwargs} + return open(path, mode, **local_kwargs) + + +def _verify_logprobs_metadata(data: bytes, *, tar_path: str, expected_hash: Optional[str]) -> None: + """Validate the per-tar dataset hash stored in ``_meta.json``.""" + if expected_hash is None: + return + saved_hash = json.loads(data).get("hash") + if saved_hash != expected_hash: + raise RuntimeError( + f"Teacher tar {tar_path} was saved with hash {saved_hash} " + f"but the current student run has hash {expected_hash}. " + "Data does not align!" + ) + + +def iter_logprobs_tar_entries( + tar_path: str, *, start_iteration: int = 0, expected_hash: Optional[str] = None +) -> Iterator[LogprobsTarEntry]: + """Stream cached-logits payload members from a batched tar archive. + + The tar format is written by :class:`LogitsSaverHooks`: a leading + ``_meta.json`` member followed by ``{iteration}.pt.zst`` payloads. + Members before *start_iteration* are skipped before their payload bytes + are materialized in Python. + """ + metadata_seen = False + + with open_logit_file(tar_path, "rb", prefetch_file=True) as stream: + with tarfile.open(fileobj=stream, mode="r|*") as tar: + for member in tar: + if not member.isreg(): + continue + + name = member.name + if name == META_TAR_MEMBER: + extracted = tar.extractfile(member) + if extracted is None: + raise RuntimeError(f"Could not read metadata member in '{tar_path}'") + _verify_logprobs_metadata( + extracted.read(), tar_path=tar_path, expected_hash=expected_hash + ) + metadata_seen = True + continue + + match = LOGPROBS_TAR_MEMBER_RE.match(name) + if match is None: + continue + + if expected_hash is not None and not metadata_seen: + raise RuntimeError( + f"Teacher tar {tar_path} does not contain leading " + f"{META_TAR_MEMBER}; cannot verify data alignment." + ) + + iteration = int(match.group("iter")) + if iteration < start_iteration: + continue + + extracted = tar.extractfile(member) + if extracted is None: + raise RuntimeError(f"Could not read log-probs member '{name}' in '{tar_path}'") + yield LogprobsTarEntry(iteration=iteration, data=extracted.read()) + + if expected_hash is not None and not metadata_seen: + raise RuntimeError( + f"Teacher tar {tar_path} does not contain {META_TAR_MEMBER}; " + "cannot verify data alignment." + ) + + +def decode_logprobs_payload(data: bytes) -> Tuple[List[torch.Tensor], List[torch.Tensor]]: + """Decode one zstd-compressed cached-logits payload.""" + data = zstandard.ZstdDecompressor().decompress(data) + tensors = torch.load(io.BytesIO(data), weights_only=True) + indices_list = [ + unpack_indices(low, bit17) for low, bit17 in zip(tensors["indices_low"], tensors["bit_17"]) + ] + return tensors["values"], indices_list + + +def detect_saved_dp_size(logprobs_dir: str) -> Optional[int]: + """Scan *logprobs_dir* for batched tars and return the saved DP world size.""" + dp_ranks_found: set[int] = set() + for path in storage_glob_with_caching(logprobs_dir, "*.tar"): + fname = storage_basename(path) + if match := BATCHED_TAR_RE.match(fname): + dp_ranks_found.add(int(match.group("dp"))) + if not dp_ranks_found: + return None + return max(dp_ranks_found) + 1 + + +# NOTE: This function is for interactive debugging purposes +def load_log_probs_from_tar( + tar_path: str, iteration: int +) -> Tuple[List[torch.Tensor], List[torch.Tensor]]: + """Load one iteration from a specific batched tar shard.""" + for entry in iter_logprobs_tar_entries(tar_path, start_iteration=iteration): + if entry.iteration == iteration: + return decode_logprobs_payload(entry.data) + if entry.iteration > iteration: + break + + raise FileNotFoundError(f"No log-probs member found for iteration {iteration} in '{tar_path}'") + + +class TarShardPrefetcher: + """Asynchronously materialize whole tar shards into the MSC cache.""" + + def __init__(self, *, enabled: bool, depth: int = 2, max_workers: Optional[int] = None): + self.enabled = bool(enabled and depth > 0) + self.depth = depth + + self._executor: Optional[concurrent.futures.ThreadPoolExecutor] = None + self._futures: dict[str, concurrent.futures.Future] = {} + + if self.enabled: + self._executor = concurrent.futures.ThreadPoolExecutor( + max_workers=max(1, max_workers or self.depth), + thread_name_prefix="logits-tar-prefetch", + ) + + def close(self) -> None: + if self._executor is not None: + self._executor.shutdown(wait=True) + self._executor = None + self._futures.clear() + + def __enter__(self) -> "TarShardPrefetcher": + return self + + def __exit__(self, exc_type, exc, tb) -> None: + self.close() + + def schedule(self, url: str) -> None: + if not self.enabled or url in self._futures: + return + assert self._executor is not None + self._futures[url] = self._executor.submit(self._prefetch_url, url) + + def schedule_group(self, urls: Sequence[str]) -> None: + for url in urls: + self.schedule(url) + + def wait(self, url: str) -> None: + future = self._futures.pop(url, None) + if future is None: + return + start = time.monotonic() + future.result() + waited = time.monotonic() - start + if waited > 0.5: + logger.warning("Waited %.3fs for cached-logit tar shard prefetch: %s", waited, url) + + def wait_group(self, urls: Sequence[str]) -> None: + for url in urls: + self.wait(url) + + def iter_prefetched(self, groups: Iterable[Sequence[str]]) -> Iterator[Tuple[str, ...]]: + """Yield URL groups, waiting only when a prefetched group is not ready.""" + group_list = [tuple(group) for group in groups] + if not self.enabled: + yield from group_list + return + + for idx in range(min(self.depth, len(group_list))): + self.schedule_group(group_list[idx]) + + for idx, group in enumerate(group_list): + next_idx = idx + self.depth + if next_idx < len(group_list): + self.schedule_group(group_list[next_idx]) + self.wait_group(group) + yield group + + def _prefetch_url(self, url: str) -> None: + # Whole-object caching avoids tar-member range bookkeeping while still + # keeping the object download ahead of the sequential tar reader. + with open_logit_file(url, "rb", prefetch_file=True) as stream: + stream.read() diff --git a/megatron/training/theoretical_memory_usage.py b/megatron/training/theoretical_memory_usage.py index 9a4b883af54..4c55abfd22c 100644 --- a/megatron/training/theoretical_memory_usage.py +++ b/megatron/training/theoretical_memory_usage.py @@ -115,14 +115,28 @@ def compute_weight_and_optimizer_memory(args, verbose=False): shared_expert_params = ( 2 * args.hidden_size * shared_expert_ffn_hidden_size * gated_linear_multiplier ) + # Latent MoE projects tokens down before routed experts and projects them back after + # combine. Shared experts still operate on the full hidden dimension. + routed_expert_hidden_size = ( + args.moe_latent_size if args.moe_latent_size is not None else args.hidden_size + ) routed_expert_params = ( - 2 * args.hidden_size * moe_ffn_hidden_size * num_experts * gated_linear_multiplier + 2 * routed_expert_hidden_size * moe_ffn_hidden_size * num_experts * gated_linear_multiplier ) active_routed_expert_params = ( - 2 * args.hidden_size * moe_ffn_hidden_size * args.moe_router_topk * gated_linear_multiplier + 2 + * routed_expert_hidden_size + * moe_ffn_hidden_size + * args.moe_router_topk + * gated_linear_multiplier if args.num_experts is not None else 0 ) + latent_projection_params = ( + 2 * args.hidden_size * args.moe_latent_size + if args.num_experts is not None and args.moe_latent_size is not None + else 0 + ) layernorm_params = 2 * args.hidden_size * norm_size router_params = ( (args.hidden_size * num_experts) + (num_experts if args.add_bias_linear else 0) @@ -142,6 +156,7 @@ def compute_weight_and_optimizer_memory(args, verbose=False): attention_params + shared_expert_params + routed_expert_params + + latent_projection_params + layernorm_params + router_params + shared_expert_gate_params @@ -150,6 +165,7 @@ def compute_weight_and_optimizer_memory(args, verbose=False): attention_params + shared_expert_params + active_routed_expert_params + + latent_projection_params + layernorm_params + router_params + shared_expert_gate_params @@ -212,7 +228,8 @@ def compute_weight_and_optimizer_memory(args, verbose=False): ) * num_dense_layers + (attention_params + shared_expert_params) * num_moe_layers replicated_params_in_transformer_block = ( layernorm_params * num_dense_layers - + (layernorm_params + router_params + shared_expert_gate_params) * num_moe_layers + + (layernorm_params + latent_projection_params + router_params + shared_expert_gate_params) + * num_moe_layers + final_layernorm ) expert_sharded_params_in_transformer_block = routed_expert_params * num_moe_layers @@ -221,7 +238,8 @@ def compute_weight_and_optimizer_memory(args, verbose=False): ) * mtp_num_dense_layers + (attention_params + shared_expert_params) * mtp_num_moe_layers replicated_params_in_mtp_block = ( layernorm_params * mtp_num_dense_layers - + (layernorm_params + router_params + shared_expert_gate_params) * mtp_num_moe_layers + + (layernorm_params + latent_projection_params + router_params + shared_expert_gate_params) + * mtp_num_moe_layers ) expert_sharded_params_in_mtp_block = routed_expert_params * mtp_num_moe_layers diff --git a/megatron/training/training.py b/megatron/training/training.py index eaa1b8b95d3..039ab1c1937 100644 --- a/megatron/training/training.py +++ b/megatron/training/training.py @@ -12,8 +12,8 @@ # Startup timestamps for tracking program initialization phases _STARTUP_TIMESTAMPS = { 'program_start': None, # Set by entry script before imports - 'main_entry': None, # Set by entry script at start of __main__ - 'pretrain_entry': None, # Set at top of pretrain() + 'main_entry': None, # Set by entry script at start of __main__ + 'pretrain_entry': None, # Set at top of pretrain() } @@ -65,66 +65,23 @@ def set_startup_timestamps(program_start=None, main_entry=None): logging.basicConfig(handlers=[CustomHandler()], level=logging.INFO) from .theoretical_memory_usage import report_theoretical_memory -_LEGACY_TRAIN_START_TIME = time.time() # NOTE(asolergi-nv): Legacy timestamp +_LEGACY_TRAIN_START_TIME = time.time() # NOTE(asolergi-nv): Legacy timestamp import torch try: from megatron.rl import rl_utils + from megatron.rl.rl_profiling import ( + RL_LOGGABLE_TIMER_NAMES, + initialize_rl_profiler, + log_iteration_profile, + shutdown_rl_profiler, + ) + has_rl_utils = True except ImportError: has_rl_utils = False - -# Canonical list of RL timer names to include in timers_to_log. -# When the profiling branch is merged, this will be imported from rl_profiling -# as RL_LOGGABLE_TIMER_NAMES instead of being defined here. -RL_LOGGABLE_TIMER_NAMES = [ - # Top-level RL phases - 'rl/rollout-collection', - 'rl/prepare-data-for-update', - # Rollout collection breakdown - 'rl/inference-setup', - 'rl/collect-rollouts', - 'rl/sync-rollouts', - 'rl/suspend-engine', - # Optimizer offload/restore - 'rl/offload-optimizer-before-inference', - 'rl/restore-optimizer-after-inference', - 'rl/offload-kv-cache-after-inference', - 'rl/restore-kv-cache-before-inference', - # Fine-grained offload/restore breakdown - 'rl/restore/grad-buffers', - 'rl/restore/optimizer-state', - 'rl/restore/wait-for-transfers', - 'rl/offload/grad-buffers', - 'rl/offload/optimizer-state', - # Weight prefetching - 'rl/prefetch-weights-to-gpu', - 'rl/prefetch-weights-to-cpu', - # Data preparation - 'rl/compute-group-stats', - 'rl/prepare-advantages', - 'rl/prepare-trajectories', - 'rl/get-ltor-masks', - 'rl/create-dataloader', - 'rl/sequence-packing', - 'rl/align-inference-logprobs', - 'rl/log-wandb-tb', - 'rl/pack-sequences', - 'rl/regather-trajectories', - # Logprobs computation - 'rl/compute-logprobs', - 'rl/compute-old-logprobs', - 'rl/compute-ref-logprobs', - 'rl/get-logprobs', - 'rl/forward-pass', - 'rl/log-softmax', - # Inference / cuda graphs - 'rl/build-cuda-graphs', - 'rl/wait-for-decode-only', -] - try: from modelopt.torch.distill.plugins.megatron import get_tensor_shapes_adjust_fn_for_distillation @@ -150,22 +107,20 @@ def set_startup_timestamps(program_start=None, main_entry=None): from megatron.core.optimizer.optimizer import param_group_identifier_keys from megatron.core.optimizer.optimizer_cuda_graph import OptimizerCudaGraphWrapper from megatron.core.optimizer.qk_clip import clip_qk +from megatron.core.pipeline_parallel.p2p_communication import P2PCommunicator from megatron.core.pipeline_parallel.utils import ( is_pp_first_stage, is_pp_last_stage, is_vp_first_stage, is_vp_last_stage, ) -from megatron.core.process_groups_config import ProcessGroupCollection +from megatron.core.process_groups_config import ( + MultiModuleProcessGroupCollection, + ProcessGroupCollection, +) from megatron.core.transformer.cuda_graphs import TECudaGraphHelper from megatron.core.transformer.module import Float16Module from megatron.core.transformer.moe.paged_stash import PagedStashRunner -from megatron.core.distributed import DistributedDataParallelConfig, TorchFullyShardedDataParallelConfig -from megatron.core.distributed import DistributedDataParallel as DDP -from megatron.core.distributed.fsdp.mcore_fsdp_adapter import FullyShardedDataParallel as megatron_FSDP -from megatron.core.optimizer.optimizer import param_group_identifier_keys - -from megatron.core.optimizer.qk_clip import clip_qk from megatron.core.utils import ( StragglerDetector, check_param_hashes_across_dp_replicas, @@ -228,6 +183,7 @@ def set_startup_timestamps(program_start=None, main_entry=None): try: from torch_memory_saver import torch_memory_saver + torch_memory_saver.hook_mode = "torch" HAVE_TORCH_MEMORY_SAVER = True except ImportError: @@ -294,7 +250,7 @@ def destroy_global_state(): def print_datetime(string, override_timestamp=None): """Note that this call will sync across all ranks. Use override_timestamp if provided; - otherwise use current timestamp.""" + otherwise use current timestamp.""" torch.distributed.barrier() if override_timestamp is None: time_str = datetime.now().strftime('%Y-%m-%d %H:%M:%S.%f') @@ -302,6 +258,7 @@ def print_datetime(string, override_timestamp=None): time_str = datetime.fromtimestamp(override_timestamp).strftime('%Y-%m-%d %H:%M:%S.%f') print_rank_0(f'[{string}] datetime: {time_str} ') + # Per-iteration packed-sequence (THD) accumulator. The tensor holds TWO stats, # both computed from the REAL ``cu_seqlens`` (i.e. unpadded sub-sequence lengths # -- ``cu_seqlens_padded`` is intentionally ignored so that neither the @@ -409,10 +366,7 @@ def consume_seqlen_stats_in_iteration() -> Tuple[Optional[float], Optional[float def num_floating_point_operations( - args, - batch_size, - seqlen_squared_sum_in_batch=None, - total_real_tokens_in_batch=None, + args, batch_size, seqlen_squared_sum_in_batch=None, total_real_tokens_in_batch=None ): """Compute the number of floating-point operations for one global batch. @@ -451,26 +405,49 @@ def mlp_layer_flops(total_tokens, hidden_size, expansion=4.0, swiglu=False): scale_factor = 3.0 / 2.0 if swiglu else 1.0 return 4 * expansion * scale_factor * total_tokens * hidden_size**2 - def moe_layer_flops(total_tokens, hidden_size, moe_ffn_hidden_size, - shared_expert_ffn_hidden_size, num_experts_routed_to, - moe_latent_size=None, swiglu=False): + def moe_layer_flops( + total_tokens, + hidden_size, + moe_ffn_hidden_size, + shared_expert_ffn_hidden_size, + num_experts_routed_to, + moe_latent_size=None, + swiglu=False, + ): """Calculate FLOPs for an MoE layer.""" scale_factor = 3.0 / 2.0 if swiglu else 1.0 if moe_latent_size is None: - routed_flops = (4 * total_tokens * hidden_size * - moe_ffn_hidden_size * num_experts_routed_to * scale_factor) + routed_flops = ( + 4 + * total_tokens + * hidden_size + * moe_ffn_hidden_size + * num_experts_routed_to + * scale_factor + ) else: # Routed experts run on moe_latent_size. - routed_flops = (4 * total_tokens * moe_latent_size * - moe_ffn_hidden_size * num_experts_routed_to * scale_factor) + routed_flops = ( + 4 + * total_tokens + * moe_latent_size + * moe_ffn_hidden_size + * num_experts_routed_to + * scale_factor + ) # Up proj and down proj. - routed_flops += (4 * total_tokens * hidden_size * moe_latent_size) + routed_flops += 4 * total_tokens * hidden_size * moe_latent_size shared_flops = 4 * total_tokens * hidden_size * shared_expert_ffn_hidden_size * scale_factor return routed_flops + shared_flops def attn_layer_flops( - total_tokens, seqlen_squared_sum, hidden_size, num_heads, gqa=True, - gqa_groups=8, kv_channels=None, + total_tokens, + seqlen_squared_sum, + hidden_size, + num_heads, + gqa=True, + gqa_groups=8, + kv_channels=None, ): """Calculate FLOPs for an attention layer. @@ -486,13 +463,13 @@ def attn_layer_flops( # 4 * total_tokens * h * p * (h + h*(g/n)): QKV + output projections (fwd*3, with FMA*2). # 2 * sum(L^2) * h * p: core attention (causal mask -> /2 cancels with FMA *2). return ( - 4 * total_tokens * hidden_size * p - * (hidden_size + hidden_size * (g / num_heads)) + 4 * total_tokens * hidden_size * p * (hidden_size + hidden_size * (g / num_heads)) + 2 * seqlen_squared_sum * hidden_size * p ) - def mamba_layer_flops(total_tokens, hidden_size, state_dim=16, - head_dim=64, num_groups=1, num_heads=128): + def mamba_layer_flops( + total_tokens, hidden_size, state_dim=16, head_dim=64, num_groups=1, num_heads=128 + ): """Calculate FLOPs for a Mamba layer.""" # Note (rwaleffe): flops estimate for scan should be updated based on new SSD kernels, # but small percent of overall layer flops @@ -503,67 +480,115 @@ def mamba_layer_flops(total_tokens, hidden_size, state_dim=16, nheads = d_in // head_dim return ( ( - 2 - * total_tokens - * hidden_size - * (2 * d_in + 2 * num_groups * state_dim + nheads) + 2 * total_tokens * hidden_size * (2 * d_in + 2 * num_groups * state_dim + nheads) ) # in_proj + (7 * total_tokens * d_in * state_dim) # scan + (2 * total_tokens * d_in * hidden_size) # out_proj ) - def gdn_layer_flops(total_tokens, hidden_size, - qk_head_dim=128, v_head_dim=128, - num_qk_heads=16, num_v_heads=32, - conv_kernel_dim=4): + def gdn_layer_flops( + total_tokens, + hidden_size, + qk_head_dim=128, + v_head_dim=128, + num_qk_heads=16, + num_v_heads=32, + conv_kernel_dim=4, + ): """Calculate FLOPs for a Gated Delta Net (GDN) layer.""" qk_dim = qk_head_dim * num_qk_heads v_dim = v_head_dim * num_v_heads return ( - 2 * total_tokens * ( + 2 + * total_tokens + * ( # in_proj: hidden_size -> (2*qk_dim + 2*v_dim + 2*num_v_heads) hidden_size * (2 * qk_dim + 2 * v_dim + 2 * num_v_heads) # conv1d + conv_kernel_dim * (2 * qk_dim + v_dim) # gated delta rule: KK^T, VK^T, S(a(I-bKK^T)), and SQ - + num_v_heads * (v_head_dim ** 2) * 4 + + num_v_heads * (v_head_dim**2) * 4 # out_proj: v_dim -> hidden_size + hidden_size * v_dim ) ) - def hybrid_flops(total_tokens, seqlen_squared_sum, hidden_size, - num_attn_layers, num_mamba_layers, num_mlp_layers, num_moe_layers, - num_gdn_layers=0, - mamba_state_dim=128, mamba_head_dim=64, - mamba_num_groups=8, mamba_num_heads=128, - num_attn_heads=32, gqa=True, - gqa_groups=8, kv_channels=None, - mlp_expansion=4.0, swiglu=False, - moe_latent_size=None, - moe_ffn_hidden_size=2048, shared_expert_ffn_hidden_size=2048, num_experts_routed_to=1, - gdn_qk_head_dim=128, gdn_v_head_dim=128, - gdn_num_qk_heads=16, gdn_num_v_heads=32, - gdn_conv_kernel_dim=4, - vocab_size=256000, mtp_num_layers=0): + def hybrid_flops( + total_tokens, + seqlen_squared_sum, + hidden_size, + num_attn_layers, + num_mamba_layers, + num_mlp_layers, + num_moe_layers, + num_gdn_layers=0, + mamba_state_dim=128, + mamba_head_dim=64, + mamba_num_groups=8, + mamba_num_heads=128, + num_attn_heads=32, + gqa=True, + gqa_groups=8, + kv_channels=None, + mlp_expansion=4.0, + swiglu=False, + moe_latent_size=None, + moe_ffn_hidden_size=2048, + shared_expert_ffn_hidden_size=2048, + num_experts_routed_to=1, + gdn_qk_head_dim=128, + gdn_v_head_dim=128, + gdn_num_qk_heads=16, + gdn_num_v_heads=32, + gdn_conv_kernel_dim=4, + vocab_size=256000, + mtp_num_layers=0, + ): """Calculate total FLOPs for the hybrid model.""" flops_fwd = ( - num_attn_layers * attn_layer_flops(total_tokens, seqlen_squared_sum, - hidden_size, num_attn_heads, gqa, - gqa_groups, kv_channels) + - num_mlp_layers * mlp_layer_flops(total_tokens, hidden_size, - mlp_expansion, swiglu) + - num_mamba_layers * mamba_layer_flops(total_tokens, hidden_size, - mamba_state_dim, mamba_head_dim, - mamba_num_groups, mamba_num_heads) + - num_moe_layers * moe_layer_flops(total_tokens, hidden_size, moe_ffn_hidden_size, - shared_expert_ffn_hidden_size, num_experts_routed_to, - moe_latent_size, swiglu) + - num_gdn_layers * gdn_layer_flops(total_tokens, hidden_size, - gdn_qk_head_dim, gdn_v_head_dim, - gdn_num_qk_heads, gdn_num_v_heads, - gdn_conv_kernel_dim) + - (2 * total_tokens * hidden_size * vocab_size * (1 + mtp_num_layers)) # logits computation + num_attn_layers + * attn_layer_flops( + total_tokens, + seqlen_squared_sum, + hidden_size, + num_attn_heads, + gqa, + gqa_groups, + kv_channels, + ) + + num_mlp_layers * mlp_layer_flops(total_tokens, hidden_size, mlp_expansion, swiglu) + + num_mamba_layers + * mamba_layer_flops( + total_tokens, + hidden_size, + mamba_state_dim, + mamba_head_dim, + mamba_num_groups, + mamba_num_heads, + ) + + num_moe_layers + * moe_layer_flops( + total_tokens, + hidden_size, + moe_ffn_hidden_size, + shared_expert_ffn_hidden_size, + num_experts_routed_to, + moe_latent_size, + swiglu, + ) + + num_gdn_layers + * gdn_layer_flops( + total_tokens, + hidden_size, + gdn_qk_head_dim, + gdn_v_head_dim, + gdn_num_qk_heads, + gdn_num_v_heads, + gdn_conv_kernel_dim, + ) + + ( + 2 * total_tokens * hidden_size * vocab_size * (1 + mtp_num_layers) + ) # logits computation ) return flops_fwd * 3 @@ -660,13 +685,9 @@ def transformer_flops(): ## Full core attention is replaced by sparse attention and is accounted ## for in the dsv4_hybrid branch below. q_term = args.q_lora_rank * ( - args.hidden_size - + args.num_attention_heads * args.v_head_dim - + 1 # q norm + args.hidden_size + args.num_attention_heads * args.v_head_dim + 1 # q norm ) - kv_term = ( - args.hidden_size * args.v_head_dim + args.v_head_dim - ) # kv proj + kv norm + kv_term = args.hidden_size * args.v_head_dim + args.v_head_dim # kv proj + kv norm ## Grouped low-rank output projection: ## wo_a: (n_head * v_head_dim) -> (o_groups * o_lora_rank) ## linear_proj: (o_groups * o_lora_rank) -> hidden @@ -721,9 +742,7 @@ def transformer_flops(): forward_backward_expansion_factor * fma_expansion_factor * ( - args.num_attention_heads - * (args.qk_head_dim + args.qk_pos_emb_head_dim) - / 2 + args.num_attention_heads * (args.qk_head_dim + args.qk_pos_emb_head_dim) / 2 + args.num_attention_heads * args.v_head_dim / 2 ) ) @@ -748,8 +767,7 @@ def transformer_flops(): + gate_projection_size ) ## out proj - + query_projection_size - * args.hidden_size + + query_projection_size * args.hidden_size ) ) # Core-attention (L^2) part: ``QK^T`` and ``(softmax(QK^T)) V``. @@ -768,8 +786,8 @@ def transformer_flops(): if isinstance(args.linear_attention_freq, int): linear_attention_pattern = [ # [1,1,...,1,0,1,1,...,1,0,...] - 0 if ((i + 1) % args.linear_attention_freq == 0) - else 1 for i in range(num_layers) + 0 if ((i + 1) % args.linear_attention_freq == 0) else 1 + for i in range(num_layers) ] elif isinstance(args.linear_attention_freq, list): linear_attention_pattern = args.linear_attention_freq @@ -806,18 +824,13 @@ def transformer_flops(): * fma_expansion_factor * ( ## in proj - args.hidden_size - * (2 * qk_dim + 2 * v_dim + 2 * num_v_heads) + args.hidden_size * (2 * qk_dim + 2 * v_dim + 2 * num_v_heads) ## conv1d - + args.linear_conv_kernel_dim - * (2 * qk_dim + v_dim) + + args.linear_conv_kernel_dim * (2 * qk_dim + v_dim) ## gated delta rule - + num_v_heads - * (v_head_dim ** 2) - * 4 # KK^T, VK^T, S(a(I-bKK^T)), and SQ + + num_v_heads * (v_head_dim**2) * 4 # KK^T, VK^T, S(a(I-bKK^T)), and SQ ## out proj - + args.hidden_size - * v_dim + + args.hidden_size * v_dim ) ) else: @@ -836,9 +849,7 @@ def transformer_flops(): num_standard_attention_layers = num_layers compress_ratios = args.csa_compress_ratios - assert compress_ratios is not None, ( - "csa_compress_ratios must be set for dsv4_hybrid" - ) + assert compress_ratios is not None, "csa_compress_ratios must be set for dsv4_hybrid" assert len(compress_ratios) == num_layers, ( f"Invalid length of csa_compress_ratios: {len(compress_ratios)}, " f"expected num_layers + mtp_num_layers ({num_layers})." @@ -880,24 +891,22 @@ def transformer_flops(): # ---- r=4 layers: sparse attention + indexer ---- if n_layers_r4 > 0: - assert args.dsa_indexer_n_heads is not None, ( - "dsa_indexer_n_heads must be set for dsv4_hybrid with ratio==4 layers." - ) - assert args.dsa_indexer_head_dim is not None, ( - "dsa_indexer_head_dim must be set for dsv4_hybrid with ratio==4 layers." - ) - assert args.dsa_indexer_topk is not None, ( - "dsa_indexer_topk must be set for dsv4_hybrid with ratio==4 layers." - ) + assert ( + args.dsa_indexer_n_heads is not None + ), "dsa_indexer_n_heads must be set for dsv4_hybrid with ratio==4 layers." + assert ( + args.dsa_indexer_head_dim is not None + ), "dsa_indexer_head_dim must be set for dsv4_hybrid with ratio==4 layers." + assert ( + args.dsa_indexer_topk is not None + ), "dsa_indexer_topk must be set for dsv4_hybrid with ratio==4 layers." idx_n_heads = args.dsa_indexer_n_heads idx_head_dim = args.dsa_indexer_head_dim idx_topk = args.dsa_indexer_topk effective_topk_4 = min(idx_topk, seq_len // 4) avg_comp_4 = effective_topk_4 * (1 - effective_topk_4 * 4 / (2 * seq_len)) - sparse_attn_r4 = ( - n_layers_r4 * n_head * (window + avg_comp_4) * v_head_dim * 2 - ) + sparse_attn_r4 = n_layers_r4 * n_head * (window + avg_comp_4) * v_head_dim * 2 # Indexer token-linear: compressor (coff=2, wkv + wgate), Q proj, # weights proj. @@ -913,9 +922,7 @@ def transformer_flops(): indexer_token_term = 0 indexer_scoring_core = 0 - sparse_attn_token_term = ( - sparse_attn_r0 + sparse_attn_r4 + sparse_attn_r128_window - ) + sparse_attn_token_term = sparse_attn_r0 + sparse_attn_r4 + sparse_attn_r128_window dsv4_hybrid_extra_term = ( forward_backward_expansion_factor @@ -957,8 +964,7 @@ def transformer_flops(): * args.hidden_size * ( # dense layer (deepseek v2, v3 style) - (args.ffn_hidden_size * ffn_expansion_factor) - * num_dense_layers + (args.ffn_hidden_size * ffn_expansion_factor) * num_dense_layers # routed experts + ( (moe_ffn_hidden_size * num_experts_routed_to * ffn_expansion_factor) @@ -976,8 +982,7 @@ def transformer_flops(): ) * num_moe_layers # Shared Experts. - + (shared_expert_ffn_hidden_size * ffn_expansion_factor) - * num_moe_layers + + (shared_expert_ffn_hidden_size * ffn_expansion_factor) * num_moe_layers ) # Self Attention (token-linear part). + self_attn_term @@ -1015,6 +1020,7 @@ def transformer_flops(): Symbols, get_hybrid_layer_counts, ) + num_mamba_layers, num_gdn_layers, num_attn_layers, num_mlp_layers, num_moe_layers = ( itemgetter(Symbols.MAMBA, Symbols.GDN, Symbols.ATTENTION, Symbols.MLP, Symbols.MOE)( get_hybrid_layer_counts(args.hybrid_layer_pattern) @@ -1045,10 +1051,16 @@ def transformer_flops(): mlp_expansion=args.ffn_hidden_size / args.hidden_size, swiglu=args.swiglu, moe_latent_size=args.moe_latent_size, - moe_ffn_hidden_size=(args.moe_ffn_hidden_size if args.moe_ffn_hidden_size is not None - else args.ffn_hidden_size), - shared_expert_ffn_hidden_size=(0 if args.moe_shared_expert_intermediate_size is None - else args.moe_shared_expert_intermediate_size), + moe_ffn_hidden_size=( + args.moe_ffn_hidden_size + if args.moe_ffn_hidden_size is not None + else args.ffn_hidden_size + ), + shared_expert_ffn_hidden_size=( + 0 + if args.moe_shared_expert_intermediate_size is None + else args.moe_shared_expert_intermediate_size + ), num_experts_routed_to=args.moe_router_topk, gdn_qk_head_dim=args.linear_key_head_dim or 128, gdn_v_head_dim=args.linear_value_head_dim or 128, @@ -1102,7 +1114,9 @@ def _get_field(string, type): # save_checkpoint was called directly (without save_checkpoint_and_time), # which writes "Saved async checkpoint" but not "Saving async checkpoint". if latest_num_floating_point_operations_uncommitted is not None: - latest_num_floating_point_operations = latest_num_floating_point_operations_uncommitted + latest_num_floating_point_operations = ( + latest_num_floating_point_operations_uncommitted + ) latest_num_floating_point_operations_uncommitted = None if world_size_in_line != args.world_size: # Re-start search if we see a different world size. @@ -1116,8 +1130,10 @@ def _get_field(string, type): assert ( start_time is not None and start_num_floating_point_operations is not None ), "Should have seen at least one 'Starting job' entry with same world_size" - print_rank_0(f"megatron.training.get_start_time_from_progress_log: " - f"{start_time=}, {start_num_floating_point_operations=}") + print_rank_0( + f"megatron.training.get_start_time_from_progress_log: " + f"{start_time=}, {start_num_floating_point_operations=}" + ) return datetime.strptime(start_time, '%Y-%m-%d %H:%M:%S'), start_num_floating_point_operations @@ -1134,6 +1150,7 @@ def preprocess_common_state_dict(common_state_dict): preprocessed_common_state_dict['args']['use_distributed_optimizer'] and "optimizer" in preprocessed_common_state_dict ): + def reorder_inner_param_groups(optimizer_state_dict): # When distributed optimizer loading, source param groups will be reordered, # so we reorder the param groups here to prevent warning. @@ -1250,8 +1267,8 @@ def pretrain( if args.fine_grained_activation_offloading: from megatron.core.pipeline_parallel.utils import set_ideal_affinity_for_current_gpu - set_ideal_affinity_for_current_gpu() + set_ideal_affinity_for_current_gpu() if cfg_container.logger.log_progress: append_to_progress_log(args.save, "Starting job") @@ -1271,7 +1288,9 @@ def pretrain( # Initialize program_start_global with a fallback value in case set_startup_timestamps() wasn't called program_start_global = _TRAIN_START_TIME if _STARTUP_TIMESTAMPS['program_start'] is not None: - program_start_global = torch.tensor([_STARTUP_TIMESTAMPS['program_start']], dtype=torch.double, device='cuda') + program_start_global = torch.tensor( + [_STARTUP_TIMESTAMPS['program_start']], dtype=torch.double, device='cuda' + ) torch.distributed.all_reduce(program_start_global, op=torch.distributed.ReduceOp.MIN) program_start_global = program_start_global.item() set_startup_timestamps(program_start=program_start_global) @@ -1291,26 +1310,41 @@ def pretrain( # Print basic megatron init time (using global min start) # NOTE(asolergi-nv): This is not entirely accurate, but we keep it for backwards compatibility. print_rank_0( - 'time to initialize megatron (seconds): {:.3f}'.format(megatron_init_end - _LEGACY_TRAIN_START_TIME) + 'time to initialize megatron (seconds): {:.3f}'.format( + megatron_init_end - _LEGACY_TRAIN_START_TIME + ) ) # Note, not entirely accurate as rank 0 might not be the first or last to hit these timestamps - print_datetime('after in-process setup and before initialize_megatron', timestamp_after_inprocess_setup) - print_datetime('after in-job setup and before initialize_megatron', timestamp_after_in_job_setup) + print_datetime( + 'after in-process setup and before initialize_megatron', timestamp_after_inprocess_setup + ) + print_datetime( + 'after in-job setup and before initialize_megatron', timestamp_after_in_job_setup + ) if program_start is not None and main_entry is not None and pretrain_entry is not None: # Inject startup deltas into timers startup_timers = { - 'startup-program-entry-spread': program_start - program_start_global, # Local program start timestamp vs the global earliest program start timestamp - 'startup-library-setup': main_entry - program_start, # Local library imports - 'startup-program-setup': pretrain_entry - main_entry, # Local __main__ entry to pretrain entry - 'startup-in-process-setup': timestamp_after_inprocess_setup - pretrain_entry, # Local in-process setup - 'startup-in-job-setup': timestamp_after_in_job_setup - timestamp_after_inprocess_setup, # Local in-job setup - 'startup-initialize-megatron': timestamp_after_initialize_megatron - timestamp_after_in_job_setup, # Local initialize megatron - 'startup-set-jit-fusion-options': timestamp_after_set_jit_fusion_options - timestamp_after_initialize_megatron, # Local set JIT fusion options - 'all-reduce-start-timestamps-tensor': megatron_init_end - timestamp_after_set_jit_fusion_options, # 2x All-reduce, first collective call - 'startup-megatron-init-local': megatron_init_end - pretrain_entry, # Local megatron init - 'startup-megatron-init-global': megatron_init_end - program_start_global, # Local megatron init vs the global earliest program start timestamp + 'startup-program-entry-spread': program_start + - program_start_global, # Local program start timestamp vs the global earliest program start timestamp + 'startup-library-setup': main_entry - program_start, # Local library imports + 'startup-program-setup': pretrain_entry + - main_entry, # Local __main__ entry to pretrain entry + 'startup-in-process-setup': timestamp_after_inprocess_setup + - pretrain_entry, # Local in-process setup + 'startup-in-job-setup': timestamp_after_in_job_setup + - timestamp_after_inprocess_setup, # Local in-job setup + 'startup-initialize-megatron': timestamp_after_initialize_megatron + - timestamp_after_in_job_setup, # Local initialize megatron + 'startup-set-jit-fusion-options': timestamp_after_set_jit_fusion_options + - timestamp_after_initialize_megatron, # Local set JIT fusion options + 'all-reduce-start-timestamps-tensor': megatron_init_end + - timestamp_after_set_jit_fusion_options, # 2x All-reduce, first collective call + 'startup-megatron-init-local': megatron_init_end + - pretrain_entry, # Local megatron init + 'startup-megatron-init-global': megatron_init_end + - program_start_global, # Local megatron init vs the global earliest program start timestamp } for name, delta in startup_timers.items(): timers(name, log_level=0).set_elapsed(delta) @@ -1353,7 +1387,8 @@ def pretrain( if cfg_container.checkpoint.replication: repl_strategy = CliqueReplicationStrategy.from_replication_params( - cfg_container.checkpoint.replication_jump, cfg_container.checkpoint.replication_factor + cfg_container.checkpoint.replication_jump, + cfg_container.checkpoint.replication_factor, ) else: repl_strategy = None @@ -1385,7 +1420,7 @@ def pretrain( or args.rl_inference_expert_model_parallel_size is not None or args.rl_inference_expert_tensor_model_parallel_size is not None ): - from megatron.rl.parallel_utils import build_inference_pg_collection + from megatron.core.inference.shards import build_inference_pg_collection print_rank_0( "Building separate RL inference model with custom parallelism: " @@ -1406,7 +1441,9 @@ def pretrain( # Build an isolated inference config so training config remains unchanged inference_config = copy.deepcopy(model_cfg) if args.rl_inference_tensor_model_parallel_size is not None: - inference_config.tensor_model_parallel_size = args.rl_inference_tensor_model_parallel_size + inference_config.tensor_model_parallel_size = ( + args.rl_inference_tensor_model_parallel_size + ) if args.rl_inference_pipeline_model_parallel_size is not None: inference_config.pipeline_model_parallel_size = ( args.rl_inference_pipeline_model_parallel_size @@ -1470,11 +1507,15 @@ def pretrain( valid_data_iterator = [] test_data_iterator = [] for vp_stage in range(len(model)): - dataset_provider_parameters = inspect.signature(train_valid_test_dataset_provider).parameters - assert "vp_stage" in dataset_provider_parameters, \ - "vp_stage must be a kwarg in train_valid_test_dataset_provider when using virtual pipeline parallelism" - vp_stage_train_valid_test_dataset_provider = \ - functools.partial(train_valid_test_dataset_provider, vp_stage=vp_stage) + dataset_provider_parameters = inspect.signature( + train_valid_test_dataset_provider + ).parameters + assert ( + "vp_stage" in dataset_provider_parameters + ), "vp_stage must be a kwarg in train_valid_test_dataset_provider when using virtual pipeline parallelism" + vp_stage_train_valid_test_dataset_provider = functools.partial( + train_valid_test_dataset_provider, vp_stage=vp_stage + ) if getattr(train_valid_test_dataset_provider, 'is_distributed', False): vp_stage_train_valid_test_dataset_provider.is_distributed = True iterators = build_train_valid_test_data_iterators( @@ -1513,6 +1554,30 @@ def pretrain( # Add job name to the wandb config to make it easier to run more singleton dependency jobs. wandb_writer.config.update({'slurm_job_name': os.getenv("SLURM_JOB_NAME", "N/A")}) + # Initialize RL profiler if enabled + if args.rl_profile: + # Determine output directory: use rl_profile_dir, or save/profiles, or ./profiles + profile_dir = getattr(args, 'rl_profile_dir', None) + if profile_dir is None: + if args.save: + profile_dir = os.path.join(args.save, 'profiles') + else: + profile_dir = './profiles' + + # Generate run ID from job name or timestamp + run_id = os.getenv("SLURM_JOB_ID", None) + if run_id is None: + run_id = datetime.now().strftime("%Y%m%d_%H%M%S") + + initialize_rl_profiler( + output_dir=profile_dir, + run_id=run_id, + enabled=True, + log_to_wandb=(wandb_writer is not None), + log_to_tensorboard=(get_tensorboard_writer() is not None), + ) + print_rank_0(f'[RLProfiler] Profiling enabled, output: {profile_dir}') + if not cfg_container.validation.skip_train or args.perform_rl_step: if cfg_container.validation.skip_train: print_rank_0('RL inference-only mode (--skip-train --perform-rl-step) ...') @@ -1538,7 +1603,12 @@ def pretrain( print_datetime('after training is done') - if not cfg_container.validation.skip_train and cfg_container.checkpoint.save and iteration != 0 and iteration % cfg_container.checkpoint.save_interval != 0: + if ( + not cfg_container.validation.skip_train + and cfg_container.checkpoint.save + and iteration != 0 + and iteration % cfg_container.checkpoint.save_interval != 0 + ): save_checkpoint_and_time( iteration, model, @@ -1546,7 +1616,7 @@ def pretrain( opt_param_scheduler, num_floating_point_operations_so_far, checkpointing_context, - train_data_iterator=train_data_iterator + train_data_iterator=train_data_iterator, ) one_logger and one_logger.log_metrics( @@ -1581,11 +1651,16 @@ def pretrain( ) else: evaluate_and_print_results( - prefix, forward_step_func, - valid_data_iterator, model, - iteration, process_non_loss_data_func, model_cfg, - verbose=True, write_to_tensorboard=not cfg_container.validation.skip_train, - non_loss_data_func=non_loss_data_func + prefix, + forward_step_func, + valid_data_iterator, + model, + iteration, + process_non_loss_data_func, + model_cfg, + verbose=True, + write_to_tensorboard=not cfg_container.validation.skip_train, + non_loss_data_func=non_loss_data_func, ) if args.do_test: @@ -1646,6 +1721,24 @@ def update_train_iters(args): print_rank_0(f'setting training iterations to {args.train_iters}') +def resolve_ddp_bucket_size(ddp_config, dp_cp_group, overlap_grad_reduce, num_parameters): + """Resolve the absolute DDP grad-bucket size. + + With ``num_buckets`` set: ``num_parameters // num_buckets``; else if no explicit + ``bucket_size``: ``max(40_000_000, 1_000_000 * dp_cp_size)``; if ``overlap_grad_reduce`` + is False: ``None`` (overrides the above). + """ + if ddp_config.num_buckets is not None: + bucket_size = num_parameters // ddp_config.num_buckets + elif ddp_config.bucket_size is None: + bucket_size = max(40000000, 1000000 * get_pg_size(dp_cp_group)) + else: + bucket_size = ddp_config.bucket_size + if not overlap_grad_reduce: + bucket_size = None + return bucket_size + + def wrap_model_chunks_with_ddp( model_chunks, config, @@ -1691,8 +1784,9 @@ def wrap_model_chunks_with_ddp( to DDP. ``False`` keeps LayerWise on its legacy sync path. DP: The DDP class to construct (``DistributedDataParallel`` or an FSDP variant). - pg_collection: Optional :class:`ProcessGroupCollection`. Forwarded to - FSDP-style DPs only. + pg_collection: Optional :class:`ProcessGroupCollection`. When provided, + forwarded to both standard DDP and FSDP variants, and used to source + DP world sizes for layout computation. bucket_sizes: Optional per-chunk bucket size override; defaults to ``[ddp_config.bucket_size] * len(model_chunks)``. disable_bucketing_per_chunk: Optional per-chunk disable_bucketing flag; @@ -1723,10 +1817,17 @@ def wrap_model_chunks_with_ddp( else: compute_layout = None if compute_layout is not None: - data_parallel_world_size = mpu.get_data_parallel_world_size( - with_context_parallel=True + layout_pgs = ( + pg_collection + if pg_collection is not None + else ProcessGroupCollection.use_mpu_process_groups() + ) + assert layout_pgs.dp_cp is not None, ( + "wrap_model_chunks_with_ddp requires a dp_cp process group to size " + "the distributed-optimizer parameter layout" ) - expert_data_parallel_world_size = mpu.get_expert_data_parallel_world_size() + data_parallel_world_size = get_pg_size(layout_pgs.dp_cp) + expert_data_parallel_world_size = get_pg_size(getattr(layout_pgs, "expt_dp", None)) for i, (chunk, bucket_size) in enumerate(zip(model_chunks, bucket_sizes)): all_params = [p for p in chunk.parameters() if p.requires_grad] per_chunk_layouts[i] = compute_layout( @@ -1743,7 +1844,8 @@ def wrap_model_chunks_with_ddp( model_chunks, per_chunk_layouts, disable_bucketing_per_chunk ): chunk_kwargs = {} - if pg_collection is not None and DP is not DDP: + # TorchFSDP takes process_group, not pg_collection. + if pg_collection is not None and not (HAVE_FSDP2 and DP is torch_FSDP): chunk_kwargs["pg_collection"] = pg_collection if layout is not None: chunk_kwargs["full_param_layout"] = layout @@ -1759,7 +1861,13 @@ def wrap_model_chunks_with_ddp( return wrapped -def get_model(model_provider_func, model_type=ModelType.encoder_or_decoder, wrap_with_ddp=True, config=None, pg_collection=None): +def get_model( + model_provider_func, + model_type=ModelType.encoder_or_decoder, + wrap_with_ddp=True, + config=None, + pg_collection=None, +): """Build the model.""" args = get_args() args.model_type = model_type @@ -1767,10 +1875,13 @@ def get_model(model_provider_func, model_type=ModelType.encoder_or_decoder, wrap pg_collection = ProcessGroupCollection.use_mpu_process_groups() if args.create_all_gather_group: - timeout = timedelta(minutes=args.distributed_timeout_minutes) if args.distributed_timeout_minutes else None + timeout = ( + timedelta(minutes=args.distributed_timeout_minutes) + if args.distributed_timeout_minutes + else None + ) dp_cp_ag, expt_dp_ag = create_all_gather_groups( - for_expert_parallelism=(args.expert_model_parallel_size > 1), - timeout=timeout, + for_expert_parallelism=(args.expert_model_parallel_size > 1), timeout=timeout ) pg_collection.dp_cp_ag = dp_cp_ag pg_collection.expt_dp_ag = expt_dp_ag @@ -1829,7 +1940,6 @@ def build_model(): model.model_type = model_type return model - if args.init_model_with_meta_device: with torch.device('meta'): model = build_model() @@ -1839,6 +1949,15 @@ def build_model(): if not isinstance(model, list): model = [model] + # For rare operations like post-training logits saving + if args.freeze_all_layers: + for model_module in model: + model_module.requires_grad_(False) + # Additionally freeze expert biases of routers + for module in model_module.modules(): + if hasattr(module, "frozen_expert_bias"): + module.frozen_expert_bias = True + # Set tensor model parallel attributes if not set. # Only parameters that are already tensor model parallel have these # attributes set for them. We should make sure the default attributes @@ -1855,9 +1974,7 @@ def build_model(): print( ' > number of parameters on (tensor, pipeline) ' 'model parallel rank ({}, {}): {}'.format( - get_pg_rank(pg_collection.tp), - get_pg_rank(pg_collection.pp), - num_parameters, + get_pg_rank(pg_collection.tp), get_pg_rank(pg_collection.pp), num_parameters ), flush=True, ) @@ -1879,7 +1996,10 @@ def build_model(): # Materialize tensors on meta device (GPU allocation) if not using FSDP2 and not using Megatron FSDP. if args.init_model_with_meta_device and not args.use_torch_fsdp2 and not args.use_megatron_fsdp: - model = [to_empty_if_meta_device(model_module, device=torch.device("cuda")) for model_module in model] + model = [ + to_empty_if_meta_device(model_module, device=torch.device("cuda")) + for model_module in model + ] # Before TE2.x: The model_module.bfloat16()/model_module.half() above will call the inplace # copy of TE's Float8Tensor, which will write an unwanted value (amax calculated @@ -1901,21 +2021,10 @@ def build_model(): ddp_config = get_megatron_ddp_config(args) if not getattr(args, "use_torch_fsdp2", False): - if ddp_config.num_buckets is not None: - ddp_config.bucket_size = num_parameters // ddp_config.num_buckets - # In the Megatron FSDP and DDP use path, we need to initialize the bucket size. - # If bucket_size is not provided as an input, use sane default. - # If using very large dp_sizes, make buckets larger to ensure that chunks used in NCCL - # ring-reduce implementations are large enough to remain bandwidth-bound rather than - # latency-bound. - if ddp_config.bucket_size is None: - ddp_config.bucket_size = max( - 40000000, 1000000 * get_pg_size(pg_collection.dp_cp) - ) - # Set bucket_size to infinity if overlap_grad_reduce is False. - if not ddp_config.overlap_grad_reduce: - ddp_config.bucket_size = None + ddp_config.bucket_size = resolve_ddp_bucket_size( + ddp_config, pg_collection.dp_cp, ddp_config.overlap_grad_reduce, num_parameters + ) # Compute per-chunk bucket sizes / disable_bucketing flags. Bucketing is # disabled for non-first chunks, when overlap_param_gather_with_optimizer_step @@ -1944,9 +2053,7 @@ def build_model(): use_layer_wise_distributed_optimizer=getattr( args, 'use_layer_wise_distributed_optimizer', False ), - use_layer_wise_param_layout=getattr( - args, 'use_layer_wise_param_layout', True - ), + use_layer_wise_param_layout=getattr(args, 'use_layer_wise_param_layout', True), DP=DP, pg_collection=pg_collection if args.use_megatron_fsdp else None, bucket_sizes=per_chunk_bucket_sizes, @@ -2035,6 +2142,7 @@ def get_megatron_optimizer_config(args: Any) -> OptimizerConfig: return config, config_overrides + def get_megatron_ddp_config(args: argparse.Namespace) -> DistributedDataParallelConfig: """Return an MCore DDPConfig from the argparse arguments.""" @@ -2052,9 +2160,12 @@ def get_megatron_ddp_config(args: argparse.Namespace) -> DistributedDataParallel kwargs["num_buckets"] = args.ddp_num_buckets kwargs["bucket_size"] = args.ddp_bucket_size kwargs["pad_buckets_for_high_nccl_busbw"] = args.ddp_pad_buckets_for_high_nccl_busbw - kwargs["reduce_scatter_with_fp32_accumulation"] = args.ddp_reduce_scatter_with_fp32_accumulation - kwargs["param_name_patterns_for_fp32_local_accumulation"] = \ - tuple(args.ddp_param_name_patterns_for_fp32_local_accumulation) + kwargs["reduce_scatter_with_fp32_accumulation"] = ( + args.ddp_reduce_scatter_with_fp32_accumulation + ) + kwargs["param_name_patterns_for_fp32_local_accumulation"] = tuple( + args.ddp_param_name_patterns_for_fp32_local_accumulation + ) kwargs["average_in_collective"] = args.ddp_average_in_collective # Megatron-FSDP arguments. kwargs["megatron_fsdp_main_params_dtype"] = args.megatron_fsdp_main_params_dtype @@ -2076,9 +2187,7 @@ def get_megatron_ddp_config(args: argparse.Namespace) -> DistributedDataParallel def setup_model_and_optimizer( - model_provider_func, - model_type, - checkpointing_context=None, + model_provider_func, model_type, checkpointing_context=None, pg_collection=None ): """Setup model and optimizer.""" args = get_args() @@ -2091,10 +2200,32 @@ def setup_model_and_optimizer( has_rl_optimizer = args.perform_rl_step and not args.no_load_optim skip_optimizer = not (has_normal_optimizer or has_rl_optimizer) wrap_with_ddp = not skip_optimizer - model = get_model(model_provider_func, model_type, wrap_with_ddp=wrap_with_ddp) + model = get_model( + model_provider_func, model_type, wrap_with_ddp=wrap_with_ddp, pg_collection=pg_collection + ) unwrapped_model = unwrap_model(model) - one_logger and one_logger.log_metrics({"app_build_optimzer_start_time": one_logger_utils.get_timestamp_in_ms()}) + if args.logits_save_dir is not None: + from megatron.training.distillation import LogitsSaverHooks + + logits_saver = LogitsSaverHooks( + save_dir=args.logits_save_dir, + k=args.logits_save_top_k, + p=args.logits_save_top_p, + min_k=args.logits_save_top_p_min_k, + save_dtype=args.logits_save_dtype, + ) + logits_saver.attach_hooks(unwrapped_model[-1]) + + if args.logits_load_dir is not None: + from megatron.training.distillation import StudentLogitsCapture + + student_logits_capture = StudentLogitsCapture() + student_logits_capture.attach_hooks(unwrapped_model[-1]) + + one_logger and one_logger.log_metrics( + {"app_build_optimzer_start_time": one_logger_utils.get_timestamp_in_ms()} + ) if skip_optimizer: optimizer, opt_param_scheduler = None, None # In RL inference-only mode, train_iters must still be set despite having no optimizer. @@ -2125,7 +2256,9 @@ def setup_model_and_optimizer( ) opt_param_scheduler = get_optimizer_param_scheduler(optimizer) - one_logger and one_logger.log_metrics({"app_build_optimzer_finish_time": one_logger_utils.get_timestamp_in_ms()}) + one_logger and one_logger.log_metrics( + {"app_build_optimzer_finish_time": one_logger_utils.get_timestamp_in_ms()} + ) if args.moe_use_upcycling: torch.distributed.barrier() @@ -2260,8 +2393,15 @@ def dummy_train_step(data_iterator): is_hybrid_cp = args.dynamic_context_parallel BATCH_KEYS = [ - "tokens", "labels", "loss_mask", "position_ids", "attention_mask", - "cu_seqlens", "cu_seqlens_padded", "max_seqlen", "local_cp_size", + "tokens", + "labels", + "loss_mask", + "position_ids", + "attention_mask", + "cu_seqlens", + "cu_seqlens_padded", + "max_seqlen", + "local_cp_size", "hybrid_cp_group", ] @@ -2274,7 +2414,11 @@ def dummy_train_step(data_iterator): if tp_rank == 0: batch = next(data_iterator) for key in BATCH_KEYS: - batch[key] = batch[key].cuda(non_blocking=True) if key in batch and batch[key] is not None else None + batch[key] = ( + batch[key].cuda(non_blocking=True) + if key in batch and batch[key] is not None + else None + ) batch = get_batch_on_this_tp_rank( batch, broadcast_src_rank=mpu.get_tensor_model_parallel_src_rank(), @@ -2299,27 +2443,50 @@ def dummy_train_step(data_iterator): ) -def train_step(forward_step_func, data_iterator, model, optimizer, opt_param_scheduler, config, forward_backward_func, iteration=None, pg_collection: Optional[ProcessGroupCollection] = None): +def train_step( + forward_step_func, + data_iterator, + model, + optimizer, + opt_param_scheduler, + config, + forward_backward_func, + iteration=None, + pg_collection: Optional[ProcessGroupCollection] = None, + p2p_communicator: Optional[P2PCommunicator] = None, + schedule_pg_collection: Optional[MultiModuleProcessGroupCollection] = None, +): """Single training step. pg_collection: optional per-module :class:`ProcessGroupCollection`; None uses the mpu globals, otherwise it must define mp, pp, and dp_cp. + p2p_communicator: optional communicator forwarded to the schedule for cross-grid P2P; None + preserves the default behavior. + schedule_pg_collection: optional per-module groups forwarded to the schedule for the + cross-grid case; None preserves the default behavior. """ args = get_args() timers = get_timers() num_microbatches = get_num_microbatches() rerun_state_machine = get_rerun_state_machine() - save_params_in_this_iteration = (args.save_params_interval is not None and - (iteration + 1) % args.save_params_interval == 0) - save_activations_in_this_iteration = (args.save_activations_interval is not None and - (iteration + 1) % args.save_activations_interval == 0) - save_tpe_in_this_iteration = (args.save_tokens_per_expert_interval is not None and - (iteration + 1) % args.save_tokens_per_expert_interval == 0) - save_wgrads_in_this_iteration = (args.save_wgrads_interval is not None and - (iteration + 1) % args.save_wgrads_interval == 0) - save_dgrads_in_this_iteration = (args.save_dgrads_interval is not None and - (iteration + 1) % args.save_dgrads_interval == 0) + save_params_in_this_iteration = ( + args.save_params_interval is not None and (iteration + 1) % args.save_params_interval == 0 + ) + save_activations_in_this_iteration = ( + args.save_activations_interval is not None + and (iteration + 1) % args.save_activations_interval == 0 + ) + save_tpe_in_this_iteration = ( + args.save_tokens_per_expert_interval is not None + and (iteration + 1) % args.save_tokens_per_expert_interval == 0 + ) + save_wgrads_in_this_iteration = ( + args.save_wgrads_interval is not None and (iteration + 1) % args.save_wgrads_interval == 0 + ) + save_dgrads_in_this_iteration = ( + args.save_dgrads_interval is not None and (iteration + 1) % args.save_dgrads_interval == 0 + ) while rerun_state_machine.should_run_forward_backward(data_iterator): # Set grad to zero. for model_chunk in model: @@ -2369,7 +2536,7 @@ def train_step(forward_step_func, data_iterator, model, optimizer, opt_param_sch enable_tokens_per_expert_logging(model, args.save) if save_dgrads_in_this_iteration: enable_dgrad_logging(model, args.save) - if config.sequence_packing_scheduler is not None: + if getattr(config, 'sequence_packing_scheduler', None) is not None: # Dynamic-CP / sequence packing (dev feature): produce the per-step packed # iterator and recompute num_microbatches. wrap_data_iterator returns a # RerunDataIterator-compatible iterator so the rerun-state-machine validation @@ -2395,6 +2562,8 @@ def train_step(forward_step_func, data_iterator, model, optimizer, opt_param_sch forward_only=False, adjust_tensor_shapes_fn=adjust_tensor_shapes_fn, force_all_reduce=save_wgrads_in_this_iteration, + p2p_communicator=p2p_communicator, + pg_collection=schedule_pg_collection, ) if save_activations_in_this_iteration: save_activations(iteration + 1) @@ -2473,9 +2642,9 @@ def _save_state_dict(attr_name, label): if pg_collection is None: pg_collection = ProcessGroupCollection.use_mpu_process_groups() for _required in ("mp", "pp", "dp_cp"): - assert getattr(pg_collection, _required, None) is not None, ( - f"pg_collection passed to train_step must define {_required}" - ) + assert ( + getattr(pg_collection, _required, None) is not None + ), f"pg_collection passed to train_step must define {_required}" mp_group = pg_collection.mp dp_cp_group = pg_collection.dp_cp is_last_stage = is_pp_last_stage(pg_collection.pp) @@ -2610,37 +2779,41 @@ def training_log( # Logging. timers_to_log = [] if args.timing_log_level >= 1: - timers_to_log.extend([ - 'forward-backward', - 'layernorm-grads-all-reduce', - 'embedding-grads-all-reduce', - 'all-grads-sync', - 'params-all-gather', - 'optimizer-copy-to-main-grad', - 'optimizer-unscale-and-check-inf', - 'optimizer-clip-main-grad', - 'optimizer-count-zeros', - 'optimizer-inner-step', - 'optimizer-copy-main-to-model-params', - 'optimizer', - ]) + timers_to_log.extend( + [ + 'forward-backward', + 'layernorm-grads-all-reduce', + 'embedding-grads-all-reduce', + 'all-grads-sync', + 'params-all-gather', + 'optimizer-copy-to-main-grad', + 'optimizer-unscale-and-check-inf', + 'optimizer-clip-main-grad', + 'optimizer-count-zeros', + 'optimizer-inner-step', + 'optimizer-copy-main-to-model-params', + 'optimizer', + ] + ) if args.timing_log_level >= 2: - timers_to_log.extend([ - 'batch-generator', - 'forward-compute', - 'backward-compute', - 'forward-recv', - 'forward-send', - 'backward-recv', - 'backward-send', - 'forward-send-forward-recv', - 'forward-send-backward-recv', - 'backward-send-forward-recv', - 'backward-send-backward-recv', - 'forward-backward-send-forward-backward-recv', - ]) + timers_to_log.extend( + [ + 'batch-generator', + 'forward-compute', + 'backward-compute', + 'forward-recv', + 'forward-send', + 'backward-recv', + 'backward-send', + 'forward-send-forward-recv', + 'forward-send-backward-recv', + 'backward-send-forward-recv', + 'backward-send-backward-recv', + 'forward-backward-send-forward-backward-recv', + ] + ) # Add timers from RL loop if needed. - if getattr(args, 'perform_rl_step', False): + if args.perform_rl_step: timers_to_log.extend(RL_LOGGABLE_TIMER_NAMES) # Calculate batch size. @@ -2653,13 +2826,17 @@ def training_log( # learning rate will be None on ranks without trainable params, so we must gather across mp ranks learning_rate: float | None = reduce_max_stat_across_model_parallel_group(learning_rate) + if learning_rate is None and args.freeze_all_layers: + learning_rate = 0.0 # Tensorboard values. if writer and (iteration % args.tensorboard_log_interval == 0): if wandb_writer: wandb_writer.log({'samples vs steps': args.consumed_train_samples}, iteration) if learning_rate is not None: writer.add_scalar('learning-rate', learning_rate, iteration) - writer.add_scalar('learning-rate vs samples', learning_rate, args.consumed_train_samples) + writer.add_scalar( + 'learning-rate vs samples', learning_rate, args.consumed_train_samples + ) if wandb_writer: wandb_writer.log({'learning-rate': learning_rate}, iteration) if args.skipped_train_samples > 0: @@ -2710,10 +2887,14 @@ def training_log( if wandb_writer: wandb_writer.log({'params-norm': params_norm}, iteration) if args.perform_rl_step: - grpo_collection_iteration = iteration // (args.grpo_iterations * ( ( args.grpo_samples_per_iteration )// args.global_batch_size )) + grpo_collection_iteration = iteration // ( + args.grpo_iterations * ((args.grpo_samples_per_iteration) // args.global_batch_size) + ) writer.add_scalar('grpo_collection_iteration', grpo_collection_iteration, iteration) if wandb_writer: - wandb_writer.log({'grpo_collection_iteration': grpo_collection_iteration}, iteration) + wandb_writer.log( + {'grpo_collection_iteration': grpo_collection_iteration}, iteration + ) if args.log_memory_to_tensorboard: mem_stats = torch.cuda.memory_stats() writer.add_scalar( @@ -2751,6 +2932,7 @@ def training_log( Symbols, get_hybrid_layer_counts, ) + layers = itemgetter(Symbols.MOE)(get_hybrid_layer_counts(args.hybrid_layer_pattern)) else: layers = args.num_layers @@ -2795,7 +2977,9 @@ def training_log( # Dump memory snapshot and print metrics to stdout. if iteration % args.log_interval == 0 or is_first_iteration: - if args.record_memory_history and (is_last_rank() or torch.distributed.get_backend() == 'fake'): + if args.record_memory_history and ( + is_last_rank() or torch.distributed.get_backend() == 'fake' + ): snapshot = torch.cuda.memory._snapshot() from pickle import dump @@ -2810,9 +2994,7 @@ def training_log( batch_size, seqlen_squared_sum_in_batch=seqlen_squared_sum_in_batch, total_real_tokens_in_batch=total_real_tokens_in_batch, - ) / ( - elapsed_time_per_iteration * 10**12 * args.world_size - ) + ) / (elapsed_time_per_iteration * 10**12 * args.world_size) one_logger_utils.track_e2e_metrics(args.log_throughput, throughput) @@ -2882,7 +3064,7 @@ def training_log( # RL token throughput metrics. if args.perform_rl_step: log_string += rl_utils.log_rl_throughput_metrics( - args, batch_size, elapsed_time_per_iteration, iteration, wandb_writer, + args, batch_size, elapsed_time_per_iteration, iteration, wandb_writer ) if should_reset: @@ -2902,13 +3084,33 @@ def training_log( if iteration > (loaded_iteration + 1): # Make sure the memory after the second iteration is reported to include optimizer state memory. report_memory_flag = False - if args.log_memory_interval is not None and iteration % args.log_memory_interval == 0 and \ - not reported_memory_in_this_iteration: + if ( + args.log_memory_interval is not None + and iteration % args.log_memory_interval == 0 + and not reported_memory_in_this_iteration + ): report_memory(f'(after {iteration} iterations)') + # Log RL profiling data if enabled (must be before timers.log which resets timers). + # Token throughput metrics are read from RLRuntimeState automatically. + if args.rl_profile: + log_iteration_profile( + iteration=iteration, + timers=timers, + elapsed_time_ms=elapsed_time_per_iteration * 1000.0, + throughput_tflops=throughput if args.log_throughput else None, + global_batch_size=batch_size, + wandb_writer=wandb_writer, + tb_writer=writer, + ) + # Write timers to wandb, don't reset the counts. if args.log_timers_to_tensorboard: - timers.write(timers_to_log, writer, iteration, normalizer=args.log_interval, reset=False) - timers.write(timers_to_log, wandb_writer, iteration, normalizer=args.log_interval, reset=False) + timers.write( + timers_to_log, writer, iteration, normalizer=args.log_interval, reset=False + ) + timers.write( + timers_to_log, wandb_writer, iteration, normalizer=args.log_interval, reset=False + ) # Log timers to stdout timers.log(timers_to_log, normalizer=args.log_interval, reset=should_reset) @@ -2945,7 +3147,7 @@ def compute_throughputs_and_append_to_progress_log(iteration, num_floating_point f"Job throughput: {job_throughput:.1f} TFLOP/s/GPU\t" f"Cumulative throughput: {cumulative_throughput:.1f} TFLOP/s/GPU\t" f"Floating-point operations: {num_floating_point_operations_so_far:.2e}\t" - f"Tokens (in billions): {tokens_so_far / 10**9:.2f}" + f"Tokens (in billions): {tokens_so_far / 10**9:.2f}", ) @@ -2966,10 +3168,12 @@ def force_param_sync(model_chunks: list[DDP]) -> None: assert isinstance(model_chunk, DDP) model_chunk.start_param_sync(force_sync=True) + # Only report memory for first 3 checkpoint saves. num_checkpoints_memory_reported = 0 MAX_NUM_CHECKPOINTS_MEMORY_REPORTED = 3 + def save_checkpoint_and_time( iteration, model, @@ -2998,6 +3202,13 @@ def save_checkpoint_and_time( # Log E2E metrics before save-checkpoint one_logger_utils.track_e2e_metrics() + # Free overlap param-gather buffers and release cached GPU memory so + # that the async checkpoint worker process has enough GPU headroom for + # D2H tensor transfers. + for model_chunk in model: + if hasattr(model_chunk, 'free_overlap_buffers'): + model_chunk.free_overlap_buffers() + torch.cuda.empty_cache() global num_checkpoints_memory_reported, MAX_NUM_CHECKPOINTS_MEMORY_REPORTED should_report_memory = num_checkpoints_memory_reported < MAX_NUM_CHECKPOINTS_MEMORY_REPORTED @@ -3054,9 +3265,7 @@ def _run_gpu_sniff_test(tag): from megatron.core.process_groups_config import ProcessGroupCollection from megatron.training.gpu_sniff_test import run_gpu_sniff_test - pg_collection = ProcessGroupCollection.use_mpu_process_groups( - required_pgs=['ep', 'dp', 'tp'], - ) + pg_collection = ProcessGroupCollection.use_mpu_process_groups(required_pgs=['ep', 'dp', 'tp']) print_datetime(f'running GPU sniff test ({tag})') timers = get_timers() timers('gpu-sniff-test', log_level=0).start(barrier=True) @@ -3073,7 +3282,7 @@ def post_training_step_callbacks( iteration, prof, num_floating_point_operations_since_last_log_event, - nsys_nvtx_context = None, + nsys_nvtx_context=None, ): """Run all post-training-step functions (e.g., FT heartbeats, GC).""" args = get_args() @@ -3111,8 +3320,7 @@ def post_training_step_callbacks( if ( args.profile and iteration == args.profile_step_end - and (len(args.profile_ranks) == 0 or - torch.distributed.get_rank() in args.profile_ranks) + and (len(args.profile_ranks) == 0 or torch.distributed.get_rank() in args.profile_ranks) ): # Disable NVTX range when profiling ends. if args.nvtx_ranges: @@ -3128,10 +3336,7 @@ def post_training_step_callbacks( nsys_nvtx_context.__exit__(None, None, None) # GPU sniff test. - if ( - args.gpu_sniff_test_interval is not None - and iteration % args.gpu_sniff_test_interval == 0 - ): + if args.gpu_sniff_test_interval is not None and iteration % args.gpu_sniff_test_interval == 0: _run_gpu_sniff_test(f'iteration {iteration:7d}') # Manual garbage collection. @@ -3231,12 +3436,8 @@ def checkpoint_and_decide_exit( return True # Exit based on iterations. - if ( - args.exit_interval - and iteration % args.exit_interval == 0 - ) or ( - args.phase_transition_iterations - and iteration in args.phase_transition_iterations + if (args.exit_interval and iteration % args.exit_interval == 0) or ( + args.phase_transition_iterations and iteration in args.phase_transition_iterations ): if args.save and not saved_checkpoint: save_checkpoint_and_time( @@ -3267,8 +3468,16 @@ def train( checkpointing_context, non_loss_data_func, inference_model=None, + p2p_communicator: Optional[P2PCommunicator] = None, + schedule_pg_collection: Optional[MultiModuleProcessGroupCollection] = None, ): - """Training function: run train_step desired number of times, run validation, checkpoint.""" + """Training function: run train_step desired number of times, run validation, checkpoint. + + p2p_communicator: optional communicator forwarded to the schedule for cross-grid P2P; None + preserves the default behavior. + schedule_pg_collection: optional per-module groups forwarded to the schedule for the + cross-grid case; None preserves the default behavior. + """ args = get_args() timers = get_timers() fault_injector_kwargs = {} @@ -3314,29 +3523,31 @@ def train( args.load = None args.finetune = True load_checkpoint( - model, - None, # Don't load optimizer state - None, # Don't load scheduler state - checkpointing_context=checkpointing_context, - skip_load_to_model_and_opt=HAVE_FSDP2 - and getattr(args, "use_torch_fsdp2", False) - and args.ckpt_format == "torch_dist", - ) - ref_state_dict = {k: (v.cpu() if v is not None else v) for k, v in model[0].state_dict().items()} + model, + None, # Don't load optimizer state + None, # Don't load scheduler state + checkpointing_context=checkpointing_context, + skip_load_to_model_and_opt=HAVE_FSDP2 + and getattr(args, "use_torch_fsdp2", False) + and args.ckpt_format == "torch_dist", + ) + ref_state_dict = { + k: (v.cpu() if v is not None else v) for k, v in model[0].state_dict().items() + } # Reload RL training checkpoint weights args.load = load args.finetune = finetune print_rank_0("> Reloading RL training checkpoint...") load_checkpoint( - model, - None, - None, - checkpointing_context=checkpointing_context, - skip_load_to_model_and_opt=HAVE_FSDP2 - and getattr(args, "use_torch_fsdp2", False) - and args.ckpt_format == "torch_dist", - ) + model, + None, + None, + checkpointing_context=checkpointing_context, + skip_load_to_model_and_opt=HAVE_FSDP2 + and getattr(args, "use_torch_fsdp2", False) + and args.ckpt_format == "torch_dist", + ) args.no_load_optim = no_load_optim @@ -3394,8 +3605,10 @@ def train( # Make sure rerun_state_machine has the right iteration loaded from checkpoint. rerun_state_machine = get_rerun_state_machine() if rerun_state_machine.current_iteration != iteration: - print_rank_0(f"Overwriting rerun_state_machine.current_iteration from " - f"{rerun_state_machine.current_iteration} to {iteration}...") + print_rank_0( + f"Overwriting rerun_state_machine.current_iteration from " + f"{rerun_state_machine.current_iteration} to {iteration}..." + ) rerun_state_machine.current_iteration = iteration # Track E2E metrics at the start of training. @@ -3490,11 +3703,7 @@ def train( if args.moe_expert_rank_capacity_factor is not None: copy_main_params = args.reuse_grad_buf_for_mxfp8_param_ag and args.overlap_param_gather forward_backward_func = PagedStashRunner( - config, - copy_main_params, - model, - optimizer, - forward_backward_func, + config, copy_main_params, model, optimizer, forward_backward_func ) if args.optimizer_cuda_graph: optimizer.step = OptimizerCudaGraphWrapper( @@ -3526,23 +3735,26 @@ def get_e2e_base_metrics(): one_logger.store_set('get_e2e_base_metrics', get_e2e_base_metrics) prof = None - nsys_nvtx_context = None # reference to context for nsys profiling, so it can be cleaned up + nsys_nvtx_context = None # reference to context for nsys profiling, so it can be cleaned up if ( args.profile - and (len(args.profile_ranks) == 0 or - torch.distributed.get_rank() in args.profile_ranks) + and (len(args.profile_ranks) == 0 or torch.distributed.get_rank() in args.profile_ranks) and args.use_pytorch_profiler ): if args.pytorch_profiler_collect_chakra: et_dir = Path(f"{args.tensorboard_dir}/../chakra") et_dir.mkdir(parents=True, exist_ok=True) - et = torch.profiler.ExecutionTraceObserver().register_callback(f"{et_dir}/rank-{torch.distributed.get_rank()}.json.gz") + et = torch.profiler.ExecutionTraceObserver().register_callback( + f"{et_dir}/rank-{torch.distributed.get_rank()}.json.gz" + ) else: et = None + def trace_handler(p): profile_dir = Path(f"{args.tensorboard_dir}/../torch_profile") profile_dir.mkdir(parents=True, exist_ok=True) p.export_chrome_trace(f"{profile_dir}/rank-{torch.distributed.get_rank()}.json.gz") + prof = torch.profiler.profile( schedule=torch.profiler.schedule( wait=max(args.profile_step_start - 1, 0), @@ -3590,9 +3802,9 @@ def trace_handler(p): # Run training iterations till done. buffered_rollouts = None while iteration < args.train_iters: - if (args.profile - and (len(args.profile_ranks) == 0 or - torch.distributed.get_rank() in args.profile_ranks)): + if args.profile and ( + len(args.profile_ranks) == 0 or torch.distributed.get_rank() in args.profile_ranks + ): # Enable NVTX range when profiling starts and nvtx_ranges is set. if iteration == args.profile_step_start and args.nvtx_ranges: configure_nvtx_profiling(True) @@ -3600,7 +3812,9 @@ def trace_handler(p): prof.step() elif iteration == args.profile_step_start: torch.cuda.check_error(torch.cuda.cudart().cudaProfilerStart()) - nsys_nvtx_context = torch.autograd.profiler.emit_nvtx(record_shapes=args.record_shapes) + nsys_nvtx_context = torch.autograd.profiler.emit_nvtx( + record_shapes=args.record_shapes + ) nsys_nvtx_context.__enter__() ft_integration.on_checkpointing_start() @@ -3609,7 +3823,10 @@ def trace_handler(p): # Update the timeout for all process groups after initialization # We update the timeout after the first successful iteration, # which takes longer than others usually - if args.distributed_timeout_seconds_after_init is not None and iteration == start_iteration+1: + if ( + args.distributed_timeout_seconds_after_init is not None + and iteration == start_iteration + 1 + ): # TODO: some dynamic timeout setting is required # based on the iteration time considering interval-based steps (e.g. eval, checkpoint) # e.g. timeout for normal iterations vs timeout for iterations with checkpoint @@ -3685,7 +3902,11 @@ def trace_handler(p): torch.cuda.empty_cache() with torch.no_grad(): train_data_iterator = rl_utils.get_grpo_data_iterator( - model, inference_model, optimizer, iteration, ref_state_dict, + model, + inference_model, + optimizer, + iteration, + ref_state_dict, grpo_iterations=args.grpo_iterations, grpo_prompts_per_step=args.grpo_prompts_per_step, grpo_group_size=args.grpo_group_size, @@ -3727,7 +3948,17 @@ def trace_handler(p): seqlen_sum_this_global_batch, seqlen_squared_sum_this_global_batch, ) = train_step( - forward_step_func, train_data_iterator, model, optimizer, opt_param_scheduler, config, forward_backward_func, iteration=iteration + forward_step_func, + train_data_iterator, + model, + optimizer, + opt_param_scheduler, + config, + forward_backward_func, + iteration=iteration, + pg_collection=model_pg_collection, + p2p_communicator=p2p_communicator, + schedule_pg_collection=schedule_pg_collection, ) ft_integration.on_training_step_end() if _maybe_raise_workload_exception is not None and iteration != start_iteration: @@ -3735,8 +3966,9 @@ def trace_handler(p): # Fault delay timing can start at the end of iteration N. Self-firing faults # (signals, GIL, GPU) may then manifest in iteration N or N+1 depending on the # configured delay; workload-exception faults manifest on a later poll. - if _maybe_raise_workload_exception is not None and should_setup_fault_injection_at_iteration( - fault_injector_config, iteration + if ( + _maybe_raise_workload_exception is not None + and should_setup_fault_injection_at_iteration(fault_injector_config, iteration) ): setup_fault_injection(fault_injector_config) if should_checkpoint: @@ -3786,7 +4018,7 @@ def trace_handler(p): getattr(args, "fsdp_manual_registration", False) and getattr(args, "nccl_ub", False) and getattr(args, "use_megatron_fsdp", False) - and iteration == start_iteration + 1 + and iteration == start_iteration + 1 ): for model_chunk in model: if isinstance(model_chunk, megatron_FSDP) and getattr( @@ -3823,7 +4055,7 @@ def trace_handler(p): else: assert num_skipped_samples_in_batch == 0 args.skipped_train_samples += num_skipped_samples_in_batch - if config.sequence_packing_scheduler is not None and not args.skip_train: + if getattr(config, 'sequence_packing_scheduler', None) is not None and not args.skip_train: # Scheduler-based packing does not feed the packed-sequence accumulator. # The scheduler already computed these from the real per-sample lengths # before CP padding/rerouting, so use them directly here. @@ -3883,8 +4115,12 @@ def trace_handler(p): is_first_iteration = False # Evaluation. - if args.eval_interval and iteration % args.eval_interval == 0 and args.do_valid \ - and (args.start_eval_at_iter is None or iteration >= args.start_eval_at_iter): + if ( + args.eval_interval + and iteration % args.eval_interval == 0 + and args.do_valid + and (args.start_eval_at_iter is None or iteration >= args.start_eval_at_iter) + ): if args.log_energy: energy_monitor.pause() timers('interval-time').stop() @@ -3926,14 +4162,23 @@ def trace_handler(p): training_model=rl_training_model, ) else: - evaluate_and_print_results(prefix, forward_step_func, - valid_data_iterator, model, - iteration, process_non_loss_data_func, - config, verbose=False, write_to_tensorboard=True, - non_loss_data_func=non_loss_data_func) + evaluate_and_print_results( + prefix, + forward_step_func, + valid_data_iterator, + model, + iteration, + process_non_loss_data_func, + config, + verbose=False, + write_to_tensorboard=True, + non_loss_data_func=non_loss_data_func, + ) eval_duration += timers('eval-time').elapsed() - eval_iterations += sum(args.eval_iters) if isinstance(args.eval_iters, list) else args.eval_iters + eval_iterations += ( + sum(args.eval_iters) if isinstance(args.eval_iters, list) else args.eval_iters + ) timers('eval-time').stop() one_logger_utils.track_e2e_metrics() @@ -4006,6 +4251,10 @@ def trace_handler(p): print_rank_0(f"Total training energy (GPU): {total_energy / 1e6:.3f} MJ") energy_monitor.shutdown() + # Shutdown RL profiler and export summary + if args.rl_profile: + shutdown_rl_profiler() + # If any exit conditions (signal handler, duration, iterations) have been reached, exit. if should_exit: # Deregister NCCL user-buffer memory pools before exit. @@ -4017,7 +4266,9 @@ def trace_handler(p): if isinstance(model_module, DDP): for buf in model_module.buffers + model_module.expert_parallel_buffers: if getattr(buf, 'nccl_mem_pool', None) is not None: - nccl_allocator.deregister_mem_pool(buf.nccl_mem_pool, buf.data_parallel_group) + nccl_allocator.deregister_mem_pool( + buf.nccl_mem_pool, buf.data_parallel_group + ) wandb_writer = get_wandb_writer() if wandb_writer: wandb_writer.finish() @@ -4072,11 +4323,7 @@ def evaluate( if args.moe_expert_rank_capacity_factor is not None: copy_main_params = args.reuse_grad_buf_for_mxfp8_param_ag and args.overlap_param_gather forward_backward_func = PagedStashRunner( - config, - copy_main_params, - model, - None, - forward_backward_func, + config, copy_main_params, model, None, forward_backward_func ) if has_nvidia_modelopt: @@ -4127,7 +4374,9 @@ def evaluate( # Reduce across processes. for key in loss_dicts[0].keys(): if key not in total_loss_dict: - total_loss_dict[key] = torch.tensor([0.0, 0.0], dtype=torch.float, device='cuda') + total_loss_dict[key] = torch.tensor( + [0.0, 0.0], dtype=torch.float, device='cuda' + ) val = [x[key].view(-1) for x in loss_dicts] if val[0].numel() == 2: @@ -4137,19 +4386,17 @@ def evaluate( val = val[:, 0] / val[:, 1].clamp(min=1) val = val.mean() torch.distributed.all_reduce( - val, - group=mpu.get_data_parallel_group(with_context_parallel=True) + val, group=mpu.get_data_parallel_group(with_context_parallel=True) ) val /= torch.distributed.get_world_size( group=mpu.get_data_parallel_group(with_context_parallel=True) ) total_loss_dict[key][0] += val total_loss_dict[key][1] += 1 - else : + else: val = torch.vstack(val).sum(dim=0) torch.distributed.all_reduce( - val, - group=mpu.get_data_parallel_group(with_context_parallel=True) + val, group=mpu.get_data_parallel_group(with_context_parallel=True) ) total_loss_dict[key] += val elif val[0].numel() == 1: @@ -4250,11 +4497,13 @@ def evaluate_and_print_results( eval_iters = args.eval_iters if args.validation_set_names: - assert args.multiple_validation_sets, \ - "--validation-set-names requires --multiple-validation-sets" - assert len(args.validation_set_names) == len(data_iterators), \ - f"Number of --validation-set-names ({len(args.validation_set_names)}) must match " \ + assert ( + args.multiple_validation_sets + ), "--validation-set-names requires --multiple-validation-sets" + assert len(args.validation_set_names) == len(data_iterators), ( + f"Number of --validation-set-names ({len(args.validation_set_names)}) must match " f"the number of validation datasets ({len(data_iterators)})" + ) for index, (iterator, iterations) in enumerate(zip(data_iterators, eval_iters)): suffix = "" @@ -4282,7 +4531,9 @@ def evaluate_and_print_results( ppl = math.exp(min(20, total_loss_dict[key].item())) string += '{} PPL: {:.6E} | '.format(key, ppl) if writer: - writer.add_scalar('{} validation{}'.format(key, suffix), total_loss_dict[key].item(), iteration) + writer.add_scalar( + '{} validation{}'.format(key, suffix), total_loss_dict[key].item(), iteration + ) writer.add_scalar( '{} validation{} vs samples'.format(key, suffix), total_loss_dict[key].item(), @@ -4291,11 +4542,14 @@ def evaluate_and_print_results( if args.log_validation_ppl_to_tensorboard: writer.add_scalar('{} validation{} ppl'.format(key, suffix), ppl, iteration) writer.add_scalar( - '{} validation{} ppl vs samples'.format(key, suffix), ppl, args.consumed_train_samples + '{} validation{} ppl vs samples'.format(key, suffix), + ppl, + args.consumed_train_samples, ) if wandb_writer and is_last_rank(): wandb_writer.log( - {'{} validation{}'.format(key, suffix): total_loss_dict[key].item()}, iteration + {'{} validation{}'.format(key, suffix): total_loss_dict[key].item()}, + iteration, ) if process_non_loss_data_func is not None and writer and is_last_rank(): @@ -4350,7 +4604,11 @@ def get_train_valid_test_num_samples(): # Get train_samples in current phase. if args.phase_transition_iterations: - phase_transition_samples = [0] + [t * args.global_batch_size for t in args.phase_transition_iterations] + [args.train_samples] + phase_transition_samples = ( + [0] + + [t * args.global_batch_size for t in args.phase_transition_iterations] + + [args.train_samples] + ) current_sample = args.iteration * args.global_batch_size last_transition_sample = max(s for s in phase_transition_samples if s <= current_sample) next_transition_sample = min(s for s in phase_transition_samples if s > current_sample) @@ -4361,7 +4619,9 @@ def get_train_valid_test_num_samples(): return (train_samples_in_current_phase, eval_samples, test_samples) -def build_train_valid_test_datasets(build_train_valid_test_datasets_provider, train_valid_test_num_samples=None): +def build_train_valid_test_datasets( + build_train_valid_test_datasets_provider, train_valid_test_num_samples=None +): """Build pretraining datasets.""" if train_valid_test_num_samples is None: train_valid_test_num_samples = get_train_valid_test_num_samples() @@ -4400,8 +4660,14 @@ def build_train_valid_test_data_loaders(build_train_valid_test_datasets_provider # Get consumed train samples in this phase. if args.phase_transition_iterations: - last_transition = max(iteration for iteration in (0, *args.phase_transition_iterations) if iteration <= args.iteration) - consumed_train_samples_in_current_phase = (args.iteration - last_transition) * args.global_batch_size + last_transition = max( + iteration + for iteration in (0, *args.phase_transition_iterations) + if iteration <= args.iteration + ) + consumed_train_samples_in_current_phase = ( + args.iteration - last_transition + ) * args.global_batch_size else: consumed_train_samples_in_current_phase = args.consumed_train_samples @@ -4418,17 +4684,21 @@ def build_train_valid_test_data_loaders(build_train_valid_test_datasets_provider valid_dataloaders = None test_dataloader = None do_train = (args.train_iters or 0) > 0 - do_valid = (args.full_validation or args.eval_iters > 0) - do_test = (args.full_validation or args.eval_iters > 0) + do_valid = args.full_validation or args.eval_iters > 0 + do_test = args.full_validation or args.eval_iters > 0 else: # Build datasets. - train_ds, valid_ds, test_ds = build_train_valid_test_datasets(build_train_valid_test_datasets_provider) + train_ds, valid_ds, test_ds = build_train_valid_test_datasets( + build_train_valid_test_datasets_provider + ) valid_ds = [valid_ds] if not isinstance(valid_ds, list) else valid_ds if args.skip_train: train_dataloader = None else: - train_dataloader = build_pretraining_data_loader(train_ds, consumed_train_samples_in_current_phase) + train_dataloader = build_pretraining_data_loader( + train_ds, consumed_train_samples_in_current_phase + ) valid_dataloaders = [] for valid_d in valid_ds: if args.skip_train or args.full_validation: @@ -4437,13 +4707,19 @@ def build_train_valid_test_data_loaders(build_train_valid_test_datasets_provider if args.multiple_validation_sets: # TODO(bnorick): for multiple validation sets without full validation, args.consumed_valid_samples is not # correct and needs to be calculated/set per validation set - raise NotImplementedError("--multiple-validation-sets currently requires --full-validation") - valid_dataloaders.append(build_pretraining_data_loader(valid_d, args.consumed_valid_samples)) + raise NotImplementedError( + "--multiple-validation-sets currently requires --full-validation" + ) + valid_dataloaders.append( + build_pretraining_data_loader(valid_d, args.consumed_valid_samples) + ) if not args.multiple_validation_sets: assert len(valid_dataloaders) == 1 test_dataloader = build_pretraining_data_loader(test_ds, 0) do_train = train_dataloader is not None and (args.skip_train or args.train_iters > 0) - do_valid = valid_dataloaders is not None and (args.full_validation or args.eval_iters > 0) + do_valid = valid_dataloaders is not None and ( + args.full_validation or args.eval_iters > 0 + ) do_test = test_dataloader is not None and (args.full_validation or args.eval_iters > 0) flags = torch.tensor( @@ -4503,7 +4779,9 @@ def _get_iterator(dataloader_type, dataloader): args.eval_iters = [None] * len(valid_dataloaders) else: local_eval_iters = [len(dl) for dl in valid_dataloaders] - eval_iters_tensor = torch.tensor(local_eval_iters, dtype=torch.long, device='cuda') + eval_iters_tensor = torch.tensor( + local_eval_iters, dtype=torch.long, device='cuda' + ) torch.distributed.all_reduce( eval_iters_tensor, op=torch.distributed.ReduceOp.MAX, @@ -4512,7 +4790,9 @@ def _get_iterator(dataloader_type, dataloader): args.eval_iters = eval_iters_tensor.tolist() else: local_eval_iters = len(valid_dataloaders[0]) - eval_iters_tensor = torch.tensor([local_eval_iters], dtype=torch.long, device='cuda') + eval_iters_tensor = torch.tensor( + [local_eval_iters], dtype=torch.long, device='cuda' + ) torch.distributed.all_reduce( eval_iters_tensor, op=torch.distributed.ReduceOp.MAX, diff --git a/tests/functional_tests/test_cases/bert/bert_release_sm/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/bert/bert_release_sm/golden_values_dev_dgx_gb200.json index 812804fe033..eeb4c44f62b 100644 --- a/tests/functional_tests/test_cases/bert/bert_release_sm/golden_values_dev_dgx_gb200.json +++ b/tests/functional_tests/test_cases/bert/bert_release_sm/golden_values_dev_dgx_gb200.json @@ -4,407 +4,407 @@ "end_step": 2000, "step_interval": 5, "values": { - "1": 10.57016, - "5": 10.57457, - "10": 10.5895, - "15": 10.5701, - "20": 10.13778, - "25": 9.49357, - "30": 9.4056, - "35": 9.1618, - "40": 9.08637, - "45": 8.96435, - "50": 8.67005, - "55": 8.77614, - "60": 8.51912, - "65": 8.52143, - "70": 8.20591, - "75": 8.31295, - "80": 8.00603, - "85": 7.79543, - "90": 7.70836, - "95": 7.57266, - "100": 7.53591, - "105": 7.35517, - "110": 7.44978, - "115": 7.20792, - "120": 6.95942, - "125": 7.27887, - "130": 7.01311, - "135": 6.80831, - "140": 7.21623, - "145": 7.00076, - "150": 7.08728, - "155": 6.98439, - "160": 6.99868, - "165": 7.03626, - "170": 6.92491, - "175": 6.87684, - "180": 6.7349, - "185": 6.92182, - "190": 7.09627, - "195": 6.95652, - "200": 6.88737, - "205": 6.88212, - "210": 6.75061, - "215": 6.9828, - "220": 6.66761, - "225": 6.77153, - "230": 6.73327, - "235": 6.84111, - "240": 6.58632, - "245": 7.08674, - "250": 6.60849, - "255": 6.65796, - "260": 6.74518, - "265": 6.62708, - "270": 6.59842, - "275": 6.82877, - "280": 6.53927, - "285": 6.5668, - "290": 6.74581, - "295": 6.73208, - "300": 6.43994, - "305": 6.34549, - "310": 6.95999, - "315": 6.93016, - "320": 6.86096, - "325": 6.66038, - "330": 6.63878, - "335": 6.64048, - "340": 6.58574, - "345": 6.53944, - "350": 6.396, - "355": 6.51173, - "360": 6.56525, - "365": 6.61797, - "370": 6.52094, - "375": 6.64693, - "380": 6.45276, - "385": 6.40779, - "390": 6.46608, - "395": 6.25858, - "400": 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"315": "nan", @@ -1716,7 +1716,7 @@ "385": "nan", "390": "nan", "395": "nan", - "400": 0.38578, + "400": 0.42279, "405": "nan", "410": "nan", "415": "nan", @@ -1736,7 +1736,7 @@ "485": "nan", "490": "nan", "495": "nan", - "500": 0.38197, + "500": 0.36059, "505": "nan", "510": "nan", "515": "nan", @@ -1756,7 +1756,7 @@ "585": "nan", "590": "nan", "595": "nan", - "600": 0.40009, + "600": 0.38088, "605": "nan", "610": "nan", "615": "nan", @@ -1776,7 +1776,7 @@ "685": "nan", "690": "nan", "695": "nan", - "700": 0.4229, + "700": 0.39751, "705": "nan", "710": "nan", "715": "nan", @@ -1796,7 +1796,7 @@ "785": "nan", "790": "nan", "795": "nan", - "800": 0.41403, + "800": 0.38202, "805": "nan", "810": "nan", "815": "nan", @@ -1816,7 +1816,7 @@ "885": "nan", "890": "nan", "895": "nan", - "900": 0.43862, + "900": 0.37039, "905": "nan", "910": "nan", "915": "nan", @@ -1836,7 +1836,7 @@ "985": "nan", "990": "nan", "995": "nan", - "1000": 0.40449, + "1000": 0.33949, "1005": "nan", "1010": "nan", "1015": "nan", @@ -1856,7 +1856,7 @@ "1085": "nan", "1090": "nan", "1095": "nan", - "1100": 0.37487, + "1100": 0.39225, "1105": "nan", "1110": "nan", "1115": "nan", @@ -1876,7 +1876,7 @@ "1185": "nan", "1190": "nan", "1195": "nan", - "1200": 0.38459, + "1200": 0.34522, "1205": "nan", "1210": "nan", "1215": "nan", @@ -1896,7 +1896,7 @@ "1285": "nan", "1290": "nan", "1295": "nan", - "1300": 0.40402, + "1300": 0.35245, "1305": "nan", "1310": "nan", "1315": "nan", @@ -1916,7 +1916,7 @@ "1385": "nan", "1390": "nan", "1395": "nan", - "1400": 0.37795, + "1400": 0.36286, "1405": "nan", "1410": "nan", "1415": "nan", @@ -1936,7 +1936,7 @@ "1485": "nan", "1490": "nan", "1495": "nan", - "1500": 0.38043, + "1500": 0.35874, "1505": "nan", "1510": "nan", "1515": "nan", @@ -1956,7 +1956,7 @@ "1585": "nan", "1590": "nan", "1595": "nan", - "1600": 0.38498, + "1600": 0.3661, "1605": "nan", "1610": "nan", "1615": "nan", @@ -1976,7 +1976,7 @@ "1685": "nan", "1690": "nan", "1695": "nan", - "1700": 0.38993, + "1700": 0.35475, "1705": "nan", "1710": "nan", "1715": "nan", @@ -1996,7 +1996,7 @@ "1785": "nan", "1790": "nan", "1795": "nan", - "1800": 0.37943, + "1800": 0.36443, "1805": "nan", "1810": "nan", "1815": "nan", @@ -2016,7 +2016,7 @@ "1885": "nan", "1890": "nan", "1895": "nan", - "1900": 0.38578, + "1900": 0.37497, "1905": "nan", "1910": "nan", "1915": "nan", @@ -2036,7 +2036,7 @@ "1985": "nan", "1990": "nan", "1995": "nan", - "2000": 0.44172 + "2000": 0.3754 } } } \ No newline at end of file diff --git a/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release_sm_gb200/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release_sm_gb200/golden_values_dev_dgx_gb200.json index e05f23ef303..ba80eeeb746 100644 --- a/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release_sm_gb200/golden_values_dev_dgx_gb200.json +++ b/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release_sm_gb200/golden_values_dev_dgx_gb200.json @@ -5,2606 +5,2606 @@ 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@@ -10456,7 +10456,7 @@ "85": "nan", "90": "nan", "95": "nan", - "100": 1.52575, + "100": 1.71014, "105": "nan", "110": "nan", "115": "nan", @@ -10476,7 +10476,7 @@ "185": "nan", "190": "nan", "195": "nan", - "200": 1.22241, + "200": 1.62718, "205": "nan", "210": "nan", "215": "nan", @@ -10496,7 +10496,7 @@ "285": "nan", "290": "nan", "295": "nan", - "300": 1.68719, + "300": 1.2702, "305": "nan", "310": "nan", "315": "nan", @@ -10516,7 +10516,7 @@ "385": "nan", "390": "nan", "395": "nan", - "400": 2.3415, + "400": 1.2744, "405": "nan", "410": "nan", "415": "nan", @@ -10536,7 +10536,7 @@ "485": "nan", "490": "nan", "495": "nan", - "500": 1.22139, + "500": 1.2753, "505": "nan", "510": "nan", "515": "nan", @@ -10556,7 +10556,7 @@ "585": "nan", "590": "nan", "595": "nan", - "600": 1.64801, + "600": 1.27482, "605": "nan", "610": "nan", "615": "nan", @@ -10576,7 +10576,7 @@ "685": "nan", "690": "nan", "695": "nan", - "700": 1.59272, + "700": 1.2733, "705": "nan", "710": "nan", "715": "nan", @@ -10596,7 +10596,7 @@ "785": "nan", "790": "nan", "795": "nan", - "800": 1.2195, + "800": 1.27074, "805": "nan", "810": "nan", "815": "nan", @@ -10616,7 +10616,7 @@ "885": "nan", "890": "nan", "895": "nan", - "900": 1.62873, + "900": 1.30714, "905": "nan", "910": "nan", "915": "nan", @@ -10636,7 +10636,7 @@ "985": "nan", "990": "nan", "995": "nan", - "1000": 1.64961, + "1000": 1.26005, "1005": "nan", "1010": "nan", "1015": "nan", @@ -10656,7 +10656,7 @@ "1085": "nan", "1090": "nan", "1095": "nan", - "1100": 1.62131, + "1100": 1.27723, "1105": "nan", "1110": "nan", "1115": "nan", @@ -10676,7 +10676,7 @@ "1185": "nan", "1190": "nan", "1195": "nan", - "1200": 1.46576, + "1200": 1.25058, "1205": "nan", "1210": "nan", "1215": "nan", @@ -10696,7 +10696,7 @@ "1285": "nan", "1290": "nan", "1295": "nan", - "1300": 1.52481, + "1300": 1.24397, "1305": "nan", "1310": "nan", "1315": "nan", @@ -10716,7 +10716,7 @@ "1385": "nan", "1390": "nan", "1395": "nan", - "1400": 1.18935, + "1400": 1.2408, "1405": "nan", "1410": "nan", "1415": "nan", @@ -10736,7 +10736,7 @@ "1485": "nan", "1490": "nan", "1495": "nan", - "1500": 1.81193, + "1500": 1.25446, "1505": "nan", "1510": "nan", "1515": "nan", @@ -10756,7 +10756,7 @@ "1585": "nan", "1590": "nan", "1595": "nan", - "1600": 1.18361, + "1600": 1.2661, "1605": "nan", "1610": "nan", "1615": "nan", @@ -10776,7 +10776,7 @@ "1685": "nan", "1690": "nan", "1695": "nan", - "1700": 1.8225, + "1700": 1.22942, "1705": "nan", "1710": "nan", "1715": "nan", @@ -10796,7 +10796,7 @@ "1785": "nan", "1790": "nan", "1795": "nan", - "1800": 1.17987, + "1800": 1.22752, "1805": "nan", "1810": "nan", "1815": "nan", @@ -10816,7 +10816,7 @@ "1885": "nan", "1890": "nan", "1895": "nan", - "1900": 1.51305, + "1900": 1.22607, "1905": "nan", "1910": "nan", "1915": "nan", @@ -10836,7 +10836,7 @@ "1985": "nan", "1990": "nan", "1995": "nan", - "2000": 1.50011, + "2000": 1.22363, "2005": "nan", "2010": "nan", "2015": "nan", @@ -10856,7 +10856,7 @@ "2085": "nan", "2090": "nan", "2095": "nan", - "2100": 1.57718, + "2100": 1.23337, "2105": "nan", "2110": "nan", "2115": "nan", @@ -10876,7 +10876,7 @@ "2185": "nan", "2190": "nan", "2195": "nan", - "2200": 1.45196, + "2200": 1.22183, "2205": "nan", "2210": "nan", "2215": "nan", @@ -10896,7 +10896,7 @@ "2285": "nan", "2290": "nan", "2295": "nan", - "2300": 1.26751, + "2300": 1.2197, "2305": "nan", "2310": "nan", "2315": "nan", @@ -10916,7 +10916,7 @@ "2385": "nan", "2390": "nan", "2395": "nan", - "2400": 1.67576, + "2400": 1.21928, "2405": "nan", "2410": "nan", "2415": "nan", @@ -10936,7 +10936,7 @@ "2485": "nan", "2490": "nan", "2495": "nan", - "2500": 1.45152, + "2500": 1.21849, "2505": "nan", "2510": "nan", "2515": "nan", @@ -10956,7 +10956,7 @@ "2585": "nan", "2590": "nan", "2595": "nan", - "2600": 1.17207, + "2600": 1.27008, "2605": "nan", "2610": "nan", "2615": "nan", @@ -10976,7 +10976,7 @@ "2685": "nan", "2690": "nan", "2695": "nan", - "2700": 1.47473, + "2700": 1.21676, "2705": "nan", "2710": "nan", "2715": "nan", @@ -10996,7 +10996,7 @@ "2785": "nan", "2790": "nan", "2795": "nan", - "2800": 1.46554, + "2800": 1.21711, "2805": "nan", "2810": "nan", "2815": "nan", @@ -11016,7 +11016,7 @@ "2885": "nan", "2890": "nan", "2895": "nan", - "2900": 1.47107, + "2900": 1.21623, "2905": "nan", "2910": "nan", "2915": "nan", @@ -11036,7 +11036,7 @@ "2985": "nan", "2990": "nan", "2995": "nan", - "3000": 1.47313, + "3000": 1.21687, "3005": "nan", "3010": "nan", "3015": "nan", @@ -11056,7 +11056,7 @@ "3085": "nan", "3090": "nan", "3095": "nan", - "3100": 1.58134, + "3100": 1.22627, "3105": "nan", "3110": "nan", "3115": "nan", @@ -11076,7 +11076,7 @@ "3185": "nan", "3190": "nan", "3195": "nan", - "3200": 1.47784, + "3200": 1.2148, "3205": "nan", "3210": "nan", "3215": "nan", @@ -11096,7 +11096,7 @@ "3285": "nan", "3290": "nan", "3295": "nan", - "3300": 1.53281, + "3300": 1.21412, "3305": "nan", "3310": "nan", "3315": "nan", @@ -11116,7 +11116,7 @@ "3385": "nan", "3390": "nan", "3395": "nan", - "3400": 1.16771, + "3400": 1.31817, "3405": "nan", "3410": "nan", "3415": "nan", @@ -11136,7 +11136,7 @@ "3485": "nan", "3490": "nan", "3495": "nan", - "3500": 1.7677, + "3500": 1.21374, "3505": "nan", "3510": "nan", "3515": "nan", @@ -11156,7 +11156,7 @@ "3585": "nan", "3590": "nan", "3595": "nan", - "3600": 1.16778, + "3600": 1.21375, "3605": "nan", "3610": "nan", "3615": "nan", @@ -11176,7 +11176,7 @@ "3685": "nan", "3690": "nan", "3695": "nan", - "3700": 1.52902, + "3700": 1.21349, "3705": "nan", "3710": "nan", "3715": "nan", @@ -11196,7 +11196,7 @@ "3785": "nan", "3790": "nan", "3795": "nan", - "3800": 1.45421, + "3800": 1.21305, "3805": "nan", "3810": "nan", "3815": "nan", @@ -11216,7 +11216,7 @@ "3885": "nan", "3890": "nan", "3895": "nan", - "3900": 1.48924, + "3900": 1.21299, "3905": "nan", "3910": "nan", "3915": "nan", @@ -11236,7 +11236,7 @@ "3985": "nan", "3990": "nan", "3995": "nan", - "4000": 1.49248, + "4000": 1.24938, "4005": "nan", "4010": "nan", "4015": "nan", @@ -11256,7 +11256,7 @@ "4085": "nan", "4090": "nan", "4095": "nan", - "4100": 1.24661, + "4100": 1.22417, "4105": "nan", "4110": "nan", "4115": "nan", @@ -11276,7 +11276,7 @@ "4185": "nan", "4190": "nan", "4195": "nan", - "4200": 1.49468, + "4200": 1.21303, "4205": "nan", "4210": "nan", "4215": "nan", @@ -11296,7 +11296,7 @@ "4285": "nan", "4290": "nan", "4295": "nan", - "4300": 1.16555, + "4300": 1.21437, "4305": "nan", "4310": "nan", "4315": "nan", @@ -11316,7 +11316,7 @@ "4385": "nan", "4390": "nan", "4395": "nan", - "4400": 1.47217, + "4400": 1.21375, "4405": "nan", "4410": "nan", "4415": "nan", @@ -11336,7 +11336,7 @@ "4485": "nan", "4490": "nan", "4495": "nan", - "4500": 1.16663, + "4500": 1.21319, "4505": "nan", "4510": "nan", "4515": "nan", @@ -11356,7 +11356,7 @@ "4585": "nan", "4590": "nan", "4595": "nan", - "4600": 1.43693, + "4600": 1.21401, "4605": "nan", "4610": "nan", "4615": "nan", @@ -11376,7 +11376,7 @@ "4685": "nan", "4690": "nan", "4695": "nan", - "4700": 1.16482, + "4700": 1.21317, "4705": "nan", "4710": "nan", "4715": "nan", @@ -11396,7 +11396,7 @@ "4785": "nan", "4790": "nan", "4795": "nan", - "4800": 1.48482, + "4800": 1.21361, "4805": "nan", "4810": "nan", "4815": "nan", @@ -11416,7 +11416,7 @@ "4885": "nan", "4890": "nan", "4895": "nan", - "4900": 1.16433, + "4900": 1.21309, "4905": "nan", "4910": "nan", "4915": "nan", @@ -11436,7 +11436,7 @@ "4985": "nan", "4990": "nan", "4995": "nan", - "5000": 1.46317, + "5000": 1.21161, "5005": "nan", "5010": "nan", "5015": "nan", @@ -11456,7 +11456,7 @@ "5085": "nan", "5090": "nan", "5095": "nan", - "5100": 1.25189, + "5100": 1.22286, "5105": "nan", "5110": "nan", "5115": "nan", @@ -11476,7 +11476,7 @@ "5185": "nan", "5190": "nan", "5195": "nan", - "5200": 1.16439, + "5200": 1.2113, "5205": "nan", "5210": "nan", "5215": "nan", @@ -11496,7 +11496,7 @@ "5285": "nan", "5290": "nan", "5295": "nan", - "5300": 1.50195, + "5300": 1.2106, "5305": "nan", "5310": "nan", "5315": "nan", @@ -11516,7 +11516,7 @@ "5385": "nan", "5390": "nan", "5395": "nan", - "5400": 1.16391, + "5400": 1.21148, "5405": "nan", "5410": "nan", "5415": "nan", @@ -11536,7 +11536,7 @@ "5485": "nan", "5490": "nan", "5495": "nan", - "5500": 1.49666, + "5500": 1.21098, "5505": "nan", "5510": "nan", "5515": "nan", @@ -11556,7 +11556,7 @@ "5585": "nan", "5590": "nan", "5595": "nan", - "5600": 1.16446, + "5600": 1.21116, "5605": "nan", "5610": "nan", "5615": "nan", @@ -11576,7 +11576,7 @@ "5685": "nan", "5690": "nan", "5695": "nan", - "5700": 1.42544, + "5700": 1.2342, "5705": "nan", "5710": "nan", "5715": "nan", @@ -11596,7 +11596,7 @@ "5785": "nan", "5790": "nan", "5795": "nan", - "5800": 1.18903, + "5800": 1.21204, "5805": "nan", "5810": "nan", "5815": "nan", @@ -11616,7 +11616,7 @@ "5885": "nan", "5890": "nan", "5895": "nan", - "5900": 1.16376, + "5900": 1.22803, "5905": "nan", "5910": "nan", "5915": "nan", @@ -11636,7 +11636,7 @@ "5985": "nan", "5990": "nan", "5995": "nan", - "6000": 1.48894, + "6000": 1.21049, "6005": "nan", "6010": "nan", "6015": "nan", @@ -11656,7 +11656,7 @@ "6085": "nan", "6090": "nan", "6095": "nan", - "6100": 1.56874, + "6100": 1.22148, "6105": "nan", "6110": "nan", "6115": "nan", @@ -11676,7 +11676,7 @@ "6185": "nan", "6190": "nan", "6195": "nan", - "6200": 1.16367, + "6200": 1.21103, "6205": "nan", "6210": "nan", "6215": "nan", @@ -11696,7 +11696,7 @@ "6285": "nan", "6290": "nan", "6295": "nan", - "6300": 1.49027, + "6300": 1.21108, "6305": "nan", "6310": "nan", "6315": "nan", @@ -11716,7 +11716,7 @@ "6385": "nan", "6390": "nan", "6395": "nan", - "6400": 1.16275, + "6400": 1.21018, "6405": "nan", "6410": "nan", "6415": "nan", @@ -11736,7 +11736,7 @@ "6485": "nan", "6490": "nan", "6495": "nan", - "6500": 1.78937, + "6500": 1.20963, "6505": "nan", "6510": "nan", "6515": "nan", @@ -11756,7 +11756,7 @@ "6585": "nan", "6590": "nan", "6595": "nan", - "6600": 1.16414, + "6600": 1.20979, "6605": "nan", "6610": "nan", "6615": "nan", @@ -11776,7 +11776,7 @@ "6685": "nan", "6690": "nan", "6695": "nan", - "6700": 1.16369, + "6700": 1.21056, "6705": "nan", "6710": "nan", "6715": "nan", @@ -11796,7 +11796,7 @@ "6785": "nan", "6790": "nan", "6795": "nan", - "6800": 1.87916, + "6800": 1.20968, "6805": "nan", "6810": "nan", "6815": "nan", @@ -11816,7 +11816,7 @@ "6885": "nan", "6890": "nan", "6895": "nan", - "6900": 1.74932, + "6900": 1.21001, "6905": "nan", "6910": "nan", "6915": "nan", @@ -11836,7 +11836,7 @@ "6985": "nan", "6990": "nan", "6995": "nan", - "7000": 1.26768, + "7000": 1.21046, "7005": "nan", "7010": "nan", "7015": "nan", @@ -11856,7 +11856,7 @@ "7085": "nan", "7090": "nan", "7095": "nan", - "7100": 1.56361, + "7100": 1.2212, "7105": "nan", "7110": "nan", "7115": "nan", @@ -11876,7 +11876,7 @@ "7185": "nan", "7190": "nan", "7195": "nan", - "7200": 1.46637, + "7200": 1.2103, "7205": "nan", "7210": "nan", "7215": "nan", @@ -11896,7 +11896,7 @@ "7285": "nan", "7290": "nan", "7295": "nan", - "7300": 1.47082, + "7300": 1.20953, "7305": "nan", "7310": "nan", "7315": "nan", @@ -11916,7 +11916,7 @@ "7385": "nan", "7390": "nan", "7395": "nan", - "7400": 1.46294, + "7400": 1.21122, "7405": "nan", "7410": "nan", "7415": "nan", @@ -11936,7 +11936,7 @@ "7485": "nan", "7490": "nan", "7495": "nan", - "7500": 1.48069, + "7500": 1.21007, "7505": "nan", "7510": "nan", "7515": "nan", @@ -11956,7 +11956,7 @@ "7585": "nan", "7590": "nan", "7595": "nan", - "7600": 1.44277, + "7600": 1.20903, "7605": "nan", "7610": "nan", "7615": "nan", @@ -11976,7 +11976,7 @@ "7685": "nan", "7690": "nan", "7695": "nan", - "7700": 1.44688, + "7700": 1.2102, "7705": "nan", "7710": "nan", "7715": "nan", @@ -11996,7 +11996,7 @@ "7785": "nan", "7790": "nan", "7795": "nan", - "7800": 1.46189, + "7800": 1.21118, "7805": "nan", "7810": "nan", "7815": "nan", @@ -12016,7 +12016,7 @@ "7885": "nan", "7890": "nan", "7895": "nan", - "7900": 1.48119, + "7900": 1.18271, "7905": "nan", "7910": "nan", "7915": "nan", @@ -12036,7 +12036,7 @@ "7985": "nan", "7990": "nan", "7995": "nan", - "8000": 1.16319, + "8000": 1.18278, "8005": "nan", "8010": "nan", "8015": "nan", @@ -12056,7 +12056,7 @@ "8085": "nan", "8090": "nan", "8095": "nan", - "8100": 1.56123, + "8100": 1.19535, "8105": "nan", "8110": "nan", "8115": "nan", @@ -12076,7 +12076,7 @@ "8185": "nan", "8190": "nan", "8195": "nan", - "8200": 1.47947, + "8200": 1.18013, "8205": "nan", "8210": "nan", "8215": "nan", @@ -12096,7 +12096,7 @@ "8285": "nan", "8290": "nan", "8295": "nan", - "8300": 1.16329, + "8300": 1.18099, "8305": "nan", "8310": "nan", "8315": "nan", @@ -12116,7 +12116,7 @@ "8385": "nan", "8390": "nan", "8395": "nan", - "8400": 1.76645, + "8400": 1.1799, "8405": "nan", "8410": "nan", "8415": "nan", @@ -12136,7 +12136,7 @@ "8485": "nan", "8490": "nan", "8495": "nan", - "8500": 1.24745, + "8500": 1.18092, "8505": "nan", "8510": "nan", "8515": "nan", @@ -12156,7 +12156,7 @@ "8585": "nan", "8590": "nan", "8595": "nan", - "8600": 1.50472, + "8600": 1.18226, "8605": "nan", "8610": "nan", "8615": "nan", @@ -12176,7 +12176,7 @@ "8685": "nan", "8690": "nan", "8695": "nan", - "8700": 1.2351, + "8700": 1.18146, "8705": "nan", "8710": "nan", "8715": "nan", @@ -12196,7 +12196,7 @@ "8785": "nan", "8790": "nan", "8795": "nan", - "8800": 1.23483, + "8800": 1.18185, "8805": "nan", "8810": "nan", "8815": "nan", @@ -12216,7 +12216,7 @@ "8885": "nan", "8890": "nan", "8895": "nan", - "8900": 1.44634, + "8900": 1.18128, "8905": "nan", "8910": "nan", "8915": "nan", @@ -12236,7 +12236,7 @@ "8985": "nan", "8990": "nan", "8995": "nan", - "9000": 1.28102, + "9000": 1.18167, "9005": "nan", "9010": "nan", "9015": "nan", @@ -12256,7 +12256,7 @@ "9085": "nan", "9090": "nan", "9095": "nan", - "9100": 1.9791, + "9100": 1.19421, "9105": "nan", "9110": "nan", "9115": "nan", @@ -12276,7 +12276,7 @@ "9185": "nan", "9190": "nan", "9195": "nan", - "9200": 1.23709, + "9200": 1.18189, "9205": "nan", "9210": "nan", "9215": "nan", @@ -12296,7 +12296,7 @@ "9285": "nan", "9290": "nan", "9295": "nan", - "9300": 1.23759, + "9300": 1.18199, "9305": "nan", "9310": "nan", "9315": "nan", @@ -12316,7 +12316,7 @@ "9385": "nan", "9390": "nan", "9395": "nan", - "9400": 1.99173, + "9400": 1.28396, "9405": "nan", "9410": "nan", "9415": "nan", @@ -12336,7 +12336,7 @@ "9485": "nan", "9490": "nan", "9495": "nan", - "9500": 1.53391, + "9500": 1.18178, "9505": "nan", "9510": "nan", "9515": "nan", @@ -12356,7 +12356,7 @@ "9585": "nan", "9590": "nan", "9595": "nan", - "9600": 1.59234, + "9600": 1.18065, "9605": "nan", "9610": "nan", "9615": "nan", @@ -12376,7 +12376,7 @@ "9685": "nan", "9690": "nan", "9695": "nan", - "9700": 1.23829, + "9700": 1.18059, "9705": "nan", "9710": "nan", "9715": "nan", @@ -12396,7 +12396,7 @@ "9785": "nan", "9790": "nan", "9795": "nan", - "9800": 1.94369, + "9800": 1.18017, "9805": "nan", "9810": "nan", "9815": "nan", @@ -12416,7 +12416,7 @@ "9885": "nan", "9890": "nan", "9895": "nan", - "9900": 1.54022, + "9900": 1.17993, "9905": "nan", "9910": "nan", "9915": "nan", @@ -12436,7 +12436,7 @@ "9985": "nan", "9990": "nan", "9995": "nan", - "10000": 1.61649, + "10000": 1.18037, "10005": "nan", "10010": "nan", "10015": "nan", @@ -12456,7 +12456,7 @@ "10085": "nan", "10090": "nan", "10095": "nan", - "10100": 1.85307, + "10100": 1.19175, "10105": "nan", "10110": "nan", "10115": "nan", @@ -12476,7 +12476,7 @@ "10185": "nan", "10190": "nan", "10195": "nan", - "10200": 1.3129, + "10200": 1.18041, "10205": "nan", "10210": "nan", "10215": "nan", @@ -12496,7 +12496,7 @@ "10285": "nan", "10290": "nan", "10295": "nan", - "10300": 1.57995, + "10300": 1.21494, "10305": "nan", "10310": "nan", "10315": "nan", @@ -12516,7 +12516,7 @@ "10385": "nan", "10390": "nan", "10395": "nan", - "10400": 1.55986, + "10400": 1.18038, "10405": "nan", "10410": "nan", "10415": "nan", @@ -12536,7 +12536,7 @@ "10485": "nan", "10490": "nan", "10495": "nan", - "10500": 1.54981, + "10500": 1.17977, "10505": "nan", "10510": "nan", "10515": "nan", @@ -12556,7 +12556,7 @@ "10585": "nan", "10590": "nan", "10595": "nan", - "10600": 1.61207, + "10600": 1.17976, "10605": "nan", "10610": "nan", "10615": "nan", @@ -12576,7 +12576,7 @@ "10685": "nan", "10690": "nan", "10695": "nan", - "10700": 1.56274, + "10700": 1.17938, "10705": "nan", "10710": "nan", "10715": "nan", @@ -12596,7 +12596,7 @@ "10785": "nan", "10790": "nan", "10795": "nan", - "10800": 1.60491, + "10800": 1.17963, "10805": "nan", "10810": "nan", "10815": "nan", @@ -12616,7 +12616,7 @@ "10885": "nan", "10890": "nan", "10895": "nan", - "10900": 1.54037, + "10900": 1.45287, "10905": "nan", "10910": "nan", "10915": "nan", @@ -12636,7 +12636,7 @@ "10985": "nan", "10990": "nan", "10995": "nan", - "11000": 1.53164, + "11000": 1.80032, "11005": "nan", "11010": "nan", "11015": "nan", @@ -12656,7 +12656,7 @@ "11085": "nan", "11090": "nan", "11095": "nan", - "11100": 1.6827, + "11100": 1.66249, "11105": "nan", "11110": "nan", "11115": "nan", @@ -12676,7 +12676,7 @@ "11185": "nan", "11190": "nan", "11195": "nan", - "11200": 1.24755, + "11200": 1.23699, "11205": "nan", "11210": "nan", "11215": "nan", @@ -12696,7 +12696,7 @@ "11285": "nan", "11290": "nan", "11295": "nan", - "11300": 1.58769, + "11300": 1.22207, "11305": "nan", "11310": "nan", "11315": "nan", @@ -12716,7 +12716,7 @@ "11385": "nan", "11390": "nan", "11395": "nan", - "11400": 1.24695, + "11400": 1.22275, "11405": "nan", "11410": "nan", "11415": "nan", @@ -12736,7 +12736,7 @@ "11485": "nan", "11490": "nan", "11495": "nan", - "11500": 1.26924, + "11500": 1.22202, "11505": "nan", "11510": "nan", "11515": "nan", @@ -12756,7 +12756,7 @@ "11585": "nan", "11590": "nan", "11595": "nan", - "11600": 1.58092, + "11600": 1.2226, "11605": "nan", "11610": "nan", "11615": "nan", @@ -12776,7 +12776,7 @@ "11685": "nan", "11690": "nan", "11695": "nan", - "11700": 1.2414, + "11700": 1.22375, "11705": "nan", "11710": "nan", "11715": "nan", @@ -12796,7 +12796,7 @@ "11785": "nan", "11790": "nan", "11795": "nan", - "11800": 1.55653, + "11800": 1.22643, "11805": "nan", "11810": "nan", "11815": "nan", @@ -12816,7 +12816,7 @@ "11885": "nan", "11890": "nan", "11895": "nan", - "11900": 1.24347, + "11900": 1.23176, "11905": "nan", "11910": "nan", "11915": "nan", @@ -12836,7 +12836,7 @@ "11985": "nan", "11990": "nan", "11995": "nan", - "12000": 1.26258, + "12000": 1.26473, "12005": "nan", "12010": "nan", "12015": "nan", @@ -12856,7 +12856,7 @@ "12085": "nan", "12090": "nan", "12095": "nan", - "12100": 1.31137, + "12100": 1.2899, "12105": "nan", "12110": "nan", "12115": "nan", @@ -12876,7 +12876,7 @@ "12185": "nan", "12190": "nan", "12195": "nan", - "12200": 1.2351, + "12200": 1.22001, "12205": "nan", "12210": "nan", "12215": "nan", @@ -12896,7 +12896,7 @@ "12285": "nan", "12290": "nan", "12295": "nan", - "12300": 1.23767, + "12300": 1.22282, "12305": "nan", "12310": "nan", "12315": "nan", @@ -12916,7 +12916,7 @@ "12385": "nan", "12390": "nan", "12395": "nan", - "12400": 1.50314, + "12400": 1.21989, "12405": "nan", "12410": "nan", "12415": "nan", @@ -12936,7 +12936,7 @@ "12485": "nan", "12490": "nan", "12495": "nan", - "12500": 1.44228, + "12500": 1.22028, "12505": "nan", "12510": "nan", "12515": "nan", @@ -12956,7 +12956,7 @@ "12585": "nan", "12590": "nan", "12595": "nan", - "12600": 1.14538, + "12600": 1.21944, "12605": "nan", "12610": "nan", "12615": "nan", @@ -12976,7 +12976,7 @@ "12685": "nan", "12690": "nan", "12695": "nan", - "12700": 1.47378, + "12700": 1.21936, "12705": "nan", "12710": "nan", "12715": "nan", @@ -12996,7 +12996,7 @@ "12785": "nan", "12790": "nan", "12795": "nan", - "12800": 1.14584, + "12800": 1.2196, "12805": "nan", "12810": "nan", "12815": "nan", @@ -13016,7 +13016,7 @@ "12885": "nan", "12890": "nan", "12895": "nan", - "12900": 1.16693, + "12900": 1.21972, "12905": "nan", "12910": "nan", "12915": "nan", @@ -13036,7 +13036,7 @@ "12985": "nan", "12990": "nan", "12995": "nan", - "13000": 1.47183 + "13000": 1.21957 } } } \ No newline at end of file diff --git a/tests/functional_tests/test_cases/mixtral/deepseekv3_proxy_flex_tp1pp4emp16etp1cp1_release_sm/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/mixtral/deepseekv3_proxy_flex_tp1pp4emp16etp1cp1_release_sm/golden_values_dev_dgx_h100.json index 7128b0fe3b7..8d882e26792 100644 --- a/tests/functional_tests/test_cases/mixtral/deepseekv3_proxy_flex_tp1pp4emp16etp1cp1_release_sm/golden_values_dev_dgx_h100.json +++ b/tests/functional_tests/test_cases/mixtral/deepseekv3_proxy_flex_tp1pp4emp16etp1cp1_release_sm/golden_values_dev_dgx_h100.json @@ -4,1914 +4,1914 @@ "end_step": 9535, "step_interval": 5, "values": { - "1": 13.8975, - "5": 13.89167, - "10": 13.85847, - "15": 13.84968, - "20": 13.74133, - "25": 13.71288, - "30": 13.39133, - "35": 13.3242, - "40": 13.23301, - "45": 13.11661, - "50": 12.53562, - "55": 12.34932, - "60": 12.16994, - "65": 12.0086, - "70": 11.82944, + "1": 13.89735, + "5": 13.89189, + "10": 13.85846, + "15": 13.84935, + "20": 13.74127, + "25": 13.71285, 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100644 --- a/tests/functional_tests/test_cases/mixtral/deepseekv3_proxy_flex_tp2pp2emp16etp1cp1_gb_200_release_sm/golden_values_dev_dgx_gb200.json +++ b/tests/functional_tests/test_cases/mixtral/deepseekv3_proxy_flex_tp2pp2emp16etp1cp1_gb_200_release_sm/golden_values_dev_dgx_gb200.json @@ -4,1914 +4,1914 @@ "end_step": 9535, "step_interval": 5, "values": { - "1": 13.90137, - "5": 13.90372, - "10": 13.87493, - "15": 13.86127, - "20": 13.76245, - "25": 13.72657, - "30": 13.42477, - "35": 13.35267, - "40": 13.2701, - "45": 13.15966, - "50": 12.56281, - "55": 12.38218, - "60": 12.19755, - "65": 12.03141, - "70": 11.8598, - "75": 11.55717, - "80": 11.35361, - "85": 11.13845, - "90": 10.99346, - "95": 10.83936, - "100": 10.62267, - "105": 10.49232, - "110": 10.28024, - "115": 10.06877, - "120": 9.91657, - "125": 9.7739, - "130": 9.68549, - "135": 9.60673, - "140": 9.37968, - "145": 9.37166, - "150": 9.21319, - "155": 9.13417, - "160": 9.05577, - "165": 8.9384, - "170": 8.88972, - "175": 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"990": "nan", "995": "nan", - "1000": 0.27626, + "1000": 0.37236, "1005": "nan", "1010": "nan", "1015": "nan", @@ -8256,7 +8256,7 @@ "1085": "nan", "1090": "nan", "1095": "nan", - "1100": 0.29956, + "1100": 0.28998, "1105": "nan", "1110": "nan", "1115": "nan", @@ -8276,7 +8276,7 @@ "1185": "nan", "1190": "nan", "1195": "nan", - "1200": 0.31326, + "1200": 0.25349, "1205": "nan", "1210": "nan", "1215": "nan", @@ -8296,7 +8296,7 @@ "1285": "nan", "1290": "nan", "1295": "nan", - "1300": 0.31493, + "1300": 0.27708, "1305": "nan", "1310": "nan", "1315": "nan", @@ -8316,7 +8316,7 @@ "1385": "nan", "1390": "nan", "1395": "nan", - "1400": 0.34858, + "1400": 0.24158, "1405": "nan", "1410": "nan", "1415": "nan", @@ -8336,7 +8336,7 @@ "1485": "nan", "1490": "nan", "1495": "nan", - "1500": 0.29301, + "1500": 0.27573, "1505": "nan", "1510": "nan", "1515": "nan", @@ -8356,7 +8356,7 @@ "1585": "nan", "1590": "nan", "1595": "nan", - "1600": 0.31075, + "1600": 0.32898, "1605": "nan", "1610": "nan", "1615": "nan", @@ -8376,7 +8376,7 @@ "1685": "nan", "1690": "nan", "1695": "nan", - "1700": 0.29068, + "1700": 0.27423, "1705": "nan", "1710": "nan", "1715": "nan", @@ -8396,7 +8396,7 @@ "1785": "nan", "1790": "nan", "1795": "nan", - "1800": 0.28353, + "1800": 0.29768, "1805": "nan", "1810": "nan", "1815": "nan", @@ -8416,7 +8416,7 @@ "1885": "nan", "1890": "nan", "1895": "nan", - "1900": 0.3545, + "1900": 0.28706, "1905": "nan", "1910": "nan", "1915": "nan", @@ -8436,7 +8436,7 @@ "1985": "nan", "1990": "nan", "1995": "nan", - "2000": 0.33465, + "2000": 0.26932, "2005": "nan", "2010": "nan", "2015": "nan", @@ -8456,7 +8456,7 @@ "2085": "nan", "2090": "nan", "2095": "nan", - "2100": 0.37474, + "2100": 0.34287, "2105": "nan", "2110": "nan", "2115": "nan", @@ -8476,7 +8476,7 @@ "2185": "nan", "2190": "nan", "2195": "nan", - "2200": 0.34016, + "2200": 0.29744, "2205": "nan", "2210": "nan", "2215": "nan", @@ -8496,7 +8496,7 @@ "2285": "nan", "2290": "nan", "2295": "nan", - "2300": 0.28193, + "2300": 0.28378, "2305": "nan", "2310": "nan", "2315": "nan", @@ -8516,7 +8516,7 @@ "2385": "nan", "2390": "nan", "2395": "nan", - "2400": 0.29414, + "2400": 0.30694, "2405": "nan", "2410": "nan", "2415": "nan", @@ -8536,7 +8536,7 @@ "2485": "nan", "2490": "nan", "2495": "nan", - "2500": 0.30252, + "2500": 0.27079, "2505": "nan", "2510": "nan", "2515": "nan", @@ -8556,7 +8556,7 @@ "2585": "nan", "2590": "nan", "2595": "nan", - "2600": 0.3, + "2600": 0.2869, "2605": "nan", "2610": "nan", "2615": "nan", @@ -8576,7 +8576,7 @@ "2685": "nan", "2690": "nan", "2695": "nan", - "2700": 0.37179, + "2700": 0.28124, "2705": "nan", "2710": "nan", "2715": "nan", @@ -8596,7 +8596,7 @@ "2785": "nan", "2790": "nan", "2795": "nan", - "2800": 0.29533, + "2800": 0.26056, "2805": "nan", "2810": "nan", "2815": "nan", @@ -8616,7 +8616,7 @@ "2885": "nan", "2890": "nan", "2895": "nan", - "2900": 0.3087, + "2900": 0.2997, "2905": "nan", "2910": "nan", "2915": "nan", @@ -8636,7 +8636,7 @@ "2985": "nan", "2990": "nan", "2995": "nan", - "3000": 0.29196, + "3000": 0.33471, "3005": "nan", "3010": "nan", "3015": "nan", @@ -8656,7 +8656,7 @@ "3085": "nan", "3090": "nan", "3095": "nan", - "3100": 0.28467, + "3100": 0.26821, "3105": "nan", "3110": "nan", "3115": "nan", @@ -8676,7 +8676,7 @@ "3185": "nan", "3190": "nan", "3195": "nan", - "3200": 0.33348, + "3200": 0.31266, "3205": "nan", "3210": "nan", "3215": "nan", @@ -8696,7 +8696,7 @@ "3285": "nan", "3290": "nan", "3295": "nan", - "3300": 0.36884, + "3300": 0.28142, "3305": "nan", "3310": "nan", "3315": "nan", @@ -8716,7 +8716,7 @@ "3385": "nan", "3390": "nan", "3395": "nan", - "3400": 0.29896, + "3400": 0.27249, "3405": "nan", "3410": "nan", "3415": "nan", @@ -8736,7 +8736,7 @@ "3485": "nan", "3490": "nan", "3495": "nan", - "3500": 0.2837, + "3500": 0.26879, "3505": "nan", "3510": "nan", "3515": "nan", @@ -8756,7 +8756,7 @@ "3585": "nan", "3590": "nan", "3595": "nan", - "3600": 0.28047, + "3600": 0.29884, "3605": "nan", "3610": "nan", "3615": "nan", @@ -8776,7 +8776,7 @@ "3685": "nan", "3690": "nan", "3695": "nan", - "3700": 0.30244, + "3700": 0.264, "3705": "nan", "3710": "nan", "3715": "nan", @@ -8796,7 +8796,7 @@ "3785": "nan", "3790": "nan", "3795": "nan", - "3800": 0.37995, + "3800": 0.32004, "3805": "nan", "3810": "nan", "3815": "nan", @@ -8816,7 +8816,7 @@ "3885": "nan", "3890": "nan", "3895": "nan", - "3900": 0.47394, + "3900": 0.3049, "3905": "nan", "3910": "nan", "3915": "nan", @@ -8836,7 +8836,7 @@ "3985": "nan", "3990": "nan", "3995": "nan", - "4000": 0.45949, + "4000": 0.27809, "4005": "nan", "4010": "nan", "4015": "nan", @@ -8856,7 +8856,7 @@ "4085": "nan", "4090": "nan", "4095": "nan", - "4100": 0.39132, + "4100": 0.28798, "4105": "nan", "4110": "nan", "4115": "nan", @@ -8876,7 +8876,7 @@ "4185": "nan", "4190": "nan", "4195": "nan", - "4200": 0.27918, + "4200": 0.31101, "4205": "nan", "4210": "nan", "4215": "nan", @@ -8896,7 +8896,7 @@ "4285": "nan", "4290": "nan", "4295": "nan", - "4300": 0.28269, + "4300": 0.3058, "4305": "nan", "4310": "nan", "4315": "nan", @@ -8916,7 +8916,7 @@ "4385": "nan", "4390": "nan", "4395": "nan", - "4400": 0.30371, + "4400": 0.29792, "4405": "nan", "4410": "nan", "4415": "nan", @@ -8936,7 +8936,7 @@ "4485": "nan", "4490": "nan", "4495": "nan", - "4500": 0.30502, + "4500": 0.2689, "4505": "nan", "4510": "nan", "4515": "nan", @@ -8956,7 +8956,7 @@ "4585": "nan", "4590": "nan", "4595": "nan", - "4600": 0.36183, + "4600": 0.25838, "4605": "nan", "4610": "nan", "4615": "nan", @@ -8976,7 +8976,7 @@ "4685": "nan", "4690": "nan", "4695": "nan", - "4700": 0.32098, + "4700": 0.25194, "4705": "nan", "4710": "nan", "4715": "nan", @@ -8996,7 +8996,7 @@ "4785": "nan", "4790": "nan", "4795": "nan", - "4800": 0.30509, + "4800": 0.26903, "4805": "nan", "4810": "nan", "4815": "nan", @@ -9016,7 +9016,7 @@ "4885": "nan", "4890": "nan", "4895": "nan", - "4900": 0.30503, + "4900": 0.26116, "4905": "nan", "4910": "nan", "4915": "nan", @@ -9036,7 +9036,7 @@ "4985": "nan", "4990": "nan", "4995": "nan", - "5000": 0.28233, + "5000": 0.29103, "5005": "nan", "5010": "nan", "5015": "nan", @@ -9056,7 +9056,7 @@ "5085": "nan", "5090": "nan", "5095": "nan", - "5100": 0.2813, + "5100": 0.25864, "5105": "nan", "5110": "nan", "5115": "nan", @@ -9076,7 +9076,7 @@ "5185": "nan", "5190": "nan", "5195": "nan", - "5200": 0.40299, + "5200": 0.32394, "5205": "nan", "5210": "nan", "5215": "nan", @@ -9096,7 +9096,7 @@ "5285": "nan", "5290": "nan", "5295": "nan", - "5300": 0.31713, + "5300": 0.2896, "5305": "nan", "5310": "nan", "5315": "nan", @@ -9116,7 +9116,7 @@ "5385": "nan", "5390": "nan", "5395": "nan", - "5400": 0.284, + "5400": 0.25624, "5405": "nan", "5410": "nan", "5415": "nan", @@ -9136,7 +9136,7 @@ "5485": "nan", "5490": "nan", "5495": "nan", - "5500": 0.28358, + "5500": 0.26317, "5505": "nan", "5510": "nan", "5515": "nan", @@ -9156,7 +9156,7 @@ "5585": "nan", "5590": "nan", "5595": "nan", - "5600": 0.3057, + "5600": 0.27198, "5605": "nan", "5610": "nan", "5615": "nan", @@ -9176,7 +9176,7 @@ "5685": "nan", "5690": "nan", "5695": "nan", - "5700": 0.3616, + "5700": 0.25643, "5705": "nan", "5710": "nan", "5715": "nan", @@ -9196,7 +9196,7 @@ "5785": "nan", "5790": "nan", "5795": "nan", - "5800": 0.34267, + "5800": 0.31639, "5805": "nan", "5810": "nan", "5815": "nan", @@ -9216,7 +9216,7 @@ "5885": "nan", "5890": "nan", "5895": "nan", - "5900": 0.28387, + "5900": 0.26555, "5905": "nan", "5910": "nan", "5915": "nan", @@ -9236,7 +9236,7 @@ "5985": "nan", "5990": "nan", "5995": "nan", - "6000": 0.30908, + "6000": 0.31065, "6005": "nan", "6010": "nan", "6015": "nan", @@ -9256,7 +9256,7 @@ "6085": "nan", "6090": "nan", "6095": "nan", - "6100": 0.37056, + "6100": 0.25496, "6105": "nan", "6110": "nan", "6115": "nan", @@ -9276,7 +9276,7 @@ "6185": "nan", "6190": "nan", "6195": "nan", - "6200": 0.29676, + "6200": 0.2982, "6205": "nan", "6210": "nan", "6215": "nan", @@ -9296,7 +9296,7 @@ "6285": "nan", "6290": "nan", "6295": "nan", - "6300": 0.28095, + "6300": 0.29392, "6305": "nan", "6310": "nan", "6315": "nan", @@ -9316,7 +9316,7 @@ "6385": "nan", "6390": "nan", "6395": "nan", - "6400": 0.30156, + "6400": 0.28817, "6405": "nan", "6410": "nan", "6415": "nan", @@ -9336,7 +9336,7 @@ "6485": "nan", "6490": "nan", "6495": "nan", - "6500": 0.29416, + "6500": 0.25475, "6505": "nan", "6510": "nan", "6515": "nan", @@ -9356,7 +9356,7 @@ "6585": "nan", "6590": "nan", "6595": "nan", - "6600": 0.38742, + "6600": 0.29871, "6605": "nan", "6610": "nan", "6615": "nan", @@ -9376,7 +9376,7 @@ "6685": "nan", "6690": "nan", "6695": "nan", - "6700": 0.28391, + "6700": 0.25288, "6705": "nan", "6710": "nan", "6715": "nan", @@ -9396,7 +9396,7 @@ "6785": "nan", "6790": "nan", "6795": "nan", - "6800": 0.30487, + "6800": 0.2556, "6805": "nan", "6810": "nan", "6815": "nan", @@ -9416,7 +9416,7 @@ "6885": "nan", "6890": "nan", "6895": "nan", - "6900": 0.29208, + "6900": 0.27188, "6905": "nan", "6910": "nan", "6915": "nan", @@ -9436,7 +9436,7 @@ "6985": "nan", "6990": "nan", "6995": "nan", - "7000": 0.29311, + "7000": 0.26986, "7005": "nan", "7010": "nan", "7015": "nan", @@ -9456,7 +9456,7 @@ "7085": "nan", "7090": "nan", "7095": "nan", - "7100": 0.28388, + "7100": 0.31668, "7105": "nan", "7110": "nan", "7115": "nan", @@ -9476,7 +9476,7 @@ "7185": "nan", "7190": "nan", "7195": "nan", - "7200": 0.39837, + "7200": 0.28041, "7205": "nan", "7210": "nan", "7215": "nan", @@ -9496,7 +9496,7 @@ "7285": "nan", "7290": "nan", "7295": "nan", - "7300": 0.29632, + "7300": 0.28314, "7305": "nan", "7310": "nan", "7315": "nan", @@ -9516,7 +9516,7 @@ "7385": "nan", "7390": "nan", "7395": "nan", - "7400": 0.29124, + "7400": 0.29607, "7405": "nan", "7410": "nan", "7415": "nan", @@ -9536,7 +9536,7 @@ "7485": "nan", "7490": "nan", "7495": "nan", - "7500": 0.28501, + "7500": 0.26655, "7505": "nan", "7510": "nan", "7515": "nan", @@ -9556,7 +9556,7 @@ "7585": "nan", "7590": "nan", "7595": "nan", - "7600": 0.29897, + "7600": 0.25728, "7605": "nan", "7610": "nan", "7615": "nan", @@ -9576,7 +9576,7 @@ "7685": "nan", "7690": "nan", "7695": "nan", - "7700": 0.37889, + "7700": 0.2782, "7705": "nan", "7710": "nan", "7715": "nan", @@ -9596,7 +9596,7 @@ "7785": "nan", "7790": "nan", "7795": "nan", - "7800": 0.29964, + "7800": 0.27021, "7805": "nan", "7810": "nan", "7815": "nan", @@ -9616,7 +9616,7 @@ "7885": "nan", "7890": "nan", "7895": "nan", - "7900": 0.28896, + "7900": 0.28852, "7905": "nan", "7910": "nan", "7915": "nan", @@ -9636,7 +9636,7 @@ "7985": "nan", "7990": "nan", "7995": "nan", - "8000": 0.29143, + "8000": 0.26955, "8005": "nan", "8010": "nan", "8015": "nan", @@ -9656,7 +9656,7 @@ "8085": "nan", "8090": "nan", "8095": "nan", - "8100": 0.33636, + "8100": 0.2779, "8105": "nan", "8110": "nan", "8115": "nan", @@ -9676,7 +9676,7 @@ "8185": "nan", "8190": "nan", "8195": "nan", - "8200": 0.28154, + "8200": 0.26932, "8205": "nan", "8210": "nan", "8215": "nan", @@ -9696,7 +9696,7 @@ "8285": "nan", "8290": "nan", "8295": "nan", - "8300": 0.2875, + "8300": 0.27803, "8305": "nan", "8310": "nan", "8315": "nan", @@ -9716,7 +9716,7 @@ "8385": "nan", "8390": "nan", "8395": "nan", - "8400": 0.29238, + "8400": 0.27747, "8405": "nan", "8410": "nan", "8415": "nan", @@ -9736,7 +9736,7 @@ "8485": "nan", "8490": "nan", "8495": "nan", - "8500": 0.30846, + "8500": 0.28351, "8505": "nan", "8510": "nan", "8515": "nan", @@ -9756,7 +9756,7 @@ "8585": "nan", "8590": "nan", "8595": "nan", - "8600": 0.35999, + "8600": 0.27633, "8605": "nan", "8610": "nan", "8615": "nan", @@ -9776,7 +9776,7 @@ "8685": "nan", "8690": "nan", "8695": "nan", - "8700": 0.29321, + "8700": 0.24843, "8705": "nan", "8710": "nan", "8715": "nan", @@ -9796,7 +9796,7 @@ "8785": "nan", "8790": "nan", "8795": "nan", - "8800": 0.362, + "8800": 0.2598, "8805": "nan", "8810": "nan", "8815": "nan", @@ -9816,7 +9816,7 @@ "8885": "nan", "8890": "nan", "8895": "nan", - "8900": 0.29259, + "8900": 0.28227, "8905": "nan", "8910": "nan", "8915": "nan", @@ -9836,7 +9836,7 @@ "8985": "nan", "8990": "nan", "8995": "nan", - "9000": 0.28762, + "9000": 0.28951, "9005": "nan", "9010": "nan", "9015": "nan", @@ -9856,7 +9856,7 @@ "9085": "nan", "9090": "nan", "9095": "nan", - "9100": 0.29108, + "9100": 0.26014, "9105": "nan", "9110": "nan", "9115": "nan", @@ -9876,7 +9876,7 @@ "9185": "nan", "9190": "nan", "9195": "nan", - "9200": 0.37687, + "9200": 0.28659, "9205": "nan", "9210": "nan", "9215": "nan", @@ -9896,7 +9896,7 @@ "9285": "nan", "9290": "nan", "9295": "nan", - "9300": 0.302, + "9300": 0.26467, "9305": "nan", "9310": "nan", "9315": "nan", @@ -9916,7 +9916,7 @@ "9385": "nan", "9390": "nan", "9395": "nan", - "9400": 0.28363, + "9400": 0.29121, "9405": "nan", "9410": "nan", "9415": "nan", @@ -9936,7 +9936,7 @@ "9485": "nan", "9490": "nan", "9495": "nan", - "9500": 0.28919, + "9500": 0.25063, "9505": "nan", "9510": "nan", "9515": "nan", @@ -9956,7 +9956,7 @@ "9585": "nan", "9590": "nan", "9595": "nan", - "9600": 0.30407, + "9600": 0.26633, "9605": "nan", "9610": "nan", "9615": "nan", @@ -9976,7 +9976,7 @@ "9685": "nan", "9690": "nan", "9695": "nan", - "9700": 0.36124, + "9700": 0.25729, "9705": "nan", "9710": "nan", "9715": "nan", @@ -9996,7 +9996,7 @@ "9785": "nan", "9790": "nan", "9795": "nan", - "9800": 0.28658, + "9800": 0.29544, "9805": "nan", "9810": "nan", "9815": "nan", @@ -10016,7 +10016,7 @@ "9885": "nan", "9890": "nan", "9895": "nan", - "9900": 0.2893, + "9900": 0.29919, "9905": "nan", "9910": "nan", "9915": "nan", @@ -10036,7 +10036,7 @@ "9985": "nan", "9990": "nan", "9995": "nan", - "10000": 0.29697 + "10000": 0.27986 } } } \ No newline at end of file diff --git a/tests/functional_tests/test_cases/t5/t5_release_sm/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/t5/t5_release_sm/golden_values_dev_dgx_h100.json index 94e5a1b1e47..33cdb10fe52 100644 --- a/tests/functional_tests/test_cases/t5/t5_release_sm/golden_values_dev_dgx_h100.json +++ b/tests/functional_tests/test_cases/t5/t5_release_sm/golden_values_dev_dgx_h100.json @@ 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"nan", @@ -8156,7 +8156,7 @@ "585": "nan", "590": "nan", "595": "nan", - "600": 0.16453, + "600": 0.27728, "605": "nan", "610": "nan", "615": "nan", @@ -8176,7 +8176,7 @@ "685": "nan", "690": "nan", "695": "nan", - "700": 0.19407, + "700": 0.25126, "705": "nan", "710": "nan", "715": "nan", @@ -8196,7 +8196,7 @@ "785": "nan", "790": "nan", "795": "nan", - "800": 0.23124, + "800": 0.22567, "805": "nan", "810": "nan", "815": "nan", @@ -8216,7 +8216,7 @@ "885": "nan", "890": "nan", "895": "nan", - "900": 0.19614, + "900": 0.28606, "905": "nan", "910": "nan", "915": "nan", @@ -8236,7 +8236,7 @@ "985": "nan", "990": "nan", "995": "nan", - "1000": 0.16586, + "1000": 0.27342, "1005": "nan", "1010": "nan", "1015": "nan", @@ -8256,7 +8256,7 @@ "1085": "nan", "1090": "nan", "1095": "nan", - "1100": 0.16901, + "1100": 0.22711, "1105": "nan", "1110": "nan", "1115": "nan", @@ -8276,7 +8276,7 @@ "1185": "nan", "1190": "nan", "1195": "nan", - "1200": 0.21643, + "1200": 0.19882, "1205": "nan", "1210": "nan", "1215": "nan", @@ -8296,7 +8296,7 @@ "1285": "nan", "1290": "nan", "1295": "nan", - "1300": 0.19756, + "1300": 0.20212, "1305": "nan", "1310": "nan", "1315": "nan", @@ -8316,7 +8316,7 @@ "1385": "nan", "1390": "nan", "1395": "nan", - "1400": 0.19925, + "1400": 0.21131, "1405": "nan", "1410": "nan", "1415": "nan", @@ -8336,7 +8336,7 @@ "1485": "nan", "1490": "nan", "1495": "nan", - "1500": 0.16554, + "1500": 0.24687, "1505": "nan", "1510": "nan", "1515": "nan", @@ -8356,7 +8356,7 @@ "1585": "nan", "1590": "nan", "1595": "nan", - "1600": 0.17563, + "1600": 0.22234, "1605": "nan", "1610": "nan", "1615": "nan", @@ -8376,7 +8376,7 @@ "1685": "nan", "1690": "nan", "1695": "nan", - "1700": 0.2172, + "1700": 0.22337, "1705": "nan", "1710": "nan", "1715": "nan", @@ -8396,7 +8396,7 @@ "1785": "nan", "1790": "nan", "1795": "nan", - "1800": 0.17392, + "1800": 0.22232, "1805": "nan", "1810": "nan", "1815": "nan", @@ -8416,7 +8416,7 @@ "1885": "nan", "1890": "nan", "1895": "nan", - "1900": 0.20554, + "1900": 0.22733, "1905": "nan", "1910": "nan", "1915": "nan", @@ -8436,7 +8436,7 @@ "1985": "nan", "1990": "nan", "1995": "nan", - "2000": 0.18468, + "2000": 0.22483, "2005": "nan", "2010": "nan", "2015": "nan", @@ -8456,7 +8456,7 @@ "2085": "nan", "2090": "nan", "2095": "nan", - "2100": 0.20159, + "2100": 0.24478, "2105": "nan", "2110": "nan", "2115": "nan", @@ -8476,7 +8476,7 @@ "2185": "nan", "2190": "nan", "2195": "nan", - "2200": 0.18773, + "2200": 0.21828, "2205": "nan", "2210": "nan", "2215": "nan", @@ -8496,7 +8496,7 @@ "2285": "nan", "2290": "nan", "2295": "nan", - "2300": 0.17279, + "2300": 0.24435, "2305": "nan", "2310": "nan", "2315": "nan", @@ -8516,7 +8516,7 @@ "2385": "nan", "2390": "nan", "2395": "nan", - "2400": 0.16274, + "2400": 0.21397, "2405": "nan", "2410": "nan", "2415": "nan", @@ -8536,7 +8536,7 @@ "2485": "nan", "2490": "nan", "2495": "nan", - "2500": 0.1968, + "2500": 0.19514, "2505": "nan", "2510": "nan", "2515": "nan", @@ -8556,7 +8556,7 @@ "2585": "nan", "2590": "nan", "2595": "nan", - "2600": 0.18491, + "2600": 0.22849, "2605": "nan", "2610": "nan", "2615": "nan", @@ -8576,7 +8576,7 @@ "2685": "nan", "2690": "nan", "2695": "nan", - "2700": 0.21214, + "2700": 0.18141, "2705": "nan", "2710": "nan", "2715": "nan", @@ -8596,7 +8596,7 @@ "2785": "nan", "2790": "nan", "2795": "nan", - "2800": 0.19074, + "2800": 0.22042, "2805": "nan", "2810": "nan", "2815": "nan", @@ -8616,7 +8616,7 @@ "2885": "nan", "2890": "nan", "2895": "nan", - "2900": 0.16448, + "2900": 0.21384, "2905": "nan", "2910": "nan", "2915": "nan", @@ -8636,7 +8636,7 @@ "2985": "nan", "2990": "nan", "2995": "nan", - "3000": 0.2131, + "3000": 0.23119, "3005": "nan", "3010": "nan", "3015": "nan", @@ -8656,7 +8656,7 @@ "3085": "nan", "3090": "nan", "3095": "nan", - "3100": 0.16974, + "3100": 0.23156, "3105": "nan", "3110": "nan", "3115": "nan", @@ -8676,7 +8676,7 @@ "3185": "nan", "3190": "nan", "3195": "nan", - "3200": 0.19537, + "3200": 0.22504, "3205": "nan", "3210": "nan", "3215": "nan", @@ -8696,7 +8696,7 @@ "3285": "nan", "3290": "nan", "3295": "nan", - "3300": 0.20036, + "3300": 0.20113, "3305": "nan", "3310": "nan", "3315": "nan", @@ -8716,7 +8716,7 @@ "3385": "nan", "3390": "nan", "3395": "nan", - "3400": 0.18441, + "3400": 0.21395, "3405": "nan", "3410": "nan", "3415": "nan", @@ -8736,7 +8736,7 @@ "3485": "nan", "3490": "nan", "3495": "nan", - "3500": 0.20315, + "3500": 0.18644, "3505": "nan", "3510": "nan", "3515": "nan", @@ -8756,7 +8756,7 @@ "3585": "nan", "3590": "nan", "3595": "nan", - "3600": 0.17402, + "3600": 0.20486, "3605": "nan", "3610": "nan", "3615": "nan", @@ -8776,7 +8776,7 @@ "3685": "nan", "3690": "nan", "3695": "nan", - "3700": 0.17364, + "3700": 0.22227, "3705": "nan", "3710": "nan", "3715": "nan", @@ -8796,7 +8796,7 @@ "3785": "nan", "3790": "nan", "3795": "nan", - "3800": 0.2033, + "3800": 0.22967, "3805": "nan", "3810": "nan", "3815": "nan", @@ -8816,7 +8816,7 @@ "3885": "nan", "3890": "nan", "3895": "nan", - "3900": 0.22678, + "3900": 0.21997, "3905": "nan", "3910": "nan", "3915": "nan", @@ -8836,7 +8836,7 @@ "3985": "nan", "3990": "nan", "3995": "nan", - "4000": 0.19275, + "4000": 0.23379, "4005": "nan", "4010": "nan", "4015": "nan", @@ -8856,7 +8856,7 @@ "4085": "nan", "4090": "nan", "4095": "nan", - "4100": 0.23807, + "4100": 0.1994, "4105": "nan", "4110": "nan", "4115": "nan", @@ -8876,7 +8876,7 @@ "4185": "nan", "4190": "nan", "4195": "nan", - "4200": 0.20671, + "4200": 0.23747, "4205": "nan", "4210": "nan", "4215": "nan", @@ -8896,7 +8896,7 @@ "4285": "nan", "4290": "nan", "4295": "nan", - "4300": 0.16643, + "4300": 0.21478, "4305": "nan", "4310": "nan", "4315": "nan", @@ -8916,7 +8916,7 @@ "4385": "nan", "4390": "nan", "4395": "nan", - "4400": 0.16303, + "4400": 0.22376, "4405": "nan", "4410": "nan", "4415": "nan", @@ -8936,7 +8936,7 @@ "4485": "nan", "4490": "nan", "4495": "nan", - "4500": 0.17779, + "4500": 0.20637, "4505": "nan", "4510": "nan", "4515": "nan", @@ -8956,7 +8956,7 @@ "4585": "nan", "4590": "nan", "4595": "nan", - "4600": 0.22795, + "4600": 0.21933, "4605": "nan", "4610": "nan", "4615": "nan", @@ -8976,7 +8976,7 @@ "4685": "nan", "4690": "nan", "4695": "nan", - "4700": 0.22139, + "4700": 0.187, "4705": "nan", "4710": "nan", "4715": "nan", @@ -8996,7 +8996,7 @@ "4785": "nan", "4790": "nan", "4795": "nan", - "4800": 0.17577, + "4800": 0.20386, "4805": "nan", "4810": "nan", "4815": "nan", @@ -9016,7 +9016,7 @@ "4885": "nan", "4890": "nan", "4895": "nan", - "4900": 0.16843, + "4900": 0.20359, "4905": "nan", "4910": "nan", "4915": "nan", @@ -9036,7 +9036,7 @@ "4985": "nan", "4990": "nan", "4995": "nan", - "5000": 0.19058, + "5000": 0.24232, "5005": "nan", "5010": "nan", "5015": "nan", @@ -9056,7 +9056,7 @@ "5085": "nan", "5090": "nan", "5095": "nan", - "5100": 0.18888, + "5100": 0.20164, "5105": "nan", "5110": "nan", "5115": "nan", @@ -9076,7 +9076,7 @@ "5185": "nan", "5190": "nan", "5195": "nan", - "5200": 0.22058, + "5200": 0.22261, "5205": "nan", "5210": "nan", "5215": "nan", @@ -9096,7 +9096,7 @@ "5285": "nan", "5290": "nan", "5295": "nan", - "5300": 0.19832, + "5300": 0.21371, "5305": "nan", "5310": "nan", "5315": "nan", @@ -9116,7 +9116,7 @@ "5385": "nan", "5390": "nan", "5395": "nan", - "5400": 0.17353, + "5400": 0.20618, "5405": "nan", "5410": "nan", "5415": "nan", @@ -9136,7 +9136,7 @@ "5485": "nan", "5490": "nan", "5495": "nan", - "5500": 0.19281, + "5500": 0.20212, "5505": "nan", "5510": "nan", "5515": "nan", @@ -9156,7 +9156,7 @@ "5585": "nan", "5590": "nan", "5595": "nan", - "5600": 0.17416, + "5600": 0.20779, "5605": "nan", "5610": "nan", "5615": "nan", @@ -9176,7 +9176,7 @@ "5685": "nan", "5690": "nan", "5695": "nan", - "5700": 0.19599, + "5700": 0.18696, "5705": "nan", "5710": "nan", "5715": "nan", @@ -9196,7 +9196,7 @@ "5785": "nan", "5790": "nan", "5795": "nan", - "5800": 0.20922, + "5800": 0.21884, "5805": "nan", "5810": "nan", "5815": "nan", @@ -9216,7 +9216,7 @@ "5885": "nan", "5890": "nan", "5895": "nan", - "5900": 0.19895, + "5900": 0.21955, "5905": "nan", "5910": "nan", "5915": "nan", @@ -9236,7 +9236,7 @@ "5985": "nan", "5990": "nan", "5995": "nan", - "6000": 0.19545, + "6000": 0.22183, "6005": "nan", "6010": "nan", "6015": "nan", @@ -9256,7 +9256,7 @@ "6085": "nan", "6090": "nan", "6095": "nan", - "6100": 0.23681, + "6100": 0.20021, "6105": "nan", "6110": "nan", "6115": "nan", @@ -9276,7 +9276,7 @@ "6185": "nan", "6190": "nan", "6195": "nan", - "6200": 0.1802, + "6200": 0.21125, "6205": "nan", "6210": "nan", "6215": "nan", @@ -9296,7 +9296,7 @@ "6285": "nan", "6290": "nan", "6295": "nan", - "6300": 0.16751, + "6300": 0.23925, "6305": "nan", "6310": "nan", "6315": "nan", @@ -9316,7 +9316,7 @@ "6385": "nan", "6390": "nan", "6395": "nan", - "6400": 0.17259, + "6400": 0.21829, "6405": "nan", "6410": "nan", "6415": "nan", @@ -9336,7 +9336,7 @@ "6485": "nan", "6490": "nan", "6495": "nan", - "6500": 0.1706, + "6500": 0.18924, "6505": "nan", "6510": "nan", "6515": "nan", @@ -9356,7 +9356,7 @@ "6585": "nan", "6590": "nan", "6595": "nan", - "6600": 0.26332, + "6600": 0.23132, "6605": "nan", "6610": "nan", "6615": "nan", @@ -9376,7 +9376,7 @@ "6685": "nan", "6690": "nan", "6695": "nan", - "6700": 0.19305, + "6700": 0.18455, "6705": "nan", "6710": "nan", "6715": "nan", @@ -9396,7 +9396,7 @@ "6785": "nan", "6790": "nan", "6795": "nan", - "6800": 0.16693, + "6800": 0.22014, "6805": "nan", "6810": "nan", "6815": "nan", @@ -9416,7 +9416,7 @@ "6885": "nan", "6890": "nan", "6895": "nan", - "6900": 0.16853, + "6900": 0.2012, "6905": "nan", "6910": "nan", "6915": "nan", @@ -9436,7 +9436,7 @@ "6985": "nan", "6990": "nan", "6995": "nan", - "7000": 0.18932, + "7000": 0.19878, "7005": "nan", "7010": "nan", "7015": "nan", @@ -9456,7 +9456,7 @@ "7085": "nan", "7090": "nan", "7095": "nan", - "7100": 0.18663, + "7100": 0.23544, "7105": "nan", "7110": "nan", "7115": "nan", @@ -9476,7 +9476,7 @@ "7185": "nan", "7190": "nan", "7195": "nan", - "7200": 0.24374, + "7200": 0.22137, "7205": "nan", "7210": "nan", "7215": "nan", @@ -9496,7 +9496,7 @@ "7285": "nan", "7290": "nan", "7295": "nan", - "7300": 0.18209, + "7300": 0.18641, "7305": "nan", "7310": "nan", "7315": "nan", @@ -9516,7 +9516,7 @@ "7385": "nan", "7390": "nan", "7395": "nan", - "7400": 0.18073, + "7400": 0.22043, "7405": "nan", "7410": "nan", "7415": "nan", @@ -9536,7 +9536,7 @@ "7485": "nan", "7490": "nan", "7495": "nan", - "7500": 0.19253, + "7500": 0.20606, "7505": "nan", "7510": "nan", "7515": "nan", @@ -9556,7 +9556,7 @@ "7585": "nan", "7590": "nan", "7595": "nan", - "7600": 0.17325, + "7600": 0.21034, "7605": "nan", "7610": "nan", "7615": "nan", @@ -9576,7 +9576,7 @@ "7685": "nan", "7690": "nan", "7695": "nan", - "7700": 0.20404, + "7700": 0.21377, "7705": "nan", "7710": "nan", "7715": "nan", @@ -9596,7 +9596,7 @@ "7785": "nan", "7790": "nan", "7795": "nan", - "7800": 0.20846, + "7800": 0.21334, "7805": "nan", "7810": "nan", "7815": "nan", @@ -9616,7 +9616,7 @@ "7885": "nan", "7890": "nan", "7895": "nan", - "7900": 0.19782, + "7900": 0.21413, "7905": "nan", "7910": "nan", "7915": "nan", @@ -9636,7 +9636,7 @@ "7985": "nan", "7990": "nan", "7995": "nan", - "8000": 0.18096, + "8000": 0.21876, "8005": "nan", "8010": "nan", "8015": "nan", @@ -9656,7 +9656,7 @@ "8085": "nan", "8090": "nan", "8095": "nan", - "8100": 0.22632, + "8100": 0.20527, "8105": "nan", "8110": "nan", "8115": "nan", @@ -9676,7 +9676,7 @@ "8185": "nan", "8190": "nan", "8195": "nan", - "8200": 0.17636, + "8200": 0.19562, "8205": "nan", "8210": "nan", "8215": "nan", @@ -9696,7 +9696,7 @@ "8285": "nan", "8290": "nan", "8295": "nan", - "8300": 0.16595, + "8300": 0.22975, "8305": "nan", "8310": "nan", "8315": "nan", @@ -9716,7 +9716,7 @@ "8385": "nan", "8390": "nan", "8395": "nan", - "8400": 0.16216, + "8400": 0.20564, "8405": "nan", "8410": "nan", "8415": "nan", @@ -9736,7 +9736,7 @@ "8485": "nan", "8490": "nan", "8495": "nan", - "8500": 0.18604, + "8500": 0.20158, "8505": "nan", "8510": "nan", "8515": "nan", @@ -9756,7 +9756,7 @@ "8585": "nan", "8590": "nan", "8595": "nan", - "8600": 0.24804, + "8600": 0.22377, "8605": "nan", "8610": "nan", "8615": "nan", @@ -9776,7 +9776,7 @@ "8685": "nan", "8690": "nan", "8695": "nan", - "8700": 0.17814, + "8700": 0.19409, "8705": "nan", "8710": "nan", "8715": "nan", @@ -9796,7 +9796,7 @@ "8785": "nan", "8790": "nan", "8795": "nan", - "8800": 0.16206, + "8800": 0.19264, "8805": "nan", "8810": "nan", "8815": "nan", @@ -9816,7 +9816,7 @@ "8885": "nan", "8890": "nan", "8895": "nan", - "8900": 0.17354, + "8900": 0.19207, "8905": "nan", "8910": "nan", "8915": "nan", @@ -9836,7 +9836,7 @@ "8985": "nan", "8990": "nan", "8995": "nan", - "9000": 0.19415, + "9000": 0.21374, "9005": "nan", "9010": "nan", "9015": "nan", @@ -9856,7 +9856,7 @@ "9085": "nan", "9090": "nan", "9095": "nan", - "9100": 0.18542, + "9100": 0.20926, "9105": "nan", "9110": "nan", "9115": "nan", @@ -9876,7 +9876,7 @@ "9185": "nan", "9190": "nan", "9195": "nan", - "9200": 0.23006, + "9200": 0.20711, "9205": "nan", "9210": "nan", "9215": "nan", @@ -9896,7 +9896,7 @@ "9285": "nan", "9290": "nan", "9295": "nan", - "9300": 0.16977, + "9300": 0.1998, "9305": "nan", "9310": "nan", "9315": "nan", @@ -9916,7 +9916,7 @@ "9385": "nan", "9390": "nan", "9395": "nan", - "9400": 0.17647, + "9400": 0.22438, "9405": "nan", "9410": "nan", "9415": "nan", @@ -9936,7 +9936,7 @@ "9485": "nan", "9490": "nan", "9495": "nan", - "9500": 0.19239, + "9500": 0.21453, "9505": "nan", "9510": "nan", "9515": "nan", @@ -9956,7 +9956,7 @@ "9585": "nan", "9590": "nan", "9595": "nan", - "9600": 0.17461, + "9600": 0.19704, "9605": "nan", "9610": "nan", "9615": "nan", @@ -9976,7 +9976,7 @@ "9685": "nan", "9690": "nan", "9695": "nan", - "9700": 0.19822, + "9700": 0.20792, "9705": "nan", "9710": "nan", "9715": "nan", @@ -9996,7 +9996,7 @@ "9785": "nan", "9790": "nan", "9795": "nan", - "9800": 0.20847, + "9800": 0.22644, "9805": "nan", "9810": "nan", "9815": "nan", @@ -10016,7 +10016,7 @@ "9885": "nan", "9890": "nan", "9895": "nan", - "9900": 0.19319, + "9900": 0.21189, "9905": "nan", "9910": "nan", "9915": "nan", @@ -10036,7 +10036,7 @@ "9985": "nan", "9990": "nan", "9995": "nan", - "10000": 0.18154 + "10000": 0.20788 } } } \ No newline at end of file diff --git a/tests/unit_tests/distributed/megatron_fsdp/test_dbuffer.py b/tests/unit_tests/distributed/megatron_fsdp/test_dbuffer.py new file mode 100644 index 00000000000..a6ae56dc852 --- /dev/null +++ b/tests/unit_tests/distributed/megatron_fsdp/test_dbuffer.py @@ -0,0 +1,573 @@ +# Copyright (c) 2026, NVIDIA CORPORATION. All rights reserved. + +"""Unit tests for Megatron-FSDP DBuffer.""" + +from collections.abc import Iterable +from typing import cast + +import pytest +import torch +import torch.distributed as dist +from torch.distributed.device_mesh import init_device_mesh + +from megatron.core.distributed.fsdp.src.megatron_fsdp.experimental.dbuffer import ( + DBuffer, + Flat, + Partial, + Replicate, +) + + +def _same_tensors_on_all_ranks(device: torch.device) -> list[torch.Tensor]: + return [ + torch.arange(21, dtype=torch.float32, device=device).reshape(7, 3), + torch.arange(10, dtype=torch.float32, device=device).reshape(2, 5) + 100, + torch.arange(7, dtype=torch.float32, device=device) + 200, + ] + + +def _assert_dbuffer_local_tensors_close(buffer: DBuffer, expected: Iterable[torch.Tensor]) -> None: + for index, tensor in enumerate(expected): + torch.testing.assert_close(buffer.get_local_tensor(index), tensor) + + +@pytest.mark.distributed +def test_dbuffer_layout_pads_to_lcm_times_dp_size_and_fills_gaps(distributed_setup): + """DBuffer layout returns element offsets and pads to LCM * DP size.""" + if distributed_setup.world_size < 2: + pytest.skip("DBuffer layout test requires at least 2 ranks.") + + mesh = init_device_mesh(distributed_setup.device.type, (2,)) + shapes = [torch.Size((5, 4)), torch.Size((2, 6)), torch.Size((3,))] + + buffer = DBuffer( + mesh=mesh, + placements=[Replicate()], + tensor_shapes=shapes, + dtype=torch.float32, + device=distributed_setup.device, + ) + + assert buffer.layout.tensor_shapes == tuple(shapes) + assert buffer.layout.tensor_to_offset == (0, 24, 20) + assert buffer.layout.size == 48 + + +@pytest.mark.distributed +def test_dbuffer_layout_aligns_fragment_offsets_to_rows(distributed_setup): + """DBuffer layout keeps small tensors aligned to their non-leading dimensions.""" + if distributed_setup.world_size < 2: + pytest.skip("DBuffer layout test requires at least 2 ranks.") + + mesh = init_device_mesh(distributed_setup.device.type, (2,)) + shapes = [torch.Size((4, 4)), torch.Size((1, 6))] + + buffer = DBuffer( + mesh=mesh, + placements=[Replicate()], + tensor_shapes=shapes, + dtype=torch.float32, + device=distributed_setup.device, + ) + + assert buffer.layout.tensor_to_offset == (0, 18) + assert buffer.layout.size == 24 + + +@pytest.mark.distributed +def test_compute_layout_fills_lcm_padding_gaps(distributed_setup): + """LCM packing fills row-aligned padding gaps on a 5-rank flat-sharded mesh.""" + if distributed_setup.world_size < 5: + pytest.skip("LCM padding-gap layout test requires at least 5 ranks.") + + # P0-P4 are zero-based logical tensor names matching tensor indices. + shapes = [ + torch.Size((2, 6)), # P0 + torch.Size((4, 4)), # P1 + torch.Size((4, 4)), # P2 + torch.Size((1, 2)), # P3 + torch.Size((1, 6)), # P4 + ] + + mesh = init_device_mesh(distributed_setup.device.type, (5,)) + if mesh.get_coordinate() is None: + pytest.skip("Rank is outside the 5-rank DBuffer mesh.") + + buffer = DBuffer( + mesh=mesh, + placements=[Flat()], + tensor_shapes=shapes, + dtype=torch.float32, + device=distributed_setup.device, + ) + layout = buffer.layout + expected_local_shapes_by_rank = [ + [(2, 6), (0, 4), (0, 4), (0, 2), (0, 6)], + [(0, 6), (3, 4), (0, 4), (0, 2), (0, 6)], + [(0, 6), (1, 4), (1, 4), (1, 2), (0, 6)], + [(0, 6), (0, 4), (3, 4), (0, 2), (0, 6)], + [(0, 6), (0, 4), (0, 4), (0, 2), (1, 6)], + ] + + assert layout.tensor_shapes == tuple(shapes) + assert layout.tensor_to_offset == (0, 12, 32, 28, 48) + assert layout.size == 60 + expected_local_shapes = expected_local_shapes_by_rank[mesh.get_local_rank(0)] + for index, expected_shape in enumerate(expected_local_shapes): + assert buffer.get_local_tensor(index).shape == torch.Size(expected_shape) + assert buffer.get_dtensor(index).shape == shapes[index] + + +@pytest.mark.distributed +def test_constructor_allocates_local_buffer(distributed_setup): + """DBuffer allocates local storage from shape, mesh, placement, dtype, and device.""" + mesh = init_device_mesh(distributed_setup.device.type, (distributed_setup.world_size,)) + tensor_shapes = [torch.Size((7, 3)), torch.Size((2, 5)), torch.Size((7,))] + mesh_size = mesh.size() + + replicated_buffer = DBuffer( + mesh=mesh, + placements=[Replicate()], + tensor_shapes=tensor_shapes, + dtype=torch.float32, + device=distributed_setup.device, + ) + sharded_buffer = DBuffer( + mesh=mesh, + placements=[Flat()], + tensor_shapes=tensor_shapes, + dtype=torch.float32, + device=distributed_setup.device, + ) + + assert replicated_buffer.layout == sharded_buffer.layout + assert replicated_buffer.layout.tensor_shapes == tuple(tensor_shapes) + assert replicated_buffer.offset == 0 + expected_sharded_local_numel = replicated_buffer.layout.size // distributed_setup.world_size + assert sharded_buffer.offset == distributed_setup.rank * expected_sharded_local_numel + assert replicated_buffer.local_buffer.numel() == replicated_buffer.layout.size + assert ( + sharded_buffer.local_buffer.numel() + == replicated_buffer.layout.size // distributed_setup.world_size + ) + assert sharded_buffer.layout.size % (15 * mesh_size) == 0 + assert replicated_buffer.dtype == torch.float32 + assert sharded_buffer.local_buffer.device == distributed_setup.device + + +@pytest.mark.distributed +def test_from_local_reuses_required_local_buffer(distributed_setup): + """DBuffer.from_local reuses caller-provided local storage without allocation.""" + mesh = init_device_mesh(distributed_setup.device.type, (distributed_setup.world_size,)) + tensors = _same_tensors_on_all_ranks(distributed_setup.device) + replicated_buffer = DBuffer.distribute_tensors(tensors, mesh, [Replicate()]) + local_numel = replicated_buffer.layout.size // distributed_setup.world_size + offset = distributed_setup.rank * local_numel + local_buffer = replicated_buffer.local_buffer.narrow(0, offset, local_numel) + + sharded_buffer = DBuffer.from_local( + local_buffer, mesh, iter([Flat()]), replicated_buffer.layout.tensor_shapes + ) + + assert sharded_buffer.placements == (Flat(),) + assert sharded_buffer.layout == replicated_buffer.layout + assert sharded_buffer.offset == offset + assert sharded_buffer.local_buffer.data_ptr() == local_buffer.data_ptr() + _assert_dbuffer_local_tensors_close(sharded_buffer.allgather(0), tensors) + + +@pytest.mark.distributed +def test_replicate_get_local_tensor_and_dtensor(distributed_setup): + """Replicated DBuffer returns full local tensors and replicated DTensors.""" + mesh = init_device_mesh(distributed_setup.device.type, (distributed_setup.world_size,)) + tensors = _same_tensors_on_all_ranks(distributed_setup.device) + + buffer = DBuffer.distribute_tensors(tensors, mesh, [Replicate()]) + + _assert_dbuffer_local_tensors_close(buffer, tensors) + dtensor = buffer.get_dtensor(0) + torch.testing.assert_close(dtensor.to_local(), tensors[0], rtol=0, atol=0) + + +@pytest.mark.distributed +def test_distribute_tensors_moves_inputs_to_mesh_device(distributed_setup): + """distribute_tensors moves full input tensors to the mesh device type.""" + mesh = init_device_mesh(distributed_setup.device.type, (distributed_setup.world_size,)) + tensors = _same_tensors_on_all_ranks(torch.device("cpu")) + + buffer = DBuffer.distribute_tensors(tensors, mesh, [Replicate()]) + + assert buffer.local_buffer.device == distributed_setup.device + _assert_dbuffer_local_tensors_close( + buffer, [tensor.to(distributed_setup.device) for tensor in tensors] + ) + + +@pytest.mark.distributed +def test_sharded_allgather_round_trip(distributed_setup): + """Sharded buffers round-trip through all-gather as contiguous tensor fragments.""" + mesh = init_device_mesh(distributed_setup.device.type, (distributed_setup.world_size,)) + tensors = _same_tensors_on_all_ranks(distributed_setup.device) + + sharded_buffer = DBuffer.distribute_tensors(tensors, mesh, [Flat()]) + layout = sharded_buffer.layout + for index, tensor in enumerate(tensors): + local_tensor = sharded_buffer.get_local_tensor(index) + assert local_tensor.shape[1:] == tensor.shape[1:] + assert local_tensor.is_contiguous() + + replicated_buffer = sharded_buffer.allgather(0) + + assert replicated_buffer.layout == layout + _assert_dbuffer_local_tensors_close(replicated_buffer, tensors) + + +@pytest.mark.distributed +def test_sharded_allgather_into_existing_buffer(distributed_setup): + """Sharded buffers can all-gather directly into a preallocated replicated buffer.""" + mesh = init_device_mesh(distributed_setup.device.type, (distributed_setup.world_size,)) + tensors = _same_tensors_on_all_ranks(distributed_setup.device) + sharded_buffer = DBuffer.distribute_tensors(tensors, mesh, [Flat()]) + destination = DBuffer( + mesh=mesh, + placements=[Replicate()], + tensor_shapes=sharded_buffer.layout.tensor_shapes, + dtype=sharded_buffer.dtype, + device=sharded_buffer.local_buffer.device, + ) + destination_data_ptr = destination.local_buffer.data_ptr() + + result = sharded_buffer.allgather(0, out=destination) + + assert result is destination + assert destination.local_buffer.data_ptr() == destination_data_ptr + _assert_dbuffer_local_tensors_close(destination, tensors) + + +@pytest.mark.distributed +def test_mesh_axis_must_be_non_negative_int(distributed_setup): + """DBuffer communication methods require explicit non-negative integer mesh axes.""" + mesh = init_device_mesh(distributed_setup.device.type, (distributed_setup.world_size,)) + buffer = DBuffer( + mesh=mesh, + placements=[Replicate()], + tensor_shapes=[torch.Size((4,))], + dtype=torch.float32, + device=distributed_setup.device, + ) + + with pytest.raises(TypeError, match="Mesh axis must be an int"): + buffer.allgather(cast(int, "dp")) + with pytest.raises(TypeError, match="Mesh axis must be an int"): + buffer.allgather(True) + with pytest.raises(ValueError, match="Mesh axis -1 is out of bounds"): + buffer.allgather(-1) + + +@pytest.mark.distributed +def test_replicate_scatter_round_trip(distributed_setup): + """Replicated buffers locally chunk into sharded buffers and all-gather back.""" + mesh = init_device_mesh(distributed_setup.device.type, (distributed_setup.world_size,)) + tensors = _same_tensors_on_all_ranks(distributed_setup.device) + + replicated_buffer = DBuffer.distribute_tensors(tensors, mesh, [Replicate()]) + sharded_buffer = replicated_buffer.scatter(0, Flat()) + redistribute_destination = DBuffer( + mesh=mesh, + placements=[Flat()], + tensor_shapes=replicated_buffer.layout.tensor_shapes, + dtype=replicated_buffer.dtype, + device=replicated_buffer.local_buffer.device, + ) + redistributed_sharded_buffer = replicated_buffer.redistribute( + [Flat()], out=redistribute_destination + ) + + assert sharded_buffer.placements == (Flat(),) + assert redistributed_sharded_buffer is redistribute_destination + assert redistributed_sharded_buffer.placements == (Flat(),) + expected_sharded_local_numel = replicated_buffer.layout.size // distributed_setup.world_size + assert sharded_buffer.offset == distributed_setup.rank * expected_sharded_local_numel + assert ( + sharded_buffer.local_buffer.untyped_storage() + is replicated_buffer.local_buffer.untyped_storage() + ) + torch.testing.assert_close( + sharded_buffer.local_buffer, redistributed_sharded_buffer.local_buffer, rtol=0, atol=0 + ) + _assert_dbuffer_local_tensors_close(sharded_buffer.allgather(0), tensors) + + +@pytest.mark.distributed +def test_partial_allreduce(distributed_setup): + """Partial buffers all-reduce into replicated buffers.""" + mesh = init_device_mesh(distributed_setup.device.type, (distributed_setup.world_size,)) + rank_scale = float(distributed_setup.rank + 1) + tensors = [ + torch.full((5, 3), rank_scale, dtype=torch.float32, device=distributed_setup.device), + torch.full((4,), rank_scale * 10, dtype=torch.float32, device=distributed_setup.device), + ] + partial_buffer = DBuffer.distribute_tensors(tensors, mesh, [Partial()]) + + replicated_buffer = partial_buffer.allreduce(0) + + scale_sum = float(distributed_setup.world_size * (distributed_setup.world_size + 1) // 2) + expected = [ + torch.full((5, 3), scale_sum, dtype=torch.float32, device=distributed_setup.device), + torch.full((4,), scale_sum * 10, dtype=torch.float32, device=distributed_setup.device), + ] + _assert_dbuffer_local_tensors_close(replicated_buffer, expected) + + +@pytest.mark.distributed +def test_partial_allreduce_average(distributed_setup): + """Partial buffers can all-reduce with AVG.""" + mesh = init_device_mesh(distributed_setup.device.type, (distributed_setup.world_size,)) + rank_scale = float(distributed_setup.rank + 1) + tensors = [ + torch.full((5, 3), rank_scale, dtype=torch.float32, device=distributed_setup.device), + torch.full((4,), rank_scale * 10, dtype=torch.float32, device=distributed_setup.device), + ] + partial_buffer = DBuffer.distribute_tensors( + tensors, mesh, [Partial(reduce_op=dist.ReduceOp.AVG)] + ) + + destination = DBuffer( + mesh=mesh, + placements=[Replicate()], + tensor_shapes=partial_buffer.layout.tensor_shapes, + dtype=partial_buffer.dtype, + device=partial_buffer.local_buffer.device, + ) + replicated_buffer = partial_buffer.allreduce(0, out=destination) + + assert replicated_buffer is destination + scale_average = float(distributed_setup.world_size + 1) / 2.0 + expected = [ + torch.full((5, 3), scale_average, dtype=torch.float32, device=distributed_setup.device), + torch.full((4,), scale_average * 10, dtype=torch.float32, device=distributed_setup.device), + ] + _assert_dbuffer_local_tensors_close(replicated_buffer, expected) + + +@pytest.mark.distributed +def test_partial_reduce_scatter_to_flat(distributed_setup): + """Partial buffers reduce-scatter into sharded buffers.""" + mesh = init_device_mesh(distributed_setup.device.type, (distributed_setup.world_size,)) + rank_scale = float(distributed_setup.rank + 1) + tensors = [ + torch.full((5, 3), rank_scale, dtype=torch.float32, device=distributed_setup.device), + torch.full((4,), rank_scale * 10, dtype=torch.float32, device=distributed_setup.device), + ] + partial_buffer = DBuffer.distribute_tensors(tensors, mesh, [Partial()]) + layout = partial_buffer.layout + + destination = DBuffer( + mesh=mesh, + placements=[Flat()], + tensor_shapes=partial_buffer.layout.tensor_shapes, + dtype=partial_buffer.dtype, + device=partial_buffer.local_buffer.device, + ) + sharded_buffer = partial_buffer.reduce_scatter(0, Flat(), out=destination) + replicated_buffer = sharded_buffer.allgather(0) + + assert sharded_buffer is destination + assert sharded_buffer.placements == (Flat(),) + assert sharded_buffer.layout == layout + assert replicated_buffer.layout == layout + scale_sum = float(distributed_setup.world_size * (distributed_setup.world_size + 1) // 2) + expected_tensors = [ + torch.full((5, 3), scale_sum, dtype=torch.float32, device=distributed_setup.device), + torch.full((4,), scale_sum * 10, dtype=torch.float32, device=distributed_setup.device), + ] + _assert_dbuffer_local_tensors_close(replicated_buffer, expected_tensors) + + +@pytest.mark.distributed +def test_partial_reduce_scatter_to_flat_average(distributed_setup): + """Partial buffers can reduce-scatter with AVG.""" + mesh = init_device_mesh(distributed_setup.device.type, (distributed_setup.world_size,)) + rank_scale = float(distributed_setup.rank + 1) + tensors = [ + torch.full((5, 3), rank_scale, dtype=torch.float32, device=distributed_setup.device), + torch.full((4,), rank_scale * 10, dtype=torch.float32, device=distributed_setup.device), + ] + partial_buffer = DBuffer.distribute_tensors( + tensors, mesh, [Partial(reduce_op=dist.ReduceOp.AVG)] + ) + layout = partial_buffer.layout + + sharded_buffer = partial_buffer.reduce_scatter(0, Flat()) + replicated_buffer = sharded_buffer.allgather(0) + + assert sharded_buffer.placements == (Flat(),) + assert sharded_buffer.layout == layout + assert replicated_buffer.layout == layout + scale_average = float(distributed_setup.world_size + 1) / 2.0 + expected_tensors = [ + torch.full((5, 3), scale_average, dtype=torch.float32, device=distributed_setup.device), + torch.full((4,), scale_average * 10, dtype=torch.float32, device=distributed_setup.device), + ] + _assert_dbuffer_local_tensors_close(replicated_buffer, expected_tensors) + + +@pytest.mark.distributed +def test_get_dtensor_from_sharded_buffer(distributed_setup): + """Sharded DBuffer exposes per-tensor local shards as DTensors.""" + mesh = init_device_mesh(distributed_setup.device.type, (distributed_setup.world_size,)) + tensors = _same_tensors_on_all_ranks(distributed_setup.device) + sharded_buffer = DBuffer.distribute_tensors(tensors, mesh, [Flat()]) + + dtensor = sharded_buffer.get_dtensor(0) + + torch.testing.assert_close( + dtensor.to_local(), sharded_buffer.get_local_tensor(0), rtol=0, atol=0 + ) + assert dtensor.shape == tensors[0].shape + + +@pytest.mark.distributed +def test_2d_mesh_replicate_flat_round_trip(distributed_setup): + """A 2D mesh can replicate on one axis and flat-shard on the other.""" + if distributed_setup.world_size < 4 or distributed_setup.world_size % 2 != 0: + pytest.skip("2D DBuffer test requires an even world size of at least 4.") + + tensors = _same_tensors_on_all_ranks(distributed_setup.device) + mesh = init_device_mesh( + distributed_setup.device.type, + (2, distributed_setup.world_size // 2), + mesh_dim_names=("replicate", "flat"), + ) + + sharded_buffer = DBuffer.distribute_tensors(tensors, mesh, [Replicate(), Flat()]) + replicated_buffer = sharded_buffer.allgather(1) + + _assert_dbuffer_local_tensors_close(replicated_buffer, tensors) + + +@pytest.mark.distributed +def test_2d_mesh_flat_before_replicate_is_rejected(distributed_setup): + """Flat axes must be a suffix to keep every local buffer contiguous.""" + if distributed_setup.world_size < 4 or distributed_setup.world_size % 2 != 0: + pytest.skip("2D DBuffer test requires an even world size of at least 4.") + + mesh = init_device_mesh( + distributed_setup.device.type, + (2, distributed_setup.world_size // 2), + mesh_dim_names=("flat", "replicate"), + ) + + with pytest.raises(ValueError, match="Flat placements must be a suffix"): + DBuffer( + mesh=mesh, + placements=[Flat(), Replicate()], + tensor_shapes=[torch.Size((6, 4))], + dtype=torch.float32, + device=distributed_setup.device, + ) + + +@pytest.mark.distributed +def test_2d_mesh_shards_across_all_ranks(distributed_setup): + """Multiple Flat axes shard local storage by the product of their mesh sizes.""" + if distributed_setup.world_size < 4 or distributed_setup.world_size % 2 != 0: + pytest.skip("2D DBuffer test requires an even world size of at least 4.") + + tensors = _same_tensors_on_all_ranks(distributed_setup.device) + mesh = init_device_mesh( + distributed_setup.device.type, + (2, distributed_setup.world_size // 2), + mesh_dim_names=("dp_outer", "dp_inner"), + ) + fully_sharded_buffer = DBuffer.distribute_tensors(tensors, mesh, [Flat(), Flat()]) + + assert fully_sharded_buffer.layout.tensor_shapes == tuple(tensor.shape for tensor in tensors) + expected_local_numel = fully_sharded_buffer.layout.size // mesh.size() + expected_inner_axis_shard_numel = fully_sharded_buffer.layout.size // mesh.size(1) + expected_offset = ( + mesh.get_local_rank(1) * expected_inner_axis_shard_numel + + mesh.get_local_rank(0) * expected_local_numel + ) + assert fully_sharded_buffer.offset == expected_offset + assert ( + fully_sharded_buffer.local_buffer.numel() == fully_sharded_buffer.layout.size // mesh.size() + ) + for index, _ in enumerate(tensors): + assert fully_sharded_buffer.get_local_tensor(index).is_contiguous() + + +@pytest.mark.distributed +def test_2d_mesh_partial_flat_reduce_scatter_to_flat_flat(distributed_setup): + """Partial+Flat reduce-scatter reduces the existing Flat local shard.""" + if distributed_setup.world_size < 4 or distributed_setup.world_size % 2 != 0: + pytest.skip("2D DBuffer test requires an even world size of at least 4.") + + mesh = init_device_mesh( + distributed_setup.device.type, + (2, distributed_setup.world_size // 2), + mesh_dim_names=("dp_outer", "dp_inner"), + ) + outer_scale = float(mesh.get_local_rank(0) + 1) + tensors = [ + torch.full((6, 2), outer_scale, dtype=torch.float32, device=distributed_setup.device), + torch.full((4,), outer_scale * 10, dtype=torch.float32, device=distributed_setup.device), + ] + + partial_sharded_buffer = DBuffer.distribute_tensors(tensors, mesh, [Partial(), Flat()]) + fully_sharded_buffer = partial_sharded_buffer.reduce_scatter(0, Flat()) + replicated_buffer = fully_sharded_buffer.allgather(0).allgather(1) + + assert fully_sharded_buffer.placements == (Flat(), Flat()) + expected_local_numel = fully_sharded_buffer.layout.size // mesh.size() + expected_inner_axis_shard_numel = fully_sharded_buffer.layout.size // mesh.size(1) + expected_offset = ( + mesh.get_local_rank(1) * expected_inner_axis_shard_numel + + mesh.get_local_rank(0) * expected_local_numel + ) + assert fully_sharded_buffer.offset == expected_offset + assert ( + fully_sharded_buffer.local_buffer.numel() + == partial_sharded_buffer.local_buffer.numel() // 2 + ) + + outer_scale_sum = float(mesh.size(0) * (mesh.size(0) + 1) // 2) + expected = [ + torch.full((6, 2), outer_scale_sum, dtype=torch.float32, device=distributed_setup.device), + torch.full( + (4,), outer_scale_sum * 10, dtype=torch.float32, device=distributed_setup.device + ), + ] + _assert_dbuffer_local_tensors_close(replicated_buffer, expected) + + +@pytest.mark.distributed +def test_2d_mesh_replicate_flat_scatter_to_flat_flat(distributed_setup): + """Replicate+Flat scatter chunks the existing Flat local shard.""" + if distributed_setup.world_size < 4 or distributed_setup.world_size % 2 != 0: + pytest.skip("2D DBuffer test requires an even world size of at least 4.") + + tensors = _same_tensors_on_all_ranks(distributed_setup.device) + mesh = init_device_mesh( + distributed_setup.device.type, + (2, distributed_setup.world_size // 2), + mesh_dim_names=("dp_outer", "dp_inner"), + ) + + replicated_sharded_buffer = DBuffer.distribute_tensors(tensors, mesh, [Replicate(), Flat()]) + fully_sharded_buffer = replicated_sharded_buffer.scatter(0, Flat()) + replicated_buffer = fully_sharded_buffer.allgather(0).allgather(1) + + assert fully_sharded_buffer.placements == (Flat(), Flat()) + expected_local_numel = fully_sharded_buffer.layout.size // mesh.size() + expected_inner_axis_shard_numel = fully_sharded_buffer.layout.size // mesh.size(1) + expected_offset = ( + mesh.get_local_rank(1) * expected_inner_axis_shard_numel + + mesh.get_local_rank(0) * expected_local_numel + ) + assert fully_sharded_buffer.offset == expected_offset + assert ( + fully_sharded_buffer.local_buffer.numel() + == replicated_sharded_buffer.local_buffer.numel() // 2 + ) + _assert_dbuffer_local_tensors_close(replicated_buffer, tensors) diff --git a/tests/unit_tests/inference/test_inference_shards.py b/tests/unit_tests/inference/test_inference_shards.py new file mode 100644 index 00000000000..22444566d87 --- /dev/null +++ b/tests/unit_tests/inference/test_inference_shards.py @@ -0,0 +1,75 @@ +# Copyright (c) 2026, NVIDIA CORPORATION. All rights reserved. + +"""Shard spec parsing/validation (CPU; no torch.distributed).""" + +import pytest + +from megatron.core.inference.shards_spec import ( + InferenceShardSpec, + normalize_shard_specs, + parse_inference_shards_spec, +) + + +def test_shard_spec_objects_match_string_parsing(): + objs = [InferenceShardSpec(tp=2, role="prefill"), InferenceShardSpec(tp=1, dp=2, role="decode")] + assert normalize_shard_specs(objs, 4) == parse_inference_shards_spec( + "tp=2,role=prefill+tp=1,dp=2,role=decode", 4 + ) + # expt_tp defaults to tp; raw dicts also accepted + assert InferenceShardSpec(tp=4).to_dict()["expt_tp"] == 4 + assert normalize_shard_specs([{"tp": 1, "role": "prefill"}, {"tp": 1, "role": "decode"}], 2) + # bad role rejected at construction + with pytest.raises(ValueError): + InferenceShardSpec(tp=1, role="both") + + +# -------------------------------------------------------------------------- +# spec parsing +# -------------------------------------------------------------------------- +def test_parse_defaults_and_dp_and_role(): + specs = parse_inference_shards_spec("tp=2,role=prefill+tp=1,dp=2,role=decode", world_size=4) + # parser returns InferenceShardSpec with defaults filled (expt_tp -> tp). + assert specs[0] == InferenceShardSpec(tp=2, role="prefill") + assert specs[1] == InferenceShardSpec(tp=1, dp=2, role="decode") + # dict form (serialization / external consumers) carries the resolved keys. + assert specs[0].to_dict() == { + "tp": 2, + "pp": 1, + "ep": 1, + "dp": 1, + "expt_tp": 2, + "role": "prefill", + } + + +def test_parse_partitions_world_with_dp(): + # world must equal sum(tp*pp*dp): 2 + (1*1*2) = 4 + parse_inference_shards_spec("tp=2,role=prefill+tp=1,dp=2,role=decode", world_size=4) + with pytest.raises(AssertionError): + parse_inference_shards_spec("tp=2,role=prefill+tp=1,dp=2,role=decode", world_size=5) + + +def test_parse_rejects_bad_role_and_unknown_key(): + with pytest.raises(AssertionError): + parse_inference_shards_spec("tp=1,role=both", world_size=1) + with pytest.raises(AssertionError): + parse_inference_shards_spec("tp=1,foo=2", world_size=1) + + +def test_plus_and_semicolon_separators_equivalent(): + a = parse_inference_shards_spec("tp=2,role=prefill+tp=1,role=decode", world_size=3) + b = parse_inference_shards_spec("tp=2,role=prefill;tp=1,role=decode", world_size=3) + assert a == b + + +def test_cp_accepted_only_when_one(): + # cp is a recognized key (clear error, not "unknown key") but must be 1: + # inference shards don't context-parallelize. + assert parse_inference_shards_spec("tp=2,cp=1", world_size=2) == [ + InferenceShardSpec(tp=2, cp=1) + ] + with pytest.raises(ValueError): + InferenceShardSpec(tp=1, cp=2) + with pytest.raises(ValueError): + parse_inference_shards_spec("tp=1,cp=2", world_size=1) diff --git a/tests/unit_tests/models/mimo/test_mimo_hetero_runtime.py b/tests/unit_tests/models/mimo/test_mimo_hetero_runtime.py new file mode 100644 index 00000000000..5a56b4717f0 --- /dev/null +++ b/tests/unit_tests/models/mimo/test_mimo_hetero_runtime.py @@ -0,0 +1,161 @@ +# Copyright (c) 2026, NVIDIA CORPORATION. All rights reserved. + +"""Tests for MIMO per-rank runtime setup (RNG seeding, bucket sizing, DDP wrapping).""" + +import argparse + +import pytest +import torch + +from examples.mimo.training.runtime import configure_module_rng, wrap_active_modules_with_ddp +from examples.mimo.training.topology import ModuleGridSpec, create_topology +from megatron.core.distributed import DistributedDataParallel, DistributedDataParallelConfig +from megatron.core.models.mimo.config.base_configs import MimoModelConfig +from megatron.core.models.mimo.config.role import MIMO_LANGUAGE_MODULE_KEY +from megatron.core.models.mimo.model.base import MimoModel +from megatron.core.tensor_parallel.random import get_cuda_rng_tracker +from megatron.core.transformer.module import Float16Module +from megatron.core.utils import unwrap_model +from megatron.training.training import resolve_ddp_bucket_size +from tests.unit_tests.models.mimo.test_mimo_1f1b_schedule import ( + get_language_model_spec, + get_vision_submodules_spec, +) +from tests.unit_tests.test_utilities import Utils + +ENCODER = "images" + + +def _args(**overrides): + base = dict( + seed=1234, image_token_id=100, fp32=True, ddp_num_buckets=None, ddp_bucket_size=None + ) + base.update(overrides) + return argparse.Namespace(**base) + + +def _build_unwrapped_mimo_model(topo, bf16=False): + """Build a bare (un-DDP-wrapped) MimoModel over a HeteroTopology's per-module PGCs.""" + mimo_config = MimoModelConfig( + language_model_spec=get_language_model_spec( + num_layers=2, + hidden_size=16, + num_attention_heads=4, + vocab_size=128, + seq_len=8, + pg_collection=topo.module_pgs[MIMO_LANGUAGE_MODULE_KEY], + bf16=bf16, + ), + modality_submodules_spec={ + ENCODER: get_vision_submodules_spec( + num_layers=2, + hidden_size=16, + num_attention_heads=4, + language_hidden_size=16, + pg_collection=topo.module_pgs[ENCODER], + bf16=bf16, + ) + }, + special_token_ids={ENCODER: 50257}, + module_to_grid_map=topo.grids, + ) + mimo_model = MimoModel(mimo_config) + mimo_model.to(torch.device("cuda")) + return mimo_model + + +def _eight_gpu_topology(): + """Encoder dp=4 at ranks 0-3; language dp=4 at ranks 4-7 (non-colocated, tiles world).""" + return create_topology( + [ + ModuleGridSpec(name=ENCODER, num_ranks=4, rank_offset=0), + ModuleGridSpec(name=MIMO_LANGUAGE_MODULE_KEY, num_ranks=4, rank_offset=4), + ] + ) + + +@pytest.mark.parametrize( + "config, overlap, num_params, expected", + [ + # num_buckets divides the param count. + (DistributedDataParallelConfig(num_buckets=4), True, 128, 128 // 4), + # explicit bucket_size passes through. + (DistributedDataParallelConfig(bucket_size=4096), True, 256, 4096), + # overlap off -> None, regardless of bucket_size. + (DistributedDataParallelConfig(bucket_size=4096), False, 256, None), + # no explicit size with group=None (dp size 1) -> the sane default. + (DistributedDataParallelConfig(), True, 256, max(40_000_000, 1_000_000)), + ], +) +def test_resolve_ddp_bucket_size(config, overlap, num_params, expected): + """The MIMO wrap delegates bucket sizing to this shared get_model helper.""" + assert resolve_ddp_bucket_size(config, None, overlap, num_params) == expected + + +@pytest.mark.skipif(torch.cuda.device_count() < 8, reason="requires 8 GPUs") +class TestRuntimeDistributed: + def setup_method(self, method): + Utils.initialize_distributed() + + def teardown_method(self, method): + Utils.destroy_model_parallel() + + def test_distinct_offsets_give_distinct_rng_states(self): + # Encoder tp=2,dp=2 at 0-3; language tp=2,pp=2 at 4-7. Each rank seeds the one + # module it participates in; distinct role offsets must reseed every tracked state. + topo = create_topology( + [ + ModuleGridSpec(name=ENCODER, num_ranks=4, tp=2, rank_offset=0), + ModuleGridSpec( + name=MIMO_LANGUAGE_MODULE_KEY, num_ranks=4, tp=2, pp=2, rank_offset=4 + ), + ] + ) + try: + module = MIMO_LANGUAGE_MODULE_KEY if torch.distributed.get_rank() >= 4 else ENCODER + pgc = topo.module_pgs[module] + configure_module_rng(_args(), pgc, role_seed_offset=10) + states_a = get_cuda_rng_tracker().get_states() + configure_module_rng(_args(), pgc, role_seed_offset=20) + states_b = get_cuda_rng_tracker().get_states() + assert set(states_a) == set(states_b) + for name in states_a: + assert not torch.equal(states_a[name], states_b[name]) + finally: + topo.destroy() + + def test_active_module_is_ddp_over_its_own_grid(self): + topo = _eight_gpu_topology() + try: + mimo_model = _build_unwrapped_mimo_model(topo) + wrap_active_modules_with_ddp(_args(), mimo_model, topo) + # Non-colocated: each rank owns exactly one active module (language XOR encoder). + if torch.distributed.get_rank() < 4: + active = mimo_model.modality_submodules[ENCODER] + assert mimo_model.language_model is None + else: + active = mimo_model.language_model + assert ENCODER not in mimo_model.modality_submodules + assert isinstance(active, DistributedDataParallel) + finally: + topo.destroy() + + def test_bf16_wraps_in_float16module_and_freezes_targets(self): + topo = _eight_gpu_topology() + try: + # bf16 -> Float16Module wrap; --freeze-vit freezes the encoder backbone only. + mimo_model = _build_unwrapped_mimo_model(topo, bf16=True) + wrap_active_modules_with_ddp(_args(fp32=False, freeze_vit=True), mimo_model, topo) + + if torch.distributed.get_rank() < 4: + active = mimo_model.modality_submodules[ENCODER] + # Float16Module sits under DDP, above the bare submodule. + assert isinstance(active.module, Float16Module) + submodule = unwrap_model(active) + # --freeze-vit froze the encoder backbone, not the projector. + assert all(not p.requires_grad for p in submodule.encoders.parameters()) + assert all(p.requires_grad for p in submodule.input_projections.parameters()) + else: + assert isinstance(mimo_model.language_model.module, Float16Module) + finally: + topo.destroy() diff --git a/tests/unit_tests/models/mimo/test_mimo_zero_grad_buffer.py b/tests/unit_tests/models/mimo/test_mimo_zero_grad_buffer.py new file mode 100644 index 00000000000..ba7fb8d6fa4 --- /dev/null +++ b/tests/unit_tests/models/mimo/test_mimo_zero_grad_buffer.py @@ -0,0 +1,33 @@ +# Copyright (c) 2026, NVIDIA CORPORATION. All rights reserved. + +"""CPU-only tests for MimoModel.zero_grad_buffer fan-out.""" + +from types import SimpleNamespace +from unittest.mock import MagicMock + +from megatron.core.models.mimo.model.base import MimoModel + + +def _stub(language_model, modality_submodules): + """Minimal stand-in carrying the real zero_grad_buffer / _active_submodules.""" + stub = SimpleNamespace(language_model=language_model, modality_submodules=modality_submodules) + stub.zero_grad_buffer = MimoModel.zero_grad_buffer.__get__(stub) + stub._active_submodules = MimoModel._active_submodules.__get__(stub) + return stub + + +def test_zero_grad_buffer_fans_out_to_present_submodules(): + language_model = MagicMock() + vision = MagicMock() + _stub(language_model, {"vision": vision}).zero_grad_buffer() + + language_model.zero_grad_buffer.assert_called_once_with() + vision.zero_grad_buffer.assert_called_once_with() + + +def test_zero_grad_buffer_skips_none_submodules(): + vision = MagicMock() + # Encoder-only rank: language_model is None, plus a None modality entry. + _stub(None, {"vision": vision, "audio": None}).zero_grad_buffer() + + vision.zero_grad_buffer.assert_called_once_with() diff --git a/tests/unit_tests/models/test_hybrid_moe_model.py b/tests/unit_tests/models/test_hybrid_moe_model.py index 44f0ec8d2fd..a86d7fa2144 100644 --- a/tests/unit_tests/models/test_hybrid_moe_model.py +++ b/tests/unit_tests/models/test_hybrid_moe_model.py @@ -166,6 +166,7 @@ "mamba_num_groups": 8, "mamba_num_heads": 64, "mamba_state_dim": 128, + "mamba_training_ssm_states_dtype": None, "masked_softmax_fusion": True, "memory_efficient_layer_norm": False, "mhc_init_gating_factor": 0.01, @@ -181,6 +182,7 @@ "moe_expert_rank_capacity_factor": None, "moe_ffn_hidden_size": 1856, "moe_flex_dispatcher_backend": "deepep", + "moe_grad_scale_func": None, "moe_grouped_gemm": True, "moe_hybridep_num_sms": None, "moe_hybridep_num_sms_preprocessing": 108, diff --git a/tests/unit_tests/pipeline_parallel/test_fine_grained_activation_offloading.py b/tests/unit_tests/pipeline_parallel/test_fine_grained_activation_offloading.py index 8508d8c14b5..df93c7a69bc 100644 --- a/tests/unit_tests/pipeline_parallel/test_fine_grained_activation_offloading.py +++ b/tests/unit_tests/pipeline_parallel/test_fine_grained_activation_offloading.py @@ -10,6 +10,7 @@ from megatron.core.models.gpt.gpt_layer_specs import get_gpt_layer_with_transformer_engine_spec from megatron.core.models.gpt.gpt_model import GPTModel +from megatron.core.pipeline_parallel.fine_grained_activation_offload import ChunkOffloadHandler from megatron.core.pipeline_parallel.fine_grained_activation_offload import ( FineGrainedActivationOffloadingInterface as off_interface, ) @@ -33,6 +34,52 @@ def _reset_cuda_memory() -> None: torch.cuda.synchronize() +def _make_chunk_handler_for_offload_checker(min_offloaded_tensor_size: int = 1): + handler = ChunkOffloadHandler.__new__(ChunkOffloadHandler) + handler.min_offloaded_tensor_size = min_offloaded_tensor_size + return handler + + +def test_chunk_offload_handler_skips_non_offloadable_tensor_types(): + handler = _make_chunk_handler_for_offload_checker() + + cpu_tensor = torch.empty(1024) + assert not handler.tensor_need_offloading_checker(cpu_tensor) + assert handler.tensor_push(cpu_tensor) is cpu_tensor + assert handler.tensor_pop(cpu_tensor) is cpu_tensor + + parameter = torch.nn.Parameter(torch.empty(1024)) + assert not handler.tensor_need_offloading_checker(parameter) + assert handler.tensor_push(parameter) is parameter + assert handler.tensor_pop(parameter) is parameter + + try: + from torch._subclasses.fake_tensor import FakeTensorMode + except ImportError: + pytest.skip("FakeTensorMode is not available in this PyTorch version.") + + with FakeTensorMode(): + fake_tensor = torch.empty(1024, device="cuda") + assert not handler.tensor_need_offloading_checker(fake_tensor) + assert handler.tensor_push(fake_tensor) is fake_tensor + assert handler.tensor_pop(fake_tensor) is fake_tensor + + +@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA is required for offload check.") +def test_chunk_offload_handler_respects_tensor_offloading_activation_opt_out(): + handler = _make_chunk_handler_for_offload_checker() + + tensor = torch.empty(1024, device="cuda") + assert handler.tensor_need_offloading_checker(tensor) + + tensor._TE_do_not_offload = True + assert not handler.tensor_need_offloading_checker(tensor) + + tensor = torch.empty(1024, device="cuda") + tensor.offloading_activation = False + assert not handler.tensor_need_offloading_checker(tensor) + + def _build_gpt_model( *, seed: int, diff --git a/tests/unit_tests/test_argument_utils.py b/tests/unit_tests/test_argument_utils.py index f698e879951..7c0b30d3d56 100644 --- a/tests/unit_tests/test_argument_utils.py +++ b/tests/unit_tests/test_argument_utils.py @@ -651,6 +651,33 @@ def test_handled_unsupported_unions(self): args = parser.parse_args(['--unsupported-with-metadata', 'baz']) +class TestMegatronNetworkArgumentGeneration: + """Test Megatron's TransformerConfig-derived argument group.""" + + def test_transformer_callback_fields_are_not_registered_as_cli_args(self): + """Callback fields are runtime hooks, not CLI-provided values.""" + from megatron.training.arguments import _add_network_size_args + + parser = ArgumentParser() + _add_network_size_args(parser) + + destinations = {action.dest for action in parser._actions} + callback_fields = { + "timers", + "finalize_model_grads_func", + "grad_scale_func", + "moe_grad_scale_func", + "no_sync_func", + "grad_sync_func", + "param_sync_func", + } + + assert destinations.isdisjoint(callback_fields) + args = parser.parse_args([]) + for field_name in callback_fields: + assert not hasattr(args, field_name) + + # --------------------------------------------------------------------------- # Tests for pretrain_cfg_container_from_args # --------------------------------------------------------------------------- diff --git a/tests/unit_tests/test_wrap_model_chunks_with_ddp.py b/tests/unit_tests/test_wrap_model_chunks_with_ddp.py new file mode 100644 index 00000000000..0b7e7f6adad --- /dev/null +++ b/tests/unit_tests/test_wrap_model_chunks_with_ddp.py @@ -0,0 +1,92 @@ +# Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. + +"""Tests for ``wrap_model_chunks_with_ddp`` pg_collection threading. + +These cover the training-loop helper directly (not the MIMO models), verifying +that an explicit ``pg_collection`` built from a ``HyperCommGrid`` (no +``parallel_state`` globals) is forwarded to standard ``DistributedDataParallel``, +and that the default ``pg_collection=None`` path still falls back to MPU groups. +""" + +import pytest +import torch +import torch.nn as nn + +from megatron.core import parallel_state +from megatron.core.distributed import DistributedDataParallel, DistributedDataParallelConfig +from megatron.core.hyper_comm_grid import HyperCommGrid +from megatron.core.process_groups_config import ProcessGroupCollection +from megatron.core.transformer import TransformerConfig +from tests.unit_tests.test_utilities import Utils + +pytestmark = pytest.mark.skipif(torch.cuda.device_count() < 8, reason="Requires 8 GPUs") + + +class _TinyModel(nn.Module): + def __init__(self): + super().__init__() + self.fc1 = nn.Linear(16, 8) + + def forward(self, x): + return self.fc1(x) + + +def _make_chunk(): + return _TinyModel().bfloat16().cuda() + + +def _config(): + return TransformerConfig(num_attention_heads=1, num_layers=1) + + +class TestExplicitPgCollection: + """Drive the helper with a HyperCommGrid-derived pgc, no parallel_state.""" + + def test_explicit_pg_collection_forwarded_to_ddp(self): + from megatron.training.training import wrap_model_chunks_with_ddp + + Utils.initialize_distributed() # torch.distributed only, no model-parallel state + # One pure-DP grid over the 8 ranks; tp/pp/ep are size-1 (no model parallelism). + grid = HyperCommGrid([1, 1, 1, 8], ["tp", "pp", "ep", "dp"], backend="nccl") + dp = grid.create_pg("dp") + pg_collection = ProcessGroupCollection( + tp=grid.create_pg("tp"), + pp=grid.create_pg("pp"), + ep=grid.create_pg("ep"), + dp=dp, + dp_cp=dp, + expt_dp=dp, + ) + + ddp_config = DistributedDataParallelConfig(use_distributed_optimizer=True) + wrapped = wrap_model_chunks_with_ddp( + [_make_chunk()], _config(), ddp_config, pg_collection=pg_collection + ) + + chunk = wrapped[0] + assert isinstance(chunk, DistributedDataParallel) + # DDP was constructed against the grid-derived dp_cp group, not parallel_state. + assert chunk.dp_cp_group is dp + grid.destroy() + + +class TestDefaultPath: + """Isolated back-compat check: pg_collection=None falls back to MPU globals.""" + + def teardown_method(self, method): + Utils.destroy_model_parallel() + + def test_default_path_uses_mpu(self): + from megatron.training.training import wrap_model_chunks_with_ddp + + Utils.initialize_model_parallel( + tensor_model_parallel_size=1, pipeline_model_parallel_size=1 + ) + ddp_config = DistributedDataParallelConfig(use_distributed_optimizer=True) + wrapped = wrap_model_chunks_with_ddp([_make_chunk()], _config(), ddp_config) + + chunk = wrapped[0] + assert isinstance(chunk, DistributedDataParallel) + assert chunk.dp_cp_group is parallel_state.get_data_parallel_group( + with_context_parallel=True + ) diff --git a/tests/unit_tests/training/test_setup_model_and_optimizer_mimo.py b/tests/unit_tests/training/test_setup_model_and_optimizer_mimo.py new file mode 100644 index 00000000000..bbeb27fb914 --- /dev/null +++ b/tests/unit_tests/training/test_setup_model_and_optimizer_mimo.py @@ -0,0 +1,21 @@ +# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. + +"""get_megatron_optimizer dispatches a MimoModel to get_mimo_optimizer.""" + +from types import SimpleNamespace +from unittest import mock + +from megatron.core.models.mimo.model.base import MimoModel +from megatron.core.optimizer import get_megatron_optimizer + + +def test_get_megatron_optimizer_dispatches_mimo_model(): + # __new__ gives an isinstance-true MimoModel without running its heavy __init__. + fake_mimo = MimoModel.__new__(MimoModel) + sentinel = object() + with mock.patch( + "megatron.core.models.mimo.optimizer.get_mimo_optimizer", return_value=sentinel + ) as get_mimo: + result = get_megatron_optimizer(SimpleNamespace(optimizer="adam"), [fake_mimo]) + get_mimo.assert_called_once() + assert result is sentinel diff --git a/tests/unit_tests/training/test_train_step_schedule_plumbing.py b/tests/unit_tests/training/test_train_step_schedule_plumbing.py new file mode 100644 index 00000000000..4d032ad8385 --- /dev/null +++ b/tests/unit_tests/training/test_train_step_schedule_plumbing.py @@ -0,0 +1,70 @@ +# Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. + +"""train_step forwards p2p_communicator and schedule pg_collection to forward_backward_func.""" + +from types import SimpleNamespace +from unittest import mock + +from megatron.training import training as training_mod + + +class _Rerun: + """Run the forward/backward body once, then ask train_step to exit before optimizer.step.""" + + _ran = False + + def should_run_forward_backward(self, data_iterator): + run, self._ran = not self._ran, True + return run + + def should_checkpoint_and_exit(self): + return False, True, 0 # (checkpoint, exit, code) + + +def _run(**kwargs): + args = SimpleNamespace( + save_params_interval=None, + save_activations_interval=None, + save_tokens_per_expert_interval=None, + save_wgrads_interval=None, + save_dgrads_interval=None, + reuse_grad_buf_for_mxfp8_param_ag=False, + overlap_param_gather=False, + seq_length=8, + global_batch_size=1, + micro_batch_size=1, + decoder_seq_length=None, + empty_unused_memory_level=0, + ) + captured = {} + model = [SimpleNamespace(force_all_reduce=False, zero_grad_buffer=lambda: None)] + with ( + mock.patch.object(training_mod, "get_args", return_value=args), + mock.patch.object(training_mod, "get_timers", return_value=mock.MagicMock()), + mock.patch.object(training_mod, "get_rerun_state_machine", return_value=_Rerun()), + mock.patch.object(training_mod, "get_num_microbatches", return_value=1), + mock.patch.object(training_mod, "has_nvidia_modelopt", False), + ): + training_mod.train_step( + forward_step_func=lambda *a, **k: None, + data_iterator=iter([]), + model=model, + optimizer=SimpleNamespace(zero_grad=lambda: None), + opt_param_scheduler=None, + config=SimpleNamespace(), + forward_backward_func=lambda **kw: captured.update(kw) or [], + iteration=0, + **kwargs, + ) + return captured + + +def test_train_step_forwards_schedule_plumbing(): + p2p, pg = object(), object() + captured = _run(p2p_communicator=p2p, schedule_pg_collection=pg) + assert captured["p2p_communicator"] is p2p and captured["pg_collection"] is pg + + +def test_train_step_defaults_to_none(): + captured = _run() + assert captured["p2p_communicator"] is None and captured["pg_collection"] is None diff --git a/tests/unit_tests/training/test_weight_and_optimizer_memory.py b/tests/unit_tests/training/test_weight_and_optimizer_memory.py index c172fde39ae..19a532c35c2 100644 --- a/tests/unit_tests/training/test_weight_and_optimizer_memory.py +++ b/tests/unit_tests/training/test_weight_and_optimizer_memory.py @@ -17,6 +17,7 @@ def _make_args(**overrides): hidden_size=8, kv_channels=4, moe_ffn_hidden_size=16, + moe_latent_size=None, moe_layer_freq=[0, 1], moe_router_topk=1, moe_shared_expert_gate=False, @@ -88,3 +89,21 @@ def test_weight_and_optimizer_memory_decreases_with_expert_parallelism(): ] assert memories[0] > memories[1] > memories[2] > memories[3] + + +def test_weight_and_optimizer_memory_accounts_for_latent_moe_experts(): + args = _make_args(moe_latent_size=4) + + # Latent MoE routes experts through moe_latent_size instead of hidden_size. + # The hidden<->latent projections are duplicated non-expert params. + tp_sharded_params_on_rank = ((256 + 256) + (256 + 128) + 256) / 2 + replicated_params_on_rank = 16 + (16 + 64 + 32) + 8 + expert_sharded_params_on_rank = 512 / (4 * 2) + + # DP = 32 // 2(TP) = 16 + # EDP = 32 // 4(ETP) // 2(EP) = 4 + expected_memory = (tp_sharded_params_on_rank + replicated_params_on_rank) * ( + 6 + 12 / 16 + ) + expert_sharded_params_on_rank * (6 + 12 / 4) + + assert math.isclose(compute_weight_and_optimizer_memory(args), expected_memory) diff --git a/tests/unit_tests/transformer/moe/test_grouped_mlp.py b/tests/unit_tests/transformer/moe/test_grouped_mlp.py index ce3a4bfede4..51ded378af4 100644 --- a/tests/unit_tests/transformer/moe/test_grouped_mlp.py +++ b/tests/unit_tests/transformer/moe/test_grouped_mlp.py @@ -167,6 +167,7 @@ def __call__(self, hidden_states, fc1_tokens, probs, fc2_tokens): moe_token_dispatcher_type=None, moe_flex_dispatcher_backend=None, moe_paged_stash=False, + delay_offload_until_cuda_graph=False, ) module._fused_ops = None fused_ops = FakeFusedOps() diff --git a/tools/trigger_internal_ci.md b/tools/trigger_internal_ci.md index 77239bcbace..5a6e949b523 100644 --- a/tools/trigger_internal_ci.md +++ b/tools/trigger_internal_ci.md @@ -40,6 +40,7 @@ python tools/trigger_internal_ci.py \ [--functional-test-scope mr] \ [--functional-test-repeat 5] \ [--functional-test-cases all] \ + [--functional-test-time-limit 14400] \ [--dry-run] ``` @@ -50,6 +51,7 @@ python tools/trigger_internal_ci.py \ | `--functional-test-scope` | `mr` | `FUNCTIONAL_TEST_SCOPE` pipeline variable | | `--functional-test-repeat` | `5` | `FUNCTIONAL_TEST_REPEAT` pipeline variable | | `--functional-test-cases` | `all` | `FUNCTIONAL_TEST_CASES` pipeline variable | +| `--functional-test-time-limit` | *(scope-dependent)* | `FUNCTIONAL_TEST_TIME_LIMIT` pipeline variable, in seconds. Defaults to `14400` (4h) for the long-running `release` and `weekly` scopes; left unset otherwise. | | `--dry-run` | off | Print what would happen without pushing or triggering | ## Example