Will LinandClaude Fable 5
34346779d1
Extend the training-side regional-compile port (internal 863e87342, GO
verdict vsa_gate/compile_ab/VERDICT.md) to inference so every H3 run can
compile the 52 transformer blocks. Opt-in and off by default:
- New FastVideoArgs.inference_torch_compile (CLI --inference-torch-compile),
env FASTVIDEO_INFERENCE_TORCH_COMPILE=1 folded in __post_init__ (the
attention_backend parse-once pattern), reachable through
PipelineSelection.experimental {"inference_torch_compile": true} exactly
like the VSA_sparsity / VSA_tile_size knobs.
- maybe_load_fsdp_model applies the compile right after the transformer
loads: per-_compile_conditions block, fullgraph=True + inductor
options.emulate_precision_casts injected, no user kwargs needed (the
compile-A/B verdict recipe). prepare_for_compile still runs first, so the
H3 fusion-inertness warning of #1735 is preserved.
- _regional_compile_unsupported_reason ported from the training loader:
VSA / VSA-H3 backends and the FASTVIDEO_DISABLE_ATTENTION_COMPILE=1
escape hatch degrade the transformer to eager with one warning; FLASH_ATTN
on flash-attn 3 is rejected with an actionable message. FA4/FA2/SDPA
compile through their existing custom-op boundaries (forward-only is
enough at inference; the training port's backward custom ops are not
needed under no_grad).
- FASTVIDEO_DISABLE_ATTENTION_COMPILE default flipped to 0 (attention is
traced by default), matching the training port and upstream #1718 —
eager runs are unaffected (torch.compiler.disable is inert outside
dynamo).
- enable_torch_compile + inference_torch_compile together skip the
pipeline-level DiT compile (the loader already owns those forwards).
- examples: --inference-torch-compile on basic_minimax_h3_t2v.py (dense
target) and basic_fasth3.py (exercises the VSA degrade path).
- CPU-safe contract tests for the guard and the kwargs injection.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
607 lines
28 KiB
Python
607 lines
28 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# Adapted from torchtune
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# Copyright 2024 The TorchTune Authors.
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# Copyright 2025 The FastVideo Authors.
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from __future__ import annotations
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import os
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import contextlib
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from collections.abc import Callable, Generator
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from itertools import chain
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from typing import Any
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import torch
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from torch import nn
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from torch.distributed import DeviceMesh, init_device_mesh
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from torch.distributed._tensor import distribute_tensor
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from torch.distributed.fsdp import (CPUOffloadPolicy, FSDPModule, MixedPrecisionPolicy, fully_shard)
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from torch.nn.modules.module import _IncompatibleKeys
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from fastvideo.logger import init_logger
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from fastvideo.models.loader.utils import (get_param_names_mapping, hf_to_custom_state_dict)
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from fastvideo.models.loader.weight_utils import safetensors_weights_iterator
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from fastvideo.utils import set_mixed_precision_policy, is_pin_memory_available
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logger = init_logger(__name__)
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def _maybe_quantize_model(model: nn.Module) -> None:
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"""Quantize NVFP4- or FP8-tagged linear layers in-place after weights are loaded.
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Walks the module tree once, looking for layers whose ``quant_method``
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is an :class:`NVFP4QuantizeMethod` or :class:`FP8QuantizeMethod` (attached
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at construction time by the respective ``get_quant_method``). When at least
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one such layer exists, calls the matching conversion function to register
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quantized weight buffers on each targeted layer.
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The walk returns on the first quantized layer found so unquantized callers
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pay only an ``isinstance`` check per module. Both imports are deferred so
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this is a no-op on hosts without the relevant backends.
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QAT-*train* linears (``nvfp4_qat_train``) need no weight conversion — the
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master weight stays full precision — but their attachment is otherwise
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silent, so the same walk emits a one-line receipt with the count of
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linears that actually carry the QAT method.
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"""
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# Defer imports: these modules pull in heavy symbols at module-load time.
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from fastvideo.layers.linear import LinearBase
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from fastvideo.layers.quantization.nvfp4_config import (
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NVFP4QuantizeMethod,
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convert_model_to_nvfp4,
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)
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from fastvideo.layers.quantization.nvfp4_qat_config import (
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NVFP4QATQuantizeMethod,
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convert_model_to_fp4,
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)
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from fastvideo.layers.quantization.nvfp4_qat_train_config import (
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NVFP4QATTrainQuantizeMethod, )
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from fastvideo.layers.quantization.fp8_config import (
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FP8QuantizeMethod,
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convert_model_to_fp8,
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)
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qat_train_attached = 0
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qat_train_skipped = 0
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for mod in model.modules():
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qm = getattr(mod, "quant_method", None)
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if isinstance(qm, NVFP4QuantizeMethod):
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logger.info("Converting loaded model weights for NVFP4 linear layers")
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convert_model_to_nvfp4(model)
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return
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if isinstance(qm, NVFP4QATQuantizeMethod):
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logger.info("Converting loaded model weights for NVFP4-QAT linear layers")
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convert_model_to_fp4(model)
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return
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if isinstance(qm, FP8QuantizeMethod):
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logger.info("Converting loaded model weights for FP8 linear layers")
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convert_model_to_fp8(model)
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return
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# QAT-train configs are mutually exclusive with the inference schemes
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# above (one quant_config per model), so when they're active the loop
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# always runs to completion and the counts below are model-wide.
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if isinstance(qm, NVFP4QATTrainQuantizeMethod):
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qat_train_attached += 1
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elif isinstance(mod, LinearBase):
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qat_train_skipped += 1
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if qat_train_attached:
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logger.info("NVFP4 QAT: attached %d linears (%d skipped by prefix filter)", qat_train_attached,
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qat_train_skipped)
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# TODO(PY): move this to utils elsewhere
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@contextlib.contextmanager
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def set_default_dtype(dtype: torch.dtype) -> Generator[None, None, None]:
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"""
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Context manager to set torch's default dtype.
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Args:
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dtype (torch.dtype): The desired default dtype inside the context manager.
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Returns:
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ContextManager: context manager for setting default dtype.
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Example:
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>>> with set_default_dtype(torch.bfloat16):
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>>> x = torch.tensor([1, 2, 3])
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>>> x.dtype
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torch.bfloat16
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"""
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old_dtype = torch.get_default_dtype()
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torch.set_default_dtype(dtype)
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try:
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yield
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finally:
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torch.set_default_dtype(old_dtype)
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# Supports optional torch.compile for FSDP-wrapped models during training
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def maybe_load_fsdp_model(
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model_cls: type[nn.Module],
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init_params: dict[str, Any],
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weight_dir_list: list[str],
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device: torch.device,
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hsdp_replicate_dim: int,
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hsdp_shard_dim: int,
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default_dtype: torch.dtype,
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param_dtype: torch.dtype,
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reduce_dtype: torch.dtype,
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strict: bool = True,
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cpu_offload: bool = False,
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fsdp_inference: bool = False,
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output_dtype: torch.dtype | None = None,
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training_mode: bool = True,
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pin_cpu_memory: bool = True,
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enable_torch_compile: bool = False,
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torch_compile_kwargs: dict[str, Any] | None = None,
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inference_regional_compile: bool = False,
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) -> torch.nn.Module:
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"""
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Load the model with FSDP if is training, else load the model without FSDP.
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"""
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# NOTE(will): cast_forward_inputs=True shouldn't be needed as we are
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# manually casting the inputs to the model
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mp_policy = MixedPrecisionPolicy(param_dtype, reduce_dtype, output_dtype, cast_forward_inputs=False)
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set_mixed_precision_policy(
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param_dtype=param_dtype,
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reduce_dtype=reduce_dtype,
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output_dtype=output_dtype,
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mp_policy=mp_policy,
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)
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logger.info("Loading model with default_dtype: %s", default_dtype)
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with set_default_dtype(default_dtype), torch.device("meta"):
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model = model_cls(**init_params)
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dtype_selector = getattr(model, "_get_parameter_dtype", None)
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has_mixed_parameter_dtypes = callable(dtype_selector) and any(
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dtype_selector(name, param_dtype) != param_dtype for name, _ in model.named_parameters())
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if training_mode and has_mixed_parameter_dtypes:
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raise NotImplementedError("FSDP training with model-selected mixed parameter dtypes requires "
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"separate gradient synchronization for replicated parameters.")
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# Check if we should use FSDP
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use_fsdp = training_mode or fsdp_inference
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# Disable FSDP for MPS as it's not compatible
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from fastvideo.platforms import current_platform
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if current_platform.is_mps():
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use_fsdp = False
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logger.info("Disabling FSDP for MPS platform as it's not compatible")
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if use_fsdp:
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pin_cpu_memory = pin_cpu_memory and is_pin_memory_available()
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world_size = hsdp_replicate_dim * hsdp_shard_dim
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if not training_mode and not fsdp_inference:
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hsdp_replicate_dim = world_size
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hsdp_shard_dim = 1
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if current_platform.is_npu():
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with torch.device("cpu"):
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device_mesh = init_device_mesh(
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"npu",
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# (Replicate(), Shard(dim=0))
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mesh_shape=(hsdp_replicate_dim, hsdp_shard_dim),
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mesh_dim_names=("replicate", "shard"),
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)
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else:
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device_mesh = init_device_mesh(
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"cuda",
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# (Replicate(), Shard(dim=0))
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mesh_shape=(hsdp_replicate_dim, hsdp_shard_dim),
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mesh_dim_names=("replicate", "shard"),
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)
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shard_model(model,
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cpu_offload=cpu_offload,
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reshard_after_forward=True,
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mp_policy=mp_policy,
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mesh=device_mesh,
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fsdp_shard_conditions=model._fsdp_shard_conditions,
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pin_cpu_memory=pin_cpu_memory)
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weight_iterator = safetensors_weights_iterator(weight_dir_list, to_cpu=True)
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param_names_mapping_fn = get_param_names_mapping(model.param_names_mapping)
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load_model_from_full_model_state_dict(
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model,
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weight_iterator,
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device,
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default_dtype,
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strict=strict,
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cpu_offload=cpu_offload,
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param_names_mapping=param_names_mapping_fn,
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)
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if hasattr(model, "materialize_non_persistent_buffers"):
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model.materialize_non_persistent_buffers(device=device, dtype=default_dtype)
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for n, p in chain(model.named_parameters(), model.named_buffers()):
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if p.is_meta:
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raise RuntimeError(f"Unexpected param or buffer {n} on meta device.")
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# Avoid unintended computation graph accumulation during inference
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if isinstance(p, torch.nn.Parameter):
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p.requires_grad = False
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# Post-load weight quantization. We detect the active scheme by the
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# ``quant_method`` attached to each linear layer at construction time
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# (via ``QuantizationConfig.get_quant_method``). The loader's
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# responsibility is just to materialize the quantized weight buffers
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# from the freshly-loaded bf16 weights. No-op when no quantized layers
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# are present (lazy imports inside the helper).
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_maybe_quantize_model(model)
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compile_in_loader = enable_torch_compile and training_mode
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if compile_in_loader:
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compile_kwargs = torch_compile_kwargs or {}
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logger.info("Enabling torch.compile for FSDP training module with kwargs=%s", compile_kwargs)
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model = torch.compile(model, **compile_kwargs)
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logger.info("torch.compile enabled for %s", type(model).__name__)
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elif inference_regional_compile and not training_mode:
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# Inference-side counterpart of the #1718 training regional compile:
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# per-block fullgraph compile right after the transformer loads, no
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# user kwargs needed (fullgraph + emulate_precision_casts injected).
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unsupported = _regional_compile_unsupported_reason(init_params)
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if unsupported is not None:
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logger.warning(
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"inference_torch_compile requested but disabled: %s. "
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"Inference continues in eager mode.", unsupported)
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else:
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prepare_for_compile = getattr(model, "prepare_for_compile", None)
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if callable(prepare_for_compile):
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logger.info("Running prepare_for_compile for %s", type(model).__name__)
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prepare_for_compile()
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_compile_model_regions(model, torch_compile_kwargs or {})
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return model
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def _regional_compile_unsupported_reason(init_params: dict[str, Any]) -> str | None:
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"""Return why regional fullgraph compile cannot run, or None if it can.
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FA3's grad-enabled attention path deliberately routes to the raw
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autograd.Function at the cost of a dynamo graph break (see
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flash_attn_default.py) — under regional ``fullgraph=True`` that break is
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a hard RuntimeError at the first compiled forward. FA2 and FA4 route
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through traceable custom ops and are compile-safe.
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The VSA backends (Triton block-sparse kernels behind sequence-parallel
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all-to-alls plus a host-synced metadata guard) are likewise not
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fullgraph-traceable; a VSA-backed transformer (e.g. the FastH3 student)
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falls back to eager while compile-safe dense loads still compile.
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"""
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try:
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from fastvideo.attention.layer import _attention_compile_disabled
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except Exception: # pragma: no cover - attention stack not importable
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pass
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else:
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if _attention_compile_disabled():
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# The escape hatch wraps attention forwards in
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# torch.compiler.disable, which is a hard dynamo error inside a
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# fullgraph region ("Skip inlining `torch.compiler.disable()`d
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# function"). Degrade to eager instead, matching the hatch's
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# debugging intent.
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return ("FASTVIDEO_DISABLE_ATTENTION_COMPILE=1 keeps attention "
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"forwards out of compiled graphs via torch.compiler."
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"disable, which fullgraph regional compile cannot trace; "
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"this model stays eager")
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config = init_params.get("config")
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resolved = getattr(config, "_resolved_attention_backend", None)
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resolved_name = getattr(resolved, "name", "")
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if resolved_name in ("VIDEO_SPARSE_ATTN", "VIDEO_SPARSE_ATTN_H3"):
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return (f"attention backend resolved to {resolved_name}, whose Triton "
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"kernels, sequence-parallel collectives, and sync metadata "
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"guard graph-break (incompatible with fullgraph regional "
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"compile); this model stays eager")
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if resolved is None or resolved_name != "FLASH_ATTN":
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return None
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try:
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from fastvideo.attention.utils.flash_attn_default import fa_version
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except Exception: # pragma: no cover - flash-attn stack not importable
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return None
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if fa_version == "3":
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return ("attention backend resolved to FLASH_ATTN with flash-attn 3, "
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"whose grad-enabled path graph-breaks (incompatible with "
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"fullgraph regional compile); use FA2, FA4 (FASTVIDEO_FA4=1), "
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"or TORCH_SDPA for compiled runs")
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return None
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def _compile_model_regions(model: nn.Module, compile_kwargs: dict[str, Any]) -> int:
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"""Compile repeated mathematical regions of a loaded model.
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Only the selected module ``forward`` is replaced. This keeps activation
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checkpoint wrappers structurally transparent while any module-level hooks
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(FSDP pre/post, layerwise offload) execute outside the compiled region.
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"""
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compile_conditions = getattr(model, "_compile_conditions", None)
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if not compile_conditions:
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raise ValueError(f"{type(model).__name__} does not declare _compile_conditions")
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if compile_kwargs.get("fullgraph", True) is not True:
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raise ValueError("Regional compile requires fullgraph=True")
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if "mode" in compile_kwargs:
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# torch.compile forbids passing both `mode` and `options`, and
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# regional compile always injects options (emulate_precision_casts)
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# for bf16 numerics parity. Fail here with an actionable message
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# instead of letting torch raise a mode/options conflict about an
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# `options` key the user never wrote.
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raise ValueError("Regional compile sets inductor options "
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"(emulate_precision_casts) and cannot be combined "
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"with torch_compile_kwargs['mode']. Remove 'mode' or "
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"express its effect via torch_compile_kwargs['options'].")
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kwargs = {**compile_kwargs, "fullgraph": True}
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options = {"emulate_precision_casts": True}
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options.update(kwargs.get("options") or {})
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kwargs["options"] = options
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compiled_count = 0
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for name, submodule in list(model.named_modules()):
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if not name:
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continue
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if any(condition(name, submodule) for condition in compile_conditions):
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# Activation checkpoint wrappers are control-flow boundaries, not
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# mathematical regions. Keep their saved-tensor/recompute logic
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# eager and compile only the repeated block they own.
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compile_target = getattr(submodule, "_checkpoint_wrapped_module", submodule)
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compile_target.forward = torch.compile(compile_target.forward, **kwargs)
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compiled_count += 1
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if compiled_count == 0:
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raise ValueError(f"No submodules in {type(model).__name__} matched _compile_conditions")
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logger.info(
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"Enabled regional torch.compile for %d submodules in %s with kwargs=%s",
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compiled_count,
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type(model).__name__,
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kwargs,
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)
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return compiled_count
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def shard_model(
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model,
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*,
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cpu_offload: bool,
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reshard_after_forward: bool = True,
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mp_policy: MixedPrecisionPolicy | None = MixedPrecisionPolicy(), # noqa
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mesh: DeviceMesh | None = None,
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fsdp_shard_conditions: list[Callable[[str, nn.Module], bool]] = [], # noqa
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pin_cpu_memory: bool = True,
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) -> None:
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"""
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Utility to shard a model with FSDP using the PyTorch Distributed fully_shard API.
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This method will over the model's named modules from the bottom-up and apply shard modules
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based on whether they meet any of the criteria from shard_conditions.
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Args:
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model (TransformerDecoder): Model to shard with FSDP.
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shard_conditions (List[Callable[[str, nn.Module], bool]]): A list of functions to determine
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which modules to shard with FSDP. Each function should take module name (relative to root)
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and the module itself, returning True if FSDP should shard the module and False otherwise.
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If any of shard_conditions return True for a given module, it will be sharded by FSDP.
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cpu_offload (bool): If set to True, FSDP will offload parameters, gradients, and optimizer
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states to CPU.
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reshard_after_forward (bool): Whether to reshard parameters and buffers after
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the forward pass. Setting this to True corresponds to the FULL_SHARD sharding strategy
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from FSDP1, while setting it to False corresponds to the SHARD_GRAD_OP sharding strategy.
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mesh (Optional[DeviceMesh]): Device mesh to use for FSDP sharding under multiple parallelism.
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Default to None.
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fsdp_shard_conditions (List[Callable[[str, nn.Module], bool]]): A list of functions to determine
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which modules to shard with FSDP.
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pin_cpu_memory (bool): If set to True, FSDP will pin the CPU memory of the offloaded parameters.
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Raises:
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ValueError: If no layer modules were sharded, indicating that no shard_condition was triggered.
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"""
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# Check if we should use size-based filtering
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use_size_filtering = os.environ.get("FASTVIDEO_FSDP2_AUTOWRAP", "0") == "1"
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if not fsdp_shard_conditions:
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logger.warning("No FSDP shard conditions provided; nothing will be sharded.")
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return
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default_param_dtype = getattr(mp_policy, "param_dtype", None)
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dtype_selector = getattr(model, "_get_parameter_dtype", None)
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ignored_params: set[nn.Parameter] = set()
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if callable(dtype_selector) and default_param_dtype is not None:
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ignored_params = {
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parameter
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for name, parameter in model.named_parameters()
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if dtype_selector(name, default_param_dtype) != default_param_dtype
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}
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|
named_modules = list(model.named_modules())
|
|
ignored_params_by_module = {
|
|
id(module): ignored_params.intersection(set(module.parameters()))
|
|
for _, module in named_modules
|
|
}
|
|
|
|
fsdp_kwargs = {
|
|
"reshard_after_forward": reshard_after_forward,
|
|
"mesh": mesh,
|
|
"mp_policy": mp_policy,
|
|
}
|
|
if cpu_offload:
|
|
fsdp_kwargs["offload_policy"] = CPUOffloadPolicy(pin_memory=pin_cpu_memory)
|
|
|
|
# iterating in reverse to start with
|
|
# lowest-level modules first
|
|
num_layers_sharded = 0
|
|
|
|
if use_size_filtering:
|
|
# Size-based filtering mode
|
|
min_params = int(os.environ.get("FASTVIDEO_FSDP2_MIN_PARAMS", "10000000"))
|
|
logger.info("Using size-based filtering with threshold: %.2fM", min_params / 1e6)
|
|
|
|
for n, m in reversed(named_modules):
|
|
if any([shard_condition(n, m) for shard_condition in fsdp_shard_conditions]):
|
|
# Count all parameters
|
|
param_count = sum(p.numel() for p in m.parameters(recurse=True))
|
|
|
|
# Skip small modules
|
|
if param_count < min_params:
|
|
logger.info("Skipping module %s (%.2fM params < %.2fM threshold)", n, param_count / 1e6,
|
|
min_params / 1e6)
|
|
continue
|
|
|
|
# Shard this module
|
|
logger.info("Sharding module %s (%.2fM params)", n, param_count / 1e6)
|
|
module_kwargs = fsdp_kwargs
|
|
local_ignored_params = ignored_params_by_module[id(m)]
|
|
if local_ignored_params:
|
|
module_kwargs = {**fsdp_kwargs, "ignored_params": local_ignored_params}
|
|
fully_shard(m, **module_kwargs)
|
|
num_layers_sharded += 1
|
|
else:
|
|
# Shard all modules matching conditions
|
|
for n, m in reversed(named_modules):
|
|
if any([shard_condition(n, m) for shard_condition in fsdp_shard_conditions]):
|
|
module_kwargs = fsdp_kwargs
|
|
local_ignored_params = ignored_params_by_module[id(m)]
|
|
if local_ignored_params:
|
|
module_kwargs = {**fsdp_kwargs, "ignored_params": local_ignored_params}
|
|
fully_shard(m, **module_kwargs)
|
|
num_layers_sharded += 1
|
|
|
|
if num_layers_sharded == 0:
|
|
raise ValueError("No layer modules were sharded. Please check if shard conditions are working as expected.")
|
|
|
|
# Finally shard the entire model to account for any stragglers
|
|
root_kwargs = fsdp_kwargs
|
|
if ignored_params:
|
|
root_kwargs = {**fsdp_kwargs, "ignored_params": ignored_params}
|
|
fully_shard(model, **root_kwargs)
|
|
|
|
|
|
# TODO(PY): device mesh for cfg parallel
|
|
def load_model_from_full_model_state_dict(
|
|
model: FSDPModule | torch.nn.Module,
|
|
full_sd_iterator: Generator[tuple[str, torch.Tensor], None, None],
|
|
device: torch.device,
|
|
param_dtype: torch.dtype,
|
|
strict: bool = False,
|
|
cpu_offload: bool = False,
|
|
param_names_mapping: Callable[[str], tuple[str, Any, Any]] | None = None,
|
|
training_mode: bool = True,
|
|
) -> _IncompatibleKeys:
|
|
"""
|
|
Converting full state dict into a sharded state dict
|
|
and loading it into FSDP model (if training) or normal huggingface model
|
|
Args:
|
|
model (Union[FSDPModule, torch.nn.Module]): Model to generate fully qualified names for cpu_state_dict
|
|
full_sd_iterator (Generator): an iterator yielding (param_name, tensor) pairs
|
|
device (torch.device): device used to move full state dict tensors
|
|
param_dtype (torch.dtype): dtype used to move full state dict tensors
|
|
strict (bool): flag to check if to load the model in strict mode
|
|
cpu_offload (bool): flag to check if FSDP offload is enabled
|
|
param_names_mapping (Optional[Callable[[str], str]]): a function that maps full param name to sharded param name
|
|
training_mode (bool): apply FSDP only for training
|
|
Returns:
|
|
``NamedTuple`` with ``missing_keys`` and ``unexpected_keys`` fields:
|
|
* **missing_keys** is a list of str containing the missing keys
|
|
* **unexpected_keys** is a list of str containing the unexpected keys
|
|
|
|
Raises:
|
|
NotImplementedError: If got FSDP with more than 1D.
|
|
"""
|
|
meta_sd = model.state_dict()
|
|
named_parameters = dict(model.named_parameters())
|
|
named_buffers = dict(model.named_buffers())
|
|
sharded_sd = {}
|
|
custom_param_sd, reverse_param_names_mapping = hf_to_custom_state_dict(full_sd_iterator,
|
|
param_names_mapping) # type: ignore
|
|
for target_param_name, full_tensor in custom_param_sd.items():
|
|
meta_sharded_param = meta_sd.get(target_param_name)
|
|
if meta_sharded_param is None:
|
|
# Some checkpoints include extra entries that are not part of the
|
|
# instantiated model's state_dict (e.g. `_extra_state` keys from
|
|
# some FSDP checkpoint formats). These can be safely skipped.
|
|
if (target_param_name.endswith("._extra_state") or target_param_name.endswith("_extra_state")):
|
|
logger.warning(
|
|
"Skipping non-parameter checkpoint key: %s",
|
|
target_param_name,
|
|
)
|
|
continue
|
|
|
|
# For non-strict loads, treat this as an "unexpected key" and skip it
|
|
# (mirrors torch.nn.Module.load_state_dict(strict=False)).
|
|
if not strict:
|
|
logger.warning(
|
|
"Skipping unexpected checkpoint key (not present in model): %s",
|
|
target_param_name,
|
|
)
|
|
continue
|
|
|
|
raise ValueError(
|
|
f"Parameter {target_param_name} not found in custom model state dict. The hf to custom mapping may be incorrect."
|
|
)
|
|
target_dtype = param_dtype
|
|
dtype_selector = getattr(model, "_get_parameter_dtype", None)
|
|
if callable(dtype_selector):
|
|
target_dtype = dtype_selector(target_param_name, param_dtype)
|
|
if not hasattr(meta_sharded_param, "device_mesh"):
|
|
full_tensor = full_tensor.to(device=device, dtype=target_dtype)
|
|
target_param = named_parameters.get(target_param_name)
|
|
weight_loader = getattr(target_param, "weight_loader", None)
|
|
# Gated on a shape mismatch: only fused/stacked params with a custom
|
|
# weight_loader (e.g. Qwen3's merged QKV/gate-up) take this path.
|
|
# Existing models whose unsharded params match the checkpoint shape
|
|
# fall through to the original `sharded_tensor = full_tensor` below.
|
|
if target_param is not None and callable(weight_loader) and tuple(target_param.shape) != tuple(
|
|
full_tensor.shape):
|
|
loaded_param = nn.Parameter(torch.empty(tuple(target_param.shape), device=device, dtype=target_dtype),
|
|
requires_grad=False)
|
|
for attr_name, attr_value in vars(target_param).items():
|
|
setattr(loaded_param, attr_name, attr_value)
|
|
weight_loader(loaded_param, full_tensor)
|
|
sharded_tensor = loaded_param.data
|
|
else:
|
|
# In cases where parts of the model aren't sharded, some parameters will be plain tensors.
|
|
sharded_tensor = full_tensor
|
|
else:
|
|
full_tensor = full_tensor.to(device=device, dtype=target_dtype)
|
|
sharded_tensor = distribute_tensor(
|
|
full_tensor,
|
|
meta_sharded_param.device_mesh,
|
|
meta_sharded_param.placements,
|
|
)
|
|
if cpu_offload:
|
|
sharded_tensor = sharded_tensor.cpu()
|
|
if target_param_name in named_buffers:
|
|
sharded_sd[target_param_name] = sharded_tensor
|
|
else:
|
|
sharded_sd[target_param_name] = nn.Parameter(sharded_tensor)
|
|
|
|
model.reverse_param_names_mapping = reverse_param_names_mapping
|
|
unused_keys = set(meta_sd.keys()) - set(sharded_sd.keys())
|
|
if unused_keys:
|
|
logger.warning("Found unloaded parameters in meta state dict: %s", unused_keys)
|
|
|
|
# List of allowed parameter name patterns
|
|
ALLOWED_NEW_PARAM_PATTERNS = ["gate_compress", "proj_l"] # Can be extended as needed
|
|
for new_param_name in unused_keys:
|
|
if not any(pattern in new_param_name for pattern in ALLOWED_NEW_PARAM_PATTERNS):
|
|
logger.error("Unsupported new parameter: %s. Allowed patterns: %s", new_param_name,
|
|
ALLOWED_NEW_PARAM_PATTERNS)
|
|
raise ValueError(f"New parameter '{new_param_name}' is not supported. "
|
|
f"Currently only parameters containing {ALLOWED_NEW_PARAM_PATTERNS} are allowed.")
|
|
meta_sharded_param = meta_sd.get(new_param_name)
|
|
target_dtype = param_dtype
|
|
dtype_selector = getattr(model, "_get_parameter_dtype", None)
|
|
if callable(dtype_selector):
|
|
target_dtype = dtype_selector(new_param_name, param_dtype)
|
|
if not hasattr(meta_sharded_param, "device_mesh"):
|
|
# Initialize with zeros
|
|
sharded_tensor = torch.zeros_like(meta_sharded_param, device=device, dtype=target_dtype)
|
|
else:
|
|
# Initialize with zeros and distribute
|
|
full_tensor = torch.zeros_like(meta_sharded_param, device=device, dtype=target_dtype)
|
|
sharded_tensor = distribute_tensor(
|
|
full_tensor,
|
|
meta_sharded_param.device_mesh,
|
|
meta_sharded_param.placements,
|
|
)
|
|
if cpu_offload:
|
|
sharded_tensor = sharded_tensor.cpu()
|
|
sharded_sd[new_param_name] = nn.Parameter(sharded_tensor)
|
|
|
|
# choose `assign=True` since we cannot call `copy_` on meta tensor
|
|
return model.load_state_dict(sharded_sd, strict=strict, assign=True)
|