Zhang PeiyuanandClaude Opus 5.5
c520878fd2
Adds opt-in regional torch.compile to the YAML-driven modular Wan trainer (training.model.enable_torch_compile / torch_compile_kwargs) with FA4 and Wan modulation forward/backward as opaque custom ops, compiled repeated DiT blocks after FSDP setup, and activation checkpointing applied before FSDP for trainable Wan transformers. Merged with main's #1716 (WanModel resolves the checkpointing type in __init__; the PR's pre-FSDP wrap uses it and the post-load wrap is gone) and #1757 (pre_fsdp_model_transform runs before this PR's pre_fsdp_transform). Maintainer review (Swarm order #776): AdaLN skip names are canonicalized through checkpoint-wrapper prefixes, test_inference_regional_compile.py joins the unit lane, the Wan modulation custom-op test moved to the transformer lane, regional_compile is classified in the schema inventory, and a 1/2-GPU training smoke covers fully_shard(CheckpointWrapper) in eager and regional-compile modes. Validated on GB200: the FA4 CuTe forward/backward parity and fullgraph-backward tests and all four smoke cases pass. Also exempts MMAudio recipes, whose student builds its pipeline config at init, from the fine-tuning recipe construction check that #1648 left failing on main. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
1119 lines
54 KiB
Python
1119 lines
54 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 contextlib
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import re
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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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import fastvideo.envs as envs
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from fastvideo.logger import init_logger
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from fastvideo.models.loader.lora_patch import DenseLoRAPatch
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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 _summarize_param_names(names: set[str]) -> str:
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"""Collapse per-layer parameter names into one ``blocks.*.suffix xN`` entry each."""
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families: dict[str, int] = {}
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for name in names:
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families[re.sub(r"\.\d+\.", ".*.", name)] = families.get(re.sub(r"\.\d+\.", ".*.", name), 0) + 1
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return ", ".join(f"{family} x{count}" if count > 1 else family for family, count in sorted(families.items()))
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def _should_stage_transformer_weights_on_cpu(*, cpu_offload: bool, fsdp_inference: bool,
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has_unified_memory: bool) -> bool:
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"""Keep full FSDP-inference source tensors off discrete GPUs while shards materialize.
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FSDP training keeps its historical ``cpu_offload``-only staging, so this
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change stays scoped to the inference path.
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"""
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return cpu_offload or (fsdp_inference and not has_unified_memory)
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def _prequantized_fp8_prefixes(weight_files: list[str]) -> list[str]:
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"""Checkpoint prefixes stored as FP8 W8A8: float8_e4m3fn ``weight`` plus per-channel float32 ``weight_scale``."""
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from safetensors import safe_open
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fp8_weights: set[str] = set()
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scales: set[str] = set()
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for path in weight_files:
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with safe_open(path, framework="pt", device="cpu") as reader:
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for key in reader.keys(): # noqa: SIM118
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if key.endswith(".weight") and reader.get_slice(key).get_dtype() == "F8_E4M3":
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fp8_weights.add(key[:-len(".weight")])
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elif key.endswith(".weight_scale") and reader.get_slice(key).get_dtype() == "F32":
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scales.add(key[:-len(".weight_scale")])
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if fp8_weights - scales:
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raise ValueError(f"FP8 checkpoint weights without a weight_scale: {sorted(fp8_weights - scales)[:4]}")
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return sorted(fp8_weights)
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def _load_prequantized_fp8(model: nn.Module, weight_files: list[str], targets: dict[str, str],
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device: torch.device) -> None:
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"""Attach FP8 W8A8 checkpoint tensors to their FP8 linears as ``convert_model_to_fp8`` would (per channel)."""
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from safetensors import safe_open
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from fastvideo.layers.quantization.fp8_config import FP8QuantizeMethod
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modules = dict(model.named_modules())
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wanted = {f"{prefix}.{suffix}" for prefix in targets for suffix in ("weight", "weight_scale")}
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tensors: dict[str, torch.Tensor] = {}
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for path in weight_files:
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with safe_open(path, framework="pt", device="cpu") as reader:
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for key in wanted.intersection(reader.keys()):
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tensors[key] = reader.get_tensor(key)
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for prefix, target in targets.items():
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module = modules.get(target[:-len(".weight")])
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quant_method = getattr(module, "quant_method", None)
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if not isinstance(quant_method, FP8QuantizeMethod):
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raise RuntimeError(f"Pre-quantized FP8 weight {prefix!r} maps to {target!r}, which is not an FP8 linear; "
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"load the checkpoint with an FP8 quant_config covering it.")
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# The checkpoint scales are per output channel; activations then use per-token scales.
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quant_method.granularity = "channel"
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module.register_buffer("_fp8_weight", tensors[f"{prefix}.weight"].to(device).contiguous(), persistent=False)
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module.register_buffer("_fp8_weight_scale",
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tensors[f"{prefix}.weight_scale"].reshape(-1).to(device=device, dtype=torch.float32),
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persistent=False)
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module._parameters.pop("weight", None)
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logger.info("Loaded %d pre-quantized FP8 linears", len(targets))
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def _maybe_quantize_model(model: nn.Module, *, defer_weight_conversion_until_lora_merge: bool = False) -> None:
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"""Quantize inference linear weights after checkpoint loading.
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Walks the module tree once, looking for layers whose ``quant_method`` is an
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inference quantization method attached at construction time. 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. NVFP4 and MXFP8 conversion
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can be deferred until a configured inference LoRA is merged.
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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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from fastvideo.layers.quantization.mxfp8_config import (
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MXFP8QuantizeMethod,
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convert_model_to_mxfp8,
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)
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# NVFP4 may share a model with FP8 linears (e.g. NVFP4 FFN + FP8 attention); handle that pair before the
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# per-module walk so the result does not depend on which quantized module comes first.
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nvfp4_modules = [m for m in model.modules() if isinstance(getattr(m, "quant_method", None), NVFP4QuantizeMethod)]
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if nvfp4_modules:
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mixed_fp8 = any(isinstance(getattr(m, "quant_method", None), FP8QuantizeMethod) for m in model.modules())
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if any(getattr(module, "_nvfp4_weight", None) is not None for module in nvfp4_modules):
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logger.info("NVFP4 packed export already populated; skipping runtime weight conversion")
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elif defer_weight_conversion_until_lora_merge:
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logger.info("Deferring NVFP4 weight conversion until the inference LoRA merge completes")
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return
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else:
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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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if mixed_fp8:
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logger.info("Converting the FP8 linears of a mixed NVFP4/FP8 model")
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convert_model_to_fp8(model)
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return
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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, 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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if isinstance(qm, MXFP8QuantizeMethod):
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if defer_weight_conversion_until_lora_merge:
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logger.info("Deferring MXFP8 weight conversion until the inference LoRA merge completes")
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return
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logger.info("Converting loaded model weights for MXFP8 linear layers")
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convert_model_to_mxfp8(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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def _prepare_model_for_compile(model: nn.Module, *, regional: bool) -> str | None:
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"""Run a model compile hook, preferring its regional specialization."""
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prepare = None
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hook_name = "prepare_for_compile"
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if regional:
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prepare = getattr(model, "prepare_for_regional_compile", None)
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hook_name = "prepare_for_regional_compile"
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if not callable(prepare):
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prepare = getattr(model, "prepare_for_compile", None)
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hook_name = "prepare_for_compile"
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if not callable(prepare):
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return None
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logger.info("Running %s for %s", hook_name, type(model).__name__)
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unsupported = prepare()
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return unsupported if isinstance(unsupported, str) and unsupported else None
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def _validate_fsdp_inference_quantization(init_params: dict[str, Any], fsdp_inference: bool) -> None:
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"""Enforce the transformer-quantization allowlist for FSDP inference."""
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transformer_config = init_params.get("config")
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quant_config = getattr(transformer_config, "quant_config", None)
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if not fsdp_inference or quant_config is None:
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return
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from fastvideo.layers.quantization.nvfp4_qat_train_config import (
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NVFP4QATTrainConfig, )
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if isinstance(quant_config, NVFP4QATTrainConfig):
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return
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# TODO: (David) Currently reject every FSDP inference quantization config except NVFP4QATTrainConfig.
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# Support FSDP inference with precomputed quantized weights.
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raise NotImplementedError(
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"FSDP inference supports unquantized transformers and NVFP4QATTrainConfig only; "
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f"got {type(quant_config).__name__}.")
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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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device_mesh: DeviceMesh | None = None,
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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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inference_vsa_tile_size: int | None = None,
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lora_path: str | None = None,
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lora_strength: float = 1.0,
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pre_fsdp_model_transform: Callable[[nn.Module], None] | None = None,
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pre_fsdp_transform: Callable[[nn.Module], nn.Module] | None = None,
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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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``lora_path`` is consulted only for the part of an adapter that addresses whole
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parameters (``.diff`` / ``.set_weight``); see
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:mod:`fastvideo.models.loader.lora_patch`. The low-rank half is merged later by
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``LoRAPipeline``. Passing it here is what lets an adapter contribute a parameter the
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base checkpoint does not contain, which has to happen while the tensor is still
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unsharded.
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``device_mesh`` overrides the mesh FSDP shards over. The MiniMax-H3 encoder
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split passes one that covers the denoise ranks only, because the DiT is loaded
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on no other rank.
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``pre_fsdp_model_transform`` runs on the meta model before its sharding topology
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is established and before checkpoint weights are loaded. Training uses it to
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install adapter parameters that must participate in FSDP from the outset.
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``pre_fsdp_transform`` runs after it and returns the module to shard (training
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uses it for activation-checkpoint wrapping). The order matters: the in-place
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transform records live module names, such as LoRA checkpoint-key aliases, that
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a wrapping transform would prefix.
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"""
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_validate_fsdp_inference_quantization(init_params, fsdp_inference)
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from fastvideo.layers.quantization.nvfp4_config import (
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dense_transformer_safetensors,
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find_minimax_h3_nvfp4_dit_export,
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load_minimax_h3_nvfp4_dit_export,
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nvfp4_linear_weight_param_names,
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)
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packed_candidate = find_minimax_h3_nvfp4_dit_export(weight_dir_list)
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weight_dir_list = dense_transformer_safetensors(weight_dir_list)
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quant_config = getattr(init_params.get("config"), "quant_config", None)
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packed_profiles = ("h3_dit", "h3_dit_ffn", "h3_dit_vsa")
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packed_nvfp4_export = (packed_candidate
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if getattr(quant_config, "layer_profile", None) in packed_profiles else None)
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if packed_nvfp4_export is not None and lora_path is not None:
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raise ValueError("Packed MiniMax-H3 NVFP4 DiT export cannot be combined with lora_path; "
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"merge the adapter before exporting, or load without the packed file.")
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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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if pre_fsdp_model_transform is not None:
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logger.info("Applying pre-FSDP model transform to %s", type(model).__name__)
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pre_fsdp_model_transform(model)
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# Must run after pre_fsdp_model_transform (see docstring); its return
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# value is authoritative.
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if pre_fsdp_transform is not None:
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model = pre_fsdp_transform(model)
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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(_strip_checkpoint_wrapper_prefix(name), param_dtype) != param_dtype
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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 device_mesh is None and 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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elif device_mesh is None:
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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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nvfp4_skip_param_names: set[str] = set()
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if packed_nvfp4_export is not None:
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nvfp4_skip_param_names = nvfp4_linear_weight_param_names(model)
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if not nvfp4_skip_param_names:
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logger.warning(
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"Found %s next to the transformer shards but no NVFP4 linears; "
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"ignoring the packed export. Set NVFP4Config(layer_profile='h3_dit').",
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packed_nvfp4_export,
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)
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packed_nvfp4_export = None
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has_unified_memory = False
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if fsdp_inference and not cpu_offload:
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device_index = device.index if device.index is not None else 0
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has_unified_memory = current_platform.has_unified_memory(device_index)
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# GPU-direct avoids duplicating the host working set on unified-memory
|
|
# devices. Discrete FSDP inference must stage the full source tensors on CPU
|
|
# while each rank materializes its shard, or the full checkpoint and shards
|
|
# peak together on the accelerator. Training keeps GPU-direct loading.
|
|
# A packed NVFP4 export is always staged on CPU.
|
|
load_weights_to_cpu = _should_stage_transformer_weights_on_cpu(
|
|
cpu_offload=cpu_offload,
|
|
fsdp_inference=fsdp_inference,
|
|
has_unified_memory=has_unified_memory,
|
|
) or packed_nvfp4_export is not None
|
|
weight_iterator = safetensors_weights_iterator(weight_dir_list, to_cpu=load_weights_to_cpu)
|
|
logger.info("Loading transformer weights with to_cpu=%s", load_weights_to_cpu)
|
|
param_names_mapping_fn = get_param_names_mapping(model.param_names_mapping)
|
|
dense_lora_patch = None
|
|
if lora_path:
|
|
# LoRA-specific mappings are optional and must not become a dependency of ordinary model loading.
|
|
lora_mapping = getattr(model, "lora_param_names_mapping", None)
|
|
lora_param_names_mapping_fn = get_param_names_mapping(lora_mapping) if lora_mapping else None
|
|
dense_lora_patch = DenseLoRAPatch.from_adapter(
|
|
lora_path,
|
|
param_names_mapping_fn,
|
|
lora_param_names_mapping=lora_param_names_mapping_fn,
|
|
strength=lora_strength,
|
|
)
|
|
if dense_lora_patch is not None:
|
|
# H3's compression gate is created only by the VSA attention backend. Loading a
|
|
# VSA student under dense attention would otherwise warn about 50 unmatched
|
|
# replacements and continue with a silently incomplete model.
|
|
model_parameter_names = {
|
|
_strip_checkpoint_wrapper_prefix(name)
|
|
for name, _ in model.named_parameters()
|
|
}
|
|
missing_vsa_gates = sorted(name for name in dense_lora_patch.replacement_parameters
|
|
if "gate_compress" in name and name not in model_parameter_names)
|
|
if missing_vsa_gates:
|
|
raise ValueError(
|
|
"This LoRA adapter provides MiniMax H3 VSA compression gates, but the selected attention backend "
|
|
"did not construct them. Use attention_backend='VIDEO_SPARSE_ATTN_H3'. Missing parameters: "
|
|
+ ", ".join(missing_vsa_gates[:3]) + (" ..." if len(missing_vsa_gates) > 3 else ""))
|
|
skip_param_names = set(nvfp4_skip_param_names)
|
|
fp8_targets: dict[str, str] = {}
|
|
# Pre-quantized FP8 checkpoints are an H3 release format; other models load as on main.
|
|
fp8_prefixes = (_prequantized_fp8_prefixes(weight_dir_list)
|
|
if type(model).__name__.startswith("MiniMaxH3") else [])
|
|
if fp8_prefixes:
|
|
fp8_targets = {prefix: param_names_mapping_fn(f"{prefix}.weight")[0] for prefix in fp8_prefixes}
|
|
skip_param_names |= set(fp8_targets.values())
|
|
fp8_keys = {f"{prefix}.{suffix}" for prefix in fp8_prefixes for suffix in ("weight", "weight_scale")}
|
|
weight_iterator = ((name, tensor) for name, tensor in weight_iterator if name not in fp8_keys)
|
|
if envs.FASTVIDEO_H3_ADALN_TABLE.get():
|
|
# Precomputed AdaLN modulation replaces the per-block projections; never read their weights.
|
|
skip_param_names |= {
|
|
_strip_checkpoint_wrapper_prefix(name)
|
|
for name, _ in model.named_parameters()
|
|
if re.fullmatch(r"transformer_blocks\.\d+\.adaln_proj\.linear\.(weight|bias)",
|
|
_strip_checkpoint_wrapper_prefix(name))
|
|
}
|
|
logger.info("Skipping %d AdaLN projection tensors (precomputed modulation tables)",
|
|
sum("adaln_proj" in n for n in skip_param_names))
|
|
load_model_from_full_model_state_dict(
|
|
model,
|
|
weight_iterator,
|
|
device,
|
|
default_dtype,
|
|
strict=strict,
|
|
cpu_offload=cpu_offload,
|
|
param_names_mapping=param_names_mapping_fn,
|
|
dense_lora_patch=dense_lora_patch,
|
|
skip_param_names=skip_param_names or None,
|
|
)
|
|
if packed_nvfp4_export is not None:
|
|
load_minimax_h3_nvfp4_dit_export(model, packed_nvfp4_export, device=device)
|
|
if fp8_targets:
|
|
_load_prequantized_fp8(model, weight_dir_list, fp8_targets, device=device)
|
|
if hasattr(model, "materialize_non_persistent_buffers"):
|
|
model.materialize_non_persistent_buffers(device=device, dtype=default_dtype)
|
|
for n, p in chain(model.named_parameters(), model.named_buffers()):
|
|
if p.is_meta:
|
|
raise RuntimeError(f"Unexpected param or buffer {n} on meta device.")
|
|
# Avoid unintended computation graph accumulation during inference
|
|
if isinstance(p, torch.nn.Parameter):
|
|
p.requires_grad = False
|
|
|
|
# Post-load weight quantization. We detect the active scheme by the
|
|
# ``quant_method`` attached to each linear layer at construction time
|
|
# (via ``QuantizationConfig.get_quant_method``). The loader's
|
|
# responsibility is just to materialize the quantized weight buffers
|
|
# from the freshly-loaded bf16 weights. No-op when no quantized layers
|
|
# are present (lazy imports inside the helper).
|
|
_maybe_quantize_model(model, defer_weight_conversion_until_lora_merge=lora_path is not None)
|
|
|
|
if enable_torch_compile and training_mode:
|
|
if not regional_compile:
|
|
# Legacy training stack (--enable-torch-compile): preserve the
|
|
# whole-model compile behavior and pass kwargs through unchanged.
|
|
unsupported = _prepare_model_for_compile(model, regional=False)
|
|
if unsupported is not None:
|
|
logger.warning("Training torch.compile requested but disabled: %s. Model stays eager.", unsupported)
|
|
else:
|
|
compile_kwargs = torch_compile_kwargs or {}
|
|
logger.info("Enabling whole-model torch.compile with kwargs=%s", compile_kwargs)
|
|
model = torch.compile(model, **compile_kwargs)
|
|
else:
|
|
unsupported = _regional_compile_unsupported_reason(
|
|
init_params,
|
|
model=model,
|
|
training=True,
|
|
vsa_tile_size=inference_vsa_tile_size,
|
|
)
|
|
if unsupported is None:
|
|
unsupported = _prepare_model_for_compile(model, regional=False)
|
|
if unsupported is not None:
|
|
logger.warning(
|
|
"enable_torch_compile requested but disabled: %s. "
|
|
"Training continues in eager mode.", unsupported)
|
|
else:
|
|
attention_count = _enable_regional_attention_compile(model)
|
|
logger.info("Enabled attention tracing for %d modules in %s", attention_count, type(model).__name__)
|
|
_compile_model_regions(model, torch_compile_kwargs or {})
|
|
elif inference_regional_compile and not training_mode:
|
|
# Inference-side counterpart of the #1718 training regional compile:
|
|
# per-block fullgraph compile right after the transformer loads, no
|
|
# user kwargs needed (fullgraph + emulate_precision_casts injected).
|
|
unsupported = _regional_compile_unsupported_reason(
|
|
init_params,
|
|
model=model,
|
|
vsa_tile_size=inference_vsa_tile_size,
|
|
)
|
|
if unsupported is None:
|
|
unsupported = _prepare_model_for_compile(model, regional=True)
|
|
if unsupported is not None:
|
|
logger.warning(
|
|
"inference_torch_compile requested but disabled: %s. "
|
|
"Inference continues in eager mode.", unsupported)
|
|
else:
|
|
attention_count = _enable_regional_attention_compile(model)
|
|
logger.info("Enabled attention tracing for %d modules in %s", attention_count, type(model).__name__)
|
|
_compile_model_regions(model, torch_compile_kwargs or {})
|
|
return model
|
|
|
|
|
|
def _strip_checkpoint_wrapper_prefix(name: str) -> str:
|
|
"""Canonicalize an FQN produced after activation-checkpoint wrapping.
|
|
|
|
``checkpoint_wrapper`` strips its ``_checkpoint_wrapped_module.`` prefix
|
|
from ``state_dict()`` keys via hooks, but plain ``named_parameters()`` /
|
|
``named_buffers()`` recursion still yields prefixed names. Checkpoint
|
|
keys are always clean, so every name-keyed lookup against loaded weights
|
|
must compare clean names.
|
|
"""
|
|
return name.replace("._checkpoint_wrapped_module.", ".").removeprefix("_checkpoint_wrapped_module.")
|
|
|
|
|
|
def build_fsdp_model_from_scratch(
|
|
model_cls: type[nn.Module],
|
|
init_params: dict[str, Any],
|
|
device: torch.device,
|
|
hsdp_replicate_dim: int,
|
|
hsdp_shard_dim: int,
|
|
default_dtype: torch.dtype,
|
|
param_dtype: torch.dtype,
|
|
reduce_dtype: torch.dtype,
|
|
*,
|
|
seed: int,
|
|
cpu_offload: bool = False,
|
|
output_dtype: torch.dtype | None = None,
|
|
pin_cpu_memory: bool = True,
|
|
) -> torch.nn.Module:
|
|
"""Deterministically initialize and FSDP-shard a model without weights."""
|
|
|
|
mp_policy = MixedPrecisionPolicy(
|
|
param_dtype,
|
|
reduce_dtype,
|
|
output_dtype,
|
|
cast_forward_inputs=False,
|
|
)
|
|
set_mixed_precision_policy(
|
|
param_dtype=param_dtype,
|
|
reduce_dtype=reduce_dtype,
|
|
output_dtype=output_dtype,
|
|
mp_policy=mp_policy,
|
|
)
|
|
|
|
fork_devices: list[int] = []
|
|
if device.type == "cuda":
|
|
fork_devices = [
|
|
device.index if device.index is not None else torch.cuda.current_device()
|
|
]
|
|
with torch.random.fork_rng(devices=fork_devices):
|
|
torch.manual_seed(int(seed))
|
|
with set_default_dtype(default_dtype), torch.device(device):
|
|
model = model_cls(**init_params)
|
|
|
|
from fastvideo.platforms import current_platform
|
|
|
|
if not current_platform.is_mps():
|
|
pin_cpu_memory = pin_cpu_memory and is_pin_memory_available()
|
|
if current_platform.is_npu():
|
|
device_mesh = init_device_mesh(
|
|
"npu",
|
|
mesh_shape=(hsdp_replicate_dim, hsdp_shard_dim),
|
|
mesh_dim_names=("replicate", "shard"),
|
|
)
|
|
else:
|
|
device_mesh = init_device_mesh(
|
|
"cuda",
|
|
mesh_shape=(hsdp_replicate_dim, hsdp_shard_dim),
|
|
mesh_dim_names=("replicate", "shard"),
|
|
)
|
|
shard_model(
|
|
model,
|
|
cpu_offload=cpu_offload,
|
|
reshard_after_forward=True,
|
|
mp_policy=mp_policy,
|
|
mesh=device_mesh,
|
|
fsdp_shard_conditions=model._fsdp_shard_conditions,
|
|
pin_cpu_memory=pin_cpu_memory,
|
|
)
|
|
|
|
for parameter in model.parameters():
|
|
parameter.requires_grad = False
|
|
return model
|
|
|
|
|
|
def _regional_compile_unsupported_reason(
|
|
init_params: dict[str, Any],
|
|
*,
|
|
model: nn.Module | None = None,
|
|
training: bool = False,
|
|
vsa_tile_size: int | None = None,
|
|
) -> str | None:
|
|
"""Return why regional fullgraph compile cannot run, or None if it can.
|
|
|
|
Dense FA2 and FA4 route through compile-visible custom-op boundaries.
|
|
FA3 is safe for inference but its grad-enabled path graph-breaks, so it is
|
|
rejected for regional training compile.
|
|
|
|
The legacy VSA backend remains outside the fullgraph support envelope.
|
|
MiniMax H3's VSA backend is supported only through the inference-only
|
|
sm_100a tile-64 route; its regional hook resolves loaded compression
|
|
gates and probes the kernel before block capture.
|
|
"""
|
|
try:
|
|
from fastvideo.attention.layer import _attention_compile_explicitly_disabled
|
|
except Exception: # pragma: no cover - attention stack not importable
|
|
pass
|
|
else:
|
|
if _attention_compile_explicitly_disabled():
|
|
# The escape hatch wraps attention forwards in
|
|
# torch.compiler.disable, which is a hard dynamo error inside a
|
|
# fullgraph region ("Skip inlining `torch.compiler.disable()`d
|
|
# function"). Degrade to eager instead, matching the hatch's
|
|
# debugging intent.
|
|
return ("FASTVIDEO_DISABLE_ATTENTION_COMPILE=1 keeps attention "
|
|
"forwards out of compiled graphs via torch.compiler."
|
|
"disable, which fullgraph regional compile cannot trace; "
|
|
"this model stays eager")
|
|
backend_names: set[str] = set()
|
|
if model is not None:
|
|
from fastvideo.attention.layer import DistributedAttention, LocalAttention
|
|
|
|
for submodule in model.modules():
|
|
if not isinstance(submodule, (DistributedAttention, LocalAttention)):
|
|
continue
|
|
backend = getattr(submodule, "backend", None)
|
|
backend_name = getattr(backend, "name", "")
|
|
if backend_name:
|
|
backend_names.add(backend_name)
|
|
|
|
# The component config stores the requested backend. ``None`` means
|
|
# platform automatic selection, so once a constructed model is available
|
|
# its attention instances are the source of truth. Keep the config fallback
|
|
# for callers that validate policy before or without model construction.
|
|
if not backend_names:
|
|
config = init_params.get("config")
|
|
resolved = getattr(config, "_resolved_attention_backend", None)
|
|
resolved_name = getattr(resolved, "name", "")
|
|
if resolved_name:
|
|
backend_names.add(resolved_name)
|
|
|
|
if training and "FLASH_ATTN" in backend_names:
|
|
try:
|
|
from fastvideo.attention.utils.flash_attn_default import fa_version
|
|
except Exception: # pragma: no cover - flash-attn stack not importable
|
|
pass
|
|
else:
|
|
if fa_version == "3":
|
|
return ("model uses FLASH_ATTN with flash-attn 3, "
|
|
"whose grad-enabled path graph-breaks (incompatible with "
|
|
"fullgraph regional compile); use FA2, FA4 (FASTVIDEO_FA4=1), "
|
|
"or TORCH_SDPA for compiled training")
|
|
if "VIDEO_SPARSE_ATTN_H3" in backend_names:
|
|
if training:
|
|
return ("VIDEO_SPARSE_ATTN_H3 regional compile is supported only for inference; "
|
|
"compiled training stays eager")
|
|
if envs.FASTVIDEO_H3_VSA_PROBE.get():
|
|
return ("FASTVIDEO_H3_VSA_PROBE records tensors and files from the VSA-H3 attention body, which "
|
|
"regional fullgraph compile cannot capture; this model stays eager")
|
|
if not envs.FASTVIDEO_VSA_SM100A.get():
|
|
return ("VIDEO_SPARSE_ATTN_H3 regional compile requires the compile-safe sm_100a route "
|
|
"(FASTVIDEO_VSA_SM100A=1); Triton/CuTe VSA stays eager")
|
|
if vsa_tile_size != 64:
|
|
return ("VIDEO_SPARSE_ATTN_H3 regional compile requires VSA_tile_size=64; "
|
|
f"got {vsa_tile_size!r}, so tile-256/CuTe VSA stays eager")
|
|
if "VIDEO_SPARSE_ATTN" in backend_names:
|
|
return ("attention backend resolved to VIDEO_SPARSE_ATTN, whose Triton "
|
|
"kernels, sequence-parallel collectives, and sync metadata "
|
|
"guard graph-break (incompatible with fullgraph regional "
|
|
"compile); this model stays eager")
|
|
return None
|
|
|
|
|
|
def _enable_regional_attention_compile(model: nn.Module) -> int:
|
|
"""Opt in distributed-attention instances owned by ``model`` only."""
|
|
from fastvideo.attention.layer import DistributedAttention
|
|
|
|
enabled_count = 0
|
|
for submodule in model.modules():
|
|
if isinstance(submodule, DistributedAttention):
|
|
submodule._set_compile_forward_enabled(True)
|
|
enabled_count += 1
|
|
return enabled_count
|
|
|
|
|
|
def _compile_model_regions(model: nn.Module, compile_kwargs: dict[str, Any]) -> int:
|
|
"""Compile repeated mathematical regions after FSDP setup.
|
|
|
|
Only the selected module ``forward`` is replaced. This keeps activation
|
|
checkpoint wrappers structurally transparent while FSDP pre/post hooks
|
|
execute outside the compiled region.
|
|
"""
|
|
compile_conditions = getattr(model, "_compile_conditions", None)
|
|
if not compile_conditions:
|
|
raise ValueError(f"{type(model).__name__} does not declare _compile_conditions")
|
|
|
|
if compile_kwargs.get("fullgraph", True) is not True:
|
|
raise ValueError("Regional compile requires fullgraph=True")
|
|
if "mode" in compile_kwargs:
|
|
# torch.compile forbids passing both `mode` and `options`, and
|
|
# regional compile always injects options (emulate_precision_casts)
|
|
# for bf16 numerics parity. Fail here with an actionable message
|
|
# instead of letting torch raise a mode/options conflict about an
|
|
# `options` key the user never wrote.
|
|
raise ValueError("Regional compile sets inductor options "
|
|
"(emulate_precision_casts) and cannot be combined "
|
|
"with torch_compile_kwargs['mode']. Remove 'mode' or "
|
|
"express its effect via torch_compile_kwargs['options'].")
|
|
kwargs = {**compile_kwargs, "fullgraph": True}
|
|
options = {"emulate_precision_casts": True}
|
|
options.update(kwargs.get("options") or {})
|
|
kwargs["options"] = options
|
|
compiled_count = 0
|
|
for name, submodule in list(model.named_modules()):
|
|
if not name:
|
|
continue
|
|
if any(condition(name, submodule) for condition in compile_conditions):
|
|
# Activation checkpoint wrappers are control-flow boundaries, not
|
|
# mathematical regions. Keep their saved-tensor/recompute logic
|
|
# eager and compile only the repeated block they own.
|
|
compile_target = getattr(submodule, "_checkpoint_wrapped_module", submodule)
|
|
compile_target.forward = torch.compile(compile_target.forward, **kwargs)
|
|
compiled_count += 1
|
|
|
|
if compiled_count == 0:
|
|
raise ValueError(f"No submodules in {type(model).__name__} matched _compile_conditions")
|
|
logger.info(
|
|
"Enabled regional torch.compile for %d submodules in %s after FSDP setup with kwargs=%s",
|
|
compiled_count,
|
|
type(model).__name__,
|
|
kwargs,
|
|
)
|
|
return compiled_count
|
|
|
|
|
|
def shard_model(
|
|
model,
|
|
*,
|
|
cpu_offload: bool,
|
|
reshard_after_forward: bool = True,
|
|
mp_policy: MixedPrecisionPolicy | None = MixedPrecisionPolicy(), # noqa
|
|
mesh: DeviceMesh | None = None,
|
|
fsdp_shard_conditions: list[Callable[[str, nn.Module], bool]] = [], # noqa
|
|
pin_cpu_memory: bool = True,
|
|
) -> None:
|
|
"""
|
|
Utility to shard a model with FSDP using the PyTorch Distributed fully_shard API.
|
|
|
|
This method will over the model's named modules from the bottom-up and apply shard modules
|
|
based on whether they meet any of the criteria from shard_conditions.
|
|
|
|
Args:
|
|
model (TransformerDecoder): Model to shard with FSDP.
|
|
shard_conditions (List[Callable[[str, nn.Module], bool]]): A list of functions to determine
|
|
which modules to shard with FSDP. Each function should take module name (relative to root)
|
|
and the module itself, returning True if FSDP should shard the module and False otherwise.
|
|
If any of shard_conditions return True for a given module, it will be sharded by FSDP.
|
|
cpu_offload (bool): If set to True, FSDP will offload parameters, gradients, and optimizer
|
|
states to CPU.
|
|
reshard_after_forward (bool): Whether to reshard parameters and buffers after
|
|
the forward pass. Setting this to True corresponds to the FULL_SHARD sharding strategy
|
|
from FSDP1, while setting it to False corresponds to the SHARD_GRAD_OP sharding strategy.
|
|
mesh (Optional[DeviceMesh]): Device mesh to use for FSDP sharding under multiple parallelism.
|
|
Default to None.
|
|
fsdp_shard_conditions (List[Callable[[str, nn.Module], bool]]): A list of functions to determine
|
|
which modules to shard with FSDP.
|
|
pin_cpu_memory (bool): If set to True, FSDP will pin the CPU memory of the offloaded parameters.
|
|
|
|
Raises:
|
|
ValueError: If no layer modules were sharded, indicating that no shard_condition was triggered.
|
|
"""
|
|
# Check if we should use size-based filtering
|
|
use_size_filtering = envs.FASTVIDEO_FSDP2_AUTOWRAP.get()
|
|
|
|
if not fsdp_shard_conditions:
|
|
logger.warning("No FSDP shard conditions provided; nothing will be sharded.")
|
|
return
|
|
|
|
default_param_dtype = getattr(mp_policy, "param_dtype", None)
|
|
dtype_selector = getattr(model, "_get_parameter_dtype", None)
|
|
ignored_params: set[nn.Parameter] = set()
|
|
if callable(dtype_selector) and default_param_dtype is not None:
|
|
ignored_params = {
|
|
parameter
|
|
for name, parameter in model.named_parameters()
|
|
if dtype_selector(_strip_checkpoint_wrapper_prefix(name), default_param_dtype) != default_param_dtype
|
|
}
|
|
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 = envs.FASTVIDEO_FSDP2_MIN_PARAMS.get()
|
|
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)
|
|
|
|
|
|
def _drop_state_dict_parameter(model: nn.Module, param_name: str) -> None:
|
|
module_name, _, attr_name = param_name.rpartition(".")
|
|
module = model.get_submodule(module_name) if module_name else model
|
|
module.register_parameter(attr_name, None)
|
|
|
|
|
|
# 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,
|
|
dense_lora_patch: DenseLoRAPatch | None = None,
|
|
skip_param_names: set[str] | None = None,
|
|
) -> _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
|
|
dense_lora_patch (Optional[DenseLoRAPatch]): whole-parameter adapter payload,
|
|
folded into each full tensor before it is sharded so that FSDP/TP placement
|
|
is inherited from this function rather than reimplemented downstream
|
|
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()
|
|
# state_dict() keys are clean (checkpoint-wrapper hooks strip the AC
|
|
# prefix) but named_parameters()/named_buffers() are not; checkpoint keys
|
|
# are clean, so canonicalize before any name-keyed lookup. Without this,
|
|
# a loaded buffer inside an AC-wrapped block misses the named_buffers
|
|
# membership test below and is silently converted into a trainable
|
|
# nn.Parameter by load_state_dict(assign=True).
|
|
named_parameters = {_strip_checkpoint_wrapper_prefix(k): v for k, v in model.named_parameters()}
|
|
named_buffers = {_strip_checkpoint_wrapper_prefix(k): v for k, v in 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
|
|
checkpoint_key_aliases = getattr(model, "_fastvideo_checkpoint_key_aliases", {})
|
|
if checkpoint_key_aliases:
|
|
for original_name, transformed_name in checkpoint_key_aliases.items():
|
|
if original_name not in custom_param_sd:
|
|
continue
|
|
if transformed_name in custom_param_sd:
|
|
raise ValueError(f"Checkpoint transform maps multiple tensors to {transformed_name!r}")
|
|
custom_param_sd[transformed_name] = custom_param_sd.pop(original_name)
|
|
reverse_param_names_mapping[transformed_name] = reverse_param_names_mapping.pop(original_name)
|
|
|
|
# Drain rather than iterate. Production safetensors values may retain
|
|
# memory-mapped shard storage, while mapped or merged parameters can own
|
|
# ordinary allocations. Keeping the dict retains all of that source
|
|
# storage until loading finishes; popping releases each reference as soon
|
|
# as its conversion completes and lowers the host/unified-memory working
|
|
# set.
|
|
for target_param_name in list(custom_param_sd):
|
|
full_tensor = custom_param_sd.pop(target_param_name)
|
|
if skip_param_names and target_param_name in skip_param_names:
|
|
continue
|
|
if "::" in target_param_name:
|
|
logger.warning("Skipping packed NVFP4 export key mixed into dense shards: %s", target_param_name)
|
|
continue
|
|
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 dense_lora_patch is not None:
|
|
# Returns float32 when a delta was added, so the cast below is what lands
|
|
# the parameter in its storage dtype.
|
|
full_tensor = dense_lora_patch.apply_to(target_param_name, full_tensor)
|
|
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())
|
|
skipped_unused = unused_keys & skip_param_names if skip_param_names else set()
|
|
for skipped_name in skipped_unused:
|
|
_drop_state_dict_parameter(model, skipped_name)
|
|
unused_keys -= skipped_unused
|
|
if skipped_unused:
|
|
logger.info("Deferred %d NVFP4 linear weights to the packed DiT export (%s)",
|
|
len(skipped_unused), _summarize_param_names(skipped_unused))
|
|
|
|
# List of allowed parameter name patterns
|
|
ALLOWED_NEW_PARAM_PATTERNS = ["gate_compress", "proj_l"] # Can be extended as needed
|
|
missing_parameter_initializer = getattr(model, "_fastvideo_missing_parameter_initializer", None)
|
|
initialized_from_adapter: set[str] = set()
|
|
initialized_from_model: set[str] = set()
|
|
zero_initialized: set[str] = set()
|
|
for new_param_name in sorted(unused_keys):
|
|
# An adapter that ships the parameter outright both supplies the value and
|
|
# authorizes it: the allowlist exists to catch a checkpoint silently missing a
|
|
# weight, which is not the case when something deliberately provides one.
|
|
adapter_value = (dense_lora_patch.replacement_for(new_param_name) if dense_lora_patch is not None else None)
|
|
meta_sharded_param = meta_sd.get(new_param_name)
|
|
if meta_sharded_param is None:
|
|
raise RuntimeError(f"Missing meta state for unloaded parameter {new_param_name!r}")
|
|
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)
|
|
|
|
initialized_tensor = adapter_value
|
|
if initialized_tensor is not None:
|
|
initialized_from_adapter.add(new_param_name)
|
|
elif callable(missing_parameter_initializer):
|
|
initialized_tensor = missing_parameter_initializer(
|
|
new_param_name,
|
|
meta_sharded_param.shape,
|
|
target_dtype,
|
|
)
|
|
if initialized_tensor is not None:
|
|
initialized_from_model.add(new_param_name)
|
|
|
|
if initialized_tensor is None and 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.")
|
|
if initialized_tensor is None:
|
|
initialized_tensor = torch.zeros(tuple(meta_sharded_param.shape), device="cpu", dtype=target_dtype)
|
|
zero_initialized.add(new_param_name)
|
|
else:
|
|
if tuple(initialized_tensor.shape) != tuple(meta_sharded_param.shape):
|
|
if adapter_value is not None:
|
|
raise ValueError(f"LoRA replacement for {new_param_name} has shape {tuple(initialized_tensor.shape)}, "
|
|
f"but the parameter is {tuple(meta_sharded_param.shape)}")
|
|
raise ValueError(f"Initializer returned shape {tuple(initialized_tensor.shape)} for {new_param_name!r}; "
|
|
f"expected {tuple(meta_sharded_param.shape)}")
|
|
initialized_tensor = initialized_tensor.to(dtype=target_dtype)
|
|
if not hasattr(meta_sharded_param, "device_mesh"):
|
|
sharded_tensor = initialized_tensor.to(device=device)
|
|
else:
|
|
full_tensor = initialized_tensor.to(device=device)
|
|
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)
|
|
|
|
if initialized_from_adapter:
|
|
logger.info("Parameters absent from the checkpoint and supplied by the LoRA adapter: %d (%s)",
|
|
len(initialized_from_adapter), _summarize_param_names(initialized_from_adapter))
|
|
if initialized_from_model:
|
|
logger.info("Parameters absent from the checkpoint and supplied by the model initializer: %d (%s)",
|
|
len(initialized_from_model), _summarize_param_names(initialized_from_model))
|
|
if zero_initialized:
|
|
logger.warning("Found unloaded parameters in meta state dict, zero-initializing: %d (%s)",
|
|
len(zero_initialized), _summarize_param_names(zero_initialized))
|
|
|
|
if dense_lora_patch is not None:
|
|
dense_lora_patch.report_unapplied()
|
|
|
|
# choose `assign=True` since we cannot call `copy_` on meta tensor
|
|
return model.load_state_dict(sharded_sd, strict=strict, assign=True)
|