wehub-resource-sync
471 lines
18 KiB
Python
471 lines
18 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import dataclasses
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import glob
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import os
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import time
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from collections.abc import Generator, Iterable
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from typing import cast
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import torch
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from torch import nn
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from transformers.utils import SAFE_WEIGHTS_INDEX_NAME
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from vllm.config import ModelConfig
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from vllm.config.load import LoadConfig
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from vllm.logger import init_logger
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from vllm.model_executor.layers.quantization.torchao import torchao_version_at_least
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from vllm.model_executor.model_loader.base_loader import BaseModelLoader
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from vllm.model_executor.model_loader.ep_weight_filter import (
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compute_local_expert_ids,
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)
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from vllm.model_executor.model_loader.weight_utils import (
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download_safetensors_index_file_from_hf,
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download_weights_from_hf,
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fastsafetensors_weights_iterator,
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filter_duplicate_safetensors_files,
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filter_files_not_needed_for_inference,
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get_quant_config,
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instanttensor_weights_iterator,
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maybe_download_from_modelscope,
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multi_thread_pt_weights_iterator,
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multi_thread_safetensors_weights_iterator,
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np_cache_weights_iterator,
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pt_weights_iterator,
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safetensors_weights_iterator,
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)
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from vllm.tracing import instrument
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from vllm.transformers_utils.repo_utils import list_filtered_repo_files
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logger = init_logger(__name__)
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class DefaultModelLoader(BaseModelLoader):
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"""Model loader that can load different file types from disk."""
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# default number of thread when enable multithread weight loading
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DEFAULT_NUM_THREADS = 8
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@dataclasses.dataclass
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class Source:
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"""A source for weights."""
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model_or_path: str
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"""The model ID or path."""
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revision: str | None
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"""The optional model revision."""
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subfolder: str | None = None
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"""The subfolder inside the model repo."""
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prefix: str = ""
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"""A prefix to prepend to all weights."""
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fall_back_to_pt: bool = True
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"""Whether .pt weights can be used."""
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allow_patterns_overrides: list[str] | None = None
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"""If defined, weights will load exclusively using these patterns."""
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counter_before_loading_weights: float = 0.0
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counter_after_loading_weights: float = 0.0
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def __init__(self, load_config: LoadConfig):
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super().__init__(load_config)
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self.local_expert_ids: set[int] | None = None
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extra_config = load_config.model_loader_extra_config
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if not isinstance(extra_config, dict):
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raise ValueError(
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f"model_loader_extra_config must be a dict for load format "
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f"{load_config.load_format}, got {type(extra_config).__name__}"
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)
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allowed_keys = {
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"enable_multithread_load",
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"num_threads",
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"enable_weights_track",
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}
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unexpected_keys = set(extra_config.keys()) - allowed_keys
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if unexpected_keys:
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raise ValueError(
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f"Unexpected extra config keys for load format "
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f"{load_config.load_format}: "
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f"{unexpected_keys}"
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)
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enable_multithread_load = extra_config.get("enable_multithread_load", False)
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if not isinstance(enable_multithread_load, bool):
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raise ValueError(
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f"enable_multithread_load must be a bool, got "
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f"{type(enable_multithread_load).__name__}"
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)
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num_threads = extra_config.get("num_threads")
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if num_threads is not None and not (
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isinstance(num_threads, int) and num_threads > 0
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):
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raise ValueError(
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f"num_threads must be a positive integer, got {num_threads!r}"
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)
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self.enable_weights_track: bool | None = extra_config.get(
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"enable_weights_track", None
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)
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# The multi-thread loader ignores safetensors_load_strategy, so reject
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# the combination instead of silently dropping the requested strategy.
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if extra_config.get("enable_multithread_load") and (
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load_config.safetensors_load_strategy not in (None, "lazy")
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):
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raise ValueError(
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"enable_multithread_load does not support "
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"safetensors_load_strategy="
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f"{load_config.safetensors_load_strategy!r}; the multi-thread "
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"loader only implements the default lazy strategy."
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)
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def _prepare_weights(
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self,
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model_name_or_path: str,
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subfolder: str | None,
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revision: str | None,
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fall_back_to_pt: bool,
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allow_patterns_overrides: list[str] | None,
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) -> tuple[str, list[str], bool]:
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"""Prepare weights for the model.
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If the model is not local, it will be downloaded."""
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model_name_or_path = (
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maybe_download_from_modelscope(model_name_or_path, revision)
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or model_name_or_path
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)
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is_local = os.path.isdir(model_name_or_path)
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load_format = self.load_config.load_format
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use_safetensors = False
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index_file = SAFE_WEIGHTS_INDEX_NAME
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# First check for 'auto' format that mistral files format are present.
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# This is to load mistral models with official format by default.
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if load_format == "auto":
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load_format = (
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"mistral"
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if len(
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list_filtered_repo_files(
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model_name_or_path=model_name_or_path,
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allow_patterns=["consolidated*.safetensors"],
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revision=revision,
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)
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)
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> 0
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else "hf"
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)
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# Some quantized models use .pt files for storing the weights.
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if load_format == "hf":
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allow_patterns = ["*.safetensors", "*.bin"]
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elif (
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load_format == "safetensors"
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or load_format == "fastsafetensors"
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or load_format == "instanttensor"
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):
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use_safetensors = True
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allow_patterns = ["*.safetensors"]
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elif load_format == "mistral":
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use_safetensors = True
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allow_patterns = ["consolidated*.safetensors"]
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index_file = "consolidated.safetensors.index.json"
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elif load_format == "pt":
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allow_patterns = ["*.pt"]
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elif load_format == "npcache":
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allow_patterns = ["*.bin"]
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else:
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raise ValueError(f"Unknown load_format: {load_format}")
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# Don't fall back to .pt for explicit safetensors formats; otherwise a
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# .pt file is matched and later opened as safetensors.
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if fall_back_to_pt and not use_safetensors:
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allow_patterns += ["*.pt"]
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if allow_patterns_overrides is not None:
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allow_patterns = allow_patterns_overrides
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if not is_local:
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hf_folder = download_weights_from_hf(
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model_name_or_path,
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self.load_config.download_dir,
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allow_patterns,
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revision,
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subfolder=subfolder,
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ignore_patterns=self.load_config.ignore_patterns,
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)
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else:
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hf_folder = model_name_or_path
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if subfolder is not None:
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hf_folder = os.path.join(hf_folder, subfolder)
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hf_weights_files: list[str] = []
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for pattern in allow_patterns:
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hf_weights_files += glob.glob(os.path.join(hf_folder, pattern))
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if len(hf_weights_files) > 0:
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if pattern.endswith(".safetensors"):
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use_safetensors = True
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break
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if use_safetensors:
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# For models like Mistral-7B-Instruct-v0.3
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# there are both sharded safetensors files and a consolidated
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# safetensors file. Using both breaks.
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# Here, we download the `model.safetensors.index.json` and filter
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# any files not found in the index.
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if not is_local:
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download_safetensors_index_file_from_hf(
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model_name_or_path,
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index_file,
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cache_dir=self.load_config.download_dir,
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subfolder=subfolder,
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revision=revision,
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)
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hf_weights_files = filter_duplicate_safetensors_files(
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hf_weights_files, hf_folder, index_file
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)
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else:
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hf_weights_files = filter_files_not_needed_for_inference(hf_weights_files)
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if len(hf_weights_files) == 0:
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raise RuntimeError(
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f"Cannot find any model weights with `{model_name_or_path}`"
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)
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return hf_folder, hf_weights_files, use_safetensors
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def _get_weights_iterator(
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self, source: "Source"
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) -> Generator[tuple[str, torch.Tensor], None, None]:
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"""Get an iterator for the model weights based on the load format."""
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extra_config = self.load_config.model_loader_extra_config
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hf_folder, hf_weights_files, use_safetensors = self._prepare_weights(
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source.model_or_path,
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source.subfolder,
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source.revision,
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source.fall_back_to_pt,
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source.allow_patterns_overrides,
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)
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if self.load_config.load_format == "npcache":
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# Currently np_cache only support *.bin checkpoints
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assert use_safetensors is False
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weights_iterator = np_cache_weights_iterator(
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source.model_or_path,
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self.load_config.download_dir,
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hf_folder,
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hf_weights_files,
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self.load_config.use_tqdm_on_load,
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)
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elif use_safetensors:
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if self.load_config.load_format == "fastsafetensors":
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weights_iterator = fastsafetensors_weights_iterator(
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hf_weights_files,
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self.load_config.use_tqdm_on_load,
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)
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elif self.load_config.load_format == "instanttensor":
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weights_iterator = instanttensor_weights_iterator(
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hf_weights_files,
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self.load_config.use_tqdm_on_load,
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)
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else:
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if extra_config.get("enable_multithread_load"):
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weights_iterator = multi_thread_safetensors_weights_iterator(
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hf_weights_files,
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self.load_config.use_tqdm_on_load,
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max_workers=extra_config.get(
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"num_threads", self.DEFAULT_NUM_THREADS
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),
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)
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else:
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weights_iterator = safetensors_weights_iterator(
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hf_weights_files,
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self.load_config.use_tqdm_on_load,
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self.load_config.safetensors_load_strategy,
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local_expert_ids=self.local_expert_ids,
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safetensors_prefetch_num_threads=(
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self.load_config.safetensors_prefetch_num_threads
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),
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safetensors_prefetch_block_size=(
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self.load_config.safetensors_prefetch_block_size
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),
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)
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else:
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if extra_config.get("enable_multithread_load"):
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weights_iterator = multi_thread_pt_weights_iterator(
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hf_weights_files,
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self.load_config.use_tqdm_on_load,
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self.load_config.pt_load_map_location,
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max_workers=extra_config.get(
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"num_threads", self.DEFAULT_NUM_THREADS
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),
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)
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else:
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weights_iterator = pt_weights_iterator(
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hf_weights_files,
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self.load_config.use_tqdm_on_load,
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self.load_config.pt_load_map_location,
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)
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if self.counter_before_loading_weights == 0.0:
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self.counter_before_loading_weights = time.perf_counter()
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# Apply the prefix.
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return ((source.prefix + name, tensor) for (name, tensor) in weights_iterator)
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def get_all_weights(
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self,
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model_config: ModelConfig,
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model: nn.Module,
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) -> Generator[tuple[str, torch.Tensor], None, None]:
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primary_weights = DefaultModelLoader.Source(
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model_config.model,
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model_config.revision,
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prefix="",
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fall_back_to_pt=getattr(model, "fall_back_to_pt_during_load", True),
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allow_patterns_overrides=getattr(model, "allow_patterns_overrides", None),
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)
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yield from self._get_weights_iterator(primary_weights)
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secondary_weights = cast(
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Iterable[DefaultModelLoader.Source],
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getattr(model, "secondary_weights", ()),
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)
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for source in secondary_weights:
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yield from self._get_weights_iterator(source)
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def download_model(self, model_config: ModelConfig) -> None:
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self._prepare_weights(
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model_name_or_path=model_config.model,
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subfolder=None,
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revision=model_config.revision,
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fall_back_to_pt=True,
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allow_patterns_overrides=None,
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)
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def _init_ep_weight_filter(self, model_config: ModelConfig) -> None:
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"""Compute local expert ids for EP weight filtering.
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When expert parallelism is active, each rank only needs a subset of
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expert weights. By computing the set upfront we can skip non-local
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expert tensors *before* reading them from disk.
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"""
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from vllm.config import get_current_vllm_config
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vllm_config = get_current_vllm_config()
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parallel_config = vllm_config.parallel_config
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if not (
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model_config.is_moe
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and parallel_config.enable_expert_parallel
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and parallel_config.enable_ep_weight_filter
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):
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return
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# When EPLB is enabled, redundant physical expert slots may map to
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# logical experts that belong to other ranks in the default partition.
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# The weight loader needs to see ALL logical expert weights so it can
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# populate these redundant slots. Skip the filter entirely.
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if parallel_config.enable_eplb:
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return
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num_experts = model_config.get_num_experts()
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if num_experts <= 0:
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return
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# EP size/rank computation mirrors FusedMoEParallelConfig.make():
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# ep_size = dp_size * pcp_size * tp_size (flattened)
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# ep_rank = dp_rank * pcp_size * tp_size + pcp_rank * tp_size + tp_rank
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from vllm.distributed import (
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get_dp_group,
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get_pcp_group,
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get_tensor_model_parallel_rank,
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)
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dp_size = parallel_config.data_parallel_size
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tp_size = parallel_config.tensor_parallel_size
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pcp_size = parallel_config.prefill_context_parallel_size
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dp_rank = get_dp_group().rank_in_group if dp_size > 1 else 0
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tp_rank = get_tensor_model_parallel_rank() if tp_size > 1 else 0
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pcp_rank = get_pcp_group().rank_in_group if pcp_size > 1 else 0
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ep_size = dp_size * pcp_size * tp_size
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ep_rank = dp_rank * pcp_size * tp_size + pcp_rank * tp_size + tp_rank
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self.local_expert_ids = compute_local_expert_ids(
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num_experts,
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ep_size,
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ep_rank,
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placement=parallel_config.expert_placement_strategy,
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)
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if self.local_expert_ids is not None:
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logger.info_once(
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"EP weight filter: ep_size=%d, ep_rank=%d, loading %d/%d experts",
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ep_size,
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ep_rank,
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len(self.local_expert_ids),
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num_experts,
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)
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@instrument(span_name="Load weights")
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def load_weights(self, model: nn.Module, model_config: ModelConfig) -> None:
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if model_config.quantization == "torchao":
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quant_config = get_quant_config(model_config, self.load_config)
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if (
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hasattr(quant_config, "is_checkpoint_torchao_serialized")
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and quant_config.is_checkpoint_torchao_serialized
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and torchao_version_at_least("0.15.0")
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):
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self.load_config.safetensors_load_strategy = "torchao"
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self._init_ep_weight_filter(model_config)
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loaded_weights = model.load_weights(self.get_all_weights(model_config, model))
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self.counter_after_loading_weights = time.perf_counter()
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logger.info_once(
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"Loading weights took %.2f seconds",
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self.counter_after_loading_weights - self.counter_before_loading_weights,
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)
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# We only enable strict check for non-quantized models
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# that have loaded weights tracking by default.
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default_enable_weights_track = (
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model_config.quantization is None and loaded_weights is not None
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)
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enable_weights_track = (
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self.enable_weights_track
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if self.enable_weights_track is not None
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else default_enable_weights_track
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)
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if enable_weights_track:
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self.track_weights_loading(model, loaded_weights)
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def track_weights_loading(
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self, model: nn.Module, loaded_weights: set[str] | None
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) -> None:
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weights_to_load = {name for name, _ in model.named_parameters()}
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if loaded_weights is not None:
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# ignore online quantization scales
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for name, module in model.named_modules():
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quant_method = getattr(module, "quant_method", None)
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has_online_quant = getattr(quant_method, "uses_meta_device", False)
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has_postprocess_quant = getattr(
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quant_method, "process_weights_after_loading", None
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)
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# ignore kv_cache scale and online quant scale,
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# which can be missing in checkpoints
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if has_online_quant or has_postprocess_quant:
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for param_name, _ in module.named_parameters():
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full_name = f"{name}.{param_name}" if name else param_name
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loaded_weights.add(full_name)
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weights_not_loaded = weights_to_load - loaded_weights
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if weights_not_loaded:
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raise ValueError(
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"Following weights were not initialized from "
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f"checkpoint: {weights_not_loaded}"
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)
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