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https://git.datalinker.icu/vllm-project/vllm.git
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327 lines
12 KiB
Python
327 lines
12 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.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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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.platforms import current_platform
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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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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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extra_config = load_config.model_loader_extra_config
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allowed_keys = {"enable_multithread_load", "num_threads"}
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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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def _prepare_weights(
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self,
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model_name_or_path: str,
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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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# Some quantized models use .pt files for storing the weights.
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if load_format == "auto":
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allow_patterns = ["*.safetensors", "*.bin"]
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elif load_format == "safetensors" or load_format == "fastsafetensors":
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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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if fall_back_to_pt:
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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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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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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 == "*.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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self.load_config.download_dir,
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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.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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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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)
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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 current_platform.is_tpu():
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from vllm.platforms.tpu import USE_TPU_INFERENCE
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if not USE_TPU_INFERENCE:
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# In PyTorch XLA, we should call `torch_xla.sync`
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# frequently so that not too many ops are accumulated
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# in the XLA program.
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import torch_xla
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def _xla_weights_iterator(iterator: Generator):
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for weights in iterator:
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yield weights
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torch_xla.sync(wait=False)
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weights_iterator = _xla_weights_iterator(weights_iterator)
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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_config.model,
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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 load_weights(self, model: nn.Module, model_config: ModelConfig) -> None:
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if model_config.quantization == "torchao" and torchao_version_at_least(
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"0.14.0"
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):
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self.load_config.safetensors_load_strategy = "torchao"
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weights_to_load = {name for name, _ in model.named_parameters()}
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# if we don't have `model.weight_metadata_and_attr_saved` defined and
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# set to True, it means that this is either offline quantization case
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# or the first run of online quantization
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# see online_quantization.py for detailed notes
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offline_quantization_or_first_run_of_online_quantization = not getattr(
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model, "weight_metadata_and_attr_saved", False
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)
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if model_config.quantization is None:
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# model is not quantized
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loaded_weights = model.load_weights(
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self.get_all_weights(model_config, model)
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)
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elif offline_quantization_or_first_run_of_online_quantization:
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# case 1: offline quantized checkpoint
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# case 2: Step I1 first run of weight loading with
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# online quantization
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# see online_quantization.py for detailed notes
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loaded_weights = model.load_weights(
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self.get_all_weights(model_config, model)
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)
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else:
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# to avoid circular dependency
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from vllm.model_executor.model_loader.online_quantization import (
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load_weights_and_online_quantize,
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)
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# subsequent runs of weight loading with online
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# quantization
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loaded_weights = load_weights_and_online_quantize(self, model, model_config)
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self.counter_after_loading_weights = time.perf_counter()
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logger.info(
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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 currently.
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if model_config.quantization is None and loaded_weights is not None:
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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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