Co-authored-by: HandH1998 <1335248067@qq.com>
This commit is contained in:
Yineng Zhang
640
python/sglang/srt/model_loader/weight_utils.py
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640
python/sglang/srt/model_loader/weight_utils.py
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@@ -0,0 +1,640 @@
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# Adapted from https://github.com/vllm-project/vllm/blob/v0.6.4.post1/vllm/model_executor/model_loader/weight_utils.py
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"""Utilities for downloading and initializing model weights."""
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import fnmatch
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import glob
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import hashlib
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import json
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import logging
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import os
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import tempfile
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from collections import defaultdict
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from typing import Any, Callable, Dict, Generator, List, Optional, Tuple, Union
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import filelock
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import gguf
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import huggingface_hub.constants
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import numpy as np
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import torch
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from huggingface_hub import HfFileSystem, hf_hub_download, snapshot_download
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from safetensors.torch import load_file, safe_open, save_file
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from tqdm.auto import tqdm
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from vllm.distributed import get_tensor_model_parallel_rank
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from sglang.srt.configs.load_config import LoadConfig
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from sglang.srt.configs.model_config import ModelConfig
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from sglang.srt.layers.quantization import QuantizationConfig, get_quantization_config
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from sglang.srt.utils import print_warning_once
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logger = logging.getLogger(__name__)
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# use system-level temp directory for file locks, so that multiple users
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# can share the same lock without error.
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# lock files in the temp directory will be automatically deleted when the
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# system reboots, so users will not complain about annoying lock files
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temp_dir = tempfile.gettempdir()
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def enable_hf_transfer():
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"""automatically activates hf_transfer"""
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if "HF_HUB_ENABLE_HF_TRANSFER" not in os.environ:
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try:
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# enable hf hub transfer if available
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import hf_transfer # type: ignore # noqa
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huggingface_hub.constants.HF_HUB_ENABLE_HF_TRANSFER = True
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except ImportError:
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pass
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enable_hf_transfer()
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class DisabledTqdm(tqdm):
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs, disable=True)
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def get_lock(model_name_or_path: str, cache_dir: Optional[str] = None):
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lock_dir = cache_dir or temp_dir
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os.makedirs(os.path.dirname(lock_dir), exist_ok=True)
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model_name = model_name_or_path.replace("/", "-")
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hash_name = hashlib.sha256(model_name.encode()).hexdigest()
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# add hash to avoid conflict with old users' lock files
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lock_file_name = hash_name + model_name + ".lock"
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# mode 0o666 is required for the filelock to be shared across users
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lock = filelock.FileLock(os.path.join(lock_dir, lock_file_name), mode=0o666)
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return lock
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def _shared_pointers(tensors):
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ptrs = defaultdict(list)
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for k, v in tensors.items():
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ptrs[v.data_ptr()].append(k)
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failing = []
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for _, names in ptrs.items():
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if len(names) > 1:
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failing.append(names)
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return failing
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def convert_bin_to_safetensor_file(
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pt_filename: str,
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sf_filename: str,
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) -> None:
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loaded = torch.load(pt_filename, map_location="cpu")
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if "state_dict" in loaded:
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loaded = loaded["state_dict"]
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shared = _shared_pointers(loaded)
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for shared_weights in shared:
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for name in shared_weights[1:]:
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loaded.pop(name)
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# For tensors to be contiguous
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loaded = {k: v.contiguous() for k, v in loaded.items()}
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dirname = os.path.dirname(sf_filename)
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os.makedirs(dirname, exist_ok=True)
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save_file(loaded, sf_filename, metadata={"format": "pt"})
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# check file size
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sf_size = os.stat(sf_filename).st_size
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pt_size = os.stat(pt_filename).st_size
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if (sf_size - pt_size) / pt_size > 0.01:
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raise RuntimeError(
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f"""The file size different is more than 1%:
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- {sf_filename}: {sf_size}
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- {pt_filename}: {pt_size}
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"""
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)
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# check if the tensors are the same
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reloaded = load_file(sf_filename)
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for k in loaded:
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pt_tensor = loaded[k]
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sf_tensor = reloaded[k]
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if not torch.equal(pt_tensor, sf_tensor):
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raise RuntimeError(f"The output tensors do not match for key {k}")
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# TODO(woosuk): Move this to other place.
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def get_quant_config(
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model_config: ModelConfig, load_config: LoadConfig
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) -> QuantizationConfig:
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quant_cls = get_quantization_config(model_config.quantization)
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# GGUF doesn't have config file
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if model_config.quantization == "gguf":
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return quant_cls.from_config({})
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# Read the quantization config from the HF model config, if available.
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hf_quant_config = getattr(model_config.hf_config, "quantization_config", None)
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# some vision model may keep quantization_config in their text_config
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hf_text_config = getattr(model_config.hf_config, "text_config", None)
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if hf_quant_config is None and hf_text_config is not None:
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hf_quant_config = getattr(hf_text_config, "quantization_config", None)
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if hf_quant_config is None:
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# compressed-tensors uses a compressions_config
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hf_quant_config = getattr(model_config.hf_config, "compression_config", None)
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if hf_quant_config is not None:
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return quant_cls.from_config(hf_quant_config)
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# In case of bitsandbytes/QLoRA, get quant config from the adapter model.
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if model_config.quantization == "bitsandbytes":
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if (
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not load_config.model_loader_extra_config
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or "qlora_adapter_name_or_path" not in load_config.model_loader_extra_config
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):
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return quant_cls.from_config({"adapter_name_or_path": ""})
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model_name_or_path = load_config.model_loader_extra_config[
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"qlora_adapter_name_or_path"
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]
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else:
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model_name_or_path = model_config.model_path
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is_local = os.path.isdir(model_name_or_path)
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if not is_local:
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# Download the config files.
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with get_lock(model_name_or_path, load_config.download_dir):
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hf_folder = snapshot_download(
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model_name_or_path,
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revision=model_config.revision,
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allow_patterns="*.json",
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cache_dir=load_config.download_dir,
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local_files_only=huggingface_hub.constants.HF_HUB_OFFLINE,
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tqdm_class=DisabledTqdm,
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)
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else:
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hf_folder = model_name_or_path
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possible_config_filenames = quant_cls.get_config_filenames()
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# If the quantization config is not found, use the default config.
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if not possible_config_filenames:
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return quant_cls()
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config_files = glob.glob(os.path.join(hf_folder, "*.json"))
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quant_config_files = [
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f for f in config_files if any(f.endswith(x) for x in possible_config_filenames)
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]
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if len(quant_config_files) == 0:
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raise ValueError(f"Cannot find the config file for {model_config.quantization}")
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if len(quant_config_files) > 1:
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raise ValueError(
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f"Found multiple config files for {model_config.quantization}: "
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f"{quant_config_files}"
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)
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quant_config_file = quant_config_files[0]
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with open(quant_config_file) as f:
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config = json.load(f)
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if model_config.quantization == "bitsandbytes":
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config["adapter_name_or_path"] = model_name_or_path
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elif model_config.quantization == "modelopt":
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if config["producer"]["name"] == "modelopt":
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return quant_cls.from_config(config)
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else:
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raise ValueError(
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f"Unsupported quantization config"
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f" found for {model_config.quantization} in {f}."
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)
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return quant_cls.from_config(config)
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def download_weights_from_hf(
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model_name_or_path: str,
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cache_dir: Optional[str],
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allow_patterns: List[str],
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revision: Optional[str] = None,
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ignore_patterns: Optional[Union[str, List[str]]] = None,
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) -> str:
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"""Download model weights from Hugging Face Hub.
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Args:
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model_name_or_path (str): The model name or path.
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cache_dir (Optional[str]): The cache directory to store the model
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weights. If None, will use HF defaults.
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allow_patterns (List[str]): The allowed patterns for the
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weight files. Files matched by any of the patterns will be
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downloaded.
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revision (Optional[str]): The revision of the model.
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ignore_patterns (Optional[Union[str, List[str]]]): The patterns to
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filter out the weight files. Files matched by any of the patterns
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will be ignored.
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Returns:
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str: The path to the downloaded model weights.
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"""
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if not huggingface_hub.constants.HF_HUB_OFFLINE:
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# Before we download we look at that is available:
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fs = HfFileSystem()
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file_list = fs.ls(model_name_or_path, detail=False, revision=revision)
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# depending on what is available we download different things
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for pattern in allow_patterns:
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matching = fnmatch.filter(file_list, pattern)
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if len(matching) > 0:
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allow_patterns = [pattern]
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break
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logger.info("Using model weights format %s", allow_patterns)
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# Use file lock to prevent multiple processes from
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# downloading the same model weights at the same time.
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with get_lock(model_name_or_path, cache_dir):
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hf_folder = snapshot_download(
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model_name_or_path,
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allow_patterns=allow_patterns,
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ignore_patterns=ignore_patterns,
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cache_dir=cache_dir,
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tqdm_class=DisabledTqdm,
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revision=revision,
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local_files_only=huggingface_hub.constants.HF_HUB_OFFLINE,
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)
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return hf_folder
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def download_safetensors_index_file_from_hf(
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model_name_or_path: str,
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index_file: str,
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cache_dir: Optional[str],
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revision: Optional[str] = None,
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) -> None:
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"""Download hf safetensors index file from Hugging Face Hub.
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Args:
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model_name_or_path (str): The model name or path.
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cache_dir (Optional[str]): The cache directory to store the model
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weights. If None, will use HF defaults.
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revision (Optional[str]): The revision of the model.
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"""
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# Use file lock to prevent multiple processes from
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# downloading the same model weights at the same time.
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with get_lock(model_name_or_path, cache_dir):
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try:
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# Download the safetensors index file.
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hf_hub_download(
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repo_id=model_name_or_path,
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filename=index_file,
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cache_dir=cache_dir,
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revision=revision,
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local_files_only=huggingface_hub.constants.HF_HUB_OFFLINE,
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)
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# If file not found on remote or locally, we should not fail since
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# only some models will have index_file.
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except huggingface_hub.utils.EntryNotFoundError:
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logger.info("No %s found in remote.", index_file)
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except huggingface_hub.utils.LocalEntryNotFoundError:
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logger.info("No %s found in local cache.", index_file)
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# For models like Mistral-7B-v0.3, there are both sharded
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# safetensors files and a consolidated safetensors file.
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# Passing both of these to the weight loader functionality breaks.
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# So, we use the index_file to
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# look up which safetensors files should be used.
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def filter_duplicate_safetensors_files(
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hf_weights_files: List[str], hf_folder: str, index_file: str
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) -> List[str]:
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# model.safetensors.index.json is a mapping from keys in the
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# torch state_dict to safetensors file holding that weight.
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index_file_name = os.path.join(hf_folder, index_file)
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if not os.path.isfile(index_file_name):
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return hf_weights_files
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# Iterate through the weight_map (weight_name: safetensors files)
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# to identify weights that we should use.
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with open(index_file_name) as f:
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weight_map = json.load(f)["weight_map"]
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weight_files_in_index = set()
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for weight_name in weight_map:
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weight_files_in_index.add(os.path.join(hf_folder, weight_map[weight_name]))
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# Filter out any fields that are not found in the index file.
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hf_weights_files = [f for f in hf_weights_files if f in weight_files_in_index]
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return hf_weights_files
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def filter_files_not_needed_for_inference(hf_weights_files: List[str]) -> List[str]:
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"""
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Exclude files that are not needed for inference.
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See https://github.com/huggingface/transformers/blob/v4.34.0/src/transformers/trainer.py#L227-L233
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"""
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blacklist = [
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"training_args.bin",
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"optimizer.bin",
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"optimizer.pt",
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"scheduler.pt",
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"scaler.pt",
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]
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hf_weights_files = [
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f for f in hf_weights_files if not any(f.endswith(x) for x in blacklist)
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]
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return hf_weights_files
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# explicitly use pure text format, with a newline at the end
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# this makes it impossible to see the animation in the progress bar
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# but will avoid messing up with ray or multiprocessing, which wraps
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# each line of output with some prefix.
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_BAR_FORMAT = "{desc}: {percentage:3.0f}% Completed | {n_fmt}/{total_fmt} [{elapsed}<{remaining}, {rate_fmt}]\n" # noqa: E501
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def np_cache_weights_iterator(
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model_name_or_path: str,
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cache_dir: Optional[str],
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hf_folder: str,
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hf_weights_files: List[str],
|
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) -> Generator[Tuple[str, torch.Tensor], None, None]:
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"""Iterate over the weights in the model np files.
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Will dump the model weights to numpy files if they are not already dumped.
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"""
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enable_tqdm = (
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not torch.distributed.is_initialized() or torch.distributed.get_rank() == 0
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)
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# Convert the model weights from torch tensors to numpy arrays for
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# faster loading.
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np_folder = os.path.join(hf_folder, "np")
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os.makedirs(np_folder, exist_ok=True)
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weight_names_file = os.path.join(np_folder, "weight_names.json")
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# Use file lock to prevent multiple processes from
|
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# dumping the same model weights to numpy at the same time.
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with get_lock(model_name_or_path, cache_dir):
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if not os.path.exists(weight_names_file):
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weight_names: List[str] = []
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for bin_file in tqdm(
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hf_weights_files,
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desc="Loading np_cache checkpoint shards",
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disable=not enable_tqdm,
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bar_format=_BAR_FORMAT,
|
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):
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state = torch.load(bin_file, map_location="cpu")
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for name, param in state.items():
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param_path = os.path.join(np_folder, name)
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with open(param_path, "wb") as f:
|
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np.save(f, param.cpu().detach().numpy())
|
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weight_names.append(name)
|
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with open(weight_names_file, "w") as f:
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json.dump(weight_names, f)
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|
||||
with open(weight_names_file) as f:
|
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weight_names = json.load(f)
|
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|
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for name in weight_names:
|
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param_path = os.path.join(np_folder, name)
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with open(param_path, "rb") as f:
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param = np.load(f)
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yield name, torch.from_numpy(param)
|
||||
|
||||
|
||||
def safetensors_weights_iterator(
|
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hf_weights_files: List[str],
|
||||
) -> Generator[Tuple[str, torch.Tensor], None, None]:
|
||||
"""Iterate over the weights in the model safetensor files."""
|
||||
enable_tqdm = (
|
||||
not torch.distributed.is_initialized() or torch.distributed.get_rank() == 0
|
||||
)
|
||||
for st_file in tqdm(
|
||||
hf_weights_files,
|
||||
desc="Loading safetensors checkpoint shards",
|
||||
disable=not enable_tqdm,
|
||||
bar_format=_BAR_FORMAT,
|
||||
):
|
||||
with safe_open(st_file, framework="pt") as f:
|
||||
for name in f.keys(): # noqa: SIM118
|
||||
param = f.get_tensor(name)
|
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yield name, param
|
||||
|
||||
|
||||
def pt_weights_iterator(
|
||||
hf_weights_files: List[str],
|
||||
) -> Generator[Tuple[str, torch.Tensor], None, None]:
|
||||
"""Iterate over the weights in the model bin/pt files."""
|
||||
enable_tqdm = (
|
||||
not torch.distributed.is_initialized() or torch.distributed.get_rank() == 0
|
||||
)
|
||||
for bin_file in tqdm(
|
||||
hf_weights_files,
|
||||
desc="Loading pt checkpoint shards",
|
||||
disable=not enable_tqdm,
|
||||
bar_format=_BAR_FORMAT,
|
||||
):
|
||||
state = torch.load(bin_file, map_location="cpu")
|
||||
yield from state.items()
|
||||
del state
|
||||
torch.cuda.empty_cache()
|
||||
|
||||
|
||||
def get_gguf_extra_tensor_names(
|
||||
gguf_file: str, gguf_to_hf_name_map: Dict[str, str]
|
||||
) -> List[str]:
|
||||
reader = gguf.GGUFReader(gguf_file)
|
||||
expected_gguf_keys = set(gguf_to_hf_name_map.keys())
|
||||
exact_gguf_keys = set([tensor.name for tensor in reader.tensors])
|
||||
extra_keys = expected_gguf_keys - exact_gguf_keys
|
||||
return [gguf_to_hf_name_map[key] for key in extra_keys]
|
||||
|
||||
|
||||
def gguf_quant_weights_iterator(
|
||||
gguf_file: str, gguf_to_hf_name_map: Dict[str, str]
|
||||
) -> Generator[Tuple[str, torch.Tensor], None, None]:
|
||||
"""
|
||||
Iterate over the quant weights in the model gguf files and convert
|
||||
them to torch tensors
|
||||
"""
|
||||
|
||||
reader = gguf.GGUFReader(gguf_file)
|
||||
|
||||
for tensor in reader.tensors:
|
||||
if tensor.name in gguf_to_hf_name_map:
|
||||
weight_type = tensor.tensor_type
|
||||
name = gguf_to_hf_name_map[tensor.name]
|
||||
|
||||
if weight_type.name != "F32":
|
||||
weight_type_name = name.replace("weight", "qweight_type")
|
||||
weight_type = torch.tensor(weight_type)
|
||||
yield weight_type_name, weight_type
|
||||
|
||||
for tensor in reader.tensors:
|
||||
if tensor.name in gguf_to_hf_name_map:
|
||||
weight = tensor.data
|
||||
weight_type = tensor.tensor_type
|
||||
name = gguf_to_hf_name_map[tensor.name]
|
||||
|
||||
if weight_type.name != "F32":
|
||||
name = name.replace("weight", "qweight")
|
||||
param = torch.tensor(weight)
|
||||
yield name, param
|
||||
|
||||
|
||||
def convert_pyslice_to_tensor(x: Any) -> torch.Tensor:
|
||||
"""convert PySafeSlice object from safetensors to torch.Tensor
|
||||
|
||||
PySafeSlice object supports indexing, which is done before loading the
|
||||
actual tensor and can reduce the amount of memory being read into the
|
||||
memory. However, it does not support more advanced functionalities
|
||||
like `.view()` or `.t()`. Therefore, if we need to modify the loaded
|
||||
tensor with these more complicated operators, we need to convert to
|
||||
tensor first.
|
||||
"""
|
||||
if not isinstance(x, torch.Tensor):
|
||||
x = x[:]
|
||||
return x
|
||||
|
||||
|
||||
def default_weight_loader(param: torch.Tensor, loaded_weight: torch.Tensor) -> None:
|
||||
"""Default weight loader."""
|
||||
try:
|
||||
if param.numel() == 1 and loaded_weight.numel() == 1:
|
||||
# Sometimes scalar values aren't considered tensors with shapes
|
||||
# so if both param and loaded_weight are a scalar,
|
||||
# "broadcast" instead of copy
|
||||
param.data.fill_(loaded_weight.item())
|
||||
else:
|
||||
assert param.size() == loaded_weight.size(), (
|
||||
f"Attempted to load weight ({loaded_weight.size()}) "
|
||||
f"into parameter ({param.size()})"
|
||||
)
|
||||
|
||||
param.data.copy_(loaded_weight)
|
||||
except Exception:
|
||||
# NOTE: This exception is added for the purpose of setting breakpoint to
|
||||
# debug weight loading issues.
|
||||
raise
|
||||
|
||||
|
||||
def row_parallel_weight_loader(
|
||||
param: torch.Tensor, loaded_weight: torch.Tensor
|
||||
) -> None:
|
||||
"""Load weights that are row-parallelized."""
|
||||
tp_rank = get_tensor_model_parallel_rank()
|
||||
shard_dim = 0 if param.dim() != 1 else None
|
||||
|
||||
if shard_dim is not None:
|
||||
shard_size = param.data.shape[shard_dim]
|
||||
start_idx = tp_rank * shard_size
|
||||
loaded_weight = loaded_weight.narrow(shard_dim, start_idx, shard_size)
|
||||
|
||||
return default_weight_loader(param, loaded_weight)
|
||||
|
||||
|
||||
LoaderFunction = Callable[[torch.Tensor, torch.Tensor], torch.Tensor]
|
||||
|
||||
|
||||
def sharded_weight_loader(shard_axis: int) -> LoaderFunction:
|
||||
"""Create a weight loader that shards the weights along the given axis"""
|
||||
|
||||
def loader(param: torch.Tensor, loaded_weight: torch.Tensor) -> None:
|
||||
tp_rank = get_tensor_model_parallel_rank()
|
||||
|
||||
shard_size = param.data.shape[shard_axis]
|
||||
start_idx = tp_rank * shard_size
|
||||
loaded_weight = loaded_weight.narrow(shard_axis, start_idx, shard_size)
|
||||
|
||||
return default_weight_loader(param, loaded_weight)
|
||||
|
||||
return loader
|
||||
|
||||
|
||||
def composed_weight_loader(
|
||||
loader: LoaderFunction, fn: Callable[[torch.Tensor], torch.Tensor]
|
||||
) -> LoaderFunction:
|
||||
"""Create a weight loader that post-processes the weights after loading"""
|
||||
|
||||
def composed_loader(param: torch.Tensor, loaded_weight: torch.Tensor) -> None:
|
||||
loader(param, loaded_weight)
|
||||
param.data.copy_(fn(param))
|
||||
return
|
||||
|
||||
return composed_loader
|
||||
|
||||
|
||||
def initialize_dummy_weights(
|
||||
model: torch.nn.Module,
|
||||
low: float = -1e-3,
|
||||
high: float = 1e-3,
|
||||
seed: int = 1234,
|
||||
) -> None:
|
||||
"""Initialize model weights with random values.
|
||||
|
||||
The model weights must be randomly initialized for accurate performance
|
||||
measurements. Additionally, the model weights should not cause NaNs in the
|
||||
forward pass. We empirically found that initializing the weights with
|
||||
values between -1e-3 and 1e-3 works well for most models.
|
||||
|
||||
We use per-parameter random seed, so that dummy weights are consistent,
|
||||
even if the model is partitioned across multiple devices. When the seed
|
||||
is fixed, the random values generated by this function only depends on
|
||||
the parameter's number of elements and its data type.
|
||||
"""
|
||||
for param in model.state_dict().values():
|
||||
if torch.is_floating_point(param):
|
||||
generator = torch.Generator(device=param.data.device)
|
||||
generator.manual_seed(seed)
|
||||
if torch.finfo(param.data.dtype).bits < 16:
|
||||
# uniform_ doesn't support < 16-bit datatypes (FP8)
|
||||
dtype = param.data.dtype
|
||||
tmp_param = param.data.to(torch.float16)
|
||||
tmp_param = tmp_param.uniform_(low, high, generator=generator).to(dtype)
|
||||
param.data.copy_(tmp_param)
|
||||
else:
|
||||
param.uniform_(low, high, generator=generator)
|
||||
|
||||
|
||||
def maybe_remap_kv_scale_name(name: str, params_dict: dict) -> Optional[str]:
|
||||
"""Remap the name of FP8 k/v_scale parameters.
|
||||
|
||||
This function handles the remapping of FP8 k/v_scale parameter names.
|
||||
It detects if the given name ends with a suffix and attempts to remap
|
||||
it to the expected name format in the model. If the remapped name is not
|
||||
found in the params_dict, a warning is printed and None is returned.
|
||||
|
||||
Args:
|
||||
name (str): The original loaded checkpoint parameter name.
|
||||
params_dict (dict): Dictionary containing the model's named parameters.
|
||||
|
||||
Returns:
|
||||
str: The remapped parameter name if successful, or the original name
|
||||
if no remapping is needed.
|
||||
None: If the remapped name is not found in params_dict.
|
||||
"""
|
||||
if name.endswith(".kv_scale"):
|
||||
print_warning_once(
|
||||
"DEPRECATED. Found kv_scale in the checkpoint. "
|
||||
"This format is deprecated in favor of separate k_scale and "
|
||||
"v_scale tensors and will be removed in a future release. "
|
||||
"Functionally, we will remap kv_scale to k_scale and duplicate "
|
||||
"k_scale to v_scale"
|
||||
)
|
||||
# NOTE: we remap the deprecated kv_scale to k_scale
|
||||
remapped_name = name.replace(".kv_scale", ".attn.k_scale")
|
||||
if remapped_name not in params_dict:
|
||||
print_warning_once(
|
||||
f"Found kv_scale in the checkpoint (e.g. {name}), "
|
||||
"but not found the expected name in the model "
|
||||
f"(e.g. {remapped_name}). kv_scale is "
|
||||
"not loaded."
|
||||
)
|
||||
return None
|
||||
return remapped_name
|
||||
|
||||
possible_scale_names = [".k_scale", ".v_scale"]
|
||||
for scale_name in possible_scale_names:
|
||||
if name.endswith(scale_name):
|
||||
remapped_name = name.replace(scale_name, f".attn{scale_name}")
|
||||
if remapped_name not in params_dict:
|
||||
print_warning_once(
|
||||
f"Found {scale_name} in the checkpoint (e.g. {name}), "
|
||||
"but not found the expected name in the model "
|
||||
f"(e.g. {remapped_name}). {scale_name} is "
|
||||
"not loaded."
|
||||
)
|
||||
return None
|
||||
return remapped_name
|
||||
|
||||
# If there were no matches, return the untouched param name
|
||||
return name
|
||||
Reference in New Issue
Block a user