# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project """Utilities for downloading and initializing model weights.""" import fnmatch import glob import hashlib import json import os import tempfile import time from collections import defaultdict from collections.abc import Generator from pathlib import Path from typing import Any, Callable, Optional, Union import filelock import huggingface_hub.constants import numpy as np import torch from huggingface_hub import HfFileSystem, hf_hub_download, snapshot_download from safetensors.torch import load_file, safe_open, save_file from tqdm.auto import tqdm from vllm.config import LoadConfig, ModelConfig from vllm.distributed import get_tensor_model_parallel_rank from vllm.logger import init_logger from vllm.model_executor.layers.quantization import (QuantizationConfig, get_quantization_config) from vllm.platforms import current_platform from vllm.utils import PlaceholderModule try: from runai_model_streamer import SafetensorsStreamer except (ImportError, OSError): # see https://github.com/run-ai/runai-model-streamer/issues/26 # OSError will be raised on arm64 platform runai_model_streamer = PlaceholderModule( "runai_model_streamer") # type: ignore[assignment] SafetensorsStreamer = runai_model_streamer.placeholder_attr( "SafetensorsStreamer") try: import gguf except ImportError: gguf = PlaceholderModule("gguf") try: from fastsafetensors import SafeTensorsFileLoader, SingleGroup except ImportError: fastsafetensors = PlaceholderModule("fastsafetensors") SafeTensorsFileLoader = fastsafetensors.placeholder_attr( "SafeTensorsFileLoader") SingleGroup = fastsafetensors.placeholder_attr("SingleGroup") logger = init_logger(__name__) # use system-level temp directory for file locks, so that multiple users # can share the same lock without error. # lock files in the temp directory will be automatically deleted when the # system reboots, so users will not complain about annoying lock files temp_dir = tempfile.gettempdir() def enable_hf_transfer(): """automatically activates hf_transfer """ if "HF_HUB_ENABLE_HF_TRANSFER" not in os.environ: try: # enable hf hub transfer if available import hf_transfer # type: ignore # noqa huggingface_hub.constants.HF_HUB_ENABLE_HF_TRANSFER = True except ImportError: pass enable_hf_transfer() class DisabledTqdm(tqdm): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs, disable=True) def get_lock(model_name_or_path: Union[str, Path], cache_dir: Optional[str] = None): lock_dir = cache_dir or temp_dir model_name_or_path = str(model_name_or_path) os.makedirs(os.path.dirname(lock_dir), exist_ok=True) model_name = model_name_or_path.replace("/", "-") hash_name = hashlib.sha256(model_name.encode()).hexdigest() # add hash to avoid conflict with old users' lock files lock_file_name = hash_name + model_name + ".lock" # mode 0o666 is required for the filelock to be shared across users lock = filelock.FileLock(os.path.join(lock_dir, lock_file_name), mode=0o666) return lock def _shared_pointers(tensors): ptrs = defaultdict(list) for k, v in tensors.items(): ptrs[v.data_ptr()].append(k) failing = [] for _, names in ptrs.items(): if len(names) > 1: failing.append(names) return failing def convert_bin_to_safetensor_file( pt_filename: str, sf_filename: str, ) -> None: loaded = torch.load(pt_filename, map_location="cpu", weights_only=True) if "state_dict" in loaded: loaded = loaded["state_dict"] shared = _shared_pointers(loaded) for shared_weights in shared: for name in shared_weights[1:]: loaded.pop(name) # For tensors to be contiguous loaded = {k: v.contiguous() for k, v in loaded.items()} dirname = os.path.dirname(sf_filename) os.makedirs(dirname, exist_ok=True) save_file(loaded, sf_filename, metadata={"format": "pt"}) # check file size sf_size = os.stat(sf_filename).st_size pt_size = os.stat(pt_filename).st_size if (sf_size - pt_size) / pt_size > 0.01: raise RuntimeError(f"""The file size different is more than 1%: - {sf_filename}: {sf_size} - {pt_filename}: {pt_size} """) # check if the tensors are the same reloaded = load_file(sf_filename) for k in loaded: pt_tensor = loaded[k] sf_tensor = reloaded[k] if not torch.equal(pt_tensor, sf_tensor): raise RuntimeError(f"The output tensors do not match for key {k}") # TODO(woosuk): Move this to other place. def get_quant_config(model_config: ModelConfig, load_config: LoadConfig) -> QuantizationConfig: quant_cls = get_quantization_config(model_config.quantization) # GGUF doesn't have config file if model_config.quantization in ("gguf", "inc"): return quant_cls() # Read the quantization config from the HF model config, if available. hf_quant_config = getattr(model_config.hf_config, "quantization_config", None) # some vision model may keep quantization_config in their text_config hf_text_config = getattr(model_config.hf_config, "text_config", None) if hf_quant_config is None and hf_text_config is not None: hf_quant_config = getattr(hf_text_config, "quantization_config", None) if hf_quant_config is None: # compressed-tensors uses a compressions_config hf_quant_config = getattr(model_config.hf_config, "compression_config", None) if hf_quant_config is not None: return quant_cls.from_config(hf_quant_config) # Inflight BNB quantization if model_config.quantization == "bitsandbytes": return quant_cls.from_config({}) is_local = os.path.isdir(model_config.model) if not is_local: # Download the config files. with get_lock(model_config.model, load_config.download_dir): hf_folder = snapshot_download( model_config.model, revision=model_config.revision, allow_patterns="*.json", cache_dir=load_config.download_dir, local_files_only=huggingface_hub.constants.HF_HUB_OFFLINE, tqdm_class=DisabledTqdm, ) else: hf_folder = model_config.model possible_config_filenames = quant_cls.get_config_filenames() # If the quantization config is not found, use the default config. if not possible_config_filenames: return quant_cls() config_files = glob.glob(os.path.join(hf_folder, "*.json")) quant_config_files = [ f for f in config_files if any( f.endswith(x) for x in possible_config_filenames) ] if len(quant_config_files) == 0: raise ValueError( f"Cannot find the config file for {model_config.quantization}") if len(quant_config_files) > 1: raise ValueError( f"Found multiple config files for {model_config.quantization}: " f"{quant_config_files}") quant_config_file = quant_config_files[0] with open(quant_config_file) as f: config = json.load(f) if model_config.quantization == "bitsandbytes": config["adapter_name_or_path"] = model_config.model elif model_config.quantization == "modelopt": if config["producer"]["name"] == "modelopt": return quant_cls.from_config(config) else: raise ValueError( f"Unsupported quantization config" f" found for {model_config.quantization} in {f}.") return quant_cls.from_config(config) def get_sparse_attention_config( model_config: ModelConfig, load_config: LoadConfig, sparse_attention_config_filename: str = "sparse_attention_config.json", ) -> dict[str, Any]: model_name_or_path = model_config.model is_local = os.path.isdir(model_name_or_path) if not is_local: # Download the config files. with get_lock(model_name_or_path, load_config.download_dir): hf_folder = snapshot_download( model_name_or_path, revision=model_config.revision, allow_patterns="*.json", cache_dir=load_config.download_dir, local_files_only=huggingface_hub.constants.HF_HUB_OFFLINE, tqdm_class=DisabledTqdm, ) else: hf_folder = model_name_or_path config_file = os.path.join(hf_folder, sparse_attention_config_filename) if not os.path.exists(config_file): return {} # Load the sparse attention config. with open(config_file) as f: config = json.load(f) logger.info("Loaded sparse attention config from %s", config_file) return config def download_weights_from_hf( model_name_or_path: str, cache_dir: Optional[str], allow_patterns: list[str], revision: Optional[str] = None, ignore_patterns: Optional[Union[str, list[str]]] = None, ) -> str: """Download model weights from Hugging Face Hub. Args: model_name_or_path (str): The model name or path. cache_dir (Optional[str]): The cache directory to store the model weights. If None, will use HF defaults. allow_patterns (list[str]): The allowed patterns for the weight files. Files matched by any of the patterns will be downloaded. revision (Optional[str]): The revision of the model. ignore_patterns (Optional[Union[str, list[str]]]): The patterns to filter out the weight files. Files matched by any of the patterns will be ignored. Returns: str: The path to the downloaded model weights. """ local_only = huggingface_hub.constants.HF_HUB_OFFLINE if not local_only: # Before we download we look at that is available: fs = HfFileSystem() file_list = fs.ls(model_name_or_path, detail=False, revision=revision) # depending on what is available we download different things for pattern in allow_patterns: matching = fnmatch.filter(file_list, pattern) if len(matching) > 0: allow_patterns = [pattern] break logger.info("Using model weights format %s", allow_patterns) # Use file lock to prevent multiple processes from # downloading the same model weights at the same time. with get_lock(model_name_or_path, cache_dir): start_time = time.perf_counter() hf_folder = snapshot_download( model_name_or_path, allow_patterns=allow_patterns, ignore_patterns=ignore_patterns, cache_dir=cache_dir, tqdm_class=DisabledTqdm, revision=revision, local_files_only=local_only, ) time_taken = time.perf_counter() - start_time if time_taken > 0.5: logger.info("Time spent downloading weights for %s: %.6f seconds", model_name_or_path, time_taken) return hf_folder def download_safetensors_index_file_from_hf( model_name_or_path: str, index_file: str, cache_dir: Optional[str], revision: Optional[str] = None, ) -> None: """Download hf safetensors index file from Hugging Face Hub. Args: model_name_or_path (str): The model name or path. index_file (str): The safetensors index file name cache_dir (Optional[str]): The cache directory to store the model weights. If None, will use HF defaults. revision (Optional[str]): The revision of the model. """ # Use file lock to prevent multiple processes from # downloading the same model weights at the same time. with get_lock(model_name_or_path, cache_dir): try: # Download the safetensors index file. hf_hub_download( repo_id=model_name_or_path, filename=index_file, cache_dir=cache_dir, revision=revision, local_files_only=huggingface_hub.constants.HF_HUB_OFFLINE, ) # If file not found on remote or locally, we should not fail since # only some models will have index_file. except huggingface_hub.utils.LocalEntryNotFoundError: logger.info("No %s found in local cache.", index_file) except huggingface_hub.utils.EntryNotFoundError: logger.info("No %s found in remote.", index_file) # For models like Mistral-7B-v0.3, there are both sharded # safetensors files and a consolidated safetensors file. # Passing both of these to the weight loader functionality breaks. # So, we use the index_file to # look up which safetensors files should be used. def filter_duplicate_safetensors_files(hf_weights_files: list[str], hf_folder: str, index_file: str) -> list[str]: # model.safetensors.index.json is a mapping from keys in the # torch state_dict to safetensors file holding that weight. index_file_name = os.path.join(hf_folder, index_file) if not os.path.isfile(index_file_name): return hf_weights_files # Iterate through the weight_map (weight_name: safetensors files) # to identify weights that we should use. with open(index_file_name) as f: weight_map = json.load(f)["weight_map"] weight_files_in_index = set() for weight_name in weight_map: weight_files_in_index.add( os.path.join(hf_folder, weight_map[weight_name])) # Filter out any fields that are not found in the index file. hf_weights_files = [ f for f in hf_weights_files if f in weight_files_in_index ] return hf_weights_files def filter_files_not_needed_for_inference( hf_weights_files: list[str]) -> list[str]: """ Exclude files that are not needed for inference. See https://github.com/huggingface/transformers/blob/v4.34.0/src/transformers/trainer.py#L227-L233 """ blacklist = [ "training_args.bin", "optimizer.bin", "optimizer.pt", "scheduler.pt", "scaler.pt", ] hf_weights_files = [ f for f in hf_weights_files if not any(f.endswith(x) for x in blacklist) ] return hf_weights_files # explicitly use pure text format, with a newline at the end # this makes it impossible to see the animation in the progress bar # but will avoid messing up with ray or multiprocessing, which wraps # each line of output with some prefix. _BAR_FORMAT = "{desc}: {percentage:3.0f}% Completed | {n_fmt}/{total_fmt} [{elapsed}<{remaining}, {rate_fmt}]\n" # noqa: E501 def enable_tqdm(use_tqdm_on_load: bool): return use_tqdm_on_load and (not torch.distributed.is_initialized() or torch.distributed.get_rank() == 0) def np_cache_weights_iterator( model_name_or_path: str, cache_dir: Optional[str], hf_folder: str, hf_weights_files: list[str], use_tqdm_on_load: bool, ) -> Generator[tuple[str, torch.Tensor], None, None]: """Iterate over the weights in the model np files. Will dump the model weights to numpy files if they are not already dumped. """ # Convert the model weights from torch tensors to numpy arrays for # faster loading. np_folder = os.path.join(hf_folder, "np") os.makedirs(np_folder, exist_ok=True) weight_names_file = os.path.join(np_folder, "weight_names.json") # Use file lock to prevent multiple processes from # dumping the same model weights to numpy at the same time. with get_lock(model_name_or_path, cache_dir): if not os.path.exists(weight_names_file): weight_names: list[str] = [] for bin_file in tqdm( hf_weights_files, desc="Loading np_cache checkpoint shards", disable=not enable_tqdm(use_tqdm_on_load), bar_format=_BAR_FORMAT, ): state = torch.load(bin_file, map_location="cpu", weights_only=True) for name, param in state.items(): param_path = os.path.join(np_folder, name) with open(param_path, "wb") as f: np.save(f, param.cpu().detach().numpy()) weight_names.append(name) with open(weight_names_file, "w") as f: json.dump(weight_names, f) with open(weight_names_file) as f: weight_names = json.load(f) for name in weight_names: param_path = os.path.join(np_folder, name) with open(param_path, "rb") as f: param = np.load(f) yield name, torch.from_numpy(param) def safetensors_weights_iterator( hf_weights_files: list[str], use_tqdm_on_load: bool, ) -> Generator[tuple[str, torch.Tensor], None, None]: """Iterate over the weights in the model safetensor files.""" for st_file in tqdm( hf_weights_files, desc="Loading safetensors checkpoint shards", disable=not enable_tqdm(use_tqdm_on_load), 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) yield name, param def runai_safetensors_weights_iterator( hf_weights_files: list[str], use_tqdm_on_load: bool, ) -> Generator[tuple[str, torch.Tensor], None, None]: """Iterate over the weights in the model safetensor files.""" with SafetensorsStreamer() as streamer: streamer.stream_files(hf_weights_files) total_tensors = sum( len(tensors_meta) for tensors_meta in streamer.files_to_tensors_metadata.values()) tensor_iter = tqdm( streamer.get_tensors(), total=total_tensors, desc="Loading safetensors using Runai Model Streamer", bar_format=_BAR_FORMAT, disable=not enable_tqdm(use_tqdm_on_load), ) yield from tensor_iter def fastsafetensors_weights_iterator( hf_weights_files: list[str], use_tqdm_on_load: bool, ) -> Generator[tuple[str, torch.Tensor], None, None]: """Iterate over the weights in the model safetensor files using fastsafetensor library.""" if torch.distributed.is_initialized(): pg = torch.distributed.group.WORLD else: pg = SingleGroup() device = torch.device(f'cuda:{pg.rank()}') weight_files_sub_lists = [ hf_weights_files[i:i + pg.size()] for i in range(0, len(hf_weights_files), pg.size()) ] for f_list in tqdm( weight_files_sub_lists, desc="Loading safetensors using Fastsafetensor loader", disable=not enable_tqdm(use_tqdm_on_load), bar_format=_BAR_FORMAT, ): loader = SafeTensorsFileLoader(pg, device) rank_file_map = {i: [f] for i, f in enumerate(f_list)} loader.add_filenames(rank_file_map) try: fb = loader.copy_files_to_device() try: keys = list(fb.key_to_rank_lidx.keys()) for k in keys: t = fb.get_tensor(k) yield k, t finally: fb.close() finally: loader.close() def pt_weights_iterator( hf_weights_files: list[str], use_tqdm_on_load: bool, pt_load_map_location: Union[str, dict[str, str]] = "cpu", ) -> Generator[tuple[str, torch.Tensor], None, None]: """Iterate over the weights in the model bin/pt files.""" for bin_file in tqdm( hf_weights_files, desc="Loading pt checkpoint shards", disable=not enable_tqdm(use_tqdm_on_load), bar_format=_BAR_FORMAT, ): state = torch.load(bin_file, map_location=pt_load_map_location, weights_only=True) yield from state.items() del state 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], None] 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): if current_platform.is_tpu(): generator = torch.Generator(device="cpu") generator.manual_seed(seed) # Note: The param.uniform_ function cannot be used in this # context because it demands more TPU HBM than directly copying # from a CPU tensor. # Note: We avoid using torch.rank_like as it doesn't currently # support the generator argument. param.copy_((high - low) * torch.rand(param.shape, generator=generator, dtype=param.dtype, layout=param.layout, requires_grad=param.requires_grad, device="cpu") + low) torch._sync(param) continue 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"): logger.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: logger.warning_once( "Found kv_scale in the checkpoint (e.g. %s), but not found the expected name in the model (e.g. %s). kv_scale is not loaded.", # noqa: E501 name, remapped_name, ) return None return remapped_name # Define scale name mapping patterns in order of precedence scale_mapping_patterns = [ # ModelOpt format: .self_attn.{k,v}_proj.{k,v}_scale -> # .self_attn.attn.{k,v}_scale (r"\.self_attn\.([kv])_proj\.([kv])_scale$", r".self_attn.attn.\2_scale"), # QKV proj format: .self_attn.qkv_proj.{k,v}_scale -> # .self_attn.attn.{k,v}_scale (r"\.self_attn\.qkv_proj\.([kv])_scale$", r".self_attn.attn.\1_scale"), # Qwen3 MoE format: .self_attn.qkqkv_proj.{k,v}_scale -> # .self_attn.attn.{k,v}_scale (r"\.self_attn\.qkqkv_proj\.([kv])_scale$", r".self_attn.attn.\1_scale" ), # Default format: .{k,v}_scale -> .attn.{k,v}_scale (r"\.([kv])_scale$", r".attn.\1_scale"), ] # Check if name ends with k_scale or v_scale if name.endswith((".k_scale", ".v_scale")): import regex as re for pattern, replacement in scale_mapping_patterns: if re.search(pattern, name): remapped_name = re.sub(pattern, replacement, name) if remapped_name not in params_dict: scale_type = name.split(".")[-1] logger.warning_once( "Found %s in the checkpoint (e.g. %s), but not found the expected name in the model (e.g. %s). %s is not loaded.", # noqa: E501 scale_type, name, remapped_name, scale_type, ) return None return remapped_name # If there were no matches, return the untouched param name return name