# SPDX-License-Identifier: Apache-2.0 # Adapted from vllm: https://github.com/vllm-project/vllm/blob/v0.7.3/vllm/model_executor/model_loader/weight_utils.py """Utilities for downloading and initializing model weights.""" import hashlib import json import os import tempfile from pathlib import Path from typing import Generator, List, Optional, Tuple, Union import filelock import huggingface_hub.constants import torch from safetensors.torch import safe_open from tqdm.auto import tqdm from fastvideo.v1.logger import init_logger 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() -> None: """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 # 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 safetensors_weights_iterator( 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) 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", weights_only=True) yield from state.items() del state 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 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( 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"] modelopt_scale_names = [ ".self_attn.k_proj.k_scale", ".self_attn.v_proj.v_scale" ] for scale_name in possible_scale_names: if name.endswith(scale_name): if any(mo_scale_name in name for mo_scale_name in modelopt_scale_names): remapped_name = name.replace( f".self_attn.{scale_name[1]}_proj{scale_name}", f".self_attn.attn{scale_name}") else: remapped_name = name.replace(scale_name, f".attn{scale_name}") if remapped_name not in params_dict: logger.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