vllm.model_executor.model_loader.weight_utils ¶

def _init_loader(
    pg: torch.distributed.ProcessGroup,
    device: torch.device,
    f_list: list[str],
    *,
    nogds: bool = False,
):
    loader = SafeTensorsFileLoader(pg, device, nogds=nogds)
    rank_file_map = {i: [f] for i, f in enumerate(f_list)}
    loader.add_filenames(rank_file_map)
    return loader

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

@contextmanager
def atomic_writer(filepath: Union[str, Path],
                  mode: str = 'w',
                  encoding: Optional[str] = None) -> Generator[IO]:
    """
    Context manager that provides an atomic file writing routine.

    The context manager writes to a temporary file and, if successful,
    atomically replaces the original file.

    Args:
        filepath (str or Path): The path to the file to write.
        mode (str): The file mode for the temporary file (e.g., 'w', 'wb').
        encoding (str): The encoding for text mode.

    Yields:
        file object: A handle to the temporary file.
    """
    # Create a temporary file in the same directory as the target file
    # to ensure it's on the same filesystem for an atomic replace.
    temp_dir = os.path.dirname(filepath)
    temp_fd, temp_path = tempfile.mkstemp(dir=temp_dir)

    try:
        # Open the temporary file for writing
        with os.fdopen(temp_fd, mode=mode, encoding=encoding) as temp_file:
            yield temp_file

        # If the 'with' block completes successfully,
        # perform the atomic replace.
        os.replace(temp_path, filepath)

    except Exception:
        logger.exception(
            "Error during atomic write. Original file '%s' not modified",
            filepath)
        raise
    finally:
        # Clean up the temporary file if it still exists.
        if os.path.exists(temp_path):
            os.remove(temp_path)

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 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}")

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 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)

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.
    """
    assert len(allow_patterns) > 0
    local_only = huggingface_hub.constants.HF_HUB_OFFLINE
    if not local_only:
        # Attempt to reduce allow_patterns to a single pattern
        # so we only have to call snapshot_download once.
        try:
            fs = HfFileSystem()
            file_list = fs.ls(model_name_or_path,
                              detail=False,
                              revision=revision)

            # Use the first pattern found in the HF repo's files.
            for pattern in allow_patterns:
                matching = fnmatch.filter(file_list, pattern)
                if len(matching) > 0:
                    allow_patterns = [pattern]
                break
        except Exception as e:
            logger.warning(
                "Failed to get file list for '%s'. Trying each pattern in "
                "allow_patterns individually until weights have been "
                "downloaded. Error: %s", model_name_or_path, e)

    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()
        for allow_pattern in allow_patterns:
            hf_folder = snapshot_download(
                model_name_or_path,
                allow_patterns=allow_pattern,
                ignore_patterns=ignore_patterns,
                cache_dir=cache_dir,
                tqdm_class=DisabledTqdm,
                revision=revision,
                local_files_only=local_only,
            )
            # If we have downloaded weights for this allow_pattern,
            # we don't need to check the rest.
            if any(Path(hf_folder).glob(allow_pattern)):
                break
        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 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

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 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())
    ]

    nogds = False

    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 = _init_loader(pg, device, f_list, nogds=nogds)
        try:
            try:
                fb = loader.copy_files_to_device()
            except RuntimeError as e:
                if "gds" not in str(e):
                    raise

                loader.close()
                nogds = True
                logger.warning_once(
                    "GDS not enabled, setting `nogds=True`.\n"
                    "For more information, see: https://github.com/foundation-model-stack/fastsafetensors?tab=readme-ov-file#basic-api-usages"
                )
                loader = _init_loader(pg, device, f_list, nogds=nogds)
                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 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

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 get_gguf_weight_type_map(
        gguf_file: str, gguf_to_hf_name_map: dict[str, str]) -> dict[str, str]:
    """
    Return GGUF mapped weight's name and its quant type
    """
    reader = gguf.GGUFReader(gguf_file)
    return {
        gguf_to_hf_name_map[tensor.name]: tensor.tensor_type.name
        for tensor in reader.tensors if tensor.name in gguf_to_hf_name_map
    }

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 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({})
    model_name_or_path = maybe_download_from_modelscope(
        model_config.model,
        revision=model_config.revision,
        download_dir=load_config.download_dir,
        allow_patterns=["*.json"],
    ) or model_config.model
    is_local = os.path.isdir(model_name_or_path)
    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_name_or_path

    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 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 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_download_from_modelscope(
        model: str,
        revision: Optional[str] = None,
        download_dir: Optional[str] = None,
        ignore_patterns: Optional[Union[str, list[str]]] = None,
        allow_patterns: Optional[Union[list[str],
                                       str]] = None) -> Optional[str]:
    """Download model from ModelScope hub if VLLM_USE_MODELSCOPE is True.

        Returns the path to the downloaded model, or None if the model is not
        downloaded from ModelScope."""
    if envs.VLLM_USE_MODELSCOPE:
        # download model from ModelScope hub,
        # lazy import so that modelscope is not required for normal use.
        # pylint: disable=C.
        from modelscope.hub.snapshot_download import snapshot_download

        # Use file lock to prevent multiple processes from
        # downloading the same model weights at the same time.
        with get_lock(model, download_dir):
            if not os.path.exists(model):
                model_path = snapshot_download(
                    model_id=model,
                    cache_dir=download_dir,
                    local_files_only=huggingface_hub.constants.HF_HUB_OFFLINE,
                    revision=revision,
                    ignore_file_pattern=ignore_patterns,
                    allow_patterns=allow_patterns,
                )
            else:
                model_path = model
        return model_path
    return None

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

def multi_thread_pt_weights_iterator(
    hf_weights_files: list[str],
    use_tqdm_on_load: bool,
    pt_load_map_location: Union[str, dict[str, str]] = "cpu",
    max_workers: int = 4,
) -> Generator[tuple[str, torch.Tensor], None, None]:
    """Multi-Thread iterate over the weights in the model bin/pt files."""

    def _load_file(bin_file: str):
        return torch.load(bin_file,
                          map_location=pt_load_map_location,
                          weights_only=True)

    with concurrent.futures.ThreadPoolExecutor(
            max_workers=max_workers) as executor:
        futures = [
            executor.submit(_load_file, bin_file)
            for bin_file in hf_weights_files
        ]
        futures_iter = tqdm(
            concurrent.futures.as_completed(futures),
            total=len(hf_weights_files),
            desc="Multi-thread loading pt checkpoint shards",
            disable=not enable_tqdm(use_tqdm_on_load),
            bar_format=_BAR_FORMAT,
        )

        for future in futures_iter:
            state = future.result()
            yield from state.items()
            del state