hao-ai-lab-FastVideo/fastvideo/models/loader/weight_utils.py

# 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 glob
import hashlib
import json
import os
import tempfile
from collections.abc import Generator
from pathlib import Path

import filelock
import huggingface_hub.constants
import torch
import torch.distributed as dist
from safetensors.torch import safe_open
from tqdm.auto import tqdm

import fastvideo.distributed.parallel_state as parallel_state
from fastvideo.logger import init_logger

SAFETENSORS_TO_TORCH_DTYPE = {
    'F16': torch.float16,
    'BF16': torch.bfloat16,
    'F32': torch.float32,
    'F64': torch.float64,
    'I8': torch.int8,
    'I16': torch.int16,
    'I32': torch.int32,
    'I64': torch.int64,
    'U8': torch.uint8,
    'BOOL': torch.bool,
    'F8_E4M3': torch.float8_e4m3fn,
    'F8_E5M2': torch.float8_e5m2,
}

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: str | Path, cache_dir: str | None = 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


SAFE_WEIGHTS_INDEX_NAME = "model.safetensors.index.json"


def resolve_safetensors_files(model_path: str) -> list[str]:
    """Discover safetensors files in a model directory."""
    files = sorted(glob.glob(os.path.join(model_path, "*.safetensors")))
    if not files:
        raise FileNotFoundError(f"No .safetensors files found in {model_path}")
    index_file = os.path.join(model_path, SAFE_WEIGHTS_INDEX_NAME)
    if os.path.exists(index_file):
        files = filter_duplicate_safetensors_files(files, model_path, SAFE_WEIGHTS_INDEX_NAME)
    return 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 _get_initialized_node_group():
    # Single-rank / pytest paths may not have initialized the node group.
    # Fall through to the old get_local_torch_device() behavior when uninitialized.
    if torch.distributed.is_initialized() and parallel_state._NODE is not None:
        return parallel_state.get_node_group()
    return None


def _is_node_leader(node_group) -> bool:
    """Return True on the rank that reads the checkpoint files for its node.

    The node group broadcasts from its rank 0, so that rank reads. The local
    rank is the CUDA device ordinal, and a node can have no rank on device 0
    (for example, Ray workers on GPUs 2 and 3 of a 4-GPU node).
    """
    if node_group is not None:
        return node_group.rank_in_group == 0
    return int(os.environ.get("LOCAL_RANK", 0)) == 0


def safetensors_weights_iterator(hf_weights_files: list[str],
                                 to_cpu: bool = False,
                                 broadcast: bool = True,
                                 async_broadcast: bool = False) -> Generator[tuple[str, torch.Tensor], None, None]:
    """Iterate over the weights in the model safetensor files.
    Args:
        hf_weights_files: List of safetensor files to load.
        to_cpu: Whether to load the weights to CPU. If False, will load to the GPU device bound to the current
            process.
        broadcast: Whether node-group rank 0 should read GPU weights and broadcast them to the other ranks of the node.
        async_broadcast: Whether to overlap loading from disk and broadcasting to other ranks. If True,
            must iterate over all the weights before use. Only used when broadcast is True and to_cpu is False.
    """
    node_group = _get_initialized_node_group()
    is_leader = _is_node_leader(node_group)
    device = str(parallel_state.get_local_torch_device()) if not to_cpu else "cpu"
    enable_tqdm = not torch.distributed.is_initialized() or is_leader
    if to_cpu or not broadcast or node_group is None:
        async_broadcast = False

    handles = []
    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", device=device) as f:
            for name in f.keys():  # noqa: SIM118
                if to_cpu:
                    param = f.get_tensor(name)
                elif broadcast and node_group is not None:
                    if is_leader:
                        param = f.get_tensor(name)
                    else:
                        sl = f.get_slice(name)
                        shape = sl.get_shape()
                        dtype = SAFETENSORS_TO_TORCH_DTYPE[sl.get_dtype()]
                        param = torch.empty(shape, device=device, dtype=dtype)
                    # broadcast to local ranks
                    # TODO(Wenxuan): scatter instead of broadcast
                    if node_group.world_size > 1:
                        group = node_group.device_group
                        if async_broadcast:
                            handle = dist.broadcast(param,
                                                    src=dist.get_global_rank(group, 0),
                                                    async_op=True,
                                                    group=group)
                            handles.append(handle)
                        else:
                            dist.broadcast(param, src=dist.get_global_rank(group, 0), group=group)
                else:
                    param = f.get_tensor(name)
                yield name, param

        if async_broadcast:
            for handle in handles:
                handle.wait()
            handles.clear()


def pt_weights_iterator(hf_weights_files: list[str],
                        to_cpu: bool = False,
                        broadcast: bool = True) -> Generator[tuple[str, torch.Tensor], None, None]:
    """Iterate over the weights in the model bin/pt files.

    Args:
        hf_weights_files: List of bin/pt files to load.
        to_cpu: Whether to load the weights to CPU.
        broadcast: Accepted for API symmetry. PT weights are loaded through
            torch.load and do not use the safetensors broadcast path.
    """
    node_group = _get_initialized_node_group()
    device = str(parallel_state.get_local_torch_device()) if not to_cpu else "cpu"
    enable_tqdm = not torch.distributed.is_initialized() or _is_node_leader(node_group)
    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=device, 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) -> str | None:
    """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