aphrodite-engine/aphrodite/modeling/hf_downloader.py

"""Utilities for downloading and initializing model weights."""
import filelock
import glob
import json
import os
from collections import defaultdict
from typing import Any, Iterator, List, Optional, Tuple

from huggingface_hub import snapshot_download
from safetensors.torch import load_file, save_file, safe_open
import numpy as np
import torch
from tqdm.auto import tqdm
from transformers import PretrainedConfig

from aphrodite.common.logger import init_logger
from aphrodite.modeling.quantization_utils import get_quant_class
from aphrodite.modeling.quantization_utils.base import QuantizationConfig

logger = init_logger(__name__)


class Disabledtqdm(tqdm):  # pylint: disable=inconsistent-mro

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs, disable=True)


def get_lock(model_name_or_path: str, cache_dir: Optional[str] = None):
    lock_dir = cache_dir if cache_dir is not None else "/tmp"
    lock_file_name = model_name_or_path.replace("/", "-") + ".lock"
    lock = filelock.FileLock(os.path.join(lock_dir, lock_file_name))
    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")
    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: Move this to other place.
def get_quant_config(
    quantization: str,
    model_name_or_path: str,
    hf_config: PretrainedConfig,
    cache_dir: Optional[str] = None,
) -> QuantizationConfig:
    if quantization == "gptq" and hasattr(hf_config, "quantization_config"):
        config = hf_config.quantization_config
        return get_quant_class(quantization).from_config(config)

    is_local = os.path.isdir(model_name_or_path)
    if not is_local:
        # Download the config files.
        with get_lock(model_name_or_path, cache_dir):
            hf_folder = snapshot_download(model_name_or_path,
                                          allow_patterns="*.json",
                                          cache_dir=cache_dir,
                                          tqdm_class=Disabledtqdm)
    else:
        hf_folder = model_name_or_path
    config_files = glob.glob(os.path.join(hf_folder, "*.json"))

    quant_cls = get_quant_class(quantization)
    quant_config_files = [
        f for f in config_files if any(
            f.endswith(x) for x in quant_cls.get_config_filenames())
    ]
    if len(quant_config_files) == 0:
        raise ValueError(f"Cannot find the config file for {quantization}")
    if len(quant_config_files) > 1:
        raise ValueError(f"Found multiple config files for {quantization}: "
                         f"{quant_config_files}")

    quant_config_file = quant_config_files[0]
    with open(quant_config_file, "r") as f:
        config = json.load(f)
    return quant_cls.from_config(config)


def prepare_hf_model_weights(
    model_name_or_path: str,
    cache_dir: Optional[str] = None,
    use_safetensors: bool = False,
    fall_back_to_pt: bool = True,
    revision: Optional[str] = None,
) -> Tuple[str, List[str], bool]:
    # Download model weights from huggingface.
    is_local = os.path.isdir(model_name_or_path)
    if use_safetensors:
        allow_patterns = ["*.safetensors"]
    else:
        # Some quantized models use .pt files for storing the weights.
        allow_patterns = ["*.bin", "*.pt"]
    if not is_local:
        # 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):
            hf_folder = snapshot_download(model_name_or_path,
                                          allow_patterns=allow_patterns,
                                          cache_dir=cache_dir,
                                          tqdm_class=Disabledtqdm,
                                          revision=revision)
    else:
        hf_folder = model_name_or_path
    hf_weights_files: List[str] = []
    for pattern in allow_patterns:
        hf_weights_files += glob.glob(os.path.join(hf_folder, pattern))
    if not use_safetensors:
        blacklist = [
            "training_args.bin",
            "optimizer.bin",
            "optimizer.pt",
            "scheduler.pt",
            "scaler.pt",
            "trainer_state.json",
        ]
        hf_weights_files = [
            f for f in hf_weights_files
            if not any(f.endswith(x) for x in blacklist)
        ]

    if len(hf_weights_files) == 0 and use_safetensors and fall_back_to_pt:
        return prepare_hf_model_weights(model_name_or_path,
                                        cache_dir=cache_dir,
                                        use_safetensors=False,
                                        fall_back_to_pt=False,
                                        revision=revision)

    if len(hf_weights_files) == 0:
        raise RuntimeError(
            f"Cannot find any model weights with `{model_name_or_path}`")

    return hf_folder, hf_weights_files, use_safetensors


def hf_model_weights_iterator(
    model_name_or_path: str,
    cache_dir: Optional[str] = None,
    load_format: str = "auto",
    revision: Optional[str] = None,
) -> Iterator[Tuple[str, torch.Tensor]]:
    use_safetensors = False
    use_np_cache = False
    fall_back_to_pt = False
    if load_format == "auto":
        use_safetensors = True
        fall_back_to_pt = True
    elif load_format == "safetensors":
        use_safetensors = True
    elif load_format == "pt":
        pass
    elif load_format == "npcache":
        use_np_cache = True
    else:
        raise ValueError(f"Unknown load_format: {load_format}")

    hf_folder, hf_weights_files, use_safetensors = prepare_hf_model_weights(
        model_name_or_path,
        cache_dir=cache_dir,
        use_safetensors=use_safetensors,
        fall_back_to_pt=fall_back_to_pt,
        revision=revision)

    if use_np_cache:
        # Currently np_cache only support *.bin checkpoints
        assert use_safetensors is False

        # 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 = []
                for bin_file in hf_weights_files:
                    state = torch.load(bin_file, map_location="cpu")
                    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, "r") 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)
    elif use_safetensors:
        for st_file in hf_weights_files:
            with safe_open(st_file, framework="pt") as f:
                for name in f.keys():
                    param = f.get_slice(name)
                    yield name, param
    else:
        for bin_file in hf_weights_files:
            state = torch.load(bin_file, map_location="cpu")
            for name, param in state.items():
                yield name, param
            del state
            torch.cuda.empty_cache()


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 load_padded_tensor_parallel_vocab(
    param: torch.Tensor,
    loaded_weight: Any,  # `torch.Tensor` or `PySafeSlice`
    tensor_model_parallel_rank: int,
) -> None:
    shard_size = param.shape[0]
    start_idx = tensor_model_parallel_rank * shard_size
    end_idx = (tensor_model_parallel_rank + 1) * shard_size
    loaded_weight = loaded_weight[start_idx:end_idx]
    loaded_weight = convert_pyslice_to_tensor(loaded_weight)
    param.data[:loaded_weight.shape[0]].copy_(loaded_weight)


def load_tensor_parallel_weights(
    param: torch.Tensor,
    loaded_weight: Any,  # `torch.Tensor` or `PySafeSlice`
    param_name: str,
    column_parallel_weight_names: List[str],
    row_parallel_weight_names: List[str],
    tensor_model_parallel_rank: int,
) -> None:
    for p in column_parallel_weight_names:
        if p in param_name:
            shard_size = param.shape[0]
            start_idx = tensor_model_parallel_rank * shard_size
            end_idx = (tensor_model_parallel_rank + 1) * shard_size
            loaded_weight = loaded_weight[start_idx:end_idx]
            break
    for p in row_parallel_weight_names:
        if p in param_name:
            shard_size = param.shape[-1]
            start_idx = tensor_model_parallel_rank * shard_size
            end_idx = (tensor_model_parallel_rank + 1) * shard_size
            if isinstance(loaded_weight, torch.Tensor):
                loaded_weight = loaded_weight[..., start_idx:end_idx]
            else:
                index = [slice(None)] * (len(loaded_weight.get_shape()) -
                                         1) + [slice(start_idx, end_idx)]
                loaded_weight = loaded_weight[index]
            break

    loaded_weight = convert_pyslice_to_tensor(loaded_weight)
    assert param.shape == loaded_weight.shape, (
        f"{param_name} shape mismatch between model and checkpoint: "
        f"{param.shape} != {loaded_weight.shape}")
    param.data.copy_(loaded_weight)


def initialize_dummy_weights(
    model: torch.nn.Module,
    low: float = -1e-3,
    high: float = 1e-3,
) -> 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.
    """
    for param in model.state_dict().values():
        if torch.is_floating_point(param):
            param.data.uniform_(low, high)


def get_parallel_weight(model: torch.nn.Module):
    if model.quant_config is None:
        column_weight_suffixes = ["weight", "bias"]
        row_weight_suffixes = ["weight"]
    else:
        column_weight_suffixes = (
            model.quant_config.get_col_parallel_tensor_names())
        row_weight_suffixes = (
            model.quant_config.get_row_parallel_tensor_names())

    column_parallel_weights: List[str] = []
    for layer in model.column_parallel_layers:
        for suffix in column_weight_suffixes:
            column_parallel_weights.append(f"{layer}.{suffix}")
    row_parallel_weights: List[str] = []
    for layer in model.row_parallel_layers:
        for suffix in row_weight_suffixes:
            row_parallel_weights.append(f"{layer}.{suffix}")

    if hasattr(model, "parallel_vocab_layers"):
        for layer in model.parallel_vocab_layers:
            for suffix in ["weight", "bias"]:
                column_parallel_weights.append(f"{layer}.{suffix}")
    return column_parallel_weights, row_parallel_weights