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93 lines
3.4 KiB
Python
93 lines
3.4 KiB
Python
import contextlib
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import json
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import os
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import re
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import tempfile
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from contextvars import ContextVar
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from typing import List, Optional, Tuple, Union
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import torch
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from hivemind.utils.logging import get_logger
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from transformers import BloomPreTrainedModel, modeling_utils
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from petals.utils.version import get_compatible_model_repo
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logger = get_logger(__name__)
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class FromPretrainedMixin:
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@classmethod
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def from_pretrained(
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cls,
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model_name_or_path: Union[str, os.PathLike, None],
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*args,
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low_cpu_mem_usage: Optional[bool] = None,
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torch_dtype: Optional[Union[str, torch.dtype]] = None,
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**kwargs,
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):
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model_name_or_path = get_compatible_model_repo(model_name_or_path)
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if low_cpu_mem_usage is None:
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low_cpu_mem_usage = True
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if torch_dtype is None:
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# torch_dtype=None gives torch.float32 in transformers>=4.26.0. In contrast,
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# torch_dtype="auto" attempts to (1) use config.torch_dtype (if exists), (2) use dtype of the weights.
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torch_dtype = "auto"
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with ignore_keys(cls._keys_to_ignore_on_load_unexpected):
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return super().from_pretrained(
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model_name_or_path, *args, low_cpu_mem_usage=low_cpu_mem_usage, torch_dtype=torch_dtype, **kwargs
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)
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from_pretrained.__doc__ = BloomPreTrainedModel.from_pretrained.__doc__.replace(
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"low_cpu_mem_usage(`bool`, *optional*)",
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"low_cpu_mem_usage(`bool`, *optional*, defaults to `True` in Petals)",
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).replace(
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"torch_dtype (`str` or `torch.dtype`, *optional*)",
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'torch_dtype (`str` or `torch.dtype`, *optional*, defaults to `"auto"` in Petals)',
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)
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_ignored_keys = ContextVar("ignored_keys", default=None)
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@contextlib.contextmanager
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def ignore_keys(patterns: List[str]):
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token = _ignored_keys.set(patterns)
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try:
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yield
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finally:
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_ignored_keys.reset(token)
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def patched_get_checkpoint_shard_files(
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pretrained_model_name_or_path, index_filename, *args, **kwargs
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) -> Tuple[List[str], dict]:
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"""Same as modeling_utils.get_checkpoint_shard_files(), but does not download shards for the ignored keys."""
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should_ignore_keys = _ignored_keys.get() is not None
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tempdir_ctx = tempfile.TemporaryDirectory() if should_ignore_keys else contextlib.nullcontext()
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with tempdir_ctx as tempdir:
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if should_ignore_keys:
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with open(index_filename) as f:
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index = json.load(f)
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n_original_shards = len(set(index["weight_map"].values()))
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index["weight_map"] = {
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param_name: filename
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for param_name, filename in index["weight_map"].items()
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if all(re.search(pattern, param_name) is None for pattern in _ignored_keys.get())
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}
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n_loaded_shards = len(set(index["weight_map"].values()))
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logger.debug(f"Loading {n_loaded_shards} shards out of {n_original_shards}")
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# Replace the original index with a patched JSON, where ignored keys are removed
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index_filename = os.path.join(tempdir, "pytorch_model.bin.index.json")
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with open(index_filename, "w") as f:
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json.dump(index, f)
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return original_get_checkpoint_shard_files(pretrained_model_name_or_path, index_filename, *args, **kwargs)
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original_get_checkpoint_shard_files = modeling_utils.get_checkpoint_shard_files
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modeling_utils.get_checkpoint_shard_files = patched_get_checkpoint_shard_files
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