93 lines
3.7 KiB
Python
93 lines
3.7 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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"""Utilities for selecting and loading models."""
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import contextlib
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import re
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from collections import defaultdict
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from collections.abc import Callable, Iterator
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from typing import Any
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import torch
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from fastvideo.logger import init_logger
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logger = init_logger(__name__)
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@contextlib.contextmanager
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def set_default_torch_dtype(dtype: torch.dtype):
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"""Sets the default torch dtype to the given dtype."""
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old_dtype = torch.get_default_dtype()
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torch.set_default_dtype(dtype)
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try:
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yield
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finally:
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torch.set_default_dtype(old_dtype)
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def get_param_names_mapping(mapping_dict: dict[str, str]) -> Callable[[str], tuple[str, Any, Any]]:
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"""
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Creates a mapping function that transforms parameter names using regex patterns.
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Args:
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mapping_dict (Dict[str, str]): Dictionary mapping regex patterns to replacement patterns
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param_name (str): The parameter name to be transformed
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Returns:
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Callable[[str], str]: A function that maps parameter names from source to target format
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"""
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def mapping_fn(name: str) -> tuple[str, Any, Any]:
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# Try to match and transform the name using the regex patterns in mapping_dict
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for pattern, replacement in mapping_dict.items():
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match = re.match(pattern, name)
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if match:
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merge_index = None
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total_splitted_params = None
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if isinstance(replacement, tuple):
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merge_index = replacement[1]
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total_splitted_params = replacement[2]
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replacement = replacement[0]
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name = re.sub(pattern, replacement, name)
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return name, merge_index, total_splitted_params
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# If no pattern matches, return the original name
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return name, None, None
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return mapping_fn
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def hf_to_custom_state_dict(
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hf_param_sd: dict[str, torch.Tensor] | Iterator[tuple[str, torch.Tensor]],
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param_names_mapping: Callable[[str], tuple[str, Any, Any]]
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) -> tuple[dict[str, torch.Tensor], dict[str, tuple[str, Any, Any]]]:
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"""
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Converts a Hugging Face parameter state dictionary to a custom parameter state dictionary.
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Args:
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hf_param_sd (Dict[str, torch.Tensor]): The Hugging Face parameter state dictionary
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param_names_mapping (Callable[[str], tuple[str, Any, Any]]): A function that maps parameter names from source to target format
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Returns:
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custom_param_sd (Dict[str, torch.Tensor]): The custom formatted parameter state dict
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reverse_param_names_mapping (Dict[str, Tuple[str, Any, Any]]): Maps back from custom to hf
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"""
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custom_param_sd = {}
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to_merge_params = defaultdict(dict) # type: ignore
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reverse_param_names_mapping = {}
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if isinstance(hf_param_sd, dict):
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hf_param_sd = hf_param_sd.items() # type: ignore
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for source_param_name, full_tensor in hf_param_sd: # type: ignore
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target_param_name, merge_index, num_params_to_merge = param_names_mapping(source_param_name)
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reverse_param_names_mapping[target_param_name] = (source_param_name, merge_index, num_params_to_merge)
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if merge_index is not None:
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to_merge_params[target_param_name][merge_index] = full_tensor
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if len(to_merge_params[target_param_name]) == num_params_to_merge:
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# cat at output dim according to the merge_index order
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sorted_tensors = [to_merge_params[target_param_name][i] for i in range(num_params_to_merge)]
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full_tensor = torch.cat(sorted_tensors, dim=0)
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del to_merge_params[target_param_name]
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else:
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continue
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custom_param_sd[target_param_name] = full_tensor
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return custom_param_sd, reverse_param_names_mapping
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