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232 lines (199 loc) 路 8.41 KB
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# coding=utf-8
# Copyright 2024 The HuggingFace Inc. team.
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import importlib
import inspect
import os
from collections import OrderedDict
from pathlib import Path
from typing import List, Optional, Union
import safetensors
import torch
from huggingface_hub.utils import EntryNotFoundError
from ..utils import (
SAFE_WEIGHTS_INDEX_NAME,
SAFETENSORS_FILE_EXTENSION,
WEIGHTS_INDEX_NAME,
_add_variant,
_get_model_file,
is_accelerate_available,
is_torch_version,
logging,
)
logger = logging.get_logger(__name__)
_CLASS_REMAPPING_DICT = {
"Transformer2DModel": {
"ada_norm_zero": "DiTTransformer2DModel",
"ada_norm_single": "PixArtTransformer2DModel",
}
}
if is_accelerate_available():
from accelerate import infer_auto_device_map
from accelerate.utils import get_balanced_memory, get_max_memory, set_module_tensor_to_device
# Adapted from `transformers` (see modeling_utils.py)
def _determine_device_map(model: torch.nn.Module, device_map, max_memory, torch_dtype):
if isinstance(device_map, str):
no_split_modules = model._get_no_split_modules(device_map)
device_map_kwargs = {"no_split_module_classes": no_split_modules}
if device_map != "sequential":
max_memory = get_balanced_memory(
model,
dtype=torch_dtype,
low_zero=(device_map == "balanced_low_0"),
max_memory=max_memory,
**device_map_kwargs,
)
else:
max_memory = get_max_memory(max_memory)
device_map_kwargs["max_memory"] = max_memory
device_map = infer_auto_device_map(model, dtype=torch_dtype, **device_map_kwargs)
return device_map
def _fetch_remapped_cls_from_config(config, old_class):
previous_class_name = old_class.__name__
remapped_class_name = _CLASS_REMAPPING_DICT.get(previous_class_name).get(config["norm_type"], None)
# Details:
# https://github.com/huggingface/diffusers/pull/7647#discussion_r1621344818
if remapped_class_name:
# load diffusers library to import compatible and original scheduler
diffusers_library = importlib.import_module(__name__.split(".")[0])
remapped_class = getattr(diffusers_library, remapped_class_name)
logger.info(
f"Changing class object to be of `{remapped_class_name}` type from `{previous_class_name}` type."
f"This is because `{previous_class_name}` is scheduled to be deprecated in a future version. Note that this"
" DOESN'T affect the final results."
)
return remapped_class
else:
return old_class
def load_state_dict(checkpoint_file: Union[str, os.PathLike], variant: Optional[str] = None):
"""
Reads a checkpoint file, returning properly formatted errors if they arise.
"""
try:
file_extension = os.path.basename(checkpoint_file).split(".")[-1]
if file_extension == SAFETENSORS_FILE_EXTENSION:
return safetensors.torch.load_file(checkpoint_file, device="cpu")
else:
weights_only_kwarg = {"weights_only": True} if is_torch_version(">=", "1.13") else {}
return torch.load(
checkpoint_file,
map_location="cpu",
**weights_only_kwarg,
)
except Exception as e:
try:
with open(checkpoint_file) as f:
if f.read().startswith("version"):
raise OSError(
"You seem to have cloned a repository without having git-lfs installed. Please install "
"git-lfs and run `git lfs install` followed by `git lfs pull` in the folder "
"you cloned."
)
else:
raise ValueError(
f"Unable to locate the file {checkpoint_file} which is necessary to load this pretrained "
"model. Make sure you have saved the model properly."
) from e
except (UnicodeDecodeError, ValueError):
raise OSError(
f"Unable to load weights from checkpoint file for '{checkpoint_file}' " f"at '{checkpoint_file}'. "
)
def load_model_dict_into_meta(
model,
state_dict: OrderedDict,
device: Optional[Union[str, torch.device]] = None,
dtype: Optional[Union[str, torch.dtype]] = None,
model_name_or_path: Optional[str] = None,
) -> List[str]:
device = device or torch.device("cpu")
dtype = dtype or torch.float32
accepts_dtype = "dtype" in set(inspect.signature(set_module_tensor_to_device).parameters.keys())
unexpected_keys = []
empty_state_dict = model.state_dict()
for param_name, param in state_dict.items():
if param_name not in empty_state_dict:
unexpected_keys.append(param_name)
continue
if empty_state_dict[param_name].shape != param.shape:
model_name_or_path_str = f"{model_name_or_path} " if model_name_or_path is not None else ""
raise ValueError(
f"Cannot load {model_name_or_path_str}because {param_name} expected shape {empty_state_dict[param_name]}, but got {param.shape}. If you want to instead overwrite randomly initialized weights, please make sure to pass both `low_cpu_mem_usage=False` and `ignore_mismatched_sizes=True`. For more information, see also: https://github.com/huggingface/diffusers/issues/1619#issuecomment-1345604389 as an example."
)
if accepts_dtype:
set_module_tensor_to_device(model, param_name, device, value=param, dtype=dtype)
else:
set_module_tensor_to_device(model, param_name, device, value=param)
return unexpected_keys
def _load_state_dict_into_model(model_to_load, state_dict: OrderedDict) -> List[str]:
# Convert old format to new format if needed from a PyTorch state_dict
# copy state_dict so _load_from_state_dict can modify it
state_dict = state_dict.copy()
error_msgs = []
# PyTorch's `_load_from_state_dict` does not copy parameters in a module's descendants
# so we need to apply the function recursively.
def load(module: torch.nn.Module, prefix: str = ""):
args = (state_dict, prefix, {}, True, [], [], error_msgs)
module._load_from_state_dict(*args)
for name, child in module._modules.items():
if child is not None:
load(child, prefix + name + ".")
load(model_to_load)
return error_msgs
def _fetch_index_file(
is_local,
pretrained_model_name_or_path,
subfolder,
use_safetensors,
cache_dir,
variant,
force_download,
resume_download,
proxies,
local_files_only,
token,
revision,
user_agent,
commit_hash,
):
if is_local:
index_file = Path(
pretrained_model_name_or_path,
subfolder or "",
_add_variant(SAFE_WEIGHTS_INDEX_NAME if use_safetensors else WEIGHTS_INDEX_NAME, variant),
)
else:
index_file_in_repo = Path(
subfolder or "",
_add_variant(SAFE_WEIGHTS_INDEX_NAME if use_safetensors else WEIGHTS_INDEX_NAME, variant),
).as_posix()
try:
index_file = _get_model_file(
pretrained_model_name_or_path,
weights_name=index_file_in_repo,
cache_dir=cache_dir,
force_download=force_download,
resume_download=resume_download,
proxies=proxies,
local_files_only=local_files_only,
token=token,
revision=revision,
subfolder=subfolder,
user_agent=user_agent,
commit_hash=commit_hash,
)
index_file = Path(index_file)
except (EntryNotFoundError, EnvironmentError):
index_file = None
return index_file