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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 ..quantizers.quantization_config import QuantizationMethod
from ..utils import (
SAFE_WEIGHTS_INDEX_NAME,
SAFETENSORS_FILE_EXTENSION,
WEIGHTS_INDEX_NAME,
_add_variant,
_get_model_file,
deprecate,
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, keep_in_fp32_modules=[], hf_quantizer=None
):
if isinstance(device_map, str):
special_dtypes = {}
if hf_quantizer is not None:
special_dtypes.update(hf_quantizer.get_special_dtypes_update(model, torch_dtype))
special_dtypes.update(
{
name: torch.float32
for name, _ in model.named_parameters()
if any(m in name for m in keep_in_fp32_modules)
}
)
target_dtype = torch_dtype
if hf_quantizer is not None:
target_dtype = hf_quantizer.adjust_target_dtype(target_dtype)
no_split_modules = model._get_no_split_modules(device_map)
device_map_kwargs = {"no_split_module_classes": no_split_modules}
if "special_dtypes" in inspect.signature(infer_auto_device_map).parameters:
device_map_kwargs["special_dtypes"] = special_dtypes
elif len(special_dtypes) > 0:
logger.warning(
"This model has some weights that should be kept in higher precision, you need to upgrade "
"`accelerate` to properly deal with them (`pip install --upgrade accelerate`)."
)
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)
if hf_quantizer is not None:
max_memory = hf_quantizer.adjust_max_memory(max_memory)
device_map_kwargs["max_memory"] = max_memory
device_map = infer_auto_device_map(model, dtype=target_dtype, **device_map_kwargs)
if hf_quantizer is not None:
hf_quantizer.validate_environment(device_map=device_map)
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.
"""
# TODO: We merge the sharded checkpoints in case we're doing quantization. We can revisit this change
# when refactoring the _merge_sharded_checkpoints() method later.
if isinstance(checkpoint_file, dict):
return checkpoint_file
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,
hf_quantizer=None,
keep_in_fp32_modules=None,
) -> List[str]:
if device is not None and not isinstance(device, (str, torch.device)):
raise ValueError(f"Expected device to have type `str` or `torch.device`, but got {type(device)=}.")
if hf_quantizer is None:
device = device or torch.device("cpu")
dtype = dtype or torch.float32
is_quantized = hf_quantizer is not None
is_quant_method_bnb = getattr(model, "quantization_method", None) == QuantizationMethod.BITS_AND_BYTES
accepts_dtype = "dtype" in set(inspect.signature(set_module_tensor_to_device).parameters.keys())
empty_state_dict = model.state_dict()
unexpected_keys = [param_name for param_name in state_dict if param_name not in empty_state_dict]
for param_name, param in state_dict.items():
if param_name not in empty_state_dict:
continue
set_module_kwargs = {}
# We convert floating dtypes to the `dtype` passed. We also want to keep the buffers/params
# in int/uint/bool and not cast them.
# TODO: revisit cases when param.dtype == torch.float8_e4m3fn
if torch.is_floating_point(param):
if (
keep_in_fp32_modules is not None
and any(
module_to_keep_in_fp32 in param_name.split(".") for module_to_keep_in_fp32 in keep_in_fp32_modules
)
and dtype == torch.float16
):
param = param.to(torch.float32)
if accepts_dtype:
set_module_kwargs["dtype"] = torch.float32
else:
param = param.to(dtype)
if accepts_dtype:
set_module_kwargs["dtype"] = dtype
# bnb params are flattened.
if empty_state_dict[param_name].shape != param.shape:
if (
is_quant_method_bnb
and hf_quantizer.pre_quantized
and hf_quantizer.check_if_quantized_param(model, param, param_name, state_dict, param_device=device)
):
hf_quantizer.check_quantized_param_shape(param_name, empty_state_dict[param_name].shape, param.shape)
elif not is_quant_method_bnb:
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 is_quantized and (
hf_quantizer.check_if_quantized_param(model, param, param_name, state_dict, param_device=device)
):
hf_quantizer.create_quantized_param(model, param, param_name, device, state_dict, unexpected_keys)
else:
if accepts_dtype:
set_module_tensor_to_device(model, param_name, device, value=param, **set_module_kwargs)
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,
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,
proxies=proxies,
local_files_only=local_files_only,
token=token,
revision=revision,
subfolder=None,
user_agent=user_agent,
commit_hash=commit_hash,
)
index_file = Path(index_file)
except (EntryNotFoundError, EnvironmentError):
index_file = None
return index_file
# Adapted from
# https://github.com/bghira/SimpleTuner/blob/cea2457ab063f6dedb9e697830ae68a96be90641/helpers/training/save_hooks.py#L64
def _merge_sharded_checkpoints(sharded_ckpt_cached_folder, sharded_metadata):
weight_map = sharded_metadata.get("weight_map", None)
if weight_map is None:
raise KeyError("'weight_map' key not found in the shard index file.")
# Collect all unique safetensors files from weight_map
files_to_load = set(weight_map.values())
is_safetensors = all(f.endswith(".safetensors") for f in files_to_load)
merged_state_dict = {}
# Load tensors from each unique file
for file_name in files_to_load:
part_file_path = os.path.join(sharded_ckpt_cached_folder, file_name)
if not os.path.exists(part_file_path):
raise FileNotFoundError(f"Part file {file_name} not found.")
if is_safetensors:
with safetensors.safe_open(part_file_path, framework="pt", device="cpu") as f:
for tensor_key in f.keys():
if tensor_key in weight_map:
merged_state_dict[tensor_key] = f.get_tensor(tensor_key)
else:
merged_state_dict.update(torch.load(part_file_path, weights_only=True, map_location="cpu"))
return merged_state_dict
def _fetch_index_file_legacy(
is_local,
pretrained_model_name_or_path,
subfolder,
use_safetensors,
cache_dir,
variant,
force_download,
proxies,
local_files_only,
token,
revision,
user_agent,
commit_hash,
):
if is_local:
index_file = Path(
pretrained_model_name_or_path,
subfolder or "",
SAFE_WEIGHTS_INDEX_NAME if use_safetensors else WEIGHTS_INDEX_NAME,
).as_posix()
splits = index_file.split(".")
split_index = -3 if ".cache" in index_file else -2
splits = splits[:-split_index] + [variant] + splits[-split_index:]
index_file = ".".join(splits)
if os.path.exists(index_file):
deprecation_message = f"This serialization format is now deprecated to standardize the serialization format between `transformers` and `diffusers`. We recommend you to remove the existing files associated with the current variant ({variant}) and re-obtain them by running a `save_pretrained()`."
deprecate("legacy_sharded_ckpts_with_variant", "1.0.0", deprecation_message, standard_warn=False)
index_file = Path(index_file)
else:
index_file = None
else:
if variant is not None:
index_file_in_repo = Path(
subfolder or "",
SAFE_WEIGHTS_INDEX_NAME if use_safetensors else WEIGHTS_INDEX_NAME,
).as_posix()
splits = index_file_in_repo.split(".")
split_index = -2
splits = splits[:-split_index] + [variant] + splits[-split_index:]
index_file_in_repo = ".".join(splits)
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,
proxies=proxies,
local_files_only=local_files_only,
token=token,
revision=revision,
subfolder=None,
user_agent=user_agent,
commit_hash=commit_hash,
)
index_file = Path(index_file)
deprecation_message = f"This serialization format is now deprecated to standardize the serialization format between `transformers` and `diffusers`. We recommend you to remove the existing files associated with the current variant ({variant}) and re-obtain them by running a `save_pretrained()`."
deprecate("legacy_sharded_ckpts_with_variant", "1.0.0", deprecation_message, standard_warn=False)
except (EntryNotFoundError, EnvironmentError):
index_file = None
return index_file