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# coding=utf-8
# Copyright 2025 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 array import array
from collections import OrderedDict
from pathlib import Path
from typing import Dict, Iterator, List, Optional, Tuple, Union
import safetensors
import torch
from huggingface_hub import DDUFEntry
from huggingface_hub.utils import EntryNotFoundError
from ..utils import (
GGUF_FILE_EXTENSION,
SAFE_WEIGHTS_INDEX_NAME,
SAFETENSORS_FILE_EXTENSION,
WEIGHTS_INDEX_NAME,
_add_variant,
_get_model_file,
deprecate,
is_accelerate_available,
is_gguf_available,
is_torch_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,
dduf_entries: Optional[Dict[str, DDUFEntry]] = None,
disable_mmap: bool = False,
):
"""
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:
if dduf_entries:
# tensors are loaded on cpu
with dduf_entries[checkpoint_file].as_mmap() as mm:
return safetensors.torch.load(mm)
if disable_mmap:
return safetensors.torch.load(open(checkpoint_file, "rb").read())
else:
return safetensors.torch.load_file(checkpoint_file, device="cpu")
elif file_extension == GGUF_FILE_EXTENSION:
return load_gguf_checkpoint(checkpoint_file)
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,
named_buffers: Optional[Iterator[Tuple[str, torch.Tensor]]] = 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
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.
# gguf quants have a different shape based on the type of quantization applied
if empty_state_dict[param_name].shape != param.shape:
if (
is_quantized
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], param)
else:
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].shape}, 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)
if named_buffers is None:
return unexpected_keys
for param_name, param in named_buffers:
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,
dduf_entries: Optional[Dict[str, DDUFEntry]] = None,
):
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,
dduf_entries=dduf_entries,
)
if not dduf_entries:
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, dduf_entries: Optional[Dict[str, DDUFEntry]] = None
):
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 dduf_entries:
if part_file_path not in dduf_entries:
raise FileNotFoundError(f"Part file {file_name} not found.")
else:
if not os.path.exists(part_file_path):
raise FileNotFoundError(f"Part file {file_name} not found.")
if is_safetensors:
if dduf_entries:
with dduf_entries[part_file_path].as_mmap() as mm:
tensors = safetensors.torch.load(mm)
merged_state_dict.update(tensors)
else:
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,
dduf_entries: Optional[Dict[str, DDUFEntry]] = None,
):
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,
dduf_entries=dduf_entries,
)
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
def _gguf_parse_value(_value, data_type):
if not isinstance(data_type, list):
data_type = [data_type]
if len(data_type) == 1:
data_type = data_type[0]
array_data_type = None
else:
if data_type[0] != 9:
raise ValueError("Received multiple types, therefore expected the first type to indicate an array.")
data_type, array_data_type = data_type
if data_type in [0, 1, 2, 3, 4, 5, 10, 11]:
_value = int(_value[0])
elif data_type in [6, 12]:
_value = float(_value[0])
elif data_type in [7]:
_value = bool(_value[0])
elif data_type in [8]:
_value = array("B", list(_value)).tobytes().decode()
elif data_type in [9]:
_value = _gguf_parse_value(_value, array_data_type)
return _value
def load_gguf_checkpoint(gguf_checkpoint_path, return_tensors=False):
"""
Load a GGUF file and return a dictionary of parsed parameters containing tensors, the parsed tokenizer and config
attributes.
Args:
gguf_checkpoint_path (`str`):
The path the to GGUF file to load
return_tensors (`bool`, defaults to `True`):
Whether to read the tensors from the file and return them. Not doing so is faster and only loads the
metadata in memory.
"""
if is_gguf_available() and is_torch_available():
import gguf
from gguf import GGUFReader
from ..quantizers.gguf.utils import SUPPORTED_GGUF_QUANT_TYPES, GGUFParameter
else:
logger.error(
"Loading a GGUF checkpoint in PyTorch, requires both PyTorch and GGUF>=0.10.0 to be installed. Please see "
"https://pytorch.org/ and https://github.com/ggerganov/llama.cpp/tree/master/gguf-py for installation instructions."
)
raise ImportError("Please install torch and gguf>=0.10.0 to load a GGUF checkpoint in PyTorch.")
reader = GGUFReader(gguf_checkpoint_path)
parsed_parameters = {}
for tensor in reader.tensors:
name = tensor.name
quant_type = tensor.tensor_type
# if the tensor is a torch supported dtype do not use GGUFParameter
is_gguf_quant = quant_type not in [gguf.GGMLQuantizationType.F32, gguf.GGMLQuantizationType.F16]
if is_gguf_quant and quant_type not in SUPPORTED_GGUF_QUANT_TYPES:
_supported_quants_str = "\n".join([str(type) for type in SUPPORTED_GGUF_QUANT_TYPES])
raise ValueError(
(
f"{name} has a quantization type: {str(quant_type)} which is unsupported."
"\n\nCurrently the following quantization types are supported: \n\n"
f"{_supported_quants_str}"
"\n\nTo request support for this quantization type please open an issue here: https://github.com/huggingface/diffusers"
)
)
weights = torch.from_numpy(tensor.data.copy())
parsed_parameters[name] = GGUFParameter(weights, quant_type=quant_type) if is_gguf_quant else weights
return parsed_parameters