# 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 inspect
import itertools
import json
import os
import re
from collections import OrderedDict
from functools import partial
from pathlib import Path
from typing import Any, Callable, List, Optional, Tuple, Union
import safetensors
import torch
from huggingface_hub import create_repo, split_torch_state_dict_into_shards
from huggingface_hub.utils import validate_hf_hub_args
from torch import Tensor, nn
from .. import __version__
from ..utils import (
CONFIG_NAME,
FLAX_WEIGHTS_NAME,
SAFE_WEIGHTS_INDEX_NAME,
SAFETENSORS_WEIGHTS_NAME,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
_add_variant,
_get_checkpoint_shard_files,
_get_model_file,
deprecate,
is_accelerate_available,
is_torch_version,
logging,
)
from ..utils.hub_utils import (
PushToHubMixin,
load_or_create_model_card,
populate_model_card,
)
from .model_loading_utils import (
_determine_device_map,
_fetch_index_file,
_load_state_dict_into_model,
load_model_dict_into_meta,
load_state_dict,
)
logger = logging.get_logger(__name__)
_REGEX_SHARD = re.compile(r"(.*?)-\d{5}-of-\d{5}")
if is_torch_version(">=", "1.9.0"):
_LOW_CPU_MEM_USAGE_DEFAULT = True
else:
_LOW_CPU_MEM_USAGE_DEFAULT = False
if is_accelerate_available():
import accelerate
def get_parameter_device(parameter: torch.nn.Module) -> torch.device:
try:
parameters_and_buffers = itertools.chain(parameter.parameters(), parameter.buffers())
return next(parameters_and_buffers).device
except StopIteration:
# For torch.nn.DataParallel compatibility in PyTorch 1.5
def find_tensor_attributes(module: torch.nn.Module) -> List[Tuple[str, Tensor]]:
tuples = [(k, v) for k, v in module.__dict__.items() if torch.is_tensor(v)]
return tuples
gen = parameter._named_members(get_members_fn=find_tensor_attributes)
first_tuple = next(gen)
return first_tuple[1].device
def get_parameter_dtype(parameter: torch.nn.Module) -> torch.dtype:
try:
params = tuple(parameter.parameters())
if len(params) > 0:
return params[0].dtype
buffers = tuple(parameter.buffers())
if len(buffers) > 0:
return buffers[0].dtype
except StopIteration:
# For torch.nn.DataParallel compatibility in PyTorch 1.5
def find_tensor_attributes(module: torch.nn.Module) -> List[Tuple[str, Tensor]]:
tuples = [(k, v) for k, v in module.__dict__.items() if torch.is_tensor(v)]
return tuples
gen = parameter._named_members(get_members_fn=find_tensor_attributes)
first_tuple = next(gen)
return first_tuple[1].dtype
class ModelMixin(torch.nn.Module, PushToHubMixin):
r"""
Base class for all models.
[`ModelMixin`] takes care of storing the model configuration and provides methods for loading, downloading and
saving models.
- **config_name** ([`str`]) -- Filename to save a model to when calling [`~models.ModelMixin.save_pretrained`].
"""
config_name = CONFIG_NAME
_automatically_saved_args = ["_diffusers_version", "_class_name", "_name_or_path"]
_supports_gradient_checkpointing = False
_keys_to_ignore_on_load_unexpected = None
_no_split_modules = None
def __init__(self):
super().__init__()
def __getattr__(self, name: str) -> Any:
"""The only reason we overwrite `getattr` here is to gracefully deprecate accessing
config attributes directly. See https://github.com/huggingface/diffusers/pull/3129 We need to overwrite
__getattr__ here in addition so that we don't trigger `torch.nn.Module`'s __getattr__':
https://pytorch.org/docs/stable/_modules/torch/nn/modules/module.html#Module
"""
is_in_config = "_internal_dict" in self.__dict__ and hasattr(self.__dict__["_internal_dict"], name)
is_attribute = name in self.__dict__
if is_in_config and not is_attribute:
deprecation_message = f"Accessing config attribute `{name}` directly via '{type(self).__name__}' object attribute is deprecated. Please access '{name}' over '{type(self).__name__}'s config object instead, e.g. 'unet.config.{name}'."
deprecate("direct config name access", "1.0.0", deprecation_message, standard_warn=False, stacklevel=3)
return self._internal_dict[name]
# call PyTorch's https://pytorch.org/docs/stable/_modules/torch/nn/modules/module.html#Module
return super().__getattr__(name)
@property
def is_gradient_checkpointing(self) -> bool:
"""
Whether gradient checkpointing is activated for this model or not.
"""
return any(hasattr(m, "gradient_checkpointing") and m.gradient_checkpointing for m in self.modules())
def enable_gradient_checkpointing(self) -> None:
"""
Activates gradient checkpointing for the current model (may be referred to as *activation checkpointing* or
*checkpoint activations* in other frameworks).
"""
if not self._supports_gradient_checkpointing:
raise ValueError(f"{self.__class__.__name__} does not support gradient checkpointing.")
self.apply(partial(self._set_gradient_checkpointing, value=True))
def disable_gradient_checkpointing(self) -> None:
"""
Deactivates gradient checkpointing for the current model (may be referred to as *activation checkpointing* or
*checkpoint activations* in other frameworks).
"""
if self._supports_gradient_checkpointing:
self.apply(partial(self._set_gradient_checkpointing, value=False))
def set_use_npu_flash_attention(self, valid: bool) -> None:
r"""
Set the switch for the npu flash attention.
"""
def fn_recursive_set_npu_flash_attention(module: torch.nn.Module):
if hasattr(module, "set_use_npu_flash_attention"):
module.set_use_npu_flash_attention(valid)
for child in module.children():
fn_recursive_set_npu_flash_attention(child)
for module in self.children():
if isinstance(module, torch.nn.Module):
fn_recursive_set_npu_flash_attention(module)
def enable_npu_flash_attention(self) -> None:
r"""
Enable npu flash attention from torch_npu
"""
self.set_use_npu_flash_attention(True)
def disable_npu_flash_attention(self) -> None:
r"""
disable npu flash attention from torch_npu
"""
self.set_use_npu_flash_attention(False)
def set_use_memory_efficient_attention_xformers(
self, valid: bool, attention_op: Optional[Callable] = None
) -> None:
# Recursively walk through all the children.
# Any children which exposes the set_use_memory_efficient_attention_xformers method
# gets the message
def fn_recursive_set_mem_eff(module: torch.nn.Module):
if hasattr(module, "set_use_memory_efficient_attention_xformers"):
module.set_use_memory_efficient_attention_xformers(valid, attention_op)
for child in module.children():
fn_recursive_set_mem_eff(child)
for module in self.children():
if isinstance(module, torch.nn.Module):
fn_recursive_set_mem_eff(module)
def enable_xformers_memory_efficient_attention(self, attention_op: Optional[Callable] = None) -> None:
r"""
Enable memory efficient attention from [xFormers](https://facebookresearch.github.io/xformers/).
When this option is enabled, you should observe lower GPU memory usage and a potential speed up during
inference. Speed up during training is not guaranteed.
⚠️ When memory efficient attention and sliced attention are both enabled, memory efficient attention takes
precedent.
Parameters:
attention_op (`Callable`, *optional*):
Override the default `None` operator for use as `op` argument to the
[`memory_efficient_attention()`](https://facebookresearch.github.io/xformers/components/ops.html#xformers.ops.memory_efficient_attention)
function of xFormers.
Examples:
```py
>>> import torch
>>> from diffusers import UNet2DConditionModel
>>> from xformers.ops import MemoryEfficientAttentionFlashAttentionOp
>>> model = UNet2DConditionModel.from_pretrained(
... "stabilityai/stable-diffusion-2-1", subfolder="unet", torch_dtype=torch.float16
... )
>>> model = model.to("cuda")
>>> model.enable_xformers_memory_efficient_attention(attention_op=MemoryEfficientAttentionFlashAttentionOp)
```
"""
self.set_use_memory_efficient_attention_xformers(True, attention_op)
def disable_xformers_memory_efficient_attention(self) -> None:
r"""
Disable memory efficient attention from [xFormers](https://facebookresearch.github.io/xformers/).
"""
self.set_use_memory_efficient_attention_xformers(False)
def save_pretrained(
self,
save_directory: Union[str, os.PathLike],
is_main_process: bool = True,
save_function: Optional[Callable] = None,
safe_serialization: bool = True,
variant: Optional[str] = None,
max_shard_size: Union[int, str] = "10GB",
push_to_hub: bool = False,
**kwargs,
):
"""
Save a model and its configuration file to a directory so that it can be reloaded using the
[`~models.ModelMixin.from_pretrained`] class method.
Arguments:
save_directory (`str` or `os.PathLike`):
Directory to save a model and its configuration file to. Will be created if it doesn't exist.
is_main_process (`bool`, *optional*, defaults to `True`):
Whether the process calling this is the main process or not. Useful during distributed training and you
need to call this function on all processes. In this case, set `is_main_process=True` only on the main
process to avoid race conditions.
save_function (`Callable`):
The function to use to save the state dictionary. Useful during distributed training when you need to
replace `torch.save` with another method. Can be configured with the environment variable
`DIFFUSERS_SAVE_MODE`.
safe_serialization (`bool`, *optional*, defaults to `True`):
Whether to save the model using `safetensors` or the traditional PyTorch way with `pickle`.
variant (`str`, *optional*):
If specified, weights are saved in the format `pytorch_model..bin`.
max_shard_size (`int` or `str`, defaults to `"10GB"`):
The maximum size for a checkpoint before being sharded. Checkpoints shard will then be each of size
lower than this size. If expressed as a string, needs to be digits followed by a unit (like `"5GB"`).
If expressed as an integer, the unit is bytes. Note that this limit will be decreased after a certain
period of time (starting from Oct 2024) to allow users to upgrade to the latest version of `diffusers`.
This is to establish a common default size for this argument across different libraries in the Hugging
Face ecosystem (`transformers`, and `accelerate`, for example).
push_to_hub (`bool`, *optional*, defaults to `False`):
Whether or not to push your model to the Hugging Face Hub after saving it. You can specify the
repository you want to push to with `repo_id` (will default to the name of `save_directory` in your
namespace).
kwargs (`Dict[str, Any]`, *optional*):
Additional keyword arguments passed along to the [`~utils.PushToHubMixin.push_to_hub`] method.
"""
if os.path.isfile(save_directory):
logger.error(f"Provided path ({save_directory}) should be a directory, not a file")
return
weights_name = SAFETENSORS_WEIGHTS_NAME if safe_serialization else WEIGHTS_NAME
weights_name = _add_variant(weights_name, variant)
weight_name_split = weights_name.split(".")
if len(weight_name_split) in [2, 3]:
weights_name_pattern = weight_name_split[0] + "{suffix}." + ".".join(weight_name_split[1:])
else:
raise ValueError(f"Invalid {weights_name} provided.")
os.makedirs(save_directory, exist_ok=True)
if push_to_hub:
commit_message = kwargs.pop("commit_message", None)
private = kwargs.pop("private", False)
create_pr = kwargs.pop("create_pr", False)
token = kwargs.pop("token", None)
repo_id = kwargs.pop("repo_id", save_directory.split(os.path.sep)[-1])
repo_id = create_repo(repo_id, exist_ok=True, private=private, token=token).repo_id
# Only save the model itself if we are using distributed training
model_to_save = self
# Attach architecture to the config
# Save the config
if is_main_process:
model_to_save.save_config(save_directory)
# Save the model
state_dict = model_to_save.state_dict()
# Save the model
state_dict_split = split_torch_state_dict_into_shards(
state_dict, max_shard_size=max_shard_size, filename_pattern=weights_name_pattern
)
# Clean the folder from a previous save
if is_main_process:
for filename in os.listdir(save_directory):
if filename in state_dict_split.filename_to_tensors.keys():
continue
full_filename = os.path.join(save_directory, filename)
if not os.path.isfile(full_filename):
continue
weights_without_ext = weights_name_pattern.replace(".bin", "").replace(".safetensors", "")
weights_without_ext = weights_without_ext.replace("{suffix}", "")
filename_without_ext = filename.replace(".bin", "").replace(".safetensors", "")
# make sure that file to be deleted matches format of sharded file, e.g. pytorch_model-00001-of-00005
if (
filename.startswith(weights_without_ext)
and _REGEX_SHARD.fullmatch(filename_without_ext) is not None
):
os.remove(full_filename)
for filename, tensors in state_dict_split.filename_to_tensors.items():
shard = {tensor: state_dict[tensor] for tensor in tensors}
filepath = os.path.join(save_directory, filename)
if safe_serialization:
# At some point we will need to deal better with save_function (used for TPU and other distributed
# joyfulness), but for now this enough.
safetensors.torch.save_file(shard, filepath, metadata={"format": "pt"})
else:
torch.save(shard, filepath)
if state_dict_split.is_sharded:
index = {
"metadata": state_dict_split.metadata,
"weight_map": state_dict_split.tensor_to_filename,
}
save_index_file = SAFE_WEIGHTS_INDEX_NAME if safe_serialization else WEIGHTS_INDEX_NAME
save_index_file = os.path.join(save_directory, _add_variant(save_index_file, variant))
# Save the index as well
with open(save_index_file, "w", encoding="utf-8") as f:
content = json.dumps(index, indent=2, sort_keys=True) + "\n"
f.write(content)
logger.info(
f"The model is bigger than the maximum size per checkpoint ({max_shard_size}) and is going to be "
f"split in {len(state_dict_split.filename_to_tensors)} checkpoint shards. You can find where each parameters has been saved in the "
f"index located at {save_index_file}."
)
else:
path_to_weights = os.path.join(save_directory, weights_name)
logger.info(f"Model weights saved in {path_to_weights}")
if push_to_hub:
# Create a new empty model card and eventually tag it
model_card = load_or_create_model_card(repo_id, token=token)
model_card = populate_model_card(model_card)
model_card.save(Path(save_directory, "README.md").as_posix())
self._upload_folder(
save_directory,
repo_id,
token=token,
commit_message=commit_message,
create_pr=create_pr,
)
@classmethod
@validate_hf_hub_args
def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]], **kwargs):
r"""
Instantiate a pretrained PyTorch model from a pretrained model configuration.
The model is set in evaluation mode - `model.eval()` - by default, and dropout modules are deactivated. To
train the model, set it back in training mode with `model.train()`.
Parameters:
pretrained_model_name_or_path (`str` or `os.PathLike`, *optional*):
Can be either:
- A string, the *model id* (for example `google/ddpm-celebahq-256`) of a pretrained model hosted on
the Hub.
- A path to a *directory* (for example `./my_model_directory`) containing the model weights saved
with [`~ModelMixin.save_pretrained`].
cache_dir (`Union[str, os.PathLike]`, *optional*):
Path to a directory where a downloaded pretrained model configuration is cached if the standard cache
is not used.
torch_dtype (`str` or `torch.dtype`, *optional*):
Override the default `torch.dtype` and load the model with another dtype. If `"auto"` is passed, the
dtype is automatically derived from the model's weights.
force_download (`bool`, *optional*, defaults to `False`):
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
resume_download:
Deprecated and ignored. All downloads are now resumed by default when possible. Will be removed in v1
of Diffusers.
proxies (`Dict[str, str]`, *optional*):
A dictionary of proxy servers to use by protocol or endpoint, for example, `{'http': 'foo.bar:3128',
'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
output_loading_info (`bool`, *optional*, defaults to `False`):
Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages.
local_files_only(`bool`, *optional*, defaults to `False`):
Whether to only load local model weights and configuration files or not. If set to `True`, the model
won't be downloaded from the Hub.
token (`str` or *bool*, *optional*):
The token to use as HTTP bearer authorization for remote files. If `True`, the token generated from
`diffusers-cli login` (stored in `~/.huggingface`) is used.
revision (`str`, *optional*, defaults to `"main"`):
The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier
allowed by Git.
from_flax (`bool`, *optional*, defaults to `False`):
Load the model weights from a Flax checkpoint save file.
subfolder (`str`, *optional*, defaults to `""`):
The subfolder location of a model file within a larger model repository on the Hub or locally.
mirror (`str`, *optional*):
Mirror source to resolve accessibility issues if you're downloading a model in China. We do not
guarantee the timeliness or safety of the source, and you should refer to the mirror site for more
information.
device_map (`str` or `Dict[str, Union[int, str, torch.device]]`, *optional*):
A map that specifies where each submodule should go. It doesn't need to be defined for each
parameter/buffer name; once a given module name is inside, every submodule of it will be sent to the
same device. Defaults to `None`, meaning that the model will be loaded on CPU.
Set `device_map="auto"` to have 🤗 Accelerate automatically compute the most optimized `device_map`. For
more information about each option see [designing a device
map](https://hf.co/docs/accelerate/main/en/usage_guides/big_modeling#designing-a-device-map).
max_memory (`Dict`, *optional*):
A dictionary device identifier for the maximum memory. Will default to the maximum memory available for
each GPU and the available CPU RAM if unset.
offload_folder (`str` or `os.PathLike`, *optional*):
The path to offload weights if `device_map` contains the value `"disk"`.
offload_state_dict (`bool`, *optional*):
If `True`, temporarily offloads the CPU state dict to the hard drive to avoid running out of CPU RAM if
the weight of the CPU state dict + the biggest shard of the checkpoint does not fit. Defaults to `True`
when there is some disk offload.
low_cpu_mem_usage (`bool`, *optional*, defaults to `True` if torch version >= 1.9.0 else `False`):
Speed up model loading only loading the pretrained weights and not initializing the weights. This also
tries to not use more than 1x model size in CPU memory (including peak memory) while loading the model.
Only supported for PyTorch >= 1.9.0. If you are using an older version of PyTorch, setting this
argument to `True` will raise an error.
variant (`str`, *optional*):
Load weights from a specified `variant` filename such as `"fp16"` or `"ema"`. This is ignored when
loading `from_flax`.
use_safetensors (`bool`, *optional*, defaults to `None`):
If set to `None`, the `safetensors` weights are downloaded if they're available **and** if the
`safetensors` library is installed. If set to `True`, the model is forcibly loaded from `safetensors`
weights. If set to `False`, `safetensors` weights are not loaded.
To use private or [gated models](https://huggingface.co/docs/hub/models-gated#gated-models), log-in with
`huggingface-cli login`. You can also activate the special
["offline-mode"](https://huggingface.co/diffusers/installation.html#offline-mode) to use this method in a
firewalled environment.
Example:
```py
from diffusers import UNet2DConditionModel
unet = UNet2DConditionModel.from_pretrained("runwayml/stable-diffusion-v1-5", subfolder="unet")
```
If you get the error message below, you need to finetune the weights for your downstream task:
```bash
Some weights of UNet2DConditionModel were not initialized from the model checkpoint at runwayml/stable-diffusion-v1-5 and are newly initialized because the shapes did not match:
- conv_in.weight: found shape torch.Size([320, 4, 3, 3]) in the checkpoint and torch.Size([320, 9, 3, 3]) in the model instantiated
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
```
"""
cache_dir = kwargs.pop("cache_dir", None)
ignore_mismatched_sizes = kwargs.pop("ignore_mismatched_sizes", False)
force_download = kwargs.pop("force_download", False)
from_flax = kwargs.pop("from_flax", False)
resume_download = kwargs.pop("resume_download", None)
proxies = kwargs.pop("proxies", None)
output_loading_info = kwargs.pop("output_loading_info", False)
local_files_only = kwargs.pop("local_files_only", None)
token = kwargs.pop("token", None)
revision = kwargs.pop("revision", None)
torch_dtype = kwargs.pop("torch_dtype", None)
subfolder = kwargs.pop("subfolder", None)
device_map = kwargs.pop("device_map", None)
max_memory = kwargs.pop("max_memory", None)
offload_folder = kwargs.pop("offload_folder", None)
offload_state_dict = kwargs.pop("offload_state_dict", False)
low_cpu_mem_usage = kwargs.pop("low_cpu_mem_usage", _LOW_CPU_MEM_USAGE_DEFAULT)
variant = kwargs.pop("variant", None)
use_safetensors = kwargs.pop("use_safetensors", None)
allow_pickle = False
if use_safetensors is None:
use_safetensors = True
allow_pickle = True
if low_cpu_mem_usage and not is_accelerate_available():
low_cpu_mem_usage = False
logger.warning(
"Cannot initialize model with low cpu memory usage because `accelerate` was not found in the"
" environment. Defaulting to `low_cpu_mem_usage=False`. It is strongly recommended to install"
" `accelerate` for faster and less memory-intense model loading. You can do so with: \n```\npip"
" install accelerate\n```\n."
)
if device_map is not None and not is_accelerate_available():
raise NotImplementedError(
"Loading and dispatching requires `accelerate`. Please make sure to install accelerate or set"
" `device_map=None`. You can install accelerate with `pip install accelerate`."
)
# Check if we can handle device_map and dispatching the weights
if device_map is not None and not is_torch_version(">=", "1.9.0"):
raise NotImplementedError(
"Loading and dispatching requires torch >= 1.9.0. Please either update your PyTorch version or set"
" `device_map=None`."
)
if low_cpu_mem_usage is True and not is_torch_version(">=", "1.9.0"):
raise NotImplementedError(
"Low memory initialization requires torch >= 1.9.0. Please either update your PyTorch version or set"
" `low_cpu_mem_usage=False`."
)
if low_cpu_mem_usage is False and device_map is not None:
raise ValueError(
f"You cannot set `low_cpu_mem_usage` to `False` while using device_map={device_map} for loading and"
" dispatching. Please make sure to set `low_cpu_mem_usage=True`."
)
# change device_map into a map if we passed an int, a str or a torch.device
if isinstance(device_map, torch.device):
device_map = {"": device_map}
elif isinstance(device_map, str) and device_map not in ["auto", "balanced", "balanced_low_0", "sequential"]:
try:
device_map = {"": torch.device(device_map)}
except RuntimeError:
raise ValueError(
"When passing device_map as a string, the value needs to be a device name (e.g. cpu, cuda:0) or "
f"'auto', 'balanced', 'balanced_low_0', 'sequential' but found {device_map}."
)
elif isinstance(device_map, int):
if device_map < 0:
raise ValueError(
"You can't pass device_map as a negative int. If you want to put the model on the cpu, pass device_map = 'cpu' "
)
else:
device_map = {"": device_map}
if device_map is not None:
if low_cpu_mem_usage is None:
low_cpu_mem_usage = True
elif not low_cpu_mem_usage:
raise ValueError("Passing along a `device_map` requires `low_cpu_mem_usage=True`")
if low_cpu_mem_usage:
if device_map is not None and not is_torch_version(">=", "1.10"):
# The max memory utils require PyTorch >= 1.10 to have torch.cuda.mem_get_info.
raise ValueError("`low_cpu_mem_usage` and `device_map` require PyTorch >= 1.10.")
# Load config if we don't provide a configuration
config_path = pretrained_model_name_or_path
user_agent = {
"diffusers": __version__,
"file_type": "model",
"framework": "pytorch",
}
# load config
config, unused_kwargs, commit_hash = cls.load_config(
config_path,
cache_dir=cache_dir,
return_unused_kwargs=True,
return_commit_hash=True,
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,
**kwargs,
)
# Determine if we're loading from a directory of sharded checkpoints.
is_sharded = False
index_file = None
is_local = os.path.isdir(pretrained_model_name_or_path)
index_file = _fetch_index_file(
is_local=is_local,
pretrained_model_name_or_path=pretrained_model_name_or_path,
subfolder=subfolder or "",
use_safetensors=use_safetensors,
cache_dir=cache_dir,
variant=variant,
force_download=force_download,
resume_download=resume_download,
proxies=proxies,
local_files_only=local_files_only,
token=token,
revision=revision,
user_agent=user_agent,
commit_hash=commit_hash,
)
if index_file is not None and index_file.is_file():
is_sharded = True
if is_sharded and from_flax:
raise ValueError("Loading of sharded checkpoints is not supported when `from_flax=True`.")
# load model
model_file = None
if from_flax:
model_file = _get_model_file(
pretrained_model_name_or_path,
weights_name=FLAX_WEIGHTS_NAME,
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,
)
model = cls.from_config(config, **unused_kwargs)
# Convert the weights
from .modeling_pytorch_flax_utils import load_flax_checkpoint_in_pytorch_model
model = load_flax_checkpoint_in_pytorch_model(model, model_file)
else:
if is_sharded:
sharded_ckpt_cached_folder, sharded_metadata = _get_checkpoint_shard_files(
pretrained_model_name_or_path,
index_file,
cache_dir=cache_dir,
proxies=proxies,
resume_download=resume_download,
local_files_only=local_files_only,
token=token,
user_agent=user_agent,
revision=revision,
subfolder=subfolder or "",
)
elif use_safetensors and not is_sharded:
try:
model_file = _get_model_file(
pretrained_model_name_or_path,
weights_name=_add_variant(SAFETENSORS_WEIGHTS_NAME, variant),
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,
)
except IOError as e:
logger.error(f"An error occurred while trying to fetch {pretrained_model_name_or_path}: {e}")
if not allow_pickle:
raise
logger.warning(
"Defaulting to unsafe serialization. Pass `allow_pickle=False` to raise an error instead."
)
if model_file is None and not is_sharded:
model_file = _get_model_file(
pretrained_model_name_or_path,
weights_name=_add_variant(WEIGHTS_NAME, variant),
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,
)
if low_cpu_mem_usage:
# Instantiate model with empty weights
with accelerate.init_empty_weights():
model = cls.from_config(config, **unused_kwargs)
# if device_map is None, load the state dict and move the params from meta device to the cpu
if device_map is None and not is_sharded:
param_device = "cpu"
state_dict = load_state_dict(model_file, variant=variant)
model._convert_deprecated_attention_blocks(state_dict)
# move the params from meta device to cpu
missing_keys = set(model.state_dict().keys()) - set(state_dict.keys())
if len(missing_keys) > 0:
raise ValueError(
f"Cannot load {cls} from {pretrained_model_name_or_path} because the following keys are"
f" missing: \n {', '.join(missing_keys)}. \n Please make sure to pass"
" `low_cpu_mem_usage=False` and `device_map=None` if you want to randomly initialize"
" those weights or else make sure your checkpoint file is correct."
)
unexpected_keys = load_model_dict_into_meta(
model,
state_dict,
device=param_device,
dtype=torch_dtype,
model_name_or_path=pretrained_model_name_or_path,
)
if cls._keys_to_ignore_on_load_unexpected is not None:
for pat in cls._keys_to_ignore_on_load_unexpected:
unexpected_keys = [k for k in unexpected_keys if re.search(pat, k) is None]
if len(unexpected_keys) > 0:
logger.warning(
f"Some weights of the model checkpoint were not used when initializing {cls.__name__}: \n {[', '.join(unexpected_keys)]}"
)
else: # else let accelerate handle loading and dispatching.
# Load weights and dispatch according to the device_map
# by default the device_map is None and the weights are loaded on the CPU
force_hook = True
device_map = _determine_device_map(model, device_map, max_memory, torch_dtype)
if device_map is None and is_sharded:
# we load the parameters on the cpu
device_map = {"": "cpu"}
force_hook = False
try:
accelerate.load_checkpoint_and_dispatch(
model,
model_file if not is_sharded else sharded_ckpt_cached_folder,
device_map,
max_memory=max_memory,
offload_folder=offload_folder,
offload_state_dict=offload_state_dict,
dtype=torch_dtype,
force_hooks=force_hook,
strict=True,
)
except AttributeError as e:
# When using accelerate loading, we do not have the ability to load the state
# dict and rename the weight names manually. Additionally, accelerate skips
# torch loading conventions and directly writes into `module.{_buffers, _parameters}`
# (which look like they should be private variables?), so we can't use the standard hooks
# to rename parameters on load. We need to mimic the original weight names so the correct
# attributes are available. After we have loaded the weights, we convert the deprecated
# names to the new non-deprecated names. Then we _greatly encourage_ the user to convert
# the weights so we don't have to do this again.
if "'Attention' object has no attribute" in str(e):
logger.warning(
f"Taking `{str(e)}` while using `accelerate.load_checkpoint_and_dispatch` to mean {pretrained_model_name_or_path}"
" was saved with deprecated attention block weight names. We will load it with the deprecated attention block"
" names and convert them on the fly to the new attention block format. Please re-save the model after this conversion,"
" so we don't have to do the on the fly renaming in the future. If the model is from a hub checkpoint,"
" please also re-upload it or open a PR on the original repository."
)
model._temp_convert_self_to_deprecated_attention_blocks()
accelerate.load_checkpoint_and_dispatch(
model,
model_file if not is_sharded else sharded_ckpt_cached_folder,
device_map,
max_memory=max_memory,
offload_folder=offload_folder,
offload_state_dict=offload_state_dict,
dtype=torch_dtype,
force_hooks=force_hook,
strict=True,
)
model._undo_temp_convert_self_to_deprecated_attention_blocks()
else:
raise e
loading_info = {
"missing_keys": [],
"unexpected_keys": [],
"mismatched_keys": [],
"error_msgs": [],
}
else:
model = cls.from_config(config, **unused_kwargs)
state_dict = load_state_dict(model_file, variant=variant)
model._convert_deprecated_attention_blocks(state_dict)
model, missing_keys, unexpected_keys, mismatched_keys, error_msgs = cls._load_pretrained_model(
model,
state_dict,
model_file,
pretrained_model_name_or_path,
ignore_mismatched_sizes=ignore_mismatched_sizes,
)
loading_info = {
"missing_keys": missing_keys,
"unexpected_keys": unexpected_keys,
"mismatched_keys": mismatched_keys,
"error_msgs": error_msgs,
}
if torch_dtype is not None and not isinstance(torch_dtype, torch.dtype):
raise ValueError(
f"{torch_dtype} needs to be of type `torch.dtype`, e.g. `torch.float16`, but is {type(torch_dtype)}."
)
elif torch_dtype is not None:
model = model.to(torch_dtype)
model.register_to_config(_name_or_path=pretrained_model_name_or_path)
# Set model in evaluation mode to deactivate DropOut modules by default
model.eval()
if output_loading_info:
return model, loading_info
return model
@classmethod
def _load_pretrained_model(
cls,
model,
state_dict: OrderedDict,
resolved_archive_file,
pretrained_model_name_or_path: Union[str, os.PathLike],
ignore_mismatched_sizes: bool = False,
):
# Retrieve missing & unexpected_keys
model_state_dict = model.state_dict()
loaded_keys = list(state_dict.keys())
expected_keys = list(model_state_dict.keys())
original_loaded_keys = loaded_keys
missing_keys = list(set(expected_keys) - set(loaded_keys))
unexpected_keys = list(set(loaded_keys) - set(expected_keys))
# Make sure we are able to load base models as well as derived models (with heads)
model_to_load = model
def _find_mismatched_keys(
state_dict,
model_state_dict,
loaded_keys,
ignore_mismatched_sizes,
):
mismatched_keys = []
if ignore_mismatched_sizes:
for checkpoint_key in loaded_keys:
model_key = checkpoint_key
if (
model_key in model_state_dict
and state_dict[checkpoint_key].shape != model_state_dict[model_key].shape
):
mismatched_keys.append(
(checkpoint_key, state_dict[checkpoint_key].shape, model_state_dict[model_key].shape)
)
del state_dict[checkpoint_key]
return mismatched_keys
if state_dict is not None:
# Whole checkpoint
mismatched_keys = _find_mismatched_keys(
state_dict,
model_state_dict,
original_loaded_keys,
ignore_mismatched_sizes,
)
error_msgs = _load_state_dict_into_model(model_to_load, state_dict)
if len(error_msgs) > 0:
error_msg = "\n\t".join(error_msgs)
if "size mismatch" in error_msg:
error_msg += (
"\n\tYou may consider adding `ignore_mismatched_sizes=True` in the model `from_pretrained` method."
)
raise RuntimeError(f"Error(s) in loading state_dict for {model.__class__.__name__}:\n\t{error_msg}")
if len(unexpected_keys) > 0:
logger.warning(
f"Some weights of the model checkpoint at {pretrained_model_name_or_path} were not used when"
f" initializing {model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are"
f" initializing {model.__class__.__name__} from the checkpoint of a model trained on another task"
" or with another architecture (e.g. initializing a BertForSequenceClassification model from a"
" BertForPreTraining model).\n- This IS NOT expected if you are initializing"
f" {model.__class__.__name__} from the checkpoint of a model that you expect to be exactly"
" identical (initializing a BertForSequenceClassification model from a"
" BertForSequenceClassification model)."
)
else:
logger.info(f"All model checkpoint weights were used when initializing {model.__class__.__name__}.\n")
if len(missing_keys) > 0:
logger.warning(
f"Some weights of {model.__class__.__name__} were not initialized from the model checkpoint at"
f" {pretrained_model_name_or_path} and are newly initialized: {missing_keys}\nYou should probably"
" TRAIN this model on a down-stream task to be able to use it for predictions and inference."
)
elif len(mismatched_keys) == 0:
logger.info(
f"All the weights of {model.__class__.__name__} were initialized from the model checkpoint at"
f" {pretrained_model_name_or_path}.\nIf your task is similar to the task the model of the"
f" checkpoint was trained on, you can already use {model.__class__.__name__} for predictions"
" without further training."
)
if len(mismatched_keys) > 0:
mismatched_warning = "\n".join(
[
f"- {key}: found shape {shape1} in the checkpoint and {shape2} in the model instantiated"
for key, shape1, shape2 in mismatched_keys
]
)
logger.warning(
f"Some weights of {model.__class__.__name__} were not initialized from the model checkpoint at"
f" {pretrained_model_name_or_path} and are newly initialized because the shapes did not"
f" match:\n{mismatched_warning}\nYou should probably TRAIN this model on a down-stream task to be"
" able to use it for predictions and inference."
)
return model, missing_keys, unexpected_keys, mismatched_keys, error_msgs
@classmethod
def _get_signature_keys(cls, obj):
parameters = inspect.signature(obj.__init__).parameters
required_parameters = {k: v for k, v in parameters.items() if v.default == inspect._empty}
optional_parameters = set({k for k, v in parameters.items() if v.default != inspect._empty})
expected_modules = set(required_parameters.keys()) - {"self"}
return expected_modules, optional_parameters
# Adapted from `transformers` modeling_utils.py
def _get_no_split_modules(self, device_map: str):
"""
Get the modules of the model that should not be spit when using device_map. We iterate through the modules to
get the underlying `_no_split_modules`.
Args:
device_map (`str`):
The device map value. Options are ["auto", "balanced", "balanced_low_0", "sequential"]
Returns:
`List[str]`: List of modules that should not be split
"""
_no_split_modules = set()
modules_to_check = [self]
while len(modules_to_check) > 0:
module = modules_to_check.pop(-1)
# if the module does not appear in _no_split_modules, we also check the children
if module.__class__.__name__ not in _no_split_modules:
if isinstance(module, ModelMixin):
if module._no_split_modules is None:
raise ValueError(
f"{module.__class__.__name__} does not support `device_map='{device_map}'`. To implement support, the model "
"class needs to implement the `_no_split_modules` attribute."
)
else:
_no_split_modules = _no_split_modules | set(module._no_split_modules)
modules_to_check += list(module.children())
return list(_no_split_modules)
@property
def device(self) -> torch.device:
"""
`torch.device`: The device on which the module is (assuming that all the module parameters are on the same
device).
"""
return get_parameter_device(self)
@property
def dtype(self) -> torch.dtype:
"""
`torch.dtype`: The dtype of the module (assuming that all the module parameters have the same dtype).
"""
return get_parameter_dtype(self)
def num_parameters(self, only_trainable: bool = False, exclude_embeddings: bool = False) -> int:
"""
Get number of (trainable or non-embedding) parameters in the module.
Args:
only_trainable (`bool`, *optional*, defaults to `False`):
Whether or not to return only the number of trainable parameters.
exclude_embeddings (`bool`, *optional*, defaults to `False`):
Whether or not to return only the number of non-embedding parameters.
Returns:
`int`: The number of parameters.
Example:
```py
from diffusers import UNet2DConditionModel
model_id = "runwayml/stable-diffusion-v1-5"
unet = UNet2DConditionModel.from_pretrained(model_id, subfolder="unet")
unet.num_parameters(only_trainable=True)
859520964
```
"""
if exclude_embeddings:
embedding_param_names = [
f"{name}.weight"
for name, module_type in self.named_modules()
if isinstance(module_type, torch.nn.Embedding)
]
non_embedding_parameters = [
parameter for name, parameter in self.named_parameters() if name not in embedding_param_names
]
return sum(p.numel() for p in non_embedding_parameters if p.requires_grad or not only_trainable)
else:
return sum(p.numel() for p in self.parameters() if p.requires_grad or not only_trainable)
def _convert_deprecated_attention_blocks(self, state_dict: OrderedDict) -> None:
deprecated_attention_block_paths = []
def recursive_find_attn_block(name, module):
if hasattr(module, "_from_deprecated_attn_block") and module._from_deprecated_attn_block:
deprecated_attention_block_paths.append(name)
for sub_name, sub_module in module.named_children():
sub_name = sub_name if name == "" else f"{name}.{sub_name}"
recursive_find_attn_block(sub_name, sub_module)
recursive_find_attn_block("", self)
# NOTE: we have to check if the deprecated parameters are in the state dict
# because it is possible we are loading from a state dict that was already
# converted
for path in deprecated_attention_block_paths:
# group_norm path stays the same
# query -> to_q
if f"{path}.query.weight" in state_dict:
state_dict[f"{path}.to_q.weight"] = state_dict.pop(f"{path}.query.weight")
if f"{path}.query.bias" in state_dict:
state_dict[f"{path}.to_q.bias"] = state_dict.pop(f"{path}.query.bias")
# key -> to_k
if f"{path}.key.weight" in state_dict:
state_dict[f"{path}.to_k.weight"] = state_dict.pop(f"{path}.key.weight")
if f"{path}.key.bias" in state_dict:
state_dict[f"{path}.to_k.bias"] = state_dict.pop(f"{path}.key.bias")
# value -> to_v
if f"{path}.value.weight" in state_dict:
state_dict[f"{path}.to_v.weight"] = state_dict.pop(f"{path}.value.weight")
if f"{path}.value.bias" in state_dict:
state_dict[f"{path}.to_v.bias"] = state_dict.pop(f"{path}.value.bias")
# proj_attn -> to_out.0
if f"{path}.proj_attn.weight" in state_dict:
state_dict[f"{path}.to_out.0.weight"] = state_dict.pop(f"{path}.proj_attn.weight")
if f"{path}.proj_attn.bias" in state_dict:
state_dict[f"{path}.to_out.0.bias"] = state_dict.pop(f"{path}.proj_attn.bias")
def _temp_convert_self_to_deprecated_attention_blocks(self) -> None:
deprecated_attention_block_modules = []
def recursive_find_attn_block(module):
if hasattr(module, "_from_deprecated_attn_block") and module._from_deprecated_attn_block:
deprecated_attention_block_modules.append(module)
for sub_module in module.children():
recursive_find_attn_block(sub_module)
recursive_find_attn_block(self)
for module in deprecated_attention_block_modules:
module.query = module.to_q
module.key = module.to_k
module.value = module.to_v
module.proj_attn = module.to_out[0]
# We don't _have_ to delete the old attributes, but it's helpful to ensure
# that _all_ the weights are loaded into the new attributes and we're not
# making an incorrect assumption that this model should be converted when
# it really shouldn't be.
del module.to_q
del module.to_k
del module.to_v
del module.to_out
def _undo_temp_convert_self_to_deprecated_attention_blocks(self) -> None:
deprecated_attention_block_modules = []
def recursive_find_attn_block(module) -> None:
if hasattr(module, "_from_deprecated_attn_block") and module._from_deprecated_attn_block:
deprecated_attention_block_modules.append(module)
for sub_module in module.children():
recursive_find_attn_block(sub_module)
recursive_find_attn_block(self)
for module in deprecated_attention_block_modules:
module.to_q = module.query
module.to_k = module.key
module.to_v = module.value
module.to_out = nn.ModuleList([module.proj_attn, nn.Dropout(module.dropout)])
del module.query
del module.key
del module.value
del module.proj_attn
class LegacyModelMixin(ModelMixin):
r"""
A subclass of `ModelMixin` to resolve class mapping from legacy classes (like `Transformer2DModel`) to more
pipeline-specific classes (like `DiTTransformer2DModel`).
"""
@classmethod
@validate_hf_hub_args
def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]], **kwargs):
# To prevent depedency import problem.
from .model_loading_utils import _fetch_remapped_cls_from_config
# Create a copy of the kwargs so that we don't mess with the keyword arguments in the downstream calls.
kwargs_copy = kwargs.copy()
cache_dir = kwargs.pop("cache_dir", None)
force_download = kwargs.pop("force_download", False)
resume_download = kwargs.pop("resume_download", None)
proxies = kwargs.pop("proxies", None)
local_files_only = kwargs.pop("local_files_only", None)
token = kwargs.pop("token", None)
revision = kwargs.pop("revision", None)
subfolder = kwargs.pop("subfolder", None)
# Load config if we don't provide a configuration
config_path = pretrained_model_name_or_path
user_agent = {
"diffusers": __version__,
"file_type": "model",
"framework": "pytorch",
}
# load config
config, _, _ = cls.load_config(
config_path,
cache_dir=cache_dir,
return_unused_kwargs=True,
return_commit_hash=True,
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,
**kwargs,
)
# resolve remapping
remapped_class = _fetch_remapped_cls_from_config(config, cls)
return remapped_class.from_pretrained(pretrained_model_name_or_path, **kwargs_copy)