# coding=utf-8

# Copyright 2023 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 os

from functools import partial

from typing import Any, Callable, List, Optional, Tuple, Union

import torch

from torch import Tensor, device

from .. import __version__

from ..utils import (

CONFIG_NAME,

DIFFUSERS_CACHE,

FLAX_WEIGHTS_NAME,

HF_HUB_OFFLINE,

SAFETENSORS_WEIGHTS_NAME,

WEIGHTS_NAME,

_add_variant,

_get_model_file,

deprecate,

is_accelerate_available,

is_safetensors_available,

is_torch_version,

logging,

)

logger = logging.get_logger(__name__)

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

from accelerate.utils import set_module_tensor_to_device

from accelerate.utils.versions import is_torch_version

if is_safetensors_available():

import safetensors

def get_parameter_device(parameter: torch.nn.Module):

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):

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

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:

if os.path.basename(checkpoint_file) == _add_variant(WEIGHTS_NAME, variant):

return torch.load(checkpoint_file, map_location="cpu")

else:

return safetensors.torch.load_file(checkpoint_file, device="cpu")

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}'. "

"If you tried to load a PyTorch model from a TF 2.0 checkpoint, please set from_tf=True."

)

def _load_state_dict_into_model(model_to_load, state_dict):

# 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=""):

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

class ModelMixin(torch.nn.Module):

r"""

Base class for all models.

[`ModelMixin`] takes care of storing the configuration of the models and handles methods for loading, downloading

and saving models.

- **config_name** ([`str`]) -- A filename under which the model should be stored when calling

[`~models.ModelMixin.save_pretrained`].

"""

config_name = CONFIG_NAME

_automatically_saved_args = ["_diffusers_version", "_class_name", "_name_or_path"]

_supports_gradient_checkpointing = False

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.

Note that in other frameworks this feature can be referred to as "activation checkpointing" or "checkpoint

activations".

"""

return any(hasattr(m, "gradient_checkpointing") and m.gradient_checkpointing for m in self.modules())

def enable_gradient_checkpointing(self):

"""

Activates gradient checkpointing for the current model.

Note that in other frameworks this feature can be referred to as "activation checkpointing" or "checkpoint

activations".

"""

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):

"""

Deactivates gradient checkpointing for the current model.

Note that in other frameworks this feature can be referred to as "activation checkpointing" or "checkpoint

activations".

"""

if self._supports_gradient_checkpointing:

self.apply(partial(self._set_gradient_checkpointing, value=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):

r"""

Enable memory efficient attention as implemented in xformers.

When this option is enabled, you should observe lower GPU memory usage and a potential speed up at inference

time. Speed up at training time is not guaranteed.

Warning: When Memory Efficient Attention and Sliced attention are both enabled, the Memory Efficient Attention

is used.

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):

r"""

Disable memory efficient attention as implemented in 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: Callable = None,

safe_serialization: bool = False,

variant: Optional[str] = None,

):

"""

Save a model and its configuration file to a directory, so that it can be re-loaded using the

`[`~models.ModelMixin.from_pretrained`]` class method.

Arguments:

save_directory (`str` or `os.PathLike`):

Directory to which to save. 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 when in distributed training like

TPUs and 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 on distributed training like TPUs when one

need to replace `torch.save` by another method. Can be configured with the environment variable

`DIFFUSERS_SAVE_MODE`.

safe_serialization (`bool`, *optional*, defaults to `False`):

Whether to save the model using `safetensors` or the traditional PyTorch way (that uses `pickle`).

variant (`str`, *optional*):

If specified, weights are saved in the format pytorch_model.<variant>.bin.

"""

if safe_serialization and not is_safetensors_available():

raise ImportError("`safe_serialization` requires the `safetensors library: `pip install safetensors`.")

if os.path.isfile(save_directory):

logger.error(f"Provided path ({save_directory}) should be a directory, not a file")

return

os.makedirs(save_directory, exist_ok=True)

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()

weights_name = SAFETENSORS_WEIGHTS_NAME if safe_serialization else WEIGHTS_NAME

weights_name = _add_variant(weights_name, variant)

# Save the model

if safe_serialization:

safetensors.torch.save_file(

state_dict, os.path.join(save_directory, weights_name), metadata={"format": "pt"}

)

else:

torch.save(state_dict, os.path.join(save_directory, weights_name))

logger.info(f"Model weights saved in {os.path.join(save_directory, weights_name)}")

@classmethod

def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]], **kwargs):

r"""

Instantiate a pretrained pytorch model from a pre-trained model configuration.

The model is set in evaluation mode by default using `model.eval()` (Dropout modules are deactivated). To train

the model, you should first set it back in training mode with `model.train()`.

The warning *Weights from XXX not initialized from pretrained model* means that the weights of XXX do not come

pretrained with the rest of the model. It is up to you to train those weights with a downstream fine-tuning

task.

The warning *Weights from XXX not used in YYY* means that the layer XXX is not used by YYY, therefore those

weights are discarded.

Parameters:

pretrained_model_name_or_path (`str` or `os.PathLike`, *optional*):

Can be either:

- A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co.

Valid model ids should have an organization name, like `google/ddpm-celebahq-256`.

- A path to a *directory* containing model weights saved using [`~ModelMixin.save_config`], e.g.,

`./my_model_directory/`.

cache_dir (`Union[str, os.PathLike]`, *optional*):

Path to a directory in which a downloaded pretrained model configuration should be cached if the

standard cache should not be used.

torch_dtype (`str` or `torch.dtype`, *optional*):

Override the default `torch.dtype` and load the model under this dtype. If `"auto"` is passed the dtype

will be 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 (`bool`, *optional*, defaults to `False`):

Whether or not to delete incompletely received files. Will attempt to resume the download if such a

file exists.

proxies (`Dict[str, str]`, *optional*):

A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'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 or not to only look at local files (i.e., do not try to download the model).

use_auth_token (`str` or *bool*, *optional*):

The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated

when running `diffusers-cli login` (stored in `~/.huggingface`).

revision (`str`, *optional*, defaults to `"main"`):

The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a

git-based system for storing models and other artifacts on huggingface.co, so `revision` can be 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 `""`):

In case the relevant files are located inside a subfolder of the model repo (either remote in

huggingface.co or downloaded locally), you can specify the folder name here.

mirror (`str`, *optional*):

Mirror source to accelerate downloads in China. If you are from China and have an accessibility

problem, you can set this option to resolve it. Note that we do not guarantee the timeliness or safety.

Please 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 refined to each

parameter/buffer name, once a given module name is inside, every submodule of it will be sent to the

same device.

To have Accelerate compute the most optimized `device_map` automatically, set `device_map="auto"`. 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 to 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*):

If the `device_map` contains any value `"disk"`, the folder where we will offload weights.

offload_state_dict (`bool`, *optional*):

If `True`, will temporarily offload the CPU state dict to the hard drive to avoid getting 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 by not initializing the weights and only loading the pre-trained weights. This

also tries to not use more than 1x model size in CPU memory (including peak memory) while loading the

model. This is only supported when torch version >= 1.9.0. If you are using an older version of torch,

setting this argument to `True` will raise an error.

variant (`str`, *optional*):

If specified load weights from `variant` filename, *e.g.* pytorch_model.<variant>.bin. `variant` is

ignored when using `from_flax`.

use_safetensors (`bool`, *optional*, defaults to `None`):

If set to `None`, the `safetensors` weights will be downloaded if they're available **and** if the

`safetensors` library is installed. If set to `True`, the model will be forcibly loaded from

`safetensors` weights. If set to `False`, loading will *not* use `safetensors`.

<Tip>

It is required to be logged in (`huggingface-cli login`) when you want to use private or [gated

models](https://huggingface.co/docs/hub/models-gated#gated-models).

</Tip>

<Tip>

Activate the special ["offline-mode"](https://huggingface.co/diffusers/installation.html#offline-mode) to use

this method in a firewalled environment.

</Tip>

"""

cache_dir = kwargs.pop("cache_dir", DIFFUSERS_CACHE)

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", False)

proxies = kwargs.pop("proxies", None)

output_loading_info = kwargs.pop("output_loading_info", False)

local_files_only = kwargs.pop("local_files_only", HF_HUB_OFFLINE)

use_auth_token = kwargs.pop("use_auth_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)

if use_safetensors and not is_safetensors_available():

raise ValueError(

"`use_safetensors`=True but safetensors is not installed. Please install safetensors with `pip install safetenstors"

)

allow_pickle = False

if use_safetensors is None:

use_safetensors = is_safetensors_available()

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`."

)

# 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,

use_auth_token=use_auth_token,

revision=revision,

subfolder=subfolder,

device_map=device_map,

max_memory=max_memory,

offload_folder=offload_folder,

offload_state_dict=offload_state_dict,

user_agent=user_agent,

**kwargs,

)

# 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,

use_auth_token=use_auth_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 use_safetensors:

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,

use_auth_token=use_auth_token,

revision=revision,

subfolder=subfolder,

user_agent=user_agent,

commit_hash=commit_hash,

)

except IOError as e:

if not allow_pickle:

raise e

pass

if model_file is None:

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,

use_auth_token=use_auth_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:

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."

)

empty_state_dict = model.state_dict()

for param_name, param in state_dict.items():

accepts_dtype = "dtype" in set(

inspect.signature(set_module_tensor_to_device).parameters.keys()

)

if empty_state_dict[param_name].shape != param.shape:

raise ValueError(

f"Cannot load {pretrained_model_name_or_path} 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, param_device, value=param, dtype=torch_dtype

)

else:

set_module_tensor_to_device(model, param_name, param_device, value=param)

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

accelerate.load_checkpoint_and_dispatch(

model,

model_file,

device_map,

max_memory=max_memory,

offload_folder=offload_folder,

offload_state_dict=offload_state_dict,

dtype=torch_dtype,

)

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,

resolved_archive_file,

pretrained_model_name_or_path,

ignore_mismatched_sizes=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

@property

def device(self) -> 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 (optionally, trainable or non-embeddings) 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-embeddings parameters

Returns:

`int`: The number of parameters.

"""

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):

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")