# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, 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.
"""Configuration base class and utilities."""
import copy
import json
import math
import os
from typing import TYPE_CHECKING, Any, TypeVar, Union
from huggingface_hub import create_repo
from packaging import version
from . import __version__
from .dynamic_module_utils import custom_object_save
from .generation.configuration_utils import GenerationConfig
from .modeling_gguf_pytorch_utils import load_gguf_checkpoint
from .modeling_rope_utils import RotaryEmbeddingConfigMixin
from .utils import (
CONFIG_NAME,
PushToHubMixin,
cached_file,
copy_func,
extract_commit_hash,
is_torch_available,
logging,
)
from .utils.generic import is_timm_config_dict
if TYPE_CHECKING:
import torch
logger = logging.get_logger(__name__)
# type hinting: specifying the type of config class that inherits from PreTrainedConfig
SpecificPreTrainedConfigType = TypeVar("SpecificPreTrainedConfigType", bound="PreTrainedConfig")
_FLOAT_TAG_KEY = "__float__"
_FLOAT_TAG_VALUES = {"Infinity": float("inf"), "-Infinity": float("-inf"), "NaN": float("nan")}
class PreTrainedConfig(PushToHubMixin, RotaryEmbeddingConfigMixin):
# no-format
r"""
Base class for all configuration classes. Handles a few parameters common to all models' configurations as well as
methods for loading/downloading/saving configurations.
A configuration file can be loaded and saved to disk. Loading the configuration file and using this file to
initialize a model does **not** load the model weights. It only affects the model's configuration.
Class attributes (overridden by derived classes):
- **model_type** (`str`) -- An identifier for the model type, serialized into the JSON file, and used to recreate
the correct object in [`~transformers.AutoConfig`].
- **has_no_defaults_at_init** (`bool`) -- Whether the config class can be initialized without providing input arguments.
Some configurations requires inputs to be defined at init and have no default values, usually these are composite configs,
(but not necessarily) such as [`~transformers.EncoderDecoderConfig`] or [`~RagConfig`]. They have to be initialized from
two or more configs of type [`~transformers.PreTrainedConfig`].
- **keys_to_ignore_at_inference** (`list[str]`) -- A list of keys to ignore by default when looking at dictionary
outputs of the model during inference.
- **attribute_map** (`dict[str, str]`) -- A dict that maps model specific attribute names to the standardized
naming of attributes.
- **base_model_tp_plan** (`dict[str, Any]`) -- A dict that maps sub-modules FQNs of a base model to a tensor
parallel plan applied to the sub-module when `model.tensor_parallel` is called.
- **base_model_pp_plan** (`dict[str, tuple[list[str]]]`) -- A dict that maps child-modules of a base model to a
pipeline parallel plan that enables users to place the child-module on the appropriate device.
Common attributes (present in all subclasses):
- **vocab_size** (`int`) -- The number of tokens in the vocabulary, which is also the first dimension of the
embeddings matrix (this attribute may be missing for models that don't have a text modality like ViT).
- **hidden_size** (`int`) -- The hidden size of the model.
- **num_attention_heads** (`int`) -- The number of attention heads used in the multi-head attention layers of the
model.
- **num_hidden_layers** (`int`) -- The number of blocks in the model.
Setting parameters for sequence generation in the model config is deprecated. For backward compatibility, loading
some of them will still be possible, but attempting to overwrite them will throw an exception -- you should set
them in a [~transformers.GenerationConfig]. Check the documentation of [~transformers.GenerationConfig] for more
information about the individual parameters.
Arg:
name_or_path (`str`, *optional*, defaults to `""`):
Store the string that was passed to [`PreTrainedModel.from_pretrained`] as `pretrained_model_name_or_path`
if the configuration was created with such a method.
output_hidden_states (`bool`, *optional*, defaults to `False`):
Whether or not the model should return all hidden-states.
output_attentions (`bool`, *optional*, defaults to `False`):
Whether or not the model should returns all attentions.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not the model should return a [`~transformers.utils.ModelOutput`] instead of a plain tuple.
is_encoder_decoder (`bool`, *optional*, defaults to `False`):
Whether the model is used as an encoder/decoder or not.
chunk_size_feed_forward (`int`, *optional*, defaults to `0`):
The chunk size of all feed forward layers in the residual attention blocks. A chunk size of `0` means that
the feed forward layer is not chunked. A chunk size of n means that the feed forward layer processes `n` <
sequence_length embeddings at a time. For more information on feed forward chunking, see [How does Feed
Forward Chunking work?](../glossary.html#feed-forward-chunking).
> Parameters for fine-tuning tasks
architectures (`list[str]`, *optional*):
Model architectures that can be used with the model pretrained weights.
id2label (`dict[int, str]`, *optional*):
A map from index (for instance prediction index, or target index) to label.
label2id (`dict[str, int]`, *optional*):
A map from label to index for the model.
num_labels (`int`, *optional*):
Number of labels to use in the last layer added to the model, typically for a classification task.
problem_type (`str`, *optional*):
Problem type for `XxxForSequenceClassification` models. Can be one of `"regression"`,
`"single_label_classification"` or `"multi_label_classification"`.
> PyTorch specific parameters
dtype (`str`, *optional*):
The `dtype` of the weights. This attribute can be used to initialize the model to a non-default `dtype`
(which is normally `float32`) and thus allow for optimal storage allocation. For example, if the saved
model is `float16`, ideally we want to load it back using the minimal amount of memory needed to load
`float16` weights.
"""
model_type: str = ""
base_config_key: str = ""
sub_configs: dict[str, type["PreTrainedConfig"]] = {}
has_no_defaults_at_init: bool = False
attribute_map: dict[str, str] = {}
base_model_tp_plan: dict[str, Any] | None = None
base_model_pp_plan: dict[str, tuple[list[str]]] | None = None
base_model_ep_plan: dict[str, tuple[list[str]]] | None = None
_auto_class: str | None = None
def __setattr__(self, key, value):
if key in super().__getattribute__("attribute_map"):
key = super().__getattribute__("attribute_map")[key]
super().__setattr__(key, value)
def __getattribute__(self, key):
if key != "attribute_map" and key in super().__getattribute__("attribute_map"):
key = super().__getattribute__("attribute_map")[key]
return super().__getattribute__(key)
def __init__(
self,
*,
# All models common arguments
output_hidden_states: bool = False,
output_attentions: bool = False,
return_dict: bool = True,
dtype: Union[str, "torch.dtype"] | None = None,
# Common arguments
chunk_size_feed_forward: int = 0,
is_encoder_decoder: bool = False,
# Fine-tuning task arguments
architectures: list[str] | None = None,
id2label: dict[int, str] | None = None,
label2id: dict[str, int] | None = None,
num_labels: int | None = None,
problem_type: str | None = None,
**kwargs,
):
# Validation for some arguments
if label2id is not None and not isinstance(label2id, dict):
raise ValueError("Argument label2id should be a dictionary.")
if id2label is not None and not isinstance(id2label, dict):
raise ValueError("Argument id2label should be a dictionary.")
if num_labels is not None and id2label is not None and len(id2label) != num_labels:
logger.warning(
f"You passed `num_labels={num_labels}` which is incompatible to "
f"the `id2label` map of length `{len(id2label)}`."
)
if problem_type is not None and problem_type not in (
"regression",
"single_label_classification",
"multi_label_classification",
):
raise ValueError(
f"The config parameter `problem_type` was not understood: received {problem_type} "
"but only 'regression', 'single_label_classification' and 'multi_label_classification' are valid."
)
# BC for the `torch_dtype` argument instead of the simpler `dtype`
# Do not warn, as it would otherwise always be triggered since most configs on the hub have `torch_dtype`
if (torch_dtype := kwargs.pop("torch_dtype", None)) is not None:
# If both are provided, keep `dtype`
dtype = dtype if dtype is not None else torch_dtype
if dtype is not None and isinstance(dtype, str) and is_torch_available():
# we will start using self.dtype in v5, but to be consistent with
# from_pretrained's dtype arg convert it to an actual torch.dtype object
import torch
dtype = getattr(torch, dtype)
# BC for rotary embeddings. We will pop out legacy keys from kwargs and rename to new format
if hasattr(self, "rope_parameters"):
ignore_keys_at_rope_validation = kwargs.pop("ignore_keys_at_rope_validation", None)
kwargs = self.convert_rope_params_to_dict(
ignore_keys_at_rope_validation=ignore_keys_at_rope_validation, **kwargs
)
# Attributes common for all models
self.return_dict = return_dict
self.output_hidden_states = output_hidden_states
self.dtype = dtype
self._output_attentions = output_attentions # has public property
# Less common kwargs, only used by some models
self.chunk_size_feed_forward = chunk_size_feed_forward
# Encoder-decoder models attributes
self.is_encoder_decoder = is_encoder_decoder
# Fine-tuning task attributes
self.architectures = architectures
self.id2label = id2label
self.label2id = label2id
self.problem_type = problem_type
if self.id2label is None:
self._create_id_label_maps(num_labels if num_labels is not None else 2)
else:
# Keys are always strings in JSON so convert ids to int here.
self.id2label = {int(key): value for key, value in self.id2label.items()}
# Parameters for sequence generation saved in the config are popped instead of loading them.
for parameter_name in GenerationConfig._get_default_generation_params().keys():
kwargs.pop(parameter_name, None)
# Name or path to the pretrained checkpoint
self._name_or_path = str(kwargs.pop("name_or_path", ""))
self._commit_hash = kwargs.pop("_commit_hash", None)
# Attention implementation to use, if relevant (it sets it recursively on sub-configs)
self._attn_implementation: str | None = kwargs.pop("attn_implementation", None)
# Experts implementation to use, if relevant (it sets it recursively on sub-configs)
self._experts_implementation = kwargs.pop("experts_implementation", None)
# Drop the transformers version info
self.transformers_version = kwargs.pop("transformers_version", None)
# Additional attributes without default values
for key, value in kwargs.items():
try:
setattr(self, key, value)
except AttributeError as err:
logger.error(f"Can't set {key} with value {value} for {self}")
raise err
def _create_id_label_maps(self, num_labels: int):
self.id2label = {i: f"LABEL_{i}" for i in range(num_labels)}
self.label2id = dict(zip(self.id2label.values(), self.id2label.keys()))
@property
def name_or_path(self) -> str | None:
return getattr(self, "_name_or_path", None)
@name_or_path.setter
def name_or_path(self, value):
self._name_or_path = str(value) # Make sure that name_or_path is a string (for JSON encoding)
@property
def output_attentions(self):
"""
`bool`: Whether or not the model should returns all attentions.
"""
return self._output_attentions
@output_attentions.setter
def output_attentions(self, value: bool):
# If we set `output_attentions` explicitly before the attn implementation, dispatch eager
if value and self._attn_implementation is None:
self._attn_implementation = "eager"
if value and self._attn_implementation != "eager":
raise ValueError(
"The `output_attentions` attribute is not supported when using the `attn_implementation` set to "
f"{self._attn_implementation}. Please set it to 'eager' instead."
)
self._output_attentions = value
@property
def use_return_dict(self) -> bool:
"""
`bool`: Whether or not return [`~utils.ModelOutput`] instead of tuples.
"""
return self.return_dict
@property
def num_labels(self) -> int:
"""
`int`: The number of labels for classification models.
"""
return len(self.id2label)
@num_labels.setter
def num_labels(self, num_labels: int):
# we do not store `num_labels` attribute in config, but instead
# compute it based on the length of the `id2label` map
if self.id2label is None or self.num_labels != num_labels:
self._create_id_label_maps(num_labels)
@property
def _attn_implementation(self):
return self._attn_implementation_internal
@_attn_implementation.setter
def _attn_implementation(self, value: str | dict | None):
"""We set it recursively on the sub-configs as well"""
# Set if for current config
current_attn = getattr(self, "_attn_implementation", None)
attn_implementation = value if not isinstance(value, dict) else value.get("", current_attn)
self._attn_implementation_internal = attn_implementation
# Set it recursively on the subconfigs
for subconfig_key in self.sub_configs:
subconfig = getattr(self, subconfig_key, None)
if subconfig is not None:
current_subconfig_attn = getattr(subconfig, "_attn_implementation", None)
sub_implementation = (
value if not isinstance(value, dict) else value.get(subconfig_key, current_subconfig_attn)
)
subconfig._attn_implementation = sub_implementation
@property
def _experts_implementation(self):
return self._experts_implementation_internal
@_experts_implementation.setter
def _experts_implementation(self, value: str | dict | None):
"""We set it recursively on the sub-configs as well"""
# Set if for current config
current_moe = getattr(self, "_experts_implementation", None)
experts_implementation = value if not isinstance(value, dict) else value.get("", current_moe)
self._experts_implementation_internal = experts_implementation
# Set it recursively on the subconfigs
for subconfig_key in self.sub_configs:
subconfig = getattr(self, subconfig_key, None)
if subconfig is not None:
current_subconfig_moe = getattr(subconfig, "_experts_implementation", None)
sub_implementation = (
value if not isinstance(value, dict) else value.get(subconfig_key, current_subconfig_moe)
)
subconfig._experts_implementation = sub_implementation
@property
def torch_dtype(self):
logger.warning_once("`torch_dtype` is deprecated! Use `dtype` instead!")
return self.dtype
@torch_dtype.setter
def torch_dtype(self, value):
logger.warning_once("`torch_dtype` is deprecated! Use `dtype` instead!")
self.dtype = value
@property
def rope_scaling(self):
return self.rope_parameters
@rope_scaling.setter
def rope_scaling(self, value):
self.rope_parameters = value
def save_pretrained(self, save_directory: str | os.PathLike, push_to_hub: bool = False, **kwargs):
"""
Save a configuration object to the directory `save_directory`, so that it can be re-loaded using the
[`~PreTrainedConfig.from_pretrained`] class method.
Args:
save_directory (`str` or `os.PathLike`):
Directory where the configuration JSON file will be saved (will be created if it does not exist).
push_to_hub (`bool`, *optional*, defaults to `False`):
Whether or not to push your model to the Hugging Face model 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 key word arguments passed along to the [`~utils.PushToHubMixin.push_to_hub`] method.
"""
if os.path.isfile(save_directory):
raise AssertionError(f"Provided path ({save_directory}) should be a directory, not a file")
generation_parameters = self._get_generation_parameters()
if len(generation_parameters) > 0:
raise ValueError(
"Some generation parameters are set in the model config. These should go into `model.generation_config`"
f"as opposed to `model.config`. \nGeneration parameters found: {str(generation_parameters)}",
)
os.makedirs(save_directory, exist_ok=True)
if push_to_hub:
commit_message = kwargs.pop("commit_message", None)
repo_id = kwargs.pop("repo_id", save_directory.split(os.path.sep)[-1])
repo_id = create_repo(repo_id, exist_ok=True, **kwargs).repo_id
files_timestamps = self._get_files_timestamps(save_directory)
# This attribute is important to know on load, but should not be serialized on save.
if "transformers_weights" in self:
delattr(self, "transformers_weights")
# If we have a custom config, we copy the file defining it in the folder and set the attributes so it can be
# loaded from the Hub.
if self._auto_class is not None:
custom_object_save(self, save_directory, config=self)
# If we save using the predefined names, we can load using `from_pretrained`
output_config_file = os.path.join(save_directory, CONFIG_NAME)
self.to_json_file(output_config_file, use_diff=True)
logger.info(f"Configuration saved in {output_config_file}")
if push_to_hub:
self._upload_modified_files(
save_directory,
repo_id,
files_timestamps,
commit_message=commit_message,
token=kwargs.get("token"),
)
@classmethod
def from_pretrained(
cls: type[SpecificPreTrainedConfigType],
pretrained_model_name_or_path: str | os.PathLike,
cache_dir: str | os.PathLike | None = None,
force_download: bool = False,
local_files_only: bool = False,
token: str | bool | None = None,
revision: str = "main",
**kwargs,
) -> SpecificPreTrainedConfigType:
r"""
Instantiate a [`PreTrainedConfig`] (or a derived class) from a pretrained model configuration.
Args:
pretrained_model_name_or_path (`str` or `os.PathLike`):
This can be either:
- a string, the *model id* of a pretrained model configuration hosted inside a model repo on
huggingface.co.
- a path to a *directory* containing a configuration file saved using the
[`~PreTrainedConfig.save_pretrained`] method, e.g., `./my_model_directory/`.
- a path or url to a saved configuration JSON *file*, e.g., `./my_model_directory/configuration.json`.
cache_dir (`str` or `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.
force_download (`bool`, *optional*, defaults to `False`):
Whether or not to force to (re-)download the configuration files and override the cached versions if
they exist.
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.
token (`str` or `bool`, *optional*):
The token to use as HTTP bearer authorization for remote files. If `True`, or not specified, will use
the token generated when running `hf auth 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.
To test a pull request you made on the Hub, you can pass `revision="refs/pr/"`.
return_unused_kwargs (`bool`, *optional*, defaults to `False`):
If `False`, then this function returns just the final configuration object.
If `True`, then this functions returns a `Tuple(config, unused_kwargs)` where *unused_kwargs* is a
dictionary consisting of the key/value pairs whose keys are not configuration attributes: i.e., the
part of `kwargs` which has not been used to update `config` and is otherwise ignored.
subfolder (`str`, *optional*, defaults to `""`):
In case the relevant files are located inside a subfolder of the model repo on huggingface.co, you can
specify the folder name here.
kwargs (`dict[str, Any]`, *optional*):
The values in kwargs of any keys which are configuration attributes will be used to override the loaded
values. Behavior concerning key/value pairs whose keys are *not* configuration attributes is controlled
by the `return_unused_kwargs` keyword parameter.
Returns:
[`PreTrainedConfig`]: The configuration object instantiated from this pretrained model.
Examples:
```python
# We can't instantiate directly the base class *PreTrainedConfig* so let's show the examples on a
# derived class: BertConfig
config = BertConfig.from_pretrained(
"google-bert/bert-base-uncased"
) # Download configuration from huggingface.co and cache.
config = BertConfig.from_pretrained(
"./test/saved_model/"
) # E.g. config (or model) was saved using *save_pretrained('./test/saved_model/')*
config = BertConfig.from_pretrained("./test/saved_model/my_configuration.json")
config = BertConfig.from_pretrained("google-bert/bert-base-uncased", output_attentions=True, foo=False)
assert config.output_attentions == True
config, unused_kwargs = BertConfig.from_pretrained(
"google-bert/bert-base-uncased", output_attentions=True, foo=False, return_unused_kwargs=True
)
assert config.output_attentions == True
assert unused_kwargs == {"foo": False}
```"""
kwargs["cache_dir"] = cache_dir
kwargs["force_download"] = force_download
kwargs["local_files_only"] = local_files_only
kwargs["revision"] = revision
config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
if cls.base_config_key and cls.base_config_key in config_dict:
config_dict = config_dict[cls.base_config_key]
if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
# sometimes the config has no `base_config_key` if the config is used in several composite models
# e.g. LlamaConfig. In that case we try to see if there is match in `model_type` before raising a warning
for v in config_dict.values():
if isinstance(v, dict) and v.get("model_type") == cls.model_type:
config_dict = v
# raise warning only if we still can't see a match in `model_type`
if config_dict["model_type"] != cls.model_type:
logger.warning(
f"You are using a model of type `{config_dict['model_type']}` to instantiate a model of type "
f"`{cls.model_type}`. This may be expected if you are loading a checkpoint that shares a subset "
f"of the architecture (e.g., loading a `sam2_video` checkpoint into `Sam2Model`), but is otherwise "
f"not supported and can yield errors. Please verify that the checkpoint is compatible with the "
f"model you are instantiating."
)
return cls.from_dict(config_dict, **kwargs)
@classmethod
def get_config_dict(
cls, pretrained_model_name_or_path: str | os.PathLike, **kwargs
) -> tuple[dict[str, Any], dict[str, Any]]:
"""
From a `pretrained_model_name_or_path`, resolve to a dictionary of parameters, to be used for instantiating a
[`PreTrainedConfig`] using `from_dict`.
Parameters:
pretrained_model_name_or_path (`str` or `os.PathLike`):
The identifier of the pre-trained checkpoint from which we want the dictionary of parameters.
Returns:
`tuple[Dict, Dict]`: The dictionary(ies) that will be used to instantiate the configuration object.
"""
original_kwargs = copy.deepcopy(kwargs)
# Get config dict associated with the base config file
config_dict, kwargs = cls._get_config_dict(pretrained_model_name_or_path, **kwargs)
if config_dict is None:
return {}, kwargs
if "_commit_hash" in config_dict:
original_kwargs["_commit_hash"] = config_dict["_commit_hash"]
# That config file may point us toward another config file to use.
if "configuration_files" in config_dict:
configuration_file = get_configuration_file(config_dict["configuration_files"])
config_dict, kwargs = cls._get_config_dict(
pretrained_model_name_or_path, _configuration_file=configuration_file, **original_kwargs
)
return config_dict, kwargs
@classmethod
def _get_config_dict(
cls, pretrained_model_name_or_path: str | os.PathLike, **kwargs
) -> tuple[dict[str, Any], dict[str, Any]]:
cache_dir = kwargs.pop("cache_dir", None)
force_download = kwargs.pop("force_download", False)
proxies = kwargs.pop("proxies", None)
token = kwargs.pop("token", None)
local_files_only = kwargs.pop("local_files_only", False)
revision = kwargs.pop("revision", None)
trust_remote_code = kwargs.pop("trust_remote_code", None)
subfolder = kwargs.pop("subfolder", "")
from_pipeline = kwargs.pop("_from_pipeline", None)
from_auto_class = kwargs.pop("_from_auto", False)
commit_hash = kwargs.pop("_commit_hash", None)
gguf_file = kwargs.get("gguf_file")
if trust_remote_code is True:
logger.warning(
"The argument `trust_remote_code` is to be used with Auto classes. It has no effect here and is"
" ignored."
)
user_agent = {"file_type": "config", "from_auto_class": from_auto_class}
if from_pipeline is not None:
user_agent["using_pipeline"] = from_pipeline
pretrained_model_name_or_path = str(pretrained_model_name_or_path)
is_local = os.path.isdir(pretrained_model_name_or_path)
if os.path.isfile(os.path.join(subfolder, pretrained_model_name_or_path)):
# Special case when pretrained_model_name_or_path is a local file
resolved_config_file = pretrained_model_name_or_path
is_local = True
else:
configuration_file = kwargs.pop("_configuration_file", CONFIG_NAME) if gguf_file is None else gguf_file
try:
# Load from local folder or from cache or download from model Hub and cache
resolved_config_file = cached_file(
pretrained_model_name_or_path,
configuration_file,
cache_dir=cache_dir,
force_download=force_download,
proxies=proxies,
local_files_only=local_files_only,
token=token,
user_agent=user_agent,
revision=revision,
subfolder=subfolder,
_commit_hash=commit_hash,
)
if resolved_config_file is None:
return None, kwargs
commit_hash = extract_commit_hash(resolved_config_file, commit_hash)
except OSError:
# Raise any environment error raise by `cached_file`. It will have a helpful error message adapted to
# the original exception.
raise
except Exception:
# For any other exception, we throw a generic error.
raise OSError(
f"Can't load the configuration of '{pretrained_model_name_or_path}'. If you were trying to load it"
" from 'https://huggingface.co/models', make sure you don't have a local directory with the same"
f" name. Otherwise, make sure '{pretrained_model_name_or_path}' is the correct path to a directory"
f" containing a {configuration_file} file"
)
try:
if gguf_file:
config_dict = load_gguf_checkpoint(resolved_config_file, return_tensors=False)["config"]
else:
# Load config dict
config_dict = cls._dict_from_json_file(resolved_config_file)
config_dict["_commit_hash"] = commit_hash
except (json.JSONDecodeError, UnicodeDecodeError):
raise OSError(f"It looks like the config file at '{resolved_config_file}' is not a valid JSON file.")
if is_local:
logger.info(f"loading configuration file {resolved_config_file}")
else:
logger.info(f"loading configuration file {configuration_file} from cache at {resolved_config_file}")
# timm models are not saved with the model_type in the config file
if "model_type" not in config_dict and is_timm_config_dict(config_dict):
config_dict["model_type"] = "timm_wrapper"
return config_dict, kwargs
@classmethod
def from_dict(
cls: type[SpecificPreTrainedConfigType], config_dict: dict[str, Any], **kwargs
) -> SpecificPreTrainedConfigType:
"""
Instantiates a [`PreTrainedConfig`] from a Python dictionary of parameters.
Args:
config_dict (`dict[str, Any]`):
Dictionary that will be used to instantiate the configuration object. Such a dictionary can be
retrieved from a pretrained checkpoint by leveraging the [`~PreTrainedConfig.get_config_dict`] method.
kwargs (`dict[str, Any]`):
Additional parameters from which to initialize the configuration object.
Returns:
[`PreTrainedConfig`]: The configuration object instantiated from those parameters.
"""
return_unused_kwargs = kwargs.pop("return_unused_kwargs", False)
# Those arguments may be passed along for our internal telemetry.
# We remove them so they don't appear in `return_unused_kwargs`.
kwargs.pop("_from_auto", None)
kwargs.pop("_from_pipeline", None)
# The commit hash might have been updated in the `config_dict`, we don't want the kwargs to erase that update.
if "_commit_hash" in kwargs and "_commit_hash" in config_dict:
kwargs["_commit_hash"] = config_dict["_commit_hash"]
# For BC on the old `torch_dtype`
if (torch_dtype := kwargs.pop("torch_dtype", None)) is not None:
logger.warning_once("`torch_dtype` is deprecated! Use `dtype` instead!")
# If both are present, use `dtype`
kwargs["dtype"] = kwargs.get("dtype", torch_dtype)
# We remove them from kwargs so that they do not appear in `return_unused_kwargs`.
config_dict["attn_implementation"] = kwargs.pop("attn_implementation", None)
config_dict["experts_implementation"] = kwargs.pop("experts_implementation", None)
config = cls(**config_dict)
# Update config with kwargs if needed
if "num_labels" in kwargs and "id2label" in kwargs:
num_labels = kwargs["num_labels"]
id2label = kwargs["id2label"] if kwargs["id2label"] is not None else []
if len(id2label) != num_labels:
raise ValueError(
f"You passed along `num_labels={num_labels}` with an incompatible id to label map: "
f"{kwargs['id2label']}. Since those arguments are inconsistent with each other, you should remove "
"one of them."
)
to_remove = []
for key, value in kwargs.items():
if hasattr(config, key):
current_attr = getattr(config, key)
# To authorize passing a custom subconfig as kwarg in models that have nested configs.
# We need to update only custom kwarg values instead and keep other attributes in subconfig.
if isinstance(current_attr, PreTrainedConfig) and isinstance(value, dict):
current_attr_updated = current_attr.to_dict()
current_attr_updated.update(value)
value = current_attr.__class__(**current_attr_updated)
setattr(config, key, value)
if key != "dtype":
to_remove.append(key)
for key in to_remove:
kwargs.pop(key, None)
logger.info(f"Model config {config}")
if return_unused_kwargs:
return config, kwargs
else:
return config
@classmethod
def from_json_file(
cls: type[SpecificPreTrainedConfigType], json_file: str | os.PathLike
) -> SpecificPreTrainedConfigType:
"""
Instantiates a [`PreTrainedConfig`] from the path to a JSON file of parameters.
Args:
json_file (`str` or `os.PathLike`):
Path to the JSON file containing the parameters.
Returns:
[`PreTrainedConfig`]: The configuration object instantiated from that JSON file.
"""
config_dict = cls._dict_from_json_file(json_file)
return cls(**config_dict)
@classmethod
def _dict_from_json_file(cls, json_file: str | os.PathLike):
with open(json_file, encoding="utf-8") as reader:
text = reader.read()
config_dict = json.loads(text)
return cls._decode_special_floats(config_dict)
@classmethod
def _encode_special_floats(cls, obj: Any) -> Any:
"""
Iterates over the passed object and encode specific floats that cannot be JSON-serialized. Python's JSON
engine saves floats like `Infinity` (+/-) or `NaN` which are not compatible with other JSON engines.
It serializes floats like `Infinity` as an object: `{'__float__': Infinity}`.
"""
if isinstance(obj, float):
if math.isnan(obj):
return {_FLOAT_TAG_KEY: "NaN"}
if obj == float("inf"):
return {_FLOAT_TAG_KEY: "Infinity"}
if obj == float("-inf"):
return {_FLOAT_TAG_KEY: "-Infinity"}
return obj
if isinstance(obj, dict):
return {k: cls._encode_special_floats(v) for k, v in obj.items()}
if isinstance(obj, (list, tuple)):
return [cls._encode_special_floats(v) for v in obj]
return obj
@classmethod
def _decode_special_floats(cls, obj: Any) -> Any:
"""
Iterates over the passed object and decode specific floats that cannot be JSON-serialized. Python's JSON
engine saves floats like `Infinity` (+/-) or `NaN` which are not compatible with other JSON engines.
This method deserializes objects like `{'__float__': Infinity}` to their float values like `Infinity`.
"""
if isinstance(obj, dict):
if set(obj.keys()) == {_FLOAT_TAG_KEY} and isinstance(obj[_FLOAT_TAG_KEY], str):
tag = obj[_FLOAT_TAG_KEY]
if tag in _FLOAT_TAG_VALUES:
return _FLOAT_TAG_VALUES[tag]
return obj
return {k: cls._decode_special_floats(v) for k, v in obj.items()}
if isinstance(obj, list):
return [cls._decode_special_floats(v) for v in obj]
return obj
def __eq__(self, other):
return isinstance(other, PreTrainedConfig) and (self.__dict__ == other.__dict__)
def __repr__(self):
return f"{self.__class__.__name__} {self.to_json_string()}"
def __iter__(self):
yield from self.__dict__
def to_diff_dict(self) -> dict[str, Any]:
"""
Removes all attributes from the configuration that correspond to the default config attributes for
better readability, while always retaining the `config` attribute from the class. Serializes to a
Python dictionary.
Returns:
dict[str, Any]: Dictionary of all the attributes that make up this configuration instance.
"""
config_dict = self.to_dict()
# Get the default config dict (from a fresh PreTrainedConfig instance)
default_config_dict = PreTrainedConfig().to_dict()
# get class specific config dict
class_config_dict = self.__class__().to_dict() if not self.has_no_defaults_at_init else {}
serializable_config_dict = {}
# Only serialize values that differ from the default config,
# except always keep the 'config' attribute.
for key, value in config_dict.items():
if (
isinstance(getattr(self, key, None), PreTrainedConfig)
and key in class_config_dict
and isinstance(class_config_dict[key], dict)
):
# For nested configs we need to clean the diff recursively
diff = recursive_diff_dict(value, default_config_dict, config_obj=getattr(self, key, None))
if "model_type" in value:
# Needs to be set even if it's not in the diff
diff["model_type"] = value["model_type"]
serializable_config_dict[key] = diff
elif (
key not in default_config_dict
or key == "transformers_version"
or key == "vocab_file"
or value != default_config_dict[key]
or (key in default_config_dict and value != class_config_dict.get(key, value))
):
serializable_config_dict[key] = value
self._remove_keys_not_serialized(serializable_config_dict)
# Key removed only in diff dict
if "_name_or_path" in serializable_config_dict:
del serializable_config_dict["_name_or_path"]
if hasattr(self, "quantization_config"):
serializable_config_dict["quantization_config"] = (
self.quantization_config.to_dict()
if not isinstance(self.quantization_config, dict) and self.quantization_config is not None
else self.quantization_config
)
self.dict_dtype_to_str(serializable_config_dict)
return serializable_config_dict
def to_dict(self) -> dict[str, Any]:
"""
Serializes this instance to a Python dictionary.
Returns:
`dict[str, Any]`: Dictionary of all the attributes that make up this configuration instance.
"""
output = copy.deepcopy(self.__dict__)
if hasattr(self.__class__, "model_type"):
output["model_type"] = self.__class__.model_type
# Transformers version when serializing the model
output["transformers_version"] = __version__
for key, value in output.items():
# Deal with nested configs like CLIP
if isinstance(value, PreTrainedConfig):
value = value.to_dict()
del value["transformers_version"]
output[key] = value
self._remove_keys_not_serialized(output)
if hasattr(self, "quantization_config"):
output["quantization_config"] = (
self.quantization_config.to_dict()
if not isinstance(self.quantization_config, dict) and self.quantization_config is not None
else self.quantization_config
)
self.dict_dtype_to_str(output)
return output
def to_json_string(self, use_diff: bool = True) -> str:
"""
Serializes this instance to a JSON string.
Args:
use_diff (`bool`, *optional*, defaults to `True`):
If set to `True`, only the difference between the config instance and the default `PreTrainedConfig()`
is serialized to JSON string.
Returns:
`str`: String containing all the attributes that make up this configuration instance in JSON format.
"""
if use_diff is True:
config_dict = self.to_diff_dict()
else:
config_dict = self.to_dict()
# Handle +/-Infinity and NaNs
config_dict = self._encode_special_floats(config_dict)
return json.dumps(config_dict, indent=2, sort_keys=True) + "\n"
def to_json_file(self, json_file_path: str | os.PathLike, use_diff: bool = True):
"""
Save this instance to a JSON file.
Args:
json_file_path (`str` or `os.PathLike`):
Path to the JSON file in which this configuration instance's parameters will be saved.
use_diff (`bool`, *optional*, defaults to `True`):
If set to `True`, only the difference between the config instance and the default `PreTrainedConfig()`
is serialized to JSON file.
"""
with open(json_file_path, "w", encoding="utf-8") as writer:
writer.write(self.to_json_string(use_diff=use_diff))
def update(self, config_dict: dict[str, Any]):
"""
Updates attributes of this class with attributes from `config_dict`.
Args:
config_dict (`dict[str, Any]`): Dictionary of attributes that should be updated for this class.
"""
for key, value in config_dict.items():
setattr(self, key, value)
def update_from_string(self, update_str: str):
"""
Updates attributes of this class with attributes from `update_str`.
The expected format is ints, floats and strings as is, and for booleans use `true` or `false`. For example:
"n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"
The keys to change have to already exist in the config object.
Args:
update_str (`str`): String with attributes that should be updated for this class.
"""
d = dict(x.split("=") for x in update_str.split(","))
for k, v in d.items():
if not hasattr(self, k):
raise ValueError(f"key {k} isn't in the original config dict")
old_v = getattr(self, k)
if isinstance(old_v, bool):
if v.lower() in ["true", "1", "y", "yes"]:
v = True
elif v.lower() in ["false", "0", "n", "no"]:
v = False
else:
raise ValueError(f"can't derive true or false from {v} (key {k})")
elif isinstance(old_v, int):
v = int(v)
elif isinstance(old_v, float):
v = float(v)
elif not isinstance(old_v, str):
raise TypeError(
f"You can only update int, float, bool or string values in the config, got {v} for key {k}"
)
setattr(self, k, v)
def dict_dtype_to_str(self, d: dict[str, Any]) -> None:
"""
Checks whether the passed dictionary and its nested dicts have a *dtype* key and if it's not None,
converts torch.dtype to a string of just the type. For example, `torch.float32` get converted into *"float32"*
string, which can then be stored in the json format.
"""
if d.get("dtype") is not None:
if isinstance(d["dtype"], dict):
d["dtype"] = {k: str(v).split(".")[-1] for k, v in d["dtype"].items()}
# models like Emu3 can have "dtype" as token in config's vocabulary map,
# so we also exclude int type here to avoid error in this special case.
elif not isinstance(d["dtype"], (str, int)):
d["dtype"] = str(d["dtype"]).split(".")[1]
for value in d.values():
if isinstance(value, dict):
self.dict_dtype_to_str(value)
def _remove_keys_not_serialized(self, d: dict[str, Any]) -> None:
"""
Checks and removes if there are any keys in the dict that should not be serialized when saving the config.
Runs recursive check on the dict, to remove from all sub configs.
"""
if "_is_quantized" in d:
del d["_is_quantized"]
if "_auto_class" in d:
del d["_auto_class"]
if "_output_attentions" in d:
d["output_attentions"] = d.pop("_output_attentions")
if "_commit_hash" in d:
del d["_commit_hash"]
if "_attn_implementation_internal" in d:
del d["_attn_implementation_internal"]
if "_experts_implementation_internal" in d:
del d["_experts_implementation_internal"]
# Do not serialize `base_model_tp_plan` for now
if "base_model_tp_plan" in d:
del d["base_model_tp_plan"]
# Do not serialize `base_model_pp_plan` for now
if "base_model_pp_plan" in d:
del d["base_model_pp_plan"]
for value in d.values():
if isinstance(value, dict):
self._remove_keys_not_serialized(value)
@classmethod
def register_for_auto_class(cls, auto_class="AutoConfig"):
"""
Register this class with a given auto class. This should only be used for custom configurations as the ones in
the library are already mapped with `AutoConfig`.
Args:
auto_class (`str` or `type`, *optional*, defaults to `"AutoConfig"`):
The auto class to register this new configuration with.
"""
if not isinstance(auto_class, str):
auto_class = auto_class.__name__
import transformers.models.auto as auto_module
if not hasattr(auto_module, auto_class):
raise ValueError(f"{auto_class} is not a valid auto class.")
cls._auto_class = auto_class
def _get_generation_parameters(self) -> dict[str, Any]:
"""
Checks if there are generation parameters in `PreTrainedConfig` instance. Note that
we should not save generation params in PreTrainedConfig, and we will raise error
if there are any.
"""
generation_params = {}
default_config = self.__class__().to_dict() if not self.has_no_defaults_at_init else {}
for key in GenerationConfig._get_default_generation_params().keys():
if key == "use_cache":
continue # common key for most models
if hasattr(self, key) and getattr(self, key) is not None and key not in default_config:
generation_params[key] = getattr(self, key)
return generation_params
def get_text_config(self, decoder=None, encoder=None) -> "PreTrainedConfig":
"""
Returns the text config related to the text input (encoder) or text output (decoder) of the model. The
`decoder` and `encoder` input arguments can be used to specify which end of the model we are interested in,
which is useful on models that have both text input and output modalities.
There are three possible outcomes of using this method:
1. On most models, it returns the original config instance itself.
2. On newer (2024+) composite models, it returns the text section of the config, which is nested under a set
of valid names.
3. On older (2023-) composite models, it discards decoder-only parameters when `encoder=True` and vice-versa.
Args:
decoder (`Optional[bool]`, *optional*):
If set to `True`, then only search for decoder config names.
encoder (`Optional[bool]`, *optional*):
If set to `True`, then only search for encoder config names.
"""
return_both = decoder == encoder # both unset or both set -> search all possible names
decoder_possible_text_config_names = ("decoder", "generator", "text_config")
encoder_possible_text_config_names = ("text_encoder",)
if return_both:
possible_text_config_names = encoder_possible_text_config_names + decoder_possible_text_config_names
elif decoder:
possible_text_config_names = decoder_possible_text_config_names
else:
possible_text_config_names = encoder_possible_text_config_names
valid_text_config_names = []
for text_config_name in possible_text_config_names:
if hasattr(self, text_config_name):
text_config = getattr(self, text_config_name, None)
if text_config is not None:
valid_text_config_names += [text_config_name]
if len(valid_text_config_names) > 1:
raise ValueError(
f"Multiple valid text configs were found in the model config: {valid_text_config_names}. In this "
"case, using `get_text_config()` would be ambiguous. Please specify the desired text config directly, "
"e.g. `text_config = config.sub_config_name`"
)
elif len(valid_text_config_names) == 1:
config_to_return = getattr(self, valid_text_config_names[0])
else:
config_to_return = self
# handle legacy models with flat config structure, when we only want one of the configs
if not return_both and len(valid_text_config_names) == 0 and config_to_return.is_encoder_decoder:
config_to_return = copy.deepcopy(config_to_return)
prefix_to_discard = "encoder" if decoder else "decoder"
prefix_to_keep = "decoder" if decoder else "encoder"
for key in config_to_return.to_dict():
# NOTE: We don't want to discard the key if it is mapped from a different attribute name at read time
if key.startswith(prefix_to_discard) and key not in config_to_return.attribute_map.values():
delattr(config_to_return, key)
if key.startswith(prefix_to_keep):
# [encoder/decoder]_layers -> num_hidden_layers
if key == prefix_to_keep + "_layers":
new_key = "num_hidden_layers"
# [encoder/decoder]_attention_heads -> num_attention_heads
elif key == prefix_to_keep + "_attention_heads":
new_key = "num_attention_heads"
# e.g. encoder_hidden_act -> hidden_act
else:
new_key = key[len(prefix_to_keep) + 1 :]
# Does the class map the new key into a different attribute name at read time? if so, let's write
# into that attribute instead
if new_key in config_to_return.attribute_map:
new_key = config_to_return.attribute_map[new_key]
value = getattr(config_to_return, key)
delattr(config_to_return, key)
setattr(config_to_return, new_key, value)
return config_to_return
def get_configuration_file(configuration_files: list[str]) -> str:
"""
Get the configuration file to use for this version of transformers.
Args:
configuration_files (`list[str]`): The list of available configuration files.
Returns:
`str`: The configuration file to use.
"""
configuration_files_map = {}
for file_name in configuration_files:
if file_name.startswith("config.") and file_name.endswith(".json") and file_name != "config.json":
v = file_name.removeprefix("config.").removesuffix(".json")
configuration_files_map[v] = file_name
available_versions = sorted(configuration_files_map.keys())
# Defaults to FULL_CONFIGURATION_FILE and then try to look at some newer versions.
configuration_file = CONFIG_NAME
transformers_version = version.parse(__version__)
for v in available_versions:
if version.parse(v) <= transformers_version:
configuration_file = configuration_files_map[v]
else:
# No point going further since the versions are sorted.
break
return configuration_file
def recursive_diff_dict(dict_a, dict_b, config_obj=None):
"""
Helper function to recursively take the diff between two nested dictionaries. The resulting diff only contains the
values from `dict_a` that are different from values in `dict_b`.
dict_b : the default config dictionary. We want to remove values that are in this one
"""
diff = {}
default = config_obj.__class__().to_dict() if config_obj is not None else {}
for key, value in dict_a.items():
obj_value = getattr(config_obj, str(key), None)
if isinstance(obj_value, PreTrainedConfig) and key in dict_b and isinstance(dict_b[key], dict):
diff_value = recursive_diff_dict(value, dict_b[key], config_obj=obj_value)
diff[key] = diff_value
elif key not in dict_b or (value != default[key]):
diff[key] = value
return diff
PreTrainedConfig.push_to_hub = copy_func(PreTrainedConfig.push_to_hub)
if PreTrainedConfig.push_to_hub.__doc__ is not None:
PreTrainedConfig.push_to_hub.__doc__ = PreTrainedConfig.push_to_hub.__doc__.format(
object="config", object_class="AutoConfig", object_files="configuration file"
)
# The alias is only here for BC - we did not have the correct CamelCasing before
PretrainedConfig = PreTrainedConfig
ALLOWED_ATTENTION_LAYER_TYPES = (
"full_attention",
"sliding_attention",
"chunked_attention",
"linear_attention", # used in minimax
)
ALLOWED_MLP_LAYER_TYPES = (
"sparse",
"dense",
)
def layer_type_validation(layer_types: list[str], num_hidden_layers: int | None = None, attention: bool = True):
"""Check that `layer_types` is correctly defined."""
allowed_layer_types = ALLOWED_ATTENTION_LAYER_TYPES if attention else ALLOWED_MLP_LAYER_TYPES
if not all(layer_type in allowed_layer_types for layer_type in layer_types):
raise ValueError(f"The `layer_types` entries must be in {allowed_layer_types}")
if num_hidden_layers is not None and num_hidden_layers != len(layer_types):
raise ValueError(
f"`num_hidden_layers` ({num_hidden_layers}) must be equal to the number of layer types "
f"({len(layer_types)})"
)