Transformers documentation

Models

Transformers

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Models

The base classes PreTrainedModel, TFPreTrainedModel, and FlaxPreTrainedModel implement the common methods for loading/saving a model either from a local file or directory, or from a pretrained model configuration provided by the library (downloaded from HuggingFace’s AWS S3 repository).

PreTrainedModel and TFPreTrainedModel also implement a few methods which are common among all the models to:

resize the input token embeddings when new tokens are added to the vocabulary
prune the attention heads of the model.

The other methods that are common to each model are defined in ModuleUtilsMixin (for the PyTorch models) and ~modeling_tf_utils.TFModuleUtilsMixin (for the TensorFlow models) or for text generation, GenerationMixin (for the PyTorch models), TFGenerationMixin (for the TensorFlow models) and FlaxGenerationMixin (for the Flax/JAX models).

PreTrainedModel

class transformers.PreTrainedModel

( config: PretrainedConfig *inputs **kwargs )

Base class for all models.

PreTrainedModel takes care of storing the configuration of the models and handles methods for loading, downloading and saving models as well as a few methods common to all models to:

resize the input embeddings,
prune heads in the self-attention heads.

Class attributes (overridden by derived classes):

config_class (PretrainedConfig) — A subclass of PretrainedConfig to use as configuration class for this model architecture.
load_tf_weights (Callable) — A python method for loading a TensorFlow checkpoint in a PyTorch model, taking as arguments:
- model (PreTrainedModel) — An instance of the model on which to load the TensorFlow checkpoint.
- config (PreTrainedConfig) — An instance of the configuration associated to the model.
- path (str) — A path to the TensorFlow checkpoint.
base_model_prefix (str) — A string indicating the attribute associated to the base model in derived classes of the same architecture adding modules on top of the base model.
is_parallelizable (bool) — A flag indicating whether this model supports model parallelization.
main_input_name (str) — The name of the principal input to the model (often input_ids for NLP models, pixel_values for vision models and input_values for speech models).

push_to_hub

( repo_id: str use_temp_dir: typing.Optional[bool] = None commit_message: typing.Optional[str] = None private: typing.Optional[bool] = None use_auth_token: typing.Union[bool, str, NoneType] = None max_shard_size: typing.Union[int, str, NoneType] = '10GB' create_pr: bool = False **deprecated_kwargs )

Parameters

repo_id (str) — The name of the repository you want to push your model to. It should contain your organization name when pushing to a given organization.
use_temp_dir (bool, optional) — Whether or not to use a temporary directory to store the files saved before they are pushed to the Hub. Will default to True if there is no directory named like repo_id, False otherwise.
commit_message (str, optional) — Message to commit while pushing. Will default to "Upload model".
private (bool, optional) — Whether or not the repository created should be private.
use_auth_token (bool or str, optional) — The token to use as HTTP bearer authorization for remote files. If True, will use the token generated when running huggingface-cli login (stored in ~/.huggingface). Will default to True if repo_url is not specified.
max_shard_size (int or str, optional, defaults to "10GB") — Only applicable for models. 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 "5MB").
create_pr (bool, optional, defaults to False) — Whether or not to create a PR with the uploaded files or directly commit.

Upload the model file to the 🤗 Model Hub while synchronizing a local clone of the repo in repo_path_or_name.

Examples:

from transformers import AutoModel

model = AutoModel.from_pretrained("bert-base-cased")

# Push the model to your namespace with the name "my-finetuned-bert".
model.push_to_hub("my-finetuned-bert")

# Push the model to an organization with the name "my-finetuned-bert".
model.push_to_hub("huggingface/my-finetuned-bert")

can_generate

( ) → bool

Returns

bool

Whether this model can generate sequences with .generate().

Returns whether this model can generate sequences with .generate().

disable_input_require_grads

( )

Removes the _require_grads_hook.

enable_input_require_grads

( )

Enables the gradients for the input embeddings. This is useful for fine-tuning adapter weights while keeping the model weights fixed.

from_pretrained

( pretrained_model_name_or_path: typing.Union[str, os.PathLike, NoneType] *model_args **kwargs )

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 can be located at the root-level, like bert-base-uncased, or namespaced under a user or organization name, like dbmdz/bert-base-german-cased.
- A path to a directory containing model weights saved using save_pretrained(), e.g., ./my_model_directory/.
- A path or url to a tensorflow index checkpoint file (e.g, ./tf_model/model.ckpt.index). In this case, from_tf should be set to True and a configuration object should be provided as config argument. This loading path is slower than converting the TensorFlow checkpoint in a PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.
- A path or url to a model folder containing a flax checkpoint file in .msgpack format (e.g, ./flax_model/ containing flax_model.msgpack). In this case, from_flax should be set to True.
- None if you are both providing the configuration and state dictionary (resp. with keyword arguments config and state_dict).
model_args (sequence of positional arguments, optional) — All remaining positional arguments will be passed to the underlying model’s __init__ method.
config (Union[PretrainedConfig, str, os.PathLike], optional) — Can be either:
- an instance of a class derived from PretrainedConfig,
- a string or path valid as input to from_pretrained().
Configuration for the model to use instead of an automatically loaded configuration. Configuration can be automatically loaded when:
- The model is a model provided by the library (loaded with the model id string of a pretrained model).
- The model was saved using save_pretrained() and is reloaded by supplying the save directory.
- The model is loaded by supplying a local directory as pretrained_model_name_or_path and a configuration JSON file named config.json is found in the directory.
state_dict (Dict[str, torch.Tensor], optional) — A state dictionary to use instead of a state dictionary loaded from saved weights file.

This option can be used if you want to create a model from a pretrained configuration but load your own weights. In this case though, you should check if using save_pretrained() and from_pretrained() is not a simpler option.
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.
from_tf (bool, optional, defaults to False) — Load the model weights from a TensorFlow checkpoint save file (see docstring of pretrained_model_name_or_path argument).
from_flax (bool, optional, defaults to False) — Load the model weights from a Flax checkpoint save file (see docstring of pretrained_model_name_or_path argument).
ignore_mismatched_sizes (bool, optional, defaults to False) — Whether or not to raise an error if some of the weights from the checkpoint do not have the same size as the weights of the model (if for instance, you are instantiating a model with 10 labels from a checkpoint with 3 labels).
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 ot 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, or not specified, will use the token generated when running huggingface-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.

To test a pull request you made on the Hub, you can pass `revision=“refs/pr/“.
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.
_fast_init(bool, optional, defaults to True) — Whether or not to disable fast initialization.

One should only disable _fast_init to ensure backwards compatibility with transformers.__version__ < 4.6.0 for seeded model initialization. This argument will be removed at the next major version. See pull request 11471 for more information.

Parameters for big model inference

low_cpu_mem_usage(bool, optional) — Tries to not use more than 1x model size in CPU memory (including peak memory) while loading the model. This is an experimental feature and a subject to change at any moment.
torch_dtype (str or torch.dtype, optional) — Override the default torch.dtype and load the model under a specific dtype. The different options are:
1. torch.float16 or torch.bfloat16 or torch.float: load in a specified dtype, ignoring the model’s config.torch_dtype if one exists. If not specified
  - the model will get loaded in torch.float (fp32).
2. "auto" - A torch_dtype entry in the config.json file of the model will be attempted to be used. If this entry isn’t found then next check the dtype of the first weight in the checkpoint that’s of a floating point type and use that as dtype. This will load the model using the dtype it was saved in at the end of the training. It can’t be used as an indicator of how the model was trained. Since it could be trained in one of half precision dtypes, but saved in fp32.
For some models the dtype they were trained in is unknown - you may try to check the model’s paper or reach out to the authors and ask them to add this information to the model’s card and to insert the torch_dtype entry in config.json on the hub.
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.
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.
load_in_8bit (bool, optional, defaults to False) — If True, will convert the loaded model into mixed-8bit quantized model. To use this feature please install bitsandbytes compiled with your CUDA version by running pip install -i https://test.pypi.org/simple/ bitsandbytes-cudaXXX where XXX is your CUDA version (e.g. 11.6 = 116). Make also sure that you have enough GPU RAM to store half of the model size since the 8bit modules are not compiled and adapted for CPUs.
quantization_config (Dict, optional) — A dictionary of configuration parameters for the bitsandbytes library and loading the model using advanced features such as offloading in fp32 on CPU or on disk.
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.
variant (str, optional) — If specified load weights from variant filename, e.g. pytorch_model..bin. variant is ignored when using from_tf or from_flax.
kwargs (remaining dictionary of keyword arguments, optional) — Can be used to update the configuration object (after it being loaded) and initiate the model (e.g., output_attentions=True). Behaves differently depending on whether a config is provided or automatically loaded:
- If a configuration is provided with config, **kwargs will be directly passed to the underlying model’s __init__ method (we assume all relevant updates to the configuration have already been done)
- If a configuration is not provided, kwargs will be first passed to the configuration class initialization function (from_pretrained()). Each key of kwargs that corresponds to a configuration attribute will be used to override said attribute with the supplied kwargs value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s __init__ function.

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.

Activate the special “offline-mode” to use this method in a firewalled environment.

Examples:

>>> from transformers import BertConfig, BertModel

>>> # Download model and configuration from huggingface.co and cache.
>>> model = BertModel.from_pretrained("bert-base-uncased")
>>> # Model was saved using *save_pretrained('./test/saved_model/')* (for example purposes, not runnable).
>>> model = BertModel.from_pretrained("./test/saved_model/")
>>> # Update configuration during loading.
>>> model = BertModel.from_pretrained("bert-base-uncased", output_attentions=True)
>>> assert model.config.output_attentions == True
>>> # Loading from a TF checkpoint file instead of a PyTorch model (slower, for example purposes, not runnable).
>>> config = BertConfig.from_json_file("./tf_model/my_tf_model_config.json")
>>> model = BertModel.from_pretrained("./tf_model/my_tf_checkpoint.ckpt.index", from_tf=True, config=config)
>>> # Loading from a Flax checkpoint file instead of a PyTorch model (slower)
>>> model = BertModel.from_pretrained("bert-base-uncased", from_flax=True)

low_cpu_mem_usage algorithm:

This is an experimental function that loads the model using ~1x model size CPU memory

Here is how it works:

save which state_dict keys we have
drop state_dict before the model is created, since the latter takes 1x model size CPU memory
after the model has been instantiated switch to the meta device all params/buffers that are going to be replaced from the loaded state_dict
load state_dict 2nd time
replace the params/buffers from the state_dict

Currently, it can’t handle deepspeed ZeRO stage 3 and ignores loading errors

get_input_embeddings

( ) → nn.Module

Returns

nn.Module

A torch module mapping vocabulary to hidden states.

Returns the model’s input embeddings.

get_memory_footprint

( return_buffers = True )

Parameters

return_buffers (bool, optional, defaults to True) — Whether to return the size of the buffer tensors in the computation of the memory footprint. Buffers are tensors that do not require gradients and not registered as parameters. E.g. mean and std in batch norm layers. Please see: https://discuss.pytorch.org/t/what-pytorch-means-by-buffers/120266/2

Get the memory footprint of a model. This will return the memory footprint of the current model in bytes. Useful to benchmark the memory footprint of the current model and design some tests. Solution inspired from the PyTorch discussions: https://discuss.pytorch.org/t/gpu-memory-that-model-uses/56822/2

get_output_embeddings

( ) → nn.Module

Returns

nn.Module

A torch module mapping hidden states to vocabulary.

Returns the model’s output embeddings.

gradient_checkpointing_disable

( )

Deactivates gradient checkpointing for the current model.

Note that in other frameworks this feature can be referred to as “activation checkpointing” or “checkpoint activations”.

gradient_checkpointing_enable

( )

Activates gradient checkpointing for the current model.

Note that in other frameworks this feature can be referred to as “activation checkpointing” or “checkpoint activations”.

init_weights

( )

If needed prunes and maybe initializes weights. If using a custom PreTrainedModel, you need to implement any initialization logic in _init_weights.

post_init

( )

A method executed at the end of each Transformer model initialization, to execute code that needs the model’s modules properly initialized (such as weight initialization).

prune_heads

( heads_to_prune: typing.Dict[int, typing.List[int]] )

Parameters

heads_to_prune (Dict[int, List[int]]) — Dictionary with keys being selected layer indices (int) and associated values being the list of heads to prune in said layer (list of int). For instance {1: [0, 2], 2: [2, 3]} will prune heads 0 and 2 on layer 1 and heads 2 and 3 on layer 2.

Prunes heads of the base model.

register_for_auto_class

( auto_class = 'AutoModel' )

Parameters

auto_class (str or type, optional, defaults to "AutoModel") — The auto class to register this new model with.

Register this class with a given auto class. This should only be used for custom models as the ones in the library are already mapped with an auto class.

This API is experimental and may have some slight breaking changes in the next releases.

resize_token_embeddings

( new_num_tokens: typing.Optional[int] = None ) → torch.nn.Embedding

Parameters

new_num_tokens (int, optional) — The number of new tokens in the embedding matrix. Increasing the size will add newly initialized vectors at the end. Reducing the size will remove vectors from the end. If not provided or None, just returns a pointer to the input tokens torch.nn.Embedding module of the model without doing anything.

Returns

torch.nn.Embedding

Pointer to the input tokens Embeddings Module of the model.

Resizes input token embeddings matrix of the model if new_num_tokens != config.vocab_size.

Takes care of tying weights embeddings afterwards if the model class has a tie_weights() method.

save_pretrained

( save_directory: typing.Union[str, os.PathLike] is_main_process: bool = True state_dict: typing.Optional[dict] = None save_function: typing.Callable = <function save at 0x7fa160442c10> push_to_hub: bool = False max_shard_size: typing.Union[int, str] = '10GB' safe_serialization: bool = False variant: typing.Optional[str] = None **kwargs )

Parameters

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.
state_dict (nested dictionary of torch.Tensor) — The state dictionary of the model to save. Will default to self.state_dict(), but can be used to only save parts of the model or if special precautions need to be taken when recovering the state dictionary of a model (like when using model parallelism).
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.
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).
max_shard_size (int or str, optional, 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 "5MB").

If a single weight of the model is bigger than max_shard_size, it will be in its own checkpoint shard which will be bigger than max_shard_size.
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..bin.

kwargs — Additional key word arguments passed along to the push_to_hub() method.

Save a model and its configuration file to a directory, so that it can be re-loaded using the from_pretrained() class method.

set_input_embeddings

( value: Module )

Parameters

value (nn.Module) — A module mapping vocabulary to hidden states.

Set model’s input embeddings.

tie_weights

( )

Tie the weights between the input embeddings and the output embeddings.

If the torchscript flag is set in the configuration, can’t handle parameter sharing so we are cloning the weights instead.

Large model loading

In Transformers 4.20.0, the from_pretrained() method has been reworked to accommodate large models using Accelerate. This requires Accelerate >= 0.9.0 and PyTorch >= 1.9.0. Instead of creating the full model, then loading the pretrained weights inside it (which takes twice the size of the model in RAM, one for the randomly initialized model, one for the weights), there is an option to create the model as an empty shell, then only materialize its parameters when the pretrained weights are loaded.

This option can be activated with low_cpu_mem_usage=True. The model is first created on the Meta device (with empty weights) and the state dict is then loaded inside it (shard by shard in the case of a sharded checkpoint). This way the maximum RAM used is the full size of the model only.

from transformers import AutoModelForSeq2SeqLM

t0pp = AutoModelForSeq2SeqLM.from_pretrained("bigscience/T0pp", low_cpu_mem_usage=True)

Moreover, you can directly place the model on different devices if it doesn’t fully fit in RAM (only works for inference for now). With device_map="auto", Accelerate will determine where to put each layer to maximize the use of your fastest devices (GPUs) and offload the rest on the CPU, or even the hard drive if you don’t have enough GPU RAM (or CPU RAM). Even if the model is split across several devices, it will run as you would normally expect.

When passing a device_map, low_cpu_mem_usage is automatically set to True, so you don’t need to specify it:

from transformers import AutoModelForSeq2SeqLM

t0pp = AutoModelForSeq2SeqLM.from_pretrained("bigscience/T0pp", device_map="auto")

You can inspect how the model was split across devices by looking at its hf_device_map attribute:

t0pp.hf_device_map

{'shared': 0,
 'decoder.embed_tokens': 0,
 'encoder': 0,
 'decoder.block.0': 0,
 'decoder.block.1': 1,
 'decoder.block.2': 1,
 'decoder.block.3': 1,
 'decoder.block.4': 1,
 'decoder.block.5': 1,
 'decoder.block.6': 1,
 'decoder.block.7': 1,
 'decoder.block.8': 1,
 'decoder.block.9': 1,
 'decoder.block.10': 1,
 'decoder.block.11': 1,
 'decoder.block.12': 1,
 'decoder.block.13': 1,
 'decoder.block.14': 1,
 'decoder.block.15': 1,
 'decoder.block.16': 1,
 'decoder.block.17': 1,
 'decoder.block.18': 1,
 'decoder.block.19': 1,
 'decoder.block.20': 1,
 'decoder.block.21': 1,
 'decoder.block.22': 'cpu',
 'decoder.block.23': 'cpu',
 'decoder.final_layer_norm': 'cpu',
 'decoder.dropout': 'cpu',
 'lm_head': 'cpu'}

You can also write your own device map following the same format (a dictionary layer name to device). It should map all parameters of the model to a given device, but you don’t have to detail where all the submosules of one layer go if that layer is entirely on the same device. For instance, the following device map would work properly for T0pp (as long as you have the GPU memory):

device_map = {"shared": 0, "encoder": 0, "decoder": 1, "lm_head": 1}

Another way to minimize the memory impact of your model is to instantiate it at a lower precision dtype (like torch.float16) or use direct quantization techniques as described below.

Model Instantiation dtype

Under Pytorch a model normally gets instantiated with torch.float32 format. This can be an issue if one tries to load a model whose weights are in fp16, since it’d require twice as much memory. To overcome this limitation, you can either explicitly pass the desired dtype using torch_dtype argument:

model = T5ForConditionalGeneration.from_pretrained("t5", torch_dtype=torch.float16)

or, if you want the model to always load in the most optimal memory pattern, you can use the special value "auto", and then dtype will be automatically derived from the model’s weights:

model = T5ForConditionalGeneration.from_pretrained("t5", torch_dtype="auto")

Models instantiated from scratch can also be told which dtype to use with:

config = T5Config.from_pretrained("t5")
model = AutoModel.from_config(config)

Due to Pytorch design, this functionality is only available for floating dtypes.

ModuleUtilsMixin

class transformers.modeling_utils.ModuleUtilsMixin

( )

A few utilities for torch.nn.Modules, to be used as a mixin.

add_memory_hooks

( )

Add a memory hook before and after each sub-module forward pass to record increase in memory consumption.

Increase in memory consumption is stored in a mem_rss_diff attribute for each module and can be reset to zero with model.reset_memory_hooks_state().

estimate_tokens

( input_dict: typing.Dict[str, typing.Union[torch.Tensor, typing.Any]] ) → int

Parameters

inputs (dict) — The model inputs.

Returns

int

The total number of tokens.

Helper function to estimate the total number of tokens from the model inputs.

floating_point_ops

( input_dict: typing.Dict[str, typing.Union[torch.Tensor, typing.Any]] exclude_embeddings: bool = True ) → int

Parameters

batch_size (int) — The batch size for the forward pass.
sequence_length (int) — The number of tokens in each line of the batch.
exclude_embeddings (bool, optional, defaults to True) — Whether or not to count embedding and softmax operations.

Returns

int

The number of floating-point operations.

Get number of (optionally, non-embeddings) floating-point operations for the forward and backward passes of a batch with this transformer model. Default approximation neglects the quadratic dependency on the number of tokens (valid if 12 * d_model << sequence_length) as laid out in this paper section 2.1. Should be overridden for transformers with parameter re-use e.g. Albert or Universal Transformers, or if doing long-range modeling with very high sequence lengths.

get_extended_attention_mask

( attention_mask: Tensor input_shape: typing.Tuple[int] device: device = None dtype: torch.float32 = None )

Parameters

attention_mask (torch.Tensor) — Mask with ones indicating tokens to attend to, zeros for tokens to ignore.
input_shape (Tuple[int]) — The shape of the input to the model.

Makes broadcastable attention and causal masks so that future and masked tokens are ignored.

get_head_mask

( head_mask: typing.Optional[torch.Tensor] num_hidden_layers: int is_attention_chunked: bool = False )

Parameters

head_mask (torch.Tensor with shape [num_heads] or [num_hidden_layers x num_heads], optional) — The mask indicating if we should keep the heads or not (1.0 for keep, 0.0 for discard).
num_hidden_layers (int) — The number of hidden layers in the model. is_attention_chunked — (bool, optional, defaults to False): Whether or not the attentions scores are computed by chunks or not.

Prepare the head mask if needed.

invert_attention_mask

( encoder_attention_mask: Tensor ) → torch.Tensor

Parameters

encoder_attention_mask (torch.Tensor) — An attention mask.

Returns

torch.Tensor

The inverted attention mask.

Invert an attention mask (e.g., switches 0. and 1.).

num_parameters

( only_trainable: bool = False exclude_embeddings: bool = False ) → int

Parameters

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.

Get number of (optionally, trainable or non-embeddings) parameters in the module.

reset_memory_hooks_state

( )

Reset the mem_rss_diff attribute of each module (see add_memory_hooks()).

TFPreTrainedModel

class transformers.TFPreTrainedModel

( *args **kwargs )

Base class for all TF models.

TFPreTrainedModel takes care of storing the configuration of the models and handles methods for loading, downloading and saving models as well as a few methods common to all models to:

resize the input embeddings,
prune heads in the self-attention heads.

Class attributes (overridden by derived classes):

config_class (PretrainedConfig) — A subclass of PretrainedConfig to use as configuration class for this model architecture.
base_model_prefix (str) — A string indicating the attribute associated to the base model in derived classes of the same architecture adding modules on top of the base model.
main_input_name (str) — The name of the principal input to the model (often input_ids for NLP models, pixel_values for vision models and input_values for speech models).

push_to_hub

( repo_id: str use_temp_dir: typing.Optional[bool] = None commit_message: typing.Optional[str] = None private: typing.Optional[bool] = None max_shard_size: typing.Union[int, str, NoneType] = '10GB' use_auth_token: typing.Union[bool, str, NoneType] = None create_pr: bool = False **base_model_card_args )

Parameters

repo_id (str) — The name of the repository you want to push your model to. It should contain your organization name when pushing to a given organization.
use_temp_dir (bool, optional) — Whether or not to use a temporary directory to store the files saved before they are pushed to the Hub. Will default to True if there is no directory named like repo_id, False otherwise.
commit_message (str, optional) — Message to commit while pushing. Will default to "Upload model".
private (bool, optional) — Whether or not the repository created should be private.
use_auth_token (bool or str, optional) — The token to use as HTTP bearer authorization for remote files. If True, will use the token generated when running huggingface-cli login (stored in ~/.huggingface). Will default to True if repo_url is not specified.
max_shard_size (int or str, optional, defaults to "10GB") — Only applicable for models. 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 "5MB").
create_pr (bool, optional, defaults to False) — Whether or not to create a PR with the uploaded files or directly commit.

Upload the model files to the 🤗 Model Hub while synchronizing a local clone of the repo in repo_path_or_name.

Examples:

from transformers import TFAutoModel

model = TFAutoModel.from_pretrained("bert-base-cased")

# Push the model to your namespace with the name "my-finetuned-bert".
model.push_to_hub("my-finetuned-bert")

# Push the model to an organization with the name "my-finetuned-bert".
model.push_to_hub("huggingface/my-finetuned-bert")

can_generate

( ) → bool

Returns

bool

Whether this model can generate sequences with .generate().

Returns whether this model can generate sequences with .generate().

compile

( optimizer = 'rmsprop' loss = 'passthrough' metrics = None loss_weights = None weighted_metrics = None run_eagerly = None steps_per_execution = None **kwargs )

This is a thin wrapper that sets the model’s loss output head as the loss if the user does not specify a loss function themselves.

create_model_card

( output_dir model_name: str language: typing.Optional[str] = None license: typing.Optional[str] = None tags: typing.Optional[str] = None finetuned_from: typing.Optional[str] = None tasks: typing.Optional[str] = None dataset_tags: typing.Union[str, typing.List[str], NoneType] = None dataset: typing.Union[str, typing.List[str], NoneType] = None dataset_args: typing.Union[str, typing.List[str], NoneType] = None )

Parameters

output_dir (str or os.PathLike) — The folder in which to create the model card.
model_name (str, optional) — The name of the model.
language (str, optional) — The language of the model (if applicable)
license (str, optional) — The license of the model. Will default to the license of the pretrained model used, if the original model given to the Trainer comes from a repo on the Hub.
tags (str or List[str], optional) — Some tags to be included in the metadata of the model card.
finetuned_from (str, optional) — The name of the model used to fine-tune this one (if applicable). Will default to the name of the repo of the original model given to the Trainer (if it comes from the Hub).
tasks (str or List[str], optional) — One or several task identifiers, to be included in the metadata of the model card.
dataset_tags (str or List[str], optional) — One or several dataset tags, to be included in the metadata of the model card.
dataset (str or List[str], optional) — One or several dataset identifiers, to be included in the metadata of the model card.
dataset_args (str or List[str], optional) — One or several dataset arguments, to be included in the metadata of the model card.

Creates a draft of a model card using the information available to the Trainer.

eager_serving

( inputs )

Parameters

inputs (Dict[str, tf.Tensor]) — The input of the saved model as a dictionary of tensors.

Method used for serving the model. Intended not to be compiled with a tf.function decorator so that we can use it to generate multiple signatures later.

from_pretrained

( pretrained_model_name_or_path *model_args **kwargs )

Parameters

pretrained_model_name_or_path (str, optional) — Can be either:
- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co. Valid model ids can be located at the root-level, like bert-base-uncased, or namespaced under a user or organization name, like dbmdz/bert-base-german-cased.
- A path to a directory containing model weights saved using save_pretrained(), e.g., ./my_model_directory/.
- A path or url to a PyTorch state_dict save file (e.g, ./pt_model/pytorch_model.bin). In this case, from_pt should be set to True and a configuration object should be provided as config argument. This loading path is slower than converting the PyTorch model in a TensorFlow model using the provided conversion scripts and loading the TensorFlow model afterwards.
- None if you are both providing the configuration and state dictionary (resp. with keyword arguments config and state_dict).
model_args (sequence of positional arguments, optional) — All remaining positional arguments will be passed to the underlying model’s __init__ method.
config (Union[PretrainedConfig, str], optional) — Can be either:
- an instance of a class derived from PretrainedConfig,
- a string valid as input to from_pretrained().
Configuration for the model to use instead of an automatically loaded configuration. Configuration can be automatically loaded when:
- The model is a model provided by the library (loaded with the model id string of a pretrained model).
- The model was saved using save_pretrained() and is reloaded by supplying the save directory.
- The model is loaded by supplying a local directory as pretrained_model_name_or_path and a configuration JSON file named config.json is found in the directory.
from_pt (bool, optional, defaults to False) — Load the model weights from a PyTorch state_dict save file (see docstring of pretrained_model_name_or_path argument).
ignore_mismatched_sizes (bool, optional, defaults to False) — Whether or not to raise an error if some of the weights from the checkpoint do not have the same size as the weights of the model (if for instance, you are instantiating a model with 10 labels from a checkpoint with 3 labels).
cache_dir (str, 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 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 ot 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 (e.g., not try doanloading the model).
use_auth_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 huggingface-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.

Instantiate a pretrained TF 2.0 model from a pre-trained model configuration.

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.

Examples:

>>> from transformers import BertConfig, TFBertModel

>>> # Download model and configuration from huggingface.co and cache.
>>> model = TFBertModel.from_pretrained("bert-base-uncased")
>>> # Model was saved using *save_pretrained('./test/saved_model/')* (for example purposes, not runnable).
>>> model = TFBertModel.from_pretrained("./test/saved_model/")
>>> # Update configuration during loading.
>>> model = TFBertModel.from_pretrained("bert-base-uncased", output_attentions=True)
>>> assert model.config.output_attentions == True
>>> # Loading from a Pytorch model file instead of a TensorFlow checkpoint (slower, for example purposes, not runnable).
>>> config = BertConfig.from_json_file("./pt_model/my_pt_model_config.json")
>>> model = TFBertModel.from_pretrained("./pt_model/my_pytorch_model.bin", from_pt=True, config=config)

get_bias

( ) → tf.Variable

Returns

tf.Variable

The weights representing the bias, None if not an LM model.

Dict of bias attached to an LM head. The key represents the name of the bias attribute.

get_head_mask

( head_mask: typing.Optional[tensorflow.python.framework.ops.Tensor] num_hidden_layers: int )

Parameters

head_mask (tf.Tensor with shape [num_heads] or [num_hidden_layers x num_heads], optional) — The mask indicating if we should keep the heads or not (1.0 for keep, 0.0 for discard).
num_hidden_layers (int) — The number of hidden layers in the model.

Prepare the head mask if needed.

get_input_embeddings

( ) → tf.Variable

Returns

tf.Variable

The embeddings layer mapping vocabulary to hidden states.

Returns the model’s input embeddings layer.

get_lm_head

( ) → tf.keras.layers.Layer

Returns

tf.keras.layers.Layer

The LM head layer if the model has one, None if not.

The LM Head layer. This method must be overwritten by all the models that have a lm head.

get_output_embeddings

( ) → tf.Variable

Returns

tf.Variable

The new weights mapping vocabulary to hidden states.

Returns the model’s output embeddings

get_output_layer_with_bias

( ) → tf.keras.layers.Layer

Returns

tf.keras.layers.Layer

The layer that handles the bias, None if not an LM model.

Get the layer that handles a bias attribute in case the model has an LM head with weights tied to the embeddings

get_prefix_bias_name

( ) → str

Returns

str

The _prefix name of the bias.

Get the concatenated _prefix name of the bias from the model name to the parent layer

load_repo_checkpoint

( repo_path_or_name ) → dict

Parameters

repo_path_or_name (str) — Can either be a repository name for your {object} in the Hub or a path to a local folder (in which case the repository will have the name of that local folder).

Returns

dict

A dictionary of extra metadata from the checkpoint, most commonly an “epoch” count.

Loads a saved checkpoint (model weights and optimizer state) from a repo. Returns the current epoch count when the checkpoint was made.

prepare_tf_dataset

( dataset: datasets.Dataset batch_size: int = 8 shuffle: bool = True tokenizer: typing.Optional[ForwardRef('PreTrainedTokenizerBase')] = None collate_fn: typing.Optional[typing.Callable] = None collate_fn_args: typing.Union[typing.Dict[str, typing.Any], NoneType] = None drop_remainder: typing.Optional[bool] = None prefetch: bool = True ) → Dataset

Parameters

dataset (Any) — A [~datasets.Dataset] to be wrapped as a tf.data.Dataset.
batch_size (int, defaults to 8) — The size of batches to return.
shuffle (bool, defaults to True) — Whether to return samples from the dataset in random order. Usually True for training datasets and False for validation/test datasets.
tokenizer (PreTrainedTokenizerBase, optional) — A PreTrainedTokenizer that will be used to pad samples to create batches. Has no effect if a specific collate_fn is passed instead.
collate_fn (Callable, optional) — A function that collates samples from the dataset into a single batch. Defaults to DefaultDataCollator if no tokenizer is supplied or DataCollatorWithPadding if a tokenizer is passed.
collate_fn_args (Dict[str, Any], optional) — A dict of arguments to pass to the collate_fn alongside the list of samples.
drop_remainder (bool, optional) — Whether to drop the final batch, if the batch_size does not evenly divide the dataset length. Defaults to the same setting as shuffle.
prefetch (bool, defaults to True) — Whether to add prefetching to the end of the tf.data pipeline. This is almost always beneficial for performance, but can be disabled in edge cases.

Returns

Dataset

A tf.data.Dataset which is ready to pass to the Keras API.

Wraps a HuggingFace Dataset as a tf.data.Dataset with collation and batching. This method is designed to create a “ready-to-use” dataset that can be passed directly to Keras methods like fit() without further modification. The method will drop columns from the dataset if they don’t match input names for the model. If you want to specify the column names to return rather than using the names that match this model, we recommend using Dataset.to_tf_dataset() instead.

prune_heads

( heads_to_prune )

Parameters

heads_to_prune (Dict[int, List[int]]) — Dictionary with keys being selected layer indices (int) and associated values being the list of heads to prune in said layer (list of int). For instance {1: [0, 2], 2: [2, 3]} will prune heads 0 and 2 on layer 1 and heads 2 and 3 on layer 2.

Prunes heads of the base model.

register_for_auto_class

( auto_class = 'TFAutoModel' )

Parameters

auto_class (str or type, optional, defaults to "TFAutoModel") — The auto class to register this new model with.

Register this class with a given auto class. This should only be used for custom models as the ones in the library are already mapped with an auto class.

This API is experimental and may have some slight breaking changes in the next releases.

resize_token_embeddings

( new_num_tokens: typing.Optional[int] = None ) → tf.Variable or tf.keras.layers.Embedding

Parameters

new_num_tokens (int, optional) — The number of new tokens in the embedding matrix. Increasing the size will add newly initialized vectors at the end. Reducing the size will remove vectors from the end. If not provided or None, just returns a pointer to the input tokens without doing anything.

Returns

tf.Variable or tf.keras.layers.Embedding

Pointer to the input tokens of the model.

Resizes input token embeddings matrix of the model if new_num_tokens != config.vocab_size.

Takes care of tying weights embeddings afterwards if the model class has a tie_weights() method.

save_pretrained

( save_directory saved_model = False version = 1 push_to_hub = False signatures = None max_shard_size: typing.Union[int, str] = '10GB' create_pr: bool = False safe_serialization: bool = False **kwargs )

Parameters

save_directory (str) — Directory to which to save. Will be created if it doesn’t exist.
saved_model (bool, optional, defaults to False) — If the model has to be saved in saved model format as well or not.
version (int, optional, defaults to 1) — The version of the saved model. A saved model needs to be versioned in order to be properly loaded by TensorFlow Serving as detailed in the official documentation https://www.tensorflow.org/tfx/serving/serving_basic
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).
signatures (dict or tf.function, optional) — Model’s signature used for serving. This will be passed to the signatures argument of model.save().
max_shard_size (int or str, optional, 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 "5MB").

If a single weight of the model is bigger than max_shard_size, it will be in its own checkpoint shard which will be bigger than max_shard_size.
create_pr (bool, optional, defaults to False) — Whether or not to create a PR with the uploaded files or directly commit.
safe_serialization (bool, optional, defaults to False) — Whether to save the model using safetensors or the traditional PyTorch way (that uses pickle).

kwargs — Additional key word arguments passed along to the push_to_hub() method.

Save a model and its configuration file to a directory, so that it can be re-loaded using the from_pretrained() class method.

serving

( inputs )

Parameters

inputs (Dict[str, tf.Tensor]) — The input of the saved model as a dictionary of tensors.

Method used for serving the model.

serving_output

( output )

Parameters

output (TFBaseModelOutput) — The output returned by the model.

Prepare the output of the saved model. Each model must implement this function.

set_bias

( value )

Parameters

value (Dict[tf.Variable]) — All the new bias attached to an LM head.

Set all the bias in the LM head.

set_input_embeddings

( value )

Parameters

value (tf.Variable) — The new weights mapping hidden states to vocabulary.

Set model’s input embeddings

set_output_embeddings

( value )

Parameters

value (tf.Variable) — The new weights mapping hidden states to vocabulary.

Set model’s output embeddings

test_step

( data )

A modification of Keras’s default train_step that correctly handles matching outputs to labels for our models and supports directly training on the loss output head. In addition, it ensures input keys are copied to the labels where appropriate. It will also copy label keys into the input dict when using the dummy loss, to ensure that they are available to the model during the forward pass.

train_step

( data )

A modification of Keras’s default train_step that correctly handles matching outputs to labels for our models and supports directly training on the loss output head. In addition, it ensures input keys are copied to the labels where appropriate. It will also copy label keys into the input dict when using the dummy loss, to ensure that they are available to the model during the forward pass.

TFModelUtilsMixin

class transformers.modeling_tf_utils.TFModelUtilsMixin

( )

A few utilities for tf.keras.Model, to be used as a mixin.

num_parameters

( only_trainable: bool = False ) → int

Parameters

only_trainable (bool, optional, defaults to False) — Whether or not to return only the number of trainable parameters

Returns

int

The number of parameters.

Get the number of (optionally, trainable) parameters in the model.

FlaxPreTrainedModel

class transformers.FlaxPreTrainedModel

( config: PretrainedConfig module: Module input_shape: typing.Tuple = (1, 1) seed: int = 0 dtype: dtype = <class 'jax.numpy.float32'> _do_init: bool = True )

Base class for all models.

FlaxPreTrainedModel takes care of storing the configuration of the models and handles methods for loading, downloading and saving models.

Class attributes (overridden by derived classes):

config_class (PretrainedConfig) — A subclass of PretrainedConfig to use as configuration class for this model architecture.
base_model_prefix (str) — A string indicating the attribute associated to the base model in derived classes of the same architecture adding modules on top of the base model.
main_input_name (str) — The name of the principal input to the model (often input_ids for NLP models, pixel_values for vision models and input_values for speech models).

push_to_hub

( repo_id: str use_temp_dir: typing.Optional[bool] = None commit_message: typing.Optional[str] = None private: typing.Optional[bool] = None use_auth_token: typing.Union[bool, str, NoneType] = None max_shard_size: typing.Union[int, str, NoneType] = '10GB' create_pr: bool = False **deprecated_kwargs )

Parameters

repo_id (str) — The name of the repository you want to push your model to. It should contain your organization name when pushing to a given organization.
use_temp_dir (bool, optional) — Whether or not to use a temporary directory to store the files saved before they are pushed to the Hub. Will default to True if there is no directory named like repo_id, False otherwise.
commit_message (str, optional) — Message to commit while pushing. Will default to "Upload model".
private (bool, optional) — Whether or not the repository created should be private.
use_auth_token (bool or str, optional) — The token to use as HTTP bearer authorization for remote files. If True, will use the token generated when running huggingface-cli login (stored in ~/.huggingface). Will default to True if repo_url is not specified.
max_shard_size (int or str, optional, defaults to "10GB") — Only applicable for models. 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 "5MB").
create_pr (bool, optional, defaults to False) — Whether or not to create a PR with the uploaded files or directly commit.

Upload the model checkpoint to the 🤗 Model Hub while synchronizing a local clone of the repo in repo_path_or_name.

Examples:

from transformers import FlaxAutoModel

model = FlaxAutoModel.from_pretrained("bert-base-cased")

# Push the model to your namespace with the name "my-finetuned-bert".
model.push_to_hub("my-finetuned-bert")

# Push the model to an organization with the name "my-finetuned-bert".
model.push_to_hub("huggingface/my-finetuned-bert")

can_generate

( )

Returns whether this model can generate sequences with .generate(). Returns: bool: Whether this model can generate sequences with .generate().

from_pretrained

( pretrained_model_name_or_path: typing.Union[str, os.PathLike] dtype: dtype = <class 'jax.numpy.float32'> *model_args **kwargs )

Parameters

pretrained_model_name_or_path (str or os.PathLike) — Can be either:
- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co. Valid model ids can be located at the root-level, like bert-base-uncased, or namespaced under a user or organization name, like dbmdz/bert-base-german-cased.
- A path to a directory containing model weights saved using save_pretrained(), e.g., ./my_model_directory/.
- A path or url to a pt index checkpoint file (e.g, ./tf_model/model.ckpt.index). In this case, from_pt should be set to True.
dtype (jax.numpy.dtype, optional, defaults to jax.numpy.float32) — The data type of the computation. Can be one of jax.numpy.float32, jax.numpy.float16 (on GPUs) and jax.numpy.bfloat16 (on TPUs).

This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If specified all the computation will be performed with the given dtype.

Note that this only specifies the dtype of the computation and does not influence the dtype of model parameters.

If you wish to change the dtype of the model parameters, see to_fp16() and to_bf16().
model_args (sequence of positional arguments, optional) — All remaining positional arguments will be passed to the underlying model’s __init__ method.
config (Union[PretrainedConfig, str, os.PathLike], optional) — Can be either:
- an instance of a class derived from PretrainedConfig,
- a string or path valid as input to from_pretrained().
Configuration for the model to use instead of an automatically loaded configuration. Configuration can be automatically loaded when:
- The model is a model provided by the library (loaded with the model id string of a pretrained model).
- The model was saved using save_pretrained() and is reloaded by supplying the save directory.
- The model is loaded by supplying a local directory as pretrained_model_name_or_path and a configuration JSON file named config.json is found in the 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.
from_pt (bool, optional, defaults to False) — Load the model weights from a PyTorch checkpoint save file (see docstring of pretrained_model_name_or_path argument).
ignore_mismatched_sizes (bool, optional, defaults to False) — Whether or not to raise an error if some of the weights from the checkpoint do not have the same size as the weights of the model (if for instance, you are instantiating a model with 10 labels from a checkpoint with 3 labels).
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.
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, or not specified, will use the token generated when running huggingface-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.

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

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.

Examples:

>>> from transformers import BertConfig, FlaxBertModel

>>> # Download model and configuration from huggingface.co and cache.
>>> model = FlaxBertModel.from_pretrained("bert-base-cased")
>>> # Model was saved using *save_pretrained('./test/saved_model/')* (for example purposes, not runnable).
>>> model = FlaxBertModel.from_pretrained("./test/saved_model/")
>>> # Loading from a PyTorch checkpoint file instead of a PyTorch model (slower, for example purposes, not runnable).
>>> config = BertConfig.from_json_file("./pt_model/config.json")
>>> model = FlaxBertModel.from_pretrained("./pt_model/pytorch_model.bin", from_pt=True, config=config)

load_flax_sharded_weights

( shard_files ) → Dict

Parameters

shard_files (List[str] — The list of shard files to load.

Returns

Dict

A nested dictionary of the model parameters, in the expected format for flax models : {'model': {'params': {'...'}}}.

This is the same as flax.serialization.from_bytes (https:lax.readthedocs.io/en/latest/_modules/flax/serialization.html#from_bytes) but for a sharded checkpoint.

This load is performed efficiently: each checkpoint shard is loaded one by one in RAM and deleted after being loaded in the model.

register_for_auto_class

( auto_class = 'FlaxAutoModel' )

Parameters

auto_class (str or type, optional, defaults to "FlaxAutoModel") — The auto class to register this new model with.

Register this class with a given auto class. This should only be used for custom models as the ones in the library are already mapped with an auto class.

This API is experimental and may have some slight breaking changes in the next releases.

save_pretrained

( save_directory: typing.Union[str, os.PathLike] params = None push_to_hub = False max_shard_size = '10GB' **kwargs )

Parameters

save_directory (str or os.PathLike) — Directory to which to save. Will be created if it doesn’t 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).
max_shard_size (int or str, optional, 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 "5MB").

If a single weight of the model is bigger than max_shard_size, it will be in its own checkpoint shard which will be bigger than max_shard_size.

kwargs — Additional key word arguments passed along to the push_to_hub() method.

Save a model and its configuration file to a directory, so that it can be re-loaded using the [from_pretrained()](/docs/transformers/v4.28.1/en/main_classes/model#transformers.FlaxPreTrainedModel.from_pretrained) class method

to_bf16

( params: typing.Union[typing.Dict, flax.core.frozen_dict.FrozenDict] mask: typing.Any = None )

Parameters

params (Union[Dict, FrozenDict]) — A PyTree of model parameters.
mask (Union[Dict, FrozenDict]) — A PyTree with same structure as the params tree. The leaves should be booleans, True for params you want to cast, and should be False for those you want to skip.

Cast the floating-point params to jax.numpy.bfloat16. This returns a new params tree and does not cast the params in place.

This method can be used on TPU to explicitly convert the model parameters to bfloat16 precision to do full half-precision training or to save weights in bfloat16 for inference in order to save memory and improve speed.

Examples:

>>> from transformers import FlaxBertModel

>>> # load model
>>> model = FlaxBertModel.from_pretrained("bert-base-cased")
>>> # By default, the model parameters will be in fp32 precision, to cast these to bfloat16 precision
>>> model.params = model.to_bf16(model.params)
>>> # If you want don't want to cast certain parameters (for example layer norm bias and scale)
>>> # then pass the mask as follows
>>> from flax import traverse_util

>>> model = FlaxBertModel.from_pretrained("bert-base-cased")
>>> flat_params = traverse_util.flatten_dict(model.params)
>>> mask = {
...     path: (path[-2] != ("LayerNorm", "bias") and path[-2:] != ("LayerNorm", "scale"))
...     for path in flat_params
... }
>>> mask = traverse_util.unflatten_dict(mask)
>>> model.params = model.to_bf16(model.params, mask)

to_fp16

( params: typing.Union[typing.Dict, flax.core.frozen_dict.FrozenDict] mask: typing.Any = None )

Parameters

params (Union[Dict, FrozenDict]) — A PyTree of model parameters.
mask (Union[Dict, FrozenDict]) — A PyTree with same structure as the params tree. The leaves should be booleans, True for params you want to cast, and should be False for those you want to skip

Cast the floating-point parmas to jax.numpy.float16. This returns a new params tree and does not cast the params in place.

This method can be used on GPU to explicitly convert the model parameters to float16 precision to do full half-precision training or to save weights in float16 for inference in order to save memory and improve speed.

Examples:

>>> from transformers import FlaxBertModel

>>> # load model
>>> model = FlaxBertModel.from_pretrained("bert-base-cased")
>>> # By default, the model params will be in fp32, to cast these to float16
>>> model.params = model.to_fp16(model.params)
>>> # If you want don't want to cast certain parameters (for example layer norm bias and scale)
>>> # then pass the mask as follows
>>> from flax import traverse_util

>>> model = FlaxBertModel.from_pretrained("bert-base-cased")
>>> flat_params = traverse_util.flatten_dict(model.params)
>>> mask = {
...     path: (path[-2] != ("LayerNorm", "bias") and path[-2:] != ("LayerNorm", "scale"))
...     for path in flat_params
... }
>>> mask = traverse_util.unflatten_dict(mask)
>>> model.params = model.to_fp16(model.params, mask)

to_fp32

( params: typing.Union[typing.Dict, flax.core.frozen_dict.FrozenDict] mask: typing.Any = None )

Parameters

params (Union[Dict, FrozenDict]) — A PyTree of model parameters.
mask (Union[Dict, FrozenDict]) — A PyTree with same structure as the params tree. The leaves should be booleans, True for params you want to cast, and should be False for those you want to skip

Cast the floating-point parmas to jax.numpy.float32. This method can be used to explicitly convert the model parameters to fp32 precision. This returns a new params tree and does not cast the params in place.

Examples:

>>> from transformers import FlaxBertModel

>>> # Download model and configuration from huggingface.co
>>> model = FlaxBertModel.from_pretrained("bert-base-cased")
>>> # By default, the model params will be in fp32, to illustrate the use of this method,
>>> # we'll first cast to fp16 and back to fp32
>>> model.params = model.to_f16(model.params)
>>> # now cast back to fp32
>>> model.params = model.to_fp32(model.params)

Pushing to the Hub

class transformers.utils.PushToHubMixin

( )

A Mixin containing the functionality to push a model or tokenizer to the hub.

push_to_hub

( repo_id: str use_temp_dir: typing.Optional[bool] = None commit_message: typing.Optional[str] = None private: typing.Optional[bool] = None use_auth_token: typing.Union[bool, str, NoneType] = None max_shard_size: typing.Union[int, str, NoneType] = '10GB' create_pr: bool = False **deprecated_kwargs )

Parameters

repo_id (str) — The name of the repository you want to push your {object} to. It should contain your organization name when pushing to a given organization.
use_temp_dir (bool, optional) — Whether or not to use a temporary directory to store the files saved before they are pushed to the Hub. Will default to True if there is no directory named like repo_id, False otherwise.
commit_message (str, optional) — Message to commit while pushing. Will default to "Upload {object}".
private (bool, optional) — Whether or not the repository created should be private.
use_auth_token (bool or str, optional) — The token to use as HTTP bearer authorization for remote files. If True, will use the token generated when running huggingface-cli login (stored in ~/.huggingface). Will default to True if repo_url is not specified.
max_shard_size (int or str, optional, defaults to "10GB") — Only applicable for models. 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 "5MB").
create_pr (bool, optional, defaults to False) — Whether or not to create a PR with the uploaded files or directly commit.

Upload the {object_files} to the 🤗 Model Hub while synchronizing a local clone of the repo in repo_path_or_name.

Examples:

from transformers import {object_class}

{object} = {object_class}.from_pretrained("bert-base-cased")

# Push the {object} to your namespace with the name "my-finetuned-bert".
{object}.push_to_hub("my-finetuned-bert")

# Push the {object} to an organization with the name "my-finetuned-bert".
{object}.push_to_hub("huggingface/my-finetuned-bert")

Sharded checkpoints

transformers.modeling_utils.load_sharded_checkpoint

( model folder strict = True prefer_safe = True ) → NamedTuple

Parameters

model (torch.nn.Module) — The model in which to load the checkpoint.
folder (str or os.PathLike) — A path to a folder containing the sharded checkpoint.
strict (bool, *optional, defaults to True`) — Whether to strictly enforce that the keys in the model state dict match the keys in the sharded checkpoint.
prefer_safe (bool, optional, defaults to False) — If both safetensors and PyTorch save files are present in checkpoint and prefer_safe is True, the safetensors files will be loaded. Otherwise, PyTorch files are always loaded when possible.

Returns

NamedTuple

A named tuple with missing_keys and unexpected_keys fields

missing_keys is a list of str containing the missing keys
unexpected_keys is a list of str containing the unexpected keys

This is the same as torch.nn.Module.load_state_dict but for a sharded checkpoint.

This load is performed efficiently: each checkpoint shard is loaded one by one in RAM and deleted after being loaded in the model.

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