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模型

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模型

基类 PreTrainedModel、TFPreTrainedModel 和 FlaxPreTrainedModel 实现了从本地文件或目录加载/保存模型的常用方法，或者从库上提供的预训练模型配置（从 HuggingFace 的 AWS S3 存储库下载）加载模型。

PreTrainedModel 和 TFPreTrainedModel 还实现了一些所有模型共有的方法：

  • 在向量词嵌入增加新词汇时调整输入标记（token）的大小
  • 对模型的注意力头进行修剪。

其他的通用方法在 ModuleUtilsMixin（用于 PyTorch 模型）和 ~modeling_tf_utils.TFModuleUtilsMixin（用于 TensorFlow 模型）中定义；文本生成方面的方法则定义在 GenerationMixin（用于 PyTorch 模型）、TFGenerationMixin（用于 TensorFlow 模型）和 FlaxGenerationMixin（用于 Flax/JAX 模型）中。

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 token: typing.Union[bool, str, NoneType] = None max_shard_size: typing.Union[int, str, NoneType] = '5GB' create_pr: bool = False safe_serialization: bool = True revision: str = None commit_description: str = None tags: typing.Optional[typing.List[str]] = None **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 to make the repo private. If None (default), the repo will be public unless the organization’s default is private. This value is ignored if the repo already exists.
  • 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 "5GB") — 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"). We default it to "5GB" so that users can easily load models on free-tier Google Colab instances without any CPU OOM issues.
  • 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 True) — Whether or not to convert the model weights in safetensors format for safer serialization.
  • revision (str, optional) — Branch to push the uploaded files to.
  • commit_description (str, optional) — The description of the commit that will be created
  • tags (List[str], optional) — List of tags to push on the Hub.

Upload the model file to the 🤗 Model Hub.

Examples:

from transformers import AutoModel

model = AutoModel.from_pretrained("google-bert/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")

add_model_tags

< >

( tags: typing.Union[typing.List[str], str] )

Parameters

  • tags (Union[List[str], str]) — The desired tags to inject in the model

Add custom tags into the model that gets pushed to the Hugging Face Hub. Will not overwrite existing tags in the model.

Examples:

from transformers import AutoModel

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

model.add_model_tags(["custom", "custom-bert"])

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

can_generate

< >

( ) → bool

Returns

bool

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

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

dequantize

< >

( )

Potentially dequantize the model in case it has been quantized by a quantization method that support dequantization.

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 config: typing.Union[transformers.configuration_utils.PretrainedConfig, str, os.PathLike, NoneType] = None cache_dir: typing.Union[str, os.PathLike, NoneType] = None ignore_mismatched_sizes: bool = False force_download: bool = False local_files_only: bool = False token: typing.Union[str, bool, NoneType] = None revision: str = 'main' use_safetensors: typing.Optional[bool] = None weights_only: bool = True **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.
    • 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:

    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 — Deprecated and ignored. All downloads are now resumed by default when possible. Will be removed in v5 of Transformers.
  • 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).
  • 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/<pr_number>".
  • 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.
  • attn_implementation (str, optional) — The attention implementation to use in the model (if relevant). Can be any of "eager" (manual implementation of the attention), "sdpa" (using F.scaled_dot_product_attention), or "flash_attention_2" (using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual "eager" implementation.

Parameters for big model inference

  • low_cpu_mem_usage(bool, optional) — Tries not to use more than 1x model size in CPU memory (including peak memory) while loading the model. Generally should be combined with a device_map (such as "auto") for best results. This is an experimental feature and a subject to change at any moment.

    If the model weights are in the same precision as the model loaded in, `low_cpu_mem_usage` (without `device_map`) is redundant and will not provide any benefit in regards to CPU memory usage. However, this should still be enabled if you are passing in a `device_map`.
  • 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.

    3. A string that is a valid torch.dtype. E.g. “float32” loads the model in torch.float32, “float16” loads in torch.float16 etc.

    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]] or int or 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. If we only pass the device (e.g., "cpu", "cuda:1", "mps", or a GPU ordinal rank like 1) on which the model will be allocated, the device map will map the entire model to this device. Passing device_map = 0 means put the whole model on GPU 0.

    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.
  • offload_buffers (bool, optional) — Whether or not to offload the buffers with the model parameters.
  • quantization_config (Union[QuantizationConfigMixin,Dict], optional) — A dictionary of configuration parameters or a QuantizationConfigMixin object for quantization (e.g bitsandbytes, gptq). There may be other quantization-related kwargs, including load_in_4bit and load_in_8bit, which are parsed by QuantizationConfigParser. Supported only for bitsandbytes quantizations and not preferred. consider inserting all such arguments into quantization_config instead.
  • 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.
  • use_safetensors (bool, optional, defaults to None) — Whether or not to use safetensors checkpoints. Defaults to None. If not specified and safetensors is not installed, it will be set to False.
  • weights_only (bool, optional, defaults to True) — Indicates whether unpickler should be restricted to loading only tensors, primitive types, dictionaries and any types added via torch.serialization.add_safe_globals(). When set to False, we can load wrapper tensor subclass weights.
  • 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.

If model weights are the same precision as the base model (and is a supported model), weights will be lazily loaded in using the meta device and brought into memory once an input is passed through that layer regardless of low_cpu_mem_usage.

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("google-bert/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("google-bert/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("google-bert/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:

  1. save which state_dict keys we have
  2. drop state_dict before the model is created, since the latter takes 1x model size CPU memory
  3. 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
  4. load state_dict 2nd time
  5. replace the params/buffers from the state_dict

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

get_compiled_call

< >

( compile_config: CompileConfig )

Return a torch.compile‘d version of self.__call__. This is useful to dynamically choose between non-compiled/compiled forward during inference, especially to switch between prefill (where we don’t want to use compiled version to avoid recomputing the graph with new shapes) and iterative decoding (where we want the speed-ups of compiled version with static shapes).

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

< >

( gradient_checkpointing_kwargs = None )

Parameters

  • gradient_checkpointing_kwargs (dict, optional) — Additional keyword arguments passed along to the torch.utils.checkpoint.checkpoint function.

Activates gradient checkpointing for the current model.

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

We pass the __call__ method of the modules instead of forward because __call__ attaches all the hooks of the module. https://discuss.pytorch.org/t/any-different-between-model-input-and-model-forward-input/3690/2

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 pad_to_multiple_of: typing.Optional[int] = None mean_resizing: bool = True ) → torch.nn.Embedding

Parameters

  • new_num_tokens (int, optional) — The new number of 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.
  • pad_to_multiple_of (int, optional) — If set will pad the embedding matrix to a multiple of the provided value.If new_num_tokens is set to None will just pad the embedding to a multiple of pad_to_multiple_of.

    This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta), or on TPUs which benefit from having sequence lengths be a multiple of 128. For more details about this, or help on choosing the correct value for resizing, refer to this guide: https://docs.nvidia.com/deeplearning/performance/dl-performance-matrix-multiplication/index.html#requirements-tc

  • mean_resizing (bool) — Whether to initialize the added embeddings from a multivariate normal distribution that has old embeddings’ mean and covariance or to initialize them with a normal distribution that has a mean of zero and std equals config.initializer_range.

    Setting mean_resizing to True is useful when increasing the size of the embeddings of causal language models, where the generated tokens’ probabilities won’t be affected by the added embeddings because initializing the new embeddings with the old embeddings’ mean will reduce the kl-divergence between the next token probability before and after adding the new embeddings. Refer to this article for more information: https://nlp.stanford.edu/~johnhew/vocab-expansion.html

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.

reverse_bettertransformer

< >

( ) → PreTrainedModel

Returns

PreTrainedModel

The model converted back to the original modeling.

Reverts the transformation from to_bettertransformer() so that the original modeling is used, for example in order to save the model.

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 0x7f3c17942f80> push_to_hub: bool = False max_shard_size: typing.Union[int, str] = '5GB' safe_serialization: bool = True variant: typing.Optional[str] = None token: typing.Union[str, bool, NoneType] = None save_peft_format: bool = True **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 "5GB") — 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"). We default it to 5GB in order for models to be able to run easily on free-tier google colab instances without CPU OOM issues.

    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 True) — 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.
  • 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).
  • save_peft_format (bool, optional, defaults to True) — For backward compatibility with PEFT library, in case adapter weights are attached to the model, all keys of the state dict of adapters needs to be pre-pended with base_model.model. Advanced users can disable this behaviours by setting save_peft_format to False.
  • kwargs (Dict[str, Any], optional) — 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.

tensor_parallel

< >

( device_mesh )

Parameters

  • device_mesh (torch.distributed.DeviceMesh) — The device mesh to use for tensor parallelism.

Tensor parallelize the model across the given device mesh.

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.

to_bettertransformer

< >

( ) → PreTrainedModel

Returns

PreTrainedModel

The model converted to BetterTransformer.

Converts the model to use PyTorch’s native attention implementation, integrated to Transformers through Optimum library. Only a subset of all Transformers models are supported.

PyTorch’s attention fastpath allows to speed up inference through kernel fusions and the use of nested tensors. Detailed benchmarks can be found in this blog post.

warn_if_padding_and_no_attention_mask

< >

( input_ids attention_mask )

Shows a one-time warning if the input_ids appear to contain padding and no attention mask was given.

大模型加载

在 Transformers 4.20.0 中，from_pretrained() 方法已重新设计，以适应使用 Accelerate 加载大型模型的场景。这需要您使用的 Accelerate 和 PyTorch 版本满足： Accelerate >= 0.9.0， PyTorch >= 1.9.0。除了创建完整模型，然后在其中加载预训练权重（这会占用两倍于模型大小的内存空间，一个用于随机初始化模型，一个用于预训练权重），我们提供了一种选项，将模型创建为空壳，然后只有在加载预训练权重时才实例化其参数。

您可以使用 low_cpu_mem_usage=True 激活此选项。首先，在 Meta 设备上创建模型（带有空权重），然后将状态字典加载到其中（在分片检查点的情况下逐片加载）。这样，最大使用的内存占用仅为模型的完整大小。

from transformers import AutoModelForSeq2SeqLM

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

此外，如果内存不足以放下加载整个模型（目前仅适用于推理），您可以直接将模型放置在不同的设备上。使用 device_map="auto"，Accelerate 将确定将每一层放置在哪个设备上，以最大化使用最快的设备（GPU），并将其余部分卸载到 CPU，甚至硬盘上（如果您没有足够的 GPU 内存 或 CPU 内存）。即使模型分布在几个设备上，它也将像您通常期望的那样运行。

在传递 device_map 时，low_cpu_mem_usage 会自动设置为 True，因此您不需要指定它：

from transformers import AutoModelForSeq2SeqLM

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

您可以通过 hf_device_map 属性来查看模型是如何在设备上分割的：

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'}

您还可以按照相同的格式（一个层名称到设备的映射关系的字典）编写自己的设备映射规则。它应该将模型的所有参数映射到给定的设备上，如果该层的所有子模块都在同一设备上，您不必详细说明其中所有子模块的位置。例如，以下设备映射对于 T0pp 将正常工作（只要您有 GPU 内存）：

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

另一种减少模型内存影响的方法是以较低精度的 dtype（例如 torch.float16）实例化它，或者使用下面介绍的直接量化技术。

模型实例化 dtype

在 PyTorch 下，模型通常以 torch.float32 格式实例化。如果尝试加载权重为 fp16 的模型，这可能会导致问题，因为它将需要两倍的内存。为了克服此限制，您可以使用 torch_dtype 参数显式传递所需的 dtype：

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

或者，如果您希望模型始终以最优的内存模式加载，则可以使用特殊值 "auto"，然后 dtype 将自动从模型的权重中推导出：

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

也可以通过以下方式告知从头开始实例化的模型要使用哪种 dtype：

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

由于 PyTorch 的设计，此功能仅适用于浮点类型。

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

< >

( config *inputs **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: Optional[bool] = None commit_message: Optional[str] = None private: Optional[bool] = None max_shard_size: Optional[Union[int, str]] = '10GB' token: Optional[Union[bool, str]] = None use_auth_token: Optional[Union[bool, str]] = 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 to make the repo private. If None (default), the repo will be public unless the organization’s default is private. This value is ignored if the repo already exists.
  • 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("google-bert/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 = 'auto_with_warning' 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: Optional[str] = None license: Optional[str] = None tags: Optional[str] = None finetuned_from: Optional[str] = None tasks: Optional[str] = None dataset_tags: Optional[Union[str, List[str]]] = None dataset: Optional[Union[str, List[str]]] = None dataset_args: Optional[Union[str, List[str]]] = 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.

from_pretrained

< >

( pretrained_model_name_or_path: Optional[Union[str, os.PathLike]] *model_args config: Optional[Union[PretrainedConfig, str, os.PathLike]] = None cache_dir: Optional[Union[str, os.PathLike]] = None ignore_mismatched_sizes: bool = False force_download: bool = False local_files_only: bool = False token: Optional[Union[str, bool]] = None revision: str = 'main' use_safetensors: bool = None **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.
    • 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:

    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 — Deprecated and ignored. All downloads are now resumed by default when possible. Will be removed in v5 of Transformers.
  • 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 downloading the model).
  • 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("google-bert/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("google-bert/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: tf.Tensor | None 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

< >

( ) → keras.layers.Layer

Returns

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

< >

( ) → keras.layers.Layer

Returns

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

prepare_tf_dataset

< >

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

Parameters

  • dataset (Any) — A [~datasets.Dataset] to be wrapped as a tf.data.Dataset.
  • batch_size (int, optional, 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: Optional[int] = None ) → tf.Variable or 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 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: Union[int, str] = '5GB' create_pr: bool = False safe_serialization: bool = False token: Optional[Union[str, bool]] = None **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 TensorFlow way (that uses h5).
  • 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).
  • kwargs (Dict[str, Any], optional) — 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

  • Method used for serving the model. Does not have a specific signature, but will be specialized as concrete —
  • functions when saving with save_pretrained. — inputs (Dict[str, tf.Tensor]): The input of the saved model as a dictionary of tensors.

serving_output

< >

( output )

Prepare the output of the saved model. Can be overridden if specific serving modifications are required.

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 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 token: typing.Union[bool, str, NoneType] = None max_shard_size: typing.Union[int, str, NoneType] = '5GB' create_pr: bool = False safe_serialization: bool = True revision: str = None commit_description: str = None tags: typing.Optional[typing.List[str]] = None **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 to make the repo private. If None (default), the repo will be public unless the organization’s default is private. This value is ignored if the repo already exists.
  • 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 "5GB") — 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"). We default it to "5GB" so that users can easily load models on free-tier Google Colab instances without any CPU OOM issues.
  • 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 True) — Whether or not to convert the model weights in safetensors format for safer serialization.
  • revision (str, optional) — Branch to push the uploaded files to.
  • commit_description (str, optional) — The description of the commit that will be created
  • tags (List[str], optional) — List of tags to push on the Hub.

Upload the model checkpoint to the 🤗 Model Hub.

Examples:

from transformers import FlaxAutoModel

model = FlaxAutoModel.from_pretrained("google-bert/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 config: typing.Union[transformers.configuration_utils.PretrainedConfig, str, os.PathLike, NoneType] = None cache_dir: typing.Union[str, os.PathLike, NoneType] = None ignore_mismatched_sizes: bool = False force_download: bool = False local_files_only: bool = False token: typing.Union[str, bool, NoneType] = None revision: str = 'main' **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.
    • 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:

    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 — Deprecated and ignored. All downloads are now resumed by default when possible. Will be removed in v5 of Transformers.
  • 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).
  • 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("google-bert/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' token: typing.Union[str, bool, NoneType] = None safe_serialization: bool = False **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.
  • 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).
  • kwargs (Dict[str, Any], optional) — Additional key word arguments passed along to the push_to_hub() method.
  • safe_serialization (bool, optional, defaults to False) — Whether to save the model using safetensors or through msgpack.

Save a model and its configuration file to a directory, so that it can be re-loaded using the [from_pretrained()](/docs/transformers/v4.47.0/zh/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("google-bert/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("google-bert/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("google-bert/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("google-bert/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("google-bert/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)

推送到 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 token: typing.Union[bool, str, NoneType] = None max_shard_size: typing.Union[int, str, NoneType] = '5GB' create_pr: bool = False safe_serialization: bool = True revision: str = None commit_description: str = None tags: typing.Optional[typing.List[str]] = None **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 to make the repo private. If None (default), the repo will be public unless the organization’s default is private. This value is ignored if the repo already exists.
  • 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 "5GB") — 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"). We default it to "5GB" so that users can easily load models on free-tier Google Colab instances without any CPU OOM issues.
  • 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 True) — Whether or not to convert the model weights in safetensors format for safer serialization.
  • revision (str, optional) — Branch to push the uploaded files to.
  • commit_description (str, optional) — The description of the commit that will be created
  • tags (List[str], optional) — List of tags to push on the Hub.

Upload the {object_files} to the 🤗 Model Hub.

Examples:

from transformers import {object_class}

{object} = {object_class}.from_pretrained("google-bert/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")

分片检查点

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.

< > Update on GitHub

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