#
Custom Layers and Utilities

This page lists all the custom layers used by the library, as well as the utility functions and classes it provides for modeling.

Most of those are only useful if you are studying the code of the models in the library.

##
Layers

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class transformers.GradientCheckpointingLayer

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( *args **kwargs )

Base class for layers with gradient checkpointing.

This class enables gradient checkpointing functionality for a layer. By default, gradient checkpointing is disabled (`gradient_checkpointing = False`). When `model.set_gradient_checkpointing()` is called, gradient checkpointing is enabled by setting `gradient_checkpointing = True` and assigning a checkpointing function to `_gradient_checkpointing_func`.

Important:

When using gradient checkpointing with `use_reentrant=True`, inputs that require gradients (e.g. hidden states) must be passed as positional arguments (`*args`) rather than keyword arguments to properly propagate gradients.

Example:

    >>> # Correct - hidden_states passed as positional arg
    >>> out = self.layer(hidden_states, attention_mask=attention_mask)

    >>> # Incorrect - hidden_states passed as keyword arg
    >>> out = self.layer(hidden_states=hidden_states, attention_mask=attention_mask)

##
Attention Functions

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class transformers.AttentionInterface

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( )

Dict-like object keeping track of allowed attention functions. You can easily add a new attention function with a call to `register()`. If a model needs to locally overwrite an existing attention function, say `sdpa`, it needs to declare a new instance of this class inside the `modeling_<model>.py`, and declare it on that instance.

####
register

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( key: str value: typing.Callable )

##
Rotary Position Embedding Functions

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transformers.dynamic_rope_update

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( rope_forward )

Parameters

- rope_forward (Callable) — The forward pass of the RoPE implementation.

Decorator function to update the RoPE parameters in the forward pass, if the model is using a dynamic RoPE (i.e. a RoPE implementation that may recompute its frequencies in the forward pass).

##
Pytorch custom modules

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class transformers.Conv1D

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( nf nx )

Parameters

- nf (`int`) — The number of output features.
- nx (`int`) — The number of input features.

1D-convolutional layer as defined by Radford et al. for OpenAI GPT (and also used in GPT-2).

Basically works like a linear layer but the weights are transposed.

##
PyTorch Helper Functions

####
transformers.apply_chunking_to_forward

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( forward_fn: Callable[..., torch.Tensor] chunk_size: int chunk_dim: int *input_tensors ) →
`torch.Tensor`

Parameters

- forward_fn (`Callable[..., torch.Tensor]`) — The forward function of the model.
- chunk_size (`int`) — The chunk size of a chunked tensor: `num_chunks = len(input_tensors[0]) / chunk_size`.
- chunk_dim (`int`) — The dimension over which the `input_tensors` should be chunked.
- input_tensors (`Tuple[torch.Tensor]`) — The input tensors of `forward_fn` which will be chunked

Returns

`torch.Tensor`

A tensor with the same shape as the `forward_fn` would have given if applied`.

This function chunks the `input_tensors` into smaller input tensor parts of size `chunk_size` over the dimension `chunk_dim`. It then applies a layer `forward_fn` to each chunk independently to save memory.

If the `forward_fn` is independent across the `chunk_dim` this function will yield the same result as directly applying `forward_fn` to `input_tensors`.

Examples:

    # rename the usual forward() fn to forward_chunk()
    def forward_chunk(self, hidden_states):
        hidden_states = self.decoder(hidden_states)
        return hidden_states

    # implement a chunked forward function
    def forward(self, hidden_states):
        return apply_chunking_to_forward(self.forward_chunk, self.chunk_size_lm_head, self.seq_len_dim, hidden_states)

####
transformers.pytorch_utils.find_pruneable_heads_and_indices

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( heads: list[int] n_heads: int head_size: int already_pruned_heads: set[int] ) →
`Tuple[Set[int], torch.LongTensor]`

Parameters

- heads (`List[int]`) — List of the indices of heads to prune.
- n_heads (`int`) — The number of heads in the model.
- head_size (`int`) — The size of each head.
- already_pruned_heads (`Set[int]`) — A set of already pruned heads.

Returns

`Tuple[Set[int], torch.LongTensor]`

A tuple with the indices of heads to prune taking `already_pruned_heads` into account and the indices of rows/columns to keep in the layer weight.

Finds the heads and their indices taking `already_pruned_heads` into account.

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transformers.prune_layer

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( layer: nn.Linear | Conv1D index: torch.LongTensor dim: int | None = None ) →
`torch.nn.Linear` or Conv1D

Parameters

- layer (`Union[torch.nn.Linear, Conv1D]`) — The layer to prune.
- index (`torch.LongTensor`) — The indices to keep in the layer.
- dim (`int`, optional) — The dimension on which to keep the indices.

Returns

`torch.nn.Linear` or Conv1D

The pruned layer as a new layer with `requires_grad=True`.

Prune a Conv1D or linear layer to keep only entries in index.

Used to remove heads.

####
transformers.pytorch_utils.prune_conv1d_layer

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( layer: Conv1D index: torch.LongTensor dim: int = 1 ) →
Conv1D

Parameters

- layer (Conv1D) — The layer to prune.
- index (`torch.LongTensor`) — The indices to keep in the layer.
- dim (`int`, optional, defaults to 1) — The dimension on which to keep the indices.

Returns

Conv1D

The pruned layer as a new layer with `requires_grad=True`.

Prune a Conv1D layer to keep only entries in index. A Conv1D work as a Linear layer (see e.g. BERT) but the weights are transposed.

Used to remove heads.

####
transformers.pytorch_utils.prune_linear_layer

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( layer: nn.Linear index: torch.LongTensor dim: int = 0 ) →
`torch.nn.Linear`

Parameters

- layer (`torch.nn.Linear`) — The layer to prune.
- index (`torch.LongTensor`) — The indices to keep in the layer.
- dim (`int`, optional, defaults to 0) — The dimension on which to keep the indices.

Returns

`torch.nn.Linear`

The pruned layer as a new layer with `requires_grad=True`.

Prune a linear layer to keep only entries in index.

Used to remove heads.

##
TensorFlow custom layers

###
class transformers.modeling_tf_utils.TFConv1D

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( nf nx initializer_range = 0.02 **kwargs )

Parameters

- nf (`int`) — The number of output features.
- nx (`int`) — The number of input features.
- initializer_range (`float`, optional, defaults to 0.02) — The standard deviation to use to initialize the weights.
- kwargs (`Dict[str, Any]`, optional) — Additional keyword arguments passed along to the `__init__` of `keras.layers.Layer`.

1D-convolutional layer as defined by Radford et al. for OpenAI GPT (and also used in GPT-2).

Basically works like a linear layer but the weights are transposed.

###
class transformers.TFSequenceSummary

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( config: PretrainedConfig initializer_range: float = 0.02 **kwargs )

Parameters

- config (PretrainedConfig) — The config used by the model. Relevant arguments in the config class of the model are (refer to the actual config class of your model for the default values it uses):

  - summary_type (`str`) — The method to use to make this summary. Accepted values are:

    - `"last"` — Take the last token hidden state (like XLNet)
    - `"first"` — Take the first token hidden state (like Bert)
    - `"mean"` — Take the mean of all tokens hidden states
    - `"cls_index"` — Supply a Tensor of classification token position (GPT/GPT-2)
    - `"attn"` — Not implemented now, use multi-head attention

  - summary_use_proj (`bool`) — Add a projection after the vector extraction.

  - summary_proj_to_labels (`bool`) — If `True`, the projection outputs to `config.num_labels` classes (otherwise to `config.hidden_size`).

  - summary_activation (`Optional[str]`) — Set to `"tanh"` to add a tanh activation to the output, another string or `None` will add no activation.

  - summary_first_dropout (`float`) — Optional dropout probability before the projection and activation.

  - summary_last_dropout (`float`)— Optional dropout probability after the projection and activation.

- initializer_range (`float`, optional, defaults to 0.02) — The standard deviation to use to initialize the weights.
- kwargs (`Dict[str, Any]`, optional) — Additional keyword arguments passed along to the `__init__` of `keras.layers.Layer`.

Compute a single vector summary of a sequence hidden states.

##
TensorFlow loss functions

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class transformers.modeling_tf_utils.TFCausalLanguageModelingLoss

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( )

Loss function suitable for causal language modeling (CLM), that is, the task of guessing the next token.

Any label of -100 will be ignored (along with the corresponding logits) in the loss computation.

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class transformers.modeling_tf_utils.TFMaskedLanguageModelingLoss

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( )

Loss function suitable for masked language modeling (MLM), that is, the task of guessing the masked tokens.

Any label of -100 will be ignored (along with the corresponding logits) in the loss computation.

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class transformers.modeling_tf_utils.TFMultipleChoiceLoss

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( )

Loss function suitable for multiple choice tasks.

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class transformers.modeling_tf_utils.TFQuestionAnsweringLoss

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( )

Loss function suitable for question answering.

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class transformers.modeling_tf_utils.TFSequenceClassificationLoss

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( )

Loss function suitable for sequence classification.

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class transformers.modeling_tf_utils.TFTokenClassificationLoss

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( )

Loss function suitable for token classification.

Any label of -100 will be ignored (along with the corresponding logits) in the loss computation.

##
TensorFlow Helper Functions

####
transformers.modeling_tf_utils.get_initializer

< source >

( initializer_range: float = 0.02 ) →
`keras.initializers.TruncatedNormal`

Parameters

- initializer_range (float, defaults to 0.02) — Standard deviation of the initializer range.

Returns

`keras.initializers.TruncatedNormal`

The truncated normal initializer.

Creates a `keras.initializers.TruncatedNormal` with the given range.

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transformers.modeling_tf_utils.keras_serializable

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( )

Parameters

- cls (a `keras.layers.Layers subclass`) — Typically a `TF.MainLayer` class in this project, in general must accept a `config` argument to its initializer.

Decorate a Keras Layer class to support Keras serialization.

This is done by:

1. Adding a `transformers_config` dict to the Keras config dictionary in `get_config` (called by Keras at serialization time.
2. Wrapping `__init__` to accept that `transformers_config` dict (passed by Keras at deserialization time) and convert it to a config object for the actual layer initializer.
3. Registering the class as a custom object in Keras (if the Tensorflow version supports this), so that it does not need to be supplied in `custom_objects` in the call to `keras.models.load_model`.

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transformers.shape_list

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( tensor: typing.Union[tensorflow.python.framework.tensor.Tensor, numpy.ndarray] ) →
`List[int]`

Parameters

- tensor (`tf.Tensor` or `np.ndarray`) — The tensor we want the shape of.

Returns

`List[int]`

The shape of the tensor as a list.

Deal with dynamic shape in tensorflow cleanly.

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