Transformers documentation
カスタムレイヤーとユーティリティ
カスタムレイヤーとユーティリティ
このページには、ライブラリで使用されるすべてのカスタム レイヤーと、モデリングに提供されるユーティリティ関数がリストされます。
これらのほとんどは、ライブラリ内のモデルのコードを研究する場合にのみ役に立ちます。
Pytorch custom modules
class transformers.Conv1D
< source >( nf nx )
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
< source >( 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 theinput_tensorsshould be chunked. - input_tensors (
Tuple[torch.Tensor]) — The input tensors offorward_fnwhich 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
< source >( 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.
transformers.prune_layer
< source >( 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
< source >( layer: Conv1D index: torch.LongTensor dim: int = 1 ) → Conv1D
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
< source >( layer: nn.Linear index: torch.LongTensor dim: int = 0 ) → torch.nn.Linear
Prune a linear layer to keep only entries in index.
Used to remove heads.
TensorFlow custom layers
class transformers.modeling_tf_utils.TFConv1D
< source >( 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__ofkeras.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
< source >( 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) — IfTrue, the projection outputs toconfig.num_labelsclasses (otherwise toconfig.hidden_size). -
summary_activation (
Optional[str]) — Set to"tanh"to add a tanh activation to the output, another string orNonewill 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__ofkeras.layers.Layer.
Compute a single vector summary of a sequence hidden states.
TensorFlow loss functions
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.
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.
Loss function suitable for multiple choice tasks.
Loss function suitable for question answering.
Loss function suitable for sequence classification.
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
Creates a keras.initializers.TruncatedNormal with the given range.
transformers.modeling_tf_utils.keras_serializable
< source >( )
Decorate a Keras Layer class to support Keras serialization.
This is done by:
- Adding a
transformers_configdict to the Keras config dictionary inget_config(called by Keras at serialization time. - Wrapping
__init__to accept thattransformers_configdict (passed by Keras at deserialization time) and convert it to a config object for the actual layer initializer. - 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_objectsin the call tokeras.models.load_model.
transformers.shape_list
< source >( tensor: typing.Union[tensorflow.python.framework.tensor.Tensor, numpy.ndarray] ) → List[int]
Deal with dynamic shape in tensorflow cleanly.