espnet2.asr.encoder.longformer_encoder.LongformerEncoder

source

class espnet2.asr.encoder.longformer_encoder.LongformerEncoder(input_size: int, output_size: int = 256, attention_heads: int = 4, linear_units: int = 2048, num_blocks: int = 6, dropout_rate: float = 0.1, positional_dropout_rate: float = 0.1, attention_dropout_rate: float = 0.0, input_layer: str = 'conv2d', normalize_before: bool = True, concat_after: bool = False, positionwise_layer_type: str = 'linear', positionwise_conv_kernel_size: int = 3, macaron_style: bool = False, rel_pos_type: str = 'legacy', pos_enc_layer_type: str = 'abs_pos', selfattention_layer_type: str = 'lf_selfattn', activation_type: str = 'swish', use_cnn_module: bool = True, zero_triu: bool = False, cnn_module_kernel: int = 31, padding_idx: int = -1, interctc_layer_idx: List[int] = [], interctc_use_conditioning: bool = False, attention_windows: list = [100, 100, 100, 100, 100, 100], attention_dilation: list = [1, 1, 1, 1, 1, 1], attention_mode: str = 'sliding_chunks')

Bases: ConformerEncoder

Longformer SA Conformer encoder module.

• Parameters:
  • input_size (int) – Input dimension.
  • output_size (int) – Dimension of attention.
  • attention_heads (int) – The number of heads of multi head attention.
  • linear_units (int) – The number of units of position-wise feed forward.
  • num_blocks (int) – The number of decoder blocks.
  • dropout_rate (float) – Dropout rate.
  • attention_dropout_rate (float) – Dropout rate in attention.
  • positional_dropout_rate (float) – Dropout rate after adding positional encoding.
  • input_layer (Union *[*str , torch.nn.Module ]) – Input layer type.
  • normalize_before (bool) – Whether to use layer_norm before the first block.
  • concat_after (bool) – Whether to concat attention layer’s input and output. If True, additional linear will be applied. i.e. x -> x + linear(concat(x, att(x))) If False, no additional linear will be applied. i.e. x -> x + att(x)
  • positionwise_layer_type (str) – “linear”, “conv1d”, or “conv1d-linear”.
  • positionwise_conv_kernel_size (int) – Kernel size of positionwise conv1d layer.
  • rel_pos_type (str) – Whether to use the latest relative positional encoding or the legacy one. The legacy relative positional encoding will be deprecated in the future. More Details can be found in https://github.com/espnet/espnet/pull/2816.
  • encoder_pos_enc_layer_type (str) – Encoder positional encoding layer type.
  • encoder_attn_layer_type (str) – Encoder attention layer type.
  • activation_type (str) – Encoder activation function type.
  • macaron_style (bool) – Whether to use macaron style for positionwise layer.
  • use_cnn_module (bool) – Whether to use convolution module.
  • zero_triu (bool) – Whether to zero the upper triangular part of attention matrix.
  • cnn_module_kernel (int) – Kernerl size of convolution module.
  • padding_idx (int) – Padding idx for input_layer=embed.
  • attention_windows (list) – Layer-wise attention window sizes for longformer self-attn
  • attention_dilation (list) – Layer-wise attention dilation sizes for longformer self-attn
  • attention_mode (str) – Implementation for longformer self-attn. Default=”sliding_chunks” Choose ‘n2’, ‘tvm’ or ‘sliding_chunks’. More details in https://github.com/allenai/longformer

Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(xs_pad: Tensor, ilens: Tensor, prev_states: Tensor | None = None, ctc: CTC | None = None, return_all_hs: bool = False) → Tuple[Tensor, Tensor, Tensor | None]

Calculate forward propagation.

• Parameters:
  • xs_pad (torch.Tensor) – Input tensor (#batch, L, input_size).
  • ilens (torch.Tensor) – Input length (#batch).
  • prev_states (torch.Tensor) – Not to be used now.
  • ctc (CTC) – ctc module for intermediate CTC loss
  • return_all_hs (bool) – whether to return all hidden states
• Returns: Output tensor (#batch, L, output_size). torch.Tensor: Output length (#batch). torch.Tensor: Not to be used now.
• Return type: torch.Tensor

output_size() → int