Source code for espnet2.asr.encoder.longformer_encoder

# Copyright 2020 Tomoki Hayashi
#  Apache 2.0  (

"""Conformer encoder definition."""

from typing import List, Optional, Tuple

import torch
from typeguard import typechecked

from espnet2.asr.ctc import CTC
from espnet2.asr.encoder.conformer_encoder import ConformerEncoder
from espnet.nets.pytorch_backend.conformer.convolution import ConvolutionModule
from espnet.nets.pytorch_backend.conformer.encoder_layer import EncoderLayer
from espnet.nets.pytorch_backend.nets_utils import get_activation, make_pad_mask
from espnet.nets.pytorch_backend.transformer.embedding import PositionalEncoding
from espnet.nets.pytorch_backend.transformer.layer_norm import LayerNorm
from espnet.nets.pytorch_backend.transformer.multi_layer_conv import (
from espnet.nets.pytorch_backend.transformer.positionwise_feed_forward import (
from espnet.nets.pytorch_backend.transformer.repeat import repeat
from espnet.nets.pytorch_backend.transformer.subsampling import (

[docs]class LongformerEncoder(ConformerEncoder): """Longformer SA Conformer encoder module. Args: 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 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 """ @typechecked def __init__( self, 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", ): super().__init__(input_size) self._output_size = output_size activation = get_activation(activation_type) if pos_enc_layer_type == "abs_pos": pos_enc_class = PositionalEncoding else: raise ValueError( "incorrect or unknown pos_enc_layer: " + pos_enc_layer_type + "Use abs_pos" ) if len(attention_dilation) != num_blocks: raise ValueError( "incorrect attention_dilation parameter of length" + str(len(attention_dilation)) + " does not match num_blocks" + str(num_blocks) ) if len(attention_windows) != num_blocks: raise ValueError( "incorrect attention_windows parameter of length" + str(len(attention_windows)) + " does not match num_blocks" + str(num_blocks) ) if attention_mode != "tvm" and max(attention_dilation) != 1: raise ValueError( "incorrect attention mode for dilation: " + attention_mode + "Use attention_mode=tvm with Cuda Kernel" ) if input_layer == "linear": self.embed = torch.nn.Sequential( torch.nn.Linear(input_size, output_size), torch.nn.LayerNorm(output_size), torch.nn.Dropout(dropout_rate), pos_enc_class(output_size, positional_dropout_rate), ) elif input_layer == "conv2d": self.embed = Conv2dSubsampling( input_size, output_size, dropout_rate, pos_enc_class(output_size, positional_dropout_rate), ) elif input_layer == "conv2d1": self.embed = Conv2dSubsampling1( input_size, output_size, dropout_rate, pos_enc_class(output_size, positional_dropout_rate), ) elif input_layer == "conv2d2": self.embed = Conv2dSubsampling2( input_size, output_size, dropout_rate, pos_enc_class(output_size, positional_dropout_rate), ) elif input_layer == "conv2d6": self.embed = Conv2dSubsampling6( input_size, output_size, dropout_rate, pos_enc_class(output_size, positional_dropout_rate), ) elif input_layer == "conv2d8": self.embed = Conv2dSubsampling8( input_size, output_size, dropout_rate, pos_enc_class(output_size, positional_dropout_rate), ) elif input_layer == "embed": self.embed = torch.nn.Sequential( torch.nn.Embedding(input_size, output_size, padding_idx=padding_idx), pos_enc_class(output_size, positional_dropout_rate), ) elif isinstance(input_layer, torch.nn.Module): self.embed = torch.nn.Sequential( input_layer, pos_enc_class(output_size, positional_dropout_rate), ) elif input_layer is None: self.embed = torch.nn.Sequential( pos_enc_class(output_size, positional_dropout_rate) ) else: raise ValueError("unknown input_layer: " + input_layer) self.normalize_before = normalize_before if positionwise_layer_type == "linear": positionwise_layer = PositionwiseFeedForward positionwise_layer_args = ( output_size, linear_units, dropout_rate, activation, ) elif positionwise_layer_type == "conv1d": positionwise_layer = MultiLayeredConv1d positionwise_layer_args = ( output_size, linear_units, positionwise_conv_kernel_size, dropout_rate, ) elif positionwise_layer_type == "conv1d-linear": positionwise_layer = Conv1dLinear positionwise_layer_args = ( output_size, linear_units, positionwise_conv_kernel_size, dropout_rate, ) else: raise NotImplementedError("Support only linear or conv1d.") self.selfattention_layer_type = selfattention_layer_type if selfattention_layer_type == "lf_selfattn": assert pos_enc_layer_type == "abs_pos" from longformer.longformer import LongformerConfig from espnet.nets.pytorch_backend.transformer.longformer_attention import ( LongformerAttention, ) encoder_selfattn_layer = LongformerAttention config = LongformerConfig( attention_window=attention_windows, attention_dilation=attention_dilation, autoregressive=False, num_attention_heads=attention_heads, hidden_size=output_size, attention_probs_dropout_prob=dropout_rate, attention_mode=attention_mode, ) encoder_selfattn_layer_args = (config,) else: raise ValueError( "incompatible or unknown encoder_attn_layer: " + selfattention_layer_type + " Use lf_selfattn" ) convolution_layer = ConvolutionModule convolution_layer_args = (output_size, cnn_module_kernel, activation) self.encoders = repeat( num_blocks, lambda layer_id: EncoderLayer( output_size, encoder_selfattn_layer(*(encoder_selfattn_layer_args + (layer_id,))), positionwise_layer(*positionwise_layer_args), positionwise_layer(*positionwise_layer_args) if macaron_style else None, convolution_layer(*convolution_layer_args) if use_cnn_module else None, dropout_rate, normalize_before, concat_after, ), ) if self.normalize_before: self.after_norm = LayerNorm(output_size) self.interctc_layer_idx = interctc_layer_idx if len(interctc_layer_idx) > 0: assert 0 < min(interctc_layer_idx) and max(interctc_layer_idx) < num_blocks self.interctc_use_conditioning = interctc_use_conditioning self.conditioning_layer = None
[docs] def output_size(self) -> int: return self._output_size
[docs] def forward( self, xs_pad: torch.Tensor, ilens: torch.Tensor, prev_states: torch.Tensor = None, ctc: CTC = None, return_all_hs: bool = False, ) -> Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]: """Calculate forward propagation. Args: 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: torch.Tensor: Output tensor (#batch, L, output_size). torch.Tensor: Output length (#batch). torch.Tensor: Not to be used now. """ masks = (~make_pad_mask(ilens)[:, None, :]).to(xs_pad.device) if ( isinstance(self.embed, Conv2dSubsampling) or isinstance(self.embed, Conv2dSubsampling1) or isinstance(self.embed, Conv2dSubsampling2) or isinstance(self.embed, Conv2dSubsampling6) or isinstance(self.embed, Conv2dSubsampling8) ): short_status, limit_size = check_short_utt(self.embed, xs_pad.size(1)) if short_status: raise TooShortUttError( f"has {xs_pad.size(1)} frames and is too short for subsampling " + f"(it needs more than {limit_size} frames), return empty results", xs_pad.size(1), limit_size, ) xs_pad, masks = self.embed(xs_pad, masks) else: xs_pad = self.embed(xs_pad) if self.selfattention_layer_type == "lf_selfattn": seq_len = xs_pad.shape[1] attention_window = ( max([x.self_attn.attention_window for x in self.encoders]) * 2 ) padding_len = ( attention_window - seq_len % attention_window ) % attention_window xs_pad = torch.nn.functional.pad( xs_pad, (0, 0, 0, padding_len), "constant", 0 ) masks = torch.nn.functional.pad(masks, (0, padding_len), "constant", False) intermediate_outs = [] if len(self.interctc_layer_idx) == 0: for layer_idx, encoder_layer in enumerate(self.encoders): xs_pad, masks = encoder_layer(xs_pad, masks) if return_all_hs: intermediate_outs.append(xs_pad) else: for layer_idx, encoder_layer in enumerate(self.encoders): xs_pad, masks = encoder_layer(xs_pad, masks) if layer_idx + 1 in self.interctc_layer_idx: encoder_out = xs_pad if isinstance(encoder_out, tuple): encoder_out = encoder_out[0] # intermediate outputs are also normalized if self.normalize_before: encoder_out = self.after_norm(encoder_out) intermediate_outs.append((layer_idx + 1, encoder_out)) if self.interctc_use_conditioning: ctc_out = ctc.softmax(encoder_out) if isinstance(xs_pad, tuple): x, pos_emb = xs_pad x = x + self.conditioning_layer(ctc_out) xs_pad = (x, pos_emb) else: xs_pad = xs_pad + self.conditioning_layer(ctc_out) if isinstance(xs_pad, tuple): xs_pad = xs_pad[0] if self.normalize_before: xs_pad = self.after_norm(xs_pad) olens = masks.squeeze(1).sum(1) if len(intermediate_outs) > 0: return (xs_pad, intermediate_outs), olens, None return xs_pad, olens, None