Source code for espnet2.asr.encoder.contextual_block_transformer_encoder

# Copyright 2020 Emiru Tsunoo
#  Apache 2.0  (http://www.apache.org/licenses/LICENSE-2.0)

"""Encoder definition."""
import math
from typing import Optional, Tuple

import torch
from typeguard import check_argument_types

from espnet2.asr.encoder.abs_encoder import AbsEncoder
from espnet.nets.pytorch_backend.nets_utils import make_pad_mask
from espnet.nets.pytorch_backend.transformer.attention import MultiHeadedAttention
from espnet.nets.pytorch_backend.transformer.contextual_block_encoder_layer import (
    ContextualBlockEncoderLayer,
)
from espnet.nets.pytorch_backend.transformer.embedding import StreamPositionalEncoding
from espnet.nets.pytorch_backend.transformer.layer_norm import LayerNorm
from espnet.nets.pytorch_backend.transformer.multi_layer_conv import (
    Conv1dLinear,
    MultiLayeredConv1d,
)
from espnet.nets.pytorch_backend.transformer.positionwise_feed_forward import (
    PositionwiseFeedForward,
)
from espnet.nets.pytorch_backend.transformer.repeat import repeat
from espnet.nets.pytorch_backend.transformer.subsampling_without_posenc import (
    Conv2dSubsamplingWOPosEnc,
)


[docs]class ContextualBlockTransformerEncoder(AbsEncoder): """Contextual Block Transformer encoder module. Details in Tsunoo et al. "Transformer ASR with contextual block processing" (https://arxiv.org/abs/1910.07204) Args: input_size: input dim output_size: dimension of attention attention_heads: the number of heads of multi head attention linear_units: the number of units of position-wise feed forward num_blocks: the number of encoder blocks dropout_rate: dropout rate attention_dropout_rate: dropout rate in attention positional_dropout_rate: dropout rate after adding positional encoding input_layer: input layer type pos_enc_class: PositionalEncoding or ScaledPositionalEncoding normalize_before: whether to use layer_norm before the first block concat_after: 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: linear of conv1d positionwise_conv_kernel_size: kernel size of positionwise conv1d layer padding_idx: padding_idx for input_layer=embed block_size: block size for contextual block processing hop_Size: hop size for block processing look_ahead: look-ahead size for block_processing init_average: whether to use average as initial context (otherwise max values) ctx_pos_enc: whether to use positional encoding to the context vectors """ 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: Optional[str] = "conv2d", pos_enc_class=StreamPositionalEncoding, normalize_before: bool = True, concat_after: bool = False, positionwise_layer_type: str = "linear", positionwise_conv_kernel_size: int = 1, padding_idx: int = -1, block_size: int = 40, hop_size: int = 16, look_ahead: int = 16, init_average: bool = True, ctx_pos_enc: bool = True, ): assert check_argument_types() super().__init__() self._output_size = output_size self.pos_enc = pos_enc_class(output_size, positional_dropout_rate) 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), torch.nn.ReLU(), ) self.subsample = 1 elif input_layer == "conv2d": self.embed = Conv2dSubsamplingWOPosEnc( input_size, output_size, dropout_rate, kernels=[3, 3], strides=[2, 2] ) self.subsample = 4 elif input_layer == "conv2d6": self.embed = Conv2dSubsamplingWOPosEnc( input_size, output_size, dropout_rate, kernels=[3, 5], strides=[2, 3] ) self.subsample = 6 elif input_layer == "conv2d8": self.embed = Conv2dSubsamplingWOPosEnc( input_size, output_size, dropout_rate, kernels=[3, 3, 3], strides=[2, 2, 2], ) self.subsample = 8 elif input_layer == "embed": self.embed = torch.nn.Sequential( torch.nn.Embedding(input_size, output_size, padding_idx=padding_idx), ) self.subsample = 1 elif input_layer is None: self.embed = None self.subsample = 1 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, ) 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.encoders = repeat( num_blocks, lambda lnum: ContextualBlockEncoderLayer( output_size, MultiHeadedAttention( attention_heads, output_size, attention_dropout_rate ), positionwise_layer(*positionwise_layer_args), dropout_rate, num_blocks, normalize_before, concat_after, ), ) if self.normalize_before: self.after_norm = LayerNorm(output_size) # for block processing self.block_size = block_size self.hop_size = hop_size self.look_ahead = look_ahead self.init_average = init_average self.ctx_pos_enc = ctx_pos_enc
[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, is_final=True, infer_mode=False, ) -> Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]: """Embed positions in tensor. Args: xs_pad: input tensor (B, L, D) ilens: input length (B) prev_states: Not to be used now. infer_mode: whether to be used for inference. This is used to distinguish between forward_train (train and validate) and forward_infer (decode). Returns: position embedded tensor and mask """ if self.training or not infer_mode: return self.forward_train(xs_pad, ilens, prev_states) else: return self.forward_infer(xs_pad, ilens, prev_states, is_final)
[docs] def forward_train( self, xs_pad: torch.Tensor, ilens: torch.Tensor, prev_states: torch.Tensor = None, ) -> Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]: """Embed positions in tensor. Args: xs_pad: input tensor (B, L, D) ilens: input length (B) prev_states: Not to be used now. Returns: position embedded tensor and mask """ masks = (~make_pad_mask(ilens)[:, None, :]).to(xs_pad.device) if isinstance(self.embed, Conv2dSubsamplingWOPosEnc): xs_pad, masks = self.embed(xs_pad, masks) elif self.embed is not None: xs_pad = self.embed(xs_pad) # create empty output container total_frame_num = xs_pad.size(1) ys_pad = xs_pad.new_zeros(xs_pad.size()) past_size = self.block_size - self.hop_size - self.look_ahead # block_size could be 0 meaning infinite # apply usual encoder for short sequence if self.block_size == 0 or total_frame_num <= self.block_size: xs_pad, masks, _, _, _, _, _ = self.encoders( self.pos_enc(xs_pad), masks, False, None, None ) if self.normalize_before: xs_pad = self.after_norm(xs_pad) olens = masks.squeeze(1).sum(1) return xs_pad, olens, None # start block processing cur_hop = 0 block_num = math.ceil( float(total_frame_num - past_size - self.look_ahead) / float(self.hop_size) ) bsize = xs_pad.size(0) addin = xs_pad.new_zeros( bsize, block_num, xs_pad.size(-1) ) # additional context embedding vecctors # first step if self.init_average: # initialize with average value addin[:, 0, :] = xs_pad.narrow(1, cur_hop, self.block_size).mean(1) else: # initialize with max value addin[:, 0, :] = xs_pad.narrow(1, cur_hop, self.block_size).max(1) cur_hop += self.hop_size # following steps while cur_hop + self.block_size < total_frame_num: if self.init_average: # initialize with average value addin[:, cur_hop // self.hop_size, :] = xs_pad.narrow( 1, cur_hop, self.block_size ).mean(1) else: # initialize with max value addin[:, cur_hop // self.hop_size, :] = xs_pad.narrow( 1, cur_hop, self.block_size ).max(1) cur_hop += self.hop_size # last step if cur_hop < total_frame_num and cur_hop // self.hop_size < block_num: if self.init_average: # initialize with average value addin[:, cur_hop // self.hop_size, :] = xs_pad.narrow( 1, cur_hop, total_frame_num - cur_hop ).mean(1) else: # initialize with max value addin[:, cur_hop // self.hop_size, :] = xs_pad.narrow( 1, cur_hop, total_frame_num - cur_hop ).max(1) if self.ctx_pos_enc: addin = self.pos_enc(addin) xs_pad = self.pos_enc(xs_pad) # set up masks mask_online = xs_pad.new_zeros( xs_pad.size(0), block_num, self.block_size + 2, self.block_size + 2 ) mask_online.narrow(2, 1, self.block_size + 1).narrow( 3, 0, self.block_size + 1 ).fill_(1) xs_chunk = xs_pad.new_zeros( bsize, block_num, self.block_size + 2, xs_pad.size(-1) ) # fill the input # first step left_idx = 0 block_idx = 0 xs_chunk[:, block_idx, 1 : self.block_size + 1] = xs_pad.narrow( -2, left_idx, self.block_size ) left_idx += self.hop_size block_idx += 1 # following steps while left_idx + self.block_size < total_frame_num and block_idx < block_num: xs_chunk[:, block_idx, 1 : self.block_size + 1] = xs_pad.narrow( -2, left_idx, self.block_size ) left_idx += self.hop_size block_idx += 1 # last steps last_size = total_frame_num - left_idx xs_chunk[:, block_idx, 1 : last_size + 1] = xs_pad.narrow( -2, left_idx, last_size ) # fill the initial context vector xs_chunk[:, 0, 0] = addin[:, 0] xs_chunk[:, 1:, 0] = addin[:, 0 : block_num - 1] xs_chunk[:, :, self.block_size + 1] = addin # forward ys_chunk, mask_online, _, _, _, _, _ = self.encoders( xs_chunk, mask_online, False, xs_chunk ) # copy output # first step offset = self.block_size - self.look_ahead - self.hop_size + 1 left_idx = 0 block_idx = 0 cur_hop = self.block_size - self.look_ahead ys_pad[:, left_idx:cur_hop] = ys_chunk[:, block_idx, 1 : cur_hop + 1] left_idx += self.hop_size block_idx += 1 # following steps while left_idx + self.block_size < total_frame_num and block_idx < block_num: ys_pad[:, cur_hop : cur_hop + self.hop_size] = ys_chunk[ :, block_idx, offset : offset + self.hop_size ] cur_hop += self.hop_size left_idx += self.hop_size block_idx += 1 ys_pad[:, cur_hop:total_frame_num] = ys_chunk[ :, block_idx, offset : last_size + 1, : ] if self.normalize_before: ys_pad = self.after_norm(ys_pad) olens = masks.squeeze(1).sum(1) return ys_pad, olens, None
[docs] def forward_infer( self, xs_pad: torch.Tensor, ilens: torch.Tensor, prev_states: torch.Tensor = None, is_final: bool = True, ) -> Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]: """Embed positions in tensor. Args: xs_pad: input tensor (B, L, D) ilens: input length (B) prev_states: Not to be used now. Returns: position embedded tensor and mask """ if prev_states is None: prev_addin = None buffer_before_downsampling = None ilens_buffer = None buffer_after_downsampling = None n_processed_blocks = 0 past_encoder_ctx = None else: prev_addin = prev_states["prev_addin"] buffer_before_downsampling = prev_states["buffer_before_downsampling"] ilens_buffer = prev_states["ilens_buffer"] buffer_after_downsampling = prev_states["buffer_after_downsampling"] n_processed_blocks = prev_states["n_processed_blocks"] past_encoder_ctx = prev_states["past_encoder_ctx"] bsize = xs_pad.size(0) assert bsize == 1 if prev_states is not None: xs_pad = torch.cat([buffer_before_downsampling, xs_pad], dim=1) ilens += ilens_buffer if is_final: buffer_before_downsampling = None else: n_samples = xs_pad.size(1) // self.subsample - 1 if n_samples < 2: next_states = { "prev_addin": prev_addin, "buffer_before_downsampling": xs_pad, "ilens_buffer": ilens, "buffer_after_downsampling": buffer_after_downsampling, "n_processed_blocks": n_processed_blocks, "past_encoder_ctx": past_encoder_ctx, } return ( xs_pad.new_zeros(bsize, 0, self._output_size), xs_pad.new_zeros(bsize), next_states, ) n_res_samples = xs_pad.size(1) % self.subsample + self.subsample * 2 buffer_before_downsampling = xs_pad.narrow( 1, xs_pad.size(1) - n_res_samples, n_res_samples ) xs_pad = xs_pad.narrow(1, 0, n_samples * self.subsample) ilens_buffer = ilens.new_full( [1], dtype=torch.long, fill_value=n_res_samples ) ilens = ilens.new_full( [1], dtype=torch.long, fill_value=n_samples * self.subsample ) if isinstance(self.embed, Conv2dSubsamplingWOPosEnc): xs_pad, _ = self.embed(xs_pad, None) elif self.embed is not None: xs_pad = self.embed(xs_pad) # create empty output container if buffer_after_downsampling is not None: xs_pad = torch.cat([buffer_after_downsampling, xs_pad], dim=1) total_frame_num = xs_pad.size(1) if is_final: past_size = self.block_size - self.hop_size - self.look_ahead block_num = math.ceil( float(total_frame_num - past_size - self.look_ahead) / float(self.hop_size) ) buffer_after_downsampling = None else: if total_frame_num <= self.block_size: next_states = { "prev_addin": prev_addin, "buffer_before_downsampling": buffer_before_downsampling, "ilens_buffer": ilens_buffer, "buffer_after_downsampling": xs_pad, "n_processed_blocks": n_processed_blocks, "past_encoder_ctx": past_encoder_ctx, } return ( xs_pad.new_zeros(bsize, 0, self._output_size), xs_pad.new_zeros(bsize), next_states, ) overlap_size = self.block_size - self.hop_size block_num = max(0, xs_pad.size(1) - overlap_size) // self.hop_size res_frame_num = xs_pad.size(1) - self.hop_size * block_num buffer_after_downsampling = xs_pad.narrow( 1, xs_pad.size(1) - res_frame_num, res_frame_num ) xs_pad = xs_pad.narrow(1, 0, block_num * self.hop_size + overlap_size) # block_size could be 0 meaning infinite # apply usual encoder for short sequence assert self.block_size > 0 if n_processed_blocks == 0 and total_frame_num <= self.block_size and is_final: xs_chunk = self.pos_enc(xs_pad).unsqueeze(1) xs_pad, _, _, _, _, _, _ = self.encoders( xs_chunk, None, True, None, None, True ) xs_pad = xs_pad.squeeze(0) if self.normalize_before: xs_pad = self.after_norm(xs_pad) return xs_pad, None, None # start block processing xs_chunk = xs_pad.new_zeros( bsize, block_num, self.block_size + 2, xs_pad.size(-1) ) for i in range(block_num): cur_hop = i * self.hop_size chunk_length = min(self.block_size, total_frame_num - cur_hop) addin = xs_pad.narrow(1, cur_hop, chunk_length) if self.init_average: addin = addin.mean(1, keepdim=True) else: addin = addin.max(1, keepdim=True) if self.ctx_pos_enc: addin = self.pos_enc(addin, i + n_processed_blocks) if prev_addin is None: prev_addin = addin xs_chunk[:, i, 0] = prev_addin xs_chunk[:, i, -1] = addin chunk = self.pos_enc( xs_pad.narrow(1, cur_hop, chunk_length), cur_hop + self.hop_size * n_processed_blocks, ) xs_chunk[:, i, 1 : chunk_length + 1] = chunk prev_addin = addin # mask setup, it should be the same to that of forward_train mask_online = xs_pad.new_zeros( xs_pad.size(0), block_num, self.block_size + 2, self.block_size + 2 ) mask_online.narrow(2, 1, self.block_size + 1).narrow( 3, 0, self.block_size + 1 ).fill_(1) ys_chunk, _, _, _, past_encoder_ctx, _, _ = self.encoders( xs_chunk, mask_online, True, past_encoder_ctx ) # remove addin ys_chunk = ys_chunk.narrow(2, 1, self.block_size) offset = self.block_size - self.look_ahead - self.hop_size if is_final: if n_processed_blocks == 0: y_length = xs_pad.size(1) else: y_length = xs_pad.size(1) - offset else: y_length = block_num * self.hop_size if n_processed_blocks == 0: y_length += offset ys_pad = xs_pad.new_zeros((xs_pad.size(0), y_length, xs_pad.size(2))) if n_processed_blocks == 0: ys_pad[:, 0:offset] = ys_chunk[:, 0, 0:offset] for i in range(block_num): cur_hop = i * self.hop_size if n_processed_blocks == 0: cur_hop += offset if i == block_num - 1 and is_final: chunk_length = min(self.block_size - offset, ys_pad.size(1) - cur_hop) else: chunk_length = self.hop_size ys_pad[:, cur_hop : cur_hop + chunk_length] = ys_chunk[ :, i, offset : offset + chunk_length ] if self.normalize_before: ys_pad = self.after_norm(ys_pad) if is_final: next_states = None else: next_states = { "prev_addin": prev_addin, "buffer_before_downsampling": buffer_before_downsampling, "ilens_buffer": ilens_buffer, "buffer_after_downsampling": buffer_after_downsampling, "n_processed_blocks": n_processed_blocks + block_num, "past_encoder_ctx": past_encoder_ctx, } return ys_pad, None, next_states