451 lines
19 KiB
Python
451 lines
19 KiB
Python
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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# Copyright 2019 Mobvoi Inc. All Rights Reserved.
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# Author: di.wu@mobvoi.com (DI WU)
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"""Encoder definition."""
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from typing import Tuple, List, Optional
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import torch
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from typeguard import check_argument_types
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from wenet.transformer.attention import MultiHeadedAttention
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from wenet.transformer.attention import RelPositionMultiHeadedAttention
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from wenet.transformer.convolution import ConvolutionModule
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from wenet.transformer.embedding import PositionalEncoding
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from wenet.transformer.embedding import RelPositionalEncoding
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from wenet.transformer.embedding import NoPositionalEncoding
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from wenet.transformer.encoder_layer import TransformerEncoderLayer
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from wenet.transformer.encoder_layer import ConformerEncoderLayer
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from wenet.transformer.positionwise_feed_forward import PositionwiseFeedForward
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from wenet.transformer.subsampling import Conv2dSubsampling4
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from wenet.transformer.subsampling import Conv2dSubsampling6
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from wenet.transformer.subsampling import Conv2dSubsampling8
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from wenet.transformer.subsampling import LinearNoSubsampling
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from wenet.utils.common import get_activation
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from wenet.utils.mask import make_pad_mask
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from wenet.utils.mask import add_optional_chunk_mask
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class BaseEncoder(torch.nn.Module):
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def __init__(
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self,
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input_size: int,
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output_size: int = 256,
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attention_heads: int = 4,
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linear_units: int = 2048,
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num_blocks: int = 6,
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dropout_rate: float = 0.1,
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positional_dropout_rate: float = 0.1,
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attention_dropout_rate: float = 0.0,
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input_layer: str = "conv2d",
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pos_enc_layer_type: str = "abs_pos",
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normalize_before: bool = True,
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concat_after: bool = False,
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static_chunk_size: int = 0,
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use_dynamic_chunk: bool = False,
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global_cmvn: torch.nn.Module = None,
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use_dynamic_left_chunk: bool = False,
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):
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"""
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Args:
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input_size (int): input dim
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output_size (int): dimension of attention
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attention_heads (int): the number of heads of multi head attention
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linear_units (int): the hidden units number of position-wise feed
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forward
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num_blocks (int): the number of decoder blocks
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dropout_rate (float): dropout rate
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attention_dropout_rate (float): dropout rate in attention
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positional_dropout_rate (float): dropout rate after adding
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positional encoding
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input_layer (str): input layer type.
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optional [linear, conv2d, conv2d6, conv2d8]
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pos_enc_layer_type (str): Encoder positional encoding layer type.
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opitonal [abs_pos, scaled_abs_pos, rel_pos, no_pos]
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normalize_before (bool):
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True: use layer_norm before each sub-block of a layer.
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False: use layer_norm after each sub-block of a layer.
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concat_after (bool): whether to concat attention layer's input
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and output.
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True: x -> x + linear(concat(x, att(x)))
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False: x -> x + att(x)
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static_chunk_size (int): chunk size for static chunk training and
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decoding
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use_dynamic_chunk (bool): whether use dynamic chunk size for
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training or not, You can only use fixed chunk(chunk_size > 0)
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or dyanmic chunk size(use_dynamic_chunk = True)
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global_cmvn (Optional[torch.nn.Module]): Optional GlobalCMVN module
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use_dynamic_left_chunk (bool): whether use dynamic left chunk in
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dynamic chunk training
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"""
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assert check_argument_types()
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super().__init__()
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self._output_size = output_size
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if pos_enc_layer_type == "abs_pos":
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pos_enc_class = PositionalEncoding
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elif pos_enc_layer_type == "rel_pos":
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pos_enc_class = RelPositionalEncoding
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elif pos_enc_layer_type == "no_pos":
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pos_enc_class = NoPositionalEncoding
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else:
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raise ValueError("unknown pos_enc_layer: " + pos_enc_layer_type)
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if input_layer == "linear":
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subsampling_class = LinearNoSubsampling
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elif input_layer == "conv2d":
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subsampling_class = Conv2dSubsampling4
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elif input_layer == "conv2d6":
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subsampling_class = Conv2dSubsampling6
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elif input_layer == "conv2d8":
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subsampling_class = Conv2dSubsampling8
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else:
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raise ValueError("unknown input_layer: " + input_layer)
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self.global_cmvn = global_cmvn
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self.embed = subsampling_class(
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input_size,
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output_size,
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dropout_rate,
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pos_enc_class(output_size, positional_dropout_rate),
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)
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self.normalize_before = normalize_before
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self.after_norm = torch.nn.LayerNorm(output_size, eps=1e-12)
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self.static_chunk_size = static_chunk_size
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self.use_dynamic_chunk = use_dynamic_chunk
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self.use_dynamic_left_chunk = use_dynamic_left_chunk
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def output_size(self) -> int:
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return self._output_size
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def forward(
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self,
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xs: torch.Tensor,
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xs_lens: torch.Tensor,
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decoding_chunk_size: int = 0,
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num_decoding_left_chunks: int = -1,
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) -> Tuple[torch.Tensor, torch.Tensor]:
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"""Embed positions in tensor.
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Args:
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xs: padded input tensor (B, T, D)
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xs_lens: input length (B)
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decoding_chunk_size: decoding chunk size for dynamic chunk
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0: default for training, use random dynamic chunk.
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<0: for decoding, use full chunk.
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>0: for decoding, use fixed chunk size as set.
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num_decoding_left_chunks: number of left chunks, this is for decoding,
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the chunk size is decoding_chunk_size.
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>=0: use num_decoding_left_chunks
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<0: use all left chunks
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Returns:
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encoder output tensor xs, and subsampled masks
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xs: padded output tensor (B, T' ~= T/subsample_rate, D)
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masks: torch.Tensor batch padding mask after subsample
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(B, 1, T' ~= T/subsample_rate)
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"""
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masks = ~make_pad_mask(xs_lens).unsqueeze(1) # (B, 1, T)
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if self.global_cmvn is not None:
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xs = self.global_cmvn(xs)
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xs, pos_emb, masks = self.embed(xs, masks)
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mask_pad = masks # (B, 1, T/subsample_rate)
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chunk_masks = add_optional_chunk_mask(xs, masks,
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self.use_dynamic_chunk,
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self.use_dynamic_left_chunk,
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decoding_chunk_size,
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self.static_chunk_size,
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num_decoding_left_chunks)
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for layer in self.encoders:
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xs, chunk_masks, _ = layer(xs, chunk_masks, pos_emb, mask_pad)
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if self.normalize_before:
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xs = self.after_norm(xs)
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# Here we assume the mask is not changed in encoder layers, so just
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# return the masks before encoder layers, and the masks will be used
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# for cross attention with decoder later
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return xs, masks
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def forward_chunk(
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self,
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xs: torch.Tensor,
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offset: int,
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required_cache_size: int,
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subsampling_cache: Optional[torch.Tensor] = None,
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elayers_output_cache: Optional[List[torch.Tensor]] = None,
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conformer_cnn_cache: Optional[List[torch.Tensor]] = None,
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) -> Tuple[torch.Tensor, torch.Tensor, List[torch.Tensor],
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List[torch.Tensor]]:
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""" Forward just one chunk
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Args:
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xs (torch.Tensor): chunk input
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offset (int): current offset in encoder output time stamp
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required_cache_size (int): cache size required for next chunk
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compuation
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>=0: actual cache size
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<0: means all history cache is required
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subsampling_cache (Optional[torch.Tensor]): subsampling cache
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elayers_output_cache (Optional[List[torch.Tensor]]):
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transformer/conformer encoder layers output cache
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conformer_cnn_cache (Optional[List[torch.Tensor]]): conformer
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cnn cache
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Returns:
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torch.Tensor: output of current input xs
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torch.Tensor: subsampling cache required for next chunk computation
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List[torch.Tensor]: encoder layers output cache required for next
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chunk computation
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List[torch.Tensor]: conformer cnn cache
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"""
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assert xs.size(0) == 1
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# tmp_masks is just for interface compatibility
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tmp_masks = torch.ones(1,
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xs.size(1),
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device=xs.device,
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dtype=torch.bool)
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tmp_masks = tmp_masks.unsqueeze(1)
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if self.global_cmvn is not None:
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xs = self.global_cmvn(xs)
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xs, pos_emb, _ = self.embed(xs, tmp_masks, offset)
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if subsampling_cache is not None:
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cache_size = subsampling_cache.size(1)
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xs = torch.cat((subsampling_cache, xs), dim=1)
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else:
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cache_size = 0
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pos_emb = self.embed.position_encoding(offset - cache_size, xs.size(1))
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if required_cache_size < 0:
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next_cache_start = 0
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elif required_cache_size == 0:
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next_cache_start = xs.size(1)
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else:
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next_cache_start = max(xs.size(1) - required_cache_size, 0)
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r_subsampling_cache = xs[:, next_cache_start:, :]
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# Real mask for transformer/conformer layers
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masks = torch.ones(1, xs.size(1), device=xs.device, dtype=torch.bool)
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masks = masks.unsqueeze(1)
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r_elayers_output_cache = []
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r_conformer_cnn_cache = []
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for i, layer in enumerate(self.encoders):
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if elayers_output_cache is None:
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attn_cache = None
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else:
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attn_cache = elayers_output_cache[i]
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if conformer_cnn_cache is None:
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cnn_cache = None
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else:
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cnn_cache = conformer_cnn_cache[i]
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xs, _, new_cnn_cache = layer(xs,
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masks,
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pos_emb,
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output_cache=attn_cache,
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cnn_cache=cnn_cache)
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r_elayers_output_cache.append(xs[:, next_cache_start:, :])
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r_conformer_cnn_cache.append(new_cnn_cache)
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if self.normalize_before:
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xs = self.after_norm(xs)
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return (xs[:, cache_size:, :], r_subsampling_cache,
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r_elayers_output_cache, r_conformer_cnn_cache)
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def forward_chunk_by_chunk(
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self,
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xs: torch.Tensor,
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decoding_chunk_size: int,
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num_decoding_left_chunks: int = -1,
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) -> Tuple[torch.Tensor, torch.Tensor]:
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""" Forward input chunk by chunk with chunk_size like a streaming
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fashion
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Here we should pay special attention to computation cache in the
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streaming style forward chunk by chunk. Three things should be taken
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into account for computation in the current network:
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1. transformer/conformer encoder layers output cache
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2. convolution in conformer
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3. convolution in subsampling
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However, we don't implement subsampling cache for:
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1. We can control subsampling module to output the right result by
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overlapping input instead of cache left context, even though it
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wastes some computation, but subsampling only takes a very
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small fraction of computation in the whole model.
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2. Typically, there are several covolution layers with subsampling
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in subsampling module, it is tricky and complicated to do cache
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with different convolution layers with different subsampling
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rate.
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3. Currently, nn.Sequential is used to stack all the convolution
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layers in subsampling, we need to rewrite it to make it work
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with cache, which is not prefered.
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Args:
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xs (torch.Tensor): (1, max_len, dim)
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chunk_size (int): decoding chunk size
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"""
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assert decoding_chunk_size > 0
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# The model is trained by static or dynamic chunk
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assert self.static_chunk_size > 0 or self.use_dynamic_chunk
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subsampling = self.embed.subsampling_rate
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context = self.embed.right_context + 1 # Add current frame
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stride = subsampling * decoding_chunk_size
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decoding_window = (decoding_chunk_size - 1) * subsampling + context
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num_frames = xs.size(1)
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subsampling_cache: Optional[torch.Tensor] = None
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elayers_output_cache: Optional[List[torch.Tensor]] = None
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conformer_cnn_cache: Optional[List[torch.Tensor]] = None
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outputs = []
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offset = 0
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required_cache_size = decoding_chunk_size * num_decoding_left_chunks
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# Feed forward overlap input step by step
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for cur in range(0, num_frames - context + 1, stride):
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end = min(cur + decoding_window, num_frames)
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chunk_xs = xs[:, cur:end, :]
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(y, subsampling_cache, elayers_output_cache,
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conformer_cnn_cache) = self.forward_chunk(chunk_xs, offset,
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required_cache_size,
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subsampling_cache,
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elayers_output_cache,
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conformer_cnn_cache)
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outputs.append(y)
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offset += y.size(1)
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ys = torch.cat(outputs, 1)
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masks = torch.ones(1, ys.size(1), device=ys.device, dtype=torch.bool)
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masks = masks.unsqueeze(1)
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return ys, masks
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class TransformerEncoder(BaseEncoder):
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"""Transformer encoder module."""
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def __init__(
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self,
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input_size: int,
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output_size: int = 256,
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attention_heads: int = 4,
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linear_units: int = 2048,
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num_blocks: int = 6,
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dropout_rate: float = 0.1,
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positional_dropout_rate: float = 0.1,
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attention_dropout_rate: float = 0.0,
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input_layer: str = "conv2d",
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pos_enc_layer_type: str = "abs_pos",
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normalize_before: bool = True,
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concat_after: bool = False,
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static_chunk_size: int = 0,
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use_dynamic_chunk: bool = False,
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global_cmvn: torch.nn.Module = None,
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use_dynamic_left_chunk: bool = False,
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):
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""" Construct TransformerEncoder
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See Encoder for the meaning of each parameter.
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"""
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assert check_argument_types()
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super().__init__(input_size, output_size, attention_heads,
|
||
|
|
linear_units, num_blocks, dropout_rate,
|
||
|
|
positional_dropout_rate, attention_dropout_rate,
|
||
|
|
input_layer, pos_enc_layer_type, normalize_before,
|
||
|
|
concat_after, static_chunk_size, use_dynamic_chunk,
|
||
|
|
global_cmvn, use_dynamic_left_chunk)
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||
|
|
self.encoders = torch.nn.ModuleList([
|
||
|
|
TransformerEncoderLayer(
|
||
|
|
output_size,
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||
|
|
MultiHeadedAttention(attention_heads, output_size,
|
||
|
|
attention_dropout_rate),
|
||
|
|
PositionwiseFeedForward(output_size, linear_units,
|
||
|
|
dropout_rate), dropout_rate,
|
||
|
|
normalize_before, concat_after) for _ in range(num_blocks)
|
||
|
|
])
|
||
|
|
|
||
|
|
|
||
|
|
class ConformerEncoder(BaseEncoder):
|
||
|
|
"""Conformer encoder module."""
|
||
|
|
def __init__(
|
||
|
|
self,
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||
|
|
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",
|
||
|
|
pos_enc_layer_type: str = "rel_pos",
|
||
|
|
normalize_before: bool = True,
|
||
|
|
concat_after: bool = False,
|
||
|
|
static_chunk_size: int = 0,
|
||
|
|
use_dynamic_chunk: bool = False,
|
||
|
|
global_cmvn: torch.nn.Module = None,
|
||
|
|
use_dynamic_left_chunk: bool = False,
|
||
|
|
positionwise_conv_kernel_size: int = 1,
|
||
|
|
macaron_style: bool = True,
|
||
|
|
selfattention_layer_type: str = "rel_selfattn",
|
||
|
|
activation_type: str = "swish",
|
||
|
|
use_cnn_module: bool = True,
|
||
|
|
cnn_module_kernel: int = 15,
|
||
|
|
causal: bool = False,
|
||
|
|
cnn_module_norm: str = "batch_norm",
|
||
|
|
):
|
||
|
|
"""Construct ConformerEncoder
|
||
|
|
|
||
|
|
Args:
|
||
|
|
input_size to use_dynamic_chunk, see in BaseEncoder
|
||
|
|
positionwise_conv_kernel_size (int): Kernel size of positionwise
|
||
|
|
conv1d layer.
|
||
|
|
macaron_style (bool): Whether to use macaron style for
|
||
|
|
positionwise layer.
|
||
|
|
selfattention_layer_type (str): Encoder attention layer type,
|
||
|
|
the parameter has no effect now, it's just for configure
|
||
|
|
compatibility.
|
||
|
|
activation_type (str): Encoder activation function type.
|
||
|
|
use_cnn_module (bool): Whether to use convolution module.
|
||
|
|
cnn_module_kernel (int): Kernel size of convolution module.
|
||
|
|
causal (bool): whether to use causal convolution or not.
|
||
|
|
"""
|
||
|
|
assert check_argument_types()
|
||
|
|
super().__init__(input_size, output_size, attention_heads,
|
||
|
|
linear_units, num_blocks, dropout_rate,
|
||
|
|
positional_dropout_rate, attention_dropout_rate,
|
||
|
|
input_layer, pos_enc_layer_type, normalize_before,
|
||
|
|
concat_after, static_chunk_size, use_dynamic_chunk,
|
||
|
|
global_cmvn, use_dynamic_left_chunk)
|
||
|
|
activation = get_activation(activation_type)
|
||
|
|
|
||
|
|
# self-attention module definition
|
||
|
|
if pos_enc_layer_type == "no_pos":
|
||
|
|
encoder_selfattn_layer = MultiHeadedAttention
|
||
|
|
else:
|
||
|
|
encoder_selfattn_layer = RelPositionMultiHeadedAttention
|
||
|
|
encoder_selfattn_layer_args = (
|
||
|
|
attention_heads,
|
||
|
|
output_size,
|
||
|
|
attention_dropout_rate,
|
||
|
|
)
|
||
|
|
# feed-forward module definition
|
||
|
|
positionwise_layer = PositionwiseFeedForward
|
||
|
|
positionwise_layer_args = (
|
||
|
|
output_size,
|
||
|
|
linear_units,
|
||
|
|
dropout_rate,
|
||
|
|
activation,
|
||
|
|
)
|
||
|
|
# convolution module definition
|
||
|
|
convolution_layer = ConvolutionModule
|
||
|
|
convolution_layer_args = (output_size, cnn_module_kernel, activation,
|
||
|
|
cnn_module_norm, causal)
|
||
|
|
|
||
|
|
self.encoders = torch.nn.ModuleList([
|
||
|
|
ConformerEncoderLayer(
|
||
|
|
output_size,
|
||
|
|
encoder_selfattn_layer(*encoder_selfattn_layer_args),
|
||
|
|
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,
|
||
|
|
) for _ in range(num_blocks)
|
||
|
|
])
|