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| # This module is from [WeNet](https://github.com/wenet-e2e/wenet). | |
| # ## Citations | |
| # ```bibtex | |
| # @inproceedings{yao2021wenet, | |
| # title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, | |
| # author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, | |
| # booktitle={Proc. Interspeech}, | |
| # year={2021}, | |
| # address={Brno, Czech Republic }, | |
| # organization={IEEE} | |
| # } | |
| # @article{zhang2022wenet, | |
| # title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, | |
| # author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, | |
| # journal={arXiv preprint arXiv:2203.15455}, | |
| # year={2022} | |
| # } | |
| # | |
| """SqueezeformerEncoderLayer definition.""" | |
| import torch | |
| import torch.nn as nn | |
| from typing import Optional, Tuple | |
| class SqueezeformerEncoderLayer(nn.Module): | |
| """Encoder layer module. | |
| Args: | |
| size (int): Input dimension. | |
| self_attn (torch.nn.Module): Self-attention module instance. | |
| `MultiHeadedAttention` or `RelPositionMultiHeadedAttention` | |
| instance can be used as the argument. | |
| feed_forward1 (torch.nn.Module): Feed-forward module instance. | |
| `PositionwiseFeedForward` instance can be used as the argument. | |
| conv_module (torch.nn.Module): Convolution module instance. | |
| `ConvlutionModule` instance can be used as the argument. | |
| feed_forward2 (torch.nn.Module): Feed-forward module instance. | |
| `PositionwiseFeedForward` instance can be used as the argument. | |
| dropout_rate (float): Dropout rate. | |
| normalize_before (bool): | |
| True: use layer_norm before each sub-block. | |
| False: use layer_norm after each sub-block. | |
| """ | |
| def __init__( | |
| self, | |
| size: int, | |
| self_attn: torch.nn.Module, | |
| feed_forward1: Optional[nn.Module] = None, | |
| conv_module: Optional[nn.Module] = None, | |
| feed_forward2: Optional[nn.Module] = None, | |
| normalize_before: bool = False, | |
| dropout_rate: float = 0.1, | |
| concat_after: bool = False, | |
| ): | |
| super(SqueezeformerEncoderLayer, self).__init__() | |
| self.size = size | |
| self.self_attn = self_attn | |
| self.layer_norm1 = nn.LayerNorm(size) | |
| self.ffn1 = feed_forward1 | |
| self.layer_norm2 = nn.LayerNorm(size) | |
| self.conv_module = conv_module | |
| self.layer_norm3 = nn.LayerNorm(size) | |
| self.ffn2 = feed_forward2 | |
| self.layer_norm4 = nn.LayerNorm(size) | |
| self.normalize_before = normalize_before | |
| self.dropout = nn.Dropout(dropout_rate) | |
| self.concat_after = concat_after | |
| if concat_after: | |
| self.concat_linear = nn.Linear(size + size, size) | |
| else: | |
| self.concat_linear = nn.Identity() | |
| def forward( | |
| self, | |
| x: torch.Tensor, | |
| mask: torch.Tensor, | |
| pos_emb: torch.Tensor, | |
| mask_pad: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool), | |
| att_cache: torch.Tensor = torch.zeros((0, 0, 0, 0)), | |
| cnn_cache: torch.Tensor = torch.zeros((0, 0, 0, 0)), | |
| ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: | |
| # self attention module | |
| residual = x | |
| if self.normalize_before: | |
| x = self.layer_norm1(x) | |
| x_att, new_att_cache = self.self_attn(x, x, x, mask, pos_emb, att_cache) | |
| if self.concat_after: | |
| x_concat = torch.cat((x, x_att), dim=-1) | |
| x = residual + self.concat_linear(x_concat) | |
| else: | |
| x = residual + self.dropout(x_att) | |
| if not self.normalize_before: | |
| x = self.layer_norm1(x) | |
| # ffn module | |
| residual = x | |
| if self.normalize_before: | |
| x = self.layer_norm2(x) | |
| x = self.ffn1(x) | |
| x = residual + self.dropout(x) | |
| if not self.normalize_before: | |
| x = self.layer_norm2(x) | |
| # conv module | |
| new_cnn_cache = torch.zeros((0, 0, 0), dtype=x.dtype, device=x.device) | |
| residual = x | |
| if self.normalize_before: | |
| x = self.layer_norm3(x) | |
| x, new_cnn_cache = self.conv_module(x, mask_pad, cnn_cache) | |
| x = residual + self.dropout(x) | |
| if not self.normalize_before: | |
| x = self.layer_norm3(x) | |
| # ffn module | |
| residual = x | |
| if self.normalize_before: | |
| x = self.layer_norm4(x) | |
| x = self.ffn2(x) | |
| # we do not use dropout here since it is inside feed forward function | |
| x = residual + self.dropout(x) | |
| if not self.normalize_before: | |
| x = self.layer_norm4(x) | |
| return x, mask, new_att_cache, new_cnn_cache | |