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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} | |
| # } | |
| # | |
| """Encoder self-attention layer definition.""" | |
| from typing import Optional, Tuple | |
| import torch | |
| from torch import nn | |
| class TransformerEncoderLayer(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_forward (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: to use layer_norm after each sub-block. | |
| """ | |
| def __init__( | |
| self, | |
| size: int, | |
| self_attn: torch.nn.Module, | |
| feed_forward: torch.nn.Module, | |
| dropout_rate: float, | |
| normalize_before: bool = True, | |
| ): | |
| """Construct an EncoderLayer object.""" | |
| super().__init__() | |
| self.self_attn = self_attn | |
| self.feed_forward = feed_forward | |
| self.norm1 = nn.LayerNorm(size, eps=1e-5) | |
| self.norm2 = nn.LayerNorm(size, eps=1e-5) | |
| self.dropout = nn.Dropout(dropout_rate) | |
| self.size = size | |
| self.normalize_before = normalize_before | |
| 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]: | |
| """Compute encoded features. | |
| Args: | |
| x (torch.Tensor): (#batch, time, size) | |
| mask (torch.Tensor): Mask tensor for the input (#batch, time，time), | |
| (0, 0, 0) means fake mask. | |
| pos_emb (torch.Tensor): just for interface compatibility | |
| to ConformerEncoderLayer | |
| mask_pad (torch.Tensor): does not used in transformer layer, | |
| just for unified api with conformer. | |
| att_cache (torch.Tensor): Cache tensor of the KEY & VALUE | |
| (#batch=1, head, cache_t1, d_k * 2), head * d_k == size. | |
| cnn_cache (torch.Tensor): Convolution cache in conformer layer | |
| (#batch=1, size, cache_t2), not used here, it's for interface | |
| compatibility to ConformerEncoderLayer. | |
| Returns: | |
| torch.Tensor: Output tensor (#batch, time, size). | |
| torch.Tensor: Mask tensor (#batch, time, time). | |
| torch.Tensor: att_cache tensor, | |
| (#batch=1, head, cache_t1 + time, d_k * 2). | |
| torch.Tensor: cnn_cahce tensor (#batch=1, size, cache_t2). | |
| """ | |
| residual = x | |
| if self.normalize_before: | |
| x = self.norm1(x) | |
| x_att, new_att_cache = self.self_attn(x, x, x, mask, cache=att_cache) | |
| x = residual + self.dropout(x_att) | |
| if not self.normalize_before: | |
| x = self.norm1(x) | |
| residual = x | |
| if self.normalize_before: | |
| x = self.norm2(x) | |
| x = residual + self.dropout(self.feed_forward(x)) | |
| if not self.normalize_before: | |
| x = self.norm2(x) | |
| fake_cnn_cache = torch.zeros((0, 0, 0), dtype=x.dtype, device=x.device) | |
| return x, mask, new_att_cache, fake_cnn_cache | |
| class ConformerEncoderLayer(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_forward (torch.nn.Module): Feed-forward module instance. | |
| `PositionwiseFeedForward` instance can be used as the argument. | |
| feed_forward_macaron (torch.nn.Module): Additional 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. | |
| 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_forward: Optional[nn.Module] = None, | |
| feed_forward_macaron: Optional[nn.Module] = None, | |
| conv_module: Optional[nn.Module] = None, | |
| dropout_rate: float = 0.1, | |
| normalize_before: bool = True, | |
| ): | |
| """Construct an EncoderLayer object.""" | |
| super().__init__() | |
| self.self_attn = self_attn | |
| self.feed_forward = feed_forward | |
| self.feed_forward_macaron = feed_forward_macaron | |
| self.conv_module = conv_module | |
| self.norm_ff = nn.LayerNorm(size, eps=1e-5) # for the FNN module | |
| self.norm_mha = nn.LayerNorm(size, eps=1e-5) # for the MHA module | |
| if feed_forward_macaron is not None: | |
| self.norm_ff_macaron = nn.LayerNorm(size, eps=1e-5) | |
| self.ff_scale = 0.5 | |
| else: | |
| self.ff_scale = 1.0 | |
| if self.conv_module is not None: | |
| self.norm_conv = nn.LayerNorm(size, eps=1e-5) # for the CNN module | |
| self.norm_final = nn.LayerNorm( | |
| size, eps=1e-5 | |
| ) # for the final output of the block | |
| self.dropout = nn.Dropout(dropout_rate) | |
| self.size = size | |
| self.normalize_before = normalize_before | |
| 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]: | |
| """Compute encoded features. | |
| Args: | |
| x (torch.Tensor): (#batch, time, size) | |
| mask (torch.Tensor): Mask tensor for the input (#batch, time，time), | |
| (0, 0, 0) means fake mask. | |
| pos_emb (torch.Tensor): positional encoding, must not be None | |
| for ConformerEncoderLayer. | |
| mask_pad (torch.Tensor): batch padding mask used for conv module. | |
| (#batch, 1，time), (0, 0, 0) means fake mask. | |
| att_cache (torch.Tensor): Cache tensor of the KEY & VALUE | |
| (#batch=1, head, cache_t1, d_k * 2), head * d_k == size. | |
| cnn_cache (torch.Tensor): Convolution cache in conformer layer | |
| (#batch=1, size, cache_t2) | |
| Returns: | |
| torch.Tensor: Output tensor (#batch, time, size). | |
| torch.Tensor: Mask tensor (#batch, time, time). | |
| torch.Tensor: att_cache tensor, | |
| (#batch=1, head, cache_t1 + time, d_k * 2). | |
| torch.Tensor: cnn_cahce tensor (#batch, size, cache_t2). | |
| """ | |
| # whether to use macaron style | |
| if self.feed_forward_macaron is not None: | |
| residual = x | |
| if self.normalize_before: | |
| x = self.norm_ff_macaron(x) | |
| x = residual + self.ff_scale * self.dropout(self.feed_forward_macaron(x)) | |
| if not self.normalize_before: | |
| x = self.norm_ff_macaron(x) | |
| # multi-headed self-attention module | |
| residual = x | |
| if self.normalize_before: | |
| x = self.norm_mha(x) | |
| x_att, new_att_cache = self.self_attn(x, x, x, mask, pos_emb, att_cache) | |
| x = residual + self.dropout(x_att) | |
| if not self.normalize_before: | |
| x = self.norm_mha(x) | |
| # convolution module | |
| # Fake new cnn cache here, and then change it in conv_module | |
| new_cnn_cache = torch.zeros((0, 0, 0), dtype=x.dtype, device=x.device) | |
| if self.conv_module is not None: | |
| residual = x | |
| if self.normalize_before: | |
| x = self.norm_conv(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.norm_conv(x) | |
| # feed forward module | |
| residual = x | |
| if self.normalize_before: | |
| x = self.norm_ff(x) | |
| x = residual + self.ff_scale * self.dropout(self.feed_forward(x)) | |
| if not self.normalize_before: | |
| x = self.norm_ff(x) | |
| if self.conv_module is not None: | |
| x = self.norm_final(x) | |
| return x, mask, new_att_cache, new_cnn_cache | |