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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} | |
| # } | |
| # | |
| """Decoder self-attention layer definition.""" | |
| from typing import Optional, Tuple | |
| import torch | |
| from torch import nn | |
| class DecoderLayer(nn.Module): | |
| """Single decoder layer module. | |
| Args: | |
| size (int): Input dimension. | |
| self_attn (torch.nn.Module): Self-attention module instance. | |
| `MultiHeadedAttention` instance can be used as the argument. | |
| src_attn (torch.nn.Module): Inter-attention module instance. | |
| `MultiHeadedAttention` instance can be used as the argument. | |
| If `None` is passed, Inter-attention is not used, such as | |
| CIF, GPT, and other decoder only model. | |
| 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: nn.Module, | |
| src_attn: Optional[nn.Module], | |
| feed_forward: nn.Module, | |
| dropout_rate: float, | |
| normalize_before: bool = True, | |
| ): | |
| """Construct an DecoderLayer object.""" | |
| super().__init__() | |
| self.size = size | |
| self.self_attn = self_attn | |
| self.src_attn = src_attn | |
| self.feed_forward = feed_forward | |
| self.norm1 = nn.LayerNorm(size, eps=1e-5) | |
| self.norm2 = nn.LayerNorm(size, eps=1e-5) | |
| self.norm3 = nn.LayerNorm(size, eps=1e-5) | |
| self.dropout = nn.Dropout(dropout_rate) | |
| self.normalize_before = normalize_before | |
| def forward( | |
| self, | |
| tgt: torch.Tensor, | |
| tgt_mask: torch.Tensor, | |
| memory: torch.Tensor, | |
| memory_mask: torch.Tensor, | |
| cache: Optional[torch.Tensor] = None, | |
| ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: | |
| """Compute decoded features. | |
| Args: | |
| tgt (torch.Tensor): Input tensor (#batch, maxlen_out, size). | |
| tgt_mask (torch.Tensor): Mask for input tensor | |
| (#batch, maxlen_out). | |
| memory (torch.Tensor): Encoded memory | |
| (#batch, maxlen_in, size). | |
| memory_mask (torch.Tensor): Encoded memory mask | |
| (#batch, maxlen_in). | |
| cache (torch.Tensor): cached tensors. | |
| (#batch, maxlen_out - 1, size). | |
| Returns: | |
| torch.Tensor: Output tensor (#batch, maxlen_out, size). | |
| torch.Tensor: Mask for output tensor (#batch, maxlen_out). | |
| torch.Tensor: Encoded memory (#batch, maxlen_in, size). | |
| torch.Tensor: Encoded memory mask (#batch, maxlen_in). | |
| """ | |
| residual = tgt | |
| if self.normalize_before: | |
| tgt = self.norm1(tgt) | |
| if cache is None: | |
| tgt_q = tgt | |
| tgt_q_mask = tgt_mask | |
| else: | |
| # compute only the last frame query keeping dim: max_time_out -> 1 | |
| assert cache.shape == ( | |
| tgt.shape[0], | |
| tgt.shape[1] - 1, | |
| self.size, | |
| ), "{cache.shape} == {(tgt.shape[0], tgt.shape[1] - 1, self.size)}" | |
| tgt_q = tgt[:, -1:, :] | |
| residual = residual[:, -1:, :] | |
| tgt_q_mask = tgt_mask[:, -1:, :] | |
| x = residual + self.dropout(self.self_attn(tgt_q, tgt, tgt, tgt_q_mask)[0]) | |
| if not self.normalize_before: | |
| x = self.norm1(x) | |
| if self.src_attn is not None: | |
| residual = x | |
| if self.normalize_before: | |
| x = self.norm2(x) | |
| x = residual + self.dropout( | |
| self.src_attn(x, memory, memory, memory_mask)[0] | |
| ) | |
| if not self.normalize_before: | |
| x = self.norm2(x) | |
| residual = x | |
| if self.normalize_before: | |
| x = self.norm3(x) | |
| x = residual + self.dropout(self.feed_forward(x)) | |
| if not self.normalize_before: | |
| x = self.norm3(x) | |
| if cache is not None: | |
| x = torch.cat([cache, x], dim=1) | |
| return x, tgt_mask, memory, memory_mask | |