text: #!/usr/bin/env python3
  # -*- coding: utf-8 -*-
  
  # Copyright 2019 Mobvoi Inc. All Rights Reserved.
  # Author: di.wu@mobvoi.com (DI WU)
  """Encoder self-attention layer definition."""
  
  from typing import Optional, Tuple
  
  import torch
  from torch import nn
  from wenet.transformer.slice_helper import slice_helper
  
  
  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.
          concat_after (bool): Whether to concat attention layer's input and
              output.
              True: x -> x + linear(concat(x, att(x)))
              False: x -> x + att(x)
  
      """
      def __init__(
          self,
          size: int,
          self_attn: torch.nn.Module,
          feed_forward: torch.nn.Module,
          dropout_rate: float,
          normalize_before: bool = True,
          concat_after: bool = False,
      ):
          """Construct an EncoderLayer object."""
          super().__init__()
          self.self_attn = self_attn
          self.feed_forward = feed_forward
          self.norm1 = nn.LayerNorm(size, eps=1e-12)
          self.norm2 = nn.LayerNorm(size, eps=1e-12)
          self.dropout = nn.Dropout(dropout_rate)
          self.size = size
          self.normalize_before = normalize_before
          self.concat_after = concat_after
          # concat_linear may be not used in forward fuction,
          # but will be saved in the *.pt
          self.concat_linear = nn.Linear(size + size, size)
      
      def set_onnx_mode(self, onnx_mode=False):
          self.onnx_mode = onnx_mode
  
      def forward(
          self,
          x: torch.Tensor,
          mask: torch.Tensor,
          pos_emb: torch.Tensor,
          mask_pad: Optional[torch.Tensor] = None,
          output_cache: Optional[torch.Tensor] = None,
          cnn_cache: Optional[torch.Tensor] = None,
      ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
          """Compute encoded features.
  
          Args:
              x (torch.Tensor): Input tensor (#batch, time, size).
              mask (torch.Tensor): Mask tensor for the input (#batch, time).
              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.
              output_cache (torch.Tensor): Cache tensor of the output
                  (#batch, time2, size), time2 < time in x.
              cnn_cache (torch.Tensor): not used here, it's for interface
                  compatibility to ConformerEncoderLayer
          Returns:
              torch.Tensor: Output tensor (#batch, time, size).
              torch.Tensor: Mask tensor (#batch, time).
  
          """
          residual = x
          if self.normalize_before:
              x = self.norm1(x)
  
          if output_cache is None:
              x_q = x
          else:
              assert output_cache.size(0) == x.size(0)
              assert output_cache.size(2) == self.size
              assert output_cache.size(1) < x.size(1)
              chunk = x.size(1) - output_cache.size(1)
              if self.onnx_mode:
                  x_q = slice_helper(x, chunk)
                  residual = slice_helper(residual, chunk)
                  mask = slice_helper(mask, chunk)
              else:
                  x_q = x[:, -chunk:, :]
                  residual = residual[:, -chunk:, :]
                  mask = mask[:, -chunk:, :]
  
          if self.concat_after:
              x_concat = torch.cat((x, self.self_attn(x_q, x, x, mask)), dim=-1)
              x = residual + self.concat_linear(x_concat)
          else:
              x = residual + self.dropout(self.self_attn(x_q, x, x, mask))
          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)
  
          if output_cache is not None:
              x = torch.cat([output_cache, x], dim=1)
  
          fake_cnn_cache = torch.tensor([0.0], dtype=x.dtype, device=x.device)
          return x, mask, 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.
          concat_after (bool): Whether to concat attention layer's input and
              output.
              True: x -> x + linear(concat(x, att(x)))
              False: x -> x + att(x)
      """
      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,
          concat_after: bool = False,
      ):
          """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-12)  # for the FNN module
          self.norm_mha = nn.LayerNorm(size, eps=1e-12)  # for the MHA module
          if feed_forward_macaron is not None:
              self.norm_ff_macaron = nn.LayerNorm(size, eps=1e-12)
              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-12)  # for the CNN module
              self.norm_final = nn.LayerNorm(
                  size, eps=1e-12)  # for the final output of the block
          self.dropout = nn.Dropout(dropout_rate)
          self.size = size
          self.normalize_before = normalize_before
          self.concat_after = concat_after
          self.concat_linear = nn.Linear(size + size, size)
  
      def forward(
          self,
          x: torch.Tensor,
          mask: torch.Tensor,
          pos_emb: torch.Tensor,
          mask_pad: Optional[torch.Tensor] = None,
          output_cache: Optional[torch.Tensor] = None,
          cnn_cache: Optional[torch.Tensor] = None,
          onnx_mode: bool = False,
      ) -> Tuple[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).
              pos_emb (torch.Tensor): positional encoding, must not be None
                  for ConformerEncoderLayer.
              mask_pad (torch.Tensor): batch padding mask used for conv module.
              output_cache (torch.Tensor): Cache tensor of the output
                  (#batch, time2, size), time2 < time in x.
              cnn_cache (torch.Tensor): Convolution cache in conformer layer
          Returns:
              torch.Tensor: Output tensor (#batch, time, size).
              torch.Tensor: Mask tensor (#batch, time).
          """
  
          # 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)
  
          if output_cache is None:
              x_q = x
          else:
              assert output_cache.size(0) == x.size(0)
              assert output_cache.size(2) == self.size
              assert output_cache.size(1) < x.size(1)
  
              # chunk = x.size(1) - output_cache.size(1)
              if onnx_mode:
                  chunk = x.size(1) - output_cache.size(1) + 1
                  chunk = torch.tensor(chunk)
              else:
                  chunk = x.size(1) - output_cache.size(1)
              if onnx_mode:
                  x_q = slice_helper(x, chunk)
                  residual = slice_helper(residual, chunk)
                  mask = slice_helper(mask, chunk)
              else:
                  x_q = x[:, -chunk:, :]
                  residual = residual[:, -chunk:, :]
                  mask = mask[:, -chunk:, :]
  
          x_att = self.self_attn(x_q, x, x, mask, pos_emb)
          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.norm_mha(x)
  
          # convolution module
          # Fake new cnn cache here, and then change it in conv_module
          new_cnn_cache = torch.tensor([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)
  
          if output_cache is not None:
              x = torch.cat([output_cache, x], dim=1)
  
          return x, mask, new_cnn_cache
  