274 lines
10 KiB
Python
274 lines
10 KiB
Python
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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# Copyright 2020 Johns Hopkins University (Shinji Watanabe)
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# Northwestern Polytechnical University (Pengcheng Guo)
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# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
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"""Encoder self-attention layer definition."""
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import torch
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from torch import nn
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from funasr.models.transformer.layer_norm import LayerNorm
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from torch.autograd import Variable
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class Encoder_Conformer_Layer(nn.Module):
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"""Encoder layer module.
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Args:
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size (int): Input dimension.
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self_attn (torch.nn.Module): Self-attention module instance.
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`MultiHeadedAttention` or `RelPositionMultiHeadedAttention` instance
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can be used as the argument.
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feed_forward (torch.nn.Module): Feed-forward module instance.
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`PositionwiseFeedForward`, `MultiLayeredConv1d`, or `Conv1dLinear` instance
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can be used as the argument.
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feed_forward_macaron (torch.nn.Module): Additional feed-forward module instance.
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`PositionwiseFeedForward`, `MultiLayeredConv1d`, or `Conv1dLinear` instance
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can be used as the argument.
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conv_module (torch.nn.Module): Convolution module instance.
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`ConvlutionModule` instance can be used as the argument.
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dropout_rate (float): Dropout rate.
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normalize_before (bool): Whether to use layer_norm before the first block.
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concat_after (bool): Whether to concat attention layer's input and output.
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if True, additional linear will be applied.
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i.e. x -> x + linear(concat(x, att(x)))
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if False, no additional linear will be applied. i.e. x -> x + att(x)
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"""
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def __init__(
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self,
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size,
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self_attn,
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feed_forward,
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feed_forward_macaron,
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conv_module,
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dropout_rate,
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normalize_before=True,
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concat_after=False,
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cca_pos=0,
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):
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"""Construct an Encoder_Conformer_Layer object."""
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super(Encoder_Conformer_Layer, self).__init__()
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self.self_attn = self_attn
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self.feed_forward = feed_forward
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self.feed_forward_macaron = feed_forward_macaron
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self.conv_module = conv_module
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self.norm_ff = LayerNorm(size) # for the FNN module
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self.norm_mha = LayerNorm(size) # for the MHA module
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if feed_forward_macaron is not None:
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self.norm_ff_macaron = LayerNorm(size)
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self.ff_scale = 0.5
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else:
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self.ff_scale = 1.0
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if self.conv_module is not None:
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self.norm_conv = LayerNorm(size) # for the CNN module
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self.norm_final = LayerNorm(size) # for the final output of the block
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self.dropout = nn.Dropout(dropout_rate)
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self.size = size
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self.normalize_before = normalize_before
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self.concat_after = concat_after
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self.cca_pos = cca_pos
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if self.concat_after:
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self.concat_linear = nn.Linear(size + size, size)
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def forward(self, x_input, mask, cache=None):
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"""Compute encoded features.
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Args:
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x_input (Union[Tuple, torch.Tensor]): Input tensor w/ or w/o pos emb.
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- w/ pos emb: Tuple of tensors [(#batch, time, size), (1, time, size)].
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- w/o pos emb: Tensor (#batch, time, size).
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mask (torch.Tensor): Mask tensor for the input (#batch, time).
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cache (torch.Tensor): Cache tensor of the input (#batch, time - 1, size).
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Returns:
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torch.Tensor: Output tensor (#batch, time, size).
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torch.Tensor: Mask tensor (#batch, time).
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"""
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if isinstance(x_input, tuple):
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x, pos_emb = x_input[0], x_input[1]
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else:
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x, pos_emb = x_input, None
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# whether to use macaron style
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if self.feed_forward_macaron is not None:
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residual = x
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if self.normalize_before:
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x = self.norm_ff_macaron(x)
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x = residual + self.ff_scale * self.dropout(self.feed_forward_macaron(x))
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if not self.normalize_before:
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x = self.norm_ff_macaron(x)
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# multi-headed self-attention module
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residual = x
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if self.normalize_before:
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x = self.norm_mha(x)
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if cache is None:
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x_q = x
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else:
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assert cache.shape == (x.shape[0], x.shape[1] - 1, self.size)
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x_q = x[:, -1:, :]
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residual = residual[:, -1:, :]
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mask = None if mask is None else mask[:, -1:, :]
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if self.cca_pos < 2:
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if pos_emb is not None:
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x_att = self.self_attn(x_q, x, x, pos_emb, mask)
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else:
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x_att = self.self_attn(x_q, x, x, mask)
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else:
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x_att = self.self_attn(x_q, x, x, mask)
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if self.concat_after:
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x_concat = torch.cat((x, x_att), dim=-1)
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x = residual + self.concat_linear(x_concat)
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else:
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x = residual + self.dropout(x_att)
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if not self.normalize_before:
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x = self.norm_mha(x)
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# convolution module
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if self.conv_module is not None:
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residual = x
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if self.normalize_before:
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x = self.norm_conv(x)
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x = residual + self.dropout(self.conv_module(x))
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if not self.normalize_before:
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x = self.norm_conv(x)
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# feed forward module
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residual = x
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if self.normalize_before:
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x = self.norm_ff(x)
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x = residual + self.ff_scale * self.dropout(self.feed_forward(x))
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if not self.normalize_before:
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x = self.norm_ff(x)
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if self.conv_module is not None:
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x = self.norm_final(x)
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if cache is not None:
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x = torch.cat([cache, x], dim=1)
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if pos_emb is not None:
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return (x, pos_emb), mask
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return x, mask
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class EncoderLayer(nn.Module):
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"""Encoder layer module.
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Args:
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size (int): Input dimension.
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self_attn (torch.nn.Module): Self-attention module instance.
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`MultiHeadedAttention` or `RelPositionMultiHeadedAttention` instance
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can be used as the argument.
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feed_forward (torch.nn.Module): Feed-forward module instance.
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`PositionwiseFeedForward`, `MultiLayeredConv1d`, or `Conv1dLinear` instance
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can be used as the argument.
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feed_forward_macaron (torch.nn.Module): Additional feed-forward module instance.
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`PositionwiseFeedForward`, `MultiLayeredConv1d`, or `Conv1dLinear` instance
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can be used as the argument.
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conv_module (torch.nn.Module): Convolution module instance.
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`ConvlutionModule` instance can be used as the argument.
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dropout_rate (float): Dropout rate.
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normalize_before (bool): Whether to use layer_norm before the first block.
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concat_after (bool): Whether to concat attention layer's input and output.
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if True, additional linear will be applied.
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i.e. x -> x + linear(concat(x, att(x)))
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if False, no additional linear will be applied. i.e. x -> x + att(x)
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"""
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def __init__(
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self,
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size,
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self_attn_cros_channel,
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self_attn_conformer,
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feed_forward_csa,
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feed_forward_macaron_csa,
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conv_module_csa,
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dropout_rate,
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normalize_before=True,
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concat_after=False,
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):
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"""Construct an EncoderLayer object."""
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super(EncoderLayer, self).__init__()
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self.encoder_cros_channel_atten = self_attn_cros_channel
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self.encoder_csa = Encoder_Conformer_Layer(
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size,
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self_attn_conformer,
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feed_forward_csa,
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feed_forward_macaron_csa,
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conv_module_csa,
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dropout_rate,
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normalize_before,
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concat_after,
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cca_pos=0,
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)
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self.norm_mha = LayerNorm(size) # for the MHA module
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self.dropout = nn.Dropout(dropout_rate)
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def forward(self, x_input, mask, channel_size, cache=None):
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"""Compute encoded features.
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Args:
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x_input (Union[Tuple, torch.Tensor]): Input tensor w/ or w/o pos emb.
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- w/ pos emb: Tuple of tensors [(#batch, time, size), (1, time, size)].
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- w/o pos emb: Tensor (#batch, time, size).
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mask (torch.Tensor): Mask tensor for the input (#batch, time).
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cache (torch.Tensor): Cache tensor of the input (#batch, time - 1, size).
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Returns:
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torch.Tensor: Output tensor (#batch, time, size).
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torch.Tensor: Mask tensor (#batch, time).
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"""
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if isinstance(x_input, tuple):
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x, pos_emb = x_input[0], x_input[1]
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else:
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x, pos_emb = x_input, None
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residual = x
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x = self.norm_mha(x)
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t_leng = x.size(1)
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d_dim = x.size(2)
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x_new = x.reshape(-1, channel_size, t_leng, d_dim).transpose(1, 2) # x_new B*T * C * D
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x_k_v = x_new.new(x_new.size(0), x_new.size(1), 5, x_new.size(2), x_new.size(3))
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pad_before = Variable(torch.zeros(x_new.size(0), 2, x_new.size(2), x_new.size(3))).type(
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x_new.type()
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)
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pad_after = Variable(torch.zeros(x_new.size(0), 2, x_new.size(2), x_new.size(3))).type(
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x_new.type()
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)
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x_pad = torch.cat([pad_before, x_new, pad_after], 1)
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x_k_v[:, :, 0, :, :] = x_pad[:, 0:-4, :, :]
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x_k_v[:, :, 1, :, :] = x_pad[:, 1:-3, :, :]
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x_k_v[:, :, 2, :, :] = x_pad[:, 2:-2, :, :]
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x_k_v[:, :, 3, :, :] = x_pad[:, 3:-1, :, :]
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x_k_v[:, :, 4, :, :] = x_pad[:, 4:, :, :]
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x_new = x_new.reshape(-1, channel_size, d_dim)
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x_k_v = x_k_v.reshape(-1, 5 * channel_size, d_dim)
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x_att = self.encoder_cros_channel_atten(x_new, x_k_v, x_k_v, None)
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x_att = (
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x_att.reshape(-1, t_leng, channel_size, d_dim)
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.transpose(1, 2)
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.reshape(-1, t_leng, d_dim)
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)
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x = residual + self.dropout(x_att)
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if pos_emb is not None:
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x_input = (x, pos_emb)
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else:
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x_input = x
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x_input, mask = self.encoder_csa(x_input, mask)
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return x_input, mask, channel_size
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