135 lines
4.4 KiB
Python
135 lines
4.4 KiB
Python
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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# Copyright 2021 Mobvoi Inc. All Rights Reserved.
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# Author: di.wu@mobvoi.com (DI WU)
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"""ConvolutionModule definition."""
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from typing import Optional, Tuple
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import torch
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from torch import nn
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from typeguard import check_argument_types
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class ConvolutionModule(nn.Module):
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"""ConvolutionModule in Conformer model."""
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def __init__(self,
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channels: int,
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kernel_size: int = 15,
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activation: nn.Module = nn.ReLU(),
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norm: str = "batch_norm",
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causal: bool = False,
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bias: bool = True):
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"""Construct an ConvolutionModule object.
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Args:
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channels (int): The number of channels of conv layers.
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kernel_size (int): Kernel size of conv layers.
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causal (int): Whether use causal convolution or not
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"""
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assert check_argument_types()
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super().__init__()
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self.pointwise_conv1 = nn.Conv1d(
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channels,
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2 * channels,
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kernel_size=1,
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stride=1,
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padding=0,
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bias=bias,
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)
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# self.lorder is used to distinguish if it's a causal convolution,
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# if self.lorder > 0: it's a causal convolution, the input will be
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# padded with self.lorder frames on the left in forward.
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# else: it's a symmetrical convolution
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if causal:
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padding = 0
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self.lorder = kernel_size - 1
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else:
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# kernel_size should be an odd number for none causal convolution
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assert (kernel_size - 1) % 2 == 0
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padding = (kernel_size - 1) // 2
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self.lorder = 0
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self.depthwise_conv = nn.Conv1d(
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channels,
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channels,
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kernel_size,
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stride=1,
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padding=padding,
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groups=channels,
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bias=bias,
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)
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assert norm in ['batch_norm', 'layer_norm']
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if norm == "batch_norm":
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self.use_layer_norm = False
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self.norm = nn.BatchNorm1d(channels)
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else:
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self.use_layer_norm = True
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self.norm = nn.LayerNorm(channels)
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self.pointwise_conv2 = nn.Conv1d(
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channels,
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channels,
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kernel_size=1,
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stride=1,
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padding=0,
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bias=bias,
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)
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self.activation = activation
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def forward(
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self,
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x: torch.Tensor,
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mask_pad: Optional[torch.Tensor] = None,
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cache: Optional[torch.Tensor] = None,
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) -> Tuple[torch.Tensor, torch.Tensor]:
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"""Compute convolution module.
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Args:
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x (torch.Tensor): Input tensor (#batch, time, channels).
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mask_pad (torch.Tensor): used for batch padding (#batch, 1, time)
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cache (torch.Tensor): left context cache, it is only
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used in causal convolution
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Returns:
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torch.Tensor: Output tensor (#batch, time, channels).
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"""
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# exchange the temporal dimension and the feature dimension
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x = x.transpose(1, 2) # (#batch, channels, time)
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# mask batch padding
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if mask_pad is not None:
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x.masked_fill_(~mask_pad, 0.0)
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if self.lorder > 0:
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if cache is None:
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x = nn.functional.pad(x, (self.lorder, 0), 'constant', 0.0)
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else:
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assert cache.size(0) == x.size(0)
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assert cache.size(1) == x.size(1)
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x = torch.cat((cache, x), dim=2)
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assert (x.size(2) > self.lorder)
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new_cache = x[:, :, -self.lorder:]
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else:
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# It's better we just return None if no cache is requried,
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# However, for JIT export, here we just fake one tensor instead of
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# None.
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new_cache = torch.tensor([0.0], dtype=x.dtype, device=x.device)
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# GLU mechanism
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x = self.pointwise_conv1(x) # (batch, 2*channel, dim)
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x = nn.functional.glu(x, dim=1) # (batch, channel, dim)
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# 1D Depthwise Conv
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x = self.depthwise_conv(x)
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if self.use_layer_norm:
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x = x.transpose(1, 2)
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x = self.activation(self.norm(x))
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if self.use_layer_norm:
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x = x.transpose(1, 2)
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x = self.pointwise_conv2(x)
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# mask batch padding
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if mask_pad is not None:
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x.masked_fill_(~mask_pad, 0.0)
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return x.transpose(1, 2), new_cache
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