yeyupiaoling
3 years ago
parent
1c156bfe4d
commit
c1df5b7985
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from typing import Tuple
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import paddle
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from paddle import nn
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from paddle.nn import initializer as I
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from typeguard import check_argument_types
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__all__ = ['ConvolutionModule2']
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from paddlespeech.s2t import masked_fill
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from paddlespeech.s2t.modules.align import Conv1D, BatchNorm1D, LayerNorm
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class ConvolutionModule2(nn.Layer):
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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.Layer=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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adaptive_scale: bool=False,
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init_weights: bool=False):
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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.bias = bias
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self.channels = channels
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self.kernel_size = kernel_size
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self.adaptive_scale = adaptive_scale
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ada_scale = self.create_parameter(
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[1, 1, channels], default_initializer=I.Constant(1.0))
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self.add_parameter('ada_scale', ada_scale)
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ada_bias = self.create_parameter(
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[1, 1, channels], default_initializer=I.Constant(0.0))
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self.add_parameter('ada_bias', ada_bias)
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self.pointwise_conv1 = 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_attr=None
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if bias else False, # None for True, using bias as default config
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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 = 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_attr=None
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if bias else False, # None for True, using bias as default config
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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 = BatchNorm1D(channels)
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else:
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self.use_layer_norm = True
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self.norm = LayerNorm(channels)
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self.pointwise_conv2 = 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_attr=None
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if bias else False, # None for True, using bias as default config
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)
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self.activation = activation
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if init_weights:
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self.init_weights()
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def init_weights(self):
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pw_max = self.channels**-0.5
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dw_max = self.kernel_size**-0.5
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self.pointwise_conv1._param_attr = paddle.nn.initializer.Uniform(
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low=-pw_max, high=pw_max)
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if self.bias:
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self.pointwise_conv1._bias_attr = paddle.nn.initializer.Uniform(
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low=-pw_max, high=pw_max)
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self.depthwise_conv._param_attr = paddle.nn.initializer.Uniform(
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low=-dw_max, high=dw_max)
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if self.bias:
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self.depthwise_conv._bias_attr = paddle.nn.initializer.Uniform(
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low=-dw_max, high=dw_max)
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self.pointwise_conv2._param_attr = paddle.nn.initializer.Uniform(
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low=-pw_max, high=pw_max)
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if self.bias:
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self.pointwise_conv2._bias_attr = paddle.nn.initializer.Uniform(
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low=-pw_max, high=pw_max)
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def forward(
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self,
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x: paddle.Tensor,
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mask_pad: paddle.Tensor=paddle.ones([0, 0, 0], dtype=paddle.bool),
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cache: paddle.Tensor=paddle.zeros([0, 0, 0]),
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) -> Tuple[paddle.Tensor, paddle.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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(0, 0, 0) means fake mask.
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cache (torch.Tensor): left context cache, it is only
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used in causal convolution (#batch, channels, cache_t),
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(0, 0, 0) meas fake cache.
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Returns:
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torch.Tensor: Output tensor (#batch, time, channels).
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"""
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if self.adaptive_scale:
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x = self.ada_scale * x + self.ada_bias
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# exchange the temporal dimension and the feature dimension
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x = x.transpose([0, 2, 1]) # [B, C, T]
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# mask batch padding
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if mask_pad.shape[2] > 0: # time > 0
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x = masked_fill(x, mask_pad, 0.0)
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if self.lorder > 0:
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if cache.shape[2] == 0: # cache_t == 0
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x = nn.functional.pad(
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x, [self.lorder, 0], 'constant', 0.0, data_format='NCL')
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else:
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assert cache.shape[0] == x.shape[0] # B
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assert cache.shape[1] == x.shape[1] # C
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x = paddle.concat((cache, x), axis=2)
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assert (x.shape[2] > self.lorder)
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new_cache = x[:, :, -self.lorder:] # [B, C, T]
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else:
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# It's better we just return None if no cache is required,
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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 = paddle.zeros([0, 0, 0], dtype=x.dtype)
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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, axis=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([0, 2, 1]) # [B, T, C]
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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([0, 2, 1]) # [B, C, T]
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x = self.pointwise_conv2(x)
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# mask batch padding
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if mask_pad.shape[2] > 0: # time > 0
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x = masked_fill(x, mask_pad, 0.0)
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x = x.transpose([0, 2, 1]) # [B, T, C]
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return x, new_cache
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