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183 lines
6.9 KiB
183 lines
6.9 KiB
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# Modified from espnet(https://github.com/espnet/espnet)
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"""Encoder self-attention layer definition."""
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import paddle
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from paddle import nn
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from paddlespeech.t2s.modules.layer_norm import LayerNorm
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class EncoderLayer(nn.Layer):
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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 (nn.Layer): 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 (nn.Layer): 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 (nn.Layer): 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 (nn.Layer): 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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stochastic_depth_rate (float): Proability to skip this layer.
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During training, the layer may skip residual computation and return input
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as-is with given probability.
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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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stochastic_depth_rate=0.0, ):
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"""Construct an EncoderLayer object."""
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super().__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(
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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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if self.concat_after:
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self.concat_linear = nn.Linear(size + size, size)
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self.stochastic_depth_rate = stochastic_depth_rate
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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, 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(Tensor): Mask tensor for the input (#batch, time).
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cache (Tensor):
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Returns:
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Tensor: Output tensor (#batch, time, size).
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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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skip_layer = False
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# with stochastic depth, residual connection `x + f(x)` becomes
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# `x <- x + 1 / (1 - p) * f(x)` at training time.
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stoch_layer_coeff = 1.0
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if self.training and self.stochastic_depth_rate > 0:
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skip_layer = paddle.rand(1).item() < self.stochastic_depth_rate
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stoch_layer_coeff = 1.0 / (1 - self.stochastic_depth_rate)
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if skip_layer:
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if cache is not None:
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x = paddle.concat([cache, x], axis=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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# 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 + stoch_layer_coeff * self.ff_scale * self.dropout(
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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 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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if self.concat_after:
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x_concat = paddle.concat((x, x_att), axis=-1)
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x = residual + stoch_layer_coeff * self.concat_linear(x_concat)
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else:
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x = residual + stoch_layer_coeff * 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 + stoch_layer_coeff * 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 + stoch_layer_coeff * self.ff_scale * self.dropout(
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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 = paddle.concat([cache, x], axis=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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