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226 lines
8.2 KiB
226 lines
8.2 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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import logging
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from paddle import nn
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from parakeet.modules.fastspeech2_transformer.attention import MultiHeadedAttention
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from parakeet.modules.fastspeech2_transformer.embedding import PositionalEncoding
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from parakeet.modules.fastspeech2_transformer.encoder_layer import EncoderLayer
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from parakeet.modules.fastspeech2_transformer.multi_layer_conv import Conv1dLinear
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from parakeet.modules.fastspeech2_transformer.multi_layer_conv import MultiLayeredConv1d
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from parakeet.modules.fastspeech2_transformer.positionwise_feed_forward import PositionwiseFeedForward
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from parakeet.modules.fastspeech2_transformer.repeat import repeat
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class Encoder(nn.Layer):
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"""Transformer encoder module.
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Parameters
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----------
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idim : int
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Input dimension.
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attention_dim : int
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Dimention of attention.
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attention_heads : int
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The number of heads of multi head attention.
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linear_units : int
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The number of units of position-wise feed forward.
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num_blocks : int
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The number of decoder blocks.
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dropout_rate : float
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Dropout rate.
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positional_dropout_rate : float
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Dropout rate after adding positional encoding.
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attention_dropout_rate : float
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Dropout rate in attention.
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input_layer : Union[str, paddle.nn.Layer]
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Input layer type.
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pos_enc_class : paddle.nn.Layer
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Positional encoding module class.
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`PositionalEncoding `or `ScaledPositionalEncoding`
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normalize_before : bool
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Whether to use layer_norm before the first block.
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concat_after : bool
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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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positionwise_layer_type : str
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"linear", "conv1d", or "conv1d-linear".
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positionwise_conv_kernel_size : int
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Kernel size of positionwise conv1d layer.
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selfattention_layer_type : str
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Encoder attention layer type.
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padding_idx : int
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Padding idx for input_layer=embed.
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"""
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def __init__(
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self,
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idim,
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attention_dim=256,
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attention_heads=4,
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linear_units=2048,
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num_blocks=6,
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dropout_rate=0.1,
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positional_dropout_rate=0.1,
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attention_dropout_rate=0.0,
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input_layer="conv2d",
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pos_enc_class=PositionalEncoding,
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normalize_before=True,
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concat_after=False,
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positionwise_layer_type="linear",
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positionwise_conv_kernel_size=1,
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selfattention_layer_type="selfattn",
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padding_idx=-1, ):
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"""Construct an Encoder object."""
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super(Encoder, self).__init__()
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self.conv_subsampling_factor = 1
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if input_layer == "linear":
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self.embed = nn.Sequential(
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nn.Linear(idim, attention_dim, bias_attr=True),
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nn.LayerNorm(attention_dim),
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nn.Dropout(dropout_rate),
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nn.ReLU(),
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pos_enc_class(attention_dim, positional_dropout_rate), )
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elif input_layer == "embed":
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self.embed = nn.Sequential(
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nn.Embedding(idim, attention_dim, padding_idx=padding_idx),
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pos_enc_class(attention_dim, positional_dropout_rate), )
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elif isinstance(input_layer, nn.Layer):
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self.embed = nn.Sequential(
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input_layer,
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pos_enc_class(attention_dim, positional_dropout_rate), )
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elif input_layer is None:
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self.embed = nn.Sequential(
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pos_enc_class(attention_dim, positional_dropout_rate))
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else:
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raise ValueError("unknown input_layer: " + input_layer)
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self.normalize_before = normalize_before
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positionwise_layer, positionwise_layer_args = self.get_positionwise_layer(
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positionwise_layer_type,
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attention_dim,
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linear_units,
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dropout_rate,
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positionwise_conv_kernel_size, )
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if selfattention_layer_type in [
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"selfattn",
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"rel_selfattn",
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"legacy_rel_selfattn",
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]:
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logging.info("encoder self-attention layer type = self-attention")
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encoder_selfattn_layer = MultiHeadedAttention
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encoder_selfattn_layer_args = [
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(attention_heads, attention_dim, attention_dropout_rate, )
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] * num_blocks
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else:
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raise NotImplementedError(selfattention_layer_type)
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self.encoders = repeat(
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num_blocks,
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lambda lnum: EncoderLayer(
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attention_dim,
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encoder_selfattn_layer(*encoder_selfattn_layer_args[lnum]),
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positionwise_layer(*positionwise_layer_args),
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dropout_rate,
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normalize_before,
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concat_after, ), )
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if self.normalize_before:
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self.after_norm = nn.LayerNorm(attention_dim)
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def get_positionwise_layer(
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self,
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positionwise_layer_type="linear",
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attention_dim=256,
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linear_units=2048,
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dropout_rate=0.1,
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positionwise_conv_kernel_size=1, ):
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"""Define positionwise layer."""
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if positionwise_layer_type == "linear":
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positionwise_layer = PositionwiseFeedForward
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positionwise_layer_args = (attention_dim, linear_units,
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dropout_rate)
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elif positionwise_layer_type == "conv1d":
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positionwise_layer = MultiLayeredConv1d
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positionwise_layer_args = (attention_dim, linear_units,
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positionwise_conv_kernel_size,
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dropout_rate, )
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elif positionwise_layer_type == "conv1d-linear":
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positionwise_layer = Conv1dLinear
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positionwise_layer_args = (attention_dim, linear_units,
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positionwise_conv_kernel_size,
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dropout_rate, )
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else:
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raise NotImplementedError("Support only linear or conv1d.")
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return positionwise_layer, positionwise_layer_args
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def forward(self, xs, masks):
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"""Encode input sequence.
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Parameters
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----------
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xs : paddle.Tensor
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Input tensor (#batch, time, idim).
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masks : paddle.Tensor
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Mask tensor (#batch, time).
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Returns
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----------
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paddle.Tensor
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Output tensor (#batch, time, attention_dim).
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paddle.Tensor
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Mask tensor (#batch, time).
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"""
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xs = self.embed(xs)
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xs, masks = self.encoders(xs, masks)
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if self.normalize_before:
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xs = self.after_norm(xs)
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return xs, masks
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def forward_one_step(self, xs, masks, cache=None):
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"""Encode input frame.
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Parameters
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----------
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xs : paddle.Tensor
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Input tensor.
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masks : paddle.Tensor
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Mask tensor.
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cache : List[paddle.Tensor]
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List of cache tensors.
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Returns
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----------
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paddle.Tensor
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Output tensor.
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paddle.Tensor
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Mask tensor.
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List[paddle.Tensor]
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List of new cache tensors.
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"""
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xs = self.embed(xs)
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if cache is None:
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cache = [None for _ in range(len(self.encoders))]
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new_cache = []
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for c, e in zip(cache, self.encoders):
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xs, masks = e(xs, masks, cache=c)
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new_cache.append(xs)
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if self.normalize_before:
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xs = self.after_norm(xs)
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return xs, masks, new_cache
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