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275 lines
12 KiB
275 lines
12 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 definition."""
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import logging
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import paddle
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from paddlespeech.t2s.modules.conformer.convolution import ConvolutionModule
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from paddlespeech.t2s.modules.conformer.encoder_layer import EncoderLayer
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from paddlespeech.t2s.modules.layer_norm import LayerNorm
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from paddlespeech.t2s.modules.nets_utils import get_activation
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from paddlespeech.t2s.modules.transformer.attention import LegacyRelPositionMultiHeadedAttention
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from paddlespeech.t2s.modules.transformer.attention import MultiHeadedAttention
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from paddlespeech.t2s.modules.transformer.attention import RelPositionMultiHeadedAttention
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from paddlespeech.t2s.modules.transformer.embedding import LegacyRelPositionalEncoding
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from paddlespeech.t2s.modules.transformer.embedding import PositionalEncoding
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from paddlespeech.t2s.modules.transformer.embedding import RelPositionalEncoding
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from paddlespeech.t2s.modules.transformer.embedding import ScaledPositionalEncoding
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from paddlespeech.t2s.modules.transformer.multi_layer_conv import Conv1dLinear
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from paddlespeech.t2s.modules.transformer.multi_layer_conv import MultiLayeredConv1d
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from paddlespeech.t2s.modules.transformer.positionwise_feed_forward import PositionwiseFeedForward
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from paddlespeech.t2s.modules.transformer.repeat import repeat
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from paddlespeech.t2s.modules.transformer.subsampling import Conv2dSubsampling
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class Encoder(paddle.nn.Layer):
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"""Conformer 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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Dimension 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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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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macaron_style : bool
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Whether to use macaron style for positionwise layer.
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pos_enc_layer_type : str
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Encoder positional encoding layer type.
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selfattention_layer_type : str
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Encoder attention layer type.
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activation_type : str
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Encoder activation function type.
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use_cnn_module : bool
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Whether to use convolution module.
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zero_triu : bool
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Whether to zero the upper triangular part of attention matrix.
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cnn_module_kernel : int
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Kernerl size of convolution module.
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padding_idx : int
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Padding idx for input_layer=embed.
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stochastic_depth_rate : float
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Maximum probability to skip the encoder layer.
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intermediate_layers : Union[List[int], None]
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indices of intermediate CTC layer.
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indices start from 1.
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if not None, intermediate outputs are returned (which changes return type
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signature.)
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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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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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macaron_style=False,
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pos_enc_layer_type="abs_pos",
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selfattention_layer_type="selfattn",
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activation_type="swish",
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use_cnn_module=False,
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zero_triu=False,
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cnn_module_kernel=31,
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padding_idx=-1,
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stochastic_depth_rate=0.0,
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intermediate_layers=None, ):
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"""Construct an Encoder object."""
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super(Encoder, self).__init__()
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activation = get_activation(activation_type)
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if pos_enc_layer_type == "abs_pos":
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pos_enc_class = PositionalEncoding
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elif pos_enc_layer_type == "scaled_abs_pos":
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pos_enc_class = ScaledPositionalEncoding
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elif pos_enc_layer_type == "rel_pos":
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assert selfattention_layer_type == "rel_selfattn"
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pos_enc_class = RelPositionalEncoding
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elif pos_enc_layer_type == "legacy_rel_pos":
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pos_enc_class = LegacyRelPositionalEncoding
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assert selfattention_layer_type == "legacy_rel_selfattn"
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else:
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raise ValueError("unknown pos_enc_layer: " + pos_enc_layer_type)
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self.conv_subsampling_factor = 1
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if input_layer == "linear":
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self.embed = paddle.nn.Sequential(
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paddle.nn.Linear(idim, attention_dim),
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paddle.nn.LayerNorm(attention_dim),
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paddle.nn.Dropout(dropout_rate),
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pos_enc_class(attention_dim, positional_dropout_rate), )
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elif input_layer == "conv2d":
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self.embed = Conv2dSubsampling(
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idim,
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attention_dim,
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dropout_rate,
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pos_enc_class(attention_dim, positional_dropout_rate), )
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self.conv_subsampling_factor = 4
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elif input_layer == "embed":
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self.embed = paddle.nn.Sequential(
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paddle.nn.Embedding(
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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, paddle.nn.Layer):
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self.embed = paddle.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 = paddle.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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# self-attention module definition
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if selfattention_layer_type == "selfattn":
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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 = (attention_heads, attention_dim,
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attention_dropout_rate, )
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elif selfattention_layer_type == "legacy_rel_selfattn":
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assert pos_enc_layer_type == "legacy_rel_pos"
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encoder_selfattn_layer = LegacyRelPositionMultiHeadedAttention
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encoder_selfattn_layer_args = (attention_heads, attention_dim,
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attention_dropout_rate, )
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elif selfattention_layer_type == "rel_selfattn":
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logging.info(
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"encoder self-attention layer type = relative self-attention")
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assert pos_enc_layer_type == "rel_pos"
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encoder_selfattn_layer = RelPositionMultiHeadedAttention
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encoder_selfattn_layer_args = (attention_heads, attention_dim,
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attention_dropout_rate, zero_triu, )
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else:
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raise ValueError("unknown encoder_attn_layer: " +
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selfattention_layer_type)
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# feed-forward module definition
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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, activation, )
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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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# convolution module definition
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convolution_layer = ConvolutionModule
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convolution_layer_args = (attention_dim, cnn_module_kernel, activation)
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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),
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positionwise_layer(*positionwise_layer_args),
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positionwise_layer(*positionwise_layer_args) if macaron_style else None,
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convolution_layer(*convolution_layer_args) if use_cnn_module else None,
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dropout_rate,
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normalize_before,
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concat_after,
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stochastic_depth_rate * float(1 + lnum) / num_blocks, ), )
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if self.normalize_before:
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self.after_norm = LayerNorm(attention_dim)
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self.intermediate_layers = intermediate_layers
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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): Mask tensor (#batch, 1, 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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if isinstance(self.embed, (Conv2dSubsampling)):
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xs, masks = self.embed(xs, masks)
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else:
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xs = self.embed(xs)
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if self.intermediate_layers is None:
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xs, masks = self.encoders(xs, masks)
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else:
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intermediate_outputs = []
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for layer_idx, encoder_layer in enumerate(self.encoders):
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xs, masks = encoder_layer(xs, masks)
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if (self.intermediate_layers is not None and
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layer_idx + 1 in self.intermediate_layers):
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# intermediate branches also require normalization.
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encoder_output = xs
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if isinstance(encoder_output, tuple):
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encoder_output = encoder_output[0]
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if self.normalize_before:
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encoder_output = self.after_norm(encoder_output)
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intermediate_outputs.append(encoder_output)
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if isinstance(xs, tuple):
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xs = xs[0]
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if self.normalize_before:
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xs = self.after_norm(xs)
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if self.intermediate_layers is not None:
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return xs, masks, intermediate_outputs
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return xs, masks
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