# Copyright 2019 Shigeki Karita
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# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
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"""Transformer encoder definition."""
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from typing import List
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from typing import Optional
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from typing import 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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import logging
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from funasr.models.ctc import CTC
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from funasr.models.encoder.abs_encoder import AbsEncoder
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from funasr.modules.attention import MultiHeadedAttention
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from funasr.modules.embedding import PositionalEncoding
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from funasr.modules.layer_norm import LayerNorm
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from funasr.modules.multi_layer_conv import Conv1dLinear
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from funasr.modules.multi_layer_conv import MultiLayeredConv1d
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from funasr.modules.nets_utils import make_pad_mask
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from funasr.modules.positionwise_feed_forward import (
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PositionwiseFeedForward, # noqa: H301
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)
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from funasr.modules.repeat import repeat
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from funasr.modules.nets_utils import rename_state_dict
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from funasr.modules.dynamic_conv import DynamicConvolution
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from funasr.modules.dynamic_conv2d import DynamicConvolution2D
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from funasr.modules.lightconv import LightweightConvolution
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from funasr.modules.lightconv2d import LightweightConvolution2D
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from funasr.modules.subsampling import Conv2dSubsampling
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from funasr.modules.subsampling import Conv2dSubsampling2
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from funasr.modules.subsampling import Conv2dSubsampling6
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from funasr.modules.subsampling import Conv2dSubsampling8
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from funasr.modules.subsampling import TooShortUttError
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from funasr.modules.subsampling import check_short_utt
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class EncoderLayer(nn.Module):
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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 (torch.nn.Module): 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 (torch.nn.Module): 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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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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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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):
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"""Construct an EncoderLayer object."""
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super(EncoderLayer, self).__init__()
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self.self_attn = self_attn
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self.feed_forward = feed_forward
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self.norm1 = LayerNorm(size)
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self.norm2 = LayerNorm(size)
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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, mask, cache=None):
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"""Compute encoded features.
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Args:
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x_input (torch.Tensor): Input tensor (#batch, time, size).
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mask (torch.Tensor): Mask tensor for the input (#batch, time).
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cache (torch.Tensor): Cache tensor of the input (#batch, time - 1, size).
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Returns:
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torch.Tensor: Output tensor (#batch, time, size).
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torch.Tensor: Mask tensor (#batch, time).
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"""
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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 = torch.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 = torch.cat([cache, x], dim=1)
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return x, mask
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residual = x
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if self.normalize_before:
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x = self.norm1(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 self.concat_after:
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x_concat = torch.cat((x, self.self_attn(x_q, x, x, mask)), dim=-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(
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self.self_attn(x_q, x, x, mask)
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)
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if not self.normalize_before:
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x = self.norm1(x)
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residual = x
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if self.normalize_before:
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x = self.norm2(x)
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x = residual + stoch_layer_coeff * self.dropout(self.feed_forward(x))
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if not self.normalize_before:
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x = self.norm2(x)
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if cache is not None:
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x = torch.cat([cache, x], dim=1)
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return x, mask
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class TransformerEncoder(AbsEncoder):
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"""Transformer encoder module.
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Args:
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input_size: input dim
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output_size: dimension of attention
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attention_heads: the number of heads of multi head attention
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linear_units: the number of units of position-wise feed forward
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num_blocks: the number of decoder blocks
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dropout_rate: dropout rate
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attention_dropout_rate: dropout rate in attention
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positional_dropout_rate: dropout rate after adding positional encoding
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input_layer: input layer type
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pos_enc_class: PositionalEncoding or ScaledPositionalEncoding
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normalize_before: whether to use layer_norm before the first block
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concat_after: 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.
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i.e. x -> x + att(x)
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positionwise_layer_type: linear of conv1d
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positionwise_conv_kernel_size: kernel size of positionwise conv1d layer
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padding_idx: 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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input_size: int,
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output_size: int = 256,
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attention_heads: int = 4,
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linear_units: int = 2048,
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num_blocks: int = 6,
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dropout_rate: float = 0.1,
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positional_dropout_rate: float = 0.1,
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attention_dropout_rate: float = 0.0,
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input_layer: Optional[str] = "conv2d",
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pos_enc_class=PositionalEncoding,
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normalize_before: bool = True,
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concat_after: bool = False,
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positionwise_layer_type: str = "linear",
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positionwise_conv_kernel_size: int = 1,
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padding_idx: int = -1,
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interctc_layer_idx: List[int] = [],
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interctc_use_conditioning: bool = False,
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):
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assert check_argument_types()
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super().__init__()
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self._output_size = output_size
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if input_layer == "linear":
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self.embed = torch.nn.Sequential(
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torch.nn.Linear(input_size, output_size),
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torch.nn.LayerNorm(output_size),
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torch.nn.Dropout(dropout_rate),
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torch.nn.ReLU(),
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pos_enc_class(output_size, positional_dropout_rate),
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)
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elif input_layer == "conv2d":
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self.embed = Conv2dSubsampling(input_size, output_size, dropout_rate)
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elif input_layer == "conv2d2":
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self.embed = Conv2dSubsampling2(input_size, output_size, dropout_rate)
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elif input_layer == "conv2d6":
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self.embed = Conv2dSubsampling6(input_size, output_size, dropout_rate)
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elif input_layer == "conv2d8":
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self.embed = Conv2dSubsampling8(input_size, output_size, dropout_rate)
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elif input_layer == "embed":
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self.embed = torch.nn.Sequential(
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torch.nn.Embedding(input_size, output_size, padding_idx=padding_idx),
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pos_enc_class(output_size, positional_dropout_rate),
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)
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elif input_layer is None:
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if input_size == output_size:
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self.embed = None
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else:
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self.embed = torch.nn.Linear(input_size, output_size)
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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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if positionwise_layer_type == "linear":
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positionwise_layer = PositionwiseFeedForward
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positionwise_layer_args = (
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output_size,
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linear_units,
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dropout_rate,
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)
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elif positionwise_layer_type == "conv1d":
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positionwise_layer = MultiLayeredConv1d
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positionwise_layer_args = (
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output_size,
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linear_units,
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positionwise_conv_kernel_size,
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dropout_rate,
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)
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elif positionwise_layer_type == "conv1d-linear":
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positionwise_layer = Conv1dLinear
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positionwise_layer_args = (
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output_size,
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linear_units,
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positionwise_conv_kernel_size,
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dropout_rate,
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)
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else:
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raise NotImplementedError("Support only linear or conv1d.")
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self.encoders = repeat(
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num_blocks,
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lambda lnum: EncoderLayer(
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output_size,
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MultiHeadedAttention(
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attention_heads, output_size, attention_dropout_rate
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),
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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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),
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)
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if self.normalize_before:
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self.after_norm = LayerNorm(output_size)
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self.interctc_layer_idx = interctc_layer_idx
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if len(interctc_layer_idx) > 0:
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assert 0 < min(interctc_layer_idx) and max(interctc_layer_idx) < num_blocks
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self.interctc_use_conditioning = interctc_use_conditioning
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self.conditioning_layer = None
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def output_size(self) -> int:
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return self._output_size
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def forward(
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self,
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xs_pad: torch.Tensor,
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ilens: torch.Tensor,
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prev_states: torch.Tensor = None,
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ctc: CTC = None,
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) -> Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]:
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"""Embed positions in tensor.
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Args:
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xs_pad: input tensor (B, L, D)
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ilens: input length (B)
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prev_states: Not to be used now.
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Returns:
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position embedded tensor and mask
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"""
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masks = (~make_pad_mask(ilens)[:, None, :]).to(xs_pad.device)
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if self.embed is None:
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xs_pad = xs_pad
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elif (
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isinstance(self.embed, Conv2dSubsampling)
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or isinstance(self.embed, Conv2dSubsampling2)
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or isinstance(self.embed, Conv2dSubsampling6)
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or isinstance(self.embed, Conv2dSubsampling8)
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):
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short_status, limit_size = check_short_utt(self.embed, xs_pad.size(1))
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if short_status:
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raise TooShortUttError(
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f"has {xs_pad.size(1)} frames and is too short for subsampling "
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+ f"(it needs more than {limit_size} frames), return empty results",
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xs_pad.size(1),
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limit_size,
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)
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xs_pad, masks = self.embed(xs_pad, masks)
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else:
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xs_pad = self.embed(xs_pad)
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intermediate_outs = []
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if len(self.interctc_layer_idx) == 0:
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xs_pad, masks = self.encoders(xs_pad, masks)
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else:
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for layer_idx, encoder_layer in enumerate(self.encoders):
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xs_pad, masks = encoder_layer(xs_pad, masks)
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if layer_idx + 1 in self.interctc_layer_idx:
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encoder_out = xs_pad
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# intermediate outputs are also normalized
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if self.normalize_before:
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encoder_out = self.after_norm(encoder_out)
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intermediate_outs.append((layer_idx + 1, encoder_out))
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if self.interctc_use_conditioning:
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ctc_out = ctc.softmax(encoder_out)
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xs_pad = xs_pad + self.conditioning_layer(ctc_out)
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if self.normalize_before:
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xs_pad = self.after_norm(xs_pad)
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olens = masks.squeeze(1).sum(1)
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if len(intermediate_outs) > 0:
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return (xs_pad, intermediate_outs), olens, None
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return xs_pad, olens, None
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def _pre_hook(
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state_dict,
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prefix,
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local_metadata,
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strict,
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missing_keys,
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unexpected_keys,
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error_msgs,
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):
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# https://github.com/espnet/espnet/commit/21d70286c354c66c0350e65dc098d2ee236faccc#diff-bffb1396f038b317b2b64dd96e6d3563
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rename_state_dict(prefix + "input_layer.", prefix + "embed.", state_dict)
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# https://github.com/espnet/espnet/commit/3d422f6de8d4f03673b89e1caef698745ec749ea#diff-bffb1396f038b317b2b64dd96e6d3563
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rename_state_dict(prefix + "norm.", prefix + "after_norm.", state_dict)
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class TransformerEncoder_s0(torch.nn.Module):
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"""Transformer encoder module.
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Args:
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idim (int): Input dimension.
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attention_dim (int): Dimension of attention.
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attention_heads (int): The number of heads of multi head attention.
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conv_wshare (int): The number of kernel of convolution. Only used in
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selfattention_layer_type == "lightconv*" or "dynamiconv*".
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conv_kernel_length (Union[int, str]): Kernel size str of convolution
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(e.g. 71_71_71_71_71_71). Only used in selfattention_layer_type
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== "lightconv*" or "dynamiconv*".
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conv_usebias (bool): Whether to use bias in convolution. Only used in
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selfattention_layer_type == "lightconv*" or "dynamiconv*".
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linear_units (int): The number of units of position-wise feed forward.
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num_blocks (int): The number of decoder blocks.
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dropout_rate (float): Dropout rate.
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positional_dropout_rate (float): Dropout rate after adding positional encoding.
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attention_dropout_rate (float): Dropout rate in attention.
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input_layer (Union[str, torch.nn.Module]): Input layer type.
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pos_enc_class (torch.nn.Module): Positional encoding module class.
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`PositionalEncoding `or `ScaledPositionalEncoding`
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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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positionwise_layer_type (str): "linear", "conv1d", or "conv1d-linear".
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positionwise_conv_kernel_size (int): Kernel size of positionwise conv1d layer.
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selfattention_layer_type (str): Encoder attention layer type.
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padding_idx (int): Padding idx for input_layer=embed.
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stochastic_depth_rate (float): Maximum probability to skip the encoder layer.
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intermediate_layers (Union[List[int], None]): 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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conv_wshare=4,
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conv_kernel_length="11",
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conv_usebias=False,
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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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stochastic_depth_rate=0.0,
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intermediate_layers=None,
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ctc_softmax=None,
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conditioning_layer_dim=None,
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):
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"""Construct an Encoder object."""
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super(TransformerEncoder_s0, self).__init__()
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self._register_load_state_dict_pre_hook(_pre_hook)
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self.conv_subsampling_factor = 1
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if input_layer == "linear":
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self.embed = torch.nn.Sequential(
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torch.nn.Linear(idim, attention_dim),
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torch.nn.LayerNorm(attention_dim),
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torch.nn.Dropout(dropout_rate),
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torch.nn.ReLU(),
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pos_enc_class(attention_dim, positional_dropout_rate),
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)
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elif input_layer == "conv2d":
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self.embed = Conv2dSubsampling(idim, attention_dim, dropout_rate)
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self.conv_subsampling_factor = 4
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elif input_layer == "conv2d-scaled-pos-enc":
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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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)
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self.conv_subsampling_factor = 4
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elif input_layer == "conv2d6":
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self.embed = Conv2dSubsampling6(idim, attention_dim, dropout_rate)
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self.conv_subsampling_factor = 6
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elif input_layer == "conv2d8":
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self.embed = Conv2dSubsampling8(idim, attention_dim, dropout_rate)
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self.conv_subsampling_factor = 8
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elif input_layer == "embed":
|
self.embed = torch.nn.Sequential(
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torch.nn.Embedding(idim, attention_dim, padding_idx=padding_idx),
|
pos_enc_class(attention_dim, positional_dropout_rate),
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)
|
elif isinstance(input_layer, torch.nn.Module):
|
self.embed = torch.nn.Sequential(
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input_layer,
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pos_enc_class(attention_dim, positional_dropout_rate),
|
)
|
elif input_layer is None:
|
self.embed = torch.nn.Sequential(
|
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
|
positionwise_layer, positionwise_layer_args = self.get_positionwise_layer(
|
positionwise_layer_type,
|
attention_dim,
|
linear_units,
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dropout_rate,
|
positionwise_conv_kernel_size,
|
)
|
if selfattention_layer_type in [
|
"selfattn",
|
"rel_selfattn",
|
"legacy_rel_selfattn",
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]:
|
logging.info("encoder self-attention layer type = self-attention")
|
encoder_selfattn_layer = MultiHeadedAttention
|
encoder_selfattn_layer_args = [
|
(
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attention_heads,
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attention_dim,
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attention_dropout_rate,
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)
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] * num_blocks
|
elif selfattention_layer_type == "lightconv":
|
logging.info("encoder self-attention layer type = lightweight convolution")
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encoder_selfattn_layer = LightweightConvolution
|
encoder_selfattn_layer_args = [
|
(
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conv_wshare,
|
attention_dim,
|
attention_dropout_rate,
|
int(conv_kernel_length.split("_")[lnum]),
|
False,
|
conv_usebias,
|
)
|
for lnum in range(num_blocks)
|
]
|
elif selfattention_layer_type == "lightconv2d":
|
logging.info(
|
"encoder self-attention layer "
|
"type = lightweight convolution 2-dimensional"
|
)
|
encoder_selfattn_layer = LightweightConvolution2D
|
encoder_selfattn_layer_args = [
|
(
|
conv_wshare,
|
attention_dim,
|
attention_dropout_rate,
|
int(conv_kernel_length.split("_")[lnum]),
|
False,
|
conv_usebias,
|
)
|
for lnum in range(num_blocks)
|
]
|
elif selfattention_layer_type == "dynamicconv":
|
logging.info("encoder self-attention layer type = dynamic convolution")
|
encoder_selfattn_layer = DynamicConvolution
|
encoder_selfattn_layer_args = [
|
(
|
conv_wshare,
|
attention_dim,
|
attention_dropout_rate,
|
int(conv_kernel_length.split("_")[lnum]),
|
False,
|
conv_usebias,
|
)
|
for lnum in range(num_blocks)
|
]
|
elif selfattention_layer_type == "dynamicconv2d":
|
logging.info(
|
"encoder self-attention layer type = dynamic convolution 2-dimensional"
|
)
|
encoder_selfattn_layer = DynamicConvolution2D
|
encoder_selfattn_layer_args = [
|
(
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conv_wshare,
|
attention_dim,
|
attention_dropout_rate,
|
int(conv_kernel_length.split("_")[lnum]),
|
False,
|
conv_usebias,
|
)
|
for lnum in range(num_blocks)
|
]
|
else:
|
raise NotImplementedError(selfattention_layer_type)
|
|
self.encoders = repeat(
|
num_blocks,
|
lambda lnum: EncoderLayer(
|
attention_dim,
|
encoder_selfattn_layer(*encoder_selfattn_layer_args[lnum]),
|
positionwise_layer(*positionwise_layer_args),
|
dropout_rate,
|
normalize_before,
|
concat_after,
|
stochastic_depth_rate * float(1 + lnum) / num_blocks,
|
),
|
)
|
if self.normalize_before:
|
self.after_norm = LayerNorm(attention_dim)
|
|
self.intermediate_layers = intermediate_layers
|
self.use_conditioning = True if ctc_softmax is not None else False
|
if self.use_conditioning:
|
self.ctc_softmax = ctc_softmax
|
self.conditioning_layer = torch.nn.Linear(
|
conditioning_layer_dim, attention_dim
|
)
|
|
def get_positionwise_layer(
|
self,
|
positionwise_layer_type="linear",
|
attention_dim=256,
|
linear_units=2048,
|
dropout_rate=0.1,
|
positionwise_conv_kernel_size=1,
|
):
|
"""Define positionwise layer."""
|
if positionwise_layer_type == "linear":
|
positionwise_layer = PositionwiseFeedForward
|
positionwise_layer_args = (attention_dim, linear_units, dropout_rate)
|
elif positionwise_layer_type == "conv1d":
|
positionwise_layer = MultiLayeredConv1d
|
positionwise_layer_args = (
|
attention_dim,
|
linear_units,
|
positionwise_conv_kernel_size,
|
dropout_rate,
|
)
|
elif positionwise_layer_type == "conv1d-linear":
|
positionwise_layer = Conv1dLinear
|
positionwise_layer_args = (
|
attention_dim,
|
linear_units,
|
positionwise_conv_kernel_size,
|
dropout_rate,
|
)
|
else:
|
raise NotImplementedError("Support only linear or conv1d.")
|
return positionwise_layer, positionwise_layer_args
|
|
def forward(self, xs, masks):
|
"""Encode input sequence.
|
|
Args:
|
xs (torch.Tensor): Input tensor (#batch, time, idim).
|
masks (torch.Tensor): Mask tensor (#batch, time).
|
|
Returns:
|
torch.Tensor: Output tensor (#batch, time, attention_dim).
|
torch.Tensor: Mask tensor (#batch, time).
|
|
"""
|
if isinstance(
|
self.embed,
|
(Conv2dSubsampling, Conv2dSubsampling6, Conv2dSubsampling8),
|
):
|
xs, masks = self.embed(xs, masks)
|
else:
|
xs = self.embed(xs)
|
|
if self.intermediate_layers is None:
|
xs, masks = self.encoders(xs, masks)
|
else:
|
intermediate_outputs = []
|
for layer_idx, encoder_layer in enumerate(self.encoders):
|
xs, masks = encoder_layer(xs, masks)
|
|
if (
|
self.intermediate_layers is not None
|
and layer_idx + 1 in self.intermediate_layers
|
):
|
encoder_output = xs
|
# intermediate branches also require normalization.
|
if self.normalize_before:
|
encoder_output = self.after_norm(encoder_output)
|
intermediate_outputs.append(encoder_output)
|
|
if self.use_conditioning:
|
intermediate_result = self.ctc_softmax(encoder_output)
|
xs = xs + self.conditioning_layer(intermediate_result)
|
|
if self.normalize_before:
|
xs = self.after_norm(xs)
|
|
if self.intermediate_layers is not None:
|
return xs, masks, intermediate_outputs
|
return xs, masks
|
|
def forward_one_step(self, xs, masks, cache=None):
|
"""Encode input frame.
|
|
Args:
|
xs (torch.Tensor): Input tensor.
|
masks (torch.Tensor): Mask tensor.
|
cache (List[torch.Tensor]): List of cache tensors.
|
|
Returns:
|
torch.Tensor: Output tensor.
|
torch.Tensor: Mask tensor.
|
List[torch.Tensor]: List of new cache tensors.
|
|
"""
|
if isinstance(self.embed, Conv2dSubsampling):
|
xs, masks = self.embed(xs, masks)
|
else:
|
xs = self.embed(xs)
|
if cache is None:
|
cache = [None for _ in range(len(self.encoders))]
|
new_cache = []
|
for c, e in zip(cache, self.encoders):
|
xs, masks = e(xs, masks, cache=c)
|
new_cache.append(xs)
|
if self.normalize_before:
|
xs = self.after_norm(xs)
|
return xs, masks, new_cache
|
