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# Copyright 2019 Shigeki Karita
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""Transformer encoder definition."""
from typing import List
from typing import Optional
from typing import Tuple
import torch
from torch import nn
import logging
from funasr_detach.models.transformer.attention import MultiHeadedAttention
from funasr_detach.models.transformer.embedding import PositionalEncoding
from funasr_detach.models.transformer.layer_norm import LayerNorm
from funasr_detach.models.transformer.utils.multi_layer_conv import Conv1dLinear
from funasr_detach.models.transformer.utils.multi_layer_conv import MultiLayeredConv1d
from funasr_detach.models.transformer.utils.nets_utils import make_pad_mask
from funasr_detach.models.transformer.positionwise_feed_forward import (
PositionwiseFeedForward, # noqa: H301
)
from funasr_detach.models.transformer.utils.repeat import repeat
from funasr_detach.models.transformer.utils.dynamic_conv import DynamicConvolution
from funasr_detach.models.transformer.utils.dynamic_conv2d import DynamicConvolution2D
from funasr_detach.models.transformer.utils.lightconv import LightweightConvolution
from funasr_detach.models.transformer.utils.lightconv2d import LightweightConvolution2D
from funasr_detach.models.transformer.utils.subsampling import Conv2dSubsampling
from funasr_detach.models.transformer.utils.subsampling import Conv2dSubsampling2
from funasr_detach.models.transformer.utils.subsampling import Conv2dSubsampling6
from funasr_detach.models.transformer.utils.subsampling import Conv2dSubsampling8
from funasr_detach.models.transformer.utils.subsampling import TooShortUttError
from funasr_detach.models.transformer.utils.subsampling import check_short_utt
class EncoderLayer(nn.Module):
"""Encoder layer module.
Args:
size (int): Input dimension.
self_attn (torch.nn.Module): Self-attention module instance.
`MultiHeadedAttention` or `RelPositionMultiHeadedAttention` instance
can be used as the argument.
feed_forward (torch.nn.Module): Feed-forward module instance.
`PositionwiseFeedForward`, `MultiLayeredConv1d`, or `Conv1dLinear` instance
can be used as the argument.
dropout_rate (float): Dropout rate.
normalize_before (bool): Whether to use layer_norm before the first block.
concat_after (bool): Whether to concat attention layer's input and output.
if True, additional linear will be applied.
i.e. x -> x + linear(concat(x, att(x)))
if False, no additional linear will be applied. i.e. x -> x + att(x)
stochastic_depth_rate (float): Proability to skip this layer.
During training, the layer may skip residual computation and return input
as-is with given probability.
"""
def __init__(
self,
size,
self_attn,
feed_forward,
dropout_rate,
normalize_before=True,
concat_after=False,
stochastic_depth_rate=0.0,
):
"""Construct an EncoderLayer object."""
super(EncoderLayer, self).__init__()
self.self_attn = self_attn
self.feed_forward = feed_forward
self.norm1 = LayerNorm(size)
self.norm2 = LayerNorm(size)
self.dropout = nn.Dropout(dropout_rate)
self.size = size
self.normalize_before = normalize_before
self.concat_after = concat_after
if self.concat_after:
self.concat_linear = nn.Linear(size + size, size)
self.stochastic_depth_rate = stochastic_depth_rate
def forward(self, x, mask, cache=None):
"""Compute encoded features.
Args:
x_input (torch.Tensor): Input tensor (#batch, time, size).
mask (torch.Tensor): Mask tensor for the input (#batch, time).
cache (torch.Tensor): Cache tensor of the input (#batch, time - 1, size).
Returns:
torch.Tensor: Output tensor (#batch, time, size).
torch.Tensor: Mask tensor (#batch, time).
"""
skip_layer = False
# with stochastic depth, residual connection `x + f(x)` becomes
# `x <- x + 1 / (1 - p) * f(x)` at training time.
stoch_layer_coeff = 1.0
if self.training and self.stochastic_depth_rate > 0:
skip_layer = torch.rand(1).item() < self.stochastic_depth_rate
stoch_layer_coeff = 1.0 / (1 - self.stochastic_depth_rate)
if skip_layer:
if cache is not None:
x = torch.cat([cache, x], dim=1)
return x, mask
residual = x
if self.normalize_before:
x = self.norm1(x)
if cache is None:
x_q = x
else:
assert cache.shape == (x.shape[0], x.shape[1] - 1, self.size)
x_q = x[:, -1:, :]
residual = residual[:, -1:, :]
mask = None if mask is None else mask[:, -1:, :]
if self.concat_after:
x_concat = torch.cat((x, self.self_attn(x_q, x, x, mask)), dim=-1)
x = residual + stoch_layer_coeff * self.concat_linear(x_concat)
else:
x = residual + stoch_layer_coeff * self.dropout(
self.self_attn(x_q, x, x, mask)
)
if not self.normalize_before:
x = self.norm1(x)
residual = x
if self.normalize_before:
x = self.norm2(x)
x = residual + stoch_layer_coeff * self.dropout(self.feed_forward(x))
if not self.normalize_before:
x = self.norm2(x)
if cache is not None:
x = torch.cat([cache, x], dim=1)
return x, mask
class TransformerEncoder_lm(nn.Module):
"""Transformer encoder module.
Args:
idim (int): Input dimension.
attention_dim (int): Dimension of attention.
attention_heads (int): The number of heads of multi head attention.
conv_wshare (int): The number of kernel of convolution. Only used in
selfattention_layer_type == "lightconv*" or "dynamiconv*".
conv_kernel_length (Union[int, str]): Kernel size str of convolution
(e.g. 71_71_71_71_71_71). Only used in selfattention_layer_type
== "lightconv*" or "dynamiconv*".
conv_usebias (bool): Whether to use bias in convolution. Only used in
selfattention_layer_type == "lightconv*" or "dynamiconv*".
linear_units (int): The number of units of position-wise feed forward.
num_blocks (int): The number of decoder blocks.
dropout_rate (float): Dropout rate.
positional_dropout_rate (float): Dropout rate after adding positional encoding.
attention_dropout_rate (float): Dropout rate in attention.
input_layer (Union[str, torch.nn.Module]): Input layer type.
pos_enc_class (torch.nn.Module): Positional encoding module class.
`PositionalEncoding `or `ScaledPositionalEncoding`
normalize_before (bool): Whether to use layer_norm before the first block.
concat_after (bool): Whether to concat attention layer's input and output.
if True, additional linear will be applied.
i.e. x -> x + linear(concat(x, att(x)))
if False, no additional linear will be applied. i.e. x -> x + att(x)
positionwise_layer_type (str): "linear", "conv1d", or "conv1d-linear".
positionwise_conv_kernel_size (int): Kernel size of positionwise conv1d layer.
selfattention_layer_type (str): Encoder attention layer type.
padding_idx (int): Padding idx for input_layer=embed.
stochastic_depth_rate (float): Maximum probability to skip the encoder layer.
intermediate_layers (Union[List[int], None]): indices of intermediate CTC layer.
indices start from 1.
if not None, intermediate outputs are returned (which changes return type
signature.)
"""
def __init__(
self,
idim,
attention_dim=256,
attention_heads=4,
conv_wshare=4,
conv_kernel_length="11",
conv_usebias=False,
linear_units=2048,
num_blocks=6,
dropout_rate=0.1,
positional_dropout_rate=0.1,
attention_dropout_rate=0.0,
input_layer="conv2d",
pos_enc_class=PositionalEncoding,
normalize_before=True,
concat_after=False,
positionwise_layer_type="linear",
positionwise_conv_kernel_size=1,
selfattention_layer_type="selfattn",
padding_idx=-1,
stochastic_depth_rate=0.0,
intermediate_layers=None,
ctc_softmax=None,
conditioning_layer_dim=None,
):
"""Construct an Encoder object."""
super().__init__()
self.conv_subsampling_factor = 1
if input_layer == "linear":
self.embed = torch.nn.Sequential(
torch.nn.Linear(idim, attention_dim),
torch.nn.LayerNorm(attention_dim),
torch.nn.Dropout(dropout_rate),
torch.nn.ReLU(),
pos_enc_class(attention_dim, positional_dropout_rate),
)
elif input_layer == "conv2d":
self.embed = Conv2dSubsampling(idim, attention_dim, dropout_rate)
self.conv_subsampling_factor = 4
elif input_layer == "conv2d-scaled-pos-enc":
self.embed = Conv2dSubsampling(
idim,
attention_dim,
dropout_rate,
pos_enc_class(attention_dim, positional_dropout_rate),
)
self.conv_subsampling_factor = 4
elif input_layer == "conv2d6":
self.embed = Conv2dSubsampling6(idim, attention_dim, dropout_rate)
self.conv_subsampling_factor = 6
elif input_layer == "conv2d8":
self.embed = Conv2dSubsampling8(idim, attention_dim, dropout_rate)
self.conv_subsampling_factor = 8
elif input_layer == "embed":
self.embed = torch.nn.Sequential(
torch.nn.Embedding(idim, attention_dim, padding_idx=padding_idx),
pos_enc_class(attention_dim, positional_dropout_rate),
)
elif isinstance(input_layer, torch.nn.Module):
self.embed = torch.nn.Sequential(
input_layer,
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)
)
else:
raise ValueError("unknown input_layer: " + input_layer)
self.normalize_before = normalize_before
positionwise_layer, positionwise_layer_args = self.get_positionwise_layer(
positionwise_layer_type,
attention_dim,
linear_units,
dropout_rate,
positionwise_conv_kernel_size,
)
if selfattention_layer_type in [
"selfattn",
"rel_selfattn",
"legacy_rel_selfattn",
]:
logging.info("encoder self-attention layer type = self-attention")
encoder_selfattn_layer = MultiHeadedAttention
encoder_selfattn_layer_args = [
(
attention_heads,
attention_dim,
attention_dropout_rate,
)
] * num_blocks
elif selfattention_layer_type == "lightconv":
logging.info("encoder self-attention layer type = lightweight convolution")
encoder_selfattn_layer = LightweightConvolution
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 == "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 = [
(
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
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