Source code for espnet2.asr.encoder.conformer_encoder
# Copyright 2020 Tomoki Hayashi
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""Conformer encoder definition."""
import logging
from typing import List, Optional, Tuple, Union
import torch
from typeguard import check_argument_types
from espnet2.asr.ctc import CTC
from espnet2.asr.encoder.abs_encoder import AbsEncoder
from espnet.nets.pytorch_backend.conformer.convolution import ConvolutionModule
from espnet.nets.pytorch_backend.conformer.encoder_layer import EncoderLayer
from espnet.nets.pytorch_backend.nets_utils import get_activation, make_pad_mask
from espnet.nets.pytorch_backend.transformer.attention import (
LegacyRelPositionMultiHeadedAttention,
MultiHeadedAttention,
RelPositionMultiHeadedAttention,
)
from espnet.nets.pytorch_backend.transformer.embedding import (
LegacyRelPositionalEncoding,
PositionalEncoding,
RelPositionalEncoding,
ScaledPositionalEncoding,
)
from espnet.nets.pytorch_backend.transformer.layer_norm import LayerNorm
from espnet.nets.pytorch_backend.transformer.multi_layer_conv import (
Conv1dLinear,
MultiLayeredConv1d,
)
from espnet.nets.pytorch_backend.transformer.positionwise_feed_forward import (
PositionwiseFeedForward,
)
from espnet.nets.pytorch_backend.transformer.repeat import repeat
from espnet.nets.pytorch_backend.transformer.subsampling import (
Conv2dSubsampling,
Conv2dSubsampling1,
Conv2dSubsampling2,
Conv2dSubsampling6,
Conv2dSubsampling8,
TooShortUttError,
check_short_utt,
)
[docs]class ConformerEncoder(AbsEncoder):
"""Conformer encoder module.
Args:
input_size (int): Input dimension.
output_size (int): Dimension of attention.
attention_heads (int): The number of heads of multi head attention.
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.
attention_dropout_rate (float): Dropout rate in attention.
positional_dropout_rate (float): Dropout rate after adding positional encoding.
input_layer (Union[str, torch.nn.Module]): Input layer type.
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.
rel_pos_type (str): Whether to use the latest relative positional encoding or
the legacy one. The legacy relative positional encoding will be deprecated
in the future. More Details can be found in
https://github.com/espnet/espnet/pull/2816.
encoder_pos_enc_layer_type (str): Encoder positional encoding layer type.
encoder_attn_layer_type (str): Encoder attention layer type.
activation_type (str): Encoder activation function type.
macaron_style (bool): Whether to use macaron style for positionwise layer.
use_cnn_module (bool): Whether to use convolution module.
zero_triu (bool): Whether to zero the upper triangular part of attention matrix.
cnn_module_kernel (int): Kernerl size of convolution module.
padding_idx (int): Padding idx for input_layer=embed.
"""
def __init__(
self,
input_size: int,
output_size: int = 256,
attention_heads: int = 4,
linear_units: int = 2048,
num_blocks: int = 6,
dropout_rate: float = 0.1,
positional_dropout_rate: float = 0.1,
attention_dropout_rate: float = 0.0,
input_layer: str = "conv2d",
normalize_before: bool = True,
concat_after: bool = False,
positionwise_layer_type: str = "linear",
positionwise_conv_kernel_size: int = 3,
macaron_style: bool = False,
rel_pos_type: str = "legacy",
pos_enc_layer_type: str = "rel_pos",
selfattention_layer_type: str = "rel_selfattn",
activation_type: str = "swish",
use_cnn_module: bool = True,
zero_triu: bool = False,
cnn_module_kernel: int = 31,
padding_idx: int = -1,
interctc_layer_idx: List[int] = [],
interctc_use_conditioning: bool = False,
stochastic_depth_rate: Union[float, List[float]] = 0.0,
layer_drop_rate: float = 0.0,
max_pos_emb_len: int = 5000,
):
assert check_argument_types()
super().__init__()
self._output_size = output_size
if rel_pos_type == "legacy":
if pos_enc_layer_type == "rel_pos":
pos_enc_layer_type = "legacy_rel_pos"
if selfattention_layer_type == "rel_selfattn":
selfattention_layer_type = "legacy_rel_selfattn"
elif rel_pos_type == "latest":
assert selfattention_layer_type != "legacy_rel_selfattn"
assert pos_enc_layer_type != "legacy_rel_pos"
else:
raise ValueError("unknown rel_pos_type: " + rel_pos_type)
activation = get_activation(activation_type)
if pos_enc_layer_type == "abs_pos":
pos_enc_class = PositionalEncoding
elif pos_enc_layer_type == "scaled_abs_pos":
pos_enc_class = ScaledPositionalEncoding
elif pos_enc_layer_type == "rel_pos":
assert selfattention_layer_type == "rel_selfattn"
pos_enc_class = RelPositionalEncoding
elif pos_enc_layer_type == "legacy_rel_pos":
assert selfattention_layer_type == "legacy_rel_selfattn"
pos_enc_class = LegacyRelPositionalEncoding
logging.warning(
"Using legacy_rel_pos and it will be deprecated in the future."
)
else:
raise ValueError("unknown pos_enc_layer: " + pos_enc_layer_type)
if input_layer == "linear":
self.embed = torch.nn.Sequential(
torch.nn.Linear(input_size, output_size),
torch.nn.LayerNorm(output_size),
torch.nn.Dropout(dropout_rate),
pos_enc_class(output_size, positional_dropout_rate, max_pos_emb_len),
)
elif input_layer == "conv2d":
self.embed = Conv2dSubsampling(
input_size,
output_size,
dropout_rate,
pos_enc_class(output_size, positional_dropout_rate, max_pos_emb_len),
)
elif input_layer == "conv2d1":
self.embed = Conv2dSubsampling1(
input_size,
output_size,
dropout_rate,
pos_enc_class(output_size, positional_dropout_rate, max_pos_emb_len),
)
elif input_layer == "conv2d2":
self.embed = Conv2dSubsampling2(
input_size,
output_size,
dropout_rate,
pos_enc_class(output_size, positional_dropout_rate, max_pos_emb_len),
)
elif input_layer == "conv2d6":
self.embed = Conv2dSubsampling6(
input_size,
output_size,
dropout_rate,
pos_enc_class(output_size, positional_dropout_rate, max_pos_emb_len),
)
elif input_layer == "conv2d8":
self.embed = Conv2dSubsampling8(
input_size,
output_size,
dropout_rate,
pos_enc_class(output_size, positional_dropout_rate, max_pos_emb_len),
)
elif input_layer == "embed":
self.embed = torch.nn.Sequential(
torch.nn.Embedding(input_size, output_size, padding_idx=padding_idx),
pos_enc_class(output_size, positional_dropout_rate, max_pos_emb_len),
)
elif isinstance(input_layer, torch.nn.Module):
self.embed = torch.nn.Sequential(
input_layer,
pos_enc_class(output_size, positional_dropout_rate, max_pos_emb_len),
)
elif input_layer is None:
self.embed = torch.nn.Sequential(
pos_enc_class(output_size, positional_dropout_rate, max_pos_emb_len)
)
else:
raise ValueError("unknown input_layer: " + input_layer)
self.normalize_before = normalize_before
if positionwise_layer_type == "linear":
positionwise_layer = PositionwiseFeedForward
positionwise_layer_args = (
output_size,
linear_units,
dropout_rate,
activation,
)
elif positionwise_layer_type == "conv1d":
positionwise_layer = MultiLayeredConv1d
positionwise_layer_args = (
output_size,
linear_units,
positionwise_conv_kernel_size,
dropout_rate,
)
elif positionwise_layer_type == "conv1d-linear":
positionwise_layer = Conv1dLinear
positionwise_layer_args = (
output_size,
linear_units,
positionwise_conv_kernel_size,
dropout_rate,
)
else:
raise NotImplementedError("Support only linear or conv1d.")
if selfattention_layer_type == "selfattn":
encoder_selfattn_layer = MultiHeadedAttention
encoder_selfattn_layer_args = (
attention_heads,
output_size,
attention_dropout_rate,
)
elif selfattention_layer_type == "legacy_rel_selfattn":
assert pos_enc_layer_type == "legacy_rel_pos"
encoder_selfattn_layer = LegacyRelPositionMultiHeadedAttention
encoder_selfattn_layer_args = (
attention_heads,
output_size,
attention_dropout_rate,
)
logging.warning(
"Using legacy_rel_selfattn and it will be deprecated in the future."
)
elif selfattention_layer_type == "rel_selfattn":
assert pos_enc_layer_type == "rel_pos"
encoder_selfattn_layer = RelPositionMultiHeadedAttention
encoder_selfattn_layer_args = (
attention_heads,
output_size,
attention_dropout_rate,
zero_triu,
)
else:
raise ValueError("unknown encoder_attn_layer: " + selfattention_layer_type)
convolution_layer = ConvolutionModule
convolution_layer_args = (output_size, cnn_module_kernel, activation)
if isinstance(stochastic_depth_rate, float):
stochastic_depth_rate = [stochastic_depth_rate] * num_blocks
if len(stochastic_depth_rate) != num_blocks:
raise ValueError(
f"Length of stochastic_depth_rate ({len(stochastic_depth_rate)}) "
f"should be equal to num_blocks ({num_blocks})"
)
self.encoders = repeat(
num_blocks,
lambda lnum: EncoderLayer(
output_size,
encoder_selfattn_layer(*encoder_selfattn_layer_args),
positionwise_layer(*positionwise_layer_args),
positionwise_layer(*positionwise_layer_args) if macaron_style else None,
convolution_layer(*convolution_layer_args) if use_cnn_module else None,
dropout_rate,
normalize_before,
concat_after,
stochastic_depth_rate[lnum],
),
layer_drop_rate,
)
if self.normalize_before:
self.after_norm = LayerNorm(output_size)
self.interctc_layer_idx = interctc_layer_idx
if len(interctc_layer_idx) > 0:
assert 0 < min(interctc_layer_idx) and max(interctc_layer_idx) < num_blocks
self.interctc_use_conditioning = interctc_use_conditioning
self.conditioning_layer = None
[docs] def forward(
self,
xs_pad: torch.Tensor,
ilens: torch.Tensor,
prev_states: torch.Tensor = None,
ctc: CTC = None,
) -> Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]:
"""Calculate forward propagation.
Args:
xs_pad (torch.Tensor): Input tensor (#batch, L, input_size).
ilens (torch.Tensor): Input length (#batch).
prev_states (torch.Tensor): Not to be used now.
Returns:
torch.Tensor: Output tensor (#batch, L, output_size).
torch.Tensor: Output length (#batch).
torch.Tensor: Not to be used now.
"""
masks = (~make_pad_mask(ilens)[:, None, :]).to(xs_pad.device)
if (
isinstance(self.embed, Conv2dSubsampling)
or isinstance(self.embed, Conv2dSubsampling1)
or isinstance(self.embed, Conv2dSubsampling2)
or isinstance(self.embed, Conv2dSubsampling6)
or isinstance(self.embed, Conv2dSubsampling8)
):
short_status, limit_size = check_short_utt(self.embed, xs_pad.size(1))
if short_status:
raise TooShortUttError(
f"has {xs_pad.size(1)} frames and is too short for subsampling "
+ f"(it needs more than {limit_size} frames), return empty results",
xs_pad.size(1),
limit_size,
)
xs_pad, masks = self.embed(xs_pad, masks)
else:
xs_pad = self.embed(xs_pad)
intermediate_outs = []
if len(self.interctc_layer_idx) == 0:
xs_pad, masks = self.encoders(xs_pad, masks)
else:
for layer_idx, encoder_layer in enumerate(self.encoders):
xs_pad, masks = encoder_layer(xs_pad, masks)
if layer_idx + 1 in self.interctc_layer_idx:
encoder_out = xs_pad
if isinstance(encoder_out, tuple):
encoder_out = encoder_out[0]
# intermediate outputs are also normalized
if self.normalize_before:
encoder_out = self.after_norm(encoder_out)
intermediate_outs.append((layer_idx + 1, encoder_out))
if self.interctc_use_conditioning:
ctc_out = ctc.softmax(encoder_out)
if isinstance(xs_pad, tuple):
x, pos_emb = xs_pad
x = x + self.conditioning_layer(ctc_out)
xs_pad = (x, pos_emb)
else:
xs_pad = xs_pad + self.conditioning_layer(ctc_out)
if isinstance(xs_pad, tuple):
xs_pad = xs_pad[0]
if self.normalize_before:
xs_pad = self.after_norm(xs_pad)
olens = masks.squeeze(1).sum(1)
if len(intermediate_outs) > 0:
return (xs_pad, intermediate_outs), olens, None
return xs_pad, olens, None