Source code for espnet2.asr.encoder.e_branchformer_encoder
# Copyright 2022 Kwangyoun Kim (ASAPP inc.)
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""E-Branchformer encoder definition.
Reference:
Kwangyoun Kim, Felix Wu, Yifan Peng, Jing Pan,
Prashant Sridhar, Kyu J. Han, Shinji Watanabe,
"E-Branchformer: Branchformer with Enhanced merging
for speech recognition," in SLT 2022.
"""
import logging
from typing import Optional, Tuple
import torch
from typeguard import typechecked
from espnet2.asr.ctc import CTC
from espnet2.asr.encoder.abs_encoder import AbsEncoder
from espnet2.asr.layers.cgmlp import ConvolutionalGatingMLP
from espnet2.asr.layers.fastformer import FastSelfAttention
from espnet.nets.pytorch_backend.nets_utils import get_activation, make_pad_mask
from espnet.nets.pytorch_backend.transformer.attention import ( # noqa: H301
LegacyRelPositionMultiHeadedAttention,
MultiHeadedAttention,
RelPositionMultiHeadedAttention,
)
from espnet.nets.pytorch_backend.transformer.embedding import ( # noqa: H301
LegacyRelPositionalEncoding,
PositionalEncoding,
RelPositionalEncoding,
ScaledPositionalEncoding,
)
from espnet.nets.pytorch_backend.transformer.layer_norm import LayerNorm
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 (
Conv1dSubsampling1,
Conv1dSubsampling2,
Conv1dSubsampling3,
Conv2dSubsampling,
Conv2dSubsampling1,
Conv2dSubsampling2,
Conv2dSubsampling6,
Conv2dSubsampling8,
TooShortUttError,
check_short_utt,
)
[docs]class EBranchformerEncoderLayer(torch.nn.Module):
"""E-Branchformer encoder layer module.
Args:
size (int): model dimension
attn: standard self-attention or efficient attention
cgmlp: ConvolutionalGatingMLP
feed_forward: feed-forward module, optional
feed_forward: macaron-style feed-forward module, optional
dropout_rate (float): dropout probability
merge_conv_kernel (int): kernel size of the depth-wise conv in merge module
"""
def __init__(
self,
size: int,
attn: torch.nn.Module,
cgmlp: torch.nn.Module,
feed_forward: Optional[torch.nn.Module],
feed_forward_macaron: Optional[torch.nn.Module],
dropout_rate: float,
merge_conv_kernel: int = 3,
):
super().__init__()
self.size = size
self.attn = attn
self.cgmlp = cgmlp
self.feed_forward = feed_forward
self.feed_forward_macaron = feed_forward_macaron
self.ff_scale = 1.0
if self.feed_forward is not None:
self.norm_ff = LayerNorm(size)
if self.feed_forward_macaron is not None:
self.ff_scale = 0.5
self.norm_ff_macaron = LayerNorm(size)
self.norm_mha = LayerNorm(size) # for the MHA module
self.norm_mlp = LayerNorm(size) # for the MLP module
self.norm_final = LayerNorm(size) # for the final output of the block
self.dropout = torch.nn.Dropout(dropout_rate)
self.depthwise_conv_fusion = torch.nn.Conv1d(
size + size,
size + size,
kernel_size=merge_conv_kernel,
stride=1,
padding=(merge_conv_kernel - 1) // 2,
groups=size + size,
bias=True,
)
self.merge_proj = torch.nn.Linear(size + size, size)
[docs] def forward(self, x_input, mask, cache=None):
"""Compute encoded features.
Args:
x_input (Union[Tuple, torch.Tensor]): Input tensor w/ or w/o pos emb.
- w/ pos emb: Tuple of tensors [(#batch, time, size), (1, time, size)].
- w/o pos emb: Tensor (#batch, time, size).
mask (torch.Tensor): Mask tensor for the input (#batch, 1, 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).
"""
if cache is not None:
raise NotImplementedError("cache is not None, which is not tested")
if isinstance(x_input, tuple):
x, pos_emb = x_input[0], x_input[1]
else:
x, pos_emb = x_input, None
if self.feed_forward_macaron is not None:
residual = x
x = self.norm_ff_macaron(x)
x = residual + self.ff_scale * self.dropout(self.feed_forward_macaron(x))
# Two branches
x1 = x
x2 = x
# Branch 1: multi-headed attention module
x1 = self.norm_mha(x1)
if isinstance(self.attn, FastSelfAttention):
x_att = self.attn(x1, mask)
else:
if pos_emb is not None:
x_att = self.attn(x1, x1, x1, pos_emb, mask)
else:
x_att = self.attn(x1, x1, x1, mask)
x1 = self.dropout(x_att)
# Branch 2: convolutional gating mlp
x2 = self.norm_mlp(x2)
if pos_emb is not None:
x2 = (x2, pos_emb)
x2 = self.cgmlp(x2, mask)
if isinstance(x2, tuple):
x2 = x2[0]
x2 = self.dropout(x2)
# Merge two branches
x_concat = torch.cat([x1, x2], dim=-1)
x_tmp = x_concat.transpose(1, 2)
x_tmp = self.depthwise_conv_fusion(x_tmp)
x_tmp = x_tmp.transpose(1, 2)
x = x + self.dropout(self.merge_proj(x_concat + x_tmp))
if self.feed_forward is not None:
# feed forward module
residual = x
x = self.norm_ff(x)
x = residual + self.ff_scale * self.dropout(self.feed_forward(x))
x = self.norm_final(x)
if pos_emb is not None:
return (x, pos_emb), mask
return x, mask
[docs]class EBranchformerEncoder(AbsEncoder):
"""E-Branchformer encoder module."""
@typechecked
def __init__(
self,
input_size: int,
output_size: int = 256,
attention_heads: int = 4,
attention_layer_type: str = "rel_selfattn",
pos_enc_layer_type: str = "rel_pos",
rel_pos_type: str = "latest",
cgmlp_linear_units: int = 2048,
cgmlp_conv_kernel: int = 31,
use_linear_after_conv: bool = False,
gate_activation: str = "identity",
num_blocks: int = 12,
dropout_rate: float = 0.1,
positional_dropout_rate: float = 0.1,
attention_dropout_rate: float = 0.0,
input_layer: Optional[str] = "conv2d",
zero_triu: bool = False,
padding_idx: int = -1,
layer_drop_rate: float = 0.0,
max_pos_emb_len: int = 5000,
use_ffn: bool = False,
macaron_ffn: bool = False,
ffn_activation_type: str = "swish",
linear_units: int = 2048,
positionwise_layer_type: str = "linear",
merge_conv_kernel: int = 3,
interctc_layer_idx=None,
interctc_use_conditioning: bool = False,
):
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 attention_layer_type == "rel_selfattn":
attention_layer_type = "legacy_rel_selfattn"
elif rel_pos_type == "latest":
assert attention_layer_type != "legacy_rel_selfattn"
assert pos_enc_layer_type != "legacy_rel_pos"
else:
raise ValueError("unknown rel_pos_type: " + rel_pos_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 attention_layer_type == "rel_selfattn"
pos_enc_class = RelPositionalEncoding
elif pos_enc_layer_type == "legacy_rel_pos":
assert attention_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 == "conv1d1":
self.embed = Conv1dSubsampling1(
input_size,
output_size,
dropout_rate,
pos_enc_class(output_size, positional_dropout_rate, max_pos_emb_len),
)
elif input_layer == "conv1d2":
self.embed = Conv1dSubsampling2(
input_size,
output_size,
dropout_rate,
pos_enc_class(output_size, positional_dropout_rate, max_pos_emb_len),
)
elif input_layer == "conv1d3":
self.embed = Conv1dSubsampling3(
input_size,
output_size,
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:
if input_size == output_size:
self.embed = torch.nn.Sequential(
pos_enc_class(output_size, positional_dropout_rate, max_pos_emb_len)
)
else:
self.embed = torch.nn.Linear(input_size, output_size)
else:
raise ValueError("unknown input_layer: " + input_layer)
activation = get_activation(ffn_activation_type)
if positionwise_layer_type == "linear":
positionwise_layer = PositionwiseFeedForward
positionwise_layer_args = (
output_size,
linear_units,
dropout_rate,
activation,
)
elif positionwise_layer_type is None:
logging.warning("no macaron ffn")
else:
raise ValueError("Support only linear.")
if attention_layer_type == "selfattn":
encoder_selfattn_layer = MultiHeadedAttention
encoder_selfattn_layer_args = (
attention_heads,
output_size,
attention_dropout_rate,
)
elif attention_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 attention_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,
)
elif attention_layer_type == "fast_selfattn":
assert pos_enc_layer_type in ["abs_pos", "scaled_abs_pos"]
encoder_selfattn_layer = FastSelfAttention
encoder_selfattn_layer_args = (
output_size,
attention_heads,
attention_dropout_rate,
)
else:
raise ValueError("unknown encoder_attn_layer: " + attention_layer_type)
cgmlp_layer = ConvolutionalGatingMLP
cgmlp_layer_args = (
output_size,
cgmlp_linear_units,
cgmlp_conv_kernel,
dropout_rate,
use_linear_after_conv,
gate_activation,
)
self.encoders = repeat(
num_blocks,
lambda lnum: EBranchformerEncoderLayer(
output_size,
encoder_selfattn_layer(*encoder_selfattn_layer_args),
cgmlp_layer(*cgmlp_layer_args),
positionwise_layer(*positionwise_layer_args) if use_ffn else None,
(
positionwise_layer(*positionwise_layer_args)
if use_ffn and macaron_ffn
else None
),
dropout_rate,
merge_conv_kernel,
),
layer_drop_rate,
)
self.after_norm = LayerNorm(output_size)
if interctc_layer_idx is None:
interctc_layer_idx = []
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,
max_layer: int = 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.
ctc (CTC): Intermediate CTC module.
max_layer (int): Layer depth below which InterCTC is applied.
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, Conv1dSubsampling1)
or isinstance(self.embed, Conv1dSubsampling2)
or isinstance(self.embed, Conv1dSubsampling3)
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)
elif self.embed is not None:
xs_pad = self.embed(xs_pad)
intermediate_outs = []
if len(self.interctc_layer_idx) == 0:
if max_layer is not None and 0 <= max_layer < len(self.encoders):
for layer_idx, encoder_layer in enumerate(self.encoders):
xs_pad, masks = encoder_layer(xs_pad, masks)
if layer_idx >= max_layer:
break
else:
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_outs.append((layer_idx + 1, encoder_out))
if self.interctc_use_conditioning:
ctc_out = ctc.softmax(encoder_out)
if isinstance(xs_pad, tuple):
xs_pad = list(xs_pad)
xs_pad[0] = xs_pad[0] + self.conditioning_layer(ctc_out)
xs_pad = tuple(xs_pad)
else:
xs_pad = xs_pad + self.conditioning_layer(ctc_out)
if isinstance(xs_pad, tuple):
xs_pad = xs_pad[0]
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