# This module is from [WeNet](https://github.com/wenet-e2e/wenet).
# ## Citations
# ```bibtex
# @inproceedings{yao2021wenet,
# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit},
# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin},
# booktitle={Proc. Interspeech},
# year={2021},
# address={Brno, Czech Republic },
# organization={IEEE}
# }
# @article{zhang2022wenet,
# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit},
# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei},
# journal={arXiv preprint arXiv:2203.15455},
# year={2022}
# }
#
"""Encoder definition."""
from typing import Tuple, Optional, List, Union
import torch
import logging
import torch.nn.functional as F
from modules.wenet_extractor.transformer.positionwise_feed_forward import (
PositionwiseFeedForward,
)
from modules.wenet_extractor.transformer.embedding import PositionalEncoding
from modules.wenet_extractor.transformer.embedding import RelPositionalEncoding
from modules.wenet_extractor.transformer.embedding import NoPositionalEncoding
from modules.wenet_extractor.transformer.subsampling import Conv2dSubsampling4
from modules.wenet_extractor.transformer.subsampling import Conv2dSubsampling6
from modules.wenet_extractor.transformer.subsampling import Conv2dSubsampling8
from modules.wenet_extractor.transformer.subsampling import LinearNoSubsampling
from modules.wenet_extractor.transformer.attention import MultiHeadedAttention
from modules.wenet_extractor.transformer.attention import (
RelPositionMultiHeadedAttention,
)
from modules.wenet_extractor.transformer.encoder_layer import ConformerEncoderLayer
from modules.wenet_extractor.efficient_conformer.subsampling import Conv2dSubsampling2
from modules.wenet_extractor.efficient_conformer.convolution import ConvolutionModule
from modules.wenet_extractor.efficient_conformer.attention import (
GroupedRelPositionMultiHeadedAttention,
)
from modules.wenet_extractor.efficient_conformer.encoder_layer import (
StrideConformerEncoderLayer,
)
from modules.wenet_extractor.utils.common import get_activation
from modules.wenet_extractor.utils.mask import make_pad_mask
from modules.wenet_extractor.utils.mask import add_optional_chunk_mask
class EfficientConformerEncoder(torch.nn.Module):
"""Conformer encoder module."""
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",
pos_enc_layer_type: str = "rel_pos",
normalize_before: bool = True,
static_chunk_size: int = 0,
use_dynamic_chunk: bool = False,
global_cmvn: torch.nn.Module = None,
use_dynamic_left_chunk: bool = False,
macaron_style: bool = True,
activation_type: str = "swish",
use_cnn_module: bool = True,
cnn_module_kernel: int = 15,
causal: bool = False,
cnn_module_norm: str = "batch_norm",
stride_layer_idx: Optional[Union[int, List[int]]] = 3,
stride: Optional[Union[int, List[int]]] = 2,
group_layer_idx: Optional[Union[int, List[int], tuple]] = (0, 1, 2, 3),
group_size: int = 3,
stride_kernel: bool = True,
**kwargs,
):
"""Construct Efficient Conformer Encoder
Args:
input_size to use_dynamic_chunk, see in BaseEncoder
macaron_style (bool): Whether to use macaron style for
positionwise layer.
activation_type (str): Encoder activation function type.
use_cnn_module (bool): Whether to use convolution module.
cnn_module_kernel (int): Kernel size of convolution module.
causal (bool): whether to use causal convolution or not.
stride_layer_idx (list): layer id with StrideConv, start from 0
stride (list): stride size of each StrideConv in efficient conformer
group_layer_idx (list): layer id with GroupedAttention, start from 0
group_size (int): group size of every GroupedAttention layer
stride_kernel (bool): default True. True: recompute cnn kernels with stride.
"""
super().__init__()
self._output_size = output_size
if pos_enc_layer_type == "abs_pos":
pos_enc_class = PositionalEncoding
elif pos_enc_layer_type == "rel_pos":
pos_enc_class = RelPositionalEncoding
elif pos_enc_layer_type == "no_pos":
pos_enc_class = NoPositionalEncoding
else:
raise ValueError("unknown pos_enc_layer: " + pos_enc_layer_type)
if input_layer == "linear":
subsampling_class = LinearNoSubsampling
elif input_layer == "conv2d2":
subsampling_class = Conv2dSubsampling2
elif input_layer == "conv2d":
subsampling_class = Conv2dSubsampling4
elif input_layer == "conv2d6":
subsampling_class = Conv2dSubsampling6
elif input_layer == "conv2d8":
subsampling_class = Conv2dSubsampling8
else:
raise ValueError("unknown input_layer: " + input_layer)
logging.info(
f"input_layer = {input_layer}, " f"subsampling_class = {subsampling_class}"
)
self.global_cmvn = global_cmvn
self.embed = subsampling_class(
input_size,
output_size,
dropout_rate,
pos_enc_class(output_size, positional_dropout_rate),
)
self.input_layer = input_layer
self.normalize_before = normalize_before
self.after_norm = torch.nn.LayerNorm(output_size, eps=1e-5)
self.static_chunk_size = static_chunk_size
self.use_dynamic_chunk = use_dynamic_chunk
self.use_dynamic_left_chunk = use_dynamic_left_chunk
activation = get_activation(activation_type)
self.num_blocks = num_blocks
self.attention_heads = attention_heads
self.cnn_module_kernel = cnn_module_kernel
self.global_chunk_size = 0
self.chunk_feature_map = 0
# efficient conformer configs
self.stride_layer_idx = (
[stride_layer_idx] if type(stride_layer_idx) == int else stride_layer_idx
)
self.stride = [stride] if type(stride) == int else stride
self.group_layer_idx = (
[group_layer_idx] if type(group_layer_idx) == int else group_layer_idx
)
self.grouped_size = group_size # group size of every GroupedAttention layer
assert len(self.stride) == len(self.stride_layer_idx)
self.cnn_module_kernels = [cnn_module_kernel] # kernel size of each StridedConv
for i in self.stride:
if stride_kernel:
self.cnn_module_kernels.append(self.cnn_module_kernels[-1] // i)
else:
self.cnn_module_kernels.append(self.cnn_module_kernels[-1])
logging.info(
f"stride_layer_idx= {self.stride_layer_idx}, "
f"stride = {self.stride}, "
f"cnn_module_kernel = {self.cnn_module_kernels}, "
f"group_layer_idx = {self.group_layer_idx}, "
f"grouped_size = {self.grouped_size}"
)
# feed-forward module definition
positionwise_layer = PositionwiseFeedForward
positionwise_layer_args = (
output_size,
linear_units,
dropout_rate,
activation,
)
# convolution module definition
convolution_layer = ConvolutionModule
# encoder definition
index = 0
layers = []
for i in range(num_blocks):
# self-attention module definition
if i in self.group_layer_idx:
encoder_selfattn_layer = GroupedRelPositionMultiHeadedAttention
encoder_selfattn_layer_args = (
attention_heads,
output_size,
attention_dropout_rate,
self.grouped_size,
)
else:
if pos_enc_layer_type == "no_pos":
encoder_selfattn_layer = MultiHeadedAttention
else:
encoder_selfattn_layer = RelPositionMultiHeadedAttention
encoder_selfattn_layer_args = (
attention_heads,
output_size,
attention_dropout_rate,
)
# conformer module definition
if i in self.stride_layer_idx:
# conformer block with downsampling
convolution_layer_args_stride = (
output_size,
self.cnn_module_kernels[index],
activation,
cnn_module_norm,
causal,
True,
self.stride[index],
)
layers.append(
StrideConformerEncoderLayer(
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_stride)
if use_cnn_module
else None,
torch.nn.AvgPool1d(
kernel_size=self.stride[index],
stride=self.stride[index],
padding=0,
ceil_mode=True,
count_include_pad=False,
), # pointwise_conv_layer
dropout_rate,
normalize_before,
)
)
index = index + 1
else:
# conformer block
convolution_layer_args_normal = (
output_size,
self.cnn_module_kernels[index],
activation,
cnn_module_norm,
causal,
)
layers.append(
ConformerEncoderLayer(
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_normal)
if use_cnn_module
else None,
dropout_rate,
normalize_before,
)
)
self.encoders = torch.nn.ModuleList(layers)
def set_global_chunk_size(self, chunk_size):
"""Used in ONNX export."""
logging.info(f"set global chunk size: {chunk_size}, default is 0.")
self.global_chunk_size = chunk_size
if self.embed.subsampling_rate == 2:
self.chunk_feature_map = 2 * self.global_chunk_size + 1
elif self.embed.subsampling_rate == 6:
self.chunk_feature_map = 6 * self.global_chunk_size + 5
elif self.embed.subsampling_rate == 8:
self.chunk_feature_map = 8 * self.global_chunk_size + 7
else:
self.chunk_feature_map = 4 * self.global_chunk_size + 3
def output_size(self) -> int:
return self._output_size
def calculate_downsampling_factor(self, i: int) -> int:
factor = 1
for idx, stride_idx in enumerate(self.stride_layer_idx):
if i > stride_idx:
factor *= self.stride[idx]
return factor
def forward(
self,
xs: torch.Tensor,
xs_lens: torch.Tensor,
decoding_chunk_size: int = 0,
num_decoding_left_chunks: int = -1,
) -> Tuple[torch.Tensor, torch.Tensor]:
"""Embed positions in tensor.
Args:
xs: padded input tensor (B, T, D)
xs_lens: input length (B)
decoding_chunk_size: decoding chunk size for dynamic chunk
0: default for training, use random dynamic chunk.
<0: for decoding, use full chunk.
>0: for decoding, use fixed chunk size as set.
num_decoding_left_chunks: number of left chunks, this is for decoding,
the chunk size is decoding_chunk_size.
>=0: use num_decoding_left_chunks
<0: use all left chunks
Returns:
encoder output tensor xs, and subsampled masks
xs: padded output tensor (B, T' ~= T/subsample_rate, D)
masks: torch.Tensor batch padding mask after subsample
(B, 1, T' ~= T/subsample_rate)
"""
T = xs.size(1)
masks = ~make_pad_mask(xs_lens, T).unsqueeze(1) # (B, 1, T)
if self.global_cmvn is not None:
xs = self.global_cmvn(xs)
xs, pos_emb, masks = self.embed(xs, masks)
mask_pad = masks # (B, 1, T/subsample_rate)
chunk_masks = add_optional_chunk_mask(
xs,
masks,
self.use_dynamic_chunk,
self.use_dynamic_left_chunk,
decoding_chunk_size,
self.static_chunk_size,
num_decoding_left_chunks,
)
index = 0 # traverse stride
for i, layer in enumerate(self.encoders):
# layer return : x, mask, new_att_cache, new_cnn_cache
xs, chunk_masks, _, _ = layer(xs, chunk_masks, pos_emb, mask_pad)
if i in self.stride_layer_idx:
masks = masks[:, :, :: self.stride[index]]
chunk_masks = chunk_masks[
:, :: self.stride[index], :: self.stride[index]
]
mask_pad = masks
pos_emb = pos_emb[:, :: self.stride[index], :]
index = index + 1
if self.normalize_before:
xs = self.after_norm(xs)
# Here we assume the mask is not changed in encoder layers, so just
# return the masks before encoder layers, and the masks will be used
# for cross attention with decoder later
return xs, masks
def forward_chunk(
self,
xs: torch.Tensor,
offset: int,
required_cache_size: int,
att_cache: torch.Tensor = torch.zeros(0, 0, 0, 0),
cnn_cache: torch.Tensor = torch.zeros(0, 0, 0, 0),
att_mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool),
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""Forward just one chunk
Args:
xs (torch.Tensor): chunk input
offset (int): current offset in encoder output time stamp
required_cache_size (int): cache size required for next chunk
compuation
>=0: actual cache size
<0: means all history cache is required
att_cache (torch.Tensor): cache tensor for KEY & VALUE in
transformer/conformer attention, with shape
(elayers, head, cache_t1, d_k * 2), where
`head * d_k == hidden-dim` and
`cache_t1 == chunk_size * num_decoding_left_chunks`.
cnn_cache (torch.Tensor): cache tensor for cnn_module in conformer,
(elayers, b=1, hidden-dim, cache_t2), where
`cache_t2 == cnn.lorder - 1`
att_mask : mask matrix of self attention
Returns:
torch.Tensor: output of current input xs
torch.Tensor: subsampling cache required for next chunk computation
List[torch.Tensor]: encoder layers output cache required for next
chunk computation
List[torch.Tensor]: conformer cnn cache
"""
assert xs.size(0) == 1
# using downsampling factor to recover offset
offset *= self.calculate_downsampling_factor(self.num_blocks + 1)
chunk_masks = torch.ones(1, xs.size(1), device=xs.device, dtype=torch.bool)
chunk_masks = chunk_masks.unsqueeze(1) # (1, 1, xs-time)
real_len = 0
if self.global_chunk_size > 0:
# for ONNX decode simulation， padding xs to chunk_size
real_len = xs.size(1)
pad_len = self.chunk_feature_map - real_len
xs = F.pad(xs, (0, 0, 0, pad_len), value=0.0)
chunk_masks = F.pad(chunk_masks, (0, pad_len), value=0.0)
if self.global_cmvn is not None:
xs = self.global_cmvn(xs)
# NOTE(xcsong): Before embed, shape(xs) is (b=1, time, mel-dim)
xs, pos_emb, chunk_masks = self.embed(xs, chunk_masks, offset)
elayers, cache_t1 = att_cache.size(0), att_cache.size(2)
chunk_size = xs.size(1)
attention_key_size = cache_t1 + chunk_size
# NOTE(xcsong): After embed, shape(xs) is (b=1, chunk_size, hidden-dim)
# shape(pos_emb) = (b=1, chunk_size, emb_size=output_size=hidden-dim)
if required_cache_size < 0:
next_cache_start = 0
elif required_cache_size == 0:
next_cache_start = attention_key_size
else:
next_cache_start = max(attention_key_size - required_cache_size, 0)
r_att_cache = []
r_cnn_cache = []
mask_pad = torch.ones(1, xs.size(1), device=xs.device, dtype=torch.bool)
mask_pad = mask_pad.unsqueeze(1) # batchPad (b=1, 1, time=chunk_size)
if self.global_chunk_size > 0:
# for ONNX decode simulation
pos_emb = self.embed.position_encoding(
offset=max(offset - cache_t1, 0), size=cache_t1 + self.global_chunk_size
)
att_mask[:, :, -self.global_chunk_size :] = chunk_masks
mask_pad = chunk_masks.to(torch.bool)
else:
pos_emb = self.embed.position_encoding(
offset=offset - cache_t1, size=attention_key_size
)
max_att_len, max_cnn_len = 0, 0 # for repeat_interleave of new_att_cache
for i, layer in enumerate(self.encoders):
factor = self.calculate_downsampling_factor(i)
# NOTE(xcsong): Before layer.forward
# shape(att_cache[i:i + 1]) is (1, head, cache_t1, d_k * 2),
# shape(cnn_cache[i]) is (b=1, hidden-dim, cache_t2)
# shape(new_att_cache) = [ batch, head, time2, outdim//head * 2 ]
att_cache_trunc = 0
if xs.size(1) + att_cache.size(2) / factor > pos_emb.size(1):
# The time step is not divisible by the downsampling multiple
att_cache_trunc = (
xs.size(1) + att_cache.size(2) // factor - pos_emb.size(1) + 1
)
xs, _, new_att_cache, new_cnn_cache = layer(
xs,
att_mask,
pos_emb,
mask_pad=mask_pad,
att_cache=att_cache[i : i + 1, :, ::factor, :][
:, :, att_cache_trunc:, :
],
cnn_cache=cnn_cache[i, :, :, :] if cnn_cache.size(0) > 0 else cnn_cache,
)
if i in self.stride_layer_idx:
# compute time dimension for next block
efficient_index = self.stride_layer_idx.index(i)
att_mask = att_mask[
:, :: self.stride[efficient_index], :: self.stride[efficient_index]
]
mask_pad = mask_pad[
:, :: self.stride[efficient_index], :: self.stride[efficient_index]
]
pos_emb = pos_emb[:, :: self.stride[efficient_index], :]
# shape(new_att_cache) = [batch, head, time2, outdim]
new_att_cache = new_att_cache[:, :, next_cache_start // factor :, :]
# shape(new_cnn_cache) = [1, batch, outdim, cache_t2]
new_cnn_cache = new_cnn_cache.unsqueeze(0)
# use repeat_interleave to new_att_cache
new_att_cache = new_att_cache.repeat_interleave(repeats=factor, dim=2)
# padding new_cnn_cache to cnn.lorder for casual convolution
new_cnn_cache = F.pad(
new_cnn_cache, (self.cnn_module_kernel - 1 - new_cnn_cache.size(3), 0)
)
if i == 0:
# record length for the first block as max length
max_att_len = new_att_cache.size(2)
max_cnn_len = new_cnn_cache.size(3)
# update real shape of att_cache and cnn_cache
r_att_cache.append(new_att_cache[:, :, -max_att_len:, :])
r_cnn_cache.append(new_cnn_cache[:, :, :, -max_cnn_len:])
if self.normalize_before:
xs = self.after_norm(xs)
# NOTE(xcsong): shape(r_att_cache) is (elayers, head, ?, d_k * 2),
# ? may be larger than cache_t1, it depends on required_cache_size
r_att_cache = torch.cat(r_att_cache, dim=0)
# NOTE(xcsong): shape(r_cnn_cache) is (e, b=1, hidden-dim, cache_t2)
r_cnn_cache = torch.cat(r_cnn_cache, dim=0)
if self.global_chunk_size > 0 and real_len:
chunk_real_len = (
real_len
// self.embed.subsampling_rate
// self.calculate_downsampling_factor(self.num_blocks + 1)
)
# Keeping 1 more timestep can mitigate information leakage
# from the encoder caused by the padding
xs = xs[:, : chunk_real_len + 1, :]
return xs, r_att_cache, r_cnn_cache
def forward_chunk_by_chunk(
self,
xs: torch.Tensor,
decoding_chunk_size: int,
num_decoding_left_chunks: int = -1,
use_onnx=False,
) -> Tuple[torch.Tensor, torch.Tensor]:
"""Forward input chunk by chunk with chunk_size like a streaming
fashion
Here we should pay special attention to computation cache in the
streaming style forward chunk by chunk. Three things should be taken
into account for computation in the current network:
1. transformer/conformer encoder layers output cache
2. convolution in conformer
3. convolution in subsampling
However, we don't implement subsampling cache for:
1. We can control subsampling module to output the right result by
overlapping input instead of cache left context, even though it
wastes some computation, but subsampling only takes a very
small fraction of computation in the whole model.
2. Typically, there are several covolution layers with subsampling
in subsampling module, it is tricky and complicated to do cache
with different convolution layers with different subsampling
rate.
3. Currently, nn.Sequential is used to stack all the convolution
layers in subsampling, we need to rewrite it to make it work
with cache, which is not prefered.
Args:
xs (torch.Tensor): (1, max_len, dim)
decoding_chunk_size (int): decoding chunk size
num_decoding_left_chunks (int):
use_onnx (bool): True for simulating ONNX model inference.
"""
assert decoding_chunk_size > 0
# The model is trained by static or dynamic chunk
assert self.static_chunk_size > 0 or self.use_dynamic_chunk
subsampling = self.embed.subsampling_rate
context = self.embed.right_context + 1 # Add current frame
stride = subsampling * decoding_chunk_size
decoding_window = (decoding_chunk_size - 1) * subsampling + context
num_frames = xs.size(1)
outputs = []
offset = 0
required_cache_size = decoding_chunk_size * num_decoding_left_chunks
if use_onnx:
logging.info("Simulating for ONNX runtime ...")
att_cache: torch.Tensor = torch.zeros(
(
self.num_blocks,
self.attention_heads,
required_cache_size,
self.output_size() // self.attention_heads * 2,
),
device=xs.device,
)
cnn_cache: torch.Tensor = torch.zeros(
(self.num_blocks, 1, self.output_size(), self.cnn_module_kernel - 1),
device=xs.device,
)
self.set_global_chunk_size(chunk_size=decoding_chunk_size)
else:
logging.info("Simulating for JIT runtime ...")
att_cache: torch.Tensor = torch.zeros((0, 0, 0, 0), device=xs.device)
cnn_cache: torch.Tensor = torch.zeros((0, 0, 0, 0), device=xs.device)
# Feed forward overlap input step by step
for cur in range(0, num_frames - context + 1, stride):
end = min(cur + decoding_window, num_frames)
logging.info(
f"-->> frame chunk msg: cur={cur}, "
f"end={end}, num_frames={end-cur}, "
f"decoding_window={decoding_window}"
)
if use_onnx:
att_mask: torch.Tensor = torch.ones(
(1, 1, required_cache_size + decoding_chunk_size),
dtype=torch.bool,
device=xs.device,
)
if cur == 0:
att_mask[:, :, :required_cache_size] = 0
else:
att_mask: torch.Tensor = torch.ones(
(0, 0, 0), dtype=torch.bool, device=xs.device
)
chunk_xs = xs[:, cur:end, :]
(y, att_cache, cnn_cache) = self.forward_chunk(
chunk_xs, offset, required_cache_size, att_cache, cnn_cache, att_mask
)
outputs.append(y)
offset += y.size(1)
ys = torch.cat(outputs, 1)
masks = torch.ones(1, 1, ys.size(1), device=ys.device, dtype=torch.bool)
return ys, masks