# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Encoder definition."""
from typing import List
from typing import Optional
from typing import Tuple
import paddle
from paddle import nn
from typeguard import check_argument_types
from deepspeech.modules.activation import get_activation
from deepspeech.modules.attention import MultiHeadedAttention
from deepspeech.modules.attention import RelPositionMultiHeadedAttention
from deepspeech.modules.conformer_convolution import ConvolutionModule
from deepspeech.modules.embedding import PositionalEncoding
from deepspeech.modules.embedding import RelPositionalEncoding
from deepspeech.modules.encoder_layer import ConformerEncoderLayer
from deepspeech.modules.encoder_layer import TransformerEncoderLayer
from deepspeech.modules.mask import add_optional_chunk_mask
from deepspeech.modules.mask import make_non_pad_mask
from deepspeech.modules.positionwise_feed_forward import PositionwiseFeedForward
from deepspeech.modules.subsampling import Conv2dSubsampling4
from deepspeech.modules.subsampling import Conv2dSubsampling6
from deepspeech.modules.subsampling import Conv2dSubsampling8
from deepspeech.modules.subsampling import LinearNoSubsampling
from deepspeech.utils.log import Log
logger = Log(__name__).getlog()
__all__ = ["BaseEncoder", 'TransformerEncoder', "ConformerEncoder"]
class BaseEncoder(nn.Layer):
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="abs_pos",
normalize_before: bool=True,
concat_after: bool=False,
static_chunk_size: int=0,
use_dynamic_chunk: bool=False,
global_cmvn: paddle.nn.Layer=None,
use_dynamic_left_chunk: bool=False, ):
"""
Args:
input_size (int): input dim, d_feature
output_size (int): dimension of attention, d_model
attention_heads (int): the number of heads of multi head attention
linear_units (int): the hidden units number of position-wise feed
forward
num_blocks (int): the number of encoder 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 (str): input layer type.
optional [linear, conv2d, conv2d6, conv2d8]
pos_enc_layer_type (str): Encoder positional encoding layer type.
opitonal [abs_pos, scaled_abs_pos, rel_pos]
normalize_before (bool):
True: use layer_norm before each sub-block of a layer.
False: use layer_norm after each sub-block of a layer.
concat_after (bool): whether to concat attention layer's input
and output.
True: x -> x + linear(concat(x, att(x)))
False: x -> x + att(x)
static_chunk_size (int): chunk size for static chunk training and
decoding
use_dynamic_chunk (bool): whether use dynamic chunk size for
training or not, You can only use fixed chunk(chunk_size > 0)
or dyanmic chunk size(use_dynamic_chunk = True)
global_cmvn (Optional[paddle.nn.Layer]): Optional GlobalCMVN layer
use_dynamic_left_chunk (bool): whether use dynamic left chunk in
dynamic chunk training
"""
assert check_argument_types()
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
else:
raise ValueError("unknown pos_enc_layer: " + pos_enc_layer_type)
if input_layer == "linear":
subsampling_class = LinearNoSubsampling
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)
self.global_cmvn = global_cmvn
self.embed = subsampling_class(
idim=input_size,
odim=output_size,
dropout_rate=dropout_rate,
pos_enc_class=pos_enc_class(
d_model=output_size, dropout_rate=positional_dropout_rate), )
self.normalize_before = normalize_before
self.after_norm = nn.LayerNorm(output_size, epsilon=1e-12)
self.static_chunk_size = static_chunk_size
self.use_dynamic_chunk = use_dynamic_chunk
self.use_dynamic_left_chunk = use_dynamic_left_chunk
def output_size(self) -> int:
return self._output_size
def forward(
self,
xs: paddle.Tensor,
xs_lens: paddle.Tensor,
decoding_chunk_size: int=0,
num_decoding_left_chunks: int=-1,
) -> Tuple[paddle.Tensor, paddle.Tensor]:
"""Embed positions in tensor.
Args:
xs: padded input tensor (B, L, 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, lens and mask
"""
masks = make_non_pad_mask(xs_lens).unsqueeze(1) # (B, 1, L)
if self.global_cmvn is not None:
xs = self.global_cmvn(xs)
#TODO(Hui Zhang): self.embed(xs, masks, offset=0), stride_slice not support bool tensor
xs, pos_emb, masks = self.embed(xs, masks.type_as(xs), offset=0)
#TODO(Hui Zhang): remove mask.astype, stride_slice not support bool tensor
masks = masks.astype(paddle.bool)
#TODO(Hui Zhang): mask_pad = ~masks
mask_pad = masks.logical_not()
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)
for layer in self.encoders:
xs, chunk_masks, _ = layer(xs, chunk_masks, pos_emb, mask_pad)
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: paddle.Tensor,
offset: int,
required_cache_size: int,
subsampling_cache: Optional[paddle.Tensor]=None,
elayers_output_cache: Optional[List[paddle.Tensor]]=None,
conformer_cnn_cache: Optional[List[paddle.Tensor]]=None,
) -> Tuple[paddle.Tensor, paddle.Tensor, List[paddle.Tensor], List[
paddle.Tensor]]:
""" Forward just one chunk
Args:
xs (paddle.Tensor): chunk input, [B=1, T, D]
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
subsampling_cache (Optional[paddle.Tensor]): subsampling cache
elayers_output_cache (Optional[List[paddle.Tensor]]):
transformer/conformer encoder layers output cache
conformer_cnn_cache (Optional[List[paddle.Tensor]]): conformer
cnn cache
Returns:
paddle.Tensor: output of current input xs
paddle.Tensor: subsampling cache required for next chunk computation
List[paddle.Tensor]: encoder layers output cache required for next
chunk computation
List[paddle.Tensor]: conformer cnn cache
"""
assert xs.size(0) == 1 # batch size must be one
# tmp_masks is just for interface compatibility
# TODO(Hui Zhang): stride_slice not support bool tensor
# tmp_masks = paddle.ones([1, xs.size(1)], dtype=paddle.bool)
tmp_masks = paddle.ones([1, xs.size(1)], dtype=paddle.int32)
tmp_masks = tmp_masks.unsqueeze(1) #[B=1, C=1, T]
if self.global_cmvn is not None:
xs = self.global_cmvn(xs)
xs, pos_emb, _ = self.embed(
xs, tmp_masks, offset=offset) #xs=(B, T, D), pos_emb=(B=1, T, D)
if subsampling_cache is not None:
cache_size = subsampling_cache.size(1) #T
xs = paddle.cat((subsampling_cache, xs), dim=1)
else:
cache_size = 0
# only used when using `RelPositionMultiHeadedAttention`
pos_emb = self.embed.position_encoding(
offset=offset - cache_size, size=xs.size(1))
if required_cache_size < 0:
next_cache_start = 0
elif required_cache_size == 0:
next_cache_start = xs.size(1)
else:
next_cache_start = xs.size(1) - required_cache_size
r_subsampling_cache = xs[:, next_cache_start:, :]
# Real mask for transformer/conformer layers
masks = paddle.ones([1, xs.size(1)], dtype=paddle.bool)
masks = masks.unsqueeze(1) #[B=1, L'=1, T]
r_elayers_output_cache = []
r_conformer_cnn_cache = []
for i, layer in enumerate(self.encoders):
attn_cache = None if elayers_output_cache is None else elayers_output_cache[
i]
cnn_cache = None if conformer_cnn_cache is None else conformer_cnn_cache[
i]
xs, _, new_cnn_cache = layer(
xs,
masks,
pos_emb,
output_cache=attn_cache,
cnn_cache=cnn_cache)
r_elayers_output_cache.append(xs[:, next_cache_start:, :])
r_conformer_cnn_cache.append(new_cnn_cache)
if self.normalize_before:
xs = self.after_norm(xs)
return (xs[:, cache_size:, :], r_subsampling_cache,
r_elayers_output_cache, r_conformer_cnn_cache)
def forward_chunk_by_chunk(
self,
xs: paddle.Tensor,
decoding_chunk_size: int,
num_decoding_left_chunks: int=-1,
) -> Tuple[paddle.Tensor, paddle.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 (paddle.Tensor): (1, max_len, dim)
chunk_size (int): decoding chunk size.
num_left_chunks (int): decoding with num left chunks.
"""
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
# feature stride and window for `subsampling` module
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)
required_cache_size = decoding_chunk_size * num_decoding_left_chunks
subsampling_cache: Optional[paddle.Tensor] = None
elayers_output_cache: Optional[List[paddle.Tensor]] = None
conformer_cnn_cache: Optional[List[paddle.Tensor]] = None
outputs = []
offset = 0
# Feed forward overlap input step by step
for cur in range(0, num_frames - context + 1, stride):
end = min(cur + decoding_window, num_frames)
chunk_xs = xs[:, cur:end, :]
(y, subsampling_cache, elayers_output_cache,
conformer_cnn_cache) = self.forward_chunk(
chunk_xs, offset, required_cache_size, subsampling_cache,
elayers_output_cache, conformer_cnn_cache)
outputs.append(y)
offset += y.size(1)
ys = paddle.cat(outputs, 1)
# fake mask, just for jit script and compatibility with `forward` api
masks = paddle.ones([1, ys.size(1)], dtype=paddle.bool)
masks = masks.unsqueeze(1)
return ys, masks
class TransformerEncoder(BaseEncoder):
"""Transformer 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="abs_pos",
normalize_before: bool=True,
concat_after: bool=False,
static_chunk_size: int=0,
use_dynamic_chunk: bool=False,
global_cmvn: nn.Layer=None,
use_dynamic_left_chunk: bool=False, ):
""" Construct TransformerEncoder
See Encoder for the meaning of each parameter.
"""
assert check_argument_types()
super().__init__(input_size, output_size, attention_heads, linear_units,
num_blocks, dropout_rate, positional_dropout_rate,
attention_dropout_rate, input_layer,
pos_enc_layer_type, normalize_before, concat_after,
static_chunk_size, use_dynamic_chunk, global_cmvn,
use_dynamic_left_chunk)
self.encoders = nn.ModuleList([
TransformerEncoderLayer(
size=output_size,
self_attn=MultiHeadedAttention(attention_heads, output_size,
attention_dropout_rate),
feed_forward=PositionwiseFeedForward(output_size, linear_units,
dropout_rate),
dropout_rate=dropout_rate,
normalize_before=normalize_before,
concat_after=concat_after) for _ in range(num_blocks)
])
class ConformerEncoder(BaseEncoder):
"""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,
concat_after: bool=False,
static_chunk_size: int=0,
use_dynamic_chunk: bool=False,
global_cmvn: nn.Layer=None,
use_dynamic_left_chunk: bool=False,
positionwise_conv_kernel_size: int=1,
macaron_style: bool=True,
selfattention_layer_type: str="rel_selfattn",
activation_type: str="swish",
use_cnn_module: bool=True,
cnn_module_kernel: int=15,
causal: bool=False,
cnn_module_norm: str="batch_norm", ):
"""Construct ConformerEncoder
Args:
input_size to use_dynamic_chunk, see in BaseEncoder
positionwise_conv_kernel_size (int): Kernel size of positionwise
conv1d layer.
macaron_style (bool): Whether to use macaron style for
positionwise layer.
selfattention_layer_type (str): Encoder attention layer type,
the parameter has no effect now, it's just for configure
compatibility.
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.
cnn_module_norm (str): cnn conv norm type, Optional['batch_norm','layer_norm']
"""
assert check_argument_types()
super().__init__(input_size, output_size, attention_heads, linear_units,
num_blocks, dropout_rate, positional_dropout_rate,
attention_dropout_rate, input_layer,
pos_enc_layer_type, normalize_before, concat_after,
static_chunk_size, use_dynamic_chunk, global_cmvn,
use_dynamic_left_chunk)
activation = get_activation(activation_type)
# self-attention module definition
encoder_selfattn_layer = RelPositionMultiHeadedAttention
encoder_selfattn_layer_args = (attention_heads, output_size,
attention_dropout_rate)
# feed-forward module definition
positionwise_layer = PositionwiseFeedForward
positionwise_layer_args = (output_size, linear_units, dropout_rate,
activation)
# convolution module definition
convolution_layer = ConvolutionModule
convolution_layer_args = (output_size, cnn_module_kernel, activation,
cnn_module_norm, causal)
self.encoders = nn.ModuleList([
ConformerEncoderLayer(
size=output_size,
self_attn=encoder_selfattn_layer(*encoder_selfattn_layer_args),
feed_forward=positionwise_layer(*positionwise_layer_args),
feed_forward_macaron=positionwise_layer(
*positionwise_layer_args) if macaron_style else None,
conv_module=convolution_layer(*convolution_layer_args)
if use_cnn_module else None,
dropout_rate=dropout_rate,
normalize_before=normalize_before,
concat_after=concat_after) for _ in range(num_blocks)
])