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Running
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Zero
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# Copyright (c) 2019 Shigeki Karita
# 2020 Mobvoi Inc (Binbin Zhang)
#
# 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.
"""Decoder self-attention layer definition."""
from typing import Dict, Optional, Tuple
import torch
from torch import nn
from wenet.transformer.attention import T_CACHE
from wenet.utils.class_utils import WENET_NORM_CLASSES
class DecoderLayer(nn.Module):
"""Single decoder layer module.
Args:
size (int): Input dimension.
self_attn (torch.nn.Module): Self-attention module instance.
`MultiHeadedAttention` instance can be used as the argument.
src_attn (torch.nn.Module): Inter-attention module instance.
`MultiHeadedAttention` instance can be used as the argument.
If `None` is passed, Inter-attention is not used, such as
CIF, GPT, and other decoder only model.
feed_forward (torch.nn.Module): Feed-forward module instance.
`PositionwiseFeedForward` instance can be used as the argument.
dropout_rate (float): Dropout rate.
normalize_before (bool):
True: use layer_norm before each sub-block.
False: to use layer_norm after each sub-block.
"""
def __init__(
self,
size: int,
self_attn: nn.Module,
src_attn: Optional[nn.Module],
feed_forward: nn.Module,
dropout_rate: float,
normalize_before: bool = True,
layer_norm_type: str = 'layer_norm',
norm_eps: float = 1e-5,
):
"""Construct an DecoderLayer object."""
super().__init__()
self.size = size
self.self_attn = self_attn
self.src_attn = src_attn
self.feed_forward = feed_forward
assert layer_norm_type in ['layer_norm', 'rms_norm']
self.norm1 = WENET_NORM_CLASSES[layer_norm_type](size, eps=norm_eps)
self.norm2 = WENET_NORM_CLASSES[layer_norm_type](size, eps=norm_eps)
self.norm3 = WENET_NORM_CLASSES[layer_norm_type](size, eps=norm_eps)
self.dropout = nn.Dropout(dropout_rate)
self.normalize_before = normalize_before
def forward(
self,
tgt: torch.Tensor,
tgt_mask: torch.Tensor,
memory: torch.Tensor,
memory_mask: torch.Tensor,
cache: Optional[Dict[str, Optional[T_CACHE]]] = None
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
"""Compute decoded features.
Args:
tgt (torch.Tensor): Input tensor (#batch, maxlen_out, size).
tgt_mask (torch.Tensor): Mask for input tensor
(#batch, maxlen_out).
memory (torch.Tensor): Encoded memory
(#batch, maxlen_in, size).
memory_mask (torch.Tensor): Encoded memory mask
(#batch, maxlen_in).
cache (torch.Tensor): cached tensors.
(#batch, maxlen_out - 1, size).
Returns:
torch.Tensor: Output tensor (#batch, maxlen_out, size).
torch.Tensor: Mask for output tensor (#batch, maxlen_out).
torch.Tensor: Encoded memory (#batch, maxlen_in, size).
torch.Tensor: Encoded memory mask (#batch, maxlen_in).
"""
if cache is not None:
att_cache = cache['self_att_cache']
cross_att_cache = cache['cross_att_cache']
else:
att_cache, cross_att_cache = None, None
residual = tgt
if self.normalize_before:
tgt = self.norm1(tgt)
if att_cache is None:
tgt_q = tgt
tgt_q_mask = tgt_mask
att_cache = (torch.empty(0, 0, 0, 0), torch.empty(0, 0, 0, 0))
else:
tgt_q = tgt[:, -1:, :]
residual = residual[:, -1:, :]
tgt_q_mask = tgt_mask[:, -1:, :]
x, new_att_cache = self.self_attn(
tgt_q,
tgt_q,
tgt_q,
tgt_q_mask,
cache=att_cache,
)
if cache is not None:
cache['self_att_cache'] = new_att_cache
x = residual + self.dropout(x)
if not self.normalize_before:
x = self.norm1(x)
if self.src_attn is not None:
residual = x
if self.normalize_before:
x = self.norm2(x)
if cross_att_cache is None:
cross_att_cache = (torch.empty(0, 0, 0,
0), torch.empty(0, 0, 0, 0))
x, new_cross_cache = self.src_attn(x,
memory,
memory,
memory_mask,
cache=cross_att_cache)
if cache is not None:
cache['cross_att_cache'] = new_cross_cache
x = residual + self.dropout(x)
if not self.normalize_before:
x = self.norm2(x)
residual = x
if self.normalize_before:
x = self.norm3(x)
x = residual + self.dropout(self.feed_forward(x))
if not self.normalize_before:
x = self.norm3(x)
return x, tgt_mask, memory, memory_mask
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