133 lines
4.7 KiB
Python
133 lines
4.7 KiB
Python
# Copyright (c) 2019 Shigeki Karita
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# 2020 Mobvoi Inc (Binbin Zhang)
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Decoder self-attention layer definition."""
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from typing import Optional, Tuple
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import torch
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from torch import nn
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class DecoderLayer(nn.Module):
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"""Single decoder layer module.
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Args:
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size (int): Input dimension.
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self_attn (torch.nn.Module): Self-attention module instance.
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`MultiHeadedAttention` instance can be used as the argument.
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src_attn (torch.nn.Module): Inter-attention module instance.
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`MultiHeadedAttention` instance can be used as the argument.
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If `None` is passed, Inter-attention is not used, such as
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CIF, GPT, and other decoder only model.
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feed_forward (torch.nn.Module): Feed-forward module instance.
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`PositionwiseFeedForward` instance can be used as the argument.
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dropout_rate (float): Dropout rate.
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normalize_before (bool):
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True: use layer_norm before each sub-block.
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False: to use layer_norm after each sub-block.
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"""
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def __init__(
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self,
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size: int,
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self_attn: nn.Module,
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src_attn: Optional[nn.Module],
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feed_forward: nn.Module,
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dropout_rate: float,
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normalize_before: bool = True,
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):
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"""Construct an DecoderLayer object."""
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super().__init__()
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self.size = size
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self.self_attn = self_attn
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self.src_attn = src_attn
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self.feed_forward = feed_forward
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self.norm1 = nn.LayerNorm(size, eps=1e-5)
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self.norm2 = nn.LayerNorm(size, eps=1e-5)
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self.norm3 = nn.LayerNorm(size, eps=1e-5)
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self.dropout = nn.Dropout(dropout_rate)
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self.normalize_before = normalize_before
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def forward(
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self,
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tgt: torch.Tensor,
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tgt_mask: torch.Tensor,
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memory: torch.Tensor,
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memory_mask: torch.Tensor,
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cache: Optional[torch.Tensor] = None
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) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
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"""Compute decoded features.
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Args:
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tgt (torch.Tensor): Input tensor (#batch, maxlen_out, size).
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tgt_mask (torch.Tensor): Mask for input tensor
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(#batch, maxlen_out).
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memory (torch.Tensor): Encoded memory
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(#batch, maxlen_in, size).
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memory_mask (torch.Tensor): Encoded memory mask
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(#batch, maxlen_in).
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cache (torch.Tensor): cached tensors.
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(#batch, maxlen_out - 1, size).
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Returns:
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torch.Tensor: Output tensor (#batch, maxlen_out, size).
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torch.Tensor: Mask for output tensor (#batch, maxlen_out).
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torch.Tensor: Encoded memory (#batch, maxlen_in, size).
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torch.Tensor: Encoded memory mask (#batch, maxlen_in).
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"""
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residual = tgt
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if self.normalize_before:
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tgt = self.norm1(tgt)
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if cache is None:
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tgt_q = tgt
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tgt_q_mask = tgt_mask
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else:
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# compute only the last frame query keeping dim: max_time_out -> 1
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assert cache.shape == (
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tgt.shape[0],
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tgt.shape[1] - 1,
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self.size,
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), "{cache.shape} == {(tgt.shape[0], tgt.shape[1] - 1, self.size)}"
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tgt_q = tgt[:, -1:, :]
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residual = residual[:, -1:, :]
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tgt_q_mask = tgt_mask[:, -1:, :]
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x = residual + self.dropout(
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self.self_attn(tgt_q, tgt, tgt, tgt_q_mask)[0])
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if not self.normalize_before:
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x = self.norm1(x)
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if self.src_attn is not None:
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residual = x
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if self.normalize_before:
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x = self.norm2(x)
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x = residual + self.dropout(
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self.src_attn(x, memory, memory, memory_mask)[0])
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if not self.normalize_before:
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x = self.norm2(x)
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residual = x
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if self.normalize_before:
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x = self.norm3(x)
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x = residual + self.dropout(self.feed_forward(x))
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if not self.normalize_before:
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x = self.norm3(x)
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if cache is not None:
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x = torch.cat([cache, x], dim=1)
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return x, tgt_mask, memory, memory_mask
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