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135 lines
4.6 KiB
Python
135 lines
4.6 KiB
Python
# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang)
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#
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# See ../../../../LICENSE for clarification regarding multiple authors
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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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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from scaling import Balancer
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class Decoder(nn.Module):
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"""This class modifies the stateless decoder from the following paper:
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RNN-transducer with stateless prediction network
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https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9054419
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It removes the recurrent connection from the decoder, i.e., the prediction
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network. Different from the above paper, it adds an extra Conv1d
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right after the embedding layer.
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TODO: Implement https://arxiv.org/pdf/2109.07513.pdf
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"""
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def __init__(
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self,
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vocab_size: int,
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decoder_dim: int,
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blank_id: int,
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context_size: int,
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):
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"""
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Args:
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vocab_size:
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Number of tokens of the modeling unit including blank.
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decoder_dim:
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Dimension of the input embedding, and of the decoder output.
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blank_id:
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The ID of the blank symbol.
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context_size:
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Number of previous words to use to predict the next word.
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1 means bigram; 2 means trigram. n means (n+1)-gram.
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"""
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super().__init__()
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self.embedding = nn.Embedding(
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num_embeddings=vocab_size,
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embedding_dim=decoder_dim,
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)
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# the balancers are to avoid any drift in the magnitude of the
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# embeddings, which would interact badly with parameter averaging.
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self.balancer = Balancer(
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decoder_dim,
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channel_dim=-1,
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min_positive=0.0,
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max_positive=1.0,
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min_abs=0.5,
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max_abs=1.0,
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prob=0.05,
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)
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self.blank_id = blank_id
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assert context_size >= 1, context_size
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self.context_size = context_size
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self.vocab_size = vocab_size
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if context_size > 1:
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self.conv = nn.Conv1d(
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in_channels=decoder_dim,
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out_channels=decoder_dim,
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kernel_size=context_size,
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padding=0,
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groups=decoder_dim // 4, # group size == 4
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bias=False,
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)
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self.balancer2 = Balancer(
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decoder_dim,
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channel_dim=-1,
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min_positive=0.0,
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max_positive=1.0,
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min_abs=0.5,
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max_abs=1.0,
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prob=0.05,
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)
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else:
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# To avoid `RuntimeError: Module 'Decoder' has no attribute 'conv'`
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# when inference with torch.jit.script and context_size == 1
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self.conv = nn.Identity()
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self.balancer2 = nn.Identity()
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def forward(self, y: torch.Tensor, need_pad: bool = True) -> torch.Tensor:
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"""
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Args:
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y:
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A 2-D tensor of shape (N, U).
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need_pad:
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True to left pad the input. Should be True during training.
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False to not pad the input. Should be False during inference.
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Returns:
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Return a tensor of shape (N, U, decoder_dim).
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"""
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y = y.to(torch.int64)
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# this stuff about clamp() is a temporary fix for a mismatch
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# at utterance start, we use negative ids in beam_search.py
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embedding_out = self.embedding(y.clamp(min=0)) * (y >= 0).unsqueeze(-1)
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embedding_out = self.balancer(embedding_out)
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if self.context_size > 1:
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embedding_out = embedding_out.permute(0, 2, 1)
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if need_pad is True:
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embedding_out = F.pad(embedding_out, pad=(self.context_size - 1, 0))
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else:
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# During inference time, there is no need to do extra padding
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# as we only need one output
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assert embedding_out.size(-1) == self.context_size
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embedding_out = self.conv(embedding_out)
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embedding_out = embedding_out.permute(0, 2, 1)
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embedding_out = F.relu(embedding_out)
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embedding_out = self.balancer2(embedding_out)
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return embedding_out
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