181 lines
6.5 KiB
Python
181 lines
6.5 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(torch.nn.Module):
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"""
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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 network.
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Different from the above paper, it adds an extra Conv1d right after the embedding layer.
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"""
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def __init__(
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self, vocab_size: int, decoder_dim: int, context_size: int, device: torch.device,
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) -> None:
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"""
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Decoder initialization.
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Parameters
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----------
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vocab_size : int
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A number of tokens or modeling units, includes blank.
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decoder_dim : int
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A dimension of the decoder embeddings, and the decoder output.
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context_size : int
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A 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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device : torch.device
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The device used to store the layer weights. Should be
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either torch.device("cpu") or torch.device("cuda").
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"""
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super().__init__()
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self.embedding = torch.nn.Embedding(vocab_size, decoder_dim)
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if context_size < 1:
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raise ValueError(
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'RNN-T decoder context size should be an integer greater '
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f'or equal than 1, but got {context_size}.',
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)
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self.context_size = context_size
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self.conv = torch.nn.Conv1d(
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decoder_dim,
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decoder_dim,
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context_size,
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groups=decoder_dim // 4,
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bias=False,
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device=device,
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)
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def forward(self, y: torch.Tensor) -> torch.Tensor:
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"""
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Does a forward pass of the stateless Decoder module. Returns an output decoder tensor.
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Parameters
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----------
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y : torch.Tensor[torch.int32]
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The input integer tensor of shape (N, context_size).
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The module input that corresponds to the last context_size decoded token indexes.
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Returns
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-------
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torch.Tensor[torch.float32]
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An output float tensor of shape (N, 1, decoder_dim).
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"""
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# this stuff about clamp() is a fix for a mismatch at utterance start,
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# we use negative ids in RNN-T decoding.
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embedding_out = self.embedding(y.clamp(min=0)) * (y >= 0).unsqueeze(2)
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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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embedding_out = self.conv(embedding_out)
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embedding_out = embedding_out.permute(0, 2, 1)
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embedding_out = torch.nn.functional.relu(embedding_out)
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return embedding_out
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class DecoderModule(torch.nn.Module):
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"""
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A helper module to combine decoder, decoder projection, and joiner inference together.
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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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joiner_dim: int,
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context_size: int,
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beam: int,
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device: torch.device,
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) -> None:
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"""
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DecoderModule initialization.
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Parameters
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----------
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vocab_size:
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A number of tokens or modeling units, includes blank.
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decoder_dim : int
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A dimension of the decoder embeddings, and the decoder output.
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joiner_dim : int
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Input joiner dimension.
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context_size : int
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A 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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beam : int
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A decoder beam.
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device : torch.device
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The device used to store the layer weights. Should be
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either torch.device("cpu") or torch.device("cuda").
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"""
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super().__init__()
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self.decoder = Decoder(vocab_size, decoder_dim, context_size, device)
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self.decoder_proj = torch.nn.Linear(decoder_dim, joiner_dim, device=device)
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self.joiner = Joiner(joiner_dim, vocab_size, device)
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self.vocab_size = vocab_size
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self.beam = beam
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def forward(
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self, decoder_input: torch.Tensor, encoder_out: torch.Tensor, hyps_log_prob: torch.Tensor,
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) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
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"""
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Does a forward pass of the stateless Decoder module. Returns an output decoder tensor.
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Parameters
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----------
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decoder_input : torch.Tensor[torch.int32]
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The input integer tensor of shape (num_hyps, context_size).
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The module input that corresponds to the last context_size decoded token indexes.
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encoder_out : torch.Tensor[torch.float32]
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An output tensor from the encoder after projection of shape (num_hyps, joiner_dim).
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hyps_log_prob : torch.Tensor[torch.float32]
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Hypothesis probabilities in a logarithmic scale of shape (num_hyps, 1).
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Returns
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-------
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torch.Tensor[torch.float32]
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A float output tensor of logit token probabilities of shape (num_hyps, vocab_size).
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"""
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decoder_out = self.decoder(decoder_input)
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decoder_out = self.decoder_proj(decoder_out)
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logits = self.joiner(encoder_out, decoder_out[:, 0, :])
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tokens_log_prob = torch.log_softmax(logits, dim=1)
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log_probs = (tokens_log_prob + hyps_log_prob).reshape(-1)
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hyps_topk_log_prob, topk_indexes = log_probs.topk(self.beam)
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topk_hyp_indexes = torch.floor_divide(topk_indexes, self.vocab_size).to(torch.int32)
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topk_token_indexes = torch.remainder(topk_indexes, self.vocab_size).to(torch.int32)
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tokens_topk_prob = torch.exp(tokens_log_prob.reshape(-1)[topk_indexes])
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return hyps_topk_log_prob, tokens_topk_prob, topk_hyp_indexes, topk_token_indexes |