# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang) # # See ../../../../LICENSE for clarification regarding multiple authors # # 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. import torch import torch.nn as nn import torch.nn.functional as F from scaling import Balancer class Decoder(torch.nn.Module): """ This class modifies the stateless decoder from the following paper: RNN-transducer with stateless prediction network https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9054419 It removes the recurrent connection from the decoder, i.e., the prediction network. Different from the above paper, it adds an extra Conv1d right after the embedding layer. """ def __init__( self, vocab_size: int, decoder_dim: int, context_size: int, device: torch.device, ) -> None: """ Decoder initialization. Parameters ---------- vocab_size : int A number of tokens or modeling units, includes blank. decoder_dim : int A dimension of the decoder embeddings, and the decoder output. context_size : int A number of previous words to use to predict the next word. 1 means bigram; 2 means trigram. n means (n+1)-gram. device : torch.device The device used to store the layer weights. Should be either torch.device("cpu") or torch.device("cuda"). """ super().__init__() self.embedding = torch.nn.Embedding(vocab_size, decoder_dim) if context_size < 1: raise ValueError( 'RNN-T decoder context size should be an integer greater ' f'or equal than 1, but got {context_size}.', ) self.context_size = context_size self.conv = torch.nn.Conv1d( decoder_dim, decoder_dim, context_size, groups=decoder_dim // 4, bias=False, device=device, ) def forward(self, y: torch.Tensor) -> torch.Tensor: """ Does a forward pass of the stateless Decoder module. Returns an output decoder tensor. Parameters ---------- y : torch.Tensor[torch.int32] The input integer tensor of shape (N, context_size). The module input that corresponds to the last context_size decoded token indexes. Returns ------- torch.Tensor[torch.float32] An output float tensor of shape (N, 1, decoder_dim). """ # this stuff about clamp() is a fix for a mismatch at utterance start, # we use negative ids in RNN-T decoding. embedding_out = self.embedding(y.clamp(min=0)) * (y >= 0).unsqueeze(2) if self.context_size > 1: embedding_out = embedding_out.permute(0, 2, 1) embedding_out = self.conv(embedding_out) embedding_out = embedding_out.permute(0, 2, 1) embedding_out = torch.nn.functional.relu(embedding_out) return embedding_out class DecoderModule(torch.nn.Module): """ A helper module to combine decoder, decoder projection, and joiner inference together. """ def __init__( self, vocab_size: int, decoder_dim: int, joiner_dim: int, context_size: int, beam: int, device: torch.device, ) -> None: """ DecoderModule initialization. Parameters ---------- vocab_size: A number of tokens or modeling units, includes blank. decoder_dim : int A dimension of the decoder embeddings, and the decoder output. joiner_dim : int Input joiner dimension. context_size : int A number of previous words to use to predict the next word. 1 means bigram; 2 means trigram. n means (n+1)-gram. beam : int A decoder beam. device : torch.device The device used to store the layer weights. Should be either torch.device("cpu") or torch.device("cuda"). """ super().__init__() self.decoder = Decoder(vocab_size, decoder_dim, context_size, device) self.decoder_proj = torch.nn.Linear(decoder_dim, joiner_dim, device=device) self.joiner = Joiner(joiner_dim, vocab_size, device) self.vocab_size = vocab_size self.beam = beam def forward( self, decoder_input: torch.Tensor, encoder_out: torch.Tensor, hyps_log_prob: torch.Tensor, ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: """ Does a forward pass of the stateless Decoder module. Returns an output decoder tensor. Parameters ---------- decoder_input : torch.Tensor[torch.int32] The input integer tensor of shape (num_hyps, context_size). The module input that corresponds to the last context_size decoded token indexes. encoder_out : torch.Tensor[torch.float32] An output tensor from the encoder after projection of shape (num_hyps, joiner_dim). hyps_log_prob : torch.Tensor[torch.float32] Hypothesis probabilities in a logarithmic scale of shape (num_hyps, 1). Returns ------- torch.Tensor[torch.float32] A float output tensor of logit token probabilities of shape (num_hyps, vocab_size). """ decoder_out = self.decoder(decoder_input) decoder_out = self.decoder_proj(decoder_out) logits = self.joiner(encoder_out, decoder_out[:, 0, :]) tokens_log_prob = torch.log_softmax(logits, dim=1) log_probs = (tokens_log_prob + hyps_log_prob).reshape(-1) hyps_topk_log_prob, topk_indexes = log_probs.topk(self.beam) topk_hyp_indexes = torch.floor_divide(topk_indexes, self.vocab_size).to(torch.int32) topk_token_indexes = torch.remainder(topk_indexes, self.vocab_size).to(torch.int32) tokens_topk_prob = torch.exp(tokens_log_prob.reshape(-1)[topk_indexes]) return hyps_topk_log_prob, tokens_topk_prob, topk_hyp_indexes, topk_token_indexes