mirror of
https://github.com/k2-fsa/icefall.git
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121 lines
3.9 KiB
Python
121 lines
3.9 KiB
Python
# Copyright (c) 2021 Xiaomi Corporation (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 logging
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import torch
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import torch.nn.functional as F
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from icefall.utils import make_pad_mask
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class RnnLmModel(torch.nn.Module):
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def __init__(
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self,
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vocab_size: int,
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embedding_dim: int,
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hidden_dim: int,
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num_layers: int,
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tie_weights: bool = False,
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):
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"""
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Args:
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vocab_size:
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Vocabulary size of BPE model.
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embedding_dim:
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Input embedding dimension.
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hidden_dim:
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Hidden dimension of RNN layers.
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num_layers:
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Number of RNN layers.
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tie_weights:
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True to share the weights between the input embedding layer and the
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last output linear layer. See https://arxiv.org/abs/1608.05859
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and https://arxiv.org/abs/1611.01462
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"""
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super().__init__()
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self.input_embedding = torch.nn.Embedding(
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num_embeddings=vocab_size,
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embedding_dim=embedding_dim,
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)
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self.rnn = torch.nn.LSTM(
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input_size=embedding_dim,
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hidden_size=hidden_dim,
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num_layers=num_layers,
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batch_first=True,
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)
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self.output_linear = torch.nn.Linear(
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in_features=hidden_dim, out_features=vocab_size
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)
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self.vocab_size = vocab_size
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if tie_weights:
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logging.info("Tying weights")
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assert embedding_dim == hidden_dim, (embedding_dim, hidden_dim)
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self.output_linear.weight = self.input_embedding.weight
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else:
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logging.info("Not tying weights")
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def forward(
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self, x: torch.Tensor, y: torch.Tensor, lengths: torch.Tensor
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) -> torch.Tensor:
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"""
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Args:
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x:
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A 2-D tensor with shape (N, L). Each row
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contains token IDs for a sentence and starts with the SOS token.
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y:
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A shifted version of `x` and with EOS appended.
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lengths:
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A 1-D tensor of shape (N,). It contains the sentence lengths
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before padding.
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Returns:
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Return a 2-D tensor of shape (N, L) containing negative log-likelihood
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loss values. Note: Loss values for padding positions are set to 0.
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"""
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assert x.ndim == y.ndim == 2, (x.ndim, y.ndim)
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assert lengths.ndim == 1, lengths.ndim
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assert x.shape == y.shape, (x.shape, y.shape)
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batch_size = x.size(0)
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assert lengths.size(0) == batch_size, (lengths.size(0), batch_size)
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# embedding is of shape (N, L, embedding_dim)
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embedding = self.input_embedding(x)
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# Note: We use batch_first==True
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rnn_out, _ = self.rnn(embedding)
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logits = self.output_linear(rnn_out)
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# Note: No need to use `log_softmax()` here
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# since F.cross_entropy() expects unnormalized probabilities
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# nll_loss is of shape (N*L,)
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# nll -> negative log-likelihood
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nll_loss = F.cross_entropy(
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logits.reshape(-1, self.vocab_size), y.reshape(-1), reduction="none"
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)
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# Set loss values for padding positions to 0
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mask = make_pad_mask(lengths).reshape(-1)
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nll_loss.masked_fill_(mask, 0)
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nll_loss = nll_loss.reshape(batch_size, -1)
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return nll_loss
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