mirror of
https://github.com/k2-fsa/icefall.git
synced 2025-12-11 06:55:27 +00:00
90 lines
2.9 KiB
Python
90 lines
2.9 KiB
Python
#!/usr/bin/env python3
|
|
# Copyright 2023 Xiaomi Corp. (authors: Daniel Povey)
|
|
#
|
|
# 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 logging
|
|
import random
|
|
import torch
|
|
from torch import nn, Tensor
|
|
|
|
class Decoder(nn.Module):
|
|
"""
|
|
"""
|
|
def __init__(self,
|
|
embed_dim: int,
|
|
hidden_dim: int,
|
|
vocab_size: int):
|
|
"""
|
|
A 'decoder' that computes the probability of symbols in a language modeling task.
|
|
"""
|
|
super().__init__()
|
|
|
|
|
|
self.to_hidden = nn.Linear(
|
|
embed_dim, hidden_dim, bias=False,
|
|
)
|
|
# no padding, will manually pad on the left so it is causal.
|
|
self.depthwise_conv = nn.Conv1d(
|
|
in_channels=hidden_dim,
|
|
out_channels=hidden_dim,
|
|
groups=hidden_dim,
|
|
kernel_size=3
|
|
)
|
|
self.activation = nn.Tanh()
|
|
self.hidden_to_vocab = nn.Linear(
|
|
hidden_dim, vocab_size,
|
|
)
|
|
|
|
self.bypass = nn.Linear(
|
|
embed_dim, vocab_size, bias=False,
|
|
)
|
|
|
|
|
|
def forward(self,
|
|
labels: Tensor,
|
|
encoder_embed: Tensor) -> Tensor:
|
|
"""
|
|
Compute log-probs.
|
|
Args:
|
|
labels: the labels, a Tensor of integer type of shape (batch_size, seq_len);
|
|
encoder_embed: the embeddings from the encoder, of shape (seq_len, batch_size, embed_dim)
|
|
|
|
Returns:
|
|
returns the log-probs for each symbol, in a Tensor of shape (batch_size, seq_len).
|
|
"""
|
|
(batch_size, seq_len) = labels.shape
|
|
(num_chunks, _batch_size, embed_dim) = encoder_embed.shape
|
|
|
|
assert batch_size == _batch_size
|
|
|
|
bypass = self.bypass(encoder_embed)
|
|
|
|
x = self.to_hidden(encoder_embed) # (seq_len, batch_size, hidden_dim)
|
|
x = x.permute(1, 2, 0) # (N,C,H) = (batch_size, hidden_dim, seq_len)
|
|
x = torch.nn.functional.pad(x, (2, 0)) # pad left with 2 frames.
|
|
x = self.depthwise_conv(x)
|
|
x = x.permute(0, 2, 1) # (batch_size, seq_len, hidden_dim)
|
|
x = self.activation(x)
|
|
x = self.hidden_to_vocab(x) # (batch_size, seq_len, vocab_size)
|
|
|
|
x = x + bypass.transpose(0, 1)
|
|
x = x.log_softmax(dim=-1)
|
|
|
|
logprobs = torch.gather(x, dim=-1, index=labels.unsqueeze(-1)).squeeze(-1) # (batch_size, seq_len)
|
|
|
|
return logprobs
|