mirror of
https://github.com/k2-fsa/icefall.git
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115 lines
3.3 KiB
Python
115 lines
3.3 KiB
Python
# Copyright (c) 2022 Xiaomi Corporation (authors: Xiaoyu Yang)
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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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from typing import Optional, Tuple
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import torch
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import torch.nn.functional as F
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from icefall.transformer_lm.encoder import Transformer
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from icefall.utils import AttributeDict, add_eos, add_sos, make_pad_mask
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class TransformerLM(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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d_model: int,
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dim_feedforward: int,
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nhead: int = 8,
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num_layers: int = 16,
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tie_weights: bool = True,
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dropout: float = 0.1,
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emb_dropout_rate: float = 0.0,
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params: AttributeDict = None,
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):
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super().__init__()
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self.vocab_size = vocab_size
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self.params = params
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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.encoder = Transformer(
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input_dim=embedding_dim,
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d_model=d_model,
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dim_feedforward=dim_feedforward,
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nhead=nhead,
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num_layers=num_layers,
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dropout_rate=dropout,
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)
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self.output_linear = torch.nn.Linear(
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in_features=d_model, out_features=vocab_size
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)
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if tie_weights:
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logging.info("Tying weights")
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assert d_model == embedding_dim, (d_model, embedding_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,
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x: torch.Tensor,
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y: torch.Tensor,
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x_lens: torch.Tensor,
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return_logits: bool = False,
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):
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"""Forward transformer language model
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Args:
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x (torch.Tensor): Input tokens (B,L)
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y (torch.Tensor): Output tokens (with EOS appended) (B,L)
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x_lens (torch.Tensor): Length of input tokens before padding (B,)
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return_logits (bool, optional): Return logits instead of NLL
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"""
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x = self.input_embedding(x)
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x, x_lens = self.encoder(x, x_lens)
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logits = self.output_linear(x)
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if return_logits:
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return logits
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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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mask = make_pad_mask(x_lens).reshape(-1)
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nll_loss.masked_fill_(mask, 0)
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return nll_loss
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def score_token(self, x: torch.Tensor, x_lens: torch.Tensor, state=None):
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bs = x.size(0)
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state = None
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logits = self.forward(x, x, x_lens, return_logits=True)
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index = torch.arange(bs)
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last_logits = logits[index, x_lens - 1, :]
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return last_logits.log_softmax(-1), state
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