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7d56685734
* merge upstream * initial commit for zipformer_ctc * remove unwanted changes * remove changes to other recipe * fix zipformer softlink * fix for JIT export * add missing file * fix symbolic links * update results * Update RESULTS.md Address comments from @csukuangfj --------- Co-authored-by: zr_jin <peter.jin.cn@gmail.com>
299 lines
10 KiB
Python
299 lines
10 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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from typing import List
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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 label_smoothing import LabelSmoothingLoss
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from torch.nn.utils.rnn import pad_sequence
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from transformer import PositionalEncoding, TransformerDecoderLayer
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class Decoder(nn.Module):
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"""This class implements Transformer based decoder for an attention-based encoder-decoder
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model.
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"""
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def __init__(
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self,
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num_layers: int,
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num_classes: int,
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d_model: int = 256,
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nhead: int = 4,
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dim_feedforward: int = 2048,
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dropout: float = 0.1,
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normalize_before: bool = True,
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):
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"""
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Args:
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num_layers:
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Number of layers.
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num_classes:
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Number of tokens of the modeling unit including blank.
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d_model:
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Dimension of the input embedding, and of the decoder output.
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"""
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super().__init__()
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if num_layers > 0:
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self.decoder_num_class = num_classes # bpe model already has sos/eos symbol
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self.decoder_embed = nn.Embedding(
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num_embeddings=self.decoder_num_class, embedding_dim=d_model
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)
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self.decoder_pos = PositionalEncoding(d_model, dropout)
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decoder_layer = TransformerDecoderLayer(
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d_model=d_model,
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nhead=nhead,
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dim_feedforward=dim_feedforward,
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dropout=dropout,
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normalize_before=normalize_before,
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)
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if normalize_before:
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decoder_norm = nn.LayerNorm(d_model)
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else:
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decoder_norm = None
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self.decoder = nn.TransformerDecoder(
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decoder_layer=decoder_layer,
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num_layers=num_layers,
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norm=decoder_norm,
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)
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self.decoder_output_layer = torch.nn.Linear(d_model, self.decoder_num_class)
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self.decoder_criterion = LabelSmoothingLoss()
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else:
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self.decoder_criterion = None
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@torch.jit.export
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def forward(
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self,
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memory: torch.Tensor,
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memory_key_padding_mask: torch.Tensor,
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token_ids: List[List[int]],
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sos_id: int,
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eos_id: int,
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) -> torch.Tensor:
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"""
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Args:
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memory:
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It's the output of the encoder with shape (T, N, C)
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memory_key_padding_mask:
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The padding mask from the encoder.
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token_ids:
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A list-of-list IDs. Each sublist contains IDs for an utterance.
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The IDs can be either phone IDs or word piece IDs.
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sos_id:
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sos token id
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eos_id:
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eos token id
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Returns:
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A scalar, the **sum** of label smoothing loss over utterances
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in the batch without any normalization.
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"""
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ys_in = add_sos(token_ids, sos_id=sos_id)
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ys_in = [torch.tensor(y) for y in ys_in]
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ys_in_pad = pad_sequence(ys_in, batch_first=True, padding_value=float(eos_id))
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ys_out = add_eos(token_ids, eos_id=eos_id)
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ys_out = [torch.tensor(y) for y in ys_out]
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ys_out_pad = pad_sequence(ys_out, batch_first=True, padding_value=float(-1))
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device = memory.device
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ys_in_pad = ys_in_pad.to(device)
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ys_out_pad = ys_out_pad.to(device)
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tgt_mask = generate_square_subsequent_mask(ys_in_pad.shape[-1]).to(device)
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tgt_key_padding_mask = decoder_padding_mask(ys_in_pad, ignore_id=eos_id)
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# TODO: Use length information to create the decoder padding mask
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# We set the first column to False since the first column in ys_in_pad
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# contains sos_id, which is the same as eos_id in our current setting.
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tgt_key_padding_mask[:, 0] = False
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tgt = self.decoder_embed(ys_in_pad) # (N, T) -> (N, T, C)
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tgt = self.decoder_pos(tgt)
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tgt = tgt.permute(1, 0, 2) # (N, T, C) -> (T, N, C)
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pred_pad = self.decoder(
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tgt=tgt,
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memory=memory,
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tgt_mask=tgt_mask,
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tgt_key_padding_mask=tgt_key_padding_mask,
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memory_key_padding_mask=memory_key_padding_mask,
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) # (T, N, C)
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pred_pad = pred_pad.permute(1, 0, 2) # (T, N, C) -> (N, T, C)
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pred_pad = self.decoder_output_layer(pred_pad) # (N, T, C)
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decoder_loss = self.decoder_criterion(pred_pad, ys_out_pad)
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return decoder_loss
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@torch.jit.export
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def decoder_nll(
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self,
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memory: torch.Tensor,
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memory_key_padding_mask: torch.Tensor,
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token_ids: List[torch.Tensor],
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sos_id: int,
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eos_id: int,
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) -> torch.Tensor:
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"""
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Args:
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memory:
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It's the output of the encoder with shape (T, N, C)
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memory_key_padding_mask:
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The padding mask from the encoder.
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token_ids:
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A list-of-list IDs (e.g., word piece IDs).
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Each sublist represents an utterance.
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sos_id:
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The token ID for SOS.
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eos_id:
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The token ID for EOS.
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Returns:
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A 2-D tensor of shape (len(token_ids), max_token_length)
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representing the cross entropy loss (i.e., negative log-likelihood).
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"""
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# The common part between this function and decoder_forward could be
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# extracted as a separate function.
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if isinstance(token_ids[0], torch.Tensor):
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# This branch is executed by torchscript in C++.
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# See https://github.com/k2-fsa/k2/pull/870
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# https://github.com/k2-fsa/k2/blob/3c1c18400060415b141ccea0115fd4bf0ad6234e/k2/torch/bin/attention_rescore.cu#L286
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token_ids = [tolist(t) for t in token_ids]
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ys_in = add_sos(token_ids, sos_id=sos_id)
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ys_in = [torch.tensor(y) for y in ys_in]
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ys_in_pad = pad_sequence(ys_in, batch_first=True, padding_value=float(eos_id))
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ys_out = add_eos(token_ids, eos_id=eos_id)
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ys_out = [torch.tensor(y) for y in ys_out]
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ys_out_pad = pad_sequence(ys_out, batch_first=True, padding_value=float(-1))
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device = memory.device
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ys_in_pad = ys_in_pad.to(device, dtype=torch.int64)
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ys_out_pad = ys_out_pad.to(device, dtype=torch.int64)
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tgt_mask = generate_square_subsequent_mask(ys_in_pad.shape[-1]).to(device)
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tgt_key_padding_mask = decoder_padding_mask(ys_in_pad, ignore_id=eos_id)
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# TODO: Use length information to create the decoder padding mask
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# We set the first column to False since the first column in ys_in_pad
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# contains sos_id, which is the same as eos_id in our current setting.
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tgt_key_padding_mask[:, 0] = False
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tgt = self.decoder_embed(ys_in_pad) # (B, T) -> (B, T, F)
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tgt = self.decoder_pos(tgt)
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tgt = tgt.permute(1, 0, 2) # (B, T, F) -> (T, B, F)
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pred_pad = self.decoder(
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tgt=tgt,
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memory=memory,
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tgt_mask=tgt_mask,
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tgt_key_padding_mask=tgt_key_padding_mask,
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memory_key_padding_mask=memory_key_padding_mask,
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) # (T, B, F)
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pred_pad = pred_pad.permute(1, 0, 2) # (T, B, F) -> (B, T, F)
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pred_pad = self.decoder_output_layer(pred_pad) # (B, T, F)
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# nll: negative log-likelihood
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nll = torch.nn.functional.cross_entropy(
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pred_pad.view(-1, self.decoder_num_class),
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ys_out_pad.view(-1),
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ignore_index=-1,
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reduction="none",
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)
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nll = nll.view(pred_pad.shape[0], -1)
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return nll
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def add_sos(token_ids: List[List[int]], sos_id: int) -> List[List[int]]:
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"""Prepend sos_id to each utterance.
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Args:
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token_ids:
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A list-of-list of token IDs. Each sublist contains
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token IDs (e.g., word piece IDs) of an utterance.
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sos_id:
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The ID of the SOS token.
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Return:
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Return a new list-of-list, where each sublist starts
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with SOS ID.
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"""
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return [[sos_id] + utt for utt in token_ids]
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def add_eos(token_ids: List[List[int]], eos_id: int) -> List[List[int]]:
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"""Append eos_id to each utterance.
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Args:
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token_ids:
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A list-of-list of token IDs. Each sublist contains
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token IDs (e.g., word piece IDs) of an utterance.
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eos_id:
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The ID of the EOS token.
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Return:
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Return a new list-of-list, where each sublist ends
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with EOS ID.
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"""
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return [utt + [eos_id] for utt in token_ids]
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def decoder_padding_mask(ys_pad: torch.Tensor, ignore_id: int = -1) -> torch.Tensor:
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"""Generate a length mask for input.
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The masked position are filled with True,
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Unmasked positions are filled with False.
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Args:
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ys_pad:
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padded tensor of dimension (batch_size, input_length).
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ignore_id:
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the ignored number (the padding number) in ys_pad
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Returns:
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Tensor:
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a bool tensor of the same shape as the input tensor.
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"""
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ys_mask = ys_pad == ignore_id
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return ys_mask
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def generate_square_subsequent_mask(sz: int) -> torch.Tensor:
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"""Generate a square mask for the sequence. The masked positions are
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filled with float('-inf'). Unmasked positions are filled with float(0.0).
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The mask can be used for masked self-attention.
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For instance, if sz is 3, it returns::
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tensor([[0., -inf, -inf],
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[0., 0., -inf],
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[0., 0., 0]])
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Args:
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sz: mask size
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Returns:
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A square mask of dimension (sz, sz)
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"""
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mask = (torch.triu(torch.ones(sz, sz)) == 1).transpose(0, 1)
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mask = (
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mask.float()
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.masked_fill(mask == 0, float("-inf"))
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.masked_fill(mask == 1, float(0.0))
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)
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return mask
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def tolist(t: torch.Tensor) -> List[int]:
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"""Used by jit"""
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return torch.jit.annotate(List[int], t.tolist())
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