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https://github.com/k2-fsa/icefall.git
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1135 lines
37 KiB
Python
1135 lines
37 KiB
Python
# Copyright (c) 2021 University of Chinese Academy of Sciences (author: Han Zhu)
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# Apache 2.0
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import math
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from typing import Dict, List, Optional, Tuple
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import k2
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import torch
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import torch.nn as nn
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from subsampling import Conv2dSubsampling, VggSubsampling
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from icefall.utils import get_texts
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# Note: TorchScript requires Dict/List/etc. to be fully typed.
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Supervisions = Dict[str, torch.Tensor]
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class Transformer(nn.Module):
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def __init__(
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self,
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num_features: int,
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num_classes: int,
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subsampling_factor: int = 4,
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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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num_encoder_layers: int = 12,
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num_decoder_layers: int = 6,
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dropout: float = 0.1,
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normalize_before: bool = True,
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vgg_frontend: bool = False,
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mmi_loss: bool = True,
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use_feat_batchnorm: bool = False,
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) -> None:
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"""
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Args:
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num_features:
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The input dimension of the model.
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num_classes:
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The output dimension of the model.
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subsampling_factor:
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Number of output frames is num_in_frames // subsampling_factor.
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Currently, subsampling_factor MUST be 4.
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d_model:
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Attention dimension.
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nhead:
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Number of heads in multi-head attention.
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Must satisfy d_model // nhead == 0.
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dim_feedforward:
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The output dimension of the feedforward layers in encoder/decoder.
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num_encoder_layers:
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Number of encoder layers.
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num_decoder_layers:
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Number of decoder layers.
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dropout:
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Dropout in encoder/decoder.
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normalize_before:
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If True, use pre-layer norm; False to use post-layer norm.
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vgg_frontend:
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True to use vgg style frontend for subsampling.
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mmi_loss:
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use_feat_batchnorm:
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True to use batchnorm for the input layer.
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"""
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super().__init__()
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self.use_feat_batchnorm = use_feat_batchnorm
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if use_feat_batchnorm:
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self.feat_batchnorm = nn.BatchNorm1d(num_features)
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self.num_features = num_features
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self.num_classes = num_classes
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self.subsampling_factor = subsampling_factor
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if subsampling_factor != 4:
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raise NotImplementedError("Support only 'subsampling_factor=4'.")
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# self.encoder_embed converts the input of shape [N, T, num_classes]
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# to the shape [N, T//subsampling_factor, d_model].
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# That is, it does two things simultaneously:
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# (1) subsampling: T -> T//subsampling_factor
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# (2) embedding: num_classes -> d_model
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if vgg_frontend:
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self.encoder_embed = VggSubsampling(num_features, d_model)
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else:
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self.encoder_embed = Conv2dSubsampling(num_features, d_model)
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self.encoder_pos = PositionalEncoding(d_model, dropout)
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encoder_layer = TransformerEncoderLayer(
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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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encoder_norm = nn.LayerNorm(d_model)
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else:
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encoder_norm = None
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self.encoder = nn.TransformerEncoder(
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encoder_layer=encoder_layer,
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num_layers=num_encoder_layers,
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norm=encoder_norm,
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)
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# TODO(fangjun): remove dropout
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self.encoder_output_layer = nn.Sequential(
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nn.Dropout(p=dropout), nn.Linear(d_model, num_classes)
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)
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if num_decoder_layers > 0:
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if mmi_loss:
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self.decoder_num_class = (
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self.num_classes + 1
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) # +1 for the sos/eos symbol
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else:
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self.decoder_num_class = (
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self.num_classes
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) # 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_decoder_layers,
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norm=decoder_norm,
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)
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self.decoder_output_layer = torch.nn.Linear(
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d_model, self.decoder_num_class
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)
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self.decoder_criterion = LabelSmoothingLoss(self.decoder_num_class)
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else:
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self.decoder_criterion = None
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def forward(
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self, x: torch.Tensor, supervision: Optional[Supervisions] = None
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) -> Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]:
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"""
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Args:
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x:
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The input tensor. Its shape is [N, T, C].
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supervision:
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Supervision in lhotse format.
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See https://github.com/lhotse-speech/lhotse/blob/master/lhotse/dataset/speech_recognition.py#L32 # noqa
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(CAUTION: It contains length information, i.e., start and number of
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frames, before subsampling)
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Returns:
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Return a tuple containing 3 tensors:
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- CTC output for ctc decoding. Its shape is [N, T, C]
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- Encoder output with shape [T, N, C]. It can be used as key and
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value for the decoder.
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- Encoder output padding mask. It can be used as
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memory_key_padding_mask for the decoder. Its shape is [N, T].
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It is None if `supervision` is None.
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"""
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if self.use_feat_batchnorm:
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x = x.permute(0, 2, 1) # [N, T, C] -> [N, C, T]
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x = self.feat_batchnorm(x)
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x = x.permute(0, 2, 1) # [N, C, T] -> [N, T, C]
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encoder_memory, memory_key_padding_mask = self.encode(x, supervision)
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x = self.encoder_output(encoder_memory)
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return x, encoder_memory, memory_key_padding_mask
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def encode(
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self, x: torch.Tensor, supervisions: Optional[Supervisions] = None
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) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
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"""
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Args:
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x:
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The model input. Its shape is [N, T, C].
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supervisions:
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Supervision in lhotse format.
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See https://github.com/lhotse-speech/lhotse/blob/master/lhotse/dataset/speech_recognition.py#L32 # noqa
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CAUTION: It contains length information, i.e., start and number of
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frames, before subsampling
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It is read directly from the batch, without any sorting. It is used
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to compute encoder padding mask, which is used as memory key padding
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mask for the decoder.
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Returns:
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Return a tuple with two tensors:
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- The encoder output, with shape [T, N, C]
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- encoder padding mask, with shape [N, T].
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The mask is None if `supervisions` is None.
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It is used as memory key padding mask in the decoder.
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"""
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x = self.encoder_embed(x)
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x = self.encoder_pos(x)
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x = x.permute(1, 0, 2) # (N, T, C) -> (T, N, C)
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mask = encoder_padding_mask(x.size(0), supervisions)
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mask = mask.to(x.device) if mask is not None else None
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x = self.encoder(x, src_key_padding_mask=mask) # (T, N, C)
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return x, mask
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def encoder_output(self, x: torch.Tensor) -> torch.Tensor:
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"""
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Args:
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x:
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The output tensor from the transformer encoder.
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Its shape is [T, N, C]
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Returns:
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Return a tensor that can be used for CTC decoding.
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Its shape is [N, T, C]
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"""
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x = self.encoder_output_layer(x)
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x = x.permute(1, 0, 2) # (T, N, C) ->(N, T, C)
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x = nn.functional.log_softmax(x, dim=-1) # (N, T, C)
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return x
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def decoder_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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supervision: Optional[Supervisions] = None,
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L_inv: Optional[k2.Fsa] = None,
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word_table: Optional[k2.SymbolTable] = None,
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oov_str: Optional[str] = None,
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token_ids: List[List[int]] = None,
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sos_id: Optional[int] = None,
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eos_id: Optional[int] = None,
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) -> torch.Tensor:
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"""
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Note:
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If phone based lexicon is used, the following arguments are required:
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- supervision
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- L_inv
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- word_table
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- oov_str
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If BPE based lexicon is used, the following arguments are required:
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- token_ids
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- sos_id
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- eos_id
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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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supervision:
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Supervision in lhotse format.
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See https://github.com/lhotse-speech/lhotse/blob/master/lhotse/dataset/speech_recognition.py#L32 # noqa
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(CAUTION: It contains length information, i.e., start and number of
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frames, before subsampling)
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L_inv:
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It is an FSA with labels being word IDs and aux_labels being
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token IDs (e.g., phone IDs or word piece IDs).
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word_table:
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Word table providing mapping between words and IDs.
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oov_str:
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The OOV word, e.g., '<UNK>'
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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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if supervision is not None and word_table is not None:
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batch_text = get_normal_transcripts(
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supervision, word_table, oov_str
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)
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ys_in_pad, ys_out_pad = add_sos_eos(
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batch_text,
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L_inv,
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sos_id,
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eos_id,
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)
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elif token_ids is not None:
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_sos = torch.tensor([sos_id])
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_eos = torch.tensor([eos_id])
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ys_in = [
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torch.cat([_sos, torch.tensor(y)], dim=0) for y in token_ids
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]
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ys_out = [
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torch.cat([torch.tensor(y), _eos], dim=0) for y in token_ids
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]
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ys_in_pad = pad_list(ys_in, eos_id)
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ys_out_pad = pad_list(ys_out, -1)
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else:
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raise ValueError("Invalid input for decoder self attention")
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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(
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device
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)
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tgt_key_padding_mask = decoder_padding_mask(ys_in_pad)
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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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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[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 (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 token_ids is not None:
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_sos = torch.tensor([sos_id])
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_eos = torch.tensor([eos_id])
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ys_in = [
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torch.cat([_sos, torch.tensor(y)], dim=0) for y in token_ids
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]
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ys_out = [
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torch.cat([torch.tensor(y), _eos], dim=0) for y in token_ids
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]
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ys_in_pad = pad_list(ys_in, eos_id)
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ys_out_pad = pad_list(ys_out, -1)
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else:
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raise ValueError("Invalid input for decoder self attention")
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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(
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device
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)
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tgt_key_padding_mask = decoder_padding_mask(ys_in_pad)
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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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class TransformerEncoderLayer(nn.Module):
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"""
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Modified from torch.nn.TransformerEncoderLayer.
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Add support of normalize_before,
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i.e., use layer_norm before the first block.
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Args:
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d_model:
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the number of expected features in the input (required).
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nhead:
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the number of heads in the multiheadattention models (required).
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dim_feedforward:
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the dimension of the feedforward network model (default=2048).
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dropout:
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the dropout value (default=0.1).
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activation:
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the activation function of intermediate layer, relu or
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gelu (default=relu).
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normalize_before:
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whether to use layer_norm before the first block.
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Examples::
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>>> encoder_layer = TransformerEncoderLayer(d_model=512, nhead=8)
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>>> src = torch.rand(10, 32, 512)
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>>> out = encoder_layer(src)
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"""
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|
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def __init__(
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self,
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d_model: int,
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nhead: int,
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dim_feedforward: int = 2048,
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dropout: float = 0.1,
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activation: str = "relu",
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normalize_before: bool = True,
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) -> None:
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super(TransformerEncoderLayer, self).__init__()
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self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=0.0)
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# Implementation of Feedforward model
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self.linear1 = nn.Linear(d_model, dim_feedforward)
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self.dropout = nn.Dropout(dropout)
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self.linear2 = nn.Linear(dim_feedforward, d_model)
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self.norm1 = nn.LayerNorm(d_model)
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self.norm2 = nn.LayerNorm(d_model)
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self.dropout1 = nn.Dropout(dropout)
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self.dropout2 = nn.Dropout(dropout)
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self.activation = _get_activation_fn(activation)
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self.normalize_before = normalize_before
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def __setstate__(self, state):
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if "activation" not in state:
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state["activation"] = nn.functional.relu
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super(TransformerEncoderLayer, self).__setstate__(state)
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|
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def forward(
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self,
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src: torch.Tensor,
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src_mask: Optional[torch.Tensor] = None,
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src_key_padding_mask: Optional[torch.Tensor] = None,
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) -> torch.Tensor:
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"""
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Pass the input through the encoder layer.
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Args:
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src: the sequence to the encoder layer (required).
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src_mask: the mask for the src sequence (optional).
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src_key_padding_mask: the mask for the src keys per batch (optional)
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Shape:
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src: (S, N, E).
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src_mask: (S, S).
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src_key_padding_mask: (N, S).
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S is the source sequence length, T is the target sequence length,
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N is the batch size, E is the feature number
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"""
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residual = src
|
|
if self.normalize_before:
|
|
src = self.norm1(src)
|
|
src2 = self.self_attn(
|
|
src,
|
|
src,
|
|
src,
|
|
attn_mask=src_mask,
|
|
key_padding_mask=src_key_padding_mask,
|
|
)[0]
|
|
src = residual + self.dropout1(src2)
|
|
if not self.normalize_before:
|
|
src = self.norm1(src)
|
|
|
|
residual = src
|
|
if self.normalize_before:
|
|
src = self.norm2(src)
|
|
src2 = self.linear2(self.dropout(self.activation(self.linear1(src))))
|
|
src = residual + self.dropout2(src2)
|
|
if not self.normalize_before:
|
|
src = self.norm2(src)
|
|
return src
|
|
|
|
|
|
class TransformerDecoderLayer(nn.Module):
|
|
"""
|
|
Modified from torch.nn.TransformerDecoderLayer.
|
|
Add support of normalize_before,
|
|
i.e., use layer_norm before the first block.
|
|
|
|
Args:
|
|
d_model:
|
|
the number of expected features in the input (required).
|
|
nhead:
|
|
the number of heads in the multiheadattention models (required).
|
|
dim_feedforward:
|
|
the dimension of the feedforward network model (default=2048).
|
|
dropout:
|
|
the dropout value (default=0.1).
|
|
activation:
|
|
the activation function of intermediate layer, relu or
|
|
gelu (default=relu).
|
|
|
|
Examples::
|
|
>>> decoder_layer = nn.TransformerDecoderLayer(d_model=512, nhead=8)
|
|
>>> memory = torch.rand(10, 32, 512)
|
|
>>> tgt = torch.rand(20, 32, 512)
|
|
>>> out = decoder_layer(tgt, memory)
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
d_model: int,
|
|
nhead: int,
|
|
dim_feedforward: int = 2048,
|
|
dropout: float = 0.1,
|
|
activation: str = "relu",
|
|
normalize_before: bool = True,
|
|
) -> None:
|
|
super(TransformerDecoderLayer, self).__init__()
|
|
self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=0.0)
|
|
self.src_attn = nn.MultiheadAttention(d_model, nhead, dropout=0.0)
|
|
# Implementation of Feedforward model
|
|
self.linear1 = nn.Linear(d_model, dim_feedforward)
|
|
self.dropout = nn.Dropout(dropout)
|
|
self.linear2 = nn.Linear(dim_feedforward, d_model)
|
|
|
|
self.norm1 = nn.LayerNorm(d_model)
|
|
self.norm2 = nn.LayerNorm(d_model)
|
|
self.norm3 = nn.LayerNorm(d_model)
|
|
self.dropout1 = nn.Dropout(dropout)
|
|
self.dropout2 = nn.Dropout(dropout)
|
|
self.dropout3 = nn.Dropout(dropout)
|
|
|
|
self.activation = _get_activation_fn(activation)
|
|
|
|
self.normalize_before = normalize_before
|
|
|
|
def __setstate__(self, state):
|
|
if "activation" not in state:
|
|
state["activation"] = nn.functional.relu
|
|
super(TransformerDecoderLayer, self).__setstate__(state)
|
|
|
|
def forward(
|
|
self,
|
|
tgt: torch.Tensor,
|
|
memory: torch.Tensor,
|
|
tgt_mask: Optional[torch.Tensor] = None,
|
|
memory_mask: Optional[torch.Tensor] = None,
|
|
tgt_key_padding_mask: Optional[torch.Tensor] = None,
|
|
memory_key_padding_mask: Optional[torch.Tensor] = None,
|
|
) -> torch.Tensor:
|
|
"""Pass the inputs (and mask) through the decoder layer.
|
|
|
|
Args:
|
|
tgt:
|
|
the sequence to the decoder layer (required).
|
|
memory:
|
|
the sequence from the last layer of the encoder (required).
|
|
tgt_mask:
|
|
the mask for the tgt sequence (optional).
|
|
memory_mask:
|
|
the mask for the memory sequence (optional).
|
|
tgt_key_padding_mask:
|
|
the mask for the tgt keys per batch (optional).
|
|
memory_key_padding_mask:
|
|
the mask for the memory keys per batch (optional).
|
|
|
|
Shape:
|
|
tgt: (T, N, E).
|
|
memory: (S, N, E).
|
|
tgt_mask: (T, T).
|
|
memory_mask: (T, S).
|
|
tgt_key_padding_mask: (N, T).
|
|
memory_key_padding_mask: (N, S).
|
|
S is the source sequence length, T is the target sequence length,
|
|
N is the batch size, E is the feature number
|
|
"""
|
|
residual = tgt
|
|
if self.normalize_before:
|
|
tgt = self.norm1(tgt)
|
|
tgt2 = self.self_attn(
|
|
tgt,
|
|
tgt,
|
|
tgt,
|
|
attn_mask=tgt_mask,
|
|
key_padding_mask=tgt_key_padding_mask,
|
|
)[0]
|
|
tgt = residual + self.dropout1(tgt2)
|
|
if not self.normalize_before:
|
|
tgt = self.norm1(tgt)
|
|
|
|
residual = tgt
|
|
if self.normalize_before:
|
|
tgt = self.norm2(tgt)
|
|
tgt2 = self.src_attn(
|
|
tgt,
|
|
memory,
|
|
memory,
|
|
attn_mask=memory_mask,
|
|
key_padding_mask=memory_key_padding_mask,
|
|
)[0]
|
|
tgt = residual + self.dropout2(tgt2)
|
|
if not self.normalize_before:
|
|
tgt = self.norm2(tgt)
|
|
|
|
residual = tgt
|
|
if self.normalize_before:
|
|
tgt = self.norm3(tgt)
|
|
tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt))))
|
|
tgt = residual + self.dropout3(tgt2)
|
|
if not self.normalize_before:
|
|
tgt = self.norm3(tgt)
|
|
return tgt
|
|
|
|
|
|
def _get_activation_fn(activation: str):
|
|
if activation == "relu":
|
|
return nn.functional.relu
|
|
elif activation == "gelu":
|
|
return nn.functional.gelu
|
|
|
|
raise RuntimeError(
|
|
"activation should be relu/gelu, not {}".format(activation)
|
|
)
|
|
|
|
|
|
class PositionalEncoding(nn.Module):
|
|
"""This class implements the positional encoding
|
|
proposed in the following paper:
|
|
|
|
- Attention Is All You Need: https://arxiv.org/pdf/1706.03762.pdf
|
|
|
|
PE(pos, 2i) = sin(pos / (10000^(2i/d_modle))
|
|
PE(pos, 2i+1) = cos(pos / (10000^(2i/d_modle))
|
|
|
|
Note::
|
|
|
|
1 / (10000^(2i/d_model)) = exp(-log(10000^(2i/d_model)))
|
|
= exp(-1* 2i / d_model * log(100000))
|
|
= exp(2i * -(log(10000) / d_model))
|
|
"""
|
|
|
|
def __init__(self, d_model: int, dropout: float = 0.1) -> None:
|
|
"""
|
|
Args:
|
|
d_model:
|
|
Embedding dimension.
|
|
dropout:
|
|
Dropout probability to be applied to the output of this module.
|
|
"""
|
|
super().__init__()
|
|
self.d_model = d_model
|
|
self.xscale = math.sqrt(self.d_model)
|
|
self.dropout = nn.Dropout(p=dropout)
|
|
self.pe = None
|
|
|
|
def extend_pe(self, x: torch.Tensor) -> None:
|
|
"""Extend the time t in the positional encoding if required.
|
|
|
|
The shape of `self.pe` is [1, T1, d_model]. The shape of the input x
|
|
is [N, T, d_model]. If T > T1, then we change the shape of self.pe
|
|
to [N, T, d_model]. Otherwise, nothing is done.
|
|
|
|
Args:
|
|
x:
|
|
It is a tensor of shape [N, T, C].
|
|
Returns:
|
|
Return None.
|
|
"""
|
|
if self.pe is not None:
|
|
if self.pe.size(1) >= x.size(1):
|
|
if self.pe.dtype != x.dtype or self.pe.device != x.device:
|
|
self.pe = self.pe.to(dtype=x.dtype, device=x.device)
|
|
return
|
|
pe = torch.zeros(x.size(1), self.d_model, dtype=torch.float32)
|
|
position = torch.arange(0, x.size(1), dtype=torch.float32).unsqueeze(1)
|
|
div_term = torch.exp(
|
|
torch.arange(0, self.d_model, 2, dtype=torch.float32)
|
|
* -(math.log(10000.0) / self.d_model)
|
|
)
|
|
pe[:, 0::2] = torch.sin(position * div_term)
|
|
pe[:, 1::2] = torch.cos(position * div_term)
|
|
pe = pe.unsqueeze(0)
|
|
# Now pe is of shape [1, T, d_model], where T is x.size(1)
|
|
self.pe = pe.to(device=x.device, dtype=x.dtype)
|
|
|
|
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
|
"""
|
|
Add positional encoding.
|
|
|
|
Args:
|
|
x:
|
|
Its shape is [N, T, C]
|
|
|
|
Returns:
|
|
Return a tensor of shape [N, T, C]
|
|
"""
|
|
self.extend_pe(x)
|
|
x = x * self.xscale + self.pe[:, : x.size(1), :]
|
|
return self.dropout(x)
|
|
|
|
|
|
class Noam(object):
|
|
"""
|
|
Implements Noam optimizer.
|
|
|
|
Proposed in
|
|
"Attention Is All You Need", https://arxiv.org/pdf/1706.03762.pdf
|
|
|
|
Modified from
|
|
https://github.com/espnet/espnet/blob/master/espnet/nets/pytorch_backend/transformer/optimizer.py # noqa
|
|
|
|
Args:
|
|
params:
|
|
iterable of parameters to optimize or dicts defining parameter groups
|
|
model_size:
|
|
attention dimension of the transformer model
|
|
factor:
|
|
learning rate factor
|
|
warm_step:
|
|
warmup steps
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
params,
|
|
model_size: int = 256,
|
|
factor: float = 10.0,
|
|
warm_step: int = 25000,
|
|
weight_decay=0,
|
|
) -> None:
|
|
"""Construct an Noam object."""
|
|
self.optimizer = torch.optim.Adam(
|
|
params, lr=0, betas=(0.9, 0.98), eps=1e-9, weight_decay=weight_decay
|
|
)
|
|
self._step = 0
|
|
self.warmup = warm_step
|
|
self.factor = factor
|
|
self.model_size = model_size
|
|
self._rate = 0
|
|
|
|
@property
|
|
def param_groups(self):
|
|
"""Return param_groups."""
|
|
return self.optimizer.param_groups
|
|
|
|
def step(self):
|
|
"""Update parameters and rate."""
|
|
self._step += 1
|
|
rate = self.rate()
|
|
for p in self.optimizer.param_groups:
|
|
p["lr"] = rate
|
|
self._rate = rate
|
|
self.optimizer.step()
|
|
|
|
def rate(self, step=None):
|
|
"""Implement `lrate` above."""
|
|
if step is None:
|
|
step = self._step
|
|
return (
|
|
self.factor
|
|
* self.model_size ** (-0.5)
|
|
* min(step ** (-0.5), step * self.warmup ** (-1.5))
|
|
)
|
|
|
|
def zero_grad(self):
|
|
"""Reset gradient."""
|
|
self.optimizer.zero_grad()
|
|
|
|
def state_dict(self):
|
|
"""Return state_dict."""
|
|
return {
|
|
"_step": self._step,
|
|
"warmup": self.warmup,
|
|
"factor": self.factor,
|
|
"model_size": self.model_size,
|
|
"_rate": self._rate,
|
|
"optimizer": self.optimizer.state_dict(),
|
|
}
|
|
|
|
def load_state_dict(self, state_dict):
|
|
"""Load state_dict."""
|
|
for key, value in state_dict.items():
|
|
if key == "optimizer":
|
|
self.optimizer.load_state_dict(state_dict["optimizer"])
|
|
else:
|
|
setattr(self, key, value)
|
|
|
|
|
|
class LabelSmoothingLoss(nn.Module):
|
|
"""
|
|
Label-smoothing loss. KL-divergence between q_{smoothed ground truth prob.}(w)
|
|
and p_{prob. computed by model}(w) is minimized.
|
|
Modified from
|
|
https://github.com/espnet/espnet/blob/master/espnet/nets/pytorch_backend/transformer/label_smoothing_loss.py # noqa
|
|
|
|
Args:
|
|
size: the number of class
|
|
padding_idx: padding_idx: ignored class id
|
|
smoothing: smoothing rate (0.0 means the conventional CE)
|
|
normalize_length: normalize loss by sequence length if True
|
|
criterion: loss function to be smoothed
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
size: int,
|
|
padding_idx: int = -1,
|
|
smoothing: float = 0.1,
|
|
normalize_length: bool = False,
|
|
criterion: nn.Module = nn.KLDivLoss(reduction="none"),
|
|
) -> None:
|
|
"""Construct an LabelSmoothingLoss object."""
|
|
super(LabelSmoothingLoss, self).__init__()
|
|
self.criterion = criterion
|
|
self.padding_idx = padding_idx
|
|
assert 0.0 < smoothing <= 1.0
|
|
self.confidence = 1.0 - smoothing
|
|
self.smoothing = smoothing
|
|
self.size = size
|
|
self.true_dist = None
|
|
self.normalize_length = normalize_length
|
|
|
|
def forward(self, x: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
|
|
"""
|
|
Compute loss between x and target.
|
|
|
|
Args:
|
|
x:
|
|
prediction of dimension
|
|
(batch_size, input_length, number_of_classes).
|
|
target:
|
|
target masked with self.padding_id of
|
|
dimension (batch_size, input_length).
|
|
|
|
Returns:
|
|
A scalar tensor containing the loss without normalization.
|
|
"""
|
|
assert x.size(2) == self.size
|
|
# batch_size = x.size(0)
|
|
x = x.view(-1, self.size)
|
|
target = target.view(-1)
|
|
with torch.no_grad():
|
|
true_dist = x.clone()
|
|
true_dist.fill_(self.smoothing / (self.size - 1))
|
|
ignore = target == self.padding_idx # (B,)
|
|
total = len(target) - ignore.sum().item()
|
|
target = target.masked_fill(ignore, 0) # avoid -1 index
|
|
true_dist.scatter_(1, target.unsqueeze(1), self.confidence)
|
|
kl = self.criterion(torch.log_softmax(x, dim=1), true_dist)
|
|
# denom = total if self.normalize_length else batch_size
|
|
denom = total if self.normalize_length else 1
|
|
return kl.masked_fill(ignore.unsqueeze(1), 0).sum() / denom
|
|
|
|
|
|
def encoder_padding_mask(
|
|
max_len: int, supervisions: Optional[Supervisions] = None
|
|
) -> Optional[torch.Tensor]:
|
|
"""Make mask tensor containing indexes of padded part.
|
|
|
|
TODO::
|
|
This function **assumes** that the model uses
|
|
a subsampling factor of 4. We should remove that
|
|
assumption later.
|
|
|
|
Args:
|
|
max_len:
|
|
Maximum length of input features.
|
|
CAUTION: It is the length after subsampling.
|
|
supervisions:
|
|
Supervision in lhotse format.
|
|
See https://github.com/lhotse-speech/lhotse/blob/master/lhotse/dataset/speech_recognition.py#L32 # noqa
|
|
(CAUTION: It contains length information, i.e., start and number of
|
|
frames, before subsampling)
|
|
|
|
Returns:
|
|
Tensor: Mask tensor of dimension (batch_size, input_length), True denote the masked indices.
|
|
"""
|
|
if supervisions is None:
|
|
return None
|
|
|
|
supervision_segments = torch.stack(
|
|
(
|
|
supervisions["sequence_idx"],
|
|
supervisions["start_frame"],
|
|
supervisions["num_frames"],
|
|
),
|
|
1,
|
|
).to(torch.int32)
|
|
|
|
lengths = [
|
|
0 for _ in range(int(supervision_segments[:, 0].max().item()) + 1)
|
|
]
|
|
for idx in range(supervision_segments.size(0)):
|
|
# Note: TorchScript doesn't allow to unpack tensors as tuples
|
|
sequence_idx = supervision_segments[idx, 0].item()
|
|
start_frame = supervision_segments[idx, 1].item()
|
|
num_frames = supervision_segments[idx, 2].item()
|
|
lengths[sequence_idx] = start_frame + num_frames
|
|
|
|
lengths = [((i - 1) // 2 - 1) // 2 for i in lengths]
|
|
bs = int(len(lengths))
|
|
seq_range = torch.arange(0, max_len, dtype=torch.int64)
|
|
seq_range_expand = seq_range.unsqueeze(0).expand(bs, max_len)
|
|
# Note: TorchScript doesn't implement Tensor.new()
|
|
seq_length_expand = torch.tensor(
|
|
lengths, device=seq_range_expand.device, dtype=seq_range_expand.dtype
|
|
).unsqueeze(-1)
|
|
mask = seq_range_expand >= seq_length_expand
|
|
|
|
return mask
|
|
|
|
|
|
def decoder_padding_mask(
|
|
ys_pad: torch.Tensor, ignore_id: int = -1
|
|
) -> torch.Tensor:
|
|
"""Generate a length mask for input.
|
|
|
|
The masked position are filled with bool(True),
|
|
Unmasked positions are filled with bool(False).
|
|
|
|
Args:
|
|
ys_pad:
|
|
padded tensor of dimension (batch_size, input_length).
|
|
ignore_id:
|
|
the ignored number (the padding number) in ys_pad
|
|
|
|
Returns:
|
|
Tensor:
|
|
a bool tensor of the same shape as the input tensor.
|
|
"""
|
|
ys_mask = ys_pad == ignore_id
|
|
return ys_mask
|
|
|
|
|
|
def get_normal_transcripts(
|
|
supervision: Supervisions, words: k2.SymbolTable, oov: str = "<UNK>"
|
|
) -> List[List[int]]:
|
|
"""Get normal transcripts (1 input recording has 1 transcript)
|
|
from lhotse cut format.
|
|
|
|
Achieved by concatenating the transcripts corresponding to the
|
|
same recording.
|
|
|
|
Args:
|
|
supervision:
|
|
Supervision in lhotse format.
|
|
See https://github.com/lhotse-speech/lhotse/blob/master/lhotse/dataset/speech_recognition.py#L32 # noqa
|
|
words:
|
|
The word symbol table.
|
|
oov:
|
|
Out of vocabulary word.
|
|
|
|
Returns:
|
|
List[List[int]]: List of concatenated transcripts, length is batch_size
|
|
"""
|
|
|
|
texts = [
|
|
[token if token in words else oov for token in text.split(" ")]
|
|
for text in supervision["text"]
|
|
]
|
|
texts_ids = [[words[token] for token in text] for text in texts]
|
|
|
|
batch_text = [
|
|
[] for _ in range(int(supervision["sequence_idx"].max().item()) + 1)
|
|
]
|
|
for sequence_idx, text in zip(supervision["sequence_idx"], texts_ids):
|
|
batch_text[sequence_idx] = batch_text[sequence_idx] + text
|
|
return batch_text
|
|
|
|
|
|
def generate_square_subsequent_mask(sz: int) -> torch.Tensor:
|
|
"""Generate a square mask for the sequence. The masked positions are
|
|
filled with float('-inf'). Unmasked positions are filled with float(0.0).
|
|
|
|
Args:
|
|
sz: mask size
|
|
|
|
Returns:
|
|
A square mask of dimension (sz, sz)
|
|
"""
|
|
mask = (torch.triu(torch.ones(sz, sz)) == 1).transpose(0, 1)
|
|
mask = (
|
|
mask.float()
|
|
.masked_fill(mask == 0, float("-inf"))
|
|
.masked_fill(mask == 1, float(0.0))
|
|
)
|
|
return mask
|
|
|
|
|
|
def add_sos_eos(
|
|
ys: List[List[int]],
|
|
L_inv: k2.Fsa,
|
|
sos_id: int,
|
|
eos_id: int,
|
|
ignore_id: int = -1,
|
|
) -> Tuple[torch.Tensor, torch.Tensor]:
|
|
"""Add <sos> and <eos> labels.
|
|
|
|
Args:
|
|
ys:
|
|
Batch of unpadded target sequences (i.e., word IDs)
|
|
L_inv:
|
|
Its labels are words, while its aux_labels are tokens.
|
|
sos_id:
|
|
index of <sos>
|
|
eos_id:
|
|
index of <eos>
|
|
ignore_id:
|
|
value for padding
|
|
|
|
Returns:
|
|
Return a tuple containing two tensors:
|
|
- Input of transformer decoder.
|
|
Padded tensor of dimension (batch_size, max_length).
|
|
- Output of transformer decoder.
|
|
Padded tensor of dimension (batch_size, max_length).
|
|
"""
|
|
|
|
_sos = torch.tensor([sos_id])
|
|
_eos = torch.tensor([eos_id])
|
|
ys = get_hierarchical_targets(ys, L_inv)
|
|
ys_in = [torch.cat([_sos, y], dim=0) for y in ys]
|
|
ys_out = [torch.cat([y, _eos], dim=0) for y in ys]
|
|
return pad_list(ys_in, eos_id), pad_list(ys_out, ignore_id)
|
|
|
|
|
|
def pad_list(ys: List[torch.Tensor], pad_value: float) -> torch.Tensor:
|
|
"""Perform padding for the list of tensors.
|
|
|
|
Args:
|
|
ys:
|
|
List of tensors. len(ys) = batch_size.
|
|
pad_value:
|
|
Value for padding.
|
|
|
|
Returns:
|
|
Tensor: Padded tensor (batch_size, max_length, `*`).
|
|
|
|
Examples:
|
|
>>> x = [torch.ones(4), torch.ones(2), torch.ones(1)]
|
|
>>> x
|
|
[tensor([1., 1., 1., 1.]), tensor([1., 1.]), tensor([1.])]
|
|
>>> pad_list(x, 0)
|
|
tensor([[1., 1., 1., 1.],
|
|
[1., 1., 0., 0.],
|
|
[1., 0., 0., 0.]])
|
|
|
|
"""
|
|
n_batch = len(ys)
|
|
max_len = max(x.size(0) for x in ys)
|
|
pad = ys[0].new_full((n_batch, max_len, *ys[0].size()[1:]), pad_value)
|
|
|
|
for i in range(n_batch):
|
|
pad[i, : ys[i].size(0)] = ys[i]
|
|
|
|
return pad
|
|
|
|
|
|
def get_hierarchical_targets(
|
|
ys: List[List[int]], L_inv: Optional[k2.Fsa] = None
|
|
) -> List[torch.Tensor]:
|
|
"""Get hierarchical transcripts (i.e., phone level transcripts) from
|
|
transcripts (i.e., word level transcripts).
|
|
|
|
Args:
|
|
ys:
|
|
Word level transcripts. Each sublist is a transcript of an utterance.
|
|
L_inv:
|
|
Its labels are words, while its aux_labels are tokens.
|
|
|
|
Returns:
|
|
List[torch.Tensor]:
|
|
Token level transcripts.
|
|
"""
|
|
|
|
if L_inv is None:
|
|
return [torch.tensor(y) for y in ys]
|
|
|
|
device = L_inv.device
|
|
|
|
transcripts = k2.create_fsa_vec(
|
|
[k2.linear_fsa(x, device=device) for x in ys]
|
|
)
|
|
transcripts_with_self_loops = k2.add_epsilon_self_loops(transcripts)
|
|
|
|
transcripts_lexicon = k2.intersect(
|
|
L_inv, transcripts_with_self_loops, treat_epsilons_specially=False
|
|
)
|
|
# Don't call invert_() above because we want to return phone IDs,
|
|
# which is the `aux_labels` of transcripts_lexicon
|
|
transcripts_lexicon = k2.remove_epsilon(transcripts_lexicon)
|
|
transcripts_lexicon = k2.top_sort(transcripts_lexicon)
|
|
|
|
transcripts_lexicon = k2.shortest_path(
|
|
transcripts_lexicon, use_double_scores=True
|
|
)
|
|
|
|
ys = get_texts(transcripts_lexicon)
|
|
ys = [torch.tensor(y) for y in ys]
|
|
|
|
return ys
|