mirror of
https://github.com/k2-fsa/icefall.git
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1d44da845b
* Begin to add RNN-T training for librispeech. * Copy files from conformer_ctc. Will edit it. * Use conformer/transformer model as encoder. * Begin to add training script. * Add training code. * Remove long utterances to avoid OOM when a large max_duraiton is used. * Begin to add decoding script. * Add decoding script. * Minor fixes. * Add beam search. * Use LSTM layers for the encoder. Need more tunings. * Use stateless decoder. * Minor fixes to make it ready for merge. * Fix README. * Update RESULT.md to include RNN-T Conformer. * Minor fixes. * Fix tests. * Minor fixes. * Minor fixes. * Fix tests.
430 lines
14 KiB
Python
430 lines
14 KiB
Python
# Copyright 2021 University of Chinese Academy of Sciences (author: Han Zhu)
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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 math
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from typing import Optional, Tuple
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import torch
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import torch.nn as nn
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from transducer.encoder_interface import EncoderInterface
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from transducer.subsampling import Conv2dSubsampling, VggSubsampling
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from icefall.utils import make_pad_mask
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class Transformer(EncoderInterface):
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def __init__(
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self,
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num_features: int,
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output_dim: 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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dropout: float = 0.1,
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normalize_before: bool = True,
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vgg_frontend: bool = False,
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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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output_dim:
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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.
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num_encoder_layers:
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Number of encoder layers.
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dropout:
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Dropout in encoder.
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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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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.output_dim = output_dim
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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_features)
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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_features -> 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, output_dim)
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)
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def forward(
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self, x: torch.Tensor, x_lens: torch.Tensor
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) -> Tuple[torch.Tensor, 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 (batch_size, seq_len, feature_dim).
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x_lens:
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A tensor of shape (batch_size,) containing the number of frames in
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`x` before padding.
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Returns:
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Return a tuple containing 2 tensors:
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- logits, its shape is (batch_size, output_seq_len, output_dim)
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- logit_lens, a tensor of shape (batch_size,) containing the number
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of frames in `logits` before padding.
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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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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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# Caution: We assume the subsampling factor is 4!
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lengths = ((x_lens - 1) // 2 - 1) // 2
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assert x.size(0) == lengths.max().item()
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mask = make_pad_mask(lengths)
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x = self.encoder(x, src_key_padding_mask=mask) # (T, N, C)
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logits = self.encoder_output_layer(x)
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logits = logits.permute(1, 0, 2) # (T, N, C) ->(N, T, C)
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return logits, lengths
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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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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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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
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if self.normalize_before:
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src = self.norm1(src)
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src2 = self.self_attn(
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src,
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src,
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src,
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attn_mask=src_mask,
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key_padding_mask=src_key_padding_mask,
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)[0]
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src = residual + self.dropout1(src2)
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if not self.normalize_before:
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src = self.norm1(src)
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residual = src
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if self.normalize_before:
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src = self.norm2(src)
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src2 = self.linear2(self.dropout(self.activation(self.linear1(src))))
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src = residual + self.dropout2(src2)
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if not self.normalize_before:
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src = self.norm2(src)
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return src
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def _get_activation_fn(activation: str):
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if activation == "relu":
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return nn.functional.relu
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elif activation == "gelu":
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return nn.functional.gelu
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raise RuntimeError(
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"activation should be relu/gelu, not {}".format(activation)
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)
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class PositionalEncoding(nn.Module):
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"""This class implements the positional encoding
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proposed in the following paper:
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- Attention Is All You Need: https://arxiv.org/pdf/1706.03762.pdf
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PE(pos, 2i) = sin(pos / (10000^(2i/d_modle))
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PE(pos, 2i+1) = cos(pos / (10000^(2i/d_modle))
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Note::
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1 / (10000^(2i/d_model)) = exp(-log(10000^(2i/d_model)))
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= exp(-1* 2i / d_model * log(100000))
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= exp(2i * -(log(10000) / d_model))
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"""
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def __init__(self, d_model: int, dropout: float = 0.1) -> None:
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"""
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Args:
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d_model:
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Embedding dimension.
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dropout:
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Dropout probability to be applied to the output of this module.
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"""
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super().__init__()
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self.d_model = d_model
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self.xscale = math.sqrt(self.d_model)
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self.dropout = nn.Dropout(p=dropout)
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# not doing: self.pe = None because of errors thrown by torchscript
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self.pe = torch.zeros(1, 0, self.d_model, dtype=torch.float32)
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def extend_pe(self, x: torch.Tensor) -> None:
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"""Extend the time t in the positional encoding if required.
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The shape of `self.pe` is (1, T1, d_model). The shape of the input x
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is (N, T, d_model). If T > T1, then we change the shape of self.pe
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to (N, T, d_model). Otherwise, nothing is done.
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Args:
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x:
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It is a tensor of shape (N, T, C).
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Returns:
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Return None.
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"""
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if self.pe is not None:
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if self.pe.size(1) >= x.size(1):
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self.pe = self.pe.to(dtype=x.dtype, device=x.device)
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return
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pe = torch.zeros(x.size(1), self.d_model, dtype=torch.float32)
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position = torch.arange(0, x.size(1), dtype=torch.float32).unsqueeze(1)
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div_term = torch.exp(
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torch.arange(0, self.d_model, 2, dtype=torch.float32)
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* -(math.log(10000.0) / self.d_model)
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)
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pe[:, 0::2] = torch.sin(position * div_term)
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pe[:, 1::2] = torch.cos(position * div_term)
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pe = pe.unsqueeze(0)
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# Now pe is of shape (1, T, d_model), where T is x.size(1)
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self.pe = pe.to(device=x.device, dtype=x.dtype)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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"""
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Add positional encoding.
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Args:
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x:
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Its shape is (N, T, C)
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Returns:
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Return a tensor of shape (N, T, C)
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"""
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self.extend_pe(x)
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x = x * self.xscale + self.pe[:, : x.size(1), :]
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return self.dropout(x)
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class Noam(object):
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"""
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Implements Noam optimizer.
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Proposed in
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"Attention Is All You Need", https://arxiv.org/pdf/1706.03762.pdf
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Modified from
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https://github.com/espnet/espnet/blob/master/espnet/nets/pytorch_backend/transformer/optimizer.py # noqa
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Args:
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params:
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iterable of parameters to optimize or dicts defining parameter groups
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model_size:
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attention dimension of the transformer model
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factor:
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learning rate factor
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warm_step:
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warmup steps
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"""
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def __init__(
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self,
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params,
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model_size: int = 256,
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factor: float = 10.0,
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warm_step: int = 25000,
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weight_decay=0,
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) -> None:
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"""Construct an Noam object."""
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self.optimizer = torch.optim.Adam(
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params, lr=0, betas=(0.9, 0.98), eps=1e-9, weight_decay=weight_decay
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)
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self._step = 0
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self.warmup = warm_step
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self.factor = factor
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self.model_size = model_size
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self._rate = 0
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@property
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def param_groups(self):
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"""Return param_groups."""
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return self.optimizer.param_groups
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def step(self):
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"""Update parameters and rate."""
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self._step += 1
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rate = self.rate()
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for p in self.optimizer.param_groups:
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p["lr"] = rate
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self._rate = rate
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self.optimizer.step()
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def rate(self, step=None):
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"""Implement `lrate` above."""
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if step is None:
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step = self._step
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return (
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self.factor
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* self.model_size ** (-0.5)
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* min(step ** (-0.5), step * self.warmup ** (-1.5))
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)
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def zero_grad(self):
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"""Reset gradient."""
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self.optimizer.zero_grad()
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def state_dict(self):
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"""Return state_dict."""
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return {
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"_step": self._step,
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"warmup": self.warmup,
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"factor": self.factor,
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"model_size": self.model_size,
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"_rate": self._rate,
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"optimizer": self.optimizer.state_dict(),
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}
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def load_state_dict(self, state_dict):
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"""Load state_dict."""
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for key, value in state_dict.items():
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if key == "optimizer":
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self.optimizer.load_state_dict(state_dict["optimizer"])
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else:
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setattr(self, key, value)
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