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#!/usr/bin/env python3
# Copyright (c) 2021 University of Chinese Academy of Sciences (author: Han Zhu)
#
# See ../../../../LICENSE for clarification regarding multiple authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import math
import warnings
from typing import Optional, Tuple
import torch
from torch import Tensor, nn
from transformer import Transformer
from icefall.utils import make_pad_mask
class Conformer(Transformer):
"""
Args:
num_features (int): Number of input features
output_dim (int): Number of output dimension
subsampling_factor (int): subsampling factor of encoder (the convolution layers before transformers)
d_model (int): attention dimension
nhead (int): number of head
dim_feedforward (int): feedforward dimention
num_encoder_layers (int): number of encoder layers
dropout (float): dropout rate
cnn_module_kernel (int): Kernel size of convolution module
normalize_before (bool): whether to use layer_norm before the first block.
vgg_frontend (bool): whether to use vgg frontend.
"""
def __init__(
self,
num_features: int,
output_dim: int,
subsampling_factor: int = 4,
d_model: int = 256,
nhead: int = 4,
dim_feedforward: int = 2048,
num_encoder_layers: int = 12,
dropout: float = 0.1,
cnn_module_kernel: int = 31,
normalize_before: bool = True,
vgg_frontend: bool = False,
) -> None:
super(Conformer, self).__init__(
num_features=num_features,
output_dim=output_dim,
subsampling_factor=subsampling_factor,
d_model=d_model,
nhead=nhead,
dim_feedforward=dim_feedforward,
num_encoder_layers=num_encoder_layers,
dropout=dropout,
normalize_before=normalize_before,
vgg_frontend=vgg_frontend,
)
self.encoder_pos = RelPositionalEncoding(d_model, dropout)
encoder_layer = ConformerEncoderLayer(
d_model,
nhead,
dim_feedforward,
dropout,
cnn_module_kernel,
normalize_before,
)
self.encoder = ConformerEncoder(encoder_layer, num_encoder_layers)
self.normalize_before = normalize_before
if self.normalize_before:
self.after_norm = nn.LayerNorm(d_model)
else:
# Note: TorchScript detects that self.after_norm could be used inside forward()
# and throws an error without this change.
self.after_norm = identity
def forward(
self, x: torch.Tensor, x_lens: torch.Tensor
) -> Tuple[torch.Tensor, torch.Tensor]:
"""
Args:
x:
The input tensor. Its shape is (batch_size, seq_len, feature_dim).
x_lens:
A tensor of shape (batch_size,) containing the number of frames in
`x` before padding.
Returns:
Return a tuple containing 2 tensors:
- logits, its shape is (batch_size, output_seq_len, output_dim)
- logit_lens, a tensor of shape (batch_size,) containing the number
of frames in `logits` before padding.
"""
x = self.encoder_embed(x)
x, pos_emb = self.encoder_pos(x)
x = x.permute(1, 0, 2) # (N, T, C) -> (T, N, C)
# Caution: We assume the subsampling factor is 4!
lengths = ((x_lens - 1) // 2 - 1) // 2
assert x.size(0) == lengths.max().item()
mask = make_pad_mask(lengths)
x = self.encoder(x, pos_emb, src_key_padding_mask=mask) # (T, N, C)
if self.normalize_before:
x = self.after_norm(x)
logits = self.encoder_output_layer(x)
logits = logits.permute(1, 0, 2) # (T, N, C) ->(N, T, C)
return logits, lengths
class ConformerEncoderLayer(nn.Module):
"""
ConformerEncoderLayer is made up of self-attn, feedforward and convolution networks.
See: "Conformer: Convolution-augmented Transformer for Speech Recognition"
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).
cnn_module_kernel (int): Kernel size of convolution module.
normalize_before: whether to use layer_norm before the first block.
Examples::
>>> encoder_layer = ConformerEncoderLayer(d_model=512, nhead=8)
>>> src = torch.rand(10, 32, 512)
>>> pos_emb = torch.rand(32, 19, 512)
>>> out = encoder_layer(src, pos_emb)
"""
def __init__(
self,
d_model: int,
nhead: int,
dim_feedforward: int = 2048,
dropout: float = 0.1,
cnn_module_kernel: int = 31,
normalize_before: bool = True,
) -> None:
super(ConformerEncoderLayer, self).__init__()
self.self_attn = RelPositionMultiheadAttention(
d_model, nhead, dropout=0.0
)
self.feed_forward = nn.Sequential(
nn.Linear(d_model, dim_feedforward),
Swish(),
nn.Dropout(dropout),
nn.Linear(dim_feedforward, d_model),
)
self.feed_forward_macaron = nn.Sequential(
nn.Linear(d_model, dim_feedforward),
Swish(),
nn.Dropout(dropout),
nn.Linear(dim_feedforward, d_model),
)
self.conv_module = ConvolutionModule(d_model, cnn_module_kernel)
self.norm_ff_macaron = nn.LayerNorm(
d_model
) # for the macaron style FNN module
self.norm_ff = nn.LayerNorm(d_model) # for the FNN module
self.norm_mha = nn.LayerNorm(d_model) # for the MHA module
self.ff_scale = 0.5
self.norm_conv = nn.LayerNorm(d_model) # for the CNN module
self.norm_final = nn.LayerNorm(
d_model
) # for the final output of the block
self.dropout = nn.Dropout(dropout)
self.normalize_before = normalize_before
def forward(
self,
src: Tensor,
pos_emb: Tensor,
src_mask: Optional[Tensor] = None,
src_key_padding_mask: Optional[Tensor] = None,
) -> Tensor:
"""
Pass the input through the encoder layer.
Args:
src: the sequence to the encoder layer (required).
pos_emb: Positional embedding tensor (required).
src_mask: the mask for the src sequence (optional).
src_key_padding_mask: the mask for the src keys per batch (optional).
Shape:
src: (S, N, E).
pos_emb: (N, 2*S-1, E)
src_mask: (S, S).
src_key_padding_mask: (N, S).
S is the source sequence length, N is the batch size, E is the feature number
"""
# macaron style feed forward module
residual = src
if self.normalize_before:
src = self.norm_ff_macaron(src)
src = residual + self.ff_scale * self.dropout(
self.feed_forward_macaron(src)
)
if not self.normalize_before:
src = self.norm_ff_macaron(src)
# multi-headed self-attention module
residual = src
if self.normalize_before:
src = self.norm_mha(src)
src_att = self.self_attn(
src,
src,
src,
pos_emb=pos_emb,
attn_mask=src_mask,
key_padding_mask=src_key_padding_mask,
)[0]
src = residual + self.dropout(src_att)
if not self.normalize_before:
src = self.norm_mha(src)
# convolution module
residual = src
if self.normalize_before:
src = self.norm_conv(src)
src = residual + self.dropout(self.conv_module(src))
if not self.normalize_before:
src = self.norm_conv(src)
# feed forward module
residual = src
if self.normalize_before:
src = self.norm_ff(src)
src = residual + self.ff_scale * self.dropout(self.feed_forward(src))
if not self.normalize_before:
src = self.norm_ff(src)
if self.normalize_before:
src = self.norm_final(src)
return src
class ConformerEncoder(nn.TransformerEncoder):
r"""ConformerEncoder is a stack of N encoder layers
Args:
encoder_layer: an instance of the ConformerEncoderLayer() class (required).
num_layers: the number of sub-encoder-layers in the encoder (required).
norm: the layer normalization component (optional).
Examples::
>>> encoder_layer = ConformerEncoderLayer(d_model=512, nhead=8)
>>> conformer_encoder = ConformerEncoder(encoder_layer, num_layers=6)
>>> src = torch.rand(10, 32, 512)
>>> pos_emb = torch.rand(32, 19, 512)
>>> out = conformer_encoder(src, pos_emb)
"""
def __init__(
self, encoder_layer: nn.Module, num_layers: int, norm: nn.Module = None
) -> None:
super(ConformerEncoder, self).__init__(
encoder_layer=encoder_layer, num_layers=num_layers, norm=norm
)
def forward(
self,
src: Tensor,
pos_emb: Tensor,
mask: Optional[Tensor] = None,
src_key_padding_mask: Optional[Tensor] = None,
) -> Tensor:
r"""Pass the input through the encoder layers in turn.
Args:
src: the sequence to the encoder (required).
pos_emb: Positional embedding tensor (required).
mask: the mask for the src sequence (optional).
src_key_padding_mask: the mask for the src keys per batch (optional).
Shape:
src: (S, N, E).
pos_emb: (N, 2*S-1, E)
mask: (S, S).
src_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
"""
output = src
for mod in self.layers:
output = mod(
output,
pos_emb,
src_mask=mask,
src_key_padding_mask=src_key_padding_mask,
)
if self.norm is not None:
output = self.norm(output)
return output
class RelPositionalEncoding(torch.nn.Module):
"""Relative positional encoding module.
See : Appendix B in "Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context"
Modified from https://github.com/espnet/espnet/blob/master/espnet/nets/pytorch_backend/transformer/embedding.py
Args:
d_model: Embedding dimension.
dropout_rate: Dropout rate.
max_len: Maximum input length.
"""
def __init__(
self, d_model: int, dropout_rate: float, max_len: int = 5000
) -> None:
"""Construct an PositionalEncoding object."""
super(RelPositionalEncoding, self).__init__()
self.d_model = d_model
self.xscale = math.sqrt(self.d_model)
self.dropout = torch.nn.Dropout(p=dropout_rate)
self.pe = None
self.extend_pe(torch.tensor(0.0).expand(1, max_len))
def extend_pe(self, x: Tensor) -> None:
"""Reset the positional encodings."""
if self.pe is not None:
# self.pe contains both positive and negative parts
# the length of self.pe is 2 * input_len - 1
if self.pe.size(1) >= x.size(1) * 2 - 1:
# Note: TorchScript doesn't implement operator== for torch.Device
if self.pe.dtype != x.dtype or str(self.pe.device) != str(
x.device
):
self.pe = self.pe.to(dtype=x.dtype, device=x.device)
return
# Suppose `i` means to the position of query vecotr and `j` means the
# position of key vector. We use position relative positions when keys
# are to the left (i>j) and negative relative positions otherwise (i<j).
pe_positive = torch.zeros(x.size(1), self.d_model)
pe_negative = torch.zeros(x.size(1), self.d_model)
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_positive[:, 0::2] = torch.sin(position * div_term)
pe_positive[:, 1::2] = torch.cos(position * div_term)
pe_negative[:, 0::2] = torch.sin(-1 * position * div_term)
pe_negative[:, 1::2] = torch.cos(-1 * position * div_term)
# Reserve the order of positive indices and concat both positive and
# negative indices. This is used to support the shifting trick
# as in "Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context"
pe_positive = torch.flip(pe_positive, [0]).unsqueeze(0)
pe_negative = pe_negative[1:].unsqueeze(0)
pe = torch.cat([pe_positive, pe_negative], dim=1)
self.pe = pe.to(device=x.device, dtype=x.dtype)
def forward(self, x: torch.Tensor) -> Tuple[Tensor, Tensor]:
"""Add positional encoding.
Args:
x (torch.Tensor): Input tensor (batch, time, `*`).
Returns:
torch.Tensor: Encoded tensor (batch, time, `*`).
torch.Tensor: Encoded tensor (batch, 2*time-1, `*`).
"""
self.extend_pe(x)
x = x * self.xscale
pos_emb = self.pe[
:,
self.pe.size(1) // 2
- x.size(1)
+ 1 : self.pe.size(1) // 2 # noqa E203
+ x.size(1),
]
return self.dropout(x), self.dropout(pos_emb)
class RelPositionMultiheadAttention(nn.Module):
r"""Multi-Head Attention layer with relative position encoding
See reference: "Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context"
Args:
embed_dim: total dimension of the model.
num_heads: parallel attention heads.
dropout: a Dropout layer on attn_output_weights. Default: 0.0.
Examples::
>>> rel_pos_multihead_attn = RelPositionMultiheadAttention(embed_dim, num_heads)
>>> attn_output, attn_output_weights = multihead_attn(query, key, value, pos_emb)
"""
def __init__(
self,
embed_dim: int,
num_heads: int,
dropout: float = 0.0,
) -> None:
super(RelPositionMultiheadAttention, self).__init__()
self.embed_dim = embed_dim
self.num_heads = num_heads
self.dropout = dropout
self.head_dim = embed_dim // num_heads
assert (
self.head_dim * num_heads == self.embed_dim
), "embed_dim must be divisible by num_heads"
self.in_proj = nn.Linear(embed_dim, 3 * embed_dim, bias=True)
self.out_proj = nn.Linear(embed_dim, embed_dim, bias=True)
# linear transformation for positional encoding.
self.linear_pos = nn.Linear(embed_dim, embed_dim, bias=False)
# these two learnable bias are used in matrix c and matrix d
# as described in "Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context" Section 3.3
self.pos_bias_u = nn.Parameter(torch.Tensor(num_heads, self.head_dim))
self.pos_bias_v = nn.Parameter(torch.Tensor(num_heads, self.head_dim))
self._reset_parameters()
def _reset_parameters(self) -> None:
nn.init.xavier_uniform_(self.in_proj.weight)
nn.init.constant_(self.in_proj.bias, 0.0)
nn.init.constant_(self.out_proj.bias, 0.0)
nn.init.xavier_uniform_(self.pos_bias_u)
nn.init.xavier_uniform_(self.pos_bias_v)
def forward(
self,
query: Tensor,
key: Tensor,
value: Tensor,
pos_emb: Tensor,
key_padding_mask: Optional[Tensor] = None,
need_weights: bool = True,
attn_mask: Optional[Tensor] = None,
) -> Tuple[Tensor, Optional[Tensor]]:
r"""
Args:
query, key, value: map a query and a set of key-value pairs to an output.
pos_emb: Positional embedding tensor
key_padding_mask: if provided, specified padding elements in the key will
be ignored by the attention. When given a binary mask and a value is True,
the corresponding value on the attention layer will be ignored. When given
a byte mask and a value is non-zero, the corresponding value on the attention
layer will be ignored
need_weights: output attn_output_weights.
attn_mask: 2D or 3D mask that prevents attention to certain positions. A 2D mask will be broadcasted for all
the batches while a 3D mask allows to specify a different mask for the entries of each batch.
Shape:
- Inputs:
- query: :math:`(L, N, E)` where L is the target sequence length, N is the batch size, E is
the embedding dimension.
- key: :math:`(S, N, E)`, where S is the source sequence length, N is the batch size, E is
the embedding dimension.
- value: :math:`(S, N, E)` where S is the source sequence length, N is the batch size, E is
the embedding dimension.
- pos_emb: :math:`(N, 2*L-1, E)` where L is the target sequence length, N is the batch size, E is
the embedding dimension.
- key_padding_mask: :math:`(N, S)` where N is the batch size, S is the source sequence length.
If a ByteTensor is provided, the non-zero positions will be ignored while the position
with the zero positions will be unchanged. If a BoolTensor is provided, the positions with the
value of ``True`` will be ignored while the position with the value of ``False`` will be unchanged.
- attn_mask: 2D mask :math:`(L, S)` where L is the target sequence length, S is the source sequence length.
3D mask :math:`(N*num_heads, L, S)` where N is the batch size, L is the target sequence length,
S is the source sequence length. attn_mask ensure that position i is allowed to attend the unmasked
positions. If a ByteTensor is provided, the non-zero positions are not allowed to attend
while the zero positions will be unchanged. If a BoolTensor is provided, positions with ``True``
is not allowed to attend while ``False`` values will be unchanged. If a FloatTensor
is provided, it will be added to the attention weight.
- Outputs:
- attn_output: :math:`(L, N, E)` where L is the target sequence length, N is the batch size,
E is the embedding dimension.
- attn_output_weights: :math:`(N, L, S)` where N is the batch size,
L is the target sequence length, S is the source sequence length.
"""
return self.multi_head_attention_forward(
query,
key,
value,
pos_emb,
self.embed_dim,
self.num_heads,
self.in_proj.weight,
self.in_proj.bias,
self.dropout,
self.out_proj.weight,
self.out_proj.bias,
training=self.training,
key_padding_mask=key_padding_mask,
need_weights=need_weights,
attn_mask=attn_mask,
)
def rel_shift(self, x: Tensor) -> Tensor:
"""Compute relative positional encoding.
Args:
x: Input tensor (batch, head, time1, 2*time1-1).
time1 means the length of query vector.
Returns:
Tensor: tensor of shape (batch, head, time1, time2)
(note: time2 has the same value as time1, but it is for
the key, while time1 is for the query).
"""
(batch_size, num_heads, time1, n) = x.shape
assert n == 2 * time1 - 1
# Note: TorchScript requires explicit arg for stride()
batch_stride = x.stride(0)
head_stride = x.stride(1)
time1_stride = x.stride(2)
n_stride = x.stride(3)
return x.as_strided(
(batch_size, num_heads, time1, time1),
(batch_stride, head_stride, time1_stride - n_stride, n_stride),
storage_offset=n_stride * (time1 - 1),
)
def multi_head_attention_forward(
self,
query: Tensor,
key: Tensor,
value: Tensor,
pos_emb: Tensor,
embed_dim_to_check: int,
num_heads: int,
in_proj_weight: Tensor,
in_proj_bias: Tensor,
dropout_p: float,
out_proj_weight: Tensor,
out_proj_bias: Tensor,
training: bool = True,
key_padding_mask: Optional[Tensor] = None,
need_weights: bool = True,
attn_mask: Optional[Tensor] = None,
) -> Tuple[Tensor, Optional[Tensor]]:
r"""
Args:
query, key, value: map a query and a set of key-value pairs to an output.
pos_emb: Positional embedding tensor
embed_dim_to_check: total dimension of the model.
num_heads: parallel attention heads.
in_proj_weight, in_proj_bias: input projection weight and bias.
dropout_p: probability of an element to be zeroed.
out_proj_weight, out_proj_bias: the output projection weight and bias.
training: apply dropout if is ``True``.
key_padding_mask: if provided, specified padding elements in the key will
be ignored by the attention. This is an binary mask. When the value is True,
the corresponding value on the attention layer will be filled with -inf.
need_weights: output attn_output_weights.
attn_mask: 2D or 3D mask that prevents attention to certain positions. A 2D mask will be broadcasted for all
the batches while a 3D mask allows to specify a different mask for the entries of each batch.
Shape:
Inputs:
- query: :math:`(L, N, E)` where L is the target sequence length, N is the batch size, E is
the embedding dimension.
- key: :math:`(S, N, E)`, where S is the source sequence length, N is the batch size, E is
the embedding dimension.
- value: :math:`(S, N, E)` where S is the source sequence length, N is the batch size, E is
the embedding dimension.
- pos_emb: :math:`(N, 2*L-1, E)` or :math:`(1, 2*L-1, E)` where L is the target sequence
length, N is the batch size, E is the embedding dimension.
- key_padding_mask: :math:`(N, S)` where N is the batch size, S is the source sequence length.
If a ByteTensor is provided, the non-zero positions will be ignored while the zero positions
will be unchanged. If a BoolTensor is provided, the positions with the
value of ``True`` will be ignored while the position with the value of ``False`` will be unchanged.
- attn_mask: 2D mask :math:`(L, S)` where L is the target sequence length, S is the source sequence length.
3D mask :math:`(N*num_heads, L, S)` where N is the batch size, L is the target sequence length,
S is the source sequence length. attn_mask ensures that position i is allowed to attend the unmasked
positions. If a ByteTensor is provided, the non-zero positions are not allowed to attend
while the zero positions will be unchanged. If a BoolTensor is provided, positions with ``True``
are not allowed to attend while ``False`` values will be unchanged. If a FloatTensor
is provided, it will be added to the attention weight.
Outputs:
- attn_output: :math:`(L, N, E)` where L is the target sequence length, N is the batch size,
E is the embedding dimension.
- attn_output_weights: :math:`(N, L, S)` where N is the batch size,
L is the target sequence length, S is the source sequence length.
"""
tgt_len, bsz, embed_dim = query.size()
assert embed_dim == embed_dim_to_check
assert key.size(0) == value.size(0) and key.size(1) == value.size(1)
head_dim = embed_dim // num_heads
assert (
head_dim * num_heads == embed_dim
), "embed_dim must be divisible by num_heads"
scaling = float(head_dim) ** -0.5
if torch.equal(query, key) and torch.equal(key, value):
# self-attention
q, k, v = nn.functional.linear(
query, in_proj_weight, in_proj_bias
).chunk(3, dim=-1)
elif torch.equal(key, value):
# encoder-decoder attention
# This is inline in_proj function with in_proj_weight and in_proj_bias
_b = in_proj_bias
_start = 0
_end = embed_dim
_w = in_proj_weight[_start:_end, :]
if _b is not None:
_b = _b[_start:_end]
q = nn.functional.linear(query, _w, _b)
# This is inline in_proj function with in_proj_weight and in_proj_bias
_b = in_proj_bias
_start = embed_dim
_end = None
_w = in_proj_weight[_start:, :]
if _b is not None:
_b = _b[_start:]
k, v = nn.functional.linear(key, _w, _b).chunk(2, dim=-1)
else:
# This is inline in_proj function with in_proj_weight and in_proj_bias
_b = in_proj_bias
_start = 0
_end = embed_dim
_w = in_proj_weight[_start:_end, :]
if _b is not None:
_b = _b[_start:_end]
q = nn.functional.linear(query, _w, _b)
# This is inline in_proj function with in_proj_weight and in_proj_bias
_b = in_proj_bias
_start = embed_dim
_end = embed_dim * 2
_w = in_proj_weight[_start:_end, :]
if _b is not None:
_b = _b[_start:_end]
k = nn.functional.linear(key, _w, _b)
# This is inline in_proj function with in_proj_weight and in_proj_bias
_b = in_proj_bias
_start = embed_dim * 2
_end = None
_w = in_proj_weight[_start:, :]
if _b is not None:
_b = _b[_start:]
v = nn.functional.linear(value, _w, _b)
if attn_mask is not None:
assert (
attn_mask.dtype == torch.float32
or attn_mask.dtype == torch.float64
or attn_mask.dtype == torch.float16
or attn_mask.dtype == torch.uint8
or attn_mask.dtype == torch.bool
), "Only float, byte, and bool types are supported for attn_mask, not {}".format(
attn_mask.dtype
)
if attn_mask.dtype == torch.uint8:
warnings.warn(
"Byte tensor for attn_mask is deprecated. Use bool tensor instead."
)
attn_mask = attn_mask.to(torch.bool)
if attn_mask.dim() == 2:
attn_mask = attn_mask.unsqueeze(0)
if list(attn_mask.size()) != [1, query.size(0), key.size(0)]:
raise RuntimeError(
"The size of the 2D attn_mask is not correct."
)
elif attn_mask.dim() == 3:
if list(attn_mask.size()) != [
bsz * num_heads,
query.size(0),
key.size(0),
]:
raise RuntimeError(
"The size of the 3D attn_mask is not correct."
)
else:
raise RuntimeError(
"attn_mask's dimension {} is not supported".format(
attn_mask.dim()
)
)
# attn_mask's dim is 3 now.
# convert ByteTensor key_padding_mask to bool
if (
key_padding_mask is not None
and key_padding_mask.dtype == torch.uint8
):
warnings.warn(
"Byte tensor for key_padding_mask is deprecated. Use bool tensor instead."
)
key_padding_mask = key_padding_mask.to(torch.bool)
q = q.contiguous().view(tgt_len, bsz, num_heads, head_dim)
k = k.contiguous().view(-1, bsz, num_heads, head_dim)
v = v.contiguous().view(-1, bsz * num_heads, head_dim).transpose(0, 1)
src_len = k.size(0)
if key_padding_mask is not None:
assert key_padding_mask.size(0) == bsz, "{} == {}".format(
key_padding_mask.size(0), bsz
)
assert key_padding_mask.size(1) == src_len, "{} == {}".format(
key_padding_mask.size(1), src_len
)
q = q.transpose(0, 1) # (batch, time1, head, d_k)
pos_emb_bsz = pos_emb.size(0)
assert pos_emb_bsz in (1, bsz) # actually it is 1
p = self.linear_pos(pos_emb).view(pos_emb_bsz, -1, num_heads, head_dim)
p = p.transpose(1, 2) # (batch, head, 2*time1-1, d_k)
q_with_bias_u = (q + self.pos_bias_u).transpose(
1, 2
) # (batch, head, time1, d_k)
q_with_bias_v = (q + self.pos_bias_v).transpose(
1, 2
) # (batch, head, time1, d_k)
# compute attention score
# first compute matrix a and matrix c
# as described in "Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context" Section 3.3
k = k.permute(1, 2, 3, 0) # (batch, head, d_k, time2)
matrix_ac = torch.matmul(
q_with_bias_u, k
) # (batch, head, time1, time2)
# compute matrix b and matrix d
matrix_bd = torch.matmul(
q_with_bias_v, p.transpose(-2, -1)
) # (batch, head, time1, 2*time1-1)
matrix_bd = self.rel_shift(matrix_bd)
attn_output_weights = (
matrix_ac + matrix_bd
) * scaling # (batch, head, time1, time2)
attn_output_weights = attn_output_weights.view(
bsz * num_heads, tgt_len, -1
)
assert list(attn_output_weights.size()) == [
bsz * num_heads,
tgt_len,
src_len,
]
if attn_mask is not None:
if attn_mask.dtype == torch.bool:
attn_output_weights.masked_fill_(attn_mask, float("-inf"))
else:
attn_output_weights += attn_mask
if key_padding_mask is not None:
attn_output_weights = attn_output_weights.view(
bsz, num_heads, tgt_len, src_len
)
attn_output_weights = attn_output_weights.masked_fill(
key_padding_mask.unsqueeze(1).unsqueeze(2),
float("-inf"),
)
attn_output_weights = attn_output_weights.view(
bsz * num_heads, tgt_len, src_len
)
attn_output_weights = nn.functional.softmax(attn_output_weights, dim=-1)
attn_output_weights = nn.functional.dropout(
attn_output_weights, p=dropout_p, training=training
)
attn_output = torch.bmm(attn_output_weights, v)
assert list(attn_output.size()) == [bsz * num_heads, tgt_len, head_dim]
attn_output = (
attn_output.transpose(0, 1)
.contiguous()
.view(tgt_len, bsz, embed_dim)
)
attn_output = nn.functional.linear(
attn_output, out_proj_weight, out_proj_bias
)
if need_weights:
# average attention weights over heads
attn_output_weights = attn_output_weights.view(
bsz, num_heads, tgt_len, src_len
)
return attn_output, attn_output_weights.sum(dim=1) / num_heads
else:
return attn_output, None
class ConvolutionModule(nn.Module):
"""ConvolutionModule in Conformer model.
Modified from https://github.com/espnet/espnet/blob/master/espnet/nets/pytorch_backend/conformer/convolution.py
Args:
channels (int): The number of channels of conv layers.
kernel_size (int): Kernerl size of conv layers.
bias (bool): Whether to use bias in conv layers (default=True).
"""
def __init__(
self, channels: int, kernel_size: int, bias: bool = True
) -> None:
"""Construct an ConvolutionModule object."""
super(ConvolutionModule, self).__init__()
# kernerl_size should be a odd number for 'SAME' padding
assert (kernel_size - 1) % 2 == 0
self.pointwise_conv1 = nn.Conv1d(
channels,
2 * channels,
kernel_size=1,
stride=1,
padding=0,
bias=bias,
)
self.depthwise_conv = nn.Conv1d(
channels,
channels,
kernel_size,
stride=1,
padding=(kernel_size - 1) // 2,
groups=channels,
bias=bias,
)
self.norm = nn.LayerNorm(channels)
self.pointwise_conv2 = nn.Conv1d(
channels,
channels,
kernel_size=1,
stride=1,
padding=0,
bias=bias,
)
self.activation = Swish()
def forward(self, x: Tensor) -> Tensor:
"""Compute convolution module.
Args:
x: Input tensor (#time, batch, channels).
Returns:
Tensor: Output tensor (#time, batch, channels).
"""
# exchange the temporal dimension and the feature dimension
x = x.permute(1, 2, 0) # (#batch, channels, time).
# GLU mechanism
x = self.pointwise_conv1(x) # (batch, 2*channels, time)
x = nn.functional.glu(x, dim=1) # (batch, channels, time)
# 1D Depthwise Conv
x = self.depthwise_conv(x)
# x is (batch, channels, time)
x = x.permute(0, 2, 1)
x = self.norm(x)
x = x.permute(0, 2, 1)
x = self.activation(x)
x = self.pointwise_conv2(x) # (batch, channel, time)
return x.permute(2, 0, 1)
class Swish(torch.nn.Module):
"""Construct an Swish object."""
def forward(self, x: Tensor) -> Tensor:
"""Return Swich activation function."""
return x * torch.sigmoid(x)
def identity(x):
return x

1
egs/aishell/ASR/pruned_transducer_stateless/conformer.py Symbolic link
View File

@ -0,0 +1 @@
../transducer_stateless/conformer.py

43
egs/aishell/ASR/pruned_transducer_stateless/encoder_interface.py
View File

@ -1,43 +0,0 @@
# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang)
#
# See ../../../../LICENSE for clarification regarding multiple authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Tuple
import torch
import torch.nn as nn
class EncoderInterface(nn.Module):
def forward(
self, x: torch.Tensor, x_lens: torch.Tensor
) -> Tuple[torch.Tensor, torch.Tensor]:
"""
Args:
x:
A tensor of shape (batch_size, input_seq_len, num_features)
containing the input features.
x_lens:
A tensor of shape (batch_size,) containing the number of frames
in `x` before padding.
Returns:
Return a tuple containing two tensors:
- encoder_out, a tensor of (batch_size, out_seq_len, output_dim)
containing unnormalized probabilities, i.e., the output of a
linear layer.
- encoder_out_lens, a tensor of shape (batch_size,) containing
the number of frames in `encoder_out` before padding.
"""
raise NotImplementedError("Please implement it in a subclass")

1
egs/aishell/ASR/pruned_transducer_stateless/encoder_interface.py Symbolic link
View File

@ -0,0 +1 @@
../transducer_stateless/encoder_interface.py

11
egs/aishell/ASR/pruned_transducer_stateless/joiner.py
View File

@ -16,6 +16,7 @@
import torch
import torch.nn as nn
import torch.nn.functional as F
class Joiner(nn.Module):
@ -31,9 +32,9 @@ class Joiner(nn.Module):
"""
Args:
encoder_out:
The pruned output from the encoder. Its shape is (N, T, s_range, C).
Output from the encoder. Its shape is (N, T, s_range, C).
decoder_out:
The pruned output from the decoder. Its shape is (N, T, s_range, C).
Output from the decoder. Its shape is (N, T, s_range, C).
Returns:
Return a tensor of shape (N, T, s_range, C).
"""
@ -42,10 +43,8 @@ class Joiner(nn.Module):
logit = encoder_out + decoder_out
logit = self.inner_linear(logit)
logit = self.inner_linear(torch.tanh(logit))
logit = torch.tanh(logit)
output = self.output_linear(logit)
output = self.output_linear(F.relu(logit))
return output

80
egs/aishell/ASR/pruned_transducer_stateless/model.py
View File

@ -14,6 +14,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import k2
import torch
import torch.nn as nn
@ -32,9 +33,6 @@ class Transducer(nn.Module):
encoder: EncoderInterface,
decoder: nn.Module,
joiner: nn.Module,
prune_range: int = 3,
lm_scale: float = 0.0,
am_scale: float = 0.0,
):
"""
Args:
@ -51,20 +49,6 @@ class Transducer(nn.Module):
It has two inputs with shapes: (N, T, C) and (N, U, C). Its
output shape is (N, T, U, C). Note that its output contains
unnormalized probs, i.e., not processed by log-softmax.
prune_range:
The prune range for rnnt loss, it means how many symbols(context)
we are considering for each frame to compute the loss.
am_scale:
The scale to smooth the loss with am (output of encoder network)
part
lm_scale:
The scale to smooth the loss with lm (output of predictor network)
part
Note:
Regarding am_scale & lm_scale, it will make the loss-function one of
the form:
lm_scale * lm_probs + am_scale * am_probs +
(1-lm_scale-am_scale) * combined_probs
"""
super().__init__()
assert isinstance(encoder, EncoderInterface), type(encoder)
@ -73,15 +57,15 @@ class Transducer(nn.Module):
self.encoder = encoder
self.decoder = decoder
self.joiner = joiner
self.prune_range = prune_range
self.lm_scale = lm_scale
self.am_scale = am_scale
def forward(
self,
x: torch.Tensor,
x_lens: torch.Tensor,
y: k2.RaggedTensor,
prune_range: int = 5,
am_scale: float = 0.0,
lm_scale: float = 0.0,
) -> torch.Tensor:
"""
Args:
@ -93,8 +77,23 @@ class Transducer(nn.Module):
y:
A ragged tensor with 2 axes [utt][label]. It contains labels of each
utterance.
prune_range:
The prune range for rnnt loss, it means how many symbols(context)
we are considering for each frame to compute the loss.
am_scale:
The scale to smooth the loss with am (output of encoder network)
part
lm_scale:
The scale to smooth the loss with lm (output of predictor network)
part
Returns:
Return the transducer loss.
Note:
Regarding am_scale & lm_scale, it will make the loss-function one of
the form:
lm_scale * lm_probs + am_scale * am_probs +
(1-lm_scale-am_scale) * combined_probs
"""
assert x.ndim == 3, x.shape
assert x_lens.ndim == 1, x_lens.shape
@ -112,11 +111,14 @@ class Transducer(nn.Module):
blank_id = self.decoder.blank_id
sos_y = add_sos(y, sos_id=blank_id)
# sos_y_padded: [B, S + 1], start with SOS.
sos_y_padded = sos_y.pad(mode="constant", padding_value=blank_id)
# decoder_out: [B, S + 1, C]
decoder_out = self.decoder(sos_y_padded)
# Note: y does not start with SOS
# y_padded : [B, S]
y_padded = y.pad(mode="constant", padding_value=0)
y_padded = y_padded.to(torch.int64)
@ -127,27 +129,41 @@ class Transducer(nn.Module):
boundary[:, 3] = x_lens
simple_loss, (px_grad, py_grad) = k2.rnnt_loss_smoothed(
decoder_out,
encoder_out,
y_padded,
blank_id,
lm_only_scale=self.lm_scale,
am_only_scale=self.am_scale,
lm=decoder_out,
am=encoder_out,
symbols=y_padded,
termination_symbol=blank_id,
lm_only_scale=lm_scale,
am_only_scale=am_scale,
boundary=boundary,
reduction="sum",
return_grad=True,
)
# ranges : [B, T, prune_range]
ranges = k2.get_rnnt_prune_ranges(
px_grad, py_grad, boundary, self.prune_range
)
am_pruned, lm_pruned = k2.do_rnnt_pruning(
encoder_out, decoder_out, ranges
px_grad=px_grad,
py_grad=py_grad,
boundary=boundary,
s_range=prune_range,
)
# am_pruned : [B, T, prune_range, C]
# lm_pruned : [B, T, prune_range, C]
am_pruned, lm_pruned = k2.do_rnnt_pruning(
am=encoder_out, lm=decoder_out, ranges=ranges
)
# logits : [B, T, prune_range, C]
logits = self.joiner(am_pruned, lm_pruned)
pruned_loss = k2.rnnt_loss_pruned(
logits, y_padded, ranges, blank_id, boundary
logits=logits,
symbols=y_padded,
ranges=ranges,
termination_symbol=blank_id,
boundary=boundary,
reduction="sum",
)
return (-torch.sum(simple_loss), -torch.sum(pruned_loss))
return (simple_loss, pruned_loss)

37
egs/aishell/ASR/pruned_transducer_stateless/train.py
View File

@ -140,7 +140,7 @@ def get_parser():
parser.add_argument(
"--lm-scale",
type=float,
default=0.5,
default=0.25,
help="The scale to smooth the loss with lm "
"(output of prediction network) part.",
)
@ -152,7 +152,22 @@ def get_parser():
help="The scale to smooth the loss with am (output of encoder network)"
"part.",
)
parser.add_argument(
"--simple-loss-scale",
type=float,
default=0.5,
help="To get pruning ranges, we will calculate a simple version"
"loss(joiner is just addition), this simple loss also uses for"
"training (as a regularization item). We will scale the simple loss"
"with this parameter before adding to the final loss.",
)
parser.add_argument(
"--seed",
type=int,
default=42,
help="The seed for random generators intended for reproducibility",
)
return parser
@ -213,13 +228,13 @@ def get_params() -> AttributeDict:
# parameters for conformer
"feature_dim": 80,
"subsampling_factor": 4,
"attention_dim": 256,
"attention_dim": 512,
"nhead": 4,
"dim_feedforward": 1024,
"dim_feedforward": 2048,
"num_encoder_layers": 12,
"vgg_frontend": False,
# parameters for decoder
"embedding_dim": 256,
"embedding_dim": 512,
# parameters for Noam
"warm_step": 30000,
"env_info": get_env_info(),
@ -272,9 +287,6 @@ def get_transducer_model(params: AttributeDict) -> nn.Module:
encoder=encoder,
decoder=decoder,
joiner=joiner,
prune_range=params.prune_range,
lm_scale=params.lm_scale,
am_scale=params.am_scale,
)
return model
@ -403,8 +415,15 @@ def compute_loss(
y = k2.RaggedTensor(y).to(device)
with torch.set_grad_enabled(is_training):
simple_loss, pruned_loss = model(x=feature, x_lens=feature_lens, y=y)
loss = simple_loss + pruned_loss
simple_loss, pruned_loss = model(
x=feature,
x_lens=feature_lens,
y=y,
prune_range=params.prune_range,
lm_scale=params.lm_scale,
am_scale=params.am_scale,
)
loss = params.simple_loss_scale * simple_loss + pruned_loss
assert loss.requires_grad == is_training

418
egs/aishell/ASR/pruned_transducer_stateless/transformer.py
View File

@ -1,418 +0,0 @@
# Copyright 2021 University of Chinese Academy of Sciences (author: Han Zhu)
#
# See ../../../../LICENSE for clarification regarding multiple authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import math
from typing import Optional, Tuple
import torch
import torch.nn as nn
from encoder_interface import EncoderInterface
from subsampling import Conv2dSubsampling, VggSubsampling
from icefall.utils import make_pad_mask
class Transformer(EncoderInterface):
def __init__(
self,
num_features: int,
output_dim: int,
subsampling_factor: int = 4,
d_model: int = 256,
nhead: int = 4,
dim_feedforward: int = 2048,
num_encoder_layers: int = 12,
dropout: float = 0.1,
normalize_before: bool = True,
vgg_frontend: bool = False,
) -> None:
"""
Args:
num_features:
The input dimension of the model.
output_dim:
The output dimension of the model.
subsampling_factor:
Number of output frames is num_in_frames // subsampling_factor.
Currently, subsampling_factor MUST be 4.
d_model:
Attention dimension.
nhead:
Number of heads in multi-head attention.
Must satisfy d_model // nhead == 0.
dim_feedforward:
The output dimension of the feedforward layers in encoder.
num_encoder_layers:
Number of encoder layers.
dropout:
Dropout in encoder.
normalize_before:
If True, use pre-layer norm; False to use post-layer norm.
vgg_frontend:
True to use vgg style frontend for subsampling.
"""
super().__init__()
self.num_features = num_features
self.output_dim = output_dim
self.subsampling_factor = subsampling_factor
if subsampling_factor != 4:
raise NotImplementedError("Support only 'subsampling_factor=4'.")
# self.encoder_embed converts the input of shape (N, T, num_features)
# to the shape (N, T//subsampling_factor, d_model).
# That is, it does two things simultaneously:
# (1) subsampling: T -> T//subsampling_factor
# (2) embedding: num_features -> d_model
if vgg_frontend:
self.encoder_embed = VggSubsampling(num_features, d_model)
else:
self.encoder_embed = Conv2dSubsampling(num_features, d_model)
self.encoder_pos = PositionalEncoding(d_model, dropout)
encoder_layer = TransformerEncoderLayer(
d_model=d_model,
nhead=nhead,
dim_feedforward=dim_feedforward,
dropout=dropout,
normalize_before=normalize_before,
)
if normalize_before:
encoder_norm = nn.LayerNorm(d_model)
else:
encoder_norm = None
self.encoder = nn.TransformerEncoder(
encoder_layer=encoder_layer,
num_layers=num_encoder_layers,
norm=encoder_norm,
)
# TODO(fangjun): remove dropout
self.encoder_output_layer = nn.Sequential(
nn.Dropout(p=dropout), nn.Linear(d_model, output_dim)
)
def forward(
self, x: torch.Tensor, x_lens: torch.Tensor
) -> Tuple[torch.Tensor, torch.Tensor]:
"""
Args:
x:
The input tensor. Its shape is (batch_size, seq_len, feature_dim).
x_lens:
A tensor of shape (batch_size,) containing the number of frames in
`x` before padding.
Returns:
Return a tuple containing 2 tensors:
- logits, its shape is (batch_size, output_seq_len, output_dim)
- logit_lens, a tensor of shape (batch_size,) containing the number
of frames in `logits` before padding.
"""
x = self.encoder_embed(x)
x = self.encoder_pos(x)
x = x.permute(1, 0, 2) # (N, T, C) -> (T, N, C)
# Caution: We assume the subsampling factor is 4!
lengths = ((x_lens - 1) // 2 - 1) // 2
assert x.size(0) == lengths.max().item()
mask = make_pad_mask(lengths)
x = self.encoder(x, src_key_padding_mask=mask) # (T, N, C)
logits = self.encoder_output_layer(x)
logits = logits.permute(1, 0, 2) # (T, N, C) ->(N, T, C)
return logits, lengths
class TransformerEncoderLayer(nn.Module):
"""
Modified from torch.nn.TransformerEncoderLayer.
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).
normalize_before:
whether to use layer_norm before the first block.
Examples::
>>> encoder_layer = TransformerEncoderLayer(d_model=512, nhead=8)
>>> src = torch.rand(10, 32, 512)
>>> out = encoder_layer(src)
"""
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(TransformerEncoderLayer, self).__init__()
self.self_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.dropout1 = nn.Dropout(dropout)
self.dropout2 = 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(TransformerEncoderLayer, self).__setstate__(state)
def forward(
self,
src: torch.Tensor,
src_mask: Optional[torch.Tensor] = None,
src_key_padding_mask: Optional[torch.Tensor] = None,
) -> torch.Tensor:
"""
Pass the input through the encoder layer.
Args:
src: the sequence to the encoder layer (required).
src_mask: the mask for the src sequence (optional).
src_key_padding_mask: the mask for the src keys per batch (optional)
Shape:
src: (S, N, E).
src_mask: (S, S).
src_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 = 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
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)
# not doing: self.pe = None because of errors thrown by torchscript
self.pe = torch.zeros(1, 0, self.d_model, dtype=torch.float32)
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):
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)

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../transducer_stateless/transformer.py