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# 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 Optional
import torch
import torch.nn as nn
import torch.nn.functional as F
from subsampling import ScaledConv1d
from torch import Tensor
class Decoder(nn.Module):
"""This class modifies the stateless decoder from the following paper:
RNN-transducer with stateless prediction network
https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9054419
It removes the recurrent connection from the decoder, i.e., the prediction
network. Different from the above paper, it adds an extra Conv1d
right after the embedding layer.
TODO: Implement https://arxiv.org/pdf/2109.07513.pdf
"""
def __init__(
self,
vocab_size: int,
embedding_dim: int,
blank_id: int,
context_size: int,
):
"""
Args:
vocab_size:
Number of tokens of the modeling unit including blank.
embedding_dim:
Dimension of the input embedding.
blank_id:
The ID of the blank symbol.
context_size:
Number of previous words to use to predict the next word.
1 means bigram; 2 means trigram. n means (n+1)-gram.
"""
super().__init__()
self.embedding = ScaledEmbedding(
num_embeddings=vocab_size,
embedding_dim=embedding_dim,
padding_idx=blank_id,
)
self.blank_id = blank_id
assert context_size >= 1, context_size
self.context_size = context_size
if context_size > 1:
self.conv = ScaledConv1d(
in_channels=embedding_dim,
out_channels=embedding_dim,
kernel_size=context_size,
padding=0,
groups=embedding_dim,
bias=False,
)
def forward(self, y: torch.Tensor, need_pad: bool = True) -> torch.Tensor:
"""
Args:
y:
A 2-D tensor of shape (N, U).
need_pad:
True to left pad the input. Should be True during training.
False to not pad the input. Should be False during inference.
Returns:
Return a tensor of shape (N, U, embedding_dim).
"""
y = y.to(torch.int64)
embedding_out = self.embedding(y)
if self.context_size > 1:
embedding_out = embedding_out.permute(0, 2, 1)
if need_pad is True:
embedding_out = F.pad(embedding_out, pad=(self.context_size - 1, 0))
else:
# During inference time, there is no need to do extra padding
# as we only need one output
assert embedding_out.size(-1) == self.context_size
embedding_out = self.conv(embedding_out)
embedding_out = embedding_out.permute(0, 2, 1)
return embedding_out
class ScaledEmbedding(nn.Module):
r"""A simple lookup table that stores embeddings of a fixed dictionary and size.
This module is often used to store word embeddings and retrieve them using indices.
The input to the module is a list of indices, and the output is the corresponding
word embeddings.
Args:
num_embeddings (int): size of the dictionary of embeddings
embedding_dim (int): the size of each embedding vector
padding_idx (int, optional): If given, pads the output with the embedding vector at :attr:`padding_idx`
(initialized to zeros) whenever it encounters the index.
max_norm (float, optional): If given, each embedding vector with norm larger than :attr:`max_norm`
is renormalized to have norm :attr:`max_norm`.
norm_type (float, optional): The p of the p-norm to compute for the :attr:`max_norm` option. Default ``2``.
scale_grad_by_freq (boolean, optional): If given, this will scale gradients by the inverse of frequency of
the words in the mini-batch. Default ``False``.
sparse (bool, optional): If ``True``, gradient w.r.t. :attr:`weight` matrix will be a sparse tensor.
See Notes for more details regarding sparse gradients.
Attributes:
weight (Tensor): the learnable weights of the module of shape (num_embeddings, embedding_dim)
initialized from :math:`\mathcal{N}(0, 1)`
Shape:
- Input: :math:`(*)`, LongTensor of arbitrary shape containing the indices to extract
- Output: :math:`(*, H)`, where `*` is the input shape and :math:`H=\text{embedding\_dim}`
.. note::
Keep in mind that only a limited number of optimizers support
sparse gradients: currently it's :class:`optim.SGD` (`CUDA` and `CPU`),
:class:`optim.SparseAdam` (`CUDA` and `CPU`) and :class:`optim.Adagrad` (`CPU`)
.. note::
With :attr:`padding_idx` set, the embedding vector at
:attr:`padding_idx` is initialized to all zeros. However, note that this
vector can be modified afterwards, e.g., using a customized
initialization method, and thus changing the vector used to pad the
output. The gradient for this vector from :class:`~torch.nn.Embedding`
is always zero.
Examples::
>>> # an Embedding module containing 10 tensors of size 3
>>> embedding = nn.Embedding(10, 3)
>>> # a batch of 2 samples of 4 indices each
>>> input = torch.LongTensor([[1,2,4,5],[4,3,2,9]])
>>> embedding(input)
tensor([[[-0.0251, -1.6902, 0.7172],
[-0.6431, 0.0748, 0.6969],
[ 1.4970, 1.3448, -0.9685],
[-0.3677, -2.7265, -0.1685]],
[[ 1.4970, 1.3448, -0.9685],
[ 0.4362, -0.4004, 0.9400],
[-0.6431, 0.0748, 0.6969],
[ 0.9124, -2.3616, 1.1151]]])
>>> # example with padding_idx
>>> embedding = nn.Embedding(10, 3, padding_idx=0)
>>> input = torch.LongTensor([[0,2,0,5]])
>>> embedding(input)
tensor([[[ 0.0000, 0.0000, 0.0000],
[ 0.1535, -2.0309, 0.9315],
[ 0.0000, 0.0000, 0.0000],
[-0.1655, 0.9897, 0.0635]]])
"""
__constants__ = [
"num_embeddings",
"embedding_dim",
"padding_idx",
"scale_grad_by_freq",
"sparse",
]
num_embeddings: int
embedding_dim: int
padding_idx: int
scale_grad_by_freq: bool
weight: Tensor
sparse: bool
def __init__(
self,
num_embeddings: int,
embedding_dim: int,
padding_idx: Optional[int] = None,
scale_grad_by_freq: bool = False,
sparse: bool = False,
scale_speed: float = 5.0,
) -> None:
super(ScaledEmbedding, self).__init__()
self.num_embeddings = num_embeddings
self.embedding_dim = embedding_dim
if padding_idx is not None:
if padding_idx > 0:
assert (
padding_idx < self.num_embeddings
), "Padding_idx must be within num_embeddings"
elif padding_idx < 0:
assert (
padding_idx >= -self.num_embeddings
), "Padding_idx must be within num_embeddings"
padding_idx = self.num_embeddings + padding_idx
self.padding_idx = padding_idx
self.scale_grad_by_freq = scale_grad_by_freq
self.scale_speed = scale_speed
self.scale = nn.Parameter(torch.zeros(())) # see reset_parameters()
self.sparse = sparse
self.weight = nn.Parameter(torch.Tensor(num_embeddings, embedding_dim))
self.reset_parameters()
def reset_parameters(self) -> None:
nn.init.normal_(self.weight, std=0.05)
nn.init.constant_(self.scale, torch.tensor(1.0 / 0.05).log() / self.scale_speed)
if self.padding_idx is not None:
with torch.no_grad():
self.weight[self.padding_idx].fill_(0)
def forward(self, input: Tensor) -> Tensor:
scale = (self.scale * self.scale_speed).exp()
if input.numel() < self.num_embeddings:
return (
F.embedding(
input,
self.weight,
self.padding_idx,
None,
2.0, # None, 2.0 relate to normalization
self.scale_grad_by_freq,
self.sparse,
)
* scale
)
else:
return F.embedding(
input,
self.weight * scale,
self.padding_idx,
None,
2.0, # None, 2.0 relates to normalization
self.scale_grad_by_freq,
self.sparse,
)
def extra_repr(self) -> str:
s = "{num_embeddings}, {embedding_dim}, scale_speed={scale_speed}, scale={scale}"
if self.padding_idx is not None:
s += ", padding_idx={padding_idx}"
if self.scale_grad_by_freq is not False:
s += ", scale_grad_by_freq={scale_grad_by_freq}"
if self.sparse is not False:
s += ", sparse=True"
return s.format(**self.__dict__)
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