mirror of
https://github.com/k2-fsa/icefall.git
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285 lines
9.0 KiB
Python
285 lines
9.0 KiB
Python
# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang)
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#
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# See ../../../../LICENSE for clarification regarding multiple authors
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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Apply layer normalization to the output of each gate in LSTM/GRU.
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This file uses
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https://github.com/pytorch/pytorch/blob/master/benchmarks/fastrnns/custom_lstms.py
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as a reference.
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"""
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import math
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from typing import List, Optional, Tuple
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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# TODO(fangjun): Support projection, see https://arxiv.org/pdf/1402.1128.pdf
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class LayerNormLSTMCell(nn.Module):
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"""This class places a `nn.LayerNorm` after the output of
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each gate (right before the activation).
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See the following paper for more details
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'Improving RNN Transducer Modeling for End-to-End Speech Recognition'
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https://arxiv.org/abs/1909.12415
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"""
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def __init__(
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self,
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input_size: int,
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hidden_size: int,
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bias: bool = True,
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ln: nn.Module = nn.LayerNorm,
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device=None,
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dtype=None,
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):
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"""
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Args:
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input_size:
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The number of expected features in the input `x`. `x` should
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be of shape (batch_size, input_size).
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hidden_size:
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The number of features in the hidden state `h` and `c`.
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Both `h` and `c` are of shape (batch_size, hidden_size).
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bias:
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If ``False``, then the cell does not use bias weights
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`bias_ih` and `bias_hh`.
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ln:
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Defaults to `nn.LayerNorm`. The output of all gates are processed
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by `ln`. We pass it as an argument so that we can replace it
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with `nn.Identity` at the testing time.
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"""
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super().__init__()
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factory_kwargs = {"device": device, "dtype": dtype}
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self.input_size = input_size
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self.hidden_size = hidden_size
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self.bias = bias
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self.weight_ih = nn.Parameter(
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torch.empty((4 * hidden_size, input_size), **factory_kwargs)
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)
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self.weight_hh = nn.Parameter(
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torch.empty((4 * hidden_size, hidden_size), **factory_kwargs)
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)
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if bias:
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self.bias_ih = nn.Parameter(
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torch.empty(4 * hidden_size, **factory_kwargs)
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)
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self.bias_hh = nn.Parameter(
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torch.empty(4 * hidden_size, **factory_kwargs)
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)
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else:
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self.register_parameter("bias_ih", None)
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self.register_parameter("bias_hh", None)
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self.layernorm_i = ln(hidden_size)
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self.layernorm_f = ln(hidden_size)
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self.layernorm_cx = ln(hidden_size)
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self.layernorm_cy = ln(hidden_size)
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self.layernorm_o = ln(hidden_size)
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self.reset_parameters()
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def forward(
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self,
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input: torch.Tensor,
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state: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
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) -> Tuple[torch.Tensor, torch.Tensor]:
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"""
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Args:
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input:
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A 2-D tensor of shape (batch_size, input_size).
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state:
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If not ``None``, it contains the hidden state (h, c); both
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are of shape (batch_size, hidden_size). If ``None``, it uses
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zeros for `h` and `c`.
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Returns:
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Return two tensors:
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- `next_h`: It is of shape (batch_size, hidden_size) containing the
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next hidden state for each element in the batch.
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- `next_c`: It is of shape (batch_size, hidden_size) containing the
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next cell state for each element in the batch.
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"""
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if state is None:
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zeros = torch.zeros(
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input.size(0),
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self.hidden_size,
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dtype=input.dtype,
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device=input.device,
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)
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state = (zeros, zeros)
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hx, cx = state
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gates = F.linear(input, self.weight_ih, self.bias_ih) + F.linear(
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hx, self.weight_hh, self.bias_hh
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)
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in_gate, forget_gate, cell_gate, out_gate = gates.chunk(chunks=4, dim=1)
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in_gate = self.layernorm_i(in_gate)
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forget_gate = self.layernorm_f(forget_gate)
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cell_gate = self.layernorm_cx(cell_gate)
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out_gate = self.layernorm_o(out_gate)
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in_gate = torch.sigmoid(in_gate)
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forget_gate = torch.sigmoid(forget_gate)
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cell_gate = torch.tanh(cell_gate)
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out_gate = torch.sigmoid(out_gate)
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cy = (forget_gate * cx) + (in_gate * cell_gate)
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cy = self.layernorm_cy(cy)
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hy = out_gate * torch.tanh(cy)
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return hy, cy
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def extra_repr(self) -> str:
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s = "{input_size}, {hidden_size}"
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if "bias" in self.__dict__ and self.bias is not True:
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s += ", bias={bias}"
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return s.format(**self.__dict__)
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def reset_parameters(self) -> None:
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stdv = 1.0 / math.sqrt(self.hidden_size)
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for weight in self.parameters():
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nn.init.uniform_(weight, -stdv, stdv)
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class LayerNormLSTMLayer(nn.Module):
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def __init__(
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self,
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input_size: int,
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hidden_size: int,
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bias: bool = True,
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ln: nn.Module = nn.LayerNorm,
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device=None,
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dtype=None,
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):
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"""
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See the args in LayerNormLSTMCell
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"""
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super().__init__()
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self.cell = LayerNormLSTMCell(
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input_size=input_size,
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hidden_size=hidden_size,
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bias=bias,
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ln=ln,
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device=device,
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dtype=dtype,
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)
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def forward(
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self,
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input: torch.Tensor,
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state: Tuple[torch.Tensor, torch.Tensor],
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) -> Tuple[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
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"""
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Args:
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input:
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A 3-D tensor of shape (batch_size, seq_len, input_size).
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Caution:
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We use `batch_first=True` here.
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state:
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If not ``None``, it contains the hidden state (h, c) of this layer.
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Both are of shape (batch_size, hidden_size).
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Note:
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We did not annotate `state` with `Optional[Tuple[...]]` since
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torchscript will complain.
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Return:
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- output, a tensor of shape (batch_size, seq_len, hidden_size)
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- (next_h, next_c) containing the hidden state of this layer
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"""
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inputs = input.unbind(1)
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outputs = torch.jit.annotate(List[torch.Tensor], [])
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for i in range(len(inputs)):
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state = self.cell(inputs[i], state)
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outputs += [state[0]]
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return torch.stack(outputs, dim=1), state
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class LayerNormLSTM(nn.Module):
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def __init__(
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self,
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input_size: int,
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hidden_size: int,
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num_layers: int,
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bias: bool = True,
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ln: nn.Module = nn.LayerNorm,
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device=None,
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dtype=None,
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):
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"""
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See the args in LSTMLayer.
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"""
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super().__init__()
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assert num_layers >= 1
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factory_kwargs = dict(
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hidden_size=hidden_size,
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bias=bias,
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ln=ln,
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device=device,
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dtype=dtype,
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)
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first_layer = LayerNormLSTMLayer(
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input_size=input_size, **factory_kwargs
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)
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layers = [first_layer]
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for i in range(1, num_layers):
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layers.append(
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LayerNormLSTMLayer(
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input_size=hidden_size,
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**factory_kwargs,
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)
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)
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self.layers = nn.ModuleList(layers)
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self.num_layers = num_layers
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def forward(
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self,
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input: torch.Tensor,
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states: List[Tuple[torch.Tensor, torch.Tensor]],
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) -> Tuple[torch.Tensor, List[Tuple[torch.Tensor, torch.Tensor]]]:
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"""
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Args:
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input:
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A 3-D tensor of shape (batch_size, seq_len, input_size).
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Caution:
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We use `batch_first=True` here.
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states:
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One state per layer. Each entry contains the hidden state (h, c)
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for a layer. Both are of shape (batch_size, hidden_size).
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Returns:
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Return a tuple containing:
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- output: A tensor of shape (batch_size, seq_len, hidden_size)
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- List[(next_h, next_c)] containing the hidden states for all layers
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"""
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output_states = torch.jit.annotate(
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List[Tuple[torch.Tensor, torch.Tensor]], []
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)
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output = input
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for i, rnn_layer in enumerate(self.layers):
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state = states[i]
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output, out_state = rnn_layer(output, state)
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output_states += [out_state]
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return output, output_states
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