1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
|
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# 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.
# ==============================================================================
"""A simple LSTM layer with benchmarks.
This sets up a simple LSTM (Long Short Term Memory) layer, unrolled to a fixed
length sequence. The only deviation from standard LSTM cells is that
activations are clipped, inspired by the GNMT machine translation model.
The GNMT paper has more details: https://arxiv.org/abs/1609.08144
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import random_ops
from tensorflow.python.ops import variables
def Clip(x):
"""Clips x to the range [-1., 1.]."""
return math_ops.maximum(math_ops.minimum(x, 1.), -1.)
def LSTMCellWeightsShape(num_inputs, num_nodes):
"""Returns the shape of the weights for a single LSTM cell."""
# Dimension 0 accounts for combining x with the previous m state.
# Dimension 1 accounts for the in value and the (in, forget, out) gates.
return [num_inputs + num_nodes, 4 * num_nodes]
def LSTMCell(weights, m_prev, c_prev, x, pad):
"""Unrolls a single LSTM cell with clipped activations forward by one step.
Args:
weights: Weight matrix with shape LSTMCellWeightsShape.
m_prev: Previous m states with shape [batch_size, num_nodes].
c_prev: Previous c states with shape [batch_size, num_nodes].
x: Input with shape [batch_size, num_inputs].
pad: Padding with shape [batch_size, 1]. Each padding value is either
0 or 1, where 1 indicates padding; i.e. the input is shorter than the
sequence length, and the (m, c) states should simply be passed through
from the previous states.
Returns:
The next (m, c) states, each with shape [batch_size, num_nodes].
"""
# Apply weights to the input and previous hidden state.
# The matmul here is the "big" operation.
xm = array_ops.concat([x, m_prev], 1)
xmw = math_ops.matmul(xm, weights)
# Element-wise ops for the standard LSTM cell, with clipped activations.
# XLA can fuse these operations into a single loop.
in_value, in_gate, forget_gate, out_gate = array_ops.split(
value=xmw, num_or_size_splits=4, axis=1)
in_value = math_ops.tanh(in_value)
in_gate = math_ops.sigmoid(in_gate)
forget_gate = math_ops.sigmoid(forget_gate)
out_gate = math_ops.sigmoid(out_gate)
c_next = Clip(Clip(forget_gate * c_prev) + Clip(in_gate * in_value))
m_next = Clip(out_gate * c_next)
# Account for padding.
c_next = c_prev * pad + c_next * (1.0 - pad)
m_next = m_prev * pad + m_next * (1.0 - pad)
return m_next, c_next
def LSTMLayer(cell_name, weights, m, c, x_seq, pad_seq):
"""Unrolls a layer of LSTM cells forward by the sequence length.
The sequence length is determined by the length of x_seq and pad_seq, which
must be the same.
Args:
cell_name: Base name of each cell.
weights: Weight matrix with shape LSTMCellWeightsShape.
m: Initial m states with shape [batch_size, num_nodes].
c: Initial c states with shape [batch_size, num_nodes].
x_seq: List of inputs, each with shape [batch_size, num_inputs].
The length of the list is the sequence length.
pad_seq: List of paddings, each with shape [batch_size, 1].
The length of the list is the sequence length.
Each padding value is either 0 or 1, where 1 indicates padding;
i.e. the input is shorter than the sequence length.
Returns:
List of per-sequence-step outputs, each with shape [batch_size, num_nodes].
Raises:
ValueError: If len(x_seq) != len(pad_seq).
"""
if len(x_seq) != len(pad_seq):
raise ValueError('length of x_seq(%d) != pad_seq(%d)' %
(len(x_seq), len(pad_seq)))
out_seq = []
for seq in range(len(x_seq)):
with ops.name_scope('%s_%d' % (cell_name, seq)):
m, c = LSTMCell(weights, m, c, x_seq[seq], pad_seq[seq])
out_seq.append(array_ops.identity(m, name='out'))
return out_seq
def RandomVar(shape, name=None):
"""Returns a variable of the given shape initialized to random values."""
return variables.VariableV1(
random_ops.random_uniform(shape), dtype=dtypes.float32, name=name)
def RandomInputs(batch_size, seq_length, num_inputs):
"""Returns randomly initialized (x_seq, pad_seq) sequences."""
x_seq = []
pad_seq = []
with ops.name_scope('inputs'):
for seq in range(seq_length):
x_seq.append(RandomVar([batch_size, num_inputs], name='x_seq_%d' % seq))
# Real padding values are always a sequence of 0 followed by a
# sequence of 1, but random values are fine for benchmarking.
pad_seq.append(RandomVar([batch_size, 1], name='pad_seq_%d' % seq))
return x_seq, pad_seq
def BuildLSTMLayer(batch_size, seq_length, num_inputs, num_nodes):
"""Builds a single LSTM layer with random weights and inputs.
Args:
batch_size: Inputs are fed in batches of this size.
seq_length: The sequence length to unroll the LSTM layer.
num_inputs: Dimension of inputs that are fed into each LSTM cell.
num_nodes: The number of nodes in each LSTM cell.
Returns:
(out_seq, weights) pair. The out_seq is a list of per-sequence-step
outputs, each with shape [batch_size, num_nodes]. The weights are a list of
weight variables that may be trained.
"""
weights = RandomVar(
LSTMCellWeightsShape(num_inputs, num_nodes), name='weights')
m = array_ops.zeros([batch_size, num_nodes], name='init_m')
c = array_ops.zeros([batch_size, num_nodes], name='init_c')
x_seq, pad_seq = RandomInputs(batch_size, seq_length, num_inputs)
out_seq = LSTMLayer('lstm', weights, m, c, x_seq, pad_seq)
return out_seq, [weights]
|
