File size: 10,134 Bytes
97b6013 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 |
# Copyright 2019 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.
# ==============================================================================
"""Quantization related ops for LSTM."""
from __future__ import absolute_import
from __future__ import division
import tensorflow.compat.v1 as tf
from tensorflow.contrib import framework as contrib_framework
from tensorflow.contrib import layers as contrib_layers
from tensorflow.python.training import moving_averages
def _quant_var(
name,
initializer_val,
vars_collection=tf.GraphKeys.MOVING_AVERAGE_VARIABLES,
):
"""Create an var for storing the min/max quantization range."""
return contrib_framework.model_variable(
name,
shape=[],
initializer=tf.constant_initializer(initializer_val),
collections=[vars_collection],
trainable=False)
def quantizable_concat(inputs,
axis,
is_training,
is_quantized=True,
default_min=0,
default_max=6,
ema_decay=0.999,
scope='quantized_concat'):
"""Concat replacement with quantization option.
Allows concat inputs to share the same min max ranges,
from experimental/gazelle/synthetic/model/tpu/utils.py.
Args:
inputs: list of tensors to concatenate.
axis: dimension along which to concatenate.
is_training: true if the graph is a training graph.
is_quantized: flag to enable/disable quantization.
default_min: default min value for fake quant op.
default_max: default max value for fake quant op.
ema_decay: the moving average decay for the quantization variables.
scope: Optional scope for variable_scope.
Returns:
Tensor resulting from concatenation of input tensors
"""
if is_quantized:
with tf.variable_scope(scope):
tf.logging.info('inputs: {}'.format(inputs))
for t in inputs:
tf.logging.info(t)
min_var = _quant_var('min', default_min)
max_var = _quant_var('max', default_max)
if not is_training:
# If we are building an eval graph just use the values in the variables.
quant_inputs = [
tf.fake_quant_with_min_max_vars(t, min_var, max_var) for t in inputs
]
tf.logging.info('min_val: {}'.format(min_var))
tf.logging.info('max_val: {}'.format(max_var))
else:
concat_tensors = tf.concat(inputs, axis=axis)
tf.logging.info('concat_tensors: {}'.format(concat_tensors))
# TFLite requires that 0.0 is always in the [min; max] range.
range_min = tf.minimum(
tf.reduce_min(concat_tensors), 0.0, name='SafeQuantRangeMin')
range_max = tf.maximum(
tf.reduce_max(concat_tensors), 0.0, name='SafeQuantRangeMax')
# Otherwise we need to keep track of the moving averages of the min and
# of the elements of the input tensor max.
min_val = moving_averages.assign_moving_average(
min_var,
range_min,
ema_decay,
name='AssignMinEma')
max_val = moving_averages.assign_moving_average(
max_var,
range_max,
ema_decay,
name='AssignMaxEma')
tf.logging.info('min_val: {}'.format(min_val))
tf.logging.info('max_val: {}'.format(max_val))
quant_inputs = [
tf.fake_quant_with_min_max_vars(t, min_val, max_val) for t in inputs
]
tf.logging.info('quant_inputs: {}'.format(quant_inputs))
outputs = tf.concat(quant_inputs, axis=axis)
tf.logging.info('outputs: {}'.format(outputs))
else:
outputs = tf.concat(inputs, axis=axis)
return outputs
def quantizable_separable_conv2d(inputs,
num_outputs,
kernel_size,
is_quantized=True,
depth_multiplier=1,
stride=1,
activation_fn=tf.nn.relu6,
normalizer_fn=None,
weights_initializer=None,
pointwise_initializer=None,
scope=None):
"""Quantization friendly backward compatible separable conv2d.
This op has the same API is separable_conv2d. The main difference is that an
additional BiasAdd is manually inserted after the depthwise conv, such that
the depthwise bias will not have name conflict with pointwise bias. The
motivation of this op is that quantization script need BiasAdd in order to
recognize the op, in which a native call to separable_conv2d do not create
for the depthwise conv.
Args:
inputs: A tensor of size [batch_size, height, width, channels].
num_outputs: The number of pointwise convolution output filters. If is
None, then we skip the pointwise convolution stage.
kernel_size: A list of length 2: [kernel_height, kernel_width] of the
filters. Can be an int if both values are the same.
is_quantized: flag to enable/disable quantization.
depth_multiplier: The number of depthwise convolution output channels for
each input channel. The total number of depthwise convolution output
channels will be equal to num_filters_in * depth_multiplier.
stride: A list of length 2: [stride_height, stride_width], specifying the
depthwise convolution stride. Can be an int if both strides are the same.
activation_fn: Activation function. The default value is a ReLU function.
Explicitly set it to None to skip it and maintain a linear activation.
normalizer_fn: Normalization function to use instead of biases.
weights_initializer: An initializer for the depthwise weights.
pointwise_initializer: An initializer for the pointwise weights.
scope: Optional scope for variable_scope.
Returns:
Tensor resulting from concatenation of input tensors
"""
if is_quantized:
outputs = contrib_layers.separable_conv2d(
inputs,
None,
kernel_size,
depth_multiplier=depth_multiplier,
stride=1,
activation_fn=None,
normalizer_fn=None,
biases_initializer=None,
weights_initializer=weights_initializer,
pointwise_initializer=None,
scope=scope)
outputs = contrib_layers.bias_add(
outputs, trainable=True, scope='%s_bias' % scope)
outputs = contrib_layers.conv2d(
outputs,
num_outputs, [1, 1],
activation_fn=activation_fn,
stride=stride,
normalizer_fn=normalizer_fn,
weights_initializer=pointwise_initializer,
scope=scope)
else:
outputs = contrib_layers.separable_conv2d(
inputs,
num_outputs,
kernel_size,
depth_multiplier=depth_multiplier,
stride=stride,
activation_fn=activation_fn,
normalizer_fn=normalizer_fn,
weights_initializer=weights_initializer,
pointwise_initializer=pointwise_initializer,
scope=scope)
return outputs
def quantize_op(inputs,
is_training=True,
is_quantized=True,
default_min=0,
default_max=6,
ema_decay=0.999,
scope='quant'):
"""Inserts a fake quantization op after inputs.
Args:
inputs: A tensor of size [batch_size, height, width, channels].
is_training: true if the graph is a training graph.
is_quantized: flag to enable/disable quantization.
default_min: default min value for fake quant op.
default_max: default max value for fake quant op.
ema_decay: the moving average decay for the quantization variables.
scope: Optional scope for variable_scope.
Returns:
Tensor resulting from quantizing the input tensors.
"""
if not is_quantized:
return inputs
with tf.variable_scope(scope):
min_var = _quant_var('min', default_min)
max_var = _quant_var('max', default_max)
if not is_training:
# Just use variables in the checkpoint.
return tf.fake_quant_with_min_max_vars(inputs, min_var, max_var)
# While training, collect EMAs of ranges seen, store in min_var, max_var.
# TFLite requires that 0.0 is always in the [min; max] range.
range_min = tf.minimum(tf.reduce_min(inputs), 0.0, 'SafeQuantRangeMin')
# We set the lower_bound of max_range to prevent range collapse.
range_max = tf.maximum(tf.reduce_max(inputs), 1e-5, 'SafeQuantRangeMax')
min_val = moving_averages.assign_moving_average(
min_var, range_min, ema_decay, name='AssignMinEma')
max_val = moving_averages.assign_moving_average(
max_var, range_max, ema_decay, name='AssignMaxEma')
return tf.fake_quant_with_min_max_vars(inputs, min_val, max_val)
def fixed_quantize_op(inputs, is_quantized=True,
fixed_min=0.0, fixed_max=6.0, scope='quant'):
"""Inserts a fake quantization op with fixed range after inputs.
Args:
inputs: A tensor of size [batch_size, height, width, channels].
is_quantized: flag to enable/disable quantization.
fixed_min: fixed min value for fake quant op.
fixed_max: fixed max value for fake quant op.
scope: Optional scope for variable_scope.
Returns:
Tensor resulting from quantizing the input tensors.
"""
if not is_quantized:
return inputs
with tf.variable_scope(scope):
# Just use fixed quantization range.
return tf.fake_quant_with_min_max_args(inputs, fixed_min, fixed_max)
|