File size: 11,805 Bytes
516a027 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 |
# Copyright 2021 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.
# ==============================================================================
"""Registry responsible for built-in keras classes."""
import tensorflow as tf
from tensorflow_model_optimization.python.core.keras.compat import keras
from tensorflow_model_optimization.python.core.quantization.keras import quant_ops
from tensorflow_model_optimization.python.core.quantization.keras import quantizers
from tensorflow_model_optimization.python.core.quantization.keras.default_8bit import (
default_8bit_quantize_registry,)
from tensorflow_model_optimization.python.core.quantization.keras.default_8bit import (
default_8bit_quantizers,)
layers = keras.layers
class _PrunePreserveInfo(object):
"""PrunePreserveInfo."""
def __init__(self, weight_attrs, quantize_config_attrs):
"""Initializes PrunePreserveInfo.
Args:
weight_attrs: list of sparsity preservable weight attributes of layer.
quantize_config_attrs: list of quantization configuration class name.
"""
self.weight_attrs = weight_attrs
self.quantize_config_attrs = quantize_config_attrs
class PrunePreserveQuantizeRegistry():
"""PrunePreserveQuantizeRegistry responsible for built-in keras layers."""
# The keys represent built-in keras layers; the first values represent the
# the variables within the layers which hold the kernel weights, second
# values represent the class name of quantization configuration for layers.
# This decide the weights of layers with quantization configurations are
# sparsity preservable.
_LAYERS_CONFIG_MAP = {
layers.Conv2D:
_PrunePreserveInfo(['kernel'], ['Default8BitConvQuantizeConfig']),
layers.Dense:
_PrunePreserveInfo(['kernel'], ['Default8BitQuantizeConfig']),
# DepthwiseConv2D is supported with 8bit qat, but not with prune,
# thus for DepthwiseConv2D PQAT, weights sparsity preserve is disabled.
layers.DepthwiseConv2D:
_PrunePreserveInfo(['depthwise_kernel'], ['Default8BitQuantizeConfig']),
# layers that supported with prune, but not yet with QAT
# layers.Conv1D:
# _PrunePreserveInfo(['kernel'], []),
# layers.Conv2DTranspose:
# _PrunePreserveInfo(['kernel'], []),
# layers.Conv3D:
# _PrunePreserveInfo(['kernel'], []),
# layers.Conv3DTranspose:
# _PrunePreserveInfo(['kernel'], []),
# layers.LocallyConnected1D:
# _PrunePreserveInfo(['kernel'], ['Default8BitQuantizeConfig']),
# layers.LocallyConnected2D:
# _PrunePreserveInfo(['kernel'], ['Default8BitQuantizeConfig']),
# SeparableConv need verify from 8bit qat
# layers.SeparableConv1D:
# _PrunePreserveInfo(['pointwise_kernel'], \
# ['Default8BitConvQuantizeConfig']),
# layers.SeparableConv2D:
# _PrunePreserveInfo(['pointwise_kernel'], \
# ['Default8BitConvQuantizeConfig']),
# Embedding need verify from 8bit qat
# layers.Embedding: _PrunePreserveInfo(['embeddings'], []),
}
_DISABLE_PRUNE_PRESERVE = frozenset({
layers.DepthwiseConv2D,
})
def __init__(self):
self._config_quantizer_map = {
'Default8BitQuantizeConfig':
PrunePreserveDefault8BitWeightsQuantizer(),
'Default8BitConvQuantizeConfig':
PrunePreserveDefault8BitConvWeightsQuantizer(),
}
@classmethod
def _no_trainable_weights(cls, layer):
"""Returns whether this layer has trainable weights.
Args:
layer: The layer to check for trainable weights.
Returns:
True/False whether the layer has trainable weights.
"""
return not layer.trainable_weights
@classmethod
def _disable_prune_preserve(cls, layer):
"""Returns whether disable this layer for prune preserve.
Args:
layer: The layer to check for disable.
Returns:
True/False whether disable this layer for prune preserve.
"""
return layer.__class__ in cls._DISABLE_PRUNE_PRESERVE
@classmethod
def supports(cls, layer):
"""Returns whether the registry supports this layer type.
Args:
layer: The layer to check for support.
Returns:
True/False whether the layer type is supported.
"""
# layers without trainable weights are considered supported,
# e.g., ReLU, Softmax, and AveragePooling2D.
if cls._no_trainable_weights(layer):
return True
if layer.__class__ in cls._LAYERS_CONFIG_MAP:
return True
return False
@classmethod
def _weight_names(cls, layer):
"""Gets the weight names."""
if cls._no_trainable_weights(layer):
return []
return cls._LAYERS_CONFIG_MAP[layer.__class__].weight_attrs
@classmethod
def get_sparsity_preservable_weights(cls, layer):
"""Gets sparsity preservable weights from keras layer.
Args:
layer: instance of keras layer
Returns:
List of sparsity preservable weights
"""
return [getattr(layer, weight) for weight in cls._weight_names(layer)]
@classmethod
def get_suppport_quantize_config_names(cls, layer):
"""Gets class name of supported quantize config for layer.
Args:
layer: instance of keras layer
Returns:
List of supported quantize config class name.
"""
# layers without trainable weights don't need quantize_config for pqat
if cls._no_trainable_weights(layer):
return []
return cls._LAYERS_CONFIG_MAP[layer.__class__].quantize_config_attrs
def apply_sparsity_preserve_quantize_config(self, layer, quantize_config):
"""Applies weights sparsity preservation.
Args:
layer: The layer to check for support.
quantize_config: quantization config to check for support,
apply sparsity preservation to pruned weights
Raises:
ValueError when layer is supported does not have quantization config.
Returns:
Returns quantize_config with addon sparsity preserve weight_quantizer.
"""
if self.supports(layer):
if (self._no_trainable_weights(layer) or
self._disable_prune_preserve(layer)):
return quantize_config
if (quantize_config.__class__.__name__
in self._LAYERS_CONFIG_MAP[layer.__class__].quantize_config_attrs):
quantize_config.weight_quantizer = self._config_quantizer_map[
quantize_config.__class__.__name__]
else:
raise ValueError('Configuration {} is not supported for Layer {}.'
.format(str(quantize_config.__class__.__name__),
str(layer.__class__.__name__)))
else:
raise ValueError('Layer {} is not supported.'.format(
str(layer.__class__.__name__)))
return quantize_config
class Default8bitPrunePreserveQuantizeRegistry(PrunePreserveQuantizeRegistry):
"""Default 8 bit PrunePreserveQuantizeRegistry."""
def get_quantize_config(self, layer):
"""Returns the quantization config with addon sparsity.
Args:
layer: input layer to return quantize config for.
Returns:
Returns the quantization config with sparsity preserve weight_quantizer.
"""
quantize_config = (default_8bit_quantize_registry
.Default8BitQuantizeRegistry()
.get_quantize_config(layer))
prune_aware_quantize_config = self.apply_sparsity_preserve_quantize_config(
layer, quantize_config)
return prune_aware_quantize_config
class PrunePreserveDefaultWeightsQuantizer(quantizers.LastValueQuantizer):
"""Quantize weights while preserve sparsity."""
def __init__(self, num_bits, per_axis, symmetric, narrow_range):
"""Initializes PrunePreserveDefaultWeightsQuantizer.
Args:
num_bits: Number of bits for quantization
per_axis: Whether to apply per_axis quantization. The last dimension is
used as the axis.
symmetric: If true, use symmetric quantization limits instead of training
the minimum and maximum of each quantization range separately.
narrow_range: In case of 8 bits, narrow_range nudges the quantized range
to be [-127, 127] instead of [-128, 127]. This ensures symmetric range
has 0 as the centre.
"""
quantizers.LastValueQuantizer.__init__(self, num_bits, per_axis, symmetric,
narrow_range)
def _build_sparsity_mask(self, name, layer):
weights = getattr(layer.layer, name)
sparsity_mask = tf.math.divide_no_nan(weights, weights)
return {'sparsity_mask': sparsity_mask}
def build(self, tensor_shape, name, layer):
"""Constructs mask to preserve weights sparsity.
Args:
tensor_shape: Shape of weights which needs to be quantized.
name: Name of weights in layer.
layer: quantization wrapped keras layer.
Returns:
Dictionary of constructed sparsity mask and
quantization params, the dictionary will be passed
to __call__ function.
"""
result = self._build_sparsity_mask(name, layer)
result.update(
super(PrunePreserveDefaultWeightsQuantizer,
self).build(tensor_shape, name, layer))
return result
def __call__(self, inputs, training, weights, **kwargs):
"""Applies sparsity preserved quantization to the input tensor.
Args:
inputs: Input tensor (layer's weights) to be quantized.
training: Whether the graph is currently training.
weights: Dictionary of weights (params) the quantizer can use to
quantize the tensor (layer's weights). This contains the weights
created in the `build` function.
**kwargs: Additional variables which may be passed to the quantizer.
Returns:
quantized tensor.
"""
prune_preserve_inputs = tf.multiply(inputs, weights['sparsity_mask'])
return quant_ops.LastValueQuantize(
prune_preserve_inputs,
weights['min_var'],
weights['max_var'],
is_training=training,
num_bits=self.num_bits,
per_channel=self.per_axis,
symmetric=self.symmetric,
narrow_range=self.narrow_range,
)
class PrunePreserveDefault8BitWeightsQuantizer(
PrunePreserveDefaultWeightsQuantizer):
"""PrunePreserveWeightsQuantizer for default 8bit weights."""
def __init__(self):
super(PrunePreserveDefault8BitWeightsQuantizer,
self).__init__(num_bits=8,
per_axis=False,
symmetric=True,
narrow_range=True)
class PrunePreserveDefault8BitConvWeightsQuantizer(
PrunePreserveDefaultWeightsQuantizer,
default_8bit_quantizers.Default8BitConvWeightsQuantizer,):
"""PrunePreserveWeightsQuantizer for default 8bit Conv2D/DepthwiseConv2D weights."""
# pylint: disable=super-init-not-called
def __init__(self):
# Skip PrunePreserveDefaultWeightsQuantizer since they have the same super.
default_8bit_quantizers.Default8BitConvWeightsQuantizer.__init__(self)
def build(self, tensor_shape, name, layer):
result = PrunePreserveDefaultWeightsQuantizer._build_sparsity_mask(
self, name, layer)
result.update(
default_8bit_quantizers.Default8BitConvWeightsQuantizer.build(
self, tensor_shape, name, layer))
return result
|