Added dygraph quantization.

paddleslim/quant/dy_quanter.py

+# Copyright (c) 2019  PaddlePaddle 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.
+
+import copy
+import logging
+
+import paddle
+import paddle.fluid as fluid
+
+from paddle.fluid.contrib.slim.quantization import ImperativeQuantAware
+
+from ..common import get_logger
+_logger = get_logger(__name__, level=logging.INFO)
+
+WEIGHT_QUANTIZATION_TYPES = [
+    'abs_max', 'channel_wise_abs_max', 'range_abs_max',
+    'moving_average_abs_max'
+]
+WEIGHT_QUANTIZATION_TYPES_TENSORRT = ['channel_wise_abs_max']
+
+ACTIVATION_QUANTIZATION_TYPES = [
+    'abs_max', 'range_abs_max', 'moving_average_abs_max'
+]
+
+ACTIVATION_QUANTIZATION_TYPES_TENSORRT = [
+    'range_abs_max', 'moving_average_abs_max'
+]
+
+_quant_config_default = {
+    # weight quantize type, default is 'channel_wise_abs_max'
+    'weight_quantize_type': 'channel_wise_abs_max',
+    # activation quantize type, default is 'moving_average_abs_max'
+    'activation_quantize_type': 'moving_average_abs_max',
+    # weight quantize bit num, default is 8
+    'weight_bits': 8,
+    # activation quantize bit num, default is 8
+    'activation_bits': 8,
+    # Layer of type in quantize_layer_types, will be quantized
+    'quantize_layer_types': ['Conv2D', 'Linear', 'ReLU', 'Pool2D', 'LeakyReLU'],
+    # data type after quantization, such as 'uint8', 'int8', etc. default is 'int8'
+    'dtype': 'int8',
+    # window size for 'range_abs_max' quantization. defaulf is 10000
+    'window_size': 10000,
+    # The decay coefficient of moving average, default is 0.9
+    'moving_rate': 0.9,
+}
+
+
+
+def _parse_configs(user_config):
+    """
+    check if user's configs are valid.
+    Args:
+        user_config(dict): user's config.
+    Return:
+        configs(dict): final configs will be used.
+    """
+
+    configs = copy.deepcopy(_quant_config_default)
+    configs.update(user_config)
+
+    assert isinstance(configs['for_tensorrt'], bool) and isinstance(
+        configs['is_full_quantize'],
+        bool), "'for_tensorrt' and 'is_full_quantize' must both be bool'"
+
+    # check if configs is valid
+    if configs['for_tensorrt']:
+        weight_types = WEIGHT_QUANTIZATION_TYPES_TENSORRT
+        activation_types = ACTIVATION_QUANTIZATION_TYPES_TENSORRT
+        platform = 'TensorRT'
+    else:
+        weight_types = WEIGHT_QUANTIZATION_TYPES
+        activation_types = WEIGHT_QUANTIZATION_TYPES
+        platform = 'PaddleLite'
+    assert configs['weight_quantize_type'] in weight_types, \
+        "Unknown weight_quantize_type: {}. {} only supports {} ".format(configs['weight_quantize_type'],
+                platform, weight_types)
+
+    assert configs['activation_quantize_type'] in activation_types, \
+        "Unknown activation_quantize_type: {}. {} only supports {}".format(configs['activation_quantize_type'],
+                platform, activation_types)
+
+    assert isinstance(configs['weight_bits'], int), \
+        "weight_bits must be int value."
+
+    assert (configs['weight_bits'] >= 1 and configs['weight_bits'] <= 16), \
+        "weight_bits should be between 1 and 16."
+
+    assert isinstance(configs['activation_bits'], int), \
+        "activation_bits must be int value."
+
+    assert (configs['activation_bits'] >= 1 and configs['activation_bits'] <= 16), \
+        "activation_bits should be between 1 and 16."
+
+    assert isinstance(configs['dtype'], str), \
+        "dtype must be a str."
+
+    assert isinstance(configs['window_size'], int), \
+        "window_size must be int value, window size for 'range_abs_max' quantization, default is 10000."
+
+    assert isinstance(configs['moving_rate'], float), \
+        "moving_rate must be float value, The decay coefficient of moving average, default is 0.9."
+
+    return configs
+
+def dy_quant_aware(model,
+                config=None,
+                scope=None,
+                for_test=False,
+                weight_quantize_func=None,
+                act_quantize_func=None,
+                weight_preprocess_func=None,
+                act_preprocess_func=None,
+                optimizer_func=None):
+        
+        imperative_qat = ImperativeQuantAware(
+            weight_quantize_type='abs_max',
+            activation_quantize_type='moving_average_abs_max',
+            quantizable_layer_type=[
+                'Conv2D', 'Linear', 'ReLU', 'Pool2D', 'LeakyReLU'
+            ])
+
+        imperative_qat.quantize(model)
+
+        return model
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