Merge branch 'develop' into 'develop'

new quant aware api

See merge request !21

paddleslim/quant/__init__.py

@@ -12,4 +12,5 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from .quanter import quant_aware, quant_post, convert
 from .quant_embedding import quant_embedding

paddleslim/quant/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 paddle
+import paddle.fluid as fluid
+from paddle.fluid.framework import IrGraph
+from paddle.fluid.contrib.slim.quantization import QuantizationTransformPass
+from paddle.fluid.contrib.slim.quantization import QuantizationFreezePass
+from paddle.fluid.contrib.slim.quantization import ConvertToInt8Pass
+from paddle.fluid.contrib.slim.quantization import TransformForMobilePass
+from paddle.fluid import core
+
+WEIGHT_QUANTIZATION_TYPES=['abs_max', 'channel_wise_abs_max', 'range_abs_max', 'moving_average_abs_max']
+ACTIVATION_QUANTIZATION_TYPES=['abs_max','range_abs_max', 'moving_average_abs_max']
+VALID_DTYPES = ['int8']
+
+_quant_config_default = {
+    # weight quantize type, default is 'abs_max'
+    'weight_quantize_type': 'abs_max',
+    # activation quantize type, default is 'abs_max'
+    'activation_quantize_type': 'abs_max',
+    # weight quantize bit num, default is 8
+    'weight_bits': 8,
+    # activation quantize bit num, default is 8
+    'activation_bits': 8,
+    # ops of name_scope in not_quant_pattern list, will not be quantized
+    'not_quant_pattern': ['skip_quant'],
+    # ops of type in quantize_op_types, will be quantized
+    'quantize_op_types': ['conv2d', 'depthwise_conv2d', 'mul'],
+    # 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,
+    # if set quant_weight_only True, then only quantize parameters of layers which need to be quantized,
+    # and activations will not be quantized.
+    'quant_weight_only': False
+}
+
+
+def _parse_configs(user_config):
+    """
+    check user configs is valid, and set default value if user not config.
+    Args:
+        user_config(dict):the config of user.
+    Return:
+        configs(dict): final configs will be used.
+    """
+
+    configs = copy.deepcopy(_quant_config_default)
+    configs.update(user_config)
+
+    # check configs is valid
+    assert configs['weight_quantize_type'] in WEIGHT_QUANTIZATION_TYPES, \
+        "Unknown weight_quantize_type: '%s'. It can only be " + " ".join(WEIGHT_QUANTIZATION_TYPES)
+
+    assert configs['activation_quantize_type'] in ACTIVATION_QUANTIZATION_TYPES, \
+        "Unknown activation_quantize_type: '%s'. It can only be " + " ".join(ACTIVATION_QUANTIZATION_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['not_quant_pattern'], list), \
+        "not_quant_pattern must be a list"
+
+    assert isinstance(configs['quantize_op_types'], list), \
+        "quantize_op_types must be a list"
+
+    assert isinstance(configs['dtype'], str), \
+        "dtype must be a str."
+
+    assert (configs['dtype'] in VALID_DTYPES), \
+        "dtype can only be " + " ".join(VALID_DTYPES)
+
+    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."
+
+    assert isinstance(configs['quant_weight_only'], bool), \
+        "quant_weight_only must be bool value, if set quant_weight_only True, " \
+        "then only quantize parameters of layers which need to be quantized, " \
+        " and activations will not be quantized."
+
+    return configs
+
+
+def quant_aware(program, place, config, scope=None, for_test=False):
+    """
+    add trainable quantization ops in program.
+    Args:
+        program(fluid.Program): program
+        scope(fluid.Scope): the scope to store var, it's should be the value of program's scope, usually it's fluid.global_scope().
+        place(fluid.CPUPlace or fluid.CUDAPlace): place
+        config(dict): configs for quantization, default values are in quant_config_default dict.
+        for_test: if program is test program, for_test should be set True, else False.
+    Return:
+        fluid.Program: user can finetune this quantization program to enhance the accuracy.
+    """
+
+    scope = fluid.global_scope() if not scope else scope
+    assert isinstance(config, dict), "config must be dict"
+
+    assert 'weight_quantize_type' in config.keys(
+    ), 'weight_quantize_type must be configured'
+    assert 'activation_quantize_type' in config.keys(
+    ), 'activation_quantize_type must be configured'
+
+    config = _parse_configs(config)
+    main_graph = IrGraph(core.Graph(program.desc), for_test=for_test)
+
+    transform_pass = QuantizationTransformPass(
+        scope=scope,
+        place=place,
+        weight_bits=config['weight_bits'],
+        activation_bits=config['activation_bits'],
+        activation_quantize_type=config['activation_quantize_type'],
+        weight_quantize_type=config['weight_quantize_type'],
+        window_size=config['window_size'],
+        moving_rate=config['moving_rate'],
+        quantizable_op_type=config['quantize_op_types'],
+        skip_pattern=config['not_quant_pattern'])
+
+    transform_pass.apply(main_graph)
+
+    if for_test:
+        quant_program = main_graph.to_program()
+    else:
+        quant_program = fluid.CompiledProgram(main_graph.graph)
+    return quant_program
+
+
+def quant_post(program, place, config, scope=None):
+    """
+    add quantization ops in program. the program returned is not trainable.
+    Args:
+        program(fluid.Program): program
+        scope(fluid.Scope): the scope to store var, it's should be the value of program's scope, usually it's fluid.global_scope().
+        place(fluid.CPUPlace or fluid.CUDAPlace): place
+        config(dict): configs for quantization, default values are in quant_config_default dict.
+        for_test: is for test program.
+    Return:
+        fluid.Program: the quantization program is not trainable.
+    """
+    pass
+
+
+def convert(program, scope, place, config, save_int8=False):
+    """
+    add quantization ops in program. the program returned is not trainable.
+    Args:
+        program(fluid.Program): program
+        scope(fluid.Scope): the scope to store var, when is None will use fluid.global_scope()
+        place(fluid.CPUPlace or fluid.CUDAPlace): place
+        config(dict): configs for quantization, default values are in quant_config_default dict.
+        save_int8: is export int8 freezed program.
+    Return:
+        fluid.Program: freezed program which can be used for inference.
+                       parameters is float32 type, but it's value in int8 range.
+        fluid.Program: freezed int8 program which can be used for inference.
+                       if save_int8 is False, this value is None.
+    """
+
+    test_graph = IrGraph(core.Graph(program.desc), for_test=True)
+
+    # Freeze the graph after training by adjusting the quantize
+    # operators' order for the inference.
+    freeze_pass = QuantizationFreezePass(
+        scope=scope,
+        place=place,
+        weight_quantize_type=config['weight_quantize_type'])
+    freeze_pass.apply(test_graph)
+    freezed_program = test_graph.to_program()
+
+    if save_int8:
+        convert_int8_pass = ConvertToInt8Pass(
+            scope=fluid.global_scope(), place=place)
+        convert_int8_pass.apply(test_graph)
+        freezed_program_int8 = test_graph.to_program()
+        return freezed_program, freezed_program_int8
+    else:
+        return freezed_program