# 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 os import copy import json import logging 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.contrib.slim.quantization import PostTrainingQuantization from paddle.fluid.contrib.slim.quantization import AddQuantDequantPass from paddle.fluid.contrib.slim.quantization import OutScaleForTrainingPass from paddle.fluid.contrib.slim.quantization import OutScaleForInferencePass from paddle.fluid import core from paddle.fluid.contrib.slim.quantization import WeightQuantization 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' ] VALID_DTYPES = ['int8'] TRANSFORM_PASS_OP_TYPES = QuantizationTransformPass._supported_quantizable_op_type QUANT_DEQUANT_PASS_OP_TYPES = AddQuantDequantPass._supported_quantizable_op_type TENSORRT_OP_TYPES = [ 'mul', 'conv2d', 'pool2d', 'depthwise_conv2d', 'elementwise_add', 'leaky_relu' ] VARS_MAPPING_TABLE = './mapping_table_for_saving_inference_model' _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, # 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 True, 'quantize_op_types' will be TENSORRT_OP_TYPES 'for_tensorrt': False, # if True, 'quantoze_op_types' will be TRANSFORM_PASS_OP_TYPES + QUANT_DEQUANT_PASS_OP_TYPES 'is_full_quantize': False } def load_dict(): with open(VARS_MAPPING_TABLE, 'r') as file: data = file.read() data = json.loads(data) return data def save_dict(table): with open(VARS_MAPPING_TABLE, 'w') as file: file.write(json.dumps(table)) 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['not_quant_pattern'], (list, str)), \ "not_quant_pattern must be list or str" assert isinstance(configs['quantize_op_types'], list), \ "quantize_op_types must be a list" if configs['for_tensorrt']: configs['quantize_op_types'] = TENSORRT_OP_TYPES elif configs['is_full_quantize']: configs[ 'quantize_op_types'] = TRANSFORM_PASS_OP_TYPES + QUANT_DEQUANT_PASS_OP_TYPES else: for op_type in configs['quantize_op_types']: assert (op_type in QUANT_DEQUANT_PASS_OP_TYPES) or ( op_type in TRANSFORM_PASS_OP_TYPES), "{} is not support, \ now support op types are {}".format( op_type, TRANSFORM_PASS_OP_TYPES + QUANT_DEQUANT_PASS_OP_TYPES) 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." return configs def quant_aware(program, place, 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, executor=None, return_program=False): """Add quantization and dequantization operators to "program" for quantization training or testing. Args: program(fluid.Program): training or testing ``program``. place(fluid.CPUPlace or fluid.CUDAPlace): This parameter represents the executor run on which device. config(dict, optional): configs for quantization. if None, will use default config. Default: None. scope(fluid.Scope): Scope records the mapping between variable names and variables, similar to brackets in programming languages. Usually users can use `fluid.global_scope `_. When ``None`` will use `fluid.global_scope() `_ . Default: ``None``. for_test(bool): If the 'program' parameter is a test program, this parameter should be set to ``True``. Otherwise, set to ``False``.Default: False weight_quantize_func(function): Function that defines how to quantize weight. Using this can quickly test if user's quantization method works or not. In this function, user should both define quantization function and dequantization function, that is, the function's input is non-quantized weight and function returns dequantized weight. If None, will use quantization op defined by 'weight_quantize_type'. Default is None. act_quantize_func(function): Function that defines how to quantize activation. Using this can quickly test if user's quantization method works or not. In this function, user should both define quantization and dequantization process, that is, the function's input is non-quantized activation and function returns dequantized activation. If None, will use quantization op defined by 'activation_quantize_type'. Default is None. weight_preprocess_func(function): Function that defines how to preprocess weight before quantization. Using this can quickly test if user's preprocess method works or not. The function's input is non-quantized weight and function returns processed weight to be quantized. If None, the weight will be quantized directly. Default is None. act_preprocess_func(function): Function that defines how to preprocess activation before quantization. Using this can quickly test if user's preprocess method works or not. The function's input is non-quantized activation and function returns processed activation to be quantized. If None, the activation will be quantized directly. Default is None. optimizer_func(function): Fuction return a optimizer. When 'is_test' is False and user want to use self-defined quantization function and preprocess function, this function must be set. Default is None. exe(Fluid.Executor): If user want to use self-defined quantization function and preprocess function, exe must be set for initialization. Default is None. return_program(bool): If user want return value is a Program rather than Compiled Program, This argument should be set True. Default is False. Returns: fluid.CompiledProgram | fluid.Program: Program with quantization and dequantization ``operators`` """ scope = fluid.global_scope() if not scope else scope if config is None: config = _quant_config_default else: assert isinstance(config, dict), "config must be dict" config = _parse_configs(config) _logger.info("quant_aware config {}".format(config)) main_graph = IrGraph(core.Graph(program.desc), for_test=for_test) transform_pass_ops = [] quant_dequant_ops = [] for op_type in config['quantize_op_types']: if op_type in TRANSFORM_PASS_OP_TYPES: transform_pass_ops.append(op_type) elif op_type in QUANT_DEQUANT_PASS_OP_TYPES: quant_dequant_ops.append(op_type) if len(transform_pass_ops) > 0: 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=transform_pass_ops, skip_pattern=config['not_quant_pattern'], weight_quantize_func=weight_quantize_func, act_quantize_func=act_quantize_func, weight_preprocess_func=weight_preprocess_func, act_preprocess_func=act_preprocess_func, optimizer_func=optimizer_func, executor=executor) transform_pass.apply(main_graph) if len(quant_dequant_ops) > 0: quant_dequant_pass = AddQuantDequantPass( scope=scope, place=place, moving_rate=config['moving_rate'], quant_bits=config['activation_bits'], skip_pattern=config['not_quant_pattern'], quantizable_op_type=quant_dequant_ops) quant_dequant_pass.apply(main_graph) out_scale_training_pass = OutScaleForTrainingPass( scope=scope, place=place, moving_rate=config['moving_rate']) out_scale_training_pass.apply(main_graph) if (weight_preprocess_func is not None or act_preprocess_func is not None) and not for_test: _logger.info( "When a preprocess_func is used in quant_aware, Need to save a mapping table to match variable names in the convert phase." ) _logger.info("The mapping table is saved as '{}'.".format( VARS_MAPPING_TABLE)) save_dict(main_graph.out_node_mapping_table) if for_test or return_program: quant_program = main_graph.to_program() else: quant_program = fluid.CompiledProgram(main_graph.graph) return quant_program def quant_post_static( executor, model_dir, quantize_model_path, batch_generator=None, sample_generator=None, model_filename=None, params_filename=None, save_model_filename='__model__', save_params_filename='__params__', batch_size=16, batch_nums=None, scope=None, algo='KL', quantizable_op_type=["conv2d", "depthwise_conv2d", "mul"], is_full_quantize=False, weight_bits=8, activation_bits=8, activation_quantize_type='range_abs_max', weight_quantize_type='channel_wise_abs_max', is_use_cache_file=False, cache_dir="./temp_post_training"): """ The function utilizes static post training quantization method to quantize the fp32 model. It uses calibrate data to calculate the scale factor of quantized variables, and inserts fake quantization and dequantization operators to obtain the quantized model. Args: executor(fluid.Executor): The executor to load, run and save the quantized model. model_dir(str): The path of fp32 model that will be quantized, and the model and params that saved by ``fluid.io.save_inference_model`` are under the path. quantize_model_path(str): The path to save quantized model using api ``fluid.io.save_inference_model``. batch_generator(Python Generator): The batch generator provides calibrate data for DataLoader, and it returns a batch every time. For sample_generator and batch_generator, only one can be set. Beisdes, batch_generator supports lod tensor. sample_generator(Python Generator): The sample generator provides calibrate data for DataLoader, and it only returns a sample every time. model_filename(str, optional): The name of model file. If parameters are saved in separate files, set it as 'None'. Default: 'None'. params_filename(str, optional): The name of params file. When all parameters are saved in a single file, set it as filename. If parameters are saved in separate files, set it as 'None'. Default : 'None'. save_model_filename(str): The name of model file to save the quantized inference program. Default: '__model__'. save_params_filename(str): The name of file to save all related parameters. If it is set None, parameters will be saved in separate files. Default: '__params__'. batch_size(int, optional): The batch size of DataLoader, default is 16. batch_nums(int, optional): If batch_nums is not None, the number of calibrate data is 'batch_size*batch_nums'. If batch_nums is None, use all data generated by sample_generator as calibrate data. scope(fluid.Scope, optional): The scope to run program, use it to load and save variables. If scope is None, will use fluid.global_scope(). algo(str, optional): If algo=KL, use KL-divergenc method to get the more precise scale factor. If algo='direct', use abs_max method to get the scale factor. Default: 'KL'. quantizable_op_type(list[str], optional): The list of op types that will be quantized. Default: ["conv2d", "depthwise_conv2d", "mul"]. weight_bits(int, optional): quantization bit number for weights. activation_bits(int): quantization bit number for activation. activation_quantize_type(str): quantization type for activation, now support 'range_abs_max', 'moving_average_abs_max' and 'abs_max'. This parameter only specifies the fake ops in quantized model. If it is 'range_abs_max' or 'moving_average_abs_max', we save the scale obtained by post training quantization in fake ops. If it is 'abs_max', the scale will not be saved in fake ops. weight_quantize_type(str): quantization type for weights, support 'abs_max' and 'channel_wise_abs_max'. Compared to 'abs_max', the model accuracy is usually higher when using 'channel_wise_abs_max'. is_full_quantize(bool): if True, apply quantization to all supported quantizable op type. If False, only apply quantization to the input quantizable_op_type. Default is False. is_use_cache_file(bool): If False, all temp data will be saved in memory. If True, all temp data will be saved to disk. Defalut: False. cache_dir(str): When 'is_use_cache_file' is True, temp data will be save in 'cache_dir'. Default is './temp_post_training'. Returns: None """ post_training_quantization = PostTrainingQuantization( executor=executor, sample_generator=sample_generator, batch_generator=batch_generator, model_dir=model_dir, model_filename=model_filename, params_filename=params_filename, batch_size=batch_size, batch_nums=batch_nums, scope=scope, algo=algo, quantizable_op_type=quantizable_op_type, is_full_quantize=is_full_quantize, weight_bits=weight_bits, activation_bits=activation_bits, activation_quantize_type=activation_quantize_type, weight_quantize_type=weight_quantize_type, is_use_cache_file=is_use_cache_file, cache_dir=cache_dir) post_training_quantization.quantize() post_training_quantization.save_quantized_model( quantize_model_path, model_filename=save_model_filename, params_filename=save_params_filename) # We have changed the quant_post to quant_post_static. # For compatibility, we keep quant_post api for now, and it will be # deprecated in the future. quant_post = quant_post_static def convert(program, place, config=None, scope=None, save_int8=False): """ convert quantized and well-trained ``program`` to final quantized ``program``that can be used to save ``inference model``. Args: program(fluid.Program): quantized and well-trained ``test program``. place(fluid.CPUPlace or fluid.CUDAPlace): This parameter represents the executor run on which device. config(dict, optional): configs for convert. if set None, will use default config. It must be same with config that used in 'quant_aware'. Default is None. scope(fluid.Scope, optional): Scope records the mapping between variable names and variables, similar to brackets in programming languages. Usually users can use `fluid.global_scope `_. When ``None`` will use `fluid.global_scope() `_ . Default: ``None``. save_int8: Whether to return ``program`` which model parameters' dtype is ``int8``. This parameter can only be used to get model size. Default: ``False``. Returns: Tuple : freezed program which can be used for inference. when ``save_int8`` is False, return ``freezed_program(fluid.Program)``. when ``save_int8`` is True, return ``freezed_program(fluid.Program)`` and ``freezed_program_int8(fluid.Program)`` """ scope = fluid.global_scope() if not scope else scope if config is None: config = _quant_config_default else: assert isinstance(config, dict), "config must be dict" config = _parse_configs(config) _logger.info("convert config {}".format(config)) test_graph = IrGraph(core.Graph(program.desc), for_test=True) out_scale_infer_pass = OutScaleForInferencePass(scope=scope) out_scale_infer_pass.apply(test_graph) # Freeze the graph after training by adjusting the quantize # operators' order for the inference. freeze_pass = QuantizationFreezePass( scope=scope, place=place, weight_bits=config['weight_bits'], activation_bits=config['activation_bits'], weight_quantize_type=config['weight_quantize_type']) if os.path.exists(VARS_MAPPING_TABLE): test_graph.out_node_mapping_table = load_dict() freeze_pass.apply(test_graph) freezed_program = test_graph.to_program() if save_int8: convert_int8_pass = ConvertToInt8Pass(scope=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 def quant_post_dynamic(model_dir, save_model_dir, model_filename=None, params_filename=None, save_model_filename=None, save_params_filename=None, quantizable_op_type=["conv2d", "mul"], weight_bits=8, generate_test_model=False): ''' The function utilizes static post training quantization method to quantize the fp32 model. In details, it quantizes the weight of some ops from float32 to int8/16. For the quantized model, there are two kinds of calculation method in the reference stage. Firstly, the quantized weight will be dequantized to float32, and then apply the float32 calculation. Secondly, collect the quantized scales of the inputs, and then apply the int8 calculation. Args: model_dir(str): The path of the fp32 model that will be quantized, and the model and params files are under the path. save_model_dir(str): The path to save the quantized model. model_filename(str, optional): The name of file used to load the inference program. If it is None, the default filename '__model__' will be used. Default is 'None'. params_filename(str, optional): The name of file used to load all parameters. When all parameters were saved in a single binary file, set it as the real filename. If parameters were saved in separate files, set it as 'None'. Default is 'None'. save_model_dir(str): The path used to save the quantized model. save_model_filename(str, optional): The name of file to save the inference program. If it is None, the default filename '__model__' will be used. Default is 'None'. save_params_filename(str, optional): The name of file to save all parameters. If it is None, parameters were saved in separate files. If it is not None, all parameters were saved in a single binary file. quantizable_op_type(list[str], optional): The list of ops that will be quantized, and the quantized ops should be contained in ["conv2d", "depthwise_conv2d", "mul"]. Default is ["conv2d", "depthwise_conv2d", "mul"]. weight_bits(int, optional): The bits for the quantized weight, and it should be 8 or 16. Default is 8. generate_test_model(bool, optional): If set generate_test_model as True, it saves a fake quantized model, in which the weights are quantized and dequantized. We can use PaddlePaddle to load the fake quantized model and test the accuracy on GPU or CPU. ''' weight_quant = WeightQuantization( model_dir=model_dir, model_filename=model_filename, params_filename=params_filename) weight_quant.quantize_weight_to_int( save_model_dir=save_model_dir, save_model_filename=save_model_filename, save_params_filename=save_params_filename, quantizable_op_type=quantizable_op_type, weight_bits=weight_bits, generate_test_model=generate_test_model) # We have changed the quant_post_only_weight to quant_post_dynamic. # For compatibility, we keep quant_post_only_weight api for now, # and it will be deprecated in the future. quant_post_only_weight = quant_post_dynamic