# 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 logging import sys import numpy as np from .... import Executor from .... import io from .... import core from ....compiler import CompiledProgram from ....compiler import BuildStrategy from ....framework import IrGraph from ..core.strategy import Strategy from .quantization_pass import * __all__ = ['QuantizationStrategy'] logging.basicConfig(format='%(asctime)s-%(levelname)s: %(message)s') _logger = logging.getLogger(__name__) _logger.setLevel(logging.INFO) class QuantizationStrategy(Strategy): """ The strategy for Quantization. """ def __init__(self, start_epoch=0, end_epoch=0, float_model_save_path=None, mobile_model_save_path=None, int8_model_save_path=None, activation_bits=8, weight_bits=8, activation_quantize_type='abs_max', weight_quantize_type='abs_max', save_in_nodes=None, save_out_nodes=None): """ Args: start_epoch(int): The 'on_epoch_begin' function will be called in start_epoch. default: 0 end_epoch(int): The 'on_epoch_end' function will be called in end_epoch. default: 0 float_model_save_path(str): The path to save model with float weights. None means it doesn't save float model. defalut: None. mobile_model_save_path(str): The path to save model for paddle-mobile execution. None means it doesn't save mobile model. defalut: None. int8_model_save_path(str): The path to save model with int8_t weight. None means it doesn't save int8 model. defalut: None. activation_bits(int): quantization bit number for activation. default: 8. weight_bits(int): quantization bit number for weights. The bias is not quantized. default: 8. activation_quantize_type(str): quantization type for activation, now support 'abs_max', 'range_abs_max' and 'moving_average_abs_max'. If use 'abs_max' mode, the quantization scale will be calculated dynamically each step in both training and testing period. If use 'range_abs_max', a static quantization scale will be calculated during training and used in inference. weight_quantize_type (str): quantization type for weights, support 'abs_max' and 'channel_wise_abs_max'. The 'range_abs_max' usually is not used for weight, since weights are fixed once the model is well trained. save_in_nodes(list): A list of variable names used to prune graph for saving inference model. save_out_nodes(list): A list of variable names used to prune graph for saving inference model. """ super(QuantizationStrategy, self).__init__(start_epoch, end_epoch) self.start_epoch = start_epoch self.end_epoch = end_epoch self.float_model_save_path = float_model_save_path self.mobile_model_save_path = mobile_model_save_path self.int8_model_save_path = int8_model_save_path self.activation_bits = activation_bits self.weight_bits = weight_bits self.activation_quantize_type = activation_quantize_type self.weight_quantize_type = weight_quantize_type self.save_out_nodes = save_out_nodes self.save_in_nodes = save_in_nodes def on_epoch_begin(self, context): """ Insert fake_quantize_op and fake_dequantize_op before trainging and testing. """ super(QuantizationStrategy, self).on_compression_begin(context) if self.start_epoch == context.epoch_id: _logger.info('QuantizationStrategy::on_epoch_begin') train_ir_graph = IrGraph( core.Graph(context.optimize_graph.program.desc), for_test=False) test_ir_graph = IrGraph( core.Graph(context.eval_graph.program.desc), for_test=True) transform_pass = QuantizationTransformPass( scope=context.scope, place=context.place, weight_bits=self.weight_bits, activation_bits=self.activation_bits, activation_quantize_type=self.activation_quantize_type, weight_quantize_type=self.weight_quantize_type) transform_pass.apply(train_ir_graph) transform_pass.apply(test_ir_graph) build_strategy = BuildStrategy() build_strategy.enable_inplace = False build_strategy.memory_optimize = False # for quantization training context.optimize_graph.compiled_graph = CompiledProgram( train_ir_graph.graph).with_data_parallel( loss_name=context.optimize_graph.out_nodes['loss'], build_strategy=build_strategy) # for evaluation. And program compiled from ir graph must be with data parallel. context.eval_graph.compiled_graph = CompiledProgram( test_ir_graph.graph).with_data_parallel( build_strategy=build_strategy) # for saving inference model after training context.put('quantization_test_ir_graph_backup', test_ir_graph) _logger.info('Finish QuantizationStrategy::on_epoch_begin') def on_epoch_end(self, context): """ Free and save inference model. """ super(QuantizationStrategy, self).on_compression_end(context) if context.epoch_id == self.end_epoch: _logger.info('QuantizationStrategy::on_epoch_end') test_ir_graph = context.get('quantization_test_ir_graph_backup') # freeze the graph after training freeze_pass = QuantizationFreezePass( scope=context.scope, place=context.place, weight_bits=self.weight_bits, activation_bits=self.activation_bits, weight_quantize_type=self.weight_quantize_type) freeze_pass.apply(test_ir_graph) # for other strategies context.eval_graph.program = test_ir_graph.to_program() if self.save_out_nodes == None: out_vars = [ context.eval_graph.var(var_name)._var for var_name in context.eval_graph.out_nodes.values() ] else: out_vars = [ context.eval_graph.var(var_name)._var for var_name in self.save_out_nodes ] if self.save_in_nodes == None: in_vars = list(context.eval_graph.out_nodes.values()) else: in_vars = self.save_in_nodes # save float model if self.float_model_save_path: executor = Executor(context.place) io.save_inference_model( self.float_model_save_path, in_vars, out_vars, executor, main_program=test_ir_graph.to_program(), model_filename='model', params_filename='weights', export_for_deployment=True) # save int8 model if self.int8_model_save_path: convert_int8_pass = ConvertToInt8Pass( scope=context.scope, place=context.place) convert_int8_pass.apply(test_ir_graph) executor = Executor(context.place) io.save_inference_model( self.int8_model_save_path, in_vars, out_vars, executor, main_program=test_ir_graph.to_program(), model_filename='model', params_filename='weights', export_for_deployment=True) # save mobile model if self.mobile_model_save_path: if not self.int8_model_save_path: # convert the weights as int8_t type convert_int8_pass = ConvertToInt8Pass( scope=context.scope, place=context.place) convert_int8_pass.apply(test_ir_graph) # make some changes on the graph for the mobile inference mobile_pass = TransformForMobilePass() mobile_pass.apply(test_ir_graph) executor = Executor(context.place) io.save_inference_model( self.mobile_model_save_path, in_vars, out_vars, executor, main_program=test_ir_graph.to_program(), model_filename='model', params_filename='weights', export_for_deployment=True) _logger.info('Finish QuantizationStrategy::on_epoch_end')