# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserve. # # 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 os import sys import numpy as np import inspect import shutil from collections import namedtuple, Iterable import platform import paddle import paddle.distributed.fleet as fleet if platform.system().lower() == 'linux': from ..quant import quant_post_hpo from ..quant.quanter import convert, quant_post from ..common.recover_program import recover_inference_program from ..common import get_logger from ..common.patterns import get_patterns from ..analysis import TableLatencyPredictor from .create_compressed_program import build_distill_program, build_quant_program, build_prune_program, remove_unused_var_nodes from .strategy_config import ProgramInfo, merge_config from .auto_strategy import prepare_strategy, get_final_quant_config, create_strategy_config, create_train_config _logger = get_logger(__name__, level=logging.INFO) class AutoCompression: def __init__(self, model_dir, model_filename, params_filename, save_dir, train_dataloader, train_config=None, strategy_config=None, target_speedup=None, eval_callback=None, eval_dataloader=None, deploy_hardware='gpu'): """ Compress inference model automatically. Args: model_dir(str): The path of inference model that will be compressed, and the model and params that saved by ``paddle.static.io.save_inference_model`` are under the path. 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_dir(str): The path to save compressed model. train_data_loader(Python Generator, Paddle.io.DataLoader): The Generator or Dataloader provides train data, and it could return a batch every time. train_config(dict, optional): The train config in the compression process, the key can reference ``_ . Only one strategy(quant_post with hyperparameter optimization) can set train_config to None. Default: None. strategy_config(dict, list(dict), optional): The strategy config. You can set single config to get multi-strategy config, such as 1. set ``Quantization`` and ``Distillation`` to get quant_aware and distillation compress config. The Quantization config can reference `https://github.com/PaddlePaddle/PaddleSlim/blob/develop/paddleslim/auto_compression/strategy_config.py#L24`_ . The Distillation config can reference `https://github.com/PaddlePaddle/PaddleSlim/blob/develop/paddleslim/auto_compression/strategy_config.py#L39`_ . 2. set ``Quantization`` and ``HyperParameterOptimization`` to get quant_post and hyperparameter optimization compress config. The Quantization config can reference `https://github.com/PaddlePaddle/PaddleSlim/blob/develop/paddleslim/auto_compression/strategy_config.py#L24`_ . The HyperParameterOptimization config can reference `https://github.com/PaddlePaddle/PaddleSlim/blob/develop/paddleslim/auto_compression/strategy_config.py#L73`_ . 3. set ``Prune`` and ``Distillation`` to get prune and distillation compress config. The Prune config can reference `https://github.com/PaddlePaddle/PaddleSlim/blob/develop/paddleslim/auto_compression/strategy_config.py#L82`_ . The Distillation config can reference `https://github.com/PaddlePaddle/PaddleSlim/blob/develop/paddleslim/auto_compression/strategy_config.py#L39`_ . 4. set ``UnstructurePrune`` and ``Distillation`` to get unstructureprune and distillation compress config. The UnstructurePrune config can reference `https://github.com/PaddlePaddle/PaddleSlim/blob/develop/paddleslim/auto_compression/strategy_config.py#L91`_ . The Distillation config can reference `https://github.com/PaddlePaddle/PaddleSlim/blob/develop/paddleslim/auto_compression/strategy_config.py#L39`_ . 5. set ``Distillation`` to use one teacher modol to distillation student model. The Distillation config can reference `https://github.com/PaddlePaddle/PaddleSlim/blob/develop/paddleslim/auto_compression/strategy_config.py#L39`_ . 6. set ``MultiTeacherDistillation`` to use multi-teacher to distillation student model. The MultiTeacherDistillation config can reference `https://github.com/PaddlePaddle/PaddleSlim/blob/develop/paddleslim/auto_compression/strategy_config.py#L56`_ . If set to None, will choose a strategy automatically. Default: None. target_speedup(float, optional): target speedup ratio by the way of auto compress. Default: None. eval_callback(function, optional): eval function, define by yourself to return the metric of the inference program, can be used to judge the metric of compressed model. The documents of how to write eval function is `https://github.com/PaddlePaddle/PaddleSlim/blob/develop/docs/zh_cn/api_cn/static/auto-compression/custom_function.rst`_ . ``eval_callback`` and ``eval_dataloader`` cannot be None at the same time. Dafault: None. eval_dataloader(paddle.io.Dataloader, optional): The Generator or Dataloader provides eval data, and it could return a batch every time. ``eval_callback`` and ``eval_dataloader`` cannot be None at the same time. Dafault: None. deploy_hardware(str, optional): The hardware you want to deploy. Default: 'gpu'. """ self.model_dir = model_dir if model_filename == 'None': model_filename = None self.model_filename = model_filename if params_filename == 'None': params_filename = None self.params_filename = params_filename base_path = os.path.basename(os.path.normpath(save_dir)) parent_path = os.path.abspath(os.path.join(save_dir, os.pardir)) base_path = base_path + '_temp' self.save_dir = os.path.join(parent_path, base_path) self.final_dir = save_dir self.strategy_config = strategy_config self.train_config = train_config self.train_dataloader = train_dataloader self.target_speedup = target_speedup self.eval_function = eval_callback self.eval_dataloader = eval_dataloader paddle.enable_static() if deploy_hardware in TableLatencyPredictor.hardware_list: self.deploy_hardware = deploy_hardware else: self.deploy_hardware = None self._exe, self._places = self._prepare_envs() self.model_type = self._get_model_type(self._exe, model_dir, model_filename, params_filename) if self.train_config is not None and self.train_config.use_fleet: fleet.init(is_collective=True) if self.strategy_config is None: strategy_config = prepare_strategy( self.model_dir, self.model_filename, self.params_filename, self.target_speedup, self.deploy_hardware, self.model_type) self.strategy_config = strategy_config elif isinstance(self.strategy_config, dict): self.strategy_config = [self.strategy_config] elif isinstance(self.strategy_config, str): strategy_config = create_strategy_config(self.strategy_config, self.model_type) self._strategy, self._config = self._prepare_strategy( self.strategy_config) # If train_config is None, set default train_config if self.train_config is None: self.train_config = create_train_config(self.strategy_config, self.model_type) def _prepare_envs(self): devices = paddle.device.get_device().split(':')[0] places = paddle.device._convert_to_place(devices) exe = paddle.static.Executor(places) return exe, places def _get_model_type(self, exe, model_dir, model_filename, params_filename): [inference_program, _, _]= paddle.fluid.io.load_inference_model( \ dirname=model_dir, \ model_filename=model_filename, params_filename=params_filename, executor=exe) _, _, model_type = get_patterns(inference_program) return model_type def _prepare_strategy(self, strategy_config): if not isinstance(strategy_config, list): strategy_config = list(list(strategy_config)) strategy = [] config = [] for strategy_c in strategy_config: quant_config = strategy_c.get("Quantization", None) hpo_config = strategy_c.get("HyperParameterOptimization", None) prune_config = strategy_c.get("Prune", None) unstructure_prune_config = strategy_c.get("UnstructurePrune", None) single_teacher_distill_config = strategy_c.get("Distillation", None) if single_teacher_distill_config is not None and single_teacher_distill_config.teacher_model_dir is None: single_teacher_distill_config = single_teacher_distill_config._replace( teacher_model_dir=self.model_dir, teacher_model_filename=self.model_filename, teacher_params_filename=self.params_filename) multi_teacher_distill_config = strategy_c.get( "MultiTeacherDistillation", None) assert (single_teacher_distill_config is None) or (multi_teacher_distill_config is None), \ "Distillation and MultiTeacherDistillation cannot be set at the same time." self._distill_config = single_teacher_distill_config if \ single_teacher_distill_config is not None else \ multi_teacher_distill_config ### case1: quant_config & hpo_config ==> PTQ & HPO if quant_config is not None and hpo_config is not None: strategy.append('ptq_hpo') config.append(merge_config(quant_config, hpo_config)) ### case2: quant_config & distill config ==> QAT & Distill elif quant_config is not None and self._distill_config is not None: strategy.append('qat_dis') config.append(merge_config(quant_config, self._distill_config)) ### case3: prune_config & distill config elif prune_config is not None and self._distill_config is not None: strategy.append('prune_dis') config.append(merge_config(prune_config, self._distill_config)) ### case4: unstructure_config & distill config elif unstructure_prune_config is not None and self._distill_config is not None: strategy.append('unstructure_prune_dis') config.append( merge_config(unstructure_prune_config, self._distill_config)) ### case4: distill_config elif self._distill_config is not None: if single_teacher_distill_config is not None: strategy.append('single_teacher_dis') config.append(single_teacher_distill_config) else: strategy.append('multi_teacher_dis') config.append(multi_teacher_distill_config) ### case N: todo else: raise NotImplementedError( "Not Implemented {} be set at the same time now".format( strategy_c.keys())) return strategy, config def _prepare_fleet_strategy(train_config): build_strategy = paddle.static.BuildStrategy() exec_strategy = paddle.static.ExecutionStrategy() strategy = fleet.DistributedStrategy() strategy.build_strategy = build_strategy if train_config.recompute_config is not None: strategy.recompute = True strategy.recompute_configs = { ** train_config.recompute_config} if train_config.sharding_config is not None: strategy.sharding = True strategy.sharding_configs = { ** train_config.sharding_config} if train_config.amp_config is not None: strategy.amp = True strategy.amp_configs = { ** train_config.amp_config} return strategy def _prepare_program(self, program, feed_target_names, fetch_targets, patterns, default_distill_node_pair, strategy, config): train_program = recover_inference_program(program) startup_program = paddle.static.Program() train_program_info = ProgramInfo(startup_program, train_program, feed_target_names, fetch_targets) config_dict = dict(config._asdict()) if "prune_strategy" in config_dict and config_dict[ "prune_strategy"] == "gmp" and config_dict[ 'gmp_config'] is None: _logger.info( "Calculating the iterations per epoch……(It will take some time)") # NOTE:XXX: This way of calculating the iters needs to be improved. iters_per_epoch = len(list(self.train_dataloader())) total_iters = self.train_config.epochs * iters_per_epoch config_dict['gmp_config'] = { 'stable_iterations': 0, 'pruning_iterations': 0.45 * total_iters, 'tunning_iterations': 0.45 * total_iters, 'resume_iteration': -1, 'pruning_steps': 100, 'initial_ratio': 0.15, } ### add prune program self._pruner = None if 'prune' in strategy: self._pruner, train_program_info = build_prune_program( self._exe, self._places, config_dict, train_program_info, strategy, patterns, self.eval_dataloader) if self.train_config.use_fleet: dist_strategy = _prepare_fleet_strategy(self.train_config) else: dist_strategy = None ### add distill program if 'dis' in strategy: train_program_info, test_program_info = build_distill_program( self._exe, self._places, config_dict, self.train_config._asdict(), train_program_info, pruner=self._pruner, dist_strategy=dist_strategy, default_distill_node_pair=default_distill_node_pair) self._quant_config = None ### add quant_aware program, quant always is last step if 'qat' in strategy: train_program_info, test_program_info, self._quant_config = build_quant_program( self._exe, self._places, config_dict, train_program_info, test_program_info) if self.train_config.sparse_model: from ..prune.unstructured_pruner import UnstructuredPruner # NOTE: The initialization parameter of this pruner doesn't work, it is only used to call the 'set_static_masks' function self._pruner = UnstructuredPruner( train_program_info.program, mode='ratio', ratio=0.75, prune_params_type='conv1x1_only', place=self._places) self._pruner.set_static_masks() # Fixed model sparsity self._exe.run(train_program_info.startup_program) if (not self.train_config.use_fleet ) and self.train_config.amp_config is not None: if hasattr(self.train_config.amp_config, 'use_pure_fp16' ) and self.train_config.amp_config.use_pure_fp16: train_program_info.optimizer.amp_init( self._places, scope=paddle.static.global_scope()) if 'prune_algo' in config_dict and config_dict['prune_algo'] == 'asp': ### prune weight in scope self._pruner.prune_model(train_program_info.program) if not self.train_config.use_fleet: train_program_info = self._compiled_program(train_program_info, strategy) test_program_info = self._compiled_program(test_program_info, self._strategy) return train_program_info, test_program_info def _compiled_program(self, program_info, strategy): compiled_prog = paddle.static.CompiledProgram(program_info.program) build_strategy = paddle.static.BuildStrategy() exec_strategy = paddle.static.ExecutionStrategy() if 'qat' in strategy: build_strategy.memory_optimize = False build_strategy.enable_inplace = False build_strategy.fuse_all_reduce_ops = False build_strategy.sync_batch_norm = False compiled_prog = compiled_prog.with_data_parallel( loss_name=program_info.fetch_targets[0].name, build_strategy=build_strategy, exec_strategy=exec_strategy) program_info.program = compiled_prog return program_info def compress(self): for strategy_idx, ( strategy, config) in enumerate(zip(self._strategy, self._config)): self.single_strategy_compress(strategy, config, strategy_idx) if strategy == 'ptq_hpo' and config.max_quant_count == 1 and platform.system( ).lower() == 'linux': ptq_loss = quant_post_hpo.g_min_emd_loss final_quant_config = get_final_quant_config( ptq_loss, mode='DistilQuant') quant_strategy, quant_config = self._prepare_strategy( final_quant_config) self.single_strategy_compress(quant_strategy[0], quant_config[0], strategy_idx) tmp_model_path = os.path.join( self.save_dir, 'strategy_{}'.format(str(strategy_idx + 1))) final_model_path = os.path.join(self.final_dir) if not os.path.exists(final_model_path): os.makedirs(final_model_path) tmp_model_file = os.path.join(tmp_model_path, 'model.pdmodel') tmp_params_file = os.path.join(tmp_model_path, 'model.pdiparams') final_model_file = os.path.join(final_model_path, 'model.pdmodel') final_params_file = os.path.join(final_model_path, 'model.pdiparams') shutil.move(tmp_model_file, final_model_file) shutil.move(tmp_params_file, final_params_file) _logger.info( "==> Finished the ACT process and the final model is saved in:{}". format(final_model_path)) os._exit(0) def single_strategy_compress(self, strategy, config, strategy_idx): # start compress, including train/eval model # TODO: add the emd loss of evaluation model. if strategy == 'quant_post': quant_post( self._exe, model_dir=self.model_dir, quantize_model_path=os.path.join( self.save_dir, 'strategy_{}'.format(str(strategy_idx + 1))), data_loader=self.train_dataloader, model_filename=self.model_filename, params_filename=self.params_filename, save_model_filename=self.model_filename, save_params_filename=self.params_filename, batch_size=1, batch_nums=config.batch_num, algo=config.ptq_algo, round_type='round', bias_correct=config.bias_correct, hist_percent=config.hist_percent, quantizable_op_type=config.quantize_op_types, is_full_quantize=config.is_full_quantize, weight_bits=config.weight_bits, activation_bits=config.activation_bits, activation_quantize_type='range_abs_max', weight_quantize_type=config.weight_quantize_type, onnx_format=False) elif strategy == 'ptq_hpo': if platform.system().lower() != 'linux': raise NotImplementedError( "post-quant-hpo is not support in system other than linux") quant_post_hpo.quant_post_hpo( self._exe, self._places, model_dir=self.model_dir, quantize_model_path=os.path.join( self.save_dir, 'strategy_{}'.format(str(strategy_idx + 1))), train_dataloader=self.train_dataloader, eval_dataloader=self.eval_dataloader, eval_function=self.eval_function, model_filename=self.model_filename, params_filename=self.params_filename, save_model_filename=self.model_filename, save_params_filename=self.params_filename, quantizable_op_type=config.quantize_op_types, weight_bits=config.weight_bits, activation_bits=config.activation_bits, weight_quantize_type=config.weight_quantize_type, is_full_quantize=config.is_full_quantize, algo=config.ptq_algo, bias_correct=config.bias_correct, hist_percent=config.hist_percent, batch_size=[1], batch_num=config.batch_num, runcount_limit=config.max_quant_count) else: assert 'dis' in strategy, "Only support optimizer compressed model by distillation loss." if strategy_idx == 0: model_dir = self.model_dir else: model_dir = os.path.join( self.save_dir, 'strategy_{}'.format(str(strategy_idx))) [inference_program, feed_target_names, fetch_targets]= paddle.fluid.io.load_inference_model( \ dirname=model_dir, \ model_filename=self.model_filename, params_filename=self.params_filename, executor=self._exe) ### used to check whether the dataloader is right self.metric_before_compressed = None if self.eval_function is not None and self.train_config.origin_metric is not None: _logger.info("start to test metric before compress") metric = self.eval_function(self._exe, inference_program, feed_target_names, fetch_targets) _logger.info("metric of compressed model is: {}".format(metric)) buf = 0.05 if metric < (float(self.train_config.origin_metric) - buf) or \ metric > (float(self.train_config.origin_metric) + buf): raise RuntimeError("target metric of pretrained model is {}, \ but now is {}, Please check the format of evaluation dataset \ or check the origin_metric in train_config" .format(\ self.train_config.origin_metric, metric)) self.metric_before_compressed = metric patterns, default_distill_node_pair, _ = get_patterns( inference_program) train_program_info, test_program_info = self._prepare_program( inference_program, feed_target_names, fetch_targets, patterns, default_distill_node_pair, strategy, config) if 'unstructure' in self._strategy: test_program_info.program._program = remove_unused_var_nodes( test_program_info.program._program) test_program_info = self._start_train(train_program_info, test_program_info, strategy) self._save_model(test_program_info, strategy, strategy_idx) def _start_train(self, train_program_info, test_program_info, strategy): best_metric = -1.0 for epoch_id in range(self.train_config.epochs): for batch_id, data in enumerate(self.train_dataloader()): np_probs_float, = self._exe.run(train_program_info.program, \ feed=data, \ fetch_list=train_program_info.fetch_targets) if 'unstructure' in strategy: self._pruner.step() if self.train_config.logging_iter is None: logging_iter = 10 else: logging_iter = self.train_config.logging_iter if batch_id % int(logging_iter) == 0: _logger.info("epoch: {}, batch: {}, loss: {}".format( epoch_id, batch_id, np_probs_float)) if batch_id % int( self.train_config.eval_iter) == 0 and batch_id != 0: if self.eval_function is not None: # GMP pruner step 3: update params before summrizing sparsity, saving model or evaluation. if 'unstructure' in strategy: self._pruner.update_params() metric = self.eval_function( self._exe, test_program_info.program, test_program_info.feed_target_names, test_program_info.fetch_targets) _logger.info( "epoch: {}, batch: {} metric of compressed model is: {}, best metric of compressed model is {}". format(epoch_id, batch_id, metric, best_metric)) if metric > best_metric: paddle.static.save( program=test_program_info.program._program, model_path=os.path.join(self.save_dir, 'best_model')) best_metric = metric if self.metric_before_compressed is not None and float( abs(best_metric - self.metric_before_compressed) ) / self.metric_before_compressed <= 0.005: break if self.train_config.target_metric is not None: if metric > float(self.train_config.target_metric): break else: _logger.warning( "Not set eval function, so unable to test accuracy performance." ) if 'unstructure' in self._strategy or self.train_config.sparse_model: self._pruner.update_params() return test_program_info def _save_model(self, test_program_info, strategy, strategy_idx): test_program = test_program_info.program._program if isinstance( test_program_info.program, paddle.static.CompiledProgram) else test_program_info.program if os.path.exists(os.path.join(self.save_dir, 'best_model.pdparams')): paddle.static.load(test_program, os.path.join(self.save_dir, 'best_model')) os.remove(os.path.join(self.save_dir, 'best_model.pdmodel')) os.remove(os.path.join(self.save_dir, 'best_model.pdopt')) os.remove(os.path.join(self.save_dir, 'best_model.pdparams')) if 'qat' in strategy: float_program, int8_program = convert(test_program_info.program._program, self._places, self._quant_config, \ scope=paddle.static.global_scope(), \ save_int8=True) test_program_info.program = float_program model_dir = os.path.join(self.save_dir, 'strategy_{}'.format(str(strategy_idx + 1))) if not os.path.exists(model_dir): os.makedirs(model_dir) paddle.fluid.io.save_inference_model( dirname=str(model_dir), feeded_var_names=test_program_info.feed_target_names, target_vars=test_program_info.fetch_targets, executor=self._exe, main_program=test_program, model_filename='model.pdmodel', params_filename='model.pdiparams')