import os import sys import logging import paddle import argparse import functools import math import time import numpy as np import paddle.fluid as fluid sys.path.append(os.path.dirname("__file__")) sys.path.append( os.path.join(os.path.dirname("__file__"), os.path.pardir, os.path.pardir)) from paddleslim.common import get_logger, get_distribution, pdf from paddleslim.analysis import flops from paddleslim.quant import quant_aware, quant_post, convert from paddleslim.quant import pact_thres import models from utility import add_arguments, print_arguments from paddle.fluid.layer_helper import LayerHelper quantization_model_save_dir = './quantization_models/' _logger = get_logger(__name__, level=logging.INFO) parser = argparse.ArgumentParser(description=__doc__) add_arg = functools.partial(add_arguments, argparser=parser) # yapf: disable add_arg('batch_size', int, 64 * 4, "Minibatch size.") add_arg('use_gpu', bool, True, "Whether to use GPU or not.") add_arg('model', str, "MobileNet", "The target model.") add_arg('pretrained_model', str, "../pretrained_model/MobileNetV1_pretrained", "Whether to use pretrained model.") add_arg('lr', float, 0.0001, "The learning rate used to fine-tune pruned model.") add_arg('lr_strategy', str, "piecewise_decay", "The learning rate decay strategy.") add_arg('l2_decay', float, 3e-5, "The l2_decay parameter.") add_arg('momentum_rate', float, 0.9, "The value of momentum_rate.") add_arg('num_epochs', int, 1, "The number of total epochs.") add_arg('total_images', int, 1281167, "The number of total training images.") parser.add_argument('--step_epochs', nargs='+', type=int, default=[30, 60, 90], help="piecewise decay step") add_arg('config_file', str, None, "The config file for compression with yaml format.") add_arg('data', str, "imagenet", "Which data to use. 'mnist' or 'imagenet'") add_arg('log_period', int, 10, "Log period in batches.") add_arg('checkpoint_dir', str, "output", "checkpoint save dir") add_arg('use_pact', bool, True, "Whether to use PACT or not.") # yapf: enable model_list = [m for m in dir(models) if "__" not in m] def piecewise_decay(args): step = int(math.ceil(float(args.total_images) / args.batch_size)) bd = [step * e for e in args.step_epochs] lr = [args.lr * (0.1**i) for i in range(len(bd) + 1)] learning_rate = fluid.layers.piecewise_decay(boundaries=bd, values=lr) optimizer = fluid.optimizer.Momentum( learning_rate=learning_rate, momentum=args.momentum_rate, regularization=fluid.regularizer.L2Decay(args.l2_decay)) return optimizer def cosine_decay(args): step = int(math.ceil(float(args.total_images) / args.batch_size)) learning_rate = fluid.layers.cosine_decay( learning_rate=args.lr, step_each_epoch=step, epochs=args.num_epochs) optimizer = fluid.optimizer.Momentum( learning_rate=learning_rate, momentum=args.momentum_rate, regularization=fluid.regularizer.L2Decay(args.l2_decay)) return optimizer def create_optimizer(args): if args.lr_strategy == "piecewise_decay": return piecewise_decay(args) elif args.lr_strategy == "cosine_decay": return cosine_decay(args) def compress(args): # 1. quantization configs quant_config = { # 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, } train_reader = None test_reader = None if args.data == "mnist": import paddle.dataset.mnist as reader train_reader = reader.train() val_reader = reader.test() class_dim = 10 image_shape = "1,28,28" elif args.data == "imagenet": import imagenet_reader as reader train_reader = reader.train() val_reader = reader.val() class_dim = 1000 image_shape = "3,224,224" else: raise ValueError("{} is not supported.".format(args.data)) image_shape = [int(m) for m in image_shape.split(",")] assert args.model in model_list, "{} is not in lists: {}".format(args.model, model_list) image = fluid.layers.data(name='image', shape=image_shape, dtype='float32') if args.use_pact: image.stop_gradient = False label = fluid.layers.data(name='label', shape=[1], dtype='int64') # model definition model = models.__dict__[args.model]() out = model.net(input=image, class_dim=class_dim) cost = fluid.layers.cross_entropy(input=out, label=label) avg_cost = fluid.layers.mean(x=cost) acc_top1 = fluid.layers.accuracy(input=out, label=label, k=1) acc_top5 = fluid.layers.accuracy(input=out, label=label, k=5) train_prog = fluid.default_main_program() val_program = fluid.default_main_program().clone(for_test=True) place = fluid.CUDAPlace(0) if args.use_gpu else fluid.CPUPlace() opt = create_optimizer(args) opt.minimize(avg_cost) exe = fluid.Executor(place) exe.run(fluid.default_startup_program()) val_reader = paddle.fluid.io.batch(val_reader, batch_size=args.batch_size) train_reader = paddle.fluid.io.batch( train_reader, batch_size=args.batch_size, drop_last=True) train_loader = fluid.io.DataLoader.from_generator( feed_list=[image, label], capacity=512, use_double_buffer=True, iterable=True) valid_loader = fluid.io.DataLoader.from_generator( feed_list=[image, label], capacity=512, use_double_buffer=True, iterable=True) places = fluid.cuda_places() if args.use_gpu else fluid.cpu_places() train_loader.set_sample_list_generator(train_reader, place) valid_loader.set_sample_list_generator(val_reader, place) # get all activations distribution act_names = [ var.name for var in list(train_prog.list_vars()) if not var.persistable and 'generated_var' not in var.name and '@GRAD' not in var.name ] var_dist = get_distribution(train_prog, act_names, exe, train_loader) train_loader.set_sample_list_generator(train_reader, places) # draw histogram pdf(var_dist, pdf_save_dir='var_dist_pdf') # calculate appropriate pact clip threshold pact_alphas = pact_thres(var_dist) # 2. quantization transform programs (training aware) # Make some quantization transforms in the graph before training and testing. # According to the weight and activation quantization type, the graph will be added # some fake quantize operators and fake dequantize operators. def pact(x): helper = LayerHelper("pact", **locals()) dtype = 'float32' init_thres = pact_alphas[x.name.split('_tmp_input')[0]] u_param_attr = fluid.ParamAttr( name=x.name + '_pact', initializer=fluid.initializer.ConstantInitializer(value=init_thres), regularizer=fluid.regularizer.L2Decay(0.0001), learning_rate=1) u_param = helper.create_parameter( attr=u_param_attr, shape=[1], dtype=dtype) x = fluid.layers.elementwise_sub( x, fluid.layers.relu(fluid.layers.elementwise_sub(x, u_param))) x = fluid.layers.elementwise_add( x, fluid.layers.relu(fluid.layers.elementwise_sub(-u_param, x))) return x def get_optimizer(): return fluid.optimizer.MomentumOptimizer(0.0001, 0.9) if args.use_pact: act_preprocess_func = pact optimizer_func = get_optimizer executor = exe else: act_preprocess_func = None optimizer_func = None executor = None val_program = quant_aware( val_program, place, quant_config, scope=None, act_preprocess_func=act_preprocess_func, optimizer_func=optimizer_func, executor=executor, for_test=True) compiled_train_prog = quant_aware( train_prog, place, quant_config, scope=None, act_preprocess_func=act_preprocess_func, optimizer_func=optimizer_func, executor=executor, for_test=False) assert os.path.exists( args.pretrained_model), "pretrained_model doesn't exist" if args.pretrained_model: def if_exist(var): return os.path.exists(os.path.join(args.pretrained_model, var.name)) fluid.io.load_vars(exe, args.pretrained_model, predicate=if_exist) def test(epoch, program): batch_id = 0 acc_top1_ns = [] acc_top5_ns = [] for data in valid_loader(): start_time = time.time() acc_top1_n, acc_top5_n = exe.run( program, feed=data, fetch_list=[acc_top1.name, acc_top5.name]) end_time = time.time() if batch_id % args.log_period == 0: _logger.info( "Eval epoch[{}] batch[{}] - acc_top1: {}; acc_top5: {}; time: {}". format(epoch, batch_id, np.mean(acc_top1_n), np.mean(acc_top5_n), end_time - start_time)) acc_top1_ns.append(np.mean(acc_top1_n)) acc_top5_ns.append(np.mean(acc_top5_n)) batch_id += 1 _logger.info("Final eval epoch[{}] - acc_top1: {}; acc_top5: {}".format( epoch, np.mean(np.array(acc_top1_ns)), np.mean(np.array(acc_top5_ns)))) return np.mean(np.array(acc_top1_ns)) def train(epoch, compiled_train_prog): batch_id = 0 for data in train_loader(): start_time = time.time() loss_n, acc_top1_n, acc_top5_n = exe.run( compiled_train_prog, feed=data, fetch_list=[avg_cost.name, acc_top1.name, acc_top5.name]) end_time = time.time() loss_n = np.mean(loss_n) acc_top1_n = np.mean(acc_top1_n) acc_top5_n = np.mean(acc_top5_n) if batch_id % args.log_period == 0: _logger.info( "epoch[{}]-batch[{}] - loss: {}; acc_top1: {}; acc_top5: {}; time: {}". format(epoch, batch_id, loss_n, acc_top1_n, acc_top5_n, end_time - start_time)) if args.use_pact and batch_id % 1000 == 0: threshold = {} for var in val_program.list_vars(): if 'pact' in var.name: array = np.array(fluid.global_scope().find_var(var.name) .get_tensor()) threshold[var.name] = array[0] _logger.info(threshold) batch_id += 1 build_strategy = fluid.BuildStrategy() build_strategy.memory_optimize = False build_strategy.enable_inplace = False build_strategy.fuse_all_reduce_ops = False build_strategy.sync_batch_norm = False exec_strategy = fluid.ExecutionStrategy() compiled_train_prog = compiled_train_prog.with_data_parallel( loss_name=avg_cost.name, build_strategy=build_strategy, exec_strategy=exec_strategy) # train loop best_acc1 = 0.0 best_epoch = 0 for i in range(args.num_epochs): train(i, compiled_train_prog) acc1 = test(i, val_program) fluid.io.save_persistables( exe, dirname=os.path.join(args.checkpoint_dir, str(i)), main_program=val_program) if acc1 > best_acc1: best_acc1 = acc1 best_epoch = i fluid.io.save_persistables( exe, dirname=os.path.join(args.checkpoint_dir, 'best_model'), main_program=val_program) if os.path.exists(os.path.join(args.checkpoint_dir, 'best_model')): fluid.io.load_persistables( exe, dirname=os.path.join(args.checkpoint_dir, 'best_model'), main_program=val_program) # 3. Freeze the graph after training by adjusting the quantize # operators' order for the inference. # The dtype of float_program's weights is float32, but in int8 range. float_program, int8_program = convert(val_program, place, quant_config, \ scope=None, \ save_int8=True) print("eval best_model after convert") final_acc1 = test(best_epoch, float_program) _logger.info("final acc:{}".format(final_acc1)) # 4. Save inference model model_path = os.path.join(quantization_model_save_dir, args.model, 'act_' + quant_config['activation_quantize_type'] + '_w_' + quant_config['weight_quantize_type']) float_path = os.path.join(model_path, 'float') int8_path = os.path.join(model_path, 'int8') if not os.path.isdir(model_path): os.makedirs(model_path) fluid.io.save_inference_model( dirname=float_path, feeded_var_names=[image.name], target_vars=[out], executor=exe, main_program=float_program, model_filename=float_path + '/model', params_filename=float_path + '/params') fluid.io.save_inference_model( dirname=int8_path, feeded_var_names=[image.name], target_vars=[out], executor=exe, main_program=int8_program, model_filename=int8_path + '/model', params_filename=int8_path + '/params') def main(): args = parser.parse_args() print_arguments(args) compress(args) if __name__ == '__main__': main()