# 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. """Finetuning on classification tasks.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import os import time import multiprocessing import paddle.fluid as fluid import reader.task_reader as task_reader from model.ernie import ErnieConfig from finetune.sequence_label import create_model, evaluate from optimization import optimization from utils.args import print_arguments from utils.init import init_pretraining_params, init_checkpoint from finetune_args import parser args = parser.parse_args() def main(args): ernie_config = ErnieConfig(args.ernie_config_path) ernie_config.print_config() gpu_id = 0 gpus = fluid.core.get_cuda_device_count() if args.is_distributed: gpus = os.getenv("FLAGS_selected_gpus").split(",") gpu_id = int(gpus[0]) if args.use_cuda: place = fluid.CUDAPlace(gpu_id) #dev_count = int(os.getenv("PADDLE_TRAINERS_NUM")) if args.is_distributed else gpus dev_count = len(gpus) if args.is_distributed else gpus else: place = fluid.CPUPlace() dev_count = int(os.environ.get('CPU_NUM', multiprocessing.cpu_count())) reader = task_reader.SequenceLabelReader( vocab_path=args.vocab_path, label_map_config=args.label_map_config, max_seq_len=args.max_seq_len, do_lower_case=args.do_lower_case, in_tokens=args.in_tokens, tokenizer=args.tokenizer) if not (args.do_train or args.do_val or args.do_test): raise ValueError("For args `do_train`, `do_val` and `do_test`, at " "least one of them must be True.") startup_prog = fluid.Program() if args.random_seed is not None: startup_prog.random_seed = args.random_seed if args.do_train: trainers_num = int(os.getenv("PADDLE_TRAINERS_NUM")) train_data_generator = reader.data_generator( input_file=args.train_set, batch_size=args.batch_size, epoch=args.epoch, dev_count=trainers_num, shuffle=True, phase="train") num_train_examples = reader.get_num_examples(args.train_set) if args.in_tokens: max_train_steps = args.epoch * num_train_examples // ( args.batch_size // args.max_seq_len) // trainers_num else: max_train_steps = args.epoch * num_train_examples // args.batch_size // trainers_num warmup_steps = int(max_train_steps * args.warmup_proportion) print("Device count: %d, gpu_id: %d" % (trainers_num, gpu_id)) print("Num train examples: %d" % num_train_examples) print("Max train steps: %d" % max_train_steps) print("Num warmup steps: %d" % warmup_steps) train_program = fluid.Program() with fluid.program_guard(train_program, startup_prog): with fluid.unique_name.guard(): train_pyreader, graph_vars = create_model( args, pyreader_name='train_reader', ernie_config=ernie_config) scheduled_lr = optimization( loss=graph_vars["loss"], warmup_steps=warmup_steps, num_train_steps=max_train_steps, learning_rate=args.learning_rate, train_program=train_program, startup_prog=startup_prog, weight_decay=args.weight_decay, scheduler=args.lr_scheduler, use_fp16=args.use_fp16, use_dynamic_loss_scaling=args.use_dynamic_loss_scaling, init_loss_scaling=args.init_loss_scaling, incr_every_n_steps=args.incr_every_n_steps, decr_every_n_nan_or_inf=args.decr_every_n_nan_or_inf, incr_ratio=args.incr_ratio, decr_ratio=args.decr_ratio) """ fluid.memory_optimize( input_program=train_program, skip_opt_set=[ graph_vars["loss"].name, graph_vars["labels"].name, graph_vars["infers"].name, graph_vars["seq_lens"].name ]) """ if args.verbose: if args.in_tokens: lower_mem, upper_mem, unit = fluid.contrib.memory_usage( program=train_program, batch_size=args.batch_size // args.max_seq_len) else: lower_mem, upper_mem, unit = fluid.contrib.memory_usage( program=train_program, batch_size=args.batch_size) print("Theoretical memory usage in training: %.3f - %.3f %s" % (lower_mem, upper_mem, unit)) if args.do_val or args.do_test: test_prog = fluid.Program() with fluid.program_guard(test_prog, startup_prog): with fluid.unique_name.guard(): test_pyreader, graph_vars = create_model( args, pyreader_name='test_reader', ernie_config=ernie_config) test_prog = test_prog.clone(for_test=True) nccl2_num_trainers = 1 nccl2_trainer_id = 0 print("args.is_distributed:", args.is_distributed) if args.is_distributed: trainer_id = int(os.getenv("PADDLE_TRAINER_ID", "0")) worker_endpoints_env = os.getenv("PADDLE_TRAINER_ENDPOINTS") current_endpoint = os.getenv("PADDLE_CURRENT_ENDPOINT") worker_endpoints = worker_endpoints_env.split(",") trainers_num = len(worker_endpoints) print("worker_endpoints:{} trainers_num:{} current_endpoint:{} \ trainer_id:{}".format(worker_endpoints, trainers_num, current_endpoint, trainer_id)) # prepare nccl2 env. config = fluid.DistributeTranspilerConfig() config.mode = "nccl2" t = fluid.DistributeTranspiler(config=config) t.transpile( trainer_id, trainers=worker_endpoints_env, current_endpoint=current_endpoint, program=train_program, startup_program=startup_prog) nccl2_num_trainers = trainers_num nccl2_trainer_id = trainer_id exe = fluid.Executor(place) exe.run(startup_prog) if args.do_train: if args.init_checkpoint and args.init_pretraining_params: print( "WARNING: args 'init_checkpoint' and 'init_pretraining_params' " "both are set! Only arg 'init_checkpoint' is made valid.") if args.init_checkpoint: init_checkpoint( exe, args.init_checkpoint, main_program=startup_prog, use_fp16=args.use_fp16) elif args.init_pretraining_params: init_pretraining_params( exe, args.init_pretraining_params, main_program=startup_prog, use_fp16=args.use_fp16) elif args.do_val or args.do_test: if not args.init_checkpoint: raise ValueError("args 'init_checkpoint' should be set if" "only doing validation or testing!") init_checkpoint( exe, args.init_checkpoint, main_program=startup_prog, use_fp16=args.use_fp16) if args.do_train: exec_strategy = fluid.ExecutionStrategy() if args.use_fast_executor: exec_strategy.use_experimental_executor = True exec_strategy.num_threads = dev_count exec_strategy.num_iteration_per_drop_scope = args.num_iteration_per_drop_scope train_exe = fluid.ParallelExecutor( use_cuda=args.use_cuda, loss_name=graph_vars["loss"].name, exec_strategy=exec_strategy, main_program=train_program, num_trainers=nccl2_num_trainers, trainer_id=nccl2_trainer_id) train_pyreader.decorate_tensor_provider(train_data_generator) else: train_exe = None test_exe = exe test_dev_count = 1 if args.do_val or args.do_test: if args.use_multi_gpu_test: test_dev_count = min(trainers_num, 8) if args.do_train: train_pyreader.start() steps = 0 #if warmup_steps > 0: # graph_vars["learning_rate"] = scheduled_lr time_begin = time.time() skip_steps = args.skip_steps * nccl2_num_trainers while True: try: steps += nccl2_num_trainers if steps % skip_steps == 0: outputs = evaluate(train_exe, train_program, train_pyreader, graph_vars, args.num_labels, "train", dev_count) if args.verbose: verbose = "train pyreader queue size: %d, " % train_pyreader.queue.size( ) #verbose += "learning rate: %f" % ( # outputs["learning_rate"] # if warmup_steps > 0 else args.learning_rate) print(verbose) current_example, current_epoch = reader.get_train_progress() time_end = time.time() used_time = time_end - time_begin print("epoch: %d, progress: %d/%d, step: %d, loss: %f, " "f1: %f, precision: %f, recall: %f, speed: %f steps/s" % (current_epoch, current_example, num_train_examples, steps, outputs["loss"], outputs["f1"], outputs["precision"], outputs["recall"], args.skip_steps / used_time)) time_begin = time.time() else: train_exe.run(fetch_list=[]) if nccl2_trainer_id == 0: if steps % args.save_steps == 0: save_path = os.path.join(args.checkpoints, "step_" + str(steps)) fluid.io.save_persistables(exe, save_path, train_program) if steps % args.validation_steps == 0: # evaluate dev set if args.do_val: test_pyreader.decorate_tensor_provider( reader.data_generator( args.dev_set, batch_size=args.batch_size, epoch=1, dev_count=1, shuffle=False)) evaluate(exe, test_prog, test_pyreader, graph_vars, args.num_labels, "dev") # evaluate test set if args.do_test: test_pyreader.decorate_tensor_provider( reader.data_generator( args.test_set, batch_size=args.batch_size, epoch=1, dev_count=1, shuffle=False)) evaluate(exe, test_prog, test_pyreader, graph_vars, args.num_labels, "test") except fluid.core.EOFException: save_path = os.path.join(args.checkpoints, "step_" + str(steps)) fluid.io.save_persistables(exe, save_path, train_program) train_pyreader.reset() break # final eval on dev set if args.do_val: test_pyreader.decorate_tensor_provider( reader.data_generator( args.dev_set, batch_size=args.batch_size, epoch=1, dev_count=1, shuffle=False)) print("Final validation result:") evaluate(exe, test_prog, test_pyreader, graph_vars, args.num_labels, "dev") # final eval on test set if args.do_test: test_pyreader.decorate_tensor_provider( reader.data_generator( args.test_set, batch_size=args.batch_size, epoch=1, dev_count=1, shuffle=False)) print("Final test result:") evaluate(exe, test_prog, test_pyreader, graph_vars, args.num_labels, "test") if __name__ == '__main__': print_arguments(args) main(args)