# Copyright (c) 2021 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 numpy as np from reader.classification_reader import ClassifyReader from model.unimo_finetune import UNIMOConfig from model.tokenization import GptBpeTokenizer from finetune.classifier import create_model, evaluate, predict from utils.optimization import optimization from utils.utils import get_time from utils.args import print_arguments from utils.init import init_pretraining_params, init_checkpoint from args.classification_args import parser args = parser.parse_args() def main(args): """main""" model_config = UNIMOConfig(args.unimo_config_path) model_config.print_config() gpu_id = 0 gpus = fluid.core.get_cuda_device_count() if args.is_distributed and os.getenv("FLAGS_selected_gpus") is not None: gpu_list = os.getenv("FLAGS_selected_gpus").split(",") gpus = len(gpu_list) gpu_id = int(gpu_list[0]) if args.use_cuda: place = fluid.CUDAPlace(gpu_id) dev_count = gpus else: place = fluid.CPUPlace() dev_count = int(os.environ.get('CPU_NUM', multiprocessing.cpu_count())) tokenizer = GptBpeTokenizer(vocab_file=args.unimo_vocab_file, encoder_json_file=args.encoder_json_file, vocab_bpe_file=args.vocab_bpe_file, do_lower_case=args.do_lower_case) data_reader = ClassifyReader(tokenizer, args) if not (args.do_train or args.do_val or args.do_val_hard \ or args.do_test or args.do_test_hard or args.do_diagnostic): raise ValueError("For args `do_train`, `do_val`, `do_val_hard`, `do_test`," \ " `do_test_hard` and `do_diagnostic`, 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", "1")) train_data_generator = data_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 = data_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" % (dev_count, 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', config=model_config) scheduled_lr, loss_scaling = optimization( loss=graph_vars["loss"], warmup_steps=warmup_steps, num_train_steps=max_train_steps, learning_rate=args.learning_rate, train_program=train_program, 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, beta1=args.beta1, beta2=args.beta2, epsilon=args.epsilon) 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_val_hard or args.do_test or args.do_test_hard \ or args.do_pred or args.do_pred_hard or args.do_diagnostic: 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', config=model_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" if args.nccl_comm_num > 1: config.nccl_comm_num = args.nccl_comm_num if args.use_hierarchical_allreduce and trainers_num > args.hierarchical_allreduce_inter_nranks: config.use_hierarchical_allreduce = args.use_hierarchical_allreduce config.hierarchical_allreduce_inter_nranks = args.hierarchical_allreduce_inter_nranks assert config.hierarchical_allreduce_inter_nranks > 1 assert trainers_num % config.hierarchical_allreduce_inter_nranks == 0 config.hierarchical_allreduce_exter_nranks = \ trainers_num / config.hierarchical_allreduce_inter_nranks t = fluid.DistributeTranspiler(config=config) t.transpile( trainer_id, trainers=worker_endpoints_env, current_endpoint=current_endpoint, program=train_program if args.do_train else test_prog, 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=train_program) elif args.init_pretraining_params: init_pretraining_params( exe, args.init_pretraining_params, main_program=train_program) elif args.do_val or args.do_val_hard or args.do_test or args.do_test_hard \ or args.do_pred or args.do_pred_hard or args.do_diagnostic: 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) 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 if args.do_val or args.do_val_hard or args.do_test or args.do_test_hard \ or args.do_pred or args.do_pred_hard or args.do_diagnostic: if args.use_multi_gpu_test: test_exe = fluid.ParallelExecutor( use_cuda=args.use_cuda, main_program=test_prog, share_vars_from=train_exe) dev_ret_history = [] # (steps, key_eval, eval) dev_hard_ret_history = [] # (steps, key_eval, eval) test_ret_history = [] # (steps, key_eval, eval) test_hard_ret_history = [] # (steps, key_eval, eval) 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 while True: try: steps += 1 if steps % skip_steps == 0: train_fetch_list = [ graph_vars["loss"].name, graph_vars["accuracy"].name, graph_vars["num_seqs"].name ] if "learning_rate" in graph_vars: train_fetch_list.append(graph_vars["learning_rate"].name) res = train_exe.run(fetch_list=train_fetch_list) outputs = {"loss": np.mean(res[0])} if "learning_rate" in graph_vars: outputs["learning_rate"] = float(res[3][0]) 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 = data_reader.get_train_progress() time_end = time.time() used_time = time_end - time_begin print("%s - epoch: %d, progress: %d/%d, step: %d, ave loss: %f, speed: %f steps/s" % \ (get_time(), current_epoch, current_example, num_train_examples, steps, \ outputs["loss"], 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 and args.save_checkpoints: 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( data_reader.data_generator( args.dev_set, batch_size=args.batch_size, epoch=1, dev_count=1, shuffle=False)) outputs = evaluate(args, test_exe, test_prog, test_pyreader, graph_vars, "dev") dev_ret_history.append((steps, outputs['key_eval'], outputs[outputs['key_eval']])) # evaluate dev_hard set if args.do_val_hard: test_pyreader.decorate_tensor_provider( data_reader.data_generator( args.dev_hard_set, batch_size=args.batch_size, epoch=1, dev_count=1, shuffle=False)) outputs = evaluate(args, test_exe, test_prog, test_pyreader, graph_vars, "dev_hard") dev_hard_ret_history.append((steps, outputs['key_eval'], outputs[outputs['key_eval']])) # evaluate test set if args.do_test: test_pyreader.decorate_tensor_provider( data_reader.data_generator( args.test_set, batch_size=args.batch_size, epoch=1, dev_count=1, shuffle=False)) outputs = evaluate(args, test_exe, test_prog, test_pyreader, graph_vars, "test") test_ret_history.append((steps, outputs['key_eval'], outputs[outputs['key_eval']])) # evaluate test_hard set if args.do_test_hard: test_pyreader.decorate_tensor_provider( data_reader.data_generator( args.test_hard_set, batch_size=args.batch_size, epoch=1, dev_count=1, shuffle=False)) outputs = evaluate(args, test_exe, test_prog, test_pyreader, graph_vars, "test_hard") test_hard_ret_history.append((steps, outputs['key_eval'], outputs[outputs['key_eval']])) # pred diagnostic set if args.do_diagnostic: test_pyreader.decorate_tensor_provider( data_reader.data_generator( args.diagnostic_set, batch_size=args.batch_size, epoch=1, dev_count=1, shuffle=False)) qids, preds, probs = predict(test_exe, test_prog, test_pyreader, graph_vars, dev_count=1) save_path = args.pred_save + '.diagnostic.' + str(steps) + '.txt' print("testing {}, save to {}".format(args.diagnostic_set, save_path)) with open(save_path, 'w') as f: for id, s, p in zip(qids, preds, probs): f.write('{}\t{}\t{}\n'.format(id, s, p)) # pred test set if args.do_pred: test_pyreader.decorate_tensor_provider( data_reader.data_generator( args.test_set, batch_size=args.batch_size, epoch=1, dev_count=1, shuffle=False)) qids, preds, probs = predict(test_exe, test_prog, test_pyreader, graph_vars, dev_count=1) save_path = args.pred_save + '.test.' + str(steps) + '.txt' print("testing {}, save to {}".format(args.test_set, save_path)) with open(save_path, 'w') as f: for id, s, p in zip(qids, preds, probs): f.write('{}\t{}\t{}\n'.format(id, s, p)) # pred test hard set if args.do_pred_hard: test_pyreader.decorate_tensor_provider( data_reader.data_generator( args.test_hard_set, batch_size=args.batch_size, epoch=1, dev_count=1, shuffle=False)) qids, preds, probs = predict(test_exe, test_prog, test_pyreader, graph_vars, dev_count=1) save_path = args.pred_save + '.test_hard.' + str(steps) + '.txt' print("testing {}, save to {}".format(args.test_hard_set, save_path)) with open(save_path, 'w') as f: for id, s, p in zip(qids, preds, probs): f.write('{}\t{}\t{}\n'.format(id, s, p)) except fluid.core.EOFException: if args.save_checkpoints: save_path = os.path.join(args.checkpoints, "step_" + str(steps)) fluid.io.save_persistables(exe, save_path, train_program) train_pyreader.reset() break if nccl2_trainer_id == 0: # final pred on diagnostic set if args.do_diagnostic: test_pyreader.decorate_tensor_provider( data_reader.data_generator( args.diagnostic_set, batch_size=args.batch_size, epoch=1, dev_count=1, shuffle=False)) qids, preds, probs = predict(test_exe, test_prog, test_pyreader, graph_vars, dev_count=1) save_path = args.pred_save + '.diagnostic.' + str(steps) + '.txt' print("testing {}, save to {}".format(args.diagnostic_set, save_path)) with open(save_path, 'w') as f: for id, s, p in zip(qids, preds, probs): f.write('{}\t{}\t{}\n'.format(id, s, p)) # final pred on test set if args.do_pred: test_pyreader.decorate_tensor_provider( data_reader.data_generator( args.test_set, batch_size=args.batch_size, epoch=1, dev_count=1, shuffle=False)) qids, preds, probs = predict(test_exe, test_prog, test_pyreader, graph_vars, dev_count=1) save_path = args.pred_save + '.test.' + str(steps) + '.txt' print("testing {}, save to {}".format(args.test_set, save_path)) with open(save_path, 'w') as f: for id, s, p in zip(qids, preds, probs): f.write('{}\t{}\t{}\n'.format(id, s, p)) # final pred on test_hard set if args.do_pred_hard: test_pyreader.decorate_tensor_provider( data_reader.data_generator( args.test_hard_set, batch_size=args.batch_size, epoch=1, dev_count=1, shuffle=False)) qids, preds, probs = predict(test_exe, test_prog, test_pyreader, graph_vars, dev_count=1) save_path = args.pred_save + '.test_hard.' + str(steps) + '.txt' print("testing {}, save to {}".format(args.test_hard_set, save_path)) with open(save_path, 'w') as f: for id, s, p in zip(qids, preds, probs): f.write('{}\t{}\t{}\n'.format(id, s, p)) # final eval on test set if args.do_test: test_pyreader.decorate_tensor_provider( data_reader.data_generator( args.test_set, batch_size=args.batch_size, epoch=1, dev_count=1, shuffle=False)) print("Final test result:") outputs = evaluate(args, test_exe, test_prog, test_pyreader, graph_vars, "test") test_ret_history.append((steps, outputs['key_eval'], outputs[outputs['key_eval']])) test_ret_history = sorted(test_ret_history, key=lambda a: a[2], reverse=True) print("Best testing result: step %d %s %f" % ( test_ret_history[0][0], test_ret_history[0][1], test_ret_history[0][2])) # final eval on test hard set if args.do_test_hard: test_pyreader.decorate_tensor_provider( data_reader.data_generator( args.test_hard_set, batch_size=args.batch_size, epoch=1, dev_count=1, shuffle=False)) print("Final test_hard result:") outputs = evaluate(args, test_exe, test_prog, test_pyreader, graph_vars, "test_hard") test_hard_ret_history.append((steps, outputs['key_eval'], outputs[outputs['key_eval']])) test_hard_ret_history = sorted(test_hard_ret_history, key=lambda a: a[2], reverse=True) print("Best testing hard result: step %d %s %f" % ( test_hard_ret_history[0][0], test_hard_ret_history[0][1], test_hard_ret_history[0][2])) # final eval on dev set if args.do_val: test_pyreader.decorate_tensor_provider( data_reader.data_generator( args.dev_set, batch_size=args.batch_size, epoch=1, dev_count=1, shuffle=False)) print("Final validation result:") outputs = evaluate(args, test_exe, test_prog, test_pyreader, graph_vars, "dev") dev_ret_history.append((steps, outputs['key_eval'], outputs[outputs['key_eval']])) dev_ret_history = sorted(dev_ret_history, key=lambda a: a[2], reverse=True) print("Best validation result: step %d %s %f" % ( dev_ret_history[0][0], dev_ret_history[0][1], dev_ret_history[0][2])) # final eval on dev hard set if args.do_val_hard: test_pyreader.decorate_tensor_provider( data_reader.data_generator( args.dev_hard_set, batch_size=args.batch_size, epoch=1, dev_count=1, shuffle=False)) print("Final validation_hard result:") outputs = evaluate(args, test_exe, test_prog, test_pyreader, graph_vars, "dev_hard") dev_hard_ret_history.append((steps, outputs['key_eval'], outputs[outputs['key_eval']])) dev_hard_ret_history = sorted(dev_hard_ret_history, key=lambda a: a[2], reverse=True) print("Best validation_hard result: step %d %s %f" % ( dev_hard_ret_history[0][0], dev_hard_ret_history[0][1], dev_hard_ret_history[0][2])) if __name__ == '__main__': print_arguments(args) main(args)