def re_use_some(tri_dict, suf): ernie_config = tri_dict["ernie_config"] startup_prog = tri_dict["startup_prog"] args = tri_dict["args"] labels_map = tri_dict["labels_map"] place = tri_dict["place"] reader = task_reader.RoleSequenceLabelReader( vocab_path=args.vocab_path, labels_map=labels_map, max_seq_len=args.max_seq_len, do_lower_case=args.do_lower_case, in_tokens=args.in_tokens, random_seed=args.random_seed, task_id=args.task_id) startup_prog = fluid.Program() if args.random_seed is not None: startup_prog.random_seed = args.random_seed 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(): # TODO pyreader_name 再次调整为不同 test_pyreader, graph_vars = create_model( args, pyreader_name='test_reader_role' + suf, ernie_config=ernie_config) test_prog = test_prog.clone(for_test=True) nccl2_num_trainers = 1 nccl2_trainer_id = 0 exe = fluid.Executor(place) exe.run(startup_prog) if 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) return reader, startup_prog, test_prog, test_pyreader, graph_vars, exe
def get_role_init_dict(args, suf): """main""" # log = logging.getLogger() # prepare_logger(log) log = logging.getLogger(__name__) check_cuda(args.use_cuda) labels_map = {} # label for line in utils.read_by_lines(args.label_map_config): arr = line.split("\t") labels_map[arr[0]] = int(arr[1]) args.num_labels = len(labels_map) print("=========ERNIE CONFIG============") ernie_config = ErnieConfig(args.ernie_config_path) # ernie_config.print_config() print("=========ERNIE CONFIG============") if args.use_cuda: dev_list = fluid.cuda_places() place = dev_list[0] print("==============place==================", place) # place = dev_list[1] dev_count = len(dev_list) else: place = fluid.CPUPlace() dev_count = int(os.environ.get('CPU_NUM', multiprocessing.cpu_count())) print("==============place, dev_count==================", place, dev_count) reader = task_reader.RoleSequenceLabelReader( vocab_path=args.vocab_path, labels_map=labels_map, max_seq_len=args.max_seq_len, do_lower_case=args.do_lower_case, in_tokens=args.in_tokens, random_seed=args.random_seed, task_id=args.task_id) 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_val or args.do_test: test_prog = fluid.Program() with fluid.program_guard(test_prog, startup_prog): with fluid.unique_name.guard(): # TODO pyreader_name 再次调整为不同 test_pyreader, graph_vars = create_model( args, pyreader_name='test_reader_role' + suf, ernie_config=ernie_config) test_prog = test_prog.clone(for_test=True) nccl2_num_trainers = 1 nccl2_trainer_id = 0 exe = fluid.Executor(place) exe.run(startup_prog) if 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) trigger_dict = dict() trigger_dict['log'] = log trigger_dict['args'] = args trigger_dict['labels_map'] = labels_map trigger_dict['ernie_config'] = ernie_config trigger_dict['place'] = place trigger_dict['dev_count'] = dev_count trigger_dict['reader'] = reader trigger_dict['startup_prog'] = startup_prog trigger_dict['test_prog'] = test_prog trigger_dict['test_pyreader'] = test_pyreader trigger_dict['graph_vars'] = graph_vars trigger_dict['nccl2_num_trainers'] = nccl2_num_trainers trigger_dict['nccl2_trainer_id'] = nccl2_trainer_id trigger_dict['exe'] = exe return trigger_dict
def main(args): ernie_config = ErnieConfig(args.ernie_config_path) ernie_config.print_config() if args.use_cuda: place = fluid.CUDAPlace(int(os.getenv('FLAGS_selected_gpus', '0'))) dev_count = fluid.core.get_cuda_device_count() else: place = fluid.CPUPlace() dev_count = int(os.environ.get('CPU_NUM', multiprocessing.cpu_count())) exe = fluid.Executor(place) 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, random_seed=args.random_seed) 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: train_data_generator = reader.data_generator( input_file=args.train_set, batch_size=args.batch_size, epoch=args.epoch, 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) // dev_count else: max_train_steps = args.epoch * num_train_examples // args.batch_size // dev_count warmup_steps = int(max_train_steps * args.warmup_proportion) print("Device count: %d" % dev_count) 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, loss_scaling=args.loss_scaling) 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) 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) train_pyreader.decorate_tensor_provider(train_data_generator) else: train_exe = None if args.do_val or args.do_test: test_exe = fluid.ParallelExecutor(use_cuda=args.use_cuda, main_program=test_prog, share_vars_from=train_exe) if args.do_train: train_pyreader.start() steps = 0 if warmup_steps > 0: graph_vars["learning_rate"] = scheduled_lr time_begin = time.time() while True: try: steps += 1 if steps % args.skip_steps != 0: train_exe.run(fetch_list=[]) else: 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["lr"] 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() 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, 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, 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, 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, shuffle=False)) print("Final test result:") evaluate(exe, test_prog, test_pyreader, graph_vars, args.num_labels, "test")
def main(args): """main""" reader = task_reader.RoleSequenceLabelReader( vocab_path=args.vocab_path, labels_map=labels_map, max_seq_len=args.max_seq_len, do_lower_case=args.do_lower_case, in_tokens=args.in_tokens, random_seed=args.random_seed, task_id=args.task_id) 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: train_data_generator = reader.data_generator( input_file=args.train_set, batch_size=args.batch_size, epoch=args.epoch, shuffle=True, phase="train") num_train_examples = reader.get_num_examples(args.train_set) if args.in_tokens: if args.batch_size < args.max_seq_len: raise ValueError( 'if in_tokens=True, batch_size should greater than max_sqelen, got batch_size:%d seqlen:%d' % (args.batch_size, args.max_seq_len)) max_train_steps = args.epoch * num_train_examples // ( args.batch_size // args.max_seq_len) // dev_count else: max_train_steps = args.epoch * num_train_examples // args.batch_size // dev_count warmup_steps = int(max_train_steps * args.warmup_proportion) print("Device count: %d" % dev_count) 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, 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, 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) 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 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 if args.do_val or args.do_test: test_exe = fluid.ParallelExecutor(use_cuda=args.use_cuda, main_program=test_prog, share_vars_from=train_exe) if args.do_train: train_pyreader.start() steps = 0 graph_vars["learning_rate"] = scheduled_lr time_begin = time.time() while True: try: steps += 1 if steps % args.skip_steps != 0: train_exe.run(fetch_list=[]) else: fetch_list = [ graph_vars["num_infer"].name, graph_vars["num_label"].name, graph_vars["num_correct"].name, graph_vars["loss"].name, graph_vars['learning_rate'].name, ] out = train_exe.run(fetch_list=fetch_list) num_infer, num_label, num_correct, np_loss, np_lr = out lr = float(np_lr[0]) loss = np_loss.mean() precision, recall, f1 = calculate_f1( num_label, num_infer, num_correct) if args.verbose: print( "train pyreader queue size: %d, learning rate: %f" % (train_pyreader.queue.size(), lr if warmup_steps > 0 else args.learning_rate)) current_example, current_epoch = reader.get_train_progress( ) time_end = time.time() used_time = time_end - time_begin print( u"【train】epoch: {}, step: {}, loss: {:.6f}, " "f1: {:.4f}, precision: {:.4f}, recall: {:.4f}, speed: {:.3f} steps/s" .format(current_epoch, steps, float(loss), float(f1), float(precision), float(recall), args.skip_steps / used_time)) time_begin = time.time() 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: precision, recall, f1 = evaluate_wrapper( reader, exe, test_prog, test_pyreader, graph_vars, current_epoch, steps) print( u"【dev】precision {:.4f} , recall {:.4f}, f1-score {:.4f}" .format(float(precision), float(recall), float(f1))) # evaluate test set if args.do_test: precision, recall, f1 = evaluate_wrapper( reader, exe, test_prog, test_pyreader, graph_vars, current_epoch, steps) print( u"【test】precision {:.4f} , recall {:.4f}, f1-score {:.4f}" .format(float(precision), float(recall), float(f1))) except fluid.core.EOFException: save_path = os.path.join(args.checkpoints, "final_model") fluid.io.save_persistables(exe, save_path, train_program) train_pyreader.reset() break # final eval on dev set if args.do_val: precision, recall, f1 = evaluate_wrapper(reader, exe, test_prog, test_pyreader, graph_vars, 1, 'final') print(u"【dev】precision {:.4f} , recall {:.4f}, f1-score {:.4f}".format( float(precision), float(recall), float(f1))) if args.do_test: test_ret = predict_wrapper(reader, exe, test_prog, test_pyreader, graph_vars, 1, 'final') utils.write_by_lines(args.trigger_pred_save_path, test_ret)
def main(args): ernie_config = ErnieConfig(args.ernie_config_path) ernie_config.print_config() if args.use_cuda: dev_list = fluid.cuda_places() place = dev_list[0] dev_count = len(dev_list) 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, random_seed=args.random_seed, task_id=args.task_id) 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: train_data_generator = reader.data_generator( input_file=args.train_set, batch_size=args.batch_size, epoch=args.epoch, shuffle=True, phase="train") num_train_examples = reader.get_num_examples(args.train_set) if args.in_tokens: if args.batch_size < args.max_seq_len: raise ValueError( 'if in_tokens=True, batch_size should greater than max_sqelen, got batch_size:%d seqlen:%d' % (args.batch_size, args.max_seq_len)) max_train_steps = args.epoch * num_train_examples // ( args.batch_size // args.max_seq_len) // dev_count else: max_train_steps = args.epoch * num_train_examples // args.batch_size // dev_count warmup_steps = int(max_train_steps * args.warmup_proportion) log.info("Device count: %d" % dev_count) log.info("Num train examples: %d" % num_train_examples) log.info("Max train steps: %d" % max_train_steps) log.info("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, 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, 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) 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) log.info("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 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) log.info("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 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: log.info( "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.set_batch_generator(train_data_generator) else: train_exe = None if args.do_val or args.do_test: test_exe = fluid.ParallelExecutor(use_cuda=args.use_cuda, main_program=test_prog, share_vars_from=train_exe) if args.do_train: train_pyreader.start() steps = 0 graph_vars["learning_rate"] = scheduled_lr time_begin = time.time() while True: try: steps += 1 if steps % args.skip_steps != 0: train_exe.run(fetch_list=[]) else: fetch_list = [ graph_vars["num_infer"].name, graph_vars["num_label"].name, graph_vars["num_correct"].name, graph_vars["loss"].name, graph_vars['learning_rate'].name, ] out = train_exe.run(fetch_list=fetch_list) num_infer, num_label, num_correct, np_loss, np_lr = out lr = float(np_lr[0]) loss = np_loss.mean() precision, recall, f1 = calculate_f1( num_label, num_infer, num_correct) if args.verbose: log.info( "train pyreader queue size: %d, learning rate: %f" % (train_pyreader.queue.size(), lr if warmup_steps > 0 else args.learning_rate)) current_example, current_epoch = reader.get_train_progress( ) time_end = time.time() used_time = time_end - time_begin log.info( "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, loss, f1, precision, recall, args.skip_steps / used_time)) time_begin = time.time() if nccl2_trainer_id == 0 and 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 nccl2_trainer_id == 0 and steps % args.validation_steps == 0: # evaluate dev set if args.do_val: evaluate_wrapper(reader, exe, test_prog, test_pyreader, graph_vars, current_epoch, steps) # evaluate test set if args.do_test: predict_wrapper(reader, exe, test_prog, test_pyreader, graph_vars, current_epoch, steps) 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 nccl2_trainer_id == 0 and args.do_val: if not args.do_train: current_example, current_epoch = reader.get_train_progress() evaluate_wrapper(reader, exe, test_prog, test_pyreader, graph_vars, current_epoch, 'final') if nccl2_trainer_id == 0 and args.do_test: if not args.do_train: current_example, current_epoch = reader.get_train_progress() predict_wrapper(reader, exe, test_prog, test_pyreader, graph_vars, current_epoch, 'final')