def main(): parser = argparse.ArgumentParser() ## Required parameters parser.add_argument( "--model", default="bert", type=str, required=True, help="The model used for pretraining. Currently support bert or electra" ) parser.add_argument( "--config_file", "--cf", help="pointer to the configuration file of the experiment", type=str, required=True) parser.add_argument( "--config_file_path", default=None, type=str, required=True, help="The blob storage directory where config file is located.") parser.add_argument( "--data_dir", default=None, type=str, required=True, help= "The input data dir. Should contain the .tsv files (or other data files) for the task." ) parser.add_argument("--task_name", default=None, type=str, required=True, help="The name of the task to train.") parser.add_argument( "--output_dir", default=None, type=str, required=True, help="The output directory where the model checkpoints will be written." ) ## Other parameters parser.add_argument( "--checkpoint_file", default=None, type=str, help= "The path to checkpoint file which will be used to initializ the model parameters." ) parser.add_argument( "--max_seq_length", default=128, type=int, help= "The maximum total input sequence length after WordPiece tokenization. \n" "Sequences longer than this will be truncated, and sequences shorter \n" "than this will be padded.") parser.add_argument("--do_train", default=False, action='store_true', help="Whether to run training.") parser.add_argument("--do_eval", default=False, action='store_true', help="Whether to run eval on the dev set.") parser.add_argument( "--do_lower_case", default=False, action='store_true', help="Set this flag if you are using an uncased model.") parser.add_argument("--train_batch_size", default=32, type=int, help="Total batch size for training.") parser.add_argument("--eval_batch_size", default=8, type=int, help="Total batch size for eval.") parser.add_argument("--learning_rate", default=5e-5, type=float, help="The initial learning rate for Adam.") parser.add_argument("--num_train_epochs", default=3.0, type=float, help="Total number of training epochs to perform.") parser.add_argument( "--warmup_proportion", default=0.1, type=float, help= "Proportion of training to perform linear learning rate warmup for. " "E.g., 0.1 = 10%% of training.") parser.add_argument("--no_cuda", default=False, action='store_true', help="Whether not to use CUDA when available") parser.add_argument("--local_rank", type=int, default=-1, help="local_rank for distributed training on gpus") parser.add_argument('--seed', type=int, default=42, help="random seed for initialization") parser.add_argument( '--gradient_accumulation_steps', type=int, default=1, help= "Number of updates steps to accumulate before performing a backward/update pass." ) parser.add_argument( '--optimize_on_cpu', default=False, action='store_true', help= "Whether to perform optimization and keep the optimizer averages on CPU" ) parser.add_argument( '--fp16', default=False, action='store_true', help="Whether to use 16-bit float precision instead of 32-bit") parser.add_argument( '--loss_scale', type=float, default=128, help= 'Loss scaling, positive power of 2 values can improve fp16 convergence.' ) parser.add_argument('--step_per_log', type=int, default=5, help='Number of updates steps to log metrics.') parser.add_argument( "--process_count_per_node", default=1, type=int, help="Total number of process count to launch per node.") args = parser.parse_args() #run = Run.get_context() processors = { "cola": ColaProcessor, "mnli": MnliProcessor, "mrpc": MrpcProcessor, "qqp": QQPProcessor, "qnli": QNLIProcessor, "sst2": SST2Processor, "stsb": STSBProcessor, "rte": RTEProcessor, } comm = DistributedCommunicator( accumulation_step=args.gradient_accumulation_steps) rank = comm.rank local_rank = comm.local_rank world_size = comm.world_size is_master = rank == 0 # Prepare logger job_id = rutils.get_current_time() logger = rutils.FileLogging('%s_bert_fine_tune_%d' % (job_id, local_rank)) logger.info("job id: %s" % job_id) logger.info(rutils.parser_args_to_dict(args)) logger.info( "world size: {}, local rank: {}, global rank: {}, fp16: {}".format( world_size, local_rank, rank, args.fp16)) torch.cuda.set_device(local_rank) device = torch.device("cuda", local_rank) hostname = socket.gethostname() n_gpu = torch.cuda.device_count() logger.info("host: {}, device: {}, n_gpu: {}".format( hostname, device, n_gpu)) # extract config job_config = BertJobConfiguration( config_file_path=os.path.join(args.config_file_path, args.config_file)) #if os.path.exists(args.output_dir) and os.listdir(args.output_dir): # raise ValueError("Output directory () already exists and is not empty.") #os.makedirs(args.output_dir, exist_ok=True) output_model_file = os.path.join(args.output_dir, job_id + "_pytorch_model_fine_tune.bin") if args.gradient_accumulation_steps < 1: raise ValueError( "Invalid gradient_accumulation_steps parameter: {}, should be >= 1" .format(args.gradient_accumulation_steps)) if local_rank == -1: args.train_batch_size = int(args.train_batch_size / args.gradient_accumulation_steps) random.seed(args.seed) np.random.seed(args.seed) torch.manual_seed(args.seed) if not args.do_train and not args.do_eval: raise ValueError( "At least one of `do_train` or `do_eval` must be True.") task_name = args.task_name.lower() is_master = (local_rank == -1 or rank == 0) if task_name not in processors: raise ValueError("Task not found: %s" % (task_name)) processor = processors[task_name]() label_list = processor.get_labels() tokenizer = BertTokenizer.from_pretrained(job_config.get_token_file_type(), do_lower_case=args.do_lower_case) train_examples = None num_train_steps = None if args.do_train: train_examples = processor.get_train_examples(args.data_dir) num_train_steps = int( len(train_examples) / args.train_batch_size / args.gradient_accumulation_steps * args.num_train_epochs) num_labels = len(processor.get_labels()) # Prepare model model_name = args.model model_config = job_config.get_model_config() if model_name == 'bert': config = BertConfig(**model_config) config.vocab_size = len(tokenizer.vocab) model = BertForSequenceClassification(config, num_labels=num_labels) elif model_name == 'electra': config = ElectraConfig(**model_config) config.vocab_size = len(tokenizer.vocab) model = ElectraForSequenceClassification(config, num_labels=num_labels) #model = BertForSequenceClassification.from_pretrained(args.bert_model, # cache_dir=PYTORCH_PRETRAINED_BERT_CACHE / 'distributed_{}'.format(local_rank), num_labels=num_labels) # Load checkpoint if specified #import pdb;pdb.set_trace() if os.path.exists(str(args.checkpoint_file)): state_dict = torch.load(args.checkpoint_file) if model_name == 'bert': model.bert.load_state_dict(state_dict) elif model_name == 'electra': model.electra.load_state_dict(state_dict) logger.info("Set the model parameter from the checkpoint %s" % args.checkpoint_file) if args.fp16: model.half() model.to(device) comm.register_model(model, args.fp16) if args.do_train: param_optimizer = list(model.named_parameters()) # hack to remove pooler, which is not used # thus it produce None grad that break apex param_optimizer = [n for n in param_optimizer if 'pooler' not in n[0]] no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight'] optimizer_grouped_parameters = [{ 'params': [ p for n, p in param_optimizer if not any(nd in n for nd in no_decay) ], 'weight_decay': 0.01 }, { 'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay': 0.0 }] t_total = num_train_steps // world_size if args.fp16: try: from apex.optimizers import FP16_Optimizer from apex.optimizers import FusedAdam except ImportError: raise ImportError( "Please install apex from https://www.github.com/nvidia/apex to run this." ) optimizer = FusedAdam(optimizer_grouped_parameters, lr=args.learning_rate, bias_correction=False, max_grad_norm=1.0) if args.loss_scale == 0: optimizer = FP16_Optimizer(optimizer, dynamic_loss_scale=True) else: optimizer = FP16_Optimizer(optimizer, static_loss_scale=args.loss_scale) else: optimizer = BertAdam(optimizer_grouped_parameters, lr=args.learning_rate, warmup=args.warmup_proportion, t_total=t_total) if is_master: logger.info('lr: {}'.format(np.float(args.learning_rate))) train_features = convert_examples_to_features(train_examples, label_list, args.max_seq_length, tokenizer, logger) logger.info("***** Running training *****") logger.info(" Num examples = %d" % (len(train_examples))) logger.info(" Batch size = %d" % (args.train_batch_size)) logger.info(" Num steps = %d" % (num_train_steps)) all_input_ids = torch.tensor([f.input_ids for f in train_features], dtype=torch.long) all_input_mask = torch.tensor([f.input_mask for f in train_features], dtype=torch.long) all_segment_ids = torch.tensor([f.segment_ids for f in train_features], dtype=torch.long) all_label_ids = torch.tensor([f.label_id for f in train_features], dtype=torch.long) train_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids) if local_rank != -1 and world_size > 1: train_sampler = DistributedSampler(train_data) else: train_sampler = RandomSampler(train_data) train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=args.train_batch_size) global_step, tr_loss = 0, 0 model.train() for _ in trange(int(args.num_train_epochs), desc="Epoch"): for _, batch in enumerate(tqdm(train_dataloader, desc="Iteration")): batch = tuple(t.to(device) for t in batch) input_ids, input_mask, segment_ids, label_ids = batch loss = model(input_ids, segment_ids, input_mask, label_ids) loss = loss / args.gradient_accumulation_steps loss.backward() global_step += 1 tr_loss += loss.item() if comm.synchronize(): lr_this_step = args.learning_rate * warmup_linear( global_step / t_total, args.warmup_proportion) for param_group in optimizer.param_groups: param_group['lr'] = lr_this_step optimizer.step() model.zero_grad() if is_master and (global_step + 1) % args.step_per_log == 0: logger.info('train_loss: {}'.format( np.float(tr_loss / args.step_per_log))) tr_loss = 0 if is_master: # Save a trained model torch.save(model.state_dict(), output_model_file) logger.info('model checkpoint saved at %s' % output_model_file) if args.do_eval and is_master: eval_examples = processor.get_dev_examples(args.data_dir) eval_features = convert_examples_to_features(eval_examples, label_list, args.max_seq_length, tokenizer, logger) logger.info("***** Running evaluation *****") logger.info(" Num examples = %d" % len(eval_examples)) logger.info(" Batch size = %d" % args.eval_batch_size) all_input_ids = torch.tensor([f.input_ids for f in eval_features], dtype=torch.long) all_input_mask = torch.tensor([f.input_mask for f in eval_features], dtype=torch.long) all_segment_ids = torch.tensor([f.segment_ids for f in eval_features], dtype=torch.long) all_label_ids = torch.tensor([f.label_id for f in eval_features], dtype=torch.long) eval_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids) # Run prediction for full data eval_sampler = SequentialSampler(eval_data) eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=args.eval_batch_size) model.eval() eval_loss, eval_accuracy = 0, 0 nb_eval_steps, nb_eval_examples = 0, 0 for input_ids, input_mask, segment_ids, label_ids in eval_dataloader: input_ids = input_ids.to(device) input_mask = input_mask.to(device) segment_ids = segment_ids.to(device) label_ids = label_ids.to(device) with torch.no_grad(): tmp_eval_loss = model(input_ids, segment_ids, input_mask, label_ids) logits = model(input_ids, segment_ids, input_mask) logits = logits.detach().cpu().numpy() label_ids = label_ids.to('cpu').numpy() tmp_eval_accuracy = accuracy(logits, label_ids) eval_loss += tmp_eval_loss.mean().item() eval_accuracy += tmp_eval_accuracy nb_eval_examples += input_ids.size(0) nb_eval_steps += 1 eval_loss = eval_loss / nb_eval_steps eval_accuracy = eval_accuracy / nb_eval_examples result = {'eval_loss': eval_loss, 'eval_accuracy': eval_accuracy} logger.info("***** Eval results *****") for key in sorted(result.keys()): logger.info(" %s = %s" % (key, str(result[key])))
def main(): parser = argparse.ArgumentParser() ## Required parameters parser.add_argument( "--data_dir", default=None, type=str, required=True, help= "The input data dir. Should contain the .tsv files (or other data files) for the task." ) parser.add_argument( "--bert_config_file", default=None, type=str, required=True, help= "The config json file corresponding to the pre-trained BERT model. \n" "This specifies the model architecture.") parser.add_argument("--task_name", default=None, type=str, required=True, help="The name of the task to train.") parser.add_argument( "--vocab_file", default=None, type=str, required=True, help="The vocabulary file that the BERT model was trained on.") parser.add_argument( "--output_dir", default=None, type=str, required=True, help="The output directory where the model checkpoints will be written." ) ## Other parameters parser.add_argument( "--init_checkpoint", default=None, type=str, help="Initial checkpoint (usually from a pre-trained BERT model).") parser.add_argument( "--do_lower_case", default=False, action='store_true', help= "Whether to lower case the input text. True for uncased models, False for cased models." ) parser.add_argument( "--max_seq_length", default=128, type=int, help= "The maximum total input sequence length after WordPiece tokenization. \n" "Sequences longer than this will be truncated, and sequences shorter \n" "than this will be padded.") parser.add_argument("--do_train", default=False, action='store_true', help="Whether to run training.") parser.add_argument("--do_eval", default=False, action='store_true', help="Whether to run eval on the dev set.") parser.add_argument("--train_batch_size", default=32, type=int, help="Total batch size for training.") parser.add_argument("--eval_batch_size", default=8, type=int, help="Total batch size for eval.") parser.add_argument("--learning_rate", default=5e-5, type=float, help="The initial learning rate for Adam.") parser.add_argument("--num_train_epochs", default=3.0, type=float, help="Total number of training epochs to perform.") parser.add_argument( "--warmup_proportion", default=0.1, type=float, help= "Proportion of training to perform linear learning rate warmup for. " "E.g., 0.1 = 10%% of training.") parser.add_argument("--save_checkpoints_steps", default=1000, type=int, help="How often to save the model checkpoint.") parser.add_argument("--no_cuda", default=False, action='store_true', help="Whether not to use CUDA when available") parser.add_argument("--local_rank", type=int, default=-1, help="local_rank for distributed training on gpus") parser.add_argument('--seed', type=int, default=42, help="random seed for initialization") parser.add_argument( '--gradient_accumulation_steps', type=int, default=1, help= "Number of updates steps to accumualte before performing a backward/update pass." ) args = parser.parse_args() processors = { "dream": dreamProcessor, } if args.local_rank == -1 or args.no_cuda: device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu") n_gpu = torch.cuda.device_count() else: device = torch.device("cuda", args.local_rank) n_gpu = 1 # Initializes the distributed backend which will take care of sychronizing nodes/GPUs torch.distributed.init_process_group(backend='nccl') logger.info("device %s n_gpu %d distributed training %r", device, n_gpu, bool(args.local_rank != -1)) if args.gradient_accumulation_steps < 1: raise ValueError( "Invalid gradient_accumulation_steps parameter: {}, should be >= 1" .format(args.gradient_accumulation_steps)) args.train_batch_size = int(args.train_batch_size / args.gradient_accumulation_steps) random.seed(args.seed) np.random.seed(args.seed) torch.manual_seed(args.seed) if n_gpu > 0: torch.cuda.manual_seed_all(args.seed) if not args.do_train and not args.do_eval: raise ValueError( "At least one of `do_train` or `do_eval` must be True.") bert_config = BertConfig.from_json_file(args.bert_config_file) if args.max_seq_length > bert_config.max_position_embeddings: raise ValueError( "Cannot use sequence length {} because the BERT model was only trained up to sequence length {}" .format(args.max_seq_length, bert_config.max_position_embeddings)) if os.path.exists(args.output_dir) and os.listdir(args.output_dir): if args.do_train: raise ValueError( "Output directory ({}) already exists and is not empty.". format(args.output_dir)) else: os.makedirs(args.output_dir, exist_ok=True) task_name = args.task_name.lower() if task_name not in processors: raise ValueError("Task not found: %s" % (task_name)) processor = processors[task_name]() label_list = processor.get_labels() tokenizer = tokenization.FullTokenizer(vocab_file=args.vocab_file, do_lower_case=args.do_lower_case) train_examples = None num_train_steps = None if args.do_train: train_examples = processor.get_train_examples(args.data_dir) num_train_steps = int( len(train_examples) / n_class / args.train_batch_size / args.gradient_accumulation_steps * args.num_train_epochs) model = BertForSequenceClassification( bert_config, 1 if n_class > 1 else len(label_list)) if args.init_checkpoint is not None: model.bert.load_state_dict( torch.load(args.init_checkpoint, map_location='cpu')) model.to(device) if args.local_rank != -1: model = torch.nn.parallel.DistributedDataParallel( model, device_ids=[args.local_rank], output_device=args.local_rank) elif n_gpu > 1: model = torch.nn.DataParallel(model) no_decay = ['bias', 'gamma', 'beta'] optimizer_parameters = [{ 'params': [p for n, p in model.named_parameters() if n not in no_decay], 'weight_decay_rate': 0.01 }, { 'params': [p for n, p in model.named_parameters() if n in no_decay], 'weight_decay_rate': 0.0 }] optimizer = BERTAdam(optimizer_parameters, lr=args.learning_rate, warmup=args.warmup_proportion, t_total=num_train_steps) global_step = 0 if args.do_train: train_features = convert_examples_to_features(train_examples, label_list, args.max_seq_length, tokenizer) logger.info("***** Running training *****") logger.info(" Num examples = %d", len(train_examples)) logger.info(" Batch size = %d", args.train_batch_size) logger.info(" Num steps = %d", num_train_steps) input_ids = [] input_mask = [] segment_ids = [] label_id = [] for f in train_features: input_ids.append([]) input_mask.append([]) segment_ids.append([]) for i in range(n_class): input_ids[-1].append(f[i].input_ids) input_mask[-1].append(f[i].input_mask) segment_ids[-1].append(f[i].segment_ids) label_id.append([f[0].label_id]) all_input_ids = torch.tensor(input_ids, dtype=torch.long) all_input_mask = torch.tensor(input_mask, dtype=torch.long) all_segment_ids = torch.tensor(segment_ids, dtype=torch.long) all_label_ids = torch.tensor(label_id, dtype=torch.long) train_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids) if args.local_rank == -1: train_sampler = RandomSampler(train_data) else: train_sampler = DistributedSampler(train_data) train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=args.train_batch_size) model.train() for _ in trange(int(args.num_train_epochs), desc="Epoch"): tr_loss = 0 nb_tr_examples, nb_tr_steps = 0, 0 for step, batch in enumerate( tqdm(train_dataloader, desc="Iteration")): batch = tuple(t.to(device) for t in batch) input_ids, input_mask, segment_ids, label_ids = batch loss, _ = model(input_ids, segment_ids, input_mask, label_ids, n_class) if n_gpu > 1: loss = loss.mean() # mean() to average on multi-gpu. if args.gradient_accumulation_steps > 1: loss = loss / args.gradient_accumulation_steps loss.backward() tr_loss += loss.item() nb_tr_examples += input_ids.size(0) nb_tr_steps += 1 if (step + 1) % args.gradient_accumulation_steps == 0: optimizer.step() # We have accumulated enought gradients model.zero_grad() global_step += 1 torch.save(model.state_dict(), os.path.join(args.output_dir, "model.pt")) model.load_state_dict(torch.load(os.path.join(args.output_dir, "model.pt"))) if args.do_eval: eval_examples = processor.get_dev_examples(args.data_dir) eval_features = convert_examples_to_features(eval_examples, label_list, args.max_seq_length, tokenizer) logger.info("***** Running evaluation *****") logger.info(" Num examples = %d", len(eval_examples)) logger.info(" Batch size = %d", args.eval_batch_size) input_ids = [] input_mask = [] segment_ids = [] label_id = [] for f in eval_features: input_ids.append([]) input_mask.append([]) segment_ids.append([]) for i in range(n_class): input_ids[-1].append(f[i].input_ids) input_mask[-1].append(f[i].input_mask) segment_ids[-1].append(f[i].segment_ids) label_id.append([f[0].label_id]) all_input_ids = torch.tensor(input_ids, dtype=torch.long) all_input_mask = torch.tensor(input_mask, dtype=torch.long) all_segment_ids = torch.tensor(segment_ids, dtype=torch.long) all_label_ids = torch.tensor(label_id, dtype=torch.long) eval_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids) if args.local_rank == -1: eval_sampler = SequentialSampler(eval_data) else: eval_sampler = DistributedSampler(eval_data) eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=args.eval_batch_size) model.eval() eval_loss, eval_accuracy = 0, 0 nb_eval_steps, nb_eval_examples = 0, 0 logits_all = [] for input_ids, input_mask, segment_ids, label_ids in eval_dataloader: input_ids = input_ids.to(device) input_mask = input_mask.to(device) segment_ids = segment_ids.to(device) label_ids = label_ids.to(device) with torch.no_grad(): tmp_eval_loss, logits = model(input_ids, segment_ids, input_mask, label_ids, n_class) logits = logits.detach().cpu().numpy() label_ids = label_ids.to('cpu').numpy() for i in range(len(logits)): logits_all += [logits[i]] tmp_eval_accuracy = accuracy(logits, label_ids.reshape(-1)) eval_loss += tmp_eval_loss.mean().item() eval_accuracy += tmp_eval_accuracy nb_eval_examples += input_ids.size(0) nb_eval_steps += 1 eval_loss = eval_loss / nb_eval_steps eval_accuracy = eval_accuracy / nb_eval_examples if args.do_train: result = { 'eval_loss': eval_loss, 'eval_accuracy': eval_accuracy, 'global_step': global_step, 'loss': tr_loss / nb_tr_steps } else: result = {'eval_loss': eval_loss, 'eval_accuracy': eval_accuracy} output_eval_file = os.path.join(args.output_dir, "eval_results_dev.txt") with open(output_eval_file, "w") as writer: logger.info("***** Eval results *****") for key in sorted(result.keys()): logger.info(" %s = %s", key, str(result[key])) writer.write("%s = %s\n" % (key, str(result[key]))) output_eval_file = os.path.join(args.output_dir, "logits_dev.txt") with open(output_eval_file, "w") as f: for i in range(len(logits_all)): for j in range(len(logits_all[i])): f.write(str(logits_all[i][j])) if j == len(logits_all[i]) - 1: f.write("\n") else: f.write(" ") eval_examples = processor.get_test_examples(args.data_dir) eval_features = convert_examples_to_features(eval_examples, label_list, args.max_seq_length, tokenizer) logger.info("***** Running evaluation *****") logger.info(" Num examples = %d", len(eval_examples)) logger.info(" Batch size = %d", args.eval_batch_size) input_ids = [] input_mask = [] segment_ids = [] label_id = [] for f in eval_features: input_ids.append([]) input_mask.append([]) segment_ids.append([]) for i in range(n_class): input_ids[-1].append(f[i].input_ids) input_mask[-1].append(f[i].input_mask) segment_ids[-1].append(f[i].segment_ids) label_id.append([f[0].label_id]) all_input_ids = torch.tensor(input_ids, dtype=torch.long) all_input_mask = torch.tensor(input_mask, dtype=torch.long) all_segment_ids = torch.tensor(segment_ids, dtype=torch.long) all_label_ids = torch.tensor(label_id, dtype=torch.long) eval_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids) if args.local_rank == -1: eval_sampler = SequentialSampler(eval_data) else: eval_sampler = DistributedSampler(eval_data) eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=args.eval_batch_size) model.eval() eval_loss, eval_accuracy = 0, 0 nb_eval_steps, nb_eval_examples = 0, 0 logits_all = [] for input_ids, input_mask, segment_ids, label_ids in eval_dataloader: input_ids = input_ids.to(device) input_mask = input_mask.to(device) segment_ids = segment_ids.to(device) label_ids = label_ids.to(device) with torch.no_grad(): tmp_eval_loss, logits = model(input_ids, segment_ids, input_mask, label_ids, n_class) logits = logits.detach().cpu().numpy() label_ids = label_ids.to('cpu').numpy() for i in range(len(logits)): logits_all += [logits[i]] tmp_eval_accuracy = accuracy(logits, label_ids.reshape(-1)) eval_loss += tmp_eval_loss.mean().item() eval_accuracy += tmp_eval_accuracy nb_eval_examples += input_ids.size(0) nb_eval_steps += 1 eval_loss = eval_loss / nb_eval_steps eval_accuracy = eval_accuracy / nb_eval_examples if args.do_train: result = { 'eval_loss': eval_loss, 'eval_accuracy': eval_accuracy, 'global_step': global_step, 'loss': tr_loss / nb_tr_steps } else: result = {'eval_loss': eval_loss, 'eval_accuracy': eval_accuracy} output_eval_file = os.path.join(args.output_dir, "eval_results_test.txt") with open(output_eval_file, "w") as writer: logger.info("***** Eval results *****") for key in sorted(result.keys()): logger.info(" %s = %s", key, str(result[key])) writer.write("%s = %s\n" % (key, str(result[key]))) output_eval_file = os.path.join(args.output_dir, "logits_test.txt") with open(output_eval_file, "w") as f: for i in range(len(logits_all)): for j in range(len(logits_all[i])): f.write(str(logits_all[i][j])) if j == len(logits_all[i]) - 1: f.write("\n") else: f.write(" ")