From 50b7e52a7fc5fe777a3d70d0e7971b30c700418b Mon Sep 17 00:00:00 2001 From: thomwolf Date: Wed, 10 Jul 2019 15:33:34 +0200 Subject: [PATCH] WIP examples --- examples/run_glue.py | 216 +++++++++--------- examples/run_squad.py | 289 ++++++++++++++++--------- examples/utils.py | 61 ++++++ pytorch_transformers/modeling_xlnet.py | 2 +- pytorch_transformers/optimization.py | 2 + 5 files changed, 361 insertions(+), 209 deletions(-) create mode 100644 examples/utils.py diff --git a/examples/run_glue.py b/examples/run_glue.py index 547a4e4698..1e14a3e183 100644 --- a/examples/run_glue.py +++ b/examples/run_glue.py @@ -37,7 +37,7 @@ from pytorch_transformers import (BertForSequenceClassification, XLNetForSequenc XLNET_PRETRAINED_MODEL_ARCHIVE_MAP, XLM_PRETRAINED_MODEL_ARCHIVE_MAP) from pytorch_transformers import (BertTokenizer, XLNetTokenizer, XLMTokenizer) -from pytorch_transformers.optimization import BertAdam, WarmupLinearSchedule +from pytorch_transformers.optimization import BertAdam from utils_glue import processors, output_modes, convert_examples_to_features, compute_metrics @@ -60,12 +60,12 @@ TOKENIZER_CLASSES = { 'xlm': XLMTokenizer, } -def train(args, train_dataset, model): +def train(args, train_dataset, model, tokenizer): """ Train the model """ if args.local_rank in [-1, 0]: tb_writer = SummaryWriter() - args.train_batch_size = args.train_batch_size // args.gradient_accumulation_steps + args.train_batch_size = args.per_gpu_train_batch_size * args.n_gpu train_sampler = RandomSampler(train_dataset) if args.local_rank == -1 else DistributedSampler(train_dataset) train_dataloader = DataLoader(train_dataset, sampler=train_sampler, batch_size=args.train_batch_size) @@ -76,42 +76,36 @@ def train(args, train_dataset, model): num_train_optimization_steps = len(train_dataloader) // args.gradient_accumulation_steps * args.num_train_epochs # Prepare optimizer - param_optimizer = list(model.named_parameters()) - no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight'] + no_decay = ['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} + {'params': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)], 'weight_decay': args.weight_decay}, + {'params': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], 'weight_decay': 0.0} ] + optimizer = BertAdam(optimizer_grouped_parameters, lr=args.learning_rate, + t_total=num_train_optimization_steps, warmup=args.warmup_proportion) if args.fp16: try: - from apex.optimizers import FP16_Optimizer, FusedAdam + from apex import amp except ImportError: raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.") - 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) - warmup_linear = WarmupLinearSchedule(warmup=args.warmup_proportion, t_total=num_train_optimization_steps) - - else: - optimizer = BertAdam(optimizer_grouped_parameters, lr=args.learning_rate, warmup=args.warmup_proportion, - t_total=num_train_optimization_steps) + model, optimizer = amp.initialize(model, optimizer, opt_level=args.fp16_opt_level) # Train! logger.info("***** Running training *****") logger.info(" Num examples = %d", len(train_dataset)) logger.info(" Num Epochs = %d", args.num_train_epochs) - logger.info(" Batch size = %d", args.train_batch_size) + logger.info(" Instantaneous batch size per GPU = %d", args.per_gpu_train_batch_size) + logger.info(" Total train batch size (w. parallel, distributed & accumulation) = %d", + args.train_batch_size * args.gradient_accumulation_steps * (torch.distributed.get_world_size() if args.local_rank != -1 else 1)) logger.info(" Gradient Accumulation steps = %d", args.gradient_accumulation_steps) logger.info(" Total optimization steps = %d", num_train_optimization_steps) global_step = 0 - tr_loss = 0 - model.train() + tr_loss, logging_loss = 0.0, 0.0 optimizer.zero_grad() for _ in trange(int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0]): for step, batch in enumerate(tqdm(train_dataloader, desc="Iteration", disable=args.local_rank not in [-1, 0])): + model.train() batch = tuple(t.to(args.device) for t in batch) inputs = {'input_ids': batch[0], 'attention_mask': batch[1], @@ -125,23 +119,25 @@ def train(args, train_dataset, model): if args.gradient_accumulation_steps > 1: loss = loss / args.gradient_accumulation_steps - loss.backward() if not args.fp16 else optimizer.backward(loss) + if args.fp16: + with amp.scale_loss(loss, optimizer) as scaled_loss: + scaled_loss.backward() + else: + loss.backward() tr_loss += loss.item() if (step + 1) % args.gradient_accumulation_steps == 0: - if args.fp16: - # modify learning rate with special warm up BERT uses - # if args.fp16 is False, BertAdam is used that handles this automatically - lr_this_step = args.learning_rate * warmup_linear.get_lr(global_step, args.warmup_proportion) - for param_group in optimizer.param_groups: - param_group['lr'] = lr_this_step optimizer.step() optimizer.zero_grad() global_step += 1 - if args.local_rank in [-1, 0]: - if not args.fp16: - tb_writer.add_scalar('lr', optimizer.get_lr()[0], global_step) - tb_writer.add_scalar('loss', loss.item(), global_step) + if args.local_rank in [-1, 0] and args.logging_steps > 0 and global_step % args.logging_steps == 0: + if args.local_rank == -1: # Only evaluate on single GPU otherwise metrics may not average well + results = evaluate(args, model, tokenizer) + for key, value in results.items(): + tb_writer.add_scalar('eval_{}'.format(key), value, global_step) + tb_writer.add_scalar('lr', optimizer.get_lr()[0], global_step) + tb_writer.add_scalar('loss', (tr_loss - logging_loss)/args.logging_steps, global_step) + logging_loss = tr_loss if args.max_steps > 0 and global_step > args.max_steps: break if args.max_steps > 0 and global_step > args.max_steps: @@ -150,62 +146,71 @@ def train(args, train_dataset, model): return global_step, tr_loss / global_step -def evalutate(args, eval_task, eval_output_dir, dataset, model): - """ Evaluate the model """ - if not os.path.exists(eval_output_dir) and args.local_rank in [-1, 0]: - os.makedirs(eval_output_dir) +def evaluate(args, model, tokenizer): + # Loop to handle MNLI double evaluation (matched, mis-matched) + eval_task_names = ("mnli", "mnli-mm") if args.task_name == "mnli" else (args.task_name,) + eval_outputs_dirs = (args.output_dir, args.output_dir + '-MM') if args.task_name == "mnli" else (args.output_dir,) - # Note that DistributedSampler samples randomly - eval_sampler = SequentialSampler(dataset) if args.local_rank == -1 else DistributedSampler(dataset) - eval_dataloader = DataLoader(dataset, sampler=eval_sampler, batch_size=args.eval_batch_size) + results = {} + for eval_task, eval_output_dir in zip(eval_task_names, eval_outputs_dirs): + eval_dataset = load_and_cache_examples(args, eval_task, tokenizer, evaluate=True) - # Eval! - logger.info("***** Running evaluation *****") - logger.info(" Num examples = %d", len(dataset)) - logger.info(" Batch size = %d", args.eval_batch_size) - model.eval() - eval_loss = 0 - nb_eval_steps = 0 - preds = None - out_label_ids = None - for batch in tqdm(eval_dataloader, desc="Evaluating"): - batch = tuple(t.to(args.device) for t in batch) + """ Evaluate the model """ + if not os.path.exists(eval_output_dir) and args.local_rank in [-1, 0]: + os.makedirs(eval_output_dir) - with torch.no_grad(): - inputs = {'input_ids': batch[0], - 'attention_mask': batch[1], - 'token_type_ids': batch[2] if args.model_type in ['bert', 'xlnet'] else None, # XLM don't use segment_ids - 'labels': batch[3]} - outputs = model(**inputs) - tmp_eval_loss, logits = outputs[:2] + # Note that DistributedSampler samples randomly + eval_sampler = SequentialSampler(eval_dataset) if args.local_rank == -1 else DistributedSampler(eval_dataset) + eval_dataloader = DataLoader(eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size) - eval_loss += tmp_eval_loss.mean().item() - nb_eval_steps += 1 - if preds is None: - preds = logits.detach().cpu().numpy() - out_label_ids = inputs['labels'].detach().cpu().numpy() - else: - preds = np.append(preds, logits.detach().cpu().numpy(), axis=0) - out_label_ids = np.append(out_label_ids, inputs['labels'].detach().cpu().numpy(), axis=0) + # Eval! + logger.info("***** Running evaluation *****") + logger.info(" Num examples = %d", len(eval_dataset)) + logger.info(" Batch size = %d", args.eval_batch_size) + model.eval() + eval_loss = 0 + nb_eval_steps = 0 + preds = None + out_label_ids = None + for batch in tqdm(eval_dataloader, desc="Evaluating"): + batch = tuple(t.to(args.device) for t in batch) - eval_loss = eval_loss / nb_eval_steps - if args.output_mode == "classification": - preds = np.argmax(preds, axis=1) - elif args.output_mode == "regression": - preds = np.squeeze(preds) - result = compute_metrics(eval_task, preds, out_label_ids) + with torch.no_grad(): + inputs = {'input_ids': batch[0], + 'attention_mask': batch[1], + 'token_type_ids': batch[2] if args.model_type in ['bert', 'xlnet'] else None, # XLM don't use segment_ids + 'labels': batch[3]} + outputs = model(**inputs) + tmp_eval_loss, logits = outputs[:2] - output_eval_file = os.path.join(eval_output_dir, "eval_results.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]))) + eval_loss += tmp_eval_loss.mean().item() + nb_eval_steps += 1 + if preds is None: + preds = logits.detach().cpu().numpy() + out_label_ids = inputs['labels'].detach().cpu().numpy() + else: + preds = np.append(preds, logits.detach().cpu().numpy(), axis=0) + out_label_ids = np.append(out_label_ids, inputs['labels'].detach().cpu().numpy(), axis=0) - return result + eval_loss = eval_loss / nb_eval_steps + if args.output_mode == "classification": + preds = np.argmax(preds, axis=1) + elif args.output_mode == "regression": + preds = np.squeeze(preds) + result = compute_metrics(eval_task, preds, out_label_ids) + results.update(result) + + output_eval_file = os.path.join(eval_output_dir, "eval_results.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]))) + + return results -def load_and_cache_examples(args, task, tokenizer, evaluate=False): +def load_and_cache_examples(args, task, tokenizer, evaluate=False, overwrite_cache=False): processor = processors[task]() output_mode = output_modes[task] # Load data features from cache or dataset file @@ -214,7 +219,7 @@ def load_and_cache_examples(args, task, tokenizer, evaluate=False): list(filter(None, args.model_name.split('/'))).pop(), str(args.max_seq_length), str(task))) - if os.path.exists(cached_features_file): + if os.path.exists(cached_features_file) and not args.overwrite_cache: logger.info("Loading features from cached file %s", cached_features_file) features = torch.load(cached_features_file) else: @@ -270,39 +275,44 @@ def main(): help="Whether to run eval on the dev set.") parser.add_argument("--do_lower_case", 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("--per_gpu_train_batch_size", default=8, type=int, + help="Batch size per GPU for training.") parser.add_argument("--eval_batch_size", default=8, type=int, help="Total batch size for eval.") 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("--learning_rate", default=5e-5, type=float, help="The initial learning rate for Adam.") + parser.add_argument("--weight_decay", default=0.0, type=float, + help="Weight deay if we apply some.") parser.add_argument("--num_train_epochs", default=3.0, type=float, help="Total number of training epochs to perform.") parser.add_argument("--max_steps", default=-1, type=int, help="If > 0: set total number of training steps to perform. Override num_train_epochs.") parser.add_argument("--warmup_proportion", default=0.1, type=float, help="Proportion of training with linear learning rate warmup (0.1 = 10%% of training).") + + parser.add_argument('--logging_steps', type=int, default=100, + help="Log every X updates steps.") parser.add_argument("--no_cuda", action='store_true', help="Avoid using CUDA when available") parser.add_argument('--overwrite_output_dir', action='store_true', help="Overwrite the content of the output directory") + parser.add_argument('--overwrite_cache', action='store_true', + help="Overwrite the cached training and evaluation sets") parser.add_argument('--seed', type=int, default=42, help="random seed for initialization") parser.add_argument('--fp16', action='store_true', - help="Whether to use 16-bit float precision instead of 32-bit") - parser.add_argument('--loss_scale', type=float, default=0, - help="Loss scaling to improve fp16 numeric stability. Only used when fp16 set to True.\n" - "0 (default value): dynamic loss scaling.\n" - "Positive power of 2: static loss scaling value.\n") - + help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit") + parser.add_argument('--fp16_opt_level', type=str, default='O1', + help="For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']." + "See details at https://nvidia.github.io/apex/amp.html") parser.add_argument("--local_rank", type=int, default=-1, - help="local_rank for distributed training on gpus") - - parser.add_argument('--server_ip', type=str, default='', help="Can be used for distant debugging.") - parser.add_argument('--server_port', type=str, default='', help="Can be used for distant debugging.") + help="For distributed training: local_rank") + parser.add_argument('--server_ip', type=str, default='', help="For distant debugging.") + parser.add_argument('--server_port', type=str, default='', help="For distant debugging.") args = parser.parse_args() if os.path.exists(args.output_dir) and os.listdir(args.output_dir) and args.do_train and not args.overwrite_output_dir: @@ -362,13 +372,10 @@ def main(): if args.local_rank == 0: torch.distributed.barrier() - # Distributed, parrallel and fp16 model - if args.fp16: - model.half() + # Distributed and parrallel training model.to(args.device) if args.local_rank != -1: - model = torch.nn.parallel.DistributedDataParallel(model, - device_ids=[args.local_rank], + model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.local_rank], output_device=args.local_rank, find_unused_parameters=True) elif args.n_gpu > 1: @@ -377,7 +384,7 @@ def main(): # Training if args.do_train: train_dataset = load_and_cache_examples(args, args.task_name, tokenizer, evaluate=False) - global_step, tr_loss = train(args, train_dataset, model) + global_step, tr_loss = train(args, train_dataset, model, tokenizer) logger.info(" global_step = %s, average loss = %s", global_step, tr_loss) @@ -402,17 +409,10 @@ def main(): model.to(args.device) # Evaluation - if args.do_eval and (args.local_rank == -1 or torch.distributed.get_rank() == 0): - # Handle MNLI double evaluation - eval_task_names = ("mnli", "mnli-mm") if args.task_name == "mnli" else (args.task_name,) - eval_outputs_dirs = (args.output_dir, args.output_dir + '-MM') if args.task_name == "mnli" else (args.output_dir,) + if args.do_eval and args.local_rank in [-1, 0]: + results = evaluate(args, model, tokenizer) - for eval_task, eval_output_dir in zip(eval_task_names, eval_outputs_dirs): - eval_dataset = load_and_cache_examples(args, eval_task, tokenizer, evaluate=True) - - result = evalutate(args, eval_task, eval_output_dir, eval_dataset, model) - - return result + return results if __name__ == "__main__": diff --git a/examples/run_squad.py b/examples/run_squad.py index d6d7279cb8..7f063109e3 100644 --- a/examples/run_squad.py +++ b/examples/run_squad.py @@ -33,36 +33,156 @@ from tqdm import tqdm, trange from tensorboardX import SummaryWriter -from pytorch_transformers import WEIGHTS_NAME, CONFIG_NAME -from pytorch_transformers.modeling_bert import BertForQuestionAnswering -from pytorch_transformers.optimization import BertAdam, WarmupLinearSchedule -from pytorch_transformers.tokenization_bert import BertTokenizer +from pytorch_transformers import (BertForQuestionAnswering, XLNetForQuestionAnswering, + XLMForQuestionAnswering, BERT_PRETRAINED_MODEL_ARCHIVE_MAP, + XLNET_PRETRAINED_MODEL_ARCHIVE_MAP, XLM_PRETRAINED_MODEL_ARCHIVE_MAP) +from pytorch_transformers import (BertTokenizer, XLNetTokenizer, + XLMTokenizer) from utils_squad import read_squad_examples, convert_examples_to_features, RawResult, write_predictions -if sys.version_info[0] == 2: - import cPickle as pickle -else: - import pickle - logger = logging.getLogger(__name__) +ALL_MODELS = sum((tuple(m.keys()) for m in (BERT_PRETRAINED_MODEL_ARCHIVE_MAP, + XLNET_PRETRAINED_MODEL_ARCHIVE_MAP, + XLM_PRETRAINED_MODEL_ARCHIVE_MAP)), ()) + +MODEL_CLASSES = { + 'bert': BertForQuestionAnswering, + 'xlnet': XLNetForQuestionAnswering, + 'xlm': XLMForQuestionAnswering, +} + +TOKENIZER_CLASSES = { + 'bert': BertTokenizer, + 'xlnet': XLNetTokenizer, + 'xlm': XLMTokenizer, +} + +def train(args, train_dataset, model): + """ Train the model """ + if args.local_rank in [-1, 0]: + tb_writer = SummaryWriter() + + args.train_batch_size = args.train_batch_size // args.gradient_accumulation_steps + train_sampler = RandomSampler(train_dataset) if args.local_rank == -1 else DistributedSampler(train_dataset) + train_dataloader = DataLoader(train_dataset, sampler=train_sampler, batch_size=args.train_batch_size) + + if args.max_steps > 0: + num_train_optimization_steps = args.max_steps + args.num_train_epochs = args.max_steps // (len(train_dataloader) // args.gradient_accumulation_steps) + 1 + else: + num_train_optimization_steps = len(train_dataloader) // args.gradient_accumulation_steps * args.num_train_epochs + + # Prepare optimizer + no_decay = ['bias', 'LayerNorm.weight'] + optimizer_grouped_parameters = [ + {'params': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)], 'weight_decay': 0.01}, + {'params': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], 'weight_decay': 0.0} + ] + optimizer = BertAdam(optimizer_grouped_parameters, lr=args.learning_rate, + t_total=num_train_optimization_steps, warmup=args.warmup_proportion) + if args.fp16: + try: + from apex import amp + except ImportError: + raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.") + model, optimizer = amp.initialize(model, optimizer, opt_level=args.fp16_opt_level) + + # Train! + logger.info("***** Running training *****") + logger.info(" Num examples = %d", len(train_dataset)) + logger.info(" Num Epochs = %d", args.num_train_epochs) + logger.info(" Batch size = %d", args.train_batch_size) + logger.info(" Total batch size (distributed) = %d", args.train_batch_size * (torch.distributed.get_world_size() if args.local_rank != -1 else 1)) + logger.info(" Gradient Accumulation steps = %d", args.gradient_accumulation_steps) + logger.info(" Total optimization steps = %d", num_train_optimization_steps) + + global_step = 0 + tr_loss, logging_loss = 0.0, 0.0 + model.train() + optimizer.zero_grad() + for _ in trange(int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0]): + for step, batch in enumerate(tqdm(train_dataloader, desc="Iteration", disable=args.local_rank not in [-1, 0])): + batch = tuple(t.to(args.device) for t in batch) + inputs = {'input_ids': batch[0], + 'attention_mask': batch[1], + 'token_type_ids': batch[2] if args.model_type in ['bert', 'xlnet'] else None, # XLM don't use segment_ids + 'labels': batch[3]} + ouputs = model(**inputs) + loss = ouputs[0] + + +def evalutate(args, dataset, model): + """ Evaluate the model """ + + + +def load_and_cache_examples(args, tokenizer, training=True): + """ Load data features from cache or dataset file. """ + cached_features_file = os.path.join(args.data_dir, 'cached_{}_{}_{}_{}'.format( + 'dev' if evaluate else 'train', + list(filter(None, args.model_name.split('/'))).pop(), + str(args.max_seq_length), + str(task))) + if os.path.exists(cached_features_file): + logger.info("Loading features from cached file %s", cached_features_file) + features = torch.load(cached_features_file) + else: + logger.info("Creating features from dataset file at %s", args.data_dir) + label_list = processor.get_labels() + examples = read_squad_examples(input_file=args.train_file if training else args.predict_file, + is_training=training, + version_2_with_negative=args.version_2_with_negative) + features = convert_examples_to_features( + examples=examples, + tokenizer=tokenizer, + max_seq_length=args.max_seq_length, + doc_stride=args.doc_stride, + max_query_length=args.max_query_length, + is_training=training) + if args.local_rank in [-1, 0]: + logger.info("Num orig examples = %d", len(examples)) + logger.info("Num split examples = %d", len(features)) + logger.info("Saving features into cached file %s", cached_features_file) + torch.save(features, cached_features_file) + + # Convert to Tensors and build dataset + 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) + if training: + all_start_positions = torch.tensor([f.start_position for f in train_features], dtype=torch.long) + all_end_positions = torch.tensor([f.end_position for f in train_features], dtype=torch.long) + dataset = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_start_positions, all_end_positions) + else: + all_example_index = torch.arange(all_input_ids.size(0), dtype=torch.long) + dataset = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_example_index) + + return dataset + def main(): parser = argparse.ArgumentParser() ## Required parameters - parser.add_argument("--bert_model", default=None, type=str, required=True, - help="Bert pre-trained model selected in the list: bert-base-uncased, " - "bert-large-uncased, bert-base-cased, bert-large-cased, bert-base-multilingual-uncased, " - "bert-base-multilingual-cased, bert-base-chinese.") + parser.add_argument("--train_file", default=None, type=str, required=True, + help="SQuAD json for training. E.g., train-v1.1.json") + parser.add_argument("--predict_file", default=None, type=str, required=True, + help="SQuAD json for predictions. E.g., dev-v1.1.json or test-v1.1.json") + parser.add_argument("--model_name", default=None, type=str, required=True, + help="Bert/XLNet/XLM pre-trained model selected in the list: " + ", ".join(ALL_MODELS)) parser.add_argument("--output_dir", default=None, type=str, required=True, help="The output directory where the model checkpoints and predictions will be written.") ## Other parameters - parser.add_argument("--train_file", default=None, type=str, help="SQuAD json for training. E.g., train-v1.1.json") - parser.add_argument("--predict_file", default=None, type=str, - help="SQuAD json for predictions. E.g., dev-v1.1.json or test-v1.1.json") + parser.add_argument('--version_2_with_negative', action='store_true', + help='If true, the SQuAD examples contain some that do not have an answer.') + parser.add_argument('--null_score_diff_threshold', type=float, default=0.0, + help="If null_score - best_non_null is greater than the threshold predict null.") + parser.add_argument('--overwrite_output_dir', action='store_true', + help="Overwrite the content of the output directory") + parser.add_argument("--max_seq_length", default=384, type=int, help="The maximum total input sequence length after WordPiece tokenization. Sequences " "longer than this will be truncated, and sequences shorter than this will be padded.") @@ -71,65 +191,53 @@ def main(): parser.add_argument("--max_query_length", default=64, type=int, help="The maximum number of tokens for the question. Questions longer than this will " "be truncated to this length.") - parser.add_argument("--do_train", action='store_true', help="Whether to run training.") - parser.add_argument("--do_predict", 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("--predict_batch_size", default=8, type=int, help="Total batch size for predictions.") - parser.add_argument("--learning_rate", default=5e-5, type=float, help="The initial learning rate for Adam.") + parser.add_argument("--do_train", action='store_true', + help="Whether to run training.") + parser.add_argument("--do_predict", action='store_true', + help="Whether to run eval on the dev set.") + parser.add_argument("--do_lower_case", action='store_true', + help="Whether to lower case the input text. True for uncased models, False for cased models.") + + parser.add_argument("--train_batch_size", default=32, type=int, + help="Total batch size for training.") + parser.add_argument("--predict_batch_size", default=8, type=int, + help="Total batch size for predictions.") + parser.add_argument("--learning_rate", default=5e-5, type=float, + help="The initial learning rate for Adam.") + 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("--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.") + help="Proportion of training with linear learning rate warmup (0.1 = 10%% of training).") parser.add_argument("--n_best_size", default=20, type=int, - help="The total number of n-best predictions to generate in the nbest_predictions.json " - "output file.") + help="The total number of n-best predictions to generate in the nbest_predictions.json output file.") parser.add_argument("--max_answer_length", default=30, type=int, help="The maximum length of an answer that can be generated. This is needed because the start " "and end predictions are not conditioned on one another.") parser.add_argument("--verbose_logging", action='store_true', help="If true, all of the warnings related to data processing will be printed. " "A number of warnings are expected for a normal SQuAD evaluation.") - parser.add_argument("--no_cuda", - action='store_true', + + parser.add_argument("--no_cuda", action='store_true', help="Whether not to use CUDA when available") - parser.add_argument('--seed', - type=int, - default=42, + 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("--do_lower_case", - action='store_true', - help="Whether to lower case the input text. True for uncased models, False for cased models.") - parser.add_argument("--local_rank", - type=int, - default=-1, + parser.add_argument("--local_rank", type=int, default=-1, help="local_rank for distributed training on gpus") - parser.add_argument('--fp16', - action='store_true', - help="Whether to use 16-bit float precision instead of 32-bit") - parser.add_argument('--overwrite_output_dir', - action='store_true', - help="Overwrite the content of the output directory") - parser.add_argument('--loss_scale', - type=float, default=0, - help="Loss scaling to improve fp16 numeric stability. Only used when fp16 set to True.\n" - "0 (default value): dynamic loss scaling.\n" - "Positive power of 2: static loss scaling value.\n") - parser.add_argument('--version_2_with_negative', - action='store_true', - help='If true, the SQuAD examples contain some that do not have an answer.') - parser.add_argument('--null_score_diff_threshold', - type=float, default=0.0, - help="If null_score - best_non_null is greater than the threshold predict null.") + parser.add_argument('--fp16', action='store_true', + help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit") + parser.add_argument('--fp16_opt_level', type=str, default='O1', + help="For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']." + "See details at https://nvidia.github.io/apex/amp.html") parser.add_argument('--server_ip', type=str, default='', help="Can be used for distant debugging.") parser.add_argument('--server_port', type=str, default='', help="Can be used for distant debugging.") args = parser.parse_args() print(args) + if os.path.exists(args.output_dir) and os.listdir(args.output_dir) and args.do_train and not args.overwrite_output_dir: + raise ValueError("Output directory ({}) already exists and is not empty. Use --overwrite_output_dir to overcome.".format(args.output_dir)) + if args.server_ip and args.server_port: # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script import ptvsd @@ -137,71 +245,52 @@ def main(): ptvsd.enable_attach(address=(args.server_ip, args.server_port), redirect_output=True) ptvsd.wait_for_attach() + # Setup CUDA, GPU & distributed training 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: + args.n_gpu = torch.cuda.device_count() + else: # Initializes the distributed backend which will take care of sychronizing nodes/GPUs torch.cuda.set_device(args.local_rank) 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') + args.n_gpu = 1 + args.device = device - logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s - %(message)s', - datefmt = '%m/%d/%Y %H:%M:%S', - level = logging.INFO if args.local_rank in [-1, 0] else logging.WARN) - - logger.info("device: {} n_gpu: {}, distributed training: {}, 16-bits training: {}".format( - device, n_gpu, bool(args.local_rank != -1), args.fp16)) - - 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 = args.train_batch_size // args.gradient_accumulation_steps + # Setup logging + logging.basicConfig(level = logging.INFO if args.local_rank in [-1, 0] else logging.WARN) + logger.warning("Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s", + args.local_rank, device, args.n_gpu, bool(args.local_rank != -1), args.fp16) + # Setup seeds random.seed(args.seed) np.random.seed(args.seed) torch.manual_seed(args.seed) - if n_gpu > 0: + if args.n_gpu > 0: torch.cuda.manual_seed_all(args.seed) - if not args.do_train and not args.do_predict: - raise ValueError("At least one of `do_train` or `do_predict` must be True.") - - if args.do_train: - if not args.train_file: - raise ValueError( - "If `do_train` is True, then `train_file` must be specified.") - if args.do_predict: - if not args.predict_file: - raise ValueError( - "If `do_predict` is True, then `predict_file` must be specified.") - - if os.path.exists(args.output_dir) and os.listdir(args.output_dir) and args.do_train and not args.overwrite_output_dir: - raise ValueError("Output directory {} already exists and is not empty. Use --overwrite_output_dir to overcome.".format(args.output_dir)) - if not os.path.exists(args.output_dir): - os.makedirs(args.output_dir) - + # Load pretrained model and tokenizer if args.local_rank not in [-1, 0]: - torch.distributed.barrier() # Make sure only the first process in distributed training will download model & vocab + torch.distributed.barrier() # Make sure only 1st process in distributed training download model & vocab + + args.model_type = args.model_name.lower().split('-')[0] + tokenizer_class = TOKENIZER_CLASSES[args.model_type] + model_class = MODEL_CLASSES[args.model_type] + tokenizer = tokenizer_class.from_pretrained(args.model_name, do_lower_case=args.do_lower_case) + model = model_class.from_pretrained(args.model_name, num_labels=num_labels) - tokenizer = BertTokenizer.from_pretrained(args.bert_model, do_lower_case=args.do_lower_case) - model = BertForQuestionAnswering.from_pretrained(args.bert_model) if args.local_rank == 0: torch.distributed.barrier() - if args.fp16: - model.half() - model.to(device) + # Distributed and parrallel training + model.to(args.device) if args.local_rank != -1: - model = torch.nn.parallel.DistributedDataParallel(model, - device_ids=[args.local_rank], + model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.local_rank], output_device=args.local_rank, find_unused_parameters=True) - elif n_gpu > 1: + elif args.n_gpu > 1: model = torch.nn.DataParallel(model) + # Training if args.do_train: if args.local_rank in [-1, 0]: tb_writer = SummaryWriter() diff --git a/examples/utils.py b/examples/utils.py new file mode 100644 index 0000000000..e4b7263efa --- /dev/null +++ b/examples/utils.py @@ -0,0 +1,61 @@ +# Copyright (c) 2019-present, the HuggingFace Inc. authors. +# All rights reserved. This source code is licensed under the BSD-style +# license found in the LICENSE file in the root directory of this source tree. +import logging +import os +from tqdm import tqdm +from pprint import pformat + +import torch + +from ignite.engine import Engine, Events +from ignite.handlers import ModelCheckpoint +from ignite.metrics import RunningAverage +from ignite.contrib.handlers import ProgressBar +from ignite.contrib.handlers.tensorboard_logger import OptimizerParamsHandler, OutputHandler, TensorboardLogger + + +def average_distributed_scalar(scalar, args): + """ Average a scalar over nodes if we are in distributed training. + We use this for distributed evaluation. + Beware, such averages only works for metrics which are additive with regard + to the evaluation dataset, e.g. accuracy, log probabilities. + Doesn't work for ratio metrics like F1. + """ + if args.local_rank == -1: + return scalar + scalar_t = torch.tensor(scalar, dtype=torch.float, device=args.device) / torch.distributed.get_world_size() + torch.distributed.all_reduce(scalar_t, op=torch.distributed.ReduceOp.SUM) + return scalar_t.item() + + +def add_logging_and_checkpoint_saving(trainer, evaluator, metrics, model, optimizer, args, prefix=""): + """ Add to a PyTorch ignite training engine tensorboard logging, + progress bar with average loss, checkpoint saving and save training config. + """ + # Add progress bar with average loss + RunningAverage(output_transform=lambda x: x).attach(trainer, prefix + "loss") + pbar = ProgressBar(persist=True) + pbar.attach(trainer, metric_names=[prefix + "loss"]) + evaluator.add_event_handler(Events.COMPLETED, lambda _: + pbar.log_message("Validation: %s" % pformat(evaluator.state.metrics))) + + # Add tensorboard logging with training and evaluation metrics + tb_logger = TensorboardLogger(log_dir=None) + tb_logger.attach(trainer, log_handler=OutputHandler(tag="training", metric_names=[prefix + "loss"]), + event_name=Events.ITERATION_COMPLETED) + tb_logger.attach(trainer, log_handler=OptimizerParamsHandler(optimizer), + event_name=Events.ITERATION_STARTED) + @evaluator.on(Events.COMPLETED) + def tb_log_metrics(engine): + for name in metrics.keys(): + tb_logger.writer.add_scalar(name, engine.state.metrics[name], trainer.state.iteration) + + # Add checkpoint saving after each epoch - take care of distributed encapsulation ('getattr()') + checkpoint_handler = ModelCheckpoint(tb_logger.writer.log_dir, 'checkpoint', save_interval=1, n_saved=3) + trainer.add_event_handler(Events.EPOCH_COMPLETED, checkpoint_handler, {'mymodel': getattr(model, 'module', model)}) + + # Save training configuration + torch.save(args, os.path.join(tb_logger.writer.log_dir, CONFIG_NAME)) + + return checkpoint_handler, tb_logger diff --git a/pytorch_transformers/modeling_xlnet.py b/pytorch_transformers/modeling_xlnet.py index e0b3fb0661..1782cb2f84 100644 --- a/pytorch_transformers/modeling_xlnet.py +++ b/pytorch_transformers/modeling_xlnet.py @@ -393,7 +393,7 @@ class XLNetRelativeAttention(nn.Module): x = x[1:, ...] x = x.reshape(x_size[0], x_size[1] - 1, x_size[2], x_size[3]) # x = x[:, 0:klen, :, :] - x = torch.index_select(x, 1, torch.arange(klen)) + x = torch.index_select(x, 1, torch.arange(klen, device=x.device, dtype=torch.long)) return x diff --git a/pytorch_transformers/optimization.py b/pytorch_transformers/optimization.py index d13dd45c6b..b2f2e43b1c 100644 --- a/pytorch_transformers/optimization.py +++ b/pytorch_transformers/optimization.py @@ -227,6 +227,8 @@ class BertAdam(Optimizer): lr = [] for group in self.param_groups: for p in group['params']: + if p.grad is None: + continue state = self.state[p] if len(state) == 0: return [0]