Merge pull request #2134 from bkkaggle/saving-and-resuming

closes #1960 Add saving and resuming functionality for remaining examples

examples/run_glue.py

@@ -112,6 +112,13 @@ def train(args, train_dataset, model, tokenizer):
 
     optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon)
     scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=t_total)
+
+    # Check if saved optimizer or scheduler states exist
+    if os.path.isfile(os.path.join(args.model_name_or_path, 'optimizer.pt')) and os.path.isfile(os.path.join(args.model_name_or_path, 'scheduler.pt')):
+        # Load in optimizer and scheduler states
+        optimizer.load_state_dict(torch.load(os.path.join(args.model_name_or_path, 'optimizer.pt')))
+        scheduler.load_state_dict(torch.load(os.path.join(args.model_name_or_path, 'scheduler.pt')))
+
     if args.fp16:
         try:
             from apex import amp
@@ -140,13 +147,33 @@ def train(args, train_dataset, model, tokenizer):
     logger.info("  Total optimization steps = %d", t_total)
 
     global_step = 0
+    epochs_trained = 0
+    steps_trained_in_current_epoch = 0
+    # Check if continuing training from a checkpoint
+    if os.path.exists(args.model_name_or_path):
+        # set global_step to gobal_step of last saved checkpoint from model path
+        global_step = int(args.model_name_or_path.split('-')[-1].split('/')[0])
+        epochs_trained = global_step // (len(train_dataloader) // args.gradient_accumulation_steps)
+        steps_trained_in_current_epoch = global_step % (len(train_dataloader) // args.gradient_accumulation_steps)
+
+        logger.info("  Continuing training from checkpoint, will skip to saved global_step")
+        logger.info("  Continuing training from epoch %d", epochs_trained)
+        logger.info("  Continuing training from global step %d", global_step)
+        logger.info("  Will skip the first %d steps in the first epoch", steps_trained_in_current_epoch)
+
     tr_loss, logging_loss = 0.0, 0.0
     model.zero_grad()
-    train_iterator = trange(int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0])
+    train_iterator = trange(epochs_trained, int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0])
     set_seed(args)  # Added here for reproductibility (even between python 2 and 3)
     for _ in train_iterator:
         epoch_iterator = tqdm(train_dataloader, desc="Iteration", disable=args.local_rank not in [-1, 0])
         for step, batch in enumerate(epoch_iterator):
+
+            # Skip past any already trained steps if resuming training
+            if steps_trained_in_current_epoch > 0:
+                steps_trained_in_current_epoch -= 1
+                continue
+
             model.train()
             batch = tuple(t.to(args.device) for t in batch)
             inputs = {'input_ids':      batch[0],
@@ -205,9 +232,15 @@ def train(args, train_dataset, model, tokenizer):
                         os.makedirs(output_dir)
                     model_to_save = model.module if hasattr(model, 'module') else model  # Take care of distributed/parallel training
                     model_to_save.save_pretrained(output_dir)
+                    tokenizer.save_pretrained(output_dir)
+
                     torch.save(args, os.path.join(output_dir, 'training_args.bin'))
                     logger.info("Saving model checkpoint to %s", output_dir)
 
+                    torch.save(optimizer.state_dict(), os.path.join(output_dir, 'optimizer.pt'))
+                    torch.save(scheduler.state_dict(), os.path.join(output_dir, 'scheduler.pt'))
+                    logger.info("Saving optimizer and scheduler states to %s", output_dir)
+
             if args.max_steps > 0 and global_step > args.max_steps:
                 epoch_iterator.close()
                 break

examples/run_ner.py

@@ -88,6 +88,13 @@ def train(args, train_dataset, model, tokenizer, labels, pad_token_label_id):
     ]
     optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon)
     scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=t_total)
+
+    # Check if saved optimizer or scheduler states exist
+    if os.path.isfile(os.path.join(args.model_name_or_path, 'optimizer.pt')) and os.path.isfile(os.path.join(args.model_name_or_path, 'scheduler.pt')):
+        # Load in optimizer and scheduler states
+        optimizer.load_state_dict(torch.load(os.path.join(args.model_name_or_path, 'optimizer.pt')))
+        scheduler.load_state_dict(torch.load(os.path.join(args.model_name_or_path, 'scheduler.pt')))
+
     if args.fp16:
         try:
             from apex import amp
@@ -117,13 +124,33 @@ def train(args, train_dataset, model, tokenizer, labels, pad_token_label_id):
     logger.info("  Total optimization steps = %d", t_total)
 
     global_step = 0
+    epochs_trained = 0
+    steps_trained_in_current_epoch = 0
+    # Check if continuing training from a checkpoint
+    if os.path.exists(args.model_name_or_path):
+        # set global_step to gobal_step of last saved checkpoint from model path
+        global_step = int(args.model_name_or_path.split('-')[-1].split('/')[0])
+        epochs_trained = global_step // (len(train_dataloader) // args.gradient_accumulation_steps)
+        steps_trained_in_current_epoch = global_step % (len(train_dataloader) // args.gradient_accumulation_steps)
+
+        logger.info("  Continuing training from checkpoint, will skip to saved global_step")
+        logger.info("  Continuing training from epoch %d", epochs_trained)
+        logger.info("  Continuing training from global step %d", global_step)
+        logger.info("  Will skip the first %d steps in the first epoch", steps_trained_in_current_epoch)
+
     tr_loss, logging_loss = 0.0, 0.0
     model.zero_grad()
-    train_iterator = trange(int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0])
+    train_iterator = trange(epochs_trained, int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0])
     set_seed(args)  # Added here for reproductibility (even between python 2 and 3)
     for _ in train_iterator:
         epoch_iterator = tqdm(train_dataloader, desc="Iteration", disable=args.local_rank not in [-1, 0])
         for step, batch in enumerate(epoch_iterator):
+
+            # Skip past any already trained steps if resuming training
+            if steps_trained_in_current_epoch > 0:
+                steps_trained_in_current_epoch -= 1
+                continue
+
             model.train()
             batch = tuple(t.to(args.device) for t in batch)
             inputs = {"input_ids": batch[0],
@@ -175,9 +202,15 @@ def train(args, train_dataset, model, tokenizer, labels, pad_token_label_id):
                         os.makedirs(output_dir)
                     model_to_save = model.module if hasattr(model, "module") else model  # Take care of distributed/parallel training
                     model_to_save.save_pretrained(output_dir)
+                    tokenizer.save_pretrained(output_dir)
+
                     torch.save(args, os.path.join(output_dir, "training_args.bin"))
                     logger.info("Saving model checkpoint to %s", output_dir)
 
+                    torch.save(optimizer.state_dict(), os.path.join(output_dir, 'optimizer.pt'))
+                    torch.save(scheduler.state_dict(), os.path.join(output_dir, 'scheduler.pt'))
+                    logger.info("Saving optimizer and scheduler states to %s", output_dir)
+
             if args.max_steps > 0 and global_step > args.max_steps:
                 epoch_iterator.close()
                 break

examples/run_squad.py

@@ -27,7 +27,8 @@ import glob
 import timeit
 import numpy as np
 import torch
-from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler, TensorDataset)
+from torch.utils.data import (
+    DataLoader, RandomSampler, SequentialSampler, TensorDataset)
 from torch.utils.data.distributed import DistributedSampler
 
 try:
@@ -65,6 +66,7 @@ MODEL_CLASSES = {
     'albert': (AlbertConfig, AlbertForQuestionAnswering, AlbertTokenizer),
 }
 
+
 def set_seed(args):
     random.seed(args.seed)
     np.random.seed(args.seed)
@@ -72,40 +74,60 @@ def set_seed(args):
     if args.n_gpu > 0:
         torch.cuda.manual_seed_all(args.seed)
 
+
 def to_list(tensor):
     return tensor.detach().cpu().tolist()
 
+
 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.per_gpu_train_batch_size * max(1, 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)
+    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:
         t_total = args.max_steps
-        args.num_train_epochs = args.max_steps // (len(train_dataloader) // args.gradient_accumulation_steps) + 1
+        args.num_train_epochs = args.max_steps // (
+            len(train_dataloader) // args.gradient_accumulation_steps) + 1
     else:
-        t_total = len(train_dataloader) // args.gradient_accumulation_steps * args.num_train_epochs
+        t_total = len(
+            train_dataloader) // args.gradient_accumulation_steps * args.num_train_epochs
 
     # Prepare optimizer and schedule (linear warmup and decay)
     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': 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}
+        {'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 = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon)
-    scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=t_total)
+    optimizer = AdamW(optimizer_grouped_parameters,
+                      lr=args.learning_rate, eps=args.adam_epsilon)
+    scheduler = get_linear_schedule_with_warmup(
+        optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=t_total)
+
+    # Check if saved optimizer or scheduler states exist
+    if os.path.isfile(os.path.join(args.model_name_or_path, 'optimizer.pt')) and os.path.isfile(os.path.join(args.model_name_or_path, 'scheduler.pt')):
+        # Load in optimizer and scheduler states
+        optimizer.load_state_dict(torch.load(
+            os.path.join(args.model_name_or_path, 'optimizer.pt')))
+        scheduler.load_state_dict(torch.load(
+            os.path.join(args.model_name_or_path, 'scheduler.pt')))
 
     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.")
+            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)
+        model, optimizer = amp.initialize(
+            model, optimizer, opt_level=args.fp16_opt_level)
 
     # multi-gpu training (should be after apex fp16 initialization)
     if args.n_gpu > 1:
@@ -121,21 +143,50 @@ def train(args, train_dataset, model, tokenizer):
     logger.info("***** Running training *****")
     logger.info("  Num examples = %d", len(train_dataset))
     logger.info("  Num Epochs = %d", args.num_train_epochs)
-    logger.info("  Instantaneous batch size per GPU = %d", args.per_gpu_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("  Gradient Accumulation steps = %d",
+                args.gradient_accumulation_steps)
     logger.info("  Total optimization steps = %d", t_total)
 
     global_step = 1
+    epochs_trained = 0
+    steps_trained_in_current_epoch = 0
+    # Check if continuing training from a checkpoint
+    if os.path.exists(args.model_name_or_path):
+        # set global_step to gobal_step of last saved checkpoint from model path
+        global_step = int(args.model_name_or_path.split('-')[-1].split('/')[0])
+        epochs_trained = global_step // (len(train_dataloader) //
+                                         args.gradient_accumulation_steps)
+        steps_trained_in_current_epoch = global_step % (
+            len(train_dataloader) // args.gradient_accumulation_steps)
+
+        logger.info(
+            "  Continuing training from checkpoint, will skip to saved global_step")
+        logger.info("  Continuing training from epoch %d", epochs_trained)
+        logger.info("  Continuing training from global step %d", global_step)
+        logger.info("  Will skip the first %d steps in the first epoch",
+                    steps_trained_in_current_epoch)
+
     tr_loss, logging_loss = 0.0, 0.0
     model.zero_grad()
-    train_iterator = trange(int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0])
-    set_seed(args)  # Added here for reproductibility (even between python 2 and 3)
+    train_iterator = trange(epochs_trained, int(
+        args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0])
+    # Added here for reproductibility (even between python 2 and 3)
+    set_seed(args)
 
     for _ in train_iterator:
-        epoch_iterator = tqdm(train_dataloader, desc="Iteration", disable=args.local_rank not in [-1, 0])
+        epoch_iterator = tqdm(train_dataloader, desc="Iteration",
+                              disable=args.local_rank not in [-1, 0])
         for step, batch in enumerate(epoch_iterator):
+
+            # Skip past any already trained steps if resuming training
+            if steps_trained_in_current_epoch > 0:
+                steps_trained_in_current_epoch -= 1
+                continue
+
             model.train()
             batch = tuple(t.to(args.device) for t in batch)
 
@@ -153,7 +204,8 @@ def train(args, train_dataset, model, tokenizer):
                 if args.version_2_with_negative:
                     inputs.update({'is_impossible': batch[7]})
             outputs = model(**inputs)
-            loss = outputs[0]  # model outputs are always tuple in transformers (see doc)
+            # model outputs are always tuple in transformers (see doc)
+            loss = outputs[0]
 
             if args.n_gpu > 1:
                 loss = loss.mean()  # mean() to average on multi-gpu parallel (not distributed) training
@@ -169,9 +221,11 @@ def train(args, train_dataset, model, tokenizer):
             tr_loss += loss.item()
             if (step + 1) % args.gradient_accumulation_steps == 0:
                 if args.fp16:
-                    torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), args.max_grad_norm)
+                    torch.nn.utils.clip_grad_norm_(
+                        amp.master_params(optimizer), args.max_grad_norm)
                 else:
-                    torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm)
+                    torch.nn.utils.clip_grad_norm_(
+                        model.parameters(), args.max_grad_norm)
 
                 optimizer.step()
                 scheduler.step()  # Update learning rate schedule
@@ -180,24 +234,41 @@ def train(args, train_dataset, model, tokenizer):
 
                 # Log metrics
                 if args.local_rank in [-1, 0] and args.logging_steps > 0 and global_step % args.logging_steps == 0:
-                    if args.local_rank == -1 and args.evaluate_during_training:  # Only evaluate when single GPU otherwise metrics may not average well
+                    # Only evaluate when single GPU otherwise metrics may not average well
+                    if args.local_rank == -1 and args.evaluate_during_training:
                         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', scheduler.get_lr()[0], global_step)
-                    tb_writer.add_scalar('loss', (tr_loss - logging_loss)/args.logging_steps, global_step)
+                            tb_writer.add_scalar(
+                                'eval_{}'.format(key), value, global_step)
+                    tb_writer.add_scalar(
+                        'lr', scheduler.get_lr()[0], global_step)
+                    tb_writer.add_scalar(
+                        'loss', (tr_loss - logging_loss)/args.logging_steps, global_step)
                     logging_loss = tr_loss
 
                 # Save model checkpoint
                 if args.local_rank in [-1, 0] and args.save_steps > 0 and global_step % args.save_steps == 0:
-                    output_dir = os.path.join(args.output_dir, 'checkpoint-{}'.format(global_step))
+                    output_dir = os.path.join(
+                        args.output_dir, 'checkpoint-{}'.format(global_step))
                     if not os.path.exists(output_dir):
                         os.makedirs(output_dir)
-                    model_to_save = model.module if hasattr(model, 'module') else model  # Take care of distributed/parallel training
+                    # Take care of distributed/parallel training
+                    model_to_save = model.module if hasattr(
+                        model, 'module') else model
                     model_to_save.save_pretrained(output_dir)
-                    torch.save(args, os.path.join(output_dir, 'training_args.bin'))
+                    tokenizer.save_pretrained(output_dir)
+
+                    torch.save(args, os.path.join(
+                        output_dir, 'training_args.bin'))
                     logger.info("Saving model checkpoint to %s", output_dir)
 
+                    torch.save(optimizer.state_dict(), os.path.join(
+                        output_dir, 'optimizer.pt'))
+                    torch.save(scheduler.state_dict(), os.path.join(
+                        output_dir, 'scheduler.pt'))
+                    logger.info(
+                        "Saving optimizer and scheduler states to %s", output_dir)
+
             if args.max_steps > 0 and global_step > args.max_steps:
                 epoch_iterator.close()
                 break
@@ -212,7 +283,8 @@ def train(args, train_dataset, model, tokenizer):
 
 
 def evaluate(args, model, tokenizer, prefix=""):
-    dataset, examples, features = load_and_cache_examples(args, tokenizer, evaluate=True, output_examples=True)
+    dataset, examples, features = load_and_cache_examples(
+        args, tokenizer, evaluate=True, output_examples=True)
 
     if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]:
         os.makedirs(args.output_dir)
@@ -221,7 +293,8 @@ def evaluate(args, model, tokenizer, prefix=""):
 
     # Note that DistributedSampler samples randomly
     eval_sampler = SequentialSampler(dataset)
-    eval_dataloader = DataLoader(dataset, sampler=eval_sampler, batch_size=args.eval_batch_size)
+    eval_dataloader = DataLoader(
+        dataset, sampler=eval_sampler, batch_size=args.eval_batch_size)
 
     # multi-gpu evaluate
     if args.n_gpu > 1 and not isinstance(model, torch.nn.DataParallel):
@@ -284,21 +357,27 @@ def evaluate(args, model, tokenizer, prefix=""):
             all_results.append(result)
 
     evalTime = timeit.default_timer() - start_time
-    logger.info("  Evaluation done in total %f secs (%f sec per example)", evalTime, evalTime / len(dataset))
+    logger.info("  Evaluation done in total %f secs (%f sec per example)",
+                evalTime, evalTime / len(dataset))
 
     # Compute predictions
-    output_prediction_file = os.path.join(args.output_dir, "predictions_{}.json".format(prefix))
-    output_nbest_file = os.path.join(args.output_dir, "nbest_predictions_{}.json".format(prefix))
+    output_prediction_file = os.path.join(
+        args.output_dir, "predictions_{}.json".format(prefix))
+    output_nbest_file = os.path.join(
+        args.output_dir, "nbest_predictions_{}.json".format(prefix))
 
     if args.version_2_with_negative:
-        output_null_log_odds_file = os.path.join(args.output_dir, "null_odds_{}.json".format(prefix))
+        output_null_log_odds_file = os.path.join(
+            args.output_dir, "null_odds_{}.json".format(prefix))
     else:
         output_null_log_odds_file = None
 
     # XLNet and XLM use a more complex post-processing procedure
     if args.model_type in ['xlnet', 'xlm']:
-        start_n_top = model.config.start_n_top if hasattr(model, "config") else model.module.config.start_n_top
-        end_n_top = model.config.end_n_top if hasattr(model, "config") else model.module.config.end_n_top
+        start_n_top = model.config.start_n_top if hasattr(
+            model, "config") else model.module.config.start_n_top
+        end_n_top = model.config.end_n_top if hasattr(
+            model, "config") else model.module.config.end_n_top
 
         predictions = compute_predictions_log_probs(examples, features, all_results, args.n_best_size,
                                                     args.max_answer_length, output_prediction_file,
@@ -315,9 +394,11 @@ def evaluate(args, model, tokenizer, prefix=""):
     results = squad_evaluate(examples, predictions)
     return results
 
+
 def load_and_cache_examples(args, tokenizer, evaluate=False, output_examples=False):
     if args.local_rank not in [-1, 0] and not evaluate:
-        torch.distributed.barrier()  # Make sure only the first process in distributed training process the dataset, and the others will use the cache
+        # Make sure only the first process in distributed training process the dataset, and the others will use the cache
+        torch.distributed.barrier()
 
     # Load data features from cache or dataset file
     input_dir = args.data_dir if args.data_dir else "."
@@ -329,7 +410,8 @@ def load_and_cache_examples(args, tokenizer, evaluate=False, output_examples=Fal
 
     # Init features and dataset from cache if it exists
     if os.path.exists(cached_features_file) and not args.overwrite_cache and not output_examples:
-        logger.info("Loading features from cached file %s", cached_features_file)
+        logger.info("Loading features from cached file %s",
+                    cached_features_file)
         features_and_dataset = torch.load(cached_features_file)
         features, dataset = features_and_dataset["features"], features_and_dataset["dataset"]
     else:
@@ -339,16 +421,18 @@ def load_and_cache_examples(args, tokenizer, evaluate=False, output_examples=Fal
             try:
                 import tensorflow_datasets as tfds
             except ImportError:
-                raise ImportError("If not data_dir is specified, tensorflow_datasets needs to be installed.")
+                raise ImportError(
+                    "If not data_dir is specified, tensorflow_datasets needs to be installed.")
 
             if args.version_2_with_negative:
-                logger.warn("tensorflow_datasets does not handle version 2 of SQuAD.")
+                logger.warn(
+                    "tensorflow_datasets does not handle version 2 of SQuAD.")
 
             tfds_examples = tfds.load("squad")
-            examples = SquadV1Processor().get_examples_from_dataset(tfds_examples, evaluate=evaluate)
+            examples = SquadV1Processor().get_examples_from_dataset(
+                tfds_examples, evaluate=evaluate)
         else:
             processor = SquadV2Processor() if args.version_2_with_negative else SquadV1Processor()
-
             if evaluate:
                 examples = processor.get_dev_examples(args.data_dir, filename=args.predict_file)
             else:
@@ -366,11 +450,14 @@ def load_and_cache_examples(args, tokenizer, evaluate=False, output_examples=Fal
         )
 
         if args.local_rank in [-1, 0]:
-            logger.info("Saving features into cached file %s", cached_features_file)
-            torch.save({"features": features, "dataset": dataset}, cached_features_file)
+            logger.info("Saving features into cached file %s",
+                        cached_features_file)
+            torch.save({"features": features, "dataset": dataset},
+                       cached_features_file)
 
     if args.local_rank == 0 and not evaluate:
-        torch.distributed.barrier()  # Make sure only the first process in distributed training process the dataset, and the others will use the cache
+        # Make sure only the first process in distributed training process the dataset, and the others will use the cache
+        torch.distributed.barrier()
 
     if output_examples:
         return dataset, examples, features
@@ -380,7 +467,7 @@ def load_and_cache_examples(args, tokenizer, evaluate=False, output_examples=Fal
 def main():
     parser = argparse.ArgumentParser()
 
-    ## Required parameters
+    # Required parameters
     parser.add_argument("--model_type", default=None, type=str, required=True,
                         help="Model type selected in the list: " + ", ".join(MODEL_CLASSES.keys()))
     parser.add_argument("--model_name_or_path", default=None, type=str, required=True,
@@ -388,7 +475,7 @@ def main():
     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
+    # Other parameters
     parser.add_argument("--data_dir", default=None, type=str,
                         help="The input data dir. Should contain the .json files for the task." +
                              "If no data dir or train/predict files are specified, will run with tensorflow_datasets.")
@@ -485,19 +572,22 @@ def main():
     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:
-        raise ValueError("Output directory ({}) already exists and is not empty. Use --overwrite_output_dir to overcome.".format(args.output_dir))
+        raise ValueError(
+            "Output directory ({}) already exists and is not empty. Use --overwrite_output_dir to overcome.".format(args.output_dir))
 
     # Setup distant debugging if needed
     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
         print("Waiting for debugger attach")
-        ptvsd.enable_attach(address=(args.server_ip, args.server_port), redirect_output=True)
+        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")
+        device = torch.device(
+            "cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
         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)
@@ -507,9 +597,9 @@ def main():
     args.device = device
 
     # Setup logging
-    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)
+    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.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)
 
@@ -518,7 +608,8 @@ def main():
 
     # 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
+        # Make sure only the first process in distributed training will download model & vocab
+        torch.distributed.barrier()
 
     args.model_type = args.model_type.lower()
     config_class, model_class, tokenizer_class = MODEL_CLASSES[args.model_type]
@@ -528,12 +619,14 @@ def main():
                                                 do_lower_case=args.do_lower_case,
                                                 cache_dir=args.cache_dir if args.cache_dir else None)
     model = model_class.from_pretrained(args.model_name_or_path,
-                                        from_tf=bool('.ckpt' in args.model_name_or_path),
+                                        from_tf=bool(
+                                            '.ckpt' in args.model_name_or_path),
                                         config=config,
                                         cache_dir=args.cache_dir if args.cache_dir else None)
 
     if args.local_rank == 0:
-        torch.distributed.barrier()  # Make sure only the first process in distributed training will download model & vocab
+        # Make sure only the first process in distributed training will download model & vocab
+        torch.distributed.barrier()
 
     model.to(args.device)
 
@@ -547,14 +640,16 @@ def main():
             import apex
             apex.amp.register_half_function(torch, 'einsum')
         except ImportError:
-            raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.")
+            raise ImportError(
+                "Please install apex from https://www.github.com/nvidia/apex to use fp16 training.")
 
     # Training
     if args.do_train:
-        train_dataset = load_and_cache_examples(args, tokenizer, evaluate=False, output_examples=False)
+        train_dataset = load_and_cache_examples(
+            args, tokenizer, evaluate=False, output_examples=False)
         global_step, tr_loss = train(args, train_dataset, model, tokenizer)
-        logger.info(" global_step = %s, average loss = %s", global_step, tr_loss)
-
+        logger.info(" global_step = %s, average loss = %s",
+                    global_step, tr_loss)
 
     # Save the trained model and the tokenizer
     if args.do_train and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
@@ -565,7 +660,8 @@ def main():
         logger.info("Saving model checkpoint to %s", args.output_dir)
         # Save a trained model, configuration and tokenizer using `save_pretrained()`.
         # They can then be reloaded using `from_pretrained()`
-        model_to_save = model.module if hasattr(model, 'module') else model  # Take care of distributed/parallel training
+        # Take care of distributed/parallel training
+        model_to_save = model.module if hasattr(model, 'module') else model
         model_to_save.save_pretrained(args.output_dir)
         tokenizer.save_pretrained(args.output_dir)
 
@@ -573,15 +669,15 @@ def main():
         torch.save(args, os.path.join(args.output_dir, 'training_args.bin'))
 
         # Load a trained model and vocabulary that you have fine-tuned
-        model = model_class.from_pretrained(args.output_dir, force_download=True)
-        tokenizer = tokenizer_class.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case)
+        model = model_class.from_pretrained(
+            args.output_dir, force_download=True)
+        tokenizer = tokenizer_class.from_pretrained(
+            args.output_dir, do_lower_case=args.do_lower_case)
         model.to(args.device)
 
-
     # Evaluation - we can ask to evaluate all the checkpoints (sub-directories) in a directory
     results = {}
     if args.do_eval and args.local_rank in [-1, 0]:
-
         if args.do_train:
             logger.info("Loading checkpoints saved during training for evaluation")
             checkpoints = [args.output_dir]
@@ -596,14 +692,17 @@ def main():
 
         for checkpoint in checkpoints:
             # Reload the model
-            global_step = checkpoint.split('-')[-1] if len(checkpoints) > 1 else ""
-            model = model_class.from_pretrained(checkpoint, force_download=True)
+            global_step = checkpoint.split(
+                '-')[-1] if len(checkpoints) > 1 else ""
+            model = model_class.from_pretrained(
+                checkpoint, force_download=True)
             model.to(args.device)
 
             # Evaluate
             result = evaluate(args, model, tokenizer, prefix=global_step)
 
-            result = dict((k + ('_{}'.format(global_step) if global_step else ''), v) for k, v in result.items())
+            result = dict((k + ('_{}'.format(global_step) if global_step else ''), v)
+                          for k, v in result.items())
             results.update(result)
 
     logger.info("Results: {}".format(results))

examples/run_xnli.py

@@ -92,6 +92,13 @@ def train(args, train_dataset, model, tokenizer):
         ]
     optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon)
     scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=t_total)
+
+    # Check if saved optimizer or scheduler states exist
+    if os.path.isfile(os.path.join(args.model_name_or_path, 'optimizer.pt')) and os.path.isfile(os.path.join(args.model_name_or_path, 'scheduler.pt')):
+        # Load in optimizer and scheduler states
+        optimizer.load_state_dict(torch.load(os.path.join(args.model_name_or_path, 'optimizer.pt')))
+        scheduler.load_state_dict(torch.load(os.path.join(args.model_name_or_path, 'scheduler.pt')))
+
     if args.fp16:
         try:
             from apex import amp
@@ -120,13 +127,32 @@ def train(args, train_dataset, model, tokenizer):
     logger.info("  Total optimization steps = %d", t_total)
 
     global_step = 0
+    epochs_trained = 0
+    steps_trained_in_current_epoch = 0
+    # Check if continuing training from a checkpoint
+    if os.path.exists(args.model_name_or_path):
+        # set global_step to gobal_step of last saved checkpoint from model path
+        global_step = int(args.model_name_or_path.split('-')[-1].split('/')[0])
+        epochs_trained = global_step // (len(train_dataloader) // args.gradient_accumulation_steps)
+        steps_trained_in_current_epoch = global_step % (len(train_dataloader) // args.gradient_accumulation_steps)
+
+        logger.info("  Continuing training from checkpoint, will skip to saved global_step")
+        logger.info("  Continuing training from epoch %d", epochs_trained)
+        logger.info("  Continuing training from global step %d", global_step)
+        logger.info("  Will skip the first %d steps in the first epoch", steps_trained_in_current_epoch)
+
     tr_loss, logging_loss = 0.0, 0.0
     model.zero_grad()
-    train_iterator = trange(int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0])
+    train_iterator = trange(epochs_trained, int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0])
     set_seed(args)  # Added here for reproductibility (even between python 2 and 3)
     for _ in train_iterator:
         epoch_iterator = tqdm(train_dataloader, desc="Iteration", disable=args.local_rank not in [-1, 0])
         for step, batch in enumerate(epoch_iterator):
+            # Skip past any already trained steps if resuming training
+            if steps_trained_in_current_epoch > 0:
+                steps_trained_in_current_epoch -= 1
+                continue
+
             model.train()
             batch = tuple(t.to(args.device) for t in batch)
             inputs = {'input_ids':      batch[0],
@@ -177,9 +203,15 @@ def train(args, train_dataset, model, tokenizer):
                         os.makedirs(output_dir)
                     model_to_save = model.module if hasattr(model, 'module') else model  # Take care of distributed/parallel training
                     model_to_save.save_pretrained(output_dir)
+                    tokenizer.save_pretrained(output_dir)
+
                     torch.save(args, os.path.join(output_dir, 'training_args.bin'))
                     logger.info("Saving model checkpoint to %s", output_dir)
 
+                    torch.save(optimizer.state_dict(), os.path.join(output_dir, 'optimizer.pt'))
+                    torch.save(scheduler.state_dict(), os.path.join(output_dir, 'scheduler.pt'))
+                    logger.info("Saving optimizer and scheduler states to %s", output_dir)
+
             if args.max_steps > 0 and global_step > args.max_steps:
                 epoch_iterator.close()
                 break