Line-by-line text dataset (including padding)

examples/run_lm_finetuning.py

@@ -32,6 +32,7 @@ from typing import Dict, List, Tuple
 
 import numpy as np
 import torch
+from torch.nn.utils.rnn import pad_sequence
 from torch.utils.data import DataLoader, Dataset, RandomSampler, SequentialSampler
 from torch.utils.data.distributed import DistributedSampler
 from tqdm import tqdm, trange
@@ -83,7 +84,7 @@ MODEL_CLASSES = {
 
 
 class TextDataset(Dataset):
-    def __init__(self, tokenizer: PreTrainedTokenizer, args, file_path="train", block_size=512):
+    def __init__(self, tokenizer: PreTrainedTokenizer, args, file_path: str, block_size=512):
         assert os.path.isfile(file_path)
         directory, filename = os.path.split(file_path)
         cached_features_file = os.path.join(
@@ -120,13 +121,32 @@ class TextDataset(Dataset):
         return torch.tensor(self.examples[item])
 
 
+class LineByLineTextDataset(Dataset):
+    def __init__(self, tokenizer: PreTrainedTokenizer, args, file_path: str, block_size=512):
+        assert os.path.isfile(file_path)
+        # Here, we do not cache the features, operating under the assumption
+        # that we will soon use fast multithreaded tokenizers from the
+        # `tokenizers` repo everywhere =)
+        logger.info("Creating features from dataset file at %s", file_path)
+
+        with open(file_path, encoding="utf-8") as f:
+            lines = [line for line in f.read().splitlines() if len(line) > 0]
+
+        self.examples = tokenizer.batch_encode_plus(lines, max_length=block_size)["input_ids"]
+
+    def __len__(self):
+        return len(self.examples)
+
+    def __getitem__(self, i):
+        return torch.tensor(self.examples[i])
+
+
 def load_and_cache_examples(args, tokenizer, evaluate=False):
-    return TextDataset(
+    file_path = args.eval_data_file if evaluate else args.train_data_file
-        tokenizer,
+    if args.line_by_line:
-        args,
+        return LineByLineTextDataset(tokenizer, args, file_path=file_path, block_size=args.block_size)
-        file_path=args.eval_data_file if evaluate else args.train_data_file,
+    else:
-        block_size=args.block_size,
+        return TextDataset(tokenizer, args, file_path=file_path, block_size=args.block_size)
-    )
 
 
 def set_seed(args):
@@ -182,6 +202,8 @@ def mask_tokens(inputs: torch.Tensor, tokenizer: PreTrainedTokenizer, args) -> T
         tokenizer.get_special_tokens_mask(val, already_has_special_tokens=True) for val in labels.tolist()
     ]
     probability_matrix.masked_fill_(torch.tensor(special_tokens_mask, dtype=torch.bool), value=0.0)
+    padding_mask = labels.eq(tokenizer.pad_token_id)
+    probability_matrix.masked_fill_(padding_mask, value=0.0)
     masked_indices = torch.bernoulli(probability_matrix).bool()
     labels[~masked_indices] = -100  # We only compute loss on masked tokens
 
@@ -204,8 +226,14 @@ def train(args, train_dataset, model: PreTrainedModel, tokenizer: PreTrainedToke
         tb_writer = SummaryWriter()
 
     args.train_batch_size = args.per_gpu_train_batch_size * max(1, args.n_gpu)
+
+    def collate(examples: List[torch.Tensor]):
+        return pad_sequence(examples, batch_first=True, padding_value=tokenizer.pad_token_id)
+
     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_dataloader = DataLoader(
+        train_dataset, sampler=train_sampler, batch_size=args.train_batch_size, collate_fn=collate
+    )
 
     if args.max_steps > 0:
         t_total = args.max_steps
@@ -391,8 +419,14 @@ def evaluate(args, model: PreTrainedModel, tokenizer: PreTrainedTokenizer, prefi
 
     args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu)
     # Note that DistributedSampler samples randomly
+
+    def collate(examples: List[torch.Tensor]):
+        return pad_sequence(examples, batch_first=True, padding_value=tokenizer.pad_token_id)
+
     eval_sampler = SequentialSampler(eval_dataset)
-    eval_dataloader = DataLoader(eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size)
+    eval_dataloader = DataLoader(
+        eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size, collate_fn=collate
+    )
 
     # multi-gpu evaluate
     if args.n_gpu > 1:
@@ -456,11 +490,14 @@ def main():
         type=str,
         help="An optional input evaluation data file to evaluate the perplexity on (a text file).",
     )
-
+    parser.add_argument(
+        "--line_by_line",
+        action="store_true",
+        help="Whether distinct lines of text in the dataset are to be handled as distinct sequences.",
+    )
     parser.add_argument(
         "--should_continue", action="store_true", help="Whether to continue from latest checkpoint in output_dir"
     )
-    
     parser.add_argument(
         "--model_name_or_path",
         default=None,