correct the truncation and padding of dataset

examples/run_seq2seq_finetuning.py

@@ -104,9 +104,11 @@ class TextDataset(Dataset):
                     except IndexError:  # skip ill-formed stories
                         continue
 
-                story = tokenizer.convert_tokens_to_ids(tokenizer.tokenize(story))
                 summary = tokenizer.convert_tokens_to_ids(tokenizer.tokenize(summary))
-                story_seq, summary_seq = _fit_to_block_size(story, summary, block_size)
+                summary_seq = _fit_to_block_size(summary, block_size)
+
+                story = tokenizer.convert_tokens_to_ids(tokenizer.tokenize(story))
+                story_seq = _fit_to_block_size(story, block_size)
 
                 self.examples.append(
                     tokenizer.add_special_token_sequence_pair(story_seq, summary_seq)
@@ -170,45 +172,15 @@ def _add_missing_period(line):
     return line + "."
 
 
-def _fit_to_block_size(src_sequence, tgt_sequence, block_size):
+def _fit_to_block_size(sequence, block_size):
     """ Adapt the source and target sequences' lengths to the block size.
-
-    If the concatenated sequence (source + target + 3 special tokens) would be
-    longer than the block size we use the 75% / 25% rule followed in [1]. For a
-    block size of 512 this means limiting the source sequence's length to 384
-    and the target sequence's length to 128.
-
-    Attributes:
-        src_sequence (list): a list of ids that maps to the tokens of the
-            source sequence.
-        tgt_sequence (list): a list of ids that maps to the tokens of the
-            target sequence.
-        block_size (int): the model's block size.
-
-    [1] Dong, Li, et al. "Unified Language Model Pre-training for Natural
-    Language Understanding and Generation." arXiv preprint arXiv:1905.03197 (2019).
+    If the sequence is shorter than the block size we pad it with -1 ids
+    which correspond to padding tokens.
     """
-    SRC_MAX_LENGTH = int(0.75 * block_size) - 2  # CLS and EOS token
-    TGT_MAX_LENGTH = block_size - (SRC_MAX_LENGTH + 2) - 1  # EOS token
-
-    # We dump the examples that are too small to fit in the block size for the
-    # sake of simplicity. You can modify this by adding model-specific padding.
-    if len(src_sequence) + len(tgt_sequence) + 3 < block_size:
-        return None
-
-    if len(src_sequence) > SRC_MAX_LENGTH:
-        if len(tgt_sequence) > TGT_MAX_LENGTH:
-            src_sequence = src_sequence[:SRC_MAX_LENGTH]
-            tgt_sequence = tgt_sequence[:TGT_MAX_LENGTH]
+    if len(sequence) > block_size:
+        return sequence[:block_size]
     else:
-            remain_size = block_size - len(tgt_sequence) - 3
-            src_sequence = src_sequence[:remain_size]
-    else:
-        if len(tgt_sequence) > TGT_MAX_LENGTH:
-            remain_size = block_size - len(src_sequence) - 3
-            tgt_sequence = tgt_sequence[:remain_size]
-
-    return src_sequence, tgt_sequence
+        return sequence.extend([-1] * [block_size - len(sequence)])
 
 
 def load_and_cache_examples(args, tokenizer):