fix attetion mask with packing

src/axolotl/datasets.py

@@ -79,11 +79,13 @@ class ConstantLengthDataset(IterableDataset):
         buffer = {"input_ids": [], "attention_mask": [], "labels": []}
         buffer_len = 0
         for dataset in self.datasets:
+            idx = 0
             iterator = iter(dataset)
             more_examples = True
             while more_examples:
                 try:
                     example = next(iterator)
+                    idx += 1
                 except StopIteration:
                     more_examples = False
                     example = None
@@ -124,6 +126,7 @@ class ConstantLengthDataset(IterableDataset):
                         "labels": [],
                     }
                     buffer_len = 0
+                    idx = 1
 
                 if example:
                     # FIXME
@@ -132,11 +135,6 @@ class ConstantLengthDataset(IterableDataset):
                         input_ids = example["input_ids"]
                         attention_mask = example["attention_mask"]
                         labels = example["labels"]
-                        if (
-                            buffer["input_ids"]
-                            and input_ids[0] == self.tokenizer.bos_token_id
-                        ):
-                            attention_mask[0] = 0
 
                         if add_concat_token:
                             input_ids.append(self.concat_token_id)
@@ -147,7 +145,7 @@ class ConstantLengthDataset(IterableDataset):
                             input_ids, dtype=self.tokens_dtype
                         )
                         attention_mask_with_concat = torch.tensor(
-                            attention_mask, dtype=self.tokens_dtype
+                            [idx * m for m in attention_mask], dtype=torch.int16
                         )
                         labels_with_concat = torch.tensor(
                             labels, dtype=self.tokens_dtype

src/axolotl/utils/data.py

@@ -405,14 +405,13 @@ def load_prepare_datasets(
                         private=True,
                     )
     else:
-        # dataset_train = load_tokenized_prepared_datasets(
-        dataset = load_tokenized_prepared_datasets(
+        dataset_train = load_tokenized_prepared_datasets(
             "train", tokenizer, cfg, default_dataset_prepared_path
         )
-        # dataset_test = load_tokenized_prepared_datasets(
-        #     "test", tokenizer, cfg, default_dataset_prepared_path
-        # )
-        # dataset = DatasetDict({"train": dataset_train, "test": dataset_test})
+        dataset_test = load_tokenized_prepared_datasets(
+            "test", tokenizer, cfg, default_dataset_prepared_path
+        )
+        dataset = DatasetDict({"train": dataset_train, "test": dataset_test})
 
     if cfg.dataset_shard_num and cfg.dataset_shard_idx is not None:
         logging.info(

tests/test_packed_dataset.py

@@ -27,7 +27,7 @@ class TestPacking(unittest.TestCase):
             }
         )
 
-    def test_resets_attention(self):
+    def test_increments_attention(self):
         prompter = AlpacaPrompter("chat")
         strat = AlpacaPromptTokenizingStrategy(
             prompter,
@@ -58,7 +58,7 @@ class TestPacking(unittest.TestCase):
 
         # but subsequent one does
         assert example["input_ids"][next_bos_index] == self.tokenizer.bos_token_id
-        assert example["attention_mask"][next_bos_index] == 0
+        assert example["attention_mask"][next_bos_index] == 2
 
 
 if __name__ == "__main__":