remove debugging, use gpt2 since starcoder requires consent

src/axolotl/datasets.py

@@ -132,8 +132,12 @@ class ConstantLengthDataset(IterableDataset):
                         attention_mask = example["attention_mask"]
                         labels = example["labels"]
                         if (
-                            buffer["input_ids"]
-                            and input_ids[0] == self.tokenizer.bos_token_id
+                            (
+                                buffer["input_ids"]
+                                and input_ids[0] == self.tokenizer.bos_token_id
+                            )
+                            or self.tokenizer.bos_token_id
+                            == self.tokenizer.eos_token_id
                         ):
                             attention_mask[0] = 0
 

src/axolotl/utils/tokenization.py

@@ -34,3 +34,5 @@ def check_example_labels(example, tokenizer):
 
     logging.info(" ".join(colored_tokens))
     logging.info("\n\n\n")
+
+    print(" ".join(colored_tokens))

tests/test_packed_dataset.py

@@ -11,7 +11,57 @@ from axolotl.prompt_tokenizers import AlpacaPromptTokenizingStrategy
 from axolotl.prompters import AlpacaPrompter
 
 
-class TestPacking(unittest.TestCase):
+class TestGpt2Packing(unittest.TestCase):
+    """
+    Test class for packing dataset sequences
+    """
+
+    def setUp(self) -> None:
+        # pylint: disable=duplicate-code
+        self.tokenizer = AutoTokenizer.from_pretrained("gpt2")
+        self.tokenizer.add_special_tokens(
+            {
+                "bos_token": "<|endoftext|>",
+                "eos_token": "<|endoftext|>",
+                "unk_token": "<|endoftext|>",
+            }
+        )
+        self.tokenizer.bos_token_id = 0
+        self.tokenizer.eos_token_id = 0
+        self.tokenizer.unk_token_id = 0
+
+    def test_resets_attention(self):
+        prompter = AlpacaPrompter("chat")
+        strat = AlpacaPromptTokenizingStrategy(
+            prompter,
+            self.tokenizer,
+            False,
+            2048,
+        )
+        dateset = load_dataset(
+            "json",
+            data_files=str(Path(__file__).parent / "fixtures/alpaca/alpaca.json"),
+        )["train"]
+        dataset = Dataset.from_list(list(TokenizedPromptDataset(strat, dateset)))
+
+        constant_len_dataset = ConstantLengthDataset(
+            self.tokenizer,
+            [dataset],
+            seq_length=2048,
+        )
+        packed_dataset = Dataset.from_list(list(constant_len_dataset))
+
+        example = packed_dataset[0]
+        # tokenizers where eos and bos tokens are the same, don't have a bos token
+        next_eos_index = (
+            example["input_ids"][1:].index(self.tokenizer.eos_token_id) + 1
+        )  # add one since we sliced
+
+        assert example["input_ids"][next_eos_index] == self.tokenizer.eos_token_id
+        assert example["attention_mask"][next_eos_index + 1] == 0
+
+
+class TestLlamaPacking(unittest.TestCase):
     """
     Test class for packing dataset sequences
     """