Fix token counting to allow there to be no attention mask

llmfoundry/data/text_data.py

@@ -325,9 +325,10 @@ def get_tokens_per_batch_func(
     """
 
     def get_num_samples_in_batch(batch: Batch) -> int:
-        if not isinstance(batch, Mapping) or 'attention_mask' not in batch:
+        if not isinstance(batch, Mapping) or ('attention_mask' not in batch and
+                                              'input_ids' not in batch):
             raise ValueError(
-                'get_tokens_per_batch_func() requires a batch with an attention_mask key'
+                'get_tokens_per_batch_func() requires a batch with an attention_mask key or an input_ids key'
             )
 
         if not decoder_only and 'decoder_attention_mask' not in batch:
@@ -336,7 +337,10 @@ def get_num_samples_in_batch(batch: Batch) -> int:
             )
 
         # Count number of non padding tokens in batch
-        input_ids_tokens = int(torch.sum(batch['attention_mask']).item())
+        if 'attention_mask' in batch:
+            input_ids_tokens = int(torch.sum(batch['attention_mask']).item())
+        else:
+            input_ids_tokens = batch['input_ids'].numel()
 
         # For encoder decoder models only
         decoder_input_ids_tokens = 0

tests/data/test_dataloader.py

@@ -642,16 +642,17 @@ def test_token_counting_func(pad_token_id: int, batch_size: int,
     assert actual_token_count == expected_token_count
 
 
-@pytest.mark.parametrize(
-    'dataloader_type',
-    ['finetuning-hf', 'finetuning-streaming', 'denoising', 'text'])
+@pytest.mark.parametrize('dataloader_type,tensor_input',
+                         [('finetuning-hf', False),
+                          ('finetuning-streaming', False), ('denoising', False),
+                          ('text', True), ('text', False)])
 @pytest.mark.parametrize('pad_token_id', [100, None])
 @pytest.mark.parametrize('batch_size', [1, 8])
 @pytest.mark.parametrize('model_max_length', [1024])
 @pytest.mark.parametrize('padding_side', ['left'])
 def test_token_counting_func_dataloader_setting(
-        dataloader_type: str, pad_token_id: Optional[int], batch_size: int,
-        model_max_length: int, padding_side: str,
+        dataloader_type: str, tensor_input: bool, pad_token_id: Optional[int],
+        batch_size: int, model_max_length: int, padding_side: str,
         monkeypatch: pytest.MonkeyPatch):
     gptt = transformers.AutoTokenizer.from_pretrained('gpt2')
     gptt.pad_token_id = pad_token_id if pad_token_id is not None else gptt.eos_token_id
@@ -661,9 +662,11 @@ def test_token_counting_func_dataloader_setting(
     batch_strings = []
     expected_token_count = 0
     for _ in range(batch_size):
+        # Get randomly different lengths if we are going to add padding
         sample_length = random.randint(
             1, model_max_length //
-            4) if pad_token_id is not None else model_max_length // 4
+            4) if (pad_token_id is not None and
+                   not tensor_input) else model_max_length // 4
         batch_strings.append(' '.join(['hello'] * sample_length))
         expected_token_count += sample_length
 
@@ -672,13 +675,18 @@ def test_token_counting_func_dataloader_setting(
         for b in batch_strings
     ]
 
+    if tensor_input:
+        batch_tokenized = [
+            torch.tensor(b['input_ids']) for b in batch_tokenized
+        ]
+
     if dataloader_type == 'denoising':
         expected_token_count += 2 * batch_size  # for the two eos tokens
         expected_token_count += 5 * batch_size  # for the corruption prefix tokens
 
     if dataloader_type in {'finetuning-hf', 'finetuning-streaming'}:
         for b in batch_tokenized:
-            b['labels'] = b['input_ids'].copy()
+            b['labels'] = b['input_ids'].copy()  # type: ignore
         expected_token_count *= 2
         expected_token_count += 1 * batch_size  # for the eos token