Merge branch 'main' into shashank/seq_id_flash_attn

llmfoundry/data/finetuning/tasks.py

@@ -47,25 +47,46 @@ def preprocessing_fn(example: Dict) -> Dict[str, str]:
 
 __all__ = ['dataset_constructor']
 
+_ALLOWED_RESPONSE_KEYS = {'response', 'completion'}
+_ALLOWED_PROMPT_KEYS = {'prompt'}
+
 
 def _tokenize_formatted_example(
         example: Dict[str, Any],
         tokenizer: PreTrainedTokenizerBase) -> Dict[str, List[int]]:
-    if ('prompt' not in example) or ('response' not in example):
+    """Tokenize a formatted example and validate expected keys."""
+    example_keys = set(example.keys())
+    prompt_keys = example_keys.intersection(_ALLOWED_PROMPT_KEYS)
+    response_keys = example_keys.intersection(_ALLOWED_RESPONSE_KEYS)
+
+    if len(prompt_keys) != 1:
+        raise KeyError(
+            f'Unable to tokenize example because {len(prompt_keys)} of the allowed prompt keys ' +\
+            f'were present in {example_keys=}. Please specify exactly one. {_ALLOWED_PROMPT_KEYS=}'
+        )
+
+    if len(response_keys) != 1:
         raise KeyError(
-            'Unable to tokenize example because it has not been properly formatted. ' +\
-            '"prompt" and "response" are required keys but at least one was missing ' +\
-            f'from {example=}.'
+            f'Unable to tokenize example because {len(response_keys)} of the allowed response keys ' +\
+            f'were present in {example_keys=}. Please specify exactly one. {_ALLOWED_RESPONSE_KEYS=}'
         )
-    if not isinstance(example['prompt'], str):
+
+    prompt_key = prompt_keys.pop()
+    response_key = response_keys.pop()
+    prompt = example[prompt_key]
+    response = example[response_key]
+
+    if not isinstance(prompt, str):
         raise TypeError(
-            f'Unable to tokenize example because "prompt" was not a string. {example=}'
+            f'Unable to tokenize example because {prompt_key} was not a string. {example=}'
         )
-    if not isinstance(example['response'], str):
+
+    if not isinstance(response, str):
         raise TypeError(
-            f'Unable to tokenize example because "response" was not a string. {example=}'
+            f'Unable to tokenize example because {response_key} was not a string. {example=}'
         )
-    return tokenizer(text=example['prompt'], text_target=example['response'])
+
+    return tokenizer(text=prompt, text_target=response)
 
 
 class StreamingFinetuningDataset(StreamingDataset):

tests/data/test_dataloader.py

@@ -21,6 +21,9 @@
 from llmfoundry import (build_finetuning_dataloader,
                         build_text_denoising_dataloader)
 from llmfoundry.data import build_dataloader
+from llmfoundry.data.finetuning.tasks import (_ALLOWED_PROMPT_KEYS,
+                                              _ALLOWED_RESPONSE_KEYS,
+                                              _tokenize_formatted_example)
 from llmfoundry.data.text_data import (ConcatenatedSequenceCollatorWrapper,
                                        build_text_dataloader,
                                        get_tokens_per_batch_func)
@@ -355,10 +358,8 @@ def test_finetuning_dataloader_small_data(dataset_size: int,
     if (dist.get_world_size() * device_batch_size > dataset_size) and drop_last:
         error_context = pytest.raises(ValueError, match='Your dataset')
     if invalid_dataset:
-        error_context = pytest.raises(
-            TypeError,
-            match='Unable to tokenize example because "prompt" was not a string'
-        )
+        error_context = pytest.raises(TypeError,
+                                      match='Unable to tokenize example')
 
     with error_context:
         _ = build_finetuning_dataloader(cfg, tokenizer, device_batch_size)
@@ -367,6 +368,39 @@ def test_finetuning_dataloader_small_data(dataset_size: int,
         shutil.rmtree(tiny_dataset_folder_path)
 
 
+def test_tokenize_example_malformed():
+    no_keys = {}
+    no_prompt_key = {'response': 'response'}
+    no_response_key = {'prompt': 'prompt'}
+    extra_keys_with_prompt = {'prompt': 'prompt', 'extra': 'extra'}
+    extra_keys_with_response = {'response': 'response', 'extra': 'extra'}
+    multiple_allowed_response_keys = {
+        'prompt': 'prompt',
+        'response': 'response',
+        'completion': 'completion'
+    }
+
+    malformed_examples = [
+        no_keys, no_prompt_key, no_response_key, extra_keys_with_prompt,
+        extra_keys_with_response, multiple_allowed_response_keys
+    ]
+
+    for example in malformed_examples:
+        with pytest.raises(KeyError):
+            _tokenize_formatted_example(example, MagicMock())
+
+
+def test_tokenize_example_well_formed():
+    tokenizer = transformers.AutoTokenizer.from_pretrained('gpt2')
+
+    for prompt_key in _ALLOWED_PROMPT_KEYS:
+        for response_key in _ALLOWED_RESPONSE_KEYS:
+            example = {prompt_key: 'prompt', response_key: 'response'}
+            tokenized_example = _tokenize_formatted_example(example, tokenizer)
+            assert 'input_ids' in tokenized_example
+            assert 'labels' in tokenized_example
+
+
 @pytest.mark.parametrize('split', ['train', 'custom', 'data'])
 def test_finetuning_dataloader_custom_split(tmp_path: pathlib.Path, split: str):
     tokenizer_name = 'gpt2'