Fix kv-cacheing and bsz > 1 in eval recipe

recipes/configs/eleuther_evaluation.yaml

@@ -33,7 +33,6 @@ tasks: ["truthfulqa_mc2"]
 limit: null
 max_seq_length: 4096
 batch_size: 8
-# It is recommended to set enable_kv_cache=False for long-context models like Llama3.1
 enable_kv_cache: True
 
 # Quantization specific args

recipes/eleuther_eval.py

@@ -46,7 +46,6 @@ class _EvalWrapper(HFLM):
         max_seq_length (int): The maximum sequence length to use.
         batch_size (int): The batch size per GPU to use.
         dtype (torch.dtype): dtype for the model caches during generation.
-        enable_kv_cache (bool): Whether to enable KV cache for generation.
     """
 
     def __init__(
@@ -58,15 +57,13 @@ def __init__(
         max_seq_length: int = 4096,
         batch_size: int = 8,
         dtype: torch.dtype = torch.float32,
-        enable_kv_cache: bool = True,
     ):
         super().__init__(pretrained="gpt2", device=str(device))
         self._model = model
         self._tokenizer = tokenizer
         self._max_seq_length = max_seq_length
         self._batch_size = batch_size
         self._dtype = dtype
-        self._enable_kv_cache = enable_kv_cache
 
     @property
     def model(self):
@@ -92,10 +89,6 @@ def batch_size(self):
     def device(self):
         return self._device
 
-    @property
-    def enable_kv_cache(self):
-        return self._enable_kv_cache
-
     def tok_encode(self, text: str, **kwargs) -> List[int]:
         # Note on add_bos flag: setting to False as this gives better results, for example
         # +1% on truthfulqa_mc2 with a LoRA finetune. lit-gpt also sets this to False,
@@ -131,19 +124,15 @@ def _model_generate(
     ) -> torch.Tensor:
         curr_batch_size = context.size(0)
 
-        if curr_batch_size > 1:
-            raise ValueError(
-                f"Got a batch size of '{curr_batch_size}'. Batch size > 1 is not supported for "
-                "generation. See https://github.com/pytorch/torchtune/issues/1250 for more info."
-            )
-
-        # Setup caches for a given batch size
-        # Technically this is not necessary, but it's a good way to ensure that
-        # the caches won't error on a different batch size. In addition, caches
-        # are not needed for a regular model call, so we just setup here
-        if self.enable_kv_cache:
-            with context.device:
-                self._model.setup_caches(batch_size=curr_batch_size, dtype=self._dtype)
+        # if we've recieved fewer than self._batch_size samples in the current
+        # batch we need to pad the batch out. here we're padding the end of the
+        # current batch to the correct length. this is because when we use static
+        # KV-caches, the model will expect a fixed batch size for all samples.
+        maybe_padded_context = torch.nn.functional.pad(
+            context,
+            (0, 0, 0, self._batch_size - curr_batch_size),
+            value=self._tokenizer.eos_id,  # pad with one of the tokenizer's stop tokens so generation can stop early
+        )
 
         temperature = generation_kwargs.get("temperature", 0.0)
         do_sample = generation_kwargs.get("do_sample", False)
@@ -156,14 +145,14 @@ def _model_generate(
 
         toks, _ = generation.generate(
             self._model,
-            context,
+            maybe_padded_context,
             max_generated_tokens=self.max_gen_toks,
             temperature=temperature,
             top_k=None,  # do_sample is not supported currently
             stop_tokens=self._tokenizer.stop_tokens,
         )
         self._model.reset_caches()
-        return torch.tensor(toks, dtype=torch.int32)
+        return toks[:curr_batch_size]
 
 
 class EleutherEvalRecipe(EvalRecipeInterface):
@@ -175,7 +164,6 @@ class EleutherEvalRecipe(EvalRecipeInterface):
     Features:
         - Single GPU evaluation. Multi-GPU evaluation is currently not supported.
         - Loading model in fp32 or bf16. Fp16 is currently not supported.
-        - Any task from the EleutherAI eval harness that is *not* free generation
 
     We recommend launching evaluation using the tune CLI:
 
@@ -198,6 +186,9 @@ def setup(self) -> None:
         self._quantization_mode = training.get_quantizer_mode(self._quantizer)
         self._enable_kv_cache = self._cfg.get("enable_kv_cache", True)
 
+        self._batch_size = self._cfg.batch_size
+        self._max_seq_length = self._cfg.get("max_seq_length", 4096)
+
         training.set_seed(seed=self._cfg.seed)
 
         checkpointer = config.instantiate(self._cfg.checkpointer)
@@ -253,10 +244,9 @@ def evaluate(self) -> None:
             self._model,
             self._tokenizer,
             device=self._device,
-            max_seq_length=self._cfg.max_seq_length,
-            batch_size=self._cfg.batch_size,
+            max_seq_length=self._max_seq_length,
+            batch_size=self._batch_size,
             dtype=self._dtype,
-            enable_kv_cache=self._enable_kv_cache,
         )
 
         # Task initialization API changed between v0.4.1 and 0.4.2
@@ -268,6 +258,24 @@ def evaluate(self) -> None:
         task_manager = TaskManager(include_path=self._cfg.get("include_path", None))
         task_dict = get_task_dict(self._tasks, task_manager)
 
+        task_types = set([task.OUTPUT_TYPE for _, task in task_dict.items()])
+        if len(task_types) > 1 and "generate_until" in task_types:
+            raise RuntimeError(
+                "Evaluating on multiple task types where any one task involves "
+                "generation is currently not supported. See the issue below for more info: "
+                "https://github.com/pytorch/torchtune/issues/1621"
+            )
+
+        # Setup caches for a given batch size
+        if self._enable_kv_cache and "generate_until" in task_types:
+            with self._device:
+                self._model.setup_caches(
+                    batch_size=self._batch_size,
+                    dtype=self._dtype,
+                    decoder_max_seq_len=self._max_seq_length
+                    + model_eval_wrapper.max_gen_toks,
+                )
+
         logger.info(f"Running evaluation on {self._tasks} tasks.")
         output = evaluate(
             model_eval_wrapper,

tests/recipes/test_eleuther_eval.py

@@ -21,7 +21,11 @@
 class TestEleutherEval:
     @pytest.mark.parametrize(
         "eval_name, expected_acc, bsz",
-        [("truthfulqa_gen", 0.1, 1), ("truthfulqa_mc2", 0.3, 8)],
+        [
+            ("truthfulqa_gen", 0.1, 8),
+            ("truthfulqa_gen", 0.1, 1),
+            ("truthfulqa_mc2", 0.4, 8),
+        ],
     )
     @pytest.mark.integration_test
     def test_torchtune_checkpoint_eval_results(
@@ -31,7 +35,8 @@ def test_torchtune_checkpoint_eval_results(
         ckpt_path = Path(CKPT_MODEL_PATHS[ckpt])
         ckpt_dir = ckpt_path.parent
 
-        # TODO @joecummings bsz > 1 isn't supported for generation tasks, update test once integrated
+        # explicitly setting limit to an odd number here to ensure generation tasks
+        # work with KV-cacheing + bsz > 1 - we'll recieve batches of size 8, 8, 5
         cmd = f"""
         tune run eleuther_eval \
             --config eleuther_evaluation \
@@ -43,7 +48,7 @@ def test_torchtune_checkpoint_eval_results(
             checkpointer.model_type=LLAMA2 \
             tokenizer.path=/tmp/test-artifacts/tokenizer.model \
             tokenizer.prompt_template=null \
-            limit=10 \
+            limit=21 \
             dtype=fp32 \
             device=cpu \
             tasks=[{eval_name}]\
@@ -62,12 +67,12 @@ def test_torchtune_checkpoint_eval_results(
         # v0.4.2 format
         # |    Tasks     |Version|Filter|n-shot|Metric|Value |   |Stderr|
         # |--------------|------:|------|-----:|------|-----:|---|-----:|
-        # |truthfulqa_mc2|      2|none  |     0|acc   |0.3469|±  |0.1444|
+        # |truthfulqa_mc2|      2|none  |     0|acc   |0.4497|±  |0.1067|
 
         # v0.4.3 format
         # |    Tasks     |Version|Filter|n-shot|Metric|   |Value |   |Stderr|
         # |--------------|------:|------|-----:|------|---|-----:|---|-----:|
-        # |truthfulqa_mc2|      2|none  |     0|acc   |↑  |0.3469|±  |0.1444|
+        # |truthfulqa_mc2|      2|none  |     0|acc   |↑  |0.4497|±  |0.1067|
 
         # The below RegEx command will pick up both formats
         search_results = re.search(