vlm: do not allow max_model_len overflow (#962)

aphrodite/engine/aphrodite_engine.py

@@ -1842,10 +1842,22 @@ class AphroditeEngine:
 
     def _validate_model_inputs(self, inputs: Union[LLMInputs,
                                                    EncoderDecoderLLMInputs]):
-        prompt_key = "encoder_prompt_token_ids" \
-            if self.is_encoder_decoder_model() else "prompt_token_ids"
-        if not inputs.get(prompt_key):
+        if self.is_encoder_decoder_model():
+            prompt_ids = inputs.get("encoder_prompt_token_ids")
+        else:
+            prompt_ids = inputs.get("prompt_token_ids")
+        if prompt_ids is None or len(prompt_ids) == 0:
             raise ValueError("Prompt cannot be empty")
+        if self.model_config.multimodal_config is not None:
+            max_prompt_len = self.model_config.max_model_len
+            if len(prompt_ids) > max_prompt_len:
+                raise ValueError(
+                    f"The prompt (total length {len(prompt_ids)}) is too long "
+                    f"to fit into the model (context length {max_prompt_len}). "
+                    "Make sure that `max_model_len` is no smaller than the "
+                    "number of text tokens plus multimodal tokens. For image "
+                    "inputs, the number of image tokens depends on the number "
+                    "of images, and possibly their aspect ratios as well.")
 
 
 setup_logger()

tests/models/test_llava.py

@@ -179,3 +179,18 @@ def test_models(hf_runner, aphrodite_runner, image_assets, model, size_factors,
         num_logprobs=num_logprobs,
         tensor_parallel_size=1,
     )
+
+
+@pytest.mark.parametrize("model", models)
+def test_context_length_too_short(aphrodite_runner, image_assets, model):
+    images = [asset.pil_image for asset in image_assets]
+    with pytest.raises(ValueError, match="too long to fit into the model"):
+        aphrodite_model = aphrodite_runner(
+            model,
+            max_model_len=128,  # LLaVA has a feature size of 576
+            enforce_eager=True,
+        )
+        with aphrodite_model:
+            aphrodite_model.generate_greedy([HF_IMAGE_PROMPTS[0]],
+                                       max_tokens=1,
+                                       images=[images[0]])