[Bugfix] Proper input validation for multi-modal encoder-decoder models (#16156)

Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>

examples/offline_inference/encoder_decoder_multimodal.py

@@ -56,7 +56,7 @@ def run_florence2():
 def run_mllama():
     engine_args = EngineArgs(
         model="meta-llama/Llama-3.2-11B-Vision-Instruct",
-        max_model_len=4096,
+        max_model_len=8192,
         max_num_seqs=2,
         limit_mm_per_prompt={"image": 1},
         dtype="half",

examples/offline_inference/vision_language.py

@@ -556,7 +556,7 @@ def run_mllama(questions: list[str], modality: str) -> ModelRequestData:
     # The configuration below has been confirmed to launch on a single L40 GPU.
     engine_args = EngineArgs(
         model=model_name,
-        max_model_len=4096,
+        max_model_len=8192,
         max_num_seqs=2,
         disable_mm_preprocessor_cache=args.disable_mm_preprocessor_cache,
     )

examples/offline_inference/vision_language_multi_image.py

@@ -318,8 +318,8 @@ def load_mllama(question: str, image_urls: list[str]) -> ModelRequestData:
     # The configuration below has been confirmed to launch on a single L40 GPU.
     engine_args = EngineArgs(
         model=model_name,
-        max_model_len=4096,
+        max_model_len=8192,
-        max_num_seqs=16,
+        max_num_seqs=2,
         limit_mm_per_prompt={"image": len(image_urls)},
     )
 

tests/engine/test_short_mm_context.py

@@ -18,7 +18,8 @@ models = ["llava-hf/llava-1.5-7b-hf"]
 def test_context_length_too_short(vllm_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"):
+    with pytest.raises(ValueError,
+                       match="longer than the maximum model length"):
         vllm_model = vllm_runner(
             model,
             max_model_len=128,  # LLaVA has a feature size of 576

tests/entrypoints/llm/test_prompt_validation.py

@@ -15,7 +15,7 @@ def v1(run_with_both_engines):
 
 def test_empty_prompt():
     llm = LLM(model="openai-community/gpt2", enforce_eager=True)
-    with pytest.raises(ValueError, match='Prompt cannot be empty'):
+    with pytest.raises(ValueError, match='decoder prompt cannot be empty'):
         llm.generate([""])
 
 

tests/entrypoints/openai/test_prompt_validation.py

@@ -17,7 +17,7 @@ async def test_empty_prompt():
         client = remote_server.get_async_client()
 
         with pytest.raises(openai.BadRequestError,
-                           match=re.compile('.+Prompt cannot be empty.+')):
+                           match="decoder prompt cannot be empty"):
             await client.completions.create(model=model_name,
                                             prompt="",
                                             max_tokens=5,

tests/models/encoder_decoder/vision_language/test_mllama.py

@@ -211,7 +211,7 @@ def _run_test(
     # max_model_len should be greater than image_feature_size
     with vllm_runner(model,
                      dtype=dtype,
-                     max_model_len=4096,
+                     max_model_len=8192,
                      max_num_seqs=3,
                      tensor_parallel_size=tensor_parallel_size,
                      distributed_executor_backend=distributed_executor_backend,
@@ -422,7 +422,7 @@ def test_bnb_regression(
     llm = LLM(
         model=model,
         dtype=dtype,
-        max_model_len=4096,
+        max_model_len=8192,
         max_num_seqs=2,
         quantization="bitsandbytes",
     )
@@ -475,7 +475,7 @@ def test_explicit_implicit_prompt(
     llm = LLM(
         model=model,
         dtype=dtype,
-        max_model_len=4096,
+        max_model_len=8192,
         max_num_seqs=2,
         tensor_parallel_size=1,
     )
@@ -506,7 +506,7 @@ def test_regression(vllm_runner, image_assets, model, dtype, max_tokens,
     with global_force_attn_backend_context_manager(attn_backend), vllm_runner(
             model,
             dtype=dtype,
-            max_model_len=4096,
+            max_model_len=8192,
             max_num_seqs=2,
             tensor_parallel_size=1,
             limit_mm_per_prompt={"image":

vllm/engine/llm_engine.py

@@ -8,7 +8,7 @@ from contextlib import contextmanager
 from dataclasses import dataclass
 from functools import partial
 from typing import (TYPE_CHECKING, Any, Callable, ClassVar, Deque, Dict,
-                    Iterable, List, Mapping, NamedTuple, Optional)
+                    Iterable, List, Literal, Mapping, NamedTuple, Optional)
 from typing import Sequence as GenericSequence
 from typing import Set, Type, Union, cast, overload
 
@@ -30,7 +30,7 @@ from vllm.entrypoints.openai.logits_processors import (
     get_logits_processors as get_openai_logits_processors)
 from vllm.executor.executor_base import ExecutorBase
 from vllm.inputs import (INPUT_REGISTRY, InputRegistry, ProcessorInputs,
-                         PromptType)
+                         PromptType, SingletonInputs)
 from vllm.inputs.parse import is_token_prompt, split_enc_dec_inputs
 from vllm.inputs.preprocess import InputPreprocessor
 from vllm.logger import init_logger
@@ -40,6 +40,7 @@ from vllm.model_executor.guided_decoding import (
     get_local_guided_decoding_logits_processor)
 from vllm.model_executor.layers.sampler import SamplerOutput
 from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalRegistry
+from vllm.multimodal.processing import EncDecMultiModalProcessor
 from vllm.outputs import (PoolingRequestOutput, RequestOutput,
                           RequestOutputFactory)
 from vllm.pooling_params import PoolingParams
@@ -2029,29 +2030,57 @@ class LLMEngine:
                                lora_request: Optional[LoRARequest]):
         encoder_inputs, decoder_inputs = split_enc_dec_inputs(inputs)
 
-        # For encoder-decoder multimodal models, the max_prompt_len
+        if encoder_inputs is not None:
-        # restricts the decoder prompt length
+            self._validate_model_input(encoder_inputs,
-        if self.model_config.is_multimodal_model:
+                                       lora_request,
-            prompt_inputs = decoder_inputs
+                                       prompt_type="encoder")
-        else:
+
-            prompt_inputs = encoder_inputs or decoder_inputs
+        self._validate_model_input(decoder_inputs,
+                                   lora_request,
+                                   prompt_type="decoder")
+
+    def _validate_model_input(
+        self,
+        prompt_inputs: SingletonInputs,
+        lora_request: Optional[LoRARequest],
+        *,
+        prompt_type: Literal["encoder", "decoder"],
+    ):
+        if prompt_type == "encoder" and self.tokenizer is not None:
+            tokenizer = self.tokenizer.get_lora_tokenizer(lora_request)
+            model_config = self.model_config
+
+            if model_config.is_multimodal_model:
+                mm_registry = self.input_preprocessor.mm_registry
+                mm_processor = mm_registry.create_processor(
+                    model_config, tokenizer=tokenizer)
+                assert isinstance(mm_processor, EncDecMultiModalProcessor)
+
+                if mm_processor.pad_dummy_encoder_prompt:
+                    return  # Skip encoder length check for Whisper
 
         prompt_ids = prompt_inputs["prompt_token_ids"]
 
-        if prompt_ids is None or len(prompt_ids) == 0:
+        if not prompt_ids:
-            raise ValueError("Prompt cannot be empty")
+            raise ValueError(f"The {prompt_type} prompt cannot be empty")
 
-        if self.model_config.is_multimodal_model:
         max_prompt_len = self.model_config.max_model_len
-
+        if len(prompt_ids) >= max_prompt_len:
-            if len(prompt_ids) > max_prompt_len:
+            if self.model_config.is_multimodal_model:
-                raise ValueError(
+                suggestion = (
-                    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.")
+            else:
+                suggestion = (
+                    "Make sure that `max_model_len` is no smaller than the "
+                    "number of text tokens.")
+
+            raise ValueError(
+                f"The {prompt_type} prompt (length {len(prompt_ids)}) is "
+                f"longer than the maximum model length of {max_prompt_len}. "
+                f"{suggestion}")
 
             # TODO: Find out how many placeholder tokens are there so we can
             # check that chunked prefill does not truncate them

vllm/multimodal/profiling.py

@@ -213,8 +213,12 @@ class MultiModalProfiler(Generic[_I]):
 
         total_len = len(encoder_prompt_token_ids)
 
-        # Encoder-decoder multimodal models only support v0
+        processor = cast(EncDecMultiModalProcessor, self.processor)
-        if total_len > seq_len:
+        if processor.pad_dummy_encoder_prompt:
+            num_tokens_to_pad = max(total_len, seq_len) - total_len
+            encoder_prompt_token_ids.extend([0] * num_tokens_to_pad)
+        # NOTE: Whisper allows total_len > seq_len.
+        elif total_len > seq_len and not envs.VLLM_USE_V1:
             # `max_num_batched_tokens` is defined by `SchedulerConfig`
             logger.warning_once(
                 "The encoder sequence length used for profiling ("
@@ -229,11 +233,6 @@ class MultiModalProfiler(Generic[_I]):
                 "increase `max_model_len`, reduce `max_num_seqs`, "
                 "and/or reduce `mm_counts`.")
 
-        processor = cast(EncDecMultiModalProcessor, self.processor)
-        if processor.pad_dummy_encoder_prompt:
-            num_tokens_to_pad = max(total_len, seq_len) - total_len
-            encoder_prompt_token_ids.extend([0] * num_tokens_to_pad)
-
         return DummyEncoderData(encoder_prompt_token_ids)
 
     def get_decoder_dummy_data(

vllm/v1/engine/processor.py

@@ -2,16 +2,17 @@
 
 import time
 from collections.abc import Mapping
-from typing import Optional, Union
+from typing import Literal, Optional, Union
 
 from vllm.config import VllmConfig
-from vllm.inputs import ProcessorInputs, PromptType
+from vllm.inputs import ProcessorInputs, PromptType, SingletonInputs
 from vllm.inputs.parse import split_enc_dec_inputs
 from vllm.inputs.preprocess import InputPreprocessor
 from vllm.lora.request import LoRARequest
 from vllm.multimodal import (MULTIMODAL_REGISTRY, MultiModalKwargs,
                              MultiModalRegistry)
 from vllm.multimodal.inputs import PlaceholderRange
+from vllm.multimodal.processing import EncDecMultiModalProcessor
 from vllm.multimodal.utils import merge_and_sort_multimodal_metadata
 from vllm.pooling_params import PoolingParams
 from vllm.prompt_adapter.request import PromptAdapterRequest
@@ -287,41 +288,62 @@ class Processor:
                                lora_request: Optional[LoRARequest] = None):
         encoder_inputs, decoder_inputs = split_enc_dec_inputs(inputs)
 
-        # For encoder-decoder multimodal models, the max_prompt_len
+        if encoder_inputs is not None:
-        # restricts the decoder prompt length
+            self._validate_model_input(encoder_inputs,
-        if self.model_config.is_multimodal_model:
+                                       lora_request,
-            prompt_inputs = decoder_inputs
+                                       prompt_type="encoder")
-        else:
+
-            prompt_inputs = encoder_inputs or decoder_inputs
+        self._validate_model_input(decoder_inputs,
+                                   lora_request,
+                                   prompt_type="decoder")
+
+    def _validate_model_input(
+        self,
+        prompt_inputs: SingletonInputs,
+        lora_request: Optional[LoRARequest],
+        *,
+        prompt_type: Literal["encoder", "decoder"],
+    ):
+        tokenizer = self.tokenizer.get_lora_tokenizer(lora_request)
+
+        if prompt_type == "encoder":
+            model_config = self.model_config
+
+            if model_config.is_multimodal_model:
+                mm_registry = self.input_preprocessor.mm_registry
+                mm_processor = mm_registry.create_processor(
+                    model_config, tokenizer=tokenizer)
+                assert isinstance(mm_processor, EncDecMultiModalProcessor)
+
+                if mm_processor.pad_dummy_encoder_prompt:
+                    return  # Skip encoder length check for Whisper
 
         prompt_ids = prompt_inputs["prompt_token_ids"]
 
-        if prompt_ids is None or len(prompt_ids) == 0:
+        if not prompt_ids:
-            raise ValueError("Prompt cannot be empty")
+            raise ValueError(f"The {prompt_type} prompt cannot be empty")
 
         max_input_id = max(prompt_ids)
-        max_allowed = self.tokenizer.get_lora_tokenizer(
+        if max_input_id > tokenizer.max_token_id:
-            lora_request).max_token_id
+            raise ValueError(f"Token id {max_input_id} is out of vocabulary")
-        if max_input_id > max_allowed:
-            raise ValueError(
-                "Token id {} is out of vocabulary".format(max_input_id))
 
-        if len(prompt_ids) >= self.model_config.max_model_len:
-            raise ValueError(
-                f"Prompt length of {len(prompt_ids)} is longer than the "
-                f"maximum model length of {self.model_config.max_model_len}.")
-
-        if self.model_config.is_multimodal_model:
         max_prompt_len = self.model_config.max_model_len
-
+        if len(prompt_ids) >= max_prompt_len:
-            if len(prompt_ids) > max_prompt_len:
+            if self.model_config.is_multimodal_model:
-                raise ValueError(
+                suggestion = (
-                    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.")
+            else:
+                suggestion = (
+                    "Make sure that `max_model_len` is no smaller than the "
+                    "number of text tokens.")
+
+            raise ValueError(
+                f"The {prompt_type} prompt (length {len(prompt_ids)}) is "
+                f"longer than the maximum model length of {max_prompt_len}. "
+                f"{suggestion}")
 
             # TODO: Find out how many placeholder tokens are there so we can
             # check that chunked prefill does not truncate them