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[Misc] Clean up validation logic in input processor (#34144)

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Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>
This commit is contained in:
Cyrus Leung
2026-02-11 11:29:29 +08:00
committed by GitHub
parent 066c6da6a0
commit b5dcb372e4
3 changed files with 72 additions and 86 deletions
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1
tests/v1/engine/test_process_multi_modal_uuids.py
View File

@@ -20,7 +20,6 @@ def _build_input_processor(
) -> InputProcessor:
model_config = ModelConfig(
model="Qwen/Qwen2.5-VL-3B-Instruct",
skip_tokenizer_init=True,
max_model_len=128,
mm_processor_cache_gb=mm_cache_gb,
)

1
vllm/multimodal/encoder_budget.py
View File

@@ -62,6 +62,7 @@ class MultiModalBudget:
processor = mm_registry.create_processor(model_config, cache=cache)
self.cache = cache
self.processor = processor
mm_config = model_config.get_multimodal_config()
enable_mm_embeds = mm_config is not None and mm_config.enable_mm_embeds

156
vllm/v1/engine/input_processor.py
View File

@@ -72,13 +72,15 @@ class InputProcessor:
self.mm_registry = mm_registry
self.mm_processor_cache = mm_registry.processor_cache_from_config(vllm_config)
self.mm_encoder_cache_size: int | None = None
if (
mm_registry.supports_multimodal_inputs(model_config)
and not model_config.skip_tokenizer_init
):
self.supports_mm_inputs = mm_registry.supports_multimodal_inputs(model_config)
self.mm_encoder_cache_size = 0
self.skip_prompt_length_check = False
if self.supports_mm_inputs:
mm_budget = MultiModalBudget(vllm_config, mm_registry)
self.mm_encoder_cache_size = mm_budget.encoder_cache_size
self.skip_prompt_length_check = (
mm_budget.processor.info.skip_prompt_length_check
)
mm_budget.reset_cache() # Not used anymore
self.input_preprocessor = InputPreprocessor(
@@ -670,76 +672,25 @@ class InputProcessor:
resumable=resumable,
)
def _validate_model_inputs(
self, encoder_inputs: SingletonInputs | None, decoder_inputs: SingletonInputs
):
if encoder_inputs is not None:
self._validate_model_input(encoder_inputs, prompt_type="encoder")
self._validate_model_input(decoder_inputs, prompt_type="decoder")
def _validate_model_input(
def _validate_prompt_len(
self,
prompt_inputs: SingletonInputs,
*,
prompt_len: int,
prompt_type: Literal["encoder", "decoder"],
):
if self.skip_prompt_length_check:
return
if prompt_len == 0 and prompt_type == "decoder":
raise ValueError(f"The {prompt_type} prompt cannot be empty")
model_config = self.model_config
prompt_ids = (
None
if prompt_inputs["type"] == "embeds"
else prompt_inputs["prompt_token_ids"]
max_prompt_len = (
model_config.max_model_len
if prompt_type == "decoder"
else self.mm_encoder_cache_size
)
prompt_embeds = (
prompt_inputs["prompt_embeds"]
if prompt_inputs["type"] == "embeds"
else None
)
prompt_len = length_from_prompt_token_ids_or_embeds(prompt_ids, prompt_embeds)
if not prompt_ids:
if prompt_type == "encoder" and model_config.is_multimodal_model:
pass # Mllama may have empty encoder inputs for text-only data
elif prompt_inputs["type"] == "embeds":
pass # Prompt embeds should not have prompt_ids.
else:
raise ValueError(f"The {prompt_type} prompt cannot be empty")
tokenizer = self.tokenizer
if tokenizer is not None:
max_input_id = max(prompt_ids or (), default=0)
# NOTE: tokenizer.max_token_id is the tokenizer’s vocab size while
# self.model_config.get_vocab_size() is the model’s vocab size.
# For Qwen3 models, the language model has extra tokens that do
# not exist in the tokenizer, and vice versa for multimodal
# placeholder tokens in some multimodal models.
# See https://github.com/QwenLM/Qwen3/issues/29#issuecomment-1933720399 # noqa: E501
# and https://github.com/vllm-project/vllm/pull/22471#discussion_r2312251421 # noqa: E501
# Here we take the max of the two to determine if a token id is
# truly out-of-vocabulary.
if max_input_id > max(
tokenizer.max_token_id, self.model_config.get_vocab_size() - 1
):
raise ValueError(f"Token id {max_input_id} is out of vocabulary")
max_prompt_len = self.model_config.max_model_len
if prompt_len > max_prompt_len:
if model_config.is_multimodal_model:
mm_registry = self.input_preprocessor.mm_registry
model_cls = mm_registry._get_model_cls(model_config)
factories = model_cls._processor_factory
ctx = mm_registry._create_processing_ctx(
model_config,
tokenizer=tokenizer,
)
mm_info = factories.info(ctx)
if mm_info.skip_prompt_length_check:
return
if model_config.is_multimodal_model:
if self.supports_mm_inputs:
suggestion = (
"Make sure that `max_model_len` is no smaller than the "
"number of text tokens plus multimodal tokens. For image "
@@ -757,17 +708,7 @@ class InputProcessor:
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
# max_batch_len = self.scheduler_config.max_num_batched_tokens
if (
prompt_len == max_prompt_len
and prompt_type == "decoder"
and not model_config.is_multimodal_model
and self.model_config.runner_type != "pooling"
):
elif prompt_len == max_prompt_len and model_config.runner_type == "generate":
suggestion = (
"Make sure that `max_model_len` is no smaller than the "
"number of text tokens (prompt + requested output tokens)."
@@ -778,11 +719,29 @@ class InputProcessor:
f"model length of {max_prompt_len}. {suggestion}"
)
if (
prompt_type == "decoder"
and prompt_inputs["type"] == "multimodal"
and self.mm_encoder_cache_size is not None
):
def _validate_model_input(
self,
prompt_inputs: SingletonInputs,
prompt_type: Literal["encoder", "decoder"],
) -> None:
model_config = self.model_config
tokenizer = self.tokenizer
prompt_ids = (
None
if prompt_inputs["type"] == "embeds"
else prompt_inputs["prompt_token_ids"]
)
prompt_embeds = (
prompt_inputs["prompt_embeds"]
if prompt_inputs["type"] == "embeds"
else None
)
prompt_len = length_from_prompt_token_ids_or_embeds(prompt_ids, prompt_embeds)
self._validate_prompt_len(prompt_len, prompt_type)
if prompt_inputs["type"] == "multimodal":
decoder_mm_positions = prompt_inputs["mm_placeholders"]
for modality, mm_positions in decoder_mm_positions.items():
for mm_position in mm_positions:
@@ -797,6 +756,33 @@ class InputProcessor:
f"by setting --limit-mm-per-prompt at startup."
)
if prompt_ids and tokenizer is not None:
max_input_id = max(prompt_ids, default=0)
# NOTE: tokenizer.max_token_id is the tokenizer’s vocab size while
# self.model_config.get_vocab_size() is the model’s vocab size.
# For Qwen3 models, the language model has extra tokens that do
# not exist in the tokenizer, and vice versa for multimodal
# placeholder tokens in some multimodal models.
# See https://github.com/QwenLM/Qwen3/issues/29#issuecomment-1933720399 # noqa: E501
# and https://github.com/vllm-project/vllm/pull/22471#discussion_r2312251421 # noqa: E501
# Here we take the max of the two to determine if a token id is
# truly out-of-vocabulary.
model_vocab_size = model_config.get_vocab_size()
if max_input_id > max(tokenizer.max_token_id, model_vocab_size - 1):
raise ValueError(f"Token id {max_input_id} is out of vocabulary")
def _validate_model_inputs(
self,
encoder_inputs: SingletonInputs | None,
decoder_inputs: SingletonInputs,
):
if encoder_inputs is not None:
self._validate_model_input(encoder_inputs, prompt_type="encoder")
self._validate_model_input(decoder_inputs, prompt_type="decoder")
def stat_mm_cache(self) -> MultiModalCacheStats | None:
return self.input_preprocessor.stat_mm_cache()