class InputProcessor:
def __init__(
self,
vllm_config: VllmConfig,
mm_registry: MultiModalRegistry = MULTIMODAL_REGISTRY,
) -> None:
self.vllm_config = vllm_config
self.model_config = model_config = vllm_config.model_config
self.cache_config = vllm_config.cache_config
self.lora_config = vllm_config.lora_config
self.scheduler_config = vllm_config.scheduler_config
self.structured_outputs_config = vllm_config.structured_outputs_config
self.observability_config = vllm_config.observability_config
self.generation_config_fields = model_config.try_get_generation_config()
self.mm_registry = mm_registry
self.mm_processor_cache = mm_registry.processor_cache_from_config(vllm_config)
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(
model_config,
self.observability_config,
mm_registry,
mm_processor_cache=self.mm_processor_cache,
)
@property
def tokenizer(self) -> TokenizerLike | None:
return self.input_preprocessor.tokenizer
def get_tokenizer(self) -> TokenizerLike:
return self.input_preprocessor.get_tokenizer()
@property
def renderer(self) -> BaseRenderer:
return self.input_preprocessor.renderer
def _validate_logprobs(
self,
params: SamplingParams,
) -> None:
max_logprobs = self.model_config.max_logprobs
if max_logprobs == -1:
max_logprobs = self.model_config.get_vocab_size()
# Validate sample logprobs.
if params.logprobs:
num_logprobs = params.logprobs
if num_logprobs == -1:
num_logprobs = self.model_config.get_vocab_size()
if num_logprobs > max_logprobs:
raise VLLMValidationError(
f"Requested sample logprobs of {num_logprobs}, "
f"which is greater than max allowed: {max_logprobs}",
parameter="logprobs",
value=num_logprobs,
)
# Validate prompt logprobs.
if params.prompt_logprobs:
num_prompt_logprobs = params.prompt_logprobs
if num_prompt_logprobs == -1:
num_prompt_logprobs = self.model_config.get_vocab_size()
if num_prompt_logprobs > max_logprobs:
raise VLLMValidationError(
f"Requested prompt logprobs of {num_prompt_logprobs}, "
f"which is greater than max allowed: {max_logprobs}",
parameter="prompt_logprobs",
value=num_prompt_logprobs,
)
def _validate_sampling_params(
self,
params: SamplingParams,
) -> None:
self._validate_structured_output(params)
self._validate_logit_bias(params)
if params.allowed_token_ids is None:
return
if not params.allowed_token_ids:
raise ValueError("allowed_token_ids is not None and empty!")
if self.tokenizer is None:
# When skip_tokenizer_init=True, we can't validate token IDs
# Skip validation and let the model handle invalid tokens
return
vocab_size = len(self.tokenizer)
if not all(0 <= tid < vocab_size for tid in params.allowed_token_ids):
raise ValueError("allowed_token_ids contains out-of-vocab token id!")
def _validate_logit_bias(
self,
params: SamplingParams,
) -> None:
"""Validate logit_bias token IDs are within vocabulary range."""
if not params.logit_bias:
return
vocab_size = self.model_config.get_vocab_size()
invalid_token_ids = []
for token_id in params.logit_bias:
if token_id < 0 or token_id >= vocab_size:
invalid_token_ids.append(token_id)
if invalid_token_ids:
raise VLLMValidationError(
f"token_id(s) {invalid_token_ids} in logit_bias contain "
f"out-of-vocab token ids. Vocabulary size: {vocab_size}",
parameter="logit_bias",
value=invalid_token_ids,
)
def _validate_supported_sampling_params(
self,
params: SamplingParams,
) -> None:
# Logits processors not supported.
if params.logits_processors:
raise ValueError(
"vLLM V1 does not support per request user-provided logits processors."
)
# Some sampling parameters are not yet compatible with spec decoding.
if self.vllm_config.speculative_config is not None and (
params.min_tokens > 1 or params.min_p > _SAMPLING_EPS or params.logit_bias
):
raise ValueError(
"The min_tokens, min_p, and logit_bias sampling parameters "
"are not yet supported with speculative decoding."
)
def _validate_params(
self,
params: SamplingParams | PoolingParams,
# TODO: Validate generation tasks as well once `supported_tasks`
# is passed to all `process_inputs` calls
supported_tasks: tuple[SupportedTask, ...] | None,
):
"""
Validate supported SamplingParam.
Should raise ValueError if unsupported for API Server.
"""
if isinstance(params, PoolingParams):
if supported_tasks is None:
raise RuntimeError("`supported_tasks` must be passed for pooling")
supported_pooling_tasks = [
task for task in supported_tasks if task in POOLING_TASKS
]
if params.task is None:
if not supported_pooling_tasks:
raise ValueError("Pooling tasks are not supported")
if "token_embed" in supported_pooling_tasks:
params.task = "token_embed"
elif "token_classify" in supported_pooling_tasks:
params.task = "token_classify"
elif "plugin" in supported_pooling_tasks:
params.task = "plugin"
if params.task not in supported_pooling_tasks:
raise ValueError(
f"Unsupported task: {params.task!r} "
f"Supported tasks: {supported_pooling_tasks}"
)
params.verify(self.model_config)
return
self._validate_logprobs(params)
self._validate_sampling_params(params)
self._validate_supported_sampling_params(params)
def _parse_mm_items(self, mm_data: MultiModalDataDict) -> MultiModalDataItems:
mm_processor = self.input_preprocessor._get_mm_processor()
return mm_processor.info.parse_mm_data(mm_data)
def _validate_singleton_mm_uuids(self, prompt: SingletonPrompt) -> None:
if not isinstance(prompt, dict):
return
mm_data = cast(MultiModalDataDict, prompt.get("multi_modal_data") or {})
mm_uuids = cast(MultiModalUUIDDict, prompt.get("multi_modal_uuids") or {})
if not mm_data and not mm_uuids:
return
mm_data_parsed = self._parse_mm_items(
{k: v for k, v in mm_data.items() if v is not None}
)
mm_uuids_parsed = {
k: [v] if isinstance(v, str) else v
for k, v in mm_uuids.items()
if v is not None
}
# NOTE: Include the keys corresponding to `None`
modalities = mm_data.keys() | mm_uuids.keys()
for modality in modalities:
data_items = cast(
ModalityDataItems | list[Any], mm_data_parsed.get(modality, [])
)
uuid_items = cast(list[str | None], mm_uuids_parsed.get(modality, []))
if len(data_items) > 0:
if len(uuid_items) > 0 and len(data_items) != len(uuid_items):
raise ValueError(
f"If given, multi_modal_uuids[{modality!r}] must have "
f"same length as multi_modal_data[{modality!r}], but "
f"got {len(uuid_items)} vs {len(data_items)}."
)
for i, item in enumerate(data_items):
if item is None:
if not uuid_items:
raise ValueError(
f"multi_modal_data[{modality!r}][{i}] is empty but "
f"multi_modal_uuids[{modality!r}] is missing."
)
if uuid_items[i] is None:
raise ValueError(
f"multi_modal_data[{modality!r}][{i}] is empty but "
f"multi_modal_uuids[{modality!r}][{i}] is missing."
)
else:
if len(uuid_items) == 0:
raise ValueError(
f"multi_modal_data[{modality!r}] is empty but "
f"multi_modal_uuids[{modality!r}] is missing."
)
def _validate_mm_uuids(self, prompt: PromptType | DictPrompt | TokPrompt) -> None:
"""
Validate that user-provided multi_modal_uuids align with
multi_modal_data in the incoming request prompt(s).
Only checks lengths; `None` entries are allowed and will be
auto-hashed downstream.
"""
if isinstance(prompt, dict) and "encoder_prompt" in prompt:
self._validate_singleton_mm_uuids(prompt["encoder_prompt"]) # type: ignore[typeddict-item]
if (dec_prompt := prompt["decoder_prompt"]) is not None: # type: ignore[typeddict-item]
self._validate_singleton_mm_uuids(dec_prompt)
else:
self._validate_singleton_mm_uuids(prompt)
def _validate_lora(self, lora_request: LoRARequest | None) -> None:
if lora_request is None:
return
# LoRA request passed in while LoRA is not enabled
if not self.lora_config:
raise ValueError(
f"Got lora_request {lora_request} but LoRA is not enabled!"
)
if self.tokenizer is not None:
logger.warning_once(
"vLLM has deprecated support for supporting different "
"tokenizers for different LoRAs. By default, vLLM uses base "
"model's tokenizer. If you are using a LoRA "
"with its own tokenizer, consider specifying `--tokenizer "
"[lora_path]` to use the LoRA tokenizer."
)
def _validate_structured_output(self, params: SamplingParams) -> None:
if not params.structured_outputs or not self.structured_outputs_config:
return
if self.model_config.skip_tokenizer_init and params.structured_outputs:
raise ValueError(
"Structured outputs requires a tokenizer so it can't be used with 'skip_tokenizer_init'" # noqa: E501
)
backend = self.structured_outputs_config.backend
if _backend := params.structured_outputs._backend:
# Request-level backend selection is not supported.
# The values may differ if `params` is reused and was set
# to a specific backend based on `auto` behavior in a previous
# request. We remember that it was set as a result of `auto`
# using the `_backend_was_auto` field set in the params.
if backend != _backend and not (
backend == "auto" and params.structured_outputs._backend_was_auto
):
raise ValueError(
"Request-level structured output backend selection is not "
f"supported. The request specified '{_backend}', but vLLM "
f"was initialised with '{backend}'. This error can be "
"resolved by removing '_backend' from the request."
)
else:
params.structured_outputs._backend = backend
# Request content validation
if (
isinstance(params.structured_outputs.choice, list)
and not params.structured_outputs.choice
):
# It is invalid for choice to be an empty list
raise ValueError(
f"Choice '{params.structured_outputs.choice}' cannot be an empty list" # noqa: E501
)
# Reject empty string grammar early to avoid engine-side crashes
if (
isinstance(params.structured_outputs.grammar, str)
and params.structured_outputs.grammar.strip() == ""
):
raise ValueError("structured_outputs.grammar cannot be an empty string")
if backend.startswith("xgrammar"):
# xgrammar with no fallback
validate_xgrammar_grammar(params)
elif backend.startswith("guidance"):
# TODO: ideally we would have the LLTokenizer here as Lark syntax
# allows <|special_token|> and similar, see
# https://github.com/guidance-ai/llguidance/blob/main/docs/syntax.md#special-tokens
# Without tokenizer these are disallowed in grammars.
if isinstance(self.tokenizer, MistralTokenizer):
raise ValueError(
"Mistral tokenizer is not supported for the 'guidance' "
"structured output backend. Please use ['xgrammar', 'outlines'] "
"backends or tokenizer_mode='hf' instead."
)
validate_guidance_grammar(params, tokenizer=None)
elif backend == "outlines":
# outlines backend
validate_structured_output_request_outlines(params)
elif backend == "lm-format-enforcer":
# lm format enforcer backend
if isinstance(self.tokenizer, MistralTokenizer):
raise ValueError(
"Mistral tokenizer is not supported for the 'lm-format-enforcer' "
"structured output backend. Please use ['xgrammar', 'outlines'] "
"backends or tokenizer_mode='hf' instead."
)
validate_structured_output_request_lm_format_enforcer(params)
else:
# NOTE: backend must be "auto" here, because we have
# checked supported_backends above.
# In this mode, we set opinionated defaults based on what we think
# will satisfy the most use cases without having to worry about
# this setting. We include fallback behavior here, but not with any
# other setting where a specific backend was specified.
try:
validate_xgrammar_grammar(params)
params.structured_outputs._backend = "xgrammar"
except ValueError:
# The request either failed validation
# or includes some jsonschema feature(s) that
# are not supported in xgrammar.
# Check if schema has features unsupported by guidance
so_params = params.structured_outputs
skip_guidance = False
if so_params.json:
if isinstance(so_params.json, str):
import json
schema = json.loads(so_params.json)
else:
schema = so_params.json
skip_guidance = has_guidance_unsupported_json_features(schema)
if isinstance(self.tokenizer, MistralTokenizer) or skip_guidance:
# Fall back to outlines if the tokenizer is Mistral
# or if schema contains features unsupported by guidance
validate_structured_output_request_outlines(params)
params.structured_outputs._backend = "outlines"
else:
# Fall back to guidance by default.
validate_guidance_grammar(params, tokenizer=None)
params.structured_outputs._backend = "guidance"
# Remember that this backend was set automatically
params.structured_outputs._backend_was_auto = True
# Run post-init validation. This is also important to ensure subsequent
# roundtrip serialization/deserialization won't fail.
params.structured_outputs.__post_init__()
def _extract_singleton_mm_data(
self, prompt: SingletonPrompt
) -> MultiModalDataDict | None:
if not isinstance(prompt, dict):
return None
return prompt.get("multi_modal_data")
def _extract_mm_data(
self, prompt: PromptType | DictPrompt | TokPrompt
) -> MultiModalDataDict | None:
if isinstance(prompt, dict) and "encoder_prompt" in prompt:
return self._extract_singleton_mm_data(prompt["encoder_prompt"]) # type: ignore[typeddict-item]
else:
return self._extract_singleton_mm_data(prompt)
def _maybe_build_mm_uuids(
self,
request_id: str,
prompt: PromptType | DictPrompt | TokPrompt,
) -> MultiModalUUIDDict | None:
"""Build per-item multimodal hash overrides when enabled. In this case,
multimodal data items are identified by their request id, modality and
index rather than their content.
Returns a dictionary of modality -> list[str] of overrides, or None if
disabled or no multimodal data is present.
"""
mm_data = self._extract_mm_data(prompt)
if not mm_data:
return None
mm_items = self._parse_mm_items(
{k: v for k, v in mm_data.items() if v is not None}
)
return {
modality: [f"{request_id}-{modality}-{i}" for i in range(data_count)]
for modality, data_count in mm_items.get_all_counts().items()
}
def _get_mm_identifier(
self,
mm_hash: str,
lora_request: LoRARequest | None,
) -> str:
"""
When enable_tower_connector_lora is True, multi-modal embeddings
vary depending on the LoRA request. Therefore, the mm_hash must be
generated based on the LoRA request to prevent incorrect cache hits.
"""
if (
lora_request is None
or self.lora_config is None
or not self.lora_config.enable_tower_connector_lora
):
return mm_hash
return f"{lora_request.lora_name}:{mm_hash}"
@staticmethod
def assign_request_id(request: EngineCoreRequest):
"""Replace the externally supplied request ID with an internal request ID
that adds 8 random characters in order to ensure uniquness.
"""
if request.external_req_id is not None:
raise ValueError(
"The external_req_id field should not be set on EngineCoreRequests"
" passed to vLLM; use the request_id field."
)
request.external_req_id = request.request_id
request.request_id = f"{request.external_req_id}-{random_uuid():.8}"
def process_inputs(
self,
request_id: str,
prompt: PromptType | DictPrompt | TokPrompt,
params: SamplingParams | PoolingParams,
arrival_time: float | None = None,
lora_request: LoRARequest | None = None,
tokenization_kwargs: dict[str, Any] | None = None,
trace_headers: Mapping[str, str] | None = None,
priority: int = 0,
data_parallel_rank: int | None = None,
supported_tasks: tuple[SupportedTask, ...] | None = None,
resumable: bool = False,
) -> EngineCoreRequest:
self._validate_lora(lora_request)
self._validate_params(params, supported_tasks)
parallel_config = self.vllm_config.parallel_config
dp_size = parallel_config.data_parallel_size
dp_local_size = parallel_config.data_parallel_size_local
num_ranks = dp_local_size if parallel_config.local_engines_only else dp_size
if data_parallel_rank is not None and not (0 <= data_parallel_rank < num_ranks):
raise ValueError(
f"data_parallel_rank {data_parallel_rank} "
f"is out of range [0, {num_ranks})."
)
if arrival_time is None:
arrival_time = time.time()
# Optionally generate multimodal hash overrides to avoid hashing
# multimodal data items by their content as their identifiers.
# NOTE: when users explicitly turn off BOTH prefix caching and input
# processing caching, no multimodal features or embeddings will be
# reused across requests, therefore identifying multimodal data items
# by their content is no longer necessary, and we create uuids with
# request id-modality-index as multimodal hash overrides.
if (
self.model_config.multimodal_config
and self.model_config.multimodal_config.mm_processor_cache_gb == 0
and not self.cache_config.enable_prefix_caching
):
mm_uuids = self._maybe_build_mm_uuids(request_id, prompt)
else:
# Otherwise, use user-provided uuids as multimodal hash overrides
# if provided.
self._validate_mm_uuids(prompt)
if isinstance(prompt, dict):
mm_uuids = cast(
MultiModalUUIDDict | None, prompt.get("multi_modal_uuids")
)
else:
mm_uuids = None
# Process inputs, which includes:
# 1. Tokenize text prompt, with LoRA request if one exists.
# 2. For multimodal models with a merged preprocessor, preprocess
# multimodal data and expand prompt token ids accordingly.
with set_request_id(request_id), set_default_torch_num_threads():
processed_inputs: ProcessorInputs = self.input_preprocessor.preprocess(
prompt,
tokenization_kwargs=tokenization_kwargs,
mm_uuids=mm_uuids,
)
from vllm.platforms import current_platform
current_platform.validate_request(
prompt=prompt,
params=params,
processed_inputs=processed_inputs,
)
eos_token_id = self.input_preprocessor.get_eos_token_id()
encoder_inputs, decoder_inputs = split_enc_dec_inputs(processed_inputs)
self._validate_model_inputs(encoder_inputs, decoder_inputs)
# Mypy can be conservative for TypedDict unions; normalize access.
if decoder_inputs["type"] == "embeds":
prompt_token_ids = None
prompt_embeds = decoder_inputs["prompt_embeds"]
else:
prompt_token_ids = decoder_inputs["prompt_token_ids"]
prompt_embeds = None
sampling_params = None
pooling_params = None
if isinstance(params, SamplingParams):
# TODO: can we avoid cloning here in multiproc case?
sampling_params = params.clone()
# If unset max tokens, then generate up to the max_model_len.
if sampling_params.max_tokens is None:
seq_len = length_from_prompt_token_ids_or_embeds(
prompt_token_ids, prompt_embeds
)
sampling_params.max_tokens = self.model_config.max_model_len - seq_len
sampling_params.update_from_generation_config(
self.generation_config_fields, eos_token_id
)
if self.tokenizer is not None:
sampling_params.update_from_tokenizer(self.tokenizer)
else:
pooling_params = params.clone()
# Multimodal related.
mm_features: list[MultiModalFeatureSpec] | None = None
if decoder_inputs["type"] == "multimodal":
decoder_mm_inputs = decoder_inputs["mm_kwargs"]
decoder_mm_positions = decoder_inputs["mm_placeholders"]
decoder_mm_hashes = decoder_inputs["mm_hashes"]
# Merge and flatten multimodal placeholders, hashes and inputs
# from dictionaries to lists, and sort them by each item's position
# in the input sequence.
sorted_mm_idxs = argsort_mm_positions(decoder_mm_positions)
mm_features = []
for modality, idx in sorted_mm_idxs:
base_mm_hash = decoder_mm_hashes[modality][idx]
mm_features.append(
MultiModalFeatureSpec(
data=decoder_mm_inputs[modality][idx],
modality=modality,
identifier=self._get_mm_identifier(
base_mm_hash,
lora_request,
),
mm_position=decoder_mm_positions[modality][idx],
mm_hash=base_mm_hash,
)
)
return EngineCoreRequest(
request_id=request_id,
prompt_token_ids=prompt_token_ids,
prompt_embeds=prompt_embeds,
mm_features=mm_features,
sampling_params=sampling_params,
pooling_params=pooling_params,
eos_token_id=eos_token_id,
arrival_time=arrival_time,
lora_request=lora_request,
cache_salt=decoder_inputs.get("cache_salt"),
priority=priority,
data_parallel_rank=data_parallel_rank,
trace_headers=trace_headers,
resumable=resumable,
)
def _validate_prompt_len(
self,
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
max_prompt_len = (
model_config.max_model_len
if prompt_type == "decoder"
else self.mm_encoder_cache_size
)
if prompt_len > max_prompt_len:
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 "
"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 {prompt_len}) is "
f"longer than the maximum model length of {max_prompt_len}. "
f"{suggestion}"
)
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)."
)
raise ValueError(
f"The {prompt_type} prompt (length {prompt_len}) plus the number of "
f"requested output tokens (at least 1) is longer than the maximum "
f"model length of {max_prompt_len}. {suggestion}"
)
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:
embed_length = mm_position.get_num_embeds()
if embed_length > self.mm_encoder_cache_size:
raise ValueError(
f"The {prompt_type} prompt contains a(n) {modality} item "
f"with length {embed_length}, which exceeds the "
f"pre-allocated encoder cache size "
f"{self.mm_encoder_cache_size}. Please reduce the input "
f"size or increase the encoder cache size "
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()
def clear_mm_cache(self) -> None:
self.input_preprocessor.clear_mm_cache()
def close(self) -> None:
if self.mm_processor_cache is not None:
self.mm_processor_cache.close()