vllm.v1.engine.processor

class Processor:

    def __init__(
        self,
        vllm_config: VllmConfig,
        tokenizer: TokenizerGroup,
        mm_registry: MultiModalRegistry = MULTIMODAL_REGISTRY,
    ):

        self.vllm_config = vllm_config
        self.model_config = vllm_config.model_config
        self.cache_config = vllm_config.cache_config
        self.lora_config = vllm_config.lora_config
        self.decoding_config = vllm_config.decoding_config
        self.tokenizer = tokenizer

        self.generation_config_fields = (
            self.model_config.try_get_generation_config())

        self.mm_registry = mm_registry
        self.mm_processor_cache = processor_cache_from_config(
            vllm_config, mm_registry)

        self.input_preprocessor = InputPreprocessor(
            self.model_config,
            self.tokenizer,
            mm_registry,
            mm_processor_cache=self.mm_processor_cache,
        )

    def _validate_logprobs(
        self,
        params: SamplingParams,
    ) -> None:
        max_logprobs = self.model_config.max_logprobs
        if max_logprobs == -1:
            return
        # Validate sample logprobs.
        if params.logprobs and (params.logprobs == -1
                                or params.logprobs > max_logprobs):
            raise ValueError(
                f"Requested sample logprobs of {params.logprobs}, "
                f"which is greater than max allowed: {max_logprobs}")

        # Validate prompt logprobs.
        if params.prompt_logprobs and params.prompt_logprobs > max_logprobs:
            raise ValueError(
                f"Requested prompt logprobs of {params.prompt_logprobs}, "
                f"which is greater than max allowed: {max_logprobs}")

    def _validate_sampling_params(
        self,
        params: SamplingParams,
        lora_request: Optional[LoRARequest],
    ) -> 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
        tokenizer = self.tokenizer.get_lora_tokenizer(lora_request)
        vocab_size = len(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 ValueError(
                f"token_id(s) {invalid_token_ids} in logit_bias contain "
                f"out-of-vocab token ids. Vocabulary size: {vocab_size}")

    def _validate_supported_sampling_params(
        self,
        params: SamplingParams,
    ) -> None:
        # Best of not yet supported.
        if params.best_of is not None and params.best_of > 1:
            raise ValueError("vLLM V1 does not yet support best_of.")
        # Logits processors not supported.
        if params.logits_processors:
            raise ValueError("vLLM V1 does not support per request "
                             "user provided logits processors.")

    def _validate_params(
        self,
        params: Union[SamplingParams, PoolingParams],
        lora_request: Optional[LoRARequest],
    ):
        """
        Validate supported SamplingParam.
        Should raise ValueError if unsupported for API Server.
        """

        if isinstance(params, PoolingParams):
            return

        self._validate_logprobs(params)
        self._validate_sampling_params(params, lora_request)
        self._validate_supported_sampling_params(params)

    def _validate_multi_modal_uuids(self, prompt: PromptType) -> 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.
        """

        def _validate_single_prompt(single_prompt: Union[dict, str]) -> None:
            if not isinstance(single_prompt, dict):
                return
            mm_data = single_prompt.get("multi_modal_data")
            mm_uuids = single_prompt.get("multi_modal_uuids")
            if not mm_data or not mm_uuids:
                return

            for modality, items in mm_data.items():
                if modality in mm_uuids:
                    data_len = len(items) if isinstance(items, list) else 1
                    uuid_len = len(mm_uuids[modality]) if isinstance(
                        mm_uuids[modality], list) else 1
                    if uuid_len != data_len:
                        raise ValueError(
                            f"multi_modal_uuids for modality '{modality}' "
                            "must have same length as data: got "
                            f"{uuid_len} uuids vs "
                            f"{data_len} items.")
                else:
                    raise ValueError(
                        f"multi_modal_uuids for modality '{modality}' must "
                        "be provided if multi_modal_data is provided.")

        # Handle explicit encoder/decoder prompts or singleton prompt
        if isinstance(prompt, dict) and "encoder_prompt" in prompt:
            enc = prompt.get("encoder_prompt")
            dec = prompt.get("decoder_prompt")
            if enc is not None:
                _validate_single_prompt(enc)
            if dec is not None:
                _validate_single_prompt(dec)
        else:
            _validate_single_prompt(prompt)  # type: ignore[arg-type]

    def _validate_lora(self, lora_request: Optional[LoRARequest]) -> None:
        if lora_request is not None and not self.lora_config:
            raise ValueError(f"Got lora_request {lora_request} but LoRA is "
                             "not enabled!")

    def _validate_structured_output(self, params: SamplingParams) -> None:
        if not params.guided_decoding or not self.decoding_config:
            return

        if self.model_config.skip_tokenizer_init and params.guided_decoding:
            raise ValueError(
                "Structured outputs requires a tokenizer so it can't be used with 'skip_tokenizer_init'"  # noqa: E501
            )

        engine_level_backend = self.decoding_config.backend
        if params.guided_decoding.backend:
            # Request-level backend selection is not supported in V1.
            # 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 `_auto` option set on the backend in the params.
            if (params.guided_decoding.backend != engine_level_backend
                    and not (engine_level_backend == "auto"
                             and params.guided_decoding.backend_was_auto)):
                raise ValueError(
                    "Request-level structured output backend selection is no "
                    "longer supported. The request specified "
                    f"'{params.guided_decoding.backend}', but vLLM was "
                    f"initialised with '{engine_level_backend}'. This error "
                    "can be resolved by removing backend selection from the "
                    "request.")
        else:
            params.guided_decoding.backend = engine_level_backend

        # Request content validation
        if (isinstance(params.guided_decoding.choice, list)
                and not params.guided_decoding.choice):
            # It is invalid for choice to be an empty list
            raise ValueError(f"Choice '{params.guided_decoding.choice}' "
                             "cannot be an empty list")

        if engine_level_backend.startswith("xgrammar"):
            # xgrammar with no fallback
            validate_xgrammar_grammar(params)
        elif engine_level_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.
            validate_guidance_grammar(params, tokenizer=None)
        elif engine_level_backend == "outlines":
            # outlines backend
            validate_structured_output_request_outlines(params)
        elif engine_level_backend == "lm-format-enforcer":
            # lm format enforcer backend
            validate_structured_output_request_lm_format_enforcer(params)
        else:
            # NOTE: engine_level_backend must be "auto" here, because we have
            # checked supported_backends above.
            # "auto" is an opt-in to opinionated behavior where we try to
            # choose a backend based on request contents. This is not the
            # default as it is less predictable and subject to change
            # between releases as feature support changes.
            try:
                validate_xgrammar_grammar(params)
                params.guided_decoding.backend = "xgrammar"
            except ValueError:
                # The request either failed validation
                # or includes some jsonschema feature(s) that
                # are not supported in xgrammar. Fall back to guidance.
                validate_guidance_grammar(params, tokenizer=None)
                params.guided_decoding.backend = "guidance"
            # Remember that this backend was set automatically
            params.guided_decoding.backend_was_auto = True

    def _maybe_build_mm_hash_overrides(
        self,
        request_id: str,
        prompt: PromptType,
    ) -> Optional[dict[str, list[str]]]:
        """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.
        """

        def _extract_mm_data(p: PromptType):
            if isinstance(p, dict) and "encoder_prompt" in p:
                enc = p.get("encoder_prompt")
                if isinstance(enc, dict):
                    return enc.get("multi_modal_data")
                return None
            if isinstance(p, dict):
                return p.get("multi_modal_data")
            return None

        mm_data = _extract_mm_data(prompt)
        if not mm_data:
            return None

        overrides: dict[str, list[str]] = {}
        for modality, data in mm_data.items():
            n = len(data) if isinstance(data, list) else 1
            overrides[modality] = [
                f"{request_id}-{modality}-{i}" for i in range(n)
            ]
        return overrides

    def process_inputs(
        self,
        request_id: str,
        prompt: PromptType,
        params: Union[SamplingParams, PoolingParams],
        arrival_time: Optional[float] = None,
        lora_request: Optional[LoRARequest] = None,
        tokenization_kwargs: Optional[dict[str, Any]] = None,
        trace_headers: Optional[Mapping[str, str]] = None,
        priority: int = 0,
        data_parallel_rank: Optional[int] = None,
    ) -> tuple[Optional[str], EngineCoreRequest]:

        # TODO(woosuk): Support pooling models.
        # TODO(woosuk): Support encoder-decoder models.
        self._validate_lora(lora_request)
        self._validate_params(params, lora_request)
        if trace_headers is not None:
            raise ValueError("V1 does not support tracing yet.")

        data_parallel_size = self.vllm_config.parallel_config.data_parallel_size
        if data_parallel_rank is not None and not (0 <= data_parallel_rank <
                                                   data_parallel_size):
            raise ValueError(f"data_parallel_rank {data_parallel_rank} "
                             f"is out of range [0, {data_parallel_size}).")

        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_hash_overrides = self._maybe_build_mm_hash_overrides(
                request_id, prompt)
        else:
            # Otherwise, use user-provided uuids as multimodal hash overrides
            # if provided.
            self._validate_multi_modal_uuids(prompt)
            if isinstance(prompt, dict):
                mm_hash_overrides = prompt.get("multi_modal_uuids")
            else:
                mm_hash_overrides = 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.
        processed_inputs: ProcessorInputs = self.input_preprocessor.preprocess(
            prompt,
            tokenization_kwargs=tokenization_kwargs,
            lora_request=lora_request,
            mm_hash_overrides=mm_hash_overrides,
        )
        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(lora_request)

        self._validate_model_inputs(processed_inputs, lora_request)

        encoder_inputs, decoder_inputs = split_enc_dec_inputs(processed_inputs)

        # TODO: Impl encoder-decoder
        if encoder_inputs is not None:
            raise NotImplementedError

        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:
                sampling_params.max_tokens = (
                    self.model_config.max_model_len -
                    len(decoder_inputs["prompt_token_ids"]))
            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.get_lora_tokenizer(lora_request))
        else:
            pooling_params = params.clone()

        # Multimodal related.
        mm_features: Optional[list[MultiModalFeatureSpec]] = 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:
                mm_features.append(
                    MultiModalFeatureSpec(
                        data=decoder_mm_inputs[modality][idx],
                        modality=modality,
                        identifier=decoder_mm_hashes[modality][idx],
                        mm_position=decoder_mm_positions[modality][idx]))

        return decoder_inputs.get("prompt"), EngineCoreRequest(
            request_id=request_id,
            prompt_token_ids=decoder_inputs["prompt_token_ids"],
            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,
        )

    def _validate_model_inputs(self,
                               inputs: ProcessorInputs,
                               lora_request: Optional[LoRARequest] = None):
        encoder_inputs, decoder_inputs = split_enc_dec_inputs(inputs)

        if encoder_inputs is not None:
            self._validate_model_input(encoder_inputs,
                                       lora_request,
                                       prompt_type="encoder")

        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"],
    ):
        model_config = self.model_config

        prompt_ids = prompt_inputs["prompt_token_ids"]
        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
            else:
                raise ValueError(f"The {prompt_type} prompt cannot be empty")

        if self.model_config.skip_tokenizer_init:
            tokenizer = None
        else:
            tokenizer = self.tokenizer.get_lora_tokenizer(lora_request)
            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.
            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 len(prompt_ids) > max_prompt_len:
            if prompt_type == "encoder" and 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 and Donut

            if model_config.is_multimodal_model:
                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 {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
            # max_batch_len = self.scheduler_config.max_num_batched_tokens

    def clear_cache(self) -> None:
        self.input_preprocessor.clear_cache()