processor.py

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

import time
from collections.abc import Mapping, Sequence
from typing import Any, Literal, Optional, Union

from vllm.config import VllmConfig
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
from vllm.sampling_params import SamplingParams
from vllm.transformers_utils.tokenizer_group import TokenizerGroup
from vllm.v1.engine import EngineCoreRequest
from vllm.v1.engine.mm_input_cache import MirroredProcessingCache
from vllm.v1.structured_output.backend_guidance import (
    validate_guidance_grammar)
from vllm.v1.structured_output.backend_xgrammar import (
    validate_xgrammar_grammar)


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

        self.mm_input_cache_client = MirroredProcessingCache(self.model_config)

        # Multi-modal hasher (for images)
        self.use_hash = self.mm_input_cache_client.use_cache or \
            self.cache_config.enable_prefix_caching

    @property
    def mm_registry(self):
        return self.input_preprocessor.mm_registry

    def _validate_logprobs(
        self,
        params: SamplingParams,
    ) -> None:
        max_logprobs = self.model_config.max_logprobs
        # Validate sample logprobs.
        if params.logprobs and 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!")
        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_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

        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 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)
        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 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,
        prompt_adapter_request: Optional[PromptAdapterRequest] = 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 priority != 0:
            raise ValueError("V1 does not support priority yet.")
        if trace_headers is not None:
            raise ValueError("V1 does not support tracing yet.")
        if prompt_adapter_request is not None:
            raise ValueError("V1 does not support prompt_adapter_request.")

        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()

        # 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.
        # 3. Apply prompt adapter to prompt token ids if one exists.
        processed_inputs: ProcessorInputs = self.input_preprocessor.preprocess(
            prompt,
            tokenization_kwargs=tokenization_kwargs,
            lora_request=lora_request,
            prompt_adapter_request=prompt_adapter_request,
            return_mm_hashes=self.use_hash,
        )
        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)
            sampling_params.update_from_tokenizer(
                self.tokenizer.get_lora_tokenizer(lora_request))
        else:
            pooling_params = params.clone()

        # Multimodal related.
        sorted_mm_inputs: Optional[Sequence[Optional[MultiModalKwargs]]] = None
        sorted_mm_positions: Optional[list[PlaceholderRange]] = None
        sorted_mm_hashes: Optional[list[str]] = None
        if decoder_inputs["type"] == "multimodal":
            decoder_mm_inputs = decoder_inputs["mm_kwargs"]

            # 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_item_modalities,
                sorted_mm_positions,
                sorted_mm_hashes,
            ) = merge_and_sort_multimodal_metadata(
                decoder_inputs["mm_placeholders"],
                decoder_inputs["mm_hashes"] if self.use_hash else None,
            )

            # The output of merged multi-modal processor (`decoder_mm_inputs`)
            # is a single MultiModalKwargs for all items from all modalities.
            # This code flattens kwargs for individual items in a list and
            # sorts them by each item's position in the input sequence if there
            # are multiple modalities.
            unique_modalities = set(sorted_item_modalities)
            if len(unique_modalities) > 1:
                orig_sorted_mm_inputs = []
                used_indices = {modality: 0 for modality in unique_modalities}

                for modality in sorted_item_modalities:
                    items = decoder_mm_inputs.get_items(modality)
                    item = items[used_indices[modality]]

                    orig_sorted_mm_inputs.append(
                        MultiModalKwargs.from_items([item]))
                    used_indices[modality] += 1
            else:
                orig_sorted_mm_inputs = [
                    MultiModalKwargs.from_items([item]) for item in
                    decoder_mm_inputs.get_items(sorted_item_modalities[0])
                ]

            if sorted_mm_hashes is not None:
                sorted_mm_inputs = self.mm_input_cache_client.get_and_update_p0(
                    orig_sorted_mm_inputs, sorted_mm_hashes)
            else:
                sorted_mm_inputs = orig_sorted_mm_inputs

        return decoder_inputs.get("prompt"), EngineCoreRequest(
            request_id=request_id,
            prompt_token_ids=decoder_inputs["prompt_token_ids"],
            mm_inputs=sorted_mm_inputs,
            mm_hashes=sorted_mm_hashes,
            mm_placeholders=sorted_mm_positions,
            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"),
            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
        tokenizer = self.tokenizer.get_lora_tokenizer(lora_request)

        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")

        max_input_id = max(prompt_ids, default=0)
        if max_input_id > tokenizer.max_token_id:
            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

            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