init

vllm_vacc/vllm/v1/engine/processor.py

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+
+
+import time
+from collections.abc import Mapping
+from typing import Any, Literal, Optional, Union
+
+import torch
+
+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.logger import init_logger
+from vllm.lora.request import LoRARequest
+from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalRegistry
+from vllm.multimodal.cache import processor_cache_from_config
+from vllm.multimodal.inputs import MultiModalFeatureSpec, MultiModalUUIDDict
+from vllm.multimodal.processing import EncDecMultiModalProcessor
+from vllm.multimodal.utils import argsort_mm_positions
+from vllm.pooling_params import PoolingParams
+from vllm.sampling_params import SamplingParams
+from vllm.transformers_utils.tokenizer import AnyTokenizer
+from vllm.utils import length_from_prompt_token_ids_or_embeds
+from vllm.v1.engine import EngineCoreRequest
+from vllm.v1.structured_output.backend_guidance import (
+    validate_guidance_grammar)
+from vllm.v1.structured_output.backend_lm_format_enforcer import (
+    validate_structured_output_request_lm_format_enforcer)
+from vllm.v1.structured_output.backend_outlines import (
+    validate_structured_output_request_outlines)
+from vllm.v1.structured_output.backend_xgrammar import (
+    validate_xgrammar_grammar)
+
+logger = init_logger(__name__)
+
+
+class Processor:
+
+    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.
+        self._validate_lora(lora_request)
+        self._validate_params(params)
+
+        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_uuids = self._maybe_build_mm_uuids(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_uuids = 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.
+        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 does not always properly infer the types of some elements of
+        # discriminated unions of TypedDicts, because of how it handles
+        # inheritance of TypedDict. If we explicitly extract the items we want
+        # we can avoid type errors from using `dict.get` later in the method.
+        prompt_str: Optional[str] = None if decoder_inputs[
+            "type"] == "embeds" else decoder_inputs.get("prompt")
+        prompt_token_ids = decoder_inputs[
+            "prompt_token_ids"] if decoder_inputs["type"] != "embeds" else None
+        prompt_embeds = decoder_inputs["prompt_embeds"] if decoder_inputs[
+            "type"] == "embeds" else None
+        deepstack_input_embeds = decoder_inputs["deepstack_input_embeds"] if decoder_inputs[
+            "type"] == "embeds" else None
+        
+        # for deepstack_input_embeds in llm.generate method
+        if isinstance(deepstack_input_embeds, dict):
+            all_tensors = []
+            for key in deepstack_input_embeds:
+                if isinstance(deepstack_input_embeds[key], torch.Tensor):
+                    all_tensors.append(deepstack_input_embeds[key].unsqueeze(0))
+            if len(all_tensors) > 0:
+                deepstack_input_embeds = torch.concatenate(all_tensors, 0)
+
+        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: 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 prompt_str, EngineCoreRequest(
+            request_id=request_id,
+            prompt_token_ids=prompt_token_ids,
+            prompt_embeds=prompt_embeds,
+            deepstack_input_embeds=deepstack_input_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,
+        )