b1340f9d55
SUMMARY: * avoid crashing the engine when we get an input longer than max_model_len FIX #12567(*link existing issues this PR will resolve*)
229 lines
9.5 KiB
Python
229 lines
9.5 KiB
Python
import time
|
|
from typing import Mapping, Optional, Union
|
|
|
|
from vllm.config import CacheConfig, LoRAConfig, ModelConfig
|
|
from vllm.inputs import (INPUT_REGISTRY, InputRegistry, ProcessorInputs,
|
|
PromptType, SingletonInputsAdapter)
|
|
from vllm.inputs.parse import is_encoder_decoder_inputs
|
|
from vllm.inputs.preprocess import InputPreprocessor
|
|
from vllm.lora.request import LoRARequest
|
|
from vllm.multimodal import (MULTIMODAL_REGISTRY, MultiModalHasher,
|
|
MultiModalKwargs, MultiModalRegistry)
|
|
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 BaseTokenizerGroup
|
|
from vllm.v1.engine import EngineCoreRequest
|
|
from vllm.v1.engine.mm_input_mapper import MMInputMapperClient
|
|
|
|
|
|
class Processor:
|
|
|
|
def __init__(
|
|
self,
|
|
model_config: ModelConfig,
|
|
cache_config: CacheConfig,
|
|
lora_config: Optional[LoRAConfig],
|
|
tokenizer: BaseTokenizerGroup,
|
|
input_registry: InputRegistry = INPUT_REGISTRY,
|
|
mm_registry: MultiModalRegistry = MULTIMODAL_REGISTRY,
|
|
):
|
|
|
|
self.model_config = model_config
|
|
self.lora_config = lora_config
|
|
self.tokenizer = tokenizer
|
|
|
|
self.generation_config_fields = model_config.try_get_generation_config(
|
|
)
|
|
self.input_preprocessor = InputPreprocessor(model_config,
|
|
self.tokenizer,
|
|
mm_registry)
|
|
self.input_processor = input_registry.create_input_processor(
|
|
model_config)
|
|
|
|
# Multi-modal (huggingface) input mapper
|
|
self.mm_input_mapper_client = MMInputMapperClient(model_config)
|
|
|
|
# Multi-modal hasher (for images)
|
|
self.use_hash = (not model_config.disable_mm_preprocessor_cache) or \
|
|
cache_config.enable_prefix_caching
|
|
|
|
def process_inputs(
|
|
self,
|
|
request_id: str,
|
|
prompt: PromptType,
|
|
params: Union[SamplingParams, PoolingParams],
|
|
arrival_time: Optional[float] = None,
|
|
lora_request: Optional[LoRARequest] = None,
|
|
trace_headers: Optional[Mapping[str, str]] = None,
|
|
prompt_adapter_request: Optional[PromptAdapterRequest] = None,
|
|
priority: int = 0,
|
|
) -> EngineCoreRequest:
|
|
|
|
# TODO(woosuk): Support pooling models.
|
|
# TODO(woosuk): Check max_logprobs
|
|
# TODO(woosuk): Support encoder-decoder models.
|
|
|
|
if lora_request is not None and not self.lora_config:
|
|
raise ValueError(f"Got lora_request {lora_request} but LoRA is "
|
|
"not enabled!")
|
|
if arrival_time is None:
|
|
arrival_time = time.time()
|
|
assert priority == 0, "vLLM V1 does not support priority at the moment."
|
|
assert trace_headers is None, "vLLM V1 does not support tracing yet."
|
|
|
|
# Process inputs.
|
|
preprocessed_inputs = self.input_preprocessor.preprocess(
|
|
prompt,
|
|
request_id=request_id,
|
|
lora_request=lora_request,
|
|
prompt_adapter_request=prompt_adapter_request,
|
|
)
|
|
processed_inputs = self.input_processor(preprocessed_inputs)
|
|
self._validate_model_inputs(processed_inputs)
|
|
eos_token_id = self.input_preprocessor.get_eos_token_id(lora_request)
|
|
|
|
if is_encoder_decoder_inputs(processed_inputs):
|
|
decoder_inputs = SingletonInputsAdapter(
|
|
processed_inputs["decoder"])
|
|
encoder_inputs = SingletonInputsAdapter(
|
|
processed_inputs["encoder"])
|
|
else:
|
|
decoder_inputs = SingletonInputsAdapter(processed_inputs)
|
|
encoder_inputs = None
|
|
|
|
# TODO: Impl encoder-decoder
|
|
if encoder_inputs is not None:
|
|
raise NotImplementedError
|
|
|
|
assert isinstance(params, SamplingParams)
|
|
# TODO: can we avoid cloning here in multiproc case
|
|
sampling_params = params.clone()
|
|
sampling_params.update_from_generation_config(
|
|
self.generation_config_fields, eos_token_id)
|
|
|
|
# Multimodal related.
|
|
# Compute MM hashes (if enabled)
|
|
mm_hashes = None
|
|
if self.use_hash:
|
|
# Use mm_hashes from processed inputs if the model has merged
|
|
# input processor.
|
|
if decoder_inputs.multi_modal_hashes:
|
|
mm_hashes = decoder_inputs.multi_modal_hashes
|
|
# Fallback to using MultiModalHasher directly.
|
|
else:
|
|
mm_hashes = MultiModalHasher.hash_prompt_mm_data(prompt)
|
|
|
|
# For merged preprocessor, mm_data is already mm_inputs
|
|
precomputed_mm_inputs: Optional[list[MultiModalKwargs]] = None
|
|
decoder_mm_data = decoder_inputs.multi_modal_data
|
|
if isinstance(decoder_mm_data, MultiModalKwargs):
|
|
# The output of merged multi-modal processor (`decoder_mm_data`)
|
|
# contains the kwargs for all items from all modalities.
|
|
# This code separates them so that there is one set of kwargs
|
|
# per item per modality.
|
|
precomputed_mm_inputs = [
|
|
MultiModalKwargs.from_items([item])
|
|
for modality in decoder_mm_data.modalities
|
|
for item in decoder_mm_data.get_items(modality)
|
|
]
|
|
|
|
mm_positions = decoder_inputs.multi_modal_placeholders
|
|
|
|
# Last-mile processing of multimodal metadata and inputs.
|
|
if mm_positions:
|
|
|
|
# Merge and flatten multimodal placeholders, hashes and inputs
|
|
# from dictionaries to lists, and sort them by each item's position
|
|
# in the input sequence.
|
|
# NOTE: interleaved modalities are not supported.
|
|
(
|
|
sorted_modalities,
|
|
sorted_mm_positions,
|
|
sorted_mm_hashes,
|
|
) = merge_and_sort_multimodal_metadata(
|
|
mm_positions,
|
|
mm_hashes,
|
|
)
|
|
|
|
# NOTE: Sort multimodal inputs/kwargs ONLY IF there are multiple
|
|
# modalities involved AND the model supports merged input processor.
|
|
if len(sorted_modalities) > 1 and precomputed_mm_inputs:
|
|
|
|
modality_order_dict = {
|
|
modality: order
|
|
for order, modality in enumerate(sorted_modalities)
|
|
}
|
|
|
|
# Sanity check to make sure each multimodal input has only one
|
|
# modality key.
|
|
for mm_input in precomputed_mm_inputs:
|
|
assert len(mm_input.modalities) == 1
|
|
|
|
# Sort MultiModalKwags to match sorted_mm_positions
|
|
precomputed_mm_inputs = sorted(
|
|
precomputed_mm_inputs,
|
|
key=lambda mm_input: modality_order_dict[list(
|
|
mm_input.modalities)[0]])
|
|
|
|
# Apply mm input cache update (and input mapper if necessary).
|
|
sorted_mm_inputs = self.mm_input_mapper_client.process_inputs(
|
|
mm_data=decoder_mm_data,
|
|
mm_hashes=sorted_mm_hashes,
|
|
mm_processor_kwargs=decoder_inputs.mm_processor_kwargs,
|
|
precomputed_mm_inputs=precomputed_mm_inputs,
|
|
)
|
|
else:
|
|
sorted_mm_inputs = None
|
|
sorted_mm_hashes = None
|
|
sorted_mm_positions = None
|
|
|
|
return EngineCoreRequest(
|
|
request_id=request_id,
|
|
prompt=decoder_inputs.prompt,
|
|
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,
|
|
eos_token_id=eos_token_id,
|
|
arrival_time=arrival_time,
|
|
lora_request=lora_request,
|
|
)
|
|
|
|
def _validate_model_inputs(self, inputs: ProcessorInputs):
|
|
if is_encoder_decoder_inputs(inputs):
|
|
# For encoder-decoder multimodal models, the max_prompt_len
|
|
# restricts the decoder prompt length
|
|
prompt_inputs = inputs["decoder" if self.model_config.
|
|
is_multimodal_model else "encoder"]
|
|
else:
|
|
prompt_inputs = inputs
|
|
|
|
prompt_ids = SingletonInputsAdapter(prompt_inputs).prompt_token_ids
|
|
|
|
if prompt_ids is None or len(prompt_ids) == 0:
|
|
raise ValueError("Prompt cannot be empty")
|
|
|
|
if len(prompt_ids) >= self.model_config.max_model_len:
|
|
raise ValueError(
|
|
f"Prompt length of {len(prompt_ids)} is longer than the "
|
|
f"maximum model length of {self.model_config.max_model_len}.")
|
|
|
|
if self.model_config.is_multimodal_model:
|
|
max_prompt_len = self.model_config.max_model_len
|
|
|
|
if len(prompt_ids) > max_prompt_len:
|
|
raise ValueError(
|
|
f"The prompt (total length {len(prompt_ids)}) is too long "
|
|
f"to fit into the model (context length {max_prompt_len}). "
|
|
"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.")
|
|
|
|
# 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
|