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[V1] Remove legacy input registry (#15673)

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Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>
This commit is contained in:
Cyrus Leung 2025-03-28 14:34:34 +08:00 committed by GitHub
parent 8693e47e6a
commit 355f66348c
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17 changed files with 132 additions and 153 deletions
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7
tests/models/multimodal/processing/test_h2ovl.py
View File

@ -10,7 +10,6 @@ from transformers import PretrainedConfig
from vllm.multimodal import MULTIMODAL_REGISTRY
from vllm.multimodal.image import rescale_image_size
from vllm.multimodal.processing import BaseMultiModalProcessor
from vllm.transformers_utils.tokenizer import cached_tokenizer_from_config
from ....conftest import _ImageAssets
from ...utils import build_model_context
@ -156,11 +155,7 @@ def test_processor_override(
mm_processor_kwargs=mm_processor_kwargs if kwargs_on_init else None,
limit_mm_per_prompt={"image": len(size_factors)},
)
tokenizer = cached_tokenizer_from_config(ctx.model_config)
processor = MULTIMODAL_REGISTRY.create_processor(
ctx.model_config,
tokenizer=tokenizer,
)
processor = MULTIMODAL_REGISTRY.create_processor(ctx.model_config)
hf_processor_mm_kwargs = {} if kwargs_on_init else mm_processor_kwargs
min_num = min_dynamic_patch if dynamic_image_size else 1

7
tests/models/multimodal/processing/test_idefics3.py
View File

@ -4,7 +4,6 @@ import pytest
from transformers import Idefics3Config
from vllm.multimodal import MULTIMODAL_REGISTRY
from vllm.transformers_utils.tokenizer import cached_tokenizer_from_config
from ....conftest import _ImageAssets
from ...utils import build_model_context
@ -38,11 +37,7 @@ def test_processor_override(
mm_processor_kwargs=mm_processor_kwargs if kwargs_on_init else None,
limit_mm_per_prompt={"image": num_imgs},
)
tokenizer = cached_tokenizer_from_config(ctx.model_config)
processor = MULTIMODAL_REGISTRY.create_processor(
ctx.model_config,
tokenizer=tokenizer,
)
processor = MULTIMODAL_REGISTRY.create_processor(ctx.model_config)
hf_processor_mm_kwargs = {} if kwargs_on_init else mm_processor_kwargs
# Build the image str / prompt based on the number of images we pass

7
tests/models/multimodal/processing/test_internvl.py
View File

@ -10,7 +10,6 @@ from transformers import PretrainedConfig
from vllm.multimodal import MULTIMODAL_REGISTRY
from vllm.multimodal.image import rescale_image_size
from vllm.multimodal.processing import BaseMultiModalProcessor
from vllm.transformers_utils.tokenizer import cached_tokenizer_from_config
from ....conftest import _ImageAssets
from ...utils import build_model_context
@ -113,11 +112,7 @@ def test_processor_override(
mm_processor_kwargs=mm_processor_kwargs if kwargs_on_init else None,
limit_mm_per_prompt={"image": len(size_factors)},
)
tokenizer = cached_tokenizer_from_config(ctx.model_config)
processor = MULTIMODAL_REGISTRY.create_processor(
ctx.model_config,
tokenizer=tokenizer,
)
processor = MULTIMODAL_REGISTRY.create_processor(ctx.model_config)
hf_processor_mm_kwargs = {} if kwargs_on_init else mm_processor_kwargs
min_num = min_dynamic_patch if dynamic_image_size else 1

16
tests/models/multimodal/processing/test_llava_next.py
View File

@ -10,7 +10,6 @@ from pqdm.threads import pqdm
from vllm.multimodal import MULTIMODAL_REGISTRY
from vllm.multimodal.parse import ImageSize
from vllm.multimodal.processing import BaseMultiModalProcessor
from vllm.transformers_utils.tokenizer import cached_tokenizer_from_config
from ...utils import build_model_context
@ -40,10 +39,7 @@ def test_processor_max_tokens(model_id):
mm_processor_kwargs=None,
limit_mm_per_prompt={"image": 1},
)
processor = MULTIMODAL_REGISTRY.create_processor(
ctx.model_config,
tokenizer=cached_tokenizer_from_config(ctx.model_config),
)
processor = MULTIMODAL_REGISTRY.create_processor(ctx.model_config)
info = processor.info
seen_aspect_ratios = set[float]()
@ -139,10 +135,7 @@ def test_processor_prompt_replacements_regression(model_id, num_imgs):
mm_processor_kwargs=None,
limit_mm_per_prompt={"image": num_imgs},
)
processor = MULTIMODAL_REGISTRY.create_processor(
ctx.model_config,
tokenizer=cached_tokenizer_from_config(ctx.model_config),
)
processor = MULTIMODAL_REGISTRY.create_processor(ctx.model_config)
image_ratios = [(171, 152), (184, 161), (198, 176), (333, 296), (369, 328),
(488, 183), (2560, 1669)]
@ -168,10 +161,7 @@ def test_processor_prompt_replacements_all(model_id, num_imgs):
mm_processor_kwargs=None,
limit_mm_per_prompt={"image": num_imgs},
)
processor = MULTIMODAL_REGISTRY.create_processor(
ctx.model_config,
tokenizer=cached_tokenizer_from_config(ctx.model_config),
)
processor = MULTIMODAL_REGISTRY.create_processor(ctx.model_config)
seen_aspect_ratios = set[float]()
image_sizes = list[ImageSize]()

16
tests/models/multimodal/processing/test_llava_onevision.py
View File

@ -10,7 +10,6 @@ from pqdm.threads import pqdm
from vllm.multimodal import MULTIMODAL_REGISTRY
from vllm.multimodal.parse import ImageSize
from vllm.multimodal.processing import BaseMultiModalProcessor
from vllm.transformers_utils.tokenizer import cached_tokenizer_from_config
from ...utils import build_model_context
@ -41,10 +40,7 @@ def test_processor_max_tokens(model_id):
mm_processor_kwargs=None,
limit_mm_per_prompt={"image": 1},
)
processor = MULTIMODAL_REGISTRY.create_processor(
ctx.model_config,
tokenizer=cached_tokenizer_from_config(ctx.model_config),
)
processor = MULTIMODAL_REGISTRY.create_processor(ctx.model_config)
info = processor.info
seen_aspect_ratios = set[float]()
@ -139,10 +135,7 @@ def test_processor_prompt_replacements_regression(model_id, num_imgs):
mm_processor_kwargs=None,
limit_mm_per_prompt={"image": num_imgs},
)
processor = MULTIMODAL_REGISTRY.create_processor(
ctx.model_config,
tokenizer=cached_tokenizer_from_config(ctx.model_config),
)
processor = MULTIMODAL_REGISTRY.create_processor(ctx.model_config)
image_ratios = [(171, 152), (184, 161), (198, 176), (333, 296), (369, 328),
(488, 183), (2560, 1669)]
@ -169,10 +162,7 @@ def test_processor_prompt_replacements_all(model_id, num_imgs):
mm_processor_kwargs=None,
limit_mm_per_prompt={"image": num_imgs},
)
processor = MULTIMODAL_REGISTRY.create_processor(
ctx.model_config,
tokenizer=cached_tokenizer_from_config(ctx.model_config),
)
processor = MULTIMODAL_REGISTRY.create_processor(ctx.model_config)
seen_aspect_ratios = set[float]()
image_sizes = list[ImageSize]()

7
tests/models/multimodal/processing/test_phi3v.py
View File

@ -3,7 +3,6 @@
import pytest
from vllm.multimodal import MULTIMODAL_REGISTRY
from vllm.transformers_utils.tokenizer import cached_tokenizer_from_config
from ....conftest import _ImageAssets
from ...utils import build_model_context
@ -39,11 +38,7 @@ def test_processor_override(
mm_processor_kwargs=mm_processor_kwargs if kwargs_on_init else None,
limit_mm_per_prompt={"image": num_imgs},
)
tokenizer = cached_tokenizer_from_config(ctx.model_config)
processor = MULTIMODAL_REGISTRY.create_processor(
ctx.model_config,
tokenizer=tokenizer,
)
processor = MULTIMODAL_REGISTRY.create_processor(ctx.model_config)
hf_processor_mm_kwargs = {} if kwargs_on_init else mm_processor_kwargs
# Build the image str / prompt based on the number of images we pass

8
tests/models/multimodal/processing/test_qwen2_vl.py
View File

@ -3,7 +3,6 @@
import pytest
from vllm.multimodal import MULTIMODAL_REGISTRY
from vllm.transformers_utils.tokenizer import cached_tokenizer_from_config
from ....conftest import _ImageAssets
from ...utils import build_model_context
@ -34,11 +33,8 @@ def test_processor_override(
mm_processor_kwargs=mm_processor_kwargs if kwargs_on_init else None,
limit_mm_per_prompt={"image": num_imgs},
)
tokenizer = cached_tokenizer_from_config(ctx.model_config)
processor = MULTIMODAL_REGISTRY.create_processor(
ctx.model_config,
tokenizer=tokenizer,
)
processor = MULTIMODAL_REGISTRY.create_processor(ctx.model_config)
tokenizer = processor.info.get_tokenizer()
hf_processor_mm_kwargs = {} if kwargs_on_init else mm_processor_kwargs
# Build the image str / prompt based on the number of images we pass

18
tests/multimodal/test_processing.py
View File

@ -28,8 +28,7 @@ from vllm.multimodal.processing import (PlaceholderFeaturesInfo,
replace_token_matches)
# yapf: enable
from vllm.multimodal.profiling import MultiModalProfiler
from vllm.transformers_utils.tokenizer import (AnyTokenizer,
cached_tokenizer_from_config)
from vllm.transformers_utils.tokenizer import AnyTokenizer
from vllm.utils import full_groupby
from .utils import random_image
@ -955,10 +954,7 @@ def test_limit_mm_per_prompt_dummy(model_id, limit, num_supported, is_valid):
limit_mm_per_prompt=limit_mm_per_prompt,
)
processor = MULTIMODAL_REGISTRY.create_processor(
model_config,
tokenizer=cached_tokenizer_from_config(model_config),
)
processor = MULTIMODAL_REGISTRY.create_processor(model_config)
profiler = MultiModalProfiler(processor)
mock_supported_mm_limits = MagicMock(return_value={"image": num_supported})
@ -994,10 +990,7 @@ def test_limit_mm_per_prompt_apply(model_id, num_images, limit, is_valid):
limit_mm_per_prompt=limit_mm_per_prompt,
)
processor = MULTIMODAL_REGISTRY.create_processor(
model_config,
tokenizer=cached_tokenizer_from_config(model_config),
)
processor = MULTIMODAL_REGISTRY.create_processor(model_config)
rng = np.random.RandomState(0)
image = random_image(rng, min_wh=128, max_wh=256)
@ -1066,10 +1059,7 @@ def test_hf_processor_kwargs(model_id, call_kwargs, expected_kwargs):
revision=None,
)
processor = MULTIMODAL_REGISTRY.create_processor(
model_config,
tokenizer=cached_tokenizer_from_config(model_config),
)
processor = MULTIMODAL_REGISTRY.create_processor(model_config)
orig_get_hf_processor = processor.info.get_hf_processor
def get_hf_processor(self, **kwargs):

12
vllm/inputs/preprocess.py
View File

@ -261,13 +261,13 @@ class InputPreprocessor:
# initialized without a tokenizer while using also multi-modal
# input.
if not self.tokenizer:
tokenizer = None
tokenizer = object() # Dummy
else:
tokenizer_group = self.get_tokenizer_group()
tokenizer = tokenizer_group.get_lora_tokenizer(lora_request)
mm_processor = self.mm_registry.create_processor(
self.model_config, tokenizer)
mm_processor = self.mm_registry.create_processor(self.model_config,
tokenizer=tokenizer)
if mm_processor_kwargs is None:
mm_processor_kwargs = {}
@ -288,14 +288,14 @@ class InputPreprocessor:
# initialized without a tokenizer while using also multi-modal
# input.
if not self.tokenizer:
tokenizer = None
tokenizer = object() # Dummy
else:
tokenizer_group = self.get_tokenizer_group()
tokenizer = await tokenizer_group.get_lora_tokenizer_async(
lora_request)
mm_processor = self.mm_registry.create_processor(
self.model_config, tokenizer)
mm_processor = self.mm_registry.create_processor(self.model_config,
tokenizer=tokenizer)
if mm_processor_kwargs is None:
mm_processor_kwargs = {}

25
vllm/inputs/registry.py
View File

@ -13,8 +13,7 @@ from typing_extensions import TypeVar, assert_never
from vllm.logger import init_logger
from vllm.transformers_utils.processor import cached_processor_from_config
from vllm.transformers_utils.tokenizer import (AnyTokenizer,
cached_tokenizer_from_config)
from vllm.transformers_utils.tokenizer import AnyTokenizer
from vllm.utils import (ClassRegistry, get_allowed_kwarg_only_overrides,
resolve_mm_processor_kwargs)
@ -329,17 +328,27 @@ class InputRegistry:
from vllm.model_executor.model_loader import get_model_architecture
from vllm.multimodal import MultiModalKwargs
from vllm.multimodal.profiling import MultiModalProfiler
from vllm.sequence import SequenceData
if mm_registry.has_processor(model_config):
tokenizer = cached_tokenizer_from_config(model_config)
processor = mm_registry.create_processor(model_config,
tokenizer,
disable_cache=True)
profiler = MultiModalProfiler(processor)
dummy_data_factory = (profiler.get_encoder_dummy_data
if is_encoder_data else
profiler.get_decoder_dummy_data)
dummy_data = dummy_data_factory(seq_len)
dummy_data_v1 = (profiler.get_encoder_dummy_data(seq_len)
if is_encoder_data else
profiler.get_decoder_dummy_data(seq_len))
_seq_data = SequenceData.from_seqs(
dummy_data_v1.prompt_token_ids) # type: ignore[attr-defined]
dummy_data = DummyData(
seq_data=_seq_data,
multi_modal_data=getattr(dummy_data_v1, "multi_modal_data",
None),
multi_modal_placeholders=getattr(dummy_data_v1,
"multi_modal_placeholders",
None),
)
else:
model_cls, _ = get_model_architecture(model_config)
if is_encoder_data:

55
vllm/multimodal/profiling.py
View File

@ -3,18 +3,18 @@
from abc import ABC, abstractmethod
from collections.abc import Mapping
from dataclasses import dataclass, field
from typing import Generic, TypeVar, cast
from typing import Generic, NamedTuple, TypeVar, cast
import numpy as np
import numpy.typing as npt
from PIL import Image
import vllm.envs as envs
from vllm.inputs import DummyData
from vllm.logger import init_logger
from .inputs import (MultiModalDataDict, MultiModalEncDecInputs,
MultiModalInputs)
MultiModalInputs, MultiModalKwargs,
MultiModalPlaceholderDict)
from .processing import BaseMultiModalProcessor, BaseProcessingInfo
logger = init_logger(__name__)
@ -31,6 +31,20 @@ class ProcessorInputs:
hf_processor_mm_kwargs: Mapping[str, object] = field(default_factory=dict)
class DummyEncoderData(NamedTuple):
"""Dummy data used for profiling."""
prompt_token_ids: list[int]
class DummyDecoderData(NamedTuple):
"""Dummy data used for profiling."""
prompt_token_ids: list[int]
multi_modal_data: MultiModalKwargs
multi_modal_placeholders: MultiModalPlaceholderDict
_I = TypeVar("_I", bound=BaseProcessingInfo)
@ -179,13 +193,7 @@ class MultiModalProfiler(Generic[_I]):
"tokens.")
return mm_inputs, total_placeholders_by_modality
def get_encoder_dummy_data(
self,
seq_len: int,
) -> DummyData:
# Avoid circular import
from vllm.sequence import SequenceData
def get_encoder_dummy_data(self, seq_len: int) -> DummyEncoderData:
mm_inputs, _ = self.get_and_validate_mm_inputs(seq_len)
mm_inputs = cast(MultiModalEncDecInputs, mm_inputs)
@ -197,19 +205,9 @@ class MultiModalProfiler(Generic[_I]):
num_tokens_to_pad = max(total_len, seq_len) - total_len
encoder_prompt_token_ids.extend([0] * num_tokens_to_pad)
return DummyData(
seq_data=SequenceData.from_seqs(encoder_prompt_token_ids),
multi_modal_data=None,
multi_modal_placeholders=None,
)
def get_decoder_dummy_data(
self,
seq_len: int,
) -> DummyData:
# Avoid circular import
from vllm.sequence import SequenceData
return DummyEncoderData(encoder_prompt_token_ids)
def get_decoder_dummy_data(self, seq_len: int) -> DummyDecoderData:
(mm_inputs, total_placeholders_by_modality
) = self.get_and_validate_mm_inputs(seq_len)
@ -231,16 +229,11 @@ class MultiModalProfiler(Generic[_I]):
"and/or reduce `mm_counts`.", seq_len, total_len,
total_placeholders_by_modality)
return DummyData(
seq_data=SequenceData.from_prompt_token_counts((0, seq_len)),
multi_modal_data=None,
multi_modal_placeholders=None,
)
if total_len < seq_len:
prompt_token_ids.extend([0] * (seq_len - total_len))
prompt_token_ids.extend([0] * (seq_len - len(prompt_token_ids)))
return DummyData(
seq_data=SequenceData.from_seqs(prompt_token_ids),
return DummyDecoderData(
prompt_token_ids=prompt_token_ids,
multi_modal_data=mm_inputs["mm_kwargs"],
multi_modal_placeholders=mm_inputs["mm_placeholders"],
)

63
vllm/multimodal/registry.py
View File

@ -21,7 +21,8 @@ from .image import ImagePlugin
from .inputs import MultiModalDataDict, MultiModalKwargs, NestedTensors
from .processing import (BaseMultiModalProcessor, BaseProcessingInfo,
ProcessingCache)
from .profiling import BaseDummyInputsBuilder, MultiModalProfiler
from .profiling import (BaseDummyInputsBuilder, DummyDecoderData,
DummyEncoderData, MultiModalProfiler)
from .video import VideoPlugin
if TYPE_CHECKING:
@ -256,10 +257,7 @@ class MultiModalRegistry:
on underlying model configuration.
"""
if self.has_processor(model_config):
tokenizer = cached_tokenizer_from_config(model_config)
processor = self.create_processor(model_config,
tokenizer,
disable_cache=True)
processor = self.create_processor(model_config, disable_cache=True)
seq_len = model_config.max_model_len
mm_limits = self.get_mm_limits_per_prompt(model_config)
return processor.info.get_mm_max_tokens_per_item(
@ -373,10 +371,7 @@ class MultiModalRegistry:
This should be called after :meth:`init_mm_limits_per_prompt`.
"""
if self.has_processor(model_config):
tokenizer = cached_tokenizer_from_config(model_config)
processor = self.create_processor(model_config,
tokenizer,
disable_cache=True)
processor = self.create_processor(model_config, disable_cache=True)
profiler = MultiModalProfiler(processor)
return profiler.get_mm_limits()
@ -436,8 +431,8 @@ class MultiModalRegistry:
def create_processor(
self,
model_config: "ModelConfig",
tokenizer: AnyTokenizer,
*,
tokenizer: Optional[AnyTokenizer] = None,
disable_cache: Optional[bool] = None,
) -> BaseMultiModalProcessor[BaseProcessingInfo]:
"""
@ -446,6 +441,8 @@ class MultiModalRegistry:
See also:
:ref:`mm-processing`
"""
if tokenizer is None:
tokenizer = cached_tokenizer_from_config(model_config)
if disable_cache is None:
disable_cache = model_config.disable_mm_preprocessor_cache
@ -456,3 +453,49 @@ class MultiModalRegistry:
cache = None if disable_cache else self._processing_cache
return factories.build_processor(ctx, cache=cache)
def get_decoder_dummy_data(
self,
model_config: "ModelConfig",
seq_len: int,
) -> DummyDecoderData:
"""
Create dummy data for profiling the memory usage of a model.
The model is identified by ``model_config``.
"""
processor = self.create_processor(model_config, disable_cache=True)
profiler = MultiModalProfiler(processor)
dummy_data = profiler.get_decoder_dummy_data(seq_len)
# Having more tokens is over-conservative but otherwise fine
token_ids = dummy_data.prompt_token_ids
if len(token_ids) < seq_len:
raise AssertionError(
f"Expected at least {seq_len} dummy tokens for profiling, "
f"but found {len(token_ids)} tokens instead.")
return dummy_data
def get_encoder_dummy_data(
self,
model_config: "ModelConfig",
seq_len: int,
) -> DummyEncoderData:
"""
Create dummy data for profiling the memory usage of a model.
The model is identified by ``model_config``.
"""
processor = self.create_processor(model_config, disable_cache=True)
profiler = MultiModalProfiler(processor)
dummy_data = profiler.get_encoder_dummy_data(seq_len)
# Having more tokens is over-conservative but otherwise fine
token_ids = dummy_data.prompt_token_ids
if len(token_ids) < seq_len:
logger.warning_once(
f"Expected at least {seq_len} dummy encoder tokens for "
f"profiling, but found {len(token_ids)} tokens instead.")
return dummy_data

7
vllm/v1/engine/async_llm.py
View File

@ -14,10 +14,11 @@ from vllm.config import ModelConfig, VllmConfig
from vllm.engine.arg_utils import AsyncEngineArgs
from vllm.engine.protocol import EngineClient
from vllm.envs import VLLM_V1_OUTPUT_PROC_CHUNK_SIZE
from vllm.inputs import INPUT_REGISTRY, InputRegistry, PromptType
from vllm.inputs import PromptType
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.outputs import RequestOutput
from vllm.pooling_params import PoolingParams
from vllm.prompt_adapter.request import PromptAdapterRequest
@ -48,7 +49,7 @@ class AsyncLLM(EngineClient):
executor_class: type[Executor],
log_stats: bool,
usage_context: UsageContext = UsageContext.ENGINE_CONTEXT,
input_registry: InputRegistry = INPUT_REGISTRY,
mm_registry: MultiModalRegistry = MULTIMODAL_REGISTRY,
use_cached_outputs: bool = False,
log_requests: bool = True,
start_engine_loop: bool = True,
@ -90,7 +91,7 @@ class AsyncLLM(EngineClient):
self.processor = Processor(
vllm_config=vllm_config,
tokenizer=self.tokenizer,
input_registry=input_registry,
mm_registry=mm_registry,
)
# OutputProcessor (converts EngineCoreOutputs --> RequestOutput).

4
vllm/v1/engine/llm_engine.py
View File

@ -11,7 +11,7 @@ from vllm.config import ParallelConfig, VllmConfig
from vllm.distributed import stateless_destroy_torch_distributed_process_group
from vllm.engine.arg_utils import EngineArgs
from vllm.engine.metrics_types import StatLoggerBase
from vllm.inputs import INPUT_REGISTRY, InputRegistry, PromptType
from vllm.inputs import PromptType
from vllm.logger import init_logger
from vllm.lora.request import LoRARequest
from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalRegistry
@ -44,7 +44,6 @@ class LLMEngine:
log_stats: bool,
usage_context: UsageContext = UsageContext.ENGINE_CONTEXT,
stat_loggers: Optional[dict[str, StatLoggerBase]] = None,
input_registry: InputRegistry = INPUT_REGISTRY,
mm_registry: MultiModalRegistry = MULTIMODAL_REGISTRY,
use_cached_outputs: bool = False,
multiprocess_mode: bool = False,
@ -80,7 +79,6 @@ class LLMEngine:
# Processor (convert Inputs --> EngineCoreRequests)
self.processor = Processor(vllm_config=vllm_config,
tokenizer=self.tokenizer,
input_registry=input_registry,
mm_registry=mm_registry)
# OutputProcessor (convert EngineCoreOutputs --> RequestOutput).

22
vllm/v1/engine/processor.py
View File

@ -5,8 +5,7 @@ from collections.abc import Mapping
from typing import Optional, Union
from vllm.config import VllmConfig
from vllm.inputs import (INPUT_REGISTRY, InputRegistry, ProcessorInputs,
PromptType, SingletonInputsAdapter)
from vllm.inputs import ProcessorInputs, PromptType
from vllm.inputs.parse import split_enc_dec_inputs
from vllm.inputs.preprocess import InputPreprocessor
from vllm.lora.request import LoRARequest
@ -31,7 +30,6 @@ class Processor:
self,
vllm_config: VllmConfig,
tokenizer: BaseTokenizerGroup,
input_registry: InputRegistry = INPUT_REGISTRY,
mm_registry: MultiModalRegistry = MULTIMODAL_REGISTRY,
):
@ -210,7 +208,6 @@ class Processor:
self._validate_model_inputs(processed_inputs, lora_request)
encoder_inputs, decoder_inputs = split_enc_dec_inputs(processed_inputs)
decoder_inputs = SingletonInputsAdapter(decoder_inputs)
# TODO: Impl encoder-decoder
if encoder_inputs is not None:
@ -221,8 +218,9 @@ class Processor:
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.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(
@ -232,8 +230,8 @@ class Processor:
sorted_mm_inputs: Optional[list[MultiModalKwargs]] = None
sorted_mm_positions: Optional[list[PlaceholderRange]] = None
sorted_mm_hashes: Optional[list[str]] = None
if (decoder_mm_inputs := decoder_inputs.multi_modal_data):
assert isinstance(decoder_mm_inputs, MultiModalKwargs)
if decoder_inputs["type"] == "multimodal":
decoder_mm_inputs = decoder_inputs["mm_kwargs"]
# The output of merged multi-modal processor (`decoder_mm_inputs`)
# contains the kwargs for all items from all modalities.
@ -254,8 +252,8 @@ class Processor:
sorted_mm_positions,
sorted_mm_hashes,
) = merge_and_sort_multimodal_metadata(
decoder_inputs.multi_modal_placeholders,
decoder_inputs.multi_modal_hashes if self.use_hash else None,
decoder_inputs["mm_placeholders"],
decoder_inputs["mm_hashes"] if self.use_hash else None,
)
# NOTE: Sort multimodal inputs/kwargs ONLY IF there are multiple
@ -281,8 +279,8 @@ class Processor:
return EngineCoreRequest(
request_id=request_id,
prompt=decoder_inputs.prompt,
prompt_token_ids=decoder_inputs.prompt_token_ids,
prompt=decoder_inputs.get("prompt"),
prompt_token_ids=decoder_inputs["prompt_token_ids"],
mm_inputs=sorted_mm_inputs,
mm_hashes=sorted_mm_hashes,
mm_placeholders=sorted_mm_positions,

9
vllm/v1/worker/gpu_model_runner.py
View File

@ -15,7 +15,6 @@ from vllm.attention.layer import Attention
from vllm.config import CompilationLevel, VllmConfig
from vllm.distributed.parallel_state import get_pp_group, graph_capture
from vllm.forward_context import set_forward_context
from vllm.inputs import INPUT_REGISTRY
from vllm.logger import init_logger
from vllm.model_executor.layers.fused_moe import FusedMoE
from vllm.model_executor.layers.rotary_embedding import MRotaryEmbedding
@ -130,7 +129,6 @@ class GPUModelRunner(LoRAModelRunnerMixin):
self.cascade_attn_enabled = not self.model_config.disable_cascade_attn
# Multi-modal data support
self.input_registry = INPUT_REGISTRY
self.mm_registry = MULTIMODAL_REGISTRY
self.uses_mrope = model_config.uses_mrope
@ -1473,16 +1471,11 @@ class GPUModelRunner(LoRAModelRunnerMixin):
encoder_budget, max_num_mm_items, dummy_data_modality)
# Create dummy batch of multimodal inputs.
dummy_request_data = self.input_registry.dummy_data_for_profiling(
dummy_request_data = self.mm_registry.get_decoder_dummy_data(
model_config=self.model_config,
seq_len=self.max_num_tokens,
mm_registry=self.mm_registry,
)
dummy_mm_data = dummy_request_data.multi_modal_data
if not isinstance(dummy_mm_data, MultiModalKwargs):
# TODO: Delete this check once input mapper is fully removed.
raise RuntimeError(
"Legacy input mapper is not supported in V1")
# Dummy data definition may contain multiple multimodal items
# (e.g, multiple images) for a single request, therefore here we

2
vllm/v1/worker/tpu_model_runner.py
View File

@ -17,7 +17,6 @@ from vllm.attention.backends.abstract import AttentionType
from vllm.attention.layer import Attention
from vllm.config import VllmConfig
from vllm.forward_context import set_forward_context
from vllm.inputs import INPUT_REGISTRY
from vllm.logger import init_logger
from vllm.model_executor.model_loader import get_model
from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalKwargs
@ -102,7 +101,6 @@ class TPUModelRunner:
self.hidden_size = model_config.get_hidden_size()
# Multi-modal data support
self.input_registry = INPUT_REGISTRY
self.mm_registry = MULTIMODAL_REGISTRY
self.uses_mrope = model_config.uses_mrope
# TODO: Support M-RoPE (e.g, Qwen2-VL)