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[Core] Rename input data types (#8688)

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Cyrus Leung 2024-10-16 18:49:37 +08:00 committed by GitHub
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32 changed files with 438 additions and 340 deletions
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2
docs/source/dev/input_processing/model_inputs_index.rst
View File

@ -25,7 +25,7 @@ Module Contents
LLM Engine Inputs
-----------------
.. autoclass:: vllm.inputs.LLMInputs
.. autoclass:: vllm.inputs.DecoderOnlyInputs
:members:
:show-inheritance:

28
tests/models/decoder_only/vision_language/test_phi3v.py
View File

@ -1,12 +1,12 @@
import os
import re
from typing import Callable, List, Optional, Tuple, Type
from typing import List, Optional, Tuple, Type
import pytest
import torch
from transformers import AutoImageProcessor, AutoTokenizer
from vllm.inputs import InputContext, LLMInputs
from vllm.inputs import InputContext, token_inputs
from vllm.model_executor.models.phi3v import _IMAGE_TOKEN_ID
from vllm.multimodal import MultiModalRegistry
from vllm.multimodal.utils import rescale_image_size
@ -311,7 +311,7 @@ def test_input_mapper_override(model: str, image_assets: _ImageAssets,
(4, 781),
(16, 2653),
])
def test_max_tokens_override(get_max_phi3v_image_tokens: Callable, model: str,
def test_max_tokens_override(get_max_phi3v_image_tokens, model: str,
num_crops: int, expected_max_tokens: int):
"""Ensure get_max_phi3v_image_tokens handles num_crops properly."""
# NOTE: mm_processor_kwargs on the context in this test is unused, since
@ -343,8 +343,8 @@ def test_max_tokens_override(get_max_phi3v_image_tokens: Callable, model: str,
(16, 2653, 1),
(16, 2653, 2),
])
def test_dummy_data_override(dummy_data_for_phi3v: Callable, model: str,
num_crops: int, toks_per_img: int, num_imgs: int):
def test_dummy_data_override(dummy_data_for_phi3v, model: str, num_crops: int,
toks_per_img: int, num_imgs: int):
"""Ensure dummy_data_for_phi3v handles num_crops properly."""
# Same as the previous test - don't initialize mm_processor_kwargs
# in this test and assume that the kwargs will be correctly expanded by
@ -374,7 +374,7 @@ def test_dummy_data_override(dummy_data_for_phi3v: Callable, model: str,
(16, 1921, 1),
(16, 1921, 2),
])
def test_input_processor_override(input_processor_for_phi3v: Callable,
def test_input_processor_override(input_processor_for_phi3v,
image_assets: _ImageAssets, model: str,
num_crops: int, expected_toks_per_img: int,
num_imgs: int):
@ -393,16 +393,14 @@ def test_input_processor_override(input_processor_for_phi3v: Callable,
prompt = f"<|user|>\n{img_str}<|end|>\n<|assistant|>\n"
images = [image_assets[0].pil_image] * num_imgs
llm_inputs = LLMInputs(prompt_token_ids=tokenizer.encode(prompt),
prompt=prompt,
multi_modal_data={"image": images})
inputs = token_inputs(prompt_token_ids=tokenizer.encode(prompt),
prompt=prompt,
multi_modal_data={"image": images})
proc_llm_inputs = input_processor_for_phi3v(
ctx=ctx,
llm_inputs=llm_inputs,
num_crops=num_crops,
)
processed_inputs = input_processor_for_phi3v(ctx,
inputs,
num_crops=num_crops)
# Ensure we have the right number of placeholders per num_crops size
img_tok_count = proc_llm_inputs["prompt_token_ids"].count(_IMAGE_TOKEN_ID)
img_tok_count = processed_inputs["prompt_token_ids"].count(_IMAGE_TOKEN_ID)
assert img_tok_count == expected_toks_per_img * num_imgs

12
tests/models/decoder_only/vision_language/test_qwen.py
View File

@ -5,7 +5,7 @@ import pytest
import torch
from PIL.Image import Image
from vllm.inputs import InputContext, LLMInputs
from vllm.inputs import InputContext, token_inputs
from vllm.multimodal.base import MultiModalInputs
from vllm.multimodal.utils import cached_get_tokenizer, rescale_image_size
@ -71,12 +71,12 @@ def test_input_processor_valid_mm_data(input_processor_for_qwen,
"""Happy cases for image inputs to Qwen's multimodal input processor."""
prompt = "".join(
[f"Picture {num}: <img></img>\n" for num in range(1, num_images + 1)])
inputs = LLMInputs(
inputs = token_inputs(
prompt=prompt,
# When processing multimodal data for a multimodal model, the qwen
# input processor will overwrite the provided prompt_token_ids with
# the image prompts
prompt_token_ids=None,
prompt_token_ids=[],
multi_modal_data={"image": torch.rand(num_images, TOKS_PER_IMG, 4096)},
)
proc_inputs = input_processor_for_qwen(qwen_vl_context, inputs)
@ -134,9 +134,9 @@ def test_input_processor_invalid_mm_data(input_processor_for_qwen,
trust_remote_code=True)
prompt = "Picture 1: <img></img>\n"
prompt_token_ids = tokenizer.encode(prompt)
inputs = LLMInputs(prompt=prompt,
prompt_token_ids=prompt_token_ids,
multi_modal_data=mm_data)
inputs = token_inputs(prompt=prompt,
prompt_token_ids=prompt_token_ids,
multi_modal_data=mm_data)
# Should fail since we have too many or too few dimensions for embeddings
with pytest.raises(ValueError):
input_processor_for_qwen(qwen_vl_context, inputs)

18
tests/multimodal/test_processor_kwargs.py
View File

@ -5,7 +5,7 @@ from unittest.mock import patch
import pytest
import torch
from vllm.inputs import InputContext, LLMInputs
from vllm.inputs import DecoderOnlyInputs, InputContext, token_inputs
from vllm.inputs.registry import InputRegistry
from vllm.multimodal import MultiModalRegistry
from vllm.sequence import VLLM_TOKEN_ID_ARRAY_TYPE, SequenceData
@ -31,7 +31,7 @@ def use_processor_mock():
"""Patches the internal model input processor with an override callable."""
def custom_processor(ctx: InputContext,
llm_inputs: LLMInputs,
inputs: DecoderOnlyInputs,
*,
num_crops=DEFAULT_NUM_CROPS):
# For testing purposes, we don't worry about the llm inputs / return
@ -84,7 +84,7 @@ def test_default_processor_is_a_noop():
dummy_registry = InputRegistry()
ctx = build_model_context(DUMMY_MODEL_ID)
processor = dummy_registry.create_input_processor(ctx.model_config)
proc_inputs = LLMInputs(prompt_token_ids=[], prompt="")
proc_inputs = token_inputs(prompt_token_ids=[], prompt="")
proc_outputs = processor(inputs=proc_inputs)
assert proc_inputs is proc_outputs
@ -125,9 +125,9 @@ def test_input_processor_kwargs(use_processor_mock, init_num_crops,
ctx = build_model_context(DUMMY_MODEL_ID, mm_processor_kwargs=init_kwargs)
processor = dummy_registry.create_input_processor(ctx.model_config)
num_crops_val = processor(
LLMInputs(prompt_token_ids=[],
prompt="",
mm_processor_kwargs=inference_kwargs))
token_inputs(prompt_token_ids=[],
prompt="",
mm_processor_kwargs=inference_kwargs))
assert num_crops_val == expected_seq_count
@ -154,9 +154,9 @@ def test_processor_with_sad_kwarg_overrides(use_processor_mock,
processor = dummy_registry.create_input_processor(ctx.model_config)
# Should filter out the inference time kwargs
num_crops_val = processor(
LLMInputs(prompt_token_ids=[],
prompt="",
mm_processor_kwargs=mm_processor_kwargs))
token_inputs(prompt_token_ids=[],
prompt="",
mm_processor_kwargs=mm_processor_kwargs))
assert num_crops_val == DEFAULT_NUM_CROPS

10
vllm/engine/llm_engine.py
View File

@ -29,8 +29,8 @@ from vllm.entrypoints.openai.logits_processors import get_logits_processors
from vllm.executor.executor_base import ExecutorBase
from vllm.executor.gpu_executor import GPUExecutor
from vllm.executor.ray_utils import initialize_ray_cluster
from vllm.inputs import (INPUT_REGISTRY, EncoderDecoderLLMInputs,
InputRegistry, LLMInputs, PromptType)
from vllm.inputs import (INPUT_REGISTRY, DecoderOnlyInputs,
EncoderDecoderInputs, InputRegistry, PromptType)
from vllm.inputs.preprocess import InputPreprocessor
from vllm.logger import init_logger
from vllm.lora.request import LoRARequest
@ -635,7 +635,7 @@ class LLMEngine:
def _add_processed_request(
self,
request_id: str,
processed_inputs: Union[LLMInputs, EncoderDecoderLLMInputs],
processed_inputs: Union[DecoderOnlyInputs, EncoderDecoderInputs],
params: Union[SamplingParams, PoolingParams],
arrival_time: float,
lora_request: Optional[LoRARequest],
@ -1855,8 +1855,8 @@ class LLMEngine:
def is_embedding_model(self):
return self.model_config.is_embedding_model
def _validate_model_inputs(self, inputs: Union[LLMInputs,
EncoderDecoderLLMInputs]):
def _validate_model_inputs(self, inputs: Union[DecoderOnlyInputs,
EncoderDecoderInputs]):
if self.model_config.is_multimodal_model:
# For encoder-decoder multimodal models, the max_prompt_len
# restricts the decoder prompt length

37
vllm/inputs/__init__.py
View File

@ -1,7 +1,8 @@
from .data import (EncoderDecoderLLMInputs, ExplicitEncoderDecoderPrompt,
LLMInputs, PromptType, SingletonPrompt, TextPrompt,
TokensPrompt, build_explicit_enc_dec_prompt,
to_enc_dec_tuple_list, zip_enc_dec_prompts)
from .data import (DecoderOnlyInputs, EncoderDecoderInputs,
ExplicitEncoderDecoderPrompt, PromptType, SingletonInputs,
SingletonPrompt, TextPrompt, TokenInputs, TokensPrompt,
build_explicit_enc_dec_prompt, to_enc_dec_tuple_list,
token_inputs, zip_enc_dec_prompts)
from .registry import InputContext, InputRegistry
INPUT_REGISTRY = InputRegistry()
@ -19,8 +20,11 @@ __all__ = [
"PromptType",
"SingletonPrompt",
"ExplicitEncoderDecoderPrompt",
"LLMInputs",
"EncoderDecoderLLMInputs",
"TokenInputs",
"token_inputs",
"SingletonInputs",
"DecoderOnlyInputs",
"EncoderDecoderInputs",
"build_explicit_enc_dec_prompt",
"to_enc_dec_tuple_list",
"zip_enc_dec_prompts",
@ -31,9 +35,9 @@ __all__ = [
def __getattr__(name: str):
if name == "PromptInput":
import warnings
import warnings
if name == "PromptInput":
msg = ("PromptInput has been renamed to PromptType. "
"The original name will be removed in an upcoming version.")
@ -41,4 +45,21 @@ def __getattr__(name: str):
return PromptType
if name == "LLMInputs":
msg = ("LLMInputs has been renamed to DecoderOnlyInputs. "
"The original name will be removed in an upcoming version.")
warnings.warn(DeprecationWarning(msg), stacklevel=2)
return DecoderOnlyInputs
if name == "EncoderDecoderLLMInputs":
msg = (
"EncoderDecoderLLMInputs has been renamed to EncoderDecoderInputs. "
"The original name will be removed in an upcoming version.")
warnings.warn(DeprecationWarning(msg), stacklevel=2)
return EncoderDecoderInputs
raise AttributeError(f"module {__name__!r} has no attribute {name!r}")

78
vllm/inputs/data.py
View File

@ -1,5 +1,5 @@
from typing import (TYPE_CHECKING, Any, Dict, Generic, Iterable, List,
Optional, Tuple, Union)
Optional, Tuple, Union, cast)
from typing_extensions import NotRequired, TypedDict, TypeVar
@ -51,7 +51,7 @@ class TokensPrompt(TypedDict):
SingletonPrompt = Union[str, TextPrompt, TokensPrompt]
"""
Set of possible schemas for a single LLM input:
Set of possible schemas for a single prompt:
- A text prompt (:class:`str` or :class:`TextPrompt`)
- A tokenized prompt (:class:`TokensPrompt`)
@ -120,13 +120,8 @@ both decoder-only and encoder/decoder input types:
"""
class LLMInputs(TypedDict):
"""
The inputs in :class:`~vllm.LLMEngine` before they are
passed to the model executor.
This specifies the data required for decoder-only models.
"""
class TokenInputs(TypedDict):
"""Represents token-based inputs."""
prompt_token_ids: List[int]
"""The token IDs of the prompt."""
@ -150,7 +145,40 @@ class LLMInputs(TypedDict):
"""
class EncoderDecoderLLMInputs(LLMInputs):
def token_inputs(
prompt_token_ids: List[int],
prompt: Optional[str] = None,
multi_modal_data: Optional["MultiModalDataDict"] = None,
mm_processor_kwargs: Optional[Dict[str, Any]] = None,
) -> TokenInputs:
"""Construct :class:`TokenInputs` from optional values."""
inputs = TokenInputs(prompt_token_ids=prompt_token_ids)
if prompt is not None:
inputs["prompt"] = prompt
if multi_modal_data is not None:
inputs["multi_modal_data"] = multi_modal_data
if mm_processor_kwargs is not None:
inputs["mm_processor_kwargs"] = mm_processor_kwargs
return inputs
SingletonInputs = TokenInputs
"""
A processed :class:`SingletonPrompt` which can be passed to
:class:`vllm.sequence.Sequence`.
"""
DecoderOnlyInputs = TokenInputs
"""
The inputs in :class:`~vllm.LLMEngine` before they are
passed to the model executor.
This specifies the data required for decoder-only models.
"""
class EncoderDecoderInputs(TokenInputs):
"""
The inputs in :class:`~vllm.LLMEngine` before they are
passed to the model executor.
@ -204,11 +232,12 @@ def zip_enc_dec_prompts(
be zipped with the encoder/decoder prompts.
"""
if mm_processor_kwargs is None:
mm_processor_kwargs = {}
if isinstance(mm_processor_kwargs, Dict):
mm_processor_kwargs = cast(Dict[str, Any], {})
if isinstance(mm_processor_kwargs, dict):
return [
build_explicit_enc_dec_prompt(encoder_prompt, decoder_prompt,
mm_processor_kwargs)
build_explicit_enc_dec_prompt(
encoder_prompt, decoder_prompt,
cast(Dict[str, Any], mm_processor_kwargs))
for (encoder_prompt,
decoder_prompt) in zip(enc_prompts, dec_prompts)
]
@ -229,9 +258,9 @@ def to_enc_dec_tuple_list(
def __getattr__(name: str):
if name == "PromptInput":
import warnings
import warnings
if name == "PromptInput":
msg = ("PromptInput has been renamed to PromptType. "
"The original name will be removed in an upcoming version.")
@ -239,4 +268,21 @@ def __getattr__(name: str):
return PromptType
if name == "LLMInputs":
msg = ("LLMInputs has been renamed to DecoderOnlyInputs. "
"The original name will be removed in an upcoming version.")
warnings.warn(DeprecationWarning(msg), stacklevel=2)
return DecoderOnlyInputs
if name == "EncoderDecoderLLMInputs":
msg = (
"EncoderDecoderLLMInputs has been renamed to EncoderDecoderInputs. "
"The original name will be removed in an upcoming version.")
warnings.warn(DeprecationWarning(msg), stacklevel=2)
return EncoderDecoderInputs
raise AttributeError(f"module {__name__!r} has no attribute {name!r}")

12
vllm/inputs/parse.py
View File

@ -4,9 +4,9 @@ from typing_extensions import TypeIs
from vllm.utils import is_list_of
from .data import (EncoderDecoderLLMInputs, ExplicitEncoderDecoderPrompt,
LLMInputs, PromptType, SingletonPrompt, TextPrompt,
TokensPrompt)
from .data import (DecoderOnlyInputs, EncoderDecoderInputs,
ExplicitEncoderDecoderPrompt, PromptType, SingletonPrompt,
TextPrompt, TokensPrompt)
class ParsedText(TypedDict):
@ -100,7 +100,7 @@ def is_explicit_encoder_decoder_prompt(
return isinstance(prompt, dict) and "encoder_prompt" in prompt
def is_valid_encoder_decoder_llm_inputs(
inputs: Union[LLMInputs, EncoderDecoderLLMInputs],
) -> TypeIs[EncoderDecoderLLMInputs]:
def is_encoder_decoder_inputs(
inputs: Union[DecoderOnlyInputs, EncoderDecoderInputs],
) -> TypeIs[EncoderDecoderInputs]:
return "encoder_prompt_token_ids" in inputs

36
vllm/inputs/preprocess.py
View File

@ -10,7 +10,7 @@ from vllm.prompt_adapter.request import PromptAdapterRequest
from vllm.transformers_utils.tokenizer_group import BaseTokenizerGroup
from vllm.utils import print_warning_once
from .data import (EncoderDecoderLLMInputs, LLMInputs, PromptType,
from .data import (DecoderOnlyInputs, EncoderDecoderInputs, PromptType,
SingletonPrompt)
from .parse import is_explicit_encoder_decoder_prompt, parse_singleton_prompt
@ -306,7 +306,7 @@ class InputPreprocessor:
encoder_comps: PromptComponents,
decoder_comps: DecoderPromptComponents,
mm_processor_kwargs: Dict[str, Any],
) -> EncoderDecoderLLMInputs:
) -> EncoderDecoderInputs:
encoder_prompt, encoder_prompt_ids, encoder_mm_data, _ = encoder_comps
decoder_prompt, decoder_prompt_ids, decoder_mm_data, _ = decoder_comps
@ -324,7 +324,7 @@ class InputPreprocessor:
decoder_prompt_ids,
force_bos=(encoder_mm_data is None and decoder_mm_data is None)))
return EncoderDecoderLLMInputs(
return EncoderDecoderInputs(
prompt_token_ids=decoder_prompt_ids,
prompt=decoder_prompt,
multi_modal_data=decoder_mm_data,
@ -338,11 +338,11 @@ class InputPreprocessor:
self,
prompt: PromptType,
request_id: str,
) -> EncoderDecoderLLMInputs:
) -> EncoderDecoderInputs:
'''
For encoder/decoder models only:
Process an input prompt into an
:class:`EncoderDecoderLLMInputs` instance.
:class:`EncoderDecoderInputs` instance.
There are two types of input prompts:
singleton prompts which carry only the
@ -369,7 +369,7 @@ class InputPreprocessor:
Returns:
* :class:`EncoderDecoderLLMInputs` instance
* :class:`EncoderDecoderInputs` instance
'''
encoder_comps: PromptComponents
@ -411,7 +411,7 @@ class InputPreprocessor:
self,
prompt: PromptType,
request_id: str,
) -> EncoderDecoderLLMInputs:
) -> EncoderDecoderInputs:
"""Async version of :meth:`_process_encoder_decoder_prompt`."""
encoder_comps: PromptComponents
decoder_comps: DecoderPromptComponents
@ -455,17 +455,17 @@ class InputPreprocessor:
self,
prompt_comps: PromptComponents,
prompt_adapter_request: Optional[PromptAdapterRequest],
) -> LLMInputs:
) -> DecoderOnlyInputs:
(prompt, prompt_token_ids, multi_modal_data,
mm_processor_kwargs) = prompt_comps
prompt_token_ids = self._apply_prompt_adapter(
prompt_token_ids, prompt_adapter_request=prompt_adapter_request)
return LLMInputs(prompt_token_ids=prompt_token_ids,
prompt=prompt,
multi_modal_data=multi_modal_data,
mm_processor_kwargs=mm_processor_kwargs)
return DecoderOnlyInputs(prompt_token_ids=prompt_token_ids,
prompt=prompt,
multi_modal_data=multi_modal_data,
mm_processor_kwargs=mm_processor_kwargs)
def _process_decoder_only_prompt(
self,
@ -473,10 +473,10 @@ class InputPreprocessor:
request_id: str,
lora_request: Optional[LoRARequest] = None,
prompt_adapter_request: Optional[PromptAdapterRequest] = None,
) -> LLMInputs:
) -> DecoderOnlyInputs:
'''
For decoder-only models:
Process an input prompt into an :class:`LLMInputs` instance.
Process an input prompt into an :class:`DecoderOnlyInputs` instance.
Arguments:
@ -487,7 +487,7 @@ class InputPreprocessor:
Returns:
* :class:`LLMInputs` instance
* :class:`DecoderOnlyInputs` instance
'''
prompt_comps = self._extract_prompt_components(
@ -507,7 +507,7 @@ class InputPreprocessor:
request_id: str,
lora_request: Optional[LoRARequest] = None,
prompt_adapter_request: Optional[PromptAdapterRequest] = None,
) -> LLMInputs:
) -> DecoderOnlyInputs:
"""Async version of :meth:`_process_decoder_only_prompt`."""
prompt_comps = await self._extract_prompt_components_async(
prompt,
@ -526,7 +526,7 @@ class InputPreprocessor:
request_id: str,
lora_request: Optional[LoRARequest] = None,
prompt_adapter_request: Optional[PromptAdapterRequest] = None,
) -> Union[LLMInputs, EncoderDecoderLLMInputs]:
) -> Union[DecoderOnlyInputs, EncoderDecoderInputs]:
"""Preprocess the input prompt."""
if self.is_encoder_decoder_model():
# Encoder-decoder model requires special mapping of
@ -554,7 +554,7 @@ class InputPreprocessor:
request_id: str,
lora_request: Optional[LoRARequest] = None,
prompt_adapter_request: Optional[PromptAdapterRequest] = None,
) -> Union[LLMInputs, EncoderDecoderLLMInputs]:
) -> Union[DecoderOnlyInputs, EncoderDecoderInputs]:
"""Async version of :meth:`preprocess`."""
if self.is_encoder_decoder_model():
# Encoder-decoder model requires special mapping of

15
vllm/inputs/registry.py
View File

@ -12,7 +12,7 @@ from vllm.logger import init_logger
from vllm.utils import (get_allowed_kwarg_only_overrides, print_warning_once,
resolve_mm_processor_kwargs)
from .data import LLMInputs
from .data import DecoderOnlyInputs
if TYPE_CHECKING:
from vllm.config import ModelConfig
@ -100,7 +100,7 @@ class _MultiModalCounts(UserDict):
raise KeyError(msg) from exc
InputProcessor = Callable[[InputContext, LLMInputs], LLMInputs]
InputProcessor = Callable[[InputContext, DecoderOnlyInputs], DecoderOnlyInputs]
"""Preprocess the inputs to the model."""
@ -134,7 +134,7 @@ class InputRegistry:
# Avoid circular import
from vllm.sequence import SequenceData
dummy_seq_data = SequenceData.from_token_counts((0, seq_len))
dummy_seq_data = SequenceData.from_prompt_token_counts((0, seq_len))
dummy_multi_modal_data = None
return dummy_seq_data, dummy_multi_modal_data
@ -245,8 +245,11 @@ class InputRegistry:
return seq_data, mm_data
def _default_input_processor(self, ctx: InputContext,
inputs: LLMInputs) -> LLMInputs:
def _default_input_processor(
self,
ctx: InputContext,
inputs: DecoderOnlyInputs,
) -> DecoderOnlyInputs:
"""The default input processor is a no-op."""
return inputs
@ -279,7 +282,7 @@ class InputRegistry:
.get(model_cls, self._default_input_processor)
def process_input(self, model_config: "ModelConfig",
inputs: LLMInputs) -> LLMInputs:
inputs: DecoderOnlyInputs) -> DecoderOnlyInputs:
"""
Apply an input processor to an instance of model inputs.

20
vllm/model_executor/models/blip.py
View File

@ -10,7 +10,7 @@ from transformers.models.blip.modeling_blip import BlipAttention
from vllm.config import ModelConfig
from vllm.distributed import divide, get_tensor_model_parallel_world_size
from vllm.inputs import LLMInputs
from vllm.inputs import DecoderOnlyInputs, token_inputs
from vllm.model_executor.layers.activation import get_act_fn
from vllm.model_executor.layers.linear import (ColumnParallelLinear,
QKVParallelLinear,
@ -63,7 +63,7 @@ def dummy_seq_data_for_blip(
else:
image_feature_size = image_feature_size_override
return SequenceData.from_token_counts(
return SequenceData.from_prompt_token_counts(
(image_token_id, image_feature_size * num_images),
(0, seq_len - image_feature_size * num_images),
)
@ -89,14 +89,14 @@ def dummy_image_for_blip(
def input_processor_for_blip(
model_config: ModelConfig,
hf_config: Union[BlipVisionConfig, Blip2VisionConfig],
llm_inputs: LLMInputs,
inputs: DecoderOnlyInputs,
*,
image_token_id: int,
image_feature_size_override: Optional[int] = None,
):
multi_modal_data = llm_inputs.get("multi_modal_data")
multi_modal_data = inputs.get("multi_modal_data")
if multi_modal_data is None or "image" not in multi_modal_data:
return llm_inputs
return inputs
tokenizer = cached_get_tokenizer(model_config.tokenizer)
@ -107,16 +107,16 @@ def input_processor_for_blip(
new_prompt, new_token_ids = repeat_and_pad_placeholder_tokens(
tokenizer,
llm_inputs.get("prompt"),
llm_inputs["prompt_token_ids"],
inputs.get("prompt"),
inputs["prompt_token_ids"],
placeholder_token_id=image_token_id,
repeat_count=image_feature_size,
)
# NOTE: Create a defensive copy of the original inputs
return LLMInputs(prompt_token_ids=new_token_ids,
prompt=new_prompt,
multi_modal_data=multi_modal_data)
return token_inputs(prompt_token_ids=new_token_ids,
prompt=new_prompt,
multi_modal_data=multi_modal_data)
# Adapted from https://github.com/huggingface/transformers/blob/v4.39.0/src/transformers/models/blip/modeling_blip.py#L164 # noqa

21
vllm/model_executor/models/blip2.py
View File

@ -9,7 +9,8 @@ from transformers import (Blip2Config, Blip2QFormerConfig, Blip2VisionConfig,
from vllm.attention import AttentionMetadata
from vllm.config import CacheConfig, MultiModalConfig
from vllm.inputs import INPUT_REGISTRY, InputContext, LLMInputs
from vllm.inputs import (INPUT_REGISTRY, DecoderOnlyInputs, InputContext,
token_inputs)
from vllm.model_executor.layers.activation import get_act_fn
from vllm.model_executor.layers.quantization import QuantizationConfig
from vllm.model_executor.layers.sampler import Sampler, SamplerOutput
@ -421,7 +422,7 @@ def dummy_seq_data_for_blip2(
else:
image_feature_size = image_feature_size_override
return SequenceData.from_token_counts(
return SequenceData.from_prompt_token_counts(
(image_token_id, image_feature_size * num_images),
(0, seq_len - image_feature_size * num_images),
)
@ -449,10 +450,10 @@ def dummy_data_for_blip2(ctx: InputContext, seq_len: int,
raise NotImplementedError(msg)
def input_processor_for_blip2(ctx: InputContext, llm_inputs: LLMInputs):
multi_modal_data = llm_inputs.get("multi_modal_data")
def input_processor_for_blip2(ctx: InputContext, inputs: DecoderOnlyInputs):
multi_modal_data = inputs.get("multi_modal_data")
if multi_modal_data is None or "image" not in multi_modal_data:
return llm_inputs
return inputs
hf_config = ctx.get_hf_config(Blip2Config)
image_feature_size = get_blip2_image_feature_size(hf_config)
@ -460,15 +461,15 @@ def input_processor_for_blip2(ctx: InputContext, llm_inputs: LLMInputs):
# The original model places image tokens at the front
# https://github.com/huggingface/transformers/blob/v4.41.2/src/transformers/models/blip_2/modeling_blip_2.py#L1514
new_token_ids = [BLIP2_IMAGE_TOKEN_ID] * image_feature_size
new_token_ids += llm_inputs["prompt_token_ids"]
new_token_ids += inputs["prompt_token_ids"]
new_prompt = llm_inputs.get("prompt")
new_prompt = inputs.get("prompt")
if new_prompt is not None:
new_prompt = BLIP2_IMAGE_TOKEN * image_feature_size + new_prompt
return LLMInputs(prompt_token_ids=new_token_ids,
prompt=new_prompt,
multi_modal_data=multi_modal_data)
return token_inputs(prompt_token_ids=new_token_ids,
prompt=new_prompt,
multi_modal_data=multi_modal_data)
@MULTIMODAL_REGISTRY.register_image_input_mapper()

22
vllm/model_executor/models/chameleon.py
View File

@ -11,7 +11,8 @@ from transformers import ChameleonConfig, ChameleonVQVAEConfig
from vllm.attention import Attention, AttentionMetadata
from vllm.config import CacheConfig, MultiModalConfig
from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
from vllm.inputs import INPUT_REGISTRY, InputContext, LLMInputs
from vllm.inputs import (INPUT_REGISTRY, DecoderOnlyInputs, InputContext,
token_inputs)
from vllm.model_executor.layers.activation import SiluAndMul
from vllm.model_executor.layers.layernorm import RMSNorm
from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,
@ -69,7 +70,7 @@ def dummy_seq_data_for_chameleon(
else:
image_feature_size = image_feature_size_override
return SequenceData.from_token_counts(
return SequenceData.from_prompt_token_counts(
(image_token_id, image_feature_size * num_images),
(0, seq_len - image_feature_size * num_images),
)
@ -106,7 +107,8 @@ def dummy_data_for_chameleon(ctx: InputContext, seq_len: int,
return seq_data, mm_data
def input_processor_for_chameleon(ctx: InputContext, llm_inputs: LLMInputs):
def input_processor_for_chameleon(ctx: InputContext,
inputs: DecoderOnlyInputs):
"""
Processing input prompt to insert required tokens for image placeholder.
@ -114,16 +116,16 @@ def input_processor_for_chameleon(ctx: InputContext, llm_inputs: LLMInputs):
See https://github.com/huggingface/transformers/blob/0fdea8607d7e01eb0e38a1ebeb7feee30a22f0cf/src/transformers/models/chameleon/processing_chameleon.py#L58
""" # noqa
multi_modal_data = llm_inputs.get("multi_modal_data")
multi_modal_data = inputs.get("multi_modal_data")
if multi_modal_data is None or "image" not in multi_modal_data:
return llm_inputs
return inputs
model_config = ctx.model_config
tokenizer = cached_get_tokenizer(model_config.tokenizer)
new_prompt, new_token_ids = repeat_and_pad_placeholder_tokens(
tokenizer,
llm_inputs.get("prompt"),
llm_inputs["prompt_token_ids"],
inputs.get("prompt"),
inputs["prompt_token_ids"],
placeholder_token_id=CHAMELEON_IMAGE_TOKEN_ID,
repeat_count=CHAMELEON_IMAGE_SEQ_LENGTH,
pad_token_left=CHAMELEON_IMAGE_START_TOKEN_ID,
@ -137,9 +139,9 @@ def input_processor_for_chameleon(ctx: InputContext, llm_inputs: LLMInputs):
new_token_ids += [CHAMELEON_SEP_TOKEN_ID]
# NOTE: Create a defensive copy of the original inputs
return LLMInputs(prompt_token_ids=new_token_ids,
prompt=new_prompt,
multi_modal_data=multi_modal_data)
return token_inputs(prompt_token_ids=new_token_ids,
prompt=new_prompt,
multi_modal_data=multi_modal_data)
class ChameleonLayerNorm(nn.LayerNorm):

22
vllm/model_executor/models/chatglm.py
View File

@ -14,7 +14,7 @@ from torch.nn import LayerNorm
from vllm.attention import Attention, AttentionMetadata
from vllm.config import CacheConfig, LoRAConfig, MultiModalConfig
from vllm.distributed import get_tensor_model_parallel_world_size
from vllm.inputs import INPUT_REGISTRY, InputContext, LLMInputs
from vllm.inputs import INPUT_REGISTRY, DecoderOnlyInputs, InputContext
from vllm.logger import init_logger
from vllm.model_executor.layers.activation import SiluAndMul
from vllm.model_executor.layers.layernorm import RMSNorm
@ -149,20 +149,20 @@ def find_all_positions(input_ids: List[int], target: int) -> List[int]:
return [index for index, value in enumerate(input_ids) if value == target]
def input_processor_for_glmv(ctx: InputContext, llm_inputs: LLMInputs):
def input_processor_for_glmv(ctx: InputContext, inputs: DecoderOnlyInputs):
hf_config = ctx.get_hf_config(ChatGLMConfig)
vision_config = getattr(hf_config, 'vision_config', None)
if vision_config is None:
return llm_inputs
return inputs
elif isinstance(vision_config, dict):
image_placeholder_length = calculate_image_placeholder(vision_config)
else:
msg = f"Unsupported vision config: {type(vision_config)}"
raise NotImplementedError(msg)
input_ids = llm_inputs.get("prompt_token_ids")
position_ids = llm_inputs.get("position_ids")
input_ids = inputs.get("prompt_token_ids")
position_ids = inputs.get("position_ids")
tokenizer = cached_get_tokenizer(
ctx.model_config.model,
trust_remote_code=ctx.model_config.trust_remote_code)
@ -171,15 +171,15 @@ def input_processor_for_glmv(ctx: InputContext, llm_inputs: LLMInputs):
raw_batch_data = tokenizer.apply_chat_template(
conversation=[{
"role": "user",
"image": llm_inputs['multi_modal_data']["image"],
"content": llm_inputs['prompt']
"image": inputs['multi_modal_data']["image"],
"content": inputs['prompt']
}],
add_generation_prompt=True,
tokenize=True,
return_tensors="pt",
return_dict=True).data
except Exception:
logger.error("Failed to process content (%s)", llm_inputs['prompt'])
logger.error("Failed to process content (%s)", inputs['prompt'])
raise
input_ids = raw_batch_data['input_ids'][0].tolist()
@ -214,9 +214,9 @@ def input_processor_for_glmv(ctx: InputContext, llm_inputs: LLMInputs):
assert len(new_input_ids) == len(new_position_ids)
llm_inputs["prompt_token_ids"] = new_input_ids
llm_inputs["position_ids"] = new_position_ids
return llm_inputs
inputs["prompt_token_ids"] = new_input_ids
inputs["position_ids"] = new_position_ids
return inputs
class GLMAttention(nn.Module):

20
vllm/model_executor/models/clip.py
View File

@ -11,7 +11,7 @@ from transformers.models.clip.modeling_clip import CLIPSdpaAttention
from vllm.config import ModelConfig
from vllm.distributed import divide, get_tensor_model_parallel_world_size
from vllm.inputs import LLMInputs
from vllm.inputs import DecoderOnlyInputs, token_inputs
from vllm.model_executor.layers.activation import get_act_fn
from vllm.model_executor.layers.linear import (ColumnParallelLinear,
QKVParallelLinear,
@ -62,7 +62,7 @@ def dummy_seq_data_for_clip(
else:
image_feature_size = image_feature_size_override
return SequenceData.from_token_counts(
return SequenceData.from_prompt_token_counts(
(image_token_id, image_feature_size * num_images),
(0, seq_len - image_feature_size * num_images),
)
@ -106,14 +106,14 @@ def dummy_video_for_clip(
def input_processor_for_clip(
model_config: ModelConfig,
hf_config: CLIPVisionConfig,
llm_inputs: LLMInputs,
inputs: DecoderOnlyInputs,
*,
image_token_id: int,
image_feature_size_override: Optional[Union[int, List[int]]] = None,
):
multi_modal_data = llm_inputs.get("multi_modal_data")
multi_modal_data = inputs.get("multi_modal_data")
if multi_modal_data is None or "image" not in multi_modal_data:
return llm_inputs
return inputs
tokenizer = cached_get_tokenizer(model_config.tokenizer)
@ -130,16 +130,16 @@ def input_processor_for_clip(
new_prompt, new_token_ids = repeat_and_pad_placeholder_tokens(
tokenizer,
llm_inputs.get("prompt"),
llm_inputs["prompt_token_ids"],
inputs.get("prompt"),
inputs["prompt_token_ids"],
placeholder_token_id=image_token_id,
repeat_count=image_feature_size,
)
# NOTE: Create a defensive copy of the original inputs
return LLMInputs(prompt_token_ids=new_token_ids,
prompt=new_prompt,
multi_modal_data=multi_modal_data)
return token_inputs(prompt_token_ids=new_token_ids,
prompt=new_prompt,
multi_modal_data=multi_modal_data)
# Adapted from https://github.com/huggingface/transformers/blob/v4.39.0/src/transformers/models/clip/modeling_clip.py#L164 # noqa

19
vllm/model_executor/models/fuyu.py
View File

@ -27,7 +27,8 @@ from transformers import FuyuConfig, FuyuImageProcessor
from vllm.attention import AttentionMetadata
from vllm.config import CacheConfig, MultiModalConfig
from vllm.inputs import INPUT_REGISTRY, InputContext, LLMInputs
from vllm.inputs import (INPUT_REGISTRY, DecoderOnlyInputs, InputContext,
token_inputs)
from vllm.model_executor.layers.linear import ColumnParallelLinear
from vllm.model_executor.layers.quantization import QuantizationConfig
from vllm.model_executor.layers.sampler import SamplerOutput
@ -149,10 +150,10 @@ def _fuyu_image_preprocess(image_processor: FuyuImageProcessor,
return model_image_input
def input_processor_for_fuyu(ctx: InputContext, llm_inputs: LLMInputs):
multi_modal_data = llm_inputs.get("multi_modal_data")
def input_processor_for_fuyu(ctx: InputContext, inputs: DecoderOnlyInputs):
multi_modal_data = inputs.get("multi_modal_data")
if multi_modal_data is None or "image" not in multi_modal_data:
return llm_inputs
return inputs
model_config = ctx.model_config
image_data = multi_modal_data["image"]
@ -176,8 +177,8 @@ def input_processor_for_fuyu(ctx: InputContext, llm_inputs: LLMInputs):
raise TypeError(f"Invalid image type: {type(image_data)}")
# process prompts
prompt = llm_inputs.get("prompt")
prompt_token_ids = llm_inputs["prompt_token_ids"]
prompt = inputs.get("prompt")
prompt_token_ids = inputs["prompt_token_ids"]
tokenizer = cached_get_tokenizer(model_config.model)
# dim0 is batch_size, dim1 is subseq_size which will always be 1
image_input_ids: List[List[
@ -190,9 +191,9 @@ def input_processor_for_fuyu(ctx: InputContext, llm_inputs: LLMInputs):
new_prompt_token_ids = image_input_ids + bos_token + prompt_token_ids[
1:] + boa_token
return LLMInputs(prompt=new_prompt,
prompt_token_ids=new_prompt_token_ids,
multi_modal_data=new_multi_modal_data)
return token_inputs(prompt=new_prompt,
prompt_token_ids=new_prompt_token_ids,
multi_modal_data=new_multi_modal_data)
def input_mapper_for_fuyu(ctx: InputContext, data: object):

21
vllm/model_executor/models/internvl.py
View File

@ -17,7 +17,8 @@ from transformers import PretrainedConfig
from vllm.attention import AttentionMetadata
from vllm.config import CacheConfig, MultiModalConfig
from vllm.inputs import INPUT_REGISTRY, InputContext, LLMInputs
from vllm.inputs import (INPUT_REGISTRY, DecoderOnlyInputs, InputContext,
token_inputs)
from vllm.model_executor.layers.quantization import QuantizationConfig
from vllm.model_executor.layers.sampler import Sampler, SamplerOutput
from vllm.model_executor.models.intern_vit import InternVisionModel
@ -276,13 +277,13 @@ class InternVLInputPipeline:
def input_processor(
self,
ctx: InputContext,
llm_inputs: LLMInputs,
inputs: DecoderOnlyInputs,
*,
max_dynamic_patch: Optional[int] = None,
) -> LLMInputs:
multi_modal_data = llm_inputs.get("multi_modal_data")
) -> DecoderOnlyInputs:
multi_modal_data = inputs.get("multi_modal_data")
if multi_modal_data is None or "image" not in multi_modal_data:
return llm_inputs
return inputs
model_config = ctx.model_config
hf_config = ctx.get_hf_config()
@ -311,8 +312,8 @@ class InternVLInputPipeline:
model_config.tokenizer,
trust_remote_code=model_config.trust_remote_code)
prompt = llm_inputs.get("prompt")
prompt_token_ids = llm_inputs["prompt_token_ids"]
prompt = inputs.get("prompt")
prompt_token_ids = inputs["prompt_token_ids"]
if prompt is None:
prompt = tokenizer.decode(prompt_token_ids)
@ -320,9 +321,9 @@ class InternVLInputPipeline:
num_patches)
new_prompt_token_ids = tokenizer.encode(new_prompt)
return LLMInputs(prompt=prompt,
prompt_token_ids=new_prompt_token_ids,
multi_modal_data=multi_modal_data)
return token_inputs(prompt=prompt,
prompt_token_ids=new_prompt_token_ids,
multi_modal_data=multi_modal_data)
def input_mapper(
self,

12
vllm/model_executor/models/llava.py
View File

@ -9,7 +9,7 @@ from transformers import CLIPVisionConfig, LlavaConfig, SiglipVisionConfig
from vllm.attention import AttentionMetadata
from vllm.config import CacheConfig, MultiModalConfig
from vllm.inputs import INPUT_REGISTRY, InputContext, LLMInputs
from vllm.inputs import INPUT_REGISTRY, DecoderOnlyInputs, InputContext
from vllm.model_executor.layers.activation import get_act_fn
from vllm.model_executor.layers.quantization import QuantizationConfig
from vllm.model_executor.layers.sampler import Sampler, SamplerOutput
@ -125,10 +125,10 @@ def dummy_data_for_llava(ctx: InputContext, seq_len: int,
raise NotImplementedError(msg)
def input_processor_for_llava(ctx: InputContext, llm_inputs: LLMInputs):
multi_modal_data = llm_inputs.get("multi_modal_data")
def input_processor_for_llava(ctx: InputContext, inputs: DecoderOnlyInputs):
multi_modal_data = inputs.get("multi_modal_data")
if multi_modal_data is None or "image" not in multi_modal_data:
return llm_inputs
return inputs
model_config = ctx.model_config
hf_config = ctx.get_hf_config(LlavaConfig)
@ -151,7 +151,7 @@ def input_processor_for_llava(ctx: InputContext, llm_inputs: LLMInputs):
return input_processor_for_clip(
model_config,
vision_config,
llm_inputs,
inputs,
image_token_id=hf_config.image_token_index,
image_feature_size_override=image_feature_size,
)
@ -159,7 +159,7 @@ def input_processor_for_llava(ctx: InputContext, llm_inputs: LLMInputs):
return input_processor_for_siglip(
model_config,
vision_config,
llm_inputs,
inputs,
image_token_id=hf_config.image_token_index,
image_feature_size_override=image_feature_size,
)

13
vllm/model_executor/models/llava_next.py
View File

@ -12,7 +12,7 @@ from typing_extensions import NotRequired
from vllm.attention import AttentionMetadata
from vllm.config import CacheConfig, MultiModalConfig
from vllm.inputs import INPUT_REGISTRY, InputContext, LLMInputs
from vllm.inputs import INPUT_REGISTRY, DecoderOnlyInputs, InputContext
from vllm.model_executor.layers.quantization import QuantizationConfig
from vllm.model_executor.layers.sampler import Sampler, SamplerOutput
from vllm.model_executor.sampling_metadata import SamplingMetadata
@ -201,10 +201,11 @@ def dummy_data_for_llava_next(ctx: InputContext, seq_len: int,
raise NotImplementedError(msg)
def input_processor_for_llava_next(ctx: InputContext, llm_inputs: LLMInputs):
multi_modal_data = llm_inputs.get("multi_modal_data")
def input_processor_for_llava_next(ctx: InputContext,
inputs: DecoderOnlyInputs):
multi_modal_data = inputs.get("multi_modal_data")
if multi_modal_data is None or "image" not in multi_modal_data:
return llm_inputs
return inputs
model_config = ctx.model_config
hf_config = ctx.get_hf_config(LlavaNextConfig)
@ -239,7 +240,7 @@ def input_processor_for_llava_next(ctx: InputContext, llm_inputs: LLMInputs):
return input_processor_for_clip(
model_config,
vision_config,
llm_inputs,
inputs,
image_token_id=hf_config.image_token_index,
image_feature_size_override=image_feature_size,
)
@ -247,7 +248,7 @@ def input_processor_for_llava_next(ctx: InputContext, llm_inputs: LLMInputs):
return input_processor_for_siglip(
model_config,
vision_config,
llm_inputs,
inputs,
image_token_id=hf_config.image_token_index,
image_feature_size_override=image_feature_size,
)

19
vllm/model_executor/models/llava_next_video.py
View File

@ -11,7 +11,8 @@ from transformers import (CLIPVisionConfig, LlavaNextVideoConfig,
from vllm.attention import AttentionMetadata
from vllm.config import CacheConfig, MultiModalConfig
from vllm.inputs import INPUT_REGISTRY, InputContext, LLMInputs
from vllm.inputs import (INPUT_REGISTRY, DecoderOnlyInputs, InputContext,
token_inputs)
from vllm.model_executor.layers.activation import get_act_fn
from vllm.model_executor.layers.quantization import QuantizationConfig
from vllm.model_executor.layers.sampler import Sampler, SamplerOutput
@ -139,10 +140,10 @@ def dummy_data_for_llava_next_video(ctx: InputContext, seq_len: int,
def input_processor_for_llava_next_video(ctx: InputContext,
llm_inputs: LLMInputs):
multi_modal_data = llm_inputs.get("multi_modal_data")
inputs: DecoderOnlyInputs):
multi_modal_data = inputs.get("multi_modal_data")
if multi_modal_data is None or "video" not in multi_modal_data:
return llm_inputs
return inputs
video_data = multi_modal_data["video"]
model_config = ctx.model_config
@ -160,15 +161,15 @@ def input_processor_for_llava_next_video(ctx: InputContext,
new_prompt, new_token_ids = repeat_and_pad_placeholder_tokens(
tokenizer,
llm_inputs.get("prompt"),
llm_inputs["prompt_token_ids"],
inputs.get("prompt"),
inputs["prompt_token_ids"],
placeholder_token_id=hf_config.video_token_index,
repeat_count=video_feature_size,
)
return LLMInputs(prompt_token_ids=new_token_ids,
prompt=new_prompt,
multi_modal_data=multi_modal_data)
return token_inputs(prompt_token_ids=new_token_ids,
prompt=new_prompt,
multi_modal_data=multi_modal_data)
elif is_list_of(video_data, np.ndarray):
raise NotImplementedError(

42
vllm/model_executor/models/llava_onevision.py
View File

@ -15,8 +15,8 @@ from typing_extensions import NotRequired
from vllm.attention import AttentionMetadata
from vllm.config import CacheConfig, MultiModalConfig
from vllm.inputs import INPUT_REGISTRY, InputContext, LLMInputs
from vllm.logger import init_logger
from vllm.inputs import (INPUT_REGISTRY, DecoderOnlyInputs, InputContext,
token_inputs)
from vllm.model_executor.layers.activation import get_act_fn
from vllm.model_executor.layers.quantization import QuantizationConfig
from vllm.model_executor.layers.sampler import Sampler, SamplerOutput
@ -37,8 +37,6 @@ from .siglip import (SiglipVisionModel, dummy_seq_data_for_siglip,
from .utils import (AutoWeightsLoader, flatten_bn, init_vllm_registered_model,
merge_multimodal_embeddings)
logger = init_logger(__name__)
# Result in the max possible feature size (2x2 grid of 336x336px tiles)
MAX_IMAGE_FEATURE_SIZE_HEIGHT = MAX_IMAGE_FEATURE_SIZE_WIDTH = 448
@ -252,10 +250,10 @@ def dummy_data_for_llava_onevision(ctx: InputContext, seq_len: int,
def input_processor_when_multimodal_input_image(ctx: InputContext,
llm_inputs: LLMInputs):
multi_modal_data = llm_inputs.get("multi_modal_data")
inputs: DecoderOnlyInputs):
multi_modal_data = inputs.get("multi_modal_data")
if multi_modal_data is None or "image" not in multi_modal_data:
return llm_inputs
return inputs
model_config = ctx.model_config
hf_config = ctx.get_hf_config(LlavaOnevisionConfig)
@ -290,7 +288,7 @@ def input_processor_when_multimodal_input_image(ctx: InputContext,
return input_processor_for_clip(
model_config,
vision_config,
llm_inputs,
inputs,
image_token_id=hf_config.image_token_index,
image_feature_size_override=image_feature_size,
)
@ -298,7 +296,7 @@ def input_processor_when_multimodal_input_image(ctx: InputContext,
return input_processor_for_siglip(
model_config,
vision_config,
llm_inputs,
inputs,
image_token_id=hf_config.image_token_index,
image_feature_size_override=image_feature_size,
)
@ -308,10 +306,10 @@ def input_processor_when_multimodal_input_image(ctx: InputContext,
def input_processor_when_multimodal_input_video(ctx: InputContext,
llm_inputs: LLMInputs):
multi_modal_data = llm_inputs.get("multi_modal_data")
inputs: DecoderOnlyInputs):
multi_modal_data = inputs.get("multi_modal_data")
if multi_modal_data is None or "video" not in multi_modal_data:
return llm_inputs
return inputs
video_data = multi_modal_data["video"]
model_config = ctx.model_config
@ -326,15 +324,15 @@ def input_processor_when_multimodal_input_video(ctx: InputContext,
new_prompt, new_token_ids = repeat_and_pad_placeholder_tokens(
tokenizer,
llm_inputs.get("prompt"),
llm_inputs["prompt_token_ids"],
inputs.get("prompt"),
inputs["prompt_token_ids"],
placeholder_token_id=hf_config.video_token_index,
repeat_count=video_feature_size,
)
return LLMInputs(prompt_token_ids=new_token_ids,
prompt=new_prompt,
multi_modal_data=multi_modal_data)
return token_inputs(prompt_token_ids=new_token_ids,
prompt=new_prompt,
multi_modal_data=multi_modal_data)
elif is_list_of(video_data, np.ndarray):
raise NotImplementedError(
@ -345,15 +343,15 @@ def input_processor_when_multimodal_input_video(ctx: InputContext,
def input_processor_for_llava_onevision(ctx: InputContext,
llm_inputs: LLMInputs):
multi_modal_data = llm_inputs.get("multi_modal_data")
inputs: DecoderOnlyInputs):
multi_modal_data = inputs.get("multi_modal_data")
if multi_modal_data is None or ("video" not in multi_modal_data
and "image" not in multi_modal_data):
return llm_inputs
return inputs
if "image" in multi_modal_data:
return input_processor_when_multimodal_input_image(ctx, llm_inputs)
return input_processor_when_multimodal_input_image(ctx, inputs)
if "video" in multi_modal_data:
return input_processor_when_multimodal_input_video(ctx, llm_inputs)
return input_processor_when_multimodal_input_video(ctx, inputs)
msg = "Unsupported multi data type"
raise NotImplementedError(msg)

18
vllm/model_executor/models/minicpmv.py
View File

@ -36,7 +36,8 @@ from typing_extensions import NotRequired
from vllm.attention import AttentionMetadata
from vllm.config import CacheConfig, LoRAConfig, MultiModalConfig
from vllm.inputs import INPUT_REGISTRY, InputContext, LLMInputs
from vllm.inputs import (INPUT_REGISTRY, DecoderOnlyInputs, InputContext,
token_inputs)
from vllm.model_executor.layers.logits_processor import LogitsProcessor
from vllm.model_executor.layers.quantization import QuantizationConfig
from vllm.model_executor.layers.resampler import (BaseResampler, Resampler2,
@ -256,7 +257,7 @@ def get_max_minicpmv_image_tokens(ctx: InputContext):
def dummy_seq_data_for_minicpmv(seq_len: int, num_images: int):
return SequenceData.from_token_counts((0, seq_len))
return SequenceData.from_prompt_token_counts((0, seq_len))
def dummy_image_for_minicpmv(ctx: InputContext, hf_config: PretrainedConfig,
@ -279,10 +280,10 @@ def dummy_data_for_minicpmv(ctx: InputContext, seq_len: int,
return seq_data, mm_data
def input_processor_for_minicpmv(ctx: InputContext, llm_inputs: LLMInputs):
multi_modal_data = llm_inputs.get("multi_modal_data")
def input_processor_for_minicpmv(ctx: InputContext, inputs: DecoderOnlyInputs):
multi_modal_data = inputs.get("multi_modal_data")
if multi_modal_data is None or "image" not in multi_modal_data:
return llm_inputs
return inputs
model_config = ctx.model_config
version = get_version_by_config(model_config.hf_config)
tokenizer = cached_get_tokenizer(
@ -297,8 +298,8 @@ def input_processor_for_minicpmv(ctx: InputContext, llm_inputs: LLMInputs):
return image_processor. \
get_slice_image_placeholder(image_size, num_image)
prompt = llm_inputs.get("prompt")
token_ids = llm_inputs.get("prompt_token_ids")
prompt = inputs.get("prompt")
token_ids = inputs.get("prompt_token_ids")
if prompt is None:
prompt = tokenizer.decode(token_ids)
@ -332,12 +333,11 @@ def input_processor_for_minicpmv(ctx: InputContext, llm_inputs: LLMInputs):
_build_image_input(ctx, image) for image in images
]
llm_inputs = LLMInputs(
return token_inputs(
prompt_token_ids=new_token_ids,
prompt=new_prompt,
multi_modal_data=multi_modal_data,
)
return llm_inputs
def input_mapper_for_minicpmv(ctx: InputContext, data: object):

52
vllm/model_executor/models/mllama.py
View File

@ -14,7 +14,6 @@
# limitations under the License.
"""PyTorch Mllama model."""
import math
from array import array
from typing import (Iterable, List, Literal, Mapping, Optional, Tuple,
TypedDict, Union)
@ -37,7 +36,8 @@ from vllm.attention import Attention, AttentionMetadata, AttentionType
from vllm.attention.ops.paged_attn import PagedAttention
from vllm.config import CacheConfig, MultiModalConfig
from vllm.distributed import get_tensor_model_parallel_world_size
from vllm.inputs import INPUT_REGISTRY, InputContext, LLMInputs
from vllm.inputs import (INPUT_REGISTRY, DecoderOnlyInputs,
EncoderDecoderInputs, InputContext)
from vllm.logger import init_logger
from vllm.model_executor.layers.layernorm import RMSNorm
from vllm.model_executor.layers.linear import (ColumnParallelLinear,
@ -51,7 +51,7 @@ from vllm.model_executor.layers.vocab_parallel_embedding import (
from vllm.model_executor.model_loader.weight_utils import default_weight_loader
from vllm.model_executor.sampling_metadata import SamplingMetadata
from vllm.multimodal import MULTIMODAL_REGISTRY
from vllm.sequence import VLLM_TOKEN_ID_ARRAY_TYPE, SequenceData
from vllm.sequence import SequenceData
from .clip import CLIPMLP
from .interfaces import SupportsMultiModal
@ -86,24 +86,24 @@ def _get_num_image_in_last_group(prompt_token_ids: List[int]) -> int:
return num_images
def input_processor_for_mllama(ctx: InputContext, llm_inputs: LLMInputs):
def input_processor_for_mllama(ctx: InputContext,
inputs: Union[DecoderOnlyInputs,
EncoderDecoderInputs]):
# move encoder_prompt to prompt
if llm_inputs.get("prompt") is None:
llm_inputs["prompt"] = llm_inputs["encoder_prompt"]
llm_inputs["prompt_token_ids"] = llm_inputs["encoder_prompt_token_ids"]
if inputs.get("prompt") is None:
inputs["prompt"] = inputs["encoder_prompt"]
inputs["prompt_token_ids"] = inputs["encoder_prompt_token_ids"]
# process multi-modal data
assert "decoder_multi_modal_data" not in llm_inputs, \
"multi-modal data should be put in encoder message of mllama"
multi_modal_data = llm_inputs.get("encoder_multi_modal_data")
multi_modal_data = inputs.get("encoder_multi_modal_data")
if multi_modal_data is None or "image" not in multi_modal_data \
or multi_modal_data["image"] is None:
# text-only
llm_inputs["encoder_prompt"] = ""
llm_inputs["encoder_prompt_token_ids"] = []
llm_inputs["encoder_multi_modal_data"] = {}
return llm_inputs
inputs["encoder_prompt"] = ""
inputs["encoder_prompt_token_ids"] = []
inputs["encoder_multi_modal_data"] = {}
return inputs
if isinstance(multi_modal_data['image'], Image.Image):
multi_modal_data['image'] = [multi_modal_data['image']]
@ -111,7 +111,7 @@ def input_processor_for_mllama(ctx: InputContext, llm_inputs: LLMInputs):
# are attended by the decoded tokens, we only need to
# get the number of tiles for those images.
num_decode_images = _get_num_image_in_last_group(
llm_inputs["prompt_token_ids"])
inputs["prompt_token_ids"])
hf_config = ctx.model_config.hf_config
num_tiles = 0
for image in multi_modal_data["image"][::-1]:
@ -137,11 +137,10 @@ def input_processor_for_mllama(ctx: InputContext, llm_inputs: LLMInputs):
"chunk size should be multiple of 14"
token_per_chunk = (hf_config.vision_config.image_size // 14)**2 + 1
num_tokens = num_tiles * token_per_chunk
llm_inputs["encoder_prompt"] = MLLAMA_IMAGE_TOKEN * num_tokens
llm_inputs["encoder_prompt_token_ids"] = [MLLAMA_IMAGE_TOKEN_ID
] * num_tokens
inputs["encoder_prompt"] = MLLAMA_IMAGE_TOKEN * num_tokens
inputs["encoder_prompt_token_ids"] = [MLLAMA_IMAGE_TOKEN_ID] * num_tokens
return llm_inputs
return inputs
def get_max_mllama_image_tokens(ctx: InputContext) -> int:
@ -154,17 +153,18 @@ def dummy_decoder_seq_data(seq_len: int, num_images: int):
# <|image|> * num_images + 0 * (seq_len - num_images)
assert seq_len >= num_images, \
"seq_len should be greater than or equal to num_images"
token_ids = array(VLLM_TOKEN_ID_ARRAY_TYPE,
[MLLAMA_IMAGE_TOKEN_ID]) * num_images
token_ids += array(VLLM_TOKEN_ID_ARRAY_TYPE, [0]) * (seq_len - num_images)
return SequenceData(token_ids)
return SequenceData.from_prompt_token_counts(
(MLLAMA_IMAGE_TOKEN_ID, num_images),
(0, seq_len - num_images),
)
def dummy_encoder_seq_data(ctx: InputContext, num_images: int):
num_tokens = get_max_mllama_image_tokens(ctx) * num_images
token_ids = array(VLLM_TOKEN_ID_ARRAY_TYPE,
[MLLAMA_IMAGE_TOKEN_ID]) * num_tokens
return SequenceData(token_ids)
return SequenceData.from_prompt_token_counts(
(MLLAMA_IMAGE_TOKEN_ID, num_tokens))
def dummy_image(num_images: int, ):

17
vllm/model_executor/models/molmo.py
View File

@ -23,7 +23,8 @@ from vllm.distributed import (get_pp_group, get_tensor_model_parallel_rank,
get_tensor_model_parallel_world_size,
split_tensor_along_last_dim,
tensor_model_parallel_all_gather)
from vllm.inputs import INPUT_REGISTRY, InputContext, LLMInputs
from vllm.inputs import (INPUT_REGISTRY, DecoderOnlyInputs, InputContext,
token_inputs)
from vllm.model_executor import SamplingMetadata
from vllm.model_executor.layers.activation import QuickGELU, SiluAndMul
from vllm.model_executor.layers.layernorm import RMSNorm
@ -945,9 +946,9 @@ def pad_images(
return images, image_input_idx, image_masks
def input_processor_for_molmo(ctx: InputContext, llm_inputs: LLMInputs):
prompt = llm_inputs.get("prompt", None)
multi_modal_data = llm_inputs.get("multi_modal_data", None)
def input_processor_for_molmo(ctx: InputContext, inputs: DecoderOnlyInputs):
prompt = inputs.get("prompt", None)
multi_modal_data = inputs.get("multi_modal_data", None)
if multi_modal_data is not None:
image = multi_modal_data.get("image", None)
else:
@ -965,9 +966,7 @@ def input_processor_for_molmo(ctx: InputContext, llm_inputs: LLMInputs):
elif prompt is not None:
out = processor.process(prompt, image)
else:
out = processor.process(None,
image,
tokens=llm_inputs["prompt_token_ids"])
out = processor.process(None, image, tokens=inputs["prompt_token_ids"])
image_processor = processor.image_processor
max_total_crops = 1 + image_processor.max_crops
@ -1020,9 +1019,9 @@ def input_processor_for_molmo(ctx: InputContext, llm_inputs: LLMInputs):
multi_modal_data = dict(image=image_data)
return LLMInputs(
return token_inputs(
prompt_token_ids=out["input_ids"],
prompt=llm_inputs["prompt"],
prompt=inputs["prompt"],
multi_modal_data=multi_modal_data,
)

20
vllm/model_executor/models/paligemma.py
View File

@ -7,7 +7,8 @@ from transformers import PaliGemmaConfig
from vllm.attention import AttentionMetadata
from vllm.config import CacheConfig, MultiModalConfig
from vllm.inputs import INPUT_REGISTRY, InputContext, LLMInputs
from vllm.inputs import (INPUT_REGISTRY, DecoderOnlyInputs, InputContext,
token_inputs)
from vllm.logger import init_logger
from vllm.model_executor.layers.quantization import QuantizationConfig
from vllm.model_executor.layers.sampler import SamplerOutput
@ -68,7 +69,8 @@ def dummy_data_for_paligemma(ctx: InputContext, seq_len: int,
return seq_data, mm_data
def input_processor_for_paligemma(ctx: InputContext, llm_inputs: LLMInputs):
def input_processor_for_paligemma(ctx: InputContext,
inputs: DecoderOnlyInputs):
"""
The correct prompt format needs to be:
@ -77,9 +79,9 @@ def input_processor_for_paligemma(ctx: InputContext, llm_inputs: LLMInputs):
See https://github.com/huggingface/transformers/blob/25245ec26dc29bcf6102e1b4ddd0dfd02e720cf5/src/transformers/models/paligemma/processing_paligemma.py#L55
""" # noqa
multi_modal_data = llm_inputs.get("multi_modal_data")
multi_modal_data = inputs.get("multi_modal_data")
if multi_modal_data is None or "image" not in multi_modal_data:
return llm_inputs
return inputs
model_config = ctx.model_config
hf_config = ctx.get_hf_config(PaliGemmaConfig)
@ -91,8 +93,8 @@ def input_processor_for_paligemma(ctx: InputContext, llm_inputs: LLMInputs):
image_token_str_pad = image_token_str * image_feature_size
image_token_ids_pad = [hf_config.image_token_index] * image_feature_size
orig_prompt = llm_inputs.get("prompt")
orig_prompt_ids = llm_inputs.get("prompt_token_ids")
orig_prompt = inputs.get("prompt")
orig_prompt_ids = inputs.get("prompt_token_ids")
if orig_prompt is not None and image_token_str in orig_prompt:
logger.warning(
@ -106,9 +108,9 @@ def input_processor_for_paligemma(ctx: InputContext, llm_inputs: LLMInputs):
new_token_ids = image_token_ids_pad + orig_prompt_ids + [108] #newline
# NOTE: Create a defensive copy of the original inputs
return LLMInputs(prompt_token_ids=new_token_ids,
prompt=new_prompt,
multi_modal_data=multi_modal_data)
return token_inputs(prompt_token_ids=new_token_ids,
prompt=new_prompt,
multi_modal_data=multi_modal_data)
class PaliGemmaMultiModalProjector(nn.Module):

20
vllm/model_executor/models/phi3v.py
View File

@ -27,7 +27,8 @@ from transformers import CLIPVisionConfig, PretrainedConfig
from vllm.attention import AttentionMetadata
from vllm.config import CacheConfig, ModelConfig, MultiModalConfig
from vllm.inputs import INPUT_REGISTRY, InputContext, LLMInputs
from vllm.inputs import (INPUT_REGISTRY, DecoderOnlyInputs, InputContext,
token_inputs)
from vllm.logger import init_logger
from vllm.model_executor.layers.pooler import Pooler, PoolingType
from vllm.model_executor.layers.quantization import QuantizationConfig
@ -410,12 +411,12 @@ def _get_image_placeholder_token_id_candidates(
def input_processor_for_phi3v(ctx: InputContext,
llm_inputs: LLMInputs,
inputs: DecoderOnlyInputs,
*,
num_crops: Optional[int] = None):
multi_modal_data = llm_inputs.get("multi_modal_data")
multi_modal_data = inputs.get("multi_modal_data")
if multi_modal_data is None or "image" not in multi_modal_data:
return llm_inputs
return inputs
model_config = ctx.model_config
hf_config = ctx.get_hf_image_processor_config()
@ -447,7 +448,7 @@ def input_processor_for_phi3v(ctx: InputContext,
else:
raise TypeError(f"Invalid image type: {type(image_data)}")
prompt = llm_inputs.get("prompt")
prompt = inputs.get("prompt")
if prompt is None:
# for async server request, we assume prompt and its token_ids is always
# in correct format. And num_image_tags == len(image_data) always True.
@ -464,7 +465,7 @@ def input_processor_for_phi3v(ctx: InputContext,
image_data), "The count of image_placeholder not match image's"
new_prompt = prompt
prompt_token_ids = llm_inputs["prompt_token_ids"].copy()
prompt_token_ids = inputs["prompt_token_ids"].copy()
print("prompt_token_ids (old)", prompt_token_ids)
@ -506,10 +507,9 @@ def input_processor_for_phi3v(ctx: InputContext,
new_token_ids.append(token_id)
# NOTE: Create a defensive copy of the original inputs
llm_inputs = LLMInputs(prompt_token_ids=new_token_ids,
prompt=new_prompt,
multi_modal_data=multi_modal_data)
return llm_inputs
return token_inputs(prompt_token_ids=new_token_ids,
prompt=new_prompt,
multi_modal_data=multi_modal_data)
@MULTIMODAL_REGISTRY.register_image_input_mapper()

14
vllm/model_executor/models/pixtral.py
View File

@ -14,7 +14,7 @@ from xformers.ops.fmha.attn_bias import BlockDiagonalMask
from vllm.attention import AttentionMetadata
from vllm.config import CacheConfig, MultiModalConfig
from vllm.inputs import INPUT_REGISTRY, InputContext, LLMInputs
from vllm.inputs import INPUT_REGISTRY, DecoderOnlyInputs, InputContext
from vllm.model_executor.layers.layernorm import RMSNorm
from vllm.model_executor.layers.quantization import QuantizationConfig
from vllm.model_executor.layers.sampler import Sampler, SamplerOutput
@ -62,7 +62,7 @@ def dummy_data_for_pixtral(ctx: InputContext, seq_len: int,
image_feature_size = (size**2) // (patch_size**2)
num_image_tokens = image_feature_size * num_images
seq_data = SequenceData.from_token_counts(
seq_data = SequenceData.from_prompt_token_counts(
(image_token_id, num_image_tokens),
(0, seq_len - num_image_tokens),
)
@ -102,8 +102,8 @@ def input_mapper_for_pixtral(ctx: InputContext,
return MultiModalInputs({"images": images})
def input_processor_for_pixtral(ctx: InputContext, llm_inputs: LLMInputs):
multi_modal_data = llm_inputs.get("multi_modal_data")
def input_processor_for_pixtral(ctx: InputContext, inputs: DecoderOnlyInputs):
multi_modal_data = inputs.get("multi_modal_data")
if multi_modal_data is not None and "image" in multi_modal_data:
tokenizer = cached_get_tokenizer(
ctx.model_config.tokenizer,
@ -112,15 +112,15 @@ def input_processor_for_pixtral(ctx: InputContext, llm_inputs: LLMInputs):
mm_encoder = tokenizer.mistral.instruct_tokenizer.mm_encoder
image_token_id = mm_encoder.special_ids.img
if image_token_id not in llm_inputs['prompt_token_ids']:
if image_token_id not in inputs['prompt_token_ids']:
raise ValueError(
(f"You've passed {llm_inputs=} without {image_token_id=}"
(f"You've passed {inputs=} without {image_token_id=}"
" Make sure to process your input via mistral_common's"
" tokenizer or pass a chat completion request. For more"
" For more info, see: "
"https://github.com/vllm-project/vllm/issues/8411."))
return llm_inputs
return inputs
@MULTIMODAL_REGISTRY.register_image_input_mapper(input_mapper_for_pixtral)

25
vllm/model_executor/models/qwen.py
View File

@ -22,7 +22,8 @@ from transformers import PretrainedConfig
from vllm.attention import Attention, AttentionMetadata
from vllm.config import CacheConfig, MultiModalConfig
from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
from vllm.inputs import INPUT_REGISTRY, InputContext, LLMInputs
from vllm.inputs import (INPUT_REGISTRY, DecoderOnlyInputs, InputContext,
token_inputs)
from vllm.logger import init_logger
from vllm.model_executor.layers.activation import SiluAndMul, get_act_fn
from vllm.model_executor.layers.layernorm import RMSNorm
@ -652,30 +653,30 @@ def get_image_text(image_num: int, padding: bool) -> str:
def input_processor_for_qwen(ctx: InputContext,
llm_inputs: LLMInputs) -> LLMInputs:
inputs: DecoderOnlyInputs) -> DecoderOnlyInputs:
"""Processes the inputs, which may or may not be multimodal.
Multimodal inputs will only be processed if the model has a "visual"
component in its model config, otherwise they'll be ignored.
Args:
ctx: Context of the loaded model.
llm_inputs: LLM inputs which may have a multi_modal_data attribute.
inputs: LLM inputs which may have a multi_modal_data attribute.
Returns:
If the model is language only or not multimodal inputs were provided,
returns llm_inputs unmodified. Otherwise, processes the multimodal
returns inputs unmodified. Otherwise, processes the multimodal
images / image embeddings and adds the fixed-length image placeholders.
"""
multi_modal_data = llm_inputs.get("multi_modal_data")
multi_modal_data = inputs.get("multi_modal_data")
# Only process images if we have multimodal data and a visual config
hf_config = ctx.get_hf_config()
if (multi_modal_data is None or "image" not in multi_modal_data
or not hasattr(hf_config, "visual")):
return llm_inputs
return inputs
prompt = llm_inputs.get("prompt")
prompt_token_ids = llm_inputs["prompt_token_ids"]
prompt = inputs.get("prompt")
prompt_token_ids = inputs["prompt_token_ids"]
model_config = ctx.model_config
tokenizer = cached_get_tokenizer(
model_config.tokenizer,
@ -713,9 +714,9 @@ def input_processor_for_qwen(ctx: InputContext,
new_prompt_token_ids = tokenizer.encode(new_prompt)
return LLMInputs(prompt=new_prompt,
prompt_token_ids=new_prompt_token_ids,
multi_modal_data=multi_modal_data)
return token_inputs(prompt=new_prompt,
prompt_token_ids=new_prompt_token_ids,
multi_modal_data=multi_modal_data)
def input_mapper_for_qwen(ctx: InputContext, data: object) -> MultiModalInputs:
@ -822,7 +823,7 @@ def dummy_data_for_qwen(
# The presence of a visual config indicates this is a multimodal model.
# If we don't have it, the model is considered an LLM for warmup purposes.
if not hasattr(hf_config, "visual"):
seq_data = SequenceData.from_token_counts((0, seq_len))
seq_data = SequenceData.from_prompt_token_counts((0, seq_len))
mm_data = None
return seq_data, mm_data

25
vllm/model_executor/models/qwen2_vl.py
View File

@ -46,7 +46,8 @@ from vllm.attention.selector import (_Backend, backend_name_to_enum,
from vllm.config import CacheConfig, MultiModalConfig
from vllm.distributed import get_pp_group, parallel_state
from vllm.distributed import utils as dist_utils
from vllm.inputs import INPUT_REGISTRY, InputContext, LLMInputs
from vllm.inputs import (INPUT_REGISTRY, DecoderOnlyInputs, InputContext,
token_inputs)
from vllm.logger import init_logger
from vllm.model_executor import SamplingMetadata
from vllm.model_executor.layers.activation import QuickGELU
@ -716,7 +717,7 @@ def dummy_data_for_qwen2_vl(
hf_config = ctx.get_hf_config(Qwen2VLConfig)
dummy_seqdata = SequenceData.from_token_counts(
dummy_seqdata = SequenceData.from_prompt_token_counts(
(hf_config.vision_start_token_id, 1),
(hf_config.image_token_id, max_llm_image_tokens),
(hf_config.vision_end_token_id, 1),
@ -799,11 +800,13 @@ def _expand_pad_tokens(inputs: list, token_id: int, make_batched_fn: Callable,
return prompt_token_ids_with_data
def input_processor_for_qwen2_vl(ctx: InputContext,
llm_inputs: LLMInputs) -> LLMInputs:
multi_modal_data = llm_inputs.get("multi_modal_data", None)
def input_processor_for_qwen2_vl(
ctx: InputContext,
inputs: DecoderOnlyInputs,
) -> DecoderOnlyInputs:
multi_modal_data = inputs.get("multi_modal_data", None)
if multi_modal_data is None:
return llm_inputs
return inputs
image_inputs = multi_modal_data.get("image", None)
video_inputs = multi_modal_data.get("video", None)
@ -817,7 +820,7 @@ def input_processor_for_qwen2_vl(ctx: InputContext,
# `transformers.models.qwen2_vl.processing_qwen2_vl.Qwen2VLProcessor`.
#
# The following code is equivalent to:
# prompt = llm_inputs["prompt"]
# prompt = inputs["prompt"]
# inputs = processor(text=[prompt],
# images=image_inputs,
# videos=video_inputs,
@ -825,9 +828,9 @@ def input_processor_for_qwen2_vl(ctx: InputContext,
# return_tensors="pt")
# prompt_token_ids = inputs["input_ids"][0].tolist()
prompt_token_ids = llm_inputs.get("prompt_token_ids", None)
prompt_token_ids = inputs.get("prompt_token_ids", None)
if prompt_token_ids is None:
prompt = llm_inputs["prompt"]
prompt = inputs["prompt"]
prompt_token_ids = processor.tokenizer(
prompt,
padding=True,
@ -868,9 +871,9 @@ def input_processor_for_qwen2_vl(ctx: InputContext,
image_processor,
prompt_token_ids)
return LLMInputs(
return token_inputs(
prompt_token_ids=prompt_token_ids,
prompt=llm_inputs["prompt"],
prompt=inputs["prompt"],
multi_modal_data=multi_modal_data,
)

16
vllm/model_executor/models/siglip.py
View File

@ -13,7 +13,7 @@ from transformers.models.siglip.modeling_siglip import SiglipSdpaAttention
from vllm.config import ModelConfig
from vllm.distributed import divide, get_tensor_model_parallel_world_size
from vllm.inputs import LLMInputs
from vllm.inputs import DecoderOnlyInputs, token_inputs
from vllm.model_executor.layers.activation import get_act_fn
from vllm.model_executor.layers.linear import (ColumnParallelLinear,
QKVParallelLinear,
@ -67,7 +67,7 @@ def dummy_seq_data_for_siglip(
else:
image_feature_size = image_feature_size_override
return SequenceData.from_token_counts(
return SequenceData.from_prompt_token_counts(
(image_token_id, image_feature_size * num_images),
(0, seq_len - image_feature_size * num_images),
)
@ -111,14 +111,14 @@ def dummy_video_for_siglip(
def input_processor_for_siglip(
model_config: ModelConfig,
hf_config: SiglipVisionConfig,
llm_inputs: LLMInputs,
inputs: DecoderOnlyInputs,
*,
image_token_id: int,
image_feature_size_override: Optional[Union[int, List[int]]] = None,
):
multi_modal_data = llm_inputs.get("multi_modal_data")
multi_modal_data = inputs.get("multi_modal_data")
if multi_modal_data is None or "image" not in multi_modal_data:
return llm_inputs
return inputs
tokenizer = cached_get_tokenizer(model_config.tokenizer)
@ -135,14 +135,14 @@ def input_processor_for_siglip(
new_prompt, new_token_ids = repeat_and_pad_placeholder_tokens(
tokenizer,
llm_inputs.get("prompt"),
llm_inputs["prompt_token_ids"],
inputs.get("prompt"),
inputs["prompt_token_ids"],
placeholder_token_id=image_token_id,
repeat_count=image_feature_size,
)
# NOTE: Create a defensive copy of the original inputs
return LLMInputs(
return token_inputs(
prompt_token_ids=new_token_ids,
prompt=new_prompt,
multi_modal_data=multi_modal_data,

18
vllm/model_executor/models/ultravox.py
View File

@ -18,7 +18,7 @@ from transformers.models.whisper.modeling_whisper import WhisperEncoder
from vllm.attention import AttentionMetadata
from vllm.config import CacheConfig, MultiModalConfig
from vllm.inputs import INPUT_REGISTRY
from vllm.inputs.data import LLMInputs
from vllm.inputs.data import DecoderOnlyInputs, token_inputs
from vllm.inputs.registry import InputContext
from vllm.model_executor.layers.activation import SiluAndMul, get_act_fn
from vllm.model_executor.layers.layernorm import RMSNorm
@ -156,10 +156,10 @@ def input_mapper_for_ultravox(ctx: InputContext, data: object):
return MultiModalInputs({"audio_features": audio_features})
def input_processor_for_ultravox(ctx: InputContext, llm_inputs: LLMInputs):
multi_modal_data = llm_inputs.get("multi_modal_data")
def input_processor_for_ultravox(ctx: InputContext, inputs: DecoderOnlyInputs):
multi_modal_data = inputs.get("multi_modal_data")
if multi_modal_data is None or "audio" not in multi_modal_data:
return llm_inputs
return inputs
feature_extractor = whisper_feature_extractor(ctx)
audios = multi_modal_data["audio"]
@ -196,16 +196,16 @@ def input_processor_for_ultravox(ctx: InputContext, llm_inputs: LLMInputs):
new_prompt, new_token_ids = repeat_and_pad_placeholder_tokens(
tokenizer,
llm_inputs.get("prompt"),
llm_inputs["prompt_token_ids"],
inputs.get("prompt"),
inputs["prompt_token_ids"],
placeholder_token_id=_AUDIO_PLACEHOLDER_TOKEN,
repeat_count=audio_token_counts,
)
# NOTE: Create a defensive copy of the original inputs
return LLMInputs(prompt_token_ids=new_token_ids,
prompt=new_prompt,
multi_modal_data=multi_modal_data)
return token_inputs(prompt_token_ids=new_token_ids,
prompt=new_prompt,
multi_modal_data=multi_modal_data)
class StackAudioFrames(nn.Module):

74
vllm/sequence.py
View File

@ -13,8 +13,7 @@ from typing import Set, Tuple, Union, cast
import msgspec
import torch
from vllm.inputs import EncoderDecoderLLMInputs, LLMInputs
from vllm.inputs.parse import is_valid_encoder_decoder_llm_inputs
from vllm.inputs.parse import is_encoder_decoder_inputs
from vllm.lora.request import LoRARequest
from vllm.pooling_params import PoolingParams
from vllm.prompt_adapter.request import PromptAdapterRequest
@ -22,6 +21,7 @@ from vllm.sampling_params import SamplingParams
from vllm.spec_decode.metrics import SpecDecodeWorkerMetrics
if TYPE_CHECKING:
from vllm.inputs import SingletonInputs
from vllm.multimodal.base import MultiModalDataDict
VLLM_TOKEN_ID_ARRAY_TYPE = "l"
@ -29,6 +29,11 @@ VLLM_TOKEN_ID_ARRAY_TYPE = "l"
VLLM_INVALID_TOKEN_ID = -1
def array_full(token_id: int, count: int):
""":class:`array` equivalent of :func:`numpy.full`."""
return array(VLLM_TOKEN_ID_ARRAY_TYPE, [token_id]) * count
# We use dataclass for now because it is used for
# openai server output, and msgspec is not serializable.
# TODO(sang): Fix it.
@ -173,22 +178,34 @@ class SequenceData(msgspec.Struct,
_mrope_position_delta: Optional[int] = None
@staticmethod
def from_token_counts(*token_counts: Tuple[int, int]) -> "SequenceData":
def from_prompt_token_counts(
*token_counts: Tuple[int, int]) -> "SequenceData":
"""
Construct a :class:`SequenceData` instance by concatenating
prompt token sequences.
Each tuple represents one token sequence, expressed in the form
:code:`(token_id, count)`.
"""
if len(token_counts) == 0:
return SequenceData.from_seqs([])
arrs = [
array(VLLM_TOKEN_ID_ARRAY_TYPE, [token_id]) * count
for token_id, count in token_counts
]
prompt_token_ids_arr = reduce(
array.__iadd__,
(array_full(token_id, count) for token_id, count in token_counts),
)
return SequenceData(reduce(array.__add__, arrs))
return SequenceData(prompt_token_ids_arr)
@staticmethod
def from_seqs(
prompt_token_ids: GenericSequence[int],
output_token_ids: Optional[GenericSequence[int]] = None,
) -> "SequenceData":
"""
Construct a :class:`SequenceData` instance from prompt and output
token sequences.
"""
prompt_token_ids_arr = array(VLLM_TOKEN_ID_ARRAY_TYPE,
prompt_token_ids)
@ -362,14 +379,14 @@ class SequenceData(msgspec.Struct,
class Sequence:
"""Stores the data, status, and block information of a sequence.
The sequence is constructed from the LLMInputs instance passed
in through the `inputs` constructor argument.
The sequence is constructed from the :code:`SingletonInputs` instance
passed in through the :code:`inputs` constructor argument.
For encoder/decoder models, LLMInputs encapsulates both a
For encoder/decoder models, SingletonInputs encapsulates both a
decoder and encoder prompt, creating an ambiguity about which
prompt to construct the sequence from. The `from_decoder_prompt`
constructor argument signals whether to construct the Sequence
from the LLMInputs decoder prompt, or encoder prompt.
from the SingletonInputs decoder prompt, or encoder prompt.
Args:
seq_id: The ID of the sequence.
@ -379,16 +396,16 @@ class Sequence:
eos_token_id: The end-of-sequence (EOS) token id recognized by this LLM.
lora_request: LoRA request.
prompt_adapter_request: Prompt Adapter request.
from_decoder_prompt: Construct Sequence from LLMInputs decoder prompt
(True) or encoder prompt (False.) Must be True
for decoder-only model.
from_decoder_prompt: Construct Sequence from SingletonInputs decoder
prompt (True) or encoder prompt (False.) Must be
True for decoder-only model.
"""
def __init__(
self,
seq_id: int,
inputs: "LLMInputs",
inputs: "SingletonInputs",
block_size: int,
eos_token_id: Optional[int] = None,
lora_request: Optional[LoRARequest] = None,
@ -404,19 +421,19 @@ class Sequence:
self.from_decoder_prompt = from_decoder_prompt
# For decoder-only models, a Sequence is constructed
# from an LLMInputs instance (the `inputs` arg.)
# from an DecoderOnlyInputs instance (the `inputs` arg.)
#
# For encoder/decoder models the same `inputs`
# instance could be utilized to construct either an
# encoder sequence or a decoder sequence, because
# `LLMInputs` has both decoder- and encoder-oriented
# `DecoderOnlyInputs` has both decoder- and encoder-oriented
# member variables (i.e. it encapsulates both an encoder
# and a decoder prompt.) The decision of which type of sequence
# to generate is determined by the `from_decoder_prompt` argument.
#
# When constructing a encoder sequence
# (`from_decoder_prompt` False) it matters that
# the `LLMInputs` instance stored in `inputs` is valid
# the `DecoderOnlyInputs` instance stored in `inputs` is valid
# in the sense that its encoder-related member variables are
# populated; below, an exception is raised if this is
# not the case.
@ -424,8 +441,7 @@ class Sequence:
# When constructing a decoder sequence (`from_decoder_prompt` True)
# it does not matter whether `inputs` has its encoder-related
# member variables populated.
if not (from_decoder_prompt
or is_valid_encoder_decoder_llm_inputs(inputs)):
if not (from_decoder_prompt or is_encoder_decoder_inputs(inputs)):
raise ValueError("Cannot extract encoder input prompt from "
f"invalid input {inputs}; did you forget the "
"encoder input prompt fields?")
@ -471,15 +487,19 @@ class Sequence:
@property
def multi_modal_data(self) -> "MultiModalDataDict":
if self.inputs.get("multi_modal_data") and self.inputs.get(
"encoder_multi_modal_data"):
inputs = self.inputs
if (inputs.get("multi_modal_data")
and inputs.get("encoder_multi_modal_data")):
raise ValueError(
"Multi-modal data in both encoder and decoder is not supported."
)
inputs = self.inputs
return self.inputs.get("multi_modal_data") or (cast(
EncoderDecoderLLMInputs,
inputs).get("encoder_multi_modal_data")) or {}
return cast(
"MultiModalDataDict",
(inputs.get("multi_modal_data")
or inputs.get("encoder_multi_modal_data") or {}),
)
@property
def mm_processor_kwargs(self) -> Dict[str, Any]: