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[V1] Scatter and gather placeholders in the model runner (#15712)

Browse Source 
Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>
Signed-off-by: mgoin <mgoin64@gmail.com>
Signed-off-by: Roger Wang <ywang@roblox.com>
Co-authored-by: mgoin <mgoin64@gmail.com>
Co-authored-by: Roger Wang <ywang@roblox.com>
This commit is contained in:
Cyrus Leung 2025-04-05 05:26:44 +08:00 committed by GitHub
parent 651cf0fec1
commit f5722a5052
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42 changed files with 496 additions and 942 deletions
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16
docs/source/contributing/model/multimodal.md
View File

@ -860,8 +860,8 @@ prompt_tokens, prompts_length = _tokenize_prompts_with_image_and_batch(
)
```
To accommodate this, instead of a string you can return an instance of {class}`~vllm.multimodal.processing.PromptUpdateDetails`
with different `full` and `feature` attributes:
To assign the vision embeddings to only the image tokens, instead of a string
you can return an instance of {class}`~vllm.multimodal.processing.PromptUpdateDetails`:
```python
hf_config = self.info.get_hf_config()
@ -879,9 +879,9 @@ def get_replacement_fuyu(item_idx: int):
image_tokens = ([_IMAGE_TOKEN_ID] * ncols +
[_NEWLINE_TOKEN_ID]) * nrows
return PromptUpdateDetails(
full=image_tokens + [bos_token_id],
features=image_tokens,
return PromptUpdateDetails.select_token_id(
image_tokens + [bos_token_id],
embed_token_id=_IMAGE_TOKEN_ID,
)
```
@ -914,9 +914,9 @@ def _get_prompt_updates(
image_tokens = ([_IMAGE_TOKEN_ID] * ncols +
[_NEWLINE_TOKEN_ID]) * nrows
return PromptUpdateDetails(
full=image_tokens + [bos_token_id],
features=image_tokens,
return PromptUpdateDetails.select_token_id(
image_tokens + [bos_token_id],
embed_token_id=_IMAGE_TOKEN_ID,
)
return [

3
docs/source/models/supported_models.md
View File

@ -989,9 +989,6 @@ See [this page](#generative-models) for more information on how to use generativ
<sup>+</sup> Multiple items can be inputted per text prompt for this modality.
:::{important}
To use Gemma3 series models, you have to install Hugging Face Transformers library from source via
`pip install git+https://github.com/huggingface/transformers`.
Pan-and-scan image pre-processing is currently supported on V0 (but not V1).
You can enable it by passing `--mm-processor-kwargs '{"do_pan_and_scan": True}'`.
:::

2
examples/offline_inference/audio_language.py
View File

@ -47,7 +47,7 @@ def run_minicpmo(question: str, audio_count: int) -> ModelRequestData:
model=model_name,
trust_remote_code=True,
max_model_len=4096,
max_num_seqs=5,
max_num_seqs=2,
limit_mm_per_prompt={"audio": audio_count},
)

5
tests/models/decoder_only/audio_language/test_ultravox.py
View File

@ -55,7 +55,10 @@ def server(request, audio_assets):
for key, value in request.param.items()
]
with RemoteOpenAIServer(MODEL_NAME, args) as remote_server:
with RemoteOpenAIServer(MODEL_NAME,
args,
env_dict={"VLLM_AUDIO_FETCH_TIMEOUT":
"30"}) as remote_server:
yield remote_server

2
tests/models/decoder_only/vision_language/test_models.py
View File

@ -167,7 +167,7 @@ VLM_TEST_SETTINGS = {
"cherry_blossom": "<image>What is the season?", # noqa: E501
}),
multi_image_prompt="<image><image>Describe the two images in detail.", # noqa: E501
max_model_len=8192,
max_model_len=4096,
max_num_seqs=2,
auto_cls=AutoModelForImageTextToText,
vllm_runner_kwargs={"mm_processor_kwargs": {"crop_to_patches": True}}

26
tests/models/decoder_only/vision_language/test_pixtral.py
View File

@ -176,6 +176,8 @@ def test_chat(
model,
dtype=dtype,
tokenizer_mode="mistral",
load_format="mistral",
config_format="mistral",
max_model_len=max_model_len,
limit_mm_per_prompt=LIMIT_MM_PER_PROMPT,
) as vllm_model:
@ -198,22 +200,14 @@ def test_chat(
@large_gpu_test(min_gb=48)
@pytest.mark.parametrize(
"prompt,expected_ranges",
[(_create_engine_inputs_hf(IMG_URLS[:1]), [{
"offset": 11,
"length": 494
}]),
(_create_engine_inputs_hf(IMG_URLS[1:4]), [{
"offset": 11,
"length": 266
}, {
"offset": 277,
"length": 1056
}, {
"offset": 1333,
"length": 418
}])])
@pytest.mark.parametrize("prompt,expected_ranges",
[(_create_engine_inputs_hf(IMG_URLS[:1]),
[PlaceholderRange(offset=11, length=494)]),
(_create_engine_inputs_hf(IMG_URLS[1:4]), [
PlaceholderRange(offset=11, length=266),
PlaceholderRange(offset=277, length=1056),
PlaceholderRange(offset=1333, length=418)
])])
def test_multi_modal_placeholders(vllm_runner, prompt,
expected_ranges: list[PlaceholderRange],
monkeypatch) -> None:

4
tests/models/multimodal/processing/test_llava_next.py
View File

@ -92,8 +92,8 @@ def _validate_image_prompt_replacements_one(
first_placeholder = image_placeholders[0]
# NOTE: There is a BOS token
assert first_placeholder["offset"] == 1
assert first_placeholder["length"] == (
assert first_placeholder.offset == 1
assert first_placeholder.length == (
len(processed_inputs["prompt_token_ids"]) - 1) // num_imgs
except Exception as exc:

4
tests/models/multimodal/processing/test_llava_onevision.py
View File

@ -92,8 +92,8 @@ def _validate_image_prompt_replacements_one(
first_placeholder = image_placeholders[0]
assert first_placeholder["offset"] == 0
assert first_placeholder["length"] == len(
assert first_placeholder.offset == 0
assert first_placeholder.length == len(
processed_inputs["prompt_token_ids"]) // num_imgs
except Exception as exc:
failed_size_excs.append((image_size, exc))

4
tests/models/registry.py
View File

@ -277,7 +277,9 @@ _MULTIMODAL_EXAMPLE_MODELS = {
trust_remote_code=True,
hf_overrides={"architectures": ["GLM4VForCausalLM"]}), # noqa: E501
"H2OVLChatModel": _HfExamplesInfo("h2oai/h2ovl-mississippi-800m",
extras={"2b": "h2oai/h2ovl-mississippi-2b"}), # noqa: E501
extras={"2b": "h2oai/h2ovl-mississippi-2b"}, # noqa: E501
max_transformers_version="4.48", # noqa: E501
transformers_version_reason="HF model is not compatible."), # noqa: E501
"InternVLChatModel": _HfExamplesInfo("OpenGVLab/InternVL2-1B",
extras={"2B": "OpenGVLab/InternVL2-2B"}, # noqa: E501
trust_remote_code=True),

9
tests/multimodal/test_processing.py
View File

@ -785,6 +785,7 @@ def test_find_update_tokens(
item_idx=0,
start_idx=6,
tokens=[32000, 32000],
is_embed=None,
),
],
"pattern_4": [
@ -793,6 +794,7 @@ def test_find_update_tokens(
item_idx=0,
start_idx=3,
tokens=[32000],
is_embed=None,
),
],
}
@ -807,12 +809,14 @@ def test_find_update_tokens(
item_idx=0,
start_idx=1,
tokens=[32000, 32000],
is_embed=None,
),
PlaceholderFeaturesInfo(
modality="pattern_1",
item_idx=1,
start_idx=5,
tokens=[32000, 32000],
is_embed=None,
),
],
"pattern_3": [
@ -821,6 +825,7 @@ def test_find_update_tokens(
item_idx=0,
start_idx=7,
tokens=[1550, 918, 1550],
is_embed=None,
),
],
# No match for pattern_4 as it has lower priority than pattern_1
@ -835,12 +840,14 @@ def test_find_update_tokens(
item_idx=0,
start_idx=1,
tokens=[32000, 32000],
is_embed=None,
),
PlaceholderFeaturesInfo(
modality="pattern_1",
item_idx=1,
start_idx=3,
tokens=[32000, 32000],
is_embed=None,
),
],
"pattern_4": [
@ -849,6 +856,7 @@ def test_find_update_tokens(
item_idx=0,
start_idx=5,
tokens=[32000],
is_embed=None,
),
],
"pattern_3": [
@ -857,6 +865,7 @@ def test_find_update_tokens(
item_idx=0,
start_idx=6,
tokens=[1550, 918, 1550],
is_embed=None,
),
],
}

46
tests/v1/core/test_kv_cache_utils.py
View File

@ -3,7 +3,7 @@
import pytest
import torch
from vllm.multimodal.inputs import MultiModalKwargs
from vllm.multimodal.inputs import MultiModalKwargs, PlaceholderRange
from vllm.sampling_params import SamplingParams
from vllm.utils import sha256
# disable yapf here as it formats differently than isort such that both fail
@ -158,13 +158,10 @@ def test_generate_block_hash_extra_keys():
request = make_request(
request_id=0,
prompt_token_ids=[_ for _ in range(20)],
mm_positions=[{
"offset": 0,
"length": 5
}, {
"offset": 10,
"length": 5
}],
mm_positions=[
PlaceholderRange(offset=0, length=5),
PlaceholderRange(offset=10, length=5),
],
mm_hashes=["hash1", "hash2"],
)
@ -222,13 +219,10 @@ def test_hash_request_tokens(hash_fn):
request = make_request(
request_id=0,
prompt_token_ids=[_ for _ in range(6)],
mm_positions=[{
"offset": 0,
"length": 3
}, {
"offset": 3,
"length": 3
}],
mm_positions=[
PlaceholderRange(offset=0, length=3),
PlaceholderRange(offset=3, length=3),
],
mm_hashes=["hash1", "hash2"],
)
@ -253,25 +247,19 @@ def test_hash_tokens_different_mm_input(hash_fn):
request1 = make_request(
request_id=0,
prompt_token_ids=[_ for _ in range(6)],
mm_positions=[{
"offset": 0,
"length": 3
}, {
"offset": 3,
"length": 3
}],
mm_positions=[
PlaceholderRange(offset=0, length=3),
PlaceholderRange(offset=3, length=3),
],
mm_hashes=["hash1", "hash2"],
)
request2 = make_request(
request_id=1,
prompt_token_ids=[_ for _ in range(6)],
mm_positions=[{
"offset": 0,
"length": 3
}, {
"offset": 3,
"length": 3
}],
mm_positions=[
PlaceholderRange(offset=0, length=3),
PlaceholderRange(offset=3, length=3),
],
mm_hashes=["hash3", "hash2"],
)
block_size = 3

71
vllm/model_executor/models/aya_vision.py
View File

@ -27,7 +27,7 @@ from vllm.multimodal.processing import (BaseMultiModalProcessor,
BaseProcessingInfo,
MultiModalFieldConfig,
PromptReplacement, PromptUpdate,
encode_tokens)
PromptUpdateDetails)
from vllm.multimodal.profiling import BaseDummyInputsBuilder, ProcessorInputs
from vllm.sequence import IntermediateTensors
@ -35,7 +35,6 @@ from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsPP
from .siglip import SiglipVisionModel
from .utils import (AutoWeightsLoader, flatten_bn, init_vllm_registered_model,
maybe_prefix, merge_multimodal_embeddings)
from .vision import scatter_patch_features, select_patch_features
class AyaVisionImagePixelInputs(TypedDict):
@ -51,13 +50,6 @@ class AyaVisionImagePixelInputs(TypedDict):
num_patches: torch.Tensor
"""Shape: `(batch_size * num_images)`"""
embed_is_patch: Union[torch.Tensor, list[torch.Tensor]]
"""
A boolean mask indicating which image embeddings correspond to patch tokens.
Shape: `(batch_size * num_images, num_embeds)`
"""
class AyaVisionMultiModalProjector(nn.Module):
@ -135,21 +127,20 @@ class AyaVisionProcessingInfo(BaseProcessingInfo):
def get_max_image_tokens(self) -> int:
hf_processor = self.get_hf_processor()
image_processor = hf_processor.image_processor
image_size = self.get_image_size_with_most_features()
tokenizer = hf_processor.tokenizer
num_patches = self.get_num_patches(
image_width=image_size.width,
image_height=image_size.height,
size=image_processor.size,
min_patches=image_processor.min_patches,
max_patches=image_processor.max_patches)
image_string = hf_processor._prompt_split_image(num_patches)
x = encode_tokens(
tokenizer,
image_string,
add_special_tokens=False,
max_patches=image_processor.max_patches,
)
return len(x)
img_patches_per_tile = (hf_processor.img_size //
hf_processor.patch_size)**2
return num_patches * img_patches_per_tile
def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
return {"image": None}
@ -221,7 +212,6 @@ class AyaVisionMultiModalProcessor(
hf_processor = self.info.get_hf_processor(**mm_kwargs)
image_processor = hf_processor.image_processor
hf_config = self.info.get_hf_config()
# HF processor pops the `num_patches` kwarg, which is needed by vLLM
if (images :=
mm_data.get("images")) is not None and '<image>' in prompt:
@ -234,6 +224,7 @@ class AyaVisionMultiModalProcessor(
parsed_images.get_image_size(i)
for i in range(len(parsed_images))
]
num_patches = [
self.info.get_num_patches(
image_width=image_size.width,
@ -243,20 +234,6 @@ class AyaVisionMultiModalProcessor(
max_patches=image_processor.max_patches)
for image_size in image_sizes
]
image_tokens_list = [
hf_processor._prompt_split_image(num_patch)
for num_patch in num_patches
]
tokenizer = self.info.get_tokenizer()
image_token_ids = [
tokenizer.encode(image_tokens, add_special_tokens=False)
for image_tokens in image_tokens_list
]
embed_is_patch = [
torch.tensor(image_repl_tokens) == hf_config.image_token_index
for image_repl_tokens in image_token_ids
]
processed_outputs["embed_is_patch"] = embed_is_patch
processed_outputs["num_patches"] = torch.tensor(num_patches)
return processed_outputs
@ -271,7 +248,6 @@ class AyaVisionMultiModalProcessor(
pixel_values=MultiModalFieldConfig.flat_from_sizes(
"image", num_patches),
num_patches=MultiModalFieldConfig.batched("image"),
embed_is_patch=MultiModalFieldConfig.batched("image"),
image_embeds=MultiModalFieldConfig.batched("image"),
)
@ -283,6 +259,7 @@ class AyaVisionMultiModalProcessor(
) -> Sequence[PromptUpdate]:
hf_processor = self.info.get_hf_processor(**hf_processor_mm_kwargs)
image_token = hf_processor.image_token
img_patch_token = hf_processor.img_patch_token
image_processor = hf_processor.image_processor
def get_replacement(item_idx: int):
@ -294,8 +271,11 @@ class AyaVisionMultiModalProcessor(
image_height=image_size.height,
size=image_processor.size,
min_patches=image_processor.min_patches,
max_patches=image_processor.max_patches)
return hf_processor._prompt_split_image(num_patches=num_patches)
max_patches=image_processor.max_patches,
)
repl = hf_processor._prompt_split_image(num_patches=num_patches)
return PromptUpdateDetails.select_text(repl, img_patch_token)
return [
PromptReplacement(
@ -424,7 +404,6 @@ class AyaVisionForConditionalGeneration(nn.Module, SupportsMultiModal,
self, **kwargs: object) -> Optional[AyaVisionImagePixelInputs]:
pixel_values = kwargs.pop("pixel_values", None)
num_patches = kwargs.pop("num_patches", None)
embed_is_patch = kwargs.pop("embed_is_patch", None)
image_embeds = kwargs.pop("image_embeds", None)
assert image_embeds is None, "Aya Vision does not support image_embeds."
@ -436,18 +415,13 @@ class AyaVisionForConditionalGeneration(nn.Module, SupportsMultiModal,
raise ValueError("Incorrect type of num_patches. "
f"Got type: {type(num_patches)}")
if not isinstance(embed_is_patch, (torch.Tensor, list)):
raise ValueError("Incorrect type of embed_is_patch. "
f"Got type: {type(embed_is_patch)}")
pixel_values = flatten_bn(pixel_values, concat=True)
num_patches = flatten_bn(num_patches, concat=True)
embed_is_patch = flatten_bn(embed_is_patch)
return AyaVisionImagePixelInputs(
type="pixel_values",
pixel_values=self._validate_pixel_values(pixel_values),
num_patches=num_patches,
embed_is_patch=embed_is_patch,
)
def get_multimodal_embeddings(
@ -455,11 +429,8 @@ class AyaVisionForConditionalGeneration(nn.Module, SupportsMultiModal,
image_input = self._parse_and_validate_image_input(**kwargs)
if image_input is None:
return None
image_features = self._process_image_input(image_input, **kwargs)
return scatter_patch_features(
image_features,
image_input["embed_is_patch"],
)
return self._process_image_input(image_input, **kwargs)
def get_input_embeddings(
self,
@ -471,9 +442,9 @@ class AyaVisionForConditionalGeneration(nn.Module, SupportsMultiModal,
inputs_embeds = merge_multimodal_embeddings(
input_ids=input_ids,
inputs_embeds=inputs_embeds,
multimodal_embeddings=select_patch_features(
multimodal_embeddings),
placeholder_token_id=self.config.image_token_index)
multimodal_embeddings=multimodal_embeddings,
placeholder_token_id=self.config.image_token_index,
)
return inputs_embeds

6
vllm/model_executor/models/chameleon.py
View File

@ -162,9 +162,9 @@ class ChameleonMultiModalProcessor(
PromptReplacement(
modality="image",
target=[image_token_id],
replacement=PromptUpdateDetails(
full=([image_start_id] + image_tokens + [image_end_id]),
features=image_tokens,
replacement=PromptUpdateDetails.select_token_id(
[image_start_id] + image_tokens + [image_end_id],
embed_token_id=image_token_id,
),
)
]

85
vllm/model_executor/models/fuyu.py
View File

@ -18,7 +18,7 @@
""" PyTorch Fuyu model."""
import math
from collections.abc import Iterable, Mapping, Sequence
from typing import Literal, Optional, Set, Tuple, TypedDict, Union
from typing import Literal, Optional, Set, Tuple, TypedDict
import torch
import torch.nn as nn
@ -43,7 +43,6 @@ from vllm.sequence import IntermediateTensors
from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsPP
from .utils import (AutoWeightsLoader, flatten_bn, maybe_prefix,
merge_multimodal_embeddings)
from .vision import scatter_patch_features, select_patch_features
# Cannot find the following 2 numbers from hf config.
_IMAGE_TOKEN_ID = 71011
@ -66,14 +65,6 @@ class FuyuImagePatchInputs(TypedDict):
flattened just like `flat_data`.
"""
embed_is_patch: Union[torch.Tensor, list[torch.Tensor]]
"""
A boolean mask indicating which image embeddings correspond
to patch tokens.
Shape: `(batch_size * num_images, num_embeds)`
"""
class FuyuProcessingInfo(BaseProcessingInfo):
@ -94,15 +85,7 @@ class FuyuProcessingInfo(BaseProcessingInfo):
seq_len: int,
mm_counts: Mapping[str, int],
) -> Mapping[str, int]:
target_width, target_height = self.get_image_size_with_most_features()
max_ncols, max_nrows = self.get_image_feature_grid_size(
image_width=target_width,
image_height=target_height,
)
max_image_tokens = (max_ncols + 1) * max_nrows
return {"image": max_image_tokens}
return {"image": self.get_max_image_tokens()}
def get_image_feature_grid_size(
self,
@ -128,11 +111,32 @@ class FuyuProcessingInfo(BaseProcessingInfo):
nrows = math.ceil(image_height / patch_height)
return ncols, nrows
def get_num_image_tokens(
self,
*,
image_width: int,
image_height: int,
) -> int:
ncols, nrows = self.get_image_feature_grid_size(
image_width=image_width,
image_height=image_height,
)
return ncols * nrows
def get_image_size_with_most_features(self) -> ImageSize:
image_processor = self.get_image_processor()
return ImageSize(width=image_processor.size["width"],
height=image_processor.size["height"])
def get_max_image_tokens(self) -> int:
target_width, target_height = self.get_image_size_with_most_features()
return self.get_num_image_tokens(
image_width=target_width,
image_height=target_height,
)
class FuyuDummyInputsBuilder(BaseDummyInputsBuilder[FuyuProcessingInfo]):
@ -192,19 +196,6 @@ class FuyuMultiModalProcessor(BaseMultiModalProcessor[FuyuProcessingInfo]):
processed_outputs["image_patches"] = image_patches[0]
# get patch grid size for each image
embed_is_patch = []
for image in images:
ncols, nrows = self.info.get_image_feature_grid_size(
image_width=image.width,
image_height=image.height,
)
mask = torch.tensor(([True] * ncols + [False]) * nrows)
embed_is_patch.append(mask)
processed_outputs["embed_is_patch"] = embed_is_patch
return processed_outputs
def _apply_hf_processor_tokens_only(
@ -224,8 +215,7 @@ class FuyuMultiModalProcessor(BaseMultiModalProcessor[FuyuProcessingInfo]):
hf_inputs: BatchFeature,
hf_processor_mm_kwargs: Mapping[str, object],
) -> Mapping[str, MultiModalFieldConfig]:
return dict(image_patches=MultiModalFieldConfig.batched("image"),
embed_is_patch=MultiModalFieldConfig.batched("image"))
return dict(image_patches=MultiModalFieldConfig.batched("image"))
def _get_prompt_updates(
self,
@ -252,9 +242,9 @@ class FuyuMultiModalProcessor(BaseMultiModalProcessor[FuyuProcessingInfo]):
image_tokens = ([_IMAGE_TOKEN_ID] * ncols +
[_NEWLINE_TOKEN_ID]) * nrows
return PromptUpdateDetails(
full=image_tokens + [bos_token_id],
features=image_tokens,
return PromptUpdateDetails.select_token_id(
image_tokens + [bos_token_id],
embed_token_id=_IMAGE_TOKEN_ID,
)
return [
@ -329,20 +319,13 @@ class FuyuForCausalLM(nn.Module, SupportsMultiModal, SupportsPP):
raise ValueError("Incorrect type of image patches. "
f"Got type: {type(image_patches)}")
embed_is_patch = kwargs.pop("embed_is_patch")
if not isinstance(embed_is_patch, (torch.Tensor, list)):
raise ValueError("Incorrect type of embed_is_patch. "
f"Got type: {type(embed_is_patch)}")
image_patches_flat = flatten_bn(image_patches)
embed_is_patch = flatten_bn(embed_is_patch)
return FuyuImagePatchInputs(
type="image_patches",
flat_data=self._validate_pixel_values(
flatten_bn(image_patches_flat, concat=True)),
patches_per_image=[x.size(0) for x in image_patches_flat],
embed_is_patch=embed_is_patch,
)
return None
@ -364,12 +347,7 @@ class FuyuForCausalLM(nn.Module, SupportsMultiModal, SupportsPP):
if image_input is None:
return None
image_features = self._process_image_input(image_input)
return scatter_patch_features(
image_features,
image_input["embed_is_patch"],
)
return self._process_image_input(image_input)
def get_input_embeddings(
self,
@ -379,8 +357,11 @@ class FuyuForCausalLM(nn.Module, SupportsMultiModal, SupportsPP):
inputs_embeds = self.language_model.get_input_embeddings(input_ids)
if multimodal_embeddings is not None:
inputs_embeds = merge_multimodal_embeddings(
input_ids, inputs_embeds,
select_patch_features(multimodal_embeddings), _IMAGE_TOKEN_ID)
input_ids,
inputs_embeds,
multimodal_embeddings,
_IMAGE_TOKEN_ID,
)
return inputs_embeds
def forward(

83
vllm/model_executor/models/gemma3_mm.py
View File

@ -25,7 +25,7 @@ from vllm.multimodal.processing import (BaseMultiModalProcessor,
PlaceholderFeaturesInfo,
PromptReplacement, PromptTargetMatch,
PromptUpdate, PromptUpdateDetails,
encode_tokens, find_mm_placeholders,
find_mm_placeholders,
replace_token_matches)
# yapf: enable
from vllm.multimodal.profiling import BaseDummyInputsBuilder, ProcessorInputs
@ -36,7 +36,6 @@ from .interfaces import (MultiModalEmbeddings, SupportsLoRA,
from .siglip import SiglipVisionModel
from .utils import (AutoWeightsLoader, flatten_bn, init_vllm_registered_model,
maybe_prefix, merge_multimodal_embeddings)
from .vision import scatter_patch_features, select_patch_features
logger = init_logger(__name__)
@ -54,14 +53,6 @@ class Gemma3ImagePixelInputs(TypedDict):
num_patches: torch.Tensor
"""Shape: `(batch_size * num_images)`"""
embed_is_patch: Union[torch.Tensor, list[torch.Tensor]]
"""
A boolean mask indicating which image embeddings correspond
to patch tokens.
Shape: `(batch_size * num_images, num_embeds)`
"""
Gemma3ImageInputs = Gemma3ImagePixelInputs
@ -183,7 +174,7 @@ class Gemma3ProcessingInfo(BaseProcessingInfo):
if processor is None:
processor = self.get_hf_processor()
image_token = processor.boi_token
boi_token = processor.boi_token
num_crops = self.get_num_crops(
image_width=image_width,
@ -192,19 +183,21 @@ class Gemma3ProcessingInfo(BaseProcessingInfo):
)
if num_crops == 0:
image_text = image_token
image_text = boi_token
else:
crops_image_tokens = " ".join(image_token
for _ in range(num_crops))
crops_image_tokens = " ".join(boi_token for _ in range(num_crops))
image_text = (
f"Here is the original image {image_token} and here are some "
f"Here is the original image {boi_token} and here are some "
f"crops to help you see better {crops_image_tokens}")
repl_full = image_text.replace(image_token,
repl_full = image_text.replace(boi_token,
processor.full_image_sequence)
repl_features = repl_full.strip("\n")
return PromptUpdateDetails(full=repl_full, features=repl_features)
tokenizer = processor.tokenizer
vocab = tokenizer.get_vocab()
image_token_id = vocab[tokenizer.image_token]
return PromptUpdateDetails.select_token_id(repl_full, image_token_id)
def get_num_image_tokens(
self,
@ -213,19 +206,17 @@ class Gemma3ProcessingInfo(BaseProcessingInfo):
image_height: int,
processor: Optional[Gemma3Processor],
) -> int:
tokenizer = self.get_tokenizer()
image_repl = self.get_image_repl(
if processor is None:
processor = self.get_hf_processor()
num_crops = self.get_num_crops(
image_width=image_width,
image_height=image_height,
processor=processor,
)
image_seq_len = processor.image_seq_length
image_repl_tokens = encode_tokens(
tokenizer,
image_repl.features,
add_special_tokens=False,
)
return len(image_repl_tokens)
return (num_crops + 1) * image_seq_len
def get_image_size_with_most_features(self) -> ImageSize:
processor = self.get_hf_processor()
@ -301,28 +292,6 @@ class Gemma3MultiModalProcessor(BaseMultiModalProcessor[Gemma3ProcessingInfo]):
]
hf_processor = self.info.get_hf_processor(**mm_kwargs)
image_repl_features = [
self.info.get_image_repl(image_width=size.width,
image_height=size.height,
processor=hf_processor).features
for size in image_sizes
]
tokenizer = self.info.get_tokenizer()
image_repls_feature_tokens = [
tokenizer.encode(image_repl, add_special_tokens=False)
for image_repl in image_repl_features
]
vocab = tokenizer.get_vocab()
image_token_id = vocab[tokenizer.image_token]
embed_is_patch = [
torch.tensor(image_repl_tokens) == image_token_id
for image_repl_tokens in image_repls_feature_tokens
]
processed_outputs["embed_is_patch"] = embed_is_patch
num_crops = [
self.info.get_num_crops(image_width=size.width,
image_height=size.height,
@ -344,7 +313,6 @@ class Gemma3MultiModalProcessor(BaseMultiModalProcessor[Gemma3ProcessingInfo]):
pixel_values=MultiModalFieldConfig.flat_from_sizes(
"image", num_crops + 1),
num_crops=MultiModalFieldConfig.batched("image"),
embed_is_patch=MultiModalFieldConfig.batched("image"),
)
def _get_prompt_updates(
@ -454,6 +422,7 @@ class Gemma3MultiModalProcessor(BaseMultiModalProcessor[Gemma3ProcessingInfo]):
item_idx=p.item_idx,
start_idx=repl_orig_idxs[p.start_idx],
tokens=p.tokens,
is_embed=p.is_embed,
) for p in placeholders
]
for modality, placeholders in repls.items()
@ -572,7 +541,6 @@ class Gemma3ForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP,
self, **kwargs: object) -> Optional[Gemma3ImageInputs]:
pixel_values = kwargs.pop("pixel_values", None)
num_crops = kwargs.pop("num_crops", None)
embed_is_patch = kwargs.pop("embed_is_patch", None)
image_embeds = kwargs.pop("image_embeds", None)
assert image_embeds is None, "Gemma3 does not support image_embeds."
if pixel_values is None:
@ -586,19 +554,13 @@ class Gemma3ForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP,
raise ValueError("Incorrect type of num_crops. "
f"Got type: {type(num_crops)}")
if not isinstance(embed_is_patch, (torch.Tensor, list)):
raise ValueError("Incorrect type of embed_is_patch. "
f"Got type: {type(embed_is_patch)}")
pixel_values = flatten_bn(pixel_values, concat=True)
num_crops = flatten_bn(num_crops, concat=True)
embed_is_patch = flatten_bn(embed_is_patch)
return Gemma3ImagePixelInputs(
type="pixel_values",
pixel_values=self._validate_pixel_values(pixel_values),
num_patches=num_crops + 1,
embed_is_patch=embed_is_patch,
)
def _image_pixels_to_features(
@ -635,12 +597,7 @@ class Gemma3ForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP,
if image_input is None:
return None
image_features = self._process_image_input(image_input)
return scatter_patch_features(
image_features,
image_input["embed_is_patch"],
)
return self._process_image_input(image_input)
def get_input_embeddings(
self,
@ -652,7 +609,7 @@ class Gemma3ForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP,
inputs_embeds = merge_multimodal_embeddings(
input_ids,
inputs_embeds,
select_patch_features(multimodal_embeddings),
multimodal_embeddings,
self.config.image_token_index,
)
return inputs_embeds

2
vllm/model_executor/models/h2ovl.py
View File

@ -257,7 +257,7 @@ class H2OVLProcessor(BaseInternVLProcessor):
repl_features = IMG_CONTEXT * feature_size
repl_full = IMG_START + repl_features + IMG_END
return PromptUpdateDetails(full=repl_full, features=repl_features)
return PromptUpdateDetails.select_text(repl_full, IMG_CONTEXT)
def resolve_min_max_num(
self,

82
vllm/model_executor/models/idefics3.py
View File

@ -41,7 +41,7 @@ from vllm.multimodal.processing import (BaseMultiModalProcessor,
MultiModalDataItems,
MultiModalFieldConfig,
PromptReplacement, PromptUpdate,
encode_tokens)
PromptUpdateDetails)
# yapf: enable
from vllm.multimodal.profiling import BaseDummyInputsBuilder, ProcessorInputs
from vllm.sequence import IntermediateTensors
@ -54,7 +54,6 @@ from .interfaces import MultiModalEmbeddings, SupportsLoRA, SupportsMultiModal
from .llama import LlamaModel
from .utils import (AutoWeightsLoader, flatten_bn, maybe_prefix,
merge_multimodal_embeddings)
from .vision import scatter_patch_features, select_patch_features
class Idefics3ImagePixelInputs(TypedDict):
@ -69,14 +68,6 @@ class Idefics3ImagePixelInputs(TypedDict):
num_patches: torch.Tensor
"""Shape: `(batch_size * num_images)`"""
embed_is_patch: Union[torch.Tensor, list[torch.Tensor]]
"""
A boolean mask indicating which image embeddings correspond
to patch tokens.
Shape: `(batch_size * num_images, num_embeds)`
"""
class Idefics3ImageEmbeddingInputs(TypedDict):
type: Literal["image_embeds"]
@ -86,14 +77,6 @@ class Idefics3ImageEmbeddingInputs(TypedDict):
`hidden_size` must match the hidden size of language model backbone.
"""
embed_is_patch: Union[torch.Tensor, list[torch.Tensor]]
"""
A boolean mask indicating which image embeddings correspond
to patch tokens.
Shape: `(batch_size * num_images, num_embeds)`
"""
ImageInputs = Union[Idefics3ImagePixelInputs, Idefics3ImageEmbeddingInputs]
@ -275,19 +258,16 @@ class Idefics3ProcessingInfo(BaseProcessingInfo):
image_height: int,
processor: Optional[Idefics3Processor],
) -> int:
tokenizer = self.get_tokenizer()
image_repl = self.get_image_repl(
if processor is None:
processor = self.get_hf_processor()
num_patches = self.get_num_patches(
image_width=image_width,
image_height=image_height,
processor=processor,
)
image_repl_tokens = encode_tokens(
tokenizer,
image_repl,
add_special_tokens=False,
)
return len(image_repl_tokens)
return num_patches * processor.image_seq_len
def get_image_size_with_most_features(self) -> ImageSize:
processor = self.get_hf_processor()
@ -364,28 +344,6 @@ class Idefics3MultiModalProcessor(
]
hf_processor = self.info.get_hf_processor(**mm_kwargs)
image_repl_features = [
self.info.get_image_repl(image_width=size.width,
image_height=size.height,
processor=hf_processor)
for size in image_sizes
]
tokenizer = self.info.get_tokenizer()
image_repls_feature_tokens = [
tokenizer.encode(image_repl, add_special_tokens=False)
for image_repl in image_repl_features
]
vocab = tokenizer.get_vocab()
image_token_id = vocab[hf_processor.image_token.content]
embed_is_patch = [
torch.tensor(image_repl_tokens) == image_token_id
for image_repl_tokens in image_repls_feature_tokens
]
processed_outputs["embed_is_patch"] = embed_is_patch
num_patches = [
self.info.get_num_patches(
image_width=size.width,
@ -415,7 +373,6 @@ class Idefics3MultiModalProcessor(
"image", num_patches),
image_embeds=MultiModalFieldConfig.batched("image"),
num_patches=MultiModalFieldConfig.batched("image"),
embed_is_patch=MultiModalFieldConfig.batched("image"),
)
def _get_prompt_updates(
@ -427,17 +384,22 @@ class Idefics3MultiModalProcessor(
hf_processor = self.info.get_hf_processor(**hf_processor_mm_kwargs)
image_token = hf_processor.image_token.content
def get_replacement_idefics3(item_idx: int) -> str:
def get_replacement_idefics3(item_idx: int) -> PromptUpdateDetails:
images = mm_items.get_items("image", ImageProcessorItems)
image_size = images.get_image_size(item_idx)
return self.info.get_image_repl(
image_repl = self.info.get_image_repl(
image_width=image_size.width,
image_height=image_size.height,
processor=hf_processor,
)
return PromptUpdateDetails.select_text(
image_repl,
embed_text=image_token,
)
return [
PromptReplacement(
modality="image",
@ -675,13 +637,6 @@ class Idefics3ForConditionalGeneration(nn.Module, SupportsMultiModal,
if pixel_values is None and image_embeds is None:
return None
embed_is_patch = kwargs.pop("embed_is_patch")
if not isinstance(embed_is_patch, (torch.Tensor, list)):
raise ValueError("Incorrect type of embed_is_patch. "
f"Got type: {type(embed_is_patch)}")
embed_is_patch = flatten_bn(embed_is_patch)
if image_embeds is not None:
if not isinstance(image_embeds, (torch.Tensor, list)):
raise ValueError("Incorrect type of image embeddings. "
@ -690,7 +645,6 @@ class Idefics3ForConditionalGeneration(nn.Module, SupportsMultiModal,
return Idefics3ImageEmbeddingInputs(
type="image_embeds",
data=flatten_bn(image_embeds, concat=True),
embed_is_patch=embed_is_patch,
)
if pixel_values is not None:
@ -718,7 +672,6 @@ class Idefics3ForConditionalGeneration(nn.Module, SupportsMultiModal,
pixel_values=self._validate_pixel_values(pixel_values),
pixel_attention_mask=pixel_attention_mask,
num_patches=num_patches,
embed_is_patch=embed_is_patch,
)
raise AssertionError("This line should be unreachable.")
@ -754,12 +707,7 @@ class Idefics3ForConditionalGeneration(nn.Module, SupportsMultiModal,
if image_input is None:
return None
image_features = self._process_image_input(image_input)
return scatter_patch_features(
image_features,
image_input["embed_is_patch"],
)
return self._process_image_input(image_input)
def get_input_embeddings(
self,
@ -771,7 +719,7 @@ class Idefics3ForConditionalGeneration(nn.Module, SupportsMultiModal,
inputs_embeds = merge_multimodal_embeddings(
input_ids,
inputs_embeds,
select_patch_features(multimodal_embeddings),
multimodal_embeddings,
self.config.image_token_id,
)
return inputs_embeds

43
vllm/model_executor/models/internvl.py
View File

@ -39,7 +39,6 @@ from vllm.transformers_utils.tokenizer import AnyTokenizer
from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsPP
from .utils import (AutoWeightsLoader, flatten_bn, init_vllm_registered_model,
maybe_prefix, merge_multimodal_embeddings)
from .vision import scatter_patch_features, select_patch_features
IMG_START = '<img>'
IMG_END = '</img>'
@ -60,14 +59,6 @@ class InternVLImagePixelInputs(TypedDict):
num_patches: torch.Tensor
"""Shape: `(batch_size * num_images)`"""
embed_is_patch: Union[torch.Tensor, list[torch.Tensor]]
"""
A boolean mask indicating which image embeddings correspond
to patch tokens.
Shape: `(batch_size * num_images, num_embeds)`
"""
class InternVLImageEmbeddingInputs(TypedDict):
type: Literal["image_embeds"]
@ -419,24 +410,12 @@ class BaseInternVLProcessor(ABC):
torch.tensor([len(item) for item in pixel_values_lst]),
}
tokenizer = self.tokenizer
image_token_id = self.image_token_id
embed_is_patch = list[torch.Tensor]()
for pixel_values in pixel_values_lst:
num_patches = pixel_values.shape[0]
feature_size = num_patches * self.num_image_token
image_repl = self.get_image_repl(feature_size, num_patches)
feature_tokens = tokenizer.encode(image_repl.features,
add_special_tokens=False)
text = [t.replace('<image>', image_repl.full, 1) for t in text]
embed_is_patch.append(
torch.tensor(feature_tokens) == image_token_id)
image_inputs["embed_is_patch"] = embed_is_patch
text_inputs = self.tokenizer(text)
@ -460,7 +439,7 @@ class InternVLProcessor(BaseInternVLProcessor):
repl_features = IMG_CONTEXT * feature_size
repl_full = IMG_START + repl_features + IMG_END
return PromptUpdateDetails(full=repl_full, features=repl_features)
return PromptUpdateDetails.select_text(repl_full, IMG_CONTEXT)
class BaseInternVLProcessingInfo(BaseProcessingInfo):
@ -599,7 +578,6 @@ class InternVLMultiModalProcessor(BaseMultiModalProcessor[_I]):
pixel_values_flat=MultiModalFieldConfig.flat_from_sizes(
"image", image_num_patches),
image_num_patches=MultiModalFieldConfig.batched("image"),
embed_is_patch=MultiModalFieldConfig.batched("image"),
image_embeds=MultiModalFieldConfig.batched("image"),
image_token_id=MultiModalFieldConfig.shared("image", num_images),
)
@ -831,7 +809,6 @@ class InternVLChatModel(nn.Module, SupportsMultiModal, SupportsPP):
self, **kwargs: object) -> Optional[InternVLImageInputs]:
pixel_values_flat = kwargs.pop("pixel_values_flat", None)
image_num_patches = kwargs.pop("image_num_patches", None)
embed_is_patch = kwargs.pop("embed_is_patch", None)
image_embeds = kwargs.pop("image_embeds", None)
if pixel_values_flat is None and image_embeds is None:
@ -860,20 +837,14 @@ class InternVLChatModel(nn.Module, SupportsMultiModal, SupportsPP):
raise ValueError("Incorrect type of image_num_patches. "
f"Got type: {type(image_num_patches)}")
if not isinstance(embed_is_patch, (torch.Tensor, list)):
raise ValueError("Incorrect type of embed_is_patch. "
f"Got type: {type(embed_is_patch)}")
pixel_values_flat = flatten_bn(pixel_values_flat, concat=True)
image_num_patches = flatten_bn(image_num_patches, concat=True)
embed_is_patch = flatten_bn(embed_is_patch)
return InternVLImagePixelInputs(
type="pixel_values",
pixel_values_flat=self._validate_pixel_values(
pixel_values_flat),
num_patches=image_num_patches,
embed_is_patch=embed_is_patch,
)
raise AssertionError("This line should be unreachable.")
@ -919,15 +890,7 @@ class InternVLChatModel(nn.Module, SupportsMultiModal, SupportsPP):
if image_input is None:
return None
image_features = self._process_image_input(image_input)
if image_input["type"] != "pixel_values":
return image_features
return scatter_patch_features(
image_features,
image_input["embed_is_patch"],
)
return self._process_image_input(image_input)
def get_input_embeddings(
self,
@ -941,7 +904,7 @@ class InternVLChatModel(nn.Module, SupportsMultiModal, SupportsPP):
inputs_embeds = merge_multimodal_embeddings(
input_ids,
inputs_embeds,
select_patch_features(multimodal_embeddings),
multimodal_embeddings,
self.img_context_token_id,
)
return inputs_embeds

55
vllm/model_executor/models/llava.py
View File

@ -32,7 +32,8 @@ from vllm.multimodal.parse import (ImageEmbeddingItems, ImageProcessorItems,
ImageSize, MultiModalDataItems)
from vllm.multimodal.processing import (BaseMultiModalProcessor,
BaseProcessingInfo, ProcessingCache,
PromptReplacement, PromptUpdate)
PromptReplacement, PromptUpdate,
PromptUpdateDetails)
from vllm.multimodal.profiling import BaseDummyInputsBuilder, ProcessorInputs
from vllm.sequence import IntermediateTensors
@ -42,8 +43,7 @@ from .pixtral import PixtralHFEncoderInfo, PixtralHFVisionModel
from .siglip import SiglipVisionModel
from .utils import (AutoWeightsLoader, flatten_bn, init_vllm_registered_model,
maybe_prefix, merge_multimodal_embeddings)
from .vision import (get_vision_encoder_info, scatter_patch_features,
select_patch_features)
from .vision import get_vision_encoder_info
class LlavaImagePixelInputs(TypedDict):
@ -67,14 +67,6 @@ class PixtralHFImagePixelInputs(TypedDict):
in which case the data is passed as a list instead of a batched tensor.
"""
embed_is_patch: Union[torch.Tensor, list[torch.Tensor]]
"""
A boolean mask indicating which image embeddings correspond
to patch tokens.
Shape: `(batch_size * num_images, num_embeds)`
"""
class LlavaImageEmbeddingInputs(TypedDict):
type: Literal["image_embeds"]
@ -343,23 +335,6 @@ class PixtralHFMultiModalProcessor(
for p, (h, w) in zip(pixel_values, image_sizes)
]
hf_config = self.info.get_hf_config()
vision_config = hf_config.vision_config
assert isinstance(vision_config, PixtralVisionConfig)
encoder_info = PixtralHFEncoderInfo(vision_config)
tile_sizes = [
encoder_info.get_patch_grid_size(
image_width=pixel_value.shape[-1],
image_height=pixel_value.shape[-2],
) for pixel_value in processed_outputs["pixel_values"]
]
embed_is_patch = [
torch.tensor(([True] * ncols + [False]) * nrows)
for ncols, nrows in tile_sizes
]
processed_outputs["embed_is_patch"] = embed_is_patch
return processed_outputs
def _get_mm_fields_config(
@ -369,7 +344,6 @@ class PixtralHFMultiModalProcessor(
) -> Mapping[str, MultiModalFieldConfig]:
return dict(
pixel_values=MultiModalFieldConfig.batched("image"),
embed_is_patch=MultiModalFieldConfig.batched("image"),
image_embeds=MultiModalFieldConfig.batched("image"),
)
@ -404,7 +378,7 @@ class PixtralHFMultiModalProcessor(
tokens = ([image_token_id] * ncols + [image_break_id]) * nrows
tokens[-1] = image_end_id
return tokens
return PromptUpdateDetails.select_token_id(tokens, image_token_id)
return [
PromptReplacement(
@ -612,17 +586,9 @@ class LlavaForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP):
f"Got type: {type(pixel_values)}")
if self.config.vision_config.model_type == "pixtral":
embed_is_patch = kwargs.pop("embed_is_patch")
if not isinstance(embed_is_patch, (torch.Tensor, list)):
raise ValueError("Incorrect type of embed_is_patch. "
f"Got type: {type(embed_is_patch)}")
embed_is_patch = flatten_bn(embed_is_patch)
return PixtralHFImagePixelInputs(
type="pixel_values_pixtral",
pixel_values=flatten_bn(pixel_values),
embed_is_patch=embed_is_patch,
)
return LlavaImagePixelInputs(
@ -714,16 +680,7 @@ class LlavaForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP):
if image_input is None:
return None
image_features = self._process_image_input(image_input)
if image_input["type"] != "pixel_values_pixtral":
# The path is used for pixtral (V0 only) and llava (V0/V1)
return image_features
return scatter_patch_features(
image_features,
image_input["embed_is_patch"],
)
return self._process_image_input(image_input)
def get_input_embeddings(
self,
@ -735,7 +692,7 @@ class LlavaForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP):
inputs_embeds = merge_multimodal_embeddings(
input_ids,
inputs_embeds,
select_patch_features(multimodal_embeddings),
multimodal_embeddings,
self.config.image_token_index,
)
return inputs_embeds

74
vllm/model_executor/models/minicpmo.py
View File

@ -40,7 +40,8 @@ from vllm.multimodal.parse import (AudioItem, AudioProcessorItems,
DictEmbeddingItems, ModalityData,
ModalityDataItems, MultiModalDataItems,
MultiModalDataParser)
from vllm.multimodal.processing import PromptReplacement, PromptUpdate
from vllm.multimodal.processing import (PromptReplacement, PromptUpdate,
PromptUpdateDetails)
from vllm.multimodal.profiling import ProcessorInputs
from .minicpmv import (_MAX_FRAMES_PER_VIDEO, MiniCPMV2_6,
@ -50,7 +51,6 @@ from .minicpmv import (_MAX_FRAMES_PER_VIDEO, MiniCPMV2_6,
_minicpmv_field_config)
from .utils import (AutoWeightsLoader, cast_overflow_tensors, flatten_bn,
maybe_prefix)
from .vision import scatter_patch_features
CPU_DEVICE = torch.device("cpu")
@ -73,14 +73,6 @@ class MiniCPMOAudioFeatureInputs(TypedDict):
which equals to `audio_features.shape[-1]`
"""
embed_is_patch: Union[torch.Tensor, list[torch.Tensor]]
"""
A boolean mask indicating which audio embeddings correspond
to patch tokens.
Shape: `(batch_size * num_audios, num_embeds)`
"""
class MiniCPMOAudioEmbeddingInputs(TypedDict):
type: Literal["audio_embeds"]
@ -93,14 +85,6 @@ class MiniCPMOAudioEmbeddingInputs(TypedDict):
Length of each slice may vary, so pass it as a list.
"""
embed_is_patch: Union[torch.Tensor, list[torch.Tensor]]
"""
A boolean mask indicating which audio embeddings correspond
to patch tokens.
Shape: `(batch_size * num_audios, num_embeds)`
"""
MiniCPMOAudioInputs = Union[MiniCPMOAudioFeatureInputs,
MiniCPMOAudioEmbeddingInputs]
@ -115,7 +99,6 @@ def _minicpmo_field_config(hf_inputs: Mapping[str, torch.Tensor]):
audio_features=MultiModalFieldConfig.batched("audio"),
audio_feature_lens=MultiModalFieldConfig.batched("audio"),
audio_embeds=MultiModalFieldConfig.batched("audio"),
audio_embed_is_patch=MultiModalFieldConfig.batched("audio"),
audio_token_id=MultiModalFieldConfig.shared("audio", num_audios),
)
@ -197,8 +180,7 @@ class MiniCPMOProcessingInfo(MiniCPMVProcessingInfo):
pool_step = self.get_default_audio_pool_step()
fbank_feat_in_chunk = 100
cnn_feat_in_chunk = (fbank_feat_in_chunk - 1) // 2 + 1
num_audio_tokens = (cnn_feat_in_chunk - pool_step) // pool_step + 1
return num_audio_tokens + 2 # <audio>(<unk>*N)</audio>
return (cnn_feat_in_chunk - pool_step) // pool_step + 1
def get_max_audio_chunks_with_most_features(self) -> int:
return 30
@ -209,8 +191,7 @@ class MiniCPMOProcessingInfo(MiniCPMVProcessingInfo):
def get_audio_len_by_num_chunks(self, num_chunks: int) -> int:
sampling_rate = self.get_default_audio_sampling_rate()
# exclude <audio> </audio>
num_tokens_per_chunk = self.get_max_audio_tokens_per_chunk() - 2
num_tokens_per_chunk = self.get_max_audio_tokens_per_chunk()
return int(num_chunks * sampling_rate / num_tokens_per_chunk) + 1
def get_num_frames_with_most_features(
@ -295,13 +276,6 @@ class MiniCPMOMultiModalProcessor(
if isinstance(parsed_audios, MiniCPMOAudioEmbeddingItems):
audio_inputs = {}
audio_lens = [
self.info.get_audio_len_by_num_chunks(
sum(map(len,
parsed_audios.get(i)["audio_embeds"])))
for i in range(len(parsed_audios))
]
else:
audio_inputs = self._base_call_hf_processor(
prompts=[self.info.audio_pattern] * len(parsed_audios),
@ -323,27 +297,7 @@ class MiniCPMOMultiModalProcessor(
]
audio_inputs["audio_features"] = unpadded_audio_features
audio_lens = [
parsed_audios.get_audio_length(i)
for i in range(len(parsed_audios))
]
audio_repl_features = [
self.get_audio_prompt_texts(audio_len) for audio_len in audio_lens
]
tokenizer = self.info.get_tokenizer()
audio_repls_feature_tokens = [
tokenizer.encode(audio_repl, add_special_tokens=False)
for audio_repl in audio_repl_features
]
embed_is_patch = [
self.get_embed_is_patch(audio_repl_tokens)
for audio_repl_tokens in audio_repls_feature_tokens
]
audio_inputs["audio_embed_is_patch"] = embed_is_patch
unk_token_id = tokenizer.get_vocab()["<unk>"]
audio_inputs["audio_token_id"] = torch.tensor(unk_token_id)
@ -384,7 +338,10 @@ class MiniCPMOMultiModalProcessor(
else:
audio_len = audios.get_audio_length(item_idx)
return self.get_audio_prompt_texts(audio_len)
return PromptUpdateDetails.select_text(
self.get_audio_prompt_texts(audio_len),
"<unk>",
)
return [
*base_updates,
@ -713,13 +670,6 @@ class MiniCPMO(MiniCPMV2_6):
assert isinstance(audio_token_id, torch.Tensor)
self.mm_token_ids.add(audio_token_id.flatten().unique().item())
audio_embed_is_patch = kwargs.pop("audio_embed_is_patch")
if not isinstance(audio_embed_is_patch, (torch.Tensor, list)):
raise ValueError("Incorrect type of audio_embed_is_patch. "
f"Got type: {type(audio_embed_is_patch)}")
audio_embed_is_patch = flatten_bn(audio_embed_is_patch)
if audio_embeds is not None:
if not isinstance(audio_embeds, (torch.Tensor, list)):
raise ValueError("Incorrect type of audio_embeds. "
@ -730,7 +680,6 @@ class MiniCPMO(MiniCPMV2_6):
return MiniCPMOAudioEmbeddingInputs(
type="audio_embeds",
audio_embeds=audio_embeds_flat,
embed_is_patch=audio_embed_is_patch,
)
if not isinstance(audio_features, (torch.Tensor, list)):
@ -749,7 +698,6 @@ class MiniCPMO(MiniCPMV2_6):
type="audio_features",
audio_features=audio_features_flat,
audio_feature_lens=audio_feature_lens_flat,
embed_is_patch=audio_embed_is_patch,
)
def _parse_and_validate_multimodal_inputs(self, **kwargs: object) -> dict:
@ -781,10 +729,6 @@ class MiniCPMO(MiniCPMV2_6):
if modality == "audios":
audio_input = modalities["audios"]
audio_features = self._process_audio_input(audio_input)
multimodal_embeddings += tuple(
scatter_patch_features(
audio_features,
audio_input["embed_is_patch"],
))
multimodal_embeddings += tuple(audio_features)
return multimodal_embeddings

142
vllm/model_executor/models/minicpmv.py
View File

@ -56,7 +56,7 @@ from vllm.multimodal.parse import (DictEmbeddingItems, ImageItem,
VideoItem, VideoProcessorItems)
from vllm.multimodal.processing import (BaseMultiModalProcessor,
BaseProcessingInfo, PromptReplacement,
PromptUpdate)
PromptUpdate, PromptUpdateDetails)
from vllm.multimodal.profiling import BaseDummyInputsBuilder, ProcessorInputs
from vllm.platforms import current_platform
from vllm.sequence import IntermediateTensors
@ -67,7 +67,6 @@ from .interfaces import (MultiModalEmbeddings, SupportsLoRA,
SupportsMultiModal, SupportsPP)
from .utils import (AutoWeightsLoader, flatten_bn, maybe_prefix,
merge_multimodal_embeddings)
from .vision import scatter_patch_features, select_patch_features
# For profile run
_MAX_FRAMES_PER_VIDEO = 16
@ -90,14 +89,6 @@ class MiniCPMVImagePixelInputs(TypedDict):
This should be in `(height, width)` format.
"""
embed_is_patch: Union[torch.Tensor, list[torch.Tensor]]
"""
A boolean mask indicating which image embeddings correspond
to patch tokens.
Shape: `(batch_size * num_images, num_embeds)`
"""
num_slices: torch.Tensor
"""Shape: `(batch_size * num_images)`"""
@ -112,14 +103,6 @@ class MiniCPMVImageEmbeddingInputs(TypedDict):
instead of a batched tensor.
"""
embed_is_patch: Union[torch.Tensor, list[torch.Tensor]]
"""
A boolean mask indicating which image embeddings correspond
to patch tokens.
Shape: `(batch_size * num_images, num_embeds)`
"""
MiniCPMVImageInputs = Union[MiniCPMVImagePixelInputs,
MiniCPMVImageEmbeddingInputs]
@ -245,12 +228,10 @@ def _minicpmv_field_config(hf_inputs: Mapping[str, torch.Tensor]):
image_sizes=MultiModalFieldConfig.batched("image"),
tgt_sizes=MultiModalFieldConfig.batched("image"),
image_embeds=MultiModalFieldConfig.batched("image"),
embed_is_patch=MultiModalFieldConfig.batched("image"),
video_pixel_values=MultiModalFieldConfig.batched("video"),
video_image_sizes=MultiModalFieldConfig.batched("video"),
video_tgt_sizes=MultiModalFieldConfig.batched("video"),
video_embeds=MultiModalFieldConfig.batched("video"),
video_embed_is_patch=MultiModalFieldConfig.batched("video"),
image_token_id=MultiModalFieldConfig.shared("image", num_images),
video_token_id=MultiModalFieldConfig.shared("video", num_videos),
)
@ -398,22 +379,43 @@ class MiniCPMVProcessingInfo(BaseProcessingInfo):
use_image_id=use_image_id,
)
def get_sliced_grid(
self,
image_size: ImageSize,
# For MiniCPM V/O 2.6
max_slice_nums: Optional[int] = None,
) -> Optional[tuple[int, int]]:
image_processor = self.get_image_processor()
version = self.get_model_version()
if version == (2, 0) or version == (2, 5):
return image_processor.get_sliced_grid(image_size)
if max_slice_nums is None:
max_slice_nums = image_processor.max_slice_nums
return image_processor.get_sliced_grid(
image_size,
max_slice_nums=max_slice_nums,
)
def get_num_image_tokens(
self,
image_size: ImageSize,
max_slice_nums: Optional[int] = None,
use_image_id: bool = True,
) -> int:
tokenizer = self.get_tokenizer()
image_placeholders = self.get_slice_image_placeholder(
image_processor = self.get_image_processor()
grid = self.get_sliced_grid(
image_size,
max_slice_nums=max_slice_nums,
use_image_id=use_image_id,
)
image_token_ids = tokenizer.encode(image_placeholders,
add_special_tokens=False)
if grid is None:
ncols = nrows = 0
else:
ncols, nrows = grid
return len(image_token_ids)
return (ncols * nrows + 1) * image_processor.image_feature_size
def get_max_image_tokens(self) -> int:
image_size = self.get_image_size_with_most_features()
@ -433,7 +435,6 @@ class MiniCPMVProcessingInfo(BaseProcessingInfo):
return self.get_num_image_tokens(
frame_size,
max_slice_nums=self.get_video_max_slice_num(),
use_image_id=False,
)
def get_max_video_tokens(
@ -539,14 +540,6 @@ class MiniCPMVMultiModalProcessor(BaseMultiModalProcessor[_I]):
use_image_id=False,
) * num_frames
def get_embed_is_patch(
self,
input_ids: list[int],
) -> torch.Tensor:
tokenizer = self.info.get_tokenizer()
unk_token_id = tokenizer.get_vocab()["<unk>"]
return torch.tensor(input_ids) == unk_token_id
def process_images(
self,
mm_data: Mapping[str, object],
@ -570,26 +563,7 @@ class MiniCPMVMultiModalProcessor(BaseMultiModalProcessor[_I]):
out_keys={"pixel_values", "image_sizes", "tgt_sizes"},
)
image_sizes = [
parsed_images.get_image_size(i) for i in range(len(parsed_images))
]
image_repl_features = [
self.get_image_prompt_texts(size, idx)
for idx, size in enumerate(image_sizes)
]
tokenizer = self.info.get_tokenizer()
image_repls_feature_tokens = [
tokenizer.encode(image_repl, add_special_tokens=False)
for image_repl in image_repl_features
]
embed_is_patch = [
self.get_embed_is_patch(image_repl_tokens)
for image_repl_tokens in image_repls_feature_tokens
]
image_inputs["embed_is_patch"] = embed_is_patch
unk_token_id = tokenizer.get_vocab()["<unk>"]
image_inputs["image_token_id"] = torch.tensor(unk_token_id)
@ -625,31 +599,9 @@ class MiniCPMVMultiModalProcessor(BaseMultiModalProcessor[_I]):
out_keys={"pixel_values", "image_sizes", "tgt_sizes"},
)
frame_sizes = [
parsed_videos.get_frame_size(i) for i in range(len(parsed_videos))
]
num_frames = [
parsed_videos.get_num_frames(i) for i in range(len(parsed_videos))
]
video_repl_features = [
self.get_video_prompt_texts(size, nframes)
for size, nframes in zip(frame_sizes, num_frames)
]
tokenizer = self.info.get_tokenizer()
video_repls_feature_tokens = [
tokenizer.encode(video_repl, add_special_tokens=False)
for video_repl in video_repl_features
]
embed_is_patch = [
self.get_embed_is_patch(video_repl_tokens)
for video_repl_tokens in video_repls_feature_tokens
]
video_inputs["embed_is_patch"] = embed_is_patch
video_inputs = {f"video_{k}": v for k, v in video_inputs.items()}
tokenizer = self.info.get_tokenizer()
unk_token_id = tokenizer.get_vocab()["<unk>"]
video_inputs["video_token_id"] = torch.tensor(unk_token_id)
@ -740,7 +692,10 @@ class MiniCPMVMultiModalProcessor(BaseMultiModalProcessor[_I]):
image_size = images.get_image_size(item_idx)
return self.get_image_prompt_texts(image_size, item_idx)
return PromptUpdateDetails.select_text(
self.get_image_prompt_texts(image_size, item_idx),
"<unk>",
)
def get_video_replacement(item_idx: int):
videos = mm_items.get_items(
@ -749,7 +704,10 @@ class MiniCPMVMultiModalProcessor(BaseMultiModalProcessor[_I]):
frame_size = videos.get_frame_size(item_idx)
num_frames = videos.get_num_frames(item_idx)
return self.get_video_prompt_texts(frame_size, num_frames)
return PromptUpdateDetails.select_text(
self.get_video_prompt_texts(frame_size, num_frames),
"<unk>",
)
get_replacement = {
"image": get_image_replacement,
@ -832,14 +790,6 @@ class MiniCPMVBaseModel(nn.Module, SupportsMultiModal, SupportsPP):
assert isinstance(image_token_id, torch.Tensor)
self.mm_token_ids.add(image_token_id.flatten().unique().item())
embed_is_patch = kwargs.pop("embed_is_patch")
if not isinstance(embed_is_patch, (torch.Tensor, list)):
raise ValueError(
f"Incorrect type of embed_is_patch for {modality=}. "
f"Got type: {type(embed_is_patch)}")
embed_is_patch = flatten_bn(embed_is_patch)
if image_embeds is not None:
if not isinstance(image_embeds, (torch.Tensor, list)):
raise ValueError(
@ -851,7 +801,6 @@ class MiniCPMVBaseModel(nn.Module, SupportsMultiModal, SupportsPP):
return MiniCPMVImageEmbeddingInputs(
type="image_embeds",
image_embeds=image_embeds_flat,
embed_is_patch=embed_is_patch,
)
if not isinstance(pixel_values, (torch.Tensor, list)):
@ -879,7 +828,6 @@ class MiniCPMVBaseModel(nn.Module, SupportsMultiModal, SupportsPP):
type="pixel_values",
pixel_values=pixel_values_flat,
tgt_sizes=tgt_sizes_flat,
embed_is_patch=embed_is_patch,
num_slices=num_slices_flat,
)
@ -936,19 +884,11 @@ class MiniCPMVBaseModel(nn.Module, SupportsMultiModal, SupportsPP):
if modality == "images":
image_input = modalities["images"]
image_features = self._process_vision_input(image_input)
multimodal_embeddings += tuple(
scatter_patch_features(
image_features,
image_input["embed_is_patch"],
))
multimodal_embeddings += tuple(image_features)
if modality == "videos":
video_input = modalities["videos"]
video_features = self._process_vision_input(video_input)
multimodal_embeddings += tuple(
scatter_patch_features(
video_features,
video_input["embed_is_patch"],
))
multimodal_embeddings += tuple(video_features)
return multimodal_embeddings
@ -971,7 +911,7 @@ class MiniCPMVBaseModel(nn.Module, SupportsMultiModal, SupportsPP):
inputs_embeds = merge_multimodal_embeddings(
input_ids,
inputs_embeds,
select_patch_features(multimodal_embeddings),
multimodal_embeddings,
list(self.mm_token_ids),
)
return inputs_embeds

50
vllm/model_executor/models/mistral3.py
View File

@ -27,7 +27,8 @@ from vllm.multimodal.parse import (ImageProcessorItems, ImageSize,
MultiModalDataItems)
from vllm.multimodal.processing import (BaseMultiModalProcessor,
BaseProcessingInfo, ProcessingCache,
PromptReplacement, PromptUpdate)
PromptReplacement, PromptUpdate,
PromptUpdateDetails)
from vllm.multimodal.profiling import BaseDummyInputsBuilder, ProcessorInputs
from vllm.sequence import IntermediateTensors
@ -35,8 +36,7 @@ from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsPP
from .pixtral import PixtralHFEncoderInfo, PixtralHFVisionModel
from .utils import (AutoWeightsLoader, flatten_bn, init_vllm_registered_model,
maybe_prefix, merge_multimodal_embeddings)
from .vision import (get_vision_encoder_info, scatter_patch_features,
select_patch_features)
from .vision import get_vision_encoder_info
class Mistral3ImagePixelInputs(TypedDict):
@ -49,14 +49,6 @@ class Mistral3ImagePixelInputs(TypedDict):
in which case the data is passed as a list instead of a batched tensor.
"""
embed_is_patch: Union[torch.Tensor, list[torch.Tensor]]
"""
A boolean mask indicating which image embeddings correspond
to patch tokens.
Shape: `(batch_size, num_images, num_embeds)`
"""
class Mistral3PatchMerger(nn.Module):
"""
@ -266,23 +258,6 @@ class Mistral3MultiModalProcessor(
p[:, :h, :w] for p, (h, w) in zip(pixel_values, image_sizes)
]
hf_config = self.info.get_hf_config()
vision_config = hf_config.vision_config
assert isinstance(vision_config, PixtralVisionConfig)
encoder_info = PixtralHFEncoderInfo(vision_config)
tile_sizes = [
encoder_info.get_patch_grid_size(
image_width=pixel_value.shape[-1],
image_height=pixel_value.shape[-2],
) for pixel_value in processed_outputs["pixel_values"]
]
embed_is_patch = [
torch.tensor(([True] * ncols + [False]) * nrows)
for ncols, nrows in tile_sizes
]
processed_outputs["embed_is_patch"] = embed_is_patch
return processed_outputs
def _get_mm_fields_config(
@ -292,7 +267,6 @@ class Mistral3MultiModalProcessor(
) -> Mapping[str, MultiModalFieldConfig]:
return dict(
pixel_values=MultiModalFieldConfig.batched("image"),
embed_is_patch=MultiModalFieldConfig.batched("image"),
image_embeds=MultiModalFieldConfig.batched("image"),
)
@ -327,7 +301,7 @@ class Mistral3MultiModalProcessor(
tokens = ([image_token_id] * ncols + [image_break_id]) * nrows
tokens[-1] = image_end_id
return tokens
return PromptUpdateDetails.select_token_id(tokens, image_token_id)
return [
PromptReplacement(
@ -418,8 +392,6 @@ def init_vision_tower_for_llava(
)
# TODO(mgoin): Support V1, there are issues with image batching/chunking
# that need to be resolved first.
@MULTIMODAL_REGISTRY.register_processor(
_build_mistral3_processor,
info=_build_mistral3_info,
@ -509,16 +481,9 @@ class Mistral3ForConditionalGeneration(nn.Module, SupportsMultiModal,
raise ValueError("Incorrect type of pixel values. "
f"Got type: {type(pixel_values)}")
assert self.config.vision_config.model_type == "pixtral"
embed_is_patch = kwargs.pop("embed_is_patch")
if not isinstance(embed_is_patch, (torch.Tensor, list)):
raise ValueError("Incorrect type of embed_is_patch. "
f"Got type: {type(embed_is_patch)}")
return Mistral3ImagePixelInputs(
type="pixel_values_pixtral",
pixel_values=flatten_bn(pixel_values),
embed_is_patch=flatten_bn(embed_is_patch),
)
def _process_image_input(
@ -557,10 +522,7 @@ class Mistral3ForConditionalGeneration(nn.Module, SupportsMultiModal,
vision_embeddings = self._process_image_input(image_input)
return scatter_patch_features(
vision_embeddings,
image_input["embed_is_patch"],
)
return vision_embeddings
def get_input_embeddings(
self,
@ -572,7 +534,7 @@ class Mistral3ForConditionalGeneration(nn.Module, SupportsMultiModal,
inputs_embeds = merge_multimodal_embeddings(
input_ids,
inputs_embeds,
select_patch_features(multimodal_embeddings),
multimodal_embeddings,
self.config.image_token_index,
)
return inputs_embeds

74
vllm/model_executor/models/molmo.py
View File

@ -46,7 +46,8 @@ from vllm.multimodal.parse import (ImageProcessorItems, ImageSize,
MultiModalDataItems)
from vllm.multimodal.processing import (BaseMultiModalProcessor,
BaseProcessingInfo, PromptIndexTargets,
PromptInsertion, PromptUpdate)
PromptInsertion, PromptUpdate,
PromptUpdateDetails)
from vllm.multimodal.profiling import BaseDummyInputsBuilder, ProcessorInputs
from vllm.sequence import IntermediateTensors
@ -56,7 +57,6 @@ from .utils import (AutoWeightsLoader, WeightsMapper, flatten_bn,
is_pp_missing_parameter,
make_empty_intermediate_tensors_factory, make_layers,
maybe_prefix, merge_multimodal_embeddings)
from .vision import scatter_patch_features, select_patch_features
# TODO: hard-coded for now. Consider making it configurable.
VIT_LAYERS = [-2, -9]
@ -84,14 +84,6 @@ class MolmoImageInputs(TypedDict):
Shape: `(batch_size * num_images, num_crops, num_patch)`
"""
embed_is_patch: Union[torch.Tensor, list[torch.Tensor]]
"""
A boolean mask indicating which image embeddings correspond
to patch tokens.
Shape: `(batch_size * num_images, num_embeds)`
"""
num_crops: torch.Tensor
"""Shape: `(batch_size * num_images)`"""
@ -1146,30 +1138,6 @@ class MolmoProcessorWrapper:
if image_input_idx is not None:
feat_is_patch = image_input_idx >= 0
input_is_embed = torch.isin(
input_ids,
torch.tensor([
self.image_patch_id,
self.im_col_id,
self.im_start_id,
self.im_end_id,
]),
)
embed_ids = input_ids[input_is_embed]
embed_is_patch = embed_ids == self.image_patch_id
assert embed_is_patch.sum() == feat_is_patch.sum()
# image_tokens = extra_joint + joint
# Both `extra_joint` and `joint` have `im_start_id` and `im_end_id`
embed_start = torch.nonzero(embed_ids == self.im_start_id)[::2, 0]
embed_end = torch.nonzero(embed_ids == self.im_end_id)[1::2, 0]
assert len(embed_start) == len(embed_end) == len(images)
embed_is_patch = [
embed_is_patch[start:end + 1]
for start, end in zip(embed_start, embed_end)
]
tilings = [
self.select_tiling(
image_width=image.size[0],
@ -1181,7 +1149,6 @@ class MolmoProcessorWrapper:
assert num_crops.sum() == len(feat_is_patch)
outputs["feat_is_patch"] = feat_is_patch
outputs["embed_is_patch"] = embed_is_patch
outputs["num_crops"] = num_crops
outputs["img_patch_id"] = self.image_patch_id
@ -1220,17 +1187,13 @@ class MolmoProcessingInfo(BaseProcessingInfo):
)
pooling_size = processor.pooling_size
base_image_input_size = processor.base_image_input_size
base_image_input_d = processor.image_patch_size
image_token_length_w = processor.image_token_length_w
image_token_length_h = processor.image_token_length_h
crop_patches = base_image_input_size[0] // base_image_input_d
extra = image_token_length_w * image_token_length_h
joint = ((ncols + 1) // pooling_size) * ((nrows + 1) // pooling_size)
per_row = ncols // pooling_size + 1
joint = per_row * (nrows // pooling_size) + 2
image_token_length = (crop_patches + pooling_size - 1) // pooling_size
resize = (image_token_length + 1) * image_token_length + 2
return resize + joint
return extra + joint
def get_max_image_tokens(self) -> int:
target_width, target_height = self.get_image_size_with_most_features()
@ -1328,7 +1291,6 @@ class MolmoMultiModalProcessor(BaseMultiModalProcessor[MolmoProcessingInfo]):
"image", num_crops),
feat_is_patch=MultiModalFieldConfig.flat_from_sizes(
"image", num_crops),
embed_is_patch=MultiModalFieldConfig.batched("image"),
num_crops=MultiModalFieldConfig.batched("image"),
img_patch_id=MultiModalFieldConfig.shared("image", num_images),
)
@ -1368,8 +1330,10 @@ class MolmoMultiModalProcessor(BaseMultiModalProcessor[MolmoProcessingInfo]):
joint = ([img_start_id] + joint_row *
((nrows + 1) // pooling_size) + [img_end_id])
image_tokens = extra_joint + joint
return image_tokens
return PromptUpdateDetails.select_token_id(
extra_joint + joint,
embed_token_id=img_patch_id,
)
return [
PromptInsertion(
@ -1475,11 +1439,6 @@ class MolmoForCausalLM(nn.Module, SupportsMultiModal, SupportsPP, SupportsLoRA,
raise ValueError("Incorrect type of feat_is_patch. "
f"Got type: {type(feat_is_patch)}")
embed_is_patch = kwargs.pop("embed_is_patch", None)
if not isinstance(embed_is_patch, (torch.Tensor, list)):
raise ValueError("Incorrect type of embed_is_patch. "
f"Got type: {type(embed_is_patch)}")
num_crops = kwargs.pop("num_crops", None)
if not isinstance(num_crops, (torch.Tensor, list)):
raise ValueError("Incorrect type of num_crops. "
@ -1491,14 +1450,12 @@ class MolmoForCausalLM(nn.Module, SupportsMultiModal, SupportsPP, SupportsLoRA,
f"Got type: {type(img_patch_id)}")
self.img_patch_id = img_patch_id.flatten().unique().item()
embed_is_patch = flatten_bn(embed_is_patch)
num_crops = flatten_bn(num_crops, concat=True)
return MolmoImageInputs(
images=images,
image_masks=image_masks,
feat_is_patch=feat_is_patch,
embed_is_patch=embed_is_patch,
num_crops=num_crops,
)
@ -1537,12 +1494,7 @@ class MolmoForCausalLM(nn.Module, SupportsMultiModal, SupportsPP, SupportsLoRA,
if image_input is None:
return None
image_features = self._process_image_input(image_input)
return scatter_patch_features(
image_features,
image_input["embed_is_patch"],
)
return self._process_image_input(image_input)
def get_input_embeddings(
self,
@ -1556,7 +1508,7 @@ class MolmoForCausalLM(nn.Module, SupportsMultiModal, SupportsPP, SupportsLoRA,
inputs_embeds = merge_multimodal_embeddings(
input_ids,
inputs_embeds,
select_patch_features(multimodal_embeddings),
multimodal_embeddings,
self.img_patch_id,
)
return inputs_embeds

32
vllm/model_executor/models/nvlm_d.py
View File

@ -57,7 +57,7 @@ class NVLMProcessor(BaseInternVLProcessor):
# when trying to find "<tile" as a subsequence of "<Image><tile"
repl = "<Image>" + features + "</Image>"
return PromptUpdateDetails(full=repl, features=repl)
return PromptUpdateDetails.select_text(repl, IMG_PAD)
class NVLMProcessingInfo(BaseInternVLProcessingInfo):
@ -84,31 +84,6 @@ class NVLMProcessingInfo(BaseInternVLProcessingInfo):
**kwargs,
)
def get_max_image_tokens(self) -> int:
hf_processor = self.get_hf_processor()
tokenizer = hf_processor.tokenizer
max_num_patches = hf_processor.max_dynamic_patch
# we need +1 here because max_dynamic_patch in config doesn't
# include the thumbnail patch
tile_pos_identifiers = [
f"<tile_{i+1}>" for i in range(max_num_patches)
]
if hf_processor.use_thumbnail and max_num_patches != 1:
tile_pos_identifiers += ["<tile_global_thumbnail>"]
# "<Image><tile" is tokenized as ["<Image", "><", "tile"]
# so we include <tile_1> in the start_str
start_str = "<Image>" + tile_pos_identifiers.pop(0)
end_str = "</Image>"
start_token_len = len(tokenizer.encode(start_str))
end_token_len = len(tokenizer.encode(end_str))
tile_token_len = sum(
len(tokenizer.encode(identifier))
for identifier in tile_pos_identifiers)
non_image_tokens_num = start_token_len + end_token_len + tile_token_len
return super().get_max_image_tokens() + non_image_tokens_num
class NVLMDummyInputsBuilder(InternVLDummyInputsBuilder[NVLMProcessingInfo]):
@ -177,10 +152,7 @@ class NVLMMultiModalProcessor(InternVLMultiModalProcessor[NVLMProcessingInfo]):
repl = hf_processor.get_image_repl(feature_size, num_patches)
return PromptUpdateDetails(
full=repl.full + "\n",
features=repl.features + "\n",
)
return PromptUpdateDetails.select_text(repl.full + "\n", IMG_PAD)
# See note in dummy data regarding why we have the extra newline
return [

6
vllm/model_executor/models/paligemma.py
View File

@ -162,9 +162,9 @@ class PaliGemmaMultiModalProcessor(
modality="image",
target=PromptIndexTargets.prefix(
[bos_token_id] if tokenizer.add_bos_token else []),
insertion=PromptUpdateDetails(
full=image_tokens + [bos_token_id],
features=image_tokens,
insertion=PromptUpdateDetails.select_token_id(
image_tokens + [bos_token_id],
embed_token_id=image_token_id,
),
)
]

11
vllm/model_executor/models/phi3v.py
View File

@ -40,8 +40,7 @@ from vllm.multimodal.parse import (ImageEmbeddingItems, ImageProcessorItems,
from vllm.multimodal.processing import (BaseMultiModalProcessor,
BaseProcessingInfo, BoundPromptUpdate,
PlaceholderFeaturesInfo,
PromptReplacement, PromptUpdate,
PromptUpdateDetails)
PromptReplacement, PromptUpdate)
# yapf: enable
from vllm.multimodal.profiling import BaseDummyInputsBuilder, ProcessorInputs
from vllm.sequence import IntermediateTensors
@ -443,12 +442,7 @@ class Phi3VMultiModalProcessor(BaseMultiModalProcessor[Phi3VProcessingInfo]):
processor=hf_processor,
)
image_tokens = [_IMAGE_TOKEN_ID] * num_image_tokens
return PromptUpdateDetails(
full=image_tokens,
features=image_tokens,
)
return [_IMAGE_TOKEN_ID] * num_image_tokens
num_images = mm_items.get_count("image", strict=False)
@ -517,6 +511,7 @@ class Phi3VMultiModalProcessor(BaseMultiModalProcessor[Phi3VProcessingInfo]):
item_idx=p.item_idx,
start_idx=p.start_idx - 1,
tokens=p.tokens,
is_embed=p.is_embed,
) for p in ps
]
for modality, ps in placeholders.items()

48
vllm/model_executor/models/pixtral.py
View File

@ -37,7 +37,7 @@ from vllm.multimodal.parse import (ImageProcessorItems, ImageSize,
MultiModalDataItems)
from vllm.multimodal.processing import (BaseMultiModalProcessor,
BaseProcessingInfo, PromptReplacement,
PromptUpdate)
PromptUpdate, PromptUpdateDetails)
from vllm.multimodal.profiling import BaseDummyInputsBuilder, ProcessorInputs
from vllm.sequence import IntermediateTensors
from vllm.transformers_utils.tokenizer import (MistralTokenizer,
@ -46,8 +46,7 @@ from vllm.transformers_utils.tokenizer import (MistralTokenizer,
from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsPP
from .utils import (flatten_bn, init_vllm_registered_model, maybe_prefix,
merge_multimodal_embeddings)
from .vision import (VisionEncoderInfo, resolve_visual_encoder_outputs,
scatter_patch_features, select_patch_features)
from .vision import VisionEncoderInfo, resolve_visual_encoder_outputs
try:
from xformers import ops as xops
@ -68,14 +67,6 @@ class PixtralImagePixelInputs(TypedDict):
The result of stacking :attr:`ImageEncoding.tokens` from each prompt.
"""
embed_is_patch: Union[torch.Tensor, list[torch.Tensor]]
"""
A boolean mask indicating which image embeddings correspond
to patch tokens.
Shape: `(batch_size * num_images, num_embeds)`
"""
class PixtralProcessorAdapter:
"""
@ -144,11 +135,8 @@ class PixtralProcessorAdapter:
"For more info, see: "
"https://github.com/vllm-project/vllm/issues/8411.")
image_token_id = self.image_token_id
images_processed = list[torch.Tensor]()
images_tokens = list[torch.Tensor]()
images_embed_is_patch = list[torch.Tensor]()
for image in images:
image_inputs = self.image_processor(ImageChunk(image=image))
@ -157,12 +145,10 @@ class PixtralProcessorAdapter:
images_processed.append(image_processed)
images_tokens.append(image_tokens)
images_embed_is_patch.append(image_tokens == image_token_id)
return {
"input_ids": torch.cat(images_tokens)[None].expand(len(text), -1),
"images": images_processed,
"embed_is_patch": images_embed_is_patch,
}
@ -213,7 +199,7 @@ class PixtralProcessingInfo(BaseProcessingInfo):
ncols, nrows = processor.image_processor._image_to_num_tokens(
Image.new("RGB", (image_width, image_height)))
return (ncols + 1) * nrows
return ncols * nrows
def get_image_size_with_most_features(self) -> ImageSize:
image_processor = self.get_hf_processor().image_processor
@ -263,10 +249,7 @@ class PixtralMultiModalProcessor(BaseMultiModalProcessor[PixtralProcessingInfo]
hf_inputs: Mapping[str, NestedTensors],
hf_processor_mm_kwargs: Mapping[str, object],
) -> Mapping[str, MultiModalFieldConfig]:
return dict(
images=MultiModalFieldConfig.batched("image"),
embed_is_patch=MultiModalFieldConfig.batched("image"),
)
return dict(images=MultiModalFieldConfig.batched("image"))
def _get_prompt_updates(
self,
@ -290,7 +273,7 @@ class PixtralMultiModalProcessor(BaseMultiModalProcessor[PixtralProcessingInfo]
tokens = ([image_token_id] * ncols + [image_break_id]) * nrows
tokens[-1] = image_end_id
return tokens
return PromptUpdateDetails.select_token_id(tokens, image_token_id)
return [
PromptReplacement(
@ -381,17 +364,9 @@ class PixtralForConditionalGeneration(nn.Module, SupportsMultiModal,
raise ValueError("Incorrect type of images. "
f"Got type: {type(images)}")
embed_is_patch = kwargs.pop("embed_is_patch")
if not isinstance(embed_is_patch, (torch.Tensor, list)):
raise ValueError("Incorrect type of embed_is_patch. "
f"Got type: {type(embed_is_patch)}")
embed_is_patch = flatten_bn(embed_is_patch)
return PixtralImagePixelInputs(
type="pixel_values",
images=flatten_bn(images),
embed_is_patch=embed_is_patch,
)
def _process_image_input(
@ -427,12 +402,7 @@ class PixtralForConditionalGeneration(nn.Module, SupportsMultiModal,
if image_input is None:
return None
image_features = self._process_image_input(image_input)
return scatter_patch_features(
image_features,
image_input["embed_is_patch"],
)
return self._process_image_input(image_input)
def get_input_embeddings(
self,
@ -444,7 +414,7 @@ class PixtralForConditionalGeneration(nn.Module, SupportsMultiModal,
inputs_embeds = merge_multimodal_embeddings(
input_ids,
inputs_embeds,
select_patch_features(multimodal_embeddings),
multimodal_embeddings,
self.vision_args.image_token_id,
)
return inputs_embeds
@ -963,9 +933,7 @@ class PixtralHFEncoderInfo(VisionEncoderInfo[PixtralVisionConfig]):
image_width=image_width,
image_height=image_height,
)
# Consider the image_break_token
return (ncols + 1) * nrows
return ncols * nrows
def get_max_image_tokens(self) -> int:
image_size = self.get_image_size()

6
vllm/model_executor/models/qwen2_audio.py
View File

@ -229,9 +229,9 @@ class Qwen2AudioMultiModalProcessor(
audio_tokens = [audio_token_id] * num_features
return PromptUpdateDetails(
full=[audio_bos_id] + audio_tokens + [audio_eos_id],
features=audio_tokens,
return PromptUpdateDetails.select_token_id(
[audio_bos_id] + audio_tokens + [audio_eos_id],
embed_token_id=audio_token_id,
)
return [

6
vllm/model_executor/models/qwen_vl.py
View File

@ -647,9 +647,9 @@ class QwenVLMultiModalProcessor(BaseMultiModalProcessor[QwenVLProcessingInfo]):
PromptReplacement(
modality="image",
target=[img_start_id, img_end_id],
replacement=PromptUpdateDetails(
full=[img_start_id] + image_tokens + [img_end_id],
features=image_tokens,
replacement=PromptUpdateDetails.select_token_id(
[img_start_id] + image_tokens + [img_end_id],
embed_token_id=img_pad_id,
),
)
]

42
vllm/model_executor/models/skyworkr1v.py
View File

@ -40,7 +40,6 @@ from vllm.transformers_utils.tokenizer import AnyTokenizer
from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsPP
from .utils import (AutoWeightsLoader, flatten_bn, init_vllm_registered_model,
maybe_prefix, merge_multimodal_embeddings)
from .vision import scatter_patch_features, select_patch_features
IMG_START = '<img>'
IMG_END = '</img>'
@ -61,14 +60,6 @@ class SkyworkR1VImagePixelInputs(TypedDict):
num_patches: torch.Tensor
"""Shape: `(batch_size * num_images)`"""
embed_is_patch: Union[torch.Tensor, list[torch.Tensor]]
"""
A boolean mask indicating which image embeddings correspond
to patch tokens.
Shape: `(batch_size * num_images, num_embeds)`
"""
class SkyworkR1VImageEmbeddingInputs(TypedDict):
type: Literal["image_embeds"]
@ -419,24 +410,13 @@ class BaseSkyworkR1VProcessor(ABC):
torch.tensor([len(item) for item in pixel_values_lst]),
}
tokenizer = self.tokenizer
image_token_id = self.image_token_id
embed_is_patch = list[torch.Tensor]()
for pixel_values in pixel_values_lst:
num_patches = pixel_values.shape[0]
feature_size = num_patches * self.num_image_token
image_repl = self.get_image_repl(feature_size, num_patches)
feature_tokens = tokenizer.encode(image_repl.features,
add_special_tokens=False)
text = [t.replace('<image>', image_repl.full, 1) for t in text]
embed_is_patch.append(
torch.tensor(feature_tokens) == image_token_id)
image_inputs["embed_is_patch"] = embed_is_patch
text_inputs = self.tokenizer(text)
@ -460,7 +440,7 @@ class SkyworkR1VProcessor(BaseSkyworkR1VProcessor):
repl_features = IMG_CONTEXT * feature_size
repl_full = IMG_START + repl_features + IMG_END
return PromptUpdateDetails(full=repl_full, features=repl_features)
return PromptUpdateDetails.select_text(repl_full, IMG_CONTEXT)
class BaseSkyworkR1VProcessingInfo(BaseProcessingInfo):
@ -599,7 +579,6 @@ class SkyworkR1VMultiModalProcessor(BaseMultiModalProcessor[_I]):
pixel_values_flat=MultiModalFieldConfig.flat_from_sizes(
"image", image_num_patches),
image_num_patches=MultiModalFieldConfig.batched("image"),
embed_is_patch=MultiModalFieldConfig.batched("image"),
image_embeds=MultiModalFieldConfig.batched("image"),
image_token_id=MultiModalFieldConfig.shared("image", num_images),
)
@ -835,7 +814,6 @@ class SkyworkR1VChatModel(nn.Module, SupportsMultiModal, SupportsPP):
self, **kwargs: object) -> Optional[SkyworkR1VImageInputs]:
pixel_values_flat = kwargs.pop("pixel_values_flat", None)
image_num_patches = kwargs.pop("image_num_patches", None)
embed_is_patch = kwargs.pop("embed_is_patch", None)
image_embeds = kwargs.pop("image_embeds", None)
if pixel_values_flat is None and image_embeds is None:
@ -864,20 +842,14 @@ class SkyworkR1VChatModel(nn.Module, SupportsMultiModal, SupportsPP):
raise ValueError("Incorrect type of image_num_patches. "
f"Got type: {type(image_num_patches)}")
if not isinstance(embed_is_patch, (torch.Tensor, list)):
raise ValueError("Incorrect type of embed_is_patch. "
f"Got type: {type(embed_is_patch)}")
pixel_values_flat = flatten_bn(pixel_values_flat, concat=True)
image_num_patches = flatten_bn(image_num_patches, concat=True)
embed_is_patch = flatten_bn(embed_is_patch)
return SkyworkR1VImagePixelInputs(
type="pixel_values",
pixel_values_flat=self._validate_pixel_values(
pixel_values_flat),
num_patches=image_num_patches,
embed_is_patch=embed_is_patch,
)
raise AssertionError("This line should be unreachable.")
@ -923,15 +895,7 @@ class SkyworkR1VChatModel(nn.Module, SupportsMultiModal, SupportsPP):
if image_input is None:
return None
image_features = self._process_image_input(image_input)
if image_input["type"] != "pixel_values":
return image_features
return scatter_patch_features(
image_features,
image_input["embed_is_patch"],
)
return self._process_image_input(image_input)
def get_input_embeddings(
self,
@ -945,7 +909,7 @@ class SkyworkR1VChatModel(nn.Module, SupportsMultiModal, SupportsPP):
inputs_embeds = merge_multimodal_embeddings(
input_ids,
inputs_embeds,
select_patch_features(multimodal_embeddings),
multimodal_embeddings,
self.img_context_token_id,
)
return inputs_embeds

77
vllm/model_executor/models/vision.py
View File

@ -1,8 +1,7 @@
# SPDX-License-Identifier: Apache-2.0
from abc import ABC, abstractmethod
from collections.abc import Sequence
from typing import Final, Generic, Optional, Protocol, TypeVar, Union, cast
from typing import Final, Generic, Optional, Protocol, TypeVar, Union
import torch
from transformers import PretrainedConfig
@ -10,12 +9,9 @@ from transformers import PretrainedConfig
import vllm.envs as envs
from vllm.attention.selector import (backend_name_to_enum,
get_global_forced_attn_backend)
from vllm.jsontree import JSONTree, json_map_leaves
from vllm.logger import init_logger
from vllm.platforms import _Backend, current_platform
from .interfaces import MultiModalEmbeddings
logger = init_logger(__name__)
_C = TypeVar("_C", bound=PretrainedConfig)
@ -155,74 +151,3 @@ def resolve_visual_encoder_outputs(
if post_layer_norm is not None and uses_last_layer:
hs_pool[-1] = post_layer_norm(encoder_outputs)
return torch.cat(hs_pool, dim=-1)
def scatter_patch_features(
patches: Union[torch.Tensor, Sequence[torch.Tensor]],
embed_is_patch: Union[torch.Tensor, Sequence[torch.Tensor]],
) -> tuple[torch.Tensor, ...]:
"""
Scatter the patch features into a contiguous tensor that corresponds
to the embedding tokens defined by the multimodal processor.
The rest of the values in the tensor are set to NaN so that they
can be filtered out by :func`select_patch_features`.
Args:
patches: The patch features for each image.
Shape: `(num_images, <patch_dims>, feature_depth)`
embed_is_patch: A boolean mask indicating which image embeddings
correspond to patch tokens for each image.
Shape: `(num_images, num_embeds)`
Note:
The original code only considers patch tokens as feature
tokens, but our processor considers all image-related tokens
as feature tokens because the feature tokens need to be
consecutive in `input_ids`.
Example:
A simplified example for one image:
.. code-block::
Embedding tokens (from HF processor):
[<start> <patch> <patch> <col> <patch> <patch> <col> <end> ]
embed_is_patch (from HF processor):
[ False True True False True True False False ]
Encoder outputs (from model):
[ p1 p2 p3 p4 ]
The resulting embedding tensor is:
[ nan p1 p2 nan p3 p4 nan nan ]
"""
if len(patches) != len(embed_is_patch):
raise ValueError(f"Inconsistent num_images: {len(patches)=} vs. "
f"{len(embed_is_patch)=}")
def get_embed_one(patches_one: torch.Tensor, e_is_patch: torch.Tensor):
embed_one = patches_one.new_full(
(e_is_patch.shape[0], patches_one.shape[-1]),
fill_value=torch.nan,
)
embed_one[e_is_patch] = patches_one
return embed_one
return tuple(
get_embed_one(patches_one, e_is_patch)
for patches_one, e_is_patch in zip(patches, embed_is_patch))
def select_patch_features(
multimodal_embeddings: MultiModalEmbeddings) -> MultiModalEmbeddings:
"""
Given the outputs of :func:`scatter_patch_features`, return only
the values that correspond to patch features.
"""
selected_features = json_map_leaves(
lambda x: x[~x.isnan()].view(-1, *x.shape[1:]),
cast(JSONTree[torch.Tensor], multimodal_embeddings),
)
return cast(MultiModalEmbeddings, selected_features)

4
vllm/multimodal/base.py
View File

@ -385,8 +385,8 @@ class MultiModalPlaceholderMap:
for placeholder_dict, mm_item in zip(multi_modal_placeholders,
multi_modal_items):
placeholder = range(
placeholder_dict["offset"],
placeholder_dict["offset"] + placeholder_dict["length"],
placeholder_dict.offset,
placeholder_dict.offset + placeholder_dict.length,
)
intersection = range(
max(positions.start, placeholder.start),

32
vllm/multimodal/inputs.py
View File

@ -109,7 +109,8 @@ The built-in modalities are defined by :class:`MultiModalDataBuiltins`.
"""
class PlaceholderRange(TypedDict):
@dataclass(frozen=True)
class PlaceholderRange:
"""
Placeholder location information for multi-modal data.
@ -121,8 +122,8 @@ class PlaceholderRange(TypedDict):
.. code-block::
A: { "offset": 0, "length": 4 }
B: { "offset": 5, "length": 4 }
A: PlaceholderRange(offset=0, length=4)
B: PlaceholderRange(offset=5, length=4)
"""
offset: int
@ -131,6 +132,31 @@ class PlaceholderRange(TypedDict):
length: int
"""The length of the placeholder."""
is_embed: Optional[torch.Tensor] = None
"""
A boolean mask of shape `(length,)` indicating which positions
between `offset` and `offset + length` to assign embeddings to.
"""
def get_num_embeds(self) -> int:
if self.is_embed is None:
return self.length
return int(self.is_embed.sum().item())
def __eq__(self, other: object) -> bool:
if not isinstance(other, self.__class__):
return False
if not (self.offset, self.length) == (other.offset, other.length):
return False
if self.is_embed is None:
return other.is_embed is None
if other.is_embed is None:
return self.is_embed is None
return nested_tensors_equal(self.is_embed, other.is_embed)
NestedTensors = Union[list["NestedTensors"], list[torch.Tensor], torch.Tensor,
tuple[torch.Tensor, ...]]

75
vllm/multimodal/processing.py
View File

@ -108,16 +108,46 @@ class PromptUpdateDetails(Generic[_S]):
full: _S
"""The full content."""
features: _S
is_embed: Optional[Callable[["_BoundPromptSequence"], torch.Tensor]] = None
"""
The part of the content that corresponds to feature placeholders;
this will be replaced by the output of the vision encoder during model
inference.
Given :attr:`full`, return a boolean mask of shape `(len(full),)`
indicating which positions of `full` to assign embeddings to.
`None` (default) means to assign embeddings to all positions of `full`.
The embeddings are obtained by calling
:class:`SupportsMultiModal.get_multimodal_embeddings`.
"""
@staticmethod
def from_seq(seq: _S) -> "PromptUpdateDetails[_S]":
return PromptUpdateDetails(full=seq, features=seq)
return PromptUpdateDetails(full=seq)
@staticmethod
def select_text(
seq: _S,
embed_text: str,
) -> "PromptUpdateDetails[_S]":
def is_embed(full: "_BoundPromptSequence") -> torch.Tensor:
embed_token_ids = encode_tokens(full.tokenizer, embed_text)
return torch.isin(
torch.tensor(full.token_ids),
torch.tensor(embed_token_ids),
)
return PromptUpdateDetails(full=seq, is_embed=is_embed)
@staticmethod
def select_token_id(
seq: _S,
embed_token_id: int,
) -> "PromptUpdateDetails[_S]":
return PromptUpdateDetails(
full=seq,
is_embed=lambda f: torch.tensor(f.token_ids) == embed_token_id,
)
PromptUpdateInfo = Union[PromptSeq, PromptUpdateDetails]
@ -406,7 +436,7 @@ class _BoundPromptSequence:
@dataclass
class _BoundPromptContent:
full: _BoundPromptSequence
features: _BoundPromptSequence
is_embed: Optional[Callable[["_BoundPromptSequence"], torch.Tensor]]
@dataclass
@ -466,10 +496,8 @@ class BoundPromptUpdate:
bound_full = _BoundPromptSequence.from_seq(self.tokenizer,
content.full)
bound_features = _BoundPromptSequence.from_seq(self.tokenizer,
content.features)
bound_content = _BoundPromptContent(full=bound_full,
features=bound_features)
is_embed=content.is_embed)
if cache_key is not None:
self._content_cache[cache_key] = bound_content
@ -605,15 +633,19 @@ class PlaceholderFeaturesInfo:
item_idx: int
start_idx: int
tokens: list[int]
is_embed: Optional[torch.Tensor]
@property
def length(self) -> int:
return len(self.tokens)
def to_range(self) -> PlaceholderRange:
# TODO: Is it worth it to optimize this by stripping the
# leading and ending positions where `is_embed=False`?
return PlaceholderRange(
offset=self.start_idx,
length=self.length,
is_embed=self.is_embed,
)
@ -806,22 +838,17 @@ def _iter_placeholders(
continue
if prompt[start_idx:end_idx_full] == content_tokens_full:
content_tokens_feat = content.features.token_ids
content_is_embed = content.is_embed
if content_is_embed is not None:
content_is_embed = content_is_embed(content.full)
try:
match = next(
iter_token_matches(content_tokens_full,
content_tokens_feat))
yield PlaceholderFeaturesInfo(
modality=modality,
item_idx=item_idx,
start_idx=start_idx + match.start_idx,
tokens=content_tokens_feat,
)
except StopIteration:
raise AssertionError(
f"{content_tokens_feat=} should be a "
f"subsequence of {content_tokens_full=}") from None
yield PlaceholderFeaturesInfo(
modality=modality,
item_idx=item_idx,
start_idx=start_idx,
tokens=content_tokens_full,
is_embed=content_is_embed,
)
# Exclude overlapping matches
start_idx = end_idx_full

2
vllm/multimodal/profiling.py
View File

@ -180,7 +180,7 @@ class MultiModalProfiler(Generic[_I]):
placeholders_by_modality = mm_inputs["mm_placeholders"]
total_placeholders_by_modality = {
modality: sum(item["length"] for item in placeholders)
modality: sum(item.get_num_embeds() for item in placeholders)
for modality, placeholders in placeholders_by_modality.items()
}
expected_placeholders_by_modality = {

2
vllm/multimodal/utils.py
View File

@ -340,7 +340,7 @@ def merge_and_sort_multimodal_metadata(
all_items.append((modality, placeholder, hash_value))
# Sort all items by offset
all_items.sort(key=lambda x: x[1]['offset'])
all_items.sort(key=lambda x: x[1].offset)
# Split into separate lists
sorted_modalities = [item[0] for item in all_items]

7
vllm/v1/core/kv_cache_utils.py
View File

@ -310,8 +310,7 @@ def _gen_mm_extra_hash_keys(request: Request, start_token_idx: int,
# Note that we assume mm_positions is sorted by offset.
# We do not need to check all mm inputs if the start token index is out of
# range. This usually happens in the late prefill phase and decoding phase.
if mm_positions[-1]["offset"] + mm_positions[-1][
"length"] < start_token_idx:
if mm_positions[-1].offset + mm_positions[-1].length < start_token_idx:
return extra_keys, start_mm_idx
# Support start_mm_idx == -1 to indicate the last mm input.
@ -322,8 +321,8 @@ def _gen_mm_extra_hash_keys(request: Request, start_token_idx: int,
curr_mm_idx = start_mm_idx
while mm_positions and curr_mm_idx < len(mm_positions):
assert mm_hashes[curr_mm_idx] is not None
offset = mm_positions[curr_mm_idx]["offset"]
length = mm_positions[curr_mm_idx]["length"]
offset = mm_positions[curr_mm_idx].offset
length = mm_positions[curr_mm_idx].length
if end_token_idx > offset:
if start_token_idx > offset + length:
# This block has passed the current mm input.

8
vllm/v1/core/sched/scheduler.py
View File

@ -505,8 +505,8 @@ class Scheduler(SchedulerInterface):
assert mm_positions is not None
assert len(mm_positions) > 0
for i, pos_info in enumerate(mm_positions):
start_pos = pos_info["offset"]
num_encoder_tokens = pos_info["length"]
start_pos = pos_info.offset
num_encoder_tokens = pos_info.length
# The encoder output is needed if the two ranges overlap:
# [num_computed_tokens, num_computed_tokens + num_new_tokens) and
@ -596,8 +596,8 @@ class Scheduler(SchedulerInterface):
if cached_encoder_input_ids:
for input_id in list(cached_encoder_input_ids):
mm_positions = request.mm_positions[input_id]
start_pos = mm_positions["offset"]
num_tokens = mm_positions["length"]
start_pos = mm_positions.offset
num_tokens = mm_positions.length
if start_pos + num_tokens <= request.num_computed_tokens:
# The encoder output is already processed and stored
# in the decoder's KV cache.

2
vllm/v1/request.py
View File

@ -121,7 +121,7 @@ class Request:
def get_num_encoder_tokens(self, input_id: int) -> int:
assert input_id < len(self.mm_positions)
num_tokens = self.mm_positions[input_id]["length"]
num_tokens = self.mm_positions[input_id].length
return num_tokens
@property

60
vllm/v1/worker/gpu_model_runner.py
View File

@ -19,7 +19,8 @@ from vllm.logger import init_logger
from vllm.model_executor.layers.fused_moe import FusedMoE
from vllm.model_executor.layers.rotary_embedding import MRotaryEmbedding
from vllm.model_executor.model_loader import get_model
from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalKwargs
from vllm.multimodal import MULTIMODAL_REGISTRY
from vllm.multimodal.inputs import MultiModalKwargs, PlaceholderRange
from vllm.multimodal.utils import group_mm_inputs_by_modality
from vllm.sampling_params import SamplingType
from vllm.sequence import IntermediateTensors
@ -43,7 +44,8 @@ from vllm.v1.utils import bind_kv_cache
from vllm.v1.worker.gpu_input_batch import CachedRequestState, InputBatch
from vllm.v1.worker.lora_model_runner_mixin import LoRAModelRunnerMixin
from .utils import sanity_check_mm_encoder_outputs
from .utils import (gather_mm_placeholders, sanity_check_mm_encoder_outputs,
scatter_mm_placeholders)
if TYPE_CHECKING:
import xgrammar as xgr
@ -829,19 +831,22 @@ class GPUModelRunner(LoRAModelRunnerMixin):
)
return metadata
def _execute_encoder(self, scheduler_output: "SchedulerOutput"):
def _execute_mm_encoder(self, scheduler_output: "SchedulerOutput"):
scheduled_encoder_inputs = scheduler_output.scheduled_encoder_inputs
if not scheduled_encoder_inputs:
return
# Batch the multi-modal inputs.
mm_inputs: list[MultiModalKwargs] = []
req_input_ids: list[tuple[str, int]] = []
mm_inputs = list[MultiModalKwargs]()
req_ids_pos = list[tuple[str, int, PlaceholderRange]]()
for req_id, encoder_input_ids in scheduled_encoder_inputs.items():
req_state = self.requests[req_id]
for input_id in encoder_input_ids:
for input_id, pos_info in zip(
encoder_input_ids,
req_state.mm_positions,
):
mm_inputs.append(req_state.mm_inputs[input_id])
req_input_ids.append((req_id, input_id))
req_ids_pos.append((req_id, input_id, pos_info))
# Batch mm inputs as much as we can: if a request in the batch has
# multiple modalities or a different modality than the previous one,
@ -877,16 +882,23 @@ class GPUModelRunner(LoRAModelRunnerMixin):
encoder_outputs.append(output)
# Cache the encoder outputs.
for (req_id, input_id), output in zip(req_input_ids, encoder_outputs):
for (req_id, input_id, pos_info), output in zip(
req_ids_pos,
encoder_outputs,
):
if req_id not in self.encoder_cache:
self.encoder_cache[req_id] = {}
self.encoder_cache[req_id][input_id] = output
def _gather_encoder_outputs(
self.encoder_cache[req_id][input_id] = scatter_mm_placeholders(
output,
is_embed=pos_info.is_embed,
)
def _gather_mm_embeddings(
self,
scheduler_output: "SchedulerOutput",
) -> list[torch.Tensor]:
encoder_outputs: list[torch.Tensor] = []
mm_embeds: list[torch.Tensor] = []
for req_id in self.input_batch.req_ids:
num_scheduled_tokens = scheduler_output.num_scheduled_tokens[
req_id]
@ -894,8 +906,8 @@ class GPUModelRunner(LoRAModelRunnerMixin):
num_computed_tokens = req_state.num_computed_tokens
mm_positions = req_state.mm_positions
for i, pos_info in enumerate(mm_positions):
start_pos = pos_info["offset"]
num_encoder_tokens = pos_info["length"]
start_pos = pos_info.offset
num_encoder_tokens = pos_info.length
# The encoder output is needed if the two ranges overlap:
# [num_computed_tokens,
@ -917,8 +929,16 @@ class GPUModelRunner(LoRAModelRunnerMixin):
assert req_id in self.encoder_cache
assert i in self.encoder_cache[req_id]
encoder_output = self.encoder_cache[req_id][i]
encoder_outputs.append(encoder_output[start_idx:end_idx])
return encoder_outputs
if (is_embed := pos_info.is_embed) is not None:
is_embed = is_embed[start_idx:end_idx]
mm_embeds_item = gather_mm_placeholders(
encoder_output[start_idx:end_idx],
is_embed=is_embed,
)
mm_embeds.append(mm_embeds_item)
return mm_embeds
def get_model(self) -> nn.Module:
return self.model
@ -983,10 +1003,10 @@ class GPUModelRunner(LoRAModelRunnerMixin):
if self.is_multimodal_model:
# Run the multimodal encoder if any.
self._execute_encoder(scheduler_output)
encoder_outputs = self._gather_encoder_outputs(scheduler_output)
self._execute_mm_encoder(scheduler_output)
mm_embeds = self._gather_mm_embeddings(scheduler_output)
else:
encoder_outputs = []
mm_embeds = []
# Prepare the decoder inputs.
attn_metadata, logits_indices, spec_decode_metadata = (
@ -1008,9 +1028,9 @@ class GPUModelRunner(LoRAModelRunnerMixin):
# embeddings), we always use embeddings (rather than token ids)
# as input to the multimodal model, even when the input is text.
input_ids = self.input_ids[:num_scheduled_tokens]
if encoder_outputs:
if mm_embeds:
inputs_embeds = self.model.get_input_embeddings(
input_ids, encoder_outputs)
input_ids, mm_embeds)
else:
inputs_embeds = self.model.get_input_embeddings(input_ids)
# TODO(woosuk): Avoid the copy. Optimize.

85
vllm/v1/worker/tpu_model_runner.py
View File

@ -19,7 +19,8 @@ from vllm.config import VllmConfig
from vllm.forward_context import set_forward_context
from vllm.logger import init_logger
from vllm.model_executor.model_loader import get_model
from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalKwargs
from vllm.multimodal import MULTIMODAL_REGISTRY
from vllm.multimodal.inputs import MultiModalKwargs, PlaceholderRange
from vllm.multimodal.utils import group_mm_inputs_by_modality
from vllm.sampling_params import SamplingType
from vllm.sequence import IntermediateTensors
@ -36,7 +37,8 @@ from vllm.v1.sample.tpu.sampler import Sampler as TPUSampler
from vllm.v1.utils import bind_kv_cache
from vllm.v1.worker.gpu_input_batch import CachedRequestState, InputBatch
from .utils import sanity_check_mm_encoder_outputs
from .utils import (gather_mm_placeholders, sanity_check_mm_encoder_outputs,
scatter_mm_placeholders)
if TYPE_CHECKING:
from vllm.v1.core.sched.output import SchedulerOutput
@ -507,19 +509,47 @@ class TPUModelRunner:
logits_indices = logits_indices.to(self.device)
return attn_metadata, logits_indices
def _execute_encoder(self, scheduler_output: "SchedulerOutput"):
def _scatter_placeholders(
self,
embeds: torch.Tensor,
is_embed: Optional[torch.Tensor],
) -> torch.Tensor:
if is_embed is None:
return embeds
placeholders = embeds.new_full(
(is_embed.shape[0], embeds.shape[-1]),
fill_value=torch.nan,
)
placeholders[is_embed] = embeds
return placeholders
def _gather_placeholders(
self,
placeholders: torch.Tensor,
is_embed: Optional[torch.Tensor],
) -> torch.Tensor:
if is_embed is None:
return placeholders
return placeholders[is_embed]
def _execute_mm_encoder(self, scheduler_output: "SchedulerOutput"):
scheduled_encoder_inputs = scheduler_output.scheduled_encoder_inputs
if not scheduled_encoder_inputs:
return
# Batch the multi-modal inputs.
mm_inputs: list[MultiModalKwargs] = []
req_input_ids: list[tuple[str, int]] = []
mm_inputs = list[MultiModalKwargs]()
req_ids_pos = list[tuple[str, int, PlaceholderRange]]()
for req_id, encoder_input_ids in scheduled_encoder_inputs.items():
req_state = self.requests[req_id]
for input_id in encoder_input_ids:
for input_id, pos_info in zip(
encoder_input_ids,
req_state.mm_positions,
):
mm_inputs.append(req_state.mm_inputs[input_id])
req_input_ids.append((req_id, input_id))
req_ids_pos.append((req_id, input_id, pos_info))
# Batch mm inputs as much as we can: if a request in the batch has
# multiple modalities or a different modality than the previous one,
@ -555,16 +585,23 @@ class TPUModelRunner:
encoder_outputs.append(output)
# Cache the encoder outputs.
for (req_id, input_id), output in zip(req_input_ids, encoder_outputs):
for (req_id, input_id, pos_info), output in zip(
req_ids_pos,
encoder_outputs,
):
if req_id not in self.encoder_cache:
self.encoder_cache[req_id] = {}
self.encoder_cache[req_id][input_id] = output
def _gather_encoder_outputs(
self.encoder_cache[req_id][input_id] = scatter_mm_placeholders(
output,
is_embed=pos_info.is_embed,
)
def _gather_mm_embeddings(
self,
scheduler_output: "SchedulerOutput",
) -> list[torch.Tensor]:
encoder_outputs: list[torch.Tensor] = []
mm_embeds: list[torch.Tensor] = []
for req_id in self.input_batch.req_ids:
num_scheduled_tokens = scheduler_output.num_scheduled_tokens[
req_id]
@ -572,8 +609,8 @@ class TPUModelRunner:
num_computed_tokens = req_state.num_computed_tokens
mm_positions = req_state.mm_positions
for i, pos_info in enumerate(mm_positions):
start_pos = pos_info["offset"]
num_encoder_tokens = pos_info["length"]
start_pos = pos_info.offset
num_encoder_tokens = pos_info.length
# The encoder output is needed if the two ranges overlap:
# [num_computed_tokens,
@ -595,8 +632,16 @@ class TPUModelRunner:
assert req_id in self.encoder_cache
assert i in self.encoder_cache[req_id]
encoder_output = self.encoder_cache[req_id][i]
encoder_outputs.append(encoder_output[start_idx:end_idx])
return encoder_outputs
if (is_embed := pos_info.is_embed) is not None:
is_embed = is_embed[start_idx:end_idx]
mm_embeds_item = gather_mm_placeholders(
encoder_output[start_idx:end_idx],
is_embed=is_embed,
)
mm_embeds.append(mm_embeds_item)
return mm_embeds
@torch.no_grad()
def execute_model(
@ -612,10 +657,10 @@ class TPUModelRunner:
if self.is_multimodal_model:
# Run the multimodal encoder if any.
self._execute_encoder(scheduler_output)
encoder_outputs = self._gather_encoder_outputs(scheduler_output)
self._execute_mm_encoder(scheduler_output)
mm_embeds = self._gather_mm_embeddings(scheduler_output)
else:
encoder_outputs = []
mm_embeds = []
# Prepare inputs
attn_metadata, logits_indices = self._prepare_inputs(scheduler_output)
@ -623,9 +668,9 @@ class TPUModelRunner:
# NOTE(woosuk): To unify token ids and soft tokens (vision
# embeddings), we always use embeddings (rather than token ids)
# as input to the multimodal model, even when the input is text.
if encoder_outputs:
if mm_embeds:
inputs_embeds = self.model.get_input_embeddings(
self.input_ids, encoder_outputs)
self.input_ids, mm_embeds)
else:
inputs_embeds = self.model.get_input_embeddings(self.input_ids)
input_ids = None

45
vllm/v1/worker/utils.py
View File

@ -1,4 +1,6 @@
# SPDX-License-Identifier: Apache-2.0
from typing import Optional
import torch
@ -27,3 +29,46 @@ def sanity_check_mm_encoder_outputs(
f"but got tensors with shapes {[e.shape for e in mm_embeddings]} "
"instead. This is most likely due to incorrect implementation "
"of the model's `get_multimodal_embeddings` method.")
def scatter_mm_placeholders(
embeds: torch.Tensor,
is_embed: Optional[torch.Tensor],
) -> torch.Tensor:
"""
Scatter the multimodal embeddings into a contiguous tensor that represents
the placeholder tokens.
:class:`vllm.multimodal.processing.PromptUpdateDetails.is_embed`.
Args:
embeds: The multimodal embeddings.
Shape: `(num_embeds, embed_dim)`
is_embed: A boolean mask indicating which positions in the placeholder
tokens need to be filled with multimodal embeddings.
Shape: `(num_placeholders, num_embeds)`
"""
if is_embed is None:
return embeds
placeholders = embeds.new_full(
(is_embed.shape[0], embeds.shape[-1]),
fill_value=torch.nan,
)
placeholders[is_embed] = embeds
return placeholders
def gather_mm_placeholders(
placeholders: torch.Tensor,
is_embed: Optional[torch.Tensor],
) -> torch.Tensor:
"""
Reconstructs the embeddings from the placeholder tokens.
This is the operation of :func:`scatter_mm_placeholders`.
"""
if is_embed is None:
return placeholders
return placeholders[is_embed]