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Unverified Commit 377d10bd authored by Cyrus Leung's avatar Cyrus Leung Committed by GitHub
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[VLM][Bugfix] Pass processor kwargs properly on init (#13516) 


Signed-off-by: default avatarDarkLight1337 <tlleungac@connect.ust.hk>
parent 52ce14d3
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Showing with 383 additions and 288 deletions
examples/offline_inference/vision_language_multi_image.py
View file @ 377d10bd
......@@ -85,6 +85,7 @@ def load_h2ovl(question: str, image_urls: List[str]) -> ModelRequestData:
trust_remote_code=True,
max_model_len=8192,
limit_mm_per_prompt={"image": len(image_urls)},
mm_processor_kwargs={"max_dynamic_patch": 4},
)
placeholders = "\n".join(f"Image-{i}: <image>\n"
......
tests/models/multimodal/processing/test_common.py
View file @ 377d10bd
......@@ -10,7 +10,7 @@ from vllm.config import ModelConfig
from vllm.inputs import InputProcessingContext
from vllm.multimodal import MULTIMODAL_REGISTRY
from vllm.multimodal.processing import ProcessingCache
from vllm.multimodal.utils import cached_get_tokenizer
from vllm.transformers_utils.tokenizer import cached_tokenizer_from_config
from ....multimodal.utils import random_audio, random_image, random_video
from ...registry import HF_EXAMPLE_MODELS
......@@ -42,10 +42,7 @@ def _test_processing_correctness(
factories = MULTIMODAL_REGISTRY._processor_factories[model_cls]
ctx = InputProcessingContext(
model_config,
tokenizer=cached_get_tokenizer(
model_config.tokenizer,
trust_remote_code=model_info.trust_remote_code,
),
tokenizer=cached_tokenizer_from_config(model_config),
)
# Ensure that it can fit all of the data
cache = ProcessingCache(capacity=1 << 30)
......
tests/models/multimodal/processing/test_h2ovl.py
View file @ 377d10bd
# SPDX-License-Identifier: Apache-2.0
"""Tests for H2OVL's multimodal preprocessing kwargs."""
from typing import Optional
from typing import Mapping, Optional
import pytest
from PIL import Image
from transformers import PretrainedConfig
from vllm.multimodal import MULTIMODAL_REGISTRY
from vllm.multimodal.image import rescale_image_size
from vllm.multimodal.utils import cached_get_tokenizer
from vllm.multimodal.processing import BaseMultiModalProcessor
from vllm.transformers_utils.tokenizer import cached_tokenizer_from_config
from ....conftest import _ImageAssets
from ...utils import build_model_context
def _get_expected_num_patches(
config: PretrainedConfig,
image: Image.Image,
num_imgs: int,
min_num: int,
max_num: int,
):
from vllm.model_executor.models.h2ovl import (calculate_h2ovl_targets,
get_h2ovl_target_ratios)
width, height = image.size
# Calculate the expected number of blocks
if num_imgs == 1 and config.use_msac:
# First pass
blocks1, _, _, aspect_ratio = calculate_h2ovl_targets(
orig_width=width,
orig_height=height,
target_ratios=get_h2ovl_target_ratios(
min_num=1,
max_num=max_num,
prior_aspect_ratio=None,
),
image_size=config.vision_config.image_size,
use_thumbnail=False, # Thumbnail is handled separately
)
# Second pass
blocks2, _, _, _ = calculate_h2ovl_targets(
orig_width=width,
orig_height=height,
target_ratios=get_h2ovl_target_ratios(
min_num=3,
max_num=max_num,
prior_aspect_ratio=aspect_ratio,
),
image_size=config.vision_config.image_size,
use_thumbnail=False,
)
# Add thumbnail if use_thumbnail is True and total_blocks > 1
if config.use_thumbnail:
blocks1 += 1 if blocks1 > 1 else 0
blocks2 += 1 if blocks2 > 1 else 0
# Total blocks is the sum of blocks from both passes minus
# overlapping
total_blocks = blocks1 + blocks2 - 1
return total_blocks
blocks, _, _, _ = calculate_h2ovl_targets(
orig_width=width,
orig_height=height,
target_ratios=get_h2ovl_target_ratios(
min_num,
max_num,
prior_aspect_ratio=None,
),
image_size=config.vision_config.image_size,
use_thumbnail=False,
)
expected_num_patches = blocks
if config.use_thumbnail and expected_num_patches > 1:
expected_num_patches += 1
return expected_num_patches
def _run_check(
processor: BaseMultiModalProcessor,
images: list[Image.Image],
min_num: int,
max_num: int,
mm_processor_kwargs: Mapping[str, object],
):
tokenizer = processor.info.get_tokenizer()
config = processor.info.get_hf_config()
mm_data = {"image": images}
total_expected_num_patches = sum(
_get_expected_num_patches(config, image, len(images), min_num, max_num)
for image in images)
processed_inputs = processor.apply("<image>" * len(images), mm_data,
mm_processor_kwargs)
# Ensure we have the right number of placeholders per num_crops size
image_token_id = tokenizer.convert_tokens_to_ids("<IMG_CONTEXT>")
img_tok_count = processed_inputs["prompt_token_ids"].count(image_token_id)
pixel_shape = processed_inputs["mm_kwargs"]["pixel_values_flat"].shape
assert img_tok_count == 256 * total_expected_num_patches
assert pixel_shape[0] == total_expected_num_patches
@pytest.mark.parametrize("model_id", [
"h2oai/h2ovl-mississippi-800m",
"h2oai/h2ovl-mississippi-2b",
......@@ -25,118 +126,54 @@ from ...utils import build_model_context
[1.0, 1.0, 1.0],
# Multi-scale
[0.25, 0.5, 1.0],
[4.0, 2.0, 1.0],
],
)
@pytest.mark.parametrize("max_dynamic_patch", [1, 2, 4, 8])
@pytest.mark.parametrize(
("min_dynamic_patch", "max_dynamic_patch"),
[(1, 1), (1, 2), (1, 4), (1, 8), (2, 4), (4, 8)],
)
@pytest.mark.parametrize("dynamic_image_size", [True, False])
@pytest.mark.parametrize("num_imgs", [1, 2])
@pytest.mark.parametrize("kwargs_on_init", [True, False])
def test_processor_override(
model_id: str,
image_assets: _ImageAssets,
size_factors: list[int],
min_dynamic_patch: int,
max_dynamic_patch: int,
dynamic_image_size: Optional[bool],
num_imgs: int,
kwargs_on_init: bool,
):
from vllm.model_executor.models.h2ovl import (calculate_h2ovl_targets,
get_h2ovl_target_ratios)
mm_processor_kwargs = {
"min_dynamic_patch": min_dynamic_patch,
"max_dynamic_patch": max_dynamic_patch,
"dynamic_image_size": dynamic_image_size,
}
ctx = build_model_context(
model_name=model_id,
tokenizer_name=model_id,
trust_remote_code=True,
mm_processor_kwargs=None,
limit_mm_per_prompt={"image": num_imgs},
)
tokenizer = cached_get_tokenizer(
ctx.model_config.tokenizer,
trust_remote_code=ctx.model_config.trust_remote_code,
mm_processor_kwargs=mm_processor_kwargs if kwargs_on_init else None,
limit_mm_per_prompt={"image": len(size_factors)},
)
tokenizer = cached_tokenizer_from_config(ctx.model_config)
processor = MULTIMODAL_REGISTRY.create_processor(
ctx.model_config,
tokenizer=tokenizer,
)
hf_processor_mm_kwargs = {} if kwargs_on_init else mm_processor_kwargs
config = processor.info.get_hf_config()
use_msac = config.use_msac
mm_processor_kwargs = {
"max_dynamic_patch": max_dynamic_patch,
}
if dynamic_image_size is not None:
mm_processor_kwargs["dynamic_image_size"] = dynamic_image_size
min_num = config.min_dynamic_patch
min_num = min_dynamic_patch if dynamic_image_size else 1
max_num = max_dynamic_patch if dynamic_image_size else 1
# Build the image str / prompt based on the number of images we pass
prompt = "<image>" * num_imgs
for asset in image_assets:
for factor in size_factors:
image = rescale_image_size(asset.pil_image, factor)
mm_data = {"image": [image] * num_imgs}
width, height = image.size
# Calculate the expected number of blocks
if num_imgs == 1 and use_msac:
# First pass
blocks1, _, _, aspect_ratio = calculate_h2ovl_targets(
orig_width=width,
orig_height=height,
target_ratios=get_h2ovl_target_ratios(
min_num,
max_num,
prior_aspect_ratio=None,
),
image_size=config.vision_config.image_size,
use_thumbnail=False, # Thumbnail is handled separately
)
# Second pass
blocks2, _, _, _ = calculate_h2ovl_targets(
orig_width=width,
orig_height=height,
target_ratios=get_h2ovl_target_ratios(
min_num,
max_num,
prior_aspect_ratio=aspect_ratio,
),
image_size=config.vision_config.image_size,
use_thumbnail=False,
)
# Add thumbnail if use_thumbnail is True and total_blocks > 1
if config.use_thumbnail:
blocks1 += 1 if blocks1 > 1 else 0
blocks2 += 1 if blocks2 > 1 else 0
# Total blocks is the sum of blocks from both passes minus
# overlapping
total_blocks = blocks1 + blocks2 - 1
expected_num_patches = total_blocks
else:
blocks, _, _, _ = calculate_h2ovl_targets(
orig_width=width,
orig_height=height,
target_ratios=get_h2ovl_target_ratios(
min_num,
max_num,
prior_aspect_ratio=None,
),
image_size=config.vision_config.image_size,
use_thumbnail=False,
)
expected_num_patches = blocks
if config.use_thumbnail and expected_num_patches != 1:
expected_num_patches += 1
processed_inputs = processor.apply(prompt, mm_data,
mm_processor_kwargs)
pixel_shape = (
processed_inputs["mm_kwargs"]["pixel_values_flat"].shape)
assert pixel_shape[0] == expected_num_patches * num_imgs
_run_check(
processor,
[
rescale_image_size(image_assets[0].pil_image, f)
for f in size_factors
],
min_num,
max_num,
hf_processor_mm_kwargs,
)
tests/models/multimodal/processing/test_idefics3.py
View file @ 377d10bd
......@@ -4,7 +4,7 @@ import pytest
from transformers import Idefics3Config
from vllm.multimodal import MULTIMODAL_REGISTRY
from vllm.multimodal.utils import cached_get_tokenizer
from vllm.transformers_utils.tokenizer import cached_tokenizer_from_config
from ....conftest import _ImageAssets
from ...utils import build_model_context
......@@ -22,9 +22,15 @@ models = ["HuggingFaceM4/Idefics3-8B-Llama3"]
])
# yapf: enable
@pytest.mark.parametrize("num_imgs", [1, 2])
def test_processor_override(image_assets: _ImageAssets, model: str,
mm_processor_kwargs: dict[str, object],
expected_toks_per_img: int, num_imgs: int):
@pytest.mark.parametrize("kwargs_on_init", [True, False])
def test_processor_override(
image_assets: _ImageAssets,
model: str,
mm_processor_kwargs: dict[str, object],
expected_toks_per_img: int,
num_imgs: int,
kwargs_on_init: bool,
):
"""Ensure input_processor_for_idefics3 handles num_crops properly."""
# Same as the previous test - don't initialize mm_processor_kwargs
# in this test and assume that the kwargs will be correctly expanded by
......@@ -33,15 +39,15 @@ def test_processor_override(image_assets: _ImageAssets, model: str,
model_name=model,
tokenizer_name=model,
trust_remote_code=True,
mm_processor_kwargs=None,
mm_processor_kwargs=mm_processor_kwargs if kwargs_on_init else None,
limit_mm_per_prompt={"image": num_imgs},
)
tokenizer = cached_get_tokenizer(ctx.model_config.tokenizer)
tokenizer = cached_tokenizer_from_config(ctx.model_config)
processor = MULTIMODAL_REGISTRY.create_processor(
ctx.model_config,
tokenizer=tokenizer,
)
hf_processor = processor.info.get_hf_processor(**mm_processor_kwargs)
hf_processor_mm_kwargs = {} if kwargs_on_init else mm_processor_kwargs
# Build the image str / prompt based on the number of images we pass
placeholders = "<image>" if num_imgs == 1 else "\n".join(
......@@ -54,8 +60,10 @@ def test_processor_override(image_assets: _ImageAssets, model: str,
dummy_image = image_assets[0].pil_image.resize(dummy_image_size)
mm_data = {"image": [dummy_image] * num_imgs}
processed_inputs = processor.apply(prompt, mm_data, mm_processor_kwargs)
processed_inputs = processor.apply(prompt, mm_data, hf_processor_mm_kwargs)
# Ensure the placeholders format are correct
hf_processor = processor.info.get_hf_processor(**hf_processor_mm_kwargs)
hf_processed_inputs = hf_processor(text=prompt, images=mm_data["image"])
assert processed_inputs["prompt_token_ids"] == hf_processed_inputs[
"input_ids"][0]
......
tests/models/multimodal/processing/test_internvl.py
View file @ 377d10bd
# SPDX-License-Identifier: Apache-2.0
"""Tests for InternVL's multimodal preprocessing kwargs."""
from typing import Optional
from typing import Mapping, Optional
import pytest
from PIL import Image
from transformers import PretrainedConfig
from vllm.multimodal import MULTIMODAL_REGISTRY
from vllm.multimodal.utils import cached_get_tokenizer
from vllm.multimodal.image import rescale_image_size
from vllm.multimodal.processing import BaseMultiModalProcessor
from vllm.transformers_utils.tokenizer import cached_tokenizer_from_config
from ....conftest import _ImageAssets
from ...utils import build_model_context
def _get_expected_num_patches(
config: PretrainedConfig,
image: Image.Image,
num_imgs: int,
min_num: int,
max_num: int,
):
from vllm.model_executor.models.internvl import (
calculate_internvl_targets, get_internvl_target_ratios)
width, height = image.size
blocks, _, _ = calculate_internvl_targets(
orig_width=width,
orig_height=height,
target_ratios=get_internvl_target_ratios(
min_num,
max_num,
),
image_size=config.vision_config.image_size,
use_thumbnail=False,
)
expected_num_patches = blocks
if config.use_thumbnail and expected_num_patches > 1:
expected_num_patches += 1
return expected_num_patches
def _run_check(
processor: BaseMultiModalProcessor,
images: list[Image.Image],
min_num: int,
max_num: int,
mm_processor_kwargs: Mapping[str, object],
):
tokenizer = processor.info.get_tokenizer()
config = processor.info.get_hf_config()
mm_data = {"image": images}
total_expected_num_patches = sum(
_get_expected_num_patches(config, image, len(images), min_num, max_num)
for image in images)
processed_inputs = processor.apply("<image>" * len(images), mm_data,
mm_processor_kwargs)
# Ensure we have the right number of placeholders per num_crops size
image_token_id = tokenizer.convert_tokens_to_ids("<IMG_CONTEXT>")
img_tok_count = processed_inputs["prompt_token_ids"].count(image_token_id)
pixel_shape = processed_inputs["mm_kwargs"]["pixel_values_flat"].shape
assert img_tok_count == 256 * total_expected_num_patches
assert pixel_shape[0] == total_expected_num_patches
@pytest.mark.parametrize("model_id", ["OpenGVLab/InternVL2-2B"])
@pytest.mark.parametrize("max_dynamic_patch", [1, 4])
@pytest.mark.parametrize("dynamic_image_size", [True, False, None])
@pytest.mark.parametrize("num_imgs", [1, 2])
@pytest.mark.parametrize(
"size_factors",
[
# Single-scale
[1.0],
# Single-scale, batched
[1.0, 1.0, 1.0],
# Multi-scale
[0.25, 0.5, 1.0],
[4.0, 2.0, 1.0],
],
)
@pytest.mark.parametrize(
("min_dynamic_patch", "max_dynamic_patch"),
[(1, 1), (1, 2), (1, 4), (1, 8), (2, 4), (4, 8)],
)
@pytest.mark.parametrize("dynamic_image_size", [True, False])
@pytest.mark.parametrize("kwargs_on_init", [True, False])
def test_processor_override(
model_id: str,
image_assets: _ImageAssets,
size_factors: list[int],
min_dynamic_patch: int,
max_dynamic_patch: int,
dynamic_image_size: Optional[bool],
num_imgs: int,
kwargs_on_init: bool,
):
mm_processor_kwargs = {
"min_dynamic_patch": min_dynamic_patch,
"max_dynamic_patch": max_dynamic_patch,
"dynamic_image_size": dynamic_image_size,
}
ctx = build_model_context(
model_name=model_id,
tokenizer_name=model_id,
trust_remote_code=True,
mm_processor_kwargs=None,
limit_mm_per_prompt={"image": num_imgs},
)
tokenizer = cached_get_tokenizer(
ctx.model_config.tokenizer,
trust_remote_code=ctx.model_config.trust_remote_code,
mm_processor_kwargs=mm_processor_kwargs if kwargs_on_init else None,
limit_mm_per_prompt={"image": len(size_factors)},
)
tokenizer = cached_tokenizer_from_config(ctx.model_config)
processor = MULTIMODAL_REGISTRY.create_processor(
ctx.model_config,
tokenizer=tokenizer,
)
hf_processor_mm_kwargs = {} if kwargs_on_init else mm_processor_kwargs
mm_processor_kwargs = {
"max_dynamic_patch": max_dynamic_patch,
}
if dynamic_image_size is not None:
mm_processor_kwargs["dynamic_image_size"] = dynamic_image_size
min_num = min_dynamic_patch if dynamic_image_size else 1
max_num = max_dynamic_patch if dynamic_image_size else 1
# Build the image str / prompt based on the number of images we pass
prompt = "<image>" * num_imgs
image = image_assets[0].pil_image.resize((448 * 2, 448 * 2))
mm_data = {"image": [image] * num_imgs}
expected_num_patches = max_dynamic_patch + 1 if max_dynamic_patch > 1 else 1
if dynamic_image_size is False:
expected_num_patches = 1
processed_inputs = processor.apply(prompt, mm_data, mm_processor_kwargs)
# Ensure we have the right number of placeholders per num_crops size
image_token_id = tokenizer.convert_tokens_to_ids("<IMG_CONTEXT>")
img_tok_count = processed_inputs["prompt_token_ids"].count(image_token_id)
pixel_shape = processed_inputs["mm_kwargs"]["pixel_values_flat"].shape
assert img_tok_count == 256 * expected_num_patches * num_imgs
assert pixel_shape[0] == expected_num_patches * num_imgs
_run_check(
processor,
[
rescale_image_size(image_assets[0].pil_image, f)
for f in size_factors
],
min_num,
max_num,
hf_processor_mm_kwargs,
)
tests/models/multimodal/processing/test_llava_next.py
View file @ 377d10bd
......@@ -10,7 +10,7 @@ from pqdm.threads import pqdm
from vllm.multimodal import MULTIMODAL_REGISTRY
from vllm.multimodal.parse import ImageSize
from vllm.multimodal.processing import BaseMultiModalProcessor
from vllm.multimodal.utils import cached_get_tokenizer
from vllm.transformers_utils.tokenizer import cached_tokenizer_from_config
from ...utils import build_model_context
......@@ -43,10 +43,7 @@ def test_processor_max_tokens(model_id):
)
processor = MULTIMODAL_REGISTRY.create_processor(
ctx.model_config,
tokenizer=cached_get_tokenizer(
ctx.model_config.tokenizer,
trust_remote_code=ctx.model_config.trust_remote_code,
),
tokenizer=cached_tokenizer_from_config(ctx.model_config),
)
info = processor.info
......@@ -146,10 +143,7 @@ def test_processor_prompt_replacements_regression(model_id, num_imgs):
)
processor = MULTIMODAL_REGISTRY.create_processor(
ctx.model_config,
tokenizer=cached_get_tokenizer(
ctx.model_config.tokenizer,
trust_remote_code=ctx.model_config.trust_remote_code,
),
tokenizer=cached_tokenizer_from_config(ctx.model_config),
)
image_ratios = [(171, 152), (184, 161), (198, 176), (333, 296), (369, 328),
......@@ -179,10 +173,7 @@ def test_processor_prompt_replacements_all(model_id, num_imgs):
)
processor = MULTIMODAL_REGISTRY.create_processor(
ctx.model_config,
tokenizer=cached_get_tokenizer(
ctx.model_config.tokenizer,
trust_remote_code=ctx.model_config.trust_remote_code,
),
tokenizer=cached_tokenizer_from_config(ctx.model_config),
)
seen_aspect_ratios = set[float]()
......
tests/models/multimodal/processing/test_llava_onevision.py
View file @ 377d10bd
......@@ -10,7 +10,7 @@ from pqdm.threads import pqdm
from vllm.multimodal import MULTIMODAL_REGISTRY
from vllm.multimodal.parse import ImageSize
from vllm.multimodal.processing import BaseMultiModalProcessor
from vllm.multimodal.utils import cached_get_tokenizer
from vllm.transformers_utils.tokenizer import cached_tokenizer_from_config
from ...utils import build_model_context
......@@ -44,10 +44,7 @@ def test_processor_max_tokens(model_id):
)
processor = MULTIMODAL_REGISTRY.create_processor(
ctx.model_config,
tokenizer=cached_get_tokenizer(
ctx.model_config.tokenizer,
trust_remote_code=ctx.model_config.trust_remote_code,
),
tokenizer=cached_tokenizer_from_config(ctx.model_config),
)
info = processor.info
......@@ -146,10 +143,7 @@ def test_processor_prompt_replacements_regression(model_id, num_imgs):
)
processor = MULTIMODAL_REGISTRY.create_processor(
ctx.model_config,
tokenizer=cached_get_tokenizer(
ctx.model_config.tokenizer,
trust_remote_code=ctx.model_config.trust_remote_code,
),
tokenizer=cached_tokenizer_from_config(ctx.model_config),
)
image_ratios = [(171, 152), (184, 161), (198, 176), (333, 296), (369, 328),
......@@ -180,10 +174,7 @@ def test_processor_prompt_replacements_all(model_id, num_imgs):
)
processor = MULTIMODAL_REGISTRY.create_processor(
ctx.model_config,
tokenizer=cached_get_tokenizer(
ctx.model_config.tokenizer,
trust_remote_code=ctx.model_config.trust_remote_code,
),
tokenizer=cached_tokenizer_from_config(ctx.model_config),
)
seen_aspect_ratios = set[float]()
......
tests/models/multimodal/processing/test_phi3v.py
View file @ 377d10bd
......@@ -3,7 +3,7 @@
import pytest
from vllm.multimodal import MULTIMODAL_REGISTRY
from vllm.multimodal.utils import cached_get_tokenizer
from vllm.transformers_utils.tokenizer import cached_tokenizer_from_config
from ....conftest import _ImageAssets
from ...utils import build_model_context
......@@ -21,12 +21,14 @@ from ...utils import build_model_context
])
# yapf: enable
@pytest.mark.parametrize("num_imgs", [1, 2])
@pytest.mark.parametrize("kwargs_on_init", [True, False])
def test_processor_override(
image_assets: _ImageAssets,
model_id: str,
mm_processor_kwargs: dict[str, int],
expected_toks_per_img: int,
num_imgs: int,
kwargs_on_init: bool,
):
"""Ensure input_processor_for_phi3v handles num_crops properly."""
# Avoid initializing CUDA early
......@@ -36,23 +38,22 @@ def test_processor_override(
model_name=model_id,
tokenizer_name=model_id,
trust_remote_code=True,
mm_processor_kwargs=mm_processor_kwargs if kwargs_on_init else None,
limit_mm_per_prompt={"image": num_imgs},
)
tokenizer = cached_get_tokenizer(
ctx.model_config.tokenizer,
trust_remote_code=ctx.model_config.trust_remote_code,
)
tokenizer = cached_tokenizer_from_config(ctx.model_config)
processor = MULTIMODAL_REGISTRY.create_processor(
ctx.model_config,
tokenizer=tokenizer,
)
hf_processor_mm_kwargs = {} if kwargs_on_init else mm_processor_kwargs
# Build the image str / prompt based on the number of images we pass
img_str = "".join([f"<|image_{idx}|>\n" for idx in range(1, num_imgs + 1)])
prompt = f"<|user|>\n{img_str}<|end|>\n<|assistant|>\n"
mm_data = {"image": [image_assets[0].pil_image] * num_imgs}
processed_inputs = processor.apply(prompt, mm_data, mm_processor_kwargs)
processed_inputs = processor.apply(prompt, mm_data, hf_processor_mm_kwargs)
# Ensure we have the right number of placeholders per num_crops size
img_tok_count = processed_inputs["prompt_token_ids"].count(_IMAGE_TOKEN_ID)
......
tests/models/multimodal/processing/test_qwen2_vl.py
View file @ 377d10bd
......@@ -3,7 +3,7 @@
import pytest
from vllm.multimodal import MULTIMODAL_REGISTRY
from vllm.multimodal.utils import cached_get_tokenizer
from vllm.transformers_utils.tokenizer import cached_tokenizer_from_config
from ....conftest import _ImageAssets
from ...utils import build_model_context
......@@ -18,6 +18,7 @@ from ...utils import build_model_context
])
# yapf: enable
@pytest.mark.parametrize("num_imgs", [1, 2])
@pytest.mark.parametrize("kwargs_on_init", [True, False])
def test_processor_override(
image_assets: _ImageAssets,
model_id: str,
......@@ -25,31 +26,30 @@ def test_processor_override(
expected_toks_per_img: int,
expected_pixels_shape: tuple[int, int],
num_imgs: int,
kwargs_on_init: bool,
):
"""Ensure Qwen2VLMultiModalProcessor handles min/max pixels properly."""
ctx = build_model_context(
model_name=model_id,
tokenizer_name=model_id,
mm_processor_kwargs=None,
mm_processor_kwargs=mm_processor_kwargs if kwargs_on_init else None,
limit_mm_per_prompt={"image": num_imgs},
)
tokenizer = cached_get_tokenizer(
ctx.model_config.tokenizer,
trust_remote_code=ctx.model_config.trust_remote_code,
)
tokenizer = cached_tokenizer_from_config(ctx.model_config)
processor = MULTIMODAL_REGISTRY.create_processor(
ctx.model_config,
tokenizer=tokenizer,
)
hf_processor_mm_kwargs = {} if kwargs_on_init else mm_processor_kwargs
# Build the image str / prompt based on the number of images we pass
prompt = "<|vision_start|><|image_pad|><|vision_end|>" * num_imgs
mm_data = {"image": [image_assets[0].pil_image] * num_imgs}
processed_inputs = processor.apply(prompt, mm_data, mm_processor_kwargs)
processed_inputs = processor.apply(prompt, mm_data, hf_processor_mm_kwargs)
# Ensure we have the right number of placeholders per num_crops size
hf_processor = processor.info.get_hf_processor(**mm_processor_kwargs)
hf_processor = processor.info.get_hf_processor(**hf_processor_mm_kwargs)
image_token_id = tokenizer.convert_tokens_to_ids(hf_processor.image_token)
img_tok_count = processed_inputs["prompt_token_ids"].count(image_token_id)
pixel_shape = processed_inputs["mm_kwargs"]["pixel_values"].shape
......
tests/models/utils.py
View file @ 377d10bd
......@@ -248,13 +248,16 @@ def check_logprobs_close(
warnings.warn(fail_msg, stacklevel=2)
def build_model_context(model_name: str,
task: TaskOption = "auto",
tokenizer_name: Optional[str] = None,
trust_remote_code: bool = False,
dtype: Optional[Union[str, torch.dtype]] = None,
mm_processor_kwargs: Optional[Dict] = None,
limit_mm_per_prompt: Optional[Dict] = None):
def build_model_context(
model_name: str,
task: TaskOption = "auto",
tokenizer_name: Optional[str] = None,
trust_remote_code: bool = False,
dtype: Optional[Union[str, torch.dtype]] = None,
mm_processor_kwargs: Optional[Dict] = None,
limit_mm_per_prompt: Optional[Dict] = None,
disable_mm_preprocessor_cache: bool = True,
):
"""Creates an InputContext for a given model.
Args:
......@@ -283,5 +286,6 @@ def build_model_context(model_name: str,
seed=0,
mm_processor_kwargs=mm_processor_kwargs,
limit_mm_per_prompt=limit_mm_per_prompt,
disable_mm_preprocessor_cache=disable_mm_preprocessor_cache,
)
return InputContext(model_config)
tests/multimodal/test_processing.py
View file @ 377d10bd
......@@ -22,8 +22,8 @@ from vllm.multimodal.processing import (PlaceholderFeaturesInfo,
replace_token_matches)
# yapf: enable
from vllm.multimodal.profiling import MultiModalProfiler
from vllm.multimodal.utils import cached_get_tokenizer
from vllm.transformers_utils.tokenizer import AnyTokenizer
from vllm.transformers_utils.tokenizer import (AnyTokenizer,
cached_tokenizer_from_config)
from vllm.utils import full_groupby
from .utils import random_image
......@@ -577,7 +577,7 @@ def test_limit_mm_per_prompt_dummy(model_id, limit, num_supported, is_valid):
processor = MULTIMODAL_REGISTRY.create_processor(
model_config,
tokenizer=cached_get_tokenizer(model_config.tokenizer),
tokenizer=cached_tokenizer_from_config(model_config),
)
profiler = MultiModalProfiler(processor)
......@@ -617,7 +617,7 @@ def test_limit_mm_per_prompt_apply(model_id, num_images, limit, is_valid):
processor = MULTIMODAL_REGISTRY.create_processor(
model_config,
tokenizer=cached_get_tokenizer(model_config.tokenizer),
tokenizer=cached_tokenizer_from_config(model_config),
)
rng = np.random.RandomState(0)
......@@ -689,7 +689,7 @@ def test_hf_processor_kwargs(model_id, call_kwargs, expected_kwargs):
processor = MULTIMODAL_REGISTRY.create_processor(
model_config,
tokenizer=cached_get_tokenizer(model_config.tokenizer),
tokenizer=cached_tokenizer_from_config(model_config),
)
orig_get_hf_processor = processor.info.get_hf_processor
......
vllm/inputs/registry.py
View file @ 377d10bd
......@@ -11,8 +11,9 @@ from transformers import BatchFeature, PretrainedConfig, ProcessorMixin
from typing_extensions import TypeVar, assert_never
from vllm.logger import init_logger
from vllm.transformers_utils.processor import cached_get_processor
from vllm.transformers_utils.tokenizer import AnyTokenizer
from vllm.transformers_utils.processor import cached_processor_from_config
from vllm.transformers_utils.tokenizer import (AnyTokenizer,
cached_tokenizer_from_config)
from vllm.utils import (ClassRegistry, get_allowed_kwarg_only_overrides,
resolve_mm_processor_kwargs)
......@@ -27,19 +28,9 @@ if TYPE_CHECKING:
logger = init_logger(__name__)
C = TypeVar("C", bound=PretrainedConfig, default=PretrainedConfig)
P = TypeVar("P", bound=ProcessorMixin, default=ProcessorMixin)
class HashableDict(dict):
"""
A dictionary that can be hashed by lru_cache.
"""
# NOTE: pythonic dict is not hashable,
# we override on it directly for simplicity
def __hash__(self) -> int: # type: ignore[override]
return hash(frozenset(self.items()))
_T = TypeVar("_T")
_C = TypeVar("_C", bound=PretrainedConfig, default=PretrainedConfig)
_P = TypeVar("_P", bound=ProcessorMixin, default=ProcessorMixin)
@dataclass(frozen=True)
......@@ -54,9 +45,9 @@ class InputContext:
def get_hf_config(
self,
typ: Union[type[C], tuple[type[C], ...]] = PretrainedConfig,
typ: Union[type[_C], tuple[type[_C], ...]] = PretrainedConfig,
/,
) -> C:
) -> _C:
"""
Get the HuggingFace configuration
(:class:`transformers.PretrainedConfig`) of the model,
......@@ -94,10 +85,10 @@ class InputContext:
def get_hf_processor(
self,
typ: Union[type[P], tuple[type[P], ...]] = ProcessorMixin,
typ: Union[type[_P], tuple[type[_P], ...]] = ProcessorMixin,
/,
**kwargs: object,
) -> P:
) -> _P:
"""
Get the HuggingFace processor
(:class:`transformers.ProcessorMixin`) of the model,
......@@ -106,33 +97,29 @@ class InputContext:
Raises:
TypeError: If the processor is not of the specified type.
"""
return cached_processor_from_config(
self.model_config,
processor_cls=typ,
**kwargs,
)
def init_processor(
self,
typ: type[_T],
/,
**kwargs: object,
) -> _T:
"""
Initialize a HuggingFace-like processor class, merging the
keyword arguments with those in the model's configuration.
"""
base_kwargs = self.model_config.mm_processor_kwargs
if base_kwargs is None:
base_kwargs = {}
merged_kwargs = {**base_kwargs, **kwargs}
if isinstance(typ, type):
merged_kwargs["processor_cls"] = typ
# NOTE: Pythonic dict is not hashable and will raise unhashable type
# error when calling `cached_get_processor`, therefore we need to
# wrap it to a hashable dict.
for key, value in merged_kwargs.items():
if isinstance(value, dict):
merged_kwargs[key] = HashableDict(value)
hf_processor = cached_get_processor(
self.model_config.model,
trust_remote_code=self.model_config.trust_remote_code,
**merged_kwargs,
)
if not isinstance(hf_processor, typ):
raise TypeError("Invalid type of HuggingFace processor. "
f"Expected type: {typ}, but "
f"found type: {type(hf_processor)}")
return hf_processor
return typ(**merged_kwargs)
@dataclass(frozen=True)
......@@ -142,10 +129,10 @@ class InputProcessingContext(InputContext):
def get_hf_processor(
self,
typ: Union[type[P], tuple[type[P], ...]] = ProcessorMixin,
typ: Union[type[_P], tuple[type[_P], ...]] = ProcessorMixin,
/,
**kwargs: object,
) -> P:
) -> _P:
return super().get_hf_processor(
typ,
tokenizer=self.tokenizer,
......@@ -341,13 +328,9 @@ class InputRegistry:
from vllm.model_executor.model_loader import get_model_architecture
from vllm.multimodal import MultiModalKwargs
from vllm.multimodal.profiling import MultiModalProfiler
from vllm.multimodal.utils import cached_get_tokenizer
if mm_registry.has_processor(model_config):
tokenizer = cached_get_tokenizer(
model_config.tokenizer,
trust_remote_code=model_config.trust_remote_code,
)
tokenizer = cached_tokenizer_from_config(model_config)
processor = mm_registry.create_processor(model_config, tokenizer)
profiler = MultiModalProfiler(processor)
dummy_data = profiler.get_dummy_data(
......
vllm/model_executor/models/aria.py
View file @ 377d10bd
......@@ -400,8 +400,8 @@ class AriaProcessingInfo(BaseProcessingInfo):
def get_vision_config(self):
return self.get_hf_config().vision_config
def get_hf_processor(self):
return self.ctx.get_hf_processor(AriaProcessor)
def get_hf_processor(self, **kwargs: object):
return self.ctx.get_hf_processor(AriaProcessor, **kwargs)
def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
return {"image": None}
......
vllm/model_executor/models/chameleon.py
View file @ 377d10bd
......@@ -58,8 +58,8 @@ class ChameleonProcessingInfo(BaseProcessingInfo):
def get_hf_config(self):
return self.ctx.get_hf_config(ChameleonConfig)
def get_hf_processor(self):
return self.ctx.get_hf_processor(ChameleonProcessor)
def get_hf_processor(self, **kwargs: object):
return self.ctx.get_hf_processor(ChameleonProcessor, **kwargs)
def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
return {"image": 1}
......
vllm/model_executor/models/deepseek_vl2.py
View file @ 377d10bd
......@@ -28,13 +28,13 @@ from vllm.multimodal.parse import (ImageEmbeddingItems, ImageProcessorItems,
from vllm.multimodal.processing import (BaseMultiModalProcessor,
BaseProcessingInfo, PromptReplacement)
from vllm.multimodal.profiling import BaseDummyInputsBuilder, ProcessorInputs
from vllm.multimodal.utils import cached_get_tokenizer
from vllm.sequence import IntermediateTensors
from vllm.transformers_utils.configs.deepseek_vl2 import (DeepseekVLV2Config,
MlpProjectorConfig,
VisionEncoderConfig)
from vllm.transformers_utils.processors.deepseek_vl2 import (
DeepseekVLV2Processor)
from vllm.transformers_utils.tokenizer import cached_tokenizer_from_config
from vllm.utils import is_list_of
from .interfaces import SupportsMultiModal, SupportsPP
......@@ -133,8 +133,8 @@ class DeepseekVL2ProcessingInfo(BaseProcessingInfo):
def get_hf_config(self):
return self.ctx.get_hf_config(DeepseekVLV2Config)
def get_hf_processor(self) -> DeepseekVLV2Processor:
return self.ctx.get_hf_processor(DeepseekVLV2Processor)
def get_hf_processor(self, **kwargs: object):
return self.ctx.get_hf_processor(DeepseekVLV2Processor, **kwargs)
def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
return {"image": None}
......@@ -308,13 +308,8 @@ class DeepseekVLV2ForCausalLM(nn.Module, SupportsMultiModal, SupportsPP):
self.text_config = config.text_config
model_config = vllm_config.model_config
tokenizer = cached_get_tokenizer(
model_config.tokenizer,
tokenizer_mode=model_config.tokenizer_mode,
tokenizer_revision=model_config.tokenizer_revision,
trust_remote_code=model_config.trust_remote_code,
)
self.image_token_id = tokenizer.vocab.get(_IMAGE_TOKEN)
tokenizer = cached_tokenizer_from_config(model_config)
self.image_token_id = tokenizer.vocab[_IMAGE_TOKEN]
self.vision = self._init_vision_module(self.vision_config,
quant_config,
......
vllm/model_executor/models/fuyu.py
View file @ 377d10bd
......@@ -71,8 +71,8 @@ class FuyuProcessingInfo(BaseProcessingInfo):
def get_hf_config(self):
return self.ctx.get_hf_config(FuyuConfig)
def get_hf_processor(self):
return self.ctx.get_hf_processor(FuyuProcessor)
def get_hf_processor(self, **kwargs: object):
return self.ctx.get_hf_processor(FuyuProcessor, **kwargs)
def get_image_processor(self) -> FuyuImageProcessor:
return self.get_hf_processor().image_processor
......
vllm/model_executor/models/glm4v.py
View file @ 377d10bd
......@@ -416,18 +416,15 @@ class GLM4VProcessor:
class GLM4VProcessingInfo(BaseProcessingInfo):
def get_tokenizer(self):
tokenizer = self.ctx.tokenizer
assert isinstance(tokenizer, PreTrainedTokenizer)
return tokenizer
def get_hf_config(self):
return self.ctx.get_hf_config(ChatGLMConfig)
def get_hf_processor(self) -> GLM4VProcessor:
return GLM4VProcessor(
self.get_hf_config(),
self.get_tokenizer(),
def get_hf_processor(self, **kwargs: object) -> GLM4VProcessor:
return self.ctx.init_processor(
GLM4VProcessor,
config=self.get_hf_config(),
tokenizer=self.get_tokenizer(),
**kwargs,
)
def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
......
vllm/model_executor/models/gritlm.py
View file @ 377d10bd
......@@ -15,9 +15,9 @@ from vllm.model_executor.layers.pooler import PoolerHead
from vllm.model_executor.models.llama import LlamaForCausalLM
from vllm.model_executor.pooling_metadata import (PoolingMetadata,
PoolingTensors)
from vllm.multimodal.utils import cached_get_tokenizer
from vllm.sequence import (IntermediateTensors, PoolerOutput,
PoolingSequenceGroupOutput)
from vllm.transformers_utils.tokenizer import cached_tokenizer_from_config
logger = init_logger(__name__)
......@@ -29,12 +29,7 @@ class GritLMPooler(nn.Module):
self.model_config = model_config
tokenizer = cached_get_tokenizer(
self.model_config.tokenizer,
tokenizer_mode=self.model_config.tokenizer_mode,
tokenizer_revision=self.model_config.tokenizer_revision,
trust_remote_code=self.model_config.trust_remote_code,
)
tokenizer = cached_tokenizer_from_config(self.model_config)
# Collect the tokens needed for pattern matching.
# "▁<" is different from "_<". The former uses "▁" to indicate that
......
vllm/model_executor/models/h2ovl.py
View file @ 377d10bd
......@@ -41,6 +41,7 @@ def resolve_h2ovl_min_max_num(
dynamic_image_size: bool,
use_thumbnail: bool,
) -> tuple[int, int]:
min_dynamic_patch = min_dynamic_patch if dynamic_image_size else 1
max_dynamic_patch = max_dynamic_patch if dynamic_image_size else 1
if use_thumbnail and max_dynamic_patch != 1:
......@@ -190,7 +191,7 @@ def image_to_pixel_values_h2ovl(
pixel_values1, aspect_ratio1 = _preprocess_image(
image,
input_size=input_size,
min_num=min_num,
min_num=1,
max_num=max_num,
use_thumbnail=True,
prior_aspect_ratio=None,
......@@ -199,7 +200,7 @@ def image_to_pixel_values_h2ovl(
pixel_values2, _ = _preprocess_image(
image,
input_size=input_size,
min_num=3, # Hardcoded value
min_num=3,
max_num=max_num,
use_thumbnail=True,
prior_aspect_ratio=aspect_ratio1,
......@@ -228,6 +229,7 @@ class H2OVLProcessor(BaseInternVLProcessor):
config: PretrainedConfig,
tokenizer: AnyTokenizer,
*,
min_dynamic_patch: Optional[int] = None,
max_dynamic_patch: Optional[int] = None,
dynamic_image_size: Optional[bool] = None,
use_msac: Optional[bool] = None,
......@@ -235,6 +237,7 @@ class H2OVLProcessor(BaseInternVLProcessor):
super().__init__(
config,
tokenizer,
min_dynamic_patch=min_dynamic_patch,
max_dynamic_patch=max_dynamic_patch,
dynamic_image_size=dynamic_image_size,
)
......@@ -267,11 +270,13 @@ class H2OVLProcessor(BaseInternVLProcessor):
def resolve_min_max_num(
self,
*,
min_dynamic_patch: Optional[int] = None,
max_dynamic_patch: Optional[int] = None,
dynamic_image_size: Optional[bool] = None,
use_thumbnail: Optional[bool] = None,
) -> tuple[int, int]:
min_dynamic_patch = self.min_dynamic_patch
min_dynamic_patch = (self.min_dynamic_patch if min_dynamic_patch
is None else min_dynamic_patch)
max_dynamic_patch = (self.max_dynamic_patch if max_dynamic_patch
is None else max_dynamic_patch)
dynamic_image_size = (self.dynamic_image_size if dynamic_image_size
......@@ -289,18 +294,21 @@ class H2OVLProcessor(BaseInternVLProcessor):
def resolve_target_ratios(
self,
*,
min_dynamic_patch: Optional[int] = None,
max_dynamic_patch: Optional[int] = None,
dynamic_image_size: Optional[bool] = None,
use_thumbnail: Optional[bool] = None,
prior_aspect_ratio: Optional[tuple[int, int]] = None,
override_min_num: Optional[int] = None,
) -> list[tuple[int, int]]:
min_num, max_num = self.resolve_min_max_num(
min_dynamic_patch=min_dynamic_patch,
max_dynamic_patch=max_dynamic_patch,
dynamic_image_size=dynamic_image_size,
use_thumbnail=use_thumbnail,
)
if prior_aspect_ratio: # hardcoded value for second pass of use_msac
min_num = 3
if override_min_num is not None:
min_num = override_min_num
return get_h2ovl_target_ratios(
min_num,
......@@ -322,6 +330,7 @@ class H2OVLProcessor(BaseInternVLProcessor):
if use_msac:
target_ratios_1 = self.resolve_target_ratios(
use_thumbnail=False, # Applied in calculate_targets
override_min_num=1,
)
num_patches_1, _, _, aspect_ratio_1 = calculate_h2ovl_targets(
orig_width=image_width,
......@@ -334,6 +343,7 @@ class H2OVLProcessor(BaseInternVLProcessor):
target_ratios_2 = self.resolve_target_ratios(
use_thumbnail=False, # Applied in calculate_targets
prior_aspect_ratio=aspect_ratio_1,
override_min_num=3,
)
num_patches_2, _, _, _ = calculate_h2ovl_targets(
orig_width=image_width,
......@@ -361,12 +371,14 @@ class H2OVLProcessor(BaseInternVLProcessor):
def _images_to_pixel_values_lst(
self,
images: list[Image.Image],
min_dynamic_patch: Optional[int] = None,
max_dynamic_patch: Optional[int] = None,
dynamic_image_size: Optional[bool] = None,
) -> list[torch.Tensor]:
use_msac = self.use_msac if len(images) == 1 else False
min_num, max_num = self.resolve_min_max_num(
min_dynamic_patch=min_dynamic_patch,
max_dynamic_patch=max_dynamic_patch,
dynamic_image_size=dynamic_image_size,
use_thumbnail=False, # Applied in image_to_pixel_values
......@@ -389,14 +401,23 @@ class H2OVLProcessingInfo(BaseInternVLProcessingInfo):
def get_hf_processor(
self,
*,
min_dynamic_patch: Optional[int] = None,
max_dynamic_patch: Optional[int] = None,
dynamic_image_size: Optional[bool] = None,
**kwargs: object,
) -> H2OVLProcessor:
return H2OVLProcessor(
self.get_hf_config(),
self.get_tokenizer(),
max_dynamic_patch=max_dynamic_patch,
dynamic_image_size=dynamic_image_size,
if min_dynamic_patch is not None:
kwargs["min_dynamic_patch"] = min_dynamic_patch
if max_dynamic_patch is not None:
kwargs["max_dynamic_patch"] = max_dynamic_patch
if dynamic_image_size is not None:
kwargs["dynamic_image_size"] = dynamic_image_size
return self.ctx.init_processor(
H2OVLProcessor,
config=self.get_hf_config(),
tokenizer=self.get_tokenizer(),
**kwargs,
)
def get_mm_max_tokens_per_item(
......
vllm/model_executor/models/idefics3.py
View file @ 377d10bd
......@@ -83,13 +83,15 @@ ImageInputs = Union[Idefics3ImagePixelInputs, Idefics3ImageEmbeddingInputs]
class Idefics3ProcessingInfo(BaseProcessingInfo):
def get_hf_processor(
self,
*,
size: Optional[Dict[str, int]] = None) -> Idefics3Processor:
self,
*,
size: Optional[Dict[str, int]] = None,
**kwargs: object,
) -> Idefics3Processor:
if size is not None:
return self.ctx.get_hf_processor(Idefics3Processor, size=size)
kwargs["size"] = size
return self.ctx.get_hf_processor(Idefics3Processor)
return self.ctx.get_hf_processor(Idefics3Processor, **kwargs)
def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
return {"image": None}
......
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