biondizzle/vllm
1
0
Fork 0
Code Issues Pull Requests Actions 2 Packages Projects Releases Wiki Activity

[Core] Dynamic image size support for VLMs (#5276)

Browse Source 
Signed-off-by: Xiaowei Jiang <xwjiang2010@gmail.com>
Co-authored-by: Xiaowei Jiang <xwjiang2010@gmail.com>
Co-authored-by: ywang96 <ywang@roblox.com>
Co-authored-by: xwjiang2010 <87673679+xwjiang2010@users.noreply.github.com>
Co-authored-by: Roger Wang <136131678+ywang96@users.noreply.github.com>
This commit is contained in:
Cyrus Leung
2024-07-03 11:34:00 +08:00
committed by GitHub
parent 482045ee77
commit 9831aec49f
38 changed files with 1453 additions and 664 deletions
Download Patch File Download Diff File Expand all files Collapse all files

127
tests/models/test_llava_next.py
View File

@@ -1,12 +1,15 @@
from typing import List, Tuple
import re
from typing import List, Optional, Tuple
import pytest
from transformers import AutoTokenizer
from vllm.config import VisionLanguageConfig
from vllm.multimodal.utils import rescale_image_size
from vllm.sequence import SampleLogprobs
from ..conftest import IMAGE_ASSETS
from .utils import check_outputs_equal
from .utils import check_logprobs_close
pytestmark = pytest.mark.vlm
@@ -15,21 +18,20 @@ _PREFACE = (
"The assistant gives helpful, detailed, and polite answers to the human's "
"questions.")
# The image token is placed before "user" on purpose so that the test can pass
HF_IMAGE_PROMPTS = IMAGE_ASSETS.prompts({
"stop_sign":
f"{_PREFACE} <image>\nUSER: What's the content of the image?\nASSISTANT:",
f"{_PREFACE} USER: <image>\nWhat's the content of the image? ASSISTANT:",
"cherry_blossom":
f"{_PREFACE} <image>\nUSER: What is the season?\nASSISTANT:",
f"{_PREFACE} USER: <image>\nWhat is the season? ASSISTANT:",
"boardwalk":
f"{_PREFACE} USER: <image>\nWhat's in this image? ASSISTANT:",
})
def iter_llava_next_configs(model_name: str):
# Need to use the max possible feature size for profile_run
image_hw_to_feature_size = {
(336, 336): 1176,
(672, 672): 2928,
(1344, 336): 1944,
(336, 1344): 1890,
(336, 336): 2928,
}
for (h, w), f in image_hw_to_feature_size.items():
@@ -47,37 +49,55 @@ model_and_vl_config = [
]
def vllm_to_hf_output(vllm_output: Tuple[List[int], str],
def vllm_to_hf_output(vllm_output: Tuple[List[int], str,
Optional[SampleLogprobs]],
vlm_config: VisionLanguageConfig, model_id: str):
"""Sanitize vllm output to be comparable with hf output.
The function reduces `input_ids` from 1, 32000, 32000, ..., 32000,
x1, x2, x3 ... to 1, 32000, x1, x2, x3 ...
It also reduces `output_str` from "<image><image>bla" to "bla".
"""
output_ids, output_str = vllm_output
output_ids, output_str, out_logprobs = vllm_output
image_token_id = vlm_config.image_token_id
tokenizer = AutoTokenizer.from_pretrained(model_id)
image_token_str = tokenizer.decode(image_token_id)
eos_token_id = tokenizer.eos_token_id
hf_output_ids = [
token_id for idx, token_id in enumerate(output_ids)
if token_id != image_token_id or output_ids[idx - 1] != image_token_id
]
hf_output_str = output_str \
.replace(image_token_str * vlm_config.image_feature_size, " ")
return hf_output_ids, hf_output_str
hf_output_str = re.sub(fr"({image_token_str})+", "", output_str)
assert hf_output_str[0] == " "
hf_output_str = hf_output_str[1:]
if hf_output_ids[-1] == eos_token_id:
hf_output_str = hf_output_str + tokenizer.decode(eos_token_id)
return hf_output_ids, hf_output_str, out_logprobs
@pytest.mark.xfail(
reason="Inconsistent image processor being used due to lack "
"of support for dynamic image token replacement")
@pytest.mark.parametrize("model_and_config", model_and_vl_config)
@pytest.mark.parametrize(
"size_factors",
[
# No image
[],
# Single-scale
[1.0],
# Single-scale, batched
[1.0, 1.0, 1.0],
# Multi-scale
[0.25, 0.5, 1.0],
],
)
@pytest.mark.parametrize("dtype", ["half"])
@pytest.mark.parametrize("max_tokens", [128])
@pytest.mark.parametrize("num_logprobs", [5])
def test_models(hf_runner, vllm_runner, image_assets, model_and_config,
dtype: str, max_tokens: int) -> None:
size_factors, dtype: str, max_tokens: int,
num_logprobs: int) -> None:
"""Inference result should be the same between hf and vllm.
All the image fixtures for the test is under tests/images.
@@ -88,37 +108,46 @@ def test_models(hf_runner, vllm_runner, image_assets, model_and_config,
The text output is sanitized to be able to compare with hf.
"""
model_id, vlm_config = model_and_config
hf_images = [asset.for_hf() for asset in image_assets]
vllm_images = [asset.for_vllm() for asset in image_assets]
images = [asset.pil_image for asset in image_assets]
inputs_per_image = [(
[prompt for _ in size_factors],
[rescale_image_size(image, factor) for factor in size_factors],
) for image, prompt in zip(images, HF_IMAGE_PROMPTS)]
# max_model_len should be greater than image_feature_size
with vllm_runner(model_id,
dtype=dtype,
max_model_len=4096,
enforce_eager=True,
**vlm_config.as_cli_args_dict()) as vllm_model:
vllm_outputs_per_image = [
vllm_model.generate_greedy_logprobs(prompts,
max_tokens,
num_logprobs=num_logprobs,
images=images)
for prompts, images in inputs_per_image
]
with hf_runner(model_id, dtype=dtype, is_vision_model=True) as hf_model:
hf_outputs = hf_model.generate_greedy(HF_IMAGE_PROMPTS,
max_tokens,
images=hf_images)
hf_outputs_per_image = [
hf_model.generate_greedy_logprobs_limit(prompts,
max_tokens,
num_logprobs=num_logprobs,
images=images)
for prompts, images in inputs_per_image
]
vllm_image_prompts = [
p.replace("<image>", "<image>" * vlm_config.image_feature_size)
for p in HF_IMAGE_PROMPTS
]
with vllm_runner(
model_id,
dtype=dtype,
# should be greater than image_feature_size
max_model_len=4096,
enforce_eager=True,
**vlm_config.as_cli_args_dict(),
) as vllm_model:
vllm_outputs = vllm_model.generate_greedy(vllm_image_prompts,
max_tokens,
images=vllm_images)
check_outputs_equal(
hf_outputs,
[
vllm_to_hf_output(vllm_output, vlm_config, model_id)
for vllm_output in vllm_outputs
],
name_0="hf",
name_1="vllm",
)
for hf_outputs, vllm_outputs in zip(hf_outputs_per_image,
vllm_outputs_per_image):
# TODO: Check whether using original CLIPVisionModel can improve
# consistency against HF
check_logprobs_close(
outputs_0_lst=hf_outputs,
outputs_1_lst=[
vllm_to_hf_output(vllm_output, vlm_config, model_id)
for vllm_output in vllm_outputs
],
name_0="hf",
name_1="vllm",
)