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

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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
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104
tests/models/test_llava.py
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@@ -4,18 +4,21 @@ 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, HfRunner, VllmRunner, _ImageAssets
from .utils import check_outputs_equal
from .utils import check_logprobs_close
pytestmark = pytest.mark.vlm
# The image token is placed before "user" on purpose so that the test can pass
HF_IMAGE_PROMPTS = IMAGE_ASSETS.prompts({
"stop_sign":
"<image>\nUSER: What's the content of the image?\nASSISTANT:",
"USER: <image>\nWhat's the content of the image?\nASSISTANT:",
"cherry_blossom":
"<image>\nUSER: What is the season?\nASSISTANT:",
"USER: <image>\nWhat is the season?\nASSISTANT:",
"boardwalk":
"USER: <image>\nWhat's in this image?\nASSISTANT:",
})
@@ -37,27 +40,34 @@ 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, "")
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
return hf_output_ids, hf_output_str, out_logprobs
def run_test(
@@ -66,8 +76,10 @@ def run_test(
image_assets: _ImageAssets,
model_and_config: Tuple[str, VisionLanguageConfig],
*,
size_factors: List[float],
dtype: str,
max_tokens: int,
num_logprobs: int,
tensor_parallel_size: int,
distributed_executor_backend: Optional[str] = None,
):
@@ -81,61 +93,85 @@ def run_test(
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]
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)]
# NOTE: take care of the order. run vLLM first, and then run HF.
# vLLM needs a fresh new process without cuda initialization.
# if we run HF first, the cuda initialization will be done and it
# will hurt multiprocessing backend with fork method (the default method).
# max_model_len should be greater than image_feature_size
with vllm_runner(model_id,
dtype=dtype,
tensor_parallel_size=tensor_parallel_size,
distributed_executor_backend=distributed_executor_backend,
enforce_eager=True,
**vlm_config.as_cli_args_dict()) as vllm_model:
# NOTE: `asset.for_vllm` will call `torch.cuda.device_count()`
# we must put it inside the vllm_runner context manager
# i.e. after creating vLLM instance.
vllm_images = [asset.for_vllm() for asset in image_assets]
vllm_image_prompts = [
p.replace("<image>", "<image>" * vlm_config.image_feature_size)
for p in HF_IMAGE_PROMPTS
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
]
vllm_outputs = vllm_model.generate_greedy(vllm_image_prompts,
max_tokens,
images=vllm_images)
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
]
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",
)
@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:
run_test(
hf_runner,
vllm_runner,
image_assets,
model_and_config,
size_factors=size_factors,
dtype=dtype,
max_tokens=max_tokens,
num_logprobs=num_logprobs,
tensor_parallel_size=1,
)