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[Model] Multi-input support for LLaVA (#8238)

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This commit is contained in:
Cyrus Leung
2024-09-07 10:57:24 +08:00
committed by GitHub
parent 41e95c5247
commit 2f707fcb35
10 changed files with 176 additions and 45 deletions
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141
tests/models/test_llava.py
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@@ -1,4 +1,4 @@
from typing import List, Optional, Tuple, Type
from typing import List, Optional, Tuple, Type, overload
import pytest
from transformers import (AutoConfig, AutoModelForVision2Seq, AutoTokenizer,
@@ -8,11 +8,14 @@ from vllm.multimodal.utils import rescale_image_size
from vllm.sequence import SampleLogprobs
from vllm.utils import STR_DTYPE_TO_TORCH_DTYPE
from ..conftest import IMAGE_ASSETS, HfRunner, VllmRunner, _ImageAssets
from ..conftest import (IMAGE_ASSETS, HfRunner, PromptImageInput, VllmRunner,
_ImageAssets)
from .utils import check_logprobs_close
pytestmark = pytest.mark.vlm
_LIMIT_IMAGE_PER_PROMPT = 4
HF_IMAGE_PROMPTS = IMAGE_ASSETS.prompts({
"stop_sign":
"USER: <image>\nWhat's the content of the image?\nASSISTANT:",
@@ -52,6 +55,7 @@ def vllm_to_hf_output(vllm_output: Tuple[List[int], str,
return hf_output_ids, hf_output_str, out_logprobs
@overload
def run_test(
hf_runner: Type[HfRunner],
vllm_runner: Type[VllmRunner],
@@ -64,6 +68,78 @@ def run_test(
num_logprobs: int,
tensor_parallel_size: int,
distributed_executor_backend: Optional[str] = None,
):
...
@overload
def run_test(
hf_runner: Type[HfRunner],
vllm_runner: Type[VllmRunner],
image_assets: _ImageAssets,
model: str,
*,
sizes: List[Tuple[int, int]],
dtype: str,
max_tokens: int,
num_logprobs: int,
tensor_parallel_size: int,
distributed_executor_backend: Optional[str] = None,
):
...
def run_test(
hf_runner: Type[HfRunner],
vllm_runner: Type[VllmRunner],
image_assets: _ImageAssets,
model: str,
*,
size_factors: Optional[List[float]] = None,
sizes: Optional[List[Tuple[int, int]]] = None,
dtype: str,
max_tokens: int,
num_logprobs: int,
tensor_parallel_size: int,
distributed_executor_backend: Optional[str] = None,
):
images = [asset.pil_image for asset in image_assets]
if size_factors is not None:
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)]
elif sizes is not None:
inputs_per_image = [(
[prompt for _ in sizes],
[image.resize(size) for size in sizes],
) for image, prompt in zip(images, HF_IMAGE_PROMPTS)]
else:
raise ValueError("You must provide either `size_factors` or `sizes`")
_run_test(hf_runner,
vllm_runner,
inputs_per_image,
model,
dtype=dtype,
max_tokens=max_tokens,
num_logprobs=num_logprobs,
tensor_parallel_size=tensor_parallel_size,
distributed_executor_backend=distributed_executor_backend)
def _run_test(
hf_runner: Type[HfRunner],
vllm_runner: Type[VllmRunner],
inputs: List[Tuple[List[str], PromptImageInput]],
model: str,
*,
dtype: str,
max_tokens: int,
num_logprobs: int,
tensor_parallel_size: int,
distributed_executor_backend: Optional[str] = None,
):
"""Inference result should be the same between hf and vllm.
@@ -85,13 +161,6 @@ def run_test(
else:
mantis_processor = None
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
@@ -100,15 +169,18 @@ def run_test(
# max_model_len should be greater than image_feature_size
with vllm_runner(model,
dtype=dtype,
max_model_len=4096,
tensor_parallel_size=tensor_parallel_size,
distributed_executor_backend=distributed_executor_backend,
enforce_eager=True) as vllm_model:
enforce_eager=True,
limit_mm_per_prompt={"image": _LIMIT_IMAGE_PER_PROMPT
}) 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
for prompts, images in inputs
]
if mantis_processor is not None:
@@ -131,7 +203,7 @@ def run_test(
max_tokens,
num_logprobs=num_logprobs,
images=images)
for prompts, images in inputs_per_image
for prompts, images in inputs
]
for hf_outputs, vllm_outputs in zip(hf_outputs_per_image,
@@ -181,6 +253,51 @@ def test_models(hf_runner, vllm_runner, image_assets, model, size_factors,
)
@pytest.mark.parametrize("model", models)
@pytest.mark.parametrize("dtype", ["half"])
@pytest.mark.parametrize("max_tokens", [128])
@pytest.mark.parametrize("num_logprobs", [5])
def test_models_multiple_image_inputs(hf_runner, vllm_runner, image_assets,
model, dtype, max_tokens,
num_logprobs) -> None:
stop_sign = image_assets[0].pil_image
cherry_blossom = image_assets[1].pil_image
inputs = [(
[
"USER: <image><image>\nDescribe 2 images.\nASSISTANT:",
"USER: <image><image>\nDescribe 2 images.\nASSISTANT:",
"USER: <image><image><image><image>\nDescribe 4 images.\nASSISTANT:", # noqa: E501
"USER: <image>\nWhat is the season?\nASSISTANT:",
],
[
[stop_sign, cherry_blossom],
# Images with different sizes and aspect-ratios
[
rescale_image_size(stop_sign, 0.1),
stop_sign,
],
[
stop_sign,
rescale_image_size(stop_sign, 0.25),
cherry_blossom.resize((183, 488)),
cherry_blossom.resize((488, 183))
],
cherry_blossom,
])]
_run_test(
hf_runner,
vllm_runner,
inputs,
model,
dtype=dtype,
max_tokens=max_tokens,
num_logprobs=num_logprobs,
tensor_parallel_size=1,
)
@pytest.mark.parametrize("model", models)
def test_context_length_too_short(vllm_runner, image_assets, model):
images = [asset.pil_image for asset in image_assets]