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

[CI/Build] Add Model Tests for Qwen2-VL (#9846)

Browse Source 
Signed-off-by: Alex-Brooks <Alex.Brooks@ibm.com>
Co-authored-by: Cyrus Leung <cyrus.tl.leung@gmail.com>
Co-authored-by: DarkLight1337 <tlleungac@connect.ust.hk>
This commit is contained in:
Alex Brooks
2024-10-31 10:10:52 -06:00
committed by GitHub
parent 5608e611c2
commit 16b8f7a86f
9 changed files with 106 additions and 52 deletions
Download Patch File Download Diff File Expand all files Collapse all files

2
tests/models/decoder_only/vision_language/mm_processor_kwargs/test_qwen2_vl.py
View File

@@ -17,7 +17,7 @@ MAX_PIXELS = "max_pixels"
# Fixtures lazy import to avoid initializing CUDA during test collection
# NOTE: Qwen2vl supports multiple input modalities, so it registers multiple
# NOTE: Qwen2VL supports multiple input modalities, so it registers multiple
# input mappers.
@pytest.fixture()
def image_input_mapper_for_qwen2_vl():

101
tests/models/decoder_only/vision_language/test_models.py
View File

@@ -75,6 +75,63 @@ COMMON_BROADCAST_SETTINGS = {
# this is a good idea for checking your command first, since tests are slow.
VLM_TEST_SETTINGS = {
#### Core tests to always run in the CI
"llava": VLMTestInfo(
models=["llava-hf/llava-1.5-7b-hf"],
test_type=(
VLMTestType.EMBEDDING,
VLMTestType.IMAGE,
VLMTestType.CUSTOM_INPUTS
),
prompt_formatter=lambda img_prompt: f"USER: {img_prompt}\nASSISTANT:",
convert_assets_to_embeddings=model_utils.get_llava_embeddings,
max_model_len=4096,
auto_cls=AutoModelForVision2Seq,
vllm_output_post_proc=model_utils.llava_image_vllm_to_hf_output,
custom_test_opts=[CustomTestOptions(
inputs=custom_inputs.multi_image_multi_aspect_ratio_inputs(
formatter=lambda img_prompt: f"USER: {img_prompt}\nASSISTANT:"
),
limit_mm_per_prompt={"image": 4},
)],
marks=[pytest.mark.core_model],
),
"paligemma": VLMTestInfo(
models=["google/paligemma-3b-mix-224"],
test_type=VLMTestType.IMAGE,
prompt_formatter=identity,
img_idx_to_prompt = lambda idx: "",
# Paligemma uses its own sample prompts because the default one fails
single_image_prompts=IMAGE_ASSETS.prompts({
"stop_sign": "caption es",
"cherry_blossom": "What is in the picture?",
}),
auto_cls=AutoModelForVision2Seq,
postprocess_inputs=model_utils.get_key_type_post_processor(
"pixel_values"
),
vllm_output_post_proc=model_utils.paligemma_vllm_to_hf_output,
dtype="half" if current_platform.is_rocm() else ("half", "float"),
marks=[pytest.mark.core_model],
),
"qwen2_vl": VLMTestInfo(
models=["Qwen/Qwen2-VL-2B-Instruct"],
test_type=(
VLMTestType.IMAGE,
VLMTestType.MULTI_IMAGE,
VLMTestType.VIDEO
),
prompt_formatter=lambda img_prompt: f"<|im_start|>User\n{img_prompt}<|im_end|>\n<|im_start|>assistant\n", # noqa: E501
img_idx_to_prompt=lambda idx: "<|vision_start|><|image_pad|><|vision_end|>", # noqa: E501
video_idx_to_prompt=lambda idx: "<|vision_start|><|video_pad|><|vision_end|>", # noqa: E501
max_model_len=4096,
max_num_seqs=2,
auto_cls=AutoModelForVision2Seq,
vllm_output_post_proc=model_utils.qwen2_vllm_to_hf_output,
marks=[pytest.mark.core_model],
image_size_factors=[(), (0.25,), (0.25, 0.25, 0.25), (0.25, 0.2, 0.15)],
),
#### Extended model tests
"blip2": VLMTestInfo(
models=["Salesforce/blip2-opt-2.7b"],
test_type=VLMTestType.IMAGE,
@@ -151,25 +208,6 @@ VLM_TEST_SETTINGS = {
use_tokenizer_eos=True,
patch_hf_runner=model_utils.internvl_patch_hf_runner,
),
"llava": VLMTestInfo(
models=["llava-hf/llava-1.5-7b-hf"],
test_type=(
VLMTestType.EMBEDDING,
VLMTestType.IMAGE,
VLMTestType.CUSTOM_INPUTS
),
prompt_formatter=lambda img_prompt: f"USER: {img_prompt}\nASSISTANT:",
convert_assets_to_embeddings=model_utils.get_llava_embeddings,
max_model_len=4096,
auto_cls=AutoModelForVision2Seq,
vllm_output_post_proc=model_utils.llava_image_vllm_to_hf_output,
custom_test_opts=[CustomTestOptions(
inputs=custom_inputs.multi_image_multi_aspect_ratio_inputs(
formatter=lambda img_prompt: f"USER: {img_prompt}\nASSISTANT:"
),
limit_mm_per_prompt={"image": 4},
)],
),
"llava_next": VLMTestInfo(
models=["llava-hf/llava-v1.6-mistral-7b-hf"],
test_type=(VLMTestType.IMAGE, VLMTestType.CUSTOM_INPUTS),
@@ -200,12 +238,12 @@ VLM_TEST_SETTINGS = {
vllm_output_post_proc=model_utils.llava_onevision_vllm_to_hf_output,
# Llava-one-vision tests fixed sizes & the default size factors
image_sizes=[((1669, 2560), (2560, 1669), (183, 488), (488, 183))],
runner_mm_key="videos",
custom_test_opts=[CustomTestOptions(
inputs=custom_inputs.multi_video_multi_aspect_ratio_inputs(
formatter=lambda vid_prompt: f"<|im_start|>user\n{vid_prompt}<|im_end|>\n<|im_start|>assistant\n", # noqa: E501
),
limit_mm_per_prompt={"video": 4},
runner_mm_key="videos",
)],
),
# FIXME
@@ -218,9 +256,11 @@ VLM_TEST_SETTINGS = {
auto_cls=AutoModelForVision2Seq,
vllm_output_post_proc=model_utils.llava_video_vllm_to_hf_output,
image_sizes=[((1669, 2560), (2560, 1669), (183, 488), (488, 183))],
runner_mm_key="videos",
marks=[
pytest.mark.skip(reason="LLava next video tests currently fail.")
pytest.mark.skipif(
transformers.__version__.startswith("4.46"),
reason="Model broken with changes in transformers 4.46"
)
],
),
"minicpmv": VLMTestInfo(
@@ -234,23 +274,6 @@ VLM_TEST_SETTINGS = {
postprocess_inputs=model_utils.wrap_inputs_post_processor,
hf_output_post_proc=model_utils.minicmpv_trunc_hf_output,
),
"paligemma": VLMTestInfo(
models=["google/paligemma-3b-mix-224"],
test_type=VLMTestType.IMAGE,
prompt_formatter=identity,
img_idx_to_prompt = lambda idx: "",
# Paligemma uses its own sample prompts because the default one fails
single_image_prompts=IMAGE_ASSETS.prompts({
"stop_sign": "caption es",
"cherry_blossom": "What is in the picture?",
}),
auto_cls=AutoModelForVision2Seq,
postprocess_inputs=model_utils.get_key_type_post_processor(
"pixel_values"
),
vllm_output_post_proc=model_utils.paligemma_vllm_to_hf_output,
dtype="half" if current_platform.is_rocm() else ("half", "float"),
),
# Tests for phi3v currently live in another file because of a bug in
# transformers. Once this issue is fixed, we can enable them here instead.
# https://github.com/huggingface/transformers/issues/34307

11
tests/models/decoder_only/vision_language/vlm_utils/model_utils.py
View File

@@ -56,6 +56,17 @@ def qwen_vllm_to_hf_output(
return output_ids, hf_output_str, out_logprobs
def qwen2_vllm_to_hf_output(
vllm_output: RunnerOutput,
model: str) -> Tuple[List[int], str, Optional[SampleLogprobs]]:
"""Sanitize vllm output [qwen2 models] to be comparable with hf output."""
output_ids, output_str, out_logprobs = vllm_output
hf_output_str = output_str + "<|im_end|>"
return output_ids, hf_output_str, out_logprobs
def llava_image_vllm_to_hf_output(vllm_output: RunnerOutput,
model: str) -> RunnerOutput:
config = AutoConfig.from_pretrained(model)

11
tests/models/decoder_only/vision_language/vlm_utils/runners.py
View File

@@ -29,6 +29,7 @@ def run_single_image_test(*, tmp_path: PosixPath, model_test_info: VLMTestInfo,
num_logprobs=test_case.num_logprobs,
limit_mm_per_prompt={"image": 1},
distributed_executor_backend=test_case.distributed_executor_backend,
runner_mm_key="images",
**model_test_info.get_non_parametrized_runner_kwargs())
@@ -51,6 +52,7 @@ def run_multi_image_test(*, tmp_path: PosixPath, model_test_info: VLMTestInfo,
num_logprobs=test_case.num_logprobs,
limit_mm_per_prompt={"image": len(image_assets)},
distributed_executor_backend=test_case.distributed_executor_backend,
runner_mm_key="images",
**model_test_info.get_non_parametrized_runner_kwargs())
@@ -74,6 +76,7 @@ def run_embedding_test(*, model_test_info: VLMTestInfo,
limit_mm_per_prompt={"image": 1},
vllm_embeddings=vllm_embeddings,
distributed_executor_backend=test_case.distributed_executor_backend,
runner_mm_key="images",
**model_test_info.get_non_parametrized_runner_kwargs())
@@ -101,6 +104,7 @@ def run_video_test(
num_logprobs=test_case.num_logprobs,
limit_mm_per_prompt={"video": len(video_assets)},
distributed_executor_backend=test_case.distributed_executor_backend,
runner_mm_key="videos",
**model_test_info.get_non_parametrized_runner_kwargs())
@@ -115,7 +119,11 @@ def run_custom_inputs_test(*, model_test_info: VLMTestInfo,
inputs = test_case.custom_test_opts.inputs
limit_mm_per_prompt = test_case.custom_test_opts.limit_mm_per_prompt
assert inputs is not None and limit_mm_per_prompt is not None
runner_mm_key = test_case.custom_test_opts.runner_mm_key
# Inputs, limit_mm_per_prompt, and runner_mm_key should all be set
assert inputs is not None
assert limit_mm_per_prompt is not None
assert runner_mm_key is not None
core.run_test(
hf_runner=hf_runner,
@@ -127,4 +135,5 @@ def run_custom_inputs_test(*, model_test_info: VLMTestInfo,
num_logprobs=test_case.num_logprobs,
limit_mm_per_prompt=limit_mm_per_prompt,
distributed_executor_backend=test_case.distributed_executor_backend,
runner_mm_key=runner_mm_key,
**model_test_info.get_non_parametrized_runner_kwargs())

6
tests/models/decoder_only/vision_language/vlm_utils/types.py
View File

@@ -52,6 +52,8 @@ class SizeType(Enum):
class CustomTestOptions(NamedTuple):
inputs: List[Tuple[List[str], List[Union[List[Image], Image]]]]
limit_mm_per_prompt: Dict[str, int]
# kwarg to pass multimodal data in as to vllm/hf runner instances.
runner_mm_key: str = "images"
class ImageSizeWrapper(NamedTuple):
@@ -141,9 +143,6 @@ class VLMTestInfo(NamedTuple):
Callable[[PosixPath, str, Union[List[ImageAsset], _ImageAssets]],
str]] = None # noqa: E501
# kwarg to pass multimodal data in as to vllm/hf runner instances
runner_mm_key: str = "images"
# Allows configuring a test to run with custom inputs
custom_test_opts: Optional[List[CustomTestOptions]] = None
@@ -168,7 +167,6 @@ class VLMTestInfo(NamedTuple):
"get_stop_token_ids": self.get_stop_token_ids,
"model_kwargs": self.model_kwargs,
"patch_hf_runner": self.patch_hf_runner,
"runner_mm_key": self.runner_mm_key,
}