diff --git a/.buildkite/test-pipeline.yaml b/.buildkite/test-pipeline.yaml index 21cbd9ba1378..4edd1cadfb2f 100644 --- a/.buildkite/test-pipeline.yaml +++ b/.buildkite/test-pipeline.yaml @@ -62,7 +62,6 @@ steps: mirror_hardwares: [amd] commands: - - pytest -v -s test_inputs.py - pytest -v -s entrypoints -m llm - pytest -v -s entrypoints -m openai @@ -79,6 +78,13 @@ steps: - python3 llava_example.py - python3 tensorize_vllm_model.py --model facebook/opt-125m serialize --serialized-directory /tmp/ --suffix v1 && python3 tensorize_vllm_model.py --model facebook/opt-125m deserialize --path-to-tensors /tmp/vllm/facebook/opt-125m/v1/model.tensors +- label: Inputs Test + #mirror_hardwares: [amd] + commands: + - bash ../.buildkite/download-images.sh + - pytest -v -s test_inputs.py + - pytest -v -s multimodal + - label: Kernels Test %N #mirror_hardwares: [amd] command: pytest -v -s kernels --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT diff --git a/tests/multimodal/test_processor.py b/tests/multimodal/test_processor.py index 4aeae633d07f..3df28e782dd8 100644 --- a/tests/multimodal/test_processor.py +++ b/tests/multimodal/test_processor.py @@ -6,8 +6,10 @@ from vllm.multimodal import MULTIMODAL_REGISTRY from vllm.multimodal.image import ImagePixelData +from ..conftest import _STR_DTYPE_TO_TORCH_DTYPE -@pytest.mark.parametrize("dtype", ["half", "bfloat16", "float"]) + +@pytest.mark.parametrize("dtype", ["half", "float"]) def test_clip_image_processor(hf_images, dtype): MODEL_NAME = "llava-hf/llava-1.5-7b-hf" IMAGE_HEIGHT = IMAGE_WIDTH = 33 @@ -36,8 +38,8 @@ def test_clip_image_processor(hf_images, dtype): for image in hf_images: hf_result = hf_processor.preprocess( image, - return_tensors="np", - ) + return_tensors="pt", + ).to(dtype=_STR_DTYPE_TO_TORCH_DTYPE[dtype]) vllm_result = MULTIMODAL_REGISTRY.process_input( ImagePixelData(image), model_config=model_config, @@ -45,7 +47,8 @@ def test_clip_image_processor(hf_images, dtype): ) assert hf_result.keys() == vllm_result.keys() - for key, hf_arr in hf_result.items(): + for key, hf_tensor in hf_result.items(): + hf_arr: np.ndarray = hf_tensor.numpy() vllm_arr: np.ndarray = vllm_result[key].numpy() assert hf_arr.shape == vllm_arr.shape, f"Failed for key={key}"