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[Misc] Clean up input processing (#17582)

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Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>
This commit is contained in:
Cyrus Leung
2025-05-02 23:11:53 +08:00
committed by GitHub
parent 3a500cd0b6
commit cb234955df
9 changed files with 359 additions and 285 deletions
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14
tests/models/multimodal/pooling/test_intern_vit.py
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@@ -6,6 +6,7 @@ from huggingface_hub import snapshot_download
from transformers import AutoConfig, AutoModel, CLIPImageProcessor
from vllm.distributed import cleanup_dist_env_and_memory
from vllm.utils import STR_DTYPE_TO_TORCH_DTYPE
from ....conftest import ImageTestAssets
@@ -14,6 +15,7 @@ from ....conftest import ImageTestAssets
DOWNLOAD_PATTERN = ["*.json", "*.py", "*.safetensors", "*.txt", "*.model"]
@torch.inference_mode()
def run_intern_vit_test(
image_assets: ImageTestAssets,
model_id: str,
@@ -21,11 +23,12 @@ def run_intern_vit_test(
dtype: str,
):
model = snapshot_download(model_id, allow_patterns=DOWNLOAD_PATTERN)
torch_dtype = STR_DTYPE_TO_TORCH_DTYPE[dtype]
img_processor = CLIPImageProcessor.from_pretrained(model)
images = [asset.pil_image for asset in image_assets]
pixel_values = [
img_processor(images, return_tensors='pt').pixel_values.to(dtype)
img_processor(images, return_tensors='pt').pixel_values.to(torch_dtype)
for images in images
]
@@ -34,7 +37,7 @@ def run_intern_vit_test(
config.norm_type = "rms_norm"
hf_model = AutoModel.from_pretrained(model,
torch_dtype=dtype,
torch_dtype=torch_dtype,
trust_remote_code=True).to("cuda")
hf_outputs_per_image = [
hf_model(pixel_value.to("cuda")).last_hidden_state
@@ -48,7 +51,7 @@ def run_intern_vit_test(
del hf_model
cleanup_dist_env_and_memory()
vllm_model = vllm_model.to("cuda", dtype)
vllm_model = vllm_model.to("cuda", torch_dtype)
vllm_outputs_per_image = [
vllm_model(pixel_values=pixel_value.to("cuda"))
for pixel_value in pixel_values
@@ -66,9 +69,8 @@ def run_intern_vit_test(
"OpenGVLab/InternViT-300M-448px",
"OpenGVLab/InternViT-6B-448px-V1-5",
])
@pytest.mark.parametrize("dtype", [torch.half])
@torch.inference_mode()
def test_models(image_assets, model_id, dtype: str) -> None:
@pytest.mark.parametrize("dtype", ["half"])
def test_models(dist_init, image_assets, model_id, dtype: str) -> None:
run_intern_vit_test(
image_assets,
model_id,