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Convert formatting to use ruff instead of yapf + isort (#26247)

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Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>
This commit is contained in:
Harry Mellor
2025-10-05 15:06:22 +01:00
committed by GitHub
parent 17edd8a807
commit d6953beb91
1508 changed files with 115244 additions and 94146 deletions
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53
tests/models/multimodal/pooling/test_llava_next.py
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@@ -24,9 +24,10 @@ from ...utils import check_embeddings_close
# built with LAPACK support.
pytestmark = pytest.mark.skipif(
not current_platform.is_cuda(),
reason="Llava Next model uses op that is only supported in CUDA")
reason="Llava Next model uses op that is only supported in CUDA",
)
llama3_template = '<|start_header_id|>user<|end_header_id|>\n\n{}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n \n' # noqa: E501
llama3_template = "<|start_header_id|>user<|end_header_id|>\n\n{}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n \n" # noqa: E501
HF_TEXT_PROMPTS = [
# T -> X
@@ -34,18 +35,21 @@ HF_TEXT_PROMPTS = [
"The label of the object is stop sign\nSummary above sentence in one word: " # noqa: E501
),
# T -> X
llama3_template.format(
"cherry blossom\nSummary above sentence in one word: "),
llama3_template.format("cherry blossom\nSummary above sentence in one word: "),
]
HF_IMAGE_PROMPTS = IMAGE_ASSETS.prompts({
# I -> X
"stop_sign":
llama3_template.format("<image>\nSummary above image in one word: "),
# I -> X
"cherry_blossom":
llama3_template.format("<image>\nSummary above image in one word: "),
})
HF_IMAGE_PROMPTS = IMAGE_ASSETS.prompts(
{
# I -> X
"stop_sign": llama3_template.format(
"<image>\nSummary above image in one word: "
),
# I -> X
"cherry_blossom": llama3_template.format(
"<image>\nSummary above image in one word: "
),
}
)
MODELS = ["royokong/e5-v"]
@@ -63,23 +67,22 @@ def _run_test(
# 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).
with vllm_runner(model,
runner="pooling",
dtype=dtype,
max_model_len=4096,
enforce_eager=True) as vllm_model:
with vllm_runner(
model, runner="pooling", dtype=dtype, max_model_len=4096, enforce_eager=True
) as vllm_model:
vllm_outputs = vllm_model.embed(input_texts, images=input_images)
with hf_runner(model, dtype=dtype,
auto_cls=AutoModelForImageTextToText) as hf_model:
with hf_runner(
model, dtype=dtype, auto_cls=AutoModelForImageTextToText
) as hf_model:
# Patch the issue where generation_config.json is missing
hf_model.processor.patch_size = \
hf_model.model.config.vision_config.patch_size
hf_model.processor.patch_size = hf_model.model.config.vision_config.patch_size
# Patch the issue where image_token_id
# exceeds the maximum allowed vocab size
hf_model.model.resize_token_embeddings(
hf_model.model.language_model.vocab_size + 1)
hf_model.model.language_model.vocab_size + 1
)
all_inputs = hf_model.get_inputs(input_texts, images=input_images)
@@ -91,8 +94,7 @@ def _run_test(
return_dict=True,
output_hidden_states=True,
)
pooled_output = F.normalize(outputs.hidden_states[-1][0, -1, :],
dim=-1)
pooled_output = F.normalize(outputs.hidden_states[-1][0, -1, :], dim=-1)
all_outputs.append(pooled_output.tolist())
@@ -142,8 +144,7 @@ def test_models_image(
dtype: str,
) -> None:
input_texts_images = [
(text, asset.pil_image)
for text, asset in zip(HF_IMAGE_PROMPTS, image_assets)
(text, asset.pil_image) for text, asset in zip(HF_IMAGE_PROMPTS, image_assets)
]
input_texts = [text for text, _ in input_texts_images]
input_images = [image for _, image in input_texts_images]