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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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123
tests/lora/test_qwen2vl.py
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@@ -37,7 +37,8 @@ class Qwen2VLTester:
"<|im_start|>system\nYou are a helpful assistant.<|im_end|>"
"\n<|im_start|>user\n<|vision_start|><|image_pad|><|vision_end|>"
"What is in the image?<|im_end|>\n"
"<|im_start|>assistant\n")
"<|im_start|>assistant\n"
)
def __init__(self, config: TestConfig):
self.config = config
@@ -56,68 +57,68 @@ class Qwen2VLTester:
max_model_len=self.config.max_model_len,
)
def run_test(self,
images: list[ImageAsset],
expected_outputs: list[str],
lora_id: Optional[int] = None,
temperature: float = 0,
max_tokens: int = 5):
def run_test(
self,
images: list[ImageAsset],
expected_outputs: list[str],
lora_id: Optional[int] = None,
temperature: float = 0,
max_tokens: int = 5,
):
sampling_params = vllm.SamplingParams(
temperature=temperature,
max_tokens=max_tokens,
)
inputs = [{
"prompt": self.PROMPT_TEMPLATE,
"multi_modal_data": {
"image": asset.pil_image
},
} for asset in images]
lora_request = LoRARequest(str(lora_id), lora_id,
self.config.lora_path)
outputs = self.llm.generate(inputs,
sampling_params,
lora_request=lora_request)
generated_texts = [
output.outputs[0].text.strip() for output in outputs
inputs = [
{
"prompt": self.PROMPT_TEMPLATE,
"multi_modal_data": {"image": asset.pil_image},
}
for asset in images
]
lora_request = LoRARequest(str(lora_id), lora_id, self.config.lora_path)
outputs = self.llm.generate(inputs, sampling_params, lora_request=lora_request)
generated_texts = [output.outputs[0].text.strip() for output in outputs]
# Validate outputs
for generated, expected in zip(generated_texts, expected_outputs):
assert expected.startswith(
generated), f"Generated text {generated} doesn't "
assert expected.startswith(generated), (
f"Generated text {generated} doesn't "
)
f"match expected pattern {expected}"
def run_beam_search_test(self,
images: list[ImageAsset],
expected_outputs: list[list[str]],
lora_id: Optional[int] = None,
temperature: float = 0,
beam_width: int = 2,
max_tokens: int = 5):
def run_beam_search_test(
self,
images: list[ImageAsset],
expected_outputs: list[list[str]],
lora_id: Optional[int] = None,
temperature: float = 0,
beam_width: int = 2,
max_tokens: int = 5,
):
beam_search_params = BeamSearchParams(
beam_width=beam_width, max_tokens=max_tokens, temperature=temperature
)
beam_search_params = BeamSearchParams(beam_width=beam_width,
max_tokens=max_tokens,
temperature=temperature)
inputs = [
{
"prompt": self.PROMPT_TEMPLATE,
"multi_modal_data": {"image": asset.pil_image},
}
for asset in images
]
inputs = [{
"prompt": self.PROMPT_TEMPLATE,
"multi_modal_data": {
"image": asset.pil_image
},
} for asset in images]
lora_request = LoRARequest(str(lora_id), lora_id,
self.config.lora_path)
outputs = self.llm.beam_search(inputs,
beam_search_params,
lora_request=lora_request)
lora_request = LoRARequest(str(lora_id), lora_id, self.config.lora_path)
outputs = self.llm.beam_search(
inputs, beam_search_params, lora_request=lora_request
)
for output_obj, expected_outs in zip(outputs, expected_outputs):
output_texts = [seq.text for seq in output_obj.sequences]
assert output_texts == expected_outs, \
f"Generated texts {output_texts} do not match expected {expected_outs}" # noqa: E501
assert output_texts == expected_outs, (
f"Generated texts {output_texts} do not match expected {expected_outs}"
) # noqa: E501
TEST_IMAGES = [
@@ -144,27 +145,25 @@ QWEN25VL_MODEL_PATH = "Qwen/Qwen2.5-VL-3B-Instruct"
@pytest.mark.xfail(
current_platform.is_rocm(),
reason="Qwen2-VL dependency xformers incompatible with ROCm")
reason="Qwen2-VL dependency xformers incompatible with ROCm",
)
def test_qwen2vl_lora(qwen2vl_lora_files):
"""Test Qwen 2.0 VL model with LoRA"""
config = TestConfig(model_path=QWEN2VL_MODEL_PATH,
lora_path=qwen2vl_lora_files)
config = TestConfig(model_path=QWEN2VL_MODEL_PATH, lora_path=qwen2vl_lora_files)
tester = Qwen2VLTester(config)
# Test with different LoRA IDs
for lora_id in [1, 2]:
tester.run_test(TEST_IMAGES,
expected_outputs=EXPECTED_OUTPUTS,
lora_id=lora_id)
tester.run_test(TEST_IMAGES, expected_outputs=EXPECTED_OUTPUTS, lora_id=lora_id)
@pytest.mark.xfail(
current_platform.is_rocm(),
reason="Qwen2-VL dependency xformers incompatible with ROCm")
reason="Qwen2-VL dependency xformers incompatible with ROCm",
)
def test_qwen2vl_lora_beam_search(qwen2vl_lora_files):
"""Test Qwen 2.0 VL model with LoRA through beam search."""
config = TestConfig(model_path=QWEN2VL_MODEL_PATH,
lora_path=qwen2vl_lora_files)
config = TestConfig(model_path=QWEN2VL_MODEL_PATH, lora_path=qwen2vl_lora_files)
tester = Qwen2VLTester(config)
# Test with different LoRA IDs
@@ -176,7 +175,8 @@ def test_qwen2vl_lora_beam_search(qwen2vl_lora_files):
tester.run_beam_search_test(
[ImageAsset("cherry_blossom")],
expected_outputs=EXPECTED_BEAM_SEARCH_OUTPUTS,
lora_id=lora_id)
lora_id=lora_id,
)
@pytest.mark.xfail(
@@ -185,12 +185,9 @@ def test_qwen2vl_lora_beam_search(qwen2vl_lora_files):
)
def test_qwen25vl_lora(qwen25vl_lora_files):
"""Test Qwen 2.5 VL model with LoRA"""
config = TestConfig(model_path=QWEN25VL_MODEL_PATH,
lora_path=qwen25vl_lora_files)
config = TestConfig(model_path=QWEN25VL_MODEL_PATH, lora_path=qwen25vl_lora_files)
tester = Qwen2VLTester(config)
# Test with different LoRA IDs
for lora_id in [1, 2]:
tester.run_test(TEST_IMAGES,
expected_outputs=EXPECTED_OUTPUTS,
lora_id=lora_id)
tester.run_test(TEST_IMAGES, expected_outputs=EXPECTED_OUTPUTS, lora_id=lora_id)