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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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92
tests/models/multimodal/generation/test_qwen2_5_vl.py
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@@ -17,14 +17,15 @@ def qwen2_5_vl_chat_template(*query):
return f"<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\n{''.join(query)}<|im_end|><|im_start|>assistant\n" # noqa: E501
VIDEO_PROMPTS = VIDEO_ASSETS.prompts({
"baby_reading":
qwen2_5_vl_chat_template(
VIDEO_PLACEHOLDER,
"Describe this video with a short sentence ",
"(no more than 20 words)",
),
})
VIDEO_PROMPTS = VIDEO_ASSETS.prompts(
{
"baby_reading": qwen2_5_vl_chat_template(
VIDEO_PLACEHOLDER,
"Describe this video with a short sentence ",
"(no more than 20 words)",
),
}
)
@pytest.mark.core_model
@@ -33,10 +34,15 @@ VIDEO_PROMPTS = VIDEO_ASSETS.prompts({
@pytest.mark.parametrize("num_frames", [16])
@pytest.mark.parametrize("dtype", [target_dtype])
@pytest.mark.parametrize("max_tokens", [128])
def test_qwen2_5_vl_evs_functionality(vllm_runner, video_assets, model,
video_pruning_rate: float,
num_frames: int, dtype: str,
max_tokens: int) -> None:
def test_qwen2_5_vl_evs_functionality(
vllm_runner,
video_assets,
model,
video_pruning_rate: float,
num_frames: int,
dtype: str,
max_tokens: int,
) -> None:
"""Test EVS (Efficient Video Sampling) functionality with different
pruning rates.
"""
@@ -51,19 +57,18 @@ def test_qwen2_5_vl_evs_functionality(vllm_runner, video_assets, model,
videos = [sampled_vids[0]]
# Initialize model with EVS configuration
with vllm_runner(model,
runner="generate",
max_model_len=4000,
max_num_seqs=1,
dtype=dtype,
limit_mm_per_prompt={"video": 1},
tensor_parallel_size=1,
video_pruning_rate=video_pruning_rate) as vllm_model:
with vllm_runner(
model,
runner="generate",
max_model_len=4000,
max_num_seqs=1,
dtype=dtype,
limit_mm_per_prompt={"video": 1},
tensor_parallel_size=1,
video_pruning_rate=video_pruning_rate,
) as vllm_model:
# Generate output - this should not crash
outputs = vllm_model.generate_greedy(prompts,
max_tokens,
videos=videos)
outputs = vllm_model.generate_greedy(prompts, max_tokens, videos=videos)
# Basic validation that we got a response
assert len(outputs) == 1
@@ -83,10 +88,15 @@ def test_qwen2_5_vl_evs_functionality(vllm_runner, video_assets, model,
@pytest.mark.parametrize("num_frames", [16])
@pytest.mark.parametrize("dtype", [target_dtype])
@pytest.mark.parametrize("max_tokens", [128])
def test_qwen2_5_vl_evs_batched_videos(vllm_runner, video_assets, model,
video_pruning_rate: float,
num_frames: int, dtype: str,
max_tokens: int) -> None:
def test_qwen2_5_vl_evs_batched_videos(
vllm_runner,
video_assets,
model,
video_pruning_rate: float,
num_frames: int,
dtype: str,
max_tokens: int,
) -> None:
"""Test EVS functionality with batched videos.
This test validates that:
@@ -102,23 +112,21 @@ def test_qwen2_5_vl_evs_batched_videos(vllm_runner, video_assets, model,
# Test batched videos
prompts = [VIDEO_PROMPTS[0], VIDEO_PROMPTS[0]]
videos = [sampled_vids[0],
sampled_vids[0]] # Use same video twice for testing
videos = [sampled_vids[0], sampled_vids[0]] # Use same video twice for testing
# Initialize model with EVS configuration
with vllm_runner(model,
runner="generate",
max_model_len=4000,
max_num_seqs=2,
dtype=dtype,
limit_mm_per_prompt={"video": 2},
tensor_parallel_size=1,
video_pruning_rate=video_pruning_rate) as vllm_model:
with vllm_runner(
model,
runner="generate",
max_model_len=4000,
max_num_seqs=2,
dtype=dtype,
limit_mm_per_prompt={"video": 2},
tensor_parallel_size=1,
video_pruning_rate=video_pruning_rate,
) as vllm_model:
# Generate output - this should not crash
outputs = vllm_model.generate_greedy(prompts,
max_tokens,
videos=videos)
outputs = vllm_model.generate_greedy(prompts, max_tokens, videos=videos)
# Basic validation that we got responses for both videos
assert len(outputs) == 2