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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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165
tests/v1/e2e/test_spec_decode.py
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@@ -48,19 +48,17 @@ def get_test_prompts(mm_enabled: bool):
give no other output than that simple sentence without quotes.
"""
elif kind == "mm":
placeholders = [{
"type": "image_url",
"image_url": {
"url":
f"{VLLM_S3_BUCKET_URL}/{VLM_IMAGES_DIR}/stop_sign.jpg"
},
}]
placeholders = [
{
"type": "image_url",
"image_url": {
"url": f"{VLLM_S3_BUCKET_URL}/{VLM_IMAGES_DIR}/stop_sign.jpg"
},
}
]
prompt = [
*placeholders,
{
"type": "text",
"text": "The meaning of the image is"
},
{"type": "text", "text": "The meaning of the image is"},
]
else:
raise ValueError(f"Unknown prompt type: {kind}")
@@ -84,10 +82,10 @@ def test_ngram_correctness(
sampling_config: SamplingParams,
model_name: str,
):
'''
"""
Compare the outputs of an original LLM and a speculative LLM
should be the same when using ngram speculative decoding.
'''
"""
test_prompts = get_test_prompts(mm_enabled=False)
ref_llm = LLM(model=model_name, max_model_len=1024)
@@ -129,32 +127,77 @@ def test_ngram_correctness(
["model_setup", "mm_enabled"],
[
(("eagle3", "Qwen/Qwen3-8B", "AngelSlim/Qwen3-8B_eagle3", 1), False),
pytest.param(("eagle3", "Qwen/Qwen2.5-VL-7B-Instruct",
"Rayzl/qwen2.5-vl-7b-eagle3-sgl", 1),
False,
marks=pytest.mark.skip(reason="Skipping due to its " \
"head_dim not being a a multiple of 32")),
(("eagle", "meta-llama/Llama-3.1-8B-Instruct",
"yuhuili/EAGLE-LLaMA3.1-Instruct-8B", 1), False),
(("eagle3", "meta-llama/Llama-3.1-8B-Instruct",
"yuhuili/EAGLE3-LLaMA3.1-Instruct-8B", 1), False),
pytest.param(("eagle", "meta-llama/Llama-4-Scout-17B-16E-Instruct",
"morgendave/EAGLE-Llama-4-Scout-17B-16E-Instruct", 4),
False,
marks=large_gpu_mark(min_gb=80)), # works on 4x H100
pytest.param(("eagle", "meta-llama/Llama-4-Scout-17B-16E-Instruct",
"morgendave/EAGLE-Llama-4-Scout-17B-16E-Instruct", 4),
True,
marks=large_gpu_mark(min_gb=80)), # works on 4x H100
(("eagle", "eagle618/deepseek-v3-random",
"eagle618/eagle-deepseek-v3-random", 1), False),
pytest.param(
(
"eagle3",
"Qwen/Qwen2.5-VL-7B-Instruct",
"Rayzl/qwen2.5-vl-7b-eagle3-sgl",
1,
),
False,
marks=pytest.mark.skip(
reason="Skipping due to its head_dim not being a a multiple of 32"
),
),
(
(
"eagle",
"meta-llama/Llama-3.1-8B-Instruct",
"yuhuili/EAGLE-LLaMA3.1-Instruct-8B",
1,
),
False,
),
(
(
"eagle3",
"meta-llama/Llama-3.1-8B-Instruct",
"yuhuili/EAGLE3-LLaMA3.1-Instruct-8B",
1,
),
False,
),
pytest.param(
(
"eagle",
"meta-llama/Llama-4-Scout-17B-16E-Instruct",
"morgendave/EAGLE-Llama-4-Scout-17B-16E-Instruct",
4,
),
False,
marks=large_gpu_mark(min_gb=80),
), # works on 4x H100
pytest.param(
(
"eagle",
"meta-llama/Llama-4-Scout-17B-16E-Instruct",
"morgendave/EAGLE-Llama-4-Scout-17B-16E-Instruct",
4,
),
True,
marks=large_gpu_mark(min_gb=80),
), # works on 4x H100
(
(
"eagle",
"eagle618/deepseek-v3-random",
"eagle618/eagle-deepseek-v3-random",
1,
),
False,
),
],
ids=[
"qwen3_eagle3", "qwen2_5_vl_eagle3", "llama3_eagle", "llama3_eagle3",
"llama4_eagle", "llama4_eagle_mm", "deepseek_eagle"
])
@pytest.mark.parametrize("attn_backend",
get_attn_backend_list_based_on_platform())
"qwen3_eagle3",
"qwen2_5_vl_eagle3",
"llama3_eagle",
"llama3_eagle3",
"llama4_eagle",
"llama4_eagle_mm",
"deepseek_eagle",
],
)
@pytest.mark.parametrize("attn_backend", get_attn_backend_list_based_on_platform())
def test_eagle_correctness(
monkeypatch: pytest.MonkeyPatch,
sampling_config: SamplingParams,
@@ -166,15 +209,16 @@ def test_eagle_correctness(
# TODO: Fix this flaky test
pytest.skip(
"TREE_ATTN is flaky in the test disable for now until it can be "
"resolved (see https://github.com/vllm-project/vllm/issues/22922)")
"resolved (see https://github.com/vllm-project/vllm/issues/22922)"
)
# Generate test prompts inside the function instead of using fixture
test_prompts = get_test_prompts(mm_enabled)
'''
"""
Compare the outputs of a original LLM and a speculative LLM
should be the same when using eagle speculative decoding.
model_setup: (method, model_name, eagle_model_name, tp_size)
'''
"""
with monkeypatch.context() as m:
if "Llama-4-Scout" in model_setup[1] and attn_backend == "FLASH_ATTN":
# Scout requires default backend selection
@@ -185,18 +229,20 @@ def test_eagle_correctness(
m.setenv("VLLM_MLA_DISABLE", "1")
m.setenv("VLLM_ATTENTION_BACKEND", attn_backend)
if (attn_backend == "TRITON_ATTN" and not current_platform.is_rocm()):
pytest.skip("TRITON_ATTN does not support "
"multi-token eagle spec decode on current platform")
if attn_backend == "TRITON_ATTN" and not current_platform.is_rocm():
pytest.skip(
"TRITON_ATTN does not support "
"multi-token eagle spec decode on current platform"
)
if attn_backend == "FLASH_ATTN" and current_platform.is_rocm():
m.setenv("VLLM_ROCM_USE_AITER", "1")
method, model_name, spec_model_name, tp_size = model_setup
ref_llm = LLM(model=model_name,
max_model_len=2048,
tensor_parallel_size=tp_size)
ref_llm = LLM(
model=model_name, max_model_len=2048, tensor_parallel_size=tp_size
)
ref_outputs = ref_llm.chat(test_prompts, sampling_config)
del ref_llm
torch.cuda.empty_cache()
@@ -233,11 +279,14 @@ def test_eagle_correctness(
cleanup_dist_env_and_memory()
@pytest.mark.parametrize(["model_setup", "mm_enabled"], [
(("mtp", "XiaomiMiMo/MiMo-7B-Base", 1), False),
(("mtp", "ZixiQi/DeepSeek-V3-4layers-MTP-FP8", 1), False),
],
ids=["mimo", "deepseek"])
@pytest.mark.parametrize(
["model_setup", "mm_enabled"],
[
(("mtp", "XiaomiMiMo/MiMo-7B-Base", 1), False),
(("mtp", "ZixiQi/DeepSeek-V3-4layers-MTP-FP8", 1), False),
],
ids=["mimo", "deepseek"],
)
def test_mtp_correctness(
monkeypatch: pytest.MonkeyPatch,
sampling_config: SamplingParams,
@@ -246,21 +295,23 @@ def test_mtp_correctness(
):
# Generate test prompts inside the function instead of using fixture
test_prompts = get_test_prompts(mm_enabled)
'''
"""
Compare the outputs of a original LLM and a speculative LLM
should be the same when using MTP speculative decoding.
model_setup: (method, model_name, tp_size)
'''
"""
with monkeypatch.context() as m:
m.setenv("VLLM_USE_V1", "1")
m.setenv("VLLM_MLA_DISABLE", "1")
method, model_name, tp_size = model_setup
ref_llm = LLM(model=model_name,
max_model_len=2048,
tensor_parallel_size=tp_size,
trust_remote_code=True)
ref_llm = LLM(
model=model_name,
max_model_len=2048,
tensor_parallel_size=tp_size,
trust_remote_code=True,
)
ref_outputs = ref_llm.chat(test_prompts, sampling_config)
del ref_llm
torch.cuda.empty_cache()