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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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59
tests/v1/tpu/test_sampler.py
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@@ -10,21 +10,20 @@ from vllm.sampling_params import SamplingParams
@pytest.mark.parametrize("model_name", ["Qwen/Qwen2.5-1.5B-Instruct"])
@pytest.mark.skipif(not current_platform.is_tpu(),
reason="This test needs a TPU")
@pytest.mark.skipif(not current_platform.is_tpu(), reason="This test needs a TPU")
def test_sampler_different(model_name: str):
"""
Test significantly different sampling params to assert the model produces
Test significantly different sampling params to assert the model produces
different results.
"""
llm = LLM(model_name,
enforce_eager=False,
max_num_seqs=1,
max_model_len=512,
max_num_batched_tokens=256)
prompts = [
"Write a short story about a robot that dreams for the first time."
]
llm = LLM(
model_name,
enforce_eager=False,
max_num_seqs=1,
max_model_len=512,
max_num_batched_tokens=256,
)
prompts = ["Write a short story about a robot that dreams for the first time."]
sampling_params = SamplingParams(temperature=0.9, min_p=0.2, max_tokens=64)
output = llm.generate(prompts, sampling_params)
@@ -47,7 +46,9 @@ def test_sampler_different(model_name: str):
max_tokens=64,
# Vary number of ks
top_k=random.randint(4, 12),
top_p=random.random()) for _ in range(B)
top_p=random.random(),
)
for _ in range(B)
]
# Make sure first two reqs have the same K/P
sampling_params[0] = sampling_params[1]
@@ -61,20 +62,18 @@ def test_sampler_different(model_name: str):
@pytest.mark.parametrize("model_name", ["Qwen/Qwen2.5-1.5B-Instruct"])
# TODO TPU will appear busy if we fan-out test params here
@pytest.mark.parametrize("n_prompts", [1])
@pytest.mark.skipif(not current_platform.is_tpu(),
reason="This test needs a TPU")
@pytest.mark.skipif(not current_platform.is_tpu(), reason="This test needs a TPU")
def test_logprobs(model_name: str, n_prompts: int):
"""
Request top logprobs with different sampling settings and check
that results contains the requested number, ordered ascendingly.
that results contains the requested number, ordered ascendingly.
"""
def check_num_logprobs(logprobs, expected_num: int):
for step in logprobs:
prev_logp = 1.0
# order by rank
sorted_step = dict(
sorted(step.items(), key=lambda item: item[1].rank))
sorted_step = dict(sorted(step.items(), key=lambda item: item[1].rank))
# Can contain the sampled token
assert len(step) == expected_num or len(step) == expected_num + 1
@@ -84,23 +83,23 @@ def test_logprobs(model_name: str, n_prompts: int):
prev_logp = logp.logprob
assert logp.rank == rankno + 1
llm = LLM(model_name,
enforce_eager=False,
max_num_seqs=1,
max_model_len=128,
max_num_batched_tokens=128)
llm = LLM(
model_name,
enforce_eager=False,
max_num_seqs=1,
max_model_len=128,
max_num_batched_tokens=128,
)
prompts = [
"Write a short story about a robot that dreams for the first time."
] * n_prompts
greedy_sampling_params = SamplingParams(temperature=0.0, max_tokens=64,\
logprobs=4)
regular_sampling_params = SamplingParams(temperature=0.4, max_tokens=64,\
logprobs=4)
topkp_sampling_params = SamplingParams(temperature=0.4, max_tokens=64,\
logprobs=4, top_k=12, top_p=0.5)
greedy_sampling_params = SamplingParams(temperature=0.0, max_tokens=64, logprobs=4)
regular_sampling_params = SamplingParams(temperature=0.4, max_tokens=64, logprobs=4)
topkp_sampling_params = SamplingParams(
temperature=0.4, max_tokens=64, logprobs=4, top_k=12, top_p=0.5
)
for sp in [greedy_sampling_params, regular_sampling_params, \
topkp_sampling_params]:
for sp in [greedy_sampling_params, regular_sampling_params, topkp_sampling_params]:
output = llm.generate(prompts, sp)
for o in output:
check_num_logprobs(o.outputs[0].logprobs, 4)