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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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45
tests/v1/generation/test_batch_invariance.py
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@@ -72,7 +72,7 @@ def test_v1_generation_is_deterministic_across_batch_sizes_with_needle():
Notes:
- Use seeded stochastic sampling with a fixed seed to test determinism.
- Outputs are intentionally longer and sampled at higher temperature/top_p
to produce a more random-sounding phrase, yet remain deterministic by
to produce a more random-sounding phrase, yet remain deterministic by
seed.
- Keep max_tokens and max_model_len bounded for speed and memory use.
"""
@@ -103,7 +103,7 @@ def test_v1_generation_is_deterministic_across_batch_sizes_with_needle():
seed=20240919,
)
needle_prompt = ("There once was a ")
needle_prompt = "There once was a "
llm_bs1 = None
llm_bsN = None
@@ -158,13 +158,16 @@ def test_v1_generation_is_deterministic_across_batch_sizes_with_needle():
passes = num_trials - mismatches
# Dump how many passed vs failed
print(f"[determinism] total={num_trials}, passed={passes}, "
f"failed={mismatches}, batch_size={batch_size}")
print(
f"[determinism] total={num_trials}, passed={passes}, "
f"failed={mismatches}, batch_size={batch_size}"
)
if mismatches > 0:
pytest.fail(
f"Nondeterministic outputs detected: {mismatches} failed out "
f"of {num_trials} trials (batch_size={batch_size}).")
f"of {num_trials} trials (batch_size={batch_size})."
)
finally:
# Ensure engines are shutdown to free GPU/VRAM across test sessions
@@ -197,8 +200,7 @@ def _extract_step_logprobs(request_output):
reason="Requires CUDA to match production inference path.",
)
def test_logprobs_bitwise_batch_invariance_bs1_vs_bs2():
#model_name = os.getenv("VLLM_TEST_MODEL", "facebook/opt-125m")
# model_name = os.getenv("VLLM_TEST_MODEL", "facebook/opt-125m")
model_name = os.getenv("VLLM_TEST_MODEL", "Qwen/Qwen3-1.7B")
tp_size = int(os.getenv("VLLM_TEST_TP_SIZE", "1"))
@@ -230,8 +232,10 @@ def test_logprobs_bitwise_batch_invariance_bs1_vs_bs2():
assert len(outs) == 1
step_logprobs = _extract_step_logprobs(outs[0])
if step_logprobs is None:
pytest.skip("Logits are not available on RequestOutput; "
"enable logprobs return to run this test.")
pytest.skip(
"Logits are not available on RequestOutput; "
"enable logprobs return to run this test."
)
bs1_logprobs_per_prompt.append(step_logprobs)
# BS=2: run prompts in a batch and collect logprobs per step for each
@@ -242,24 +246,29 @@ def test_logprobs_bitwise_batch_invariance_bs1_vs_bs2():
for o in outs_batched:
step_logprobs = _extract_step_logprobs(o)
if step_logprobs is None:
pytest.skip("Logits are not available on RequestOutput; "
"enable logprobs return to run this test.")
pytest.skip(
"Logits are not available on RequestOutput; "
"enable logprobs return to run this test."
)
bs2_logprobs_per_prompt.append(step_logprobs)
# Compare step-by-step logprobs for each prompt between BS=1 and BS=2 runs.
for i, (logprobs_bs1, logprobs_bs2) in enumerate(
zip(bs1_logprobs_per_prompt, bs2_logprobs_per_prompt)):
zip(bs1_logprobs_per_prompt, bs2_logprobs_per_prompt)
):
assert len(logprobs_bs1) == len(logprobs_bs2), (
f"Different number of generation steps for prompt index {i}: "
f"{len(logprobs_bs1)} (BS=1) vs {len(logprobs_bs2)} (BS=2)")
f"{len(logprobs_bs1)} (BS=1) vs {len(logprobs_bs2)} (BS=2)"
)
for t, (a, b) in enumerate(zip(logprobs_bs1, logprobs_bs2)):
assert a.shape == b.shape, (
f"Logits shape mismatch at prompt {i}, step {t}: "
f"{a.shape} vs {b.shape}")
f"Logits shape mismatch at prompt {i}, step {t}: {a.shape} vs {b.shape}"
)
# Bitwise exact equality.
assert torch.equal(
a, b), (f"Bitwise logprobs mismatch at prompt {i}, step {t} "
f"(dtype={a.dtype}, shape={a.shape}).")
assert torch.equal(a, b), (
f"Bitwise logprobs mismatch at prompt {i}, step {t} "
f"(dtype={a.dtype}, shape={a.shape})."
)
def LLM_with_max_seqs(