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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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90
tests/v1/tpu/test_basic.py
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@@ -4,6 +4,7 @@
Run `pytest tests/v1/tpu/test_basic.py`.
"""
from __future__ import annotations
from typing import TYPE_CHECKING
@@ -32,8 +33,9 @@ MAX_NUM_REQS = [16, 1024]
# TENSOR_PARALLEL_SIZES = [1, 4]
@pytest.mark.skipif(not current_platform.is_tpu(),
reason="This is a basic test for TPU only")
@pytest.mark.skipif(
not current_platform.is_tpu(), reason="This is a basic test for TPU only"
)
@pytest.mark.parametrize("model", MODELS)
@pytest.mark.parametrize("max_tokens", [5])
@pytest.mark.parametrize("tensor_parallel_size", TENSOR_PARALLEL_SIZES)
@@ -46,32 +48,36 @@ def test_basic(
tensor_parallel_size: int,
max_num_seqs: int,
) -> None:
prompt = "The next numbers of the sequence " + ", ".join(
str(i) for i in range(1024)) + " are:"
prompt = (
"The next numbers of the sequence "
+ ", ".join(str(i) for i in range(1024))
+ " are:"
)
example_prompts = [prompt]
with monkeypatch.context() as m:
m.setenv("VLLM_USE_V1", "1")
with vllm_runner(
model,
# Note: max_num_batched_tokens == 1024 is needed here to
# actually test chunked prompt
max_num_batched_tokens=1024,
max_model_len=8192,
gpu_memory_utilization=0.7,
max_num_seqs=max_num_seqs,
tensor_parallel_size=tensor_parallel_size) as vllm_model:
vllm_outputs = vllm_model.generate_greedy(example_prompts,
max_tokens)
model,
# Note: max_num_batched_tokens == 1024 is needed here to
# actually test chunked prompt
max_num_batched_tokens=1024,
max_model_len=8192,
gpu_memory_utilization=0.7,
max_num_seqs=max_num_seqs,
tensor_parallel_size=tensor_parallel_size,
) as vllm_model:
vllm_outputs = vllm_model.generate_greedy(example_prompts, max_tokens)
output = vllm_outputs[0][1]
assert "1024" in output or "0, 1" in output
@pytest.mark.skip(reason="Temporarily disabled due to timeout")
@pytest.mark.skipif(not current_platform.is_tpu(),
reason="This is a basic test for TPU only")
@pytest.mark.skipif(
not current_platform.is_tpu(), reason="This is a basic test for TPU only"
)
@pytest.mark.parametrize("max_tokens", [8])
@pytest.mark.parametrize("max_num_seqs", [16])
def test_phi3(
@@ -96,9 +102,9 @@ def test_phi3(
with monkeypatch.context() as m:
m.setenv("VLLM_USE_V1", "1")
with vllm_runner(model,
max_num_batched_tokens=256,
max_num_seqs=max_num_seqs) as vllm_model:
with vllm_runner(
model, max_num_batched_tokens=256, max_num_seqs=max_num_seqs
) as vllm_model:
vllm_outputs = vllm_model.generate_greedy(prompts, max_tokens)
# vllm_outputs is a list of tuples whose first element is the token id
# and the second element is the output (including the prompt).
@@ -110,10 +116,11 @@ def test_phi3(
TP_SIZE_8 = 8
@pytest.mark.skipif(not current_platform.is_tpu(),
reason="This is a test for TPU only")
@pytest.mark.skipif(tpu.num_available_chips() < TP_SIZE_8,
reason=f"This test requires {TP_SIZE_8} TPU chips.")
@pytest.mark.skipif(not current_platform.is_tpu(), reason="This is a test for TPU only")
@pytest.mark.skipif(
tpu.num_available_chips() < TP_SIZE_8,
reason=f"This test requires {TP_SIZE_8} TPU chips.",
)
def test_gemma3_27b_with_text_input_and_tp(
vllm_runner: type[VllmRunner],
monkeypatch: pytest.MonkeyPatch,
@@ -137,10 +144,11 @@ def test_gemma3_27b_with_text_input_and_tp(
m.setenv("VLLM_USE_V1", "1")
with vllm_runner(
model,
max_num_batched_tokens=256,
max_num_seqs=max_num_seqs,
tensor_parallel_size=tensor_parallel_size) as vllm_model:
model,
max_num_batched_tokens=256,
max_num_seqs=max_num_seqs,
tensor_parallel_size=tensor_parallel_size,
) as vllm_model:
vllm_outputs = vllm_model.generate_greedy(prompts, max_tokens)
# vllm_outputs is a list of tuples whose first element is the token id
# and the second element is the output (including the prompt).
@@ -149,8 +157,9 @@ def test_gemma3_27b_with_text_input_and_tp(
assert answer in generated_text
@pytest.mark.skipif(not current_platform.is_tpu(),
reason="This is a basic test for TPU only")
@pytest.mark.skipif(
not current_platform.is_tpu(), reason="This is a basic test for TPU only"
)
def test_w8a8_quantization(
vllm_runner: type[VllmRunner],
monkeypatch: pytest.MonkeyPatch,
@@ -160,22 +169,25 @@ def test_w8a8_quantization(
tensor_parallel_size = 1
max_num_seqs = 4
prompt = "The next numbers of the sequence " + ", ".join(
str(i) for i in range(1024)) + " are:"
prompt = (
"The next numbers of the sequence "
+ ", ".join(str(i) for i in range(1024))
+ " are:"
)
example_prompts = [prompt]
with monkeypatch.context() as m:
m.setenv("VLLM_USE_V1", "1")
with vllm_runner(
model,
max_num_batched_tokens=64,
max_model_len=4096,
gpu_memory_utilization=0.7,
max_num_seqs=max_num_seqs,
tensor_parallel_size=tensor_parallel_size) as vllm_model:
vllm_outputs = vllm_model.generate_greedy(example_prompts,
max_tokens)
model,
max_num_batched_tokens=64,
max_model_len=4096,
gpu_memory_utilization=0.7,
max_num_seqs=max_num_seqs,
tensor_parallel_size=tensor_parallel_size,
) as vllm_model:
vllm_outputs = vllm_model.generate_greedy(example_prompts, max_tokens)
output = vllm_outputs[0][1]
assert "1024" in output or "0, 1" in output