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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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75
tests/v1/test_serial_utils.py
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@@ -9,18 +9,21 @@ import numpy as np
import pytest
import torch
from vllm.multimodal.inputs import (MultiModalBatchedField,
MultiModalFieldElem, MultiModalFlatField,
MultiModalKwargsItem,
MultiModalKwargsItems,
MultiModalSharedField, NestedTensors)
from vllm.multimodal.inputs import (
MultiModalBatchedField,
MultiModalFieldElem,
MultiModalFlatField,
MultiModalKwargsItem,
MultiModalKwargsItems,
MultiModalSharedField,
NestedTensors,
)
from vllm.v1.serial_utils import MsgpackDecoder, MsgpackEncoder
pytestmark = pytest.mark.cpu_test
class UnrecognizedType(UserDict):
def __init__(self, an_int: int):
super().__init__()
self.an_int = an_int
@@ -47,10 +50,7 @@ def test_encode_decode(monkeypatch: pytest.MonkeyPatch):
m.setenv("VLLM_ALLOW_INSECURE_SERIALIZATION", "1")
obj = MyType(
tensor1=torch.randint(low=0,
high=100,
size=(1024, ),
dtype=torch.int32),
tensor1=torch.randint(low=0, high=100, size=(1024,), dtype=torch.int32),
a_string="hello",
list_of_tensors=[
torch.rand((1, 10), dtype=torch.float32),
@@ -58,8 +58,9 @@ def test_encode_decode(monkeypatch: pytest.MonkeyPatch):
torch.tensor(1984), # test scalar too
# Make sure to test bf16 which numpy doesn't support.
torch.rand((3, 5, 1000), dtype=torch.bfloat16),
torch.tensor([float("-inf"), float("inf")] * 1024,
dtype=torch.bfloat16),
torch.tensor(
[float("-inf"), float("inf")] * 1024, dtype=torch.bfloat16
),
],
numpy_array=np.arange(512),
unrecognized=UnrecognizedType(33),
@@ -103,22 +104,24 @@ class MyRequest(msgspec.Struct):
def test_multimodal_kwargs():
e1 = MultiModalFieldElem("audio", "a0",
torch.zeros(1000, dtype=torch.bfloat16),
MultiModalBatchedField())
e1 = MultiModalFieldElem(
"audio", "a0", torch.zeros(1000, dtype=torch.bfloat16), MultiModalBatchedField()
)
e2 = MultiModalFieldElem(
"video",
"v0",
[torch.zeros(1000, dtype=torch.int8) for _ in range(4)],
MultiModalFlatField(
[[slice(1, 2, 3), slice(4, 5, 6)], [slice(None, 2)]], 0),
MultiModalFlatField([[slice(1, 2, 3), slice(4, 5, 6)], [slice(None, 2)]], 0),
)
e3 = MultiModalFieldElem(
"image", "i0", torch.zeros(1000, dtype=torch.int32), MultiModalSharedField(4)
)
e3 = MultiModalFieldElem("image", "i0", torch.zeros(1000,
dtype=torch.int32),
MultiModalSharedField(4))
e4 = MultiModalFieldElem(
"image", "i1", torch.zeros(1000, dtype=torch.int32),
MultiModalFlatField([slice(1, 2, 3), slice(4, 5, 6)], 2))
"image",
"i1",
torch.zeros(1000, dtype=torch.int32),
MultiModalFlatField([slice(1, 2, 3), slice(4, 5, 6)], 2),
)
audio = MultiModalKwargsItem.from_elems([e1])
video = MultiModalKwargsItem.from_elems([e2])
image = MultiModalKwargsItem.from_elems([e3, e4])
@@ -164,16 +167,14 @@ def assert_equal(obj1: MyType, obj2: MyType):
assert torch.equal(obj1.tensor1, obj2.tensor1)
assert obj1.a_string == obj2.a_string
assert all(
torch.equal(a, b)
for a, b in zip(obj1.list_of_tensors, obj2.list_of_tensors))
torch.equal(a, b) for a, b in zip(obj1.list_of_tensors, obj2.list_of_tensors)
)
assert np.array_equal(obj1.numpy_array, obj2.numpy_array)
assert obj1.unrecognized.an_int == obj2.unrecognized.an_int
assert torch.equal(obj1.small_f_contig_tensor, obj2.small_f_contig_tensor)
assert torch.equal(obj1.large_f_contig_tensor, obj2.large_f_contig_tensor)
assert torch.equal(obj1.small_non_contig_tensor,
obj2.small_non_contig_tensor)
assert torch.equal(obj1.large_non_contig_tensor,
obj2.large_non_contig_tensor)
assert torch.equal(obj1.small_non_contig_tensor, obj2.small_non_contig_tensor)
assert torch.equal(obj1.large_non_contig_tensor, obj2.large_non_contig_tensor)
assert torch.equal(obj1.empty_tensor, obj2.empty_tensor)
@@ -210,8 +211,9 @@ def test_tensor_serialization():
decoded = decoder.decode(encoded)
# Verify the decoded tensor matches the original
assert torch.allclose(
tensor, decoded), "Decoded tensor does not match the original tensor."
assert torch.allclose(tensor, decoded), (
"Decoded tensor does not match the original tensor."
)
def test_numpy_array_serialization():
@@ -229,13 +231,12 @@ def test_numpy_array_serialization():
decoded = decoder.decode(encoded)
# Verify the decoded array matches the original
assert np.allclose(
array,
decoded), "Decoded numpy array does not match the original array."
assert np.allclose(array, decoded), (
"Decoded numpy array does not match the original array."
)
class CustomClass:
def __init__(self, value):
self.value = value
@@ -244,7 +245,8 @@ class CustomClass:
def test_custom_class_serialization_allowed_with_pickle(
monkeypatch: pytest.MonkeyPatch):
monkeypatch: pytest.MonkeyPatch,
):
"""Test that serializing a custom class succeeds when allow_pickle=True."""
with monkeypatch.context() as m:
@@ -261,8 +263,7 @@ def test_custom_class_serialization_allowed_with_pickle(
decoded = decoder.decode(encoded)
# Verify the decoded object matches the original
assert obj == decoded, (
"Decoded object does not match the original object.")
assert obj == decoded, "Decoded object does not match the original object."
def test_custom_class_serialization_disallowed_without_pickle():