biondizzle/vllm
1
0
Fork 0
Code Issues Pull Requests Actions 2 Packages Projects Releases Wiki Activity

Convert formatting to use ruff instead of yapf + isort (#26247)

Browse Source 
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
Download Patch File Download Diff File Expand all files Collapse all files

34
tests/multimodal/test_inputs.py
View File

@@ -9,8 +9,7 @@ from vllm.multimodal.inputs import MultiModalKwargs, NestedTensors
pytestmark = pytest.mark.cpu_test
def assert_nested_tensors_equal(expected: NestedTensors,
actual: NestedTensors):
def assert_nested_tensors_equal(expected: NestedTensors, actual: NestedTensors):
assert type(expected) == type(actual) # noqa: E721
if isinstance(expected, torch.Tensor):
assert torch.equal(expected, actual)
@@ -19,8 +18,9 @@ def assert_nested_tensors_equal(expected: NestedTensors,
assert_nested_tensors_equal(expected_item, actual_item)
def assert_multimodal_inputs_equal(expected: MultiModalKwargs,
actual: MultiModalKwargs):
def assert_multimodal_inputs_equal(
expected: MultiModalKwargs, actual: MultiModalKwargs
):
assert set(expected.keys()) == set(actual.keys())
for key in expected:
assert_nested_tensors_equal(expected[key], actual[key])
@@ -52,19 +52,10 @@ def test_multimodal_input_batch_nested_tensors():
a = torch.rand([2, 3])
b = torch.rand([2, 3])
c = torch.rand([2, 3])
result = MultiModalKwargs.batch([{
"image": [a]
}, {
"image": [b]
}, {
"image": [c]
}])
assert_multimodal_inputs_equal(result, {
"image":
torch.stack([a.unsqueeze(0),
b.unsqueeze(0),
c.unsqueeze(0)])
})
result = MultiModalKwargs.batch([{"image": [a]}, {"image": [b]}, {"image": [c]}])
assert_multimodal_inputs_equal(
result, {"image": torch.stack([a.unsqueeze(0), b.unsqueeze(0), c.unsqueeze(0)])}
)
def test_multimodal_input_batch_heterogeneous_lists():
@@ -73,8 +64,8 @@ def test_multimodal_input_batch_heterogeneous_lists():
c = torch.rand([1, 2, 3])
result = MultiModalKwargs.batch([{"image": [a, b]}, {"image": [c]}])
assert_multimodal_inputs_equal(
result,
{"image": [torch.stack([a, b]), c.unsqueeze(0)]})
result, {"image": [torch.stack([a, b]), c.unsqueeze(0)]}
)
def test_multimodal_input_batch_multiple_batchable_lists():
@@ -84,9 +75,8 @@ def test_multimodal_input_batch_multiple_batchable_lists():
d = torch.rand([1, 2, 3])
result = MultiModalKwargs.batch([{"image": [a, b]}, {"image": [c, d]}])
assert_multimodal_inputs_equal(
result,
{"image": torch.stack([torch.stack([a, b]),
torch.stack([c, d])])})
result, {"image": torch.stack([torch.stack([a, b]), torch.stack([c, d])])}
)
def test_multimodal_input_batch_mixed_stacking_depths():