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[0/N] Rename MultiModalInputs to MultiModalKwargs (#10040)

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Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>
This commit is contained in:
Cyrus Leung
2024-11-09 11:31:02 +08:00
committed by GitHub
parent d7edca1dee
commit e0191a95d8
32 changed files with 151 additions and 121 deletions
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22
tests/multimodal/test_base.py
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@@ -1,6 +1,6 @@
import torch
from vllm.multimodal.base import MultiModalInputs, NestedTensors
from vllm.multimodal.base import MultiModalKwargs, NestedTensors
def assert_nested_tensors_equal(expected: NestedTensors,
@@ -13,8 +13,8 @@ def assert_nested_tensors_equal(expected: NestedTensors,
assert_nested_tensors_equal(expected_item, actual_item)
def assert_multimodal_inputs_equal(expected: MultiModalInputs,
actual: MultiModalInputs):
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])
@@ -22,7 +22,7 @@ def assert_multimodal_inputs_equal(expected: MultiModalInputs,
def test_multimodal_input_batch_single_tensor():
t = torch.rand([1, 2])
result = MultiModalInputs.batch([{"image": t}])
result = MultiModalKwargs.batch([{"image": t}])
assert_multimodal_inputs_equal(result, {"image": t.unsqueeze(0)})
@@ -30,7 +30,7 @@ def test_multimodal_input_batch_multiple_tensors():
a = torch.rand([1, 1, 2])
b = torch.rand([1, 1, 2])
c = torch.rand([1, 1, 2])
result = MultiModalInputs.batch([{"image": a}, {"image": b}, {"image": c}])
result = MultiModalKwargs.batch([{"image": a}, {"image": b}, {"image": c}])
assert_multimodal_inputs_equal(result, {"image": torch.stack([a, b, c])})
@@ -38,7 +38,7 @@ def test_multimodal_input_batch_multiple_heterogeneous_tensors():
a = torch.rand([1, 2, 2])
b = torch.rand([1, 3, 2])
c = torch.rand([1, 4, 2])
result = MultiModalInputs.batch([{"image": a}, {"image": b}, {"image": c}])
result = MultiModalKwargs.batch([{"image": a}, {"image": b}, {"image": c}])
assert_multimodal_inputs_equal(result, {"image": [a, b, c]})
@@ -46,7 +46,7 @@ def test_multimodal_input_batch_nested_tensors():
a = torch.rand([2, 3])
b = torch.rand([2, 3])
c = torch.rand([2, 3])
result = MultiModalInputs.batch([{
result = MultiModalKwargs.batch([{
"image": [a]
}, {
"image": [b]
@@ -65,7 +65,7 @@ def test_multimodal_input_batch_heterogeneous_lists():
a = torch.rand([1, 2, 3])
b = torch.rand([1, 2, 3])
c = torch.rand([1, 2, 3])
result = MultiModalInputs.batch([{"image": [a, b]}, {"image": [c]}])
result = MultiModalKwargs.batch([{"image": [a, b]}, {"image": [c]}])
assert_multimodal_inputs_equal(
result,
{"image": [torch.stack([a, b]), c.unsqueeze(0)]})
@@ -76,7 +76,7 @@ def test_multimodal_input_batch_multiple_batchable_lists():
b = torch.rand([1, 2, 3])
c = torch.rand([1, 2, 3])
d = torch.rand([1, 2, 3])
result = MultiModalInputs.batch([{"image": [a, b]}, {"image": [c, d]}])
result = MultiModalKwargs.batch([{"image": [a, b]}, {"image": [c, d]}])
assert_multimodal_inputs_equal(
result,
{"image": torch.stack([torch.stack([a, b]),
@@ -88,8 +88,8 @@ def test_multimodal_input_batch_mixed_stacking_depths():
b = torch.rand([1, 3, 3])
c = torch.rand([1, 4, 3])
result = MultiModalInputs.batch([{"image": [a, b]}, {"image": [c]}])
result = MultiModalKwargs.batch([{"image": [a, b]}, {"image": [c]}])
assert_multimodal_inputs_equal(result, {"image": [[a, b], c.unsqueeze(0)]})
result = MultiModalInputs.batch([{"image": [a]}, {"image": [b, c]}])
result = MultiModalKwargs.batch([{"image": [a]}, {"image": [b, c]}])
assert_multimodal_inputs_equal(result, {"image": [a.unsqueeze(0), [b, c]]})