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127 lines
3.7 KiB
Python
127 lines
3.7 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import pytest
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import torch
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from vllm.model_executor.models.phi3v import Phi3VImagePixelInputs
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def test_tensor_schema_valid_tensor():
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Phi3VImagePixelInputs(
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data=torch.randn(16, 64, 3, 32, 32),
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image_sizes=torch.randint(0, 256, (16, 2)),
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)
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def test_tensor_schema_optional_fields():
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Phi3VImagePixelInputs(
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data=torch.randn(16, 64, 3, 32, 32),
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image_sizes=None,
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)
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Phi3VImagePixelInputs(data=torch.randn(16, 64, 3, 32, 32), )
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def test_tensor_schema_constant_dim_failure():
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with pytest.raises(ValueError, match="dim\\[2\\] expected 3, got 4"):
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Phi3VImagePixelInputs(
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data=torch.randn(16, 64, 4, 32, 32), # dim[2] = 4
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image_sizes=torch.randint(0, 256, (16, 2)),
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)
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def test_tensor_schema_symbolic_dim_mismatch():
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with pytest.raises(ValueError, match="expected 'bn'=12, got 16"):
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Phi3VImagePixelInputs(
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data=torch.randn(12, 64, 3, 32, 32),
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image_sizes=torch.randint(0, 256, (16, 2)),
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)
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def test_tensor_schema_list_tensor_valid():
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Phi3VImagePixelInputs(
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data=[torch.randn(64, 3, 32, 32) for _ in range(16)],
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image_sizes=torch.randint(0, 256, (16, 2)),
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)
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def test_tensor_schema_variable_patch_counts_valid():
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# Each image has a different number of patches (p)
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# Each tensor has shape (p, 3, 32, 32)
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data = [
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torch.randn(16, 3, 32, 32), # p = 16
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torch.randn(32, 3, 32, 32), # p = 32
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torch.randn(64, 3, 32, 32), # p = 64
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]
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image_sizes = torch.randint(0, 256, (3, 2)) # bn = 3
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Phi3VImagePixelInputs(
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data=data,
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image_sizes=image_sizes,
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)
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def test_tensor_schema_tuple_tensor_valid():
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Phi3VImagePixelInputs(
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data=tuple(torch.randn(64, 3, 32, 32) for _ in range(16)),
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image_sizes=torch.randint(0, 256, (16, 2)),
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)
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def test_tensor_schema_inconsistent_shapes_in_list():
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with pytest.raises(ValueError, match="contains inconsistent shapes"):
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Phi3VImagePixelInputs(
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data=[torch.randn(64, 3, 32, 32),
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torch.randn(64, 3, 16, 16)] +
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[torch.randn(64, 3, 32, 32) for _ in range(14)],
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image_sizes=torch.randint(0, 256, (16, 2)),
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)
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def test_tensor_schema_empty_list():
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with pytest.raises(ValueError, match="is an empty list"):
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Phi3VImagePixelInputs(
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data=[],
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image_sizes=torch.randint(0, 256, (0, 2)),
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)
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def test_tensor_schema_validation_disabled_skips_shape_check():
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# This should NOT raise, because validation is turned off
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# This would normally fail (dim[2] should be 3, not 4)
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Phi3VImagePixelInputs(
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data=torch.randn(16, 64, 4, 32, 32),
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image_sizes=torch.randint(0, 256, (16, 2)),
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validate=False,
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)
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def test_tensor_schema_with_valid_resolve_binding_dims():
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data = torch.randn(16, 64, 3, 336, 336) # h=336, w=336
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image_sizes = torch.randint(0, 256, (16, 2))
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Phi3VImagePixelInputs(
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data=data,
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image_sizes=image_sizes,
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resolve_bindings={
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"h": 336,
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"w": 336
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},
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)
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def test_tensor_schema_with_invalid_resolve_binding_dims():
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data = torch.randn(16, 64, 3, 36, 36) # h=36, w=36
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image_sizes = torch.randint(0, 256, (16, 2))
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# Should raise because 'h' and 'w' don't match resolve bindings
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with pytest.raises(ValueError, match="dim\\[3\\] expected 336, got 36"):
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Phi3VImagePixelInputs(
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data=data,
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image_sizes=image_sizes,
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resolve_bindings={
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"h": 336,
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"w": 336
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},
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)
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