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[Backend Tester] Add permute, transpose, and masked_fill tests #12850
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,101 @@ | ||
| # Copyright (c) Meta Platforms, Inc. and affiliates. | ||
| # All rights reserved. | ||
| # | ||
| # This source code is licensed under the BSD-style license found in the | ||
| # LICENSE file in the root directory of this source tree. | ||
|
|
||
| # pyre-unsafe | ||
|
|
||
| from typing import Union | ||
|
|
||
| import torch | ||
| from executorch.backends.test.suite.flow import TestFlow | ||
|
|
||
| from executorch.backends.test.suite.operators import ( | ||
| dtype_test, | ||
| operator_test, | ||
| OperatorTest, | ||
| ) | ||
|
|
||
|
|
||
| class MaskedFillModel(torch.nn.Module): | ||
| def __init__(self, value: Union[float, int]): | ||
| super().__init__() | ||
| self.value = value | ||
|
|
||
| def forward(self, x, mask): | ||
| return x.masked_fill(mask, self.value) | ||
|
|
||
|
|
||
| @operator_test | ||
| class MaskedFill(OperatorTest): | ||
| @dtype_test | ||
| def test_masked_fill_dtype(self, flow: TestFlow, dtype) -> None: | ||
| mask = torch.randint(0, 2, (16, 32), dtype=torch.bool) | ||
| self._test_op( | ||
| MaskedFillModel(value=0.0), | ||
| ( | ||
| torch.rand(16, 32).to(dtype), | ||
| mask, | ||
| ), | ||
| flow, | ||
| ) | ||
|
|
||
| def test_masked_fill_different_values(self, flow: TestFlow) -> None: | ||
| mask = torch.randint(0, 2, (16, 32), dtype=torch.bool) | ||
|
|
||
| self._test_op( | ||
| MaskedFillModel(value=5.0), | ||
| ( | ||
| torch.randn(16, 32), | ||
| mask, | ||
| ), | ||
| flow, | ||
| ) | ||
|
|
||
| self._test_op( | ||
| MaskedFillModel(value=-5.0), | ||
| ( | ||
| torch.randn(16, 32), | ||
| mask, | ||
| ), | ||
| flow, | ||
| ) | ||
|
|
||
| self._test_op( | ||
| MaskedFillModel(value=1), | ||
| ( | ||
| torch.randn(16, 32), | ||
| mask, | ||
| ), | ||
| flow, | ||
| ) | ||
|
|
||
| def test_masked_fill_different_shapes(self, flow: TestFlow) -> None: | ||
| self._test_op( | ||
| MaskedFillModel(value=0.0), | ||
| ( | ||
| torch.randn(512), | ||
| torch.randint(0, 2, (512,), dtype=torch.bool), | ||
| ), | ||
| flow, | ||
| ) | ||
|
|
||
| self._test_op( | ||
| MaskedFillModel(value=0.0), | ||
| ( | ||
| torch.randn(4, 8, 16), | ||
| torch.randint(0, 2, (4, 8, 16), dtype=torch.bool), | ||
| ), | ||
| flow, | ||
| ) | ||
|
|
||
| def test_masked_fill_broadcast(self, flow: TestFlow) -> None: | ||
| self._test_op( | ||
| MaskedFillModel(value=0.0), | ||
| ( | ||
| torch.randn(16, 32), | ||
| torch.randint(0, 2, (32,), dtype=torch.bool), | ||
| ), | ||
| flow, | ||
| ) |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,109 @@ | ||
| # Copyright (c) Meta Platforms, Inc. and affiliates. | ||
| # All rights reserved. | ||
| # | ||
| # This source code is licensed under the BSD-style license found in the | ||
| # LICENSE file in the root directory of this source tree. | ||
|
|
||
| # pyre-unsafe | ||
|
|
||
| from typing import List | ||
|
|
||
| import torch | ||
| from executorch.backends.test.suite.flow import TestFlow | ||
|
|
||
| from executorch.backends.test.suite.operators import ( | ||
| dtype_test, | ||
| operator_test, | ||
| OperatorTest, | ||
| ) | ||
|
|
||
|
|
||
| class PermuteModel(torch.nn.Module): | ||
| def __init__(self, dims: List[int]): | ||
| super().__init__() | ||
| self.dims = dims | ||
|
|
||
| def forward(self, x): | ||
| return x.permute(self.dims) | ||
|
|
||
|
|
||
| @operator_test | ||
| class Permute(OperatorTest): | ||
| @dtype_test | ||
| def test_permute_dtype(self, flow: TestFlow, dtype) -> None: | ||
| self._test_op( | ||
| PermuteModel(dims=[1, 0]), | ||
| (torch.rand(20, 32).to(dtype),), | ||
| flow, | ||
| ) | ||
|
|
||
| def test_permute_3d(self, flow: TestFlow) -> None: | ||
| self._test_op( | ||
| PermuteModel(dims=[2, 0, 1]), | ||
| (torch.randn(8, 10, 12),), | ||
| flow, | ||
| ) | ||
|
|
||
| self._test_op( | ||
| PermuteModel(dims=[1, 2, 0]), | ||
| (torch.randn(8, 10, 12),), | ||
| flow, | ||
| ) | ||
|
|
||
| self._test_op( | ||
| PermuteModel(dims=[0, 2, 1]), | ||
| (torch.randn(8, 10, 12),), | ||
| flow, | ||
| ) | ||
|
|
||
| def test_permute_4d(self, flow: TestFlow) -> None: | ||
| self._test_op( | ||
| PermuteModel(dims=[3, 2, 1, 0]), | ||
| (torch.randn(4, 6, 8, 10),), | ||
| flow, | ||
| ) | ||
|
|
||
| self._test_op( | ||
| PermuteModel(dims=[0, 2, 1, 3]), | ||
| (torch.randn(4, 6, 8, 10),), | ||
| flow, | ||
| ) | ||
|
|
||
| def test_permute_identity(self, flow: TestFlow) -> None: | ||
| self._test_op( | ||
| PermuteModel(dims=[0, 1]), | ||
| (torch.randn(20, 32),), | ||
| flow, | ||
| ) | ||
|
|
||
| self._test_op( | ||
| PermuteModel(dims=[0, 1, 2]), | ||
| (torch.randn(8, 10, 12),), | ||
| flow, | ||
| ) | ||
|
|
||
| def test_permute_negative_dims(self, flow: TestFlow) -> None: | ||
| self._test_op( | ||
| PermuteModel(dims=[-1, -3, -2, -4]), | ||
| (torch.randn(4, 6, 8, 10),), | ||
| flow, | ||
| ) | ||
|
|
||
| self._test_op( | ||
| PermuteModel(dims=[-4, -2, -3, -1]), | ||
| (torch.randn(4, 6, 8, 10),), | ||
| flow, | ||
| ) | ||
|
|
||
| def test_permute_different_shapes(self, flow: TestFlow) -> None: | ||
| self._test_op( | ||
| PermuteModel(dims=[0]), | ||
| (torch.randn(512),), | ||
| flow, | ||
| ) | ||
|
|
||
| self._test_op( | ||
| PermuteModel(dims=[4, 3, 2, 1, 0]), | ||
| (torch.randn(2, 3, 4, 5, 6),), | ||
| flow, | ||
| ) | ||
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,143 @@ | ||
| # Copyright (c) Meta Platforms, Inc. and affiliates. | ||
| # All rights reserved. | ||
| # | ||
| # This source code is licensed under the BSD-style license found in the | ||
| # LICENSE file in the root directory of this source tree. | ||
|
|
||
| # pyre-unsafe | ||
|
|
||
|
|
||
| import torch | ||
| from executorch.backends.test.suite.flow import TestFlow | ||
|
|
||
| from executorch.backends.test.suite.operators import ( | ||
| dtype_test, | ||
| operator_test, | ||
| OperatorTest, | ||
| ) | ||
|
|
||
|
|
||
| class TransposeModel(torch.nn.Module): | ||
| def __init__(self, dim0: int, dim1: int): | ||
| super().__init__() | ||
| self.dim0 = dim0 | ||
| self.dim1 = dim1 | ||
|
|
||
| def forward(self, x): | ||
| return torch.transpose(x, self.dim0, self.dim1) | ||
|
|
||
|
|
||
| @operator_test | ||
| class Transpose(OperatorTest): | ||
| @dtype_test | ||
| def test_transpose_dtype(self, flow: TestFlow, dtype) -> None: | ||
| self._test_op( | ||
| TransposeModel(dim0=0, dim1=1), | ||
| (torch.rand(20, 32).to(dtype),), | ||
| flow, | ||
| ) | ||
|
|
||
| def test_transpose_basic(self, flow: TestFlow) -> None: | ||
| self._test_op( | ||
| TransposeModel(dim0=0, dim1=1), | ||
| (torch.randn(20, 32),), | ||
| flow, | ||
| ) | ||
|
|
||
| def test_transpose_3d(self, flow: TestFlow) -> None: | ||
| self._test_op( | ||
| TransposeModel(dim0=0, dim1=1), | ||
| (torch.randn(8, 10, 12),), | ||
| flow, | ||
| ) | ||
|
|
||
| self._test_op( | ||
| TransposeModel(dim0=0, dim1=2), | ||
| (torch.randn(8, 10, 12),), | ||
| flow, | ||
| ) | ||
|
|
||
| self._test_op( | ||
| TransposeModel(dim0=1, dim1=2), | ||
| (torch.randn(8, 10, 12),), | ||
| flow, | ||
| ) | ||
|
|
||
| def test_transpose_4d(self, flow: TestFlow) -> None: | ||
| self._test_op( | ||
| TransposeModel(dim0=0, dim1=3), | ||
| (torch.randn(4, 6, 8, 10),), | ||
| flow, | ||
| ) | ||
|
|
||
| self._test_op( | ||
| TransposeModel(dim0=1, dim1=2), | ||
| (torch.randn(4, 6, 8, 10),), | ||
| flow, | ||
| ) | ||
|
|
||
| def test_transpose_identity(self, flow: TestFlow) -> None: | ||
| self._test_op( | ||
| TransposeModel(dim0=0, dim1=0), | ||
| (torch.randn(20, 32),), | ||
| flow, | ||
| ) | ||
| self._test_op( | ||
| TransposeModel(dim0=1, dim1=1), | ||
| (torch.randn(20, 32),), | ||
| flow, | ||
| ) | ||
|
|
||
| self._test_op( | ||
| TransposeModel(dim0=0, dim1=0), | ||
| (torch.randn(8, 10, 12),), | ||
| flow, | ||
| ) | ||
| self._test_op( | ||
| TransposeModel(dim0=1, dim1=1), | ||
| (torch.randn(8, 10, 12),), | ||
| flow, | ||
| ) | ||
| self._test_op( | ||
| TransposeModel(dim0=2, dim1=2), | ||
| (torch.randn(8, 10, 12),), | ||
| flow, | ||
| ) | ||
|
|
||
| def test_transpose_negative_dims(self, flow: TestFlow) -> None: | ||
| self._test_op( | ||
| TransposeModel(dim0=-3, dim1=-1), | ||
| (torch.randn(8, 10, 12),), | ||
| flow, | ||
| ) | ||
|
|
||
| self._test_op( | ||
| TransposeModel(dim0=-2, dim1=-1), | ||
| (torch.randn(8, 10, 12),), | ||
| flow, | ||
| ) | ||
|
|
||
| def test_transpose_different_shapes(self, flow: TestFlow) -> None: | ||
| self._test_op( | ||
| TransposeModel(dim0=0, dim1=1), | ||
| (torch.randn(20, 32),), | ||
| flow, | ||
| ) | ||
|
|
||
| self._test_op( | ||
| TransposeModel(dim0=0, dim1=2), | ||
| (torch.randn(8, 10, 12),), | ||
| flow, | ||
| ) | ||
|
|
||
| self._test_op( | ||
| TransposeModel(dim0=1, dim1=3), | ||
| (torch.randn(4, 6, 8, 10),), | ||
| flow, | ||
| ) | ||
|
|
||
| self._test_op( | ||
| TransposeModel(dim0=0, dim1=4), | ||
| (torch.randn(2, 3, 4, 5, 6),), | ||
| flow, | ||
| ) |
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some test generator or Facto or even a meta class to generate permutations for permute might be better?
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Makes sense. Should be easy to use itertools for this, I think. I'll take this as a follow-up.