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‎backends/test/suite/operators/test_amax.py‎

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# (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
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# pyre-strict
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from typing import Callable, List, Optional, Tuple, Union
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import torch
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from executorch.backends.test.compliance_suite import (
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dtype_test,
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operator_test,
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OperatorTest,
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)
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class AmaxModel(torch.nn.Module):
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def __init__(
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self,
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dim: Optional[Union[int, Tuple[int, ...], List[int]]] = None,
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keepdim: bool = False
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):
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super().__init__()
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self.dim = dim
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self.keepdim = keepdim
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def forward(self, x):
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return torch.amax(x, dim=self.dim, keepdim=self.keepdim)
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@operator_test
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class TestAmax(OperatorTest):
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@dtype_test
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def test_amax_dtype(self, dtype, tester_factory: Callable) -> None:
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# Test with different dtypes
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model = AmaxModel().to(dtype)
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self._test_op(model, (torch.rand(10, 10).to(dtype),), tester_factory)
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def test_amax_basic(self, tester_factory: Callable) -> None:
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# Basic test with default parameters (global reduction)
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self._test_op(AmaxModel(), (torch.randn(10, 10),), tester_factory)
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def test_amax_dim(self, tester_factory: Callable) -> None:
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# Test with different dimensions
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# 2D tensor, dim=0
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self._test_op(AmaxModel(dim=0), (torch.randn(5, 10),), tester_factory)
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# 2D tensor, dim=1
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self._test_op(AmaxModel(dim=1), (torch.randn(5, 10),), tester_factory)
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# 3D tensor, dim=0
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self._test_op(AmaxModel(dim=0), (torch.randn(3, 4, 5),), tester_factory)
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# 3D tensor, dim=1
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self._test_op(AmaxModel(dim=1), (torch.randn(3, 4, 5),), tester_factory)
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# 3D tensor, dim=2
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self._test_op(AmaxModel(dim=2), (torch.randn(3, 4, 5),), tester_factory)
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# 4D tensor, dim=1
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self._test_op(AmaxModel(dim=1), (torch.randn(2, 3, 4, 5),), tester_factory)
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# Negative dim (last dimension)
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self._test_op(AmaxModel(dim=-1), (torch.randn(3, 4, 5),), tester_factory)
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# Negative dim (second-to-last dimension)
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self._test_op(AmaxModel(dim=-2), (torch.randn(3, 4, 5),), tester_factory)
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def test_amax_multi_dim(self, tester_factory: Callable) -> None:
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# Test with multiple dimensions
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# 3D tensor, dim=(0, 1)
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self._test_op(AmaxModel(dim=(0, 1)), (torch.randn(3, 4, 5),), tester_factory)
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# 3D tensor, dim=(0, 2)
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self._test_op(AmaxModel(dim=(0, 2)), (torch.randn(3, 4, 5),), tester_factory)
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# 3D tensor, dim=(1, 2)
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self._test_op(AmaxModel(dim=(1, 2)), (torch.randn(3, 4, 5),), tester_factory)
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# 4D tensor, dim=(1, 3)
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self._test_op(AmaxModel(dim=(1, 3)), (torch.randn(2, 3, 4, 5),), tester_factory)
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# 4D tensor, dim=(0, 2)
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self._test_op(AmaxModel(dim=(0, 2)), (torch.randn(2, 3, 4, 5),), tester_factory)
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# 4D tensor, dim=(-1, -3)
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self._test_op(AmaxModel(dim=(-1, -3)), (torch.randn(2, 3, 4, 5),), tester_factory)
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# 4D tensor, all dimensions
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self._test_op(AmaxModel(dim=(0, 1, 2, 3)), (torch.randn(2, 3, 4, 5),), tester_factory)
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def test_amax_keepdim(self, tester_factory: Callable) -> None:
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# Test with keepdim=True
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# 2D tensor, dim=0, keepdim=True
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self._test_op(AmaxModel(dim=0, keepdim=True), (torch.randn(5, 10),), tester_factory)
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# 2D tensor, dim=1, keepdim=True
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self._test_op(AmaxModel(dim=1, keepdim=True), (torch.randn(5, 10),), tester_factory)
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# 3D tensor, dim=1, keepdim=True
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self._test_op(AmaxModel(dim=1, keepdim=True), (torch.randn(3, 4, 5),), tester_factory)
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# 4D tensor, dim=2, keepdim=True
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self._test_op(AmaxModel(dim=2, keepdim=True), (torch.randn(2, 3, 4, 5),), tester_factory)
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# Multiple dimensions with keepdim=True
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self._test_op(AmaxModel(dim=(1, 2), keepdim=True), (torch.randn(3, 4, 5),), tester_factory)
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def test_amax_shapes(self, tester_factory: Callable) -> None:
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# Test with different tensor shapes
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# 1D tensor
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self._test_op(AmaxModel(), (torch.randn(20),), tester_factory)
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self._test_op(AmaxModel(dim=0), (torch.randn(20),), tester_factory)
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# 2D tensor
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self._test_op(AmaxModel(), (torch.randn(5, 10),), tester_factory)
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# 3D tensor
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self._test_op(AmaxModel(), (torch.randn(3, 4, 5),), tester_factory)
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# 4D tensor
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self._test_op(AmaxModel(), (torch.randn(2, 3, 4, 5),), tester_factory)
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# 5D tensor
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self._test_op(AmaxModel(), (torch.randn(2, 2, 3, 4, 5),), tester_factory)
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def test_amax_values(self, tester_factory: Callable) -> None:
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# Test with different value patterns
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# Tensor with clear maximum
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x = torch.tensor([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
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self._test_op(AmaxModel(), (x,), tester_factory)
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self._test_op(AmaxModel(dim=0), (x,), tester_factory)
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self._test_op(AmaxModel(dim=1), (x,), tester_factory)
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# Tensor with duplicate maximum values
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x = torch.tensor([[3.0, 2.0, 3.0], [6.0, 6.0, 5.0]])
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self._test_op(AmaxModel(), (x,), tester_factory)
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self._test_op(AmaxModel(dim=0), (x,), tester_factory)
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self._test_op(AmaxModel(dim=1), (x,), tester_factory)
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# Tensor with negative values
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x = torch.tensor([[-3.0, -2.0, -1.0], [-6.0, -5.0, -4.0]])
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self._test_op(AmaxModel(), (x,), tester_factory)
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self._test_op(AmaxModel(dim=0), (x,), tester_factory)
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self._test_op(AmaxModel(dim=1), (x,), tester_factory)
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# Tensor with mixed positive and negative values
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x = torch.tensor([[-3.0, 2.0, -1.0], [6.0, -5.0, 4.0]])
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self._test_op(AmaxModel(), (x,), tester_factory)
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self._test_op(AmaxModel(dim=0), (x,), tester_factory)
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self._test_op(AmaxModel(dim=1), (x,), tester_factory)
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def test_amax_edge_cases(self, tester_factory: Callable) -> None:
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# Test edge cases
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# Tensor with all same values
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x = torch.ones(3, 4)
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self._test_op(AmaxModel(), (x,), tester_factory)
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self._test_op(AmaxModel(dim=0), (x,), tester_factory)
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self._test_op(AmaxModel(dim=1), (x,), tester_factory)
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# Zero tensor
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x = torch.zeros(3, 4)
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self._test_op(AmaxModel(), (x,), tester_factory)
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self._test_op(AmaxModel(dim=0), (x,), tester_factory)
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self._test_op(AmaxModel(dim=1), (x,), tester_factory)
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# Tensor with infinity
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x = torch.tensor([[1.0, float('inf'), 3.0], [4.0, 5.0, float('inf')]])
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self._test_op(AmaxModel(), (x,), tester_factory)
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self._test_op(AmaxModel(dim=0), (x,), tester_factory)
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self._test_op(AmaxModel(dim=1), (x,), tester_factory)
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# Tensor with negative infinity
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x = torch.tensor([[1.0, float('-inf'), 3.0], [4.0, 5.0, float('-inf')]])
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self._test_op(AmaxModel(), (x,), tester_factory)
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self._test_op(AmaxModel(dim=0), (x,), tester_factory)
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self._test_op(AmaxModel(dim=1), (x,), tester_factory)
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# Tensor with NaN (NaN should be propagated)
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x = torch.tensor([[1.0, float('nan'), 3.0], [4.0, 5.0, float('nan')]])
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self._test_op(AmaxModel(), (x,), tester_factory)
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self._test_op(AmaxModel(dim=0), (x,), tester_factory)
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self._test_op(AmaxModel(dim=1), (x,), tester_factory)
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# Single element tensor
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x = torch.tensor([5.0])
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self._test_op(AmaxModel(), (x,), tester_factory)
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self._test_op(AmaxModel(dim=0), (x,), tester_factory)
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def test_amax_scalar(self, tester_factory: Callable) -> None:
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# Test with scalar input (1-element tensor)
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self._test_op(AmaxModel(), (torch.tensor([5.0]),), tester_factory)
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self._test_op(AmaxModel(dim=0), (torch.tensor([5.0]),), tester_factory)

‎backends/test/suite/operators/test_amin.py‎

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# (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
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# pyre-strict
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from typing import Callable, List, Optional, Tuple, Union
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import torch
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from executorch.backends.test.compliance_suite import (
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dtype_test,
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operator_test,
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OperatorTest,
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)
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class AminModel(torch.nn.Module):
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def __init__(
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self,
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dim: Optional[Union[int, Tuple[int, ...], List[int]]] = None,
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keepdim: bool = False
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):
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super().__init__()
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self.dim = dim
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self.keepdim = keepdim
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def forward(self, x):
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return torch.amin(x, dim=self.dim, keepdim=self.keepdim)
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@operator_test
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class TestAmin(OperatorTest):
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@dtype_test
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def test_amin_dtype(self, dtype, tester_factory: Callable) -> None:
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# Test with different dtypes
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model = AminModel().to(dtype)
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self._test_op(model, (torch.rand(10, 10).to(dtype),), tester_factory)
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def test_amin_basic(self, tester_factory: Callable) -> None:
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# Basic test with default parameters (global reduction)
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self._test_op(AminModel(), (torch.randn(10, 10),), tester_factory)
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def test_amin_dim(self, tester_factory: Callable) -> None:
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# Test with different dimensions
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# 2D tensor, dim=0
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self._test_op(AminModel(dim=0), (torch.randn(5, 10),), tester_factory)
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# 2D tensor, dim=1
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self._test_op(AminModel(dim=1), (torch.randn(5, 10),), tester_factory)
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# 3D tensor, dim=0
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self._test_op(AminModel(dim=0), (torch.randn(3, 4, 5),), tester_factory)
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# 3D tensor, dim=1
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self._test_op(AminModel(dim=1), (torch.randn(3, 4, 5),), tester_factory)
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# 3D tensor, dim=2
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self._test_op(AminModel(dim=2), (torch.randn(3, 4, 5),), tester_factory)
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# 4D tensor, dim=1
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self._test_op(AminModel(dim=1), (torch.randn(2, 3, 4, 5),), tester_factory)
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# Negative dim (last dimension)
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self._test_op(AminModel(dim=-1), (torch.randn(3, 4, 5),), tester_factory)
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# Negative dim (second-to-last dimension)
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self._test_op(AminModel(dim=-2), (torch.randn(3, 4, 5),), tester_factory)
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def test_amin_multi_dim(self, tester_factory: Callable) -> None:
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# Test with multiple dimensions
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# 3D tensor, dim=(0, 1)
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self._test_op(AminModel(dim=(0, 1)), (torch.randn(3, 4, 5),), tester_factory)
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# 3D tensor, dim=(0, 2)
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self._test_op(AminModel(dim=(0, 2)), (torch.randn(3, 4, 5),), tester_factory)
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# 3D tensor, dim=(1, 2)
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self._test_op(AminModel(dim=(1, 2)), (torch.randn(3, 4, 5),), tester_factory)
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# 4D tensor, dim=(1, 3)
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self._test_op(AminModel(dim=(1, 3)), (torch.randn(2, 3, 4, 5),), tester_factory)
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# 4D tensor, dim=(0, 2)
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self._test_op(AminModel(dim=(0, 2)), (torch.randn(2, 3, 4, 5),), tester_factory)
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# 4D tensor, dim=(-1, -3)
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self._test_op(AminModel(dim=(-1, -3)), (torch.randn(2, 3, 4, 5),), tester_factory)
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# 4D tensor, all dimensions
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self._test_op(AminModel(dim=(0, 1, 2, 3)), (torch.randn(2, 3, 4, 5),), tester_factory)
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def test_amin_keepdim(self, tester_factory: Callable) -> None:
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# Test with keepdim=True
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# 2D tensor, dim=0, keepdim=True
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self._test_op(AminModel(dim=0, keepdim=True), (torch.randn(5, 10),), tester_factory)
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# 2D tensor, dim=1, keepdim=True
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self._test_op(AminModel(dim=1, keepdim=True), (torch.randn(5, 10),), tester_factory)
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# 3D tensor, dim=1, keepdim=True
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self._test_op(AminModel(dim=1, keepdim=True), (torch.randn(3, 4, 5),), tester_factory)
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# 4D tensor, dim=2, keepdim=True
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self._test_op(AminModel(dim=2, keepdim=True), (torch.randn(2, 3, 4, 5),), tester_factory)
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# Multiple dimensions with keepdim=True
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self._test_op(AminModel(dim=(1, 2), keepdim=True), (torch.randn(3, 4, 5),), tester_factory)
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def test_amin_shapes(self, tester_factory: Callable) -> None:
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# Test with different tensor shapes
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# 1D tensor
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self._test_op(AminModel(), (torch.randn(20),), tester_factory)
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self._test_op(AminModel(dim=0), (torch.randn(20),), tester_factory)
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# 2D tensor
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self._test_op(AminModel(), (torch.randn(5, 10),), tester_factory)
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# 3D tensor
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self._test_op(AminModel(), (torch.randn(3, 4, 5),), tester_factory)
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# 4D tensor
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self._test_op(AminModel(), (torch.randn(2, 3, 4, 5),), tester_factory)
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# 5D tensor
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self._test_op(AminModel(), (torch.randn(2, 2, 3, 4, 5),), tester_factory)
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def test_amin_values(self, tester_factory: Callable) -> None:
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# Test with different value patterns
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# Tensor with clear minimum
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x = torch.tensor([[6.0, 5.0, 4.0], [3.0, 2.0, 1.0]])
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self._test_op(AminModel(), (x,), tester_factory)
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self._test_op(AminModel(dim=0), (x,), tester_factory)
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self._test_op(AminModel(dim=1), (x,), tester_factory)
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# Tensor with duplicate minimum values
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x = torch.tensor([[3.0, 2.0, 2.0], [1.0, 1.0, 5.0]])
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self._test_op(AminModel(), (x,), tester_factory)
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self._test_op(AminModel(dim=0), (x,), tester_factory)
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self._test_op(AminModel(dim=1), (x,), tester_factory)
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# Tensor with negative values
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x = torch.tensor([[-3.0, -2.0, -1.0], [-6.0, -5.0, -4.0]])
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self._test_op(AminModel(), (x,), tester_factory)
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self._test_op(AminModel(dim=0), (x,), tester_factory)
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self._test_op(AminModel(dim=1), (x,), tester_factory)
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# Tensor with mixed positive and negative values
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x = torch.tensor([[-3.0, 2.0, -1.0], [6.0, -5.0, 4.0]])
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self._test_op(AminModel(), (x,), tester_factory)
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self._test_op(AminModel(dim=0), (x,), tester_factory)
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self._test_op(AminModel(dim=1), (x,), tester_factory)
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def test_amin_edge_cases(self, tester_factory: Callable) -> None:
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# Test edge cases
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# Tensor with all same values
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x = torch.ones(3, 4)
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self._test_op(AminModel(), (x,), tester_factory)
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self._test_op(AminModel(dim=0), (x,), tester_factory)
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self._test_op(AminModel(dim=1), (x,), tester_factory)
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# Zero tensor
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x = torch.zeros(3, 4)
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self._test_op(AminModel(), (x,), tester_factory)
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self._test_op(AminModel(dim=0), (x,), tester_factory)
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self._test_op(AminModel(dim=1), (x,), tester_factory)
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# Tensor with infinity
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x = torch.tensor([[1.0, float('inf'), 3.0], [4.0, 5.0, float('inf')]])
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self._test_op(AminModel(), (x,), tester_factory)
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self._test_op(AminModel(dim=0), (x,), tester_factory)
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self._test_op(AminModel(dim=1), (x,), tester_factory)
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# Tensor with negative infinity
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x = torch.tensor([[1.0, float('-inf'), 3.0], [4.0, 5.0, float('-inf')]])
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self._test_op(AminModel(), (x,), tester_factory)
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self._test_op(AminModel(dim=0), (x,), tester_factory)
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self._test_op(AminModel(dim=1), (x,), tester_factory)
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# Tensor with NaN (NaN should be propagated)
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x = torch.tensor([[1.0, float('nan'), 3.0], [4.0, 5.0, float('nan')]])
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self._test_op(AminModel(), (x,), tester_factory)
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self._test_op(AminModel(dim=0), (x,), tester_factory)
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self._test_op(AminModel(dim=1), (x,), tester_factory)
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# Single element tensor
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x = torch.tensor([5.0])
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self._test_op(AminModel(), (x,), tester_factory)
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self._test_op(AminModel(dim=0), (x,), tester_factory)
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def test_amin_scalar(self, tester_factory: Callable) -> None:
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# Test with scalar input (1-element tensor)
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self._test_op(AminModel(), (torch.tensor([5.0]),), tester_factory)
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self._test_op(AminModel(dim=0), (torch.tensor([5.0]),), tester_factory)

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