Update

[ghstack-poisoned]

Author: GregoryComer

backends/test/compliance_suite/operators/test_log_softmax.py

@@ -0,0 +1,48 @@
+# (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
+
+# pyre-strict
+
+from typing import Callable
+
+import torch
+
+from executorch.backends.test.compliance_suite import (
+    dtype_test,
+    operator_test,
+    OperatorTest,
+)
+
+class Model(torch.nn.Module):
+    def __init__(self, dim=-1):
+        super().__init__()
+        self.dim = dim
+
+    def forward(self, x):
+        return torch.nn.functional.log_softmax(x, dim=self.dim)
+
+@operator_test
+class TestLogSoftmax(OperatorTest):
+    @dtype_test
+    def test_log_softmax_dtype(self, dtype, tester_factory: Callable) -> None:
+        self._test_op(Model(), ((torch.rand(2, 10) * 100).to(dtype),), tester_factory)
+        
+    def test_log_softmax_f32_dim_last(self, tester_factory: Callable) -> None:
+        # Default dim is -1 (last dimension)
+        self._test_op(Model(), (torch.randn(3, 4, 5),), tester_factory)
+
+    def test_log_softmax_f32_dim_first(self, tester_factory: Callable) -> None:
+        # Test with dim=0 (first dimension)
+        self._test_op(Model(dim=0), (torch.randn(3, 4, 5),), tester_factory)
+        
+    def test_log_softmax_f32_dim_middle(self, tester_factory: Callable) -> None:
+        # Test with dim=1 (middle dimension)
+        self._test_op(Model(dim=1), (torch.randn(3, 4, 5),), tester_factory)
+    
+    def test_log_softmax_f32_1d_tensor(self, tester_factory: Callable) -> None:
+        # Test with 1D tensor
+        self._test_op(Model(), (torch.randn(10),), tester_factory)
+        
+    def test_log_softmax_f32_large_values(self, tester_factory: Callable) -> None:
+        # Test with large values to check numerical stability
+        x = torch.tensor([[1000.0, 0.0, -1000.0]])
+        self._test_op(Model(), (x,), tester_factory)

backends/test/compliance_suite/operators/test_softmax.py

@@ -0,0 +1,48 @@
+# (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
+
+# pyre-strict
+
+from typing import Callable
+
+import torch
+
+from executorch.backends.test.compliance_suite import (
+    dtype_test,
+    operator_test,
+    OperatorTest,
+)
+
+class Model(torch.nn.Module):
+    def __init__(self, dim=-1):
+        super().__init__()
+        self.dim = dim
+
+    def forward(self, x):
+        return torch.nn.functional.softmax(x, dim=self.dim)
+
+@operator_test
+class TestSoftmax(OperatorTest):
+    @dtype_test
+    def test_softmax_dtype(self, dtype, tester_factory: Callable) -> None:
+        self._test_op(Model(), ((torch.rand(2, 10) * 100).to(dtype),), tester_factory)
+        
+    def test_softmax_f32_dim_last(self, tester_factory: Callable) -> None:
+        # Default dim is -1 (last dimension)
+        self._test_op(Model(), (torch.randn(3, 4, 5),), tester_factory)
+
+    def test_softmax_f32_dim_first(self, tester_factory: Callable) -> None:
+        # Test with dim=0 (first dimension)
+        self._test_op(Model(dim=0), (torch.randn(3, 4, 5),), tester_factory)
+        
+    def test_softmax_f32_dim_middle(self, tester_factory: Callable) -> None:
+        # Test with dim=1 (middle dimension)
+        self._test_op(Model(dim=1), (torch.randn(3, 4, 5),), tester_factory)
+    
+    def test_softmax_f32_1d_tensor(self, tester_factory: Callable) -> None:
+        # Test with 1D tensor
+        self._test_op(Model(), (torch.randn(10),), tester_factory)
+        
+    def test_softmax_f32_large_values(self, tester_factory: Callable) -> None:
+        # Test with large values to check numerical stability
+        x = torch.tensor([[1000.0, 0.0, -1000.0]])
+        self._test_op(Model(), (x,), tester_factory)

backends/test/compliance_suite/operators/test_softmax2d.py

@@ -0,0 +1,49 @@
+# (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
+
+# pyre-strict
+
+from typing import Callable
+
+import torch
+
+from executorch.backends.test.compliance_suite import (
+    dtype_test,
+    operator_test,
+    OperatorTest,
+)
+
+class Model(torch.nn.Module):
+    def forward(self, x):
+        # softmax2d is equivalent to softmax with dim=1 for 4D inputs
+        return torch.nn.functional.softmax(x, dim=1)
+
+@operator_test
+class TestSoftmax2d(OperatorTest):
+    @dtype_test
+    def test_softmax2d_dtype(self, dtype, tester_factory: Callable) -> None:
+        # Input must be 4D (N, C, H, W)
+        self._test_op(Model(), ((torch.rand(2, 3, 4, 5) * 100).to(dtype),), tester_factory)
+        
+    def test_softmax2d_f32_various_shapes(self, tester_factory: Callable) -> None:
+        # Test with different shapes
+        self._test_op(Model(), (torch.randn(1, 3, 8, 8),), tester_factory)
+        
+    def test_softmax2d_f32_single_channel(self, tester_factory: Callable) -> None:
+        # Test with single channel (C=1)
+        self._test_op(Model(), (torch.randn(2, 1, 4, 4),), tester_factory)
+        
+    def test_softmax2d_f32_many_channels(self, tester_factory: Callable) -> None:
+        # Test with many channels
+        self._test_op(Model(), (torch.randn(2, 16, 4, 4),), tester_factory)
+    
+    def test_softmax2d_f32_single_batch(self, tester_factory: Callable) -> None:
+        # Test with single batch (N=1)
+        self._test_op(Model(), (torch.randn(1, 3, 4, 4),), tester_factory)
+        
+    def test_softmax2d_f32_large_values(self, tester_factory: Callable) -> None:
+        # Test with large values to check numerical stability
+        x = torch.zeros(2, 3, 2, 2)
+        x[:, 0] = 1000.0  # First channel has large positive values
+        x[:, 1] = 0.0     # Second channel has zeros
+        x[:, 2] = -1000.0 # Third channel has large negative values
+        self._test_op(Model(), (x,), tester_factory)