Added tests to test for deterministic_maxpool3d

test/test_nn.py

@@ -52,6 +52,7 @@
 from torch.testing._internal.common_cuda import tf32_on_and_off, tf32_is_not_fp32, tf32_off, tf32_on
 from torch.types import _TensorOrTensors
 from torch.testing._internal.common_mkldnn import bf32_on_and_off
+from torch.nn.functional import deterministic_max_pool3d
 
 AMPERE_OR_ROCM = TEST_WITH_ROCM or tf32_is_not_fp32()
 
@@ -7165,6 +7166,42 @@ def test_preserves_memory_format(self):
         nhwc_padded = torch.constant_pad_nd(nhwc_tensor, [1, 2], 0.5)
         self.assertTrue(nhwc_padded.is_contiguous(memory_format=torch.channels_last))
 
+class TestDeterministicMaxPool3D(unittest.TestCase):
+    def setUp(self):
+        # This method will be called before each test.
+        self.input_tensor = torch.randn(1, 1, 4, 4, 4, dtype=torch.double)
+        self.kernel_size = 2
+        self.stride = 2
+        self.padding = 0
+        self.dilation = 1
+        self.ceil_mode = False
+
+    def test_basic_functionality(self):
+        # Test the basic functionality of the deterministic max pooling
+        deterministic_output = deterministic_max_pool3d(
+            self.input_tensor, self.kernel_size, self.stride, self.padding, 
+            self.dilation, self.ceil_mode, deterministic=False
+        )
+        expected_output = torch.nn.functional.max_pool3d(
+            self.input_tensor, self.kernel_size, self.stride, self.padding, 
+            self.dilation, self.ceil_mode
+        )
+        self.assertTrue(torch.allclose(deterministic_output, expected_output),
+                        "The deterministic function does not match expected output.")
+
+    def test_deterministic_output(self):
+        # Test that deterministic flag provides the same output on multiple runs
+        output1 = deterministic_max_pool3d(
+            self.input_tensor, self.kernel_size, self.stride, self.padding, 
+            self.dilation, self.ceil_mode, deterministic=True
+        )
+        output2 = deterministic_max_pool3d(
+            self.input_tensor, self.kernel_size, self.stride, self.padding, 
+            self.dilation, self.ceil_mode, deterministic=True
+        )
+        self.assertTrue(torch.allclose(output1, output2),
+                        "Deterministic outputs are not identical on repeated runs.")
+
 
 class TestAddRelu(TestCase):
     def test_add_relu(self):