adding test cases and correcting empty__stride decomposition

py/torch_tensorrt/dynamo/lowering/_decompositions.py

@@ -232,7 +232,7 @@ def select_scatter_decomposition(
 def empty_strided_decomposition(*args, **kwargs) -> torch.Tensor:
     empty_size = args[0]
     empty_stride = args[1]
-    return torch.as_strided(torch.empty(empty_size), empty_stride)
+    return torch.as_strided(torch.empty(empty_size), empty_size, empty_stride)
 
 
 def get_decompositions(

tests/py/dynamo/lowering/test_decompositions.py

@@ -1,6 +1,8 @@
 import torch
 import torch_tensorrt
+from parameterized import parameterized
 from torch.testing._internal.common_utils import TestCase, run_tests
+from parameterized import parameterized
 
 from ..testing_utilities import DECIMALS_OF_AGREEMENT, lower_graph_testing
 
@@ -868,6 +870,99 @@ def forward(self, x, src, dim, index):
             f"Select_scatter TRT outputs don't match with the original model.",
         )
 
+        empty_ops = [
+        (
+            "empty_stride_one_dimension_firstcase",
+            [5, 5],
+            [1, 2],
+            None,
+        ),
+        (
+            "empty_stride_two_dimension_secondcase",
+            [5, 5],
+            [2, 2],
+            None,
+        ),
+        (
+            "empty_three_dimension",
+            [8, 8, 8],
+            [1, 2, 3],
+            torch.int32,
+        ),
+    ]
+
+    @parameterized.expand(
+        [(empty_op[0], empty_op[1], empty_op[2], empty_op[3]) for empty_op in empty_ops]
+    )
+    def test_empty_stride(self, _, shape_or_input, stride, data_type):
+        class TestModule(torch.nn.Module):
+            def __init__(self):
+                super().__init__()
+
+            def forward(self, input):
+                # The add operation is added otherwise it returns an empty graph post lowering passes
+                add_tensor = torch.ops.aten.add(input[0], input[0])
+                shape_or_input[0] = input[0].shape[0]
+                empty_strided = torch.ops.aten.empty_strided.default(
+                    shape_or_input, stride, dtype=data_type
+                )
+                add_tensor = empty_strided.cuda() + add_tensor
+                return add_tensor
+
+        # Operations expected to be included in the traced graph after decompositions
+        unexpected_ops = {
+            torch.ops.aten.empty_strided.default,
+            torch.ops.aten.empty_permuted.default,
+        }
+        expected_ops = {torch.ops.aten.add.Tensor}
+
+        input = [torch.randint(1, 3, shape_or_input, dtype=torch.int32).cuda()]
+        inputs = [input]
+
+        fx_graph = torch.fx.symbolic_trace(TestModule())
+
+        unexpected_ops_seen, expected_ops_unseen = lower_graph_testing(
+            fx_graph,
+            inputs,
+            expected_ops=expected_ops,
+            unexpected_ops=unexpected_ops,
+            min_block_size=2,
+        )
+
+        torch._dynamo.reset()
+
+        self.assertEqual(
+            len(unexpected_ops_seen),
+            0,
+            f"The following unexpected ops were encountered: {unexpected_ops_seen}",
+        )
+
+        self.assertEqual(
+            len(expected_ops_unseen),
+            0,
+            f"The following expected ops were not encountered: {expected_ops_unseen}",
+        )
+
+        torch._dynamo.reset()
+
+        # Validate that the results between Torch and Torch-TRT are similar
+        optimized_model = torch_tensorrt.compile(
+            fx_graph,
+            "torch_compile",
+            inputs,
+            min_block_size=1,
+            truncate_double=True,
+            pass_through_build_failures=True,
+        )
+        optimized_model_results = optimized_model(*inputs).detach().cpu()
+        torch_model_results = fx_graph(*inputs).detach().cpu()
+
+        self.assertEqual(
+            optimized_model_results.shape,
+            torch_model_results.shape,
+            f"The optimized model results shape and torch model results shape should be equal in empty_stride",
+        )
+
 
 if __name__ == "__main__":
     run_tests()