Review comments- adding cases for stride, correcting validator and changing call to torch.ops.aten.empty.memory_format

Author: apbose

py/torch_tensorrt/dynamo/conversion/ops_evaluators.py

@@ -123,8 +123,6 @@ def aten_ops_randperm(
 
 def empty_validator(empty_node: Node) -> bool:
     layout = empty_node.kwargs.get("layout", None)
-    pin_memory = empty_node.kwargs.get("pin_memory", None)
-    memory_format = empty_node.kwargs.get("memory_format", None)
     if layout is not None:
         _LOGGER.debug(f"Currently we don't support specifying layout, got {layout}.")
         return False

tests/py/dynamo/conversion/test_empty_aten.py

@@ -2,7 +2,7 @@
 import torch
 import torch.nn as nn
 import torch_tensorrt
-from harness import DispatchTestCase
+from .harness import DispatchTestCase
 from parameterized import parameterized
 from torch.testing._internal.common_utils import run_tests
 
@@ -75,16 +75,16 @@
 
 class TestRandConverter(DispatchTestCase):
     @parameterized.expand(
-        [(empty_op[0], empty_op[1], empty_op[2], empty_op[3]) for empty_op in empty_ops]
+        [(empty_op[0], empty_op[1], empty_op[2], empty_op[3], empty_op[4]) for empty_op in empty_ops]
     )
-    def test_empty(self, name, shape_or_input, data_type, device):
+    def test_empty(self, name, shape_or_input, data_type, device, memory_format):
         class TestModule(nn.Module):
             def __init__(self):
                 super().__init__()
 
             def forward(self, x):
                 shape_or_input[0] = x.shape[0]
-                return torch.empty(shape_or_input)
+                return torch.ops.aten.empty.memory_format(shape_or_input, dtype = data_type, memory_format = layout, device = device)
 
         empty_model = TestModule()