From 8f027788a004b493c7f5cf5219cc633331d733f7 Mon Sep 17 00:00:00 2001
From: lanluo-nvidia <lanl@nvidia.com>
Date: Sat, 22 Nov 2025 20:20:19 -0800
Subject: [PATCH 1/6] change trt_ep_path so that parallel test execution won't
 mix up

---
 tests/py/dynamo/models/test_export_serde.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/tests/py/dynamo/models/test_export_serde.py b/tests/py/dynamo/models/test_export_serde.py
index c5b007e34b..1d58f4fb3f 100644
--- a/tests/py/dynamo/models/test_export_serde.py
+++ b/tests/py/dynamo/models/test_export_serde.py
@@ -141,6 +141,8 @@ def test_no_compile(ir):
     This tests export serde functionality on a model
     which won't convert to TRT because of min_block_size=5 constraint
     """
+    tmp_dir = tempfile.mkdtemp(prefix="test_no_compile")
+    trt_ep_path = os.path.join(tmp_dir, "trt.ep")
 
     class MyModule(torch.nn.Module):
         def __init__(self):

From 2fddc94ea21105fcf00e57c3e81ed8fa55e1c645 Mon Sep 17 00:00:00 2001
From: lanluo-nvidia <lanl@nvidia.com>
Date: Sat, 22 Nov 2025 19:34:52 -0800
Subject: [PATCH 2/6] change trt_ep_path so that parallel test execution won't
 mix up

---
 .../dynamo/models/test_export_kwargs_serde.py | 15 ++++++---
 tests/py/dynamo/models/test_export_serde.py   | 31 +++++++++++++++++--
 tests/py/dynamo/models/test_model_refit.py    |  6 ++--
 tests/py/dynamo/models/test_reexport.py       | 29 +++++++++++++++--
 4 files changed, 68 insertions(+), 13 deletions(-)

diff --git a/tests/py/dynamo/models/test_export_kwargs_serde.py b/tests/py/dynamo/models/test_export_kwargs_serde.py
index dabbad3cc8..3efe498223 100644
--- a/tests/py/dynamo/models/test_export_kwargs_serde.py
+++ b/tests/py/dynamo/models/test_export_kwargs_serde.py
@@ -75,7 +75,8 @@ def forward(self, x, b=5, c=None, d=None):
     )
 
     # Save the module
-    trt_ep_path = os.path.join(tempfile.gettempdir(), "compiled.ep")
+    tmp_dir = tempfile.mkdtemp(prefix="test_custom_model")
+    trt_ep_path = os.path.join(tmp_dir, "compiled.ep")
     torchtrt.save(trt_gm, trt_ep_path, retrace=False)
     # Clean up model env
     torch._dynamo.reset()
@@ -137,7 +138,8 @@ def forward(self, x, b=5, c=None, d=None):
     )
 
     # Save the module
-    trt_ep_path = os.path.join(tempfile.gettempdir(), "compiled.ep")
+    tmp_dir = tempfile.mkdtemp(prefix="test_custom_model_with_dynamo_trace")
+    trt_ep_path = os.path.join(tmp_dir, "compiled.ep")
     torchtrt.save(trt_gm, trt_ep_path, retrace=False)
     # Clean up model env
     torch._dynamo.reset()
@@ -208,7 +210,8 @@ def forward(self, x, b=5, c=None, d=None):
     )
 
     # Save the module
-    trt_ep_path = os.path.join(tempfile.gettempdir(), "compiled.ep")
+    tmp_dir = tempfile.mkdtemp(prefix="test_custom_model_with_dynamo_trace_dynamic")
+    trt_ep_path = os.path.join(tmp_dir, "compiled.ep")
     torchtrt.save(trt_gm, trt_ep_path, retrace=False)
     # Clean up model env
     torch._dynamo.reset()
@@ -298,7 +301,8 @@ def forward(self, x, b=None, c=None, d=None, e=[]):
         msg=f"CustomKwargs Module TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
     )
     # Save the module
-    trt_ep_path = os.path.join(tempfile.gettempdir(), "compiled.ep")
+    tmp_dir = tempfile.mkdtemp(prefix="test_custom_model_with_dynamo_trace_kwarg_dynamic")
+    trt_ep_path = os.path.join(tmp_dir, "compiled.ep")
     torchtrt.save(trt_gm, trt_ep_path, retrace=False)
     # Clean up model env
     torch._dynamo.reset()
@@ -388,7 +392,8 @@ def forward(self, x, b=None, c=None, d=None, e=[]):
         msg=f"CustomKwargs Module TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
     )
     # Save the module
-    trt_ep_path = os.path.join(tempfile.gettempdir(), "compiled.ep")
+    tmp_dir = tempfile.mkdtemp(prefix="test_custom_model_with_dynamo_trace_kwarg_dynamic")
+    trt_ep_path = os.path.join(tmp_dir, "compiled.ep")
     torchtrt.save(trt_gm, trt_ep_path, retrace=False)
     # Clean up model env
     torch._dynamo.reset()
diff --git a/tests/py/dynamo/models/test_export_serde.py b/tests/py/dynamo/models/test_export_serde.py
index 1d58f4fb3f..1979fca298 100644
--- a/tests/py/dynamo/models/test_export_serde.py
+++ b/tests/py/dynamo/models/test_export_serde.py
@@ -17,9 +17,6 @@
 if importlib.util.find_spec("torchvision"):
     import torchvision.models as models
 
-trt_ep_path = os.path.join(tempfile.gettempdir(), "trt.ep")
-
-
 @pytest.mark.unit
 @pytest.mark.critical
 def test_base_full_compile(ir):
@@ -27,6 +24,8 @@ def test_base_full_compile(ir):
     This tests export serde functionality on a base model
     which is fully TRT convertible
     """
+    tmp_dir = tempfile.mkdtemp(prefix="test_base_full_compile")
+    trt_ep_path = os.path.join(tmp_dir, "trt.ep")
 
     class MyModule(torch.nn.Module):
         def __init__(self):
@@ -82,6 +81,9 @@ def test_base_full_compile_multiple_outputs(ir):
     with multiple outputs which is fully TRT convertible
     """
 
+    tmp_dir = tempfile.mkdtemp(prefix="test_base_full_compile_multiple_outputs")
+    trt_ep_path = os.path.join(tmp_dir, "trt.ep")
+
     class MyModule(torch.nn.Module):
         def __init__(self):
             super().__init__()
@@ -204,6 +206,9 @@ def test_hybrid_relu_fallback(ir):
     fallback
     """
 
+    tmp_dir = tempfile.mkdtemp(prefix="test_hybrid_relu_fallback")
+    trt_ep_path = os.path.join(tmp_dir, "trt.ep")
+
     class MyModule(torch.nn.Module):
         def __init__(self):
             super().__init__()
@@ -264,6 +269,9 @@ def test_resnet18(ir):
     """
     This tests export save and load functionality on Resnet18 model
     """
+    tmp_dir = tempfile.mkdtemp(prefix="test_resnet18")
+    trt_ep_path = os.path.join(tmp_dir, "trt.ep")
+
     model = models.resnet18().eval().cuda()
     input = torch.randn((1, 3, 224, 224)).to("cuda")
 
@@ -309,6 +317,9 @@ def test_resnet18_cpu_offload(ir):
     """
     This tests export save and load functionality on Resnet18 model
     """
+    tmp_dir = tempfile.mkdtemp(prefix="test_resnet18_cpu_offload")
+    trt_ep_path = os.path.join(tmp_dir, "trt.ep")
+
     model = models.resnet18().eval().cuda()
     input = torch.randn((1, 3, 224, 224)).to("cuda")
 
@@ -361,6 +372,9 @@ def test_resnet18_dynamic(ir):
     """
     This tests export save and load functionality on Resnet18 model
     """
+    tmp_dir = tempfile.mkdtemp(prefix="test_resnet18_dynamic")
+    trt_ep_path = os.path.join(tmp_dir, "trt.ep")
+
     model = models.resnet18().eval().cuda()
     input = torch.randn((1, 3, 224, 224)).to("cuda")
 
@@ -401,6 +415,9 @@ def test_resnet18_torch_exec_ops_serde(ir):
     """
     This tests export save and load functionality on Resnet18 model
     """
+    tmp_dir = tempfile.mkdtemp(prefix="test_resnet18_torch_exec_ops_serde")
+    trt_ep_path = os.path.join(tmp_dir, "trt.ep")
+
     model = models.resnet18().eval().cuda()
     input = torch.randn((1, 3, 224, 224)).to("cuda")
 
@@ -434,6 +451,9 @@ def test_hybrid_conv_fallback(ir):
     model where a conv (a weighted layer)  has been forced to fallback to Pytorch.
     """
 
+    tmp_dir = tempfile.mkdtemp(prefix="test_hybrid_conv_fallback")
+    trt_ep_path = os.path.join(tmp_dir, "trt.ep")
+
     class MyModule(torch.nn.Module):
         def __init__(self):
             super().__init__()
@@ -495,6 +515,9 @@ def test_hybrid_conv_fallback_cpu_offload(ir):
     model where a conv (a weighted layer)  has been forced to fallback to Pytorch.
     """
 
+    tmp_dir = tempfile.mkdtemp(prefix="test_hybrid_conv_fallback_cpu_offload")
+    trt_ep_path = os.path.join(tmp_dir, "trt.ep")
+
     class MyModule(torch.nn.Module):
         def __init__(self):
             super().__init__()
@@ -557,6 +580,8 @@ def test_arange_export(ir):
     Here the arange output is a static constant (which is registered as input to the graph)
     in the exporter.
     """
+    tmp_dir = tempfile.mkdtemp(prefix="test_arange_export")
+    trt_ep_path = os.path.join(tmp_dir, "trt.ep")
 
     class MyModule(torch.nn.Module):
         def __init__(self):
diff --git a/tests/py/dynamo/models/test_model_refit.py b/tests/py/dynamo/models/test_model_refit.py
index e6b7f6e2a4..fe6d4c2ce5 100644
--- a/tests/py/dynamo/models/test_model_refit.py
+++ b/tests/py/dynamo/models/test_model_refit.py
@@ -532,7 +532,8 @@ def test_refit_one_engine_bert_with_weightmap():
 )
 @pytest.mark.unit
 def test_refit_one_engine_inline_runtime_with_weightmap():
-    trt_ep_path = os.path.join(tempfile.gettempdir(), "compiled.ep")
+    tmp_dir = tempfile.mkdtemp(prefix="test_refit_one_engine_inline_runtime_with_weightmap")
+    trt_ep_path = os.path.join(tmp_dir, "compiled.ep")
     model = models.resnet18(pretrained=False).eval().to("cuda")
     model2 = models.resnet18(pretrained=True).eval().to("cuda")
     inputs = [torch.randn((1, 3, 224, 224)).to("cuda")]
@@ -889,7 +890,8 @@ def test_refit_one_engine_bert_without_weightmap():
 )
 @pytest.mark.unit
 def test_refit_one_engine_inline_runtime_without_weightmap():
-    trt_ep_path = os.path.join(tempfile.gettempdir(), "compiled.ep")
+    tmp_dir = tempfile.mkdtemp(prefix="test_refit_one_engine_inline_runtime_without_weightmap")
+    trt_ep_path = os.path.join(tmp_dir, "compiled.ep")
     model = models.resnet18(pretrained=True).eval().to("cuda")
     model2 = models.resnet18(pretrained=False).eval().to("cuda")
     inputs = [torch.randn((1, 3, 224, 224)).to("cuda")]
diff --git a/tests/py/dynamo/models/test_reexport.py b/tests/py/dynamo/models/test_reexport.py
index 7c414def52..e8149dbe2e 100644
--- a/tests/py/dynamo/models/test_reexport.py
+++ b/tests/py/dynamo/models/test_reexport.py
@@ -14,8 +14,6 @@
 if importlib.util.find_spec("torchvision"):
     import torchvision.models as models
 
-trt_ep_path = os.path.join(tempfile.gettempdir(), "trt.ep")
-
 
 @pytest.mark.unit
 @pytest.mark.critical
@@ -56,6 +54,9 @@ def forward(self, x):
 
     # Reexport
     trt_exp_program = torch.export.export(trt_module, (input,), strict=False)
+    tmp_dir = tempfile.mkdtemp(prefix="test_base_full_compile")
+    trt_ep_path = os.path.join(tmp_dir, "trt.ep")
+
     torch.export.save(trt_exp_program, trt_ep_path)
 
     deser_trt_module = torchtrt.load(trt_ep_path).module()
@@ -82,6 +83,9 @@ def test_base_full_compile_multiple_outputs(ir):
     with multiple outputs which is fully TRT convertible
     """
 
+    tmp_dir = tempfile.mkdtemp(prefix="test_base_full_compile_multiple_outputs")
+    trt_ep_path = os.path.join(tmp_dir, "trt.ep")
+
     class MyModule(torch.nn.Module):
         def __init__(self):
             super().__init__()
@@ -156,7 +160,8 @@ def forward(self, x):
             conv = conv * 0.5
             relu = self.relu(conv)
             return conv, relu
-
+    tmp_dir = tempfile.mkdtemp(prefix="test_no_compile")
+    trt_ep_path = os.path.join(tmp_dir, "trt.ep")
     model = MyModule().eval().cuda()
     input = torch.randn((1, 3, 224, 224)).to("cuda")
 
@@ -207,6 +212,9 @@ def test_hybrid_relu_fallback(ir):
     fallback
     """
 
+    tmp_dir = tempfile.mkdtemp(prefix="test_hybrid_relu_fallback")
+    trt_ep_path = os.path.join(tmp_dir, "trt.ep")
+
     class MyModule(torch.nn.Module):
         def __init__(self):
             super().__init__()
@@ -270,6 +278,9 @@ def test_resnet18(ir):
     """
     This tests export save and load functionality on Resnet18 model
     """
+    tmp_dir = tempfile.mkdtemp(prefix="test_resnet18")
+    trt_ep_path = os.path.join(tmp_dir, "trt.ep")
+
     model = models.resnet18().eval().cuda()
     input = torch.randn((1, 3, 224, 224)).to("cuda")
 
@@ -315,6 +326,8 @@ def test_hybrid_conv_fallback(ir):
     This tests export save and load functionality on a hybrid
     model where a conv (a weighted layer)  has been forced to fallback to Pytorch.
     """
+    tmp_dir = tempfile.mkdtemp(prefix="test_hybrid_conv_fallback")
+    trt_ep_path = os.path.join(tmp_dir, "trt.ep")
 
     class MyModule(torch.nn.Module):
         def __init__(self):
@@ -378,6 +391,8 @@ def test_arange_export(ir):
     Here the arange output is a static constant (which is registered as input to the graph)
     in the exporter.
     """
+    tmp_dir = tempfile.mkdtemp(prefix="test_arange_export")
+    trt_ep_path = os.path.join(tmp_dir, "trt.ep")
 
     class MyModule(torch.nn.Module):
         def __init__(self):
@@ -440,6 +455,8 @@ def test_resnet18_dynamic(ir):
     """
     This tests export save and load functionality on Resnet18 model with dynamic shapes
     """
+    tmp_dir = tempfile.mkdtemp(prefix="test_resnet18_dynamic")
+    trt_ep_path = os.path.join(tmp_dir, "trt.ep")
     model = models.resnet18().eval().cuda()
     input_bs2 = torch.randn((2, 3, 224, 224)).to("cuda")
 
@@ -514,6 +531,8 @@ def test_resnet18_dynamic_fallback(ir):
     """
     This tests export save and load functionality on Resnet18 model with dynamic shapes and fallback
     """
+    tmp_dir = tempfile.mkdtemp(prefix="test_resnet18_dynamic_fallback")
+    trt_ep_path = os.path.join(tmp_dir, "trt.ep")
     model = models.resnet18().eval().cuda()
     input_bs2 = torch.randn((2, 3, 224, 224)).to("cuda")
 
@@ -588,6 +607,8 @@ def test_bitwise_and_dynamic_fallback(ir):
     """
     This tests export save and load functionality on a bitwise_and model with dynamic shapes and fallback
     """
+    tmp_dir = tempfile.mkdtemp(prefix="test_bitwise_and_dynamic_fallback")
+    trt_ep_path = os.path.join(tmp_dir, "trt.ep")
 
     class bitwise_and(torch.nn.Module):
         def forward(self, lhs_val, rhs_val):
@@ -668,6 +689,8 @@ def test_random_dynamic_fallback(ir):
     """
     This tests export save and load functionality on a random model with dynamic shapes and fallback
     """
+    tmp_dir = tempfile.mkdtemp(prefix="test_random_dynamic_fallback")
+    trt_ep_path = os.path.join(tmp_dir, "trt.ep")
 
     class NeuralNetwork(nn.Module):
         def __init__(self):

From f66d951f7e5225a33f968e9669ed7ec86169d174 Mon Sep 17 00:00:00 2001
From: lanluo-nvidia <lanl@nvidia.com>
Date: Sat, 22 Nov 2025 19:04:36 -0800
Subject: [PATCH 3/6] restore nonezero test for thor

---
 py/torch_tensorrt/dynamo/conversion/aten_ops_converters.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/py/torch_tensorrt/dynamo/conversion/aten_ops_converters.py b/py/torch_tensorrt/dynamo/conversion/aten_ops_converters.py
index 9ee84643c9..2acebea673 100644
--- a/py/torch_tensorrt/dynamo/conversion/aten_ops_converters.py
+++ b/py/torch_tensorrt/dynamo/conversion/aten_ops_converters.py
@@ -429,7 +429,7 @@ def index_nonbool_validator(
     node: Node, settings: Optional[CompilationSettings] = None
 ) -> bool:
     # for thor and tensorrt_rtx, we don't support boolean indices, due to nonzero op not supported
-    if is_thor() or ENABLED_FEATURES.tensorrt_rtx:
+    if ENABLED_FEATURES.tensorrt_rtx:
         index = node.args[1]
         for ind in index:
             if ind is not None:

From c35de076858c8fcda76e82a197370ec65b26a7cd Mon Sep 17 00:00:00 2001
From: lanluo-nvidia <lanl@nvidia.com>
Date: Sat, 22 Nov 2025 18:14:23 -0800
Subject: [PATCH 4/6] restore nonezero test for thor

---
 .../py/dynamo/conversion/test_arange_aten.py  |  6 ++---
 .../py/dynamo/conversion/test_cumsum_aten.py  |  6 ++---
 tests/py/dynamo/conversion/test_index_aten.py | 22 ++++++++-----------
 .../py/dynamo/conversion/test_nonzero_aten.py |  6 ++---
 tests/py/dynamo/conversion/test_sym_size.py   |  5 -----
 .../dynamo/runtime/test_output_allocator.py   | 10 ++++-----
 6 files changed, 23 insertions(+), 32 deletions(-)

diff --git a/tests/py/dynamo/conversion/test_arange_aten.py b/tests/py/dynamo/conversion/test_arange_aten.py
index 968611a4ec..d7b5298be5 100644
--- a/tests/py/dynamo/conversion/test_arange_aten.py
+++ b/tests/py/dynamo/conversion/test_arange_aten.py
@@ -5,14 +5,14 @@
 import torch_tensorrt
 from parameterized import parameterized
 from torch.testing._internal.common_utils import run_tests
-from torch_tensorrt._utils import is_tegra_platform, is_thor
+from torch_tensorrt._utils import is_tegra_platform
 
 from .harness import DispatchTestCase
 
 
 @unittest.skipIf(
-    is_thor() or is_tegra_platform(),
-    "Skipped on Thor and Tegra platforms",
+    is_tegra_platform(),
+    "Skipped on Tegra platforms",
 )
 class TestArangeConverter(DispatchTestCase):
     @parameterized.expand(
diff --git a/tests/py/dynamo/conversion/test_cumsum_aten.py b/tests/py/dynamo/conversion/test_cumsum_aten.py
index a677212cb1..e5e0d7df29 100644
--- a/tests/py/dynamo/conversion/test_cumsum_aten.py
+++ b/tests/py/dynamo/conversion/test_cumsum_aten.py
@@ -5,14 +5,14 @@
 import torch_tensorrt
 from parameterized import parameterized
 from torch.testing._internal.common_utils import run_tests
-from torch_tensorrt._utils import is_tegra_platform, is_thor
+from torch_tensorrt._utils import is_tegra_platform
 
 from .harness import DispatchTestCase
 
 
 @unittest.skipIf(
-    is_thor() or is_tegra_platform(),
-    "Skipped on Thor and Tegra platforms",
+    is_tegra_platform(),
+    "Skipped on Tegra platforms",
 )
 class TestCumsumConverter(DispatchTestCase):
     @parameterized.expand(
diff --git a/tests/py/dynamo/conversion/test_index_aten.py b/tests/py/dynamo/conversion/test_index_aten.py
index e34dc48dd5..ee6b61305f 100644
--- a/tests/py/dynamo/conversion/test_index_aten.py
+++ b/tests/py/dynamo/conversion/test_index_aten.py
@@ -6,7 +6,7 @@
 from parameterized import parameterized
 from torch.testing._internal.common_utils import run_tests
 from torch_tensorrt import ENABLED_FEATURES, Input
-from torch_tensorrt._utils import is_tegra_platform, is_thor
+from torch_tensorrt._utils import is_tegra_platform
 
 from .harness import DispatchTestCase
 
@@ -114,8 +114,8 @@ def forward(self, input):
         ]
     )
     @unittest.skipIf(
-        is_thor() or ENABLED_FEATURES.tensorrt_rtx,
-        "Skipped on Thor or tensorrt_rtx due to nonzero not supported",
+        ENABLED_FEATURES.tensorrt_rtx,
+        "Skipped on tensorrt_rtx due to nonzero not supported",
     )
     def test_index_constant_bool_mask(self, _, index, input):
         class TestModule(torch.nn.Module):
@@ -149,8 +149,8 @@ def forward(self, x, index0):
         )
 
     @unittest.skipIf(
-        is_thor() or ENABLED_FEATURES.tensorrt_rtx,
-        "Skipped on Thor or tensorrt_rtx due to nonzero not supported",
+        iENABLED_FEATURES.tensorrt_rtx,
+        "Skipped on tensorrt_rtx due to nonzero not supported",
     )
     def test_index_zero_two_dim_ITensor_mask(self):
         class TestModule(nn.Module):
@@ -163,10 +163,6 @@ def forward(self, x, index0):
         index0 = torch.tensor([True, False])
         self.run_test(TestModule(), [input, index0], enable_passes=True)
 
-    @unittest.skipIf(
-        is_thor(),
-        "Skipped on Thor due to nonzero not supported",
-    )
     def test_index_zero_index_three_dim_ITensor(self):
         class TestModule(nn.Module):
             def forward(self, x, index0):
@@ -180,8 +176,8 @@ def forward(self, x, index0):
         self.run_test(TestModule(), [input, index0])
 
     @unittest.skipIf(
-        is_thor() or ENABLED_FEATURES.tensorrt_rtx,
-        "Skipped on Thor or tensorrt_rtx due to nonzero not supported",
+        ENABLED_FEATURES.tensorrt_rtx,
+        "Skipped on tensorrt_rtx due to nonzero not supported",
     )
     def test_index_zero_index_three_dim_mask_ITensor(self):
         class TestModule(nn.Module):
@@ -252,8 +248,8 @@ def forward(self, input):
 
 
 @unittest.skipIf(
-    torch_tensorrt.ENABLED_FEATURES.tensorrt_rtx or is_thor() or is_tegra_platform(),
-    "nonzero is not supported for tensorrt_rtx",
+    torch_tensorrt.ENABLED_FEATURES.tensorrt_rtx or is_tegra_platform(),
+    "nonzero is not supported for tensorrt_rtx or Tegra platforms",
 )
 class TestIndexDynamicInputNonDynamicIndexConverter(DispatchTestCase):
     def test_index_input_non_dynamic_index_dynamic(self):
diff --git a/tests/py/dynamo/conversion/test_nonzero_aten.py b/tests/py/dynamo/conversion/test_nonzero_aten.py
index 641cc7c098..646f000a8d 100644
--- a/tests/py/dynamo/conversion/test_nonzero_aten.py
+++ b/tests/py/dynamo/conversion/test_nonzero_aten.py
@@ -6,14 +6,14 @@
 from parameterized import parameterized
 from torch.testing._internal.common_utils import run_tests
 from torch_tensorrt import Input
-from torch_tensorrt._utils import is_tegra_platform, is_thor
+from torch_tensorrt._utils import is_tegra_platform
 
 from .harness import DispatchTestCase
 
 
 @unittest.skipIf(
-    torch_tensorrt.ENABLED_FEATURES.tensorrt_rtx or is_thor() or is_tegra_platform(),
-    "nonzero is not supported for tensorrt_rtx",
+    torch_tensorrt.ENABLED_FEATURES.tensorrt_rtx or is_tegra_platform(),
+    "nonzero is not supported for tensorrt_rtx or Tegra platforms",
 )
 class TestNonZeroConverter(DispatchTestCase):
     @parameterized.expand(
diff --git a/tests/py/dynamo/conversion/test_sym_size.py b/tests/py/dynamo/conversion/test_sym_size.py
index 770dd75504..968ecd322b 100644
--- a/tests/py/dynamo/conversion/test_sym_size.py
+++ b/tests/py/dynamo/conversion/test_sym_size.py
@@ -4,15 +4,10 @@
 import torch.nn as nn
 from parameterized import parameterized
 from torch.testing._internal.common_utils import run_tests
-from torch_tensorrt._utils import is_thor
 
 from .harness import DispatchTestCase
 
 
-@unittest.skipIf(
-    is_thor(),
-    "Skipped on Thor",
-)
 class TestSymSizeConverter(DispatchTestCase):
     @parameterized.expand(
         [
diff --git a/tests/py/dynamo/runtime/test_output_allocator.py b/tests/py/dynamo/runtime/test_output_allocator.py
index ce00135c99..28d8ad3e94 100644
--- a/tests/py/dynamo/runtime/test_output_allocator.py
+++ b/tests/py/dynamo/runtime/test_output_allocator.py
@@ -5,7 +5,7 @@
 import torch_tensorrt
 from parameterized import parameterized
 from torch.testing._internal.common_utils import TestCase, run_tests
-from torch_tensorrt._utils import is_tegra_platform, is_thor
+from torch_tensorrt._utils import is_tegra_platform
 
 from ..testing_utilities import DECIMALS_OF_AGREEMENT
 
@@ -45,7 +45,7 @@ def forward(self, input):
 
 
 @unittest.skipIf(
-    torch_tensorrt.ENABLED_FEATURES.tensorrt_rtx or is_thor() or is_tegra_platform(),
+    torch_tensorrt.ENABLED_FEATURES.tensorrt_rtx or is_tegra_platform(),
     "TensorRT RTX does not support nonzero which are required for this test",
 )
 class TestOutputAllocatorStaticModel(TestCase):
@@ -158,7 +158,7 @@ def test_combination_of_cg_and_oa(self, _, use_python_runtime):
 
 
 @unittest.skipIf(
-    torch_tensorrt.ENABLED_FEATURES.tensorrt_rtx or is_thor() or is_tegra_platform(),
+    torch_tensorrt.ENABLED_FEATURES.tensorrt_rtx or s_tegra_platform(),
     "TensorRT RTX does not support nonzero which are required for this test",
 )
 class TestOutputAllocatorDDSModel(TestCase):
@@ -268,7 +268,7 @@ def test_combination_of_cg_and_oa(self, _, use_python_runtime):
 
 
 @unittest.skipIf(
-    torch_tensorrt.ENABLED_FEATURES.tensorrt_rtx or is_thor() or is_tegra_platform(),
+    torch_tensorrt.ENABLED_FEATURES.tensorrt_rtx or is_tegra_platform(),
     "TensorRT RTX does not support nonzero which are required for this test",
 )
 class TestOutputAllocatorDDSOpWithReductionOpModel(TestCase):
@@ -382,7 +382,7 @@ def test_combination_of_cg_and_oa(self, _, use_python_runtime):
 
 
 @unittest.skipIf(
-    torch_tensorrt.ENABLED_FEATURES.tensorrt_rtx or is_thor() or is_tegra_platform(),
+    torch_tensorrt.ENABLED_FEATURES.tensorrt_rtx or is_tegra_platform(),
     "TensorRT RTX does not support nonzero which are required for this test",
 )
 class TestOutputAllocatorDDSModelWithGraphBreak(TestCase):

From 1669fd708f5b2a55994453686cd372e0a8b646c6 Mon Sep 17 00:00:00 2001
From: lanluo-nvidia <lanl@nvidia.com>
Date: Sat, 22 Nov 2025 20:46:18 -0800
Subject: [PATCH 5/6] test

---
 tests/py/dynamo/conversion/test_index_aten.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/py/dynamo/conversion/test_index_aten.py b/tests/py/dynamo/conversion/test_index_aten.py
index ee6b61305f..fd8c35de53 100644
--- a/tests/py/dynamo/conversion/test_index_aten.py
+++ b/tests/py/dynamo/conversion/test_index_aten.py
@@ -149,7 +149,7 @@ def forward(self, x, index0):
         )
 
     @unittest.skipIf(
-        iENABLED_FEATURES.tensorrt_rtx,
+        ENABLED_FEATURES.tensorrt_rtx,
         "Skipped on tensorrt_rtx due to nonzero not supported",
     )
     def test_index_zero_two_dim_ITensor_mask(self):

From e553b1c285ecadd02ab0b6b916a86e6c5ba90d94 Mon Sep 17 00:00:00 2001
From: lanluo-nvidia <lanl@nvidia.com>
Date: Sat, 22 Nov 2025 21:06:49 -0800
Subject: [PATCH 6/6] lint

---
 tests/py/dynamo/models/test_export_kwargs_serde.py | 8 ++++++--
 tests/py/dynamo/models/test_export_serde.py        | 1 +
 tests/py/dynamo/models/test_model_refit.py         | 8 ++++++--
 tests/py/dynamo/models/test_reexport.py            | 1 +
 4 files changed, 14 insertions(+), 4 deletions(-)

diff --git a/tests/py/dynamo/models/test_export_kwargs_serde.py b/tests/py/dynamo/models/test_export_kwargs_serde.py
index 3efe498223..81121cbccd 100644
--- a/tests/py/dynamo/models/test_export_kwargs_serde.py
+++ b/tests/py/dynamo/models/test_export_kwargs_serde.py
@@ -301,7 +301,9 @@ def forward(self, x, b=None, c=None, d=None, e=[]):
         msg=f"CustomKwargs Module TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
     )
     # Save the module
-    tmp_dir = tempfile.mkdtemp(prefix="test_custom_model_with_dynamo_trace_kwarg_dynamic")
+    tmp_dir = tempfile.mkdtemp(
+        prefix="test_custom_model_with_dynamo_trace_kwarg_dynamic"
+    )
     trt_ep_path = os.path.join(tmp_dir, "compiled.ep")
     torchtrt.save(trt_gm, trt_ep_path, retrace=False)
     # Clean up model env
@@ -392,7 +394,9 @@ def forward(self, x, b=None, c=None, d=None, e=[]):
         msg=f"CustomKwargs Module TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
     )
     # Save the module
-    tmp_dir = tempfile.mkdtemp(prefix="test_custom_model_with_dynamo_trace_kwarg_dynamic")
+    tmp_dir = tempfile.mkdtemp(
+        prefix="test_custom_model_with_dynamo_trace_kwarg_dynamic"
+    )
     trt_ep_path = os.path.join(tmp_dir, "compiled.ep")
     torchtrt.save(trt_gm, trt_ep_path, retrace=False)
     # Clean up model env
diff --git a/tests/py/dynamo/models/test_export_serde.py b/tests/py/dynamo/models/test_export_serde.py
index 1979fca298..8c00d1ec3d 100644
--- a/tests/py/dynamo/models/test_export_serde.py
+++ b/tests/py/dynamo/models/test_export_serde.py
@@ -17,6 +17,7 @@
 if importlib.util.find_spec("torchvision"):
     import torchvision.models as models
 
+
 @pytest.mark.unit
 @pytest.mark.critical
 def test_base_full_compile(ir):
diff --git a/tests/py/dynamo/models/test_model_refit.py b/tests/py/dynamo/models/test_model_refit.py
index fe6d4c2ce5..c9d4595d43 100644
--- a/tests/py/dynamo/models/test_model_refit.py
+++ b/tests/py/dynamo/models/test_model_refit.py
@@ -532,7 +532,9 @@ def test_refit_one_engine_bert_with_weightmap():
 )
 @pytest.mark.unit
 def test_refit_one_engine_inline_runtime_with_weightmap():
-    tmp_dir = tempfile.mkdtemp(prefix="test_refit_one_engine_inline_runtime_with_weightmap")
+    tmp_dir = tempfile.mkdtemp(
+        prefix="test_refit_one_engine_inline_runtime_with_weightmap"
+    )
     trt_ep_path = os.path.join(tmp_dir, "compiled.ep")
     model = models.resnet18(pretrained=False).eval().to("cuda")
     model2 = models.resnet18(pretrained=True).eval().to("cuda")
@@ -890,7 +892,9 @@ def test_refit_one_engine_bert_without_weightmap():
 )
 @pytest.mark.unit
 def test_refit_one_engine_inline_runtime_without_weightmap():
-    tmp_dir = tempfile.mkdtemp(prefix="test_refit_one_engine_inline_runtime_without_weightmap")
+    tmp_dir = tempfile.mkdtemp(
+        prefix="test_refit_one_engine_inline_runtime_without_weightmap"
+    )
     trt_ep_path = os.path.join(tmp_dir, "compiled.ep")
     model = models.resnet18(pretrained=True).eval().to("cuda")
     model2 = models.resnet18(pretrained=False).eval().to("cuda")
diff --git a/tests/py/dynamo/models/test_reexport.py b/tests/py/dynamo/models/test_reexport.py
index e8149dbe2e..0ede5a9226 100644
--- a/tests/py/dynamo/models/test_reexport.py
+++ b/tests/py/dynamo/models/test_reexport.py
@@ -160,6 +160,7 @@ def forward(self, x):
             conv = conv * 0.5
             relu = self.relu(conv)
             return conv, relu
+
     tmp_dir = tempfile.mkdtemp(prefix="test_no_compile")
     trt_ep_path = os.path.join(tmp_dir, "trt.ep")
     model = MyModule().eval().cuda()