reenable back thor test

Author: lanluo-nvidia

py/torch_tensorrt/dynamo/conversion/aten_ops_converters.py

@@ -10,7 +10,7 @@
 from torch.fx.node import Argument, Node, Target
 from torch_tensorrt import ENABLED_FEATURES
 from torch_tensorrt._features import needs_not_tensorrt_rtx
-from torch_tensorrt._utils import is_tensorrt_version_supported, is_thor
+from torch_tensorrt._utils import is_tensorrt_version_supported
 from torch_tensorrt.dynamo._settings import CompilationSettings
 from torch_tensorrt.dynamo._SourceIR import SourceIR
 from torch_tensorrt.dynamo.conversion import impl
@@ -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:
@@ -3621,18 +3621,10 @@ def aten_ops_full(
     )
 
 
-def nonzero_validator(
-    node: Node, settings: Optional[CompilationSettings] = None
-) -> bool:
-    return not is_thor()
-
-
 # currently nonzero is not supported for tensorrt_rtx
 # TODO: lan to add the nonzero support once tensorrt_rtx team has added the support
-# TODO: apbose to remove the capability validator once thor bug resolve in NGC
 @dynamo_tensorrt_converter(
     torch.ops.aten.nonzero.default,
-    capability_validator=nonzero_validator,
     supports_dynamic_shapes=True,
     requires_output_allocator=True,
 )

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(

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(

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",
+        ENABLED_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):

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(

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(
         [

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 +301,10 @@ 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 +394,10 @@ 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()

tests/py/dynamo/models/test_export_serde.py

@@ -17,8 +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
@@ -27,6 +25,8 @@ 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 +82,9 @@ 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__()
@@ -141,6 +144,8 @@ 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):
@@ -202,6 +207,9 @@ 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__()
@@ -262,6 +270,9 @@ 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")
 
@@ -307,6 +318,9 @@ 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")
 
@@ -359,6 +373,9 @@ 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")
 
@@ -399,6 +416,9 @@ 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")
 
@@ -432,6 +452,9 @@ 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__()
@@ -493,6 +516,9 @@ 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__()
@@ -555,6 +581,8 @@ 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):

tests/py/dynamo/models/test_model_refit.py

@@ -532,7 +532,10 @@ 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 +892,10 @@ 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")]