fix: Repair version checking system for Torch

py/torch_tensorrt/__init__.py

@@ -94,7 +94,7 @@ def _find_lib(name, paths):
 
 from torch_tensorrt import fx
 
-if version.parse(torch.__version__) >= version.parse("2.1.dev"):
+if version.parse(sanitized_torch_version()) >= version.parse("2.1.dev"):
     from torch_tensorrt import dynamo
     from torch_tensorrt.dynamo import backend
 

py/torch_tensorrt/_util.py

@@ -30,3 +30,11 @@ def get_build_info() -> str:
 
 def set_device(gpu_id):
     _C.set_device(gpu_id)
+
+
+def sanitized_torch_version() -> str:
+    return (
+        torch.__version__
+        if ".nv" not in torch.__version__
+        else torch.__version__.split(".nv")[0]
+    )

py/torch_tensorrt/dynamo/__init__.py

@@ -1,6 +1,6 @@
-import torch
 from packaging import version
+from torch_tensorrt._util import sanitized_torch_version
 
-if version.parse(torch.__version__) >= version.parse("2.1.dev"):
+if version.parse(sanitized_torch_version()) >= version.parse("2.1.dev"):
     from torch_tensorrt.dynamo import fx_ts_compat
     from .backend import compile

py/torch_tensorrt/fx/test/passes/test_fix_reshape_batch_dim.py

@@ -11,6 +11,7 @@
 from torch.testing._internal.common_utils import run_tests, TestCase
 from torch_tensorrt.fx.passes.lower_basic_pass import fix_reshape_batch_dim
 from torch_tensorrt.fx.tracer.acc_tracer import acc_tracer
+from torch_tensorrt._util import sanitized_torch_version
 
 _LOGGER = logging.getLogger(__name__)
 
@@ -43,7 +44,9 @@ def forward(self, x, y):
     %reshape : [num_users=1] = call_function[target=torch_tensorrt.fx.tracer.acc_tracer.acc_ops.reshape](args = (), kwargs = {input: %y, acc_out_ty: ((%getitem_1, -1, 3), None, None, None, None, None, None)})
     return reshape
 """
-        if version.parse(torch.__version__) < version.parse("2.1.0.dev20230620"):
+        if version.parse(sanitized_torch_version()) < version.parse(
+            "2.1.0.dev20230620"
+        ):
             expected_graph = expected_graph.replace("num_users", "#users")
 
         assert (

py/torch_tensorrt/fx/test/passes/test_remove_duplicate_output_args.py

@@ -8,6 +8,8 @@
 import torch.nn as nn
 
 import torch_tensorrt.fx.passes.remove_duplicate_output_args as dedup
+from torch_tensorrt._util import sanitized_torch_version
+
 from torch.testing._internal.common_utils import run_tests, TestCase
 
 _LOGGER = logging.getLogger(__name__)
@@ -57,7 +59,9 @@ def is_leaf_module(self, m, qn):
     return add
 """.strip()
 
-        if version.parse(torch.__version__) < version.parse("2.1.0.dev20230620"):
+        if version.parse(sanitized_torch_version()) < version.parse(
+            "2.1.0.dev20230620"
+        ):
             ttop_graph_expected = ttop_graph_expected.replace("num_users", "#users")
 
         assert (
@@ -71,7 +75,9 @@ def is_leaf_module(self, m, qn):
     return (x,)
 """.strip()
 
-        if version.parse(torch.__version__) < version.parse("2.1.0.dev20230620"):
+        if version.parse(sanitized_torch_version()) < version.parse(
+            "2.1.0.dev20230620"
+        ):
             ttop_a_graph_expected = ttop_a_graph_expected.replace("num_users", "#users")
 
         assert (

py/torch_tensorrt/fx/tracer/dispatch_tracer/aten_tracer.py

@@ -3,10 +3,11 @@
 from contextlib import contextmanager
 from typing import Any, Callable, Dict, Generator, List, Optional, Set, Tuple, Union
 from packaging import version
+from torch_tensorrt._util import sanitized_torch_version
 
 import torch
 
-if version.parse(torch.__version__) >= version.parse("2.dev"):
+if version.parse(sanitized_torch_version()) >= version.parse("2.dev"):
     import torch._dynamo as torchdynamo
 
 from torch.fx.passes.infra.pass_base import PassResult

py/torch_tensorrt/fx/utils.py

@@ -12,7 +12,7 @@
     replace_op_with_indices,
     run_const_fold,
 )
-
+from torch_tensorrt._util import sanitized_torch_version
 from .types import Shape, TRTDataType
 
 
@@ -160,7 +160,7 @@ def nested_decorator(f: Callable):
         def function_wrapper(*args, **kwargs):
             # Parse minimum and current Torch versions
             min_version = version.parse(min_torch_version)
-            current_version = version.parse(torch.__version__)
+            current_version = version.parse(sanitized_torch_version())
 
             if current_version < min_version:
                 raise AssertionError(