Allow passing traced torch.nn.Modules into torch_mlir.compile

python/test/compile_api/already_traced.py

@@ -0,0 +1,28 @@
+# Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
+# See https://llvm.org/LICENSE.txt for license information.
+# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+# Also available under a BSD-style license. See LICENSE.
+
+# RUN: %PYTHON %s | FileCheck %s
+
+import torch
+import torch_mlir
+
+class BasicModule(torch.nn.Module):
+    def forward(self, x):
+        return torch.ops.aten.sin(x)
+
+example_arg = torch.ones(2, 3)
+example_args = torch_mlir.ExampleArgs.get(example_arg)
+
+traced = torch.jit.trace(BasicModule(), example_arg)
+print(torch_mlir.compile(traced, example_args))
+# CHECK: module
+# CHECK-DAG: func.func @forward
+
+traced = torch.jit.trace(BasicModule(), example_arg)
+try:
+    # CHECK: Model does not have exported method 'nonexistent', requested in `example_args`. Consider adding `@torch.jit.export` to the method definition.
+    torch_mlir.compile(traced, torch_mlir.ExampleArgs().add_method("nonexistent", example_arg))
+except Exception as e:
+    print(e)

python/torch_mlir/__init__.py

@@ -314,7 +314,7 @@ def compile(model: torch.nn.Module,
     # backend. This separation should be visible at the Python API level, and
     # we can implement a deliberately simplified API like `torch_mlir.compile`
     # on top of those building blocks.
-    if isinstance(model, torch.jit._script.RecursiveScriptModule):
+    if isinstance(model, torch.jit.ScriptModule):
         # If the user already converted the model to JIT IR themselves, just
         # do some basic error checking, but take the model as-is.
         for method_name in example_args._get_methods():