Fix misuse of normalize_function api

python/test/eager_mode/normalize_args_kwargs.py

@@ -0,0 +1,84 @@
+# 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
+
+from torch_mlir.eager_mode.torch_mlir_dispatch import normalize_args_kwargs
+
+
+def run_test(test, XFAIL=False, XPASS=False):
+    try:
+        test()
+        print(("X" if XPASS else "") + f"PASS - {test.__name__}")
+    except Exception as e:
+        print(("X" if XFAIL else "") + f"FAIL - {test.__name__}")
+        print(e)
+
+
+# CHECK: PASS - should_normalize
+def should_normalize():
+    target = torch.ops.aten.max_pool2d_with_indices.default.overloadpacket
+    args = (torch.randn((1, 3, 32, 32)),)
+    kwargs = {"kernel_size": [3, 3]}
+    golden = {
+        "kernel_size": [3, 3],
+        # This is due to the schema for max_pool2d_with_indices defining
+        # the stride arg as int[2] stride=[].
+        "stride": [],
+        "padding": [0, 0],
+        "dilation": [1, 1],
+        "ceil_mode": False,
+    }
+
+    new_args, new_kwargs = normalize_args_kwargs(target, args, kwargs)
+    for arg, new_arg in zip(args, new_args):
+        assert torch.allclose(arg, new_arg)
+    for k, v in new_kwargs.items():
+        assert v == golden[k]
+
+
+# CHECK: FAIL - shouldnt_normalize1
+# CHECK: Couldn't normalize args and kwargs
+def shouldnt_normalize1():
+    target = torch.ops.aten.max_pool2d_with_indices.default.overloadpacket
+    args = (torch.randn((1, 3, 32, 32)),)
+    kwargs = {"stride": []}
+    normalize_args_kwargs(target, args, kwargs)
+
+
+# This next two tests are XPASS because of https://github.com/pytorch/pytorch/issues/75342
+# I.e., they should fail but in fact they pass because of the upstream bug.
+# The reason for the bug is a fast path branch in operator_schemas.normalize_function
+# that doesn't do rigorous type checking, and hence lets type mistmatches slip through.
+# TODO(max): change these to FAIL when the upstream bug is fixed.
+
+# CHECK: XPASS - shouldnt_normalize2
+def shouldnt_normalize2():
+    target = torch.ops.aten.max_pool2d_with_indices.default.overloadpacket
+    args = (torch.randn((1, 3, 32, 32)),)
+    kwargs = {"kernel_size": []}
+    normalize_args_kwargs(target, args, kwargs)
+
+
+# CHECK: XPASS - shouldnt_normalize3
+def shouldnt_normalize3():
+    target = torch.ops.aten.max_pool2d_with_indices.default.overloadpacket
+    args = (torch.randn((1, 3, 32, 32)),)
+    kwargs = {"kernel_size": [3, 3], "padding": None}
+    normalize_args_kwargs(target, args, kwargs)
+
+
+def main():
+    run_test(should_normalize)
+    run_test(shouldnt_normalize1)
+    run_test(shouldnt_normalize2, XPASS=True)
+    run_test(shouldnt_normalize3, XPASS=True)
+
+
+if __name__ == "__main__":
+    main()

python/torch_mlir/eager_mode/torch_mlir_dispatch.py

@@ -57,8 +57,10 @@ def normalize_args_kwargs(target: Callable, args: Tuple[Any], kwargs: Dict[str,
 
     arg_types = map_aggregate(args, type)
     assert isinstance(arg_types, tuple)
-    arg_types = tuple([create_type_hint(i) for i in arg_types])
-    kwarg_types = {k: type(v) for k, v in kwargs.items()}
+    arg_types = map_aggregate(map_aggregate(args, type), create_type_hint)
+    kwarg_types = {
+        k: create_type_hint(map_aggregate(v, type)) for k, v in kwargs.items()
+    }
 
     new_args_and_kwargs = normalize_function(
         target, args, kwargs, arg_types, kwarg_types, normalize_to_only_use_kwargs=False