Turn on python dispatcher for EdgeOpArgValidator (#3809)

Summary:
Pull Request resolved: #3809

Possibly fixes #3659

We need to enable the python dispatcher so that expand_copy and view_copy will go through the correct meta kernels

Reviewed By: larryliu0820

Differential Revision: D58091304

fbshipit-source-id: f8907ee130720b01c629d55f222eb5a7e63a34bd
(cherry picked from commit ab6f177)

exir/program/test/test_program.py

@@ -10,7 +10,7 @@
 from typing import Any, Dict
 
 import torch
-from executorch.exir import ExecutorchBackendConfig
+from executorch.exir import EdgeCompileConfig, ExecutorchBackendConfig
 from executorch.exir.backend.test.op_partitioner_demo import AddMulPartitionerDemo
 from executorch.exir.dialects._ops import ops as exir_ops
 from executorch.exir.error import ExportError
@@ -26,7 +26,7 @@
 from executorch.extension.pybindings.portable_lib import (
     _load_for_executorch_from_buffer,
 )
-from torch.export import export, ExportedProgram
+from torch.export import Dim, export, ExportedProgram
 
 from torch.library import impl, Library
 
@@ -225,6 +225,38 @@ def test_edge_manager_transform(self):
             original_res,  # x * y + x
         )
 
+    def test_issue_3659(self):
+
+        class Mul(torch.nn.Module):
+            def __init__(self):
+                super(Mul, self).__init__()
+
+            def forward(self, x: torch.Tensor, y: torch.Tensor):
+                return torch.matmul(x, y)
+
+            def get_eager_model(self) -> torch.nn.Module:
+                return self
+
+            def get_example_inputs(self):
+                return (torch.randn(1, 3, 10), torch.randn(1, 10, 3))
+
+            def get_dynamic_shapes(self):
+                dim1_x = Dim("Dot_dim1_x", min=2, max=100)
+                dim2_x = Dim("Dot_dim2_x", min=2, max=100)
+                return {"x": {1: dim1_x, 2: dim2_x}, "y": {1: dim2_x, 2: dim1_x}}
+
+        model = Mul()
+        ep = torch.export.export(
+            model, model.get_example_inputs(), dynamic_shapes=model.get_dynamic_shapes()
+        )
+
+        to_edge(
+            ep,
+            compile_config=EdgeCompileConfig(
+                _check_ir_validity=True,
+            ),
+        )
+
     def test_transform_dict_api(self):
         edge_manager = to_edge(get_exported_programs(), get_config_methods())
 

exir/verification/verifier.py

@@ -17,6 +17,7 @@
     EdgeOpArgValidator,
     RunHigherOrderOperatorError,
 )
+from torch._dispatch.python import enable_python_dispatcher
 
 from torch._export.verifier import SpecViolationError, Verifier
 from torch._ops import OpOverload
@@ -119,7 +120,8 @@ def _check_tensor_args_matching_op_allowed_dtype(gm: GraphModule) -> None:
     validator = EdgeOpArgValidator(gm)
     inputs = _get_inputs(gm)
     try:
-        validator.run(*inputs)
+        with enable_python_dispatcher():
+            validator.run(*inputs)
     except RunHigherOrderOperatorError:
         # NB: ignore higher order operator in the graph.
         # If we lower a graph module to delegate and then compose it with some other graph module, retrace it,