Skip argmin/argmax with dim=None in CoreML partitioner

backends/apple/coreml/partition/coreml_partitioner.py

@@ -116,6 +116,23 @@ def should_override_support(self, node) -> bool:
             )
             return True
 
+        # https://github.com/pytorch/executorch/issues/11715
+        # argmin/argmax with dim=None reduces over the flattened input, which
+        # CoreML does not support and causes intermittent process crashes.
+        if node.target in [
+            torch.ops.aten.argmax.default,
+            torch.ops.aten.argmin.default,
+            exir_ops.edge.aten.argmax.default,
+            exir_ops.edge.aten.argmin.default,
+        ]:
+            dim = node.args[1] if len(node.args) >= 2 else node.kwargs.get("dim", None)
+            if dim is None:
+                self.log_once(
+                    "torch.ops.aten.{argmax, argmin}.default with dim=None is "
+                    "not supported by CoreML.  Overriding op support."
+                )
+                return True
+
         # TODO: enable this after bugs in ExecuTorch's partitioner are fixed
         # # If lower_full_graph=False, do not partition nodes with symbolic args because it can result in symbolic args
         # # in the placeholders due to partitioning, which CoreML does not support

backends/apple/coreml/test/test_coreml_partitioner.py

@@ -338,6 +338,51 @@ def forward(self, x):
                 torch.allclose(et_outputs, eager_outputs, atol=1e-02, rtol=1e-02)
             )
 
+    def test_argmax_argmin_dim_none_is_skipped(self):
+        """
+        Regression test for https://github.com/pytorch/executorch/issues/11715.
+
+        argmax/argmin with dim=None reduces over the flattened tensor, which
+        CoreML does not support; the resulting model intermittently crashes
+        the process at runtime.  The partitioner must reject these so they
+        fall back to the portable backend, while still delegating the
+        ordinary dim=int form.
+        """
+
+        class FlatModel(torch.nn.Module):
+            def forward(self, x):
+                return torch.argmax(x, dim=None, keepdim=False) + torch.argmin(
+                    x, dim=None
+                )
+
+        ep = torch.export.export(FlatModel().eval(), (torch.randn(10, 10),), strict=True)
+        edge = executorch.exir.to_edge_transform_and_lower(
+            ep, partitioner=[CoreMLPartitioner()]
+        )
+        op_names = [
+            n.target.__name__
+            for n in edge.exported_program().graph.nodes
+            if n.op == "call_function"
+        ]
+        self.assertIn("aten.argmax.default", op_names)
+        self.assertIn("aten.argmin.default", op_names)
+
+        class DimModel(torch.nn.Module):
+            def forward(self, x):
+                return torch.argmax(x, dim=1)
+
+        ep = torch.export.export(DimModel().eval(), (torch.randn(10, 10),), strict=True)
+        edge = executorch.exir.to_edge_transform_and_lower(
+            ep, partitioner=[CoreMLPartitioner()]
+        )
+        op_names = [
+            n.target.__name__
+            for n in edge.exported_program().graph.nodes
+            if n.op == "call_function"
+        ]
+        self.assertIn("executorch_call_delegate", op_names)
+        self.assertNotIn("aten.argmax.default", op_names)
+
     def test_deprecation_warning_for_to_backend_workflow(self):
         """
         Test that the deprecated to_edge + to_backend workflow shows a deprecation warning.