Add basic ambiguity check in the tests (pytorch#9371)

### Summary
This is also an example of how to use the new dim_order APIs to detect
ambiguity when coming from PyTorch Tensor to ExecuTorch.

### Test plan
```
python -m unittest exir.tests.test_memory_format_ops_pass.TestMemoryFormatOpsPass
```

Author: digantdesai

exir/tests/test_memory_format_ops_pass.py

@@ -24,6 +24,7 @@
 from executorch.exir.pass_base import ExportPass, ProxyValue
 
 from executorch.exir.tests.test_memory_format_ops_pass_utils import (
+    AmbiguousDimOrderError,
     MemoryFormatOpsPassTestUtils,
     MemoryFormatTestSet,
     PropagateToCopyChannalsLastModule,
@@ -124,8 +125,34 @@ def test_op_dim_order_propagation(self) -> None:
                 target_memory_format=torch.channels_last,
                 _load_for_executorch_from_buffer=_load_for_executorch_from_buffer,
             ),
+            check_unambiguous_dim_order=True,
         )
 
+    def test_op_dim_order_propagation_ambiguous(self) -> None:
+        try:
+            MemoryFormatOpsPassTestUtils.memory_format_test_runner(
+                self,
+                MemoryFormatTestSet(
+                    module=PropagateToCopyChannalsLastModule().eval(),
+                    op=torch.ops.aten._to_copy.default,
+                    sample_input=(
+                        torch.rand_like(
+                            torch.zeros(
+                                [2, 1, 2, 2]
+                            ),  # Ambiguous shape should trigger AmbiguousDimOrderError!
+                            dtype=torch.float32,
+                            memory_format=torch.contiguous_format,
+                        ),
+                    ),
+                    target_memory_format=torch.channels_last,
+                    _load_for_executorch_from_buffer=_load_for_executorch_from_buffer,
+                ),
+                check_unambiguous_dim_order=True,
+            )
+            AssertionError("Should have raised AmbiguousDimOrderError")
+        except AmbiguousDimOrderError:
+            pass  # Expected error
+
     # Only test dim order replacement result in lean mode test.
     # This test is irrelevant with operator mode.
     def test_dim_order_replacement(self) -> None:

exir/tests/test_memory_format_ops_pass_utils.py

@@ -20,8 +20,11 @@
     is_channel_last_dim_order,
     is_contiguous_dim_order,
 )
+from executorch.exir.pass_base import ExportPass
 
 from torch.export import export
+
+from torch.fx.passes.infra.pass_manager import PassManager
 from torch.testing import FileCheck
 from torch.utils._pytree import tree_flatten
 
@@ -99,10 +102,67 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
         return t1 * t2
 
 
+class AmbiguousDimOrderError(RuntimeError):
+    pass
+
+
+def assert_unambiguous_dim_order(gm):
+    class ExampleNOPPass(ExportPass):
+        """
+        Does nothing!
+        """
+
+        def call_operator(self, op, args, kwargs, meta):
+            return super().call_operator(
+                op,
+                args,
+                kwargs,
+                meta,
+            )
+
+    # This is an example of how one can detect ambiguous dim_order anywhere in the graph.
+    # You can be surgical and only detect it in the nodes you are interested in or something else.
+    def detect_ambiguity(gm):
+        """
+        Check every node's output tensor dim_order and raise if it is ambiguous for a list of formats.
+        """
+
+        def get_tensors(node: torch.fx.Node) -> torch.Tensor:
+            val = node.meta["val"]
+            if isinstance(val, torch.Tensor):
+                return [val]
+            elif isinstance(val, (list, tuple)):
+                return [tensor for tensor in val if isinstance(tensor, torch.Tensor)]
+            return []
+
+        for node in gm.graph.nodes:
+            if node.op == "call_function":
+                for tensor in get_tensors(node):
+                    # Let's make sure dim_order is not ambiguous, raise otherwise.
+                    # This is raising because we can't do anything about it.
+                    # The right course of follow up action is to ask user to try with a different example input.
+                    try:
+                        _ = tensor.dim_order(
+                            ambiguity_check=[
+                                torch.contiguous_format,
+                                torch.channels_last,
+                            ]
+                        )
+                    except Exception:
+                        raise AmbiguousDimOrderError
+
+    # any pass or passes, just using MemoryFormatOpsPass as an example
+    dim_order_pass_manager = PassManager(passes=[ExampleNOPPass()])
+    dim_order_pass_manager.add_checks(detect_ambiguity)
+    dim_order_pass_manager(gm)
+
+
 class MemoryFormatOpsPassTestUtils:
     @staticmethod
     def memory_format_test_runner(
-        test_class: unittest.TestCase, test_set: MemoryFormatTestSet
+        test_class: unittest.TestCase,
+        test_set: MemoryFormatTestSet,
+        check_unambiguous_dim_order: bool = False,
     ):
         before = export(
             test_set.module, test_set.sample_input, strict=True
@@ -121,6 +181,9 @@ def memory_format_test_runner(
                 before, compile_config=EdgeCompileConfig(_skip_dim_order=False)
             )
 
+        if check_unambiguous_dim_order:
+            assert_unambiguous_dim_order(epm.exported_program().graph_module)
+
         # check memory format ops, if needed
         if test_set.op_level_check:
             aten_op_str, edge_op_str = MemoryFormatOps2Str[test_set.op]