[executorch] Add logs for helping debug address space overflow issue

exir/emit/_emitter.py

@@ -79,6 +79,7 @@
     TensorShapeDynamism,
 )
 from executorch.exir.tensor import (
+    AddressSpaceOverflowException,
     layout_enum,
     make_allocation_info,
     make_tensor_value,
@@ -349,7 +350,20 @@ def _tensor_spec_to_evalue(self, spec: TensorSpec) -> EValue:
                 self.node,
                 f"Non-const tensor should be an activation tensor: mem_offset {spec.mem_offset}",
             )
-            allocation_info = make_allocation_info(spec.mem_id, spec.mem_offset)
+            try:
+                allocation_info = make_allocation_info(spec.mem_id, spec.mem_offset)
+            except AddressSpaceOverflowException as e:
+                raise InternalError(
+                    self._emit_node_specific_error(
+                        self.node,
+                        (
+                            f"{e}\nHint: If you are using a memory pass based on dynamic shape bounds, "
+                            f"such as ConstraintBasedSymShapeEvalPass, this may be the cause of an "
+                            f"unbacked SymInt with its upper bound lazily set to 2^64-1 (uint64 max) "
+                            "during torch.export()."
+                        ),
+                    )
+                )
 
         if spec.const:
             # Tensor with a blob we need to serialize. May not actually be constant at runtime
@@ -1527,7 +1541,6 @@ def placeholder(
         is_user_input = True
 
         if isinstance(target, str) and isinstance(spec, TensorSpec):
-
             fqn, is_mutable_buffer = self._find_fqn_for_placeholder(target, spec)
 
             # From the fqn find the corresponding tensor

exir/passes/sym_shape_eval_pass.py

@@ -181,7 +181,7 @@ class HintBasedSymShapeEvalPass(PassBase):
 
     Warning: if you're using torch.export with constrain API, this method doesn't respect the input constraints.
 
-    Not inherit from ExportPass since we simply need a way to iterate thru
+    Not inherited from ExportPass since we simply need a way to iterate thru
     every node's output. PassBase is easier for that purpose.
     """
 
@@ -245,7 +245,7 @@ class ConstraintBasedSymShapeEvalPass(PassBase):
     formula. We should convert those symbolic formula to concrete value for
     static/upperbound tensors so we can properly do memory planning for them.
 
-    Not inherit from ExportPass since we simply need a way to iterate thru
+    Not inherited from ExportPass since we simply need a way to iterate through
     every node's output. PassBase is easier for that purpose.
     """
 

exir/tensor.py

@@ -22,6 +22,10 @@
 from executorch.exir.sym_util import eval_shape
 
 
+class AddressSpaceOverflowException(Exception):
+    pass
+
+
 def num_bytes_from_shape_and_dtype(shape: torch.Size, dtype: torch.dtype) -> int:
     """
     Assume the tensor is a contiguous one.
@@ -297,7 +301,9 @@ def make_allocation_info(mem_id: int, mem_offset: int) -> schema.AllocationDetai
     memory_offset_low = mem_offset & ((1 << 32) - 1)
     memory_offset_high = mem_offset >> 32
     if memory_offset_high >= 1 << 32:
-        raise ValueError(f"mem_offset {mem_offset} does not fit in 64 bits")
+        raise AddressSpaceOverflowException(
+            f"mem_offset {mem_offset} does not fit in 64 bits"
+        )
 
     allocation_info = schema.AllocationDetails(
         memory_id=mem_id,

exir/tests/test_tensor.py

@@ -171,7 +171,7 @@ def test_allocation_info_fails(self) -> None:
         )
         for test_case in test_cases:
             kwargs = test_case[0]
-            with self.assertRaisesRegex(ValueError, test_case[1], msg=f"{kwargs}"):
+            with self.assertRaisesRegex(Exception, test_case[1], msg=f"{kwargs}"):
                 make_allocation_info(**kwargs)
 
     def test_contiguous_stride_from_shape(self) -> None: