Improve error message returned in case of memory planning failure.

Summary: as titled.

Differential Revision: D78047056

Author: hsharma35

backends/cadence/aot/memory_planning.py

@@ -17,6 +17,7 @@
     ConstraintsGenPass,
     get_aligned_offset,
     MemoryPlanningAlgo,
+    MemoryPlanningAlgoFailure,
     MemoryPlanningState,
 )
 from executorch.backends.cadence.aot.utils import MemoryConfig
@@ -90,7 +91,7 @@ def plan(
         ):
             self.plan_spec(spec, state)
             if not state.is_placed(spec):
-                raise MemoryError(f"Cannot fit {spec} in any memory hierarchy")
+                raise MemoryPlanningAlgoFailure(f"Cannot fit {spec} {spec.allocated_memory=} in any memory hierarchy for {self.memory_config}")
 
         return state
 
@@ -162,7 +163,7 @@ def plan(
         ):
             self.plan_spec(spec, state)
             if not state.is_placed(spec):
-                raise MemoryError(f"Cannot fit {spec} in any memory hierarchy")
+                raise MemoryPlanningAlgoFailure(f"Cannot fit {spec} in any memory hierarchy for {self.memory_config}")
 
         logging.debug(
             f"greedy by size for offset calculation with hierarchy returns bufsizes: {state.bufsizes}"

backends/cadence/aot/memory_planning_algo.py

@@ -18,6 +18,9 @@
 from executorch.exir.tensor import TensorSpec
 from torch.export.exported_program import ExportGraphSignature
 
+class MemoryPlanningAlgoFailure(Exception):
+    pass
+
 
 def get_aligned_offset(pre_aligned_offset: int, alignment: int) -> int:
     return int(math.ceil(pre_aligned_offset / alignment) * alignment)