Added Cadence memory planning example

backends/cadence/aot/TARGETS

@@ -40,6 +40,7 @@ python_library(
         ":utils",
         ":ops_registrations",
         ":replace_ops",
+        ":memory_planning",
         "//caffe2:torch",
         "//executorch/backends/cadence/aot/quantizer:fusion_pass",
         "//executorch/backends/cadence/aot/quantizer:quantizer",

backends/cadence/aot/compiler.py

@@ -12,22 +12,34 @@
 
 import executorch.backends.cadence.aot.ops_registrations  # noqa
 import torch
+from executorch.backends.cadence.aot.memory_planning import (
+    CadenceMemoryPlanning,
+    print_memory_planning_info,
+)
 from executorch.backends.cadence.aot.quantizer.fusion_pass import QuantFusion
 from executorch.backends.cadence.aot.quantizer.quantizer import CadenceQuantizer
 
 from executorch.backends.cadence.aot.replace_ops import ReplaceSafeSoftmaxWithSoftmax
-from executorch.backends.cadence.aot.utils import model_gm_has_SDPA, model_is_quantized
+from executorch.backends.cadence.aot.utils import (
+    get_default_memory_config,
+    MemoryConfig,
+    model_gm_has_SDPA,
+    model_is_quantized,
+)
 from executorch.backends.transforms.decompose_sdpa import (
     DecomposeScaledDotProductAttention,
 )
 from executorch.devtools import generate_etrecord
 from executorch.exir import (
     EdgeCompileConfig,
     EdgeProgramManager,
+    ExecutorchBackendConfig,
     ExecutorchProgramManager,
     to_edge,
 )
 from executorch.exir.pass_base import PassResult
+from executorch.exir.passes import ToOutVarPass
+from executorch.exir.passes.sym_shape_eval_pass import HintBasedSymShapeEvalPass
 from torch._inductor.decomposition import remove_decompositions
 from torch.ao.quantization.pt2e.export_utils import model_is_exported
 from torch.ao.quantization.quantize_pt2e import convert_pt2e, prepare_pt2e
@@ -263,6 +275,10 @@ def export_to_executorch_gen_etrecord(
     inputs: tuple[object, ...],
     output_dir: Optional[str] = None,
     opt_level: int = 1,
+    mem_algo: int = 0,
+    alloc_graph_input: bool = True,
+    alloc_graph_output: bool = True,
+    memory_config: Optional[MemoryConfig] = None,
     dump_graphs: bool = False,
 ) -> ExecutorchProgramManager:
     cadence_passes = get_cadence_passes(opt_level)
@@ -281,8 +297,36 @@ def export_to_executorch_gen_etrecord(
         cadence_prog_manager.exported_program().graph_module,
     )
 
+    if memory_config is None:
+        memory_config = get_default_memory_config()
+
+    memory_planning_pass = CadenceMemoryPlanning(
+        memory_config,
+        opt_level=opt_level,
+        mem_algo=mem_algo,
+        alloc_graph_input=alloc_graph_input,
+        alloc_graph_output=alloc_graph_output,
+    )
+
     # Get executorch program after Cadence specific passes
-    exec_prog: ExecutorchProgramManager = cadence_prog_manager.to_executorch()
+    exec_prog: ExecutorchProgramManager = cadence_prog_manager.to_executorch(
+        ExecutorchBackendConfig(
+            memory_planning_pass=memory_planning_pass,
+            emit_stacktrace=False,
+            to_out_var_pass=ToOutVarPass(),
+            extract_delegate_segments=False,
+            sym_shape_eval_pass=HintBasedSymShapeEvalPass(),
+        ),
+    )
+
+    print_memory_planning_info(
+        exec_prog,
+        memory_config,
+        opt_level,
+        alloc_graph_input,
+        alloc_graph_output,
+    )
+
     if output_dir:
         _gen_etrecord(edge_prog_manager, exec_prog, Path(output_dir))
     else: