Clean up lowering APIs

Summary: Changes some types in `export_to_edge`, add an `export_to_cadence` function, and refactor some calls. Mostly preemptive work before adding a lot of AoT passes.

Differential Revision: D57579738

Author: mcremon-meta

backends/cadence/aot/TARGETS

@@ -0,0 +1,32 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+load("@fbcode_macros//build_defs:python_library.bzl", "python_library")
+
+oncall("odai_jarvis")
+
+python_library(
+    name = "compiler",
+    srcs = [
+        "compiler.py",
+    ],
+    deps = [
+        ":passes",
+        "//caffe2:torch",
+        "//executorch/exir:lib",
+    ],
+)
+
+python_library(
+    name = "passes",
+    srcs = [
+        "passes.py",
+    ],
+    deps = [
+        "//executorch/exir:pass_base",
+        "//executorch/exir/dialects:lib",
+    ],
+)

backends/cadence/aot/compiler.py

@@ -9,15 +9,20 @@
 
 import torch
 
+from executorch.backends.cadence.aot.passes import (
+    ReplacePT2DequantWithCadenceDequant,
+    ReplacePT2QuantWithCadenceQuant,
+)
 from executorch.exir import EdgeCompileConfig, EdgeProgramManager, to_edge
 
 from torch.export import export
 from torch.export.exported_program import ExportedProgram
 
 
+# Export the model and lower it to an ExportedProgram (in aten IR)
 def export_program(
     model: torch.nn.Module,
-    inputs: Any,
+    inputs: Tuple[Any, ...],
 ) -> ExportedProgram:
     assert isinstance(model, torch.nn.Module), "model should be an nn.Module"
 
@@ -37,12 +42,14 @@ def export_program(
     return export(model, inputs)
 
 
-# Export the model and lower it it edge IR.
+# Export the model and lower it to an EdgeProgramManager (in edge IR).
 def export_to_edge(
     model: torch.nn.Module,
-    inputs: Any,
+    inputs: Tuple[Any, ...],
     dump_graphs: bool = False,
-) -> Tuple[EdgeProgramManager, ExportedProgram]:
+) -> EdgeProgramManager:
+    assert isinstance(model, torch.nn.Module), "model should be an nn.Module"
+
     # Export the model into an ExportedProgram.
     expo_program = export_program(model, inputs)
 
@@ -51,12 +58,32 @@ def export_to_edge(
 
     # Call to_edge to convert the graph to edge IR.
     edge_prog_manager = to_edge(
-        expo_program, compile_config=EdgeCompileConfig(_check_ir_validity=False)
+        expo_program,
+        compile_config=EdgeCompileConfig(
+            _check_ir_validity=False, _skip_dim_order=True
+        ),
     )
 
     if dump_graphs:
         logging.info(
             f"Edge graph:\n{edge_prog_manager.exported_program().graph_module.graph}"
         )
 
-    return edge_prog_manager, expo_program
+    return edge_prog_manager
+
+
+# Export the model and lower it to an EdgeProgramManager (in edge IR), and
+# apply passes specific to Cadence DSP execution.
+def export_to_cadence(
+    model: torch.nn.Module,
+    inputs: Tuple[Any, ...],
+    dump_graphs: bool = False,
+) -> EdgeProgramManager:
+    edge_program_manager = export_to_edge(model, inputs)
+
+    # Run a couple required passes for quant/dequant ops
+    cadence_program_manager = edge_program_manager.transform(
+        [ReplacePT2QuantWithCadenceQuant(), ReplacePT2DequantWithCadenceDequant()]
+    )
+
+    return cadence_program_manager

backends/cadence/aot/export_example.py

@@ -13,11 +13,7 @@
 import os
 from typing import Any, Tuple
 
-from executorch.backends.cadence.aot.compiler import export_to_edge
-from executorch.backends.cadence.aot.passes import (
-    ReplacePT2DequantWithCadenceDequant,
-    ReplacePT2QuantWithCadenceQuant,
-)
+from executorch.backends.cadence.aot.compiler import export_to_cadence, export_to_edge
 from executorch.backends.cadence.aot.quantizer.fusion_pass import QuantFusion
 from executorch.backends.cadence.aot.quantizer.quantizer import CadenceQuantizer
 from executorch.exir import ExecutorchProgramManager
@@ -68,13 +64,11 @@ def export_model(
     patterns = [q.pattern for q in quantizer.quantizers]
     QuantFusion(patterns)(converted_model)
 
-    # Get edge program (note: the name will change to export_to_cadence in future PRs)
-    edge_prog_manager, expo_prog = export_to_edge(converted_model, example_inputs)
+    # Get edge program
+    edge_prog_manager = export_to_edge(converted_model, example_inputs)
 
-    # Run a couple required passes for quant/dequant ops
-    cadence_prog_manager = edge_prog_manager.transform(
-        [ReplacePT2QuantWithCadenceQuant(), ReplacePT2DequantWithCadenceDequant()]
-    )
+    # Get edge program after Cadence specific passes
+    cadence_prog_manager = export_to_cadence(converted_model, example_inputs)
 
     exec_prog = cadence_prog_manager.to_executorch()
 
@@ -84,7 +78,6 @@ def export_model(
 
     # Print some information to terminal
     print_ops_info(
-        expo_prog.graph_module,
         edge_prog_manager.exported_program().graph_module,
         cadence_prog_manager.exported_program().graph_module,
     )

backends/cadence/aot/utils.py

@@ -71,29 +71,15 @@ def get_ops_count(graph_module: torch.fx.GraphModule) -> Dict[str, int]:
 # from export, from to_edge, and from Jarvis. Print the available
 # implementations for each op, and error out if the op is not supported.
 def print_ops_info(
-    export_gm: torch.fx.GraphModule,
     to_edge_gm: torch.fx.GraphModule,
     jarvis_gm: torch.fx.GraphModule,
 ):
-    export_ops_count = get_ops_count(export_gm)
     to_edge_ops_count = get_ops_count(to_edge_gm)
     jarvis_ops_count = get_ops_count(jarvis_gm)
 
-    # De-duplicate the "<op>" and "<op>_copy" ops
-    keys_to_delete_and_add = []
-    for k1 in export_ops_count:
-        for k2 in {**to_edge_ops_count, **jarvis_ops_count}:
-            if k2.startswith(k1):
-                keys_to_delete_and_add.append((k1, k2))
-                break
-
-    for k in keys_to_delete_and_add:
-        export_ops_count[k[1]] = export_ops_count[k[0]]
-        del export_ops_count[k[0]]
-
     removed_ops = []
     # Get the counts of the ops that are removed from the final graph
-    for k in {**export_ops_count, **to_edge_ops_count}:
+    for k in to_edge_ops_count:
         if k not in jarvis_ops_count:
             removed_ops.append(k)
 
@@ -103,7 +89,6 @@ def print_ops_info(
             op,
             jarvis_ops_count[op],
             to_edge_ops_count[op] if op in to_edge_ops_count else 0,
-            export_ops_count[op] if op in export_ops_count else 0,
         ]
         for op in jarvis_ops_count
     ]
@@ -115,7 +100,6 @@ def print_ops_info(
             op,
             0,
             to_edge_ops_count[op] if op in to_edge_ops_count else 0,
-            export_ops_count[op] if op in export_ops_count else 0,
         ]
         for op in removed_ops
     ]