Add experimental API for preserving ops from decomposition

exir/program/_program.py

@@ -9,12 +9,13 @@
 import copy
 import io
 import logging
-from typing import Any, Dict, List, Optional, Sequence, Set, TextIO, Union
+from typing import Any, Dict, List, Optional, Sequence, Set, TextIO, Tuple, Union
 
 import torch
 import torch._export
 from executorch.exir._serialize import _serialize_pte_binary
 from executorch.exir._serialize._cord import Cord
+from executorch.exir._warnings import experimental
 from executorch.exir.backend.backend_api import to_backend
 from executorch.exir.backend.partitioner import Partitioner
 from executorch.exir.capture._config import EdgeCompileConfig, ExecutorchBackendConfig
@@ -1057,6 +1058,54 @@ def to_edge_transform_and_lower(
     return edge_manager
 
 
+@experimental(
+    """
+    This is an experimental API which overloads to_edge by preserving specified ops to not be decomposed. 
+    This function will be combined with to_edge in the future.
+    """
+)
+def to_edge_with_preserved_ops(
+    programs: Union[ExportedProgram, Dict[str, ExportedProgram]],
+    constant_methods: Optional[Dict[str, Any]] = None,
+    compile_config: Optional[EdgeCompileConfig] = None,
+    preserve_ops: Tuple[torch._ops.OpOverload, ...] = (),
+) -> "EdgeProgramManager":
+    """
+    :func:`to_edge` constructs an EdgeProgramManager from a set of exported programs in
+    ATen dialect. Upon construction those programs are transformed into edge dialect.
+
+    Args:
+        programs: Can be a single ExportedProgram or a dictionary mapping function names to their corresponding ExportedPrograms. If only a single ExportedProgram is provided it will be assigned the name "forward".
+        constant_methods: An optional dictionary of method name to the constant value returned by that method in eager mode. Often used to store config information on Edge models.
+        compile_config: An optional argument used to provide greater control over the transformation to edge dialect process.
+        preserve_ops: An argument used to specify ops that should not be decomposed.
+
+    Returns:
+        EdgeProgramManager
+    """
+    assert not isinstance(constant_methods, EdgeCompileConfig)
+    config = compile_config or EdgeCompileConfig()
+    if not isinstance(programs, dict):
+        aten_programs = {"forward": programs}
+    else:
+        aten_programs = programs
+
+    edge_programs: Dict[str, ExportedProgram] = {}
+
+    for name, program in aten_programs.items():
+        # Decompose to Core ATen
+        program = program.run_decompositions(
+            _default_decomposition_table(), _preserve_ops=preserve_ops
+        )
+        edge_programs[name] = _generate_edge_program(
+            name, config, program, list(preserve_ops)
+        )
+
+    return EdgeProgramManager(
+        edge_programs, constant_methods, config, list(preserve_ops)
+    )
+
+
 def to_edge(
     programs: Union[ExportedProgram, Dict[str, ExportedProgram]],
     constant_methods: Optional[Dict[str, Any]] = None,

exir/program/test/test_program.py

@@ -26,6 +26,7 @@
     ExecutorchProgramManager,
     to_edge,
     to_edge_transform_and_lower,
+    to_edge_with_preserved_ops,
 )
 from executorch.exir.tracer import _default_decomposition_table
 from executorch.exir.verification.verifier import EXIREdgeDialectVerifier
@@ -716,3 +717,89 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
             )
         except SpecViolationError:
             self.fail("Should not error out on linalg_vector_norm op")
+
+    def _test_to_edge_with_preserved_ops(
+        self, program, preserved_ops, expected_preserved_ops
+    ):
+        edge = to_edge_with_preserved_ops(program, preserve_ops=preserved_ops)
+
+        def count_nodes(graph_module, target):
+            count = 0
+            for node in graph_module.graph.nodes:
+                if node.op == "call_function" and node.target in target:
+                    count += 1
+            return count
+
+        aten_ops_non_decomposed = count_nodes(
+            program.graph_module,
+            preserved_ops,
+        )
+
+        edge_ops_non_decomposed = count_nodes(
+            edge.exported_program().graph_module,
+            expected_preserved_ops,
+        )
+
+        self.assertEqual(aten_ops_non_decomposed, edge_ops_non_decomposed)
+
+    def test_to_edge_with_single_preserved_op(self):
+        model = TestLinear()
+        program = torch.export.export(model, model._get_random_inputs())
+
+        ops_not_to_decompose = [
+            torch.ops.aten.linear.default,
+        ]
+        expected_non_decomposed_edge_ops = [
+            exir_ops.edge.aten.linear.default,
+        ]
+
+        self._test_to_edge_with_preserved_ops(
+            program, ops_not_to_decompose, expected_non_decomposed_edge_ops
+        )
+
+    def test_to_edge_with_partial_ops_preserved(self):
+        model = TestLinearSDPACombined()
+        program = torch.export.export(model, model._get_random_inputs())
+
+        ops_not_to_decompose = [
+            torch.ops.aten.linear.default,
+        ]
+        expected_non_decomposed_edge_ops = [
+            exir_ops.edge.aten.linear.default,
+        ]
+
+        self._test_to_edge_with_preserved_ops(
+            program, ops_not_to_decompose, expected_non_decomposed_edge_ops
+        )
+
+    def test_to_edge_with_multiple_ops_preserved(self):
+        model = TestLinearSDPACombined()
+        program = torch.export.export(model, model._get_random_inputs())
+
+        ops_not_to_decompose = [
+            torch.ops.aten.linear.default,
+            torch.ops.aten.scaled_dot_product_attention.default,
+        ]
+        expected_non_decomposed_edge_ops = [
+            exir_ops.edge.aten.linear.default,
+            exir_ops.edge.aten.scaled_dot_product_attention.default,
+        ]
+
+        self._test_to_edge_with_preserved_ops(
+            program, ops_not_to_decompose, expected_non_decomposed_edge_ops
+        )
+
+    def test_to_edge_with_preserved_ops_not_in_model(self):
+        model = TestSDPA()
+        program = torch.export.export(model, model._get_random_inputs())
+
+        ops_not_to_decompose = [
+            torch.ops.aten.linear.default,
+        ]
+        expected_non_decomposed_edge_ops = [
+            exir_ops.edge.aten.linear.default,
+        ]
+
+        self._test_to_edge_with_preserved_ops(
+            program, ops_not_to_decompose, expected_non_decomposed_edge_ops
+        )