to_edge_transform_and_lower

exir/backend/partitioner.py

@@ -7,7 +7,9 @@
 from abc import ABC, abstractmethod
 from dataclasses import dataclass
 from types import MappingProxyType
-from typing import Dict, List, Mapping, NamedTuple, Union
+from typing import Callable, Dict, List, Mapping, NamedTuple, Optional, Tuple, Union
+
+import torch
 
 from executorch.exir.backend.backend_details import enforcedmethod
 from executorch.exir.backend.compile_spec_schema import CompileSpec
@@ -91,3 +93,21 @@ def partition(self, exported_program: ExportedProgram) -> PartitionResult:
             PartitionResult: includes the tagged graph and the delegation spec to indicate what backend_id and compile_spec is used for each node and the tag created by the backend developers.
         """
         pass
+
+    def ops_to_not_decompose(
+        self,
+    ) -> Tuple[List[torch._ops.OpOverload], Optional[Callable[[torch.fx.Node], bool]]]:
+        """
+        Returns a list of operator names that should not be decomposed. When these ops are
+        registered and the `to_backend` is invoked through to_edge_transform_and_lower it will be
+        guaranteed that the program that the backend receives will not have any of these ops
+        decomposed.
+
+        Returns:
+            List[torch._ops.OpOverload]: a list of operator names that should not be decomposed.
+            Optional[Callable[[torch.fx.Node], bool]]]: an optional callable, acting as a filter, that users can provide
+            which will be called for each node in the graph that users can use as a filter for certain
+            nodes that should be continued to be decomposed even though the op they correspond to is
+            in the list returned by ops_to_not_decompose.
+        """
+        return ([], None)

exir/backend/test/backend_with_compiler_demo.py

@@ -83,15 +83,19 @@ def preprocess(
         processed_bytes = ""
         number_of_instruction = 0
         debug_handle_map = {}
+        match_ops = [
+            exir_ops.edge.aten.sin.default,
+            exir_ops.edge.aten.mm.default,
+            exir_ops.edge.aten.add.Tensor,
+            torch.ops.aten.sin.default,
+            exir_ops.edge.aten.linear.default,
+            exir_ops.edge.aten.scaled_dot_product_attention.default,
+        ]
+
         for node in edge_program.graph.nodes:
             if node.op == "call_function":
                 # TODO(gasoonjia): remove the support of torch.ops.aten.sin.default after migrate serde to edge dialect.
-                if (
-                    node.target == exir_ops.edge.aten.sin.default
-                    or node.target == exir_ops.edge.aten.mm.default
-                    or node.target == exir_ops.edge.aten.add.Tensor
-                    or node.target == torch.ops.aten.sin.default
-                ):
+                if node.target in match_ops:
                     simple_op = DemoOp(
                         node.target.__name__,
                         int(torch.prod(torch.tensor(node.meta["val"].shape), 0).item()),

exir/backend/test/op_partitioner_demo.py

@@ -4,7 +4,7 @@
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
 
-from typing import Dict, final
+from typing import Callable, Dict, final, List, Optional, Tuple
 
 import torch
 from executorch.exir.backend.canonical_partitioners.pattern_op_partitioner import (
@@ -71,6 +71,7 @@ def _partition_graph_module(
         for _, submodule, _ in get_control_flow_submodules(graph_module):
             ret_partition_tags = self._partition_graph_module(submodule)
             partition_tags.update(ret_partition_tags)
+
         return partition_tags
 
     def partition(self, exported_program: ExportedProgram) -> PartitionResult:
@@ -121,3 +122,74 @@ def partition(self, edge_exported_program: ExportedProgram) -> PartitionResult:
         return PartitionResult(
             tagged_exported_program=edge_exported_program, partition_tags=partition_tags
         )
+
+
+ops_not_to_decompose = [
+    torch.ops.aten.linear.default,
+    torch.ops.aten.scaled_dot_product_attention.default,
+]
+
+edge_ops_non_decomposed = [
+    exir_ops.edge.aten.linear.default,
+    exir_ops.edge.aten.scaled_dot_product_attention.default,
+]
+
+
+class OpsToNotDecomposeOperatorSupport(OperatorSupportBase):
+    def is_node_supported(self, submodules, node: torch.fx.Node) -> bool:
+        return node.op == "call_function" and node.target in edge_ops_non_decomposed
+
+
+@final
+class NonDecompTestPartitioner(Partitioner):
+    """
+    Partitions all add/mul nodes regardless of order
+    """
+
+    def __init__(self) -> None:
+        self.op_support = any_chain(OpsToNotDecomposeOperatorSupport())
+        self.delegation_spec = DelegationSpec(
+            BackendWithCompilerDemo.__name__,
+            [CompileSpec("max_value", bytes([4]))],
+        )
+
+    def ops_to_not_decompose(
+        self,
+    ) -> Tuple[List[torch._ops.OpOverload], Optional[Callable[[torch.fx.Node], bool]]]:
+        def filter_ops(node: torch.fx.Node) -> bool:
+            if node.op == "call_function" and node.target in ops_not_to_decompose:
+                if len(node.args) == 3:
+                    # This means that linear has a bias which is the only linear we support in this
+                    # demo partitioner.
+                    return True
+                else:
+                    return False
+
+            return True
+
+        return (ops_not_to_decompose, filter_ops)
+
+    def _partition_graph_module(
+        self,
+        graph_module: torch.fx.GraphModule,
+    ) -> Dict[str, DelegationSpec]:
+        partition_tags: Dict[str, DelegationSpec] = {}
+        partition_list = generate_pattern_op_partitions(
+            graph_module, op_support=self.op_support
+        )
+        for partition in partition_list:
+            for node in partition.nodes:
+                delegation_tag = f"tag{partition.id}"
+                node.meta["delegation_tag"] = delegation_tag
+                partition_tags[delegation_tag] = self.delegation_spec
+
+        for _, submodule, _ in get_control_flow_submodules(graph_module):
+            ret_partition_tags = self._partition_graph_module(submodule)
+            partition_tags.update(ret_partition_tags)
+        return partition_tags
+
+    def partition(self, exported_program: ExportedProgram) -> PartitionResult:
+        partition_tags = self._partition_graph_module(exported_program.graph_module)
+        return PartitionResult(
+            tagged_exported_program=exported_program, partition_tags=partition_tags
+        )

exir/program/TARGETS

@@ -21,6 +21,7 @@ python_library(
     deps = [
         "//caffe2:torch",
         "//executorch/exir:error",
+        "//executorch/exir:graph_module",
         "//executorch/exir:pass_manager",
         "//executorch/exir:print_program",
         "//executorch/exir:schema",
@@ -36,6 +37,7 @@ python_library(
         "//executorch/exir/passes:normalize_view_copy_base_pass",
         "//executorch/exir/passes:remove_graph_asserts_pass",
         "//executorch/exir/passes:remove_mixed_type_operators",
+        "//executorch/exir/passes:replace_aten_with_edge_pass",
         "//executorch/exir/passes:replace_view_copy_with_view_pass",
         "//executorch/exir/passes:spec_prop_pass",
         "//executorch/exir/verification:verifier",

exir/program/__init__.py

@@ -9,6 +9,7 @@
 from executorch.exir.program._fake_program import get_fake_program
 from executorch.exir.program._program import (
     _to_edge,
+    _to_edge_transform_and_lower,
     edge_to_executorch_passes,
     EdgeProgramManager,
     ExecutorchProgram,
@@ -22,6 +23,7 @@
     "ExecutorchProgram",
     "_to_edge",
     "to_edge",
+    "_to_edge_transform_and_lower",
     "edge_to_executorch_passes",
     "EdgeProgramManager",
     "ExecutorchProgramManager",