[ExecuTorch][to_backend] add AllNodePartitioner

exir/backend/canonical_partitioners/TARGETS

@@ -7,6 +7,7 @@ runtime.python_library(
     srcs = [
         "duplicate_dequant_node_pass.py",
         "pattern_op_partitioner.py",
+        "all_node_partitioner.py",
     ],
     visibility = [
         "//executorch/...",

exir/backend/canonical_partitioners/all_node_partitioner.py

@@ -0,0 +1,55 @@
+# 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.
+
+from typing import Dict, List
+
+import torch
+from executorch.exir.backend.backend_details import ExportedProgram
+from executorch.exir.backend.compile_spec_schema import CompileSpec
+from executorch.exir.backend.partitioner import (
+    DelegationSpec,
+    Partitioner,
+    PartitionResult,
+)
+from torch._export.utils import is_buffer, is_lifted_tensor_constant, is_param
+
+
+def is_non_tensor_placeholder(node: torch.fx.Node, ep: ExportedProgram) -> bool:
+    """
+    Returns true if the node is a placeholder node and it is not a tensor
+    """
+    return node.op == "placeholder" and not (
+        is_param(ep, node) or is_buffer(ep, node) or is_lifted_tensor_constant(ep, node)
+    )
+
+
+class AllNodePartitioner(Partitioner):
+    def __init__(
+        self,
+        backend_id: str,
+        compile_specs: List[CompileSpec],
+    ):
+        """
+        Partitioner that lowers every single node in the graph module to the
+        specified backend_id
+        """
+        super().__init__()
+        self.delegation_spec = DelegationSpec(backend_id, compile_specs)
+
+    def partition(self, exported_program: ExportedProgram) -> PartitionResult:
+        # tag all nodes
+        partition_tags: Dict[str, DelegationSpec] = {}
+        for node in exported_program.graph_module.graph.nodes:
+            if is_non_tensor_placeholder(node, exported_program) or node.op == "output":
+                continue
+
+            delegation_tag = self.delegation_spec.backend_id
+            node.meta["delegation_tag"] = delegation_tag
+            partition_tags[delegation_tag] = self.delegation_spec
+
+        return PartitionResult(
+            tagged_exported_program=exported_program, partition_tags=partition_tags
+        )

exir/backend/test/test_backends.py

@@ -10,7 +10,11 @@
 
 import executorch.exir as exir
 import torch
+from executorch.exir import to_edge
 from executorch.exir.backend.backend_api import LoweredBackendModule, to_backend
+from executorch.exir.backend.canonical_partitioners.all_node_partitioner import (
+    AllNodePartitioner,
+)
 from executorch.exir.backend.compile_spec_schema import CompileSpec
 from executorch.exir.backend.partitioner import (
     DelegationSpec,
@@ -1266,3 +1270,178 @@ def forward(self, x: List[torch.Tensor]):
 
         gm = exir.capture(ComposedM(), inputs, exir.CaptureConfig()).to_edge()
         gm(*inputs)
+
+    def test_to_backend_delegation_spec(self):
+        class SinModule(torch.nn.Module):
+            def __init__(self):
+                super().__init__()
+
+            def forward(self, x):
+                return [torch.sin(x)]
+
+        sin_module = SinModule()
+        model_inputs = (torch.ones(1),)
+        max_value = model_inputs[0].shape[0]
+
+        partitioner = AllNodePartitioner(
+            "BackendWithCompilerDemo", [CompileSpec("max_value", bytes([max_value]))]
+        )
+
+        edgeir_m = to_edge(torch.export.export(sin_module, model_inputs))
+        edgeir_m = edgeir_m.to_backend(partitioner)
+        exec_prog = edgeir_m.to_executorch()
+        graph_module = exec_prog.exported_program().graph_module
+        # Check that there is not an aten.sin node.
+        self.assertTrue(
+            exir_ops.edge.aten.sin
+            not in {node.target for node in graph_module.graph.nodes}
+        )
+
+        # Check that there exists a call_delegate, representing the call to the
+        # delegated function
+        FileCheck().check("torch.ops.higher_order.executorch_call_delegate").run(
+            graph_module.code
+        )
+        lowered_submodules = get_lowered_submodules(graph_module)
+        self.assertEqual(len(lowered_submodules), 1)
+
+        for node in graph_module.graph.nodes:
+            if node.op == "call_function" and node.target == executorch_call_delegate:
+                # Check that first arg is lowered_module_{unique_id}
+                self.assertEqual(node.args[0].target, "lowered_module_0")
+
+        program = exec_prog.executorch_program
+
+        # Check the program can be printed
+        print_program(program)
+
+        # Check the backend delegate
+        self.check_backend_delegate(
+            program=program,
+            delegate=program.execution_plan[0].delegates[0],
+            expected_id=BackendWithCompilerDemo.__name__,
+            expected_processed=b"1version:0#op:demo::aten.sin.default, numel:1, dtype:torch.float32<debug_handle>2#",
+        )
+
+        # Check the delegate instruction
+        self.assertTrue(
+            isinstance(
+                program.execution_plan[0].chains[0].instructions[0].instr_args,
+                DelegateCall,
+            )
+        )
+        buff = exec_prog.buffer
+
+        executorch_module = _load_for_executorch_from_buffer(buff)
+        model_inputs = torch.ones(1)
+        model_outputs = executorch_module.forward([model_inputs])
+        self.assertEqual(
+            model_inputs,
+            torch.ones(1),
+        )
+        expected_output = 0.8333 * torch.ones(1)
+
+        self.assertTrue(
+            torch.allclose(model_outputs[0], expected_output, atol=1e-03, rtol=1e-03)
+        )
+
+    def test_to_backend_multimethod_delegation_spec(self):
+        class SinModule(torch.nn.Module):
+            def __init__(self):
+                super().__init__()
+
+            def forward(self, x):
+                return torch.sin(x)
+
+            def inputs(self):
+                return (torch.ones(1),)
+
+        class AddMulModule(torch.nn.Module):
+            def __init__(self):
+                super().__init__()
+
+            def forward(self, a, x, b):
+                y = torch.mm(a, x)
+                z = torch.add(y, b)
+                return z
+
+            def inputs(self):
+                return (torch.ones(2, 2), 2 * torch.ones(2, 2), 3 * torch.ones(2, 2))
+
+        sin_module = SinModule()
+        max_value_sin = sin_module.inputs()[0].shape[0]
+        sin_partitioner = AllNodePartitioner(
+            "BackendWithCompilerDemo",
+            [CompileSpec("max_value", bytes([max_value_sin]))],
+        )
+
+        add_mul_module = AddMulModule()
+        max_value_add_mul = add_mul_module.inputs()[0].shape[0]
+        add_mul_partitioner = AllNodePartitioner(
+            "BackendWithCompilerDemo",
+            [CompileSpec("max_value", bytes([max_value_add_mul]))],
+        )
+
+        edgeir_m = to_edge(
+            {
+                "sin": torch.export.export(sin_module, sin_module.inputs()),
+                "add_mul": torch.export.export(add_mul_module, add_mul_module.inputs()),
+            }
+        )
+        edgeir_m = edgeir_m.to_backend(
+            {
+                "sin": sin_partitioner,
+                "add_mul": add_mul_partitioner,
+            }
+        )
+        exec_prog = edgeir_m.to_executorch()
+
+        for method_name in ["sin", "add_mul"]:
+            graph_module = exec_prog.exported_program(method_name).graph_module
+            # Check delegated nodes are gone
+            self.assertTrue(
+                exir_ops.edge.aten.sin
+                not in {node.target for node in graph_module.graph.nodes}
+            )
+            self.assertTrue(
+                exir_ops.edge.aten.add
+                not in {node.target for node in graph_module.graph.nodes}
+            )
+            self.assertTrue(
+                exir_ops.edge.aten.mm
+                not in {node.target for node in graph_module.graph.nodes}
+            )
+            # Check that there exists a call_delegate, representing the call to the
+            # delegated function
+            FileCheck().check("torch.ops.higher_order.executorch_call_delegate").run(
+                graph_module.code
+            )
+            lowered_submodules = get_lowered_submodules(graph_module)
+            self.assertEqual(len(lowered_submodules), 1)
+
+        program = exec_prog.executorch_program
+
+        # Check the program can be printed
+        print_program(program)
+
+        buff = exec_prog.buffer
+
+        executorch_module = _load_for_executorch_from_buffer(buff)
+
+        for method_name, module in {
+            "sin": sin_module,
+            "add_mul": add_mul_module,
+        }.items():
+            inputs_flattened, _ = tree_flatten(module.inputs())
+            model_outputs = executorch_module.run_method(
+                method_name, tuple(inputs_flattened)
+            )
+
+            if method_name == "sin":
+                # backend with compiler demo does a taylor approximation of sin
+                ref_output = 0.8333 * torch.ones(1)
+            else:
+                ref_output = module(*module.inputs())
+            self.assertTrue(
+                torch.allclose(model_outputs[0], ref_output, atol=1e-03, rtol=1e-03)
+            )

exir/backend/test/test_backends_lifted.py

@@ -11,6 +11,9 @@
 import torch
 from executorch.exir import to_edge
 from executorch.exir.backend.backend_api import LoweredBackendModule, to_backend
+from executorch.exir.backend.canonical_partitioners.all_node_partitioner import (
+    AllNodePartitioner,
+)
 from executorch.exir.backend.compile_spec_schema import CompileSpec
 from executorch.exir.backend.partitioner import (
     DelegationSpec,
@@ -138,6 +141,18 @@ def forward(self, x):
 
         self.assertTrue(torch.allclose(new_res, expected_res))
 
+        # Test same flow but through edge_program_manager
+        edgeir_m = to_edge(export(sin_module, model_inputs, strict=True))
+        loweredir_m = edgeir_m.to_backend(
+            AllNodePartitioner(BackendWithCompilerDemo.__name__, [])
+        )
+        lowered_sin_module = get_lowered_submodules(
+            loweredir_m.exported_program().graph_module
+        )[0][1]
+
+        new_res = lowered_sin_module(*model_inputs)[0]
+
+        self.assertTrue(torch.allclose(new_res, expected_res))
         # TODO(tkaruturi): emitting single LoweredBackendModule
         # program = to_edge(export(graph_module)).to_exectorch()._emitter_output.program
 

exir/backend/test/test_compatibility.py

@@ -10,6 +10,9 @@
 from executorch.exir import to_edge
 from executorch.exir._serialize import _serialize_pte_binary
 from executorch.exir.backend.backend_api import to_backend
+from executorch.exir.backend.canonical_partitioners.all_node_partitioner import (
+    AllNodePartitioner,
+)
 from executorch.exir.backend.compile_spec_schema import CompileSpec
 from executorch.exir.backend.test.backend_with_compiler_demo import (
     BackendWithCompilerDemo,
@@ -65,3 +68,49 @@ def forward(self, x):
             "loading method forward failed with error 0x30",
         ):
             executorch_module = _load_for_executorch_from_buffer(buff)
+
+    def test_compatibility_in_runtime_edge_program_manager(self):
+        class SinModule(torch.nn.Module):
+            def __init__(self):
+                super().__init__()
+
+            def forward(self, x):
+                return torch.sin(x)
+
+        sin_module = SinModule()
+        model_inputs = (torch.ones(1),)
+        edgeir_m = to_edge(export(sin_module, model_inputs, strict=True))
+        max_value = model_inputs[0].shape[0]
+        compile_specs = [CompileSpec("max_value", bytes([max_value]))]
+        lowered_edge_irm = edgeir_m.to_backend(
+            AllNodePartitioner("BackendWithCompilerDemo", compile_specs)
+        )
+        exec_prog = lowered_edge_irm.to_executorch()
+
+        buff = exec_prog.buffer
+
+        # The demo backend works well
+        executorch_module = _load_for_executorch_from_buffer(buff)
+        model_inputs = torch.ones(1)
+        _ = executorch_module.forward([model_inputs])
+
+        prog = exec_prog.executorch_program
+        # Rewrite the delegate version number from 0 to 1.
+        prog.backend_delegate_data[0].data = bytes(
+            "1version:1#op:demo::aten.sin.default, numel:1, dtype:torch.float32<debug_handle>1#",
+            encoding="utf8",
+        )
+
+        # Generate the .pte file with the wrong version.
+        buff = bytes(
+            _serialize_pte_binary(
+                program=prog,
+            )
+        )
+
+        # Throw runtime error with error code 0x30, meaning delegate is incompatible.
+        with self.assertRaisesRegex(
+            RuntimeError,
+            "loading method forward failed with error 0x30",
+        ):
+            executorch_module = _load_for_executorch_from_buffer(buff)

exir/program/TARGETS

@@ -31,6 +31,7 @@ python_library(
         "//executorch/exir/_serialize:lib",
         "//executorch/exir/backend:backend_api",
         "//executorch/exir/backend:partitioner",
+        "//executorch/exir/backend/canonical_partitioners:canonical_partitioner_lib",
         "//executorch/exir/capture:config",
         "//executorch/exir/emit:emit",
         "//executorch/exir/emit:lib",