Add AoT apis (#345)

Summary:

Added long term aot api for executorch.

Consists of 3 stages based on the dialect the program is in,

ATen
Edge
Executorch

Unified api for multiple entry point and single entry point

Differential Revision: D49163694

Author: JacobSzwejbka

exir/__init__.py

@@ -18,11 +18,14 @@
 from executorch.exir.program import (
     _to_edge,
     edge_to_executorch_passes,
+    EdgeProgramManager,
     ExecutorchProgram,
+    ExecutorchProgramManager,
     ExirExportedProgram,
     multi_method_program_to_executorch,
     MultiMethodExecutorchProgram,
     MultiMethodExirExportedProgram,
+    to_edge,
 )
 from executorch.exir.tracer import ExirDynamoConfig
 from torch._export import (  # lots of people are doing from exir import CallSpec, ExportGraphSignature, ExportedProgram which seems wrong
@@ -45,6 +48,9 @@
     "ExecutorchProgram",
     "ExportGraphSignature",
     "_to_edge",
+    "to_edge",
+    "EdgeProgramManager",
+    "ExecutorchProgramManager",
     "edge_to_executorch_passes",
     "MultiMethodExirExportedProgram",
     "MultiMethodExecutorchProgram",

exir/backend/backend_api.py

@@ -8,10 +8,9 @@
 import logging
 from contextlib import contextmanager
 from functools import singledispatch
-from typing import Dict, Generator, List, Type, Union
+from typing import Generator, List, Type
 
 import torch
-from executorch.exir import MultiMethodExirExportedProgram
 
 from executorch.exir.backend.backend_details import BackendDetails, PreprocessResult
 from executorch.exir.backend.compile_spec_schema import CompileSpec
@@ -320,59 +319,3 @@ def to_backend(
         copy.deepcopy(edge_program.equality_constraints),
         copy.deepcopy(edge_program.module_call_graph),
     )
-
-
-def to_backend_multiple(
-    multi_method_program: MultiMethodExirExportedProgram,
-    partitioner: Union[Dict[str, Type[TPartitioner]], Type[TPartitioner]],
-) -> MultiMethodExirExportedProgram:
-    """
-    Returns a semantically-equivalent program to the one given as input (represented
-    as a graph module in Edge dialect), but with portions of each method in the
-    program targeted for delegation as determined by the partitioner.
-
-    Args:
-        MultiMethodExirExportedProgram: A multiple method exported program in Edge dialect.
-
-        partitioner: The partitioner can either be a Partitioner subclass, or a
-            dictionary mapping method names to Partitioner subclass. If it is a
-            Partitioner subclass, all methods in the given multi-method exported
-            program will be lowered using the given partitioner. If it is a
-            dictionary, only method names specified in the dictionary will be
-            lowered with the given partitioner.
-
-            THe Partitioner subclass is in charge with tagging portions of the
-            input program for delegation. A valid partitioner must have
-            partition_tags: Dict[str, DelegationSpec], where each key is a tag
-            name and the nodes with same tag will be fused a one subgraph and
-            delegated to backend specififed in delegation spec.
-
-    Returns:
-        MultiMethodExirExportedProgram: The input program, with some portions
-        targeted for delegation in each method of the program.
-    """
-    if not (isinstance(partitioner, dict) or issubclass(partitioner, Partitioner)):
-        raise TypeError(
-            "partitioner should either be a dictionary of method names to"
-            + "partitioner subclass, or a partitioner subclass."
-        )
-
-    method_name_to_delegated_program = {}
-    for method_name, prog in multi_method_program.methods().items():
-        if isinstance(partitioner, dict):
-            if method_name in partitioner:
-                method_name_to_delegated_program[method_name] = prog
-                method_name_to_delegated_program[
-                    method_name
-                ].exported_program = to_backend(
-                    prog.exported_program, partitioner[method_name]
-                )
-            else:
-                method_name_to_delegated_program[method_name] = prog
-        else:
-            method_name_to_delegated_program[method_name] = prog
-            method_name_to_delegated_program[method_name].exported_program = to_backend(
-                prog.exported_program, partitioner
-            )
-
-    return MultiMethodExirExportedProgram(method_name_to_delegated_program)

exir/backend/test/test_backends.py

@@ -11,11 +11,7 @@
 import torch
 import torch.fx as fx
 from executorch.exir import multi_method_program_to_executorch
-from executorch.exir.backend.backend_api import (
-    LoweredBackendModule,
-    to_backend,
-    to_backend_multiple,
-)
+from executorch.exir.backend.backend_api import LoweredBackendModule, to_backend
 from executorch.exir.backend.compile_spec_schema import CompileSpec
 from executorch.exir.backend.partitioner import (
     DelegationSpec,
@@ -1212,137 +1208,3 @@ def forward(self, x: List[torch.Tensor]):
 
         gm = exir.capture(ComposedM(), inputs, exir.CaptureConfig()).to_edge()
         gm(*inputs)
-
-    def test_lower_multiple(self) -> None:
-        class MultipleMethodModule(torch.nn.Module):
-            def __init__(self) -> None:
-                super().__init__()
-
-            def forward(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
-                return x + y * y
-
-            def method1(self, x: torch.Tensor) -> torch.Tensor:
-                return x + x - x
-
-            def method2(
-                self, x: torch.Tensor, y: torch.Tensor, z: torch.Tensor
-            ) -> torch.Tensor:
-                return x + y - z
-
-        module = MultipleMethodModule()
-        method_name_to_args = {
-            "forward": (torch.rand(2, 2), torch.rand(2, 2)),
-            "method1": (torch.rand(2, 2),),
-            "method2": (torch.rand(2, 2), torch.rand(2, 2), torch.rand(2, 2)),
-        }
-
-        multi_method_prog = exir.capture_multiple(
-            module, method_name_to_args, exir.CaptureConfig()
-        ).to_edge()
-
-        lowered_multi_method_prog = to_backend_multiple(
-            multi_method_prog, AddMulPartitionerDemo
-        )
-
-        for method_name, args in method_name_to_args.items():
-            exported_prog = lowered_multi_method_prog.find_method(method_name)
-            self.assertIsNotNone(exported_prog)
-            exported_gm = exported_prog.exported_program.graph_module
-            self.assertIsInstance(exported_gm, fx.GraphModule)
-
-            eager_method = getattr(module, method_name)
-            eager_results = eager_method(*args)
-            exported_results = exported_gm(*args)
-            self.assertTrue(torch.allclose(eager_results, exported_results[0]))
-
-            add_nodes = [
-                node
-                for node in exported_gm.graph.nodes
-                if node.op == "call_function"
-                and node.target == exir_ops.edge.aten.add.Tensor
-            ]
-            self.assertEqual(len(add_nodes), 0)
-
-            lowered_submods = get_lowered_submodules(exported_gm)
-            self.assertEqual(len(lowered_submods), 1)
-
-        _ = multi_method_program_to_executorch(lowered_multi_method_prog)
-
-    def test_lower_multiple_selective(self) -> None:
-        class MultipleMethodModule(torch.nn.Module):
-            def __init__(self) -> None:
-                super().__init__()
-
-            def forward(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
-                return x + y * y
-
-            def method1(self, x: torch.Tensor) -> torch.Tensor:
-                return x + x - x
-
-            def method2(
-                self, x: torch.Tensor, y: torch.Tensor, z: torch.Tensor
-            ) -> torch.Tensor:
-                return x + y - z
-
-        module = MultipleMethodModule()
-        method_name_to_args = {
-            "forward": (torch.rand(2, 2), torch.rand(2, 2)),
-            "method1": (torch.rand(2, 2),),
-            "method2": (torch.rand(2, 2), torch.rand(2, 2), torch.rand(2, 2)),
-        }
-
-        multi_method_prog = exir.capture_multiple(
-            module, method_name_to_args, exir.CaptureConfig()
-        ).to_edge()
-
-        method_name_to_partitioners = {
-            "forward": AddMulPartitionerDemo,
-            "method1": AddMulPartitionerDemo,
-        }
-        lowered_multi_method_prog = to_backend_multiple(
-            multi_method_prog, method_name_to_partitioners
-        )
-
-        for method_name, args in method_name_to_args.items():
-            if method_name == "method2":
-                break
-
-            exported_prog = lowered_multi_method_prog.find_method(method_name)
-            self.assertIsNotNone(exported_prog)
-            exported_gm = exported_prog.exported_program.graph_module
-            self.assertIsInstance(exported_gm, fx.GraphModule)
-
-            eager_method = getattr(module, method_name)
-            eager_results = eager_method(*args)
-            exported_results = exported_gm(*args)
-            self.assertTrue(torch.allclose(eager_results, exported_results[0]))
-
-            add_nodes = [
-                node
-                for node in exported_gm.graph.nodes
-                if node.op == "call_function"
-                and node.target == exir_ops.edge.aten.add.Tensor
-            ]
-            self.assertEqual(len(add_nodes), 0)
-
-            lowered_submods = get_lowered_submodules(exported_gm)
-            self.assertEqual(len(lowered_submods), 1)
-
-        # Check that method2 had nothing lowered
-        method2_prog = lowered_multi_method_prog.find_method("method2")
-        self.assertIsNotNone(method2_prog)
-        method2_gm = method2_prog.exported_program.graph_module
-        self.assertIsInstance(method2_gm, fx.GraphModule)
-        add_nodes = [
-            node
-            for node in method2_gm.graph.nodes
-            if node.op == "call_function"
-            and node.target == exir_ops.edge.aten.add.Tensor
-        ]
-        self.assertEqual(len(add_nodes), 1)
-
-        lowered_submods = get_lowered_submodules(method2_gm)
-        self.assertEqual(len(lowered_submods), 0)
-
-        # Check we can export to executorch properly
-        _ = multi_method_program_to_executorch(lowered_multi_method_prog)

exir/backend/test/test_backends_lifted.py

@@ -11,11 +11,7 @@
 import torch
 import torch.fx as fx
 from executorch.exir import multi_method_program_to_executorch
-from executorch.exir.backend.backend_api import (
-    LoweredBackendModule,
-    to_backend,
-    to_backend_multiple,
-)
+from executorch.exir.backend.backend_api import LoweredBackendModule, to_backend
 from executorch.exir.backend.compile_spec_schema import CompileSpec
 from executorch.exir.backend.partitioner import (
     DelegationSpec,
@@ -1257,137 +1253,3 @@ def forward(self, x: List[torch.Tensor]):
 
         gm = exir.capture(ComposedM(), inputs, get_testing_capture_config()).to_edge()
         gm(*inputs)
-
-    def test_lower_multiple(self) -> None:
-        class MultipleMethodModule(torch.nn.Module):
-            def __init__(self) -> None:
-                super().__init__()
-
-            def forward(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
-                return x + y * y
-
-            def method1(self, x: torch.Tensor) -> torch.Tensor:
-                return x + x - x
-
-            def method2(
-                self, x: torch.Tensor, y: torch.Tensor, z: torch.Tensor
-            ) -> torch.Tensor:
-                return x + y - z
-
-        module = MultipleMethodModule()
-        method_name_to_args = {
-            "forward": (torch.rand(2, 2), torch.rand(2, 2)),
-            "method1": (torch.rand(2, 2),),
-            "method2": (torch.rand(2, 2), torch.rand(2, 2), torch.rand(2, 2)),
-        }
-
-        multi_method_prog = exir.capture_multiple(
-            module, method_name_to_args, get_testing_capture_config()
-        ).to_edge()
-
-        lowered_multi_method_prog = to_backend_multiple(
-            multi_method_prog, AddMulPartitionerDemo
-        )
-
-        for method_name, args in method_name_to_args.items():
-            exported_prog = lowered_multi_method_prog.find_method(method_name)
-            self.assertIsNotNone(exported_prog)
-            exported_gm = exported_prog.exported_program.graph_module
-            self.assertIsInstance(exported_gm, fx.GraphModule)
-
-            eager_method = getattr(module, method_name)
-            eager_results = eager_method(*args)
-            exported_results = exported_gm(*args)
-            self.assertTrue(torch.allclose(eager_results, exported_results[0]))
-
-            add_nodes = [
-                node
-                for node in exported_gm.graph.nodes
-                if node.op == "call_function"
-                and node.target == exir_ops.edge.aten.add.Tensor
-            ]
-            self.assertEqual(len(add_nodes), 0)
-
-            lowered_submods = get_lowered_submodules(exported_gm)
-            self.assertEqual(len(lowered_submods), 1)
-
-        _ = multi_method_program_to_executorch(lowered_multi_method_prog)
-
-    def test_lower_multiple_selective(self) -> None:
-        class MultipleMethodModule(torch.nn.Module):
-            def __init__(self) -> None:
-                super().__init__()
-
-            def forward(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
-                return x + y * y
-
-            def method1(self, x: torch.Tensor) -> torch.Tensor:
-                return x + x - x
-
-            def method2(
-                self, x: torch.Tensor, y: torch.Tensor, z: torch.Tensor
-            ) -> torch.Tensor:
-                return x + y - z
-
-        module = MultipleMethodModule()
-        method_name_to_args = {
-            "forward": (torch.rand(2, 2), torch.rand(2, 2)),
-            "method1": (torch.rand(2, 2),),
-            "method2": (torch.rand(2, 2), torch.rand(2, 2), torch.rand(2, 2)),
-        }
-
-        multi_method_prog = exir.capture_multiple(
-            module, method_name_to_args, get_testing_capture_config()
-        ).to_edge()
-
-        method_name_to_partitioners = {
-            "forward": AddMulPartitionerDemo,
-            "method1": AddMulPartitionerDemo,
-        }
-        lowered_multi_method_prog = to_backend_multiple(
-            multi_method_prog, method_name_to_partitioners
-        )
-
-        for method_name, args in method_name_to_args.items():
-            if method_name == "method2":
-                break
-
-            exported_prog = lowered_multi_method_prog.find_method(method_name)
-            self.assertIsNotNone(exported_prog)
-            exported_gm = exported_prog.exported_program.graph_module
-            self.assertIsInstance(exported_gm, fx.GraphModule)
-
-            eager_method = getattr(module, method_name)
-            eager_results = eager_method(*args)
-            exported_results = exported_gm(*args)
-            self.assertTrue(torch.allclose(eager_results, exported_results[0]))
-
-            add_nodes = [
-                node
-                for node in exported_gm.graph.nodes
-                if node.op == "call_function"
-                and node.target == exir_ops.edge.aten.add.Tensor
-            ]
-            self.assertEqual(len(add_nodes), 0)
-
-            lowered_submods = get_lowered_submodules(exported_gm)
-            self.assertEqual(len(lowered_submods), 1)
-
-        # Check that method2 had nothing lowered
-        method2_prog = lowered_multi_method_prog.find_method("method2")
-        self.assertIsNotNone(method2_prog)
-        method2_gm = method2_prog.exported_program.graph_module
-        self.assertIsInstance(method2_gm, fx.GraphModule)
-        add_nodes = [
-            node
-            for node in method2_gm.graph.nodes
-            if node.op == "call_function"
-            and node.target == exir_ops.edge.aten.add.Tensor
-        ]
-        self.assertEqual(len(add_nodes), 1)
-
-        lowered_submods = get_lowered_submodules(method2_gm)
-        self.assertEqual(len(lowered_submods), 0)
-
-        # Check we can export to executorch properly
-        _ = multi_method_program_to_executorch(lowered_multi_method_prog)