# Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # Copyright 2025 Arm Limited and/or its affiliates. # # This source code is licensed under the BSD-style license found in the # LICENSE file in the root directory of this source tree. # pyre-unsafe import copy import io import logging import os from typing import Any, Dict, List, Optional, Sequence, Set, TextIO, Tuple, Type, Union import torch import torch._export from executorch.exir._serialize._cord import Cord from executorch.exir._serialize._named_data_store import ( NamedDataStore, NamedDataStoreOutput, ) from executorch.exir._serialize._serialize import serialize_for_executorch from executorch.exir._serialize.data_serializer import DataSerializer from executorch.exir._warnings import experimental from executorch.exir.backend.backend_api import ( MethodProgramsPartitionerSpec, to_backend, ) from executorch.exir.backend.partitioner import Partitioner from executorch.exir.capture._config import EdgeCompileConfig, ExecutorchBackendConfig from executorch.exir.delegate import executorch_call_delegate, is_lowered_module from executorch.exir.emit import emit_program, EmitterOutput from executorch.exir.emit._emitter import _DelegateDebugIdentifierMap from executorch.exir.error import ExportError from executorch.exir.graph_module import get_control_flow_submodules from executorch.exir.operator.convert import _pybind_schema_to_native_schema from executorch.exir.operator.util import _QUANT_PRIMITIVES from executorch.exir.pass_base import PassBase from executorch.exir.pass_manager import PassType from executorch.exir.passes import ( base_post_op_replace_passes, base_pre_op_replace_passes, dead_code_elimination_pass, EdgeToBackendOpsPass, MemoryFormatOpsPass, OpReplacePass, remove_unused_parameters_pass, ) from executorch.exir.passes.external_constants_pass import ( external_constants_pass, external_mutable_weights_pass, ) from executorch.exir.passes.insert_write_back_for_buffers_pass import ( insert_write_back_for_buffers_pass, ) from executorch.exir.passes.normalize_view_copy_base_pass import ( NormalizeViewCopyBasePass, ) from executorch.exir.passes.quant_fusion_pass import quant_fusion_and_const_prop_pass from executorch.exir.passes.remove_graph_asserts_pass import ( RemoveGraphAssertsPass, RemoveNonCoreAtenOpGraphAssertsPass, ) from executorch.exir.passes.remove_mixed_type_operators import RemoveMixedTypeOperators from executorch.exir.passes.replace_aten_with_edge_pass import aten_to_edge from executorch.exir.passes.replace_view_copy_with_view_pass import ( ReplaceViewCopyWithViewPass, ) from executorch.exir.passes.spec_prop_pass import SpecPropPass from executorch.exir.passes.weights_to_outputs_pass import weights_to_outputs_pass from executorch.exir.print_program import pretty_print, print_program from executorch.exir.schema import Program from executorch.exir.tracer import _default_decomposition_table from executorch.exir.verification.verifier import ( EXIRATenDialectVerifier, EXIREdgeDialectVerifier, get_aten_verifier, ) from executorch.extension.flat_tensor.serialize.serialize import FlatTensorSerializer from torch._export.passes import ReplaceViewOpsWithViewCopyOpsPass from torch._export.verifier import Verifier from torch.export import ExportedProgram from torch.export._remove_auto_functionalized_pass import ( unsafe_remove_auto_functionalized_pass, ) from torch.export.exported_program import ( ConstantArgument, ExportGraphSignature, InputKind, InputSpec, OutputSpec, TensorArgument, ) from torch.fx import _pytree as fx_pytree from torch.fx._compatibility import compatibility from torch.fx.passes.infra.pass_manager import PassManager from torch.utils import _pytree as pytree Val = Any from typing import Any, Callable from torch.library import Library try: from executorch.exir.program.fb.logger import et_logger except ImportError: # Define a stub decorator that does nothing def et_logger(api_name: str) -> Callable[[Any], Any]: def decorator(func: Callable[..., Any]) -> Callable[..., Any]: def wrapper(self: Any, *args: Any, **kwargs: Any) -> Any: return func(self, *args, **kwargs) return wrapper return decorator # This is the reserved namespace that is used to register ops to that will # be prevented from being decomposed during to_edge_transform_and_lower. edge_no_decomp_namespace = "EDGE_DO_NOT_DECOMP" lib = Library(edge_no_decomp_namespace, "DEF") # Map from aten ops to the transformed ops registered in the edge_no_decomp_namespace. aten_op_to_transform_op = {} # Map from the transformed ops registered in the edge_no_decomp_namespace to aten ops. transform_op_to_aten_op = {} def _get_updated_range_constraints(gm): def get_shape_env(gm): vals = [ node.meta["val"] for node in gm.graph.nodes if node.meta.get("val", None) is not None ] from torch._guards import detect_fake_mode # type: ignore[21] fake_mode = detect_fake_mode(vals) if fake_mode is not None: return fake_mode.shape_env for v in vals: if isinstance(v, torch.SymInt): return v.node.shape_env shape_env = get_shape_env(gm) if shape_env is None: return {} range_constraints = { k: v for k, v in shape_env.var_to_range.items() if k not in shape_env.replacements } # Only when we have an unbacked symint, and it's used as constructor inputs, # runtime_var_to_range will make a difference compated to var_to_range. # e.g. [2, oo) -> [0, oo) for k, v in shape_env.var_to_range.items(): if k not in shape_env.replacements: range_constraints[k] = v return range_constraints def _get_updated_graph_signature( old_signature: ExportGraphSignature, new_gm: torch.fx.GraphModule, ) -> ExportGraphSignature: """ Update the graph signature's user_input/user_outputs. """ new_input_specs = [] i = 0 for node in new_gm.graph.nodes: if node.op != "placeholder": continue assert i < len( old_signature.input_specs ), "Number of inputs changed after transformation" old_input_spec = old_signature.input_specs[i] arg = ( old_input_spec.arg if isinstance(old_input_spec.arg, ConstantArgument) # pyre-fixme[20]: Argument `class_fqn` expected. else type(old_input_spec.arg)(node.name) ) new_input_specs.append( InputSpec( old_input_spec.kind, arg, old_input_spec.target, persistent=old_input_spec.persistent, ) ) i += 1 output_node = list(new_gm.graph.nodes)[-1] assert output_node.op == "output" new_output_specs = [] for i, node in enumerate(output_node.args[0]): assert i < len( old_signature.output_specs ), "Number of outputs changed after transformation" old_output_spec = old_signature.output_specs[i] arg = ( old_output_spec.arg if isinstance(old_output_spec.arg, ConstantArgument) # pyre-fixme[20]: Argument `class_fqn` expected. else type(old_output_spec.arg)(node.name) ) new_output_specs.append( OutputSpec(old_output_spec.kind, arg, old_output_spec.target) ) new_signature = ExportGraphSignature( input_specs=new_input_specs, output_specs=new_output_specs ) return new_signature def _transform( self, *passes: PassType, override_verifiers: None | list[Type[Verifier]] = None, ) -> "ExportedProgram": """ Transforms the program according to the provided passes. Args: self: The ExportedProgram instance to transform *passes: A sequence of passes to apply to the program override_verifiers: Optional list of verifier classes to use instead of the default verifiers. This is needed if the transforms yields illegal graph that the default verifier cannot handle. Returns: ExportedProgram: A new ExportedProgram with the transformations applied, or self if no changes were made """ # A user friendly check to avoid vararg surprises, PEP 3102 assert not any( isinstance(p, (list, Verifier)) for p in passes ), f"Expected all passes to be of PassType, not list or Verifier. Use override_verifiers kwarg instead. Got: {list(passes)}" pm = PassManager(list(passes)) res = pm(self.graph_module) transformed_gm = res.graph_module if res is not None else self.graph_module assert transformed_gm is not None if transformed_gm is self.graph_module and not res.modified: return self return _update_exported_program_graph_module( self, transformed_gm, override_verifiers ) def _update_exported_program_graph_module( exported_program: ExportedProgram, gm: torch.fx.GraphModule, override_verifiers: None | list[Type[Verifier]] = None, ) -> "ExportedProgram": transformed_ep = ExportedProgram( root=gm, graph=gm.graph, graph_signature=_get_updated_graph_signature( exported_program.graph_signature, gm ), state_dict=exported_program.state_dict, range_constraints=_get_updated_range_constraints(gm), module_call_graph=copy.deepcopy(exported_program._module_call_graph), example_inputs=exported_program.example_inputs, constants=exported_program.constants, verifiers=override_verifiers or [exported_program.verifier], ) transformed_ep.graph_module.meta.update(exported_program.graph_module.meta) transformed_ep.graph_module.meta.update(gm.meta) return transformed_ep def _copy_module(new_prog, new_gm): new_prog.meta.update(new_gm.meta) new_prog.graph = new_gm.graph submodules = [name for name, _ in new_prog.named_children()] for name in submodules: delattr(new_prog, name) for name, mod in new_gm.named_children(): setattr(new_prog, name, mod) for node in new_gm.graph.nodes: if node.op == "get_attr": t = getattr(new_gm, node.target, None) if isinstance(t, torch.Tensor): setattr(new_prog, node.target, t) def lift_constant_tensor_pass(ep): """ Takes an ExportedProgram and returns the ExportedProgram modified in-place, with the constant tensors as buffers. """ if len([node for node in ep.graph.nodes if node.op == "placeholder"]) == 0: return ep graph_signature = ep.graph_signature buffers = list(graph_signature.buffers) fake_mode = list(ep.graph.nodes)[0].meta["val"].fake_mode first_user_input = None lifted_constants = [] for node in ep.graph.nodes: if node.op == "placeholder" and node.name in graph_signature.user_inputs: first_user_input = node break for node in ep.graph.nodes: if node.op == "get_attr": constant_tensor = getattr(ep.graph_module, node.target) if not isinstance(constant_tensor, torch.Tensor): continue constant_tensor_fqn = f"_lifted_tensor_constant{len(buffers)}" with ep.graph.inserting_before(first_user_input): # Insert the constant node before the first user input const_placeholder_node = ep.graph.placeholder(constant_tensor_fqn) for k, v in node.meta.items(): const_placeholder_node.meta[k] = v if fake_mode is not None: const_placeholder_node.meta["val"] = fake_mode.from_tensor( constant_tensor, static_shapes=True ) else: const_placeholder_node.meta["val"] = constant_tensor const_placeholder_node.meta["val"].constant = constant_tensor node.replace_all_uses_with(const_placeholder_node) ep.graph.erase_node(node) # Add the constant as a buffer to the graph signature lifted_constants.append( InputSpec( kind=InputKind.BUFFER, arg=TensorArgument(name=const_placeholder_node.name), target=constant_tensor_fqn, persistent=True, ) ) buffers.append(constant_tensor_fqn) ep.state_dict[constant_tensor_fqn] = constant_tensor new_input_specs = [] for s in graph_signature.input_specs: if s.kind == InputKind.USER_INPUT and len(lifted_constants) > 0: new_input_specs.extend(lifted_constants) lifted_constants.clear() new_input_specs.append(s) if len(lifted_constants) > 0: new_input_specs = lifted_constants + new_input_specs ep.graph_signature.input_specs = new_input_specs ep.graph_module.recompile() return ep # Stub to ease migration from `transform` to private `_transform` def transform_exported_program(ep, *passes: PassType) -> ExportedProgram: if hasattr(ep, "_transform"): return ep._transform(*passes) else: return ep.transform(*passes) class HackedUpExportedProgramDONOTUSE(ExportedProgram): def __init__( self, root, graph, graph_signature, call_spec, state_dict, range_constraints, module_call_graph, example_inputs, verifier, ): super().__init__( root=root, graph=graph, graph_signature=graph_signature, state_dict=state_dict, range_constraints=range_constraints, module_call_graph=module_call_graph, example_inputs=example_inputs, verifier=verifier, ) def __call__(self, *args: Any, **kwargs: Any) -> Any: import torch._export.error as error if self.call_spec.in_spec is not None: user_args = args try: args = fx_pytree.tree_flatten_spec(user_args, self.call_spec.in_spec) # type: ignore[assignment] except Exception: _, received_spec = pytree.tree_flatten(user_args) raise error.InternalError( "Trying to flatten user inputs with exported input tree spec: \n" f"{self.call_spec.in_spec}\n" "but actually got inputs with tree spec of: \n" f"{received_spec}" ) ordered_params = tuple( self.state_dict[name] for name in self.graph_signature.parameters ) ordered_buffers = tuple( self.state_dict[name] for name in self.graph_signature.buffers ) with torch.no_grad(): # NOTE: calling convention is first params, then buffers, then args as user supplied them. # See: torch/_functorch/aot_autograd.py#L1034 res = torch.fx.Interpreter(self.graph_module).run( *ordered_params, *ordered_buffers, *args, enable_io_processing=False ) if self.call_spec.out_spec is not None: mutation = self.graph_signature.buffers_to_mutate num_mutated = len(mutation) mutated_buffers = res[:num_mutated] # Exclude dependency token from final result. assertion_dep_token = self.graph_signature.assertion_dep_token if assertion_dep_token is not None: assertion_dep_token_index = list(assertion_dep_token.keys())[0] res = res[:assertion_dep_token_index] res = res[num_mutated:] try: res = pytree.tree_unflatten(res, self.call_spec.out_spec) except Exception: _, received_spec = pytree.tree_flatten(res) raise error.InternalError( "Trying to flatten user outputs with exported output tree spec: \n" f"{self.call_spec.out_spec}\n" "but actually got outputs with tree spec of: \n" f"{received_spec}" ) finally: ix = 0 for buffer in self.graph_signature.buffers_to_mutate.values(): self.state_dict[buffer] = mutated_buffers[ix] ix += 1 return res @compatibility(is_backward_compatible=False) class ExirExportedProgram: def __init__( self, exported_program: ExportedProgram, after_to_edge_passes: bool, ): self.exported_program = exported_program # Add a flag to denote whehter to_edge is called on this program # to detect misusage of directly calling to_executorch without to_edge self.after_to_edge_passes = after_to_edge_passes def transform(self, *passes: PassType) -> "ExirExportedProgram": self.exported_program = _transform(self.exported_program, *passes) return self def __call__(self, *args: Any) -> Any: return self.exported_program.module()(*args) # TODO(ycao): Change this to a composable function. def to_edge( self, config: Optional[EdgeCompileConfig] = None ) -> "ExirExportedProgram": config = config or EdgeCompileConfig() assert isinstance( self.exported_program.graph_module, torch.fx.GraphModule ), f"type is instead: {type(self.exported_program.graph_module).__name__}" return _to_edge(self, config) def dump(self) -> None: print(self.exported_program.graph_module.graph) def to_executorch( self, config: Optional[ExecutorchBackendConfig] = None, ) -> "ExecutorchProgram": if not self.after_to_edge_passes: raise RuntimeError("Must run to_edge before to_executorch.") config = config or ExecutorchBackendConfig() new_gm = self.exported_program.graph_module for p in edge_to_executorch_passes(config): new_gm_res = p(new_gm) assert new_gm_res is not None new_gm = new_gm_res.graph_module # This is tech debt on tech debt. memory planning pass inherits from some pass infra for GMs. # This isnt enough info now so i cant use call I have to use some new function 'run'. # Existing user passes dont use run so Im just cheating here because they dont need to work on mutable buffers yet. # After exir.capture is gone I will clean up the memory planning infra to be consistent. # Frankly all of exir has big code quality issues because of the migrations that need to be addressed. new_gm_res = config.memory_planning_pass(new_gm) # pyre-ignore[29] assert new_gm_res is not None new_gm = new_gm_res.graph_module new_prog = ExirExportedProgram( copy.deepcopy(self.exported_program), self.after_to_edge_passes ) _copy_module(new_prog.exported_program.graph_module, new_gm) executorch_prog = ExecutorchProgram( new_prog, emit_stacktrace=config.emit_stacktrace, extract_delegate_segments=config.extract_delegate_segments, segment_alignment=config.segment_alignment, constant_tensor_alignment=config.constant_tensor_alignment, delegate_alignment=config.delegate_alignment, ) executorch_prog.graph_module.meta.update(new_gm.meta) executorch_prog.graph_module.meta.update( self.exported_program.graph_module.meta ) return executorch_prog def __deepcopy__( self, memo: Optional[Dict[int, Any]] = None ) -> "ExirExportedProgram": new_eep = ExirExportedProgram( copy.deepcopy(self.exported_program, memo), self.after_to_edge_passes, ) return new_eep @compatibility(is_backward_compatible=False) class ExecutorchProgram: def __init__( self, exir_exported_program: ExirExportedProgram, emit_stacktrace: bool, extract_delegate_segments: bool, segment_alignment: int, constant_tensor_alignment: Optional[int] = None, delegate_alignment: Optional[int] = None, ) -> None: if not exir_exported_program.after_to_edge_passes: raise RuntimeError( "Need to call prog.to_edge prior to constructing ExecutorchProgram." ) self.exported_program = exir_exported_program.exported_program self._pte_data: Optional[Cord] = None self._tensor_data: Optional[Dict[str, Cord]] = None self._buffer: Optional[bytes] = None self._emitter_output: Optional[EmitterOutput] = None self._emit_stacktrace: bool = emit_stacktrace self._extract_delegate_segments: bool = extract_delegate_segments self._segment_alignment: int = segment_alignment self._constant_tensor_alignment: Optional[int] = constant_tensor_alignment self._delegate_alignment: Optional[int] = delegate_alignment self._data_serializer: DataSerializer = FlatTensorSerializer() def _get_emitter_output(self) -> EmitterOutput: if self._emitter_output is None: self._emitter_output = emit_program( self.exported_program, self._emit_stacktrace ) return self._emitter_output def _get_pte_data(self) -> Cord: if self._pte_data is None: self._pte_data, self._tensor_data = serialize_for_executorch( self._get_emitter_output(), ExecutorchBackendConfig( extract_delegate_segments=self._extract_delegate_segments, segment_alignment=self._segment_alignment, constant_tensor_alignment=self._constant_tensor_alignment, delegate_alignment=self._delegate_alignment, ), self._data_serializer, ) assert self._pte_data is not None return self._pte_data @property def buffer(self) -> bytes: """Returns the serialized ExecuTorch binary as a byte string. Note that the call to `buffer` may allocate a very large amount of contiguous memory, depending on the model size. If writing to a file, use `write_to_file` which won't incur additional copies. """ # TODO(T181494963): update pybinding to remove buffer cache, which can consume large # amounts of memory longer than necessary. if self._buffer is None: self._buffer = bytes(self._get_pte_data()) return self._buffer @property def program(self) -> Program: return self._get_emitter_output().program @property def debug_handle_map(self) -> Dict[int, Union[int, List[int]]]: if self._emitter_output: return self._emitter_output.debug_handle_map return {} @property def delegate_map( self, ) -> Dict[str, Dict[int, Dict[str, Union[str, _DelegateDebugIdentifierMap]]]]: if self._emitter_output: return self._emitter_output.method_to_delegate_debug_id_map return {} @property def graph_module(self) -> torch.fx.GraphModule: return self.exported_program.graph_module # TODO (zhxchen17) Change this to property. def dump_graph_module(self) -> torch.fx.GraphModule: return self.exported_program.graph_module def dump_exported_program(self) -> ExportedProgram: return self.exported_program def write_to_file(self, open_file: io.BufferedIOBase) -> None: """ Writes the serialized ExecuTorch binary to the file at `open_file`. Prefer to use this over `buffer`, as it writes to file without copying into a contiguous block of memory first, reducing the peak memory usage. """ self._get_pte_data().write_to_file(open_file) def write_tensor_data_to_file(self, outdir) -> None: """ Writes the serialized ExecuTorch data files to the directory at `outdir`. """ assert self._tensor_data is not None # pyre-ignore[16]: `Optional` has no attribute `items`. for filename, cord in self._tensor_data.items(): with open(os.path.join(outdir, f"{filename}.ptd"), "wb") as f: logging.info(f"Writing data file to {filename}.ptd") cord.write_to_file(f) def _get_aten_to_edge_passes(config: EdgeCompileConfig): # TODO: the last two passes for aten_to_edge need to be eliminated_dead_code -> debug_handle_generator. After enable # use_edge_op it can be moved to aten_to_edge_passes before eliminated_dead_code pass. Also ExportPass doesn't play # well with node.meta, meaning after some passes permuting operators, we may lose some information in node.meta. # It might be regenerated in SpecPropPass so it may not be visiable. However debug handle will be lost. pre_op_replace_passes = base_pre_op_replace_passes + ( [] if config._skip_type_promotion else [RemoveMixedTypeOperators()] ) post_op_replace_passes = base_post_op_replace_passes return pre_op_replace_passes, post_op_replace_passes def _to_edge(ep, config: EdgeCompileConfig) -> "ExirExportedProgram": if config._check_ir_validity: try: EXIRATenDialectVerifier()(ep.exported_program.graph_module) except ExportError: logging.info( "If a particular operator failed core ATen IR check, please consider adding it to the exception list. " "Add the operator to _core_aten_ops_exception_list in EdgeCompileConfig. This is the recommended way " "to resolve this type of failure, so that the rest of the IR validation check can still be performed.\n" "If you'd like to disable IR validation checking, please set _check_ir_validity in EdgeCompileConfig, " "like *.to_edge(exir.EdgeCompileConfig(_check_ir_validity=False))." ) raise dialect = ep.exported_program.dialect if dialect == "ATEN": ep = ExirExportedProgram( ExportedProgram( root=ep.exported_program.graph_module, graph=ep.exported_program.graph_module.graph, graph_signature=ep.exported_program.graph_signature, state_dict=ep.exported_program.state_dict, range_constraints=ep.exported_program.range_constraints, module_call_graph=ep.exported_program.module_call_graph, example_inputs=ep.exported_program.example_inputs, constants=ep.exported_program.constants, verifiers=[ get_aten_verifier( config=config, ) ], ), False, ) pre_op_replace_passes, post_op_replace_passes = _get_aten_to_edge_passes(config) new_ep = copy.deepcopy(ep).transform(*pre_op_replace_passes) if dialect == "ATEN": new_ep.exported_program = lift_constant_tensor_pass(new_ep.exported_program) new_gm = new_ep.exported_program.graph_module if config._use_edge_ops: new_gm_res = OpReplacePass()(new_gm) assert new_gm_res is not None new_gm = new_gm_res.graph_module if not config._skip_dim_order: new_gm_res = MemoryFormatOpsPass()(new_gm) assert new_gm_res is not None new_gm = new_gm_res.graph_module for p in post_op_replace_passes: new_gm_res = p(new_gm) assert new_gm_res is not None new_gm = new_gm_res.graph_module new_ep.exported_program = ExportedProgram( root=new_gm, graph=new_gm.graph, graph_signature=_get_updated_graph_signature( new_ep.exported_program.graph_signature, new_gm ), state_dict=new_ep.exported_program.state_dict, range_constraints=new_ep.exported_program.range_constraints, module_call_graph=new_ep.exported_program.module_call_graph, example_inputs=new_ep.exported_program.example_inputs, constants=new_ep.exported_program.constants, verifiers=[ EXIREdgeDialectVerifier( edge_compile_config=config, class_only=True, ) ], ) new_ep.after_to_edge_passes = True return new_ep def pre_memory_planning_passes( config: ExecutorchBackendConfig, name: Optional[str] = None ) -> List[PassType]: """ Returns a list of passes to run before memory planning. Get the sym shape eval pass based on the method name, if the pass is not in the dict, use the default pass. """ # Handle symbolic shape eval pass if isinstance(config.sym_shape_eval_pass, dict): default_pass = ExecutorchBackendConfig().sym_shape_eval_pass if not name: sym_shape_eval_pass = default_pass # pyre-ignore: Undefined attribute [16] sym_shape_eval_pass = config.sym_shape_eval_pass.get(name, default_pass) elif isinstance(config.sym_shape_eval_pass, PassBase): sym_shape_eval_pass = config.sym_shape_eval_pass else: raise RuntimeError( f"sym_shape_eval_pass must be a dict or a PassBase, got {config.sym_shape_eval_pass}" ) if config.remove_view_copy: return [ NormalizeViewCopyBasePass(), dead_code_elimination_pass, ReplaceViewCopyWithViewPass(), sym_shape_eval_pass, config.to_out_var_pass, ] else: return [ sym_shape_eval_pass, config.to_out_var_pass, ] def edge_to_executorch_passes( config: ExecutorchBackendConfig, name: Optional[str] = None ) -> List[PassType]: """ Returns a list of passes to lower from edge to executorch. Get the pre memory planning passes based on the method name, if the pass is not in the dict, use the default pass. """ passes: List[PassType] = [ *config.passes, SpecPropPass(), # ExecuTorch backend ops are unable to handle unbacked symints. So after # this pass, passes cannot be Interpreter-based, because it will fail if # there exists an unbacked symint operation. EdgeToBackendOpsPass(), RemoveGraphAssertsPass(), ] + pre_memory_planning_passes(config, name) return passes def _generate_edge_program( name: str, config: EdgeCompileConfig, program: ExportedProgram, ops_set_to_not_decompose: Optional[List[torch._ops.OpOverload]] = None, ) -> ExportedProgram: # Remove invalid assert ops, such as _assert_tensor_metadata gm = program.graph_module gm_res = RemoveNonCoreAtenOpGraphAssertsPass()(gm) assert gm_res is not None gm = gm_res.graph_module # Remove unused parameters program = remove_unused_parameters_pass(program) if config._check_ir_validity: try: EXIRATenDialectVerifier( edge_compile_config=config, class_only=False, exception_list=ops_set_to_not_decompose, )(gm) except ExportError as e: logging.info(f"Input program {name} is not in ATen dialect.") raise e pre_op_replace_passes, post_op_replace_passes = _get_aten_to_edge_passes(config) passes = [] passes.append( ReplaceViewOpsWithViewCopyOpsPass() ) # TODO move inside aten_to_edge passes after all users are migrated off v1 capture passes.extend(pre_op_replace_passes) if config._use_edge_ops: passes.append(OpReplacePass()) if not config._skip_dim_order: passes.append(MemoryFormatOpsPass()) for p in passes: gm_res = p(gm) assert gm_res is not None gm = gm_res.graph_module edge_program = ExportedProgram( root=gm, graph=gm.graph, graph_signature=_get_updated_graph_signature(program.graph_signature, gm), state_dict=program.state_dict, range_constraints=program.range_constraints, module_call_graph=program.module_call_graph, example_inputs=program.example_inputs, constants=program.constants, verifiers=[ EXIREdgeDialectVerifier( edge_compile_config=config, class_only=True, exception_list=ops_set_to_not_decompose, ) ], ) # Lift the tensor constants created in ScalarToTensorPass edge_program = lift_constant_tensor_pass(edge_program) edge_program = _transform(edge_program, *post_op_replace_passes) return edge_program def _replace_aten_ops_with_transformed_ops( name: str, program: ExportedProgram, partitioner, ): ops_to_not_decompose = set() partitioners = partitioner.get(name) if partitioners is None: return # Iterate through the graph and replace the aten ops with the corresponding # transformed ops. for partitioner in partitioners: ops_set_to_not_decompose, check_op_support = partitioner.ops_to_not_decompose( program ) ops_set_to_not_decompose = _remove_invalid_ops_for_not_decompose( ops_set_to_not_decompose ) for op_aten in ops_set_to_not_decompose: _register_no_decomp_op(op_aten) for node in program.graph.nodes: is_op_supported = check_op_support(node) if check_op_support else True if ( node.op == "call_function" and node.target in ops_set_to_not_decompose and is_op_supported ): ops_to_not_decompose.add(node.target) node.target = aten_op_to_transform_op[node.target] for _, submod, _ in get_control_flow_submodules(program.graph_module): for node in submod.graph.nodes: is_op_supported = check_op_support(node) if check_op_support else True if ( node.op == "call_function" and node.target in ops_set_to_not_decompose and is_op_supported ): ops_to_not_decompose.add(node.target) node.target = aten_op_to_transform_op[node.target] return ops_to_not_decompose def _restore_transformed_ops_to_aten_ops(program: ExportedProgram): # Iterate through the graph and replace back the transformed ops with their # corresponding aten ops. for node in program.graph.nodes: if node.op == "call_function" and str(node.target) in transform_op_to_aten_op: node.target = transform_op_to_aten_op[str(node.target)] for _, submod, _ in get_control_flow_submodules(program.graph_module): for node in submod.graph.nodes: if ( node.op == "call_function" and str(node.target) in transform_op_to_aten_op ): node.target = transform_op_to_aten_op[str(node.target)] # Returns the op in edge_no_decomp_namespace namespace for the aten # op that is passed in. def _get_transformed_op(op_aten): op_name = op_aten._schema.name.split("::")[1] overload_name = op_aten._schema.overload_name assert hasattr( torch.ops, edge_no_decomp_namespace ), f"Couldn't find {edge_no_decomp_namespace} in torch.ops. Please make sure the Library has been registered." op_namespace = getattr(torch.ops, edge_no_decomp_namespace) op = getattr(op_namespace, op_name) return getattr(op, overload_name) # Registers the op in edge_no_decomp_namespace namespace for the aten # op that is passed in if it is not already cached in the table. def _register_no_decomp_op(op_aten): # Check if the op is already cached in the table. If not, then we need to # create a new op in the edge_no_decomp_namespace namespace. if aten_op_to_transform_op.get(op_aten) is None and isinstance( op_aten, torch._ops.OpOverload ): # Extract the schema from the aten op. op_schema = str(op_aten._schema).split("::")[1] op_name = op_aten._schema.name.split("::")[1] # Define an op in the edge_no_decomp_namespace namespace with the aten schema. lib.define(op_schema) # Define the implementation of the op in the edge_no_decomp_namespace namespace. # Important to note that the implementation of the op is the same as the aten op. overload_name = op_aten._schema.overload_name if overload_name != "": op_name += "." + overload_name lib.impl(op_name, op_aten, "CompositeExplicitAutograd") # Cache the aten op and transformed op in their corresponding tables for future use. aten_op_to_transform_op[op_aten] = _get_transformed_op(op_aten) transform_op_to_aten_op[str(aten_op_to_transform_op[op_aten])] = op_aten def _sanity_check_graph_for_non_decomp_ops( name: str, program: ExportedProgram, ops_set_to_not_decompose, check_op_support, generate_error=False, partitioner_name=None, ): warning_str_end = "" if partitioner_name is not None: warning_str_end += f"This op was registered by the partitioner {partitioner_name} to not be decomposed.\n" warning_str_end += f"The following ops: {ops_set_to_not_decompose} were specified to not be decomposed in {name}." # Check that the ops that were registered to not be decomposed are not present in the # graph anymore as the transform passes and backends should have consumed them by now. ops_set_to_not_decompose = { aten_to_edge(op) for op in ops_set_to_not_decompose }.union(ops_set_to_not_decompose) quant_primitives = {aten_to_edge(op) for op in _QUANT_PRIMITIVES} for node in program.graph_module.graph.nodes: is_op_supported = check_op_support(node) if check_op_support else True if ( node.op == "call_function" and node.target in ops_set_to_not_decompose and node.target not in quant_primitives ) and is_op_supported: warning_str = ( f"Node {node} with op {node.target} was not decomposed or delegated.\n" + warning_str_end ) if generate_error: raise RuntimeError(warning_str) else: logging.warning(warning_str) for _, submod, _ in get_control_flow_submodules(program.graph_module): for node in submod.graph.nodes: is_op_supported = check_op_support(node) if check_op_support else True if ( node.op == "call_function" and node.target in ops_set_to_not_decompose and node.target not in quant_primitives ) and is_op_supported: warning_str = ( f"Node {node} with op {node.target} was not decomposed or delegated.\n" + warning_str_end ) if generate_error: raise RuntimeError(warning_str) else: logging.warning(warning_str) def _remove_invalid_ops_for_not_decompose( ops_to_not_decompose: List[torch._ops.OpOverload], ) -> List[torch._ops.OpOverload]: # To address https://github.com/pytorch/executorch/issues/8781 def keep(op): # Explicit allow list allow_list = [] try: # Ops in torch.ops.quant are not always loaded, so we use try/except # Aliases output, but we need to allow it for XNNPACK allow_list.append(torch.ops.torchao.choose_qparams_affine.default) except: pass if op in allow_list: return True schema = op._schema native_schema = _pybind_schema_to_native_schema(schema) if native_schema is None: logging.warn( f"Torchgen is not able to parse the schema of {op._schema}. This is not fatal." ) else: if native_schema.is_mutable: logging.warn( f"Op {op} was requested for preservation by partitioner. This request is ignored because it is mutable." ) return False if native_schema.aliased_return_names() != [None]: logging.warn( f"Op {op} was requested for preservation by partitioner. This request is ignored because it aliases output." ) return False # Explicit block list of ops that don't work if asked for # preservation if op in [ # Hits infinte recursion error when op is in # EDGE_DO_NOT_DECOMP namespace torch.ops.aten._to_copy.default, # scalar to tensor type promotion does not work on ops # in EDGE_DO_NOT_DECOMP namespace torch.ops.aten.mul.Tensor, torch.ops.aten.add.Tensor, torch.ops.aten.sub.Tensor, torch.ops.aten.div.Tensor, torch.ops.aten.item.default, torch.ops.aten._local_scalar_dense.default, torch.ops.aten.unbind.int, torch.ops.aten.split_with_sizes.default, ]: logging.warn( f"Op {op} was requested for preservation by partitioner. This request is ignored because it is in a blocklist." ) return False return True return list(filter(keep, ops_to_not_decompose)) def _gen_edge_manager_for_partitioners( partitioner: Dict[str, List[Partitioner]], aten_programs: Dict[str, ExportedProgram], config: EdgeCompileConfig, constant_methods: Optional[Dict[str, Any]], ) -> "EdgeProgramManager": """ Generates EdgeProgramManager for subsequent lowering to the partitioners specified by partitioner. The EdgeProgramManager is generated from aten_programs. Partitioners specify what nodes should not be decomposed from the original aten programs. This is done through two passes of run_decompositions. - First pass preserves all aten_targets specified by partitioners to preserve them from nested decompositions - Second pass uses check_op fn provided by partitioners to perform additional checks on nodes with preserved aten targets. They are then replaces with transformed ops to keep them through the second pass of decompositions """ ops_set_to_not_decompose_by_program = {} edge_programs: Dict[str, ExportedProgram] = {} for name, program in aten_programs.items(): if partitioner is not None: # preserve all ops listed by all partitioners first all_ops_no_decomp = set() for curr_partitioner in partitioner.get(name, []): curr_ops_no_decomp, _ = curr_partitioner.ops_to_not_decompose(program) curr_ops_no_decomp = _remove_invalid_ops_for_not_decompose( curr_ops_no_decomp ) all_ops_no_decomp |= set(curr_ops_no_decomp) table = _default_decomposition_table() for op in all_ops_no_decomp: table.pop(op, None) program = program.run_decompositions(table) # Among all the preserved aten ops, use the check_op_fn to do an additional # check on which ops need to be preserved and which ops need to be decomposed # Those which are truly preserved will be replaced with transformed ops ops_set_to_not_decompose_by_program[name] = ( _replace_aten_ops_with_transformed_ops(name, program, partitioner) or [] ) program = program.run_decompositions(_default_decomposition_table()) _restore_transformed_ops_to_aten_ops(program) edge_programs[name] = program edge_programs[name] = _generate_edge_program( name, config, program, list(ops_set_to_not_decompose_by_program.get(name, [])), ) edge_manager = EdgeProgramManager( edge_programs, constant_methods, config, list(set().union(*ops_set_to_not_decompose_by_program.values())), ) return edge_manager def collect_named_data_store_from_exported_program( exported_program: ExportedProgram, named_data_store: NamedDataStore, ) -> None: """ Collects all the named data store outputs found within the exported program and adds them to named_data_store. """ # collected all the named data into the named data store for deduplication def collect_named_data_store_outputs( graph_module: torch.fx.GraphModule, ) -> None: for node in graph_module.graph.nodes: if node.target == executorch_call_delegate: lbm = getattr(graph_module, node.args[0].target) assert is_lowered_module(lbm) data_store_output = lbm.named_data_store_output if data_store_output is not None: named_data_store.merge_named_data_store(data_store_output) for _, submod, _ in get_control_flow_submodules(graph_module): collect_named_data_store_outputs(submod) collect_named_data_store_outputs(exported_program.graph_module) @et_logger("to_edge_transform_and_lower") def to_edge_transform_and_lower( programs: Union[ExportedProgram, Dict[str, ExportedProgram]], transform_passes: Optional[ Union[Sequence[PassType], Dict[str, Sequence[PassType]]] ] = None, partitioner: Optional[ Union[List[Partitioner], Dict[str, List[Partitioner]]] ] = None, constant_methods: Optional[Dict[str, Any]] = None, compile_config: Optional[EdgeCompileConfig] = None, ) -> "EdgeProgramManager": """ :func:`to_edge_transform_and_lower` constructs an EdgeProgramManager from a set of exported programs in ATen dialect. It differs fundamentally from to_edge in that it combines the conversion of the ATen dialect to the edge dialect program, then running the transformation passes and then subsequently lowering the programs to their corresponding backends all into a single API. This is fundamentally useful for lowering to backends that have ops registered that they do not want to be decomposed and thus rely on matching with these non-decomposed ops. For these sorts of backends this is the *only* API that should be used to lower to the edge dialect. Using a combination of to_edge(...) and to_backend(...) will result in inconsistent or wrong behavior. This API is the primary recommended way to lower to the CPU based XNNPack backend. 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". transform_passes: The passes can either be a list of passes, or a dictionary mapping method names to lists of passes. If it is just a list of passes, all methods in the given EdgeProgramManager will be transformed with the provided passes. If it is a dictionary, only method names specified in the dictionary will be transformed with their corresponding passes. partitioner: The partitioner can either be a Partitioner subclass instance, or a dictionary mapping method names to Partitioner subclass instance. If it is a Partitioner subclass, all programs in the given EdgeProgramManager 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. 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. 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 if not isinstance(partitioner, dict) and partitioner is not None: partitioner = {name: partitioner for name in aten_programs.keys()} elif partitioner is None: partitioner = {name: [] for name in aten_programs.keys()} edge_manager = _gen_edge_manager_for_partitioners( partitioner, aten_programs, config, constant_methods ) if transform_passes is not None: edge_manager = edge_manager.transform(transform_passes) max_num_partitioners = 0 for partitioner_list in partitioner.values(): max_num_partitioners = max(max_num_partitioners, len(partitioner_list)) for i in range(max_num_partitioners): method_to_partitioner = {} for name, partitioner_list in partitioner.items(): if i < len(partitioner_list): method_to_partitioner[name] = partitioner_list[i] edge_manager = edge_manager.to_backend(method_to_partitioner) for name, program in edge_manager._edge_programs.items(): ops_set_to_not_decompose: Set[torch._ops.OpOverload] = set() partitioners = partitioner.get(name, []) for curr_partitioner in partitioners: curr_op_set, check_op_support = curr_partitioner.ops_to_not_decompose( program ) curr_op_set = _remove_invalid_ops_for_not_decompose(curr_op_set) ops_set_to_not_decompose = ops_set_to_not_decompose.union(curr_op_set) _sanity_check_graph_for_non_decomp_ops( name, program, ops_set_to_not_decompose, check_op_support, partitioner_name=curr_partitioner.__class__.__name__, generate_error=True, ) if config._check_ir_validity: EXIREdgeDialectVerifier( edge_compile_config=config, class_only=True, exception_list=list(ops_set_to_not_decompose), )()(program.graph_module) 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 table = _default_decomposition_table() for op in preserve_ops: table.pop(op, None) program = program.run_decompositions(table) edge_programs[name] = _generate_edge_program( name, config, program, list(preserve_ops) ) return EdgeProgramManager( edge_programs, constant_methods, config, list(preserve_ops) ) @et_logger("to_edge") def to_edge( programs: Union[ExportedProgram, Dict[str, ExportedProgram]], constant_methods: Optional[Dict[str, Any]] = None, compile_config: Optional[EdgeCompileConfig] = None, ) -> "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. 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()) edge_programs[name] = _generate_edge_program(name, config, program) return EdgeProgramManager(edge_programs, constant_methods, config) class EdgeProgramManager: """ Package of one or more `ExportedPrograms` in Edge dialect. Designed to simplify lowering to ExecuTorch. See: https://pytorch.org/executorch/main/ir-exir Allows easy applications of transforms across a collection of exported programs including the delegation of subgraphs. Manages the second link in the lowering chain of ATen -> Edge -> ExecuTorch. """ def __init__( self, edge_programs: Union[ExportedProgram, Dict[str, ExportedProgram]], constant_methods: Optional[Dict[str, Any]] = None, compile_config: Optional[EdgeCompileConfig] = None, ops_set_to_not_decompose: Optional[List[torch._ops.OpOverload]] = None, ): """ Should not be called directly by users. User should use :func:'to_edge' instead. Constructs an EdgeProgramManager from an existing set of exported programs in edge dialect. """ self.compile_config = compile_config or EdgeCompileConfig() if not isinstance(edge_programs, dict): edge_programs = {"forward": edge_programs} for name, program in edge_programs.items(): try: EXIREdgeDialectVerifier( edge_compile_config=self.compile_config, exception_list=ops_set_to_not_decompose, )(program.graph_module) except ExportError as e: logging.info(f"Input program {name} is not in aten dialect.") raise e self._edge_programs: Dict[str, ExportedProgram] = edge_programs self._config_methods = constant_methods self._named_data_store = NamedDataStore() for _, program in self._edge_programs.items(): collect_named_data_store_from_exported_program( program, self._named_data_store ) @property def methods(self) -> Set[str]: """ Returns the set of methods in this EdgeProgramManager. """ return set(self._edge_programs.keys()) @property def config_methods(self) -> Set[str]: """ Returns the set of config methods in this EdgeProgramManager. """ return set(self._config_methods.keys()) if self._config_methods else set() def exported_program(self, method_name: str = "forward") -> ExportedProgram: """ Returns the ExportedProgram specified by 'method_name'. """ return self._edge_programs[method_name] @et_logger("transform") def transform( self, passes: Union[Sequence[PassType], Dict[str, Sequence[PassType]]], compile_config: Optional[EdgeCompileConfig] = None, ) -> "EdgeProgramManager": """ Transforms the program according to the provided passes. Args: passes: The passes can either be a list of passes, or a dictionary mapping method names to lists of passes. If it is just a list of passes, all methods in the given EdgeProgramManager will be transformed with the provided passes. If it is a dictionary, only method names specified in the dictionary will be transformed with their corresponding passes. compile_config: Compile config to use for veriy the correctness of model graph after each pass. If not specified, the compile config of the calling EdgeProgramManager will be used. It will be used in as compile config of returned EdgeProgramManager. Returns: EdgeProgramManager: A copy of the calling EdgeProgramManager with the transformations applied. """ compile_config = compile_config or self.compile_config new_programs: Dict[str, ExportedProgram] = {} if isinstance(passes, dict): for name, program in self._edge_programs.items(): if name in passes.keys(): new_programs[name] = _transform(program, *passes[name]) EXIREdgeDialectVerifier(edge_compile_config=compile_config)( new_programs[name].graph_module ) else: new_programs[name] = copy.deepcopy(program) else: # apply passes to every method for name, program in self._edge_programs.items(): new_programs[name] = _transform(program, *passes) EXIREdgeDialectVerifier(edge_compile_config=compile_config)( new_programs[name].graph_module ) return EdgeProgramManager( new_programs, copy.deepcopy(self._config_methods), compile_config ) @et_logger("to_backend") def to_backend( self, partitioner: Union[Partitioner, Dict[str, Partitioner]], ) -> "EdgeProgramManager": """ Returns a semantically-equivalent program to the one given as input, but with portions of each program in the EdgeProgramManager targeted for delegation as determined by the partitioner. Args: partitioner: The partitioner can either be a Partitioner subclass instance, or a dictionary mapping method names to Partitioner subclass instance. If it is a Partitioner subclass, all programs in the given EdgeProgramManager 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 instance is in charge with tagging portions of the input program for delegation. A valid partitioner must return PartitionerResult including valid 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: EdgeProgramManager: A copy of the calling EdgeProgramManager with the specified subgraphs lowered. """ new_edge_programs: Dict[str, ExportedProgram] = {} method_to_partitioner: Dict[str, Partitioner] = {} if not isinstance(partitioner, dict): method_to_partitioner = {name: partitioner for name in self._edge_programs} else: method_to_partitioner = partitioner method_to_programs_and_partitioners = MethodProgramsPartitionerSpec( self._edge_programs, method_to_partitioner, ) new_edge_programs = to_backend(method_to_programs_and_partitioners) config = EdgeCompileConfig(_check_ir_validity=False) return EdgeProgramManager( new_edge_programs, copy.deepcopy(self._config_methods), config, ) @et_logger("to_executorch") def to_executorch( self, config: Optional[ExecutorchBackendConfig] = None, ) -> "ExecutorchProgramManager": """ Transforms the program to the ExecuTorch backend. Args: config: An optional argument used to provide greater control over the transformation to the ExecuTorch backend. Returns: ExecutorchProgramManager: A manager representing the state of the EdgeProgramManager after it has been transformed to the ExecuTorch backend. """ config = config if config else ExecutorchBackendConfig() execution_programs: Dict[str, ExportedProgram] = {} for name, program in self._edge_programs.items(): if config.do_quant_fusion_and_const_prop: if program.graph_signature.backward_signature is not None: raise Exception( "Cannot run do_quant_fusion_and_const_prop on a graph with a backward signature intended for on-device training." " Please set do_quant_fusion_and_const_prop to False in the ExecutorchBackendConfig." ) program = quant_fusion_and_const_prop_pass(program) program = weights_to_outputs_pass(program) program = unsafe_remove_auto_functionalized_pass(program) gm, new_signature = insert_write_back_for_buffers_pass(program) new_gm = program.graph_module for p in edge_to_executorch_passes(config, name): new_gm_res = p(new_gm) assert new_gm_res is not None new_gm = new_gm_res.graph_module if isinstance(p, SpecPropPass): # Note that this is a hacky way to get around the fact that # placeholder nodes corresponding to the parameters of the graph module # shall not participate in memory planning. It increases runtime memory # footprint. # Proper way would be to have ExportPass work with ExportedProgram # instead of GraphModule. This is because ExportPass should work # on top of the export artifact of torch.export whichi s ExportedProgram. # Working with GraphModule does not provide all the information contained # in the ExportedProgram # TODO(who?) p.update_placeholder_tensor_specs(program, new_gm) # Extract constants if the config says too. if config.external_constants: new_gm_res = external_constants_pass(new_gm) new_gm = new_gm_res.graph_module elif config.external_mutable_weights: new_gm_res = external_mutable_weights_pass(new_gm, program) new_gm = new_gm_res.graph_module if isinstance(config.memory_planning_pass, dict): memory_planning_pass = config.memory_planning_pass.get( name, ExecutorchBackendConfig().memory_planning_pass ) else: memory_planning_pass = config.memory_planning_pass # TODO(jakeszwe): Follow up with compiler on if the deepcopy is necessary and if so how to make it work if hasattr(memory_planning_pass, "run"): new_gm_res = memory_planning_pass.run( # pyre-ignore[16] new_gm, new_signature ) else: new_gm_res = memory_planning_pass(new_gm) # pyre-ignore[29] # WARNING: DO NOT ADD ANY MORE PASSES AFTER MEMORY PLANNING PASS. # THERE ARE A LOT OF ASSUMPTIONS IN THE STACK THAT MEMORY PLANNING IS THE LAST PASS BEFORE THE EMITTER. assert new_gm_res is not None new_gm = new_gm_res.graph_module _copy_module(program.graph_module, new_gm) execution_programs[name] = program return ExecutorchProgramManager( execution_programs, self._config_methods, config, self._named_data_store.get_named_data_store_output(), ) class ExecutorchProgramManager: """ Package of one or more `ExportedPrograms` in Execution dialect. Designed to simplify lowering to ExecuTorch. See: https://pytorch.org/executorch/main/ir-exir When the ExecutorchProgramManager is constructed the ExportedPrograms in execution dialect are used to form the executorch binary (in a process called emission) and then serialized to a buffer. Manages the final link in the lowering chain of ATen -> Edge -> ExecuTorch. """ def __init__( self, execution_programs: Dict[str, ExportedProgram], config_methods: Optional[Dict[str, Any]] = None, backend_config: Optional[ExecutorchBackendConfig] = None, named_data: Optional[NamedDataStoreOutput] = None, ): """ End users should not call this constructor directly. Instead, they should use :func:'to_executorch' to construct an ExecutorchProgramManager. Constructs an ExecutorchProgramManager from a set of exported programs in execution dialect. Args: execution_programs: A dictionary of method name to the corresponding ExportedProgram. config_methods: A dictionary of method name to the config value returned by that method in eager mode. backend_config: An optional argument used to provide greater control over the emission and serialization. """ # Set up methods self._execution_programs: Dict[str, ExportedProgram] = execution_programs self._config_methods: Optional[Dict[str, Any]] = config_methods # Named data from EdgeProgramManager self._named_data: Optional[NamedDataStoreOutput] = named_data backend_config = backend_config or ExecutorchBackendConfig() # Emit methods self._emitter_output: EmitterOutput = emit_program( self._execution_programs, backend_config.emit_stacktrace, self._config_methods, backend_config.emit_mutable_buffer_names, ) # Serialize emitter output, ready to be written to a file. self._data_serializer = FlatTensorSerializer() self._pte_data, self._tensor_data = serialize_for_executorch( self._emitter_output, backend_config, self._data_serializer, self._named_data, ) self._buffer: Optional[bytes] = None @property def methods(self) -> Set[str]: """ Returns the set of methods in this ExecutorchProgramManager. """ return set(self._execution_programs.keys()) @property def config_methods(self) -> Set[str]: """ Returns the set of config methods in this ExecutorchProgramManager. """ return set(self._config_methods.keys()) if self._config_methods else set() def exported_program(self, method_name: str = "forward") -> ExportedProgram: """ Returns the ExportedProgram specified by 'method_name'. """ return self._execution_programs[method_name] def dump_executorch_program( self, verbose: bool = False, out: Optional[TextIO] = None ) -> None: """ Prints the ExecuTorch binary in a human readable format. Args: verbose (bool): If False prints the binary in a condensed format. If True prints the binary 1-1 with the specification in the schema. out: If None, prints to stdout. If non-None, writes the string to that stream object. It can be a file object, a StringIO object, or any other TextIO subclass. """ if verbose: pretty_print(self._emitter_output.program, out=out) else: print_program(self._emitter_output.program, out=out) @property def debug_handle_map(self) -> Dict[int, Union[int, List[int]]]: return self._emitter_output.debug_handle_map @property def delegate_map( self, ) -> Dict[str, Dict[int, Dict[str, Union[str, _DelegateDebugIdentifierMap]]]]: return self._emitter_output.method_to_delegate_debug_id_map @property def executorch_program(self) -> Program: """ Returns the object that represents the ExecuTorch binary before serialization. """ return self._emitter_output.program @property def buffer(self) -> bytes: """Returns the serialized ExecuTorch binary as a byte string. Note that the call to `buffer` may allocate a very large amount of contiguous memory, depending on the model size. If writing to a file, use `write_to_file` which won't incur additional copies. """ # TODO(T181494963): update pybinding to remove buffer cache, which can consume large # amounts of memory longer than necessary. if self._buffer is None: self._buffer = bytes(self._pte_data) return self._buffer def write_to_file(self, open_file: io.BufferedIOBase) -> None: """ Writes the serialized ExecuTorch binary to the file at `open_file`. Prefer to use this over `buffer`, as it writes to file without copying into a contiguous block of memory first, reducing the peak memory usage. """ self._pte_data.write_to_file(open_file) def write_tensor_data_to_file(self, outdir) -> None: """ Writes the serialized ExecuTorch data files to the directory at `outdir`. """ assert self._tensor_data is not None for filename, cord in self._tensor_data.items(): with open(os.path.join(outdir, f"{filename}.ptd"), "wb") as f: logging.info(f"Writing data file to {filename}") cord.write_to_file(f) def save(self, path: str) -> None: """ Saves the serialized ExecuTorch binary to the file at `path`. """ if path[-4:] != ".pte": logging.error(f"Path {path} does not end with .pte") raise ValueError(f"Path {path} does not end with .pte") try: with open(path, "wb") as file: self.write_to_file(file) logging.info(f"Saved exported program to {path}") except Exception as e: logging.error(f"Error while saving to {path}: {e}")