torch-mlir/python/torch_mlir/fx.py

# Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
# See https://llvm.org/LICENSE.txt for license information.
# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
# Also available under a BSD-style license. See LICENSE.

from typing import Optional, Union, Dict, Tuple, Any, Callable

import warnings

import torch
import torch.export
import torch.nn as nn
from torch.export import ExportedProgram

from torch_mlir.extras.fx_importer import FxImporter, FxImporterHooks
from torch_mlir import ir
from torch_mlir.dialects import torch as torch_d
from torch_mlir.extras.fx_decomp_util import get_decomposition_table
from torch_mlir.compiler_utils import (
    OutputType,
    run_pipeline_with_repro_report,
    lower_mlir_module,
)


def _module_lowering(
    verbose,
    output_type,
    torch_mod,
    extra_library_file_name=None,
):

    if output_type == OutputType.TORCH:
        if verbose:
            print(torch_mod)
        return torch_mod
    # TODO: pass extra_library_file_name by caller
    if extra_library_file_name is None:
        extra_library_file_name = ""
    option_string = "{extra-library=" + extra_library_file_name + "}"
    run_pipeline_with_repro_report(
        torch_mod,
        f"builtin.module(torchdynamo-export-to-torch-backend-pipeline{option_string})",
        "Lowering TorchFX IR -> Torch Backend IR",
        enable_ir_printing=verbose,
    )
    return lower_mlir_module(verbose, output_type, torch_mod)


def export_and_import(
    f: Union[nn.Module, ExportedProgram],
    *args,
    output_type: Union[str, OutputType] = OutputType.TORCH,
    fx_importer: Optional[FxImporter] = None,
    dynamic_shapes: Optional[Union[Dict[str, Any], Tuple[Any]]] = None,
    experimental_support_mutation: bool = False,
    import_symbolic_shape_expressions: bool = False,
    hooks: Optional[FxImporterHooks] = None,
    decomposition_table: Optional[Dict[torch._ops.OperatorBase, Callable]] = None,
    func_name: str = "main",
    enable_graph_printing: bool = False,
    enable_ir_printing: bool = False,
    **kwargs,
):
    context = ir.Context()
    torch_d.register_dialect(context)

    if fx_importer is None:
        fx_importer = FxImporter(context=context, hooks=hooks)
    if isinstance(f, ExportedProgram):
        prog = f
    else:
        prog = torch.export.export(f, args, kwargs, dynamic_shapes=dynamic_shapes)
    if decomposition_table is None:
        decomposition_table = get_decomposition_table()
    if decomposition_table:
        prog = prog.run_decompositions(decomposition_table)
    if enable_graph_printing:
        prog.graph_module.print_readable()
    if experimental_support_mutation:
        if torch.__version__ < "2.3.0.dev20240207":
            warnings.warn("Mutable program import only supported on PyTorch 2.3+")
        fx_importer.import_program(
            prog,
            func_name=func_name,
            import_symbolic_shape_expressions=import_symbolic_shape_expressions,
        )
    else:
        fx_importer.import_frozen_program(
            prog,
            func_name=func_name,
            import_symbolic_shape_expressions=import_symbolic_shape_expressions,
        )

    return _module_lowering(
        enable_ir_printing, OutputType.get(output_type), fx_importer.module
    )


def stateless_fx_import(
    gm: torch.fx.GraphModule,
    output_type: Union[str, OutputType] = OutputType.TORCH,
    fx_importer: Optional[FxImporter] = None,
    hooks: Optional[FxImporterHooks] = None,
    model_name: str = "main",
    enable_graph_printing: bool = False,
    enable_ir_printing: bool = False,
):
    if enable_graph_printing:
        gm.print_readable()
    context = ir.Context()
    torch_d.register_dialect(context)
    if fx_importer is None:
        fx_importer = FxImporter(context=context, hooks=hooks)
    fx_importer.import_stateless_graph(gm.graph, func_name=model_name)
    return _module_lowering(
        enable_ir_printing, OutputType.get(output_type), fx_importer.module
    )