[FxImporter] Add backend lowering to Fx API (#3288)

projects/pt1/python/torch_mlir_e2e_test/configs/fx_importer_backend.py

@@ -3,8 +3,6 @@
 # SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
 # Also available under a BSD-style license. See LICENSE.
 
-from typing import Union, Optional, Sequence
-
 import numpy as np
 import torch
 import torch.utils._pytree as pytree
@@ -12,15 +10,6 @@ from torch.export.graph_signature import OutputSpec, OutputKind
 from torch.export import ExportedProgram
 
 from torch_mlir import fx
-from torch_mlir.compiler_utils import (
-    run_pipeline_with_repro_report,
-    lower_mlir_module,
-    OutputType,
-)
-from torch_mlir.torchscript import (
-    BACKEND_LEGAL_OPS,
-    _canon_extra_library,
-)
 from torch_mlir_e2e_test.configs.utils import (
     recursively_convert_to_numpy,
     recursively_convert_from_numpy,
@@ -39,53 +28,6 @@ def refine_result_type(_result):
         raise ValueError(f"Unhandled return type {type(_result)}")
 
 
-def jit(
-    prog: ExportedProgram,
-    func_name: str,
-    output_type: Union[str, "OutputType"] = OutputType.TORCH,
-    backend_legal_ops: Optional[Sequence[str]] = None,
-    extra_library=None,
-    verbose: bool = False,
-):
-    if extra_library is None:
-        extra_library = []
-    mlir_module = None
-
-    extra_library_file_name = _canon_extra_library(extra_library)
-    output_type = OutputType.get(output_type)
-    if backend_legal_ops is not None:
-        if output_type != OutputType.TORCH:
-            raise Exception(
-                "`backend_legal_ops` is only valid with the " "`torch` output type"
-            )
-        backend_legal_ops = list(sorted(set(backend_legal_ops)))
-    else:
-        backend_legal_ops = BACKEND_LEGAL_OPS.get(output_type, [])
-
-    option_string = (
-        "{backend-legal-ops="
-        + ",".join(backend_legal_ops)
-        + " extra-library="
-        + extra_library_file_name
-        + "}"
-    )
-
-    mlir_module = fx.export_and_import(prog, func_name=func_name)
-    assert mlir_module is not None
-    run_pipeline_with_repro_report(
-        mlir_module,
-        f"builtin.module(torch-simplification-pipeline)",
-        "Simplification pipeline for torch dialect",
-    )
-    run_pipeline_with_repro_report(
-        mlir_module,
-        f"builtin.module(torch-function-to-torch-backend-pipeline{option_string})",
-        "Lowering TorchFX IR -> Torch Backend IR",
-    )
-
-    return lower_mlir_module(verbose, output_type, mlir_module)
-
-
 class FxImporterTestConfig(TestConfig):
     """TestConfig that runs the torch.nn.Module with Fx Importer"""
 
@@ -100,11 +42,11 @@ class FxImporterTestConfig(TestConfig):
     def run(self, artifact: torch.nn.Module, trace: Trace) -> Trace:
         result: Trace = []
         for item in trace:
-            prog = torch.export.export(artifact, tuple(item.inputs))
+            prog: ExportedProgram = torch.export.export(artifact, tuple(item.inputs))
-            module = jit(
+            module = fx.export_and_import(
                 prog,
-                func_name=artifact.__class__.__name__,
                 output_type=self._output_type,
+                func_name=artifact.__class__.__name__,
             )
             module = self._backend.compile(module)
             backend_module = self._backend.load(module)

python/torch_mlir/fx.py

@@ -16,11 +16,50 @@ 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,
+    backend_legal_ops=None,
+    extra_library_file_name=None,
+):
+
+    if output_type == OutputType.TORCH:
+        if verbose:
+            print(torch_mod)
+        return torch_mod
+    # TODO: pass backend_legal_ops/extra_library_file_name by caller
+    if backend_legal_ops is None:
+        backend_legal_ops = []
+    if extra_library_file_name is None:
+        extra_library_file_name = ""
+    option_string = (
+        "{backend-legal-ops="
+        + ",".join(backend_legal_ops)
+        + " extra-library="
+        + extra_library_file_name
+        + "}"
+    )
+    run_pipeline_with_repro_report(
+        torch_mod,
+        f"builtin.module(torch-function-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,
@@ -28,6 +67,7 @@ def export_and_import(
     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()
@@ -52,15 +92,19 @@ def export_and_import(
     else:
         fx_importer.import_frozen_program(prog, func_name=func_name)
 
-    return fx_importer.module
+    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()
@@ -69,4 +113,6 @@ def stateless_fx_import(
     if fx_importer is None:
         fx_importer = FxImporter(context=context, hooks=hooks)
     fx_importer.import_stateless_graph(gm.graph, func_name=model_name)
-    return fx_importer.module
+    return _module_lowering(
+        enable_ir_printing, OutputType.get(output_type), fx_importer.module
+    )