Add make_fx_tosa variant to end2end tests (llvm#2240)

* Add make_fx_tosa variant to end2end tests

* e2e_testing/xfail_sets.py: Add make_fx_tosa xfail for stable

build_tools/python_deploy/build_linux_packages.sh

@@ -300,7 +300,10 @@ function test_in_tree() {
       exit 1
       ;;
     esac
-
+
+  echo ":::: Run make_fx + TOSA e2e integration tests"
+  python -m e2e_testing.main --config=make_fx_tosa -v
+
   echo ":::: Run TorchDynamo e2e integration tests"
   python -m e2e_testing.main --config=torchdynamo -v
 

e2e_testing/main.py

@@ -29,6 +29,7 @@
 
 from .xfail_sets import (
     LINALG_XFAIL_SET,
+    MAKE_FX_TOSA_PASS_SET,
     STABLEHLO_PASS_SET,
     TOSA_PASS_SET,
     LTC_XFAIL_SET,
@@ -42,7 +43,7 @@
 register_all_tests()
 
 def _get_argparse():
-    config_choices = ["native_torch", "torchscript", "linalg", "stablehlo", "tosa", "lazy_tensor_core", "torchdynamo"]
+    config_choices = ["native_torch", "torchscript", "linalg", "stablehlo", "make_fx_tosa", "tosa", "lazy_tensor_core", "torchdynamo"]
     parser = argparse.ArgumentParser(description="Run torchscript e2e tests.")
     parser.add_argument("-c", "--config",
         choices=config_choices,
@@ -94,6 +95,10 @@ def main():
         config = TosaBackendTestConfig(LinalgOnTensorsTosaBackend())
         xfail_set = all_test_unique_names - TOSA_PASS_SET
         crashing_set = set()
+    elif args.config == "make_fx_tosa":
+        config = TosaBackendTestConfig(LinalgOnTensorsTosaBackend(), use_make_fx=True)
+        xfail_set = all_test_unique_names - MAKE_FX_TOSA_PASS_SET
+        crashing_set = set()
     elif args.config == "stablehlo":
         config = StablehloBackendTestConfig(LinalgOnTensorsStablehloBackend())
         xfail_set = all_test_unique_names - STABLEHLO_PASS_SET

e2e_testing/xfail_sets.py

@@ -11,6 +11,7 @@
 # might be used to keep more elaborate sets of testing configurations).
 
 from torch_mlir_e2e_test.test_suite import COMMON_TORCH_MLIR_LOWERING_XFAILS
+from torch_mlir._version import torch_version_for_comparison, version
 
 LINALG_XFAIL_SET = COMMON_TORCH_MLIR_LOWERING_XFAILS
 
@@ -1113,6 +1114,41 @@
     "ChunkListUnpackUneven_Module_basic",
 }
 
+MAKE_FX_TOSA_PASS_SET = (TOSA_PASS_SET | {
+### Tests additionally passing in make_fx_tosa
+    "NativeGroupNormBackwardModule_basic",
+    "TensorFloatModule_basic",
+    "TensorIntModule_basic",
+}) - {
+### Test failing in make_fx_tosa but not in tosa
+
+    # failed to lower torch.aten.empty.memory_format
+    "BatchNorm1DModule_basic",
+    "BatchNorm1DWith2DInputModule_basic",
+    "BatchNorm2DModule_basic",
+    "BatchNorm3DModule_basic",
+    "BatchNorm1DStaticShapeModule_basic",
+
+    # Dynamic shape, has extra unsupported broadcast ops
+    "Matmul_3d",
+
+    # failed to legalize operation 'torch.aten.max_pool2d_with_indices
+    "MaxPool2dEmptyStrideStaticModule_basic",
+    "MaxPool2dStaticCeilModeTrueModule_basic",
+    "MaxPool2dStaticModule_basic",
+    "ResNet18StaticModule_basic",
+
+    # Unimplemented operator 'aten._index_put_impl_.hacked_twin'
+    "IndexPutImpl1DFloatNonAccumulateModule_basic",
+    "IndexPutImpl1DIntNonAccumulateModule_basic",
+}
+
+if torch_version_for_comparison() < version.parse("2.1.0.dev"):
+    MAKE_FX_TOSA_PASS_SET -= {
+        # 'tensor.expand_shape' op expected rank expansion, but found source rank 1 >= result rank 1
+        "ReshapeCollapseModule_basic",
+    }
+
 LTC_CRASHING_SET = {
     # https://github.com/llvm/torch-mlir/issues/2186
     "Add_Module_basic"

python/torch_mlir/__init__.py

@@ -13,6 +13,8 @@
 from torch._functorch.compile_utils import strip_overloads
 import torch
 import torch.fx
+from torch_mlir.dynamo import _get_decomposition_table
+from torch.fx.experimental.proxy_tensor import make_fx
 
 from .compiler_utils import run_pipeline_with_repro_report
 from torch_mlir.dialects.torch.importer.jit_ir import ClassAnnotator, ImportOptions, ModuleBuilder
@@ -225,8 +227,11 @@ def _get_for_tracing(
                         # they know what they are doing and that their trace is
                         # correct for any specific concrete size.
                         shape = [s if s != -1 else 7 for s in arg.shape]
-                        example_args_for_trace.append(
-                            torch.ones(*shape, dtype=arg.dtype))
+                        if len(shape) == 0:
+                            example_args_for_trace.append(torch.tensor(1))
+                        else:
+                            example_args_for_trace.append(
+                                torch.ones(*shape, dtype=arg.dtype))
                     else:
                         assert isinstance(arg, torch.Tensor)
                         example_args_for_trace.append(arg)
@@ -313,7 +318,8 @@ def compile(model: torch.nn.Module,
             ignore_traced_shapes=False,
             backend_legal_ops: Optional[Sequence[str]] = None,
             extra_library: Iterable[Callable] = [],
-            verbose: bool = False):
+            verbose: bool = False,
+            use_make_fx: bool = False):
     """Convert a PyTorch model to MLIR.
 
     Args:
@@ -367,6 +373,13 @@ def compile(model: torch.nn.Module,
     else:
         backend_legal_ops = BACKEND_LEGAL_OPS.get(output_type, [])
 
+    if use_make_fx:
+        args = example_args._get_for_tracing(use_tracing=True, ignore_traced_shapes=True)["forward"]
+        model = make_fx(
+           model,
+           decomposition_table=_get_decomposition_table())(*args)
+
+
     # For FX-based models, automatically strip overloads.
     if isinstance(model, torch.fx.GraphModule):
         strip_overloads(model)

python/torch_mlir_e2e_test/configs/tosa_backend.py

@@ -23,14 +23,15 @@ class TosaBackendTestConfig(TestConfig):
     This class handles all the common lowering that torch-mlir does before
     reaching the linalg-on-tensors abstraction level.
     """
-    def __init__(self, backend: TosaBackend):
+    def __init__(self, backend: TosaBackend, use_make_fx: bool = False):
         super().__init__()
         self.backend = backend
+        self.use_make_fx = use_make_fx
 
     def compile(self, program: torch.nn.Module) -> Any:
         example_args = convert_annotations_to_placeholders(program.forward)
         module = torch_mlir.compile(
-            program, example_args, output_type="tosa")
+            program, example_args, output_type="tosa", use_make_fx=self.use_make_fx)
 
         return self.backend.compile(module)