Revert "E2E support for AtenRemainderScalarOp (#1119)" (#1190)

This reverts commit 34e207eeb5.

e2e_testing/torchscript/xfail_sets.py

@@ -179,9 +179,6 @@ TOSA_PASS_SET = {
     "ArgmaxModule_keepDim",
     "ArgmaxModule_with_dim",
     "_LogSoftmaxModuleStable_basic",    
-    "ElementwiseRemainderScalarModule_Int_Float_basic",
-    "ElementwiseRemainderScalarModule_Float_basic",
-    "ElementwiseRemainderScalarModule_Int_basic",
 }
 
 LTC_XFAIL_SET = {

include/torch-mlir/Dialect/Torch/IR/GeneratedTorchOps.td

@@ -7883,30 +7883,6 @@ def Torch_AtenRemainderIntOp : Torch_Op<"aten.remainder.int", [
   let hasFolder = 1;
 }
 
-def Torch_AtenRemainderScalarOp : Torch_Op<"aten.remainder.Scalar", [
-    AllowsTypeRefinement,
-    HasValueSemantics,
-    ReadOnly
-  ]> {
-  let summary = "Generated op for `aten::remainder.Scalar : (Tensor, Scalar) -> (Tensor)`";
-  let arguments = (ins
-    AnyTorchTensorType:$self,
-    AnyTorchScalarType:$other
-  );
-  let results = (outs
-    AnyTorchTensorType:$result
-  );
-  let hasCustomAssemblyFormat = 1;
-  let extraClassDefinition = [{
-    ParseResult AtenRemainderScalarOp::parse(OpAsmParser &parser, OperationState &result) {
-      return parseDefaultTorchOp(parser, result, 2, 1);
-    }
-    void AtenRemainderScalarOp::print(OpAsmPrinter &printer) {
-      printDefaultTorchOp(printer, *this, 2, 1);
-    }
-  }];
-}
-
 def Torch_AtenAddIntOp : Torch_Op<"aten.add.int", [
     AllowsTypeRefinement,
     HasValueSemantics,

lib/Conversion/TorchToLinalg/Uncategorized.cpp

@@ -803,23 +803,6 @@ static Value createLinalgPayloadCalculationForElementwiseOp(
     Value other = convertScalarToDtype(b, loc, operands[1], dtype);
     return b.create<arith::DivFOp>(loc, self, other);
   }
-  if (auto remScalar = dyn_cast<AtenRemainderScalarOp>(op)) {
-    Type newResultType = converter->convertType(remScalar.getType())
-                             .cast<RankedTensorType>()
-                             .getElementType();
-
-    Value self = convertScalarToDtype(b, loc, payloadArgs[0], newResultType);
-    Value other = convertScalarToDtype(b, loc, operands[1], newResultType);
-    Value result;
-
-    if (newResultType.isa<mlir::FloatType>()) {
-      result = b.create<arith::RemFOp>(loc, self, other);
-    } else {
-      result = b.create<arith::RemSIOp>(loc, self, other);
-    }
-
-    return result;
-  }
   if (auto reciprocal = dyn_cast<AtenReciprocalOp>(op)) {
     Type dtype = converter->convertType(reciprocal.getType())
                      .cast<RankedTensorType>()
@@ -960,14 +943,14 @@ public:
              AtenMinimumOp, AtenMaximumOp, AtenToDtypeOp, AtenClampOp,
              AtenRsubScalarOp, AtenMulScalarOp, AtenLogOp, AtenErfOp,
              AtenSqrtOp, AtenFloorOp, AtenPowTensorScalarOp, AtenLog2Op,
-             AtenLog1pOp, AtenRsqrtOp, AtenDivScalarOp, AtenRemainderScalarOp,
+             AtenLog1pOp, AtenRsqrtOp, AtenDivScalarOp, AtenAbsOp,
-             AtenAbsOp, AtenReciprocalOp, AtenBitwiseAndTensorOp,
+             AtenReciprocalOp, AtenBitwiseAndTensorOp, AtenGtScalarOp,
-             AtenGtScalarOp, AtenGeScalarOp, AtenEqScalarOp, AtenLtScalarOp,
+             AtenGeScalarOp, AtenEqScalarOp, AtenLtScalarOp, AtenLeScalarOp,
-             AtenLeScalarOp, AtenWhereSelfOp, AtenCeilOp, AtenGtTensorOp,
+             AtenWhereSelfOp, AtenCeilOp, AtenGtTensorOp, AtenEqTensorOp,
-             AtenEqTensorOp, AtenLtTensorOp, AtenSubScalarOp, AtenAddScalarOp,
+             AtenLtTensorOp, AtenSubScalarOp, AtenAddScalarOp, AtenThresholdOp,
-             AtenThresholdOp, AtenThresholdBackwardOp, AtenCloneOp, AtenSinOp,
+             AtenThresholdBackwardOp, AtenCloneOp, AtenSinOp, AtenCosOp,
-             AtenCosOp, AtenNeScalarOp, AtenNegOp, AtenMaskedFillScalarOp,
+             AtenNeScalarOp, AtenNegOp, AtenMaskedFillScalarOp, AtenLogicalOrOp,
-             AtenLogicalOrOp, AtenTriuOp>(op))
+             AtenTriuOp>(op))
       return rewriter.notifyMatchFailure(op, "not a supported elementwise op");
 
     if (failed(verifyLinalgCompatibleTypes(op, rewriter)))
@@ -1698,14 +1681,14 @@ void mlir::torch::torch_to_linalg::populateUncategorizedPatternsAndLegality(
       AtenTanhOp, AtenReluOp, AtenLeakyReluOp, AtenGeluOp, AtenGeluBackwardOp,
       AtenAddTensorOp, AtenMulTensorOp, AtenDivTensorOp, AtenDivTensorModeOp,
       AtenSubTensorOp, AtenLerpTensorOp, AtenSigmoidOp, AtenMinimumOp,
-      AtenMaximumOp, AtenToDtypeOp, AtenClampOp, AtenRsubScalarOp, AtenLogOp,
+      AtenAtan2Op, AtenMaximumOp, AtenToDtypeOp, AtenClampOp, AtenRsubScalarOp,
-      AtenErfOp, AtenSqrtOp, AtenFloorOp, AtenCeilOp, AtenPowTensorScalarOp,
+      AtenLogOp, AtenErfOp, AtenSqrtOp, AtenFloorOp, AtenCeilOp,
-      AtenLog2Op, AtenLog1pOp, AtenRsqrtOp, AtenAbsOp, AtenReciprocalOp,
+      AtenPowTensorScalarOp, AtenLog2Op, AtenLog1pOp, AtenRsqrtOp, AtenAbsOp,
-      AtenBitwiseAndTensorOp, AtenGtScalarOp, AtenGeScalarOp, AtenEqScalarOp,
+      AtenReciprocalOp, AtenBitwiseAndTensorOp, AtenGtScalarOp, AtenGeScalarOp,
-      AtenLtScalarOp, AtenLeScalarOp, AtenWhereSelfOp, AtenGtTensorOp,
+      AtenEqScalarOp, AtenLtScalarOp, AtenLeScalarOp, AtenWhereSelfOp,
-      AtenEqTensorOp, AtenLtTensorOp, AtenThresholdOp, AtenThresholdBackwardOp,
+      AtenGtTensorOp, AtenEqTensorOp, AtenLtTensorOp, AtenThresholdOp,
-      AtenCloneOp, AtenSinOp, AtenCosOp, AtenNeScalarOp, AtenMaskedFillScalarOp,
+      AtenThresholdBackwardOp, AtenCloneOp, AtenSinOp, AtenCosOp,
-      AtenLogicalOrOp, AtenTriuOp, AtenRemainderScalarOp>();
+      AtenNeScalarOp, AtenMaskedFillScalarOp, AtenLogicalOrOp, AtenTriuOp>();
   patterns.add<ConvertElementwiseOp>(typeConverter, context);
   target.addIllegalOp<AtenNllLossForwardOp>();
   patterns.add<ConvertAtenDetachOp>(typeConverter, context);

lib/Dialect/Torch/Transforms/RefineTypes.cpp

@@ -770,7 +770,7 @@ void TypeAnalysis::visitOperation(Operation *op,
   // Promote LHS with scalar RHS.
   if (isa<AtenAddScalarOp, AtenSubScalarOp, AtenMulScalarOp, AtenDivScalarOp,
           AtenFmodScalarOp, AtenFloorDivideScalarOp, AtenPowTensorScalarOp,
-          AtenRsubScalarOp, AtenLeakyReluOp, AtenRemainderScalarOp>(op)) {
+          AtenRsubScalarOp, AtenLeakyReluOp>(op)) {
     auto lhs = operands[0]->getValue();
     Value scalar = op->getOperand(1);
     auto knowledge =

lib/Dialect/Torch/Transforms/ShapeLibrary.cpp

@@ -5441,10 +5441,6 @@ module {
     %0 = call @__torch__.torch.jit._shape_functions.unary(%arg0) : (!torch.list<int>) -> !torch.list<int>
     return %0 : !torch.list<int>
   }
-  func.func @"__torch_mlir_shape_fn.aten.remainder.Scalar"(%arg0: !torch.list<int>, %arg1: !torch.float) -> !torch.list<int> {
-    %0 = call @__torch__.torch.jit._shape_functions.unary(%arg0) : (!torch.list<int>) -> !torch.list<int>
-    return %0 : !torch.list<int>
-  }
   func.func @"__torch_mlir_shape_fn.aten.to.dtype"(%arg0: !torch.list<int>, %arg1: !torch.int, %arg2: !torch.bool, %arg3: !torch.bool, %arg4: !torch.optional<int>) -> !torch.list<int> {
     %0 = call @__torch__.torch.jit._shape_functions.unary(%arg0) : (!torch.list<int>) -> !torch.list<int>
     return %0 : !torch.list<int>

python/torch_mlir/dialects/torch/importer/jit_ir/build_tools/shape_lib_gen.py

@@ -478,9 +478,6 @@ def aten〇mul〇Scalar(self: List[int], other: float) -> List[int]:
 def aten〇div〇Scalar(self: List[int], other: float) -> List[int]:
     return upstream_shape_functions.unary(self)
 
-def aten〇remainder〇Scalar(self: List[int], other: float) -> List[int]:
-    return upstream_shape_functions.unary(self)
-
 def aten〇floor_divide〇Scalar(self: List[int], other: float) -> List[int]:
     return upstream_shape_functions.unary(self)
 

python/torch_mlir/dialects/torch/importer/jit_ir/build_tools/torch_ods_gen.py

@@ -549,7 +549,6 @@ def emit_ops(emitter_td: TextEmitter, registry: Registry):
     emit("aten::eq.int : (int, int) -> (bool)", has_folder=True)
     emit("aten::floordiv.int : (int, int) -> (int)", has_folder=True)
     emit("aten::remainder.int : (int, int) -> (int)", has_folder=True)
-    emit("aten::remainder.Scalar : (Tensor, Scalar) -> (Tensor)")
     emit("aten::add.int : (int, int) -> (int)", has_folder=True)
     emit("aten::sub.int : (int, int) -> (int)", has_folder=True)
     emit("aten::mul.int : (int, int) -> (int)", has_folder=True)

python/torch_mlir_e2e_test/test_suite/elementwise.py

@@ -1304,70 +1304,6 @@ class ElementwiseDivScalarModule(torch.nn.Module):
 def ElementwiseDivScalarModule_basic(module, tu: TestUtils):
     module.forward(tu.rand(3, 4))
 
-# ==============================================================================
-
-
-class ElementwiseRemainderScalarModule_Int_Float(torch.nn.Module):
-
-    def __init__(self):
-        super().__init__()
-
-    @export
-    @annotate_args([
-        None,
-        ([-1], torch.int64, True),
-    ])
-    def forward(self, x):
-        return torch.remainder(x, 2.0)
-
-
-@register_test_case(module_factory=lambda: ElementwiseRemainderScalarModule_Int_Float())
-def ElementwiseRemainderScalarModule_Int_Float_basic(module, tu: TestUtils):
-    module.forward(torch.randint(10, (3,)))
-
-
-# ==============================================================================
-
-
-class ElementwiseRemainderScalarModule_Float(torch.nn.Module):
-
-    def __init__(self):
-        super().__init__()
-
-    @export
-    @annotate_args([
-        None,
-        ([-1, -1], torch.float, True),
-    ])
-    def forward(self, x):
-        return torch.remainder(x, 2.0)
-
-
-@register_test_case(module_factory=lambda: ElementwiseRemainderScalarModule_Float())
-def ElementwiseRemainderScalarModule_Float_basic(module, tu: TestUtils):
-    module.forward(torch.rand(10, 3))
-
-
-# ==============================================================================
-
-class ElementwiseRemainderScalarModule_Int(torch.nn.Module):
-
-    def __init__(self):
-        super().__init__()
-
-    @export
-    @annotate_args([
-        None,
-        ([-1, -1], torch.int64, True),
-    ])
-    def forward(self, x):
-        return torch.remainder(x, 2)
-
-
-@register_test_case(module_factory=lambda: ElementwiseRemainderScalarModule_Int())
-def ElementwiseRemainderScalarModule_Int_basic(module, tu: TestUtils):
-    module.forward(torch.randint(10, (3, 2)))
-
 
 # ==============================================================================