E2E support for AtenRemainderScalarOp (#1200)

e2e_testing/torchscript/xfail_sets.py

@@ -179,7 +179,7 @@ TOSA_PASS_SET = {
     "TransposeIntNegDimsModule_basic",
     "ArgmaxModule_keepDim",
     "ArgmaxModule_with_dim",
-    "_LogSoftmaxModuleStable_basic",    
+    "_LogSoftmaxModuleStable_basic",
 }
 
 LTC_XFAIL_SET = {
@@ -338,4 +338,8 @@ LTC_XFAIL_SET = {
     "ViewCollapseDynamicWithAtenSizeIntModule_basic",
     "AtenEmbeddingBagSumExample_basic",
     "Aten_EmbeddingBagExample_basic",
+    "ElementwiseRemainderScalarModule_Int_Float_basic",
+    "ElementwiseRemainderScalarModule_Float_basic",
+    "ElementwiseRemainderScalarModule_Int_basic",
+    "ElementwiseRemainderScalarModule_Bool_basic",
 }

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

@@ -7910,6 +7910,30 @@ 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

@@ -803,6 +803,26 @@ 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 if (newResultType.isa<mlir::IntegerType>()) {
+      result = b.create<arith::RemSIOp>(loc, self, other);
+    } else {
+      remScalar.emitError(
+          "Unsupported type encountered for AtenRemainderScalarOp.");
+    }
+
+    return result;
+  }
   if (auto reciprocal = dyn_cast<AtenReciprocalOp>(op)) {
     Type dtype = converter->convertType(reciprocal.getType())
                      .cast<RankedTensorType>()
@@ -943,14 +963,14 @@ public:
              AtenMinimumOp, AtenMaximumOp, AtenToDtypeOp, AtenClampOp,
              AtenRsubScalarOp, AtenMulScalarOp, AtenLogOp, AtenErfOp,
              AtenSqrtOp, AtenFloorOp, AtenPowTensorScalarOp, AtenLog2Op,
-             AtenLog1pOp, AtenRsqrtOp, AtenDivScalarOp, AtenAbsOp,
-             AtenReciprocalOp, AtenBitwiseAndTensorOp, AtenGtScalarOp,
-             AtenGeScalarOp, AtenEqScalarOp, AtenLtScalarOp, AtenLeScalarOp,
-             AtenWhereSelfOp, AtenCeilOp, AtenGtTensorOp, AtenEqTensorOp,
-             AtenLtTensorOp, AtenSubScalarOp, AtenAddScalarOp, AtenThresholdOp,
-             AtenThresholdBackwardOp, AtenCloneOp, AtenSinOp, AtenCosOp,
-             AtenNeScalarOp, AtenNegOp, AtenMaskedFillScalarOp, AtenLogicalOrOp,
-             AtenTriuOp>(op))
+             AtenLog1pOp, AtenRsqrtOp, AtenDivScalarOp, AtenRemainderScalarOp,
+             AtenAbsOp, AtenReciprocalOp, AtenBitwiseAndTensorOp,
+             AtenGtScalarOp, AtenGeScalarOp, AtenEqScalarOp, AtenLtScalarOp,
+             AtenLeScalarOp, AtenWhereSelfOp, AtenCeilOp, AtenGtTensorOp,
+             AtenEqTensorOp, AtenLtTensorOp, AtenSubScalarOp, AtenAddScalarOp,
+             AtenThresholdOp, AtenThresholdBackwardOp, AtenCloneOp, AtenSinOp,
+             AtenCosOp, AtenNeScalarOp, AtenNegOp, AtenMaskedFillScalarOp,
+             AtenLogicalOrOp, AtenTriuOp>(op))
       return rewriter.notifyMatchFailure(op, "not a supported elementwise op");
 
     if (failed(verifyLinalgCompatibleTypes(op, rewriter)))
@@ -1688,7 +1708,7 @@ void mlir::torch::torch_to_linalg::populateUncategorizedPatternsAndLegality(
       AtenEqScalarOp, AtenLtScalarOp, AtenLeScalarOp, AtenWhereSelfOp,
       AtenGtTensorOp, AtenEqTensorOp, AtenLtTensorOp, AtenThresholdOp,
       AtenThresholdBackwardOp, AtenCloneOp, AtenSinOp, AtenCosOp,
-      AtenNeScalarOp, AtenMaskedFillScalarOp, AtenLogicalOrOp, AtenTriuOp>();
+      AtenNeScalarOp, AtenMaskedFillScalarOp, AtenLogicalOrOp, AtenTriuOp, AtenRemainderScalarOp>();
   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>(op)) {
+          AtenRsubScalarOp, AtenLeakyReluOp, AtenRemainderScalarOp>(op)) {
     auto lhs = operands[0]->getValue();
     Value scalar = op->getOperand(1);
     auto knowledge =

lib/Dialect/Torch/Transforms/ShapeLibrary.cpp

@@ -5441,6 +5441,10 @@ 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

@@ -481,6 +481,9 @@ 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

@@ -550,6 +550,7 @@ 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

@@ -1304,6 +1304,90 @@ 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.int32, 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,), dtype=torch.int32))
+
+
+# ==============================================================================
+
+
+class ElementwiseRemainderScalarModule_Float(torch.nn.Module):
+
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args([
+        None,
+        ([-1, -1], torch.float32, 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.int32, 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), dtype=torch.int32))
+
+# ==============================================================================
+
+class ElementwiseRemainderScalarModule_Bool(torch.nn.Module):
+
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args([
+        None,
+        ([-1], torch.bool, True),
+    ])
+    def forward(self, x):
+        return torch.remainder(x, 2)
+
+
+@register_test_case(module_factory=lambda: ElementwiseRemainderScalarModule_Bool())
+def ElementwiseRemainderScalarModule_Bool_basic(module, tu: TestUtils):
+    module.forward(torch.tensor([True, False, True, True, True]))
+
 
 # ==============================================================================