[MLIR] Add sincos op to math dialect

mlir/include/mlir/Dialect/Math/IR/MathOps.td

@@ -510,6 +510,43 @@ def Math_SinhOp : Math_FloatUnaryOp<"sinh"> {
   let hasFolder = 1;
 }
 
+//===----------------------------------------------------------------------===//
+// SinCosOp
+//===----------------------------------------------------------------------===//
+
+def Math_SincosOp : Math_Op<"sincos",
+    [SameOperandsAndResultShape,
+     DeclareOpInterfaceMethods<ArithFastMathInterface>,
+     AllTypesMatch<["operand", "sin", "cos"]>]> {
+  let summary = "sine and cosine of the specified value";
+  let description = [{
+    The `sincos` operation computes both the sine and cosine of a given value
+    simultaneously. It takes one operand of floating point type (i.e., scalar,
+    tensor or vector) and returns two results of the same type. This operation
+    can be more efficient than computing sine and cosine separately when both
+    values are needed.
+
+    Example:
+
+    ```mlir
+    // Scalar sine and cosine values.
+    %sin, %cos = math.sincos %input : f64
+    ```
+  }];
+
+  let arguments = (ins FloatLike:$operand,
+      DefaultValuedAttr<Arith_FastMathAttr,
+                        "::mlir::arith::FastMathFlags::none">:$fastmath);
+  let results = (outs FloatLike:$sin, FloatLike:$cos);
+
+  let assemblyFormat = [{ $operand (`fastmath` `` $fastmath^)?
+                          attr-dict `:` type($operand) }];
+
+  let extraClassDeclaration = [{
+    std::optional<SmallVector<int64_t, 4>> getShapeForUnroll();
+  }];
+}
+
 //===----------------------------------------------------------------------===//
 // CountLeadingZerosOp
 //===----------------------------------------------------------------------===//

mlir/lib/Conversion/GPUToNVVM/LowerGpuOpsToNVVMOps.cpp

@@ -436,7 +436,7 @@ void mlir::configureGpuToNVVMConversionLegality(ConversionTarget &target) {
                       LLVM::FAbsOp, LLVM::FCeilOp, LLVM::FFloorOp, LLVM::FRemOp,
                       LLVM::LogOp, LLVM::Log10Op, LLVM::Log2Op, LLVM::PowOp,
                       LLVM::RoundEvenOp, LLVM::RoundOp, LLVM::SinOp,
-                      LLVM::SqrtOp>();
+                      LLVM::SincosOp, LLVM::SqrtOp>();
 
   // TODO: Remove once we support replacing non-root ops.
   target.addLegalOp<gpu::YieldOp, gpu::GPUModuleOp>();
@@ -466,6 +466,100 @@ void mlir::configureGpuToNVVMTypeConverter(LLVMTypeConverter &converter) {
   });
 }
 
+struct SincosOpLowering : public ConvertOpToLLVMPattern<math::SincosOp> {
+  using ConvertOpToLLVMPattern<math::SincosOp>::ConvertOpToLLVMPattern;
+
+  LogicalResult
+  matchAndRewrite(math::SincosOp op, OpAdaptor adaptor,
+                  ConversionPatternRewriter &rewriter) const override {
+    Location loc = op.getLoc();
+    Value input = adaptor.getOperand();
+    Type inputType = input.getType();
+    auto convertedInput = maybeExt(input, rewriter);
+    auto computeType = convertedInput.getType();
+
+    StringRef sincosFunc;
+    if (isa<Float32Type>(computeType)) {
+      const arith::FastMathFlags flag = op.getFastmath();
+      const bool useApprox =
+          mlir::arith::bitEnumContainsAny(flag, arith::FastMathFlags::afn);
+      sincosFunc = useApprox ? "__nv_fast_sincosf" : "__nv_sincosf";
+    } else if (isa<Float64Type>(computeType)) {
+      sincosFunc = "__nv_sincos";
+    } else {
+      return rewriter.notifyMatchFailure(op,
+                                         "unsupported operand type for sincos");
+    }
+
+    auto ptrType = LLVM::LLVMPointerType::get(rewriter.getContext());
+
+    Value sinPtr, cosPtr;
+    {
+      OpBuilder::InsertionGuard guard(rewriter);
+      auto *scope =
+          op->getParentWithTrait<mlir::OpTrait::AutomaticAllocationScope>();
+      assert(scope && "Expected op to be inside automatic allocation scope");
+      rewriter.setInsertionPointToStart(&scope->getRegion(0).front());
+      auto one = rewriter.create<LLVM::ConstantOp>(
+          loc, rewriter.getI32Type(), rewriter.getI32IntegerAttr(1));
+      sinPtr =
+          rewriter.create<LLVM::AllocaOp>(loc, ptrType, computeType, one, 0);
+      cosPtr =
+          rewriter.create<LLVM::AllocaOp>(loc, ptrType, computeType, one, 0);
+    }
+
+    createSincosCall(rewriter, loc, sincosFunc, convertedInput, sinPtr, cosPtr,
+                     op);
+
+    auto sinResult = rewriter.create<LLVM::LoadOp>(loc, computeType, sinPtr);
+    auto cosResult = rewriter.create<LLVM::LoadOp>(loc, computeType, cosPtr);
+
+    rewriter.replaceOp(op, {maybeTrunc(sinResult, inputType, rewriter),
+                            maybeTrunc(cosResult, inputType, rewriter)});
+    return success();
+  }
+
+private:
+  Value maybeExt(Value operand, PatternRewriter &rewriter) const {
+    if (isa<Float16Type, BFloat16Type>(operand.getType()))
+      return rewriter.create<LLVM::FPExtOp>(
+          operand.getLoc(), Float32Type::get(rewriter.getContext()), operand);
+    return operand;
+  }
+
+  Value maybeTrunc(Value operand, Type type, PatternRewriter &rewriter) const {
+    if (operand.getType() != type)
+      return rewriter.create<LLVM::FPTruncOp>(operand.getLoc(), type, operand);
+    return operand;
+  }
+
+  void createSincosCall(ConversionPatternRewriter &rewriter, Location loc,
+                        StringRef funcName, Value input, Value sinPtr,
+                        Value cosPtr, Operation *op) const {
+    auto voidType = LLVM::LLVMVoidType::get(rewriter.getContext());
+    auto ptrType = sinPtr.getType();
+
+    SmallVector<Type> operandTypes = {input.getType(), ptrType, ptrType};
+    auto funcType = LLVM::LLVMFunctionType::get(voidType, operandTypes);
+
+    auto funcAttr = StringAttr::get(op->getContext(), funcName);
+    auto funcOp =
+        SymbolTable::lookupNearestSymbolFrom<LLVM::LLVMFuncOp>(op, funcAttr);
+
+    if (!funcOp) {
+      auto parentFunc = op->getParentOfType<FunctionOpInterface>();
+      assert(parentFunc && "expected there to be a parent function");
+      OpBuilder b(parentFunc);
+
+      auto globalloc = loc->findInstanceOfOrUnknown<FileLineColLoc>();
+      funcOp = LLVM::LLVMFuncOp::create(b, globalloc, funcName, funcType);
+    }
+
+    SmallVector<Value> callOperands = {input, sinPtr, cosPtr};
+    rewriter.create<LLVM::CallOp>(loc, funcOp, callOperands);
+  }
+};
+
 template <typename OpTy>
 static void populateOpPatterns(const LLVMTypeConverter &converter,
                                RewritePatternSet &patterns,
@@ -589,6 +683,9 @@ void mlir::populateLibDeviceConversionPatterns(
                                   "__nv_tan", "__nv_fast_tanf");
   populateOpPatterns<math::TanhOp>(converter, patterns, benefit, "__nv_tanhf",
                                    "__nv_tanh");
+
+  // Custom pattern for sincos since it returns two values
+  patterns.add<SincosOpLowering>(converter, benefit);
 }
 
 void mlir::populateGpuToNVVMConversionPatterns(

mlir/lib/Conversion/MathToLLVM/MathToLLVM.cpp

@@ -121,6 +121,38 @@ using CountTrailingZerosOpLowering =
                           LLVM::CountTrailingZerosOp>;
 using AbsIOpLowering = IntOpWithFlagLowering<math::AbsIOp, LLVM::AbsOp>;
 
+// A `sincos` is converted into `llvm.intr.sincos` followed by extractvalue ops.
+struct SincosOpLowering : public ConvertOpToLLVMPattern<math::SincosOp> {
+  using ConvertOpToLLVMPattern<math::SincosOp>::ConvertOpToLLVMPattern;
+
+  LogicalResult
+  matchAndRewrite(math::SincosOp op, OpAdaptor adaptor,
+                  ConversionPatternRewriter &rewriter) const override {
+    const LLVMTypeConverter &typeConverter = *this->getTypeConverter();
+    mlir::Location loc = op.getLoc();
+    mlir::Type operandType = adaptor.getOperand().getType();
+    mlir::Type llvmOperandType = typeConverter.convertType(operandType);
+    mlir::Type sinType = typeConverter.convertType(op.getSin().getType());
+    mlir::Type cosType = typeConverter.convertType(op.getCos().getType());
+    if (!llvmOperandType || !sinType || !cosType)
+      return failure();
+
+    ConvertFastMath<math::SincosOp, LLVM::SincosOp> attrs(op);
+
+    auto structType = LLVM::LLVMStructType::getLiteral(
+        rewriter.getContext(), {llvmOperandType, llvmOperandType});
+
+    auto sincosOp = rewriter.create<LLVM::SincosOp>(
+        loc, structType, adaptor.getOperand(), attrs.getAttrs());
+
+    auto sinValue = LLVM::ExtractValueOp::create(rewriter, loc, sincosOp, 0);
+    auto cosValue = LLVM::ExtractValueOp::create(rewriter, loc, sincosOp, 1);
+
+    rewriter.replaceOp(op, {sinValue, cosValue});
+    return success();
+  }
+};
+
 // A `expm1` is converted into `exp - 1`.
 struct ExpM1OpLowering : public ConvertOpToLLVMPattern<math::ExpM1Op> {
   using ConvertOpToLLVMPattern<math::ExpM1Op>::ConvertOpToLLVMPattern;
@@ -393,6 +425,7 @@ void mlir::populateMathToLLVMConversionPatterns(
     RoundEvenOpLowering,
     RoundOpLowering,
     RsqrtOpLowering,
+    SincosOpLowering,
     SinOpLowering,
     SinhOpLowering,
     ASinOpLowering,

mlir/lib/Dialect/Math/IR/MathOps.cpp

@@ -284,6 +284,16 @@ OpFoldResult math::SinhOp::fold(FoldAdaptor adaptor) {
       });
 }
 
+//===----------------------------------------------------------------------===//
+// SinCosOp getShapeForUnroll
+//===----------------------------------------------------------------------===//
+
+std::optional<SmallVector<int64_t, 4>> math::SincosOp::getShapeForUnroll() {
+  if (auto vt = mlir::dyn_cast<VectorType>(getOperand().getType()))
+    return llvm::to_vector<4>(vt.getShape());
+  return std::nullopt;
+}
+
 //===----------------------------------------------------------------------===//
 // CountLeadingZerosOp folder
 //===----------------------------------------------------------------------===//

mlir/test/Conversion/GPUToNVVM/gpu-to-nvvm.mlir

@@ -1109,3 +1109,42 @@ gpu.module @test_module_55 {
     func.return %result32, %result64 : f32, f64
   }
 }
+
+gpu.module @test_module_56 {
+  // CHECK: gpu.module @test_module_56
+
+  // CHECK-DAG: llvm.func @__nv_sincosf(f32, !llvm.ptr, !llvm.ptr)
+  // CHECK-DAG: llvm.func @__nv_sincos(f64, !llvm.ptr, !llvm.ptr)
+
+  // CHECK-LABEL: func @gpu_sincos
+  // CHECK-SAME: %[[ARG_f16:.*]]: f16, %[[ARG_f32:.*]]: f32, %[[ARG_f64:.*]]: f64
+  func.func @gpu_sincos(%arg_f16 : f16, %arg_f32 : f32, %arg_f64 : f64) -> (f16, f16, f32, f32, f64, f64) {
+    // CHECK-COUNT-6: llvm.alloca
+    // CHECK: %[[ARG_f16_ext:.*]] = llvm.fpext %[[ARG_f16]] : f16 to f32
+    // CHECK: llvm.call @__nv_sincosf(%[[ARG_f16_ext]], %{{.+}}, %{{.+}}) : (f32, !llvm.ptr, !llvm.ptr) -> ()
+    // CHECK-COUNT-2: llvm.fptrunc
+    // CHECK: llvm.call @__nv_sincosf(%[[ARG_f32]], %{{.+}}, %{{.+}}) : (f32, !llvm.ptr, !llvm.ptr) -> ()
+    // CHECK: llvm.call @__nv_sincos(%[[ARG_f64]], %{{.+}}, %{{.+}}) : (f64, !llvm.ptr, !llvm.ptr) -> ()
+    %sin16, %cos16 = math.sincos %arg_f16 : f16
+    %sin32, %cos32 = math.sincos %arg_f32 : f32
+    %sin64, %cos64 = math.sincos %arg_f64 : f64
+    func.return %sin16, %cos16, %sin32, %cos32, %sin64, %cos64 : f16, f16, f32, f32, f64, f64
+  }
+
+  // CHECK: llvm.func @__nv_fast_sincosf(f32, !llvm.ptr, !llvm.ptr)
+
+  // CHECK-LABEL: func @gpu_sincos_fastmath
+  // CHECK-SAME: %[[ARG_f16:.*]]: f16, %[[ARG_f32:.*]]: f32, %[[ARG_f64:.*]]: f64
+  func.func @gpu_sincos_fastmath(%arg_f16 : f16, %arg_f32 : f32, %arg_f64 : f64) -> (f16, f16, f32, f32, f64, f64) {
+    // CHECK-COUNT-6: llvm.alloca
+    // CHECK: %[[ARG_f16_ext:.*]] = llvm.fpext %[[ARG_f16]] : f16 to f32
+    // CHECK: llvm.call @__nv_fast_sincosf(%[[ARG_f16_ext]], %{{.+}}, %{{.+}}) : (f32, !llvm.ptr, !llvm.ptr) -> ()
+    // CHECK-COUNT-2: llvm.fptrunc
+    // CHECK: llvm.call @__nv_fast_sincosf(%[[ARG_f32]], %{{.+}}, %{{.+}}) : (f32, !llvm.ptr, !llvm.ptr) -> ()
+    // CHECK: llvm.call @__nv_sincos(%[[ARG_f64]], %{{.+}}, %{{.+}}) : (f64, !llvm.ptr, !llvm.ptr) -> ()
+    %sin16, %cos16 = math.sincos %arg_f16 fastmath<afn> : f16
+    %sin32, %cos32 = math.sincos %arg_f32 fastmath<afn> : f32
+    %sin64, %cos64 = math.sincos %arg_f64 fastmath<afn> : f64
+    func.return %sin16, %cos16, %sin32, %cos32, %sin64, %cos64 : f16, f16, f32, f32, f64, f64
+  }
+}

mlir/test/Conversion/MathToLLVM/math-to-llvm.mlir

@@ -230,6 +230,16 @@ func.func @trigonometrics(%arg0: f32) {
 
 // -----
 
+// CHECK-LABEL: func @sincos
+// CHECK-SAME: [[ARG0:%.+]]: f32
+func.func @sincos(%arg0: f32) {
+  // CHECK: llvm.intr.sincos([[ARG0]]) : (f32) -> !llvm.struct<(f32, f32)>
+  %0:2 = math.sincos %arg0 : f32
+  func.return
+}
+
+// -----
+
 // CHECK-LABEL: func @inverse_trigonometrics
 // CHECK-SAME: [[ARG0:%.+]]: f32
 func.func @inverse_trigonometrics(%arg0: f32) {

mlir/test/Dialect/Math/ops.mlir

@@ -62,6 +62,18 @@ func.func @sin(%f: f32, %v: vector<4xf32>, %t: tensor<4x4x?xf32>) {
   return
 }
 
+// CHECK-LABEL: func @sincos(
+// CHECK-SAME:            %[[F:.*]]: f32, %[[V:.*]]: vector<4xf32>, %[[T:.*]]: tensor<4x4x?xf32>)
+func.func @sincos(%f: f32, %v: vector<4xf32>, %t: tensor<4x4x?xf32>) {
+  // CHECK: %{{.*}} = math.sincos %[[F]] : f32
+  %0:2 = math.sincos %f : f32
+  // CHECK: %{{.*}} = math.sincos %[[V]] : vector<4xf32>
+  %1:2 = math.sincos %v : vector<4xf32>
+  // CHECK: %{{.*}} = math.sincos %[[T]] : tensor<4x4x?xf32>
+  %2:2 = math.sincos %t : tensor<4x4x?xf32>
+  return
+}
+
 // CHECK-LABEL: func @erf(
 // CHECK-SAME:            %[[F:.*]]: f32, %[[V:.*]]: vector<4xf32>, %[[T:.*]]: tensor<4x4x?xf32>)
 func.func @erf(%f: f32, %v: vector<4xf32>, %t: tensor<4x4x?xf32>) {