Rewrite tanh using TanhOp, add log, cos

src/dialect/onnx/gen_doc.py

@@ -267,7 +267,7 @@ def gen_schema(schema) :
                         'Add', 'Mul', 'Div', 'Sub', 'And', 'Or', 'Xor',
                         'Sum', 'Max', 'Min', 'MatMul', 'Gemm', 'LeakyRelu',
                         'Elu', 'Selu', 'HardSigmoid', 'Reshape', 'Reciprocal',
-                        'Identity']
+                        'Identity', 'Cos', 'Log']
     CanonicalList=['Add', 'Identity']
     line_indent = '  '
 

src/dialect/onnx/onnx_ops.cpp

@@ -91,6 +91,18 @@ void ONNXCoshOp::inferShapes() {
   getResult()->setType(getOperand()->getType());
 }
 
+//===----------------------------------------------------------------------===//
+// Cos
+/// Infer the output shape of the ONNXCosOp. This method is required by the
+/// shape inference interface.
+void ONNXCosOp::inferShapes() { getResult()->setType(getOperand()->getType()); }
+
+//===----------------------------------------------------------------------===//
+// Log
+/// Infer the output shape of the ONNXLogOp. This method is required by the
+/// shape inference interface.
+void ONNXLogOp::inferShapes() { getResult()->setType(getOperand()->getType()); }
+
 //===----------------------------------------------------------------------===//
 // HardSigmoid
 /// Infer the output shape of the ONNXHardSigmoidOp. This method is required by

src/dialect/onnx/onnxop.inc

@@ -361,7 +361,7 @@ def ONNXConvTransposeOp:ONNX_Op<"ConvTranspose",
 }
 
 def ONNXCosOp:ONNX_Op<"Cos", 
-    [NoSideEffect]> {
+    [NoSideEffect, DeclareOpInterfaceMethods<ShapeInferenceOpInterface>]> {
   let summary = "ONNX Cos operation";
   let description = [{
     "Calculates the cosine of the given input tensor, element-wise."
@@ -1216,7 +1216,7 @@ def ONNXLessOp:ONNX_Op<"Less",
 }
 
 def ONNXLogOp:ONNX_Op<"Log", 
-    [NoSideEffect]> {
+    [NoSideEffect, DeclareOpInterfaceMethods<ShapeInferenceOpInterface>]> {
   let summary = "ONNX Log operation";
   let description = [{
     "Calculates the natural log of the given input tensor, element-wise."

src/pass/lower_frontend_to_krnl.cpp

@@ -286,6 +286,24 @@ struct ScalarOp<ONNXSumOp> {
   using IOp = AddIOp;
 };
 
+template <>
+struct ScalarOp<ONNXTanhOp> {
+  using FOp = TanhOp;
+  using IOp = TanhOp; // not use
+};
+
+template <>
+struct ScalarOp<ONNXCosOp> {
+  using FOp = CosOp;
+  using IOp = CosOp; // not use
+};
+
+template <>
+struct ScalarOp<ONNXLogOp> {
+  using FOp = LogOp;
+  using IOp = LogOp; // not use
+};
+
 template <typename ElementwiseNaryOp>
 using ScalarFOp = typename ScalarOp<ElementwiseNaryOp>::FOp;
 template <typename ElementwiseNaryOp>
@@ -314,28 +332,6 @@ Value mapToLowerScalarOp(Operation *op, ArrayRef<Type> result_types,
   }
 }
 
-//===----------------------------------------------------------------------===//
-// Scalar unary ops for lowering ONNXTanhOp
-//===----------------------------------------------------------------------===//
-template <>
-Value mapToLowerScalarOp<ONNXTanhOp>(Operation *op, ArrayRef<Type> result_types,
-                                     ArrayRef<Value> operands,
-                                     ConversionPatternRewriter &rewriter) {
-  // ONNXTanhOp(%X) = DivFOp(SubFOp(ExpOp(%X), ExpOp(NegFOp(%X))),
-  //                         AddFOp(ExpOp(%X), ExpOp(NegFOp(%X))))
-  auto loc = op->getLoc();
-  Value operand = operands[0];
-
-  auto zero = rewriter.create<ConstantOp>(loc, rewriter.getF32FloatAttr(0.0f));
-  auto neg = rewriter.create<SubFOp>(loc, zero, operand);
-  auto exp = rewriter.create<ExpOp>(loc, operand);
-  auto negExp = rewriter.create<ExpOp>(loc, neg);
-  auto diff = rewriter.create<SubFOp>(loc, exp, negExp);
-  auto sum = rewriter.create<AddFOp>(loc, exp, negExp);
-  auto result = rewriter.create<DivFOp>(loc, diff, sum);
-
-  return result;
-}
 
 //===----------------------------------------------------------------------===//
 // Scalar unary ops for lowering ONNXSinhOp
@@ -982,6 +978,8 @@ void FrontendToKrnlLoweringPass::runOnModule() {
                   ONNXElementwiseUnaryOpLowering<mlir::ONNXTanhOp>,
                   ONNXElementwiseUnaryOpLowering<mlir::ONNXSinhOp>,
                   ONNXElementwiseUnaryOpLowering<mlir::ONNXCoshOp>,
+                  ONNXElementwiseUnaryOpLowering<mlir::ONNXCosOp>,
+                  ONNXElementwiseUnaryOpLowering<mlir::ONNXLogOp>,
                   ONNXElementwiseUnaryOpLowering<mlir::ONNXSigmoidOp>,
                   ONNXElementwiseUnaryOpLowering<mlir::ONNXHardSigmoidOp>,
                   ONNXElementwiseUnaryOpLowering<mlir::ONNXEluOp>,

src/pass/shape_inference_pass.cpp

@@ -92,6 +92,8 @@ public:
         op->getName().getStringRef() != "onnx.Tanh" &&
         op->getName().getStringRef() != "onnx.Sinh" &&
         op->getName().getStringRef() != "onnx.Cosh" &&
+        op->getName().getStringRef() != "onnx.Cos" &&
+        op->getName().getStringRef() != "onnx.Log" &&
         op->getName().getStringRef() != "onnx.Sigmoid" &&
         op->getName().getStringRef() != "onnx.HardSigmoid" &&
         op->getName().getStringRef() != "onnx.Elu" &&

test/mlir/onnx/onnx_lowering.mlir

@@ -159,14 +159,8 @@ func @test_tanh(%arg0 : tensor<?x10xf32>) -> tensor<*xf32> {
   // CHECK: [[DIM_2:%.+]] = dim %arg0, 0 : memref<?x10xf32>
   // CHECK: krnl.iterate([[OPT_LOOPS]]#0, [[OPT_LOOPS]]#1) with ([[DEF_LOOPS]]#0 -> %arg1 = 0 to [[DIM_2]], [[DEF_LOOPS]]#1 -> %arg2 = 0 to 10) {
   // CHECK: [[LOAD:%.+]] = load %arg0[%arg1, %arg2] : memref<?x10xf32>
-  // CHECK: [[ZERO:%.+]] = constant {{0.+}} : f32
+  // CHECK: [[TANH:%.+]] = tanh [[LOAD]] : f32
-  // CHECK: [[NLOAD:%.+]] = subf [[ZERO]], [[LOAD]] : f32
+  // CHECK: store [[TANH]], [[RES]][%arg1, %arg2] : memref<?x10xf32>
-  // CHECK: [[EXP:%.+]] = exp [[LOAD]] : f32
-  // CHECK: [[NEXP:%.+]] = exp [[NLOAD]] : f32
-  // CHECK: [[DIVIDEND:%.+]] = subf [[EXP]], [[NEXP]] : f32
-  // CHECK: [[DIVISOR:%.+]] = addf [[EXP]], [[NEXP]] : f32
-  // CHECK: [[TANH_RES:%.+]] = divf [[DIVIDEND]], [[DIVISOR]] : f32
-  // CHECK: store [[TANH_RES]], [[RES]][%arg1, %arg2] : memref<?x10xf32>
   // CHECK: return [[RES]] : memref<?x10xf32>
 }
 
@@ -220,6 +214,44 @@ func @test_cosh(%arg0 : tensor<?x10xf32>) -> tensor<*xf32> {
   // CHECK: return [[RES]] : memref<?x10xf32>
 }
 
+func @test_cos(%arg0 : tensor<?x10xf32>) -> tensor<*xf32> {
+  %0 = "onnx.Cos"(%arg0) : (tensor<?x10xf32>) -> tensor<*xf32>
+  "std.return"(%0) : (tensor<*xf32>) -> ()
+
+  // CHECK-LABEL: test_cos
+  // CHECK: [[DIM_0:%.+]] = dim %arg0, 0 : memref<?x10xf32>
+  // CHECK: [[RES:%.+]] = alloc([[DIM_0]]) : memref<?x10xf32>
+  // CHECK: [[DEF_LOOPS:%.+]]:2 = krnl.define_loops 2
+  // CHECK: [[OPT_LOOPS:%.+]]:2 = krnl.optimize_loops  {
+  // CHECK:   krnl.return_loops [[DEF_LOOPS]]#0, [[DEF_LOOPS]]#1
+  // CHECK: } : () -> (!krnl.loop, !krnl.loop)
+  // CHECK: [[DIM_2:%.+]] = dim %arg0, 0 : memref<?x10xf32>
+  // CHECK: krnl.iterate([[OPT_LOOPS]]#0, [[OPT_LOOPS]]#1) with ([[DEF_LOOPS]]#0 -> %arg1 = 0 to [[DIM_2]], [[DEF_LOOPS]]#1 -> %arg2 = 0 to 10) {
+  // CHECK: [[LOAD:%.+]] = load %arg0[%arg1, %arg2] : memref<?x10xf32>
+  // CHECK: [[COS:%.+]] = cos [[LOAD]] : f32
+  // CHECK: store [[COS]], [[RES]][%arg1, %arg2] : memref<?x10xf32>
+  // CHECK: return [[RES]] : memref<?x10xf32>
+}
+
+func @test_log(%arg0 : tensor<?x10xf32>) -> tensor<*xf32> {
+  %0 = "onnx.Log"(%arg0) : (tensor<?x10xf32>) -> tensor<*xf32>
+  "std.return"(%0) : (tensor<*xf32>) -> ()
+
+  // CHECK-LABEL: test_log
+  // CHECK: [[DIM_0:%.+]] = dim %arg0, 0 : memref<?x10xf32>
+  // CHECK: [[RES:%.+]] = alloc([[DIM_0]]) : memref<?x10xf32>
+  // CHECK: [[DEF_LOOPS:%.+]]:2 = krnl.define_loops 2
+  // CHECK: [[OPT_LOOPS:%.+]]:2 = krnl.optimize_loops  {
+  // CHECK:   krnl.return_loops [[DEF_LOOPS]]#0, [[DEF_LOOPS]]#1
+  // CHECK: } : () -> (!krnl.loop, !krnl.loop)
+  // CHECK: [[DIM_2:%.+]] = dim %arg0, 0 : memref<?x10xf32>
+  // CHECK: krnl.iterate([[OPT_LOOPS]]#0, [[OPT_LOOPS]]#1) with ([[DEF_LOOPS]]#0 -> %arg1 = 0 to [[DIM_2]], [[DEF_LOOPS]]#1 -> %arg2 = 0 to 10) {
+  // CHECK: [[LOAD:%.+]] = load %arg0[%arg1, %arg2] : memref<?x10xf32>
+  // CHECK: [[LOG:%.+]] = log [[LOAD]] : f32
+  // CHECK: store [[LOG]], [[RES]][%arg1, %arg2] : memref<?x10xf32>
+  // CHECK: return [[RES]] : memref<?x10xf32>
+}
+
 func @test_sigmoid(%arg0 : tensor<?x10xf32>) -> tensor<*xf32> {
   %0 = "onnx.Sigmoid"(%arg0) : (tensor<?x10xf32>) -> tensor<*xf32>
   "std.return"(%0) : (tensor<*xf32>) -> ()