Merge branch 'master' into matmul-shape

src/dialect/onnx/gen_doc.py

@@ -267,7 +267,8 @@ def gen_schema(schema) :
                         'Add', 'Mul', 'Div', 'Sub', 'And', 'Or', 'Xor',
                         'Sum', 'Max', 'Min', 'MatMul', 'Gemm', 'LeakyRelu',
                         'Elu', 'Selu', 'HardSigmoid', 'Reshape', 'Reciprocal',
-                        'Identity', 'Cos', 'Log', 'Transpose', 'Softmax']
+                        'Identity', 'Cos', 'Log', 'Transpose', 'Softmax',
+                        'Softplus', 'Softsign']
     CanonicalList=['Add', 'Identity']
     line_indent = '  '
 

src/dialect/onnx/onnx_ops.cpp

@@ -166,6 +166,22 @@ void ONNXSoftmaxOp::inferShapes() {
   getResult().setType(getOperand().getType());
 }
 
+//===----------------------------------------------------------------------===//
+// Softplus
+/// Infer the output shape of the ONNXSoftplusOp. This method is required by
+/// the shape inference interface.
+void ONNXSoftplusOp::inferShapes() {
+  getResult().setType(getOperand().getType());
+}
+
+//===----------------------------------------------------------------------===//
+// Softsign
+/// Infer the output shape of the ONNXSoftsignOp. This method is required by
+/// the shape inference interface.
+void ONNXSoftsignOp::inferShapes() {
+  getResult().setType(getOperand().getType());
+}
+
 //===----------------------------------------------------------------------===//
 // Add
 /// Infer the output shape of the ONNXAddOp. This method is required by the

src/dialect/onnx/onnxop.inc

@@ -2863,7 +2863,7 @@ def ONNXSoftmaxOp:ONNX_Op<"Softmax",
 }
 
 def ONNXSoftplusOp:ONNX_Op<"Softplus", 
-    [NoSideEffect]> {
+    [NoSideEffect, DeclareOpInterfaceMethods<ShapeInferenceOpInterface>]> {
   let summary = "ONNX Softplus operation";
   let description = [{
     "Softplus takes one input data (Tensor<T>) and produces one output data"
@@ -2875,7 +2875,7 @@ def ONNXSoftplusOp:ONNX_Op<"Softplus",
 }
 
 def ONNXSoftsignOp:ONNX_Op<"Softsign", 
-    [NoSideEffect]> {
+    [NoSideEffect, DeclareOpInterfaceMethods<ShapeInferenceOpInterface>]> {
   let summary = "ONNX Softsign operation";
   let description = [{
     "Calculates the softsign (x/(1+|x|)) of the given input tensor element-wise."

src/pass/lower_frontend_to_krnl.cpp

@@ -570,6 +570,46 @@ Value mapToLowerScalarOp<ONNXReciprocalOp>(
   return result;
 }
 
+//===----------------------------------------------------------------------===//
+// Scalar unary ops for lowering ONNXSoftplusOp
+//===----------------------------------------------------------------------===//
+template <>
+Value mapToLowerScalarOp<ONNXSoftplusOp>(
+    Operation *op, ArrayRef<Type> result_types, ArrayRef<Value> operands,
+    ConversionPatternRewriter &rewriter) {
+  // ONNXSoftplusOp(%X) = LogOp(AddFOp(ExpOp(%X), ConstantOp 1))
+  auto loc = op->getLoc();
+  Value operand = operands[0];
+  auto elementType = result_types[0];
+
+  auto exp = rewriter.create<ExpOp>(loc, operand);
+  auto one = rewriter.create<ConstantOp>(loc, FloatAttr::get(elementType, 1));
+  auto add = rewriter.create<AddFOp>(loc, exp, one);
+  auto result = rewriter.create<LogOp>(loc, add);
+
+  return result;
+}
+
+//===----------------------------------------------------------------------===//
+// Scalar unary ops for lowering ONNXSoftsignOp
+//===----------------------------------------------------------------------===//
+template <>
+Value mapToLowerScalarOp<ONNXSoftsignOp>(
+    Operation *op, ArrayRef<Type> result_types, ArrayRef<Value> operands,
+    ConversionPatternRewriter &rewriter) {
+  // ONNXSoftsignOp(%X) = DivFOp(ConstantOp 1, %X)
+  auto loc = op->getLoc();
+  Value operand = operands[0];
+  auto elementType = result_types[0];
+
+  auto abs = rewriter.create<AbsFOp>(loc, operand);
+  auto one = rewriter.create<ConstantOp>(loc, FloatAttr::get(elementType, 1));
+  auto add = rewriter.create<AddFOp>(loc, abs, one);
+  auto result = rewriter.create<DivFOp>(loc, operand, add);
+
+  return result;
+}
+
 //===----------------------------------------------------------------------===//
 // Scalar unary ops for lowering ONNXMaxOp
 //===----------------------------------------------------------------------===//
@@ -1214,6 +1254,8 @@ void FrontendToKrnlLoweringPass::runOnModule() {
                   ONNXElementwiseUnaryOpLowering<mlir::ONNXLeakyReluOp>,
                   ONNXElementwiseUnaryOpLowering<mlir::ONNXSeluOp>,
                   ONNXElementwiseUnaryOpLowering<mlir::ONNXReciprocalOp>,
+                  ONNXElementwiseUnaryOpLowering<mlir::ONNXSoftplusOp>,
+                  ONNXElementwiseUnaryOpLowering<mlir::ONNXSoftsignOp>,
                   ONNXElementwiseVariadicOpLowering<mlir::ONNXAddOp>,
                   ONNXElementwiseVariadicOpLowering<mlir::ONNXMulOp>,
                   ONNXElementwiseVariadicOpLowering<mlir::ONNXDivOp>,

src/pass/shape_inference_pass.cpp

@@ -101,6 +101,8 @@ public:
         op->getName().getStringRef() != "onnx.LeakyRelu" &&
         op->getName().getStringRef() != "onnx.Selu" &&
         op->getName().getStringRef() != "onnx.Reciprocal" &&
+        op->getName().getStringRef() != "onnx.Softplus" &&
+        op->getName().getStringRef() != "onnx.Softsign" &&
         op->getName().getStringRef() != "onnx.Mul" &&
         op->getName().getStringRef() != "onnx.Add" &&
         op->getName().getStringRef() != "onnx.Div" &&

test/backend/test.py

@@ -146,6 +146,14 @@ test_to_enable = [
     # Reciprocal Op:
     "test_reciprocal_cpu",
     "test_reciprocal_example_cpu",
+
+    # SoftplusOp:
+    "test_softplus_cpu",
+    "test_softplus_example_cpu",
+
+    # SoftsignOp:
+    "test_softsign_cpu",
+    "test_softsign_example_cpu",
 ]
 
 # Extract name of all test cases.

test/mlir/onnx/onnx_lowering.mlir

@@ -508,6 +508,50 @@ func @test_reciprocal(%arg0 : tensor<?x10xf32>) -> tensor<*xf32> {
   // CHECK: return [[RES]] : memref<?x10xf32>
 }
 
+func @test_softplus(%arg0 : tensor<?x10xf32>) -> tensor<*xf32> {
+  %0 = "onnx.Softplus"(%arg0) : (tensor<?x10xf32>) -> tensor<*xf32>
+  "std.return"(%0) : (tensor<*xf32>) -> ()
+
+  // CHECK-LABEL: test_softplus
+  // 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: [[EXP:%.+]] = exp [[LOAD]] : f32
+  // CHECK: [[ONE:%.+]] = constant {{1.+}} : f32
+  // CHECK: [[ADD:%.+]] = addf [[EXP]], [[ONE]] : f32
+  // CHECK: [[SOFTPLUS_RES:%.+]] = log [[ADD]] : f32
+  // CHECK: store [[SOFTPLUS_RES]], [[RES]][%arg1, %arg2] : memref<?x10xf32>
+  // CHECK: return [[RES]] : memref<?x10xf32>
+}
+
+func @test_softsign(%arg0 : tensor<?x10xf32>) -> tensor<*xf32> {
+  %0 = "onnx.Softsign"(%arg0) : (tensor<?x10xf32>) -> tensor<*xf32>
+  "std.return"(%0) : (tensor<*xf32>) -> ()
+
+  // CHECK-LABEL: test_softsign
+  // 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: [[ABS:%.+]] = absf [[LOAD]] : f32
+  // CHECK: [[ONE:%.+]] = constant {{1.+}} : f32
+  // CHECK: [[ADD:%.+]] = addf [[ABS]], [[ONE]] : f32
+  // CHECK: [[SOFTSIGN_RES:%.+]] = divf [[LOAD]], [[ADD]] : f32
+  // CHECK: store [[SOFTSIGN_RES]], [[RES]][%arg1, %arg2] : memref<?x10xf32>
+  // CHECK: return [[RES]] : memref<?x10xf32>
+}
+
 func @test_add_with_broadcasting(%arg0 : tensor<?xf32>, %arg1 : tensor<?x10xf32>) -> tensor<*xf32> {
   %0 = "onnx.Add"(%arg0, %arg1) : (tensor<?xf32>, tensor<?x10xf32>) -> tensor<*xf32>
   "std.return"(%0) : (tensor<*xf32>) -> ()