feat(ONNXTOTosa): legalization of ONNXXorOp and ONNXBitwiseXorOp.

Xilinx · Jan 4, 2024 · 835d343 · 835d343
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diff --git a/src/Conversion/ONNXToTOSA/Math/Elementwise.cpp b/src/Conversion/ONNXToTOSA/Math/Elementwise.cpp
@@ -442,6 +442,10 @@ static void populateLoweringONNXElementwiseBinaryTemplateOpToTOSAPattern(
           IsBool>,
       ONNXBinaryElementwiseOpLoweringToTOSA<ONNXBitwiseOrOp,
           mlir::tosa::BitwiseOrOp, IsInt>,
+      ONNXBinaryElementwiseOpLoweringToTOSA<ONNXXorOp, mlir::tosa::LogicalXorOp,
+          IsBool>,
+      ONNXBinaryElementwiseOpLoweringToTOSA<ONNXBitwiseXorOp,
+          mlir::tosa::BitwiseXorOp, IsInt>,
       ONNXBinaryElementwiseOpLoweringToTOSA<ONNXAddOp, mlir::tosa::AddOp,
           IsIntOrFloat>,
       ONNXBinaryElementwiseOpLoweringToTOSA<ONNXSubOp, mlir::tosa::SubOp,

diff --git a/test/mlir/conversion/onnx_to_tosa/Math/Elementwise.mlir b/test/mlir/conversion/onnx_to_tosa/Math/Elementwise.mlir
@@ -555,4 +555,47 @@ func.func @test_bitwise_or_broadcast(%arg0: tensor<13x21x1xi64>, %arg1: tensor<1
 // CHECK:           [[VAR_0_:%.+]] = "tosa.reshape"([[PARAM_1_]]) <{new_shape = array<i64: 1, 1, 1>}> : (tensor<1xi64>) -> tensor<1x1x1xi64>
 // CHECK:           [[VAR_1_:%.+]] = "tosa.bitwise_or"([[PARAM_0_]], [[VAR_0_]]) : (tensor<13x21x1xi64>, tensor<1x1x1xi64>) -> tensor<13x21x1xi64>
 // CHECK:           return [[VAR_1_]] : tensor<13x21x1xi64>
+
+}
+
+// -----
+
+func.func @test_xor(%arg0: tensor<13x21x1xi1>, %arg1: tensor<13x21x1xi1>) -> tensor<13x21x1xi1> {
+  %0 = "onnx.Xor"(%arg0, %arg1) : (tensor<13x21x1xi1>, tensor<13x21x1xi1>) -> tensor<13x21x1xi1>
+  "func.return"(%0) : (tensor<13x21x1xi1>) -> ()
+// CHECK-LABEL:  func @test_xor
+// CHECK-SAME:   ([[PARAM_0_:%.+]]: tensor<13x21x1xi1>, [[PARAM_1_:%.+]]: tensor<13x21x1xi1>) -> tensor<13x21x1xi1> {
+// CHECK-NEXT:      [[VAR_0_:%.+]] = "tosa.logical_xor"([[PARAM_0_]], [[PARAM_1_]]) : (tensor<13x21x1xi1>, tensor<13x21x1xi1>) -> tensor<13x21x1xi1>
+}
+
+// -----
+
+func.func @test_xor_broadcast(%arg0: tensor<13x21x1xi1>, %arg1: tensor<1xi1>) -> tensor<13x21x1xi1> {
+  %0 = "onnx.Xor"(%arg0, %arg1) : (tensor<13x21x1xi1>, tensor<1xi1>) -> tensor<13x21x1xi1>
+  "func.return"(%0) : (tensor<13x21x1xi1>) -> ()
+// CHECK-LABEL:  func.func @test_xor_broadcast
+// CHECK-SAME:   ([[PARAM_0_:%.+]]: tensor<13x21x1xi1>, [[PARAM_1_:%.+]]: tensor<1xi1>) -> tensor<13x21x1xi1> {
+// CHECK:           [[VAR_0_:%.+]] = "tosa.reshape"([[PARAM_1_]]) <{new_shape = array<i64: 1, 1, 1>}> : (tensor<1xi1>) -> tensor<1x1x1xi1>
+// CHECK:           [[VAR_1_:%.+]] = "tosa.logical_xor"([[PARAM_0_]], [[VAR_0_]]) : (tensor<13x21x1xi1>, tensor<1x1x1xi1>) -> tensor<13x21x1xi1>
+// CHECK:           return [[VAR_1_]] : tensor<13x21x1xi1>
+}
+// -----
+
+func.func @test_bitwise_xor(%arg0: tensor<13x21x1xi64>, %arg1: tensor<13x21x1xi64>) -> tensor<13x21x1xi64> {
+  %0 = "onnx.BitwiseXor"(%arg0, %arg1) : (tensor<13x21x1xi64>, tensor<13x21x1xi64>) -> tensor<13x21x1xi64>
+  "func.return"(%0) : (tensor<13x21x1xi64>) -> ()
+// CHECK-LABEL:  func @test_bitwise_xor
+// CHECK-SAME:   ([[PARAM_0_:%.+]]: tensor<13x21x1xi64>, [[PARAM_1_:%.+]]: tensor<13x21x1xi64>) -> tensor<13x21x1xi64> {
+// CHECK-NEXT:      [[VAR_0_:%.+]] = "tosa.bitwise_xor"([[PARAM_0_]], [[PARAM_1_]]) : (tensor<13x21x1xi64>, tensor<13x21x1xi64>) -> tensor<13x21x1xi64>
+}
+// -----
+
+func.func @test_bitwise_xor_broadcast(%arg0: tensor<13x21x1xi64>, %arg1: tensor<1xi64>) -> tensor<13x21x1xi64> {
+  %0 = "onnx.BitwiseXor"(%arg0, %arg1) : (tensor<13x21x1xi64>, tensor<1xi64>) -> tensor<13x21x1xi64>
+  "func.return"(%0) : (tensor<13x21x1xi64>) -> ()
+// CHECK-LABEL:  func.func @test_bitwise_xor_broadcast
+// CHECK-SAME:   ([[PARAM_0_:%.+]]: tensor<13x21x1xi64>, [[PARAM_1_:%.+]]: tensor<1xi64>) -> tensor<13x21x1xi64> {
+// CHECK:           [[VAR_0_:%.+]] = "tosa.reshape"([[PARAM_1_]]) <{new_shape = array<i64: 1, 1, 1>}> : (tensor<1xi64>) -> tensor<1x1x1xi64>
+// CHECK:           [[VAR_1_:%.+]] = "tosa.bitwise_xor"([[PARAM_0_]], [[VAR_0_]]) : (tensor<13x21x1xi64>, tensor<1x1x1xi64>) -> tensor<13x21x1xi64>
+// CHECK:           return [[VAR_1_]] : tensor<13x21x1xi64>
 }