diff --git a/src/Conversion/ONNXToTOSA/Math/Elementwise.cpp b/src/Conversion/ONNXToTOSA/Math/Elementwise.cpp
index 612632e47a..476cd53a75 100644
--- a/src/Conversion/ONNXToTOSA/Math/Elementwise.cpp
+++ b/src/Conversion/ONNXToTOSA/Math/Elementwise.cpp
@@ -31,6 +31,20 @@ struct TOSADialectOp<ONNXNegOp> {
   using Op = mlir::tosa::NegateOp;
 };
 
+struct AbsIOSupportedTypes {
+  static LogicalResult checkType(
+      ConversionPatternRewriter &rewriter, Type scalarType, Operation *op) {
+    if (!mlir::isa<mlir::BFloat16Type, mlir::Float16Type, mlir::Float32Type>(
+            scalarType) &&
+        !scalarType.isSignlessInteger(/*width=*/32)) {
+      return rewriter.notifyMatchFailure(op,
+          "this operation only supports signless 32 integer or fp16, fp32"
+          " or bf16");
+    }
+    return success();
+  }
+};
+
 struct IsIntOrFloat {
   static LogicalResult checkType(
       ConversionPatternRewriter &rewriter, Type scalarType, Operation *op) {
@@ -333,7 +347,9 @@ static void populateLoweringONNXElementwiseUnaryTemplateOpToTOSAPattern(
       ONNXElementwiseUnaryOpLoweringToTOSA<ONNXTanhOp, mlir::tosa::TanhOp,
           IsIntOrFloat, IsIntOrFloat>,
       ONNXElementwiseUnaryOpLoweringToTOSA<ONNXSigmoidOp, mlir::tosa::SigmoidOp,
-          IsIntOrFloat, IsIntOrFloat>>(typeConverter, ctx);
+          IsIntOrFloat, IsIntOrFloat>,
+      ONNXElementwiseUnaryOpLoweringToTOSA<ONNXAbsOp, mlir::tosa::AbsOp,
+          AbsIOSupportedTypes, AbsIOSupportedTypes>>(typeConverter, ctx);
 }
 
 void populateLoweringONNXElementwiseOpToTOSAPattern(ConversionTarget &target,
diff --git a/test/mlir/conversion/onnx_to_tosa/Math/Elementwise.mlir b/test/mlir/conversion/onnx_to_tosa/Math/Elementwise.mlir
index fe10cddb1c..8a967039e0 100644
--- a/test/mlir/conversion/onnx_to_tosa/Math/Elementwise.mlir
+++ b/test/mlir/conversion/onnx_to_tosa/Math/Elementwise.mlir
@@ -297,3 +297,25 @@ func.func @test_div_decomposed_broadcast(%arg0: tensor<13x21x1xf32>, %arg1: tens
 // CHECK-NEXT:      [[VAR_1_:%.+]] = "tosa.reshape"([[VAR_0_]]) <{new_shape = array<i64: 1, 1, 1>}> : (tensor<1xf32>) -> tensor<1x1x1xf32>
 // CHECK-NEXT:      [[VAR_2_:%.+]] = "tosa.mul"([[PARAM_0_]], [[VAR_1_]]) <{shift = 0 : i32}> : (tensor<13x21x1xf32>, tensor<1x1x1xf32>) -> tensor<13x21x1xf32>
 }
+
+// -----
+
+func.func @test_abs_i32(%arg0: tensor<3xi32>) -> tensor<3xi32> {
+  %0 = "onnx.Abs"(%arg0) : (tensor<3xi32>) -> tensor<3xi32>
+  return %0 : tensor<3xi32>
+// CHECK-LABEL:  func @test_abs_i32
+// CHECK-SAME:   ([[PARAM_0_:%.+]]: tensor<3xi32>) -> tensor<3xi32>
+// CHECK-NEXT:     [[VAR_0_:%.+]] = "tosa.abs"([[PARAM_0_]]) : (tensor<3xi32>) -> tensor<3xi32>
+// CHECK-NEXT:     return [[VAR_0_]] : tensor<3xi32>
+// CHECK-NEXT:   }
+}
+
+func.func @test_abs_bf16(%arg0: tensor<3xbf16>) -> tensor<3xbf16> {
+  %0 = "onnx.Abs"(%arg0) : (tensor<3xbf16>) -> tensor<3xbf16>
+  return %0 : tensor<3xbf16>
+// CHECK-LABEL:  func @test_abs_bf16
+// CHECK-SAME:   ([[PARAM_0_:%.+]]: tensor<3xbf16>) -> tensor<3xbf16>
+// CHECK-NEXT:     [[VAR_0_:%.+]] = "tosa.abs"([[PARAM_0_]]) : (tensor<3xbf16>) -> tensor<3xbf16>
+// CHECK-NEXT:     return [[VAR_0_]] : tensor<3xbf16>
+// CHECK-NEXT:   }
+}