diff --git a/src/Conversion/ONNXToKrnl/Tensor/Constant.cpp b/src/Conversion/ONNXToKrnl/Tensor/Constant.cpp
index 19ef38abbd..dfdc35d910 100644
--- a/src/Conversion/ONNXToKrnl/Tensor/Constant.cpp
+++ b/src/Conversion/ONNXToKrnl/Tensor/Constant.cpp
@@ -39,8 +39,20 @@ struct ONNXConstantOpLowering : public OpConversionPattern<ONNXConstantOp> {
 
     // Emit the constant global in Krnl dialect.
     MultiDialectBuilder<KrnlBuilder> create(rewriter, loc);
-    Value constantGlobal = create.krnl.constant(
-        memRefType, "constant_", constantOp.getValue().value());
+    mlir::Attribute constValAttr = constantOp.getValue().value();
+    if (memRefType.getElementType().isa<krnl::StringType>()) {
+      // If the onnx.ConstantOp has string type value attribute,
+      // The element type of the value attribute of krnl.global op should be
+      // "!krnl.string" instead of "!onnx.String".
+      ShapedType constStrType = RankedTensorType::get(
+          memRefType.getShape(), krnl::StringType::get(rewriter.getContext()));
+      SmallVector<StringRef> constStrVector(
+          constValAttr.dyn_cast<DenseElementsAttr>().getValues<StringAttr>());
+      ArrayRef<StringRef> constStrValues(constStrVector);
+      constValAttr = mlir::DenseElementsAttr::get(constStrType, constStrValues);
+    }
+    Value constantGlobal =
+        create.krnl.constant(memRefType, "constant_", constValAttr);
 
     // Replace this operation with the generated krnl.global.
     rewriter.replaceOp(op, constantGlobal);
diff --git a/test/mlir/conversion/onnx_to_krnl/Tensor/Constant.mlir b/test/mlir/conversion/onnx_to_krnl/Tensor/Constant.mlir
index 7585ebe59a..772143937f 100644
--- a/test/mlir/conversion/onnx_to_krnl/Tensor/Constant.mlir
+++ b/test/mlir/conversion/onnx_to_krnl/Tensor/Constant.mlir
@@ -8,3 +8,39 @@ func.func private @test_constant_dense_2d_value(%arg0: tensor<1xf32>) -> tensor<
   // CHECK: return [[GLOBAL]] : memref<3x2xf32>
 }
 
+// -----
+
+func.func @test_constant_string() -> tensor<!onnx.String> {
+  %0 = onnx.Constant dense<"1"> : tensor<!onnx.String>
+  "func.return"(%0) : (tensor<!onnx.String>) -> ()
+  // mlir2FileCheck.py
+  // CHECK-LABEL:  func.func @test_constant_string
+  // CHECK-SAME:   () -> memref<!krnl.string> {
+  // CHECK:           [[VAR_0_:%.+]] = "krnl.global"() {name = "constant_{{[0-9]+}}", shape = [], value = dense<"1"> : tensor<!krnl.string>} : () -> memref<!krnl.string>
+  // CHECK:           return [[VAR_0_]] : memref<!krnl.string>
+}
+
+// -----
+
+func.func @test_constant_string_3elem() -> tensor<3x!onnx.String> {
+  %0 = onnx.Constant dense<["1", "2", "3"]> : tensor<3x!onnx.String>
+  "func.return"(%0) : (tensor<3x!onnx.String>) -> ()
+  // mlir2FileCheck.py
+  // CHECK-LABEL:  func.func @test_constant_string_3elem
+  // CHECK-SAME:   () -> memref<3x!krnl.string> {
+  // CHECK:           [[VAR_0_:%.+]] = "krnl.global"() {name = "constant_{{[0-9]+}}", shape = [3], value = dense<["1", "2", "3"]> : tensor<3x!krnl.string>} : () -> memref<3x!krnl.string>
+  // CHECK:           return [[VAR_0_]] : memref<3x!krnl.string>
+}
+
+// -----
+
+func.func @test_constant_string_3elem2() -> tensor<3x!onnx.String> {
+  %0 = onnx.Constant dense<"1"> : tensor<3x!onnx.String>
+  "func.return"(%0) : (tensor<3x!onnx.String>) -> ()
+  // mlir2FileCheck.py
+  // CHECK-LABEL:  func.func @test_constant_string_3elem2
+  // CHECK-SAME:   () -> memref<3x!krnl.string> {
+  // CHECK:           [[VAR_0_:%.+]] = "krnl.global"() {name = "constant_{{[0-9]+}}", shape = [3], value = dense<"1"> : tensor<3x!krnl.string>} : () -> memref<3x!krnl.string>
+  // CHECK:           return [[VAR_0_]] : memref<3x!krnl.string>
+  // CHECK:         }
+}