diff --git a/src/Accelerators/NNPA/Conversion/ONNXToZHigh/ONNXLegalityCheck.cpp b/src/Accelerators/NNPA/Conversion/ONNXToZHigh/ONNXLegalityCheck.cpp
index cf25d27d8c..5ea377887c 100644
--- a/src/Accelerators/NNPA/Conversion/ONNXToZHigh/ONNXLegalityCheck.cpp
+++ b/src/Accelerators/NNPA/Conversion/ONNXToZHigh/ONNXLegalityCheck.cpp
@@ -41,8 +41,10 @@ bool isValidElementType(Value val) {
 /// detect whether the shapes are exactly the same or not. Hence, return false.
 /// Also, check the ranks of two tensors, they must be in range of (0, 4].
 bool haveSameStaticShape(Value value1, Value value2) {
-  auto valueType1 = value1.getType().cast<ShapedType>();
-  auto valueType2 = value2.getType().cast<ShapedType>();
+  ShapedType valueType1 = value1.getType().cast<ShapedType>();
+  ShapedType valueType2 = value2.getType().cast<ShapedType>();
+  if (!valueType1.hasRank() || !valueType2.hasRank())
+    return false;
   // Different rank, return false.
   if (valueType1.getRank() != valueType2.getRank())
     return false;
@@ -360,8 +362,9 @@ template <>
 bool isSuitableForZDNN<ONNXSoftmaxOp>(ONNXSoftmaxOp op) {
   if (!isValidElementType(op.input()))
     return false;
-  return ((op.axis() == 1 || op.axis() == -1) &&
-          (op.input().getType().cast<ShapedType>().getRank() == 2));
+  ShapedType inputType = op.getType().cast<ShapedType>();
+  return (op.axis() == 1 || op.axis() == -1) && inputType.hasRank() &&
+         (inputType.getRank() == 2);
 }
 
 /// Check legality for ONNXRelu.
@@ -369,7 +372,8 @@ template <>
 bool isSuitableForZDNN<ONNXReluOp>(ONNXReluOp op) {
   if (!isValidElementType(op.X()))
     return false;
-  return (op.X().getType().cast<ShapedType>().getRank() <= 4);
+  ShapedType xType = op.X().getType().cast<ShapedType>();
+  return xType.hasRank() && (xType.getRank() <= 4);
 }
 
 /// Check legality for ONNXTanh.
@@ -377,7 +381,8 @@ template <>
 bool isSuitableForZDNN<ONNXTanhOp>(ONNXTanhOp op) {
   if (!isValidElementType(op.input()))
     return false;
-  return (op.input().getType().cast<ShapedType>().getRank() <= 4);
+  ShapedType inputType = op.getType().cast<ShapedType>();
+  return inputType.hasRank() && (inputType.getRank() <= 4);
 }
 
 /// Check legality for ONNXSigmoid.
@@ -385,7 +390,8 @@ template <>
 bool isSuitableForZDNN<ONNXSigmoidOp>(ONNXSigmoidOp op) {
   if (!isValidElementType(op.X()))
     return false;
-  return (op.X().getType().cast<ShapedType>().getRank() <= 4);
+  ShapedType xType = op.X().getType().cast<ShapedType>();
+  return xType.hasRank() && (xType.getRank() <= 4);
 }
 
 /// Check legality for ONNXLog.
@@ -393,7 +399,8 @@ template <>
 bool isSuitableForZDNN<ONNXLogOp>(ONNXLogOp op) {
   if (!isValidElementType(op.input()))
     return false;
-  return (op.input().getType().cast<ShapedType>().getRank() <= 4);
+  ShapedType inputType = op.input().getType().cast<ShapedType>();
+  return inputType.hasRank() && (inputType.getRank() <= 4);
 }
 
 /// Check legality for ONNXExp.
@@ -401,7 +408,8 @@ template <>
 bool isSuitableForZDNN<ONNXExpOp>(ONNXExpOp op) {
   if (!isValidElementType(op.input()))
     return false;
-  return (op.input().getType().cast<ShapedType>().getRank() <= 4);
+  ShapedType inputType = op.input().getType().cast<ShapedType>();
+  return inputType.hasRank() && (inputType.getRank() <= 4);
 }
 
 /// Check legality for ONNXMatMul.
diff --git a/src/Accelerators/NNPA/Conversion/ONNXToZHigh/ONNXToZHigh.td b/src/Accelerators/NNPA/Conversion/ONNXToZHigh/ONNXToZHigh.td
index 9afcb9c72d..4e17d4c339 100644
--- a/src/Accelerators/NNPA/Conversion/ONNXToZHigh/ONNXToZHigh.td
+++ b/src/Accelerators/NNPA/Conversion/ONNXToZHigh/ONNXToZHigh.td
@@ -33,7 +33,16 @@ def IsNoneType : Constraint<CPred<"(($_self).getType().isa<NoneType>())">>;
 def IsNotNoneType : Constraint<CPred<"(!($_self).getType().isa<NoneType>())">>;
 
 class HasRankOf<int rank> : Constraint<
-  CPred<"$0.getType().isa<ShapedType>() && $0.getType().cast<ShapedType>().getRank() == " # rank>
+  CPred<"$0.getType().isa<ShapedType>() && "
+        "$0.getType().cast<ShapedType>().hasRank() && "
+        "$0.getType().cast<ShapedType>().getRank() == " # rank>
+>;
+
+def IsBiasNoneOr1D : Constraint<
+  CPred<"$_self.getType().isa<NoneType>() || "
+        " ($_self.getType().isa<ShapedType>() && "
+	"  $_self.getType().cast<ShapedType>().hasRank() && "
+	"  $_self.getType().cast<ShapedType>().getRank() == 1)">
 >;
 
 class VariadicSizeIs<int N> : Constraint<
@@ -536,14 +545,15 @@ def normalizeONNXGemmTransBPattern : Pat<
   (addBenefit 1)
 >;
 
-def replaceONNXGemmBias1DPattern : Pat<
+
+def replaceONNXGemmBiasNoneOr1DPattern : Pat<
   (ONNXGemmOp $a, $b, $c, $_, $_, $_, $_),
   (ZHighUnstickOp
      (ZHighMatMulOp
         (ZHighStickOp $a, (_2DLayoutAttr)),
         (ZHighStickOp $b, (_2DLayoutAttr)),
         (ZHighStickOp $c, (_1DLayoutAttr)))),
-  [(HasRankOf<1> $c)],
+  [(IsBiasNoneOr1D:$c)],
   (addBenefit 0)
 >;
 
diff --git a/test/mlir/accelerators/nnpa/conversion/onnx-to-zhigh/gemm.mlir b/test/mlir/accelerators/nnpa/conversion/onnx-to-zhigh/gemm.mlir
index 91ef05ccc0..60b143baed 100644
--- a/test/mlir/accelerators/nnpa/conversion/onnx-to-zhigh/gemm.mlir
+++ b/test/mlir/accelerators/nnpa/conversion/onnx-to-zhigh/gemm.mlir
@@ -1,5 +1,23 @@
 // RUN: onnx-mlir-opt --maccel=NNPA --shape-inference --convert-onnx-to-zhigh --canonicalize %s -split-input-file | FileCheck %s
 
+func @test_gemm_bias_none(%arg0 : tensor<10x5xf32>, %arg1 : tensor<5x10xf32>) -> tensor<*xf32> {
+  %bias = "onnx.NoValue"() {value} : () -> none
+  %0 ="onnx.Gemm"(%arg0, %arg1, %bias) {alpha = 1.0 : f32, beta = 1.0 : f32, transA = 0 : si64, transB = 0 : si64} : (tensor<10x5xf32>, tensor<5x10xf32>, none) -> tensor<*xf32>
+ "func.return"(%0) : (tensor<*xf32>) -> ()
+
+// CHECK-LABEL:  func @test_gemm_bias_none
+// CHECK-SAME:   ([[PARAM_0_:%.+]]: tensor<10x5xf32>, [[PARAM_1_:%.+]]: tensor<5x10xf32>) -> tensor<10x10xf32> {
+// CHECK-DAG:       [[VAR_0_:%.+]] = "zhigh.Stick"([[PARAM_0_]]) {layout = "2D"} : (tensor<10x5xf32>) -> tensor<10x5xf32, #zhigh.encoding<{dataLayout = "2D"}>>
+// CHECK-DAG:       [[VAR_1_:%.+]] = "zhigh.Stick"([[PARAM_1_]]) {layout = "2D"} : (tensor<5x10xf32>) -> tensor<5x10xf32, #zhigh.encoding<{dataLayout = "2D"}>>
+// CHECK-DAG:       [[VAR_2_:%.+]] = "onnx.NoValue"() {value} : () -> none
+// CHECK:           [[VAR_3_:%.+]] = "zhigh.MatMul"([[VAR_0_]], [[VAR_1_]], [[VAR_2_]]) : (tensor<10x5xf32, #zhigh.encoding<{dataLayout = "2D"}>>, tensor<5x10xf32, #zhigh.encoding<{dataLayout = "2D"}>>, none) -> tensor<*xf32>
+// CHECK:           [[VAR_4_:%.+]] = "zhigh.Unstick"([[VAR_3_]]) : (tensor<*xf32>) -> tensor<10x10xf32>
+// CHECK:           return [[VAR_4_]] : tensor<10x10xf32>
+// CHECK:         }
+}
+
+// -----
+
 func @test_gemm_bias_1d(%arg0 : tensor<10x5xf32>, %arg1 : tensor<5x10xf32>, %arg2: tensor<10xf32>) -> tensor<*xf32> {
   %0 ="onnx.Gemm"(%arg0, %arg1, %arg2) {alpha = 1.0 : f32, beta = 1.0 : f32, transA = 0 : si64, transB = 0 : si64} : (tensor<10x5xf32>, tensor<5x10xf32>, tensor<10xf32>) -> tensor<*xf32>
  "func.return"(%0) : (tensor<*xf32>) -> ()