diff --git a/src/Conversion/ONNXToStableHlo/ConvertONNXToStableHlo.cpp b/src/Conversion/ONNXToStableHlo/ConvertONNXToStableHlo.cpp
index 082a53c17b..4f5d15b09c 100644
--- a/src/Conversion/ONNXToStableHlo/ConvertONNXToStableHlo.cpp
+++ b/src/Conversion/ONNXToStableHlo/ConvertONNXToStableHlo.cpp
@@ -42,7 +42,9 @@ void populateONNXToStableHloConversionPattern(
   populateLoweringONNXExpandOpToStableHloPattern(patterns, ctx);
   populateLoweringONNXFlattenOpToStableHloPattern(patterns, ctx);
   populateLoweringONNXGatherOpToStableHloPattern(patterns, ctx);
+  populateLoweringONNXGatherElementsOpToStableHloPattern(patterns, ctx);
   populateLoweringONNXIdentityOpToStableHloPattern(patterns, ctx);
+  populateLoweringONNXPadOpToStableHloPattern(patterns, ctx);
   populateLoweringONNXReshapeOpToStableHloPattern(patterns, ctx);
   populateLoweringONNXShapeOpToStableHloPattern(patterns, ctx);
   populateLoweringONNXSliceOpToStableHloPattern(patterns, ctx);
@@ -92,7 +94,8 @@ void FrontendToStableHloLoweringPass::runOnOperation() {
   // Added affine as some affine maps are generated by IndexExpression. It could
   // be disabled and/or replaced by shape max/min.
   target.addLegalDialect<stablehlo::StablehloDialect, func::FuncDialect,
-      arith::ArithDialect, shape::ShapeDialect, mlir::affine::AffineDialect>();
+      arith::ArithDialect, shape::ShapeDialect, mlir::affine::AffineDialect,
+      tensor::TensorDialect>();
   // Needed to support unsigned int computations. To be removed if we use a
   // scheme that does not rely on the UnrealizedConversionCastOp.
   target.addLegalOp<::mlir::UnrealizedConversionCastOp>();
diff --git a/src/Conversion/ONNXToStableHlo/Tensor/GatherElements.cpp b/src/Conversion/ONNXToStableHlo/Tensor/GatherElements.cpp
index 9100eb93b2..1b320421bd 100644
--- a/src/Conversion/ONNXToStableHlo/Tensor/GatherElements.cpp
+++ b/src/Conversion/ONNXToStableHlo/Tensor/GatherElements.cpp
@@ -57,17 +57,31 @@ struct ONNXGatherElementsOpLoweringToStableHlo : public ConversionPattern {
     Value zero = getShapedZero(loc, rewriter, indices);
     Value inputShape = rewriter.create<shape::ShapeOfOp>(loc, data);
     Value indicesShape = rewriter.create<shape::ShapeOfOp>(loc, indices);
-    Value axisDimSize =
-        rewriter.create<shape::GetExtentOp>(loc, inputShape, axisLit);
-    axisDimSize =
-        rewriter.create<arith::IndexCastOp>(loc, indexElemType, axisDimSize);
-    axisDimSize = rewriter.create<tensor::FromElementsOp>(loc, axisDimSize);
-    axisDimSize = rewriter.create<stablehlo::ReshapeOp>(loc,
-        RankedTensorType::get(SmallVector<int64_t>{}, indexElemType),
-        axisDimSize);
-    Value broadcastedAxisDimSize =
-        rewriter.create<stablehlo::DynamicBroadcastInDimOp>(loc, indicesType,
-            axisDimSize, indicesShape, rewriter.getI64TensorAttr({}));
+    Value broadcastedAxisDimSize, axisDimSize;
+    if (inputType.hasStaticShape()) {
+      axisDimSize = rewriter.create<stablehlo::ConstantOp>(
+          loc, rewriter.getIntegerAttr(
+                   indexElemType, inputType.getDimSize(axisLit)));
+    } else {
+      axisDimSize =
+          rewriter.create<shape::GetExtentOp>(loc, inputShape, axisLit);
+      axisDimSize =
+          rewriter.create<shape::GetExtentOp>(loc, inputShape, axisLit);
+      axisDimSize =
+          rewriter.create<arith::IndexCastOp>(loc, indexElemType, axisDimSize);
+      axisDimSize = rewriter.create<tensor::FromElementsOp>(loc, axisDimSize);
+      axisDimSize = rewriter.create<stablehlo::ReshapeOp>(loc,
+          RankedTensorType::get(SmallVector<int64_t>{}, indexElemType),
+          axisDimSize);
+    }
+    if (indicesType.hasStaticShape()) {
+      broadcastedAxisDimSize = rewriter.create<stablehlo::BroadcastInDimOp>(
+          loc, indicesType, axisDimSize, rewriter.getI64TensorAttr({}));
+    } else {
+      broadcastedAxisDimSize =
+          rewriter.create<stablehlo::DynamicBroadcastInDimOp>(loc, indicesType,
+              axisDimSize, indicesShape, rewriter.getI64TensorAttr({}));
+    }
     Value isNegative = rewriter.create<stablehlo::CompareOp>(
         loc, indices, zero, stablehlo::ComparisonDirection::LT);
     Value positiveIndices = rewriter.create<stablehlo::AddOp>(
diff --git a/src/Conversion/ONNXToStableHlo/Tensor/Pad.cpp b/src/Conversion/ONNXToStableHlo/Tensor/Pad.cpp
index 8e3b7162c3..9d305e5ed2 100644
--- a/src/Conversion/ONNXToStableHlo/Tensor/Pad.cpp
+++ b/src/Conversion/ONNXToStableHlo/Tensor/Pad.cpp
@@ -52,8 +52,10 @@ struct ONNXPadOpLoweringToStablehlo : public ConversionPattern {
                    rewriter.getZeroAttr(elemType)));
     } else {
       // constantValue might be 1D tensor, reshape it to scalar
-      constantValue = rewriter.create<stablehlo::ReshapeOp>(
-          loc, RankedTensorType::get({}, elemType), constantValue);
+      ShapedType constantType = constantValue.getType().cast<ShapedType>();
+      if (constantType.getRank() != 0)
+        constantValue = rewriter.create<stablehlo::ReshapeOp>(
+            loc, RankedTensorType::get({}, elemType), constantValue);
     }
     SmallVector<int64_t> edgePaddingLowVec(rank, 0);
     SmallVector<int64_t> edgePaddingHighVec(rank, 0);
@@ -91,7 +93,7 @@ struct ONNXPadOpLoweringToStablehlo : public ConversionPattern {
 
 } // namespace
 
-void populateLoweringONNXPadOpToStablehloPattern(
+void populateLoweringONNXPadOpToStableHloPattern(
     RewritePatternSet &patterns, MLIRContext *ctx) {
   patterns.insert<ONNXPadOpLoweringToStablehlo>(ctx);
 }
diff --git a/test/mlir/conversion/onnx_to_stablehlo/Tensor/GatherElements.mlir b/test/mlir/conversion/onnx_to_stablehlo/Tensor/GatherElements.mlir
index 2c2662d383..9694383137 100644
--- a/test/mlir/conversion/onnx_to_stablehlo/Tensor/GatherElements.mlir
+++ b/test/mlir/conversion/onnx_to_stablehlo/Tensor/GatherElements.mlir
@@ -4,15 +4,16 @@ func.func @main_gather_elements(%arg0: tensor<3x2xf32>, %arg1: tensor<2x2xi64>)
   %0 = "onnx.GatherElements"(%arg0, %arg1) {axis = 0 : si64} : (tensor<3x2xf32>, tensor<2x2xi64>) -> tensor<2x2xf32>
   return %0 : tensor<2x2xf32>
 // CHECK:  func.func @main_gather_elements([[PARAM_0_:%.+]]: tensor<3x2xf32>, [[PARAM_1_:%.+]]: tensor<2x2xi64>) -> tensor<2x2xf32> {
-// CHECK-DAG:    [[VAR_0_:%.+]] = stablehlo.constant dense<3> : tensor<2x2xi64>
+// CHECK-DAG:    [[CST_:%.+]] = arith.constant dense<[2, 2, 1]> : tensor<3xindex>
+// CHECK-DAG:    [[VAR_0_:%.+]] = stablehlo.constant dense<3> : tensor<i64>
 // CHECK-DAG:    [[VAR_1_:%.+]] = stablehlo.constant dense<0> : tensor<2x2xi64>
-// CHECK-DAG:    [[VAR_2_:%.+]] = stablehlo.compare  LT, [[PARAM_1_]], [[VAR_1_]],  NOTYPE : (tensor<2x2xi64>, tensor<2x2xi64>) -> tensor<2x2xi1>
-// CHECK-DAG:    [[VAR_3_:%.+]] = stablehlo.add [[PARAM_1_]], [[VAR_0_]] : tensor<2x2xi64>
-// CHECK-NEXT:   [[VAR_4_:%.+]] = stablehlo.select [[VAR_2_]], [[VAR_3_]], [[PARAM_1_]] : tensor<2x2xi1>, tensor<2x2xi64>
-// CHECK-NEXT:   [[VAR_5_:%.+]] = stablehlo.reshape [[VAR_4_]] : (tensor<2x2xi64>) -> tensor<2x2x1xi64>
-// CHECK-DAG:    [[VAR_6_:%.+]] = "stablehlo.iota"() {iota_dimension = 0 : i64} : () -> tensor<2xi64>
-// CHECK-NEXT:   [[VAR_7_:%.+]] = "stablehlo.broadcast_in_dim"([[VAR_6_]]) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<2xi64>) -> tensor<2x2x1xi64>
-// CHECK-NEXT:   [[VAR_8_:%.+]] = "stablehlo.concatenate"([[VAR_5_]], [[VAR_7_]]) {dimension = 2 : i64} : (tensor<2x2x1xi64>, tensor<2x2x1xi64>) -> tensor<2x2x2xi64>
+// CHECK-DAG:    [[VAR_2_:%.+]] = stablehlo.broadcast_in_dim [[VAR_0_]], dims = [] : (tensor<i64>) -> tensor<2x2xi64>
+// CHECK-DAG:    [[VAR_3_:%.+]] = stablehlo.compare  LT, [[PARAM_1_]], [[VAR_1_]],  NOTYPE : (tensor<2x2xi64>, tensor<2x2xi64>) -> tensor<2x2xi1>
+// CHECK-DAG:    [[VAR_4_:%.+]] = stablehlo.add [[PARAM_1_]], [[VAR_2_]] : tensor<2x2xi64>
+// CHECK-NEXT:   [[VAR_5_:%.+]] = stablehlo.select [[VAR_3_]], [[VAR_4_]], [[PARAM_1_]] : tensor<2x2xi1>, tensor<2x2xi64>
+// CHECK-DAG:    [[VAR_6_:%.+]] = stablehlo.dynamic_reshape [[VAR_5_]], [[CST_]] : (tensor<2x2xi64>, tensor<3xindex>) -> tensor<2x2x1xi64>
+// CHECK-DAG:    [[VAR_7_:%.+]] = stablehlo.dynamic_iota [[CST_]], dim = 1 : (tensor<3xindex>) -> tensor<2x2x1xi64>
+// CHECK-NEXT:   [[VAR_8_:%.+]] = stablehlo.concatenate [[VAR_6_]], [[VAR_7_]], dim = 2 : (tensor<2x2x1xi64>, tensor<2x2x1xi64>) -> tensor<2x2x2xi64>
 // CHECK-NEXT:   [[VAR_9_:%.+]] = "stablehlo.gather"([[PARAM_0_]], [[VAR_8_]]) {dimension_numbers = #stablehlo.gather<collapsed_slice_dims = [0, 1], start_index_map = [0, 1], index_vector_dim = 2>, indices_are_sorted = false, slice_sizes = dense<1> : tensor<2xi64>} : (tensor<3x2xf32>, tensor<2x2x2xi64>) -> tensor<2x2xf32>
 // CHECK-NEXT:   return [[VAR_9_]] : tensor<2x2xf32>
 }
\ No newline at end of file
diff --git a/test/mlir/conversion/onnx_to_stablehlo/Tensor/Pad.mlir b/test/mlir/conversion/onnx_to_stablehlo/Tensor/Pad.mlir
index 7d7292c691..8b2e9702f5 100644
--- a/test/mlir/conversion/onnx_to_stablehlo/Tensor/Pad.mlir
+++ b/test/mlir/conversion/onnx_to_stablehlo/Tensor/Pad.mlir
@@ -8,6 +8,6 @@ func.func @test_pad_constant(%arg0: tensor<1x3x5x5xf32>) -> tensor<1x3x7x7xf32>
   return %3 : tensor<1x3x7x7xf32>
 // CHECK-LABEL:  func.func @test_pad_constant(%arg0: tensor<1x3x5x5xf32>) -> tensor<1x3x7x7xf32> {
 // CHECK-NEXT:  %0 = stablehlo.constant dense<2.000000e+00> : tensor<f32>
-// CHECK-NEXT:  %1 = "stablehlo.pad"(%arg0, %0) {edge_padding_high = dense<[0, 0, 1, 1]> : vector<4xi64>, edge_padding_low = dense<[0, 0, 1, 1]> : vector<4xi64>, interior_padding = dense<0> : vector<4xi64>} : (tensor<1x3x5x5xf32>, tensor<f32>) -> tensor<1x3x7x7xf32>
+// CHECK-NEXT:  %1 = stablehlo.pad %arg0, %0, low = [0, 0, 1, 1], high = [0, 0, 1, 1], interior = [0, 0, 0, 0] : (tensor<1x3x5x5xf32>, tensor<f32>) -> tensor<1x3x7x7xf32>
 // CHECK-NEXT:  return %1 : tensor<1x3x7x7xf32>
 }