diff --git a/docs/BuildOnLinuxOSX.md b/docs/BuildOnLinuxOSX.md
index f2bf7858ae..21fbc37e0c 100644
--- a/docs/BuildOnLinuxOSX.md
+++ b/docs/BuildOnLinuxOSX.md
@@ -15,7 +15,7 @@ Firstly, install MLIR (as a part of LLVM-Project):
 ``` bash
 git clone -n https://github.com/llvm/llvm-project.git
 # Check out a specific branch that is known to work with ONNX-MLIR.
-cd llvm-project && git checkout e86910337f98e57f5b9253f7d80d5b916eb1d97e && cd ..
+cd llvm-project && git checkout b270525f730be6e7196667925f5a9bfa153262e9 && cd ..
 ```
 
 [same-as-file]: <> (utils/build-mlir.sh)
diff --git a/docs/BuildOnWindows.md b/docs/BuildOnWindows.md
index 2f10915561..13e2a002ec 100644
--- a/docs/BuildOnWindows.md
+++ b/docs/BuildOnWindows.md
@@ -52,7 +52,7 @@ Install MLIR (as a part of LLVM-Project):
 ```shell
 git clone -n https://github.com/llvm/llvm-project.git
 # Check out a specific branch that is known to work with ONNX-MLIR.
-cd llvm-project && git checkout e86910337f98e57f5b9253f7d80d5b916eb1d97e && cd ..
+cd llvm-project && git checkout b270525f730be6e7196667925f5a9bfa153262e9 && cd ..
 ```
 
 [same-as-file]: <> (utils/build-mlir.cmd)
diff --git a/src/Conversion/KrnlToLLVM/ConvertKrnlToLLVM.cpp b/src/Conversion/KrnlToLLVM/ConvertKrnlToLLVM.cpp
index d33abe5918..950eb65236 100644
--- a/src/Conversion/KrnlToLLVM/ConvertKrnlToLLVM.cpp
+++ b/src/Conversion/KrnlToLLVM/ConvertKrnlToLLVM.cpp
@@ -193,12 +193,23 @@ void populateAffineAndKrnlToLLVMConversion(RewritePatternSet &patterns,
   // They run it in two steps, and add additional lowerings.
 
   vector::populateVectorToVectorCanonicalizationPatterns(patterns);
+  vector::populateVectorBitCastLoweringPatterns(patterns);
   vector::populateVectorBroadcastLoweringPatterns(patterns);
   vector::populateVectorContractLoweringPatterns(
       patterns, vector::VectorTransformsOptions());
+  vector::populateVectorMaskOpLoweringPatterns(patterns);
+  vector::populateVectorShapeCastLoweringPatterns(patterns);
+  vector::populateVectorInterleaveLoweringPatterns(patterns);
   vector::populateVectorTransposeLoweringPatterns(
       patterns, vector::VectorTransformsOptions());
-  vector::populateVectorShapeCastLoweringPatterns(patterns);
+  // Vector transfer ops with rank > 1 should be lowered with VectorToSCF.
+  vector::populateVectorTransferLoweringPatterns(
+      patterns, /*maxTransferRank=*/1);
+  vector::populateVectorMaskMaterializationPatterns(
+      patterns, /*force32BitVectorIndices*/ false);
+  vector::populateVectorInsertExtractStridedSliceTransforms(patterns);
+  vector::populateVectorStepLoweringPatterns(patterns);
+  vector::populateVectorRankReducingFMAPattern(patterns);
 
   populateAffineToStdConversionPatterns(patterns);
   populateSCFToControlFlowConversionPatterns(patterns);
diff --git a/src/Conversion/ONNXToTOSA/Math/Conv2D.cpp b/src/Conversion/ONNXToTOSA/Math/Conv2D.cpp
index dc35d39099..3bede485b3 100644
--- a/src/Conversion/ONNXToTOSA/Math/Conv2D.cpp
+++ b/src/Conversion/ONNXToTOSA/Math/Conv2D.cpp
@@ -41,7 +41,7 @@ Value createConvInGroups(PatternRewriter &rewriter, Operation *op,
     const llvm::ArrayRef<int64_t> weightShape, Value &newInput,
     Value &newWeight, Value &bias, const int64_t groups,
     DenseI64ArrayAttr &pads, DenseI64ArrayAttr &strides,
-    DenseI64ArrayAttr &dilations) {
+    DenseI64ArrayAttr &dilations, TypeAttr &accType) {
   // Set up constants outside of loop
   const int64_t sizeOfSliceInput = weightShape[1];
   const int64_t sizeOfSliceKernel = weightShape[0] / groups;
@@ -72,7 +72,7 @@ Value createConvInGroups(PatternRewriter &rewriter, Operation *op,
         mlir::cast<ShapedType>(resultType).getElementType());
     Value tempConv2D = tosa::CreateOpAndInfer<mlir::tosa::Conv2DOp>(rewriter,
         op->getLoc(), newConvOutputType, newSliceInput, newSliceWeight,
-        newSliceBias, pads, strides, dilations);
+        newSliceBias, pads, strides, dilations, accType);
     // Add value to vector
     sliceValues.push_back(tempConv2D);
   }
@@ -147,6 +147,10 @@ class ONNXConvOpLoweringToTOSA : public ConversionPattern {
     DenseI64ArrayAttr newPads =
         rewriter.getDenseI64ArrayAttr({pads[0], pads[2], pads[1], pads[3]});
 
+    Type convType =
+        (resultType.isF16()) ? rewriter.getF16Type() : rewriter.getF32Type();
+    TypeAttr accType = mlir::TypeAttr::get(convType);
+
     // Handle group parameter by creating multiple convs
     const int64_t group = adaptor.getGroup();
     Value conv2D = NULL;
@@ -157,11 +161,11 @@ class ONNXConvOpLoweringToTOSA : public ConversionPattern {
 
       conv2D = tosa::CreateOpAndInfer<mlir::tosa::Conv2DOp>(rewriter,
           convOp->getLoc(), newConvOutputType, newInput, newWeight, bias,
-          newPads, strides, dilations);
+          newPads, strides, dilations, accType);
     } else {
       conv2D = createConvInGroups(rewriter, convOp, tosaBuilder, resultType,
           weightShape, newInput, newWeight, bias, group, newPads, strides,
-          dilations);
+          dilations, accType);
     }
 
     // Convert output [N,OH,OW,OC] -> [N,OC,OH,OW]
diff --git a/src/Conversion/ONNXToTOSA/ONNXToTOSALegalizeUtils.cpp b/src/Conversion/ONNXToTOSA/ONNXToTOSALegalizeUtils.cpp
index 984c60b5b2..321a2b35e2 100644
--- a/src/Conversion/ONNXToTOSA/ONNXToTOSALegalizeUtils.cpp
+++ b/src/Conversion/ONNXToTOSA/ONNXToTOSALegalizeUtils.cpp
@@ -48,7 +48,7 @@ Value buildOnnxToTosaPaddingConstOp(mlir::PatternRewriter &rewriter,
 
   // Create a new pad vec in the right format
   // ONNX : [b1, b2, b3, b4, e1, e2, e3, e4]
-  // TOSA :[[b1, e1], [b2, e2], [b3, e3], [b4, e4]]
+  // TOSA :[b1, e1, b2, e2, b3, e3, b4, e4]
 
   // Adds any initial or last vals, not included in onnxPads.
   llvm::SmallVector<int64_t, 8> tosaPads{initialVals};
@@ -59,11 +59,9 @@ Value buildOnnxToTosaPaddingConstOp(mlir::PatternRewriter &rewriter,
     tosaPads.push_back(onnxPads[i + dimSize]);
   }
   tosaPads.insert(tosaPads.end(), lastVals.begin(), lastVals.end());
-
-  // TOSA format groups dimensions by 2.
-  const unsigned int numberOfDims = tosaPads.size() / 2;
   TosaBuilder tosaBuilder(rewriter, loc);
-  return tosaBuilder.getConst(tosaPads, {numberOfDims, 2});
+  return tosaBuilder.getConst(
+      tosaPads, {static_cast<int64_t>(tosaPads.size())});
 }
 
 } // namespace tosa
diff --git a/test/mlir/conversion/krnl_to_llvm/krnl_math_function_lowering.mlir b/test/mlir/conversion/krnl_to_llvm/krnl_math_function_lowering.mlir
index d739869098..4293e3352d 100644
--- a/test/mlir/conversion/krnl_to_llvm/krnl_math_function_lowering.mlir
+++ b/test/mlir/conversion/krnl_to_llvm/krnl_math_function_lowering.mlir
@@ -16,9 +16,7 @@ func.func @test_krnl_erf_lowering(%arg0: memref<10x10xf32>) -> memref<10x10xf32>
 
 // CHECK-LABEL: test_krnl_erf_lowering
 // CHECK: [[MEMREF_IN:%.+]] = llvm.insertvalue %arg6, {{.*}}[4, 1] : !llvm.struct<(ptr, ptr, i64, array<2 x i64>, array<2 x i64>)>
-// CHECK: [[BUILTIN_CAST_0:%.+]] = builtin.unrealized_conversion_cast [[MEMREF_IN]] : !llvm.struct<(ptr, ptr, i64, array<2 x i64>, array<2 x i64>)> to memref<10x10xf32>
-// CHECK: [[BUILTIN_CAST_1:%.+]] = builtin.unrealized_conversion_cast [[BUILTIN_CAST_0]] : memref<10x10xf32> to !llvm.struct<(ptr, ptr, i64, array<2 x i64>, array<2 x i64>)>
-// CHECK: [[DATA:%.+]] = llvm.extractvalue [[BUILTIN_CAST_1]][1] : !llvm.struct<(ptr, ptr, i64, array<2 x i64>, array<2 x i64>)>
+// CHECK: [[DATA:%.+]] = llvm.extractvalue [[MEMREF_IN]][1] : !llvm.struct<(ptr, ptr, i64, array<2 x i64>, array<2 x i64>)>
 // CHECK: [[DATA_IN:%.+]] = llvm.getelementptr [[DATA]]{{.*}} : (!llvm.ptr, i64) -> !llvm.ptr, f32
 // CHECK: [[SCALAR_IN:%.+]] = llvm.load [[DATA_IN]] : !llvm.ptr
 // CHECK: [[ERF_RES:%.+]] = llvm.call @erff([[SCALAR_IN]]) : (f32) -> f32
@@ -41,9 +39,7 @@ func.func @test_krnl_acos_lowering(%arg0: memref<10x10xf32>) -> memref<10x10xf32
 
 // CHECK-LABEL: test_krnl_acos_lowering
 // CHECK: [[MEMREF_IN:%.+]] = llvm.insertvalue %arg6, {{.*}}[4, 1] : !llvm.struct<(ptr, ptr, i64, array<2 x i64>, array<2 x i64>)>
-// CHECK: [[BUILTIN_CAST_0:%.+]] = builtin.unrealized_conversion_cast [[MEMREF_IN]] : !llvm.struct<(ptr, ptr, i64, array<2 x i64>, array<2 x i64>)> to memref<10x10xf32>
-// CHECK: [[BUILTIN_CAST_1:%.+]] = builtin.unrealized_conversion_cast [[BUILTIN_CAST_0]] : memref<10x10xf32> to !llvm.struct<(ptr, ptr, i64, array<2 x i64>, array<2 x i64>)>
-// CHECK: [[DATA:%.+]] = llvm.extractvalue [[BUILTIN_CAST_1]][1] : !llvm.struct<(ptr, ptr, i64, array<2 x i64>, array<2 x i64>)>
+// CHECK: [[DATA:%.+]] = llvm.extractvalue [[MEMREF_IN]][1] : !llvm.struct<(ptr, ptr, i64, array<2 x i64>, array<2 x i64>)>
 // CHECK: [[DATA_IN:%.+]] = llvm.getelementptr [[DATA]]{{.*}} : (!llvm.ptr, i64) -> !llvm.ptr, f32
 // CHECK: [[SCALAR_IN:%.+]] = llvm.load [[DATA_IN]] : !llvm.ptr
 // CHECK: [[ACOS_RES:%.+]] = llvm.call @acosf([[SCALAR_IN]]) : (f32) -> f32
@@ -66,9 +62,7 @@ func.func @test_krnl_acosh_lowering(%arg0: memref<10x10xf32>) -> memref<10x10xf3
 
 // CHECK-LABEL: test_krnl_acosh_lowering
 // CHECK: [[MEMREF_IN:%.+]] = llvm.insertvalue %arg6, {{.*}}[4, 1] : !llvm.struct<(ptr, ptr, i64, array<2 x i64>, array<2 x i64>)>
-// CHECK: [[BUILTIN_CAST_0:%.+]] = builtin.unrealized_conversion_cast [[MEMREF_IN]] : !llvm.struct<(ptr, ptr, i64, array<2 x i64>, array<2 x i64>)> to memref<10x10xf32>
-// CHECK: [[BUILTIN_CAST_1:%.+]] = builtin.unrealized_conversion_cast [[BUILTIN_CAST_0]] : memref<10x10xf32> to !llvm.struct<(ptr, ptr, i64, array<2 x i64>, array<2 x i64>)>
-// CHECK: [[DATA:%.+]] = llvm.extractvalue [[BUILTIN_CAST_1]][1] : !llvm.struct<(ptr, ptr, i64, array<2 x i64>, array<2 x i64>)>
+// CHECK: [[DATA:%.+]] = llvm.extractvalue [[MEMREF_IN]][1] : !llvm.struct<(ptr, ptr, i64, array<2 x i64>, array<2 x i64>)>
 // CHECK: [[DATA_IN:%.+]] = llvm.getelementptr [[DATA]]{{.*}} : (!llvm.ptr, i64) -> !llvm.ptr, f32
 // CHECK: [[SCALAR_IN:%.+]] = llvm.load [[DATA_IN]] : !llvm.ptr
 // CHECK: [[ACOS_RES:%.+]] = llvm.call @acoshf([[SCALAR_IN]]) : (f32) -> f32
@@ -91,9 +85,7 @@ func.func @test_krnl_asin_lowering(%arg0: memref<10x10xf32>) -> memref<10x10xf32
 
 // CHECK-LABEL: test_krnl_asin_lowering
 // CHECK: [[MEMREF_IN:%.+]] = llvm.insertvalue %arg6, {{.*}}[4, 1] : !llvm.struct<(ptr, ptr, i64, array<2 x i64>, array<2 x i64>)>
-// CHECK: [[BUILTIN_CAST_0:%.+]] = builtin.unrealized_conversion_cast [[MEMREF_IN]] : !llvm.struct<(ptr, ptr, i64, array<2 x i64>, array<2 x i64>)> to memref<10x10xf32>
-// CHECK: [[BUILTIN_CAST_1:%.+]] = builtin.unrealized_conversion_cast [[BUILTIN_CAST_0]] : memref<10x10xf32> to !llvm.struct<(ptr, ptr, i64, array<2 x i64>, array<2 x i64>)>
-// CHECK: [[DATA:%.+]] = llvm.extractvalue [[BUILTIN_CAST_1]][1] : !llvm.struct<(ptr, ptr, i64, array<2 x i64>, array<2 x i64>)>
+// CHECK: [[DATA:%.+]] = llvm.extractvalue [[MEMREF_IN]][1] : !llvm.struct<(ptr, ptr, i64, array<2 x i64>, array<2 x i64>)>
 // CHECK: [[DATA_IN:%.+]] = llvm.getelementptr [[DATA]]{{.*}} : (!llvm.ptr, i64) -> !llvm.ptr, f32
 // CHECK: [[SCALAR_IN:%.+]] = llvm.load [[DATA_IN]] : !llvm.ptr
 // CHECK: [[ACOS_RES:%.+]] = llvm.call @asinf([[SCALAR_IN]]) : (f32) -> f32
@@ -116,9 +108,7 @@ func.func @test_krnl_asinh_lowering(%arg0: memref<10x10xf32>) -> memref<10x10xf3
 
 // CHECK-LABEL: test_krnl_asinh_lowering
 // CHECK: [[MEMREF_IN:%.+]] = llvm.insertvalue %arg6, {{.*}}[4, 1] : !llvm.struct<(ptr, ptr, i64, array<2 x i64>, array<2 x i64>)>
-// CHECK: [[BUILTIN_CAST_0:%.+]] = builtin.unrealized_conversion_cast [[MEMREF_IN]] : !llvm.struct<(ptr, ptr, i64, array<2 x i64>, array<2 x i64>)> to memref<10x10xf32>
-// CHECK: [[BUILTIN_CAST_1:%.+]] = builtin.unrealized_conversion_cast [[BUILTIN_CAST_0]] : memref<10x10xf32> to !llvm.struct<(ptr, ptr, i64, array<2 x i64>, array<2 x i64>)>
-// CHECK: [[DATA:%.+]] = llvm.extractvalue [[BUILTIN_CAST_1]][1] : !llvm.struct<(ptr, ptr, i64, array<2 x i64>, array<2 x i64>)>
+// CHECK: [[DATA:%.+]] = llvm.extractvalue [[MEMREF_IN]][1] : !llvm.struct<(ptr, ptr, i64, array<2 x i64>, array<2 x i64>)>
 // CHECK: [[DATA_IN:%.+]] = llvm.getelementptr [[DATA]]{{.*}} : (!llvm.ptr, i64) -> !llvm.ptr, f32
 // CHECK: [[SCALAR_IN:%.+]] = llvm.load [[DATA_IN]] : !llvm.ptr
 // CHECK: [[ACOS_RES:%.+]] = llvm.call @asinhf([[SCALAR_IN]]) : (f32) -> f32
@@ -141,9 +131,7 @@ func.func @test_krnl_atan_lowering(%arg0: memref<10x10xf32>) -> memref<10x10xf32
 
 // CHECK-LABEL: test_krnl_atan_lowering
 // CHECK: [[MEMREF_IN:%.+]] = llvm.insertvalue %arg6, {{.*}}[4, 1] : !llvm.struct<(ptr, ptr, i64, array<2 x i64>, array<2 x i64>)>
-// CHECK: [[BUILTIN_CAST_0:%.+]] = builtin.unrealized_conversion_cast [[MEMREF_IN]] : !llvm.struct<(ptr, ptr, i64, array<2 x i64>, array<2 x i64>)> to memref<10x10xf32>
-// CHECK: [[BUILTIN_CAST_1:%.+]] = builtin.unrealized_conversion_cast [[BUILTIN_CAST_0]] : memref<10x10xf32> to !llvm.struct<(ptr, ptr, i64, array<2 x i64>, array<2 x i64>)>
-// CHECK: [[DATA:%.+]] = llvm.extractvalue [[BUILTIN_CAST_1]][1] : !llvm.struct<(ptr, ptr, i64, array<2 x i64>, array<2 x i64>)>
+// CHECK: [[DATA:%.+]] = llvm.extractvalue [[MEMREF_IN]][1] : !llvm.struct<(ptr, ptr, i64, array<2 x i64>, array<2 x i64>)>
 // CHECK: [[DATA_IN:%.+]] = llvm.getelementptr [[DATA]]{{.*}} : (!llvm.ptr, i64) -> !llvm.ptr, f32
 // CHECK: [[SCALAR_IN:%.+]] = llvm.load [[DATA_IN]] : !llvm.ptr
 // CHECK: [[ACOS_RES:%.+]] = llvm.call @atanf([[SCALAR_IN]]) : (f32) -> f32
@@ -165,9 +153,7 @@ func.func @test_krnl_atanh_lowering(%arg0: memref<10x10xf32>) -> memref<10x10xf3
 
 // CHECK-LABEL: test_krnl_atanh_lowering
 // CHECK: [[MEMREF_IN:%.+]] = llvm.insertvalue %arg6, {{.*}}[4, 1] : !llvm.struct<(ptr, ptr, i64, array<2 x i64>, array<2 x i64>)>
-// CHECK: [[BUILTIN_CAST_0:%.+]] = builtin.unrealized_conversion_cast [[MEMREF_IN]] : !llvm.struct<(ptr, ptr, i64, array<2 x i64>, array<2 x i64>)> to memref<10x10xf32>
-// CHECK: [[BUILTIN_CAST_1:%.+]] = builtin.unrealized_conversion_cast [[BUILTIN_CAST_0]] : memref<10x10xf32> to !llvm.struct<(ptr, ptr, i64, array<2 x i64>, array<2 x i64>)>
-// CHECK: [[DATA:%.+]] = llvm.extractvalue [[BUILTIN_CAST_1]][1] : !llvm.struct<(ptr, ptr, i64, array<2 x i64>, array<2 x i64>)>
+// CHECK: [[DATA:%.+]] = llvm.extractvalue [[MEMREF_IN]][1] : !llvm.struct<(ptr, ptr, i64, array<2 x i64>, array<2 x i64>)>
 // CHECK: [[DATA_IN:%.+]] = llvm.getelementptr [[DATA]]{{.*}} : (!llvm.ptr, i64) -> !llvm.ptr, f32
 // CHECK: [[SCALAR_IN:%.+]] = llvm.load [[DATA_IN]] : !llvm.ptr
 // CHECK: [[ACOS_RES:%.+]] = llvm.call @atanhf([[SCALAR_IN]]) : (f32) -> f32
@@ -189,12 +175,9 @@ func.func @test_krnl_tan_lowering(%arg0: memref<10x10xf32>) -> memref<10x10xf32>
 
 // CHECK-LABEL: test_krnl_tan_lowering
 // CHECK: [[MEMREF_IN:%.+]] = llvm.insertvalue %arg6, {{.*}}[4, 1] : !llvm.struct<(ptr, ptr, i64, array<2 x i64>, array<2 x i64>)>
-// CHECK: [[BUILTIN_CAST_0:%.+]] = builtin.unrealized_conversion_cast [[MEMREF_IN]] : !llvm.struct<(ptr, ptr, i64, array<2 x i64>, array<2 x i64>)> to memref<10x10xf32>
-// CHECK: [[BUILTIN_CAST_1:%.+]] = builtin.unrealized_conversion_cast [[BUILTIN_CAST_0]] : memref<10x10xf32> to !llvm.struct<(ptr, ptr, i64, array<2 x i64>, array<2 x i64>)>
-// CHECK: [[DATA:%.+]] = llvm.extractvalue [[BUILTIN_CAST_1]][1] : !llvm.struct<(ptr, ptr, i64, array<2 x i64>, array<2 x i64>)>
+// CHECK: [[DATA:%.+]] = llvm.extractvalue [[MEMREF_IN]][1] : !llvm.struct<(ptr, ptr, i64, array<2 x i64>, array<2 x i64>)>
 // CHECK: [[DATA_IN:%.+]] = llvm.getelementptr [[DATA]]{{.*}} : (!llvm.ptr, i64) -> !llvm.ptr, f32
 // CHECK: [[SCALAR_IN:%.+]] = llvm.load [[DATA_IN]] : !llvm.ptr
 // CHECK: [[ACOS_RES:%.+]] = llvm.call @tanf([[SCALAR_IN]]) : (f32) -> f32
 // CHECK: [[DATA_OUT:%.+]] = llvm.getelementptr {{.*}} : (!llvm.ptr, i64) -> !llvm.ptr, f32
 // CHECK: llvm.store [[ACOS_RES]], [[DATA_OUT]] : f32, !llvm.ptr
-
diff --git a/test/mlir/conversion/onnx_to_tosa/Math/Conv.mlir b/test/mlir/conversion/onnx_to_tosa/Math/Conv.mlir
index a772d6b4a0..0437de4625 100644
--- a/test/mlir/conversion/onnx_to_tosa/Math/Conv.mlir
+++ b/test/mlir/conversion/onnx_to_tosa/Math/Conv.mlir
@@ -11,7 +11,7 @@ func.func @test_onnx_conv2d_stride_13(%arg0: tensor<5x3x256x256xf32>, %arg1 : te
 // CHECK:           %[[VAL_3:.*]] = "tosa.const"() <{value = dense<[0, 2, 3, 1]> : tensor<4xi32>}> : () -> tensor<4xi32>
 // CHECK:           %[[VAL_4:.*]] = tosa.transpose %[[VAL_0]], %[[VAL_3]] : (tensor<5x3x256x256xf32>, tensor<4xi32>) -> tensor<5x256x256x3xf32>
 // CHECK:           %[[VAL_5:.*]] = tosa.transpose %[[VAL_1]], %[[VAL_3]] : (tensor<2x3x64x64xf32>, tensor<4xi32>) -> tensor<2x64x64x3xf32>
-// CHECK:           %[[VAL_6:.*]] = tosa.conv2d %[[VAL_4]], %[[VAL_5]], %[[VAL_2]] {dilation = array<i64: 1, 1>, pad = array<i64: 1, 1, 1, 1>, stride = array<i64: 13, 13>} : (tensor<5x256x256x3xf32>, tensor<2x64x64x3xf32>, tensor<2xf32>) -> tensor<5x15x15x2xf32>
+// CHECK:           %[[VAL_6:.*]] = tosa.conv2d %[[VAL_4]], %[[VAL_5]], %[[VAL_2]] {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 1, 1, 1, 1>, stride = array<i64: 13, 13>} : (tensor<5x256x256x3xf32>, tensor<2x64x64x3xf32>, tensor<2xf32>) -> tensor<5x15x15x2xf32>
 // CHECK:           %[[VAL_7:.*]] = "tosa.const"() <{value = dense<[0, 3, 1, 2]> : tensor<4xi32>}> : () -> tensor<4xi32>
 // CHECK:           %[[VAL_8:.*]] = tosa.transpose %[[VAL_6]], %[[VAL_7]] : (tensor<5x15x15x2xf32>, tensor<4xi32>) -> tensor<5x2x15x15xf32>
 // CHECK:           return %[[VAL_8]] : tensor<5x2x15x15xf32>
@@ -29,7 +29,7 @@ func.func @test_onnx_conv2d_novalue(%arg0: tensor<5x3x256x256xf32>, %arg1 : tens
 // CHECK:           %[[VAL_3:.*]] = tosa.transpose %[[VAL_0]], %[[VAL_2]] : (tensor<5x3x256x256xf32>, tensor<4xi32>) -> tensor<5x256x256x3xf32>
 // CHECK:           %[[VAL_4:.*]] = tosa.transpose %[[VAL_1]], %[[VAL_2]] : (tensor<2x3x64x64xf32>, tensor<4xi32>) -> tensor<2x64x64x3xf32>
 // CHECK:           %[[VAL_5:.*]] = "tosa.const"() <{value = dense<0.000000e+00> : tensor<2xf32>}> : () -> tensor<2xf32>
-// CHECK:           %[[VAL_6:.*]] = tosa.conv2d %[[VAL_3]], %[[VAL_4]], %[[VAL_5]] {dilation = array<i64: 1, 1>, pad = array<i64: 1, 3, 2, 4>, stride = array<i64: 1, 1>} : (tensor<5x256x256x3xf32>, tensor<2x64x64x3xf32>, tensor<2xf32>) -> tensor<5x197x199x2xf32>
+// CHECK:           %[[VAL_6:.*]] = tosa.conv2d %[[VAL_3]], %[[VAL_4]], %[[VAL_5]] {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 1, 3, 2, 4>, stride = array<i64: 1, 1>} : (tensor<5x256x256x3xf32>, tensor<2x64x64x3xf32>, tensor<2xf32>) -> tensor<5x197x199x2xf32>
 // CHECK:           %[[VAL_7:.*]] = "tosa.const"() <{value = dense<[0, 3, 1, 2]> : tensor<4xi32>}> : () -> tensor<4xi32>
 // CHECK:           %[[VAL_8:.*]] = tosa.transpose %[[VAL_6]], %[[VAL_7]] : (tensor<5x197x199x2xf32>, tensor<4xi32>) -> tensor<5x2x197x199xf32>
 // CHECK:           return %[[VAL_8]] : tensor<5x2x197x199xf32>
@@ -47,7 +47,7 @@ func.func @test_onnx_conv2d_no_dilation_pad(%arg0: tensor<5x3x256x256xf32>, %arg
 // CHECK:           %[[VAL_3:.*]] = tosa.transpose %[[VAL_0]], %[[VAL_2]] : (tensor<5x3x256x256xf32>, tensor<4xi32>) -> tensor<5x256x256x3xf32>
 // CHECK:           %[[VAL_4:.*]] = tosa.transpose %[[VAL_1]], %[[VAL_2]] : (tensor<7x3x64x64xf32>, tensor<4xi32>) -> tensor<7x64x64x3xf32>
 // CHECK:           %[[VAL_5:.*]] = "tosa.const"() <{value = dense<0.000000e+00> : tensor<7xf32>}> : () -> tensor<7xf32>
-// CHECK:           %[[VAL_6:.*]] = tosa.conv2d %[[VAL_3]], %[[VAL_4]], %[[VAL_5]] {dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 13, 13>} : (tensor<5x256x256x3xf32>, tensor<7x64x64x3xf32>, tensor<7xf32>) -> tensor<5x15x15x7xf32>
+// CHECK:           %[[VAL_6:.*]] = tosa.conv2d %[[VAL_3]], %[[VAL_4]], %[[VAL_5]] {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 13, 13>} : (tensor<5x256x256x3xf32>, tensor<7x64x64x3xf32>, tensor<7xf32>) -> tensor<5x15x15x7xf32>
 // CHECK:           %[[VAL_7:.*]] = "tosa.const"() <{value = dense<[0, 3, 1, 2]> : tensor<4xi32>}> : () -> tensor<4xi32>
 // CHECK:           %[[VAL_8:.*]] = tosa.transpose %[[VAL_6]], %[[VAL_7]] : (tensor<5x15x15x7xf32>, tensor<4xi32>) -> tensor<5x7x15x15xf32>
 // CHECK:           return %[[VAL_8]] : tensor<5x7x15x15xf32>
@@ -65,7 +65,7 @@ func.func @test_onnx_conv2d_no_dilation_pad_stride(%arg0: tensor<5x3x256x260xf32
 // CHECK:           %[[VAL_3:.*]] = tosa.transpose %[[VAL_0]], %[[VAL_2]] : (tensor<5x3x256x260xf32>, tensor<4xi32>) -> tensor<5x256x260x3xf32>
 // CHECK:           %[[VAL_4:.*]] = tosa.transpose %[[VAL_1]], %[[VAL_2]] : (tensor<2x3x60x64xf32>, tensor<4xi32>) -> tensor<2x60x64x3xf32>
 // CHECK:           %[[VAL_5:.*]] = "tosa.const"() <{value = dense<0.000000e+00> : tensor<2xf32>}> : () -> tensor<2xf32>
-// CHECK:           %[[VAL_6:.*]] = tosa.conv2d %[[VAL_3]], %[[VAL_4]], %[[VAL_5]] {dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<5x256x260x3xf32>, tensor<2x60x64x3xf32>, tensor<2xf32>) -> tensor<5x197x197x2xf32>
+// CHECK:           %[[VAL_6:.*]] = tosa.conv2d %[[VAL_3]], %[[VAL_4]], %[[VAL_5]] {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<5x256x260x3xf32>, tensor<2x60x64x3xf32>, tensor<2xf32>) -> tensor<5x197x197x2xf32>
 // CHECK:           %[[VAL_7:.*]] = "tosa.const"() <{value = dense<[0, 3, 1, 2]> : tensor<4xi32>}> : () -> tensor<4xi32>
 // CHECK:           %[[VAL_8:.*]] = tosa.transpose %[[VAL_6]], %[[VAL_7]] : (tensor<5x197x197x2xf32>, tensor<4xi32>) -> tensor<5x2x197x197xf32>
 // CHECK:           return %[[VAL_8]] : tensor<5x2x197x197xf32>
@@ -85,19 +85,19 @@ func.func @test_onnx_conv2d_group(%arg0: tensor<5x64x256x256xf32>, %arg1 : tenso
 // CHECK:           %[[VAL_6:.*]] = tosa.slice %[[VAL_4]] {size = array<i64: 5, 256, 256, 16>, start = array<i64: 0, 0, 0, 0>} : (tensor<5x256x256x64xf32>) -> tensor<5x256x256x16xf32>
 // CHECK:           %[[VAL_7:.*]] = tosa.slice %[[VAL_5]] {size = array<i64: 3, 45, 45, 16>, start = array<i64: 0, 0, 0, 0>} : (tensor<12x45x45x16xf32>) -> tensor<3x45x45x16xf32>
 // CHECK:           %[[VAL_8:.*]] = tosa.slice %[[VAL_2]] {size = array<i64: 3>, start = array<i64: 0>} : (tensor<12xf32>) -> tensor<3xf32>
-// CHECK:           %[[VAL_9:.*]] = tosa.conv2d %[[VAL_6]], %[[VAL_7]], %[[VAL_8]] {dilation = array<i64: 1, 1>, pad = array<i64: 1, 1, 1, 1>, stride = array<i64: 13, 13>} : (tensor<5x256x256x16xf32>, tensor<3x45x45x16xf32>, tensor<3xf32>) -> tensor<5x17x17x3xf32>
+// CHECK:           %[[VAL_9:.*]] = tosa.conv2d %[[VAL_6]], %[[VAL_7]], %[[VAL_8]] {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 1, 1, 1, 1>, stride = array<i64: 13, 13>} : (tensor<5x256x256x16xf32>, tensor<3x45x45x16xf32>, tensor<3xf32>) -> tensor<5x17x17x3xf32>
 // CHECK:           %[[VAL_10:.*]] = tosa.slice %[[VAL_4]] {size = array<i64: 5, 256, 256, 16>, start = array<i64: 0, 0, 0, 16>} : (tensor<5x256x256x64xf32>) -> tensor<5x256x256x16xf32>
 // CHECK:           %[[VAL_11:.*]] = tosa.slice %[[VAL_5]] {size = array<i64: 3, 45, 45, 16>, start = array<i64: 3, 0, 0, 0>} : (tensor<12x45x45x16xf32>) -> tensor<3x45x45x16xf32>
 // CHECK:           %[[VAL_12:.*]] = tosa.slice %[[VAL_2]] {size = array<i64: 3>, start = array<i64: 3>} : (tensor<12xf32>) -> tensor<3xf32>
-// CHECK:           %[[VAL_13:.*]] = tosa.conv2d %[[VAL_10]], %[[VAL_11]], %[[VAL_12]] {dilation = array<i64: 1, 1>, pad = array<i64: 1, 1, 1, 1>, stride = array<i64: 13, 13>} : (tensor<5x256x256x16xf32>, tensor<3x45x45x16xf32>, tensor<3xf32>) -> tensor<5x17x17x3xf32>
+// CHECK:           %[[VAL_13:.*]] = tosa.conv2d %[[VAL_10]], %[[VAL_11]], %[[VAL_12]] {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 1, 1, 1, 1>, stride = array<i64: 13, 13>} : (tensor<5x256x256x16xf32>, tensor<3x45x45x16xf32>, tensor<3xf32>) -> tensor<5x17x17x3xf32>
 // CHECK:           %[[VAL_14:.*]] = tosa.slice %[[VAL_4]] {size = array<i64: 5, 256, 256, 16>, start = array<i64: 0, 0, 0, 32>} : (tensor<5x256x256x64xf32>) -> tensor<5x256x256x16xf32>
 // CHECK:           %[[VAL_15:.*]] = tosa.slice %[[VAL_5]] {size = array<i64: 3, 45, 45, 16>, start = array<i64: 6, 0, 0, 0>} : (tensor<12x45x45x16xf32>) -> tensor<3x45x45x16xf32>
 // CHECK:           %[[VAL_16:.*]] = tosa.slice %[[VAL_2]] {size = array<i64: 3>, start = array<i64: 6>} : (tensor<12xf32>) -> tensor<3xf32>
-// CHECK:           %[[VAL_17:.*]] = tosa.conv2d %[[VAL_14]], %[[VAL_15]], %[[VAL_16]] {dilation = array<i64: 1, 1>, pad = array<i64: 1, 1, 1, 1>, stride = array<i64: 13, 13>} : (tensor<5x256x256x16xf32>, tensor<3x45x45x16xf32>, tensor<3xf32>) -> tensor<5x17x17x3xf32>
+// CHECK:           %[[VAL_17:.*]] = tosa.conv2d %[[VAL_14]], %[[VAL_15]], %[[VAL_16]] {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 1, 1, 1, 1>, stride = array<i64: 13, 13>} : (tensor<5x256x256x16xf32>, tensor<3x45x45x16xf32>, tensor<3xf32>) -> tensor<5x17x17x3xf32>
 // CHECK:           %[[VAL_18:.*]] = tosa.slice %[[VAL_4]] {size = array<i64: 5, 256, 256, 16>, start = array<i64: 0, 0, 0, 48>} : (tensor<5x256x256x64xf32>) -> tensor<5x256x256x16xf32>
 // CHECK:           %[[VAL_19:.*]] = tosa.slice %[[VAL_5]] {size = array<i64: 3, 45, 45, 16>, start = array<i64: 9, 0, 0, 0>} : (tensor<12x45x45x16xf32>) -> tensor<3x45x45x16xf32>
 // CHECK:           %[[VAL_20:.*]] = tosa.slice %[[VAL_2]] {size = array<i64: 3>, start = array<i64: 9>} : (tensor<12xf32>) -> tensor<3xf32>
-// CHECK:           %[[VAL_21:.*]] = tosa.conv2d %[[VAL_18]], %[[VAL_19]], %[[VAL_20]] {dilation = array<i64: 1, 1>, pad = array<i64: 1, 1, 1, 1>, stride = array<i64: 13, 13>} : (tensor<5x256x256x16xf32>, tensor<3x45x45x16xf32>, tensor<3xf32>) -> tensor<5x17x17x3xf32>
+// CHECK:           %[[VAL_21:.*]] = tosa.conv2d %[[VAL_18]], %[[VAL_19]], %[[VAL_20]] {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 1, 1, 1, 1>, stride = array<i64: 13, 13>} : (tensor<5x256x256x16xf32>, tensor<3x45x45x16xf32>, tensor<3xf32>) -> tensor<5x17x17x3xf32>
 // CHECK:           %[[VAL_22:.*]] = tosa.concat %[[VAL_9]], %[[VAL_13]], %[[VAL_17]], %[[VAL_21]] {axis = 3 : i32} : (tensor<5x17x17x3xf32>, tensor<5x17x17x3xf32>, tensor<5x17x17x3xf32>, tensor<5x17x17x3xf32>) -> tensor<5x17x17x12xf32>
 // CHECK:           %[[VAL_23:.*]] = "tosa.const"() <{value = dense<[0, 3, 1, 2]> : tensor<4xi32>}> : () -> tensor<4xi32>
 // CHECK:           %[[VAL_24:.*]] = tosa.transpose %[[VAL_22]], %[[VAL_23]] : (tensor<5x17x17x12xf32>, tensor<4xi32>) -> tensor<5x12x17x17xf32>
@@ -115,7 +115,7 @@ func.func @test_onnx_conv2d_autopad(%arg0: tensor<5x3x125x256xf32>, %arg1 : tens
 // CHECK:           %[[VAL_3:.*]] = "tosa.const"() <{value = dense<[0, 2, 3, 1]> : tensor<4xi32>}> : () -> tensor<4xi32>
 // CHECK:           %[[VAL_4:.*]] = tosa.transpose %[[VAL_0]], %[[VAL_3]] : (tensor<5x3x125x256xf32>, tensor<4xi32>) -> tensor<5x125x256x3xf32>
 // CHECK:           %[[VAL_5:.*]] = tosa.transpose %[[VAL_1]], %[[VAL_3]] : (tensor<2x3x64x64xf32>, tensor<4xi32>) -> tensor<2x64x64x3xf32>
-// CHECK:           %[[VAL_6:.*]] = tosa.conv2d %[[VAL_4]], %[[VAL_5]], %[[VAL_2]] {dilation = array<i64: 1, 1>, pad = array<i64: 32, 31, 32, 31>, stride = array<i64: 1, 1>} : (tensor<5x125x256x3xf32>, tensor<2x64x64x3xf32>, tensor<2xf32>) -> tensor<5x125x256x2xf32>
+// CHECK:           %[[VAL_6:.*]] = tosa.conv2d %[[VAL_4]], %[[VAL_5]], %[[VAL_2]] {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 32, 31, 32, 31>, stride = array<i64: 1, 1>} : (tensor<5x125x256x3xf32>, tensor<2x64x64x3xf32>, tensor<2xf32>) -> tensor<5x125x256x2xf32>
 // CHECK:           %[[VAL_7:.*]] = "tosa.const"() <{value = dense<[0, 3, 1, 2]> : tensor<4xi32>}> : () -> tensor<4xi32>
 // CHECK:           %[[VAL_8:.*]] = tosa.transpose %[[VAL_6]], %[[VAL_7]] : (tensor<5x125x256x2xf32>, tensor<4xi32>) -> tensor<5x2x125x256xf32>
 // CHECK:           return %[[VAL_8]] : tensor<5x2x125x256xf32>
diff --git a/test/mlir/conversion/onnx_to_tosa/NN/AveragePool.mlir b/test/mlir/conversion/onnx_to_tosa/NN/AveragePool.mlir
index 8ebeaa30c6..8d9d426494 100644
--- a/test/mlir/conversion/onnx_to_tosa/NN/AveragePool.mlir
+++ b/test/mlir/conversion/onnx_to_tosa/NN/AveragePool.mlir
@@ -141,10 +141,10 @@ func.func @test_averagepool_pad_with_count_include_pad(%arg0 : tensor<5x5x32x32x
 }
 // CHECK-LABEL:  func.func @test_averagepool_pad_with_count_include_pad
 // CHECK-SAME:   ([[PARAM_0_:%.+]]: tensor<5x5x32x32xf32>) -> tensor<5x5x32x32xf32> {
-// CHECK-DAG:       [[VAR_0_:%.+]] = "tosa.const"() <{value = dense<{{.}}[0, 0], [0, 0], [1, 1], [1, 1]{{.}}> : tensor<4x2xi64>}> : () -> tensor<4x2xi64>
+// CHECK-DAG:       [[VAR_0_:%.+]] = "tosa.const"() <{value = dense<[0, 0, 0, 0, 1, 1, 1, 1]> : tensor<8xi64>}> : () -> tensor<8xi64>
 // CHECK-DAG:       [[VAR_1_:%.+]] = "tosa.const"() <{value = dense<0.000000e+00> : tensor<f32>}> : () -> tensor<f32>
 // CHECK-NOT: separator of consecutive DAGs
-// CHECK-DAG:       [[VAR_2_:%.+]] = tosa.pad [[PARAM_0_]], [[VAR_0_]], [[VAR_1_]] : (tensor<5x5x32x32xf32>, tensor<4x2xi64>, tensor<f32>) -> tensor<5x5x34x34xf32>
+// CHECK-DAG:       [[VAR_2_:%.+]] = tosa.pad [[PARAM_0_]], [[VAR_0_]], [[VAR_1_]] : (tensor<5x5x32x32xf32>, tensor<8xi64>, tensor<f32>) -> tensor<5x5x34x34xf32>
 // CHECK-DAG:       [[VAR_3_:%.+]] = "tosa.const"() <{value = dense<[0, 2, 3, 1]> : tensor<4xi32>}> : () -> tensor<4xi32>
 // CHECK:           [[VAR_4_:%.+]] = tosa.transpose [[VAR_2_]], [[VAR_3_]] : (tensor<5x5x34x34xf32>, tensor<4xi32>) -> tensor<5x34x34x5xf32>
 // CHECK-DAG:       [[VAR_5_:%.+]] = tosa.avg_pool2d [[VAR_4_]] {acc_type = f32, kernel = array<i64: 3, 3>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<5x34x34x5xf32>) -> tensor<5x32x32x5xf32>
@@ -162,10 +162,10 @@ func.func @test_averagepool_pad_nonunif_with_count_include_pad(%arg0 : tensor<5x
 }
 // CHECK-LABEL:  func.func @test_averagepool_pad_nonunif_with_count_include_pad
 // CHECK-SAME:   ([[PARAM_0_:%.+]]: tensor<5x5x32x32xf32>) -> tensor<5x5x30x34xf32> {
-// CHECK-DAG:       [[VAR_0_:%.+]] = "tosa.const"() <{value = dense<{{.}}[0, 0], [0, 0], [0, 2], [1, 3]{{.}}> : tensor<4x2xi64>}> : () -> tensor<4x2xi64>
+// CHECK-DAG:       [[VAR_0_:%.+]] = "tosa.const"() <{value = dense<[0, 0, 0, 0, 0, 2, 1, 3]> : tensor<8xi64>}> : () -> tensor<8xi64>
 // CHECK-DAG:       [[VAR_1_:%.+]] = "tosa.const"() <{value = dense<0.000000e+00> : tensor<f32>}> : () -> tensor<f32>
 // CHECK-NOT: separator of consecutive DAGs
-// CHECK-DAG:       [[VAR_2_:%.+]] = tosa.pad [[PARAM_0_]], [[VAR_0_]], [[VAR_1_]] : (tensor<5x5x32x32xf32>, tensor<4x2xi64>, tensor<f32>) -> tensor<5x5x34x36xf32>
+// CHECK-DAG:       [[VAR_2_:%.+]] = tosa.pad [[PARAM_0_]], [[VAR_0_]], [[VAR_1_]] : (tensor<5x5x32x32xf32>, tensor<8xi64>, tensor<f32>) -> tensor<5x5x34x36xf32>
 // CHECK-DAG:       [[VAR_3_:%.+]] = "tosa.const"() <{value = dense<[0, 2, 3, 1]> : tensor<4xi32>}> : () -> tensor<4xi32>
 // CHECK:           [[VAR_4_:%.+]] = tosa.transpose [[VAR_2_]], [[VAR_3_]] : (tensor<5x5x34x36xf32>, tensor<4xi32>) -> tensor<5x34x36x5xf32>
 // CHECK-DAG:       [[VAR_5_:%.+]] = tosa.avg_pool2d [[VAR_4_]] {acc_type = f32, kernel = array<i64: 5, 3>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<5x34x36x5xf32>) -> tensor<5x30x34x5xf32>
@@ -183,10 +183,10 @@ func.func @test_averagepool_strides_nonunifpad_ceil_with_count_include_pad(%arg0
 }
 // CHECK-LABEL:  func.func @test_averagepool_strides_nonunifpad_ceil_with_count_include_pad
 // CHECK-SAME:   ([[PARAM_0_:%.+]]: tensor<5x5x30x32xf32>) -> tensor<5x5x16x17xf32> {
-// CHECK-DAG:       [[VAR_0_:%.+]] = "tosa.const"() <{value = dense<{{.}}[0, 0], [0, 0], [1, 0], [2, 0]{{.}}> : tensor<4x2xi64>}> : () -> tensor<4x2xi64>
+// CHECK-DAG:       [[VAR_0_:%.+]] = "tosa.const"() <{value = dense<[0, 0, 0, 0, 1, 0, 2, 0]> : tensor<8xi64>}> : () -> tensor<8xi64>
 // CHECK-DAG:       [[VAR_1_:%.+]] = "tosa.const"() <{value = dense<0.000000e+00> : tensor<f32>}> : () -> tensor<f32>
 // CHECK-NOT: separator of consecutive DAGs
-// CHECK-DAG:       [[VAR_2_:%.+]] = tosa.pad [[PARAM_0_]], [[VAR_0_]], [[VAR_1_]] : (tensor<5x5x30x32xf32>, tensor<4x2xi64>, tensor<f32>) -> tensor<5x5x31x34xf32>
+// CHECK-DAG:       [[VAR_2_:%.+]] = tosa.pad [[PARAM_0_]], [[VAR_0_]], [[VAR_1_]] : (tensor<5x5x30x32xf32>, tensor<8xi64>, tensor<f32>) -> tensor<5x5x31x34xf32>
 // CHECK-DAG:       [[VAR_3_:%.+]] = "tosa.const"() <{value = dense<[0, 2, 3, 1]> : tensor<4xi32>}> : () -> tensor<4xi32>
 // CHECK:           [[VAR_4_:%.+]] = tosa.transpose [[VAR_2_]], [[VAR_3_]] : (tensor<5x5x31x34xf32>, tensor<4xi32>) -> tensor<5x31x34x5xf32>
 // CHECK-DAG:       [[VAR_5_:%.+]] = tosa.avg_pool2d [[VAR_4_]] {acc_type = f32, kernel = array<i64: 2, 2>, pad = array<i64: 0, 2, 0, 0>, stride = array<i64: 2, 2>} : (tensor<5x31x34x5xf32>) -> tensor<5x16x17x5xf32>
diff --git a/third_party/stablehlo b/third_party/stablehlo
index 8c7946c8c9..459e481b77 160000
--- a/third_party/stablehlo
+++ b/third_party/stablehlo
@@ -1 +1 @@
-Subproject commit 8c7946c8c94182d238600fb0775a5ba594a43bd7
+Subproject commit 459e481b77f8537aae3f8e8e8ad9550721afe202
diff --git a/utils/clone-mlir.sh b/utils/clone-mlir.sh
index 303761b48f..804dff5fda 100644
--- a/utils/clone-mlir.sh
+++ b/utils/clone-mlir.sh
@@ -1,3 +1,3 @@
 git clone -n https://github.com/llvm/llvm-project.git
 # Check out a specific branch that is known to work with ONNX-MLIR.
-cd llvm-project && git checkout e86910337f98e57f5b9253f7d80d5b916eb1d97e && cd ..
+cd llvm-project && git checkout b270525f730be6e7196667925f5a9bfa153262e9 && cd ..