diff --git a/midend/lib/Conversion/ConvVectorization/PoolingNhwcMaxVectorization.cpp b/midend/lib/Conversion/ConvVectorization/PoolingNhwcMaxVectorization.cpp
index 1f5d698ae7..280d11b226 100644
--- a/midend/lib/Conversion/ConvVectorization/PoolingNhwcMaxVectorization.cpp
+++ b/midend/lib/Conversion/ConvVectorization/PoolingNhwcMaxVectorization.cpp
@@ -84,13 +84,13 @@ class PoolingNhwcMaxVectorizationPattern : public ConversionPattern {
     // Get strides.
     SmallVector<int64_t, 2> strides = {1, 1};
     if (op->hasAttr("strides")) {
-        // 获取 "strides" 属性
-        if (auto attr = op->getAttrOfType<mlir::DenseIntElementsAttr>("strides")) {
-            strides.clear(); // 清空默认值
-            for (auto value : attr.getValues<int64_t>()) {
-                strides.push_back(value);
-            }
+      if (auto attr =
+              op->getAttrOfType<mlir::DenseIntElementsAttr>("strides")) {
+        strides.clear();
+        for (auto value : attr.getValues<int64_t>()) {
+          strides.push_back(value);
         }
+      }
     }
     bool stride1 = strides[0] != 1;
     bool stride2 = strides[1] != 1;
@@ -100,16 +100,18 @@ class PoolingNhwcMaxVectorizationPattern : public ConversionPattern {
     // // Get dilations.
     SmallVector<int64_t, 2> dilations = {1, 1};
     if (op->hasAttr("dilations")) {
-        if (auto attr = op->getAttrOfType<mlir::DenseIntElementsAttr>("dilations")) {
-            dilations.clear(); 
-            for (auto value : attr.getValues<int64_t>()) {
-                dilations.push_back(value);
-            }
+      if (auto attr =
+              op->getAttrOfType<mlir::DenseIntElementsAttr>("dilations")) {
+        dilations.clear();
+        for (auto value : attr.getValues<int64_t>()) {
+          dilations.push_back(value);
         }
+      }
     }
     bool dilated1 = dilations[0] != 1;
     bool dilated2 = dilations[1] != 1;
-    Value dilHeight = rewriter.create<arith::ConstantIndexOp>(loc, dilations[0]);
+    Value dilHeight =
+        rewriter.create<arith::ConstantIndexOp>(loc, dilations[0]);
     Value dilWidth = rewriter.create<arith::ConstantIndexOp>(loc, dilations[1]);
 
     // Get ElementType of input.
@@ -121,7 +123,7 @@ class PoolingNhwcMaxVectorizationPattern : public ConversionPattern {
     const Value c1 = rewriter.create<arith::ConstantIndexOp>(loc, 1);
     const Value c2 = rewriter.create<arith::ConstantIndexOp>(loc, 2);
     const Value c3 = rewriter.create<arith::ConstantIndexOp>(loc, 3);
-    const Value vl_step = rewriter.create<arith::ConstantIndexOp>(loc, strip);
+    const Value vlStep = rewriter.create<arith::ConstantIndexOp>(loc, strip);
     const Value zero =
         buddy::insertZeroConstantOp(ctx, rewriter, loc, elementTy);
 
@@ -139,12 +141,11 @@ class PoolingNhwcMaxVectorizationPattern : public ConversionPattern {
     Value channels = rewriter.create<memref::DimOp>(loc, output, c3);
 
     // Calculate the upper bound for vectorized processing
-    // - Subtract `vl_step` is to avoid overflow at the vectorization tail.
+    // - Subtract `vlStep` is to avoid overflow at the vectorization tail.
     // - Add 1 to ensure the final loop runs when the workload length
     //   is divisible by the vector size.
-    Value upperBound_tmp =
-        rewriter.create<arith::SubIOp>(loc, channels, vl_step);
-    Value upperBound = rewriter.create<arith::AddIOp>(loc, upperBound_tmp, c1);
+    Value upperBoundTmp = rewriter.create<arith::SubIOp>(loc, channels, vlStep);
+    Value upperBound = rewriter.create<arith::AddIOp>(loc, upperBoundTmp, c1);
 
     SmallVector<Value, 8> lowerBounds(3, c0);
     SmallVector<Value, 8> uperBounds{batch, height, width};
@@ -153,17 +154,17 @@ class PoolingNhwcMaxVectorizationPattern : public ConversionPattern {
         rewriter, loc, lowerBounds, uperBounds, steps,
         [&](OpBuilder &builder, Location loc, ValueRange ivs) {
           // Create strides variables.
-          Value tmp_ivs1 = ivs[1];
-          if(stride1){
-            tmp_ivs1 = builder.create<arith::MulIOp>(loc, ivs[1], strHeight);
+          Value tmpIvs1 = ivs[1];
+          if (stride1) {
+            tmpIvs1 = builder.create<arith::MulIOp>(loc, ivs[1], strHeight);
           }
-          Value tmp_ivs2 = ivs[2];
-          if(stride2){
-            tmp_ivs2 = builder.create<arith::MulIOp>(loc, ivs[2], strWidth);
+          Value tmpIvs2 = ivs[2];
+          if (stride2) {
+            tmpIvs2 = builder.create<arith::MulIOp>(loc, ivs[2], strWidth);
           }
           // Create strip mining loop.
-          auto iter_idx = builder.create<scf::ForOp>(
-              loc, c0, upperBound, /*Step=*/vl_step, ValueRange{c0},
+          auto iterIdx = builder.create<scf::ForOp>(
+              loc, c0, upperBound, /*Step=*/vlStep, ValueRange{c0},
               [&](OpBuilder &nestedBuilder, Location nestedLoc, Value iv,
                   ValueRange itrArgs) {
                 Value outputVector = nestedBuilder.create<vector::LoadOp>(
@@ -177,12 +178,13 @@ class PoolingNhwcMaxVectorizationPattern : public ConversionPattern {
                     [&](OpBuilder &builder, Location loc, Value iv0,
                         ValueRange itrArgs0) {
                       // Create dilated[0] variables.
-                      Value tmp_ivs3 = iv0;
-                      if(dilated1){
-                        tmp_ivs3 = builder.create<arith::MulIOp>(loc, iv0, dilHeight);
+                      Value tmpIvs3 = iv0;
+                      if (dilated1) {
+                        tmpIvs3 =
+                            builder.create<arith::MulIOp>(loc, iv0, dilHeight);
                       }
                       Value inputHeight =
-                          builder.create<arith::AddIOp>(loc, tmp_ivs1, tmp_ivs3);
+                          builder.create<arith::AddIOp>(loc, tmpIvs1, tmpIvs3);
                       auto tmp1 = builder.create<affine::AffineForOp>(
                           loc, ValueRange{c0}, builder.getDimIdentityMap(),
                           ValueRange{kernelWidth}, builder.getDimIdentityMap(),
@@ -190,12 +192,13 @@ class PoolingNhwcMaxVectorizationPattern : public ConversionPattern {
                           [&](OpBuilder &builder, Location loc, Value iv1,
                               ValueRange itrArgs1) {
                             // Create dilated[1] variables.
-                            Value tmp_ivs4 = iv1;
-                            if(dilated2){
-                              tmp_ivs4 = builder.create<arith::MulIOp>(loc, iv1, dilWidth);
+                            Value tmpIvs4 = iv1;
+                            if (dilated2) {
+                              tmpIvs4 = builder.create<arith::MulIOp>(loc, iv1,
+                                                                      dilWidth);
                             }
-                            Value inputWidth =
-                                builder.create<arith::AddIOp>(loc, tmp_ivs2,  tmp_ivs4);
+                            Value inputWidth = builder.create<arith::AddIOp>(
+                                loc, tmpIvs2, tmpIvs4);
                             Value inputVector = builder.create<vector::LoadOp>(
                                 loc, vectorTy, input,
                                 ValueRange{ivs[0], inputHeight, inputWidth,
@@ -219,12 +222,13 @@ class PoolingNhwcMaxVectorizationPattern : public ConversionPattern {
                 builder.create<vector::StoreOp>(
                     loc, tmp0.getResult(0), output,
                     ValueRange{ivs[0], ivs[1], ivs[2], iv});
-                Value idx = builder.create<arith::AddIOp>(loc, itrArgs[0], vl_step);
+                Value idx =
+                    builder.create<arith::AddIOp>(loc, itrArgs[0], vlStep);
                 builder.create<scf::YieldOp>(loc, idx);
               });
           // Compute the tail size and Process the remaining elements
           // using masked vector operations.
-          Value idx = iter_idx.getResult(0);
+          Value idx = iterIdx.getResult(0);
           Value tailSize = builder.create<arith::SubIOp>(loc, channels, idx);
           Value tailCond = rewriter.create<arith::CmpIOp>(
               loc, arith::CmpIPredicate::sgt, tailSize, c0);
@@ -245,12 +249,13 @@ class PoolingNhwcMaxVectorizationPattern : public ConversionPattern {
                 [&](OpBuilder &builder, Location loc, Value iv0,
                     ValueRange itrArgs0) {
                   // Create dilated[0] variables.
-                  Value tmp_ivs3 = iv0;
-                  if(dilated1){
-                    tmp_ivs3 = builder.create<arith::MulIOp>(loc, iv0, dilHeight);
+                  Value tmpIvs3 = iv0;
+                  if (dilated1) {
+                    tmpIvs3 =
+                        builder.create<arith::MulIOp>(loc, iv0, dilHeight);
                   }
                   Value inputHeight =
-                      builder.create<arith::AddIOp>(loc, tmp_ivs1, tmp_ivs3);
+                      builder.create<arith::AddIOp>(loc, tmpIvs1, tmpIvs3);
                   auto tmp1 = builder.create<affine::AffineForOp>(
                       loc, ValueRange{c0}, builder.getDimIdentityMap(),
                       ValueRange{kernelWidth}, builder.getDimIdentityMap(),
@@ -260,12 +265,13 @@ class PoolingNhwcMaxVectorizationPattern : public ConversionPattern {
                         // Calculate the index of the input and
                         // output.
                         // Create dilated[1] variables.
-                        Value tmp_ivs4 = iv1;
-                        if(dilated2){
-                          tmp_ivs4 = builder.create<arith::MulIOp>(loc, iv1, dilWidth);
+                        Value tmpIvs4 = iv1;
+                        if (dilated2) {
+                          tmpIvs4 =
+                              builder.create<arith::MulIOp>(loc, iv1, dilWidth);
                         }
                         Value inputWidth =
-                            builder.create<arith::AddIOp>(loc, iv1, tmp_ivs2);
+                            builder.create<arith::AddIOp>(loc, iv1, tmpIvs2);
                         // Masked load input and output.
                         Value maskedInputVec = builder.create<MaskedLoadOp>(
                             loc, vectorTy, input,