diff --git a/lib/gc/Transforms/LowerToTileVector.cpp b/lib/gc/Transforms/LowerToTileVector.cpp
index d105eaeb8..b5f92dca9 100644
--- a/lib/gc/Transforms/LowerToTileVector.cpp
+++ b/lib/gc/Transforms/LowerToTileVector.cpp
@@ -34,8 +34,7 @@ namespace {
 #define SAFE_EXPAND(X) X
 #define LDBG(X) LLVM_DEBUG(DBGS() << SAFE_EXPAND(X) << "\n")
 
-#define SUPPORT_TENSOR_OP                                                      \
-  tensor::ExpandShapeOp, tensor::CollapseShapeOp, tensor::ConcatOp
+#define SUPPORT_TENSOR_OP tensor::ConcatOp
 
 template <typename T, typename U>
 struct decay_equiv : std::is_same<typename std::decay<T>::type, U>::type {};
@@ -559,6 +558,38 @@ struct TensorUnpackConvertVectorPass : public RewritePattern {
   }
 };
 
+struct TensorPackConvertVectorPass : public RewritePattern {
+
+  explicit TensorPackConvertVectorPass(MLIRContext *context)
+      : RewritePattern(MatchAnyOpTypeTag(), /*benefit=*/1, context) {}
+  LogicalResult matchAndRewrite(Operation *op,
+                                PatternRewriter &rewriter) const override {
+
+    auto tensorPackOp = dyn_cast<tensor::PackOp>(op);
+    if (!tensorPackOp)
+      return rewriter.notifyMatchFailure(op, "Not expected operations.");
+
+    auto resultTy = cast<ShapedType>(op->getResultTypes()[0]);
+    ArrayRef<int64_t> retShape = resultTy.getShape();
+
+    if (ShapedType::isDynamicShape(retShape))
+      return rewriter.notifyMatchFailure(
+          op, "Pack operation result is dynamic shape.");
+
+    SmallVector<int64_t> inputVectorSize(
+        retShape.take_front(tensorPackOp.getSourceRank()));
+    SmallVector<bool, 5> targetVecDims(inputVectorSize.size(), false);
+
+    if (failed(linalg::vectorize(rewriter, op,
+                                 /*inputVectorSizes=*/inputVectorSize,
+                                 /*inputScalableVecDims=*/targetVecDims, false,
+                                 false)))
+      return rewriter.notifyMatchFailure(op, "Fail to vectorize.");
+
+    return success();
+  }
+};
+
 /// Some tensor operation lowering to vector.
 ///
 /// Currently support expand_shape, collapse_shape and concat_shape.
@@ -590,10 +621,8 @@ struct TensorOpConvertVectorPass : public RewritePattern {
 /// Patterns that lower to tile (virtual) vector.
 void populateLowerToTileVectorPatterns(RewritePatternSet &patterns) {
   patterns.add<OperationConvertTileVectorPass<linalg::LinalgOp>,
-               OperationConvertTileVectorPass<tensor::PackOp>>(
-      patterns.getContext());
-  patterns.add<TensorUnpackConvertVectorPass>(patterns.getContext());
-  patterns.add<TensorOpConvertVectorPass>(patterns.getContext());
+               TensorPackConvertVectorPass, TensorUnpackConvertVectorPass,
+               TensorOpConvertVectorPass>(patterns.getContext());
 }
 
 /// LowerToTileVectorPass is a pass that lowers operations to tile (virtual)
diff --git a/test/mlir/test/gc/Transforms/vectorization.mlir b/test/mlir/test/gc/Transforms/vectorization.mlir
index 2f87dffc3..acb8fec09 100644
--- a/test/mlir/test/gc/Transforms/vectorization.mlir
+++ b/test/mlir/test/gc/Transforms/vectorization.mlir
@@ -108,16 +108,12 @@ func.func @add_tensor_test4(%arg0: tensor<12x2x56x56x32xf32>, %arg1: tensor<12x2
 }
 
 // CHECK-LABEL:   @add_tensor_test5
-// CHECK:           %[[CST:.*]] = arith.constant 0.000000e+00 : f32
-// CHECK:           %[[CST_0:.*]] = arith.constant dense<1.000000e+00> : vector<1x8xf32>
+// CHECK:           %[[CST:.*]] = arith.constant dense<1.000000e+00> : vector<1x8xf32>
 // CHECK:           %[[C0:.*]] = arith.constant 0 : index
 // CHECK:           %[[EMPTY0:.*]] tensor.empty() : tensor<1x8xf32>
 // CHECK:           %[[WRITE0:.*]] = vector.transfer_write %{{.*}} {in_bounds = [true, true]} : vector<1x8xf32>, tensor<1x8xf32>
-// CHECK:           %[[extracted_slice:.*]] = tensor.extract_slice %1[0, 0] [1, 8] [1, 1] : tensor<1x8xf32> to tensor<8xf32>
-// CHECK:           %[[READ1:.*]] = vector.transfer_read %{{.*}}  {in_bounds = [true]} : tensor<8xf32>, vector<8xf32>
-// CHECK:           %[[SHAPECAST1:.*]] = vector.shape_cast %[[READ1]] : vector<8xf32> to vector<1x1x1x8xf32>
-// CHECK:           %[[EMPTY1:.*]] = tensor.empty() : tensor<1x1x1x8xf32>
-// CHECK:           %[[WRITE0:.*]] = vector.transfer_write %{{.*}} {in_bounds = [true, true, true, true]} : vector<1x1x1x8xf32>, tensor<1x1x1x8xf32>
+// CHECK:           %[[EXTRACTSLICE:.*]] = tensor.extract_slice %1[0, 0] [1, 8] [1, 1] : tensor<1x8xf32> to tensor<8xf32>
+// CHECK:           %[[expand:.*]] = tensor.expand_shape
 func.func @add_tensor_test5() -> tensor<1x1x1x8xf32> {
   %cst = arith.constant 1.000000e+00 : f32
   %init = tensor.empty() : tensor<1x8xf32>
@@ -128,12 +124,7 @@ func.func @add_tensor_test5() -> tensor<1x1x1x8xf32> {
 }
 
 // CHECK-LABEL:   @tensor_collapse_shape_test6
-// CHECK:           %[[CST:.*]] = arith.constant 0.000000e+00 : f32
-// CHECK:           %[[C0:.*]] = arith.constant 0 : index
-// CHECK:           %[[READ1:.*]] = vector.transfer_read %{{.*}}  {in_bounds = [true, true]} : tensor<2x3xf32>, vector<2x3xf32>
-// CHECK:           %[[SHAPECAST1:.*]] = vector.shape_cast %[[READ1]] :  vector<2x3xf32> to vector<6xf32>
-// CHECK:           %[[EMPTY1:.*]] = tensor.empty() : tensor<6xf32>
-// CHECK:           %[[WRITE0:.*]] = vector.transfer_write %{{.*}} {in_bounds = [true]} : vector<6xf32>, tensor<6xf32>
+// CHECK:           tensor.collapse_shape
 func.func @tensor_collapse_shape_test6(%arg0: tensor<2x3xf32>) -> tensor<6xf32> {
   %0 = tensor.collapse_shape %arg0 [[0, 1]] : tensor<2x3xf32> into tensor<6xf32>
   return %0 : tensor<6xf32>
@@ -183,13 +174,32 @@ func.func @fc_relu_test8(%lhs: tensor<512x512xf32>, %rhs: tensor<512x512xf32>,
 }
 
 
-// CHECK-LABEL:   @test_pad_dynamic_shape_test9
+// CHECK-LABEL:   @test_pack_dynamic_shape_test9
+// CHECK:           %[[C1:.*]] = arith.constant 1 : index
+// CHECK:           %[[CST:.*]] = arith.constant 0.000000e+00 : f32
+// CHECK:           %[[C0:.*]] = arith.constant 0 : index
+// CHECK:           %[[DIM0:.*]] = tensor.dim {{.*}}, %[[C0]] : tensor<?x?xf32>
+// CHECK:           %[[DIM1:.*]] = tensor.dim {{.*}}, %[[C1]] : tensor<?x?xf32>
+// CHECK:           %[[CREATEMASK:.*]] = vector.create_mask %[[DIM0]], %[[DIM1]] : vector<4x16xi1>
+// CHECK:           %[[MASKREAD:.*]] = vector.mask %[[CREATEMASK]] { vector.transfer_read {{.*}}, %[[CST]] {in_bounds = [true, true]} : tensor<?x?xf32>, vector<4x16xf32> } : vector<4x16xi1> -> vector<4x16xf32>
+// CHECK:           %[[SHAPECAST:.*]] = vector.shape_cast %[[MASKREAD]] : vector<4x16xf32> to vector<1x4x1x16xf32>
+// CHECK:           %[[TRANSPOSE:.*]] = vector.transpose %[[SHAPECAST]], [0, 2, 1, 3] : vector<1x4x1x16xf32> to vector<1x1x4x16xf32>
+// CHECK:           %[[EMPTY:.*]] = tensor.empty() : tensor<1x1x4x16xf32>
+// CHECK:           %[[WRITE0:.*]] = vector.transfer_write %[[TRANSPOSE]], %[[EMPTY]][%[[C0]], %[[C0]], %[[C0]], %[[C0]]] {in_bounds = [true, true, true, true]} : vector<1x1x4x16xf32>, tensor<1x1x4x16xf32>
+func.func @test_pack_dynamic_shape_test9(%arg0: tensor<?x?xf32>, %arg1: tensor<1x1x4x16xf32>) -> tensor<1x1x4x16xf32> {
+  %cst = arith.constant 0.0 : f32
+  %pack = tensor.pack %arg0 padding_value(%cst : f32) outer_dims_perm = [0, 1] 
+  inner_dims_pos = [0, 1] inner_tiles = [4, 16] 
+  into %arg1 : tensor<?x?xf32> -> tensor<1x1x4x16xf32>
+  return %pack : tensor<1x1x4x16xf32>
+}
+// CHECK-LABEL:   @test_pad_dynamic_shape_test10
 // CHECK:           %[[C0:.*]] = arith.constant 0 : index
 // CHECK:           %[[CST_0:.*]] = arith.constant 0.000000e+00 : f32
 // CHECK:           %[[EMPTY0:.*]] = tensor.empty() : tensor<4x16xf32>
 // CHECK:           %[[READ0:.*]] = vector.transfer_read %{{.*}} : tensor<?x?xf32>, vector<4x16xf32>
 // CHECK:           %[[WRITE0:.*]] = vector.transfer_write %{{.*}} {in_bounds = [true, true]} : vector<4x16xf32>, tensor<4x16xf32>
-func.func @test_pad_dynamic_shape_test9(%arg0: tensor<?x?xf32>) -> tensor<4x16xf32> {
+func.func @test_pad_dynamic_shape_test10(%arg0: tensor<?x?xf32>) -> tensor<4x16xf32> {
   %f0 = arith.constant 0.0 : f32
  %pad = tensor.pad %arg0 low[0, 0] high[1,1] {
   ^bb0(%arg3: index, %arg4: index):
@@ -205,13 +215,7 @@ func.func @test_add_dynamic_shape_test11(%arg0: tensor<?x?xf32>, %arg1: tensor<4
   return %1 : tensor<4x16xf32>
 }
 
-func.func @test_collapse_dynamic_shape_test12(%arg0: tensor<?x?x?xf32>) -> tensor<?x?xf32> {
-  // expected-error @+1 {{Fail to vectorize.}}
-  %0 = tensor.collapse_shape %arg0 [[0], [1, 2]]: tensor<?x?x?xf32> into tensor<?x?xf32>
-  return %0 : tensor<?x?xf32>
-}
-
-func.func @test_expand_dynamic_shape_test13(%arg0 : tensor<2x?x5xf32>, %sz0: index) ->tensor<2x6x?x5xf32> {
+func.func @test_expand_dynamic_shape_test12(%arg0 : tensor<2x?x5xf32>, %sz0: index) ->tensor<2x6x?x5xf32> {
   // expected-error @+1 {{Fail to vectorize.}}
   %0 = tensor.expand_shape %arg0 [[0], [1, 2], [3]] output_shape [2, 6, %sz0, 5] : tensor<2x?x5xf32> into tensor<2x6x?x5xf32>
   return %0 : tensor<2x6x?x5xf32>