diff --git a/lite/kernels/mlu/bridges/concat_op.cc b/lite/kernels/mlu/bridges/concat_op.cc
index 1c3c0b1e35b26950ef07f7a4d63d84e0df06c4c5..76037c3358201fb99c2a1415c1f7bfd91ef06103 100644
--- a/lite/kernels/mlu/bridges/concat_op.cc
+++ b/lite/kernels/mlu/bridges/concat_op.cc
@@ -44,9 +44,14 @@ int ConcatConverter(void* ctx, OpLite* op, KernelBase* kernel) {
 
   auto dims = output_dims.size();
   int axis = (param_axis < 0) ? (param_axis + dims) : param_axis;
-  CHECK_LE(axis, 4) << "Unsupport dims in mlu concat";
-  int nchw_to_nhwc_axis_map[4] = {0, 3, 1, 2};
-  int nhwc_axis = nchw_to_nhwc_axis_map[axis];
+  CHECK_LT(axis, dims) << "Unsupport dims in mlu concat";
+  std::vector<int> nchw2nhwc_axis(dims);
+  nchw2nhwc_axis[0] = 0;
+  if (dims > 1) nchw2nhwc_axis[1] = dims - 1;
+  for (size_t i = 2; i < dims; ++i) {
+    nchw2nhwc_axis[i] = i - 1;
+  }
+  int nhwc_axis = nchw2nhwc_axis[axis];
 
   auto output_tensor = graph->AddNode(
       out_var_name, output_dims, CNML_TENSOR, CNML_NCHW, graph->FPType());
diff --git a/lite/kernels/mlu/bridges/transpose_op.cc b/lite/kernels/mlu/bridges/transpose_op.cc
index 130e8417f1f9ad5ea4bc8c0b4ffaacf20124fae7..43f25550c26be484a452b0fc6e9a5e64226cf38c 100644
--- a/lite/kernels/mlu/bridges/transpose_op.cc
+++ b/lite/kernels/mlu/bridges/transpose_op.cc
@@ -22,12 +22,24 @@ namespace subgraph {
 namespace mlu {
 
 std::vector<int> axis_to_nhwc(const std::vector<int>& axis) {
-  CHECK_EQ(axis.size(), 4) << "Unsupport dim in mlu transpose";
-  std::vector<int> new_axis(4, 0);
-  const std::vector<int> axis_map1 = {0, 2, 3, 1};
-  const std::vector<int> axis_map2 = {0, 3, 1, 2};
+  std::vector<int> new_axis(axis.size());
+
+  std::vector<int> nhwc2nchw_axis(axis.size());
+  nhwc2nchw_axis[0] = 0;
+  if (axis.size() > 1) nhwc2nchw_axis[1] = axis.size() - 1;
+  for (size_t i = 2; i < axis.size(); ++i) {
+    nhwc2nchw_axis[i] = i - 1;
+  }
+
+  std::vector<int> nchw2nhwc_axis(axis.size());
+  nchw2nhwc_axis[0] = 0;
+  for (size_t i = 1; i < axis.size() - 1; ++i) {
+    nchw2nhwc_axis[i] = i + 1;
+  }
+  if (axis.size() > 1) nchw2nhwc_axis[axis.size() - 1] = 1;
+
   for (size_t i = 0; i < new_axis.size(); ++i) {
-    new_axis[i] = axis_map2[axis[axis_map1[i]]];
+    new_axis[i] = nhwc2nchw_axis[axis[nchw2nhwc_axis[i]]];
   }
   return new_axis;
 }
@@ -51,9 +63,6 @@ int TransposeConverter(void* ctx, OpLite* op, KernelBase* kernel) {
   auto output_dims = output->dims().Vectorize();
 
   auto axis = op_info->GetAttr<std::vector<int>>("axis");
-  while (axis.size() < 4) {
-    axis.push_back(axis.size());
-  }
   std::vector<int> axis_nhwc = axis_to_nhwc(axis);
 
   auto output_tensor = graph->AddNode(
diff --git a/lite/kernels/mlu/io_copy_compute.cc b/lite/kernels/mlu/io_copy_compute.cc
index 7178cdb109fab8cfeda797a7e6ef53af34ec834d..d11279f767d77b04a78d51c3b283ed638070b64d 100644
--- a/lite/kernels/mlu/io_copy_compute.cc
+++ b/lite/kernels/mlu/io_copy_compute.cc
@@ -173,14 +173,14 @@ REGISTER_LITE_KERNEL(
     kMLU,
     kInt8,
     kNHWC,
-    paddle::lite::kernels::mlu::IoCopyMluToHostCompute<PRECISION(kInt8)>,
-    device_to_host_kInt8)
+    paddle::lite::kernels::mlu::IoCopyHostToMluCompute<PRECISION(kInt8)>,
+    host_to_device_to_kInt8)
     .BindInput("Input",
-               {LiteType::GetTensorTy(TARGET(kMLU),
+               {LiteType::GetTensorTy(TARGET(kHost),
                                       PRECISION(kInt8),
                                       DATALAYOUT(kAny))})
     .BindOutput("Out",
-                {LiteType::GetTensorTy(TARGET(kHost),
+                {LiteType::GetTensorTy(TARGET(kMLU),
                                        PRECISION(kInt8),
                                        DATALAYOUT(kAny))})
     .Finalize();