MKLDNN layout: Support for batch norm operator

paddle/fluid/operators/batch_norm_mkldnn_op.cc

@@ -19,10 +19,17 @@ limitations under the License. */
 namespace paddle {
 namespace operators {
 
-using Tensor = framework::Tensor;
+using batch_norm_bwd = mkldnn::batch_normalization_backward;
+using batch_norm_fwd = mkldnn::batch_normalization_forward;
+using framework::DataLayout;
+using framework::Tensor;
+using mkldnn::memory;
+using mkldnn::primitive;
+using mkldnn::reorder;
+using mkldnn::stream;
 using paddle::platform::MKLDNNDeviceContext;
 using paddle::platform::MKLDNNMemDesc;
-using mkldnn::memory;
+using platform::to_void_cast;
 
 template <typename T>
 using EigenArrayMap =
@@ -64,21 +71,12 @@ void run_batch_norm_op(Args &&... args) {
   mkldnn::stream(mkldnn::stream::kind::eager).submit(pipeline).wait();
 }
 
-template <typename T>
-inline void *cast_const_to_void(const T *t) {
-  return static_cast<void *>(const_cast<T *>(t));
-}
 }  // namespace
 
 template <typename T>
 class BatchNormMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext &ctx) const override {
-    auto data_layout_str = ctx.Attr<std::string>("data_layout");
-    auto data_layout = framework::StringToDataLayout(data_layout_str);
-    PADDLE_ENFORCE(data_layout == framework::DataLayout::kNCHW,
-                   "MKLDNN batch normalization handles only NCHW data layout");
-
     const float epsilon = ctx.Attr<float>("epsilon");
     const float momentum = ctx.Attr<float>("momentum");
     const bool is_test = ctx.Attr<bool>("is_test");
@@ -99,41 +97,53 @@ class BatchNormMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
     const auto *scale = ctx.Input<Tensor>("Scale");
     const auto *shift = ctx.Input<Tensor>("Bias");
 
-    y->mutable_data<T>(ctx.GetPlace());
-    mean_out->mutable_data<T>(ctx.GetPlace());
-    variance_out->mutable_data<T>(ctx.GetPlace());
+    PADDLE_ENFORCE(x->layout() == DataLayout::kMKLDNN &&
+                       x->format() != memory::format::format_undef,
+                   "Wrong layout/format set for Input x tensor");
+
+    const T *x_data = x->data<T>();
+    const T *mean_data = mean->data<T>();
+    const T *variance_data = variance->data<T>();
+    T *y_data = y->mutable_data<T>(ctx.GetPlace());
+    T *mean_out_data = mean_out->mutable_data<T>(ctx.GetPlace());
+    T *variance_out_data = variance_out->mutable_data<T>(ctx.GetPlace());
+    T *batch_mean_data = nullptr;
+    T *batch_variance_data = nullptr;
 
     if (!is_test) {
-      batch_mean->mutable_data<T>(ctx.GetPlace());
-      batch_variance->mutable_data<T>(ctx.GetPlace());
+      batch_mean_data = batch_mean->mutable_data<T>(ctx.GetPlace());
+      batch_variance_data = batch_variance->mutable_data<T>(ctx.GetPlace());
     }
 
     auto propagation = is_test == true ? mkldnn::prop_kind::forward_scoring
                                        : mkldnn::prop_kind::forward_training;
 
-    auto dims = paddle::framework::vectorize2int(x->dims());
-
-    auto src_md =
-        MKLDNNMemDesc(dims, memory::data_type::f32, memory::format::nchw);
-    auto dst_md =
-        MKLDNNMemDesc(dims, memory::data_type::f32, memory::format::nchw);
-
-    auto src_pd = mkldnn::memory::primitive_desc{src_md, mkldnn_engine};
-    auto dst_pd = mkldnn::memory::primitive_desc{dst_md, mkldnn_engine};
-
-    auto src = mkldnn::memory{src_pd, cast_const_to_void(x->data<T>())};
-    auto dst = mkldnn::memory{dst_pd, y->data<T>()};
+    auto src_tz = paddle::framework::vectorize2int(x->dims());
+    auto scale_tz = paddle::framework::vectorize2int(scale->dims());
+    PADDLE_ENFORCE(scale_tz.size() == 1, "Dims of scale tensor is NOT 1");
+    const unsigned int ic = scale_tz[0];
 
     unsigned flags = mkldnn::use_scale_shift;
     if (is_test) flags |= mkldnn::use_global_stats;
 
+    // create mkldnn memory from input x tensor
+    auto src_memory =
+        memory({{{src_tz}, memory::data_type::f32, x->format()}, mkldnn_engine},
+               to_void_cast(x_data));
+
+    // create primitive descriptor for batch norm forward
     using bn_fwd_types = bn_type_traits<mkldnn::batch_normalization_forward>;
-    auto batch_norm_fwd_desc =
-        bn_fwd_types::op_desc{propagation, src_md, epsilon, flags};
-    auto batch_norm_fwd_pd =
-        bn_fwd_types::op_prim{batch_norm_fwd_desc, mkldnn_engine};
+    auto batch_norm_fwd_desc = bn_fwd_types::op_desc{
+        propagation, src_memory.get_primitive_desc().desc(), epsilon, flags};
+    std::shared_ptr<batch_norm_fwd::primitive_desc> batch_norm_fwd_pd =
+        std::shared_ptr<batch_norm_fwd::primitive_desc>(
+            new batch_norm_fwd::primitive_desc(batch_norm_fwd_desc,
+                                               mkldnn_engine));
 
-    const unsigned int ic = dims[1];
+    // Save the pd to be used in backward pass
+    const std::string key = ctx.op().Output("SavedMean");
+    const std::string key_batch_norm_fwd_pd = key + "@bn_fwd_pd";
+    dev_ctx.SetBlob(key_batch_norm_fwd_pd, batch_norm_fwd_pd);
 
     // MKLDNN requires a single piece of memory for scale and shift/bias data
     const size_t scaleshift_size = 2 * ic;
@@ -143,73 +153,58 @@ class BatchNormMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
     copy_to_weights(scale->data<T>(), scale->data<T>() + ic, shift->data<T>(),
                     shift->data<T>() + ic, &scaleshift_data);
 
-    auto scaleshift_memory = mkldnn::memory{
-        batch_norm_fwd_pd.weights_primitive_desc(), scaleshift_data.data()};
+    // crate mkldnn memory for weights(scale/shift)
+    auto scaleshift_memory = memory(batch_norm_fwd_pd->weights_primitive_desc(),
+                                    scaleshift_data.data());
 
-    if (is_test) {
-      auto mean_memory = mkldnn::memory{batch_norm_fwd_pd.mean_primitive_desc(),
-                                        cast_const_to_void(mean->data<T>())};
+    // create mkldnn memory for output y tensor
+    auto dst_memory = memory(batch_norm_fwd_pd->dst_primitive_desc(), y_data);
 
+    if (is_test) {
+      // create mkldnn memory for stats (as input)
+      auto mean_memory = memory(batch_norm_fwd_pd->mean_primitive_desc(),
+                                to_void_cast(mean_data));
       auto variance_memory =
-          mkldnn::memory{batch_norm_fwd_pd.variance_primitive_desc(),
-                         cast_const_to_void(variance->data<T>())};
+          memory(batch_norm_fwd_pd->variance_primitive_desc(),
+                 to_void_cast(variance_data));
 
       run_batch_norm_op<typename bn_fwd_types::op_type>(
-          batch_norm_fwd_pd, src, (const mkldnn::primitive::at &)mean_memory,
+          *batch_norm_fwd_pd, src_memory,
+          (const mkldnn::primitive::at &)mean_memory,
           (const mkldnn::primitive::at &)variance_memory, scaleshift_memory,
-          dst);
+          dst_memory);
     } else {
+      // create mkldnn memory for stats (as output)
       auto mean_memory =
-          mkldnn::memory{batch_norm_fwd_pd.mean_primitive_desc(),
-                         cast_const_to_void(batch_mean->data<T>())};
-
-      auto variance_memory =
-          mkldnn::memory{batch_norm_fwd_pd.variance_primitive_desc(),
-                         cast_const_to_void(batch_variance->data<T>())};
+          memory(batch_norm_fwd_pd->mean_primitive_desc(), batch_mean_data);
+      auto variance_memory = memory(
+          batch_norm_fwd_pd->variance_primitive_desc(), batch_variance_data);
 
-      run_batch_norm_op<bn_fwd_types::op_type>(batch_norm_fwd_pd, src,
-                                               scaleshift_memory, dst,
+      run_batch_norm_op<bn_fwd_types::op_type>(*batch_norm_fwd_pd, src_memory,
+                                               scaleshift_memory, dst_memory,
                                                mean_memory, variance_memory);
     }
 
     if (!is_test) {
-      const unsigned int in = dims[0];
-      const unsigned int sample_size = x->numel() / in / ic;
-
-      // saved_xx is use just in this batch of data
-      EigenVectorArrayMap<T> saved_mean_e(
-          batch_mean->mutable_data<T>(ctx.GetPlace()), ic);
-      EigenVectorArrayMap<T> saved_variance_e(
-          batch_variance->mutable_data<T>(ctx.GetPlace()), ic);
-      saved_mean_e.setZero();
-      saved_variance_e.setZero();
-
-      const unsigned int x_arr_size = in * ic;
-      ConstEigenArrayMap<T> x_arr(x->data<T>(), sample_size, x_arr_size);
-      for (unsigned int nc = 0; nc < x_arr_size; ++nc) {
-        saved_mean_e(nc % ic) += x_arr.col(nc).sum();
-      }
-      saved_mean_e /= in * sample_size;
-      for (unsigned int nc = 0; nc < x_arr_size; ++nc) {
-        saved_variance_e(nc % ic) +=
-            (x_arr.col(nc) - saved_mean_e(nc % ic)).matrix().squaredNorm();
-      }
-      saved_variance_e /= in * sample_size;
-
-      ConstEigenVectorArrayMap<T> mean_arr{mean->data<T>(), ic};
-      ConstEigenVectorArrayMap<T> variance_arr{variance->data<T>(), ic};
+      // mkldnn only compute stats for current batch
+      // so we need compute momentum stats via Eigen lib
+      EigenVectorArrayMap<T> batch_mean_e(batch_mean_data, ic);
+      EigenVectorArrayMap<T> batch_variance_e(batch_variance_data, ic);
+      ConstEigenVectorArrayMap<T> mean_e(mean_data, ic);
+      ConstEigenVectorArrayMap<T> variance_e{variance_data, ic};
 
-      EigenVectorArrayMap<T> running_mean_arr(
-          mean_out->mutable_data<T>(ctx.GetPlace()), ic);
-      EigenVectorArrayMap<T> running_var_arr(
-          variance_out->mutable_data<T>(ctx.GetPlace()), ic);
+      EigenVectorArrayMap<T> running_mean_e(mean_out_data, ic);
+      EigenVectorArrayMap<T> running_variance_e(variance_out_data, ic);
 
       auto one_minus_momentum = 1. - momentum;
-      running_mean_arr =
-          mean_arr * momentum + saved_mean_e * one_minus_momentum;
-      running_var_arr =
-          variance_arr * momentum + saved_variance_e * one_minus_momentum;
+      running_mean_e = mean_e * momentum + batch_mean_e * one_minus_momentum;
+      running_variance_e =
+          variance_e * momentum + batch_variance_e * one_minus_momentum;
     }
+
+    y->set_layout(DataLayout::kMKLDNN);
+    y->set_format(
+        (memory::format)dst_memory.get_primitive_desc().desc().data.format);
   }
 };
 
@@ -217,11 +212,6 @@ template <typename T>
 class BatchNormMKLDNNGradOpKernel : public paddle::framework::OpKernel<T> {
  public:
   void Compute(const paddle::framework::ExecutionContext &ctx) const override {
-    auto data_layout_str = ctx.Attr<std::string>("data_layout");
-    auto data_layout = framework::StringToDataLayout(data_layout_str);
-    PADDLE_ENFORCE(data_layout == framework::DataLayout::kNCHW,
-                   "MKLDNN batch normalization handles only NCHW data layout");
-
     auto &dev_ctx = ctx.template device_context<MKLDNNDeviceContext>();
     auto mkldnn_engine = dev_ctx.GetEngine();
 
@@ -238,88 +228,132 @@ class BatchNormMKLDNNGradOpKernel : public paddle::framework::OpKernel<T> {
     auto *diff_scale = ctx.Output<Tensor>(framework::GradVarName("Scale"));
     auto *diff_shift = ctx.Output<Tensor>(framework::GradVarName("Bias"));
 
-    diff_x->mutable_data<T>(ctx.GetPlace());
-    diff_scale->mutable_data<T>(ctx.GetPlace());
-    diff_shift->mutable_data<T>(ctx.GetPlace());
+    PADDLE_ENFORCE(diff_y->layout() == DataLayout::kMKLDNN &&
+                       diff_y->format() != memory::format::format_undef,
+                   "Wrong layout/format set for Input diff_y tensor");
+
+    const T *x_data = x->data<T>();
+    const T *diff_y_data = diff_y->data<T>();
+    const T *batch_mean_data = batch_mean->data<T>();
+    const T *batch_variance_data = batch_variance->data<T>();
+    const T *scale_data = scale->data<T>();
+    const T *shift_data = shift->data<T>();
+    T *diff_x_data = diff_x->mutable_data<T>(ctx.GetPlace());
+    T *diff_scale_data = diff_scale->mutable_data<T>(ctx.GetPlace());
+    T *diff_shift_data = diff_shift->mutable_data<T>(ctx.GetPlace());
+
+    auto src_tz = paddle::framework::vectorize2int(x->dims());
+    auto diff_src_tz = src_tz;
+    auto dst_tz = src_tz;
+    auto diff_dst_tz = dst_tz;
+    auto scale_tz = paddle::framework::vectorize2int(scale->dims());
+    PADDLE_ENFORCE(scale_tz.size() == 1, "Dims of scale tensor is NOT 1");
+
+    const unsigned int ic = scale_tz[0];
+
+    // Retrieve bn_fwd_pd from device context
+    const std::string key = ctx.op().Input("SavedMean");
+    const std::string key_batch_norm_fwd_pd = key + "@bn_fwd_pd";
+    auto batch_norm_fwd_pd =
+        std::static_pointer_cast<batch_norm_fwd::primitive_desc>(
+            dev_ctx.GetBlob(key_batch_norm_fwd_pd));
+    PADDLE_ENFORCE(batch_norm_fwd_pd != nullptr,
+                   "Fail to find batch_norm_fwd_pd in device context");
 
-    auto dims = paddle::framework::vectorize2int(x->dims());
-    unsigned flags = mkldnn::use_scale_shift | !mkldnn::use_global_stats;
+    using bn_bwd_types = bn_type_traits<mkldnn::batch_normalization_backward>;
 
-    auto src_md =
-        MKLDNNMemDesc(dims, memory::data_type::f32, memory::format::nchw);
-    auto dst_md =
-        MKLDNNMemDesc(dims, memory::data_type::f32, memory::format::nchw);
-    auto diff_src_md =
-        MKLDNNMemDesc(dims, memory::data_type::f32, memory::format::nchw);
-    auto diff_dst_md =
-        MKLDNNMemDesc(dims, memory::data_type::f32, memory::format::nchw);
+    // create mkldnn memory from input diff_y tensor
+    auto user_diff_dst_memory =
+        memory({{{diff_dst_tz}, memory::data_type::f32, diff_y->format()},
+                mkldnn_engine},
+               to_void_cast(diff_y_data));
 
-    using bn_bwd_types = bn_type_traits<mkldnn::batch_normalization_backward>;
-    using bn_fwd_types = bn_type_traits<mkldnn::batch_normalization_forward>;
+    // create mkldnn memory from input x tensor
+    auto src_memory =
+        memory({{{src_tz}, memory::data_type::f32, x->format()}, mkldnn_engine},
+               to_void_cast(x_data));
 
-    auto batch_norm_fwd_desc = bn_fwd_types::op_desc{
-        mkldnn::prop_kind::forward_training, src_md, epsilon, flags};
-    auto batch_norm_fwd_pd =
-        bn_fwd_types::op_prim{batch_norm_fwd_desc, mkldnn_engine};
+    // for diff_dst, try to use same format as dst in forward pass
+    auto diff_dst_pd = batch_norm_fwd_pd.get()->dst_primitive_desc();
+    auto diff_dst_md = diff_dst_pd.desc();
 
+    // create primitive descriptor for batch norm backward
+    unsigned flags = mkldnn::use_scale_shift;
     auto batch_norm_bwd_desc = bn_bwd_types::op_desc{
-        mkldnn::prop_kind::backward, diff_dst_md, dst_md, epsilon, flags};
+        mkldnn::prop_kind::backward, diff_dst_md,
+        src_memory.get_primitive_desc().desc(), epsilon, flags};
     auto batch_norm_bwd_pd = bn_bwd_types::op_prim{
-        batch_norm_bwd_desc, mkldnn_engine, batch_norm_fwd_pd};
-
-    auto src = mkldnn::memory{{src_md, mkldnn_engine},
-                              cast_const_to_void(x->data<T>())};
-
-    auto mean = mkldnn::memory{batch_norm_bwd_pd.mean_primitive_desc(),
-                               cast_const_to_void(batch_mean->data<T>())};
-
-    auto variance =
-        mkldnn::memory{batch_norm_bwd_pd.variance_primitive_desc(),
-                       cast_const_to_void(batch_variance->data<T>())};
-
-    auto diff_dst = mkldnn::memory{{diff_dst_md, mkldnn_engine},
-                                   cast_const_to_void(diff_y->data<T>())};
+        batch_norm_bwd_desc, mkldnn_engine, *batch_norm_fwd_pd};
+
+    // reorder user_diff_dst if it's not in preferred format
+    auto diff_dst_memory = user_diff_dst_memory;
+    primitive reorder_diff_dst;
+    bool is_diff_dst_reordered = false;
+    if (diff_dst_pd != user_diff_dst_memory.get_primitive_desc()) {
+      diff_dst_memory = memory(diff_dst_pd);
+      reorder_diff_dst = reorder(user_diff_dst_memory, diff_dst_memory);
+      is_diff_dst_reordered = true;
+    }
 
-    const unsigned int ic = dims[1];
+    // create mkldnn memory for input tensors (src/mean/variance)
+    auto mean_memory = memory(batch_norm_bwd_pd.mean_primitive_desc(),
+                              to_void_cast(batch_mean_data));
+    auto variance_memory = memory(batch_norm_bwd_pd.variance_primitive_desc(),
+                                  to_void_cast(batch_variance_data));
 
+    // MKLDNN requires a single piece of memory for scale and shift/bias data
     const size_t scaleshift_size = 2 * ic;
 
     std::vector<T> scaleshift_data;
     scaleshift_data.reserve(scaleshift_size);
-    copy_to_weights(scale->data<T>(), scale->data<T>() + ic, shift->data<T>(),
-                    shift->data<T>() + ic, &scaleshift_data);
+    copy_to_weights(scale_data, scale_data + ic, shift_data, shift_data + ic,
+                    &scaleshift_data);
 
-    auto scaleshift_memory = mkldnn::memory{
-        batch_norm_bwd_pd.weights_primitive_desc(), scaleshift_data.data()};
+    // create mkldnn memory for input tensors (scale/shift)
+    auto scaleshift_memory = memory(batch_norm_bwd_pd.weights_primitive_desc(),
+                                    scaleshift_data.data());
 
+    // create mkldnn memory for output diff weights (combined scale/shift)
     std::vector<T> diff_scaleshift_data;
     diff_scaleshift_data.reserve(scaleshift_size);
-    copy_to_weights(diff_scale->data<T>(), diff_scale->data<T>() + ic,
-                    diff_shift->data<T>(), diff_shift->data<T>() + ic,
-                    &diff_scaleshift_data);
-
     auto diff_scaleshift_memory =
-        mkldnn::memory{batch_norm_bwd_pd.diff_weights_primitive_desc(),
-                       diff_scaleshift_data.data()};
-
-    auto diff_src = mkldnn::memory{{diff_src_md, mkldnn_engine},
-                                   static_cast<void *>(diff_x->data<T>())};
-
-    run_batch_norm_op<bn_bwd_types::op_type>(
-        batch_norm_bwd_pd, src, mean, variance, diff_dst, scaleshift_memory,
-        diff_src, diff_scaleshift_memory);
-
+        memory(batch_norm_bwd_pd.diff_weights_primitive_desc(),
+               diff_scaleshift_data.data());
+
+    // here assume diff_src is in the same format of src
+    auto diff_src_memory = memory(src_memory.get_primitive_desc(), diff_x_data);
+
+    // finally create batch_norm backward primitive
+    auto batch_norm_bwd_prim =
+        batch_norm_bwd(batch_norm_bwd_pd, src_memory, mean_memory,
+                       variance_memory, diff_dst_memory, scaleshift_memory,
+                       diff_src_memory, diff_scaleshift_memory);
+
+    // execute optional reorder and batch_norm backward primitive
+    std::vector<primitive> pipeline;
+    if (is_diff_dst_reordered) pipeline.push_back(reorder_diff_dst);
+    pipeline.push_back(batch_norm_bwd_prim);
+    stream(stream::kind::eager).submit(pipeline).wait();
+
+    // copy back diff sacle/shift to output tensors (diff scale/shift)
+    diff_scaleshift_data.resize(scaleshift_size);
     auto it = std::begin(diff_scaleshift_data);
-    std::copy(it, std::next(it, ic), diff_scale->data<T>());
+    std::copy(it, std::next(it, ic), diff_scale_data);
     std::copy(std::next(it, ic), std::end(diff_scaleshift_data),
-              diff_shift->data<T>());
+              diff_shift_data);
+
+    // set layout/format of output tensors
+    diff_x->set_layout(DataLayout::kMKLDNN);
+    diff_x->set_format((memory::format)diff_src_memory.get_primitive_desc()
+                           .desc()
+                           .data.format);
   }
 };
 }  // namespace operators
 }  // namespace paddle
 
 namespace ops = paddle::operators;
-REGISTER_OP_KERNEL(batch_norm, MKLDNN, paddle::platform::CPUPlace,
+REGISTER_OP_KERNEL(batch_norm, MKLDNN, ::paddle::platform::CPUPlace,
                    ops::BatchNormMKLDNNOpKernel<float>);
-REGISTER_OP_KERNEL(batch_norm_grad, MKLDNN, paddle::platform::CPUPlace,
+REGISTER_OP_KERNEL(batch_norm_grad, MKLDNN, ::paddle::platform::CPUPlace,
                    ops::BatchNormMKLDNNGradOpKernel<float>);

paddle/fluid/operators/batch_norm_op.cc

@@ -110,19 +110,19 @@ class BatchNormOp : public framework::OperatorWithKernel {
                                          ctx.Input<Tensor>("Variance")->type()),
                       "Variance input should be of float type");
 
-    framework::LibraryType library_{framework::LibraryType::kPlain};
     // TODO(pzelazko-intel): enable MKLDNN layout when it's ready
+    framework::LibraryType library = framework::LibraryType::kPlain;
     framework::DataLayout layout = framework::DataLayout::kAnyLayout;
-
 #ifdef PADDLE_WITH_MKLDNN
-    if (library_ == framework::LibraryType::kPlain &&
+    if (library == framework::LibraryType::kPlain &&
         platform::CanMKLDNNBeUsed(ctx)) {
-      library_ = framework::LibraryType::kMKLDNN;
+      library = framework::LibraryType::kMKLDNN;
       layout = framework::DataLayout::kMKLDNN;
     }
 #endif
+
     return framework::OpKernelType(input_data_type, ctx.GetPlace(), layout,
-                                   library_);
+                                   library);
   }
 };
 
@@ -368,19 +368,21 @@ class BatchNormGradOp : public framework::OperatorWithKernel {
       PADDLE_THROW("can't find Y@GRAD");
     }
 
-    framework::LibraryType library_{framework::LibraryType::kPlain};
     // TODO(pzelazko-intel): enable MKLDNN layout when it's ready
-    framework::DataLayout layout_ = framework::DataLayout::kAnyLayout;
+    framework::LibraryType library = framework::LibraryType::kPlain;
+    framework::DataLayout layout = framework::DataLayout::kAnyLayout;
+
 #ifdef PADDLE_WITH_MKLDNN
-    if (library_ == framework::LibraryType::kPlain &&
+    if (library == framework::LibraryType::kPlain &&
         platform::CanMKLDNNBeUsed(ctx)) {
-      library_ = framework::LibraryType::kMKLDNN;
-      layout_ = framework::DataLayout::kMKLDNN;
+      library = framework::LibraryType::kMKLDNN;
+      layout = framework::DataLayout::kMKLDNN;
     }
 #endif
+
     return framework::OpKernelType(
         framework::ToDataType(ctx.Input<Tensor>("X")->type()), ctx.GetPlace(),
-        layout_, library_);
+        layout, library);
   }
 };