Changed first batch of deprecated mkldnn headers and function names to new oneDNN names (#37040)

* Change first batch of mkldnn headers and namespace names to dnnl

* Revert changes to tensor.h, which require approval

* Format changes with pre-commit

* Add int32 tests

* Fix int32 tests and call GetDataFromTensor for int32

* Fix test

paddle/fluid/framework/data_layout_transform.cc

@@ -100,21 +100,21 @@ void TransDataLayout(const OpKernelType& kernel_type_for_var,
 }
 
 #ifdef PADDLE_WITH_MKLDNN
-using mkldnn::memory;
-using mkldnn::primitive;
-using mkldnn::reorder;
+using dnnl::memory;
+using dnnl::primitive;
+using dnnl::reorder;
 
-void* GetDataFromTensor(const Tensor& tensor, mkldnn::memory::data_type type) {
+void* GetDataFromTensor(const Tensor& tensor, dnnl::memory::data_type type) {
   switch (type) {
-    case mkldnn::memory::data_type::f32:
+    case dnnl::memory::data_type::f32:
       return platform::to_void_cast(tensor.data<float>());
-    case mkldnn::memory::data_type::s8:
+    case dnnl::memory::data_type::s8:
       return platform::to_void_cast(tensor.data<int8_t>());
-    case mkldnn::memory::data_type::u8:
+    case dnnl::memory::data_type::u8:
       return platform::to_void_cast(tensor.data<unsigned char>());
-    case mkldnn::memory::data_type::s32:
+    case dnnl::memory::data_type::s32:
       return platform::to_void_cast(tensor.data<int32_t>());
-    case mkldnn::memory::data_type::bf16:
+    case dnnl::memory::data_type::bf16:
       return platform::to_void_cast(tensor.data<paddle::platform::bfloat16>());
     default:
       PADDLE_THROW(

paddle/fluid/framework/data_layout_transform.h

@@ -37,7 +37,7 @@ namespace paddle {
 namespace framework {
 
 #ifdef PADDLE_WITH_MKLDNN
-using MKLDNNDataType = mkldnn::memory::data_type;
+using MKLDNNDataType = dnnl::memory::data_type;
 
 inline MKLDNNMemoryFormat ToMKLDNNFormat(const DataLayout& layout) {
   switch (layout) {

paddle/fluid/framework/data_layout_transform_test.cc

@@ -44,7 +44,7 @@ TEST(DataTransform, DataLayoutFunction) {
 }
 
 #ifdef PADDLE_WITH_MKLDNN
-TEST(DataTransform, GetDataFromTensorDNNL) {
+TEST(DataTransformBf16, GetDataFromTensorDNNL) {
   auto place = paddle::platform::CPUPlace();
   paddle::framework::Tensor in = paddle::framework::Tensor();
   in.mutable_data<paddle::platform::bfloat16>(
@@ -55,4 +55,14 @@ TEST(DataTransform, GetDataFromTensorDNNL) {
   EXPECT_EQ(in_data, paddle::platform::to_void_cast(
                          in.data<paddle::platform::bfloat16>()));
 }
+
+TEST(DataTransformInt32, GetDataFromTensorDNNL) {
+  auto place = paddle::platform::CPUPlace();
+  paddle::framework::Tensor in = paddle::framework::Tensor();
+  in.mutable_data<int32_t>(paddle::framework::make_ddim({2, 3, 1, 2}), place);
+
+  void* in_data =
+      paddle::framework::GetDataFromTensor(in, dnnl::memory::data_type::s32);
+  EXPECT_EQ(in_data, paddle::platform::to_void_cast(in.data<int32_t>()));
+}
 #endif

paddle/fluid/framework/data_type_transform_test.cc

@@ -310,4 +310,117 @@ TEST(DataTypeTransform, CPUTransform) {
                 static_cast<paddle::platform::bfloat16>(in_data_bool[i]).x);
     }
   }
+
+  // data type transform from/to int32
+  {
+    paddle::framework::Tensor in;
+    paddle::framework::Tensor out;
+
+    int32_t* ptr =
+        in.mutable_data<int32_t>(paddle::framework::make_ddim({2, 3}), place);
+    int data_number = 2 * 3;
+
+    for (int i = 0; i < data_number; ++i) {
+      ptr[i] = i;
+    }
+
+    // transform from int32 to other data types
+    paddle::framework::TransDataType(kernel_int32, kernel_fp32, in, &out);
+    float* out_data_float = out.data<float>();
+    for (int i = 0; i < data_number; ++i) {
+      EXPECT_EQ(out_data_float[i], static_cast<float>(ptr[i]));
+    }
+
+    paddle::framework::TransDataType(kernel_int32, kernel_fp64, in, &out);
+    double* out_data_double = out.data<double>();
+    for (int i = 0; i < data_number; ++i) {
+      EXPECT_EQ(out_data_double[i], static_cast<double>(ptr[i]));
+    }
+
+    paddle::framework::TransDataType(kernel_int32, kernel_bf16, in, &out);
+    paddle::platform::bfloat16* out_data_bf16 =
+        out.data<paddle::platform::bfloat16>();
+    for (int i = 0; i < data_number; ++i) {
+      EXPECT_EQ(out_data_bf16[i],
+                static_cast<paddle::platform::bfloat16>(ptr[i]));
+    }
+
+    paddle::framework::TransDataType(kernel_int32, kernel_int64, in, &out);
+    int64_t* out_data_int64 = out.data<int64_t>();
+    for (int i = 0; i < data_number; ++i) {
+      EXPECT_EQ(out_data_int64[i], static_cast<int64_t>(ptr[i]));
+    }
+
+    paddle::framework::TransDataType(kernel_int32, kernel_bool, in, &out);
+    bool* out_data_bool = out.data<bool>();
+    for (int i = 0; i < data_number; ++i) {
+      EXPECT_EQ(out_data_bool[i], static_cast<bool>(ptr[i]));
+    }
+
+    // transform float to int32
+    float* in_data_float =
+        in.mutable_data<float>(paddle::framework::make_ddim({2, 3}), place);
+    for (int i = 0; i < data_number; ++i) {
+      in_data_float[i] = i;
+    }
+
+    paddle::framework::TransDataType(kernel_fp32, kernel_int32, in, &out);
+    ptr = out.data<int32_t>();
+    for (int i = 0; i < data_number; ++i) {
+      EXPECT_EQ(ptr[i], static_cast<int32_t>(in_data_float[i]));
+    }
+
+    // transform double to int32
+    double* in_data_double =
+        in.mutable_data<double>(paddle::framework::make_ddim({2, 3}), place);
+    for (int i = 0; i < data_number; ++i) {
+      in_data_double[i] = i;
+    }
+
+    paddle::framework::TransDataType(kernel_fp64, kernel_int32, in, &out);
+    ptr = out.data<int32_t>();
+    for (int i = 0; i < data_number; ++i) {
+      EXPECT_EQ(ptr[i], static_cast<int32_t>(in_data_double[i]));
+    }
+
+    // transform bfloat16 to int32
+    paddle::platform::bfloat16* in_data_bf16 =
+        in.mutable_data<paddle::platform::bfloat16>(
+            paddle::framework::make_ddim({2, 3}), place);
+    for (int i = 0; i < data_number; ++i) {
+      in_data_bf16[i] = i;
+    }
+
+    paddle::framework::TransDataType(kernel_bf16, kernel_int32, in, &out);
+    ptr = out.data<int32_t>();
+    for (int i = 0; i < data_number; ++i) {
+      EXPECT_EQ(ptr[i], static_cast<int32_t>(in_data_bf16[i]));
+    }
+
+    // transform int64 to int32
+    int64_t* in_data_int64 =
+        in.mutable_data<int64_t>(paddle::framework::make_ddim({2, 3}), place);
+    for (int i = 0; i < data_number; ++i) {
+      in_data_int64[i] = i;
+    }
+
+    paddle::framework::TransDataType(kernel_int64, kernel_int32, in, &out);
+    ptr = out.data<int32_t>();
+    for (int i = 0; i < data_number; ++i) {
+      EXPECT_EQ(ptr[i], static_cast<int32_t>(in_data_int64[i]));
+    }
+
+    // transform bool to int32
+    bool* in_data_bool =
+        in.mutable_data<bool>(paddle::framework::make_ddim({2, 3}), place);
+    for (int i = 0; i < data_number; ++i) {
+      in_data_bool[i] = i;
+    }
+
+    paddle::framework::TransDataType(kernel_bool, kernel_int32, in, &out);
+    ptr = out.data<int32_t>();
+    for (int i = 0; i < data_number; ++i) {
+      EXPECT_EQ(ptr[i], static_cast<int32_t>(in_data_bool[i]));
+    }
+  }
 }

paddle/fluid/operators/elementwise/mkldnn/elementwise_mkldnn_op.h

@@ -26,9 +26,9 @@ namespace operators {
 
 using framework::DataLayout;
 using framework::Tensor;
-using mkldnn::memory;
-using mkldnn::primitive;
-using mkldnn::stream;
+using dnnl::memory;
+using dnnl::primitive;
+using dnnl::stream;
 
 template <typename T, dnnl::algorithm BINARY_OP>
 class EltwiseMKLDNNKernel : public framework::OpKernel<T> {

paddle/fluid/operators/fused/mkldnn/fusion_lstm_mkldnn_op.cc

@@ -31,12 +31,11 @@ class LSTMMKLDNNHandler
  public:
   LSTMMKLDNNHandler(const paddle::framework::ExecutionContext& ctx,
                     const platform::MKLDNNDeviceContext& dev_ctx,
-                    const mkldnn::engine mkldnn_engine,
-                    platform::Place cpu_place, const LoDTensor* input,
-                    const Tensor* weight_h, const Tensor* h0, const Tensor* c0,
-                    const bool is_reverse, const int64_t N, const int64_t Ti,
-                    const int64_t IC, const int64_t OC,
-                    const std::string& unique_name)
+                    const dnnl::engine mkldnn_engine, platform::Place cpu_place,
+                    const LoDTensor* input, const Tensor* weight_h,
+                    const Tensor* h0, const Tensor* c0, const bool is_reverse,
+                    const int64_t N, const int64_t Ti, const int64_t IC,
+                    const int64_t OC, const std::string& unique_name)
       : RNNMKLDNNHandler<T, dnnl::lstm_forward, T_out>(
             ctx, dev_ctx, mkldnn_engine, ctx.GetPlace(), input, weight_h, h0,
             is_reverse, N, Ti, IC, OC, 4,

paddle/fluid/operators/fused/mkldnn/fusion_rnn_mkldnn.h

@@ -30,12 +30,11 @@ class RNNMKLDNNHandler : public platform::MKLDNNHandlerT<T, T_alg> {
  public:
   RNNMKLDNNHandler(const paddle::framework::ExecutionContext& ctx,
                    const platform::MKLDNNDeviceContext& dev_ctx,
-                   const mkldnn::engine mkldnn_engine,
-                   platform::Place cpu_place, const LoDTensor* input,
-                   const Tensor* weight_h, const Tensor* h0,
-                   const bool is_reverse, const int64_t N, const int64_t Ti,
-                   const int64_t IC, const int64_t OC, const int64_t G,
-                   const std::string& unique_name)
+                   const dnnl::engine mkldnn_engine, platform::Place cpu_place,
+                   const LoDTensor* input, const Tensor* weight_h,
+                   const Tensor* h0, const bool is_reverse, const int64_t N,
+                   const int64_t Ti, const int64_t IC, const int64_t OC,
+                   const int64_t G, const std::string& unique_name)
       : platform::MKLDNNHandlerT<T, T_alg>(
             dev_ctx, dev_ctx.GetEngine(), cpu_place,
             CreateKey(dev_ctx, unique_name, MKLDNNGetDataType<T>(), Ti)),

paddle/fluid/operators/mkldnn/axpy_handler.cc

@@ -17,7 +17,7 @@ limitations under the License. */
 #include <string>
 #include <vector>
 
-#include "mkldnn.hpp"
+#include "dnnl.hpp"
 #include "paddle/fluid/operators/mkldnn/axpy_handler.h"
 #include "paddle/fluid/platform/bfloat16.h"
 #include "paddle/fluid/platform/device_context.h"

paddle/fluid/operators/mkldnn/dequantize_mkldnn_op.cc

@@ -12,7 +12,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
 limitations under the License. */
 
-#include "mkldnn.hpp"
+#include "dnnl.hpp"
 #include "paddle/fluid/framework/data_layout_transform.h"
 #include "paddle/fluid/framework/tensor.h"
 #include "paddle/fluid/operators/dequantize_op.h"
@@ -23,13 +23,13 @@ limitations under the License. */
 namespace paddle {
 namespace operators {
 
-using mkldnn::memory;
-using mkldnn::primitive;
-using mkldnn::reorder;
+using dnnl::memory;
+using dnnl::primitive;
+using dnnl::reorder;
 using platform::to_void_cast;
 using Tensor = framework::Tensor;
 using framework::DataLayout;
-using mkldnn::stream;
+using dnnl::stream;
 using platform::GetMKLDNNFormat;
 
 template <typename T>
@@ -64,7 +64,7 @@ class DeQuantOpKernel : public framework::OpKernel<T> {
 
     auto src_tz = paddle::framework::vectorize<int64_t>(input->dims());
     auto dst_tz = paddle::framework::vectorize<int64_t>(output->dims());
-    mkldnn::memory::data_type src_dt =
+    dnnl::memory::data_type src_dt =
         paddle::framework::ToMKLDNNDataType(input->type());
     MKLDNNMemoryFormat src_fmt = input->format();
 
@@ -76,34 +76,34 @@ class DeQuantOpKernel : public framework::OpKernel<T> {
     const std::string key_src_mem = key + "@s";
     const std::string key_dst_mem = key + "@d";
 
-    std::shared_ptr<mkldnn::memory> src_memory;
-    std::shared_ptr<mkldnn::memory> dst_memory;
+    std::shared_ptr<dnnl::memory> src_memory;
+    std::shared_ptr<dnnl::memory> dst_memory;
     std::shared_ptr<reorder> reorder_p;
     reorder_p = std::static_pointer_cast<reorder>(dev_ctx.GetBlob(key_prim));
 
     if (reorder_p == nullptr) {
-      mkldnn::primitive_attr attri;
+      dnnl::primitive_attr attri;
       int mask = 0;
       float reorder_scale = 1. / scale_data;
       attri.set_output_scales(mask, {reorder_scale});
 
       if (with_shift) {
-        mkldnn::post_ops post_operations;
+        dnnl::post_ops post_operations;
         post_operations.append_sum();
         attri.set_post_ops(post_operations);
         std::fill(output_data, output_data + output->numel(), reorder_shift);
       }
 
       auto src_md = platform::MKLDNNMemDesc({src_tz}, src_dt, src_fmt);
-      src_memory = std::make_shared<mkldnn::memory>(
-          src_md, engine, to_void_cast<T>(input_data));
+      src_memory = std::make_shared<dnnl::memory>(src_md, engine,
+                                                  to_void_cast<T>(input_data));
 
       auto dst_md =
           platform::MKLDNNMemDesc({dst_tz}, memory::data_type::f32,
                                   platform::MKLDNNFormatForSize(
                                       dst_tz.size(), MKLDNNMemoryFormat::nchw));
 
-      dst_memory = std::make_shared<mkldnn::memory>(
+      dst_memory = std::make_shared<dnnl::memory>(
           dst_md, engine, to_void_cast<float>(output_data));
 
       auto reorder_pd = std::shared_ptr<reorder::primitive_desc>(
@@ -113,12 +113,12 @@ class DeQuantOpKernel : public framework::OpKernel<T> {
       dev_ctx.SetBlob(key_src_mem, src_memory);
       dev_ctx.SetBlob(key_dst_mem, dst_memory);
     } else {
-      src_memory = std::static_pointer_cast<mkldnn::memory>(
-          dev_ctx.GetBlob(key_src_mem));
+      src_memory =
+          std::static_pointer_cast<dnnl::memory>(dev_ctx.GetBlob(key_src_mem));
       src_memory->set_data_handle(to_void_cast<T>(input_data));
 
-      dst_memory = std::static_pointer_cast<mkldnn::memory>(
-          dev_ctx.GetBlob(key_dst_mem));
+      dst_memory =
+          std::static_pointer_cast<dnnl::memory>(dev_ctx.GetBlob(key_dst_mem));
       if (with_shift)
         std::fill(output_data, output_data + output->numel(), reorder_shift);
       dst_memory->set_data_handle(output->mutable_data<float>(ctx.GetPlace()));

paddle/fluid/operators/mkldnn/quantize_mkldnn_op.cc

@@ -12,7 +12,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
 limitations under the License. */
 
-#include "mkldnn.hpp"
+#include "dnnl.hpp"
 #include "paddle/fluid/framework/tensor.h"
 #include "paddle/fluid/operators/quantize_op.h"
 #include "paddle/fluid/platform/mkldnn_helper.h"
@@ -21,13 +21,13 @@ limitations under the License. */
 namespace paddle {
 namespace operators {
 
-using mkldnn::memory;
-using mkldnn::primitive;
-using mkldnn::reorder;
+using dnnl::memory;
+using dnnl::primitive;
+using dnnl::reorder;
 using platform::to_void_cast;
 using Tensor = framework::Tensor;
 using framework::DataLayout;
-using mkldnn::stream;
+using dnnl::stream;
 using platform::GetMKLDNNFormat;
 
 template <typename T>
@@ -65,19 +65,19 @@ class QuantOpKernel : public framework::OpKernel<T> {
     bool bfloat16 = ctx.Attr<bool>("bfloat16");
 
     // TODO(jczaja): Refactor with Acquire API
-    std::shared_ptr<mkldnn::memory> src_memory;
-    std::shared_ptr<mkldnn::memory> dst_memory;
+    std::shared_ptr<dnnl::memory> src_memory;
+    std::shared_ptr<dnnl::memory> dst_memory;
     std::shared_ptr<reorder> reorder_p;
 
     std::string out_layout = ctx.Attr<std::string>("output_format");
     MKLDNNMemoryFormat out_format =
         platform::data_format_to_memory_format(out_layout);
-    mkldnn::primitive_attr attri;
+    dnnl::primitive_attr attri;
     int mask = 0;
     attri.set_output_scales(mask, {scale_data});
 
     if (with_shift) {
-      mkldnn::post_ops post_operations;
+      dnnl::post_ops post_operations;
       post_operations.append_sum();
       attri.set_post_ops(post_operations);
       uint8_t* output_data = output->mutable_data<uint8_t>(ctx.GetPlace());
@@ -87,10 +87,10 @@ class QuantOpKernel : public framework::OpKernel<T> {
 
     auto src_md = platform::MKLDNNMemDesc({src_tz}, memory::data_type::f32,
                                           input->format());
-    src_memory = std::make_shared<mkldnn::memory>(src_md, engine,
+    src_memory = std::make_shared<dnnl::memory>(src_md, engine,
                                                 to_void_cast<T>(input_data));
 
-    std::shared_ptr<mkldnn::memory::desc> dst_md;
+    std::shared_ptr<dnnl::memory::desc> dst_md;
     if (bfloat16) {
       platform::SetDstMemoryQuantized<paddle::platform::bfloat16>(
           ctx, output, dst_tz, engine, dst_md, dst_memory, out_format);

paddle/fluid/operators/mkldnn/requantize_mkldnn_op.cc

@@ -12,7 +12,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
 limitations under the License. */
 
-#include "mkldnn.hpp"
+#include "dnnl.hpp"
 #include "paddle/fluid/framework/data_layout_transform.h"
 #include "paddle/fluid/framework/tensor.h"
 #include "paddle/fluid/operators/requantize_op.h"
@@ -93,7 +93,7 @@ class ReQuantOpKernel : public framework::OpKernel<T> {
       int mask = 0;
       attri.set_output_scales(mask, {reorder_scale});
       if (with_shift) {
-        mkldnn::post_ops post_operations;
+        dnnl::post_ops post_operations;
         post_operations.append_sum();
         attri.set_post_ops(post_operations);
         uint8_t* output_data = output->mutable_data<uint8_t>(ctx.GetPlace());

paddle/fluid/platform/mkldnn_helper.h

@@ -20,22 +20,22 @@ limitations under the License. */
 #include <string>
 #include <utility>
 #include <vector>
-#include "mkldnn.hpp"
+#include "dnnl.hpp"
 #include "paddle/fluid/framework/operator.h"
 #include "paddle/fluid/platform/place.h"
 #include "paddle/fluid/platform/profiler.h"
 namespace paddle {
 #ifdef PADDLE_WITH_MKLDNN
-using MKLDNNMemoryFormat = mkldnn::memory::format_tag;
+using MKLDNNMemoryFormat = dnnl::memory::format_tag;
 #endif
 namespace platform {
 
-using MKLDNNStream = mkldnn::stream;
-using MKLDNNEngine = mkldnn::engine;
-using MKLDNNMemory = mkldnn::memory;
-using MKLDNNMemoryDescriptor = mkldnn::memory::desc;
-using MKLDNNPrimitive = mkldnn::primitive;
-using MKLDNNPrimitiveDesc = mkldnn::handle<mkldnn_primitive_desc_t>;
+using MKLDNNStream = dnnl::stream;
+using MKLDNNEngine = dnnl::engine;
+using MKLDNNMemory = dnnl::memory;
+using MKLDNNMemoryDescriptor = dnnl::memory::desc;
+using MKLDNNPrimitive = dnnl::primitive;
+using MKLDNNPrimitiveDesc = dnnl::handle<dnnl_primitive_desc_t>;
 
 typedef std::unique_ptr<MKLDNNStream> MKLDNNStreamPtr;
 typedef std::unique_ptr<MKLDNNEngine> MKLDNNEnginePtr;
@@ -62,7 +62,7 @@ using tf_pd = typename Type::primitive_desc;
 template <typename Type, typename Engine, typename... Args>
 std::shared_ptr<tf_pd<Type>> MKLDNNFwdPrimitiveDesc(const Engine& e,
                                                     Args&&... args) {
-  auto desc = tf_desc<Type>(mkldnn::prop_kind::forward, (args)...);
+  auto desc = tf_desc<Type>(dnnl::prop_kind::forward, (args)...);
   auto pd = new tf_pd<Type>(desc, e);
   return std::shared_ptr<tf_pd<Type>>(pd);
 }
@@ -129,10 +129,10 @@ struct mkldnn_dummy_primitive {
   struct desc {};
 };
 
-inline mkldnn::memory::desc MKLDNNMemDesc(const std::vector<int64_t>& dims,
-                                          mkldnn::memory::data_type data_type,
+inline dnnl::memory::desc MKLDNNMemDesc(const std::vector<int64_t>& dims,
+                                        dnnl::memory::data_type data_type,
                                         MKLDNNMemoryFormat format) {
-  return mkldnn::memory::desc({dims}, data_type, format);
+  return dnnl::memory::desc({dims}, data_type, format);
 }
 
 inline void ClearMKLDNNCache(const platform::Place& place,
@@ -159,36 +159,35 @@ inline void DontClearMKLDNNCache(const platform::Place& place) {
 }
 
 template <typename Type>
-mkldnn::memory::data_type MKLDNNGetDataType() {
-  return mkldnn::memory::data_type::undef;
+dnnl::memory::data_type MKLDNNGetDataType() {
+  return dnnl::memory::data_type::undef;
 }
 
 template <>
-inline mkldnn::memory::data_type MKLDNNGetDataType<float>() {
-  return mkldnn::memory::data_type::f32;
+inline dnnl::memory::data_type MKLDNNGetDataType<float>() {
+  return dnnl::memory::data_type::f32;
 }
 template <>
-inline mkldnn::memory::data_type MKLDNNGetDataType<int32_t>() {
-  return mkldnn::memory::data_type::s32;
+inline dnnl::memory::data_type MKLDNNGetDataType<int32_t>() {
+  return dnnl::memory::data_type::s32;
 }
 template <>
-inline mkldnn::memory::data_type MKLDNNGetDataType<int8_t>() {
-  return mkldnn::memory::data_type::s8;
+inline dnnl::memory::data_type MKLDNNGetDataType<int8_t>() {
+  return dnnl::memory::data_type::s8;
 }
 template <>
-inline mkldnn::memory::data_type MKLDNNGetDataType<uint8_t>() {
-  return mkldnn::memory::data_type::u8;
+inline dnnl::memory::data_type MKLDNNGetDataType<uint8_t>() {
+  return dnnl::memory::data_type::u8;
 }
 
 template <>
-inline mkldnn::memory::data_type
-MKLDNNGetDataType<paddle::platform::bfloat16>() {
-  return mkldnn::memory::data_type::bf16;
+inline dnnl::memory::data_type MKLDNNGetDataType<paddle::platform::bfloat16>() {
+  return dnnl::memory::data_type::bf16;
 }
 
-inline void Reorder(mkldnn::memory src, mkldnn::memory dst,
-                    const mkldnn::engine& engine) {
-  auto reorder_prim = mkldnn::reorder(src, dst);
+inline void Reorder(dnnl::memory src, dnnl::memory dst,
+                    const dnnl::engine& engine) {
+  auto reorder_prim = dnnl::reorder(src, dst);
   auto& astream = platform::MKLDNNDeviceContext::tls().get_stream();
   platform::RecordEvent record_reorder("int_reorder",
                                        platform::EventRole::kUniqueOp);
@@ -196,8 +195,7 @@ inline void Reorder(mkldnn::memory src, mkldnn::memory dst,
   astream.wait();
 }
 
-inline mkldnn::memory::format_tag GetMKLDNNFormat(
-    mkldnn::memory::desc mem_desc) {
+inline dnnl::memory::format_tag GetMKLDNNFormat(dnnl::memory::desc mem_desc) {
   auto ndims = mem_desc.data.ndims;
   auto strides = mem_desc.data.format_desc.blocking.strides;
   auto inner_nblks = mem_desc.data.format_desc.blocking.inner_nblks;
@@ -205,62 +203,62 @@ inline mkldnn::memory::format_tag GetMKLDNNFormat(
   auto inner_idxs = mem_desc.data.format_desc.blocking.inner_idxs;
 
   if (ndims == 1) {
-    return mkldnn::memory::format_tag::x;
+    return dnnl::memory::format_tag::x;
   } else if (ndims == 2) {
     if (inner_nblks == 0) {
       if (strides[0] >= strides[1]) {
-        return mkldnn::memory::format_tag::nc;
+        return dnnl::memory::format_tag::nc;
       } else {
-        return mkldnn::memory::format_tag::cn;
+        return dnnl::memory::format_tag::cn;
       }
     }
   } else if (ndims == 3) {
     if (inner_nblks == 0) {
       if (strides[0] >= strides[1] && strides[1] >= strides[2]) {
-        return mkldnn::memory::format_tag::ncw;
+        return dnnl::memory::format_tag::ncw;
       } else if (strides[1] >= strides[0] && strides[0] >= strides[2]) {
-        return mkldnn::memory::format_tag::ntc;
+        return dnnl::memory::format_tag::ntc;
       } else {
-        return mkldnn::memory::format_tag::nwc;
+        return dnnl::memory::format_tag::nwc;
       }
     }
   } else if (ndims == 4) {
     if (inner_nblks == 0) {
       if (strides[0] >= strides[1] && strides[1] >= strides[2] &&
           strides[2] >= strides[3]) {
-        return mkldnn::memory::format_tag::nchw;
+        return dnnl::memory::format_tag::nchw;
       } else if (strides[2] >= strides[3] && strides[3] >= strides[1] &&
                  strides[1] >= strides[0]) {
-        return mkldnn::memory::format_tag::cdba;
+        return dnnl::memory::format_tag::cdba;
       } else {
-        return mkldnn::memory::format_tag::nhwc;
+        return dnnl::memory::format_tag::nhwc;
       }
     } else if (inner_nblks == 1) {
       if (inner_blks[0] == 16 && inner_idxs[0] == 1) {
-        return mkldnn::memory::format_tag::nChw16c;
+        return dnnl::memory::format_tag::nChw16c;
       } else if (inner_blks[0] == 8 && inner_idxs[0] == 1) {
-        return mkldnn::memory::format_tag::nChw8c;
+        return dnnl::memory::format_tag::nChw8c;
       } else if (inner_blks[0] == 8 && inner_idxs[0] == 0) {
         if (strides[0] >= strides[2] && strides[2] >= strides[3] &&
             strides[3] >= strides[1]) {
-          return mkldnn::memory::format_tag::Acdb8a;
+          return dnnl::memory::format_tag::Acdb8a;
         }
       } else if (inner_blks[0] == 4 && inner_idxs[0] == 1) {
-        return mkldnn::memory::format_tag::nChw4c;
+        return dnnl::memory::format_tag::nChw4c;
       } else if (inner_blks[0] == 16 && inner_idxs[0] == 0) {
         if (strides[0] >= strides[2] && strides[2] >= strides[3] &&
             strides[3] >= strides[1]) {
-          return mkldnn::memory::format_tag::Acdb16a;
+          return dnnl::memory::format_tag::Acdb16a;
         }
       }
     } else if (inner_nblks == 2) {
       if (inner_blks[0] == 16 && inner_blks[1] == 16) {
         if (inner_idxs[0] == 1 && inner_idxs[1] == 0) {
-          return mkldnn::memory::format_tag::OIhw16i16o;
+          return dnnl::memory::format_tag::OIhw16i16o;
         }
       } else if (inner_blks[0] == 8 && inner_blks[1] == 8) {
         if (inner_idxs[0] == 1 && inner_idxs[1] == 0) {
-          return mkldnn::memory::format_tag::OIhw8i8o;
+          return dnnl::memory::format_tag::OIhw8i8o;
         }
       }
     }
@@ -268,38 +266,38 @@ inline mkldnn::memory::format_tag GetMKLDNNFormat(
     if (inner_nblks == 0) {
       if (strides[0] >= strides[1] && strides[1] >= strides[2] &&
           strides[2] >= strides[3] && strides[3] >= strides[4]) {
-        return mkldnn::memory::format_tag::ncdhw;
+        return dnnl::memory::format_tag::ncdhw;
       } else {
-        return mkldnn::memory::format_tag::ndhwc;
+        return dnnl::memory::format_tag::ndhwc;
       }
     } else if (inner_nblks == 1) {
       if (inner_blks[0] == 8 && inner_idxs[0] == 0) {
         if (strides[0] >= strides[2] && strides[2] >= strides[3] &&
             strides[3] >= strides[4] && strides[4] >= strides[1]) {
-          return mkldnn::memory::format_tag::Acdeb8a;
+          return dnnl::memory::format_tag::Acdeb8a;
         }
         if (strides[0] >= strides[1] && strides[1] >= strides[2] &&
             strides[2] >= strides[3] && strides[3] >= strides[4]) {
-          return mkldnn::memory::format_tag::Abcde8a;
+          return dnnl::memory::format_tag::Abcde8a;
         }
       } else if (inner_blks[0] == 8 && inner_idxs[0] == 1) {
         if (strides[0] >= strides[1] && strides[1] >= strides[2] &&
             strides[2] >= strides[3] && strides[3] >= strides[4]) {
-          return mkldnn::memory::format_tag::aBcde8b;
+          return dnnl::memory::format_tag::aBcde8b;
         }
       } else if (inner_blks[0] == 16 && inner_idxs[0] == 0) {
         if (strides[0] >= strides[2] && strides[2] >= strides[3] &&
             strides[3] >= strides[4] && strides[4] >= strides[1]) {
-          return mkldnn::memory::format_tag::Acdeb16a;
+          return dnnl::memory::format_tag::Acdeb16a;
         }
         if (strides[0] >= strides[1] && strides[1] >= strides[2] &&
             strides[2] >= strides[3] && strides[3] >= strides[4]) {
-          return mkldnn::memory::format_tag::Abcde16a;
+          return dnnl::memory::format_tag::Abcde16a;
         }
       } else if (inner_blks[0] == 16 && inner_idxs[0] == 1) {
         if (strides[0] >= strides[1] && strides[1] >= strides[2] &&
             strides[2] >= strides[3] && strides[3] >= strides[4]) {
-          return mkldnn::memory::format_tag::aBcde16b;
+          return dnnl::memory::format_tag::aBcde16b;
         }
       }
     }
@@ -308,7 +306,7 @@ inline mkldnn::memory::format_tag GetMKLDNNFormat(
       if (strides[0] >= strides[1] && strides[1] >= strides[2] &&
           strides[2] >= strides[3] && strides[3] >= strides[4] &&
           strides[4] >= strides[5]) {
-        return mkldnn::memory::format_tag::abcdef;
+        return dnnl::memory::format_tag::abcdef;
       }
     }
   }
@@ -325,10 +323,10 @@ inline mkldnn::memory::format_tag GetMKLDNNFormat(
   // for (int i=0;i<inner_nblks;++i) {
   //   std::cout<<"INNER_IDXS["<<i<<"]: "<<inner_idxs[i]<<std::endl;
   // }
-  return mkldnn::memory::format_tag::undef;
+  return dnnl::memory::format_tag::undef;
 }
 
-inline mkldnn::memory::format_tag GetMKLDNNFormat(const mkldnn::memory memory) {
+inline dnnl::memory::format_tag GetMKLDNNFormat(const dnnl::memory memory) {
   auto mem_desc = memory.get_desc();
   return GetMKLDNNFormat(mem_desc);
 }
@@ -441,24 +439,24 @@ inline void AppendKey(std::string* key, const T& num) {
 
 template <>
 inline void AppendKey(std::string* key,
-                      const mkldnn::memory::format_tag& format) {
+                      const dnnl::memory::format_tag& format) {
   key->append(std::to_string(static_cast<int>(format)));
 }
 
 template <>
 inline void AppendKey(std::string* key,
-                      const mkldnn::memory::data_type& data_type) {
+                      const dnnl::memory::data_type& data_type) {
   key->append(std::to_string(static_cast<int>(data_type)));
 }
 
 template <>
-inline void AppendKey(std::string* key, const mkldnn::algorithm& algorithm) {
+inline void AppendKey(std::string* key, const dnnl::algorithm& algorithm) {
   key->append(std::to_string(static_cast<int>(algorithm)));
 }
 
 template <>
 inline void AppendKey(std::string* key,
-                      const mkldnn::normalization_flags& flags) {
+                      const dnnl::normalization_flags& flags) {
   key->append(std::to_string(static_cast<int>(flags)));
 }
 

paddle/fluid/platform/mkldnn_reuse.h

@@ -33,14 +33,14 @@ namespace platform {
 using framework::DataLayout;
 using framework::Tensor;
 using user_function = std::function<std::shared_ptr<float>(const float*)>;
-using memory = mkldnn::memory;
+using memory = dnnl::memory;
 
 template <typename T, typename TForward,
           typename TBackward = mkldnn_dummy_primitive,
           typename TBackward_params = mkldnn_dummy_primitive>
 class MKLDNNHandlerNoCachingT {
  public:
-  MKLDNNHandlerNoCachingT(mkldnn::engine engine, platform::Place cpu_place)
+  MKLDNNHandlerNoCachingT(dnnl::engine engine, platform::Place cpu_place)
       : engine_(engine), place_(cpu_place), fwd_pd_(nullptr), bwd_pd_(nullptr) {
     platform::MKLDNNDeviceContext::tls().log_lib_version();
   }
@@ -60,7 +60,7 @@ class MKLDNNHandlerNoCachingT {
     return std::make_shared<TBackward_params>(*bwd_w_pd_);
   }
 
-  std::shared_ptr<mkldnn::memory> AcquireSrcMemory(
+  std::shared_ptr<dnnl::memory> AcquireSrcMemory(
       const framework::Tensor* input) {
     const T* input_data = input->data<T>();
     return this->AcquireMemoryFromPrimitive(fwd_pd_->src_desc(),
@@ -68,33 +68,33 @@ class MKLDNNHandlerNoCachingT {
   }
 
   template <typename T_out = T>
-  std::shared_ptr<mkldnn::memory> AcquireDstMemory(framework::Tensor* output) {
+  std::shared_ptr<dnnl::memory> AcquireDstMemory(framework::Tensor* output) {
     T_out* ptr =
         output->mutable_data<T_out>(place_, fwd_pd_->dst_desc().get_size());
     return this->AcquireMemoryFromPrimitive(fwd_pd_->dst_desc(), ptr);
   }
 
   template <typename T_out = T>
-  std::shared_ptr<mkldnn::memory> AcquireDstMemory(void) {
+  std::shared_ptr<dnnl::memory> AcquireDstMemory(void) {
     return this->AcquireMemoryFromPrimitive(fwd_pd_->dst_desc());
   }
 
   template <typename T_out = T>
-  std::shared_ptr<mkldnn::memory> AcquireDstMemory(
+  std::shared_ptr<dnnl::memory> AcquireDstMemory(
       const framework::Tensor* output) {
     const T_out* output_data = output->data<T_out>();
     return this->AcquireMemoryFromPrimitive(bwd_pd_->dst_desc(),
                                             to_void_cast<T_out>(output_data));
   }
 
-  std::shared_ptr<mkldnn::memory> AcquireDiffDstMemory(
+  std::shared_ptr<dnnl::memory> AcquireDiffDstMemory(
       const framework::Tensor* diffdst) {
     const T* ptr = diffdst->data<T>();
     return this->AcquireMemoryFromPrimitive(bwd_pd_->diff_dst_desc(),
                                             to_void_cast<T>(ptr));
   }
 
-  std::shared_ptr<mkldnn::memory> AcquireDiffSrcMemory(
+  std::shared_ptr<dnnl::memory> AcquireDiffSrcMemory(
       framework::Tensor* diffsrc) {
     T* ptr =
         diffsrc->mutable_data<T>(place_, bwd_pd_->diff_src_desc().get_size());
@@ -102,7 +102,7 @@ class MKLDNNHandlerNoCachingT {
   }
 
   // Buffer of given Tensor is used for oneDNN computation
-  std::shared_ptr<mkldnn::memory> AcquireDiffWeightsMemory(
+  std::shared_ptr<dnnl::memory> AcquireDiffWeightsMemory(
       framework::Tensor* diff_weights) {
     PADDLE_ENFORCE_NOT_NULL(
         bwd_w_pd_,
@@ -115,7 +115,7 @@ class MKLDNNHandlerNoCachingT {
   }
 
   // Buffer is allocated by oneDNN to store computation results
-  std::shared_ptr<mkldnn::memory> AcquireDiffWeightsMemory(void) {
+  std::shared_ptr<dnnl::memory> AcquireDiffWeightsMemory(void) {
     PADDLE_ENFORCE_NOT_NULL(
         bwd_w_pd_,
         platform::errors::Unavailable(
@@ -179,37 +179,36 @@ class MKLDNNHandlerNoCachingT {
         bwd_desc, engine_, *fwd_pd_);
   }
 
-  std::shared_ptr<mkldnn::memory> AcquireMemoryFromPrimitive(
-      mkldnn::memory::desc md, void* ptr) {
-    return std::make_shared<mkldnn::memory>(md, engine_, ptr);
+  std::shared_ptr<dnnl::memory> AcquireMemoryFromPrimitive(
+      dnnl::memory::desc md, void* ptr) {
+    return std::make_shared<dnnl::memory>(md, engine_, ptr);
   }
 
-  std::shared_ptr<mkldnn::memory> AcquireMemoryFromPrimitive(
-      mkldnn::memory::desc md) {
-    return std::make_shared<mkldnn::memory>(md, engine_);
+  std::shared_ptr<dnnl::memory> AcquireMemoryFromPrimitive(
+      dnnl::memory::desc md) {
+    return std::make_shared<dnnl::memory>(md, engine_);
   }
 
-  void AcquireReorder(const std::shared_ptr<mkldnn::memory>& user_memory_p,
-                      const std::shared_ptr<mkldnn::memory>& target_memory_p) {
+  void AcquireReorder(const std::shared_ptr<dnnl::memory>& user_memory_p,
+                      const std::shared_ptr<dnnl::memory>& target_memory_p) {
     auto reorder_p =
-        std::make_shared<mkldnn::reorder>(*user_memory_p, *target_memory_p);
+        std::make_shared<dnnl::reorder>(*user_memory_p, *target_memory_p);
 
     auto& astream = platform::MKLDNNDeviceContext::tls().get_stream();
 
     platform::RecordEvent record_reorder("int_reorder",
                                          platform::EventRole::kUniqueOp);
-    reorder_p->execute(astream, {{MKLDNN_ARG_FROM, *user_memory_p},
-                                 {MKLDNN_ARG_TO, *target_memory_p}});
+    reorder_p->execute(astream, {{DNNL_ARG_FROM, *user_memory_p},
+                                 {DNNL_ARG_TO, *target_memory_p}});
     astream.wait();
   }
 
   template <typename F = T>
-  std::shared_ptr<mkldnn::memory> AcquireMemoryWithReorder(
-      const mkldnn::memory::desc& user_md,
-      const mkldnn::memory::desc& target_md, void* ptr,
-      bool is_persistent = false,
+  std::shared_ptr<dnnl::memory> AcquireMemoryWithReorder(
+      const dnnl::memory::desc& user_md, const dnnl::memory::desc& target_md,
+      void* ptr, bool is_persistent = false,
       std::function<std::shared_ptr<F>(const F*)> custom_reorder_func = {}) {
-    std::shared_ptr<mkldnn::memory> target_memory_p;
+    std::shared_ptr<dnnl::memory> target_memory_p;
     if (custom_reorder_func) {
       auto reordered_data =
           custom_reorder_func(reinterpret_cast<const F*>(ptr));
@@ -217,15 +216,15 @@ class MKLDNNHandlerNoCachingT {
     }
     auto user_memory_p = std::make_shared<dnnl::memory>(user_md, engine_, ptr);
     if (user_md != target_md) {
-      target_memory_p = std::make_shared<mkldnn::memory>(target_md, engine_);
+      target_memory_p = std::make_shared<dnnl::memory>(target_md, engine_);
       auto reorder_p =
           std::make_shared<dnnl::reorder>(*user_memory_p, *target_memory_p);
 
       auto& astream = platform::MKLDNNDeviceContext::tls().get_stream();
       platform::RecordEvent record_reorder("int_reorder",
                                            platform::EventRole::kUniqueOp);
-      reorder_p->execute(astream, {{MKLDNN_ARG_FROM, *user_memory_p},
-                                   {MKLDNN_ARG_TO, *target_memory_p}});
+      reorder_p->execute(astream, {{DNNL_ARG_FROM, *user_memory_p},
+                                   {DNNL_ARG_TO, *target_memory_p}});
       astream.wait();
     } else {
       target_memory_p = user_memory_p;
@@ -233,7 +232,7 @@ class MKLDNNHandlerNoCachingT {
     return target_memory_p;
   }
 
-  mkldnn::engine engine_;
+  dnnl::engine engine_;
   platform::Place place_;
   std::shared_ptr<typename TForward::primitive_desc> fwd_pd_;
   std::shared_ptr<typename TBackward::primitive_desc> bwd_pd_;
@@ -245,7 +244,7 @@ template <typename T, typename TForward,
           typename TBackward_params = mkldnn_dummy_primitive>
 class MKLDNNHandlerT {
  public:
-  MKLDNNHandlerT(const MKLDNNDeviceContext& dev_ctx, mkldnn::engine engine,
+  MKLDNNHandlerT(const MKLDNNDeviceContext& dev_ctx, dnnl::engine engine,
                  platform::Place cpu_place, const std::string& base_key)
       : dev_ctx_(dev_ctx),
         engine_(engine),
@@ -294,7 +293,7 @@ class MKLDNNHandlerT {
     return backward_p;
   }
 
-  std::shared_ptr<mkldnn::memory> AcquireSrcMemory(
+  std::shared_ptr<dnnl::memory> AcquireSrcMemory(
       const framework::Tensor* input) {
     const T* input_data = input->data<T>();
     return this->AcquireMemoryFromPrimitive(
@@ -302,7 +301,7 @@ class MKLDNNHandlerT {
   }
 
   template <typename T_out = T>
-  std::shared_ptr<mkldnn::memory> AcquireDstMemory(framework::Tensor* output) {
+  std::shared_ptr<dnnl::memory> AcquireDstMemory(framework::Tensor* output) {
     T_out* ptr =
         output->mutable_data<T_out>(place_, fwd_pd_->dst_desc().get_size());
     return this->AcquireMemoryFromPrimitive(fwd_pd_->dst_desc(), ptr,
@@ -310,12 +309,12 @@ class MKLDNNHandlerT {
   }
 
   template <typename T_out = T>
-  std::shared_ptr<mkldnn::memory> AcquireDstMemory(void) {
+  std::shared_ptr<dnnl::memory> AcquireDstMemory(void) {
     return this->AcquireMemoryFromPrimitive(fwd_pd_->dst_desc(), "@dstt_mem_p");
   }
 
   template <typename T_out = T>
-  std::shared_ptr<mkldnn::memory> AcquireDstMemory(
+  std::shared_ptr<dnnl::memory> AcquireDstMemory(
       const framework::Tensor* output) {
     const T_out* output_data = output->data<T_out>();
     return this->AcquireMemoryFromPrimitive(bwd_pd_->dst_desc(),
@@ -323,14 +322,14 @@ class MKLDNNHandlerT {
                                             "@bwd-dst_mem_p");
   }
 
-  std::shared_ptr<mkldnn::memory> AcquireDiffDstMemory(
+  std::shared_ptr<dnnl::memory> AcquireDiffDstMemory(
       const framework::Tensor* diffdst) {
     const T* ptr = diffdst->data<T>();
     return this->AcquireMemoryFromPrimitive(
         bwd_pd_->diff_dst_desc(), to_void_cast<T>(ptr), "@diff_dst_mem_p");
   }
 
-  std::shared_ptr<mkldnn::memory> AcquireDiffSrcMemory(
+  std::shared_ptr<dnnl::memory> AcquireDiffSrcMemory(
       framework::Tensor* diffsrc) {
     T* ptr =
         diffsrc->mutable_data<T>(place_, bwd_pd_->diff_src_desc().get_size());
@@ -339,7 +338,7 @@ class MKLDNNHandlerT {
   }
 
   // Buffer of given Tensor is used for oneDNN computation
-  std::shared_ptr<mkldnn::memory> AcquireDiffWeightsMemory(
+  std::shared_ptr<dnnl::memory> AcquireDiffWeightsMemory(
       framework::Tensor* diff_weights) {
     PADDLE_ENFORCE_NOT_NULL(
         bwd_w_pd_,
@@ -352,7 +351,7 @@ class MKLDNNHandlerT {
   }
 
   // Buffer is allocated by oneDNN to store computation results
-  std::shared_ptr<mkldnn::memory> AcquireDiffWeightsMemory(void) {
+  std::shared_ptr<dnnl::memory> AcquireDiffWeightsMemory(void) {
     PADDLE_ENFORCE_NOT_NULL(
         bwd_w_pd_,
         platform::errors::Unavailable(
@@ -467,19 +466,19 @@ class MKLDNNHandlerT {
     }
   }
 
-  std::shared_ptr<mkldnn::memory> AcquireMemoryFromPrimitive(
+  std::shared_ptr<dnnl::memory> AcquireMemoryFromPrimitive(
       const std::string& suffix) {
-    return std::static_pointer_cast<mkldnn::memory>(
+    return std::static_pointer_cast<dnnl::memory>(
         dev_ctx_.GetBlob(key_ + suffix));
   }
 
-  std::shared_ptr<mkldnn::memory> AcquireMemoryFromPrimitive(
-      mkldnn::memory::desc md, void* ptr, const std::string& suffix) {
+  std::shared_ptr<dnnl::memory> AcquireMemoryFromPrimitive(
+      dnnl::memory::desc md, void* ptr, const std::string& suffix) {
     const auto local_key = key_ + suffix;
     auto mem_p =
-        std::static_pointer_cast<mkldnn::memory>(dev_ctx_.GetBlob(local_key));
+        std::static_pointer_cast<dnnl::memory>(dev_ctx_.GetBlob(local_key));
     if (mem_p == nullptr) {
-      mem_p = std::make_shared<mkldnn::memory>(md, engine_, ptr);
+      mem_p = std::make_shared<dnnl::memory>(md, engine_, ptr);
       dev_ctx_.SetBlob(local_key, mem_p);
     } else {
       mem_p->set_data_handle(ptr);
@@ -487,37 +486,36 @@ class MKLDNNHandlerT {
     return mem_p;
   }
 
-  std::shared_ptr<mkldnn::memory> AcquireMemoryFromPrimitive(
-      mkldnn::memory::desc md, const std::string& suffix) {
+  std::shared_ptr<dnnl::memory> AcquireMemoryFromPrimitive(
+      dnnl::memory::desc md, const std::string& suffix) {
     const auto local_key = key_ + suffix;
     auto mem_p =
-        std::static_pointer_cast<mkldnn::memory>(dev_ctx_.GetBlob(local_key));
+        std::static_pointer_cast<dnnl::memory>(dev_ctx_.GetBlob(local_key));
     if (mem_p == nullptr) {
-      mem_p = std::make_shared<mkldnn::memory>(md, engine_);
+      mem_p = std::make_shared<dnnl::memory>(md, engine_);
       dev_ctx_.SetBlob(local_key, mem_p);
     }
     return mem_p;
   }
 
-  void AcquireReorder(const std::shared_ptr<mkldnn::memory>& user_memory_p,
-                      const std::shared_ptr<mkldnn::memory>& target_memory_p) {
+  void AcquireReorder(const std::shared_ptr<dnnl::memory>& user_memory_p,
+                      const std::shared_ptr<dnnl::memory>& target_memory_p) {
     auto reorder_p =
-        std::make_shared<mkldnn::reorder>(*user_memory_p, *target_memory_p);
+        std::make_shared<dnnl::reorder>(*user_memory_p, *target_memory_p);
 
     auto& astream = platform::MKLDNNDeviceContext::tls().get_stream();
 
     platform::RecordEvent record_reorder("int_reorder",
                                          platform::EventRole::kUniqueOp);
-    reorder_p->execute(astream, {{MKLDNN_ARG_FROM, *user_memory_p},
-                                 {MKLDNN_ARG_TO, *target_memory_p}});
+    reorder_p->execute(astream, {{DNNL_ARG_FROM, *user_memory_p},
+                                 {DNNL_ARG_TO, *target_memory_p}});
     astream.wait();
   }
 
   template <typename F = T>
-  std::shared_ptr<mkldnn::memory> AcquireMemoryWithReorder(
-      const mkldnn::memory::desc& user_md,
-      const mkldnn::memory::desc& target_md, void* ptr,
-      const std::string& suffix, bool is_persistent = false,
+  std::shared_ptr<dnnl::memory> AcquireMemoryWithReorder(
+      const dnnl::memory::desc& user_md, const dnnl::memory::desc& target_md,
+      void* ptr, const std::string& suffix, bool is_persistent = false,
       std::function<std::shared_ptr<F>(const F*)> custom_reorder_func = {},
       const std::vector<float>& scale_data = {1.0f}, int mask = 0) {
     const auto target_key = key_ + suffix + "_target";
@@ -537,7 +535,7 @@ class MKLDNNHandlerT {
       auto user_memory_p =
           std::make_shared<dnnl::memory>(user_md, engine_, ptr);
       if (user_md != target_md) {
-        target_memory_p = std::make_shared<mkldnn::memory>(target_md, engine_);
+        target_memory_p = std::make_shared<dnnl::memory>(target_md, engine_);
         dnnl::reorder::primitive_desc reorder_pdesc;
         if (is_int8<T>()) {
           dnnl::primitive_attr attr;
@@ -554,8 +552,8 @@ class MKLDNNHandlerT {
         auto& astream = platform::MKLDNNDeviceContext::tls().get_stream();
         platform::RecordEvent record_reorder("int_reorder",
                                              platform::EventRole::kUniqueOp);
-        reorder_p->execute(astream, {{MKLDNN_ARG_FROM, *user_memory_p},
-                                     {MKLDNN_ARG_TO, *target_memory_p}});
+        reorder_p->execute(astream, {{DNNL_ARG_FROM, *user_memory_p},
+                                     {DNNL_ARG_TO, *target_memory_p}});
         astream.wait();
       } else {
         target_memory_p = user_memory_p;
@@ -571,27 +569,26 @@ class MKLDNNHandlerT {
 
       // TODO(jczaja): Here we detect if reorder is cached it means it is needed
       // need to change this to get rid of keys
-      auto reorder_p = std::static_pointer_cast<mkldnn::reorder>(
+      auto reorder_p = std::static_pointer_cast<dnnl::reorder>(
           dev_ctx_.GetBlob(key_reorder_p));
       if (reorder_p != nullptr) {
         platform::RecordEvent record_reorder("int_reorder",
                                              platform::EventRole::kUniqueOp);
-        reorder_p->execute(astream, {{MKLDNN_ARG_FROM, *user_memory_p},
-                                     {MKLDNN_ARG_TO, *target_memory_p}});
+        reorder_p->execute(astream, {{DNNL_ARG_FROM, *user_memory_p},
+                                     {DNNL_ARG_TO, *target_memory_p}});
         astream.wait();
       }
     }
     return target_memory_p;
   }
 
-  std::shared_ptr<mkldnn::memory> AcquireMemory(const std::string& suffix) {
+  std::shared_ptr<dnnl::memory> AcquireMemory(const std::string& suffix) {
     const auto local_key = key_ + suffix;
-    return std::static_pointer_cast<mkldnn::memory>(
-        dev_ctx_.GetBlob(local_key));
+    return std::static_pointer_cast<dnnl::memory>(dev_ctx_.GetBlob(local_key));
   }
 
   const MKLDNNDeviceContext& dev_ctx_;
-  mkldnn::engine engine_;
+  dnnl::engine engine_;
   platform::Place place_;
   std::string key_common_;
   std::string key_;
@@ -605,7 +602,7 @@ class BinaryMKLDNNHandler
     : public platform::MKLDNNHandlerNoCachingT<T, dnnl::binary> {
  public:
   BinaryMKLDNNHandler(const dnnl::algorithm algo, const int axis,
-                      const mkldnn::engine engine, platform::Place cpu_place,
+                      const dnnl::engine engine, platform::Place cpu_place,
                       const Tensor* x, const Tensor* y, Tensor* z,
                       float scale_x, float scale_y, float scale_z,
                       const dnnl::post_ops& post_ops = dnnl::post_ops())
@@ -662,7 +659,7 @@ class BinaryMKLDNNHandler
     this->AcquireForwardPrimitiveDescriptor(attributes, algo, src0_md, src1_md,
                                             dst_md);
   }
-  std::shared_ptr<mkldnn::memory> AcquireSecondSrcMemory(
+  std::shared_ptr<dnnl::memory> AcquireSecondSrcMemory(
       const framework::Tensor* input) {
     const T* input_data = input->data<T>();
     return this->AcquireMemoryFromPrimitive(this->fwd_pd_->src1_desc(),
@@ -707,7 +704,7 @@ class BroadcastDataMKLDNNHandler
     : public platform::MKLDNNHandlerNoCachingT<T, dnnl::binary> {
  public:
   BroadcastDataMKLDNNHandler(const dnnl::algorithm algo,
-                             const mkldnn::engine engine,
+                             const dnnl::engine engine,
                              platform::Place cpu_place, const Tensor* out,
                              const Tensor* x, float scale_x, float scale_y,
                              const std::vector<int64_t>& input_dims)
@@ -735,7 +732,7 @@ class BroadcastDataMKLDNNHandler
   }
 
   template <typename T_out = T>
-  std::shared_ptr<mkldnn::memory> AcquireDstMemory(framework::Tensor* output) {
+  std::shared_ptr<dnnl::memory> AcquireDstMemory(framework::Tensor* output) {
     T_out* ptr = output->mutable_data<T_out>(
         this->place_, this->fwd_pd_->dst_desc().get_size());
     memset(ptr, 0, this->fwd_pd_->dst_desc().get_size());
@@ -748,7 +745,7 @@ class ReductionMKLDNNHandler
     : public platform::MKLDNNHandlerNoCachingT<T, dnnl::reduction> {
  public:
   ReductionMKLDNNHandler(const dnnl::algorithm algo, const float p,
-                         const float eps, const mkldnn::engine engine,
+                         const float eps, const dnnl::engine engine,
                          platform::Place cpu_place, const Tensor* x,
                          const Tensor* y, std::vector<int64_t> y_tz,
                          const dnnl::primitive_attr& attr = NULL)
@@ -777,15 +774,15 @@ class ReductionMKLDNNHandler
 
 template <typename T>
 class ActivationMKLDNNHandler
-    : public MKLDNNHandlerNoCachingT<T, mkldnn::eltwise_forward,
-                                     mkldnn::eltwise_backward> {
+    : public MKLDNNHandlerNoCachingT<T, dnnl::eltwise_forward,
+                                     dnnl::eltwise_backward> {
  public:
-  ActivationMKLDNNHandler(mkldnn::algorithm algorithm,
+  ActivationMKLDNNHandler(dnnl::algorithm algorithm,
                           const framework::ExecutionContext& ctx,
-                          const mkldnn::engine engine, Place cpu_place,
+                          const dnnl::engine engine, Place cpu_place,
                           const framework::Tensor* in_x)
-      : platform::MKLDNNHandlerNoCachingT<T, mkldnn::eltwise_forward,
-                                          mkldnn::eltwise_backward>(engine,
+      : platform::MKLDNNHandlerNoCachingT<T, dnnl::eltwise_forward,
+                                          dnnl::eltwise_backward>(engine,
                                                                   cpu_place) {
     float alpha = ctx.HasAttr("alpha") ? ctx.Attr<float>("alpha") : 0;
     float beta = ctx.HasAttr("beta") ? ctx.Attr<float>("beta") : 0;
@@ -811,7 +808,7 @@ class ActivationMKLDNNHandler
                                  : ctx.Attr<float>("max");
     } else {
       // paddle uses beta but mkldnn uses alpha for swish
-      if (algorithm == mkldnn::algorithm::eltwise_swish) {
+      if (algorithm == dnnl::algorithm::eltwise_swish) {
         std::swap(alpha, beta);
       } else if (algorithm == dnnl::algorithm::eltwise_bounded_relu) {
         alpha = ctx.Attr<float>("threshold");
@@ -827,24 +824,24 @@ class ActivationMKLDNNHandler
     auto src_tz = framework::vectorize<int64_t>(in_x->dims());
     auto src_fmt = src_tz.size() == 2 ? MKLDNNMemoryFormat::nc : in_x->format();
     auto md =
-        mkldnn::memory::desc(src_tz, platform::MKLDNNGetDataType<T>(), src_fmt);
+        dnnl::memory::desc(src_tz, platform::MKLDNNGetDataType<T>(), src_fmt);
 
-    this->AcquireForwardPrimitiveDescriptor(mkldnn::prop_kind::forward_training,
+    this->AcquireForwardPrimitiveDescriptor(dnnl::prop_kind::forward_training,
                                             algorithm, md, alpha, beta);
   }
 
-  ActivationMKLDNNHandler(mkldnn::algorithm algorithm,
+  ActivationMKLDNNHandler(dnnl::algorithm algorithm,
                           const framework::ExecutionContext& ctx,
-                          const mkldnn::engine engine, Place cpu_place,
+                          const dnnl::engine engine, Place cpu_place,
                           const framework::Tensor* in_x, const Tensor* out_grad)
-      : platform::MKLDNNHandlerNoCachingT<T, mkldnn::eltwise_forward,
-                                          mkldnn::eltwise_backward>(engine,
+      : platform::MKLDNNHandlerNoCachingT<T, dnnl::eltwise_forward,
+                                          dnnl::eltwise_backward>(engine,
                                                                   cpu_place) {
     float alpha = ctx.HasAttr("alpha") ? ctx.Attr<float>("alpha") : 0;
     float beta = ctx.HasAttr("beta") ? ctx.Attr<float>("beta") : 0;
 
     // paddle uses beta but mkldnn uses alpha for swish
-    if (algorithm == mkldnn::algorithm::eltwise_swish) {
+    if (algorithm == dnnl::algorithm::eltwise_swish) {
       std::swap(alpha, beta);
     } else if (algorithm == dnnl::algorithm::eltwise_bounded_relu) {
       alpha = ctx.Attr<float>("threshold");
@@ -870,13 +867,13 @@ class ActivationMKLDNNHandler
     auto src_md = platform::MKLDNNMemDesc(
         dims, platform::MKLDNNGetDataType<T>(), src_fmt);
 
-    this->AcquireForwardPrimitiveDescriptor(mkldnn::prop_kind::forward_training,
+    this->AcquireForwardPrimitiveDescriptor(dnnl::prop_kind::forward_training,
                                             algorithm, src_md, alpha, beta);
     this->AcquireBackwardPrimitiveDescriptor(algorithm, diff_dst_md, src_md,
                                              alpha, beta);
   }
 
-  std::shared_ptr<mkldnn::memory> AcquireBackwardSrcMemory(
+  std::shared_ptr<dnnl::memory> AcquireBackwardSrcMemory(
       const framework::Tensor* input) {
     const T* input_data = input->data<T>();
     return this->AcquireMemoryFromPrimitive(this->bwd_pd_->src_desc(),
@@ -888,7 +885,7 @@ class ReorderMKLDNNHandler {
  public:
   ReorderMKLDNNHandler(std::vector<int64_t>& dims,  // NOLINT
                        framework::proto::VarType::Type vtype,
-                       mkldnn::memory::data_type dtype, mkldnn::engine engine)
+                       dnnl::memory::data_type dtype, dnnl::engine engine)
       : dims_(dims),
         vtype_(vtype),
         vtype_dst_(vtype),
@@ -898,10 +895,9 @@ class ReorderMKLDNNHandler {
 
   ReorderMKLDNNHandler(std::vector<int64_t>& dims,  // NOLINT
                        framework::proto::VarType::Type vtype,
-                       mkldnn::memory::data_type dtype,
+                       dnnl::memory::data_type dtype,
                        framework::proto::VarType::Type vtype_dst,
-                       mkldnn::memory::data_type dtype_dst,
-                       mkldnn::engine engine)
+                       dnnl::memory::data_type dtype_dst, dnnl::engine engine)
       : dims_(dims),
         vtype_(vtype),
         vtype_dst_(vtype_dst),
@@ -909,56 +905,56 @@ class ReorderMKLDNNHandler {
         dtype_dst_(dtype_dst),
         engine_(engine) {}
 
-  std::shared_ptr<mkldnn::memory> AcquireSrcMemory(
-      const MKLDNNMemoryFormat& fmt, void* ptr) {
-    auto md = mkldnn::memory::desc(dims_, dtype_, fmt);
-    return std::make_shared<mkldnn::memory>(md, engine_, ptr);
+  std::shared_ptr<dnnl::memory> AcquireSrcMemory(const MKLDNNMemoryFormat& fmt,
+                                                 void* ptr) {
+    auto md = dnnl::memory::desc(dims_, dtype_, fmt);
+    return std::make_shared<dnnl::memory>(md, engine_, ptr);
   }
 
-  std::shared_ptr<mkldnn::memory> AcquireSubmemory(
+  std::shared_ptr<dnnl::memory> AcquireSubmemory(
       const std::vector<int64_t>& dims, const std::vector<int64_t>& offset,
-      const std::shared_ptr<mkldnn::memory>& mem_p) {
+      const std::shared_ptr<dnnl::memory>& mem_p) {
     auto sub_md = mem_p->get_desc().submemory_desc(dims, {offset});
-    auto sub_mem_p = std::make_shared<mkldnn::memory>(sub_md, engine_,
+    auto sub_mem_p = std::make_shared<dnnl::memory>(sub_md, engine_,
                                                     mem_p->get_data_handle());
     return sub_mem_p;
   }
 
-  std::shared_ptr<mkldnn::memory> AcquireDstMemory(
-      framework::Tensor* output, const MKLDNNMemoryFormat& fmt,
+  std::shared_ptr<dnnl::memory> AcquireDstMemory(framework::Tensor* output,
+                                                 const MKLDNNMemoryFormat& fmt,
                                                  platform::Place place) {
     auto dst_md = platform::MKLDNNMemDesc(dims_, dtype_dst_, fmt);
     auto dst_data = output->mutable_data(place, vtype_dst_, dst_md.get_size());
-    return std::make_shared<mkldnn::memory>(dst_md, engine_, dst_data);
+    return std::make_shared<dnnl::memory>(dst_md, engine_, dst_data);
   }
 
-  std::shared_ptr<mkldnn::memory> AcquireDstMemory(
+  std::shared_ptr<dnnl::memory> AcquireDstMemory(
       framework::Tensor* output, const std::vector<int64_t>& dims,
       const MKLDNNMemoryFormat& fmt, platform::Place place) {
     auto dst_md = platform::MKLDNNMemDesc(dims, dtype_dst_, fmt);
     auto dst_data = output->mutable_data(place, vtype_dst_, dst_md.get_size());
-    return std::make_shared<mkldnn::memory>(dst_md, engine_, dst_data);
+    return std::make_shared<dnnl::memory>(dst_md, engine_, dst_data);
   }
 
-  std::shared_ptr<mkldnn::reorder> AcquireReorder(
-      std::shared_ptr<mkldnn::memory> dst_memory_p,
-      std::shared_ptr<mkldnn::memory> src_memory_p) {
-    return std::make_shared<mkldnn::reorder>(*(src_memory_p), *(dst_memory_p));
+  std::shared_ptr<dnnl::reorder> AcquireReorder(
+      std::shared_ptr<dnnl::memory> dst_memory_p,
+      std::shared_ptr<dnnl::memory> src_memory_p) {
+    return std::make_shared<dnnl::reorder>(*(src_memory_p), *(dst_memory_p));
   }
 
  private:
   std::vector<int64_t> dims_;
   framework::proto::VarType::Type vtype_, vtype_dst_;
-  mkldnn::memory::data_type dtype_, dtype_dst_;
-  mkldnn::engine engine_;
+  dnnl::memory::data_type dtype_, dtype_dst_;
+  dnnl::engine engine_;
 };
 
 template <typename T>
 static void SetDstMemoryQuantized(
     const framework::ExecutionContext& ctx, framework::Tensor* output,
-    std::vector<int64_t> dst_tz, const mkldnn::engine& engine,
-    std::shared_ptr<mkldnn::memory::desc>& dst_md,  // NOLINT
-    std::shared_ptr<mkldnn::memory>& dst_memory,    // NOLINT
+    std::vector<int64_t> dst_tz, const dnnl::engine& engine,
+    std::shared_ptr<dnnl::memory::desc>& dst_md,  // NOLINT
+    std::shared_ptr<dnnl::memory>& dst_memory,    // NOLINT
     MKLDNNMemoryFormat output_format) {
   T* output_data = output->mutable_data<T>(ctx.GetPlace());
   const size_t dst_dims = dst_tz.size();
@@ -974,9 +970,9 @@ static void SetDstMemoryQuantized(
       {dst_tz}, paddle::framework::ToMKLDNNDataType(
                     framework::DataTypeTrait<T>::DataType()),
       dst_fmt);
-  dst_md.reset(new mkldnn::memory::desc(tmp_dst_md));
+  dst_md.reset(new dnnl::memory::desc(tmp_dst_md));
   dst_memory.reset(
-      new mkldnn::memory(*dst_md, engine, to_void_cast<T>(output_data)));
+      new dnnl::memory(*dst_md, engine, to_void_cast<T>(output_data)));
 }
 
 }  // namespace platform