Merge remote-tracking branch 'upstream/develop' into windows/build

CMakeLists.txt

@@ -71,6 +71,8 @@ option(WITH_ARM_FP16    "Use half precision support on armv8.2-a cpu"   OFF)
 option(WITH_CONTRIB     "Compile the third-party contributation"        OFF)
 option(REPLACE_ENFORCE_GLOG "Replace PADDLE_ENFORCE with glog/CHECK for better debug." OFF)
 option(WITH_ANAKIN      "Compile with Anakin library"                   OFF)
+option(ANAKIN_BUILD_FAT_BIN "Build anakin cuda fat-bin lib for all device plantform, ignored when WITH_ANAKIN=OFF" OFF)
+option(ANAKIN_BUILD_CROSS_PLANTFORM "Build anakin lib for any nvidia device plantform. ignored when WITH_ANAKIN=OFF" ON)
 option(WITH_GRPC     "Use grpc as the default rpc framework"            ${WITH_DISTRIBUTE})
 option(WITH_BRPC_RDMA     "Use brpc rdma as the rpc protocal"           OFF)
 option(ON_INFER         "Turn on inference optimization."               OFF)

cmake/external/anakin.cmake

@@ -58,19 +58,21 @@ ExternalProject_Add(
                         -DPROTOBUF_ROOT=${THIRD_PARTY_PATH}/install/protobuf
                         -DMKLML_ROOT=${THIRD_PARTY_PATH}/install/mklml
                         -DENABLE_OP_TIMER=${ANAKIN_ENABLE_OP_TIMER}
+                        -DBUILD_FAT_BIN=${ANAKIN_BUILD_FAT_BIN}
+                        -DBUILD_CROSS_PLANTFORM=${ANAKIN_BUILD_CROSS_PLANTFORM}
                         ${EXTERNAL_OPTIONAL_ARGS}
     CMAKE_CACHE_ARGS    -DCMAKE_INSTALL_PREFIX:PATH=${ANAKIN_INSTALL_DIR}
 )
 
 message(STATUS "Anakin for inference is enabled")
 message(STATUS "Anakin is set INCLUDE:${ANAKIN_INCLUDE} LIBRARY:${ANAKIN_LIBRARY}")
-
+add_dependencies(extern_anakin protobuf mklml)
 add_library(anakin_shared SHARED IMPORTED GLOBAL)
 set_property(TARGET anakin_shared PROPERTY IMPORTED_LOCATION ${ANAKIN_SHARED_LIB})
-add_dependencies(anakin_shared extern_anakin protobuf mklml)
+add_dependencies(anakin_shared extern_anakin)
 
 add_library(anakin_saber SHARED IMPORTED GLOBAL)
 set_property(TARGET anakin_saber PROPERTY IMPORTED_LOCATION ${ANAKIN_SABER_LIB})
-add_dependencies(anakin_saber extern_anakin protobuf mklml)
+add_dependencies(anakin_saber extern_anakin)
 
 list(APPEND external_project_dependencies anakin_shared anakin_saber)

paddle/fluid/framework/CMakeLists.txt

@@ -136,6 +136,11 @@ cc_library(version SRCS version.cc)
 cc_test(version_test SRCS version_test.cc DEPS version)
 
 cc_library(proto_desc SRCS var_desc.cc op_desc.cc block_desc.cc program_desc.cc DEPS shape_inference op_info operator glog version)
+cc_library(ngraph_bridge SRCS ngraph_bridge.cc DEPS operator framework_proto)
+if(NOT WIN32)
+cc_library(ngraph_operator SRCS ngraph_operator.cc DEPS ngraph_bridge operator op_info device_context tensor scope glog
+  shape_inference data_transform lod_tensor profiler)
+endif(NOT WIN32)
 
 cc_library(op_registry SRCS op_registry.cc DEPS op_proto_maker op_info operator glog proto_desc)
 nv_test(op_registry_test SRCS op_registry_test.cc DEPS op_registry)
@@ -170,7 +175,11 @@ if(WITH_DISTRIBUTE)
   set(DISTRIBUTE_COMPILE_FLAGS "-Wno-non-virtual-dtor -Wno-error=non-virtual-dtor -Wno-error=delete-non-virtual-dtor")
   set_source_files_properties(executor.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS})
 else()
+  if(NOT WIN32)
+    cc_library(executor SRCS executor.cc DEPS op_registry device_context scope framework_proto glog lod_rank_table feed_fetch_method graph_to_program_pass ngraph_operator)
+  else(NOT WIN32)
     cc_library(executor SRCS executor.cc DEPS op_registry device_context scope framework_proto glog lod_rank_table feed_fetch_method graph_to_program_pass)
+  endif(NOT WIN32)
   cc_test(test_naive_executor SRCS naive_executor_test.cc DEPS naive_executor elementwise_add_op)
 endif()
 

paddle/fluid/framework/executor.cc

@@ -17,6 +17,7 @@ limitations under the License. */
 #include "paddle/fluid/framework/feed_fetch_method.h"
 #include "paddle/fluid/framework/lod_rank_table.h"
 #include "paddle/fluid/framework/lod_tensor_array.h"
+#include "paddle/fluid/framework/ngraph_operator.h"
 #include "paddle/fluid/framework/op_registry.h"
 #include "paddle/fluid/framework/reader.h"
 #include "paddle/fluid/operators/detail/macros.h"
@@ -25,6 +26,7 @@ limitations under the License. */
 
 DECLARE_bool(benchmark);
 DEFINE_bool(use_mkldnn, false, "Use MKLDNN to run");
+DEFINE_bool(use_ngraph, false, "Use NGRAPH to run");
 
 namespace paddle {
 namespace framework {
@@ -81,6 +83,24 @@ static void DeleteUnusedTensors(const Scope& scope, const OperatorBase* op,
   }
 }
 
+static void EnableFusedOp(ExecutorPrepareContext* ctx) {
+#ifdef PADDLE_WITH_NGRAPH
+  VLOG(3) << "use_ngraph=True";
+  auto intervals = FusedOperator::FusedOpIntervals(&ctx->ops_);
+  for (auto& interval : intervals) {
+    auto* fused_op = new FusedOperator(ctx->prog_, ctx->block_id_,
+                                       interval.at(0), interval.at(1));
+    *interval[0] = std::unique_ptr<OperatorBase>(fused_op);
+  }
+  for (auto it = intervals.rbegin(); it != intervals.rend(); ++it) {
+    ctx->ops_.erase(it->at(0) + 1, it->at(1));
+  }
+#else
+  LOG(WARNING)
+      << "'NGRAPH' is not supported, Please re-compile with WITH_NGRAPH option";
+#endif
+}
+
 Executor::Executor(const platform::Place& place) : place_(place) {}
 
 void Executor::Close() {
@@ -338,6 +358,7 @@ std::unique_ptr<ExecutorPrepareContext> Executor::Prepare(
   for (auto& op_desc : block.AllOps()) {
     ctx->ops_.push_back(OpRegistry::CreateOp(*op_desc));
   }
+  if (FLAGS_use_ngraph) EnableFusedOp(ctx.get());
   return ctx;
 }
 
@@ -486,6 +507,5 @@ void Executor::EnableMKLDNN(const ProgramDesc& program) {
       << "'MKLDNN' is not supported, Please re-compile with WITH_MKLDNN option";
 #endif
 }
-
 }  // namespace framework
 }  // namespace paddle

paddle/fluid/framework/ngraph_bridge.cc

+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+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. */
+
+#ifdef PADDLE_WITH_NGRAPH
+#include <algorithm>
+#include <functional>
+
+#include "paddle/fluid/framework/ngraph_bridge.h"
+
+#include "ngraph/ngraph.hpp"
+
+namespace paddle {
+namespace framework {
+
+std::map<std::string,
+         std::function<void(const std::shared_ptr<OperatorBase>&,
+                            std::shared_ptr<std::unordered_map<
+                                std::string, std::shared_ptr<ngraph::Node>>>)>>
+    NgraphBridge::NG_NODE_MAP = {};
+
+void NgraphBridge::build_graph(const std::shared_ptr<OperatorBase>& op) {
+  auto& op_type = op->Type();
+  NG_NODE_MAP[op_type](op, ngb_node_map);
+}
+
+}  // namespace framework
+}  // namespace paddle
+#endif

paddle/fluid/framework/ngraph_bridge.h

+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+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. */
+
+#pragma once
+
+#ifdef PADDLE_WITH_NGRAPH
+
+#include <algorithm>
+#include <map>
+#include <string>
+#include <unordered_map>
+#include <vector>
+
+#include "paddle/fluid/framework/operator.h"
+#include "paddle/fluid/platform/enforce.h"
+
+#include "ngraph/ngraph.hpp"
+
+namespace paddle {
+namespace framework {
+
+class NgraphBridge {
+ public:
+  static std::map<
+      std::string,
+      std::function<void(const std::shared_ptr<OperatorBase>&,
+                         std::shared_ptr<std::unordered_map<
+                             std::string, std::shared_ptr<ngraph::Node>>>)>>
+      NG_NODE_MAP;
+
+  explicit NgraphBridge(
+      std::shared_ptr<
+          std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
+          var_node_map)
+      : ngb_node_map(var_node_map) {}
+
+  void build_graph(const std::shared_ptr<OperatorBase>& op);
+
+ private:
+  std::shared_ptr<
+      std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
+      ngb_node_map;
+};
+
+}  // namespace framework
+}  // namespace paddle
+#endif

paddle/fluid/framework/ngraph_operator.cc

+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+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. */
+
+#ifdef PADDLE_WITH_NGRAPH
+#include <glog/logging.h>
+
+#include <algorithm>
+#include <map>
+
+#include "paddle/fluid/framework/feed_fetch_type.h"
+#include "paddle/fluid/framework/ngraph_operator.h"
+#include "paddle/fluid/framework/shape_inference.h"
+#include "paddle/fluid/framework/var_desc.h"
+#include "paddle/fluid/framework/var_type.h"
+
+namespace paddle {
+namespace framework {
+
+static std::map<proto::VarType::Type, ngraph::element::Type> pd2ng_type_map = {
+    {proto::VarType::FP32, ngraph::element::f32},
+    {proto::VarType::FP64, ngraph::element::f64},
+    {proto::VarType::INT32, ngraph::element::i32},
+    {proto::VarType::INT64, ngraph::element::i64},
+    {proto::VarType::BOOL, ngraph::element::boolean},
+};
+
+typedef enum {                /* nGraph support state on ops          */
+               FULL_TRAIN,    /* Support full ops for train           */
+               PARTIAL_TRAIN, /* Support partial ops for train        */
+               FULL_TEST,     /* Support full list of ops for test    */
+               PARTIAL_TEST   /* Support partial list of ops for test */
+} op_state;
+
+class NgraphOperator {
+ public:
+  explicit NgraphOperator(const Scope& scope, const platform::Place& place,
+                          const std::vector<std::shared_ptr<OperatorBase>>& ops,
+                          const std::unordered_map<
+                              std::string, ngraph::element::Type>& var_type_map,
+                          const std::unordered_set<std::string>& persist,
+                          const std::unordered_set<std::string>& fetches,
+                          const std::unordered_set<std::string>& post_op_inputs,
+                          op_state ng_op_state)
+      : scope_(scope),
+        place_(place),
+        fused_ops_(ops),
+        var_type_map_(var_type_map),
+        persistables_(persist),
+        fetches_(fetches),
+        post_op_inputs_(post_op_inputs),
+        ng_op_state_(ng_op_state) {}
+
+  void Run(const Scope& scope, const platform::Place& place) const;
+
+ private:
+  static std::unordered_map<std::string, std::shared_ptr<ngraph::Function>>
+      func_cache;
+  const Scope& scope_;
+  const platform::Place& place_;
+  std::vector<std::shared_ptr<OperatorBase>> fused_ops_;
+  std::unordered_map<std::string, ngraph::element::Type> var_type_map_;
+  std::unordered_set<std::string> persistables_;
+  std::unordered_set<std::string> fetches_;
+  std::unordered_set<std::string> post_op_inputs_;
+  op_state ng_op_state_;
+};
+
+std::vector<std::vector<std::vector<std::unique_ptr<OperatorBase>>::iterator>>
+FusedOperator::FusedOpIntervals(
+    std::vector<std::unique_ptr<paddle::framework::OperatorBase>>* ops) {
+  std::vector<std::vector<std::vector<std::unique_ptr<OperatorBase>>::iterator>>
+      intervals;
+  if (ops->empty()) {
+    return intervals;
+  }
+  size_t size = ops->size();
+  size_t left = 0;
+  while (left < size && ops.at(left)->Type() != kFeedOpType) {
+    ++left;
+  }
+  if (left == size) {
+    return intervals;
+  }
+  while (left < size && ops->at(left)->Type() == kFeedOpType) {
+    ++left;
+  }
+
+  size_t right = left;
+  while (right < size && ops->at(right)->Type() != kFetchOpType) {
+    ++right;
+  }
+  if (right == size) {
+    return intervals;
+  }
+  if (left >= right) return intervals;
+
+  // (left, right - 1) represents indices between feed and fetch
+  size_t pivot = left;
+  while (pivot < right) {
+    auto op_type = ops->at(pivot)->Type();
+    if (paddle::framework::NgraphBridge::NG_NODE_MAP.find(op_type) ==
+        paddle::framework::NgraphBridge::NG_NODE_MAP.end()) {
+      ++pivot;
+    } else {
+      size_t start = pivot, end = start;
+      while (pivot < right &&
+             (paddle::framework::NgraphBridge::NG_NODE_MAP.find(
+                  ops.at(pivot)->Type()) !=
+              paddle::framework::NgraphBridge::NG_NODE_MAP.end())) {
+        ++pivot;
+        ++end;
+      }
+      std::vector<std::vector<std::unique_ptr<OperatorBase>>::iterator>
+          interval = {ops->begin() + start, ops->begin() + end};
+      intervals.push_back(interval);
+    }
+  }  // end while
+
+  return intervals;
+}
+
+FusedOperator::FusedOperator(
+    const ProgramDesc& prog, size_t block_id,
+    std::vector<std::unique_ptr<OperatorBase>>::iterator start,
+    std::vector<std::unique_ptr<OperatorBase>>::iterator end,
+    const std::string& type, const VariableNameMap& inputs,
+    const VariableNameMap& outputs, const AttributeMap& attrs)
+    : OperatorBase(type, inputs, outputs, attrs), pdesc(prog), block(block_id) {
+  for (std::vector<std::unique_ptr<OperatorBase>>::iterator it = start;
+       it != end; ++it) {
+    fused_ops_.push_back(std::move(*it));
+  }
+
+  for (std::vector<std::unique_ptr<OperatorBase>>::iterator it = end;
+       (*it)->Type() != kFetchOpType; ++it) {
+    for (auto& var_name_item : (*it)->Inputs()) {
+      for (auto& var_name : var_name_item.second) {
+        post_op_inputs_.insert(var_name);
+      }
+    }
+  }
+
+  if ((*(start - 1))->Type() == kFeedOpType && (*end)->Type() == kFetchOpType) {
+    is_complete = true;
+  }
+
+  Process();
+}
+
+void FusedOperator::Process() {
+  auto& bdesc = pdesc_.Block(block_);
+  for (auto& var : bdesc.AllVars()) {
+    if (!(var->GetType() == proto::VarType::SELECTED_ROWS ||
+          var->GetType() == proto::VarType::LOD_TENSOR ||
+          var->GetType() == proto::VarType::LOD_TENSOR_ARRAY)) {
+      continue;
+    }
+
+    auto var_name = var->Name();
+    if (var->Name() == framework::kEmptyVarName) {
+      continue;
+    }
+
+    if (var_name != "fetch" && var_name != "feed") {
+      auto pd_type = var->GetDataType();
+      if (pd2ng_type_map.find(pd_type) == pd2ng_type_map.end()) {
+        PADDLE_THROW("Data type of var %s not found in pd2ng_type_map",
+                     var_name);
+      }
+      var_type_map_[var_name] = pd2ng_type_map[pd_type];
+    }
+
+    if (var->Persistable()) {
+      persistables_.insert(var->Name());
+    }
+  }
+
+  for (auto* op : bdesc.AllOps()) {
+    if (op->Type() == kFetchOpType) {
+      std::string fetch_target_name = op->Input("X")[0];
+      fetches_.insert(fetch_target_name);
+    }
+  }
+}
+
+void FusedOperator::RunImpl(const Scope& scope,
+                            const platform::Place& place) const {
+  op_state ng_op_state = PARTIAL_TEST;
+  auto& bdesc = pdesc_.Block(block_);
+  for (auto* op : bdesc.AllOps()) {
+    if (op->Type().find("_grad") != std::string::npos) {
+      ng_op_state = PARTIAL_TRAIN;
+      break;
+    }
+  }
+
+  if (is_full) {
+    ng_op_state = ng_op_state == PARTIAL_TEST ? FULL_TEST : FULL_TRAIN;
+  }
+
+  NgraphOperator ngraph_op(scope, place, fused_ops_, var_type_map_,
+                           persistables_, fetches_, post_op_inputs_,
+                           ng_op_state);
+  ngraph_op.Run(scope, place);
+}
+
+}  // namespace framework
+}  // namespace paddle
+#endif

paddle/fluid/framework/ngraph_operator.h

+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+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. */
+
+#pragma once
+
+#ifdef PADDLE_WITH_NGRAPH
+
+#include <algorithm>
+#include <atomic>
+#include <string>
+#include <unordered_map>
+#include <vector>
+
+#include "paddle/fluid/framework/attribute.h"
+#include "paddle/fluid/framework/framework.pb.h"
+#include "paddle/fluid/framework/lod_tensor.h"
+#include "paddle/fluid/framework/ngraph_bridge.h"
+#include "paddle/fluid/framework/op_info.h"
+#include "paddle/fluid/framework/op_kernel_type.h"
+#include "paddle/fluid/framework/operator.h"
+#include "paddle/fluid/framework/program_desc.h"
+#include "paddle/fluid/framework/scope.h"
+#include "paddle/fluid/framework/tensor.h"
+#include "paddle/fluid/platform/variant.h"
+
+#include "ngraph/ngraph.hpp"
+
+namespace paddle {
+namespace framework {
+
+class FusedOperator : public OperatorBase {
+ public:
+  static std::vector<
+      std::vector<std::vector<std::unique_ptr<OperatorBase>>::iterator>>
+  FusedOpIntervals(
+      std::vector<std::unique_ptr<paddle::framework::OperatorBase>>* ops);
+
+  explicit FusedOperator(
+      const ProgramDesc& prog, size_t block_id,
+      std::vector<std::unique_ptr<OperatorBase>>::iterator start,
+      std::vector<std::unique_ptr<OperatorBase>>::iterator end,
+      const std::string& type = "fused_op", const VariableNameMap& inputs = {},
+      const VariableNameMap& outputs = {}, const AttributeMap& attrs = {});
+
+  void RunImpl(const Scope& scope, const platform::Place& place) const final;
+
+ private:
+  const ProgramDesc pdesc_;
+  size_t block_;
+  std::vector<std::shared_ptr<OperatorBase>> fused_ops_;
+  std::unordered_map<std::string, ngraph::element::Type> var_type_map_;
+  std::unordered_set<std::string> persistables_;
+  std::unordered_set<std::string> fetches_;
+  std::unordered_set<std::string> post_op_inputs_;
+  bool is_full_ = false;
+
+  void Process();
+};
+}  // namespace framework
+}  // namespace paddle
+#endif

paddle/fluid/memory/malloc.cc

@@ -12,6 +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 <string>
 #include <vector>
 
 #include "paddle/fluid/memory/malloc.h"
@@ -21,6 +22,7 @@ limitations under the License. */
 #include "paddle/fluid/memory/detail/buddy_allocator.h"
 #include "paddle/fluid/memory/detail/system_allocator.h"
 #include "paddle/fluid/platform/gpu_info.h"
+#include "paddle/fluid/string/printf.h"
 
 DEFINE_bool(init_allocated_mem, false,
             "It is a mistake that the values of the memory allocated by "
@@ -137,12 +139,18 @@ void* Alloc<platform::CUDAPlace>(platform::CUDAPlace place, size_t size) {
     platform::SetDeviceId(place.device);
     size_t avail, total;
     platform::GpuMemoryUsage(&avail, &total);
-    LOG(WARNING) << "Cannot allocate " << size << " bytes in GPU "
-                 << place.device << ", available " << avail << " bytes";
+    LOG(WARNING) << "Cannot allocate " << string::HumanReadableSize(size)
+                 << " in GPU " << place.device << ", available "
+                 << string::HumanReadableSize(avail);
     LOG(WARNING) << "total " << total;
-    LOG(WARNING) << "GpuMinChunkSize " << buddy_allocator->GetMinChunkSize();
-    LOG(WARNING) << "GpuMaxChunkSize " << buddy_allocator->GetMaxChunkSize();
-    LOG(WARNING) << "GPU memory used: " << Used<platform::CUDAPlace>(place);
+    LOG(WARNING) << "GpuMinChunkSize "
+                 << string::HumanReadableSize(
+                        buddy_allocator->GetMinChunkSize());
+    LOG(WARNING) << "GpuMaxChunkSize "
+                 << string::HumanReadableSize(
+                        buddy_allocator->GetMaxChunkSize());
+    LOG(WARNING) << "GPU memory used: "
+                 << string::HumanReadableSize(Used<platform::CUDAPlace>(place));
     platform::SetDeviceId(cur_dev);
   }
   if (FLAGS_init_allocated_mem) {

paddle/fluid/operators/conv_cudnn_op.cu.cc

@@ -50,12 +50,18 @@ static constexpr char kCUDNNBwdFilterAlgoCache[] = "kCUDNNBwdFilterAlgoCache";
 static constexpr size_t kCONV_CUDNN_WORKSPACE_LIMIT_BYTES =
     static_cast<size_t>(1024) * 1024 * 1024;
 
-static constexpr size_t kNUM_CUDNN_FWD_ALGS =
-    CUDNN_CONVOLUTION_BWD_FILTER_ALGO_COUNT;
+#if CUDNN_VERSION_MIN(6, 0, 5)
+static constexpr size_t kNUM_CUDNN_FWD_ALGS = CUDNN_CONVOLUTION_FWD_ALGO_COUNT;
 static constexpr size_t kNUM_CUDNN_BWD_FILTER_ALGS =
     CUDNN_CONVOLUTION_BWD_FILTER_ALGO_COUNT;
 static constexpr size_t kNUM_CUDNN_BWD_DATA_ALGS =
     CUDNN_CONVOLUTION_BWD_DATA_ALGO_COUNT;
+#else
+// cuDNN v5 has no CUDNN_CONVOLUTION_FWD_ALGO_COUNT etc.
+static constexpr size_t kNUM_CUDNN_FWD_ALGS = 7;
+static constexpr size_t kNUM_CUDNN_BWD_FILTER_ALGS = 4;
+static constexpr size_t kNUM_CUDNN_BWD_DATA_ALGS = 5;
+#endif
 
 template <typename T>
 class CUDNNConvOpKernel : public framework::OpKernel<T> {

paddle/fluid/operators/lrn_op.cc

@@ -14,6 +14,7 @@ limitations under the License. */
 
 #include "paddle/fluid/operators/lrn_op.h"
 #include <string>
+#include "paddle/fluid/operators/math/blas.h"
 #ifdef PADDLE_WITH_MKLDNN
 #include "paddle/fluid/platform/mkldnn_helper.h"
 #endif
@@ -29,34 +30,43 @@ struct LRNFunctor<platform::CPUDeviceContext, T> {
                   const framework::Tensor& input, framework::Tensor* out,
                   framework::Tensor* mid, int N, int C, int H, int W, int n,
                   T k, T alpha, T beta) {
-    auto x_v = framework::EigenVector<T>::Flatten(input);
-
-    const int start = -(n - 1) / 2;
-    const int end = start + n;
-
-    auto e_mid = framework::EigenTensor<T, 4>::From(*mid);
-    e_mid = e_mid.constant(k);
-
-    auto e_x = framework::EigenTensor<T, 4>::From(input);
-    for (int m = 0; m < N; m++) {
-      for (int i = 0; i < C; i++) {
-        for (int c = start; c < end; c++) {
-          int ch = i + c;
-          if (ch >= 0 && ch < C) {
-            auto s = e_mid.slice(Eigen::array<int, 4>({{m, i, 0, 0}}),
-                                 Eigen::array<int, 4>({{1, 1, H, W}}));
-
-            auto r = e_x.slice(Eigen::array<int, 4>({{m, ch, 0, 0}}),
-                               Eigen::array<int, 4>({{1, 1, H, W}}));
-
-            s += alpha * r.square();
+    const T* idata = input.data<T>();
+    auto place = ctx.GetPlace();
+    auto blas = math::GetBlas<platform::CPUDeviceContext, T>(ctx);
+    T* odata = out->mutable_data<T>(place);
+    T* mdata = mid->mutable_data<T>(place);
+    Tensor squared;
+    T* sdata = squared.mutable_data<T>({1, C + n - 1, H, W}, place);
+    std::memset(sdata, 0, sizeof(T) * squared.numel());
+    for (int i = 0; i < mid->numel(); ++i) {
+      mdata[i] = k;
     }
+    int img_size = H * W;
+    int fea_size = C * img_size;
+    int pre_pad = (n - 1) / 2;
+    // compute batches one by one
+    for (int i = 0; i < N; ++i) {
+      blas.VSQR(fea_size, idata + i * fea_size, sdata + pre_pad * img_size);
+      // init the first channel of mid
+      for (int c = 0; c < n; ++c) {
+        blas.AXPY(img_size, alpha, sdata + c * img_size, mdata + i * fea_size);
       }
+      for (int c = 1; c < C; ++c) {
+        // copy previous scale
+        int mid_offset = i * fea_size + c * img_size;
+        std::memcpy(mdata + mid_offset, mdata + mid_offset - img_size,
+                    img_size * sizeof(T));
+        // add last
+        blas.AXPY(img_size, alpha, sdata + (c + n - 1) * img_size,
+                  mdata + mid_offset);
+        // sub rest
+        blas.AXPY(img_size, -alpha, sdata + (c - 1) * img_size,
+                  mdata + mid_offset);
       }
     }
-
-    auto out_e = framework::EigenVector<T>::Flatten(*out);
-    out_e = x_v * e_mid.reshape(Eigen::DSizes<int, 1>(e_mid.size())).pow(-beta);
+    // compute the final output
+    blas.VPOW(mid->numel(), mdata, -beta, odata);
+    blas.VMUL(mid->numel(), odata, idata, odata);
   }
 };
 template struct LRNFunctor<platform::CPUDeviceContext, float>;
@@ -156,6 +166,9 @@ class LRNOp : public framework::OperatorWithKernel {
     auto x_dim = ctx->GetInputDim("X");
     PADDLE_ENFORCE_EQ(x_dim.size(), 4, "Input(X)'rank of LRNOp should be 4.");
 
+    int n = ctx->Attrs().Get<int>("n");
+    PADDLE_ENFORCE(n > 0 && n % 2 == 1, "n should be positive odd value");
+
     ctx->SetOutputDim("Out", x_dim);
     ctx->ShareLoD("X", /*->*/ "Out");
     ctx->SetOutputDim("MidOut", x_dim);

paddle/fluid/operators/lrn_op.h

@@ -60,7 +60,6 @@ class LRNKernel : public framework::OpKernel<T> {
     T beta = ctx.Attr<float>("beta");
     T k = ctx.Attr<float>("k");
 
-    PADDLE_ENFORCE(n > 0, "n should >= 0");
     PADDLE_ENFORCE(alpha >= 0.0, "alpha should >= 0.0");
     PADDLE_ENFORCE(beta >= 0.0, "beta should >= 0.0");
     PADDLE_ENFORCE(k >= 0.0, "k should >= 0.0");

paddle/fluid/operators/math/blas.h

@@ -152,6 +152,12 @@ class Blas {
   template <typename T>
   void VEXP(int n, const T* x, T* y) const;
 
+  template <typename T>
+  void VSQR(int n, const T* x, T* y) const;
+
+  template <typename T>
+  void VPOW(int n, const T* x, T alpha, T* y) const;
+
   template <typename T>
   void GEMV(bool trans_a, int M, int N, T alpha, const T* A, const T* B, T beta,
             T* C) const;
@@ -238,6 +244,16 @@ class BlasT : private Blas<DeviceContext> {
     Base()->template VEXP<T>(args...);
   }
 
+  template <typename... ARGS>
+  void VSQR(ARGS... args) const {
+    Base()->template VSQR<T>(args...);
+  }
+
+  template <typename... ARGS>
+  void VPOW(ARGS... args) const {
+    Base()->template VPOW<T>(args...);
+  }
+
   template <typename... ARGS>
   void GEMV(ARGS... args) const {
     Base()->template GEMV<T>(args...);

paddle/fluid/operators/math/blas_impl.h

@@ -12,6 +12,7 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 #pragma once
+#include <cmath>
 #include <limits>
 #include <vector>
 #include "paddle/fluid/operators/math/math_function.h"
@@ -102,6 +103,16 @@ struct CBlas<float> {
   static void VEXP(ARGS... args) {
     platform::dynload::vsExp(args...);
   }
+
+  template <typename... ARGS>
+  static void VSQR(ARGS... args) {
+    platform::dynload::vsSqr(args...);
+  }
+
+  template <typename... ARGS>
+  static void VPOW(ARGS... args) {
+    platform::dynload::vsPowx(args...);
+  }
 };
 
 template <>
@@ -182,6 +193,16 @@ struct CBlas<double> {
   static void VEXP(ARGS... args) {
     platform::dynload::vdExp(args...);
   }
+
+  template <typename... ARGS>
+  static void VSQR(ARGS... args) {
+    platform::dynload::vdSqr(args...);
+  }
+
+  template <typename... ARGS>
+  static void VPOW(ARGS... args) {
+    platform::dynload::vdPowx(args...);
+  }
 };
 
 #else
@@ -241,6 +262,8 @@ struct CBlas<platform::float16> {
   }
   static void VMUL(...) { PADDLE_THROW("float16 VMUL not supported on CPU"); }
   static void VEXP(...) { PADDLE_THROW("float16 VEXP not supported on CPU"); }
+  static void VSQR(...) { PADDLE_THROW("float16 VSQR not supported on CPU"); }
+  static void VPOW(...) { PADDLE_THROW("float16 VPOW not supported on CPU"); }
   static void DOT(...) { PADDLE_THROW("float16 DOT not supported on CPU"); };
   static void SCAL(...) { PADDLE_THROW("float16 SCAL not supported on CPU"); };
 #ifdef PADDLE_WITH_MKLML
@@ -398,6 +421,31 @@ void Blas<platform::CPUDeviceContext>::VEXP(int n, const T *x, T *y) const {
 #endif
 }
 
+template <>
+template <typename T>
+void Blas<platform::CPUDeviceContext>::VSQR(int n, const T *x, T *y) const {
+#ifdef PADDLE_WITH_MKLML
+  CBlas<T>::VSQR(n, x, y);
+#else
+  for (int i = 0; i < n; ++i) {
+    y[i] = std::sqrt(x[i]);
+  }
+#endif
+}
+
+template <>
+template <typename T>
+void Blas<platform::CPUDeviceContext>::VPOW(int n, const T *x, T a,
+                                            T *y) const {
+#ifdef PADDLE_WITH_MKLML
+  CBlas<T>::VPOW(n, x, a, y);
+#else
+  for (int i = 0; i < n; ++i) {
+    y[i] = std::pow(x[i], a);
+  }
+#endif
+}
+
 template <>
 template <typename T>
 T Blas<platform::CPUDeviceContext>::DOT(int n, const T *x, const T *y) const {

paddle/fluid/platform/dynload/mklml.h

@@ -76,6 +76,10 @@ extern void* mklml_dso_handle;
   __macro(vdMul);                   \
   __macro(vsExp);                   \
   __macro(vdExp);                   \
+  __macro(vsSqr);                   \
+  __macro(vdSqr);                   \
+  __macro(vsPowx);                  \
+  __macro(vdPowx);                  \
   __macro(MKL_Set_Num_Threads)
 
 MKLML_ROUTINE_EACH(DECLARE_DYNAMIC_LOAD_MKLML_WRAP);

paddle/fluid/string/printf.h

@@ -72,6 +72,7 @@
 #include <iostream>
 #include <sstream>
 #include <string>
+#include <vector>
 
 #include "tinyformat/tinyformat.h"  // https://github.com/c42f/tinyformat
 
@@ -102,5 +103,22 @@ void Printf(const char* fmt, const Args&... args) {
   Fprintf(std::cout, fmt, args...);
 }
 
+template <typename T>
+std::string HumanReadableSize(T size) {
+  size_t i = 0;
+  double f_size = static_cast<double>(size);
+  double orig = f_size;
+  const std::vector<std::string> units(
+      {"B", "kB", "MB", "GB", "TB", "PB", "EB", "ZB", "YB"});
+  while (f_size > 1024) {
+    f_size /= 1024;
+    i++;
+  }
+  if (i >= units.size()) {
+    return Sprintf("%fB", orig);
+  }
+  return Sprintf("%f%s", f_size, units[i]);
+}
+
 }  // namespace string
 }  // namespace paddle

paddle/scripts/paddle_build.sh

@@ -156,6 +156,8 @@ function cmake_gen() {
         -DWITH_INFERENCE_API_TEST=${WITH_INFERENCE_API_TEST:-ON}
         -DINFERENCE_DEMO_INSTALL_DIR=${INFERENCE_DEMO_INSTALL_DIR}
         -DWITH_ANAKIN=${WITH_ANAKIN:-OFF}
+        -DANAKIN_BUILD_FAT_BIN=${ANAKIN_BUILD_FAT_BIN:OFF}
+        -DANAKIN_BUILD_CROSS_PLANTFORM=${ANAKIN_BUILD_CROSS_PLANTFORM:ON}
         -DPY_VERSION=${PY_VERSION:-2.7}
         -DCMAKE_INSTALL_PREFIX=${INSTALL_PREFIX:-/paddle/build}
     ========================================
@@ -188,6 +190,8 @@ EOF
         -DWITH_INFERENCE_API_TEST=${WITH_INFERENCE_API_TEST:-ON} \
         -DINFERENCE_DEMO_INSTALL_DIR=${INFERENCE_DEMO_INSTALL_DIR} \
         -DWITH_ANAKIN=${WITH_ANAKIN:-OFF} \
+        -DANAKIN_BUILD_FAT_BIN=${ANAKIN_BUILD_FAT_BIN:OFF}\
+        -DANAKIN_BUILD_CROSS_PLANTFORM=${ANAKIN_BUILD_CROSS_PLANTFORM:ON}\
         -DPY_VERSION=${PY_VERSION:-2.7} \
         -DCMAKE_INSTALL_PREFIX=${INSTALL_PREFIX:-/paddle/build}
 
@@ -777,6 +781,17 @@ function main() {
         test_fluid_lib
         assert_api_spec_approvals
         ;;
+      assert_api)
+        assert_api_not_changed ${PYTHON_ABI:-""}
+        ;;
+      test_inference)
+        gen_capi_package
+        gen_fluid_lib
+        test_fluid_lib
+        ;;
+      assert_api_approvals)
+        assert_api_spec_approvals
+        ;;
       maccheck)
         cmake_gen ${PYTHON_ABI:-""}
         build_mac

python/paddle/fluid/__init__.py

@@ -112,9 +112,10 @@ def __bootstrap__():
     os.environ['OMP_NUM_THREADS'] = str(num_threads)
 
     read_env_flags = [
-        'use_pinned_memory', 'check_nan_inf', 'benchmark', 'eager_delete_scope',
-        'use_mkldnn', 'initial_cpu_memory_in_mb', 'init_allocated_mem',
-        'free_idle_memory', 'paddle_num_threads', 'dist_threadpool_size',
+        'use_pinned_memory', 'check_nan_inf', 'benchmark',
+        'eager_delete_scope', 'use_mkldnn', 'use_ngraph',
+        'initial_cpu_memory_in_mb', 'init_allocated_mem', 'free_idle_memory',
+        'paddle_num_threads', 'dist_threadpool_size',
         'eager_delete_tensor_gb', 'reader_queue_speed_test_mode'
     ]
     if os.name != 'nt':

python/paddle/fluid/layers/nn.py

@@ -6835,7 +6835,7 @@ def prelu(x, mode, param_attr=None, name=None):
         alpha_shape = x.shape
     dtype = helper.input_dtype(input_param_name='x')
     alpha = helper.create_parameter(
-        attr=param_attr,
+        attr=helper.param_attr,
         shape=alpha_shape,
         dtype='float32',
         is_bias=False,