Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into img

Fix img links errors

paddle/fluid/framework/details/multi_devices_graph_builder.cc

@@ -21,6 +21,9 @@
 #include "paddle/fluid/framework/details/nccl_all_reduce_op_handle.h"
 #endif
 
+#include <string>
+#include <vector>
+
 namespace paddle {
 namespace framework {
 namespace details {
@@ -168,6 +171,11 @@ std::unique_ptr<SSAGraph> MultiDevSSAGraphBuilder::Build(
    */
   PolishGraphToSupportDataHazards(&result);
 
+  /*
+   * Only variables should be the leaves of graph.
+   */
+  AddOutputToLeafOps(&result);
+
   if (VLOG_IS_ON(10)) {
     std::ostringstream sout;
     PrintGraphviz(*graph, sout);

paddle/fluid/framework/details/ssa_graph_builder.cc

@@ -136,6 +136,17 @@ void SSAGraphBuilder::PrintGraphviz(const SSAGraph &graph, std::ostream &sout) {
 
   sout << "}\n";
 }
+
+void SSAGraphBuilder::AddOutputToLeafOps(SSAGraph *graph) {
+  for (auto &op : graph->ops_) {
+    if (!op->outputs_.empty()) {
+      continue;
+    }
+    auto *dummy_leaf = new DummyVarHandle();
+    graph->dep_vars_.emplace(dummy_leaf);
+    op->AddOutput(dummy_leaf);
+  }
+}
 }  // namespace details
 }  // namespace framework
 }  // namespace paddle

paddle/fluid/framework/details/ssa_graph_builder.h

@@ -14,13 +14,13 @@
 
 #pragma once
 
+#include <memory>
+#include <string>
+
 #include "paddle/fluid/framework/details/ssa_graph.h"
 #include "paddle/fluid/framework/program_desc.h"
 #include "paddle/fluid/platform/place.h"
 
-#include <memory>
-#include <string>
-
 namespace paddle {
 namespace framework {
 namespace details {
@@ -52,6 +52,8 @@ class SSAGraphBuilder {
                              const std::string &each_var_name,
                              const platform::Place &place, size_t place_offset);
 
+  static void AddOutputToLeafOps(SSAGraph *graph);
+
   static void PrintGraphviz(const SSAGraph &graph, std::ostream &sout);
 };
 }  // namespace details

paddle/fluid/framework/details/threaded_ssa_graph_executor.cc

@@ -87,7 +87,6 @@ FeedFetchList ThreadedSSAGraphExecutor::Run(
 
   // Step 2. Insert FetchOps
   std::vector<std::unique_ptr<FetchOpHandle>> fetch_ops;
-  std::vector<DummyVarHandle> dummy_vars;
   FeedFetchList fetch_data(fetch_tensors.size());
 
   std::unordered_map<std::string, std::vector<VarHandleBase *>> fetched_vars;
@@ -101,13 +100,13 @@ FeedFetchList ThreadedSSAGraphExecutor::Run(
     }
   }
 
+  std::unordered_set<std::unique_ptr<VarHandleBase>> fetch_dependencies;
   for (size_t i = 0; i < fetch_tensors.size(); ++i) {
     auto &var_name = fetch_tensors[i];
     auto &vars = fetched_vars.at(var_name);
     auto *op = new FetchOpHandle(&fetch_data, i, &local_scopes_);
     fetch_ops.emplace_back(op);
 
-    // FIXME: Use new device context
     for (auto &p : places_) {
       op->dev_ctxes_[p] = fetch_ctxs_.Get(p);
     }
@@ -115,6 +114,11 @@ FeedFetchList ThreadedSSAGraphExecutor::Run(
     for (auto *var : vars) {
       op->AddInput(var);
     }
+
+    auto *fetch_dummy = new DummyVarHandle();
+    op->AddOutput(fetch_dummy);
+    fetch_dependencies.emplace(fetch_dummy);
+    InsertPendingVar(*fetch_dummy);
     InsertPendingOp(*op);
   }
 

paddle/fluid/framework/tensor.h

@@ -45,11 +45,10 @@ class Tensor {
   friend struct EigenVector;
 
  public:
-  Tensor() : offset_(0), is_pinned_(false) {}
+  Tensor() : offset_(0) {}
 
   /*! Constructor with place should only be used in pybind. */
-  explicit Tensor(const platform::Place& place)
-      : offset_(0), is_pinned_(false) {
+  explicit Tensor(const platform::Place& place) : offset_(0) {
     holder_->set_place(place);
   }
 
@@ -70,12 +69,11 @@ class Tensor {
    * @note    If not exist, then allocation.
    */
   template <typename T>
-  inline T* mutable_data(platform::Place place, bool is_pinned = false);
+  inline T* mutable_data(platform::Place place);
 
-  inline void* mutable_data(platform::Place place, std::type_index type,
-                            bool is_pinned = false);
+  inline void* mutable_data(platform::Place place, std::type_index type);
 
-  inline void* mutable_data(platform::Place place, bool is_pinned = false);
+  inline void* mutable_data(platform::Place place);
 
   /**
    * @brief     Return a pointer to mutable memory block.
@@ -86,8 +84,7 @@ class Tensor {
    * @note      If not exist, then allocation.
    */
   template <typename T>
-  inline T* mutable_data(DDim dims, platform::Place place,
-                         bool is_pinned = false);
+  inline T* mutable_data(DDim dims, platform::Place place);
 
   /*! Return the dimensions of the memory block. */
   inline const DDim& dims() const;
@@ -95,9 +92,6 @@ class Tensor {
   /*! Return the numel of the memory block. */
   inline int64_t numel() const;
 
-  /*! Return the numel of the memory block. */
-  inline bool isPinned() const;
-
   /*! Resize the dimensions of the memory block. */
   inline Tensor& Resize(const DDim& dims);
 
@@ -152,14 +146,12 @@ class Tensor {
 
   template <typename Place>
   struct PlaceholderImpl : public Placeholder {
-    PlaceholderImpl(Place place, size_t size, std::type_index type,
-                    bool is_pinned = false)
-        : ptr_(static_cast<uint8_t*>(memory::Alloc(place, size, is_pinned)),
-               memory::PODDeleter<uint8_t, Place>(place, is_pinned)),
+    PlaceholderImpl(Place place, size_t size, std::type_index type)
+        : ptr_(static_cast<uint8_t*>(memory::Alloc(place, size)),
+               memory::PODDeleter<uint8_t, Place>(place)),
           place_(place),
           size_(size),
-          type_(type),
-          is_pinned_(is_pinned) {
+          type_(type) {
       PADDLE_ENFORCE_NOT_NULL(ptr_, "Insufficient %s memory to allocation.",
                               (is_cpu_place(place_) ? "CPU" : "GPU"));
     }
@@ -182,9 +174,6 @@ class Tensor {
 
     /* the current type of memory */
     std::type_index type_;
-
-    /*! use pinned memory or not. */
-    bool is_pinned_;
   };
 
   /*! holds the memory block if allocated. */
@@ -219,7 +208,6 @@ class Tensor {
    *          PlaceHolder::ptr_ and where the tensor data really begins.
    */
   size_t offset_;
-  bool is_pinned_;
 };
 
 inline void Tensor::switch_place(platform::Place new_place) {

paddle/fluid/framework/tensor_impl.h

@@ -101,21 +101,19 @@ inline T* Tensor::data() {
 }
 
 template <typename T>
-inline T* Tensor::mutable_data(DDim dims, platform::Place place,
-                               bool is_pinned) {
+inline T* Tensor::mutable_data(DDim dims, platform::Place place) {
   static_assert(std::is_pod<T>::value, "T must be POD");
   Resize(dims);
-  return mutable_data<T>(place, is_pinned);
+  return mutable_data<T>(place);
 }
 
 template <typename T>
-inline T* Tensor::mutable_data(platform::Place place, bool is_pinned) {
+inline T* Tensor::mutable_data(platform::Place place) {
   static_assert(std::is_pod<T>::value, "T must be POD");
-  return reinterpret_cast<T*>(mutable_data(place, typeid(T), is_pinned));
+  return reinterpret_cast<T*>(mutable_data(place, typeid(T)));
 }
 
-inline void* Tensor::mutable_data(platform::Place place, std::type_index type,
-                                  bool is_pinned) {
+inline void* Tensor::mutable_data(platform::Place place, std::type_index type) {
   if (holder_ != nullptr) {
     holder_->set_type(type);
   }
@@ -129,27 +127,26 @@ inline void* Tensor::mutable_data(platform::Place place, std::type_index type,
       holder_->size() < size + offset_) {
     if (platform::is_cpu_place(place)) {
       holder_.reset(new PlaceholderImpl<platform::CPUPlace>(
-          boost::get<platform::CPUPlace>(place), size, type, is_pinned));
+          boost::get<platform::CPUPlace>(place), size, type));
     } else if (platform::is_gpu_place(place)) {
 #ifndef PADDLE_WITH_CUDA
       PADDLE_THROW("'CUDAPlace' is not supported in CPU only device.");
     }
 #else
       holder_.reset(new PlaceholderImpl<platform::CUDAPlace>(
-          boost::get<platform::CUDAPlace>(place), size, type, is_pinned));
+          boost::get<platform::CUDAPlace>(place), size, type));
     }
 #endif
     offset_ = 0;
-    is_pinned_ = is_pinned;
   }
   return reinterpret_cast<void*>(reinterpret_cast<uintptr_t>(holder_->ptr()) +
                                  offset_);
 }
 
-inline void* Tensor::mutable_data(platform::Place place, bool is_pinned) {
+inline void* Tensor::mutable_data(platform::Place place) {
   PADDLE_ENFORCE(this->holder_ != nullptr,
                  "Cannot invoke mutable data if current hold nothing");
-  return mutable_data(place, holder_->type(), is_pinned);
+  return mutable_data(place, holder_->type());
 }
 
 inline Tensor& Tensor::ShareDataWith(const Tensor& src) {
@@ -191,8 +188,6 @@ inline const DDim& Tensor::dims() const { return dims_; }
 
 inline int64_t Tensor::numel() const { return product(dims_); }
 
-inline bool Tensor::isPinned() const { return is_pinned_; }
-
 inline Tensor ReshapeToMatrix(const Tensor& src, int num_col_dims) {
   Tensor res;
   res.ShareDataWith(src);

paddle/fluid/framework/tensor_util.cc

@@ -148,6 +148,11 @@ struct AnyVisitor : public boost::static_visitor<bool> {
                  const platform::CPUPlace& cpu) const {
     return *out.data<bool>();
   }
+
+  bool GetResult(const framework::Tensor& out,
+                 const platform::CUDAPinnedPlace& cpu) const {
+    return *out.data<bool>();
+  }
 };
 
 template <typename Predicate>

paddle/fluid/memory/CMakeLists.txt

@@ -4,13 +4,17 @@ cc_library(memory SRCS memory.cc DEPS place enforce)
 cc_library(memcpy SRCS memcpy.cc DEPS place)
 
 cc_library(paddle_memory
-    DEPS
-    memory
-    memcpy
-    meta_data
-    meta_cache
-    memory_block
-    buddy_allocator
-    system_allocator)
+        DEPS
+        memory
+        memcpy
+        meta_data
+        meta_cache
+        memory_block
+        buddy_allocator
+        system_allocator)
 
 cc_test(memory_test SRCS memory_test.cc DEPS place paddle_memory)
+
+#if (WITH_GPU)
+#   nv_test(pinned_memory_test SRCS pinned_memory_test.cu  DEPS place paddle_memory)
+#endif()

paddle/fluid/memory/detail/system_allocator.cc

@@ -14,6 +14,7 @@ limitations under the License. */
 
 #include "paddle/fluid/memory/detail/system_allocator.h"
 #include "paddle/fluid/platform/assert.h"
+#include "paddle/fluid/platform/cpu_info.h"
 #include "paddle/fluid/platform/enforce.h"
 #include "paddle/fluid/platform/gpu_info.h"
 
@@ -134,21 +135,31 @@ bool GPUAllocator::UseGpu() const { return true; }
 // memory. It’s locked to a physical address.
 void* CUDAPinnedAllocator::Alloc(size_t& index, size_t size) {
   if (size <= 0) return nullptr;
-  void* p;
-  // NOTE: here, we use GpuMaxAllocSize() as the maximum memory size
+
+  // NOTE: here, we use CUDAPinnedMaxAllocSize as the maximum memory size
   // of host pinned allocation. Allocates too much would reduce
   // the amount of memory available to the underlying system for paging.
+  size_t usable =
+      paddle::platform::CUDAPinnedMaxAllocSize() - cuda_pinnd_alloc_size_;
 
-  size_t usable = paddle::platform::GpuMaxAllocSize() - fallback_alloc_size_;
-
-  if (size > usable) return nullptr;
+  if (size > usable) {
+    LOG(WARNING) << "Cannot malloc " << size / 1024.0 / 1024.0
+                 << " MB pinned memory."
+                 << ", available " << usable / 1024.0 / 1024.0 << " MB";
+    return nullptr;
+  }
 
+  void* p;
   // PINNED memory is visible to all CUDA contexts.
   cudaError_t result = cudaMallocHost(&p, size);
+
   if (result == cudaSuccess) {
-    index = 1;
-    fallback_alloc_size_ += size;
+    index = 1;  // PINNED memory
+    cuda_pinnd_alloc_size_ += size;
     return p;
+  } else {
+    LOG(WARNING) << "cudaMallocHost failed.";
+    return nullptr;
   }
 
   return nullptr;
@@ -158,8 +169,8 @@ void CUDAPinnedAllocator::Free(void* p, size_t size, size_t index) {
   cudaError_t err;
   PADDLE_ASSERT(index == 1);
 
-  PADDLE_ASSERT(fallback_alloc_size_ >= size);
-  fallback_alloc_size_ -= size;
+  PADDLE_ASSERT(cuda_pinnd_alloc_size_ >= size);
+  cuda_pinnd_alloc_size_ -= size;
   err = cudaFreeHost(p);
 
   // Purposefully allow cudaErrorCudartUnloading, because
@@ -172,7 +183,7 @@ void CUDAPinnedAllocator::Free(void* p, size_t size, size_t index) {
   }
 }
 
-bool CUDAPinnedAllocator::UseGpu() const { return true; }
+bool CUDAPinnedAllocator::UseGpu() const { return false; }
 
 #endif
 

paddle/fluid/memory/detail/system_allocator.h

@@ -21,8 +21,9 @@ namespace memory {
 namespace detail {
 
 /**
- * \brief SystemAllocator is the parent class of CPUAllocator and GPUAllocator.
- *        A BuddyAllocator object uses a SystemAllocator* pointing to the
+ * \brief SystemAllocator is the parent class of CPUAllocator,
+ *        CUDAPinnedAllocator and GPUAllocator. A BuddyAllocator
+ *        object uses a SystemAllocator* pointing to the
  *        underlying system allocator.
  */
 class SystemAllocator {
@@ -62,9 +63,7 @@ class CUDAPinnedAllocator : public SystemAllocator {
   virtual bool UseGpu() const;
 
  private:
-  size_t gpu_alloc_size_ =
-      0;  // TODO(zcd): how to define the upper limit of CUDAPinnedMemory?
-  size_t fallback_alloc_size_ = 0;
+  size_t cuda_pinnd_alloc_size_ = 0;
 };
 #endif
 

paddle/fluid/memory/memcpy.cc

@@ -56,6 +56,45 @@ void Copy<platform::CUDAPlace, platform::CUDAPlace>(
   }
 }
 
+template <>
+void Copy<platform::CPUPlace, platform::CUDAPinnedPlace>(
+    platform::CPUPlace dst_place, void* dst,
+    platform::CUDAPinnedPlace src_place, const void* src, size_t num) {
+  std::memcpy(dst, src, num);
+}
+
+template <>
+void Copy<platform::CUDAPinnedPlace, platform::CPUPlace>(
+    platform::CUDAPinnedPlace dst_place, void* dst,
+    platform::CPUPlace src_place, const void* src, size_t num) {
+  std::memcpy(dst, src, num);
+}
+
+template <>
+void Copy<platform::CUDAPinnedPlace, platform::CUDAPinnedPlace>(
+    platform::CUDAPinnedPlace dst_place, void* dst,
+    platform::CUDAPinnedPlace src_place, const void* src, size_t num) {
+  std::memcpy(dst, src, num);
+}
+
+template <>
+void Copy<platform::CUDAPinnedPlace, platform::CUDAPlace>(
+    platform::CUDAPinnedPlace dst_place, void* dst,
+    platform::CUDAPlace src_place, const void* src, size_t num,
+    cudaStream_t stream) {
+  platform::SetDeviceId(src_place.device);
+  platform::GpuMemcpyAsync(dst, src, num, cudaMemcpyDeviceToHost, stream);
+}
+
+template <>
+void Copy<platform::CUDAPlace, platform::CUDAPinnedPlace>(
+    platform::CUDAPlace dst_place, void* dst,
+    platform::CUDAPinnedPlace src_place, const void* src, size_t num,
+    cudaStream_t stream) {
+  platform::SetDeviceId(dst_place.device);
+  platform::GpuMemcpyAsync(dst, src, num, cudaMemcpyHostToDevice, stream);
+}
+
 #endif
 
 }  // namespace memory

paddle/fluid/memory/memory.cc

@@ -38,8 +38,7 @@ BuddyAllocator* GetCPUBuddyAllocator() {
 }
 
 template <>
-void* Alloc<platform::CPUPlace>(platform::CPUPlace place, size_t size,
-                                bool is_pinned) {
+void* Alloc<platform::CPUPlace>(platform::CPUPlace place, size_t size) {
   VLOG(10) << "Allocate " << size << " bytes on " << platform::Place(place);
   void* p = GetCPUBuddyAllocator()->Alloc(size);
   VLOG(10) << "  pointer=" << p;
@@ -47,8 +46,7 @@ void* Alloc<platform::CPUPlace>(platform::CPUPlace place, size_t size,
 }
 
 template <>
-void Free<platform::CPUPlace>(platform::CPUPlace place, void* p,
-                              bool is_pinned) {
+void Free<platform::CPUPlace>(platform::CPUPlace place, void* p) {
   VLOG(10) << "Free pointer=" << p << " on " << platform::Place(place);
   GetCPUBuddyAllocator()->Free(p);
 }
@@ -84,47 +82,15 @@ BuddyAllocator* GetGPUBuddyAllocator(int gpu_id) {
   return as[gpu_id];
 }
 
-BuddyAllocator* GetCUDAPinnedBuddyAllocator(int gpu_id) {
-  static BuddyAllocator** as = NULL;
-  if (as == NULL) {
-    int gpu_num = platform::GetCUDADeviceCount();
-    as = new BuddyAllocator*[gpu_num];
-    for (int gpu = 0; gpu < gpu_num; gpu++) {
-      as[gpu] = nullptr;
-    }
-  }
-  platform::SetDeviceId(gpu_id);
-  if (!as[gpu_id]) {
-    as[gpu_id] = new BuddyAllocator(new detail::CUDAPinnedAllocator,
-                                    platform::GpuMinChunkSize(),
-                                    platform::GpuMaxChunkSize());
-    VLOG(10) << "\n\nNOTE: each GPU device use "
-             << FLAGS_fraction_of_gpu_memory_to_use * 100
-             << "% of GPU memory.\n"
-             << "You can set GFlags environment variable '"
-             << "FLAGS_fraction_of_gpu_memory_to_use"
-             << "' to change the fraction of GPU usage.\n\n";
-  }
-  return as[gpu_id];
-}
-
 template <>
 size_t Used<platform::CUDAPlace>(platform::CUDAPlace place) {
   return GetGPUBuddyAllocator(place.device)->Used();
 }
 
 template <>
-void* Alloc<platform::CUDAPlace>(platform::CUDAPlace place, size_t size,
-                                 bool is_pinned) {
-  void* ptr;
-  if (is_pinned) {
-    auto* buddy_allocator = GetCUDAPinnedBuddyAllocator(place.device);
-    ptr = buddy_allocator->Alloc(size);
-  } else {
-    auto* buddy_allocator = GetGPUBuddyAllocator(place.device);
-    ptr = buddy_allocator->Alloc(size);
-  }
-
+void* Alloc<platform::CUDAPlace>(platform::CUDAPlace place, size_t size) {
+  auto* buddy_allocator = GetGPUBuddyAllocator(place.device);
+  auto* ptr = buddy_allocator->Alloc(size);
   if (ptr == nullptr) {
     int cur_dev = platform::GetCurrentDeviceId();
     platform::SetDeviceId(place.device);
@@ -142,15 +108,42 @@ void* Alloc<platform::CUDAPlace>(platform::CUDAPlace place, size_t size,
 }
 
 template <>
-void Free<platform::CUDAPlace>(platform::CUDAPlace place, void* p,
-                               bool is_pinned) {
-  if (is_pinned) {
-    GetCUDAPinnedBuddyAllocator(place.device)->Free(p);
-  } else {
-    GetGPUBuddyAllocator(place.device)->Free(p);
+void Free<platform::CUDAPlace>(platform::CUDAPlace place, void* p) {
+  GetGPUBuddyAllocator(place.device)->Free(p);
+}
+
+BuddyAllocator* GetCUDAPinnedBuddyAllocator() {
+  static BuddyAllocator* ba = NULL;
+  if (ba == NULL) {
+    ba = new BuddyAllocator(new detail::CUDAPinnedAllocator,
+                            platform::CUDAPinnedMinChunkSize(),
+                            platform::CUDAPinnedMaxChunkSize());
   }
+  return ba;
 }
 
+template <>
+size_t Used<platform::CUDAPinnedPlace>(platform::CUDAPinnedPlace place) {
+  return GetCUDAPinnedBuddyAllocator()->Used();
+}
+
+template <>
+void* Alloc<platform::CUDAPinnedPlace>(platform::CUDAPinnedPlace place,
+                                       size_t size) {
+  auto* buddy_allocator = GetCUDAPinnedBuddyAllocator();
+  void* ptr = buddy_allocator->Alloc(size);
+
+  if (ptr == nullptr) {
+    LOG(WARNING) << "cudaMallocHost Cannot allocate " << size
+                 << " bytes in CUDAPinnedPlace";
+  }
+  return ptr;
+}
+
+template <>
+void Free<platform::CUDAPinnedPlace>(platform::CUDAPinnedPlace place, void* p) {
+  GetCUDAPinnedBuddyAllocator()->Free(p);
+}
 #endif
 
 size_t Usage::operator()(const platform::CPUPlace& cpu) const {
@@ -165,6 +158,14 @@ size_t Usage::operator()(const platform::CUDAPlace& gpu) const {
 #endif
 }
 
+size_t Usage::operator()(const platform::CUDAPinnedPlace& cuda_pinned) const {
+#ifdef PADDLE_WITH_CUDA
+  return Used(cuda_pinned);
+#else
+  PADDLE_THROW("'CUDAPinnedPlace' is not supported in CPU only device.");
+#endif
+}
+
 size_t memory_usage(const platform::Place& p) {
   return boost::apply_visitor(Usage(), p);
 }

paddle/fluid/memory/memory.h

@@ -33,7 +33,7 @@ namespace memory {
  *          address is valid or not.
  */
 template <typename Place>
-void* Alloc(Place place, size_t size, bool is_pinned = false);
+void* Alloc(Place place, size_t size);
 
 /**
  * \brief   Free memory block in one place.
@@ -43,7 +43,7 @@ void* Alloc(Place place, size_t size, bool is_pinned = false);
  *
  */
 template <typename Place>
-void Free(Place place, void* ptr, bool is_pinned = false);
+void Free(Place place, void* ptr);
 
 /**
  * \brief   Total size of used memory in one place.
@@ -57,6 +57,7 @@ size_t Used(Place place);
 struct Usage : public boost::static_visitor<size_t> {
   size_t operator()(const platform::CPUPlace& cpu) const;
   size_t operator()(const platform::CUDAPlace& gpu) const;
+  size_t operator()(const platform::CUDAPinnedPlace& cuda_pinned) const;
 };
 
 size_t memory_usage(const platform::Place& p);
@@ -74,13 +75,11 @@ class PODDeleter {
   static_assert(std::is_pod<T>::value, "T must be POD");
 
  public:
-  explicit PODDeleter(Place place, bool is_pinned = false)
-      : place_(place), is_pinned_(is_pinned) {}
-  void operator()(T* ptr) { Free(place_, static_cast<void*>(ptr), is_pinned_); }
+  explicit PODDeleter(Place place) : place_(place) {}
+  void operator()(T* ptr) { Free(place_, static_cast<void*>(ptr)); }
 
  private:
   Place place_;
-  bool is_pinned_;
 };
 
 /**

paddle/fluid/memory/memory_test.cc

@@ -141,4 +141,59 @@ TEST(BuddyAllocator, GPUMultAlloc) {
   }
 }
 
+size_t align(size_t size, paddle::platform::CUDAPinnedPlace place) {
+  size += sizeof(paddle::memory::detail::Metadata);
+  size_t alignment = paddle::platform::CUDAPinnedMinChunkSize();
+  size_t remaining = size % alignment;
+  return remaining == 0 ? size : size + (alignment - remaining);
+}
+
+TEST(BuddyAllocator, CUDAPinnedAllocator) {
+  void *p = nullptr;
+
+  EXPECT_EQ(p, nullptr);
+
+  paddle::platform::CUDAPinnedPlace cpu;
+  p = paddle::memory::Alloc(cpu, 4096);
+
+  EXPECT_NE(p, nullptr);
+
+  paddle::platform::Place place = cpu;
+  EXPECT_EQ(paddle::memory::Used(cpu), paddle::memory::memory_usage(place));
+
+  paddle::memory::Free(cpu, p);
+}
+
+TEST(BuddyAllocator, CUDAPinnedMultAllocator) {
+  paddle::platform::CUDAPinnedPlace cpu;
+
+  std::unordered_map<void *, size_t> ps;
+
+  size_t total_size = paddle::memory::Used(cpu);
+  EXPECT_EQ(total_size, 0UL);
+
+  for (auto size :
+       {0, 128, 256, 1024, 4096, 16384, 65536, 262144, 1048576, 4194304}) {
+    ps[paddle::memory::Alloc(cpu, size)] = size;
+
+    // Buddy Allocator doesn't manage too large memory chunk
+    if (paddle::memory::Used(cpu) == total_size) continue;
+
+    size_t aligned_size = align(size, cpu);
+    total_size += aligned_size;
+    EXPECT_EQ(total_size, paddle::memory::Used(cpu));
+  }
+
+  for (auto p : ps) {
+    EXPECT_EQ(is_aligned(p.first), true);
+    paddle::memory::Free(cpu, p.first);
+
+    // Buddy Allocator doesn't manage too large memory chunk
+    if (paddle::memory::Used(cpu) == total_size) continue;
+
+    size_t aligned_size = align(p.second, cpu);
+    total_size -= aligned_size;
+    EXPECT_EQ(total_size, paddle::memory::Used(cpu));
+  }
+}
 #endif

paddle/fluid/memory/pinned_memory_test.cu

+/* 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. */
+#include <gtest/gtest.h>
+#include <unordered_map>
+
+#include "paddle/fluid/memory/detail/memory_block.h"
+#include "paddle/fluid/memory/detail/meta_data.h"
+#include "paddle/fluid/memory/memcpy.h"
+#include "paddle/fluid/memory/memory.h"
+
+#include "paddle/fluid/platform/cpu_info.h"
+#include "paddle/fluid/platform/gpu_info.h"
+#include "paddle/fluid/platform/place.h"
+
+// This unit test is an example comparing the performance between using pinned
+// memory and not. In general, using pinned memory will be faster.
+template <typename T>
+__global__ void Kernel(T* output, int dim) {
+  int tid = blockIdx.x * blockDim.x + threadIdx.x;
+  if (tid < dim) {
+    output[tid] = output[tid] * output[tid] / 100;
+  }
+}
+
+template <typename Place>
+float test_pinned_memory() {
+  Place cpu_place;
+  paddle::platform::CUDAPlace cuda_place;
+
+  const int data_size = 4096;
+  const int iteration = 10;
+
+  // create event start and end
+  cudaEvent_t start_e, stop_e, copying_e;
+  float elapsedTime = 0;
+  cudaEventCreate(&start_e);
+  cudaEventCreate(&stop_e);
+  cudaEventCreate(&copying_e);
+
+  // create computation stream, data copying stream
+  cudaStream_t computation_stream, copying_stream;
+  cudaStreamCreate(&computation_stream);
+  cudaStreamCreate(&copying_stream);
+
+  // create record event, pinned memory, gpu memory
+  std::vector<cudaEvent_t> record_event(iteration);
+  std::vector<float*> input_pinned_mem(iteration);
+  std::vector<float*> gpu_mem(iteration);
+  std::vector<float*> output_pinned_mem(iteration);
+
+  // initial data
+  for (int j = 0; j < iteration; ++j) {
+    cudaEventCreateWithFlags(&record_event[j], cudaEventDisableTiming);
+    cudaEventCreate(&(record_event[j]));
+    input_pinned_mem[j] = static_cast<float*>(
+        paddle::memory::Alloc(cpu_place, data_size * sizeof(float)));
+    output_pinned_mem[j] = static_cast<float*>(
+        paddle::memory::Alloc(cpu_place, data_size * sizeof(float)));
+    gpu_mem[j] = static_cast<float*>(
+        paddle::memory::Alloc(cuda_place, data_size * sizeof(float)));
+
+    for (int k = 0; k < data_size; ++k) {
+      input_pinned_mem[j][k] = k;
+    }
+  }
+
+  cudaEventRecord(start_e, computation_stream);
+
+  // computation
+  for (int m = 0; m < 30; ++m) {
+    for (int i = 0; i < iteration; ++i) {
+      // cpu -> GPU on computation stream.
+      // note: this operation is async for pinned memory.
+      paddle::memory::Copy(cuda_place, gpu_mem[i], cpu_place,
+                           input_pinned_mem[i], data_size * sizeof(float),
+                           computation_stream);
+
+      // call kernel on computation stream.
+      Kernel<<<4, 1024, 0, computation_stream>>>(gpu_mem[i], data_size);
+
+      // record event_computation on computation stream
+      cudaEventRecord(record_event[i], computation_stream);
+
+      // wait event_computation on copy stream.
+      // note: this operation is async.
+      cudaStreamWaitEvent(copying_stream, record_event[i], 0);
+
+      // copy data GPU->CPU, on copy stream.
+      // note: this operation is async for pinned memory.
+      paddle::memory::Copy(cpu_place, output_pinned_mem[i], cuda_place,
+                           gpu_mem[i], data_size * sizeof(float),
+                           copying_stream);
+    }
+  }
+
+  cudaEventRecord(copying_e, copying_stream);
+  cudaStreamWaitEvent(computation_stream, copying_e, 0);
+
+  cudaEventRecord(stop_e, computation_stream);
+
+  cudaEventSynchronize(start_e);
+  cudaEventSynchronize(stop_e);
+  cudaEventElapsedTime(&elapsedTime, start_e, stop_e);
+
+  // std::cout << cpu_place << " "
+  //          << "time consume:" << elapsedTime / 30 << std::endl;
+
+  for (int l = 0; l < iteration; ++l) {
+    for (int k = 0; k < data_size; ++k) {
+      float temp = input_pinned_mem[l][k];
+      temp = temp * temp / 100;
+      EXPECT_FLOAT_EQ(temp, output_pinned_mem[l][k]);
+    }
+  }
+
+  // destroy resource
+  cudaEventDestroy(copying_e);
+  cudaEventDestroy(start_e);
+  cudaEventDestroy(stop_e);
+  for (int j = 0; j < 10; ++j) {
+    cudaEventDestroy((record_event[j]));
+    paddle::memory::Free(cpu_place, input_pinned_mem[j]);
+    paddle::memory::Free(cpu_place, output_pinned_mem[j]);
+    paddle::memory::Free(cuda_place, gpu_mem[j]);
+  }
+  return elapsedTime / 30;
+}
+
+TEST(CPUANDCUDAPinned, CPUAllocatorAndCUDAPinnedAllocator) {
+  // Generally speaking, operation on pinned_memory is faster than that on
+  // unpinned-memory, but if this unit test fails frequently, please close this
+  // test for the time being.
+  float time1 = test_pinned_memory<paddle::platform::CPUPlace>();
+  float time2 = test_pinned_memory<paddle::platform::CUDAPinnedPlace>();
+  EXPECT_GT(time1, time2);
+}

paddle/fluid/operators/conv_op.cc

@@ -13,6 +13,10 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/fluid/operators/conv_op.h"
+
+#include <string>
+#include <vector>
+
 #ifdef PADDLE_WITH_CUDA
 #include "paddle/fluid/platform/cudnn_helper.h"
 #endif

paddle/fluid/operators/detail/bytebuffer_stream.cc

@@ -17,7 +17,7 @@ limitations under the License. */
 //       file and did some modifications so that we can send gRPC
 //       requests without too much copying of the tensor data.
 
-#include "bytebuffer_stream.h"
+#include "paddle/fluid/operators/detail/bytebuffer_stream.h"
 
 namespace paddle {
 namespace operators {

paddle/fluid/operators/detail/bytebuffer_stream.h

@@ -19,9 +19,11 @@ limitations under the License. */
 
 #pragma once
 
-#include <grpc++/grpc++.h>
+#include <vector>
+
 #include "google/protobuf/io/coded_stream.h"
 #include "google/protobuf/io/zero_copy_stream.h"
+#include "grpc++/grpc++.h"
 
 namespace grpc {
 // A ZeroCopyInputStream that reads from grpc_byte_buffer
@@ -56,7 +58,7 @@ class GrpcBufferReader final
       *data = GRPC_SLICE_START_PTR(slice_) + GRPC_SLICE_LENGTH(slice_) -
               backup_count_;
       GPR_CODEGEN_ASSERT(backup_count_ <= INT_MAX);
-      *size = (int)backup_count_;
+      *size = static_cast<int>(backup_count_);
       backup_count_ = 0;
       return true;
     }
@@ -68,7 +70,7 @@ class GrpcBufferReader final
     *data = GRPC_SLICE_START_PTR(slice_);
     // On win x64, int is only 32bit
     GPR_CODEGEN_ASSERT(GRPC_SLICE_LENGTH(slice_) <= INT_MAX);
-    byte_count_ += * size = (int)GRPC_SLICE_LENGTH(slice_);
+    byte_count_ += * size = static_cast<int>(GRPC_SLICE_LENGTH(slice_));
     return true;
   }
 

paddle/fluid/operators/detail/grpc_client.cc

@@ -14,6 +14,8 @@ limitations under the License. */
 
 #include "paddle/fluid/operators/detail/grpc_client.h"
 
+#include <sys/time.h>
+
 #include <limits>
 
 #include "paddle/fluid/framework/threadpool.h"
@@ -54,7 +56,7 @@ bool RPCClient::AsyncSendVariable(const std::string& ep,
     auto call = s->stub_g_.PrepareUnaryCall(
         s->context_.get(), "/sendrecv.SendRecvService/SendVariable", req, &cq_);
     call->StartCall();
-    call->Finish(&s->reply_, &s->status_, static_cast<void*>(s));
+    call->Finish(&s->reply_, &s->status_, reinterpret_cast<void*>(s));
   });
 
   req_count_++;
@@ -66,7 +68,7 @@ void ProcGetResponse(const VarHandle& var_h,
                      // const sendrecv::VariableMessage& ret_msg) {
                      const ::grpc::ByteBuffer& ret_msg) {
   framework::Variable* outvar = NULL;
-  DeserializeFromByteBuffer(ret_msg, *var_h.ctx, var_h.scope, outvar);
+  DeserializeFromByteBuffer(ret_msg, *var_h.ctx, var_h.scope, &outvar);
 }
 
 template <typename T>
@@ -110,7 +112,7 @@ bool RPCClient::AsyncGetVariable(const std::string& ep,
     auto call = s->stub_g_.PrepareUnaryCall(
         s->context_.get(), "/sendrecv.SendRecvService/GetVariable", buf, &cq_);
     call->StartCall();
-    call->Finish(&s->reply_, &s->status_, static_cast<void*>(s));
+    call->Finish(&s->reply_, &s->status_, reinterpret_cast<void*>(s));
   });
 
   req_count_++;
@@ -170,7 +172,7 @@ void RPCClient::AsyncSendBatchBarrier(const std::string& ep, int64_t time_out) {
   sendrecv::VariableMessage req;
   req.set_varname(BATCH_BARRIER_MESSAGE);
   auto rpc = s->stub_->AsyncSendVariable(s->context_.get(), req, &cq_);
-  rpc->Finish(&s->reply_, &s->status_, static_cast<void*>(s));
+  rpc->Finish(&s->reply_, &s->status_, reinterpret_cast<void*>(s));
   req_count_++;
 }
 
@@ -182,7 +184,7 @@ void RPCClient::AsyncSendFetchBarrier(const std::string& ep, int64_t time_out) {
   sendrecv::VariableMessage req;
   req.set_varname(FETCH_BARRIER_MESSAGE);
   auto rpc = s->stub_->AsyncGetVariable(s->context_.get(), req, &cq_);
-  rpc->Finish(&s->reply_, &s->status_, static_cast<void*>(s));
+  rpc->Finish(&s->reply_, &s->status_, reinterpret_cast<void*>(s));
   req_count_++;
 }
 

paddle/fluid/operators/detail/grpc_client.h

@@ -14,10 +14,9 @@ limitations under the License. */
 
 #pragma once
 
-#include <grpc++/grpc++.h>
-#include <grpc/support/log.h>
 #include <time.h>
-#include <chrono>
+
+#include <chrono>  // NOLINT
 #include <ctime>
 #include <functional>
 #include <iostream>
@@ -25,11 +24,11 @@ limitations under the License. */
 #include <string>
 #include <vector>
 
-#include <grpc++/generic/generic_stub.h>
-#include <grpc++/grpc++.h>
-#include <grpc++/support/byte_buffer.h>
-#include <grpc++/support/slice.h>
-
+#include "grpc++/generic/generic_stub.h"
+#include "grpc++/grpc++.h"
+#include "grpc++/support/byte_buffer.h"
+#include "grpc++/support/slice.h"
+#include "grpc/support/log.h"
 #include "paddle/fluid/framework/data_type.h"
 #include "paddle/fluid/framework/lod_tensor.h"
 #include "paddle/fluid/framework/scope.h"

paddle/fluid/operators/detail/grpc_server.cc

@@ -273,7 +273,7 @@ void AsyncGRPCServer::TryToRegisterNewPrefetchOne() {
 
 // FIXME(typhoonzero): change cq_name to enum.
 void AsyncGRPCServer::HandleRequest(::grpc::ServerCompletionQueue* cq,
-                                    std::string cq_name,
+                                    const std::string& cq_name,
                                     std::function<void()> TryToRegisterNewOne) {
   TryToRegisterNewOne();
 

paddle/fluid/operators/detail/grpc_server.h

@@ -14,10 +14,11 @@ limitations under the License. */
 
 #pragma once
 
-#include <grpc++/grpc++.h>
 #include <string>
+#include <thread>  // NOLINT
 #include <utility>
 
+#include "grpc++/grpc++.h"
 #include "paddle/fluid/framework/executor.h"
 #include "paddle/fluid/framework/lod_tensor.h"
 #include "paddle/fluid/framework/program_desc.h"
@@ -71,7 +72,8 @@ class AsyncGRPCServer final {
   void ShutDown();
 
  protected:
-  void HandleRequest(::grpc::ServerCompletionQueue *cq, std::string cq_name,
+  void HandleRequest(::grpc::ServerCompletionQueue *cq,
+                     const std::string &cq_name,
                      std::function<void()> TryToRegisterNewOne);
   void TryToRegisterNewSendOne();
   void TryToRegisterNewGetOne();

paddle/fluid/operators/detail/grpc_server_test.cc

@@ -14,7 +14,7 @@ limitations under the License. */
 
 #include <unistd.h>
 #include <string>
-#include <thread>
+#include <thread>  // NOLINT
 
 #include "gtest/gtest.h"
 #include "paddle/fluid/operators/detail/grpc_client.h"

paddle/fluid/operators/detail/proto_encoder_helper.h

@@ -19,7 +19,9 @@ limitations under the License. */
 
 #pragma once
 
-#include <grpc++/grpc++.h>
+#include <string>
+
+#include "grpc++/grpc++.h"
 #include "paddle/fluid/platform/enforce.h"
 
 namespace paddle {
@@ -142,6 +144,6 @@ class ProtoEncodeHelper {
   char* limit_;  // Just for CHECKs
 };
 
-}  // detail
-}  // operators
-}  // paddle
+}  // namespace detail
+}  // namespace operators
+}  // namespace paddle

paddle/fluid/operators/detail/sendrecvop_utils.cc

@@ -13,8 +13,10 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/fluid/operators/detail/sendrecvop_utils.h"
+
 #include <sys/time.h>
-#include <thread>
+#include <thread>  // NOLINT
+
 #include "google/protobuf/io/coded_stream.h"
 #include "google/protobuf/io/zero_copy_stream.h"
 #include "paddle/fluid/framework/data_type.h"
@@ -42,7 +44,7 @@ void SerializeToByteBuffer(const std::string& name, framework::Variable* var,
   void* buf = malloc(1024);
   void* payload = nullptr;
   size_t payload_size;
-  ProtoEncodeHelper e((char*)buf, 1024);
+  ProtoEncodeHelper e(static_cast<char*>(buf), 1024);
   e.WriteString(VarMsg::kVarnameFieldNumber, name);
   if (var->IsType<framework::LoDTensor>()) {
     e.WriteUint64(VarMsg::kTypeFieldNumber, 0);
@@ -152,7 +154,7 @@ void SerializeToByteBuffer(const std::string& name, framework::Variable* var,
       framework::proto::VarType_Type_SELECTED_ROWS) {
     auto* slr = var->GetMutable<framework::SelectedRows>();
 
-    ProtoEncodeHelper e2((char*)buf, 128);
+    ProtoEncodeHelper e2(static_cast<char*>(buf), 128);
     // NOTE: rows is of type int64_t
     size_t rows_memory_size =
         slr->rows().size() * framework::SizeOfType(typeid(int64_t));
@@ -181,10 +183,10 @@ void SerializeToByteBuffer(const std::string& name, framework::Variable* var,
 void DeserializeFromByteBuffer(const ::grpc::ByteBuffer& msg,
                                const platform::DeviceContext& ctx,
                                const framework::Scope* scope,
-                               framework::Variable*& var) {
+                               framework::Variable** var) {
   operators::detail::VariableResponse resp(scope, &ctx);
   PADDLE_ENFORCE(resp.Parse(msg) == 0, "parse bytebuffer to tensor error!");
-  var = resp.GetVar();
+  *var = resp.GetVar();
 }
 
 }  // namespace detail

paddle/fluid/operators/detail/sendrecvop_utils.h

@@ -51,7 +51,7 @@ void SerializeToByteBuffer(const std::string& name, framework::Variable* var,
 void DeserializeFromByteBuffer(const ::grpc::ByteBuffer& msg,
                                const platform::DeviceContext& ctx,
                                const framework::Scope* scope,
-                               framework::Variable*& var);
+                               framework::Variable** var);
 
 inline std::type_index ToTypeIndex(sendrecv::VariableMessage::Type type) {
   switch (type) {

paddle/fluid/operators/detail/serde_test.cc

@@ -14,9 +14,9 @@ limitations under the License. */
 
 #include <unistd.h>
 #include <string>
-#include <thread>
+#include <thread>  // NOLINT
 
-#include <google/protobuf/text_format.h>
+#include "google/protobuf/text_format.h"
 #include "gtest/gtest.h"
 #include "paddle/fluid/framework/lod_tensor.h"
 #include "paddle/fluid/framework/tensor_util.h"
@@ -107,7 +107,7 @@ void RunSerdeTestSelectedRows(platform::Place place) {
   for (int i = 0; i < tensor_numel; ++i) {
     EXPECT_FLOAT_EQ(tensor_data2[i], 32.7);
   }
-  for (int i = 0; i < rows2->size(); ++i) {
+  for (int64_t i = 0; i < rows2->size(); ++i) {
     EXPECT_EQ(rows_data2[i], i);
   }
   EXPECT_EQ(slr2->height(), 1000);

paddle/fluid/operators/detail/simple_block_queue.h

@@ -14,9 +14,9 @@ limitations under the License. */
 
 #pragma once
 
-#include <condition_variable>
+#include <condition_variable>  // NOLINT
 #include <deque>
-#include <mutex>
+#include <mutex>  // NOLINT
 
 namespace paddle {
 namespace operators {

paddle/fluid/operators/detail/variable_response.cc

@@ -112,7 +112,8 @@ bool ReadRaw(::google::protobuf::io::CodedInputStream* input,
 
 bool VariableResponse::CopyLodTensorData(
     ::google::protobuf::io::CodedInputStream* input,
-    const platform::DeviceContext& ctx, framework::DDim& dims, int length) {
+    const platform::DeviceContext& ctx, const framework::DDim& dims,
+    int length) {
   auto var = scope_->FindVar(meta_.varname());
   auto* tensor = var->GetMutable<framework::LoDTensor>();
   tensor->Resize(dims);
@@ -148,7 +149,8 @@ inline framework::DDim GetDims(
 
 bool VariableResponse::CopySelectRowsTensorData(
     ::google::protobuf::io::CodedInputStream* input,
-    const platform::DeviceContext& ctx, framework::DDim& dims, int length) {
+    const platform::DeviceContext& ctx, const framework::DDim& dims,
+    int length) {
   auto var = scope_->FindVar(meta_.varname());
   auto* slr = var->GetMutable<framework::SelectedRows>();
   slr->set_height(meta_.slr_height());

paddle/fluid/operators/detail/variable_response.h

@@ -14,6 +14,8 @@
 
 #pragma once
 
+#include <string>
+
 #include "paddle/fluid/framework/data_type.h"
 #include "paddle/fluid/framework/lod_tensor.h"
 #include "paddle/fluid/framework/scope.h"
@@ -60,14 +62,14 @@ class VariableResponse {
  private:
   bool CopySelectRowsTensorData(::google::protobuf::io::CodedInputStream* input,
                                 const platform::DeviceContext& ctx,
-                                framework::DDim& dims, int length);
+                                const framework::DDim& dims, int length);
 
   bool CopySelectRowsData(::google::protobuf::io::CodedInputStream* input,
                           const platform::DeviceContext& ctx, int length);
 
   bool CopyLodTensorData(::google::protobuf::io::CodedInputStream* input,
                          const platform::DeviceContext& ctx,
-                         framework::DDim& dims, int length);
+                         const framework::DDim& dims, int length);
 
  private:
   const framework::Scope* scope_;

paddle/fluid/operators/fc_mkldnn_op.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. */
+
+#include "paddle/fluid/framework/tensor.h"
+#include "paddle/fluid/operators/fc_op.h"
+#include "paddle/fluid/platform/device_context.h"
+#include "paddle/fluid/platform/mkldnn_helper.h"
+
+namespace paddle {
+namespace operators {
+
+using paddle::framework::Tensor;
+using paddle::platform::MKLDNNDeviceContext;
+
+template <typename T>
+class MKLDNNMD {
+ public:
+  explicit MKLDNNMD(const T* in, const T* w, bool bias)
+      : in{paddle::framework::vectorize2int(in->dims())},
+        w{paddle::framework::vectorize2int(w->dims())} {
+    with_bias_ = bias;
+  }
+
+  mkldnn::memory::desc dst() const {
+    return platform::MKLDNNMemDesc({in[0], w[1]},
+                                   mkldnn::memory::data_type::f32,
+                                   mkldnn::memory::format::nc);
+  }
+
+  mkldnn::memory::desc src() const {
+    return is_spatial()
+               ? platform::MKLDNNMemDesc({in[0], in[1], in[2], in[3]},
+                                         mkldnn::memory::data_type::f32,
+                                         mkldnn::memory::format::nchw)
+               : platform::MKLDNNMemDesc({in[0], in[1]},
+                                         mkldnn::memory::data_type::f32,
+                                         mkldnn::memory::format::nc);
+  }
+
+  mkldnn::memory::desc weights() const {
+    return is_spatial()
+               ? platform::MKLDNNMemDesc({w[1], in[1], in[2], in[3]},
+                                         mkldnn::memory::data_type::f32,
+                                         mkldnn::memory::format::oihw)
+               : platform::MKLDNNMemDesc({w[1], in[1]},
+                                         mkldnn::memory::data_type::f32,
+                                         mkldnn::memory::format::oi);
+  }
+
+  mkldnn::memory::desc bias() const {
+    return with_bias_
+               ? platform::MKLDNNMemDesc({w[1]}, mkldnn::memory::data_type::f32,
+                                         mkldnn::memory::format::format_undef)
+               : platform::MKLDNNMemDesc({}, mkldnn::memory::data_type::f32,
+                                         mkldnn::memory::format::format_undef);
+  }
+
+ private:
+  bool is_spatial() const { return in.size() > 1 && w.size() > 1; }
+
+  std::vector<int> in;
+  std::vector<int> w;
+  bool with_bias_;
+  bool is_spatial_;
+};
+
+class MKLDNNMemory {
+ public:
+  MKLDNNMemory(MKLDNNMD<Tensor>* t, const mkldnn::engine& e)
+      : md_{t}, engine_{e} {}
+  virtual ~MKLDNNMemory() = default;
+
+  template <typename Output>
+  mkldnn::memory dst(const Output* out) {
+    return mkldnn::memory({md_->dst(), engine_},
+                          static_cast<void*>(const_cast<float*>(out)));
+  }
+
+  template <typename Output>
+  mkldnn::memory dst(Output* out) {
+    return mkldnn::memory({md_->dst(), engine_}, out);
+  }
+
+  template <typename Input>
+  mkldnn::memory src(const Input* in) {
+    return mkldnn::memory({md_->src(), engine_},
+                          static_cast<void*>(const_cast<float*>(in)));
+  }
+
+  template <typename Weight>
+  mkldnn::memory weights(const Weight* w) {
+    return mkldnn::memory({md_->weights(), engine_},
+                          static_cast<void*>(const_cast<float*>(w)));
+  }
+
+  mkldnn::memory bias() {
+    return mkldnn::memory(mkldnn::memory::primitive_desc(md_->bias(), engine_));
+  }
+
+ private:
+  MKLDNNMD<Tensor>* md_;
+  const mkldnn::engine& engine_;
+};
+
+template <typename T>
+class FCMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
+  void Compute(const paddle::framework::ExecutionContext& ctx) const override {
+    PADDLE_ENFORCE(paddle::platform::is_cpu_place(ctx.GetPlace()),
+                   "It must use CPUPlace.");
+
+    auto& dev_ctx = ctx.template device_context<MKLDNNDeviceContext>();
+    const auto& mkldnn_engine = dev_ctx.GetEngine();
+
+    auto input = ctx.Input<Tensor>("Input");
+    auto w = ctx.Input<Tensor>("W");
+
+    PADDLE_ENFORCE(input->dims().size() == 2 || input->dims().size() == 4,
+                   "Input must be with 2 or 4 dimensions, i.e. NCHW");
+    PADDLE_ENFORCE(w->dims().size() == 2 || w->dims().size() == 4,
+                   "Weights must be with 2 or 4 dimensions, i.e. OI or OIHW");
+
+    bool with_bias = ctx.Attr<bool>("bias_attr");
+    MKLDNNMD<Tensor> md(input, w, with_bias);
+
+    std::shared_ptr<mkldnn::inner_product_forward::primitive_desc> pd =
+        FcFwdPrimitiveDesc(md.src(), md.weights(), md.dst(), md.bias(),
+                           with_bias, mkldnn_engine);
+
+    const std::string key = ctx.op().Output("Out");
+    const std::string key_fc_pd = key + "@fc_pd";
+
+    dev_ctx.SetBlob(key_fc_pd, pd);
+
+    MKLDNNMemory mem(&md, mkldnn_engine);
+
+    const T* input_data = input->data<T>();
+    const T* w_data = w->data<T>();
+
+    auto output = ctx.Output<Tensor>("Out");
+    T* output_data = output->mutable_data<T>(ctx.GetPlace());
+
+    auto dst_memory = mem.dst(output_data);
+    auto src_memory = mem.src(input_data);
+    auto weights_memory = mem.weights(w_data);
+    auto bias_memory = mem.bias();
+
+    auto forward = with_bias ? mkldnn::inner_product_forward(
+                                   *pd, src_memory, weights_memory, bias_memory,
+                                   dst_memory)
+                             : mkldnn::inner_product_forward(
+                                   *pd, src_memory, weights_memory, dst_memory);
+
+    std::vector<mkldnn::primitive> pipeline = {forward};
+    mkldnn::stream(mkldnn::stream::kind::eager).submit(pipeline).wait();
+  }
+
+ private:
+  std::unique_ptr<mkldnn::inner_product_forward::primitive_desc>
+  FcFwdPrimitiveDesc(const mkldnn::memory::desc& src,
+                     const mkldnn::memory::desc& weights,
+                     const mkldnn::memory::desc& dst,
+                     const mkldnn::memory::desc& bias, const bool with_bias,
+                     const mkldnn::engine& engine) const {
+    auto desc = with_bias
+                    ? mkldnn::inner_product_forward::desc(
+                          mkldnn::prop_kind::forward, src, weights, bias, dst)
+                    : mkldnn::inner_product_forward::desc(
+                          mkldnn::prop_kind::forward, src, weights, dst);
+
+    auto pd = new mkldnn::inner_product_forward::primitive_desc(desc, engine);
+    return std::unique_ptr<mkldnn::inner_product_forward::primitive_desc>(pd);
+  }
+};
+
+template <typename T>
+class FCMKLDNNGradOpKernel : public paddle::framework::OpKernel<T> {
+ public:
+  void Compute(const paddle::framework::ExecutionContext& ctx) const override {
+    PADDLE_ENFORCE(paddle::platform::is_cpu_place(ctx.GetPlace()),
+                   "It must use CPUPlace.");
+
+    auto& dev_ctx = ctx.template device_context<MKLDNNDeviceContext>();
+    const auto& mkldnn_engine = dev_ctx.GetEngine();
+
+    T* input_grad_data = nullptr;
+    T* w_grad_data = nullptr;
+
+    Tensor* input_grad = ctx.Output<Tensor>(framework::GradVarName("Input"));
+    Tensor* w_grad = ctx.Output<Tensor>(framework::GradVarName("W"));
+
+    if (input_grad) {
+      input_grad_data = input_grad->mutable_data<T>(ctx.GetPlace());
+    }
+    if (w_grad) {
+      w_grad_data = w_grad->mutable_data<T>(ctx.GetPlace());
+    }
+
+    const Tensor* input = ctx.Input<Tensor>("Input");
+    const T* input_data = input->data<T>();
+
+    const Tensor* w = ctx.Input<Tensor>("W");
+    const T* w_data = w->data<T>();
+
+    const Tensor* out_grad = ctx.Input<Tensor>(framework::GradVarName("Out"));
+    const T* out_grad_data = out_grad->data<T>();
+
+    bool with_bias = ctx.Attr<bool>("bias_attr");
+
+    MKLDNNMD<Tensor> md(input, w, with_bias);
+    MKLDNNMemory mem(&md, mkldnn_engine);
+
+    auto dst_memory = mem.dst(out_grad_data);
+    auto src_memory = mem.src(input_data);
+    auto weights_memory = mem.weights(w_data);
+    auto bias_memory = mem.bias();
+
+    const std::string key = ctx.op().Input("Out");
+    const std::string key_fc_pd = key + "@fc_pd";
+
+    auto pd =
+        std::static_pointer_cast<mkldnn::inner_product_forward::primitive_desc>(
+            dev_ctx.GetBlob(key_fc_pd));
+
+    PADDLE_ENFORCE(pd != nullptr, "Fail to find key_fc_pd in device context");
+
+    if (w_grad) {
+      auto weights_grad_memory = mem.weights(w_grad_data);
+
+      mkldnn::inner_product_backward_weights::primitive_desc bwd_weight_pd =
+          FcBwdWeightsPrimitiveDesc(md.src(), md.weights(), md.dst(), md.bias(),
+                                    with_bias, *pd, mkldnn_engine);
+
+      auto bwd_weights_prim = mkldnn::inner_product_backward_weights(
+          bwd_weight_pd, src_memory, dst_memory, weights_grad_memory,
+          bias_memory);
+
+      std::vector<mkldnn::primitive> pipeline{bwd_weights_prim};
+      mkldnn::stream(mkldnn::stream::kind::eager).submit(pipeline).wait();
+    }
+
+    if (input_grad) {
+      auto src_grad_memory = mem.src(input_grad_data);
+
+      mkldnn::inner_product_backward_data::primitive_desc bwd_data_pd =
+          FcBwdDataPrimitiveDesc(md.src(), md.weights(), md.dst(), *pd,
+                                 mkldnn_engine);
+
+      auto bwd_data_prim = mkldnn::inner_product_backward_data(
+          bwd_data_pd, dst_memory, weights_memory, src_grad_memory);
+
+      std::vector<mkldnn::primitive> pipeline{bwd_data_prim};
+      mkldnn::stream(mkldnn::stream::kind::eager).submit(pipeline).wait();
+    }
+  }
+
+ private:
+  mkldnn::inner_product_backward_weights::primitive_desc
+  FcBwdWeightsPrimitiveDesc(
+      const mkldnn::memory::desc& src, const mkldnn::memory::desc& diff_weights,
+      const mkldnn::memory::desc& diff_dst, const mkldnn::memory::desc& bias,
+      const bool with_bias,
+      const mkldnn::inner_product_forward::primitive_desc& pd,
+      const mkldnn::engine& engine) const {
+    auto bwd_weight_desc = with_bias
+                               ? mkldnn::inner_product_backward_weights::desc(
+                                     src, diff_weights, bias, diff_dst)
+                               : mkldnn::inner_product_backward_weights::desc(
+                                     src, diff_weights, bias, diff_dst);
+
+    return mkldnn::inner_product_backward_weights::primitive_desc(
+        bwd_weight_desc, engine, pd);
+  }
+
+  mkldnn::inner_product_backward_data::primitive_desc FcBwdDataPrimitiveDesc(
+      const mkldnn::memory::desc& diff_src, const mkldnn::memory::desc& weights,
+      const mkldnn::memory::desc& diff_dst,
+      const mkldnn::inner_product_forward::primitive_desc& pd,
+      const mkldnn::engine& engine) const {
+    auto bwd_data_desc =
+        mkldnn::inner_product_backward_data::desc(diff_src, weights, diff_dst);
+    return mkldnn::inner_product_backward_data::primitive_desc(bwd_data_desc,
+                                                               engine, pd);
+  }
+};
+}  // namespace operators
+}  // namespace paddle
+
+REGISTER_OP_KERNEL(fc, MKLDNN, ::paddle::platform::CPUPlace,
+                   paddle::operators::FCMKLDNNOpKernel<float>);
+
+REGISTER_OP_KERNEL(fc_grad, MKLDNN, ::paddle::platform::CPUPlace,
+                   paddle::operators::FCMKLDNNGradOpKernel<float>);

paddle/fluid/operators/fc_op.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. */
+
+#include "paddle/fluid/operators/fc_op.h"
+#include <vector>
+
+namespace paddle {
+namespace operators {
+
+void FCOp::InferShape(framework::InferShapeContext* ctx) const {
+  PADDLE_ENFORCE(ctx->HasInput("Input"),
+                 "X(Input) of Fully Connected should not be null.");
+  PADDLE_ENFORCE(ctx->HasOutput("Out"),
+                 "Out(Output) of Fully Connected should not be null.");
+  PADDLE_ENFORCE(ctx->HasInput("W"),
+                 "W(Input) of Fully Connected should not be null.");
+
+  auto in_dims = ctx->GetInputDim("Input");
+  auto w_dims = ctx->GetInputDim("W");
+  std::vector<int64_t> output_shape({in_dims[0], w_dims[1]});
+
+  PADDLE_ENFORCE(in_dims.size() == 2 || in_dims.size() == 4,
+                 "Fully Connected input should be 2-D or 4-D tensor.");
+
+  PADDLE_ENFORCE(w_dims.size() == 2 || w_dims.size() == 4,
+                 "Fully Connected input should be 2-D or 4-D tensor.");
+
+  ctx->SetOutputDim("Out", framework::make_ddim(output_shape));
+  ctx->ShareLoD("Input", "Out");
+}
+
+framework::OpKernelType FCOp::GetExpectedKernelType(
+    const framework::ExecutionContext& ctx) const {
+  framework::LibraryType library{framework::LibraryType::kMKLDNN};
+  framework::DataLayout layout{framework::DataLayout::kAnyLayout};
+
+  return framework::OpKernelType(
+      framework::ToDataType(ctx.Input<Tensor>("Input")->type()), ctx.GetPlace(),
+      layout, library);
+}
+
+void FCOpGrad::InferShape(framework::InferShapeContext* ctx) const {
+  auto in_dims = ctx->GetInputDim("Input");
+  auto w_dims = ctx->GetInputDim("W");
+
+  if (ctx->HasOutput(framework::GradVarName("Input"))) {
+    ctx->SetOutputDim(framework::GradVarName("Input"), in_dims);
+  }
+  if (ctx->HasOutput(framework::GradVarName("W"))) {
+    ctx->SetOutputDim(framework::GradVarName("W"), w_dims);
+  }
+}
+
+framework::OpKernelType FCOpGrad::GetExpectedKernelType(
+    const framework::ExecutionContext& ctx) const {
+  framework::LibraryType library{framework::LibraryType::kMKLDNN};
+  framework::DataLayout layout{framework::DataLayout::kAnyLayout};
+
+  return framework::OpKernelType(
+      framework::ToDataType(ctx.Input<Tensor>("Input")->type()), ctx.GetPlace(),
+      layout, library);
+}
+
+FCOpMaker::FCOpMaker(OpProto* proto, OpAttrChecker* op_checker)
+    : OpProtoAndCheckerMaker(proto, op_checker) {
+  AddInput("Input", "(Tensor) The input tensor of fully connected operator. ");
+  AddInput("W", "(Tensor), The second input tensor of fc op.");
+  AddOutput("Out", "(Tensor) The output tensor of fully connected operator. ");
+  AddAttr<bool>("use_mkldnn",
+                "(bool, default false) Only used in mkldnn kernel")
+      .SetDefault(false);
+  AddAttr<bool>("bias_attr", "(bool, default false) Only used in mkldnn kernel")
+      .SetDefault(false);
+  AddComment(R"DOC(
+  Fully Connected Operator.
+
+  The fully connected operation calculates the output based on the input, weights and bias attribute.
+  The size of each dimension of the parameters checked in the infer-shape.
+  The matrix of bias is generated by the mkldnn framework, when the bias_attr is True.
+  Additional parametrs are use_mkldnn and bias_attr.
+  The input(X) size and output(Out) size may be diffrent.
+
+  The fully connected layer only supports MKLDNN version
+)DOC");
+}
+
+}  // namespace operators
+}  // namespace paddle
+
+REGISTER_OP(fc, paddle::operators::FCOp, paddle::operators::FCOpMaker, fc_grad,
+            paddle::operators::FCOpGrad);

paddle/fluid/operators/fc_op.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
+
+#include "paddle/fluid/framework/op_registry.h"
+
+namespace paddle {
+namespace operators {
+
+using Tensor = framework::Tensor;
+
+class FCOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+  void InferShape(framework::InferShapeContext* ctx) const override;
+
+ protected:
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext& ctx) const override;
+};
+
+class FCOpGrad : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+  void InferShape(framework::InferShapeContext* ctx) const override;
+
+ protected:
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext& ctx) const override;
+};
+
+class FCOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  FCOpMaker(OpProto* proto, OpAttrChecker* op_checker);
+};
+
+}  // namespace operators
+}  // namespace paddle

paddle/fluid/operators/math/math_function.cc

@@ -322,6 +322,14 @@ void set_constant_with_place<platform::CPUPlace>(
                            TensorSetConstantCPU(tensor, value));
 }
 
+template <>
+void set_constant_with_place<platform::CUDAPinnedPlace>(
+    const platform::DeviceContext& context, framework::Tensor* tensor,
+    float value) {
+  framework::VisitDataType(framework::ToDataType(tensor->type()),
+                           TensorSetConstantCPU(tensor, value));
+}
+
 struct TensorSetConstantWithPlace : public boost::static_visitor<void> {
   TensorSetConstantWithPlace(const platform::DeviceContext& context,
                              framework::Tensor* tensor, float value)

paddle/fluid/operators/nccl_op_test.cu.cc

@@ -15,8 +15,8 @@ limitations under the License. */
 #include <glog/logging.h>
 #include <gtest/gtest.h>
 #include <memory>
-#include <mutex>
-#include <thread>
+#include <mutex>   // NOLINT
+#include <thread>  // NOLINT
 #include <vector>
 
 #include "paddle/fluid/framework/init.h"
@@ -43,7 +43,7 @@ const f::DDim kDims = {20, 20};
 // nccl op common tester, init communicator.
 class NCCLTester : public ::testing::Test {
  public:
-  virtual void SetUp() override {
+  void SetUp() override {
     int count = p::GetCUDADeviceCount();
     if (count <= 1) {
       LOG(WARNING)
@@ -64,7 +64,7 @@ class NCCLTester : public ::testing::Test {
     NCCLInitOp();
   }
 
-  virtual void TearDown() override {
+  void TearDown() override {
     for (auto &device_context : dev_ctxs_) {
       delete device_context;
     }

paddle/fluid/operators/reader/create_double_buffer_reader_op.cc

@@ -12,7 +12,8 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-#include <thread>
+#include <thread>  // NOLINT
+
 #include "paddle/fluid/framework/channel.h"
 #include "paddle/fluid/operators/reader/reader_op_registry.h"
 

paddle/fluid/operators/reader/open_files_op.cc

@@ -21,6 +21,22 @@ namespace reader {
 
 class MultipleReader : public framework::ReaderBase {
  public:
+  class ThreadBufferMap {
+   public:
+    std::vector<framework::LoDTensor>& operator[](
+        const std::thread::id& thread_id) {
+      std::lock_guard<std::mutex> lock(mutex_);
+      return buffer_[thread_id];
+    }
+
+    void Clear() { buffer_.clear(); }
+
+   private:
+    std::mutex mutex_;
+    std::unordered_map<std::thread::id, std::vector<framework::LoDTensor>>
+        buffer_;
+  };
+
   MultipleReader(const std::vector<std::string>& file_names,
                  const std::vector<framework::DDim>& dims, size_t thread_num)
       : file_names_(file_names), dims_(dims) {
@@ -47,28 +63,27 @@ class MultipleReader : public framework::ReaderBase {
   framework::Channel<size_t>* waiting_file_idx_;
   framework::Channel<size_t>* available_thread_idx_;
   framework::Channel<std::vector<framework::LoDTensor>>* buffer_;
-  mutable std::vector<framework::LoDTensor> local_buffer_;
+  mutable ThreadBufferMap thread_buffer_map_;
 };
 
 void MultipleReader::ReadNext(std::vector<framework::LoDTensor>* out) {
   if (!HasNext()) {
     PADDLE_THROW("There is no next data!");
   }
-
-  if (local_buffer_.empty()) {
-    buffer_->Receive(&local_buffer_);
-  }
-  *out = local_buffer_;
-  local_buffer_.clear();
+  auto& thread_local_buffer = thread_buffer_map_[std::this_thread::get_id()];
+  *out = thread_local_buffer;
+  thread_local_buffer.clear();
 }
 
 bool MultipleReader::HasNext() const {
-  return local_buffer_.empty() ? buffer_->Receive(&local_buffer_) : true;
+  auto& thread_local_buffer = thread_buffer_map_[std::this_thread::get_id()];
+  return thread_local_buffer.empty() ? buffer_->Receive(&thread_local_buffer)
+                                     : true;
 }
 
 void MultipleReader::ReInit() {
   EndScheduler();
-  local_buffer_.clear();
+  thread_buffer_map_.Clear();
   StartNewScheduler();
 }
 
@@ -176,7 +191,7 @@ class OpenFilesOp : public framework::OperatorBase {
     const auto& ranks = Attr<std::vector<int>>("ranks");
     PADDLE_ENFORCE(!shape_concat.empty() && !ranks.empty());
     PADDLE_ENFORCE_EQ(std::accumulate(ranks.begin(), ranks.end(), 0),
-                      int(shape_concat.size()),
+                      static_cast<int>(shape_concat.size()),
                       "The accumulate of all ranks should be equal to the "
                       "shape concat's length.");
     const auto& file_names = Attr<std::vector<std::string>>("file_names");

paddle/fluid/operators/reshape_op.cc

@@ -14,6 +14,9 @@ limitations under the License. */
 
 #include "paddle/fluid/operators/reshape_op.h"
 
+#include <string>
+#include <vector>
+
 namespace paddle {
 namespace operators {
 

paddle/fluid/operators/reshape_op.h

@@ -14,6 +14,9 @@ limitations under the License. */
 
 #pragma once
 
+#include <string>
+#include <vector>
+
 #include "paddle/fluid/framework/eigen.h"
 #include "paddle/fluid/framework/op_registry.h"
 

paddle/fluid/platform/cpu_info.cc

@@ -27,6 +27,11 @@ DEFINE_double(fraction_of_cpu_memory_to_use, 1,
               "Default use 100% of CPU memory for PaddlePaddle,"
               "reserve the rest for page tables, etc");
 
+DEFINE_double(
+    fraction_of_cuda_pinned_memory_to_use, 0.5,
+    "Default use 50% of CPU memory as the pinned_memory for PaddlePaddle,"
+    "reserve the rest for page tables, etc");
+
 namespace paddle {
 namespace platform {
 
@@ -62,5 +67,22 @@ size_t CpuMaxChunkSize() {
   return CpuMaxAllocSize() / 32;
 }
 
+size_t CUDAPinnedMaxAllocSize() {
+  // For distributed systems, it requires configuring and limiting
+  // the fraction of memory to use.
+  return FLAGS_fraction_of_cuda_pinned_memory_to_use * CpuTotalPhysicalMemory();
+}
+
+size_t CUDAPinnedMinChunkSize() {
+  // Allow to allocate the minimum chunk size is 64 KB.
+  return 1 << 16;
+}
+
+size_t CUDAPinnedMaxChunkSize() {
+  // Allow to allocate the maximum chunk size is roughly 1/256 of CUDA_PINNED
+  // memory.
+  return CUDAPinnedMaxAllocSize() / 256;
+}
+
 }  // namespace platform
 }  // namespace paddle

paddle/fluid/platform/cpu_info.h

@@ -22,11 +22,20 @@ namespace platform {
 //! Get the maximum allocation size for a machine.
 size_t CpuMaxAllocSize();
 
+//! Get the maximum allocation size for a machine.
+size_t CUDAPinnedMaxAllocSize();
+
 //! Get the minimum chunk size for buddy allocator.
 size_t CpuMinChunkSize();
 
 //! Get the maximum chunk size for buddy allocator.
 size_t CpuMaxChunkSize();
 
+//! Get the minimum chunk size for buddy allocator.
+size_t CUDAPinnedMinChunkSize();
+
+//! Get the maximum chunk size for buddy allocator.
+size_t CUDAPinnedMaxChunkSize();
+
 }  // namespace platform
 }  // namespace paddle

paddle/fluid/platform/device_context.cc

@@ -53,6 +53,16 @@ DeviceContextPool::DeviceContextPool(
       PADDLE_THROW(
           "'CUDAPlace' is not supported, Please re-compile with WITH_GPU "
           "option");
+#endif
+    } else if (platform::is_cuda_pinned_place(p)) {
+#ifdef PADDLE_WITH_CUDA
+      device_contexts_.emplace(
+          p,
+          PtrType(new CUDAPinnedDeviceContext(boost::get<CUDAPinnedPlace>(p))));
+#else
+      PADDLE_THROW(
+          "'CUDAPlace' is not supported, Please re-compile with WITH_GPU "
+          "option");
 #endif
     }
   }
@@ -186,6 +196,20 @@ cudnnHandle_t CUDADeviceContext::cudnn_handle() const { return cudnn_handle_; }
 
 cudaStream_t CUDADeviceContext::stream() const { return stream_; }
 
+CUDAPinnedDeviceContext::CUDAPinnedDeviceContext() {
+  eigen_device_.reset(new Eigen::DefaultDevice());
+}
+
+CUDAPinnedDeviceContext::CUDAPinnedDeviceContext(CUDAPinnedPlace place)
+    : place_(place) {
+  eigen_device_.reset(new Eigen::DefaultDevice());
+}
+
+Eigen::DefaultDevice* CUDAPinnedDeviceContext::eigen_device() const {
+  return eigen_device_.get();
+}
+
+Place CUDAPinnedDeviceContext::GetPlace() const { return place_; }
 #endif
 
 #ifdef PADDLE_WITH_MKLDNN

paddle/fluid/platform/device_context.h

@@ -118,6 +118,25 @@ struct DefaultDeviceContextType<platform::CUDAPlace> {
   using TYPE = CUDADeviceContext;
 };
 
+// Currently, CUDAPinnedDeviceContext is only used to data copying.
+class CUDAPinnedDeviceContext : public DeviceContext {
+ public:
+  CUDAPinnedDeviceContext();
+  explicit CUDAPinnedDeviceContext(CUDAPinnedPlace place);
+
+  Place GetPlace() const override;
+
+  Eigen::DefaultDevice* eigen_device() const;
+
+ private:
+  CUDAPinnedPlace place_;
+  std::unique_ptr<Eigen::DefaultDevice> eigen_device_;
+};
+
+template <>
+struct DefaultDeviceContextType<platform::CUDAPinnedPlace> {
+  using TYPE = CUDAPinnedDeviceContext;
+};
 #endif
 
 #ifdef PADDLE_WITH_MKLDNN

paddle/fluid/platform/place.cc

@@ -26,6 +26,7 @@ class PlacePrinter : public boost::static_visitor<> {
   void operator()(const CUDAPlace &p) {
     os_ << "CUDAPlace(" << p.device << ")";
   }
+  void operator()(const CUDAPinnedPlace &p) { os_ << "CUDAPinnedPlace"; }
 
  private:
   std::ostream &os_;
@@ -40,12 +41,19 @@ const Place &get_place() { return the_default_place; }
 
 const CUDAPlace default_gpu() { return CUDAPlace(0); }
 const CPUPlace default_cpu() { return CPUPlace(); }
+const CUDAPinnedPlace default_cuda_pinned() { return CUDAPinnedPlace(); }
 
 bool is_gpu_place(const Place &p) {
   return boost::apply_visitor(IsCUDAPlace(), p);
 }
 
-bool is_cpu_place(const Place &p) { return !is_gpu_place(p); }
+bool is_cpu_place(const Place &p) {
+  return boost::apply_visitor(IsCPUPlace(), p);
+}
+
+bool is_cuda_pinned_place(const Place &p) {
+  return boost::apply_visitor(IsCUDAPinnedPlace(), p);
+}
 
 bool places_are_same_class(const Place &p1, const Place &p2) {
   return p1.which() == p2.which();
@@ -53,7 +61,7 @@ bool places_are_same_class(const Place &p1, const Place &p2) {
 
 bool is_same_place(const Place &p1, const Place &p2) {
   if (places_are_same_class(p1, p2)) {
-    if (is_cpu_place(p1)) {
+    if (is_cpu_place(p1) || is_cuda_pinned_place(p1)) {
       return true;
     } else {
       return boost::get<CUDAPlace>(p1) == boost::get<CUDAPlace>(p2);

paddle/fluid/platform/place.h

@@ -45,12 +45,33 @@ struct CUDAPlace {
   int device;
 };
 
+struct CUDAPinnedPlace {
+  CUDAPinnedPlace() {}
+
+  // needed for variant equality comparison
+  inline bool operator==(const CUDAPinnedPlace &) const { return true; }
+  inline bool operator!=(const CUDAPinnedPlace &) const { return false; }
+};
+
 struct IsCUDAPlace : public boost::static_visitor<bool> {
   bool operator()(const CPUPlace &) const { return false; }
   bool operator()(const CUDAPlace &gpu) const { return true; }
+  bool operator()(const CUDAPinnedPlace &) const { return false; }
 };
 
-typedef boost::variant<CUDAPlace, CPUPlace> Place;
+struct IsCPUPlace : public boost::static_visitor<bool> {
+  bool operator()(const CPUPlace &cpu) const { return true; }
+  bool operator()(const CUDAPlace &) const { return false; }
+  bool operator()(const CUDAPinnedPlace &) const { return false; }
+};
+
+struct IsCUDAPinnedPlace : public boost::static_visitor<bool> {
+  bool operator()(const CPUPlace &) const { return false; }
+  bool operator()(const CUDAPlace &) const { return false; }
+  bool operator()(const CUDAPinnedPlace &cuda_pinned) const { return true; }
+};
+
+typedef boost::variant<CUDAPlace, CPUPlace, CUDAPinnedPlace> Place;
 
 using PlaceList = std::vector<Place>;
 
@@ -59,9 +80,11 @@ const Place &get_place();
 
 const CUDAPlace default_gpu();
 const CPUPlace default_cpu();
+const CUDAPinnedPlace default_cuda_pinned();
 
 bool is_gpu_place(const Place &);
 bool is_cpu_place(const Place &);
+bool is_cuda_pinned_place(const Place &);
 bool places_are_same_class(const Place &, const Place &);
 bool is_same_place(const Place &, const Place &);
 
@@ -95,6 +118,16 @@ struct PlaceVisitorWrapper
 #else
     PADDLE_THROW("Paddle is not compiled with CUDA. Cannot visit cuda device");
     return typename Visitor::result_type();
+#endif
+  }
+
+  typename Visitor::result_type operator()(
+      const CUDAPinnedPlace &cuda_pinned) const {
+#ifdef PADDLE_WITH_CUDA
+    return visitor_(cuda_pinned);
+#else
+    PADDLE_THROW("Paddle is not compiled with CUDA. Cannot visit cuda_pinned");
+    return typename Visitor::result_type();
 #endif
   }
 };

python/paddle/fluid/layers/nn.py

@@ -133,6 +133,8 @@ def fc(input,
         bias_attr (ParamAttr|list of ParamAttr, default None): The parameter attribute for the bias
             of this layer. If it is set to None, no bias will be added to the output units.
         act (str, default None): Activation to be applied to the output of this layer.
+        use_mkldnn(bool): Use mkldnn kernel or not, it is valid only when the mkldnn
+            library is installed. Default: False
         name (str, default None): The name of this layer.
 
     Returns:
@@ -153,38 +155,64 @@ def fc(input,
     dtype = helper.input_dtype()
 
     mul_results = []
-    for input_var, param_attr in helper.iter_inputs_and_params():
-        input_shape = input_var.shape
+    if use_mkldnn:
+        tmp = helper.create_tmp_variable(dtype)
+        input_shape = input.shape
         param_shape = [
             reduce(lambda a, b: a * b, input_shape[num_flatten_dims:], 1)
         ] + [size]
 
         w = helper.create_parameter(
-            attr=param_attr, shape=param_shape, dtype=dtype, is_bias=False)
-        tmp = helper.create_tmp_variable(dtype)
+            attr=helper.param_attr,
+            shape=param_shape,
+            dtype=dtype,
+            is_bias=False)
+        if bias_attr is None or bias_attr is False:
+            bias_attr = False
+        else:
+            bias_attr = True
         helper.append_op(
-            type="mul",
-            inputs={"X": input_var,
-                    "Y": w},
+            type="fc",
+            inputs={"Input": input,
+                    "W": w},
             outputs={"Out": tmp},
-            attrs={
-                "x_num_col_dims": num_flatten_dims,
-                "y_num_col_dims": 1,
-                'use_mkldnn': use_mkldnn
-            })
-        mul_results.append(tmp)
-
-    # sum
-    if len(mul_results) == 1:
-        pre_bias = mul_results[0]
+            attrs={"use_mkldnn": use_mkldnn,
+                   "bias_attr": bias_attr})
+        return helper.append_activation(tmp)
     else:
-        pre_bias = helper.create_tmp_variable(dtype)
-        helper.append_op(
-            type="sum", inputs={"X": mul_results}, outputs={"Out": pre_bias})
-    # add bias
-    pre_activation = helper.append_bias_op(pre_bias, dim_start=num_flatten_dims)
-    # add activation
-    return helper.append_activation(pre_activation)
+        for input_var, param_attr in helper.iter_inputs_and_params():
+            input_shape = input_var.shape
+            param_shape = [
+                reduce(lambda a, b: a * b, input_shape[num_flatten_dims:], 1)
+            ] + [size]
+
+            w = helper.create_parameter(
+                attr=param_attr, shape=param_shape, dtype=dtype, is_bias=False)
+            tmp = helper.create_tmp_variable(dtype)
+            helper.append_op(
+                type="mul",
+                inputs={"X": input_var,
+                        "Y": w},
+                outputs={"Out": tmp},
+                attrs={
+                    "x_num_col_dims": num_flatten_dims,
+                    "y_num_col_dims": 1,
+                })
+            mul_results.append(tmp)
+
+        if len(mul_results) == 1:
+            pre_bias = mul_results[0]
+        else:
+            pre_bias = helper.create_tmp_variable(dtype)
+            helper.append_op(
+                type="sum",
+                inputs={"X": mul_results},
+                outputs={"Out": pre_bias})
+        # add bias
+        pre_activation = helper.append_bias_op(
+            pre_bias, dim_start=num_flatten_dims)
+        # add activation
+        return helper.append_activation(pre_activation)
 
 
 def embedding(input,

python/paddle/fluid/tests/unittests/test_fc_op.py

+# 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.
+
+import unittest
+import numpy as np
+from op_test import OpTest
+
+
+def fully_connected_naive(input, weights, bias_data=None):
+    in_n, in_c, in_h, in_w = input.shape
+    w_h, w_c = weights.shape
+
+    x_data = np.reshape(input, [in_n, in_c * in_h * in_w])
+    w_data = np.transpose(np.reshape(weights, (w_c, in_c * in_h * in_w)))
+    result = None
+
+    if not bias_data:
+        result = np.dot(x_data, w_data)
+    else:
+        result = np.dot(x_data, w_data) + bias_data
+
+    return result
+
+
+class MatrixGenerate:
+    def __init__(self, mb, ic, oc, h, w):
+        self.input = np.random.random((mb, ic, h, w)).astype("float32")
+        self.weights = np.random.random((ic * h * w, oc)).astype("float32")
+
+
+class TestFCMKLDNNOp(OpTest):
+    def setUp(self):
+        self.op_type = "fc"
+        self.use_mkldnn = True
+        self.with_bias = True
+        self.matrix = MatrixGenerate(1, 10, 15, 3, 3)
+
+        self.inputs = {'Input': self.matrix.input, 'W': self.matrix.weights}
+
+        self.attrs = {
+            'use_mkldnn': self.use_mkldnn,
+            'with_bias': self.with_bias
+        }
+
+        self.outputs = {
+            'Out': fully_connected_naive(self.matrix.input, self.matrix.weights)
+        }
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad_normal(self):
+        self.check_grad(set(['Input', 'W']), 'Out', max_relative_error=0.9)
+
+    def test_check_grad_no_weight(self):
+        self.check_grad(
+            ['Input'], 'Out', max_relative_error=0.5, no_grad_set=set('W'))
+
+
+class TestFCMKLDNNOp1(TestFCMKLDNNOp):
+    def init_op_type(self):
+        self.matrix = MatrixGenerate(2, 15, 48, 2, 2)
+
+
+class TestFCMKLDNNOp2(TestFCMKLDNNOp):
+    def init_op_type(self):
+        self.matrix = MatrixGenerate(2, 32, 40, 1, 1)
+
+
+class TestFCMKLDNNOp3(TestFCMKLDNNOp):
+    def init_op_type(self):
+        self.matrix = MatrixGenerate(2, 2, 4, 1, 1)
+
+
+class TestFCMKLDNNOp4(TestFCMKLDNNOp):
+    def init_op_type(self):
+        self.with_bias = False
+        self.matrix = MatrixGenerate(2, 32, 48, 2, 2)
+
+
+class TestFCMKLDNNOp4(TestFCMKLDNNOp):
+    def init_op_type(self):
+        self.with_bias = False
+        self.matrix = MatrixGenerate(2, 32, 1000, 6, 6)
+
+
+if __name__ == "__main__":
+    unittest.main()