Fix a bug in getting MKLDNN memory

CMakeLists.txt

@@ -159,8 +159,12 @@ endif()
 
 if(USE_MKL_IF_AVAILABLE)
   if(USE_MKLDNN)
+    # We need to use generic archtecture. Otherwise, MKLDNN compiled in one
+    # CPU architecture (e.g., C5) can't run on another architecture (e.g., g3).
+    set(ARCH_OPT_FLAGS "-mtune=generic")
     add_subdirectory(3rdparty/mkldnn)
     include_directories(3rdparty/mkldnn/include)
+    add_definitions(-DMXNET_USE_MKLDNN=1)
     list(APPEND mxnet_LINKER_LIBS mkldnn)
   endif()
   find_package(MKL)
@@ -169,10 +173,6 @@ if(USE_MKL_IF_AVAILABLE)
     include_directories(${MKL_INCLUDE_DIR})
     include_directories(${CMAKE_CURRENT_SOURCE_DIR}/src/operator/mkl)
 
-    if(USE_MKLDNN)
-      add_definitions(-DMXNET_USE_MKLDNN=1)
-    endif()
-
     add_definitions(-DUSE_MKL=1)
     add_definitions(-DCUB_MKL=1)
     list(APPEND mxnet_LINKER_LIBS ${MKL_LIBRARIES})

Jenkinsfile

@@ -26,7 +26,7 @@ mx_lib = 'lib/libmxnet.so, lib/libmxnet.a, 3rdparty/dmlc-core/libdmlc.a, 3rdpart
 mx_dist_lib = 'lib/libmxnet.so, lib/libmxnet.a, 3rdparty/dmlc-core/libdmlc.a, 3rdparty/nnvm/lib/libnnvm.a, 3rdparty/ps-lite/build/libps.a, deps/lib/libprotobuf-lite.a, deps/lib/libzmq.a'
 // mxnet cmake libraries, in cmake builds we do not produce a libnvvm static library by default.
 mx_cmake_lib = 'build/libmxnet.so, build/libmxnet.a, build/3rdparty/dmlc-core/libdmlc.a, build/tests/mxnet_unit_tests, build/3rdparty/openmp/runtime/src/libomp.so'
-mx_cmake_mkldnn_lib = 'build/libmxnet.so, build/libmxnet.a, build/3rdparty/dmlc-core/libdmlc.a, build/tests/mxnet_unit_tests, build/3rdparty/openmp/runtime/src/libomp.so, build/3rdparty/mkldnn/src/libmkldnn.so, build/3rdparty/mkldnn/src/libmkldnn.so.0'
+mx_cmake_mkldnn_lib = 'build/libmxnet.so, build/libmxnet.a, build/3rdparty/dmlc-core/libdmlc.a, build/tests/mxnet_unit_tests, build/3rdparty/openmp/runtime/src/libomp.so, build/3rdparty/mkldnn/src/libmkldnn.so.0'
 mx_mkldnn_lib = 'lib/libmxnet.so, lib/libmxnet.a, lib/libiomp5.so, lib/libmkldnn.so.0, lib/libmklml_intel.so, 3rdparty/dmlc-core/libdmlc.a, 3rdparty/nnvm/lib/libnnvm.a'
 // command to start a docker container
 docker_run = 'tests/ci_build/ci_build.sh'
@@ -574,6 +574,17 @@ try {
         }
       }
     },
+    'Cpp: MKLDNN+GPU': {
+      node('mxnetlinux-gpu') {
+        ws('workspace/ut-cpp-mkldnn-gpu') {
+          timeout(time: max_time, unit: 'MINUTES') {
+            init_git()
+            unpack_lib('cmake_mkldnn_gpu', mx_cmake_mkldnn_lib)
+            sh "ci/build.py --nvidiadocker --platform ubuntu_gpu /work/runtime_functions.sh unittest_ubuntu_gpu_cpp"
+          }
+        }
+      }
+    },
     'R: CPU': {
       node('mxnetlinux-cpu') {
         ws('workspace/ut-r-cpu') {

ci/docker/runtime_functions.sh

@@ -323,6 +323,9 @@ build_ubuntu_gpu_cmake_mkldnn() {
         /work/mxnet
 
     ninja -v
+    # libmkldnn.so.0 is a link file. We need an actual binary file named libmkldnn.so.0.
+    cp 3rdparty/mkldnn/src/libmkldnn.so.0 3rdparty/mkldnn/src/libmkldnn.so.0.tmp
+    mv 3rdparty/mkldnn/src/libmkldnn.so.0.tmp 3rdparty/mkldnn/src/libmkldnn.so.0
 }
 
 build_ubuntu_gpu_cmake() {

src/ndarray/ndarray.cc

@@ -485,8 +485,8 @@ const mkldnn::memory *NDArray::GetMKLDNNData(
 }
 
 const mkldnn::memory *NDArray::GetMKLDNNDataReorder(
-    const mkldnn::memory::primitive_desc &desc) const {
-  if (desc.get_size() != shape().Size() * GetTypeSize(dtype_)) {
+    const mkldnn::memory::primitive_desc &new_pd) const {
+  if (new_pd.get_size() != shape().Size() * GetTypeSize(dtype_)) {
     LOG(FATAL) << "The size of NDArray doesn't match the requested MKLDNN memory desc";
     return nullptr;
   }
@@ -495,24 +495,41 @@ const mkldnn::memory *NDArray::GetMKLDNNDataReorder(
   const mkldnn::memory *mem = GetMKLDNNData();
   // If the memory descriptor matches, it's easy.
   MKLDNNStream *stream = MKLDNNStream::Get();
-  if (mem->get_primitive_desc() == desc) {
-    return GetMKLDNNExact(mem, desc);
+  if (mem->get_primitive_desc() == new_pd) {
+    return GetMKLDNNExact(mem, new_pd);
   }
 
-  mkldnn::memory::primitive_desc _desc = desc;
+  mkldnn::memory::primitive_desc _pd = new_pd;
+  mkldnn::memory::desc desc1 = mem->get_primitive_desc().desc();
+  mkldnn::memory::desc desc2 = _pd.desc();
   // Now we need to determine if we should reorder the memory.
   // If both use the default formats, we think we don't need to reorder.
-  mkldnn::memory::desc desc1 = mem->get_primitive_desc().desc();
-  mkldnn::memory::desc desc2 = _desc.desc();
   if (desc1.data.format == GetDefaultFormat(desc1) &&
       desc2.data.format == GetDefaultFormat(desc2)) {
-    mkldnn_mem_ptr ret(new mkldnn::memory(desc, mem->get_data_handle()));
+    mkldnn_mem_ptr ret(new mkldnn::memory(new_pd, mem->get_data_handle()));
     stream->RegisterMem(ret);
     return ret.get();
-  } else {
-    mkldnn::memory *ret = TmpMemMgr::Get()->Alloc(desc);
+  } else if (same_shape(desc1, desc2)) {
+    // If they have the same shape, we can reorder data directly.
+    mkldnn::memory *ret = TmpMemMgr::Get()->Alloc(new_pd);
     stream->RegisterPrim(mkldnn::reorder(*mem, *ret));
     return ret;
+  } else {
+    // If they have different shapes, we need to reshape the array first.
+    // Since this method will only be used inside an operator, we can call
+    // MKLDNNDataReshape to reshape an array.
+    TShape required_shape(desc2.data.ndims);
+    for (int i = 0; i < desc2.data.ndims; i++)
+      required_shape[i] = desc2.data.dims[i];
+    NDArray reshaped = MKLDNNDataReshape(required_shape);
+    const mkldnn::memory *ret = reshaped.GetMKLDNNData();
+    if (ret->get_primitive_desc() == new_pd) {
+      return GetMKLDNNExact(ret, new_pd);
+    } else {
+      mkldnn::memory *ret2 = TmpMemMgr::Get()->Alloc(new_pd);
+      stream->RegisterPrim(mkldnn::reorder(*ret, *ret2));
+      return ret2;
+    }
   }
 }
 
@@ -566,10 +583,15 @@ void NDArray::MKLDNNDataReorderAsync(const mkldnn::memory::primitive_desc &desc)
 
 const mkldnn::memory *NDArray::GetMKLDNNData() const {
   CHECK(storage_type() == kDefaultStorage);
-  // If this array uses MKLDNN layout, we have to make sure it's not a view.
-  // Otherwise, we'll have to change the layout inside the array.
-  if (IsMKLDNNData())
+  if (IsMKLDNNData()) {
+    // If this array uses MKLDNN layout, we have to make sure it's not a view.
+    // Otherwise, we'll have to change the layout inside the array.
     CHECK(!IsView());
+    MKLDNNStream::Get()->RegisterMem(ptr_->mkl_mem_->GetMem());
+    // If this array uses MKLDNN format, we should return now. Otherwise,
+    // SetMKLMem may mess up mkl_mem_.
+    return ptr_->mkl_mem_->GetRaw();
+  }
   ptr_->SetMKLMem(IsView() ? ptr_->storage_shape : shape_, dtype_);
   MKLDNNStream::Get()->RegisterMem(ptr_->mkl_mem_->GetMem());
   if (IsView()) {

src/operator/nn/mkldnn/mkldnn_base-inl.h

@@ -272,12 +272,11 @@ class MKLDNNStream {
   std::vector<std::shared_ptr<const mkldnn::memory> > mem_holder;
 
  public:
-  static MKLDNNStream *Get() {
-    static thread_local MKLDNNStream stream;
-    return &stream;
-  }
+  static MKLDNNStream *Get();
 
-  void RegisterPrim(const mkldnn::primitive &prim) { net.push_back(prim); }
+  void RegisterPrim(const mkldnn::primitive &prim) {
+    net.push_back(prim);
+  }
 
   void RegisterMem(std::shared_ptr<const mkldnn::memory> mem) {
     mem_holder.push_back(mem);
@@ -287,10 +286,21 @@ class MKLDNNStream {
     return !net.empty();
   }
 
-  void Submit() {
-    if (!net.empty())
+  /*
+   * After submitting mkldnn operations for execution, we need to
+   * clean up memory held by the stream. However, sometimes users
+   * might want to separate mkldnn execution and memory cleanup.
+   */
+  void Submit(bool cleanup = true) {
+    if (!net.empty()) {
       mkldnn::stream(mkldnn::stream::kind::eager).submit(net).wait();
-    net.clear();
+      net.clear();
+    }
+    if (cleanup)
+      Cleanup();
+  }
+
+  void Cleanup() {
     mem_holder.clear();
     TmpMemMgr::Get()->Reset();
   }
@@ -348,6 +358,16 @@ inline bool same_shape(const TShape &shape, const mkldnn_dims_t dims, int ndims)
   return true;
 }
 
+inline bool same_shape(const mkldnn::memory::desc &desc1,
+                       const mkldnn::memory::desc &desc2) {
+  if (desc1.data.ndims != desc2.data.ndims)
+    return false;
+  for (int i = 0; i < desc1.data.ndims; i++)
+    if (desc1.data.dims[i] != desc2.data.dims[i])
+      return false;
+  return true;
+}
+
 inline bool same_shape(const TShape &shape, int dtype,
                        const mkldnn::memory::desc &desc) {
   return same_shape(shape, desc.data.dims, desc.data.ndims)

src/operator/nn/mkldnn/mkldnn_base.cc

@@ -25,6 +25,11 @@
 
 namespace mxnet {
 
+MKLDNNStream *MKLDNNStream::Get() {
+  static thread_local MKLDNNStream stream;
+  return &stream;
+}
+
 void *AlignMem(void *mem, size_t size, size_t alignment, size_t *space) {
   if (size > *space)
     return nullptr;
@@ -57,8 +62,11 @@ mkldnn::memory *TmpMemMgr::Alloc(const mkldnn::memory::primitive_desc &pd) {
     this->curr_mem = static_cast<char *>(mem) + pd.get_size();
     return ret.get();
   } else {
-    LOG(WARNING) << "Allocate " << pd.get_size()
-        << " bytes with malloc directly";
+    // If curr_mem has been initialized and we still reach here. It means
+    // the current allocated memory isn't enough.
+    if (this->curr_mem)
+      LOG(WARNING) << "Allocate " << pd.get_size()
+          << " bytes with malloc directly";
     mkldnn_mem_ptr ret(new mkldnn::memory(pd));
     MKLDNNStream::Get()->RegisterMem(ret);
     return ret.get();

tests/cpp/include/test_core_op.h

@@ -410,7 +410,7 @@ class CoreOpExecutor : public test::op::OperatorDataInitializer<DType>
           if (bwd_node_ptr) {
             CHECK_EQ(bwd_node_ptr->inputs.size(), num_inputs);
             input_types.resize(bwd_node_ptr->inputs.size(), -1);
-            for (size_t i = 0; i < num_inputs; ++i) {
+            for (int i = 0; i < num_inputs; ++i) {
               const int map_key = bwd_node_ptr->inputs[i].index;
               CHECK(index2array.find(map_key) != index2array.end());
               const int dtype = index2array[map_key]->dtype();
@@ -421,7 +421,7 @@ class CoreOpExecutor : public test::op::OperatorDataInitializer<DType>
               output_types.emplace_back(dtype);
             }
           } else {
-            for (size_t x = 0; x < num_inputs; ++x) {
+            for (int x = 0; x < num_inputs; ++x) {
               input_types.emplace_back(default_dtype());
             }
             for (const auto &fwd_inp : backward_for_op->inputs()) {
@@ -431,10 +431,10 @@ class CoreOpExecutor : public test::op::OperatorDataInitializer<DType>
           }
         } else {
           CHECK(false);  // above always true?
-          for (size_t x = 0; x < num_inputs; ++x) {
+          for (int x = 0; x < num_inputs; ++x) {
             input_types.emplace_back(default_dtype());
           }
-          for (size_t x = 0; x < inferred_num_outputs; ++x) {
+          for (int x = 0; x < inferred_num_outputs; ++x) {
             output_types.emplace_back(default_dtype());
           }
         }
@@ -455,7 +455,7 @@ class CoreOpExecutor : public test::op::OperatorDataInitializer<DType>
             if (bwd_node_ptr) {
               input_shapes.clear();
               CHECK_EQ(bwd_node_ptr->inputs.size(), num_inputs);
-              for (size_t i = 0; i < num_inputs; ++i) {
+              for (int i = 0; i < num_inputs; ++i) {
                 const int map_key = bwd_node_ptr->inputs[i].index;
                 CHECK(index2array.find(map_key) != index2array.end());
                 const nnvm::TShape &shp = index2array[map_key]->shape();