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

CMakeLists.txt

@@ -72,6 +72,7 @@ option(WITH_INFERENCE    "Compile fluid inference library"              ON)
 option(WITH_INFERENCE_API_TEST   "Test fluid inference high-level api interface"  OFF)
 option(WITH_SYSTEM_BLAS   "Use system blas library"           OFF)
 option(PY_VERSION       "Compile PaddlePaddle with python3 support"     ${PY_VERSION})
+option(WITH_FAST_MATH   "Make use of fast math library"                 OFF)
 
 # PY_VERSION
 if(NOT PY_VERSION)

cmake/cuda.cmake

@@ -175,7 +175,10 @@ list(APPEND CUDA_NVCC_FLAGS "-std=c++11")
 list(APPEND CUDA_NVCC_FLAGS "-Xcompiler -fPIC")
 endif(NOT WIN32)
 
-list(APPEND CUDA_NVCC_FLAGS "--use_fast_math")
+if(WITH_FAST_MATH)
+  # Make use of fast math library. https://docs.nvidia.com/cuda/cuda-compiler-driver-nvcc/index.html
+  list(APPEND CUDA_NVCC_FLAGS "--use_fast_math")
+endif()
 # in cuda9, suppress cuda warning on eigen 
 list(APPEND CUDA_NVCC_FLAGS "-w")
 # Set :expt-relaxed-constexpr to suppress Eigen warnings

cmake/external/eigen.cmake

@@ -3,6 +3,14 @@ INCLUDE(ExternalProject)
 SET(EIGEN_SOURCE_DIR ${THIRD_PARTY_PATH}/eigen3)
 SET(EIGEN_INCLUDE_DIR ${EIGEN_SOURCE_DIR}/src/extern_eigen3)
 INCLUDE_DIRECTORIES(${EIGEN_INCLUDE_DIR})
+if(NOT WITH_FAST_MATH)
+  # EIGEN_FAST_MATH: https://eigen.tuxfamily.org/dox/TopicPreprocessorDirectives.html
+  # enables some optimizations which might affect the accuracy of the result. 
+  # This currently enables the SSE vectorization of sin() and cos(), 
+  # and speedups sqrt() for single precision.
+  # Defined to 1 by default. Define it to 0 to disable.
+  add_definitions(-DEIGEN_FAST_MATH=0)
+endif()
 
 if(WITH_AMD_GPU)
     ExternalProject_Add(

cmake/flags.cmake

@@ -157,6 +157,8 @@ if (APPLE)
         # On Mac OS X build fat binaries with x86_64 architectures by default.
         set (CMAKE_OSX_ARCHITECTURES "x86_64" CACHE STRING "Build architectures for OSX" FORCE)
     endif()
+    # On Mac OS X register class specifier is deprecated and will cause warning error on latest clang 10.0
+    set (COMMON_FLAGS -Wno-deprecated-register)
 endif(APPLE)
 
 if(LINUX)

paddle/fluid/framework/rw_lock.h

@@ -46,6 +46,7 @@ struct RWLock {
  private:
   pthread_rwlock_t lock_;
 };
+// TODO(paddle-dev): Support RWLock for WIN32 for correctness.
 #else
 // https://stackoverflow.com/questions/7125250/making-pthread-rwlock-wrlock-recursive
 // In windows, rw_lock seems like a hack. Use empty object and do nothing.

paddle/fluid/inference/tests/api/analyzer_resnet50_tester.cc

@@ -27,6 +27,9 @@ void SetConfig(AnalysisConfig *cfg) {
   cfg->device = 0;
   cfg->enable_ir_optim = true;
   cfg->specify_input_name = true;
+#ifdef PADDLE_WITH_MKLDNN
+  cfg->_use_mkldnn = true;
+#endif
 }
 
 void SetInput(std::vector<std::vector<PaddleTensor>> *inputs) {

paddle/fluid/operators/top_k_op.cu

@@ -256,36 +256,65 @@ __device__ __forceinline__ void BlockReduce(Pair<T>* sh_topk, int* maxid,
  * 3. go to the second setp, until one thread's topk value is null;
  * 4. go to the first setp, until get the topk value.
  */
+
 template <typename T, int MaxLength, int BlockSize>
 __global__ void KeMatrixTopK(T* output, int output_stride, int64_t* indices,
-                             const T* src, int lds, int dim, int k) {
+                             const T* src, int lds, int dim, int k,
+                             int grid_dim, int num) {
   __shared__ Pair<T> sh_topk[BlockSize];
   __shared__ int maxid[BlockSize / 2];
   const int tid = threadIdx.x;
   const int warp = threadIdx.x / 32;
-  output += blockIdx.x * output_stride;
-  indices += blockIdx.x * k;
 
-  Pair<T> topk[MaxLength];
-  int beam = MaxLength;
-  Pair<T> max;
-  bool is_empty = false;
-  bool firststep = true;
+  const int bid = blockIdx.x;
+  for (int i = bid; i < num; i += grid_dim) {
+    output += i * output_stride;
+    indices += i * k;
+
+    Pair<T> topk[MaxLength];
+    int beam = MaxLength;
+    Pair<T> max;
+    bool is_empty = false;
+    bool firststep = true;
+
+    for (int k = 0; k < MaxLength; k++) {
+      topk[k].set(-INFINITY, -1);
+    }
+    while (k) {
+      ThreadGetTopK<T, MaxLength, BlockSize>(
+          topk, &beam, k, src + i * lds, &firststep, &is_empty, &max, dim, tid);
 
-  for (int k = 0; k < MaxLength; k++) {
-    topk[k].set(-INFINITY, -1);
+      sh_topk[tid] = topk[0];
+      BlockReduce<T, MaxLength, BlockSize>(sh_topk, maxid, topk, &output,
+                                           &indices, &beam, &k, tid, warp);
+    }
   }
-  while (k) {
-    ThreadGetTopK<T, MaxLength, BlockSize>(topk, &beam, k,
-                                           src + blockIdx.x * lds, &firststep,
-                                           &is_empty, &max, dim, tid);
-
-    sh_topk[tid] = topk[0];
-    BlockReduce<T, MaxLength, BlockSize>(sh_topk, maxid, topk, &output,
-                                         &indices, &beam, &k, tid, warp);
+}
+
+inline static int GetDesiredBlockDim(int dim) {
+  if (dim > 128) {
+    return 256;
+  } else if (dim > 64) {
+    return 128;
+  } else if (dim > 32) {
+    return 64;
+  } else {
+    return 32;
   }
 }
 
+#define FIXED_BLOCK_DIM_BASE(dim, ...) \
+  case (dim): {                        \
+    constexpr auto kBlockDim = (dim);  \
+    __VA_ARGS__;                       \
+  } break
+
+#define FIXED_BLOCK_DIM(...)                \
+  FIXED_BLOCK_DIM_BASE(256, ##__VA_ARGS__); \
+  FIXED_BLOCK_DIM_BASE(128, ##__VA_ARGS__); \
+  FIXED_BLOCK_DIM_BASE(64, ##__VA_ARGS__);  \
+  FIXED_BLOCK_DIM_BASE(32, ##__VA_ARGS__)
+
 template <typename T>
 class TopkOpCUDAKernel : public framework::OpKernel<T> {
  public:
@@ -310,18 +339,26 @@ class TopkOpCUDAKernel : public framework::OpKernel<T> {
     // NOTE: pass lds and dim same to input width.
     // NOTE: old matrix implementation of stride is different to eigen.
     // TODO(typhoonzero): refine this kernel.
-    dim3 threads(256, 1);
-    dim3 grid(input_height, 1);
-
-    KeMatrixTopK<T, 5, 256><<<
-        grid, threads, 0, reinterpret_cast<const platform::CUDADeviceContext&>(
-                              ctx.device_context())
-                              .stream()>>>(
-        output_data, output->dims()[1], indices_data, input_data, input_width,
-        input_width, static_cast<int>(k));
+    const int kMaxHeight = 2048;
+    int gridx = input_height < kMaxHeight ? input_height : kMaxHeight;
+    auto& dev_ctx = ctx.cuda_device_context();
+
+    switch (GetDesiredBlockDim(input_width)) {
+      FIXED_BLOCK_DIM(
+          KeMatrixTopK<T, 5,
+                       kBlockDim><<<gridx, kBlockDim, 0, dev_ctx.stream()>>>(
+              output_data, output->dims()[1], indices_data, input_data,
+              input_width, input_width, static_cast<int>(k), gridx,
+              input_height));
+      default:
+        PADDLE_THROW("Error");
+    }
   }
 };
 
+#undef FIXED_BLOCK_DIM_BASE
+#undef FIXED_BLOCK_DIM
+
 }  // namespace operators
 }  // namespace paddle
 

paddle/fluid/operators/while_op.cc

@@ -224,10 +224,12 @@ class WhileGradOp : public framework::OperatorBase {
         if (cur_scope_iter == step_scopes->rbegin()) {
           auto *var = (*cur_scope_iter)->FindVar(inside_grad_name);
           PADDLE_ENFORCE_NOT_NULL(var, "Can not find var %s", inside_grad_name);
-          PADDLE_ENFORCE(var->IsType<framework::LoDTensorArray>() ||
-                             var->IsType<LoDTensor>(),
-                         "Currently the type of var only can be LoDTensorArray "
-                         "or LoDTensor.");
+          PADDLE_ENFORCE(
+              var->IsType<framework::LoDTensorArray>() ||
+                  var->IsType<LoDTensor>(),
+              "Currently the type of var only can be LoDTensorArray, "
+              "or LoDTensor, but the received var[%s] is %s.",
+              inside_grad_name, var->Type().name());
 
           if (var->IsType<LoDTensor>()) {
             auto &inside_tensor = var->Get<framework::LoDTensor>();

paddle/fluid/platform/gpu_info.cc

@@ -20,8 +20,11 @@ limitations under the License. */
 #include "paddle/fluid/platform/enforce.h"
 
 DEFINE_double(fraction_of_gpu_memory_to_use, 0.92,
-              "Default use 92% of GPU memory for PaddlePaddle,"
-              "reserve the rest for page tables, etc");
+              "Allocate a trunk of gpu memory that is this fraction of the "
+              "total gpu memory size. Future memory usage will be allocated "
+              "from the trunk. If the trunk doesn't have enough gpu memory, "
+              "additional trunks of the same size will be requested from gpu "
+              "until the gpu has no memory left for another trunk.");
 
 namespace paddle {
 namespace platform {

paddle/scripts/paddle_build.sh

@@ -598,7 +598,7 @@ EOF
 EOF
 
     if [[ ${WITH_GPU} == "ON"  ]]; then
-        NCCL_DEPS="apt-get install -y --allow-downgrades libnccl2=2.2.13-1+cuda${CUDA_MAJOR} libnccl-dev=2.2.13-1+cuda${CUDA_MAJOR} &&"
+        NCCL_DEPS="apt-get install -y --allow-downgrades libnccl2=2.2.13-1+cuda${CUDA_MAJOR} libnccl-dev=2.2.13-1+cuda${CUDA_MAJOR} || true"
     else
         NCCL_DEPS=""
     fi
@@ -614,9 +614,8 @@ EOF
     cat >> ${PADDLE_ROOT}/build/Dockerfile <<EOF
     ADD python/dist/*.whl /
     # run paddle version to install python packages first
-    RUN apt-get update &&\
-        ${NCCL_DEPS}\
-        apt-get install -y wget python-pip python-opencv libgtk2.0-dev dmidecode python-tk && easy_install -U pip && \
+    RUN apt-get update && ${NCCL_DEPS}
+    RUN apt-get install -y wget python-pip python-opencv libgtk2.0-dev dmidecode python-tk && easy_install -U pip && \
         pip install /*.whl; apt-get install -f -y && \
         apt-get clean -y && \
         rm -f /*.whl && \

python/paddle/fluid/layers/control_flow.py

@@ -1570,6 +1570,10 @@ class DynamicRNN(object):
 
     The dynamic RNN can mark multiple variables as its output. Use `drnn()` to
     get the output sequence.
+    
+    NOTES:
+        Currently it is not supported that setting is_sparse to True of any 
+        layers within DynamicRNN.
     """
     BEFORE_RNN = 0
     IN_RNN = 1