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

… feature/insert_reduce_to_parallel_exe

Dockerfile

@@ -32,7 +32,7 @@ RUN apt-get update && \
     automake locales clang-format swig doxygen cmake  \
     liblapack-dev liblapacke-dev \
     clang-3.8 llvm-3.8 libclang-3.8-dev \
-    net-tools libtool && \
+    net-tools libtool ccache && \
     apt-get clean -y
 
 # Install Go and glide

paddle/cuda/include/hl_base.h

@@ -12,8 +12,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
 limitations under the License. */
 
-#ifndef HL_BASE_H_
-#define HL_BASE_H_
+#pragma once
 
 #include <cstddef>
 
@@ -207,8 +206,8 @@ typedef struct {
 
 #ifdef __NVCC__
 
-#include "cuda_runtime.h"
-#include "hl_cuda.h"
+#include <cuda_runtime.h>
+#include "paddle/cuda/include/hl_cuda.h"
 #include "paddle/utils/Logging.h"
 
 extern __thread bool g_sync_flag;
@@ -228,6 +227,19 @@ extern __thread cudaStream_t default_stream;
         << "CUDA error: " << hl_get_device_error_string((size_t)err); \
   }
 
-#endif /* __NVCC__ */
+// __shfl has been deprecated as of CUDA 9.0.
+#if CUDA_VERSION < 9000
+template <typename T>
+__forceinline__ __device__ T
+__shfl_sync(unsigned, T val, int src_line, int width) {
+  return __shfl(val, src_line, width);
+}
 
-#endif /* HL_BASE_H_ */
+#define CREATE_SHFL_MASK(mask, predicate) mask = 0u;
+#else
+#define FULL_WARP_MASK 0xFFFFFFFF
+#define CREATE_SHFL_MASK(mask, predicate) \
+  mask = __ballot_sync(FULL_WARP_MASK, (predicate))
+#endif
+
+#endif  // __NVCC__

paddle/cuda/src/hl_cuda_lstm.cu

@@ -341,12 +341,15 @@ void hl_lstm_parallel_forward(real *gateValue,
 }
 
 __device__ __forceinline__ void transpose_32x32(real a[], const int idx) {
-  int addr = idx % 32;
+  const int warp_size = 32;
+  int addr = idx % warp_size;
+  unsigned mask = 0u;
+  CREATE_SHFL_MASK(mask, addr < warp_size);
 #pragma unroll
   for (int k = 1; k < 32; k++) {
     // rSrc[k] = __shfl_sync(rSrc[k], (threadIdx.x + k) % 32, 32);
-    addr = __shfl_sync(addr, (idx + 1) % 32, 32);
-    a[k] = __shfl_sync(a[k], addr, 32);
+    addr = __shfl_sync(mask, addr, (idx + 1) % 32, 32);
+    a[k] = __shfl_sync(mask, a[k], addr, 32);
   }
 
 #pragma unroll
@@ -360,10 +363,11 @@ __device__ __forceinline__ void transpose_32x32(real a[], const int idx) {
   }
 
   addr = (32 - idx) % 32;
+  CREATE_SHFL_MASK(mask, idx % 32 < warp_size);
 #pragma unroll
   for (int k = 0; k < 32; k++) {
-    a[k] = __shfl_sync(a[k], addr, 32);
-    addr = __shfl_sync(addr, (idx + 31) % 32, 32);
+    a[k] = __shfl_sync(mask, a[k], addr, 32);
+    addr = __shfl_sync(mask, addr, (idx + 31) % 32, 32);
   }
 }
 

paddle/cuda/src/hl_top_k.cu

@@ -244,13 +244,16 @@ __device__ __forceinline__ void blockReduce(Pair* shTopK,
     if (--beamSize == 0) break;
     __syncthreads();
 
+    unsigned mask = 0u;
+    // CREATE_SHFL_MASK(mask, tid < len);
+
     if (tid == maxId[0]) {
       if (beam < maxLength) {
         shTopK[tid] = topK[beam];
       }
     }
     if (maxId[0] / 32 == warp) {
-      if (__shfl_sync(beam, (maxId[0]) % 32, 32) == maxLength) break;
+      if (__shfl_sync(mask, beam, (maxId[0]) % 32, 32) == maxLength) break;
     }
   }
 }

paddle/fluid/framework/details/multi_devices_graph_builder.cc

@@ -37,7 +37,7 @@ MultiDevSSAGraphBuilder::MultiDevSSAGraphBuilder(
     const std::string &loss_var_name,
     const std::unordered_set<std::string> &params,
     const std::vector<Scope *> &local_scopes,
-    platform::NCCLContextMap *nccl_ctxs, bool skip_scale_loss,
+    platform::NCCLContextMap *nccl_ctxs, bool use_default_grad_scale,
     bool use_nccl_allreduce)
     : loss_var_name_(loss_var_name),
       places_(places),
@@ -50,7 +50,7 @@ MultiDevSSAGraphBuilder::MultiDevSSAGraphBuilder(
     const std::vector<platform::Place> &places,
     const std::string &loss_var_name,
     const std::unordered_set<std::string> &params,
-    const std::vector<Scope *> &local_scopes, bool skip_scale_loss,
+    const std::vector<Scope *> &local_scopes, bool use_default_grad_scale,
     bool use_nccl_allreduce)
     : loss_var_name_(loss_var_name),
       places_(places),
@@ -60,7 +60,7 @@ MultiDevSSAGraphBuilder::MultiDevSSAGraphBuilder(
   for (auto &p : params) {
     grad_names_.insert(GradVarName(p));
   }
-  skip_scale_loss_ = skip_scale_loss;
+  use_default_grad_scale_ = use_default_grad_scale;
 }
 
 void MultiDevSSAGraphBuilder::CreateOpHandleIOs(SSAGraph *result,
@@ -141,8 +141,8 @@ std::unique_ptr<SSAGraph> MultiDevSSAGraphBuilder::Build(
     } else if (IsDistTrainOp(*op, send_op)) {
       CreateComputationalOps(&result, *op, 1);
     } else if (IsScaleLossOp(*op)) {
-      // user can customize loss@grad if skip_scale_loss_
-      if (!skip_scale_loss_) {
+      // user can customize loss@grad if not use_default_grad_scale_
+      if (use_default_grad_scale_) {
         CreateScaleLossGradOp(&result);
       }
       is_forwarding = false;

paddle/fluid/framework/details/multi_devices_graph_builder.h

@@ -36,13 +36,13 @@ class MultiDevSSAGraphBuilder : public SSAGraphBuilder {
                           const std::unordered_set<std::string> &params,
                           const std::vector<Scope *> &local_scopes,
                           platform::NCCLContextMap *nccl_ctxs,
-                          bool skip_scale_loss, bool use_nccl_allreduce);
+                          bool use_default_grad_scale, bool use_nccl_allreduce);
 #else
   MultiDevSSAGraphBuilder(const std::vector<platform::Place> &places,
                           const std::string &loss_var_name,
                           const std::unordered_set<std::string> &params,
                           const std::vector<Scope *> &local_scopes,
-                          bool skip_scale_loss, bool use_nccl_allreduce);
+                          bool use_default_grad_scale, bool use_nccl_allreduce);
 #endif
 
   std::unique_ptr<SSAGraph> Build(const ProgramDesc &program) const override;
@@ -61,7 +61,7 @@ class MultiDevSSAGraphBuilder : public SSAGraphBuilder {
   platform::NCCLContextMap *nccl_ctxs_;
 #endif
   bool use_nccl_allreduce_;
-  bool skip_scale_loss_;
+  bool use_default_grad_scale_;
 
   bool IsScaleLossOp(const OpDesc &op) const;
 

paddle/fluid/framework/lod_tensor_test.cc

@@ -255,11 +255,11 @@ TEST(LoDTensor, RecordIO) {
     std::unique_ptr<std::istream> stream_ptr(stream);
     recordio::Scanner scanner(std::move(stream_ptr));
     auto tensors = ReadFromRecordIO(&scanner, ctx);
-    ASSERT_EQ(tensors.size(), 2);
+    ASSERT_EQ(tensors.size(), static_cast<size_t>(2));
     assert_tensor_ok(tensors[0]);
     assert_tensor_ok(tensors[1]);
     tensors = ReadFromRecordIO(&scanner, ctx);
-    ASSERT_EQ(tensors.size(), 2);
+    ASSERT_EQ(tensors.size(), static_cast<size_t>(2));
     assert_tensor_ok(tensors[0]);
     assert_tensor_ok(tensors[1]);
   }

paddle/fluid/framework/parallel_executor.cc

@@ -58,7 +58,7 @@ ParallelExecutor::ParallelExecutor(
     const std::unordered_set<std::string> &bcast_vars,
     const ProgramDesc &main_program, const std::string &loss_var_name,
     Scope *scope, const std::vector<Scope *> &local_scopes, bool allow_op_delay,
-    bool customize_scale_loss, bool use_nccl_allreduce)
+    bool use_default_grad_scale, bool use_nccl_allreduce)
     : member_(new ParallelExecutorPrivate(places)) {
   member_->global_scope_ = scope;
 
@@ -93,11 +93,11 @@ ParallelExecutor::ParallelExecutor(
 #ifdef PADDLE_WITH_CUDA
   details::MultiDevSSAGraphBuilder builder(
       member_->places_, loss_var_name, params, member_->local_scopes_,
-      member_->nccl_ctxs_.get(), customize_scale_loss, use_nccl_allreduce);
+      member_->nccl_ctxs_.get(), use_default_grad_scale, use_nccl_allreduce);
 #else
   details::MultiDevSSAGraphBuilder builder(
       member_->places_, loss_var_name, params, member_->local_scopes_,
-      customize_scale_loss, use_nccl_allreduce);
+      use_default_grad_scale, use_nccl_allreduce);
 #endif
   auto graph = builder.Build(main_program);
 

paddle/fluid/framework/parallel_executor.h

@@ -40,7 +40,7 @@ class ParallelExecutor {
                             const ProgramDesc& main_program,
                             const std::string& loss_var_name, Scope* scope,
                             const std::vector<Scope*>& local_scopes,
-                            bool allow_op_delay, bool customize_scale_loss,
+                            bool allow_op_delay, bool use_default_grad_scale,
                             bool use_nccl_allreduce);
 
   ~ParallelExecutor();

paddle/fluid/framework/selected_rows.cc

@@ -120,11 +120,11 @@ bool SelectedRows::HasKey(int64_t key) const {
                                                                    : true;
 }
 
-std::vector<int64_t> SelectedRows::Get(std::vector<int64_t> keys,
-                                       framework::Tensor* value) const {
+std::vector<std::pair<int64_t, int64_t>> SelectedRows::Get(
+    std::vector<int64_t> keys, framework::Tensor* value) const {
   PADDLE_ENFORCE(value->IsInitialized(),
                  "The value tensor should be initialized.");
-  std::vector<int64_t> non_keys;
+  std::vector<std::pair<int64_t, int64_t>> non_keys_pair;
   int64_t value_width = value_->numel() / value_->dims()[0];
   PADDLE_ENFORCE_EQ(value_width, value->numel() / value->dims()[0],
                     "output tensor should have the same shape with table "
@@ -133,15 +133,15 @@ std::vector<int64_t> SelectedRows::Get(std::vector<int64_t> keys,
   for (size_t i = 0; i < keys.size(); ++i) {
     int64_t index = Index(keys[i]);
     if (index == -1) {
-      non_keys.push_back(keys[i]);
+      non_keys_pair.push_back(std::make_pair(keys[i], static_cast<int64_t>(i)));
     } else {
       framework::VisitDataType(
           framework::ToDataType(value_->type()),
           TensorCopyVisitor(value, i * value_width, *value_.get(),
                             index * value_width, value_width));
     }
   }
-  return non_keys;
+  return non_keys_pair;
 }
 
 bool SelectedRows::Set(int64_t key, const framework::Tensor& value) {

paddle/fluid/framework/selected_rows.h

@@ -15,6 +15,7 @@ limitations under the License. */
 #pragma once
 
 #include <algorithm>
+#include <utility>
 #include <vector>
 
 #include "paddle/fluid/framework/lod_tensor.h"
@@ -78,10 +79,11 @@ class SelectedRows {
   /*
    * @brief Get value by the key list, if the
    *
-   * @return a list of keys which does not exists in table
+   * @return a list of pair which contains the non-exists key and the index in
+   * the value
    */
-  std::vector<int64_t> Get(std::vector<int64_t> keys,
-                           framework::Tensor* tensor) const;
+  std::vector<std::pair<int64_t, int64_t>> Get(std::vector<int64_t> keys,
+                                               framework::Tensor* value) const;
 
   /*
    * @brief Set a key-value pair into the table.

paddle/fluid/framework/selected_rows_test.cc

@@ -59,7 +59,7 @@ TEST_F(SelectedRowsTester, SerializeAndDeseralize) {
   ASSERT_EQ(selected_rows_->GetCompleteDims(), dst_tensor.GetCompleteDims());
 }
 
-TEST_F(SelectedRowsTester, Table) {
+TEST_F(SelectedRowsTester, SparseTable) {
   platform::CPUPlace cpu;
   SelectedRows table;
   // initialize a sparse table
@@ -87,11 +87,11 @@ TEST_F(SelectedRowsTester, Table) {
   framework::Tensor get_value;
   get_value.mutable_data<float>(framework::make_ddim({2, 100}), cpu);
   std::vector<int64_t> keys({non_key, key});
-  auto non_keys = table.Get(keys, &get_value);
+  auto non_key_pairs = table.Get(keys, &get_value);
 
   ASSERT_EQ(get_value.data<float>()[100], static_cast<float>(10));
-  ASSERT_EQ(non_keys.size(), static_cast<size_t>(1));
-  ASSERT_EQ(non_keys[0], non_key);
+  ASSERT_EQ(non_key_pairs.size(), static_cast<size_t>(1));
+  ASSERT_EQ(non_key_pairs[0].first, non_key);
 }
 
 }  // namespace framework

paddle/fluid/inference/tensorrt/engine.h

@@ -65,7 +65,7 @@ class TensorRTEngine : public EngineBase {
   // Initialize the inference network, so that TensorRT layers can add to this
   // network.
   void InitNetwork() {
-    infer_builder_.reset(createInferBuilder(logger_));
+    infer_builder_.reset(createInferBuilder(&logger_));
     infer_network_.reset(infer_builder_->createNetwork());
   }
   // After finishing adding ops, freeze this network and creates the executation

paddle/fluid/inference/tensorrt/helper.h

@@ -46,13 +46,13 @@ const int kDataTypeSize[] = {
 // The following two API are implemented in TensorRT's header file, cannot load
 // from the dynamic library. So create our own implementation and directly
 // trigger the method from the dynamic library.
-static nvinfer1::IBuilder* createInferBuilder(nvinfer1::ILogger& logger) {
+static nvinfer1::IBuilder* createInferBuilder(nvinfer1::ILogger* logger) {
   return static_cast<nvinfer1::IBuilder*>(
-      dy::createInferBuilder_INTERNAL(&logger, NV_TENSORRT_VERSION));
+      dy::createInferBuilder_INTERNAL(logger, NV_TENSORRT_VERSION));
 }
-static nvinfer1::IRuntime* createInferRuntime(nvinfer1::ILogger& logger) {
+static nvinfer1::IRuntime* createInferRuntime(nvinfer1::ILogger* logger) {
   return static_cast<nvinfer1::IRuntime*>(
-      dy::createInferRuntime_INTERNAL(&logger, NV_TENSORRT_VERSION));
+      dy::createInferRuntime_INTERNAL(logger, NV_TENSORRT_VERSION));
 }
 
 // A logger for create TensorRT infer builder.
@@ -80,7 +80,7 @@ class NaiveLogger : public nvinfer1::ILogger {
     return *x;
   }
 
-  virtual ~NaiveLogger() override {}
+  ~NaiveLogger() override {}
 };
 
 }  // namespace tensorrt