Merge branch 'develop' into core_fix_openblas_threads

Dockerfile

@@ -70,7 +70,7 @@ RUN localedef -i en_US -f UTF-8 en_US.UTF-8
 # specify sphinx version as 1.5.6 and remove -U option for [pip install -U
 # sphinx-rtd-theme] since -U option will cause sphinx being updated to newest
 # version(1.7.1 for now), which causes building documentation failed.
-RUN pip install --upgrade pip==9.0.3 && \
+RUN easy_install -U pip && \
     pip install -U wheel && \
     pip install -U docopt PyYAML sphinx==1.5.6 && \
     pip install sphinx-rtd-theme==0.1.9 recommonmark

benchmark/fluid/mnist.py

@@ -159,6 +159,7 @@ def run_benchmark(model, args):
         paddle.dataset.mnist.train(), batch_size=args.batch_size)
 
     accuracy = fluid.metrics.Accuracy()
+    train_exe = fluid.ParallelExecutor(use_cuda=True, loss_name=avg_cost.name)
     iters, num_samples, start_time = 0, 0, time.time()
     for pass_id in range(args.pass_num):
         accuracy.reset()
@@ -175,17 +176,20 @@ def run_benchmark(model, args):
             y_data = np.array(map(lambda x: x[1], data)).astype("int64")
             y_data = y_data.reshape([len(y_data), 1])
 
-            outs = exe.run(
-                fluid.default_main_program(),
+            outs = train_exe.run(
                 feed={"pixel": img_data,
                       "label": y_data},
-                fetch_list=[avg_cost, batch_acc, batch_size_tensor]
+                fetch_list=[
+                    avg_cost.name, batch_acc.name, batch_size_tensor.name
+                ]
             )  # The accuracy is the accumulation of batches, but not the current batch.
-            accuracy.update(value=outs[1], weight=outs[2])
+            accuracy.update(
+                value=np.array(np.mean(outs[1])),
+                weight=np.mean(np.array(outs[2])))
             iters += 1
             num_samples += len(y_data)
-            loss = np.array(outs[0])
-            acc = np.array(outs[1])
+            loss = np.mean(np.array(outs[0]))
+            acc = np.mean(np.array(outs[1]))
             train_losses.append(loss)
             train_accs.append(acc)
             print("Pass: %d, Iter: %d, Loss: %f, Accuracy: %f" %

benchmark/fluid/resnet.py

@@ -241,6 +241,7 @@ def run_benchmark(model, args):
     exe = fluid.Executor(place)
     exe.run(fluid.default_startup_program())
     accuracy = fluid.average.WeightedAverage()
+    train_exe = fluid.ParallelExecutor(use_cuda=True, loss_name=avg_cost.name)
     if args.use_fake_data:
         data = train_reader().next()
         image = np.array(map(lambda x: x[0].reshape(dshape), data)).astype(
@@ -264,14 +265,17 @@ def run_benchmark(model, args):
                                      data)).astype('float32')
                 label = np.array(map(lambda x: x[1], data)).astype('int64')
                 label = label.reshape([-1, 1])
-            loss, acc, weight = exe.run(
-                fluid.default_main_program(),
+            loss, acc, weight = train_exe.run(
                 feed={'data': image,
                       'label': label},
-                fetch_list=[avg_cost, batch_acc, batch_size_tensor])
+                fetch_list=[
+                    avg_cost.name, batch_acc.name, batch_size_tensor.name
+                ])
             iters += 1
             num_samples += len(label)
-            accuracy.add(value=acc, weight=weight)
+            accuracy.add(value=np.array(np.mean(acc)), weight=np.mean(weight))
+            loss = np.mean(np.array(loss))
+            acc = np.mean(np.array(acc))
             train_losses.append(loss)
             train_accs.append(acc)
             print("Pass: %d, Iter: %d, Loss: %f, Accuracy: %f" %

benchmark/fluid/vgg.py

@@ -169,6 +169,7 @@ def main():
 
     iters, num_samples, start_time = 0, 0, time.time()
     accuracy = fluid.average.WeightedAverage()
+    train_exe = fluid.ParallelExecutor(use_cuda=True, loss_name=avg_cost.name)
     for pass_id in range(args.pass_num):
         accuracy.reset()
         train_accs = []
@@ -184,14 +185,17 @@ def main():
             y_data = np.array(map(lambda x: x[1], data)).astype("int64")
             y_data = y_data.reshape([-1, 1])
 
-            loss, acc, weight = exe.run(
-                fluid.default_main_program(),
+            loss, acc, weight = train_exe.run(
                 feed={"pixel": img_data,
                       "label": y_data},
-                fetch_list=[avg_cost, batch_acc, batch_size_tensor])
-            accuracy.add(value=acc, weight=weight)
+                fetch_list=[
+                    avg_cost.name, batch_acc.name, batch_size_tensor.name
+                ])
+            accuracy.add(value=np.array(np.mean(acc)), weight=np.mean(weight))
             iters += 1
             num_samples += len(y_data)
+            loss = np.mean(np.array(loss))
+            acc = np.mean(np.array(acc))
             print(
                 "Pass = %d, Iter = %d, Loss = %f, Accuracy = %f" %
                 (pass_id, iters, loss, acc)

cmake/external/boost.cmake

@@ -24,7 +24,7 @@ set(BOOST_PROJECT       "extern_boost")
 # So we use 1.41.0 here.
 set(BOOST_VER           "1.41.0")
 set(BOOST_TAR           "boost_1_41_0")
-set(BOOST_URL           "http://paddlepaddledeps.bj.bcebos.com/${BOOST_TAR}.tar.gz")
+set(BOOST_URL           "http://paddlepaddledeps.cdn.bcebos.com/${BOOST_TAR}.tar.gz")
 set(BOOST_SOURCES_DIR ${THIRD_PARTY_PATH}/boost)
 set(BOOST_DOWNLOAD_DIR  "${BOOST_SOURCES_DIR}/src/${BOOST_PROJECT}")
 set(BOOST_INCLUDE_DIR "${BOOST_DOWNLOAD_DIR}/${BOOST_TAR}" CACHE PATH "boost include directory." FORCE)

cmake/external/eigen.cmake

@@ -21,11 +21,12 @@ else()
     ExternalProject_Add(
         extern_eigen3
         ${EXTERNAL_PROJECT_LOG_ARGS}
-        GIT_REPOSITORY  "https://github.com/RLovelett/eigen.git"
+        GIT_REPOSITORY  "https://github.com/eigenteam/eigen-git-mirror"
         # eigen on cuda9.1 missing header of math_funtions.hpp
         # https://stackoverflow.com/questions/43113508/math-functions-hpp-not-found-when-using-cuda-with-eigen
         GIT_TAG         917060c364181f33a735dc023818d5a54f60e54c
         PREFIX          ${EIGEN_SOURCE_DIR}
+        DOWNLOAD_NAME   "eigen"
         UPDATE_COMMAND  ""
         CONFIGURE_COMMAND ""
         BUILD_COMMAND     ""

cmake/external/mkldnn.cmake

@@ -53,11 +53,9 @@ ExternalProject_Add(
     ${EXTERNAL_PROJECT_LOG_ARGS}
     DEPENDS             ${MKLDNN_DEPENDS}
     GIT_REPOSITORY      "https://github.com/01org/mkl-dnn.git"
-    GIT_TAG             "v0.14"
+    GIT_TAG             "db3424ad44901513c03a1ea31ccaacdf633fbe9f"
     PREFIX              ${MKLDNN_SOURCES_DIR}
     UPDATE_COMMAND      ""
-    # Patch MKLDNN to compile with gcc 4.8, the related issue is in intel/mkl-dnn#237.
-    PATCH_COMMAND       ${CMAKE_COMMAND} -E copy_if_different ${CMAKE_CURRENT_SOURCE_DIR}/patches/mkldnn.hpp ${MKLDNN_SOURCES_DIR}/src/extern_mkldnn/include/mkldnn.hpp
     CMAKE_ARGS          -DCMAKE_INSTALL_PREFIX=${MKLDNN_INSTALL_DIR}
     CMAKE_ARGS          -DCMAKE_BUILD_TYPE=${CMAKE_BUILD_TYPE} 
     CMAKE_ARGS          -DMKLROOT=${MKLML_ROOT}

cmake/external/mklml.cmake

@@ -28,7 +28,7 @@ INCLUDE(ExternalProject)
 
 SET(MKLML_PROJECT       "extern_mklml")
 SET(MKLML_VER           "mklml_lnx_2018.0.3.20180406")
-SET(MKLML_URL           "http://paddlepaddledeps.bj.bcebos.com/${MKLML_VER}.tgz")
+SET(MKLML_URL           "http://paddlepaddledeps.cdn.bcebos.com/${MKLML_VER}.tgz")
 SET(MKLML_SOURCE_DIR    "${THIRD_PARTY_PATH}/mklml")
 SET(MKLML_DOWNLOAD_DIR  "${MKLML_SOURCE_DIR}/src/${MKLML_PROJECT}")
 SET(MKLML_DST_DIR       "mklml")

cmake/external/snappy.cmake

@@ -47,8 +47,6 @@ ExternalProject_Add(
                      -DCMAKE_INSTALL_LIBDIR:PATH=${SNAPPY_INSTALL_DIR}/lib
                      -DCMAKE_POSITION_INDEPENDENT_CODE:BOOL=ON
                      -DCMAKE_BUILD_TYPE:STRING=${THIRD_PARTY_BUILD_TYPE}
-    BUILD_COMMAND   make -j8
-    INSTALL_COMMAND make install
 )
 
 add_library(snappy STATIC IMPORTED GLOBAL)

cmake/external/snappystream.cmake

@@ -46,8 +46,6 @@ ExternalProject_Add(
                         -DCMAKE_INSTALL_PREFIX:PATH=${SNAPPYSTREAM_INSTALL_DIR}
                         -DCMAKE_INSTALL_LIBDIR:PATH=${SNAPPYSTREAM_INSTALL_DIR}/lib
                         -DCMAKE_BUILD_TYPE:STRING=${THIRD_PARTY_BUILD_TYPE}
-        BUILD_COMMAND   make -j8
-        INSTALL_COMMAND make install
         DEPENDS snappy
 )
 

cmake/inference_lib.cmake

@@ -98,6 +98,14 @@ elseif (WITH_MKLML)
     )
 endif()
 
+if(WITH_MKLDNN)
+  set(dst_dir "${CMAKE_INSTALL_PREFIX}/third_party/install/mkldnn")
+  copy(mkldnn_lib
+    SRCS ${MKLDNN_INC_DIR} ${MKLDNN_SHARED_LIB}
+    DSTS ${dst_dir} ${dst_dir}/lib
+  )
+endif()
+
 if(NOT MOBILE_INFERENCE AND NOT RPI)
   set(dst_dir "${CMAKE_INSTALL_PREFIX}/third_party/install/snappy")
   copy(snappy_lib
@@ -148,4 +156,10 @@ copy(string_lib
   DSTS ${dst_dir}/${module} ${dst_dir}/${module}/tinyformat
 )
 
+set(module "pybind")
+copy(pybind_lib
+  SRCS ${CMAKE_CURRENT_BINARY_DIR}/paddle/fluid/${module}/pybind.h
+  DSTS ${dst_dir}/${module}
+)
+
 add_custom_target(inference_lib_dist DEPENDS ${inference_lib_dist_dep}) 

doc/fluid/design/concepts/functions_operators_layers.md

@@ -40,7 +40,7 @@ template <typename T>
 class FCOp : public OperatorBase {
  public:
   void Run(...) {
-    add(mul(Input<T>("X"), Input<T>("W")), Input<T>("b");
+    add(mul(Input<T>("X"), Input<T>("W")), Input<T>("b"));
   }
 };
 REGISTER_OP(FCOp, "fc");

paddle/CMakeLists.txt

@@ -24,6 +24,6 @@ if(NOT WITH_FLUID_ONLY)
 endif()
 
 add_subdirectory(testing)
-if(NOT MOBILE_INFERENCE AND NOT RPI)
+if(NOT MOBILE_INFERENCE AND NOT RPI AND NOT WITH_C_API)
   add_subdirectory(fluid)
 endif()

paddle/fluid/framework/data_device_transform.cc

@@ -36,9 +36,11 @@ void TransDataDevice(const Tensor& in, const platform::Place& dst_place,
   VLOG(3) << "DeviceTransform in, src_place " << in.place()
           << " dst_place: " << dst_place;
   auto* dev_ctx = GetDeviceContext(in.place(), dst_place);
-  dev_ctx->Wait();
+
   TensorCopy(in, dst_place, *dev_ctx, out);
+  if (platform::is_gpu_place(in.place()) && platform::is_cpu_place(dst_place)) {
     dev_ctx->Wait();
+  }
 }
 
 }  // namespace framework

paddle/fluid/framework/data_type.cc

@@ -58,6 +58,7 @@ static DataTypeMap* InitDataTypeMap() {
   RegType(bool, proto::VarType::BOOL);
   RegType(size_t, proto::VarType::SIZE_T);
   RegType(int16_t, proto::VarType::INT16);
+  RegType(uint8_t, proto::VarType::UINT8);
 
 #undef RegType
   return retv;

paddle/fluid/framework/data_type.h

@@ -47,8 +47,14 @@ inline void VisitDataType(proto::VarType::Type type, Visitor visitor) {
     case proto::VarType::BOOL:
       visitor.template operator()<bool>();
       break;
+    case proto::VarType::UINT8:
+      visitor.template operator()<uint8_t>();
+      break;
+    case proto::VarType::INT16:
+      visitor.template operator()<int16_t>();
+      break;
     default:
-      PADDLE_THROW("Not supported");
+      PADDLE_THROW("Not supported %d", type);
   }
 }
 

paddle/fluid/framework/details/fetch_op_handle.cc

@@ -48,17 +48,18 @@ void FetchOpHandle::RunImpl() {
   WaitInputVarGenerated(platform::CPUPlace());
 
   tensors_.resize(inputs_.size());
-  auto *var_handle = static_cast<VarHandle *>(inputs_[0]);
-  auto &var_name = var_handle->name_;
   platform::CPUPlace cpu;
   auto &scopes = *local_scopes_;
 
-  for (size_t i = 0; i < scopes.size(); ++i) {
-    auto &scope = scopes[i];
-    auto *var =
-        scope->FindVar(kLocalExecScopeName)->Get<Scope *>()->FindVar(var_name);
+  for (size_t i = 0; i < inputs_.size(); ++i) {
+    auto *var_handle = static_cast<VarHandle *>(inputs_[i]);
+    auto &scope = scopes.at(var_handle->scope_idx_);
+    auto *var = scope->FindVar(kLocalExecScopeName)
+                    ->Get<Scope *>()
+                    ->FindVar(var_handle->name_);
     PADDLE_ENFORCE_NOT_NULL(var, "Cannot find variable %s in execution scope",
-                            var_name);
+                            var_handle->name_);
+
     auto &t = var->Get<framework::LoDTensor>();
     if (platform::is_gpu_place(t.place())) {
 #ifdef PADDLE_WITH_CUDA

paddle/fluid/framework/details/multi_devices_graph_builder.cc

@@ -98,7 +98,7 @@ bool MultiDevSSAGraphBuilder::IsDistTrainOp(const OpDesc &op,
     return false;
   };
 
-  if (op.Type() == "split") {
+  if (op.Type() == "split" || op.Type() == "split_byref") {
     return checker(op.OutputArgumentNames(), send_op->InputArgumentNames());
   } else if (op.Type() == "concat") {
     return checker(op.InputArgumentNames(), send_op->OutputArgumentNames());

paddle/fluid/framework/details/op_handle_base.h

@@ -70,6 +70,14 @@ class OpHandleBase {
 
   const std::vector<VarHandleBase *> &Inputs() const { return inputs_; }
 
+  size_t NoDupInputSize() const {
+    std::unordered_set<VarHandleBase *> res;
+    for (auto *var : inputs_) {
+      res.emplace(var);
+    }
+    return res.size();
+  }
+
   const std::vector<VarHandleBase *> &Outputs() const { return outputs_; }
 
  protected:

paddle/fluid/framework/details/threaded_ssa_graph_executor.cc

@@ -174,7 +174,7 @@ void ThreadedSSAGraphExecutor::InsertFetchOps(
 void ThreadedSSAGraphExecutor::InsertPendingOp(
     std::unordered_map<OpHandleBase *, size_t> *pending_ops,
     OpHandleBase *op_instance) const {
-  pending_ops->insert({op_instance, op_instance->Inputs().size()});
+  pending_ops->insert({op_instance, op_instance->NoDupInputSize()});
 }
 
 void ThreadedSSAGraphExecutor::InsertPendingVar(

paddle/fluid/framework/executor.cc

@@ -228,7 +228,8 @@ static bool has_fetch_operators(
 void Executor::Run(const ProgramDesc& program, Scope* scope,
                    std::map<std::string, const LoDTensor*>* feed_targets,
                    std::map<std::string, LoDTensor*>* fetch_targets,
-                   bool create_vars, const std::string& feed_holder_name,
+                   bool create_local_scope, bool create_vars,
+                   const std::string& feed_holder_name,
                    const std::string& fetch_holder_name) {
   platform::RecordBlock b(kProgramId);
   bool has_feed_ops =
@@ -290,8 +291,9 @@ void Executor::Run(const ProgramDesc& program, Scope* scope,
   }
 
   auto ctx = Prepare(*copy_program, 0);
-  RunPreparedContext(ctx.get(), scope, feed_targets, fetch_targets, create_vars,
-                     feed_holder_name, fetch_holder_name);
+  RunPreparedContext(ctx.get(), scope, feed_targets, fetch_targets,
+                     create_local_scope, create_vars, feed_holder_name,
+                     fetch_holder_name);
 }
 
 std::unique_ptr<ExecutorPrepareContext> Executor::Prepare(
@@ -366,8 +368,9 @@ void Executor::RunPreparedContext(ExecutorPrepareContext* ctx, Scope* scope,
 void Executor::RunPreparedContext(
     ExecutorPrepareContext* ctx, Scope* scope,
     std::map<std::string, const LoDTensor*>* feed_targets,
-    std::map<std::string, LoDTensor*>* fetch_targets, bool create_vars,
-    const std::string& feed_holder_name, const std::string& fetch_holder_name) {
+    std::map<std::string, LoDTensor*>* fetch_targets, bool create_local_scope,
+    bool create_vars, const std::string& feed_holder_name,
+    const std::string& fetch_holder_name) {
   auto& global_block = ctx->prog_.Block(ctx->block_id_);
 
   PADDLE_ENFORCE(
@@ -387,7 +390,7 @@ void Executor::RunPreparedContext(
     }
   }
 
-  RunPreparedContext(ctx, scope, create_vars, create_vars);
+  RunPreparedContext(ctx, scope, create_local_scope, create_vars);
 
   // obtain the data of fetch_targets from fetch_holder
   for (auto* op : global_block.AllOps()) {

paddle/fluid/framework/executor.h

@@ -57,7 +57,7 @@ class Executor {
   void Run(const ProgramDesc& program, Scope* scope,
            std::map<std::string, const LoDTensor*>* feed_targets,
            std::map<std::string, LoDTensor*>* fetch_targets,
-           bool create_vars = true,
+           bool create_local_scope = true, bool create_vars = true,
            const std::string& feed_holder_name = "feed",
            const std::string& fetch_holder_name = "fetch");
 
@@ -76,6 +76,7 @@ class Executor {
   void RunPreparedContext(ExecutorPrepareContext* ctx, Scope* scope,
                           std::map<std::string, const LoDTensor*>* feed_targets,
                           std::map<std::string, LoDTensor*>* fetch_targets,
+                          bool create_local_scope = true,
                           bool create_vars = true,
                           const std::string& feed_holder_name = "feed",
                           const std::string& fetch_holder_name = "fetch");

paddle/fluid/framework/framework.proto

@@ -103,6 +103,7 @@ message VarType {
     FP64 = 6;
     // Tensor<size_t> is used in C++.
     SIZE_T = 19;
+    UINT8 = 20;
 
     // Other types that may need additional descriptions
     LOD_TENSOR = 7;

paddle/fluid/framework/lod_tensor_test.cc

@@ -228,11 +228,12 @@ TEST(LoD, CheckAbsLoD) {
   ASSERT_FALSE(CheckAbsLoD(abs_lod0));
 }
 
-TEST(LoDTensor, RecordIO) {
+template <typename T>
+static void TestRecordIO() {
   LoDTensor tensor;
-  int* tmp = tensor.mutable_data<int>(make_ddim({4, 5}), platform::CPUPlace());
+  T* tmp = tensor.mutable_data<T>(make_ddim({4, 5}), platform::CPUPlace());
   for (int i = 0; i < 20; ++i) {
-    tmp[i] = i;
+    tmp[i] = static_cast<T>(i);
   }
 
   std::stringstream* stream = new std::stringstream();
@@ -247,7 +248,7 @@ TEST(LoDTensor, RecordIO) {
 
   auto assert_tensor_ok = [](const LoDTensor& tensor) {
     for (int i = 0; i < 20; ++i) {
-      ASSERT_EQ(tensor.data<int>()[i], i);
+      ASSERT_EQ(tensor.data<T>()[i], static_cast<T>(i));
     }
   };
 
@@ -265,5 +266,13 @@ TEST(LoDTensor, RecordIO) {
   }
 }
 
+TEST(LoDTensor, RecordIO) {
+  TestRecordIO<int>();
+  TestRecordIO<int16_t>();
+  TestRecordIO<uint8_t>();
+  TestRecordIO<float>();
+  TestRecordIO<double>();
+}
+
 }  // namespace framework
 }  // namespace paddle

paddle/fluid/inference/tensorrt/convert/op_converter.h

@@ -49,7 +49,7 @@ class OpConverter {
   // convert fluid block to tensorrt network
   void ConvertBlock(const framework::proto::BlockDesc& block,
                     TensorRTEngine* engine) {
-    for (size_t i = 0; i < block.ops_size(); i++) {
+    for (int i = 0; i < block.ops_size(); i++) {
       const auto& op = block.ops(i);
       OpConverter::Run(op, engine);
     }

paddle/fluid/inference/tests/test_helper.h

@@ -149,7 +149,7 @@ void TestInference(const std::string& dirname,
     state = paddle::platform::ProfilerState::kCPU;
   } else {
 #ifdef PADDLE_WITH_CUDA
-    state = paddle::platform::ProfilerState::kCUDA;
+    state = paddle::platform::ProfilerState::kAll;
     // The default device_id of paddle::platform::CUDAPlace is 0.
     // Users can get the device_id using:
     //   int device_id = place.GetDeviceId();
@@ -172,7 +172,7 @@ void TestInference(const std::string& dirname,
   }
   // Disable the profiler and print the timing information
   paddle::platform::DisableProfiler(paddle::platform::EventSortingKey::kDefault,
-                                    "load_program_profiler.txt");
+                                    "load_program_profiler");
   paddle::platform::ResetProfiler();
 
   // 3. Get the feed_target_names and fetch_target_names
@@ -208,10 +208,10 @@ void TestInference(const std::string& dirname,
     if (PrepareContext) {
       ctx = executor.Prepare(*inference_program, 0);
       executor.RunPreparedContext(ctx.get(), scope, &feed_targets,
-                                  &fetch_targets, CreateVars);
+                                  &fetch_targets, true, CreateVars);
     } else {
       executor.Run(*inference_program, scope, &feed_targets, &fetch_targets,
-                   CreateVars);
+                   true, CreateVars);
     }
 
     // Enable the profiler
@@ -236,8 +236,7 @@ void TestInference(const std::string& dirname,
 
     // Disable the profiler and print the timing information
     paddle::platform::DisableProfiler(
-        paddle::platform::EventSortingKey::kDefault,
-        "run_inference_profiler.txt");
+        paddle::platform::EventSortingKey::kDefault, "run_inference_profiler");
     paddle::platform::ResetProfiler();
   }
 

paddle/fluid/operators/CMakeLists.txt

@@ -186,11 +186,7 @@ endif()
 
 add_subdirectory(detail)
 if(WITH_DISTRIBUTE)
-    if(WITH_GPU)
-        op_library(gen_nccl_id_op DEPS nccl_common)
-    else()
-        set(DEPS_OPS ${DEPS_OPS} gen_nccl_id_op)
-    endif()
+    
     set(DISTRIBUTE_DEPS sendrecvop_grpc grpc++_unsecure grpc_unsecure gpr cares zlib protobuf)
     set(DISTRIBUTE_COMPILE_FLAGS "-Wno-non-virtual-dtor -Wno-error=non-virtual-dtor -Wno-error=delete-non-virtual-dtor")
     op_library(send_op DEPS ${DISTRIBUTE_DEPS})
@@ -207,7 +203,13 @@ if(WITH_DISTRIBUTE)
     set_source_files_properties(send_barrier_op.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS})
     set_source_files_properties(send_recv_op_test.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS})
     cc_test(test_send_recv SRCS send_recv_op_test.cc DEPS prefetch_op send_op listen_and_serv_op sum_op executor)
+    if(WITH_GPU)
         cc_test(test_send_nccl_id SRCS test_send_nccl_id.cc DEPS send_op listen_and_serv_op executor)
+        op_library(gen_nccl_id_op DEPS nccl_common sendrecvop_grpc)
+        set_source_files_properties(gen_nccl_id_op.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS})
+    else()
+        set(DEPS_OPS ${DEPS_OPS} gen_nccl_id_op)
+    endif()
 else()
     set(DEPS_OPS ${DEPS_OPS} send_op prefetch_op recv_op listen_and_serv_op send_vars_op send_barrier_op gen_nccl_id_op)
 endif()

paddle/fluid/operators/beam_search_op.h

@@ -14,10 +14,6 @@ limitations under the License. */
 
 #pragma once
 
-#ifdef PADDLE_WITH_TESTING
-#include "gtest/gtest.h"
-#endif
-
 #include <string>
 #include <vector>
 #include "paddle/fluid/framework/lod_tensor.h"

paddle/fluid/operators/detail/grpc_server.cc

@@ -184,7 +184,7 @@ class RequestPrefetch final : public RequestBase {
     framework::Scope* local_scope = &scope_->NewScope();
     auto* var = local_scope->FindVar(var_name);
     InitializeVariable(var, var_desc->GetType());
-    executor_->RunPreparedContext(prefetch_ctx_, scope_, false, false);
+    executor_->RunPreparedContext(prefetch_ctx_, scope_);
 
     SerializeToByteBuffer(var_name, var, *dev_ctx_, &reply);
 

paddle/fluid/operators/listen_and_serv_op.cc

@@ -57,8 +57,7 @@ static void ParallelExecuteBlocks(
         framework::Async([&executor, &prepared, &program, &scope, idx]() {
           int run_block = idx;  // thread local
           try {
-            executor->RunPreparedContext(prepared[run_block].get(), scope,
-                                         false, false);
+            executor->RunPreparedContext(prepared[run_block].get(), scope);
           } catch (std::exception &e) {
             LOG(ERROR) << "run sub program error " << e.what();
           }
@@ -211,8 +210,8 @@ static void AsyncUpdateThread(
     }
     auto fs = framework::Async([var_name, &executor, &v, prepared] {
       try {
-        executor->RunPreparedContext(prepared, v.second->GetMutableLocalScope(),
-                                     false, false);
+        executor->RunPreparedContext(prepared,
+                                     v.second->GetMutableLocalScope());
       } catch (std::exception &e) {
         LOG(ERROR) << "run sub program error " << e.what();
       }

paddle/fluid/operators/math/math_function.cc

@@ -38,7 +38,9 @@ template struct SetConstant<platform::CPUDeviceContext, bool>;
   template struct Transpose<platform::CPUDeviceContext, double, RANK>;     \
   template struct Transpose<platform::CPUDeviceContext, int, RANK>;        \
   template struct Transpose<platform::CPUDeviceContext, int64_t, RANK>;    \
-  template struct Transpose<platform::CPUDeviceContext, bool, RANK>;
+  template struct Transpose<platform::CPUDeviceContext, bool, RANK>;       \
+  template struct Transpose<platform::CPUDeviceContext, int16_t, RANK>;    \
+  template struct Transpose<platform::CPUDeviceContext, uint8_t, RANK>;
 
 DEFINE_CPU_TRANS(1);
 DEFINE_CPU_TRANS(2);

paddle/fluid/operators/roi_pool_op.cu

@@ -38,10 +38,10 @@ __global__ void GPUROIPoolForward(
   int index = blockIdx.x * blockDim.x + threadIdx.x;
   int offset = blockDim.x * gridDim.x;
   for (size_t i = index; i < nthreads; i += offset) {
-    int pw = index % pooled_width;
-    int ph = (index / pooled_width) % pooled_height;
-    int c = (index / pooled_width / pooled_height) % channels;
-    int n = index / pooled_width / pooled_height / channels;
+    int pw = i % pooled_width;
+    int ph = (i / pooled_width) % pooled_height;
+    int c = (i / pooled_width / pooled_height) % channels;
+    int n = i / pooled_width / pooled_height / channels;
 
     const int64_t* offset_input_rois = input_rois + n * kROISize;
     int roi_batch_ind = roi_batch_id_data[n];
@@ -52,14 +52,19 @@ __global__ void GPUROIPoolForward(
 
     int roi_width = max(roi_end_w - roi_start_w + 1, 1);
     int roi_height = max(roi_end_h - roi_start_h + 1, 1);
-    T bin_size_h = static_cast<T>(roi_height) / static_cast<T>(pooled_height);
-    T bin_size_w = static_cast<T>(roi_width) / static_cast<T>(pooled_width);
-
-    int hstart = static_cast<int>(floor(static_cast<T>(ph) * bin_size_h));
-    int wstart = static_cast<int>(floor(static_cast<T>(pw) * bin_size_w));
-    int hend = static_cast<int>(ceil(static_cast<T>(ph + 1) * bin_size_h));
-    int wend = static_cast<int>(ceil(static_cast<T>(pw + 1) * bin_size_w));
 
+    int hstart = static_cast<int>(floor(static_cast<double>(ph) *
+                                        static_cast<double>(roi_height) /
+                                        static_cast<double>(pooled_height)));
+    int wstart = static_cast<int>(floor(static_cast<double>(pw) *
+                                        static_cast<double>(roi_width) /
+                                        static_cast<double>(pooled_width)));
+    int hend = static_cast<int>(ceil(static_cast<double>(ph + 1) *
+                                     static_cast<double>(roi_height) /
+                                     static_cast<double>(pooled_height)));
+    int wend = static_cast<int>(ceil(static_cast<double>(pw + 1) *
+                                     static_cast<double>(roi_width) /
+                                     static_cast<double>(pooled_width)));
     hstart = min(max(hstart + roi_start_h, 0), height);
     hend = min(max(hend + roi_start_h, 0), height);
     wstart = min(max(wstart + roi_start_w, 0), width);
@@ -79,9 +84,9 @@ __global__ void GPUROIPoolForward(
         }
       }
     }
-    output_data[index] = maxval;
+    output_data[i] = maxval;
     if (argmax_data) {
-      argmax_data[index] = maxidx;
+      argmax_data[i] = maxidx;
     }
   }
 }
@@ -96,10 +101,10 @@ __global__ void GPUROIPoolBackward(
   int index = blockIdx.x * blockDim.x + threadIdx.x;
   int offset = blockDim.x * gridDim.x;
   for (int i = index; i < nthreads; i += offset) {
-    int pw = index % pooled_width;
-    int ph = (index / pooled_width) % pooled_height;
-    int c = (index / pooled_width / pooled_height) % channels;
-    int n = index / pooled_width / pooled_height / channels;
+    int pw = i % pooled_width;
+    int ph = (i / pooled_width) % pooled_height;
+    int c = (i / pooled_width / pooled_height) % channels;
+    int n = i / pooled_width / pooled_height / channels;
 
     int roi_batch_ind = roi_batch_id_data[n];
     int input_offset = (roi_batch_ind * channels + c) * height * width;
@@ -138,6 +143,7 @@ class GPUROIPoolOpKernel : public framework::OpKernel<T> {
     int width = in_dims[3];
 
     int rois_num = rois->dims()[0];
+
     if (rois_num == 0) return;
 
     int output_size = out->numel();

paddle/fluid/operators/send_recv_op_test.cc

@@ -92,12 +92,16 @@ void InitSelectedRowsInScope(const p::CPUPlace &place, f::Scope *scope) {
 
 void AddOp(const std::string &type, const f::VariableNameMap &inputs,
            const f::VariableNameMap &outputs, f::AttributeMap attrs,
-           f::BlockDesc *block) {
+           f::BlockDesc *block, bool is_sparse) {
   // insert output
   for (auto kv : outputs) {
     for (auto v : kv.second) {
       auto var = block->Var(v);
       var->SetDataType(f::proto::VarType::FP32);
+      var->SetPersistable(true);
+      if (is_sparse) {
+        var->SetType(f::proto::VarType::SELECTED_ROWS);
+      }
     }
   }
 
@@ -128,7 +132,8 @@ void StartServerNet(bool is_sparse, std::atomic<bool> *initialized) {
   auto *optimize_block = program.AppendBlock(root_block);
   auto *prefetch_block = program.AppendBlock(root_block);
   // X for server side tensors, RX for received tensors, must be of same shape.
-  AddOp("sum", {{"X", {"x0", "x1"}}}, {{"Out", {"Out"}}}, {}, optimize_block);
+  AddOp("sum", {{"X", {"x0", "x1"}}}, {{"Out", {"Out"}}}, {}, optimize_block,
+        is_sparse);
   f::AttributeMap attrs;
   attrs.insert({"endpoint", std::string("127.0.0.1:0")});
   attrs.insert({"Fanin", 1});

paddle/fluid/operators/smooth_l1_loss_op.cc

@@ -105,7 +105,7 @@ class SmoothL1LossGradOp : public framework::OperatorWithKernel {
   using framework::OperatorWithKernel::OperatorWithKernel;
 
   void InferShape(framework::InferShapeContext* ctx) const override {
-    auto in_dims = ctx->GetInputDim("X");
+    auto in_dims = ctx->GetInputDim("Diff");
     auto out_dims = ctx->GetInputDim(framework::GradVarName("Out"));
 
     PADDLE_ENFORCE_GE(out_dims.size(), 2,
@@ -127,12 +127,33 @@ class SmoothL1LossGradOp : public framework::OperatorWithKernel {
   }
 };
 
+class SmoothL1LossGradMaker : public framework::SingleGradOpDescMaker {
+ public:
+  using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
+
+ protected:
+  std::unique_ptr<framework::OpDesc> Apply() const override {
+    auto* op = new framework::OpDesc();
+    op->SetType("smooth_l1_loss_grad");
+    op->SetInput("InsideWeight", Input("InsideWeight"));
+    op->SetInput("OutsideWeight", Input("OutsideWeight"));
+    op->SetInput("Diff", Output("Diff"));
+    op->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
+
+    op->SetAttrMap(Attrs());
+
+    op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
+    op->SetOutput(framework::GradVarName("Y"), InputGrad("Y"));
+    return std::unique_ptr<framework::OpDesc>(op);
+  }
+};
+
 }  // namespace operators
 }  // namespace paddle
 
 namespace ops = paddle::operators;
 REGISTER_OPERATOR(smooth_l1_loss, ops::SmoothL1LossOp, ops::SmoothL1LossOpMaker,
-                  paddle::framework::DefaultGradOpDescMaker<true>);
+                  ops::SmoothL1LossGradMaker);
 REGISTER_OPERATOR(smooth_l1_loss_grad, ops::SmoothL1LossGradOp);
 REGISTER_OP_CPU_KERNEL(
     smooth_l1_loss,

paddle/fluid/platform/CMakeLists.txt

-proto_library(profiler_proto SRCS profiler.proto)
+proto_library(profiler_proto SRCS profiler.proto DEPS framework_proto)
 py_proto_compile(profiler_py_proto SRCS profiler.proto)
 
 add_custom_target(profiler_py_proto_init ALL COMMAND ${CMAKE_COMMAND} -E touch __init__.py)
@@ -49,7 +49,7 @@ nv_test(device_context_test SRCS device_context_test.cu DEPS device_context gpu_
 nv_test(cudnn_helper_test SRCS cudnn_helper_test.cc DEPS dynload_cuda)
 nv_test(transform_test SRCS transform_test.cu DEPS memory place device_context)
 
-cc_library(device_tracer SRCS device_tracer.cc DEPS boost profiler_proto ${GPU_CTX_DEPS})
+cc_library(device_tracer SRCS device_tracer.cc DEPS boost profiler_proto framework_proto ${GPU_CTX_DEPS})
 cc_library(profiler SRCS profiler.cc DEPS device_context device_tracer)
 cc_test(profiler_test SRCS profiler_test.cc DEPS profiler)
 

paddle/fluid/platform/profiler.cc

@@ -173,8 +173,9 @@ void PopEvent(const std::string& name, const DeviceContext* dev_ctx) {
 }
 
 RecordEvent::RecordEvent(const std::string& name, const DeviceContext* dev_ctx)
-    : start_ns_(PosixInNsec()) {
+    : is_enabled_(false), start_ns_(PosixInNsec()) {
   if (g_state == ProfilerState::kDisabled) return;
+  is_enabled_ = true;
   dev_ctx_ = dev_ctx;
   name_ = name;
   PushEvent(name_, dev_ctx_);
@@ -183,7 +184,7 @@ RecordEvent::RecordEvent(const std::string& name, const DeviceContext* dev_ctx)
 }
 
 RecordEvent::~RecordEvent() {
-  if (g_state == ProfilerState::kDisabled) return;
+  if (g_state == ProfilerState::kDisabled || !is_enabled_) return;
   DeviceTracer* tracer = GetDeviceTracer();
   if (tracer) {
     tracer->AddCPURecords(CurAnnotation(), start_ns_, PosixInNsec(),
@@ -193,14 +194,16 @@ RecordEvent::~RecordEvent() {
   PopEvent(name_, dev_ctx_);
 }
 
-RecordBlock::RecordBlock(int block_id) : start_ns_(PosixInNsec()) {
+RecordBlock::RecordBlock(int block_id)
+    : is_enabled_(false), start_ns_(PosixInNsec()) {
   if (g_state == ProfilerState::kDisabled) return;
+  is_enabled_ = true;
   SetCurBlock(block_id);
   name_ = string::Sprintf("block_%d", block_id);
 }
 
 RecordBlock::~RecordBlock() {
-  if (g_state == ProfilerState::kDisabled) return;
+  if (g_state == ProfilerState::kDisabled || !is_enabled_) return;
   DeviceTracer* tracer = GetDeviceTracer();
   if (tracer) {
     // We try to put all blocks at the same nested depth in the

paddle/fluid/platform/profiler.h

@@ -74,6 +74,7 @@ struct RecordEvent {
 
   ~RecordEvent();
 
+  bool is_enabled_;
   uint64_t start_ns_;
   // The device context is used by Event to get the current cuda stream.
   const DeviceContext* dev_ctx_;
@@ -89,6 +90,7 @@ struct RecordBlock {
   ~RecordBlock();
 
  private:
+  bool is_enabled_;
   std::string name_;
   uint64_t start_ns_;
 };

paddle/scripts/docker/build.sh

@@ -198,7 +198,7 @@ EOF
     # run paddle version to install python packages first
     RUN apt-get update &&\
         ${NCCL_DEPS}\
-        apt-get install -y wget python-pip dmidecode python-tk && pip install -U pip==9.0.3 && \
+        apt-get install -y wget python-pip dmidecode python-tk && easy_install -U pip && \
         pip install /*.whl; apt-get install -f -y && \
         apt-get clean -y && \
         rm -f /*.whl && \

paddle/scripts/paddle_build.sh

@@ -20,19 +20,15 @@
 #=================================================
 
 function print_usage() {
-    RED='\033[0;31m'
-    BLUE='\033[0;34m'
-    BOLD='\033[1m'
-    NONE='\033[0m'
-    
     echo -e "\n${RED}Usage${NONE}:
-    ${BOLD}$0${NONE} [OPTION]"
+    ${BOLD}${SCRIPT_NAME}${NONE} [OPTION]"
     
     echo -e "\n${RED}Options${NONE}:
     ${BLUE}build${NONE}: run build for x86 platform
     ${BLUE}build_android${NONE}: run build for android platform
     ${BLUE}build_ios${NONE}: run build for ios platform
     ${BLUE}test${NONE}: run all unit tests
+    ${BLUE}single_test${NONE}: run a single unit test
     ${BLUE}bind_test${NONE}: parallel tests bind to different GPU
     ${BLUE}doc${NONE}: generate paddle documents
     ${BLUE}html${NONE}: convert C++ source code into HTML
@@ -45,7 +41,15 @@ function print_usage() {
 }
 
 function init() {
+    RED='\033[0;31m'
+    BLUE='\033[0;34m'
+    BOLD='\033[1m'
+    NONE='\033[0m'
+
     PADDLE_ROOT="$( cd "$( dirname "${BASH_SOURCE[0]}")/../../" && pwd )"
+    if [ -z "${SCRIPT_NAME}" ]; then
+        SCRIPT_NAME=$0
+    fi
 }
 
 function cmake_gen() {
@@ -91,7 +95,6 @@ function cmake_gen() {
         -DWITH_AVX=${WITH_AVX:-OFF}
         -DWITH_GOLANG=${WITH_GOLANG:-OFF}
         -DCUDA_ARCH_NAME=${CUDA_ARCH_NAME:-All}
-        -DWITH_SWIG_PY=ON
         -DWITH_C_API=${WITH_C_API:-OFF}
         -DWITH_PYTHON=${WITH_PYTHON:-ON}
         -DWITH_SWIG_PY=${WITH_SWIG_PY:-ON}
@@ -309,6 +312,25 @@ EOF
     fi
 }
 
+function single_test() {
+    TEST_NAME=$1
+    if [ -z "${TEST_NAME}" ]; then
+        echo -e "${RED}Usage:${NONE}"
+        echo -e "${BOLD}${SCRIPT_NAME}${NONE} ${BLUE}single_test${NONE} [test_name]"
+        exit 1
+    fi
+    mkdir -p ${PADDLE_ROOT}/build
+    cd ${PADDLE_ROOT}/build
+    if [ ${WITH_TESTING:-ON} == "ON" ] ; then
+    cat <<EOF
+    ========================================
+    Running ${TEST_NAME} ...
+    ========================================
+EOF
+        ctest --output-on-failure -R ${TEST_NAME}
+    fi
+}
+
 function bind_test() {
     # the number of process to run tests
     NUM_PROC=6
@@ -383,8 +405,10 @@ EOF
 
 function gen_dockerfile() {
     # Set BASE_IMAGE according to env variables
+    CUDA_MAJOR="$(echo $CUDA_VERSION | cut -d '.' -f 1).$(echo $CUDA_VERSION | cut -d '.' -f 2)"
+    CUDNN_MAJOR=$(echo $CUDNN_VERSION | cut -d '.' -f 1)
     if [[ ${WITH_GPU} == "ON" ]]; then
-    BASE_IMAGE="nvidia/cuda:8.0-cudnn5-runtime-ubuntu16.04"
+        BASE_IMAGE="nvidia/cuda:${CUDA_MAJOR}-cudnn${CUDNN_MAJOR}-runtime-ubuntu16.04"
     else
         BASE_IMAGE="ubuntu:16.04"
     fi
@@ -393,7 +417,7 @@ function gen_dockerfile() {
     DOCKERFILE_CUDNN_DSO=""
     if [[ ${WITH_GPU:-OFF} == 'ON' ]]; then
         DOCKERFILE_GPU_ENV="ENV LD_LIBRARY_PATH /usr/lib/x86_64-linux-gnu:\${LD_LIBRARY_PATH}"
-        DOCKERFILE_CUDNN_DSO="RUN ln -s /usr/lib/x86_64-linux-gnu/libcudnn.so.5 /usr/lib/x86_64-linux-gnu/libcudnn.so"
+        DOCKERFILE_CUDNN_DSO="RUN ln -s /usr/lib/x86_64-linux-gnu/libcudnn.so.${CUDNN_MAJOR} /usr/lib/x86_64-linux-gnu/libcudnn.so"
     fi
 
     cat <<EOF
@@ -427,7 +451,7 @@ EOF
     # run paddle version to install python packages first
     RUN apt-get update &&\
         ${NCCL_DEPS}\
-        apt-get install -y wget python-pip dmidecode python-tk && pip install -U pip==9.0.3 && \
+        apt-get install -y wget python-pip dmidecode python-tk && easy_install -U pip && \
         pip install /*.whl; apt-get install -f -y && \
         apt-get clean -y && \
         rm -f /*.whl && \
@@ -468,7 +492,7 @@ function gen_fluid_inference_lib() {
     Deploying fluid inference library ...
     ========================================
 EOF
-        make inference_lib_dist
+        make -j `nproc` inference_lib_dist
     fi
 }
 
@@ -480,6 +504,7 @@ function main() {
       build)
         cmake_gen ${PYTHON_ABI:-""}
         build
+        gen_dockerfile
         ;;
       build_android)
         build_android
@@ -490,6 +515,9 @@ function main() {
       test)
         run_test
         ;;
+      single_test)
+        single_test $2
+        ;;
       bind_test)
         bind_test
         ;;
@@ -504,6 +532,7 @@ function main() {
         ;;
       capi)
         cmake_gen ${PYTHON_ABI:-""}
+        build
         gen_capi_package
         ;;
       fluid_inference_lib)

paddle/scripts/paddle_docker_build.sh

@@ -63,6 +63,7 @@ EOL
     ${DOCKER_CMD} run -it \
         --name $CONTAINER_ID \
         ${DOCKER_ENV} \
+        -e SCRIPT_NAME=$0 \
         -v $PADDLE_ROOT:/paddle \
         -v ${HOME}/.ccache:/root/.ccache \
         -w /paddle \

python/paddle/fluid/data_feeder.py

@@ -54,9 +54,9 @@ class DataToLoDTensorConverter(object):
             self.data.append(data)
         else:
             cur_lod_len = len(data)
-            lod[-1].append(lod[-1][-1] + cur_lod_len)
+            lod[0].append(lod[0][-1] + cur_lod_len)
             for each_data in data:
-                self._feed_impl_(each_data, lod[:-1], lod_level - 1)
+                self._feed_impl_(each_data, lod[1:], lod_level - 1)
 
     def done(self):
         arr = numpy.array(self.data, dtype=self.dtype).reshape(self.shape)

python/paddle/fluid/inferencer.py

@@ -12,11 +12,14 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import contextlib
+
 import core
 
 import executor
 import framework
 import io
+import parallel_executor
 import unique_name
 from trainer import check_and_get_place
 
@@ -24,40 +27,53 @@ __all__ = ['Inferencer', ]
 
 
 class Inferencer(object):
-    def __init__(self, infer_func, param_path, place=None):
+    def __init__(self, infer_func, param_path, place=None, parallel=False):
         """
         :param infer_func: a function that will return predict Variable
         :param param_path: the path where the inference model is saved by fluid.io.save_params
         :param place: place to do the inference
+        :param parallel: use parallel_executor to run the inference, it will use multi CPU/GPU.
         """
         self.param_path = param_path
         self.scope = core.Scope()
+        self.parallel = parallel
+        self.place = check_and_get_place(place)
 
         self.inference_program = framework.Program()
         with framework.program_guard(self.inference_program):
             with unique_name.guard():
                 self.predict_var = infer_func()
 
-        self.exe = executor.Executor(check_and_get_place(place))
-        with executor.scope_guard(self.scope):
+        with self._prog_and_scope_guard():
             # load params from param_path into scope
-            io.load_params(self.exe, param_path, self.inference_program)
+            io.load_params(executor.Executor(self.place), param_path)
+
+        if parallel:
+            with self._prog_and_scope_guard():
+                self.exe = parallel_executor.ParallelExecutor(
+                    use_cuda=isinstance(self.place, core.CUDAPlace),
+                    loss_name=self.predict_var.name)
+        else:
+            self.exe = executor.Executor(self.place)
 
-    def infer(self, inputs, return_numpy=True):
+    def infer(self, inputs):
         """
         :param inputs: a map of {"input_name": input_var} that will be feed into the inference program
         to get the predict value
-        :param return_numpy: if return numpy value for row tensor
         :return: the predict value of the inference model
         """
         if not isinstance(inputs, dict):
             raise ValueError(
                 "inputs should be a map of {'input_name': input_var}")
 
-        with executor.scope_guard(self.scope):
-            results = self.exe.run(self.inference_program,
-                                   feed=inputs,
-                                   fetch_list=[self.predict_var],
-                                   return_numpy=return_numpy)
+        with self._prog_and_scope_guard():
+            results = self.exe.run(feed=inputs,
+                                   fetch_list=[self.predict_var.name])
 
         return results
+
+    @contextlib.contextmanager
+    def _prog_and_scope_guard(self):
+        with framework.program_guard(main_program=self.inference_program):
+            with executor.scope_guard(self.scope):
+                yield

python/paddle/fluid/layers/detection.py

@@ -23,6 +23,7 @@ import nn
 import math
 
 __all__ = [
+    'prior_box',
     'multi_box_head',
     'bipartite_match',
     'target_assign',
@@ -564,6 +565,98 @@ def ssd_loss(location,
     return loss
 
 
+def prior_box(input,
+              image,
+              min_sizes,
+              max_sizes=None,
+              aspect_ratios=None,
+              variance=[0.1, 0.1, 0.2, 0.2],
+              flip=False,
+              clip=False,
+              steps=[0.0, 0.0],
+              offset=0.5,
+              name=None):
+    """
+    **Prior box operator**
+
+    Generate prior boxes for SSD(Single Shot MultiBox Detector) algorithm.
+    Each position of the input produce N prior boxes, N is determined by
+    the count of min_sizes, max_sizes and aspect_ratios, The size of the
+    box is in range(min_size, max_size) interval, which is generated in
+    sequence according to the aspect_ratios.
+
+    Args:
+       input(Variable): The Input Variables, the format is NCHW.
+       image(Variable): The input image data of PriorBoxOp,
+            the layout is NCHW.
+       min_sizes(list|tuple): min sizes of generated prior boxes.
+       max_sizes(list|tuple|None): max sizes of generated prior boxes.
+            Default: None.
+       aspect_ratios(list|tuple): the aspect ratios of generated prior
+            boxes. Default: None.
+       variance(list|tuple): the variances to be encoded in prior boxes.
+            Default:[0.1, 0.1, 0.2, 0.2].
+       flip(bool): Whether to flip aspect ratios. Default:False.
+       clip(bool): Whether to clip out-of-boundary boxes. Default: False.
+       step(list|turple): Prior boxes step across weight and height, If
+            step[0] == 0.0/step[1] == 0.0, the prior boxes step across
+            height/weight  of the input will be automatically calculated.
+            Default: [0.0]
+       offset(float): Prior boxes center offset. Default: 0.5
+       name(str): Name of the prior box op. Default: None.
+
+    Returns:
+        boxes(Variable): the output prior boxes of PriorBox.
+             The layout is [H, W, num_priors, 4].
+             H is the height of input, W is the width of input,
+             num_priors is the total
+             box count of each position of input.
+        Variances(Variable): the expanded variances of PriorBox.
+             The layout is [H, W, num_priors, 4].
+             H is the height of input, W is the width of input
+             num_priors is the total
+             box count of each position of input
+
+
+    Examples:
+        .. code-block:: python
+            box, var = prior_box(
+            input=conv1,
+            image=images,
+            min_sizes=[100.],
+            flip=True,
+            clip=True)
+    """
+    helper = LayerHelper("prior_box", **locals())
+    dtype = helper.input_dtype()
+
+    attrs = {
+        'min_sizes': min_sizes,
+        'aspect_ratios': aspect_ratios,
+        'variances': variance,
+        'flip': flip,
+        'clip': clip,
+        'step_w': steps[0],
+        'step_h': steps[1],
+        'offset': offset
+    }
+    if max_sizes is not None and len(max_sizes) > 0 and max_sizes[0] > 0:
+        attrs['max_sizes'] = max_sizes
+
+    box = helper.create_tmp_variable(dtype)
+    var = helper.create_tmp_variable(dtype)
+    helper.append_op(
+        type="prior_box",
+        inputs={"Input": input,
+                "Image": image},
+        outputs={"Boxes": box,
+                 "Variances": var},
+        attrs=attrs, )
+    box.stop_gradient = True
+    var.stop_gradient = True
+    return box, var
+
+
 def multi_box_head(inputs,
                    image,
                    base_size,
@@ -660,47 +753,6 @@ def multi_box_head(inputs,
             clip=True)
     """
 
-    def _prior_box_(input,
-                    image,
-                    min_sizes,
-                    max_sizes,
-                    aspect_ratios,
-                    variance,
-                    flip=False,
-                    clip=False,
-                    step_w=0.0,
-                    step_h=0.0,
-                    offset=0.5,
-                    name=None):
-        helper = LayerHelper("prior_box", **locals())
-        dtype = helper.input_dtype()
-
-        attrs = {
-            'min_sizes': min_sizes,
-            'aspect_ratios': aspect_ratios,
-            'variances': variance,
-            'flip': flip,
-            'clip': clip,
-            'step_w': step_w,
-            'step_h': step_h,
-            'offset': offset
-        }
-        if len(max_sizes) > 0 and max_sizes[0] > 0:
-            attrs['max_sizes'] = max_sizes
-
-        box = helper.create_tmp_variable(dtype)
-        var = helper.create_tmp_variable(dtype)
-        helper.append_op(
-            type="prior_box",
-            inputs={"Input": input,
-                    "Image": image},
-            outputs={"Boxes": box,
-                     "Variances": var},
-            attrs=attrs, )
-        box.stop_gradient = True
-        var.stop_gradient = True
-        return box, var
-
     def _reshape_with_axis_(input, axis=1):
         if not (axis > 0 and axis < len(input.shape)):
             raise ValueError("The axis should be smaller than "
@@ -777,11 +829,10 @@ def multi_box_head(inputs,
             aspect_ratio = aspect_ratios[i]
             if not _is_list_or_tuple_(aspect_ratio):
                 aspect_ratio = [aspect_ratio]
+        step = [step_w[i] if step_w else 0.0, step_h[i] if step_w else 0.0]
 
-        box, var = _prior_box_(input, image, min_size, max_size, aspect_ratio,
-                               variance, flip, clip, step_w[i]
-                               if step_w else 0.0, step_h[i]
-                               if step_w else 0.0, offset)
+        box, var = prior_box(input, image, min_size, max_size, aspect_ratio,
+                             variance, flip, clip, step, offset)
 
         box_results.append(box)
         var_results.append(var)

python/paddle/fluid/layers/nn.py

@@ -1329,6 +1329,8 @@ def sequence_pool(input, pool_type):
          sqrt   : out.data = [2.82, 6.93, 4.24], where 2.82=(1+3)/sqrt(2),
                     6.93=(2+4+6)/sqrt(3), 4.24=(5+1)/sqrt(2)
          max    : out.data = [3, 6, 5], where 3=max(1,3), 6=max(2,4,6), 5=max(5,1)
+         last   : out.data = [3, 6, 1], where 3=last(1,3), 6=last(2,4,6), 1=last(5,1)
+         first  : out.data = [1, 2, 5], where 1=first(1,3), 2=first(2,4,6), 5=first(5,1)
 
     Args:
         input(variable): The input variable which is a LoDTensor.
@@ -1348,6 +1350,8 @@ def sequence_pool(input, pool_type):
              sum_x = fluid.layers.sequence_pool(input=x, pool_type='sum')
              sqrt_x = fluid.layers.sequence_pool(input=x, pool_type='sqrt')
              max_x = fluid.layers.sequence_pool(input=x, pool_type='max')
+             last_x = fluid.layers.sequence_pool(input=x, pool_type='last')
+             first_x = fluid.layers.sequence_pool(input=x, pool_type='first')
     """
     helper = LayerHelper('sequence_pool', **locals())
     dtype = helper.input_dtype()
@@ -3263,35 +3267,35 @@ def smooth_l1(x, y, inside_weight=None, outside_weight=None, sigma=None):
     """
     **Smooth L1 Loss Operator. **
 
-    This operator computes the smooth l1 loss for X and Y.
+    This operator computes the smooth L1 loss for X and Y.
     The operator takes the first dimension of X and Y as batch size.
-    For each instance, it computes the smooth l1 loss element by element first
+    For each instance, it computes the smooth L1 loss element by element first
     and then sums all the losses. So the shape of Out is [batch_size, 1].
 
     Args:
         x (Variable): A tensor with rank at least 2. The input value of smooth
-            l1 loss op with shape [batch_size, dim1, ..., dimN].
+            L1 loss op with shape [batch_size, dim1, ..., dimN].
         y (Variable): A tensor with rank at least 2. The target value of smooth
-            l1 loss op with same shape as x.
+            L1 loss op with same shape as x.
         inside_weight (Variable|None):  A tensor with rank at least 2. This
             input is optional and should have same shape with x. If provided,
             the result of (x - y) will be multiplied by this tensor element by
             element.
         outside_weight (Variable|None): A tensor with rank at least 2. This
             input is optional and should have same shape with x. If provided,
-            the out smooth l1 loss will be multiplied by this tensor element
+            the out smooth L1 loss will be multiplied by this tensor element
             by element.
-        sigma (float|None): Hyper parameter of smooth l1 loss op. A float scalar
+        sigma (float|None): Hyper parameter of smooth L1 loss op. A float scalar
             with default value 1.0.
     Returns:
-        Variable: A tensor with rank be 2. The output smooth l1 loss with
+        Variable: A tensor with rank be 2. The output smooth L1 loss with
             shape [batch_size, 1].
 
     Examples:
         .. code-block:: python
 
             data = fluid.layers.data(name='data', shape=[128], dtype='float32')
-            label = fluid.layers.data(name='label', shape=[100], dtype='int64')
+            label = fluid.layers.data(name='label', shape=[100], dtype='float32')
             fc = fluid.layers.fc(input=data, size=100)
             out = fluid.layers.smooth_l1(x=fc, y=label)
     """

python/paddle/fluid/tests/book/high-level-api/CMakeLists.txt

@@ -8,3 +8,4 @@ endforeach()
 
 add_subdirectory(fit_a_line)
 add_subdirectory(recognize_digits)
+add_subdirectory(image_classification)

python/paddle/fluid/tests/book/high-level-api/fit_a_line/test_fit_a_line.py

@@ -57,22 +57,20 @@ def train(use_cuda, train_program, save_dirname):
         optimizer=fluid.optimizer.SGD(learning_rate=0.001))
 
     def event_handler(event):
-        if isinstance(event, fluid.EndEpochEvent):
+        if isinstance(event, fluid.EndStepEvent):
+            if event.step == 10:
                 test_metrics = trainer.test(
                     reader=test_reader, feed_order=['x', 'y'])
                 print test_metrics
                 '''
-            
                 ...
                 ['25.768919467926025']
                 ['15.343549569447836']
                 ...
-            
                 '''
-            if float(test_metrics[0]) < 20.0:
                 if save_dirname is not None:
                     trainer.save_params(save_dirname)
-            return
+                trainer.stop()
 
     trainer.train(
         reader=train_reader,
@@ -94,7 +92,7 @@ def infer(use_cuda, inference_program, save_dirname=None):
     tensor_x = numpy.random.uniform(0, 10, [batch_size, 13]).astype("float32")
 
     results = inferencer.infer({'x': tensor_x})
-    print("infer results: ", results[0])
+    print("infer results: ", numpy.array(results[0]))
 
 
 def main(use_cuda):

python/paddle/fluid/tests/book/high-level-api/image_classification/CMakeLists.txt

+file(GLOB TEST_OPS RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "test_*.py")
+string(REPLACE ".py" "" TEST_OPS "${TEST_OPS}")
+
+# default test
+foreach(src ${TEST_OPS})
+    py_test(${src} SRCS ${src}.py)
+endforeach()

python/paddle/fluid/tests/book/high-level-api/image_classification/cifar10_small_test_set.py

+# Copyright (c) 2016 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.
+"""
+CIFAR dataset.
+
+This module will download dataset from
+https://www.cs.toronto.edu/~kriz/cifar.html and parse train/test set into
+paddle reader creators.
+
+The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes,
+with 6000 images per class. There are 50000 training images and 10000 test
+images.
+
+The CIFAR-100 dataset is just like the CIFAR-10, except it has 100 classes
+containing 600 images each. There are 500 training images and 100 testing
+images per class.
+
+"""
+
+import cPickle
+import itertools
+import numpy
+import paddle.v2.dataset.common
+import tarfile
+
+__all__ = ['train10']
+
+URL_PREFIX = 'https://www.cs.toronto.edu/~kriz/'
+CIFAR10_URL = URL_PREFIX + 'cifar-10-python.tar.gz'
+CIFAR10_MD5 = 'c58f30108f718f92721af3b95e74349a'
+
+
+def reader_creator(filename, sub_name, batch_size=None):
+    def read_batch(batch):
+        data = batch['data']
+        labels = batch.get('labels', batch.get('fine_labels', None))
+        assert labels is not None
+        for sample, label in itertools.izip(data, labels):
+            yield (sample / 255.0).astype(numpy.float32), int(label)
+
+    def reader():
+        with tarfile.open(filename, mode='r') as f:
+            names = (each_item.name for each_item in f
+                     if sub_name in each_item.name)
+
+            batch_count = 0
+            for name in names:
+                batch = cPickle.load(f.extractfile(name))
+                for item in read_batch(batch):
+                    if isinstance(batch_size, int) and batch_count > batch_size:
+                        break
+                    batch_count += 1
+                    yield item
+
+    return reader
+
+
+def train10(batch_size=None):
+    """
+    CIFAR-10 training set creator.
+
+    It returns a reader creator, each sample in the reader is image pixels in
+    [0, 1] and label in [0, 9].
+
+    :return: Training reader creator
+    :rtype: callable
+    """
+    return reader_creator(
+        paddle.v2.dataset.common.download(CIFAR10_URL, 'cifar', CIFAR10_MD5),
+        'data_batch',
+        batch_size=batch_size)

python/paddle/fluid/tests/book/high-level-api/image_classification/notest_image_classification_resnet.py → python/paddle/fluid/tests/book/high-level-api/image_classification/test_image_classification_resnet.py

@@ -17,6 +17,7 @@ from __future__ import print_function
 import paddle
 import paddle.fluid as fluid
 import numpy
+import cifar10_small_test_set
 
 
 def resnet_cifar10(input, depth=32):
@@ -81,46 +82,50 @@ def train_network():
     cost = fluid.layers.cross_entropy(input=predict, label=label)
     avg_cost = fluid.layers.mean(cost)
     accuracy = fluid.layers.accuracy(input=predict, label=label)
-    return avg_cost, accuracy
+    return [avg_cost, accuracy]
 
 
-def train(use_cuda, save_path):
+def train(use_cuda, train_program, save_dirname):
     BATCH_SIZE = 128
     EPOCH_NUM = 1
 
     train_reader = paddle.batch(
         paddle.reader.shuffle(
-            paddle.dataset.cifar.train10(), buf_size=128 * 10),
+            cifar10_small_test_set.train10(batch_size=10), buf_size=128 * 10),
         batch_size=BATCH_SIZE)
 
     test_reader = paddle.batch(
         paddle.dataset.cifar.test10(), batch_size=BATCH_SIZE)
 
     def event_handler(event):
-        if isinstance(event, fluid.EndIteration):
-            if (event.batch_id % 10) == 0:
-                avg_cost, accuracy = trainer.test(reader=test_reader)
+        if isinstance(event, fluid.EndStepEvent):
+            avg_cost, accuracy = trainer.test(
+                reader=test_reader, feed_order=['pixel', 'label'])
 
-                print('BatchID {1:04}, Loss {2:2.2}, Acc {3:2.2}'.format(
-                    event.batch_id + 1, avg_cost, accuracy))
+            print('Loss {0:2.2}, Acc {1:2.2}'.format(avg_cost, accuracy))
 
             if accuracy > 0.01:  # Low threshold for speeding up CI
-                    trainer.params.save(save_path)
+                if save_dirname is not None:
+                    trainer.save_params(save_dirname)
                 return
 
     place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
     trainer = fluid.Trainer(
-        train_network,
+        train_func=train_program,
         optimizer=fluid.optimizer.Adam(learning_rate=0.001),
-        place=place,
-        event_handler=event_handler)
-    trainer.train(train_reader, EPOCH_NUM, event_handler=event_handler)
+        place=place)
 
+    trainer.train(
+        reader=train_reader,
+        num_epochs=EPOCH_NUM,
+        event_handler=event_handler,
+        feed_order=['pixel', 'label'])
 
-def infer(use_cuda, save_path):
-    params = fluid.Params(save_path)
+
+def infer(use_cuda, inference_program, save_dirname=None):
     place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
-    inferencer = fluid.Inferencer(inference_network, params, place=place)
+    inferencer = fluid.Inferencer(
+        infer_func=inference_program, param_path=save_dirname, place=place)
 
     # The input's dimension of conv should be 4-D or 5-D.
     # Use normilized image pixels as input data, which should be in the range
@@ -135,8 +140,14 @@ def main(use_cuda):
     if use_cuda and not fluid.core.is_compiled_with_cuda():
         return
     save_path = "image_classification_resnet.inference.model"
-    train(use_cuda, save_path)
-    infer(use_cuda, save_path)
+
+    train(
+        use_cuda=use_cuda, train_program=train_network, save_dirname=save_path)
+
+    infer(
+        use_cuda=use_cuda,
+        inference_program=inference_network,
+        save_dirname=save_path)
 
 
 if __name__ == '__main__':

python/paddle/fluid/tests/book/high-level-api/image_classification/notest_image_classification_vgg.py → python/paddle/fluid/tests/book/high-level-api/image_classification/test_image_classification_vgg.py

@@ -17,6 +17,7 @@ from __future__ import print_function
 import paddle
 import paddle.fluid as fluid
 import numpy
+import cifar10_small_test_set
 
 
 def vgg16_bn_drop(input):
@@ -60,46 +61,48 @@ def train_network():
     cost = fluid.layers.cross_entropy(input=predict, label=label)
     avg_cost = fluid.layers.mean(cost)
     accuracy = fluid.layers.accuracy(input=predict, label=label)
-    return avg_cost, accuracy
+    return [avg_cost, accuracy]
 
 
-def train(use_cuda, save_path):
+def train(use_cuda, train_program, save_dirname):
     BATCH_SIZE = 128
-    EPOCH_NUM = 1
-
     train_reader = paddle.batch(
         paddle.reader.shuffle(
-            paddle.dataset.cifar.train10(), buf_size=128 * 10),
+            cifar10_small_test_set.train10(batch_size=10), buf_size=128 * 10),
         batch_size=BATCH_SIZE)
 
     test_reader = paddle.batch(
         paddle.dataset.cifar.test10(), batch_size=BATCH_SIZE)
 
     def event_handler(event):
-        if isinstance(event, fluid.EndIteration):
-            if (event.batch_id % 10) == 0:
-                avg_cost, accuracy = trainer.test(reader=test_reader)
+        if isinstance(event, fluid.EndStepEvent):
+            avg_cost, accuracy = trainer.test(
+                reader=test_reader, feed_order=['pixel', 'label'])
 
-                print('BatchID {1:04}, Loss {2:2.2}, Acc {3:2.2}'.format(
-                    event.batch_id + 1, avg_cost, accuracy))
+            print('Loss {0:2.2}, Acc {1:2.2}'.format(avg_cost, accuracy))
 
             if accuracy > 0.01:  # Low threshold for speeding up CI
-                    trainer.params.save(save_path)
+                if save_dirname is not None:
+                    trainer.save_params(save_dirname)
                 return
 
     place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
     trainer = fluid.Trainer(
-        train_network,
-        optimizer=fluid.optimizer.Adam(learning_rate=0.001),
+        train_func=train_program,
         place=place,
-        event_handler=event_handler)
-    trainer.train(train_reader, EPOCH_NUM, event_handler=event_handler)
+        optimizer=fluid.optimizer.Adam(learning_rate=0.001))
+
+    trainer.train(
+        reader=train_reader,
+        num_epochs=1,
+        event_handler=event_handler,
+        feed_order=['pixel', 'label'])
 
 
-def infer(use_cuda, save_path):
-    params = fluid.Params(save_path)
+def infer(use_cuda, inference_program, save_dirname=None):
     place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
-    inferencer = fluid.Inferencer(inference_network, params, place=place)
+    inferencer = fluid.Inferencer(
+        infer_func=inference_program, param_path=save_dirname, place=place)
 
     # The input's dimension of conv should be 4-D or 5-D.
     # Use normilized image pixels as input data, which should be in the range
@@ -114,8 +117,14 @@ def main(use_cuda):
     if use_cuda and not fluid.core.is_compiled_with_cuda():
         return
     save_path = "image_classification_vgg.inference.model"
-    train(use_cuda, save_path)
-    infer(use_cuda, save_path)
+
+    train(
+        use_cuda=use_cuda, train_program=train_network, save_dirname=save_path)
+
+    infer(
+        use_cuda=use_cuda,
+        inference_program=inference_network,
+        save_dirname=save_path)
 
 
 if __name__ == '__main__':

python/paddle/fluid/tests/book/high-level-api/recognize_digits/test_recognize_digits_conv.py

@@ -62,31 +62,31 @@ def train(use_cuda, train_program, save_dirname):
     optimizer = fluid.optimizer.Adam(learning_rate=0.001)
 
     trainer = fluid.Trainer(
-        train_func=train_program, place=place, optimizer=optimizer)
+        train_func=train_program,
+        place=place,
+        optimizer=optimizer,
+        parallel=True)
 
     def event_handler(event):
         if isinstance(event, fluid.EndEpochEvent):
             test_reader = paddle.batch(
                 paddle.dataset.mnist.test(), batch_size=BATCH_SIZE)
-            test_metrics = trainer.test(
+            avg_cost, acc = trainer.test(
                 reader=test_reader, feed_order=['img', 'label'])
-            avg_cost_set = test_metrics[0]
-            acc_set = test_metrics[1]
-
-            # get test acc and loss
-            acc = numpy.array(acc_set).mean()
-            avg_cost = numpy.array(avg_cost_set).mean()
 
             print("avg_cost: %s" % avg_cost)
             print("acc     : %s" % acc)
 
-            if float(acc) > 0.2:  # Smaller value to increase CI speed
+            if acc > 0.2:  # Smaller value to increase CI speed
                 trainer.save_params(save_dirname)
             else:
                 print('BatchID {0}, Test Loss {1:0.2}, Acc {2:0.2}'.format(
-                    event.epoch + 1, float(avg_cost), float(acc)))
-                if math.isnan(float(avg_cost)):
+                    event.epoch + 1, avg_cost, acc))
+                if math.isnan(avg_cost):
                     sys.exit("got NaN loss, training failed.")
+        elif isinstance(event, fluid.EndStepEvent):
+            print("Step {0}, Epoch {1} Metrics {2}".format(
+                event.step, event.epoch, map(numpy.array, event.metrics)))
 
     train_reader = paddle.batch(
         paddle.reader.shuffle(
@@ -112,7 +112,7 @@ def infer(use_cuda, inference_program, save_dirname=None):
 
     results = inferencer.infer({'img': tensor_img})
 
-    print("infer results: ", results[0])
+    print("infer results: ", numpy.array(results[0]))
 
 
 def main(use_cuda):
@@ -131,4 +131,4 @@ def main(use_cuda):
 
 if __name__ == '__main__':
     # for use_cuda in (False, True):
-    main(use_cuda=False)
+    main(use_cuda=True)

python/paddle/fluid/tests/book/high-level-api/recognize_digits/test_recognize_digits_mlp.py

@@ -55,24 +55,18 @@ def train(use_cuda, train_program, save_dirname):
         if isinstance(event, fluid.EndEpochEvent):
             test_reader = paddle.batch(
                 paddle.dataset.mnist.test(), batch_size=BATCH_SIZE)
-            test_metrics = trainer.test(
+            avg_cost, acc = trainer.test(
                 reader=test_reader, feed_order=['img', 'label'])
-            avg_cost_set = test_metrics[0]
-            acc_set = test_metrics[1]
-
-            # get test acc and loss
-            acc = numpy.array(acc_set).mean()
-            avg_cost = numpy.array(avg_cost_set).mean()
 
             print("avg_cost: %s" % avg_cost)
             print("acc     : %s" % acc)
 
-            if float(acc) > 0.2:  # Smaller value to increase CI speed
+            if acc > 0.2:  # Smaller value to increase CI speed
                 trainer.save_params(save_dirname)
             else:
                 print('BatchID {0}, Test Loss {1:0.2}, Acc {2:0.2}'.format(
-                    event.epoch + 1, float(avg_cost), float(acc)))
-                if math.isnan(float(avg_cost)):
+                    event.epoch + 1, avg_cost, acc))
+                if math.isnan(avg_cost):
                     sys.exit("got NaN loss, training failed.")
 
     train_reader = paddle.batch(
@@ -99,7 +93,7 @@ def infer(use_cuda, inference_program, save_dirname=None):
 
     results = inferencer.infer({'img': tensor_img})
 
-    print("infer results: ", results[0])
+    print("infer results: ", numpy.array(results[0]))
 
 
 def main(use_cuda):

python/paddle/fluid/tests/book/high-level-api/word2vec/no_test_word2vec_new_api.py → python/paddle/fluid/tests/book/high-level-api/word2vec/test_word2vec_new_api.py

@@ -90,7 +90,7 @@ def train_program(is_sparse):
     return avg_cost
 
 
-def train(use_cuda, train_program, save_path):
+def train(use_cuda, train_program, save_dirname):
     train_reader = paddle.batch(
         paddle.dataset.imikolov.train(word_dict, N), BATCH_SIZE)
     test_reader = paddle.batch(
@@ -99,27 +99,36 @@ def train(use_cuda, train_program, save_path):
     place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
 
     def event_handler(event):
-        if isinstance(event, fluid.EndEpochEvent):
-            outs = trainer.test(reader=test_reader)
+        if isinstance(event, fluid.EndStepEvent):
+            outs = trainer.test(
+                reader=test_reader,
+                feed_order=['firstw', 'secondw', 'thirdw', 'forthw', 'nextw'])
             avg_cost = outs[0]
             print("loss= ", avg_cost)
 
-            if avg_cost < 5.0:
-                trainer.save_params(save_path)
-                return
+            if avg_cost < 10.0:
+                trainer.save_params(save_dirname)
+                trainer.stop()
+
             if math.isnan(avg_cost):
                 sys.exit("got NaN loss, training failed.")
 
     trainer = fluid.Trainer(
-        train_program, fluid.optimizer.SGD(learning_rate=0.001), place=place)
+        train_func=train_program,
+        optimizer=fluid.optimizer.SGD(learning_rate=0.001),
+        place=place)
+
     trainer.train(
-        reader=train_reader, num_epochs=1, event_handler=event_handler)
+        reader=train_reader,
+        num_epochs=1,
+        event_handler=event_handler,
+        feed_order=['firstw', 'secondw', 'thirdw', 'forthw', 'nextw'])
 
 
-def infer(use_cuda, inference_program, save_path):
+def infer(use_cuda, inference_program, save_dirname=None):
     place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
     inferencer = fluid.Inferencer(
-        infer_func=inference_program, param_path=save_path, place=place)
+        infer_func=inference_program, param_path=save_dirname, place=place)
 
     lod = [0, 1]
     first_word = create_random_lodtensor(lod, place, low=0, high=dict_size - 1)
@@ -142,9 +151,17 @@ def main(use_cuda, is_sparse):
     if use_cuda and not fluid.core.is_compiled_with_cuda():
         return
 
-    save_path = "word2vec.params"
-    train(use_cuda, partial(train_program, is_sparse), save_path)
-    infer(use_cuda, partial(inference_program, is_sparse), save_path)
+    save_path = "word2vec.inference.model"
+
+    train(
+        use_cuda=use_cuda,
+        train_program=partial(train_program, is_sparse),
+        save_dirname=save_path)
+
+    infer(
+        use_cuda=use_cuda,
+        inference_program=partial(inference_program, is_sparse),
+        save_dirname=save_path)
 
 
 if __name__ == '__main__':

python/paddle/fluid/tests/book/test_label_semantic_roles.py

@@ -182,12 +182,6 @@ def train(use_cuda, save_dirname=None, is_local=True):
     crf_decode = fluid.layers.crf_decoding(
         input=feature_out, param_attr=fluid.ParamAttr(name='crfw'))
 
-    chunk_evaluator = fluid.evaluator.ChunkEvaluator(
-        input=crf_decode,
-        label=target,
-        chunk_scheme="IOB",
-        num_chunk_types=int(math.ceil((label_dict_len - 1) / 2.0)))
-
     train_data = paddle.batch(
         paddle.reader.shuffle(
             paddle.dataset.conll05.test(), buf_size=8192),
@@ -203,7 +197,6 @@ def train(use_cuda, save_dirname=None, is_local=True):
 
     def train_loop(main_program):
         exe.run(fluid.default_startup_program())
-
         embedding_param = fluid.global_scope().find_var(
             embedding_name).get_tensor()
         embedding_param.set(
@@ -213,27 +206,19 @@ def train(use_cuda, save_dirname=None, is_local=True):
         start_time = time.time()
         batch_id = 0
         for pass_id in xrange(PASS_NUM):
-            chunk_evaluator.reset(exe)
             for data in train_data():
-                cost, precision, recall, f1_score = exe.run(
-                    main_program,
+                cost = exe.run(main_program,
                                feed=feeder.feed(data),
-                    fetch_list=[avg_cost] + chunk_evaluator.metrics)
-                pass_precision, pass_recall, pass_f1_score = chunk_evaluator.eval(
-                    exe)
+                               fetch_list=[avg_cost])
+                cost = cost[0]
 
                 if batch_id % 10 == 0:
-                    print("avg_cost:" + str(cost) + " precision:" + str(
-                        precision) + " recall:" + str(recall) + " f1_score:" +
-                          str(f1_score) + " pass_precision:" + str(
-                              pass_precision) + " pass_recall:" + str(
-                                  pass_recall) + " pass_f1_score:" + str(
-                                      pass_f1_score))
+                    print("avg_cost:" + str(cost))
                     if batch_id != 0:
                         print("second per batch: " + str((time.time(
                         ) - start_time) / batch_id))
                     # Set the threshold low to speed up the CI test
-                    if float(pass_precision) > 0.01:
+                    if float(cost) < 60.0:
                         if save_dirname is not None:
                             # TODO(liuyiqun): Change the target to crf_decode
                             fluid.io.save_inference_model(save_dirname, [

python/paddle/fluid/tests/test_data_feeder.py

@@ -13,15 +13,62 @@
 # limitations under the License.
 
 import paddle.fluid as fluid
+import unittest
 
 
-def test_converter():
+class TestDataFeeder(unittest.TestCase):
+    def test_lod_level_0_converter(self):
         img = fluid.layers.data(name='image', shape=[1, 28, 28])
         label = fluid.layers.data(name='label', shape=[1], dtype='int64')
         feeder = fluid.DataFeeder([img, label], fluid.CPUPlace())
-    result = feeder.feed([[[0] * 784, [9]], [[1] * 784, [1]]])
+        result = feeder.feed([([0] * 784, [9]), ([1] * 784, [1])])
         print(result)
 
+        self.assertEqual(result['image'].shape(), [2, 1, 28, 28])
+        self.assertEqual(result['label'].shape(), [2, 1])
+        self.assertEqual(result['image'].lod(), [])
+        self.assertEqual(result['label'].lod(), [])
+
+    def test_lod_level_1_converter(self):
+        # lod_level = 1
+        # each sentence has a different number of words
+        sentences = fluid.layers.data(
+            name='sentences', shape=[1], dtype='int64', lod_level=1)
+        label = fluid.layers.data(name='label', shape=[1], dtype='int64')
+        feeder = fluid.DataFeeder([sentences, label], fluid.CPUPlace())
+
+        # lod = [[0, 3, 5, 9]]
+        # data = [[1, 2, 3], [4, 5], [6, 7, 8, 9]]
+        # label = [1] * len(data)
+        result = feeder.feed(
+            [([1, 2, 3], [1]), ([4, 5], [1]), ([6, 7, 8, 9], [1])])
+        print(result)
+
+        self.assertEqual(result['sentences'].shape(), [9, 1])
+        self.assertEqual(result['label'].shape(), [3, 1])
+        self.assertEqual(result['sentences'].lod(), [[0, 3, 5, 9]])
+        self.assertEqual(result['label'].lod(), [])
+
+    def test_lod_level_2_converter(self):
+        # lod_level = 2
+        # paragraphs -> sentences -> words
+        paragraphs = fluid.layers.data(
+            name='paragraphs', shape=[1], dtype='int64', lod_level=2)
+        label = fluid.layers.data(name='label', shape=[1], dtype='int64')
+        feeder = fluid.DataFeeder([paragraphs, label], fluid.CPUPlace())
+
+        # lod = [[0, 2, 3], [0, 3, 5, 9]]
+        # data = [[[1, 2, 3], [4, 5]], [[6, 7, 8, 9]]]
+        # label = [1] * len(data)
+        result = feeder.feed(
+            [([[1, 2, 3], [4, 5]], [1]), ([[6, 7, 8, 9]], [1])])
+        print(result)
+
+        self.assertEqual(result['paragraphs'].shape(), [9, 1])
+        self.assertEqual(result['label'].shape(), [2, 1])
+        self.assertEqual(result['paragraphs'].lod(), [[0, 2, 3], [0, 3, 5, 9]])
+        self.assertEqual(result['label'].lod(), [])
+
 
 if __name__ == '__main__':
-    test_converter()
+    unittest.main()

python/paddle/fluid/tests/test_detection.py

@@ -109,6 +109,24 @@ class TestDetection(unittest.TestCase):
         print(str(program))
 
 
+class TestPriorBox(unittest.TestCase):
+    def test_prior_box(self):
+        data_shape = [3, 224, 224]
+        images = fluid.layers.data(
+            name='pixel', shape=data_shape, dtype='float32')
+        conv1 = fluid.layers.conv2d(images, 3, 3, 2)
+        box, var = layers.prior_box(
+            input=conv1,
+            image=images,
+            min_sizes=[100.0],
+            aspect_ratios=[1.],
+            flip=True,
+            clip=True)
+        assert len(box.shape) == 4
+        assert box.shape == var.shape
+        assert box.shape[3] == 4
+
+
 class TestMultiBoxHead(unittest.TestCase):
     def test_multi_box_head(self):
         data_shape = [3, 224, 224]

python/paddle/fluid/tests/unittests/CMakeLists.txt

@@ -28,11 +28,11 @@ function(py_test_modules TARGET_NAME)
   if(WITH_TESTING)
     set(options "")
     set(oneValueArgs "")
-    set(multiValueArgs MODULES DEPS ARGS ENVS)
+    set(multiValueArgs MODULES DEPS ENVS)
     cmake_parse_arguments(py_test_modules "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
     add_test(NAME ${TARGET_NAME}
              COMMAND env PYTHONPATH=${PADDLE_BINARY_DIR}/python ${py_test_modules_ENVS}
-             ${PYTHON_EXECUTABLE} -u -m unittest --verbose ${py_test_modules_MODULES} ${py_test_modules_ARGS}
+             ${PYTHON_EXECUTABLE} ${PADDLE_SOURCE_DIR}/tools/test_runner.py ${py_test_modules_MODULES}
              WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR})
   endif()
 endfunction()

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

@@ -52,15 +52,18 @@ class TestSendOp(unittest.TestCase):
             serv = layers.ListenAndServ(
                 "127.0.0.1:0", ["X"], optimizer_mode=False)
             with serv.do():
+                out_var = main.global_block().create_var(
+                    name="scale_0.tmp_0",
+                    psersistable=True,
+                    dtype="float32",
+                    shape=[32, 32])
                 x = layers.data(
                     shape=[32, 32],
                     dtype='float32',
                     name="X",
                     append_batch_size=False)
                 fluid.initializer.Constant(value=1.0)(x, main.global_block())
-                o = layers.scale(x=x, scale=10.0)
-            main.global_block().create_var(
-                name=o.name, psersistable=False, dtype=o.dtype, shape=o.shape)
+                layers.scale(x=x, scale=10.0, out=out_var)
 
         self.server_exe = fluid.Executor(place)
         self.server_exe.run(main)

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

@@ -24,33 +24,30 @@ BATCH_SIZE = 20
 
 
 class TestNetWithDtype(unittest.TestCase):
-    def set_network(self):
+    def setUp(self):
         self.dtype = "float64"
         self.init_dtype()
-        main = fluid.Program()
-        with fluid.program_guard(main):
-            self.x = fluid.layers.data(name='x', shape=[13], dtype=self.dtype)
-            self.y = fluid.layers.data(name='y', shape=[1], dtype=self.dtype)
-            y_predict = fluid.layers.fc(input=self.x, size=1, act=None)
 
-            cost = fluid.layers.square_error_cost(input=y_predict, label=self.y)
+    def run_net_on_place(self, place):
+        main = fluid.Program()
+        startup = fluid.Program()
+        with fluid.program_guard(main, startup):
+            x = fluid.layers.data(name='x', shape=[13], dtype=self.dtype)
+            y = fluid.layers.data(name='y', shape=[1], dtype=self.dtype)
+            y_predict = fluid.layers.fc(input=x, size=1, act=None)
+            cost = fluid.layers.square_error_cost(input=y_predict, label=y)
             avg_cost = fluid.layers.mean(cost)
-        self.program = main
-        self.fetch_list = [avg_cost]
-
             sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001)
             sgd_optimizer.minimize(avg_cost)
 
-    def run_net_on_place(self, place):
+        fetch_list = [avg_cost]
         train_reader = paddle.batch(
             paddle.dataset.uci_housing.train(), batch_size=BATCH_SIZE)
-        feeder = fluid.DataFeeder(place=place, feed_list=[self.x, self.y])
+        feeder = fluid.DataFeeder(place=place, feed_list=[x, y])
         exe = fluid.Executor(place)
-        exe.run(fluid.default_startup_program())
+        exe.run(startup)
         for data in train_reader():
-            exe.run(self.program,
-                    feed=feeder.feed(data),
-                    fetch_list=self.fetch_list)
+            exe.run(main, feed=feeder.feed(data), fetch_list=fetch_list)
             # the main program is runable, the datatype is fully supported
             break
 
@@ -58,14 +55,12 @@ class TestNetWithDtype(unittest.TestCase):
         pass
 
     def test_cpu(self):
-        self.set_network()
         place = fluid.CPUPlace()
         self.run_net_on_place(place)
 
     def test_gpu(self):
         if not core.is_compiled_with_cuda():
             return
-        self.set_network()
         place = fluid.CUDAPlace(0)
         self.run_net_on_place(place)
 

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

@@ -775,7 +775,7 @@ class TestCRFModel(unittest.TestCase):
         build_strategy = fluid.BuildStrategy()
         build_strategy.reduce_strategy = fluid.BuildStrategy.ReduceStrategy.Reduce
         self.check_network_convergence(
-            is_sparse=False, build_strategy=build_strategy)
+            is_sparse=True, build_strategy=build_strategy)
 
     def test_update_dense_parameter_reduce(self):
         build_strategy = fluid.BuildStrategy()
@@ -849,8 +849,7 @@ class TestFetchOp(unittest.TestCase):
                     assert not math.isnan(np.sum(ret[i])) and \
                            not math.isinf(np.sum(ret[i]))
 
-    @unittest.skip("this test is buggy")
-    def test_feed(self):
+    def test_fetch_op(self):
         tst_reader = paddle.batch(flowers.test(use_xmap=False), batch_size=16)
         tst_reader_iter = tst_reader()
 

python/paddle/fluid/trainer.py

@@ -12,17 +12,18 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import contextlib
 import os
+
 import core
-import framework
-import executor
+
 import data_feeder
-import contextlib
+import executor
+import framework
 import io
-import unique_name
-
 # optimizer is same as the parameter of Trainer.__init__. Rename it to opt_module
 import optimizer as opt_module
+import parallel_executor
 from transpiler import distribute_transpiler
 
 __all__ = [
@@ -48,12 +49,14 @@ class BeginStepEvent(object):
     def __init__(self, epoch_id, step_id):
         self.epoch = epoch_id
         self.step = step_id
+        self.fetch_metrics = True
 
 
 class EndStepEvent(object):
-    def __init__(self, epoch_id, step_id):
+    def __init__(self, epoch_id, step_id, metrics):
         self.epoch = epoch_id
         self.step = step_id
+        self.metrics = metrics
 
 
 def check_and_get_place(place):
@@ -87,12 +90,18 @@ class Trainer(object):
 
     Args:
         train_func(callable): A function which will return loss. The loss must be a scalar.
-        infer_func(callable): A function which will return predict, used to save inference model
         optimizer(optimizer.Optimizer): The optimizer should be an instance of Optimizer
         place: The device place of this trainer.
     """
 
-    def __init__(self, train_func, optimizer, param_path=None, place=None):
+    def __init__(self,
+                 train_func,
+                 optimizer,
+                 param_path=None,
+                 place=None,
+                 parallel=False):
+        self.__stop = False
+        self.parallel = parallel
         # 1. we need to generate a framework.Program by calling
         # program_func. Reference: fluid.program_guard in
         # test_word2vec.py
@@ -106,14 +115,14 @@ class Trainer(object):
 
         with framework.program_guard(self.train_program, self.startup_program):
             program_func_outs = train_func()
-            self.test_outputs = program_func_outs if isinstance(
+            self.train_func_outputs = program_func_outs if isinstance(
                 program_func_outs, list) else [program_func_outs]
             self.test_program = self.train_program.clone()
             if not isinstance(optimizer, opt_module.Optimizer):
                 raise TypeError(
                     "The optimizer should be an instance of Optimizer")
             # The fisrt element of program_func_outs is loss.
-            loss = self.test_outputs[0]
+            loss = self.train_func_outputs[0]
             optimize_ops, params_grads = optimizer.minimize(loss)
 
         self.place = check_and_get_place(place)
@@ -131,7 +140,40 @@ class Trainer(object):
             # load params from param_path into scope
             io.load_persistables(exe, dirname=param_path)
 
+    def _transpile_nccl2_dist(self):
+        # PADDLE_TRAINER_IPS
+        if "PADDLE_TRAINER_IPS" not in os.environ:
+            self.nccl_id_var = None
+        else:
+            self.trainer_id = int(os.getenv("PADDLE_TRAINER_ID"))
+            port = os.getenv("PADDLE_PSERVER_PORT")
+            worker_ips = os.getenv("PADDLE_TRAINER_IPS")
+            worker_endpoints = []
+            for ip in worker_ips.split(","):
+                worker_endpoints.append(':'.join([ip, port]))
+            self.num_trainers = len(worker_endpoints)
+            current_endpoint = os.getenv("POD_IP") + ":" + port
+            worker_endpoints.remove(current_endpoint)
+            # TODO(wuyi): use self.nccl_id_var, self.num_trainers and self.trainer_id
+            # in ParallelExecutor to start
+            # distributed training using NCCL2
+            self.nccl_id_var = self.startup_program.global_block().create_var(
+                name="NCCLID", persistable=True, type=core.VarDesc.VarType.RAW)
+            self.startup_program.global_block().append_op(
+                type="gen_nccl_id",
+                inputs={},
+                outputs={"NCCLID": self.nccl_id_var},
+                attrs={
+                    "endpoint": current_endpoint,
+                    "endpoint_list": worker_endpoints,
+                    "trainer_id": self.trainer_id
+                })
+
     def _dist_transpile_if_necessary(self, optimize_ops, params_grads):
+        self._transpile_nccl2_dist()
+        if self.nccl_id_var != None:
+            return
+
         if "PADDLE_TRAINING_ROLE" not in os.environ:
             return
 
@@ -169,12 +211,13 @@ class Trainer(object):
                     'TRAINING_ROLE environment variable must be either TRAINER or PSERVER'
                 )
 
-    def train(self,
-              num_epochs,
-              event_handler,
-              reader,
-              feed_order,
-              parallel=False):
+    def stop(self):
+        """
+        stop training
+        """
+        self.__stop = True
+
+    def train(self, num_epochs, event_handler, reader=None, feed_order=None):
         """
         Train the model.
 
@@ -182,25 +225,24 @@ class Trainer(object):
             num_epochs: The number of epoch. An epoch will process all data in reader
             event_handler: The event handler. A function with type (ev:Event)->void
             reader:
-            parallel: True if use multi-CPUs or multi-GPUs
             feed_order: Feeding order of reader. None will following the defining
                 order in program
 
         Returns:
 
         """
-        if parallel:
-            raise NotImplementedError(
-                "Parallel Executor version of trainer is not implemented")
-
         training_role = os.getenv("PADDLE_TRAINING_ROLE", "")
         if training_role == "PSERVER":
             with self._prog_and_scope_guard():
                 exe = executor.Executor(self.place)
                 exe.run()
                 return
-
-        self._train_by_executor(num_epochs, event_handler, reader, feed_order)
+        if self.parallel:
+            self._train_by_parallel_executor(num_epochs, event_handler, reader,
+                                             feed_order)
+        else:
+            self._train_by_executor(num_epochs, event_handler, reader,
+                                    feed_order)
 
     def test(self, reader, feed_order):
         """
@@ -212,7 +254,8 @@ class Trainer(object):
                 order in program
         """
 
-        return self._test_by_executor(reader, feed_order, self.test_outputs)
+        return self._test_by_executor(reader, feed_order,
+                                      self.train_func_outputs)
 
     def save_params(self, param_path):
         # reference: save_persistables in io.py
@@ -246,12 +289,26 @@ class Trainer(object):
             feeder = data_feeder.DataFeeder(
                 feed_list=feed_var_list, place=self.place)
             exe = executor.Executor(self.place)
+            reader = feeder.decorate_reader(reader, multi_devices=False)
+            self._train_by_any_executor(event_handler, exe, num_epochs, reader)
+
+    def _train_by_any_executor(self, event_handler, exe, num_epochs, reader):
         for epoch_id in range(num_epochs):
             event_handler(BeginEpochEvent(epoch_id))
             for step_id, data in enumerate(reader()):
-                    event_handler(BeginStepEvent(epoch_id, step_id))
-                    exe.run(feed=feeder.feed(data), fetch_list=[])
-                    event_handler(EndStepEvent(epoch_id, step_id))
+                if self.__stop:
+                    return
+                begin_event = BeginStepEvent(epoch_id, step_id)
+                event_handler(begin_event)
+                if begin_event.fetch_metrics:
+                    metrics = exe.run(feed=data,
+                                      fetch_list=[
+                                          var.name
+                                          for var in self.train_func_outputs
+                                      ])
+                else:
+                    metrics = exe.run(feed=data, fetch_list=[])
+                event_handler(EndStepEvent(epoch_id, step_id, metrics))
             event_handler(EndEpochEvent(epoch_id))
 
     def _test_by_executor(self, reader, feed_order, fetch_list):
@@ -271,6 +328,26 @@ class Trainer(object):
 
             return [x / count for x in accumulated]
 
+    def _train_by_parallel_executor(self, num_epochs, event_handler, reader,
+                                    feed_order):
+        with self._prog_and_scope_guard():
+            pe = self._get_or_create_parallel_executor()
+            feed_var_list = build_feed_var_list(self.train_program, feed_order)
+            feeder = data_feeder.DataFeeder(
+                feed_list=feed_var_list, place=self.place)
+            reader = feeder.decorate_reader(reader, multi_devices=True)
+            self._train_by_any_executor(event_handler, pe, num_epochs, reader)
+
+    def _get_parallel_executor(self):
+        return getattr(self, 'parallel_executor', None)
+
+    def _get_or_create_parallel_executor(self):
+        if self._get_parallel_executor() is None:
+            self.parallel_executor = parallel_executor.ParallelExecutor(
+                use_cuda=isinstance(self.place, core.CUDAPlace),
+                loss_name=self.train_func_outputs[0].name)
+        return self._get_parallel_executor()
+
 
 def build_feed_var_list(program, feed_order):
     if not isinstance(program, framework.Program):

tools/test_runner.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 os
+import sys
+import paddle.fluid as fluid
+import importlib
+import cStringIO
+
+
+def main():
+    sys.path.append(os.getcwd())
+    some_test_failed = False
+    for module_name in sys.argv[1:]:
+        buffer = cStringIO.StringIO()
+        main = fluid.Program()
+        startup = fluid.Program()
+        scope = fluid.core.Scope()
+        with fluid.program_guard(main, startup):
+            with fluid.scope_guard(scope):
+                with fluid.unique_name.guard():
+                    test_loader = unittest.TestLoader()
+                    module = importlib.import_module(module_name)
+                    tests = test_loader.loadTestsFromModule(module)
+                    res = unittest.TextTestRunner(stream=buffer).run(tests)
+                    if not res.wasSuccessful():
+                        some_test_failed = True
+                        print >> sys.stderr, module_name, 'failed\n', buffer.getvalue(
+                        )
+
+    if some_test_failed:
+        exit(1)
+
+
+if __name__ == '__main__':
+    main()

tools/timeline.py

@@ -171,7 +171,7 @@ if args.timeline_path:
 
 profile_paths = profile_path.split(',')
 profile_dict = dict()
-if len(profile_path) == 1:
+if len(profile_paths) == 1:
     with open(profile_path, 'r') as f:
         profile_s = f.read()
         profile_pb = profiler_pb2.Profile()