merge develop

.travis.yml

@@ -27,15 +27,6 @@ script:
     # 43min timeout
     paddle/scripts/paddle_docker_build.sh ${JOB}
     if [ $? -eq 0 ] || [ $? -eq 142 ]; then true; else exit 1; fi;
-  - |
-    if [[ "$JOB" != "doc" ]]; then exit 0; fi;
-    # For document only
-    if [[ "$TRAVIS_PULL_REQUEST" != "false" ]]; then exit 0; fi;
-    if [[ "$TRAVIS_BRANCH" != "develop"  && ! "$TRAVIS_BRANCH" =~ ^v|release/[[:digit:]]+\.[[:digit:]]+(\.[[:digit:]]+)?(-\S*)?$ ]]; then exit 0; fi;
-    export DEPLOY_DOCS_SH=https://raw.githubusercontent.com/PaddlePaddle/PaddlePaddle.org/master/scripts/deploy/deploy_docs.sh
-    export DOCS_DIR=`pwd`
-    cd ..
-    curl $DEPLOY_DOCS_SH | bash -s $CONTENT_DEC_PASSWD $TRAVIS_BRANCH $DOCS_DIR $DOCS_DIR/build/doc/
 notifications:
   email:
     on_success: change

CMakeLists.txt

@@ -200,6 +200,14 @@ include(external/snappy)    # download snappy
 include(external/snappystream)
 include(external/threadpool)
 
+if(WITH_GPU)
+    include(cuda)
+    include(tensorrt)
+    include(external/anakin)
+else()
+  set(WITH_ANAKIN OFF CACHE STRING "Anakin is valid only when GPU is set." FORCE)
+endif()
+
 include(cudnn)              # set cudnn libraries, must before configure
 include(cupti)
 include(configure)          # add paddle env configuration
@@ -228,14 +236,6 @@ set(EXTERNAL_LIBS
     ${PYTHON_LIBRARIES}
 )
 
-if(WITH_GPU)
-    include(cuda)
-    include(tensorrt)
-    include(external/anakin)
-else()
-  set(WITH_ANAKIN OFF CACHE STRING "Anakin is valid only when GPU is set." FORCE)
-endif()
-
 if(WITH_AMD_GPU)
     find_package(HIP)
     include(hip)

cmake/cudnn.cmake

@@ -21,6 +21,7 @@ list(APPEND CUDNN_CHECK_LIBRARY_DIRS
     ${CUDNN_ROOT}/lib64
     ${CUDNN_ROOT}/lib
     ${CUDNN_ROOT}/lib/${TARGET_ARCH}-linux-gnu
+    ${CUDNN_ROOT}/local/cuda-${CUDA_VERSION}/targets/${TARGET_ARCH}-linux/lib/
     $ENV{CUDNN_ROOT}
     $ENV{CUDNN_ROOT}/lib64
     $ENV{CUDNN_ROOT}/lib

cmake/generic.cmake

@@ -264,7 +264,10 @@ function(cc_test TARGET_NAME)
              WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR})
     if (${cc_test_SERIAL})
         set_property(TEST ${TARGET_NAME} PROPERTY RUN_SERIAL 1)
+
+    set_property(TEST ${TARGET_NAME} PROPERTY ENVIRONMENT FLAGS_cpu_deterministic=true)
     set_property(TEST ${TARGET_NAME} PROPERTY ENVIRONMENT FLAGS_init_allocated_mem=true)
+    set_property(TEST ${TARGET_NAME} PROPERTY ENVIRONMENT FLAGS_cudnn_deterministic=true)
     endif()
   endif()
 endfunction(cc_test)
@@ -329,7 +332,10 @@ function(nv_test TARGET_NAME)
     add_test(${TARGET_NAME} ${TARGET_NAME})
     if (nv_test_SERIAL)
         set_property(TEST ${TARGET_NAME} PROPERTY RUN_SERIAL 1)
+
+    set_property(TEST ${TARGET_NAME} PROPERTY ENVIRONMENT FLAGS_cpu_deterministic=true)
     set_property(TEST ${TARGET_NAME} PROPERTY ENVIRONMENT FLAGS_init_allocated_mem=true)
+    set_property(TEST ${TARGET_NAME} PROPERTY ENVIRONMENT FLAGS_cudnn_deterministic=true)
     endif()
   endif()
 endfunction(nv_test)
@@ -577,7 +583,9 @@ function(py_test TARGET_NAME)
     set(multiValueArgs SRCS DEPS ARGS ENVS)
     cmake_parse_arguments(py_test "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
     add_test(NAME ${TARGET_NAME}
-             COMMAND env FLAGS_init_allocated_mem=true PYTHONPATH=${PADDLE_BINARY_DIR}/python ${py_test_ENVS}
+             COMMAND env FLAGS_init_allocated_mem=true FLAGS_cudnn_deterministic=true
+             FLAGS_cpu_deterministic=true
+             PYTHONPATH=${PADDLE_BINARY_DIR}/python ${py_test_ENVS}
              ${PYTHON_EXECUTABLE} -u ${py_test_SRCS} ${py_test_ARGS}
              WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR})
   endif()

doc/fluid/howto/optimization/timeline_cn.md

 # 如何使用timeline工具做性能分析
 
-1. 在训练的主循环外加上`with profiler.profiler(...)`。运行之后，代码会在`/tmp/profile`目录下生成一个profile的记录文件。
+1. 在训练的主循环外加上`profiler.start_profiler(...)`和`profiler.stop_profiler(...)`。运行之后，代码会在`/tmp/profile`目录下生成一个profile的记录文件。
 
 	**提示：**
 	请不要在timeline记录信息时运行太多次迭代，因为timeline中的记录数量和迭代次数是成正比的。
 
 	```python
-	with profiler.profiler('All', 'total', '/tmp/profile') as prof:
-	    for pass_id in range(pass_num):
-	        for batch_id, data in enumerate(train_reader()):
-	            exe.run(fluid.default_main_program(),
-	                    feed=feeder.feed(data),
-	                    fetch_list=[])
+    for pass_id in range(pass_num):
+        for batch_id, data in enumerate(train_reader()):
+            if pass_id == 0 and batch_id == 5:
+                profiler.start_profiler("All")
+            elif pass_id == 0 and batch_id == 10:
+                profiler.stop_profiler("total", "/tmp/profile")
+            exe.run(fluid.default_main_program(),
+                    feed=feeder.feed(data),
+                    fetch_list=[])
 	            ...
 	```
 
 1. 运行`python paddle/tools/timeline.py`来处理`/tmp/profile`，这个程序默认会生成一个`/tmp/timeline`文件，你也可以用命令行参数来修改这个路径，请参考[timeline.py](https://github.com/PaddlePaddle/Paddle/blob/develop/tools/timeline.py)。
+```python
+python Paddle/tools/timeline.py --profile_path=/tmp/profile --timeline_path=timeline
+```
 
 1. 打开chrome浏览器，访问<chrome://tracing/>，用`load`按钮来加载生成的`timeline`文件。
 

doc/fluid/howto/optimization/timeline_en.md

 # how to use timeline tool to do profile
 
-1. Add `with profiler.profiler(...)` to the main training loop. After run, the code will generate a profile record file `/tmp/profile`. **Warning**: Please do not run too many batches when use profiler to record timeline information, for the profile record will grow with the batch number.
+1. Add `profiler.start_profiler(...)`和`profiler.stop_profiler(...)` to the main training loop. After run, the code will generate a profile record file `/tmp/profile`. **Warning**: Please do not run too many batches when use profiler to record timeline information, for the profile record will grow with the batch number.
 
 	```python
-	with profiler.profiler('All', 'total', '/tmp/profile') as prof:
-	    for pass_id in range(pass_num):
-	        for batch_id, data in enumerate(train_reader()):
-	            exe.run(fluid.default_main_program(),
-	                    feed=feeder.feed(data),
-	                    fetch_list=[],
-	                    use_program_cache=True)
+    for pass_id in range(pass_num):
+        for batch_id, data in enumerate(train_reader()):
+            if pass_id == 0 and batch_id == 5:
+                profiler.start_profiler("All")
+            elif pass_id == 0 and batch_id == 10:
+                profiler.stop_profiler("total", "/tmp/profile")
+            exe.run(fluid.default_main_program(),
+                    feed=feeder.feed(data),
+                    fetch_list=[])
 	            ...
 	```
 
@@ -17,6 +19,10 @@
 file `/tmp/timeline` by default. You can change the path by cmd parameter, please take a look at
 [timeline.py](https://github.com/PaddlePaddle/Paddle/blob/develop/tools/timeline.py) for details.
 
+```python
+python Paddle/tools/timeline.py --profile_path=/tmp/profile --timeline_path=timeline
+```
+
 1. Open chrome and visit <chrome://tracing/>, use `load` button to load the generated `timeline` file.
 
 	![chrome tracing](./tracing.jpeg)

paddle/fluid/framework/details/all_reduce_op_handle.cc

@@ -17,6 +17,7 @@
 #include "paddle/fluid/framework/details/container_cast.h"
 #include "paddle/fluid/framework/details/reduce_and_gather.h"
 #include "paddle/fluid/framework/details/variable_visitor.h"
+#include "paddle/fluid/platform/profiler.h"
 
 namespace paddle {
 namespace framework {
@@ -45,6 +46,7 @@ AllReduceOpHandle::AllReduceOpHandle(ir::Node *node,
 #endif
 
 void AllReduceOpHandle::RunImpl() {
+  platform::RecordEvent r("all_reduce", nullptr);
   if (NoDummyInputSize() == 1) {
     return;  // No need to all reduce when GPU count = 1;
   } else {

paddle/fluid/framework/details/build_strategy.h

@@ -21,6 +21,26 @@ namespace framework {
 namespace details {
 
 struct BuildStrategy {
+  // ParallelExecutor supports two modes of ReduceStrategy, kAllReduce and
+  // kReduce, for CPU and GPU. If you use kAllReduce, different threads
+  // optimize their parameters separately. If you use kReduce, the optimizations
+  // of parameters are distributed to different threads.
+  // For example, a model has 100 parameters and is running with four threads,
+  // if you choose kAllReduce, every thread is to optimize 100 parameters
+  // separately, if you choose kReduce, every thread is to optimize 25
+  // parameters.
+  // Of particular note is, if you use kReduce when using CPU training,
+  // all the parameters are shared between different threads. This feature will
+  // save memory.
+  // FIXME(zcd): The result of the two modes(kAllReduce and kReduce) maybe not
+  // equal for GPU. Because, the result of the different order of summing maybe
+  // different, for example, the result of `a+b+c+d` may be different with the
+  // result of `c+a+b+d`.
+  // For GPU, the implementation of kAllReduce and kReduce is adopted NCCL,
+  // so the result of kAllReduce and kReduce maybe not equal.
+  // For CPU, if you want to fix the order of summing to make the result
+  // of kAllReduce and kReduce no diff, you can add
+  // `FLAGS_cpu_deterministic=true` to env.
   enum class ReduceStrategy { kAllReduce = 0, kReduce = 1 };
 
   enum class GradientScaleStrategy {

paddle/fluid/framework/details/reduce_op_handle.cc

@@ -16,12 +16,18 @@
 #include "paddle/fluid/framework/details/container_cast.h"
 #include "paddle/fluid/framework/details/reduce_and_gather.h"
 #include "paddle/fluid/framework/details/variable_visitor.h"
+#include "paddle/fluid/platform/profiler.h"
+
+DEFINE_bool(
+    cpu_deterministic, false,
+    "Whether to make the result of computation deterministic in CPU side.");
 
 namespace paddle {
 namespace framework {
 namespace details {
 
 void ReduceOpHandle::RunImpl() {
+  platform::RecordEvent r("reduce", nullptr);
   if (places_.size() == 1) return;
   // the input and output may have dummy var.
   auto in_var_handles = DynamicCast<VarHandle>(inputs_);
@@ -89,11 +95,33 @@ void ReduceOpHandle::RunImpl() {
   } else {
     std::vector<const LoDTensor *> lod_tensors =
         GetInputValues<LoDTensor>(in_var_handles, var_scopes);
+
     if (paddle::platform::is_cpu_place(lod_tensors[0]->place())) {
       this->RunAndRecordEvent([&] {
-        ReduceLoDTensor func(lod_tensors,
-                             out_var->GetMutable<framework::LoDTensor>());
-        VisitDataType(ToDataType(lod_tensors[0]->type()), func);
+        // FIXME(zcd): The order of summing is important,
+        // especially when the type of data is float or double.
+        // For example, the result of `a+b+c+d` may be different
+        // with the result of `c+a+b+d`, so the summing order should be fixed.
+        if (!FLAGS_cpu_deterministic) {
+          ReduceLoDTensor func(lod_tensors,
+                               out_var->GetMutable<framework::LoDTensor>());
+          VisitDataType(ToDataType(lod_tensors[0]->type()), func);
+        } else {
+          // We sum lod_tensors to reduce_sum_trg which is in local_scopes_0
+          // here, but it doesn't mean reduce_sum_trg must be in local_scopes_0.
+          auto &reduce_sum_trg = *this->local_scopes_[0]
+                                      ->FindVar(kLocalExecScopeName)
+                                      ->Get<Scope *>()
+                                      ->FindVar(out_var_handle->name_)
+                                      ->GetMutable<framework::LoDTensor>();
+          ReduceLoDTensor func(lod_tensors, &reduce_sum_trg);
+          VisitDataType(ToDataType(lod_tensors[0]->type()), func);
+
+          auto trg = out_var->GetMutable<framework::LoDTensor>();
+          if (reduce_sum_trg.data<void>() != trg->data<void>()) {
+            TensorCopy(reduce_sum_trg, platform::CPUPlace(), trg);
+          }
+        }
       });
     } else if (paddle::platform::is_gpu_place(lod_tensors[0]->place())) {
 #ifdef PADDLE_WITH_CUDA

paddle/fluid/framework/details/scope_buffered_ssa_graph_executor.cc

@@ -17,6 +17,7 @@
 #include <string>
 #include <vector>
 #include "paddle/fluid/framework/executor.h"
+#include "paddle/fluid/platform/profiler.h"
 
 namespace paddle {
 namespace framework {
@@ -62,6 +63,7 @@ FeedFetchList ScopeBufferedSSAGraphExecutor::Run(
     eptr = std::current_exception();
   }
 
+  platform::RecordEvent e("ScopeBufferedSSAGraphExecutorAfterRun", nullptr);
   drop_scope_counter_ += 1;
   if (!fetch_tensors.empty() ||
       drop_scope_counter_ == strategy_.num_iteration_per_drop_scope_) {

paddle/fluid/framework/details/threaded_ssa_graph_executor.cc

@@ -15,6 +15,7 @@
 #include "paddle/fluid/framework/details/threaded_ssa_graph_executor.h"
 
 #include "paddle/fluid/framework/details/ssa_graph_builder.h"
+#include "paddle/fluid/platform/profiler.h"
 
 namespace paddle {
 namespace framework {
@@ -34,6 +35,8 @@ ThreadedSSAGraphExecutor::ThreadedSSAGraphExecutor(
 
 FeedFetchList ThreadedSSAGraphExecutor::Run(
     const std::vector<std::string> &fetch_tensors) {
+  std::unique_ptr<platform::RecordEvent> event(
+      new platform::RecordEvent("ThreadedSSAGraphExecutorPrepare", nullptr));
   std::unordered_map<OpHandleBase *, size_t> pending_ops;
   std::unordered_set<VarHandleBase *> pending_vars;
   BlockingQueue<VarHandleBase *> ready_vars;
@@ -84,6 +87,7 @@ FeedFetchList ThreadedSSAGraphExecutor::Run(
   // Clean run context
   run_op_futures_.clear();
   exception_holder_.Clear();
+  event.reset(nullptr);
 
   // Step 3. Execution
   while (!pending_vars.empty()) {

paddle/fluid/framework/operator.cc

@@ -18,6 +18,7 @@ limitations under the License. */
 
 #include "paddle/fluid/framework/data_transform.h"
 #include "paddle/fluid/framework/executor.h"
+#include "paddle/fluid/framework/lod_tensor.h"
 #include "paddle/fluid/framework/operator.h"
 #include "paddle/fluid/framework/shape_inference.h"
 #include "paddle/fluid/framework/var_type.h"
@@ -57,7 +58,11 @@ static DDim GetDims(const Scope& scope, const std::string& name,
   }
 
   if (var->IsType<LoDTensor>()) {
-    return var->Get<LoDTensor>().dims();
+    const LoDTensor& tensor = var->Get<LoDTensor>();
+    if (UNLIKELY(!tensor.IsInitialized())) {
+      return DDim({-1});
+    }
+    return tensor.dims();
   } else if (var->IsType<SelectedRows>()) {
     if (get_actual_dim) {
       return var->Get<SelectedRows>().value().dims();
@@ -74,8 +79,13 @@ static std::string GetDtype(const Scope& scope, const std::string& name) {
   if (var == nullptr) {
     return "";
   }
+
   if (var->IsType<LoDTensor>()) {
-    return DataTypeToString(ToDataType(var->Get<LoDTensor>().type()));
+    const LoDTensor& tensor = var->Get<LoDTensor>();
+    if (UNLIKELY(!tensor.IsInitialized())) {
+      return "";
+    }
+    return DataTypeToString(ToDataType(tensor.type()));
   } else if (var->IsType<SelectedRows>()) {
     return DataTypeToString(
         ToDataType(var->Get<SelectedRows>().value().type()));
@@ -106,7 +116,11 @@ static LoD GetLoD(const Scope& scope, const std::string& name) {
   }
 
   if (var->IsType<LoDTensor>()) {
-    return var->Get<LoDTensor>().lod();
+    const LoDTensor& tensor = var->Get<LoDTensor>();
+    if (UNLIKELY(!tensor.IsInitialized())) {
+      return default_lod;
+    }
+    return tensor.lod();
   } else {
     return default_lod;
   }
@@ -122,6 +136,8 @@ void OperatorBase::Run(const Scope& scope, const platform::Place& place) {
     platform::SetDeviceId(dev_id);
 #endif
   }
+  platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
+  platform::RecordEvent record_event(Type(), pool.Get(place));
   RunImpl(scope, place);
   VLOG(10) << "+ " << DebugStringEx(&scope);
 }
@@ -625,9 +641,6 @@ void OperatorWithKernel::RunImpl(const Scope& scope,
   platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
   auto* dev_ctx = pool.Get(place);
 
-  // For profiling, don't move out of this function because that will result
-  // in the failure of multi-GPU profiling.
-  platform::RecordEvent record_event(Type(), dev_ctx);
   // check if op[type] has kernel registered.
   auto& all_op_kernels = AllOpKernels();
   auto kernels_iter = all_op_kernels.find(type_);

paddle/fluid/framework/tensor.h

@@ -82,7 +82,7 @@ class Tensor {
   template <typename T>
   const T* data() const;
 
-  bool IsInitialized() const;
+  inline bool IsInitialized() const;
 
   /**
    * @brief   Return a pointer to mutable memory block.

paddle/fluid/inference/api/CMakeLists.txt

@@ -74,9 +74,10 @@ if (WITH_ANAKIN) # only needed in CI
     target_link_libraries(inference_anakin_api anakin anakin_saber_common)
     target_link_libraries(inference_anakin_api_shared anakin anakin_saber_common)
     if (WITH_TESTING)
-        cc_test(inference_anakin_test SRCS api_anakin_engine_tester.cc
-                                  ARGS --model=${ANAKIN_INSTALL_DIR}/mobilenet_v2.anakin.bin
-                                  DEPS inference_anakin_api_shared)
-        target_compile_options(inference_anakin_test BEFORE PUBLIC ${ANAKIN_COMPILE_EXTRA_FLAGS})
+        # this test is unstable, disable it first.
+        #cc_test(inference_anakin_test SRCS api_anakin_engine_tester.cc
+                                  #ARGS --model=${ANAKIN_INSTALL_DIR}/mobilenet_v2.anakin.bin
+                                  #DEPS inference_anakin_api_shared)
+        #target_compile_options(inference_anakin_test BEFORE PUBLIC ${ANAKIN_COMPILE_EXTRA_FLAGS})
      endif(WITH_TESTING)
 endif()

paddle/fluid/operators/feed_op.cc

@@ -31,7 +31,6 @@ class FeedOp : public framework::OperatorBase {
                const platform::Place &place) const override {
     // get device context from pool
     auto *dev_ctx = platform::DeviceContextPool::Instance().Get(place);
-    platform::RecordEvent record_event(Type(), dev_ctx);
 
     auto feed_var_name = Input("X");
     auto *feed_var = scope.FindVar(feed_var_name);

paddle/fluid/operators/fetch_barrier_op.cc

@@ -36,12 +36,6 @@ class FetchBarrierOp : public framework::OperatorBase {
   void RunImpl(const framework::Scope& scope,
                const platform::Place& place) const override {
     std::vector<std::string> eps = Attr<std::vector<std::string>>("endpoints");
-
-    platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
-    auto& ctx = *pool.Get(place);
-    // For profiling
-    platform::RecordEvent record_event(Type(), &ctx);
-
     distributed::RPCClient* rpc_client =
         distributed::RPCClient::GetInstance<RPCCLIENT_T>();
 

paddle/fluid/operators/fetch_op.cc

@@ -30,9 +30,6 @@ class FetchOp : public framework::OperatorBase {
  private:
   void RunImpl(const framework::Scope &scope,
                const platform::Place &place) const override {
-    platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
-    platform::RecordEvent record_event(Type(), pool.Get(place));
-
     auto fetch_var_name = Input("X");
     auto *fetch_var = scope.FindVar(fetch_var_name);
     PADDLE_ENFORCE(fetch_var != nullptr,

paddle/fluid/operators/load_op.cc

@@ -31,9 +31,6 @@ class LoadOp : public framework::OperatorBase {
  private:
   void RunImpl(const framework::Scope &scope,
                const platform::Place &place) const override {
-    auto *dev_ctx = platform::DeviceContextPool::Instance().Get(place);
-    platform::RecordEvent record_event(Type(), dev_ctx);
-
     // FIXME(yuyang18): We save variable to local file now, but we should change
     // it to save an output stream.
     auto filename = Attr<std::string>("file_path");

paddle/fluid/operators/lookup_table_op.cc

@@ -32,11 +32,16 @@ class LookupTableOp : public framework::OperatorWithKernel {
 
     auto table_dims = ctx->GetInputDim("W");
     auto ids_dims = ctx->GetInputDim("Ids");
+    int ids_rank = ids_dims.size();
 
-    PADDLE_ENFORCE_EQ(ids_dims.size(), 2);
-    PADDLE_ENFORCE_EQ(ids_dims[1], 1);
+    PADDLE_ENFORCE_EQ(table_dims.size(), 2);
+    PADDLE_ENFORCE_EQ(ids_dims[ids_rank - 1], 1,
+                      "The last dimension of the 'Ids' tensor must be 1.");
 
-    ctx->SetOutputDim("Out", {ids_dims[0], table_dims[1]});
+    auto output_dims =
+        framework::vectorize(framework::slice_ddim(ids_dims, 0, ids_rank - 1));
+    output_dims.push_back(table_dims[1]);
+    ctx->SetOutputDim("Out", framework::make_ddim(output_dims));
 
     if (ctx->GetOutputsVarType("Out")[0] ==
         framework::proto::VarType::LOD_TENSOR) {
@@ -61,8 +66,7 @@ class LookupTableOpMaker : public framework::OpProtoAndCheckerMaker {
     AddInput("Ids",
              "An input with type int32 or int64 "
              "contains the ids to be looked up in W. "
-             "Ids must be a column vector with rank = 2. "
-             "The 2nd dimension size must be 1.");
+             "The last dimension size must be 1.");
     AddOutput("Out", "The lookup results, which have the same type as W.");
     AddAttr<bool>("is_sparse",
                   "(boolean, default false) "

paddle/fluid/operators/lookup_table_op.cu

@@ -118,28 +118,31 @@ class LookupTableGradCUDAKernel : public framework::OpKernel<T> {
       auto *d_table = context.Output<SelectedRows>(framework::GradVarName("W"));
 
       auto *ids_data = ids->data<int64_t>();
-      auto ids_dim = ids->dims();
+      int64_t ids_num = ids->numel();
 
       auto stream = dev_ctx.stream();
       // copy GPU memory to CPU pinned memory
       framework::Vector<int64_t> new_rows;
-      new_rows.resize(ids_dim[0]);
+      new_rows.resize(ids_num);
       auto gpu_place = boost::get<platform::CUDAPlace>(context.GetPlace());
 
       // TODO(yuyang18): Strange code here.
       memory::Copy(platform::CPUPlace(),
                    new_rows.CUDAMutableData(context.GetPlace()), gpu_place,
-                   ids_data, ids_dim[0] * sizeof(int64_t), stream);
+                   ids_data, ids_num * sizeof(int64_t), stream);
 
       d_table->set_rows(new_rows);
 
       auto *d_table_value = d_table->mutable_value();
-      d_table_value->Resize({ids_dim[0], table->dims()[1]});
+      d_table_value->Resize({ids_num, table->dims()[1]});
       d_table_value->mutable_data<T>(context.GetPlace());
 
       auto *d_table_data = d_table_value->data<T>();
       auto *d_output_data = d_output->data<T>();
-      PADDLE_ENFORCE_EQ(d_table_value->dims(), d_output->dims());
+      auto d_output_dims = d_output->dims();
+      PADDLE_ENFORCE_EQ(
+          d_table_value->dims(),
+          framework::flatten_to_2d(d_output_dims, d_output_dims.size() - 1));
       memory::Copy(gpu_place, d_table_data, gpu_place, d_output_data,
                    d_output->numel() * sizeof(T), stream);
 

paddle/fluid/operators/lookup_table_op.h

@@ -109,17 +109,17 @@ class LookupTableGradKernel : public framework::OpKernel<T> {
       auto *d_table = context.Output<SelectedRows>(framework::GradVarName("W"));
 
       auto *ids_data = ids->data<int64_t>();
-      auto ids_dim = ids->dims();
+      int64_t ids_num = ids->numel();
 
       framework::Vector<int64_t> new_rows;
-      new_rows.reserve(ids_dim[0]);
-      for (int64_t i = 0; i < ids_dim[0]; i++) {
+      new_rows.reserve(ids_num);
+      for (int64_t i = 0; i < ids_num; i++) {
         new_rows.push_back(ids_data[i]);
       }
       d_table->set_rows(new_rows);
 
       auto *d_table_value = d_table->mutable_value();
-      d_table_value->Resize({ids_dim[0], table_dim[1]});
+      d_table_value->Resize({ids_num, table_dim[1]});
       d_table_value->mutable_data<T>(context.GetPlace());
 
       d_table->set_height(table_dim[0]);
@@ -127,7 +127,10 @@ class LookupTableGradKernel : public framework::OpKernel<T> {
       auto *d_output_data = d_output->data<T>();
       auto *d_table_data = d_table_value->data<T>();
 
-      PADDLE_ENFORCE_EQ(d_table_value->dims(), d_output->dims());
+      auto d_output_dims = d_output->dims();
+      PADDLE_ENFORCE_EQ(
+          d_table_value->dims(),
+          framework::flatten_to_2d(d_output_dims, d_output_dims.size() - 1));
       memcpy(d_table_data, d_output_data, sizeof(T) * d_output->numel());
     } else {
       auto *ids = context.Input<LoDTensor>("Ids");
@@ -135,10 +138,9 @@ class LookupTableGradKernel : public framework::OpKernel<T> {
       auto *d_table = context.Output<LoDTensor>(framework::GradVarName("W"));
 
       auto *ids_data = ids->data<int64_t>();
-      auto ids_dim = ids->dims();
 
       int N = table_dim[0];
-      int D = d_output->dims()[1];
+      int D = table_dim[1];
 
       auto *d_output_data = d_output->data<T>();
       auto *d_table_data = d_table->mutable_data<T>(context.GetPlace());

paddle/fluid/operators/parallel_do_op.cc

@@ -18,7 +18,6 @@ limitations under the License. */
 #include "paddle/fluid/framework/op_registry.h"
 #include "paddle/fluid/framework/threadpool.h"
 #include "paddle/fluid/operators/detail/safe_ref.h"
-#include "paddle/fluid/platform/profiler.h"
 
 namespace paddle {
 namespace operators {
@@ -166,8 +165,6 @@ class ParallelDoOp : public framework::OperatorBase {
 
       workers.emplace_back(
           framework::Async([program, cur_scope, place, block, place_idx] {
-            // Give the thread an id to distinguish parallel block with same id.
-            platform::RecordThread rt(static_cast<int>(place_idx) + 1);
             framework::Executor executor(place);
             executor.Run(*program, cur_scope, block->ID(),
                          false /*create_local_scope*/);
@@ -244,8 +241,6 @@ class ParallelDoGradOp : public framework::OperatorBase {
       // execute
       workers.emplace_back(
           framework::Async([program, cur_scope, place, block, i] {
-            // Give the thread an id to distinguish parallel block with same id.
-            platform::RecordThread rt(static_cast<int>(i) + 1);
             framework::Executor executor(place);
             executor.Run(*program, cur_scope, block->ID(),
                          false /*create_local_scope*/);

paddle/fluid/operators/read_op.cc

@@ -15,6 +15,7 @@
 #include "paddle/fluid/framework/op_registry.h"
 #include "paddle/fluid/framework/reader.h"
 #include "paddle/fluid/operators/detail/safe_ref.h"
+#include "paddle/fluid/platform/profiler.h"
 
 namespace paddle {
 namespace operators {
@@ -65,6 +66,12 @@ class ReadOp : public framework::OperatorBase {
             .GetMutable<framework::ReaderHolder>();
     std::vector<std::string> out_arg_names = Outputs("Out");
     std::vector<framework::LoDTensor> ins;
+
+    // For profiling
+    platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
+    auto& ctx = *pool.Get(dev_place);
+    platform::RecordEvent record_event(Type(), &ctx);
+
     reader->ReadNext(&ins);
     if (ins.empty()) {
       if (Attr<bool>("throw_eof_exp")) {

paddle/fluid/operators/recv_op.cc

@@ -40,8 +40,6 @@ class RecvOp : public framework::OperatorBase {
 
     platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
     auto& ctx = *pool.Get(place);
-    // For profiling
-    platform::RecordEvent record_event(Type(), &ctx);
 
     distributed::RPCClient* rpc_client =
         distributed::RPCClient::GetInstance<RPCCLIENT_T>();

paddle/fluid/operators/send_barrier_op.cc

@@ -39,11 +39,6 @@ class SendBarrierOp : public framework::OperatorBase {
     std::vector<std::string> eps = Attr<std::vector<std::string>>("endpoints");
     bool sync_mode = Attr<bool>("sync_mode");
 
-    platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
-    auto& ctx = *pool.Get(place);
-    // For profiling
-    platform::RecordEvent record_event(Type(), &ctx);
-
     distributed::RPCClient* rpc_client =
         distributed::RPCClient::GetInstance<RPCCLIENT_T>();
 

paddle/fluid/operators/send_op.cc

@@ -42,9 +42,6 @@ class SendOp : public framework::OperatorBase {
     platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
     auto& ctx = *pool.Get(place);
 
-    // For profiling
-    platform::RecordEvent record_event(Type(), &ctx);
-
     distributed::RPCClient* rpc_client =
         distributed::RPCClient::GetInstance<RPCCLIENT_T>();
 

paddle/fluid/operators/softmax_cudnn_op.cu.cc

@@ -30,8 +30,16 @@ class SoftmaxCUDNNKernel : public framework::OpKernel<T> {
     // allocate memory on device.
     Out->mutable_data<T>(context.GetPlace());
 
+    auto dims = X->dims();
+    auto flattened_dims = framework::flatten_to_2d(dims, dims.size() - 1);
+    framework::LoDTensor flattened_x;
+    framework::LoDTensor flattened_out;
+    flattened_x.ShareDataWith(*X).Resize(flattened_dims);
+    flattened_out.ShareDataWith(*Out).Resize(flattened_dims);
+
     math::SoftmaxCUDNNFunctor<T>()(
-        context.template device_context<platform::CUDADeviceContext>(), X, Out);
+        context.template device_context<platform::CUDADeviceContext>(),
+        &flattened_x, &flattened_out);
   }
 };
 
@@ -46,9 +54,18 @@ class SoftmaxGradCUDNNKernel : public framework::OpKernel<T> {
     // allocate memory on device.
     dX->mutable_data<T>(context.GetPlace());
 
+    auto dims = Out->dims();
+    auto flattened_dims = framework::flatten_to_2d(dims, dims.size() - 1);
+    framework::LoDTensor flattened_out;
+    framework::LoDTensor flattened_d_out;
+    framework::LoDTensor flattened_d_x;
+    flattened_out.ShareDataWith(*Out).Resize(flattened_dims);
+    flattened_d_out.ShareDataWith(*dOut).Resize(flattened_dims);
+    flattened_d_x.ShareDataWith(*dX).Resize(flattened_dims);
+
     math::SoftmaxGradCUDNNFunctor<T>()(
-        context.template device_context<platform::CUDADeviceContext>(), Out,
-        dOut, dX);
+        context.template device_context<platform::CUDADeviceContext>(),
+        &flattened_out, &flattened_d_out, &flattened_d_x);
   }
 };
 

paddle/fluid/operators/softmax_mkldnn_op.cc

@@ -26,9 +26,9 @@ using paddle::platform::MKLDNNMemDesc;
 
 using mkldnn::memory;  // Note: paddle has also "memory" namespace
 using mkldnn::primitive;
-using mkldnn::softmax_forward;
-using mkldnn::softmax_backward;
 using mkldnn::prop_kind;
+using mkldnn::softmax_backward;
+using mkldnn::softmax_forward;
 using mkldnn::stream;
 using platform::to_void_cast;
 
@@ -113,17 +113,27 @@ class SoftmaxMKLDNNKernel : public paddle::framework::OpKernel<T> {
     auto mkldnn_engine = dev_ctx.GetEngine();
     const Tensor* input = ctx.Input<Tensor>("X");
     Tensor* output = ctx.Output<Tensor>("Out");
-    PADDLE_ENFORCE(input->dims().size() == 2UL,
-                   "The input of softmax op must be a 2D matrix.");
-    const T* input_data = input->data<T>();
-    // allocate memory for output
-    T* output_data = output->mutable_data<T>(ctx.GetPlace());
-    std::vector<int> src_tz = paddle::framework::vectorize2int(input->dims());
-    std::vector<int> dst_tz = paddle::framework::vectorize2int(output->dims());
-    // MKL-DNN does support softmax over selected axis. Having 2D Tensor,
-    // we will make normalization after final eg. axis: 1
-    PADDLE_ENFORCE(((src_tz[0] == dst_tz[0]) && (src_tz[1] == dst_tz[1])),
-                   "Softmax input and output dimensions should match");
+    PADDLE_ENFORCE_EQ(
+        input->dims(), output->dims(),
+        "The shape of softmax's input and output must be identical.");
+
+    // make sure 'output' holds memory, which will be shared by
+    // 'flattened_output' later.
+    output->mutable_data<T>(ctx.GetPlace());
+
+    // flatten input and output to 2-D matrixs
+    auto dims = input->dims();  // input and output share the same shape
+    auto flattened_dims = framework::flatten_to_2d(dims, dims.size() - 1);
+    framework::Tensor flattened_input;
+    framework::Tensor flattened_output;
+    flattened_input.ShareDataWith(*input).Resize(flattened_dims);
+    flattened_output.ShareDataWith(*output).Resize(flattened_dims);
+
+    const T* input_data = flattened_input.data<T>();
+    T* output_data = flattened_output.mutable_data<T>(ctx.GetPlace());
+
+    std::vector<int> src_tz = paddle::framework::vectorize2int(flattened_dims);
+    std::vector<int> dst_tz = src_tz;
     // Same memory descriptor to be used for input and output
     memory::dims softmax_tz = {src_tz[0], src_tz[1]};
     // Generate keys for storing/retriving primitives for this operator
@@ -174,23 +184,34 @@ class SoftmaxMKLDNNGradKernel : public paddle::framework::OpKernel<T> {
     auto& dev_ctx = ctx.template device_context<MKLDNNDeviceContext>();
     auto mkldnn_engine = dev_ctx.GetEngine();
     const Tensor* output = ctx.Input<Tensor>("Out");
-    const T* dst_data = output->data<T>();
-
     auto* dout = ctx.template Input<Tensor>(framework::GradVarName("Out"));
-    const auto* diff_dst_ptr = dout->template data<T>();
-
     auto* dx =
         ctx.template Output<framework::Tensor>(framework::GradVarName("X"));
-    T* diff_src_ptr = dx->template mutable_data<T>(ctx.GetPlace());
 
-    std::vector<int> dst_tz = paddle::framework::vectorize2int(output->dims());
+    PADDLE_ENFORCE_EQ(
+        dout->dims(), dx->dims(),
+        "The shape of softmax_grad's input and output must be identical.");
+
+    // make sure 'dx' holds memory, which will be shared by 'flattened_dx'
+    // later.
+    dx->template mutable_data<T>(ctx.GetPlace());
+
+    auto dims = dout->dims();  // input and output share the same shape
+    auto flattened_dims = framework::flatten_to_2d(dims, dims.size() - 1);
+    framework::Tensor flattened_output;
+    framework::Tensor flattened_dout;
+    framework::Tensor flattened_dx;
+    flattened_output.ShareDataWith(*output).Resize(flattened_dims);
+    flattened_dout.ShareDataWith(*dout).Resize(flattened_dims);
+    flattened_dx.ShareDataWith(*dx).Resize(flattened_dims);
+
+    const T* dst_data = flattened_output.data<T>();
+    const T* diff_dst_ptr = flattened_dout.template data<T>();
+    T* diff_src_ptr = flattened_dx.template mutable_data<T>(ctx.GetPlace());
+
+    std::vector<int> dst_tz = paddle::framework::vectorize2int(flattened_dims);
     std::vector<int> src_tz(dst_tz);
-    PADDLE_ENFORCE(output->dims().size() == 2UL,
-                   "The input of softmax op must be a 2D matrix.");
-    // MKL-DNN does support softmax over selected axis. Having 2D Tensor,
-    // we will make normalization after final eg. axis: 1
-    PADDLE_ENFORCE(((src_tz[0] == dst_tz[0]) && (src_tz[1] == dst_tz[1])),
-                   "Softmax input and output dimensions should match");
+
     // Same memory descriptor to be used for input and output
     memory::dims softmax_tz = {src_tz[0], src_tz[1]};
     // Currently only supports NC data format

paddle/fluid/operators/softmax_op.cc

@@ -37,10 +37,7 @@ class SoftmaxOp : public framework::OperatorWithKernel {
     PADDLE_ENFORCE(ctx->HasOutput("Out"),
                    "Output(Out) of SoftmaxOp should not be null.");
 
-    auto x_dims = ctx->GetInputDim("X");
-    PADDLE_ENFORCE(x_dims.size() == 2UL,
-                   "The input of softmax op must be a matrix.");
-    ctx->SetOutputDim("Out", x_dims);
+    ctx->SetOutputDim("Out", ctx->GetInputDim("X"));
     ctx->ShareLoD("X", /*->*/ "Out");
   }
 
@@ -81,8 +78,8 @@ class SoftmaxOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
   void Make() override {
     AddInput("X",
-             "The input tensor of softmax. "
-             "2-D with shape [batch_size, input_feature_dimensions].");
+             "The input tensor of softmax, "
+             "whose last dimension is the input_feature_dimensions.");
     AddOutput("Out", "The normalized values with the same shape as X.")
         .Reuse("X");
     AddAttr<bool>(
@@ -105,20 +102,23 @@ class SoftmaxOpMaker : public framework::OpProtoAndCheckerMaker {
     AddComment(R"DOC(
 Softmax Operator.
 
-The input of the softmax operator is a 2-D tensor with shape N x K (N is the
-batch_size, K is the dimension of input feature). The output tensor has the
-same shape as the input tensor.
+The input of the softmax operator is a tensor of any rank. The output tensor 
+has the same shape as the input.
 
-For each row of the input tensor, the softmax operator squashes the
-K-dimensional vector of arbitrary real values to a K-dimensional vector of real
-values in the range [0, 1] that add up to 1.
+The input tensor will first be logically flattened to a 2-D matrix. The matrix's 
+second dimension(row length) is as same as the last dimension of the input 
+tensor, and the first dimension(column length) is the product of all other 
+dimensions of the input tensor. For each row of the matrix, the softmax operator 
+squashes the K-dimensional(K is the width of the matrix, which is also the size 
+of the input tensor's last dimension) vector of arbitrary real values to a 
+K-dimensional vector of real values in the range [0, 1] that add up to 1.
 It computes the exponential of the given dimension and the sum of exponential
 values of all the other dimensions in the K-dimensional vector input.
 Then the ratio of the exponential of the given dimension and the sum of
 exponential values of all the other dimensions is the output of the softmax
 operator.
 
-For each row $i$ and each column $j$ in Input(X), we have:
+For each row $i$ and each column $j$ in the matrix, we have:
     $$Out[i, j] = \frac{\exp(X[i, j])}{\sum_j(exp(X[i, j])}$$
 
 )DOC");

paddle/fluid/operators/softmax_op.h

@@ -31,8 +31,16 @@ class SoftmaxKernel : public framework::OpKernel<T> {
     // allocate memory on device.
     Out->mutable_data<T>(context.GetPlace());
 
+    auto dims = X->dims();
+    auto flattened_dims = framework::flatten_to_2d(dims, dims.size() - 1);
+    framework::LoDTensor flattened_x;
+    framework::LoDTensor flattened_out;
+    flattened_x.ShareDataWith(*X).Resize(flattened_dims);
+    flattened_out.ShareDataWith(*Out).Resize(flattened_dims);
+
     math::SoftmaxFunctor<DeviceContext, T>()(
-        context.template device_context<DeviceContext>(), X, Out);
+        context.template device_context<DeviceContext>(), &flattened_x,
+        &flattened_out);
   }
 };
 
@@ -47,8 +55,18 @@ class SoftmaxGradKernel : public framework::OpKernel<T> {
     // allocate memory on device.
     dX->mutable_data<T>(context.GetPlace());
 
+    auto dims = Out->dims();
+    auto flattened_dims = framework::flatten_to_2d(dims, dims.size() - 1);
+    framework::LoDTensor flattened_out;
+    framework::LoDTensor flattened_d_out;
+    framework::LoDTensor flattened_d_x;
+    flattened_out.ShareDataWith(*Out).Resize(flattened_dims);
+    flattened_d_out.ShareDataWith(*dOut).Resize(flattened_dims);
+    flattened_d_x.ShareDataWith(*dX).Resize(flattened_dims);
+
     math::SoftmaxGradFunctor<DeviceContext, T>()(
-        context.template device_context<DeviceContext>(), Out, dOut, dX);
+        context.template device_context<DeviceContext>(), &flattened_out,
+        &flattened_d_out, &flattened_d_x);
   }
 };
 

paddle/fluid/platform/device_tracer.cc

@@ -30,9 +30,6 @@ limitations under the License. */
 namespace paddle {
 namespace platform {
 namespace {
-// Current thread's id. Note, we don't distinguish nested threads
-// for now.
-thread_local int cur_thread_id = 0;
 // Tracking the nested block stacks of each thread.
 thread_local std::deque<int> block_id_stack;
 // Tracking the nested event stacks.
@@ -413,12 +410,5 @@ void SetCurBlock(int block_id) { block_id_stack.push_back(block_id); }
 void ClearCurBlock() { block_id_stack.pop_back(); }
 
 int BlockDepth() { return block_id_stack.size(); }
-
-void SetCurThread(int thread_id) { cur_thread_id = thread_id; }
-
-void ClearCurThread() { cur_thread_id = 0; }
-
-int CurThread() { return cur_thread_id; }
-
 }  // namespace platform
 }  // namespace paddle

paddle/fluid/platform/device_tracer.h

@@ -99,9 +99,5 @@ std::string CurAnnotation();
 void SetCurBlock(int block_id);
 void ClearCurBlock();
 int BlockDepth();
-
-void SetCurThread(int thread_id);
-void ClearCurThread();
-int CurThread();
 }  // namespace platform
 }  // namespace paddle

paddle/fluid/platform/profiler.cc

@@ -110,6 +110,8 @@ Event::Event(EventType type, std::string name, uint32_t thread_id,
   has_cuda_ = dev_ctx ? platform::is_gpu_place(dev_ctx->GetPlace()) : false;
   if (has_cuda_) {
     auto* cuda_dev_ctx = static_cast<const CUDADeviceContext*>(dev_ctx);
+    PADDLE_ENFORCE(cudaSetDevice(
+        boost::get<platform::CUDAPlace>(cuda_dev_ctx->GetPlace()).device));
     PADDLE_ENFORCE(cudaGetDevice(&device_));
     PADDLE_ENFORCE(cudaEventCreate(&event_));
     auto stream = cuda_dev_ctx->stream();
@@ -176,6 +178,7 @@ void PopEvent(const std::string& name, const DeviceContext* dev_ctx) {
 
 RecordEvent::RecordEvent(const std::string& name, const DeviceContext* dev_ctx)
     : is_enabled_(false), start_ns_(PosixInNsec()) {
+  std::lock_guard<std::mutex> l(profiler_mu);
   if (g_state == ProfilerState::kDisabled) return;
   is_enabled_ = true;
   dev_ctx_ = dev_ctx;
@@ -186,11 +189,12 @@ RecordEvent::RecordEvent(const std::string& name, const DeviceContext* dev_ctx)
 }
 
 RecordEvent::~RecordEvent() {
+  std::lock_guard<std::mutex> l(profiler_mu);
   if (g_state == ProfilerState::kDisabled || !is_enabled_) return;
   DeviceTracer* tracer = GetDeviceTracer();
   if (tracer) {
     tracer->AddCPURecords(CurAnnotation(), start_ns_, PosixInNsec(),
-                          BlockDepth(), CurThread());
+                          BlockDepth(), g_thread_id);
   }
   ClearCurAnnotation();
   PopEvent(name_, dev_ctx_);
@@ -198,6 +202,7 @@ RecordEvent::~RecordEvent() {
 
 RecordBlock::RecordBlock(int block_id)
     : is_enabled_(false), start_ns_(PosixInNsec()) {
+  std::lock_guard<std::mutex> l(profiler_mu);
   if (g_state == ProfilerState::kDisabled) return;
   is_enabled_ = true;
   SetCurBlock(block_id);
@@ -205,27 +210,18 @@ RecordBlock::RecordBlock(int block_id)
 }
 
 RecordBlock::~RecordBlock() {
+  std::lock_guard<std::mutex> l(profiler_mu);
   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
     // same timeline lane. and distinguish the using thread_id.
     tracer->AddCPURecords(name_, start_ns_, PosixInNsec(), BlockDepth(),
-                          CurThread());
+                          g_thread_id);
   }
   ClearCurBlock();
 }
 
-RecordThread::RecordThread(int thread_id) {
-  if (g_state == ProfilerState::kDisabled) return;
-  SetCurThread(thread_id);
-}
-
-RecordThread::~RecordThread() {
-  if (g_state == ProfilerState::kDisabled) return;
-  ClearCurThread();
-}
-
 void EnableProfiler(ProfilerState state) {
   PADDLE_ENFORCE(state != ProfilerState::kDisabled,
                  "Can't enbale profling, since the input state is ",

paddle/fluid/platform/profiler.h

@@ -95,11 +95,6 @@ struct RecordBlock {
   uint64_t start_ns_;
 };
 
-struct RecordThread {
-  explicit RecordThread(int thread_id);
-  ~RecordThread();
-};
-
 // Return the event list of all threads. Assumed the returned value calls
 // event_lists, event_lists[i][j] represents the j-th Event of i-th thread.
 std::vector<std::vector<Event>> GetAllEvents();

paddle/scripts/paddle_build.sh

@@ -419,6 +419,25 @@ EOF
     linkchecker doc/v2/en/html/index.html
     linkchecker doc/v2/cn/html/index.html
     linkchecker doc/v2/api/en/html/index.html
+
+    if [[ "$TRAVIS_PULL_REQUEST" != "false" ]]; then exit 0; fi;
+
+    # Deploy to the the content server if its a "develop" or "release/version" branch
+    # The "develop_doc" branch is reserved to test full deploy process without impacting the real content.
+    if [ "$TRAVIS_BRANCH" == "develop_doc" ]; then
+        PPO_SCRIPT_BRANCH=develop
+    elif [[ "$TRAVIS_BRANCH" == "develop"  ||  "$TRAVIS_BRANCH" =~ ^v|release/[[:digit:]]+\.[[:digit:]]+(\.[[:digit:]]+)?(-\S*)?$ ]]; then
+        PPO_SCRIPT_BRANCH=master
+    else
+        # Early exit, this branch doesn't require documentation build
+        return 0;
+    fi
+     # Fetch the paddlepaddle.org deploy_docs.sh from the appopriate branch
+    export DEPLOY_DOCS_SH=https://raw.githubusercontent.com/PaddlePaddle/PaddlePaddle.org/$PPO_SCRIPT_BRANCH/scripts/deploy/deploy_docs.sh
+    export PYTHONPATH=$PYTHONPATH:${PADDLE_ROOT}/build/python:/paddle/build/python
+    cd ..
+    curl $DEPLOY_DOCS_SH | bash -s $CONTENT_DEC_PASSWD $TRAVIS_BRANCH ${PADDLE_ROOT} ${PADDLE_ROOT}/build/doc/ ${PPO_SCRIPT_BRANCH}
+    cd -
 }
 
 function gen_html() {

paddle/scripts/paddle_docker_build.sh

@@ -52,6 +52,9 @@ EOL
     ${DOCKER_CMD} run -it \
         ${DOCKER_ENV} \
         -e SCRIPT_NAME=$0 \
+        -e CONTENT_DEC_PASSWD=$CONTENT_DEC_PASSWD \
+        -e TRAVIS_BRANCH=$TRAVIS_BRANCH \
+        -e TRAVIS_PULL_REQUEST=$TRAVIS_PULL_REQUEST \
         -v $PADDLE_ROOT:/paddle \
         -v ${HOME}/.ccache:/root/.ccache \
         -w /paddle \

python/paddle/batch.py

@@ -40,4 +40,10 @@ def batch(reader, batch_size, drop_last=False):
         if drop_last == False and len(b) != 0:
             yield b
 
+    # Batch size check
+    batch_size = int(batch_size)
+    if batch_size <= 0:
+        raise ValueError("batch_size should be a positive integeral value, "
+                         "but got batch_size={}".format(batch_size))
+
     return batch_reader

python/paddle/fluid/__init__.py

@@ -123,7 +123,8 @@ def __bootstrap__():
     read_env_flags = [
         'use_pinned_memory', 'check_nan_inf', 'benchmark', 'warpctc_dir',
         'eager_delete_scope', 'use_mkldnn', 'initial_cpu_memory_in_mb',
-        'init_allocated_mem', 'free_idle_memory', 'paddle_num_threads'
+        'init_allocated_mem', 'free_idle_memory', 'paddle_num_threads',
+        'cpu_deterministic'
     ]
     if core.is_compiled_with_dist():
         read_env_flags.append('rpc_deadline')

python/paddle/fluid/backward.py

@@ -572,8 +572,6 @@ def append_backward(loss, parameter_list=None, no_grad_set=None,
 
     program.current_block_idx = current_block_idx
     program._sync_with_cpp()
-    # FIXME(zcd): prevent loss.grad optimized by mem_opt.
-    loss.block.var(_append_grad_suffix_(loss.name)).persistable = True
 
     if parameter_list is not None:
         parameters = parameter_list

python/paddle/fluid/framework.py

@@ -1038,7 +1038,26 @@ class Block(object):
         global_block = self.program.global_block()
         param = Parameter(global_block, *args, **kwargs)
         if 'initializer' in kwargs:
-            kwargs['initializer'](param, self)
+
+            def _is_inited_by(block, var):
+                init_ops = []
+                for op in block.ops:
+                    if var.name in op.output_arg_names:
+                        init_ops.append(op)
+                return init_ops
+
+            initializer = kwargs['initializer']
+            init_ops = _is_inited_by(global_block, param)
+            init_ops_len = len(init_ops)
+            if init_ops_len > 1:
+                raise RuntimeError("param " + param.name +
+                                   " is inited by multiple init ops " + str(
+                                       init_ops))
+            elif init_ops_len == 1:
+                #TODO already inited, do nothing, should log a warning
+                pass
+            else:
+                initializer(param, self)
         return param
 
     def append_op(self, *args, **kwargs):

python/paddle/fluid/layers/nn.py

@@ -949,6 +949,10 @@ def dropout(x, dropout_prob, is_test=False, seed=None, name=None):
     helper = LayerHelper('dropout', **locals())
     out = helper.create_tmp_variable(dtype=x.dtype)
     mask = helper.create_tmp_variable(dtype=x.dtype, stop_gradient=True)
+
+    if (seed is None or seed == 0) and helper.main_program.random_seed != 0:
+        seed = helper.main_program.random_seed
+
     helper.append_op(
         type='dropout',
         inputs={'X': [x]},
@@ -1313,13 +1317,16 @@ def sequence_softmax(input, param_attr=None, bias_attr=None, use_cudnn=True):
 
 def softmax(input, param_attr=None, bias_attr=None, use_cudnn=True, name=None):
     """
-    The input of the softmax layer is a 2-D tensor with shape N x K (N is the
-    batch_size, K is the dimension of input feature). The output tensor has the
-    same shape as the input tensor.
+    The input of the softmax operator is a tensor of any rank. The output tensor 
+    has the same shape as the input.
 
-    For each row of the input tensor, the softmax operator squashes the
-    K-dimensional vector of arbitrary real values to a K-dimensional vector of real
-    values in the range [0, 1] that add up to 1.
+    The input tensor will first be logically flattened to a 2-D matrix. The matrix's 
+    second dimension(row length) is as same as the last dimension of the input 
+    tensor, and the first dimension(column length) is the product of all other 
+    dimensions of the input tensor. For each row of the matrix, the softmax operator 
+    squashes the K-dimensional(K is the width of the matrix, which is also the size 
+    of the input tensor's last dimension) vector of arbitrary real values to a 
+    K-dimensional vector of real values in the range [0, 1] that add up to 1.
 
     It computes the exponential of the given dimension and the sum of exponential
     values of all the other dimensions in the K-dimensional vector input.
@@ -1327,7 +1334,7 @@ def softmax(input, param_attr=None, bias_attr=None, use_cudnn=True, name=None):
     exponential values of all the other dimensions is the output of the softmax
     operator.
 
-    For each row :math:`i` and each column :math:`j` in Input(X), we have:
+    For each row :math:`i` and each column :math:`j` in the matrix, we have:
 
     .. math::
 

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

@@ -50,6 +50,8 @@ list(REMOVE_ITEM TEST_OPS test_parallel_executor_crf)
 list(REMOVE_ITEM TEST_OPS test_parallel_executor_fetch_feed)
 list(REMOVE_ITEM TEST_OPS test_dist_se_resnext)
 list(REMOVE_ITEM TEST_OPS test_dist_transformer)
+list(REMOVE_ITEM TEST_OPS test_parallel_executor_transformer)
+list(REMOVE_ITEM TEST_OPS test_image_classification_resnet)
 foreach(TEST_OP ${TEST_OPS})
     py_test_modules(${TEST_OP} MODULES ${TEST_OP})
 endforeach(TEST_OP)
@@ -64,3 +66,5 @@ py_test_modules(test_parallel_executor_crf MODULES test_parallel_executor_crf SE
 py_test_modules(test_parallel_executor_fetch_feed MODULES test_parallel_executor_fetch_feed SERIAL)
 py_test_modules(test_dist_transformer MODULES test_dist_transformer SERIAL)
 py_test_modules(test_dist_se_resnext MODULES test_dist_se_resnext SERIAL)
+py_test_modules(test_parallel_executor_transformer MODULES test_parallel_executor_transformer SERIAL)
+py_test_modules(test_image_classification_resnet MODULES test_image_classification_resnet SERIAL)

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

@@ -174,6 +174,9 @@ class SE_ResNeXt():
             padding=(filter_size - 1) / 2,
             groups=groups,
             act=None,
+            # avoid pserver CPU init differs from GPU
+            param_attr=fluid.ParamAttr(
+                initializer=fluid.initializer.Constant()),
             bias_attr=False)
         return fluid.layers.batch_norm(input=conv, act=act)
 
@@ -194,10 +197,8 @@ class SE_ResNeXt():
 
 def get_model(batch_size):
     # Input data
-    image = fluid.layers.fill_constant(
-        shape=[batch_size, 3, 224, 224], dtype='float32', value=0.0)
-    label = fluid.layers.fill_constant(
-        shape=[batch_size, 1], dtype='int64', value=0.0)
+    image = fluid.layers.data(name="data", shape=[3, 224, 224], dtype='float32')
+    label = fluid.layers.data(name="int64", shape=[1], dtype='int64')
 
     # Train program
     model = SE_ResNeXt(layers=50)
@@ -222,8 +223,10 @@ def get_model(batch_size):
     lr = [base_lr * (0.1**i) for i in range(len(bd) + 1)]
 
     optimizer = fluid.optimizer.Momentum(
-        learning_rate=fluid.layers.piecewise_decay(
-            boundaries=bd, values=lr),
+        # FIXME(typhoonzero): add back LR decay once ParallelExecutor fixed.
+        #learning_rate=fluid.layers.piecewise_decay(
+        #    boundaries=bd, values=lr),
+        learning_rate=base_lr,
         momentum=0.9,
         regularization=fluid.regularizer.L2Decay(1e-4))
     optimizer.minimize(avg_cost)
@@ -232,7 +235,7 @@ def get_model(batch_size):
     train_reader = paddle.batch(
         paddle.dataset.flowers.train(), batch_size=batch_size)
     test_reader = paddle.batch(
-        paddle.dataset.flowers.test(), batch_size=batch_size)
+        paddle.dataset.flowers.test(use_xmap=False), batch_size=batch_size)
 
     return test_program, avg_cost, train_reader, test_reader, acc_top1, out
 
@@ -256,7 +259,6 @@ class DistSeResneXt2x2:
                            trainers)
         pserver_prog = t.get_pserver_program(current_endpoint)
         startup_prog = t.get_startup_program(current_endpoint, pserver_prog)
-
         place = fluid.CPUPlace()
         exe = fluid.Executor(place)
         exe.run(startup_prog)
@@ -302,12 +304,19 @@ class DistSeResneXt2x2:
         ]
 
         feeder = fluid.DataFeeder(feed_var_list, place)
-        reader_generator = train_reader()
-        first_loss, = exe.run(fetch_list=[avg_cost.name])
+        reader_generator = test_reader()
+
+        data = next(reader_generator)
+        first_loss, = exe.run(fetch_list=[avg_cost.name],
+                              feed=feeder.feed(data))
         print(first_loss)
+
         for i in xrange(5):
-            loss, = exe.run(fetch_list=[avg_cost.name])
-        last_loss, = exe.run(fetch_list=[avg_cost.name])
+            data = next(reader_generator)
+            loss, = exe.run(fetch_list=[avg_cost.name], feed=feeder.feed(data))
+
+        data = next(reader_generator)
+        last_loss, = exe.run(fetch_list=[avg_cost.name], feed=feeder.feed(data))
         print(last_loss)
 
 

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

@@ -63,7 +63,8 @@ class TestDistBase(unittest.TestCase):
             "PATH": os.getenv("PATH"),
             "PYTHONPATH": os.getenv("PYTHONPATH"),
             "LD_LIBRARY_PATH": os.getenv("LD_LIBRARY_PATH"),
-            "FLAGS_fraction_of_gpu_memory_to_use": "0.15"
+            "FLAGS_fraction_of_gpu_memory_to_use": "0.15",
+            "FLAGS_cudnn_deterministic": "1"
         }
         # Run local to get a base line
         env_local = {"CUDA_VISIBLE_DEVICES": "0"}

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

@@ -17,8 +17,7 @@ from test_dist_base import TestDistBase
 
 class TestDistSeResneXt2x2(TestDistBase):
     def test_se_resnext(self):
-        # TODO(paddle-dev): Is the delta too large?
-        self.check_with_place("dist_se_resnext.py", delta=0.2)
+        self.check_with_place("dist_se_resnext.py")
 
 
 if __name__ == "__main__":

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

@@ -73,9 +73,18 @@ class TranspilerTest(unittest.TestCase):
 
         return self.transpiler
 
+    def transpiler_test_impl(self):
+        pass
 
-class TestBasicModel(TranspilerTest):
     def test_transpiler(self):
+        main = fluid.Program()
+        startup = fluid.Program()
+        with fluid.program_guard(main, startup):
+            self.transpiler_test_impl()
+
+
+class TestBasicModel(TranspilerTest):
+    def transpiler_test_impl(self):
         pserver, startup = self.get_pserver(self.pserver1_ep)
         pserver2, startup2 = self.get_pserver(self.pserver2_ep)
 
@@ -123,7 +132,7 @@ class TestBasicModel(TranspilerTest):
 
 
 class TestBasicModelWithLargeBlockSize(TranspilerTest):
-    def test_transpiler(self):
+    def transpiler_test_impl(self):
         config = fluid.DistributeTranspilerConfig()
         config.min_block_size = 1048576
 
@@ -148,7 +157,7 @@ class TestBasicModelWithLargeBlockSize(TranspilerTest):
                          ["sum", "scale", "sgd"])
         # confirm startup program
         self.assertEqual([op.type for op in startup.global_block().ops],
-                         ["fill_constant", "fill_constant", "fill_constant"])
+                         ["fill_constant", "fill_constant"])
         # the variable #fc_w will be split into two blocks
         fc_w_var = startup2.global_block().var("fc_w")
         self.assertEqual(fc_w_var.shape, (1000L, 1000L))
@@ -177,7 +186,7 @@ class TestNoSliceVar(TranspilerTest):
     def setUp(self):
         super(TestNoSliceVar, self).setUp()
 
-    def test_transpiler(self):
+    def transpiler_test_impl(self):
         config = fluid.DistributeTranspilerConfig()
         config.slice_var_up = False
 
@@ -212,7 +221,7 @@ class TestLRDecay(TranspilerTest):
         sgd_optimizer.minimize(avg_cost)
         return
 
-    def test_transpiler(self):
+    def transpiler_test_impl(self):
         pserver, startup = self.get_pserver(self.pserver1_ep)
         trainer = self.get_trainer()
 
@@ -242,7 +251,7 @@ class TestLRDecayConditional(TranspilerTest):
         sgd_optimizer.minimize(avg_cost)
         return
 
-    def test_transpiler(self):
+    def transpiler_test_impl(self):
         pserver, startup = self.get_pserver(self.pserver1_ep)
         trainer = self.get_trainer()
 
@@ -291,7 +300,7 @@ class TestL2Decay(TranspilerTest):
         sgd_optimizer.minimize(avg_cost)
         return
 
-    def test_transpiler(self):
+    def transpiler_test_impl(self):
         pserver, startup = self.get_pserver(self.pserver1_ep)
         trainer = self.get_trainer()
 
@@ -326,7 +335,7 @@ class TestL2DecayWithPiecewise(TranspilerTest):
         sgd_optimizer.minimize(avg_cost)
         return
 
-    def test_transpiler(self):
+    def transpiler_test_impl(self):
         pserver, startup = self.get_pserver(self.pserver1_ep)
         trainer = self.get_trainer()
 
@@ -350,5 +359,110 @@ class TestL2DecayWithPiecewise(TranspilerTest):
             ["sum", "scale", "scale", "elementwise_add", "momentum"])
 
 
+class TestDistLookupTableBase(TranspilerTest):
+    def network_with_table(self, is_sparse, is_distributed):
+        def emb_pool(ids):
+            table_size = 1000
+            emb_size = 64
+            emb = fluid.layers.embedding(
+                input=ids,
+                size=[table_size, emb_size],
+                dtype='float32',
+                param_attr='shared_w',  # share parameter
+                is_sparse=is_sparse,
+                is_distributed=is_distributed)
+            pool = fluid.layers.sequence_pool(input=emb, pool_type='average')
+            return pool
+
+        title_ids = fluid.layers.data(
+            name='title_ids', shape=[1], dtype='int64', lod_level=1)
+        brand_ids = fluid.layers.data(
+            name='brand_ids', shape=[1], dtype='int64', lod_level=1)
+        title_emb = emb_pool(title_ids)
+        brand_emb = emb_pool(brand_ids)
+        fc0 = fluid.layers.concat(input=[title_emb, brand_emb], axis=1)
+        predict = fluid.layers.fc(input=fc0,
+                                  size=2,
+                                  act=None,
+                                  param_attr=fluid.ParamAttr(name='fc_w'),
+                                  bias_attr=fluid.ParamAttr(name='fc_b'))
+
+        label = fluid.layers.data(name='label', shape=[1], dtype='int64')
+        cost = fluid.layers.cross_entropy(input=predict, label=label)
+        avg_cost = fluid.layers.mean(cost)
+        optimizer = fluid.optimizer.Adam(learning_rate=0.003)
+        optimizer.minimize(avg_cost)
+
+
+class TestLocalLookupTable(TestDistLookupTableBase):
+    def net_conf(self):
+        self.network_with_table(is_sparse=True, is_distributed=False)
+
+    def transpiler_test_impl(self):
+        pserver1, startup1 = self.get_pserver(self.pserver1_ep)
+
+        self.assertEqual(len(pserver1.blocks), 3)
+        # 0 listen_and_serv
+        # 1 optimize for fc_w or fc_b adam
+        self.assertEqual([op.type for op in pserver1.blocks[1].ops],
+                         ["sum", "scale", "adam", "scale", "scale"])
+        # 2 optimize for table adam
+        # NOTE: if param is not selected rows, the grad will scaled to grad / trainer_num
+        self.assertEqual([op.type for op in pserver1.blocks[2].ops],
+                         ["sum", "adam", "scale", "scale"])
+
+        trainer = self.get_trainer()
+        self.assertEqual(len(trainer.blocks), 1)
+        ops = [
+            'lookup_table', 'sequence_pool', 'lookup_table', 'sequence_pool',
+            'concat', 'mul', 'elementwise_add', 'cross_entropy', 'mean',
+            'fill_constant', 'mean_grad', 'cross_entropy_grad',
+            'elementwise_add_grad', 'send', 'mul_grad', 'send', 'concat_grad',
+            'sequence_pool_grad', 'lookup_table_grad', 'sequence_pool_grad',
+            'lookup_table_grad', 'sum', 'split_selected_rows', 'send',
+            'send_barrier', 'recv', 'recv', 'recv', 'fetch_barrier', 'concat'
+        ]
+        self.assertEqual([op.type for op in trainer.blocks[0].ops], ops)
+
+
+class TestDistLookupTable(TestDistLookupTableBase):
+    def net_conf(self):
+        self.network_with_table(is_sparse=True, is_distributed=True)
+
+    def transpiler_test_impl(self):
+        pserver1, startup1 = self.get_pserver(self.pserver1_ep)
+
+        self.assertEqual(len(pserver1.blocks), 6)
+        # 0 listen_and_serv
+        # 1 optimize for fc_w or fc_b adam
+        self.assertEqual([op.type for op in pserver1.blocks[1].ops],
+                         ["sum", "scale", "adam", "scale", "scale"])
+        # 2 optimize for table sgd
+        self.assertEqual([op.type for op in pserver1.blocks[2].ops],
+                         ["sum", "sgd"])
+        # 3 prefetch -> lookup_sparse_table for data0
+        self.assertEqual([op.type for op in pserver1.blocks[3].ops],
+                         ["lookup_sparse_table"])
+        # 4 prefetch -> lookup_sparse_table for data1
+        self.assertEqual([op.type for op in pserver1.blocks[4].ops],
+                         ["lookup_sparse_table"])
+        # 5 save table
+        self.assertEqual([op.type for op in pserver1.blocks[5].ops], ["save"])
+
+        trainer = self.get_trainer()
+        self.assertEqual(len(trainer.blocks), 1)
+        ops = [
+            'split_ids', 'prefetch', 'merge_ids', 'sequence_pool', 'split_ids',
+            'prefetch', 'merge_ids', 'sequence_pool', 'concat', 'mul',
+            'elementwise_add', 'cross_entropy', 'mean', 'fill_constant',
+            'mean_grad', 'cross_entropy_grad', 'elementwise_add_grad', 'send',
+            'mul_grad', 'send', 'concat_grad', 'sequence_pool_grad',
+            'lookup_table_grad', 'sequence_pool_grad', 'lookup_table_grad',
+            'sum', 'split_ids', 'send', 'send_barrier', 'recv', 'recv',
+            'fetch_barrier'
+        ]
+        self.assertEqual([op.type for op in trainer.blocks[0].ops], ops)
+
+
 if __name__ == "__main__":
     unittest.main()

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

@@ -27,12 +27,13 @@ class TestConstantInitializer(unittest.TestCase):
         """
         program = framework.Program()
         block = program.global_block()
-        block.create_parameter(
-            dtype="float32",
-            shape=[5, 10],
-            lod_level=0,
-            name="param",
-            initializer=initializer.ConstantInitializer())
+        for _ in range(2):
+            block.create_parameter(
+                dtype="float32",
+                shape=[5, 10],
+                lod_level=0,
+                name="param",
+                initializer=initializer.ConstantInitializer())
         self.assertEqual(len(block.ops), 1)
         init_op = block.ops[0]
         self.assertEqual(init_op.type, 'fill_constant')
@@ -43,12 +44,13 @@ class TestConstantInitializer(unittest.TestCase):
         """
         program = framework.Program()
         block = program.global_block()
-        block.create_parameter(
-            dtype="float32",
-            shape=[5, 10],
-            lod_level=0,
-            name="param",
-            initializer=initializer.ConstantInitializer(2.3))
+        for _ in range(2):
+            block.create_parameter(
+                dtype="float32",
+                shape=[5, 10],
+                lod_level=0,
+                name="param",
+                initializer=initializer.ConstantInitializer(2.3))
         self.assertEqual(len(block.ops), 1)
         init_op = block.ops[0]
         self.assertEqual(init_op.type, 'fill_constant')
@@ -61,12 +63,13 @@ class TestUniformInitializer(unittest.TestCase):
         """
         program = framework.Program()
         block = program.global_block()
-        block.create_parameter(
-            dtype="float32",
-            shape=[5, 10],
-            lod_level=0,
-            name="param",
-            initializer=initializer.UniformInitializer())
+        for _ in range(2):
+            block.create_parameter(
+                dtype="float32",
+                shape=[5, 10],
+                lod_level=0,
+                name="param",
+                initializer=initializer.UniformInitializer())
         self.assertEqual(len(block.ops), 1)
         init_op = block.ops[0]
         self.assertEqual(init_op.type, 'uniform_random')
@@ -80,18 +83,19 @@ class TestUniformInitializer(unittest.TestCase):
         program = framework.Program()
         program.random_seed = 123
         block = program.global_block()
-        block.create_parameter(
-            dtype="float32",
-            shape=[5, 10],
-            lod_level=0,
-            name="param",
-            initializer=initializer.UniformInitializer())
-        block.create_parameter(
-            dtype="float32",
-            shape=[5, 10],
-            lod_level=0,
-            name="param",
-            initializer=initializer.UniformInitializer(seed=456))
+        for _ in range(2):
+            block.create_parameter(
+                dtype="float32",
+                shape=[5, 10],
+                lod_level=0,
+                name="param1",
+                initializer=initializer.UniformInitializer())
+            block.create_parameter(
+                dtype="float32",
+                shape=[5, 10],
+                lod_level=0,
+                name="param2",
+                initializer=initializer.UniformInitializer(seed=456))
         init_op = block.ops[1]
         self.assertEqual(init_op.attr("seed"), 123)
         init_op1 = block.ops[0]
@@ -102,12 +106,13 @@ class TestUniformInitializer(unittest.TestCase):
         """
         program = framework.Program()
         block = program.global_block()
-        block.create_parameter(
-            dtype="float32",
-            shape=[5, 10],
-            lod_level=0,
-            name="param",
-            initializer=initializer.UniformInitializer(-4.2, 3.1, 123))
+        for _ in range(2):
+            block.create_parameter(
+                dtype="float32",
+                shape=[5, 10],
+                lod_level=0,
+                name="param",
+                initializer=initializer.UniformInitializer(-4.2, 3.1, 123))
         self.assertEqual(len(block.ops), 1)
         init_op = block.ops[0]
         self.assertEqual(init_op.type, 'uniform_random')
@@ -115,6 +120,25 @@ class TestUniformInitializer(unittest.TestCase):
         self.assertAlmostEqual(init_op.attr('max'), 3.1, delta=DELTA)
         self.assertEqual(init_op.attr('seed'), 123)
 
+    def test_uniform_initializer_two_op(self):
+        """Test uniform initializer with supplied attributes
+        """
+        program = framework.Program()
+        block = program.global_block()
+        for i in range(2):
+            block.create_parameter(
+                dtype="float32",
+                shape=[5, 10],
+                lod_level=0,
+                name="param",
+                initializer=initializer.UniformInitializer(-4.2, float(i), 123))
+        self.assertEqual(len(block.ops), 1)
+        init_op0 = block.ops[0]
+        self.assertEqual(init_op0.type, 'uniform_random')
+        self.assertAlmostEqual(init_op0.attr('min'), -4.2, delta=DELTA)
+        self.assertAlmostEqual(init_op0.attr('max'), 0.0, delta=DELTA)
+        self.assertEqual(init_op0.attr('seed'), 123)
+
 
 class TestNormalInitializer(unittest.TestCase):
     def test_normal_initializer_default_value(self):
@@ -122,12 +146,13 @@ class TestNormalInitializer(unittest.TestCase):
         """
         program = framework.Program()
         block = program.global_block()
-        block.create_parameter(
-            dtype="float32",
-            shape=[5, 10],
-            lod_level=0,
-            name="param",
-            initializer=initializer.NormalInitializer())
+        for _ in range(2):
+            block.create_parameter(
+                dtype="float32",
+                shape=[5, 10],
+                lod_level=0,
+                name="param",
+                initializer=initializer.NormalInitializer())
         self.assertEqual(len(block.ops), 1)
         init_op = block.ops[0]
         self.assertEqual(init_op.type, 'gaussian_random')
@@ -140,12 +165,13 @@ class TestNormalInitializer(unittest.TestCase):
         """
         program = framework.Program()
         block = program.global_block()
-        block.create_parameter(
-            dtype="float32",
-            shape=[5, 10],
-            lod_level=0,
-            name="param",
-            initializer=initializer.NormalInitializer(2.3, 1.9, 123))
+        for _ in range(2):
+            block.create_parameter(
+                dtype="float32",
+                shape=[5, 10],
+                lod_level=0,
+                name="param",
+                initializer=initializer.NormalInitializer(2.3, 1.9, 123))
         self.assertEqual(len(block.ops), 1)
         init_op = block.ops[0]
         self.assertEqual(init_op.type, 'gaussian_random')
@@ -161,12 +187,13 @@ class TestXavierInitializer(unittest.TestCase):
         """
         program = framework.Program()
         block = program.global_block()
-        param = block.create_parameter(
-            dtype="float32",
-            shape=[5, 10],
-            lod_level=0,
-            name="param",
-            initializer=initializer.XavierInitializer())
+        for _ in range(2):
+            param = block.create_parameter(
+                dtype="float32",
+                shape=[5, 10],
+                lod_level=0,
+                name="param",
+                initializer=initializer.XavierInitializer())
         self.assertEqual(len(block.ops), 1)
         init_op = block.ops[0]
         self.assertEqual(init_op.type, 'uniform_random')
@@ -181,12 +208,13 @@ class TestXavierInitializer(unittest.TestCase):
         """
         program = framework.Program()
         block = program.global_block()
-        param = block.create_parameter(
-            dtype="float32",
-            shape=[5, 10, 15, 20],
-            lod_level=0,
-            name="param",
-            initializer=initializer.XavierInitializer())
+        for _ in range(2):
+            param = block.create_parameter(
+                dtype="float32",
+                shape=[5, 10, 15, 20],
+                lod_level=0,
+                name="param",
+                initializer=initializer.XavierInitializer())
         self.assertEqual(len(block.ops), 1)
         init_op = block.ops[0]
         self.assertEqual(init_op.type, 'uniform_random')
@@ -203,12 +231,13 @@ class TestXavierInitializer(unittest.TestCase):
         """
         program = framework.Program()
         block = program.global_block()
-        param = block.create_parameter(
-            dtype="float32",
-            shape=[5, 10],
-            lod_level=0,
-            name="param",
-            initializer=initializer.XavierInitializer(uniform=False))
+        for _ in range(2):
+            param = block.create_parameter(
+                dtype="float32",
+                shape=[5, 10],
+                lod_level=0,
+                name="param",
+                initializer=initializer.XavierInitializer(uniform=False))
         self.assertEqual(len(block.ops), 1)
         init_op = block.ops[0]
         self.assertEqual(init_op.type, 'gaussian_random')
@@ -223,12 +252,13 @@ class TestXavierInitializer(unittest.TestCase):
         """
         program = framework.Program()
         block = program.global_block()
-        param = block.create_parameter(
-            dtype="float32",
-            shape=[5, 10, 15, 20],
-            lod_level=0,
-            name="param",
-            initializer=initializer.XavierInitializer(uniform=False))
+        for _ in range(2):
+            param = block.create_parameter(
+                dtype="float32",
+                shape=[5, 10, 15, 20],
+                lod_level=0,
+                name="param",
+                initializer=initializer.XavierInitializer(uniform=False))
         self.assertEqual(len(block.ops), 1)
         init_op = block.ops[0]
         self.assertEqual(init_op.type, 'gaussian_random')
@@ -244,13 +274,14 @@ class TestXavierInitializer(unittest.TestCase):
         """
         program = framework.Program()
         block = program.global_block()
-        block.create_parameter(
-            dtype="float32",
-            shape=[5, 10],
-            lod_level=0,
-            name="param",
-            initializer=initializer.XavierInitializer(
-                fan_in=12, fan_out=23, seed=134))
+        for _ in range(2):
+            block.create_parameter(
+                dtype="float32",
+                shape=[5, 10],
+                lod_level=0,
+                name="param",
+                initializer=initializer.XavierInitializer(
+                    fan_in=12, fan_out=23, seed=134))
         self.assertEqual(len(block.ops), 1)
         init_op = block.ops[0]
         self.assertEqual(init_op.type, 'uniform_random')
@@ -267,12 +298,13 @@ class TestMSRAInitializer(unittest.TestCase):
         """
         program = framework.Program()
         block = program.global_block()
-        param = block.create_parameter(
-            dtype="float32",
-            shape=[5, 10],
-            lod_level=0,
-            name="param",
-            initializer=initializer.MSRAInitializer())
+        for _ in range(2):
+            param = block.create_parameter(
+                dtype="float32",
+                shape=[5, 10],
+                lod_level=0,
+                name="param",
+                initializer=initializer.MSRAInitializer())
         self.assertEqual(len(block.ops), 1)
         init_op = block.ops[0]
         self.assertEqual(init_op.type, 'uniform_random')
@@ -287,12 +319,13 @@ class TestMSRAInitializer(unittest.TestCase):
         """
         program = framework.Program()
         block = program.global_block()
-        param = block.create_parameter(
-            dtype="float32",
-            shape=[5, 10, 15, 20],
-            lod_level=0,
-            name="param",
-            initializer=initializer.MSRAInitializer())
+        for _ in range(2):
+            param = block.create_parameter(
+                dtype="float32",
+                shape=[5, 10, 15, 20],
+                lod_level=0,
+                name="param",
+                initializer=initializer.MSRAInitializer())
         self.assertEqual(len(block.ops), 1)
         init_op = block.ops[0]
         self.assertEqual(init_op.type, 'uniform_random')
@@ -308,12 +341,13 @@ class TestMSRAInitializer(unittest.TestCase):
         """
         program = framework.Program()
         block = program.global_block()
-        param = block.create_parameter(
-            dtype="float32",
-            shape=[5, 10],
-            lod_level=0,
-            name="param",
-            initializer=initializer.MSRAInitializer(uniform=False))
+        for _ in range(2):
+            param = block.create_parameter(
+                dtype="float32",
+                shape=[5, 10],
+                lod_level=0,
+                name="param",
+                initializer=initializer.MSRAInitializer(uniform=False))
         self.assertEqual(len(block.ops), 1)
         init_op = block.ops[0]
         self.assertEqual(init_op.type, 'gaussian_random')
@@ -328,12 +362,13 @@ class TestMSRAInitializer(unittest.TestCase):
         """
         program = framework.Program()
         block = program.global_block()
-        param = block.create_parameter(
-            dtype="float32",
-            shape=[5, 10, 15, 20],
-            lod_level=0,
-            name="param",
-            initializer=initializer.MSRAInitializer(uniform=False))
+        for _ in range(2):
+            param = block.create_parameter(
+                dtype="float32",
+                shape=[5, 10, 15, 20],
+                lod_level=0,
+                name="param",
+                initializer=initializer.MSRAInitializer(uniform=False))
         self.assertEqual(len(block.ops), 1)
         init_op = block.ops[0]
         self.assertEqual(init_op.type, 'gaussian_random')
@@ -348,13 +383,14 @@ class TestMSRAInitializer(unittest.TestCase):
         """
         program = framework.Program()
         block = program.global_block()
-        block.create_parameter(
-            dtype="float32",
-            shape=[5, 10],
-            lod_level=0,
-            name="param",
-            initializer=initializer.MSRAInitializer(
-                fan_in=12, seed=134))
+        for _ in range(2):
+            block.create_parameter(
+                dtype="float32",
+                shape=[5, 10],
+                lod_level=0,
+                name="param",
+                initializer=initializer.MSRAInitializer(
+                    fan_in=12, seed=134))
         self.assertEqual(len(block.ops), 1)
         init_op = block.ops[0]
         self.assertEqual(init_op.type, 'uniform_random')
@@ -370,12 +406,13 @@ class TestMSRAInitializer(unittest.TestCase):
         """
         program = framework.Program()
         block = program.global_block()
-        block.create_parameter(
-            dtype="float32",
-            shape=[8, 1, 3, 3],
-            lod_level=0,
-            name="param",
-            initializer=initializer.BilinearInitializer())
+        for _ in range(2):
+            block.create_parameter(
+                dtype="float32",
+                shape=[8, 1, 3, 3],
+                lod_level=0,
+                name="param",
+                initializer=initializer.BilinearInitializer())
         self.assertEqual(len(block.ops), 1)
         init_op = block.ops[0]
         self.assertEqual(init_op.type, 'assign_value')

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

@@ -35,6 +35,22 @@ class TestLookupTableOp(OpTest):
         self.check_grad(['W'], 'Out', no_grad_set=set('Ids'))
 
 
+class TestLookupTableOpWithTensorIds(OpTest):
+    def setUp(self):
+        self.op_type = "lookup_table"
+        table = np.random.random((17, 31)).astype("float32")
+        ids = np.random.randint(
+            low=0, high=17, size=(2, 4, 5, 1)).astype("int64")
+        self.inputs = {'W': table, 'Ids': ids}
+        self.outputs = {'Out': table[ids.flatten()].reshape((2, 4, 5, 31))}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        self.check_grad(['W'], 'Out', no_grad_set=set('Ids'))
+
+
 class TestLookupTableOpWithPadding(TestLookupTableOp):
     def test_check_output(self):
         ids = np.squeeze(self.inputs['Ids'])
@@ -44,21 +60,34 @@ class TestLookupTableOpWithPadding(TestLookupTableOp):
         self.check_output()
 
     def test_check_grad(self):
-        # Since paddings are not trainable and fixed in forward, the gradient of 
+        # Since paddings are not trainable and fixed in forward, the gradient of
         # paddings makes no sense and we don't test the gradient here.
         pass
 
 
-class TestLookupTableWIsSelectedRows(OpTest):
-    def check_with_place(self, place):
-        scope = core.Scope()
+class TestLookupTableOpWithTensorIdsAndPadding(TestLookupTableOpWithTensorIds):
+    def test_check_output(self):
+        ids = self.inputs['Ids']
+        flatten_idx = ids.flatten()
+        padding_idx = np.random.choice(flatten_idx, 1)[0]
+        self.outputs['Out'][np.squeeze(ids == padding_idx)] = np.zeros(31)
+        self.attrs = {'padding_idx': long(padding_idx)}
+        self.check_output()
+
+    def test_check_grad(self):
+        # Since paddings are not trainable and fixed in forward, the gradient of
+        # paddings makes no sense and we don't test the gradient here.
+        pass
 
-        # create and initialize Id Variable
+
+class TestLookupTableWIsSelectedRows(OpTest):
+    def prepare_ids(self, scope, place):
         ids_tensor = scope.var('Ids').get_tensor()
         ids_array = np.array([[0], [4], [3], [5]]).astype("int64")
         ids_tensor.set(ids_array, place)
+        return ids_array
 
-        # create and initialize W Variable
+    def prepare_w(self, scope, place):
         rows = [0, 1, 2, 3, 4, 5, 6]
         row_numel = 12
 
@@ -71,8 +100,22 @@ class TestLookupTableWIsSelectedRows(OpTest):
         w_tensor = w_selected_rows.get_tensor()
         w_tensor.set(w_array, place)
 
-        # create Out Variable
-        out_tensor = scope.var('Out').get_tensor()
+    def create_out_tensor(self, scope, place):
+        return scope.var('Out').get_tensor()
+
+    def check_result(self, ids_array, result_array):
+        # all(): return True if all elements of the iterable are true (or if the iterable is empty)
+        for idx, row in enumerate(ids_array):
+            assert (row[0] == result_array[idx]).all()
+
+    def check_with_place(self, place):
+        scope = core.Scope()
+
+        ids_array = self.prepare_ids(scope, place)
+
+        self.prepare_w(scope, place)
+
+        out_tensor = self.create_out_tensor(scope, place)
 
         # create and run lookup_table operator
         lookup_table = Operator("lookup_table", W='W', Ids='Ids', Out='Out')
@@ -80,9 +123,8 @@ class TestLookupTableWIsSelectedRows(OpTest):
 
         # get result from Out
         result_array = np.array(out_tensor)
-        # all(): return True if all elements of the iterable are true (or if the iterable is empty)
-        for idx, row in enumerate(ids_array):
-            assert (row[0] == result_array[idx]).all()
+
+        self.check_result(ids_array, result_array)
 
     def test_w_is_selected_rows(self):
         places = [core.CPUPlace()]
@@ -91,5 +133,19 @@ class TestLookupTableWIsSelectedRows(OpTest):
             self.check_with_place(place)
 
 
+class TestLookupTableWithTensorIdsWIsSelectedRows(
+        TestLookupTableWIsSelectedRows):
+    def prepare_ids(self, scope, place):
+        ids_tensor = scope.var('Ids').get_tensor()
+        ids_array = np.random.randint(
+            low=0, high=6, size=(2, 4, 3, 1)).astype("int64")
+        ids_tensor.set(ids_array, place)
+        return ids_array
+
+    def check_result(self, ids_array, result_array):
+        for idx, row in np.ndenumerate(ids_array):
+            assert (row == result_array[idx]).all()
+
+
 if __name__ == "__main__":
     unittest.main()

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

@@ -98,16 +98,13 @@ class TestMNIST(TestParallelExecutorBase):
             fluid.recordio_writer.convert_reader_to_recordio_file(
                 MNIST_RECORDIO_FILE, reader, feeder)
 
-    def _init_data(self, random=True):
+    def _init_data(self):
         np.random.seed(5)
-        if random:
-            img = np.random.random(size=[32, 784]).astype(np.float32)
-        else:
-            img = np.ones(shape=[32, 784], dtype='float32')
+        img = np.random.random(size=[32, 784]).astype(np.float32)
         label = np.ones(shape=[32, 1], dtype='int64')
         return img, label
 
-    def _compare_reduce_and_allreduce(self, model, use_cuda, random_data=True):
+    def _compare_reduce_and_allreduce(self, model, use_cuda):
         if use_cuda and not core.is_compiled_with_cuda():
             return
         self.check_network_convergence(
@@ -115,7 +112,7 @@ class TestMNIST(TestParallelExecutorBase):
         self.check_network_convergence(
             model, use_cuda=use_cuda, allow_op_delay=True, use_reduce=True)
 
-        img, label = self._init_data(random_data)
+        img, label = self._init_data()
 
         all_reduce_first_loss, all_reduce_last_loss = self.check_network_convergence(
             model,
@@ -166,27 +163,27 @@ class TestMNIST(TestParallelExecutorBase):
         if use_cuda and not core.is_compiled_with_cuda():
             return
 
-        img, label = self._init_data(random=False)
+        img, label = self._init_data()
 
         single_first_loss, single_last_loss = self.check_network_convergence(
             method=simple_fc_net,
-            seed=1000,
+            seed=1,
             feed_dict={"image": img,
                        "label": label},
             use_cuda=use_cuda,
             use_parallel_executor=False)
         parallel_first_loss, parallel_last_loss = self.check_network_convergence(
             method=simple_fc_net,
-            seed=1000,
+            seed=1,
             feed_dict={"image": img,
                        "label": label},
             use_cuda=use_cuda,
             use_parallel_executor=True)
 
-        for p_f in parallel_first_loss:
-            self.assertAlmostEquals(p_f, single_first_loss[0], delta=1e-6)
-        for p_l in parallel_last_loss:
-            self.assertAlmostEquals(p_l, single_last_loss[0], delta=1e-6)
+        self.assertAlmostEquals(
+            np.mean(parallel_first_loss), single_first_loss, delta=1e-6)
+        self.assertAlmostEquals(
+            np.mean(parallel_last_loss), single_last_loss, delta=1e-6)
 
     def test_simple_fc_parallel_accuracy(self):
         self.check_simple_fc_parallel_accuracy(True)

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

@@ -21,6 +21,19 @@ from parallel_executor_test_base import TestParallelExecutorBase
 import unittest
 import math
 import os
+import numpy as np
+
+# FIXME(zcd): If the neural net has dropout_op, the output of ParallelExecutor
+# and Executor is different. Because, for ParallelExecutor, the dropout_op of
+# the neural net will be copied N copies(N is the number of device). This will
+# lead to the random numbers generated by ParallelExecutor and Executor are different.
+# So, if we compare the loss of ParallelExecutor and Executor, we should remove the
+# dropout_op.
+remove_dropout = False
+
+# FIXME(zcd): If the neural net has batch_norm, the output of ParallelExecutor
+# and Executor is different.
+remove_bn = False
 
 
 def squeeze_excitation(input, num_channels, reduction_ratio):
@@ -53,7 +66,8 @@ def conv_bn_layer(input, num_filters, filter_size, stride=1, groups=1,
         groups=groups,
         act=None,
         bias_attr=False)
-    return fluid.layers.batch_norm(input=conv, act=act, momentum=0.1)
+    return conv if remove_bn else fluid.layers.batch_norm(
+        input=conv, act=act, momentum=0.1)
 
 
 def shortcut(input, ch_out, stride):
@@ -92,13 +106,14 @@ def bottleneck_block(input, num_filters, stride, cardinality, reduction_ratio):
     return fluid.layers.elementwise_add(x=short, y=scale, act='relu')
 
 
-def SE_ResNeXt50Small(batch_size=2, use_feed=False):
-    assert not use_feed, "SE_ResNeXt doesn't support feed yet"
+batch_size = 12
+img_shape = [3, 224, 224]
+
 
-    img = fluid.layers.fill_constant(
-        shape=[batch_size, 3, 224, 224], dtype='float32', value=0.0)
-    label = fluid.layers.fill_constant(
-        shape=[batch_size, 1], dtype='int64', value=0.0)
+def SE_ResNeXt50Small(use_feed):
+
+    img = fluid.layers.data(name='image', shape=img_shape, dtype='float32')
+    label = fluid.layers.data(name='label', shape=[1], dtype='int64')
 
     conv = conv_bn_layer(
         input=img, num_filters=16, filter_size=3, stride=2, act='relu')
@@ -127,7 +142,8 @@ def SE_ResNeXt50Small(batch_size=2, use_feed=False):
     reshape = fluid.layers.reshape(
         x=conv, shape=[-1, shape[1], shape[2] * shape[3]])
     pool = fluid.layers.reduce_mean(input=reshape, dim=2)
-    dropout = fluid.layers.dropout(x=pool, dropout_prob=0.2)
+    dropout = pool if remove_dropout else fluid.layers.dropout(
+        x=pool, dropout_prob=0.2, seed=1)
     # Classifier layer:
     prediction = fluid.layers.fc(input=dropout, size=1000, act='softmax')
     loss = fluid.layers.cross_entropy(input=prediction, label=label)
@@ -135,75 +151,135 @@ def SE_ResNeXt50Small(batch_size=2, use_feed=False):
     return loss
 
 
-class TestResnet(TestParallelExecutorBase):
-    def check_resnet_convergence_with_learning_rate_decay(self,
-                                                          use_cuda=True,
-                                                          use_reduce=False,
-                                                          iter=20):
+def cosine_decay(learning_rate, step_each_epoch, epochs=120):
+    """
+    Applies cosine decay to the learning rate.
+    lr = 0.05 * (math.cos(epoch * (math.pi / 120)) + 1)
+    """
+    global_step = _decay_step_counter()
 
-        if use_cuda and not core.is_compiled_with_cuda():
-            return
+    with init_on_cpu():
+        epoch = ops.floor(global_step / step_each_epoch)
+        decayed_lr = learning_rate * \
+                     (ops.cos(epoch * (math.pi / epochs)) + 1)/2
+    return decayed_lr
 
-        os.environ['CPU_NUM'] = str(4)
 
-        def _cosine_decay(learning_rate, step_each_epoch, epochs=120):
-            """
-            Applies cosine decay to the learning rate.
-            lr = 0.05 * (math.cos(epoch * (math.pi / 120)) + 1)
-            """
-            global_step = _decay_step_counter()
+def optimizer(learning_rate=0.01):
+    optimizer = fluid.optimizer.Momentum(
+        learning_rate=cosine_decay(
+            learning_rate=learning_rate, step_each_epoch=2, epochs=1),
+        momentum=0.9,
+        regularization=fluid.regularizer.L2Decay(1e-4))
+    return optimizer
 
-            with init_on_cpu():
-                epoch = ops.floor(global_step / step_each_epoch)
-                decayed_lr = learning_rate * \
-                            (ops.cos(epoch * (math.pi / epochs)) + 1)/2
-            return decayed_lr
 
-        def _optimizer(learning_rate=0.01):
-            optimizer = fluid.optimizer.Momentum(
-                learning_rate=_cosine_decay(
-                    learning_rate=learning_rate, step_each_epoch=2, epochs=1),
-                momentum=0.9,
-                regularization=fluid.regularizer.L2Decay(1e-4))
-            return optimizer
+class TestResnet(TestParallelExecutorBase):
+    @classmethod
+    def setUpClass(cls):
+        os.environ['CPU_NUM'] = str(4)
+        global remove_dropout
+        global remove_bn
+        remove_dropout = False
+        remove_bn = False
+
+    def _init_data(self, batch_size=2, random=True):
+        np.random.seed(5)
+        if random:
+            img = np.random.random(
+                size=[batch_size] + img_shape).astype(np.float32)
+        else:
+            img = np.ones(shape=[batch_size] + img_shape, dtype='float32')
+        label = [np.random.randint(0, 999) for _ in range(batch_size)]
+        label = np.array(label).astype(np.int64).reshape(-1, 1)
+        return img, label
+
+    def _compare_reduce_and_allreduce(self,
+                                      model,
+                                      use_cuda,
+                                      iter=20,
+                                      delta2=1e-6):
+        if use_cuda and not core.is_compiled_with_cuda():
+            return
 
-        import functools
+        global remove_bn
+        remove_bn = True
 
-        batch_size = 2
+        img, label = self._init_data(batch_size=batch_size)
+        all_reduce_first_loss, all_reduce_last_loss = self.check_network_convergence(
+            model,
+            feed_dict={"image": img,
+                       "label": label},
+            iter=iter,
+            batch_size=batch_size,
+            use_cuda=use_cuda,
+            use_reduce=False,
+            optimizer=optimizer)
+        reduce_first_loss, reduce_last_loss = self.check_network_convergence(
+            model,
+            feed_dict={"image": img,
+                       "label": label},
+            iter=iter,
+            batch_size=batch_size,
+            use_cuda=use_cuda,
+            use_reduce=True,
+            optimizer=optimizer)
+
+        for loss in zip(all_reduce_first_loss, reduce_first_loss):
+            self.assertAlmostEquals(loss[0], loss[1], delta=1e-6)
+        for loss in zip(all_reduce_last_loss, reduce_last_loss):
+            self.assertAlmostEquals(loss[0], loss[1], delta=delta2)
+
+    def _check_resnet_convergence(self,
+                                  model,
+                                  use_cuda=True,
+                                  use_reduce=False,
+                                  iter=20,
+                                  delta2=1e-6):
+        if use_cuda and not core.is_compiled_with_cuda():
+            return
 
+        global remove_dropout
+        global remove_bn
+        remove_dropout = True
+        remove_bn = True
+
+        img, label = self._init_data(batch_size=batch_size)
         single_first_loss, single_last_loss = self.check_network_convergence(
-            functools.partial(
-                SE_ResNeXt50Small, batch_size=batch_size),
+            model,
+            feed_dict={"image": img,
+                       "label": label},
             iter=iter,
             batch_size=batch_size,
             use_cuda=use_cuda,
             use_reduce=use_reduce,
-            optimizer=_optimizer,
+            optimizer=optimizer,
             use_parallel_executor=False)
-
         parallel_first_loss, parallel_last_loss = self.check_network_convergence(
-            functools.partial(
-                SE_ResNeXt50Small, batch_size=batch_size),
+            model,
+            feed_dict={"image": img,
+                       "label": label},
             iter=iter,
             batch_size=batch_size,
             use_cuda=use_cuda,
             use_reduce=use_reduce,
-            optimizer=_optimizer)
+            optimizer=optimizer)
 
-        for p_f in parallel_first_loss:
-            self.assertAlmostEquals(p_f, single_first_loss[0], delta=1e-6)
-        for p_l in parallel_last_loss:
-            self.assertAlmostEquals(p_l, single_last_loss[0], delta=1e-6)
+        self.assertAlmostEquals(
+            np.mean(parallel_first_loss), single_first_loss[0], delta=1e-6)
+        self.assertAlmostEquals(
+            np.mean(parallel_last_loss), single_last_loss[0], delta=delta2)
 
     def test_seresnext_with_learning_rate_decay(self):
-        self.check_resnet_convergence_with_learning_rate_decay(True, False)
-        self.check_resnet_convergence_with_learning_rate_decay(
-            False, False, iter=5)
-
-    def test_seresnext_with_new_strategy_with_learning_rate_decay(self):
-        self.check_resnet_convergence_with_learning_rate_decay(True, True)
-        self.check_resnet_convergence_with_learning_rate_decay(
-            False, True, iter=5)
+        self._check_resnet_convergence(model=SE_ResNeXt50Small, use_cuda=True)
+        self._check_resnet_convergence(
+            model=SE_ResNeXt50Small, use_cuda=False, iter=2, delta2=1e-3)
+
+    def test_seresnext_with_new_strategy(self):
+        self._compare_reduce_and_allreduce(
+            model=SE_ResNeXt50Small, use_cuda=True, delta2=1e-2)
+        self._compare_reduce_and_allreduce(
+            model=SE_ResNeXt50Small, use_cuda=False, iter=5)
 
 
 if __name__ == '__main__':

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

@@ -26,15 +26,22 @@ def stable_softmax(x):
 
 
 class TestSoftmaxOp(OpTest):
+    def get_x_shape(self):
+        return [10, 10]
+
     def setUp(self):
         self.op_type = "softmax"
         self.use_cudnn = False
         self.use_mkldnn = False
         self.dtype = np.float32
         self.init_kernel_type()
+        self.shape = self.get_x_shape()
+
+        x = np.random.uniform(0.1, 1, self.shape).astype(self.dtype)
+        out = np.apply_along_axis(stable_softmax, 1,
+                                  x.reshape([-1, self.shape[-1]]))
+        out = out.reshape(self.shape)
 
-        x = np.random.uniform(0.1, 1, [10, 10]).astype(self.dtype)
-        out = np.apply_along_axis(stable_softmax, 1, x)
         self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
         self.outputs = {'Out': out}
         self.attrs = {
@@ -63,6 +70,11 @@ class TestSoftmaxOp(OpTest):
             self.check_grad(["X"], "Out", max_relative_error=0.01)
 
 
+class TestSoftmaxOp2(TestSoftmaxOp):
+    def get_x_shape(self):
+        return [2, 3, 4, 5]
+
+
 @unittest.skipIf(not core.is_compiled_with_cuda(),
                  "core is not compiled with CUDA")
 class TestSoftmaxCUDNNOp(TestSoftmaxOp):
@@ -70,6 +82,13 @@ class TestSoftmaxCUDNNOp(TestSoftmaxOp):
         self.use_cudnn = True
 
 
+@unittest.skipIf(not core.is_compiled_with_cuda(),
+                 "core is not compiled with CUDA")
+class TestSoftmaxCUDNNOp2(TestSoftmaxCUDNNOp):
+    def get_x_shape(self):
+        return [2, 3, 4, 5]
+
+
 @unittest.skipIf(not core.is_compiled_with_cuda(),
                  "core is not compiled with CUDA")
 class TestSoftmaxFP16Op(TestSoftmaxOp):
@@ -83,6 +102,13 @@ class TestSoftmaxFP16Op(TestSoftmaxOp):
                 self.check_output_with_place(place, atol=1e-3)
 
 
+@unittest.skipIf(not core.is_compiled_with_cuda(),
+                 "core is not compiled with CUDA")
+class TestSoftmaxFP16Op2(TestSoftmaxFP16Op):
+    def get_x_shape(self):
+        return [2, 3, 4, 5]
+
+
 @unittest.skipIf(not core.is_compiled_with_cuda(),
                  "core is not compiled with CUDA")
 class TestSoftmaxFP16CUDNNOp(TestSoftmaxOp):
@@ -97,10 +123,22 @@ class TestSoftmaxFP16CUDNNOp(TestSoftmaxOp):
                 self.check_output_with_place(place, atol=1e-3)
 
 
+@unittest.skipIf(not core.is_compiled_with_cuda(),
+                 "core is not compiled with CUDA")
+class TestSoftmaxFP16CUDNNOp2(TestSoftmaxFP16CUDNNOp):
+    def get_x_shape(self):
+        return [2, 3, 4, 5]
+
+
 class TestSoftmaxMKLDNNOp(TestSoftmaxOp):
     def init_kernel_type(self):
         self.use_mkldnn = True
 
 
+class TestSoftmaxMKLDNNOp2(TestSoftmaxMKLDNNOp):
+    def get_x_shape(self):
+        return [2, 3, 4, 5]
+
+
 if __name__ == "__main__":
     unittest.main()

python/paddle/fluid/transpiler/distribute_transpiler.py

@@ -896,8 +896,6 @@ class DistributeTranspiler(object):
             self.table_name
         ][0]
         table_opt_block = pserver_program.create_block(pre_block_idx)
-        # only support sgd now
-        assert table_opt_op.type == "sgd"
 
         if self.sync_mode:
             # create grad vars in pserver program
@@ -937,11 +935,12 @@ class DistributeTranspiler(object):
             "LearningRate": [lr_var]
         }
         outputs = {"ParamOut": [param_var]}
-        table_opt_block.append_op(
-            type=table_opt_op.type,
-            inputs=inputs,
-            outputs=outputs,
-            attrs=table_opt_op.attrs)
+        # only support sgd now
+        import logging
+        logging.warn(
+            "distribute lookup table only support sgd optimizer, change it's optimizer to sgd instead of "
+            + table_opt_op.type)
+        table_opt_block.append_op(type="sgd", inputs=inputs, outputs=outputs)
 
         # add table parameter gradient and it's block id to grad_to_block_id
         grad_to_block_id.append(grad_var.name + ":" + str(table_opt_block.idx))