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

CMakeLists.txt

@@ -65,6 +65,7 @@ option(REPLACE_ENFORCE_GLOG "Replace PADDLE_ENFORCE with glog/CHECK for better d
 option(WITH_ANAKIN      "Compile with Anakin library"                   OFF)
 option(WITH_GRPC     "Use grpc as the default rpc framework"            ${WITH_DISTRIBUTE})
 option(WITH_BRPC_RDMA     "Use brpc rdma as the rpc protocal"           OFF)
+option(WITH_SYSTEM_BLAS   "Use system blas library"           OFF)
 
 # CMAKE_BUILD_TYPE
 if(NOT CMAKE_BUILD_TYPE)

benchmark/fluid/args.py

@@ -125,6 +125,10 @@ def parse_args():
     parser.add_argument(
         '--use_inference_transpiler',
         action='store_true',
-        help='If set, uses inference transpiler to optimize the program.')
+        help='If set, use inference transpiler to optimize the program.')
+    parser.add_argument(
+        '--no_random',
+        action='store_true',
+        help='If set, keep the random seed and do not shuffle the data.')
     args = parser.parse_args()
     return args

benchmark/fluid/fluid_benchmark.py

@@ -132,10 +132,6 @@ def train(avg_loss, infer_prog, optimizer, train_reader, test_reader, batch_acc,
     exe.run(startup_prog)
 
     # Use inference_transpiler to speedup
-    if args.use_inference_transpiler:
-        t = fluid.InferenceTranspiler()
-        t.transpile(infer_prog, place)
-
     if not args.use_reader_op:
         feed_var_list = [
             var for var in train_prog.global_block().vars.itervalues()
@@ -186,6 +182,10 @@ def train(avg_loss, infer_prog, optimizer, train_reader, test_reader, batch_acc,
         print("Pass: %d, Loss: %f" % (pass_id, np.mean(train_losses))),
         # evaluation
         if not args.no_test and batch_acc and not args.use_reader_op:
+            if args.use_inference_transpiler:
+                t = fluid.InferenceTranspiler()
+                t.transpile(infer_prog, place)
+
             pass_test_acc = test(exe, infer_prog, test_reader, feeder,
                                  batch_acc)
             print(", Test Accuracy: %f" % pass_test_acc)
@@ -316,6 +316,8 @@ def main():
     args = parse_args()
     print_arguments(args)
     print_paddle_envs()
+    if args.no_random:
+        fluid.default_startup_program().random_seed = 1
 
     # the unique trainer id, starting from 0, needed by trainer
     # only

benchmark/fluid/models/resnet.py

@@ -197,12 +197,12 @@ def get_model(args):
     optimizer = fluid.optimizer.Momentum(learning_rate=0.01, momentum=0.9)
 
     batched_train_reader = paddle.batch(
-        paddle.reader.shuffle(
+        train_reader if args.no_random else paddle.reader.shuffle(
             train_reader, buf_size=5120),
         batch_size=args.batch_size * args.gpus,
         drop_last=True)
     batched_test_reader = paddle.batch(
-        train_reader, batch_size=args.batch_size, drop_last=True)
+        test_reader, batch_size=args.batch_size, drop_last=True)
 
     return avg_cost, inference_program, optimizer, batched_train_reader,\
                    batched_test_reader, batch_acc

cmake/cblas.cmake

@@ -83,18 +83,20 @@ else()
   set(REFERENCE_CBLAS_LIB_SEARCH_PATHS ${REFERENCE_CBLAS_ROOT}/lib)
 endif()
 
-find_path(REFERENCE_CBLAS_INCLUDE_DIR NAMES cblas.h PATHS
+if(WITH_SYSTEM_BLAS)
+  find_path(REFERENCE_CBLAS_INCLUDE_DIR NAMES cblas.h PATHS
         ${REFERENCE_CBLAS_INCLUDE_SEARCH_PATHS})
-find_library(REFERENCE_CBLAS_LIBRARY NAMES cblas PATHS
+  find_library(REFERENCE_CBLAS_LIBRARY NAMES cblas PATHS
         ${REFERENCE_CBLAS_LIB_SEARCH_PATHS})
 
-if(REFERENCE_CBLAS_INCLUDE_DIR AND REFERENCE_CBLAS_LIBRARY)
+  if(REFERENCE_CBLAS_INCLUDE_DIR AND REFERENCE_CBLAS_LIBRARY)
     set(CBLAS_FOUND ON)
     set(CBLAS_PROVIDER REFERENCE)
     set(CBLAS_INC_DIR ${REFERENCE_CBLAS_INCLUDE_DIR})
     set(CBLAS_LIBRARIES ${REFERENCE_CBLAS_LIBRARY})
     add_definitions(-DPADDLE_USE_REFERENCE_CBLAS)
     message(STATUS "Found reference-cblas (include: ${CBLAS_INC_DIR}, library: ${CBLAS_LIBRARIES})")
+  endif()
 endif()
 
 if(IOS_USE_VECLIB_FOR_BLAS AND VECLIB_FOUND)

paddle/CMakeLists.txt

@@ -7,18 +7,18 @@ if(NOT WITH_FLUID_ONLY)
   add_subdirectory(legacy/parameter)
 
   if(MOBILE_INFERENCE)
-    add_subdirectory(capi)
+    add_subdirectory(legacy/capi)
   else()
     add_subdirectory(legacy/pserver)
     add_subdirectory(trainer)
     add_subdirectory(scripts)
 
     if(WITH_C_API)
-      add_subdirectory(capi)
+      add_subdirectory(legacy/capi)
     endif()
 
     if(WITH_SWIG_PY)
-      add_subdirectory(api)
+      add_subdirectory(legacy/api)
     endif()
   endif()
 endif()

paddle/fluid/framework/details/CMakeLists.txt

@@ -25,11 +25,12 @@ else()
     cc_library(broadcast_op_handle SRCS broadcast_op_handle.cc DEPS op_handle_base scope ddim memory variable_visitor)
 endif()
 
+cc_library(data_balance_op_handle SRCS data_balance_op_handle.cc DEPS op_handle_base scope lod_tensor)
 cc_library(gather_op_handle SRCS gather_op_handle.cc DEPS op_handle_base scope ddim memory variable_visitor)
 cc_library(fuse_vars_op_handle SRCS fuse_vars_op_handle.cc DEPS op_handle_base scope)
 
 cc_library(multi_devices_graph_builder SRCS multi_devices_graph_builder.cc DEPS ssa_graph_builder computation_op_handle
-        scale_loss_grad_op_handle rpc_op_handle all_reduce_op_handle reduce_op_handle broadcast_op_handle)
+        scale_loss_grad_op_handle rpc_op_handle all_reduce_op_handle reduce_op_handle broadcast_op_handle data_balance_op_handle)
 
 
 cc_library(ssa_graph_builder_factory SRCS ssa_graph_builder_factory.cc DEPS multi_devices_graph_builder ssa_graph_printer ssa_graph_checker)

paddle/fluid/framework/details/build_strategy.h

@@ -33,6 +33,8 @@ struct BuildStrategy {
   GradientScaleStrategy gradient_scale_{GradientScaleStrategy::kCoeffNumDevice};
 
   std::string debug_graphviz_path_{""};
+
+  bool enable_data_balance_{true};
 };
 
 }  // namespace details

paddle/fluid/framework/details/data_balance_op_handle.cc

+// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle/fluid/framework/details/data_balance_op_handle.h"
+#include <algorithm>
+#include "paddle/fluid/framework/details/container_cast.h"
+
+namespace paddle {
+namespace framework {
+namespace details {
+
+#ifdef PADDLE_WITH_CUDA
+DataBalanceOpHandle::DataBalanceOpHandle(
+    const std::vector<Scope *> &local_scopes,
+    const std::vector<platform::Place> &places,
+    const platform::NCCLContextMap *ctxs)
+    : local_scopes_(local_scopes), places_(places) {
+  if (ctxs) {
+    for (auto &p : places_) {
+      this->dev_ctxes_[p] = ctxs->DevCtx(p);
+    }
+  }
+}
+#else
+DataBalanceOpHandle::DataBalanceOpHandle(
+    const std::vector<Scope *> &local_scopes,
+    const std::vector<platform::Place> &places)
+    : local_scopes_(local_scopes), places_(places) {}
+#endif
+
+std::string DataBalanceOpHandle::Name() const { return "data balance"; }
+
+std::vector<std::array<int, 3>> DataBalanceOpHandle::GetBalancePlan(
+    const std::vector<int> &device_sizes) {
+  int device_num = device_sizes.size();
+  int total_size = 0;
+  int empty_num = 0;
+  std::vector<std::array<int, 2>> size_device_vec;
+  size_device_vec.reserve(device_num);
+  for (int i = 0; i < device_num; ++i) {
+    if (device_sizes[i] == 0) {
+      ++empty_num;
+    }
+    total_size += device_sizes[i];
+    size_device_vec.push_back({{device_sizes[i], i}});
+  }
+  std::vector<std::array<int, 3>> res;
+  if (empty_num == 0) {
+    // No need to do data balance.
+    return res;
+  }
+  if (total_size < device_num) {
+    // No enough data.
+    PADDLE_THROW("There is no next data.");
+  }
+  std::sort(size_device_vec.begin(), size_device_vec.end(),
+            [](const std::array<int, 2> &a, const std::array<int, 2> &b) {
+              return a[0] > b[0];
+            });
+  int expected_device_size = total_size / device_num;
+  int src_idx = 0;
+  for (int dst_idx = device_num - empty_num; dst_idx < device_num; ++dst_idx) {
+    if (size_device_vec[src_idx][0] <= expected_device_size) {
+      ++src_idx;
+      PADDLE_ENFORCE_LT(
+          src_idx, device_num - empty_num,
+          "In current srategy an empty tensor should not be copy source.");
+    }
+    size_device_vec[src_idx][0] -= expected_device_size;
+    size_device_vec[dst_idx][0] += expected_device_size;
+    res.push_back({{size_device_vec[src_idx][1], size_device_vec[dst_idx][1],
+                    expected_device_size}});
+  }
+  return res;
+}
+
+void DataBalanceOpHandle::RunImpl() {
+  if (places_.size() == 1) {
+    return;
+  }
+  auto in_var_handles = DynamicCast<VarHandle>(inputs_);
+  auto out_var_handles = DynamicCast<VarHandle>(outputs_);
+  PADDLE_ENFORCE(in_var_handles.size() % places_.size() == 0);
+  PADDLE_ENFORCE_EQ(
+      in_var_handles.size(), out_var_handles.size(),
+      "The NoDummyInputSize and NoDummyOutputSize should be equal.");
+  int data_num = in_var_handles.size() / places_.size();
+  WaitInputVarGenerated();
+  std::vector<std::vector<LoDTensor *>> lod_tensors(data_num);
+  std::vector<int> device_sizes;
+  for (int i = 0; i < static_cast<int>(in_var_handles.size()); ++i) {
+    PADDLE_ENFORCE_EQ(in_var_handles[i]->name_, out_var_handles[i]->name_,
+                      "The name of input and output should be equal.");
+    int place_idx = i / data_num;
+    int data_idx = i % data_num;
+    auto *local_scope =
+        local_scopes_[place_idx]->FindVar(kLocalExecScopeName)->Get<Scope *>();
+    auto *tensor_var = local_scope->FindVar(in_var_handles[i]->name_);
+    PADDLE_ENFORCE(tensor_var->IsType<LoDTensor>());
+    auto *tensor = tensor_var->GetMutable<LoDTensor>();
+    lod_tensors[data_idx].push_back(tensor);
+    int ins_size =
+        tensor->lod().empty() ? tensor->dims()[0] : tensor->NumElements();
+    if (data_idx == 0) {
+      device_sizes.emplace_back(ins_size);
+    } else {
+      PADDLE_ENFORCE_EQ(
+          ins_size, device_sizes.at(place_idx),
+          "All data on the same device shall have the same batch size.");
+    }
+  }
+  const auto &balance_plan = GetBalancePlan(device_sizes);
+
+  for (const auto &trans : balance_plan) {
+    for (int data_idx = 0; data_idx < data_num; ++data_idx) {
+      LoDTensor *src_tensor = lod_tensors[data_idx][trans[0]];
+      LoDTensor *dst_tensor = lod_tensors[data_idx][trans[1]];
+      int trans_ins_size = trans[2];
+      LoD src_lod = src_tensor->lod();
+      int src_ins_size =
+          src_lod.empty() ? src_tensor->dims()[0] : src_tensor->NumElements();
+      int cut_point = src_ins_size - trans_ins_size;
+      if (!src_lod.empty()) {
+        for (auto &level : src_lod) {
+          cut_point = level[cut_point];
+        }
+      }
+      TensorCopySync(src_tensor->Slice(cut_point, src_tensor->dims()[0]),
+                     dst_tensor->place(), dst_tensor);
+      src_tensor->ShareDataWith(src_tensor->Slice(0, cut_point));
+      if (!src_lod.empty()) {
+        dst_tensor->set_lod(SliceInLevel(
+            src_lod, 0, src_ins_size - trans_ins_size, src_ins_size));
+        src_tensor->set_lod(
+            SliceInLevel(src_lod, 0, 0, src_ins_size - trans_ins_size));
+      }
+    }
+  }
+}
+
+}  // namespace details
+}  // namespace framework
+}  // namespace paddle

paddle/fluid/framework/details/data_balance_op_handle.h

+// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+
+#include <string>
+#include <vector>
+#include "paddle/fluid/framework/details/op_handle_base.h"
+#include "paddle/fluid/framework/lod_tensor.h"
+#include "paddle/fluid/framework/scope.h"
+#ifdef PADDLE_WITH_CUDA
+#include "paddle/fluid/platform/nccl_helper.h"
+#endif
+
+namespace paddle {
+namespace framework {
+namespace details {
+
+struct DataBalanceOpHandle : public OpHandleBase {
+ public:
+#ifdef PADDLE_WITH_CUDA
+  DataBalanceOpHandle(const std::vector<Scope *> &local_scopes,
+                      const std::vector<platform::Place> &places,
+                      const platform::NCCLContextMap *ctxs);
+#else
+  DataBalanceOpHandle(const std::vector<Scope *> &local_scopes,
+                      const std::vector<platform::Place> &places);
+#endif
+
+  std::string Name() const override;
+
+  bool IsMultiDeviceTransfer() override { return false; };
+
+ protected:
+  void RunImpl() override;
+
+ private:
+  // std::vector<(src_dev_id, dst_dev_id, trans_size)>
+  std::vector<std::array<int, 3>> GetBalancePlan(
+      const std::vector<int> &batch_size_per_device);
+
+  const std::vector<Scope *> local_scopes_;
+  const std::vector<platform::Place> places_;
+};
+
+}  // namespace details
+}  // namespace framework
+}  // namespace paddle

paddle/fluid/framework/details/fetch_op_handle.cc

@@ -67,8 +67,8 @@ void FetchOpHandle::RunImpl() {
 #endif
     } else {
       tensors_[i].ShareDataWith(t);
-      tensors_[i].set_lod(t.lod());
     }
+    tensors_[i].set_lod(t.lod());
   }
 
   this->WaitAndMergeCPUTensors();

paddle/fluid/framework/details/multi_devices_graph_builder.cc

@@ -20,6 +20,7 @@
 #include "paddle/fluid/framework/details/all_reduce_op_handle.h"
 #include "paddle/fluid/framework/details/broadcast_op_handle.h"
 #include "paddle/fluid/framework/details/computation_op_handle.h"
+#include "paddle/fluid/framework/details/data_balance_op_handle.h"
 #include "paddle/fluid/framework/details/multi_devices_graph_builder.h"
 #include "paddle/fluid/framework/details/reduce_op_handle.h"
 #include "paddle/fluid/framework/details/rpc_op_handle.h"
@@ -215,7 +216,14 @@ std::unique_ptr<SSAGraph> MultiDevSSAGraphBuilder::Build(
       } else {
         // This op runs on all devices, and its output may have parameter's
         // gradients.
+        if (op->Type() == "read" && strategy_.enable_data_balance_) {
+          op->SetAttr("throw_eof_exp", false);
           CreateComputationalOps(&result, *op, places_.size());
+          const auto &data_var_names = op->Output("Out");
+          InsertDataBalanceOp(&result, data_var_names);
+        } else {
+          CreateComputationalOps(&result, *op, places_.size());
+        }
 
         if (!is_forwarding && places_.size() > 1) {
           // Currently, we assume that once gradient is generated, it can be
@@ -360,6 +368,29 @@ void MultiDevSSAGraphBuilder::InsertAllReduceOp(SSAGraph *result,
   }
 }
 
+void MultiDevSSAGraphBuilder::InsertDataBalanceOp(
+    SSAGraph *result, const std::vector<std::string> &datas) const {
+#ifdef PADDLE_WITH_CUDA
+  result->ops_.emplace_back(
+      new DataBalanceOpHandle(local_scopes_, places_, nccl_ctxs_));
+#else
+  result->ops_.emplace_back(new DataBalanceOpHandle(local_scopes_, places_));
+#endif
+  auto *op_handle = result->ops_.back().get();
+  for (size_t i = 0; i < places_.size(); ++i) {
+    auto &p = places_[i];
+    SetCommunicationContext(op_handle, p);
+    for (const std::string &d_name : datas) {
+      auto &vars = result->vars_[i][d_name];
+      PADDLE_ENFORCE(!vars.empty());
+      op_handle->AddInput(vars.back().get());
+      auto var = new VarHandle(vars.size(), i, d_name, p);
+      vars.emplace_back(var);
+      op_handle->AddOutput(var);
+    }
+  }
+}
+
 bool MultiDevSSAGraphBuilder::IsParameterGradientOnce(
     const std::string &og,
     std::unordered_set<std::string> *og_has_been_broadcast) const {
@@ -512,7 +543,8 @@ void MultiDevSSAGraphBuilder::CreateRPCOp(SSAGraph *result,
     op_dev_id = GetVarDeviceID(op.InputArgumentNames()[0]);
     // the variable name which contains .block means it was splited by
     // split_byref op
-    // so that we can balance the variable blocks to all the pserver instances.
+    // so that we can balance the variable blocks to all the pserver
+    // instances.
     if (strategy_.reduce_ == BuildStrategy::ReduceStrategy::kAllReduce &&
         op.InputArgumentNames()[0].find(".block") == std::string::npos) {
       op_dev_id = GetAppropriateDeviceID(op.InputArgumentNames());

paddle/fluid/framework/details/multi_devices_graph_builder.h

@@ -101,6 +101,9 @@ class MultiDevSSAGraphBuilder : public SSAGraphBuilder {
 
   void InsertAllReduceOp(SSAGraph *result, const std::string &og) const;
 
+  void InsertDataBalanceOp(SSAGraph *result,
+                           const std::vector<std::string> &datas) const;
+
   void CreateBroadcastOp(SSAGraph *result, const std::string &p_name,
                          size_t src_dev_id) const;
 

paddle/fluid/framework/details/op_handle_base.cc

@@ -58,8 +58,10 @@ void OpHandleBase::Run(bool use_cuda) {
 
 void OpHandleBase::RecordWaitEventOnCtx(platform::DeviceContext *waited_ctx) {
 #ifdef PADDLE_WITH_CUDA
+  PADDLE_ENFORCE_NOT_NULL(waited_ctx);
   if (platform::is_cpu_place(waited_ctx->GetPlace()) || events_.empty()) {
     for (auto &dev_ctx : dev_ctxes_) {
+      PADDLE_ENFORCE_NOT_NULL(dev_ctx.second);
       dev_ctx.second->Wait();
     }
   } else {

paddle/fluid/framework/lod_tensor.cc

@@ -20,6 +20,7 @@ limitations under the License. */
 #include "paddle/fluid/framework/data_type.h"
 #include "paddle/fluid/framework/framework.pb.h"
 #include "paddle/fluid/framework/lod_tensor.h"
+#include "paddle/fluid/framework/var_type.h"
 
 #include "paddle/fluid/memory/memcpy.h"
 #include "paddle/fluid/memory/memory.h"
@@ -68,9 +69,9 @@ std::ostream &operator<<(std::ostream &os, const LoDTensor &t) {
   // only print first ten elements
   int64_t size = t.numel() < 10 ? t.numel() : 10;
   for (int64_t i = 0; i < size; ++i) {
-    if (t.type().hash_code() == typeid(float).hash_code()) {  // NOLINT
+    if (IsType<float>(t.type())) {
       os << t.data<float>()[i] << " ";
-    } else if (t.type().hash_code() == typeid(int64_t).hash_code()) {
+    } else if (IsType<int64_t>(t.type())) {
       os << t.data<int64_t>()[i] << " ";
     } else {
       PADDLE_THROW("LoDTensor data type not in [float, int64_t]");
@@ -89,6 +90,7 @@ std::string LoDToString(const LoD &lod) {
 LoD SliceInLevel(const LoD &in, size_t level, size_t elem_begin,
                  size_t elem_end) {
   PADDLE_ENFORCE_LT(level, in.size());
+  PADDLE_ENFORCE_LT(elem_begin, elem_end);
   PADDLE_ENFORCE_LT(elem_end, in[level].size());
 
   LoD res;
@@ -384,7 +386,7 @@ void LoDTensor::MergeLoDTensor(
   LoD new_lod = lod_tensors[0]->lod();
   for (size_t i = 1; i < lod_tensors.size(); ++i) {
     auto *t = lod_tensors[i];
-    PADDLE_ENFORCE_EQ(new_type.hash_code(), t->type().hash_code());
+    PADDLE_ENFORCE_EQ(new_type, t->type());
     PADDLE_ENFORCE_EQ(new_layout, t->layout());
 
     PADDLE_ENFORCE_EQ(framework::product(new_dim) / new_dim[0],
@@ -392,6 +394,7 @@ void LoDTensor::MergeLoDTensor(
     new_dim[0] += t->dims()[0];
 
     auto &lod = t->lod();
+    PADDLE_ENFORCE_EQ(new_lod.size(), lod.size());
     for (size_t j = 0; j < lod.size(); ++j) {
       auto &sub_lod = new_lod[j];
       auto &offset = sub_lod.back();

paddle/fluid/framework/op_registry.h

@@ -76,13 +76,9 @@ class OpRegistry {
 template <typename PlaceType, bool at_end, size_t I, typename... KernelType>
 struct OpKernelRegistrarFunctor;
 
-template <typename PlaceType, size_t I, typename... KernelTypes>
-struct OpKernelRegistrarFunctor<PlaceType, false, I, KernelTypes...> {
-  using KERNEL_TYPE =
-      typename std::tuple_element<I, std::tuple<KernelTypes...>>::type;
-
-  void operator()(const char* op_type, const char* library_type) const {
-    using T = typename KERNEL_TYPE::ELEMENT_TYPE;
+template <typename PlaceType, typename T, typename Func>
+inline void RegisterKernelClass(const char* op_type, const char* library_type,
+                                Func func) {
   std::string library(library_type);
   std::string data_layout = "ANYLAYOUT";
   if (library == "MKLDNN") {
@@ -91,8 +87,20 @@ struct OpKernelRegistrarFunctor<PlaceType, false, I, KernelTypes...> {
   OpKernelType key(ToDataType(std::type_index(typeid(T))), PlaceType(),
                    StringToDataLayout(data_layout),
                    StringToLibraryType(library_type));
-    OperatorWithKernel::AllOpKernels()[op_type][key].reset(new KERNEL_TYPE);
+  OperatorWithKernel::AllOpKernels()[op_type][key] = func;
+}
+
+template <typename PlaceType, size_t I, typename... KernelTypes>
+struct OpKernelRegistrarFunctor<PlaceType, false, I, KernelTypes...> {
+  using KERNEL_TYPE =
+      typename std::tuple_element<I, std::tuple<KernelTypes...>>::type;
 
+  void operator()(const char* op_type, const char* library_type) const {
+    using T = typename KERNEL_TYPE::ELEMENT_TYPE;
+    RegisterKernelClass<PlaceType, T>(
+        op_type, library_type, [](const framework::ExecutionContext& ctx) {
+          KERNEL_TYPE().Compute(ctx);
+        });
     constexpr auto size = std::tuple_size<std::tuple<KernelTypes...>>::value;
     OpKernelRegistrarFunctor<PlaceType, I + 1 == size, I + 1, KernelTypes...>
         func;
@@ -116,6 +124,47 @@ class OpKernelRegistrar : public Registrar {
   }
 };
 
+template <typename PlaceType, bool at_end, size_t I, typename... KernelType>
+struct OpKernelRegistrarFunctorEx;
+
+template <typename PlaceType, typename... DataTypeAndKernelType>
+class OpKernelRegistrarEx : public Registrar {
+ public:
+  explicit OpKernelRegistrarEx(const char* op_type, const char* library_type) {
+    OpKernelRegistrarFunctorEx<PlaceType, false, 0, DataTypeAndKernelType...>
+        func;
+    func(op_type, library_type);
+  }
+};
+
+template <typename PlaceType, size_t I, typename... DataTypeAndKernelType>
+struct OpKernelRegistrarFunctorEx<PlaceType, true, I,
+                                  DataTypeAndKernelType...> {
+  void operator()(const char* op_type, const char* library_type) const {}
+};
+
+template <typename PlaceType, size_t I, typename... DataTypeAndKernelType>
+struct OpKernelRegistrarFunctorEx<PlaceType, false, I,
+                                  DataTypeAndKernelType...> {
+  using Functor =
+      typename std::tuple_element<I + 1,
+                                  std::tuple<DataTypeAndKernelType...>>::type;
+  using T =
+      typename std::tuple_element<I,
+                                  std::tuple<DataTypeAndKernelType...>>::type;
+
+  void operator()(const char* op_type, const char* library_type) const {
+    RegisterKernelClass<PlaceType, T>(op_type, library_type, Functor());
+
+    constexpr auto size =
+        std::tuple_size<std::tuple<DataTypeAndKernelType...>>::value;
+    OpKernelRegistrarFunctorEx<PlaceType, I + 2 >= size, I + 2,
+                               DataTypeAndKernelType...>
+        func;
+    func(op_type, library_type);
+  }
+};
+
 /**
  * check if MACRO is used in GLOBAL NAMESPACE.
  */
@@ -174,6 +223,25 @@ class OpKernelRegistrar : public Registrar {
 #define REGISTER_OP_CPU_KERNEL(op_type, ...) \
   REGISTER_OP_KERNEL(op_type, CPU, ::paddle::platform::CPUPlace, __VA_ARGS__)
 
+#define REGISTER_OP_KERNEL_EX(op_type, library_type, place_class, ...)      \
+  STATIC_ASSERT_GLOBAL_NAMESPACE(                                           \
+      __reg_op_kernel_##op_type##_##library_type##__,                       \
+      "REGISTER_OP_KERNEL_EX must be called in global namespace");          \
+  static ::paddle::framework::OpKernelRegistrarEx<place_class, __VA_ARGS__> \
+      __op_kernel_registrar_##op_type##_##library_type##__(#op_type,        \
+                                                           #library_type);  \
+  int TouchOpKernelRegistrar_##op_type##_##library_type() {                 \
+    __op_kernel_registrar_##op_type##_##library_type##__.Touch();           \
+    return 0;                                                               \
+  }
+
+#define REGISTER_OP_CUDA_KERNEL_FUNCTOR(op_type, ...)                 \
+  REGISTER_OP_KERNEL_EX(op_type, CUDA, ::paddle::platform::CUDAPlace, \
+                        __VA_ARGS__)
+
+#define REGISTER_OP_CPU_KERNEL_FUNCTOR(op_type, ...) \
+  REGISTER_OP_KERNEL_EX(op_type, CPU, ::paddle::platform::CPUPlace, __VA_ARGS__)
+
 /**
  * Macro to mark what Operator and Kernel
  * we will use and tell the compiler to

paddle/fluid/framework/operator.cc

@@ -592,8 +592,7 @@ static void CheckTensorNANOrInf(const std::string& name,
   if (tensor.memory_size() == 0) {
     return;
   }
-  if (tensor.type().hash_code() != typeid(float).hash_code() &&   // NOLINT
-      tensor.type().hash_code() != typeid(double).hash_code()) {  // NOLINT
+  if (!IsType<float>(tensor.type()) && !IsType<double>(tensor.type())) {
     return;
   }
   PADDLE_ENFORCE(!framework::TensorContainsInf(tensor),
@@ -652,7 +651,7 @@ void OperatorWithKernel::RunImpl(const Scope& scope,
     dev_ctx = pool.Get(expected_kernel_key.place_);
   }
 
-  kernel_iter->second->Compute(ExecutionContext(*this, exec_scope, *dev_ctx));
+  kernel_iter->second(ExecutionContext(*this, exec_scope, *dev_ctx));
 
   if (!transfered_inplace_vars.empty()) {
     // there is inplace variable has been transfered.

paddle/fluid/framework/operator.h

@@ -347,9 +347,9 @@ class OpKernel : public OpKernelBase {
 
 class OperatorWithKernel : public OperatorBase {
  public:
+  using OpKernelFunc = std::function<void(const ExecutionContext&)>;
   using OpKernelMap =
-      std::unordered_map<OpKernelType, std::unique_ptr<OpKernelBase>,
-                         OpKernelType::Hash>;
+      std::unordered_map<OpKernelType, OpKernelFunc, OpKernelType::Hash>;
 
   OperatorWithKernel(const std::string& type, const VariableNameMap& inputs,
                      const VariableNameMap& outputs, const AttributeMap& attrs)

paddle/fluid/framework/var_type.h

@@ -24,18 +24,24 @@ limitations under the License. */
 
 namespace paddle {
 namespace framework {
+
+template <typename T>
+bool IsType(const std::type_index& type_index) {
+  return type_index == std::type_index(typeid(T));
+}
+
 inline proto::VarType::Type ToVarType(std::type_index type) {
-  if (type.hash_code() == typeid(LoDTensor).hash_code()) {
+  if (IsType<LoDTensor>(type)) {
     return proto::VarType_Type_LOD_TENSOR;
-  } else if (type.hash_code() == typeid(LoDRankTable).hash_code()) {
+  } else if (IsType<LoDRankTable>(type)) {
     return proto::VarType_Type_LOD_RANK_TABLE;
-  } else if (type.hash_code() == typeid(LoDTensorArray).hash_code()) {
+  } else if (IsType<LoDTensorArray>(type)) {
     return proto::VarType_Type_LOD_TENSOR_ARRAY;
-  } else if (type.hash_code() == typeid(SelectedRows).hash_code()) {
+  } else if (IsType<SelectedRows>(type)) {
     return proto::VarType_Type_SELECTED_ROWS;
-  } else if (type.hash_code() == typeid(ReaderHolder).hash_code()) {
+  } else if (IsType<ReaderHolder>(type)) {
     return proto::VarType_Type_READER;
-  } else if (type.hash_code() == typeid(ChannelHolder).hash_code()) {
+  } else if (IsType<ChannelHolder>(type)) {
     return proto::VarType_Type_CHANNEL;
   } else {
     PADDLE_THROW("ToVarType:Unsupported type %s", type.name());

paddle/fluid/inference/analysis/helper.h

@@ -16,6 +16,7 @@ limitations under the License. */
 
 #include <cstdio>
 #include <string>
+#include <typeindex>
 #include <unordered_map>
 #include <vector>
 
@@ -41,7 +42,7 @@ int AccuDims(Vec &&vec, int size) {
   return res;
 }
 
-#define SET_TYPE(type__) dic_[typeid(type__).hash_code()] = #type__;
+#define SET_TYPE(type__) dic_[std::type_index(typeid(type__))] = #type__;
 /*
  * Map typeid to representation.
  */
@@ -53,14 +54,14 @@ struct DataTypeNamer {
 
   template <typename T>
   const std::string &repr() const {
-    auto x = typeid(T).hash_code();
+    auto x = std::type_index(typeid(T));
     PADDLE_ENFORCE(dic_.count(x), "unknown type for representation");
     return dic_.at(x);
   }
 
-  const std::string &repr(size_t &hash) const {  // NOLINT
-    PADDLE_ENFORCE(dic_.count(hash), "unknown type for representation");
-    return dic_.at(hash);
+  const std::string &repr(const std::type_index &type) const {  // NOLINT
+    PADDLE_ENFORCE(dic_.count(type), "unknown type for representation");
+    return dic_.at(type);
   }
 
  private:
@@ -71,9 +72,7 @@ struct DataTypeNamer {
     SET_TYPE(void *);
   }
 
-  std::unordered_map<decltype(typeid(int).hash_code()),  // NOLINT
-                     std::string>
-      dic_;
+  std::unordered_map<std::type_index, std::string> dic_;
 };
 #undef SET_TYPE
 

paddle/fluid/inference/analysis/node.cc

@@ -23,9 +23,9 @@ namespace analysis {
 template <>
 std::string &NodeAttr::As<std::string>() {
   if (data_.empty()) {
-    type_hash_ = typeid(std::string).hash_code();
+    type_index_ = std::type_index(typeid(std::string));
   }
-  PADDLE_ENFORCE_EQ(type_hash_, typeid(std::string).hash_code());
+  PADDLE_ENFORCE_EQ(type_index_, std::type_index(typeid(std::string)));
   return data_;
 }
 

paddle/fluid/inference/analysis/node.h

@@ -25,6 +25,7 @@ limitations under the License. */
 #include <unordered_map>
 #include <vector>
 
+#include "paddle/fluid/framework/var_type.h"
 #include "paddle/fluid/inference/analysis/device.h"
 #include "paddle/fluid/inference/analysis/dot.h"
 #include "paddle/fluid/inference/analysis/helper.h"
@@ -57,12 +58,12 @@ struct NodeAttr {
     // init storage in the first usage.
     if (data_.empty()) {
       VLOG(4) << "resize data to " << sizeof(T);
-      type_hash_ = typeid(T).hash_code();
+      type_index_ = std::type_index(typeid(T));
       data_.resize(sizeof(T));
     }
-    PADDLE_ENFORCE(type_hash_ == typeid(T).hash_code(),
+    PADDLE_ENFORCE(framework::IsType<T>(type_index_),
                    "type not matched, origin is %s, want %s",
-                   DataTypeNamer::Global().repr(type_hash_),
+                   DataTypeNamer::Global().repr(type_index_),
                    DataTypeNamer::Global().repr<T>());
     PADDLE_ENFORCE_EQ(data_.size(), sizeof(T), "Node attr type recast error");
     return *reinterpret_cast<T *>(&data_[0]);
@@ -70,7 +71,7 @@ struct NodeAttr {
 
  private:
   std::string data_;
-  size_t type_hash_{std::numeric_limits<size_t>::max()};
+  std::type_index type_index_{typeid(NodeAttr)};
 };
 
 /*

paddle/fluid/operators/conditional_block_op.cc

@@ -14,6 +14,7 @@ limitations under the License. */
 #include <algorithm>
 #include "paddle/fluid/framework/executor.h"
 #include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/framework/var_type.h"
 
 namespace paddle {
 namespace operators {
@@ -47,7 +48,7 @@ class ConditionalOp : public framework::OperatorBase {
     if (!(ips.size() == 1UL && ips[0]->IsInitialized())) {
       PADDLE_THROW("should have one initialized input as condition");
     }
-    if (!(ips[0]->type().hash_code() == typeid(bool).hash_code() &&  // NOLINT
+    if (!(framework::IsType<bool>(ips[0]->type()) &&  // NOLINT
           ips[0]->numel() == 1)) {
       PADDLE_THROW(
           "condition input's data type should be bool, "

paddle/fluid/operators/fc_mkldnn_op.cc

@@ -115,6 +115,7 @@ class MKLDNNMemory {
 
 template <typename T>
 class FCMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
+ public:
   void Compute(const paddle::framework::ExecutionContext& ctx) const override {
     PADDLE_ENFORCE(paddle::platform::is_cpu_place(ctx.GetPlace()),
                    "It must use CPUPlace.");

paddle/fluid/operators/print_op.cc

@@ -16,6 +16,7 @@
 #include <ctime>
 
 #include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/framework/var_type.h"
 #include "paddle/fluid/framework/variable.h"
 
 namespace paddle {
@@ -62,7 +63,7 @@ struct Formater {
     }
   }
   void PrintDtype() {
-    if (dtype.hash_code() != typeid(const char).hash_code()) {
+    if (!framework::IsType<const char>(dtype)) {
       CLOG << "\tdtype: " << dtype.name() << std::endl;
     }
   }
@@ -83,15 +84,15 @@ struct Formater {
   void PrintData(size_t size) {
     PADDLE_ENFORCE_NOT_NULL(data);
     // print float
-    if (dtype.hash_code() == typeid(const float).hash_code()) {
+    if (framework::IsType<const float>(dtype)) {
       Display<float>(size);
-    } else if (dtype.hash_code() == typeid(const double).hash_code()) {
+    } else if (framework::IsType<const double>(dtype)) {
       Display<double>(size);
-    } else if (dtype.hash_code() == typeid(const int).hash_code()) {
+    } else if (framework::IsType<const int>(dtype)) {
       Display<int>(size);
-    } else if (dtype.hash_code() == typeid(const int64_t).hash_code()) {
+    } else if (framework::IsType<const int64_t>(dtype)) {
       Display<int64_t>(size);
-    } else if (dtype.hash_code() == typeid(const bool).hash_code()) {
+    } else if (framework::IsType<const bool>(dtype)) {
       Display<bool>(size);
     } else {
       CLOG << "\tdata: unprintable type: " << dtype.name() << std::endl;

paddle/fluid/operators/read_op.cc

@@ -66,9 +66,19 @@ class ReadOp : public framework::OperatorBase {
     std::vector<std::string> out_arg_names = Outputs("Out");
     std::vector<framework::LoDTensor> ins;
     reader->ReadNext(&ins);
-    PADDLE_ENFORCE(!ins.empty(), "There is no next data.");
+    if (ins.empty()) {
+      if (Attr<bool>("throw_eof_exp")) {
+        PADDLE_THROW("There is no next data.");
+      } else {
+        ins.resize(out_arg_names.size());
+        for (auto& tensor : ins) {
+          // data type is not important for subsequent DataBalanceOpHandle
+          tensor.mutable_data<float>(framework::make_ddim({0}), dev_place);
+        }
+      }
+    }
     PADDLE_ENFORCE_EQ(ins.size(), out_arg_names.size());
-    for (size_t i = 0; i < ins.size(); ++i) {
+    for (size_t i = 0; i < out_arg_names.size(); ++i) {
       auto* out =
           scope.FindVar(out_arg_names[i])->GetMutable<framework::LoDTensor>();
       out->ShareDataWith(ins[i]);
@@ -82,6 +92,10 @@ class ReadOpMaker : public framework::OpProtoAndCheckerMaker {
   void Make() override {
     AddInput("Reader", "(ReaderHolder) The executed reader.");
     AddOutput("Out", "(LoDTensor) The output data.").AsDuplicable();
+    AddAttr<bool>("throw_eof_exp",
+                  "If set true, an exception will be thrown when the Reader "
+                  "yields empty (which means there is no next data).")
+        .SetDefault(true);
     AddComment(R"DOC(
       Read Operator
 

paddle/fluid/operators/reshape_op.cc

@@ -12,14 +12,108 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
 limitations under the License. */
 
-#include "paddle/fluid/operators/reshape_op.h"
-
 #include <string>
 #include <vector>
+#include "paddle/fluid/framework/op_registry.h"
 
 namespace paddle {
 namespace operators {
 
+class ReshapeOp : public framework::OperatorWithKernel {
+ public:
+  ReshapeOp(const std::string &type, const framework::VariableNameMap &inputs,
+            const framework::VariableNameMap &outputs,
+            const framework::AttributeMap &attrs)
+      : OperatorWithKernel(type, inputs, outputs, attrs) {}
+
+  void InferShape(framework::InferShapeContext *ctx) const override {
+    PADDLE_ENFORCE(ctx->HasInput("X"),
+                   "Input(X) of ReshapeOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasOutput("Out"),
+                   "Output(Out) of ReshapeOp should not be null.");
+
+    const std::vector<int> &shape = ctx->Attrs().Get<std::vector<int>>("shape");
+    PADDLE_ENFORCE(!shape.empty(),
+                   "The shape information must be set by Attr(shape).");
+
+    if (ctx->HasInput("Shape") && ctx->IsRuntime()) {
+      // If true, set the shape of Output(Out) according to Input(Shape) in
+      // ReshapeKernel with ExecutionContext. Also check LoD in ReshapeKernel.
+      ctx->ShareLoD("X", /*->*/ "Out");
+      return;
+    }
+
+    auto x_dims = ctx->GetInputDim("X");
+    auto out_dims = ValidateShape(shape, x_dims);
+    ctx->SetOutputDim("Out", out_dims);
+    if (x_dims[0] == out_dims[0]) {
+      // Only pass LoD when the first dimension of output and Input(X)
+      // are the same.
+      ctx->ShareLoD("X", /*->*/ "Out");
+    }
+  }
+
+  static framework::DDim ValidateShape(const std::vector<int> shape,
+                                       const framework::DDim &in_dims) {
+    const int64_t in_size = framework::product(in_dims);
+    // only one dimension can be set to -1, whose size will be automatically
+    // infered.
+    const int64_t unk_dim_val = -1;
+    const int64_t copy_dim_val = 0;
+
+    std::vector<int64_t> output_shape(shape.size(), 0);
+    int64_t capacity = 1;
+    int unk_dim_idx = -1;
+    for (size_t i = 0; i < shape.size(); ++i) {
+      if (shape[i] == unk_dim_val) {
+        PADDLE_ENFORCE(
+            unk_dim_idx == -1,
+            "Only one input dimension of Attr(shape) can be unknown.");
+        unk_dim_idx = i;
+      } else if (shape[i] == copy_dim_val) {
+        PADDLE_ENFORCE(
+            static_cast<int>(i) < in_dims.size(),
+            "The index of dimension to copy from input shape must be less "
+            "than the size of input shape.");
+      } else {
+        PADDLE_ENFORCE(
+            shape[i] > 0,
+            "Each input dimension of Attr(shape) must not be negtive except "
+            "one unknown dimension.");
+      }
+
+      capacity *= (shape[i] ? shape[i] : in_dims[i]);
+      output_shape[i] =
+          (shape[i] ? static_cast<int64_t>(shape[i]) : in_dims[i]);
+    }
+
+    if (unk_dim_idx != -1) {
+      if (in_size > 0) {
+        // in_size < 0 and is un-determinate in compile time, skip the check,
+        // for example, in_dims = [-1, 8, 1, 1], shape = [-1, 3, 8],
+        // capacity = -24, in_size = -8, output_shape[0] = 0
+        // the following check will fail.
+        output_shape[unk_dim_idx] = -in_size / capacity;
+        PADDLE_ENFORCE_EQ(output_shape[unk_dim_idx] * capacity, -in_size,
+                          "Invalid shape is given.");
+      } else {
+        output_shape[unk_dim_idx] = -1;
+      }
+    } else {
+      PADDLE_ENFORCE_EQ(capacity, in_size, "Invalid shape is given.");
+    }
+    return framework::make_ddim(output_shape);
+  }
+
+ protected:
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext &ctx) const override {
+    return framework::OpKernelType(
+        framework::ToDataType(ctx.Input<framework::LoDTensor>("X")->type()),
+        ctx.device_context());
+  }
+};
+
 class ReshapeOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
   void Make() override {
@@ -107,19 +201,93 @@ class ReshapeGradOp : public framework::OperatorWithKernel {
   }
 };
 
+class ReshapeKernel {
+ public:
+  void operator()(const framework::ExecutionContext &ctx) const {
+    auto *out = ctx.Output<framework::LoDTensor>("Out");
+    auto *in = ctx.Input<framework::LoDTensor>("X");
+
+    auto *shape_tensor = ctx.HasInput("Shape")
+                             ? ctx.Input<framework::LoDTensor>("Shape")
+                             : nullptr;
+
+    framework::DDim out_dims = out->dims();
+
+    if (shape_tensor) {
+      auto *shape_data = shape_tensor->data<int>();
+      framework::Tensor cpu_shape_tensor;
+      if (platform::is_gpu_place(ctx.GetPlace())) {
+        TensorCopySync(*shape_tensor, platform::CPUPlace(), &cpu_shape_tensor);
+        shape_data = cpu_shape_tensor.data<int>();
+      }
+      auto shape =
+          std::vector<int>(shape_data, shape_data + shape_tensor->numel());
+      out_dims = ReshapeOp::ValidateShape(shape, in->dims());
+    }
+    if (!in->lod().empty()) {
+      PADDLE_ENFORCE_EQ(
+          out_dims[0], in->dims()[0],
+          "Reshape operator cannot reshape an input sequence batch "
+          "into an output sequence batch that has a different "
+          "number of time steps. Please consider using "
+          "sequence_reshape op.");
+    }
+
+    bool inplace = ctx.Attr<bool>("inplace");
+    out->Resize(out_dims);
+    if (!inplace) {
+      out->mutable_data(ctx.GetPlace(), in->type());
+      framework::TensorCopySync(*in, ctx.GetPlace(), out);
+      out->Resize(out_dims);
+    } else {
+      out->ShareDataWith(*in);
+      out->Resize(out_dims);
+    }
+  }
+};
+
+class ReshapeGradKernel {
+ public:
+  void operator()(const framework::ExecutionContext &ctx) const {
+    auto *d_out = ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
+    auto *d_x = ctx.Output<framework::Tensor>(framework::GradVarName("X"));
+
+    d_x->mutable_data(ctx.GetPlace(), d_out->type());
+    bool inplace = ctx.Attr<bool>("inplace");
+
+    auto in_dims = d_x->dims();
+    if (!inplace) {
+      framework::TensorCopy(*d_out, ctx.GetPlace(), ctx.device_context(), d_x);
+      ctx.device_context().Wait();
+      d_x->Resize(in_dims);
+    } else {
+      d_x->ShareDataWith(*d_out);
+      d_x->Resize(in_dims);
+    }
+  }
+};
+
 }  // namespace operators
 }  // namespace paddle
 namespace ops = paddle::operators;
-using CPU = paddle::platform::CPUDeviceContext;
 
 REGISTER_OPERATOR(reshape, ops::ReshapeOp, ops::ReshapeOpMaker,
                   paddle::framework::DefaultGradOpDescMaker<true>);
 REGISTER_OPERATOR(reshape_grad, ops::ReshapeGradOp);
-REGISTER_OP_CPU_KERNEL(reshape, ops::ReshapeKernel<CPU, float>,
-                       ops::ReshapeKernel<CPU, double>,
-                       ops::ReshapeKernel<CPU, int>,
-                       ops::ReshapeKernel<CPU, int64_t>);
-REGISTER_OP_CPU_KERNEL(reshape_grad, ops::ReshapeGradKernel<CPU, float>,
-                       ops::ReshapeGradKernel<CPU, double>,
-                       ops::ReshapeGradKernel<CPU, int>,
-                       ops::ReshapeGradKernel<CPU, int64_t>);
+REGISTER_OP_CPU_KERNEL_FUNCTOR(reshape, float, ops::ReshapeKernel, double,
+                               ops::ReshapeKernel, int, ops::ReshapeKernel,
+                               int64_t, ops::ReshapeKernel);
+REGISTER_OP_CPU_KERNEL_FUNCTOR(reshape_grad, float, ops::ReshapeGradKernel,
+                               double, ops::ReshapeGradKernel, int,
+                               ops::ReshapeGradKernel, int64_t,
+                               ops::ReshapeGradKernel);
+
+#ifdef PADDLE_WITH_CUDA
+REGISTER_OP_CUDA_KERNEL_FUNCTOR(reshape, float, ops::ReshapeKernel, double,
+                                ops::ReshapeKernel, int, ops::ReshapeKernel,
+                                int64_t, ops::ReshapeKernel);
+REGISTER_OP_CUDA_KERNEL_FUNCTOR(reshape_grad, float, ops::ReshapeGradKernel,
+                                double, ops::ReshapeGradKernel, int,
+                                ops::ReshapeGradKernel, int64_t,
+                                ops::ReshapeGradKernel);
+#endif

paddle/fluid/operators/reshape_op.cu

-/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License. */
-
-#include "paddle/fluid/operators/reshape_op.h"
-using CUDA = paddle::platform::CUDADeviceContext;
-
-REGISTER_OP_CUDA_KERNEL(reshape, paddle::operators::ReshapeKernel<CUDA, float>,
-                        paddle::operators::ReshapeKernel<CUDA, double>,
-                        paddle::operators::ReshapeKernel<CUDA, int>,
-                        paddle::operators::ReshapeKernel<CUDA, int64_t>);
-REGISTER_OP_CUDA_KERNEL(reshape_grad,
-                        paddle::operators::ReshapeGradKernel<CUDA, float>,
-                        paddle::operators::ReshapeGradKernel<CUDA, double>,
-                        paddle::operators::ReshapeGradKernel<CUDA, int>,
-                        paddle::operators::ReshapeGradKernel<CUDA, int64_t>);

paddle/fluid/operators/reshape_op.h

-/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License. */
-
-#pragma once
-
-#include <string>
-#include <vector>
-
-#include "paddle/fluid/framework/eigen.h"
-#include "paddle/fluid/framework/op_registry.h"
-
-namespace paddle {
-namespace operators {
-
-class ReshapeOp : public framework::OperatorWithKernel {
- public:
-  ReshapeOp(const std::string &type, const framework::VariableNameMap &inputs,
-            const framework::VariableNameMap &outputs,
-            const framework::AttributeMap &attrs)
-      : OperatorWithKernel(type, inputs, outputs, attrs) {}
-
-  void InferShape(framework::InferShapeContext *ctx) const override {
-    PADDLE_ENFORCE(ctx->HasInput("X"),
-                   "Input(X) of ReshapeOp should not be null.");
-    PADDLE_ENFORCE(ctx->HasOutput("Out"),
-                   "Output(Out) of ReshapeOp should not be null.");
-
-    const std::vector<int> &shape = ctx->Attrs().Get<std::vector<int>>("shape");
-    PADDLE_ENFORCE(!shape.empty(),
-                   "The shape information must be set by Attr(shape).");
-
-    if (ctx->HasInput("Shape") && ctx->IsRuntime()) {
-      // If true, set the shape of Output(Out) according to Input(Shape) in
-      // ReshapeKernel with ExecutionContext. Also check LoD in ReshapeKernel.
-      ctx->ShareLoD("X", /*->*/ "Out");
-      return;
-    }
-
-    auto x_dims = ctx->GetInputDim("X");
-    auto out_dims = ValidateShape(shape, x_dims);
-    ctx->SetOutputDim("Out", out_dims);
-    if (x_dims[0] == out_dims[0]) {
-      // Only pass LoD when the first dimension of output and Input(X)
-      // are the same.
-      ctx->ShareLoD("X", /*->*/ "Out");
-    }
-  }
-
-  static framework::DDim ValidateShape(const std::vector<int> shape,
-                                       const framework::DDim &in_dims) {
-    const int64_t in_size = framework::product(in_dims);
-    // only one dimension can be set to -1, whose size will be automatically
-    // infered.
-    const int64_t unk_dim_val = -1;
-    const int64_t copy_dim_val = 0;
-
-    std::vector<int64_t> output_shape(shape.size(), 0);
-    int64_t capacity = 1;
-    int unk_dim_idx = -1;
-    for (size_t i = 0; i < shape.size(); ++i) {
-      if (shape[i] == unk_dim_val) {
-        PADDLE_ENFORCE(
-            unk_dim_idx == -1,
-            "Only one input dimension of Attr(shape) can be unknown.");
-        unk_dim_idx = i;
-      } else if (shape[i] == copy_dim_val) {
-        PADDLE_ENFORCE(
-            static_cast<int>(i) < in_dims.size(),
-            "The index of dimension to copy from input shape must be less "
-            "than the size of input shape.");
-      } else {
-        PADDLE_ENFORCE(
-            shape[i] > 0,
-            "Each input dimension of Attr(shape) must not be negtive except "
-            "one unknown dimension.");
-      }
-
-      capacity *= (shape[i] ? shape[i] : in_dims[i]);
-      output_shape[i] =
-          (shape[i] ? static_cast<int64_t>(shape[i]) : in_dims[i]);
-    }
-
-    if (unk_dim_idx != -1) {
-      if (in_size > 0) {
-        // in_size < 0 and is un-determinate in compile time, skip the check,
-        // for example, in_dims = [-1, 8, 1, 1], shape = [-1, 3, 8],
-        // capacity = -24, in_size = -8, output_shape[0] = 0
-        // the following check will fail.
-        output_shape[unk_dim_idx] = -in_size / capacity;
-        PADDLE_ENFORCE_EQ(output_shape[unk_dim_idx] * capacity, -in_size,
-                          "Invalid shape is given.");
-      } else {
-        output_shape[unk_dim_idx] = -1;
-      }
-    } else {
-      PADDLE_ENFORCE_EQ(capacity, in_size, "Invalid shape is given.");
-    }
-    return framework::make_ddim(output_shape);
-  }
-
- protected:
-  framework::OpKernelType GetExpectedKernelType(
-      const framework::ExecutionContext &ctx) const override {
-    return framework::OpKernelType(
-        framework::ToDataType(ctx.Input<framework::LoDTensor>("X")->type()),
-        ctx.device_context());
-  }
-};
-
-template <typename DeviceContext, typename T>
-class ReshapeKernel : public framework::OpKernel<T> {
- public:
-  void Compute(const framework::ExecutionContext &ctx) const {
-    auto *out = ctx.Output<framework::LoDTensor>("Out");
-    auto *in = ctx.Input<framework::LoDTensor>("X");
-
-    auto *shape_tensor = ctx.HasInput("Shape")
-                             ? ctx.Input<framework::LoDTensor>("Shape")
-                             : nullptr;
-
-    framework::DDim out_dims = out->dims();
-
-    if (shape_tensor) {
-      auto *shape_data = shape_tensor->data<int>();
-      framework::Tensor cpu_shape_tensor;
-      if (platform::is_gpu_place(ctx.GetPlace())) {
-        TensorCopySync(*shape_tensor, platform::CPUPlace(), &cpu_shape_tensor);
-        shape_data = cpu_shape_tensor.data<int>();
-      }
-      auto shape =
-          std::vector<int>(shape_data, shape_data + shape_tensor->numel());
-      out_dims = ReshapeOp::ValidateShape(shape, in->dims());
-    }
-    if (!in->lod().empty()) {
-      PADDLE_ENFORCE_EQ(
-          out_dims[0], in->dims()[0],
-          "Reshape operator cannot reshape an input sequence batch "
-          "into an output sequence batch that has a different "
-          "number of time steps. Please consider using "
-          "sequence_reshape op.");
-    }
-
-    bool inplace = ctx.Attr<bool>("inplace");
-    out->Resize(out_dims);
-    if (!inplace) {
-      out->mutable_data<T>(ctx.GetPlace());
-      framework::TensorCopySync(*in, ctx.GetPlace(), out);
-      out->Resize(out_dims);
-    } else {
-      out->ShareDataWith(*in);
-      out->Resize(out_dims);
-    }
-  }
-};
-
-template <typename DeviceContext, typename T>
-class ReshapeGradKernel : public framework::OpKernel<T> {
- public:
-  void Compute(const framework::ExecutionContext &ctx) const {
-    auto *d_out = ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
-    auto *d_x = ctx.Output<framework::Tensor>(framework::GradVarName("X"));
-
-    d_x->mutable_data<T>(ctx.GetPlace());
-    bool inplace = ctx.Attr<bool>("inplace");
-
-    auto in_dims = d_x->dims();
-    if (!inplace) {
-      framework::TensorCopy(*d_out, ctx.GetPlace(), ctx.device_context(), d_x);
-      ctx.device_context().Wait();
-      d_x->Resize(in_dims);
-    } else {
-      d_x->ShareDataWith(*d_out);
-      d_x->Resize(in_dims);
-    }
-  }
-};
-}  // namespace operators
-}  // namespace paddle

paddle/fluid/operators/while_op.cc

@@ -17,6 +17,7 @@ limitations under the License. */
 #include "paddle/fluid/framework/lod_tensor_array.h"
 #include "paddle/fluid/framework/op_registry.h"
 #include "paddle/fluid/framework/operator.h"
+#include "paddle/fluid/framework/var_type.h"
 #include "paddle/fluid/operators/detail/safe_ref.h"
 
 namespace paddle {
@@ -135,15 +136,14 @@ class WhileGradOp : public framework::OperatorBase {
         auto &og_inside =
             detail::Ref(cur_scope.Var(inside_og_name),
                         "Cannot find inside gradient %s", inside_og_name);
-        if (og_outside.Type().hash_code() ==
-            typeid(framework::LoDTensor).hash_code()) {
+        if (framework::IsType<framework::LoDTensor>(og_outside.Type())) {
           auto &outside_tensor = og_outside.Get<framework::LoDTensor>();
           auto &inside_tensor =
               detail::Ref(og_inside.GetMutable<framework::LoDTensor>());
           inside_tensor.set_lod(outside_tensor.lod());
           inside_tensor.ShareDataWith(outside_tensor);
-        } else if (og_outside.Type().hash_code() ==
-                   typeid(framework::LoDTensorArray).hash_code()) {
+        } else if (framework::IsType<framework::LoDTensorArray>(
+                       og_outside.Type())) {
           auto &outside_array = og_outside.Get<framework::LoDTensorArray>();
           auto &inside_array =
               detail::Ref(og_inside.GetMutable<framework::LoDTensorArray>());

paddle/fluid/pybind/pybind.cc

@@ -643,7 +643,11 @@ All parameter, weight, gradient are variables in Paddle.
           [](const BuildStrategy &self) { return self.debug_graphviz_path_; },
           [](BuildStrategy &self, const std::string &path) {
             self.debug_graphviz_path_ = path;
-          });
+          })
+      .def_property(
+          "enable_data_balance",
+          [](const BuildStrategy &self) { return self.enable_data_balance_; },
+          [](BuildStrategy &self, bool b) { self.enable_data_balance_ = b; });
 
   pe.def(py::init<const std::vector<platform::Place> &,
                   const std::unordered_set<std::string> &,

paddle/api/Paddle.i → paddle/legacy/api/Paddle.i

@@ -2,7 +2,7 @@
 %include "std_string.i"
 %{
 #define SWIG_FILE_WITH_INIT
-#include "api/PaddleAPI.h"   
+#include "legacy/api/PaddleAPI.h"
 %}
 
 %include "exception.i"
@@ -199,4 +199,4 @@ namespace std {
 %ignore OptimizationConfigPrivate;
 %ignore ParameterTraverseCallbackPrivate;
 %include "utils/GlobalConstants.h"
-%include "api/PaddleAPI.h"
+%include "legacy/api/PaddleAPI.h"

paddle/scripts/paddle_build.sh

@@ -106,7 +106,7 @@ function cmake_gen() {
         -DWITH_FLUID_ONLY=${WITH_FLUID_ONLY:-OFF}
         -DCMAKE_EXPORT_COMPILE_COMMANDS=ON
         -DWITH_CONTRIB=${WITH_CONTRIB:-ON}
-        -DWITH_ANAKIN=${WITH_ANAKIN:-ON}
+        -DWITH_ANAKIN=${WITH_ANAKIN:-OFF}
         -DWITH_INFERENCE_DEMO=${WITH_INFERENCE_DEMO:-ON}
     ========================================
 EOF
@@ -135,7 +135,7 @@ EOF
         -DWITH_FLUID_ONLY=${WITH_FLUID_ONLY:-OFF} \
         -DCMAKE_EXPORT_COMPILE_COMMANDS=ON \
         -DWITH_CONTRIB=${WITH_CONTRIB:-ON} \
-        -DWITH_ANAKIN=${WITH_ANAKIN:-ON} \
+        -DWITH_ANAKIN=${WITH_ANAKIN:-OFF} \
         -DWITH_INFERENCE_DEMO=${WITH_INFERENCE_DEMO:-ON}
 }
 

python/paddle/fluid/io.py

@@ -18,7 +18,7 @@ import time
 import shutil
 
 from paddle.fluid.evaluator import Evaluator
-from paddle.fluid.framework import Program, Parameter, default_main_program, Variable
+from paddle.fluid.framework import Program, Parameter, default_main_program, default_startup_program, Variable
 from . import core
 
 __all__ = [
@@ -1374,3 +1374,101 @@ def get_latest_checkpoint_serial(checkpoint_dir):
         if success_num > current_dir:
             current_dir = success_num
     return current_dir
+
+
+def get_test_program(filelist, program=None, startup_program=None):
+    """
+    Transpile current train program to a program to read test dataset
+    if the program is using reader ops like "open_files_op".
+    """
+
+    def _copy_reader_var_(block, var, new_name=None):
+        if new_name == None:
+            new_name = var.name
+        new_var = block.create_var(
+            name=str(new_name), type=core.VarDesc.VarType.READER)
+        new_var.desc.set_shapes(var.desc.shapes())
+        new_var.desc.set_dtypes(var.desc.dtypes())
+        new_var.persistable = True
+        return new_var
+
+    def _get_test_reader_name(train_reader_name):
+        return train_reader_name + "_test"
+
+    def _is_reader_op(op):
+        block = op.block
+        if "Out" in op.output_names:
+            reader_out = block.vars[op.output("Out")[0]]
+            if reader_out.type == core.VarDesc.VarType.READER:
+                return True
+        return False
+
+    if program == None:
+        program = default_main_program()
+    if startup_program == None:
+        startup_program = default_startup_program()
+    startup_block = startup_program.global_block()
+
+    # 1. find out the orignal reader var name
+    startup_reader_op_list = []
+
+    for op in startup_block.ops:
+        if _is_reader_op(op):
+            startup_reader_op_list.append(op)
+
+    if len(startup_reader_op_list) == 0:
+        return program
+
+    root_reader_op = startup_reader_op_list[0]
+    train_test_reader_map = {}
+    # 2. add operators to startup to read open and read test data files
+    for op in startup_reader_op_list:
+        assert (len(op.output("Out")) == 1)
+        train_reader_name = op.output("Out")[0]
+        train_reader = startup_block.vars[train_reader_name]
+        test_reader = _copy_reader_var_(
+            startup_block,
+            train_reader,
+            new_name=_get_test_reader_name(train_reader_name))
+        train_test_reader_map[train_reader.name] = test_reader
+
+        test_op_inputs = {}
+        for name in op.input_names:
+            train_arg_names = op.input(name)
+            test_arg_vars = []
+            for arg_name in train_arg_names:
+                arg_var = train_test_reader_map[
+                    arg_name] if name == "UnderlyingReader" else startup_block.vars[
+                        arg_name]
+                test_arg_vars.append(arg_var)
+            test_op_inputs[name] = test_arg_vars
+
+        test_op = startup_block.append_op(
+            type=op.type,
+            inputs=test_op_inputs,
+            outputs={'Out': [test_reader]},
+            attrs=op.attrs)
+        # root reader op's filelist attr for read test files
+        if op.type == root_reader_op.type:
+            test_op.set_attr("file_names", filelist)
+        if op.type == "create_multi_pass_reader":
+            test_op.set_attr("pass_num", 1)
+
+    # 3. rename reader vars in inference program to different name
+    #    to avoid read from train data.
+    main_block = program.global_block()
+    for var in main_block.vars.values():
+        if var.type == core.VarDesc.VarType.READER:
+            main_block.rename_var(
+                str(var.name), str(_get_test_reader_name(var.name)))
+
+    for op in main_block.ops:
+        if op.type == root_reader_op.type:
+            test_op.set_attr("file_names", filelist)
+        if op.type == "create_multi_pass_reader":
+            test_op.set_attr("pass_num", 1)
+
+    startup_program.sync_with_cpp()
+    program.sync_with_cpp()
+
+    return program

python/paddle/fluid/tests/unittests/.gitignore

@@ -4,3 +4,5 @@ mnist_1.recordio
 mnist_2.recordio
 flowers.recordio
 wmt16.recordio
+data_balance_test.recordio
+data_balance_with_lod_test.recordio

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

+# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+import paddle.fluid as fluid
+import paddle.v2 as paddle
+import numpy as np
+
+
+class TestDataBalance(unittest.TestCase):
+    def prepare_data(self):
+        def fake_data_generator():
+            for n in xrange(self.total_ins_num):
+                yield np.ones((3, 4)) * n, n
+
+        # Prepare data
+        with fluid.program_guard(fluid.Program(), fluid.Program()):
+            reader = paddle.batch(
+                fake_data_generator, batch_size=self.batch_size)
+            feeder = fluid.DataFeeder(
+                feed_list=[
+                    fluid.layers.data(
+                        name='image', shape=[3, 4], dtype='float32'),
+                    fluid.layers.data(
+                        name='label', shape=[1], dtype='int64'),
+                ],
+                place=fluid.CPUPlace())
+            self.num_batches = fluid.recordio_writer.convert_reader_to_recordio_file(
+                self.data_file_name, reader, feeder)
+
+    def prepare_lod_data(self):
+        def fake_data_generator():
+            for n in xrange(1, self.total_ins_num + 1):
+                d1 = (np.ones((n, 3)) * n).astype('float32')
+                d2 = (np.array(n).reshape((1, 1))).astype('int32')
+                yield d1, d2
+
+        # Prepare lod data
+        with fluid.program_guard(fluid.Program(), fluid.Program()):
+            with fluid.recordio_writer.create_recordio_writer(
+                    filename=self.lod_data_file_name) as writer:
+                eof = False
+                generator = fake_data_generator()
+                while (not eof):
+                    data_batch = [
+                        np.array([]).reshape((0, 3)), np.array([]).reshape(
+                            (0, 1))
+                    ]
+                    lod = [0]
+                    for _ in xrange(self.batch_size):
+                        try:
+                            ins = generator.next()
+                        except StopIteration:
+                            eof = True
+                            break
+                        for i, d in enumerate(ins):
+                            data_batch[i] = np.concatenate(
+                                (data_batch[i], d), axis=0)
+                        lod.append(lod[-1] + ins[0].shape[0])
+                    if data_batch[0].shape[0] > 0:
+                        for i, d in enumerate(data_batch):
+                            t = fluid.LoDTensor()
+                            t.set(data_batch[i], fluid.CPUPlace())
+                            if i == 0:
+                                t.set_lod([lod])
+                            writer.append_tensor(t)
+                        writer.complete_append_tensor()
+
+    def setUp(self):
+        self.use_cuda = fluid.core.is_compiled_with_cuda()
+        self.data_file_name = './data_balance_test.recordio'
+        self.lod_data_file_name = './data_balance_with_lod_test.recordio'
+        self.total_ins_num = 50
+        self.batch_size = 10
+        self.prepare_data()
+        self.prepare_lod_data()
+
+    def main(self):
+        main_prog = fluid.Program()
+        startup_prog = fluid.Program()
+        with fluid.program_guard(main_prog, startup_prog):
+            data_reader = fluid.layers.io.open_files(
+                filenames=[self.data_file_name],
+                shapes=[[-1, 3, 4], [-1, 1]],
+                lod_levels=[0, 0],
+                dtypes=['float32', 'int64'])
+            if self.use_cuda:
+                data_reader = fluid.layers.double_buffer(data_reader)
+            image, label = fluid.layers.read_file(data_reader)
+
+            place = fluid.CUDAPlace(0) if self.use_cuda else fluid.CPUPlace()
+            exe = fluid.Executor(place)
+            exe.run(startup_prog)
+
+            parallel_exe = fluid.ParallelExecutor(
+                use_cuda=self.use_cuda, main_program=main_prog)
+
+            if (parallel_exe.device_count > self.batch_size):
+                print("WARNING: Unittest TestDataBalance skipped. \
+                    For the result is not correct when device count \
+                    is larger than batch size.")
+                exit(0)
+            fetch_list = [image.name, label.name]
+
+            data_appeared = [False] * self.total_ins_num
+            while (True):
+                try:
+                    image_val, label_val = parallel_exe.run(fetch_list,
+                                                            return_numpy=True)
+                except fluid.core.EnforceNotMet as ex:
+                    self.assertIn("There is no next data.", ex.message)
+                    break
+                ins_num = image_val.shape[0]
+                broadcasted_label = np.ones(
+                    (ins_num, 3, 4)) * label_val.reshape((ins_num, 1, 1))
+                self.assertEqual(image_val.all(), broadcasted_label.all())
+                for l in label_val:
+                    self.assertFalse(data_appeared[l[0]])
+                    data_appeared[l[0]] = True
+            for i in data_appeared:
+                self.assertTrue(i)
+
+    def main_lod(self):
+        main_prog = fluid.Program()
+        startup_prog = fluid.Program()
+        with fluid.program_guard(main_prog, startup_prog):
+            data_reader = fluid.layers.io.open_files(
+                filenames=[self.lod_data_file_name],
+                shapes=[[-1, 3], [-1, 1]],
+                lod_levels=[1, 0],
+                dtypes=['float32', 'int32'],
+                thread_num=1)
+            ins, label = fluid.layers.read_file(data_reader)
+
+            place = fluid.CUDAPlace(0) if self.use_cuda else fluid.CPUPlace()
+            exe = fluid.Executor(place)
+            exe.run(startup_prog)
+
+            parallel_exe = fluid.ParallelExecutor(
+                use_cuda=self.use_cuda, main_program=main_prog)
+
+            if (parallel_exe.device_count > self.batch_size):
+                print("WARNING: Unittest TestDataBalance skipped. \
+                    For the result is not correct when device count \
+                    is larger than batch size.")
+                exit(0)
+            fetch_list = [ins.name, label.name]
+
+            data_appeared = [False] * self.total_ins_num
+            while (True):
+                try:
+                    ins_tensor, label_tensor = parallel_exe.run(
+                        fetch_list, return_numpy=False)
+                except fluid.core.EnforceNotMet as ex:
+                    self.assertIn("There is no next data.", ex.message)
+                    break
+
+                ins_val = np.array(ins_tensor)
+                label_val = np.array(label_tensor)
+                ins_lod = ins_tensor.lod()[0]
+                self.assertEqual(ins_val.shape[1], 3)
+                self.assertEqual(label_val.shape[1], 1)
+                self.assertEqual(len(ins_lod) - 1, label_val.shape[0])
+                for i in range(0, len(ins_lod) - 1):
+                    ins_elem = ins_val[ins_lod[i]:ins_lod[i + 1]][:]
+                    label_elem = label_val[i][0]
+                    self.assertEqual(ins_elem.all(), label_elem.all())
+                    self.assertFalse(data_appeared[int(label_elem - 1)])
+                    data_appeared[int(label_elem - 1)] = True
+
+            for i in data_appeared:
+                self.assertTrue(i)
+
+    def test_all(self):
+        self.main()
+        self.main_lod()

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

@@ -15,51 +15,248 @@
 import unittest
 import paddle.fluid as fluid
 from paddle.fluid.transpiler.distribute_transpiler import delete_ops
+import traceback
 
-from transpiler_test import TranspilerTest
 
-
-class TestDistTranspiler(TranspilerTest):
+class TranspilerTest(unittest.TestCase):
     def setUp(self):
-        self.current_pserver_ep = "127.0.0.1:6174"
+        self.trainer_id = 0
+        self.trainers = 2
+        self.pservers = 2
+        # NOTE: we do not actually bind this port
+        self.pserver_eps = "127.0.0.1:6174,127.0.0.1:6175"
+        self.pserver1_ep = "127.0.0.1:6174"
+        self.pserver2_ep = "127.0.0.1:6175"
+        self.slice_var_up = True
+        self.sync_mode = True
+        self.transpiler = None
+
+    def net_conf(self):
+        x = fluid.layers.data(name='x', shape=[1000], dtype='float32')
+        y_predict = fluid.layers.fc(input=x,
+                                    size=1000,
+                                    act=None,
+                                    param_attr=fluid.ParamAttr(name='fc_w'),
+                                    bias_attr=fluid.ParamAttr(name='fc_b'))
+        y = fluid.layers.data(name='y', shape=[1], dtype='float32')
+        cost = fluid.layers.square_error_cost(input=y_predict, label=y)
+        avg_cost = fluid.layers.mean(cost)
+        sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.1)
+        sgd_optimizer.minimize(avg_cost)
+        return
+
+    def get_main_program(self):
+        main = fluid.Program()
+        with fluid.program_guard(main):
+            self.net_conf()
+        self.origin_prog = main.clone()
+        return main
+
+    def get_trainer(self):
+        t = self._transpiler_instance()
+        return t.get_trainer_program()
+
+    def get_pserver(self, ep):
+        t = self._transpiler_instance()
+        pserver = t.get_pserver_program(ep)
+        startup = t.get_startup_program(ep, pserver)
+        return pserver, startup
+
+    def _transpiler_instance(self):
+        if not self.transpiler:
+            main = self.get_main_program()
+            self.transpiler = fluid.DistributeTranspiler()
+            self.transpiler.transpile(
+                self.trainer_id,
+                program=main,
+                pservers=self.pserver_eps,
+                trainers=self.trainers,
+                slice_var_up=self.slice_var_up,
+                sync_mode=self.sync_mode)
+        return self.transpiler
 
+
+class TestBasicModel(TranspilerTest):
     def test_transpiler(self):
+        pserver, startup = self.get_pserver(self.pserver1_ep)
+        pserver2, startup2 = self.get_pserver(self.pserver2_ep)
+
         trainer = self.get_trainer()
-        pserver, startup = self.get_pserver(self.current_pserver_ep)
-        self.assertEqual([op.type for op in trainer.global_block().ops],
-                         self.get_expect_trainer_ops())
+
+        self.assertEqual([op.type for op in trainer.global_block().ops], [
+            'mul', 'elementwise_add', 'elementwise_sub', 'square', 'mean',
+            'fill_constant', 'mean_grad', 'square_grad', 'elementwise_sub_grad',
+            'elementwise_add_grad', 'send', 'mul_grad', 'split_byref', 'send',
+            'send_barrier', 'recv', 'recv', 'fetch_barrier', 'concat'
+        ])
 
         self.assertEqual(len(pserver.blocks), 3)
         # block0: listen_and_serv
         self.assertEqual([op.type for op in pserver.blocks[0].ops],
                          ["listen_and_serv"])
-        # block2: optimize pass
+        # block1~2: optimize pass
         self.assertEqual([op.type for op in pserver.blocks[1].ops],
                          ["sum", "scale", "sgd"])
-
         # confirm startup program
-
-        self.assertEqual([op.type for op in startup.global_block().ops], [
-            "fill_constant", "fill_constant", "uniform_random", "uniform_random"
-        ])
-
+        self.assertEqual([op.type for op in startup.global_block().ops],
+                         ["fill_constant", "fill_constant", "uniform_random"])
         # the variable #fc_w will be split into two blocks
         fc_w_var = startup.global_block().var("fc_w.block1")
         self.assertEqual(fc_w_var.shape, (500, 1000))
+        # all parameters should be optimized on pserver
+
+        pserver_params = []
+        for prog in [pserver, pserver2]:
+            for blk in prog.blocks:
+                for op in blk.ops:
+                    if "Param" in op.input_names:
+                        param_name = op.input("Param")[0]
+                        is_block_idx = param_name.find(".block")
+                        if is_block_idx != -1:
+                            origin_param_name = param_name[:is_block_idx]
+                        else:
+                            origin_param_name = param_name
+                        pserver_params.append(origin_param_name)
+        trainer_params = []
+        for op in self.origin_prog.global_block().ops:
+            if "Param" in op.input_names:
+                trainer_params.append(op.input("Param")[0])
+        self.assertEqual(set(pserver_params), set(trainer_params))
+
+
+class TestNoSliceVar(TranspilerTest):
+    def setUp(self):
+        super(TestNoSliceVar, self).setUp()
+        self.slice_var_up = False
+
+    def test_transpiler(self):
+        _, startup = self.get_pserver(self.pserver1_ep)
+        _, startup2 = self.get_pserver(self.pserver2_ep)
+
+        if startup.global_block().vars.has_key("fc_w"):
+            fc_w_var = startup.global_block().vars["fc_w"]
+        elif startup2.global_block().vars.has_key("fc_w"):
+            fc_w_var = startup2.global_block().vars["fc_w"]
+
+        self.assertEqual(fc_w_var.shape, (1000, 1000))
 
-    def get_expect_trainer_ops(self):
-        trainer = fluid.Program()
 
-        with fluid.program_guard(trainer):
-            optimize_ops, params_grads = self.net_conf()
+class TestLRDecay(TranspilerTest):
+    def net_conf(self):
+        x = fluid.layers.data(name='x', shape=[1000], dtype='float32')
+        y_predict = fluid.layers.fc(input=x,
+                                    size=1000,
+                                    act=None,
+                                    param_attr=fluid.ParamAttr(name='fc_w'),
+                                    bias_attr=fluid.ParamAttr(name='fc_b'))
+        y = fluid.layers.data(name='y', shape=[1], dtype='float32')
+        cost = fluid.layers.square_error_cost(input=y_predict, label=y)
+        avg_cost = fluid.layers.mean(cost)
+        sgd_optimizer = fluid.optimizer.SGD(
+            learning_rate=fluid.layers.exponential_decay(
+                learning_rate=1.0,
+                decay_steps=2100,
+                decay_rate=0.1,
+                staircase=True))
+        sgd_optimizer.minimize(avg_cost)
+        return
+
+    def test_transpiler(self):
+        pserver, startup = self.get_pserver(self.pserver1_ep)
+        trainer = self.get_trainer()
+
+        self.assertEqual(len(pserver.blocks), 4)
+        lr_decay_ops = [op.type for op in pserver.blocks[1].ops]
+        self.assertEqual(lr_decay_ops, [
+            "increment", "cast", "fill_constant", "elementwise_div", "floor",
+            "fill_constant", "elementwise_pow", "fill_constant",
+            "elementwise_mul"
+        ])
+
+
+class TestLRDecayConditional(TranspilerTest):
+    def net_conf(self):
+        x = fluid.layers.data(name='x', shape=[1000], dtype='float32')
+        y_predict = fluid.layers.fc(input=x,
+                                    size=1000,
+                                    act=None,
+                                    param_attr=fluid.ParamAttr(name='fc_w'),
+                                    bias_attr=fluid.ParamAttr(name='fc_b'))
+        y = fluid.layers.data(name='y', shape=[1], dtype='float32')
+        cost = fluid.layers.square_error_cost(input=y_predict, label=y)
+        avg_cost = fluid.layers.mean(cost)
+        sgd_optimizer = fluid.optimizer.SGD(
+            learning_rate=fluid.layers.piecewise_decay([10000, 20000],
+                                                       [1.0, 0.5, 1.0]))
+        sgd_optimizer.minimize(avg_cost)
+        return
+
+    def test_transpiler(self):
+        pserver, startup = self.get_pserver(self.pserver1_ep)
+        trainer = self.get_trainer()
+
+        serv_op = pserver.blocks[0].ops[0]
+        sub_blocks = []
+        optimize_blocks = []
+        for b in serv_op.attrs["optimize_blocks"]:
+            optimize_blocks.append(b.idx)
+        for b in pserver.blocks:
+            if b.idx not in optimize_blocks:
+                sub_blocks.append(b.idx)
+
+        self.assertEqual(len(pserver.blocks), 7)
+        lr_decay_ops = [op.type for op in pserver.blocks[1].ops]
+        self.assertEqual(lr_decay_ops, [
+            "increment", "cast", "fill_constant", "fill_constant", "less_than",
+            "logical_not", "conditional_block", "fill_constant",
+            "fill_constant", "less_than", "logical_not", "logical_and",
+            "logical_and", "conditional_block", "fill_constant",
+            "conditional_block"
+        ])
+        # test the condition blocks
+        for b in sub_blocks:
+            if b == 0:
+                continue
+            block = pserver.blocks[b]
+            self.assertEqual([op.type for op in block.ops], ["assign"])
+
+
+class TestL2Decay(TranspilerTest):
+    def net_conf(self):
+        x = fluid.layers.data(name='x', shape=[1000], dtype='float32')
+        y_predict = fluid.layers.fc(
+            input=x,
+            size=1000,
+            act=None,
+            param_attr=fluid.ParamAttr(
+                name='fc_w',
+                regularizer=fluid.regularizer.L2Decay(),
+                gradient_clip=fluid.clip.GradientClipByValue(0.1)),
+            bias_attr=fluid.ParamAttr(name='fc_b'))
+        y = fluid.layers.data(name='y', shape=[1], dtype='float32')
+        cost = fluid.layers.square_error_cost(input=y_predict, label=y)
+        avg_cost = fluid.layers.mean(cost)
+        sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.1)
+        sgd_optimizer.minimize(avg_cost)
+        return
+
+    def test_transpiler(self):
+        pserver, startup = self.get_pserver(self.pserver1_ep)
+        trainer = self.get_trainer()
+
+        self.assertEqual(len(pserver.blocks), 3)
+        self.assertEqual([op.type for op in pserver.blocks[1].ops],
+                         ["sum", "scale", "clip", "sgd"])
+        self.assertEqual(
+            [op.type for op in pserver.blocks[2].ops],
+            ["sum", "scale", "clip", "scale", "elementwise_add", "sgd"])
+        # TODO(typhoonzero): test clipping and L2Decay ops are removed from trainer
+
 
-        delete_ops(trainer.global_block(), optimize_ops)
-        ops = [op.type for op in trainer.global_block().ops] + [
-            "split_byref", "send", "send_barrier", "recv", "recv",
-            "fetch_barrier", "concat"
-        ]
-        ops.insert(ops.index("elementwise_add_grad") + 1, "send")
-        return ops
+    # FIXME(typhoonzero): need to add test for async case:
+    # see https://github.com/PaddlePaddle/Paddle/issues/11691
+class TestAsyncSGD(TranspilerTest):
+    pass
 
 
 if __name__ == "__main__":

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

-#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import numpy as np
-
-import paddle.fluid as fluid
-from paddle.fluid.transpiler.distribute_transpiler import delete_ops
-
-from transpiler_test import TranspilerTest
-
-
-class TestSimpleDistTranspiler(TranspilerTest):
-    def setUp(self):
-        self.current_pserver_ep = "127.0.0.1:6175"
-
-    def test_simple_transpiler(self):
-        np.random.seed(1)
-
-        trainer = self.get_trainer()
-        pserver, startup = self.get_pserver(self.current_pserver_ep)
-        self.assertEqual([op.type for op in trainer.global_block().ops],
-                         self.get_expect_trainer_ops())
-
-        self.assertEqual(len(pserver.blocks), 2)
-        # block0: listen_and_serv
-        self.assertEqual([op.type for op in pserver.blocks[0].ops],
-                         ["listen_and_serv"])
-        # block1: optimize pass
-        self.assertEqual([op.type for op in pserver.blocks[1].ops],
-                         ["sum", "scale", "sgd"])
-
-        # confirm startup program
-        self.assertEqual([op.type for op in startup.global_block().ops],
-                         ["fill_constant", "uniform_random", "uniform_random"])
-
-        # the variable #fc_w will NOT be splited
-        fc_w_var = startup.global_block().var("fc_w@GRAD")
-        self.assertEqual(fc_w_var.shape, (1000, 1000))
-
-        fc_w_var = startup.global_block().var("fc_w@GRAD.trainer_0")
-        self.assertEqual(fc_w_var.shape, (1000, 1000))
-
-    def get_expect_trainer_ops(self):
-        trainer = fluid.Program()
-
-        with fluid.program_guard(trainer):
-            optimize_ops, params_grads = self.net_conf()
-
-        delete_ops(trainer.global_block(), optimize_ops)
-        ops = [op.type for op in trainer.global_block().ops] + [
-            "send", "send_barrier", "recv", "recv", "fetch_barrier"
-        ]
-        ops.insert(ops.index("elementwise_add_grad") + 1, "send")
-        return ops
-
-    def _transpiler_instance(self):
-        main = self.get_main_program()
-        t = fluid.DistributeTranspiler()
-        t.transpile(
-            self.trainer_id,
-            program=main,
-            pservers=self.pserver_eps,
-            trainers=self.trainers,
-            slice_var_up=False)
-        return t
-
-
-if __name__ == "__main__":
-    unittest.main()

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

-#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import unittest
-import numpy as np
-
-import paddle.fluid as fluid
-import paddle.fluid.core as core
-import paddle.fluid.layers as layers
-
-
-class TranspilerTest(unittest.TestCase):
-    @classmethod
-    def setUpClass(self):
-        self.trainer_id = 0
-        self.trainers = 2
-        self.pservers = 2
-        self.pserver_eps = "127.0.0.1:6174,127.0.0.1:6175"
-
-    def net_conf(self):
-        x = fluid.layers.data(name='x', shape=[1000], dtype='float32')
-
-        y_predict = fluid.layers.fc(input=x,
-                                    size=1000,
-                                    act=None,
-                                    param_attr=fluid.ParamAttr(name='fc_w'))
-
-        y = fluid.layers.data(name='y', shape=[1], dtype='float32')
-
-        cost = fluid.layers.square_error_cost(input=y_predict, label=y)
-        avg_cost = fluid.layers.mean(cost)
-        sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.1)
-
-        optimize_ops, params_grads = sgd_optimizer.minimize(avg_cost)
-        return optimize_ops, params_grads
-
-    def get_main_program(self):
-        main = fluid.Program()
-
-        with fluid.program_guard(main):
-            self.net_conf()
-
-        return main
-
-    def get_trainer(self):
-        return self._transpiler_instance().get_trainer_program()
-
-    def get_pserver(self, ep):
-        t = self._transpiler_instance()
-        pserver = t.get_pserver_program(ep)
-        startup = t.get_startup_program(ep, pserver)
-        return pserver, startup
-
-    def _transpiler_instance(self):
-        main = self.get_main_program()
-        t = fluid.DistributeTranspiler()
-        t.transpile(
-            self.trainer_id,
-            program=main,
-            pservers=self.pserver_eps,
-            trainers=self.trainers)
-        return t

python/paddle/fluid/transpiler/distribute_transpiler.py

@@ -455,6 +455,8 @@ class DistributeTranspiler(object):
                     __append_optimize_op__(op, per_opt_block, grad_to_block_id,
                                            merged_var, lr_ops)
 
+        # dedup grad to ids list
+        grad_to_block_id = list(set(grad_to_block_id))
         # append global ops
         if global_ops:
             opt_state_block = pserver_program.create_block(
@@ -960,8 +962,6 @@ class DistributeTranspiler(object):
             if not block_map.has_key(varname):
                 block_map[varname] = []
             block_map[varname].append((long(offset), long(size)))
-        # Do not remove this important debug message:
-        print("block map: %s" % block_map)
 
         for varname, splited in block_map.iteritems():
             orig_var = program.global_block().var(varname)
@@ -1401,6 +1401,16 @@ class DistributeTranspiler(object):
                     break
         return lr_ops
 
+    def _is_opt_role_op(self, op):
+        # NOTE: depend on oprole to find out whether this op is for
+        # optimize
+        op_maker = core.op_proto_and_checker_maker
+        optimize_role = core.op_proto_and_checker_maker.OpRole.Optimize
+        if op_maker.kOpRoleAttrName() in op.attrs and \
+            int(op.attrs[op_maker.kOpRoleAttrName()]) == int(optimize_role):
+            return True
+        return False
+
     def _get_optimize_pass(self):
         """
         Get optimizer operators, paramters and gradients from origin_program
@@ -1413,10 +1423,7 @@ class DistributeTranspiler(object):
         params_grads = []
         origin_var_dict = self.origin_program.global_block().vars
         for op in block.ops:
-            # NOTE(Yancey1989): we can not use op role to distinguish an optimizer op
-            # or not, because all ops in optimizer sub-graph would
-            # sign the optimizer op role
-            if self._is_optimizer_op(op):
+            if self._is_opt_role_op(op):
                 opt_ops.append(op)
                 # HACK(wuyi): if we find grad vars from input of optimize
                 # ops, we may get the output of clip op. Use syntax "@GRAD"