Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into multithread-sparse-adam

test=develop

CMakeLists.txt

@@ -208,10 +208,10 @@ include(external/xxhash)    # download xxhash
 include(external/dlpack)
 include(external/snappy)    # download snappy
 include(external/snappystream) # download snappystream
+include(external/warpctc)   # download, build, install warpctc
 
 if (NOT WIN32)
-# there is no official support of warpctc, nccl, cupti in windows
-include(external/warpctc)   # download, build, install warpctc
+# there is no official support of nccl, cupti in windows
 include(cupti)
 include(external/gzstream)
 endif (NOT WIN32)

cmake/external/warpctc.cmake

@@ -26,25 +26,33 @@ SET(WARPCTC_INCLUDE_DIR "${WARPCTC_INSTALL_DIR}/include"
 # Used in unit test test_WarpCTCLayer
 SET(WARPCTC_LIB_DIR "${WARPCTC_INSTALL_DIR}/lib"
     CACHE PATH "Warp-ctc Library Directory" FORCE)
-SET(WARPCTC_LIBRARIES "${WARPCTC_INSTALL_DIR}/lib/libwarpctc${CMAKE_SHARED_LIBRARY_SUFFIX}"
-    CACHE FILEPATH "Warp-ctc Library" FORCE)
 
-IF(CMAKE_CXX_COMPILER_ID STREQUAL "Clang" OR CMAKE_CXX_COMPILER_ID STREQUAL "AppleClang" )
+IF(CMAKE_CXX_COMPILER_ID STREQUAL "Clang" OR CMAKE_CXX_COMPILER_ID STREQUAL "AppleClang" OR WIN32)
     SET(USE_OMP OFF)
 ELSE()
     SET(USE_OMP ON)
 ENDIF()
 
+IF(WIN32)
+    SET(WARPCTC_REPOSITORY "https://github.com/wopeizl/warp-ctc.git")
+ELSE()
+    SET(WARPCTC_REPOSITORY "https://github.com/dzhwinter/warp-ctc.git")
+ENDIF()
+
 ExternalProject_Add(
     extern_warpctc
     ${EXTERNAL_PROJECT_LOG_ARGS}
-    GIT_REPOSITORY  "https://github.com/dzhwinter/warp-ctc.git"
+    GIT_REPOSITORY ${WARPCTC_REPOSITORY}
     PREFIX          ${WARPCTC_SOURCES_DIR}
     UPDATE_COMMAND  ""
     CMAKE_ARGS      -DCMAKE_CXX_COMPILER=${CMAKE_CXX_COMPILER}
                     -DCMAKE_C_COMPILER=${CMAKE_C_COMPILER}
-                    -DCMAKE_CXX_FLAGS=${CMAKE_CXX_FLAGS}
                     -DCMAKE_C_FLAGS=${CMAKE_C_FLAGS}
+                    -DCMAKE_C_FLAGS_DEBUG=${CMAKE_C_FLAGS_DEBUG}
+                    -DCMAKE_C_FLAGS_RELEASE=${CMAKE_C_FLAGS_RELEASE}
+                    -DCMAKE_CXX_FLAGS=${CMAKE_CXX_FLAGS}
+                    -DCMAKE_CXX_FLAGS_RELEASE=${CMAKE_CXX_FLAGS_RELEASE}
+                    -DCMAKE_CXX_FLAGS_DEBUG=${CMAKE_CXX_FLAGS_DEBUG}
                     -DCMAKE_INSTALL_PREFIX=${WARPCTC_INSTALL_DIR}
                     -DWITH_GPU=${WITH_GPU}
                     -DWITH_OMP=${USE_OMP}
@@ -59,6 +67,18 @@ ExternalProject_Add(
                      -DCMAKE_POSITION_INDEPENDENT_CODE:BOOL=ON
                      -DCMAKE_INSTALL_PREFIX:PATH=${WARPCTC_INSTALL_DIR}
 )
+IF(WIN32)
+    IF(NOT EXISTS "${WARPCTC_INSTALL_DIR}/lib/warpctc${CMAKE_SHARED_LIBRARY_SUFFIX}")
+        add_custom_command(TARGET extern_warpctc POST_BUILD
+                COMMAND cmake -E copy ${WARPCTC_INSTALL_DIR}/bin/warpctc${CMAKE_SHARED_LIBRARY_SUFFIX} ${WARPCTC_INSTALL_DIR}/lib/warpctc${CMAKE_SHARED_LIBRARY_SUFFIX}
+                )
+    ENDIF()
+    SET(WARPCTC_LIBRARIES "${WARPCTC_INSTALL_DIR}/lib/warpctc${CMAKE_SHARED_LIBRARY_SUFFIX}"
+            CACHE FILEPATH "Warp-ctc Library" FORCE)
+else(WIN32)
+    SET(WARPCTC_LIBRARIES "${WARPCTC_INSTALL_DIR}/lib/libwarpctc${CMAKE_SHARED_LIBRARY_SUFFIX}"
+            CACHE FILEPATH "Warp-ctc Library" FORCE)
+ENDIF(WIN32)
 
 MESSAGE(STATUS "warp-ctc library: ${WARPCTC_LIBRARIES}")
 INCLUDE_DIRECTORIES(${WARPCTC_INCLUDE_DIR}) # For warpctc code to include its headers.

cmake/operators.cmake

@@ -84,7 +84,7 @@ function(op_library TARGET)
     endif()
     if (WIN32)
     # remove windows unsupported op, because windows has no nccl, no warpctc such ops.
-    foreach(windows_unsupport_op "nccl_op" "gen_nccl_id_op" "warpctc_op")
+    foreach(windows_unsupport_op "nccl_op" "gen_nccl_id_op")
         if ("${TARGET}" STREQUAL "${windows_unsupport_op}")
           return()
         endif()

paddle/fluid/API.spec

@@ -350,6 +350,22 @@ paddle.fluid.contrib.QuantizeTranspiler.__init__ ArgSpec(args=['self', 'weight_b
 paddle.fluid.contrib.QuantizeTranspiler.convert_to_int8 ArgSpec(args=['self', 'program', 'place', 'scope'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.contrib.QuantizeTranspiler.freeze_program ArgSpec(args=['self', 'program', 'place', 'fuse_bn', 'scope'], varargs=None, keywords=None, defaults=(False, None))
 paddle.fluid.contrib.QuantizeTranspiler.training_transpile ArgSpec(args=['self', 'program', 'startup_program'], varargs=None, keywords=None, defaults=(None, None))
+paddle.fluid.contrib.load_persistables_for_increment ArgSpec(args=['dirname', 'executor', 'program', 'lookup_table_var', 'lookup_table_var_path'], varargs=None, keywords=None, defaults=None)
+paddle.fluid.contrib.load_persistables_for_inference ArgSpec(args=['dirname', 'executor', 'program', 'lookup_table_var_name'], varargs=None, keywords=None, defaults=None)
+paddle.fluid.contrib.convert_dist_to_sparse_program ArgSpec(args=['program'], varargs=None, keywords=None, defaults=None)
+paddle.fluid.contrib.HDFSClient.__init__ ArgSpec(args=['self', 'hadoop_home', 'configs'], varargs=None, keywords=None, defaults=None)
+paddle.fluid.contrib.HDFSClient.delete ArgSpec(args=['self', 'hdfs_path'], varargs=None, keywords=None, defaults=None)
+paddle.fluid.contrib.HDFSClient.download ArgSpec(args=['self', 'hdfs_path', 'local_path', 'overwrite', 'unzip'], varargs=None, keywords=None, defaults=(False, False))
+paddle.fluid.contrib.HDFSClient.is_dir ArgSpec(args=['self', 'hdfs_path'], varargs=None, keywords=None, defaults=(None,))
+paddle.fluid.contrib.HDFSClient.is_exist ArgSpec(args=['self', 'hdfs_path'], varargs=None, keywords=None, defaults=(None,))
+paddle.fluid.contrib.HDFSClient.ls ArgSpec(args=['self', 'hdfs_path'], varargs=None, keywords=None, defaults=None)
+paddle.fluid.contrib.HDFSClient.lsr ArgSpec(args=['self', 'hdfs_path', 'only_file', 'sort'], varargs=None, keywords=None, defaults=(True, True))
+paddle.fluid.contrib.HDFSClient.make_local_dirs ArgSpec(args=['local_path'], varargs=None, keywords=None, defaults=None)
+paddle.fluid.contrib.HDFSClient.makedirs ArgSpec(args=['self', 'hdfs_path'], varargs=None, keywords=None, defaults=None)
+paddle.fluid.contrib.HDFSClient.rename ArgSpec(args=['self', 'hdfs_src_path', 'hdfs_dst_path', 'overwrite'], varargs=None, keywords=None, defaults=(False,))
+paddle.fluid.contrib.HDFSClient.upload ArgSpec(args=['self', 'hdfs_path', 'local_path', 'overwrite', 'retry_times'], varargs=None, keywords=None, defaults=(False, 5))
+paddle.fluid.contrib.multi_download ArgSpec(args=['client', 'hdfs_path', 'local_path', 'trainer_id', 'trainers', 'multi_processes'], varargs=None, keywords=None, defaults=(5,))
+paddle.fluid.contrib.multi_upload ArgSpec(args=['client', 'hdfs_path', 'local_path', 'multi_processes', 'overwrite', 'sync'], varargs=None, keywords=None, defaults=(5, False, True))
 paddle.fluid.transpiler.DistributeTranspiler.__init__ ArgSpec(args=['self', 'config'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.transpiler.DistributeTranspiler.get_pserver_program ArgSpec(args=['self', 'endpoint'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.transpiler.DistributeTranspiler.get_pserver_programs ArgSpec(args=['self', 'endpoint'], varargs=None, keywords=None, defaults=None)

paddle/fluid/framework/details/multi_devices_graph_pass.cc

@@ -359,7 +359,9 @@ std::unique_ptr<ir::Graph> MultiDevSSAGraphBuilder::ApplyImpl(
           BuildStrategy::GradientScaleStrategy::kCustomized) {
         // TODO(paddle-dev): Why is there no input for this op_handle?
         auto loss_grad_name = node->Op()->OutputArgumentNames()[0];
-        CreateScaleLossGradOp(&result, loss_grad_name, node->outputs[0]);
+        auto out_dtype = all_vars_.at(loss_grad_name)->GetDataType();
+        CreateScaleLossGradOp(&result, loss_grad_name, node->outputs[0],
+                              out_dtype);
       }
       // This assumes the backward generating code will ensure IsScaleLossOp
       // is true only for the op that scale the final scalar loss.
@@ -662,13 +664,13 @@ int MultiDevSSAGraphBuilder::GetVarDeviceID(
 
 void MultiDevSSAGraphBuilder::CreateScaleLossGradOp(
     ir::Graph *result, const std::string &loss_grad_name,
-    ir::Node *out_var_node) const {
+    ir::Node *out_var_node, proto::VarType::Type dtype) const {
   for (size_t i = 0; i < places_.size(); ++i) {
     // Insert ScaleCost OpHandle
     auto *dev_ctx = platform::DeviceContextPool::Instance().Get(places_[i]);
     auto *op_handle = new ScaleLossGradOpHandle(
         result->CreateEmptyNode("scale_loss_grad", ir::Node::Type::kOperation),
-        local_scopes_.size(), local_scopes_[i], places_[i], dev_ctx);
+        local_scopes_.size(), local_scopes_[i], places_[i], dev_ctx, dtype);
     result->Get<GraphOps>(kGraphOps).emplace_back(op_handle);
 
     // FIXME: Currently ScaleLossGradOp only use device_count as scale

paddle/fluid/framework/details/multi_devices_graph_pass.h

@@ -68,7 +68,8 @@ class MultiDevSSAGraphBuilder : public ir::Pass {
 
   void CreateScaleLossGradOp(ir::Graph *result,
                              const std::string &loss_grad_name,
-                             ir::Node *out_var_node) const;
+                             ir::Node *out_var_node,
+                             proto::VarType::Type dtype) const;
 
   VarHandle *CreateReduceOp(ir::Graph *result, const std::string &og,
                             int dst_dev_id) const;

paddle/fluid/framework/details/scale_loss_grad_op_handle.cc

@@ -22,39 +22,66 @@ namespace details {
 ScaleLossGradOpHandle::ScaleLossGradOpHandle(ir::Node *node, size_t num_dev,
                                              Scope *scope,
                                              platform::Place place,
-                                             platform::DeviceContext *dev_ctx)
+                                             platform::DeviceContext *dev_ctx,
+                                             proto::VarType::Type dtype)
     : OpHandleBase(node),
       coeff_(static_cast<float>(1.0 / num_dev)),
       scope_(scope),
-      place_(place) {
+      place_(place),
+      out_dtype_(dtype) {
   this->SetDeviceContext(place_, dev_ctx);
 }
 
 ScaleLossGradOpHandle::~ScaleLossGradOpHandle() {}
 
-void ScaleLossGradOpHandle::RunImpl() {
-  // Doesn't wait any event
-  std::string var_name = static_cast<VarHandle *>(this->outputs_[0])->name_;
-  auto &local_scope = *scope_->FindVar(kLocalExecScopeName)->Get<Scope *>();
+struct ScaleLossGradFunctor {
+  float coeff_;
+  Tensor *out_;
+  platform::Place place_;
+  OpHandleBase *op_handle_;
+  proto::VarType::Type out_dtype_;
+  platform::DeviceContext *ctx_;
 
-  float *tmp = local_scope.FindVar(var_name)
-                   ->GetMutable<LoDTensor>()
-                   ->mutable_data<float>(make_ddim({1}), place_);
+  ScaleLossGradFunctor(float coeff, Tensor *out, platform::Place place,
+                       OpHandleBase *op_handle, proto::VarType::Type dtype,
+                       platform::DeviceContext *ctx)
+      : coeff_(coeff), out_(out), place_(place), out_dtype_(dtype), ctx_(ctx) {}
 
+  template <typename OutT>
+  void apply() const {
+    auto *out_data = out_->mutable_data<OutT>(place_);
     if (platform::is_cpu_place(place_)) {
-    *tmp = coeff_;
+      *out_data = static_cast<OutT>(coeff_);
     } else {
 #ifdef PADDLE_WITH_CUDA
-    this->RunAndRecordEvent([&] {
-      auto stream = static_cast<platform::CUDADeviceContext *>(
-                        this->dev_ctxes_.at(place_))
-                        ->stream();
-      memory::Copy(boost::get<platform::CUDAPlace>(place_), tmp,
-                   platform::CPUPlace(), &coeff_, sizeof(float), stream);
+      OutT cast_coeff = static_cast<OutT>(coeff_);
+      auto stream = static_cast<platform::CUDADeviceContext *>(ctx_)->stream();
+      memory::Copy(boost::get<platform::CUDAPlace>(place_), out_data,
+                   platform::CPUPlace(), &cast_coeff, SizeOfType(out_dtype_),
+                   stream);
       VLOG(10) << place_ << "RUN Scale loss grad op";
-    });
+
 #endif
     }
+  }
+};
+
+void ScaleLossGradOpHandle::RunImpl() {
+  // Doesn't wait any event
+  std::string var_name = static_cast<VarHandle *>(this->outputs_[0])->name_;
+  auto &local_scope = *scope_->FindVar(kLocalExecScopeName)->Get<Scope *>();
+
+  auto *tensor = local_scope.FindVar(var_name)->GetMutable<LoDTensor>();
+  tensor->Resize(make_ddim({1}));
+
+#ifdef PADDLE_WITH_CUDA
+  ScaleLossGradFunctor func(coeff_, tensor, place_, this, out_dtype_,
+                            this->dev_ctxes_.at(place_));
+  this->RunAndRecordEvent([&] { framework::VisitDataType(out_dtype_, func); });
+#else
+  ScaleLossGradFunctor func(coeff_, tensor, place_, this, out_dtype_, nullptr);
+  framework::VisitDataType(out_dtype_, func);
+#endif
 }
 
 std::string ScaleLossGradOpHandle::Name() const { return "Scale LossGrad"; }

paddle/fluid/framework/details/scale_loss_grad_op_handle.h

@@ -26,8 +26,8 @@ namespace details {
 
 struct ScaleLossGradOpHandle : public OpHandleBase {
   ScaleLossGradOpHandle(ir::Node *node, size_t num_dev, Scope *scope,
-                        platform::Place place,
-                        platform::DeviceContext *context);
+                        platform::Place place, platform::DeviceContext *context,
+                        proto::VarType::Type dtype);
 
   ~ScaleLossGradOpHandle() final;
 
@@ -40,6 +40,7 @@ struct ScaleLossGradOpHandle : public OpHandleBase {
   float coeff_;
   Scope *scope_;
   platform::Place place_;
+  proto::VarType::Type out_dtype_;
 };
 
 }  // namespace details

paddle/fluid/framework/ir/conv_elementwise_add_mkldnn_fuse_pass.cc

@@ -24,35 +24,6 @@ namespace paddle {
 namespace framework {
 namespace ir {
 
-// The function keeps the graph consistent by replacing
-// a node 'from' in the set of inputs nodes
-// of the visited node by a node 'to'.
-void CorrectGraphEdges(Graph* graph, Node* from, Node* to) {
-  for (auto& node : GraphTraits::DFS(*graph)) {
-    auto from_in_inputs =
-        std::find(std::begin(node.inputs), std::end(node.inputs), from);
-
-    if (from_in_inputs != std::end(node.inputs)) {
-      IR_NODE_LINK_TO(to, (&node));
-
-      auto inputs = node.Op()->Inputs();
-
-      using input_type = VariableNameMap::value_type;
-
-      std::for_each(std::begin(inputs), std::end(inputs),
-                    [from, to, &node](const input_type& i) -> void {
-                      auto param_names = i.second;
-                      auto pi = std::find(std::begin(param_names),
-                                          std::end(param_names), from->Name());
-
-                      if (pi != std::end(param_names)) {
-                        node.Op()->SetInput(i.first, {to->Name()});
-                      }
-                    });
-    }
-  }
-}
-
 bool IsReachable(ir::Graph* graph, Node* from, Node* to) {
   auto find_node = [](ir::Graph* graph, const Node* node) -> Node* {
     for (auto n : graph->Nodes()) {
@@ -99,24 +70,11 @@ bool IsReachable(ir::Graph* graph, Node* from, Node* to) {
   return false;
 }
 
-boost::optional<Node*> HasBias(const Node& op, const std::string& bias_name) {
-  auto bias_input_names = op.Op()->Inputs();
-  auto bias_it = bias_input_names.find(bias_name);
-
-  if (bias_it != std::end(bias_input_names)) {
-    bool has_bias = !bias_it->second.empty();
-
-    if (has_bias) {
-      auto bias_names = bias_it->second;
-      auto bias_names_it =
-          std::find_if(std::begin(op.inputs), std::end(op.inputs),
-                       [&bias_names](Node* n) -> bool {
-                         return n->Name() == bias_names[0];
-                       });
-      return *bias_names_it;
-    }
-  }
-
+template <typename T>
+boost::optional<T> HasAttribute(const Node& op, const std::string& attr) {
+  if (op.Op()->HasAttr(attr))
+    return boost::get<T>(op.Op()->GetAttr(attr));
+  else
     return boost::none;
 }
 
@@ -151,40 +109,18 @@ void ResidualConnectionMKLDNNFusePass::IdentityFuseHandle::operator()(
 
   if (!IsReachable(graph, elementwise_add_identity, conv_output)) return;
 
-  OpDesc op_desc;
-  op_desc.SetType("conv2d");
+  auto fuse_relu = HasAttribute<bool>(*conv_op, "fuse_relu");
+  if (fuse_relu && *fuse_relu) return;
 
-  op_desc.SetInput("Input", {conv_input->Name()});
-  op_desc.SetInput("Filter", {conv_filter->Name()});
-  op_desc.SetInput("ResidualData", {elementwise_add_identity->Name()});
-  op_desc.SetOutput("Output", {conv_output->Name()});
+  conv_op->Op()->SetInput("ResidualData", {elementwise_add_identity->Name()});
+  conv_op->Op()->SetOutput("Output", {elementwise_add_out->Name()});
+  conv_op->Op()->SetAttr("fuse_residual_connection", true);
 
-  auto conv_bias = HasBias(*conv_op, "Bias");
+  GraphSafeRemoveNodes(graph, {conv_output, elementwise_add_op});
 
-  if (conv_bias) {
-    op_desc.SetInput("Bias", {(*conv_bias)->Name()});
-  }
+  IR_NODE_LINK_TO(elementwise_add_identity, conv_op);
+  IR_NODE_LINK_TO(conv_op, elementwise_add_out);
 
-  for (const auto& attr : conv_op->Op()->GetAttrMap()) {
-    op_desc.SetAttr(attr.first, attr.second);
-  }
-
-  op_desc.SetAttr("fuse_residual_connection", true);
-
-  auto fused_conv_op = graph->CreateOpNode(&op_desc);
-
-  IR_NODE_LINK_TO(conv_input, fused_conv_op);
-  IR_NODE_LINK_TO(conv_filter, fused_conv_op);
-  IR_NODE_LINK_TO(elementwise_add_identity, fused_conv_op);
-  IR_NODE_LINK_TO(fused_conv_op, conv_output);
-
-  if (conv_bias) {
-    IR_NODE_LINK_TO((*conv_bias), fused_conv_op);
-  }
-
-  CorrectGraphEdges(graph, elementwise_add_out, conv_output);
-  GraphSafeRemoveNodes(graph,
-                       {elementwise_add_out, conv_op, elementwise_add_op});
   (*fusion_stats)++;
 }
 
@@ -229,60 +165,33 @@ void ResidualConnectionMKLDNNFusePass::ProjectionFuseHandle::operator()(
 
   Node* projection_node;
   Node* residual_conv_op;
-  Node* residual_conv_input;
-  Node* residual_conv_filter;
   Node* residual_conv_output;
 
   if (IsReachable(graph, conv_x_input, conv_y_output)) {
     projection_node = conv_x_output;
     residual_conv_op = conv_y_op;
-    residual_conv_input = conv_y_input;
-    residual_conv_filter = conv_y_filter;
     residual_conv_output = conv_y_output;
   } else if (IsReachable(graph, conv_y_input, conv_x_output)) {
     projection_node = conv_y_output;
     residual_conv_op = conv_x_op;
-    residual_conv_input = conv_x_input;
-    residual_conv_filter = conv_x_filter;
     residual_conv_output = conv_x_output;
   } else {
     return;
   }
 
-  OpDesc op_desc;
-  op_desc.SetType("conv2d");
+  auto fuse_relu = HasAttribute<bool>(*residual_conv_op, "fuse_relu");
+  if (fuse_relu && *fuse_relu) return;
 
-  op_desc.SetInput("Input", {residual_conv_input->Name()});
-  op_desc.SetInput("Filter", {residual_conv_filter->Name()});
-  op_desc.SetInput("ResidualData", {projection_node->Name()});
-  op_desc.SetOutput("Output", {residual_conv_output->Name()});
+  residual_conv_op->Op()->SetInput("ResidualData", {projection_node->Name()});
+  residual_conv_op->Op()->SetOutput("Output", {elementwise_add_out->Name()});
 
-  auto residual_conv_bias = HasBias(*residual_conv_op, "Bias");
+  residual_conv_op->Op()->SetAttr("fuse_residual_connection", true);
 
-  if (residual_conv_bias) {
-    op_desc.SetInput("Bias", {(*residual_conv_bias)->Name()});
-  }
-
-  for (const auto& attr : residual_conv_op->Op()->GetAttrMap()) {
-    op_desc.SetAttr(attr.first, attr.second);
-  }
-
-  op_desc.SetAttr("fuse_residual_connection", true);
+  GraphSafeRemoveNodes(graph, {residual_conv_output, elementwise_add_op});
 
-  auto fused_conv_op = graph->CreateOpNode(&op_desc);
-
-  IR_NODE_LINK_TO(residual_conv_input, fused_conv_op);
-  IR_NODE_LINK_TO(residual_conv_filter, fused_conv_op);
-  IR_NODE_LINK_TO(projection_node, fused_conv_op);
-  IR_NODE_LINK_TO(fused_conv_op, residual_conv_output);
-
-  if (residual_conv_bias) {
-    IR_NODE_LINK_TO((*residual_conv_bias), fused_conv_op);
-  }
+  IR_NODE_LINK_TO(projection_node, residual_conv_op);
+  IR_NODE_LINK_TO(residual_conv_op, elementwise_add_out);
 
-  CorrectGraphEdges(graph, elementwise_add_out, residual_conv_output);
-  GraphSafeRemoveNodes(
-      graph, {elementwise_add_out, residual_conv_op, elementwise_add_op});
   (*fusion_stats)++;
 }
 

paddle/fluid/framework/ngraph_bridge.cc

@@ -16,100 +16,25 @@ limitations under the License. */
 #include <functional>
 #include <vector>
 
+#include "ngraph/ngraph.hpp"
 #include "paddle/fluid/framework/ngraph_bridge.h"
 #include "paddle/fluid/framework/operator.h"
+#include "paddle/fluid/operators/ngraph/ngraph_ops.h"
 #include "paddle/fluid/platform/enforce.h"
-
-#include "ngraph/ngraph.hpp"
+#include "paddle/fluid/platform/ngraph_helper.h"
 
 namespace paddle {
 namespace framework {
 
-static std::shared_ptr<ngraph::Node> GetNode(
-    const std::shared_ptr<OperatorBase>& op, const std::string name,
-    const VariableNameMap& var_map,
-    std::shared_ptr<
-        std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
-        ngb_node_map) {
-  auto& var_names = var_map.at(name);
-  PADDLE_ENFORCE_EQ(var_names.size(), 1,
-                    "op %s name %s expects one associated var", op->Type(),
-                    name);
-  if (ngb_node_map->find(var_names[0]) != ngb_node_map->end()) {
-    return (*ngb_node_map)[var_names[0]];
-  } else {
-    return nullptr;
-  }
-}
-
-static std::shared_ptr<ngraph::Node> GetInputNode(
-    const std::shared_ptr<OperatorBase>& op, const std::string name,
-    std::shared_ptr<
-        std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
-        ngb_node_map) {
-  return GetNode(op, name, op->Inputs(), ngb_node_map);
-}
-
-static std::shared_ptr<ngraph::Node> GetOutputNode(
-    const std::shared_ptr<OperatorBase>& op, const std::string name,
-    std::shared_ptr<
-        std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
-        ngb_node_map) {
-  return GetNode(op, name, op->Outputs(), ngb_node_map);
-}
-
-static void SetOutputNode(
-    const std::shared_ptr<OperatorBase>& op, const std::string name,
-    std::shared_ptr<ngraph::Node> node,
-    std::shared_ptr<
-        std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
-        ngb_node_map) {
-  auto& var_names = op->Outputs().at(name);
-  if (var_names.size() == 1) {
-    (*ngb_node_map)[var_names[0]] = node;
-  } else if (var_names.size() == 0) {
-    (*ngb_node_map)[""] = node;
-  } else {
-    PADDLE_THROW("name %s has more than 1 var_names.", name);
-  }
-}
-
-static bool HasOutput(const std::shared_ptr<OperatorBase>& op,
-                      const std::string name) {
-  auto& outputs = op->Outputs();
-  if (outputs.find(name) == outputs.end()) return false;
-  return outputs.at(name).size() > 0;
-}
-
-template <typename T>
-static void BuildBinaryNode(
-    const std::shared_ptr<OperatorBase>& op,
-    std::shared_ptr<
-        std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
-        ngb_node_map) {
-  auto x = GetInputNode(op, "X", ngb_node_map);
-  auto y = GetInputNode(op, "Y", ngb_node_map);
-  auto out = std::make_shared<T>(x, y);
-  SetOutputNode(op, "Out", out, ngb_node_map);
-}
-
-template <typename T>
-static void BuildUnaryNode(
-    const std::shared_ptr<OperatorBase>& op,
-    std::shared_ptr<
-        std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
-        ngb_node_map) {
-  auto input = GetInputNode(op, "X", ngb_node_map);
-  auto out = std::make_shared<T>(input);
-  SetOutputNode(op, "Out", out, ngb_node_map);
-}
-
 std::map<std::string,
          std::function<void(const std::shared_ptr<OperatorBase>&,
                             std::shared_ptr<std::unordered_map<
                                 std::string, std::shared_ptr<ngraph::Node>>>)>>
-    NgraphBridge::NG_NODE_MAP = {{"relu", BuildUnaryNode<ngraph::op::Relu>},
-                                 {"tanh", BuildUnaryNode<ngraph::op::Tanh>}};
+    NgraphBridge::NG_NODE_MAP = {
+        {"mul", paddle::operators::ngraphs::BuildMulNode},
+        {"mul_grad", paddle::operators::ngraphs::BuildMulGradNode},
+        {"relu", paddle::operators::ngraphs::BuildUnaryNode<ngraph::op::Relu>},
+        {"tanh", paddle::operators::ngraphs::BuildUnaryNode<ngraph::op::Tanh>}};
 
 void NgraphBridge::BuildNgNode(const std::shared_ptr<OperatorBase>& op) {
   auto& op_type = op->Type();

paddle/fluid/framework/ngraph_operator.cc

@@ -278,7 +278,8 @@ std::shared_ptr<ngraph::runtime::Backend> NgraphEngine::backend_ =
     ngraph::runtime::Backend::create("CPU");
 
 void NgraphEngine::GetNgInputShape(std::shared_ptr<OperatorBase> op) {
-  op->RuntimeInferShape(scope_, place_);
+  RuntimeContext ctx(op->Inputs(), op->Outputs(), scope_);
+  op->RuntimeInferShape(scope_, place_, ctx);
   for (auto& var_name_item : op->Inputs()) {
     for (auto& var_name : var_name_item.second) {
       auto* var = scope_.FindVar(var_name);

paddle/fluid/framework/operator.cc

@@ -139,6 +139,23 @@ static LoD GetLoD(const Scope& scope, const std::string& name) {
   }
 }
 
+RuntimeContext::RuntimeContext(const VariableNameMap& innames,
+                               const VariableNameMap& outnames,
+                               const Scope& scope) {
+  for (auto& var_name_item : innames) {
+    std::vector<Variable*>& input_vars = inputs[var_name_item.first];
+    for (auto& var_name : var_name_item.second) {
+      input_vars.push_back(scope.FindVar(var_name));
+    }
+  }
+  for (auto& var_name_item : outnames) {
+    std::vector<Variable*>& output_vars = outputs[var_name_item.first];
+    for (auto& var_name : var_name_item.second) {
+      output_vars.push_back(scope.FindVar(var_name));
+    }
+  }
+}
+
 void OperatorBase::Run(const Scope& scope, const platform::Place& place) {
   VLOG(4) << place << " " << DebugStringEx(&scope);
   if (platform::is_gpu_place(place)) {
@@ -414,11 +431,48 @@ bool ExecutionContext::HasOutput(const std::string& name) const {
   return var != nullptr;
 }
 
+const Variable* ExecutionContext::InputVar(const std::string& name) const {
+  auto it = ctx_.inputs.find(name);
+  if (it == ctx_.inputs.end()) return nullptr;
+
+  PADDLE_ENFORCE_LE(it->second.size(), 1UL,
+                    "Operator %s's input %s should contain only one variable.",
+                    op_.Type(), name);
+  return it->second.empty() ? nullptr : it->second[0];
+}
+
+const Variable* ExecutionContext::LegacyInputVar(
+    const std::string& name) const {
+  auto ipt = op_.Input(name);
+  return ipt == kEmptyVarName ? nullptr : scope_.FindVar(ipt);
+}
+
+Variable* ExecutionContext::OutputVar(const std::string& name) const {
+  auto it = ctx_.outputs.find(name);
+  if (it == ctx_.outputs.end()) return nullptr;
+
+  PADDLE_ENFORCE_LE(it->second.size(), 1UL,
+                    "Operator %s's output %s should contain only one variable.",
+                    op_.Type(), name);
+  return it->second.empty() ? nullptr : it->second[0];
+}
+
+Variable* ExecutionContext::LegacyOutputVar(const std::string& name) const {
+  auto opt = op_.Output(name);
+  return opt == kEmptyVarName ? nullptr : scope_.FindVar(opt);
+}
+
 template <>
 const Tensor* ExecutionContext::Input<Tensor>(const std::string& name) const {
   return Input<LoDTensor>(name);
 }
 
+template <>
+const Tensor* ExecutionContext::LegacyInput<Tensor>(
+    const std::string& name) const {
+  return LegacyInput<LoDTensor>(name);
+}
+
 template <>
 const std::vector<const Tensor*> ExecutionContext::MultiInput<Tensor>(
     const std::string& name) const {
@@ -443,6 +497,11 @@ Tensor* ExecutionContext::Output<Tensor>(const std::string& name) const {
   return Output<LoDTensor>(name);
 }
 
+template <>
+Tensor* ExecutionContext::LegacyOutput<Tensor>(const std::string& name) const {
+  return LegacyOutput<LoDTensor>(name);
+}
+
 template <>
 std::vector<Tensor*> ExecutionContext::MultiOutput<Tensor>(
     const std::string& name) const {
@@ -479,23 +538,22 @@ bool OpSupportGPU(const std::string& op_type) {
 
 class RuntimeInferShapeContext : public InferShapeContext {
  public:
-  RuntimeInferShapeContext(const OperatorBase& op, const Scope& scope)
-      : op_(op), scope_(scope) {}
+  RuntimeInferShapeContext(const OperatorBase& op, const Scope& scope,
+                           const RuntimeContext& ctx)
+      : op_(op), scope_(scope), ctx_(ctx) {}
 
   bool HasInput(const std::string& name) const override {
     // has only one input
-    const auto& ins = op_.Inputs();
+    const auto& ins = ctx_.inputs;
     auto it = ins.find(name);
     if (it == ins.end()) {
       return false;
     }
     const auto& in = it->second;
-    if (in.size() == 0 || in[0] == kEmptyVarName) {
-      return false;
-    }
+    if (in.size() == 0) return false;
     PADDLE_ENFORCE_EQ(in.size(), 1UL,
                       "Input %s should not have more than one inputs", name);
-    return scope_.FindVar(in[0]) != nullptr;
+    return in[0] != nullptr;
   }
 
   bool HasOutput(const std::string& name) const override {
@@ -680,6 +738,7 @@ class RuntimeInferShapeContext : public InferShapeContext {
  private:
   const OperatorBase& op_;
   const Scope& scope_;
+  const RuntimeContext& ctx_;
 };
 
 static void CheckTensorNANOrInf(const std::string& name,
@@ -698,15 +757,15 @@ static void CheckTensorNANOrInf(const std::string& name,
 }
 
 void OperatorWithKernel::RuntimeInferShape(const Scope& scope,
-                                           const platform::Place& place) const {
-  RuntimeInferShapeContext infer_shape_ctx(*this, scope);
+                                           const platform::Place& place,
+                                           const RuntimeContext& ctx) const {
+  RuntimeInferShapeContext infer_shape_ctx(*this, scope, ctx);
   this->InferShape(&infer_shape_ctx);
 }
 
 void OperatorWithKernel::RunImpl(const Scope& scope,
                                  const platform::Place& place) const {
-  RuntimeInferShapeContext infer_shape_ctx(*this, scope);
-  this->InferShape(&infer_shape_ctx);
+  RuntimeContext ctx(Inputs(), Outputs(), scope);
   platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
   auto* dev_ctx = pool.Get(place);
 
@@ -720,15 +779,8 @@ void OperatorWithKernel::RunImpl(const Scope& scope,
 
   OpKernelMap& kernels = kernels_iter->second;
 
-  // TODO(dzhwinter) : kernel fallback mechanism will be added when all the
-  // transform functions are ready.
-
-  // for (auto& candidate : kKernelPriority) {
-  //   Do selection
-  // }
-
-  auto expected_kernel_key =
-      this->GetExpectedKernelType(ExecutionContext(*this, scope, *dev_ctx));
+  auto expected_kernel_key = this->GetExpectedKernelType(
+      ExecutionContext(*this, scope, *dev_ctx, ctx));
   VLOG(3) << "expected_kernel_key:" << expected_kernel_key;
 
   auto kernel_iter = kernels.find(expected_kernel_key);
@@ -750,7 +802,7 @@ void OperatorWithKernel::RunImpl(const Scope& scope,
   // do data transformScope &transfer_scope;
   std::vector<std::string> transfered_inplace_vars;
   auto* transfer_scope =
-      TryTransferData(scope, expected_kernel_key, &transfered_inplace_vars);
+      PrepareData(scope, expected_kernel_key, &transfered_inplace_vars, &ctx);
 
   // exec scope is the scope that kernel actually executed on.
   const Scope& exec_scope =
@@ -760,7 +812,11 @@ void OperatorWithKernel::RunImpl(const Scope& scope,
     dev_ctx = pool.Get(expected_kernel_key.place_);
   }
 
-  kernel_iter->second(ExecutionContext(*this, exec_scope, *dev_ctx));
+  RuntimeInferShapeContext infer_shape_ctx(*this, exec_scope, ctx);
+  this->InferShape(&infer_shape_ctx);
+  // TODO(panyx0718): ExecutionContext should only depend on RuntimeContext
+  // not Scope. Imperative mode only pass inputs and get outputs.
+  kernel_iter->second(ExecutionContext(*this, exec_scope, *dev_ctx, ctx));
 
   if (!transfered_inplace_vars.empty()) {
     // there is inplace variable has been transfered.
@@ -784,6 +840,7 @@ void OperatorWithKernel::RunImpl(const Scope& scope,
     }
   }
 }
+
 void OperatorWithKernel::TransferInplaceVarsBack(
     const Scope& scope, const std::vector<std::string>& inplace_vars,
     const Scope& transfer_scope) const {
@@ -799,13 +856,19 @@ void OperatorWithKernel::TransferInplaceVarsBack(
   }
 }
 
-Scope* OperatorWithKernel::TryTransferData(
+Scope* OperatorWithKernel::PrepareData(
     const Scope& scope, const OpKernelType& expected_kernel_key,
-    std::vector<std::string>* transfered_inplace_vars) const {
+    std::vector<std::string>* transfered_inplace_vars,
+    RuntimeContext* ctx) const {
   Scope* new_scope = nullptr;
   for (auto& var_name_item : Inputs()) {
-    for (auto& var_name : var_name_item.second) {
+    std::vector<Variable*>& input_vars = ctx->inputs[var_name_item.first];
+
+    for (size_t i = 0; i < var_name_item.second.size(); ++i) {
+      auto& var_name = var_name_item.second[i];
       auto* var = scope.FindVar(var_name);
+      input_vars[i] = var;
+
       // Only tensor can be tranfer to another device.
       if (var == nullptr || !VarIsTensor(*var)) {
         continue;
@@ -853,6 +916,7 @@ Scope* OperatorWithKernel::TryTransferData(
       }
 
       auto* trans_var = new_scope->Var(var_name);
+      input_vars[i] = trans_var;
 
       Tensor out;
       TransformData(expected_kernel_key, kernel_type_for_var, *tensor_in, &out);

paddle/fluid/framework/operator.h

@@ -73,6 +73,15 @@ Tensor* GetMutableLoDTensorOrSelectedRowsValueFromVar(Variable* var);
 class OperatorBase;
 class ExecutionContext;
 
+class RuntimeContext {
+ public:
+  RuntimeContext(const VariableNameMap& innames,
+                 const VariableNameMap& outnames, const Scope& scope);
+
+  VariableValueMap inputs;
+  VariableValueMap outputs;
+};
+
 /**
  * OperatorBase has the basic elements that Net will call to do computation.
  * Only CreateOperator from OpRegistry will new Operator directly. User
@@ -132,7 +141,8 @@ class OperatorBase {
 
   void SetIsCalledByExecutor(bool x) { run_by_executor_ = x; }
   virtual void RuntimeInferShape(const Scope& scope,
-                                 const platform::Place& place) const {}
+                                 const platform::Place& place,
+                                 const RuntimeContext& ctx) const {}
 
  protected:
   std::string type_;
@@ -159,8 +169,9 @@ class OperatorBase {
 class ExecutionContext {
  public:
   ExecutionContext(const OperatorBase& op, const Scope& scope,
-                   const platform::DeviceContext& device_context)
-      : op_(op), scope_(scope), device_context_(device_context) {}
+                   const platform::DeviceContext& device_context,
+                   const RuntimeContext& ctx)
+      : op_(op), scope_(scope), device_context_(device_context), ctx_(ctx) {}
 
   const OperatorBase& op() const { return op_; }
 
@@ -183,15 +194,9 @@ class ExecutionContext {
     return op_.Outputs(name).size();
   }
 
-  const Variable* InputVar(const std::string& name) const {
-    auto ipt = op_.Input(name);
-    return ipt == kEmptyVarName ? nullptr : scope_.FindVar(ipt);
-  }
+  const Variable* InputVar(const std::string& name) const;
 
-  Variable* OutputVar(const std::string& name) const {
-    auto opt = op_.Output(name);
-    return opt == kEmptyVarName ? nullptr : scope_.FindVar(opt);
-  }
+  Variable* OutputVar(const std::string& name) const;
 
   const std::vector<const Variable*> MultiInputVar(
       const std::string& name) const {
@@ -230,6 +235,22 @@ class ExecutionContext {
     return var == nullptr ? nullptr : var->GetMutable<T>();
   }
 
+  template <typename T>
+  const T* LegacyInput(const std::string& name) const {
+    auto* var = LegacyInputVar(name);
+    return var == nullptr ? nullptr : &var->Get<T>();
+  }
+
+  template <typename T>
+  T* LegacyOutput(const std::string& name) const {
+    auto var = LegacyOutputVar(name);
+    return var == nullptr ? nullptr : var->GetMutable<T>();
+  }
+
+  const Variable* LegacyInputVar(const std::string& name) const;
+
+  Variable* LegacyOutputVar(const std::string& name) const;
+
   template <typename T>
   const std::vector<const T*> MultiInput(const std::string& name) const {
     auto names = op_.Inputs(name);
@@ -289,11 +310,16 @@ class ExecutionContext {
   const OperatorBase& op_;
   const Scope& scope_;
   const platform::DeviceContext& device_context_;
+  const RuntimeContext& ctx_;
 };
 
 template <>
 const Tensor* ExecutionContext::Input<Tensor>(const std::string& name) const;
 
+template <>
+const Tensor* ExecutionContext::LegacyInput<Tensor>(
+    const std::string& name) const;
+
 template <>
 const std::vector<const Tensor*> ExecutionContext::MultiInput<Tensor>(
     const std::string& name) const;
@@ -301,6 +327,9 @@ const std::vector<const Tensor*> ExecutionContext::MultiInput<Tensor>(
 template <>
 Tensor* ExecutionContext::Output<Tensor>(const std::string& name) const;
 
+template <>
+Tensor* ExecutionContext::LegacyOutput<Tensor>(const std::string& name) const;
+
 template <>
 std::vector<Tensor*> ExecutionContext::MultiOutput<Tensor>(
     const std::string& name) const;
@@ -353,8 +382,8 @@ class OperatorWithKernel : public OperatorBase {
     OpInfoMap::Instance().Get(Type()).infer_shape_(ctx);
   }
 
-  void RuntimeInferShape(const Scope& scope,
-                         const platform::Place& place) const override;
+  void RuntimeInferShape(const Scope& scope, const platform::Place& place,
+                         const RuntimeContext& ctx) const override;
 
  protected:
   virtual OpKernelType GetExpectedKernelType(const ExecutionContext& ctx) const;
@@ -374,9 +403,10 @@ class OperatorWithKernel : public OperatorBase {
    *
    * * transfered_inplace_vars is a output vector.
    */
-  Scope* TryTransferData(
-      const Scope& scope, const OpKernelType& expected_kernel_key,
-      std::vector<std::string>* transfered_inplace_vars) const;
+  Scope* PrepareData(const Scope& scope,
+                     const OpKernelType& expected_kernel_key,
+                     std::vector<std::string>* transfered_inplace_vars,
+                     RuntimeContext* ctx) const;
 
   void TransferInplaceVarsBack(const Scope& scope,
                                const std::vector<std::string>& inplace_vars,

paddle/fluid/framework/type_defs.h

@@ -28,8 +28,11 @@ class OperatorBase;
 class OpDesc;
 class InferShapeContext;
 class BlockDesc;
+class Variable;
 
 using VariableNameMap = std::map<std::string, std::vector<std::string>>;
+// TODO(panyx0718): Replace vector with something like gtl::Vector.
+using VariableValueMap = std::map<std::string, std::vector<Variable*>>;
 
 // The order should be as same as framework.proto
 using Attribute =

paddle/fluid/imperative/layer.cc

@@ -188,11 +188,13 @@ std::vector<Variable*> OpBase::ApplyGrad(framework::Scope* scope) {
   std::vector<Variable*> ret;
   for (size_t i = 0; i < input_vars_->size(); ++i) {
     bool found = false;
+    VarBase* origin_var = (*input_vars_)[i];
     for (const std::string& outvar : grad_op_desc_->OutputArgumentNames()) {
       Variable* var = scope->FindVar(outvar);
-      VarBase* origin_var = (*input_vars_)[i];
       std::string orig_var = grad_to_var_->at(outvar);
-      PADDLE_ENFORCE(origin_var->var_desc_->Name() == orig_var);
+      if (origin_var->var_desc_->Name() != orig_var) {
+        continue;
+      }
       VLOG(3) << "apply grad " << outvar << " with origin " << orig_var;
       origin_var->ApplyGrad(scope, var);
       found = true;

paddle/fluid/imperative/tracer.h

@@ -43,9 +43,12 @@ void CreateGradOp(const framework::OpDesc& op_desc,
 
 class Tracer {
  public:
-  explicit Tracer(framework::BlockDesc* root_block) : root_block_(root_block) {
+  explicit Tracer(framework::BlockDesc* root_block,
+                  framework::BlockDesc* startup_block)
+      : root_block_(root_block), startup_block_(startup_block) {
     root_scope_ = new framework::Scope();
     scopes_[root_block_] = root_scope_;
+    scopes_[startup_block_] = root_scope_;
   }
 
   virtual ~Tracer() { delete root_scope_; }
@@ -80,6 +83,8 @@ class Tracer {
       } else {
         op->pre_ops_->push_back(nullptr);
       }
+      VLOG(3) << "input vname " << vname << " "
+              << var->Get<framework::LoDTensor>().dims().size();
     }
 
     *op->output_vars_ = outputs;
@@ -98,12 +103,19 @@ class Tracer {
       outputs[i]->pre_op_ = op;
       outputs[i]->pre_op_out_idx_ = i;
     }
+
+    VLOG(3) << "tracer running " << op_desc->Type();
     op_base->Run(*scope, platform::CPUPlace());
+    if (block == startup_block_) {
+      op->grad_op_desc_ = nullptr;
+      op->grad_to_var_ = nullptr;
+    } else {
       framework::OpDesc* grad_op_desc;
       auto grad_to_var = new std::unordered_map<std::string, std::string>();
       CreateGradOp(*op_desc, {}, {block}, &grad_op_desc, grad_to_var);
       op->grad_op_desc_ = grad_op_desc;
       op->grad_to_var_ = grad_to_var;
+    }
     op->block_ = block;
   }
 
@@ -121,6 +133,7 @@ class Tracer {
  private:
   std::map<framework::BlockDesc*, framework::Scope*> scopes_;
   framework::BlockDesc* root_block_;
+  framework::BlockDesc* startup_block_;
   framework::Scope* root_scope_;
 };
 

paddle/fluid/operators/CMakeLists.txt

@@ -64,9 +64,7 @@ endif()
 set(COMMON_OP_DEPS ${OP_HEADER_DEPS})
 
 set(COMMON_OP_DEPS ${COMMON_OP_DEPS} selected_rows_functor selected_rows lod_tensor maxouting unpooling pooling lod_rank_table context_project sequence_pooling executor)
-if (NOT WIN32)
-    set(COMMON_OP_DEPS ${COMMON_OP_DEPS} dynload_warpctc)
-endif()
+set(COMMON_OP_DEPS ${COMMON_OP_DEPS} dynload_warpctc)
 set(COMMON_OP_DEPS ${COMMON_OP_DEPS} sequence_padding sequence_scale cos_sim_functor memory jit_kernel concat_and_split cross_entropy softmax vol2col im2col sampler)
 set(COMMON_OP_DEPS ${COMMON_OP_DEPS} sequence2batch lstm_compute matrix_bit_code gru_compute activation_functions)
 if (WITH_GPU)

paddle/fluid/operators/beam_search_decode_op.cc

@@ -122,7 +122,8 @@ class BeamSearchDecodeOp : public framework::OperatorBase {
     platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
     auto& dev_ctx = *pool.Get(dev_place);
 
-    framework::ExecutionContext ctx(*this, scope, dev_ctx);
+    framework::RuntimeContext run_ctx(Inputs(), Outputs(), scope);
+    framework::ExecutionContext ctx(*this, scope, dev_ctx, run_ctx);
 
     const LoDTensorArray* ids = ctx.Input<LoDTensorArray>("Ids");
     const LoDTensorArray* scores = ctx.Input<LoDTensorArray>("Scores");

paddle/fluid/operators/controlflow/parallel_do_op.cc

-/* 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 <vector>
-
-#include "paddle/fluid/framework/executor.h"
-#include "paddle/fluid/framework/op_registry.h"
-#include "paddle/fluid/framework/threadpool.h"
-#include "paddle/fluid/operators/detail/safe_ref.h"
-
-namespace paddle {
-namespace operators {
-
-static constexpr char kInputs[] = "inputs";
-static constexpr char kParameters[] = "parameters";
-static constexpr char kPlaces[] = "places";
-
-static constexpr char kOutputs[] = "outputs";
-static constexpr char kParallelScopes[] = "parallel_scopes";
-
-static constexpr char kParallelBlock[] = "sub_block";
-static constexpr char kUseNCCL[] = "use_nccl";
-
-using LoDTensor = framework::LoDTensor;
-using SelectedRows = framework::SelectedRows;
-
-static void SplitTensorAndMoveTensorToScopes(
-    const framework::Scope &scope, std::vector<framework::Scope *> *sub_scopes,
-    const std::vector<platform::Place> &places,
-    const std::vector<std::string> &names) {
-  size_t num_sub_scopes = 0;
-  for (auto &argu : names) {
-    const auto &tensor =
-        detail::Ref(scope.FindVar(argu),
-                    "Cannot find variable %s in the parent scope", argu)
-            .Get<LoDTensor>();
-    auto lod_tensors = tensor.SplitLoDTensor(places);
-
-    for (auto &lod : lod_tensors) {
-      VLOG(3) << lod.dims();
-    }
-    if (num_sub_scopes == 0) {
-      num_sub_scopes = lod_tensors.size();
-    } else {
-      PADDLE_ENFORCE_EQ(num_sub_scopes, lod_tensors.size());
-    }
-    PADDLE_ENFORCE_NE(num_sub_scopes, 0);
-    if (sub_scopes->size() == 0) {
-      sub_scopes->reserve(num_sub_scopes);
-      for (size_t i = 0; i < num_sub_scopes; ++i) {
-        sub_scopes->emplace_back(&scope.NewScope());
-      }
-    }
-
-    for (size_t i = 0; i < lod_tensors.size(); ++i) {
-      *detail::Ref(sub_scopes->at(i)->Var(argu),
-                   "Cannot find variable in the sub-scope", argu)
-           .GetMutable<LoDTensor>() = lod_tensors[i];
-    }
-  }
-}
-
-inline void CopyOrShare(const framework::Variable &src,
-                        const platform::Place &dst_place,
-                        framework::Variable *dst) {
-  if (src.IsType<LoDTensor>()) {
-    if (src.Get<LoDTensor>().place() == dst_place) {
-      dst->GetMutable<LoDTensor>()->ShareDataWith(src.Get<LoDTensor>());
-      dst->GetMutable<LoDTensor>()->set_lod(src.Get<LoDTensor>().lod());
-    } else {
-      TensorCopy(src.Get<LoDTensor>(), dst_place, dst->GetMutable<LoDTensor>());
-    }
-  } else if (src.IsType<SelectedRows>()) {
-    auto &src_sr = src.Get<SelectedRows>();
-    auto *dst_sr = dst->GetMutable<SelectedRows>();
-    dst_sr->set_height(src_sr.height());
-    if (src_sr.value().place() == dst_place) {
-      dst_sr->mutable_value()->ShareDataWith(src_sr.value());
-      dst_sr->set_rows(src_sr.rows());
-    } else {
-      TensorCopy(src_sr.value(), dst_place, dst_sr->mutable_value());
-    }
-  } else {
-    PADDLE_THROW("Expect LoDTensor/SelectedRows, get %s", src.Type().name());
-  }
-}
-
-void WaitOnPlace(const platform::Place place) {
-  platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
-  auto &dev_ctx = *pool.Get(place);
-  dev_ctx.Wait();
-}
-
-void WaitOnPlaces(const std::vector<platform::Place> places) {
-  platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
-
-  for (auto &place : places) {
-    auto &dev_ctx = *pool.Get(place);
-    dev_ctx.Wait();
-  }
-}
-
-class ParallelDoOp : public framework::OperatorBase {
- public:
-  ParallelDoOp(const std::string &type,
-               const framework::VariableNameMap &inputs,
-               const framework::VariableNameMap &outputs,
-               const framework::AttributeMap &attrs)
-      : framework::OperatorBase(type, inputs, outputs, attrs) {}
-
- private:
-  void RunImpl(const framework::Scope &scope,
-               const platform::Place &place) const override {
-    // get device context from pool
-    platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
-    auto &dev_ctx = *pool.Get(place);
-
-    auto *block = Attr<framework::BlockDesc *>(kParallelBlock);
-    auto *program = block->Program();
-
-    auto &places = scope.FindVar(Input(kPlaces))->Get<platform::PlaceList>();
-
-    auto &sub_scopes = *scope.FindVar(Output(kParallelScopes))
-                            ->GetMutable<std::vector<framework::Scope *>>();
-
-    // split input
-    SplitTensorAndMoveTensorToScopes(scope, &sub_scopes, places,
-                                     Inputs(kInputs));
-
-    // copy parameter
-    for (auto &param : Inputs(kParameters)) {
-      PADDLE_ENFORCE(scope.FindVar(param)->IsType<LoDTensor>(),
-                     "Only support parameter type as LoDTensor");
-      auto &src = scope.FindVar(param)->Get<LoDTensor>();
-
-      auto *sub_scope0 = sub_scopes[0];
-      auto *dst0 = sub_scope0->Var(param)->GetMutable<LoDTensor>();
-      dst0->ShareDataWith(src);
-
-      for (size_t i = 1; i < sub_scopes.size(); ++i) {
-        auto &place = places[i];
-        auto *sub_scope = sub_scopes[i];
-        auto *dst = sub_scope->Var(param)->GetMutable<LoDTensor>();
-        framework::TensorCopy(src, place, dst);
-      }
-    }
-    WaitOnPlaces(places);
-
-    std::vector<std::future<void>> workers;
-    workers.reserve(places.size());
-    for (size_t place_idx = 0; place_idx < sub_scopes.size(); ++place_idx) {
-      auto &place = places[place_idx];
-      auto *cur_scope = sub_scopes[place_idx];
-
-      workers.emplace_back(framework::Async([program, cur_scope, place, block] {
-        framework::Executor executor(place);
-        executor.Run(*program, cur_scope, block->ID(),
-                     false /*create_local_scope*/);
-      }));
-    }
-    for (auto &worker : workers) {
-      worker.wait();
-    }
-    WaitOnPlaces(places);
-
-    // merge output
-    for (auto &o_name : Outputs(kOutputs)) {
-      std::vector<const framework::LoDTensor *> lod_tensors;
-      lod_tensors.reserve(sub_scopes.size());
-      for (auto *sub_scope : sub_scopes) {
-        lod_tensors.emplace_back(&sub_scope->FindVar(o_name)->Get<LoDTensor>());
-      }
-
-      auto *lod_tensor_to_be_merged =
-          scope.FindVar(o_name)->GetMutable<LoDTensor>();
-      lod_tensor_to_be_merged->MergeLoDTensor(lod_tensors, dev_ctx.GetPlace());
-    }
-    WaitOnPlaces(places);
-  }
-};
-
-class ParallelDoOpProtoMaker : public framework::OpProtoAndCheckerMaker {
- public:
-  void Make() override {
-    AddInput(kInputs, "").AsDuplicable();
-    AddInput(kParameters, "").AsDuplicable();
-    AddInput(kPlaces, "");
-    AddOutput(kOutputs, "").AsDuplicable();
-    AddOutput(kParallelScopes, "");
-    AddAttr<framework::BlockDesc *>(kParallelBlock, "");
-    AddAttr<bool>(kUseNCCL, "true if we use nccl on backward")
-        .SetDefault(false);
-    AddComment(R"DOC(
-ParallelDo Operator.
-)DOC");
-  }
-};
-
-class ParallelDoGradOp : public framework::OperatorBase {
- public:
-  ParallelDoGradOp(const std::string &type,
-                   const framework::VariableNameMap &inputs,
-                   const framework::VariableNameMap &outputs,
-                   const framework::AttributeMap &attrs)
-      : framework::OperatorBase(type, inputs, outputs, attrs) {}
-
- private:
-  void RunImpl(const framework::Scope &scope,
-               const platform::Place &place) const override {
-    auto *block = Attr<framework::BlockDesc *>(kParallelBlock);
-    auto *program = block->Program();
-
-    auto &sub_scopes = scope.FindVar(Input(kParallelScopes))
-                           ->Get<std::vector<framework::Scope *>>();
-    auto &places = scope.FindVar(Input(kPlaces))->Get<platform::PlaceList>();
-
-    // feed output@grad
-    SplitTensorAndMoveTensorToScopes(
-        scope, const_cast<std::vector<framework::Scope *> *>(&sub_scopes),
-        places, Inputs(framework::GradVarName(kOutputs)));
-    WaitOnPlaces(places);
-
-    // exe run
-    std::vector<std::future<void>> workers;
-    for (size_t i = 0; i < sub_scopes.size(); ++i) {
-      auto &place = places[i];
-      auto *cur_scope = sub_scopes[i];
-
-      // execute
-      workers.emplace_back(framework::Async([program, cur_scope, place, block] {
-        framework::Executor executor(place);
-        executor.Run(*program, cur_scope, block->ID(),
-                     false /*create_local_scope*/);
-      }));
-    }
-    for (auto &worker : workers) {
-      worker.wait();
-    }
-    WaitOnPlaces(places);
-
-    // NCCL allreduce op will be added by backward,
-    // so no need to explicitly accumulate grad
-    if (!(Attr<bool>(kUseNCCL))) {
-      AccumulateGrad(scope, place, sub_scopes, places);
-    } else {
-      for (auto &place : places) {
-        PADDLE_ENFORCE(platform::is_gpu_place(place),
-                       "NCCL only supports cuda place");
-      }
-    }
-    for (auto &s : Outputs(framework::GradVarName(kParameters))) {
-      if (s == framework::kEmptyVarName) {
-        continue;
-      }
-      VLOG(3) << "Moving " << s;
-      CopyOrShare(*sub_scopes[0]->FindVar(s), place, scope.FindVar(s));
-    }
-    WaitOnPlaces(places);
-  }
-
-  void AccumulateGrad(const framework::Scope &scope,
-                      const platform::Place &place,
-                      const std::vector<framework::Scope *> &sub_scopes,
-                      const platform::PlaceList &places) const {
-    for (auto &s : Outputs(framework::GradVarName(kParameters))) {
-      if (s == framework::kEmptyVarName) {
-        continue;
-      }
-      VLOG(3) << "Accumulating " << s;
-      if (s == framework::kEmptyVarName) continue;
-      std::string tmp_name;
-      auto *tmp = sub_scopes[0]->Var(&tmp_name);
-
-      for (size_t i = 1; i < sub_scopes.size(); ++i) {
-        CopyOrShare(*sub_scopes[i]->FindVar(s), places[0], tmp);
-        WaitOnPlaces(places);
-
-        auto sum_op = framework::OpRegistry::CreateOp(
-            "sum", {{"X", {s, tmp_name}}}, {{"Out", {s}}},
-            framework::AttributeMap{{"use_mkldnn", {false}}});
-        VLOG(10) << sum_op->DebugStringEx(sub_scopes[0]);
-        sum_op->Run(*sub_scopes[0], places[0]);
-        WaitOnPlace(places[0]);
-      }
-
-      CopyOrShare(*sub_scopes[0]->FindVar(s), place, scope.FindVar(s));
-    }
-    WaitOnPlaces(places);
-  }
-};
-
-std::ostream &operator<<(std::ostream &sout,
-                         const std::vector<std::string> &strs) {
-  std::copy(strs.begin(), strs.end(),
-            std::ostream_iterator<std::string>(sout, ","));
-  return sout;
-}
-
-class ParallelDoGradOpDescMaker : public framework::SingleGradOpDescMaker {
- public:
-  using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
-
- protected:
-  virtual std::unique_ptr<framework::OpDesc> Apply() const {
-    auto *grad = new framework::OpDesc();
-    grad->SetType("parallel_do_grad");
-    for (auto &input_param : this->InputNames()) {
-      VLOG(3) << input_param;
-      grad->SetInput(input_param, this->Input(input_param));
-      if (input_param != kPlaces) {
-        grad->SetOutput(framework::GradVarName(input_param),
-                        this->InputGrad(input_param, false));
-      }
-    }
-    auto *g_block = this->grad_block_[0];
-
-    // All variable name that needed by gradient operators
-    std::unordered_set<std::string> all_inputs_in_grad_blocks;
-
-    for (size_t i = 0; i < g_block->OpSize(); ++i) {
-      auto *op = g_block->Op(i);
-      for (auto &var_name : op->InputArgumentNames()) {
-        all_inputs_in_grad_blocks.insert(var_name);
-      }
-    }
-
-    for (auto &output_param : this->OutputNames()) {
-      if (output_param == kParallelScopes) {
-        grad->SetInput(output_param, this->Output(output_param));
-        grad->SetInput(framework::GradVarName(output_param),
-                       this->Output(output_param));
-      } else {
-        grad->SetInput(output_param, this->Output(output_param));
-        std::vector<std::string> og_names;
-        for (auto &og_name : this->OutputGrad(output_param)) {
-          if (all_inputs_in_grad_blocks.count(og_name) != 0) {
-            // there are some gradient operators who need the OG. So make this
-            // OG as an input of parallel.do
-            og_names.push_back(og_name);
-          }
-          // else, there is no operator who need the OG. Do not use this OG as
-          // an input
-        }
-        grad->SetInput(framework::GradVarName(output_param), og_names);
-      }
-    }
-    grad->SetInput("Communicator", {"nccl_com__do_not_change_"});
-    grad->SetAttrMap(this->Attrs());
-    grad->SetBlockAttr(kParallelBlock, grad_block_[0]);
-
-    return std::unique_ptr<framework::OpDesc>(grad);
-  }
-};
-
-class ParallelDoGradOpShapeInference : public framework::InferShapeBase {
- public:
-  void operator()(framework::InferShapeContext *ctx) const override {
-    PADDLE_ENFORCE(ctx->HasInputs(kParameters));
-    PADDLE_ENFORCE(ctx->HasInputs(kInputs));
-    PADDLE_ENFORCE(ctx->HasInputs(kOutputs));
-
-    ctx->SetOutputsDim(framework::GradVarName(kParameters),
-                       ctx->GetInputsDim(kParameters));
-
-    auto i_dims = ctx->GetInputsDim(kInputs);
-    auto ig_names = ctx->Outputs(framework::GradVarName(kInputs));
-
-    for (size_t i = 0; i < ig_names.size(); ++i) {
-      auto &ig_name = ig_names[i];
-      if (ig_name == framework::kEmptyVarName) {
-        continue;
-      }
-
-      ctx->SetDims({ig_name}, {i_dims[i]});
-    }
-
-    auto p_dims = ctx->GetInputsDim(kParameters);
-    auto pg_names = ctx->Outputs(framework::GradVarName(kParameters));
-    for (size_t i = 0; i < pg_names.size(); ++i) {
-      auto &pg_name = pg_names[i];
-      if (pg_name == framework::kEmptyVarName) {
-        continue;
-      }
-      ctx->SetDims({pg_name}, {p_dims[i]});
-    }
-  }
-};
-
-class ParallelDoGradOpVarTypeInference : public framework::VarTypeInference {
- public:
-  void operator()(const framework::OpDesc &op_desc,
-                  framework::BlockDesc *block) const override {
-    framework::BlockDesc *sub_block =
-        boost::get<framework::BlockDesc *>(op_desc.GetAttr(kParallelBlock));
-    for (auto &out_vars : op_desc.Outputs()) {
-      for (auto &out_var : out_vars.second) {
-        auto &var = block->FindRecursiveOrCreateVar(out_var);
-        auto sub_var = sub_block->FindRecursiveOrCreateVar(out_var);
-        if (sub_var.GetType() != var.GetType()) {
-          var.SetType(sub_var.GetType());
-        }
-      }
-    }
-  }
-};
-
-}  // namespace operators
-}  // namespace paddle
-
-REGISTER_OPERATOR(parallel_do, paddle::operators::ParallelDoOp,
-                  paddle::operators::ParallelDoOpProtoMaker,
-                  paddle::operators::ParallelDoGradOpDescMaker);
-REGISTER_OPERATOR(parallel_do_grad, paddle::operators::ParallelDoGradOp,
-                  paddle::operators::ParallelDoGradOpShapeInference,
-                  paddle::operators::ParallelDoGradOpVarTypeInference);

paddle/fluid/operators/distributed/brpc_sendrecvop_utils.cc

@@ -16,6 +16,7 @@ limitations under the License. */
 #include <nccl.h>
 #endif
 #include <sys/time.h>
+#include <limits>
 #include <thread>  // NOLINT
 
 #include "paddle/fluid/framework/data_type.h"
@@ -31,7 +32,12 @@ namespace distributed {
 
 class IOBufWriter {
  public:
-  static void Append(butil::IOBuf* iobuf, int k, const char* v, int64_t vlen) {
+  static void Append(const std::string& varname, butil::IOBuf* iobuf, int k,
+                     const char* v, int64_t vlen) {
+    if (vlen >= std::numeric_limits<int>::max() || vlen < 0) {
+      LOG(FATAL) << "AppendZeroCopy varname:" << varname << ", vlen:" << vlen;
+    }
+
     iobuf->append(reinterpret_cast<char*>(&k), 4);
     iobuf->append(reinterpret_cast<char*>(&vlen), 8);
     iobuf->append(v, vlen);
@@ -87,6 +93,10 @@ class IOBufWriter {
                              int k, const char* v, int64_t vlen,
                              bool in_cuda_pinned, void (*destroy)(void*),
                              void* user_data) {
+    if (vlen >= std::numeric_limits<int>::max() || vlen < 0) {
+      LOG(FATAL) << "AppendZeroCopy varname:" << varname << ", vlen:" << vlen;
+    }
+
 #ifdef PADDLE_WITH_BRPC_RDMA
     IOBufWriter::AppendRdmaZeroCopy(varname, iobuf, k, v, vlen, in_cuda_pinned,
                                     destroy, user_data);
@@ -134,7 +144,7 @@ void SerializeToIOBuf(const std::string& name, framework::Variable* var,
     request->set_type(::sendrecv::NCCL_ID);
     const ncclUniqueId& uid = var->Get<ncclUniqueId>();
     // TODO(gongwb): use append_zero to avoid data copy.
-    IOBufWriter::Append(iobuf,
+    IOBufWriter::Append(name, iobuf,
                         sendrecv::VariableMessage::kSerializedFieldNumber,
                         uid.internal, NCCL_UNIQUE_ID_BYTES);
     return;
@@ -149,7 +159,7 @@ void SerializeToIOBuf(const std::string& name, framework::Variable* var,
   // FIXME(gongwb): it seems that can use zero copy.
   if (var_is_not_stable) {
     IOBufWriter::Append(
-        iobuf, ::sendrecv::VariableMessage::kSerializedFieldNumber,
+        name, iobuf, ::sendrecv::VariableMessage::kSerializedFieldNumber,
         static_cast<const char*>(payload->ptr()), payload->memory_size());
   } else {
     if (platform::is_gpu_place(ctx.GetPlace())) {
@@ -171,10 +181,11 @@ void SerializeToIOBuf(const std::string& name, framework::Variable* var,
 
   if (var->IsType<framework::SelectedRows>()) {
     auto* slr = var->GetMutable<framework::SelectedRows>();
-    size_t rows_memory_size =
-        slr->rows().size() * framework::SizeOfType(typeid(int64_t));
+    PADDLE_ENFORCE(VectorElemName(slr->rows()) == typeid(int64_t).name());
+    size_t rows_memory_size = slr->rows().size() * sizeof(int64_t);
 
-    IOBufWriter::Append(iobuf, ::sendrecv::VariableMessage::kRowsFieldNumber,
+    IOBufWriter::Append(name, iobuf,
+                        ::sendrecv::VariableMessage::kRowsFieldNumber,
                         reinterpret_cast<const char*>(slr->rows().data()),
                         static_cast<int64_t>(rows_memory_size));
   }

paddle/fluid/operators/distributed/grpc_client.cc

@@ -12,6 +12,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
 limitations under the License. */
 
+#include <stdlib.h>
 #include <limits>
 
 #include "glog/logging.h"  // For VLOG
@@ -420,7 +421,15 @@ void GRPCClient::Proceed() {
       sync_cond_.notify_all();
     }
   }
-  VLOG(3) << "GRPCClient Proceed end";
+
+  // Last log message
+  // Avoid using VLOG() and LOG(): in the destructor of google::LogMessage() a
+  // static Mutex log_mutex is used for synchronization, which might have been
+  // destructed at this moment.
+  if (FLAGS_v >= 3) {
+    std::string msg("GRPCClient Proceed end");
+    fwrite(msg.c_str(), msg.length(), 1, stdout);
+  }
 }
 
 std::shared_ptr<grpc::Channel> GRPCClient::GetChannel(const std::string& ep) {

paddle/fluid/operators/distributed/grpc_serde.cc

@@ -15,6 +15,7 @@ limitations under the License. */
 #ifdef PADDLE_WITH_CUDA
 #include <nccl.h>
 #endif
+#include <limits>
 #include <thread>  // NOLINT
 
 #include "google/protobuf/io/coded_stream.h"
@@ -102,6 +103,10 @@ void SerializeToByteBuffer(const std::string& name, framework::Variable* var,
 
   e.WriteVarlengthBeginning(VarMsg::kSerializedFieldNumber,
                             payload->memory_size());
+  if (payload->memory_size() >= std::numeric_limits<int>::max()) {
+    LOG(FATAL) << "AppendZeroCopy varname:" << name
+               << ", vlen:" << payload->memory_size();
+  }
   // steal reference of tensor data
   ::grpc::Slice slices[4];  // metadata, tensor, rows meta, rows
   int num_slices = 2;       // only SelectedRows have rows buffer
@@ -115,7 +120,10 @@ void SerializeToByteBuffer(const std::string& name, framework::Variable* var,
   if (var->IsType<framework::SelectedRows>()) {
     auto* slr = var->GetMutable<framework::SelectedRows>();
     ProtoEncodeHelper e2(static_cast<char*>(buf), 128);
+
+    PADDLE_ENFORCE(VectorElemName(slr->rows()) == typeid(int64_t).name());
     size_t rows_memory_size = slr->rows().size() * sizeof(int64_t);
+
     e2.WriteVarlengthBeginning(VarMsg::kRowsFieldNumber, rows_memory_size);
     slices[2] = ::grpc::Slice(e2.size());
     memcpy(const_cast<uint8_t*>(slices[2].begin()), e2.data(), e2.size());

paddle/fluid/operators/distributed/sendrecvop_utils.h

@@ -15,6 +15,7 @@ limitations under the License. */
 #pragma once
 #include <iostream>
 #include <string>
+#include <typeindex>
 #include <vector>
 
 #include "paddle/fluid/framework/data_type.h"
@@ -23,9 +24,8 @@ limitations under the License. */
 #include "paddle/fluid/framework/selected_rows.h"
 #include "paddle/fluid/framework/tensor_util.h"
 #include "paddle/fluid/framework/var_type.h"
-#include "paddle/fluid/platform/port.h"
-
 #include "paddle/fluid/operators/distributed/send_recv.pb.h"
+#include "paddle/fluid/platform/port.h"
 
 namespace paddle {
 namespace operators {
@@ -83,6 +83,11 @@ inline framework::proto::VarType::Type ToVarType(
   }
 }
 
+template <template <typename> class T, typename Elem>
+std::string VectorElemName(const T<Elem>& arg) {
+  return typeid(Elem).name();
+}
+
 }  // namespace distributed
 }  // namespace operators
 }  // namespace paddle

paddle/fluid/operators/distributed/variable_response.cc

@@ -118,7 +118,7 @@ bool VariableResponse::CopyLodTensorData(
 
   VLOG(6) << "Tensor.memory_size = " << tensor->memory_size()
           << ", Buffer Size = " << length;
-  PADDLE_ENFORCE_EQ(tensor->memory_size(), length);
+  PADDLE_ENFORCE_EQ(tensor->memory_size(), static_cast<unsigned int>(length));
   return ReadRaw(input, ctx, tensor->place(), tensor_data, length);
 }
 

paddle/fluid/operators/elementwise/elementwise_div_op.cu

@@ -12,18 +12,23 @@ 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/elementwise/elementwise_div_op.h"
+#include "paddle/fluid/platform/float16.h"
 
 namespace ops = paddle::operators;
 
 REGISTER_OP_CUDA_KERNEL(
     elementwise_div,
     ops::ElementwiseDivKernel<paddle::platform::CUDADeviceContext, float>,
+    ops::ElementwiseDivKernel<paddle::platform::CUDADeviceContext,
+                              paddle::platform::float16>,
     ops::ElementwiseDivKernel<paddle::platform::CUDADeviceContext, double>,
     ops::ElementwiseDivKernel<paddle::platform::CUDADeviceContext, int>,
     ops::ElementwiseDivKernel<paddle::platform::CUDADeviceContext, int64_t>);
 REGISTER_OP_CUDA_KERNEL(
     elementwise_div_grad,
     ops::ElementwiseDivGradKernel<paddle::platform::CUDADeviceContext, float>,
+    ops::ElementwiseDivGradKernel<paddle::platform::CUDADeviceContext,
+                                  paddle::platform::float16>,
     ops::ElementwiseDivGradKernel<paddle::platform::CUDADeviceContext, double>,
     ops::ElementwiseDivGradKernel<paddle::platform::CUDADeviceContext, int>,
     ops::ElementwiseDivGradKernel<paddle::platform::CUDADeviceContext,

paddle/fluid/operators/elementwise/elementwise_mul_op.cu

@@ -12,19 +12,21 @@ 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/elementwise/elementwise_mul_op.h"
+#include "paddle/fluid/platform/float16.h"
 
 namespace ops = paddle::operators;
+namespace plat = paddle::platform;
 
 REGISTER_OP_CUDA_KERNEL(
-    elementwise_mul,
-    ops::ElementwiseMulKernel<paddle::platform::CUDADeviceContext, float>,
-    ops::ElementwiseMulKernel<paddle::platform::CUDADeviceContext, double>,
-    ops::ElementwiseMulKernel<paddle::platform::CUDADeviceContext, int>,
-    ops::ElementwiseMulKernel<paddle::platform::CUDADeviceContext, int64_t>);
+    elementwise_mul, ops::ElementwiseMulKernel<plat::CUDADeviceContext, float>,
+    ops::ElementwiseMulKernel<plat::CUDADeviceContext, double>,
+    ops::ElementwiseMulKernel<plat::CUDADeviceContext, int>,
+    ops::ElementwiseMulKernel<plat::CUDADeviceContext, int64_t>,
+    ops::ElementwiseMulKernel<plat::CUDADeviceContext, plat::float16>);
 REGISTER_OP_CUDA_KERNEL(
     elementwise_mul_grad,
-    ops::ElementwiseMulGradKernel<paddle::platform::CUDADeviceContext, float>,
-    ops::ElementwiseMulGradKernel<paddle::platform::CUDADeviceContext, double>,
-    ops::ElementwiseMulGradKernel<paddle::platform::CUDADeviceContext, int>,
-    ops::ElementwiseMulGradKernel<paddle::platform::CUDADeviceContext,
-                                  int64_t>);
+    ops::ElementwiseMulGradKernel<plat::CUDADeviceContext, float>,
+    ops::ElementwiseMulGradKernel<plat::CUDADeviceContext, double>,
+    ops::ElementwiseMulGradKernel<plat::CUDADeviceContext, int>,
+    ops::ElementwiseMulGradKernel<plat::CUDADeviceContext, int64_t>,
+    ops::ElementwiseMulGradKernel<plat::CUDADeviceContext, plat::float16>);

paddle/fluid/operators/fill_zeros_like_op.cu.cc

@@ -14,6 +14,7 @@ limitations under the License. */
 
 #include "paddle/fluid/operators/fill_zeros_like_op.h"
 #include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/platform/float16.h"
 
 namespace ops = paddle::operators;
 REGISTER_OP_CUDA_KERNEL(
@@ -22,4 +23,6 @@ REGISTER_OP_CUDA_KERNEL(
     ops::FillZerosLikeKernel<paddle::platform::CUDADeviceContext, int64_t>,
     ops::FillZerosLikeKernel<paddle::platform::CUDADeviceContext, float>,
     ops::FillZerosLikeKernel<paddle::platform::CUDADeviceContext, double>,
+    ops::FillZerosLikeKernel<paddle::platform::CUDADeviceContext,
+                             paddle::platform::float16>,
     ops::FillZerosLikeKernel<paddle::platform::CUDADeviceContext, bool>);

paddle/fluid/operators/metrics/accuracy_op.cu

@@ -16,6 +16,7 @@ limitations under the License. */
 #include <thrust/reduce.h>
 #include "paddle/fluid/operators/metrics/accuracy_op.h"
 #include "paddle/fluid/platform/cuda_primitives.h"
+#include "paddle/fluid/platform/float16.h"
 #include "paddle/fluid/platform/gpu_info.h"
 
 namespace paddle {
@@ -94,6 +95,7 @@ class AccuracyOpCUDAKernel : public framework::OpKernel<T> {
 
 // FIXME(typhoonzero): types of T is for inference data.
 // label data is always int64
-REGISTER_OP_CUDA_KERNEL(accuracy,
-                        paddle::operators::AccuracyOpCUDAKernel<float>,
-                        paddle::operators::AccuracyOpCUDAKernel<double>);
+REGISTER_OP_CUDA_KERNEL(
+    accuracy, paddle::operators::AccuracyOpCUDAKernel<float>,
+    paddle::operators::AccuracyOpCUDAKernel<double>,
+    paddle::operators::AccuracyOpCUDAKernel<paddle::platform::float16>);

paddle/fluid/operators/mul_op.cc

@@ -49,7 +49,8 @@ class MulOp : public framework::OperatorWithKernel {
     PADDLE_ENFORCE_GT(
         y_dims.size(), y_num_col_dims,
         "The input tensor Y's rank of MulOp should be larger than "
-        "y_num_col_dims.");
+        "y_num_col_dims: %ld vs %ld",
+        y_dims.size(), y_num_col_dims);
 
     auto x_mat_dims = framework::flatten_to_2d(x_dims, x_num_col_dims);
     auto y_mat_dims = framework::flatten_to_2d(y_dims, y_num_col_dims);

paddle/fluid/operators/ngraph/ngraph_ops.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. */
+
+/*
+ * This file contains the list of the ngraph operators for Paddle.
+ *
+ * ATTENTION: It requires some C++11 features, for lower version C++ or C, we
+ * might release another API.
+ */
+
+#pragma once
+
+#include "ops/binary_unnary_op.h"
+#include "ops/mul_op.h"

paddle/fluid/operators/ngraph/ops/binary_unnary_op.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. */
+
+#ifdef PADDLE_WITH_NGRAPH
+#pragma once
+
+#include <string>
+#include "ngraph/ngraph.hpp"
+#include "paddle/fluid/platform/ngraph_helper.h"
+
+namespace paddle {
+namespace operators {
+namespace ngraphs {
+
+template <typename T>
+static void BuildBinaryNode(
+    const std::shared_ptr<paddle::framework::OperatorBase>& op,
+    std::shared_ptr<
+        std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
+        ngb_node_map) {
+  auto x = paddle::platform::GetInputNode(op, "X", ngb_node_map);
+  auto y = paddle::platform::GetInputNode(op, "Y", ngb_node_map);
+  auto out = std::make_shared<T>(x, y);
+  paddle::platform::SetOutputNode(op, "Out", out, ngb_node_map);
+}
+
+template <typename T>
+static void BuildUnaryNode(
+    const std::shared_ptr<paddle::framework::OperatorBase>& op,
+    std::shared_ptr<
+        std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
+        ngb_node_map) {
+  auto input = paddle::platform::GetInputNode(op, "X", ngb_node_map);
+  auto out = std::make_shared<T>(input);
+  paddle::platform::SetOutputNode(op, "Out", out, ngb_node_map);
+}
+
+}  // namespace ngraphs
+}  // namespace operators
+}  // namespace paddle
+#endif

paddle/fluid/operators/ngraph/ops/mul_op.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. */
+
+#ifdef PADDLE_WITH_NGRAPH
+#pragma once
+
+#include <string>
+#include "ngraph/ngraph.hpp"
+#include "paddle/fluid/platform/ngraph_helper.h"
+
+namespace paddle {
+namespace operators {
+namespace ngraphs {
+
+static void BuildMulNode(
+    const std::shared_ptr<paddle::framework::OperatorBase>& op,
+    std::shared_ptr<
+        std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
+        ngb_node_map) {
+  auto op_attrs = paddle::framework::AttrReader(op->Attrs());
+  int x_num_col_dims = op_attrs.Get<int>("x_num_col_dims");
+  int y_num_col_dims = op_attrs.Get<int>("y_num_col_dims");
+  auto x = paddle::platform::GetInputNode(op, "X", ngb_node_map);
+  auto y = paddle::platform::GetInputNode(op, "Y", ngb_node_map);
+
+  auto x_reshape = x;
+  auto y_reshape = y;
+
+  if (x->get_shape().size() > 2) {
+    auto x_2d = paddle::platform::FlattenTo2d(x->get_shape(), x_num_col_dims);
+    x_reshape = paddle::platform::NgReshaper(x, x_2d);
+  }
+
+  if (y->get_shape().size() > 2) {
+    auto y_2d = paddle::platform::FlattenTo2d(y->get_shape(), y_num_col_dims);
+    y_reshape = paddle::platform::NgReshaper(y, y_2d);
+  }
+
+  std::shared_ptr<ngraph::Node> out =
+      std::make_shared<ngraph::op::Dot>(x_reshape, y_reshape);
+
+  auto dummy_out = paddle::platform::GetOutputNode(op, "Out", ngb_node_map);
+  if (dummy_out && dummy_out->get_shape() != out->get_shape()) {
+    out = paddle::platform::NgReshaper(out, dummy_out->get_shape());
+  }
+  paddle::platform::SetOutputNode(op, "Out", out, ngb_node_map);
+}
+
+static void BuildMulGradNode(
+    const std::shared_ptr<paddle::framework::OperatorBase>& op,
+    std::shared_ptr<
+        std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
+        ngb_node_map) {
+  auto op_attrs = paddle::framework::AttrReader(op->Attrs());
+  int x_num_col_dims = op_attrs.Get<int>("x_num_col_dims");
+  int y_num_col_dims = op_attrs.Get<int>("y_num_col_dims");
+  auto x = paddle::platform::GetInputNode(op, "X", ngb_node_map);
+  auto y = paddle::platform::GetInputNode(op, "Y", ngb_node_map);
+  auto dout = paddle::platform::GetInputNode(op, "Out@GRAD", ngb_node_map);
+
+  bool is_dx = paddle::platform::HasOutput(op, "X@GRAD") ? true : false;
+  bool is_dy = paddle::platform::HasOutput(op, "Y@GRAD") ? true : false;
+
+  auto x_shape = x->get_shape();
+  auto y_shape = y->get_shape();
+
+  auto x_reshape = x;
+  auto y_reshape = y;
+
+  if (x_shape.size() > 2) {
+    auto x_2d_shape = paddle::platform::FlattenTo2d(x_shape, x_num_col_dims);
+    x_reshape = paddle::platform::NgReshaper(x, x_2d_shape);
+  }
+
+  if (y_shape.size() > 2) {
+    auto y_2d_shape = paddle::platform::FlattenTo2d(y_shape, y_num_col_dims);
+    y_reshape = paddle::platform::NgReshaper(y, y_2d_shape);
+  }
+
+  auto x_reshape_shape = x_reshape->get_shape();
+  std::reverse(x_reshape_shape.begin(), x_reshape_shape.end());
+  auto x_transpose = std::make_shared<ngraph::op::Reshape>(
+      x_reshape, ngraph::AxisVector{1, 0}, x_reshape_shape);
+
+  auto y_reshape_shape = y_reshape->get_shape();
+  std::reverse(y_reshape_shape.begin(), y_reshape_shape.end());
+  auto y_transpose = std::make_shared<ngraph::op::Reshape>(
+      y_reshape, ngraph::AxisVector{1, 0}, y_reshape_shape);
+
+  if (is_dx) {
+    if (dout->get_shape().size() > 2) {
+      auto dout_2d_shape = paddle::platform::FlattenTo2d(dout->get_shape(), 2);
+      dout = paddle::platform::NgReshaper(dout, dout_2d_shape);
+    }
+    auto dx = std::make_shared<ngraph::op::Dot>(dout, y_transpose);
+
+    if (dx->get_shape() == x_shape) {
+      paddle::platform::SetOutputNode(op, "X@GRAD", dx, ngb_node_map);
+    } else {
+      auto dx_reshape = paddle::platform::NgReshaper(dx, x_shape);
+      paddle::platform::SetOutputNode(op, "X@GRAD", dx_reshape, ngb_node_map);
+    }
+  }
+
+  if (is_dy) {
+    if (dout->get_shape().size() > 2) {
+      auto dout_2d_shape = paddle::platform::FlattenTo2d(dout->get_shape(), 2);
+      dout = paddle::platform::NgReshaper(dout, dout_2d_shape);
+    }
+    auto dy = std::make_shared<ngraph::op::Dot>(x_transpose, dout);
+
+    if (dy->get_shape() == y_shape) {
+      paddle::platform::SetOutputNode(op, "Y@GRAD", dy, ngb_node_map);
+    } else {
+      auto dy_reshape = paddle::platform::NgReshaper(dy, y_shape);
+      paddle::platform::SetOutputNode(op, "Y@GRAD", dy_reshape, ngb_node_map);
+    }
+  }
+}
+}  // namespace ngraphs
+}  // namespace operators
+}  // namespace paddle
+#endif

paddle/fluid/operators/optimizers/momentum_op.cu

@@ -14,8 +14,11 @@ limitations under the License. */
 
 #include "paddle/fluid/framework/op_registry.h"
 #include "paddle/fluid/operators/optimizers/momentum_op.h"
+#include "paddle/fluid/platform/float16.h"
 
 namespace ops = paddle::operators;
 REGISTER_OP_CUDA_KERNEL(
     momentum, ops::MomentumOpKernel<paddle::platform::CUDADeviceContext, float>,
-    ops::MomentumOpKernel<paddle::platform::CUDADeviceContext, double>);
+    ops::MomentumOpKernel<paddle::platform::CUDADeviceContext, double>,
+    ops::MomentumOpKernel<paddle::platform::CUDADeviceContext,
+                          paddle::platform::float16>);

paddle/fluid/operators/optimizers/momentum_op.h

@@ -237,7 +237,8 @@ class SparseMomentumFunctor<T, UseNesterov> {
   inline HOSTDEVICE void operator()(size_t i) {
     auto row_idx =
         math::BinarySearch<int64_t>(rows_, row_height_, i / row_numel_);
-    T g = row_idx >= 0 ? g_[row_idx * row_numel_ + i % row_numel_] : 0;
+    T g = row_idx >= 0 ? g_[row_idx * row_numel_ + i % row_numel_]
+                       : static_cast<T>(0);
     // put memory access in register
     const T p = p_[i];
     const T lr = lr_[0];
@@ -282,7 +283,8 @@ class SparseMomentumFunctor<T, NoNesterov> {
   inline HOSTDEVICE void operator()(size_t i) {
     auto row_idx =
         math::BinarySearch<int64_t>(rows_, row_height_, i / row_numel_);
-    T g = row_idx >= 0 ? g_[row_idx * row_numel_ + i % row_numel_] : 0;
+    T g = row_idx >= 0 ? g_[row_idx * row_numel_ + i % row_numel_]
+                       : static_cast<T>(0);
     // put memory access in register
     const T p = p_[i];
     const T lr = lr_[0];

paddle/fluid/operators/top_k_op.cu

@@ -16,6 +16,7 @@ limitations under the License. */
 #include "paddle/fluid/operators/top_k_op.h"
 #include "paddle/fluid/platform/assert.h"
 #include "paddle/fluid/platform/cuda_device_function.h"
+#include "paddle/fluid/platform/float16.h"
 
 namespace paddle {
 namespace operators {
@@ -150,7 +151,7 @@ __device__ __forceinline__ void ThreadGetTopK(Pair<T> topk[], int* beam,
         if (k < MaxLength - (*beam)) {
           topk[k] = topk[k + *beam];
         } else {
-          topk[k].set(-INFINITY, -1);
+          topk[k].set(-static_cast<T>(INFINITY), -1);
         }
       }
       if (!(*is_empty)) {
@@ -160,7 +161,7 @@ __device__ __forceinline__ void ThreadGetTopK(Pair<T> topk[], int* beam,
     }
 
     *max = topk[MaxLength - 1];
-    if ((*max).v == -1) *is_empty = true;
+    if ((*max).v == -static_cast<T>(1)) *is_empty = true;
     *beam = 0;
   }
 }
@@ -181,7 +182,7 @@ __device__ __forceinline__ void ThreadGetTopK(Pair<T> topk[], int* beam,
         if (k < MaxLength - *beam) {
           topk[k] = topk[k + *beam];
         } else {
-          topk[k].set(-INFINITY, -1);
+          topk[k].set(-static_cast<T>(INFINITY), -1);
         }
       }
       if (!(*is_empty)) {
@@ -278,7 +279,7 @@ __global__ void KeMatrixTopK(T* output, int output_stride, int64_t* indices,
     bool firststep = true;
 
     for (int j = 0; j < MaxLength; j++) {
-      topk[j].set(-INFINITY, -1);
+      topk[j].set(-static_cast<T>(INFINITY), -1);
     }
     while (top_num) {
       ThreadGetTopK<T, MaxLength, BlockSize>(
@@ -362,5 +363,7 @@ class TopkOpCUDAKernel : public framework::OpKernel<T> {
 }  // namespace operators
 }  // namespace paddle
 
-REGISTER_OP_CUDA_KERNEL(top_k, paddle::operators::TopkOpCUDAKernel<float>,
-                        paddle::operators::TopkOpCUDAKernel<double>);
+REGISTER_OP_CUDA_KERNEL(
+    top_k, paddle::operators::TopkOpCUDAKernel<float>,
+    paddle::operators::TopkOpCUDAKernel<double>,
+    paddle::operators::TopkOpCUDAKernel<paddle::platform::float16>);

paddle/fluid/operators/transpose_mkldnn_op.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/data_layout_transform.h"
+#include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/memory/malloc.h"
+#include "paddle/fluid/platform/mkldnn_reuse.h"
+
+namespace paddle {
+namespace operators {
+
+using Tensor = framework::Tensor;
+using framework::DataLayout;
+
+template <typename T>
+class TransposeMKLDNNOpKernel : 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.");
+    const bool is_test = ctx.Attr<bool>("is_test");
+    PADDLE_ENFORCE(
+        is_test == true,
+        "ConvTransposeMKLDNN works only for inference!. Set is_test = True");
+    auto& dev_ctx =
+        ctx.template device_context<paddle::platform::MKLDNNDeviceContext>();
+    const auto& mkldnn_engine = dev_ctx.GetEngine();
+    std::vector<int> axis = ctx.Attr<std::vector<int>>("axis");
+    int ndims = axis.size();
+    auto* input = ctx.Input<Tensor>("X");
+    auto* output = ctx.Output<Tensor>("Out");
+    const T* input_data = input->data<T>();
+
+    if (ndims == 1) {
+      output->ShareDataWith(*input);
+      return;
+    }
+
+    std::vector<int> nchw_axis(ndims, 0);
+    for (size_t i = 0; i < nchw_axis.size(); ++i) {
+      nchw_axis[i] = i;
+    }
+
+    std::vector<int> nchw_tz = paddle::framework::vectorize2int(input->dims());
+    std::string data_format = ctx.Attr<std::string>("data_format");
+
+    auto src_md =
+        input->format() != mkldnn::memory::format::nchw
+            ? platform::MKLDNNMemDesc(nchw_tz, platform::MKLDNNGetDataType<T>(),
+                                      input->format())
+            : Axis2MemoryDesc(nchw_tz, nchw_axis);
+
+    this->TransposeKernel(ctx.GetPlace(), Axis2MemoryDesc(nchw_tz, axis),
+                          src_md, output, input_data, nchw_tz, mkldnn_engine);
+  }
+
+ protected:
+  mkldnn::memory::desc Axis2MemoryDesc(std::vector<int>& nchw_tz,
+                                       std::vector<int>& axis) const {
+    mkldnn_memory_desc_t mem_fmt;
+
+    mem_fmt.primitive_kind = mkldnn_memory;
+    mem_fmt.ndims = axis.size();
+    for (unsigned int i = 0; i < nchw_tz.size(); ++i) {
+      mem_fmt.dims[i] = nchw_tz[i];  // logical dimensions (nchw format,
+                                     // regardless physical layout)
+    }
+    mem_fmt.data_type = mkldnn_f32;
+    mem_fmt.format = mkldnn_blocked;
+
+    unsigned int total_stride = 1;
+    for (int i = nchw_tz.size() - 1; i >= 0; --i) {
+      mem_fmt.layout_desc.blocking.padding_dims[i] =
+          nchw_tz[i];  // logical dimensions (nchw format, regardless physical
+                       // layout)
+      mem_fmt.layout_desc.blocking.block_dims[i] = 1;
+      mem_fmt.layout_desc.blocking.offset_padding_to_data[i] = 0;  // no offset
+      mem_fmt.layout_desc.blocking.strides[0][axis[i]] = total_stride;
+      mem_fmt.layout_desc.blocking.strides[1][axis[i]] = 1;
+      total_stride *= nchw_tz[axis[i]];
+    }
+    mem_fmt.layout_desc.blocking.offset_padding = 0;  // no initial offset
+    return mem_fmt;
+  }
+
+  void TransposeKernel(platform::Place place, mkldnn::memory::desc md_o,
+                       mkldnn::memory::desc md_i, Tensor* output,
+                       const T* data_i, std::vector<int>& nchw_dims,
+                       const mkldnn::engine& eng) const {
+    // Make Memory primitive descriptors
+    auto mpd_o = mkldnn::memory::primitive_desc(md_o, eng);
+    auto mpd_i = mkldnn::memory::primitive_desc(md_i, eng);
+
+    auto data_o = output->mutable_data<T>(
+        place, paddle::memory::Allocator::kDefault, mpd_o.get_size());
+
+    auto src = mkldnn::memory(mpd_i, (T*)(data_i));
+    auto dst = mkldnn::memory(mpd_o, data_o);
+
+    auto r = mkldnn::reorder(src, dst);
+    mkldnn::stream(mkldnn::stream::kind::eager).submit({r}).wait();
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+
+REGISTER_OP_KERNEL(transpose2, MKLDNN, ::paddle::platform::CPUPlace,
+                   ops::TransposeMKLDNNOpKernel<float>);
+REGISTER_OP_KERNEL(transpose, MKLDNN, ::paddle::platform::CPUPlace,
+                   ops::TransposeMKLDNNOpKernel<float>);

paddle/fluid/operators/transpose_op.cc

@@ -16,6 +16,10 @@ limitations under the License. */
 #include <string>
 #include <vector>
 
+#ifdef PADDLE_WITH_MKLDNN
+#include "paddle/fluid/platform/mkldnn_helper.h"
+#endif
+
 namespace paddle {
 namespace operators {
 
@@ -53,11 +57,32 @@ class TransposeOp : public framework::OperatorWithKernel {
     }
     ctx->SetOutputDim("Out", out_dims);
   }
+
+ protected:
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext &ctx) const override {
+    framework::LibraryType library_{framework::LibraryType::kPlain};
+    std::string data_format = ctx.Attr<std::string>("data_format");
+    framework::DataLayout layout_ = framework::StringToDataLayout(data_format);
+#ifdef PADDLE_WITH_MKLDNN
+    if (library_ == framework::LibraryType::kPlain &&
+        platform::CanMKLDNNBeUsed(ctx)) {
+      library_ = framework::LibraryType::kMKLDNN;
+      layout_ = framework::DataLayout::kMKLDNN;
+    }
+#endif
+    return framework::OpKernelType(ctx.Input<Tensor>("X")->type(),
+                                   ctx.GetPlace(), layout_, library_);
+  }
 };
 
 class TransposeOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
   void Make() override {
+    AddAttr<bool>("is_test",
+                  "(bool, default false) Set to true for inference only, false "
+                  "for training. Some layers may run faster when this is true.")
+        .SetDefault(false);
     AddInput(
         "X",
         "(Tensor) The input tensor, tensors with rank up to 6 are supported.");
@@ -67,6 +92,16 @@ class TransposeOpMaker : public framework::OpProtoAndCheckerMaker {
         "(vector<int>) A list of values, and the size of the list should be "
         "the same with the input tensor rank. This operator permutes the input "
         "tensor's axes according to the values given.");
+    AddAttr<bool>("use_mkldnn",
+                  "(bool, default false) Only used in mkldnn kernel")
+        .SetDefault(false);
+    AddAttr<std::string>(
+        "data_format",
+        "(string, default NCHW) Only used in "
+        "An optional string from: \"NHWC\", \"NCHW\". "
+        "Defaults to \"NHWC\". Specify the data format of the output data, "
+        "the input will be transformed automatically. ")
+        .SetDefault("AnyLayout");
     AddComment(R"DOC(
 Transpose Operator.
 
@@ -144,8 +179,18 @@ class Transpose2Op : public TransposeOp {
  protected:
   framework::OpKernelType GetExpectedKernelType(
       const framework::ExecutionContext &ctx) const override {
-    return framework::OpKernelType(ctx.Input<framework::LoDTensor>("X")->type(),
-                                   ctx.device_context());
+    framework::LibraryType library_{framework::LibraryType::kPlain};
+    std::string data_format = ctx.Attr<std::string>("data_format");
+    framework::DataLayout layout_ = framework::StringToDataLayout(data_format);
+#ifdef PADDLE_WITH_MKLDNN
+    if (library_ == framework::LibraryType::kPlain &&
+        platform::CanMKLDNNBeUsed(ctx)) {
+      library_ = framework::LibraryType::kMKLDNN;
+      layout_ = framework::DataLayout::kMKLDNN;
+    }
+#endif
+    return framework::OpKernelType(ctx.Input<Tensor>("X")->type(),
+                                   ctx.GetPlace(), layout_, library_);
   }
 };
 

paddle/fluid/platform/dynload/CMakeLists.txt

@@ -16,9 +16,7 @@ if (CUPTI_FOUND)
     list(APPEND CUDA_SRCS cupti.cc)
 endif(CUPTI_FOUND)
 nv_library(dynload_cuda SRCS ${CUDA_SRCS} DEPS dynamic_loader)
-if (NOT WIN32)
 cc_library(dynload_warpctc SRCS warpctc.cc DEPS dynamic_loader warpctc)
-endif(NOT WIN32)
 if (WITH_MKLML)
     cc_library(dynload_mklml SRCS mklml.cc DEPS dynamic_loader mklml)
 endif()

paddle/fluid/platform/dynload/cudnn.h

@@ -34,7 +34,7 @@ extern void EnforceCUDNNLoaded(const char* fn_name);
 #define DECLARE_DYNAMIC_LOAD_CUDNN_WRAP(__name)                            \
   struct DynLoad__##__name {                                               \
     template <typename... Args>                                            \
-    auto operator()(Args... args) -> decltype(__name(args...)) {           \
+    auto operator()(Args... args) -> DECLARE_TYPE(__name, args...) {       \
       using cudnn_func = decltype(&::__name);                              \
       std::call_once(cudnn_dso_flag, []() {                                \
         cudnn_dso_handle = paddle::platform::dynload::GetCUDNNDsoHandle(); \

paddle/fluid/platform/dynload/dynamic_loader.cc

@@ -201,6 +201,8 @@ void* GetCurandDsoHandle() {
 void* GetWarpCTCDsoHandle() {
 #if defined(__APPLE__) || defined(__OSX__)
   return GetDsoHandleFromSearchPath(FLAGS_warpctc_dir, "libwarpctc.dylib");
+#elif defined(_WIN32)
+  return GetDsoHandleFromSearchPath(FLAGS_warpctc_dir, "warpctc.dll");
 #else
   return GetDsoHandleFromSearchPath(FLAGS_warpctc_dir, "libwarpctc.so");
 #endif

paddle/fluid/platform/dynload/dynamic_loader.h

@@ -18,6 +18,12 @@ namespace paddle {
 namespace platform {
 namespace dynload {
 
+#ifndef _WIN32
+#define DECLARE_TYPE(__name, ...) decltype(__name(__VA_ARGS__))
+#else
+#define DECLARE_TYPE(__name, ...) decltype(auto)
+#endif
+
 void* GetCublasDsoHandle();
 void* GetCUDNNDsoHandle();
 void* GetCUPTIDsoHandle();

paddle/fluid/platform/dynload/mklml.h

@@ -34,7 +34,7 @@ extern void* mklml_dso_handle;
 #define DYNAMIC_LOAD_MKLML_WRAP(__name)                                    \
   struct DynLoad__##__name {                                               \
     template <typename... Args>                                            \
-    auto operator()(Args... args) -> decltype(__name(args...)) {           \
+    auto operator()(Args... args) -> DECLARE_TYPE(__name, args...) {       \
       using mklmlFunc = decltype(&::__name);                               \
       std::call_once(mklml_dso_flag, []() {                                \
         mklml_dso_handle = paddle::platform::dynload::GetMKLMLDsoHandle(); \

paddle/fluid/platform/dynload/tensorrt.h

@@ -33,7 +33,7 @@ extern void* tensorrt_dso_handle;
 #define DECLARE_DYNAMIC_LOAD_TENSORRT_WRAP(__name)                      \
   struct DynLoad__##__name {                                            \
     template <typename... Args>                                         \
-    auto operator()(Args... args) -> decltype(__name(args...)) {        \
+    auto operator()(Args... args) -> DECLARE_TYPE(__name, args...) {    \
       using tensorrt_func = decltype(__name(args...)) (*)(Args...);     \
       std::call_once(tensorrt_dso_flag, []() {                          \
         tensorrt_dso_handle =                                           \

paddle/fluid/platform/dynload/warpctc.h

@@ -34,7 +34,7 @@ extern void* warpctc_dso_handle;
 #define DYNAMIC_LOAD_WARPCTC_WRAP(__name)                                      \
   struct DynLoad__##__name {                                                   \
     template <typename... Args>                                                \
-    auto operator()(Args... args) -> decltype(__name(args...)) {               \
+    auto operator()(Args... args) -> DECLARE_TYPE(__name, args...) {           \
       using warpctcFunc = decltype(&::__name);                                 \
       std::call_once(warpctc_dso_flag, []() {                                  \
         warpctc_dso_handle = paddle::platform::dynload::GetWarpCTCDsoHandle(); \

paddle/fluid/platform/nccl_helper.h

@@ -23,6 +23,7 @@
 #include "paddle/fluid/framework/data_type.h"
 #include "paddle/fluid/platform/dynload/nccl.h"
 #include "paddle/fluid/platform/enforce.h"
+#include "paddle/fluid/platform/float16.h"
 
 #define NCCL_ID_VARNAME "NCCLID"
 
@@ -38,6 +39,8 @@ inline ncclDataType_t ToNCCLDataType(framework::proto::VarType::Type type) {
     return ncclInt;
   } else if (type == framework::proto::VarType::INT64) {
     return ncclInt64;
+  } else if (type == framework::proto::VarType::FP16) {
+    return ncclFloat16;
   } else {
     PADDLE_THROW("Not supported");
   }

paddle/fluid/platform/ngraph_helper.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. */
+
+#ifdef PADDLE_WITH_NGRAPH
+#pragma once
+
+#include <functional>
+#include <string>
+#include <vector>
+#include "ngraph/ngraph.hpp"
+
+namespace paddle {
+namespace platform {
+
+static ngraph::Shape FlattenTo2d(ngraph::Shape sh, int num) {
+  auto x1 = std::accumulate(std::begin(sh), std::begin(sh) + num, 1,
+                            std::multiplies<size_t>());
+  auto x2 = std::accumulate(std::begin(sh) + num, std::end(sh), 1,
+                            std::multiplies<size_t>());
+  size_t x1_l = static_cast<size_t>(x1);
+  size_t x2_l = static_cast<size_t>(x2);
+  return ngraph::Shape{x1_l, x2_l};
+}
+
+static std::shared_ptr<ngraph::Node> NgReshaper(
+    std::shared_ptr<ngraph::Node> input, ngraph::Shape shape) {
+  std::vector<size_t> input_order(input->get_shape().size());
+  std::iota(std::begin(input_order), std::end(input_order), 0);
+  return std::make_shared<ngraph::op::Reshape>(
+      input, ngraph::AxisVector(input_order), shape);
+}
+
+static std::shared_ptr<ngraph::Node> GetNode(
+    const std::shared_ptr<paddle::framework::OperatorBase>& op,
+    const std::string prm, const paddle::framework::VariableNameMap& var_map,
+    std::shared_ptr<
+        std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
+        ngb_node_map) {
+  auto& var_names = var_map.at(prm);
+  PADDLE_ENFORCE_EQ(var_names.size(), 1,
+                    "op %s prm %s expects one associated var", op->Type(), prm);
+  if (ngb_node_map->find(var_names[0]) != ngb_node_map->end()) {
+    return (*ngb_node_map)[var_names[0]];
+  } else {
+    return nullptr;
+  }
+}
+
+static std::shared_ptr<ngraph::Node> GetInputNode(
+    const std::shared_ptr<paddle::framework::OperatorBase>& op,
+    const std::string prm,
+    std::shared_ptr<
+        std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
+        ngb_node_map) {
+  return GetNode(op, prm, op->Inputs(), ngb_node_map);
+}
+
+static std::shared_ptr<ngraph::Node> GetOutputNode(
+    const std::shared_ptr<paddle::framework::OperatorBase>& op,
+    const std::string prm,
+    std::shared_ptr<
+        std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
+        ngb_node_map) {
+  return GetNode(op, prm, op->Outputs(), ngb_node_map);
+}
+
+static void SetOutputNode(
+    const std::shared_ptr<paddle::framework::OperatorBase>& op,
+    const std::string prm, std::shared_ptr<ngraph::Node> node,
+    std::shared_ptr<
+        std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
+        ngb_node_map) {
+  auto& var_names = op->Outputs().at(prm);
+  if (var_names.size() == 1) {
+    (*ngb_node_map)[var_names[0]] = node;
+  } else if (var_names.size() == 0) {
+    (*ngb_node_map)[""] = node;
+  } else {
+    PADDLE_THROW("prm %s has more than 1 var_names.", prm);
+  }
+}
+
+static bool HasOutput(
+    const std::shared_ptr<paddle::framework::OperatorBase>& op,
+    const std::string prm) {
+  auto& outputs = op->Outputs();
+  if (outputs.find(prm) == outputs.end()) return false;
+  return outputs.at(prm).size() > 0;
+}
+
+}  // namespace platform
+}  // namespace paddle
+
+#endif

paddle/fluid/platform/port.h

@@ -55,7 +55,6 @@ static void *dlsym(void *handle, const char *symbol_name) {
 
 static void *dlopen(const char *filename, int flag) {
   std::string file_name(filename);
-  file_name.replace(0, file_name.size() - 1, '/', '\\');
   HMODULE hModule = LoadLibrary(file_name.c_str());
   if (!hModule) {
     throw std::runtime_error(file_name + " not found.");

paddle/fluid/pybind/imperative.cc

@@ -24,8 +24,9 @@ namespace pybind {
 void BindTracer(pybind11::module *m) {
   pybind11::class_<imperative::Tracer>(*m, "Tracer", "")
       .def("__init__",
-           [](imperative::Tracer &self, framework::BlockDesc *root_block) {
-             new (&self) imperative::Tracer(root_block);
+           [](imperative::Tracer &self, framework::BlockDesc *root_block,
+              framework::BlockDesc *startup_block) {
+             new (&self) imperative::Tracer(root_block, startup_block);
            })
       .def("trace", &imperative::Tracer::Trace)
       .def("get_scope", &imperative::Tracer::GetScope,

paddle/scripts/paddle_build.sh

@@ -509,10 +509,10 @@ function assert_api_spec_approvals() {
       if [ ${API_CHANGE} ] && [ "${GIT_PR_ID}" != "" ]; then
           # NOTE: per_page=10000 should be ok for all cases, a PR review > 10000 is not human readable.
           APPROVALS=`curl -H "Authorization: token ${GITHUB_API_TOKEN}" https://api.github.com/repos/PaddlePaddle/Paddle/pulls/${GIT_PR_ID}/reviews?per_page=10000 | \
-          python ${PADDLE_ROOT}/tools/check_pr_approval.py 2 7845005 2887803 728699 13348433`
+          python ${PADDLE_ROOT}/tools/check_pr_approval.py 1 2887803`
           echo "current pr ${GIT_PR_ID} got approvals: ${APPROVALS}"
           if [ "${APPROVALS}" == "FALSE" ]; then
-              echo "You must have at least 2 approvals for the api change! ${API_FILE}"
+              echo "You must have panyx0718 approval for the api change! ${API_FILE}"
               exit 1
           fi
       fi
@@ -521,10 +521,10 @@ function assert_api_spec_approvals() {
     HAS_CONST_CAST=`git diff -U0 upstream/$BRANCH |grep -o -m 1 "const_cast" || true`
     if [ ${HAS_CONST_CAST} ] && [ "${GIT_PR_ID}" != "" ]; then
         APPROVALS=`curl -H "Authorization: token ${GITHUB_API_TOKEN}" https://api.github.com/repos/PaddlePaddle/Paddle/pulls/${GIT_PR_ID}/reviews?per_page=10000 | \
-        python ${PADDLE_ROOT}/tools/check_pr_approval.py 2 7845005 2887803 728699 13348433`
+        python ${PADDLE_ROOT}/tools/check_pr_approval.py 1 2887803`
         echo "current pr ${GIT_PR_ID} got approvals: ${APPROVALS}"
         if [ "${APPROVALS}" == "FALSE" ]; then
-            echo "You must have at least 2 approvals for the const_cast"
+            echo "You must have panyx0718 approval for the const_cast"
             exit 1
         fi
     fi

python/paddle/fluid/__init__.py

@@ -102,6 +102,13 @@ def __bootstrap__():
     import sys
     import os
     import platform
+
+    if os.name == 'nt':
+        third_lib_path = os.path.abspath(os.path.dirname(
+            __file__)) + os.sep + '..' + os.sep + 'libs'
+        os.environ['path'] += ';' + third_lib_path
+        sys.path.append(third_lib_path)
+
     from . import core
 
     in_test = 'unittest' in sys.modules
@@ -128,14 +135,14 @@ def __bootstrap__():
         'free_idle_memory', 'paddle_num_threads', "dist_threadpool_size",
         'eager_delete_tensor_gb', 'fast_eager_deletion_mode',
         'allocator_strategy', 'reader_queue_speed_test_mode',
-        'print_sub_graph_dir', 'pe_profile_fname', 'inner_op_parallelism',
+        'print_sub_graph_dir', 'pe_profile_fname', 'warpctc_dir',
+        'inner_op_parallelism'
         'min_param_size_to_use_multithread'
     ]
     if 'Darwin' not in sysstr:
         read_env_flags.append('use_pinned_memory')
 
     if os.name != 'nt':
-        read_env_flags.append('warpctc_dir')
         read_env_flags.append('cpu_deterministic')
 
     if core.is_compiled_with_dist():

python/paddle/fluid/backward.py

@@ -249,69 +249,6 @@ def serialize_op_decs(op_desc):
     return proto.__str__()
 
 
-def _callback_lookup_(op):
-    """
-    Only used in _append_backward_ops_
-    Build and returns a callback function for certain op. For example
-
-    parallel_do:           AllReduce
-
-    :param op:
-    :return: callback function
-    """
-    if op.type == 'parallel_do' and op.attr('use_nccl'):
-        all_vars = op.block.vars
-        param_names = set(op.input('parameters'))
-        param_names = [
-            name for name in param_names
-            if all_vars[name].stop_gradient is False
-        ]
-        param_grad_names = [n + "@GRAD" for n in param_names]
-
-        class ParallelDoCallBack(object):
-            def __init__(self, param_grad_names, parallel_scopes_name):
-                self.has_inserted_nccl_init = False
-                self.param_grad_names = param_grad_names
-                self.parallel_scopes_name = parallel_scopes_name
-
-            def __call__(self, block, context):
-                if not self.has_inserted_nccl_init:
-                    op_desc = _create_op_desc_(
-                        "ncclInit",
-                        {"parallel_scopes": self.parallel_scopes_name},
-                        {"Communicator": ['nccl_com__do_not_change_']}, {})
-                    block.program.global_block().desc.append_op().copy_from(
-                        op_desc)
-                    self.has_inserted_nccl_init = True
-
-                current_op_desc = context["__current_op_desc__"]
-                for o_param in current_op_desc.output_names():
-                    for o_argu in current_op_desc.output(o_param):
-                        if o_argu in self.param_grad_names:
-                            allreduce_out_name = o_argu + "__nccl_all_reduce__"
-                            op_desc = _create_op_desc_(
-                                "ncclReduce",
-                                {
-                                    "X": [o_argu],
-                                    "Communicator":
-                                    ['nccl_com__do_not_change_']
-                                },
-                                {"Out": [allreduce_out_name]},
-                                {"reduction": "ncclSum",
-                                 "root": 0}, )
-                            block.desc.append_op().copy_from(op_desc)
-
-                            op_desc = _create_op_desc_(
-                                "assign", {"X": [allreduce_out_name]},
-                                {"Out": [o_argu]}, {})
-                            block.desc.append_op().copy_from(op_desc)
-
-        return ParallelDoCallBack(param_grad_names,
-                                  op.output("parallel_scopes"))
-    else:
-        return None
-
-
 def _append_backward_ops_(block,
                           ops,
                           target_block,
@@ -349,15 +286,6 @@ def _append_backward_ops_(block,
             sub_block = program.block(op._block_attr_id("sub_block"))
             grad_sub_block = program._create_block()
             grad_sub_block._set_forward_block_idx(sub_block.idx)
-            cb = _callback_lookup_(op)
-            if cb is not None:
-                if callbacks is None:
-                    new_callbacks = [cb]
-                else:
-                    new_callbacks = callbacks + [_callback_lookup_(op)]
-                _append_backward_ops_(sub_block, sub_block.ops, grad_sub_block,
-                                      no_grad_dict, grad_to_var, new_callbacks)
-            else:
             _append_backward_ops_(sub_block, sub_block.ops, grad_sub_block,
                                   no_grad_dict, grad_to_var, callbacks)
 
@@ -424,9 +352,6 @@ def _append_backward_vars_(block, start_op_idx, grad_to_var, grad_info_map):
         # infer_shape and infer_type
         op_desc.infer_var_type(block.desc)
         op_desc.infer_shape(block.desc)
-        # ncclInit dones't need to set data_type
-        if op_desc.type() == 'ncclInit':
-            continue
         for arg in op_desc.output_arg_names():
             if arg in new_vars:
                 _infer_var_data_type_(arg, block)
@@ -564,8 +489,11 @@ def append_backward(loss, parameter_list=None, no_grad_set=None,
     grad_to_var = dict()
 
     op_desc = _create_op_desc_(
-        "fill_constant", {}, {"Out": [_append_grad_suffix_(loss.name)]}, {
-            "shape": [1],
+        "fill_constant",
+        {},
+        {"Out": [_append_grad_suffix_(loss.name)]},
+        {
+            "shape": [1],  # TODO(panyx0718): This can be loss.shape.
             "value": 1.0,
             "dtype": loss.dtype,
             "force_cpu": False,

python/paddle/fluid/contrib/__init__.py

@@ -22,9 +22,12 @@ from . import op_frequence
 from .op_frequence import *
 from . import quantize
 from .quantize import *
+from . import utils
+from .utils import *
 
 __all__ = []
 __all__ += decoder.__all__
 __all__ += memory_usage_calc.__all__
 __all__ += op_frequence.__all__
 __all__ += quantize.__all__
+__all__ += utils.__all__

python/paddle/fluid/contrib/utils/__init__.py

@@ -13,10 +13,11 @@
 # limitations under the License.
 
 from __future__ import print_function
-#from . import lookup_table_utils
-#from .lookup_table_utils import *
+from . import lookup_table_utils
+from .lookup_table_utils import *
 from . import hdfs_utils
 from .hdfs_utils import *
 
-#__all__ = lookup_table_utils.__all__
-__all__ = hdfs_utils.__all__
+__all__ = []
+__all__ += lookup_table_utils.__all__
+__all__ += hdfs_utils.__all__

python/paddle/fluid/contrib/utils/hdfs_utils.py

@@ -14,6 +14,7 @@
 """HDFS Utils"""
 
 import os
+import sys
 import subprocess
 import multiprocessing
 from datetime import datetime
@@ -24,7 +25,7 @@ import errno
 
 import logging
 
-__all__ = ["HDFSClient", "multi_download"]
+__all__ = ["HDFSClient", "multi_download", "multi_upload"]
 
 logging.basicConfig(format='%(asctime)s - %(levelname)s - %(message)s')
 _logger = logging.getLogger("hdfs_utils")
@@ -94,11 +95,13 @@ class HDFSClient(object):
     def upload(self, hdfs_path, local_path, overwrite=False, retry_times=5):
         """
         upload the local file to hdfs
+
         Args:
-                hdfs_path: hdfs path, target path 
-                local_path: local file path, source path
-                overwrite: will overwrite the original file
-                retry_times: max times retry to upload
+            hdfs_path(str): the hdfs file path
+            local_path(str): the local file path
+            overwrite(bool|None): will overwrite the file on HDFS or not
+            retry_times(int|5): retry times
+
         Returns:
                 True or False
         """
@@ -109,7 +112,7 @@ class HDFSClient(object):
             _logger.warn(
                 "The Local path: {} is dir and I will support it later, return".
                 format(local_path))
-            return
+            return False
 
         base = os.path.basename(local_path)
         if not self.is_exist(hdfs_path):
@@ -141,13 +144,15 @@ class HDFSClient(object):
 
     def download(self, hdfs_path, local_path, overwrite=False, unzip=False):
         """
-            download from hdfs
+        download file from HDFS
+
         Args:
-                hdfs_path: hdfs path, target path 
-                local_path: local file path, source path
-                overwrite: will remove original file and overwrite it.
-                unzip: ignore this param
-            Returns
+            hdfs_path(str): the hdfs file path
+            local_path(str): the local file path
+            overwrite(bool|None): will overwrite the file on HDFS or not
+            unzip(bool|False): if the download file is compressed by zip, unzip it or not.
+
+        Returns:
             True or False
         """
         _logger.info('Downloading %r to %r.', hdfs_path, local_path)
@@ -188,11 +193,11 @@ class HDFSClient(object):
 
     def is_exist(self, hdfs_path=None):
         """
-            whether the remote hdfs path exists?
+        whether the remote HDFS path exists
+
         Args:
-                hdfs_path: default value(${OUTPUT_PATH}/${SYS_USER_ID}/${SYS_JOB_ID}/tmp)
-                fs_name: The default values are the same as in the job configuration
-                fs_ugi: The default values are the same as in the job configuration
+            hdfs_path(str): the hdfs file path
+
         Returns:
             True or False
         """
@@ -211,11 +216,11 @@ class HDFSClient(object):
 
     def is_dir(self, hdfs_path=None):
         """
-            whether the remote hdfs path exists?
+        whether the remote HDFS path is directory
+
         Args:
-                remote_file_path: default value(${OUTPUT_PATH}/${SYS_USER_ID}/${SYS_JOB_ID}/tmp)
-                fs_name: The default values are the same as in the job configuration
-                fs_ugi: The default values are the same as in the job configuration
+            hdfs_path(str): the hdfs file path
+
         Returns:
             True or False
         """
@@ -239,15 +244,15 @@ class HDFSClient(object):
         """
         Remove a file or directory from HDFS.
 
+        whether the remote HDFS path exists
+
         Args:
-            param hdfs_path: HDFS path.
-            param recursive: Recursively delete files and directories. By default,
-            this method will raise an :class:`HdfsError` if trying to delete a
-            non-empty directory.
+        hdfs_path: HDFS path.
+
         Returns:
+            True or False
             This function returns `True` if the deletion was successful and `False` if
             no file or directory previously existed at `hdfs_path`.
-
         """
         _logger.info('Deleting %r.', hdfs_path)
 
@@ -273,16 +278,14 @@ class HDFSClient(object):
 
     def rename(self, hdfs_src_path, hdfs_dst_path, overwrite=False):
         """
-        Rename a file or folder.
+        Move a file or folder on HDFS.
+
         Args:
-            :param hdfs_src_path: Source path.
-            :param hdfs_dst_path: Destination path. If the path already exists and is
-              a directory, the source will be moved into it. If the path exists and is
-              a file, or if a parent destination directory is missing, this method will
-              raise an :class:`HdfsError`.
+        hdfs_path(str): HDFS path.
+        overwrite(bool|False): If the path already exists and overwrite is False, will return False.
+
         Returns:
-             This function returns `True` if the rename was successful and `False` if
-             rename was faild.       
+            True or False
         """
         assert hdfs_src_path is not None
         assert hdfs_dst_path is not None
@@ -320,17 +323,20 @@ class HDFSClient(object):
                 raise
 
     def makedirs(self, hdfs_path):
-        """Create a remote directory, recursively if necessary.
+        """
+        Create a remote directory, recursively if necessary.
+
         Args:
-            :param hdfs_path: Remote path. Intermediate directories will be created
-              appropriately.
+        hdfs_path(str): Remote path. Intermediate directories will be created appropriately.
+
         Returns:
-            True if make a directories was successful, False when make a directiries was failed. 
+            True or False
         """
         _logger.info('Creating directories to %r.', hdfs_path)
         assert hdfs_path is not None
 
         if self.is_exist(hdfs_path):
+            _logger.error("HDFS path is exist: {}".format(hdfs_path))
             return
 
         mkdirs_commands = ['-mkdir', hdfs_path]
@@ -346,11 +352,13 @@ class HDFSClient(object):
 
     def ls(self, hdfs_path):
         """
-        ls a hdfs_path.
+        ls directory contents about HDFS hdfs_path
+
         Args:
-            :param hdfs_path: hdfs_path will be ls.
+        hdfs_path(str): Remote HDFS path will be ls.
+
         Returns:
-             This function returns a `list` that contaion all files in the hdfs_path.        
+            List: a contents list about hdfs_path.
         """
         assert hdfs_path is not None
 
@@ -378,11 +386,15 @@ class HDFSClient(object):
 
     def lsr(self, hdfs_path, only_file=True, sort=True):
         """
-        ls a hdfs_path sort by time.
+        list directory contents about HDFS hdfs_path recursively
+
         Args:
-            :param hdfs_path: hdfs_path will be ls.
+        hdfs_path(str): Remote HDFS path.
+        only_file(bool|True): will discard folders.
+        sort(bool|True): will be sorted by create time.
+
         Returns:
-             This function returns a `list` that contaion all files sorted by time in the hdfs_path.        
+            List: a contents list about hdfs_path.
         """
 
         def sort_by_time(v1, v2):
@@ -422,61 +434,54 @@ class HDFSClient(object):
             return ret_lines
 
 
-def multi_upload(client,
+def multi_download(client,
                    hdfs_path,
                    local_path,
-                 multi_processes=5,
-                 overwrite=False):
+                   trainer_id,
+                   trainers,
+                   multi_processes=5):
     """
-    Upload file to hdfs.
+    Download files from HDFS using multi process.
+
     Args:
-        :param overwrite: will overwrite hdfs file or not
-        :param multi_processes: the upload data process at the same time, default=5
-        :param client: instance of HDFSClient
-        :param hdfs_path: path on hdfs
-        :param local_path: path on local
-    Returns:
+        client(HDFSClient): instance of HDFSClient
+        hdfs_path(str): path on hdfs
+        local_path(str): path on local
+        trainer_id(int): current trainer id
+        trainers(int): all trainers number
+        multi_processes(int|5): the download data process at the same time, default=5
 
+    Returns:
+        List:
+        Download files in local folder.
     """
 
-    def __subprocess_upload(datas):
+    def __subprocess_download(datas):
         for data in datas:
-            re_path = os.path.relpath(os.path.dirname(data), local_path)
-            hdfs_re_path = os.path.join(hdfs_path, re_path)
-            client.upload(hdfs_re_path, data, overwrite, retry_times=5)
-
-    def get_local_files(path):
-        """
-            Get all local files
-        Args:
-            path: local file path
-        Returns:
-            A list that contation all files in the path.
-        """
-        rlist = []
+            re_path = os.path.relpath(os.path.dirname(data), hdfs_path)
+            if re_path == os.curdir:
+                sub_local_re_path = local_path
+            else:
+                sub_local_re_path = os.path.join(local_path, re_path)
+            client.download(data, sub_local_re_path)
 
-        if not os.path.isdir(path):
-            return rlist
+    assert isinstance(client, HDFSClient)
 
-        for dirname, folder, files in os.walk(path):
-            for i in files:
-                t = os.path.join(dirname, i)
-                rlist.append(t)
-        return rlist
+    client.make_local_dirs(local_path)
+    _logger.info("Make local dir {} successfully".format(local_path))
 
-    assert isinstance(client, HDFSClient)
+    all_need_download = client.lsr(hdfs_path, sort=True)
+    need_download = all_need_download[trainer_id::trainers]
+    _logger.info("Get {} files From all {} files need to be download from {}".
+                 format(len(need_download), len(all_need_download), hdfs_path))
 
-    all_files = get_local_files(local_path)
-    if not all_files:
-        _logger.info("there are nothing need to upload, exit")
-        return
-    _logger.info("Start {} multi process to upload datas".format(
+    _logger.info("Start {} multi process to download datas".format(
         multi_processes))
     procs = []
     for i in range(multi_processes):
-        process_datas = all_files[i::multi_processes]
+        process_datas = need_download[i::multi_processes]
         p = multiprocessing.Process(
-            target=__subprocess_upload, args=(process_datas, ))
+            target=__subprocess_download, args=(process_datas, ))
         procs.append(p)
         p.start()
 
@@ -484,55 +489,84 @@ def multi_upload(client,
     for proc in procs:
         proc.join()
 
-    _logger.info("Finish {} multi process to upload datas".format(
+    _logger.info("Finish {} multi process to download datas".format(
         multi_processes))
 
+    local_downloads = []
+    for data in need_download:
+        data_name = os.path.basename(data)
+        re_path = os.path.relpath(os.path.dirname(data), hdfs_path)
+        if re_path == os.curdir:
+            local_re_path = os.path.join(local_path, data_name)
+        else:
+            local_re_path = os.path.join(local_path, re_path, data_name)
+        local_downloads.append(local_re_path)
+
+    return local_downloads
 
-def multi_download(client,
+
+def getfilelist(path):
+    rlist = []
+    for dir, folder, file in os.walk(path):
+        for i in file:
+            t = os.path.join(dir, i)
+            rlist.append(t)
+    for r in rlist:
+        print(r)
+
+
+def multi_upload(client,
                  hdfs_path,
                  local_path,
-                   trainer_id,
-                   trainers,
-                   file_cnt,
-                   multi_processes=5):
+                 multi_processes=5,
+                 overwrite=False,
+                 sync=True):
     """
-    multi_download
+    Upload files to HDFS using multi process.
+
     Args:
-        :param client: instance of HDFSClient
-        :param hdfs_path: path on hdfs
-        :param local_path: path on local
-        :param trainer_id: current trainer id
-        :param trainers: all trainers number
-        :param file_cnt: all file number
-        :param multi_processes: the download data process at the same time, default=5
-        :return: None
+        client(HDFSClient): instance of HDFSClient
+        hdfs_path(str): path on hdfs
+        local_path(str): path on local
+        multi_processes(int|5): the upload data process at the same time, default=5
+        overwrite(bool|False): will overwrite file on HDFS or not
+        sync(bool|True): upload files sync or not.
+
     Returns:
-        A list that be downloaded. 
+        None
     """
 
-    def __subprocess_download(datas):
+    def __subprocess_upload(datas):
         for data in datas:
-            re_path = os.path.relpath(os.path.dirname(data), hdfs_path)
-            local_re_path = os.path.join(local_path, re_path)
-            client.download(data, local_re_path)
+            re_path = os.path.relpath(os.path.dirname(data), local_path)
+            hdfs_re_path = os.path.join(hdfs_path, re_path)
+            client.upload(hdfs_re_path, data, overwrite, retry_times=5)
 
-    assert isinstance(client, HDFSClient)
+    def get_local_files(path):
+        rlist = []
 
-    client.make_local_dirs(local_path)
-    _logger.info("Make local dir {} successfully".format(local_path))
+        if not os.path.isdir(path):
+            return rlist
 
-    all_need_download = client.lsr(hdfs_path, sort=True)[:file_cnt]
-    need_download = all_need_download[trainer_id::trainers]
-    _logger.info("Get {} files From all {} files need to be download from {}".
-                 format(len(need_download), len(all_need_download), hdfs_path))
+        for dirname, folder, files in os.walk(path):
+            for i in files:
+                t = os.path.join(dirname, i)
+                rlist.append(t)
+        return rlist
 
-    _logger.info("Start {} multi process to download datas".format(
+    assert isinstance(client, HDFSClient)
+
+    all_files = get_local_files(local_path)
+    if not all_files:
+        _logger.info("there are nothing need to upload, exit")
+        return
+    _logger.info("Start {} multi process to upload datas".format(
         multi_processes))
     procs = []
     for i in range(multi_processes):
-        process_datas = need_download[i::multi_processes]
+        process_datas = all_files[i::multi_processes]
         p = multiprocessing.Process(
-            target=__subprocess_download, args=(process_datas, ))
+            target=__subprocess_upload, args=(process_datas, ))
         procs.append(p)
         p.start()
 
@@ -540,18 +574,9 @@ def multi_download(client,
     for proc in procs:
         proc.join()
 
-    _logger.info("Finish {} multi process to download datas".format(
+    _logger.info("Finish {} multi process to upload datas".format(
         multi_processes))
 
-    local_downloads = []
-    for data in need_download:
-        data_name = os.path.basename(data)
-        re_path = os.path.relpath(os.path.dirname(data), hdfs_path)
-        local_re_path = os.path.join(local_path, re_path, data_name)
-        local_downloads.append(local_re_path)
-
-    return local_downloads
-
 
 if __name__ == "__main__":
     hadoop_home = "/home/client/hadoop-client/hadoop/"

python/paddle/fluid/contrib/utils/lookup_table_utils.py

@@ -18,14 +18,12 @@ import os
 import time
 import logging
 
-import paddle
-import paddle.fluid as fluid
 from paddle.fluid import core
 from paddle.fluid import io
 from paddle.fluid import Program
 
 __all__ = [
-    "load_inference_model", "load_persistable_vars",
+    "load_persistables_for_increment", "load_persistables_for_inference",
     "convert_dist_to_sparse_program"
 ]
 
@@ -80,19 +78,28 @@ def __get_prefetch_op_tuples(main_program):
     return prefetch_op_tuples
 
 
-def convert_dist_to_sparse_program(main_program):
-    if not main_program._distributed_lookup_table:
+def convert_dist_to_sparse_program(program):
+    """
+    WARNING: this function will only be used for distributed training with distributed lookup table.
+    when we train model with distributed lookup table but want to do the local inference, we can use
+    this function to convert the train program with distributed lookup table to sparse lookup table.
+
+    :param program(Program): the program must be the trainer program, which will be get by the distribute transpiler.
+    :return:
+        program: The `program` is a Program, it's the program replace distributed lookup table to sparse lookup table.
+    """
+    if not program._distributed_lookup_table:
         _logger.warn(
             "There are no distributed lookup tables need to be converted")
         return
 
     # create table param and grad var in pserver program
-    origin_emb_var = "{}.origin".format(main_program._distributed_lookup_table)
-    emb_var = main_program._distributed_lookup_table
-    main_program.global_block()._rename_var(emb_var, origin_emb_var)
-    origin_param_var = main_program.global_block().vars[origin_emb_var]
+    origin_emb_var = "{}.origin".format(program._distributed_lookup_table)
+    emb_var = program._distributed_lookup_table
+    program.global_block()._rename_var(emb_var, origin_emb_var)
+    origin_param_var = program.global_block().vars[origin_emb_var]
 
-    param_var = main_program.global_block().create_var(
+    param_var = program.global_block().create_var(
         name=emb_var,
         shape=origin_param_var.shape,
         dtype=origin_param_var.dtype,
@@ -100,28 +107,28 @@ def convert_dist_to_sparse_program(main_program):
         persistable=True)
     # parameter must be selected rows
     param_var.desc.set_type(core.VarDesc.VarType.SELECTED_ROWS)
-    main_program._sync_with_cpp()
+    program._sync_with_cpp()
 
-    prefetch_op_tuples = __get_prefetch_op_tuples(main_program)
+    prefetch_op_tuples = __get_prefetch_op_tuples(program)
 
     split_ids_id = prefetch_op_tuples[0]
 
     for idx in range(split_ids_id + 2, split_ids_id - 1, -1):
-        main_program.global_block()._remove_op(idx)
-    main_program.desc.flush()
+        program.global_block()._remove_op(idx)
+    program.desc.flush()
 
     in_out_pairs = zip(prefetch_op_tuples[1], prefetch_op_tuples[2])
 
     for in_out_pair in in_out_pairs:
         idx = split_ids_id
-        ids = main_program.global_block().vars[in_out_pair[0]]
-        out = main_program.global_block().vars[in_out_pair[1]]
-        __insert_lookup_sparse_table_op(main_program, idx, ids, param_var, out)
-        main_program.desc.flush()
-    return main_program
+        ids = program.global_block().vars[in_out_pair[0]]
+        out = program.global_block().vars[in_out_pair[1]]
+        __insert_lookup_sparse_table_op(program, idx, ids, param_var, out)
+        program.desc.flush()
+    return program
 
 
-def load_persistable_vars(executor, dirname, program, lookup_table_var):
+def _load_persistable_vars(executor, dirname, program, lookup_table_vars):
     def _is_checkpoint_var(exclude_fluid_vars=None):
         """
         the checkpoint will not save or load all the variables.
@@ -159,7 +166,81 @@ def load_persistable_vars(executor, dirname, program, lookup_table_var):
 
         return is_valid
 
-    def _load_lookup_table_vars(executor, dirname, main_program,
+    io.load_vars(
+        executor,
+        dirname=dirname,
+        main_program=program,
+        predicate=_is_checkpoint_var(lookup_table_vars),
+        filename=None)
+
+
+def load_persistables_for_increment(dirname, executor, program,
+                                    lookup_table_var, lookup_table_var_path):
+    """
+    WARNING: this function will only be used for distributed training with distributed lookup table.
+    for increment trainning, the pserver will not only load dense variables,
+    but also load the suitable lookup table var. Because of slice lookup table
+    var with HASH, we must load the correct slice var.
+
+
+    :param dirname(str): The directory path
+    :param executor(Executor): The executor to run for loading inference model.
+    :param program(Program): The parameter server program, which will run on Pserver.
+    :param lookup_table_var: the distributed lookup tables var name.
+    :param lookup_table_var_path: the the distributed lookup tables var location.
+    :return: None
+    """
+
+    def __load_lookup_table_vars(executor, main_program, lookup_table_var,
+                                 lookup_table_var_path):
+        emb_var = main_program.global_block().var(lookup_table_var)
+
+        load_program = Program()
+        load_block = load_program.global_block()
+        load_block.append_op(
+            type='load',
+            inputs={},
+            outputs={'Out': [emb_var]},
+            attrs={'file_path': lookup_table_var_path})
+        executor.run(load_program)
+
+    if not os.path.isdir(dirname):
+        raise ValueError("There is no directory named '%s'", dirname)
+
+    if not os.path.exists(lookup_table_var_path):
+        raise ValueError("There is no file named '%s'", lookup_table_var_path)
+
+    if not isinstance(program, Program):
+        raise ValueError("program must be an instance of fluid.Program")
+
+    _logger.info("Start Load Sparse Program With "
+                 "Distributed Lookup Table Vars from {}, time = {}".format(
+                     dirname, time.ctime()))
+
+    _load_persistable_vars(executor, dirname, program, [lookup_table_var])
+    __load_lookup_table_vars(executor, program, lookup_table_var,
+                             lookup_table_var_path)
+
+    _logger.info("Finish Load Sparse Program With "
+                 "Distributed Lookup Table Vars from {}, time = {}".format(
+                     dirname, time.ctime()))
+
+
+def load_persistables_for_inference(dirname, executor, program,
+                                    lookup_table_var_name):
+    """
+    WARNING: this function will only be used for inference with distributed lookup table.
+    Inference with distributed lookup table is a little funky, this function will load distributed
+    lookup table vars into sparse var, can be used in local inference mode.
+
+    :param dirname(str): The directory path
+    :param executor(Executor): The executor to run for loading inference model.
+    :param program(Program): The parameter server program, which will run on Pserver.
+    :param lookup_table_var_name: the distributed lookup tables var name.
+    :return: None
+    """
+
+    def __load_lookup_table_vars(executor, dirname, main_program,
                                  lookup_table_vars):
         if not os.path.isdir(dirname):
             raise ValueError("There is no directory named '%s'", dirname)
@@ -209,30 +290,13 @@ def load_persistable_vars(executor, dirname, program, lookup_table_var):
         global_block.append_op(type='delete_var', inputs={'X': sums})
         executor.run(convert_program)
 
-    _logger.info("Start Load Sparse Program With "
-                 "Distributed Lookup Table Vars from {}, time = {}".format(
-                     dirname, time.ctime()))
-
-    lookup_table_vars = [lookup_table_var]
-
-    io.load_vars(
-        executor,
-        dirname=dirname,
-        main_program=program,
-        predicate=_is_checkpoint_var(lookup_table_vars),
-        filename=None)
-
-    _load_lookup_table_vars(executor, dirname, program, lookup_table_vars)
-
-    _logger.info("Finish Load Sparse Program With "
-                 "Distributed Lookup Table Vars from {}, time = {}".format(
-                     dirname, time.ctime()))
-
-
-def load_inference_model(dirname, executor, lookup_table_var_name):
     if not os.path.isdir(dirname):
         raise ValueError("There is no directory named '%s'", dirname)
 
+    if program:
+        if not isinstance(program, Program):
+            raise ValueError("program must be an instance of fluid.Program")
+    else:
         local_model = os.path.join(dirname, model_filename)
 
         with open(local_model, "rb") as f:
@@ -244,13 +308,16 @@ def load_inference_model(dirname, executor, lookup_table_var_name):
             raise ValueError("Unsupported program version: %d\n" %
                              program._version())
 
-    # Binary data also need version.
-    load_persistable_vars(executor, dirname, program, lookup_table_var_name)
+    _logger.info("Start Load Sparse Program With "
+                 "Distributed Lookup Table Vars from {}, time = {}".format(
+                     dirname, time.ctime()))
+
+    _load_persistable_vars(executor, dirname, program, [lookup_table_var_name])
+    __load_lookup_table_vars(executor, dirname, program,
+                             [lookup_table_var_name])
 
-    feed_target_names = program.desc.get_feed_target_names()
-    fetch_target_names = program.desc.get_fetch_target_names()
-    fetch_targets = [
-        program.global_block().var(name) for name in fetch_target_names
-    ]
+    _logger.info("Finish Load Sparse Program With "
+                 "Distributed Lookup Table Vars from {}, time = {}".format(
+                     dirname, time.ctime()))
 
-    return [program, feed_target_names, fetch_targets]
+    return program

python/paddle/fluid/data_feeder.py

@@ -44,6 +44,8 @@ class DataToLoDTensorConverter(object):
             self.dtype = 'int64'
         elif dtype == core.VarDesc.VarType.FP64:
             self.dtype = 'float64'
+        elif dtype == core.VarDesc.VarType.FP16:
+            self.dtype = 'float16'
         elif dtype == core.VarDesc.VarType.INT32:
             self.dtype = 'int32'
         elif dtype == core.VarDesc.VarType.UINT8:

python/paddle/fluid/framework.py

@@ -16,6 +16,7 @@ from __future__ import print_function
 
 import collections
 import contextlib
+import os
 import re
 import six
 import sys
@@ -27,6 +28,13 @@ from .proto import framework_pb2
 try:
     from . import core
 except ImportError as e:
+    if os.name == 'nt':
+        raise ImportError(
+            """NOTE: You may need to run \"set PATH=c:\python27\lib:%PATH%\"
+        if you encounters \"mkldnn.dll not found\" errors. If you have python
+        installed in other directory, replace \"c:\python27\lib" with your own
+        directory. The original error is: \n""" + cpt.get_exception_message(e))
+    else:
         raise ImportError(
             """NOTE: You may need to run \"export LD_LIBRARY_PATH=/usr/local/lib:$LD_LIBRARY_PATH\"
         if you encounters \"libmkldnn.so not found\" errors. If you have python
@@ -563,8 +571,8 @@ class Operator(object):
     OP_WITHOUT_KERNEL_SET = {
         'feed', 'fetch', 'save', 'load', 'recurrent', 'go',
         'rnn_memory_helper_grad', 'conditional_block', 'while', 'send', 'recv',
-        'listen_and_serv', 'parallel_do', 'save_combine', 'load_combine',
-        'ncclInit', 'select', 'checkpoint_notify', 'gen_nccl_id'
+        'listen_and_serv', 'save_combine', 'load_combine', 'ncclInit', 'select',
+        'checkpoint_notify', 'gen_nccl_id'
     }
 
     def __init__(self,
@@ -1316,6 +1324,9 @@ class Block(object):
     def _prepend_op(self, *args, **kwargs):
         op_desc = self.desc._prepend_op()
         op = Operator(self, op_desc, *args, **kwargs)
+        if _in_imperative_mode():
+            _imperative_tracer().trace(op.iop, [v._ivar for v in op.inputs],
+                                       [v._ivar for v in op.outputs], self.desc)
         self.ops.insert(0, op)
         return op
 

python/paddle/fluid/imperative/base.py

@@ -28,7 +28,8 @@ def enabled():
 def guard():
     train = framework.Program()
     startup = framework.Program()
-    tracer = core.Tracer(train.current_block().desc)
+    tracer = core.Tracer(train.current_block().desc,
+                         startup.current_block().desc)
     with framework.program_guard(train, startup):
         with framework.unique_name.guard():
             with framework._imperative_guard(tracer):

python/paddle/fluid/imperative/layers.py

@@ -25,11 +25,9 @@ __all__ = ['PyLayer']
 
 class PyLayer(core.Layer):
     def __init__(self):
-        pass
+        self._built = False
 
     def __call__(self, inputs):
-        # TODO(panyx0718): Support declarative mode as well.
-        assert base.enabled()
         if not isinstance(inputs, list) and not isinstance(inputs, tuple):
             inputs = [inputs]
 
@@ -37,8 +35,15 @@ class PyLayer(core.Layer):
         for x in inputs:
             py_var = base.to_variable(x)
             var_inputs.append(py_var)
+        if not self._built:
+            self._build_once(inputs)
+            self._built = True
+
         outputs = self.forward(var_inputs)
         return outputs
 
+    def _build_once(self, inputs):
+        pass
+
     def forward(self, inputs):
         return []

python/paddle/fluid/initializer.py

@@ -18,6 +18,7 @@ from . import framework
 import numpy as np
 import contextlib
 from .core import VarDesc
+from . import unique_name
 
 __all__ = [
     'Constant', 'Uniform', 'Normal', 'TruncatedNormal', 'Xavier', 'Bilinear',
@@ -207,16 +208,39 @@ class UniformInitializer(Initializer):
         # Initialization Ops should be prepended and not appended
         if self._seed == 0:
             self._seed = block.program.random_seed
+
+        # to be compatible of fp16 initalizers
+        if var.dtype == VarDesc.VarType.FP16:
+            out_dtype = VarDesc.VarType.FP32
+            out_var = block.create_var(
+                name=unique_name.generate(".".join(['gaussian_random', 'tmp'])),
+                shape=var.shape,
+                dtype=out_dtype,
+                type=VarDesc.VarType.LOD_TENSOR,
+                persistable=False)
+        else:
+            out_dtype = var.dtype
+            out_var = var
+
         op = block._prepend_op(
             type="uniform_random",
-            outputs={"Out": var},
+            outputs={"Out": out_var},
             attrs={
                 "shape": var.shape,
-                "dtype": int(var.dtype),
+                "dtype": out_dtype,
                 "min": self._low,
                 "max": self._high,
                 "seed": self._seed
             })
+
+        if var.dtype == VarDesc.VarType.FP16:
+            block.append_op(
+                type="cast",
+                inputs={"X": out_var},
+                outputs={"Out": var},
+                attrs={"in_dtype": out_var.dtype,
+                       "out_dtype": var.dtype})
+
         var.op = op
         return op
 
@@ -261,17 +285,39 @@ class NormalInitializer(Initializer):
         # Initialization Ops should be prepended and not appended
         if self._seed == 0:
             self._seed = block.program.random_seed
+
+        # to be compatible of fp16 initalizers
+        if var.dtype == VarDesc.VarType.FP16:
+            out_dtype = VarDesc.VarType.FP32
+            out_var = block.create_var(
+                name=unique_name.generate(".".join(['gaussian_random', 'tmp'])),
+                shape=var.shape,
+                dtype=out_dtype,
+                type=VarDesc.VarType.LOD_TENSOR,
+                persistable=False)
+        else:
+            out_dtype = var.dtype
+            out_var = var
+
         op = block._prepend_op(
             type="gaussian_random",
-            outputs={"Out": var},
+            outputs={"Out": out_var},
             attrs={
                 "shape": var.shape,
-                "dtype": int(var.dtype),
+                "dtype": out_dtype,
                 "mean": self._mean,
                 "std": self._std_dev,
                 "seed": self._seed,
                 "use_mkldnn": False
             })
+
+        if var.dtype == VarDesc.VarType.FP16:
+            block.append_op(
+                type="cast",
+                inputs={"X": out_var},
+                outputs={"Out": var},
+                attrs={"in_dtype": out_var.dtype,
+                       "out_dtype": var.dtype})
         var.op = op
         return op
 

python/paddle/fluid/layers/control_flow.py

@@ -226,156 +226,6 @@ class BlockGuard(object):
         return True
 
 
-class ParallelDo(object):
-    """
-    ParallelDo is used to represent multi-thread data parallel processing.
-
-    Its vanilla implementation can be shown as the following (:math:`|` means
-    single thread and :math:`||||` means multiple threads)
-
-    .. code-block:: text
-
-      In the forward pass
-        |      Split input onto different devices
-        |      Copy parameter onto different devices
-        ||||   Compute forward pass in parallel
-        |      Merge output from different devices
-
-      In the backward pass
-        |      Split output@grad onto different devices
-        ||||   Compute backward pass in parallel
-        |      accumulate param@grad from different devices to the first device
-        |      Merge input@grad from different devices
-        |      Copy param@grad to the place of parallel_do_op
-
-    Examples:
-
-    .. code-block:: python
-
-      images = fluid.layers.data(name='pixel', shape=[1, 28, 28], dtype=DTYPE)
-      label = fluid.layers.data(name='label', shape=[1], dtype='int64')
-
-      # ParallelDo version & Single-thread version
-      if thread_num > 1:
-          places = fluid.layers.get_places(thread_num)
-          pd = fluid.layers.control_flow.ParallelDo(places)
-          with pd.do():
-              images = pd.read_input(images)
-              label = pd.read_input(label)
-              predict = cnn_model(images)
-              cost = fluid.layers.cross_entropy(input=predict, label=label)
-
-              avg_cost = fluid.layers.mean(x=cost)
-              pd.write_output(avg_cost)
-
-          avg_cost = pd()
-          avg_cost = fluid.layers.mean(avg_cost)
-      else:
-          predict = cnn_model(images)
-          cost = fluid.layers.cross_entropy(input=predict, label=label)
-          avg_cost = fluid.layers.mean(x=cost)
-
-    .. warning::
-
-       It will be soon deprecated, please use ParallelExecutor instead.
-    """
-
-    def __init__(self, places, use_nccl=False, name=None):
-        warnings.warn(
-            "API ParallelDo is deprecated since 0.15.0. Please use ParallelExecutor instead.",
-            Warning)
-        self.helper = LayerHelper("parallel_do", name=name)
-        self.inputs = []
-        self.places = places
-        self.outputs = []
-        self.status = StaticRNN.BEFORE_RNN_BLOCK
-        self.use_nccl = use_nccl
-
-    def do(self):
-        return BlockGuardWithCompletion(self)
-
-    def parent_block(self):
-        prog = self.helper.main_program
-        parent_idx = prog.current_block().parent_idx
-        assert parent_idx >= 0
-        parent_block = prog.block(parent_idx)
-        return parent_block
-
-    def __call__(self, *args, **kwargs):
-        if self.status != StaticRNN.AFTER_RNN_BLOCK:
-            raise ValueError("RNN output can only be retrieved after rnn block")
-        if len(self.outputs) == 0:
-            raise ValueError("RNN has no output")
-        elif len(self.outputs) == 1:
-            return self.outputs[0]
-        else:
-            return self.outputs
-
-    def read_input(self, var):
-        self.inputs.append(var)
-        return var
-
-    def write_output(self, var):
-        self.outputs.append(var)
-
-    def get_parameters(self):
-        main_program = self.helper.main_program
-        current_block = main_program.current_block()
-        parent_block = self.parent_block()
-
-        local_inputs = set()
-        params = list()
-        for var in self.inputs:
-            local_inputs.add(var.name)
-
-        for op in current_block.ops:
-            for iname in op.input_names:
-                for in_var_name in op.input(iname):
-                    if in_var_name not in local_inputs:
-                        params.append(in_var_name)
-
-            for oname in op.output_names:
-                for out_var_name in op.output(oname):
-                    local_inputs.add(out_var_name)
-
-        params = list(set(params))
-
-        return [parent_block.var(name) for name in params]
-
-    def _complete_op(self):
-        main_program = self.helper.main_program
-        current_block = main_program.current_block()
-        parent_block = self.parent_block()
-
-        step_scope = parent_block.create_var(
-            type=core.VarDesc.VarType.STEP_SCOPES)
-
-        self.outputs = [
-            parent_block.create_var(
-                name=o.name,
-                shape=o.shape,
-                dtype=o.dtype,
-                lod_level=o.lod_level,
-                persistable=o.persistable,
-                stop_gradient=o.stop_gradient) for o in self.outputs
-        ]
-
-        inputs = [parent_block.var(i.name) for i in self.inputs]
-        outputs = [parent_block.var(o.name) for o in self.outputs]
-
-        parent_block.append_op(
-            type='parallel_do',
-            inputs={
-                'inputs': inputs,
-                'parameters': self.get_parameters(),
-                'places': self.places
-            },
-            outputs={'outputs': outputs,
-                     'parallel_scopes': [step_scope]},
-            attrs={'sub_block': current_block,
-                   'use_nccl': self.use_nccl})
-
-
 class BlockGuardWithCompletion(BlockGuard):
     """
     BlockGuardWithCompletion class.
@@ -384,9 +234,8 @@ class BlockGuardWithCompletion(BlockGuard):
     """
 
     def __init__(self, rnn):
-        if not (isinstance(rnn, StaticRNN) or isinstance(rnn, ParallelDo)):
-            raise TypeError(
-                "BlockGuardWithCompletion takes a StaticRNN or ParallelDo")
+        if not isinstance(rnn, StaticRNN):
+            raise TypeError("BlockGuardWithCompletion takes a StaticRNN")
         super(BlockGuardWithCompletion, self).__init__(rnn.helper.main_program)
         self.rnn = rnn
 

python/paddle/fluid/layers/learning_rate_scheduler.py

@@ -63,6 +63,8 @@ def noam_decay(d_model, warmup_steps):
     Returns:
         The decayed learning rate.
     """
+
+    def _lr_schedule(dtype):
         with default_main_program()._lr_schedule_guard():
             global_step = _decay_step_counter(1)
 
@@ -72,6 +74,8 @@ def noam_decay(d_model, warmup_steps):
 
         return lr_value
 
+    return _lr_schedule
+
 
 def exponential_decay(learning_rate, decay_steps, decay_rate, staircase=False):
     """
@@ -109,6 +113,8 @@ def exponential_decay(learning_rate, decay_steps, decay_rate, staircase=False):
           sgd_optimizer.minimize(avg_cost)
 
     """
+
+    def _lr_schedule(dtype):
         with default_main_program()._lr_schedule_guard():
             global_step = _decay_step_counter()
 
@@ -119,6 +125,8 @@ def exponential_decay(learning_rate, decay_steps, decay_rate, staircase=False):
 
             return decayed_lr
 
+    return _lr_schedule
+
 
 def natural_exp_decay(learning_rate, decay_steps, decay_rate, staircase=False):
     """Applies natural exponential decay to the initial learning rate.
@@ -138,6 +146,8 @@ def natural_exp_decay(learning_rate, decay_steps, decay_rate, staircase=False):
     Returns:
         The decayed learning rate
     """
+
+    def _lr_schedule(dtype):
         with default_main_program()._lr_schedule_guard():
             global_step = _decay_step_counter()
 
@@ -148,6 +158,8 @@ def natural_exp_decay(learning_rate, decay_steps, decay_rate, staircase=False):
 
             return decayed_lr
 
+    return _lr_schedule
+
 
 def inverse_time_decay(learning_rate, decay_steps, decay_rate, staircase=False):
     """
@@ -184,6 +196,8 @@ def inverse_time_decay(learning_rate, decay_steps, decay_rate, staircase=False):
                     staircase=True))
           sgd_optimizer.minimize(avg_cost)
     """
+
+    def _lr_schedule(dtype):
         with default_main_program()._lr_schedule_guard():
             global_step = _decay_step_counter()
 
@@ -195,6 +209,8 @@ def inverse_time_decay(learning_rate, decay_steps, decay_rate, staircase=False):
 
             return decayed_lr
 
+    return _lr_schedule
+
 
 def polynomial_decay(learning_rate,
                      decay_steps,
@@ -224,15 +240,17 @@ def polynomial_decay(learning_rate,
     Returns:
         Variable: The decayed learning rate
     """
+
+    def _lr_schedule(dtype, decay_steps=decay_steps):
         with default_main_program()._lr_schedule_guard():
             global_step = _decay_step_counter()
 
             if cycle:
                 div_res = ops.ceil(global_step / decay_steps)
                 zero_var = tensor.fill_constant(
-                shape=[1], dtype='float32', value=0.0)
+                    shape=[1], dtype=dtype, value=0.0)
                 one_var = tensor.fill_constant(
-                shape=[1], dtype='float32', value=1.0)
+                    shape=[1], dtype=dtype, value=1.0)
 
                 with control_flow.Switch() as switch:
                     with switch.case(global_step == zero_var):
@@ -240,13 +258,16 @@ def polynomial_decay(learning_rate,
                 decay_steps = decay_steps * div_res
             else:
                 decay_steps_var = tensor.fill_constant(
-                shape=[1], dtype='float32', value=float(decay_steps))
-            global_step = nn.elementwise_min(x=global_step, y=decay_steps_var)
+                    shape=[1], dtype=dtype, value=float(decay_steps))
+                global_step = nn.elementwise_min(
+                    x=global_step, y=decay_steps_var)
 
             decayed_lr = (learning_rate - end_learning_rate) * \
                 ((1 - global_step / decay_steps) ** power) + end_learning_rate
             return decayed_lr
 
+    return _lr_schedule
+
 
 def piecewise_decay(boundaries, values):
     """Applies piecewise decay to the initial learning rate.
@@ -273,6 +294,8 @@ def piecewise_decay(boundaries, values):
 
 
     """
+
+    def _lr_schedule(dtype):
         with default_main_program()._lr_schedule_guard():
             if len(values) - len(boundaries) != 1:
                 raise ValueError("len(values) - len(boundaries) should be 1")
@@ -306,6 +329,8 @@ def piecewise_decay(boundaries, values):
 
         return lr
 
+    return _lr_schedule
+
 
 def append_LARS(params_grads, learning_rate, weight_decay):
     """

python/paddle/fluid/layers/nn.py

@@ -29,6 +29,7 @@ from . import utils
 from .. import unique_name
 from functools import reduce
 from .. import core
+from ..imperative import layers
 
 __all__ = [
     'fc',
@@ -2797,6 +2798,10 @@ def batch_norm(input,
     helper = LayerHelper('batch_norm', **locals())
     dtype = helper.input_dtype()
 
+    # use fp32 for bn parameter
+    if dtype == core.VarDesc.VarType.FP16:
+        dtype = core.VarDesc.VarType.FP32
+
     input_shape = input.shape
     if data_layout == 'NCHW':
         channel_num = input_shape[1]
@@ -2831,7 +2836,7 @@ def batch_norm(input,
             trainable=False,
             do_model_average=do_model_average_for_mean_and_var),
         shape=param_shape,
-        dtype=input.dtype)
+        dtype=dtype)
     mean.stop_gradient = True
 
     variance = helper.create_parameter(
@@ -2841,7 +2846,7 @@ def batch_norm(input,
             trainable=False,
             do_model_average=do_model_average_for_mean_and_var),
         shape=param_shape,
-        dtype=input.dtype)
+        dtype=dtype)
     variance.stop_gradient = True
 
     # create output
@@ -9426,3 +9431,47 @@ def huber_loss(input, label, delta):
                  'Residual': residual},
         attrs={'delta': delta})
     return out
+
+
+class FC(layers.PyLayer):
+    def __init__(self,
+                 size,
+                 param_attr=None,
+                 num_flatten_dims=1,
+                 dtype=core.VarDesc.VarType.FP32):
+        super(FC, self).__init__()
+        self._size = size
+        self._num_flatten_dims = num_flatten_dims
+        self._dtype = dtype
+        self._helper = LayerHelper('FC', param_attr=param_attr)
+
+    def _build_once(self, inputs):
+        input_shape = inputs[0].shape
+        param_shape = [
+            reduce(lambda a, b: a * b, input_shape[self._num_flatten_dims:], 1)
+        ] + [self._size]
+        self._w = self._helper.create_parameter(
+            attr=self._helper.param_attr,
+            shape=param_shape,
+            dtype=self._dtype,
+            is_bias=False)
+
+    def forward(self, inputs):
+        tmp = self._helper.create_variable_for_type_inference(self._dtype)
+        self._helper.append_op(
+            type="mul",
+            inputs={"X": inputs[0],
+                    "Y": self._w},
+            outputs={"Out": tmp},
+            attrs={
+                "x_num_col_dims": self._num_flatten_dims,
+                "y_num_col_dims": 1
+            })
+
+        out = self._helper.create_variable_for_type_inference(self._dtype)
+        self._helper.append_op(
+            type="sum",
+            inputs={"X": [tmp]},
+            outputs={"Out": out},
+            attrs={"use_mkldnn": False})
+        return out

python/paddle/fluid/optimizer.py

@@ -50,17 +50,21 @@ class Optimizer(object):
 
     def __init__(self, learning_rate, regularization=None, name=None):
         if not isinstance(learning_rate, float) and \
-                not isinstance(learning_rate, framework.Variable):
-            raise TypeError("learning rate should be float or Variable")
+                not isinstance(learning_rate, framework.Variable) and \
+                not callable(learning_rate):
+            raise TypeError(
+                "learning rate should be float or Variable or callable(dtype)")
         self._name = name
         self.regularization = regularization
         self._learning_rate = learning_rate
         # the learning rate type should be inferenced from loss
         self._dtype = None
         # each program should have a independent learning rate
-        # program -> Variable(learning_rate)
+        # program -> Variable(learning_rate) or:
+        # program -> callable(return learning_rate Variable)
         self._learning_rate_map = dict()
-        if isinstance(self._learning_rate, framework.Variable):
+        if isinstance(self._learning_rate, framework.Variable) or \
+            callable(self._learning_rate):
             self._learning_rate_map[framework.default_main_program(
             )] = self._learning_rate
         # Dictionary of accumulators. Some optimizer subclasses need to
@@ -75,6 +79,11 @@ class Optimizer(object):
 
         if isinstance(lr, framework.Variable):
             return
+        elif callable(lr):
+            dtype = 'float32' if self._dtype is None else self._dtype
+            self._learning_rate_map[framework.default_main_program()] = lr(
+                dtype)
+            return
         else:
             if not isinstance(self._learning_rate, float):
                 raise TypeError(

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

@@ -15,7 +15,6 @@
 from __future__ import print_function
 
 from paddle.fluid.layers.device import get_places
-from paddle.fluid.layers.control_flow import ParallelDo
 import unittest
 import paddle.fluid as fluid
 import paddle
@@ -147,22 +146,7 @@ def train(word_dict,
         cost, acc_out, prediction = net_method(
             data, label, input_dim=dict_dim, class_dim=class_dim)
     else:
-        places = get_places()
-        pd = ParallelDo(places)
-        with pd.do():
-            cost, acc, _ = net_method(
-                pd.read_input(data),
-                pd.read_input(label),
-                input_dim=dict_dim,
-                class_dim=class_dim)
-            pd.write_output(cost)
-            pd.write_output(acc)
-
-        cost, acc = pd()
-        cost = fluid.layers.mean(cost)
-        acc_out = fluid.layers.mean(acc)
-        prediction = None
-        assert save_dirname is None
+        raise NotImplementedError()
 
     adagrad = fluid.optimizer.Adagrad(learning_rate=0.002)
     adagrad.minimize(cost)

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

@@ -25,7 +25,6 @@ import numpy
 import paddle
 import paddle.fluid as fluid
 from paddle.fluid.layers.device import get_places
-from paddle.fluid.layers.control_flow import ParallelDo
 
 BATCH_SIZE = 64
 
@@ -82,19 +81,7 @@ def train(nn_type,
         net_conf = conv_net
 
     if parallel:
-        places = get_places()
-        pd = ParallelDo(places)
-        with pd.do():
-            img_ = pd.read_input(img)
-            label_ = pd.read_input(label)
-            prediction, avg_loss, acc = net_conf(img_, label_)
-            for o in [avg_loss, acc]:
-                pd.write_output(o)
-
-        avg_loss, acc = pd()
-        # get mean loss and acc through every devices.
-        avg_loss = fluid.layers.mean(avg_loss)
-        acc = fluid.layers.mean(acc)
+        raise NotImplementedError()
     else:
         prediction, avg_loss, acc = net_conf(img, label)
 
@@ -273,7 +260,7 @@ def inject_all_tests():
     for use_cuda in (False, True):
         if use_cuda and not core.is_compiled_with_cuda():
             continue
-        for parallel in (False, True):
+        for parallel in (False, ):
             for nn_type in ('mlp', 'conv'):
                 inject_test_method(use_cuda, parallel, nn_type, True)
 

python/paddle/fluid/tests/book/test_word2vec.py

@@ -17,7 +17,6 @@ from __future__ import print_function
 import paddle
 import paddle.fluid as fluid
 from paddle.fluid.layers.device import get_places
-from paddle.fluid.layers.control_flow import ParallelDo
 import unittest
 import os
 import numpy as np
@@ -84,18 +83,7 @@ def train(use_cuda, is_sparse, is_parallel, save_dirname, is_local=True):
         avg_cost, predict_word = __network__(
             [first_word, second_word, third_word, forth_word, next_word])
     else:
-        places = get_places()
-        pd = ParallelDo(places)
-        with pd.do():
-            avg_cost, predict_word = __network__(
-                list(
-                    map(pd.read_input, [
-                        first_word, second_word, third_word, forth_word,
-                        next_word
-                    ])))
-            pd.write_output(avg_cost)
-
-        avg_cost = fluid.layers.mean(pd())
+        raise NotImplementedError()
 
     sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001)
     sgd_optimizer.minimize(avg_cost)
@@ -262,7 +250,7 @@ def inject_test_method(use_cuda, is_sparse, is_parallel):
 
 for use_cuda in (False, True):
     for is_sparse in (False, True):
-        for is_parallel in (False, True):
+        for is_parallel in (False, ):
             inject_test_method(use_cuda, is_sparse, is_parallel)
 
 if __name__ == '__main__':

python/paddle/fluid/tests/book_memory_optimization/test_memopt_fit_a_line.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.
-
-from __future__ import print_function
-
-import math
-import sys
-
-import paddle
-import paddle.fluid as fluid
-from paddle.fluid.layers.device import get_places
-from paddle.fluid.layers.control_flow import ParallelDo
-
-# need to fix random seed and training data to compare the loss
-# value accurately calculated by the default and the memory optimization
-# version.
-fluid.default_startup_program().random_seed = 111
-
-x = fluid.layers.data(name='x', shape=[13], dtype='float32')
-y = fluid.layers.data(name='y', shape=[1], dtype='float32')
-
-device_type = 'CPU'
-use_nccl = False
-place = fluid.CPUPlace()
-if fluid.core.is_compiled_with_cuda():
-    device_type = 'CUDA'
-    use_nccl = False
-    place = fluid.CUDAPlace(0)
-
-places = get_places(device_count=0, device_type=device_type)
-pd = ParallelDo(places, use_nccl=use_nccl)
-with pd.do():
-    x_ = pd.read_input(x)
-    y_ = pd.read_input(y)
-    y_predict = fluid.layers.fc(input=x_, size=1, act=None)
-    cost = fluid.layers.square_error_cost(input=y_predict, label=y_)
-    avg_cost = fluid.layers.mean(x=cost)
-    pd.write_output(avg_cost)
-
-cost = pd()
-avg_cost = fluid.layers.mean(x=cost)
-sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.01)
-sgd_optimizer.minimize(avg_cost)
-
-fluid.memory_optimize(fluid.default_main_program(), print_log=True)
-# fluid.release_memory(fluid.default_main_program())
-
-BATCH_SIZE = 200
-
-# fix the order of training data
-train_reader = paddle.batch(
-    paddle.dataset.uci_housing.train(), batch_size=BATCH_SIZE, drop_last=False)
-
-# train_reader = paddle.batch(
-#     paddle.reader.shuffle(
-#         paddle.dataset.uci_housing.train(), buf_size=500),
-#     batch_size=BATCH_SIZE)
-
-feeder = fluid.DataFeeder(place=place, feed_list=[x, y])
-exe = fluid.Executor(place)
-
-exe.run(fluid.default_startup_program())
-
-PASS_NUM = 100
-for pass_id in range(PASS_NUM):
-    for data in train_reader():
-        avg_loss_value, = exe.run(fluid.default_main_program(),
-                                  feed=feeder.feed(data),
-                                  fetch_list=[avg_cost])
-
-        if avg_loss_value[0] < 10.0:
-            exit(0)  # if avg cost less than 10.0, we think our code is good.
-        print(avg_loss_value[0])
-        if math.isnan(float(avg_loss_value)):
-            sys.exit("got NaN loss, training failed.")
-exit(1)

python/paddle/fluid/tests/unittests/ngraph/test_activation_ngraph_op.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.
+
+from __future__ import print_function
+
+import unittest
+import numpy as np
+import paddle.fluid.core as core
+from paddle.fluid.tests.unittests.op_test import OpTest
+from paddle.fluid.tests.unittests.test_activation_op import TestRelu, TestTanh
+
+
+class TestNGRAPHReluDim2(TestRelu):
+    def setUp(self):
+        super(TestNGRAPHReluDim2, self).setUp()
+
+
+class TestNGRAPHTanhDim2(TestTanh):
+    def setUp(self):
+        super(TestNGRAPHTanhDim2, self).setUp()
+
+
+class TestNGRAPHReluDim4(TestRelu):
+    def setUp(self):
+        super(TestNGRAPHReluDim4, self).setUp()
+
+        x = np.random.uniform(-1, 1, [2, 4, 3, 5]).astype("float32")
+        # The same reason with TestAbs
+        x[np.abs(x) < 0.005] = 0.02
+        out = np.maximum(x, 0)
+
+        self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
+        self.outputs = {'Out': out}
+
+
+class TestNGRAPHTanhDim4(TestTanh):
+    def setUp(self):
+        super(TestNGRAPHTanhDim4, self).setUp()
+
+        self.inputs = {
+            'X': np.random.uniform(0.1, 1, [2, 4, 3, 5]).astype("float32")
+        }
+        self.outputs = {'Out': np.tanh(self.inputs['X'])}
+
+
+if __name__ == '__main__':
+    unittest.main()

python/paddle/fluid/tests/unittests/ngraph/test_mul_ngraph_op.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.
+
+from __future__ import print_function
+
+import unittest
+from paddle.fluid.tests.unittests.test_mul_op import TestMulOp, TestMulOp2, TestFP16MulOp1, TestFP16MulOp2
+
+
+class TestNGRAPHMulOp(TestMulOp):
+    def init_dtype_type(self):
+        pass
+
+
+class TestNGRAPHMulOp2(TestMulOp2):
+    def init_dtype_type(self):
+        pass
+
+
+class TestNGRAPHFP16MulOp1(TestFP16MulOp1):
+    def init_dtype_type(self):
+        pass
+
+
+class TestNGRAPHFP16MulOp2(TestFP16MulOp2):
+    def init_dtype_type(self):
+        pass
+
+
+if __name__ == "__main__":
+    unittest.main()

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

@@ -370,6 +370,8 @@ class OpTest(unittest.TestCase):
                     return [place]
                 else:
                     return []
+            else:
+                return []
         places = [fluid.CPUPlace()]
         cpu_only = self._cpu_only if hasattr(self, '_cpu_only') else False
         if core.is_compiled_with_cuda() and core.op_support_gpu(self.op_type)\

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

@@ -22,8 +22,10 @@ from op_test import OpTest
 class TestAccuracyOp(OpTest):
     def setUp(self):
         self.op_type = "accuracy"
+        self.dtype = np.float32
+        self.init_dtype()
         n = 8192
-        infer = np.random.random((n, 1)).astype("float32")
+        infer = np.random.random((n, 1)).astype(self.dtype)
         indices = np.random.randint(0, 2, (n, 1))
         label = np.random.randint(0, 2, (n, 1))
         self.inputs = {'Out': infer, 'Indices': indices, "Label": label}
@@ -34,14 +36,25 @@ class TestAccuracyOp(OpTest):
                     num_correct += 1
                     break
         self.outputs = {
-            'Accuracy': np.array([num_correct / float(n)]).astype("float32"),
+            'Accuracy': np.array([num_correct / float(n)]).astype(self.dtype),
             'Correct': np.array([num_correct]).astype("int32"),
             'Total': np.array([n]).astype("int32")
         }
 
+    def init_dtype(self):
+        pass
+
     def test_check_output(self):
         self.check_output()
 
 
+class TestAccuracyOpFp16(TestAccuracyOp):
+    def init_dtype(self):
+        self.dtype = np.float16
+
+    def test_check_output(self):
+        self.check_output(atol=1e-3)
+
+
 if __name__ == '__main__':
     unittest.main()

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

@@ -21,14 +21,16 @@ from op_test import OpTest
 class ElementwiseDivOp(OpTest):
     def setUp(self):
         self.op_type = "elementwise_div"
+        self.dtype = np.float32
+        self.init_dtype()
         """ Warning
         CPU gradient check error!
         'X': np.random.random((32,84)).astype("float32"),
         'Y': np.random.random((32,84)).astype("float32")
         """
         self.inputs = {
-            'X': np.random.uniform(0.1, 1, [13, 17]).astype("float32"),
-            'Y': np.random.uniform(0.1, 1, [13, 17]).astype("float32")
+            'X': np.random.uniform(0.1, 1, [13, 17]).astype(self.dtype),
+            'Y': np.random.uniform(0.1, 1, [13, 17]).astype(self.dtype)
         }
         self.outputs = {'Out': np.divide(self.inputs['X'], self.inputs['Y'])}
 
@@ -46,6 +48,9 @@ class ElementwiseDivOp(OpTest):
         self.check_grad(
             ['X'], 'Out', max_relative_error=0.05, no_grad_set=set('Y'))
 
+    def init_dtype(self):
+        pass
+
 
 class TestElementwiseDivOp_scalar(ElementwiseDivOp):
     def setUp(self):
@@ -126,5 +131,21 @@ class TestElementwiseDivOp_broadcast_3(ElementwiseDivOp):
         }
 
 
+class TestElementwiseDivOpFp16(ElementwiseDivOp):
+    def init_dtype(self):
+        self.dtype = np.float16
+
+    def test_check_grad_normal(self):
+        self.check_grad(['X', 'Y'], 'Out', max_relative_error=1)
+
+    def test_check_grad_ingore_x(self):
+        self.check_grad(
+            ['Y'], 'Out', max_relative_error=1, no_grad_set=set("X"))
+
+    def test_check_grad_ingore_y(self):
+        self.check_grad(
+            ['X'], 'Out', max_relative_error=1, no_grad_set=set('Y'))
+
+
 if __name__ == '__main__':
     unittest.main()

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

@@ -135,5 +135,10 @@ class TestElementwiseMulOp_broadcast_3(ElementwiseMulOp):
         }
 
 
+class TestElementwiseMulOpFp16(ElementwiseMulOp):
+    def init_dtype(self):
+        self.dtype = np.float16
+
+
 if __name__ == '__main__':
     unittest.main()

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

@@ -22,12 +22,22 @@ from op_test import OpTest
 class TestFillZerosLikeOp(OpTest):
     def setUp(self):
         self.op_type = "fill_zeros_like"
-        self.inputs = {'X': np.random.random((219, 232)).astype("float32")}
+        self.dtype = np.float32
+        self.init_dtype()
+        self.inputs = {'X': np.random.random((219, 232)).astype(self.dtype)}
         self.outputs = {'Out': np.zeros_like(self.inputs["X"])}
 
+    def init_dtype(self):
+        pass
+
     def test_check_output(self):
         self.check_output()
 
 
+class TestFillZerosLikeOpFp16(TestFillZerosLikeOp):
+    def init_dtype(self):
+        self.dtype = np.float16
+
+
 if __name__ == "__main__":
     unittest.main()

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

@@ -12,12 +12,23 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import contextlib
 import unittest
-import sys
 import numpy as np
 
 import paddle.fluid as fluid
 from paddle.fluid import core
+from paddle.fluid.layers.nn import FC
+
+
+@contextlib.contextmanager
+def new_program_scope():
+    prog = fluid.Program()
+    startup_prog = fluid.Program()
+    scope = fluid.core.Scope()
+    with fluid.scope_guard(scope):
+        with fluid.program_guard(prog, startup_prog):
+            yield
 
 
 class MyLayer(fluid.imperative.PyLayer):
@@ -30,6 +41,23 @@ class MyLayer(fluid.imperative.PyLayer):
         return [fluid.layers.elementwise_mul(x, x)]
 
 
+class MLP(fluid.imperative.PyLayer):
+    def __init__(self):
+        super(MLP, self).__init__()
+        self._fc1 = FC(3,
+                       fluid.ParamAttr(
+                           initializer=fluid.initializer.Constant(value=0.1)))
+        self._fc2 = FC(4,
+                       fluid.ParamAttr(
+                           initializer=fluid.initializer.Constant(value=0.1)))
+
+    def forward(self, inputs):
+        x = self._fc1(inputs[0])
+        x = self._fc2(x)
+        x = fluid.layers.reduce_sum(x)
+        return x
+
+
 class TestImperative(unittest.TestCase):
     def test_layer(self):
         with fluid.imperative.guard():
@@ -39,13 +67,56 @@ class TestImperative(unittest.TestCase):
             l.forward([])
 
     def test_layer_in_out(self):
+        np_inp = np.array([1.0, 2.0, -1.0], dtype=np.float32)
         with fluid.imperative.guard():
             l = MyLayer()
-            x = l(np.array([1.0, 2.0, -1.0], dtype=np.float32))[0]
+            x = l(np_inp)[0]
             self.assertIsNotNone(x)
-            sys.stderr.write("%s output: %s\n" % (x, x._numpy()))
+            dy_out = x._numpy()
             x._backward()
-            sys.stderr.write("grad %s\n" % l._x_for_debug._gradient())
+            dy_grad = l._x_for_debug._gradient()
+
+        with new_program_scope():
+            inp = fluid.layers.data(
+                name="inp", shape=[3], append_batch_size=False)
+            l = MyLayer()
+            x = l(inp)[0]
+            param_grads = fluid.backward.append_backward(
+                x, parameter_list=[l._x_for_debug.name])[0]
+            exe = fluid.Executor(fluid.CPUPlace())
+
+            static_out, static_grad = exe.run(
+                feed={inp.name: np_inp},
+                fetch_list=[x.name, param_grads[1].name])
+
+        self.assertTrue(np.allclose(dy_out, static_out))
+        self.assertTrue(np.allclose(dy_grad, static_grad))
+
+    def test_mlp(self):
+        np_inp = np.array([[1.0, 2.0], [3.0, 4.0]], dtype=np.float32)
+        with fluid.imperative.guard():
+            mlp = MLP()
+            out = mlp(np_inp)
+            dy_out = out._numpy()
+            out._backward()
+            dy_grad = mlp._fc1._w._gradient()
+
+        with new_program_scope():
+            inp = fluid.layers.data(
+                name="inp", shape=[2, 2], append_batch_size=False)
+            mlp = MLP()
+            out = mlp(inp)
+            param_grads = fluid.backward.append_backward(
+                out, parameter_list=[mlp._fc1._w.name])[0]
+            exe = fluid.Executor(fluid.CPUPlace())
+            exe.run(fluid.default_startup_program())
+
+            static_out, static_grad = exe.run(
+                feed={inp.name: np_inp},
+                fetch_list=[out.name, param_grads[1].name])
+
+        self.assertTrue(np.allclose(dy_out, static_out))
+        self.assertTrue(np.allclose(dy_grad, static_grad))
 
 
 if __name__ == '__main__':

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

@@ -97,7 +97,7 @@ class TestLearningRateDecay(unittest.TestCase):
         startup_prog = fluid.Program()
 
         with fluid.program_guard(main_prog, startup_prog):
-            decayed_lr = fluid_decay_fn(**kwargs)
+            decayed_lr = fluid_decay_fn(**kwargs)("float32")
 
         place = fluid.CPUPlace()
         exe = fluid.Executor(place)

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

@@ -24,11 +24,13 @@ from op_test import OpTest
 class TestMomentumOp1(OpTest):
     def setUp(self):
         self.op_type = "momentum"
+        self.dtype = np.float32
+        self.init_dtype()
 
-        param = np.random.random((123, 321)).astype("float32")
-        grad = np.random.random((123, 321)).astype("float32")
-        velocity = np.zeros((123, 321)).astype("float32")
-        learning_rate = np.array([0.001]).astype("float32")
+        param = np.random.random((123, 321)).astype(self.dtype)
+        grad = np.random.random((123, 321)).astype(self.dtype)
+        velocity = np.zeros((123, 321)).astype(self.dtype)
+        learning_rate = np.array([0.001]).astype(self.dtype)
         mu = 0.0001
         use_nesterov = False
 
@@ -50,10 +52,21 @@ class TestMomentumOp1(OpTest):
 
         self.outputs = {'ParamOut': param_out, 'VelocityOut': velocity_out}
 
+    def init_dtype(self):
+        pass
+
     def test_check_output(self):
         self.check_output()
 
 
+class TestMomentumOpFp16(TestMomentumOp1):
+    def init_dtype(self):
+        self.dtype = np.float16
+
+    def test_check_output(self):
+        self.check_output(atol=1e-3)
+
+
 class TestMomentumOp2(OpTest):
     '''Test Momentum with default values for attributes
     '''

python/paddle/fluid/tests/unittests/test_parallel_op.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.
-
-from __future__ import print_function
-
-import unittest
-
-import paddle.fluid as fluid
-from paddle.fluid.layers.device import get_places
-from paddle.fluid.layers.control_flow import ParallelDo
-import paddle.fluid.profiler as profiler
-import numpy
-import six
-
-
-class BaseParallelForTest(unittest.TestCase):
-    def run_test(self, callback, feed, fetch):
-        """
-        Run the unittest for parallel.for
-        Args:
-            callback(callable): A callable function returns a generator. There
-                are two yields in the generator function. The first yield
-                returns the data layers, and the second yield returns the loss.
-                The modified data variables will be sent back during the first
-                yield.
-
-            feed(dict): The executor feeding dictionary.
-            fetch(list|basestr): The fetch name lists.
-
-        Returns:
-            None
-
-        Raises:
-            AssertionError when the computation of cpu, parallel.for in cpu,
-                gpu, parallel.for in gpu are different.
-
-        """
-        cpu = fluid.CPUPlace()
-        result_cpu = self._run_test_impl_(
-            callback=callback,
-            feed=feed,
-            fetch=fetch,
-            place=cpu,
-            use_parallel=False)
-        result_cpu_parallel = self._run_test_impl_(
-            callback=callback,
-            feed=feed,
-            fetch=fetch,
-            place=cpu,
-            use_parallel=True)
-        if fluid.core.is_compiled_with_cuda():
-            gpu = fluid.CUDAPlace(0)
-            result_gpu = self._run_test_impl_(
-                callback=callback,
-                feed=feed,
-                fetch=fetch,
-                place=gpu,
-                use_parallel=False,
-                use_gpu=True)
-            result_gpu_parallel = self._run_test_impl_(
-                callback=callback,
-                feed=feed,
-                fetch=fetch,
-                place=gpu,
-                use_parallel=True,
-                use_gpu=True)
-            result_gpu_nccl = self._run_test_impl_(
-                callback=callback,
-                feed=feed,
-                fetch=fetch,
-                place=gpu,
-                use_parallel=True,
-                use_nccl=True,
-                use_gpu=True)
-            self._assert_same_(fetch, result_cpu, result_cpu_parallel,
-                               result_gpu, result_gpu_parallel, result_gpu_nccl)
-        else:
-            self._assert_same_(fetch, result_cpu, result_cpu_parallel)
-
-    def _run_test_impl_(self,
-                        callback,
-                        feed,
-                        fetch,
-                        place,
-                        use_parallel=False,
-                        use_nccl=False,
-                        use_gpu=False):
-        """
-        Run a single test, returns the fetch values
-        Args:
-            place(Place): the computation place.
-            use_parallel(bool): Whether use parallel.for or not.
-
-        Returns:
-            Fetched numpy arrays.
-
-        """
-        if isinstance(fetch, six.string_types):
-            fetch = [fetch]
-        main = fluid.Program()
-        startup = fluid.Program()
-        # Fix seed
-        main.random_seed = 10
-        startup.random_seed = 10
-
-        with fluid.program_guard(main, startup):
-            generator = callback()
-            # Automatically insert parallel do if use_parallel = True
-            if use_parallel:
-                thread_num = fluid.core.get_cuda_device_count(
-                ) if use_gpu else 8
-                places = get_places(thread_num)
-                pd = ParallelDo(places, use_nccl=use_nccl)
-                data = next(generator)
-
-                if isinstance(data, fluid.framework.Variable):
-                    data = [data]
-
-                with pd.do():
-                    ins = list(map(pd.read_input, data))
-                    if len(ins) == 1:
-                        ins = ins[0]
-                    loss = generator.send(ins)  # patch input
-                    pd.write_output(loss)
-
-                loss = pd()
-            else:
-                data = next(generator)
-                loss = generator.send(data)
-            self.assertIsNotNone(loss)
-            avg_loss = fluid.layers.mean(loss)
-            fluid.backward.append_backward(loss=avg_loss)
-
-        exe = fluid.Executor(place)
-        exe.run(startup)
-        if use_gpu:
-            profile_type = 'GPU'
-        else:
-            profile_type = 'CPU'
-        with profiler.profiler(profile_type, 'total', '/tmp/profiler'):
-            return exe.run(main, feed=feed, fetch_list=fetch)
-
-    def _assert_same_(self, fetch, *args):
-        """
-        Assert the return values of `run_test` are same.
-        Args:
-            fetch: Fetch list. Used for print error message
-            *args: The fetch result lists of each situations.
-
-        Returns:
-            None
-
-        Raises:
-            AssertionError
-
-        """
-
-        def _impl_(a, b, fetch_id, item_id):
-            item_str = [
-                'CPU', 'ParallelCPU', 'GPU', 'ParallelGPU', 'ParallelGPUNCCL'
-            ]
-            flag = numpy.allclose(a, b, rtol=0.1, atol=1e-3)
-            self.assertTrue(flag,
-                            "The {0} are different in {1}, {2} vs {3}".format(
-                                fetch[fetch_id], item_str[item_id], a, b))
-
-        for i, items in enumerate(zip(*args)):
-            self.assertGreater(len(items), 0)
-            for j in range(1, len(items)):
-                _impl_(items[0], items[j], fetch_id=i, item_id=j)
-
-
-class ParallelOpTest(BaseParallelForTest):
-    @staticmethod
-    def __network__():
-        x = fluid.layers.data(shape=[784], dtype='float32', name='img')
-        x = yield x
-        hidden = fluid.layers.fc(input=x, size=200, param_attr='fc1.w')
-        hidden = fluid.layers.batch_norm(input=hidden)
-        loss = fluid.layers.mean(hidden)
-        yield loss
-
-    def test_simple_fc(self):
-        self.run_test(
-            callback=self.__network__,
-            feed={
-                'img': numpy.random.random(size=(51, 784)).astype('float32')
-            },
-            fetch=['fc1.w@GRAD'])
-
-    def test_fc_with_tiny_data(self):
-        self.run_test(
-            callback=self.__network__,
-            feed={'img': numpy.random.random(size=(1, 784)).astype('float32')},
-            fetch=['fc1.w@GRAD'])
-
-
-class ParallelOpTestMultipleInput(BaseParallelForTest):
-    @staticmethod
-    def __network__():
-        x = fluid.layers.data(
-            shape=[784], dtype='float32', name='img1', stop_gradient=False)
-        y = fluid.layers.data(
-            shape=[784], dtype='float32', name='img2', stop_gradient=False)
-        yield [x, y]
-        x = x + y
-        hidden1 = fluid.layers.fc(input=x, size=200, param_attr='fc1.w')
-        hidden2 = fluid.layers.fc(input=hidden1, size=200, param_attr='fc2.w')
-        hidden3 = fluid.layers.fc(input=hidden2, size=200, param_attr='fc3.w')
-        loss = fluid.layers.mean(hidden3)
-        yield loss
-
-    def test_simple_fc(self):
-        self.run_test(
-            callback=self.__network__,
-            feed={
-                'img1': numpy.random.random(size=(51, 784)).astype('float32'),
-                'img2': numpy.random.random(size=(51, 784)).astype('float32')
-            },
-            fetch=['fc1.w@GRAD', 'fc2.w@GRAD', 'fc3.w@GRAD'])
-
-
-if __name__ == '__main__':
-    unittest.main()

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

@@ -23,8 +23,11 @@ class TestTopkOp(OpTest):
     def setUp(self):
         self.set_args()
         self.op_type = "top_k"
+        self.dtype = np.float32
+        self.init_dtype()
+
         k = self.top_k
-        input = np.random.random((self.row, k)).astype("float32")
+        input = np.random.random((self.row, k)).astype(self.dtype)
         output = np.ndarray((self.row, k))
         indices = np.ndarray((self.row, k)).astype("int64")
 
@@ -38,6 +41,9 @@ class TestTopkOp(OpTest):
 
         self.outputs = {'Out': output, 'Indices': indices}
 
+    def init_dtype(self):
+        pass
+
     def set_args(self):
         self.row = 32
         self.top_k = 1
@@ -46,6 +52,11 @@ class TestTopkOp(OpTest):
         self.check_output()
 
 
+class TestTopkOpFp16(TestTopkOp):
+    def init_dtype(self):
+        self.dtype = np.float16
+
+
 class TestTopkOp3d(OpTest):
     def setUp(self):
         self.op_type = "top_k"

python/paddle/fluid/tests/unittests/test_transpose_mkldnn_op.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.
+
+from __future__ import print_function
+
+import unittest
+
+from test_transpose_op import TestTransposeOp
+
+
+class TestTransposeMKLDNN(TestTransposeOp):
+    def init_op_type(self):
+        self.op_type = "transpose2"
+        self.use_mkldnn = True
+        self.is_test = True
+        return
+
+    def test_check_grad(self):
+        return
+
+    def test_check_grad_no_input(self):
+        return
+
+    def test_check_grad_no_filter(self):
+        return
+
+
+class TestCase0MKLDNN(TestTransposeMKLDNN):
+    def initTestCase(self):
+        self.shape = (3, )
+        self.axis = (0, )
+
+
+class TestCase1a(TestTransposeMKLDNN):
+    def initTestCase(self):
+        self.shape = (3, 4, 5)
+        self.axis = (0, 2, 1)
+
+
+class TestCase1b(TestTransposeMKLDNN):
+    def initTestCase(self):
+        self.shape = (3, 4, 5)
+        self.axis = (2, 1, 0)
+
+
+class TestCase2(TestTransposeMKLDNN):
+    def initTestCase(self):
+        self.shape = (2, 3, 4, 5)
+        self.axis = (0, 2, 3, 1)
+
+
+class TestCase3(TestTransposeMKLDNN):
+    def initTestCase(self):
+        self.shape = (2, 3, 4, 5, 6)
+        self.axis = (4, 2, 3, 1, 0)
+
+
+class TestCase4(TestTransposeMKLDNN):
+    def initTestCase(self):
+        self.shape = (2, 3, 4, 5, 6, 1)
+        self.axis = (4, 2, 3, 1, 0, 5)
+
+
+if __name__ == '__main__':
+    unittest.main()

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

@@ -21,15 +21,24 @@ from op_test import OpTest
 
 class TestTransposeOp(OpTest):
     def setUp(self):
+        self.init_op_type()
         self.initTestCase()
-        self.op_type = "transpose2"
         self.inputs = {'X': np.random.random(self.shape).astype("float32")}
-        self.attrs = {'axis': list(self.axis)}
+        self.attrs = {
+            'axis': list(self.axis),
+            'use_mkldnn': self.use_mkldnn,
+            'is_test': self.is_test,
+        }
         self.outputs = {
             'XShape': np.random.random(self.shape).astype("float32"),
             'Out': self.inputs['X'].transpose(self.axis)
         }
 
+    def init_op_type(self):
+        self.op_type = "transpose2"
+        self.use_mkldnn = False
+        self.is_test = False
+
     def test_check_output(self):
         self.check_output(no_check_set=['XShape'])
 

python/paddle/fluid/transpiler/memory_optimization_transpiler.py

@@ -35,11 +35,10 @@ dtype_to_size = {
 }
 
 SUB_BLOCK_OPS = [
-    "while", "while_grad", "parallel_do", "parallel_do_grad",
-    "conditional_block", "conditional_block_grad"
+    "while", "while_grad", "conditional_block", "conditional_block_grad"
 ]
 
-SUB_BLOCK_PAIR = [("while", "while_grad"), ("parallel_do", "parallel_do_grad"),
+SUB_BLOCK_PAIR = [("while", "while_grad"),
                   ("conditional_block", "conditional_block_grad")]
 
 PRINT_LOG = False

python/setup.py.in

@@ -107,9 +107,9 @@ packages=['paddle',
           'paddle.fluid.distributed',
           'paddle.fluid.layers',
           'paddle.fluid.contrib',
-          'paddle.fluid.contrib.utils',
           'paddle.fluid.contrib.decoder',
           'paddle.fluid.contrib.quantize',
+          'paddle.fluid.contrib.utils',
           'paddle.fluid.transpiler',
           'paddle.fluid.transpiler.details']
 
@@ -160,10 +160,11 @@ if '${WITH_FLUID_ONLY}'== 'OFF':
 
 # put all thirdparty libraries in paddle.libs
 libs_path='${PADDLE_BINARY_DIR}/python/paddle/libs'
-if os.name != 'nt':
-    package_data['paddle.libs']= []
-    package_data['paddle.libs']=['libwarpctc' + ext_name]
-    shutil.copy('${WARPCTC_LIBRARIES}', libs_path)
+
+package_data['paddle.libs']= []
+package_data['paddle.libs']=[('libwarpctc' if os.name != 'nt' else 'warpctc') + ext_name]
+shutil.copy('${WARPCTC_LIBRARIES}', libs_path)
+
 if '${WITH_MKL}' == 'ON':
     shutil.copy('${MKLML_LIB}', libs_path)
     shutil.copy('${MKLML_IOMP_LIB}', libs_path)