Merge remote-tracking branch 'ups/develop' into refine/jit

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/build_strategy.cc

@@ -131,9 +131,7 @@ std::shared_ptr<ir::PassBuilder> BuildStrategy::CreatePassesFromStrategy(
 
 std::unique_ptr<ir::Graph> BuildStrategy::Apply(
     const ProgramDesc &main_program, const std::vector<platform::Place> &places,
-    const std::string &loss_var_name,
+    const std::string &loss_var_name, const std::vector<Scope *> &local_scopes,
-    const std::unordered_set<std::string> &param_names,
-    const std::vector<Scope *> &local_scopes,
 #if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
     const bool use_cuda, platform::NCCLContextMap *nccl_ctxs) const {
 #else
@@ -149,9 +147,6 @@ std::unique_ptr<ir::Graph> BuildStrategy::Apply(
       pass->SetNotOwned<const std::vector<platform::Place>>("places", &places);
       pass->Erase("loss_var_name");
       pass->SetNotOwned<const std::string>("loss_var_name", &loss_var_name);
-      pass->Erase("params");
-      pass->SetNotOwned<const std::unordered_set<std::string>>("params",
-                                                               &param_names);
       pass->Erase("local_scopes");
       pass->SetNotOwned<const std::vector<Scope *>>("local_scopes",
                                                     &local_scopes);

paddle/fluid/framework/details/build_strategy.h

@@ -106,16 +106,15 @@ struct BuildStrategy {
 
   // Apply the passes built by the pass_builder_. The passes will be
   // applied to the Program and output an ir::Graph.
-  std::unique_ptr<ir::Graph> Apply(
+  std::unique_ptr<ir::Graph> Apply(const ProgramDesc &main_program,
-      const ProgramDesc &main_program,
+                                   const std::vector<platform::Place> &places,
-      const std::vector<platform::Place> &places,
+                                   const std::string &loss_var_name,
-      const std::string &loss_var_name,
+                                   const std::vector<Scope *> &local_scopes,
-      const std::unordered_set<std::string> &param_names,
-      const std::vector<Scope *> &local_scopes,
 #if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
-      const bool use_cuda, platform::NCCLContextMap *nccl_ctxs) const;
+                                   const bool use_cuda,
+                                   platform::NCCLContextMap *nccl_ctxs) const;
 #else
-      const bool use_cuda) const;
+                                   const bool use_cuda) const;
 #endif
 
  private:

paddle/fluid/framework/details/multi_devices_graph_pass.cc

@@ -130,7 +130,6 @@ void AddOutputToLeafOps(ir::Graph *graph) {
 
 static const char kLossVarName[] = "loss_var_name";
 static const char kPlaces[] = "places";
-static const char kParams[] = "params";
 static const char kLocalScopes[] = "local_scopes";
 static const char kStrategy[] = "strategy";
 static const char kNumTrainers[] = "num_trainers";
@@ -147,9 +146,6 @@ void MultiDevSSAGraphBuilder::Init() const {
   nccl_ctxs_ = &Get<platform::NCCLContextMap>("nccl_ctxs");
 #endif
 
-  for (auto &p : Get<const std::unordered_set<std::string>>(kParams)) {
-    grad_names_.insert(GradVarName(p));
-  }
   balance_vars_.resize(places_.size(), 0);
   if (strategy_.enable_data_balance_ && places_.size() == 1) {
     LOG(WARNING) << "It is no need to enable data balance when there is only "
@@ -359,7 +355,9 @@ 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 +660,13 @@ 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
@@ -896,7 +894,6 @@ REGISTER_PASS(multi_devices_pass,
               paddle::framework::details::MultiDevSSAGraphBuilder)
     .RequirePassAttr(paddle::framework::details::kLossVarName)
     .RequirePassAttr(paddle::framework::details::kPlaces)
-    .RequirePassAttr(paddle::framework::details::kParams)
     .RequirePassAttr(paddle::framework::details::kLocalScopes)
     .RequirePassAttr(paddle::framework::details::kStrategy)
     .RequirePassAttr(paddle::framework::details::kNumTrainers);

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;
@@ -102,7 +103,6 @@ class MultiDevSSAGraphBuilder : public ir::Pass {
   mutable std::string loss_var_name_;
   mutable std::vector<platform::Place> places_;
   mutable std::vector<Scope *> local_scopes_;
-  mutable std::unordered_set<std::string> grad_names_;
 
   mutable BuildStrategy strategy_;
   mutable std::unordered_map<std::string, VarDesc *> all_vars_;

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() {}
 
+struct ScaleLossGradFunctor {
+  float coeff_;
+  Tensor *out_;
+  platform::Place place_;
+  OpHandleBase *op_handle_;
+  proto::VarType::Type out_dtype_;
+  platform::DeviceContext *ctx_;
+
+  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_)) {
+      *out_data = static_cast<OutT>(coeff_);
+    } else {
+#ifdef PADDLE_WITH_CUDA
+      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 *>();
 
-  float *tmp = local_scope.FindVar(var_name)
+  auto *tensor = local_scope.FindVar(var_name)->GetMutable<LoDTensor>();
-                   ->GetMutable<LoDTensor>()
+  tensor->Resize(make_ddim({1}));
-                   ->mutable_data<float>(make_ddim({1}), place_);
 
-  if (platform::is_cpu_place(place_)) {
-    *tmp = coeff_;
-  } else {
 #ifdef PADDLE_WITH_CUDA
-    this->RunAndRecordEvent([&] {
+  ScaleLossGradFunctor func(coeff_, tensor, place_, this, out_dtype_,
-      auto stream = static_cast<platform::CUDADeviceContext *>(
+                            this->dev_ctxes_.at(place_));
-                        this->dev_ctxes_.at(place_))
+  this->RunAndRecordEvent([&] { framework::VisitDataType(out_dtype_, func); });
-                        ->stream();
+#else
-      memory::Copy(boost::get<platform::CUDAPlace>(place_), tmp,
+  ScaleLossGradFunctor func(coeff_, tensor, place_, this, out_dtype_, nullptr);
-                   platform::CPUPlace(), &coeff_, sizeof(float), stream);
+  framework::VisitDataType(out_dtype_, func);
-      VLOG(10) << place_ << "RUN Scale loss grad op";
-    });
 #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::Place place, platform::DeviceContext *context,
-                        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/op_desc.cc

@@ -110,22 +110,125 @@ class CompileTimeInferShapeContext : public InferShapeContext {
     }
   }
 
+  std::vector<InferShapeVarPtr> GetInputVarPtrs(
+      const std::string &name) override {
+    const std::vector<std::string> arg_names = Inputs(name);
+    std::vector<InferShapeVarPtr> res;
+    res.reserve(arg_names.size());
+    std::transform(arg_names.begin(), arg_names.end(), std::back_inserter(res),
+                   [this](const std::string &name) {
+                     return block_.FindVarRecursive(name);
+                   });
+    return res;
+  }
+
+  std::vector<InferShapeVarPtr> GetOutputVarPtrs(
+      const std::string &name) override {
+    const std::vector<std::string> arg_names = Outputs(name);
+    std::vector<InferShapeVarPtr> res;
+    res.reserve(arg_names.size());
+    std::transform(arg_names.begin(), arg_names.end(), std::back_inserter(res),
+                   [this](const std::string &name) {
+                     return block_.FindVarRecursive(name);
+                   });
+    return res;
+  }
+
+  DDim GetInputDim(const std::string &name) const override {
+    const std::vector<std::string> &arg_names = Inputs(name);
+    PADDLE_ENFORCE_EQ(arg_names.size(), 1UL,
+                      "Input(%s) should hold one element, but now it holds %d",
+                      name, arg_names.size());
+    return this->GetDim(arg_names[0]);
+  }
+
+  std::vector<DDim> GetInputsDim(const std::string &name) const override {
+    const std::vector<std::string> &arg_names = Inputs(name);
+    return GetDims(arg_names);
+  }
+
   bool IsRuntime() const override;
 
+  std::vector<proto::VarType::Type> GetInputsVarType(
+      const std::string &name) const override {
+    return GetVarTypes(Inputs(name));
+  }
+
+  std::vector<proto::VarType::Type> GetOutputsVarType(
+      const std::string &name) const override {
+    return GetVarTypes(Outputs(name));
+  }
+
+  void SetOutputDim(const std::string &name, const DDim &dim) override {
+    auto &arg_names = Outputs(name);
+    PADDLE_ENFORCE_EQ(arg_names.size(), 1UL,
+                      "Output(%s) should hold one element, but now it holds %d",
+                      name, arg_names.size());
+    SetDim(arg_names[0], dim);
+  }
+
+  void SetOutputsDim(const std::string &name,
+                     const std::vector<DDim> &dims) override {
+    auto &names = Outputs(name);
+    SetDims(names, dims);
+  }
+
  protected:
-  proto::VarType::Type GetVarType(const std::string &name) const override;
+  std::vector<proto::VarType::Type> GetVarTypes(
+      const std::vector<std::string> &names) const {
+    std::vector<proto::VarType::Type> retv;
+    retv.resize(names.size());
+    std::transform(
+        names.begin(), names.end(), retv.begin(),
+        std::bind(std::mem_fn(&CompileTimeInferShapeContext::GetVarType), this,
+                  std::placeholders::_1));
+    return retv;
+  }
+
+  proto::VarType::Type GetVarType(const std::string &name) const;
+
+  DDim GetDim(const std::string &name) const {
+    auto var = block_.FindVarRecursive(name);
+    PADDLE_ENFORCE(var != nullptr, "Cannot find variable %s", name);
+    DDim res;
+    try {
+      auto shape = var->GetShape();
+      res = shape.empty() ? make_ddim({0UL}) : make_ddim(shape);
+    } catch (...) {
+      VLOG(5) << "GetDim of variable " << name << " error";
+      std::rethrow_exception(std::current_exception());
+    }
+    return res;
+  }
 
-  DDim GetDim(const std::string &name) const override;
+  std::vector<DDim> GetDims(const std::vector<std::string> &names) const {
+    std::vector<DDim> ret;
+    ret.reserve(names.size());
+    std::transform(
+        names.begin(), names.end(), std::back_inserter(ret),
+        [this](const std::string &name) { return this->GetDim(name); });
+    return ret;
+  }
+
+  void SetDim(const std::string &name, const DDim &dim);
 
-  void SetDim(const std::string &name, const DDim &dim) override;
+  void SetDims(const std::vector<std::string> &names,
+               const std::vector<DDim> &dims) {
+    size_t length = names.size();
+    PADDLE_ENFORCE_EQ(length, dims.size());
+    for (size_t i = 0; i < length; ++i) {
+      if (names[i] == framework::kEmptyVarName) {
+        continue;
+      }
+      SetDim(names[i], dims[i]);
+    }
+  }
 
   std::vector<DDim> GetRepeatedDims(const std::string &name) const override;
 
   void SetRepeatedDims(const std::string &name,
                        const std::vector<DDim> &dims) override;
 
-  InferShapeVarPtr GetVarPtr(const std::string &name) override;
-
   const OpDesc &op_;
   const BlockDesc &block_;
 };
@@ -644,20 +747,6 @@ const std::vector<std::string> &CompileTimeInferShapeContext::Outputs(
   return op_.Output(name);
 }
 
-DDim CompileTimeInferShapeContext::GetDim(const std::string &name) const {
-  auto var = block_.FindVarRecursive(name);
-  PADDLE_ENFORCE(var != nullptr, "Cannot find variable %s", name);
-  DDim res;
-  try {
-    auto shape = var->GetShape();
-    res = shape.empty() ? make_ddim({0UL}) : make_ddim(shape);
-  } catch (...) {
-    VLOG(5) << "GetDim of variable " << name << " error";
-    std::rethrow_exception(std::current_exception());
-  }
-  return res;
-}
-
 std::vector<DDim> CompileTimeInferShapeContext::GetRepeatedDims(
     const std::string &name) const {
   auto var = block_.FindVarRecursive(name);
@@ -696,10 +785,5 @@ proto::VarType::Type CompileTimeInferShapeContext::GetVarType(
   return block_.FindVarRecursive(name)->GetType();
 }
 
-InferShapeVarPtr CompileTimeInferShapeContext::GetVarPtr(
-    const std::string &name) {
-  return block_.FindVarRecursive(name);
-}
-
 }  // namespace framework
 }  // namespace paddle

paddle/fluid/framework/parallel_executor.cc

@@ -190,7 +190,6 @@ std::vector<Scope *> &ParallelExecutor::GetLocalScopes() {
 
 ParallelExecutor::ParallelExecutor(
     const std::vector<platform::Place> &places,
-    const std::unordered_set<std::string> &params,
     const std::unordered_set<std::string> &bcast_vars,
     const ProgramDesc &main_program, const std::string &loss_var_name,
     Scope *scope, const std::vector<Scope *> &local_scopes,
@@ -209,7 +208,7 @@ ParallelExecutor::ParallelExecutor(
                    "the number of places must be greater than 1.");
   }
 
-  // Step 1. Bcast the params to devs.
+  // Step 1. Bcast the bcast_vars to devs.
   // Create local scopes
   if (local_scopes.empty()) {
     member_->own_local_scope_ = true;
@@ -249,12 +248,12 @@ ParallelExecutor::ParallelExecutor(
 // ncclOp
 #if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
   std::unique_ptr<ir::Graph> graph = build_strategy.Apply(
-      main_program, member_->places_, loss_var_name, params,
+      main_program, member_->places_, loss_var_name, member_->local_scopes_,
-      member_->local_scopes_, member_->use_cuda_, member_->nccl_ctxs_.get());
+      member_->use_cuda_, member_->nccl_ctxs_.get());
 #else
   std::unique_ptr<ir::Graph> graph =
       build_strategy.Apply(main_program, member_->places_, loss_var_name,
-                           params, member_->local_scopes_, member_->use_cuda_);
+                           member_->local_scopes_, member_->use_cuda_);
 #endif
   auto max_memory_size = GetEagerDeletionThreshold();
   if (max_memory_size >= 0) {

paddle/fluid/framework/parallel_executor.h

@@ -41,7 +41,6 @@ class ParallelExecutor {
 
  public:
   explicit ParallelExecutor(const std::vector<platform::Place> &places,
-                            const std::unordered_set<std::string> &params,
                             const std::unordered_set<std::string> &bcast_vars,
                             const ProgramDesc &main_program,
                             const std::string &loss_var_name, Scope *scope,

paddle/fluid/framework/shape_inference.cc

@@ -22,20 +22,6 @@ limitations under the License. */
 namespace paddle {
 namespace framework {
 
-DDim InferShapeContext::GetInputDim(const std::string &name) const {
-  const std::vector<std::string> &arg_names = Inputs(name);
-  PADDLE_ENFORCE_EQ(arg_names.size(), 1UL,
-                    "Input(%s) should hold one element, but now it holds %d",
-                    name, arg_names.size());
-  return this->GetDim(arg_names[0]);
-}
-
-std::vector<DDim> InferShapeContext::GetInputsDim(
-    const std::string &name) const {
-  const std::vector<std::string> &arg_names = Inputs(name);
-  return GetDims(arg_names);
-}
-
 std::vector<DDim> InferShapeContext::GetReaderDims(
     const std::string &name) const {
   const std::vector<std::string> &arg_names = Inputs(name);
@@ -46,26 +32,6 @@ std::vector<DDim> InferShapeContext::GetReaderDims(
   return this->GetRepeatedDims(arg_names[0]);
 }
 
-DDim InferShapeContext::GetInputsElementDim(const std::string &name,
-                                            int idx) const {
-  const std::vector<std::string> &names = Inputs(name);
-  return this->GetDim(names[idx]);
-}
-
-void InferShapeContext::SetOutputDim(const std::string &name, const DDim &dim) {
-  auto &arg_names = Outputs(name);
-  PADDLE_ENFORCE_EQ(arg_names.size(), 1UL,
-                    "Output(%s) should hold one element, but now it holds %d",
-                    name, arg_names.size());
-  SetDim(arg_names[0], dim);
-}
-
-void InferShapeContext::SetOutputsDim(const std::string &name,
-                                      const std::vector<DDim> &dims) {
-  auto &names = Outputs(name);
-  SetDims(names, dims);
-}
-
 void InferShapeContext::SetReaderDims(const std::string &name,
                                       const std::vector<DDim> &dims) {
   const std::vector<std::string> &arg_names = Outputs(name);
@@ -76,69 +42,5 @@ void InferShapeContext::SetReaderDims(const std::string &name,
   return this->SetRepeatedDims(arg_names[0], dims);
 }
 
-std::vector<InferShapeVarPtr> InferShapeContext::GetInputVarPtrs(
-    const std::string &name) {
-  const std::vector<std::string> arg_names = Inputs(name);
-  std::vector<InferShapeVarPtr> res;
-  res.reserve(arg_names.size());
-  std::transform(
-      arg_names.begin(), arg_names.end(), std::back_inserter(res),
-      [this](const std::string &name) { return this->GetVarPtr(name); });
-  return res;
-}
-
-std::vector<InferShapeVarPtr> InferShapeContext::GetOutputVarPtrs(
-    const std::string &name) {
-  const std::vector<std::string> arg_names = Outputs(name);
-  std::vector<InferShapeVarPtr> res;
-  res.reserve(arg_names.size());
-  std::transform(
-      arg_names.begin(), arg_names.end(), std::back_inserter(res),
-      [this](const std::string &name) { return this->GetVarPtr(name); });
-  return res;
-}
-
-std::vector<DDim> InferShapeContext::GetDims(
-    const std::vector<std::string> &names) const {
-  std::vector<DDim> ret;
-  ret.reserve(names.size());
-  std::transform(
-      names.begin(), names.end(), std::back_inserter(ret),
-      [this](const std::string &name) { return this->GetDim(name); });
-  return ret;
-}
-
-void InferShapeContext::SetDims(const std::vector<std::string> &names,
-                                const std::vector<DDim> &dims) {
-  size_t length = names.size();
-  PADDLE_ENFORCE_EQ(length, dims.size());
-  for (size_t i = 0; i < length; ++i) {
-    if (names[i] == framework::kEmptyVarName) {
-      continue;
-    }
-    SetDim(names[i], dims[i]);
-  }
-}
-
-std::vector<proto::VarType::Type> InferShapeContext::GetInputsVarType(
-    const std::string &name) const {
-  return GetVarTypes(Inputs(name));
-}
-
-std::vector<proto::VarType::Type> InferShapeContext::GetOutputsVarType(
-    const std::string &name) const {
-  return GetVarTypes(Outputs(name));
-}
-
-std::vector<proto::VarType::Type> InferShapeContext::GetVarTypes(
-    const std::vector<std::string> &names) const {
-  std::vector<proto::VarType::Type> retv;
-  retv.resize(names.size());
-  std::transform(names.begin(), names.end(), retv.begin(),
-                 std::bind(std::mem_fn(&InferShapeContext::GetVarType), this,
-                           std::placeholders::_1));
-  return retv;
-}
-
 }  // namespace framework
 }  // namespace paddle

paddle/fluid/framework/shape_inference.h

@@ -33,22 +33,23 @@ class InferShapeContext {
   virtual bool HasInput(const std::string &name) const = 0;
   virtual bool HasOutput(const std::string &name) const = 0;
 
-  std::vector<proto::VarType::Type> GetInputsVarType(
+  virtual std::vector<proto::VarType::Type> GetInputsVarType(
-      const std::string &name) const;
+      const std::string &name) const = 0;
-  std::vector<proto::VarType::Type> GetOutputsVarType(
+  virtual std::vector<proto::VarType::Type> GetOutputsVarType(
-      const std::string &name) const;
+      const std::string &name) const = 0;
 
   virtual bool HasInputs(const std::string &name) const = 0;
   virtual bool HasOutputs(const std::string &name) const = 0;
 
-  DDim GetInputDim(const std::string &name) const;
+  virtual DDim GetInputDim(const std::string &name) const = 0;
-  std::vector<DDim> GetInputsDim(const std::string &name) const;
+  virtual std::vector<DDim> GetInputsDim(const std::string &name) const = 0;
-  std::vector<DDim> GetReaderDims(const std::string &name) const;
+  virtual std::vector<DDim> GetReaderDims(const std::string &name) const;
-  DDim GetInputsElementDim(const std::string &name, int idx) const;
 
-  void SetOutputDim(const std::string &name, const DDim &dim);
+  virtual void SetOutputDim(const std::string &name, const DDim &dim) = 0;
-  void SetOutputsDim(const std::string &name, const std::vector<DDim> &dims);
+  virtual void SetOutputsDim(const std::string &name,
-  void SetReaderDims(const std::string &name, const std::vector<DDim> &dims);
+                             const std::vector<DDim> &dims) = 0;
+  virtual void SetReaderDims(const std::string &name,
+                             const std::vector<DDim> &dims);
 
   virtual AttrReader Attrs() const = 0;
   virtual const std::vector<std::string> &Inputs(
@@ -67,27 +68,15 @@ class InferShapeContext {
 
   virtual bool IsRuntime() const = 0;
 
-  std::vector<InferShapeVarPtr> GetInputVarPtrs(const std::string &name);
+  virtual std::vector<InferShapeVarPtr> GetInputVarPtrs(
-  std::vector<InferShapeVarPtr> GetOutputVarPtrs(const std::string &name);
+      const std::string &name) = 0;
-  virtual InferShapeVarPtr GetVarPtr(const std::string &name) = 0;
+  virtual std::vector<InferShapeVarPtr> GetOutputVarPtrs(
-
+      const std::string &name) = 0;
-  // Note: In while op, we need this to be public
-  void SetDims(const std::vector<std::string> &names,
-               const std::vector<DDim> &dims);
 
  protected:
-  virtual DDim GetDim(const std::string &name) const = 0;
-  virtual void SetDim(const std::string &name, const DDim &dim) = 0;
   virtual std::vector<DDim> GetRepeatedDims(const std::string &name) const = 0;
   virtual void SetRepeatedDims(const std::string &name,
                                const std::vector<DDim> &dims) = 0;
-
-  std::vector<DDim> GetDims(const std::vector<std::string> &names) const;
-
-  std::vector<proto::VarType::Type> GetVarTypes(
-      const std::vector<std::string> &names) const;
-
-  virtual proto::VarType::Type GetVarType(const std::string &name) const = 0;
 };
 
 }  // namespace framework

paddle/fluid/operators/controlflow/while_op.cc

@@ -399,26 +399,41 @@ class WhileGradOpShapeInference : public framework::InferShapeBase {
     ctx->HasInputs(kOutputs);
     ctx->HasInputs(framework::GradVarName(kOutputs));
 
-    auto p_names = ctx->Inputs(kX);
     auto pg_ig_names = ctx->Outputs(kXGRAD);
-    auto var_types = ctx->GetInputsVarType(kX);
+    std::vector<framework::InferShapeVarPtr> in_var_ptrs =
-    std::vector<std::string> names_to_set;
+        ctx->GetInputVarPtrs(kX);
-    std::vector<framework::DDim> dims_to_set;
+    std::vector<framework::InferShapeVarPtr> out_var_ptrs =
-    for (size_t i = 0; i < p_names.size(); ++i) {
+        ctx->GetOutputVarPtrs(kXGRAD);
+    PADDLE_ENFORCE(in_var_ptrs.size() == out_var_ptrs.size());
+
+    for (size_t i = 0; i < in_var_ptrs.size(); ++i) {
       if (pg_ig_names[i] == framework::kEmptyVarName) {
         continue;
       }
-      auto dims = ctx->GetInputsElementDim(kX, i);
+      if (ctx->IsRuntime()) {
-      if (var_types[i] == framework::proto::VarType::LOD_TENSOR) {
+        framework::Variable *in_var =
-        names_to_set.push_back(pg_ig_names[i]);
+            boost::get<framework::Variable *>(in_var_ptrs[i]);
-        dims_to_set.push_back(dims);
+        framework::Variable *out_var =
-      } else if (var_types[i] == framework::proto::VarType::LOD_TENSOR_ARRAY) {
+            boost::get<framework::Variable *>(out_var_ptrs[i]);
-        // not sure how to set the dim of LOD_TENSOR_ARRAY
+
-        names_to_set.push_back(pg_ig_names[i]);
+        auto type = framework::ToVarType(in_var->Type());
-        dims_to_set.push_back(dims);
+        if (type == framework::proto::VarType::LOD_TENSOR) {
+          out_var->GetMutable<LoDTensor>()->Resize(
+              in_var->Get<framework::LoDTensor>().dims());
+        } else if (type == framework::proto::VarType::SELECTED_ROWS) {
+          out_var->GetMutable<framework::SelectedRows>()->set_height(
+              in_var->Get<framework::SelectedRows>().GetCompleteDims()[0]);
+        } else if (type == framework::proto::VarType::LOD_TENSOR_ARRAY) {
+          PADDLE_THROW("WhileGradOp doesn't support type %d",
+                       static_cast<int>(type));
+        }
+      } else {
+        framework::VarDesc *in_var =
+            boost::get<framework::VarDesc *>(in_var_ptrs[i]);
+        boost::get<framework::VarDesc *>(out_var_ptrs[i])
+            ->SetShape(in_var->GetShape());
       }
     }
-    ctx->SetDims(names_to_set, dims_to_set);
   }
 };
 

paddle/fluid/operators/conv_mkldnn_op.cc

@@ -155,11 +155,14 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
     auto chosen_memory_format =
         platform::data_format_to_memory_format(data_format);
 
-    if (is_conv3d) {
+    weights_format = mkldnn::memory::format::any;
-      chosen_memory_format =
+    // Check the format for user's special output
-          platform::MKLDNNFormatForSize(src_tz.size(), chosen_memory_format);
+    if (chosen_memory_format != mkldnn::memory::format::any) {
+      if (is_conv3d) {
+        chosen_memory_format =
+            platform::MKLDNNFormatForSize(src_tz.size(), chosen_memory_format);
+      }
     }
-    weights_format = GetWeightsFormat(chosen_memory_format, g, is_conv3d);
 
     auto src_md = platform::MKLDNNMemDesc(
         src_tz, platform::MKLDNNGetDataType<T>(), chosen_memory_format);
@@ -435,11 +438,14 @@ class ConvMKLDNNGradOpKernel : public paddle::framework::OpKernel<T> {
     auto chosen_memory_format =
         platform::data_format_to_memory_format(data_format);
 
-    if (is_conv3d) {
+    weights_format = mkldnn::memory::format::any;
-      chosen_memory_format =
+    // Check the format for user's special output
-          platform::MKLDNNFormatForSize(src_tz.size(), chosen_memory_format);
+    if (chosen_memory_format != mkldnn::memory::format::any) {
+      if (is_conv3d) {
+        chosen_memory_format =
+            platform::MKLDNNFormatForSize(src_tz.size(), chosen_memory_format);
+      }
     }
-    weights_format = GetWeightsFormat(chosen_memory_format, g, is_conv3d);
 
     auto src_md = platform::MKLDNNMemDesc(
         src_tz, platform::MKLDNNGetDataType<T>(), chosen_memory_format);

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/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,
+    elementwise_mul, ops::ElementwiseMulKernel<plat::CUDADeviceContext, float>,
-    ops::ElementwiseMulKernel<paddle::platform::CUDADeviceContext, float>,
+    ops::ElementwiseMulKernel<plat::CUDADeviceContext, double>,
-    ops::ElementwiseMulKernel<paddle::platform::CUDADeviceContext, double>,
+    ops::ElementwiseMulKernel<plat::CUDADeviceContext, int>,
-    ops::ElementwiseMulKernel<paddle::platform::CUDADeviceContext, int>,
+    ops::ElementwiseMulKernel<plat::CUDADeviceContext, int64_t>,
-    ops::ElementwiseMulKernel<paddle::platform::CUDADeviceContext, int64_t>);
+    ops::ElementwiseMulKernel<plat::CUDADeviceContext, plat::float16>);
 REGISTER_OP_CUDA_KERNEL(
     elementwise_mul_grad,
-    ops::ElementwiseMulGradKernel<paddle::platform::CUDADeviceContext, float>,
+    ops::ElementwiseMulGradKernel<plat::CUDADeviceContext, float>,
-    ops::ElementwiseMulGradKernel<paddle::platform::CUDADeviceContext, double>,
+    ops::ElementwiseMulGradKernel<plat::CUDADeviceContext, double>,
-    ops::ElementwiseMulGradKernel<paddle::platform::CUDADeviceContext, int>,
+    ops::ElementwiseMulGradKernel<plat::CUDADeviceContext, int>,
-    ops::ElementwiseMulGradKernel<paddle::platform::CUDADeviceContext,
+    ops::ElementwiseMulGradKernel<plat::CUDADeviceContext, int64_t>,
-                                  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,
+REGISTER_OP_CUDA_KERNEL(
-                        paddle::operators::AccuracyOpCUDAKernel<float>,
+    accuracy, paddle::operators::AccuracyOpCUDAKernel<float>,
-                        paddle::operators::AccuracyOpCUDAKernel<double>);
+    paddle::operators::AccuracyOpCUDAKernel<double>,
+    paddle::operators::AccuracyOpCUDAKernel<paddle::platform::float16>);

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>,
+REGISTER_OP_CUDA_KERNEL(
-                        paddle::operators::TopkOpCUDAKernel<double>);
+    top_k, paddle::operators::TopkOpCUDAKernel<float>,
+    paddle::operators::TopkOpCUDAKernel<double>,
+    paddle::operators::TopkOpCUDAKernel<paddle::platform::float16>);

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/pybind/pybind.cc

@@ -977,7 +977,6 @@ All parameter, weight, gradient are variables in Paddle.
                 cannot be updated after being finalized.)DOC");
 
   pe.def(py::init<const std::vector<platform::Place> &,
-                  const std::unordered_set<std::string> &,
                   const std::unordered_set<std::string> &, const ProgramDesc &,
                   const std::string &, Scope *, std::vector<Scope *> &,
                   const ExecutionStrategy &, const BuildStrategy &, size_t,