Refactor Context/Stream & Add CTC Loss

Dragon/include/core/common.h

@@ -12,6 +12,7 @@
 #ifndef DRAGON_CORE_COMMON_H_
 #define DRAGON_CORE_COMMON_H_
 
+#include <ctime>
 #include <climits>
 #include <memory>
 #include <string>
@@ -48,7 +49,26 @@ using Map = std::unordered_map<Key, Value>;
 template <typename Value>
 using Set = std::unordered_set<Value> ;
 
-#define DRAGON_VERSION 2205
+/*
+ * Define the Kernel version.
+ *
+ * | Major(2) | Minor(2) | Patch(06) |
+ */
+#define DRAGON_VERSION 2206
+
+/*
+ * Define the default random seed.
+ */
+#define DEFAULT_RNG_SEED 3
+
+/*
+ * Define the common marcos.
+ */
+#ifdef _MSC_VER
+#if _MSC_VER < 1900
+#define thread_local __declspec(thread)
+#endif
+#endif
 
 #define CONCATENATE_IMPL(s1, s2) s1##s2
 #define CONCATENATE(s1, s2) CONCATENATE_IMPL(s1,s2)

Dragon/include/core/context.h

@@ -23,21 +23,18 @@
 
 namespace dragon {
 
-class CPUObject {
-public:
-    unique_ptr<std::mt19937> rand_generator;
-};
-
 class CPUContext {
  public:
-    CPUContext(): random_seed_(3) { generator(); }
-    CPUContext(unsigned int random_seed): random_seed_(random_seed) { generator(); }
-    CPUContext(const DeviceOption& option): random_seed_(option.has_random_seed() ?
-                                                         option.random_seed() : 3) { generator(); }
+    CPUContext(): random_seed_(3) {}
+    CPUContext(unsigned int random_seed)
+        : random_seed_(random_seed)  {}
+    CPUContext(const DeviceOption& option)
+        : random_seed_(option.has_random_seed() ?
+            option.random_seed() : DEFAULT_RNG_SEED) {}
     virtual ~CPUContext() {}
 
     inline void SwitchToDevice() {}
-    inline void static FinishDeviceCompution() { return; }
+    inline void FinishDeviceCompution() {}
 
     inline static void* New(size_t nbytes) {
         void* data;
@@ -50,40 +47,54 @@ class CPUContext {
         return data;
     }
 
-    inline static void Memset(size_t nbytes, void* ptr) { memset(ptr, 0, nbytes); }
+    inline static void Memset(size_t nbytes, void* ptr) {
+        memset(ptr, 0, nbytes);
+    }
+
     template<class DstContext, class SrcContext>
-    inline static void Memcpy(size_t nbytes, void* dst, const void* src) { memcpy(dst, src, nbytes); }
+    inline static void Memcpy(
+        size_t              nbytes,
+        void*               dst,
+        const void*         src) {
+        memcpy(dst, src, nbytes);
+    }
+
     inline static void Delete(void* data) { free(data); }
 
     template<class DstContext, class SrcContext>
-    inline static void MemcpyAsync(size_t nbytes, void* dst, const void* src) { NOT_IMPLEMENTED; }
+    inline static void MemcpyAsync(
+        size_t              nbytes,
+        void*               dst,
+        const void*         src) {
+        NOT_IMPLEMENTED;
+    }
 
     template<typename T, class DstContext, class SrcContext>
-    inline static void Copy(int n, T* dst, const T* src) {
+    inline static void Copy(
+        int                 n,
+        T*                  dst,
+        const T*            src) {
         if (dst == src) return;
         //  only the basic types(e.g. int/float) can memcpy correctly
         if (std::is_fundamental<T>::value)
-            Memcpy<DstContext, SrcContext>(n * sizeof(T), (void*)dst, (const void*)src);
+            Memcpy<DstContext, SrcContext>(
+                n * sizeof(T), (void*)dst, (const void*)src);
         else for (int i = 0; i < n; i++) dst[i] = src[i];
     }
 
-    inline std::mt19937* generator() {
-        auto& generator = cpu_object_.rand_generator;
-        if (!generator.get())
-            generator.reset(new std::mt19937(random_seed_));
-        return generator.get();
-    }
+    inline int device_id() const { return 0; }
 
-    static CPUObject cpu_object_;
+    inline std::mt19937* rand_generator() {
+        if (!rand_generator_.get())
+            rand_generator_.reset(new std::mt19937(random_seed_));
+        return rand_generator_.get();
+    }
 
  private:
     unsigned int random_seed_;
+    unique_ptr<std::mt19937> rand_generator_;
 };
 
-static inline std::mt19937* rand_generator() {
-    return CPUContext::cpu_object_.rand_generator.get();
-}
-
 #define CPU_FP16_NOT_SUPPORTED \
     LOG(FATAL) << "FP16 is unsupported for CPUContext.";
 

Dragon/include/core/graph.h

@@ -26,11 +26,18 @@ class GraphBase {
         string op_type;
     };
 
-    GraphBase(const GraphDef& meta_graph, Workspace* ws);
+    GraphBase(
+        const GraphDef&         meta_graph,
+        Workspace*              ws);
     virtual ~GraphBase() {}
 
-    virtual bool Create(const GraphDef& optimized_graph, Workspace* ws) = 0;
-    virtual bool Run(const string& include, const string& exclude) = 0;
+    virtual bool Create(
+        const GraphDef&         optimized_graph,
+        Workspace*              ws) = 0;
+
+    virtual bool Run(
+        const string&           include,
+        const string&           exclude) = 0;
 
     inline string name() const { return name_; }
 
@@ -45,21 +52,31 @@ class Graph final : public GraphBase {
     Graph(const GraphDef& meta_graph, Workspace* ws);
     ~Graph() { for (auto* op : ops_) delete op; }
 
-    bool Create(const GraphDef& optimized_graph, Workspace* ws) override;
-    bool Run(const string& include, const string& exclude) override;
+    bool Create(
+        const GraphDef&         optimized_graph,
+        Workspace*              ws) override;
+
+    bool Run(
+        const string&           include,
+        const string&           exclude) override;
 
     GraphDef Prune(const GraphDef& meta_graph);
     GraphDef MakeUpdate(const GraphDef& meta_graph);
     GraphDef Share(const GraphDef& optimized_graph);
     void ShareGrads(GraphDef& optimized_graph);
 
-    void RecomputingAware(const GraphDef& optimized_graph, Workspace* ws);
+    void RecomputingAware(
+        const GraphDef&         optimized_graph,
+        Workspace*              ws);
 
     inline Workspace* ws() const { return ws_; }
 
  private:
     void ForwardShareDyeing(string u, string ancestor);
-    void ForwardPruneDyeing(string u, string leaf, vector<string> path);
+    void ForwardPruneDyeing(
+        string                  u,
+        string                  leaf,
+        vector<string>          path);
     void BackwardPruneDyeing(string v);
 
     vector<OperatorBase*> ops_;
@@ -69,8 +86,15 @@ class Graph final : public GraphBase {
     Set<string> targets_;
 };
 
-GraphBase* NewGraph(const GraphDef& meta_graph, Workspace* ws);
-DECLARE_REGISTRY(GraphRegistry, GraphBase, const GraphDef&, Workspace*);
+GraphBase* NewGraph(
+    const GraphDef&             meta_graph,
+    Workspace*                  ws);
+
+DECLARE_REGISTRY(
+    GraphRegistry,
+    GraphBase,
+    const GraphDef&,
+    Workspace*);
 
 }    // namespace dragon
 

Dragon/include/core/graph_gradient.h

@@ -18,24 +18,32 @@ namespace dragon {
 
 class GraphGradientMaker {
  public:
-    GraphGradientMaker() : cur_op_idx_(0) {}
+    GraphGradientMaker(): cur_op_idx_(0) {}
 
-    void Make(const GraphDef& forward_def,
+    void Make(
+        const GraphDef&         forward_def,
         const vector<string>&   targets,
         GraphDef&               new_def);
 
     void Share(const string& grads_prefix, GraphDef& graph);
 
-    inline void SetTerms(const Map<string, string>& terms) { terms_ = terms; }
-    inline void SetOperatorPrefix(const string& prefix) { op_prefix_ = prefix; }
-    inline void SetOperatorSuffix(const string& suffix) { op_suffix_ = suffix; }
-    inline void AddExternalGrad(const string& name) { external_grads_.insert(name); }
-    inline void AddIgnoreGrad(const string& name) { ignore_grads_.insert(name); }
+    inline void SetTerms(
+        const Map<string, string>& terms) { terms_ = terms; }
+    inline void SetOperatorPrefix(
+        const string& prefix) { op_prefix_ = prefix; }
+    inline void SetOperatorSuffix(
+        const string& suffix) { op_suffix_ = suffix; }
+    inline void AddExternalGrad(
+        const string& name) { external_grads_.insert(name); }
+    inline void AddIgnoreGrad(
+        const string& name) { ignore_grads_.insert(name); }
 
  private:
-    bool CheckGrad(const OperatorDef& forward_op,
+    bool CheckGrad(
+        const OperatorDef&              forward_op,
         const Set<string>&              targets,
         vector< pair<string, int> >&    gen_grads);
+
     string GetOperatorName();
 
     Map<string, string> terms_, inputs_to_grads_;

Dragon/include/core/mixedmem.h

@@ -19,7 +19,13 @@ namespace dragon {
 
 class MixedMemory {
  public:
-    enum State { UNINITIALIZED, STATE_AT_CPU, STATE_AT_CUDA, SWITCHED, SYNCED };
+    enum State {
+        UNINITIALIZED,
+        STATE_AT_CPU,
+        STATE_AT_CUDA,
+        SWITCHED,
+        SYNCED };
+
     MixedMemory() : cpu_ptr_(nullptr), cuda_ptr_(nullptr) {}
     MixedMemory(const TypeMeta& meta, const size_t nbytes)
         : meta_(meta), nbytes_(nbytes),
@@ -31,9 +37,6 @@ class MixedMemory {
     void* mutable_cpu_data();
     void* mutable_cuda_data();
     void set_cpu_data(void* cpu_ptr, size_t nbytes);
-#ifdef WITH_CUDA
-    void async_cuda_data(const cudaStream_t& stream);
-#endif
 
     void SwitchToDevice();
     void SwitchToCUDADevice(int device_id);

Dragon/include/core/operator.h

@@ -29,7 +29,7 @@ class Workspace;
 
 class OperatorBase {
  public:
-    OperatorBase(const OperatorDef& op_def, Workspace* ws);
+    OperatorBase(const OperatorDef& def, Workspace* ws);
     virtual ~OperatorBase() {}
 
     Tensor& Input(int idx);
@@ -38,66 +38,73 @@ class OperatorBase {
     inline size_t InputSize() { return inputs_.size(); }
     inline size_t OutputSize() { return outputs_.size(); }
 
-    void MutableOp(const OperatorDef& op_def);
+    void MutableOp(const OperatorDef& def);
     void MutableOp(const vector<string>& inputs,
                    const vector<string>& outputs,
                    const string& anchor);
 
-    inline void SwitchToPhase(const string& phase) { this->phase_ = phase; }
+    inline void SwitchToPhase(const string& phase) { phase_ = phase; }
     virtual void Run() { NOT_IMPLEMENTED; }
 
-    inline const string& name() const { return op_def_.name(); }
-    inline const string& type() const { return op_def_.type(); }
+    inline const string& name() const { return def_.name(); }
+    inline const string& type() const { return def_.type(); }
     inline const string& phase() const { return phase_; }
     inline const string& anchor() { return anchor_; }
     inline Workspace* ws() const { return ws_; }
 
     template <typename T>
-    T GetSingleArg(const string& name, const T& default_value);
+    T Arg(const string& name, const T& default_value);
 
     template <typename T>
-    vector<T> GetRepeatedArg(const string& name);
+    vector<T> Args(const string& name);
 
     inline const Map<std::string, const Argument*>& args() { return args_; }
     inline const Argument& arg(const string& name) { return *(args_[name]); }
 
     typedef Map<string, vector<OperatorBase*> > RecomputeMap;
     inline RecomputeMap& recompute_map() { return recompute_map_; }
-    void set_recompute_map(RecomputeMap recompute_map) { recompute_map_ = recompute_map; }
+    void set_recompute_map(RecomputeMap recompute_map) {
+        recompute_map_ = recompute_map; 
+    }
 
-    inline const OperatorDef& op_def() const { return op_def_; }
-    inline string DebugString() const { return op_def_.DebugString(); }
-    string DTypeHelper(const Tensor& tensor, const Set<string>& dtypes) const;
-    string DTypeHelper(const string& dtype, const Set<string>& dtypes) const;
+    inline const OperatorDef& def() const { return def_; }
+    inline string DebugString() const { return def_.DebugString(); }
+    string DTypeHelper(
+        const Tensor&           tensor,
+        const Set<string>&      dtypes) const;
+    string DTypeHelper(
+        const string&           dtype,
+        const Set<string>&      dtypes) const;
 
  protected:
     string phase_, anchor_;
     Map<std::string, const Argument*> args_;
     Map<string, vector<OperatorBase*> > recompute_map_;
     vector<Tensor*> inputs_, outputs_;
-    OperatorDef op_def_;
+    OperatorDef def_;
     Workspace* ws_;
 };
 
 template <class Context>
 class Operator : public OperatorBase {
  public:
-    Operator(const OperatorDef& op_def, Workspace* ws)
-        : OperatorBase(op_def, ws), ctx_(op_def.device_option()),
-          do_synchronize_(Operator::GetSingleArg<bool>("do_synchronize", false)),
-          recomputing_aware_(Operator::GetSingleArg<bool>("recomputing_aware", false)) {
+    Operator(const OperatorDef& def, Workspace* ws)
+        : OperatorBase(def, ws), ctx_(def.device_option()),
+          recomputing_aware_(OperatorBase::Arg<bool>(
+              "recomputing_aware", false)) {
         allow_run_ = true;
         allow_run_ &= _MPICheck();
-        allow_run_ &= (!(OutputSize() == 1 && Output(0)->name() == "ignore"));
+        allow_run_ &= (!(OutputSize() == 1 &&
+            Output(0)->name() == "ignore"));
     }
 
     virtual void Run() final {
         if (!allow_run_)  return;
         if (recomputing_aware_) MakeResource();
-        ctx_.SwitchToDevice();
+        ctx().SwitchToDevice();
         MemorySwitch();
         RunOnDevice();
-        if (do_synchronize_) ctx_.FinishDeviceCompution();
+        ctx().FinishDeviceCompution();
         if (recomputing_aware_) CleanResource();
     }
 
@@ -106,8 +113,10 @@ class Operator : public OperatorBase {
     virtual void CleanResource();
 
     void MemorySwitch() {
-        for (auto* I : inputs_) if(I->name() != "ignore") I->SwitchToDevice();
-        for (auto* O : outputs_) if(O->name() != "ignore") O->SwitchToDevice();
+        for (auto* I : inputs_)
+            if(I->name() != "ignore") I->SwitchToDevice();
+        for (auto* O : outputs_) 
+            if(O->name() != "ignore") O->SwitchToDevice();
     }
 
     virtual void RunOnDevice() = 0;
@@ -117,14 +126,15 @@ class Operator : public OperatorBase {
 
  protected:
     Context ctx_;
-    bool allow_run_, recomputing_aware_, do_synchronize_;
+    bool allow_run_, recomputing_aware_;
 
  private:
     bool _MPICheck() {
 #ifndef WITH_MPI
         return true;
 #else
-        vector<int> allow_ranks = Operator::GetRepeatedArg<int>("mpi_ranks");
+        vector<int> allow_ranks =
+            OperatorBase::Args<int>("mpi_ranks");
         if (allow_ranks.empty()) return true;
         int cur_rank;
         MPI_Comm_rank(MPI_COMM_WORLD, &cur_rank);
@@ -135,11 +145,11 @@ class Operator : public OperatorBase {
     }
 };
 
-OperatorBase* CreateOperator(const OperatorDef& op_def, Workspace* ws);
+OperatorBase* CreateOperator(const OperatorDef& def, Workspace* ws);
 
 #define USE_SIMPLE_CTOR_DTOR(name) \
-    name(const OperatorDef& op_def, Workspace* ws) \
-        : Operator<Context>(op_def, ws) {} \
+    name(const OperatorDef& def, Workspace* ws) \
+        : Operator<Context>(def, ws) {} \
     virtual ~name() {}
 
 #define USE_OPERATOR_BASE_FUNCTIONS \
@@ -150,7 +160,7 @@ OperatorBase* CreateOperator(const OperatorDef& op_def, Workspace* ws);
     using OperatorBase::type; \
     using OperatorBase::phase; \
     using OperatorBase::anchor; \
-    using OperatorBase::op_def; \
+    using OperatorBase::def; \
     using OperatorBase::InputSize; \
     using OperatorBase::OutputSize; \
     using OperatorBase::DebugString; \
@@ -162,9 +172,23 @@ OperatorBase* CreateOperator(const OperatorDef& op_def, Workspace* ws);
     using Operator<Context>::ctx; \
     using Operator<Context>::AllowRun
 
-DECLARE_REGISTRY(CPUOperatorRegistry, OperatorBase,const OperatorDef&, Workspace*);
-DECLARE_REGISTRY(CUDAOperatorRegistry, OperatorBase, const OperatorDef&, Workspace*);
-DECLARE_REGISTRY(CUDNNOperatorRegistry, OperatorBase, const OperatorDef&, Workspace*);
+DECLARE_REGISTRY(
+    CPUOperatorRegistry,
+    OperatorBase,
+    const OperatorDef&,
+    Workspace*);
+
+DECLARE_REGISTRY(
+    CUDAOperatorRegistry,
+    OperatorBase,
+    const OperatorDef&,
+    Workspace*);
+
+DECLARE_REGISTRY(
+    CUDNNOperatorRegistry,
+    OperatorBase,
+    const OperatorDef&,
+    Workspace*);
 
 #define TENSOR_FILL(tensor, shape) \
     if (tensor.count() == 0) { \
@@ -174,7 +198,7 @@ DECLARE_REGISTRY(CUDNNOperatorRegistry, OperatorBase, const OperatorDef&, Worksp
         tensor.Reshape(shape); \
         unique_ptr< Filler<T, Context> > filler(  \
             CreateFiller<T, Context>(*ws()->GetFiller(tensor.name()))); \
-        filler->Fill(&tensor); \
+        filler->Fill(&tensor, &ctx()); \
     } else { \
          TIndex count = 1; \
          for(int i = 0; i < shape.size(); i++) count *= shape[i]; \
@@ -189,7 +213,7 @@ DECLARE_REGISTRY(CUDNNOperatorRegistry, OperatorBase, const OperatorDef&, Worksp
 #define INIT_MULTIPLIER(ptr_tensor, size) { \
     ptr_tensor = ws()->CreateTensor("/share/multiplier"); \
     if (size > ptr_tensor->count()) { \
-        ptr_tensor->Reshape(vector<TIndex>(1, size)); \
+        ptr_tensor->Reshape({ size }); \
         math::Set<T, Context>(size, dragon_cast<T, float>(1.f), \
             ptr_tensor->template mutable_data<T, Context>()); \
     } \
@@ -214,12 +238,12 @@ DECLARE_REGISTRY(CUDNNOperatorRegistry, OperatorBase, const OperatorDef&, Worksp
     type argument(int idx)
 
 #define GET_ARGUMENT_WITH_DESC(type, argument, default_value) \
-    argument##_value = OperatorBase::GetSingleArg<type>(#argument, default_value); \
-    argument##_desc = OperatorBase::GetSingleArg<string>(string(#argument) + "_desc", "")
+    argument##_value = OperatorBase::Arg<type>(#argument, default_value); \
+    argument##_desc = OperatorBase::Arg<string>(string(#argument) + "_desc", "")
 
 #define GET_ARGUMENTS_WITH_DESC(type, argument) \
-    argument##_value = OperatorBase::GetRepeatedArg<type>(#argument); \
-    argument##_desc = OperatorBase::GetRepeatedArg<string>(string(#argument) + "_desc")
+    argument##_value = OperatorBase::Args<type>(#argument); \
+    argument##_desc = OperatorBase::Args<string>(string(#argument) + "_desc")
 
 #define DEFINE_ARGUMENT_WITH_DESC(type, classname, argument) \
     template <class Context> \

Dragon/include/core/operator_gradient.h

@@ -23,7 +23,8 @@ struct Gradient {
     vector<OperatorDef> ops;
     vector<string> g_inputs;
     vector<float> defaults;
-    Gradient(const vector<OperatorDef>& ops,
+    Gradient(
+        const vector<OperatorDef>&   ops,
         const vector<string>&        g_inputs,
         const vector<float>&         defaults)
         : ops(ops), g_inputs(g_inputs), defaults(defaults) {}
@@ -31,9 +32,11 @@ struct Gradient {
 
 class GradientMakerBase {
  public:
-    GradientMakerBase(const OperatorDef& def,
+    GradientMakerBase(
+        const OperatorDef&          def,
         const vector<string>&       g_outputs)
-        : def(def), g_outputs_(g_outputs), g_inputs_(def.input_size()) {}
+        : def(def), g_outputs_(g_outputs),
+          g_inputs_(def.input_size()) {}
     virtual ~GradientMakerBase() {}
 
     inline virtual bool CopyDeviceOption() const { return true; }
@@ -80,7 +83,9 @@ class GradientMakerBase {
 };
 
 //  implemented in operator.cc
-Gradient MakeGradientForOp(const OperatorDef& op_def, const vector<string>& g_outputs);
+Gradient MakeGradientForOp(
+    const OperatorDef&              op_def,
+    const vector<string>&           g_outputs);
 
 # define GRADIENT_MAKER_CTOR(name) \
     name(const OperatorDef& def, const vector<string>& g_output) \
@@ -94,12 +99,14 @@ class NoGradient : public GradientMakerBase {
     }
 };
 
-DECLARE_REGISTRY(GradientRegistry,
+DECLARE_REGISTRY(
+    GradientRegistry,
     GradientMakerBase,
     const OperatorDef&,
     const vector<string>&);
 
-DECLARE_REGISTRY(NoGradientRegistry,
+DECLARE_REGISTRY(
+    NoGradientRegistry,
     GradientMakerBase,
     const OperatorDef&,
     const vector<string>&);

Dragon/include/core/operator_schema.h

@@ -22,13 +22,24 @@ namespace dragon {
 class OpSchema {
  public:
     OpSchema()
-        : op_type_("unknown"), file_("unknown"), line_(0) { Init(); }
-    OpSchema(const string& op_type, const string& file, const int line)
-        : op_type_(op_type), file_(file), line_(line) { Init(); }
+        : op_type_("unknown"), file_("unknown"), line_(0) {
+        Init();
+    }
+
+    OpSchema(
+        const string&               op_type,
+        const string&               file, 
+        const int                   line)
+        : op_type_(op_type), file_(file), line_(line) {
+        Init();
+    }
 
     bool Verify(const OperatorDef& def) const;
 
-    inline OpSchema& IgnoreVerify() { ignore_verify_ = true; return *this; }
+    inline OpSchema& IgnoreVerify() {
+        ignore_verify_ = true; 
+        return *this; 
+    }
 
     OpSchema& Inplace(set<pair<int, int> > inplace);
     std::function<bool(int, int)> CheckInplace;
@@ -56,20 +67,26 @@ class OpSchema {
 
 class OpSchemaRegistry {
  public:
-    static OpSchema& NewSchema(const string& op_type, const string& file, const int line) {
+    static OpSchema& NewSchema(
+        const string&               op_type,
+        const string&               file,
+        const int                   line) {
         auto& m = schema_map();
         CHECK(!m.count(op_type))
-            << "\nOpSchema(" << op_type << ") has registered before."
+            << "\nOpSchema(" << op_type
+            << ") has registered before."
             << "\nat file: " << file
             << "\n   line: " << line;
-        m.emplace(std::make_pair(op_type, OpSchema(op_type, file, line)));
+        m.emplace(std::make_pair(op_type,
+            OpSchema(op_type, file, line)));
         return m[op_type];
     }
 
     static const OpSchema* Schema(const string& op_type) {
         auto& m = schema_map();
         if (m.count(op_type)) return &m[op_type];
-        else LOG(FATAL) << "OpSchema(" << op_type << ") has not registered yet.";
+        else LOG(FATAL) << "OpSchema(" << op_type
+                        << ") has not registered yet.";
         return nullptr;
     }
 

Dragon/include/core/registry.h

@@ -20,35 +20,50 @@ namespace dragon {
 
 template <class SrcType, class ObjType, class... Args>
 class Registry {
-public:
+ public:
     typedef std::function<ObjType*(Args ...)> Creator;
     void Register(const SrcType& key, Creator creator) {
-        CHECK(!registry_.count(key)) << "\nKey(" << key  << ") has already registered.";
+        CHECK(!registry_.count(key))
+            << "\nKey(" << key  << ") has already registered.";
         registry_[key] = creator;
     }
+
     ObjType* Create(const SrcType& key, Args ... args) {
-        CHECK(registry_.count(key)) << "\nKey(" << key << ") has not registered yet.";
+        CHECK(registry_.count(key))
+            << "\nKey(" << key << ") has not registered yet.";
         return registry_[key](args...);
     }
-    bool Has(const SrcType& key) { return (registry_.count(key)) != 0; }
+
+    bool Has(const SrcType& key) {
+        return (registry_.count(key)) != 0; 
+    }
+
     vector<SrcType> keys() {
         vector<SrcType> ret;
-        for (const auto& it : registry_) ret.push_back(it.first);
+        for (const auto& it : registry_)
+            ret.push_back(it.first);
         return ret;
     }
-private:
+
+ private:
     Map<SrcType, Creator> registry_;
 };
 
 template <class SrcType, class ObjType, class... Args>
 class Registerer {
-public:
-    Registerer(const SrcType& key, Registry<SrcType, ObjType, Args...>* registry,
-        typename Registry<SrcType, ObjType, Args...>::Creator creator, const string& help_msg = "") {
+ public:
+    Registerer(
+        const SrcType& key,
+        Registry<SrcType, ObjType, Args...>* registry,
+        typename Registry<SrcType, ObjType, Args...>::Creator creator,
+        const string& help_msg = "") {
         registry->Register(key, creator);
     }
+
     template <class DerivedType>
-    static ObjType* defaultCreator(Args ... args) {return new DerivedType(args...);}
+    static ObjType* defaultCreator(Args ... args) {
+        return new DerivedType(args...);
+    }
 };
 
 //  use in *.h files

Dragon/include/core/tensor.h

@@ -74,10 +74,15 @@ class Tensor {
         return ret;
     }
     inline TIndex count() const { return size_; }
-    inline TIndex count(const TIndex start) const { return count(start, ndim()); }
+    inline TIndex count(const TIndex start) const {
+        return count(start, ndim());
+    }
 
-    inline TIndex offset(const TIndex n, const TIndex c = 0,
-                         const TIndex h = 0, const TIndex w = 0) {
+    inline TIndex offset(
+        const TIndex            n,
+        const TIndex            c = 0,
+        const TIndex            h = 0,
+        const TIndex            w = 0) {
         CHECK_LE(n, dim(0));
         CHECK_LE(c, dim(1));
         CHECK_LE(h, dim(2));
@@ -95,7 +100,7 @@ class Tensor {
         return offset;
     }
 
-    inline string dim_string() const {
+    inline string DimString() const {
         if (ndim() == 0) return "(0,)";
         std::stringstream ss;
         ss << "(";
@@ -108,9 +113,18 @@ class Tensor {
     inline bool is_corrupted() const { return is_corrupted_; }
     inline void Corrupt() { is_corrupted_ = true; }
 
-    inline bool has_memory() const { return memory_ || ex_memory_ != nullptr; }
-    MixedMemory* memory() const { return own_mem_ ? memory_.get() : ex_memory_; }
-    void set_memory(MixedMemory* mem) { memory_.reset(mem); capacity_ = mem->nbytes(); }
+    inline bool has_memory() const {
+        return memory_ || ex_memory_ != nullptr; 
+    }
+
+    MixedMemory* memory() const {
+        return own_mem_ ? memory_.get() : ex_memory_;
+    }
+
+    void set_memory(MixedMemory* mem) { 
+        memory_.reset(mem); capacity_ = mem->nbytes();
+    }
+
     MixedMemory::State memory_state() const {
         MixedMemory* mem = memory();
         CHECK(mem) << "\nMemory access before allowcating.";
@@ -124,7 +138,8 @@ class Tensor {
 
     const TypeMeta& meta() const { return meta_; }
     void SetMeta(const TypeMeta& meta) { meta_ = meta; }
-    template <typename T> inline bool IsType() { return meta_.Match<T>(); }
+    template <typename T>
+    inline bool IsType() { return meta_.Match<T>(); }
 
     template <class Context>
     void mutable_data_ptr(void** data_ptr) {
@@ -132,12 +147,15 @@ class Tensor {
         if (!mem) {
             *data_ptr = nullptr;
         } else {
-            if (TypeMeta::Id<Context>() == TypeMeta::Id<CPUContext>()) {
+            if (TypeMeta::Id<Context>() ==
+                    TypeMeta::Id<CPUContext>()) {
                 *data_ptr = mem->mutable_cpu_data();
-            } else if (TypeMeta::Id<Context>() == TypeMeta::Id<CUDAContext>()) {
+            } else if (TypeMeta::Id<Context>() ==
+                    TypeMeta::Id<CUDAContext>()) {
                 *data_ptr = mem->mutable_cuda_data();
             } else {
-                LOG(FATAL) << "Unknown memory type. Only CPU or CUDA is supported.";
+                LOG(FATAL) << "Unknown memory type. "
+                           << "Only CPU or CUDA is supported.";
             }
         }
     }
@@ -146,12 +164,15 @@ class Tensor {
     const void* const_data_ptr() const {
         MixedMemory* mem = memory();
         CHECK(mem) << "\nMemory access before allowcating.";
-        if (TypeMeta::Id<Context>() == TypeMeta::Id<CPUContext>()) {
+        if (TypeMeta::Id<Context>() ==
+                TypeMeta::Id<CPUContext>()) {
              return mem->cpu_data();
-        } else if (TypeMeta::Id<Context>() == TypeMeta::Id<CUDAContext>()) {
+        } else if (TypeMeta::Id<Context>() ==
+                TypeMeta::Id<CUDAContext>()) {
              return mem->cuda_data();
         } else {
-             LOG(FATAL) << "Unknown memory type. Only CPU or CUDA are supported.";
+             LOG(FATAL) << "Unknown memory type. "
+                        << "Only CPU or CUDA are supported.";
              return nullptr;
         }
     }
@@ -164,10 +185,17 @@ class Tensor {
         if (meta_ != meta && data_ptr && !own_mem_) delete ex_memory_;
         meta_ = meta;
         CHECK_GT(size_, 0);
-        if (own_mem_) memory_.reset(new MixedMemory(meta, size_* meta_.itemsize()));
-        else ex_memory_ = new MixedMemory(meta, size_* meta_.itemsize());
-        mutable_data_ptr<Context>(&data_ptr);    //  malloc memory
-        if (meta.ctor()) meta_.ctor()(data_ptr, size_);  //  call the constructor
+        if (own_mem_) {
+            memory_.reset(new MixedMemory(
+                meta, size_* meta_.itemsize()));
+        } else {
+            ex_memory_ = new MixedMemory(
+                meta, size_* meta_.itemsize());
+        }
+        //  malloc memory
+        mutable_data_ptr<Context>(&data_ptr);
+        //  call the constructors
+        if (meta.ctor()) meta_.ctor()(data_ptr, size_);
         capacity_ = size_ * meta.itemsize();
         return data_ptr;
     }
@@ -189,8 +217,10 @@ class Tensor {
     T* mutable_data() {
         void* data_ptr;
         mutable_data_ptr<Context>(&data_ptr);
-        if (data_ptr && meta_ == TypeMeta::Make<T>()) return static_cast<T*>(data_ptr);
-        return static_cast<T*>(raw_mutable_data<Context>(TypeMeta::Make<T>()));
+        if (data_ptr && meta_ == TypeMeta::Make<T>())
+            return static_cast<T*>(data_ptr);
+        return static_cast<T*>(
+            raw_mutable_data<Context>(TypeMeta::Make<T>()));
     }
 
     template <typename T, class Context>
@@ -208,9 +238,11 @@ class Tensor {
         auto* src = other.template raw_data<SrcCTX>();
         auto* dst = raw_mutable_data<DstCTX>(other.meta_);
         if (dst == src) return;
-        if (TypeMeta::Id<DstCTX>() == TypeMeta::Id<CPUContext>()) {
+        if (TypeMeta::Id<DstCTX>() ==
+                TypeMeta::Id<CPUContext>()) {
             CPUContext::Memcpy<DstCTX, SrcCTX>(nbytes(), dst, src);
-        } else if (TypeMeta::Id<DstCTX>() == TypeMeta::Id<CUDAContext>()) {
+        } else if (TypeMeta::Id<DstCTX>() == 
+                TypeMeta::Id<CUDAContext>()) {
             CUDAContext::Memcpy<DstCTX, SrcCTX>(nbytes(), dst, src);
         }
     }

Dragon/include/core/typeid.h

@@ -82,38 +82,47 @@ class TypeMeta {
     template <typename T>
     static void Ctor(void* ptr, size_t n) {
         T* typed_ptr = static_cast<T*>(ptr);
-        for (unsigned int i = 0; i < n; i++) new(typed_ptr + i) T;
+        for (unsigned int i = 0; i < n; i++)
+            new(typed_ptr + i) T;
     }
 
     template <typename T>
     static void Copy(const void* src, void* dst, size_t n) {
         const T* typed_src = static_cast<const T*>(src);
         T* typed_dst = static_cast<T*>(dst);
-        for (unsigned int i = 0; i < n; i++) typed_dst[i] = typed_src[i];
+        for (unsigned int i = 0; i < n; i++)
+            typed_dst[i] = typed_src[i];
     }
 
     template <typename T>
     static void Dtor(void* ptr, size_t n) {
         T* typed_ptr = static_cast<T*>(ptr);
-        for (unsigned int i = 0; i < n; i++) typed_ptr[i].~T();
+        for (unsigned int i = 0; i < n; i++)
+            typed_ptr[i].~T();
     }
 
 #define FundMeta std::enable_if<std::is_fundamental<T>::value,TypeMeta>::type
+#define StructMeta std::enable_if<!std::is_fundamental<T>::value && std::is_copy_assignable<T>::value, TypeMeta>::type 
+
     template <typename T>
     static typename FundMeta Make() {
-        return TypeMeta(Id<T>(), Itemsize<T>(), nullptr, nullptr, nullptr);
+        return TypeMeta(Id<T>(), Itemsize<T>(),
+            nullptr, nullptr, nullptr);
     }
 
-#define StructMeta std::enable_if<!std::is_fundamental<T>::value && std::is_copy_assignable<T>::value, TypeMeta>::type 
-
     template<typename T>
     static typename StructMeta Make() {
-        return TypeMeta(Id<T>(), Itemsize<T>(), Ctor<T>, Copy<T>, Dtor<T>);
+        return TypeMeta(Id<T>(), Itemsize<T>(),
+            Ctor<T>, Copy<T>, Dtor<T>);
     }
 
  private:
-    TypeMeta(TypeId id, size_t itemsize, 
-             PlacementNew ctor, TypedCopy copy, TypedDestructor dtor) 
+    TypeMeta(
+        TypeId              id,
+        size_t              itemsize,
+        PlacementNew        ctor,
+        TypedCopy           copy,
+        TypedDestructor     dtor)
         : id_(id), itemsize_(itemsize),
           ctor_(ctor), copy_(copy), dtor_(dtor) {}
 

Dragon/include/core/types.h

@@ -40,8 +40,10 @@ typedef struct {
 
 #endif
 
-inline const TypeMeta& TypeStringToMeta(const std::string& str_type) {
-    static std::unordered_map<std::string, TypeMeta> s2m_type_map {
+inline const TypeMeta& TypeStringToMeta(
+    const std::string&              str_type) {
+    static std::unordered_map<std::string, TypeMeta>
+        s2m_type_map {
             { "float32", TypeMeta::Make<float>() },
             { "int32", TypeMeta::Make<int>() },
             { "int64", TypeMeta::Make<int64_t>() },
@@ -50,11 +52,14 @@ inline const TypeMeta& TypeStringToMeta(const std::string& str_type) {
             { "uint8", TypeMeta::Make<uint8_t>() } 
     };
     static TypeMeta unknown_type;
-    return s2m_type_map.count(str_type) ? s2m_type_map[str_type] : unknown_type;
+    return s2m_type_map.count(str_type) ?
+        s2m_type_map[str_type] : unknown_type;
 }
 
-inline const std::string TypeMetaToString(const TypeMeta& meta) {
-    static std::unordered_map<TypeId, std::string> m2s_type_map {
+inline const std::string TypeMetaToString(
+    const TypeMeta&                 meta) {
+    static std::unordered_map<TypeId, std::string>
+        m2s_type_map {
             { TypeMeta::Id<float>(), "float32" },
             { TypeMeta::Id<int>(), "int32" },
             { TypeMeta::Id<int64_t>(), "int64" },
@@ -62,7 +67,8 @@ inline const std::string TypeMetaToString(const TypeMeta& meta) {
             { TypeMeta::Id<float16>(), "float16" },
             { TypeMeta::Id<uint8_t>(), "uint8" } 
     };
-    return m2s_type_map.count(meta.id()) ? m2s_type_map[meta.id()] : "unknown";
+    return m2s_type_map.count(meta.id()) ?
+        m2s_type_map[meta.id()] : "unknown";
 }
 
 }    // namespace dragon

Dragon/include/core/workspace.h

@@ -39,13 +39,16 @@ class Workspace {
 
     inline void InitWorkspace() {
         CreateTensor("ignore");
-        Tensor* head = CreateTensor("/opt/mirror_stage/head");
-        head->Reshape(vector<TIndex>(1, WORKSPACE_MAX_CORRUPTED_SIZE));
-        Tensor* recompute_flag = CreateTensor("/opt/mirror_stage/recompute_flag");
-        recompute_flag->Reshape(vector<TIndex>(1, 1));
+        Tensor* head = CreateTensor(
+            "/opt/mirror_stage/head");
+        head->Reshape({ WORKSPACE_MAX_CORRUPTED_SIZE });
+        Tensor* recompute_flag = CreateTensor(
+            "/opt/mirror_stage/recompute_flag");
+        recompute_flag->Reshape({ 1 });
         recompute_flag->mutable_data<bool, CPUContext>()[0] = false;
         for (int i = 0; i < WORKSPACE_MAX_CORRUPTED_SIZE; i++) {
-            string name = "/opt/mirror_stage/buffer_" + dragon_cast<string, int>(i);
+            string name = "/opt/mirror_stage/buffer_" +
+                dragon_cast<string, int>(i);
             Tensor* buffer = CreateTensor(name);
             head->mutable_data<string, CPUContext>()[i] = "";
         }
@@ -72,7 +75,9 @@ class Workspace {
         } else { return name; }
     }
 
-    inline Tensor* TryGetTensor(const string& name, bool use_remote=true) {
+    inline Tensor* TryGetTensor(
+        const string&           name,
+        bool                    use_remote = true) {
         string query = GetTensorName(name);
         //  search local workspace
         if (tensor_map_.count(query) > 0)
@@ -87,7 +92,9 @@ class Workspace {
         return nullptr;
     }
 
-    inline bool HasTensor(const string& name, bool use_remote=true) {
+    inline bool HasTensor(
+        const string&           name,
+        bool                    use_remote = true) {
         return TryGetTensor(name, use_remote) ? true : false;
     }
 
@@ -100,7 +107,9 @@ class Workspace {
         return tensor;
     }
 
-    inline Tensor* GetTensor(const string& name, bool use_remote=true) {
+    inline Tensor* GetTensor(
+        const string&           name,
+        bool                    use_remote = true) {
         Tensor* tensor = TryGetTensor(name, use_remote);
         CHECK(tensor) << "\nTensor(" << name << ") does not exist "
                       << "in current workspace or sub-workspace.";
@@ -122,14 +131,17 @@ class Workspace {
         //  serach remote workspace
         for (auto& it : workspace_map_) {
             vector<string> sub_names = it.second->GetTensors();
-            names.insert(names.end(), sub_names.begin(), sub_names.end());
+            names.insert(names.end(),
+                sub_names.begin(), sub_names.end());
         }
         return names;
     }
 
     /******************** Filler ********************/
 
-    inline bool HasFiller(const string& name, bool use_remote=true) {
+    inline bool HasFiller(
+        const string&           name,
+        bool                    use_remote = true) {
         //  search local workspace
         bool result = filler_map_.count(name) > 0;
         if (!use_remote) return result;
@@ -140,14 +152,16 @@ class Workspace {
         return result;
     }
 
-    inline void CreateFiller(const TensorFiller filler) {
+    inline void CreateFiller(
+        const TensorFiller      filler) {
         CHECK_GT(filler.tensor().size(), 0)
             << "Tensor without a valid name can not be filled.";
         if (HasFiller(filler.tensor())) return;
         filler_map_[filler.tensor()] = filler;
     }
 
-    inline const TensorFiller* GetFiller(const string& name) {
+    inline const TensorFiller* GetFiller(
+        const string&           name) {
         //  search local workspace
         if (filler_map_.count(name) > 0)
             return &filler_map_[name];
@@ -163,11 +177,12 @@ class Workspace {
     /******************** Cache ********************/
 
     template <class Context>
-    inline vector<void*> caches(const vector<size_t>& segments) {
+    inline vector<void*> caches(
+        const vector<size_t>&   segments) {
         TIndex total_size = 0;
         for (auto& segment : segments) total_size += (TIndex)segment;
         Tensor* cacheT = CreateTensor("/share/cache");
-        cacheT->Reshape(vector<TIndex>(1, total_size));
+        cacheT->Reshape({ total_size });
         vector<void*> caches(segments.size());
         caches[0] = cacheT->template mutable_data<uint8_t, Context>();
         for (int i = 1; i < segments.size(); i++)
@@ -176,11 +191,12 @@ class Workspace {
     }
 
     template <typename T, class Context>
-    inline vector<T*> caches(const vector<TIndex>& segments) {
+    inline vector<T*> caches(
+        const vector<TIndex>&   segments) {
         TIndex total_count = 0;
         for (auto& segment : segments) total_count += segment;
         Tensor* cacheT = CreateTensor("/share/cache");
-        cacheT->Reshape(vector<TIndex>(1, total_count));
+        cacheT->Reshape({ total_count });
         vector<T*> caches(segments.size());
         caches[0] = cacheT->template mutable_data<T, Context>();
         for (int i = 1; i < segments.size(); i++)
@@ -190,12 +206,14 @@ class Workspace {
 
     /******************** Operator ********************/
 
-    inline void CreatePersistentOp(const OperatorDef& meta_op) {
+    inline void CreatePersistentOp(
+        const OperatorDef& meta_op) {
         string persistent_key;
         for (auto& arg : meta_op.arg())
             if (arg.name() == "persistent_key")
                 persistent_key = arg.s();
-        CHECK(persistent_key.size() > 0) << "\nGot empty persistent key.";
+        CHECK(persistent_key.size() > 0)
+            << "\nGot empty persistent key.";
         if (!op_map_.count(persistent_key)) {
             for (auto& input : meta_op.input()) CreateTensor(input);
             op_map_[persistent_key] = unique_ptr<OperatorBase>(
@@ -203,7 +221,9 @@ class Workspace {
         }
     }
 
-    inline void RunPersistentOp(const string& key, const string& anchor,
+    inline void RunPersistentOp(
+        const string&           key,
+        const string&           anchor,
         const vector<string>&   inputs,
         const vector<string>&   outputs) {
         CHECK(op_map_.count(key) > 0)
@@ -236,11 +256,13 @@ class Workspace {
 
     GraphBase* CreateGraph(const GraphDef& meta_graph);
 
-    void RunGraph(const string& graph_name,
+    void RunGraph(
+        const string&           graph_name,
         const string&           include,
         const string&           exclude) {
         if (!graph_map_.count(graph_name))
-            LOG(FATAL) << "Graph(" << graph_name << ") does not exist.";
+            LOG(FATAL) << "Graph(" << graph_name
+                       << ") does not exist.";
         graph_map_[graph_name]->Run(include, exclude);
     }
 
@@ -252,7 +274,8 @@ class Workspace {
 
     /******************** Utility ********************/
 
-    inline void CreateRename(const string& old_tensor,
+    inline void CreateRename(
+        const string&           old_tensor,
         const string&           new_tensor) {
         rename_map_[old_tensor] = new_tensor;
     }

Dragon/include/operators/activation/dropout_op.h

@@ -20,11 +20,11 @@ namespace dragon {
 template <class Context>
 class DropoutOp final : public Operator<Context> {
  public:
-    DropoutOp(const OperatorDef& op_def, Workspace* ws)
-        : Operator<Context>(op_def, ws),
-          use_scale(OperatorBase::GetSingleArg<bool>("scale", true)) {
+    DropoutOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws),
+          use_scale(OperatorBase::Arg<bool>("scale", true)) {
         GET_ARGUMENT_WITH_DESC(float, prob, 0.5);
-        SwitchToPhase(OperatorBase::GetSingleArg<string>("phase", ""));
+        SwitchToPhase(OperatorBase::Arg<string>("phase", ""));
     }
     USE_OPERATOR_FUNCTIONS;
 
@@ -39,11 +39,11 @@ class DropoutOp final : public Operator<Context> {
 template <class Context>
 class DropoutGradientOp final : public Operator<Context> {
  public:
-    DropoutGradientOp(const OperatorDef& op_def, Workspace* ws)
-        : Operator<Context>(op_def, ws),
-          use_scale(OperatorBase::GetSingleArg<bool>("scale", true)) {
+    DropoutGradientOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws),
+          use_scale(OperatorBase::Arg<bool>("scale", true)) {
         GET_ARGUMENT_WITH_DESC(float, prob, 0.5);
-        SwitchToPhase(OperatorBase::GetSingleArg<string>("phase", ""));
+        SwitchToPhase(OperatorBase::Arg<string>("phase", ""));
     }
     USE_OPERATOR_FUNCTIONS;
 
@@ -66,12 +66,12 @@ DEFINE_ARGUMENT_WITH_DESC(float, DropoutGradientOp, prob);
 template <class Context>
 class CuDNNDropoutOp final : public Operator<Context> {
 public:
-    CuDNNDropoutOp(const OperatorDef& op_def, Workspace* ws)
-        : Operator<Context>(op_def, ws), states_initialized(false),
-        use_scale(OperatorBase::GetSingleArg<bool>("scale", true)),
-        random_seed(op_def.device_option().random_seed()) {
+    CuDNNDropoutOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws), states_initialized(false),
+        use_scale(OperatorBase::Arg<bool>("scale", true)),
+        random_seed(DEFAULT_RNG_SEED) {
         GET_ARGUMENT_WITH_DESC(float, prob, 0.5);
-        SwitchToPhase(OperatorBase::GetSingleArg<string>("phase", ""));
+        SwitchToPhase(OperatorBase::Arg<string>("phase", ""));
         CUDNN_CHECK(cudnnCreateTensorDescriptor(&input_desc));
         CUDNN_CHECK(cudnnCreateDropoutDescriptor(&dropout_desc));
     }
@@ -97,12 +97,12 @@ public:
 template <class Context>
 class CuDNNDropoutGradientOp final : public Operator<Context> {
 public:
-    CuDNNDropoutGradientOp(const OperatorDef& op_def, Workspace* ws)
-        : Operator<Context>(op_def, ws), states_initialized(false),
-        use_scale(OperatorBase::GetSingleArg<bool>("scale", true)),
-        random_seed(op_def.device_option().random_seed()) {
+    CuDNNDropoutGradientOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws), states_initialized(false),
+        use_scale(OperatorBase::Arg<bool>("scale", true)),
+        random_seed(DEFAULT_RNG_SEED) {
         GET_ARGUMENT_WITH_DESC(float, prob, 0.5);
-        SwitchToPhase(OperatorBase::GetSingleArg<string>("phase", ""));
+        SwitchToPhase(OperatorBase::Arg<string>("phase", ""));
         CUDNN_CHECK(cudnnCreateTensorDescriptor(&input_desc));
         CUDNN_CHECK(cudnnCreateDropoutDescriptor(&dropout_desc));
     }

Dragon/include/operators/activation/elu_op.h

@@ -19,9 +19,9 @@ namespace dragon {
 template <class Context>
 class EluOp : public Operator<Context> {
  public:
-    EluOp(const OperatorDef& op_def, Workspace* ws)
-        : Operator<Context>(op_def, ws),
-          alpha(OperatorBase::GetSingleArg<float>("alpha", 1.0)) {}
+    EluOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws),
+          alpha(OperatorBase::Arg<float>("alpha", 1.0)) {}
     USE_OPERATOR_FUNCTIONS;
 
     void RunOnDevice() override;
@@ -34,9 +34,9 @@ class EluOp : public Operator<Context> {
 template <class Context>
 class EluGradientOp : public Operator<Context> {
  public:
-    EluGradientOp(const OperatorDef& op_def, Workspace* ws)
-        : Operator<Context>(op_def, ws),
-          alpha(OperatorBase::GetSingleArg<float>("alpha", 1.0)) {}
+    EluGradientOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws),
+          alpha(OperatorBase::Arg<float>("alpha", 1.0)) {}
     USE_OPERATOR_FUNCTIONS;
 
     void RunOnDevice() override;
@@ -53,8 +53,8 @@ class EluGradientOp : public Operator<Context> {
 template <class Context>
 class CuDNNEluOp final : public EluOp<Context> {
 public:
-    CuDNNEluOp(const OperatorDef& op_def, Workspace* ws) 
-        : EluOp<Context>(op_def, ws) {
+    CuDNNEluOp(const OperatorDef& def, Workspace* ws)
+        : EluOp<Context>(def, ws) {
         CUDNN_CHECK(cudnnCreateTensorDescriptor(&input_desc));
         CUDNN_CHECK(cudnnCreateTensorDescriptor(&output_desc));
         CUDNN_CHECK(cudnnCreateActivationDescriptor(&act_desc));
@@ -80,8 +80,8 @@ public:
 template <class Context>
 class CuDNNEluGradientOp final : public EluGradientOp<Context> {
  public:
-    CuDNNEluGradientOp(const OperatorDef& op_def, Workspace* ws)
-        : EluGradientOp<Context>(op_def, ws) {
+    CuDNNEluGradientOp(const OperatorDef& def, Workspace* ws)
+        : EluGradientOp<Context>(def, ws) {
         CUDNN_CHECK(cudnnCreateTensorDescriptor(&input_desc));
         CUDNN_CHECK(cudnnCreateTensorDescriptor(&output_desc));
         CUDNN_CHECK(cudnnCreateActivationDescriptor(&act_desc));

Dragon/include/operators/activation/prelu_op.h

@@ -17,12 +17,12 @@
 namespace dragon {
 
 template <class Context>
-class PReluOp : public Operator<Context> {
+class PReluOp final : public Operator<Context> {
  public:
-    PReluOp(const OperatorDef& op_def, Workspace* ws)
-        : Operator<Context>(op_def, ws),
-          channel_shared(OperatorBase::GetSingleArg<bool>("channel_shared", false)),
-          data_format(OperatorBase::GetSingleArg<string>("data_format", "NCHW")) {}
+    PReluOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws),
+          channel_shared(OperatorBase::Arg<bool>("channel_shared", false)),
+          data_format(OperatorBase::Arg<string>("data_format", "NCHW")) {}
     USE_OPERATOR_FUNCTIONS;
 
     void RunOnDevice() override;
@@ -34,12 +34,12 @@ class PReluOp : public Operator<Context> {
 };
 
 template <class Context>
-class PReluGradientOp : public Operator<Context> {
+class PReluGradientOp final : public Operator<Context> {
  public:
-    PReluGradientOp(const OperatorDef& op_def, Workspace* ws)
-        : Operator<Context>(op_def, ws),
-          channel_shared(OperatorBase::GetSingleArg<bool>("channel_shared", false)),
-          data_format(OperatorBase::GetSingleArg<string>("data_format", "NCHW")) {}
+    PReluGradientOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws),
+          channel_shared(OperatorBase::Arg<bool>("channel_shared", false)),
+          data_format(OperatorBase::Arg<string>("data_format", "NCHW")) {}
     USE_OPERATOR_FUNCTIONS;
 
     void RunOnDevice() override;

Dragon/include/operators/activation/relu_op.h

@@ -19,9 +19,9 @@ namespace dragon {
 template <class Context>
 class ReluOp : public Operator<Context> {
  public:
-    ReluOp(const OperatorDef& op_def, Workspace* ws)
-        : Operator<Context>(op_def, ws),
-          slope(OperatorBase::GetSingleArg<float>("slope", 0.0)) {}
+    ReluOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws),
+          slope(OperatorBase::Arg<float>("slope", 0.0)) {}
     USE_OPERATOR_FUNCTIONS;
 
     void RunOnDevice() override;
@@ -34,9 +34,9 @@ class ReluOp : public Operator<Context> {
 template <class Context>
 class ReluGradientOp : public Operator<Context> {
  public:
-    ReluGradientOp(const OperatorDef& op_def, Workspace* ws)
-        : Operator<Context>(op_def, ws),
-          slope(OperatorBase::GetSingleArg<float>("slope", 0.0)) {}
+    ReluGradientOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws),
+          slope(OperatorBase::Arg<float>("slope", 0.0)) {}
     USE_OPERATOR_FUNCTIONS;
 
     void RunOnDevice() override;
@@ -51,8 +51,8 @@ class ReluGradientOp : public Operator<Context> {
 template <class Context>
 class CuDNNReluOp final : public ReluOp<Context> {
 public:
-    CuDNNReluOp(const OperatorDef& op_def, Workspace* ws) 
-        : ReluOp<Context>(op_def, ws) {
+    CuDNNReluOp(const OperatorDef& def, Workspace* ws)
+        : ReluOp<Context>(def, ws) {
         CUDNN_CHECK(cudnnCreateTensorDescriptor(&input_desc));
         CUDNN_CHECK(cudnnCreateTensorDescriptor(&output_desc));
         CUDNN_CHECK(cudnnCreateActivationDescriptor(&act_desc));
@@ -78,8 +78,8 @@ public:
 template <class Context>
 class CuDNNReluGradientOp final : public ReluGradientOp<Context> {
  public:
-    CuDNNReluGradientOp(const OperatorDef& op_def, Workspace* ws)
-        : ReluGradientOp<Context>(op_def, ws) {
+    CuDNNReluGradientOp(const OperatorDef& def, Workspace* ws)
+        : ReluGradientOp<Context>(def, ws) {
         CUDNN_CHECK(cudnnCreateTensorDescriptor(&input_desc));
         CUDNN_CHECK(cudnnCreateTensorDescriptor(&output_desc));
         CUDNN_CHECK(cudnnCreateActivationDescriptor(&act_desc));

Dragon/include/operators/activation/selu_op.h

@@ -17,7 +17,7 @@
 namespace dragon {
 
 template <class Context>
-class SEluOp : public Operator<Context> {
+class SEluOp final : public Operator<Context> {
  public:
     USE_SIMPLE_CTOR_DTOR(SEluOp);
     USE_OPERATOR_FUNCTIONS;
@@ -27,7 +27,7 @@ class SEluOp : public Operator<Context> {
 };
 
 template <class Context>
-class SEluGradientOp : public Operator<Context> {
+class SEluGradientOp final : public Operator<Context> {
  public:
     USE_SIMPLE_CTOR_DTOR(SEluGradientOp);
     USE_OPERATOR_FUNCTIONS;

Dragon/include/operators/activation/sigmoid_op.h

@@ -41,8 +41,8 @@ class SigmoidGradientOp : public Operator<Context> {
 template <class Context>
 class CuDNNSigmoidOp final : public SigmoidOp<Context> {
 public:
-    CuDNNSigmoidOp(const OperatorDef& op_def, Workspace* ws) 
-        : SigmoidOp<Context>(op_def, ws) {
+    CuDNNSigmoidOp(const OperatorDef& def, Workspace* ws)
+        : SigmoidOp<Context>(def, ws) {
         CUDNN_CHECK(cudnnCreateTensorDescriptor(&input_desc));
         CUDNN_CHECK(cudnnCreateTensorDescriptor(&output_desc));
         CUDNN_CHECK(cudnnCreateActivationDescriptor(&act_desc));
@@ -68,8 +68,8 @@ public:
 template <class Context>
 class CuDNNSigmoidGradientOp final : public SigmoidGradientOp<Context> {
  public:
-    CuDNNSigmoidGradientOp(const OperatorDef& op_def, Workspace* ws)
-        : SigmoidGradientOp<Context>(op_def, ws) {
+    CuDNNSigmoidGradientOp(const OperatorDef& def, Workspace* ws)
+        : SigmoidGradientOp<Context>(def, ws) {
         CUDNN_CHECK(cudnnCreateTensorDescriptor(&input_desc));
         CUDNN_CHECK(cudnnCreateTensorDescriptor(&output_desc));
         CUDNN_CHECK(cudnnCreateActivationDescriptor(&act_desc));

Dragon/include/operators/activation/softmax_op.h

@@ -19,9 +19,9 @@ namespace dragon {
 template <class Context>
 class SoftmaxOp final : public Operator<Context> {
  public:
-    SoftmaxOp(const OperatorDef& op_def, Workspace* ws)
-        : Operator<Context>(op_def, ws),
-          axis(OperatorBase::GetSingleArg<int>("axis", 1)) {}
+    SoftmaxOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws),
+          axis(OperatorBase::Arg<int>("axis", 1)) {}
     USE_OPERATOR_FUNCTIONS;
 
     void RunOnDevice() override;
@@ -34,9 +34,9 @@ class SoftmaxOp final : public Operator<Context> {
 template <class Context>
 class SoftmaxGradientOp final : public Operator<Context> {
  public:
-    SoftmaxGradientOp(const OperatorDef& op_def, Workspace* ws)
-        : Operator<Context>(op_def, ws),
-          axis(OperatorBase::GetSingleArg<int>("axis", 1)) {}
+    SoftmaxGradientOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws),
+          axis(OperatorBase::Arg<int>("axis", 1)) {}
     USE_OPERATOR_FUNCTIONS;
 
     void RunOnDevice() override;
@@ -53,9 +53,9 @@ class SoftmaxGradientOp final : public Operator<Context> {
 template <class Context>
 class CuDNNSoftmaxOp final : public Operator<Context> {
  public:
-    CuDNNSoftmaxOp(const OperatorDef& op_def, Workspace* ws)
-        : Operator<Context>(op_def, ws),
-          axis(OperatorBase::GetSingleArg<int>("axis", 1)) {
+    CuDNNSoftmaxOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws),
+          axis(OperatorBase::Arg<int>("axis", 1)) {
         CUDNN_CHECK(cudnnCreateTensorDescriptor(&input_desc));
         CUDNN_CHECK(cudnnCreateTensorDescriptor(&output_desc));
     }
@@ -78,9 +78,9 @@ class CuDNNSoftmaxOp final : public Operator<Context> {
 template <class Context>
 class CuDNNSoftmaxGradientOp final : public Operator<Context> {
  public:
-    CuDNNSoftmaxGradientOp(const OperatorDef& op_def, Workspace* ws) 
-        : Operator<Context>(op_def, ws),
-          axis(OperatorBase::GetSingleArg<int>("axis", 1)) {
+    CuDNNSoftmaxGradientOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws),
+          axis(OperatorBase::Arg<int>("axis", 1)) {
         CUDNN_CHECK(cudnnCreateTensorDescriptor(&input_desc));
         CUDNN_CHECK(cudnnCreateTensorDescriptor(&output_desc));
     }

Dragon/include/operators/activation/tanh_op.h

@@ -41,8 +41,8 @@ class TanhGradientOp : public Operator<Context> {
 template <class Context>
 class CuDNNTanhOp final : public TanhOp<Context> {
 public:
-    CuDNNTanhOp(const OperatorDef& op_def, Workspace* ws)
-        : TanhOp<Context>(op_def, ws) {
+    CuDNNTanhOp(const OperatorDef& def, Workspace* ws)
+        : TanhOp<Context>(def, ws) {
         CUDNN_CHECK(cudnnCreateTensorDescriptor(&input_desc));
         CUDNN_CHECK(cudnnCreateTensorDescriptor(&output_desc));
         CUDNN_CHECK(cudnnCreateActivationDescriptor(&act_desc));
@@ -68,8 +68,8 @@ public:
 template <class Context>
 class CuDNNTanhGradientOp final : public TanhGradientOp<Context> {
  public:
-    CuDNNTanhGradientOp(const OperatorDef& op_def, Workspace* ws)
-        : TanhGradientOp<Context>(op_def, ws) {
+    CuDNNTanhGradientOp(const OperatorDef& def, Workspace* ws)
+        : TanhGradientOp<Context>(def, ws) {
         CUDNN_CHECK(cudnnCreateTensorDescriptor(&input_desc));
         CUDNN_CHECK(cudnnCreateTensorDescriptor(&output_desc));
         CUDNN_CHECK(cudnnCreateActivationDescriptor(&act_desc));

Dragon/include/operators/arithmetic/affine_op.h

@@ -17,12 +17,12 @@
 namespace dragon {
 
 template <class Context>
-class AffineOp : public Operator<Context> {
+class AffineOp final : public Operator<Context> {
  public:
-    AffineOp(const OperatorDef& op_def, Workspace* ws)
-        : Operator<Context>(op_def, ws),
-          axis(OperatorBase::GetSingleArg<int>("axis", 1)),
-          num_axes(OperatorBase::GetSingleArg<int>("num_axes", 1)) {}
+    AffineOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws),
+          axis(OperatorBase::Arg<int>("axis", 1)),
+          num_axes(OperatorBase::Arg<int>("num_axes", 1)) {}
     USE_OPERATOR_FUNCTIONS;
 
     void RunOnDevice() override;
@@ -36,10 +36,10 @@ class AffineOp : public Operator<Context> {
 template <class Context>
 class AffineGradientOp final : public Operator<Context> {
  public:
-    AffineGradientOp(const OperatorDef& op_def, Workspace* ws)
-        : Operator<Context>(op_def, ws),
-        axis(OperatorBase::GetSingleArg<int>("axis", 1)),
-        num_axes(OperatorBase::GetSingleArg<int>("num_axes", -1)) {}
+    AffineGradientOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws),
+          axis(OperatorBase::Arg<int>("axis", 1)),
+          num_axes(OperatorBase::Arg<int>("num_axes", -1)) {}
     USE_OPERATOR_FUNCTIONS;
 
     void RunOnDevice() override;
@@ -60,10 +60,10 @@ class AffineGradientOp final : public Operator<Context> {
 template <class Context>
 class CuDNNAffineOpBase : public Operator<Context> {
  public:
-    CuDNNAffineOpBase(const OperatorDef& op_def, Workspace* ws)
-         : Operator<Context>(op_def, ws),
-        axis(OperatorBase::GetSingleArg<int>("axis", 1)),
-        num_axes(OperatorBase::GetSingleArg<int>("num_axes", -1)) {
+    CuDNNAffineOpBase(const OperatorDef& def, Workspace* ws)
+         : Operator<Context>(def, ws),
+           axis(OperatorBase::Arg<int>("axis", 1)),
+           num_axes(OperatorBase::Arg<int>("num_axes", -1)) {
         CUDNN_CHECK(cudnnCreateTensorDescriptor(&input_desc));
         CUDNN_CHECK(cudnnCreateTensorDescriptor(&param_desc));
         CUDNN_CHECK(cudnnCreateOpTensorDescriptor(&mul_desc));
@@ -120,10 +120,10 @@ class CuDNNAffineOpBase : public Operator<Context> {
     using CuDNNAffineOpBase<Context>::reduce_desc
 
 template <class Context>
-class CuDNNAffineOp : public CuDNNAffineOpBase<Context> {
+class CuDNNAffineOp final : public CuDNNAffineOpBase<Context> {
  public:
-    CuDNNAffineOp(const OperatorDef& op_def, Workspace* ws)
-         : CuDNNAffineOpBase<Context>(op_def, ws) {}
+    CuDNNAffineOp(const OperatorDef& def, Workspace* ws)
+         : CuDNNAffineOpBase<Context>(def, ws) {}
 
     void RunOnDevice() override;
     template <typename T> void RunWithType();
@@ -133,10 +133,10 @@ class CuDNNAffineOp : public CuDNNAffineOpBase<Context> {
 };
 
 template <class Context>
-class CuDNNAffineGradientOp : public CuDNNAffineOpBase<Context> {
+class CuDNNAffineGradientOp final : public CuDNNAffineOpBase<Context> {
 public:
-    CuDNNAffineGradientOp(const OperatorDef& op_def, Workspace* ws)
-        : CuDNNAffineOpBase<Context>(op_def, ws) {}
+    CuDNNAffineGradientOp(const OperatorDef& def, Workspace* ws)
+        : CuDNNAffineOpBase<Context>(def, ws) {}
 
     void RunOnDevice() override;
     template <typename T> void ComputeScaleGradient(T* dYxX, T* dA);

Dragon/include/operators/arithmetic/clip_op.h

@@ -20,10 +20,10 @@ namespace dragon {
 template <class Context>
 class ClipOp final : public Operator<Context> {
  public:
-    ClipOp(const OperatorDef& op_def, Workspace* ws)
-        : Operator<Context>(op_def, ws),
-          low(OperatorBase::GetSingleArg<float>("low", -FLT_MAX)),
-          high(OperatorBase::GetSingleArg<float>("high", FLT_MAX)) {}
+    ClipOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws),
+          low(OperatorBase::Arg<float>("low", -FLT_MAX)),
+          high(OperatorBase::Arg<float>("high", FLT_MAX)) {}
     USE_OPERATOR_FUNCTIONS;
 
     void RunOnDevice() override;

Dragon/include/operators/arithmetic/dot_op.h

@@ -19,10 +19,10 @@ namespace dragon {
 template <class Context>
 class DotOp final : public Operator<Context> {
  public:
-    DotOp(const OperatorDef& op_def, Workspace* ws)
-        : Operator<Context>(op_def, ws),
-          TransA(OperatorBase::GetSingleArg<bool>("TransA", false)),
-          TransB(OperatorBase::GetSingleArg<bool>("TransB", false)) {}
+    DotOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws),
+          TransA(OperatorBase::Arg<bool>("TransA", false)),
+          TransB(OperatorBase::Arg<bool>("TransB", false)) {}
     USE_OPERATOR_FUNCTIONS;
 
     void RunOnDevice() override;
@@ -37,10 +37,10 @@ class DotOp final : public Operator<Context> {
 template <class Context>
 class DotGradientOp final : public Operator<Context> {
  public:
-    DotGradientOp(const OperatorDef& op_def, Workspace* ws)
-        : Operator<Context>(op_def, ws),
-        TransA(OperatorBase::GetSingleArg<bool>("TransA", false)),
-        TransB(OperatorBase::GetSingleArg<bool>("TransB", false)) {}
+    DotGradientOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws),
+          TransA(OperatorBase::Arg<bool>("TransA", false)),
+          TransB(OperatorBase::Arg<bool>("TransB", false)) {}
     USE_OPERATOR_FUNCTIONS;
 
     void RunOnDevice() override;

Dragon/include/operators/arithmetic/eltwise_op.h

@@ -19,10 +19,10 @@ namespace dragon {
 template <class Context>
 class EltwiseOp final : public Operator<Context> {
  public:
-    EltwiseOp(const OperatorDef& op_def, Workspace* ws)
-        : Operator<Context>(op_def, ws),
-          operation(OperatorBase::GetSingleArg<string>("operation", "SUM")),
-          coeffs(OperatorBase::GetRepeatedArg<float>("coeffs")) {
+    EltwiseOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws),
+          operation(OperatorBase::Arg<string>("operation", "SUM")),
+          coeffs(OperatorBase::Args<float>("coeffs")) {
         if (coeffs.size() > 0) {
             CHECK_EQ(coeffs.size(), InputSize())
                 << "\nOp has " << InputSize() << " inputs, "
@@ -43,10 +43,10 @@ class EltwiseOp final : public Operator<Context> {
 template <class Context>
 class EltwiseGradientOp final : public Operator<Context> {
  public:
-    EltwiseGradientOp(const OperatorDef& op_def, Workspace* ws) 
-        : Operator<Context>(op_def, ws),
-          operation(OperatorBase::GetSingleArg<string>("operation", "SUM")),
-          coeffs(OperatorBase::GetRepeatedArg<float>("coeff")) {
+    EltwiseGradientOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws),
+          operation(OperatorBase::Arg<string>("operation", "SUM")),
+          coeffs(OperatorBase::Args<float>("coeff")) {
         if (coeffs.size() > 0) {
             CHECK_EQ(coeffs.size(), InputSize())
                 << "\nop has " << InputSize() << " inputs, "

Dragon/include/operators/arithmetic/gram_matrix_op.h

@@ -19,9 +19,9 @@ namespace dragon {
 template <class Context>
 class GramMatrixOp final : public Operator<Context> {
  public:
-    GramMatrixOp(const OperatorDef& op_def, Workspace* ws)
-        : Operator<Context>(op_def, ws),
-          axis(OperatorBase::GetSingleArg<int>("axis", 1)) {}
+    GramMatrixOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws),
+          axis(OperatorBase::Arg<int>("axis", 1)) {}
     USE_OPERATOR_FUNCTIONS;
 
     void RunOnDevice() override;
@@ -35,9 +35,9 @@ class GramMatrixOp final : public Operator<Context> {
 template <class Context>
 class GramMatrixGradientOp final : public Operator<Context> {
  public:
-    GramMatrixGradientOp(const OperatorDef& op_def, Workspace* ws) 
-        : Operator<Context>(op_def, ws),
-          axis(OperatorBase::GetSingleArg<int>("axis", 1)) {}
+    GramMatrixGradientOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws),
+          axis(OperatorBase::Arg<int>("axis", 1)) {}
     USE_OPERATOR_FUNCTIONS;
 
     void RunOnDevice() override;

Dragon/include/operators/arithmetic/inner_product_op.h

@@ -17,13 +17,13 @@
 namespace dragon {
 
 template <class Context>
-class InnerProductOp: public Operator<Context> {
+class InnerProductOp final : public Operator<Context> {
  public:
-    InnerProductOp(const OperatorDef& op_def, Workspace *ws)
-        : Operator<Context>(op_def, ws),
-          axis(OperatorBase::GetSingleArg<int>("axis", 1)),
-          num_output(OperatorBase::GetSingleArg<int>("num_output", 0)),
-          TransW(OperatorBase::GetSingleArg<bool>("TransW", true)) {}
+    InnerProductOp(const OperatorDef& def, Workspace *ws)
+        : Operator<Context>(def, ws),
+          axis(OperatorBase::Arg<int>("axis", 1)),
+          num_output(OperatorBase::Arg<int>("num_output", 0)),
+          TransW(OperatorBase::Arg<bool>("TransW", true)) {}
     USE_OPERATOR_FUNCTIONS;
 
     void RunOnDevice();
@@ -37,11 +37,11 @@ class InnerProductOp: public Operator<Context> {
 template <class Context>
 class InnerProductGradientOp final : public Operator<Context> {
  public:
-    InnerProductGradientOp(const OperatorDef& op_def, Workspace *ws)
-        : Operator<Context>(op_def, ws),
-          axis(OperatorBase::GetSingleArg<int>("axis", 1)),
-          num_output(OperatorBase::GetSingleArg<int>("num_output", 0)),
-          TransW(OperatorBase::GetSingleArg<bool>("TransW", true)) {}
+    InnerProductGradientOp(const OperatorDef& def, Workspace *ws)
+        : Operator<Context>(def, ws),
+          axis(OperatorBase::Arg<int>("axis", 1)),
+          num_output(OperatorBase::Arg<int>("num_output", 0)),
+          TransW(OperatorBase::Arg<bool>("TransW", true)) {}
     USE_OPERATOR_FUNCTIONS;
 
     void RunOnDevice() override;

Dragon/include/operators/arithmetic/matmul_op.h

@@ -19,10 +19,10 @@ namespace dragon {
 template <class Context>
 class MatmulOp final : public Operator<Context> {
  public:
-    MatmulOp(const OperatorDef& op_def, Workspace* ws)
-        : Operator<Context>(op_def, ws),
-          TransA(OperatorBase::GetSingleArg<bool>("TransA", false)),
-          TransB(OperatorBase::GetSingleArg<bool>("TransB", false)) {}
+    MatmulOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws),
+          TransA(OperatorBase::Arg<bool>("TransA", false)),
+          TransB(OperatorBase::Arg<bool>("TransB", false)) {}
     USE_OPERATOR_FUNCTIONS;
 
     void RunOnDevice() override;
@@ -36,10 +36,10 @@ class MatmulOp final : public Operator<Context> {
 template <class Context>
 class MatmulGradientOp final : public Operator<Context> {
  public:
-    MatmulGradientOp(const OperatorDef& op_def, Workspace* ws)
-        : Operator<Context>(op_def, ws),
-        TransA(OperatorBase::GetSingleArg<bool>("TransA", false)),
-        TransB(OperatorBase::GetSingleArg<bool>("TransB", false)) {}
+    MatmulGradientOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws),
+          TransA(OperatorBase::Arg<bool>("TransA", false)),
+          TransB(OperatorBase::Arg<bool>("TransB", false)) {}
     USE_OPERATOR_FUNCTIONS;
 
     void RunOnDevice() override;

Dragon/include/operators/arithmetic/pow_op.h

@@ -17,13 +17,13 @@
 namespace dragon {
 
 template <class Context>
-class PowOp: public Operator<Context> {
+class PowOp final : public Operator<Context> {
  public:
-    PowOp(const OperatorDef& op_def, Workspace* ws)
-        : Operator<Context>(op_def, ws),
-          scale(OperatorBase::GetSingleArg<float>("scale", 1.0)),
-          shift(OperatorBase::GetSingleArg<float>("shift", 0.0)),
-          power(OperatorBase::GetSingleArg<float>("power", 1.0)) {
+    PowOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws),
+          scale(OperatorBase::Arg<float>("scale", 1.0)),
+          shift(OperatorBase::Arg<float>("shift", 0.0)),
+          power(OperatorBase::Arg<float>("power", 1.0)) {
           power_scale = power * scale;
     }
     USE_OPERATOR_FUNCTIONS;
@@ -38,11 +38,11 @@ class PowOp: public Operator<Context> {
 template <class Context>
 class PowGradientOp final : public Operator<Context> {
  public:
-    PowGradientOp(const OperatorDef& op_def, Workspace* ws)
-        : Operator<Context>(op_def, ws),
-        scale(OperatorBase::GetSingleArg<float>("scale", 1.0)),
-        shift(OperatorBase::GetSingleArg<float>("shift", 0.0)),
-        power(OperatorBase::GetSingleArg<float>("power", 1.0)) {
+    PowGradientOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws),
+        scale(OperatorBase::Arg<float>("scale", 1.0)),
+        shift(OperatorBase::Arg<float>("shift", 0.0)),
+        power(OperatorBase::Arg<float>("power", 1.0)) {
         power_scale = power * scale;
     }
     USE_OPERATOR_FUNCTIONS;

Dragon/include/operators/control_flow/compare_op.h

@@ -19,9 +19,9 @@ namespace dragon {
 template <class Context>
 class CompareOp final : public Operator<Context> {
  public:
-    CompareOp(const OperatorDef& op_def, Workspace* ws)
-        : Operator<Context>(op_def, ws),
-          operation(OperatorBase::GetSingleArg<string>("operation", "NONE")) {}
+    CompareOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws),
+          operation(OperatorBase::Arg<string>("operation", "NONE")) {}
     USE_OPERATOR_FUNCTIONS;
 
     void RunOnDevice() override;

Dragon/include/operators/control_flow/scan_op.h

@@ -19,16 +19,16 @@ namespace dragon {
 template <class Context>
 class ScanOp final: public Operator<Context> {
  public:
-    ScanOp(const OperatorDef& op_def, Workspace *ws) 
-        : Operator<Context>(op_def, ws),
-          axis(OperatorBase::GetSingleArg<int>("axis", 0)),
-          nsteps(OperatorBase::GetSingleArg<int>("nsteps", 0)),
-          step_type(OperatorBase::GetSingleArg<string>("step_type", "Static")),
-          step_tensor(OperatorBase::GetSingleArg<string>("step_tensor", "")),
-          nseqs(OperatorBase::GetSingleArg<int>("nseqs", 0)),
-          default_outputs(OperatorBase::GetRepeatedArg<string>("default_outputs")),
+    ScanOp(const OperatorDef& def, Workspace *ws)
+        : Operator<Context>(def, ws),
+          axis(OperatorBase::Arg<int>("axis", 0)),
+          nsteps(OperatorBase::Arg<int>("nsteps", 0)),
+          step_type(OperatorBase::Arg<string>("step_type", "Static")),
+          step_tensor(OperatorBase::Arg<string>("step_tensor", "")),
+          nseqs(OperatorBase::Arg<int>("nseqs", 0)),
+          default_outputs(OperatorBase::Args<string>("default_outputs")),
           nout((int)default_outputs.size()),
-          debug_mode(OperatorBase::GetSingleArg<bool>("debug_mode", false)) { 
+          debug_mode(OperatorBase::Arg<bool>("debug_mode", false)) {
         InitTemplate(); 
     }
     USE_OPERATOR_FUNCTIONS;
@@ -52,14 +52,14 @@ class ScanOp final: public Operator<Context> {
 template <class Context>
 class ScanGradientOp final: public Operator<Context> {
  public:
-    ScanGradientOp(const OperatorDef& op_def, Workspace* ws)
-        : Operator<Context>(op_def, ws),
-          axis(OperatorBase::GetSingleArg<int>("axis", 0)),
-          nsteps(OperatorBase::GetSingleArg<int>("nsteps", 0)),
-          step_type(OperatorBase::GetSingleArg<string>("step_type", "Static")),
-          step_tensor(OperatorBase::GetSingleArg<string>("step_tensor", "")),
-          forward_inputs(OperatorBase::GetRepeatedArg<string>("inputs_name")),
-          forward_outputs(OperatorBase::GetRepeatedArg<string>("outputs_name")) {
+    ScanGradientOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws),
+          axis(OperatorBase::Arg<int>("axis", 0)),
+          nsteps(OperatorBase::Arg<int>("nsteps", 0)),
+          step_type(OperatorBase::Arg<string>("step_type", "Static")),
+          step_tensor(OperatorBase::Arg<string>("step_tensor", "")),
+          forward_inputs(OperatorBase::Args<string>("inputs_name")),
+          forward_outputs(OperatorBase::Args<string>("outputs_name")) {
         //  handle GO(x)
         for (int i = 0; i < forward_outputs.size(); i++)
             terms[forward_outputs[i] + "_grad"] = Input(i + (int)OutputSize()).name();

Dragon/include/operators/loss/ctc_loss_op.h

+// ------------------------------------------------------------
+// Copyright (c) 2017-present, SeetaTech, Co.,Ltd.
+//
+// Licensed under the BSD 2-Clause License.
+// You should have received a copy of the BSD 2-Clause License
+// along with the software. If not, See,
+//
+//      <https://opensource.org/licenses/BSD-2-Clause>
+//
+// -------------------------------------------------------------
+
+#ifndef DRAGON_OPERATORS_LOSS_CTC_LOSS_OP_H_
+#define DRAGON_OPERATORS_LOSS_CTC_LOSS_OP_H_
+
+#include "core/operator.h"
+
+namespace dragon {
+
+template <class Context>
+class CTCLossOp final : public Operator<Context> {
+ public:
+     CTCLossOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws) {
+        LOG(FATAL) << "CTCLoss requires CuDNN support.";
+     }
+     USE_OPERATOR_FUNCTIONS;
+
+     void RunOnDevice() override {}
+};
+
+template <class Context>
+class CTCLossGradientOp final : public Operator<Context> {
+public:
+    CTCLossGradientOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws) {}
+    USE_OPERATOR_FUNCTIONS;
+
+    void RunOnDevice() override;
+    template <typename T> void RunWithType();
+};
+
+#ifdef WITH_CUDNN
+
+#if CUDNN_VERSION_MIN(7, 0, 0)
+
+#include "utils/cudnn_device.h"
+
+template <class Context>
+class CuDNNCTCLossOp final : public Operator<Context> {
+ public:
+    CuDNNCTCLossOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws),
+          blank_first(OperatorBase::Arg<bool>("blank_first", true)),
+          padding_mask(OperatorBase::Arg<int>("padding_mask", -1)) {
+         CUDNN_CHECK(cudnnCreateCTCLossDescriptor(&ctc_desc));
+         CUDNN_CHECK(cudnnCreateTensorDescriptor(&prob_desc));
+         CUDNN_CHECK(cudnnCreateTensorDescriptor(&grad_desc));
+         ctc_algo = CUDNN_CTC_LOSS_ALGO_DETERMINISTIC;
+     }
+     USE_OPERATOR_FUNCTIONS;
+
+     ~CuDNNCTCLossOp() {
+         CUDNN_CHECK(cudnnDestroyCTCLossDescriptor(ctc_desc));
+         CUDNN_CHECK(cudnnDestroyTensorDescriptor(prob_desc));
+         CUDNN_CHECK(cudnnDestroyTensorDescriptor(grad_desc));
+     }
+
+    void RunOnDevice() override;
+    template <typename T> void RunWithType();
+    
+    void WrapIO();
+
+ protected:
+    bool blank_first;
+    TIndex padding_mask;
+
+    cudnnCTCLossAlgo_t ctc_algo;
+    cudnnCTCLossDescriptor_t ctc_desc;
+    cudnnTensorDescriptor_t prob_desc, grad_desc;
+    size_t workspace_size;
+
+    vector<int> packed_labels, label_lengths, input_lengths;
+};
+
+#endif
+
+#endif    // WITH_CUDNN
+
+}    // namespace dragon
+
+#endif    // DRAGON_OPERATORS_LOSS_CTC_LOSS_OP_H_
\ No newline at end of file

Dragon/include/operators/loss/l1_loss_op.h

@@ -17,11 +17,12 @@
 namespace dragon {
 
 template <class Context>
-class L1LossOp : public Operator<Context> {
+class L1LossOp final : public Operator<Context> {
  public:
-    L1LossOp(const OperatorDef& op_def, Workspace* ws) 
-        : Operator<Context>(op_def, ws),
-          normalization(OperatorBase::GetSingleArg<string>("normalization", "BATCH_SIZE")) {}
+    L1LossOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws),
+          normalization(OperatorBase::Arg<string>(
+              "normalization", "BATCH_SIZE")) {}
     USE_OPERATOR_FUNCTIONS;
 
     void RunOnDevice() override;
@@ -35,9 +36,10 @@ class L1LossOp : public Operator<Context> {
 template <class Context>
 class L1LossGradientOp final : public Operator<Context> {
  public:
-    L1LossGradientOp(const OperatorDef& op_def, Workspace* ws)
-        : Operator<Context>(op_def, ws),
-          normalization(OperatorBase::GetSingleArg<string>("normalization", "BATCH_SIZE")) {}
+    L1LossGradientOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws),
+          normalization(OperatorBase::Arg<string>(
+              "normalization", "BATCH_SIZE")) {}
     USE_OPERATOR_FUNCTIONS;
 
     void RunOnDevice() override;

Dragon/include/operators/loss/l2_loss_op.h

@@ -17,11 +17,12 @@
 namespace dragon {
 
 template <class Context>
-class L2LossOp : public Operator<Context> {
+class L2LossOp final : public Operator<Context> {
  public:
-    L2LossOp(const OperatorDef& op_def, Workspace* ws) 
-        : Operator<Context>(op_def, ws),
-          normalization(OperatorBase::GetSingleArg<string>("normalization", "BATCH_SIZE")) {}
+    L2LossOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws),
+          normalization(OperatorBase::Arg<string>(
+              "normalization", "BATCH_SIZE")) {}
     USE_OPERATOR_FUNCTIONS;
 
     void RunOnDevice() override;
@@ -35,9 +36,10 @@ class L2LossOp : public Operator<Context> {
 template <class Context>
 class L2LossGradientOp final : public Operator<Context> {
  public:
-    L2LossGradientOp(const OperatorDef& op_def, Workspace* ws)
-        : Operator<Context>(op_def, ws),
-          normalization(OperatorBase::GetSingleArg<string>("normalization", "BATCH_SIZE")) {}
+    L2LossGradientOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws),
+          normalization(OperatorBase::Arg<string>(
+              "normalization", "BATCH_SIZE")) {}
     USE_OPERATOR_FUNCTIONS;
 
     void RunOnDevice() override;

Dragon/include/operators/loss/sigmoid_cross_entropy_op.h

@@ -19,9 +19,10 @@ namespace dragon {
 template <class Context>
 class SigmoidCrossEntropyOp final : public Operator<Context> {
  public:
-    SigmoidCrossEntropyOp(const OperatorDef& op_def, Workspace* ws)
-        : Operator<Context>(op_def, ws),
-          normalization(OperatorBase::GetSingleArg<string>("normalization", "VALID")) {}
+    SigmoidCrossEntropyOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws),
+          normalization(OperatorBase::Arg<string>(
+              "normalization", "VALID")) {}
     USE_OPERATOR_FUNCTIONS;
 
     void RunOnDevice() override;
@@ -35,9 +36,10 @@ class SigmoidCrossEntropyOp final : public Operator<Context> {
 template <class Context>
 class SigmoidCrossEntropyGradientOp final : public Operator<Context> {
  public:
-    SigmoidCrossEntropyGradientOp(const OperatorDef& op_def, Workspace* ws)
-        : Operator<Context>(op_def, ws),
-          normalization(OperatorBase::GetSingleArg<string>("normalization", "VALID")) {}
+    SigmoidCrossEntropyGradientOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws),
+          normalization(OperatorBase::Arg<string>(
+              "normalization", "VALID")) {}
     USE_OPERATOR_FUNCTIONS;
 
     void RunOnDevice() override;

Dragon/include/operators/loss/smooth_l1_loss_op.h

@@ -19,10 +19,11 @@ namespace dragon {
 template <class Context>
 class SmoothL1LossOp final : public Operator<Context> {
  public:
-    SmoothL1LossOp(const OperatorDef& op_def, Workspace* ws)
-        : Operator<Context>(op_def, ws),
-          beta(OperatorBase::GetSingleArg<float>("beta", 1.0)),
-          normalization(OperatorBase::GetSingleArg<string>("normalization", "BATCH_SIZE")) {}
+    SmoothL1LossOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws),
+          beta(OperatorBase::Arg<float>("beta", 1.0)),
+          normalization(OperatorBase::Arg<string>(
+              "normalization", "BATCH_SIZE")) {}
     USE_OPERATOR_FUNCTIONS;
 
     void RunOnDevice() override;
@@ -37,10 +38,11 @@ class SmoothL1LossOp final : public Operator<Context> {
 template <class Context>
 class SmoothL1LossGradientOp final : public Operator<Context> {
  public:
-    SmoothL1LossGradientOp(const OperatorDef& op_def, Workspace* ws) 
-        : Operator<Context>(op_def, ws),
-        beta(OperatorBase::GetSingleArg<float>("beta", 1.0)),
-        normalization(OperatorBase::GetSingleArg<string>("normalization", "BATCH_SIZE")) {}
+    SmoothL1LossGradientOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws),
+          beta(OperatorBase::Arg<float>("beta", 1.0)),
+          normalization(OperatorBase::Arg<string>(
+              "normalization", "BATCH_SIZE")) {}
     USE_OPERATOR_FUNCTIONS;
 
     void RunOnDevice() override;

Dragon/include/operators/loss/softmax_cross_entropy_op.h

@@ -19,11 +19,11 @@ namespace dragon {
 template <class Context>
 class SoftmaxCrossEntropyOp final : public Operator<Context> {
  public:
-    SoftmaxCrossEntropyOp(const OperatorDef& op_def, Workspace* ws) 
-        : Operator<Context>(op_def, ws),
-          axis(OperatorBase::GetSingleArg<int>("axis", 1)),
-          normalization(OperatorBase::GetSingleArg<string>("normalization", "FULL")) {
-    }
+    SoftmaxCrossEntropyOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws),
+          axis(OperatorBase::Arg<int>("axis", 1)),
+          normalization(OperatorBase::Arg<string>(
+              "normalization", "FULL")) {}
     USE_OPERATOR_FUNCTIONS;
 
     void SoftmaxRun();
@@ -41,10 +41,11 @@ class SoftmaxCrossEntropyOp final : public Operator<Context> {
 template <class Context>
 class SoftmaxCrossEntropyGradientOp final : public Operator<Context> {
  public:
-    SoftmaxCrossEntropyGradientOp(const OperatorDef& op_def, Workspace* ws) 
-        : Operator<Context>(op_def, ws),
-          axis(OperatorBase::GetSingleArg<int>("axis", 1)),
-          normalization(OperatorBase::GetSingleArg<string>("normalization", "FULL")) {}
+    SoftmaxCrossEntropyGradientOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws),
+          axis(OperatorBase::Arg<int>("axis", 1)),
+          normalization(OperatorBase::Arg<string>(
+              "normalization", "FULL")) {}
     USE_OPERATOR_FUNCTIONS;
 
     void RunOnDevice() override;

Dragon/include/operators/loss/sparse_softmax_cross_entropy_op.h

@@ -19,15 +19,16 @@ namespace dragon {
 template <class Context>
 class SparseSoftmaxCrossEntropyOp : public Operator<Context> {
  public:
-    SparseSoftmaxCrossEntropyOp(const OperatorDef& op_def, Workspace* ws)
-        : Operator<Context>(op_def, ws),
-          axis(OperatorBase::GetSingleArg<int>("axis", 1)),
-          normalization(OperatorBase::GetSingleArg<string>("normalization", "VALID")) {
-        vector<int> args = OperatorBase::GetRepeatedArg<int>("ignore_labels");
-        if (args.size()) {
-            ignore.Reshape(vector<TIndex>(1, args.size()));
-            int* ignore_data = ignore.mutable_data<int, CPUContext>();
-            for (int i = 0; i < args.size(); i++) ignore_data[i] = args[i];
+    SparseSoftmaxCrossEntropyOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws),
+          axis(OperatorBase::Arg<int>("axis", 1)),
+          normalization(OperatorBase::Arg<string>(
+              "normalization", "VALID")) {
+        vector<int> ignores = OperatorBase::Args<int>("ignore_labels");
+        if (ignores.size()) {
+            ignore.Reshape({ (TIndex)ignores.size() });
+            auto* Idata = ignore.mutable_data<int, CPUContext>();
+            for (int i = 0; i < ignores.size(); i++) Idata[i] = ignores[i];
         }
     }
     USE_OPERATOR_FUNCTIONS;
@@ -49,15 +50,16 @@ class SparseSoftmaxCrossEntropyOp : public Operator<Context> {
 template <class Context>
 class SparseSoftmaxCrossEntropyGradientOp : public Operator<Context> {
  public:
-    SparseSoftmaxCrossEntropyGradientOp(const OperatorDef& op_def, Workspace* ws)
-        : Operator<Context>(op_def, ws),
-          axis(OperatorBase::GetSingleArg<int>("axis", 1)),
-          normalization(OperatorBase::GetSingleArg<string>("normalization", "VALID")) {
-        vector<int> args = OperatorBase::GetRepeatedArg<int>("ignore_labels");
-        if (args.size()) {
-            ignore.Reshape(vector<TIndex>(1, args.size()));
-            int* ignore_data = ignore.mutable_data<int, CPUContext>();
-            for (int i = 0; i < args.size(); i++) ignore_data[i] = args[i];
+    SparseSoftmaxCrossEntropyGradientOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws),
+          axis(OperatorBase::Arg<int>("axis", 1)),
+          normalization(OperatorBase::Arg<string>(
+              "normalization", "VALID")) {
+        vector<int> ignores = OperatorBase::Args<int>("ignore_labels");
+        if (ignores.size()) {
+            ignore.Reshape({ (TIndex)ignores.size() });
+            auto* Idata = ignore.mutable_data<int, CPUContext>();
+            for (int i = 0; i < ignores.size(); i++) Idata[i] = ignores[i];
         }
     }
     USE_OPERATOR_FUNCTIONS;

Dragon/include/operators/loss/sparse_softmax_focal_loss_op.h

@@ -19,13 +19,14 @@ namespace dragon {
 template <class Context>
 class SparseSoftmaxFocalLossOp final : public SparseSoftmaxCrossEntropyOp<Context> {
  public:
-    SparseSoftmaxFocalLossOp(const OperatorDef& op_def, Workspace* ws)
-        : SparseSoftmaxCrossEntropyOp<Context>(op_def, ws),
-           axis(OperatorBase::GetSingleArg<int>("axis", 1)),
-           normalization(OperatorBase::GetSingleArg<string>("normalization", "VALID")),
-           alpha(OperatorBase::GetSingleArg<float>("alpha", 0.5)),
-           gamma(OperatorBase::GetSingleArg<float>("gamma", 0.0)),
-           neg_id(OperatorBase::GetSingleArg<int>("neg_id", -1)) {
+    SparseSoftmaxFocalLossOp(const OperatorDef& def, Workspace* ws)
+        : SparseSoftmaxCrossEntropyOp<Context>(def, ws),
+           axis(OperatorBase::Arg<int>("axis", 1)),
+           normalization(OperatorBase::Arg<string>(
+               "normalization", "VALID")),
+           alpha(OperatorBase::Arg<float>("alpha", 0.5)),
+           gamma(OperatorBase::Arg<float>("gamma", 0.0)),
+           neg_id(OperatorBase::Arg<int>("neg_id", -1)) {
         pos_alpha = alpha * 2.0;
         neg_alpha = (1 - alpha) * 2.0;
     }
@@ -46,13 +47,14 @@ class SparseSoftmaxFocalLossOp final : public SparseSoftmaxCrossEntropyOp<Contex
 template <class Context>
 class SparseSoftmaxFocalLossGradientOp final : public SparseSoftmaxCrossEntropyGradientOp<Context> {
  public:
-    SparseSoftmaxFocalLossGradientOp(const OperatorDef& op_def, Workspace* ws)
-         : SparseSoftmaxCrossEntropyGradientOp<Context>(op_def, ws),
-           axis(OperatorBase::GetSingleArg<int>("axis", 1)),
-           normalization(OperatorBase::GetSingleArg<string>("normalization", "VALID")),
-           gamma(OperatorBase::GetSingleArg<float>("gamma", 0.0)),
-           eps(OperatorBase::GetSingleArg<float>("eps", float(1e-10))),
-           neg_id(OperatorBase::GetSingleArg<int>("neg_id", -1)) {}
+    SparseSoftmaxFocalLossGradientOp(const OperatorDef& def, Workspace* ws)
+         : SparseSoftmaxCrossEntropyGradientOp<Context>(def, ws),
+           axis(OperatorBase::Arg<int>("axis", 1)),
+           normalization(OperatorBase::Arg<string>(
+               "normalization", "VALID")),
+           gamma(OperatorBase::Arg<float>("gamma", 0.0)),
+           eps(OperatorBase::Arg<float>("eps", float(1e-10))),
+           neg_id(OperatorBase::Arg<int>("neg_id", -1)) {}
     USE_OPERATOR_FUNCTIONS;
 
     void RunOnDevice() override;

Dragon/include/operators/misc/accuracy_op.h

@@ -17,17 +17,17 @@
 namespace dragon {
 
 template <class Context>
-class AccuracyOp final: public Operator<Context> {
+class AccuracyOp final : public Operator<Context> {
  public:
-    AccuracyOp(const OperatorDef& op_def, Workspace* ws)
-        : Operator<Context>(op_def, ws),
-          top_k(OperatorBase::GetSingleArg<int>("top_k", 1)),
-          axis(OperatorBase::GetSingleArg<int>("axis", 1)) {
-        vector<int> args = OperatorBase::GetRepeatedArg<int>("ignore_labels");
-        if (args.size()) {
-            ignore_labels.Reshape(vector<TIndex>(1, args.size()));
-            int* ignore_data = ignore_labels.mutable_data<int, CPUContext>();
-            for (int i = 0; i < args.size(); i++) ignore_data[i] = args[i];
+    AccuracyOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws),
+          top_k(OperatorBase::Arg<int>("top_k", 1)),
+          axis(OperatorBase::Arg<int>("axis", 1)) {
+        vector<int> ignores = OperatorBase::Args<int>("ignore_labels");
+        if (ignores.size()) {
+            ignore.Reshape({ (TIndex)ignores.size() });
+            auto* Idata = ignore.mutable_data<int, CPUContext>();
+            for (int i = 0; i < ignores.size(); i++) Idata[i] = ignores[i];
         }
     }
     USE_OPERATOR_FUNCTIONS;
@@ -37,7 +37,7 @@ class AccuracyOp final: public Operator<Context> {
 
  protected:
     TIndex top_k, axis, outer_dim, inner_dim, num_classes;
-    Tensor ignore_labels;
+    Tensor ignore;
 };
 
 }    // namespace dragon

Dragon/include/operators/misc/astype_op.h

@@ -19,10 +19,10 @@ namespace dragon {
 template <class Context>
 class AsTypeOp final : public Operator<Context> {
  public:
-    AsTypeOp(const OperatorDef& op_def, Workspace* ws)
-        : Operator<Context>(op_def, ws),
-        dtype(OperatorBase::GetSingleArg<string>("dtype", "float32")),
-        inplace(OperatorBase::GetSingleArg<bool>("inplace", false)) {}
+    AsTypeOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws),
+          dtype(OperatorBase::Arg<string>("dtype", "float32")),
+          inplace(OperatorBase::Arg<bool>("inplace", false)) {}
      USE_OPERATOR_FUNCTIONS;
 
      void RunOnDevice() override;

Dragon/include/operators/misc/gradient_op.h

@@ -19,9 +19,9 @@ namespace dragon {
 template <class Context>
 class GradientGenerateOp final: public Operator<Context> {
  public:
-    GradientGenerateOp(const OperatorDef& op_def, Workspace* ws)
-        : Operator<Context>(op_def, ws),
-          defaults(OperatorBase::GetRepeatedArg<float>("defaults")) {
+    GradientGenerateOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws),
+          defaults(OperatorBase::Args<float>("defaults")) {
         CHECK_EQ(InputSize(), OutputSize());
         CHECK_EQ(defaults.size(), OutputSize());
     }
@@ -37,8 +37,8 @@ class GradientGenerateOp final: public Operator<Context> {
 template <class Context>
 class GradientGatherOp final : public Operator<Context> {
  public:
-    GradientGatherOp(const OperatorDef& op_def, Workspace* ws)
-        : Operator<Context>(op_def, ws) {
+    GradientGatherOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws) {
         for (int i = 0; i < InputSize(); i++)
             if (Input(i).name() != "ignore") indices.push_back(i);
     }

Dragon/include/operators/misc/image_data_op.h

@@ -19,23 +19,23 @@ namespace dragon {
 template <class Context>
 class ImageDataOp final : public Operator<Context> {
  public:
-    ImageDataOp(const OperatorDef& op_def, Workspace* ws)
-         : Operator<Context>(op_def, ws),
-           dtype(OperatorBase::GetSingleArg<string>("dtype", "FLOAT32")),
-           mean_values(OperatorBase::GetRepeatedArg<float>("mean_values")),
-           std_values(OperatorBase::GetRepeatedArg<float>("std_values")),
-           data_format(OperatorBase::GetSingleArg<string>("data_format", "NCHW")) {
+    ImageDataOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws),
+          dtype(OperatorBase::Arg<string>("dtype", "FLOAT32")),
+          mean_values(OperatorBase::Args<float>("mean_values")),
+          std_values(OperatorBase::Args<float>("std_values")),
+          data_format(OperatorBase::Arg<string>("data_format", "NCHW")) {
         if (mean_values.size() > 0) {
             CHECK_EQ((int)mean_values.size(), 3)
                 << "The mean values should be a list with length 3.";
-            mean.Reshape(vector<TIndex>(1, 3));
+            mean.Reshape({ 3 });
             for (int i = 0; i < 3; i++)
                 mean.mutable_data<float, CPUContext>()[i] = mean_values[i];
         }
         if (std_values.size() > 0) {
             CHECK_EQ((int)std_values.size(), 3)
                 << "The std values should be a list with length 3.";
-            std.Reshape(vector<TIndex>(1, 3));
+            std.Reshape({ 3 });
             for (int i = 0; i < 3; i++)
                 std.mutable_data<float, CPUContext>()[i] = std_values[i];
         }

Dragon/include/operators/misc/initialize_op.h

@@ -18,11 +18,11 @@
 namespace dragon {
 
 template <class Context>
-class InitializeOp: public Operator<Context> {
+class InitializeOp : public Operator<Context> {
  public:
-    InitializeOp(const OperatorDef& op_def, Workspace* ws) 
-        : Operator<Context>(op_def, ws),
-          shape_desc(OperatorBase::GetSingleArg<string>("shape", "")) {
+    InitializeOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws),
+          shape_desc(OperatorBase::Arg<string>("shape", "")) {
         GET_ARGUMENTS_WITH_DESC(int, dims);
     }
     USE_OPERATOR_FUNCTIONS;
@@ -38,11 +38,11 @@ class InitializeOp: public Operator<Context> {
 
 template <class Context>
 class FillOp final : public InitializeOp<Context> {
-public:
-    FillOp(const OperatorDef& op_def, Workspace* ws) 
-        : InitializeOp<Context>(op_def, ws) {
+ public:
+    FillOp(const OperatorDef& def, Workspace* ws)
+        : InitializeOp<Context>(def, ws) {
         this->filler.set_type("constant");
-        this->filler.set_value(OperatorBase::GetSingleArg<float>("value", 0.0));
+        this->filler.set_value(OperatorBase::Arg<float>("value", 0.0));
     }
     USE_OPERATOR_FUNCTIONS;
 };
@@ -50,11 +50,11 @@ public:
 template <class Context>
 class RandomUniformOp final : public InitializeOp<Context> {
 public:
-    RandomUniformOp(const OperatorDef& op_def, Workspace* ws) 
-        : InitializeOp<Context>(op_def, ws) {
+    RandomUniformOp(const OperatorDef& def, Workspace* ws)
+        : InitializeOp<Context>(def, ws) {
         this->filler.set_type("uniform");
-        this->filler.set_low(OperatorBase::GetSingleArg<float>("low", -1.0));
-        this->filler.set_high(OperatorBase::GetSingleArg<float>("high", 1.0));
+        this->filler.set_low(OperatorBase::Arg<float>("low", -1.0));
+        this->filler.set_high(OperatorBase::Arg<float>("high", 1.0));
     }
     USE_OPERATOR_FUNCTIONS;
 };
@@ -62,11 +62,11 @@ public:
 template <class Context>
 class RandomNormalOp final : public InitializeOp<Context> {
 public:
-    RandomNormalOp(const OperatorDef& op_def, Workspace* ws) 
-        : InitializeOp<Context>(op_def, ws) {
+    RandomNormalOp(const OperatorDef& def, Workspace* ws)
+        : InitializeOp<Context>(def, ws) {
         this->filler.set_type("normal");
-        this->filler.set_mean(OperatorBase::GetSingleArg<float>("mean", 0.0));
-        this->filler.set_std(OperatorBase::GetSingleArg<float>("std", 1.0));
+        this->filler.set_mean(OperatorBase::Arg<float>("mean", 0.0));
+        this->filler.set_std(OperatorBase::Arg<float>("std", 1.0));
     }
     USE_OPERATOR_FUNCTIONS;
 };
@@ -74,11 +74,11 @@ public:
 template <class Context>
 class TruncatedNormalOp final : public InitializeOp<Context> {
 public:
-    TruncatedNormalOp(const OperatorDef& op_def, Workspace* ws) 
-        : InitializeOp<Context>(op_def, ws) {
+    TruncatedNormalOp(const OperatorDef& def, Workspace* ws)
+        : InitializeOp<Context>(def, ws) {
         this->filler.set_type("truncated_normal");
-        float mu = OperatorBase::GetSingleArg<float>("mean", 0.0);
-        float sigma = OperatorBase::GetSingleArg<float>("std", 1.0);
+        float mu = OperatorBase::Arg<float>("mean", 0.0);
+        float sigma = OperatorBase::Arg<float>("std", 1.0);
         this->filler.set_mean(mu);
         this->filler.set_std(sigma);
         this->filler.set_low(mu - 2 * sigma);
@@ -90,10 +90,10 @@ public:
 template <class Context>
 class GlorotUniformOp final : public InitializeOp<Context> {
 public:
-    GlorotUniformOp(const OperatorDef& op_def, Workspace* ws)
-        : InitializeOp<Context>(op_def, ws) {
-        string mode = OperatorBase::GetSingleArg<string>("mode", "fan_in");
-        float scale = OperatorBase::GetSingleArg<float>("scale", 3.0);
+    GlorotUniformOp(const OperatorDef& def, Workspace* ws)
+        : InitializeOp<Context>(def, ws) {
+        string mode = OperatorBase::Arg<string>("mode", "fan_in");
+        float scale = OperatorBase::Arg<float>("scale", 3.0);
 
         this->filler.set_type("xavier");
         if (mode == "fan_avg") {
@@ -111,10 +111,10 @@ public:
 template <class Context>
 class GlorotNormalOp final : public InitializeOp<Context> {
 public:
-    GlorotNormalOp(const OperatorDef& op_def, Workspace* ws)
-        : InitializeOp<Context>(op_def, ws) {
-        string mode = OperatorBase::GetSingleArg<string>("mode", "fan_in");
-        float scale = OperatorBase::GetSingleArg<float>("scale", 2.0);
+    GlorotNormalOp(const OperatorDef& def, Workspace* ws)
+        : InitializeOp<Context>(def, ws) {
+        string mode = OperatorBase::Arg<string>("mode", "fan_in");
+        float scale = OperatorBase::Arg<float>("scale", 2.0);
 
         this->filler.set_type("msra");
         if (mode == "fan_avg") {

Dragon/include/operators/misc/python_op.h

@@ -23,7 +23,7 @@ namespace dragon {
 template <class Context>
 class RunOp : public Operator<Context> {
  public:
-    RunOp(const OperatorDef& op_def, Workspace* ws);
+    RunOp(const OperatorDef& def, Workspace* ws);
     USE_OPERATOR_FUNCTIONS;
 
     void RunOnDevice() override;
@@ -36,16 +36,16 @@ class RunOp : public Operator<Context> {
 template <class Context>
 class TemplateOp : public RunOp<Context> {
  public:
-    TemplateOp(const OperatorDef& op_def, Workspace* ws)
-        : RunOp<Context>(op_def, ws) {}
+    TemplateOp(const OperatorDef& def, Workspace* ws)
+        : RunOp<Context>(def, ws) {}
     USE_OPERATOR_FUNCTIONS;
 };
 
 template <class Context>
-class TemplateGradientOp : public TemplateOp<Context> {
+class TemplateGradientOp final : public TemplateOp<Context> {
 public:
-    TemplateGradientOp(const OperatorDef& op_def, Workspace* ws)
-        : TemplateOp<Context>(op_def, ws) {}
+    TemplateGradientOp(const OperatorDef& def, Workspace* ws)
+        : TemplateOp<Context>(def, ws) {}
     USE_OPERATOR_FUNCTIONS;
 
     void RunOnDevice() override;

Dragon/include/operators/mpi/base_mpi_op.h

@@ -22,11 +22,11 @@ namespace dragon {
 template <class Context>
 class ModelMPIBase : public Operator<Context> {
  public:
-    ModelMPIBase(const OperatorDef& op_def, Workspace* ws)
-        : Operator<Context>(op_def, ws),
-          comm((MPI_Comm)OperatorBase::GetSingleArg<int64_t>("comm", 0)),
-          group((MPI_Group)OperatorBase::GetSingleArg<int64_t>("group", 0)),
-          dtype(OperatorBase::GetSingleArg<string>("dtype", "FLOAT32")) {
+    ModelMPIBase(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws),
+          comm((MPI_Comm)OperatorBase::Arg<int64_t>("comm", 0)),
+          group((MPI_Group)OperatorBase::Arg<int64_t>("group", 0)),
+          dtype(OperatorBase::Arg<string>("dtype", "FLOAT32")) {
 
         if (comm == MPI_COMM_NULL) return;
         MPI_Comm_size(MPI_COMM_WORLD, &world_size);
@@ -36,7 +36,7 @@ class ModelMPIBase : public Operator<Context> {
 
         MPI_Group world_group;
         MPI_Comm_group(MPI_COMM_WORLD, &world_group);
-        int world_root = OperatorBase::GetSingleArg<int>("root", 0);
+        int world_root = OperatorBase::Arg<int>("root", 0);
         MPI_Group_translate_ranks(world_group, 1, &world_root, group, &comm_root);
 
         CHECK(comm_root != MPI_UNDEFINED) << "MPI root is not included in layer group.";

Dragon/include/operators/mpi/mpi_broadcast_op.h

@@ -21,8 +21,8 @@ namespace dragon {
 template <class Context>
 class MPIBroadcastOp final : public ModelMPIBase<Context> {
  public:
-    MPIBroadcastOp(const OperatorDef& op_def, Workspace* ws)
-        : ModelMPIBase<Context>(op_def, ws) {}
+    MPIBroadcastOp(const OperatorDef& def, Workspace* ws)
+        : ModelMPIBase<Context>(def, ws) {}
     USE_OPERATOR_FUNCTIONS;
     USE_MPIMODEL_FUNCTIONS(Context);
 
@@ -33,8 +33,8 @@ class MPIBroadcastOp final : public ModelMPIBase<Context> {
 template <class Context>
 class MPIBroadcastGradientOp final : public ModelMPIBase<Context> {
 public:
-    MPIBroadcastGradientOp(const OperatorDef& op_def, Workspace* ws)
-        : ModelMPIBase<Context>(op_def, ws) {}
+    MPIBroadcastGradientOp(const OperatorDef& def, Workspace* ws)
+        : ModelMPIBase<Context>(def, ws) {}
     USE_OPERATOR_FUNCTIONS;
     USE_MPIMODEL_FUNCTIONS(Context);
 

Dragon/include/operators/mpi/mpi_gather_op.h

@@ -21,8 +21,8 @@ namespace dragon {
 template <class Context>
 class MPIGatherOp final : public ModelMPIBase<Context> {
  public:
-    MPIGatherOp(const OperatorDef& op_def, Workspace *ws)
-        : ModelMPIBase<Context>(op_def, ws) {}
+    MPIGatherOp(const OperatorDef& def, Workspace *ws)
+        : ModelMPIBase<Context>(def, ws) {}
     USE_OPERATOR_FUNCTIONS;
     USE_MPIMODEL_FUNCTIONS(Context);
 
@@ -33,8 +33,8 @@ class MPIGatherOp final : public ModelMPIBase<Context> {
 template <class Context>
 class MPIGatherGradientOp final : public ModelMPIBase<Context> {
  public:
-    MPIGatherGradientOp(const OperatorDef& op_def, Workspace *ws) 
-        : ModelMPIBase<Context>(op_def, ws) {}
+    MPIGatherGradientOp(const OperatorDef& def, Workspace *ws) 
+        : ModelMPIBase<Context>(def, ws) {}
     USE_OPERATOR_FUNCTIONS;
     USE_MPIMODEL_FUNCTIONS(Context);
 

Dragon/include/operators/ndarray/arange_op.h

@@ -19,9 +19,9 @@ namespace dragon {
 template <class Context>
 class ArangeOp final : public Operator<Context> {
  public:
-    ArangeOp(const OperatorDef& op_def, Workspace* ws)
-        : Operator<Context>(op_def, ws),
-          dtype(OperatorBase::GetSingleArg<string>("dtype", "FLOAT32")) {
+    ArangeOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws),
+          dtype(OperatorBase::Arg<string>("dtype", "FLOAT32")) {
         GET_ARGUMENT_WITH_DESC(int, start, 0);
         GET_ARGUMENT_WITH_DESC(int, stop, 0);
         GET_ARGUMENT_WITH_DESC(int, step, 1);

Dragon/include/operators/ndarray/argreduce_op.h

@@ -19,12 +19,12 @@ namespace dragon {
 template <class Context>
 class ArgReduceOp final : public Operator<Context> {
  public:
-    ArgReduceOp(const OperatorDef& op_def, Workspace* ws)
-        : Operator<Context>(op_def, ws),
-          axis(OperatorBase::GetSingleArg<int>("axis", -1)),
-          operation(OperatorBase::GetSingleArg<string>("operation", "NONE")),
-          keep_dims(OperatorBase::GetSingleArg<bool>("keep_dims", false)),
-          top_k(OperatorBase::GetSingleArg<int>("top_k", 1)) {}
+    ArgReduceOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws),
+          axis(OperatorBase::Arg<int>("axis", -1)),
+          operation(OperatorBase::Arg<string>("operation", "NONE")),
+          keep_dims(OperatorBase::Arg<bool>("keep_dims", false)),
+          top_k(OperatorBase::Arg<int>("top_k", 1)) {}
     USE_OPERATOR_FUNCTIONS;
 
     void RunOnDevice() override;

Dragon/include/operators/ndarray/concat_op.h

@@ -19,16 +19,17 @@ namespace dragon {
 template <class Context>
 class ConcatOp : public Operator<Context> {
  public:
-    ConcatOp(const OperatorDef& op_def, Workspace* ws)
-        : Operator<Context>(op_def, ws), 
-        axis(OperatorBase::GetSingleArg<int>("axis", 1)) {}
+    ConcatOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws),
+          axis(OperatorBase::Arg<int>("axis", 1)) {}
     USE_OPERATOR_FUNCTIONS;
 
     void RunOnDevice() override;
     template <typename T> void RunWithType();
 
  protected:
-    TIndex axis, outer_dim, inner_dim, x_concat_dim, y_concat_dim;
+    TIndex axis, outer_dim, inner_dim;
+    TIndex x_concat_dim, y_concat_dim;
     TIndex x_offset, y_offset, concat_offset;
     vector<TIndex> concat_dims;
 };
@@ -36,16 +37,17 @@ class ConcatOp : public Operator<Context> {
 template <class Context>
 class ConcatGradientOp : public Operator<Context> {
  public:
-    ConcatGradientOp(const OperatorDef& op_def, Workspace* ws)
-        : Operator<Context>(op_def, ws),
-        axis(OperatorBase::GetSingleArg<int>("axis", 1)) {}
+    ConcatGradientOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws),
+          axis(OperatorBase::Arg<int>("axis", 1)) {}
     USE_OPERATOR_FUNCTIONS;
 
     void RunOnDevice() override;
     template <typename T> void RunWithType();
 
  protected:
-    TIndex axis, outer_dim, inner_dim, x_concat_dim, y_concat_dim;
+    TIndex axis, outer_dim, inner_dim;
+    TIndex x_concat_dim, y_concat_dim;
     TIndex x_offset, y_offset, concat_offset;
     vector<TIndex> concat_dims;
 };

Dragon/include/operators/ndarray/crop_op.h

@@ -17,14 +17,14 @@
 namespace dragon {
 
 template <class Context>
-class CropOp: public Operator<Context> {
+class CropOp final : public Operator<Context> {
  public:
-    CropOp(const OperatorDef& op_def, Workspace* ws)
-        : Operator<Context>(op_def, ws),
-          start_axis(OperatorBase::GetSingleArg<int>("start_axis", -1)),
-          offsets(OperatorBase::GetRepeatedArg<int>("offsets")),
-          shape(OperatorBase::GetRepeatedArg<int>("shape")),
-          shape_like(OperatorBase::GetSingleArg<string>("shape_like", "")) {
+    CropOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws),
+          start_axis(OperatorBase::Arg<int>("start_axis", -1)),
+          offsets(OperatorBase::Args<int>("offsets")),
+          shape(OperatorBase::Args<int>("shape")),
+          shape_like(OperatorBase::Arg<string>("shape_like", "")) {
         GET_ARGUMENTS_WITH_DESC(int, starts);
         GET_ARGUMENTS_WITH_DESC(int, ends);
     }

Dragon/include/operators/ndarray/expand_dims_op.h

@@ -19,9 +19,9 @@ namespace dragon {
 template <class Context>
 class ExpandDimsOp final : public Operator<Context> {
  public:
-    ExpandDimsOp(const OperatorDef& op_def, Workspace* ws)
-        : Operator<Context>(op_def, ws),
-          axis(OperatorBase::GetSingleArg<int>("axis", -1)) {}
+    ExpandDimsOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws),
+          axis(OperatorBase::Arg<int>("axis", -1)) {}
     USE_OPERATOR_FUNCTIONS;
 
     void RunOnDevice() override;

Dragon/include/operators/ndarray/flatten_op.h

@@ -19,11 +19,11 @@ namespace dragon {
 template <class Context>
 class FlattenOp final : public Operator<Context> {
  public:
-    FlattenOp(const OperatorDef& op_def, Workspace* ws) 
-        : Operator<Context>(op_def, ws),
-          axis(OperatorBase::GetSingleArg<int>("axis", 0)),
-          num_axes(OperatorBase::GetSingleArg<int>("num_axes", -1)),
-          keep_axes(OperatorBase::GetSingleArg<int>("keep_axes", INT_MAX)) {}
+    FlattenOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws),
+          axis(OperatorBase::Arg<int>("axis", 0)),
+          num_axes(OperatorBase::Arg<int>("num_axes", -1)),
+          keep_axes(OperatorBase::Arg<int>("keep_axes", INT_MAX)) {}
     USE_OPERATOR_FUNCTIONS;
 
     void RunOnDevice() override;

Dragon/include/operators/ndarray/gather_op.h

@@ -19,9 +19,9 @@ namespace dragon {
 template <class Context>
 class GatherOp final : public Operator<Context> {
  public:
-    GatherOp(const OperatorDef& op_def, Workspace* ws)
-        : Operator<Context>(op_def, ws),
-          axis(OperatorBase::GetSingleArg<int>("axis", 0)) {}
+    GatherOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws),
+          axis(OperatorBase::Arg<int>("axis", 0)) {}
     USE_OPERATOR_FUNCTIONS;
 
     void RunOnDevice() override;
@@ -35,10 +35,10 @@ class GatherOp final : public Operator<Context> {
 template <class Context>
 class GatherGradientOp final : public Operator<Context> {
  public:
-    GatherGradientOp(const OperatorDef& op_def, Workspace* ws) 
-        : Operator<Context>(op_def, ws),
-          axis(OperatorBase::GetSingleArg<int>("axis", 0)),
-          acc_grad(OperatorBase::GetSingleArg<bool>("acc_gradient", false)) {}
+    GatherGradientOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws),
+          axis(OperatorBase::Arg<int>("axis", 0)),
+          acc_grad(OperatorBase::Arg<bool>("acc_gradient", false)) {}
     USE_OPERATOR_FUNCTIONS;
 
     void RunOnDevice() override;

Dragon/include/operators/ndarray/one_hot_op.h

@@ -19,11 +19,11 @@ namespace dragon {
 template <class Context>
 class OneHotOp final : public Operator < Context > {
  public:
-    OneHotOp(const OperatorDef& op_def, Workspace* ws)
-        : Operator<Context>(op_def, ws),
-          depth(OperatorBase::GetSingleArg<int>("depth", -1)),
-          on_value(OperatorBase::GetSingleArg<int>("on_value", 1)),
-          off_value(OperatorBase::GetSingleArg<int>("off_value", 0)) {}
+    OneHotOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws),
+          depth(OperatorBase::Arg<int>("depth", -1)),
+          on_value(OperatorBase::Arg<int>("on_value", 1)),
+          off_value(OperatorBase::Arg<int>("off_value", 0)) {}
     USE_OPERATOR_FUNCTIONS;
 
     void RunOnDevice() override;

Dragon/include/operators/ndarray/pad_op.h

@@ -19,12 +19,12 @@ namespace dragon {
 template <class Context>
 class PadOp final : public Operator<Context> {
  public:
-    PadOp(const OperatorDef& op_def, Workspace* ws)
-        : Operator<Context>(op_def, ws),
-          pad_l(OperatorBase::GetRepeatedArg<int>("pad_l")),
-          pad_r(OperatorBase::GetRepeatedArg<int>("pad_r")),
-          mode(OperatorBase::GetSingleArg<string>("mode", "CONSTANT")), 
-          value(OperatorBase::GetSingleArg<float>("value", 0.0f)) {
+    PadOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws),
+          pad_l(OperatorBase::Args<int>("pad_l")),
+          pad_r(OperatorBase::Args<int>("pad_r")),
+          mode(OperatorBase::Arg<string>("mode", "CONSTANT")),
+          value(OperatorBase::Arg<float>("value", 0.0f)) {
         if (pad_r.size() == 0) pad_r = pad_l;
         else CHECK_EQ(pad_l.size(), pad_r.size())
             << "The pad_l and pad_r should have the same length.";
@@ -54,11 +54,11 @@ class PadOp final : public Operator<Context> {
 template <class Context>
 class PadGradientOp final : public Operator<Context> {
  public:
-    PadGradientOp(const OperatorDef& op_def, Workspace* ws)
-        : Operator<Context>(op_def, ws),    
-          pad_l(OperatorBase::GetRepeatedArg<int>("pad_l")),
-          pad_r(OperatorBase::GetRepeatedArg<int>("pad_r")),
-          mode(OperatorBase::GetSingleArg<string>("mode", "CONSTANT")) {
+    PadGradientOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws),
+          pad_l(OperatorBase::Args<int>("pad_l")),
+          pad_r(OperatorBase::Args<int>("pad_r")),
+          mode(OperatorBase::Arg<string>("mode", "CONSTANT")) {
         if (pad_r.size() == 0) pad_r = pad_l;
         else CHECK_EQ(pad_l.size(), pad_r.size())
             << "The pad_l and pad_r should have the same length.";

Dragon/include/operators/ndarray/random_pick_op.h

@@ -17,20 +17,20 @@
 namespace dragon {
 
 template <class Context>
-class RandomPickOp : public Operator<Context> {
+class RandomPickOp final : public Operator<Context> {
  public:
-    RandomPickOp(const OperatorDef& op_def, Workspace* ws) :
-        Operator<Context>(op_def, ws),
-        axis(OperatorBase::GetSingleArg<int>("axis", 0)),
-        max_samples(OperatorBase::GetSingleArg<int>("max_samples", 1)) {}
+    RandomPickOp(const OperatorDef& def, Workspace* ws) :
+        Operator<Context>(def, ws),
+        axis(OperatorBase::Arg<int>("axis", 0)),
+        max_samples(OperatorBase::Arg<int>("max_samples", 1)) {}
     USE_OPERATOR_FUNCTIONS;
 
     void RunOnDevice() override;
     template <typename T> void RunWithType();
 
  protected:
-    TIndex axis, max_samples;
-    TIndex outer_dim, inner_dim, x_slice_dim, y_slice_dim;
+    TIndex axis, outer_dim, inner_dim, max_samples;
+    TIndex x_slice_dim, y_slice_dim;
     vector<TIndex> output_dims;
     Tensor* pick_indices;
 };
@@ -38,17 +38,17 @@ class RandomPickOp : public Operator<Context> {
 template <class Context>
 class RandomPickGradientOp final : public Operator<Context> {
 public:
-    RandomPickGradientOp(const OperatorDef& op_def, Workspace* ws)
-        : Operator<Context>(op_def, ws),
-        axis(OperatorBase::GetSingleArg<int>("axis", 0)) {}
+    RandomPickGradientOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws),
+          axis(OperatorBase::Arg<int>("axis", 0)) {}
     USE_OPERATOR_FUNCTIONS;
 
     void RunOnDevice() override;
     template <typename T> void RunWithType();
 
 protected:
-    TIndex axis;
-    TIndex outer_dim, inner_dim, x_slice_dim, y_slice_dim;
+    TIndex axis, outer_dim, inner_dim;
+    TIndex x_slice_dim, y_slice_dim;
     Tensor* pick_indices;
 };
 

Dragon/include/operators/ndarray/reduce_op.h

@@ -19,11 +19,11 @@ namespace dragon {
 template <class Context>
 class ReduceOp final : public Operator<Context> {
  public:
-    ReduceOp(const OperatorDef& op_def, Workspace* ws)
-        : Operator<Context>(op_def, ws),
-          axis(OperatorBase::GetSingleArg<int>("axis", -1)),
-          operation(OperatorBase::GetSingleArg<string>("operation", "NONE")),
-          keep_dims(OperatorBase::GetSingleArg<bool>("keep_dims", false)) {}
+    ReduceOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws),
+          axis(OperatorBase::Arg<int>("axis", -1)),
+          operation(OperatorBase::Arg<string>("operation", "NONE")),
+          keep_dims(OperatorBase::Arg<bool>("keep_dims", false)) {}
     USE_OPERATOR_FUNCTIONS;
 
     void RunOnDevice() override;
@@ -38,10 +38,10 @@ class ReduceOp final : public Operator<Context> {
 template <class Context>
 class ReduceGradientOp final : public Operator<Context> {
  public:
-    ReduceGradientOp(const OperatorDef& op_def, Workspace* ws)
-        : Operator<Context>(op_def, ws),
-        axis(OperatorBase::GetSingleArg<int>("axis", -1)),
-        operation(OperatorBase::GetSingleArg<string>("operation", "NONE")) {}
+    ReduceGradientOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws),
+          axis(OperatorBase::Arg<int>("axis", -1)),
+          operation(OperatorBase::Arg<string>("operation", "NONE")) {}
     USE_OPERATOR_FUNCTIONS;
 
     void RunOnDevice() override;

Dragon/include/operators/ndarray/repeat_op.h

@@ -17,11 +17,11 @@
 namespace dragon {
 
 template <class Context>
-class RepeatOp : public Operator<Context> {
+class RepeatOp final : public Operator<Context> {
  public:
-    RepeatOp(const OperatorDef& op_def, Workspace* ws)
-        : Operator<Context>(op_def, ws),
-          axis(OperatorBase::GetSingleArg<int>("axis", -1)) {
+    RepeatOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws),
+          axis(OperatorBase::Arg<int>("axis", -1)) {
         GET_ARGUMENT_WITH_DESC(int, repeats, 1);
     }
     USE_OPERATOR_FUNCTIONS;
@@ -35,11 +35,11 @@ class RepeatOp : public Operator<Context> {
 };
 
 template <class Context>
-class RepeatGradientOp : public Operator<Context> {
+class RepeatGradientOp final : public Operator<Context> {
  public:
-    RepeatGradientOp(const OperatorDef& op_def, Workspace* ws)
-        : Operator<Context>(op_def, ws),
-          axis(OperatorBase::GetSingleArg<int>("axis", -1)) {
+    RepeatGradientOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws),
+          axis(OperatorBase::Arg<int>("axis", -1)) {
         GET_ARGUMENT_WITH_DESC(int, repeats, 1);
     }
     USE_OPERATOR_FUNCTIONS;

Dragon/include/operators/ndarray/reshape_op.h

@@ -19,9 +19,9 @@ namespace dragon {
 template <class Context>
 class ReshapeOp final : public Operator<Context> {
  public:
-    ReshapeOp(const OperatorDef& op_def, Workspace* ws) 
-        : Operator<Context>(op_def, ws),
-          shape_like_desc(OperatorBase::GetSingleArg<string>("shape_like", "")) {
+    ReshapeOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws),
+          shape_like_desc(OperatorBase::Arg<string>("shape_like", "")) {
         GET_ARGUMENTS_WITH_DESC(int, shape);
     }
     USE_OPERATOR_FUNCTIONS;

Dragon/include/operators/ndarray/slice_op.h

@@ -17,12 +17,12 @@
 namespace dragon {
 
 template <class Context>
-class SliceOp : public Operator<Context> {
+class SliceOp final : public Operator<Context> {
  public:
-    SliceOp(const OperatorDef& op_def, Workspace* ws):
-        Operator<Context>(op_def, ws),
-        axis(OperatorBase::GetSingleArg<int>("axis", 1)),
-        nout(OperatorBase::GetSingleArg<int>("num_output", 1)) {}
+    SliceOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws),
+          axis(OperatorBase::Arg<int>("axis", 1)),
+          nout(OperatorBase::Arg<int>("num_output", 1)) {}
     USE_OPERATOR_FUNCTIONS;
 
     void RunOnDevice() override;
@@ -38,10 +38,10 @@ class SliceOp : public Operator<Context> {
 template <class Context>
 class SliceGradientOp final : public Operator<Context> {
  public:
-    SliceGradientOp(const OperatorDef& op_def, Workspace* ws):
-        Operator<Context>(op_def, ws),
-        axis(OperatorBase::GetSingleArg<int>("axis", 1)),
-        nout(OperatorBase::GetSingleArg<int>("num_output", 1)) {}
+    SliceGradientOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws),
+          axis(OperatorBase::Arg<int>("axis", 1)),
+          nout(OperatorBase::Arg<int>("num_output", 1)) {}
     USE_OPERATOR_FUNCTIONS;
 
     void RunOnDevice() override;

Dragon/include/operators/ndarray/stack_op.h

@@ -17,37 +17,37 @@
 namespace dragon {
 
 template <class Context>
-class StackOp : public Operator<Context> {
+class StackOp final : public Operator<Context> {
  public:
-    StackOp(const OperatorDef& op_def, Workspace* ws)
-        : Operator<Context>(op_def, ws),
-          axis(OperatorBase::GetSingleArg<int>("axis", 0)),
-          nin(OperatorBase::GetSingleArg<int>("num_input", 1)) {}
+    StackOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws),
+          axis(OperatorBase::Arg<int>("axis", 0)) {}
     USE_OPERATOR_FUNCTIONS;
 
     void RunOnDevice() override;
     template <typename T> void RunWithType();
 
  protected:
-    TIndex axis, nin, outer_dim, inner_dim, x_concat_dim, y_concat_dim;
+    TIndex axis, outer_dim, inner_dim;
+    TIndex x_concat_dim, y_concat_dim;
     TIndex x_offset, y_offset, concat_offset;
     vector<TIndex> stack_dims, concat_dims;
 };
 
 template <class Context>
-class StackGradientOp : public Operator<Context> {
+class StackGradientOp final : public Operator<Context> {
  public:
-    StackGradientOp(const OperatorDef& op_def, Workspace* ws) 
-        : Operator<Context>(op_def, ws),
-          axis(OperatorBase::GetSingleArg<int>("axis", 0)),
-          nin(OperatorBase::GetSingleArg<int>("num_input", 1)) {}
+    StackGradientOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws),
+          axis(OperatorBase::Arg<int>("axis", 0)) {}
     USE_OPERATOR_FUNCTIONS;
 
     void RunOnDevice() override;
     template <typename T> void RunWithType();
 
  protected:
-    TIndex axis, nin, outer_dim, inner_dim, x_concat_dim, y_concat_dim;
+    TIndex axis, outer_dim, inner_dim;
+    TIndex x_concat_dim, y_concat_dim;
     TIndex x_offset, y_offset, concat_offset;
     vector<TIndex> concat_dims;
 }; 

Dragon/include/operators/ndarray/tile_op.h

@@ -17,10 +17,10 @@
 namespace dragon {
 
 template <class Context>
-class TileOp : public Operator<Context> {
+class TileOp final : public Operator<Context> {
  public:
-    TileOp(const OperatorDef& op_def, Workspace* ws)
-        : Operator<Context>(op_def, ws) {
+    TileOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws) {
         GET_ARGUMENTS_WITH_DESC(int, multiples);
     }
     USE_OPERATOR_FUNCTIONS;
@@ -35,10 +35,10 @@ class TileOp : public Operator<Context> {
 };
 
 template <class Context>
-class TileGradientOp : public Operator<Context> {
+class TileGradientOp final : public Operator<Context> {
  public:
-    TileGradientOp(const OperatorDef& op_def, Workspace* ws) 
-        : Operator<Context>(op_def, ws) {
+    TileGradientOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws) {
         GET_ARGUMENTS_WITH_DESC(int, multiples);
     }
     USE_OPERATOR_FUNCTIONS;

Dragon/include/operators/ndarray/transpose_op.h

@@ -19,11 +19,9 @@ namespace dragon {
 template <class Context>
 class TransposeOp final: public Operator<Context> {
  public:
-    TransposeOp(const OperatorDef& op_def, Workspace* ws)
-        : Operator<Context>(op_def, ws),
-          perms(OperatorBase::GetRepeatedArg<int>("perms")) {
-        if (perms.size() > 0) reverse_dims = false;
-        else reverse_dims = true;
+    TransposeOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws) {
+        GET_ARGUMENTS_WITH_DESC(int, perms);
     }
     USE_OPERATOR_FUNCTIONS;
 
@@ -31,17 +29,17 @@ class TransposeOp final: public Operator<Context> {
     template <typename T> void RunWithType();
 
  protected:
-    vector<int> perms;
-    bool reverse_dims;
+    DECLARE_ARGUMENTS_WITH_DESC(int, perms);
     Tensor* order, *old_steps, *new_steps;
 };
 
+DEFINE_ARGUMENTS_WITH_DESC(int, TransposeOp, perms);
 
 template <class Context>
 class TransposeGradientOp final : public Operator<Context> {
  public:
-    TransposeGradientOp(const OperatorDef& op_def, Workspace* ws)
-        : Operator<Context>(op_def, ws) {}
+    TransposeGradientOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws) {}
     USE_OPERATOR_FUNCTIONS;
 
     void RunOnDevice() override;

Dragon/include/operators/norm/batch_norm_op.h

@@ -19,15 +19,15 @@
 namespace dragon {
 
 template <class Context>
-class BatchNormOp : public Operator<Context> {
+class BatchNormOp final : public Operator<Context> {
  public:
-    BatchNormOp(const OperatorDef& op_def, Workspace* ws)
-        : Operator<Context>(op_def, ws),
-          axis(OperatorBase::GetSingleArg<int>("axis", -1)),
-          momentum(OperatorBase::GetSingleArg<float>("momentum", 0.9f)),
-          eps(OperatorBase::GetSingleArg<float>("eps", 1e-3f)),
-          use_stats(OperatorBase::GetSingleArg<int>("use_stats", -1)),
-          mode(OperatorBase::GetSingleArg<string>("mode", "DEFAULT")) {
+    BatchNormOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws),
+          axis(OperatorBase::Arg<int>("axis", -1)),
+          momentum(OperatorBase::Arg<float>("momentum", 0.9f)),
+          eps(OperatorBase::Arg<float>("eps", 1e-3f)),
+          use_stats(OperatorBase::Arg<int>("use_stats", -1)),
+          mode(OperatorBase::Arg<string>("mode", "DEFAULT")) {
         if (axis != -1) 
             CHECK_EQ(axis, 1) 
                 << "\nThe axis can only be set to 1.";
@@ -51,10 +51,10 @@ class BatchNormOp : public Operator<Context> {
 template <class Context>
 class BatchNormGradientOp final : public Operator<Context> {
  public:
-    BatchNormGradientOp(const OperatorDef& op_def, Workspace *ws)
-        : Operator<Context>(op_def, ws),
-          axis(OperatorBase::GetSingleArg<int>("axis", -1)),
-          use_stats(OperatorBase::GetSingleArg<int>("use_stats", -1)) {
+    BatchNormGradientOp(const OperatorDef& def, Workspace *ws)
+        : Operator<Context>(def, ws),
+          axis(OperatorBase::Arg<int>("axis", -1)),
+          use_stats(OperatorBase::Arg<int>("use_stats", -1)) {
         if (axis != -1)
             CHECK_EQ(axis, 1)
                 << "\nThe axis can only be set to 1.";
@@ -77,12 +77,12 @@ class BatchNormGradientOp final : public Operator<Context> {
 template <class Context>
 class FusedBatchNormOp : public Operator<Context> {
  public:
-    FusedBatchNormOp(const OperatorDef& op_def, Workspace* ws)
-        : Operator<Context>(op_def, ws),
-          axis(OperatorBase::GetSingleArg<int>("axis", -1)),
-          momentum(OperatorBase::GetSingleArg<float>("momentum", 0.9f)),
-          eps(OperatorBase::GetSingleArg<float>("eps", 1e-3f)),
-          use_stats(OperatorBase::GetSingleArg<int>("use_stats", -1)) {}
+    FusedBatchNormOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws),
+          axis(OperatorBase::Arg<int>("axis", -1)),
+          momentum(OperatorBase::Arg<float>("momentum", 0.9f)),
+          eps(OperatorBase::Arg<float>("eps", 1e-3f)),
+          use_stats(OperatorBase::Arg<int>("use_stats", -1)) {}
     USE_OPERATOR_FUNCTIONS;
 
     void Setup();
@@ -102,11 +102,11 @@ class FusedBatchNormOp : public Operator<Context> {
 template <class Context>
 class FusedBatchNormGradientOp : public Operator<Context> {
  public:
-    FusedBatchNormGradientOp(const OperatorDef& op_def, Workspace* ws)
-        : Operator<Context>(op_def, ws),
-          axis(OperatorBase::GetSingleArg<int>("axis", -1)),
-          eps(OperatorBase::GetSingleArg<float>("eps", 1e-3f)),
-          use_stats(OperatorBase::GetSingleArg<int>("use_stats", -1)) {}
+    FusedBatchNormGradientOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws),
+          axis(OperatorBase::Arg<int>("axis", -1)),
+          eps(OperatorBase::Arg<float>("eps", 1e-3f)),
+          use_stats(OperatorBase::Arg<int>("use_stats", -1)) {}
     USE_OPERATOR_FUNCTIONS;
 
     void Setup();
@@ -132,9 +132,9 @@ class FusedBatchNormGradientOp : public Operator<Context> {
 template <class Context>
 class CuDNNBatchNormOp final : public FusedBatchNormOp<Context> {
  public:
-    CuDNNBatchNormOp(const OperatorDef& op_def, Workspace* ws)
-        : FusedBatchNormOp<Context>(op_def, ws),
-        eps64(OperatorBase::GetSingleArg<float>("eps", 1e-3f)) {
+    CuDNNBatchNormOp(const OperatorDef& def, Workspace* ws)
+        : FusedBatchNormOp<Context>(def, ws),
+          eps64(OperatorBase::Arg<float>("eps", 1e-3f)) {
         CUDNN_CHECK(cudnnCreateTensorDescriptor(&input_desc));
         CUDNN_CHECK(cudnnCreateTensorDescriptor(&output_desc));
         CUDNN_CHECK(cudnnCreateTensorDescriptor(&bn_desc));
@@ -169,9 +169,9 @@ class CuDNNBatchNormOp final : public FusedBatchNormOp<Context> {
 template <class Context>
 class CuDNNBatchNormGradientOp final : public FusedBatchNormGradientOp<Context> {
  public:
-    CuDNNBatchNormGradientOp(const OperatorDef& op_def, Workspace* ws)
-        : FusedBatchNormGradientOp<Context>(op_def, ws),
-        eps64(OperatorBase::GetSingleArg<float>("eps", 1e-3f)) {
+    CuDNNBatchNormGradientOp(const OperatorDef& def, Workspace* ws)
+        : FusedBatchNormGradientOp<Context>(def, ws),
+          eps64(OperatorBase::Arg<float>("eps", 1e-3f)) {
         CUDNN_CHECK(cudnnCreateTensorDescriptor(&input_desc));
         CUDNN_CHECK(cudnnCreateTensorDescriptor(&output_desc));
         CUDNN_CHECK(cudnnCreateTensorDescriptor(&bn_desc));

Dragon/include/operators/norm/batch_renorm_op.h

@@ -17,19 +17,19 @@
 namespace dragon {
 
 template <class Context>
-class BatchRenormOp : public Operator<Context> {
+class BatchRenormOp final : public Operator<Context> {
  public:
-    BatchRenormOp(const OperatorDef& op_def, Workspace* ws) 
-        : Operator<Context>(op_def, ws),
-          axis(OperatorBase::GetSingleArg<int>("axis", -1)),
-          momentum(OperatorBase::GetSingleArg<float>("momentum", 0.9f)),
-          eps(OperatorBase::GetSingleArg<float>("eps", 1e-3f)),
-          r_max(OperatorBase::GetSingleArg<float>("r_max", 3.f)),
-          d_max(OperatorBase::GetSingleArg<float>("d_max", 5.f)),
-          t_delta(OperatorBase::GetSingleArg<float>("t_delta", 1.f)),
-          use_stats(OperatorBase::GetSingleArg<int>("use_stats", -1)),
+    BatchRenormOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws),
+          axis(OperatorBase::Arg<int>("axis", -1)),
+          momentum(OperatorBase::Arg<float>("momentum", 0.9f)),
+          eps(OperatorBase::Arg<float>("eps", 1e-3f)),
+          r_max(OperatorBase::Arg<float>("r_max", 3.f)),
+          d_max(OperatorBase::Arg<float>("d_max", 5.f)),
+          t_delta(OperatorBase::Arg<float>("t_delta", 1.f)),
+          use_stats(OperatorBase::Arg<int>("use_stats", -1)),
           t_r_max(1.f), t_d_max(0.f), t_val(0.f),
-          mode(OperatorBase::GetSingleArg<string>("mode", "DEFAULT")) {
+          mode(OperatorBase::Arg<string>("mode", "DEFAULT")) {
         if (axis != -1)
             CHECK_EQ(axis, 1)
                 << "\nThe axis can only be set to 1.";
@@ -55,10 +55,10 @@ class BatchRenormOp : public Operator<Context> {
 template <class Context>
 class BatchRenormGradientOp final : public Operator<Context> {
  public:
-    BatchRenormGradientOp(const OperatorDef& op_def, Workspace *ws) 
-        : Operator<Context>(op_def, ws),
-          axis(OperatorBase::GetSingleArg<int>("axis", -1)),
-          use_stats(OperatorBase::GetSingleArg<int>("use_stats", -1)) {
+    BatchRenormGradientOp(const OperatorDef& def, Workspace *ws)
+        : Operator<Context>(def, ws),
+          axis(OperatorBase::Arg<int>("axis", -1)),
+          use_stats(OperatorBase::Arg<int>("use_stats", -1)) {
         if (axis != -1)
             CHECK_EQ(axis, 1)
                 << "\nThe axis can only be set to 1.";

Dragon/include/operators/norm/group_norm_op.h

@@ -17,13 +17,13 @@
 namespace dragon {
 
 template <class Context>
-class GroupNormOp : public Operator<Context> {
+class GroupNormOp final : public Operator<Context> {
  public:
-    GroupNormOp(const OperatorDef& op_def, Workspace* ws)
-        : Operator<Context>(op_def, ws),
-          group(OperatorBase::GetSingleArg<int>("group", 32)),
-          axis(OperatorBase::GetSingleArg<int>("axis", -1)),
-          eps(OperatorBase::GetSingleArg<float>("eps", 1e-3f)) {
+    GroupNormOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws),
+          group(OperatorBase::Arg<int>("group", 32)),
+          axis(OperatorBase::Arg<int>("axis", -1)),
+          eps(OperatorBase::Arg<float>("eps", 1e-3f)) {
         if (axis != -1) 
             CHECK_EQ(axis, 1) 
                 << "\nThe axis can only be set to 1.";
@@ -45,10 +45,10 @@ class GroupNormOp : public Operator<Context> {
 template <class Context>
 class GroupNormGradientOp final : public Operator<Context> {
  public:
-    GroupNormGradientOp(const OperatorDef& op_def, Workspace *ws)
-        : Operator<Context>(op_def, ws),
-          group(OperatorBase::GetSingleArg<int>("group", 32)),
-          axis(OperatorBase::GetSingleArg<int>("axis", -1)) {
+    GroupNormGradientOp(const OperatorDef& def, Workspace *ws)
+        : Operator<Context>(def, ws),
+          group(OperatorBase::Arg<int>("group", 32)),
+          axis(OperatorBase::Arg<int>("axis", -1)) {
         if (axis != -1)
             CHECK_EQ(axis, 1)
                 << "\nThe axis can only be set to 1.";
@@ -67,13 +67,13 @@ class GroupNormGradientOp final : public Operator<Context> {
 };
 
 template <class Context>
-class FusedGroupNormOp : public Operator<Context> {
+class FusedGroupNormOp final : public Operator<Context> {
  public:
-    FusedGroupNormOp(const OperatorDef& op_def, Workspace* ws)
-        : Operator<Context>(op_def, ws),
-          group(OperatorBase::GetSingleArg<int>("group", 32)),
-          axis(OperatorBase::GetSingleArg<int>("axis", -1)),
-          eps(OperatorBase::GetSingleArg<float>("eps", 1e-3f)) {}
+    FusedGroupNormOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws),
+          group(OperatorBase::Arg<int>("group", 32)),
+          axis(OperatorBase::Arg<int>("axis", -1)),
+          eps(OperatorBase::Arg<float>("eps", 1e-3f)) {}
     USE_OPERATOR_FUNCTIONS;
 
     void Setup();
@@ -89,12 +89,12 @@ class FusedGroupNormOp : public Operator<Context> {
 };
 
 template <class Context>
-class FusedGroupNormGradientOp : public Operator<Context> {
+class FusedGroupNormGradientOp final : public Operator<Context> {
  public:
-    FusedGroupNormGradientOp(const OperatorDef& op_def, Workspace* ws)
-        : Operator<Context>(op_def, ws),
-          group(OperatorBase::GetSingleArg<int>("group", 32)),
-          axis(OperatorBase::GetSingleArg<int>("axis", -1)) {}
+    FusedGroupNormGradientOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws),
+          group(OperatorBase::Arg<int>("group", 32)),
+          axis(OperatorBase::Arg<int>("axis", -1)) {}
     USE_OPERATOR_FUNCTIONS;
 
     void Setup();

Dragon/include/operators/norm/instance_norm_op.h

@@ -17,12 +17,12 @@
 namespace dragon {
 
 template <class Context>
-class InstanceNormOp : public Operator<Context> {
+class InstanceNormOp final : public Operator<Context> {
  public:
-    InstanceNormOp(const OperatorDef& op_def, Workspace* ws)
-        : Operator<Context>(op_def, ws),
-          axis(OperatorBase::GetSingleArg<int>("axis", -1)),
-          eps(OperatorBase::GetSingleArg<float>("eps", 1e-3f)) {
+    InstanceNormOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws),
+          axis(OperatorBase::Arg<int>("axis", -1)),
+          eps(OperatorBase::Arg<float>("eps", 1e-3f)) {
         if (axis != -1) 
             CHECK_EQ(axis, 1) << "\nThe axis can only be set to 1.";
     }
@@ -43,9 +43,9 @@ class InstanceNormOp : public Operator<Context> {
 template <class Context>
 class InstanceNormGradientOp final : public Operator<Context> {
  public:
-    InstanceNormGradientOp(const OperatorDef& op_def, Workspace *ws) 
-        : Operator<Context>(op_def, ws),
-          axis(OperatorBase::GetSingleArg<int>("axis", -1)) {
+    InstanceNormGradientOp(const OperatorDef& def, Workspace *ws)
+        : Operator<Context>(def, ws),
+          axis(OperatorBase::Arg<int>("axis", -1)) {
         if (axis != -1)
             CHECK_EQ(axis, 1) << "\nThe axis can only be set to 1.";
     }

Dragon/include/operators/norm/l2_norm_op.h

@@ -19,12 +19,12 @@ namespace dragon {
 template <class Context>
 class L2NormOp final : public Operator<Context> {
  public:
-    L2NormOp(const OperatorDef& op_def, Workspace* ws)
-        : Operator<Context>(op_def, ws),
-          axis(OperatorBase::GetSingleArg<int>("axis", 0)),
-          num_axes(OperatorBase::GetSingleArg<int>("num_axes", -1)),
-          eps(OperatorBase::GetSingleArg<float>("eps", 1e-5f)),
-          mode(OperatorBase::GetSingleArg<string>("mode", "SUM")) {}
+    L2NormOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws),
+          axis(OperatorBase::Arg<int>("axis", 0)),
+          num_axes(OperatorBase::Arg<int>("num_axes", -1)),
+          eps(OperatorBase::Arg<float>("eps", 1e-5f)),
+          mode(OperatorBase::Arg<string>("mode", "SUM")) {}
     USE_OPERATOR_FUNCTIONS;
 
     void RunOnDevice() override;
@@ -42,11 +42,11 @@ class L2NormOp final : public Operator<Context> {
 template <class Context>
 class L2NormGradientOp final : public Operator<Context> {
  public:
-    L2NormGradientOp(const OperatorDef& op_def, Workspace* ws)
-        : Operator<Context>(op_def, ws),
-          axis(OperatorBase::GetSingleArg<int>("axis", 0)),
-          num_axes(OperatorBase::GetSingleArg<int>("num_axes", -1)),
-          mode(OperatorBase::GetSingleArg<string>("mode", "SUM")) {}
+    L2NormGradientOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws),
+          axis(OperatorBase::Arg<int>("axis", 0)),
+          num_axes(OperatorBase::Arg<int>("num_axes", -1)),
+          mode(OperatorBase::Arg<string>("mode", "SUM")) {}
     USE_OPERATOR_FUNCTIONS;
 
     void RunOnDevice() override;

Dragon/include/operators/recurrent/cudnn_recurrent_op.h

@@ -49,29 +49,29 @@ class cudnnTensorDescriptors {
 template <class Context>
 class CuDNNRecurrentOpBase : public Operator<Context> {
  public:
-    CuDNNRecurrentOpBase(const OperatorDef& op_def, Workspace* ws)
-        : Operator<Context>(op_def, ws), states_initialized(false),
-        hidden_size(OperatorBase::GetSingleArg<int>("hidden_size", 0)),
-        num_layers(OperatorBase::GetSingleArg<int>("num_layers", 1)),
-        bidirectional(OperatorBase::GetSingleArg<bool>("bidirectional", false)),
-        dropout_ratio(OperatorBase::GetSingleArg<float>("dropout_ratio", 1.0)),
-        random_seed(op_def.device_option().random_seed()) {
+    CuDNNRecurrentOpBase(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws), states_initialized(false), 
+          hidden_size(OperatorBase::Arg<int>("hidden_size", 0)),
+          num_layers(OperatorBase::Arg<int>("num_layers", 1)),
+          bidirectional(OperatorBase::Arg<bool>("bidirectional", false)),
+          dropout_ratio(OperatorBase::Arg<float>("dropout_ratio", 1.0)),
+          random_seed(def.device_option().random_seed()) {
         //  determine the rnn direction
         rnn_direction = bidirectional ? CUDNN_BIDIRECTIONAL : CUDNN_UNIDIRECTIONAL;
         //  determine the rnn mode
-        const string mode = OperatorBase::GetSingleArg<string>("rnn_mode", "");
+        const string mode = OperatorBase::Arg<string>("rnn_mode", "");
         if (mode == "rnn_tanh") rnn_mode = CUDNN_RNN_TANH;
         else if (mode == "rnn_relu") rnn_mode = CUDNN_RNN_RELU;
         else if (mode == "lstm") rnn_mode = CUDNN_LSTM;
         else if (mode == "gru") rnn_mode = CUDNN_GRU;
         else LOG(FATAL) << "Unsupported rnn mode: " << mode;
         //  determine the rnn input mode
-        const string input_mode = OperatorBase::GetSingleArg<string>("rnn_input_mode", "linear");
+        const string input_mode = OperatorBase::Arg<string>("rnn_input_mode", "linear");
         if (input_mode == "skip") rnn_input_mode = CUDNN_SKIP_INPUT;
         else if (input_mode == "linear") rnn_input_mode = CUDNN_LINEAR_INPUT;
         else LOG(FATAL) << "Unsupported rnn input mode: " << input_mode;
         //  override the running phase
-        SwitchToPhase(OperatorBase::GetSingleArg<string>("phase", ""));
+        SwitchToPhase(OperatorBase::Arg<string>("phase", ""));
         CUDNN_CHECK(cudnnCreateRNNDescriptor(&rnn_desc));
         CUDNN_CHECK(cudnnCreateDropoutDescriptor(&dropout_desc));
         CUDNN_CHECK(cudnnCreateFilterDescriptor(&w_desc));
@@ -92,8 +92,7 @@ class CuDNNRecurrentOpBase : public Operator<Context> {
         CUDNN_CHECK(cudnnDestroyTensorDescriptor(cy_desc));
     }
 
-    template <typename T> void ResetDesc(Tensor* X, Tensor* Hx, Tensor* Cx,
-                                         Tensor* Y, Tensor* Hy, Tensor* Cy);
+    template <typename T> void ResetDesc();
 
  public:
     TIndex hidden_size, num_layers;
@@ -109,7 +108,7 @@ class CuDNNRecurrentOpBase : public Operator<Context> {
     cudnnFilterDescriptor_t w_desc;
     cudnnTensorDescriptor_t hx_desc, cx_desc;
     cudnnTensorDescriptor_t hy_desc, cy_desc;
-    vector<TIndex> input_dims;
+    vector<TIndex> input_dims, output_dims, hidden_dims;
     size_t workspace_size, reserve_size, states_size;
 
     std::unique_ptr<cudnnTensorDescriptors> xs_desc;
@@ -128,14 +127,16 @@ class CuDNNRecurrentOpBase : public Operator<Context> {
     using CuDNNRecurrentOpBase<Context>::xs_desc; \
     using CuDNNRecurrentOpBase<Context>::ys_desc; \
     using CuDNNRecurrentOpBase<Context>::input_dims; \
+    using CuDNNRecurrentOpBase<Context>::output_dims; \
+    using CuDNNRecurrentOpBase<Context>::hidden_dims; \
     using CuDNNRecurrentOpBase<Context>::workspace_size; \
     using CuDNNRecurrentOpBase<Context>::reserve_size
 
 template <class Context>
-class CuDNNRecurrentOp : public CuDNNRecurrentOpBase<Context> {
+class CuDNNRecurrentOp final : public CuDNNRecurrentOpBase<Context> {
  public:
-    CuDNNRecurrentOp(const OperatorDef& op_def, Workspace* ws)
-        : CuDNNRecurrentOpBase<Context>(op_def, ws) {}
+    CuDNNRecurrentOp(const OperatorDef& def, Workspace* ws)
+        : CuDNNRecurrentOpBase<Context>(def, ws) {}
     USE_CUDNN_RECURRENT_FUNCTIONS;
 
     void RunOnDevice() override;
@@ -143,10 +144,10 @@ class CuDNNRecurrentOp : public CuDNNRecurrentOpBase<Context> {
 };
 
 template <class Context>
-class CuDNNRecurrentGradientOp : public CuDNNRecurrentOpBase<Context> {
+class CuDNNRecurrentGradientOp final : public CuDNNRecurrentOpBase<Context> {
  public:
-    CuDNNRecurrentGradientOp(const OperatorDef& op_def, Workspace* ws)
-        : CuDNNRecurrentOpBase<Context>(op_def, ws) {}
+    CuDNNRecurrentGradientOp(const OperatorDef& def, Workspace* ws)
+        : CuDNNRecurrentOpBase<Context>(def, ws) {}
     USE_CUDNN_RECURRENT_FUNCTIONS;
 
     void RunOnDevice() override;

Dragon/include/operators/recurrent/lstm_cell_op.h

@@ -17,10 +17,10 @@
 namespace dragon {
 
 template <class Context>
-class LSTMCellOp : public Operator<Context> {
+class LSTMCellOp final : public Operator<Context> {
  public:
-    LSTMCellOp(const OperatorDef& op_def, Workspace* ws)
-        : Operator<Context>(op_def, ws) {}
+    LSTMCellOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws) {}
     USE_OPERATOR_FUNCTIONS;
 
     void RunOnDevice() override;
@@ -29,10 +29,10 @@ class LSTMCellOp : public Operator<Context> {
 };
 
 template <class Context>
-class LSTMCellGradientOp : public Operator<Context> {
+class LSTMCellGradientOp final : public Operator<Context> {
  public:
-    LSTMCellGradientOp(const OperatorDef& op_def, Workspace* ws)
-         : Operator<Context>(op_def, ws) {}
+    LSTMCellGradientOp(const OperatorDef& def, Workspace* ws)
+         : Operator<Context>(def, ws) {}
     USE_OPERATOR_FUNCTIONS;
 
     void RunOnDevice() override;

Dragon/include/operators/recurrent/recurrent_op.h

@@ -19,8 +19,8 @@ namespace dragon {
 template <class Context>
 class RecurrentOp : public Operator<Context> {
  public:
-    RecurrentOp(const OperatorDef& op_def, Workspace* ws)
-        : Operator<Context>(op_def, ws) {
+    RecurrentOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws) {
         LOG(FATAL) << "RNN Operators require CuDNN support.";
     }
     USE_OPERATOR_FUNCTIONS;
@@ -31,8 +31,8 @@ class RecurrentOp : public Operator<Context> {
 template <class Context>
 class RecurrentGradientOp : public Operator<Context> {
 public:
-    RecurrentGradientOp(const OperatorDef& op_def, Workspace* ws)
-        : Operator<Context>(op_def, ws) {
+    RecurrentGradientOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws) {
         LOG(FATAL) << "RNN Operators require CuDNN support.";
     }
     USE_OPERATOR_FUNCTIONS;

Dragon/include/operators/recurrent/rnn_param_op.h

@@ -17,18 +17,18 @@
 namespace dragon {
 
 template <class Context>
-class RNNParamSetOp : public Operator<Context> {
+class RNNParamSetOp final : public Operator<Context> {
  public:
-    RNNParamSetOp(const OperatorDef& op_def, Workspace* ws)
-        : Operator<Context>(op_def, ws),
-        param_type(OperatorBase::GetSingleArg<string>("param_type", "matrix")),
-        rnn_mode(OperatorBase::GetSingleArg<string>("rnn_mode", "rnn_tanh")),
-        num_layers(OperatorBase::GetSingleArg<int>("num_layers", 1)),
-        num_directions(OperatorBase::GetSingleArg<int>("num_directions", 1)),
-        input_size(OperatorBase::GetSingleArg<int>("input_size", 0)),
-        hidden_size(OperatorBase::GetSingleArg<int>("hidden_size", 0)),
-        layer_id(OperatorBase::GetSingleArg<int>("layer_id", 0)),
-        param_id(OperatorBase::GetSingleArg<int>("param_id", 0)) {
+    RNNParamSetOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws),
+          param_type(OperatorBase::Arg<string>("param_type", "matrix")),
+          rnn_mode(OperatorBase::Arg<string>("rnn_mode", "rnn_tanh")),
+          num_layers(OperatorBase::Arg<int>("num_layers", 1)),
+          num_directions(OperatorBase::Arg<int>("num_directions", 1)),
+          input_size(OperatorBase::Arg<int>("input_size", 0)),
+          hidden_size(OperatorBase::Arg<int>("hidden_size", 0)),
+          layer_id(OperatorBase::Arg<int>("layer_id", 0)),
+          param_id(OperatorBase::Arg<int>("param_id", 0)) {
         if (rnn_mode == "rnn_tanh") { num_params = 2; spliter = 1; }
         else if (rnn_mode == "rnn_relu") { num_params = 2; spliter = 1; }
         else if (rnn_mode == "lstm") { num_params = 8; spliter = 4; }

Dragon/include/operators/update/adam_update_op.h

@@ -19,8 +19,8 @@ namespace dragon {
 template <class Context>
 class AdamUpdateOp final : public UpdateOpBase<Context> {
  public:
-    AdamUpdateOp(const OperatorDef& op_def, Workspace* ws) 
-        : UpdateOpBase<Context>(op_def, ws), t(0) {}
+    AdamUpdateOp(const OperatorDef& def, Workspace* ws)
+        : UpdateOpBase<Context>(def, ws), t(0) {}
     USE_OPERATOR_FUNCTIONS;
     USE_UPDATER_FUNCTIONS(Context);
 

Dragon/include/operators/update/collective_update_op.h

@@ -19,16 +19,26 @@ namespace dragon {
 #ifdef WITH_MPI
 
 template <class Context>
-class CollectiveUpdateOp : public Operator<Context> {
+class CollectiveUpdateOp final : public Operator<Context> {
  public:
-    CollectiveUpdateOp(const OperatorDef& op_def, Workspace* ws)
-        : Operator<Context>(op_def, ws),
-          mode(OperatorBase::GetSingleArg<string>("mode", "UNKNOWN")) {
+    CollectiveUpdateOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws),
+          mode(OperatorBase::Arg<string>("mode", "UNKNOWN")) {
          InitMPI();
          if (mode.find("NCCL") != string::npos) InitNCCL();
     }
     USE_OPERATOR_FUNCTIONS;
 
+    ~CollectiveUpdateOp() {
+        /*  TODO(PhyscalX): Temporarily disable it,
+                            to avoid a unhandled error. */
+#ifdef WITH_MPI_NCCL
+        if (mode.find("NCCL") != string::npos) {
+            /* ncclCommDestroy(nccl_comm); */
+        }
+#endif
+    }
+
     void InitMPI();
     void InitNCCL();
 
@@ -48,7 +58,7 @@ class CollectiveUpdateOp : public Operator<Context> {
 
 #ifdef WITH_MPI_NCCL
     ncclComm_t nccl_comm;
-    cudaStream_t stream;
+    CUDAClosure<Context> closure;
 #endif
 };
 

Dragon/include/operators/update/moving_average_op.h

@@ -19,9 +19,9 @@ namespace dragon {
 template <class Context>
 class MovingAverageOp final : public Operator<Context> {
  public:
-    MovingAverageOp(const OperatorDef& op_def, Workspace* ws)
-        : Operator<Context>(op_def, ws), 
-          decay(OperatorBase::GetSingleArg<float>("decay", 1.0)) {}
+    MovingAverageOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws),
+          decay(OperatorBase::Arg<float>("decay", 1.0)) {}
     USE_OPERATOR_FUNCTIONS;
 
     void RunOnDevice() override;
@@ -29,7 +29,6 @@ class MovingAverageOp final : public Operator<Context> {
 
  protected:
     float decay;
-
 };
 
 }   // namespace dragon

Dragon/include/operators/update/nesterov_update_op.h

@@ -19,8 +19,8 @@ namespace dragon {
 template <class Context>
 class NesterovUpdateOp final : public UpdateOpBase<Context> {
  public:
-    NesterovUpdateOp(const OperatorDef& op_def, Workspace* ws)
-        : UpdateOpBase<Context>(op_def, ws) {}
+    NesterovUpdateOp(const OperatorDef& def, Workspace* ws)
+        : UpdateOpBase<Context>(def, ws) {}
     USE_OPERATOR_FUNCTIONS;
     USE_UPDATER_FUNCTIONS(Context);
 

Dragon/include/operators/update/rmsprop_update_op.h

@@ -19,8 +19,8 @@ namespace dragon {
 template <class Context>
 class RMSPropUpdateOp final : public UpdateOpBase<Context> {
  public:
-    RMSPropUpdateOp(const OperatorDef& op_def, Workspace* ws) 
-        : UpdateOpBase<Context>(op_def, ws) {}
+    RMSPropUpdateOp(const OperatorDef& def, Workspace* ws)
+        : UpdateOpBase<Context>(def, ws) {}
     USE_OPERATOR_FUNCTIONS;
     USE_UPDATER_FUNCTIONS(Context);
 

Dragon/include/operators/update/sgd_update_op.h

@@ -19,8 +19,8 @@ namespace dragon {
 template <class Context>
 class SGDUpdateOp final : public UpdateOpBase<Context> {
  public:
-    SGDUpdateOp(const OperatorDef& op_def, Workspace* ws) 
-        : UpdateOpBase<Context>(op_def, ws),
+    SGDUpdateOp(const OperatorDef& def, Workspace* ws)
+        : UpdateOpBase<Context>(def, ws),
         old_lr(-1.f), correction(1.f) {}
     USE_OPERATOR_FUNCTIONS;
     USE_UPDATER_FUNCTIONS(Context);

Dragon/include/operators/update/update_op_base.h

@@ -19,12 +19,12 @@ namespace dragon {
 template <class Context>
 class UpdateOpBase : public Operator<Context> {
  public:
-    UpdateOpBase(const OperatorDef& op_def, Workspace* ws)
-        : Operator<Context>(op_def, ws),
-        lr_mult(OperatorBase::GetSingleArg<float>("lr_mult", 1.0)),
-        decay_mult(OperatorBase::GetSingleArg<float>("decay_mult", 1.0)),
-        slot(OperatorBase::GetSingleArg<string>("slot", "")),
-        zero_grad(OperatorBase::GetSingleArg<bool>("zero_grad", true)) {
+    UpdateOpBase(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws),
+          lr_mult(OperatorBase::Arg<float>("lr_mult", 1.0)),
+          decay_mult(OperatorBase::Arg<float>("decay_mult", 1.0)),
+          slot(OperatorBase::Arg<string>("slot", "")),
+          zero_grad(OperatorBase::Arg<bool>("zero_grad", true)) {
         CHECK(!slot.empty()) << "\nRequired a non-empty slot";
     }
     USE_OPERATOR_FUNCTIONS;
@@ -34,8 +34,13 @@ class UpdateOpBase : public Operator<Context> {
 
     void RunOnDevice() override;
     template <typename T> void PreprocessRunWithType();
+
     virtual void ComputeRunWithFloat() = 0;
-    virtual void ComputeRunWithFloat16() { LOG(FATAL) << "This Updater does not support FP16."; }
+
+    virtual void ComputeRunWithFloat16() {
+        LOG(FATAL) << "This Updater does not support FP16."; 
+    }
+
     template <typename T> void UpdateRunWithType();
 
  protected:

Dragon/include/operators/vision/bias_add_op.h

@@ -17,11 +17,11 @@
 namespace dragon {
 
 template <class Context>
-class BiasAddOp : public Operator<Context> {
+class BiasAddOp final : public Operator<Context> {
  public:
-    BiasAddOp(const OperatorDef& op_def, Workspace* ws)
-        : Operator<Context>(op_def, ws),
-          data_format(OperatorBase::GetSingleArg<string>("data_format", "NCHW")) {}
+    BiasAddOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws),
+          data_format(OperatorBase::Arg<string>("data_format", "NCHW")) {}
     USE_OPERATOR_FUNCTIONS;
 
     void RunOnDevice() override;
@@ -35,9 +35,9 @@ class BiasAddOp : public Operator<Context> {
 template <class Context>
 class BiasAddGradientOp final : public Operator<Context> {
  public:
-    BiasAddGradientOp(const OperatorDef& op_def, Workspace* ws)
-        : Operator<Context>(op_def, ws),
-          data_format(OperatorBase::GetSingleArg<string>("data_format", "NCHW")) {}
+    BiasAddGradientOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws),
+          data_format(OperatorBase::Arg<string>("data_format", "NCHW")) {}
     USE_OPERATOR_FUNCTIONS;
 
     void RunOnDevice() override;

Dragon/include/operators/vision/bilinear_resize_op.h

@@ -17,14 +17,14 @@
 namespace dragon {
 
 template <class Context>
-class BilinearResizeOp : public Operator<Context> {
+class BilinearResizeOp final : public Operator<Context> {
  public:
-    BilinearResizeOp(const OperatorDef& op_def, Workspace* ws)
-        : Operator<Context>(op_def, ws),
-          fy(OperatorBase::GetSingleArg<float>("fy", -1.0)),
-          fx(OperatorBase::GetSingleArg<float>("fx", -1.0)),
-          shape_like_desc(OperatorBase::GetSingleArg<string>("shape_like", "")),
-          data_format(OperatorBase::GetSingleArg<string>("data_format", "NCHW")) {
+    BilinearResizeOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws),
+          fy(OperatorBase::Arg<float>("fy", -1.0)),
+          fx(OperatorBase::Arg<float>("fx", -1.0)),
+          shape_like_desc(OperatorBase::Arg<string>("shape_like", "")),
+          data_format(OperatorBase::Arg<string>("data_format", "NCHW")) {
         GET_ARGUMENTS_WITH_DESC(int, dsize);
         if (data_format == "NCHW") spatial_axis = 2;
         else if (data_format == "NHWC") spatial_axis = 1;
@@ -43,11 +43,11 @@ class BilinearResizeOp : public Operator<Context> {
 };
 
 template <class Context>
-class BilinearResizeGradientOp : public Operator<Context> {
+class BilinearResizeGradientOp final : public Operator<Context> {
  public:
-    BilinearResizeGradientOp(const OperatorDef& op_def, Workspace* ws)
-        : Operator<Context>(op_def, ws),
-          data_format(OperatorBase::GetSingleArg<string>("data_format", "NCHW")) {}
+    BilinearResizeGradientOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws),
+          data_format(OperatorBase::Arg<string>("data_format", "NCHW")) {}
     USE_OPERATOR_FUNCTIONS;
 
     void RunOnDevice() override;

Dragon/include/operators/vision/conv_op.h

@@ -53,10 +53,11 @@ class Conv2dGradientOp : public Conv2dOp<Context> {
 #include "utils/cudnn_device.h"
 
 template <class Context>
-class CuDNNConv2dOp : public Conv2dOp<Context> {
+class CuDNNConv2dOp final : public Conv2dOp<Context> {
  public:
     CuDNNConv2dOp(const OperatorDef& def, Workspace* ws)
-        : Conv2dOp<Context>(def, ws), enable_tensor_core(true) {
+        : Conv2dOp<Context>(def, ws),
+          enable_tensor_core(true) {
 #if CUDNN_VERSION_MIN(7, 0, 0)
         cudnn_group = 1;
         enable_tensor_core &= TENSOR_CORE_AVAILABLE();
@@ -64,14 +65,6 @@ class CuDNNConv2dOp : public Conv2dOp<Context> {
         cudnn_group = group;
         enable_tensor_core = false;
 #endif
-        handle = new cudnnHandle_t[cudnn_group];
-        stream = new cudaStream_t[cudnn_group];
-        ctx().SwitchToDevice();
-        for (int g = 0; g < cudnn_group; g++) {
-            CUDA_CHECK(cudaStreamCreate(&stream[g]));
-            CUDNN_CHECK(cudnnCreate(&handle[g]));
-            CUDNN_CHECK(cudnnSetStream(handle[g], stream[g]));
-        }
         CUDNN_CHECK(cudnnCreateFilterDescriptor(&filter_desc));
         CUDNN_CHECK(cudnnCreateTensorDescriptor(&input_desc));
         CUDNN_CHECK(cudnnCreateTensorDescriptor(&output_desc));
@@ -90,10 +83,6 @@ class CuDNNConv2dOp : public Conv2dOp<Context> {
         CUDNN_CHECK(cudnnDestroyTensorDescriptor(output_desc));
         CUDNN_CHECK(cudnnDestroyConvolutionDescriptor(conv_desc));
         if (HasBias()) CUDNN_CHECK(cudnnDestroyTensorDescriptor(bias_desc));
-        for (int g = 0; g < cudnn_group; g++) {
-            cudaStreamDestroy(stream[g]);
-            CUDNN_CHECK(cudnnDestroy(handle[g]));
-        }
     }
 
     void RunOnDevice() override;
@@ -101,8 +90,6 @@ class CuDNNConv2dOp : public Conv2dOp<Context> {
     template <typename T> void RunWithType();
 
  protected:
-    cudnnHandle_t* handle;
-    cudaStream_t*  stream;
     cudnnDataType_t compute_type;
     cudnnTensorFormat_t format;
     cudnnConvolutionFwdAlgo_t fwd_algo;
@@ -116,10 +103,11 @@ class CuDNNConv2dOp : public Conv2dOp<Context> {
 };
 
 template <class Context>
-class CuDNNConv2dGradientOp : public Conv2dGradientOp<Context> {
+class CuDNNConv2dGradientOp final : public Conv2dGradientOp<Context> {
  public:
     CuDNNConv2dGradientOp(const OperatorDef& def, Workspace* ws)
-        : Conv2dGradientOp<Context>(def, ws), enable_tensor_core(true) {
+        : Conv2dGradientOp<Context>(def, ws),
+          enable_tensor_core(true) {
 #if CUDNN_VERSION_MIN(7, 0, 0)
         cudnn_group = 1;
         enable_tensor_core &= TENSOR_CORE_AVAILABLE();
@@ -127,13 +115,6 @@ class CuDNNConv2dGradientOp : public Conv2dGradientOp<Context> {
         cudnn_group = group;
         enable_tensor_core = false;
 #endif
-        handle = new cudnnHandle_t[cudnn_group * 3];
-        stream = new cudaStream_t[cudnn_group * 3];
-        for (int g = 0; g < cudnn_group * 3; g++) {
-            CUDA_CHECK(cudaStreamCreate(&stream[g]));
-            CUDNN_CHECK(cudnnCreate(&handle[g]));
-            CUDNN_CHECK(cudnnSetStream(handle[g], stream[g]));
-        }
         CUDNN_CHECK(cudnnCreateFilterDescriptor(&filter_desc));
         CUDNN_CHECK(cudnnCreateTensorDescriptor(&input_desc));
         CUDNN_CHECK(cudnnCreateTensorDescriptor(&output_desc));
@@ -152,10 +133,6 @@ class CuDNNConv2dGradientOp : public Conv2dGradientOp<Context> {
         CUDNN_CHECK(cudnnDestroyTensorDescriptor(output_desc));
         CUDNN_CHECK(cudnnDestroyConvolutionDescriptor(conv_desc));
         if (HasBias()) CUDNN_CHECK(cudnnDestroyTensorDescriptor(bias_desc));
-        for (int g = 0; g < cudnn_group * 3; g++) {
-            cudaStreamDestroy(stream[g]);
-            CUDNN_CHECK(cudnnDestroy(handle[g]));
-        }
     }
 
     void RunOnDevice() override;
@@ -163,8 +140,6 @@ class CuDNNConv2dGradientOp : public Conv2dGradientOp<Context> {
     template <typename T> void RunWithType();
 
  protected:
-    cudnnHandle_t* handle;
-    cudaStream_t*  stream;
     cudnnDataType_t compute_type;
     cudnnTensorFormat_t format;
     cudnnConvolutionBwdFilterAlgo_t bwd_filter_algo;

Dragon/include/operators/vision/conv_op_base.h

@@ -21,14 +21,14 @@ namespace dragon {
 template <class Context>
 class ConvOpBase : public Operator<Context> {
  public:
-    ConvOpBase(const OperatorDef& op_def, Workspace* ws) 
-        : Operator<Context>(op_def, ws),
-          data_format(OperatorBase::GetSingleArg<string>("data_format", "NCHW")),
-          padding(OperatorBase::GetSingleArg<string>("padding", "VALID")),
-          num_output(OperatorBase::GetSingleArg<int>("num_output", 1)),
-          group(OperatorBase::GetSingleArg<int>("group", 1)) {
-        output_dims_value = OperatorBase::GetRepeatedArg<int>("output_shape");
-        output_dims_desc = OperatorBase::GetRepeatedArg<string>("output_shape_desc");
+    ConvOpBase(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws),
+          data_format(OperatorBase::Arg<string>("data_format", "NCHW")),
+          padding(OperatorBase::Arg<string>("padding", "VALID")),
+          num_output(OperatorBase::Arg<int>("num_output", 1)),
+          group(OperatorBase::Arg<int>("group", 1)) {
+        output_dims_value = OperatorBase::Args<int>("output_shape");
+        output_dims_desc = OperatorBase::Args<string>("output_shape_desc");
         if (data_format == "NCHW") spatial_axis = 2;
         else if (data_format == "NHWC") spatial_axis = 1;
         else LOG(FATAL) << "Unknown data format: " << data_format;

Dragon/include/operators/vision/conv_transpose_op.h

@@ -17,7 +17,7 @@
 namespace dragon {
 
 template <class Context>
-class Conv2dTransposeOp: public ConvOpBase<Context>  {
+class Conv2dTransposeOp : public ConvOpBase<Context> {
  public:
     Conv2dTransposeOp(const OperatorDef& def, Workspace* ws) 
         : ConvOpBase<Context>(def, ws) {
@@ -57,7 +57,7 @@ class Conv2dTransposeGradientOp : public Conv2dTransposeOp<Context> {
 #include "utils/cudnn_device.h"
 
 template <class Context>
-class CuDNNConv2dTransposeOp : public Conv2dTransposeOp<Context> {
+class CuDNNConv2dTransposeOp final : public Conv2dTransposeOp<Context> {
  public:
     CuDNNConv2dTransposeOp(const OperatorDef& def, Workspace* ws)
         : Conv2dTransposeOp<Context>(def, ws), enable_tensor_core(true) {
@@ -68,13 +68,6 @@ class CuDNNConv2dTransposeOp : public Conv2dTransposeOp<Context> {
         cudnn_group = group;
         enable_tensor_core = false;
 #endif
-        handle = new cudnnHandle_t[cudnn_group];
-        stream = new cudaStream_t[cudnn_group];
-        for (int g = 0; g < cudnn_group; g++) {
-            CUDA_CHECK(cudaStreamCreate(&stream[g]));
-            CUDNN_CHECK(cudnnCreate(&handle[g]));
-            CUDNN_CHECK(cudnnSetStream(handle[g], stream[g]));
-        }
         CUDNN_CHECK(cudnnCreateFilterDescriptor(&filter_desc));
         CUDNN_CHECK(cudnnCreateTensorDescriptor(&input_desc));
         CUDNN_CHECK(cudnnCreateTensorDescriptor(&output_desc));
@@ -93,10 +86,6 @@ class CuDNNConv2dTransposeOp : public Conv2dTransposeOp<Context> {
         CUDNN_CHECK(cudnnDestroyTensorDescriptor(output_desc));
         CUDNN_CHECK(cudnnDestroyConvolutionDescriptor(conv_desc));
         if (HasBias()) CUDNN_CHECK(cudnnDestroyTensorDescriptor(bias_desc));
-        for (int g = 0; g < cudnn_group; g++) {
-            cudaStreamDestroy(stream[g]);
-            CUDNN_CHECK(cudnnDestroy(handle[g]));
-        }
     }
 
     void RunOnDevice() override;
@@ -104,8 +93,6 @@ class CuDNNConv2dTransposeOp : public Conv2dTransposeOp<Context> {
     template <typename T> void RunWithType();
 
  protected:
-    cudnnHandle_t* handle;
-    cudaStream_t*  stream;
     cudnnDataType_t compute_type;
     cudnnTensorFormat_t format;
     cudnnConvolutionBwdDataAlgo_t fwd_algo;
@@ -119,7 +106,7 @@ class CuDNNConv2dTransposeOp : public Conv2dTransposeOp<Context> {
 };
 
 template <class Context>
-class CuDNNConv2dTransposeGradientOp : public Conv2dTransposeGradientOp<Context> {
+class CuDNNConv2dTransposeGradientOp final : public Conv2dTransposeGradientOp<Context> {
 public:
     CuDNNConv2dTransposeGradientOp(const OperatorDef& def, Workspace* ws)
         : Conv2dTransposeGradientOp<Context>(def, ws), enable_tensor_core(true) {
@@ -130,13 +117,6 @@ public:
         cudnn_group = group;
         enable_tensor_core = false;
 #endif
-        handle = new cudnnHandle_t[cudnn_group * 3];
-        stream = new cudaStream_t[cudnn_group * 3];
-        for (int g = 0; g < cudnn_group * 3; g++) {
-            CUDA_CHECK(cudaStreamCreate(&stream[g]));
-            CUDNN_CHECK(cudnnCreate(&handle[g]));
-            CUDNN_CHECK(cudnnSetStream(handle[g], stream[g]));
-        }
         CUDNN_CHECK(cudnnCreateFilterDescriptor(&filter_desc));
         CUDNN_CHECK(cudnnCreateTensorDescriptor(&input_desc));
         CUDNN_CHECK(cudnnCreateTensorDescriptor(&output_desc));
@@ -155,10 +135,6 @@ public:
         CUDNN_CHECK(cudnnDestroyTensorDescriptor(output_desc));
         CUDNN_CHECK(cudnnDestroyConvolutionDescriptor(conv_desc));
         if (HasBias()) CUDNN_CHECK(cudnnDestroyTensorDescriptor(bias_desc));
-        for (int g = 0; g < cudnn_group * 3; g++) {
-            cudaStreamDestroy(stream[g]);
-            CUDNN_CHECK(cudnnDestroy(handle[g]));
-        }
     }
 
     void RunOnDevice() override;
@@ -166,8 +142,6 @@ public:
     template <typename T> void RunWithType();
 
  protected:
-    cudnnHandle_t* handle;
-    cudaStream_t*  stream;
     cudnnDataType_t compute_type;
     cudnnTensorFormat_t format;
     cudnnConvolutionBwdFilterAlgo_t bwd_filter_algo;

Dragon/include/operators/vision/dense_concat_op.h

@@ -19,17 +19,17 @@ namespace dragon {
 template <class Context>
 class DenseConcatOp final : public ConcatOp<Context> {
  public:
-    DenseConcatOp(const OperatorDef& op_def, Workspace* ws)
-        : ConcatOp<Context>(op_def, ws) {}
+    DenseConcatOp(const OperatorDef& def, Workspace* ws)
+        : ConcatOp<Context>(def, ws) {}
     USE_OPERATOR_FUNCTIONS;
 };
 
 template <class Context>
-class DenseConcatGradientOp : public ConcatGradientOp<Context> {
+class DenseConcatGradientOp final : public ConcatGradientOp<Context> {
  public:
-    DenseConcatGradientOp(const OperatorDef& op_def, Workspace* ws)
-        : ConcatGradientOp<Context>(op_def, ws),
-          growth_rate(OperatorBase::GetSingleArg<int>("growth_rate", 0)) {}
+    DenseConcatGradientOp(const OperatorDef& def, Workspace* ws)
+        : ConcatGradientOp<Context>(def, ws),
+          growth_rate(OperatorBase::Arg<int>("growth_rate", 0)) {}
     USE_OPERATOR_FUNCTIONS;
 
     void ElimateCorruption() override;

Dragon/include/operators/vision/lrn_op.h

@@ -21,14 +21,14 @@ enum LRNMode { ACROSS_CHANNELS, WITHIN_CHANNEL };
 template <class Context>
 class LRNOp : public Operator<Context> {
  public:
-    LRNOp(const OperatorDef& op_def, Workspace* ws)
-        : Operator<Context>(op_def, ws),
-          local_size(OperatorBase::GetSingleArg<int>("local_size", 5)),
-          alpha(OperatorBase::GetSingleArg<float>("alpha", float(0.0001))),
-          beta(OperatorBase::GetSingleArg<float>("beta", float(0.75))),
-          k(OperatorBase::GetSingleArg<float>("k", float(2.0))),
-          mode(OperatorBase::GetSingleArg<string>("mode", "ACROSS_CHANNELS")),
-          data_format(OperatorBase::GetSingleArg<string>("data_format", "NCHW")) {}
+    LRNOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws),
+          local_size(OperatorBase::Arg<int>("local_size", 5)),
+          alpha(OperatorBase::Arg<float>("alpha", float(0.0001))),
+          beta(OperatorBase::Arg<float>("beta", float(0.75))),
+          k(OperatorBase::Arg<float>("k", float(2.0))),
+          mode(OperatorBase::Arg<string>("mode", "ACROSS_CHANNELS")),
+          data_format(OperatorBase::Arg<string>("data_format", "NCHW")) {}
     USE_OPERATOR_FUNCTIONS;
 
     void RunOnDevice() override;
@@ -52,14 +52,14 @@ class LRNOp : public Operator<Context> {
 template <class Context>
 class LRNGradientOp : public Operator<Context> {
  public:
-    LRNGradientOp(const OperatorDef& op_def, Workspace* ws) 
-        : Operator<Context>(op_def, ws),
-          local_size(OperatorBase::GetSingleArg<int>("local_size", 5)),
-          alpha(OperatorBase::GetSingleArg<float>("alpha", float(0.0001))),
-          beta(OperatorBase::GetSingleArg<float>("beta", float(0.75))),
-          k(OperatorBase::GetSingleArg<float>("k", float(2.0))),
-          mode(OperatorBase::GetSingleArg<string>("mode", "ACROSS_CHANNELS")),
-          data_format(OperatorBase::GetSingleArg<string>("data_format", "NCHW")) {}
+    LRNGradientOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws),
+          local_size(OperatorBase::Arg<int>("local_size", 5)),
+          alpha(OperatorBase::Arg<float>("alpha", float(0.0001))),
+          beta(OperatorBase::Arg<float>("beta", float(0.75))),
+          k(OperatorBase::Arg<float>("k", float(2.0))),
+          mode(OperatorBase::Arg<string>("mode", "ACROSS_CHANNELS")),
+          data_format(OperatorBase::Arg<string>("data_format", "NCHW")) {}
     USE_OPERATOR_FUNCTIONS;
 
     void RunOnDevice() override;
@@ -85,17 +85,15 @@ class LRNGradientOp : public Operator<Context> {
 #include "utils/cudnn_device.h"
 
 template <class Context>
-class CuDNNLRNOp : public LRNOp<Context> {
+class CuDNNLRNOp final : public LRNOp<Context> {
  public:
-    CuDNNLRNOp(const OperatorDef& op_def, Workspace* ws) 
-        : LRNOp<Context>(op_def, ws) {
+    CuDNNLRNOp(const OperatorDef& def, Workspace* ws)
+        : LRNOp<Context>(def, ws) {
         CUDNN_CHECK(cudnnCreateTensorDescriptor(&input_desc));
         CUDNN_CHECK(cudnnCreateTensorDescriptor(&output_desc));
         CUDNN_CHECK(cudnnCreateLRNDescriptor(&norm_desc));
-        CUDNN_CHECK(cudnnSetLRNDescriptor(norm_desc, this->local_size, 
-                                                     this->alpha, 
-                                                     this->beta, 
-                                                     this->k));
+        CUDNN_CHECK(cudnnSetLRNDescriptor(norm_desc,
+            this->local_size, this->alpha, this->beta, this->k));
     }
     USE_OPERATOR_FUNCTIONS;
 
@@ -114,17 +112,15 @@ class CuDNNLRNOp : public LRNOp<Context> {
 };
 
 template <class Context>
-class CuDNNLRNGradientOp : public LRNGradientOp<Context > {
+class CuDNNLRNGradientOp final : public LRNGradientOp<Context > {
  public:
-    CuDNNLRNGradientOp(const OperatorDef& op_def, Workspace* ws) :
-        LRNGradientOp<Context>(op_def, ws) {
+    CuDNNLRNGradientOp(const OperatorDef& def, Workspace* ws) 
+        : LRNGradientOp<Context>(def, ws) {
         CUDNN_CHECK(cudnnCreateTensorDescriptor(&input_desc));
         CUDNN_CHECK(cudnnCreateTensorDescriptor(&output_desc));
         CUDNN_CHECK(cudnnCreateLRNDescriptor(&norm_desc));
-        CUDNN_CHECK(cudnnSetLRNDescriptor(norm_desc, this->local_size,
-                                                     this->alpha, 
-                                                     this->beta, 
-                                                     this->k));
+        CUDNN_CHECK(cudnnSetLRNDescriptor(norm_desc,
+            this->local_size, this->alpha, this->beta, this->k));
     }
     USE_OPERATOR_FUNCTIONS;
 

Dragon/include/operators/vision/nn_resize_op.h

@@ -17,14 +17,14 @@
 namespace dragon {
 
 template <class Context>
-class NNResizeOp : public Operator<Context> {
+class NNResizeOp final : public Operator<Context> {
  public:
-    NNResizeOp(const OperatorDef& op_def, Workspace* ws)
-        : Operator<Context>(op_def, ws),
-          fy(OperatorBase::GetSingleArg<float>("fy", -1.0)),
-          fx(OperatorBase::GetSingleArg<float>("fx", -1.0)),
-          shape_like_desc(OperatorBase::GetSingleArg<string>("shape_like", "")),
-          data_format(OperatorBase::GetSingleArg<string>("data_format", "NCHW")) {
+    NNResizeOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws),
+          fy(OperatorBase::Arg<float>("fy", -1.0)),
+          fx(OperatorBase::Arg<float>("fx", -1.0)),
+          shape_like_desc(OperatorBase::Arg<string>("shape_like", "")),
+          data_format(OperatorBase::Arg<string>("data_format", "NCHW")) {
         GET_ARGUMENTS_WITH_DESC(int, dsize);
         if (data_format == "NCHW") spatial_axis = 2;
         else if (data_format == "NHWC") spatial_axis = 1;
@@ -43,11 +43,11 @@ class NNResizeOp : public Operator<Context> {
 };
 
 template <class Context>
-class NNResizeGradientOp : public Operator<Context> {
+class NNResizeGradientOp final : public Operator<Context> {
  public:
-    NNResizeGradientOp(const OperatorDef& op_def, Workspace* ws) 
-        : Operator<Context>(op_def, ws),
-          data_format(OperatorBase::GetSingleArg<string>("data_format", "NCHW")) {}
+    NNResizeGradientOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws),
+          data_format(OperatorBase::Arg<string>("data_format", "NCHW")) {}
     USE_OPERATOR_FUNCTIONS;
 
     void RunOnDevice() override;

Dragon/include/operators/vision/pooling_op.h

@@ -17,18 +17,18 @@
 namespace dragon {
 
 template <class Context>
-class Pooling2dOp: public Operator <Context> {
+class Pooling2dOp : public Operator<Context> {
  public:
-    Pooling2dOp(const OperatorDef& op_def, Workspace* ws)
-         : Operator<Context>(op_def, ws),
-        mode(OperatorBase::GetSingleArg<string>("mode", "MAX")),
-        data_format(OperatorBase::GetSingleArg<string>("data_format", "NCHW")),
-        padding(OperatorBase::GetSingleArg<string>("padding", "VALID")),
-        global_pooling(OperatorBase::GetSingleArg<bool>("global_pooling", false)),
-        ceil_mode(OperatorBase::GetSingleArg<bool>("ceil", true)) {
-        vector<int> ks = OperatorBase::GetRepeatedArg<int>("kernel_size");
-        vector<int> s = OperatorBase::GetRepeatedArg<int>("stride");
-        vector<int> p = OperatorBase::GetRepeatedArg<int>("pad");
+    Pooling2dOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws),
+          mode(OperatorBase::Arg<string>("mode", "MAX")),
+          data_format(OperatorBase::Arg<string>("data_format", "NCHW")),
+          padding(OperatorBase::Arg<string>("padding", "VALID")),
+          global_pooling(OperatorBase::Arg<bool>("global_pooling", false)),
+          ceil_mode(OperatorBase::Arg<bool>("ceil", true)) {
+        vector<int> ks = OperatorBase::Args<int>("kernel_size");
+        vector<int> s = OperatorBase::Args<int>("stride");
+        vector<int> p = OperatorBase::Args<int>("pad");
         for (int i = 0; i < 2; i++) {
             if (global_pooling) {
                 kernel_size.push_back(-1);
@@ -57,18 +57,18 @@ class Pooling2dOp: public Operator <Context> {
 };
 
 template <class Context>
-class Pooling2dGradientOp: public Operator<Context> {
+class Pooling2dGradientOp : public Operator<Context> {
  public:
-    Pooling2dGradientOp(const OperatorDef& op_def, Workspace* ws)
-         : Operator<Context>(op_def, ws),
-        mode(OperatorBase::GetSingleArg<string>("mode", "MAX")),
-        data_format(OperatorBase::GetSingleArg<string>("data_format", "NCHW")),
-        padding(OperatorBase::GetSingleArg<string>("padding", "VALID")),
-        global_pooling(OperatorBase::GetSingleArg<bool>("global_pooling", false)),
-        ceil_mode(OperatorBase::GetSingleArg<bool>("ceil", true)) {
-        vector<int> ks = OperatorBase::GetRepeatedArg<int>("kernel_size");
-        vector<int> s = OperatorBase::GetRepeatedArg<int>("stride");
-        vector<int> p = OperatorBase::GetRepeatedArg<int>("pad");
+    Pooling2dGradientOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws),
+          mode(OperatorBase::Arg<string>("mode", "MAX")),
+          data_format(OperatorBase::Arg<string>("data_format", "NCHW")),
+          padding(OperatorBase::Arg<string>("padding", "VALID")),
+          global_pooling(OperatorBase::Arg<bool>("global_pooling", false)),
+          ceil_mode(OperatorBase::Arg<bool>("ceil", true)) {
+        vector<int> ks = OperatorBase::Args<int>("kernel_size");
+        vector<int> s = OperatorBase::Args<int>("stride");
+        vector<int> p = OperatorBase::Args<int>("pad");
         for (int i = 0; i < 2; i++) {
             if (global_pooling) {
                 kernel_size.push_back(-1);
@@ -101,8 +101,8 @@ class Pooling2dGradientOp: public Operator<Context> {
 template <class Context>
 class CuDNNPooling2dOp final : public Pooling2dOp<Context> {
  public:
-    CuDNNPooling2dOp(const OperatorDef& op_def, Workspace* ws)
-        : Pooling2dOp<Context>(op_def, ws) {
+    CuDNNPooling2dOp(const OperatorDef& def, Workspace* ws)
+        : Pooling2dOp<Context>(def, ws) {
         CUDNN_CHECK(cudnnCreateTensorDescriptor(&input_desc));
         CUDNN_CHECK(cudnnCreateTensorDescriptor(&output_desc));
         CUDNN_CHECK(cudnnCreatePoolingDescriptor(&pool_desc));
@@ -136,8 +136,8 @@ class CuDNNPooling2dOp final : public Pooling2dOp<Context> {
 template <class Context>
 class CuDNNPooling2dGradientOp final : public Pooling2dGradientOp<Context> {
  public:
-    CuDNNPooling2dGradientOp(const OperatorDef& op_def, Workspace* ws)
-        : Pooling2dGradientOp<Context>(op_def, ws) {
+    CuDNNPooling2dGradientOp(const OperatorDef& def, Workspace* ws)
+        : Pooling2dGradientOp<Context>(def, ws) {
         CUDNN_CHECK(cudnnCreateTensorDescriptor(&input_desc));
         CUDNN_CHECK(cudnnCreateTensorDescriptor(&output_desc));
         CUDNN_CHECK(cudnnCreatePoolingDescriptor(&pool_desc));

Dragon/include/operators/vision/roi_align_op.h

@@ -17,14 +17,14 @@
 namespace dragon {
 
 template <class Context>
-class ROIAlignOp : public Operator<Context> {
+class ROIAlignOp final : public Operator<Context> {
  public:
-    ROIAlignOp(const OperatorDef& op_def, Workspace *ws)
-        : Operator<Context>(op_def, ws),
-          pool_h(OperatorBase::GetSingleArg<int>("pool_h", 0)),
-          pool_w(OperatorBase::GetSingleArg<int>("pool_w", 0)),
-          spatial_scale(OperatorBase::GetSingleArg<float>("spatial_scale", 1.0)),
-          sampling_ratio(OperatorBase::GetSingleArg<int>("sampling_ratio", 2)) {
+    ROIAlignOp(const OperatorDef& def, Workspace *ws)
+        : Operator<Context>(def, ws),
+          pool_h(OperatorBase::Arg<int>("pool_h", 0)),
+          pool_w(OperatorBase::Arg<int>("pool_w", 0)),
+          spatial_scale(OperatorBase::Arg<float>("spatial_scale", 1.0)),
+          sampling_ratio(OperatorBase::Arg<int>("sampling_ratio", 2)) {
         CHECK_GT(pool_h, 0) << "\npool_h must > 0";
         CHECK_GT(pool_w, 0) << "\npool_w must > 0";
     }
@@ -39,14 +39,14 @@ class ROIAlignOp : public Operator<Context> {
 };
 
 template <class Context>
-class ROIAlignGradientOp : public Operator<Context> {
+class ROIAlignGradientOp final : public Operator<Context> {
  public:
-    ROIAlignGradientOp(const OperatorDef& op_def, Workspace *ws)
-        : Operator<Context>(op_def, ws),
-          pool_h(OperatorBase::GetSingleArg<int>("pool_h", 0)),
-          pool_w(OperatorBase::GetSingleArg<int>("pool_w", 0)),
-          spatial_scale(OperatorBase::GetSingleArg<float>("spatial_scale", 1.0)),
-          sampling_ratio(OperatorBase::GetSingleArg<int>("sampling_ratio", 2)) {
+    ROIAlignGradientOp(const OperatorDef& def, Workspace *ws)
+        : Operator<Context>(def, ws),
+          pool_h(OperatorBase::Arg<int>("pool_h", 0)),
+          pool_w(OperatorBase::Arg<int>("pool_w", 0)),
+          spatial_scale(OperatorBase::Arg<float>("spatial_scale", 1.f)),
+          sampling_ratio(OperatorBase::Arg<int>("sampling_ratio", 2)) {
         CHECK_GT(pool_h, 0) << "\npool_h must > 0";
         CHECK_GT(pool_w, 0) << "\npool_w must > 0";
     }

Dragon/include/operators/vision/roi_pooling_op.h

@@ -17,13 +17,13 @@
 namespace dragon {
 
 template <class Context>
-class ROIPoolingOp : public Operator<Context> {
+class ROIPoolingOp final : public Operator<Context> {
  public:
-    ROIPoolingOp(const OperatorDef& op_def, Workspace *ws) 
-        : Operator<Context>(op_def, ws),
-          pool_h(OperatorBase::GetSingleArg<int>("pool_h", 0)),
-          pool_w(OperatorBase::GetSingleArg<int>("pool_w", 0)),
-          spatial_scale(OperatorBase::GetSingleArg<float>("spatial_scale", 1.0)) {
+    ROIPoolingOp(const OperatorDef& def, Workspace *ws) 
+        : Operator<Context>(def, ws),
+          pool_h(OperatorBase::Arg<int>("pool_h", 0)),
+          pool_w(OperatorBase::Arg<int>("pool_w", 0)),
+          spatial_scale(OperatorBase::Arg<float>("spatial_scale", 1.0)) {
         CHECK_GT(pool_h, 0) << "\npool_h must > 0";
         CHECK_GT(pool_w, 0) << "\npool_w must > 0";
     }
@@ -40,11 +40,11 @@ class ROIPoolingOp : public Operator<Context> {
 template <class Context>
 class ROIPoolingGradientOp final : public Operator<Context> {
  public:
-    ROIPoolingGradientOp(const OperatorDef& op_def, Workspace* ws)
-        : Operator<Context>(op_def, ws),
-        pool_h(OperatorBase::GetSingleArg<int>("pool_h", 0)),
-        pool_w(OperatorBase::GetSingleArg<int>("pool_w", 0)),
-        spatial_scale(OperatorBase::GetSingleArg<float>("spatial_scale", 1.0)) {}
+    ROIPoolingGradientOp(const OperatorDef& def, Workspace* ws)
+        : Operator<Context>(def, ws),
+          pool_h(OperatorBase::Arg<int>("pool_h", 0)),
+          pool_w(OperatorBase::Arg<int>("pool_w", 0)),
+          spatial_scale(OperatorBase::Arg<float>("spatial_scale", 1.f)) {}
     USE_OPERATOR_FUNCTIONS;
 
     void RunOnDevice() override;

Dragon/include/utils/caffemodel.h

@@ -33,19 +33,26 @@ using google::protobuf::io::ZeroCopyInputStream;
 using google::protobuf::io::CodedInputStream;
 using google::protobuf::io::FileInputStream;
 
-inline void WriteProtoToBinaryFile(const Message& proto, const char* filename) {
-    std::fstream output(filename, std::ios::out | std::ios::trunc | std::ios::binary);
+inline void WriteProtoToBinaryFile(
+    const Message&              proto,
+    const char*                 filename) {
+    std::fstream output(filename,
+        std::ios::out |
+            std::ios::trunc |
+                std::ios::binary);
     proto.SerializeToOstream(&output);
 }
 
-inline bool ReadProtoFromBinaryFile(const char* filename, Message* proto) {
+inline bool ReadProtoFromBinaryFile(
+    const char*                 filename,
+    Message*                    proto) {
 #ifdef _MSC_VER
     int fd = _open(filename, O_RDONLY | O_BINARY);
 #else
     int fd = open(filename, O_RDONLY);
 #endif
-    ZeroCopyInputStream *raw_input = new FileInputStream(fd);
-    CodedInputStream *coded_input = new CodedInputStream(raw_input);
+    ZeroCopyInputStream* raw_input = new FileInputStream(fd);
+    CodedInputStream* coded_input = new CodedInputStream(raw_input);
     coded_input->SetTotalBytesLimit(INT_MAX, -1);
     bool success = proto->ParseFromCodedStream(coded_input);
     delete raw_input;
@@ -54,7 +61,9 @@ inline bool ReadProtoFromBinaryFile(const char* filename, Message* proto) {
     return success;
 }
 
-inline void LoadCaffeModel(string file, Workspace* ws) {
+inline void LoadCaffeModel(
+    string                      file,
+    Workspace*                  ws) {
     NetParameter net_param;
     ReadProtoFromBinaryFile(file.c_str(), &net_param);
     LOG(INFO) << "Restore From Model @: " << file << "......";
@@ -73,36 +82,40 @@ inline void LoadCaffeModel(string file, Workspace* ws) {
                 vector<TIndex> dims;
                 for (auto dim : blob.shape().dim()) dims.push_back(dim);
                 Tensor* tensor = ws->GetTensor(tensor_name);
-                std::stringstream dim_string;
+                std::stringstream DimString;
                 if (dims.size() > 0) {
                     tensor->Reshape(dims);
                     CHECK_EQ(tensor->count(), blob.data_size())
                         << "\nTensor(" << tensor_name << ") "
                         << "failed to load, except size:  "
-                        << tensor->count() << ", loaded: " << blob.data_size();
-                    dim_string << tensor->dim_string();
+                        << tensor->count()
+                        << ", loaded: " << blob.data_size();
+                    DimString << tensor->DimString();
                 } else {
-                    tensor->Reshape(vector<TIndex>(1, blob.data_size()));
-                    dim_string << "(missing)";
+                    tensor->Reshape({ blob.data_size() });
+                    DimString << "(missing)";
                 }
                 float* Xdata = tensor->mutable_data<float, CPUContext>();
                 for (int idx = 0; idx < blob.data_size(); idx++)
                     Xdata[idx] = blob.data(idx);
                 LOG(INFO) << "Tensor(" << tensor_name << ") "
-                          << "loaded, shape: " << dim_string.str()
+                          << "loaded, shape: " << DimString.str()
                           << ", size: " << blob.data_size();
             }    
         }
     }
 }
 
-inline void SavaCaffeModel(string file, const vector<Tensor*>& tensors) {
+inline void SavaCaffeModel(
+    string                      file,
+    const vector<Tensor*>&      tensors) {
     NetParameter net_param;
     Map<string, int> layer_hash;
     int layer_idx = -1;
     for (int i = 0; i < tensors.size(); i++) {
         if (tensors[i]->count() <= 0) continue;
-        vector<string> splits = SplitString(tensors[i]->name(), "/param:");
+        vector<string> splits = SplitString(
+            tensors[i]->name(), "/param:");
         if (layer_hash.count(splits[0]) == 0) {
             layer_hash[splits[0]] = ++layer_idx;
             LayerParameter* layer = net_param.add_layer();
@@ -110,11 +123,14 @@ inline void SavaCaffeModel(string file, const vector<Tensor*>& tensors) {
         }
         BlobProto* blob = net_param.mutable_layer(layer_idx)->add_blobs();
         for (auto dim : tensors[i]->dims()) blob->mutable_shape()->add_dim(dim);
-        const float* Xdata = tensors[i]->data < float, CPUContext >();
+        const float* Xdata = tensors[i]->data<float, CPUContext>();
         for (int id = 0; id < tensors[i]->count(); id++)
             blob->mutable_data()->Add(Xdata[id]);
     }
-    std::fstream output(file, std::ios::out | std::ios::trunc | std::ios::binary);
+    std::fstream output(file,
+        std::ios::out |
+            std::ios::trunc |
+                std::ios::binary);
     CHECK(net_param.SerializeToOstream(&output));
     LOG(INFO) << "Save the model @: " << file << "......";
     LOG(INFO) << "Model format: caffemodel";

Dragon/include/utils/cast.h

@@ -26,9 +26,13 @@ template<> inline int dragon_cast<int, float>(float val) {
     return static_cast<int>(val);
 }
 
-template<> inline float dragon_cast<float, float>(float val) { return val; }
+template<> inline float dragon_cast<float, float>(float val) {
+    return val; 
+}
 
-template<> inline float16 dragon_cast<float16, float16>(float16 val) { return val; }
+template<> inline float16 dragon_cast<float16, float16>(float16 val) {
+    return val; 
+}
 
 template<> inline float16 dragon_cast<float16, float>(float val) {
     float16 ret;
@@ -67,7 +71,8 @@ template<> inline float16 dragon_cast<float16, float>(float val) {
     // Round to nearest even.
     remainder = (mantissa & lsb_m1);
     mantissa >>= shift;
-    if (remainder > lsb_s1 || (remainder == lsb_s1 && (mantissa & 0x1))) {
+    if (remainder > lsb_s1 ||
+            (remainder == lsb_s1 && (mantissa & 0x1))) {
         ++mantissa;
         if (!(mantissa & 0x3ff)) {
             ++exponent;

Dragon/include/utils/cuda_device.h

@@ -29,9 +29,9 @@ namespace dragon {
 
 #ifdef WITH_CUDA
 
-static const int CUDA_NUM_THREADS = 1024;
+static const int CUDA_THREADS = 1024;
 //  We do have a server with 10 GPUs :-)
-#define MAX_GPUS 10
+#define CUDA_MAX_DEVICES 10
 
 #define CUDA_VERSION_MIN(major, minor, patch) \
     (CUDA_VERSION >= (major * 1000 + minor * 100 + patch))
@@ -42,7 +42,8 @@ static const int CUDA_NUM_THREADS = 1024;
 #define CUDA_CHECK(condition) \
   do { \
     cudaError_t error = condition; \
-    CHECK_EQ(error, cudaSuccess) << "\n" << cudaGetErrorString(error); \
+    CHECK_EQ(error, cudaSuccess) \
+        << "\n" << cudaGetErrorString(error); \
   } while (0)
 
 #define CUBLAS_CHECK(condition) \
@@ -61,7 +62,8 @@ static const int CUDA_NUM_THREADS = 1024;
 #define NCCL_CHECK(condition) \
   do { \
     ncclResult_t status = condition; \
-    CHECK_EQ(status, ncclSuccess) << "\n" << ncclGetErrorString(status); \
+    CHECK_EQ(status, ncclSuccess) \
+        << "\n" << ncclGetErrorString(status); \
   } while (0)
 #endif  // WITH_MPI_NCCL
 
@@ -69,12 +71,10 @@ static const int CUDA_NUM_THREADS = 1024;
   for (int i = blockIdx.x * blockDim.x + threadIdx.x; \
        i < n; i += blockDim.x * gridDim.x)
 
-inline int GET_BLOCKS(const int N) {
-    return (N + CUDA_NUM_THREADS - 1) / CUDA_NUM_THREADS;
+inline int CUDA_BLOCKS(const int N) {
+    return (N + CUDA_THREADS - 1) / CUDA_THREADS;
 }
 
-#define CUDA_POST_KERNEL_CHECK CUDA_CHECK(cudaPeekAtLastError())
-
 #if CUDA_VERSION_MAX(9, 0, 0)
 #define __hdiv hdiv
 #endif
@@ -83,18 +83,19 @@ inline int CUDA_NUM_DEVICES() {
     static int count = -1;
     if (count < 0) {
         auto err = cudaGetDeviceCount(&count);
-        if (err == cudaErrorNoDevice || err == cudaErrorInsufficientDriver) count = 0;
+        if (err == cudaErrorNoDevice ||
+            err == cudaErrorInsufficientDriver) count = 0;
     }
     return count;
 }
 
-inline int CUDA_CURRENT_DEVICE() {
+inline int CUDA_DEVICE() {
     int gpu_id;
     cudaGetDevice(&gpu_id);
     return gpu_id;
 }
 
-inline int CUDA_POINTER_DEVICE(const void* ptr) {
+inline int CUDA_DEVICE(const void* ptr) {
     cudaPointerAttributes attr;
     CUDA_CHECK(cudaPointerGetAttributes(&attr, ptr));
     return attr.device;
@@ -108,16 +109,18 @@ struct CUDADeviceProps {
     vector<cudaDeviceProp> props;
 };
 
-inline const cudaDeviceProp& GetDeviceProperty(const int device_id) {
+inline const cudaDeviceProp& GetDeviceProperty(
+    const int               device_id) {
     static CUDADeviceProps props;
     CHECK_LT(device_id, (int)props.props.size())
         << "Invalid device id: " << device_id
-        << "\nDetected " << props.props.size() << " eligible cuda devices.";
+        << "\nDetected " << props.props.size()
+        << " eligible cuda devices.";
     return props.props[device_id];
 }
 
 inline bool CUDA_TRUE_FP16_AVAILABLE() {
-    int device = CUDA_CURRENT_DEVICE();
+    int device = CUDA_DEVICE();
     auto& prop = GetDeviceProperty(device);
     return prop.major >= 6;
 }
@@ -126,7 +129,7 @@ inline bool TENSOR_CORE_AVAILABLE() {
 #if CUDA_VERSION < 9000
     return false;
 #else
-    int device = CUDA_CURRENT_DEVICE();
+    int device = CUDA_DEVICE();
     auto& prop = GetDeviceProperty(device);
     return prop.major >= 7;
 #endif
@@ -134,11 +137,15 @@ inline bool TENSOR_CORE_AVAILABLE() {
 
 class DeviceGuard {
  public:
-    DeviceGuard(int newDevice) : previous_(CUDA_CURRENT_DEVICE()) {
+    DeviceGuard(int newDevice)
+        : previous_(CUDA_DEVICE()) {
         if (previous_ != newDevice)
             CUDA_CHECK(cudaSetDevice(newDevice));
     }
-    ~DeviceGuard() { CUDA_CHECK(cudaSetDevice(previous_)); }
+
+    ~DeviceGuard() {
+        CUDA_CHECK(cudaSetDevice(previous_));
+    }
 
  private:
     int previous_;

Dragon/include/utils/filler.h

@@ -21,8 +21,9 @@ namespace dragon {
 template <typename T, class Context>
 class Filler {
  public:
-    Filler(const TensorFiller& filler): filler_(filler) {}
-    virtual void Fill(Tensor* tensor) = 0;
+    Filler(const TensorFiller& filler) : filler_(filler) {}
+
+    virtual void Fill(Tensor* tensor, Context* ctx) = 0;
 
     inline TensorFiller& filler() { return filler_; }
 
@@ -30,101 +31,125 @@ class Filler {
     TensorFiller filler_;
 };
 
-
 template <typename T, class Context>
 class ConstantFiller final : public Filler<T, Context> {
  public:
-    ConstantFiller(const TensorFiller& filler): Filler<T, Context>(filler) {}
-    void Fill(Tensor* tensor) override {
+    ConstantFiller(const TensorFiller& filler)
+        : Filler<T, Context>(filler) {}
+
+    void Fill(Tensor* tensor, Context* ctx) override {
         math::Set<T, Context>(tensor->count(),
-                              dragon_cast<T, float>(this->filler().value()),
+            dragon_cast<T, float>(filler().value()),
                 tensor->mutable_data<T, Context>());
     }
+
+ protected:
+    using Filler<T, Context>::filler;
 };
 
 template <typename T, class Context>
 class NormalFiller final : public Filler<T, Context> {
  public:
-    NormalFiller(const TensorFiller& filler): Filler<T, Context>(filler) {}
-    void Fill(Tensor* tensor) override {
+    NormalFiller(const TensorFiller& filler)
+        : Filler<T, Context>(filler) {}
+
+    void Fill(Tensor* tensor, Context* ctx) override {
         math::RandomNormal<T, Context>(tensor->count(),
-                                 this->filler().mean(),
-                                  this->filler().std(),
-                   tensor->mutable_data<T, Context>());
+            filler().mean(), filler().std(),
+                tensor->mutable_data<T, Context>(), ctx);
     }
+
+ protected:
+    using Filler<T, Context>::filler;
 };
 
 template <typename T, class Context>
-class TruncatedNormalFiller final : public Filler < T, Context > {
+class TruncatedNormalFiller final : public Filler<T, Context> {
  public:
-    TruncatedNormalFiller(const TensorFiller& filler): Filler<T, Context>(filler) {}
-    void Fill(Tensor* tensor) override {
+    TruncatedNormalFiller(const TensorFiller& filler)
+        : Filler<T, Context>(filler) {}
+
+    void Fill(Tensor* tensor, Context* ctx) override {
         //  implement it on gpu is difficult
+        static CPUContext cpu_ctx;
         math::RandomTruncatedNormal<T, CPUContext>(tensor->count(),
-                                             this->filler().mean(),
-                                              this->filler().std(),
-                                              this->filler().low(),
-                                             this->filler().high(),
-                            tensor->mutable_data<T, CPUContext>());
+            filler().mean(), filler().std(),
+                filler().low(), filler().high(),
+                    tensor->mutable_data<T, CPUContext>(), &cpu_ctx);
     }
+
+ protected:
+    using Filler<T, Context>::filler;
 };
 
 template <typename T, class Context>
 class UniformFiller final : public Filler<T, Context> {
  public:
-    UniformFiller(const TensorFiller& filler) : Filler<T, Context>(filler) {}
-    void Fill(Tensor* tensor) override {
+    UniformFiller(const TensorFiller& filler) 
+        : Filler<T, Context>(filler) {}
+
+    void Fill(Tensor* tensor, Context* ctx) override {
         math::RandomUniform<T, Context>(tensor->count(),
-                                   this->filler().low(),
-                                  this->filler().high(),
-                    tensor->mutable_data<T, Context>());
+            filler().low(), filler().high(),
+                tensor->mutable_data<T, Context>(), ctx);
     }
+
+ protected:
+    using Filler<T, Context>::filler;
 };
 
 template <typename T, class Context>
 class XavierFiller final : public Filler<T, Context> {
  public:
-    XavierFiller(const TensorFiller& filler) : Filler<T, Context>(filler) {}
-    using Filler<T, Context>::filler;
-    void Fill(Tensor* tensor) override {
+    XavierFiller(const TensorFiller& filler)
+        : Filler<T, Context>(filler) {}
+
+    void Fill(Tensor* tensor, Context* ctx) override {
         int fan_in = tensor->count() / tensor->dim(0);
         int fan_out = tensor->count() / tensor->dim(1);
         float n = fan_in, scale = 3.0;
         if (filler().has_scale()) scale = filler().scale();
-        if (filler().variance_norm() == TensorFiller_VarianceNorm_FAN_AVG) {
+        if (filler().variance_norm() ==
+            TensorFiller_VarianceNorm_FAN_AVG) {
             n = (fan_in + fan_out) / float(2);
-        } else if (filler().variance_norm() == TensorFiller_VarianceNorm_FAN_OUT) {
+        } else if (filler().variance_norm() ==
+            TensorFiller_VarianceNorm_FAN_OUT) {
             n = fan_out;
         }
         float limit = std::sqrt(scale / n);
         math::RandomUniform<T, Context>(tensor->count(),
-                                                 -limit,
-                                                  limit,
-                    tensor->mutable_data<T, Context>());
+            -limit, limit, tensor->mutable_data<T, Context>(), ctx);
     }
+
+ protected:
+    using Filler<T, Context>::filler;
 };
 
 template <typename T, class Context>
 class MSRAFiller final : public Filler <T, Context> {
  public:
-    MSRAFiller(const TensorFiller& filler) : Filler<T, Context>(filler) {}
-    using Filler<T, Context>::filler;
-    void Fill(Tensor* tensor) override {
+    MSRAFiller(const TensorFiller& filler)
+        : Filler<T, Context>(filler) {}
+
+    void Fill(Tensor* tensor, Context* ctx) override {
         int fan_in = tensor->count() / tensor->dim(0);
         int fan_out = tensor->count() / tensor->dim(1);
         float n = fan_in, scale = 2.0;
         if (filler().has_scale()) scale = filler().scale();
-        if (filler().variance_norm() == TensorFiller_VarianceNorm_FAN_AVG) {
+        if (filler().variance_norm() ==
+            TensorFiller_VarianceNorm_FAN_AVG) {
             n = (fan_in + fan_out) / float(2);
-        } else if (filler().variance_norm() == TensorFiller_VarianceNorm_FAN_OUT) {
+        } else if (filler().variance_norm() == 
+            TensorFiller_VarianceNorm_FAN_OUT) {
             n = fan_out;
         }
         float std = std::sqrt(scale / n);
         math::RandomNormal<T, Context>(tensor->count(),
-                                              float(0),
-                                                   std,
-                   tensor->mutable_data<T, Context>());
+            0.f, std, tensor->mutable_data<T, Context>(), ctx);
     }
+
+ protected:
+    using Filler<T, Context>::filler;
 };
 
 

Dragon/include/utils/math_functions.h

@@ -43,14 +43,16 @@ void RandomUniform(
     const int               n,
     const float             low,
     const float             high,
-    T*                      x);
+    T*                      x,
+    Context*                ctx);
 
 template <typename T, class Context>
 void RandomNormal(
     const int               n,
     const float             mu,
     const float             sigma,
-    T*                      x);
+    T*                      x,
+    Context*                ctx);
 
 template <typename T, class Context>
 void RandomTruncatedNormal(
@@ -59,13 +61,15 @@ void RandomTruncatedNormal(
     const float             sigma,
     const float             low,
     const float             high,
-    T*                      x);
+    T*                      x,
+    Context*                ctx);
 
 template <typename T, class Context>
 void RandomBernoulli(
     const int               n,
     const float             p,
-    uint32_t*               x);
+    uint32_t*               x,
+    Context*                ctx);
 
 /******************** Level-1 ********************/
 
@@ -148,14 +152,16 @@ template <typename T, class Context>
 void Scal(
     const int               n,
     const float             alpha,
-    T*                      y);
+    T*                      y,
+    Context*                ctx);
 
 template <typename T, class Context>
 void Scale(
     const int               n,
     const float             alpha,
     const T*                x,
-    T*                      y);
+    T*                      y,
+    Context*                ctx);
 
 template <typename T, class Context>
 T StridedDot(
@@ -163,13 +169,15 @@ T StridedDot(
     const T*                a,
     const int               incx,
     const T*                b,
-    const int               incy);
+    const int               incy,
+    Context*                ctx);
 
 template <typename T, class Context>
 float Dot(
     const int               n,
     const T*                a,
-    const T*                b);
+    const T*                b,
+    Context*                ctx);
 
 template<typename T, class Context>
 float ASum(
@@ -193,7 +201,8 @@ void Axpy(
     const int               n,
     float                   alpha,
     const T*                x,
-    T*                      y);
+    T*                      y,
+    Context*                ctx);
 
 template<typename T, class Context>
 void Axpby(
@@ -201,7 +210,8 @@ void Axpby(
     float                   alpha,
     const T*                x,
     float                   beta,
-    T*                      y);
+    T*                      y,
+    Context*                ctx);
 
 /******************** Level-3 ********************/
 
@@ -217,6 +227,7 @@ void Gemm(
     const T*                B,
     const float             beta,
     T*                      C,
+    Context*                ctx,
     TensorProto_DataType    math_type = TensorProto_DataType_FLOAT);
 
 template<typename T, class Context>
@@ -229,6 +240,7 @@ void Gemv(
     const T*                x,
     const float             beta,
     T*                      y,
+    Context*                ctx,
     TensorProto_DataType    math_type = TensorProto_DataType_FLOAT);
 
 }    // namespace math

Dragon/include/utils/op_kernel.h

@@ -20,9 +20,6 @@ namespace kernel {
 
 typedef int64_t TIndex;
 
-template <typename T, class Context>
-void Empty();
-
 /******************** activation.dropout ********************/
 
 template <typename T, class Context>
@@ -32,7 +29,8 @@ void Dropout(
     T                       scale,
     const T*                x,
     uint32_t*               mask,
-    T*                      y);
+    T*                      y,
+    Context*                ctx);
 
 template <typename T, class Context>
 void DropoutGrad(
@@ -41,7 +39,8 @@ void DropoutGrad(
     T                       scale,
     const T*                dy,
     const uint32_t*         mask,
-    T*                      dx);
+    T*                      dx,
+    Context*                ctx);
 
 /******************** activation.elu ********************/
 
@@ -97,7 +96,8 @@ void PReluWGrad(
     const T*                x,
     const T*                multiplier,
     T*                      bcast_dw,
-    T*                      dw);
+    T*                      dw,
+    Context*                ctx);
 
 /******************** activation.relu ********************/
 
@@ -157,7 +157,8 @@ void Softmax(
     const T*                sum_multiplier,
     const T*                x,
     T*                      scale,
-    T*                      y);
+    T*                      y,
+    Context*                ctx);
 
 template <typename T, class Context>
 void SoftmaxGrad(
@@ -169,7 +170,8 @@ void SoftmaxGrad(
     const T*                dy,
     const T*                y,
     T*                      scale,
-    T*                      dx);
+    T*                      dx,
+    Context*                ctx);
 
 /******************** activation.tanh ********************/
 
@@ -198,7 +200,8 @@ void Affine(
     const T*                alpha,
     const T*                beta,
     const T*                beta_multiplier,
-    T*                      y);
+    T*                      y,
+    Context*                ctx);
 
 template <typename T, class Context>
 void AffineGrad(
@@ -208,7 +211,8 @@ void AffineGrad(
     const int               inner_dim,
     const T*                dy,
     const T*                alpha,
-    T*                      dx);
+    T*                      dx,
+    Context*                ctx);
 
 /******************** arithmetic.clip ********************/
 
@@ -293,7 +297,8 @@ void SparseSoftmaxCrossEntropy(
     const Ty*               labels,
     Tx*                     loss,
     Tx*                     valid,
-    Tensor*                 ignore);
+    Tensor*                 ignore,
+    Context*                ctx);
 
 template <typename Tx, typename Ty, class Context>
 void SparseSoftmaxCrossEntropyGrad(
@@ -305,7 +310,8 @@ void SparseSoftmaxCrossEntropyGrad(
     const Ty*               labels,
     Tx*                     valid,
     Tensor*                 ignore,
-    Tx*                     dx);
+    Tx*                     dx,
+    Context*                ctx);
 
 /******************** loss.sparse_softmax_focal_loss ********************/
 
@@ -585,7 +591,8 @@ void RepeatGrad(
     const int               inner_dim,
     const int               repeats,
     const T*                dy,
-    T*                      dx);
+    T*                      dx,
+    Context*                ctx);
 
 /******************** ndarray.slice ********************/
 
@@ -629,7 +636,8 @@ void TileGrad(
     const int               ex_inner_dim,
     const int               multiple,
     const T*                dy,
-    T*                      dx);
+    T*                      dx,
+    Context*                ctx);
 
 /******************** ndarray.transpose ********************/
 
@@ -733,7 +741,8 @@ void BiasAdd(
     const string&           data_format,
     const T*                bias,
     const T*                bias_multiplier,
-    T*                      y);
+    T*                      y,
+    Context*                ctx);
 
 /******************** vision.bilinear_resize ********************/
 

Dragon/include/utils/proto_utils.h

@@ -22,8 +22,11 @@ namespace dragon {
 
 using google::protobuf::Message;
 
-template <class IterableInputs,class IterableOutputs,class IterableArgs>
-inline OperatorDef MakeOperatorDef(const string& type,
+template <class IterableInputs,
+              class IterableOutputs,
+                  class IterableArgs>
+inline OperatorDef MakeOperatorDef(
+    const string&               type,
     const string&               name,
     const IterableInputs&       inputs,
     const IterableOutputs&      outputs,
@@ -36,29 +39,42 @@ inline OperatorDef MakeOperatorDef(const string& type,
     for (const string& in : inputs) def.add_input(in);
     for (const string& out : outputs) def.add_output(out);
     for (const Argument& arg : args) def.add_arg()->CopyFrom(arg);
-    if (device_option.has_device_type()) def.mutable_device_option()->CopyFrom(device_option);
+    if (device_option.has_device_type())
+        def.mutable_device_option()->CopyFrom(device_option);
     return def;
 }
 
-template <class IterableInputs, class IterableOutputs, class IterableArgs>
-inline OperatorDef MakeOperatorDef(const string& type,
+template <class IterableInputs,
+              class IterableOutputs,
+                  class IterableArgs>
+inline OperatorDef MakeOperatorDef(
+    const string&               type,
     const string&               name,
     const IterableInputs&       inputs,
     const IterableOutputs&      outputs,
     const IterableArgs&         args) {
-    return MakeOperatorDef(type, name, inputs, outputs, args, DeviceOption(), "");
+    return MakeOperatorDef(
+        type, name, inputs, outputs, args,
+            DeviceOption(), "");
 }
 
-template <class IterableInputs, class IterableOutputs>
-inline OperatorDef MakeOperatorDef(const string& type,
+template <class IterableInputs,
+              class IterableOutputs>
+inline OperatorDef MakeOperatorDef(
+    const string&               type,
     const string&               name,
     const IterableInputs&       inputs,
     const IterableOutputs&      outputs) {
-    return MakeOperatorDef(type, name, inputs, outputs, vector<Argument>(), DeviceOption(), "");
+    return MakeOperatorDef(
+        type, name, inputs, outputs,
+            vector<Argument>(), DeviceOption(), "");
 }
 
-inline void ParseProtoFromText(string text, Message* proto) {
-    google::protobuf::TextFormat::ParseFromString(text, proto);
+inline void ParseProtoFromText(
+    string                      text,
+    Message*                    proto) {
+    google::protobuf::TextFormat
+        ::ParseFromString(text, proto);
 }
 
 }    // namespace dragon