pytorch · li-roy · Jun 19, 2019 · Jun 19, 2019 · Jun 19, 2019 · Jun 20, 2019
diff --git a/aten/src/ATen/CPUTypeDefault.h b/aten/src/ATen/CPUTypeDefault.h
@@ -4,8 +4,8 @@
 namespace at {
 
 struct CAFFE2_API CPUTypeDefault : public TypeDefault {
-  CPUTypeDefault(TensorTypeId type_id, bool is_variable, bool is_undefined)
-      : TypeDefault(type_id, is_variable, is_undefined) {}
+  CPUTypeDefault()
+      : TypeDefault() {}
 };
 
 } // namespace at
diff --git a/aten/src/ATen/UndefinedType.cpp b/aten/src/ATen/UndefinedType.cpp
@@ -4,10 +4,7 @@
 namespace at {
 
 UndefinedType::UndefinedType()
-    : TypeDefault(UndefinedTensorId(), /*is_variable=*/false, /*is_undefined=*/true) {}
-Backend UndefinedType::backend() const {
-  return Backend::Undefined;
-}
+    : TypeDefault() {}
 
 const char * UndefinedType::toString() const {
   return "UndefinedType";
@@ -17,17 +14,4 @@ TypeID UndefinedType::ID() const {
   return TypeID::Undefined;
 }
 
-Type & UndefinedType::toBackend(Backend b) const {
-  if (b == Backend::Undefined) {
-    return TypeDefault::toBackend(b);
-  }
-  AT_ERROR("toBackend not implemented for UndefinedType to non-UndefinedType");
-}
-Type & UndefinedType::toScalarType(ScalarType s) const {
-  if (s == ScalarType::Undefined) {
-    return TypeDefault::toScalarType(s);
-  }
-  AT_ERROR("toScalarType not implemented for UndefinedType to non-UndefinedType");
-}
-
 }
diff --git a/aten/src/ATen/UndefinedType.h b/aten/src/ATen/UndefinedType.h
@@ -13,10 +13,7 @@ namespace at {
 
 struct UndefinedType final : public TypeDefault {
   explicit UndefinedType();
-  virtual Backend backend() const override;
   virtual const char * toString() const override;
-  virtual Type & toBackend(Backend b) const override;
-  virtual Type & toScalarType(ScalarType s) const override;
   virtual TypeID ID() const override;
 };
 

diff --git a/aten/src/ATen/core/Tensor.h b/aten/src/ATen/core/Tensor.h
@@ -349,19 +349,13 @@ class CAFFE2_API Tensor {
     return impl_->grad();
   }
 
-  void set_data(Tensor new_data);
-
-  /// Computes the gradient of current tensor w.r.t. graph leaves.
-  void backward(
-      c10::optional<Tensor> gradient = c10::nullopt,
-      bool keep_graph = false,
-      bool create_graph = false);
-
   // STOP.  Thinking of adding a method here, which only makes use
   // of other ATen methods?  Define it in native_functions.yaml.
 
   //example
   //Tensor * add(Tensor & b);
+  void backward(const Tensor & gradient={}, bool keep_graph=false, bool create_graph=false) const;
+  void set_data(const Tensor & new_data) const;
   Tensor abs() const;
   Tensor & abs_();
   Tensor acos() const;

diff --git a/aten/src/ATen/core/TensorMethods.h b/aten/src/ATen/core/TensorMethods.h
@@ -56,18 +56,15 @@ inline TensorOptions Tensor::options() const {
                         .is_variable(is_variable());
 }
 
-inline void Tensor::backward(
-    c10::optional<Tensor> gradient,
-    bool keep_graph,
-    bool create_graph) {
-  dispatch_type().backward(*this, std::move(gradient), keep_graph, create_graph);
+// all static inline to allow for inlining of the non-dynamic part of dispatch
+inline void Tensor::backward(const Tensor & gradient, bool keep_graph, bool create_graph) const {
+    static auto table = globalATenDispatch().getOpTable("aten::backward(Tensor self, Tensor? gradient=None, bool keep_graph=False, bool create_graph=False) -> void");
+    return table->getOp<void (const Tensor &, const Tensor &, bool, bool)>(tensorTypeIdToBackend(type_id()), is_variable())(*this, gradient, keep_graph, create_graph);
 }
-
-inline void Tensor::set_data(Tensor new_data) {
-  dispatch_type().set_data(*this, new_data);
+inline void Tensor::set_data(const Tensor & new_data) const {
+    static auto table = globalATenDispatch().getOpTable("aten::set_data(Tensor(a!) self, Tensor new_data) -> void");
+    return table->getOp<void (const Tensor &, const Tensor &)>(tensorTypeIdToBackend(type_id()), is_variable())(*this, new_data);
 }
-
-// all static inline to allow for inlining of the non-dynamic part of dispatch
 inline Tensor Tensor::abs() const {
     static auto table = globalATenDispatch().getOpTable("aten::abs(Tensor self) -> Tensor");
     return table->getOp<Tensor (const Tensor &)>(tensorTypeIdToBackend(type_id()), is_variable())(*this);

diff --git a/aten/src/ATen/core/Type.h b/aten/src/ATen/core/Type.h
@@ -70,86 +70,11 @@ enum class TypeID {
 };
 
 struct CAFFE2_API Type {
-  explicit Type(TensorTypeId type_id, bool is_variable, bool is_undefined)
-      : type_id_(type_id), is_variable_(is_variable), is_undefined_(is_undefined) {}
+  explicit Type() {}
 
   virtual ~Type() {}
-  virtual Backend backend() const = 0;
-  Layout layout() const noexcept { return layout_from_backend(backend()); }
-  virtual bool is_cuda() const = 0;
-  virtual bool is_hip() const = 0;
-  virtual bool is_sparse() const = 0;
-  virtual bool is_quantized() const = 0;
-  virtual bool is_distributed() const = 0;
-  bool is_variable() const noexcept { return is_variable_; }
-  bool is_undefined() const noexcept { return is_undefined_; }
   virtual const char * toString() const = 0;
-  virtual Type & toBackend(Backend b) const = 0;
-  virtual Type & toScalarType(ScalarType s) const = 0;
-  Type & toSparse() const {
-    return this->toBackend(at::toSparse(this->backend()));
-  }
-  Type & toDense() const {
-    return this->toBackend(at::toDense(this->backend()));
-  }
-  Type & cpu() const {
-    return this->toBackend(at::backendToCPU(this->backend()));
-  }
-  Type & cuda() const {
-    return this->toBackend(at::backendToCUDA(this->backend()));
-  }
-  Type & hip() const {
-    return this->toBackend(at::backendToHIP(this->backend()));
-  }
-  // contiguous IDs for all types in the system
-  // for external dispatch
   virtual TypeID ID() const = 0;
-
-  // New-style TensorTypeId that supports open registration.
-  TensorTypeId type_id() const { return type_id_; }
-
-  // NB: This will return DeviceType::CPU for Backend::SparseCPU
-  DeviceType device_type() const {
-    return backendToDeviceType(backend());
-  }
-
-  virtual void backward(
-      Tensor& self,
-      c10::optional<Tensor> gradient,
-      bool keep_graph,
-      bool create_graph) const = 0;
-  virtual void set_data(Tensor & self, Tensor new_data) const = 0;
-
-  bool operator==(const Type& other) const {
-    return this == &other;
-  }
-  bool operator!=(const Type& other) const {
-    return this != &other;
-  }
-
-  TensorOptions options(ScalarType s, int16_t device_index = -1) const {
-    return TensorOptions().dtype(s)
-                          .device(device_type(), device_index)
-                          .layout(layout())
-                          .is_variable(is_variable());
-  }
-
-  /// Constructs the `TensorOptions` from a type and a Device.  Asserts that
-  /// the device type matches the device type of the type.
-  TensorOptions options(ScalarType s, c10::optional<Device> device_opt) const {
-    if (!device_opt.has_value()) {
-      return options(s, -1);
-    } else {
-      Device device = device_opt.value();
-      AT_ASSERT(device.type() == device_type());
-      return options(s, device.index());
-    }
-  }
-
-protected:
-  TensorTypeId type_id_;
-  bool is_variable_;
-  bool is_undefined_;
 };
 
 } // namespace at
diff --git a/aten/src/ATen/cuda/CUDATypeDefault.h b/aten/src/ATen/cuda/CUDATypeDefault.h
@@ -5,8 +5,8 @@
 namespace at {
 
 struct AT_CUDA_API CUDATypeDefault : public TypeDefault {
-  CUDATypeDefault(TensorTypeId type_id, bool is_variable, bool is_undefined)
-      : TypeDefault(type_id, is_variable, is_undefined) {}
+  CUDATypeDefault()
+      : TypeDefault() {}
 };
 
 } // namespace at
diff --git a/aten/src/ATen/native/VariableMethodStubs.cpp b/aten/src/ATen/native/VariableMethodStubs.cpp
@@ -0,0 +1,16 @@
+#include <ATen/ATen.h>
+#include <ATen/NativeFunctions.h>
+
+namespace at {
+namespace native {
+
+void backward(const Tensor& self, const Tensor& gradient, bool keep_graph, bool create_graph) {
+  AT_ERROR("backward is not implemented for Tensor");
+}
+
+void set_data(const Tensor& self, const Tensor& new_data) {
+  AT_ERROR("set_data is not implemented for Tensor");
+}
+
+} // namespace native
+} // namespace at
diff --git a/aten/src/ATen/native/native_functions.yaml b/aten/src/ATen/native/native_functions.yaml
@@ -29,6 +29,12 @@
 - func: _cast_Half(Tensor self, bool non_blocking=False) -> Tensor
   variants: function
 
+- func: backward(Tensor self, Tensor? gradient=None, bool keep_graph=False, bool create_graph=False) -> void
+  variants: method
+
+- func: set_data(Tensor(a!) self, Tensor new_data) -> void
+  variants: method
+
 - func: _cudnn_ctc_loss(Tensor log_probs, Tensor targets, int[] input_lengths, int[] target_lengths, int blank, bool deterministic, bool zero_infinity) -> (Tensor, Tensor)
   dispatch:
     CUDA: _cudnn_ctc_loss

diff --git a/aten/src/ATen/templates/SparseTypeDerived.cpp b/aten/src/ATen/templates/SparseTypeDerived.cpp
@@ -29,10 +29,7 @@
 namespace at {
 
 ${Type}::${Type}()
-  : ${DeviceType}TypeDefault(${Backend}TensorId(), /*is_variable=*/false, /*is_undefined=*/false) {}
-Backend ${Type}::backend() const {
-  return Backend::${Backend};
-}
+  : ${DeviceType}TypeDefault() {}
 
 const char * ${Type}::toString() const {
   return "${Type}";

diff --git a/aten/src/ATen/templates/Tensor.h b/aten/src/ATen/templates/Tensor.h
@@ -349,14 +349,6 @@ class CAFFE2_API Tensor {
     return impl_->grad();
   }
 
-  void set_data(Tensor new_data);
-
-  /// Computes the gradient of current tensor w.r.t. graph leaves.
-  void backward(
-      c10::optional<Tensor> gradient = c10::nullopt,
-      bool keep_graph = false,
-      bool create_graph = false);
-
   // STOP.  Thinking of adding a method here, which only makes use
   // of other ATen methods?  Define it in native_functions.yaml.
 

diff --git a/aten/src/ATen/templates/TensorMethods.h b/aten/src/ATen/templates/TensorMethods.h
@@ -56,17 +56,6 @@ inline TensorOptions Tensor::options() const {
                         .is_variable(is_variable());
 }
 
-inline void Tensor::backward(
-    c10::optional<Tensor> gradient,
-    bool keep_graph,
-    bool create_graph) {
-  dispatch_type().backward(*this, std::move(gradient), keep_graph, create_graph);
-}
-
-inline void Tensor::set_data(Tensor new_data) {
-  dispatch_type().set_data(*this, new_data);
-}
-
 // all static inline to allow for inlining of the non-dynamic part of dispatch
 ${tensor_method_definitions}
 

diff --git a/aten/src/ATen/templates/Type.h b/aten/src/ATen/templates/Type.h
@@ -63,86 +63,11 @@ enum class TypeID {
 };
 
 struct CAFFE2_API Type {
-  explicit Type(TensorTypeId type_id, bool is_variable, bool is_undefined)
-      : type_id_(type_id), is_variable_(is_variable), is_undefined_(is_undefined) {}
+  explicit Type() {}
 
   virtual ~Type() {}
-  virtual Backend backend() const = 0;
-  Layout layout() const noexcept { return layout_from_backend(backend()); }
-  virtual bool is_cuda() const = 0;
-  virtual bool is_hip() const = 0;
-  virtual bool is_sparse() const = 0;
-  virtual bool is_quantized() const = 0;
-  virtual bool is_distributed() const = 0;
-  bool is_variable() const noexcept { return is_variable_; }
-  bool is_undefined() const noexcept { return is_undefined_; }
   virtual const char * toString() const = 0;
-  virtual Type & toBackend(Backend b) const = 0;
-  virtual Type & toScalarType(ScalarType s) const = 0;
-  Type & toSparse() const {
-    return this->toBackend(at::toSparse(this->backend()));
-  }
-  Type & toDense() const {
-    return this->toBackend(at::toDense(this->backend()));
-  }
-  Type & cpu() const {
-    return this->toBackend(at::backendToCPU(this->backend()));
-  }
-  Type & cuda() const {
-    return this->toBackend(at::backendToCUDA(this->backend()));
-  }
-  Type & hip() const {
-    return this->toBackend(at::backendToHIP(this->backend()));
-  }
-  // contiguous IDs for all types in the system
-  // for external dispatch
   virtual TypeID ID() const = 0;
-
-  // New-style TensorTypeId that supports open registration.
-  TensorTypeId type_id() const { return type_id_; }
-
-  // NB: This will return DeviceType::CPU for Backend::SparseCPU
-  DeviceType device_type() const {
-    return backendToDeviceType(backend());
-  }
-
-  virtual void backward(
-      Tensor& self,
-      c10::optional<Tensor> gradient,
-      bool keep_graph,
-      bool create_graph) const = 0;
-  virtual void set_data(Tensor & self, Tensor new_data) const = 0;
-
-  bool operator==(const Type& other) const {
-    return this == &other;
-  }
-  bool operator!=(const Type& other) const {
-    return this != &other;
-  }
-
-  TensorOptions options(ScalarType s, int16_t device_index = -1) const {
-    return TensorOptions().dtype(s)
-                          .device(device_type(), device_index)
-                          .layout(layout())
-                          .is_variable(is_variable());
-  }
-
-  /// Constructs the `TensorOptions` from a type and a Device.  Asserts that
-  /// the device type matches the device type of the type.
-  TensorOptions options(ScalarType s, c10::optional<Device> device_opt) const {
-    if (!device_opt.has_value()) {
-      return options(s, -1);
-    } else {
-      Device device = device_opt.value();
-      AT_ASSERT(device.type() == device_type());
-      return options(s, device.index());
-    }
-  }
-
-protected:
-  TensorTypeId type_id_;
-  bool is_variable_;
-  bool is_undefined_;
 };
 
 } // namespace at
diff --git a/aten/src/ATen/templates/TypeDefault.cpp b/aten/src/ATen/templates/TypeDefault.cpp
@@ -19,25 +19,6 @@
 
 namespace at {
 
-void TypeDefault::backward(
-    Tensor& self,
-    c10::optional<Tensor> gradient,
-    bool keep_graph,
-    bool create_graph) const {
-  AT_ERROR("backward is not implemented for Tensor");
-}
-
-void TypeDefault::set_data(Tensor & self, Tensor new_data) const {
-  AT_ERROR("set_data is not implemented for Tensor");
-}
-
-Type & TypeDefault::toBackend(Backend b) const {
-  return at::globalContext().getNonVariableType(b, ScalarType::Undefined);
-}
-Type & TypeDefault::toScalarType(ScalarType s) const {
-  return at::globalContext().getNonVariableType(backend(),s);
-}
-
 ${type_method_definitions}
 
 static auto& registerer = globalATenDispatch()