address comments

Author: icemelon

include/tvm/relay/op_attr_types.h

@@ -249,15 +249,6 @@ class OpImplementNode : public Object {
  */
 class OpImplement : public ObjectRef {
  public:
-  /*! \brief default constructor  */
-  OpImplement() {}
-  /*! \brief constructor from node pointer */
-  explicit OpImplement(ObjectPtr<Object> n) : ObjectRef(n) {}
-  /*!
-   * \brief access the internal node container
-   * \return the pointer to the internal node container
-   */
-  inline const OpImplementNode* operator->() const;
   /*!
    * \brief Invoke the operator compute function.
    * \param attrs The attribute of the primitive
@@ -278,6 +269,8 @@ class OpImplement : public ObjectRef {
   te::Schedule Schedule(const Attrs& attrs,
                         const Array<te::Tensor>& outs,
                         const Target& target);
+
+  TVM_DEFINE_OBJECT_REF_METHODS(OpImplement, ObjectRef, OpImplementNode);
 };
 
 /*!
@@ -305,18 +298,6 @@ class OpSpecializationNode : public Object {
  */
 class OpSpecialization : public ObjectRef {
  public:
-  OpSpecialization() {}
-  explicit OpSpecialization(ObjectPtr<Object> n) : ObjectRef(n) {}
-  /*!
-   * \brief access the internal node container
-   * \return the pointer to the internal node container
-   */
-  inline const OpSpecializationNode* operator->() const;
-  /*!
-   * \brief access the internal node container
-   * \return the pointer to the internal node container
-   */
-  inline OpSpecializationNode* operator->();
   /*!
    * \brief Add an implementation.
    * \param compute Compute function
@@ -325,6 +306,8 @@ class OpSpecialization : public ObjectRef {
    */
   void AddImplement(FTVMCompute fcompute, FTVMSchedule fschedule,
                     int plevel);
+
+  TVM_DEFINE_MUTABLE_OBJECT_REF_METHODS(OpSpecialization, ObjectRef, OpSpecializationNode);
 };
 
 /*!
@@ -348,49 +331,16 @@ class OpStrategyNode : public Object {
  */
 class OpStrategy : public ObjectRef {
  public:
-  /*! \brief default constructor  */
-  OpStrategy() {}
-  /*! \brief constructor from node pointer */
-  explicit OpStrategy(ObjectPtr<Object> n) : ObjectRef(n) {}
-  /*!
-   * \brief access the internal node container
-   * \return the pointer to the internal node container
-   */
-  inline const OpStrategyNode* operator->() const;
-  /*!
-   * \brief access the internal node container
-   * \return the pointer to the internal node container
-   */
-  inline OpStrategyNode* operator->();
   /*!
    * \brief Add an implementation.
    * \param compute Compute function
    * \param schedule Schedule function
    * \param plevel Priority level of this implementation.
    */
   void AddImplement(FTVMCompute fcompute, FTVMSchedule fschedule, int plevel);
-};
-
-// implementations
-inline const OpImplementNode* OpImplement::operator->() const {
-  return static_cast<const OpImplementNode*>(get());
-}
 
-inline const OpSpecializationNode* OpSpecialization::operator->() const {
-  return static_cast<const OpSpecializationNode*>(get());
-}
-
-inline OpSpecializationNode* OpSpecialization::operator->() {
-  return static_cast<OpSpecializationNode*>(get_mutable());
-}
-
-inline const OpStrategyNode* OpStrategy::operator->() const {
-  return static_cast<const OpStrategyNode*>(get());
-}
-
-inline OpStrategyNode* OpStrategy::operator->() {
-  return static_cast<OpStrategyNode*>(get_mutable());
-}
+  TVM_DEFINE_MUTABLE_OBJECT_REF_METHODS(OpStrategy, ObjectRef, OpStrategyNode);
+};
 
 }  // namespace relay
 }  // namespace tvm

python/tvm/expr.py

@@ -864,7 +864,7 @@ def __init__(self, var, value, body):
             _make.Let, var, value, body)
 
 
-@register_object
+@tvm._ffi.register_object
 class Any(PrimExpr):
     """Any node.
     """

python/tvm/relay/backend/compile_engine.py

@@ -25,7 +25,6 @@
 from ..base import register_relay_node, Object
 from ... import _api_internal
 from ... import target as _target
-from ..._ffi.function import register_func
 from ... import autotvm
 from .. import expr as _expr
 from .. import op as _op
@@ -389,7 +388,7 @@ def visit_tuple_getitem(self, t):
         return [tup[t.index]]
 
 
-@register_func("relay.backend.create_schedule")
+@tvm._ffi.register_func("relay.backend.create_schedule")
 def create_schedule(src_func, target):
     return ScheduleGetter(target).create(src_func)
 

python/tvm/relay/op/strategy/arm_cpu.py

@@ -172,7 +172,7 @@ def conv2d_transpose_strategy_arm_cpu(attrs, inputs, out_type, target):
     assert groups == 1, "only support groups == 1 for now"
     strategy = _op.OpStrategy()
     strategy.add_implement(
-        wrap_comptue_conv2d_transpose(topi.arm_cpu.conv2d_transpose_nchw),
+        wrap_compute_conv2d_transpose(topi.arm_cpu.conv2d_transpose_nchw),
         wrap_topi_schedule(topi.arm_cpu.schedule_conv2d_transpose_nchw))
     return strategy
 

python/tvm/relay/op/strategy/cuda.py

@@ -71,12 +71,6 @@ def schedule_lrn_cuda(attrs, outs, target):
     with target:
         return topi.cuda.schedule_lrn(outs)
 
-@schedule_l2_normalize.register(["cuda", "gpu"])
-def schedule_l2_normalize_cuda(attrs, outs, target):
-    """schedule L2 normalize for cuda"""
-    with target:
-        return topi.cuda.schedule_l2_normalize(outs)
-
 @conv2d_strategy.register(["cuda", "gpu"])
 def conv2d_strategy_cuda(attrs, inputs, out_type, target):
     """conv2d cuda strategy"""
@@ -197,7 +191,7 @@ def conv2d_transpose_strategy_cuda(attrs, inputs, out_type, target):
     assert groups == 1, "only support groups == 1 for now"
     strategy = _op.OpStrategy()
     strategy.add_implement(
-        wrap_comptue_conv2d_transpose(topi.cuda.conv2d_transpose_nchw),
+        wrap_compute_conv2d_transpose(topi.cuda.conv2d_transpose_nchw),
         wrap_topi_schedule(topi.cuda.schedule_conv2d_transpose_nchw))
     return strategy
 

python/tvm/relay/op/strategy/generic.py

@@ -120,13 +120,6 @@ def schedule_lrn(attrs, outs, target):
     with target:
         return topi.generic.schedule_lrn(outs)
 
-# l2_normalize
-@generic_func
-def schedule_l2_normalize(attrs, outs, target):
-    """Schedule L2 normalize op"""
-    with target:
-        return topi.generic.schedule_l2_normalize(outs)
-
 # bitpack
 @generic_func
 def schedule_bitpack(attrs, outs, target):
@@ -283,7 +276,7 @@ def deformable_conv2d_strategy(attrs, inputs, out_type, target):
     return strategy
 
 # conv2d_transpose
-def wrap_comptue_conv2d_transpose(topi_compute):
+def wrap_compute_conv2d_transpose(topi_compute):
     """wrap conv2d_transpose topi compute"""
     def compute_conv2d_transpose(attrs, inputs, out_dtype):
         """Compute definition of conv2d_transpose"""
@@ -311,7 +304,7 @@ def conv2d_transpose_strategy(attrs, inputs, out_type, target):
     assert groups == 1, "only support groups == 1 for now"
     strategy = _op.OpStrategy()
     strategy.add_implement(
-        wrap_comptue_conv2d_transpose(topi.nn.conv2d_transpose_nchw),
+        wrap_compute_conv2d_transpose(topi.nn.conv2d_transpose_nchw),
         wrap_topi_schedule(topi.generic.schedule_conv2d_transpose_nchw))
     return strategy
 

python/tvm/relay/op/strategy/hls.py

@@ -121,7 +121,7 @@ def conv2d_transpose_strategy_hls(attrs, inputs, out_type, target):
     assert groups == 1, "only support groups == 1 for now"
     strategy = _op.OpStrategy()
     strategy.add_implement(
-        wrap_comptue_conv2d_transpose(topi.nn.conv2d_transpose_nchw),
+        wrap_compute_conv2d_transpose(topi.nn.conv2d_transpose_nchw),
         wrap_topi_schedule(topi.hls.schedule_conv2d_transpose_nchw))
     return strategy
 

python/tvm/relay/op/strategy/rocm.py

@@ -28,12 +28,6 @@ def schedule_lrn_rocm(attrs, outs, target):
     with target:
         return topi.rocm.schedule_lrn(outs)
 
-@schedule_l2_normalize.register("rocm")
-def schedule_l2_normalize_rocm(attrs, outs, target):
-    """schedule L2 normalize for rocm"""
-    with target:
-        return topi.rocm.schedule_l2_normalize(outs)
-
 @conv2d_strategy.register("rocm")
 def conv2d_strategy_cuda(attrs, inputs, out_type, target):
     """conv2d cuda strategy"""

python/tvm/relay/op/strategy/x86.py

@@ -169,7 +169,7 @@ def conv2d_transpose_strategy_cpu(attrs, inputs, out_type, target):
     assert groups == 1, "only support groups == 1 for now"
     strategy = _op.OpStrategy()
     strategy.add_implement(
-        wrap_comptue_conv2d_transpose(topi.x86.conv2d_transpose_nchw),
+        wrap_compute_conv2d_transpose(topi.x86.conv2d_transpose_nchw),
         wrap_topi_schedule(topi.x86.schedule_conv2d_transpose_nchw))
     return strategy
 

python/tvm/schedule.py

@@ -650,7 +650,7 @@ def opengl(self):
         """
         _api_internal._StageOpenGL(self)
 
-@register_object
+@tvm._ffi.register_object
 class SpecializedCondition(Object):
     """Specialized condition to enable op specialization."""
     def __init__(self, conditions):