[MetaSchedule][M3a] Instruction and Trace

include/tvm/tir/schedule/instruction.h

@@ -0,0 +1,288 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+#ifndef TVM_TIR_SCHEDULE_INSTRUCTION_H_
+#define TVM_TIR_SCHEDULE_INSTRUCTION_H_
+
+#include <tvm/node/reflection.h>
+
+#include <utility>
+
+namespace tvm {
+
+// Forward declaration
+template <typename, typename>
+class AttrRegistry;
+
+namespace tir {
+
+// Forward declaration
+class Schedule;
+
+/*!
+ * \brief Type of the functor that applies the instruction to a TensorIR schedule
+ * \param sch The schedule to be applied on
+ * \param inputs The input random variables
+ * \param attrs Instruction attributes
+ * \param decision Decisions made on the instruction
+ * \return The functor returns an array of output random variables
+ */
+using FInstructionApply = runtime::TypedPackedFunc<Array<ObjectRef>(
+    Schedule sch, const Array<ObjectRef>& inputs, const Array<ObjectRef>& attrs,
+    const Optional<ObjectRef>& decision)>;
+
+/*!
+ * \brief Type of the functor that converts the instruction to a statement in python syntax
+ * \param inputs Names of the input random variables
+ * \param attrs Instruction attributes
+ * \param decisions Decisions made on the instruction
+ * \param outputs Names of the output random variables
+ * \return A string representing the python api call
+ */
+using FInstructionAsPython = runtime::TypedPackedFunc<String(
+    const Array<ObjectRef>& inputs, const Array<ObjectRef>& attrs,
+    const Optional<ObjectRef>& decision, const Array<String>& outputs)>;
+
+/*!
+ * \brief Type of the functor that serialize its attributes to JSON
+ * \param attrs The attributes to be serialized
+ * \return An array, serialized attributes
+ * \note This functor is nullable
+ */
+using FInstructionAttrsAsJSON = runtime::TypedPackedFunc<ObjectRef(Array<ObjectRef> attrs)>;
+
+/*!
+ * \brief Type of the functor that deserialize its attributes from JSON
+ * \param json_attrs The attributes to be serialized
+ * \return An array, deserialized attributes
+ * \note This functor is nullable
+ */
+using FInstructionAttrsFromJSON = runtime::TypedPackedFunc<Array<ObjectRef>(ObjectRef json_attrs)>;
+
+/*!
+ * \brief Kind of an instruction, e.g. Split, Reorder, etc.
+ * Besides the name, every kind of instruction has its own properties, including:
+ * 1) A boolean indicating if the instruction is pure, i.e. change nothing in the schedule state
+ * 2) A functor that applies the instruction to a TensorIR schedule
+ * 3) A functor that converts the instruction to a statement in python syntax
+ * 4) A functor that serialize its attributes to JSON
+ * 5) A functor that deserialize its attributes from JSON
+ *
+ * Unlike `tvm::OpNode`, `InstructionKindNode` doesn't support unstructured properties,
+ * mainly because there is no such usecase yet to add any other property.
+ */
+class InstructionKindNode : public runtime::Object {
+ public:
+  /*! \brief The name of a kind of instructions */
+  String name;
+  /*!
+   * \brief Indicates if the instruction is pure, i.e. removing it alone doesn't mutate the schedule
+   * state. For example, the instruction `GetBlock` is pure because it changes
+   * nothing, while `ComputeInline` is not because removing it leads to a different resulting
+   * schedule.
+   */
+  bool is_pure{false};
+  /*! \brief A functor that applies the instruction to a TensorIR schedule */
+  FInstructionApply f_apply_to_schedule{nullptr};
+  /*! \brief A functor that converts the instruction to a statement in python syntax */
+  FInstructionAsPython f_as_python{nullptr};
+  /*!
+   * \brief A functor that serialize its attributes to JSON
+   * \note If the functor is null, it means no conversion is needed
+   */
+  FInstructionAttrsAsJSON f_attrs_as_json{nullptr};
+  /*!
+   * \brief A functor that deserialize its attributes from JSON
+   * \note If the functor is null, it means no conversion is needed
+   */
+  FInstructionAttrsFromJSON f_attrs_from_json{nullptr};
+
+  void VisitAttrs(tvm::AttrVisitor* v) {
+    v->Visit("name", &name);
+    v->Visit("_is_pure", &is_pure);
+    // not visited: f_apply_to_schedule
+    // not visited: f_as_python
+    // not visited: f_attrs_as_json
+    // not visited: f_attrs_from_json
+  }
+
+  static constexpr const char* _type_key = "tir.InstructionKind";
+  TVM_DECLARE_FINAL_OBJECT_INFO(InstructionKindNode, runtime::Object);
+};
+
+/*!
+ * \brief Managed reference to InstructionKindNode
+ * \sa InstructionKindNode
+ */
+class InstructionKind : public runtime::ObjectRef {
+ public:
+  /*!
+   * \brief Retrieve an InstructionKind using its name
+   * \param name The registered name of the InstructionKind
+   * \return The InstructionKind retrieved
+   */
+  static InstructionKind Get(const String& name);
+  TVM_DEFINE_OBJECT_REF_METHODS(InstructionKind, runtime::ObjectRef, InstructionKindNode);
+};
+
+/*! \brief Schedule instructions each corresponds to a schedule primitive */
+class InstructionNode : public runtime::Object {
+ public:
+  /*! \brief The kind of the instruction */
+  InstructionKind kind;
+  /*!
+   * \brief The input random variables of the instruction, and the type of each element can be one
+   * of the following:
+   * - BlockRV
+   * - LoopRV
+   * - ExprRV
+   * - FloatImm
+   * - IntImm
+   * - String
+   * - null pointer
+   */
+  Array<ObjectRef> inputs;
+  /*!
+   * \brief The attributes of the instruction. Similar to attributes of an operator,
+   * attributes of an instruction are arbitrary constant metadata required by the instructions.
+   * For example, the name of the block to be retrieved in `GetBlock`.
+   */
+  Array<ObjectRef> attrs;
+  /*! \brief The output random variables of the instruction, and the type of each element can be one
+   * of the following:
+   * - BlockRV
+   * - LoopRV
+   * - ExprRV, atomic variables only, won't be constants or composite PrimExpr
+   */
+  Array<ObjectRef> outputs;
+
+  void VisitAttrs(tvm::AttrVisitor* v) {
+    v->Visit("kind", &kind);
+    v->Visit("inputs", &inputs);
+    v->Visit("attrs", &attrs);
+    v->Visit("outputs", &outputs);
+  }
+
+  static constexpr const char* _type_key = "tir.Instruction";
+  TVM_DECLARE_FINAL_OBJECT_INFO(InstructionNode, runtime::Object);
+};
+
+/*!
+ * \brief Managed reference to InstructionNode
+ * \sa InstructionNode
+ */
+class Instruction : public runtime::ObjectRef {
+ public:
+  /*!
+   * \brief Constructor
+   * \param kind The kind of the instruction
+   * \param inputs The input random variables of the instruction
+   * \param attrs The attributes of the instruction
+   * \param outputs The output random variables of the instruction
+   */
+  explicit Instruction(InstructionKind kind, Array<ObjectRef> inputs, Array<ObjectRef> attrs,
+                       Array<ObjectRef> outputs);
+
+  TVM_DEFINE_OBJECT_REF_METHODS(Instruction, runtime::ObjectRef, InstructionNode);
+};
+
+/*!
+ * \brief A helper macro to register InstructionKind, only used in `TVM_REGISTER_INST_KIND`
+ * \note This macro is not user-facing.
+ * \sa TVM_REGISTER_INST_KIND
+ */
+#define TVM_INST_KIND_REGISTER_VAR_DEF \
+  static DMLC_ATTRIBUTE_UNUSED ::tvm::tir::InstructionKindRegEntry& __make_##InstructionKind
+
+/*!
+ * \brief Register an InstructionKind
+ * \param InstructionKindName The name of the InstructionKind
+ *
+ * Example:
+ *
+ * \code
+ *
+ * TVM_REGISTER_INST_KIND("ComputeInline")
+ *     .set_is_pure(false)
+ *     .set_apply_to_schedule(ApplyToSchedule)
+ *     .set_attrs_as_json(AttrsAsJSON)
+ *     .set_attrs_from_json(AttrsFromJSON)
+ *     .set_as_python(AsPython);
+ *
+ * \endcode
+ */
+#define TVM_REGISTER_INST_KIND(InstructionKindName)             \
+  TVM_STR_CONCAT(TVM_INST_KIND_REGISTER_VAR_DEF, __COUNTER__) = \
+      ::tvm::tir::InstructionKindRegEntry::RegisterOrGet(InstructionKindName).set_name()
+
+/*! \brief An entry in the registry of InstructionKind */
+class InstructionKindRegEntry {
+ public:
+  static InstructionKindRegEntry& RegisterOrGet(const String& name);
+
+  InstructionKindRegEntry& set_name() {
+    get_mutable()->name = this->name;
+    return *this;
+  }
+
+  InstructionKindRegEntry& set_is_pure(bool is_pure) {
+    get_mutable()->is_pure = is_pure;
+    return *this;
+  }
+
+  InstructionKindRegEntry& set_apply_to_schedule(FInstructionApply f_apply_to_schedule) {
+    get_mutable()->f_apply_to_schedule = std::move(f_apply_to_schedule);
+    return *this;
+  }
+
+  InstructionKindRegEntry& set_as_python(FInstructionAsPython f_as_python) {
+    get_mutable()->f_as_python = std::move(f_as_python);
+    return *this;
+  }
+
+  InstructionKindRegEntry& set_attrs_as_json(FInstructionAttrsAsJSON f_attrs_as_json) {
+    get_mutable()->f_attrs_as_json = std::move(f_attrs_as_json);
+    return *this;
+  }
+
+  InstructionKindRegEntry& set_attrs_from_json(FInstructionAttrsFromJSON f_attrs_from_json) {
+    get_mutable()->f_attrs_from_json = std::move(f_attrs_from_json);
+    return *this;
+  }
+
+ private:
+  /*! \brief Private constructor, used only by AttrRegistry */
+  explicit InstructionKindRegEntry(uint32_t reg_index);
+  /*! \brief Get the mutable reference to the internal InstructionKind */
+  InstructionKindNode* get_mutable() const {
+    return const_cast<InstructionKindNode*>(inst_kind_.get());
+  }
+
+  /*! \brief The name of the registry entry */
+  String name;
+  /*! \brief The instruction kind */
+  InstructionKind inst_kind_;
+  template <typename, typename>
+  friend class ::tvm::AttrRegistry;
+  friend class InstructionKind;
+};
+
+}  // namespace tir
+}  // namespace tvm
+
+#endif  //  TVM_TIR_SCHEDULE_INSTRUCTION_H_

include/tvm/tir/schedule/schedule.h

@@ -180,7 +180,8 @@ class ScheduleNode : public runtime::Object {
   virtual void RemoveRV(const ExprRV& expr_rv) = 0;
 
  public:
-  /******** Block/Loop relation ********/
+  /******** Schedule: Sampling ********/
+  /******** Schedule: Get blocks & loops ********/
   /*!
    * \brief Retrieve a block in a specific function with its name
    * \param name The name of the block to be retrieved
@@ -195,7 +196,7 @@ class ScheduleNode : public runtime::Object {
    * \return A list of loops above the given block in its scope, from outer to inner
    */
   virtual Array<LoopRV> GetLoops(const BlockRV& block_rv) = 0;
-  /******** Schedule: loops manipulation ********/
+  /******** Schedule: Transform loops ********/
   /*!
    * \brief Fuse a list of consecutive loops into one. It requires:
    * 1) The loops can't have annotations or thread bindings.
@@ -215,7 +216,9 @@ class ScheduleNode : public runtime::Object {
    * \return The new loops after split
    */
   virtual Array<LoopRV> Split(const LoopRV& loop_rv, const Array<Optional<ExprRV>>& factors) = 0;
-  /******** Schedule: compute location ********/
+  /******** Schedule: Manipulate ForKind ********/
+  /******** Schedule: Insert cache stages ********/
+  /******** Schedule: Compute location ********/
   /*!
    * \brief Inline a block into its consumer(s). It requires:
    * 1) The block is a complete non-root block, which only produces one buffer
@@ -239,9 +242,7 @@ class ScheduleNode : public runtime::Object {
    * \param block The block to be inlined to its producer
    */
   virtual void ReverseComputeInline(const BlockRV& block) = 0;
-  /******** Schedule: loop binding/annotation ********/
-  /******** Schedule: cache read/write ********/
-  /******** Schedule: reduction ********/
+  /******** Schedule: Reduction ********/
   /*!
    * \brief Factorize an associative reduction block by the specified loop.
    * \details An associative reduction cannot be parallelized directly,
@@ -260,7 +261,11 @@ class ScheduleNode : public runtime::Object {
    * \return The rfactor block
    */
   virtual BlockRV RFactor(const LoopRV& loop_rv, int factor_axis) = 0;
-  /******** Schedule: blockize & tensorize ********/
+  /******** Schedule: Blockize & Tensorize ********/
+  /******** Schedule: Annotation ********/
+  /******** Schedule: Misc ********/
+  /*! \brief A no-op that marks the start of postprocessing phase of scheduling */
+  virtual void EnterPostproc() = 0;
 };
 
 /*!

include/tvm/tir/schedule/state.h

@@ -190,14 +190,6 @@ class ScheduleState : public ObjectRef {
    * and each time after calling the Replace method.
    */
   TVM_DLL explicit ScheduleState(IRModule mod, int debug_mode = 0);
-  /*!
-   * \brief Construct a schedule state from a PrimFunc
-   * \param func The PrimFunc to be scheduled. A new IRModule will be created with
-   * this specific PrimFunc as "main" function in the module to be scheduled
-   * \param debug_mode Do extra correctness checking after the class creation
-   * and each time after calling the Replace method.
-   */
-  TVM_DLL explicit ScheduleState(PrimFunc func, int debug_mode = 0);
 
   /*! \return The mutable pointer to the ScheduleStateNode */
   ScheduleStateNode* get() const { return static_cast<ScheduleStateNode*>(data_.get()); }