class Add : public Expr {
  Expr lhs;
  Expr rhs;
};

auto Add::Codegen() {
  auto lhs_val = lhs->Codegen();
  auto rhs_val = rhs->Codegen();
  return add(lhs_val, rhs_val);
}

class LetNode : public ExprNode {
 protected:
  // LetNode uses own deleter to indirectly call non-recursive destructor
  Object::FDeleter saved_deleter_;
  static void Deleter_(Object* ptr);

 public:
  /*! \brief The variable we bind to */
  Var var;
  /*! \brief The value we bind var to */
  Expr value;
  /*! \brief The body of the let binding */
  Expr body;

fn (%x) {
  %1 = log(%x)
  %2 = fn(%y) {
    add(%y, %1)
  }
  %2
}

1. ADT 的基本概念

1.1. 定义和匹配

# Defines an ADT named "Numbers"
data Numbers {
  Empty : () -> Numbers
  Single : (Tensor[(), int32]) -> Numbers
  Pair : (Tensor[(), int32], Tensor[(), int32]) -> Numbers
}
# A Numbers value can be produced using an Empty, Single, or Pair
# constructor, each with a signature given above

def @sum(%n : Numbers[]) -> Tensor[(), int32] {
   # The match expression branches on the constructor that was
   # used to produce %n. The variables in each case are bound
   # if the constructor matches that used for %n
   match(%n) {
     case Empty() { 0 }
     case Single(x) { x }
     case Pair(x, y) { x + y }
   }
}

@sum(Empty())    # evaluates to 0
@sum(Single(3))  # evaluates to 3
@sum(Pair(5, 6)) # evaluates to 11

# structurally identical constructors to Numbers
data Numbers2 {
  Empty2 : () -> Numbers2
  Single2 : (Tensor[(), int32]) -> Numbers2
  Pair2 : (Tensor[(), int32], Tensor[(), int32]) -> Numbers2
}

# the below results in a type error because Numbers2
# is a distinct type from Numbers
# fn() { @sum(Empty2()) }

1. Type

// namespace update for backward compact
// will be removed later.
using AnyNode = tvm::tir::AnyNode;
using Any = tvm::tir::Any;
using Kind = TypeKind;
using Type = tvm::Type;
using TypeNode = tvm::TypeNode;
using TypeVar = tvm::TypeVar;
using TypeVarNode = tvm::TypeVarNode;
using GlobalTypeVar = tvm::GlobalTypeVar;
using GlobalTypeVarNode = tvm::GlobalTypeVarNode;
using TupleType = tvm::TupleType;
using TupleTypeNode = tvm::TupleTypeNode;
using TypeConstraint = tvm::TypeConstraint;
using TypeConstraintNode = tvm::TypeConstraintNode;
using FuncType = tvm::FuncType;
using FuncTypeNode = tvm::FuncTypeNode;
using IncompleteType = tvm::IncompleteType;
using IncompleteTypeNode = tvm::IncompleteTypeNode;
using RelayRefType = tvm::RelayRefType;
using RelayRefTypeNode = tvm::RelayRefTypeNode;
using TensorType = tvm::TensorType;
using TensorTypeNode = tvm::TensorTypeNode;
using TypeCall = tvm::TypeCall;
using TypeCallNode = tvm::TypeCallNode;
using TypeRelation = tvm::TypeRelation;
using TypeRelationNode = tvm::TypeRelationNode;
using TypeRelationFn = tvm::TypeRelationFn;
using TypeReporter = tvm::TypeReporter;
using TypeReporterNode = tvm::TypeReporterNode;

2. Expr

namespace tvm {
namespace relay {

using Expr = tvm::RelayExpr;

class Tuple : public Expr {
 public:
  /*!
   * \brief The constructor
   * \param fields The fields of a tuple.
   * \param span The source span of the expression.
   */
  TVM_DLL explicit Tuple(tvm::Array fields, Span span = Span());

  TVM_DEFINE_OBJECT_REF_METHODS(Tuple, RelayExpr, TupleNode);
  TVM_DEFINE_OBJECT_REF_COW_METHOD(TupleNode);
};

class VarNode : public ExprNode {
 public:
  /*!
   * \brief The unique identifier of the Var.
   *
   * vid will be preserved for the same Var during type inference
   * and other rewritings, while the VarNode might be recreated
   * to attach additional information.
   * This property can be used to keep track of parameter Var
   * information across passes.
   */
  Id vid;
  /*!
   * \brief type annotaion of the variable.
   * This field records user provided type annotation of the Var.
   * This field is optional and can be None.
   */
  Type type_annotation;
  // ...
}