Relay 类型系统#

在详细介绍 Relay 的表达式语言时简要介绍了类型，但还没有描述它的类型系统。Relay 是一种静态类型和推断类型的语言，允许程序在只需要一些显式类型注解的情况下进行完全类型化。

静态类型在执行编译器优化时很有用，因为它们可以传递有关程序操作的数据的属性，例如运行时形状、数据布局和存储，而不需要运行程序。Relay Algebraic Data Types 允许轻松和灵活地组合类型，以构建可以归纳推理的数据结构，并用于编写递归函数。

Relay 类型系统具有一种 dependent typing 形式的形状。也就是说，它的类型系统在 Relay 程序中跟踪张量的形状。将张量形状作为类型允许 Relay 在编译时执行更强大的推理；特别是对于输出形状根据输入形状变化而变化的运算，Relay 可以以复杂的方式进行静态推理。将形状推断作为类型推断问题允许 Relay 在编译时推断所有张量的形状，包括在使用分支和函数调用的程序中。

Statically reasoning about shapes in this manner allows Relay to be ahead-of-time compiled and provides much more information about tensors for optimizations further in the compilation pipeline. Such optimizations can be implemented as passes, which are Relay-to-Relay AST transformations, and may use the inferred types (e.g., shape information) for making decisions about program transformations. For instance, src/relay/transforms/fuse_ops.cc gives an implementation of a pass that uses inferred tensor shapes to replace invocations of operators in a Relay program with fused operator implementations.

Reasoning about tensor types in Relay is encoded using type relations, which means that the bulk of type checking in Relay is constraint solving (ensuring that all type relations are satisfied at call sites). Type relations offer a flexible and relatively simple way of making the power of dependent typing available in Relay without greatly increasing the complexity of its type system.

Below we detail the language of types in Relay and how they are assigned to Relay expressions.

Type#

The base type for all Relay types. All Relay types are sub-classes of this base type.

See Type for its definition and documentation.

Tensor Type#

A concrete tensor type in Relay.

Tensors are typed according to data type and shape. At present, these use TVM’s data types and shapes, but in the future, Relay may include a separate AST for shapes. In particular, data types include bool, float32, int8 and various other bit widths and numbers of lanes. Shapes are given as tuples of dimensions (TVM IndexExpr), such as (5, 5); scalars are also given tuple types and have a shape of ().

Note, though, that TVM shapes can also include variables and arithmetic expressions including variables, so Relay’s constraint solving phase will attempt to find assignments to all shape variables to ensure all shapes will be concrete before running a program.

For example, here is a simple concrete tensor type corresponding to a 10-by-10 tensor of 32-bit floats:

Tensor[(10, 10), float32]

See TensorType for its definition and documentation.

Tuple Type#

A type of a tuple in Relay.

Just as a tuple is simply a sequence of values of statically known length, the type of a tuple consists of a sequence of the types corresponding to each member of the tuple.

Because a tuple type is of statically known size, the type of a tuple projection is simply the corresponding index into the tuple type.

For example, in the below code, %t is of type (Tensor[(), bool], Tensor[(10, 10), float32]) and %c is of type Tensor[(10, 10), float32].

let %t = (False, Constant(1, (10, 10), float32));
let %c = %t.1;
%c

See TupleType for its definition and documentation.

Type Parameter#

Type parameters represent placeholder types used for polymorphism in functions. Type parameters are specified according to kind, corresponding to the types those parameters are allowed to replace:

  • Type, corresponding to top-level Relay types like tensor types, tuple types, and function types

  • BaseType, corresponding to the base type of a tensor (e.g., float32, bool)

  • Shape, corresponding to a tensor shape

  • ShapeVar, corresponding to variables within a tensor shape

Relay’s type system enforces that type parameters are only allowed to appear where their kind permits them, so if type variable t is of kind Type, Tensor[t, float32] is not a valid type.

Like normal parameters, concrete arguments must be given for type parameters at call sites.

For example, s below is a type parameter of kind Shape and it will be substituted with (10, 10) at the call site below:

def @plus<s : Shape>(%t1 : Tensor[s, float32], %t2 : Tensor[s, float32]) {
     add(%t1, %t2)
}
plus<(10, 10)>(%a, %b)

See TypeVar for its definition and documentation.

Type Constraint#

This is an abstract class representing a type constraint, to be elaborated upon in further releases. Currently, type relations are the only type constraints provided; they are discussed below.

See TypeConstraint for its definition and documentation.

Function Type#

A function type in Relay, see tvm/relay/type.h for more details.

This is the type assigned to functions in Relay. A function type consists of a list of type parameters, a set of type constraints, a sequence of argument types, and a return type.

We informally write function types as: fn<type_params>(arg_types) -> ret_type where type_constraints

A type parameter in the function type may appear in the argument types or the return types. Additionally, each of the type constraints must hold at every call site of the function. The type constraints typically take the function’s argument types and the function’s return type as arguments, but may take a subset instead.

See FuncType for its definition and documentation.

Type Relation#

A type relation is the most complex type system feature in Relay. It allows users to extend type inference with new rules. We use type relations to define types for operators that work with tensor shapes in complex ways, such as broadcasting operators or flatten, allowing Relay to statically reason about the shapes in these cases.

A type relation R describes a relationship between the input and output types of a Relay function. Namely, R is a function on types that outputs true if the relationship holds and false if it fails to hold. Types given to a relation may be incomplete or include shape variables, so type inference must assign appropriate values to incomplete types and shape variables for necessary relations to hold, if such values exist.

For example we can define an identity relation to be:

Identity(I, I) :- true

It is usually convenient to type operators in Relay by defining a relation specific to that operator that encodes all the necessary constraints on the argument types and the return type. For example, we can define the relation for flatten:

Flatten(Tensor(sh, bt), O) :-
  O = Tensor(sh[0], prod(sh[1:]))

If we have a relation like Broadcast it becomes possible to type operators like add:

add : fn<t1 : Type, t2 : Type, t3 : Type>(t1, t2) -> t3
            where Broadcast

The inclusion of Broadcast above indicates that the argument types and the return type must be tensors where the shape of t3 is the broadcast of the shapes of t1 and t2. The type system will accept any argument types and return type so long as they fulfill Broadcast.

Note that the above example relations are written in Prolog-like syntax, but currently the relations must be implemented by users in C++ or Python. More specifically, Relay’s type system uses an ad hoc solver for type relations in which type relations are actually implemented as C++ or Python functions that check whether the relation holds and imperatively update any shape variables or incomplete types. In the current implementation, the functions implementing relations should return False if the relation fails to hold and True if the relation holds or if there is not enough information to determine whether it holds or not.

The functions for all the relations are run as needed (if an input is updated) until one of the following conditions holds:

  1. All relations hold and no incomplete types remain (typechecking succeeds).

  2. A relation fails to hold (a type error).

  3. A fixpoint is reached where shape variables or incomplete types remain (either a type error or more type annotations may be needed).

Presently all of the relations used in Relay are implemented in C++. See the files in src/relay/op for examples of relations implemented in C++.

See TypeRelation for its definition and documentation.

Incomplete Type#

An incomplete type is a type or portion of a type that is not yet known. This is only used during type inference. Any omitted type annotation is replaced by an incomplete type, which will be replaced by another type at a later point.

Incomplete types are known as “type variables” or “type holes” in the programming languages literature. We use the name “incomplete type” in order to more clearly distinguish them from type parameters: Type parameters must be bound to a function and are replaced with concrete type arguments (instantiated) at call sites, whereas incomplete types may appear anywhere in the program and are filled in during type inference.

See IncompleteType for its definition and documentation.

Algebraic Data Types#

Note: ADTs are not currently supported in the text format.

Algebraic data types (ADTs) are described in more detail in their overview; this section describes their implementation in the type system.

An ADT is defined by a collection of named constructors, each of which takes arguments of certain types. An instance of an ADT is a container that stores the values of the constructor arguments used to produce it as well as the name of the constructor; the values can be retrieved by deconstructing the instance by matching based on its constructor. Hence, ADTs are sometimes called “tagged unions”: like a C-style union, the contents of an instance for a given ADT may have different types in certain cases, but the constructor serves as a tag to indicate how to interpret the contents.

From the type system’s perspective, it is most pertinent that ADTs can take type parameters (constructor arguments can be type parameters, though ADT instances with different type parameters must be treated as different types) and be recursive (a constructor for an ADT can take an instance of that ADT, thus an ADT like a tree or list can be inductively built up). The representation of ADTs in the type system must be able to accommodate these facts, as the below sections will detail.

Global Type Variable#

To represent ADTs compactly and easily allow for recursive ADT definitions, an ADT definition is given a handle in the form of a global type variable that uniquely identifies it. Each ADT definition is given a fresh global type variable as a handle, so pointer equality can be used to distinguish different ADT names.

For the purposes of Relay’s type system, ADTs are differentiated by name; that means that if two ADTs have different handles, they will be considered different types even if all their constructors are structurally identical.

Recursion in an ADT definition thus follows just like recursion for a global function: the constructor can simply reference the ADT handle (global type variable) in its definition.

See GlobalTypeVar for its definition and documentation.

Definitions (Type Data)#

Besides a name, an ADT needs to store the constructors that are used to define it and any type parameters used within them. These are stored in the module, analogous to global function definitions.

While type-checking uses of ADTs, the type system sometimes must index into the module using the ADT name to look up information about constructors. For example, if a constructor is being pattern-matched in a match expression clause, the type-checker must check the constructor’s signature to ensure that any bound variables are being assigned the correct types.

See TypeData for its definition and documentation.

Type Call#

Because an ADT definition can take type parameters, Relay’s type system considers an ADT definition to be a type-level function (in that the definition takes type parameters and returns the type of an ADT instance with those type parameters). Thus, any instance of an ADT is typed using a type call, which explicitly lists the type parameters given to the ADT definition.

It is important to list the type parameters for an ADT instance, as two ADT instances built using different constructors but the same type parameters are of the same type while two ADT instances with different type parameters should not be considered the same type (e.g., a list of integers should not have the same type as a list of pairs of floating point tensors).

The “function” in the type call is the ADT handle and there must be one argument for each type parameter in the ADT definition. (An ADT definition with no arguments means that any instance will have no type arguments passed to the type call).

See TypeCall for its definition and documentation.

Example: List ADT#

This subsection uses the simple list ADT (included as a default ADT in Relay) to illustrate the constructs described in the previous sections. Its definition is as follows:

data List<a> {
  Nil : () -> List
  Cons : (a, List[a]) -> List
}

Thus, the global type variable List is the handle for the ADT. The type data for the list ADT in the module notes that List takes one type parameter and has two constructors, Nil (with signature fn<a>() -> List[a]) and Cons (with signature fn<a>(a, List[a]) -> List[a]). The recursive reference to List in the Cons constructor is accomplished by using the global type variable List in the constructor definition.

Below two instances of lists with their types given, using type calls:

Cons(1, Cons(2, Nil())) # List[Tensor[(), int32]]
Cons((1, 1), Cons((2, 2), Nil())) # List[(Tensor[(), int32], Tensor[(), int32])]

Note that Nil() can be an instance of any list because it does not take any arguments that use a type parameter. (Nevertheless, for any particular instance of Nil(), the type parameter must be specified.)

Here are two lists that are rejected by the type system because the type parameters do not match:

# attempting to put an integer on a list of int * int tuples
Cons(1, Cons((1, 1), Nil()))
# attempting to put a list of ints on a list of lists of int * int tuples
Cons(Cons(1, Cons(2, Nil())), Cons(Cons((1, 1), Cons((2, 2), Nil())), Nil()))