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Python Enhancement Proposals

PEP 695 – Type Parameter Syntax

Author:
Eric Traut <erictr at microsoft.com>
Sponsor:
Guido van Rossum <guido at python.org>
Discussions-To:
Typing-SIG list
Status:
Draft
Type:
Standards Track
Created:
15-Jun-2022
Python-Version:
3.12

Table of Contents

Abstract

This PEP specifies an improved syntax for specifying type parameters within a generic class, function, or type alias. It also introduces a new statement for declaring type aliases.

Motivation

PEP 484 introduced type variables into the language. PEP 612 built upon this concept by introducing parameter specifications, and PEP 646 added variadic type variables.

While generic types and type parameters have grown in popularity, the syntax for specifying type parameters still feels “bolted on” to Python. This is a source of confusion among Python developers.

There is consensus within the Python static typing community that it is time to provide a formal syntax and bring Python in alignment with other modern programming languages that support generic types.

An analysis of 25 popular typed Python libraries revealed that type variables (in particular, the typing.TypeVar symbol) were used in 14% of modules. This percentage is likely to increase if type parameters become less cumbersome to use.

Points of Confusion

While the use of type variables has become widespread, the manner in which they are specified within code is the source of confusion among many Python developers. There are a couple of factors that contribute to this confusion.

The scoping rules for type variables are difficult to understand. Type variables are typically allocated within the global scope, but their semantic meaning is valid only when used within the context of a generic class, function or or type alias. A single runtime instance of a type variable may be reused in multiple generic contexts, and it has a different semantic meaning in each of these contexts. This PEP proposes to eliminate this source of confusion by declaring type parameters at a natural place within a class, function or type alias declaration statement.

Generic type aliases are often misused because it is not clear to developers that a type argument must be supplied when the type alias is used. This leads to an implied type argument of Any, which is rarely the intent. This PEP proposes to add new syntax that makes generic type alias declarations clear.

PEP 483 and PEP 484 introduced the concept of “variance” for a type variable used within a generic class. Type variables can be invariant, covariant, or contravariant. The concept of variance is an advanced detail of type theory that is not well understood by most Python developers, yet they are immediately confronted with the concept today when defining their first generic class. This PEP largely eliminates the need for most developers to understand the concept of variance when defining generic classes.

When more than one type parameter is used with a generic class or type alias, the rules for type parameter ordering can be confusing. It is normally based on the order in which they first appear within a class or type alias declaration statement. However, this can be overridden in a class definition by including a “Generic” or “Protocol” base class. For example, in the class declaration class ClassA(Mapping[K, V]), the type parameters are ordered as K and then V. However, in the class declaration class ClassB(Mapping[K, V], Generic[V, K]), the type parameters are ordered as V and then K. This PEP proposes to make type parameter ordering explicit in all cases.

The practice of sharing a type variable across multiple generic contexts creates other problems today. Modern editors provide features like “find all references” and “rename all references” that operate on symbols at the semantic level. When a type parameter is shared among multiple generic classes, functions, and type aliases, all references are semantically equivalent.

Type variables defined within the global scope also need to be given a name that starts with an underscore to indicate that the variable is private to the module. Globally-defined type variables are also often given names to indicate their variance, leading to cumbersome names like “_T_contra” and “_KT_co”. The current mechanisms for allocating type variables also requires the developer to supply a redundant name in quotes (e.g. T = TypeVar("T")). This PEP eliminates the need for the redundant name and cumbersome variable names.

Defining type parameters today requires importing the TypeVar and Generic symbols from the typing module. Over the past several releases of Python, efforts have been made to eliminate the need to import typing symbols for common use cases, and the PEP furthers this goal.

Summary Examples

Defining a generic class prior to this PEP looks something like this.

from typing import Generic, TypeVar

_T_co = TypeVar("_T_co", covariant=True, bound=str)

class ClassA(Generic[_T_co]):
    ...

With the new syntax, it looks like this.

class ClassA[T: str]:
    ...

Here is an example of a generic function today.

from typing import ParamSpec, TypeVar, Callable

_P = ParamSpec("_P")
_R = TypeVar("_R")

def func(cb: Callable[_P, _R], *args: _P.args, **kwargs: _P.kwargs) -> _R:
    ...

And the new syntax.

from typing import Callable

def func[**P, R](cb: Callable[P, R], *args: P.args, **kwargs: P.kwargs) -> R:
    ...

Here is an example of a generic type alias today.

from typing import TypeAlias

_T = TypeVar("_T")

ListOrSet: TypeAlias = list[_T] | set[_T]

And with the new syntax.

type ListOrSet[T] = list[T] | set[T]

Specification

Type Parameter Declarations

We propose to add new syntax for declaring type parameters for generic classes, functions, and type aliases. The syntax adds support for a comma-delimited list of type parameters in square brackets after the name of the class, function, or type alias.

Simple (non-variadic) type variables are declared with an unadorned name. Variadic type variables are preceded by *. Parameter specifications are preceded by **.

# This generic class is parameterized by a TypeVar T, a
# TypeVarTuple Ts, and a ParamSpec P.
class ChildClass[T, *Ts, **P]: ...

There is no need to include Generic as a base class. Its inclusion as a base class is implied by the presence of type parameters, and it will automatically be included in the __mro__ and __orig_bases attributes for the class. The explicit use of a Generic base class will result in a runtime error.

class ClassA[T](Generic[T]): ...  # Runtime error

A Protocol base class with type arguments will not generate a runtime error, but type checkers should generate an error in this case because the use of type arguments is not needed, and the order of type parameters for the class are no longer dictated by their order in the Protocol base class.

class ClassA[S, T](Protocol): ... # OK

class ClassB[S, T](Protocol[S, T]): ... # Recommended type checker error

Type parameter names within a generic class, function, or type alias must be unique. Type parameters for a generic function cannot overlap the name of a function parameter. A duplicate name generates a syntax error at compile time.

class ClassA[T, *T]: ... # Syntax Error

def func1[T, **T](): ... # Syntax Error

def func2[T](T): ... # Syntax Error

Class type parameter names are not mangled if they begin with a double underscore. Mangling would not make sense because type parameters, unlike other class-scoped variables, cannot be accessed through the class dictionary, and the notion of a “private” type parameter doesn’t make sense. Other class-scoped variables are mangled if they begin with a double underscore, so the mangled name is used to determine whether there is a name collision with type parameters.

class ClassA[__T, _ClassA__S]:
    __T = 0  # OK
    __S = 0  # Syntax Error (because mangled name is _ClassA__S)

Type Parameter Scopes

A type parameter declared as part of a generic class is valid within the class body and inner scopes contained therein. Type parameters are also accessible when evaluating the argument list (base classes and any keyword arguments) that comprise the class definition. This allows base classes to be parameterized by these type parameters. Type parameters are not accessible outside of the class body, including in any class decorators.

class ClassA[T](BaseClass[T], param = Foo[T]): ...  # OK

print(T)  # Runtime error: 'T' is not defined

@dec(Foo[T])  # Runtime error: 'T' is not defined
class ClassA[T]: ...

A type parameter declared as part of a generic function is valid within the function body and any scopes contained therein. It is also valid within parameter and return type annotations. Default argument values for function parameters are evaluated outside of this scope, so type parameters are not accessible in default value expressions. Likewise, type parameters are not in scope for function decorators.

def func1[T](a: T) -> T: ...  # OK

print(T)  # Runtime error: 'T' is not defined

def func2[T](a = list[T]): ...  # Runtime error: 'T' is not defined

@dec(list[T])  # Runtime error: 'T' is not defined
def func3[T](): ...

Upper Bound Specification

For a non-variadic type parameter, an “upper bound” type can be specified through the use of a type annotation expression. If an upper bound is not specified, the upper bound is assumed to be object.

class ClassA[T: str]: ...

The specified upper bound type must use an expression form that is allowed in type annotations. More complex expression forms should be flagged as an error by a type checker. Quoted forward declarations are allowed.

The specified upper bound type must be concrete. An attempt to use a generic type should be flagged as an error by a type checker.

class ClassA[T: dict[str, int]]: ...  # OK

class ClassB[T: "ForwardDeclaration"]: ...  # OK

class ClassC[T: dict[str, V]]: ...  # Type checker error: generic type

class ClassD[T: [str, int]]: ...  # Type checker error: illegal expression form

Constrained Type Specification

For a non-variadic type parameter, a set of two or more “constrained types” can be specified through the use of a literal tuple expression that contains two or more types.

class ClassA[AnyStr: (str, bytes)]: ...  # OK

class ClassB[T: ("ForwardDeclaration", bytes)]: ...  # OK

class ClassC[T: ()]: ...  # Type checker error: two or more types required

class ClassD[T: (str, )]: ...  # Type checker error: two or more types required

t1 = (bytes, str)
class ClassE[T: t1]: ...  # Type checker error: literal tuple expression required

If the specified type is not a tuple expression or the tuple expression includes complex expression forms that are not allowed in a type annotation, a type checker should generate an error. Quoted forward declarations are allowed.

class ClassF[T: (3, bytes)]: ...  # Type checker error: invalid expression form

The specified constrained types must be concrete. An attempt to use a generic type should be flagged as an error by a type checker. This is consistent with the existing rules enforced by type checkers for a TypeVar constructor call.

class ClassG[T: (list[S], str)]: ...  # Type checker error: generic type

Generic Type Alias

We propose to introduce a new statement for declaring type aliases. Similar to class and def statements, a type statement defines a scope for type parameters.

# A non-generic type alias
type IntOrStr = int | str

# A generic type alias
type ListOrSet[T] = list[T] | set[T]

Type aliases can refer to themselves without the use of quotes.

# A type alias that refers to a forward-declared type
type AnimalOrVegetable = Animal | "Vegetable"

# A generic self-referential type alias
type RecursiveList[T] = T | list[RecursiveList[T]]

The type keyword is a new soft keyword. It is interpreted as a keyword only in this part of the grammar. In all other locations, it is assumed to be an identifier name.

Type parameters declared as part of a generic type alias are valid only when evaluating the right-hand side of the type alias.

As with typing.TypeAlias, type checkers should restrict the right-hand expression to expression forms that are allowed within type annotations. The use of more complex expression forms (call expressions, ternary operators, arithmetic operators, comparison operators, etc.) should be flagged as an error.

Type alias expressions are not allowed to use traditional type variables. Type checkers should generate an error in this case.

T = TypeVar("T")
type MyList = list[T]  # Type checker error: traditional type variable usage

We propose to deprecate the existing typing.TypeAlias introduced in PEP 613. The new syntax eliminates its need entirely.

Runtime Type Alias Class

At runtime, a type statement will generate an instance of typing.TypeAliasType. This class represents the type. Its attributes include:

  • __name__ is a str representing the name of the type alias
  • __parameters__ is a tuple of TypeVar, TypeVarTuple, or
    ParamSpec objects that parameterize the type alias if it is generic
  • __value__ is the evaluated value of the type alias

The __value__ attribute initially has a value of None while the type alias expression is evaluated. It is then updated after a successful evaluation. This allows for self-referential type aliases.

Variance Inference

We propose to eliminate the need for variance to be specified for type parameters. Instead, type checkers will infer the variance of type parameters based on their usage within a class. Type parameters can be invariant, covariant, or contravariant depending on how they are used.

Python type checkers already include the ability to determine the variance of type parameters for the purpose of validating variance within a generic protocol class. This capability can be used for all classes (whether or not they are protocols) to calculate the variance of each type parameter. This eliminates the need for most developers to understand the concept of variance. It also eliminates the need to introduce a dedicated syntax for specifying variance.

The algorithm for computing the variance of a type parameter is as follows.

For each type parameter in a generic class:

1. If the type parameter is variadic (TypeVarTuple) or a parameter specification (ParamSpec), it is always considered invariant. No further inference is needed.

2. If the type parameter comes from a traditional TypeVar declaration and is not specified as autovariance (see below), its variance is specified by the TypeVar constructor call. No further inference is needed.

3. Create two specialized versions of the class. We’ll refer to these as upper and lower specializations. In both of these specializations, replace all type parameters other than the one being inferred by a dummy type instance. In the upper specialized class, specialize the target type parameter with an object instance. In the lower specialized class, specialize the target type parameter with itself. This specialization ignores the type parameter’s upper bound or constraints.

4. Determine whether lower can be assigned to upper using normal type compatibility rules. If so, the target type parameter is covariant. If not, determine whether upper can be assigned to lower. If so, the target type parameter is contravariant. If neither of these combinations are assignable, the target type parameter is invariant.

Here is an example.

class ClassA[T1, T2, T3](list[T1]):
    def method1(self, a: T2) -> None:
        ...

    def method2(self) -> T3:
        ...

To determine the variance of T1, we specialize ClassA as follows:

upper = ClassA[object, Dummy, Dummy]
lower = ClassA[T1, Dummy, Dummy]

We find that upper is not assignable to lower nor is lower assignable to upper using standard type compatibility checks, so we can conclude that T1 is invariant.

To determine the variance of T2, we specialize ClassA as follows:

upper = ClassA[Dummy, object, Dummy]
lower = ClassA[Dummy, T2, Dummy]

Since upper is assignable to lower, T2 is contravariant.

To determine the variance of T3, we specialize ClassA as follows:

upper = ClassA[Dummy, Dummy, object]
lower = ClassA[Dummy, Dummy, T3]

Since lower is assignable to upper, T3 is covariant.

Auto Variance For TypeVar

The existing TypeVar class constructor accepts keyword parameters named covariant and contravariant. If both of these are False, the type variable is assumed to be invariant. We propose to add another keyword parameter named autovariance. A corresponding instance variable __autovariance__ can be accessed at runtime to determine whether the variance is inferred. Type variables that are implicitly allocated using the new syntax will always have __autovariance__ set to True.

A generic class that uses the traditional syntax may include combinations of type variables with explicit and inferred variance.

T1 = TypeVar("T1", autovariance=True)  # Inferred variance
T2 = TypeVar("T2")  # Invariant
T3 = TypeVar("T3", covariant=True)  # Covariant

# A type checker should infer the variance for T1 but use the
# specified variance for T2 and T3.
class ClassA(Generic[T1, T2, T3]): ...

Compatibility with Traditional TypeVars

The existing mechanism for allocating TypeVar, TypeVarTuple, and ParamSpec is retained for backward compatibility. However, these “traditional” type variables should not be combined with type parameters allocated using the new syntax. Such a combination should be flagged as an error by type checkers. This is necessary because the type parameter order is ambiguous.

It is OK to combine traditional type variables with new-style type parameters if the class, function, or type alias does not use the new syntax. The new-style type parameters must come from an outer scope in this case.

K = TypeVar("K")

class ClassA[V](dict[K, V]): ...  # Type checker error

class ClassB[K, V](dict[K, V]): ...  # OK

class ClassC[V]:
    # The use of K and V for "method1" is OK because it uses the
    # "traditional" generic function mechanism where type parameters
    # are implicit. In this case V comes from an outer scope (ClassC)
    # and K is introduced implicitly as a type parameter for "method1".
    def method1(self, a: V, b: K) -> V | K: ...

    # The use of M and K are not allowed for "method2". A type checker
    # should generate an error in this case because this method uses the
    # new syntax for type parameters, and all type parameters associated
    # with the method must be explicitly declared. In this case, ``K``
    # is not declared by "method2", nor is it supplied defined an outer
    # scope.
    def method2[M](self, a: M, b: K) -> M | K: ...

Runtime Implementation

Grammar Changes

This PEP introduces a new soft keyword type. It modifies the grammar in the following ways:

  1. Addition of optional type parameter clause in class and def statements.
type_params: '[' t=type_param_seq  ']'

type_param_seq: a[asdl_typeparam_seq*]=','.type_param+ [',']

type_param:
    | a=NAME b=[type_param_bound]
    | '*' a=NAME
    | '**' a=NAME

type_param_bound: ":" e=expression
  1. Addition of new type statement for defining type aliases.
type_alias[stmt_ty]:
    | "type" n=NAME t=[type_params] '=' b=expression {
        CHECK_VERSION(stmt_ty, 12, "Type statement is", _PyAST_TypeAlias(n->v.Name.id, t, b, EXTRA)) }

AST Changes

This PEP introduces a new AST node type called TypeAlias.

TypeAlias(identifier name, typeparam* typeparams, expr value)

It also adds an AST node that represents a type parameter.

typeparam = TypeVar(identifier name, expr? bound)
    | ParamSpec(identifier name)
    | TypeVarTuple(identifier name)

It also modifies existing AST nodes FunctionDef, AsyncFunctionDef and ClassDef to include an additional optional attribute called typeparam* that includes a list of type parameters associated with the function or class.

Compiler Changes

The compiler maintains a list of “active type variables” as it recursively generates byte codes for the program. Consider the following example.

class Outer[K, V]:
    # Active type variables are K and V

    class Inner[T]:
        # Active type variables are K, V, and T

        def method[M](self, a: M) -> M:
            # Active type variables are K, V, T, and M
            ...

An active type variable symbol cannot be used for other purposes within these scopes. This includes local parameters, local variables, variables bound from other scopes (nonlocal or global), or other type parameters. An attempt to reuse a type variable name in one of these manners results in a syntax error.

class ClassA[K, V]:
    class Inner[K]: # Syntax error: K already in use as type variable
        ...

class ClassB[K, V]:
    def method(self, K): # Syntax error: K already in use as type variable
        ...

class ClassC[T, T]: # Syntax error: T already in use as type variable
    ...

def func1[T]():
    ...

A type variable is considered “active” when compiling the arguments for a class declaration, the type annotations for a function declaration, and the right-hand expression in a type alias declaration. Type variable are not considered “active” when compiling the default argument expressions for a function declaration or decorator expressions for classes or functions.

T = list

@decorator(T) # T in decorator refers to outer variable
class ClassA[T](Base[T], metaclass=Meta[T]) # T refers to type variable
    ...

@decorator(T) # T in decorator refers to outer variable
def func1[T](a: list[T]) -> T: # T refers to type variable
    ...

def func2[T](a = T): # T in default refers to outer variable
    ...

When a class, def, or type statement includes one or more type parameters, the compiler emits byte codes to construct the corresponding typing.TypeVar, typing.TypeVarTuple, or typing.ParamSpec instances. It then builds a new tuple that includes all active type variables and stores this new tuple in a local variable. Active type variables include all type parameters declared by outer class and def scopes plus those declared by the class, def, or type statement itself. (In the reference implementation, the local variable happens to have the name __type_variables__, but this is an implementation detail. Other Python compilers or future versions of the CPython compiler may choose a different name or an entirely anonymous local variable slot for this purpose.)

When a type variable is referenced, the compiler generates opcodes that load the active type variable tuple from either the local variable or (if there are no local type variables) through the use of a new opcode called LOAD_TYPEVARS that loads the tuple of active type variables from the current function object. It then emits opcodes to index into this tuple to fetch the desired type variable.

When a new function is created, the “active type variables” tuple is copied to the C struct field func_typevars of the function object, making the type variables from outer scopes available to inner scopes of the function or class.

A new read-only attribute called __type_variables__ is available on class, function, and type alias objects. This attribute is a tuple of the active type variables that are visible within the scope of that class, function, or type alias. This attribute is used for runtime evaluation of stringified (forward referenced) type annotations that include references to type parameters. Functions like typing.get_type_hints can use this attribute to populate the locals dictionary with values for type parameters that are in scope when calling eval to evaluate the stringified expression.

Library Changes

Several classes in the typing module that are currently implemented in Python must be reimplemented in C. This includes: TypeVar, TypeVarTuple, ParamSpec, and Generic. The new class TypeAliasType (described above) also must be implemented in C.

The typing.get_type_hints must be updated to use the new __type_variables__ attribute.

Reference Implementation

This proposal is prototyped in the CPython code base in this fork.

The Pyright type checker supports the behavior described in this PEP.

Rejected Ideas

Prefix Clause

We explored various syntactic options for specifying type parameters that preceded def and class statements. One such variant we considered used a using clause as follows:

using S, T
class ClassA: ...

This option was rejected because the scoping rules for the type parameters were less clear. Also, this syntax did not interact well with class and function decorators, which are common in Python. Only one other popular programming language, C++, uses this approach.

We likewise considered prefix forms that looked like decorators (e.g., @using(S, T)). This idea was rejected because such forms would be confused with regular decorators, and they would not compose well with existing decorators. Furthermore, decorators are logically executed after the statement they are decorating, so it would be confusing for them to introduce symbols (type parameters) that are visible within the “decorated” statement, which is logically executed before the decorator itself.

Angle Brackets

Many languages that support generics make use of angle brackets. (Refer to the table at the end of Appendix A for a summary.) We explored the use of angle brackets for type parameter declarations in Python, but we ultimately rejected it for two reasons. First, angle brackets are not considered “paired” by the Python scanner, so end-of-line characters between a < and > token are retained. That means any line breaks within a list of type parameters would require the use of unsightly and cumbersome \ escape sequences. Second, Python has already established the use of square brackets for explicit specialization of a generic type (e.g., list[int]). We concluded that it would be inconsistent and confusing to use angle brackets for generic declarations but square brackets for explicit specialization. All other languages that we surveyed were consistent in this regard.

Bounds Syntax

We explored various syntactic options for specifying the bounds and constraints for a type variable. We considered, but ultimately rejected, the use of a <: token like in Scala, the use of an extends or with keyword like in various other languages, and the use of a function call syntax similar to today’s typing.TypeVar constructor. The simple colon syntax is consistent with many other programming languages (see Appendix A), and it was heavily preferred by a cross section of Python developers who were surveyed.

Explicit Variance

We considered adding syntax for specifying whether a type parameter is intended to be invariant, covariant, or contravariant. The typing.TypeVar mechanism in Python requires this. A few other languages including Scala and C# also require developers to specify the variance. We rejected this idea because variance can generally be inferred, and most modern programming languages do infer variance based on usage. Variance is an advanced topic that many developers find confusing, so we want to eliminate the need to understand this concept for most Python developers.

Name Mangling

When considering implementation options, we considered a “name mangling” approach where each type parameter was given a unique “mangled” name by the compiler. This mangled name would be based on the qualified name of the generic class, function or type alias it was associated with. This approach was rejected because qualified names are not necessarily unique, which means the mangled name would need to be based on some other randomized value. Furthermore, this approach is not compatible with techniques used for evaluating quoted (forward referenced) type annotations.

Lambda Lifting

When considering implementation options, we considered introducing a new scope and executing the class, def, or type statement within a lambda – a technique that is sometimes referred to as “lambda lifting”. We ultimately rejected this idea because it did not work well for statements within a class body (because class-scoped symbols cannot be accessed by inner scopes). It also introduced many odd behaviors for scopes that were further nested within the lambda.

Appendix A: Survey of Type Parameter Syntax

Support for generic types is found in many programming languages. In this section, we provide a survey of the options used by other popular programming languages. This is relevant because familiarity with other languages will make it easier for Python developers to understand this concept. We provide additional details here (for example, default type argument support) that may be useful when considering future extensions to the Python type system.

C++

C++ uses angle brackets in combination with keywords “template” and “typename” to declare type parameters. It uses angle brackets for specialization.

C++20 introduced the notion of generalized constraints, which can act like protocols in Python. A collection of constraints can be defined in a named entity called a “concept”.

Variance is not explicitly specified, but constraints can enforce variance.

A default type argument can be specified using the “=” operator.

// Generic class
template <typename>
class ClassA
{
    // Constraints are supported through compile-time assertions.
    static_assert(std::is_base_of<BaseClass, T>::value);

public:
    Container<T> t;
};

// Generic function with default type argument
template <typename S = int>
S func1(ClassA<S> a, S b) {};

// C++20 introduced a more generalized notion of "constraints"
// and "concepts", which are named constraints.

// A sample concept
template<typename T>
concept Hashable = requires(T a)
{
    { std::hash<T>{}(a) } -> std::convertible_to<std::size_t>;
};

// Use of a concept in a template
template<Hashable T>
void func2(T value) {}

// Alternative use of concept
template<typename T> requires Hashable<T>
void func3(T value) {}

// Alternative use of concept
template<typename T>
void func3(T value) requires Hashable<T> {}

Java

Java uses angle brackets to declare type parameters and for specialization. The “extends” keyword is used to specify an upper bound.

Java uses use-site variance. The compiler places limits on which methods and members can be accessed based on the use of a generic type. Variance is not specified explicitly.

Java provides no way to specify a default type argument.

// Generic class
public class ClassA<T> {
    public Container<T> t;

    // Generic method
    public <S extends Number> void method1(S value) { }
}

C#

C# uses angle brackets to declare type parameters and for specialization. The “where” keyword and a colon is used to specify the bound for a type parameter.

C# uses declaration-site variance using the keywords “in” and “out” for contravariance and covariance, respectively. By default, type parameters are invariant.

C# provides no way to specify a default type argument.

// Generic class with bounds on type parameters
public class ClassA<S, T>
    where T : SomeClass1
    where S : SomeClass2
{
    // Generic method
    public void MyMethod<U>(U value) where U : SomeClass3 { }
}

// Contravariant and covariant type parameters
public class ClassB<in S, out T>
{
    public T MyMethod(S value) { }
}

TypeScript

TypeScript uses angle brackets to declare type parameters and for specialization. The “extends” keyword is used to specify a bound. It can be combined with other type operators such as “keyof”.

TypeScript uses declaration-site variance. Variance is inferred from usage, not specified explicitly. TypeScript 4.7 will introduce the ability to specify variance using “in” and “out” keywords. This was added to handle extremely complex types where inference of variance was expensive.

A default type argument can be specified using the “=” operator.

TypeScript supports the “type” keyword to declare a type alias, and this syntax supports generics.

// Generic interface
interface InterfaceA<S, T extends SomeInterface1> {
    val1: S;
    val2: T;

    method1<U extends SomeInterface2>(val: U): S
}

// Generic function
function func1<T, K extends keyof T>(ojb: T, key: K) { }

// Contravariant and covariant type parameters (TypeScript 4.7)
interface InterfaceB<in S, out T> { }

// Type parameter with default
interface InterfaceC<T = SomeInterface3> { }

// Generic type alias
type MyType<T extends SomeInterface4> = Array<T>

Scala

In Scala, square brackets are used to declare type parameters. Square brackets are also used for specialization. The “<:” and “>:” operators are used to specify upper and lower bounds, respectively.

Scala uses use-site variance but also allows declaration-site variance specification. It uses a “+” or “-” prefix operator for covariance and contravariance, respectively.

Scala provides no way to specify a default type argument.

It does support higher-kinded types (type parameters that accept type type parameters).

// Generic class; type parameter has upper bound
class ClassA[A <: SomeClass1]
{
    // Generic method; type parameter has lower bound
    def method1[B >: A](val: B) ...
}

// Use of an upper and lower bound with the same type parameter
class ClassB[A >: SomeClass1 <: SomeClass2] { }

// Contravariant and covariant type parameters
class ClassC[+A, -B] { }

// Higher-kinded type
trait Collection[T[_]]
{
    def method1[A](a: A): T[A]
    def method2[B](b: T[B]): B
}

// Generic type alias
type MyType[T <: Int] = Container[T]

Swift

Swift uses angle brackets to declare type parameters and for specialization. The upper bound of a type parameter is specified using a colon.

Swift uses declaration-site variance, and variance of type parameters is inferred from their usage.

Swift provides no way to specify a default type argument.

// Generic class
class ClassA<T> {
    // Generic method
    func method1<X>(val: T) -> S { }
}

// Type parameter with upper bound constraint
class ClassB<T: SomeClass1> {}

// Generic type alias
typealias MyType<A> = Container<A>

Rust

Rust uses angle brackets to declare type parameters and for specialization. The upper bound of a type parameter is specified using a colon. Alternatively a “where” clause can specify various constraints.

Rust uses declaration-site variance, and variance of type parameters is typically inferred from their usage. In cases where a type parameter is not used within a type, variance can be specified explicitly.

A default type argument can be specified using the “=” operator.

// Generic class
struct StructA<T> {
    x: T
}

// Type parameter with bound
struct StructB<T: StructA> {}

// Type parameter with additional constraints
struct StructC<T>
where
    T: Iterator,
    T::Item: Copy
{}

// Generic function
fn func1<T>(val: &[T]) -> T { }

// Explicit variance specification
use type_variance::{Covariant, Contravariant};

struct StructD<A, R> {
    arg: Covariant<A>,
    ret: Contravariant<R>,
}

// Generic type alias
type MyType<T> = StructC<T>

Kotlin

Kotlin uses angle brackets to declare type parameters and for specialization. The upper bound of a type is specified using a colon.

Kotlin supports declaration-site variance where variance of type parameters is explicitly declared using “in” and “out” keywords. It also supports use-site variance which limits which methods and members can be used.

Kotlin provides no way to specify a default type argument.

// Generic class
class ClassA<T> { }

// Type parameter with upper bound
class ClassB<T: SomeClass1> { }

// Contravariant and covariant type parameters
class ClassC<in S, out T> { }

// Generic function
fun func1<T>() -> T {}

// Generic type alias
typealias<T> = ClassA<T>

Julia

Julia uses curly braces to declare type parameters and for specialization. The “<:” operator can be used within a “where” clause to declare upper and lower bounds on a type.

// Generic struct; type parameter with upper and lower bounds
struct StructA{T} where Int <: T <: Number
    x::T
end

// Generic function
function func1{T <: Real}(v::Container{T})

// Alternate form of generic function
function func2(v::Container{T} where T <: Real)

Summary

Decl Syntax Upper Bound Lower Bound Default Value Variance Site Variance
C++ template <> n/a n/a = n/a n/a
Java <> extends use inferred
C# <> where decl in, out
TypeScript <> extends = decl inferred, in, out
Scala [] T <: X T >: X use, decl +, -
Swift <> T: X decl inferred
Rust <> T: X, where = decl inferred, explicit
Kotlin <> T: X use, decl inferred
Julia {} T <: X X <: T n/a n/a
Python (proposed) [] T: X decl inferred

Acknowledgements

Thanks to Sebastian Rittau for kick-starting the discussions that led to this proposal, to Jukka Lehtosalo for proposing the syntax for type alias statements and to Jelle Zijlstra, Daniel Moisset, and Guido van Rossum for their valuable feedback and suggested improvements to the specification and implementation.


Source: https://github.com/python/peps/blob/main/pep-0695.rst

Last modified: 2022-07-24 23:59:02 GMT