Expressions in Relay

The Relay IR is a pure, expression-oriented language. The below sections describe the different expressions in Relay and give details of their semantics.

Dataflow and Control Fragments

For the purposes of comparing Relay to traditional computational graph-based IRs, it can be useful to consider Relay exrpessions in terms of dataflow and control fragments. Each portion of a Relay program containing expressions that only affect the dataflow can be viewed as a traditional comptuation graph when writing and expressing transformations.

The dataflow fragment covers the set of Relay expressions that do not involve control flow. That is, any portion of a program containing only the following constructs corresponds to a pure computation graph:

Control flow expressions allow the graph topology to change based on the value of previously executed expressions. The control fragment in Relay includes the following constructs:

From the point of view of a computation graph, a function is a subgraph and a function call inlines the subgraph, substituting its arguments for the free variables in the subgraph with corresponding names. Thus if a function’s body uses only dataflow constructs , a call to that function is in the dataflow fragment; conversely, if the function’s body contains control flow, a call to that function is not part of the dataflow fragment.


Inspired by LLVM, Relay explicitly distinguishes between local and global variables both in the AST and in the text format. In the text format, global and local variables are distinguished by prefixes, or sigils. Global variables are prefixed with @ and local variables with %.

This explicit distinction makes certain optimizations easier to implement. For example, inlining a global definition requires no analysis: simply substituting the definition suffices.

Global Variable

Global identifiers are prefixed by the @ sigil, such as “@global”. A global identifier always references a globally visible definition contained in the globally visible environment, known as the module. Global identifiers must be unique.

See GlobalVar for its implementation and documentation.

Local Variable

Local identifiers are prefixed by the % sigil, such as “%local”. A local identifier always references a function argument or a variable bound in a let expression, and will be scoped to the function where it appears or the let expression where it is bound, respectively.

In the below code segment, notice that %a is defined twice. This is permitted, as in most functional languages; in the scope of the second let expression, the name %a is “shadowed,” meaning all references to %a in the inner scope refer to the later defintion, while references to %a in the outer scope continue to refer to the first one.

let %a = 1;
let %b = 2 * %a;  // %b = 2
let %a = %a + %a; // %a = 2. %a is shadowed
%a + %b           // has value 2 + 2 = 4

(Note that in Relay’s implementation, each definition of a local variable creates a new Var, so a shadowed local variable, despite having the same name as one in an outer scope, will be a different object. This allows for comparing local variables by pointer identity with the knowledge that the same local variable object corresponds to a different binding site.)

See Var for its implementation and documentation.


Functions in Relay act similarly to procedures or functions in other programming languages and serve to generalize the concept of a named subgraph.

Functions are first class in Relay, which means they are expressions just like variables, constants, and tuples. Additionally, functions in Relay are higher-order, which means that a function can be passed as an argument to a function or returned by a function, as function expressions evaluate to closures (see the Closures subsection), which are values like tensors and tuples.

See Function for the definition and documentation of function nodes.


A definition minimally consists of the keyword fn, an empty set of parameters, and a body expression (Expr) contained by curly braces.

fn() { body }

A definition may contain any number of parameters. For example, a simple function that invokes the add operator:

fn(%x, %y) { add(%x, %y) }

Notice that within the function’s body, the parameters are local variables, just like those bound in a let expression.

One may also annotate explicit types on functions. For example, we can restrict the above function to only work on certain types:

fn(%x : Tensor[(10, 10), float32], %y : Tensor[(10, 10), float32])
           -> Tensor[(10, 10), float32] {
    add(%x, %y)

The above function only takes arguments of type Tensor[(10, 10), float32] and returns a value of type Tensor[(10, 10), float32]. A function parameter is just a local variable (LocalVar) optionally annotated with a type, written as %x : T.

When the type information is omitted, Relay attempts to infer the most general type for the users. This property is known as generalization: for a definition without explicit annotations, Relay attempts to assign the most general type to the parameters and return type based on the function body and call sites.

A recursive function expression can be defined using a let binding, as here:

let %fact = fn(%x : Tensor[(10, 10), float32]) -> Tensor[(10, 10), float32] {
    if (%x == Constant(0, (10, 10), float32)) {
        Constant(1, (10, 10), float32)
    } else {
        %x * %fact(%x - Constant(1, (10, 10), float32))
%fact(Constant(10, (10, 10), float32))


A function expression evaluates to a closure. Closures are values that are represented as a pair of a local environment (storing the values for all variables defined outside the scope of the function’s body) and the function itself.

For example, in the below example, the final result will be a tensor of zero values because the closure for %f stores the value of %x at the pointer where %f was defined.

let %g = fn() {
  let %x = Constant(0, (10, 10), float32);
  // %x is a free variable in the below function
  fn(%y) { %y * %x }
// the %x in %g's body is not in scope anymore
// %f is a closure where %x maps to Constant(0, (10, 10), float32)
let %f = %g();
let %x = Constant(1, (10, 10), float32);
%f(%x) // evaluates to Constant(0, (10, 10), float32)

Polymorphism and Type Relations

Note: type parameter syntax is not yet supported in the text format.

A function may also be given a set of type parameters, which can be substituted for specific types at call sites. Functions with type parameters are type polymorphic; their return type or the types of arguments they will accept can vary based on the type arguments given at call sites.

Type parameters are classified by kind and can only appear in parts of the type signature where their kind is appropriate (e.g., type parameters of kind Shape can only appear where a shape would be expected in a tensor type); for a full discussion, see the documentation on type parameters.

For example, one can define a polymorphic identity function for any Relay type as follows:

The below definition is also polymorphic, but restricts its arguments to tensor types:

Notice that the return type is omitted and will be inferred.

Note: :code:`where` syntax is not yet supported in the text format.

A function may also be subject to one or more type relations, such as in the following:

fn(%x, %y) where Broadcast { add(%x, %y) }

In the above definition, the types of %x and %y and the return type are subject to the Broadcast relation, meaning all three must be tensors and their shapes follow the elementwise broadcast relation. As with operators, the definitions of relations are not transparent to Relay and they are instead implemented externally in either C++ or Python.

As in the case of Broadcast, relations are used to express complicated constraints on types (especially tensor shapes). All function relations must hold at all call sites; type checking is thus treated as a constraint-solving problem. For more detail on type relations and their implementations, please see their section in the documentation on Relay’s type system.


An operator is a primitive operation, such as add or conv2d, not defined in the Relay language. Operators are declared in the global operator registry in C++. Many common operators are backed by TVM’s Tensor Operator Inventory (TOPI).

To register an operator a user must provide an implementation of the operator, its type, and any other desired metadata. The operator registry is a column-based store where operators are keys, so any metadata (which might be referenced by optimization passes) may be registered as a new column.

From the perspective of Relay’s type system, an operator is a function, so operators may be called like any other function and have function types. In particular, operator types are registered using a single type relation (see the documentation on type relations), typically a relation specialized to that operator. For example, the add operator is registered with the Broadcast relation, indicating that the arguments of add must be tensors and that the return type is a tensor whose shape depends on those of its arguments.

Operators are rendered without a sigil (e.g conv2d, flatten) when pretty-printing Relay programs. Operators are explicitly contained in the program and are uniquely identifiable by pointer.

Note that common arithmetic operators such as add and multiply may be written using the corresponding arithmetic operators in the text format (e.g., + or *) as syntactic sugar.

See Op for the definition and documentation of operator nodes, demonstrating the infrastructure for registering operator metadata. The other files in op give handles for generating a call to various pre-registered operators. The tutorial on adding operators to Relay shows how to add further operators into the language.


Expressions with function types in Relay are “callable,” meaning that they can be invoked via a function call. These consist of any expression that evaluates to a closure (i.e., function expressions or global functions) and Relay operators.

The syntax of calls follows that used in C-like languages, demonstrated in the example below:

let %c = 1;
let %f = fn(%x : Tensor[(), float32], %y : Tensor[(), float32]) { %x + %y + %c };
%f(10, 11)

When a closure is called (see Closures), the closure’s body is evaluated in the stored environment (i.e., using the stored values for free variables) with local variable bindings added for each argument; the final value obtained by evaluating the body is the call’s return value. Thus, in the above example, the call evaluates to 22. In the case of operators, the implementation is opaque to Relay, so the result is left up to the registered TVM implementation.

Note: type parameters are not yet supported in the text format.

A type-polymorphic function can also include type arguments at a call site. The type arguments are substituted for type parameters when type checking. If a function is type-polymorphic and type arguments are not given, type inference will attempt to infer type arguments if possible. The following code gives examples of explicit and inferred type arguments:

// %f : fn<a : Type, b : Type, c : Type>(a, b) -> c
let %x1 = %f<Tensor[(), bool], Tensor[(), bool], Tensor[(), bool)]>(True, False);
// %x1 is of type Tensor[(), bool]
let %x2 : () = %f(%x1, %x1)
// the type arguments in the second call are inferred to be <Tensor[(), bool], Tensor[(), bool], ()>

Note that all type relations in the function type must hold at each call site. Specifically, this means that the relation will be checked against the specific types of the arguments at a given call site. This is also a form of polymorphism, since there may be multiple valid assignments of argument types and a return type so long as the relation is satisfied.

For example, if we have a function %f that takes tensor arguments and has the Broadcast relation, then there are many different shapes that the arguments in the below call could have that would satisfy the type annotation:

let %x : Tensor[(100, 100, 100), float32] = %f(%a, %b);

See Call for its definition and documentation.

Module and Global Functions

Relay keeps a global data structure known as a “module” (often called an “environment” in other functional programming languages) to keep track of the definitions of global functions. In particular, the module keeps a globally accessible mapping of global variables to the function expressions they denote. The utility of the module is that it allows global functions to recursively refer to themselves or any other global function (e.g., as in mutual recursion).

Note Relay’s module is analogous to data structures for keeping track of subgraphs in computation graph-based IRs.

Global functions in Relay behave identically to the function expressions defined in Functions, but have syntactic sugar in the text format to enter their definitions into the module. Namely, a global function definition includes a global identifier and is allowed to recursively refer to that identifier in the body, as in the following example:

def @ackermann(%m : Tensor[(), int32], %n : Tensor[(), int32]) -> Tensor[(), int32] {
    if (%m == 0) {
        %n + 1
    } else if (%m > 0 && %n == 0) {
        @ackermann(%m - 1, 1)
    } else {
        @ackermann(%m - 1, @ackermann(%m, %n - 1))

This definition would result in a module entry mapping the identifier @ackermann to a function expression with the parameters, return type, and body above. Any reference to the identifier @ackermann elsewhere in the code could then look up the identifier in the module and replace the function definition as needed.

See Module for the definition and documentation of a module.


This node represents a constant tensor value (see Value for more details). A constant is represented as a NDArray, allowing Relay to utilize TVM operators for constant evaluation.

This node can also represent scalar constants, since scalars are tensors with a shape of (). In the text format, numerical and boolean literals are thus syntactic sugar for constants encoding a tensor type with a rank-zero shape.

See Constant for its definition and documentation.



The tuple node builds a finite (that is, of statically known size) sequence of heterogeneous data. These tuples match Python’s closely, and their fixed length allows for efficient projection of their members.

fn(%a : Tensor[(10, 10), float32], %b : float32, %c : Tensor[(100, 100), float32]) {
    let %tup = (%a, %b);     // type: (Tensor[(10, 10), float32], float32)
    ((%tup.0 + %tup.1), %c)  // type: (Tensor[(10, 10), float32], Tensor[(100, 100), float32])

See Tuple for its definition and documentation.


A tuple must be indexed by an integer constant in order to extract a particular member of the tuple. Projections are 0-indexed.

For example, the below projection evaluates to %b:

(%a, %b, %c).1

See TupleGetItem for its definition and documentation.

Let Bindings

A let binding is an immutable local variable binding, allowing the user to bind an expression to a name.

A let binding contains a local variable, an optional type annotation, a value, and a body expression that may reference the bound identifier. If a type annotation on the bound variable is omitted, Relay attempts to infer the most general type permitted for the variable.

The bound variable in a let expression is only in scope in its body, except when the variable defines a function expression. When a let expression creates a function, the variable is also in scope in its value to allow for recursively defined functions (see the previous subsection).

The value of a let binding is the value of the final expression after evaluating the bindings it depends on. For example, in the following example the entire expression evaluates to a tensor of shape (10, 10) where all elements are 2:

let %x : Tensor[(10, 10), float32] = Constant(1, (10, 10), float32);
%x + %x

A sequence of let bindings can be considered as a dataflow graph, where the bindings are a series of sub-graphs connected by bound variables. Since these binding sequences are pure, a pair of bindings where neither depends on the other can be safely reordered. For example, the first and second let bindings below may be evaluated in either order because neither has a dataflow dependency on the other:

let %x = %a + %b;
let %y = %c + %d;
%x * %y

See Let for its definition and documentation.

Graph Bindings

A let binding creates a named variable that is bound to the given value and scoped to the subsequent expression. By contrast, a graph binding allows for explicitly constructing dataflow graphs in a Relay program by binding an expression (graph node) directly to a temporary variable, which is not scoped. Each reference to the variable corresponds to an edge in the dataflow graph. This has the semantics of substituting the expression wherever the variable appears, even though the graph node will only be evaluated once by the compiled program.

These bindings allow for a style of programming that corresponds to that already employed by NNVM and other dataflow graph-based input formats. The fact that the variables are not scoped offers some flexibility in evaluation order compared to let bindings, though this can also introduce some ambiguity in programs (the developer introduction to the Relay IR includes more detailed discussion of this nuance).

Note: Graph bindings are not currently parsed by the text format.

In Relay’s text format, a graph binding can be written as below (note the lack of a let keyword and a semicolon):

%1 = %a + %b
%2 = %1 + %1
%2 * %2

Unlike a let binding, a graph binding is not represented as an AST node in Relay, but rather as a meta-variable referencing its AST node value. For example, a program like the above could be constructed in Relay’s Python front-end by setting Python variables equal to the corresponding Relay AST node and using the variables repeatedly, as below (a C++ program using the corresponding API bindings could accomplish the same thing):

sum1 = relay.add(a, b)
sum2 = relay.add(sum1, sum1)
relay.multiply(sum2, sum2)

For development purposes and to enable certain optimizations, Relay includes passes to convert between dataflow graphs defined using graph bindings and programs with let bindings in A-normal form, employed by many compiler optimizations from the functional programming community (see “A-Normalization: Why and How” by Matt Might for an introduction to A-normal form).


Relay has a simple if-then-else expression that allows programs to branch on a single value of type bool, i.e., a zero-rank tensor of booleans (Tensor[(), bool]).

if (%t == %u) {
} else {

Since if-then-else branches are expressions, they may appear inline wherever any other expression may be expected, like invocations of the ternary operator in C-like languages. The if-then-else expression evaluates to the value of the “then” branch if the condition value evaluates to True and evaluates to the value of the “else” branch if the condition value evaluates to False.

See If for its definition and documentation.


Program transformations (passes) in Relay may require inserting temporary state into the program AST to guide further transformations. The TempExpr node is provided as a utility to developers for this purpose; nodes inheriting from TempExpr cannot appear directly in user-provided code but may be inserted in a pass. Any TempExpr created in a pass should ideally be eliminated before the pass is complete, as a TempExpr only stores internal state and has no semantics of its own.

For an example of TempExpr being used in a pass, see src/relay/pass/, which uses TempExpr nodes to store information about operator layouts as the pass tries to rearrange operator calls.

See TempExpr for its definition and documentation.