This document describes the broadcasting semantics of XLA.

What is broadcasting?

Broadcasting is the process of making arrays with different shapes have compatible shapes for arithmetic operations. The terminology is borrowed from NumPy broadcasting.

Broadcasting may be required for operations between multi-dimensional arrays of different ranks, or between multi-dimensional arrays with different but compatible shapes. Consider the addition X+v where X is a matrix (an array of rank 2) and v is a vector (an array of rank 1). To perform element-wise addition, XLA needs to "broadcast" the vector v to the same rank as the matrix X, by replicating v a certain number of times. The vector's length has to match at least one of the dimensions of the matrix.

For example:

|1 2 3| + |7 8 9|
|4 5 6|

The matrix's dimensions are (2,3), and the vector's dimension is (3). The vector is broadcast by replicating it over rows to get:

|1 2 3| + |7 8 9| = |8  10 12|
|4 5 6|   |7 8 9|   |11 13 15|

In NumPy, this is called broadcasting.


The XLA language is as strict and explicit as possible, avoiding implicit "magical" features. Such features might make some computations slightly easier to define, but at the cost of more assumptions baked into user code that will be difficult to change in the long term. If necessary, implicit magical features can be added in client-level wrappers.

With regard to broadcasting, XLA requires explicit broadcasting specifications on operations between arrays of different ranks. This is different from NumPy, which infers the specification when possible.

Broadcasting a lower-rank array onto a higher-rank array

Scalars can always be broadcast over arrays without an explicit specification of broadcasting dimensions. An element-wise binary operation between a scalar and an array means applying the operation with the scalar to each element in the array. For example, adding a scalar to a matrix means producing a matrix in which each element is a sum of the scalar and the corresponding element of the input matrix.

|1 2 3| + 7 = |8  9  10|
|4 5 6|       |11 12 13|

Most broadcasting needs can be captured by using a tuple of dimensions on a binary operation. When the inputs to the operation have different ranks, this broadcasting tuple specifies which dimension(s) in the higher-rank array to match with the lower-rank array.

Consider the previous example. Instead of adding a scalar to a (2,3) matrix, add a vector of dimension (3) to a matrix of dimensions (2,3). Without specifying broadcasting, this operation is invalid. To correctly request matrix-vector addition, specify the broadcasting dimension to be (1), meaning the vector's dimension is matched to dimension 1 of the matrix. In 2D, if dimension 0 represents rows and dimension 1 represents columns, this means that each element of the vector becomes a column of a size matching the number of rows in the matrix:

|7 8 9| ==> |7 8 9|
            |7 8 9|

As a more complex example, consider adding a 3-element vector (dimension (3)) to a 3x3 matrix (dimensions (3,3)). There are two ways broadcasting can happen for this example:

(1) A broadcasting dimension of 1 can be used. Each vector element becomes a column and the vector is duplicated for each row in the matrix.

|7 8 9| ==> |7 8 9|
            |7 8 9|
            |7 8 9|

(2) A broadcasting dimension of 0 can be used. Each vector element becomes a row and the vector is duplicated for each column in the matrix.

 |7| ==> |7 7 7|
 |8|     |8 8 8|
 |9|     |9 9 9|

The broadcasting dimensions can be a tuple that describes how a smaller rank shape is broadcast into a larger rank shape. For example, given a 2x3x4 cuboid and a 3x4 matrix, a broadcasting tuple (1,2) means matching the matrix to dimensions 1 and 2 of the cuboid.

This type of broadcast is used in the binary ops in XlaBuilder, if the broadcast_dimensions argument is given. For example, see XlaBuilder::Add. In the XLA source code, this type of broadcasting is sometimes called "InDim" broadcasting.

Formal definition

The broadcasting attribute allows matching a lower-rank array to a higher-rank array by specifying which dimensions of the higher-rank array to match. For example, for an array with dimensions MxNxPxQ, a vector with dimension T can be matched as follows:


dim 3:          T
dim 2:        T
dim 1:      T
dim 0:    T

In each case, T has to be equal to the matching dimension of the higher-rank array. The vector's values are then broadcast from the matched dimension to all the other dimensions.

To match a TxV matrix onto the MxNxPxQ array, a pair of broadcasting dimensions is used:

dim 2,3:      T V
dim 1,2:    T V
dim 0,3:  T     V

The order of dimensions in the broadcasting tuple must be the order in which the dimensions of the lower-rank array are expected to match the dimensions of the higher-rank array. The first element in the tuple specifies which dimension in the higher-rank array has to match dimension 0 in the lower-rank array. The second element in the tuple specifies which dimension in the higher-rank array has to match dimension 1 in the lower-rank array, and so on. The order of broadcast dimensions must be strictly increasing. For example, in the previous example it is illegal to match V to N and T to P; it is also illegal to match V to both P and N.

Broadcasting similar-rank arrays with degenerate dimensions

A related problem is broadcasting two arrays that have the same rank but different dimension sizes. As with NumPy, this is only possible when the arrays are compatible. Two arrays are compatible when all their dimensions are compatible. Two dimensions are compatible if:

  • They are equal, or
  • One of them is 1 (a "degenerate" dimension)

When two compatible arrays are encountered, the result shape has the maximum of the two inputs at every dimension index.


  1. (2,1) and (2,3) broadcast to (2,3).
  2. (1,2,5) and (7,2,5) broadcast to (7,2,5).
  3. (7,2,5) and (7,1,5) broadcast to (7,2,5).
  4. (7,2,5) and (7,2,6) are incompatible and cannot be broadcast.

A special case arises, and is also supported, where each of the input arrays has a degenerate dimension at a different index. In this case, the result is an "outer operation": (2,1) and (1,3) broadcast to (2,3). For more examples, consult the NumPy documentation on broadcasting.

Broadcast composition

Broadcasting of a lower-rank array to a higher-rank array and broadcasting using degenerate dimensions can both be performed in the same binary operation. For example, a vector of size 4 and a matrix of size 1x2 can be added together using broadcast dimensions of value (0):

|1 2 3 4| + [5 6]    // [5 6] is a 1x2 matrix, not a vector.

First the vector is broadcast up to rank 2 (matrix) using the broadcast dimensions. The single value (0) in the broadcast dimensions indicates that dimension zero of the vector matches dimension zero of the matrix. This produces a matrix of size 4xM where the value M is chosen to match the corresponding dimension size in the 1x2 array. Therefore, a 4x2 matrix is produced:

|1 1| + [5 6]
|2 2|
|3 3|
|4 4|

Then "degenerate dimension broadcasting" broadcasts dimension zero of the 1x2 matrix to match the corresponding dimension size of the right hand side:

|1 1| + |5 6|     |6  7|
|2 2| + |5 6|  =  |7  8|
|3 3| + |5 6|     |8  9|
|4 4| + |5 6|     |9 10|

A more complicated example is a matrix of size 1x2 added to an array of size 4x3x1 using broadcast dimensions of (1, 2). First the 1x2 matrix is broadcast up to rank 3 using the broadcast dimensions to produce an intermediate Mx1x2 array where the dimension size M is determined by the size of the larger operand (the 4x3x1 array) producing a 4x1x2 intermediate array. The M is at dimension 0 (the left-most dimension) because the dimensions 1 and 2 are mapped to the dimensions of the original 1x2 matrix as the broadcast dimensions are (1, 2). This intermediate array can be added to the 4x3x1 matrix using broadcasting of degenerate dimensions to produce a 4x3x2 array result.