ML Community Day is November 9! Join us for updates from TensorFlow, JAX, and more Learn more

Aliasing in XLA

This document describes the aliasing API for XLA: when building an XLA program, you can specify the desired aliasing between the input and output buffers.

Defining aliasing at compile-time

For example, consider a trivial HLO module which simply adds 1 to its input:

HloModule increment

ENTRY entry {
  %p = f32[] parameter(0)
  %c = f32[] constant(1)
  ROOT %out = f32[] add(%p, %c)
}

This module will allocate two 4-byte buffers: one for the input %p, and one for the output %out.

However, it is often desirable to perform the update in-place (for example, if in the frontend generating the expression the input variable is no longer alive after the computation, as in the increment p++).

To perform such an update efficiently, you can specify the input aliasing:

HloModule increment, input_output_alias={ {}: 0 }

ENTRY entry {
  %p = f32[] parameter(0)
  %c = f32[] constant(1)
  ROOT %out = f32[] add(%p, %c)
}

The format specifies that the entire output (marked by {}) is aliased to the input parameter 0.

See the XlaBuilder::SetUpAlias API to specify the aliasing programmatically.

Defining aliasing at run-time

The aliasing defined in the previous step is specified during the compilation. During the execution, you can choose whether actually to donate the buffer using the LocalClient::RunAsync API.

Input buffers to the program are wrapped in ExecutionInput, which in turn contain a tree of MaybeOwningDeviceMemory. If memory is specified as owning (ownership of the buffer is passed to the XLA runtime), the buffer is actually donated, and the update is executed in-place, as requested by the compile-time aliasing API.

If, however, the buffer which is aliased at compile time is not donated at runtime, copy-protection kicks in: an extra output buffer O is allocated, and the contents of the input buffer P which was meant to be aliased are copied into O (so effectively the program can execute as if the buffer O was donated at runtime).

Frontend interop

TF/XLA

In clusters of TensorFlow program compiled with XLA, all resource variable updates are aliased at compile time (the aliasing at runtime depends on whether anything else holds a reference to the resource variable tensor).