# Palettization Overview#

Palettization, also referred to as weight clustering, compresses a model by clustering the model’s `float`

weights, and creating a lookup table (LUT) of centroids, and then storing the original weight values with indices pointing to the entries in the LUT.

Weights with similar values are grouped together and represented using the value of the cluster centroid they belong to, as shown in the following figure. The original weight matrix is converted to an index table in which each element points to the corresponding cluster center.

`N={1,2,3,4,6,8}`

are supported, where `N`

is the number of bits used for palettization.

## Granularity#

Figure above shows what is referred to as `per_tensor`

granularity, where the entire tensor shares a single LUT. This can lead to high approximation error for large matrices.
Starting `iOS18/macOS15`

, a mode called `per_grouped_channel`

is available , which allows a group of channels (specified by the parameter `group_size`

) to share a single LUT, thereby having multiple LUTs for the whole weight matrix. For example, a weight of shape `(1024, 1024)`

, with `group_size=16`

, will have `64`

LUTs.

## Per-channel scale#

When this mode is enabled, weights are normalized along the output channels using per channel scales before being palettized.

## Vector Palettization#

Cluster centroids can be scalar or vector values. This can be configured with `cluster_dim`

which by default is set to `1`

indicating scalar palettization.
When `cluster_dim > 1`

, it indicates 2-D clustering and each `cluster_dim`

length of weight vectors along the output channel are palettized using the same 2-D centroid. This is called vector palettization.

## Quantizing the LUT#

The values in LUT are by default `float`

. However, starting from `iOS18/macOS15`

, the LUT can be of stored in 8-bit precision as well, for further compression.

Feature Availability

Palettized weight representation for Core ML `mlprogram`

models is available in `iOS16`

/`macOS13`

/`watchOS9`

/`tvOS16`

and newer deployment target formats.