# Factorize#

class composer.algorithms.Factorize(factorize_convs=True, factorize_linears=True, min_channels=256, latent_channels=0.25, min_features=256, latent_features=128)[source]#

Decomposes linear operators into pairs of smaller linear operators.

Specifically, this algorithm replaces torch.nn.Conv2d and torch.nn.Linear modules with FactorizedConv2d and FactorizedLinear modules.

The replacement is only performed if doing so would reduce the number of multiply-adds used to compute each module’s output. For linear layers and pointwise convolutions, this means that the factorization must use an intermediate rank of less than half the input and output ranks, since it must perform two operations instead of one.

For convolutions with kernel sizes greater than 1, the threshold for factorization being worthwhile varies with kernel size. Larger kernels allow larger intermediate ranks.

See factorize_matrix() and factorize_conv2d() for more information about the factorization process. See FactorizedConv2d and FactorizedLinear for more information about the factorized modules used to replace the original modules.

Runs on Event.INIT.

Parameters
• factorize_convs (bool) – whether to try factorizing torch.nn.Conv2d modules. Default: True.

• factorize_linears (bool) – whether to try factorizing torch.nn.Linear modules. Default: True.

• min_channels (int) – if a torch.nn.Conv2d module does not have at least this many input and output channels, it will be ignored. Modules with few channels are unlikely to be accelerated by factorization due to poor hardware utilization. Default: 256.

• latent_channels (int, float) – number of latent channels to use in factorized convolutions. Can be specified as either an integer > 1 or as a float within [0, 1). In the latter case, the value is interpreted as a fraction of min(in_channels, out_channels) for each torch.nn.Conv2d module, and is converted to the equivalent integer value, with a minimum of 1. Default: 0.25.

• min_features (int) – if a torch.nn.Linear module does not have at least this many input and output features, it will be ignored. Modules with few features are unlikely to be accelerated by factorization due to poor hardware utilization. Default: 256.

• latent_features (int, float) – size of the latent space for factorized linear modules. Can be specified as either an integer > 1 or as a float within [0, 0.5). In the latter case, the value is interpreted as a fraction of min(in_features, out_features) for each torch.nn.Linear module and is converted to the equivalent integer value, with a minimum of 1. Default: 128.