Post-Training Pruning

To sparsify the weights of a Core ML model, you can use one of two configurations:

The following example shows how to use them.

OpThresholdPrunerConfig

from coremltools.optimize.coreml import (
    OpThresholdPrunerConfig,
    OptimizationConfig,
    prune_weights,
)

op_config = OpThresholdPrunerConfig(
    threshold=0.03,
    minimum_sparsity_percentile=0.55,
    weight_threshold=1024,
)
config = OptimizationConfig(global_config=op_config)
model_compressed = prune_weights(model, config=config)

OpThresholdPrunerConfig works by setting all weight values below a certain value, as specified by threshold, to zero. In the resulting weight tensor, sparse representation is used only if the proportion of values that are zero is greater than a level, as specified by minimum_sparsity_percentile. Otherwise dense format will be used.

The weight_threshold parameter specifies the minimum number of elements that the weight tensor must have for this operation to take effect. In the previous code sample, since weight_threshold=1024 was specified, all the weight tensors that have less than 1024 elements will be left untouched, while the tensors of size greater than 1024 will be sparsified according to the threshold and minimum_sparsity_percentile settings.

OpMagnitudePrunerConfig

from coremltools.optimize.coreml import (
    OpMagnitudePrunerConfig,
    OptimizationConfig,
    prune_weights,
)

op_config = OpMagnitudePrunerConfig(
    target_sparsity=0.6,
    weight_threshold=1024,
)
config = OptimizationConfig(global_config=op_config)
model_compressed = prune_weights(model, config=config)

When using OpMagnitudePrunerConfig, the lowest magnitude weight values up to the percentile as specified by target_sparsity are set to zero, and the weight is stored in the sparse format. You can also specify to apply structured block or n:m sparsity using the parameters block_size and n_m_ratio respectively in OpMagnitudePrunerConfig.

For options on how to set different pruning configs for different weights in the same network, see Customizing Ops to Compress.

For more details on the parameters available in the config, see the API Reference:

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Use Post-Training Pruning APIs for Benchmarking

Unless the weights in your Core ML model are known to have a lot of zeros, using data-free pruning is typically going to lead to a large accuracy loss for any meaningful level of sparsity. Therefore, use the prune_weights method to experiment with different patterns and levels of sparsity to see the impact on size reduction and performance, and then use the results as a guiding factor to find a config that then you prune for using fine tuning.


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