Quantizing Lottery Tickets

This is the code for QLTH, a 2-step model compression scheme that applies Post-Training Quantization on the subnetworks derived by iterative magnitude prunning used in finding ”winning tickets” or ”matching”.

LTH Experiments

All LTH experiments are conducted using the OpenLTH framework, which is located in the folder open_lth.

First do cd open_lth, then:

the Lottery Ticket without Rewinding experiment results could be reproduced with the following commands:

python open_lth.py lottery --model_name cifar_resnet_20 --default_hparams=cifar_resnet_20 --levels=10
python open_lth.py lottery --model_name cifar_resnet_56 --default_hparams=cifar_resnet_20 --levels=10
python open_lth.py lottery --model_name mnist_lenet_300_100 --default_hparams=mnist_lenet_300_100 --levels=15

and the Lottery Ticket with Rewinding experiment results could be reproduced with the following commands:

python open_lth.py lottery --model_name cifar_resnet_20 --default_hparams=cifar_resnet_20 --levels=10 --rewinding_steps=2000it
python open_lth.py lottery --model_name cifar_resnet_56 --default_hparams=cifar_resnet_20 --levels=10 --rewinding_steps=2000it

Quantization Experiments

After deriving the checkpoints for LTH experiments, the quantization experiments could be performed using the Neural Network Distiller library. The experiment scripts are located in the folder examples/classifier_compression.

First do cd examples/classifier_compression, then adjust the following command to run experiments for different settings:

python compress_classifier.py -a [MODEL_ARCH] [DATASET_PATH] \
                --resume [LOTTERY_MODEL_CHECKPOINT_PATH] \
                --evaluate --lth --pruned -mp [LOTTERY_MASK_PATH] \
                --qe --qebw [QE_NUM_BITS_WTS] --qeba [QE_NUM_BITS_ACTS] --qem [QE_MODE]

For example, the following command will run a quantization experiment on a trained ResNet 20 model with 90% of parameters pruned, using 8 bits for weights and activation quantization, and asymmetric quanzation mode:

python compress_classifier.py -a resnet20_cifar ../../../data.cifar10 \
                --resume ~/open_lth_data/lottery_6a3f90a482a5e73fa0473f151fe64163/replicate_1/level_10/main/model_best.pth \
                --evaluate --lth --pruned -mp ~/open_lth_data/lottery_6a3f90a482a5e73fa0473f151fe64163/replicate_1/level_10/main/mask.pth \
                --qe --qebw 8 --qeba 8 --qem asym_u

Some Results

This section includes parts of the results provided in the report.

LeNet 300-100 on MNIST dataset using our IMP-quantization pipeline

ResNet 20 on CIFAR10 dataset using our IMP-quantization pipeline (with rewinding in IMP)

ResNet 56 on CIFAR10 dataset using our IMP-quantization pipeline (with rewinding in IMP)