Rigging the Lottery: Making All Tickets Winners
Paper: https://arxiv.org/abs/1911.11134
15min Presentation [pml4dc] [icml]
Colabs for Calculating FLOPs of Sparse Models
Best Sparse Models
Parameters are float, so each parameter is represented with 4 bytes. Uniform sparsity distribution keeps first layer dense therefore have slightly larger size and parameters. ERK applies to all layers except for 99% sparse model, in which we set the first layer to be dense, since otherwise we observe much worse performance.
Extended Training Results
Performance of RigL increases significantly with extended training iterations. In this section we extend the training of sparse models by 5x. Note that sparse models require much less FLOPs per training iteration and therefore most of the extended trainings cost less FLOPs than baseline dense training.
Observing improving performance we wanted to understand where the performance of sparse networks saturates. Longest training we ran had 100x training length of the original 100 epoch ImageNet training. This training costs 5.8x of the original dense training FLOPS and the resulting 99% sparse Resnet-50 achieves an impressive 68.15% test accuracy (vs 5x training accuracy of 61.86%).
S. Distribution | Sparsity | Training FLOPs | Inference FLOPs | Model Size (Bytes) | Top-1 Acc | Ckpt |
---|---|---|---|---|---|---|
- (DENSE) | 0 | 3.2e18 | 8.2e9 | 102.122 | 76.8 | - |
ERK | 0.8 | 2.09x | 0.42x | 23.683 | 77.17 | link |
Uniform | 0.8 | 1.14x | 0.23x | 23.685 | 76.71 | link |
ERK | 0.9 | 1.23x | 0.24x | 13.499 | 76.42 | link |
Uniform | 0.9 | 0.66x | 0.13x | 13.532 | 75.73 | link |
ERK | 0.95 | 0.63x | 0.12x | 8.399 | 74.63 | link |
Uniform | 0.95 | 0.42x | 0.08x | 8.433 | 73.22 | link |
ERK | 0.965 | 0.45x | 0.09x | 6.904 | 72.77 | link |
Uniform | 0.965 | 0.34x | 0.07x | 6.904 | 71.31 | link |
ERK | 0.99 | 0.29x | 0.05x | 4.354 | 61.86 | link |
ERK | 0.99 | 0.58x | 0.05x | 4.354 | 63.89 | link |
ERK | 0.99 | 2.32x | 0.05x | 4.354 | 66.94 | link |
ERK | 0.99 | 5.8x | 0.05x | 4.354 | 68.15 | link |
We also ran extended training runs with MobileNet-v1. Again training 100x more, we were not able saturate the performance. Training longer consistently achieved better results.
S. Distribution | Sparsity | Training FLOPs | Inference FLOPs | Model Size (Bytes) | Top-1 Acc | Ckpt |
---|---|---|---|---|---|---|
- (DENSE) | 0 | 4.5e17 | 1.14e9 | 16.864 | 72.1 | - |
ERK | 0.89 | 1.39x | 0.21x | 2.392 | 69.31 | link |
ERK | 0.89 | 2.79x | 0.21x | 2.392 | 70.63 | link |
Uniform | 0.89 | 1.25x | 0.09x | 2.392 | 69.28 | link |
Uniform | 0.89 | 6.25x | 0.09x | 2.392 | 70.25 | link |
Uniform | 0.89 | 12.5x | 0.09x | 2.392 | 70.59 | link |
1x Training Results
S. Distribution | Sparsity | Training FLOPs | Inference FLOPs | Model Size (Bytes) | Top-1 Acc | Ckpt |
---|---|---|---|---|---|---|
ERK | 0.8 | 0.42x | 0.42x | 23.683 | 75.12 | link |
Uniform | 0.8 | 0.23x | 0.23x | 23.685 | 74.60 | link |
ERK | 0.9 | 0.24x | 0.24x | 13.499 | 73.07 | link |
Uniform | 0.9 | 0.13x | 0.13x | 13.532 | 72.02 | link |
Evaluating checkpoints
Download the checkpoints and run the evaluation on ERK checkpoints with the following:
python imagenet_train_eval.py --mode=eval_once --output_dir=path/to/ckpt/folder \
--eval_once_ckpt_prefix=model.ckpt-3200000 --use_folder_stub=False \
--training_method=rigl --mask_init_method=erdos_renyi_kernel \
--first_layer_sparsity=-1
When running checkpoints with uniform sparsity distribution use --mask_init_method=random
and --first_layer_sparsity=0
. Set
--model_architecture=mobilenet_v1
when evaluating mobilenet checkpoints.
Sparse Training Algorithms
In this repository we implement following dynamic sparsity strategies:
-
SET: Implements Sparse Evalutionary Training (SET) which corresponds to replacing low magnitude connections randomly with new ones.
-
SNFS: Implements momentum based training without sparsity re-distribution:
-
RigL: Our method, RigL, removes a fraction of connections based on weight magnitudes and activates new ones using instantaneous gradient information.
And the following one-shot pruning algorithm:
- SNIP: Single-shot Network Pruning based on connection sensitivity prunes the least salient connections before training.
We have code for following settings:
- Imagenet2012: TPU compatible code with Resnet-50 and MobileNet-v1/v2.
- CIFAR-10 with WideResNets.
- MNIST with 2 layer fully connected network.
Setup
First clone this repo.
git clone https://github.com/google-research/rigl.git
cd rigl
We use Neurips 2019 MicroNet Challenge code for counting operations and size of our networks. Let's clone the google_research repo and add current folder to the python path.
git clone https://github.com/google-research/google-research.git
mv google-research/ google_research/
export PYTHONPATH=$PYTHONPATH:$PWD
Now we can run some tests. Following script creates a virtual environment and installs the necessary libraries. Finally, it runs few tests.
bash run.sh
We need to activate the virtual environment before running an experiment. With that, we are ready to run some trivial MNIST experiments.
source env/bin/activate
python rigl/mnist/mnist_train_eval.py
You can load and verify the performance of the Resnet-50 checkpoints like following.
python rigl/imagenet_resnet/imagenet_train_eval.py --mode=eval_once --training_method=baseline --eval_batch_size=100 --output_dir=/path/to/folder --eval_once_ckpt_prefix=s80_model.ckpt-1280000 --use_folder_stub=False
We use the Official TPU Code for loading ImageNet data. First clone the tensorflow/tpu repo and then add models/ folder to the python path.
git clone https://github.com/tensorflow/tpu.git
export PYTHONPATH=$PYTHONPATH:$PWD/tpu/models/
Other Implementations
- Graphcore-TF-MNIST: with sparse matrix ops!
- Pytorch implementation by Dyllan McCreary.
- Micrograd-Pure Python: This is a toy example with pure python sparse implementation. Caution, very slow but fun.
Citation
@incollection{rigl,
author = {Evci, Utku and Gale, Trevor and Menick, Jacob and Castro, Pablo Samuel and Elsen, Erich},
booktitle = {Proceedings of Machine Learning and Systems 2020},
pages = {471--481},
title = {Rigging the Lottery: Making All Tickets Winners},
year = {2020}
}
Disclaimer
This is not an official Google product.