123 points by tensor_wiz 5 months ago flag hide 18 comments
alex_cortez 5 months ago next
Great article! I've been curious about the practicality of neural network pruning in real-world applications.
hacker1234 5 months ago next
I think it's really promising. Not only does pruning reduce model size, but it also tends to increase inference speed, which is crucial for things like embedded devices.
nvidia_engineer 5 months ago next
Yes, I have! Pruning dynamic-variant transformers is particularly interesting because you can adjust the size of the model at runtime. It's great for fine-tuning based on specific user queries or available resources.
ml_learner 5 months ago prev next
Has anyone experimented with pruning large transformer models, like BERT? I'm curious about the impact on NLP tasks.
ds_enthusiast 5 months ago prev next
I'm not convinced pruning is a better approach than quantization or using smaller network architectures to begin with. Anyone care to weigh in?
deep_mind_dev 5 months ago next
Pruning has the advantage of retaining the original model architecture and weights which, in some cases, can lead to higher performance than quantization or smaller models.
google_research 5 months ago prev next
From what I've seen, each method has its own trade-offs. It all depends on the specific use case and resources available.
openai_engineer 5 months ago prev next
What pruning algorithms have people found to work best? I'm using the lottery ticket hypothesis method and achieving decent results.
tensorflow_fan 5 months ago next
I prefer using magnitude pruning as it's computationally inexpensive and easy to implement. I've found that applying iterative pruning helps with preserving the model's accuracy post-pruning.
pytorch_junkie 5 months ago prev next
Any tips on implementing pruning in a distributed manner? I'd expect that could lead to speedups during the pruning process.
spartan_coder 5 months ago next
I'd recommend updating the pruning mask in a separate process to the model training. It prevents slowing down the training process and allows for more efficient parallelization.
big_data_dev 5 months ago prev next
Look into using techniques like model parallelism and gradient accumulation to minimize any slowdown during training and pruning.
cuda_wiz 5 months ago prev next
I'm curious, how does pruning affect finetuning a pre-trained model? I'm working on a project that involves fine-tuning a GAN model for image classification.
ml_ninja 5 months ago next
From my experience, it doesn't affect fine-tuning too much. The key is to maintain the most important weights during pruning to ensure the solution space remains similar. This was explored in a Google AI blog post as well.
f5_fan 5 months ago prev next
Depending on how you implement the pruning, it could result in unstable fine-tuning. I suggest applying a small learning rate during fine-tuning to safeguard performance.
arm_developer 5 months ago prev next
Are there any frameworks/libraries out there designed specifically to simplify the pruning process?
prune_meister 5 months ago next
Yes, there are some great ones! I recommend checking out AMPNet, TensorFlow Model Optimization Toolkit, and NVIDIA's TensorRT library for various aspects of efficient model processing, including pruning.
quant_guru 5 months ago prev next
Don't forget about the Sparsify tool that allows fine-grained pruning control. Another option is the ICLR 2021 paper 'Finding Pruning Strategies via Mixed Strategy Reinforcement Learning' which has an easy-to-implement algorithm and demo code.