101 points by pytorchai 6 months ago flag hide 16 comments
pytorch_fan 6 months ago next
Exciting news! I've been waiting for this PyTorch Quantization Library to be released. Looking forward to the potential of running AI models on mobile devices seamlessly.
john_doe 6 months ago next
Absolutely! I've been playing around with the library and the results are impressive. You can achieve up to 4x performance gain on mobile devices.
ai_enthusiast 6 months ago prev next
It's great that PyTorch is focusing on mobile and edge devices. It will open up new possibilities for AI on the go.
mike_wazowski 6 months ago prev next
I'm a bit skeptical about the performance gain claims. Has anyone done any benchmarking yet?
john_doe 6 months ago next
Yes, I've run some benchmarks and the performance gain is real. However, it may vary depending on the model architecture and the device being used.
pytorch_core_team 6 months ago prev next
We've done extensive testing and the performance gain is consistently around 2x-4x on various devices. We'll be releasing more details and benchmarks soon.
edgedev 6 months ago prev next
What about support for heterogeneous devices like the ones with CPU, GPU and DSP? Will the library take advantage of all of them?
pytorch_core_team 6 months ago next
Currently, the library focuses on on-device AI for mobile and edge devices with a single GPU. However, we're exploring options to extend support for heterogeneous devices in the future.
tensorguy 6 months ago prev next
One of the key features of this library is the ability to quantize trained models with minimal accuracy loss. I'm excited to try it out!
tech_savvy 6 months ago next
Did you try the automatic quantization feature? How was your experience?
tensorguy 6 months ago next
Yes, I did. It was surprisingly easy to use and the accuracy loss was minimal. However, I did notice some performance degradation compared to manual optimization.
pytorch_core_team 6 months ago prev next
That's great to hear! We've focused on developing an intuitive and user-friendly library. There's room for improvement on the manual optimization side, so thank you for the feedback.
efficient_code 6 months ago prev next
How does this library compare to TensorFlow Lite's quantization feature? Are there any significant differences?
pytorch_fan 6 months ago next
From my understanding, both libraries offer similar quantization capabilities. However, PyTorch Quantization Library's automatic quantization feature stands out as a convenient option for those who want to minimize the manual work and/or have limited expertise in quantization.
new_dev 6 months ago prev next
I've been trying to quantize a custom model, but I keep getting weird errors. Anyone else facing similar issues?
pytorch_core_team 6 months ago next
Oh, I'm sorry to hear that. Could you please share the error messages and a reproducible example with us? We'll try to help you out and figure out what's going on.