GPU acceleration for Apple's M1 chip?
## 🚀 Feature Hi, I was wondering if we could evaluate PyTorch's performance on Apple's new M1 chip. I'm also wondering how we could possibly optimize Pytorch's capabilities on M1 GPUs/neural engines. I know the issue of supporting acceleration frameworks outside of CUDA has been discussed in previous issues like #488..but I think this is worth a revisit. In Apple's big reveal today, we learned that Apple's on a roll with 50% of product usage growth being as a result of new users this year. Given that Apple is moving to these in-house designed chips, enhanced support for these chips could make deep learning on personal laptops a better experience for many researchers and engineers. I think this really aligns with PyTorch's theme of facilitating deep learning from research to production. I'm not quite sure how this should go down. But these could be important: 1. A study on M1 chips 2. Evaluation of Pytorch's performance on M1 chips 3. Assessment on M1's compatibility with acceleration frameworks compatible with PyTorch (best bet would be CUDA transpilation..from what I see at #488) 4. Investigating enhancements to PyTorch that can take advantage of M1's ML features. cc @VitalyFedyunin @ngimel
Posted by chris-dare on Nov 10, 2020, 10:05 PM
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GPU acceleration for Apple's M1 chip?
## 🚀 Feature Hi, I was wondering if we could evaluate PyTorch's performance on Apple's new M1 chip. I'm also wondering how we could possibly optimize Pytorch's capabilities on M1 GPUs/neural engines. I know the issue of supporting acceleration frameworks outside of CUDA has been discussed in previous issues like #488..but I think this is worth a revisit. In Apple's big reveal today, we learned that Apple's on a roll with 50% of product usage growth being as a result of new users this year. Given that Apple is moving to these in-house designed chips, enhanced support for these chips could make deep learning on personal laptops a better experience for many researchers and engineers. I think this really aligns with PyTorch's theme of facilitating deep learning from research to production. I'm not quite sure how this should go down. But these could be important: 1. A study on M1 chips 2. Evaluation of Pytorch's performance on M1 chips 3. Assessment on M1's compatibility with acceleration frameworks compatible with PyTorch (best bet would be CUDA transpilation..from what I see at #488) 4. Investigating enhancements to PyTorch that can take advantage of M1's ML features. cc @VitalyFedyunin @ngimel
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