Thanks, that is very helpful. Do you have to train the model differently for use with AITemplate? Could it be helpful for Leela Chess Zero (LC0)? I think LC0 has a generic Pytorch backend, that is several times slower than its NVidia specific CUDA backend. I'm not very clueful about this stuff though.
As @haolu7 mentioned, you could take a pre-trained model and use AITemplate to do model inference. All you need to do is to re-write the model using AITemplate frontend and map PyTorch params to AITemplate params. Besides, AITemplate has a limited operator coverage compared to mature frameworks like PyTorch so you may need to implement your own kernels if necessary (though it already supports Bert, VIT, StableDiffusion, ResNet, Detectron, and general recommendation models).
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Thanks, that is very helpful. Do you have to train the model differently for use with AITemplate? Could it be helpful for Leela Chess Zero (LC0)? I think LC0 has a generic Pytorch backend, that is several times slower than its NVidia specific CUDA backend. I'm not very clueful about this stuff though.
No, you don't need to train the model differently to use it with AITemplate. Here is an intro example to do inference with AITemplate with a very simple PyTorch model: https://facebookincubator.github.io/AITemplate/tutorial/how_.... For more advanced examples, check out https://github.com/facebookincubator/AITemplate/tree/main/ex...
As @haolu7 mentioned, you could take a pre-trained model and use AITemplate to do model inference. All you need to do is to re-write the model using AITemplate frontend and map PyTorch params to AITemplate params. Besides, AITemplate has a limited operator coverage compared to mature frameworks like PyTorch so you may need to implement your own kernels if necessary (though it already supports Bert, VIT, StableDiffusion, ResNet, Detectron, and general recommendation models).