Hi,
I've been exploring the impressive work you've done on incorporating FP8 GEMM to accelerate tensor matrix multiplication operations in TransformerEngine. The initiative is well-support by the findings in the original paper [1], where experiments indicate that models can still converge when trained with FP8 precision.
While the initial results are certainly promising, I observed that there lacks a detailed quantitative analysis regarding the potential loss in accuracy that may come from using FP8 precision. Given the training LLMs are very expensive, this absence of granular data makes it challenging to advocate for the use of FP8 in training other LLMs.
In particular, it would be helpful to see an evaluation of:
- The tensor distribution of using FP8 tensor matmul before and after FP8 cast.
- The scalability of FP8 benefits across various LLMs
Thanks!
[1] https://arxiv.org/pdf/2209.05433.pdf
Hi,
I've been exploring the impressive work you've done on incorporating FP8 GEMM to accelerate tensor matrix multiplication operations in TransformerEngine. The initiative is well-support by the findings in the original paper [1], where experiments indicate that models can still converge when trained with FP8 precision.
While the initial results are certainly promising, I observed that there lacks a detailed quantitative analysis regarding the potential loss in accuracy that may come from using FP8 precision. Given the training LLMs are very expensive, this absence of granular data makes it challenging to advocate for the use of FP8 in training other LLMs.
In particular, it would be helpful to see an evaluation of:
Thanks!
[1] https://arxiv.org/pdf/2209.05433.pdf