> You can only give it a try, but don't get your hopes high on a large context.
You may or may not know this, but: when training off-the-shelf LLMs (i.e. ones which have a huge vocabulary) what consumes a huge amount of memory usage is calculating the cross-entropy loss (which gets worse the more tokens you stuff in your batch), so always use a fused cross-entropy kernel.
For example, for a Gemma 2 model with 2B parameters at a batch size of 8k this consumes 24GB of VRAM by default (!); you can fuse your cross-entropy loss with @torch.compile and that can cut down this memory usage to something like a few gigabytes, but with a dedicated kernel this becomes a few megabytes.
> "The integration involves modifying the TransformerDecoder module in torchtune to bypass the linear layer computation, allowing the Liger Fused Linear Cross Entropy Loss to handle the forward projection weights. "
Although this wasn't integrated into PyTorch itself (but to torchtune, which is a different thing). If you're writing your own training loop you need to use a third-party kernel, e.g. the Liger kernel mentioned in the article, or Cut Cross Entropy (which is much better than the Liger one, although IIRC it has a numeric bug in one of its kernels making the results very slightly off).
> Activation would still require gigabytes for a few kb context.
For that you use activation checkpointing, and you can also offload that to the CPU in a smart way to hide the latency. Although, yes, for long context training the activations do dominate the memory usage (and quantizing them degrades things more than just quantizing weights and/or optimizer states).
You may or may not know this, but: when training off-the-shelf LLMs (i.e. ones which have a huge vocabulary) what consumes a huge amount of memory usage is calculating the cross-entropy loss (which gets worse the more tokens you stuff in your batch), so always use a fused cross-entropy kernel.
For example, for a Gemma 2 model with 2B parameters at a batch size of 8k this consumes 24GB of VRAM by default (!); you can fuse your cross-entropy loss with @torch.compile and that can cut down this memory usage to something like a few gigabytes, but with a dedicated kernel this becomes a few megabytes.