What GPU needed to finetune Large version?

[ai-forever]

What GPU needed to finetune Large version?

[OzoneReloaded]

Hello! I've managed to run finetuning on 11 gb GPU with:

gpt_options="
--hidden-size 1024
--seq-length 1024
--cpu-optimizer
--cpu_torch_adam
"

Hope it helps. @Rai220

[fen0s]

I have 16Gb GPU and get CUDA out of memory error (for batch size = 1!):

RuntimeError: CUDA out of memory. Tried to allocate 26.00 MiB (GPU 0; 14.76 GiB total capacity; 13.25 GiB already allocated; 21.44 MiB free; 13.84 GiB reserved in total by PyTorch)

Is this memory really not enough to train the large version? May be there is some tips to reduce memory using on pretraining? I using such list of parameters:

    --per_gpu_train_batch_size 1 \
    --gradient_accumulation_steps 1 \
    --overwrite_cache \
    --num_train_epochs 2 \
    --save_steps 1000 \
    --block_size 256 \
    --fp16

Apparently, optimization level of O3 helps, but I haven't quite figured out how to make it generate samples, it just outputs negative probability for some reason. The above answer is for GPT-3 large, not GPT-2 large, so...

[fen0s]

Basically what's needed is gradient checkpointing that was provided in one of transformers library versions. Not sure if I can implement it, especially considering that old versions of transformers library is used in here...

[TatianaShavrina]

Hey @Rai220 @fen0s The organizers gave participants the opportunity to get access to Cristofari. To get access, please send to [email protected] your request with brief information about your project. We will review your request and get back to you. Please note that the number of such accesses is limited. If necessary, please leave your request as early as possible.