[in progress] fp16 memory optimizations

[nwatx]

• still need to bench performance accurately (will add a bench suite soon)

• working torch.half() / floating point

• model memory optimization

• kv cache memory optimization

• clean up code

[rishikksh20]

@nwatx Hi, is fp16 gives good output and speed up than normal ?

[nwatx]

i haven't measured speed up, but from observation, it seems to improve memory consumption

[nwatx]

the output seems to be of similar quality

[Ph0rk0z]

Basically I tested this and found that there is no difference beyond just changing the KV_CACHE to fp16. Using the autocasting and stuff like that gives no benefit that I can see. I was sorta hopeful this did something I didn't, but no such luck.

On a side note, I can generate on my 2080 22g using the fp16 cache, previously it would OOM but so far it has not.

[jasonppy]

flashattention might help

[Ph0rk0z]

There's a vllm one that will work for all tensor core cards: https://github.com/vllm-project/vllm/blob/main/vllm/attention/ops/triton_flash_attention.py

current one only supports ampere+

Not sure how to wrap it around your forwards.

[aashay-sarvam]

@nwatx I am getting error while running, specifically text_tokens.half() is creating an issue