[Bug/Future] bitsandbytes higher than 0.35.0 breaks training on 8bit adamW (Windows)

[Panchovix]

Hi there! When updating bitsandbytes to any version higher than 0.35.0, all trainings get loss value of nan.

I've tested with cu116, cu117, cu118 and cu121 binaries (with torch+cu116, +cu117, +cu118 and +cu121 respectively) and the issue happens on all of them.

I know it is more for a future issue, but if somehow the binaries have to be updated, they may suffer this issue.

The bitsandbytes whls were obtained from https://github.com/jllllll/bitsandbytes-windows-webui

[enranime]

this, I thought something broke so It's seem like this to be a cause thanks!

[sdbds]

did u use fp16?

[Panchovix]

did u use fp16?

I think I was using fp16 setting on accelerate config

[sdbds]

did u use fp16?

I think I was using fp16 setting on accelerate config

fp16 has some problem in bnb over 0.35

[Panchovix]

did u use fp16?

I think I was using fp16 setting on accelerate config

fp16 has some problem in bnb over 0.35

You think if using bf16, it should work correctly? I can try in some hours.

[sdbds]

did u use fp16?

I think I was using fp16 setting on accelerate config

fp16 has some problem in bnb over 0.35

You think if using bf16, it should work correctly? I can try in some hours.

yeah, you can try bf16.
i think fp16 problem form SDXL vae.
did u train on SD1.5 or SDXL?

[Panchovix]

did u use fp16?

I think I was using fp16 setting on accelerate config

fp16 has some problem in bnb over 0.35

You think if using bf16, it should work correctly? I can try in some hours.

yeah, you can try bf16. i think fp16 problem form SDXL vae. did u train on SD1.5 or SDXL?

The issue happened in both SD1.5 and SDXL (training loss going to nan)

[Panchovix]

Just tested with BF16 on SD1.5 and it still goes to nan/1+.

bitsandbytes 0.41.0

[sdbds]

Just tested with BF16 on SD1.5 and it still goes to nan/1+.

bitsandbytes 0.41.0

bnb over 0.35 has more weight on LR
you can use less LR such as 2e-6~2e-5 on it

[2kpr]

I've known of an issue with bitsandbytes since 0.35.0 and at the time no one knew what the issue was caused by they just knew to keep bitsandbytes at 0.35.0, and with the advent of SDXL I started training again and thought that that issue might have been fixed until I trained SD v1.5 and SDXL in the last two days and got bad results, seemingly overtrainings, blotchyness, nans, etc...

So I went back and tried to track down the issue and what might have happened from bitsandbytes 0.35.0 and after, and it turns out it was the probable mistaken indentation of a section of code in the 8bit dynamic map code in bitsandbytes!

Which also explains why people for months have been seeing this issue only when using the 8bit optimizers like AdamW8bit with bitsandbytes >0.35.0, and not when using just AdamW for instance.

Here are some links related to the issue:
ShivamShrirao/diffusers#178
bitsandbytes-foundation/bitsandbytes#152

And in the second link ArrowM tracked down the issue in this commit of bitsandbytes:

This is a total shot in the dark, but I wonder if bitsandbytes/functional.py:218-223 was accidentally indented in bitsandbytes-foundation/bitsandbytes@2f2063b

And RossM noticied in the actual commit here: bitsandbytes-foundation/bitsandbytes@2f2063b#r109622091

The indent here looks clearly wrong, this repeats the additional_items for each value of exponent bits while it should apply to only the last value.

[sdbds]

I've known of an issue with bitsandbytes since 0.35.0 and at the time no one knew what the issue was caused by they just knew to keep bitsandbytes at 0.35.0, and with the advent of SDXL I started training again and thought that that issue might have been fixed until I trained SD v1.5 and SDXL in the last two days and got bad results, seemingly overtrainings, blotchyness, nans, etc...

So I went back and tried to track down the issue and what might have happened from bitsandbytes 0.35.0 and after, and it turns out it was the probable mistaken indentation of a section of code in the 8bit dynamic map code in bitsandbytes!

Which also explains why people for months have been seeing this issue only when using the 8bit optimizers like AdamW8bit with bitsandbytes >0.35.0, and not when using just AdamW for instance.

Here are some links related to the issue: ShivamShrirao/diffusers#178 TimDettmers/bitsandbytes#152

And in the second link ArrowM tracked down the issue in this commit of bitsandbytes:

This is a total shot in the dark, but I wonder if bitsandbytes/functional.py:218-223 was accidentally indented in TimDettmers/bitsandbytes@2f2063b

And RossM noticied in the actual commit here: TimDettmers/bitsandbytes@2f2063b#r109622091

The indent here looks clearly wrong, this repeats the additional_items for each value of exponent bits while it should apply to only the last value.

Thank u for reporting,so TimDettmers/bitsandbytes havent fix yet?

bitsandbytes-foundation/bitsandbytes#262
i found a pr for fixing but closed.
idk if it is bug or new feature...

[2kpr]

Thank u for reporting,so TimDettmers/bitsandbytes havent fix yet?

bitsandbytes-foundation/bitsandbytes#262
i found a pr for fixing but closed.
idk if it is bug or new feature...

It has not been fixed, no, it's been in the code since 0.35.0, and like I mentioned we knew back at the end of 2022 that 'something' happened to bitsandbytes after 0.35.0 but just didn't know what.

Seems like it shouldn't have been indented and is in fact a bug as RossM pointed out:

The indent here looks clearly wrong, this repeats the additional_items for each value of exponent bits while it should apply to only the last value.

It was probably an accident, and Tim Dettmers didn't close that PR or comment on it, so not sure why ArrowM decided to close it with no comments either.

There are currently 28 open PRs on bitsandbytes and 2 weeks ago 7 were merged, but the last time any were merged was May 7th, so it makes sense that Tim is very busy and hasn't had time to read all the issues(318)/PRs(28) to even see that someone mentioned about the indent issue from 0.35.0 and on, etc.

[Panchovix]

Thanks for the info! Sadly tried to fix the ident but I keep getting my loss to 1.0/nan.

So I'm not sure if there's something more besides this.

@sdbds I'm using
learning_rate = 0.0005 (5e-4)
text_encoder_lr=5e-5
unet_lr=0.0002125 (2.125e-4)

on bitsandbytes 0.35.0, and as far I know, at least on SD 1.5 training, if you set unet_lr, learning rate doesn't gets used. These give me good results for LoRA training at 1024x1024 in models based on SD1.5

Are these values too high for bitsandbytes 0.36.0+?