Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Quickfix: Accelerate YAML and LoRA Fused Ops #92

Merged
merged 11 commits into from
Oct 14, 2024
Prev Previous commit
Next Next commit
improve comments
Signed-off-by: Yu Chin Fabian Lim <flim@sg.ibm.com>
  • Loading branch information
fabianlim committed Oct 13, 2024
commit 50e7e20375a2b74e7083b0861898276ec724dbb4
Original file line number Diff line number Diff line change
Expand Up @@ -230,7 +230,7 @@ def augmentation(
train_args: TrainingArguments,
modifiable_args: Tuple[LoraConfig],
):
# assert that plugin requires mixed precision to be set
# - when using our prepare peft, we will enforce the mixed precision settings
assert (
train_args.bf16 is True or train_args.fp16 is True
), f"{self.__class__} requires mixed precision argument `--fp16` or `--bf16`"
Expand Down
Original file line number Diff line number Diff line change
Expand Up @@ -129,7 +129,8 @@ def augmentation(
has_quant = getattr(model, "quantization_method", None)

if has_quant:
# assert that plugin requires mixed precision to be set
# - only in the case where quant case, that we enforce the mixed precision settings
# - this is mostly for the fused-loras
assert (
train_args.bf16 is True or train_args.fp16 is True
), f"{self.__class__} requires mixed precision argument `--fp16` or `--bf16`"
Expand Down
Loading