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use LigerCrossEntropy loss with reduction=sum #578

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thisisatharva-rh wants to merge 3 commits into from
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use LigerCrossEntropy loss with reduction=sum #578

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14d9499
use LigerCrossEntropy with reduction set to sum
thisisatharva-rh May 29, 2025
9dca572
fix linting issue
thisisatharva-rh May 29, 2025
0442be5
fix import issue
thisisatharva-rh May 29, 2025
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5 changes: 5 additions & 0 deletions src/instructlab/training/main_ds.py
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Original file line number Diff line number Diff line change
Expand Up @@ -55,6 +55,7 @@
)
import torch
import torch.distributed
from torch.nn import CrossEntropyLoss

# First Party
from instructlab.training import config
Expand Down Expand Up @@ -153,6 +154,7 @@ def setup_model(
model = GPTDolomiteForCausalLM.from_pretrained(
**base_model_args,
)
model.loss_fct = CrossEntropyLoss(reduction="sum")
elif args.use_liger:
# TODO(osilkin): we duplicate some checks here because someone may run this script through
# torchrun directly and not `run_training`. To fix this, we should eventually move everything
Expand All @@ -164,6 +166,7 @@ def setup_model(
try:
# Third Party
from liger_kernel.transformers import AutoLigerKernelForCausalLM
from liger_kernel.transformers.cross_entropy import LigerCrossEntropyLoss
except ImportError as e:
raise ValueError(
"Liger kernels are not installed. Please install Liger kernels using the following command: pip install liger-kernel"
Expand All @@ -175,8 +178,10 @@ def setup_model(
model = AutoLigerKernelForCausalLM.from_pretrained(
**base_model_args, cross_entropy=True, fused_linear_cross_entropy=False
)
model.loss_fct = LigerCrossEntropyLoss(reduction="sum")
else:
model = AutoModelForCausalLM.from_pretrained(**base_model_args)
model.loss_fct = CrossEntropyLoss(reduction="sum")

# store the base model args so we can recall them later if saving a LoRA model
args.base_model_args = base_model_args
Expand Down
3 changes: 1 addition & 2 deletions src/instructlab/training/utils.py
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Expand Up @@ -425,8 +425,7 @@ def reduce_sum_forward(
shift_labels = shift_labels.view(-1)
# Ensure tensors are on the same device
shift_labels = shift_labels.to(shift_logits.device)
loss_fct = torch.nn.CrossEntropyLoss(reduction="sum")
loss = loss_fct(shift_logits, shift_labels)
loss = model.loss_fct(shift_logits, shift_labels)

if not return_dict:
return ((loss,) + output) if loss is not None else output
Expand Down