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🐯 Fix LigerKernel for SFTTrainer #2940

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Merged
merged 3 commits into from
Feb 24, 2025
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🐯 Fix LigerKernel for SFTTrainer #2940

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fd6da4c
Fix LigerKernel for SFTTrainer
lewtun Feb 23, 2025
95d6c13
Merge branch 'main' into fix-liger-sft
qgallouedec Feb 24, 2025
5be79a3
Merge branch 'main' into fix-liger-sft
lewtun Feb 24, 2025
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3 changes: 1 addition & 2 deletions trl/trainer/sft_trainer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -173,7 +173,6 @@ def __init__(
)
if isinstance(model, str):
model = self._create_model_from_path(model, args)
self.use_liger = is_liger_kernel_available() and isinstance(model, AutoLigerKernelForCausalLM)
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What if the model used is already a liger model (and args.use_liger = False)?

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Good point, but as recommended by the Liger maintainer, we shouldn't be passing Liger models at the init (just patched via the config)

Should we deprecate passing the Liger model to the trainer or would you prefer an alternative?

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It seems that this command doesn't achieve what it's supposed to:

>>> from liger_kernel.transformers import AutoLigerKernelForCausalLM
>>> model =  AutoLigerKernelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B")
Applied Liger kernels to Qwen2
Sliding Window Attention is enabled but not implemented for `sdpa`; unexpected results may be encountered.
>>> isinstance(model, AutoLigerKernelForCausalLM)
False

I don't know of an easy way to test whether a model is liger (perhaps @ByronHsu does?).

Anyway, with your change you can still pass a liger model to the trainer. But you'll need to specify use_liger=True. Which sounds good to me.

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This line doesn't work when a Liger Model is converted to PEFT before passing into the trainer. It does not respect args.use_liger either.

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Good point, but as recommended by the Liger maintainer, we shouldn't be passing Liger models at the init (just patched via the config)

Should we deprecate passing the Liger model to the trainer or would you prefer an alternative?

Deprecating it might cause some problem. LoRA+ requires the model instance to be created before hand (to create optimizer). The flag use_liger does not convert PEFT wrapped model to liger model.

model = get_peft_model(model, lora_config)
optimizer = create_loraplus_optimizer(
    model=model,
    optimizer_cls=torch.optim.AdamW,
    lr=lr,
    eps=eps,
    betas=betas,
    weight_decay=weight_decay,
    loraplus_lr_ratio=loraplus_lr_ratio,
)

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The flag use_liger does not convert PEFT wrapped model to liger model.

in sft_tariner.py:

        if args.use_liger:
            if not is_liger_kernel_available():
                raise ImportError("Please install Liger-kernel for use_liger=True")
            model = AutoLigerKernelForCausalLM.from_pretrained(model_path, **model_init_kwargs)
        else:
            model = AutoModelForCausalLM.from_pretrained(model_path, **model_init_kwargs)
        return model

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Also, many VLMs can't be loaded with AutoLigerKernelForCausalLM. For example, monkey patching apply_liger_kernel_to_qwen2_vl() is required for Qwen2-VL


# PEFT configuration and model wrapping
if peft_config is not None:
Expand Down Expand Up @@ -472,7 +471,7 @@ def compute_loss(self, model, inputs, return_outputs=False, num_items_in_batch=N
)

# Compute token accuracy if we have labels and if the model is not using Liger (no logits)
if "labels" in inputs and not self.use_liger:
if "labels" in inputs and not self.args.use_liger:
shift_logits = outputs.logits[..., :-1, :].contiguous()
shift_labels = inputs["labels"][..., 1:].contiguous()

Expand Down