Streamline KD & QAD transformers Trainers #708
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AAnoosheh
requested review from
ChenhanYu,
kevalmorabia97,
kinjalpatel27,
mxinO and
realAsma
examples/llm_qat/README.md
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| > **_NOTE:_** `launch.sh` defaults to use `LlamaDecoderLayer` as the transformer layer class. If your model uses a different class, you need to pass `--fsdp_transformer_layer_cls_to_wrap <your_layer_class>` to the `launch.sh` script. For example, for `Qwen/Qwen3-8B`, specify `--fsdp_transformer_layer_cls_to_wrap Qwen3DecoderLayer` as an additional argument. | ||
| > **_NOTE:_** The script defaults to using FSDP1. To use FSDP2, pass "--use_fsdp2 True" to the `launch.sh` script. Note that FSDP2 is less stable than FSDP1 currently. Use it with caution. | ||
| > **_NOTE:_** The script defaults to using FSDP1. To use FSDP2, pass "--backend=fsdp2" to the `launch.sh` script. Note that FSDP2 is less stable than FSDP1 currently. Use it with caution. |
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Is this statement still valid? Note that FSDP2 is less stable than FSDP1 currently. Use it with caution.
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I doubt it, but I don't have proof. I don't have proof that it is less stable either.
Signed-off-by: Asha Anoosheh <[email protected]>
Signed-off-by: Asha Anoosheh <[email protected]>
Signed-off-by: Asha Anoosheh <[email protected]>
Signed-off-by: Asha Anoosheh <[email protected]>
Signed-off-by: Asha Anoosheh <[email protected]>
Signed-off-by: Asha Anoosheh <[email protected]>
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Signed-off-by: Asha Anoosheh <[email protected]>
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Signed-off-by: Asha Anoosheh <[email protected]>
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Signed-off-by: Asha Anoosheh <[email protected]>
| model = self.accelerator.unwrap_model(self.model) | ||
| with model.hide_teacher_model(), model.hide_loss_modules(enable=not _internal_call): | ||
| return QATTrainer.save_model(self, output_dir, _internal_call, *args, **kwargs) | ||
| else: | ||
| return KDTrainer.save_model(self, output_dir, _internal_call, *args, **kwargs) |
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Do we need def save_model( for QADTrainer?
MRO for QADTrainer.save_model is `QATTrainer.save_model -> KDTrainer.save_model -> HF Trainer.svae_model right? What in particular is being achieved here now?
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If we don't respect this MRO, the fix in https://github.com/NVIDIA/Model-Optimizer/pull/546/files wont be applied for QAD checkpoints
What does this PR do?
Type of change: ? Refactor and stabilization
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