Implementation for soft offline distillation using saved top-k teacher logits#3382
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ajkv-google wants to merge 3 commits intomainfrom
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Implementation for soft offline distillation using saved top-k teacher logits#3382ajkv-google wants to merge 3 commits intomainfrom
ajkv-google wants to merge 3 commits intomainfrom
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entrpn
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Mar 11, 2026
| def __init__(self, data_dir: str, epochs: int = 100): | ||
| # Check if the user passed a directory or a direct file path | ||
| if tf.io.gfile.isdir(data_dir): | ||
| self.filepath = os.path.join(data_dir, "teacher_top_k_global.array_record") |
Collaborator
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is it ok to hardcode this file as teacher_top_k_global.array_record?
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| if __name__ == "__main__": | ||
| app.run(main) | ||
| parser = argparse.ArgumentParser() |
Collaborator
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I think these should go inside types.py to add them as part of the config.
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Description
This PR introduces an end-to-end offline distillation training pipeline. Previously, the distillation loop executed in an "online" mode, which required both the frozen Teacher model and the learning Student model to be loaded and executed simultaneously during training. This change allows the trainer to load pre-computed, top-K Teacher logits from .array_record files, which allows us to bybass the forward pass for the teacher model during the training loop.
Tests
Tested this code change by running the following command:
python3 src/maxtext/trainers/post_train/distillation/train_distill.py src/maxtext/configs/post_train/distillation.yml steps=100 tokenizer_path="/mnt/ajkv/disks/codebase/maxtext/src/maxtext/assets/tokenizers/tokenizer_llama3.tiktoken" --offline_distillation --offline_data_dir="/mnt/ajkv/disks/teacher_logits_output/teacher_top_k_global.array_record"Truncated output showing the successful run: https://paste.googleplex.com/6342987127848960#l=8.
Verified that the training happened sucessfully and finished the distillation run.
Checklist
Before submitting this PR, please make sure (put X in square brackets):
gemini-reviewlabel.