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Implement AdamW optimizer with FP8 state storage using PyTorch's native float8 format for improved precision and memory efficiency.
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The loss is still around 0.4, and nothing has improved. |
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Description
This PR adds support for AdamWFP8, the goal was to benchmark this optimizer for Z-Image Base LoRA trainings.
The optimizer Python script come from https://github.com/None9527/None_Z-image-Turbo_trainer/blob/omni/src/zimage_trainer/optimizers/adamw_fp8.py 🙂