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Fix DeepSpeed Zero 3 Saving #709

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merged 3 commits into from
Oct 19, 2023
Merged

Fix DeepSpeed Zero 3 Saving #709

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fa61055
Update train.py
tokestermw Oct 9, 2023
2004904
add zero3 check
tokestermw Oct 17, 2023
50413b2
chore: lint
winglian Oct 19, 2023
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16 changes: 16 additions & 0 deletions src/axolotl/train.py
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Original file line number Diff line number Diff line change
Expand Up @@ -134,6 +134,22 @@ def terminate_handler(_, __, model):
# only save on rank 0, otherwise it corrupts output on multi-GPU when multiple processes attempt to write the same file
if cfg.fsdp:
trainer.save_model(cfg.output_dir)
elif cfg.deepspeed:
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does this apply to all zero* stages, or just zero3 as listed in the pr title?

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this error applies only for zero3. this code change will run for all zero* stages. should work for all zero* stages however.

if we want a smaller change, i think something like this should work:
https://github.com/lm-sys/FastChat/pull/1457/files#diff-82b734e9eda6b4bac9a28b1056d4e0e0676f904e43cede16d7aa6e2d1da3e61bR155

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perhaps elif cfg.deepspeed and trainer.hf_deepspeed_config_orig.is_zero3(): just to minimize the blast radius on this change?

# Copied over from: https://github.com/huggingface/accelerate/blob/5ae611118057232f441055f7ef9ba0b0f2b8d533/docs/source/usage_guides/deepspeed.md#saving-and-loading
trainer.accelerator.wait_for_everyone()
unwrapped_model = trainer.accelerator.unwrap_model(trainer.model_wrapped)

# Saves the whole/unpartitioned fp16 model when in ZeRO Stage-3 to the output directory if
# `stage3_gather_16bit_weights_on_model_save` is True in DeepSpeed Config file or
# `zero3_save_16bit_model` is True in DeepSpeed Plugin.
# For Zero Stages 1 and 2, models are saved as usual in the output directory.
# The model name saved is `pytorch_model.bin`
unwrapped_model.save_pretrained(
cfg.output_dir,
is_main_process=trainer.accelerator.is_main_process,
save_function=trainer.accelerator.save,
state_dict=trainer.accelerator.get_state_dict(trainer.model_wrapped),
)
elif cfg.local_rank == 0:
if cfg.flash_optimum:
model = BetterTransformer.reverse(model)
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