[deepspeed] zero inference - #14253
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stas00
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November 20, 2021 02:14
sgugger
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Nov 20, 2021
sgugger
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Thanks for adding this new mode!
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| Inference: | ||
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| 1. DeepSpeed ZeRO Inference - same as Training but doesn't require Optimizer |
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Can we make a real sentence? I don't understand what this means.
| return config.get(ds_key, default) | ||
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| def del_config_sub_tree(self, ds_key_long, must_exist=False): | ||
| config = self.config |
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This method deservers a docstring.
| config = config.get(node) | ||
| if config is None: | ||
| if must_exist: | ||
| raise ValueError(f"Can't find {ds_key_long} entry in the config") |
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Might be worth printing the config as well?
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| raise ValueError(f"Can't find {ds_key_long} entry in the config") | |
| raise ValueError(f"Can't find {ds_key_long} entry in the config {config}") |
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that won't work, but I will fix it to dump the config, thank you.
| hf_deepspeed_config.trainer_config_finalize(args, model, num_training_steps) | ||
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| # resume config update - some bits like `model` and `num_training_steps` only become available during train | ||
| def deepspeed_optim_sched(trainer, hf_deepspeed_config, args, num_training_steps): |
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This new method deserves a docstring.
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
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Thanks a lot for the review and the suggestions, Sylvain. All addressed, please have another look at your convenience. Thank you. |
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This PR extends HF/DS integration to support Deepspeed Zero inference. Now we don't need to waste gpu memory on allocating the optimizer/scheduler and then dropping them. And in some cases enabling what was not possible before - in case the user doesn't have the extra gpu memory and was getting OOM on inference.
Blocking events:
@jeffra, @sgugger
The CI errors seem to be irrelevant to this PR