mirror of https://github.com/hpcaitech/ColossalAI
71 lines
3.0 KiB
Markdown
71 lines
3.0 KiB
Markdown
# Booster Checkpoint
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Author: [Hongxin Liu](https://github.com/ver217)
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**Prerequisite:**
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- [Booster API](./booster_api.md)
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## Introduction
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We've introduced the [Booster API](./booster_api.md) in the previous tutorial. In this tutorial, we will introduce how to save and load checkpoints using booster.
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## Model Checkpoint
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{{ autodoc:colossalai.booster.Booster.save_model }}
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Model must be boosted by `colossalai.booster.Booster` before saving. `checkpoint` is the path to saved checkpoint. It can be a file, if `shard=False`. Otherwise, it should be a directory. If `shard=True`, the checkpoint will be saved in a sharded way. This is useful when the checkpoint is too large to be saved in a single file. Our sharded checkpoint format is compatible with [huggingface/transformers](https://github.com/huggingface/transformers), so you can use huggingface `from_pretrained` method to load model from our sharded checkpoint.
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{{ autodoc:colossalai.booster.Booster.load_model }}
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Model must be boosted by `colossalai.booster.Booster` before loading. It will detect the checkpoint format automatically, and load in corresponding way.
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If you want to load a pretrained model from Huggingface while the model is too large to be directly loaded through `from_pretrained` on a single device, a recommended way is to download the pretrained weights to a local directory, and use `booster.load` to load from that directory after boosting the model. Also, the model should be initialized under lazy initialization context to avoid OOM. Here is an example pseudocode:
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```python
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from colossalai.lazy import LazyInitContext
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from huggingface_hub import snapshot_download
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...
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# Initialize model under lazy init context
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init_ctx = LazyInitContext(default_device=get_current_device)
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with init_ctx:
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model = LlamaForCausalLM(config)
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...
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# Wrap the model through Booster.boost
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model, optimizer, _, _, _ = booster.boost(model, optimizer)
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# download huggingface pretrained model to local directory.
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model_dir = snapshot_download(repo_id="lysandre/arxiv-nlp")
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# load model using booster.load
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booster.load(model, model_dir)
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...
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```
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## Optimizer Checkpoint
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{{ autodoc:colossalai.booster.Booster.save_optimizer }}
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Optimizer must be boosted by `colossalai.booster.Booster` before saving.
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{{ autodoc:colossalai.booster.Booster.load_optimizer }}
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Optimizer must be boosted by `colossalai.booster.Booster` before loading.
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## LR Scheduler Checkpoint
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{{ autodoc:colossalai.booster.Booster.save_lr_scheduler }}
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LR scheduler must be boosted by `colossalai.booster.Booster` before saving. `checkpoint` is the local path to checkpoint file.
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{{ autodoc:colossalai.booster.Booster.load_lr_scheduler }}
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LR scheduler must be boosted by `colossalai.booster.Booster` before loading. `checkpoint` is the local path to checkpoint file.
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## Checkpoint design
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More details about checkpoint design can be found in our discussion [A Unified Checkpoint System Design](https://github.com/hpcaitech/ColossalAI/discussions/3339).
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<!-- doc-test-command: echo -->
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