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Edit tutorial comments on PEFT / LoRA #416

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Jul 3, 2023
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Edit tutorial comments on PEFT / LoRA #416

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add more comments to the peft lora section in the tutorial
vchiley Jul 3, 2023
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updt cmts
vchiley Jul 3, 2023
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17 changes: 15 additions & 2 deletions TUTORIAL.md
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Expand Up @@ -328,8 +328,21 @@ The majority of our training setups use `triton`. -->
Updating to LLVM14 (or LLVM15) cannot be done because there are breaking changes.
What is the result of this? Although sm89+ is not **formally** supported until LLVM15, our testing on H100 GPUs shows that `attn_impl=triton` still works well and still runs fast. The only issue is that when the network is starting to run, LLVM might throw a warning like: `'sm_90' is not a recognized processor for this target (ignoring processor)`. This warning does not seem to affect performance.

### Can I finetune using PEFT / LORA?
- The LLM Foundry codebase does not directly have examples of PEFT or LORA workflows. However, our MPT model is a subclass of HuggingFace `PretrainedModel`, and we are working on adding the remaining features to enable HuggingFace’s [PEFT](https://huggingface.co/docs/peft/index) / [LORA](https://huggingface.co/docs/peft/conceptual_guides/lora) workflows for MPT.

### Can I finetune using PEFT / LoRA?
- The LLM Foundry codebase does not directly have examples of PEFT or LORA workflows. However, our MPT model is a subclass of HuggingFace `PretrainedModel`, and https://github.com/mosaicml/llm-foundry/pull/346 added required features to enable HuggingFace’s [PEFT](https://huggingface.co/docs/peft/index) / [LORA](https://huggingface.co/docs/peft/conceptual_guides/lora) workflows for MPT. MPT models with LoRA modules can be trained either using LLM Foundry or Hugging Face's [accelerate](https://huggingface.co/docs/accelerate/index). Within LLM Foundry, run (`scripts/train/train.py`), adding `lora` arguments to the config `.yaml`, like so:
<!--pytest.mark.skip-->
```yaml
lora:
args:
r: 16
lora_alpha: 32
lora_dropout: 0.05
target_modules: ['Wqkv']
```
- In the current release, these features have Beta support.
- For efficiency, The MPT model concatenates the `Q`, `K`, and `V` matrices in each attention block into a single `Wqkv` matrix that is three times wider. Currently, LoRA supports a low-rank approximation to this `Wqkv` matrix.
- Known issue: PEFT / LoRA do not directly work with FSDP.

### Can I quantize these models and/or run on CPU?
- The LLM Foundry codebase does not directly have examples of quantization or limited-resource inference. But you can check out [GGML](https://github.com/ggerganov/ggml) (same library that powers llama.cpp) which has built support for efficiently running MPT models on CPU! You _can_ load your model in 8-bit precision for inference using the [bitsandbytes library](https://github.com/TimDettmers/bitsandbytes) and Hugging Face's [accelerate](https://huggingface.co/docs/accelerate/index) via `load model = AutoModelForCausalLM.from_pretrained(model_name, load_in_8bit=True, device_map="auto", trust_remote_code=True)`, although we have not extensively benchmarked the performance (see the Hugging Face [quantization documentation](https://huggingface.co/docs/transformers/main/main_classes/quantization) for more detail).
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