176 lines
6.7 KiB
Markdown
176 lines
6.7 KiB
Markdown
# Axolotl
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#### Go ahead and axolotl questions
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## Support Matrix
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| | fp16/fp32 | fp16/fp32 w/ lora | 4bit-quant | 4bit-quant w/flash attention | flash attention | xformers attention |
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|----------|:----------|:------------------|------------|------------------------------|-----------------|--------------------|
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| llama | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
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| Pythia | ✅ | ✅ | ❌ | ❌ | ❌ | ❓ |
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| cerebras | ✅ | ✅ | ❌ | ❌ | ❌ | ❓ |
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## Getting Started
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- install python 3.9. 3.10 and above are not supported.
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- Point the config you are using to a huggingface hub dataset (see [configs/llama_7B_4bit.yml](https://github.com/winglian/axolotl/blob/main/configs/llama_7B_4bit.yml#L6-L8))
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```yaml
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datasets:
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- path: vicgalle/alpaca-gpt4
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type: alpaca
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```
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- Optionally Download some datasets, see [data/README.md](data/README.md)
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- Create a new or update the existing YAML config [config/sample.yml](config/sample.yml)
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```yaml
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# this is the huggingface model that contains *.pt, *.safetensors, or *.bin files
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# this can also be a relative path to a model on disk
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base_model: decapoda-research/llama-7b-hf-int4
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# you can specify an ignore pattern if the model repo contains more than 1 model type (*.pt, etc)
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base_model_ignore_patterns:
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# if the base_model repo on hf hub doesn't include configuration .json files,
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# you can set that here, or leave this empty to default to base_model
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base_model_config: decapoda-research/llama-7b-hf
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# If you want to specify the type of model to load, AutoModelForCausalLM is a good choice too
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model_type: AutoModelForCausalLM
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# Corresponding tokenizer for the model AutoTokenizer is a good choice
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tokenizer_type: AutoTokenizer
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# whether you are training a 4-bit quantized model
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load_4bit: true
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# this will attempt to quantize the model down to 8 bits and use adam 8 bit optimizer
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load_in_8bit: true
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# a list of one or more datasets to finetune the model with
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datasets:
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# this can be either a hf dataset, or relative path
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- path: vicgalle/alpaca-gpt4
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# The type of prompt to use for training. [alpaca, sharegpt, gpteacher, oasst, reflection]
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type: alpaca
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# axolotl attempts to save the dataset as an arrow after packing the data together so
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# subsequent training attempts load faster, relative path
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dataset_prepared_path: data/last_run_prepared
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# How much of the dataset to set aside as evaluation. 1 = 100%, 0.50 = 50%, etc
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val_set_size: 0.04
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# if you want to use lora, leave blank to train all parameters in original model
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adapter: lora
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# if you already have a lora model trained that you want to load, put that here
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lora_model_dir:
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# the maximum length of an input to train with, this should typically be less than 2048
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# as most models have a token/context limit of 2048
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sequence_len: 2048
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# max sequence length to concatenate training samples together up to
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# inspired by StackLLaMA. see https://huggingface.co/blog/stackllama#supervised-fine-tuning
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max_packed_sequence_len: 1024
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# lora hyperparameters
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lora_r: 8
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lora_alpha: 16
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lora_dropout: 0.05
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lora_target_modules:
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- q_proj
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- v_proj
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# - k_proj
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# - o_proj
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lora_fan_in_fan_out: false
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# wandb configuration if your're using it
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wandb_project:
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wandb_watch:
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wandb_run_id:
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wandb_log_model: checkpoint
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# where to save the finsihed model to
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output_dir: ./completed-model
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# training hyperparameters
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batch_size: 8
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micro_batch_size: 2
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eval_batch_size: 2
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num_epochs: 3
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warmup_steps: 100
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learning_rate: 0.00003
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# whether to mask out or include the human's prompt from the training labels
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train_on_inputs: false
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# don't use this, leads to wonky training (according to someone on the internet)
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group_by_length: false
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# Use CUDA bf16
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bf16: true
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# Use CUDA tf32
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tf32: true
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# does not work with current implementation of 4-bit LoRA
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gradient_checkpointing: false
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# stop training after this many evaluation losses have increased in a row
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# https://huggingface.co/transformers/v4.2.2/_modules/transformers/trainer_callback.html#EarlyStoppingCallback
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early_stopping_patience: 3
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# specify a scheduler to use with the optimizer. only one_cycle is supported currently
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lr_scheduler:
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# whether to use xformers attention patch https://github.com/facebookresearch/xformers:
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xformers_attention:
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# whether to use flash attention patch https://github.com/HazyResearch/flash-attention:
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flash_attention:
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# resume from a specific checkpoint dir
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resume_from_checkpoint:
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# if resume_from_checkpoint isn't set and you simply want it to start where it left off
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# be careful with this being turned on between different models
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auto_resume_from_checkpoints: false
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# don't mess with this, it's here for accelerate and torchrun
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local_rank:
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```
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- Install python dependencies with ONE of the following:
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- `pip3 install -e .[int4]` (recommended)
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- `pip3 install -e .[int4_triton]`
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- `pip3 install -e .`
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-
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- If not using `int4` or `int4_triton`, run `pip install "peft @ git+https://github.com/huggingface/peft.git"`
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- Configure accelerate `accelerate config` or update `~/.cache/huggingface/accelerate/default_config.yaml`
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```yaml
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compute_environment: LOCAL_MACHINE
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distributed_type: MULTI_GPU
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downcast_bf16: 'no'
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gpu_ids: all
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machine_rank: 0
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main_training_function: main
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mixed_precision: bf16
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num_machines: 1
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num_processes: 4
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rdzv_backend: static
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same_network: true
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tpu_env: []
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tpu_use_cluster: false
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tpu_use_sudo: false
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use_cpu: false
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```
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- Train! `accelerate launch scripts/finetune.py`, make sure to choose the correct YAML config file
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- Alternatively you can pass in the config file like: `accelerate launch scripts/finetune.py configs/llama_7B_alpaca.yml`~~
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## How to start training on Runpod in under 10 minutes
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- Choose your Docker container wisely.
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- I recommend `huggingface:transformers-pytorch-deepspeed-latest-gpu` see https://hub.docker.com/r/huggingface/transformers-pytorch-deepspeed-latest-gpu/
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- Once you start your runpod, and SSH into it:
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```shell
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export TORCH_CUDA_ARCH_LIST="7.0 7.5 8.0 8.6+PTX"
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source <(curl -s https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/dev/scripts/setup-runpod.sh)
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```
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- Once the setup script completes
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```shell
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accelerate launch scripts/finetune.py configs/quickstart.yml
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```
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- Here are some helpful environment variables you'll want to manually set if you open a new shell
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```shell
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export WANDB_MODE=offline
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export WANDB_CACHE_DIR=/workspace/data/wandb-cache
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export HF_DATASETS_CACHE="/workspace/data/huggingface-cache/datasets"
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export HUGGINGFACE_HUB_CACHE="/workspace/data/huggingface-cache/hub"
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export TRANSFORMERS_CACHE="/workspace/data/huggingface-cache/hub"
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export NCCL_P2P_DISABLE=1
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```
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