diff --git a/README.md b/README.md index 55689696e..198bf2270 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,8 @@ # Axolotl -#### Go ahead and axolotl questions +A centralized repo to train multiple architectures with different dataset types using a simple yaml file. + +Go ahead and axolotl questions!! ## Support Matrix @@ -9,41 +11,111 @@ | llama | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | | Pythia | ✅ | ✅ | ❌ | ❌ | ❌ | ❓ | | cerebras | ✅ | ✅ | ❌ | ❌ | ❌ | ❓ | +| mpt | ✅ | ❌ | ❌ | ❌ | ❌ | ❓ | ## Getting Started -- install python 3.9. 3.10 and above are not supported. -- 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)) +### Environment -```yaml -datasets: - - path: vicgalle/alpaca-gpt4 - type: alpaca -``` +- Docker + ```bash + docker pull winglian/axolotl + ``` -- Optionally Download some datasets, see [data/README.md](data/README.md) +- Conda/Pip venv + 1. install python **3.9** + 2. Install python dependencies with ONE of the following: + - `pip3 install -e .[int4]` (recommended) + - `pip3 install -e .[int4_triton]` + - `pip3 install -e .` -- Create a new or update the existing YAML config [config/sample.yml](config/sample.yml) +### Dataset + +Have a dataset in one of the following format: + +- alpaca: instruction + ```json + {"instruction": "...", "input": "...", "output": "..."} + ``` +- #TODO add others +- completion: raw corpus + ```json + {"text": "..."} + ``` + +Optionally Download some datasets, see [data/README.md](data/README.md) + +### Config + +See sample configs in [configs](configs) folder. It is recommended to duplicate and modify to your needs. The most important options are: + +- model + ```yaml + base_model: ./llama-7b-hf # local or huggingface repo + ``` + +- dataset + ```yaml + datasets: + - path: vicgalle/alpaca-gpt4 # local or huggingface repo + type: alpaca # format from above + ``` + +- loading + ```yaml + load_4bit: true + load_in_8bit: true + bf16: true + fp16: true + tf32: true + ``` + +- lora + ```yaml + adapter: lora # blank for full finetune + lora_r: 8 + lora_alpha: 16 + lora_dropout: 0.05 + lora_target_modules: + - q_proj + - v_proj + ``` + +
+ +All yaml options ```yaml # this is the huggingface model that contains *.pt, *.safetensors, or *.bin files # this can also be a relative path to a model on disk -base_model: decapoda-research/llama-7b-hf-int4 +base_model: ./llama-7b-hf # you can specify an ignore pattern if the model repo contains more than 1 model type (*.pt, etc) base_model_ignore_patterns: # if the base_model repo on hf hub doesn't include configuration .json files, # you can set that here, or leave this empty to default to base_model -base_model_config: decapoda-research/llama-7b-hf +base_model_config: ./llama-7b-hf # If you want to specify the type of model to load, AutoModelForCausalLM is a good choice too model_type: AutoModelForCausalLM # Corresponding tokenizer for the model AutoTokenizer is a good choice tokenizer_type: AutoTokenizer + # whether you are training a 4-bit quantized model load_4bit: true +gptq_groupsize: 128 # group size +gptq_model_v1: false # v1 or v2 + # this will attempt to quantize the model down to 8 bits and use adam 8 bit optimizer load_in_8bit: true + +# Use CUDA bf16 +bf16: true +# Use CUDA fp16 +fp16: true +# Use CUDA tf32 +tf32: true + # a list of one or more datasets to finetune the model with datasets: # this can be either a hf dataset, or relative path @@ -55,17 +127,19 @@ datasets: dataset_prepared_path: data/last_run_prepared # How much of the dataset to set aside as evaluation. 1 = 100%, 0.50 = 50%, etc val_set_size: 0.04 -# if you want to use lora, leave blank to train all parameters in original model -adapter: lora -# if you already have a lora model trained that you want to load, put that here -lora_model_dir: + # the maximum length of an input to train with, this should typically be less than 2048 # as most models have a token/context limit of 2048 sequence_len: 2048 # max sequence length to concatenate training samples together up to # inspired by StackLLaMA. see https://huggingface.co/blog/stackllama#supervised-fine-tuning max_packed_sequence_len: 1024 + +# if you want to use lora, leave blank to train all parameters in original model +adapter: lora +# if you already have a lora model trained that you want to load, put that here # lora hyperparameters +lora_model_dir: lora_r: 8 lora_alpha: 16 lora_dropout: 0.05 @@ -74,14 +148,24 @@ lora_target_modules: - v_proj # - k_proj # - o_proj +# - gate_proj +# - down_proj +# - up_proj +lora_modules_to_save: +# - embed_tokens +# - lm_head +lora_out_dir: # TODO: explain lora_fan_in_fan_out: false -# wandb configuration if your're using it + +# wandb configuration if you're using it wandb_project: wandb_watch: wandb_run_id: -wandb_log_model: checkpoint +wandb_log_model: # 'checkpoint' + # where to save the finsihed model to output_dir: ./completed-model + # training hyperparameters batch_size: 8 micro_batch_size: 2 @@ -89,87 +173,83 @@ eval_batch_size: 2 num_epochs: 3 warmup_steps: 100 learning_rate: 0.00003 +logging_steps: + # whether to mask out or include the human's prompt from the training labels train_on_inputs: false # don't use this, leads to wonky training (according to someone on the internet) group_by_length: false -# Use CUDA bf16 -bf16: true -# Use CUDA tf32 -tf32: true + # does not work with current implementation of 4-bit LoRA gradient_checkpointing: false + # stop training after this many evaluation losses have increased in a row # https://huggingface.co/transformers/v4.2.2/_modules/transformers/trainer_callback.html#EarlyStoppingCallback early_stopping_patience: 3 # specify a scheduler to use with the optimizer. only one_cycle is supported currently lr_scheduler: +# specify optimizer +optimizer: +# specify weight decay +weight_decay: + # whether to use xformers attention patch https://github.com/facebookresearch/xformers: xformers_attention: # whether to use flash attention patch https://github.com/HazyResearch/flash-attention: flash_attention: + # resume from a specific checkpoint dir resume_from_checkpoint: # if resume_from_checkpoint isn't set and you simply want it to start where it left off # be careful with this being turned on between different models auto_resume_from_checkpoints: false + # don't mess with this, it's here for accelerate and torchrun local_rank: +# add or change special tokens + +special_tokens: + # bos_token: "" + # eos_token: "" + # unk_token: "" + +# FSDP +fsdp: +fsdp_config: + +# Deepspeed +deepspeed: + +# TODO +torchdistx_path: + +# Debug mode +debug: ``` -- Install python dependencies with ONE of the following: +
- - `pip3 install -e .[int4]` (recommended) - - `pip3 install -e .[int4_triton]` - - `pip3 install -e .` -- -- If not using `int4` or `int4_triton`, run `pip install "peft @ git+https://github.com/huggingface/peft.git"` -- Configure accelerate `accelerate config` or update `~/.cache/huggingface/accelerate/default_config.yaml` +### Accelerate -```yaml -compute_environment: LOCAL_MACHINE -distributed_type: MULTI_GPU -downcast_bf16: 'no' -gpu_ids: all -machine_rank: 0 -main_training_function: main -mixed_precision: bf16 -num_machines: 1 -num_processes: 4 -rdzv_backend: static -same_network: true -tpu_env: [] -tpu_use_cluster: false -tpu_use_sudo: false -use_cpu: false +Configure accelerate using `accelerate config` or update `~/.cache/huggingface/accelerate/default_config.yaml` + +### Train + +Run +```bash +accelerate launch scripts/finetune.py configs/your_config.yml ``` -- Train! `accelerate launch scripts/finetune.py`, make sure to choose the correct YAML config file -- Alternatively you can pass in the config file like: `accelerate launch scripts/finetune.py configs/llama_7B_alpaca.yml`~~ +### Inference +Add `--inference` flag to train command above -## How to start training on Runpod in under 10 minutes - -- Choose your Docker container wisely. -- I recommend `huggingface:transformers-pytorch-deepspeed-latest-gpu` see https://hub.docker.com/r/huggingface/transformers-pytorch-deepspeed-latest-gpu/ -- Once you start your runpod, and SSH into it: -```shell -export TORCH_CUDA_ARCH_LIST="7.0 7.5 8.0 8.6+PTX" -source <(curl -s https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/dev/scripts/setup-runpod.sh) +If you are inferencing a pretrained LORA, pass +```bash +--lora_model_dir path/to/lora ``` -- Once the setup script completes -```shell -accelerate launch scripts/finetune.py configs/quickstart.yml -``` +### Merge LORA to base -- Here are some helpful environment variables you'll want to manually set if you open a new shell -```shell -export WANDB_MODE=offline -export WANDB_CACHE_DIR=/workspace/data/wandb-cache -export HF_DATASETS_CACHE="/workspace/data/huggingface-cache/datasets" -export HUGGINGFACE_HUB_CACHE="/workspace/data/huggingface-cache/hub" -export TRANSFORMERS_CACHE="/workspace/data/huggingface-cache/hub" -export NCCL_P2P_DISABLE=1 -``` +Add `--merge_lora --lora_model_dir="path/to/lora"` flag to train command above