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axolotl/README.md
NanoCode012 73a0b6ead5 Feat(config): Add hub_strategy (#386)
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# Axolotl
<div align="center">
<img src="image/axolotl.png" alt="axolotl" width="160">
<div>
<p>
<b>One repo to finetune them all! </b>
</p>
<p>
Go ahead and axolotl questions!!
</p>
<img src="https://github.com/OpenAccess-AI-Collective/axolotl/actions/workflows/pre-commit.yml/badge.svg?branch=main" alt="pre-commit">
<img alt="PyTest Status" src="https://github.com/OpenAccess-AI-Collective/axolotl/actions/workflows/tests.yml/badge.svg?branch=main">
</div>
</div>
## Axolotl supports
| | fp16/fp32 | lora | qlora | gptq | gptq w/ lora | gptq w/flash attn | flash attn | xformers attn |
|----------|:----------|:-----|-------|------|:-------------|-------------------|------------|---------------|
| llama | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
| Pythia | ✅ | ✅ | ✅ | ❌ | ❓ | ❌ | ❌ | ❓ |
| cerebras | ✅ | ✅ | ✅ | ❌ | ❓ | ❌ | ❌ | ✅ |
| mpt | ✅ | ❌ | ❓ | ❌ | ❓ | ❌ | ❌ | ❓ |
| falcon | ✅ | ✅ | ✅ | ❌ | ❓ | ❌ | ❌ | ✅ |
| gpt-j | ✅ | ✅ | ✅ | ❌ | ❓ | ❌ | ❓ | ✅ |
| XGen | ✅ | ❓ | ✅ | ❓ | ❓ | ❓ | ❓ | ✅
## Quickstart ⚡
**Requirements**: Python >=3.9 and Pytorch >=2.0.
```bash
git clone https://github.com/OpenAccess-AI-Collective/axolotl
pip3 install -e .
pip3 install -U git+https://github.com/huggingface/peft.git
# finetune lora
accelerate launch scripts/finetune.py examples/openllama-3b/lora.yml
# inference
accelerate launch scripts/finetune.py examples/openllama-3b/lora.yml \
--inference --lora_model_dir="./lora-out"
```
## Installation
### Environment
- Docker
```bash
docker run --gpus '"all"' --rm -it winglian/axolotl:main-py3.10-cu118-2.0.1
```
- `winglian/axolotl-runpod:main-py3.10-cu118-2.0.1`: for runpod
- `winglian/axolotl-runpod:main-py3.9-cu118-2.0.1-gptq`: for gptq
Or run on the current files for development:
```sh
docker compose up -d
```
- Conda/Pip venv
1. Install python **3.9**
2. Install pytorch stable https://pytorch.org/get-started/locally/
3. Install python dependencies with ONE of the following:
- Recommended, supports QLoRA, NO gptq/int4 support
```bash
pip3 install -e .
pip3 install -U git+https://github.com/huggingface/peft.git
```
- gptq/int4 support, NO QLoRA
```bash
pip3 install -e .[gptq]
```
- same as above but not recommended
```bash
pip3 install -e .[gptq_triton]
```
- LambdaLabs
<details>
<summary>Click to Expand</summary>
1. Install python
```bash
sudo apt update
sudo apt install -y python3.9
sudo update-alternatives --install /usr/bin/python python /usr/bin/python3.9 1
sudo update-alternatives --config python # pick 3.9 if given option
python -V # should be 3.9
```
2. Install pip
```bash
wget https://bootstrap.pypa.io/get-pip.py
python get-pip.py
```
3. Install torch
```bash
pip3 install -U torch --index-url https://download.pytorch.org/whl/cu118
```
4. Axolotl
```bash
git clone https://github.com/OpenAccess-AI-Collective/axolotl
cd axolotl
pip3 install -e . # change depend on needs
pip3 install protobuf==3.20.3
pip3 install -U requests
pip3 install -U --ignore-installed psutil
pip3 install -U scipy
pip3 install git+https://github.com/huggingface/peft.git # not for gptq
```
5. Set path
```bash
export LD_LIBRARY_PATH=/usr/lib/x86_64-linux-gnu:$LD_LIBRARY_PATH
```
</details>
### Dataset
Have dataset(s) in one of the following format (JSONL recommended):
- `alpaca`: instruction; input(optional)
```json
{"instruction": "...", "input": "...", "output": "..."}
```
- `sharegpt:chat`: conversations where `from` is `human`/`gpt`
```json
{"conversations": [{"from": "...", "value": "..."}]}
```
- `completion`: raw corpus
```json
{"text": "..."}
```
<details>
<summary>See other formats</summary>
- `jeopardy`: question and answer
```json
{"question": "...", "category": "...", "answer": "..."}
```
- `oasst`: instruction
```json
{"INSTRUCTION": "...", "RESPONSE": "..."}
```
- `gpteacher`: instruction; input(optional)
```json
{"instruction": "...", "input": "...", "response": "..."}
```
- `reflection`: instruction with reflect; input(optional)
```json
{"instruction": "...", "input": "...", "output": "...", "reflection": "...", "corrected": "..."}
```
- `explainchoice`: question, choices, (solution OR explanation)
```json
{"question": "...", "choices": ["..."], "solution": "...", "explanation": "..."}
```
- `concisechoice`: question, choices, (solution OR explanation)
```json
{"question": "...", "choices": ["..."], "solution": "...", "explanation": "..."}
```
- `summarizetldr`: article and summary
```json
{"article": "...", "summary": "..."}
```
- `alpaca_chat`: basic instruct for alpaca chat
```json
{"instruction": "...", "input": "...", "response": "..."}
```
- `alpaca_chat.load_qa`: question and answer for alpaca chat
```json
{"question": "...", "answer": "..."}
```
- `alpaca_chat.load_concise`: question and answer for alpaca chat, for concise answers
```json
{"instruction": "...", "input": "...", "response": "..."}
```
- `alpaca_chat.load_camel_ai`: question and answer for alpaca chat, for load_camel_ai
```json
{"message_1": "...", "message_2": "..."}
```
- `alpaca_w_system.load_open_orca`: support for open orca datasets with included system prompts, instruct
```json
{"system_prompt": "...", "question": "...", "response": "..."}
```
- `context_qa`: in context question answering from an article
```json
{"article": "...", "question": "...", "answer": "..."}
```
- `context_qa.load_404`: in context question answering from an article, with default response for no answer from context
```json
{"article": "...", "unanswerable_question": "..."}
```
- `creative_acr.load_answer`: instruction and revision
```json
{"instruction": "...", "revision": "..."}
```
- `creative_acr.load_critique`: critique
```json
{"scores": "...", "critiques": "...", "instruction": "...", "answer": "..."}
```
- `creative_acr.load_revise`: critique and revise
```json
{"scores": "...", "critiques": "...", "instruction": "...", "answer": "...", "revision": "..."}
```
- `pygmalion`: pygmalion
```json
{"conversations": [{"role": "...", "value": "..."}]}
```
- `sharegpt_simple.load_role`: conversations where `role` is used instead of `from`
```json
{"conversations": [{"role": "...", "value": "..."}]}
```
- `sharegpt_simple.load_guanaco`: conversations where `from` is `prompter`/`assistant` instead of default sharegpt
```json
{"conversations": [{"from": "...", "value": "..."}]}
```
- `sharegpt_jokes`: creates a chat where bot is asked to tell a joke, then explain why the joke is funny
```json
{"conversations": [{"title": "...", "text": "...", "explanation": "..."}]}
```
</details>
#### How to add custom prompts
1. Add your method to a file in [prompt_strategies](src/axolotl/prompt_strategies). Please see other files as example.
2. Use your custom file name as the dataset type `<prompt_strategies_file>.load_<load_fn>`.
Optionally, download some datasets, see [data/README.md](data/README.md)
### Config
See [examples](examples) for quick start. 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
```
Note: The code will load the right architecture.
- dataset
```yaml
sequence_len: 2048 # max token length for prompt
# huggingface repo
datasets:
- path: vicgalle/alpaca-gpt4
type: alpaca # format from earlier
# huggingface repo with specific configuration/subset
datasets:
- path: EleutherAI/pile
name: enron_emails
type: completion # format from earlier
# local
datasets:
- path: json
data_files: data.jsonl # or json
type: alpaca # format from earlier
```
- loading
```yaml
load_in_4bit: true
load_in_8bit: true
bf16: true # require >=ampere
fp16: true
tf32: true # require >=ampere
bfloat16: true # require >=ampere, use instead of bf16 when you don't want AMP (automatic mixed precision)
float16: true # use instead of fp16 when you don't want AMP
```
Note: Repo does not do 4-bit quantization.
- lora
```yaml
adapter: lora # qlora or leave blank for full finetune
lora_r: 8
lora_alpha: 16
lora_dropout: 0.05
lora_target_modules:
- q_proj
- v_proj
```
<details>
<summary>All yaml options</summary>
```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: ./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: ./llama-7b-hf
# you can specify to choose a specific model revision from huggingface hub
model_revision:
# Optional tokenizer configuration override in case you want to use a different tokenizer
# than the one defined in the base model
tokenizer_config:
# 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
# Trust remote code for untrusted source
trust_remote_code:
# use_fast option for tokenizer loading from_pretrained, default to True
tokenizer_use_fast:
# resize the model embeddings when new tokens are added to multiples of 32
# this is reported to improve training speed on some models
resize_token_embeddings_to_32x:
# whether you are training a 4-bit GPTQ quantized model
gptq: 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 bitsandbytes 4 bit
load_in_4bit:
# Use CUDA bf16
bf16: true # bool or 'full' for `bf16_full_eval`. require >=ampere
# Use CUDA fp16
fp16: true
# Use CUDA tf32
tf32: true # require >=ampere
# a list of one or more datasets to finetune the model with
datasets:
# hf dataset repo | "json" for local dataset, make sure to fill data_files
- path: vicgalle/alpaca-gpt4
# The type of prompt to use for training. [alpaca, sharegpt, gpteacher, oasst, reflection]
type: alpaca # format | format:<prompt_style> (chat/instruct) | <prompt_strategies>.load_<load_fn>
data_files: # path to source data files
shards: # number of shards to split data into
name: # name of dataset configuration to load
# axolotl attempts to save the dataset as an arrow after packing the data together so
# subsequent training attempts load faster, relative path
dataset_prepared_path: data/last_run_prepared
# push prepared dataset to hub
push_dataset_to_hub: # repo path
# push checkpoints to hub
hub_model_id: # repo path to push finetuned model
# how to push checkpoints to hub
# https://huggingface.co/docs/transformers/v4.31.0/en/main_classes/trainer#transformers.TrainingArguments.hub_strategy
hub_strategy:
# whether to use hf `use_auth_token` for loading datasets. Useful for fetching private datasets
# required to be true when used in combination with `push_dataset_to_hub`
hf_use_auth_token: # boolean
# How much of the dataset to set aside as evaluation. 1 = 100%, 0.50 = 50%, etc
val_set_size: 0.04
# Num shards for whole dataset
dataset_shard_num:
# Index of shard to use for whole dataset
dataset_shard_idx:
# 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
# FutureWarning: This will soon be DEPRECATED
max_packed_sequence_len: 1024
# use efficient multi-packing with block diagonal attention and per sequence position_ids. Recommend set to 'true'
sample_packing:
# you can set these packing optimizations AFTER starting a training at least once.
# The trainer will provide recommended values for these values.
sample_packing_eff_est:
total_num_tokens:
# if you want to use 'lora' or 'qlora' or 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
lora_target_modules:
- q_proj
- v_proj
# - k_proj
# - o_proj
# - gate_proj
# - down_proj
# - up_proj
lora_target_linear: # if true, will target all linear layers
lora_modules_to_save:
# - embed_tokens
# - lm_head
lora_out_dir:
lora_fan_in_fan_out: false
# wandb configuration if you're using it
wandb_mode: # "offline" to save run metadata locally and not sync to the server, "disabled" to turn off wandb
wandb_project: # your wandb project name
wandb_entity: # a wandb Team name if using a Team
wandb_watch:
wandb_run_id: # set the name of your wandb run
wandb_log_model: # "checkpoint" to log model to wandb Artifacts every `save_steps` or "end" to log only at the end of training
# where to save the finished model to
output_dir: ./completed-model
# training hyperparameters
gradient_accumulation_steps: 1
micro_batch_size: 2
eval_batch_size: 2
num_epochs: 3
warmup_steps: 100
learning_rate: 0.00003
logging_steps:
save_steps:
eval_steps:
save_total_limit:
# save model as safetensors (require safetensors package)
save_safetensors:
# whether to mask out or include the human's prompt from the training labels
train_on_inputs: false
# group similarly sized data to minimize padding
# may be slower to start, as it must download and sort the entire dataset
# note that training loss may have an oscillating pattern with this enabled
group_by_length: false
# Whether to use gradient checkpointing https://huggingface.co/docs/transformers/v4.18.0/en/performance#gradient-checkpointing
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 and kwargs to use with the optimizer
lr_scheduler: # 'one_cycle' | 'log_sweep' | empty for cosine
lr_scheduler_kwargs:
# for one_cycle optim
lr_div_factor: # learning rate div factor
# for log_sweep optim
log_sweep_min_lr:
log_sweep_max_lr:
# specify optimizer
optimizer:
# specify weight decay
weight_decay:
# adamw hyperparams
adam_beta1:
adam_beta2:
adam_epsilon:
# Gradient clipping max norm
max_grad_norm:
# whether to bettertransformers
flash_optimum:
# 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: # require a100 for llama
# whether to use scaled-dot-product attention
# https://pytorch.org/docs/stable/generated/torch.nn.functional.scaled_dot_product_attention.html
sdp_attention:
# Landmark attention (only llama)
landmark_attention:
# xpos RoPE see https://github.com/kaiokendev/cutoff-len-is-context-len/blob/main/util/xpos_rope_llama_monkey_patch.py
# llama only
xpos_rope:
# RoPE Scaling https://github.com/huggingface/transformers/pull/24653
rope_scaling:
type: # linear | dynamic
factor: # float
# 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: "<s>"
# eos_token: "</s>"
# unk_token: "<unk>"
# add extra tokens
tokens:
# FSDP
fsdp:
fsdp_config:
# Deepspeed
deepspeed:
# Path to torch distx for optim 'adamw_anyprecision'
torchdistx_path:
# Set padding for data collator to 'longest'
collator_pad_to_longest:
# Set to HF dataset for type: 'completion' for streaming instead of pre-tokenize
pretraining_dataset:
# Debug mode
debug:
# Seed
seed:
# Allow overwrite yml config using from cli
strict:
```
</details>
### Train
Run
```bash
accelerate launch scripts/finetune.py configs/your_config.yml
```
#### Multi-GPU
You can optionally pre-tokenize dataset with the following before finetuning:
```bash
CUDA_VISIBLE_DEVICES="" accelerate ... --prepare_ds_only
```
##### Config
- llama FSDP
```yaml
fsdp:
- full_shard
- auto_wrap
fsdp_config:
fsdp_offload_params: true
fsdp_state_dict_type: FULL_STATE_DICT
fsdp_transformer_layer_cls_to_wrap: LlamaDecoderLayer
```
- llama Deepspeed: append `ACCELERATE_USE_DEEPSPEED=true` in front of finetune command
##### Weights & Biases Logging
- wandb options
```yaml
wandb_mode:
wandb_project:
wandb_entity:
wandb_watch:
wandb_run_id:
wandb_log_model:
```
### Inference
Pass the appropriate flag to the train command:
- Pretrained LORA:
```bash
--inference --lora_model_dir="./lora-output-dir"
```
- Full weights finetune:
```bash
--inference --base_model="./completed-model"
```
- Full weights finetune w/ a prompt from a text file:
```bash
cat /tmp/prompt.txt | python scripts/finetune.py configs/your_config.yml \
--base_model="./completed-model" --inference --prompter=None --load_in_8bit=True
```
### Merge LORA to base
Add below flag to train command above
```bash
--merge_lora --lora_model_dir="./completed-model" --load_in_8bit=False --load_in_4bit=False
```
If you run out of CUDA memory, you can try to merge in system RAM with
```bash
CUDA_VISIBLE_DEVICES="" python3 scripts/finetune.py ...
```
## Common Errors 🧰
> Cuda out of memory
Please reduce any below
- `micro_batch_size`
- `eval_batch_size`
- `gradient_accumulation_steps`
- `sequence_len`
> RuntimeError: expected scalar type Float but found Half
Try set `fp16: true`
> NotImplementedError: No operator found for `memory_efficient_attention_forward` ...
Try to turn off xformers.
> accelerate config missing
It's safe to ignore it.
## Need help? 🙋♂️
Join our [Discord server](https://discord.gg/HhrNrHJPRb) where we can help you
## Badge ❤🏷️
Building something cool with Axolotl? Consider adding a badge to your model card.
```markdown
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
```
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
## Community Showcase
Open Access AI Collective
- [Minotaur 13b](https://huggingface.co/openaccess-ai-collective/minotaur-13b)
- [Manticore 13b](https://huggingface.co/openaccess-ai-collective/manticore-13b)
- [Hippogriff 30b](https://huggingface.co/openaccess-ai-collective/hippogriff-30b-chat)
PocketDoc Labs
- [Dan's PersonalityEngine 13b LoRA](https://huggingface.co/PocketDoc/Dans-PersonalityEngine-13b-LoRA)
## Contributing 🤝
Bugs? Please check for open issue else create a new [Issue](https://github.com/OpenAccess-AI-Collective/axolotl/issues/new).
PRs are **greatly welcome**!
Please run below to setup env
```bash
pip3 install -r requirements-dev.txt -r requirements-tests.txt
pre-commit install
# test
pytest tests/
```
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