Axolotl のデバッグ方法

Motivation

Axolotl is a great project for fine-tuning LLMs. I started contributing to the project, and I found that it was difficult to debug. I wanted to share some tips and tricks I learned along the way, along with configuration files for debugging with VSCode. Moreover, I think being able to debug axolotl empowers developers who encounter bugs or want to understand how the code works. I hope this document helps you get started.

This content is now part of the Axolotl docs!

I contributed this blog post’s contents as documentation for the axolotl project. You can find this content in the axolotl repo here.

General Tips

While debugging, it’s helpful to simplify your test scenario as much as possible. Here are some tips for doing so:

Note

All of these tips are incorporated into the example configuration for debugging with VSCode below.

Make sure you are using the latest version of axolotl: This project changes often and bugs get fixed fast. Check your git branch and make sure you have pulled the latest changes from main.

Eliminate Concurrency: Restrict the number of processes to 1 for both training and data preprocessing:

Set CUDA_VISIBLE_DEVICES to a single GPU, ex: export CUDA_VISIBLE_DEVICES=0.

Set dataset_processes: 1 in your axolotl config or run the training command with --dataset_processes=1.

Use a small dataset: Construct or use a small dataset from HF Hub. When using a small dataset, you will often have to make sure sample_packing: False and eval_sample_packing: False to avoid errors. If you are in a pinch and don’t have time to construct a small dataset but want to use from the HF Hub, you can shard the data (this will still tokenize the entire dataset but will only use a fraction of the data for training. For example, to shard the dataset into 20 pieces, add the following to your axolotl config):

dataset:

...

shards: 20

Use a small model: A good example of a small model is TinyLlama/TinyLlama-1.1B-Chat-v1.0.

Minimize iteration time: Make sure the training loop finishes as fast as possible, with these settings.

micro_batch_size: 1

max_steps: 1

val_set_size: 0

Clear Caches: Axolotl caches certain steps and so does the underlying HuggingFace trainer. You may want to clear some of these caches when debugging.

Data preprocessing: When debugging data preprocessing, which includes prompt template formation, you may want to delete the directory set in dataset_prepared_path: in your axolotl config. If you didn’t set this value, the default is last_run_prepared.

HF Hub: If you are debugging data preprocessing, you should clear the relevant HF cache HuggingFace cache, by deleting the appropriate ~/.cache/huggingface/datasets/... folder(s).

The recommended approach is to redirect all outputs and caches to a temporary folder and delete selected subfolders before each run. This is demonstrated in the example configuration below.

Debugging with VSCode

Background

The below example shows how to configure VSCode to debug data preprocessing of the sharegpt format. This is the format used when you have the following in your axolotl config:

datasets:

• path: <path to your sharegpt formatted dataset> # example on HF Hub: philschmid/guanaco-sharegpt-style

type: sharegpt

Important

If you are already familiar with advanced VSCode debugging, you can skip the below explanation and look at the files .vscode/launch.json and .vscode/tasks.json for an example configuration.

Tip

If you prefer to watch a video, rather than read, you can skip to the video tutorial below (but doing both is recommended).

Setup

Make sure you have an editable install of Axolotl, which ensures that changes you make to the code are reflected at runtime. Run the following commands from the root of this project:

pip3 install packaging

pip3 install -e '.[flash-attn,deepspeed]'

Remote Hosts

If you developing on a remote host, you can easily use VSCode to debug remotely. To do so, you will need to follow this remote - SSH guide. You can also see the video below on Docker and Remote SSH debugging.

Configuration

The easiest way to get started is to modify the .vscode/launch.json file in the axolotl GitHub repo. This is just an example configuration, so you may need to modify or copy it to suit your needs.

For example, to mimic the command cd devtools && CUDA_VISIBLE_DEVICES=0 accelerate launch -m axolotl.cli.train dev_sharegpt.yml, you would use the below configuration1. Note that we add additional flags that override the axolotl config and incorporate the tips above (see the comments). We also set the working directory to devtools and set the env variable HF_HOME to a temporary folder that is later partially deleted. This is because we want to delete the HF dataset cache before each run in order to ensure that the data preprocessing code is run from scratch.

// https://github.com/OpenAccess-AI-Collective/axolotl/blob/main/.vscode/launch.json

{

"version": "0.2.0",

"configurations": [

{

"name": "Debug axolotl prompt - sharegpt",

"type": "python",

"module": "accelerate.commands.launch",

"request": "launch",

"args": [

"-m", "axolotl.cli.train", "dev_sharegpt.yml",

// The flags below simplify debugging by overriding the axolotl config

// with the debugging tips above. Modify as needed.

"--dataset_processes=1", // limits data preprocessing to one process

"--max_steps=1", // limits training to just one step

"--batch_size=1", // minimizes batch size

"--micro_batch_size=1", // minimizes batch size

"--val_set_size=0", // disables validation

"--sample_packing=False", // disables sample packing which is necessary for small datasets

"--eval_sample_packing=False",// disables sample packing on eval set

"--dataset_prepared_path=temp_debug/axolotl_outputs/data", // send data outputs to a temp folder

"--output_dir=temp_debug/axolotl_outputs/model" // send model outputs to a temp folder

],

"console": "integratedTerminal", // show output in the integrated terminal

"cwd": "${workspaceFolder}/devtools", // set working directory to devtools from the root of the project

"justMyCode": true, // step through only axolotl code

"env": {"CUDA_VISIBLE_DEVICES": "0", // Since we aren't doing distributed training, we need to limit to one GPU

"HF_HOME": "${workspaceFolder}/devtools/temp_debug/.hf-cache"}, // send HF cache to a temp folder

"preLaunchTask": "cleanup-for-dataprep", // delete temp folders (see below)

}

]

}

Additional notes about this configuration:

The argument justMyCode is set to true such that you step through only the axolotl code. If you want to step into dependencies, set this to false.

The preLaunchTask: cleanup-for-dataprep is defined in .vscode/tasks.json and is used to delete the following folders before debugging, which is essential to ensure that the data pre-processing code is run from scratch:

./devtools/temp_debug/axolotl_outputs

./devtools/temp_debug/.hf-cache/datasets

Tip

You may not want to delete these folders. For example, if you are debugging model training instead of data pre-processing, you may NOT want to delete the cache or output folders. You may also need to add additional tasks to the tasks.json file depending on your use case.

Below is the ./vscode/tasks.json file that defines the cleanup-for-dataprep task. This task is run before each debugging session when you use the above configuration. Note how there are two tasks that delete the two folders mentioned above. The third task cleanup-for-dataprep is a composite task that combines the two tasks. A composite task is necessary because VSCode does not allow you to specify multiple tasks in the preLaunchTask argument of the launch.json file.

// https://github.com/OpenAccess-AI-Collective/axolotl/blob/main/.vscode/tasks.json

// this file is used by launch.json

{

"version": "2.0.0",

"tasks": [

// this task changes into the devtools directory and deletes the temp_debug/axolotl_outputs folder

{

"label": "delete-outputs",

"type": "shell",

"command": "rm -rf temp_debug/axolotl_outputs",

"options":{ "cwd": "${workspaceFolder}/devtools"},

"problemMatcher": []

},

// this task changes into the devtools directory and deletes the temp_debug/.hf-cache/datasets folder

{

"label": "delete-temp-hf-dataset-cache",

"type": "shell",

"command": "rm -rf temp_debug/.hf-cache/datasets",

"options":{ "cwd": "${workspaceFolder}/devtools"},

"problemMatcher": []

},

// this task combines the two tasks above

{

"label": "cleanup-for-dataprep",

"dependsOn": ["delete-outputs", "delete-temp-hf-dataset-cache"],

}

]

}

Customizing your debugger

Your debugging use case may differ from the example above. The easiest thing to do is to put your own axolotl config in the devtools folder and modify the launch.json file to use your config. You may also want to modify the preLaunchTask to delete different folders or not delete anything at all.

Video Tutorial

The following video tutorial walks through the above configuration and demonstrates how to debug with VSCode:

Debugging With Docker

Using official Axolotl Docker images is a great way to debug your code, and is a very popular way to use Axolotl. Attaching VSCode to Docker takes a few more steps.

Setup

On the host that is running axolotl (ex: if you are using a remote host), clone the axolotl repo and change your current directory to the root:

git clone https://github.com/OpenAccess-AI-Collective/axolotl

cd axolotl

Tip

If you already have axolotl cloned on your host, make sure you have the latest changes and change into the root of the project.

Next, run the desired docker image and mount the current directory. Below is a docker command you can run to do this:2

docker run --privileged --gpus '"all"' --shm-size 10g --rm -it --name axolotl --ipc=host --ulimit memlock=-1 --ulimit stack=67108864 --mount type=bind,src="${PWD}",target=/workspace/axolotl -v ${HOME}/.cache/huggingface:/root/.cache/huggingface winglian/axolotl:main-py3.10-cu118-2.0.1

Tip

To understand which containers are available, see the Docker section of the README and the DockerHub repo. For details of how the Docker containers are built, see axolotl’s Docker CI builds.

You will now be in the container. Next, perform an editable install of Axolotl:

pip3 install packaging

pip3 install -e '.[flash-attn,deepspeed]'

Attach To Container

Next, if you are using a remote host, Remote into this host with VSCode. If you are using a local host, you can skip this step.

Next, select Dev Containers: Attach to Running Container... using the command palette (CMD + SHIFT + P) in VSCode. You will be prompted to select a container to attach to. Select the container you just created. You will now be in the container with a working directory that is at the root of the project. Any changes you make to the code will be reflected both in the container and on the host.

Now you are ready to debug as described above (see Debugging with VSCode).

Video - Attaching To Docker On Remote Host

Here is a short video that demonstrates how to attach to a Docker container on a remote host:

Footnotes

The config actually mimics the command CUDA_VISIBLE_DEVICES=0 python -m accelerate.commands.launch -m axolotl.cli.train devtools/sharegpt.yml, but this is the same thing.↩︎

Many of the below flags are recommended best practices by Nvidia when using nvidia-container-toolkit. You can read more about these flags here.↩︎