diff --git a/.nojekyll b/.nojekyll index 4facc733c..51fdb57b7 100644 --- a/.nojekyll +++ b/.nojekyll @@ -1 +1 @@ -b19d49f2 \ No newline at end of file +59938e1e \ No newline at end of file diff --git a/docs/amd_hpc.html b/docs/amd_hpc.html index 42ac801b0..a91de7ac5 100644 --- a/docs/amd_hpc.html +++ b/docs/amd_hpc.html @@ -372,7 +372,7 @@ pre > code.sourceCode > span > a:first-child::before { text-decoration: underlin cd flash-attention export PYTHON_SITE_PACKAGES=$(python -c 'import site; print(site.getsitepackages()[0])') patch "${PYTHON_SITE_PACKAGES}/torch/utils/hipify/hipify_python.py" hipify_patch.patch -pip install . +pip install --no-build-isolation .

6. Install Axolotl

@@ -380,7 +380,7 @@ pre > code.sourceCode > span > a:first-child::before { text-decoration: underlin 
git clone https://github.com/axolotl-ai-cloud/axolotl
 cd axolotl
 pip install packaging ninja
-pip install -e .
+pip install --no-build-isolation -e .

7. Apply xformers Workaround

diff --git a/docs/dataset-formats/index.html b/docs/dataset-formats/index.html index c3c9d7966..7e53e97dd 100644 --- a/docs/dataset-formats/index.html +++ b/docs/dataset-formats/index.html @@ -363,7 +363,7 @@ Description - + Pre-training @@ -371,7 +371,7 @@ Description Data format for a pre-training completion task. - + Instruction Tuning @@ -379,7 +379,7 @@ Description Instruction tuning formats for supervised fine-tuning. - + Conversation @@ -387,7 +387,7 @@ Description Conversation format for supervised fine-tuning. - + Template-Free @@ -395,7 +395,7 @@ Description Construct prompts without a template. - + Custom Pre-Tokenized Dataset diff --git a/docs/debugging.html b/docs/debugging.html index 79cc6b5e4..bdd859aa5 100644 --- a/docs/debugging.html +++ b/docs/debugging.html @@ -407,7 +407,7 @@ pre > code.sourceCode > span > a:first-child::before { text-decoration: underlin

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]'
+pip3 install --no-build-isolation -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.

@@ -525,7 +525,7 @@ pre > code.sourceCode > span > a:first-child::before { text-decoration: underlin

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

pip3 install packaging
-pip3 install -e '.[flash-attn,deepspeed]'
+pip3 install --no-build-isolation -e '.[flash-attn,deepspeed]'

Attach To Container

diff --git a/examples/colab-notebooks/colab-axolotl-example.html b/examples/colab-notebooks/colab-axolotl-example.html index 1d5099f18..038a27a7d 100644 --- a/examples/colab-notebooks/colab-axolotl-example.html +++ b/examples/colab-notebooks/colab-axolotl-example.html @@ -358,7 +358,7 @@ window.Quarto = { assert (torch.cuda.is_available()==True)
-
!pip install axolotl[deepspeed]
+
!pip install --no-build-isolation axolotl[deepspeed]

Hugging Face login (optional)

diff --git a/index.html b/index.html index e05b7606f..7cac56d80 100644 --- a/index.html +++ b/index.html @@ -369,7 +369,7 @@ pre > code.sourceCode > span > a:first-child::before { text-decoration: underlin

Quickstart ⚡

Get started with Axolotl in just a few steps! This quickstart guide will walk you through setting up and running a basic fine-tuning task.

Requirements: Nvidia GPU (Ampere architecture or newer for bf16 and Flash Attention) or AMD GPU, Python >=3.10 and PyTorch >=2.3.1.

-
pip3 install axolotl[flash-attn,deepspeed]
+
pip3 install --no-build-isolation axolotl[flash-attn,deepspeed]
 
 # download examples and optionally deepspeed configs to the local path
 axolotl fetch examples
@@ -383,7 +383,7 @@ pre > code.sourceCode > span > a:first-child::before { text-decoration: underlin
 
git clone https://github.com/axolotl-ai-cloud/axolotl.git
 cd axolotl
 pip3 install packaging ninja
-pip3 install -e '.[flash-attn,deepspeed]'

+pip3 install --no-build-isolation -e '.[flash-attn,deepspeed]'

Axolotl CLI Usage

@@ -691,7 +691,7 @@ Docker advanced

  1. Install python >=3.10

  2. Install pytorch stable https://pytorch.org/get-started/locally/

    3. -

  3. Install Axolotl along with python dependencies bash pip3 install packaging pip3 install -e '.[flash-attn,deepspeed]'

    4. +

  4. Install Axolotl along with python dependencies bash pip3 install packaging pip3 install --no-build-isolation -e '.[flash-attn,deepspeed]'

  5. (Optional) Login to Huggingface to use gated models/datasets. bash huggingface-cli login Get the token at huggingface.co/settings/tokens

@@ -758,7 +758,7 @@ Click to Expand

Mac

Use the below instead of the install method in QuickStart.

-
pip3 install -e '.'
+
pip3 install --no-build-isolation -e '.'

More info: mac.md

diff --git a/search.json b/search.json index 3fa4c5b62..8b601fbd3 100644 --- a/search.json +++ b/search.json @@ -14,7 +14,7 @@ "href": "index.html#quickstart", "title": "Axolotl", "section": "Quickstart ⚡", - "text": "Quickstart ⚡\nGet started with Axolotl in just a few steps! This quickstart guide will walk you through setting up and running a basic fine-tuning task.\nRequirements: Nvidia GPU (Ampere architecture or newer for bf16 and Flash Attention) or AMD GPU, Python >=3.10 and PyTorch >=2.3.1.\npip3 install axolotl[flash-attn,deepspeed]\n\n# download examples and optionally deepspeed configs to the local path\naxolotl fetch examples\naxolotl fetch deepspeed_configs # OPTIONAL\n\n# finetune using lora\naxolotl train examples/llama-3/lora-1b.yml\n\nEdge Builds 🏎️\nIf you’re looking for the latest features and updates between releases, you’ll need to install from source.\ngit clone https://github.com/axolotl-ai-cloud/axolotl.git\ncd axolotl\npip3 install packaging ninja\npip3 install -e '.[flash-attn,deepspeed]'\n\n\nAxolotl CLI Usage\nWe now support a new, more streamlined CLI using click.\n# preprocess datasets - optional but recommended\nCUDA_VISIBLE_DEVICES=\"0\" axolotl preprocess examples/llama-3/lora-1b.yml\n\n# finetune lora\naxolotl train examples/llama-3/lora-1b.yml\n\n# inference\naxolotl inference examples/llama-3/lora-1b.yml \\\n --lora-model-dir=\"./outputs/lora-out\"\n\n# gradio\naxolotl inference examples/llama-3/lora-1b.yml \\\n --lora-model-dir=\"./outputs/lora-out\" --gradio\n\n# remote yaml files - the yaml config can be hosted on a public URL\n# Note: the yaml config must directly link to the **raw** yaml\naxolotl train https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/examples/llama-3/lora-1b.yml\nWe’ve also added a new command for fetching examples and deepspeed_configs to your local machine. This will come in handy when installing axolotl from PyPI.\n# Fetch example YAML files (stores in \"examples/\" folder)\naxolotl fetch examples\n\n# Fetch deepspeed config files (stores in \"deepspeed_configs/\" folder)\naxolotl fetch deepspeed_configs\n\n# Optionally, specify a destination folder\naxolotl fetch examples --dest path/to/folder\n\n\nLegacy Usage\n\n\nClick to Expand\n\nWhile the Axolotl CLI is the preferred method for interacting with axolotl, we still support the legacy -m axolotl.cli.* usage.\n# preprocess datasets - optional but recommended\nCUDA_VISIBLE_DEVICES=\"0\" python -m axolotl.cli.preprocess examples/llama-3/lora-1b.yml\n\n# finetune lora\naccelerate launch -m axolotl.cli.train examples/llama-3/lora-1b.yml\n\n# inference\naccelerate launch -m axolotl.cli.inference examples/llama-3/lora-1b.yml \\\n --lora_model_dir=\"./outputs/lora-out\"\n\n# gradio\naccelerate launch -m axolotl.cli.inference examples/llama-3/lora-1b.yml \\\n --lora_model_dir=\"./outputs/lora-out\" --gradio\n\n# remote yaml files - the yaml config can be hosted on a public URL\n# Note: the yaml config must directly link to the **raw** yaml\naccelerate launch -m axolotl.cli.train https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/examples/llama-3/lora-1b.yml", + "text": "Quickstart ⚡\nGet started with Axolotl in just a few steps! This quickstart guide will walk you through setting up and running a basic fine-tuning task.\nRequirements: Nvidia GPU (Ampere architecture or newer for bf16 and Flash Attention) or AMD GPU, Python >=3.10 and PyTorch >=2.3.1.\npip3 install --no-build-isolation axolotl[flash-attn,deepspeed]\n\n# download examples and optionally deepspeed configs to the local path\naxolotl fetch examples\naxolotl fetch deepspeed_configs # OPTIONAL\n\n# finetune using lora\naxolotl train examples/llama-3/lora-1b.yml\n\nEdge Builds 🏎️\nIf you’re looking for the latest features and updates between releases, you’ll need to install from source.\ngit clone https://github.com/axolotl-ai-cloud/axolotl.git\ncd axolotl\npip3 install packaging ninja\npip3 install --no-build-isolation -e '.[flash-attn,deepspeed]'\n\n\nAxolotl CLI Usage\nWe now support a new, more streamlined CLI using click.\n# preprocess datasets - optional but recommended\nCUDA_VISIBLE_DEVICES=\"0\" axolotl preprocess examples/llama-3/lora-1b.yml\n\n# finetune lora\naxolotl train examples/llama-3/lora-1b.yml\n\n# inference\naxolotl inference examples/llama-3/lora-1b.yml \\\n --lora-model-dir=\"./outputs/lora-out\"\n\n# gradio\naxolotl inference examples/llama-3/lora-1b.yml \\\n --lora-model-dir=\"./outputs/lora-out\" --gradio\n\n# remote yaml files - the yaml config can be hosted on a public URL\n# Note: the yaml config must directly link to the **raw** yaml\naxolotl train https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/examples/llama-3/lora-1b.yml\nWe’ve also added a new command for fetching examples and deepspeed_configs to your local machine. This will come in handy when installing axolotl from PyPI.\n# Fetch example YAML files (stores in \"examples/\" folder)\naxolotl fetch examples\n\n# Fetch deepspeed config files (stores in \"deepspeed_configs/\" folder)\naxolotl fetch deepspeed_configs\n\n# Optionally, specify a destination folder\naxolotl fetch examples --dest path/to/folder\n\n\nLegacy Usage\n\n\nClick to Expand\n\nWhile the Axolotl CLI is the preferred method for interacting with axolotl, we still support the legacy -m axolotl.cli.* usage.\n# preprocess datasets - optional but recommended\nCUDA_VISIBLE_DEVICES=\"0\" python -m axolotl.cli.preprocess examples/llama-3/lora-1b.yml\n\n# finetune lora\naccelerate launch -m axolotl.cli.train examples/llama-3/lora-1b.yml\n\n# inference\naccelerate launch -m axolotl.cli.inference examples/llama-3/lora-1b.yml \\\n --lora_model_dir=\"./outputs/lora-out\"\n\n# gradio\naccelerate launch -m axolotl.cli.inference examples/llama-3/lora-1b.yml \\\n --lora_model_dir=\"./outputs/lora-out\" --gradio\n\n# remote yaml files - the yaml config can be hosted on a public URL\n# Note: the yaml config must directly link to the **raw** yaml\naccelerate launch -m axolotl.cli.train https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/examples/llama-3/lora-1b.yml", "crumbs": [ "Home" ] @@ -64,7 +64,7 @@ "href": "index.html#advanced-setup", "title": "Axolotl", "section": "Advanced Setup", - "text": "Advanced Setup\n\nEnvironment\n\nDocker\ndocker run --gpus '\"all\"' --rm -it axolotlai/axolotl:main-latest\nOr run on the current files for development:\ndocker compose up -d\n\n[!Tip] If you want to debug axolotl or prefer to use Docker as your development environment, see the debugging guide’s section on Docker.\n\n\n\nDocker advanced\n\nA more powerful Docker command to run would be this:\ndocker 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 ${HOME}/.cache/huggingface:/root/.cache/huggingface axolotlai/axolotl:main-latest\nIt additionally: * Prevents memory issues when running e.g. deepspeed (e.g. you could hit SIGBUS/signal 7 error) through --ipc and --ulimit args. * Persists the downloaded HF data (models etc.) and your modifications to axolotl code through --mount/-v args. * The --name argument simply makes it easier to refer to the container in vscode (Dev Containers: Attach to Running Container...) or in your terminal. * The --privileged flag gives all capabilities to the container. * The --shm-size 10g argument increases the shared memory size. Use this if you see exitcode: -7 errors using deepspeed.\nMore information on nvidia website\n\n\n\nConda/Pip venv\n\nInstall python >=3.10\nInstall pytorch stable https://pytorch.org/get-started/locally/\nInstall Axolotl along with python dependencies bash pip3 install packaging pip3 install -e '.[flash-attn,deepspeed]'\n(Optional) Login to Huggingface to use gated models/datasets. bash huggingface-cli login Get the token at huggingface.co/settings/tokens\n\n\n\nCloud GPU\nFor cloud GPU providers that support docker images, use axolotlai/axolotl-cloud:main-latest\n\non Latitude.sh use this direct link\non JarvisLabs.ai use this direct link\non RunPod use this direct link\n\n\n\nBare Metal Cloud GPU\n\nLambdaLabs\n\n\nClick to Expand\n\n\nInstall python\n\nsudo apt update\nsudo apt install -y python3.10\n\nsudo update-alternatives --install /usr/bin/python python /usr/bin/python3.10 1\nsudo update-alternatives --config python # pick 3.10 if given option\npython -V # should be 3.10\n\nInstall pip\n\nwget https://bootstrap.pypa.io/get-pip.py\npython get-pip.py\n\nInstall Pytorch https://pytorch.org/get-started/locally/\nFollow instructions on quickstart.\nRun\n\npip3 install protobuf==3.20.3\npip3 install -U --ignore-installed requests Pillow psutil scipy\n\nSet path\n\nexport LD_LIBRARY_PATH=/usr/lib/x86_64-linux-gnu:$LD_LIBRARY_PATH\n\n\n\nGCP\n\n\nClick to Expand\n\nUse a Deeplearning linux OS with cuda and pytorch installed. Then follow instructions on quickstart.\nMake sure to run the below to uninstall xla.\npip uninstall -y torch_xla[tpu]\n\n\n\n\nWindows\nPlease use WSL or Docker!\n\n\nMac\nUse the below instead of the install method in QuickStart.\npip3 install -e '.'\nMore info: mac.md\n\n\nGoogle Colab\nPlease use this example notebook.\n\n\nLaunching on public clouds via SkyPilot\nTo launch on GPU instances (both on-demand and spot instances) on 7+ clouds (GCP, AWS, Azure, OCI, and more), you can use SkyPilot:\npip install \"skypilot-nightly[gcp,aws,azure,oci,lambda,kubernetes,ibm,scp]\" # choose your clouds\nsky check\nGet the example YAMLs of using Axolotl to finetune mistralai/Mistral-7B-v0.1:\ngit clone https://github.com/skypilot-org/skypilot.git\ncd skypilot/llm/axolotl\nUse one command to launch:\n# On-demand\nHF_TOKEN=xx sky launch axolotl.yaml --env HF_TOKEN\n\n# Managed spot (auto-recovery on preemption)\nHF_TOKEN=xx BUCKET=<unique-name> sky spot launch axolotl-spot.yaml --env HF_TOKEN --env BUCKET\n\n\nLaunching on public clouds via dstack\nTo launch on GPU instance (both on-demand and spot instances) on public clouds (GCP, AWS, Azure, Lambda Labs, TensorDock, Vast.ai, and CUDO), you can use dstack.\nWrite a job description in YAML as below:\n# dstack.yaml\ntype: task\n\nimage: axolotlai/axolotl-cloud:main-latest\n\nenv:\n - HUGGING_FACE_HUB_TOKEN\n - WANDB_API_KEY\n\ncommands:\n - accelerate launch -m axolotl.cli.train config.yaml\n\nports:\n - 6006\n\nresources:\n gpu:\n memory: 24GB..\n count: 2\nthen, simply run the job with dstack run command. Append --spot option if you want spot instance. dstack run command will show you the instance with cheapest price across multi cloud services:\npip install dstack\nHUGGING_FACE_HUB_TOKEN=xxx WANDB_API_KEY=xxx dstack run . -f dstack.yaml # --spot\nFor further and fine-grained use cases, please refer to the official dstack documents and the detailed description of axolotl example on the official repository.\n\n\n\nDataset\nAxolotl supports a variety of dataset formats. It is recommended to use a JSONL. The schema of the JSONL depends upon the task and the prompt template you wish to use. Instead of a JSONL, you can also use a HuggingFace dataset with columns for each JSONL field.\nSee the documentation for more information on how to use different dataset formats.\n\n\nConfig\nSee examples for quick start. It is recommended to duplicate and modify to your needs. The most important options are:\n\nmodel\nbase_model: ./llama-7b-hf # local or huggingface repo\nNote: The code will load the right architecture.\ndataset\ndatasets:\n # huggingface repo\n - path: vicgalle/alpaca-gpt4\n type: alpaca\n\n # huggingface repo with specific configuration/subset\n - path: EleutherAI/pile\n name: enron_emails\n type: completion # format from earlier\n field: text # Optional[str] default: text, field to use for completion data\n\n # huggingface repo with multiple named configurations/subsets\n - path: bigcode/commitpackft\n name:\n - ruby\n - python\n - typescript\n type: ... # unimplemented custom format\n\n # chat_template https://axolotl-ai-cloud.github.io/axolotl/docs/dataset-formats/conversation.html#chat_template\n - path: ...\n type: chat_template\n chat_template: chatml # defaults to tokenizer's chat_template\n\n # local\n - path: data.jsonl # or json\n ds_type: json # see other options below\n type: alpaca\n\n # dataset with splits, but no train split\n - path: knowrohit07/know_sql\n type: context_qa.load_v2\n train_on_split: validation\n\n # loading from s3 or gcs\n # s3 creds will be loaded from the system default and gcs only supports public access\n - path: s3://path_to_ds # Accepts folder with arrow/parquet or file path like above. Supports s3, gcs.\n ...\n\n # Loading Data From a Public URL\n # - The file format is `json` (which includes `jsonl`) by default. For different formats, adjust the `ds_type` option accordingly.\n - path: https://some.url.com/yourdata.jsonl # The URL should be a direct link to the file you wish to load. URLs must use HTTPS protocol, not HTTP.\n ds_type: json # this is the default, see other options below.\nloading\nload_in_4bit: true\nload_in_8bit: true\n\nbf16: auto # require >=ampere, auto will detect if your GPU supports this and choose automatically.\nfp16: # leave empty to use fp16 when bf16 is 'auto'. set to false if you want to fallback to fp32\ntf32: true # require >=ampere\n\nbfloat16: true # require >=ampere, use instead of bf16 when you don't want AMP (automatic mixed precision)\nfloat16: true # use instead of fp16 when you don't want AMP\nNote: Repo does not do 4-bit quantization.\nlora\nadapter: lora # 'qlora' or leave blank for full finetune\nlora_r: 8\nlora_alpha: 16\nlora_dropout: 0.05\nlora_target_modules:\n - q_proj\n - v_proj\n\n\nAll Config Options\nSee these docs for all config options.\n\n\n\nTrain\nRun\naccelerate launch -m axolotl.cli.train your_config.yml\n\n[!TIP] You can also reference a config file that is hosted on a public URL, for example accelerate launch -m axolotl.cli.train https://yourdomain.com/your_config.yml\n\n\nPreprocess dataset\nYou can optionally pre-tokenize dataset with the following before finetuning. This is recommended for large datasets.\n\nSet dataset_prepared_path: to a local folder for saving and loading pre-tokenized dataset.\n(Optional): Set push_dataset_to_hub: hf_user/repo to push it to Huggingface.\n(Optional): Use --debug to see preprocessed examples.\n\npython -m axolotl.cli.preprocess your_config.yml\n\n\nMulti-GPU\nBelow are the options available in axolotl for training with multiple GPUs. Note that DeepSpeed is the recommended multi-GPU option currently because FSDP may experience loss instability.\n\nDeepSpeed\nDeepspeed is an optimization suite for multi-gpu systems allowing you to train much larger models than you might typically be able to fit into your GPU’s VRAM. More information about the various optimization types for deepspeed is available at https://huggingface.co/docs/accelerate/main/en/usage_guides/deepspeed#what-is-integrated\nWe provide several default deepspeed JSON configurations for ZeRO stage 1, 2, and 3.\ndeepspeed: deepspeed_configs/zero1.json\naccelerate launch -m axolotl.cli.train examples/llama-2/config.yml --deepspeed deepspeed_configs/zero1.json\n\n\nFSDP\n\nllama FSDP\n\nfsdp:\n - full_shard\n - auto_wrap\nfsdp_config:\n fsdp_offload_params: true\n fsdp_state_dict_type: FULL_STATE_DICT\n fsdp_transformer_layer_cls_to_wrap: LlamaDecoderLayer\n\n\nFSDP + QLoRA\nAxolotl supports training with FSDP and QLoRA, see these docs for more information.\n\n\nWeights & Biases Logging\nMake sure your WANDB_API_KEY environment variable is set (recommended) or you login to wandb with wandb login.\n\nwandb options\n\nwandb_mode:\nwandb_project:\nwandb_entity:\nwandb_watch:\nwandb_name:\nwandb_log_model:\n\n\nComet Logging\nMake sure your COMET_API_KEY environment variable is set (recommended) or you login to wandb with comet login.\n\nwandb options\n\nuse_comet:\ncomet_api_key:\ncomet_workspace:\ncomet_project_name:\ncomet_experiment_key:\ncomet_mode:\ncomet_online:\ncomet_experiment_config:\n\n\nSpecial Tokens\nIt is important to have special tokens like delimiters, end-of-sequence, beginning-of-sequence in your tokenizer’s vocabulary. This will help you avoid tokenization issues and help your model train better. You can do this in axolotl like this:\nspecial_tokens:\n bos_token: \"<s>\"\n eos_token: \"</s>\"\n unk_token: \"<unk>\"\ntokens: # these are delimiters\n - \"<|im_start|>\"\n - \"<|im_end|>\"\nWhen you include these tokens in your axolotl config, axolotl adds these tokens to the tokenizer’s vocabulary.\n\n\nLiger Kernel\nLiger Kernel: Efficient Triton Kernels for LLM Training\nhttps://github.com/linkedin/Liger-Kernel\nLiger (LinkedIn GPU Efficient Runtime) Kernel is a collection of Triton kernels designed specifically for LLM training. It can effectively increase multi-GPU training throughput by 20% and reduces memory usage by 60%. The Liger Kernel composes well and is compatible with both FSDP and Deepspeed.\nplugins:\n - axolotl.integrations.liger.LigerPlugin\nliger_rope: true\nliger_rms_norm: true\nliger_glu_activation: true\nliger_layer_norm: true\nliger_fused_linear_cross_entropy: true\n\n\n\n\nInference Playground\nAxolotl allows you to load your model in an interactive terminal playground for quick experimentation. The config file is the same config file used for training.\nPass the appropriate flag to the inference command, depending upon what kind of model was trained:\n\nPretrained LORA:\npython -m axolotl.cli.inference examples/your_config.yml --lora_model_dir=\"./lora-output-dir\"\nFull weights finetune:\npython -m axolotl.cli.inference examples/your_config.yml --base_model=\"./completed-model\"\nFull weights finetune w/ a prompt from a text file:\ncat /tmp/prompt.txt | python -m axolotl.cli.inference examples/your_config.yml \\\n --base_model=\"./completed-model\" --prompter=None --load_in_8bit=True\n– With gradio hosting\npython -m axolotl.cli.inference examples/your_config.yml --gradio\n\nPlease use --sample_packing False if you have it on and receive the error similar to below:\n\nRuntimeError: stack expects each tensor to be equal size, but got [1, 32, 1, 128] at entry 0 and [1, 32, 8, 128] at entry 1\n\n\n\nMerge LORA to base\nThe following command will merge your LORA adapater with your base model. You can optionally pass the argument --lora_model_dir to specify the directory where your LORA adapter was saved, otherwhise, this will be inferred from output_dir in your axolotl config file. The merged model is saved in the sub-directory {lora_model_dir}/merged.\npython3 -m axolotl.cli.merge_lora your_config.yml --lora_model_dir=\"./completed-model\"\nYou may need to use the gpu_memory_limit and/or lora_on_cpu config options to avoid running out of memory. If you still run out of CUDA memory, you can try to merge in system RAM with\nCUDA_VISIBLE_DEVICES=\"\" python3 -m axolotl.cli.merge_lora ...\nalthough this will be very slow, and using the config options above are recommended instead.", + "text": "Advanced Setup\n\nEnvironment\n\nDocker\ndocker run --gpus '\"all\"' --rm -it axolotlai/axolotl:main-latest\nOr run on the current files for development:\ndocker compose up -d\n\n[!Tip] If you want to debug axolotl or prefer to use Docker as your development environment, see the debugging guide’s section on Docker.\n\n\n\nDocker advanced\n\nA more powerful Docker command to run would be this:\ndocker 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 ${HOME}/.cache/huggingface:/root/.cache/huggingface axolotlai/axolotl:main-latest\nIt additionally: * Prevents memory issues when running e.g. deepspeed (e.g. you could hit SIGBUS/signal 7 error) through --ipc and --ulimit args. * Persists the downloaded HF data (models etc.) and your modifications to axolotl code through --mount/-v args. * The --name argument simply makes it easier to refer to the container in vscode (Dev Containers: Attach to Running Container...) or in your terminal. * The --privileged flag gives all capabilities to the container. * The --shm-size 10g argument increases the shared memory size. Use this if you see exitcode: -7 errors using deepspeed.\nMore information on nvidia website\n\n\n\nConda/Pip venv\n\nInstall python >=3.10\nInstall pytorch stable https://pytorch.org/get-started/locally/\nInstall Axolotl along with python dependencies bash pip3 install packaging pip3 install --no-build-isolation -e '.[flash-attn,deepspeed]'\n(Optional) Login to Huggingface to use gated models/datasets. bash huggingface-cli login Get the token at huggingface.co/settings/tokens\n\n\n\nCloud GPU\nFor cloud GPU providers that support docker images, use axolotlai/axolotl-cloud:main-latest\n\non Latitude.sh use this direct link\non JarvisLabs.ai use this direct link\non RunPod use this direct link\n\n\n\nBare Metal Cloud GPU\n\nLambdaLabs\n\n\nClick to Expand\n\n\nInstall python\n\nsudo apt update\nsudo apt install -y python3.10\n\nsudo update-alternatives --install /usr/bin/python python /usr/bin/python3.10 1\nsudo update-alternatives --config python # pick 3.10 if given option\npython -V # should be 3.10\n\nInstall pip\n\nwget https://bootstrap.pypa.io/get-pip.py\npython get-pip.py\n\nInstall Pytorch https://pytorch.org/get-started/locally/\nFollow instructions on quickstart.\nRun\n\npip3 install protobuf==3.20.3\npip3 install -U --ignore-installed requests Pillow psutil scipy\n\nSet path\n\nexport LD_LIBRARY_PATH=/usr/lib/x86_64-linux-gnu:$LD_LIBRARY_PATH\n\n\n\nGCP\n\n\nClick to Expand\n\nUse a Deeplearning linux OS with cuda and pytorch installed. Then follow instructions on quickstart.\nMake sure to run the below to uninstall xla.\npip uninstall -y torch_xla[tpu]\n\n\n\n\nWindows\nPlease use WSL or Docker!\n\n\nMac\nUse the below instead of the install method in QuickStart.\npip3 install --no-build-isolation -e '.'\nMore info: mac.md\n\n\nGoogle Colab\nPlease use this example notebook.\n\n\nLaunching on public clouds via SkyPilot\nTo launch on GPU instances (both on-demand and spot instances) on 7+ clouds (GCP, AWS, Azure, OCI, and more), you can use SkyPilot:\npip install \"skypilot-nightly[gcp,aws,azure,oci,lambda,kubernetes,ibm,scp]\" # choose your clouds\nsky check\nGet the example YAMLs of using Axolotl to finetune mistralai/Mistral-7B-v0.1:\ngit clone https://github.com/skypilot-org/skypilot.git\ncd skypilot/llm/axolotl\nUse one command to launch:\n# On-demand\nHF_TOKEN=xx sky launch axolotl.yaml --env HF_TOKEN\n\n# Managed spot (auto-recovery on preemption)\nHF_TOKEN=xx BUCKET=<unique-name> sky spot launch axolotl-spot.yaml --env HF_TOKEN --env BUCKET\n\n\nLaunching on public clouds via dstack\nTo launch on GPU instance (both on-demand and spot instances) on public clouds (GCP, AWS, Azure, Lambda Labs, TensorDock, Vast.ai, and CUDO), you can use dstack.\nWrite a job description in YAML as below:\n# dstack.yaml\ntype: task\n\nimage: axolotlai/axolotl-cloud:main-latest\n\nenv:\n - HUGGING_FACE_HUB_TOKEN\n - WANDB_API_KEY\n\ncommands:\n - accelerate launch -m axolotl.cli.train config.yaml\n\nports:\n - 6006\n\nresources:\n gpu:\n memory: 24GB..\n count: 2\nthen, simply run the job with dstack run command. Append --spot option if you want spot instance. dstack run command will show you the instance with cheapest price across multi cloud services:\npip install dstack\nHUGGING_FACE_HUB_TOKEN=xxx WANDB_API_KEY=xxx dstack run . -f dstack.yaml # --spot\nFor further and fine-grained use cases, please refer to the official dstack documents and the detailed description of axolotl example on the official repository.\n\n\n\nDataset\nAxolotl supports a variety of dataset formats. It is recommended to use a JSONL. The schema of the JSONL depends upon the task and the prompt template you wish to use. Instead of a JSONL, you can also use a HuggingFace dataset with columns for each JSONL field.\nSee the documentation for more information on how to use different dataset formats.\n\n\nConfig\nSee examples for quick start. It is recommended to duplicate and modify to your needs. The most important options are:\n\nmodel\nbase_model: ./llama-7b-hf # local or huggingface repo\nNote: The code will load the right architecture.\ndataset\ndatasets:\n # huggingface repo\n - path: vicgalle/alpaca-gpt4\n type: alpaca\n\n # huggingface repo with specific configuration/subset\n - path: EleutherAI/pile\n name: enron_emails\n type: completion # format from earlier\n field: text # Optional[str] default: text, field to use for completion data\n\n # huggingface repo with multiple named configurations/subsets\n - path: bigcode/commitpackft\n name:\n - ruby\n - python\n - typescript\n type: ... # unimplemented custom format\n\n # chat_template https://axolotl-ai-cloud.github.io/axolotl/docs/dataset-formats/conversation.html#chat_template\n - path: ...\n type: chat_template\n chat_template: chatml # defaults to tokenizer's chat_template\n\n # local\n - path: data.jsonl # or json\n ds_type: json # see other options below\n type: alpaca\n\n # dataset with splits, but no train split\n - path: knowrohit07/know_sql\n type: context_qa.load_v2\n train_on_split: validation\n\n # loading from s3 or gcs\n # s3 creds will be loaded from the system default and gcs only supports public access\n - path: s3://path_to_ds # Accepts folder with arrow/parquet or file path like above. Supports s3, gcs.\n ...\n\n # Loading Data From a Public URL\n # - The file format is `json` (which includes `jsonl`) by default. For different formats, adjust the `ds_type` option accordingly.\n - path: https://some.url.com/yourdata.jsonl # The URL should be a direct link to the file you wish to load. URLs must use HTTPS protocol, not HTTP.\n ds_type: json # this is the default, see other options below.\nloading\nload_in_4bit: true\nload_in_8bit: true\n\nbf16: auto # require >=ampere, auto will detect if your GPU supports this and choose automatically.\nfp16: # leave empty to use fp16 when bf16 is 'auto'. set to false if you want to fallback to fp32\ntf32: true # require >=ampere\n\nbfloat16: true # require >=ampere, use instead of bf16 when you don't want AMP (automatic mixed precision)\nfloat16: true # use instead of fp16 when you don't want AMP\nNote: Repo does not do 4-bit quantization.\nlora\nadapter: lora # 'qlora' or leave blank for full finetune\nlora_r: 8\nlora_alpha: 16\nlora_dropout: 0.05\nlora_target_modules:\n - q_proj\n - v_proj\n\n\nAll Config Options\nSee these docs for all config options.\n\n\n\nTrain\nRun\naccelerate launch -m axolotl.cli.train your_config.yml\n\n[!TIP] You can also reference a config file that is hosted on a public URL, for example accelerate launch -m axolotl.cli.train https://yourdomain.com/your_config.yml\n\n\nPreprocess dataset\nYou can optionally pre-tokenize dataset with the following before finetuning. This is recommended for large datasets.\n\nSet dataset_prepared_path: to a local folder for saving and loading pre-tokenized dataset.\n(Optional): Set push_dataset_to_hub: hf_user/repo to push it to Huggingface.\n(Optional): Use --debug to see preprocessed examples.\n\npython -m axolotl.cli.preprocess your_config.yml\n\n\nMulti-GPU\nBelow are the options available in axolotl for training with multiple GPUs. Note that DeepSpeed is the recommended multi-GPU option currently because FSDP may experience loss instability.\n\nDeepSpeed\nDeepspeed is an optimization suite for multi-gpu systems allowing you to train much larger models than you might typically be able to fit into your GPU’s VRAM. More information about the various optimization types for deepspeed is available at https://huggingface.co/docs/accelerate/main/en/usage_guides/deepspeed#what-is-integrated\nWe provide several default deepspeed JSON configurations for ZeRO stage 1, 2, and 3.\ndeepspeed: deepspeed_configs/zero1.json\naccelerate launch -m axolotl.cli.train examples/llama-2/config.yml --deepspeed deepspeed_configs/zero1.json\n\n\nFSDP\n\nllama FSDP\n\nfsdp:\n - full_shard\n - auto_wrap\nfsdp_config:\n fsdp_offload_params: true\n fsdp_state_dict_type: FULL_STATE_DICT\n fsdp_transformer_layer_cls_to_wrap: LlamaDecoderLayer\n\n\nFSDP + QLoRA\nAxolotl supports training with FSDP and QLoRA, see these docs for more information.\n\n\nWeights & Biases Logging\nMake sure your WANDB_API_KEY environment variable is set (recommended) or you login to wandb with wandb login.\n\nwandb options\n\nwandb_mode:\nwandb_project:\nwandb_entity:\nwandb_watch:\nwandb_name:\nwandb_log_model:\n\n\nComet Logging\nMake sure your COMET_API_KEY environment variable is set (recommended) or you login to wandb with comet login.\n\nwandb options\n\nuse_comet:\ncomet_api_key:\ncomet_workspace:\ncomet_project_name:\ncomet_experiment_key:\ncomet_mode:\ncomet_online:\ncomet_experiment_config:\n\n\nSpecial Tokens\nIt is important to have special tokens like delimiters, end-of-sequence, beginning-of-sequence in your tokenizer’s vocabulary. This will help you avoid tokenization issues and help your model train better. You can do this in axolotl like this:\nspecial_tokens:\n bos_token: \"<s>\"\n eos_token: \"</s>\"\n unk_token: \"<unk>\"\ntokens: # these are delimiters\n - \"<|im_start|>\"\n - \"<|im_end|>\"\nWhen you include these tokens in your axolotl config, axolotl adds these tokens to the tokenizer’s vocabulary.\n\n\nLiger Kernel\nLiger Kernel: Efficient Triton Kernels for LLM Training\nhttps://github.com/linkedin/Liger-Kernel\nLiger (LinkedIn GPU Efficient Runtime) Kernel is a collection of Triton kernels designed specifically for LLM training. It can effectively increase multi-GPU training throughput by 20% and reduces memory usage by 60%. The Liger Kernel composes well and is compatible with both FSDP and Deepspeed.\nplugins:\n - axolotl.integrations.liger.LigerPlugin\nliger_rope: true\nliger_rms_norm: true\nliger_glu_activation: true\nliger_layer_norm: true\nliger_fused_linear_cross_entropy: true\n\n\n\n\nInference Playground\nAxolotl allows you to load your model in an interactive terminal playground for quick experimentation. The config file is the same config file used for training.\nPass the appropriate flag to the inference command, depending upon what kind of model was trained:\n\nPretrained LORA:\npython -m axolotl.cli.inference examples/your_config.yml --lora_model_dir=\"./lora-output-dir\"\nFull weights finetune:\npython -m axolotl.cli.inference examples/your_config.yml --base_model=\"./completed-model\"\nFull weights finetune w/ a prompt from a text file:\ncat /tmp/prompt.txt | python -m axolotl.cli.inference examples/your_config.yml \\\n --base_model=\"./completed-model\" --prompter=None --load_in_8bit=True\n– With gradio hosting\npython -m axolotl.cli.inference examples/your_config.yml --gradio\n\nPlease use --sample_packing False if you have it on and receive the error similar to below:\n\nRuntimeError: stack expects each tensor to be equal size, but got [1, 32, 1, 128] at entry 0 and [1, 32, 8, 128] at entry 1\n\n\n\nMerge LORA to base\nThe following command will merge your LORA adapater with your base model. You can optionally pass the argument --lora_model_dir to specify the directory where your LORA adapter was saved, otherwhise, this will be inferred from output_dir in your axolotl config file. The merged model is saved in the sub-directory {lora_model_dir}/merged.\npython3 -m axolotl.cli.merge_lora your_config.yml --lora_model_dir=\"./completed-model\"\nYou may need to use the gpu_memory_limit and/or lora_on_cpu config options to avoid running out of memory. If you still run out of CUDA memory, you can try to merge in system RAM with\nCUDA_VISIBLE_DEVICES=\"\" python3 -m axolotl.cli.merge_lora ...\nalthough this will be very slow, and using the config options above are recommended instead.", "crumbs": [ "Home" ] @@ -354,7 +354,7 @@ "href": "docs/debugging.html#debugging-with-vscode", "title": "Debugging", "section": "Debugging with VSCode", - "text": "Debugging with VSCode\n\nBackground\nThe below example shows how to configure VSCode to debug data preprocessing of the chat_template format. This is the format used when you have the following in your axolotl config:\ndatasets:\n - path: <path to your chat_template formatted dataset> # example on HF Hub: fozziethebeat/alpaca_messages_2k_test\n type: chat_template\n\n[!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.\n\n\n[!Tip] If you prefer to watch a video, rather than read, you can skip to the video tutorial below (but doing both is recommended).\n\n\n\nSetup\nMake 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:\npip3 install packaging\npip3 install -e '.[flash-attn,deepspeed]'\n\nRemote Hosts\nIf 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.\n\n\n\nConfiguration\nThe easiest way to get started is to modify the .vscode/launch.json file in this project. This is just an example configuration, so you may need to modify or copy it to suit your needs.\nFor example, to mimic the command cd devtools && CUDA_VISIBLE_DEVICES=0 accelerate launch -m axolotl.cli.train dev_chat_template.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.\n// .vscode/launch.json\n{\n \"version\": \"0.2.0\",\n \"configurations\": [\n {\n \"name\": \"Debug axolotl prompt - chat_template\",\n \"type\": \"python\",\n \"module\": \"accelerate.commands.launch\",\n \"request\": \"launch\",\n \"args\": [\n \"-m\", \"axolotl.cli.train\", \"dev_chat_template.yml\",\n // The flags below simplify debugging by overriding the axolotl config\n // with the debugging tips above. Modify as needed.\n \"--dataset_processes=1\", // limits data preprocessing to one process\n \"--max_steps=1\", // limits training to just one step\n \"--batch_size=1\", // minimizes batch size\n \"--micro_batch_size=1\", // minimizes batch size\n \"--val_set_size=0\", // disables validation\n \"--sample_packing=False\", // disables sample packing which is necessary for small datasets\n \"--eval_sample_packing=False\",// disables sample packing on eval set\n \"--dataset_prepared_path=temp_debug/axolotl_outputs/data\", // send data outputs to a temp folder\n \"--output_dir=temp_debug/axolotl_outputs/model\" // send model outputs to a temp folder\n ],\n \"console\": \"integratedTerminal\", // show output in the integrated terminal\n \"cwd\": \"${workspaceFolder}/devtools\", // set working directory to devtools from the root of the project\n \"justMyCode\": true, // step through only axolotl code\n \"env\": {\"CUDA_VISIBLE_DEVICES\": \"0\", // Since we aren't doing distributed training, we need to limit to one GPU\n \"HF_HOME\": \"${workspaceFolder}/devtools/temp_debug/.hf-cache\"}, // send HF cache to a temp folder\n \"preLaunchTask\": \"cleanup-for-dataprep\", // delete temp folders (see below)\n }\n ]\n}\nAdditional notes about this configuration:\n\nThe 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.\nThe 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:\n\n./devtools/temp_debug/axolotl_outputs\n./devtools/temp_debug/.hf-cache/datasets\n\n\n\n[!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.\n\nBelow 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.\n// .vscode/tasks.json\n// this file is used by launch.json\n{\n \"version\": \"2.0.0\",\n \"tasks\": [\n // this task changes into the devtools directory and deletes the temp_debug/axolotl_outputs folder\n {\n \"label\": \"delete-outputs\",\n \"type\": \"shell\",\n \"command\": \"rm -rf temp_debug/axolotl_outputs\",\n \"options\":{ \"cwd\": \"${workspaceFolder}/devtools\"},\n \"problemMatcher\": []\n },\n // this task changes into the devtools directory and deletes the `temp_debug/.hf-cache/datasets` folder\n {\n \"label\": \"delete-temp-hf-dataset-cache\",\n \"type\": \"shell\",\n \"command\": \"rm -rf temp_debug/.hf-cache/datasets\",\n \"options\":{ \"cwd\": \"${workspaceFolder}/devtools\"},\n \"problemMatcher\": []\n },\n // this task combines the two tasks above\n {\n \"label\": \"cleanup-for-dataprep\",\n \"dependsOn\": [\"delete-outputs\", \"delete-temp-hf-dataset-cache\"],\n }\n ]\n}\n\n\nCustomizing your debugger\nYour 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.\n\n\nVideo Tutorial\nThe following video tutorial walks through the above configuration and demonstrates how to debug with VSCode, (click the image below to watch):\n\n\n\nHamel Husain’s tutorial: Debugging Axolotl w/VSCode", + "text": "Debugging with VSCode\n\nBackground\nThe below example shows how to configure VSCode to debug data preprocessing of the chat_template format. This is the format used when you have the following in your axolotl config:\ndatasets:\n - path: <path to your chat_template formatted dataset> # example on HF Hub: fozziethebeat/alpaca_messages_2k_test\n type: chat_template\n\n[!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.\n\n\n[!Tip] If you prefer to watch a video, rather than read, you can skip to the video tutorial below (but doing both is recommended).\n\n\n\nSetup\nMake 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:\npip3 install packaging\npip3 install --no-build-isolation -e '.[flash-attn,deepspeed]'\n\nRemote Hosts\nIf 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.\n\n\n\nConfiguration\nThe easiest way to get started is to modify the .vscode/launch.json file in this project. This is just an example configuration, so you may need to modify or copy it to suit your needs.\nFor example, to mimic the command cd devtools && CUDA_VISIBLE_DEVICES=0 accelerate launch -m axolotl.cli.train dev_chat_template.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.\n// .vscode/launch.json\n{\n \"version\": \"0.2.0\",\n \"configurations\": [\n {\n \"name\": \"Debug axolotl prompt - chat_template\",\n \"type\": \"python\",\n \"module\": \"accelerate.commands.launch\",\n \"request\": \"launch\",\n \"args\": [\n \"-m\", \"axolotl.cli.train\", \"dev_chat_template.yml\",\n // The flags below simplify debugging by overriding the axolotl config\n // with the debugging tips above. Modify as needed.\n \"--dataset_processes=1\", // limits data preprocessing to one process\n \"--max_steps=1\", // limits training to just one step\n \"--batch_size=1\", // minimizes batch size\n \"--micro_batch_size=1\", // minimizes batch size\n \"--val_set_size=0\", // disables validation\n \"--sample_packing=False\", // disables sample packing which is necessary for small datasets\n \"--eval_sample_packing=False\",// disables sample packing on eval set\n \"--dataset_prepared_path=temp_debug/axolotl_outputs/data\", // send data outputs to a temp folder\n \"--output_dir=temp_debug/axolotl_outputs/model\" // send model outputs to a temp folder\n ],\n \"console\": \"integratedTerminal\", // show output in the integrated terminal\n \"cwd\": \"${workspaceFolder}/devtools\", // set working directory to devtools from the root of the project\n \"justMyCode\": true, // step through only axolotl code\n \"env\": {\"CUDA_VISIBLE_DEVICES\": \"0\", // Since we aren't doing distributed training, we need to limit to one GPU\n \"HF_HOME\": \"${workspaceFolder}/devtools/temp_debug/.hf-cache\"}, // send HF cache to a temp folder\n \"preLaunchTask\": \"cleanup-for-dataprep\", // delete temp folders (see below)\n }\n ]\n}\nAdditional notes about this configuration:\n\nThe 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.\nThe 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:\n\n./devtools/temp_debug/axolotl_outputs\n./devtools/temp_debug/.hf-cache/datasets\n\n\n\n[!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.\n\nBelow 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.\n// .vscode/tasks.json\n// this file is used by launch.json\n{\n \"version\": \"2.0.0\",\n \"tasks\": [\n // this task changes into the devtools directory and deletes the temp_debug/axolotl_outputs folder\n {\n \"label\": \"delete-outputs\",\n \"type\": \"shell\",\n \"command\": \"rm -rf temp_debug/axolotl_outputs\",\n \"options\":{ \"cwd\": \"${workspaceFolder}/devtools\"},\n \"problemMatcher\": []\n },\n // this task changes into the devtools directory and deletes the `temp_debug/.hf-cache/datasets` folder\n {\n \"label\": \"delete-temp-hf-dataset-cache\",\n \"type\": \"shell\",\n \"command\": \"rm -rf temp_debug/.hf-cache/datasets\",\n \"options\":{ \"cwd\": \"${workspaceFolder}/devtools\"},\n \"problemMatcher\": []\n },\n // this task combines the two tasks above\n {\n \"label\": \"cleanup-for-dataprep\",\n \"dependsOn\": [\"delete-outputs\", \"delete-temp-hf-dataset-cache\"],\n }\n ]\n}\n\n\nCustomizing your debugger\nYour 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.\n\n\nVideo Tutorial\nThe following video tutorial walks through the above configuration and demonstrates how to debug with VSCode, (click the image below to watch):\n\n\n\nHamel Husain’s tutorial: Debugging Axolotl w/VSCode", "crumbs": [ "How-To Guides", "Debugging" @@ -365,7 +365,7 @@ "href": "docs/debugging.html#debugging-with-docker", "title": "Debugging", "section": "Debugging With Docker", - "text": "Debugging With Docker\nUsing 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.\n\nSetup\nOn 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:\ngit clone https://github.com/axolotl-ai-cloud/axolotl\ncd axolotl\n\n[!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.\n\nNext, run the desired docker image and mount the current directory. Below is a docker command you can run to do this:2\ndocker 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 axolotlai/axolotl:main-py3.10-cu118-2.0.1\n\n[!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.\n\nYou will now be in the container. Next, perform an editable install of Axolotl:\npip3 install packaging\npip3 install -e '.[flash-attn,deepspeed]'\n\n\nAttach To Container\nNext, 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.\nNext, 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.\nNow you are ready to debug as described above (see Debugging with VSCode).\n\n\nVideo - Attaching To Docker On Remote Host\nHere is a short video that demonstrates how to attach to a Docker container on a remote host:\n\n\n\nHamel Husain’s tutorial: Debugging Axolotl Part 2: Attaching to Docker on a Remote Host", + "text": "Debugging With Docker\nUsing 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.\n\nSetup\nOn 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:\ngit clone https://github.com/axolotl-ai-cloud/axolotl\ncd axolotl\n\n[!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.\n\nNext, run the desired docker image and mount the current directory. Below is a docker command you can run to do this:2\ndocker 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 ${HOME}/.cache/huggingface:/root/.cache/huggingface axolotlai/axolotl:main-py3.10-cu118-2.0.1\n\n[!Tip] To understand which containers are available, see the Docker section of the README and the DockerHub repo. For details of how the Docker containers are built, see axolotl’s Docker CI builds.\n\nYou will now be in the container. Next, perform an editable install of Axolotl:\npip3 install packaging\npip3 install --no-build-isolation -e '.[flash-attn,deepspeed]'\n\n\nAttach To Container\nNext, if you are using a remote host, Remote into this host with VSCode. If you are using a local host, you can skip this step.\nNext, select Dev Containers: Attach to Running Container... using the command palette (CMD + SHIFT + P) in VSCode. You will be prompted to select a container to attach to. Select the container you just created. You will now be in the container with a working directory that is at the root of the project. Any changes you make to the code will be reflected both in the container and on the host.\nNow you are ready to debug as described above (see Debugging with VSCode).\n\n\nVideo - Attaching To Docker On Remote Host\nHere is a short video that demonstrates how to attach to a Docker container on a remote host:\n\n\n\nHamel Husain’s tutorial: Debugging Axolotl Part 2: Attaching to Docker on a Remote Host", "crumbs": [ "How-To Guides", "Debugging" @@ -785,7 +785,7 @@ "href": "docs/amd_hpc.html#setup", "title": "Training with AMD GPUs on HPC Systems", "section": "Setup", - "text": "Setup\n\n1. Install Python\nWe recommend using Miniforge, a minimal conda-based Python distribution:\ncurl -L -O \"https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-$(uname)-$(uname -m).sh\"\nbash Miniforge3-$(uname)-$(uname -m).sh\n\n\n2. Configure Python Environment\nAdd Python to your PATH and ensure it’s available at login:\necho 'export PATH=~/miniforge3/bin:$PATH' >> ~/.bashrc\necho 'if [ -f ~/.bashrc ]; then . ~/.bashrc; fi' >> ~/.bash_profile\n\n\n3. Load AMD GPU Software\nLoad the ROCm module:\nmodule load rocm/5.7.1\nNote: The specific module name and version may vary depending on your HPC system. Consult your system documentation for the correct module name.\n\n\n4. Install PyTorch\nInstall PyTorch with ROCm support:\npip install -U torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm5.7 --force-reinstall\n\n\n5. Install Flash Attention\nClone and install the Flash Attention repository:\ngit clone --recursive https://github.com/ROCmSoftwarePlatform/flash-attention.git\nexport GPU_ARCHS=\"gfx90a\"\ncd flash-attention\nexport PYTHON_SITE_PACKAGES=$(python -c 'import site; print(site.getsitepackages()[0])')\npatch \"${PYTHON_SITE_PACKAGES}/torch/utils/hipify/hipify_python.py\" hipify_patch.patch\npip install .\n\n\n6. Install Axolotl\nClone and install Axolotl:\ngit clone https://github.com/axolotl-ai-cloud/axolotl\ncd axolotl\npip install packaging ninja\npip install -e .\n\n\n7. Apply xformers Workaround\nxformers appears to be incompatible with ROCm. Apply the following workarounds: - Edit $HOME/packages/axolotl/src/axolotl/monkeypatch/llama_attn_hijack_flash.py modifying the code to always return False for SwiGLU availability from xformers. - Edit $HOME/miniforge3/lib/python3.10/site-packages/xformers/ops/swiglu_op.py replacing the “SwiGLU” function with a pass statement.\n\n\n8. Prepare Job Submission Script\nCreate a script for job submission using your HPC’s particular software (e.g. Slurm, PBS). Include necessary environment setup and the command to run Axolotl training. If the compute node(s) do(es) not have internet access, it is recommended to include\nexport TRANSFORMERS_OFFLINE=1\nexport HF_DATASETS_OFFLINE=1\n\n\n9. Download Base Model\nDownload a base model using the Hugging Face CLI:\nhuggingface-cli download meta-llama/Meta-Llama-3.1-8B --local-dir ~/hfdata/llama3.1-8B\n\n\n10. Create Axolotl Configuration\nCreate an Axolotl configuration file (YAML format) tailored to your specific training requirements and dataset. Use FSDP for multi-node training.\nNote: Deepspeed did not work at the time of testing. However, if anyone managed to get it working, please let us know.\n\n\n11. Preprocess Data\nRun preprocessing on the login node:\nCUDA_VISIBLE_DEVICES=\"\" python -m axolotl.cli.preprocess /path/to/your/config.yaml\n\n\n12. Train\nYou are now ready to submit your previously prepared job script. 🚂", + "text": "Setup\n\n1. Install Python\nWe recommend using Miniforge, a minimal conda-based Python distribution:\ncurl -L -O \"https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-$(uname)-$(uname -m).sh\"\nbash Miniforge3-$(uname)-$(uname -m).sh\n\n\n2. Configure Python Environment\nAdd Python to your PATH and ensure it’s available at login:\necho 'export PATH=~/miniforge3/bin:$PATH' >> ~/.bashrc\necho 'if [ -f ~/.bashrc ]; then . ~/.bashrc; fi' >> ~/.bash_profile\n\n\n3. Load AMD GPU Software\nLoad the ROCm module:\nmodule load rocm/5.7.1\nNote: The specific module name and version may vary depending on your HPC system. Consult your system documentation for the correct module name.\n\n\n4. Install PyTorch\nInstall PyTorch with ROCm support:\npip install -U torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm5.7 --force-reinstall\n\n\n5. Install Flash Attention\nClone and install the Flash Attention repository:\ngit clone --recursive https://github.com/ROCmSoftwarePlatform/flash-attention.git\nexport GPU_ARCHS=\"gfx90a\"\ncd flash-attention\nexport PYTHON_SITE_PACKAGES=$(python -c 'import site; print(site.getsitepackages()[0])')\npatch \"${PYTHON_SITE_PACKAGES}/torch/utils/hipify/hipify_python.py\" hipify_patch.patch\npip install --no-build-isolation .\n\n\n6. Install Axolotl\nClone and install Axolotl:\ngit clone https://github.com/axolotl-ai-cloud/axolotl\ncd axolotl\npip install packaging ninja\npip install --no-build-isolation -e .\n\n\n7. Apply xformers Workaround\nxformers appears to be incompatible with ROCm. Apply the following workarounds: - Edit $HOME/packages/axolotl/src/axolotl/monkeypatch/llama_attn_hijack_flash.py modifying the code to always return False for SwiGLU availability from xformers. - Edit $HOME/miniforge3/lib/python3.10/site-packages/xformers/ops/swiglu_op.py replacing the “SwiGLU” function with a pass statement.\n\n\n8. Prepare Job Submission Script\nCreate a script for job submission using your HPC’s particular software (e.g. Slurm, PBS). Include necessary environment setup and the command to run Axolotl training. If the compute node(s) do(es) not have internet access, it is recommended to include\nexport TRANSFORMERS_OFFLINE=1\nexport HF_DATASETS_OFFLINE=1\n\n\n9. Download Base Model\nDownload a base model using the Hugging Face CLI:\nhuggingface-cli download meta-llama/Meta-Llama-3.1-8B --local-dir ~/hfdata/llama3.1-8B\n\n\n10. Create Axolotl Configuration\nCreate an Axolotl configuration file (YAML format) tailored to your specific training requirements and dataset. Use FSDP for multi-node training.\nNote: Deepspeed did not work at the time of testing. However, if anyone managed to get it working, please let us know.\n\n\n11. Preprocess Data\nRun preprocessing on the login node:\nCUDA_VISIBLE_DEVICES=\"\" python -m axolotl.cli.preprocess /path/to/your/config.yaml\n\n\n12. Train\nYou are now ready to submit your previously prepared job script. 🚂", "crumbs": [ "How-To Guides", "Training with AMD GPUs on HPC Systems" @@ -796,7 +796,7 @@ "href": "examples/colab-notebooks/colab-axolotl-example.html", "title": "Setting up", "section": "", - "text": "import torch\n# Check so there is a gpu available, a T4(free tier) is enough to run this notebook\nassert (torch.cuda.is_available()==True)\n!pip install axolotl[deepspeed]" + "text": "import torch\n# Check so there is a gpu available, a T4(free tier) is enough to run this notebook\nassert (torch.cuda.is_available()==True)\n!pip install --no-build-isolation axolotl[deepspeed]" }, { "objectID": "examples/colab-notebooks/colab-axolotl-example.html#hugging-face-login-optional", diff --git a/sitemap.xml b/sitemap.xml index 62d98d583..ea8f5d19f 100644 --- a/sitemap.xml +++ b/sitemap.xml @@ -2,114 +2,114 @@ https://axolotl-ai-cloud.github.io/axolotl/index.html - 2024-12-09T19:20:28.871Z + 2024-12-10T21:25:37.159Z https://axolotl-ai-cloud.github.io/axolotl/src/axolotl/integrations/LICENSE.html - 2024-12-09T19:20:28.875Z + 2024-12-10T21:25:37.159Z https://axolotl-ai-cloud.github.io/axolotl/docs/nccl.html - 2024-12-09T19:20:28.859Z + 2024-12-10T21:25:37.147Z https://axolotl-ai-cloud.github.io/axolotl/docs/input_output.html - 2024-12-09T19:20:28.859Z + 2024-12-10T21:25:37.147Z https://axolotl-ai-cloud.github.io/axolotl/docs/dataset_preprocessing.html - 2024-12-09T19:20:28.859Z + 2024-12-10T21:25:37.143Z https://axolotl-ai-cloud.github.io/axolotl/docs/torchao.html - 2024-12-09T19:20:28.859Z + 2024-12-10T21:25:37.147Z https://axolotl-ai-cloud.github.io/axolotl/docs/rlhf.html - 2024-12-09T19:20:28.859Z + 2024-12-10T21:25:37.147Z https://axolotl-ai-cloud.github.io/axolotl/docs/config.html - 2024-12-09T19:20:28.859Z + 2024-12-10T21:25:37.143Z https://axolotl-ai-cloud.github.io/axolotl/docs/dataset-formats/template_free.html - 2024-12-09T19:20:28.859Z + 2024-12-10T21:25:37.143Z https://axolotl-ai-cloud.github.io/axolotl/docs/dataset-formats/conversation.html - 2024-12-09T19:20:28.859Z + 2024-12-10T21:25:37.143Z https://axolotl-ai-cloud.github.io/axolotl/docs/dataset-formats/tokenized.html - 2024-12-09T19:20:28.859Z + 2024-12-10T21:25:37.143Z https://axolotl-ai-cloud.github.io/axolotl/docs/mac.html - 2024-12-09T19:20:28.859Z + 2024-12-10T21:25:37.147Z https://axolotl-ai-cloud.github.io/axolotl/docs/multi-node.html - 2024-12-09T19:20:28.859Z + 2024-12-10T21:25:37.147Z https://axolotl-ai-cloud.github.io/axolotl/FAQS.html - 2024-12-09T19:20:28.855Z + 2024-12-10T21:25:37.143Z https://axolotl-ai-cloud.github.io/axolotl/TODO.html - 2024-12-09T19:20:28.855Z + 2024-12-10T21:25:37.143Z https://axolotl-ai-cloud.github.io/axolotl/docs/faq.html - 2024-12-09T19:20:28.859Z + 2024-12-10T21:25:37.143Z https://axolotl-ai-cloud.github.io/axolotl/docs/debugging.html - 2024-12-09T19:20:28.859Z + 2024-12-10T21:25:37.143Z https://axolotl-ai-cloud.github.io/axolotl/docs/dataset-formats/inst_tune.html - 2024-12-09T19:20:28.859Z + 2024-12-10T21:25:37.143Z https://axolotl-ai-cloud.github.io/axolotl/docs/dataset-formats/pretraining.html - 2024-12-09T19:20:28.859Z + 2024-12-10T21:25:37.143Z https://axolotl-ai-cloud.github.io/axolotl/docs/dataset-formats/index.html - 2024-12-09T19:20:28.859Z + 2024-12-10T21:25:37.143Z https://axolotl-ai-cloud.github.io/axolotl/docs/unsloth.html - 2024-12-09T19:20:28.859Z + 2024-12-10T21:25:37.147Z https://axolotl-ai-cloud.github.io/axolotl/docs/multimodal.html - 2024-12-09T19:20:28.859Z + 2024-12-10T21:25:37.147Z https://axolotl-ai-cloud.github.io/axolotl/docs/batch_vs_grad.html - 2024-12-09T19:20:28.855Z + 2024-12-10T21:25:37.143Z https://axolotl-ai-cloud.github.io/axolotl/docs/fsdp_qlora.html - 2024-12-09T19:20:28.859Z + 2024-12-10T21:25:37.143Z https://axolotl-ai-cloud.github.io/axolotl/docs/multipack.html - 2024-12-09T19:20:28.859Z + 2024-12-10T21:25:37.147Z https://axolotl-ai-cloud.github.io/axolotl/docs/amd_hpc.html - 2024-12-09T19:20:28.855Z + 2024-12-10T21:25:37.143Z https://axolotl-ai-cloud.github.io/axolotl/examples/colab-notebooks/colab-axolotl-example.html - 2024-12-09T19:20:28.859Z + 2024-12-10T21:25:37.147Z https://axolotl-ai-cloud.github.io/axolotl/src/axolotl/integrations/cut_cross_entropy/ACKNOWLEDGEMENTS.html - 2024-12-09T19:20:28.875Z + 2024-12-10T21:25:37.159Z