Compare commits
1 Commits
quantize-p
...
20231212-f
| Author | SHA1 | Date | |
|---|---|---|---|
|
5bb4a782ce |
@@ -1,16 +0,0 @@
|
||||
# yaml-language-server: $schema=https://coderabbit.ai/integrations/schema.v2.json
|
||||
language: "en-US"
|
||||
early_access: false
|
||||
reviews:
|
||||
profile: "chill"
|
||||
request_changes_workflow: false
|
||||
high_level_summary: true
|
||||
review_status: true
|
||||
collapse_walkthrough: true
|
||||
poem: false
|
||||
sequence_diagrams: false
|
||||
auto_review:
|
||||
enabled: true
|
||||
drafts: false
|
||||
chat:
|
||||
auto_reply: true
|
||||
14
.coveragerc
14
.coveragerc
@@ -1,14 +0,0 @@
|
||||
[run]
|
||||
source = axolotl
|
||||
omit =
|
||||
*/tests/*
|
||||
setup.py
|
||||
|
||||
[report]
|
||||
exclude_lines =
|
||||
pragma: no cover
|
||||
def __repr__
|
||||
raise NotImplementedError
|
||||
if __name__ == .__main__.:
|
||||
pass
|
||||
raise ImportError
|
||||
10
.github/CONTRIBUTING.md
vendored
10
.github/CONTRIBUTING.md
vendored
@@ -15,18 +15,18 @@ First of all, thank you for your interest in contributing to axolotl! We appreci
|
||||
- [Commit Messages](#commit-messages)
|
||||
- [Additional Resources](#additional-resources)
|
||||
|
||||
## Code of Conduct
|
||||
## Code of Conductcode
|
||||
|
||||
All contributors are expected to adhere to our [Code of Conduct](CODE_OF_CONDUCT.md). Please read it before participating in the axolotl community.
|
||||
|
||||
## Getting Started
|
||||
|
||||
Bugs? Please check for open issue else create a new [Issue](https://github.com/axolotl-ai-cloud/axolotl/issues/new).
|
||||
Bugs? Please check for open issue else create a new [Issue](https://github.com/OpenAccess-AI-Collective/axolotl/issues/new).
|
||||
|
||||
PRs are **greatly welcome**!
|
||||
|
||||
1. Fork the repository and clone it to your local machine.
|
||||
2. Set up the development environment by following the instructions in the [README.md](https://github.com/axolotl-ai-cloud/axolotl/tree/main/README.md) file.
|
||||
2. Set up the development environment by following the instructions in the [README.md](https://github.com/OpenAccess-AI-Collective/axolotl/tree/main/README.md) file.
|
||||
3. Explore the codebase, run tests, and verify that everything works as expected.
|
||||
|
||||
Please run below to setup env
|
||||
@@ -42,11 +42,11 @@ pytest tests/
|
||||
|
||||
### Reporting Bugs
|
||||
|
||||
If you encounter a bug or issue while using axolotl, please open a new issue on the [GitHub Issues](https://github.com/axolotl-ai-cloud/axolotl/issues) page. Provide a clear and concise description of the problem, steps to reproduce it, and any relevant error messages or logs.
|
||||
If you encounter a bug or issue while using axolotl, please open a new issue on the [GitHub Issues](https://github.com/OpenAccess-AI-Collective/axolotl/issues) page. Provide a clear and concise description of the problem, steps to reproduce it, and any relevant error messages or logs.
|
||||
|
||||
### Suggesting Enhancements
|
||||
|
||||
We welcome ideas for improvements and new features. To suggest an enhancement, open a new issue on the [GitHub Issues](https://github.com/axolotl-ai-cloud/axolotl/issues) page. Describe the enhancement in detail, explain the use case, and outline the benefits it would bring to the project.
|
||||
We welcome ideas for improvements and new features. To suggest an enhancement, open a new issue on the [GitHub Issues](https://github.com/OpenAccess-AI-Collective/axolotl/issues) page. Describe the enhancement in detail, explain the use case, and outline the benefits it would bring to the project.
|
||||
|
||||
### Submitting Pull Requests
|
||||
|
||||
|
||||
6
.github/FUNDING.yml
vendored
6
.github/FUNDING.yml
vendored
@@ -1,13 +1,13 @@
|
||||
# These are supported funding model platforms
|
||||
|
||||
github: [winglian, OpenAccess-AI-Collective] # Replace with up to 4 GitHub Sponsors-enabled usernames e.g., [user1, user2]
|
||||
github: OpenAccess-AI-Collective # Replace with up to 4 GitHub Sponsors-enabled usernames e.g., [user1, user2]
|
||||
patreon: # Replace with a single Patreon username
|
||||
open_collective: # Replace with a single Open Collective username
|
||||
ko_fi: axolotl_ai # Replace with a single Ko-fi username
|
||||
ko_fi: # Replace with a single Ko-fi username
|
||||
tidelift: # Replace with a single Tidelift platform-name/package-name e.g., npm/babel
|
||||
community_bridge: # Replace with a single Community Bridge project-name e.g., cloud-foundry
|
||||
liberapay: # Replace with a single Liberapay username
|
||||
issuehunt: # Replace with a single IssueHunt username
|
||||
otechie: # Replace with a single Otechie username
|
||||
lfx_crowdfunding: # Replace with a single LFX Crowdfunding project-name e.g., cloud-foundry
|
||||
custom: ['https://quickchart.io/qr?text=bitcoin%3Abc1qxlgwlqwfea5s2cxm42xqsfmwjct0rj8w8ea5np&size=480¢erImageUrl=https%3A%2F%2Fupload.wikimedia.org%2Fwikipedia%2Fcommons%2Fthumb%2F4%2F46%2FBitcoin.svg%2F64px-Bitcoin.svg.png'] # Replace with up to 4 custom sponsorship URLs e.g., ['link1', 'link2']
|
||||
custom: # Replace with up to 4 custom sponsorship URLs e.g., ['link1', 'link2']
|
||||
|
||||
3
.github/ISSUE_TEMPLATE/bug-report.yaml
vendored
3
.github/ISSUE_TEMPLATE/bug-report.yaml
vendored
@@ -15,7 +15,7 @@ body:
|
||||
label: "Please check that this issue hasn't been reported before."
|
||||
description: "The **Label filters** may help make your search more focussed."
|
||||
options:
|
||||
- label: "I searched previous [Bug Reports](https://github.com/axolotl-ai-cloud/axolotl/labels/bug) didn't find any similar reports."
|
||||
- label: "I searched previous [Bug Reports](https://github.com/OpenAccess-AI-Collective/axolotl/labels/bug) didn't find any similar reports."
|
||||
required: true
|
||||
|
||||
- type: textarea
|
||||
@@ -59,7 +59,6 @@ body:
|
||||
label: Config yaml
|
||||
description: |
|
||||
Please attach the config yaml!
|
||||
render: yaml
|
||||
|
||||
- type: textarea
|
||||
id: possible-solution
|
||||
|
||||
2
.github/ISSUE_TEMPLATE/config.yml
vendored
2
.github/ISSUE_TEMPLATE/config.yml
vendored
@@ -1,7 +1,7 @@
|
||||
blank_issues_enabled: false
|
||||
contact_links:
|
||||
- name: Ask a question
|
||||
url: https://github.com/axolotl-ai-cloud/axolotl/discussions/categories/q-a
|
||||
url: https://github.com/OpenAccess-AI-Collective/axolotl/discussions/categories/q-a
|
||||
about: Ask questions and discuss with other community members
|
||||
- name: Discuss the Project in Discord
|
||||
url: https://discord.gg/HhrNrHJPRb
|
||||
|
||||
2
.github/ISSUE_TEMPLATE/docs.yml
vendored
2
.github/ISSUE_TEMPLATE/docs.yml
vendored
@@ -10,7 +10,7 @@ body:
|
||||
value: |
|
||||
* Ask questions in [Discord](https://discord.gg/HhrNrHJPRb).
|
||||
* Before you file an issue read the [Contributing guide](./CONTRIBUTING.md).
|
||||
* Check to make sure someone hasn't already opened a [similar issue](https://github.com/axolotl-ai-cloud/axolotl/issues).
|
||||
* Check to make sure someone hasn't already opened a [similar issue](https://github.com/OpenAccess-AI-Collective/axolotl/issues).
|
||||
- type: textarea
|
||||
attributes:
|
||||
label: What piece of documentation is affected?
|
||||
|
||||
4
.github/ISSUE_TEMPLATE/feature-request.yaml
vendored
4
.github/ISSUE_TEMPLATE/feature-request.yaml
vendored
@@ -8,9 +8,9 @@ body:
|
||||
label: "⚠️ Please check that this feature request hasn't been suggested before."
|
||||
description: "There are two locations for previous feature requests. Please search in both. Thank you. The **Label filters** may help make your search more focussed."
|
||||
options:
|
||||
- label: "I searched previous [Ideas in Discussions](https://github.com/axolotl-ai-cloud/axolotl/discussions/categories/ideas) didn't find any similar feature requests."
|
||||
- label: "I searched previous [Ideas in Discussions](https://github.com/OpenAccess-AI-Collective/axolotl/discussions/categories/ideas) didn't find any similar feature requests."
|
||||
required: true
|
||||
- label: "I searched previous [Issues](https://github.com/axolotl-ai-cloud/axolotl/labels/enhancement) didn't find any similar feature requests."
|
||||
- label: "I searched previous [Issues](https://github.com/OpenAccess-AI-Collective/axolotl/labels/enhancement) didn't find any similar feature requests."
|
||||
required: true
|
||||
|
||||
- type: textarea
|
||||
|
||||
@@ -20,8 +20,3 @@
|
||||
## Types of changes
|
||||
|
||||
<!--- What types of changes does your code introduce? Put an `x` in all the boxes that apply: -->
|
||||
|
||||
## Social Handles (Optional)
|
||||
|
||||
<!-- Thanks for submitting a bugfix or enhancement. -->
|
||||
<!-- We'd love to show our thanks to you on Twitter & Discord if you provide your handle -->
|
||||
151
.github/workflows/base.yml
vendored
151
.github/workflows/base.yml
vendored
@@ -3,159 +3,58 @@ name: ci-cd-base
|
||||
on:
|
||||
push:
|
||||
branches:
|
||||
- "main"
|
||||
paths:
|
||||
- 'docker/Dockerfile-base'
|
||||
- 'docker/Dockerfile-uv-base'
|
||||
- '.github/workflows/base.yml'
|
||||
pull_request:
|
||||
paths:
|
||||
- 'docker/Dockerfile-base'
|
||||
- 'docker/Dockerfile-uv-base'
|
||||
- '.github/workflows/base.yml'
|
||||
workflow_dispatch:
|
||||
- "main-base"
|
||||
- "dev-base"
|
||||
|
||||
jobs:
|
||||
build-base:
|
||||
if: github.repository_owner == 'axolotl-ai-cloud'
|
||||
timeout-minutes: 480
|
||||
if: github.repository_owner == 'OpenAccess-AI-Collective'
|
||||
# this job needs to be run on self-hosted GPU runners...
|
||||
runs-on: ubuntu-latest-m
|
||||
runs-on: self-hosted
|
||||
strategy:
|
||||
fail-fast: false
|
||||
matrix:
|
||||
include:
|
||||
- cuda: "124"
|
||||
cuda_version: 12.4.1
|
||||
cudnn_version: ""
|
||||
python_version: "3.11"
|
||||
pytorch: 2.6.0
|
||||
torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
|
||||
dockerfile: "Dockerfile-base"
|
||||
- cuda: "126"
|
||||
cuda_version: 12.6.3
|
||||
cudnn_version: ""
|
||||
python_version: "3.11"
|
||||
pytorch: 2.6.0
|
||||
torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
|
||||
dockerfile: "Dockerfile-base"
|
||||
- cuda: "126"
|
||||
cuda_version: 12.6.3
|
||||
cudnn_version: ""
|
||||
python_version: "3.11"
|
||||
pytorch: 2.7.0
|
||||
torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
|
||||
dockerfile: "Dockerfile-base"
|
||||
- cuda: "126"
|
||||
cuda_version: 12.6.3
|
||||
cudnn_version: ""
|
||||
python_version: "3.11"
|
||||
pytorch: 2.7.1
|
||||
torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
|
||||
dockerfile: "Dockerfile-base"
|
||||
- cuda: "128"
|
||||
cuda_version: 12.6.3
|
||||
cudnn_version: ""
|
||||
python_version: "3.11"
|
||||
pytorch: 2.7.1
|
||||
torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
|
||||
dockerfile: "Dockerfile-base"
|
||||
- cuda: "128"
|
||||
cuda_version: 12.8.1
|
||||
cudnn_version: ""
|
||||
python_version: "3.11"
|
||||
pytorch: nightly
|
||||
torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
|
||||
dockerfile: "Dockerfile-base-nightly"
|
||||
# # "next" is for release candidates of pytorch
|
||||
# - cuda: "128"
|
||||
# cuda_version: 12.8.1
|
||||
# cudnn_version: ""
|
||||
# python_version: "3.11"
|
||||
# pytorch: next
|
||||
# torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
|
||||
# dockerfile: "Dockerfile-base-next"
|
||||
- cuda: "118"
|
||||
cuda_version: 11.8.0
|
||||
python_version: "3.9"
|
||||
pytorch: 2.0.1
|
||||
torch_cuda_arch_list: "7.0 7.5 8.0 8.6 9.0+PTX"
|
||||
- cuda: "118"
|
||||
cuda_version: 11.8.0
|
||||
python_version: "3.10"
|
||||
pytorch: 2.0.1
|
||||
torch_cuda_arch_list: "7.0 7.5 8.0 8.6 9.0+PTX"
|
||||
- cuda: "118"
|
||||
cuda_version: 11.8.0
|
||||
python_version: "3.10"
|
||||
pytorch: 2.1.0
|
||||
torch_cuda_arch_list: "7.0 7.5 8.0 8.6 9.0+PTX"
|
||||
steps:
|
||||
- name: Checkout
|
||||
uses: actions/checkout@v4
|
||||
uses: actions/checkout@v3
|
||||
- name: Docker metadata
|
||||
id: metadata
|
||||
uses: docker/metadata-action@v5
|
||||
uses: docker/metadata-action@v3
|
||||
with:
|
||||
images: |
|
||||
winglian/axolotl-base
|
||||
axolotlai/axolotl-base
|
||||
images: winglian/axolotl-base
|
||||
- name: Login to Docker Hub
|
||||
uses: docker/login-action@v2
|
||||
with:
|
||||
username: ${{ secrets.DOCKERHUB_USERNAME }}
|
||||
password: ${{ secrets.DOCKERHUB_TOKEN }}
|
||||
- name: Set up Docker Buildx
|
||||
uses: docker/setup-buildx-action@v3
|
||||
uses: docker/setup-buildx-action@v2
|
||||
- name: Build
|
||||
uses: docker/build-push-action@v4
|
||||
with:
|
||||
context: .
|
||||
file: ./docker/${{ matrix.dockerfile }}
|
||||
file: ./docker/Dockerfile-base
|
||||
push: ${{ github.event_name != 'pull_request' }}
|
||||
tags: ${{ steps.metadata.outputs.tags }}-base-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
|
||||
tags: ${{ steps.metadata.outputs.tags }}-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
|
||||
labels: ${{ steps.metadata.outputs.labels }}
|
||||
build-args: |
|
||||
CUDA_VERSION=${{ matrix.cuda_version }}
|
||||
CUDNN_VERSION=${{ matrix.cudnn_version }}
|
||||
CUDA=${{ matrix.cuda }}
|
||||
PYTHON_VERSION=${{ matrix.python_version }}
|
||||
PYTORCH_VERSION=${{ matrix.pytorch }}
|
||||
TORCH_CUDA_ARCH_LIST=${{ matrix.torch_cuda_arch_list }}
|
||||
build-base-uv:
|
||||
if: github.repository_owner == 'axolotl-ai-cloud'
|
||||
timeout-minutes: 480
|
||||
runs-on: ubuntu-latest-m
|
||||
strategy:
|
||||
fail-fast: false
|
||||
matrix:
|
||||
include:
|
||||
- cuda: "126"
|
||||
cuda_version: 12.6.3
|
||||
cudnn_version: ""
|
||||
python_version: "3.11"
|
||||
pytorch: 2.6.0
|
||||
torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
|
||||
dockerfile: "Dockerfile-uv-base"
|
||||
- cuda: "128"
|
||||
cuda_version: 12.8.1
|
||||
cudnn_version: ""
|
||||
python_version: "3.11"
|
||||
pytorch: 2.7.1
|
||||
torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
|
||||
dockerfile: "Dockerfile-uv-base"
|
||||
steps:
|
||||
- name: Checkout
|
||||
uses: actions/checkout@v4
|
||||
- name: Docker metadata
|
||||
id: metadata
|
||||
uses: docker/metadata-action@v5
|
||||
with:
|
||||
images: |
|
||||
axolotlai/axolotl-base-uv
|
||||
- name: Login to Docker Hub
|
||||
uses: docker/login-action@v2
|
||||
with:
|
||||
username: ${{ secrets.DOCKERHUB_USERNAME }}
|
||||
password: ${{ secrets.DOCKERHUB_TOKEN }}
|
||||
- name: Set up Docker Buildx
|
||||
uses: docker/setup-buildx-action@v3
|
||||
- name: Build
|
||||
uses: docker/build-push-action@v4
|
||||
with:
|
||||
context: .
|
||||
file: ./docker/${{ matrix.dockerfile }}
|
||||
push: ${{ github.event_name != 'pull_request' }}
|
||||
tags: ${{ steps.metadata.outputs.tags }}-base-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
|
||||
labels: ${{ steps.metadata.outputs.labels }}
|
||||
build-args: |
|
||||
CUDA_VERSION=${{ matrix.cuda_version }}
|
||||
CUDNN_VERSION=${{ matrix.cudnn_version }}
|
||||
CUDA=${{ matrix.cuda }}
|
||||
PYTHON_VERSION=${{ matrix.python_version }}
|
||||
PYTORCH_VERSION=${{ matrix.pytorch }}
|
||||
|
||||
34
.github/workflows/docs.yml
vendored
34
.github/workflows/docs.yml
vendored
@@ -1,34 +0,0 @@
|
||||
name: Publish Docs
|
||||
on:
|
||||
push:
|
||||
branches:
|
||||
- main
|
||||
|
||||
permissions:
|
||||
contents: write
|
||||
pages: write
|
||||
|
||||
jobs:
|
||||
build-deploy:
|
||||
runs-on: ubuntu-latest
|
||||
steps:
|
||||
- name: Check out repository
|
||||
uses: actions/checkout@v4
|
||||
- name: Set up Quarto
|
||||
uses: quarto-dev/quarto-actions/setup@v2
|
||||
- name: Setup Python
|
||||
uses: actions/setup-python@v5
|
||||
with:
|
||||
python-version: '3.11'
|
||||
- name: Install dependencies
|
||||
run: |
|
||||
python3 -m pip install jupyter quartodoc
|
||||
python3 -m pip install -e .
|
||||
- name: Build autodoc
|
||||
run: quartodoc build
|
||||
- name: Publish to GitHub Pages (and render)
|
||||
uses: quarto-dev/quarto-actions/publish@v2
|
||||
with:
|
||||
target: gh-pages
|
||||
env:
|
||||
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
|
||||
25
.github/workflows/lint.yml
vendored
25
.github/workflows/lint.yml
vendored
@@ -1,25 +0,0 @@
|
||||
name: lint
|
||||
on:
|
||||
# check on PRs, and manual triggers
|
||||
merge_group:
|
||||
pull_request:
|
||||
paths:
|
||||
- '**.py'
|
||||
- 'requirements.txt'
|
||||
- '.github/workflows/*.yml'
|
||||
- "*.[q]md"
|
||||
- "examples/**/*.y[a]?ml"
|
||||
- ".pre-commit-config.yaml"
|
||||
workflow_dispatch:
|
||||
|
||||
jobs:
|
||||
pre-commit:
|
||||
name: pre-commit
|
||||
runs-on: ubuntu-latest
|
||||
steps:
|
||||
- uses: actions/checkout@v4
|
||||
- uses: actions/setup-python@v5
|
||||
with:
|
||||
python-version: "3.11"
|
||||
cache: 'pip' # caching pip dependencies
|
||||
- uses: pre-commit/action@v3.0.1
|
||||
169
.github/workflows/main.yml
vendored
169
.github/workflows/main.yml
vendored
@@ -4,181 +4,108 @@ on:
|
||||
push:
|
||||
branches:
|
||||
- "main"
|
||||
tags:
|
||||
- "v*"
|
||||
workflow_dispatch:
|
||||
|
||||
jobs:
|
||||
build-axolotl:
|
||||
if: ${{ ! contains(github.event.commits[0].message, '[skip docker]') && github.repository_owner == 'axolotl-ai-cloud' }}
|
||||
if: github.repository_owner == 'OpenAccess-AI-Collective'
|
||||
# this job needs to be run on self-hosted GPU runners...
|
||||
strategy:
|
||||
fail-fast: false
|
||||
matrix:
|
||||
include:
|
||||
- cuda: 126
|
||||
cuda_version: 12.6.3
|
||||
python_version: "3.11"
|
||||
pytorch: 2.6.0
|
||||
- cuda: 118
|
||||
cuda_version: 11.8.0
|
||||
python_version: "3.9"
|
||||
pytorch: 2.0.1
|
||||
axolotl_extras:
|
||||
- cuda: 126
|
||||
cuda_version: 12.6.3
|
||||
python_version: "3.11"
|
||||
pytorch: 2.7.0
|
||||
axolotl_extras: vllm
|
||||
- cuda: 126
|
||||
cuda_version: 12.6.3
|
||||
python_version: "3.11"
|
||||
pytorch: 2.7.1
|
||||
- cuda: 118
|
||||
cuda_version: 11.8.0
|
||||
python_version: "3.10"
|
||||
pytorch: 2.0.1
|
||||
axolotl_extras:
|
||||
- cuda: 128
|
||||
cuda_version: 12.8.1
|
||||
python_version: "3.11"
|
||||
pytorch: 2.7.1
|
||||
is_latest: true
|
||||
- cuda: 118
|
||||
cuda_version: 11.8.0
|
||||
python_version: "3.10"
|
||||
pytorch: 2.1.0
|
||||
axolotl_extras:
|
||||
runs-on: axolotl-gpu-runner
|
||||
runs-on: [self-hosted, gpu, docker]
|
||||
steps:
|
||||
- name: Checkout
|
||||
uses: actions/checkout@v4
|
||||
uses: actions/checkout@v3
|
||||
- name: Docker metadata
|
||||
id: metadata
|
||||
uses: docker/metadata-action@v5
|
||||
uses: docker/metadata-action@v3
|
||||
with:
|
||||
images: |
|
||||
winglian/axolotl
|
||||
axolotlai/axolotl
|
||||
tags: |
|
||||
type=ref,event=branch
|
||||
type=pep440,pattern={{version}}
|
||||
- name: Set up Docker Buildx
|
||||
uses: docker/setup-buildx-action@v3
|
||||
images: winglian/axolotl
|
||||
- name: Login to Docker Hub
|
||||
uses: docker/login-action@v3
|
||||
uses: docker/login-action@v2
|
||||
with:
|
||||
username: ${{ secrets.DOCKERHUB_USERNAME }}
|
||||
password: ${{ secrets.DOCKERHUB_TOKEN }}
|
||||
# guidance for testing before pushing: https://docs.docker.com/build/ci/github-actions/test-before-push/
|
||||
- name: Build and export to Docker
|
||||
uses: docker/build-push-action@v5
|
||||
- name: Set up Docker Buildx
|
||||
uses: docker/setup-buildx-action@v2
|
||||
- name: Build
|
||||
uses: docker/build-push-action@v4
|
||||
with:
|
||||
context: .
|
||||
build-args: |
|
||||
BASE_TAG=${{ github.ref_type == 'tag' && 'main' || github.ref_name }}-base-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}
|
||||
BASE_TAG=${{ github.ref_name }}-base-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}
|
||||
CUDA=${{ matrix.cuda }}
|
||||
PYTORCH_VERSION=${{ matrix.pytorch }}
|
||||
AXOLOTL_ARGS=${{ matrix.axolotl_args }}
|
||||
AXOLOTL_EXTRAS=${{ matrix.axolotl_extras}}
|
||||
file: ./docker/Dockerfile
|
||||
push: ${{ github.event_name != 'pull_request' }}
|
||||
tags: |
|
||||
${{ steps.metadata.outputs.tags }}-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
|
||||
${{ steps.metadata.outputs.tags }}-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}
|
||||
${{ (matrix.is_latest) && format('{0}-latest', steps.metadata.outputs.tags) || '' }}
|
||||
labels: ${{ steps.metadata.outputs.labels }}
|
||||
|
||||
build-axolotl-cloud:
|
||||
build-axolotl-runpod:
|
||||
needs: build-axolotl
|
||||
if: ${{ ! contains(github.event.commits[0].message, '[skip docker]') && github.repository_owner == 'axolotl-ai-cloud' }}
|
||||
if: github.repository_owner == 'OpenAccess-AI-Collective'
|
||||
# this job needs to be run on self-hosted GPU runners...
|
||||
strategy:
|
||||
matrix:
|
||||
include:
|
||||
- cuda: 126
|
||||
cuda_version: 12.6.3
|
||||
python_version: "3.11"
|
||||
pytorch: 2.6.0
|
||||
- cuda: 118
|
||||
cuda_version: 11.8.0
|
||||
python_version: "3.9"
|
||||
pytorch: 2.0.1
|
||||
axolotl_extras:
|
||||
- cuda: 126
|
||||
cuda_version: 12.6.3
|
||||
python_version: "3.11"
|
||||
pytorch: 2.7.0
|
||||
axolotl_extras:
|
||||
- cuda: 126
|
||||
cuda_version: 12.6.3
|
||||
python_version: "3.11"
|
||||
pytorch: 2.7.1
|
||||
- cuda: 118
|
||||
cuda_version: 11.8.0
|
||||
python_version: "3.10"
|
||||
pytorch: 2.0.1
|
||||
axolotl_extras:
|
||||
is_latest: true
|
||||
- cuda: 128
|
||||
cuda_version: 12.8.1
|
||||
python_version: "3.11"
|
||||
pytorch: 2.7.1
|
||||
- cuda: 118
|
||||
cuda_version: 11.8.0
|
||||
python_version: "3.10"
|
||||
pytorch: 2.1.0
|
||||
axolotl_extras:
|
||||
runs-on: axolotl-gpu-runner
|
||||
runs-on: [self-hosted, gpu, docker]
|
||||
steps:
|
||||
- name: Checkout
|
||||
uses: actions/checkout@v4
|
||||
uses: actions/checkout@v3
|
||||
- name: Docker metadata
|
||||
id: metadata
|
||||
uses: docker/metadata-action@v5
|
||||
uses: docker/metadata-action@v3
|
||||
with:
|
||||
images: |
|
||||
winglian/axolotl-cloud
|
||||
axolotlai/axolotl-cloud
|
||||
tags: |
|
||||
type=ref,event=branch
|
||||
type=pep440,pattern={{version}}
|
||||
images: winglian/axolotl-runpod
|
||||
- name: Login to Docker Hub
|
||||
uses: docker/login-action@v3
|
||||
uses: docker/login-action@v2
|
||||
with:
|
||||
username: ${{ secrets.DOCKERHUB_USERNAME }}
|
||||
password: ${{ secrets.DOCKERHUB_TOKEN }}
|
||||
- name: Set up Docker Buildx
|
||||
uses: docker/setup-buildx-action@v3
|
||||
uses: docker/setup-buildx-action@v2
|
||||
- name: Build
|
||||
uses: docker/build-push-action@v5
|
||||
uses: docker/build-push-action@v4
|
||||
with:
|
||||
context: .
|
||||
build-args: |
|
||||
BASE_TAG=${{ github.ref_type == 'tag' && 'main' || github.ref_name }}-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
|
||||
BASE_TAG=${{ github.ref_name }}-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
|
||||
CUDA=${{ matrix.cuda }}
|
||||
file: ./docker/Dockerfile-cloud
|
||||
push: ${{ github.event_name != 'pull_request' }}
|
||||
tags: |
|
||||
${{ steps.metadata.outputs.tags }}-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
|
||||
${{ (matrix.is_latest) && format('{0}-latest', steps.metadata.outputs.tags) || '' }}
|
||||
labels: ${{ steps.metadata.outputs.labels }}
|
||||
|
||||
build-axolotl-cloud-no-tmux:
|
||||
needs: build-axolotl
|
||||
if: ${{ ! contains(github.event.commits[0].message, '[skip docker]') && github.repository_owner == 'axolotl-ai-cloud' }}
|
||||
# this job needs to be run on self-hosted GPU runners...
|
||||
strategy:
|
||||
matrix:
|
||||
include:
|
||||
- cuda: 126
|
||||
cuda_version: 12.6.3
|
||||
python_version: "3.11"
|
||||
pytorch: 2.6.0
|
||||
axolotl_extras:
|
||||
runs-on: axolotl-gpu-runner
|
||||
steps:
|
||||
- name: Checkout
|
||||
uses: actions/checkout@v4
|
||||
- name: Docker metadata
|
||||
id: metadata
|
||||
uses: docker/metadata-action@v5
|
||||
with:
|
||||
images: |
|
||||
winglian/axolotl-cloud-term
|
||||
axolotlai/axolotl-cloud-term
|
||||
tags: |
|
||||
type=ref,event=branch
|
||||
type=pep440,pattern={{version}}
|
||||
- name: Login to Docker Hub
|
||||
uses: docker/login-action@v3
|
||||
with:
|
||||
username: ${{ secrets.DOCKERHUB_USERNAME }}
|
||||
password: ${{ secrets.DOCKERHUB_TOKEN }}
|
||||
- name: Set up Docker Buildx
|
||||
uses: docker/setup-buildx-action@v3
|
||||
- name: Build
|
||||
uses: docker/build-push-action@v5
|
||||
with:
|
||||
context: .
|
||||
build-args: |
|
||||
BASE_TAG=${{ github.ref_type == 'tag' && 'main' || github.ref_name }}-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
|
||||
CUDA=${{ matrix.cuda }}
|
||||
file: ./docker/Dockerfile-cloud-no-tmux
|
||||
file: ./docker/Dockerfile-runpod
|
||||
push: ${{ github.event_name != 'pull_request' }}
|
||||
tags: |
|
||||
${{ steps.metadata.outputs.tags }}-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
|
||||
|
||||
75
.github/workflows/multi-gpu-e2e.yml
vendored
75
.github/workflows/multi-gpu-e2e.yml
vendored
@@ -1,75 +0,0 @@
|
||||
name: docker-multigpu-tests-biweekly
|
||||
|
||||
on:
|
||||
pull_request:
|
||||
paths:
|
||||
- 'tests/e2e/multigpu/**.py'
|
||||
- 'requirements.txt'
|
||||
- 'setup.py'
|
||||
- 'pyproject.toml'
|
||||
- '.github/workflows/multi-gpu-e2e.yml'
|
||||
- 'src/axolotl/core/trainers/mixins/sequence_parallel.py'
|
||||
- 'src/axolotl/utils/distributed.py'
|
||||
workflow_dispatch:
|
||||
schedule:
|
||||
- cron: '0 0 * * 1,4' # Runs at 00:00 UTC every monday & thursday
|
||||
|
||||
# Cancel jobs on the same ref if a new one is triggered
|
||||
concurrency:
|
||||
group: ${{ github.workflow }}-${{ github.ref }}
|
||||
cancel-in-progress: ${{ github.ref != 'refs/heads/main' }}
|
||||
|
||||
jobs:
|
||||
test-axolotl-multigpu:
|
||||
if: ${{ ! contains(github.event.commits[0].message, '[skip e2e]') && github.repository_owner == 'axolotl-ai-cloud' }}
|
||||
strategy:
|
||||
fail-fast: false
|
||||
matrix:
|
||||
include:
|
||||
- cuda: 126
|
||||
cuda_version: 12.6.3
|
||||
python_version: "3.11"
|
||||
pytorch: 2.6.0
|
||||
axolotl_extras:
|
||||
num_gpus: 2
|
||||
nightly_build: "true"
|
||||
- cuda: 126
|
||||
cuda_version: 12.6.3
|
||||
python_version: "3.11"
|
||||
pytorch: 2.7.0
|
||||
axolotl_extras: vllm
|
||||
num_gpus: 2
|
||||
nightly_build: "true"
|
||||
- cuda: 126
|
||||
cuda_version: 12.6.3
|
||||
python_version: "3.11"
|
||||
pytorch: 2.7.1
|
||||
axolotl_extras:
|
||||
num_gpus: 2
|
||||
nightly_build: "true"
|
||||
runs-on: [self-hosted, modal]
|
||||
timeout-minutes: 120
|
||||
steps:
|
||||
- name: Checkout
|
||||
uses: actions/checkout@v4
|
||||
- name: Install Python
|
||||
uses: actions/setup-python@v5
|
||||
with:
|
||||
python-version: "3.11"
|
||||
- name: Install Modal
|
||||
run: |
|
||||
python -m pip install --upgrade pip
|
||||
pip install modal==1.0.2 jinja2
|
||||
- name: Update env vars
|
||||
run: |
|
||||
echo "BASE_TAG=main-base-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}" >> $GITHUB_ENV
|
||||
echo "PYTORCH_VERSION=${{ matrix.pytorch}}" >> $GITHUB_ENV
|
||||
echo "AXOLOTL_ARGS=${{ matrix.axolotl_args}}" >> $GITHUB_ENV
|
||||
echo "AXOLOTL_EXTRAS=${{ matrix.axolotl_extras}}" >> $GITHUB_ENV
|
||||
echo "CUDA=${{ matrix.cuda }}" >> $GITHUB_ENV
|
||||
echo "N_GPUS=${{ matrix.num_gpus }}" >> $GITHUB_ENV
|
||||
echo "NIGHTLY_BUILD=${{ matrix.nightly_build }}" >> $GITHUB_ENV
|
||||
echo "CODECOV_TOKEN=${{ secrets.CODECOV_TOKEN }}" >> $GITHUB_ENV
|
||||
- name: Run tests job on Modal
|
||||
run: |
|
||||
modal run cicd.multigpu
|
||||
109
.github/workflows/nightlies.yml
vendored
109
.github/workflows/nightlies.yml
vendored
@@ -1,109 +0,0 @@
|
||||
name: docker-nightlies
|
||||
|
||||
on:
|
||||
workflow_dispatch:
|
||||
schedule:
|
||||
- cron: '0 0 * * *' # Runs at 00:00 UTC every day
|
||||
|
||||
jobs:
|
||||
build-axolotl:
|
||||
if: ${{ ! contains(github.event.commits[0].message, '[skip docker]') && github.repository_owner == 'axolotl-ai-cloud' }}
|
||||
strategy:
|
||||
fail-fast: false
|
||||
matrix:
|
||||
include:
|
||||
- cuda: 126
|
||||
cuda_version: 12.6.3
|
||||
python_version: "3.11"
|
||||
pytorch: 2.6.0
|
||||
axolotl_extras:
|
||||
- cuda: 126
|
||||
cuda_version: 12.6.3
|
||||
python_version: "3.11"
|
||||
pytorch: 2.7.1
|
||||
axolotl_extras:
|
||||
runs-on: axolotl-gpu-runner
|
||||
steps:
|
||||
- name: Checkout
|
||||
uses: actions/checkout@v4
|
||||
- name: Docker metadata
|
||||
id: metadata
|
||||
uses: docker/metadata-action@v5
|
||||
with:
|
||||
images: |
|
||||
winglian/axolotl
|
||||
axolotlai/axolotl
|
||||
tags: |
|
||||
type=raw,value={{ branch }}-{{ date 'YYYYMMDD' }}
|
||||
- name: Set up Docker Buildx
|
||||
uses: docker/setup-buildx-action@v3
|
||||
- name: Login to Docker Hub
|
||||
uses: docker/login-action@v3
|
||||
with:
|
||||
username: ${{ secrets.DOCKERHUB_USERNAME }}
|
||||
password: ${{ secrets.DOCKERHUB_TOKEN }}
|
||||
# guidance for testing before pushing: https://docs.docker.com/build/ci/github-actions/test-before-push/
|
||||
- name: Build and export to Docker
|
||||
uses: docker/build-push-action@v5
|
||||
with:
|
||||
context: .
|
||||
build-args: |
|
||||
BASE_TAG=${{ github.ref_name }}-base-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}
|
||||
CUDA=${{ matrix.cuda }}
|
||||
PYTORCH_VERSION=${{ matrix.pytorch }}
|
||||
AXOLOTL_ARGS=${{ matrix.axolotl_args }}
|
||||
file: ./docker/Dockerfile
|
||||
push: ${{ github.event_name != 'pull_request' }}
|
||||
tags: |
|
||||
${{ steps.metadata.outputs.tags }}-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
|
||||
labels: ${{ steps.metadata.outputs.labels }}
|
||||
|
||||
build-axolotl-cloud:
|
||||
needs: build-axolotl
|
||||
if: ${{ ! contains(github.event.commits[0].message, '[skip docker]') && github.repository_owner == 'axolotl-ai-cloud' }}
|
||||
# this job needs to be run on self-hosted GPU runners...
|
||||
strategy:
|
||||
matrix:
|
||||
include:
|
||||
- cuda: 126
|
||||
cuda_version: 12.6.3
|
||||
python_version: "3.11"
|
||||
pytorch: 2.6.0
|
||||
axolotl_extras:
|
||||
- cuda: 126
|
||||
cuda_version: 12.6.3
|
||||
python_version: "3.11"
|
||||
pytorch: 2.7.1
|
||||
axolotl_extras:
|
||||
runs-on: axolotl-gpu-runner
|
||||
steps:
|
||||
- name: Checkout
|
||||
uses: actions/checkout@v4
|
||||
- name: Docker metadata
|
||||
id: metadata
|
||||
uses: docker/metadata-action@v5
|
||||
with:
|
||||
images: |
|
||||
winglian/axolotl-cloud
|
||||
axolotlai/axolotl-cloud
|
||||
tags: |
|
||||
type=raw,value={{ branch }}-{{ date 'YYYYMMDD' }}
|
||||
- name: Login to Docker Hub
|
||||
uses: docker/login-action@v3
|
||||
with:
|
||||
username: ${{ secrets.DOCKERHUB_USERNAME }}
|
||||
password: ${{ secrets.DOCKERHUB_TOKEN }}
|
||||
- name: Set up Docker Buildx
|
||||
uses: docker/setup-buildx-action@v3
|
||||
- name: Build
|
||||
uses: docker/build-push-action@v5
|
||||
with:
|
||||
context: .
|
||||
build-args: |
|
||||
BASE_TAG=${{ github.ref_name }}-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
|
||||
CUDA=${{ matrix.cuda }}
|
||||
file: ./docker/Dockerfile-cloud
|
||||
push: ${{ github.event_name != 'pull_request' }}
|
||||
tags: |
|
||||
${{ steps.metadata.outputs.tags }}-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
|
||||
labels: ${{ steps.metadata.outputs.labels }}
|
||||
40
.github/workflows/precommit-autoupdate.yml
vendored
40
.github/workflows/precommit-autoupdate.yml
vendored
@@ -1,40 +0,0 @@
|
||||
name: Pre-commit auto-update
|
||||
|
||||
on:
|
||||
schedule:
|
||||
- cron: '0 0 * * 0' # Run weekly
|
||||
workflow_dispatch: # Manual kickoff
|
||||
|
||||
jobs:
|
||||
auto-update:
|
||||
runs-on: ubuntu-latest
|
||||
permissions:
|
||||
contents: write
|
||||
pull-requests: write
|
||||
steps:
|
||||
- uses: actions/checkout@v4
|
||||
|
||||
- uses: actions/setup-python@v5
|
||||
with:
|
||||
python-version: '3.11'
|
||||
|
||||
- name: Update pre-commit hooks
|
||||
id: update
|
||||
run: |
|
||||
pip install pre-commit
|
||||
pre-commit autoupdate
|
||||
if [[ -n $(git status --porcelain) ]]; then
|
||||
echo "changes=true" >> $GITHUB_OUTPUT
|
||||
fi
|
||||
|
||||
- name: Create Pull Request
|
||||
if: steps.update.outputs.changes == 'true'
|
||||
uses: peter-evans/create-pull-request@v6
|
||||
with:
|
||||
token: ${{ secrets.GITHUB_TOKEN }}
|
||||
branch: update/pre-commit-hooks
|
||||
delete-branch: true
|
||||
title: "chore: update pre-commit hooks"
|
||||
commit-message: "chore: update pre-commit hooks"
|
||||
body: |
|
||||
Automated PR to update pre-commit hooks to their latest versions.
|
||||
76
.github/workflows/preview-docs.yml
vendored
76
.github/workflows/preview-docs.yml
vendored
@@ -1,76 +0,0 @@
|
||||
name: Preview
|
||||
on:
|
||||
workflow_dispatch:
|
||||
pull_request:
|
||||
types: [opened, synchronize, reopened]
|
||||
|
||||
# Run the workflow only when one of these files changes
|
||||
paths:
|
||||
- '**/*.md' # any Markdown file
|
||||
- '**/*.qmd' # any Quarto file
|
||||
- '_quarto.yml'
|
||||
- docs/scripts/generate_config_docs.py
|
||||
- src/axolotl/utils/schemas/**.py
|
||||
|
||||
permissions:
|
||||
checks: write
|
||||
contents: write
|
||||
deployments: write
|
||||
issues: write
|
||||
discussions: write
|
||||
pages: write
|
||||
pull-requests: write
|
||||
statuses: write
|
||||
|
||||
jobs:
|
||||
preview:
|
||||
runs-on: ubuntu-latest
|
||||
steps:
|
||||
- name: Check out repository
|
||||
uses: actions/checkout@v4
|
||||
with:
|
||||
ref: ${{ github.event.pull_request.head.sha }}
|
||||
|
||||
- name: Set up Quarto
|
||||
uses: quarto-dev/quarto-actions/setup@v2
|
||||
|
||||
- name: Setup Python
|
||||
uses: actions/setup-python@v5
|
||||
with:
|
||||
python-version: '3.11'
|
||||
|
||||
- name: Install dependencies
|
||||
run: |
|
||||
python3 -m pip install jupyter quartodoc
|
||||
python3 -m pip install -e .
|
||||
|
||||
- name: Build autodoc
|
||||
run: quartodoc build
|
||||
|
||||
- name: Quarto render
|
||||
run: quarto render
|
||||
|
||||
- name: Netlify Publish
|
||||
uses: nwtgck/actions-netlify@v3.0
|
||||
id: netlify
|
||||
with:
|
||||
publish-dir: './_site'
|
||||
enable-pull-request-comment: false
|
||||
enable-github-deployment: false
|
||||
github-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
deploy-message: "Deployed On Netlify"
|
||||
github-deployment-environment: 'preview'
|
||||
github-deployment-description: 'Preview Deployment'
|
||||
env:
|
||||
NETLIFY_AUTH_TOKEN: ${{ secrets.NETLIFY_AUTH_TOKEN }}
|
||||
NETLIFY_SITE_ID: ${{ secrets.NETLIFY_SITE_ID }}
|
||||
|
||||
- name: Update PR with preview link
|
||||
if: ${{ steps.netlify.outcome == 'success' }}
|
||||
uses: marocchino/sticky-pull-request-comment@v2
|
||||
with:
|
||||
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
|
||||
message: |
|
||||
📖 **Documentation Preview**: ${{ steps.netlify.outputs.deploy-url }}
|
||||
|
||||
Deployed on Netlify from commit ${{ github.event.pull_request.head.sha }}
|
||||
37
.github/workflows/pypi.yml
vendored
37
.github/workflows/pypi.yml
vendored
@@ -3,27 +3,12 @@ name: publish pypi
|
||||
on:
|
||||
push:
|
||||
tags:
|
||||
- 'v*'
|
||||
workflow_dispatch:
|
||||
- '*'
|
||||
|
||||
jobs:
|
||||
setup_release:
|
||||
name: Create Release
|
||||
runs-on: ubuntu-latest
|
||||
permissions:
|
||||
contents: write
|
||||
steps:
|
||||
- name: Checkout code
|
||||
uses: actions/checkout@v4
|
||||
|
||||
- name: Create release
|
||||
env:
|
||||
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
|
||||
run: gh release create "$GITHUB_REF_NAME" --generate-notes
|
||||
pypi-publish:
|
||||
name: Upload release to PyPI
|
||||
runs-on: ubuntu-latest
|
||||
needs: [setup_release]
|
||||
environment:
|
||||
name: pypi
|
||||
url: https://pypi.org/p/axolotl
|
||||
@@ -31,30 +16,30 @@ jobs:
|
||||
id-token: write # IMPORTANT: this permission is mandatory for trusted publishing
|
||||
steps:
|
||||
- name: Check out repository code
|
||||
uses: actions/checkout@v4
|
||||
uses: actions/checkout@v3
|
||||
|
||||
- name: Setup Python
|
||||
uses: actions/setup-python@v5
|
||||
uses: actions/setup-python@v4
|
||||
with:
|
||||
python-version: "3.11"
|
||||
python-version: "3.10"
|
||||
|
||||
- name: Install dependencies
|
||||
run: |
|
||||
pip3 install wheel packaging==23.2
|
||||
pip3 install --no-build-isolation -e .
|
||||
pip3 install -r requirements-dev.txt -r requirements-tests.txt
|
||||
pip3 install wheel
|
||||
pip3 install -e .
|
||||
pip3 install -r requirements-tests.txt
|
||||
|
||||
- name: Extract tag name
|
||||
id: tag
|
||||
run: echo ::set-output name=TAG_NAME::$(echo $GITHUB_REF | cut -d / -f 3)
|
||||
|
||||
- name: Update version in setup.py
|
||||
run: |
|
||||
run: >-
|
||||
sed -i -E 's/version="([0-9.]+)",/version="${{ steps.tag.outputs.TAG_NAME }}",/g' setup.py
|
||||
|
||||
- name: Build a source dist
|
||||
run: |
|
||||
python setup.py sdist
|
||||
- name: Build a binary wheel
|
||||
run: >-
|
||||
python setup.py sdist bdist_wheel
|
||||
|
||||
- name: Publish package distributions to PyPI
|
||||
uses: pypa/gh-action-pypi-publish@release/v1
|
||||
|
||||
181
.github/workflows/tests-nightly.yml
vendored
181
.github/workflows/tests-nightly.yml
vendored
@@ -1,181 +0,0 @@
|
||||
name: Tests Nightly against upstream main
|
||||
on:
|
||||
workflow_dispatch:
|
||||
schedule:
|
||||
- cron: '0 0 * * *' # Runs at 00:00 UTC every day
|
||||
|
||||
jobs:
|
||||
pre-commit:
|
||||
name: pre-commit
|
||||
runs-on: ubuntu-latest
|
||||
steps:
|
||||
- uses: actions/checkout@v4
|
||||
- uses: actions/setup-python@v5
|
||||
with:
|
||||
python-version: "3.11"
|
||||
cache: 'pip' # caching pip dependencies
|
||||
- uses: pre-commit/action@v3.0.1
|
||||
env:
|
||||
SKIP: no-commit-to-branch
|
||||
|
||||
pytest:
|
||||
name: PyTest
|
||||
runs-on: ubuntu-latest
|
||||
strategy:
|
||||
fail-fast: false
|
||||
max-parallel: 2
|
||||
matrix:
|
||||
python_version: ["3.11"]
|
||||
pytorch_version: ["2.6.0", "2.7.0"]
|
||||
timeout-minutes: 20
|
||||
|
||||
steps:
|
||||
- name: Check out repository code
|
||||
uses: actions/checkout@v4
|
||||
|
||||
- name: Restore Cache from S3
|
||||
id: hf-cache-restore-s3
|
||||
run: |
|
||||
mkdir -p /home/runner/.cache/huggingface/hub
|
||||
curl -L https://d1dttdx32dkk5p.cloudfront.net/hf-cache.tar.zst | tar -xf - -C /home/runner/.cache/huggingface/hub/ --use-compress-program unzstd
|
||||
|
||||
- name: Setup Python
|
||||
uses: actions/setup-python@v5
|
||||
with:
|
||||
python-version: ${{ matrix.python_version }}
|
||||
cache: 'pip' # caching pip dependencies
|
||||
|
||||
- name: upgrade pip
|
||||
run: |
|
||||
pip3 install --upgrade pip
|
||||
pip3 install --upgrade packaging==23.2 setuptools==75.8.0 wheel
|
||||
|
||||
- name: Install PyTorch
|
||||
run: |
|
||||
pip3 install torch==${{ matrix.pytorch_version }}
|
||||
|
||||
- name: Update requirements.txt
|
||||
run: |
|
||||
sed -i 's#^transformers.*#transformers @ git+https://github.com/huggingface/transformers.git@main#' requirements.txt
|
||||
sed -i 's#^peft.*#peft @ git+https://github.com/huggingface/peft.git@main#' requirements.txt
|
||||
sed -i 's#^accelerate.*#accelerate @ git+https://github.com/huggingface/accelerate.git@main#' requirements.txt
|
||||
sed -i 's#^trl.*#trl @ git+https://github.com/huggingface/trl.git@main#' requirements.txt
|
||||
sed -i 's#^datasets.*#datasets @ git+https://github.com/huggingface/datasets.git@main#' requirements.txt
|
||||
|
||||
- name: Install dependencies
|
||||
run: |
|
||||
pip3 show torch
|
||||
pip3 install --no-build-isolation -U -e .
|
||||
python scripts/unsloth_install.py | sh
|
||||
python scripts/cutcrossentropy_install.py | sh
|
||||
pip3 install -r requirements-dev.txt -r requirements-tests.txt
|
||||
|
||||
- name: Make sure PyTorch version wasn't clobbered
|
||||
run: |
|
||||
python -c "import torch; assert '${{ matrix.pytorch_version }}' in torch.__version__"
|
||||
|
||||
- name: Ensure axolotl CLI was installed
|
||||
run: |
|
||||
axolotl --help
|
||||
|
||||
- name: Run tests
|
||||
run: |
|
||||
pytest -v --durations=10 -n8 --dist loadfile --ignore=tests/e2e/ --ignore=tests/patched/ --ignore=tests/cli/ tests/
|
||||
pytest -v --durations=10 tests/patched/
|
||||
pytest -v --durations=10 tests/cli/
|
||||
|
||||
- name: cleanup pip cache
|
||||
run: |
|
||||
find "$(pip cache dir)/http-v2" -type f -mtime +14 -exec rm {} \;
|
||||
|
||||
docker-e2e-tests:
|
||||
if: github.repository_owner == 'axolotl-ai-cloud'
|
||||
# this job needs to be run on self-hosted GPU runners...
|
||||
runs-on: [self-hosted, modal]
|
||||
timeout-minutes: 120
|
||||
needs: [pre-commit, pytest]
|
||||
|
||||
strategy:
|
||||
fail-fast: false
|
||||
matrix:
|
||||
include:
|
||||
- cuda: 126
|
||||
cuda_version: 12.6.3
|
||||
python_version: "3.11"
|
||||
pytorch: 2.6.0
|
||||
num_gpus: 1
|
||||
axolotl_extras:
|
||||
nightly_build: "true"
|
||||
- cuda: 126
|
||||
cuda_version: 12.6.3
|
||||
python_version: "3.11"
|
||||
pytorch: 2.7.1
|
||||
num_gpus: 1
|
||||
axolotl_extras:
|
||||
nightly_build: "true"
|
||||
steps:
|
||||
- name: Checkout
|
||||
uses: actions/checkout@v4
|
||||
- name: Install Python
|
||||
uses: actions/setup-python@v5
|
||||
with:
|
||||
python-version: "3.11"
|
||||
- name: Install Modal
|
||||
run: |
|
||||
python -m pip install --upgrade pip
|
||||
pip install modal==1.0.2 jinja2
|
||||
- name: Update env vars
|
||||
run: |
|
||||
echo "BASE_TAG=main-base-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}" >> $GITHUB_ENV
|
||||
echo "PYTORCH_VERSION=${{ matrix.pytorch}}" >> $GITHUB_ENV
|
||||
echo "AXOLOTL_ARGS=${{ matrix.axolotl_args}}" >> $GITHUB_ENV
|
||||
echo "AXOLOTL_EXTRAS=${{ matrix.axolotl_extras}}" >> $GITHUB_ENV
|
||||
echo "CUDA=${{ matrix.cuda }}" >> $GITHUB_ENV
|
||||
echo "N_GPUS=${{ matrix.num_gpus }}" >> $GITHUB_ENV
|
||||
echo "NIGHTLY_BUILD=${{ matrix.nightly_build }}" >> $GITHUB_ENV
|
||||
echo "CODECOV_TOKEN=${{ secrets.CODECOV_TOKEN }}" >> $GITHUB_ENV
|
||||
- name: Run tests job on Modal
|
||||
run: |
|
||||
modal run cicd.e2e_tests
|
||||
docker-e2e-multigpu-tests:
|
||||
if: github.repository_owner == 'axolotl-ai-cloud'
|
||||
# this job needs to be run on self-hosted GPU runners...
|
||||
runs-on: [self-hosted, modal]
|
||||
timeout-minutes: 120
|
||||
needs: [pre-commit, pytest, docker-e2e-tests]
|
||||
|
||||
strategy:
|
||||
fail-fast: false
|
||||
matrix:
|
||||
include:
|
||||
- cuda: 126
|
||||
cuda_version: 12.6.3
|
||||
python_version: "3.11"
|
||||
pytorch: 2.7.1
|
||||
num_gpus: 2
|
||||
axolotl_extras:
|
||||
nightly_build: "true"
|
||||
steps:
|
||||
- name: Checkout
|
||||
uses: actions/checkout@v4
|
||||
- name: Install Python
|
||||
uses: actions/setup-python@v5
|
||||
with:
|
||||
python-version: "3.11"
|
||||
- name: Install Modal
|
||||
run: |
|
||||
python -m pip install --upgrade pip
|
||||
pip install modal==1.0.2 jinja2
|
||||
- name: Update env vars
|
||||
run: |
|
||||
echo "BASE_TAG=main-base-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}" >> $GITHUB_ENV
|
||||
echo "PYTORCH_VERSION=${{ matrix.pytorch}}" >> $GITHUB_ENV
|
||||
echo "AXOLOTL_ARGS=${{ matrix.axolotl_args}}" >> $GITHUB_ENV
|
||||
echo "AXOLOTL_EXTRAS=${{ matrix.axolotl_extras}}" >> $GITHUB_ENV
|
||||
echo "CUDA=${{ matrix.cuda }}" >> $GITHUB_ENV
|
||||
echo "N_GPUS=${{ matrix.num_gpus }}" >> $GITHUB_ENV
|
||||
echo "NIGHTLY_BUILD=${{ matrix.nightly_build }}" >> $GITHUB_ENV
|
||||
echo "CODECOV_TOKEN=${{ secrets.CODECOV_TOKEN }}" >> $GITHUB_ENV
|
||||
- name: Run tests job on Modal
|
||||
run: |
|
||||
modal run cicd.multigpu
|
||||
295
.github/workflows/tests.yml
vendored
295
.github/workflows/tests.yml
vendored
@@ -1,326 +1,81 @@
|
||||
name: Tests
|
||||
on:
|
||||
# check on push/merge to main, PRs, and manual triggers
|
||||
merge_group:
|
||||
push:
|
||||
branches:
|
||||
- "main"
|
||||
paths:
|
||||
- '**.py'
|
||||
- 'requirements.txt'
|
||||
- '.github/workflows/*.yml'
|
||||
- 'requirements-tests.txt'
|
||||
- 'cicd/cicd.sh'
|
||||
- 'cicd/Dockerfile.jinja'
|
||||
pull_request:
|
||||
paths:
|
||||
- '**.py'
|
||||
- 'requirements.txt'
|
||||
- '.github/workflows/*.yml'
|
||||
- 'requirements-tests.txt'
|
||||
- 'cicd/cicd.sh'
|
||||
- 'cicd/Dockerfile.jinja'
|
||||
workflow_dispatch:
|
||||
|
||||
# Cancel jobs on the same ref if a new one is triggered
|
||||
concurrency:
|
||||
group: ${{ github.workflow }}-${{ github.ref }}
|
||||
cancel-in-progress: ${{ github.ref != 'refs/heads/main' }}
|
||||
|
||||
env:
|
||||
TRANSFORMERS_IS_CI: "yes"
|
||||
|
||||
jobs:
|
||||
pre-commit:
|
||||
name: pre-commit
|
||||
runs-on: ubuntu-latest
|
||||
steps:
|
||||
- uses: actions/checkout@v4
|
||||
- uses: actions/setup-python@v5
|
||||
- uses: actions/checkout@v3
|
||||
- uses: actions/setup-python@v4
|
||||
with:
|
||||
python-version: "3.11"
|
||||
python-version: "3.9"
|
||||
cache: 'pip' # caching pip dependencies
|
||||
- uses: pre-commit/action@v3.0.1
|
||||
env:
|
||||
SKIP: no-commit-to-branch
|
||||
- uses: pre-commit/action@v3.0.0
|
||||
|
||||
pytest:
|
||||
name: PyTest
|
||||
runs-on: ubuntu-latest
|
||||
# needs: [preload-cache]
|
||||
strategy:
|
||||
fail-fast: false
|
||||
matrix:
|
||||
python_version: ["3.11"]
|
||||
pytorch_version: ["2.6.0", "2.7.0", "2.7.1"]
|
||||
timeout-minutes: 20
|
||||
python_version: ["3.9", "3.10"]
|
||||
timeout-minutes: 10
|
||||
|
||||
steps:
|
||||
- name: Check out repository code
|
||||
uses: actions/checkout@v4
|
||||
|
||||
- name: Restore Cache from S3
|
||||
id: hf-cache-restore-s3
|
||||
run: |
|
||||
mkdir -p /home/runner/.cache/huggingface/hub
|
||||
curl -L https://d1dttdx32dkk5p.cloudfront.net/hf-cache.tar.zst | tar -xf - -C /home/runner/.cache/huggingface/hub/ --use-compress-program unzstd
|
||||
uses: actions/checkout@v3
|
||||
|
||||
- name: Setup Python
|
||||
uses: actions/setup-python@v5
|
||||
uses: actions/setup-python@v4
|
||||
with:
|
||||
python-version: ${{ matrix.python_version }}
|
||||
cache: 'pip' # caching pip dependencies
|
||||
|
||||
- name: upgrade pip
|
||||
run: |
|
||||
pip3 install --upgrade pip
|
||||
pip3 install --upgrade packaging==23.2 setuptools==75.8.0 wheel
|
||||
|
||||
- name: Install PyTorch
|
||||
run: |
|
||||
pip3 install torch==${{ matrix.pytorch_version }}
|
||||
|
||||
- name: Install dependencies
|
||||
run: |
|
||||
pip3 show torch
|
||||
pip3 install --no-build-isolation -U -e .
|
||||
python scripts/unsloth_install.py | sh
|
||||
python scripts/cutcrossentropy_install.py | sh
|
||||
pip3 install -r requirements-dev.txt -r requirements-tests.txt
|
||||
|
||||
- name: Make sure PyTorch version wasn't clobbered
|
||||
run: |
|
||||
python -c "import torch; assert '${{ matrix.pytorch_version }}' in torch.__version__"
|
||||
|
||||
- name: Ensure axolotl CLI was installed
|
||||
run: |
|
||||
axolotl --help
|
||||
|
||||
- name: Pre-Download dataset fixture
|
||||
run: |
|
||||
huggingface-cli download --repo-type=dataset axolotl-ai-internal/axolotl-oss-dataset-fixtures
|
||||
pip3 install -U -e .
|
||||
pip3 install -r requirements-tests.txt
|
||||
|
||||
- name: Run tests
|
||||
run: |
|
||||
pytest -v --durations=10 -n8 --dist loadfile --ignore=tests/e2e/ --ignore=tests/patched/ --ignore=tests/cli/ tests/ --cov=axolotl --cov-report=xml
|
||||
pytest -v --durations=10 tests/patched/ --cov=axolotl --cov-append --cov-report=xml
|
||||
pytest -v --durations=10 tests/cli/ --cov=axolotl --cov-append --cov-report=xml
|
||||
pytest --ignore=tests/e2e/ tests/
|
||||
|
||||
- name: Upload coverage to Codecov
|
||||
uses: codecov/codecov-action@v5
|
||||
with:
|
||||
token: ${{ secrets.CODECOV_TOKEN }}
|
||||
files: ./coverage.xml
|
||||
flags: unittests,pytorch-${{ matrix.pytorch_version }}
|
||||
fail_ci_if_error: false
|
||||
|
||||
- name: cleanup pip cache
|
||||
run: |
|
||||
find "$(pip cache dir)/http-v2" -type f -mtime +14 -exec rm {} \;
|
||||
|
||||
pytest-sdist:
|
||||
name: PyTest from Source Dist
|
||||
runs-on: ubuntu-latest
|
||||
strategy:
|
||||
fail-fast: false
|
||||
matrix:
|
||||
python_version: ["3.11"]
|
||||
pytorch_version: ["2.6.0", "2.7.0", "2.7.1"]
|
||||
e2e-test:
|
||||
name: E2E Tests
|
||||
runs-on: [self-hosted, gpu]
|
||||
timeout-minutes: 20
|
||||
needs: [pre-commit, pytest]
|
||||
|
||||
steps:
|
||||
- name: Check out repository code
|
||||
uses: actions/checkout@v4
|
||||
|
||||
- name: Restore Cache from S3
|
||||
id: hf-cache-restore-s3
|
||||
run: |
|
||||
mkdir -p /home/runner/.cache/huggingface/hub
|
||||
curl -L https://d1dttdx32dkk5p.cloudfront.net/hf-cache.tar.zst | tar -xf - -C /home/runner/.cache/huggingface/hub/ --use-compress-program unzstd
|
||||
uses: actions/checkout@v3
|
||||
|
||||
- name: Setup Python
|
||||
uses: actions/setup-python@v5
|
||||
uses: actions/setup-python@v4
|
||||
with:
|
||||
python-version: ${{ matrix.python_version }}
|
||||
cache: 'pip' # caching pip dependencies
|
||||
|
||||
- name: upgrade pip
|
||||
run: |
|
||||
pip3 install --upgrade pip
|
||||
pip3 install --upgrade packaging==23.2 setuptools==75.8.0 setuptools_scm build wheel
|
||||
|
||||
- name: Install PyTorch
|
||||
run: |
|
||||
pip3 install torch==${{ matrix.pytorch_version }}
|
||||
python-version: "3.10"
|
||||
# cache: 'pip' # caching pip dependencies
|
||||
|
||||
- name: Install dependencies
|
||||
run: |
|
||||
pip3 show torch
|
||||
python -m build --no-isolation --sdist
|
||||
pip3 install --no-build-isolation dist/axolotl*.tar.gz
|
||||
python scripts/unsloth_install.py | sh
|
||||
python scripts/cutcrossentropy_install.py | sh
|
||||
pip3 install -r requirements-dev.txt -r requirements-tests.txt
|
||||
pip3 install --extra-index-url https://download.pytorch.org/whl/cu118 -U torch==2.0.1
|
||||
pip3 uninstall -y transformers accelerate
|
||||
pip3 install -U -e .[flash-attn,mamba-ssm]
|
||||
pip3 install -r requirements-tests.txt
|
||||
|
||||
- name: Make sure PyTorch version wasn't clobbered
|
||||
- name: Run e2e tests
|
||||
run: |
|
||||
python -c "import torch; assert '${{ matrix.pytorch_version }}' in torch.__version__"
|
||||
|
||||
- name: Ensure axolotl CLI was installed
|
||||
run: |
|
||||
axolotl --help
|
||||
|
||||
- name: Show HF cache
|
||||
run: huggingface-cli scan-cache
|
||||
|
||||
- name: Run tests
|
||||
run: |
|
||||
pytest -v --durations=10 -n8 --dist loadfile --ignore=tests/e2e/ --ignore=tests/patched/ --ignore=tests/cli/ tests/
|
||||
pytest -v --durations=10 tests/patched/
|
||||
pytest -v --durations=10 tests/cli/
|
||||
|
||||
- name: cleanup pip cache
|
||||
run: |
|
||||
find "$(pip cache dir)/http-v2" -type f -mtime +14 -exec rm {} \;
|
||||
|
||||
docker-e2e-tests-1st:
|
||||
# Run this job first as a gate for running the remainder of the test matrix
|
||||
if: ${{ ! contains(github.event.commits[0].message, '[skip e2e]') && github.repository_owner == 'axolotl-ai-cloud' }}
|
||||
# this job needs to be run on self-hosted GPU runners...
|
||||
runs-on: [self-hosted, modal]
|
||||
timeout-minutes: 120
|
||||
needs: [pre-commit, pytest, pytest-sdist]
|
||||
|
||||
strategy:
|
||||
fail-fast: false
|
||||
matrix:
|
||||
include:
|
||||
- cuda: 126
|
||||
cuda_version: 12.6.3
|
||||
python_version: "3.11"
|
||||
pytorch: 2.7.1
|
||||
num_gpus: 1
|
||||
axolotl_extras:
|
||||
- cuda: 126
|
||||
cuda_version: 12.6.3
|
||||
python_version: "3.11"
|
||||
pytorch: 2.6.0
|
||||
num_gpus: 1
|
||||
axolotl_extras:
|
||||
dockerfile: "Dockerfile-uv.jinja"
|
||||
steps:
|
||||
- name: Checkout
|
||||
uses: actions/checkout@v4
|
||||
- name: Install Python
|
||||
uses: actions/setup-python@v5
|
||||
with:
|
||||
python-version: "3.11"
|
||||
- name: Install Modal
|
||||
run: |
|
||||
python -m pip install --upgrade pip
|
||||
pip install modal==1.0.2 jinja2
|
||||
- name: Update env vars
|
||||
run: |
|
||||
echo "BASE_TAG=main-base-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}" >> $GITHUB_ENV
|
||||
echo "PYTORCH_VERSION=${{ matrix.pytorch}}" >> $GITHUB_ENV
|
||||
echo "AXOLOTL_ARGS=${{ matrix.axolotl_args}}" >> $GITHUB_ENV
|
||||
echo "AXOLOTL_EXTRAS=${{ matrix.axolotl_extras}}" >> $GITHUB_ENV
|
||||
echo "CUDA=${{ matrix.cuda }}" >> $GITHUB_ENV
|
||||
echo "MODAL_IMAGE_BUILDER_VERSION=2024.10" >> $GITHUB_ENV
|
||||
echo "N_GPUS=${{ matrix.num_gpus }}" >> $GITHUB_ENV
|
||||
echo "CODECOV_TOKEN=${{ secrets.CODECOV_TOKEN }}" >> $GITHUB_ENV
|
||||
echo "E2E_DOCKERFILE=${{ matrix.dockerfile || 'Dockerfile.jinja'}}" >> $GITHUB_ENV
|
||||
- name: Run tests job on Modal
|
||||
run: |
|
||||
modal run cicd.e2e_tests
|
||||
|
||||
docker-e2e-tests:
|
||||
if: github.repository_owner == 'axolotl-ai-cloud'
|
||||
# this job needs to be run on self-hosted GPU runners...
|
||||
runs-on: [self-hosted, modal]
|
||||
timeout-minutes: 120
|
||||
# Only run the remainder of the matrix if the first e2e check passed;
|
||||
# this is to save on wasted compute costs for known failures that get caught in the first run
|
||||
needs: [pre-commit, pytest, docker-e2e-tests-1st]
|
||||
|
||||
strategy:
|
||||
fail-fast: false
|
||||
matrix:
|
||||
include:
|
||||
- cuda: 126
|
||||
cuda_version: 12.6.3
|
||||
python_version: "3.11"
|
||||
pytorch: 2.6.0
|
||||
num_gpus: 1
|
||||
axolotl_extras:
|
||||
- cuda: 128
|
||||
cuda_version: 12.8.1
|
||||
python_version: "3.11"
|
||||
pytorch: 2.7.1
|
||||
num_gpus: 1
|
||||
axolotl_extras:
|
||||
steps:
|
||||
- name: Checkout
|
||||
uses: actions/checkout@v4
|
||||
- name: Install Python
|
||||
uses: actions/setup-python@v5
|
||||
with:
|
||||
python-version: "3.11"
|
||||
- name: Install Modal
|
||||
run: |
|
||||
python -m pip install --upgrade pip
|
||||
pip install modal==1.0.2 jinja2
|
||||
- name: Update env vars
|
||||
run: |
|
||||
echo "BASE_TAG=main-base-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}" >> $GITHUB_ENV
|
||||
echo "PYTORCH_VERSION=${{ matrix.pytorch}}" >> $GITHUB_ENV
|
||||
echo "AXOLOTL_ARGS=${{ matrix.axolotl_args}}" >> $GITHUB_ENV
|
||||
echo "AXOLOTL_EXTRAS=${{ matrix.axolotl_extras}}" >> $GITHUB_ENV
|
||||
echo "CUDA=${{ matrix.cuda }}" >> $GITHUB_ENV
|
||||
echo "MODAL_IMAGE_BUILDER_VERSION=2024.10" >> $GITHUB_ENV
|
||||
echo "N_GPUS=${{ matrix.num_gpus }}" >> $GITHUB_ENV
|
||||
echo "CODECOV_TOKEN=${{ secrets.CODECOV_TOKEN }}" >> $GITHUB_ENV
|
||||
echo "E2E_DOCKERFILE=${{ matrix.dockerfile || 'Dockerfile.jinja'}}" >> $GITHUB_ENV
|
||||
- name: Run tests job on Modal
|
||||
run: |
|
||||
modal run cicd.e2e_tests
|
||||
|
||||
docker-e2e-cleanup:
|
||||
runs-on: [self-hosted, modal]
|
||||
timeout-minutes: 90
|
||||
needs: [docker-e2e-tests]
|
||||
|
||||
strategy:
|
||||
fail-fast: false
|
||||
matrix:
|
||||
include:
|
||||
- cuda: 124
|
||||
cuda_version: 12.4.1
|
||||
python_version: "3.11"
|
||||
pytorch: 2.6.0
|
||||
num_gpus: 1
|
||||
axolotl_extras:
|
||||
steps:
|
||||
- name: Checkout
|
||||
uses: actions/checkout@v4
|
||||
- name: Install Python
|
||||
uses: actions/setup-python@v5
|
||||
with:
|
||||
python-version: "3.11"
|
||||
- name: Install Modal
|
||||
run: |
|
||||
python -m pip install --upgrade pip
|
||||
pip install modal==1.0.2 jinja2
|
||||
- name: Update env vars
|
||||
run: |
|
||||
echo "BASE_TAG=main-base-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}" >> $GITHUB_ENV
|
||||
echo "PYTORCH_VERSION=${{ matrix.pytorch}}" >> $GITHUB_ENV
|
||||
echo "AXOLOTL_ARGS=${{ matrix.axolotl_args}}" >> $GITHUB_ENV
|
||||
echo "AXOLOTL_EXTRAS=${{ matrix.axolotl_extras}}" >> $GITHUB_ENV
|
||||
echo "CUDA=${{ matrix.cuda }}" >> $GITHUB_ENV
|
||||
echo "MODAL_IMAGE_BUILDER_VERSION=2024.10" >> $GITHUB_ENV
|
||||
echo "N_GPUS=${{ matrix.num_gpus }}" >> $GITHUB_ENV
|
||||
echo "CODECOV_TOKEN=${{ secrets.CODECOV_TOKEN }}" >> $GITHUB_ENV
|
||||
- name: Run tests job on Modal
|
||||
run: |
|
||||
modal run cicd.cleanup
|
||||
pytest tests/e2e/
|
||||
|
||||
25
.gitignore
vendored
25
.gitignore
vendored
@@ -1,9 +1,5 @@
|
||||
**/axolotl.egg-info
|
||||
configs
|
||||
last_run_prepared/
|
||||
outputs
|
||||
.vscode
|
||||
_site/
|
||||
|
||||
# Byte-compiled / optimized / DLL files
|
||||
__pycache__/
|
||||
@@ -134,7 +130,6 @@ venv/
|
||||
ENV/
|
||||
env.bak/
|
||||
venv.bak/
|
||||
venv3.10/
|
||||
|
||||
# Spyder project settings
|
||||
.spyderproject
|
||||
@@ -170,23 +165,3 @@ cython_debug/
|
||||
# WandB
|
||||
# wandb creates a folder to store logs for training runs
|
||||
wandb
|
||||
|
||||
# Runs
|
||||
lora-out/*
|
||||
qlora-out/*
|
||||
mlruns/*
|
||||
|
||||
/.quarto/
|
||||
prepared-datasets/
|
||||
submit.sh
|
||||
*.out*
|
||||
|
||||
# Quartodoc generated files
|
||||
objects.json
|
||||
site_libs/
|
||||
|
||||
typings/
|
||||
out/
|
||||
|
||||
# vim
|
||||
*.swp
|
||||
|
||||
@@ -1,4 +1,3 @@
|
||||
[settings]
|
||||
profile=black
|
||||
known_third_party=wandb,comet_ml
|
||||
known_local_folder=src,tests
|
||||
known_third_party=wandb
|
||||
|
||||
@@ -1,5 +1,5 @@
|
||||
[mypy]
|
||||
plugins = pydantic.mypy
|
||||
|
||||
exclude = venv
|
||||
|
||||
[mypy-alpaca_lora_4bit.*]
|
||||
@@ -11,9 +11,6 @@ ignore_errors = True
|
||||
[mypy-axolotl.models.mixtral.*]
|
||||
ignore_errors = True
|
||||
|
||||
[mypy-axolotl.integrations.liger.models.*]
|
||||
ignore_errors = True
|
||||
|
||||
[mypy-axolotl.models.phi.*]
|
||||
ignore_errors = True
|
||||
|
||||
@@ -35,9 +32,6 @@ ignore_missing_imports = True
|
||||
[mypy-bitsandbytes]
|
||||
ignore_missing_imports = True
|
||||
|
||||
[mypy-requests]
|
||||
ignore_missing_imports = True
|
||||
|
||||
[mypy-datasets]
|
||||
ignore_missing_imports = True
|
||||
|
||||
|
||||
@@ -3,40 +3,37 @@ default_language_version:
|
||||
|
||||
repos:
|
||||
- repo: https://github.com/pre-commit/pre-commit-hooks
|
||||
rev: v5.0.0
|
||||
rev: v4.4.0
|
||||
hooks:
|
||||
- id: check-yaml
|
||||
- id: end-of-file-fixer
|
||||
- id: trailing-whitespace
|
||||
- id: no-commit-to-branch
|
||||
args: ['--branch', 'main']
|
||||
- repo: https://github.com/psf/black
|
||||
rev: 25.1.0
|
||||
rev: 23.3.0
|
||||
hooks:
|
||||
- id: black
|
||||
- repo: https://github.com/pycqa/isort
|
||||
rev: 6.0.1
|
||||
rev: 5.12.0
|
||||
hooks:
|
||||
- id: isort
|
||||
- repo: https://github.com/PyCQA/flake8
|
||||
rev: 7.3.0
|
||||
rev: 6.0.0
|
||||
hooks:
|
||||
- id: flake8
|
||||
- repo: https://github.com/pylint-dev/pylint
|
||||
rev: v3.3.7
|
||||
- repo: https://github.com/PyCQA/pylint
|
||||
rev: v2.17.4
|
||||
hooks:
|
||||
- id: pylint
|
||||
- repo: https://github.com/pre-commit/mirrors-mypy
|
||||
rev: v1.16.1
|
||||
rev: v1.3.0
|
||||
hooks:
|
||||
- id: mypy
|
||||
additional_dependencies:
|
||||
[
|
||||
'types-PyYAML',
|
||||
'pydantic>=2.5.3',
|
||||
]
|
||||
- repo: https://github.com/PyCQA/bandit
|
||||
rev: 1.8.6
|
||||
rev: 1.7.5
|
||||
hooks:
|
||||
- id: bandit
|
||||
args: [
|
||||
|
||||
@@ -1,5 +1,5 @@
|
||||
[MASTER]
|
||||
init-hook="from pylint.config import find_default_config_files; import sys; sys.path.append(next(find_default_config_files()).parent.as_posix())"
|
||||
init-hook="from pylint.config import find_pylintrc; import os, sys; sys.path.append(os.path.dirname(find_pylintrc()))"
|
||||
|
||||
[TYPECHECK]
|
||||
|
||||
@@ -12,4 +12,3 @@ generated-members=numpy.*, torch.*
|
||||
disable=missing-function-docstring, line-too-long, import-error,
|
||||
too-many-arguments, too-many-locals, too-many-statements, too-many-branches, too-few-public-methods,
|
||||
too-many-instance-attributes, fixme, import-outside-toplevel, logging-fstring-interpolation,
|
||||
too-many-positional-arguments, possibly-used-before-assignment
|
||||
|
||||
161
.runpod/.gitignore
vendored
161
.runpod/.gitignore
vendored
@@ -1,161 +0,0 @@
|
||||
# Byte-compiled / optimized / DLL files
|
||||
__pycache__/
|
||||
*.py[cod]
|
||||
*$py.class
|
||||
|
||||
# C extensions
|
||||
*.so
|
||||
|
||||
# Distribution / packaging
|
||||
.Python
|
||||
build/
|
||||
develop-eggs/
|
||||
dist/
|
||||
downloads/
|
||||
eggs/
|
||||
.eggs/
|
||||
lib/
|
||||
lib64/
|
||||
parts/
|
||||
sdist/
|
||||
var/
|
||||
wheels/
|
||||
share/python-wheels/
|
||||
*.egg-info/
|
||||
.installed.cfg
|
||||
*.egg
|
||||
MANIFEST
|
||||
|
||||
# PyInstaller
|
||||
# Usually these files are written by a python script from a template
|
||||
# before PyInstaller builds the exe, so as to inject date/other infos into it.
|
||||
*.manifest
|
||||
*.spec
|
||||
|
||||
# Installer logs
|
||||
pip-log.txt
|
||||
pip-delete-this-directory.txt
|
||||
|
||||
# Unit test / coverage reports
|
||||
htmlcov/
|
||||
.tox/
|
||||
.nox/
|
||||
.coverage
|
||||
.coverage.*
|
||||
.cache
|
||||
nosetests.xml
|
||||
coverage.xml
|
||||
*.cover
|
||||
*.py,cover
|
||||
.hypothesis/
|
||||
.pytest_cache/
|
||||
cover/
|
||||
|
||||
# Translations
|
||||
*.mo
|
||||
*.pot
|
||||
|
||||
# Django stuff:
|
||||
*.log
|
||||
local_settings.py
|
||||
db.sqlite3
|
||||
db.sqlite3-journal
|
||||
|
||||
# Flask stuff:
|
||||
instance/
|
||||
.webassets-cache
|
||||
|
||||
# Scrapy stuff:
|
||||
.scrapy
|
||||
|
||||
# Sphinx documentation
|
||||
docs/_build/
|
||||
|
||||
# PyBuilder
|
||||
.pybuilder/
|
||||
target/
|
||||
|
||||
# Jupyter Notebook
|
||||
.ipynb_checkpoints
|
||||
|
||||
# IPython
|
||||
profile_default/
|
||||
ipython_config.py
|
||||
|
||||
# pyenv
|
||||
# For a library or package, you might want to ignore these files since the code is
|
||||
# intended to run in multiple environments; otherwise, check them in:
|
||||
# .python-version
|
||||
|
||||
# pipenv
|
||||
# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
|
||||
# However, in case of collaboration, if having platform-specific dependencies or dependencies
|
||||
# having no cross-platform support, pipenv may install dependencies that don't work, or not
|
||||
# install all needed dependencies.
|
||||
#Pipfile.lock
|
||||
|
||||
# poetry
|
||||
# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
|
||||
# This is especially recommended for binary packages to ensure reproducibility, and is more
|
||||
# commonly ignored for libraries.
|
||||
# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
|
||||
#poetry.lock
|
||||
|
||||
# pdm
|
||||
# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
|
||||
#pdm.lock
|
||||
# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
|
||||
# in version control.
|
||||
# https://pdm.fming.dev/#use-with-ide
|
||||
.pdm.toml
|
||||
|
||||
# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
|
||||
__pypackages__/
|
||||
|
||||
# Celery stuff
|
||||
celerybeat-schedule
|
||||
celerybeat.pid
|
||||
|
||||
# SageMath parsed files
|
||||
*.sage.py
|
||||
|
||||
# Environments
|
||||
.env
|
||||
.venv
|
||||
env/
|
||||
venv/
|
||||
ENV/
|
||||
env.bak/
|
||||
venv.bak/
|
||||
|
||||
# Spyder project settings
|
||||
.spyderproject
|
||||
.spyproject
|
||||
|
||||
# Rope project settings
|
||||
.ropeproject
|
||||
|
||||
# mkdocs documentation
|
||||
/site
|
||||
|
||||
# mypy
|
||||
.mypy_cache/
|
||||
.dmypy.json
|
||||
dmypy.json
|
||||
|
||||
# Pyre type checker
|
||||
.pyre/
|
||||
|
||||
# pytype static type analyzer
|
||||
.pytype/
|
||||
|
||||
# Cython debug symbols
|
||||
cython_debug/
|
||||
|
||||
# PyCharm
|
||||
# JetBrains specific template is maintained in a separate JetBrains.gitignore that can
|
||||
# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
|
||||
# and can be added to the global gitignore or merged into this file. For a more nuclear
|
||||
# option (not recommended) you can uncomment the following to ignore the entire idea folder.
|
||||
#.idea/
|
||||
pod/scripts/config.yaml
|
||||
@@ -1,18 +0,0 @@
|
||||
FROM axolotlai/axolotl-cloud:main-py3.11-cu124-2.6.0
|
||||
|
||||
COPY .runpod/requirements.txt /requirements.txt
|
||||
RUN --mount=type=cache,target=/root/.cache/pip \
|
||||
python3 -m pip install --upgrade pip && \
|
||||
python3 -m pip install --upgrade -r /requirements.txt
|
||||
|
||||
# Environment settings
|
||||
ARG BASE_VOLUME="/runpod-volume"
|
||||
ENV BASE_VOLUME=$BASE_VOLUME
|
||||
ENV HF_DATASETS_CACHE="${BASE_VOLUME}/huggingface-cache/datasets"
|
||||
ENV HUGGINGFACE_HUB_CACHE="${BASE_VOLUME}/huggingface-cache/hub"
|
||||
ENV TRANSFORMERS_CACHE="${BASE_VOLUME}/huggingface-cache/hub"
|
||||
|
||||
COPY .runpod/src /src
|
||||
|
||||
WORKDIR /src
|
||||
CMD ["python3", "/src/handler.py"]
|
||||
@@ -1,335 +0,0 @@
|
||||
<h1>LLM Post Training- Full fine-tune, LoRA, QLoRa etc. Llama/Mistral/Gemma and more</h1>
|
||||
|
||||
# Configuration Options
|
||||
|
||||
This document outlines all available configuration options for training models. The configuration can be provided as a JSON request.
|
||||
|
||||
## Usage
|
||||
|
||||
You can use these configuration Options:
|
||||
|
||||
1. As a JSON request body:
|
||||
|
||||
```json
|
||||
{
|
||||
"input": {
|
||||
"user_id": "user",
|
||||
"model_id": "model-name",
|
||||
"run_id": "run-id",
|
||||
"credentials": {
|
||||
"wandb_api_key": "", # add your Weights & biases key. TODO: you will be able to set this in Enviornment variables.
|
||||
"hf_token": "", # add your HF_token. TODO: you will be able to set this in Enviornment variables.
|
||||
},
|
||||
"args": {
|
||||
"base_model": "NousResearch/Llama-3.2-1B",
|
||||
// ... other options
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
## Configuration Options
|
||||
|
||||
### Model Configuration
|
||||
|
||||
| Option | Description | Default |
|
||||
| ------------------- | --------------------------------------------------------------------------------------------- | -------------------- |
|
||||
| `base_model` | Path to the base model (local or HuggingFace) | Required |
|
||||
| `base_model_config` | Configuration path for the base model | Same as base_model |
|
||||
| `revision_of_model` | Specific model revision from HuggingFace hub | Latest |
|
||||
| `tokenizer_config` | Custom tokenizer configuration path | Optional |
|
||||
| `model_type` | Type of model to load | AutoModelForCausalLM |
|
||||
| `tokenizer_type` | Type of tokenizer to use | AutoTokenizer |
|
||||
| `hub_model_id` | Repository ID where the model will be pushed on Hugging Face Hub (format: username/repo-name) | Optional |
|
||||
|
||||
## Model Family Identification
|
||||
|
||||
| Option | Default | Description |
|
||||
| -------------------------- | ------- | ------------------------------ |
|
||||
| `is_falcon_derived_model` | `false` | Whether model is Falcon-based |
|
||||
| `is_llama_derived_model` | `false` | Whether model is LLaMA-based |
|
||||
| `is_qwen_derived_model` | `false` | Whether model is Qwen-based |
|
||||
| `is_mistral_derived_model` | `false` | Whether model is Mistral-based |
|
||||
|
||||
## Model Configuration Overrides
|
||||
|
||||
| Option | Default | Description |
|
||||
| ----------------------------------------------- | ---------- | ---------------------------------- |
|
||||
| `overrides_of_model_config.rope_scaling.type` | `"linear"` | RoPE scaling type (linear/dynamic) |
|
||||
| `overrides_of_model_config.rope_scaling.factor` | `1.0` | RoPE scaling factor |
|
||||
|
||||
### Model Loading Options
|
||||
|
||||
| Option | Description | Default |
|
||||
| -------------- | ----------------------------- | ------- |
|
||||
| `load_in_8bit` | Load model in 8-bit precision | false |
|
||||
| `load_in_4bit` | Load model in 4-bit precision | false |
|
||||
| `bf16` | Use bfloat16 precision | false |
|
||||
| `fp16` | Use float16 precision | false |
|
||||
| `tf32` | Use tensor float 32 precision | false |
|
||||
|
||||
## Memory and Device Settings
|
||||
|
||||
| Option | Default | Description |
|
||||
| ------------------ | --------- | ----------------------- |
|
||||
| `gpu_memory_limit` | `"20GiB"` | GPU memory limit |
|
||||
| `lora_on_cpu` | `false` | Load LoRA on CPU |
|
||||
| `device_map` | `"auto"` | Device mapping strategy |
|
||||
| `max_memory` | `null` | Max memory per device |
|
||||
|
||||
## Training Hyperparameters
|
||||
|
||||
| Option | Default | Description |
|
||||
| ----------------------------- | --------- | --------------------------- |
|
||||
| `gradient_accumulation_steps` | `1` | Gradient accumulation steps |
|
||||
| `micro_batch_size` | `2` | Batch size per GPU |
|
||||
| `eval_batch_size` | `null` | Evaluation batch size |
|
||||
| `num_epochs` | `4` | Number of training epochs |
|
||||
| `warmup_steps` | `100` | Warmup steps |
|
||||
| `warmup_ratio` | `0.05` | Warmup ratio |
|
||||
| `learning_rate` | `0.00003` | Learning rate |
|
||||
| `lr_quadratic_warmup` | `false` | Quadratic warmup |
|
||||
| `logging_steps` | `null` | Logging frequency |
|
||||
| `eval_steps` | `null` | Evaluation frequency |
|
||||
| `evals_per_epoch` | `null` | Evaluations per epoch |
|
||||
| `save_strategy` | `"epoch"` | Checkpoint saving strategy |
|
||||
| `save_steps` | `null` | Saving frequency |
|
||||
| `saves_per_epoch` | `null` | Saves per epoch |
|
||||
| `save_total_limit` | `null` | Maximum checkpoints to keep |
|
||||
| `max_steps` | `null` | Maximum training steps |
|
||||
|
||||
### Dataset Configuration
|
||||
|
||||
```yaml
|
||||
datasets:
|
||||
- path: vicgalle/alpaca-gpt4 # HuggingFace dataset or TODO: You will be able to add the local path.
|
||||
type: alpaca # Format type (alpaca, gpteacher, oasst, etc.)
|
||||
ds_type: json # Dataset type
|
||||
data_files: path/to/data # Source data files
|
||||
train_on_split: train # Dataset split to use
|
||||
```
|
||||
|
||||
## Chat Template Settings
|
||||
|
||||
| Option | Default | Description |
|
||||
| ------------------------ | -------------------------------- | ---------------------- |
|
||||
| `chat_template` | `"tokenizer_default"` | Chat template type |
|
||||
| `chat_template_jinja` | `null` | Custom Jinja template |
|
||||
| `default_system_message` | `"You are a helpful assistant."` | Default system message |
|
||||
|
||||
## Dataset Processing
|
||||
|
||||
| Option | Default | Description |
|
||||
| ----------------------------- | -------------------------- | --------------------------------- |
|
||||
| `dataset_prepared_path` | `"data/last_run_prepared"` | Path for prepared dataset |
|
||||
| `push_dataset_to_hub` | `""` | Push dataset to HF hub |
|
||||
| `dataset_processes` | `4` | Number of preprocessing processes |
|
||||
| `dataset_keep_in_memory` | `false` | Keep dataset in memory |
|
||||
| `shuffle_merged_datasets` | `true` | Shuffle merged datasets |
|
||||
| `dataset_exact_deduplication` | `true` | Deduplicate datasets |
|
||||
|
||||
## LoRA Configuration
|
||||
|
||||
| Option | Default | Description |
|
||||
| -------------------------- | ---------------------- | ------------------------------ |
|
||||
| `adapter` | `"lora"` | Adapter type (lora/qlora) |
|
||||
| `lora_model_dir` | `""` | Directory with pretrained LoRA |
|
||||
| `lora_r` | `8` | LoRA attention dimension |
|
||||
| `lora_alpha` | `16` | LoRA alpha parameter |
|
||||
| `lora_dropout` | `0.05` | LoRA dropout |
|
||||
| `lora_target_modules` | `["q_proj", "v_proj"]` | Modules to apply LoRA |
|
||||
| `lora_target_linear` | `false` | Target all linear modules |
|
||||
| `peft_layers_to_transform` | `[]` | Layers to transform |
|
||||
| `lora_modules_to_save` | `[]` | Modules to save |
|
||||
| `lora_fan_in_fan_out` | `false` | Fan in/out structure |
|
||||
|
||||
## Optimization Settings
|
||||
|
||||
| Option | Default | Description |
|
||||
| ------------------------- | ------- | -------------------------- |
|
||||
| `train_on_inputs` | `false` | Train on input prompts |
|
||||
| `group_by_length` | `false` | Group by sequence length |
|
||||
| `gradient_checkpointing` | `false` | Use gradient checkpointing |
|
||||
| `early_stopping_patience` | `3` | Early stopping patience |
|
||||
|
||||
## Learning Rate Scheduling
|
||||
|
||||
| Option | Default | Description |
|
||||
| -------------------------- | ---------- | -------------------- |
|
||||
| `lr_scheduler` | `"cosine"` | Scheduler type |
|
||||
| `lr_scheduler_kwargs` | `{}` | Scheduler parameters |
|
||||
| `cosine_min_lr_ratio` | `null` | Minimum LR ratio |
|
||||
| `cosine_constant_lr_ratio` | `null` | Constant LR ratio |
|
||||
| `lr_div_factor` | `null` | LR division factor |
|
||||
|
||||
## Optimizer Settings
|
||||
|
||||
| Option | Default | Description |
|
||||
| ---------------------- | ------------ | ------------------- |
|
||||
| `optimizer` | `"adamw_hf"` | Optimizer choice |
|
||||
| `optim_args` | `{}` | Optimizer arguments |
|
||||
| `optim_target_modules` | `[]` | Target modules |
|
||||
| `weight_decay` | `null` | Weight decay |
|
||||
| `adam_beta1` | `null` | Adam beta1 |
|
||||
| `adam_beta2` | `null` | Adam beta2 |
|
||||
| `adam_epsilon` | `null` | Adam epsilon |
|
||||
| `max_grad_norm` | `null` | Gradient clipping |
|
||||
|
||||
## Attention Implementations
|
||||
|
||||
| Option | Default | Description |
|
||||
| -------------------------- | ------- | ----------------------------- |
|
||||
| `flash_optimum` | `false` | Use better transformers |
|
||||
| `xformers_attention` | `false` | Use xformers |
|
||||
| `flash_attention` | `false` | Use flash attention |
|
||||
| `flash_attn_cross_entropy` | `false` | Flash attention cross entropy |
|
||||
| `flash_attn_rms_norm` | `false` | Flash attention RMS norm |
|
||||
| `flash_attn_fuse_qkv` | `false` | Fuse QKV operations |
|
||||
| `flash_attn_fuse_mlp` | `false` | Fuse MLP operations |
|
||||
| `sdp_attention` | `false` | Use scaled dot product |
|
||||
| `s2_attention` | `false` | Use shifted sparse attention |
|
||||
|
||||
## Tokenizer Modifications
|
||||
|
||||
| Option | Default | Description |
|
||||
| ---------------- | ------- | ---------------------------- |
|
||||
| `special_tokens` | - | Special tokens to add/modify |
|
||||
| `tokens` | `[]` | Additional tokens |
|
||||
|
||||
## Distributed Training
|
||||
|
||||
| Option | Default | Description |
|
||||
| ----------------------- | ------- | --------------------- |
|
||||
| `fsdp` | `null` | FSDP configuration |
|
||||
| `fsdp_config` | `null` | FSDP config options |
|
||||
| `deepspeed` | `null` | Deepspeed config path |
|
||||
| `ddp_timeout` | `null` | DDP timeout |
|
||||
| `ddp_bucket_cap_mb` | `null` | DDP bucket capacity |
|
||||
| `ddp_broadcast_buffers` | `null` | DDP broadcast buffers |
|
||||
|
||||
<details>
|
||||
<summary><h3>Example Configuration Request:</h3></summary>
|
||||
|
||||
Here's a complete example for fine-tuning a LLaMA model using LoRA:
|
||||
|
||||
```json
|
||||
{
|
||||
"input": {
|
||||
"user_id": "user",
|
||||
"model_id": "llama-test",
|
||||
"run_id": "test-run",
|
||||
"credentials": {
|
||||
"wandb_api_key": "",
|
||||
"hf_token": ""
|
||||
},
|
||||
"args": {
|
||||
"base_model": "NousResearch/Llama-3.2-1B",
|
||||
"load_in_8bit": false,
|
||||
"load_in_4bit": false,
|
||||
"strict": false,
|
||||
"datasets": [
|
||||
{
|
||||
"path": "teknium/GPT4-LLM-Cleaned",
|
||||
"type": "alpaca"
|
||||
}
|
||||
],
|
||||
"dataset_prepared_path": "last_run_prepared",
|
||||
"val_set_size": 0.1,
|
||||
"output_dir": "./outputs/lora-out",
|
||||
"adapter": "lora",
|
||||
"sequence_len": 2048,
|
||||
"sample_packing": true,
|
||||
"eval_sample_packing": true,
|
||||
"pad_to_sequence_len": true,
|
||||
"lora_r": 16,
|
||||
"lora_alpha": 32,
|
||||
"lora_dropout": 0.05,
|
||||
"lora_target_modules": [
|
||||
"gate_proj",
|
||||
"down_proj",
|
||||
"up_proj",
|
||||
"q_proj",
|
||||
"v_proj",
|
||||
"k_proj",
|
||||
"o_proj"
|
||||
],
|
||||
"gradient_accumulation_steps": 2,
|
||||
"micro_batch_size": 2,
|
||||
"num_epochs": 1,
|
||||
"optimizer": "adamw_8bit",
|
||||
"lr_scheduler": "cosine",
|
||||
"learning_rate": 0.0002,
|
||||
"train_on_inputs": false,
|
||||
"group_by_length": false,
|
||||
"bf16": "auto",
|
||||
"tf32": false,
|
||||
"gradient_checkpointing": true,
|
||||
"logging_steps": 1,
|
||||
"flash_attention": true,
|
||||
"loss_watchdog_threshold": 5,
|
||||
"loss_watchdog_patience": 3,
|
||||
"warmup_steps": 10,
|
||||
"evals_per_epoch": 4,
|
||||
"saves_per_epoch": 1,
|
||||
"weight_decay": 0,
|
||||
"hub_model_id": "runpod/llama-fr-lora",
|
||||
"wandb_name": "test-run-1",
|
||||
"wandb_project": "test-run-1",
|
||||
"wandb_entity": "axo-test",
|
||||
"special_tokens": {
|
||||
"pad_token": "<|end_of_text|>"
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
### Advanced Features
|
||||
|
||||
#### Wandb Integration
|
||||
|
||||
- `wandb_project`: Project name for Weights & Biases
|
||||
- `wandb_entity`: Team name in W&B
|
||||
- `wandb_watch`: Monitor model with W&B
|
||||
- `wandb_name`: Name of the W&B run
|
||||
- `wandb_run_id`: ID for the W&B run
|
||||
|
||||
#### Performance Optimization
|
||||
|
||||
- `sample_packing`: Enable efficient sequence packing
|
||||
- `eval_sample_packing`: Use sequence packing during evaluation
|
||||
- `torch_compile`: Enable PyTorch 2.0 compilation
|
||||
- `flash_attention`: Use Flash Attention implementation
|
||||
- `xformers_attention`: Use xFormers attention implementation
|
||||
|
||||
### Available Optimizers
|
||||
|
||||
The following optimizers are supported:
|
||||
|
||||
- `adamw_hf`: HuggingFace's AdamW implementation
|
||||
- `adamw_torch`: PyTorch's AdamW
|
||||
- `adamw_torch_fused`: Fused AdamW implementation
|
||||
- `adamw_torch_xla`: XLA-optimized AdamW
|
||||
- `adamw_apex_fused`: NVIDIA Apex fused AdamW
|
||||
- `adafactor`: Adafactor optimizer
|
||||
- `adamw_anyprecision`: Anyprecision AdamW
|
||||
- `adamw_bnb_8bit`: 8-bit AdamW from bitsandbytes
|
||||
- `lion_8bit`: 8-bit Lion optimizer
|
||||
- `lion_32bit`: 32-bit Lion optimizer
|
||||
- `sgd`: Stochastic Gradient Descent
|
||||
- `adagrad`: Adagrad optimizer
|
||||
|
||||
## Notes
|
||||
|
||||
- Set `load_in_8bit: true` or `load_in_4bit: true` for memory-efficient training
|
||||
- Enable `flash_attention: true` for faster training on modern GPUs
|
||||
- Use `gradient_checkpointing: true` to reduce memory usage
|
||||
- Adjust `micro_batch_size` and `gradient_accumulation_steps` based on your GPU memory
|
||||
|
||||
For more detailed information, please refer to the [documentation](https://axolotl-ai-cloud.github.io/axolotl/docs/config-reference.html).
|
||||
|
||||
### Errors:
|
||||
|
||||
- if you face any issues with the Flash Attention-2, Delete yoor worker and Re-start.
|
||||
@@ -1,93 +0,0 @@
|
||||
{
|
||||
"title": "Axolotl Fine-Tuning",
|
||||
"description": "Serverless fine-tuning of open-source LLMs with Axolotl. Supports LoRA, QLoRA, DPO, and more using Hugging Face models and datasets.",
|
||||
"type": "serverless",
|
||||
"category": "language",
|
||||
"iconUrl": "https://avatars.githubusercontent.com/u/167502477",
|
||||
"config": {
|
||||
"runsOn": "GPU",
|
||||
"containerDiskInGb": 200,
|
||||
"gpuCount": 1,
|
||||
"allowedCudaVersions": [
|
||||
"12.8",
|
||||
"12.7",
|
||||
"12.6",
|
||||
"12.5",
|
||||
"12.4"
|
||||
],
|
||||
"presets": [],
|
||||
"env": [
|
||||
{
|
||||
"key": "TOKENIZER",
|
||||
"input": {
|
||||
"name": "Tokenizer",
|
||||
"type": "string",
|
||||
"description": "Name or path of the Hugging Face tokenizer to use.",
|
||||
"default": "",
|
||||
"advanced": true
|
||||
}
|
||||
},
|
||||
{
|
||||
"key": "MAX_NUM_SEQS",
|
||||
"input": {
|
||||
"name": "Max Num Seqs",
|
||||
"type": "number",
|
||||
"description": "Maximum number of sequences per iteration.",
|
||||
"default": 256,
|
||||
"advanced": true
|
||||
}
|
||||
},
|
||||
{
|
||||
"key": "DISABLE_LOG_STATS",
|
||||
"input": {
|
||||
"name": "Disable Log Stats",
|
||||
"type": "boolean",
|
||||
"description": "Disable logging statistics.",
|
||||
"default": false,
|
||||
"trueValue": "true",
|
||||
"falseValue": "false"
|
||||
}
|
||||
},
|
||||
{
|
||||
"key": "LOAD_FORMAT",
|
||||
"input": {
|
||||
"name": "Load Format",
|
||||
"type": "string",
|
||||
"description": "The format of the model weights to load.",
|
||||
"default": "auto",
|
||||
"options": [
|
||||
{
|
||||
"label": "auto",
|
||||
"value": "auto"
|
||||
},
|
||||
{
|
||||
"label": "pt",
|
||||
"value": "pt"
|
||||
},
|
||||
{
|
||||
"label": "safetensors",
|
||||
"value": "safetensors"
|
||||
},
|
||||
{
|
||||
"label": "npcache",
|
||||
"value": "npcache"
|
||||
},
|
||||
{
|
||||
"label": "dummy",
|
||||
"value": "dummy"
|
||||
},
|
||||
{
|
||||
"label": "tensorizer",
|
||||
"value": "tensorizer"
|
||||
},
|
||||
{
|
||||
"label": "bitsandbytes",
|
||||
"value": "bitsandbytes"
|
||||
}
|
||||
],
|
||||
"advanced": true
|
||||
}
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
@@ -1,7 +0,0 @@
|
||||
# Required Python packages get listed here, one per line.
|
||||
# Reccomended to lock the version number to avoid unexpected changes.
|
||||
|
||||
# You can also install packages from a git repository, e.g.:
|
||||
# git+https://github.com/runpod/runpod-python.git
|
||||
# To learn more, see https://pip.pypa.io/en/stable/reference/requirements-file-format/
|
||||
runpod~=1.7.0
|
||||
@@ -1,573 +0,0 @@
|
||||
# # 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:
|
||||
# # Whether to use the legacy tokenizer setting, defaults to True
|
||||
# tokenizer_legacy:
|
||||
# # 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:
|
||||
|
||||
# # Used to identify which the model is based on
|
||||
# is_falcon_derived_model:
|
||||
# is_llama_derived_model:
|
||||
# # Please note that if you set this to true, `padding_side` will be set to "left" by default
|
||||
# is_mistral_derived_model:
|
||||
# is_qwen_derived_model:
|
||||
|
||||
# # optional overrides to the base model configuration
|
||||
# model_config:
|
||||
# # RoPE Scaling https://github.com/huggingface/transformers/pull/24653
|
||||
# rope_scaling:
|
||||
# type: # linear | dynamic
|
||||
# factor: # float
|
||||
|
||||
|
||||
# # 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
|
||||
|
||||
# # No AMP (automatic mixed precision)
|
||||
# bfloat16: true # require >=ampere
|
||||
# float16: true
|
||||
|
||||
# # A list of one or more datasets to finetune the model with
|
||||
# datasets:
|
||||
# # HuggingFace dataset repo | s3://,gs:// path | "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>
|
||||
# ds_type: # Optional[str] (json|arrow|parquet|text|csv) defines the datatype when path is a file
|
||||
# data_files: # Optional[str] path to source data files
|
||||
# shards: # Optional[int] number of shards to split data into
|
||||
# name: # Optional[str] name of dataset configuration to load
|
||||
# train_on_split: train # Optional[str] name of dataset split to load from
|
||||
|
||||
# # Optional[str] fastchat conversation type, only used with type: sharegpt
|
||||
# conversation: # Options (see Conversation 'name'): https://github.com/lm-sys/FastChat/blob/main/fastchat/conversation.py
|
||||
# field_human: # Optional[str]. Human key to use for conversation.
|
||||
# field_model: # Optional[str]. Assistant key to use for conversation.
|
||||
|
||||
# # Custom user prompt
|
||||
# - path: repo
|
||||
# type:
|
||||
# # The below are defaults. only set what's needed.
|
||||
# system_prompt: ""
|
||||
# system_format: "{system}"
|
||||
# field_system: system
|
||||
# field_instruction: instruction
|
||||
# field_input: input
|
||||
# field_output: output
|
||||
|
||||
# # Customizable to be single line or multi-line
|
||||
# # 'format' can include {input}
|
||||
# format: |-
|
||||
# User: {instruction} {input}
|
||||
# Assistant:
|
||||
# # 'no_input_format' cannot include {input}
|
||||
# no_input_format: "{instruction} "
|
||||
|
||||
# # For `completion` datasets only, uses the provided field instead of `text` column
|
||||
# field:
|
||||
|
||||
# # 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
|
||||
# # The maximum number of processes to use while preprocessing your input dataset. This defaults to `os.cpu_count()`
|
||||
# # if not set.
|
||||
# dataset_processes: # defaults to os.cpu_count() if not set
|
||||
# # 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. 0 for no eval.
|
||||
# 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
|
||||
# # Pad inputs so each step uses constant sized buffers
|
||||
# # This will reduce memory fragmentation and may prevent OOMs, by re-using memory more efficiently
|
||||
# pad_to_sequence_len:
|
||||
# # 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:
|
||||
# # Set to 'false' if getting errors during eval with sample_packing on.
|
||||
# eval_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.
|
||||
# # This means after training, if you want to test the model, you should set this to the value of `lora_out_dir`.
|
||||
# lora_model_dir:
|
||||
|
||||
# # LoRA hyperparameters
|
||||
# # For more details about the following options, see:
|
||||
# # https://www.anyscale.com/blog/fine-tuning-llms-lora-or-full-parameter-an-in-depth-analysis-with-llama-2
|
||||
# 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
|
||||
|
||||
# # If you added new tokens to the tokenizer, you may need to save some LoRA modules because they need to know the new tokens.
|
||||
# # For LLaMA and Mistral, you need to save `embed_tokens` and `lm_head`. It may vary for other models.
|
||||
# # `embed_tokens` converts tokens to embeddings, and `lm_head` converts embeddings to token probabilities.
|
||||
# # https://github.com/huggingface/peft/issues/334#issuecomment-1561727994
|
||||
# lora_modules_to_save:
|
||||
# # - embed_tokens
|
||||
# # - lm_head
|
||||
|
||||
# # Once you complete training, the model will be saved to the following directory.
|
||||
# # If you merge the adapter to the base model, a subdirectory `merged` will be created under this directory.
|
||||
# # Make sure `lora_model_dir` points to this directory if you want to use the trained model.
|
||||
# lora_out_dir:
|
||||
# lora_fan_in_fan_out: false
|
||||
|
||||
# # ReLoRA configuration
|
||||
# # Must use either 'lora' or 'qlora' adapter, and does not support fsdp or deepspeed
|
||||
# relora_steps: # Number of steps per ReLoRA restart
|
||||
# relora_warmup_steps: # Number of per-restart warmup steps
|
||||
# relora_cpu_offload: # True to perform lora weight merges on cpu during restarts, for modest gpu memory savings
|
||||
|
||||
# # 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 full-finetuned model to
|
||||
# output_dir: ./completed-model
|
||||
|
||||
# # Whether to use torch.compile and which backend to use
|
||||
# torch_compile: # bool
|
||||
# torch_compile_backend: # Optional[str]
|
||||
|
||||
# # Training hyperparameters
|
||||
|
||||
# # If greater than 1, backpropagation will be skipped and the gradients will be accumulated for the given number of steps.
|
||||
# gradient_accumulation_steps: 1
|
||||
# # The number of samples to include in each batch. This is the number of samples sent to each GPU.
|
||||
# micro_batch_size: 2
|
||||
# eval_batch_size:
|
||||
# num_epochs: 4
|
||||
# warmup_steps: 100 # cannot use with warmup_ratio
|
||||
# warmup_ratio: 0.05 # cannot use with warmup_steps
|
||||
# learning_rate: 0.00003
|
||||
# lr_quadratic_warmup:
|
||||
# logging_steps:
|
||||
# save_strategy: # Set to `no` to skip checkpoint saves
|
||||
# save_steps: # Leave empty to save at each epoch
|
||||
# eval_steps: # Leave empty to eval at each epoch, integers for every N steps. decimal for fraction of total steps
|
||||
# save_total_limit: # Checkpoints saved at a time
|
||||
# # Maximum number of iterations to train for. It precedes num_epochs which means that
|
||||
# # if both are set, num_epochs will not be guaranteed.
|
||||
# # e.g., when 1 epoch is 1000 steps => `num_epochs: 2` and `max_steps: 100` will train for 100 steps
|
||||
# max_steps:
|
||||
|
||||
# eval_table_size: # Approximate number of predictions sent to wandb depending on batch size. Enabled above 0. Default is 0
|
||||
# eval_table_max_new_tokens: # Total number of tokens generated for predictions sent to wandb. Default is 128
|
||||
|
||||
# # 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' | empty for cosine
|
||||
# lr_scheduler_kwargs:
|
||||
|
||||
# # For one_cycle optim
|
||||
# lr_div_factor: # Learning rate div factor
|
||||
|
||||
# # Specify optimizer
|
||||
# # Valid values are driven by the Transformers OptimizerNames class, see:
|
||||
# # https://github.com/huggingface/transformers/blob/95b374952dc27d8511541d6f5a4e22c9ec11fb24/src/transformers/training_args.py#L134
|
||||
# #
|
||||
# # Note that not all optimizers may be available in your environment, ex: 'adamw_anyprecision' is part of
|
||||
# # torchdistx, 'adamw_bnb_8bit' is part of bnb.optim.Adam8bit, etc. When in doubt, it is recommended to start with the optimizer used
|
||||
# # in the examples/ for your model and fine-tuning use case.
|
||||
# #
|
||||
# # Valid values for 'optimizer' include:
|
||||
# # - adamw_hf
|
||||
# # - adamw_torch
|
||||
# # - adamw_torch_fused
|
||||
# # - adamw_torch_xla
|
||||
# # - adamw_apex_fused
|
||||
# # - adafactor
|
||||
# # - adamw_anyprecision
|
||||
# # - sgd
|
||||
# # - adagrad
|
||||
# # - adamw_bnb_8bit
|
||||
# # - lion_8bit
|
||||
# # - lion_32bit
|
||||
# # - paged_adamw_32bit
|
||||
# # - paged_adamw_8bit
|
||||
# # - paged_lion_32bit
|
||||
# # - paged_lion_8bit
|
||||
# optimizer:
|
||||
# # Specify weight decay
|
||||
# weight_decay:
|
||||
# # adamw hyperparams
|
||||
# adam_beta1:
|
||||
# adam_beta2:
|
||||
# adam_epsilon:
|
||||
# # Gradient clipping max norm
|
||||
# max_grad_norm:
|
||||
|
||||
# # Augmentation techniques
|
||||
# # NEFT https://arxiv.org/abs/2310.05914, set this to a number (paper default is 5) to add noise to embeddings
|
||||
# # currently only supported on Llama and Mistral
|
||||
# noisy_embedding_alpha:
|
||||
|
||||
# # 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/Dao-AILab/flash-attention:
|
||||
# flash_attention:
|
||||
# flash_attn_cross_entropy: # Whether to use flash-attention cross entropy implementation - advanced use only
|
||||
# flash_attn_rms_norm: # Whether to use flash-attention rms norm implementation - advanced use only
|
||||
# flash_attn_fuse_qkv: # Whether to fuse QKV into a single operation
|
||||
# flash_attn_fuse_mlp: # Whether to fuse part of the MLP into a single operation
|
||||
# # 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:
|
||||
|
||||
# # 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.
|
||||
# # If you add tokens here, you don't need to add them to the `tokens` list.
|
||||
# special_tokens:
|
||||
# # bos_token: "<s>"
|
||||
# # eos_token: "</s>"
|
||||
# # unk_token: "<unk>"
|
||||
|
||||
# # Add extra tokens.
|
||||
# tokens:
|
||||
|
||||
# # FSDP
|
||||
# fsdp:
|
||||
# fsdp_config:
|
||||
|
||||
# # Deepspeed config path. e.g., deepspeed/zero3.json
|
||||
# deepspeed:
|
||||
|
||||
# # Advanced DDP Arguments
|
||||
# ddp_timeout:
|
||||
# ddp_bucket_cap_mb:
|
||||
# ddp_broadcast_buffers:
|
||||
|
||||
# # Path to torch distx for optim 'adamw_anyprecision'
|
||||
# torchdistx_path:
|
||||
|
||||
# # 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:
|
||||
|
||||
|
||||
|
||||
base_model: ${BASE_MODEL}
|
||||
base_model_ignore_patterns: ${BASE_MODEL_IGNORE_PATTERNS}
|
||||
base_model_config: ${BASE_MODEL_CONFIG}
|
||||
revision_of_model: ${REVISION_OF_MODEL}
|
||||
tokenizer_config: ${TOKENIZER_CONFIG}
|
||||
model_type: ${MODEL_TYPE}
|
||||
tokenizer_type: ${TOKENIZER_TYPE}
|
||||
trust_remote_code: ${TRUST_REMOTE_CODE}
|
||||
tokenizer_use_fast: ${TOKENIZER_USE_FAST}
|
||||
tokenizer_legacy: ${TOKENIZER_LEGACY}
|
||||
resize_token_embeddings_to_32x: ${RESIZE_TOKEN_EMBEDDINGS_TO_32X}
|
||||
|
||||
is_falcon_derived_model: ${IS_FALCON_DERIVED_MODEL}
|
||||
is_llama_derived_model: ${IS_LLAMA_DERIVED_MODEL}
|
||||
is_qwen_derived_model: ${IS_QWEN_DERIVED_MODEL}
|
||||
is_mistral_derived_model: ${IS_MISTRAL_DERIVED_MODEL}
|
||||
|
||||
overrides_of_model_config:
|
||||
rope_scaling:
|
||||
type: ${ROPE_SCALING_TYPE}
|
||||
factor: ${ROPE_SCALING_FACTOR}
|
||||
|
||||
bnb_config_kwargs:
|
||||
llm_int8_has_fp16_weight: ${BNB_LLM_INT8_HAS_FP16_WEIGHT}
|
||||
bnb_4bit_quant_type: ${BNB_4BIT_QUANT_TYPE}
|
||||
bnb_4bit_use_double_quant: ${BNB_4BIT_USE_DOUBLE_QUANT}
|
||||
|
||||
gptq: ${GPTQ}
|
||||
load_in_8bit: ${LOAD_IN_8BIT}
|
||||
load_in_4bit: ${LOAD_IN_4BIT}
|
||||
bf16: ${BF16}
|
||||
fp16: ${FP16}
|
||||
tf32: ${TF32}
|
||||
bfloat16: ${BFLOAT16}
|
||||
float16: ${FLOAT16}
|
||||
|
||||
gpu_memory_limit: ${GPU_MEMORY_LIMIT}
|
||||
lora_on_cpu: ${LORA_ON_CPU}
|
||||
|
||||
datasets:
|
||||
- path: ${DATASET_PATH}
|
||||
type: ${DATASET_TYPE}
|
||||
ds_type: ${DATASET_DS_TYPE}
|
||||
data_files: ${DATASET_DATA_FILES}
|
||||
shards: ${DATASET_SHARDS}
|
||||
name: ${DATASET_NAME}
|
||||
train_on_split: ${DATASET_TRAIN_ON_SPLIT}
|
||||
revision: ${DATASET_REVISION}
|
||||
trust_remote_code: ${DATASET_TRUST_REMOTE_CODE}
|
||||
|
||||
rl: ${RL}
|
||||
dpo_use_weighting: ${DPO_USE_WEIGHTING}
|
||||
|
||||
chat_template: ${CHAT_TEMPLATE}
|
||||
chat_template_jinja: ${CHAT_TEMPLATE_JINJA}
|
||||
default_system_message: ${DEFAULT_SYSTEM_MESSAGE}
|
||||
dataset_prepared_path: ${DATASET_PREPARED_PATH}
|
||||
push_dataset_to_hub: ${PUSH_DATASET_TO_HUB}
|
||||
dataset_processes: ${DATASET_PROCESSES}
|
||||
dataset_keep_in_memory: ${DATASET_KEEP_IN_MEMORY}
|
||||
hub_model_id: ${HUB_MODEL_ID}
|
||||
hub_strategy: ${HUB_STRATEGY}
|
||||
hf_use_auth_token: ${HF_USE_AUTH_TOKEN}
|
||||
val_set_size: ${VAL_SET_SIZE}
|
||||
dataset_shard_num: ${DATASET_SHARD_NUM}
|
||||
dataset_shard_idx: ${DATASET_SHARD_IDX}
|
||||
|
||||
sequence_len: ${SEQUENCE_LEN}
|
||||
pad_to_sequence_len: ${PAD_TO_SEQUENCE_LEN}
|
||||
sample_packing: ${SAMPLE_PACKING}
|
||||
eval_sample_packing: ${EVAL_SAMPLE_PACKING}
|
||||
sample_packing_eff_est: ${SAMPLE_PACKING_EFF_EST}
|
||||
total_num_tokens: ${TOTAL_NUM_TOKENS}
|
||||
sample_packing_group_size: ${SAMPLE_PACKING_GROUP_SIZE}
|
||||
sample_packing_bin_size: ${SAMPLE_PACKING_BIN_SIZE}
|
||||
|
||||
batch_flattening: ${BATCH_FLATTENING}
|
||||
device_map: ${DEVICE_MAP}
|
||||
max_memory: ${MAX_MEMORY}
|
||||
|
||||
adapter: ${ADAPTER}
|
||||
lora_model_dir: ${LORA_MODEL_DIR}
|
||||
|
||||
lora_r: ${LORA_R}
|
||||
lora_alpha: ${LORA_ALPHA}
|
||||
lora_dropout: ${LORA_DROPOUT}
|
||||
lora_target_modules:
|
||||
- ${LORA_TARGET_MODULES}
|
||||
lora_target_linear: ${LORA_TARGET_LINEAR}
|
||||
peft_layers_to_transform: ${PEFT_LAYERS_TO_TRANSFORM}
|
||||
lora_modules_to_save: ${LORA_MODULES_TO_SAVE}
|
||||
lora_fan_in_fan_out: ${LORA_FAN_IN_FAN_OUT}
|
||||
|
||||
loraplus_lr_ratio: ${LORAPLUS_LR_RATIO}
|
||||
loraplus_lr_embedding: ${LORAPLUS_LR_EMBEDDING}
|
||||
|
||||
peft:
|
||||
loftq_config:
|
||||
loftq_bits: ${LOFTQ_BITS}
|
||||
|
||||
relora_steps: ${RELORA_STEPS}
|
||||
relora_warmup_steps: ${RELORA_WARMUP_STEPS}
|
||||
relora_anneal_steps: ${RELORA_ANNEAL_STEPS}
|
||||
relora_prune_ratio: ${RELORA_PRUNE_RATIO}
|
||||
relora_cpu_offload: ${RELORA_CPU_OFFLOAD}
|
||||
|
||||
wandb_mode: ${WANDB_MODE}
|
||||
wandb_project: ${WANDB_PROJECT}
|
||||
wandb_entity: ${WANDB_ENTITY}
|
||||
wandb_watch: ${WANDB_WATCH}
|
||||
wandb_name: ${WANDB_NAME}
|
||||
wandb_run_id: ${WANDB_RUN_ID}
|
||||
wandb_log_model: ${WANDB_LOG_MODEL}
|
||||
|
||||
mlflow_tracking_uri: ${MLFLOW_TRACKING_URI}
|
||||
mlflow_experiment_name: ${MLFLOW_EXPERIMENT_NAME}
|
||||
mlflow_run_name: ${MLFLOW_RUN_NAME}
|
||||
hf_mlflow_log_artifacts: ${HF_MLFLOW_LOG_ARTIFACTS}
|
||||
|
||||
use_comet: ${USE_COMET}
|
||||
comet_api_key: ${COMET_API_KEY}
|
||||
comet_workspace: ${COMET_WORKSPACE}
|
||||
comet_project_name: ${COMET_PROJECT_NAME}
|
||||
comet_experiment_key: ${COMET_EXPERIMENT_KEY}
|
||||
comet_mode: ${COMET_MODE}
|
||||
comet_online: ${COMET_ONLINE}
|
||||
comet_experiment_config: ${COMET_EXPERIMENT_CONFIG}
|
||||
|
||||
output_dir: ${OUTPUT_DIR}
|
||||
|
||||
torch_compile: ${TORCH_COMPILE}
|
||||
torch_compile_backend: ${TORCH_COMPILE_BACKEND}
|
||||
|
||||
gradient_accumulation_steps: ${GRADIENT_ACCUMULATION_STEPS}
|
||||
micro_batch_size: ${MICRO_BATCH_SIZE}
|
||||
eval_batch_size: ${EVAL_BATCH_SIZE}
|
||||
num_epochs: ${NUM_EPOCHS}
|
||||
warmup_steps: ${WARMUP_STEPS}
|
||||
warmup_ratio: ${WARMUP_RATIO}
|
||||
learning_rate: ${LEARNING_RATE}
|
||||
lr_quadratic_warmup: ${LR_QUADRATIC_WARMUP}
|
||||
logging_steps: ${LOGGING_STEPS}
|
||||
eval_steps: ${EVAL_STEPS}
|
||||
evals_per_epoch: ${EVALS_PER_EPOCH}
|
||||
save_strategy: ${SAVE_STRATEGY}
|
||||
save_steps: ${SAVE_STEPS}
|
||||
saves_per_epoch: ${SAVES_PER_EPOCH}
|
||||
save_total_limit: ${SAVE_TOTAL_LIMIT}
|
||||
max_steps: ${MAX_STEPS}
|
||||
|
||||
eval_table_size: ${EVAL_TABLE_SIZE}
|
||||
eval_max_new_tokens: ${EVAL_MAX_NEW_TOKENS}
|
||||
eval_causal_lm_metrics: ${EVAL_CAUSAL_LM_METRICS}
|
||||
|
||||
profiler_steps: ${PROFILER_STEPS}
|
||||
loss_watchdog_threshold: ${LOSS_WATCHDOG_THRESHOLD}
|
||||
loss_watchdog_patience: ${LOSS_WATCHDOG_PATIENCE}
|
||||
|
||||
save_safetensors: ${SAVE_SAFETENSORS}
|
||||
train_on_inputs: ${TRAIN_ON_INPUTS}
|
||||
group_by_length: ${GROUP_BY_LENGTH}
|
||||
gradient_checkpointing: ${GRADIENT_CHECKPOINTING}
|
||||
early_stopping_patience: ${EARLY_STOPPING_PATIENCE}
|
||||
|
||||
lr_scheduler: ${LR_SCHEDULER}
|
||||
lr_scheduler_kwargs: ${LR_SCHEDULER_KWARGS}
|
||||
cosine_min_lr_ratio: ${COSINE_MIN_LR_RATIO}
|
||||
cosine_constant_lr_ratio: ${COSINE_CONSTANT_LR_RATIO}
|
||||
lr_div_factor: ${LR_DIV_FACTOR}
|
||||
|
||||
optimizer: ${OPTIMIZER}
|
||||
optim_args: ${OPTIM_ARGS}
|
||||
optim_target_modules: ${OPTIM_TARGET_MODULES}
|
||||
weight_decay: ${WEIGHT_DECAY}
|
||||
adam_beta1: ${ADAM_BETA1}
|
||||
adam_beta2: ${ADAM_BETA2}
|
||||
adam_epsilon: ${ADAM_EPSILON}
|
||||
max_grad_norm: ${MAX_GRAD_NORM}
|
||||
|
||||
neftune_noise_alpha: ${NEFTUNE_NOISE_ALPHA}
|
||||
|
||||
flash_optimum: ${FLASH_OPTIMUM}
|
||||
xformers_attention: ${XFORMERS_ATTENTION}
|
||||
flash_attention: ${FLASH_ATTENTION}
|
||||
flash_attn_cross_entropy: ${FLASH_ATTN_CROSS_ENTROPY}
|
||||
flash_attn_rms_norm: ${FLASH_ATTN_RMS_NORM}
|
||||
flash_attn_fuse_qkv: ${FLASH_ATTN_FUSE_QKV}
|
||||
flash_attn_fuse_mlp: ${FLASH_ATTN_FUSE_MLP}
|
||||
sdp_attention: ${SDP_ATTENTION}
|
||||
s2_attention: ${S2_ATTENTION}
|
||||
resume_from_checkpoint: ${RESUME_FROM_CHECKPOINT}
|
||||
auto_resume_from_checkpoints: ${AUTO_RESUME_FROM_CHECKPOINTS}
|
||||
|
||||
local_rank: ${LOCAL_RANK}
|
||||
|
||||
special_tokens:
|
||||
bos_token: ${SPECIAL_TOKEN_BOS}
|
||||
eos_token: ${SPECIAL_TOKEN_EOS}
|
||||
unk_token: ${SPECIAL_TOKEN_UNK}
|
||||
pad_token: ${SPECIAL_TOKEN_PAD}
|
||||
|
||||
tokens: ${TOKENS}
|
||||
|
||||
fsdp: ${FSDP}
|
||||
fsdp_config: ${FSDP_CONFIG}
|
||||
deepspeed: ${DEEPSPEED}
|
||||
|
||||
ddp_timeout: ${DDP_TIMEOUT}
|
||||
ddp_bucket_cap_mb: ${DDP_BUCKET_CAP_MB}
|
||||
ddp_broadcast_buffers: ${DDP_BROADCAST_BUFFERS}
|
||||
|
||||
torchdistx_path: ${TORCHDISTX_PATH}
|
||||
pretraining_dataset: ${PRETRAINING_DATASET}
|
||||
debug: ${DEBUG}
|
||||
seed: ${SEED}
|
||||
strict: ${STRICT}
|
||||
@@ -1,66 +0,0 @@
|
||||
"""
|
||||
Runpod serverless entrypoint handler
|
||||
"""
|
||||
|
||||
import os
|
||||
|
||||
import runpod
|
||||
import yaml
|
||||
from huggingface_hub._login import login
|
||||
from train import train
|
||||
from utils import get_output_dir
|
||||
|
||||
BASE_VOLUME = os.environ.get("BASE_VOLUME", "/runpod-volume")
|
||||
if not os.path.exists(BASE_VOLUME):
|
||||
os.makedirs(BASE_VOLUME)
|
||||
|
||||
logger = runpod.RunPodLogger()
|
||||
|
||||
|
||||
async def handler(job):
|
||||
runpod_job_id = job["id"]
|
||||
inputs = job["input"]
|
||||
run_id = inputs.get("run_id", "default_run_id")
|
||||
args = inputs.get("args", {})
|
||||
|
||||
# Set output directory
|
||||
output_dir = os.path.join(BASE_VOLUME, get_output_dir(run_id))
|
||||
args["output_dir"] = output_dir
|
||||
|
||||
# First save args to a temporary config file
|
||||
config_path = "/workspace/test_config.yaml"
|
||||
|
||||
# Add run_name and job_id to args before saving
|
||||
args["run_name"] = run_id
|
||||
args["runpod_job_id"] = runpod_job_id
|
||||
|
||||
yaml_data = yaml.dump(args, default_flow_style=False)
|
||||
with open(config_path, "w", encoding="utf-8") as file:
|
||||
file.write(yaml_data)
|
||||
|
||||
# Handle credentials
|
||||
credentials = inputs.get("credentials", {})
|
||||
|
||||
if "wandb_api_key" in credentials:
|
||||
os.environ["WANDB_API_KEY"] = credentials["wandb_api_key"]
|
||||
if "hf_token" in credentials:
|
||||
os.environ["HF_TOKEN"] = credentials["hf_token"]
|
||||
|
||||
if os.environ.get("HF_TOKEN"):
|
||||
login(token=os.environ["HF_TOKEN"])
|
||||
else:
|
||||
logger.info("No HF_TOKEN provided. Skipping login.")
|
||||
|
||||
logger.info("Starting Training.")
|
||||
async for result in train(config_path): # Pass the config path instead of args
|
||||
logger.info(result)
|
||||
logger.info("Training Complete.")
|
||||
|
||||
# Cleanup
|
||||
if "WANDB_API_KEY" in os.environ:
|
||||
del os.environ["WANDB_API_KEY"]
|
||||
if "HF_TOKEN" in os.environ:
|
||||
del os.environ["HF_TOKEN"]
|
||||
|
||||
|
||||
runpod.serverless.start({"handler": handler, "return_aggregate_stream": True})
|
||||
@@ -1,61 +0,0 @@
|
||||
{
|
||||
"input": {
|
||||
"user_id": "user",
|
||||
"model_id": "llama-test",
|
||||
"run_id": "llama-test",
|
||||
"credentials": {
|
||||
"wandb_api_key": "",
|
||||
"hf_token": ""
|
||||
},
|
||||
"args": {
|
||||
"base_model": "NousResearch/Meta-Llama-3-8B",
|
||||
"model_type": "LlamaForCausalLM",
|
||||
"tokenizer_type": "AutoTokenizer",
|
||||
"load_in_8bit": true,
|
||||
"load_in_4bit": false,
|
||||
"strict": false,
|
||||
"datasets": [
|
||||
{
|
||||
"path": "mhenrichsen/alpaca_2k_test",
|
||||
"type": "alpaca"
|
||||
}
|
||||
],
|
||||
"val_set_size": 0.05,
|
||||
"output_dir": "./outputs/lora-out",
|
||||
"sequence_len": 4096,
|
||||
"sample_packing": true,
|
||||
"eval_sample_packing": false,
|
||||
"pad_to_sequence_len": true,
|
||||
"adapter": "lora",
|
||||
"lora_r": 32,
|
||||
"lora_alpha": 16,
|
||||
"lora_dropout": 0.05,
|
||||
"lora_target_linear": true,
|
||||
"lora_modules_to_save": [
|
||||
"embed_tokens",
|
||||
"lm_head"
|
||||
],
|
||||
"gradient_accumulation_steps": 4,
|
||||
"micro_batch_size": 2,
|
||||
"num_epochs": 1,
|
||||
"optimizer": "adamw_bnb_8bit",
|
||||
"lr_scheduler": "cosine",
|
||||
"learning_rate": 0.0002,
|
||||
"train_on_inputs": false,
|
||||
"group_by_length": false,
|
||||
"bf16": "auto",
|
||||
"tf32": false,
|
||||
"gradient_checkpointing": true,
|
||||
"logging_steps": 1,
|
||||
"flash_attention": true,
|
||||
"warmup_steps": 1,
|
||||
"evals_per_epoch": 1,
|
||||
"eval_max_new_tokens": 128,
|
||||
"saves_per_epoch": 1,
|
||||
"weight_decay": 0.0,
|
||||
"special_tokens": {
|
||||
"pad_token": "<|end_of_text|>"
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -1,45 +0,0 @@
|
||||
"""
|
||||
Runpod train entrypoint
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
|
||||
|
||||
async def train(config_path: str, gpu_id: str = "0", preprocess: bool = True):
|
||||
"""
|
||||
Run preprocessing (if enabled) and training with the given config file
|
||||
:param config_path: Path to the YAML config file
|
||||
:param gpu_id: GPU ID to use (default: "0")
|
||||
:param preprocess: Whether to run preprocessing (default: True)
|
||||
|
||||
"""
|
||||
# First check if preprocessing is needed
|
||||
if preprocess:
|
||||
# Preprocess command
|
||||
preprocess_cmd = (
|
||||
f"CUDA_VISIBLE_DEVICES={gpu_id} axolotl preprocess {config_path}"
|
||||
)
|
||||
process = await asyncio.create_subprocess_shell(
|
||||
preprocess_cmd,
|
||||
stdout=asyncio.subprocess.PIPE,
|
||||
stderr=asyncio.subprocess.STDOUT,
|
||||
)
|
||||
|
||||
if process.stdout is not None:
|
||||
async for line in process.stdout:
|
||||
yield f"Preprocessing: {line.decode().strip()}"
|
||||
await process.wait()
|
||||
yield "Preprocessing completed."
|
||||
else:
|
||||
yield "Skipping preprocessing step."
|
||||
|
||||
# Training command
|
||||
train_cmd = f"axolotl train {config_path}"
|
||||
process = await asyncio.create_subprocess_shell(
|
||||
train_cmd, stdout=asyncio.subprocess.PIPE, stderr=asyncio.subprocess.STDOUT
|
||||
)
|
||||
|
||||
if process.stdout is not None:
|
||||
async for line in process.stdout:
|
||||
yield f"Training: {line.decode().strip()}"
|
||||
await process.wait()
|
||||
@@ -1,89 +0,0 @@
|
||||
"""
|
||||
Runpod launcher utils
|
||||
"""
|
||||
|
||||
import os
|
||||
|
||||
import yaml
|
||||
|
||||
|
||||
def get_output_dir(run_id):
|
||||
path = f"fine-tuning/{run_id}"
|
||||
return path
|
||||
|
||||
|
||||
def make_valid_config(input_args):
|
||||
"""
|
||||
Creates and saves updated config file, returns the path to the new config
|
||||
:param input_args: dict of input args
|
||||
:return: str, path to the updated config file
|
||||
"""
|
||||
# Load default config
|
||||
with open("config/config.yaml", "r", encoding="utf-8") as fin:
|
||||
all_args = yaml.safe_load(fin)
|
||||
|
||||
if not input_args:
|
||||
print("No args provided, using defaults")
|
||||
else:
|
||||
all_args.update(input_args)
|
||||
|
||||
# Create updated config path
|
||||
updated_config_path = "config/updated_config.yaml"
|
||||
|
||||
# Save updated config to new file
|
||||
with open(updated_config_path, "w", encoding="utf-8") as f:
|
||||
yaml.dump(all_args, f)
|
||||
|
||||
return updated_config_path
|
||||
|
||||
|
||||
def set_config_env_vars(args: dict):
|
||||
"""
|
||||
Convert API arguments into environment variables.
|
||||
Handles nested dictionaries, lists, and special values.
|
||||
|
||||
Args:
|
||||
args (dict): The arguments dictionary from the API request
|
||||
"""
|
||||
|
||||
def process_value(value):
|
||||
"""Convert Python values to string format for environment variables"""
|
||||
if value is None:
|
||||
return ""
|
||||
if isinstance(value, bool):
|
||||
return str(value).lower()
|
||||
if isinstance(value, (list, dict)):
|
||||
return str(value)
|
||||
return str(value)
|
||||
|
||||
def set_env_vars(data, prefix=""):
|
||||
"""Recursively set environment variables from nested dictionary"""
|
||||
for key, value in data.items():
|
||||
env_key = prefix + key.upper()
|
||||
|
||||
# Handle special cases
|
||||
if isinstance(value, dict):
|
||||
# For nested dictionaries (like special_tokens)
|
||||
set_env_vars(value, f"{env_key}_")
|
||||
elif isinstance(value, list):
|
||||
# Handle list of dictionaries (like datasets)
|
||||
if value and isinstance(value[0], dict):
|
||||
for i, item in enumerate(value):
|
||||
set_env_vars(item, f"{env_key}_{i}_")
|
||||
else:
|
||||
# For simple lists (like lora_target_modules)
|
||||
os.environ[env_key] = process_value(value)
|
||||
else:
|
||||
# Handle all other cases
|
||||
os.environ[env_key] = process_value(value)
|
||||
|
||||
# Clear any existing related environment variables
|
||||
# This prevents old values from persisting
|
||||
for key in list(os.environ.keys()):
|
||||
if key.startswith(
|
||||
("BASE_MODEL", "MODEL_TYPE", "TOKENIZER_TYPE", "DATASET", "LORA_", "WANDB_")
|
||||
):
|
||||
del os.environ[key]
|
||||
|
||||
# Set new environment variables
|
||||
set_env_vars(args)
|
||||
@@ -1,86 +0,0 @@
|
||||
{
|
||||
"input": {
|
||||
"name": "quick_smoke_test_sft",
|
||||
"user_id": "user",
|
||||
"model_id": "llama-test",
|
||||
"run_id": "llama-test",
|
||||
"credentials": {
|
||||
"wandb_api_key": "",
|
||||
"hf_token": ""
|
||||
},
|
||||
"args": {
|
||||
"base_model": "HuggingFaceTB/SmolLM2-135M",
|
||||
"model_type": "AutoModelForCausalLM",
|
||||
"tokenizer_type": "AutoTokenizer",
|
||||
"load_in_4bit": true,
|
||||
"strict": false,
|
||||
"datasets": [
|
||||
{
|
||||
"path": "mhenrichsen/alpaca_2k_test",
|
||||
"type": "alpaca",
|
||||
"split": "train[:10%]"
|
||||
}
|
||||
],
|
||||
"val_set_size": 0.02,
|
||||
"output_dir": "./outputs/lora-out",
|
||||
"sequence_len": 4096,
|
||||
"sample_packing": true,
|
||||
"eval_sample_packing": false,
|
||||
"pad_to_sequence_len": true,
|
||||
"adapter": "qlora",
|
||||
"lora_r": 32,
|
||||
"lora_alpha": 64,
|
||||
"lora_dropout": 0.05,
|
||||
"lora_target_linear": true,
|
||||
"lora_modules_to_save": [
|
||||
"embed_tokens",
|
||||
"lm_head"
|
||||
],
|
||||
"gradient_accumulation_steps": 2,
|
||||
"micro_batch_size": 1,
|
||||
"num_epochs": 1,
|
||||
"optimizer": "adamw_torch_fused",
|
||||
"lr_scheduler": "cosine",
|
||||
"learning_rate": 0.0002,
|
||||
"train_on_inputs": false,
|
||||
"group_by_length": false,
|
||||
"bf16": "auto",
|
||||
"tf32": true,
|
||||
"gradient_checkpointing": true,
|
||||
"logging_steps": 1,
|
||||
"flash_attention": true,
|
||||
"warmup_steps": 1,
|
||||
"evals_per_epoch": 1,
|
||||
"eval_max_new_tokens": 128,
|
||||
"saves_per_epoch": 1,
|
||||
"weight_decay": 0.0,
|
||||
"special_tokens": {
|
||||
"pad_token": "<|endoftext|>"
|
||||
},
|
||||
"max_steps": 20
|
||||
},
|
||||
"timeout": 100000
|
||||
},
|
||||
"config": {
|
||||
"gpuTypeId": "NVIDIA GeForce RTX 4090",
|
||||
"gpuCount": 1,
|
||||
"containerDiskInGb": 200,
|
||||
"env": [
|
||||
{
|
||||
"key": "TOKENIZER",
|
||||
"value": ""
|
||||
},
|
||||
{
|
||||
"key": "DISABLE_LOG_STATS",
|
||||
"value": "true"
|
||||
}
|
||||
],
|
||||
"allowedCudaVersions": [
|
||||
"12.8",
|
||||
"12.7",
|
||||
"12.6",
|
||||
"12.5",
|
||||
"12.4"
|
||||
]
|
||||
}
|
||||
}
|
||||
@@ -1,90 +0,0 @@
|
||||
{
|
||||
"tests": [
|
||||
{
|
||||
"name": "quick_smoke_test_sft",
|
||||
"input": {
|
||||
"user_id": "user",
|
||||
"model_id": "llama-test",
|
||||
"run_id": "llama-test",
|
||||
"credentials": {
|
||||
"wandb_api_key": "",
|
||||
"hf_token": ""
|
||||
},
|
||||
"args": {
|
||||
"base_model": "HuggingFaceTB/SmolLM2-135M",
|
||||
"model_type": "AutoModelForCausalLM",
|
||||
"tokenizer_type": "AutoTokenizer",
|
||||
"load_in_4bit": true,
|
||||
"strict": false,
|
||||
"datasets": [
|
||||
{
|
||||
"path": "mhenrichsen/alpaca_2k_test",
|
||||
"type": "alpaca",
|
||||
"split": "train[:10%]"
|
||||
}
|
||||
],
|
||||
"val_set_size": 0.02,
|
||||
"output_dir": "./outputs/lora-out",
|
||||
"sequence_len": 4096,
|
||||
"sample_packing": true,
|
||||
"eval_sample_packing": false,
|
||||
"pad_to_sequence_len": true,
|
||||
"adapter": "qlora",
|
||||
"lora_r": 32,
|
||||
"lora_alpha": 64,
|
||||
"lora_dropout": 0.05,
|
||||
"lora_target_linear": true,
|
||||
"lora_modules_to_save": [
|
||||
"embed_tokens",
|
||||
"lm_head"
|
||||
],
|
||||
"gradient_accumulation_steps": 2,
|
||||
"micro_batch_size": 1,
|
||||
"num_epochs": 1,
|
||||
"optimizer": "adamw_torch_fused",
|
||||
"lr_scheduler": "cosine",
|
||||
"learning_rate": 0.0002,
|
||||
"train_on_inputs": false,
|
||||
"group_by_length": false,
|
||||
"bf16": "auto",
|
||||
"tf32": true,
|
||||
"gradient_checkpointing": true,
|
||||
"logging_steps": 1,
|
||||
"flash_attention": true,
|
||||
"warmup_steps": 1,
|
||||
"evals_per_epoch": 1,
|
||||
"eval_max_new_tokens": 128,
|
||||
"saves_per_epoch": 1,
|
||||
"weight_decay": 0.0,
|
||||
"special_tokens": {
|
||||
"pad_token": "<|endoftext|>"
|
||||
},
|
||||
"max_steps": 20
|
||||
}
|
||||
},
|
||||
"timeout": 100000
|
||||
}
|
||||
],
|
||||
"config": {
|
||||
"gpuTypeId": "NVIDIA GeForce RTX 4090",
|
||||
"gpuCount": 1,
|
||||
"containerDiskInGb": 200,
|
||||
"env": [
|
||||
{
|
||||
"key": "TOKENIZER",
|
||||
"value": ""
|
||||
},
|
||||
{
|
||||
"key": "DISABLE_LOG_STATS",
|
||||
"value": "true"
|
||||
}
|
||||
],
|
||||
"allowedCudaVersions": [
|
||||
"12.8",
|
||||
"12.7",
|
||||
"12.6",
|
||||
"12.5",
|
||||
"12.4"
|
||||
]
|
||||
}
|
||||
}
|
||||
1
.vscode/README.md
vendored
1
.vscode/README.md
vendored
@@ -1 +0,0 @@
|
||||
See [docs/debugging.md](../docs/debugging.md) for guidance on how to modify these files to debug axolotl with VSCode.
|
||||
34
.vscode/launch.json
vendored
34
.vscode/launch.json
vendored
@@ -1,34 +0,0 @@
|
||||
{
|
||||
// Use IntelliSense to learn about possible attributes.
|
||||
// Hover to view descriptions of existing attributes.
|
||||
// For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
|
||||
"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": "${workspaceFolder}/devtools/temp_debug/.hf-cache"}, // send HF cache to a temp folder
|
||||
"preLaunchTask": "cleanup-for-dataprep", // delete temp folders (see below)
|
||||
}
|
||||
]
|
||||
}
|
||||
27
.vscode/tasks.json
vendored
27
.vscode/tasks.json
vendored
@@ -1,27 +0,0 @@
|
||||
//this file is used by launch.json
|
||||
{
|
||||
"version": "2.0.0",
|
||||
"tasks": [
|
||||
// this task changes into the devtools directory and deletes the temp_debug/axolotl_outputs folder
|
||||
{
|
||||
"label": "delete-outputs",
|
||||
"type": "shell",
|
||||
"command": "rm -rf temp_debug/axolotl_outputs",
|
||||
"options":{ "cwd": "${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"],
|
||||
}
|
||||
]
|
||||
}
|
||||
@@ -1,6 +0,0 @@
|
||||
include requirements.txt
|
||||
include README.md
|
||||
include LICENSE
|
||||
include src/setuptools_axolotl_dynamic_dependencies.py
|
||||
include src/axolotl/utils/chat_templates/templates/*.jinja
|
||||
recursive-include axolotl *.py
|
||||
310
_quarto.yml
310
_quarto.yml
@@ -1,310 +0,0 @@
|
||||
project:
|
||||
type: website
|
||||
pre-render: docs/scripts/generate_config_docs.py
|
||||
|
||||
quartodoc:
|
||||
dir: docs/api
|
||||
package: axolotl
|
||||
title: API Reference
|
||||
parser: google
|
||||
|
||||
sections:
|
||||
- title: Core
|
||||
desc: Core functionality for training
|
||||
contents:
|
||||
- train
|
||||
- evaluate
|
||||
- datasets
|
||||
- convert
|
||||
- prompt_tokenizers
|
||||
- logging_config
|
||||
- core.builders.base
|
||||
- core.builders.causal
|
||||
- core.builders.rl
|
||||
- core.training_args
|
||||
- core.chat.messages
|
||||
- core.chat.format.chatml
|
||||
- core.chat.format.llama3x
|
||||
- core.chat.format.shared
|
||||
- core.datasets.chat
|
||||
- core.datasets.transforms.chat_builder
|
||||
- title: CLI
|
||||
desc: Command-line interface
|
||||
contents:
|
||||
- cli.main
|
||||
- cli.train
|
||||
- cli.evaluate
|
||||
- cli.args
|
||||
- cli.checks
|
||||
- cli.config
|
||||
- cli.inference
|
||||
- cli.merge_lora
|
||||
- cli.merge_sharded_fsdp_weights
|
||||
- cli.preprocess
|
||||
- cli.sweeps
|
||||
- cli.utils
|
||||
- cli.vllm_serve
|
||||
- cli.cloud.base
|
||||
- cli.cloud.modal_
|
||||
- cli.quantize
|
||||
- title: Trainers
|
||||
desc: Training implementations
|
||||
contents:
|
||||
- core.trainers.base
|
||||
- core.trainers.trl
|
||||
- core.trainers.mamba
|
||||
- core.trainers.relora
|
||||
- core.trainers.dpo.trainer
|
||||
- core.trainers.grpo.trainer
|
||||
- core.trainers.grpo.sampler
|
||||
- core.trainers.utils
|
||||
- title: Model Loading
|
||||
desc: Functionality for loading and patching models, tokenizers, etc.
|
||||
contents:
|
||||
- loaders.model
|
||||
- loaders.tokenizer
|
||||
- loaders.processor
|
||||
- loaders.adapter
|
||||
- loaders.patch_manager
|
||||
- loaders.constants
|
||||
- title: Mixins
|
||||
desc: Mixin classes for augmenting trainers
|
||||
contents:
|
||||
- core.trainers.mixins.optimizer
|
||||
- core.trainers.mixins.rng_state_loader
|
||||
- core.trainers.mixins.scheduler
|
||||
- title: Context Managers
|
||||
desc: Context managers for altering trainer behaviors
|
||||
contents:
|
||||
- utils.ctx_managers.sequence_parallel
|
||||
- title: Prompt Strategies
|
||||
desc: Prompt formatting strategies
|
||||
contents:
|
||||
- prompt_strategies.base
|
||||
- prompt_strategies.chat_template
|
||||
- prompt_strategies.alpaca_chat
|
||||
- prompt_strategies.alpaca_instruct
|
||||
- prompt_strategies.alpaca_w_system
|
||||
- prompt_strategies.user_defined
|
||||
- prompt_strategies.llama2_chat
|
||||
- prompt_strategies.completion
|
||||
- prompt_strategies.input_output
|
||||
- prompt_strategies.stepwise_supervised
|
||||
- prompt_strategies.metharme
|
||||
- prompt_strategies.orcamini
|
||||
- prompt_strategies.pygmalion
|
||||
- prompt_strategies.messages.chat
|
||||
- prompt_strategies.dpo.chat_template
|
||||
- prompt_strategies.dpo.llama3
|
||||
- prompt_strategies.dpo.chatml
|
||||
- prompt_strategies.dpo.zephyr
|
||||
- prompt_strategies.dpo.user_defined
|
||||
- prompt_strategies.dpo.passthrough
|
||||
- prompt_strategies.kto.llama3
|
||||
- prompt_strategies.kto.chatml
|
||||
- prompt_strategies.kto.user_defined
|
||||
- prompt_strategies.orpo.chat_template
|
||||
- prompt_strategies.bradley_terry.llama3
|
||||
- title: Kernels
|
||||
desc: Low-level performance optimizations
|
||||
contents:
|
||||
- kernels.lora
|
||||
- kernels.geglu
|
||||
- kernels.swiglu
|
||||
- kernels.quantize
|
||||
- kernels.utils
|
||||
- title: Monkey Patches
|
||||
desc: Runtime patches for model optimizations
|
||||
contents:
|
||||
- monkeypatch.llama_attn_hijack_flash
|
||||
- monkeypatch.llama_attn_hijack_xformers
|
||||
- monkeypatch.mistral_attn_hijack_flash
|
||||
- monkeypatch.multipack
|
||||
- monkeypatch.relora
|
||||
- monkeypatch.llama_expand_mask
|
||||
- monkeypatch.lora_kernels
|
||||
- monkeypatch.utils
|
||||
- monkeypatch.btlm_attn_hijack_flash
|
||||
- monkeypatch.llama_patch_multipack
|
||||
- monkeypatch.stablelm_attn_hijack_flash
|
||||
- monkeypatch.trainer_fsdp_optim
|
||||
- monkeypatch.transformers_fa_utils
|
||||
- monkeypatch.unsloth_
|
||||
- monkeypatch.data.batch_dataset_fetcher
|
||||
- monkeypatch.mixtral
|
||||
- monkeypatch.gradient_checkpointing.offload_cpu
|
||||
- monkeypatch.gradient_checkpointing.offload_disk
|
||||
- title: Utils
|
||||
desc: Utility functions
|
||||
contents:
|
||||
- utils.tokenization
|
||||
- utils.chat_templates
|
||||
- utils.lora
|
||||
- utils.model_shard_quant
|
||||
- utils.bench
|
||||
- utils.freeze
|
||||
- utils.trainer
|
||||
- utils.schedulers
|
||||
- utils.distributed
|
||||
- utils.dict
|
||||
- utils.optimizers.adopt
|
||||
- utils.data.pretraining
|
||||
- utils.data.sft
|
||||
- utils.quantization
|
||||
- title: Schemas
|
||||
desc: Pydantic data models for Axolotl config
|
||||
contents:
|
||||
- utils.schemas.config
|
||||
- utils.schemas.model
|
||||
- utils.schemas.training
|
||||
- utils.schemas.datasets
|
||||
- utils.schemas.peft
|
||||
- utils.schemas.trl
|
||||
- utils.schemas.multimodal
|
||||
- utils.schemas.integrations
|
||||
- utils.schemas.enums
|
||||
- utils.schemas.utils
|
||||
- title: Integrations
|
||||
desc: Third-party integrations and extensions
|
||||
contents:
|
||||
- integrations.base
|
||||
- integrations.cut_cross_entropy.args
|
||||
- integrations.grokfast.optimizer
|
||||
- integrations.kd.trainer
|
||||
- integrations.liger.args
|
||||
- integrations.lm_eval.args
|
||||
- integrations.spectrum.args
|
||||
- title: Common
|
||||
desc: Common utilities and shared functionality
|
||||
contents:
|
||||
- common.architectures
|
||||
- common.const
|
||||
- common.datasets
|
||||
- title: Models
|
||||
desc: Custom model implementations
|
||||
contents:
|
||||
- models.mamba.modeling_mamba
|
||||
- title: Data Processing
|
||||
desc: Data processing utilities
|
||||
contents:
|
||||
- utils.collators.core
|
||||
- utils.collators.batching
|
||||
- utils.collators.mamba
|
||||
- utils.collators.mm_chat
|
||||
- utils.samplers.multipack
|
||||
- title: Callbacks
|
||||
desc: Training callbacks
|
||||
contents:
|
||||
- utils.callbacks.perplexity
|
||||
- utils.callbacks.profiler
|
||||
- utils.callbacks.lisa
|
||||
- utils.callbacks.mlflow_
|
||||
- utils.callbacks.comet_
|
||||
- utils.callbacks.qat
|
||||
website:
|
||||
title: "Axolotl"
|
||||
description: "We make fine-tuning accessible, scalable, and fun"
|
||||
favicon: favicon.jpg
|
||||
|
||||
google-analytics: "G-9KYCVJBNMQ"
|
||||
|
||||
navbar:
|
||||
logo: image/axolotl_logo_digital_white.svg
|
||||
title: false
|
||||
background: dark
|
||||
pinned: false
|
||||
collapse: false
|
||||
tools:
|
||||
- icon: twitter
|
||||
href: https://twitter.com/axolotl_ai
|
||||
- icon: github
|
||||
href: https://github.com/axolotl-ai-cloud/axolotl/
|
||||
- icon: discord
|
||||
href: https://discord.gg/7m9sfhzaf3
|
||||
|
||||
sidebar:
|
||||
pinned: true
|
||||
collapse-level: 2
|
||||
style: docked
|
||||
contents:
|
||||
- text: Home
|
||||
href: index.qmd
|
||||
|
||||
- section: "Getting Started"
|
||||
contents:
|
||||
- docs/getting-started.qmd
|
||||
- docs/installation.qmd
|
||||
- docs/inference.qmd
|
||||
- docs/cli.qmd
|
||||
- docs/config-reference.qmd
|
||||
- text: "API Reference"
|
||||
href: docs/api
|
||||
|
||||
- section: "Dataset Formats"
|
||||
contents: docs/dataset-formats/*
|
||||
|
||||
- section: "Deployments"
|
||||
contents:
|
||||
- docs/docker.qmd
|
||||
- docs/multi-gpu.qmd
|
||||
- docs/multi-node.qmd
|
||||
- docs/ray-integration.qmd
|
||||
- docs/amd_hpc.qmd
|
||||
- docs/mac.qmd
|
||||
|
||||
- section: "How To Guides"
|
||||
contents:
|
||||
- docs/multimodal.qmd
|
||||
- docs/rlhf.qmd
|
||||
- docs/reward_modelling.qmd
|
||||
- docs/lr_groups.qmd
|
||||
- docs/lora_optims.qmd
|
||||
- docs/dataset_loading.qmd
|
||||
- docs/qat.qmd
|
||||
- docs/quantize.qmd
|
||||
|
||||
- section: "Core Concepts"
|
||||
contents:
|
||||
- docs/batch_vs_grad.qmd
|
||||
- docs/dataset_preprocessing.qmd
|
||||
- docs/multipack.qmd
|
||||
|
||||
- section: "Advanced Features"
|
||||
contents:
|
||||
- docs/fsdp_qlora.qmd
|
||||
- docs/unsloth.qmd
|
||||
- docs/torchao.qmd
|
||||
- docs/custom_integrations.qmd
|
||||
- docs/sequence_parallelism.qmd
|
||||
- docs/gradient_checkpointing.qmd
|
||||
|
||||
- section: "Troubleshooting"
|
||||
contents:
|
||||
- docs/faq.qmd
|
||||
- docs/debugging.qmd
|
||||
- docs/nccl.qmd
|
||||
|
||||
format:
|
||||
html:
|
||||
theme: darkly
|
||||
css: styles.css
|
||||
toc: true
|
||||
# Enable better handling of line breaks in markdown
|
||||
preserve-tabs: true
|
||||
html-math-method: mathjax
|
||||
# Improved markdown processing options
|
||||
md-extensions:
|
||||
- markdown_it
|
||||
- def_list
|
||||
- attr_list
|
||||
- fenced_divs
|
||||
- tables
|
||||
- html_admonition
|
||||
- lineblocks
|
||||
- fancy_lists
|
||||
# Control whitespace handling
|
||||
whitespace: preserve
|
||||
# Process newlines in paragraphs
|
||||
wrap: preserve
|
||||
# Better line break handling
|
||||
preserve-linebreaks: true
|
||||
@@ -1,52 +0,0 @@
|
||||
FROM axolotlai/axolotl-base-uv:{{ BASE_TAG }}
|
||||
|
||||
ENV TORCH_CUDA_ARCH_LIST="7.0 7.5 8.0 8.6 9.0+PTX"
|
||||
ENV AXOLOTL_EXTRAS="{{ AXOLOTL_EXTRAS }}"
|
||||
ENV AXOLOTL_ARGS="{{ AXOLOTL_ARGS }}"
|
||||
ENV CUDA="{{ CUDA }}"
|
||||
ENV PYTORCH_VERSION="{{ PYTORCH_VERSION }}"
|
||||
ENV GITHUB_REF="{{ GITHUB_REF }}"
|
||||
ENV GITHUB_SHA="{{ GITHUB_SHA }}"
|
||||
ENV NIGHTLY_BUILD="{{ NIGHTLY_BUILD }}"
|
||||
ENV HF_HOME="{{ HF_HOME }}"
|
||||
|
||||
RUN apt-get update && \
|
||||
apt-get install -y --allow-change-held-packages vim curl nano libnccl2 libnccl-dev
|
||||
|
||||
WORKDIR /workspace
|
||||
|
||||
RUN git clone --depth=1 https://github.com/axolotl-ai-cloud/axolotl.git
|
||||
|
||||
WORKDIR /workspace/axolotl
|
||||
|
||||
RUN git fetch origin +$GITHUB_REF && \
|
||||
git checkout FETCH_HEAD
|
||||
|
||||
# If AXOLOTL_EXTRAS is set, append it in brackets
|
||||
RUN if [ "$NIGHTLY_BUILD" = "true" ] ; then \
|
||||
sed -i 's#^transformers.*#transformers @ git+https://github.com/huggingface/transformers.git@main#' requirements.txt; \
|
||||
sed -i 's#^peft.*#peft @ git+https://github.com/huggingface/peft.git@main#' requirements.txt; \
|
||||
sed -i 's#^accelerate.*#accelerate @ git+https://github.com/huggingface/accelerate.git@main#' requirements.txt; \
|
||||
sed -i 's#^trl.*#trl @ git+https://github.com/huggingface/trl.git@main#' requirements.txt; \
|
||||
sed -i 's#^datasets.*#datasets @ git+https://github.com/huggingface/datasets.git@main#' requirements.txt; \
|
||||
fi
|
||||
|
||||
RUN uv pip install packaging==23.2 setuptools==75.8.0
|
||||
RUN if [ "$AXOLOTL_EXTRAS" != "" ] ; then \
|
||||
uv pip install --no-build-isolation -e .[deepspeed,flash-attn,ring-flash-attn,optimizers,ray,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \
|
||||
else \
|
||||
uv pip install --no-build-isolation -e .[deepspeed,flash-attn,ring-flash-attn,optimizers,ray] $AXOLOTL_ARGS; \
|
||||
fi
|
||||
|
||||
RUN python scripts/unsloth_install.py --uv | sh
|
||||
RUN python scripts/cutcrossentropy_install.py --uv | sh
|
||||
|
||||
# So we can test the Docker image
|
||||
RUN uv pip install -r requirements-dev.txt -r requirements-tests.txt
|
||||
|
||||
# fix so that git fetch/pull from remote works
|
||||
RUN git config remote.origin.fetch "+refs/heads/*:refs/remotes/origin/*" && \
|
||||
git config --get remote.origin.fetch
|
||||
|
||||
# helper for huggingface-login cli
|
||||
RUN git config --global credential.helper store
|
||||
@@ -1,53 +0,0 @@
|
||||
FROM axolotlai/axolotl-base:{{ BASE_TAG }}
|
||||
|
||||
ENV TORCH_CUDA_ARCH_LIST="7.0 7.5 8.0 8.6+PTX"
|
||||
ENV AXOLOTL_EXTRAS="{{ AXOLOTL_EXTRAS }}"
|
||||
ENV AXOLOTL_ARGS="{{ AXOLOTL_ARGS }}"
|
||||
ENV CUDA="{{ CUDA }}"
|
||||
ENV PYTORCH_VERSION="{{ PYTORCH_VERSION }}"
|
||||
ENV GITHUB_REF="{{ GITHUB_REF }}"
|
||||
ENV GITHUB_SHA="{{ GITHUB_SHA }}"
|
||||
ENV NIGHTLY_BUILD="{{ NIGHTLY_BUILD }}"
|
||||
ENV HF_HOME="{{ HF_HOME }}"
|
||||
ENV AXOLOTL_DATASET_PROCESSES="8"
|
||||
|
||||
RUN apt-get update && \
|
||||
apt-get install -y --allow-change-held-packages vim curl nano libnccl2 libnccl-dev
|
||||
|
||||
WORKDIR /workspace
|
||||
|
||||
RUN git clone --depth=1 https://github.com/axolotl-ai-cloud/axolotl.git
|
||||
|
||||
WORKDIR /workspace/axolotl
|
||||
|
||||
RUN git fetch origin +$GITHUB_REF && \
|
||||
git checkout FETCH_HEAD
|
||||
|
||||
# If AXOLOTL_EXTRAS is set, append it in brackets
|
||||
RUN if [ "$NIGHTLY_BUILD" = "true" ] ; then \
|
||||
sed -i 's#^transformers.*#transformers @ git+https://github.com/huggingface/transformers.git@main#' requirements.txt; \
|
||||
sed -i 's#^peft.*#peft @ git+https://github.com/huggingface/peft.git@main#' requirements.txt; \
|
||||
sed -i 's#^accelerate.*#accelerate @ git+https://github.com/huggingface/accelerate.git@main#' requirements.txt; \
|
||||
sed -i 's#^trl.*#trl @ git+https://github.com/huggingface/trl.git@main#' requirements.txt; \
|
||||
sed -i 's#^datasets.*#datasets @ git+https://github.com/huggingface/datasets.git@main#' requirements.txt; \
|
||||
fi
|
||||
|
||||
RUN pip install packaging==23.2 setuptools==75.8.0
|
||||
RUN if [ "$AXOLOTL_EXTRAS" != "" ] ; then \
|
||||
pip install --no-build-isolation -e .[deepspeed,flash-attn,ring-flash-attn,optimizers,ray,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \
|
||||
else \
|
||||
pip install --no-build-isolation -e .[deepspeed,flash-attn,ring-flash-attn,optimizers,ray] $AXOLOTL_ARGS; \
|
||||
fi
|
||||
|
||||
RUN python scripts/unsloth_install.py | sh
|
||||
RUN python scripts/cutcrossentropy_install.py | sh
|
||||
|
||||
# So we can test the Docker image
|
||||
RUN pip install -r requirements-dev.txt -r requirements-tests.txt
|
||||
|
||||
# fix so that git fetch/pull from remote works
|
||||
RUN git config remote.origin.fetch "+refs/heads/*:refs/remotes/origin/*" && \
|
||||
git config --get remote.origin.fetch
|
||||
|
||||
# helper for huggingface-login cli
|
||||
RUN git config --global credential.helper store
|
||||
55
cicd/cicd.sh
55
cicd/cicd.sh
@@ -1,55 +0,0 @@
|
||||
#!/bin/bash
|
||||
set -e
|
||||
|
||||
python -c "import torch; assert '$PYTORCH_VERSION' in torch.__version__"
|
||||
|
||||
# Run unit tests with initial coverage report
|
||||
pytest -v --durations=10 -n8 \
|
||||
--ignore=tests/e2e/ \
|
||||
--ignore=tests/patched/ \
|
||||
--ignore=tests/cli \
|
||||
/workspace/axolotl/tests/ \
|
||||
--cov=axolotl
|
||||
|
||||
# Run lora kernels tests with coverage append
|
||||
pytest -v --durations=10 \
|
||||
/workspace/axolotl/tests/e2e/patched/lora_kernels \
|
||||
--cov=axolotl \
|
||||
--cov-append
|
||||
|
||||
# Run patched tests excluding lora kernels with coverage append
|
||||
pytest --full-trace -vvv --durations=10 \
|
||||
--ignore=tests/e2e/patched/lora_kernels \
|
||||
/workspace/axolotl/tests/e2e/patched \
|
||||
--cov=axolotl \
|
||||
--cov-append
|
||||
|
||||
# Run solo tests with coverage append
|
||||
pytest -v --durations=10 -n1 \
|
||||
/workspace/axolotl/tests/e2e/solo/ \
|
||||
--cov=axolotl \
|
||||
--cov-append
|
||||
|
||||
# Run integration tests with coverage append
|
||||
pytest -v --durations=10 \
|
||||
/workspace/axolotl/tests/e2e/integrations/ \
|
||||
--cov=axolotl \
|
||||
--cov-append
|
||||
|
||||
pytest -v --durations=10 /workspace/axolotl/tests/cli \
|
||||
--cov=axolotl \
|
||||
--cov-append
|
||||
|
||||
# Run remaining e2e tests with coverage append and final report
|
||||
pytest -v --durations=10 \
|
||||
--ignore=tests/e2e/solo/ \
|
||||
--ignore=tests/e2e/patched/ \
|
||||
--ignore=tests/e2e/multigpu/ \
|
||||
--ignore=tests/e2e/integrations/ \
|
||||
--ignore=tests/cli \
|
||||
/workspace/axolotl/tests/e2e/ \
|
||||
--cov=axolotl \
|
||||
--cov-append \
|
||||
--cov-report=xml:e2e-coverage.xml
|
||||
|
||||
codecov upload-process -t $CODECOV_TOKEN -f e2e-coverage.xml -F e2e,pytorch-${PYTORCH_VERSION} || true
|
||||
@@ -1,19 +0,0 @@
|
||||
"""Modal app to run axolotl GPU cleanup"""
|
||||
|
||||
from .single_gpu import VOLUME_CONFIG, app, cicd_image, run_cmd
|
||||
|
||||
|
||||
@app.function(
|
||||
image=cicd_image,
|
||||
timeout=60 * 60,
|
||||
cpu=8.0,
|
||||
memory=131072,
|
||||
volumes=VOLUME_CONFIG,
|
||||
)
|
||||
def cleanup():
|
||||
run_cmd("./cicd/cleanup.sh", "/workspace/axolotl")
|
||||
|
||||
|
||||
@app.local_entrypoint()
|
||||
def main():
|
||||
cleanup.remote()
|
||||
@@ -1,6 +0,0 @@
|
||||
#!/bin/bash
|
||||
set -e
|
||||
|
||||
# cleanup old cache files for datasets processing and intermediate mappings
|
||||
find /workspace/data/huggingface-cache/hub/datasets -name "cache-*" -type f -mtime +1 -exec rm {} \;
|
||||
find /workspace/data/huggingface-cache/hub/datasets -name "*.lock" -type f -mtime +1 -exec rm {} \;
|
||||
@@ -1,20 +0,0 @@
|
||||
"""Modal app to run axolotl GPU tests"""
|
||||
|
||||
from .single_gpu import GPU_CONFIG, VOLUME_CONFIG, app, cicd_image, run_cmd
|
||||
|
||||
|
||||
@app.function(
|
||||
image=cicd_image,
|
||||
gpu=GPU_CONFIG,
|
||||
timeout=120 * 60, # 90 min
|
||||
cpu=8.0,
|
||||
memory=131072,
|
||||
volumes=VOLUME_CONFIG,
|
||||
)
|
||||
def cicd_pytest():
|
||||
run_cmd("./cicd/cicd.sh", "/workspace/axolotl")
|
||||
|
||||
|
||||
@app.local_entrypoint()
|
||||
def main():
|
||||
cicd_pytest.remote()
|
||||
@@ -1,83 +0,0 @@
|
||||
"""
|
||||
modal application to run axolotl gpu tests in Modal
|
||||
"""
|
||||
|
||||
# pylint: disable=duplicate-code
|
||||
|
||||
import os
|
||||
import pathlib
|
||||
import tempfile
|
||||
|
||||
import jinja2
|
||||
import modal
|
||||
from jinja2 import select_autoescape
|
||||
from modal import App, Image
|
||||
|
||||
cicd_path = pathlib.Path(__file__).parent.resolve()
|
||||
|
||||
template_loader = jinja2.FileSystemLoader(searchpath=cicd_path)
|
||||
template_env = jinja2.Environment(
|
||||
loader=template_loader, autoescape=select_autoescape()
|
||||
)
|
||||
df_template = template_env.get_template("Dockerfile.jinja")
|
||||
|
||||
df_args = {
|
||||
"AXOLOTL_EXTRAS": os.environ.get("AXOLOTL_EXTRAS", ""),
|
||||
"AXOLOTL_ARGS": os.environ.get("AXOLOTL_ARGS", ""),
|
||||
"PYTORCH_VERSION": os.environ.get("PYTORCH_VERSION", "2.6.0"),
|
||||
"BASE_TAG": os.environ.get("BASE_TAG", "main-base-py3.11-cu126-2.6.0"),
|
||||
"CUDA": os.environ.get("CUDA", "126"),
|
||||
"GITHUB_REF": os.environ.get("GITHUB_REF", "refs/heads/main"),
|
||||
"GITHUB_SHA": os.environ.get("GITHUB_SHA", ""),
|
||||
"CODECOV_TOKEN": os.environ.get("CODECOV_TOKEN", ""),
|
||||
"HF_HOME": "/workspace/data/huggingface-cache/hub",
|
||||
}
|
||||
|
||||
dockerfile_contents = df_template.render(**df_args)
|
||||
|
||||
temp_dir = tempfile.mkdtemp()
|
||||
with open(pathlib.Path(temp_dir) / "Dockerfile", "w", encoding="utf-8") as f:
|
||||
f.write(dockerfile_contents)
|
||||
|
||||
cicd_image = Image.from_dockerfile(
|
||||
pathlib.Path(temp_dir) / "Dockerfile",
|
||||
force_build=True,
|
||||
gpu="A10G",
|
||||
).env(df_args)
|
||||
|
||||
app = App("Axolotl CI/CD", secrets=[])
|
||||
|
||||
hf_cache_volume = modal.Volume.from_name(
|
||||
"axolotl-ci-hf-hub-cache", create_if_missing=True
|
||||
)
|
||||
VOLUME_CONFIG = {
|
||||
"/workspace/data/huggingface-cache/hub": hf_cache_volume,
|
||||
}
|
||||
|
||||
N_GPUS = int(os.environ.get("N_GPUS", 2))
|
||||
GPU_CONFIG = f"H100:{N_GPUS}"
|
||||
|
||||
|
||||
def run_cmd(cmd: str, run_folder: str):
|
||||
import subprocess # nosec
|
||||
|
||||
# Propagate errors from subprocess.
|
||||
if exit_code := subprocess.call(cmd.split(), cwd=run_folder): # nosec
|
||||
exit(exit_code) # pylint: disable=consider-using-sys-exit
|
||||
|
||||
|
||||
@app.function(
|
||||
image=cicd_image,
|
||||
gpu=GPU_CONFIG,
|
||||
timeout=120 * 60,
|
||||
cpu=16.0,
|
||||
memory=131072 * N_GPUS,
|
||||
volumes=VOLUME_CONFIG,
|
||||
)
|
||||
def cicd_pytest():
|
||||
run_cmd("./cicd/multigpu.sh", "/workspace/axolotl")
|
||||
|
||||
|
||||
@app.local_entrypoint()
|
||||
def main():
|
||||
cicd_pytest.remote()
|
||||
@@ -1,23 +0,0 @@
|
||||
#!/bin/bash
|
||||
set -e
|
||||
|
||||
# Only run two tests at a time to avoid OOM on GPU (with coverage collection)
|
||||
pytest -v -n2 \
|
||||
--ignore=/workspace/axolotl/tests/e2e/multigpu/solo/ \
|
||||
--ignore=/workspace/axolotl/tests/e2e/multigpu/patched/ \
|
||||
/workspace/axolotl/tests/e2e/multigpu/ \
|
||||
--cov=axolotl
|
||||
|
||||
# Run solo tests with coverage append
|
||||
pytest -v --durations=10 -n1 \
|
||||
/workspace/axolotl/tests/e2e/multigpu/solo/ \
|
||||
--cov=axolotl \
|
||||
--cov-append
|
||||
|
||||
pytest -v --durations=10 -n1 /workspace/axolotl/tests/e2e/multigpu/patched/ \
|
||||
--cov=axolotl \
|
||||
--cov-append \
|
||||
--cov-report=xml:multigpu-coverage.xml
|
||||
|
||||
# Upload coverage to Codecov
|
||||
codecov upload-process -t "${CODECOV_TOKEN}" -f multigpu-coverage.xml -F multigpu,docker-tests,pytorch-${PYTORCH_VERSION} || true
|
||||
@@ -1,70 +0,0 @@
|
||||
"""Modal app to run axolotl GPU tests"""
|
||||
|
||||
# pylint: disable=duplicate-code
|
||||
|
||||
import os
|
||||
import pathlib
|
||||
import tempfile
|
||||
|
||||
import jinja2
|
||||
import modal
|
||||
import modal.experimental
|
||||
from jinja2 import select_autoescape
|
||||
from modal import App
|
||||
|
||||
cicd_path = pathlib.Path(__file__).parent.resolve()
|
||||
|
||||
template_loader = jinja2.FileSystemLoader(searchpath=cicd_path)
|
||||
template_env = jinja2.Environment(
|
||||
loader=template_loader, autoescape=select_autoescape()
|
||||
)
|
||||
dockerfile = os.environ.get("E2E_DOCKERFILE", "Dockerfile.jinja")
|
||||
df_template = template_env.get_template(dockerfile)
|
||||
|
||||
df_args = {
|
||||
"AXOLOTL_EXTRAS": os.environ.get("AXOLOTL_EXTRAS", ""),
|
||||
"AXOLOTL_ARGS": os.environ.get("AXOLOTL_ARGS", ""),
|
||||
"PYTORCH_VERSION": os.environ.get("PYTORCH_VERSION", "2.6.0"),
|
||||
"BASE_TAG": os.environ.get("BASE_TAG", "main-base-py3.11-cu126-2.6.0"),
|
||||
"CUDA": os.environ.get("CUDA", "126"),
|
||||
"GITHUB_REF": os.environ.get("GITHUB_REF", "refs/heads/main"),
|
||||
"GITHUB_SHA": os.environ.get("GITHUB_SHA", ""),
|
||||
"NIGHTLY_BUILD": os.environ.get("NIGHTLY_BUILD", ""),
|
||||
"CODECOV_TOKEN": os.environ.get("CODECOV_TOKEN", ""),
|
||||
"HF_HOME": "/workspace/data/huggingface-cache/hub",
|
||||
"PYTHONUNBUFFERED": os.environ.get("PYTHONUNBUFFERED", "1"),
|
||||
"DEEPSPEED_LOG_LEVEL": os.environ.get("DEEPSPEED_LOG_LEVEL", "WARNING"),
|
||||
}
|
||||
|
||||
dockerfile_contents = df_template.render(**df_args)
|
||||
|
||||
temp_dir = tempfile.mkdtemp()
|
||||
with open(pathlib.Path(temp_dir) / "Dockerfile", "w", encoding="utf-8") as f:
|
||||
f.write(dockerfile_contents)
|
||||
|
||||
cicd_image = modal.experimental.raw_dockerfile_image(
|
||||
pathlib.Path(temp_dir) / "Dockerfile",
|
||||
# context_mount=None,
|
||||
force_build=True,
|
||||
# gpu="A10G",
|
||||
).env(df_args)
|
||||
|
||||
app = App("Axolotl CI/CD", secrets=[])
|
||||
|
||||
hf_cache_volume = modal.Volume.from_name(
|
||||
"axolotl-ci-hf-hub-cache", create_if_missing=True
|
||||
)
|
||||
VOLUME_CONFIG = {
|
||||
"/workspace/data/huggingface-cache/hub": hf_cache_volume,
|
||||
}
|
||||
|
||||
N_GPUS = int(os.environ.get("N_GPUS", 1))
|
||||
GPU_CONFIG = f"L40S:{N_GPUS}"
|
||||
|
||||
|
||||
def run_cmd(cmd: str, run_folder: str):
|
||||
import subprocess # nosec
|
||||
|
||||
# Propagate errors from subprocess.
|
||||
if exit_code := subprocess.call(cmd.split(), cwd=run_folder): # nosec
|
||||
exit(exit_code) # pylint: disable=consider-using-sys-exit
|
||||
57
codecov.yml
57
codecov.yml
@@ -1,57 +0,0 @@
|
||||
codecov:
|
||||
require_ci_to_pass: yes
|
||||
notify:
|
||||
wait_for_ci: true
|
||||
|
||||
coverage:
|
||||
precision: 2
|
||||
round: down
|
||||
range: "70...100"
|
||||
status:
|
||||
project:
|
||||
default:
|
||||
# basic
|
||||
target: auto
|
||||
threshold: 0%
|
||||
base: auto
|
||||
# advanced
|
||||
branches: null
|
||||
if_no_uploads: error
|
||||
if_not_found: success
|
||||
if_ci_failed: error
|
||||
only_pulls: true
|
||||
flags: null
|
||||
paths: null
|
||||
informational: true
|
||||
patch:
|
||||
default:
|
||||
# basic
|
||||
target: auto
|
||||
threshold: 0%
|
||||
base: auto
|
||||
# advanced
|
||||
branches: null
|
||||
if_no_uploads: error
|
||||
if_not_found: success
|
||||
if_ci_failed: error
|
||||
only_pulls: false
|
||||
flags: null
|
||||
paths: null
|
||||
|
||||
parsers:
|
||||
gcov:
|
||||
branch_detection:
|
||||
conditional: yes
|
||||
loop: yes
|
||||
method: no
|
||||
macro: no
|
||||
|
||||
comment:
|
||||
layout: "reach,diff,flags,files,footer"
|
||||
behavior: default
|
||||
require_changes: no
|
||||
require_base: no
|
||||
require_head: yes
|
||||
|
||||
github_checks:
|
||||
annotations: false
|
||||
@@ -15,12 +15,16 @@
|
||||
"hysteresis": 2,
|
||||
"min_loss_scale": 1
|
||||
},
|
||||
"compile": {
|
||||
"disable": false,
|
||||
"backend": "inductor"
|
||||
"optimizer": {
|
||||
"type": "AdamW",
|
||||
"params": {
|
||||
"lr": "auto",
|
||||
"betas": "auto",
|
||||
"eps": "auto",
|
||||
"weight_decay": "auto"
|
||||
}
|
||||
},
|
||||
"gradient_accumulation_steps": "auto",
|
||||
"gradient_clipping": "auto",
|
||||
"train_batch_size": "auto",
|
||||
"train_micro_batch_size_per_gpu": "auto",
|
||||
"wall_clock_breakdown": false
|
||||
@@ -19,8 +19,16 @@
|
||||
"hysteresis": 2,
|
||||
"min_loss_scale": 1
|
||||
},
|
||||
"optimizer": {
|
||||
"type": "AdamW",
|
||||
"params": {
|
||||
"lr": "auto",
|
||||
"betas": "auto",
|
||||
"eps": "auto",
|
||||
"weight_decay": "auto"
|
||||
}
|
||||
},
|
||||
"gradient_accumulation_steps": "auto",
|
||||
"gradient_clipping": "auto",
|
||||
"train_batch_size": "auto",
|
||||
"train_micro_batch_size_per_gpu": "auto",
|
||||
"wall_clock_breakdown": false
|
||||
@@ -23,8 +23,16 @@
|
||||
"hysteresis": 2,
|
||||
"min_loss_scale": 1
|
||||
},
|
||||
"optimizer": {
|
||||
"type": "AdamW",
|
||||
"params": {
|
||||
"lr": "auto",
|
||||
"betas": "auto",
|
||||
"eps": "auto",
|
||||
"weight_decay": "auto"
|
||||
}
|
||||
},
|
||||
"gradient_accumulation_steps": "auto",
|
||||
"gradient_clipping": "auto",
|
||||
"train_batch_size": "auto",
|
||||
"train_micro_batch_size_per_gpu": "auto",
|
||||
"wall_clock_breakdown": false
|
||||
@@ -1,23 +0,0 @@
|
||||
{
|
||||
"zero_optimization": {
|
||||
"stage": 1,
|
||||
"overlap_comm": true
|
||||
},
|
||||
"bf16": {
|
||||
"enabled": "auto"
|
||||
},
|
||||
"fp16": {
|
||||
"enabled": "auto",
|
||||
"auto_cast": false,
|
||||
"loss_scale": 0,
|
||||
"initial_scale_power": 32,
|
||||
"loss_scale_window": 1000,
|
||||
"hysteresis": 2,
|
||||
"min_loss_scale": 1
|
||||
},
|
||||
"gradient_accumulation_steps": "auto",
|
||||
"gradient_clipping": "auto",
|
||||
"train_batch_size": "auto",
|
||||
"train_micro_batch_size_per_gpu": "auto",
|
||||
"wall_clock_breakdown": false
|
||||
}
|
||||
@@ -1,31 +0,0 @@
|
||||
{
|
||||
"compile": {
|
||||
"disable": false,
|
||||
"backend": "inductor"
|
||||
},
|
||||
"zero_optimization": {
|
||||
"stage": 2,
|
||||
"offload_optimizer": {
|
||||
"device": "cpu"
|
||||
},
|
||||
"contiguous_gradients": true,
|
||||
"overlap_comm": true
|
||||
},
|
||||
"bf16": {
|
||||
"enabled": "auto"
|
||||
},
|
||||
"fp16": {
|
||||
"enabled": "auto",
|
||||
"auto_cast": false,
|
||||
"loss_scale": 0,
|
||||
"initial_scale_power": 32,
|
||||
"loss_scale_window": 1000,
|
||||
"hysteresis": 2,
|
||||
"min_loss_scale": 1
|
||||
},
|
||||
"gradient_accumulation_steps": "auto",
|
||||
"gradient_clipping": "auto",
|
||||
"train_batch_size": "auto",
|
||||
"train_micro_batch_size_per_gpu": "auto",
|
||||
"wall_clock_breakdown": false
|
||||
}
|
||||
@@ -1,22 +0,0 @@
|
||||
{
|
||||
"zero_optimization": {
|
||||
"stage": 3,
|
||||
"overlap_comm": true,
|
||||
"contiguous_gradients": true,
|
||||
"sub_group_size": 0,
|
||||
"reduce_bucket_size": "auto",
|
||||
"stage3_prefetch_bucket_size": "auto",
|
||||
"stage3_param_persistence_threshold": "auto",
|
||||
"stage3_max_live_parameters": 0,
|
||||
"stage3_max_reuse_distance": 0,
|
||||
"stage3_gather_16bit_weights_on_model_save": true
|
||||
},
|
||||
"bf16": {
|
||||
"enabled": true
|
||||
},
|
||||
"gradient_accumulation_steps": "auto",
|
||||
"gradient_clipping": "auto",
|
||||
"train_batch_size": "auto",
|
||||
"train_micro_batch_size_per_gpu": "auto",
|
||||
"wall_clock_breakdown": false
|
||||
}
|
||||
@@ -1,32 +0,0 @@
|
||||
{
|
||||
"zero_force_ds_cpu_optimizer": false,
|
||||
"zero_allow_untested_optimizer": true,
|
||||
"zero_optimization": {
|
||||
"stage": 3,
|
||||
"offload_optimizer": {
|
||||
"device": "cpu",
|
||||
"pin_memory": true
|
||||
},
|
||||
"offload_param": {
|
||||
"device": "cpu",
|
||||
"pin_memory": true
|
||||
},
|
||||
"overlap_comm": true,
|
||||
"contiguous_gradients": true,
|
||||
"sub_group_size": 0,
|
||||
"reduce_bucket_size": "auto",
|
||||
"stage3_prefetch_bucket_size": "auto",
|
||||
"stage3_param_persistence_threshold": "auto",
|
||||
"stage3_max_live_parameters": 0,
|
||||
"stage3_max_reuse_distance": 0,
|
||||
"stage3_gather_16bit_weights_on_model_save": true
|
||||
},
|
||||
"bf16": {
|
||||
"enabled": true
|
||||
},
|
||||
"gradient_accumulation_steps": "auto",
|
||||
"gradient_clipping": "auto",
|
||||
"train_batch_size": "auto",
|
||||
"train_micro_batch_size_per_gpu": "auto",
|
||||
"wall_clock_breakdown": false
|
||||
}
|
||||
@@ -1,28 +0,0 @@
|
||||
{
|
||||
"zero_force_ds_cpu_optimizer": false,
|
||||
"zero_allow_untested_optimizer": true,
|
||||
"zero_optimization": {
|
||||
"stage": 3,
|
||||
"offload_param": {
|
||||
"device": "cpu",
|
||||
"pin_memory": true
|
||||
},
|
||||
"overlap_comm": true,
|
||||
"contiguous_gradients": true,
|
||||
"sub_group_size": 0,
|
||||
"reduce_bucket_size": "auto",
|
||||
"stage3_prefetch_bucket_size": "auto",
|
||||
"stage3_param_persistence_threshold": "auto",
|
||||
"stage3_max_live_parameters": 0,
|
||||
"stage3_max_reuse_distance": 0,
|
||||
"stage3_gather_16bit_weights_on_model_save": true
|
||||
},
|
||||
"bf16": {
|
||||
"enabled": true
|
||||
},
|
||||
"gradient_accumulation_steps": "auto",
|
||||
"gradient_clipping": "auto",
|
||||
"train_batch_size": "auto",
|
||||
"train_micro_batch_size_per_gpu": "auto",
|
||||
"wall_clock_breakdown": false
|
||||
}
|
||||
@@ -1 +0,0 @@
|
||||
This directory contains example config files that might be useful for debugging. Please see [docs/debugging.qmd](../docs/debugging.qmd) for more information.
|
||||
@@ -1,48 +0,0 @@
|
||||
# Example config for debugging the chat_template prompt format
|
||||
base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
|
||||
model_type: LlamaForCausalLM
|
||||
tokenizer_type: LlamaTokenizer
|
||||
|
||||
load_in_8bit: true
|
||||
load_in_4bit: false
|
||||
|
||||
datasets:
|
||||
- path: fozziethebeat/alpaca_messages_2k_test
|
||||
type: chat_template
|
||||
shards: 10
|
||||
val_set_size: 0
|
||||
output_dir: temp_debug/axolotl_outputs/model
|
||||
dataset_prepared_path: temp_debug/axolotl_outputs/data
|
||||
dataset_processes: 1
|
||||
|
||||
sequence_len: 4096
|
||||
sample_packing: false
|
||||
pad_to_sequence_len: true
|
||||
|
||||
adapter: lora
|
||||
lora_model_dir:
|
||||
lora_r: 32
|
||||
lora_alpha: 16
|
||||
lora_dropout: 0.05
|
||||
lora_target_linear: true
|
||||
lora_fan_in_fan_out:
|
||||
|
||||
micro_batch_size: 1
|
||||
num_epochs: 1
|
||||
max_steps: 10
|
||||
optimizer: adamw_bnb_8bit
|
||||
lr_scheduler: cosine
|
||||
learning_rate: 0.0002
|
||||
|
||||
train_on_inputs: false
|
||||
group_by_length: false
|
||||
bf16: false
|
||||
fp16: true
|
||||
tf32: false
|
||||
|
||||
gradient_checkpointing: true
|
||||
logging_steps: 1
|
||||
flash_attention: true
|
||||
|
||||
warmup_steps: 10
|
||||
weight_decay: 0.0
|
||||
@@ -1,36 +1,31 @@
|
||||
ARG BASE_TAG=main-base
|
||||
FROM axolotlai/axolotl-base:$BASE_TAG
|
||||
FROM winglian/axolotl-base:$BASE_TAG
|
||||
|
||||
ARG TORCH_CUDA_ARCH_LIST="7.0 7.5 8.0 8.6+PTX"
|
||||
ARG AXOLOTL_EXTRAS=""
|
||||
ARG AXOLOTL_ARGS=""
|
||||
ARG CUDA="118"
|
||||
ARG PYTORCH_VERSION="2.1.2"
|
||||
ENV BNB_CUDA_VERSION=$CUDA
|
||||
ARG PYTORCH_VERSION="2.0.1"
|
||||
|
||||
ENV PYTORCH_VERSION=$PYTORCH_VERSION
|
||||
|
||||
RUN apt-get update && \
|
||||
apt-get install -y --allow-change-held-packages vim curl nano libnccl2 libnccl-dev rsync s3fs
|
||||
apt-get install -y vim curl
|
||||
|
||||
WORKDIR /workspace
|
||||
|
||||
RUN git clone --depth=1 https://github.com/axolotl-ai-cloud/axolotl.git
|
||||
RUN git clone --depth=1 https://github.com/OpenAccess-AI-Collective/axolotl.git
|
||||
|
||||
WORKDIR /workspace/axolotl
|
||||
|
||||
# If AXOLOTL_EXTRAS is set, append it in brackets
|
||||
RUN sed -i "s/torch==.*/torch==$PYTORCH_VERSION/" requirements.txt
|
||||
RUN if [ "$AXOLOTL_EXTRAS" != "" ] ; then \
|
||||
pip install --no-build-isolation -e .[deepspeed,flash-attn,ring-flash-attn,optimizers,ray,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \
|
||||
pip install -e .[deepspeed,flash-attn,$AXOLOTL_EXTRAS]; \
|
||||
else \
|
||||
pip install --no-build-isolation -e .[deepspeed,flash-attn,ring-flash-attn,optimizers,ray] $AXOLOTL_ARGS; \
|
||||
pip install -e .[deepspeed,flash-attn]; \
|
||||
fi
|
||||
|
||||
RUN python scripts/unsloth_install.py | sh
|
||||
RUN python scripts/cutcrossentropy_install.py | sh
|
||||
|
||||
# So we can test the Docker image
|
||||
RUN pip install pytest
|
||||
|
||||
# fix so that git fetch/pull from remote works
|
||||
RUN git config remote.origin.fetch "+refs/heads/*:refs/remotes/origin/*" && \
|
||||
git config --get remote.origin.fetch
|
||||
|
||||
@@ -3,12 +3,12 @@ ARG CUDNN_VERSION="8"
|
||||
ARG UBUNTU_VERSION="22.04"
|
||||
ARG MAX_JOBS=4
|
||||
|
||||
FROM nvidia/cuda:$CUDA_VERSION-cudnn$CUDNN_VERSION-devel-ubuntu$UBUNTU_VERSION AS base-builder
|
||||
FROM nvidia/cuda:$CUDA_VERSION-cudnn$CUDNN_VERSION-devel-ubuntu$UBUNTU_VERSION as base-builder
|
||||
|
||||
ENV PATH="/root/miniconda3/bin:${PATH}"
|
||||
|
||||
ARG PYTHON_VERSION="3.10"
|
||||
ARG PYTORCH_VERSION="2.1.2"
|
||||
ARG PYTHON_VERSION="3.9"
|
||||
ARG PYTORCH_VERSION="2.0.1"
|
||||
ARG CUDA="118"
|
||||
ARG TORCH_CUDA_ARCH_LIST="7.0 7.5 8.0 8.6 9.0+PTX"
|
||||
|
||||
@@ -16,7 +16,7 @@ ENV PYTHON_VERSION=$PYTHON_VERSION
|
||||
ENV TORCH_CUDA_ARCH_LIST=$TORCH_CUDA_ARCH_LIST
|
||||
|
||||
RUN apt-get update \
|
||||
&& apt-get install -y wget git build-essential ninja-build git-lfs libaio-dev pkg-config && rm -rf /var/lib/apt/lists/* \
|
||||
&& apt-get install -y wget git build-essential ninja-build git-lfs libaio-dev && rm -rf /var/lib/apt/lists/* \
|
||||
&& wget \
|
||||
https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh \
|
||||
&& mkdir /root/.conda \
|
||||
@@ -28,16 +28,10 @@ ENV PATH="/root/miniconda3/envs/py${PYTHON_VERSION}/bin:${PATH}"
|
||||
|
||||
WORKDIR /workspace
|
||||
|
||||
RUN python3 -m pip install --upgrade pip && pip3 install -U packaging==23.2 setuptools==75.8.0 wheel && \
|
||||
python3 -m pip install --no-cache-dir -U torch==${PYTORCH_VERSION}+cu${CUDA} torchvision --extra-index-url https://download.pytorch.org/whl/cu$CUDA && \
|
||||
python3 -m pip install --no-cache-dir "causal_conv1d @ git+https://github.com/Dao-AILab/causal-conv1d.git@main" && \
|
||||
python3 -m pip install --no-cache-dir "mamba_ssm @ git+https://github.com/state-spaces/mamba.git@main"
|
||||
RUN python3 -m pip install --upgrade pip && pip3 install packaging && \
|
||||
python3 -m pip install --no-cache-dir -U torch==${PYTORCH_VERSION}+cu${CUDA} deepspeed-kernels --extra-index-url https://download.pytorch.org/whl/cu$CUDA
|
||||
|
||||
RUN git lfs install --skip-repo && \
|
||||
pip3 install awscli && \
|
||||
# The base image ships with `pydantic==1.8.2` which is not working
|
||||
pip3 install -U --no-cache-dir pydantic==1.10.10
|
||||
|
||||
RUN if [ "$PYTORCH_VERSION" = "2.6.0" ] && [ "$CUDA" = "124" ] ; then \
|
||||
FLASH_ATTENTION_FORCE_BUILD="TRUE" pip3 install --no-build-isolation flash-attn==2.8.0.post2; \
|
||||
fi
|
||||
|
||||
@@ -1,38 +0,0 @@
|
||||
ARG CUDA_VERSION="12.8.1"
|
||||
ARG CUDNN_VERSION="8"
|
||||
ARG UBUNTU_VERSION="22.04"
|
||||
ARG MAX_JOBS=4
|
||||
|
||||
FROM nvidia/cuda:$CUDA_VERSION-cudnn$CUDNN_VERSION-devel-ubuntu$UBUNTU_VERSION AS base-builder
|
||||
|
||||
ENV PATH="/root/miniconda3/bin:${PATH}"
|
||||
|
||||
ARG PYTHON_VERSION="3.11"
|
||||
ARG PYTORCH_VERSION="next"
|
||||
ARG CUDA="128"
|
||||
ARG TORCH_CUDA_ARCH_LIST="7.0 7.5 8.0 8.6 9.0+PTX"
|
||||
|
||||
ENV PYTHON_VERSION=$PYTHON_VERSION
|
||||
ENV TORCH_CUDA_ARCH_LIST=$TORCH_CUDA_ARCH_LIST
|
||||
|
||||
RUN apt-get update \
|
||||
&& apt-get install -y wget git build-essential ninja-build git-lfs libaio-dev pkg-config && rm -rf /var/lib/apt/lists/* \
|
||||
&& wget \
|
||||
https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh \
|
||||
&& mkdir /root/.conda \
|
||||
&& bash Miniconda3-latest-Linux-x86_64.sh -b \
|
||||
&& rm -f Miniconda3-latest-Linux-x86_64.sh \
|
||||
&& conda create -n "py${PYTHON_VERSION}" python="${PYTHON_VERSION}"
|
||||
|
||||
ENV PATH="/root/miniconda3/envs/py${PYTHON_VERSION}/bin:${PATH}"
|
||||
|
||||
WORKDIR /workspace
|
||||
|
||||
RUN python3 -m pip install --upgrade pip && pip3 install packaging && \
|
||||
python3 -m pip install --no-cache-dir -U torch==2.7.1 --extra-index-url https://download.pytorch.org/whl/test/cu$CUDA && \
|
||||
python3 -m pip install --no-cache-dir "causal_conv1d @ git+https://github.com/Dao-AILab/causal-conv1d.git@main" && \
|
||||
python3 -m pip install --no-cache-dir "mamba_ssm @ git+https://github.com/state-spaces/mamba.git@main"
|
||||
|
||||
RUN git lfs install --skip-repo && \
|
||||
pip3 install awscli && \
|
||||
pip3 install -U --no-cache-dir pydantic==2.10.6
|
||||
@@ -1,39 +0,0 @@
|
||||
ARG CUDA_VERSION="12.8.1"
|
||||
ARG CUDNN_VERSION="8"
|
||||
ARG UBUNTU_VERSION="22.04"
|
||||
ARG MAX_JOBS=4
|
||||
|
||||
FROM nvidia/cuda:$CUDA_VERSION-cudnn$CUDNN_VERSION-devel-ubuntu$UBUNTU_VERSION AS base-builder
|
||||
|
||||
ENV PATH="/root/miniconda3/bin:${PATH}"
|
||||
|
||||
ARG PYTHON_VERSION="3.11"
|
||||
ARG PYTORCH_VERSION="nightly"
|
||||
ARG CUDA="128"
|
||||
ARG TORCH_CUDA_ARCH_LIST="7.0 7.5 8.0 8.6 9.0+PTX"
|
||||
|
||||
ENV PYTHON_VERSION=$PYTHON_VERSION
|
||||
ENV TORCH_CUDA_ARCH_LIST=$TORCH_CUDA_ARCH_LIST
|
||||
|
||||
RUN apt-get update \
|
||||
&& apt-get install -y wget git build-essential ninja-build git-lfs libaio-dev pkg-config && rm -rf /var/lib/apt/lists/* \
|
||||
&& wget \
|
||||
https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh \
|
||||
&& mkdir /root/.conda \
|
||||
&& bash Miniconda3-latest-Linux-x86_64.sh -b \
|
||||
&& rm -f Miniconda3-latest-Linux-x86_64.sh \
|
||||
&& conda create -n "py${PYTHON_VERSION}" python="${PYTHON_VERSION}"
|
||||
|
||||
ENV PATH="/root/miniconda3/envs/py${PYTHON_VERSION}/bin:${PATH}"
|
||||
|
||||
WORKDIR /workspace
|
||||
|
||||
RUN python3 -m pip install --upgrade pip && pip3 install packaging && \
|
||||
python3 -m pip install --no-cache-dir -U torch --extra-index-url https://download.pytorch.org/whl/nightly/cu$CUDA && \
|
||||
python3 -m pip install --no-cache-dir "causal_conv1d @ git+https://github.com/Dao-AILab/causal-conv1d.git@main" && \
|
||||
python3 -m pip install --no-cache-dir "mamba_ssm @ git+https://github.com/state-spaces/mamba.git@main"
|
||||
|
||||
RUN git lfs install --skip-repo && \
|
||||
pip3 install awscli && \
|
||||
# The base image ships with `pydantic==1.8.2` which is not working
|
||||
pip3 install -U --no-cache-dir pydantic==1.10.10
|
||||
@@ -1,27 +0,0 @@
|
||||
ARG BASE_TAG=main
|
||||
FROM axolotlai/axolotl:$BASE_TAG
|
||||
|
||||
ENV HF_DATASETS_CACHE="/workspace/data/huggingface-cache/datasets"
|
||||
ENV HF_HUB_CACHE="/workspace/data/huggingface-cache/hub"
|
||||
ENV HF_HOME="/workspace/data/huggingface-cache/hub"
|
||||
ENV HF_HUB_ENABLE_HF_TRANSFER="1"
|
||||
|
||||
EXPOSE 8888
|
||||
EXPOSE 22
|
||||
|
||||
COPY scripts/cloud-entrypoint.sh /root/cloud-entrypoint.sh
|
||||
COPY scripts/motd /etc/motd
|
||||
|
||||
RUN pip install jupyterlab notebook ipywidgets && \
|
||||
jupyter lab clean
|
||||
RUN apt install --yes --no-install-recommends openssh-server tmux iproute2 nvtop && \
|
||||
mkdir -p ~/.ssh && \
|
||||
chmod 700 ~/.ssh && \
|
||||
printf "\n[[ -z \"\$TMUX\" ]] && { tmux attach-session -t ssh_tmux || tmux new-session -s ssh_tmux; exit; }\n" >> ~/.bashrc && \
|
||||
printf "[ ! -z \"\$TERM\" -a -r /etc/motd ] && cat /etc/motd\n" >> ~/.bashrc && \
|
||||
chmod +x /workspace/axolotl/scripts/cloud-entrypoint.sh && \
|
||||
chmod +x /root/cloud-entrypoint.sh && \
|
||||
echo 'set-option -g history-limit 5000' >> ~/.tmux.conf
|
||||
|
||||
ENTRYPOINT ["/root/cloud-entrypoint.sh"]
|
||||
CMD ["sleep", "infinity"]
|
||||
@@ -1,26 +0,0 @@
|
||||
ARG BASE_TAG=main
|
||||
FROM axolotlai/axolotl:$BASE_TAG
|
||||
|
||||
ENV HF_DATASETS_CACHE="/workspace/data/huggingface-cache/datasets"
|
||||
ENV HF_HUB_CACHE="/workspace/data/huggingface-cache/hub"
|
||||
ENV HF_HOME="/workspace/data/huggingface-cache/hub"
|
||||
ENV HF_HUB_ENABLE_HF_TRANSFER="1"
|
||||
|
||||
EXPOSE 8888
|
||||
EXPOSE 22
|
||||
|
||||
COPY scripts/cloud-entrypoint-term.sh /root/cloud-entrypoint.sh
|
||||
COPY scripts/motd /etc/motd
|
||||
|
||||
RUN pip install jupyterlab notebook ipywidgets && \
|
||||
jupyter lab clean
|
||||
RUN apt install --yes --no-install-recommends openssh-server tmux sudo && \
|
||||
pip3 install -U --no-cache-dir grpcio ray[default]==2.9.3 && \
|
||||
mkdir -p ~/.ssh && \
|
||||
chmod 700 ~/.ssh && \
|
||||
printf "[ ! -z \"\$TERM\" -a -r /etc/motd ] && cat /etc/motd\n" >> ~/.bashrc && \
|
||||
chmod +x /workspace/axolotl/scripts/cloud-entrypoint.sh && \
|
||||
chmod +x /root/cloud-entrypoint.sh
|
||||
|
||||
ENTRYPOINT ["/root/cloud-entrypoint.sh"]
|
||||
CMD ["sleep", "infinity"]
|
||||
19
docker/Dockerfile-runpod
Normal file
19
docker/Dockerfile-runpod
Normal file
@@ -0,0 +1,19 @@
|
||||
ARG BASE_TAG=main
|
||||
FROM winglian/axolotl:$BASE_TAG
|
||||
|
||||
ENV HF_DATASETS_CACHE="/workspace/data/huggingface-cache/datasets"
|
||||
ENV HUGGINGFACE_HUB_CACHE="/workspace/data/huggingface-cache/hub"
|
||||
ENV TRANSFORMERS_CACHE="/workspace/data/huggingface-cache/hub"
|
||||
ENV HF_HOME="/workspace/data/huggingface-cache/hub"
|
||||
|
||||
COPY scripts/runpod-entrypoint.sh /root/runpod-entrypoint.sh
|
||||
|
||||
RUN apt install --yes --no-install-recommends openssh-server tmux && \
|
||||
mkdir -p ~/.ssh && \
|
||||
chmod 700 ~/.ssh && \
|
||||
printf "\n[[ -z \"\$TMUX\" ]] && { tmux attach-session -t ssh_tmux || tmux new-session -s ssh_tmux; exit; }\n" >> ~/.bashrc && \
|
||||
chmod +x /workspace/axolotl/scripts/runpod-entrypoint.sh && \
|
||||
chmod +x /root/runpod-entrypoint.sh
|
||||
|
||||
ENTRYPOINT ["/root/runpod-entrypoint.sh"]
|
||||
CMD ["sleep", "infinity"]
|
||||
@@ -1,40 +0,0 @@
|
||||
ARG BASE_TAG=main-base
|
||||
FROM axolotlai/axolotl-base:$BASE_TAG
|
||||
|
||||
ARG TORCH_CUDA_ARCH_LIST="7.0 7.5 8.0 8.6+PTX"
|
||||
ARG AXOLOTL_EXTRAS=""
|
||||
ARG AXOLOTL_ARGS=""
|
||||
ARG CUDA="118"
|
||||
ARG PYTORCH_VERSION="2.1.2"
|
||||
ARG GITHUB_REF="main"
|
||||
|
||||
ENV PYTORCH_VERSION=$PYTORCH_VERSION
|
||||
|
||||
RUN apt-get update && \
|
||||
apt-get install -y --allow-change-held-packages vim curl nano libnccl2 libnccl-dev
|
||||
|
||||
WORKDIR /workspace
|
||||
|
||||
RUN git clone --depth=1 https://github.com/axolotl-ai-cloud/axolotl.git
|
||||
|
||||
WORKDIR /workspace/axolotl
|
||||
|
||||
RUN git fetch origin +$GITHUB_REF && \
|
||||
git checkout FETCH_HEAD
|
||||
|
||||
# If AXOLOTL_EXTRAS is set, append it in brackets
|
||||
RUN if [ "$AXOLOTL_EXTRAS" != "" ] ; then \
|
||||
pip install --no-build-isolation -e .[deepspeed,flash-attn,mamba-ssm,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \
|
||||
else \
|
||||
pip install --no-build-isolation -e .[deepspeed,flash-attn,mamba-ssm] $AXOLOTL_ARGS; \
|
||||
fi
|
||||
|
||||
# So we can test the Docker image
|
||||
RUN pip install pytest
|
||||
|
||||
# fix so that git fetch/pull from remote works
|
||||
RUN git config remote.origin.fetch "+refs/heads/*:refs/remotes/origin/*" && \
|
||||
git config --get remote.origin.fetch
|
||||
|
||||
# helper for huggingface-login cli
|
||||
RUN git config --global credential.helper store
|
||||
@@ -1,36 +0,0 @@
|
||||
ARG CUDA_VERSION="12.6.3"
|
||||
ARG CUDNN_VERSION=""
|
||||
ARG UBUNTU_VERSION="22.04"
|
||||
ARG MAX_JOBS=4
|
||||
|
||||
FROM nvidia/cuda:$CUDA_VERSION-cudnn$CUDNN_VERSION-devel-ubuntu$UBUNTU_VERSION AS base-builder
|
||||
|
||||
ARG PYTHON_VERSION="3.11"
|
||||
ARG PYTORCH_VERSION="2.6.0"
|
||||
ARG CUDA="126"
|
||||
ARG TORCH_CUDA_ARCH_LIST="7.0 7.5 8.0 8.6 9.0+PTX"
|
||||
|
||||
ENV PYTHON_VERSION=$PYTHON_VERSION
|
||||
ENV TORCH_CUDA_ARCH_LIST=$TORCH_CUDA_ARCH_LIST
|
||||
ENV UV_TORCH_BACKEND="cu${CUDA}"
|
||||
|
||||
RUN apt-get update \
|
||||
&& apt-get install -y wget git build-essential ninja-build git-lfs libaio-dev pkg-config curl && rm -rf /var/lib/apt/lists/* \
|
||||
&& git lfs install --skip-repo \
|
||||
&& curl -LsSf https://astral.sh/uv/install.sh | sh
|
||||
|
||||
ENV PATH="/root/.local/bin:${PATH}"
|
||||
|
||||
RUN uv python install ${PYTHON_VERSION}
|
||||
|
||||
WORKDIR /workspace
|
||||
|
||||
RUN uv venv --no-project --relocatable axolotl-venv
|
||||
|
||||
ENV PATH="/workspace/axolotl-venv/bin:${PATH}"
|
||||
|
||||
RUN uv pip install packaging setuptools wheel psutil \
|
||||
&& uv pip install torch==${PYTORCH_VERSION} \
|
||||
&& uv pip install --no-build-isolation "causal_conv1d @ git+https://github.com/Dao-AILab/causal-conv1d.git@main" \
|
||||
&& uv pip install "mamba_ssm @ git+https://github.com/state-spaces/mamba.git@main" \
|
||||
&& uv pip install awscli pydantic
|
||||
5
docs/.gitignore
vendored
5
docs/.gitignore
vendored
@@ -1,5 +0,0 @@
|
||||
/.quarto/
|
||||
_site/
|
||||
/api/*.qmd
|
||||
/api/*.html
|
||||
config-reference.qmd
|
||||
108
docs/amd_hpc.qmd
108
docs/amd_hpc.qmd
@@ -1,108 +0,0 @@
|
||||
---
|
||||
title: AMD GPUs on HPC Systems
|
||||
description: A comprehensive guide for using Axolotl on distributed systems with AMD GPUs
|
||||
---
|
||||
|
||||
This guide provides step-by-step instructions for installing and configuring Axolotl on a High-Performance Computing (HPC) environment equipped with AMD GPUs.
|
||||
|
||||
## Setup
|
||||
|
||||
### 1. Install Python
|
||||
|
||||
We recommend using Miniforge, a minimal conda-based Python distribution:
|
||||
|
||||
```bash
|
||||
curl -L -O "https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-$(uname)-$(uname -m).sh"
|
||||
bash Miniforge3-$(uname)-$(uname -m).sh
|
||||
```
|
||||
|
||||
### 2. Configure Python Environment
|
||||
Add Python to your PATH and ensure it's available at login:
|
||||
|
||||
```bash
|
||||
echo 'export PATH=~/miniforge3/bin:$PATH' >> ~/.bashrc
|
||||
echo 'if [ -f ~/.bashrc ]; then . ~/.bashrc; fi' >> ~/.bash_profile
|
||||
```
|
||||
|
||||
### 3. Load AMD GPU Software
|
||||
|
||||
Load the ROCm module:
|
||||
|
||||
```bash
|
||||
module load rocm/5.7.1
|
||||
```
|
||||
|
||||
Note: The specific module name and version may vary depending on your HPC system. Consult your system documentation for the correct module name.
|
||||
|
||||
### 4. Install PyTorch
|
||||
|
||||
Install PyTorch with ROCm support:
|
||||
|
||||
```bash
|
||||
pip install -U torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm5.7 --force-reinstall
|
||||
```
|
||||
|
||||
### 5. Install Flash Attention
|
||||
|
||||
Clone and install the Flash Attention repository:
|
||||
|
||||
```bash
|
||||
git clone --recursive https://github.com/ROCmSoftwarePlatform/flash-attention.git
|
||||
export GPU_ARCHS="gfx90a"
|
||||
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 --no-build-isolation .
|
||||
```
|
||||
|
||||
### 6. Install Axolotl
|
||||
|
||||
Clone and install Axolotl:
|
||||
|
||||
```bash
|
||||
git clone https://github.com/axolotl-ai-cloud/axolotl
|
||||
cd axolotl
|
||||
pip install packaging ninja
|
||||
pip install --no-build-isolation -e .
|
||||
```
|
||||
|
||||
### 7. Apply xformers Workaround
|
||||
|
||||
xformers 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.
|
||||
|
||||
### 8. Prepare Job Submission Script
|
||||
|
||||
Create 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
|
||||
|
||||
```bash
|
||||
export TRANSFORMERS_OFFLINE=1
|
||||
export HF_DATASETS_OFFLINE=1
|
||||
```
|
||||
|
||||
### 9. Download Base Model
|
||||
|
||||
Download a base model using the Hugging Face CLI:
|
||||
|
||||
```bash
|
||||
huggingface-cli download meta-llama/Meta-Llama-3.1-8B --local-dir ~/hfdata/llama3.1-8B
|
||||
```
|
||||
|
||||
### 10. Create Axolotl Configuration
|
||||
|
||||
Create an Axolotl configuration file (YAML format) tailored to your specific training requirements and dataset. Use FSDP for multi-node training.
|
||||
|
||||
Note: Deepspeed did not work at the time of testing. However, if anyone managed to get it working, please let us know.
|
||||
|
||||
### 11. Preprocess Data
|
||||
|
||||
Run preprocessing on the login node:
|
||||
|
||||
```bash
|
||||
CUDA_VISIBLE_DEVICES="" python -m axolotl.cli.preprocess /path/to/your/config.yaml
|
||||
```
|
||||
|
||||
### 12. Train
|
||||
|
||||
You are now ready to submit your previously prepared job script. 🚂
|
||||
@@ -1,59 +0,0 @@
|
||||
---
|
||||
title: Batch size vs Gradient accumulation
|
||||
description: Understanding of batch size and gradient accumulation steps
|
||||
---
|
||||
|
||||
Gradient accumulation means accumulating gradients over several mini-batches and updating the model weights afterward. When the samples in each batch are diverse, this technique doesn't significantly impact learning.
|
||||
|
||||
This method allows for effective training with larger effective batch sizes without needing proportionally larger memory. Here's why:
|
||||
|
||||
1. **Memory Consumption with Batch Size**: The primary reason increasing the batch size impacts memory is due to the storage requirements for intermediate activations. When you forward propagate a batch through a network, you have to store the activations at each layer for each sample in the batch, because these activations are used during backpropagation to compute gradients. Therefore, larger batches mean more activations, leading to greater GPU memory consumption.
|
||||
|
||||
2. **Gradient Accumulation**: With gradient accumulation, you're effectively simulating a larger batch size by accumulating gradients over several smaller batches (or micro-batches). However, at any given time, you're only forward and backward propagating a micro-batch. This means you only store activations for the micro-batch, not the full accumulated batch. As a result, you can simulate the effect of a larger batch size without the memory cost of storing activations for a large batch.
|
||||
|
||||
**Example 1:**
|
||||
Micro batch size: 3
|
||||
Gradient accumulation steps: 2
|
||||
Number of GPUs: 3
|
||||
Total batch size = 3 * 2 * 3 = 18
|
||||
|
||||
```
|
||||
| GPU 1 | GPU 2 | GPU 3 |
|
||||
|----------------|----------------|----------------|
|
||||
| S1, S2, S3 | S4, S5, S6 | S7, S8, S9 |
|
||||
| e1, e2, e3 | e4, e5, e6 | e7, e8, e9 |
|
||||
|----------------|----------------|----------------|
|
||||
| → (accumulate) | → (accumulate) | → (accumulate) |
|
||||
|----------------|----------------|----------------|
|
||||
| S10, S11, S12 | S13, S14, S15 | S16, S17, S18 |
|
||||
| e10, e11, e12 | e13, e14, e15 | e16, e17, e18 |
|
||||
|----------------|----------------|----------------|
|
||||
| → (apply) | → (apply) | → (apply) |
|
||||
|
||||
Accumulated gradient for the weight w1 after the second iteration (considering all GPUs):
|
||||
Total gradient for w1 = e1 + e2 + e3 + e4 + e5 + e6 + e7 + e8 + e9 + e10 + e11 + e12 + e13 + e14 + e15 + e16 + e17 + e18
|
||||
|
||||
Weight update for w1:
|
||||
w1_new = w1_old - learning rate x (Total gradient for w1 / 18)
|
||||
```
|
||||
|
||||
**Example 2:**
|
||||
Micro batch size: 2
|
||||
Gradient accumulation steps: 1
|
||||
Number of GPUs: 3
|
||||
Total batch size = 2 * 1 * 3 = 6
|
||||
|
||||
```
|
||||
| GPU 1 | GPU 2 | GPU 3 |
|
||||
|-----------|-----------|-----------|
|
||||
| S1, S2 | S3, S4 | S5, S6 |
|
||||
| e1, e2 | e3, e4 | e5, e6 |
|
||||
|-----------|-----------|-----------|
|
||||
| → (apply) | → (apply) | → (apply) |
|
||||
|
||||
Accumulated gradient for the weight w1 (considering all GPUs):
|
||||
Total gradient for w1 = e1 + e2 + e3 + e4 + e5 + e6
|
||||
|
||||
Weight update for w1:
|
||||
w1_new = w1_old - learning rate × (Total gradient for w1 / 6)
|
||||
```
|
||||
323
docs/cli.qmd
323
docs/cli.qmd
@@ -1,323 +0,0 @@
|
||||
---
|
||||
title: "Command Line Interface (CLI)"
|
||||
format:
|
||||
html:
|
||||
toc: true
|
||||
toc-expand: 2
|
||||
toc-depth: 3
|
||||
execute:
|
||||
enabled: false
|
||||
---
|
||||
|
||||
The Axolotl CLI provides a streamlined interface for training and fine-tuning large language models. This guide covers
|
||||
the CLI commands, their usage, and common examples.
|
||||
|
||||
|
||||
## Basic Commands
|
||||
|
||||
All Axolotl commands follow this general structure:
|
||||
|
||||
```bash
|
||||
axolotl <command> [config.yml] [options]
|
||||
```
|
||||
|
||||
The config file can be local or a URL to a raw YAML file.
|
||||
|
||||
## Command Reference
|
||||
|
||||
### fetch
|
||||
|
||||
Downloads example configurations and deepspeed configs to your local machine.
|
||||
|
||||
```bash
|
||||
# Get example YAML files
|
||||
axolotl fetch examples
|
||||
|
||||
# Get deepspeed config files
|
||||
axolotl fetch deepspeed_configs
|
||||
|
||||
# Specify custom destination
|
||||
axolotl fetch examples --dest path/to/folder
|
||||
```
|
||||
|
||||
### preprocess
|
||||
|
||||
Preprocesses and tokenizes your dataset before training. This is recommended for large datasets.
|
||||
|
||||
```bash
|
||||
# Basic preprocessing
|
||||
axolotl preprocess config.yml
|
||||
|
||||
# Preprocessing with one GPU
|
||||
CUDA_VISIBLE_DEVICES="0" axolotl preprocess config.yml
|
||||
|
||||
# Debug mode to see processed examples
|
||||
axolotl preprocess config.yml --debug
|
||||
|
||||
# Debug with limited examples
|
||||
axolotl preprocess config.yml --debug --debug-num-examples 5
|
||||
```
|
||||
|
||||
Configuration options:
|
||||
|
||||
```yaml
|
||||
dataset_prepared_path: Local folder for saving preprocessed data
|
||||
push_dataset_to_hub: HuggingFace repo to push preprocessed data (optional)
|
||||
```
|
||||
|
||||
### train
|
||||
|
||||
Trains or fine-tunes a model using the configuration specified in your YAML file.
|
||||
|
||||
```bash
|
||||
# Basic training
|
||||
axolotl train config.yml
|
||||
|
||||
# Train and set/override specific options
|
||||
axolotl train config.yml \
|
||||
--learning-rate 1e-4 \
|
||||
--micro-batch-size 2 \
|
||||
--num-epochs 3
|
||||
|
||||
# Training without accelerate
|
||||
axolotl train config.yml --no-accelerate
|
||||
|
||||
# Resume training from checkpoint
|
||||
axolotl train config.yml --resume-from-checkpoint path/to/checkpoint
|
||||
```
|
||||
|
||||
It is possible to run sweeps over multiple hyperparameters by passing in a sweeps config.
|
||||
|
||||
```bash
|
||||
# Basic training with sweeps
|
||||
axolotl train config.yml --sweep path/to/sweep.yaml
|
||||
```
|
||||
|
||||
Example sweep config:
|
||||
```yaml
|
||||
_:
|
||||
# This section is for dependent variables we need to fix
|
||||
- load_in_8bit: false
|
||||
load_in_4bit: false
|
||||
adapter: lora
|
||||
- load_in_8bit: true
|
||||
load_in_4bit: false
|
||||
adapter: lora
|
||||
|
||||
# These are independent variables
|
||||
learning_rate: [0.0003, 0.0006]
|
||||
lora_r:
|
||||
- 16
|
||||
- 32
|
||||
lora_alpha:
|
||||
- 16
|
||||
- 32
|
||||
- 64
|
||||
```
|
||||
|
||||
|
||||
|
||||
### inference
|
||||
|
||||
Runs inference using your trained model in either CLI or Gradio interface mode.
|
||||
|
||||
```bash
|
||||
# CLI inference with LoRA
|
||||
axolotl inference config.yml --lora-model-dir="./outputs/lora-out"
|
||||
|
||||
# CLI inference with full model
|
||||
axolotl inference config.yml --base-model="./completed-model"
|
||||
|
||||
# Gradio web interface
|
||||
axolotl inference config.yml --gradio \
|
||||
--lora-model-dir="./outputs/lora-out"
|
||||
|
||||
# Inference with input from file
|
||||
cat prompt.txt | axolotl inference config.yml \
|
||||
--base-model="./completed-model"
|
||||
```
|
||||
|
||||
### merge-lora
|
||||
|
||||
Merges trained LoRA adapters into the base model.
|
||||
|
||||
```bash
|
||||
# Basic merge
|
||||
axolotl merge-lora config.yml
|
||||
|
||||
# Specify LoRA directory (usually used with checkpoints)
|
||||
axolotl merge-lora config.yml --lora-model-dir="./lora-output/checkpoint-100"
|
||||
|
||||
# Merge using CPU (if out of GPU memory)
|
||||
CUDA_VISIBLE_DEVICES="" axolotl merge-lora config.yml
|
||||
```
|
||||
|
||||
Configuration options:
|
||||
|
||||
```yaml
|
||||
gpu_memory_limit: Limit GPU memory usage
|
||||
lora_on_cpu: Load LoRA weights on CPU
|
||||
```
|
||||
|
||||
### merge-sharded-fsdp-weights
|
||||
|
||||
Merges sharded FSDP model checkpoints into a single combined checkpoint.
|
||||
|
||||
```bash
|
||||
# Basic merge
|
||||
axolotl merge-sharded-fsdp-weights config.yml
|
||||
```
|
||||
|
||||
### evaluate
|
||||
|
||||
Evaluates a model's performance (loss etc) on the train and eval datasets.
|
||||
|
||||
```bash
|
||||
# Basic evaluation
|
||||
axolotl evaluate config.yml
|
||||
```
|
||||
|
||||
### lm-eval
|
||||
|
||||
Runs LM Evaluation Harness on your model.
|
||||
|
||||
```bash
|
||||
# Basic evaluation
|
||||
axolotl lm-eval config.yml
|
||||
```
|
||||
|
||||
Configuration options:
|
||||
|
||||
```yaml
|
||||
# List of tasks to evaluate
|
||||
lm_eval_tasks:
|
||||
- arc_challenge
|
||||
- hellaswag
|
||||
lm_eval_batch_size: # Batch size for evaluation
|
||||
output_dir: # Directory to save evaluation results
|
||||
```
|
||||
|
||||
See [LM Eval Harness](https://github.com/EleutherAI/lm-evaluation-harness) for more details.
|
||||
|
||||
### delinearize-llama4
|
||||
|
||||
Delinearizes a Llama 4 linearized model into a regular HuggingFace Llama 4 model. This only works with the non-quantized linearized model.
|
||||
|
||||
```bash
|
||||
axolotl delinearize-llama4 --model path/to/model_dir --output path/to/output_dir
|
||||
```
|
||||
|
||||
This would be necessary to use with other frameworks. If you have an adapter, merge it with the non-quantized linearized model before delinearizing.
|
||||
|
||||
### quantize
|
||||
|
||||
Quantizes a model using the quantization configuration specified in your YAML file.
|
||||
|
||||
```bash
|
||||
axolotl quantize config.yml
|
||||
```
|
||||
|
||||
See [Quantization](./quantize.qmd) for more details.
|
||||
|
||||
|
||||
## Legacy CLI Usage
|
||||
|
||||
While the new Click-based CLI is preferred, Axolotl still supports the legacy module-based CLI:
|
||||
|
||||
```bash
|
||||
# Preprocess
|
||||
python -m axolotl.cli.preprocess config.yml
|
||||
|
||||
# Train
|
||||
accelerate launch -m axolotl.cli.train config.yml
|
||||
|
||||
# Inference
|
||||
accelerate launch -m axolotl.cli.inference config.yml \
|
||||
--lora_model_dir="./outputs/lora-out"
|
||||
|
||||
# Gradio interface
|
||||
accelerate launch -m axolotl.cli.inference config.yml \
|
||||
--lora_model_dir="./outputs/lora-out" --gradio
|
||||
```
|
||||
|
||||
::: {.callout-important}
|
||||
When overriding CLI parameters in the legacy CLI, use same notation as in yaml file (e.g., `--lora_model_dir`).
|
||||
|
||||
**Note:** This differs from the new Click-based CLI, which uses dash notation (e.g., `--lora-model-dir`). Keep this in mind if you're referencing newer documentation or switching between CLI versions.
|
||||
:::
|
||||
|
||||
## Remote Compute with Modal Cloud
|
||||
|
||||
Axolotl supports running training and inference workloads on Modal cloud infrastructure. This is configured using a
|
||||
cloud YAML file alongside your regular Axolotl config.
|
||||
|
||||
### Cloud Configuration
|
||||
|
||||
Create a cloud config YAML with your Modal settings:
|
||||
|
||||
```yaml
|
||||
# cloud_config.yml
|
||||
provider: modal
|
||||
gpu: a100 # Supported: l40s, a100-40gb, a100-80gb, a10g, h100, t4, l4
|
||||
gpu_count: 1 # Number of GPUs to use
|
||||
timeout: 86400 # Maximum runtime in seconds (24 hours)
|
||||
branch: main # Git branch to use (optional)
|
||||
|
||||
volumes: # Persistent storage volumes
|
||||
- name: axolotl-cache
|
||||
mount: /workspace/cache
|
||||
- name: axolotl-data
|
||||
mount: /workspace/data
|
||||
- name: axolotl-artifacts
|
||||
mount: /workspace/artifacts
|
||||
|
||||
secrets: # Secrets to inject
|
||||
- WANDB_API_KEY
|
||||
- HF_TOKEN
|
||||
```
|
||||
|
||||
### Running on Modal Cloud
|
||||
|
||||
Commands that support the --cloud flag:
|
||||
|
||||
```bash
|
||||
# Preprocess on cloud
|
||||
axolotl preprocess config.yml --cloud cloud_config.yml
|
||||
|
||||
# Train on cloud
|
||||
axolotl train config.yml --cloud cloud_config.yml
|
||||
|
||||
# Train without accelerate on cloud
|
||||
axolotl train config.yml --cloud cloud_config.yml --no-accelerate
|
||||
|
||||
# Run lm-eval on cloud
|
||||
axolotl lm-eval config.yml --cloud cloud_config.yml
|
||||
```
|
||||
|
||||
### Cloud Configuration Options
|
||||
|
||||
```yaml
|
||||
provider: # compute provider, currently only `modal` is supported
|
||||
gpu: # GPU type to use
|
||||
gpu_count: # Number of GPUs (default: 1)
|
||||
memory: # RAM in GB (default: 128)
|
||||
timeout: # Maximum runtime in seconds
|
||||
timeout_preprocess: # Preprocessing timeout
|
||||
branch: # Git branch to use
|
||||
docker_tag: # Custom Docker image tag
|
||||
volumes: # List of persistent storage volumes
|
||||
|
||||
# Environment variables to pass. Can be specified in two ways:
|
||||
# 1. As a string: Will load the value from the host computer's environment variables
|
||||
# 2. As a key-value pair: Will use the specified value directly
|
||||
# Example:
|
||||
# env:
|
||||
# - CUSTOM_VAR # Loads from host's $CUSTOM_VAR
|
||||
# - {CUSTOM_VAR: "value"} # Uses "value" directly
|
||||
env:
|
||||
|
||||
# Secrets to inject. Same input format as `env` but for sensitive data.
|
||||
secrets:
|
||||
# - HF_TOKEN
|
||||
# - WANDB_API_KEY
|
||||
```
|
||||
@@ -1,121 +0,0 @@
|
||||
---
|
||||
title: Custom Integrations
|
||||
toc: true
|
||||
toc-depth: 3
|
||||
---
|
||||
|
||||
```{python}
|
||||
#| echo: false
|
||||
|
||||
import os
|
||||
import re
|
||||
|
||||
def process_readme(integration_name):
|
||||
try:
|
||||
path = f'../src/axolotl/integrations/{integration_name}/README.md'
|
||||
with open(path, 'r') as f:
|
||||
txt = f.read()
|
||||
# Remove h1 headings
|
||||
txt = re.sub(r'^# .*\n?', '', txt, flags=re.MULTILINE)
|
||||
# Convert h2 to h3
|
||||
txt = re.sub(r'^## ', '### ', txt, flags=re.MULTILINE)
|
||||
return txt
|
||||
except FileNotFoundError:
|
||||
return None
|
||||
|
||||
def print_section(name, folder_name):
|
||||
output = f"\n## {name}\n"
|
||||
content = process_readme(folder_name)
|
||||
if content:
|
||||
output += content
|
||||
output += f"\nPlease see reference [here](https://github.com/axolotl-ai-cloud/axolotl/tree/main/src/axolotl/integrations/{folder_name})\n"
|
||||
return output
|
||||
```
|
||||
|
||||
```{python}
|
||||
#| output: asis
|
||||
#| echo: false
|
||||
|
||||
# Introduction text
|
||||
print("""
|
||||
Axolotl adds custom features through `integrations`. They are located within the `src/axolotl/integrations` directory.
|
||||
|
||||
To enable them, please check the respective documentations.
|
||||
""")
|
||||
|
||||
# Sections
|
||||
sections = [
|
||||
("Cut Cross Entropy", "cut_cross_entropy"),
|
||||
("Grokfast", "grokfast"),
|
||||
("Knowledge Distillation (KD)", "kd"),
|
||||
("Liger Kernels", "liger"),
|
||||
("Language Model Evaluation Harness (LM Eval)", "lm_eval"),
|
||||
("Spectrum", "spectrum"),
|
||||
("LLMCompressor", "llm_compressor")
|
||||
]
|
||||
|
||||
for folder_name in os.listdir("../src/axolotl/integrations/"):
|
||||
if folder_name in [path for name, path in sections]:
|
||||
# skip if already in sections
|
||||
continue
|
||||
if os.path.exists(f"../src/axolotl/integrations/{folder_name}/README.md"):
|
||||
# grab the first heading in README.md as the section name
|
||||
with open(f"../src/axolotl/integrations/{folder_name}/README.md", "r") as f:
|
||||
txt = f.read()
|
||||
matches = re.search(r'^# (.*)\n?', txt, flags=re.MULTILINE)
|
||||
if matches:
|
||||
name = matches.group(1)
|
||||
else:
|
||||
continue
|
||||
sections.append((name, folder_name))
|
||||
|
||||
# sort sections by name
|
||||
sections = sorted(sections, key=lambda x: x[0])
|
||||
|
||||
for section_name, folder_name in sections:
|
||||
print(print_section(section_name, folder_name))
|
||||
```
|
||||
|
||||
## Adding a new integration
|
||||
|
||||
Plugins can be used to customize the behavior of the training pipeline through [hooks](https://en.wikipedia.org/wiki/Hooking). See [`axolotl.integrations.BasePlugin`](https://github.com/axolotl-ai-cloud/axolotl/blob/main/src/axolotl/integrations/base.py) for the possible hooks.
|
||||
|
||||
To add a new integration, please follow these steps:
|
||||
|
||||
1. Create a new folder in the `src/axolotl/integrations` directory.
|
||||
2. Add any relevant files (`LICENSE`, `README.md`, `ACKNOWLEDGEMENTS.md`, etc.) to the new folder.
|
||||
3. Add `__init__.py` and `args.py` files to the new folder.
|
||||
- `__init__.py` should import the integration and hook into the appropriate functions.
|
||||
- `args.py` should define the arguments for the integration.
|
||||
4. (If applicable) Add CPU tests under `tests/integrations` or GPU tests under `tests/e2e/integrations`.
|
||||
|
||||
::: {.callout-tip}
|
||||
|
||||
See [src/axolotl/integrations/cut_cross_entropy](https://github.com/axolotl-ai-cloud/axolotl/tree/main/src/axolotl/integrations/cut_cross_entropy) for a minimal integration example.
|
||||
|
||||
:::
|
||||
|
||||
::: {.callout-warning}
|
||||
|
||||
If you could not load your integration, please ensure you are pip installing in editable mode.
|
||||
|
||||
```bash
|
||||
pip install -e .
|
||||
```
|
||||
|
||||
and correctly spelled the integration name in the config file.
|
||||
|
||||
```yaml
|
||||
plugins:
|
||||
- axolotl.integrations.your_integration_name.YourIntegrationPlugin
|
||||
```
|
||||
|
||||
:::
|
||||
|
||||
::: {.callout-note}
|
||||
|
||||
It is not necessary to place your integration in the `integrations` folder. It can be in any location, so long as it's installed in a package in your python env.
|
||||
|
||||
See this repo for an example: [https://github.com/axolotl-ai-cloud/diff-transformer](https://github.com/axolotl-ai-cloud/diff-transformer)
|
||||
|
||||
:::
|
||||
@@ -1,321 +0,0 @@
|
||||
---
|
||||
title: Conversation
|
||||
description: Conversation format for supervised fine-tuning.
|
||||
order: 3
|
||||
---
|
||||
|
||||
## chat_template
|
||||
|
||||
Chat Template strategy uses a jinja2 template that converts a list of messages into a prompt. Support using tokenizer's template, a supported template, or custom jinja2.
|
||||
|
||||
```{.json filename="data.jsonl"}
|
||||
{"messages": [{"role": "...", "content": "..."}, {"role": "...", "content": "..."}, ...]}
|
||||
```
|
||||
|
||||
See [configs](../config-reference.qmd) for full configs and supported templates.
|
||||
|
||||
### Migrating from sharegpt
|
||||
|
||||
Most configs can be adapted as follows:
|
||||
|
||||
```yaml
|
||||
# old
|
||||
chat_template: chatml
|
||||
datasets:
|
||||
- path: ...
|
||||
type: sharegpt
|
||||
conversation: chatml
|
||||
|
||||
# new (if using tokenizer's chat_template)
|
||||
datasets:
|
||||
- path: ...
|
||||
type: chat_template
|
||||
|
||||
field_messages: conversations
|
||||
message_property_mappings:
|
||||
role: from
|
||||
content: value
|
||||
|
||||
# new (if setting a new chat_template like chatml, gemma, etc)
|
||||
chat_template: chatml
|
||||
datasets:
|
||||
- path: ...
|
||||
type: chat_template
|
||||
|
||||
field_messages: conversations
|
||||
message_property_mappings:
|
||||
role: from
|
||||
content: value
|
||||
```
|
||||
|
||||
We recommend checking the below examples for other usecases.
|
||||
|
||||
### Examples
|
||||
|
||||
#### Training on last message
|
||||
|
||||
(Legacy) Using the default chat template in the tokenizer_config.json on OpenAI messages format, training on only last message.
|
||||
|
||||
```yaml
|
||||
datasets:
|
||||
- path: ...
|
||||
type: chat_template
|
||||
roles_to_train:
|
||||
train_on_eos:
|
||||
```
|
||||
|
||||
::: {.callout-tip}
|
||||
If you receive an error like "`chat_template` choice is `tokenizer_default` but tokenizer's `chat_template` is null.", it means the tokenizer does not have a default `chat_template`. Follow the examples below instead to set a custom `chat_template`.
|
||||
:::
|
||||
|
||||
#### Overriding default chat template
|
||||
|
||||
Using the `gemma` chat template to override the tokenizer_config.json's chat template on OpenAI messages format, training on all assistant messages.
|
||||
|
||||
```yaml
|
||||
chat_template: gemma # this overwrites the tokenizer's chat_template
|
||||
datasets:
|
||||
- path: ...
|
||||
type: chat_template
|
||||
roles_to_train: ["assistant"] # default value
|
||||
```
|
||||
|
||||
::: {.callout-note}
|
||||
If you want to use built-in chat_template, use `chat_template: tokenizer_default` (this is set by default).
|
||||
:::
|
||||
|
||||
#### Using default chat template with fallback
|
||||
|
||||
Using the tokenizer_config.json's chat template or `chatml` as fallback if the former's chat template does not exist, on OpenAI messages format, training on all assistant messages.
|
||||
|
||||
```yaml
|
||||
chat_template: tokenizer_default_fallback_chatml # this overwrites the tokenizer's chat_template
|
||||
datasets:
|
||||
- path: ...
|
||||
type: chat_template
|
||||
```
|
||||
|
||||
#### Custom Jinja template
|
||||
|
||||
Using a custom jinja template on OpenAI messages format, training on all assistant messages.
|
||||
|
||||
```yaml
|
||||
# chat_template: jinja # `jinja` will be implied if the `chat_template_jinja` is set and this field is empty
|
||||
chat_template_jinja: "{{ bos_token }}{% for message in messages %}{% if (message['role'] == 'system') %}{{'<|system|>' + '\n' + message['content'] + '<|end|>' + '\n'}}{% elif (message['role'] == 'user') %}{{'<|user|>' + '\n' + message['content'] + '<|end|>' + '\n' + '<|assistant|>' + '\n'}}{% elif message['role'] == 'assistant' %}{{message['content'] + '<|end|>' + '\n'}}{% endif %}{% endfor %}"
|
||||
|
||||
datasets:
|
||||
- path: ...
|
||||
type: chat_template
|
||||
```
|
||||
|
||||
::: {.callout-important}
|
||||
Please make sure that your `tokenizer.eos_token` is same as EOS (End-of-Sequence) token in template. Otherwise, set `eos_token` under `special_tokens: `.
|
||||
:::
|
||||
|
||||
#### Using template with different token for EOT and EOS
|
||||
|
||||
- If you are using a template that has a different EOT (End-of-Turn) token from EOS token or multiple EOT tokens (like Mistral V7 Tekken), set the `eot_tokens: ` config. The handling of EOT tokens follows `train_on_eos: ` which defaults to turn.
|
||||
|
||||
```yaml
|
||||
eot_tokens:
|
||||
- "[/INST]"
|
||||
# - "[/SYSTEM_PROMPT]"
|
||||
|
||||
datasets:
|
||||
- path: ...
|
||||
type: chat_template
|
||||
|
||||
# optional
|
||||
train_on_eot: turn # defaults read from train_on_eos (which defaults to turn)
|
||||
```
|
||||
|
||||
::: {.callout-tip}
|
||||
See [config documentation](../config-reference.qmd) for detailed explanations of "turn", "last", and "all" options for training on tokens.
|
||||
:::
|
||||
|
||||
::: {.callout-note}
|
||||
Using `eot_tokens` requires each token that exists in `chat_template` to be a single token in the tokenizer. Otherwise, the tokenizer will split the token and cause unexpected behavior.
|
||||
|
||||
You can add those tokens as new tokens under `tokens: ` or (recommended) override unused added_tokens via `added_tokens_overrides: `. See [config](../config-reference.qmd) for more details.
|
||||
:::
|
||||
|
||||
- Continuing from the previous example, if you want to train on all EOT token trainable turns but only last EOS token, set `train_on_eos: last`.
|
||||
|
||||
```yaml
|
||||
eot_tokens:
|
||||
- "[/INST]"
|
||||
# ...
|
||||
|
||||
datasets:
|
||||
- path: ...
|
||||
type: chat_template
|
||||
|
||||
train_on_eos: last
|
||||
train_on_eot: turn
|
||||
```
|
||||
|
||||
::: {.callout-tip}
|
||||
If EOS token only appears at the end of a prompt, `train_on_eos: last` is equivalent to `train_on_eos: turn`. Therefore, generally, you can leave them to their defaults and omit them.
|
||||
:::
|
||||
|
||||
|
||||
#### Using tool use
|
||||
|
||||
Instead of passing `tools` via the system prompt, an alternative method would be to have the `tools` in a separate column and loaded via `chat_template` to let the template dynamically build it.
|
||||
|
||||
```json
|
||||
{
|
||||
"tools": [
|
||||
{
|
||||
"type": "...",
|
||||
"function": {
|
||||
"name": "...",
|
||||
"description": "...",
|
||||
"parameters": {
|
||||
"type": "...",
|
||||
"properties": {
|
||||
// ...
|
||||
},
|
||||
"required": ["..."],
|
||||
},
|
||||
},
|
||||
},
|
||||
],
|
||||
"messages": [
|
||||
// ...
|
||||
{
|
||||
"role": "assistant", // call the function via assistant
|
||||
"tool_calls": [
|
||||
{
|
||||
"id": "...", // required only for mistral
|
||||
"type": "function",
|
||||
"function": {
|
||||
"name": "...",
|
||||
"arguments": {
|
||||
"...": "...",
|
||||
}
|
||||
}
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
"role": "tool",
|
||||
"tool_call_id": "...", // required only for mistral
|
||||
"name": "...",
|
||||
"content": "..."
|
||||
},
|
||||
],
|
||||
}
|
||||
```
|
||||
|
||||
::: {.callout-note}
|
||||
Tools need to follow [JSON schema](https://json-schema.org/learn/getting-started-step-by-step).
|
||||
:::
|
||||
|
||||
```yaml
|
||||
chat_template: llama4
|
||||
datasets:
|
||||
- path: ...
|
||||
type: chat_template
|
||||
# field_tools: tools # default is `tools`
|
||||
```
|
||||
|
||||
::: {.callout-tip}
|
||||
Look into the `chat_template` you are using to see if it supports `tools` and what the expected role is for the tool answer. In the example above, the tool answer is expected to be in the `tool` or `ipython` role for `llama4` template.
|
||||
:::
|
||||
|
||||
|
||||
#### Using fine-grained control over token masking
|
||||
|
||||
(Advanced) Using fine-grained control over tokens and turns to train in a conversation
|
||||
|
||||
For a data sample that looks like:
|
||||
|
||||
```{.json filename="data.jsonl"}
|
||||
{
|
||||
"conversations": [
|
||||
{"from": "system", "value": "You are an AI assistant.", "train": false},
|
||||
{"from": "human", "value": "Hello", "train": false},
|
||||
{"from": "assistant", "value": "Hello", "train": true},
|
||||
{"from": "human", "value": "How are you?", "train": true},
|
||||
{
|
||||
"from": "assistant",
|
||||
"value": "I'm doing very well, thank you!",
|
||||
"train_detail": [
|
||||
{"begin_offset": 0, "end_offset": 8, "train": false},
|
||||
{"begin_offset": 9, "end_offset": 18, "train": true},
|
||||
{"begin_offset": 19, "end_offset": 30, "train": false},
|
||||
],
|
||||
},
|
||||
{
|
||||
"from": "human",
|
||||
"value": "I'm doing very well, thank you!",
|
||||
"train": true,
|
||||
},
|
||||
{"from": "assistant", "value": "Hi there!", "train": true}
|
||||
]
|
||||
}
|
||||
```
|
||||
|
||||
The configuration would look like:
|
||||
|
||||
```yaml
|
||||
datasets:
|
||||
- path: ...
|
||||
type: chat_template
|
||||
chat_template: tokenizer_default
|
||||
field_messages: conversations
|
||||
message_property_mappings:
|
||||
role: from
|
||||
content: value
|
||||
roles_to_train: []
|
||||
train_on_eos: turn
|
||||
message_field_training: train
|
||||
message_field_training_detail: train_detail
|
||||
```
|
||||
|
||||
::: {.callout-tip}
|
||||
It is not necessary to set both `message_field_training` and `message_field_training_detail` at once.
|
||||
:::
|
||||
|
||||
#### Reasoning split
|
||||
|
||||
(For Qwen3 template only) Enable reasoning split, where the reasoning is split from the content and passed as a separate field into the template.
|
||||
|
||||
```yaml
|
||||
datasets:
|
||||
- path: ...
|
||||
type: chat_template
|
||||
chat_template: qwen3
|
||||
split_thinking: true
|
||||
```
|
||||
|
||||
For example, a content can look like:
|
||||
|
||||
```json
|
||||
{
|
||||
"content": "<think>Some thinking outputs</think>Output after thinking."
|
||||
}
|
||||
```
|
||||
|
||||
After split, it will look like:
|
||||
|
||||
```json
|
||||
{
|
||||
"reasoning_content": "Some thinking outputs",
|
||||
"content": "Output after thinking..."
|
||||
}
|
||||
```
|
||||
|
||||
|
||||
## sharegpt
|
||||
|
||||
::: {.callout-important}
|
||||
ShareGPT is deprecated!. Please see [chat_template](#chat_template) section.
|
||||
:::
|
||||
|
||||
## pygmalion
|
||||
|
||||
```{.json filename="data.jsonl"}
|
||||
{"conversations": [{"role": "...", "value": "..."}]}
|
||||
```
|
||||
@@ -1,495 +0,0 @@
|
||||
---
|
||||
title: Dataset Formats
|
||||
description: Guide to Dataset Formats in Axolotl
|
||||
back-to-top-navigation: true
|
||||
toc: true
|
||||
toc-depth: 5
|
||||
---
|
||||
|
||||
|
||||
Axolotl is a training framework that aims to make the process convenient yet flexible to users by simply passing a config yaml file.
|
||||
|
||||
As there are a lot of available options in Axolotl, this guide aims to provide an simplify the user experience to choosing the proper choice.
|
||||
|
||||
Axolotl supports 3 kinds of training methods: pre-training, supervised fine-tuning, and preference-based post-training (e.g. DPO, ORPO, PRMs). Each method has their own dataset format which are described below.
|
||||
|
||||
::: {.callout-tip}
|
||||
|
||||
This guide will mainly use JSONL as an introduction. Please refer to the [dataset loading docs](../dataset_loading.qmd) to understand how to load datasets from other sources.
|
||||
|
||||
For `pretraining_dataset:` specifically, please refer to the [Pre-training section](#pre-training).
|
||||
:::
|
||||
|
||||
## Pre-training
|
||||
|
||||
When aiming to train on large corpora of text datasets, pre-training is your go-to choice. Due to the size of these datasets, downloading the entire-datasets before beginning training would be prohibitively time-consuming. Axolotl supports [streaming](https://huggingface.co/docs/datasets/en/stream) to only load batches into memory at a time.
|
||||
|
||||
A sample format for a pre-training dataset is as follows:
|
||||
|
||||
```json
|
||||
{"text": "first row"}
|
||||
{"text": "second row"}
|
||||
...
|
||||
```
|
||||
|
||||
It is typically recommended to save your dataset as `.jsonl` due to its flexibility and simplicity.
|
||||
|
||||
Axolotl supports loading from a Hugging Face hub repo or from local files.
|
||||
|
||||
### Pre-training from Hugging Face hub datasets
|
||||
|
||||
As an example, to train using a Hugging Face dataset `hf_org/name`, you can pass the following config:
|
||||
|
||||
```yaml
|
||||
pretraining_dataset: hf_org/name
|
||||
```
|
||||
|
||||
### Pre-training from local dataset files
|
||||
|
||||
Given a few corpus files: `A.jsonl`, `B.jsonl`, and `C.jsonl`, your config will look like the below:
|
||||
|
||||
```yaml
|
||||
pretraining_dataset:
|
||||
- path: json
|
||||
data_files:
|
||||
- A.jsonl
|
||||
- B.jsonl
|
||||
- C.jsonl
|
||||
```
|
||||
|
||||
While we recommend `.jsonl`, you can also use the other formats (`csv`, `parquet`, `arrow`, `SQL`, `Webdataset`) that are supported by [`Dataset.load_dataset`](https://huggingface.co/docs/datasets/loading#local-and-remote-files)
|
||||
|
||||
### Pre-training without streaming
|
||||
|
||||
On the rare case that the dataset is small and can be loaded entirely into memory, another approach to running pre-training is to use the `completion` format. This would mean that the entire dataset is pre-tokenized instead of on-demand in streaming.
|
||||
|
||||
One benefit of this is that the tokenization can be performed separately on a CPU-only machine, and then transferred to a GPU machine for training to save costs.
|
||||
|
||||
From Hugging Face:
|
||||
|
||||
```yaml
|
||||
datasets:
|
||||
- path: hf_org/name
|
||||
type: completion
|
||||
```
|
||||
|
||||
From local files:
|
||||
|
||||
```yaml
|
||||
datasets:
|
||||
- path: A.jsonl
|
||||
type: completion
|
||||
|
||||
- path: B.jsonl
|
||||
type: completion
|
||||
```
|
||||
|
||||
::: {.callout-important}
|
||||
For `completion` only, Axolotl would split texts if it exceeds the context length into multiple smaller prompts. If you are interested in having this for `pretraining_dataset` too, please let us know or help make a PR!
|
||||
:::
|
||||
|
||||
### Pre-training dataset configuration tips
|
||||
|
||||
#### Setting max_steps
|
||||
|
||||
When using streaming for large datasets, Axolotl does not know in advance how large the dataset is and does not know when to stop.
|
||||
|
||||
Therefore, it is necessary to set `max_steps: int` in your config for pre-training to run, so that Axolotl knows when to stop training.
|
||||
|
||||
One step is equal to `sequence_len * micro_batch_size * gradient_accumulation_steps * total_num_gpus` tokens.
|
||||
|
||||
#### Group_by_length
|
||||
|
||||
It is recommended to leave this off if downloading from Hugging Face hub as it would download the entire dataset which can be very large.
|
||||
|
||||
### Reference
|
||||
|
||||
Please see docs [here](pretraining.qmd).
|
||||
|
||||
## Supervised fine-tuning (SFT)
|
||||
|
||||
Supervised fine-tuning is the process of training models to respond to an instruction or chat input.
|
||||
|
||||
As there are a wide variety of dataset formats, Axolotl tries to support a majority of the formats available in public datasets.
|
||||
|
||||
Axolotl provides four approaches for loading datasets, however, it's easier to work backwards from the dataset you have available to figure out which approach to use.
|
||||
|
||||
A flow chart is as follows:
|
||||
|
||||
1. Do you already have the dataset tokenized? If yes, check [Pre-Tokenized Dataset](#pre-tokenized-dataset).
|
||||
|
||||
2. Do you want to format the dataset yourself and manually choose each section to mask? If yes, check [Template Free Dataset](#template-free-dataset)
|
||||
|
||||
3. Is your dataset in a "conversation" format, containing a `list[messages]`? If yes, check [Conversation Dataset](#conversation-dataset)
|
||||
|
||||
4. Is your dataset in an "instruct" format, containing `{ instruction, response }`? If yes, check [Instruction Dataset](#instruction-dataset)
|
||||
|
||||
If you went through the flow chart and did not find one that matches, it is recommended to preprocess your dataset into one of the above or create a thread on Github Discussion.
|
||||
|
||||
::: {.callout-tip}
|
||||
You can mix and match within each approach or across approaches to train a model on a variety of datasets.
|
||||
:::
|
||||
|
||||
### Pre-Tokenized Dataset
|
||||
|
||||
We suggest this approach when you want to bring your own tokenized dataset.
|
||||
|
||||
Axolotl expects the dataset to have three keys:
|
||||
|
||||
- `input_ids`: from tokenizing formatted prompt
|
||||
- `attention_mask`: for masking padding. If you don't add padding, it would be equal to `len(input_ids) * [1]`
|
||||
- `labels`: this is the same as `input_ids`, however, if you want to mask certain tokens, you would set those indices to `-100`.
|
||||
|
||||
::: {.callout-tip}
|
||||
Make sure to add BOS/EOS tokens to your prompt and mask it appropriately.
|
||||
:::
|
||||
|
||||
A config for this would look like:
|
||||
|
||||
```yaml
|
||||
datasets:
|
||||
- path: A.jsonl
|
||||
type:
|
||||
```
|
||||
|
||||
::: {.callout-note}
|
||||
`type: ` is empty!
|
||||
:::
|
||||
|
||||
Reference: [Pre-Tokenized Dataset Documentation](tokenized.qmd).
|
||||
|
||||
### Template Free Dataset
|
||||
|
||||
We reccomend this approach when you want granular control over the prompt formatting, special tokens, and masking, whilst letting Axolotl handle the tokenization. This is very useful if your dataset has unique prompts that differ across samples and where one single general template wouldn't suffice.
|
||||
|
||||
In the example below, you could see that there is no proper structure. At the same time, it's very flexible as there are no constraints on how your prompt can look.
|
||||
|
||||
```json
|
||||
{
|
||||
"segments": [
|
||||
{
|
||||
"label": true,
|
||||
"text": "<s>Hello\n"
|
||||
},
|
||||
{
|
||||
"label": true,
|
||||
"text": "hi there!. "
|
||||
},
|
||||
{
|
||||
"label": false,
|
||||
"text": "goodbye "
|
||||
},
|
||||
{
|
||||
"label": true,
|
||||
"text": "farewell</s>"
|
||||
}
|
||||
]
|
||||
}
|
||||
```
|
||||
|
||||
Each prompt must be have a key called `segments` which is a list of `{ text, label }`.
|
||||
|
||||
```yaml
|
||||
datasets:
|
||||
- path: A.jsonl
|
||||
type: input_output
|
||||
```
|
||||
|
||||
Reference: [Template Free Documentation](template_free.qmd).
|
||||
|
||||
### Conversation Dataset
|
||||
|
||||
`conversation` messages are a list of messages which usually contain a `role` and `content` key.
|
||||
|
||||
::: {.callout-tip}
|
||||
Fun fact: Axolotl synonymously refers to "chat" messages as `conversation` messages due to how FastChat initially used this term to build a widely used [fastchat conversation](https://github.com/lm-sys/FastChat/blob/main/fastchat/conversation.py) method for formatting chat messages prior to the creation of `chat_templates`.
|
||||
:::
|
||||
|
||||
#### What are `chat_templates`?
|
||||
|
||||
The current most popular and convenient method for inference is to use `chat_templates` for formatting prompts. Axolotl supports using `chat_templates` for training to ensure that the model performs in the same environment as in inference.
|
||||
|
||||
Here's a quick rundown on `chat_template`: A `chat_template` is a Jinja2 template which formats a list of messages into a prompt.
|
||||
|
||||
An example of a prompt formatted into a popular template called ChatML can be seen below:
|
||||
|
||||
Single prompt (pretty-printed):
|
||||
```json
|
||||
{
|
||||
"messages": [
|
||||
{
|
||||
"role": "user",
|
||||
"content": "Hi"
|
||||
},
|
||||
{
|
||||
"role": "assistant",
|
||||
"content": "How can I help you?"
|
||||
},
|
||||
{
|
||||
"role": "user",
|
||||
"content": "Can you add 3+5?"
|
||||
},
|
||||
{
|
||||
"role": "assistant",
|
||||
"content": "The answer is 8."
|
||||
}
|
||||
]
|
||||
}
|
||||
```
|
||||
|
||||
The ChatML template is as follows:
|
||||
```jinja2
|
||||
{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}
|
||||
```
|
||||
|
||||
The above prompt formatted into this template will result in:
|
||||
|
||||
```
|
||||
<|im_start|>user
|
||||
Hi<|im_end|>
|
||||
<|im_start|>assistant
|
||||
How can I help you?<|im_end|>
|
||||
<|im_start|>user
|
||||
Can you add 3+5?<|im_end|>
|
||||
<|im_start|>assistant
|
||||
The answer is 8.<|im_end|>
|
||||
```
|
||||
|
||||
By using delimiters (`<|im_start|>` and `<|im_end|>`), a prompt separates different speakers which helps the model identify which portion belongs to whom.
|
||||
|
||||
#### Common Conversation Dataset formats
|
||||
|
||||
Older conversation datasets with the following format are colloquially called `sharegpt` datasets.
|
||||
|
||||
```json
|
||||
{"conversations": [{"from": "...", "value": "..."}]}
|
||||
```
|
||||
|
||||
Newer conversation datasets usually follow the OpenAI format.
|
||||
|
||||
```json
|
||||
{"messages": [{"role": "...", "content": "..."}]}
|
||||
```
|
||||
|
||||
Axolotl supports both as well as allowing customization of any kind of key.
|
||||
|
||||
#### Chat Template Usage
|
||||
|
||||
To properly use this method, it is important to identify three things:
|
||||
|
||||
1. Which `chat_template` would you use?
|
||||
|
||||
2. What are the keys in your dataset, and what are the possible roles? For example, in OpenAI format, the keys would be `messages`, `role`, and `content`, respectively, whereas the possible roles are `system`, `user`, and `assistant`.
|
||||
|
||||
3. What do you want to mask? For instance, only assistant messages, only last message, or nothing.
|
||||
|
||||
##### Choosing a `chat_template`
|
||||
|
||||
There are a lot of `chat_templates` out there. Axolotl supports the common ones: [supported chat templates](https://github.com/axolotl-ai-cloud/axolotl/blob/860609392184cf62a7e0ca676658b170e059ce6c/src/axolotl/utils/chat_templates.py#L17). For example, to use ChatML, it would be `chat_template: chatml`.
|
||||
|
||||
However, it is also possible to use the already configured template within the tokenizer by specifying `chat_template: tokenizer_default`. If you want a fallback (in case some tokenizer does not have it pre-configured), you can do `chat_template: tokenizer_default_fallback_chatml` to fallback to the ChatML template if a tokenizer template was not found.
|
||||
|
||||
One last but powerful approach is to bring your own template. This can be set via:
|
||||
|
||||
```yaml
|
||||
chat_template_jinja: # your template
|
||||
```
|
||||
|
||||
##### Setting `chat_template` dataset keys
|
||||
|
||||
We currently default to OpenAI format for dataset keys, so if that's your current dataset format, there's nothing to do here.
|
||||
|
||||
If your dataset format is different, here are the keys you should check (with their defaults):
|
||||
|
||||
```yaml
|
||||
datasets:
|
||||
...
|
||||
field_messages: messages # this should point to the key containing the list of conversations
|
||||
message_property_mappings: # this is a mapping from keys in your dataset to keys in chat_template
|
||||
role: role
|
||||
content: content
|
||||
```
|
||||
|
||||
In some `chat_templates` (e.g. [Gemma](https://huggingface.co/google/gemma-2b-it/blob/main/tokenizer_config.json#L1507)), the roles are hardcoded to `user` and `assistant`. Consequently, you may find it necessary to map the roles in your dataset to these above. We currently have some defaults that should work for common datasets, but if you get a `KeyError`, it would be necessary to add mapping for your roles. Here is an example of how it would look like:
|
||||
|
||||
```yaml
|
||||
datasets:
|
||||
...
|
||||
roles:
|
||||
assistant:
|
||||
- gpt
|
||||
- model
|
||||
user:
|
||||
- human
|
||||
```
|
||||
|
||||
In the example above, all `gpt` and `model` values are converted to `assistant`. All `human` values are converted to `user.`
|
||||
|
||||
##### Handling masking
|
||||
|
||||
The common use case for `chat_template` is for chat messages, therefore, it is common to mask all non-assistant messages. Assistant messages refer to the bot messages that you want the model to learn on.
|
||||
|
||||
To train on all `assistant` messages, you would set the following configs.
|
||||
|
||||
```yaml
|
||||
datasets:
|
||||
...
|
||||
roles_to_train: ["assistant"]
|
||||
train_on_eos: "turn"
|
||||
```
|
||||
|
||||
The `train_on_eos` config means that it would mask all EOS tokens for turns that aren't assistant-turns. The other options are: `all` and `last` to choose which EOS to train on.
|
||||
|
||||
Perhaps, you want to train on `assistant` and `narrator` roles, you can simply add `narrator` to the list of `roles_to_train`. You would also need to add it to the mapping of `roles` above.
|
||||
|
||||
```yaml
|
||||
datasets:
|
||||
...
|
||||
roles_to_train: ["assistant", "narrator"]
|
||||
roles:
|
||||
assistant:
|
||||
- gpt
|
||||
- model
|
||||
user:
|
||||
- human
|
||||
narrator: ["narrator"]
|
||||
```
|
||||
|
||||
::: {.callout-tip}
|
||||
As chat_templates may use hardcoded EOS/EOT tokens that are different from the tokenizer's EOS, it is highly recommended to set them. For example, `ChatML` uses `<|im_end|>` to end turns.
|
||||
|
||||
```yaml
|
||||
special_tokens:
|
||||
eos_token: <|im_end|>
|
||||
```
|
||||
|
||||
:::
|
||||
|
||||
##### Applying `chat_template`
|
||||
|
||||
Once all the above steps are completed, you could combine all these configs together to form a bespoke configuration for your custom dataset.
|
||||
|
||||
```yaml
|
||||
datasets:
|
||||
- path: A.jsonl
|
||||
type: chat_template
|
||||
|
||||
# step 1
|
||||
chat_template: chatml
|
||||
|
||||
# step 2
|
||||
field_messages: messages
|
||||
message_property_mappings:
|
||||
role: role
|
||||
content: content
|
||||
|
||||
roles:
|
||||
assistant:
|
||||
- gpt
|
||||
- model
|
||||
- assistant
|
||||
user:
|
||||
- human
|
||||
- user
|
||||
|
||||
# step 3
|
||||
roles_to_train: ["assistant"]
|
||||
train_on_eos: "turn"
|
||||
|
||||
special_tokens:
|
||||
eos_token: <|im_end|>
|
||||
```
|
||||
|
||||
If this config were to be applied to the sample dataset above, the output would look as such (which can be retrieved via `axolotl preprocess config.yaml --debug`):
|
||||
|
||||
```
|
||||
<|im_start|>(-100, 128256) user(-100, 882)
|
||||
(-100, 198) Hi(-100, 13347) <|im_end|>(-100, 128257)
|
||||
(-100, 198) <|im_start|>(-100, 128256) assistant(-100, 78191)
|
||||
(-100, 198) How(4438, 4438) can(649, 649) I(358, 358) help(1520, 1520) you(499, 499) ?(30, 30) <|im_end|>(128257, 128257)
|
||||
(-100, 198) <|im_start|>(-100, 128256) user(-100, 882)
|
||||
(-100, 198) Can(-100, 6854) you(-100, 499) add(-100, 923) (-100, 220) 3(-100, 18) +(-100, 10) 5(-100, 20) ?(-100, 30) <|im_end|>(-100, 128257)
|
||||
(-100, 198) <|im_start|>(-100, 128256) assistant(-100, 78191)
|
||||
(-100, 198) The(791, 791) answer(4320, 4320) is(374, 374) (220, 220) 8(23, 23) .(13, 13) <|im_end|>(128257, 128257)
|
||||
(-100, 198)
|
||||
```
|
||||
|
||||
The first number refers to the label, the second refers to the `token_id`. For example, `-100` labels appear on non-assistant portions, meaning that they are masked during. For assistant portions, the label is the same as the `token_id`.
|
||||
|
||||
::: {.callout-note}
|
||||
|
||||
If during `preprocess`, there are a lot of warnings of `Could not find content __ boundary`, please check the FAQ section for [chat_templates](../faq.qmd#chat-templates).
|
||||
|
||||
:::
|
||||
|
||||
#### Reference
|
||||
|
||||
Please see docs [here](conversation.qmd).
|
||||
|
||||
### Instruction Dataset
|
||||
|
||||
Instruction datasets are used to train instruction-following models and comprise a prompt, containing an instruction, and a single response. In contrast to chat datasets which may be multi-turn, instruct datasets are typically single-turn.
|
||||
|
||||
An example is of a common format called Alpaca:
|
||||
```json
|
||||
{"instruction": "...", "input": "...", "output": "..."}
|
||||
```
|
||||
|
||||
Using those keys, a prompt can be built based on it.
|
||||
```
|
||||
Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
|
||||
|
||||
### Instruction:
|
||||
{instruction}
|
||||
|
||||
### Input:
|
||||
{input}
|
||||
|
||||
### Response:
|
||||
{output}
|
||||
```
|
||||
|
||||
This can be configured as such:
|
||||
```yaml
|
||||
datasets:
|
||||
- path: A.jsonl
|
||||
type: alpaca
|
||||
```
|
||||
|
||||
Axolotl supports many kinds of instruction dataset. All of them can be found in the [Instruction Dataset Documentation](inst_tune.qmd) with their respective type and sample row format.
|
||||
|
||||
#### Custom Instruct Prompt Format
|
||||
|
||||
Due to the myriad possibilities of instruction formats, Axolotl allows customizing your own instruction format without having to dive into the code directly.
|
||||
|
||||
In the example below, a sample row is used to output in `mistral_v1` format.
|
||||
```json
|
||||
{"input": "...", "output": "..."}
|
||||
```
|
||||
|
||||
```yaml
|
||||
datasets:
|
||||
- path: repo
|
||||
type:
|
||||
system_prompt: ""
|
||||
|
||||
field_system:
|
||||
field_instruction: input
|
||||
field_input:
|
||||
field_output: output
|
||||
|
||||
# multi-line example with input
|
||||
format: |-
|
||||
[INST] {instruction} {input} [/INST]
|
||||
|
||||
# single-line example without input
|
||||
no_input_format: "[INST] {instruction} [/INST]"
|
||||
```
|
||||
|
||||
The config sets that the `field_instruction` is actually named `input`, and the `field_input` is empty as we don't have an `input` in this sample. Generally, `instruction` can be thought as the question to the model, and `input` as the additional information with `output` being the response. It is not necessary to have an `input` nor `system`. In the end, the most important part is to understand what format you want it to look like and how you can customize this to your use case.
|
||||
|
||||
Reference: [Custom Instruct Prompt Format Documentation](inst_tune.qmd#how-to-add-custom-prompt-format).
|
||||
|
||||
## Reinforcement Learning from Human Feedback (RLHF)
|
||||
|
||||
As there are multiple RLHF methods with their own dataset requirements. Please see [RLHF documentation](../rlhf.qmd) for more detail.
|
||||
@@ -1,189 +0,0 @@
|
||||
---
|
||||
title: Instruction Tuning
|
||||
description: Instruction tuning formats for supervised fine-tuning.
|
||||
order: 2
|
||||
---
|
||||
|
||||
## alpaca
|
||||
|
||||
instruction; input(optional)
|
||||
|
||||
```{.json filename="data.jsonl"}
|
||||
{"instruction": "...", "input": "...", "output": "..."}
|
||||
```
|
||||
|
||||
## jeopardy
|
||||
|
||||
question and answer
|
||||
|
||||
```{.json filename="data.jsonl"}
|
||||
{"question": "...", "category": "...", "answer": "..."}
|
||||
```
|
||||
|
||||
## oasst
|
||||
|
||||
instruction
|
||||
|
||||
```{.json filename="data.jsonl"}
|
||||
{"INSTRUCTION": "...", "RESPONSE": "..."}
|
||||
```
|
||||
|
||||
## gpteacher
|
||||
|
||||
instruction; input(optional)
|
||||
|
||||
```{.json filename="data.jsonl"}
|
||||
{"instruction": "...", "input": "...", "response": "..."}
|
||||
```
|
||||
|
||||
## reflection
|
||||
|
||||
instruction with reflect; input(optional)
|
||||
|
||||
```{.json filename="data.jsonl"}
|
||||
{"instruction": "...", "input": "...", "output": "...", "reflection": "...", "corrected": "..."}
|
||||
```
|
||||
|
||||
## explainchoice
|
||||
|
||||
question, choices, (solution OR explanation)
|
||||
|
||||
```{.json filename="data.jsonl"}
|
||||
{"question": "...", "choices": ["..."], "solution": "...", "explanation": "..."}
|
||||
```
|
||||
|
||||
## concisechoice
|
||||
|
||||
question, choices, (solution OR explanation)
|
||||
|
||||
```{.json filename="data.jsonl"}
|
||||
{"question": "...", "choices": ["..."], "solution": "...", "explanation": "..."}
|
||||
```
|
||||
|
||||
## summarizetldr
|
||||
|
||||
article and summary
|
||||
|
||||
```{.json filename="data.jsonl"}
|
||||
{"article": "...", "summary": "..."}
|
||||
```
|
||||
|
||||
## alpaca_chat
|
||||
|
||||
basic instruct for alpaca chat
|
||||
|
||||
```{.json filename="data.jsonl"}
|
||||
{"instruction": "...", "input": "...", "response": "..."}
|
||||
```
|
||||
|
||||
## alpaca_chat.load_qa
|
||||
|
||||
question and answer for alpaca chat
|
||||
|
||||
```{.json filename="data.jsonl"}
|
||||
{"question": "...", "answer": "..."}
|
||||
```
|
||||
|
||||
## alpaca_chat.load_concise
|
||||
|
||||
question and answer for alpaca chat, for concise answers
|
||||
|
||||
```{.json filename="data.jsonl"}
|
||||
{"instruction": "...", "input": "...", "response": "..."}
|
||||
```
|
||||
|
||||
## alpaca_chat.load_camel_ai
|
||||
|
||||
question and answer for alpaca chat, for load_camel_ai
|
||||
|
||||
```{.json filename="data.jsonl"}
|
||||
{"message_1": "...", "message_2": "..."}
|
||||
```
|
||||
|
||||
## alpaca_w_system.load_open_orca
|
||||
|
||||
support for open orca datasets with included system prompts, instruct
|
||||
|
||||
```{.json filename="data.jsonl"}
|
||||
{"system_prompt": "...", "question": "...", "response": "..."}
|
||||
```
|
||||
|
||||
## context_qa
|
||||
|
||||
in context question answering from an article
|
||||
|
||||
```{.json filename="data.jsonl"}
|
||||
{"article": "...", "question": "...", "answer": "..."}
|
||||
```
|
||||
|
||||
## context_qa.load_v2
|
||||
|
||||
in context question answering (alternate)
|
||||
|
||||
```{.json filename="data.jsonl"}
|
||||
{"context": "...", "question": "...", "answer": "..."}
|
||||
```
|
||||
|
||||
## context_qa.load_404
|
||||
|
||||
in context question answering from an article, with default response for no answer from context
|
||||
|
||||
```{.json filename="data.jsonl"}
|
||||
{"article": "...", "unanswerable_question": "..."}
|
||||
```
|
||||
|
||||
## creative_acr.load_answer
|
||||
|
||||
instruction and revision
|
||||
|
||||
```{.json filename="data.jsonl"}
|
||||
{"instruction": "...", "revision": "..."}
|
||||
```
|
||||
|
||||
## creative_acr.load_critique
|
||||
|
||||
critique
|
||||
|
||||
```{.json filename="data.jsonl"}
|
||||
{"scores": "...", "critiques": "...", "instruction": "...", "answer": "..."}
|
||||
```
|
||||
|
||||
## creative_acr.load_revise
|
||||
|
||||
critique and revise
|
||||
|
||||
```{.json filename="data.jsonl"}
|
||||
{"scores": "...", "critiques": "...", "instruction": "...", "answer": "...", "revision": "..."}
|
||||
```
|
||||
|
||||
## metharme
|
||||
|
||||
instruction, adds additional eos tokens
|
||||
|
||||
```{.json filename="data.jsonl"}
|
||||
{"prompt": "...", "generation": "..."}
|
||||
```
|
||||
|
||||
## How to add custom prompt format
|
||||
|
||||
For a dataset that is preprocessed for instruction purposes:
|
||||
|
||||
```{.json filename="data.jsonl"}
|
||||
{"input": "...", "output": "..."}
|
||||
```
|
||||
|
||||
You can use this example in your YAML config:
|
||||
|
||||
```{.yaml filename="config.yaml"}
|
||||
datasets:
|
||||
- path: repo
|
||||
type:
|
||||
system_prompt: ""
|
||||
field_system: system
|
||||
field_instruction: input
|
||||
field_output: output
|
||||
format: "[INST] {instruction} [/INST]"
|
||||
no_input_format: "[INST] {instruction} [/INST]"
|
||||
```
|
||||
|
||||
See full config options under [here](../config-reference.qmd).
|
||||
@@ -1,32 +0,0 @@
|
||||
---
|
||||
title: Pre-training
|
||||
description: Data format for a pre-training completion task.
|
||||
order: 1
|
||||
---
|
||||
|
||||
For pretraining, there is no prompt template or roles. The only required field is `text`:
|
||||
|
||||
```{.json filename="data.jsonl"}
|
||||
{"text": "first row"}
|
||||
{"text": "second row"}
|
||||
...
|
||||
```
|
||||
|
||||
:::{.callout-note}
|
||||
|
||||
### Streaming is recommended for large datasets
|
||||
|
||||
Axolotl usually loads the entire dataset into memory. This will be challenging for large datasets. Use the following config to enable streaming:
|
||||
|
||||
```{.yaml filename="config.yaml"}
|
||||
pretraining_dataset:
|
||||
- name:
|
||||
path:
|
||||
split:
|
||||
text_column: # column in dataset with the data, usually `text`
|
||||
type: pretrain
|
||||
trust_remote_code:
|
||||
skip: # number of rows of data to skip over from the beginning
|
||||
```
|
||||
|
||||
:::
|
||||
@@ -1,26 +0,0 @@
|
||||
---
|
||||
title: Stepwise Supervised Format
|
||||
description: Format for datasets with stepwise completions and labels
|
||||
order: 3
|
||||
---
|
||||
|
||||
## Stepwise Supervised
|
||||
|
||||
The stepwise supervised format is designed for chain-of-thought (COT) reasoning
|
||||
datasets where each example contains multiple completion steps and a preference label
|
||||
for each step.
|
||||
|
||||
### Example
|
||||
|
||||
Here's a simple example of a stepwise supervised dataset entry:
|
||||
|
||||
```json
|
||||
{
|
||||
"prompt": "Which number is larger, 9.8 or 9.11?",
|
||||
"completions": [
|
||||
"The fractional part of 9.8 is 0.8, while the fractional part of 9.11 is 0.11.",
|
||||
"Since 0.11 is greater than 0.8, the number 9.11 is larger than 9.8."
|
||||
],
|
||||
"labels": [true, false]
|
||||
}
|
||||
```
|
||||
@@ -1,239 +0,0 @@
|
||||
---
|
||||
title: Template-Free
|
||||
description: Construct prompts without a template.
|
||||
toc: true
|
||||
toc-depth: 3
|
||||
order: 4
|
||||
---
|
||||
|
||||
## Background {#sec-background}
|
||||
|
||||
### Masking Inputs {#masking-inputs}
|
||||
|
||||
One of the most popular features of
|
||||
[axolotl](https://github.com/axolotl-ai-cloud/axolotl) is
|
||||
setting the following configuration value:
|
||||
|
||||
|
||||
```yaml
|
||||
train_on_inputs: false
|
||||
```
|
||||
|
||||
If you declare a [dataset formats](https://github.com/axolotl-ai-cloud/axolotl?tab=readme-ov-file#dataset)
|
||||
such as `alpaca` or `chatml`, axolotl knows what is an input
|
||||
(i.e. human) vs. an output (i.e. the assistant) and masks the input
|
||||
labels so that your model can focus on predicting the outputs only.
|
||||
|
||||
### You may not want prompt templates {#sec-you-may-not-want-prompt-templates}
|
||||
|
||||
However, there are many situations where you don't want to use one of
|
||||
these formats or templates. This is because they can:
|
||||
|
||||
- Add unnecessary boilerplate to your prompts.
|
||||
- Create artifacts like special delimiters `<|im_start|>` that can
|
||||
quickly become footguns if you don't include them correctly at
|
||||
inference time.
|
||||
- Enforce a *chat* interface when you do not want one. Sometimes you
|
||||
just want to fine-tune a model to a very specific task and do NOT
|
||||
want multi-turn conversations, roles, etc.
|
||||
- Limit you to only certain roles that the template allows.
|
||||
|
||||
### The `input_output` format {#sec-the-inputoutput-format}
|
||||
|
||||
You can construct your prompts without a template by using the
|
||||
`input_output` format, by setting `type: input_output` in your
|
||||
configuration file like this:
|
||||
|
||||
**config.yml**
|
||||
|
||||
```yaml
|
||||
train_on_inputs: false # Mask segments of your data
|
||||
datasets:
|
||||
- path: output.jsonl
|
||||
type: input_output # use template free prompt construction
|
||||
```
|
||||
|
||||
Unlike `type: completion`, which is also template-free,
|
||||
`type: input_output` allows you to mask segments of your text. More
|
||||
details on how this works are described below.
|
||||
|
||||
## Usage {#sec-usage}
|
||||
|
||||
This is how you can use the `input_output` format:
|
||||
|
||||
### 1. Prepare Data {#sec-1-prepare-data}
|
||||
|
||||
To use the `input_output` format, collect your data in the following
|
||||
format into a jsonl file (below is the first row from the file
|
||||
`output`.jsonl` pretty printed):
|
||||
|
||||
```bash
|
||||
$ head -n1 output.jsonl | python -m json.tool
|
||||
```
|
||||
|
||||
:::{.cell-output .cell-output-stdout}
|
||||
{
|
||||
"segments": [
|
||||
{
|
||||
"label": true,
|
||||
"text": "<s>Hello\n"
|
||||
},
|
||||
{
|
||||
"label": true,
|
||||
"text": "hi there!. "
|
||||
},
|
||||
{
|
||||
"label": false,
|
||||
"text": "goodbye "
|
||||
},
|
||||
{
|
||||
"label": true,
|
||||
"text": "farewell</s>"
|
||||
}
|
||||
]
|
||||
}
|
||||
:::
|
||||
|
||||
Set `label:false` when you want to mask a segment of text so that the
|
||||
model isn't trained on it. Some things to keep in mind:
|
||||
|
||||
> [!IMPORTANT]
|
||||
> 1. **EOS, BOS, spaces, newlines etc. are entirely up to you. Axolotl
|
||||
concatenates all the segments as-is.** The tokenizer doesn't add
|
||||
anything additional. Notice how I added spaces, newlines, `<s>`
|
||||
(BOS), and `</s>` (EOS) myself.
|
||||
> 2. Make sure you check the materialized output to validate that the
|
||||
prompt is getting assembled how you like.
|
||||
|
||||
### 2. Use `type: input_output` {#sec-2-use-type-inputoutput}
|
||||
|
||||
Let's materialize data with our `output.jsonl` file by setting
|
||||
`type: input_output` in our axolotl config:
|
||||
|
||||
```yaml
|
||||
# training_config.yaml
|
||||
base_model: mistralai/Mistral-7B-v0.1
|
||||
data_seed: 49
|
||||
seed: 49
|
||||
|
||||
datasets:
|
||||
- path: output.jsonl
|
||||
type: input_output
|
||||
val_set_size: 0.1
|
||||
|
||||
sequence_len: 896
|
||||
sample_packing: false
|
||||
|
||||
micro_batch_size: 2
|
||||
gradient_accumulation_steps: 3
|
||||
eval_batch_size: 2
|
||||
num_epochs: 1
|
||||
learning_rate: 0.0002
|
||||
|
||||
train_on_inputs: false
|
||||
special_tokens:
|
||||
bos_token: "<s>"
|
||||
eos_token: "</s>"
|
||||
unk_token: "<unk>"
|
||||
```
|
||||
|
||||
You can use the following command to materialize your data. The
|
||||
`--debug` flag will print the tokens, along with the labels so you can
|
||||
verify that the correct items are being ignored:
|
||||
|
||||
```bash
|
||||
axolotl preprocess training_config.yaml --debug
|
||||
|
||||
...
|
||||
[2024-03-05 23:36:46,969] [INFO] [axolotl.check_example_labels:35] [PID:607731] [RANK:0] <s>(1, 1) Hello(22557, 22557)
|
||||
(13, 13) hi(12014, 12014) there(736, 736) !(28808, 28808) .(28723, 28723) (28705, 28705) good(-100, 1179) bye(-100, 17664) (-100, 28705) fare(19111, 19111) well(5458, 5458) </s>(2, 2)
|
||||
|
||||
```
|
||||
|
||||
The format is `decoded_token`(`label`, `token_id`), for example,
|
||||
`<s>(1, 1)` means that the token is `<s>`, the label is `1` and the
|
||||
token_id is `1`. When the label is `-100` then that token is ignored for
|
||||
training.
|
||||
|
||||
### 3. Check the prompts {#sec-3-check-the-prompts}
|
||||
|
||||
Here is another way to check the materialized output:
|
||||
|
||||
```python
|
||||
from transformers import AutoTokenizer
|
||||
from datasets import load_from_disk
|
||||
import yaml
|
||||
|
||||
directory = !ls last_run_prepared/
|
||||
with open('training_config.yaml', 'r') as f:
|
||||
cfg = yaml.safe_load(f)
|
||||
model_id = cfg['base_model']
|
||||
tok = AutoTokenizer.from_pretrained(model_id)
|
||||
ds = load_from_disk(f'last_run_prepared/{directory[0]}/')
|
||||
```
|
||||
|
||||
```python
|
||||
>>> row = ds[0]
|
||||
>>> print(tok.decode(row['input_ids']))
|
||||
<s> Hello
|
||||
hi there!. goodbye farewell</s>
|
||||
```
|
||||
|
||||
We can check that the right tokens are ignored by comparing the labels
|
||||
to each token:
|
||||
|
||||
```python
|
||||
import pandas as pd
|
||||
pd.DataFrame([{'token': tok.decode(i), 'label': l, 'id':i} for i,l in
|
||||
zip(row['input_ids'], row['labels'])])
|
||||
```
|
||||
|
||||
| token | label | id |
|
||||
|-------|-------|-------|
|
||||
| 0 | \<s\> | 1 |
|
||||
| 1 | Hello | 22557 |
|
||||
| 2 | \\n | 13 |
|
||||
| 3 | hi | 12014 |
|
||||
| 4 | there | 736 |
|
||||
| 5 | ! | 28808 |
|
||||
| 6 | . | 28723 |
|
||||
| 7 | | 28705 |
|
||||
| 8 | good | -100 |
|
||||
| 9 | bye | -100 |
|
||||
| 10 | | -100 |
|
||||
| 11 | fare | 19111 |
|
||||
| 12 | well | 5458 |
|
||||
| 13 | \</s\>| 2 |
|
||||
|
||||
|
||||
|
||||
If we look at the input data, the above table seems correct! (The jsonl
|
||||
version is repeated below for reference):
|
||||
|
||||
|
||||
```bash
|
||||
$ head -n1 output.jsonl | python -m json.tool
|
||||
```
|
||||
|
||||
:::{.cell-output .cell-output-stdout}
|
||||
{
|
||||
"segments": [
|
||||
{
|
||||
"label": true,
|
||||
"text": "<s>Hello\n"
|
||||
},
|
||||
{
|
||||
"label": true,
|
||||
"text": "hi there!. "
|
||||
},
|
||||
{
|
||||
"label": false,
|
||||
"text": "goodbye "
|
||||
},
|
||||
{
|
||||
"label": true,
|
||||
"text": "farewell</s>"
|
||||
}
|
||||
]
|
||||
}
|
||||
:::
|
||||
@@ -1,28 +0,0 @@
|
||||
---
|
||||
title: Custom Pre-Tokenized Dataset
|
||||
description: How to use a custom pre-tokenized dataset.
|
||||
order: 5
|
||||
---
|
||||
|
||||
- Pass an empty `type:` in your axolotl config.
|
||||
- Columns in Dataset must be exactly `input_ids`, `attention_mask`, `labels`
|
||||
- To indicate that a token should be ignored during training, set its corresponding label to `-100`.
|
||||
- You must add BOS and EOS, and make sure that you are training on EOS by not setting its label to -100.
|
||||
- For pretraining, do not truncate/pad documents to the context window length.
|
||||
- For instruction training, documents must be truncated/padded as desired.
|
||||
|
||||
Sample config:
|
||||
|
||||
```{.yaml filename="config.yml"}
|
||||
datasets:
|
||||
- path: /path/to/your/file.jsonl
|
||||
ds_type: json
|
||||
type:
|
||||
```
|
||||
|
||||
Sample jsonl:
|
||||
|
||||
```jsonl
|
||||
{"input_ids":[271,299,99],"attention_mask":[1,1,1],"labels":[271,-100,99]}
|
||||
{"input_ids":[87,227,8383,12],"attention_mask":[1,1,1,1],"labels":[87,227,8383,12]}
|
||||
```
|
||||
@@ -1,268 +0,0 @@
|
||||
---
|
||||
title: Dataset Loading
|
||||
description: Understanding how to load datasets from different sources
|
||||
back-to-top-navigation: true
|
||||
toc: true
|
||||
toc-depth: 5
|
||||
---
|
||||
|
||||
## Overview
|
||||
|
||||
Datasets can be loaded in a number of different ways depending on the how it is saved (the extension of the file) and where it is stored.
|
||||
|
||||
## Loading Datasets
|
||||
|
||||
We use the `datasets` library to load datasets and a mix of `load_dataset` and `load_from_disk` to load them.
|
||||
|
||||
You may recognize the similar named configs between `load_dataset` and the `datasets` section of the config file.
|
||||
|
||||
```yaml
|
||||
datasets:
|
||||
- path:
|
||||
name:
|
||||
data_files:
|
||||
split:
|
||||
revision:
|
||||
trust_remote_code:
|
||||
```
|
||||
|
||||
::: {.callout-tip}
|
||||
|
||||
Do not feel overwhelmed by the number of options here. A lot of them are optional. In fact, the most common config to use would be `path` and sometimes `data_files`.
|
||||
|
||||
:::
|
||||
|
||||
This matches the API of [`datasets.load_dataset`](https://github.com/huggingface/datasets/blob/0b5998ac62f08e358f8dcc17ec6e2f2a5e9450b6/src/datasets/load.py#L1838-L1858), so if you're familiar with that, you will feel right at home.
|
||||
|
||||
For HuggingFace's guide to load different dataset types, see [here](https://huggingface.co/docs/datasets/loading).
|
||||
|
||||
For full details on the config, see [config-reference.qmd](config-reference.qmd).
|
||||
|
||||
::: {.callout-note}
|
||||
|
||||
You can set multiple datasets in the config file by more than one entry under `datasets`.
|
||||
|
||||
```yaml
|
||||
datasets:
|
||||
- path: /path/to/your/dataset
|
||||
- path: /path/to/your/other/dataset
|
||||
```
|
||||
|
||||
:::
|
||||
|
||||
### Local dataset
|
||||
|
||||
#### Files
|
||||
|
||||
To load a JSON file, you would do something like this:
|
||||
|
||||
```python
|
||||
from datasets import load_dataset
|
||||
|
||||
dataset = load_dataset("json", data_files="data.json")
|
||||
```
|
||||
|
||||
Which translates to the following config:
|
||||
|
||||
```yaml
|
||||
datasets:
|
||||
- path: data.json
|
||||
ds_type: json
|
||||
```
|
||||
|
||||
In the example above, it can be seen that we can just point the `path` to the file or directory along with the `ds_type` to load the dataset.
|
||||
|
||||
This works for CSV, JSON, Parquet, and Arrow files.
|
||||
|
||||
::: {.callout-tip}
|
||||
|
||||
If `path` points to a file and `ds_type` is not specified, we will automatically infer the dataset type from the file extension, so you could omit `ds_type` if you'd like.
|
||||
|
||||
:::
|
||||
|
||||
#### Directory
|
||||
|
||||
If you're loading a directory, you can point the `path` to the directory.
|
||||
|
||||
Then, you have two options:
|
||||
|
||||
##### Loading entire directory
|
||||
|
||||
You do not need any additional configs.
|
||||
|
||||
We will attempt to load in the following order:
|
||||
- datasets saved with `datasets.save_to_disk`
|
||||
- loading entire directory of files (such as with parquet/arrow files)
|
||||
|
||||
```yaml
|
||||
datasets:
|
||||
- path: /path/to/your/directory
|
||||
```
|
||||
|
||||
##### Loading specific files in directory
|
||||
|
||||
Provide `data_files` with a list of files to load.
|
||||
|
||||
```yaml
|
||||
datasets:
|
||||
# single file
|
||||
- path: /path/to/your/directory
|
||||
ds_type: csv
|
||||
data_files: file1.csv
|
||||
|
||||
# multiple files
|
||||
- path: /path/to/your/directory
|
||||
ds_type: json
|
||||
data_files:
|
||||
- file1.jsonl
|
||||
- file2.jsonl
|
||||
|
||||
# multiple files for parquet
|
||||
- path: /path/to/your/directory
|
||||
ds_type: parquet
|
||||
data_files:
|
||||
- file1.parquet
|
||||
- file2.parquet
|
||||
|
||||
```
|
||||
|
||||
### HuggingFace Hub
|
||||
|
||||
The method you use to load the dataset depends on how the dataset was created, whether a folder was uploaded directly or a HuggingFace Dataset was pushed.
|
||||
|
||||
::: {.callout-note}
|
||||
|
||||
If you're using a private dataset, you will need to enable the `hf_use_auth_token` flag in the root-level of the config file.
|
||||
|
||||
:::
|
||||
|
||||
#### Folder uploaded
|
||||
|
||||
This would mean that the dataset is a single file or file(s) uploaded to the Hub.
|
||||
|
||||
```yaml
|
||||
datasets:
|
||||
- path: org/dataset-name
|
||||
data_files:
|
||||
- file1.jsonl
|
||||
- file2.jsonl
|
||||
```
|
||||
|
||||
#### HuggingFace Dataset
|
||||
|
||||
This means that the dataset is created as a HuggingFace Dataset and pushed to the Hub via `datasets.push_to_hub`.
|
||||
|
||||
```yaml
|
||||
datasets:
|
||||
- path: org/dataset-name
|
||||
```
|
||||
|
||||
::: {.callout-note}
|
||||
|
||||
There are some other configs which may be required like `name`, `split`, `revision`, `trust_remote_code`, etc depending on the dataset.
|
||||
|
||||
:::
|
||||
|
||||
### Remote Filesystems
|
||||
|
||||
Via the `storage_options` config under `load_dataset`, you can load datasets from remote filesystems like S3, GCS, Azure, and OCI.
|
||||
|
||||
::: {.callout-warning}
|
||||
|
||||
This is currently experimental. Please let us know if you run into any issues!
|
||||
|
||||
:::
|
||||
|
||||
The only difference between the providers is that you need to prepend the path with the respective protocols.
|
||||
|
||||
```yaml
|
||||
datasets:
|
||||
# Single file
|
||||
- path: s3://bucket-name/path/to/your/file.jsonl
|
||||
|
||||
# Directory
|
||||
- path: s3://bucket-name/path/to/your/directory
|
||||
```
|
||||
|
||||
For directory, we load via `load_from_disk`.
|
||||
|
||||
#### S3
|
||||
|
||||
Prepend the path with `s3://`.
|
||||
|
||||
The credentials are pulled in the following order:
|
||||
|
||||
- `AWS_ACCESS_KEY_ID`, `AWS_SECRET_ACCESS_KEY`, and `AWS_SESSION_TOKEN` environment variables
|
||||
- from the `~/.aws/credentials` file
|
||||
- for nodes on EC2, the IAM metadata provider
|
||||
|
||||
::: {.callout-note}
|
||||
|
||||
We assume you have credentials setup and not using anonymous access. If you want to use anonymous access, let us know! We may have to open a config option for this.
|
||||
|
||||
:::
|
||||
|
||||
Other environment variables that can be set can be found in [boto3 docs](https://boto3.amazonaws.com/v1/documentation/api/latest/guide/configuration.html#using-environment-variables)
|
||||
|
||||
#### GCS
|
||||
|
||||
Prepend the path with `gs://` or `gcs://`.
|
||||
|
||||
The credentials are loaded in the following order:
|
||||
|
||||
- gcloud credentials
|
||||
- for nodes on GCP, the google metadata service
|
||||
- anonymous access
|
||||
|
||||
#### Azure
|
||||
|
||||
##### Gen 1
|
||||
|
||||
Prepend the path with `adl://`.
|
||||
|
||||
Ensure you have the following environment variables set:
|
||||
|
||||
- `AZURE_STORAGE_TENANT_ID`
|
||||
- `AZURE_STORAGE_CLIENT_ID`
|
||||
- `AZURE_STORAGE_CLIENT_SECRET`
|
||||
|
||||
##### Gen 2
|
||||
|
||||
Prepend the path with `abfs://` or `az://`.
|
||||
|
||||
Ensure you have the following environment variables set:
|
||||
|
||||
- `AZURE_STORAGE_ACCOUNT_NAME`
|
||||
- `AZURE_STORAGE_ACCOUNT_KEY`
|
||||
|
||||
Other environment variables that can be set can be found in [adlfs docs](https://github.com/fsspec/adlfs?tab=readme-ov-file#setting-credentials)
|
||||
|
||||
#### OCI
|
||||
|
||||
Prepend the path with `oci://`.
|
||||
|
||||
It would attempt to read in the following order:
|
||||
|
||||
- `OCIFS_IAM_TYPE`, `OCIFS_CONFIG_LOCATION`, and `OCIFS_CONFIG_PROFILE` environment variables
|
||||
- when on OCI resource, resource principal
|
||||
|
||||
Other environment variables:
|
||||
|
||||
- `OCI_REGION_METADATA`
|
||||
|
||||
Please see the [ocifs docs](https://ocifs.readthedocs.io/en/latest/getting-connected.html#Using-Environment-Variables).
|
||||
|
||||
### HTTPS
|
||||
|
||||
The path should start with `https://`.
|
||||
|
||||
```yaml
|
||||
datasets:
|
||||
- path: https://path/to/your/dataset/file.jsonl
|
||||
```
|
||||
|
||||
This must be publically accessible.
|
||||
|
||||
## Next steps
|
||||
|
||||
Now that you know how to load datasets, you can learn more on how to load your specific dataset format into your target output format [dataset formats docs](dataset-formats).
|
||||
@@ -1,44 +0,0 @@
|
||||
---
|
||||
title: Dataset Preprocessing
|
||||
description: How datasets are processed
|
||||
---
|
||||
|
||||
## Overview
|
||||
|
||||
Dataset pre-processing is the step where Axolotl takes each dataset you've configured alongside
|
||||
the [dataset format](dataset-formats) and prompt strategies to:
|
||||
|
||||
- parse the dataset based on the *dataset format*
|
||||
- transform the dataset to how you would interact with the model based on the *prompt strategy*
|
||||
- tokenize the dataset based on the configured model & tokenizer
|
||||
- shuffle and merge multiple datasets together if using more than one
|
||||
|
||||
The processing of the datasets can happen one of two ways:
|
||||
|
||||
1. Before kicking off training by calling `axolotl preprocess config.yaml --debug`
|
||||
2. When training is started
|
||||
|
||||
### What are the benefits of pre-processing?
|
||||
|
||||
When training interactively or for sweeps
|
||||
(e.g. you are restarting the trainer often), processing the datasets can oftentimes be frustratingly
|
||||
slow. Pre-processing will cache the tokenized/formatted datasets according to a hash of dependent
|
||||
training parameters so that it will intelligently pull from its cache when possible.
|
||||
|
||||
The path of the cache is controlled by `dataset_prepared_path:` and is often left blank in example
|
||||
YAMLs as this leads to a more robust solution that prevents unexpectedly reusing cached data.
|
||||
|
||||
If `dataset_prepared_path:` is left empty, when training, the processed dataset will be cached in a
|
||||
default path of `./last_run_prepared/`, but will ignore anything already cached there. By explicitly
|
||||
setting `dataset_prepared_path: ./last_run_prepared`, the trainer will use whatever pre-processed
|
||||
data is in the cache.
|
||||
|
||||
### What are the edge cases?
|
||||
|
||||
Let's say you are writing a custom prompt strategy or using a user-defined
|
||||
prompt template. Because the trainer cannot readily detect these changes, we cannot change the
|
||||
calculated hash value for the pre-processed dataset.
|
||||
|
||||
If you have `dataset_prepared_path: ...` set
|
||||
and change your prompt templating logic, it may not pick up the changes you made and you will be
|
||||
training over the old prompt.
|
||||
@@ -1,247 +0,0 @@
|
||||
---
|
||||
title: Debugging
|
||||
description: How to debug Axolotl
|
||||
---
|
||||
|
||||
|
||||
This document provides some tips and tricks for debugging Axolotl. It also provides an example configuration for debugging with VSCode. A good debugging setup is essential to understanding how Axolotl code works behind the scenes.
|
||||
|
||||
## Table of Contents
|
||||
|
||||
- [General Tips](#general-tips)
|
||||
- [Debugging with VSCode](#debugging-with-vscode)
|
||||
- [Background](#background)
|
||||
- [Configuration](#configuration)
|
||||
- [Customizing your debugger](#customizing-your-debugger)
|
||||
- [Video Tutorial](#video-tutorial)
|
||||
- [Debugging With Docker](#debugging-with-docker)
|
||||
- [Setup](#setup)
|
||||
- [Attach To Container](#attach-to-container)
|
||||
- [Video - Attaching To Docker On Remote Host](#video---attaching-to-docker-on-remote-host)
|
||||
|
||||
## General Tips
|
||||
|
||||
While debugging it's helpful to simplify your test scenario as much as possible. Here are some tips for doing so:
|
||||
|
||||
> [!Important]
|
||||
> All of these tips are incorporated into the [example configuration](#configuration) for debugging with VSCode below.
|
||||
|
||||
1. **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`.
|
||||
1. **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`.
|
||||
2. **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):
|
||||
|
||||
```yaml
|
||||
datasets:
|
||||
...
|
||||
shards: 20
|
||||
```
|
||||
|
||||
3. **Use a small model**: A good example of a small model is [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0).
|
||||
4. **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`
|
||||
5. **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](https://huggingface.co/docs/datasets/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 `chat_template` format. This is the format used when you have the following in your axolotl config:
|
||||
|
||||
```yaml
|
||||
datasets:
|
||||
- path: <path to your chat_template formatted dataset> # example on HF Hub: fozziethebeat/alpaca_messages_2k_test
|
||||
type: chat_template
|
||||
```
|
||||
|
||||
>[!Important]
|
||||
> If you are already familiar with advanced VSCode debugging, you can skip the below explanation and look at the files [.vscode/launch.json](../.vscode/launch.json) and [.vscode/tasks.json](../.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](#video-tutorial) below (but doing both is recommended).
|
||||
|
||||
### Setup
|
||||
|
||||
Make sure you have an [editable install](https://setuptools.pypa.io/en/latest/userguide/development_mode.html) 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:
|
||||
|
||||
```bash
|
||||
pip3 install packaging
|
||||
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](https://code.visualstudio.com/docs/remote/ssh). You can also see the video below on [Docker and Remote SSH debugging](#video---attaching-to-docker-on-remote-host).
|
||||
|
||||
|
||||
### Configuration
|
||||
|
||||
The easiest way to get started is to modify the [.vscode/launch.json](../.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.
|
||||
|
||||
For 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 configuration[^1]. 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.
|
||||
|
||||
```json
|
||||
// .vscode/launch.json
|
||||
{
|
||||
"version": "0.2.0",
|
||||
"configurations": [
|
||||
{
|
||||
"name": "Debug axolotl prompt - chat_template",
|
||||
"type": "python",
|
||||
"module": "accelerate.commands.launch",
|
||||
"request": "launch",
|
||||
"args": [
|
||||
"-m", "axolotl.cli.train", "dev_chat_template.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](../.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](../.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.
|
||||
|
||||
```json
|
||||
// .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, (click the image below to watch):
|
||||
|
||||
<div style="text-align: center; line-height: 0;">
|
||||
|
||||
<a href="https://youtu.be/xUUB11yeMmc" target="_blank"
|
||||
title="How to debug Axolotl (for fine tuning LLMs)"><img
|
||||
src="https://i.ytimg.com/vi/xUUB11yeMmc/maxresdefault.jpg"
|
||||
style="border-radius: 10px; display: block; margin: auto;" width="560" height="315" /></a>
|
||||
|
||||
<figcaption style="font-size: smaller;"><a href="https://hamel.dev">Hamel Husain's</a> tutorial: <a href="https://www.youtube.com/watch?v=xUUB11yeMmc">Debugging Axolotl w/VSCode</a></figcaption>
|
||||
|
||||
</div>
|
||||
<br>
|
||||
|
||||
## Debugging With Docker
|
||||
|
||||
Using [official Axolotl Docker images](https://hub.docker.com/r/axolotlai/axolotl/tags) 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:
|
||||
|
||||
```bash
|
||||
git clone https://github.com/axolotl-ai-cloud/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]
|
||||
|
||||
```bash
|
||||
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 axolotlai/axolotl:main-py3.10-cu118-2.0.1
|
||||
```
|
||||
|
||||
>[!Tip]
|
||||
> To understand which containers are available, see the [Docker section of the README](../README.md#docker) and the [DockerHub repo](https://hub.docker.com/r/axolotlai/axolotl/tags). For details of how the Docker containers are built, see axolotl's [Docker CI builds](../.github/workflows/main.yml).
|
||||
|
||||
You will now be in the container. Next, perform an editable install of Axolotl:
|
||||
|
||||
```bash
|
||||
pip3 install packaging
|
||||
pip3 install --no-build-isolation -e '.[flash-attn,deepspeed]'
|
||||
```
|
||||
|
||||
### Attach To Container
|
||||
|
||||
Next, if you are using a remote host, [Remote into this host with VSCode](https://code.visualstudio.com/docs/remote/ssh). 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](#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:
|
||||
|
||||
<div style="text-align: center; line-height: 0;">
|
||||
|
||||
<a href="https://youtu.be/0AuoR7QnHR0" target="_blank"
|
||||
title="Debugging Axolotl Part 2: Attaching to Docker on a Remote Host"><img
|
||||
src="https://i.ytimg.com/vi/0AuoR7QnHR0/hqdefault.jpg"
|
||||
style="border-radius: 10px; display: block; margin: auto;" width="560" height="315" /></a>
|
||||
|
||||
<figcaption style="font-size: smaller;"><a href="https://hamel.dev">Hamel Husain's</a> tutorial: <a href="https://youtu.be/0AuoR7QnHR0">Debugging Axolotl Part 2: Attaching to Docker on a Remote Host
|
||||
</a></figcaption>
|
||||
|
||||
</div>
|
||||
<br>
|
||||
|
||||
[^1]: The config actually mimics the command `CUDA_VISIBLE_DEVICES=0 python -m accelerate.commands.launch -m axolotl.cli.train devtools/chat_template.yml`, but this is the same thing.
|
||||
|
||||
[^2]: Many of the below flags are recommended best practices by Nvidia when using nvidia-container-toolkit. You can read more about these flags [here](https://docs.nvidia.com/deeplearning/frameworks/user-guide/index.html).
|
||||
146
docs/docker.qmd
146
docs/docker.qmd
@@ -1,146 +0,0 @@
|
||||
---
|
||||
title: "Docker"
|
||||
format:
|
||||
html:
|
||||
toc: true
|
||||
toc-depth: 4
|
||||
---
|
||||
|
||||
This section describes the different Docker images that are released by AxolotlAI at [Docker Hub](https://hub.docker.com/u/axolotlai).
|
||||
|
||||
::: {.callout-important}
|
||||
For Blackwell GPUs, please use the tags with PyTorch 2.7.1 and CUDA 12.8.
|
||||
:::
|
||||
|
||||
## Base
|
||||
|
||||
The base image is the most minimal image that can install Axolotl. It is based on the `nvidia/cuda` image. It includes python, torch, git, git-lfs, awscli, pydantic, and more.
|
||||
|
||||
#### Image
|
||||
|
||||
```
|
||||
axolotlai/axolotl-base
|
||||
```
|
||||
|
||||
Link: [Docker Hub](https://hub.docker.com/r/axolotlai/axolotl-base)
|
||||
|
||||
#### Tags format
|
||||
|
||||
```bash
|
||||
main-base-py{python_version}-cu{cuda_version}-{pytorch_version}
|
||||
```
|
||||
|
||||
Tags examples:
|
||||
|
||||
- `main-base-py3.11-cu128-2.7.1`
|
||||
- `main-base-py3.11-cu126-2.7.1`
|
||||
- `main-base-py3.11-cu126-2.7.0`
|
||||
- `main-base-py3.11-cu126-2.6.0`
|
||||
- `main-base-py3.11-cu124-2.6.0`
|
||||
|
||||
## Main
|
||||
|
||||
The main image is the image that is used to run Axolotl. It is based on the `axolotlai/axolotl-base` image and includes the Axolotl codebase, dependencies, and more.
|
||||
|
||||
#### Image
|
||||
|
||||
```
|
||||
axolotlai/axolotl
|
||||
```
|
||||
|
||||
Link: [Docker Hub](https://hub.docker.com/r/axolotlai/axolotl)
|
||||
|
||||
#### Tags format {#sec-main-tags}
|
||||
|
||||
```bash
|
||||
# on push to main
|
||||
main-py{python_version}-cu{cuda_version}-{pytorch_version}
|
||||
|
||||
# latest main (currently torch 2.6.0, python 3.11, cuda 12.4)
|
||||
main-latest
|
||||
|
||||
# nightly build
|
||||
{branch}-{date_in_YYYYMMDD}-py{python_version}-cu{cuda_version}-{pytorch_version}
|
||||
|
||||
# tagged release
|
||||
{version}
|
||||
```
|
||||
|
||||
:::{.callout-tip}
|
||||
|
||||
There may be some extra tags appended to the image, like `-vllm` which installs those packages.
|
||||
|
||||
:::
|
||||
|
||||
Tags examples:
|
||||
|
||||
- `main-py3.11-cu128-2.7.1`
|
||||
- `main-py3.11-cu126-2.7.1`
|
||||
- `main-py3.11-cu126-2.7.0`
|
||||
- `main-py3.11-cu126-2.6.0`
|
||||
- `main-py3.11-cu124-2.6.0`
|
||||
- `main-latest`
|
||||
- `main-20250303-py3.11-cu124-2.6.0`
|
||||
- `main-20250303-py3.11-cu126-2.6.0`
|
||||
- `0.10.1`
|
||||
|
||||
## Cloud
|
||||
|
||||
The cloud image is the image that is used to run Axolotl in the cloud. It is based on the `axolotlai/axolotl` image and sets ENV variables like HuggingFace cache directories for volume mounts, tmux, and more for different cloud providers.
|
||||
|
||||
:::{.callout-tip}
|
||||
|
||||
Jupyter lab is run by default. Set `JUPYTER_DISABLE=1` in the environment variables to disable it.
|
||||
|
||||
:::
|
||||
|
||||
#### Image
|
||||
|
||||
```
|
||||
axolotlai/axolotl-cloud
|
||||
```
|
||||
|
||||
Link: [Docker Hub](https://hub.docker.com/r/axolotlai/axolotl-cloud)
|
||||
|
||||
#### Tags format
|
||||
|
||||
This uses the same tags as the [`main` image](#sec-main-tags).
|
||||
|
||||
#### Environment variables
|
||||
|
||||
- `JUPYTER_DISABLE`: Disable Jupyter lab.
|
||||
- `JUPYTER_PASSWORD`: Set a password for the Jupyter lab.
|
||||
- `PUBLIC_KEY` / `SSH_KEY`: Add a public key for the SSH service.
|
||||
|
||||
#### Volume mounts
|
||||
|
||||
:::{.callout-tip}
|
||||
|
||||
We recommend mounting volumes to `/workspace/data` for data persistence. `/workspace/axolotl` contains the source code and is ephemeral.
|
||||
|
||||
:::
|
||||
|
||||
- `/workspace/data/axolotl-artifacts`: Directory to store Axolotl artifacts.
|
||||
- `/workspace/data/huggingface-cache`: Directory to store HuggingFace cache.
|
||||
|
||||
## Cloud-no-tmux
|
||||
|
||||
This is the same as the [`cloud` image](#sec-cloud) but without tmux.
|
||||
|
||||
#### Image
|
||||
|
||||
```
|
||||
axolotlai/axolotl-cloud-term
|
||||
```
|
||||
|
||||
Link: [Docker Hub](https://hub.docker.com/r/axolotlai/axolotl-cloud-term)
|
||||
|
||||
:::{.callout-note}
|
||||
|
||||
The naming may be a bit confusing as it has `-term` appended to the end.
|
||||
|
||||
:::
|
||||
|
||||
#### Tags format
|
||||
|
||||
This uses the same tags as the [`cloud` image](#sec-cloud-tags).
|
||||
18
docs/faq.md
Normal file
18
docs/faq.md
Normal file
@@ -0,0 +1,18 @@
|
||||
# Axolotl FAQ's
|
||||
|
||||
|
||||
> The trainer stopped and hasn't progressed in several minutes.
|
||||
|
||||
Usually an issue with the GPU's communicating with each other. See the [NCCL doc](../docs/nccl.md)
|
||||
|
||||
> Exitcode -9
|
||||
|
||||
This usually happens when you run out of system RAM.
|
||||
|
||||
> Exitcode -7 while using deepspeed
|
||||
|
||||
Try upgrading deepspeed w: `pip install -U deepspeed`
|
||||
|
||||
> AttributeError: 'DummyOptim' object has no attribute 'step'
|
||||
|
||||
You may be using deepspeed with single gpu. Please don't set `deepspeed:` in yaml or cli.
|
||||
138
docs/faq.qmd
138
docs/faq.qmd
@@ -1,138 +0,0 @@
|
||||
---
|
||||
title: FAQ
|
||||
description: Frequently asked questions
|
||||
---
|
||||
|
||||
### General
|
||||
|
||||
**Q: The trainer stopped and hasn't progressed in several minutes.**
|
||||
|
||||
> A: Usually an issue with the GPUs communicating with each other. See the [NCCL doc](nccl.qmd)
|
||||
|
||||
**Q: exitcode: -9**
|
||||
|
||||
> A: This usually happens when you run out of system RAM.
|
||||
|
||||
**Q: exitcode: -7 while using deepspeed**
|
||||
|
||||
> A: Try upgrading deepspeed w: `pip install -U deepspeed`
|
||||
|
||||
**Q: AttributeError: 'DummyOptim' object has no attribute 'step'**
|
||||
|
||||
**Q: ModuleNotFoundError: No module named 'mpi4py' using single GPU with deepspeed**
|
||||
|
||||
> A: You may be using deepspeed with single gpu. Please remove the `deepspeed:` section in the yaml file or `--deepspeed` CLI flag.
|
||||
|
||||
**Q: The codes is stuck on saving preprocessed datasets.**
|
||||
|
||||
> A: This is usually an issue with the GPU. This can be resolved through setting the os environment variable `CUDA_VISIBLE_DEVICES=0`. If you are on runpod, this is usually a pod issue. Starting a new pod should take care of it.
|
||||
|
||||
**Q: Received mismatch error on merge adapters / loading adapters between torch.Size of checkpoint and model.**
|
||||
|
||||
> A: This is likely due to vocab size mismatch. By default, Axolotl expands the model's embeddings if the tokenizer has more tokens than the model. Please use the `axolotl merge-lora` command to merge the adapters instead of using your own scripts.
|
||||
|
||||
> On the other hand, if the model has more tokens than the tokenizer, Axolotl does not shrink the model's embeddings unless `shrink_embeddings: true` is set in the config.
|
||||
|
||||
**Q: How to call Axolotl via custom python scripts?**
|
||||
|
||||
> A: Since Axolotl is just Python, please see `src/axolotl/cli/main.py` on how each command is called.
|
||||
|
||||
**Q: How to know the value to use for `fsdp_transformer_layer_cls_to_wrap`?**
|
||||
|
||||
> A: This is the class name of the transformer layer to wrap with FSDP. For example, for `LlamaForCausalLM`, the value is `LlamaDecoderLayer`. To find this for a specific model, check the model's `PreTrainedModel` definition and look for `_no_split_modules` variable in the `modeling_<model_name>.py` file within `transformers` library.
|
||||
|
||||
**Q: ValueError: Asking to pad but the tokenizer does not have a padding token. Please select a token to use as pad_token**
|
||||
|
||||
> A: This is because the tokenizer does not have a padding token. Please add a padding token to the tokenizer via:
|
||||
|
||||
> ```yaml
|
||||
> special_tokens:
|
||||
> # str. If you're not sure, set to same as `eos_token`.
|
||||
> pad_token: "..."
|
||||
> ```
|
||||
|
||||
**Q: `IterableDataset error` or `KeyError: 'input_ids'` when using `preprocess` CLI**
|
||||
|
||||
> A: This is because you may be using `preprocess` CLI with `pretraining_dataset:` or `skip_prepare_dataset: true` respectively. Please use `axolotl train` CLI directly instead as these datasets are prepared on demand.
|
||||
|
||||
**Q: vLLM is not working with Axolotl**
|
||||
|
||||
> A: We currently recommend torch 2.6.0 for use with `vllm`. Please ensure you use the right version. For Docker, please use the `main-py3.11-cu124-2.6.0` tag.
|
||||
|
||||
**Q: FA2 2.8.0 `undefined symbol` runtime error on CUDA 12.4**
|
||||
|
||||
> A: There seems to be a wheel issue with FA2 2.8.0 on CUDA 12.4. Try CUDA 12.6 instead or downgrade to FA2 2.7.4. Please refer to the upstream issue: https://github.com/Dao-AILab/flash-attention/issues/1717.
|
||||
|
||||
### Chat templates
|
||||
|
||||
**Q: `jinja2.exceptions.UndefinedError: 'dict object' has no attribute 'content' / 'role' / ____`**
|
||||
|
||||
> A: This means that the property mapping for the stated attribute does not exist when building `chat_template` prompt. For example, if `no attribute 'content'`, please check you have added the correct mapping for `content` under `message_property_mappings`.
|
||||
|
||||
**Q: `Empty template generated for turn ___`**
|
||||
|
||||
> A: The `content` is empty for that turn.
|
||||
|
||||
**Q: `Could not find content start/end boundary for turn __`**
|
||||
|
||||
> A: The specific turn's start/end could not be detected. Please ensure you have set the `eos_token` following your `chat_template`. Otherwise, this could be a `chat_template` which doesn't use proper boundaries for each turn (like system). On the rare occurrence, make sure your content is not `[[dummy_message]]`. Please let us know about this.
|
||||
|
||||
**Q: `Content end boundary is before start boundary for turn ___`**
|
||||
|
||||
> A: This is an edge case which should not occur. Please create an Issue if this happens.
|
||||
|
||||
**Q: `Content end boundary is the same as start boundary for turn ___. This is likely an empty turn.`**
|
||||
|
||||
> A: This is likely an empty turn.
|
||||
|
||||
**Q: The EOS token is incorrectly being masked or not being masked / `EOS token __ not found in chat template`.**
|
||||
|
||||
> A: There can be two reasons:
|
||||
|
||||
> 1. This is because of the mismatch between `tokenizer.eos_token` and EOS token in template. Please make sure to set `eos_token: ` under `special_tokens: ` to the same EOS token as in template.
|
||||
|
||||
> 2. The EOS token is not in the template. Please check if your template is correct. As an example, `phi_35` template does not use its dedicated EOS token `<|endoftext|>` at the end.
|
||||
|
||||
**Q: "`chat_template` choice is `tokenizer_default` but tokenizer's `chat_template` is null. Please add a `chat_template` in tokenizer config"**
|
||||
|
||||
> A: This is because the tokenizer does not have a chat template. Please add a chat template in the tokenizer config. See [chat_template](dataset-formats/conversation.qmd#chat-template) for more details.
|
||||
|
||||
**Q: The EOT token(s) are incorrectly being masked or not being masked / `EOT token __ not found in chat template`.**
|
||||
|
||||
> A: There can be two reasons:
|
||||
|
||||
> 1. The EOT token is different from the EOS token and was not specified under `eot_tokens: `. Please set `eot_tokens: ` to the same EOT token(s) as in template.
|
||||
|
||||
> 2. There is more than one EOT token per turn in the template. Please raise an issue with examples as we recognize this as an edge case.
|
||||
|
||||
**Q: `EOT token encoding failed. Please check if the token is valid and can be encoded.`**
|
||||
|
||||
> A: There could be some issue with the tokenizer or unicode encoding. Please raise an issue with examples with the EOT token & tokenizer causing the issue.
|
||||
|
||||
**Q: `EOT token __ is encoded as multiple tokens.`**
|
||||
|
||||
> A: This is because the EOT token is encoded as multiple tokens which can cause unexpected behavior. Please add it under `tokens: ` or (recommended) override unused added_tokens via `added_tokens_overrides: `.
|
||||
|
||||
**Q: `Conflict between train_on_eos and train_on_eot. eos_token is in eot_tokens and train_on_eos != train_on_eot`**
|
||||
|
||||
> A: This is because the EOS token is in the `eot_tokens: ` while mismatch between `train_on_eos: ` and `train_on_eot: `. This will cause one to override the other. Please ensure that `train_on_eos: ` and `train_on_eot: ` are the same or remove the EOS token from `eot_tokens: `.
|
||||
|
||||
**Q: If `eot_tokens: ` is not provided, what happens?**
|
||||
|
||||
> A: If `eot_tokens: ` is not provided, the default behavior is the same as before. EOS tokens used to delimit turns are masked/unmasked depending on whether the turn is trainable.
|
||||
|
||||
> Internally, `eot_tokens: tokenizer.eos_token` and `train_on_eot: train_on_eos` (which defaults to `turn`). This transition helps clarify the naming and behavior of EOT/EOS tokens.
|
||||
|
||||
**Q: `Data processing error: CAS service error`**
|
||||
|
||||
> A: Try disabling XET with `export HF_HUB_DISABLE_XET=1`
|
||||
|
||||
**Q: `torch._inductor.exc.LoweringException: NoValidChoicesError: No choices to select, please consider adding ATEN into max_autotune_gemm_backends config (defined in torch/_inductor/config.py) to allow at least one choice. `**
|
||||
|
||||
> A: Depending on the version of torch, you may need to include this in your YAML:
|
||||
|
||||
> ```yaml
|
||||
> flex_attn_compile_kwargs:
|
||||
> dynamic: false
|
||||
> mode: max-autotune-no-cudagraphs
|
||||
> ```
|
||||
@@ -1,43 +0,0 @@
|
||||
---
|
||||
title: "FDSP + QLoRA"
|
||||
description: Use FSDP with QLoRA to fine-tune large LLMs on consumer GPUs.
|
||||
format:
|
||||
html:
|
||||
toc: true
|
||||
---
|
||||
|
||||
## Background
|
||||
|
||||
Using FSDP with QLoRA is essential for **fine-tuning larger (70b+ parameter) LLMs on consumer GPUs.** For example, you can use FSDP + QLoRA to train a 70b model on two 24GB GPUs[^1].
|
||||
|
||||
Below, we describe how to use this feature in Axolotl.
|
||||
|
||||
## Usage
|
||||
|
||||
To enable `QLoRA` with `FSDP`, you need to perform the following steps:
|
||||
|
||||
> ![Tip]
|
||||
> See the [example config](#example-config) file in addition to reading these instructions.
|
||||
|
||||
1. Set `adapter: qlora` in your axolotl config file.
|
||||
2. Enable FSDP in your axolotl config, as [described here](multi-gpu.qmd#sec-fsdp).
|
||||
3. Use one of the supported model types: `llama`, `mistral` or `mixtral`.
|
||||
|
||||
## Example Config
|
||||
|
||||
[examples/llama-2/qlora-fsdp.yml](../examples/llama-2/qlora-fsdp.yml) contains an example of how to enable QLoRA + FSDP in axolotl.
|
||||
|
||||
## References
|
||||
|
||||
- [PR #1378](https://github.com/axolotl-ai-cloud/axolotl/pull/1378) enabling QLoRA in FSDP in Axolotl.
|
||||
- [Blog Post](https://www.answer.ai/posts/2024-03-06-fsdp-qlora.html) from the [Answer.AI](https://www.answer.ai/) team describing the work that enabled QLoRA in FSDP.
|
||||
- Related HuggingFace PRs Enabling FDSP + QLoRA:
|
||||
- Accelerate [PR#2544](https://github.com/huggingface/accelerate/pull/2544 )
|
||||
- Transformers [PR#29587](https://github.com/huggingface/transformers/pull/29587)
|
||||
- TRL [PR#1416](https://github.com/huggingface/trl/pull/1416)
|
||||
- PEFT [PR#1550](https://github.com/huggingface/peft/pull/1550)
|
||||
|
||||
|
||||
|
||||
|
||||
[^1]: This was enabled by [this work](https://www.answer.ai/posts/2024-03-06-fsdp-qlora.html) from the Answer.AI team.
|
||||
@@ -1,186 +0,0 @@
|
||||
---
|
||||
title: "Quickstart"
|
||||
format:
|
||||
html:
|
||||
toc: true
|
||||
toc-depth: 3
|
||||
number-sections: true
|
||||
execute:
|
||||
enabled: false
|
||||
---
|
||||
|
||||
This guide will walk you through your first model fine-tuning project with Axolotl.
|
||||
|
||||
## Quick Example {#sec-quick-example}
|
||||
|
||||
Let's start by fine-tuning a small language model using LoRA. This example uses a 1B parameter model to ensure it runs on most GPUs.
|
||||
Assuming `axolotl` is installed (if not, see our [Installation Guide](installation.qmd))
|
||||
|
||||
1. Download example configs:
|
||||
```bash
|
||||
axolotl fetch examples
|
||||
```
|
||||
|
||||
2. Run the training:
|
||||
```bash
|
||||
axolotl train examples/llama-3/lora-1b.yml
|
||||
```
|
||||
|
||||
That's it! Let's understand what just happened.
|
||||
|
||||
## Understanding the Process {#sec-understanding}
|
||||
|
||||
### The Configuration File {#sec-config}
|
||||
|
||||
The YAML configuration file controls everything about your training. Here's what (part of) our example config looks like:
|
||||
|
||||
```yaml
|
||||
base_model: NousResearch/Llama-3.2-1B
|
||||
|
||||
load_in_8bit: true
|
||||
adapter: lora
|
||||
|
||||
datasets:
|
||||
- path: teknium/GPT4-LLM-Cleaned
|
||||
type: alpaca
|
||||
dataset_prepared_path: last_run_prepared
|
||||
val_set_size: 0.1
|
||||
output_dir: ./outputs/lora-out
|
||||
```
|
||||
|
||||
::: {.callout-tip}
|
||||
`load_in_8bit: true` and `adapter: lora` enables LoRA adapter finetuning.
|
||||
|
||||
- To perform Full finetuning, remove these two lines.
|
||||
- To perform QLoRA finetuning, replace with `load_in_4bit: true` and `adapter: qlora`.
|
||||
:::
|
||||
|
||||
See our [config options](config-reference.qmd) for more details.
|
||||
|
||||
### Training {#sec-training}
|
||||
|
||||
When you run `axolotl train`, Axolotl:
|
||||
|
||||
1. Downloads the base model
|
||||
2. (If specified) applies QLoRA/LoRA adapter layers
|
||||
3. Loads and processes the dataset
|
||||
4. Runs the training loop
|
||||
5. Saves the trained model and / or LoRA weights
|
||||
|
||||
## Your First Custom Training {#sec-custom}
|
||||
|
||||
Let's modify the example for your own data:
|
||||
|
||||
1. Create a new config file `my_training.yml`:
|
||||
|
||||
```yaml
|
||||
base_model: NousResearch/Nous-Hermes-llama-1b-v1
|
||||
|
||||
load_in_8bit: true
|
||||
adapter: lora
|
||||
|
||||
# Training settings
|
||||
micro_batch_size: 2
|
||||
num_epochs: 3
|
||||
learning_rate: 0.0003
|
||||
|
||||
# Your dataset
|
||||
datasets:
|
||||
- path: my_data.jsonl # Your local data file
|
||||
type: alpaca # Or other format
|
||||
```
|
||||
|
||||
This specific config is for LoRA fine-tuning a model with instruction tuning data using
|
||||
the `alpaca` dataset format, which has the following format:
|
||||
|
||||
```json
|
||||
{
|
||||
"instruction": "Write a description of alpacas.",
|
||||
"input": "",
|
||||
"output": "Alpacas are domesticated South American camelids..."
|
||||
}
|
||||
```
|
||||
|
||||
Please see our [Dataset Formats](dataset-formats) for more dataset formats and how to
|
||||
format them.
|
||||
|
||||
2. Prepare your JSONL data in the specified format (in this case, the expected `alpaca`
|
||||
format):
|
||||
|
||||
```json
|
||||
{"instruction": "Classify this text", "input": "I love this!", "output": "positive"}
|
||||
{"instruction": "Classify this text", "input": "Not good at all", "output": "negative"}
|
||||
```
|
||||
|
||||
3. Run the training:
|
||||
|
||||
```bash
|
||||
axolotl train my_training.yml
|
||||
```
|
||||
|
||||
## Common Tasks {#sec-common-tasks}
|
||||
|
||||
::: {.callout-tip}
|
||||
|
||||
The same yaml file is used for training, inference, and merging.
|
||||
|
||||
:::
|
||||
|
||||
### Testing Your Model {#sec-testing}
|
||||
|
||||
After training, test your model:
|
||||
|
||||
```bash
|
||||
axolotl inference my_training.yml --lora-model-dir="./outputs/lora-out"
|
||||
```
|
||||
|
||||
More details can be found in [Inference](inference.qmd).
|
||||
|
||||
### Using a UI {#sec-ui}
|
||||
|
||||
Launch a Gradio interface:
|
||||
|
||||
```bash
|
||||
axolotl inference my_training.yml --lora-model-dir="./outputs/lora-out" --gradio
|
||||
```
|
||||
|
||||
### Preprocessing Data {#sec-preprocessing}
|
||||
|
||||
For large datasets, preprocess first:
|
||||
|
||||
```bash
|
||||
axolotl preprocess my_training.yml
|
||||
```
|
||||
|
||||
Please make sure to set `dataset_prepared_path: ` in your config to set the path to save the prepared dataset.
|
||||
|
||||
More details can be found in [Dataset Preprocessing](dataset_preprocessing.qmd).
|
||||
|
||||
### Merging LoRA weights {#sec-merging-lora}
|
||||
|
||||
To merge the LoRA weights back into the base model, run:
|
||||
|
||||
```bash
|
||||
axolotl merge-lora my_training.yml --lora-model-dir="./outputs/lora-out"
|
||||
```
|
||||
|
||||
The merged model will be saved in the `{output_dir}/merged` directory.
|
||||
|
||||
More details can be found in [Merging LoRA weights](inference.qmd#sec-merging).
|
||||
|
||||
## Next Steps {#sec-next-steps}
|
||||
|
||||
Now that you have the basics, you might want to:
|
||||
|
||||
- Try different model architectures
|
||||
- Experiment with hyperparameters
|
||||
- Use more advanced training methods
|
||||
- Scale up to larger models
|
||||
|
||||
Check our other guides for details on these topics:
|
||||
|
||||
- [Configuration Guide](config-reference.qmd) - Full configuration options
|
||||
- [Dataset Loading](dataset_loading.qmd) - Loading datasets from various sources
|
||||
- [Dataset Formats](dataset-formats) - Working with different data formats
|
||||
- [Multi-GPU Training](multi-gpu.qmd)
|
||||
- [Multi-Node Training](multi-node.qmd)
|
||||
@@ -1,29 +0,0 @@
|
||||
---
|
||||
title: Gradient Checkpointing and Activation Offloading
|
||||
---
|
||||
|
||||
Gradient checkpointing and activation offloading are techniques used to optimize the performance of deep learning
|
||||
models by reducing the memory footprint and improving computational efficiency.
|
||||
|
||||
### Enabling Gradient Checkpointing
|
||||
|
||||
```yaml
|
||||
gradient_checkpointing: true
|
||||
```
|
||||
|
||||
### Enabling Activation Offloading
|
||||
|
||||
```yaml
|
||||
gradient_checkpointing: true # required for activation offloading
|
||||
activation_offloading: true
|
||||
```
|
||||
|
||||
Activation offloading variants:
|
||||
|
||||
The default `activation_offloading: true` offloads activations to CPU and uses CUDA streams
|
||||
to overlap the communications and computations when offloading.
|
||||
|
||||
The `activation_offloading: legacy` naively offloads activations to CPU and without additional optimizations.
|
||||
|
||||
For resource constrained environments with limited CPU memory, `activation_offloading: disk` offloads
|
||||
activations to disk instead of CPU RAM so that much larger context lengths can be trained with minimal memory.
|
||||
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|
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|
Before Width: | Height: | Size: 292 KiB |
@@ -1,151 +0,0 @@
|
||||
---
|
||||
title: "Inference and Merging"
|
||||
format:
|
||||
html:
|
||||
toc: true
|
||||
toc-depth: 3
|
||||
number-sections: true
|
||||
execute:
|
||||
enabled: false
|
||||
---
|
||||
|
||||
This guide covers how to use your trained models for inference, including model loading, interactive testing, merging adapters, and common troubleshooting steps.
|
||||
|
||||
## Quick Start {#sec-quickstart}
|
||||
|
||||
::: {.callout-tip}
|
||||
Use the same config used for training on inference/merging.
|
||||
:::
|
||||
|
||||
### Basic Inference {#sec-basic}
|
||||
|
||||
::: {.panel-tabset}
|
||||
|
||||
## LoRA Models
|
||||
|
||||
```{.bash}
|
||||
axolotl inference your_config.yml --lora-model-dir="./lora-output-dir"
|
||||
```
|
||||
|
||||
## Full Fine-tuned Models
|
||||
|
||||
```{.bash}
|
||||
axolotl inference your_config.yml --base-model="./completed-model"
|
||||
```
|
||||
|
||||
:::
|
||||
|
||||
## Advanced Usage {#sec-advanced}
|
||||
|
||||
### Gradio Interface {#sec-gradio}
|
||||
|
||||
Launch an interactive web interface:
|
||||
|
||||
```{.bash}
|
||||
axolotl inference your_config.yml --gradio
|
||||
```
|
||||
|
||||
### File-based Prompts {#sec-file-prompts}
|
||||
|
||||
Process prompts from a text file:
|
||||
|
||||
```{.bash}
|
||||
cat /tmp/prompt.txt | axolotl inference your_config.yml \
|
||||
--base-model="./completed-model" --prompter=None
|
||||
```
|
||||
|
||||
### Memory Optimization {#sec-memory}
|
||||
|
||||
For large models or limited memory:
|
||||
|
||||
```{.bash}
|
||||
axolotl inference your_config.yml --load-in-8bit=True
|
||||
```
|
||||
|
||||
## Merging LoRA Weights {#sec-merging}
|
||||
|
||||
Merge LoRA adapters with the base model:
|
||||
|
||||
```{.bash}
|
||||
axolotl merge-lora your_config.yml --lora-model-dir="./completed-model"
|
||||
```
|
||||
|
||||
### Memory Management for Merging {#sec-memory-management}
|
||||
|
||||
::: {.panel-tabset}
|
||||
|
||||
## Configuration Options
|
||||
|
||||
```{.yaml}
|
||||
gpu_memory_limit: 20GiB # Adjust based on your GPU
|
||||
lora_on_cpu: true # Process on CPU if needed
|
||||
```
|
||||
|
||||
## Force CPU Merging
|
||||
|
||||
```{.bash}
|
||||
CUDA_VISIBLE_DEVICES="" axolotl merge-lora ...
|
||||
```
|
||||
|
||||
:::
|
||||
|
||||
## Tokenization {#sec-tokenization}
|
||||
|
||||
### Common Issues {#sec-tokenization-issues}
|
||||
|
||||
::: {.callout-warning}
|
||||
Tokenization mismatches between training and inference are a common source of problems.
|
||||
:::
|
||||
|
||||
To debug:
|
||||
|
||||
1. Check training tokenization:
|
||||
```{.bash}
|
||||
axolotl preprocess your_config.yml --debug
|
||||
```
|
||||
|
||||
2. Verify inference tokenization by decoding tokens before model input
|
||||
|
||||
3. Compare token IDs between training and inference
|
||||
|
||||
### Special Tokens {#sec-special-tokens}
|
||||
|
||||
Configure special tokens in your YAML:
|
||||
|
||||
```{.yaml}
|
||||
special_tokens:
|
||||
bos_token: "<s>"
|
||||
eos_token: "</s>"
|
||||
unk_token: "<unk>"
|
||||
tokens:
|
||||
- "<|im_start|>"
|
||||
- "<|im_end|>"
|
||||
```
|
||||
|
||||
## Troubleshooting {#sec-troubleshooting}
|
||||
|
||||
### Common Problems {#sec-common-problems}
|
||||
|
||||
::: {.panel-tabset}
|
||||
|
||||
## Memory Issues
|
||||
|
||||
- Use 8-bit loading
|
||||
- Reduce batch sizes
|
||||
- Try CPU offloading
|
||||
|
||||
## Token Issues
|
||||
|
||||
- Verify special tokens
|
||||
- Check tokenizer settings
|
||||
- Compare training and inference preprocessing
|
||||
|
||||
## Performance Issues
|
||||
|
||||
- Verify model loading
|
||||
- Check prompt formatting
|
||||
- Ensure temperature/sampling settings
|
||||
|
||||
:::
|
||||
|
||||
For more details, see our [debugging guide](debugging.qmd).
|
||||
@@ -1,6 +0,0 @@
|
||||
---
|
||||
title: Template-free prompt construction
|
||||
description: "Template-free prompt construction with the `input_output` format"
|
||||
---
|
||||
|
||||
The documentation moved to [here](dataset-formats/template_free.qmd).
|
||||
Some files were not shown because too many files have changed in this diff Show More
Reference in New Issue
Block a user