test

Co-authored-by: NanoCode012 <nano@axolotl.ai>

.github/workflows/base.yml

@@ -1,16 +1,6 @@
 name: ci-cd-base
 
 on:
-  push:
-    branches:
-      - "main"
-    paths:
-      - 'Dockerfile-base'
-      - '.github/workflows/base.yml'
-  pull_request:
-    paths:
-      - 'Dockerfile-base'
-      - '.github/workflows/base.yml'
   workflow_dispatch:
 
 jobs:
@@ -37,7 +27,7 @@ jobs:
           - cuda: "124"
             cuda_version: 12.4.1
             cudnn_version: ""
-            python_version: "3.10"
+            python_version: "3.11"
             pytorch: 2.4.1
             torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
           - cuda: "124"
@@ -54,21 +44,19 @@ jobs:
             torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 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:

.github/workflows/docs.yml

@@ -17,7 +17,7 @@ jobs:
         - name: Set up Quarto
           uses: quarto-dev/quarto-actions/setup@v2
         - name: Setup Python
-          uses: actions/setup-python@v5
+          uses: actions/setup-python@v3
           with:
             python-version: '3.10'
         - name: install dependencies

.github/workflows/lint.yml

@@ -15,9 +15,9 @@ jobs:
     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: "3.10"
           cache: 'pip' # caching pip dependencies
-      - uses: pre-commit/action@v3.0.1
+      - uses: pre-commit/action@v3.0.0

.github/workflows/main.yml

@@ -4,13 +4,11 @@ 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: ${{ ! contains(github.event.commits[0].message, '[skip docker]]') && github.repository_owner == 'axolotl-ai-cloud' }}
     strategy:
       fail-fast: false
       matrix:
@@ -34,7 +32,7 @@ jobs:
           - cuda: 124
             cuda_version: 12.4.1
             python_version: "3.11"
-            pytorch: 2.5.1
+            pytorch: 2.5.0
             axolotl_extras:
     runs-on: axolotl-gpu-runner
     steps:
@@ -44,12 +42,7 @@ jobs:
         id: metadata
         uses: docker/metadata-action@v5
         with:
-          images: |
-            winglian/axolotl
-            axolotlai/axolotl
-          tags: |
-            type=ref,event=branch
-            type=pep440,pattern={{version}}
+          images: winglian/axolotl
       - name: Set up Docker Buildx
         uses: docker/setup-buildx-action@v3
       - name: Login to Docker Hub
@@ -63,7 +56,7 @@ jobs:
         with:
           context: .
           build-args: |
-            BASE_TAG=${{ 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 }}
@@ -77,7 +70,7 @@ jobs:
 
   build-axolotl-cloud:
     needs: build-axolotl
-    if: ${{ ! contains(github.event.commits[0].message, '[skip docker]') && github.repository_owner == 'axolotl-ai-cloud' }}
+    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:
@@ -101,7 +94,7 @@ jobs:
           - cuda: 124
             cuda_version: 12.4.1
             python_version: "3.11"
-            pytorch: 2.5.1
+            pytorch: 2.5.0
             axolotl_extras:
     runs-on: axolotl-gpu-runner
     steps:
@@ -111,25 +104,20 @@ jobs:
         id: metadata
         uses: docker/metadata-action@v5
         with:
-          images: |
-            winglian/axolotl-cloud
-            axolotlai/axolotl-cloud
-          tags: |
-            type=ref,event=branch
-            type=pep440,pattern={{version}}
+          images: winglian/axolotl-cloud
       - name: Login to Docker Hub
         uses: docker/login-action@v3
         with:
           username: ${{ secrets.DOCKERHUB_USERNAME }}
           password: ${{ secrets.DOCKERHUB_TOKEN }}
       - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3
+        uses: docker/setup-buildx-action@v2
       - name: Build
         uses: docker/build-push-action@v5
         with:
           context: .
           build-args: |
-            BASE_TAG=${{ github.ref_type == 'tag' && 'main' || github.ref_name }}-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
+            BASE_TAG=${{ 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' }}
@@ -140,7 +128,7 @@ jobs:
 
   build-axolotl-cloud-no-tmux:
     needs: build-axolotl
-    if: ${{ ! contains(github.event.commits[0].message, '[skip docker]') && github.repository_owner == 'axolotl-ai-cloud' }}
+    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:
@@ -158,25 +146,20 @@ jobs:
         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}}
+          images: winglian/axolotl-cloud-term
       - name: Login to Docker Hub
         uses: docker/login-action@v3
         with:
           username: ${{ secrets.DOCKERHUB_USERNAME }}
           password: ${{ secrets.DOCKERHUB_TOKEN }}
       - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3
+        uses: docker/setup-buildx-action@v2
       - name: Build
         uses: docker/build-push-action@v5
         with:
           context: .
           build-args: |
-            BASE_TAG=${{ github.ref_type == 'tag' && 'main' || github.ref_name }}-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
+            BASE_TAG=${{ 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-no-tmux
           push: ${{ github.event_name != 'pull_request' }}

.github/workflows/multi-gpu-e2e.yml

@@ -8,14 +8,9 @@ on:
   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' }}
+    if: ${{ ! contains(github.event.commits[0].message, '[skip docker]]') && github.repository_owner == 'axolotl-ai-cloud' }}
     strategy:
       fail-fast: false
       matrix:
@@ -36,7 +31,7 @@ jobs:
           - cuda: 124
             cuda_version: 12.4.1
             python_version: "3.11"
-            pytorch: 2.5.1
+            pytorch: 2.5.0
             axolotl_extras:
             num_gpus: 2
             nightly_build: "true"

.github/workflows/nightlies.yml

@@ -7,7 +7,7 @@ on:
 
 jobs:
   build-axolotl:
-    if: ${{ ! contains(github.event.commits[0].message, '[skip docker]') && github.repository_owner == 'axolotl-ai-cloud' }}
+    if: ${{ ! contains(github.event.commits[0].message, '[skip docker]]') && github.repository_owner == 'axolotl-ai-cloud' }}
     strategy:
       fail-fast: false
       matrix:
@@ -31,7 +31,7 @@ jobs:
           - cuda: 124
             cuda_version: 12.4.1
             python_version: "3.11"
-            pytorch: 2.5.1
+            pytorch: 2.5.0
             axolotl_extras:
     runs-on: axolotl-gpu-runner
     steps:
@@ -41,9 +41,7 @@ jobs:
         id: metadata
         uses: docker/metadata-action@v5
         with:
-          images: |
-            winglian/axolotl
-            axolotlai/axolotl
+          images: winglian/axolotl
           tags: |
             type=raw,value={{ branch }}-{{ date 'YYYYMMDD' }}
       - name: Set up Docker Buildx
@@ -71,7 +69,7 @@ jobs:
 
   build-axolotl-cloud:
     needs: build-axolotl
-    if: ${{ ! contains(github.event.commits[0].message, '[skip docker]') && github.repository_owner == 'axolotl-ai-cloud' }}
+    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:
@@ -95,7 +93,7 @@ jobs:
           - cuda: 124
             cuda_version: 12.4.1
             python_version: "3.11"
-            pytorch: 2.5.1
+            pytorch: 2.5.0
             axolotl_extras:
     runs-on: axolotl-gpu-runner
     steps:
@@ -105,9 +103,7 @@ jobs:
         id: metadata
         uses: docker/metadata-action@v5
         with:
-          images: |
-            winglian/axolotl-cloud
-            axolotlai/axolotl-cloud
+          images: winglian/axolotl-cloud
           tags: |
             type=raw,value={{ branch }}-{{ date 'YYYYMMDD' }}
       - name: Login to Docker Hub
@@ -116,7 +112,7 @@ jobs:
           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
         with:

.github/workflows/pypi.yml

@@ -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
@@ -31,10 +16,10 @@ 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: "3.10"
 
@@ -52,9 +37,9 @@ jobs:
         run: |
           sed -i -E 's/version="([0-9.]+)",/version="${{ steps.tag.outputs.TAG_NAME }}",/g' setup.py
 
-      - name: Build a source dist
+      - name: Build a binary wheel
         run: |
-          python setup.py sdist
+          python setup.py sdist bdist_wheel
 
       - name: Publish package distributions to PyPI
         uses: pypa/gh-action-pypi-publish@release/v1

.github/workflows/tests-nightly.yml

@@ -9,12 +9,12 @@ jobs:
     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: "3.10"
           cache: 'pip' # caching pip dependencies
-      - uses: pre-commit/action@v3.0.1
+      - uses: pre-commit/action@v3.0.0
         env:
           SKIP: no-commit-to-branch
 
@@ -23,23 +23,17 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       fail-fast: false
-      max-parallel: 2
       matrix:
         python_version: ["3.10", "3.11"]
-        pytorch_version: ["2.3.1", "2.4.1", "2.5.1"]
-        exclude:
-          - python_version: "3.10"
-            pytorch_version: "2.4.1"
-          - python_version: "3.10"
-            pytorch_version: "2.5.1"
+        pytorch_version: ["2.3.1", "2.4.1", "2.5.0"]
     timeout-minutes: 20
 
     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: ${{ matrix.python_version }}
           cache: 'pip' # caching pip dependencies
@@ -54,25 +48,17 @@ jobs:
           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 install --upgrade pip
           pip3 install --upgrade packaging
           pip3 install -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: Ensure axolotl CLI was installed
-        run: |
-          axolotl --help
-
       - name: Run tests
         run: |
-          pytest -n8 --dist loadfile --ignore=tests/e2e/ --ignore=tests/patched/ tests/
-          pytest tests/patched/
+          pytest --ignore=tests/e2e/ tests/
 
       - name: cleanup pip cache
         run: |
@@ -106,7 +92,7 @@ jobs:
           - cuda: 124
             cuda_version: 12.4.1
             python_version: "3.11"
-            pytorch: 2.5.1
+            pytorch: 2.5.0
             num_gpus: 1
             axolotl_extras:
             nightly_build: "true"

.github/workflows/tests.yml

@@ -8,35 +8,24 @@ on:
       - '**.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' }}
-
 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: "3.10"
           cache: 'pip' # caching pip dependencies
-      - uses: pre-commit/action@v3.0.1
+      - uses: pre-commit/action@v3.0.0
         env:
           SKIP: no-commit-to-branch
 
@@ -45,23 +34,17 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       fail-fast: false
-      max-parallel: 2
       matrix:
         python_version: ["3.10", "3.11"]
-        pytorch_version: ["2.3.1", "2.4.1", "2.5.1"]
-        exclude:
-          - python_version: "3.10"
-            pytorch_version: "2.4.1"
-          - python_version: "3.10"
-            pytorch_version: "2.5.1"
+        pytorch_version: ["2.3.1", "2.4.1", "2.5.0"]
     timeout-minutes: 20
 
     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: ${{ matrix.python_version }}
           cache: 'pip' # caching pip dependencies
@@ -79,81 +62,22 @@ jobs:
         run: |
           pip3 show torch
           pip3 install -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: Ensure axolotl CLI was installed
-        run: |
-          axolotl --help
-
       - name: Run tests
         run: |
-          pytest -v -n8 --dist loadfile --ignore=tests/e2e/ --ignore=tests/patched/ tests/
-          pytest -v tests/patched/
-
-      - 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
-      max-parallel: 1
-      matrix:
-        python_version: ["3.11"]
-        pytorch_version: ["2.4.1", "2.5.1"]
-    timeout-minutes: 20
-
-    steps:
-      - name: Check out repository code
-        uses: actions/checkout@v4
-
-      - name: Setup Python
-        uses: actions/setup-python@v5
-        with:
-          python-version: ${{ matrix.python_version }}
-          cache: 'pip' # caching pip dependencies
-
-      - name: upgrade pip
-        run: |
-          pip3 install --upgrade pip
-          pip3 install --upgrade packaging setuptools wheel
-
-      - name: Install PyTorch
-        run: |
-          pip3 install torch==${{ matrix.pytorch_version }}
-
-      - name: Install dependencies
-        run: |
-          pip3 show torch
-          python3 setup.py sdist
-          pip3 install 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
-
-      - name: Ensure axolotl CLI was installed
-        run: |
-          axolotl --help
-
-      - name: Run tests
-        run: |
-          pytest -v -n8 --dist loadfile --ignore=tests/e2e/ --ignore=tests/patched/ tests/
-          pytest -v tests/patched/
+          pytest --ignore=tests/e2e/ tests/
 
       - name: cleanup pip cache
         run: |
           find "$(pip cache dir)/http-v2" -type f -mtime +14 -exec rm {} \;
 
   docker-e2e-tests-1st:
-    if: ${{ ! contains(github.event.commits[0].message, '[skip e2e]') && github.repository_owner == 'axolotl-ai-cloud' }}
+    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: 90
-    needs: [pre-commit, pytest, pytest-sdist]
+    needs: [pre-commit, pytest]
 
     strategy:
       fail-fast: false
@@ -208,7 +132,7 @@ jobs:
           - cuda: 124
             cuda_version: 12.4.1
             python_version: "3.11"
-            pytorch: 2.5.1
+            pytorch: 2.5.0
             num_gpus: 1
             axolotl_extras:
     steps:

.gitignore

@@ -182,6 +182,3 @@ submit.sh
 
 typings/
 out/
-
-# vim
-*.swp

MANIFEST.in

@@ -1,4 +0,0 @@
-include requirements.txt
-include README.md
-include LICENSE
-recursive-include axolotl *.py

README.md

@@ -1,25 +1,8 @@
-<p align="center">
-    <picture>
-        <source media="(prefers-color-scheme: dark)" srcset="image/axolotl_logo_digital_white.svg">
-        <source media="(prefers-color-scheme: light)" srcset="image/axolotl_logo_digital_black.svg">
-        <img alt="Axolotl" src="image/axolotl_logo_digital_black.svg" width="400" height="104" style="max-width: 100%;">
-    </picture>
-</p>
+# Axolotl
 
-<p align="center">
-    <img src="https://img.shields.io/github/license/axolotl-ai-cloud/axolotl.svg?color=blue" alt="GitHub License">
-    <img src="https://github.com/axolotl-ai-cloud/axolotl/actions/workflows/tests.yml/badge.svg" alt="tests">
-    <a href="https://github.com/axolotl-ai-cloud/axolotl/releases"><img src="https://img.shields.io/github/release/axolotl-ai-cloud/axolotl.svg" alt="Releases"></a>
-    <br/>
-    <a href="https://github.com/axolotl-ai-cloud/axolotl/graphs/contributors"><img src="https://img.shields.io/github/contributors-anon/axolotl-ai-cloud/axolotl?color=yellow&style=flat-square" alt="contributors" style="height: 20px;"></a>
-    <img src="https://img.shields.io/github/stars/axolotl-ai-cloud/axolotl" alt="GitHub Repo stars">
-    <br/>
-    <a href="https://discord.com/invite/HhrNrHJPRb"><img src="https://img.shields.io/badge/discord-7289da.svg?style=flat-square&logo=discord" alt="discord" style="height: 20px;"></a>
-    <a href="https://twitter.com/axolotl_ai"><img src="https://img.shields.io/twitter/follow/axolotl_ai?style=social" alt="twitter" style="height: 20px;"></a>
-    <br/>
-    <img src="https://github.com/axolotl-ai-cloud/axolotl/actions/workflows/tests-nightly.yml/badge.svg" alt="tests-nightly">
-    <img src="https://github.com/axolotl-ai-cloud/axolotl/actions/workflows/multi-gpu-e2e.yml/badge.svg" alt="multigpu-semi-weekly tests">
-</p>
+![tests](https://github.com/axolotl-ai-cloud/axolotl/actions/workflows/tests.yml/badge.svg)
+![tests-nightly](https://github.com/axolotl-ai-cloud/axolotl/actions/workflows/tests-nightly.yml/badge.svg)
+![multigpu-semi-weekly tests](https://github.com/axolotl-ai-cloud/axolotl/actions/workflows/multi-gpu-e2e.yml/badge.svg)
 
 Axolotl is a tool designed to streamline the fine-tuning of various AI models, offering support for multiple configurations and architectures.
 
@@ -45,13 +28,9 @@ Features:
 ## Table of Contents
 - [Axolotl](#axolotl)
   - [Table of Contents](#table-of-contents)
-  - [Quickstart ⚡](#quickstart-)
-    - [Edge Builds](#edge-builds-)
-    - [Axolotl CLI Usage](#axolotl-cli-usage)
-  - [Badge ❤🏷️](#badge-️)
-  - [Contributing 🤝](#contributing-)
-  - [Sponsors 🤝❤](#sponsors-)
   - [Axolotl supports](#axolotl-supports)
+  - [Quickstart ⚡](#quickstart-)
+    - [Usage](#usage)
   - [Advanced Setup](#advanced-setup)
     - [Environment](#environment)
       - [Docker](#docker)
@@ -83,12 +62,20 @@ Features:
     - [Tokenization Mismatch b/w Inference \& Training](#tokenization-mismatch-bw-inference--training)
   - [Debugging Axolotl](#debugging-axolotl)
   - [Need help? 🙋](#need-help-)
+  - [Badge ❤🏷️](#badge-️)
+  - [Community Showcase](#community-showcase)
+  - [Contributing 🤝](#contributing-)
+  - [Sponsors 🤝❤](#sponsors-)
+      - [💎 Diamond Sponsors - Contact directly](#-diamond-sponsors---contact-directly)
+      - [🥇 Gold Sponsors - $5000/mo](#-gold-sponsors---5000mo)
+      - [🥈 Silver Sponsors - $1000/mo](#-silver-sponsors---1000mo)
+      - [🥉 Bronze Sponsors - $500/mo](#-bronze-sponsors---500mo)
 
 </td>
 <td>
 
 <div align="center">
-  <img src="image/axolotl_symbol_digital_white.svg" alt="axolotl" width="160">
+  <img src="image/axolotl.png" alt="axolotl" width="160">
   <div>
     <p>
       <b>Axolotl provides a unified repository for fine-tuning <br />a variety of AI models with ease</b>
@@ -105,148 +92,6 @@ Features:
 </tr>
 </table>
 
-## Quickstart ⚡
-
-Get started with Axolotl in just a few steps! This quickstart guide will walk you through setting up and running a basic fine-tuning task.
-
-**Requirements**: *Nvidia* GPU (Ampere architecture or newer for `bf16` and Flash Attention) or *AMD* GPU, Python >=3.10 and PyTorch >=2.3.1.
-
-```bash
-pip3 install axolotl[flash-attn,deepspeed]
-
-# download examples and optionally deepspeed configs to the local path
-axolotl fetch examples
-axolotl fetch deepspeed_configs  # OPTIONAL
-
-# finetune using lora
-axolotl train examples/llama-3/lora-1b.yml
-```
-
-### Edge Builds 🏎️
-
-If you're looking for the latest features and updates between releases, you'll need to install
-from source.
-
-```bash
-git clone https://github.com/axolotl-ai-cloud/axolotl.git
-cd axolotl
-pip3 install packaging ninja
-pip3 install -e '.[flash-attn,deepspeed]'
-```
-
-### Axolotl CLI Usage
-We now support a new, more streamlined CLI using [click](https://click.palletsprojects.com/en/stable/).
-
-```bash
-# preprocess datasets - optional but recommended
-CUDA_VISIBLE_DEVICES="0" axolotl preprocess examples/llama-3/lora-1b.yml
-
-# finetune lora
-axolotl train examples/llama-3/lora-1b.yml
-
-# inference
-axolotl inference examples/llama-3/lora-1b.yml \
-    --lora-model-dir="./outputs/lora-out"
-
-# gradio
-axolotl inference examples/llama-3/lora-1b.yml \
-    --lora-model-dir="./outputs/lora-out" --gradio
-
-# remote yaml files - the yaml config can be hosted on a public URL
-# Note: the yaml config must directly link to the **raw** yaml
-axolotl train https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/examples/llama-3/lora-1b.yml
-```
-
-We've also added a new command for fetching `examples` and `deepspeed_configs` to your
-local machine. This will come in handy when installing `axolotl` from PyPI.
-
-```bash
-# Fetch example YAML files (stores in "examples/" folder)
-axolotl fetch examples
-
-# Fetch deepspeed config files (stores in "deepspeed_configs/" folder)
-axolotl fetch deepspeed_configs
-
-# Optionally, specify a destination folder
-axolotl fetch examples --dest path/to/folder
-```
-
-### Legacy Usage
-<details>
-
-<summary>Click to Expand</summary>
-
-While the Axolotl CLI is the preferred method for interacting with axolotl, we
-still support the legacy `-m axolotl.cli.*` usage.
-
-```bash
-# preprocess datasets - optional but recommended
-CUDA_VISIBLE_DEVICES="0" python -m axolotl.cli.preprocess examples/llama-3/lora-1b.yml
-
-# finetune lora
-accelerate launch -m axolotl.cli.train examples/llama-3/lora-1b.yml
-
-# inference
-accelerate launch -m axolotl.cli.inference examples/llama-3/lora-1b.yml \
-    --lora_model_dir="./outputs/lora-out"
-
-# gradio
-accelerate launch -m axolotl.cli.inference examples/llama-3/lora-1b.yml \
-    --lora_model_dir="./outputs/lora-out" --gradio
-
-# remote yaml files - the yaml config can be hosted on a public URL
-# Note: the yaml config must directly link to the **raw** yaml
-accelerate launch -m axolotl.cli.train https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/examples/llama-3/lora-1b.yml
-```
-
-</details>
-
-## Badge ❤🏷️
-
-Building something cool with Axolotl? Consider adding a badge to your model card.
-
-```markdown
-[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
-```
-
-[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
-
-## Sponsors 🤝❤
-
-If you love axolotl, consider sponsoring the project by reaching out directly to [wing@axolotl.ai](mailto:wing@axolotl.ai).
-
----
-
-- [Modal](https://modal.com/) Modal lets you run data/AI jobs in the cloud, by just writing a few lines of Python. Customers use Modal to deploy Gen AI models at large scale, fine-tune LLM models, run protein folding simulations, and much more.
-
----
-
-## Contributing 🤝
-
-Please read the [contributing guide](./.github/CONTRIBUTING.md)
-
-Bugs? Please check the [open issues](https://github.com/axolotl-ai-cloud/axolotl/issues/bug) else create a new Issue.
-
-PRs are **greatly welcome**!
-
-Please run the quickstart instructions followed by the below to setup env:
-```bash
-pip3 install -r requirements-dev.txt -r requirements-tests.txt
-pre-commit install
-
-# test
-pytest tests/
-
-# optional: run against all files
-pre-commit run --all-files
-```
-
-Thanks to all of our contributors to date. Help drive open source AI progress forward by contributing to Axolotl.
-
-<a href="https://github.com/axolotl-ai-cloud/axolotl/graphs/contributors">
-  <img src="https://contrib.rocks/image?repo=openaccess-ai-collective/axolotl" alt="contributor chart by https://contrib.rocks"/>
-</a>
-
 ## Axolotl supports
 
 |             | fp16/fp32 | lora | qlora | gptq | gptq w/flash attn | flash attn | xformers attn |
@@ -272,6 +117,41 @@ Thanks to all of our contributors to date. Help drive open source AI progress fo
 ❌: not supported
 ❓: untested
 
+## Quickstart ⚡
+
+Get started with Axolotl in just a few steps! This quickstart guide will walk you through setting up and running a basic fine-tuning task.
+
+**Requirements**: Nvidia GPU (Ampere architecture or newer for `bf16` and Flash Attention), Python >=3.10 and PyTorch >=2.3.1.
+
+```bash
+git clone https://github.com/axolotl-ai-cloud/axolotl
+cd axolotl
+
+pip3 install packaging ninja
+pip3 install -e '.[flash-attn,deepspeed]'
+```
+
+### Usage
+```bash
+# preprocess datasets - optional but recommended
+CUDA_VISIBLE_DEVICES="" python -m axolotl.cli.preprocess examples/openllama-3b/lora.yml
+
+# finetune lora
+accelerate launch -m axolotl.cli.train examples/openllama-3b/lora.yml
+
+# inference
+accelerate launch -m axolotl.cli.inference examples/openllama-3b/lora.yml \
+    --lora_model_dir="./outputs/lora-out"
+
+# gradio
+accelerate launch -m axolotl.cli.inference examples/openllama-3b/lora.yml \
+    --lora_model_dir="./outputs/lora-out" --gradio
+
+# remote yaml files - the yaml config can be hosted on a public URL
+# Note: the yaml config must directly link to the **raw** yaml
+accelerate launch -m axolotl.cli.train https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/examples/openllama-3b/lora.yml
+```
+
 ## Advanced Setup
 
 ### Environment
@@ -279,7 +159,7 @@ Thanks to all of our contributors to date. Help drive open source AI progress fo
 #### Docker
 
   ```bash
-  docker run --gpus '"all"' --rm -it axolotlai/axolotl:main-latest
+  docker run --gpus '"all"' --rm -it winglian/axolotl:main-latest
   ```
 
   Or run on the current files for development:
@@ -298,7 +178,7 @@ Thanks to all of our contributors to date. Help drive open source AI progress fo
   A more powerful Docker command to run would be this:
 
   ```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 ${HOME}/.cache/huggingface:/root/.cache/huggingface axolotlai/axolotl:main-latest
+docker run --privileged --gpus '"all"' --shm-size 10g --rm -it --name axolotl --ipc=host --ulimit memlock=-1 --ulimit stack=67108864 --mount type=bind,src="${PWD}",target=/workspace/axolotl -v ${HOME}/.cache/huggingface:/root/.cache/huggingface winglian/axolotl:main-latest
   ```
 
   It additionally:
@@ -330,7 +210,7 @@ docker run --privileged --gpus '"all"' --shm-size 10g --rm -it --name axolotl --
 
 #### Cloud GPU
 
-For cloud GPU providers that support docker images, use [`axolotlai/axolotl-cloud:main-latest`](https://hub.docker.com/r/axolotlai/axolotl-cloud/tags)
+For cloud GPU providers that support docker images, use [`winglian/axolotl-cloud:main-latest`](https://hub.docker.com/r/winglian/axolotl-cloud/tags)
 
 - on Latitude.sh use this [direct link](https://latitude.sh/blueprint/989e0e79-3bf6-41ea-a46b-1f246e309d5c)
 - on JarvisLabs.ai use this [direct link](https://jarvislabs.ai/templates/axolotl)
@@ -439,7 +319,7 @@ Write a job description in YAML as below:
 # dstack.yaml
 type: task
 
-image: axolotlai/axolotl-cloud:main-latest
+image: winglian/axolotl-cloud:main-20240429-py3.11-cu121-2.2.2
 
 env:
   - HUGGING_FACE_HUB_TOKEN
@@ -503,10 +383,11 @@ See [examples](examples) for quick start. It is recommended to duplicate and mod
         - typescript
       type: ... # unimplemented custom format
 
-      # chat_template https://axolotl-ai-cloud.github.io/axolotl/docs/dataset-formats/conversation.html#chat_template
+      # fastchat conversation (deprecation soon, use chat_template https://axolotl-ai-cloud.github.io/axolotl/docs/dataset-formats/conversation.html#chat_template)
+      # See 'conversation' options: https://github.com/lm-sys/FastChat/blob/main/fastchat/conversation.py
     - path: ...
-      type: chat_template
-      chat_template: chatml # defaults to tokenizer's chat_template
+      type: sharegpt
+      conversation: chatml # default: vicuna_v1.1
 
       # local
     - path: data.jsonl # or json
@@ -789,6 +670,86 @@ See [this debugging guide](docs/debugging.qmd) for tips on debugging Axolotl, al
 
 ## Need help? 🙋
 
-Join our [Discord server](https://discord.gg/HhrNrHJPRb) where our community members can help you.
+Join our [Discord server](https://discord.gg/HhrNrHJPRb) where we our community members can help you.
 
-Need dedicated support? Please contact us at [✉️wing@axolotl.ai](ailto:wing@axolotl.ai) for dedicated support options.
+Need dedicated support? Please contact us at [✉️wing@openaccessaicollective.org](mailto:wing@openaccessaicollective.org) for dedicated support options.
+
+## Badge ❤🏷️
+
+Building something cool with Axolotl? Consider adding a badge to your model card.
+
+```markdown
+[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
+```
+
+[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
+
+## Community Showcase
+
+Check out some of the projects and models that have been built using Axolotl! Have a model you'd like to add to our Community Showcase? Open a PR with your model.
+
+Open Access AI Collective
+- [Minotaur 13b](https://huggingface.co/openaccess-ai-collective/minotaur-13b-fixed)
+- [Manticore 13b](https://huggingface.co/openaccess-ai-collective/manticore-13b)
+- [Hippogriff 30b](https://huggingface.co/openaccess-ai-collective/hippogriff-30b-chat)
+
+PocketDoc Labs
+- [Dan's PersonalityEngine 13b LoRA](https://huggingface.co/PocketDoc/Dans-PersonalityEngine-13b-LoRA)
+
+## Contributing 🤝
+
+Please read the [contributing guide](./.github/CONTRIBUTING.md)
+
+Bugs? Please check the [open issues](https://github.com/axolotl-ai-cloud/axolotl/issues/bug) else create a new Issue.
+
+PRs are **greatly welcome**!
+
+Please run the quickstart instructions followed by the below to setup env:
+```bash
+pip3 install -r requirements-dev.txt -r requirements-tests.txt
+pre-commit install
+
+# test
+pytest tests/
+
+# optional: run against all files
+pre-commit run --all-files
+```
+
+Thanks to all of our contributors to date. Help drive open source AI progress forward by contributing to Axolotl.
+
+<a href="https://github.com/axolotl-ai-cloud/axolotl/graphs/contributors">
+  <img src="https://contrib.rocks/image?repo=openaccess-ai-collective/axolotl" alt="contributor chart by https://contrib.rocks"/>
+</a>
+
+## Sponsors 🤝❤
+
+OpenAccess AI Collective is run by volunteer contributors such as [winglian](https://github.com/winglian),
+[NanoCode012](https://github.com/NanoCode012), [tmm1](https://github.com/tmm1),
+[mhenrichsen](https://github.com/mhenrichsen), [casper-hansen](https://github.com/casper-hansen),
+[hamelsmu](https://github.com/hamelsmu) and many more who help us accelerate forward by fixing bugs, answering
+community questions and implementing new features. Axolotl needs donations from sponsors for the compute needed to
+run our unit & integration tests, troubleshooting community issues, and providing bounties. If you love axolotl,
+consider sponsoring the project via [GitHub Sponsors](https://github.com/sponsors/OpenAccess-AI-Collective),
+[Ko-fi](https://ko-fi.com/axolotl_ai) or reach out directly to
+[wing@openaccessaicollective.org](mailto:wing@openaccessaicollective.org).
+
+---
+
+#### 💎 Diamond Sponsors - [Contact directly](mailto:wing@openaccessaicollective.org)
+
+---
+
+#### 🥇 Gold Sponsors - $5000/mo
+
+---
+
+#### 🥈 Silver Sponsors - $1000/mo
+
+---
+
+#### 🥉 Bronze Sponsors - $500/mo
+
+ - [JarvisLabs.ai](https://jarvislabs.ai)
+
+---

cicd/Dockerfile.jinja

@@ -1,9 +1,10 @@
-FROM axolotlai/axolotl-base:{{ BASE_TAG }}
+FROM winglian/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 BNB_CUDA_VERSION="{{ CUDA }}"
 ENV PYTORCH_VERSION="{{ PYTORCH_VERSION }}"
 ENV GITHUB_REF="{{ GITHUB_REF }}"
 ENV GITHUB_SHA="{{ GITHUB_SHA }}"
@@ -27,7 +28,6 @@ RUN if [ "$NIGHTLY_BUILD" = "true" ] ; then \
         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 if [ "$AXOLOTL_EXTRAS" != "" ] ; then \
@@ -36,9 +36,6 @@ RUN if [ "$AXOLOTL_EXTRAS" != "" ] ; then \
         pip install -e .[deepspeed,flash-attn,optimizers] $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
 

cicd/cicd.sh

@@ -1,8 +1,6 @@
 #!/bin/bash
 set -e
 
-pytest -v --durations=10 -n8 --ignore=tests/e2e/ --ignore=tests/patched/ /workspace/axolotl/tests/
-# pytest -v --durations=10 -n8 --dist loadfile /workspace/axolotl/tests/patched/
-pytest -v --durations=10 -n1 --dist loadfile /workspace/axolotl/tests/e2e/patched/
-pytest -v --durations=10 -n1 --dist loadfile /workspace/axolotl/tests/e2e/integrations/
-pytest -v --durations=10 --ignore=tests/e2e/patched/ --ignore=tests/e2e/multigpu/ --ignore=tests/e2e/integrations/ /workspace/axolotl/tests/e2e/
+pytest -n4 --ignore=tests/e2e/ /workspace/axolotl/tests/
+pytest -n1 --dist loadfile -v /workspace/axolotl/tests/e2e/patched/ /workspace/axolotl/tests/e2e/integrations/
+pytest --ignore=tests/e2e/patched/ --ignore=tests/e2e/multigpu/ --ignore=tests/e2e/integrations/ /workspace/axolotl/tests/e2e/

cicd/multigpu.py

@@ -10,7 +10,7 @@ import tempfile
 import jinja2
 import modal
 from jinja2 import select_autoescape
-from modal import App, Image
+from modal import Image, Stub
 
 cicd_path = pathlib.Path(__file__).parent.resolve()
 
@@ -46,7 +46,7 @@ cicd_image = (
     .pip_install("fastapi==0.110.0", "pydantic==2.6.3")
 )
 
-app = App("Axolotl CI/CD", secrets=[])
+stub = Stub("Axolotl CI/CD", secrets=[])
 
 
 N_GPUS = int(os.environ.get("N_GPUS", 2))
@@ -61,7 +61,7 @@ def run_cmd(cmd: str, run_folder: str):
         exit(exit_code)  # pylint: disable=consider-using-sys-exit
 
 
-@app.function(
+@stub.function(
     image=cicd_image,
     gpu=GPU_CONFIG,
     timeout=60 * 60,
@@ -72,6 +72,6 @@ def cicd_pytest():
     run_cmd("./cicd/multigpu.sh", "/workspace/axolotl")
 
 
-@app.local_entrypoint()
+@stub.local_entrypoint()
 def main():
     cicd_pytest.remote()

cicd/multigpu.sh

@@ -2,4 +2,4 @@
 set -e
 
 # only run one test at a time so as not to OOM the GPU
-pytest -v -n2 /workspace/axolotl/tests/e2e/multigpu/
+pytest -n1 /workspace/axolotl/tests/e2e/multigpu/

cicd/tests.py

@@ -10,7 +10,7 @@ import tempfile
 import jinja2
 import modal
 from jinja2 import select_autoescape
-from modal import App, Image
+from modal import Image, Stub
 
 cicd_path = pathlib.Path(__file__).parent.resolve()
 
@@ -40,7 +40,6 @@ with open(pathlib.Path(temp_dir) / "Dockerfile", "w", encoding="utf-8") as f:
 cicd_image = (
     Image.from_dockerfile(
         pathlib.Path(temp_dir) / "Dockerfile",
-        context_mount=None,
         force_build=True,
         gpu="A10G",
     )
@@ -48,7 +47,7 @@ cicd_image = (
     .pip_install("fastapi==0.110.0", "pydantic==2.6.3")
 )
 
-app = App("Axolotl CI/CD", secrets=[])
+stub = Stub("Axolotl CI/CD", secrets=[])
 
 
 N_GPUS = int(os.environ.get("N_GPUS", 1))
@@ -63,7 +62,7 @@ def run_cmd(cmd: str, run_folder: str):
         exit(exit_code)  # pylint: disable=consider-using-sys-exit
 
 
-@app.function(
+@stub.function(
     image=cicd_image,
     gpu=GPU_CONFIG,
     timeout=60 * 60,
@@ -74,6 +73,6 @@ def cicd_pytest():
     run_cmd("./cicd/cicd.sh", "/workspace/axolotl")
 
 
-@app.local_entrypoint()
+@stub.local_entrypoint()
 def main():
     cicd_pytest.remote()

devtools/dev_chat_template.yml

@@ -1,4 +1,4 @@
-# Example config for debugging the chat_template prompt format
+# Example config for debugging the sharegpt prompt format
 base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
 model_type: LlamaForCausalLM
 tokenizer_type: LlamaTokenizer

docker/Dockerfile

@@ -1,10 +1,11 @@
 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"
+ENV BNB_CUDA_VERSION=$CUDA
 ARG PYTORCH_VERSION="2.1.2"
 
 ENV PYTORCH_VERSION=$PYTORCH_VERSION
@@ -25,9 +26,6 @@ RUN if [ "$AXOLOTL_EXTRAS" != "" ] ; then \
         pip install -e .[deepspeed,flash-attn,optimizers] $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 pytest
 

docker/Dockerfile-base

@@ -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 \
@@ -29,9 +29,7 @@ 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==${PYTORCH_VERSION}+cu${CUDA} --extra-index-url https://download.pytorch.org/whl/cu$CUDA && \
-    python3 -m pip install --no-cache-dir "causal_conv1d @ git+https://github.com/Dao-AILab/causal-conv1d.git@main" && \
-    python3 -m pip install --no-cache-dir "mamba_ssm @ git+https://github.com/state-spaces/mamba.git@main"
+    python3 -m pip install --no-cache-dir -U torch==${PYTORCH_VERSION}+cu${CUDA} --extra-index-url https://download.pytorch.org/whl/cu$CUDA
 
 RUN git lfs install --skip-repo && \
     pip3 install awscli && \

docker/Dockerfile-cloud

@@ -1,8 +1,8 @@
 ARG BASE_TAG=main
-FROM axolotlai/axolotl:$BASE_TAG
+FROM winglian/axolotl:$BASE_TAG
 
 ENV HF_DATASETS_CACHE="/workspace/data/huggingface-cache/datasets"
-ENV HF_HUB_CACHE="/workspace/data/huggingface-cache/hub"
+ENV HUGGINGFACE_HUB_CACHE="/workspace/data/huggingface-cache/hub"
 ENV HF_HOME="/workspace/data/huggingface-cache/hub"
 ENV HF_HUB_ENABLE_HF_TRANSFER="1"
 

docker/Dockerfile-cloud-no-tmux

@@ -1,8 +1,8 @@
 ARG BASE_TAG=main
-FROM axolotlai/axolotl:$BASE_TAG
+FROM winglian/axolotl:$BASE_TAG
 
 ENV HF_DATASETS_CACHE="/workspace/data/huggingface-cache/datasets"
-ENV HF_HUB_CACHE="/workspace/data/huggingface-cache/hub"
+ENV HUGGINGFACE_HUB_CACHE="/workspace/data/huggingface-cache/hub"
 ENV HF_HOME="/workspace/data/huggingface-cache/hub"
 ENV HF_HUB_ENABLE_HF_TRANSFER="1"
 

docker/Dockerfile-tests

@@ -1,10 +1,11 @@
 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"
+ENV BNB_CUDA_VERSION=$CUDA
 ARG PYTORCH_VERSION="2.1.2"
 ARG GITHUB_REF="main"
 

docs/config.qmd

@@ -83,7 +83,7 @@ lora_on_cpu: true
 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, gpteacher, oasst, reflection]
+    # 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
@@ -91,7 +91,15 @@ datasets:
     name: # Optional[str] name of dataset configuration to load
     train_on_split: train # Optional[str] name of dataset split to load from
     revision: # Optional[str] The specific revision of the dataset to use when loading from the Hugging Face Hub. This can be a commit hash, tag, or branch name. If not specified, the latest version will be used. This parameter is ignored for local datasets.
-    trust_remote_code: # Optional[bool] Trust remote code for untrusted source
+
+    # 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.
+    # Add additional keys from your dataset as input or output roles
+    roles:
+      input: # Optional[List[str]]. These will be masked based on train_on_input
+      output: # Optional[List[str]].
 
   # Custom user instruction prompt
   - path: repo
@@ -162,9 +170,6 @@ datasets:
 # The same applies to the `test_datasets` option and the `pretraining_dataset` option. Default is true.
 shuffle_merged_datasets: true
 
-Deduplicates datasets and test_datasets with identical entries.
-dataset_exact_deduplication: true
-
 # A list of one or more datasets to eval the model with.
 # You can use either test_datasets, or val_set_size, but not both.
 test_datasets:
@@ -178,8 +183,6 @@ test_datasets:
 
 # use RL training: 'dpo', 'ipo', 'kto'
 rl:
-# whether to perform weighting if doing DPO training. Boolean.
-dpo_use_weighting:
 
 # The name of the chat template to use for training, following values are supported:
 # - tokenizer_default: Uses the chat template that is available in the tokenizer_config.json. If the chat template is not available in the tokenizer, it will raise an error. This is the default value.
@@ -409,7 +412,6 @@ lr_div_factor: # Learning rate div factor
 # - adamw_torch_fused
 # - adamw_torch_xla
 # - adamw_apex_fused
-# - adopt_adamw (an EXPERIMENTAL optimizer, only for torch version >= 2.5.1)
 # - adafactor
 # - adamw_anyprecision
 # - sgd

docs/dataset-formats/conversation.qmd

@@ -6,8 +6,33 @@ order: 3
 
 ## sharegpt
 
-IMPORTANT: ShareGPT is deprecated!. Please see `chat_template` section below.
+UPDATE: ShareGPT is being deprecated in the next release. Please see `chat_template` section below.
 
+conversations where `from` is `human`/`gpt`. (optional: first row with role `system` to override default system prompt)
+
+```{.json filename="data.jsonl"}
+{"conversations": [{"from": "...", "value": "..."}]}
+```
+
+Note: `type: sharegpt` opens special configs:
+- `conversation`: enables conversions to many Conversation types. Refer to the 'name' [here](https://github.com/lm-sys/FastChat/blob/main/fastchat/conversation.py) for options.
+- `roles`: allows you to specify the roles for input and output. This is useful for datasets with custom roles such as `tool` etc to support masking.
+- `field_human`: specify the key to use instead of `human` in the conversation.
+- `field_model`: specify the key to use instead of `gpt` in the conversation.
+
+```yaml
+datasets:
+    path: ...
+    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.
+    # Add additional keys from your dataset as input or output roles
+    roles:
+      input: # Optional[List[str]]. These will be masked based on train_on_input
+      output: # Optional[List[str]].
+```
 
 ## pygmalion
 
@@ -15,6 +40,38 @@ IMPORTANT: ShareGPT is deprecated!. Please see `chat_template` section below.
 {"conversations": [{"role": "...", "value": "..."}]}
 ```
 
+## sharegpt.load_role
+
+conversations where `role` is used instead of `from`
+
+```{.json filename="data.jsonl"}
+{"conversations": [{"role": "...", "value": "..."}]}
+```
+
+## sharegpt.load_guanaco
+
+conversations where `from` is `prompter` `assistant` instead of default sharegpt
+
+```{.json filename="data.jsonl"}
+{"conversations": [{"from": "...", "value": "..."}]}
+```
+
+## sharegpt.load_ultrachat
+
+conversations where the turns field is 'messages', human is 'user' and gpt is 'assistant'.
+
+```{.json filename="data.jsonl"}
+{"messages": [{"user": "...", "assistant": "..."}]}
+```
+
+## sharegpt_jokes
+
+creates a chat where bot is asked to tell a joke, then explain why the joke is funny
+
+```{.json filename="data.jsonl"}
+{"conversations": [{"title": "...", "text": "...", "explanation": "..."}]}
+```
+
 
 ## chat_template
 

docs/debugging.qmd

@@ -185,7 +185,7 @@ style="border-radius: 10px; display: block; margin: auto;" width="560" height="3
 
 ## 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.
+Using [official Axolotl Docker images](https://hub.docker.com/r/winglian/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
 
@@ -202,11 +202,11 @@ cd axolotl
 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 ${HOME}/.cache/huggingface:/root/.cache/huggingface axolotlai/axolotl:main-py3.10-cu118-2.0.1
+docker run --privileged --gpus '"all"' --shm-size 10g --rm -it --name axolotl --ipc=host --ulimit memlock=-1 --ulimit stack=67108864 --mount type=bind,src="${PWD}",target=/workspace/axolotl -v ${HOME}/.cache/huggingface:/root/.cache/huggingface winglian/axolotl:main-py3.10-cu118-2.0.1
 ```
 
 >[!Tip]
-> To understand which containers are available, see the [Docker section of the README](../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).
+> 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/winglian/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:
 

docs/rlhf.qmd

@@ -52,26 +52,6 @@ datasets:
     type: chat_template.argilla
 ```
 
-
-#### KTO
-
-```yaml
-rl: kto
-rl_beta: 0.5
-kto_desirable_weight: 0.2
-
-remove_unused_columns: false
-
-datasets:
-  - path: argilla/ultrafeedback-binarized-preferences-cleaned-kto
-    type: llama3.ultra
-    split: train
-
-gradient_checkpointing: true
-gradient_checkpointing_kwargs:
-  use_reentrant: true
-```
-
 #### Using local dataset files
 ```yaml
 datasets:

docs/unsloth.qmd

@@ -11,10 +11,12 @@ standard industry baselines.
 
 ### Installation
 
-The following will install the correct unsloth and extras from source.
+The following will install unsloth from source and downgrade xformers as unsloth is incompatible with the most up
+to date libraries.
 
 ```bash
-python scripts/unsloth_install.py | sh
+pip install --no-deps "unsloth @ git+https://github.com/unslothai/unsloth.git"
+pip install --no-deps --force-reinstall xformers==0.0.26.post1
 ```
 
 ### Using unsloth w Axolotl

examples/colab-notebooks/colab-axolotl-example.ipynb

@@ -2,15 +2,19 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "id": "AKjdG7tbTb-n"
+   },
    "source": [
-    "## Setting up"
+    "# Example notebook for running Axolotl on google colab"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "id": "RcbNpOgWRcii"
+   },
    "outputs": [],
    "source": [
     "import torch\n",
@@ -18,76 +22,82 @@
     "assert (torch.cuda.is_available()==True)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "h3nLav8oTRA5"
+   },
+   "source": [
+    "## Install Axolotl and dependencies"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "3c3yGAwnOIdi",
+    "outputId": "e3777b5a-40ef-424f-e181-62dfecd1dd01"
+   },
    "outputs": [],
    "source": [
-    "!pip install axolotl[deepspeed]"
+    "!pip install -e git+https://github.com/axolotl-ai-cloud/axolotl#egg=axolotl\n",
+    "!pip install flash-attn==\"2.5.0\"\n",
+    "!pip install deepspeed==\"0.13.1\"!pip install mlflow==\"2.13.0\""
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "id": "BW2MFr7HTjub"
+   },
    "source": [
-    "## Hugging Face login (optional)"
+    "## Create an yaml config file"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Example configuration"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "id": "9pkF2dSoQEUN"
+   },
    "outputs": [],
    "source": [
     "import yaml\n",
     "\n",
+    "# Your YAML string\n",
     "yaml_string = \"\"\"\n",
-    "base_model: NousResearch/Meta-Llama-3.1-8B\n",
+    "base_model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T\n",
+    "model_type: LlamaForCausalLM\n",
+    "tokenizer_type: LlamaTokenizer\n",
     "\n",
     "load_in_8bit: false\n",
     "load_in_4bit: true\n",
     "strict: false\n",
     "\n",
     "datasets:\n",
-    "  - path: tatsu-lab/alpaca\n",
+    "  - path: mhenrichsen/alpaca_2k_test\n",
     "    type: alpaca\n",
-    "dataset_prepared_path: last_run_prepared\n",
+    "dataset_prepared_path:\n",
     "val_set_size: 0.05\n",
-    "output_dir: ./outputs/lora-out\n",
-    "\n",
-    "sequence_len: 2048\n",
-    "sample_packing: true\n",
-    "eval_sample_packing: true\n",
-    "pad_to_sequence_len: true\n",
+    "output_dir: ./outputs/qlora-out\n",
     "\n",
     "adapter: qlora\n",
     "lora_model_dir:\n",
+    "\n",
+    "sequence_len: 4096\n",
+    "sample_packing: true\n",
+    "eval_sample_packing: false\n",
+    "pad_to_sequence_len: true\n",
+    "\n",
     "lora_r: 32\n",
     "lora_alpha: 16\n",
     "lora_dropout: 0.05\n",
+    "lora_target_modules:\n",
     "lora_target_linear: true\n",
     "lora_fan_in_fan_out:\n",
-    "lora_modules_to_save:\n",
-    "  - embed_tokens\n",
-    "  - lm_head\n",
     "\n",
     "wandb_project:\n",
     "wandb_entity:\n",
@@ -95,12 +105,12 @@
     "wandb_name:\n",
     "wandb_log_model:\n",
     "\n",
-    "gradient_accumulation_steps: 2\n",
-    "micro_batch_size: 1\n",
-    "num_epochs: 1\n",
-    "optimizer: paged_adamw_8bit\n",
+    "gradient_accumulation_steps: 4\n",
+    "micro_batch_size: 2\n",
+    "num_epochs: 4\n",
+    "optimizer: paged_adamw_32bit\n",
     "lr_scheduler: cosine\n",
-    "learning_rate: 2e-5\n",
+    "learning_rate: 0.0002\n",
     "\n",
     "train_on_inputs: false\n",
     "group_by_length: false\n",
@@ -111,15 +121,13 @@
     "gradient_checkpointing: true\n",
     "early_stopping_patience:\n",
     "resume_from_checkpoint:\n",
+    "local_rank:\n",
     "logging_steps: 1\n",
     "xformers_attention:\n",
-    "flash_attention: false\n",
-    "sdp_attention: true\n",
+    "flash_attention: true\n",
     "\n",
-    "warmup_steps: 1\n",
-    "max_steps: 25\n",
-    "evals_per_epoch: 1\n",
-    "eval_table_size:\n",
+    "warmup_steps: 10\n",
+    "evals_per_epoch: 4\n",
     "saves_per_epoch: 1\n",
     "debug:\n",
     "deepspeed:\n",
@@ -127,9 +135,8 @@
     "fsdp:\n",
     "fsdp_config:\n",
     "special_tokens:\n",
-    "  pad_token: <|end_of_text|>\n",
-    "\"\"\"\n",
     "\n",
+    "\"\"\"\n",
     "\n",
     "# Convert the YAML string to a Python dictionary\n",
     "yaml_dict = yaml.safe_load(yaml_string)\n",
@@ -139,124 +146,31 @@
     "\n",
     "# Write the YAML file\n",
     "with open(file_path, 'w') as file:\n",
-    "    yaml.dump(yaml_dict, file)"
+    "    yaml.dump(yaml_dict, file)\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "id": "bidoj8YLTusD"
+   },
    "source": [
-    "Above we have a configuration file with base LLM model and datasets specified, among many other things. Axolotl can automatically detect whether the specified datasets are on HuggingFace repo or local machine.\n",
-    "\n",
-    "The Axolotl configuration options encompass model and dataset selection, data pre-processing, and training. Let's go through them line by line:\n",
-    "\n",
-    "*   \"base model\": String value, specifies the underlying pre-trained LLM that will be used for finetuning\n",
-    "\n",
-    "Next we have options for model weights quantization. Quantization allows for reduction in occupied memory on GPUs.\n",
-    "\n",
-    "*   \"load_in_8bit\": Boolean value, whether to quantize the model weights into 8-bit integer.\n",
-    "\n",
-    "*   \"load_in_4bit\": Boolean value, whether to quantize the model weights into 4-bit integer.\n",
-    "\n",
-    "*   \"strict\": Boolean value. If false, it allows for overriding established configuration options in the yaml file when executing in command-line interface.\n",
-    "\n",
-    "*   \"datasets\": a list of dicts that contain path and type of data sets as well as other optional configurations where datasets are concerned. Supports multiple datasets.\n",
-    "\n",
-    "*   \"val_set_size\": Either a float value less than one or an integer less than the total size of dataset. Sets the size of validation set from the whole dataset. If float, sets the proportion of the dataset assigned for validation. If integer, sets the direct size of validation set.\n",
-    "\n",
-    "*   \"output_dir\": String value. Path of trained model.\n",
-    "\n",
-    "For data preprocessing:\n",
-    "\n",
-    "*   \"sequence_len\": Integer. Specifies the maximum sequence length of the input. Typically 2048 or less.\n",
-    "\n",
-    "*   \"pad_to_sequence_len\": Boolean. Padding input to maximum sequence length.\n",
-    "\n",
-    "*   \"sample_packing\": Boolean. Specifies whether to use multi-packing with block diagonal attention.\n",
-    "\n",
-    "*   \"special_tokens\": Python dict, optional. Allows users to specify the additional special tokens to be ignored by the tokenizer.\n",
-    "\n",
-    "For LoRA configuration and its hyperparamters:\n",
-    "\n",
-    "*   \"adapter\": String. Either \"lora\" or \"qlora\", depending on user's choice.\n",
-    "\n",
-    "*   \"lora_model_dir\": String, Optional. Path to directory that contains LoRA model, if there is already a trained LoRA model the user would like to use.\n",
-    "\n",
-    "*   \"lora_r\": Integer. Refers to the rank of LoRA decomposition matrices. Higher value will reduce LoRA efficiency. Recommended to be set to 8.\n",
-    "\n",
-    "*   \"lora_alpha\": Integer. Scale the weight matrices by $\\frac{\\text{lora_alpha}}{\\text{lora_r}}$Recommended to be fixed at 16.\n",
-    "\n",
-    "*   \"lora_dropout\": Float that is 1 or less. The dropout probability of a lora layer.\n",
-    "\n",
-    "*   \"lora_target_linear\": Boolean. If true, lora will target all linear modules in the transformers architecture.\n",
-    "\n",
-    "*   \"lora_modules_to_save\": If you added new tokens to the tokenizer, you may need to save some LoRA modules because they need to know the new tokens.\n",
-    "\n",
-    "See [LoRA](https://arxiv.org/abs/2106.09685) for detailed explanation of LoRA implementation.\n",
-    "\n",
-    "For the training configurations:\n",
-    "\n",
-    "*   \"gradient_accumulation_steps\": Integer. The number of steps over which to accumulate gradient for batch training. E.g. if 2, backprop is performed every two steps.\n",
-    "\n",
-    "*   \"micro_batch_size\": Integer. Batch size per gpu / gradient_accumulation_steps\n",
-    "\n",
-    "*   \"num_epochs\": Integer. Number of epochs. One epoch is when training has looped over every batch in the whole data set once.\n",
-    "\n",
-    "*   \"optimizer\": The optimizer to use for the training.\n",
-    "\n",
-    "*   \"learning_rate\": The learning rate.\n",
-    "\n",
-    "*   \"lr_scheduler\": The learning rate scheduler to use for adjusting learning rate during training.\n",
-    "\n",
-    "*   \"train_on_inputs\": Boolean. Whether to ignore or include the user's prompt from the training labels.\n",
-    "\n",
-    "*   \"group_by_length\": Boolean. Whether to group similarly sized data to minimize padding.\n",
-    "\n",
-    "*   \"bf16\": Either \"auto\", \"true\", or \"false\". Whether to use CUDA bf16 floating point format. If set to \"auto\", will automatically apply bf16 should the gpu supports it.\n",
-    "\n",
-    "*   \"fp16\": Optional. Specifies whether to use CUDA fp16. Automatically set to true if \"bf16\" is set to true. Otherwise false.\n",
-    "\n",
-    "*   \"tf32\": Boolean. Whether to use CUDA tf32. Will override bf16.\n",
-    "\n",
-    "*   \"gradient_checkpointing\": Boolean. Whether to use gradient checkpointing https://huggingface.co/docs/transformers/v4.18.0/en/performance#gradient-checkpointing\n",
-    "\n",
-    "*   \"gradient_checkpointing_kwargs\": Python Dict. Fed into the trainer.\n",
-    "\n",
-    "*   \"logging_steps\": Integer. Log training information over every specified number of steps.\n",
-    "\n",
-    "*   \"flash_attention\": Boolean. Whether to use the [flash attention](https://github.com/Dao-AILab/flash-attention) mechanism.\n",
-    "\n",
-    "*   \"sdp_attention\": Boolean. Whether to use the Scaled Dot Product attention mechanism (the attention mechanism in the [original implementation](https://arxiv.org/abs/1706.03762) of transformers.)\n",
-    "\n",
-    "*   \"warmup_steps\": Integer. The number of pre-training steps where a very low learning rate is used.\n",
-    "\n",
-    "*   \"evals_per_epoch\": Integer. Number of evaluations to be performed within one training epoch.\n",
-    "\n",
-    "*   \"saves_per_epoch\": Integer. Number of times the model is saved in one training epoch.\n",
-    "\n",
-    "*   \"weight_decay\": Positive Float. Sets the \"strength\" of weight decay (i.e. setting the coefficient of L2 regularization)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The above is but a snippet aiming to get users familiarized with the types of streamlined configuration options axolotl provides. For a full list of configuration options, see [here](https://axolotl-ai-cloud.github.io/axolotl/docs/config.html)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Train the model"
+    "## Launch the training"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "ydTI2Jk2RStU",
+    "outputId": "d6d0df17-4b53-439c-c802-22c0456d301b"
+   },
    "outputs": [],
    "source": [
+    "# By using the ! the comand will be executed as a bash command\n",
     "!accelerate launch -m axolotl.cli.train /content/test_axolotl.yaml"
    ]
   },
@@ -264,7 +178,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Predict with trained model"
+    "## Play with inference"
    ]
   },
   {
@@ -273,85 +187,36 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "# By using the ! the comand will be executed as a bash command\n",
     "!accelerate launch -m axolotl.cli.inference /content/test_axolotl.yaml \\\n",
-    "    --lora_model_dir=\"./outputs/lora-out\" --gradio"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Deeper Dive"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "It is also helpful to gain some familiarity over some of the core inner workings of axolotl"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Configuration Normalization"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Axolotl uses a custom Dict class, called ```DictDefault```\n",
-    "to store configurations specified in the yaml configuration file (into a Python variable named ```cfg```). The definition for this custom Dict can be found in the [utils/dict.py](https://github.com/axolotl-ai-cloud/axolotl/blob/main/src/axolotl/utils/dict.py)\n",
-    "\n",
-    "```DictDefault``` is amended such that calling a missing key from it will result in a ```None``` return type. This is important because if some configuration options aren't specified by the user, the ```None``` type allows Axolotl to perform boolean operations to determine the default settings for missing configurations. For more examples on how this is done, check out [utils/config/__init__.py](https://github.com/axolotl-ai-cloud/axolotl/blob/main/src/axolotl/utils/config/__init__.py)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Loading Models, Tokenizers, and Trainer"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If we inspect [cli.train.py](https://github.com/axolotl-ai-cloud/axolotl/blob/main/src/axolotl/cli/train.py), we will find that most of the heavy lifting were done by the function ```train()``` which is itself imported from [src/axolotl/train.py](https://github.com/axolotl-ai-cloud/axolotl/blob/main/src/axolotl/train.py).\n",
-    "\n",
-    "```train()``` takes care of loading the appropriate tokenizer and pre-trained model through ```load_model()``` and ```load_tokenizer()``` from [src/axolotl/utils/models.py](https://github.com/axolotl-ai-cloud/axolotl/blob/main/src/axolotl/utils/models.py) respectively.\n",
-    "\n",
-    "```load_tokenizer()``` loads in the appropriate tokenizer given the desired model, as well as chat templates.\n",
-    "\n",
-    "```ModelLoader``` class follows after tokenizer has been selected. It will automatically discern the base model type, load in the desired model, as well as applying model-appropriate attention mechanism modifications (e.g. flash attention). Depending on which base model the user chooses in the configuration, ```ModelLoader``` will utilize the corresponding \"attention hijacking\" script. For example, if the user specified the base model to be ```NousResearch/Meta-Llama-3.1-8B```, which is of llama type, and set ```flash_attn``` to ```True```, ```ModelLoader``` will load in [llama_attn_hijack_flash.py](https://github.com/axolotl-ai-cloud/axolotl/blob/main/src/axolotl/monkeypatch/llama_attn_hijack_flash.py). For a list of supported attention hijacking, please refer to the directory [/src/axolotl/monkeypatch/](https://github.com/axolotl-ai-cloud/axolotl/tree/main/src/axolotl/monkeypatch)\n",
-    "\n",
-    "Another important operation encompassed in ```train()``` is setting up the training that takes into account of user-specified traning configurations (e.g. num_epochs, optimizer) through the use of ```setup_trainer()``` from [/src/axolotl/utils/trainer.py](https://github.com/axolotl-ai-cloud/axolotl/blob/main/src/axolotl/utils/trainer.py), which in turn relies on modules from [/src/axolotl/core/trainer_builder.py](https://github.com/axolotl-ai-cloud/axolotl/blob/main/src/axolotl/core/trainer_builder.py).\n",
-    "```trainer_builder.py``` provides a list of trainer object options bespoke for the task type (Causal or Reinforcement learning ('dpo', 'ipo', 'kto') )"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Monkey patch\n",
-    "\n",
-    "The [Monkey patch directory](https://github.com/axolotl-ai-cloud/axolotl/tree/main/src/axolotl/monkeypatch) is where model architecture/optimization patching scripts are stored (these are modifications that are not implemented in the official releases, hence the name monkey patch). It includes attention jacking, ReLoRA, and unsloth optimization."
+    "    --qlora_model_dir=\"./qlora-out\" --gradio"
    ]
   }
  ],
  "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "gpuType": "T4",
+   "provenance": []
+  },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
   "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
    "name": "python",
-   "version": "3.9.6"
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.1"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }

examples/llama-3/lora-1b-deduplicate-dpo.yml

@@ -1,95 +0,0 @@
-base_model: meta-llama/Llama-3.2-1B
-model_type: LlamaForCausalLM
-tokenizer_type: AutoTokenizer
-
-load_in_8bit: true
-load_in_4bit: false
-strict: false
-
-chat_template: llama3
-rl: dpo
-datasets:
-  - path: fozziethebeat/alpaca_messages_2k_dpo_test
-    type: chat_template.default
-    field_messages: conversation
-    field_chosen: chosen
-    field_rejected: rejected
-    message_field_role: role
-    message_field_content: content
-    roles:
-      system:
-        - system
-      user:
-        - user
-      assistant:
-        - assistant
-  - path: fozziethebeat/alpaca_messages_2k_dpo_test
-    type: chat_template.default
-    field_messages: conversation
-    field_chosen: chosen
-    field_rejected: rejected
-    message_field_role: role
-    message_field_content: content
-    roles:
-      system:
-        - system
-      user:
-        - user
-      assistant:
-        - assistant
-
-dataset_exact_deduplication: true
-dataset_prepared_path:
-val_set_size: 0
-output_dir: ./outputs/lora-out
-
-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:
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 4
-micro_batch_size: 2
-num_epochs: 4
-optimizer: adamw_bnb_8bit
-lr_scheduler: cosine
-learning_rate: 0.0002
-
-train_on_inputs: false
-group_by_length: false
-bf16: auto
-fp16:
-tf32: false
-
-gradient_checkpointing: true
-early_stopping_patience:
-resume_from_checkpoint:
-local_rank:
-logging_steps: 1
-xformers_attention:
-flash_attention: true
-s2_attention:
-
-warmup_steps: 10
-evals_per_epoch: 4
-eval_table_size:
-eval_max_new_tokens: 128
-saves_per_epoch: 1
-debug:
-deepspeed:
-weight_decay: 0.0
-fsdp:
-fsdp_config:

examples/llama-3/lora-1b-deduplicate-sft.yml

@@ -1,76 +0,0 @@
-base_model: meta-llama/Llama-3.2-1B
-model_type: LlamaForCausalLM
-tokenizer_type: AutoTokenizer
-
-load_in_8bit: true
-load_in_4bit: false
-strict: false
-
-datasets:
-  - path: mhenrichsen/alpaca_2k_test
-    type: alpaca
-  - path: mhenrichsen/alpaca_2k_test
-    type: alpaca
-dataset_prepared_path:
-val_set_size: 0.0
-output_dir: ./outputs/lora-out
-
-dataset_exact_deduplication: true
-test_value: true
-
-sequence_len: 4096
-sample_packing: true
-eval_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:
-lora_modules_to_save:
-  - embed_tokens
-  - lm_head
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 4
-micro_batch_size: 2
-num_epochs: 4
-optimizer: adamw_bnb_8bit
-lr_scheduler: cosine
-learning_rate: 0.0002
-
-train_on_inputs: false
-group_by_length: false
-bf16: auto
-fp16:
-tf32: false
-
-gradient_checkpointing: true
-early_stopping_patience:
-resume_from_checkpoint:
-local_rank:
-logging_steps: 1
-xformers_attention:
-flash_attention: true
-s2_attention:
-
-warmup_steps: 10
-evals_per_epoch: 4
-eval_table_size:
-eval_max_new_tokens: 128
-saves_per_epoch: 1
-debug:
-deepspeed:
-weight_decay: 0.0
-fsdp:
-fsdp_config:
-special_tokens:
-   pad_token: <|end_of_text|>

examples/llama-3/lora-1b.yml

@@ -1,74 +0,0 @@
-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
-lora_model_dir:
-
-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_fan_in_fan_out:
-lora_target_modules:
-  - gate_proj
-  - down_proj
-  - up_proj
-  - q_proj
-  - v_proj
-  - k_proj
-  - o_proj
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-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
-fp16:
-tf32: false
-
-gradient_checkpointing: true
-early_stopping_patience:
-resume_from_checkpoint:
-local_rank:
-logging_steps: 1
-xformers_attention:
-flash_attention: true
-
-loss_watchdog_threshold: 5.0
-loss_watchdog_patience: 3
-
-warmup_steps: 10
-evals_per_epoch: 4
-saves_per_epoch: 1
-debug:
-deepspeed:
-weight_decay: 0.0
-fsdp:
-fsdp_config:
-special_tokens:
-  pad_token: "<|end_of_text|>"

examples/llama-3/qlora-1b-kto.yaml

@@ -1,75 +0,0 @@
-base_model: meta-llama/Llama-3.2-1B
-
-load_in_8bit: false
-load_in_4bit: true
-strict: false
-
-rl: kto
-rl_beta: 0.5
-kto_desirable_weight: 0.2
-
-datasets:
-  - path: argilla/ultrafeedback-binarized-preferences-cleaned-kto
-    type: llama3.ultra
-    split: train
-dataset_prepared_path: last_run_prepared
-val_set_size: 0.0
-output_dir: ./outputs/qlora-out
-
-remove_unused_columns: false
-
-adapter: qlora
-lora_model_dir:
-
-sequence_len: 2048
-sample_packing: false  # not supported with kto
-eval_sample_packing: false
-pad_to_sequence_len: false
-
-lora_r: 32
-lora_alpha: 64
-lora_dropout: 0.05
-lora_target_linear: true
-lora_fan_in_fan_out:
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 1
-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
-fp16:
-tf32: true
-
-gradient_checkpointing: true
-gradient_checkpointing_kwargs:
-  use_reentrant: true
-early_stopping_patience:
-resume_from_checkpoint:
-local_rank:
-logging_steps: 1
-xformers_attention:
-flash_attention: true
-
-warmup_steps: 20
-evals_per_epoch: 4
-eval_table_size:
-eval_max_new_tokens: 128
-saves_per_epoch: 1
-debug:
-deepspeed:
-weight_decay: 0.0
-fsdp:
-fsdp_config:
-special_tokens:
-  pad_token: "<|end_of_text|>"

examples/llama-3/qlora-1b.yml

@@ -1,4 +1,4 @@
-base_model: NousResearch/Llama-3.2-1B
+base_model: meta-llama/Llama-3.2-1B
 
 load_in_8bit: false
 load_in_4bit: true
@@ -22,6 +22,7 @@ pad_to_sequence_len: true
 lora_r: 32
 lora_alpha: 16
 lora_dropout: 0.05
+lora_target_linear: true
 lora_fan_in_fan_out:
 lora_target_modules:
   - gate_proj

examples/mistral/mistral-dpo-qlora.yml

@@ -1,93 +0,0 @@
-#Note that we are switching from the regular chat template to chatml.
-#If you experience problems with the special tokens, training for more epochs can help.
-#After training, merge the model before inference otherwise you might
-#face problems with the special tokens.
-
-base_model: mistralai/Mistral-7B-Instruct-v0.2
-model_type: MistralForCausalLM
-tokenizer_type: LlamaTokenizer
-
-load_in_8bit: false
-load_in_4bit: true
-strict: false
-
-chat_template: chatml
-rl: dpo
-datasets:
-  - path: olivermolenschot/alpaca_messages_dpo_test
-    type: chat_template.default
-    field_messages: conversation
-    field_chosen: chosen
-    field_rejected: rejected
-    message_field_role: role
-    message_field_content: content
-
-dataset_prepared_path:
-val_set_size: 0.05
-output_dir: ./outputs/dpo-qlora
-
-sequence_len: 2048
-sample_packing: false
-pad_to_sequence_len: true
-
-adapter: qlora
-lora_model_dir:
-lora_r: 8
-lora_alpha: 16
-lora_dropout: 0.2
-lora_target_linear: true
-lora_fan_in_fan_out:
-
-lora_target_modules:
-  - gate_proj
-  - down_proj
-  - up_proj
-  - q_proj
-  - v_proj
-  - k_proj
-  - o_proj
-lora_modules_to_save:
- - embed_tokens
- - lm_head
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 4
-micro_batch_size: 16
-num_epochs: 6
-optimizer: adamw_bnb_8bit
-lr_scheduler: cosine
-learning_rate: 0.0001
-
-train_on_inputs: false
-group_by_length: false
-bf16: auto
-fp16:
-tf32: false
-
-gradient_checkpointing: true
-early_stopping_patience:
-resume_from_checkpoint:
-local_rank:
-logging_steps: 1
-xformers_attention:
-flash_attention: false
-s2_attention:
-
-warmup_steps: 10
-evals_per_epoch: 4
-eval_table_size:
-eval_max_new_tokens: 128
-saves_per_epoch: 1
-debug:
-deepspeed:
-weight_decay: 0.0
-fsdp:
-fsdp_config:
-special_tokens:
-  bos_token: "<|im_start|>"
-  eos_token: "<|im_end|>"

examples/qwen2/dpo.yaml

@@ -1,67 +0,0 @@
-base_model: Qwen/Qwen2.5-0.5B
-
-strict: false
-
-chat_template: qwen_25
-rl: dpo
-datasets:
-  - path: fozziethebeat/alpaca_messages_2k_dpo_test
-    type: chat_template.default
-    field_messages: conversation
-    field_chosen: chosen
-    field_rejected: rejected
-    message_field_role: role
-    message_field_content: content
-    roles:
-      system:
-        - system
-      user:
-        - user
-      assistant:
-        - assistant
-
-dataset_prepared_path:
-val_set_size: 0.0
-output_dir: ./outputs/dpo-out
-
-sequence_len: 2048
-sample_packing: false
-pad_to_sequence_len: true
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 4
-micro_batch_size: 2
-num_epochs: 4
-optimizer: adamw_bnb_8bit
-lr_scheduler: cosine
-learning_rate: 0.0002
-
-train_on_inputs: false
-group_by_length: false
-bf16: auto
-fp16:
-tf32: false
-
-gradient_checkpointing: true
-early_stopping_patience:
-resume_from_checkpoint:
-local_rank:
-logging_steps: 1
-xformers_attention:
-flash_attention: true
-
-warmup_steps: 10
-evals_per_epoch: 4
-eval_table_size:
-eval_max_new_tokens: 128
-saves_per_epoch: 1
-debug:
-deepspeed:
-weight_decay: 0.0
-fsdp:
-fsdp_config:

image/axolotl_logo_digital_black.svg

@@ -1,19 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
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image/axolotl_wordmark_digital_black.svg

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-        <path class="cls-1" d="M460.6,197.8c-18,0-32.6-14.6-32.6-32.6V12.5h24.1v152.6c0,4.7,3.8,8.5,8.5,8.5h16.8v24.1h-16.8Z"/>
-        <path class="cls-1" d="M722.8,198.1V45.2c0-4.7-3.8-8.5-8.5-8.5h-16.8V12.5h16.8c18,0,32.6,14.6,32.6,32.6v152.9h-24.1Z"/>
-        <path class="cls-1" d="M665.2,198.1c-18,0-32.6-14.6-32.6-32.6v-85.1h-20.3v-22.1h20.3V12.9h24.1v45.3h30.2v22.1h-30.2v85.1c0,4.7,3.8,8.5,8.5,8.5h21.7v24.1h-21.7Z"/>
-      </g>
-    </g>
-  </g>
-</svg>

pyproject.toml

@@ -1,19 +0,0 @@
-[build-system]
-requires = ["setuptools>=64", "wheel", "setuptools_scm>=8"]
-build-backend = "setuptools.build_meta"
-
-[project]
-name = "axolotl"
-dynamic = ["version", "dependencies", "optional-dependencies"]
-description = "LLM Trainer"
-readme = "README.md"
-requires-python = ">=3.10"
-
-[project.scripts]
-axolotl = "axolotl.cli.main:main"
-
-[project.urls]
-Homepage = "https://axolotl-ai-cloud.github.io/axolotl/"
-Repository = "https://github.com/axolotl-ai-cloud/axolotl.git"
-
-[tool.setuptools_scm]

requirements-dev.txt

@@ -2,3 +2,4 @@ pre-commit
 black
 mypy
 types-requests
+tbparse

requirements-tests.txt

@@ -1,5 +1,2 @@
 pytest
 pytest-xdist
-pytest-retry
-pytest-sugar
-tbparse

requirements.txt

@@ -1,30 +1,22 @@
 --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/
-
-# START section of dependencies that don't install on Darwin/MacOS
-bitsandbytes==0.45.0
-triton>=2.3.0
-mamba-ssm==1.2.0.post1
-flash-attn==2.7.0.post2
-xformers>=0.0.23.post1
-autoawq==0.2.7.post3
-liger-kernel==0.4.2
-# END section
-
 packaging==23.2
-peft==0.14.0
-transformers>=4.46.3
+peft==0.13.2
+transformers==4.46.2
 tokenizers>=0.20.1
-accelerate==1.2.0
-datasets==3.1.0
-deepspeed==0.16.1
+bitsandbytes==0.44.1
+accelerate==1.1.0
+datasets==3.0.1
+deepspeed==0.15.3
 pydantic==2.6.3
 addict
 fire
 PyYAML>=6.0
 requests
+flash-attn==2.6.3
 sentencepiece
 wandb
 einops
+xformers>=0.0.23.post1
 optimum==1.16.2
 hf_transfer
 colorama
@@ -34,18 +26,24 @@ numpy>=1.24.4,<=2.0.1
 evaluate==0.4.1
 scipy
 scikit-learn==1.4.2
-nvidia-ml-py==12.560.30
+pynvml
 art
+fschat @ git+https://github.com/lm-sys/FastChat.git@27a05b04a35510afb1d767ae7e5990cbd278f8fe
 gradio==3.50.2
 tensorboard
 python-dotenv==1.0.1
+autoawq>=0.2.5
+triton>=2.3.0
+liger-kernel==0.4.0
+
+mamba-ssm==1.2.0.post1
 
 # remote filesystems
 s3fs>=2024.5.0
 gcsfs>=2024.5.0
 # adlfs
 
-trl==0.12.1
+trl @ git+https://github.com/huggingface/trl.git@31d02cfb795284591a084416b9dcb7bef5d08924
 zstandard==0.22.0
 fastcore
 
@@ -56,4 +54,3 @@ immutabledict==4.2.0
 antlr4-python3-runtime==4.13.2
 
 torchao==0.5.0
-schedulefree==1.3.0

scripts/cloud-entrypoint.sh

@@ -2,7 +2,7 @@
 
 # Export specific ENV variables to /etc/rp_environment
 echo "Exporting environment variables..."
-printenv | grep -E '^HF_|^BNB_|^CUDA_|^NCCL_|^NV|^RUNPOD_|^PATH=|^_=' | sed 's/^\([^=]*\)=\(.*\)$/export \1="\2"/' | grep -v 'printenv' >> /etc/rp_environment
+printenv | grep -E '^RUNPOD_|^PATH=|^_=' | sed 's/^\(.*\)=\(.*\)$/export \1="\2"/' >> /etc/rp_environment
 echo 'source /etc/rp_environment' >> ~/.bashrc
 
 add_keys_to_authorized() {

scripts/cutcrossentropy_install.py

@@ -1,28 +0,0 @@
-"""Script to output the correct installation command for cut-cross-entropy."""
-import importlib.util
-import sys
-
-try:
-    import torch
-except ImportError as exc:
-    raise ImportError("Install torch via `pip install torch`") from exc
-from packaging.version import Version as V
-
-v = V(torch.__version__)
-
-# no cut-cross-entropy support for torch < 2.4.0
-if v < V("2.4.0"):
-    print("")
-    sys.exit(0)
-
-cce_spec = importlib.util.find_spec("cut_cross_entropy")
-
-UNINSTALL_PREFIX = ""
-if cce_spec:
-    if not importlib.util.find_spec("cut_cross_entropy.transformers"):
-        UNINSTALL_PREFIX = "pip uninstall -y cut-cross-entropy && "
-
-print(
-    UNINSTALL_PREFIX
-    + 'pip install "cut-cross-entropy @ git+https://github.com/apple/ml-cross-entropy.git@9c297c905f55b73594b5d650722d1e78183b77bd"'
-)

scripts/unsloth_install.py

@@ -1,36 +0,0 @@
-# noqa
-# pylint: skip-file
-try:
-    import torch
-except ImportError:
-    raise ImportError("Install torch via `pip install torch`")
-from packaging.version import Version as V
-
-v = V(torch.__version__)
-cuda = str(torch.version.cuda)
-try:
-    is_ampere = torch.cuda.get_device_capability()[0] >= 8
-except RuntimeError:
-    is_ampere = False
-if cuda != "12.1" and cuda != "11.8" and cuda != "12.4":
-    raise RuntimeError(f"CUDA = {cuda} not supported!")
-if v <= V("2.1.0"):
-    raise RuntimeError(f"Torch = {v} too old!")
-elif v <= V("2.1.1"):
-    x = "cu{}{}-torch211"
-elif v <= V("2.1.2"):
-    x = "cu{}{}-torch212"
-elif v < V("2.3.0"):
-    x = "cu{}{}-torch220"
-elif v < V("2.4.0"):
-    x = "cu{}{}-torch230"
-elif v < V("2.5.0"):
-    x = "cu{}{}-torch240"
-elif v < V("2.6.0"):
-    x = "cu{}{}-torch250"
-else:
-    raise RuntimeError(f"Torch = {v} too new!")
-x = x.format(cuda.replace(".", ""), "-ampere" if is_ampere else "")
-print(
-    f'pip install unsloth-zoo==2024.11.7 && pip install --no-deps "unsloth[{x}]==2024.11.9"'
-)

setup.py

@@ -1,10 +1,8 @@
 """setup.py for axolotl"""
-import ast
-import os
+
 import platform
 import re
 from importlib.metadata import PackageNotFoundError, version
-from pathlib import Path
 
 from setuptools import find_packages, setup
 
@@ -41,10 +39,7 @@ def parse_requirements():
         else:
             # detect the version of torch already installed
             # and set it so dependencies don't clobber the torch version
-            try:
-                torch_version = version("torch")
-            except PackageNotFoundError:
-                torch_version = "2.5.1"
+            torch_version = version("torch")
             _install_requires.append(f"torch=={torch_version}")
 
             version_match = re.match(r"^(\d+)\.(\d+)(?:\.(\d+))?", torch_version)
@@ -59,10 +54,6 @@ def parse_requirements():
 
             if (major, minor) >= (2, 5):
                 _install_requires.pop(_install_requires.index(xformers_version))
-                if patch == 0:
-                    _install_requires.append("xformers==0.0.28.post2")
-                else:
-                    _install_requires.append("xformers==0.0.28.post3")
                 _install_requires.pop(_install_requires.index(autoawq_version))
             elif (major, minor) >= (2, 4):
                 if patch == 0:
@@ -93,39 +84,27 @@ def parse_requirements():
     return _install_requires, _dependency_links
 
 
-def get_package_version():
-    with open(
-        Path(os.path.dirname(os.path.abspath(__file__)))
-        / "src"
-        / "axolotl"
-        / "__init__.py",
-        "r",
-        encoding="utf-8",
-    ) as fin:
-        version_match = re.search(r"^__version__\s*=\s*(.*)$", fin.read(), re.MULTILINE)
-    version_ = ast.literal_eval(version_match.group(1))
-    return version_
-
-
 install_requires, dependency_links = parse_requirements()
 
+
 setup(
-    version=get_package_version(),
+    name="axolotl",
+    version="0.4.1",
+    description="LLM Trainer",
+    long_description="Axolotl is a tool designed to streamline the fine-tuning of various AI models, offering support for multiple configurations and architectures.",
     package_dir={"": "src"},
-    packages=find_packages("src"),
+    packages=find_packages(),
     install_requires=install_requires,
     dependency_links=dependency_links,
-    entry_points={
-        "console_scripts": [
-            "axolotl=axolotl.cli.main:main",
-        ],
-    },
     extras_require={
         "flash-attn": [
-            "flash-attn==2.7.0.post2",
+            "flash-attn==2.6.3",
+        ],
+        "fused-dense-lib": [
+            "fused-dense-lib  @ git+https://github.com/Dao-AILab/flash-attention@v2.6.2#subdirectory=csrc/fused_dense_lib",
         ],
         "deepspeed": [
-            "deepspeed==0.16.1",
+            "deepspeed==0.14.4",
             "deepspeed-kernels",
         ],
         "mamba-ssm": [

src/axolotl/__init__.py

@@ -1,3 +0,0 @@
-"""Axolotl - Train and fine-tune large language models"""
-
-__version__ = "0.6.0"

src/axolotl/cli/__init__.py

@@ -27,17 +27,14 @@ from transformers.utils import is_torch_bf16_gpu_available
 from transformers.utils.import_utils import _is_package_available
 
 from axolotl.common.cli import TrainerCliArgs, load_model_and_tokenizer
+from axolotl.integrations.base import PluginManager
 from axolotl.logging_config import configure_logging
 from axolotl.train import TrainDatasetMeta
-from axolotl.utils.chat_templates import (
-    get_chat_template,
-    get_chat_template_from_config,
-)
+from axolotl.utils.chat_templates import get_chat_template
 from axolotl.utils.comet_ import setup_comet_env_vars
 from axolotl.utils.config import (
     normalize_cfg_datasets,
     normalize_config,
-    prepare_plugins,
     validate_config,
 )
 from axolotl.utils.data import load_prepare_dpo_datasets, prepare_dataset
@@ -100,8 +97,8 @@ def print_dep_versions():
         print("*" * 40)
         print("**** Axolotl Dependency Versions *****")
         for pkg in packages:
-            pkg_version = _is_package_available(pkg, return_version=True)
-            print(f"{pkg: >{max_len}}: {pkg_version[1]: <15}")
+            version = _is_package_available(pkg, return_version=True)
+            print(f"{pkg: >{max_len}}: {version[1]: <15}")
         print("*" * 40)
 
 
@@ -139,7 +136,7 @@ def check_remote_config(config: Union[str, Path]):
         with open(output_path, "wb") as file:
             file.write(content)
         LOG.info(
-            f"Using the following config obtained from {config}: \n\n{content.decode('utf-8')}\n"
+            f"Using the following config obtained from {config}:\n\n{content.decode('utf-8')}\n"
         )
         return output_path
 
@@ -193,19 +190,18 @@ def do_inference(
 ):
     model, tokenizer = load_model_and_tokenizer(cfg=cfg, cli_args=cli_args)
     prompter = cli_args.prompter
+    default_tokens = {"unk_token": "<unk>", "bos_token": "<s>", "eos_token": "</s>"}
+
+    for token, symbol in default_tokens.items():
+        # If the token isn't already specified in the config, add it
+        if not (cfg.special_tokens and token in cfg.special_tokens):
+            tokenizer.add_special_tokens({token: symbol})
 
     prompter_module = None
-    chat_template_str = None
     if prompter:
         prompter_module = getattr(
             importlib.import_module("axolotl.prompters"), prompter
         )
-    elif cfg.chat_template:
-        chat_template_str = get_chat_template(cfg.chat_template)
-    elif cfg.datasets[0].type == "chat_template":
-        chat_template_str = get_chat_template_from_config(
-            cfg=cfg, ds_cfg=cfg.datasets[0], tokenizer=tokenizer
-        )
 
     model = model.to(cfg.device, dtype=cfg.torch_dtype)
 
@@ -215,31 +211,13 @@ def do_inference(
         instruction = get_multi_line_input()
         if not instruction:
             return
-
         if prompter_module:
             prompt: str = next(
                 prompter_module().build_prompt(instruction=instruction.strip("\n"))
             )
         else:
             prompt = instruction.strip()
-
-        if chat_template_str:
-            batch = tokenizer.apply_chat_template(
-                [
-                    {
-                        "role": "user",
-                        "content": prompt,
-                    }
-                ],
-                return_tensors="pt",
-                add_special_tokens=True,
-                add_generation_prompt=True,
-                chat_template=chat_template_str,
-                tokenize=True,
-                return_dict=True,
-            )
-        else:
-            batch = tokenizer(prompt, return_tensors="pt", add_special_tokens=True)
+        batch = tokenizer(prompt, return_tensors="pt", add_special_tokens=True)
 
         print("=" * 40)
         model.eval()
@@ -279,6 +257,13 @@ def do_inference_gradio(
 
     model, tokenizer = load_model_and_tokenizer(cfg=cfg, cli_args=cli_args)
     prompter = cli_args.prompter
+    # default_tokens = {"unk_token": "<unk>", "bos_token": "<s>", "eos_token": "</s>"}
+    default_tokens: Dict[str, str] = {}
+
+    for token, symbol in default_tokens.items():
+        # If the token isn't already specified in the config, add it
+        if not (cfg.special_tokens and token in cfg.special_tokens):
+            tokenizer.add_special_tokens({token: symbol})
 
     prompter_module = None
     chat_template_str = None
@@ -380,7 +365,7 @@ def choose_config(path: Path):
 
     if len(yaml_files) == 1:
         print(f"Using default YAML file '{yaml_files[0]}'")
-        return str(yaml_files[0])
+        return yaml_files[0]
 
     print("Choose a YAML file:")
     for idx, file in enumerate(yaml_files):
@@ -391,7 +376,7 @@ def choose_config(path: Path):
         try:
             choice = int(input("Enter the number of your choice: "))
             if 1 <= choice <= len(yaml_files):
-                chosen_file = str(yaml_files[choice - 1])
+                chosen_file = yaml_files[choice - 1]
             else:
                 print("Invalid choice. Please choose a number from the list.")
         except ValueError:
@@ -426,14 +411,17 @@ def load_cfg(config: Union[str, Path] = Path("examples/"), **kwargs):
 
     cfg.axolotl_config_path = config
 
+    if cfg.get("plugins"):
+        plugin_manager = PluginManager.get_instance()
+        for plugin_name in cfg["plugins"]:
+            plugin_manager.register(plugin_name)
+
     try:
         device_props = torch.cuda.get_device_properties("cuda")
         gpu_version = "sm_" + str(device_props.major) + str(device_props.minor)
     except:  # pylint: disable=bare-except # noqa: E722
         gpu_version = None
 
-    prepare_plugins(cfg)
-
     cfg = validate_config(
         cfg,
         capabilities={
@@ -441,9 +429,6 @@ def load_cfg(config: Union[str, Path] = Path("examples/"), **kwargs):
             "n_gpu": int(os.environ.get("WORLD_SIZE", 1)),
             "compute_capability": gpu_version,
         },
-        env_capabilities={
-            "torch_version": str(torch.__version__).split("+", maxsplit=1)[0],
-        },
     )
 
     prepare_optim_env(cfg)

src/axolotl/cli/inference.py

@@ -2,7 +2,6 @@
 CLI to run inference on a trained model
 """
 from pathlib import Path
-from typing import Union
 
 import fire
 import transformers
@@ -17,10 +16,10 @@ from axolotl.cli import (
 from axolotl.common.cli import TrainerCliArgs
 
 
-def do_cli(config: Union[Path, str] = Path("examples/"), gradio=False, **kwargs):
+def do_cli(config: Path = Path("examples/"), gradio=False, **kwargs):
     # pylint: disable=duplicate-code
     print_axolotl_text_art()
-    parsed_cfg = load_cfg(config, inference=True, **kwargs)
+    parsed_cfg = load_cfg(config, **kwargs)
     parsed_cfg.sample_packing = False
     parser = transformers.HfArgumentParser((TrainerCliArgs))
     parsed_cli_args, _ = parser.parse_args_into_dataclasses(

src/axolotl/cli/main.py

@@ -1,233 +0,0 @@
-"""CLI definition for various axolotl commands."""
-# pylint: disable=redefined-outer-name
-import subprocess  # nosec B404
-from typing import Optional
-
-import click
-
-import axolotl
-from axolotl.cli.utils import (
-    add_options_from_config,
-    add_options_from_dataclass,
-    build_command,
-    fetch_from_github,
-)
-from axolotl.common.cli import PreprocessCliArgs, TrainerCliArgs
-from axolotl.utils.config.models.input.v0_4_1 import AxolotlInputConfig
-
-
-@click.group()
-@click.version_option(version=axolotl.__version__, prog_name="axolotl")
-def cli():
-    """Axolotl CLI - Train and fine-tune large language models"""
-
-
-@cli.command()
-@click.argument("config", type=click.Path(exists=True, path_type=str))
-@add_options_from_dataclass(PreprocessCliArgs)
-@add_options_from_config(AxolotlInputConfig)
-def preprocess(config: str, **kwargs):
-    """Preprocess datasets before training."""
-    kwargs = {k: v for k, v in kwargs.items() if v is not None}
-
-    from axolotl.cli.preprocess import do_cli
-
-    do_cli(config=config, **kwargs)
-
-
-@cli.command()
-@click.argument("config", type=click.Path(exists=True, path_type=str))
-@click.option(
-    "--accelerate/--no-accelerate",
-    default=True,
-    help="Use accelerate launch for multi-GPU training",
-)
-@add_options_from_dataclass(TrainerCliArgs)
-@add_options_from_config(AxolotlInputConfig)
-def train(config: str, accelerate: bool, **kwargs):
-    """Train or fine-tune a model."""
-    kwargs = {k: v for k, v in kwargs.items() if v is not None}
-
-    if accelerate:
-        base_cmd = ["accelerate", "launch", "-m", "axolotl.cli.train"]
-        if config:
-            base_cmd.append(config)
-        cmd = build_command(base_cmd, kwargs)
-        subprocess.run(cmd, check=True)  # nosec B603
-    else:
-        from axolotl.cli.train import do_cli
-
-        do_cli(config=config, **kwargs)
-
-
-@cli.command()
-@click.argument("config", type=click.Path(exists=True, path_type=str))
-@click.option(
-    "--accelerate/--no-accelerate",
-    default=True,
-    help="Use accelerate launch for multi-GPU inference",
-)
-@click.option(
-    "--lora-model-dir",
-    type=click.Path(exists=True, path_type=str),
-    help="Directory containing LoRA model",
-)
-@click.option(
-    "--base-model",
-    type=click.Path(exists=True, path_type=str),
-    help="Path to base model for non-LoRA models",
-)
-@click.option("--gradio", is_flag=True, help="Launch Gradio interface")
-@click.option("--load-in-8bit", is_flag=True, help="Load model in 8-bit mode")
-@add_options_from_dataclass(TrainerCliArgs)
-@add_options_from_config(AxolotlInputConfig)
-def inference(
-    config: str,
-    accelerate: bool,
-    lora_model_dir: Optional[str] = None,
-    base_model: Optional[str] = None,
-    **kwargs,
-):
-    """Run inference with a trained model."""
-    kwargs = {k: v for k, v in kwargs.items() if v is not None}
-    del kwargs["inference"]  # interferes with inference.do_cli
-
-    if lora_model_dir:
-        kwargs["lora_model_dir"] = lora_model_dir
-    if base_model:
-        kwargs["output_dir"] = base_model
-
-    if accelerate:
-        base_cmd = ["accelerate", "launch", "-m", "axolotl.cli.inference"]
-        if config:
-            base_cmd.append(config)
-        cmd = build_command(base_cmd, kwargs)
-        subprocess.run(cmd, check=True)  # nosec B603
-    else:
-        from axolotl.cli.inference import do_cli
-
-        do_cli(config=config, **kwargs)
-
-
-@cli.command()
-@click.argument("config", type=click.Path(exists=True, path_type=str))
-@click.option(
-    "--accelerate/--no-accelerate",
-    default=False,
-    help="Use accelerate launch for multi-GPU operations",
-)
-@click.option(
-    "--model-dir",
-    type=click.Path(exists=True, path_type=str),
-    help="Directory containing model weights to shard",
-)
-@click.option(
-    "--save-dir",
-    type=click.Path(path_type=str),
-    help="Directory to save sharded weights",
-)
-@add_options_from_dataclass(TrainerCliArgs)
-@add_options_from_config(AxolotlInputConfig)
-def shard(config: str, accelerate: bool, **kwargs):
-    """Shard model weights."""
-    kwargs = {k: v for k, v in kwargs.items() if v is not None}
-
-    if accelerate:
-        base_cmd = ["accelerate", "launch", "-m", "axolotl.cli.shard"]
-        if config:
-            base_cmd.append(config)
-        cmd = build_command(base_cmd, kwargs)
-        subprocess.run(cmd, check=True)  # nosec B603
-    else:
-        from axolotl.cli.shard import do_cli
-
-        do_cli(config=config, **kwargs)
-
-
-@cli.command()
-@click.argument("config", type=click.Path(exists=True, path_type=str))
-@click.option(
-    "--accelerate/--no-accelerate",
-    default=True,
-    help="Use accelerate launch for weight merging",
-)
-@click.option(
-    "--model-dir",
-    type=click.Path(exists=True, path_type=str),
-    help="Directory containing sharded weights",
-)
-@click.option(
-    "--save-path", type=click.Path(path_type=str), help="Path to save merged weights"
-)
-@add_options_from_dataclass(TrainerCliArgs)
-@add_options_from_config(AxolotlInputConfig)
-def merge_sharded_fsdp_weights(config: str, accelerate: bool, **kwargs):
-    """Merge sharded FSDP model weights."""
-    kwargs = {k: v for k, v in kwargs.items() if v is not None}
-
-    if accelerate:
-        base_cmd = [
-            "accelerate",
-            "launch",
-            "-m",
-            "axolotl.cli.merge_sharded_fsdp_weights",
-        ]
-        if config:
-            base_cmd.append(config)
-        cmd = build_command(base_cmd, kwargs)
-        subprocess.run(cmd, check=True)  # nosec B603
-    else:
-        from axolotl.cli.merge_sharded_fsdp_weights import do_cli
-
-        do_cli(config=config, **kwargs)
-
-
-@cli.command()
-@click.argument("config", type=click.Path(exists=True, path_type=str))
-@click.option(
-    "--lora-model-dir",
-    type=click.Path(exists=True, path_type=str),
-    help="Directory containing the LoRA model to merge",
-)
-@click.option(
-    "--output-dir",
-    type=click.Path(path_type=str),
-    help="Directory to save the merged model",
-)
-def merge_lora(
-    config: str,
-    lora_model_dir: Optional[str] = None,
-    output_dir: Optional[str] = None,
-):
-    """Merge a trained LoRA into a base model"""
-    kwargs = {}
-    if lora_model_dir:
-        kwargs["lora_model_dir"] = lora_model_dir
-    if output_dir:
-        kwargs["output_dir"] = output_dir
-
-    from axolotl.cli.merge_lora import do_cli
-
-    do_cli(config=config, **kwargs)
-
-
-@cli.command()
-@click.argument("directory", type=click.Choice(["examples", "deepspeed_configs"]))
-@click.option("--dest", help="Destination directory")
-def fetch(directory: str, dest: Optional[str]):
-    """
-    Fetch example configs or other resources.
-
-    Available directories:
-    - examples: Example configuration files
-    - deepspeed_configs: DeepSpeed configuration files
-    """
-    fetch_from_github(f"{directory}/", dest)
-
-
-def main():
-    cli()
-
-
-if __name__ == "__main__":
-    main()

src/axolotl/cli/merge_lora.py

@@ -2,7 +2,6 @@
 CLI to run merge a trained LoRA into a base model
 """
 from pathlib import Path
-from typing import Union
 
 import fire
 import transformers
@@ -12,7 +11,7 @@ from axolotl.cli import do_merge_lora, load_cfg, print_axolotl_text_art
 from axolotl.common.cli import TrainerCliArgs
 
 
-def do_cli(config: Union[Path, str] = Path("examples/"), **kwargs):
+def do_cli(config: Path = Path("examples/"), **kwargs):
     # pylint: disable=duplicate-code
     print_axolotl_text_art()
     parser = transformers.HfArgumentParser((TrainerCliArgs))

src/axolotl/cli/merge_sharded_fsdp_weights.py

@@ -177,7 +177,7 @@ def merge_fsdp_weights(
     state.wait_for_everyone()
 
 
-def do_cli(config: Union[Path, str] = Path("examples/"), **kwargs):
+def do_cli(config: Path = Path("examples/"), **kwargs):
     # pylint: disable=duplicate-code
     print_axolotl_text_art()
     parser = transformers.HfArgumentParser((TrainerCliArgs))

src/axolotl/cli/preprocess.py

@@ -23,6 +23,10 @@ from axolotl.cli import (
 )
 from axolotl.common.cli import PreprocessCliArgs
 from axolotl.common.const import DEFAULT_DATASET_PREPARED_PATH
+from axolotl.prompt_strategies.sharegpt import (
+    register_chatml_template,
+    register_llama3_template,
+)
 from axolotl.utils.trainer import disable_datasets_caching
 
 LOG = logging.getLogger("axolotl.cli.preprocess")
@@ -40,6 +44,23 @@ def do_cli(config: Union[Path, str] = Path("examples/"), **kwargs):
         return_remaining_strings=True
     )
 
+    if parsed_cfg.chat_template == "chatml":
+        if parsed_cfg.default_system_message:
+            LOG.info(
+                f"ChatML set. Adding default system message: {parsed_cfg.default_system_message}"
+            )
+            register_chatml_template(parsed_cfg.default_system_message)
+        else:
+            register_chatml_template()
+    elif parsed_cfg.chat_template == "llama3":
+        if parsed_cfg.default_system_message:
+            LOG.info(
+                f"LLaMA-3 set. Adding default system message: {parsed_cfg.default_system_message}"
+            )
+            register_llama3_template(parsed_cfg.default_system_message)
+        else:
+            register_llama3_template()
+
     if not parsed_cfg.dataset_prepared_path:
         msg = (
             Fore.RED

src/axolotl/cli/train.py

@@ -19,6 +19,10 @@ from axolotl.cli import (
 )
 from axolotl.common.cli import TrainerCliArgs
 from axolotl.integrations.base import PluginManager
+from axolotl.prompt_strategies.sharegpt import (
+    register_chatml_template,
+    register_llama3_template,
+)
 from axolotl.train import train
 
 LOG = logging.getLogger("axolotl.cli.train")
@@ -38,6 +42,21 @@ def do_train(cfg, cli_args) -> None:
     print_axolotl_text_art()
     check_accelerate_default_config()
     check_user_token()
+    if cfg.chat_template == "chatml" and cfg.default_system_message:
+        LOG.info(
+            f"ChatML set. Adding default system message: {cfg.default_system_message}"
+        )
+        register_chatml_template(cfg.default_system_message)
+    else:
+        register_chatml_template()
+
+    if cfg.chat_template == "llama3" and cfg.default_system_message:
+        LOG.info(
+            f"LLaMA-3 set. Adding default system message: {cfg.default_system_message}"
+        )
+        register_llama3_template(cfg.default_system_message)
+    else:
+        register_llama3_template()
 
     if cfg.rl:  # and cfg.rl != "orpo":
         dataset_meta = load_rl_datasets(cfg=cfg, cli_args=cli_args)

src/axolotl/cli/utils.py

@@ -1,218 +0,0 @@
-"""Utility methods for axoltl CLI."""
-import concurrent.futures
-import dataclasses
-import hashlib
-import json
-import logging
-from pathlib import Path
-from types import NoneType
-from typing import Any, Dict, List, Optional, Tuple, Type, Union, get_args, get_origin
-
-import click
-import requests
-from pydantic import BaseModel
-
-LOG = logging.getLogger("axolotl.cli.utils")
-
-
-def add_options_from_dataclass(config_class: Type[Any]):
-    """Create Click options from the fields of a dataclass."""
-
-    def decorator(function):
-        # Process dataclass fields in reverse order for correct option ordering
-        for field in reversed(dataclasses.fields(config_class)):
-            field_type = field.type
-
-            if get_origin(field_type) is Union and type(None) in get_args(field_type):
-                field_type = next(
-                    t for t in get_args(field_type) if not isinstance(t, NoneType)
-                )
-
-            if field_type == bool:
-                field_name = field.name.replace("_", "-")
-                option_name = f"--{field_name}/--no-{field_name}"
-                function = click.option(
-                    option_name,
-                    default=field.default,
-                    help=field.metadata.get("description"),
-                )(function)
-            else:
-                option_name = f"--{field.name.replace('_', '-')}"
-                function = click.option(
-                    option_name,
-                    type=field_type,
-                    default=field.default,
-                    help=field.metadata.get("description"),
-                )(function)
-        return function
-
-    return decorator
-
-
-def add_options_from_config(config_class: Type[BaseModel]):
-    """Create Click options from the fields of a Pydantic model."""
-
-    def decorator(function):
-        # Process model fields in reverse order for correct option ordering
-        for name, field in reversed(config_class.model_fields.items()):
-            if field.annotation == bool:
-                field_name = name.replace("_", "-")
-                option_name = f"--{field_name}/--no-{field_name}"
-                function = click.option(
-                    option_name, default=None, help=field.description
-                )(function)
-            else:
-                option_name = f"--{name.replace('_', '-')}"
-                function = click.option(
-                    option_name, default=None, help=field.description
-                )(function)
-        return function
-
-    return decorator
-
-
-def build_command(base_cmd: List[str], options: Dict[str, Any]) -> List[str]:
-    """Build command list from base command and options."""
-    cmd = base_cmd.copy()
-
-    for key, value in options.items():
-        if value is None:
-            continue
-
-        key = key.replace("_", "-")
-
-        if isinstance(value, bool):
-            if value:
-                cmd.append(f"--{key}")
-        else:
-            cmd.extend([f"--{key}", str(value)])
-
-    return cmd
-
-
-def download_file(
-    file_info: tuple, raw_base_url: str, dest_path: Path, dir_prefix: str
-) -> Tuple[str, str]:
-    """
-    Download a single file and return its processing status.
-
-    Args:
-        file_info: Tuple of (file_path, remote_sha)
-        raw_base_url: Base URL for raw GitHub content
-        dest_path: Local destination directory
-        dir_prefix: Directory prefix to filter files
-
-    Returns:
-        Tuple of (file_path, status) where status is 'new', 'updated', or 'unchanged'
-    """
-    file_path, remote_sha = file_info
-    raw_url = f"{raw_base_url}/{file_path}"
-    dest_file = dest_path / file_path.split(dir_prefix)[-1]
-
-    # Check if file exists and needs updating
-    if dest_file.exists():
-        with open(dest_file, "rb") as file:
-            content = file.read()
-            # Calculate git blob SHA
-            blob = b"blob " + str(len(content)).encode() + b"\0" + content
-            local_sha = hashlib.sha1(blob, usedforsecurity=False).hexdigest()
-
-        if local_sha == remote_sha:
-            print(f"Skipping {file_path} (unchanged)")
-            return file_path, "unchanged"
-
-        print(f"Updating {file_path}")
-        status = "new"
-    else:
-        print(f"Downloading {file_path}")
-        status = "new"
-
-    # Create directories if needed
-    dest_file.parent.mkdir(parents=True, exist_ok=True)
-
-    # Download and save file
-    try:
-        response = requests.get(raw_url, timeout=30)
-        response.raise_for_status()
-
-        with open(dest_file, "wb") as file:
-            file.write(response.content)
-
-        return file_path, status
-    except (requests.RequestException, IOError) as request_error:
-        print(f"Error downloading {file_path}: {str(request_error)}")
-        return file_path, "error"
-
-
-def fetch_from_github(
-    dir_prefix: str, dest_dir: Optional[str] = None, max_workers: int = 5
-) -> None:
-    """
-    Sync files from a specific directory in the GitHub repository.
-    Only downloads files that don't exist locally or have changed.
-
-    Args:
-        dir_prefix: Directory prefix to filter files (e.g., 'examples/', 'deepspeed_configs/')
-        dest_dir: Local destination directory
-        max_workers: Maximum number of concurrent downloads
-    """
-    api_url = "https://api.github.com/repos/axolotl-ai-cloud/axolotl/git/trees/main?recursive=1"
-    raw_base_url = "https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main"
-
-    # Get repository tree with timeout
-    response = requests.get(api_url, timeout=30)
-    response.raise_for_status()
-    tree = json.loads(response.text)
-
-    # Filter for files and get their SHA
-    files = {
-        item["path"]: item["sha"]
-        for item in tree["tree"]
-        if item["type"] == "blob" and item["path"].startswith(dir_prefix)
-    }
-
-    if not files:
-        raise click.ClickException(f"No files found in {dir_prefix}")
-
-    # Default destination directory is the last part of dir_prefix
-    default_dest = Path(dir_prefix.rstrip("/"))
-    dest_path = Path(dest_dir) if dest_dir else default_dest
-
-    # Keep track of processed files for summary
-    files_processed: Dict[str, List[str]] = {
-        "new": [],
-        "updated": [],
-        "unchanged": [],
-        "error": [],
-    }
-
-    # Process files in parallel using ThreadPoolExecutor
-    with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor:
-        future_to_file = {
-            executor.submit(
-                download_file,
-                (file_path, remote_sha),
-                raw_base_url,
-                dest_path,
-                dir_prefix,
-            ): file_path
-            for file_path, remote_sha in files.items()
-        }
-
-        # Process completed tasks as they finish
-        for future in concurrent.futures.as_completed(future_to_file):
-            file_path = future_to_file[future]
-            try:
-                file_path, status = future.result()
-                files_processed[status].append(file_path)
-            except (requests.RequestException, IOError) as request_error:
-                print(f"Error processing {file_path}: {str(request_error)}")
-                files_processed["error"].append(file_path)
-
-    # Log summary
-    LOG.info("\nSync Summary:")
-    LOG.info(f"New files: {len(files_processed['new'])}")
-    LOG.info(f"Updated files: {len(files_processed['updated'])}")
-    LOG.info(f"Unchanged files: {len(files_processed['unchanged'])}")
-    if files_processed["error"]:
-        LOG.info(f"Failed files: {len(files_processed['error'])}")

src/axolotl/core/tokenizer_utils.py

@@ -3,88 +3,36 @@ helper functions for fixing the embeddings/tokenizer
 """
 
 # Copyright 2023-present Daniel Han-Chen & the Unsloth team. All rights reserved.
-# GNU LESSER GENERAL PUBLIC LICENSE
-# Version 3, 29 June 2007
 #
-# Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
-# Everyone is permitted to copy and distribute verbatim copies
-# of this license document, but changing it is not allowed.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 
 import gc
 import itertools
-import logging
-from collections import Counter
 
-import datasets
 import numpy as np
 import torch
 
-LOG = logging.getLogger("axolotl.core.tokenizer_utils")
 
-
-@torch.inference_mode()
-def fix_untrained_tokens(  # pylint: disable=too-many-return-statements
-    model, tokenizer, train_dataset, ignored_tokenizer_names=None, eps=1e-16
-):
+@torch.inference_mode
+def fix_untrained_tokens(model, tokenizer, train_dataset, eps=1e-16):
     """
-    Llama-3 for eg has untrained vectors in the base model.
-    These include <|eot_id|>, <|start_header_id|>, <|end_header_id|>
-    We reset them to the mean of the rest of the tokens
+    Many of the newer models have reserved tokens that are not trained.
     """
-    # Code licensed under LGPL
     embedding_matrix = model.get_input_embeddings().weight
     lm_head_matrix = model.get_output_embeddings().weight
-    chat_template = getattr(tokenizer, "chat_template", None)
-    tokenizer = tokenizer.tokenizer if hasattr(tokenizer, "tokenizer") else tokenizer
-
-    # Ignore some model checks for now
-    if not ignored_tokenizer_names:
-        ignored_tokenizer_names = []
-    if (
-        model.config._name_or_path  # pylint: disable=protected-access
-        in ignored_tokenizer_names
-    ):
-        return
-
-    # Sometimes the sizes can be different like in vision models
-    # Ie <image> is in input, but not in output
-    min_size = min(embedding_matrix.shape[1], lm_head_matrix.shape[1])
-    embedding_matrix = embedding_matrix[:, :min_size]
-    lm_head_matrix = lm_head_matrix[:, :min_size]
 
     # Get untrained tokens
-    indicator_untrained1 = torch.amax(embedding_matrix, axis=1) <= eps
-    # Check lm_head as well
-
-    # Does NOT work for Llama 3.1!!
-    indicator_untrained2 = torch.amax(lm_head_matrix, axis=1) <= eps
-
-    # We instead check for repeated vectors
-    lm_head_where = torch.where(indicator_untrained1)[0]
-    lm_head_bad = lm_head_matrix[lm_head_where]
-    lm_head_bad = lm_head_bad.cpu().float().numpy().round(3)
-    counter = Counter()
-    for row in lm_head_bad:
-        counter[hash(row.data.tobytes())] += 1
-    counter = Counter({k: c for k, c in counter.items() if c >= 2})
-
-    lm_head_where = lm_head_where.cpu().numpy()
-    final_bad_lm_head = []
-    for j, row in enumerate(lm_head_bad):
-        if hash(row.data.tobytes()) in counter:
-            final_bad_lm_head.append(lm_head_where[j])
-    indicator_untrained2 = indicator_untrained2 | torch.zeros_like(indicator_untrained2)
-    indicator_untrained2[final_bad_lm_head] = True
-
-    # Combine both checks
-    indicator_untrained = indicator_untrained1 & indicator_untrained2
-
-    # Remove pad token possibility
-    if hasattr(tokenizer, "pad_token_id"):
-        pad_token_id = tokenizer.pad_token_id
-        if pad_token_id is not None and pad_token_id < indicator_untrained.shape[0]:
-            indicator_untrained[pad_token_id] = False
-
+    indicator_untrained = torch.amax(embedding_matrix, axis=1) <= eps
     where_untrained = torch.where(indicator_untrained)[0]
     n_untrained = where_untrained.shape[0]
     n_trained = embedding_matrix.shape[0] - n_untrained
@@ -92,9 +40,10 @@ def fix_untrained_tokens(  # pylint: disable=too-many-return-statements
     # Get set and actual tokens
     where_untrained = where_untrained.tolist()
     if len(where_untrained) == 0:
-        return
+        return False
 
     # Remove untrained indices where it's longer
+
     where_untrained_set = frozenset(where_untrained)
     actual_bad_tokens = tokenizer.convert_ids_to_tokens(where_untrained)
     # Remove None items in actual_bad_tokens
@@ -104,14 +53,10 @@ def fix_untrained_tokens(  # pylint: disable=too-many-return-statements
     if_bad_first = False
     if_bad_second = False
     # Check tokenizer's chat template for any untrained tokens
+    chat_template = getattr(tokenizer, "chat_template", None)
     if chat_template is not None:
         if_bad_first = any(x in chat_template for x in actual_bad_tokens)
 
-    if isinstance(train_dataset, datasets.IterableDataset):
-        # Skip the check, since the code below assumes
-        # an indexable dataset
-        return
-
     # Check the first 250, last 250 input_ids
     size_dataset = len(train_dataset)
     size = min(size_dataset, 250)
@@ -138,69 +83,7 @@ def fix_untrained_tokens(  # pylint: disable=too-many-return-statements
 
     # Check if bad tokens exists!
     if not if_bad_first and not if_bad_second:
-        return
-
-    # Check if lm_head / embed_token are trainable!
-    bad_not_trainable = False
-    if not embedding_matrix.requires_grad:
-        bad_not_trainable = True
-    if not lm_head_matrix.requires_grad:
-        bad_not_trainable = True
-
-    if bad_not_trainable:  # pylint: disable=too-many-nested-blocks
-        final_bad_items = []
-
-        # Re-check the first 250, last 250 input_ids
-        size_dataset = len(train_dataset)
-        size = min(size_dataset, 250)
-        for j in range(size):
-            input_ids = train_dataset[j]
-            if "input_ids" in input_ids:
-                input_ids = input_ids["input_ids"]
-                for item in input_ids:
-                    if item in where_untrained_set:
-                        final_bad_items.append(item)
-
-        # Re-check last 250
-        left = max(size_dataset - 250, 0)
-        for j in range(left, size_dataset):
-            input_ids = train_dataset[j]
-            if "input_ids" in input_ids:
-                input_ids = input_ids["input_ids"]
-                for item in input_ids:
-                    if item in where_untrained_set:
-                        final_bad_items.append(item)
-
-        # If no bad tokens, possibly chat template itself has issues?
-        if len(final_bad_items) == 0:
-            # Recheck 2000 and last 2000 items
-            size_dataset = len(train_dataset)
-            size = min(size_dataset, 2000)
-            for j in range(size):
-                input_ids = train_dataset[j]
-                if "input_ids" in input_ids:
-                    input_ids = input_ids["input_ids"]
-                    for item in input_ids:
-                        if item in where_untrained_set:
-                            final_bad_items.append(item)
-
-            # Re-check last 2000
-            left = max(size_dataset - 2000, 0)
-            for j in range(left, size_dataset):
-                input_ids = train_dataset[j]
-                if "input_ids" in input_ids:
-                    input_ids = input_ids["input_ids"]
-                    for item in input_ids:
-                        if item in where_untrained_set:
-                            final_bad_items.append(item)
-
-            # Most likely false signal!
-            if len(final_bad_items) == 0:
-                return
-
-        raise ValueError(
-            f"Untrained tokens of [{list(set(final_bad_items))}] found, but embed_tokens & lm_head not trainable, causing NaNs. "
-        )
+        return False
 
     # Count all the possible bad tokens
     final_counts = np.zeros(
@@ -214,23 +97,6 @@ def fix_untrained_tokens(  # pylint: disable=too-many-return-statements
 
     train_dataset.map(mapping, batched=True, desc="Counting untrained tokens")
 
-    # Get counts for untrained tokens
-    counts_untrained = final_counts[where_untrained]
-    # Identify untrained tokens seen in train_dataset
-    indices_seen_in_train = np.where(counts_untrained > 0)[0]
-    tokens_to_update = [where_untrained[i] for i in indices_seen_in_train]
-
-    if len(tokens_to_update) == 0:
-        LOG.info(
-            "No untrained tokens found in train_dataset. No embeddings were modified."
-        )
-        return
-
-    # Log the token IDs that are being rescaled
-    LOG.info(
-        f"Rescaling embeddings for tokens seen in train_dataset: {tokens_to_update}"
-    )
-
     # Get sum of all items
     sum_embedding = torch.sum(embedding_matrix, dtype=torch.float32, axis=0)
     sum_lm_head = torch.sum(lm_head_matrix, dtype=torch.float32, axis=0)
@@ -247,26 +113,38 @@ def fix_untrained_tokens(  # pylint: disable=too-many-return-statements
     mean_embedding = sum_embedding / n_trained
     mean_lm_head = sum_lm_head / n_trained
 
-    # Compute scaling for tokens to update
-    scaling = counts_untrained[indices_seen_in_train] / max(final_counts.max(), 1)
+    # Scale each to be equal to 1/max_frequency. Also set some to 0 if none seen
+    scaling = final_counts[where_untrained] / max(final_counts.max(), 1)
     scaling = torch.tensor(scaling, device=mean_embedding.device).unsqueeze(1)
+    mean_embedding = (
+        mean_embedding.repeat(
+            (
+                n_untrained,
+                1,
+            )
+        )
+        * scaling
+    )
+    mean_lm_head = (
+        mean_lm_head.repeat(
+            (
+                n_untrained,
+                1,
+            )
+        )
+        * scaling
+    )
+    where_null = scaling.ravel() == 0
+    mean_embedding[where_null] = 0
+    mean_lm_head[where_null] = 0
 
-    # Prepare mean embeddings for tokens to update
-    mean_embedding_repeated = (
-        mean_embedding.unsqueeze(0).repeat(len(tokens_to_update), 1) * scaling
-    )
-    mean_lm_head_repeated = (
-        mean_lm_head.unsqueeze(0).repeat(len(tokens_to_update), 1) * scaling
-    )
-
-    # Update embeddings only for tokens seen in train_dataset
-    embedding_matrix[tokens_to_update] = mean_embedding_repeated.to(
-        embedding_matrix.dtype
-    )
-    lm_head_matrix[tokens_to_update] = mean_lm_head_repeated.to(lm_head_matrix.dtype)
+    # Set them to the mean
+    embedding_matrix[where_untrained] = mean_embedding.to(embedding_matrix.dtype)
+    lm_head_matrix[where_untrained] = mean_lm_head.to(lm_head_matrix.dtype)
 
     # Clean up
     for _ in range(3):
         gc.collect()
         torch.cuda.empty_cache()
-    return
+
+    return True

src/axolotl/core/trainer_builder.py

@@ -22,7 +22,6 @@ from typing import Any, Dict, List, Literal, Optional, Type, Union
 import torch
 import transformers
 from datasets import Dataset
-from packaging import version
 from peft.optimizers import create_loraplus_optimizer
 from torch import nn
 from torch.optim.lr_scheduler import OneCycleLR
@@ -108,22 +107,6 @@ def _sanitize_kwargs_for_tagging(tag_names, kwargs=None):
     return kwargs
 
 
-def _sanitize_kwargs_for_ds_tagging(dataset_tags, kwargs=None):
-    if isinstance(dataset_tags, str):
-        dataset_tags = [dataset_tags]
-
-    if (dataset_tags is not None) and (kwargs is not None):
-        if "dataset_tags" not in kwargs:
-            kwargs["dataset_tags"] = dataset_tags
-        elif "dataset_tags" in kwargs and isinstance(kwargs["dataset_tags"], list):
-            kwargs["dataset_tags"].extend(dataset_tags)
-        elif "dataset_tags" in kwargs and isinstance(kwargs["dataset_tags"], str):
-            dataset_tags.append(kwargs["dataset_tags"])
-            kwargs["dataset_tags"] = dataset_tags
-
-    return kwargs
-
-
 @dataclass
 class AxolotlTrainingMixins:
     """
@@ -237,14 +220,6 @@ class AxolotlTrainingMixins:
         default=1e-6,
         metadata={"help": "loraplus learning rate for lora embedding layers."},
     )
-    embedding_lr_scale: Optional[float] = field(
-        default=None,
-        metadata={"help": "Scale the learning rate for the embedding layers."},
-    )
-    embedding_lr: Optional[float] = field(
-        default=None,
-        metadata={"help": "absolute learning rate for the embedding layers."},
-    )
     qlora: bool = field(
         default=False,
         metadata={"help": "whether this is a qlora training"},
@@ -411,7 +386,7 @@ class SchedulerMixin(Trainer):
                     min_lr_ratio=self.args.cosine_min_lr_ratio,
                 )
             else:
-                return super().create_scheduler(num_training_steps, optimizer=optimizer)
+                return super().create_scheduler(num_training_steps, optimizer)
         else:
             if use_cosine_quadratic:
                 LOG.warning("axolotl's cosine scheduler with quadratic warmup not used (e.g., because of deepspeed).")
@@ -435,12 +410,10 @@ class AxolotlTrainer(SchedulerMixin, Trainer):
         *_args,
         bench_data_collator=None,
         eval_data_collator=None,
-        dataset_tags=None,
         **kwargs,
     ):
         self.bench_data_collator = bench_data_collator
         self.eval_data_collator = eval_data_collator
-        self.dataset_tags = dataset_tags
         super().__init__(*_args, **kwargs)
         self.train_data_collator = self.data_collator
         self._stored_metrics = defaultdict(lambda: defaultdict(list))
@@ -462,75 +435,38 @@ class AxolotlTrainer(SchedulerMixin, Trainer):
     def create_optimizer(self):
         if (
             self.args.loraplus_lr_ratio is None
-            and self.args.embedding_lr_scale is None
-            and self.args.embedding_lr is None
             and self.args.alternate_optimizer
-            not in [
-                "optimi_adamw",
-                "ao_adamw_8bit",
-                "ao_adamw_4bit",
-                "ao_adamw_fp8",
-                "adopt_adamw",
-            ]
+            not in ["optimi_adamw", "ao_adamw_8bit", "ao_adamw_4bit", "ao_adamw_fp8"]
         ):
             return super().create_optimizer()
 
         opt_model = self.model_wrapped if is_sagemaker_mp_enabled() else self.model
         if self.optimizer is None:  # pylint: disable=access-member-before-definition
             decay_parameters = self.get_decay_parameter_names(opt_model)
-            params = {
-                "to_weight_decay": {},  # LayerNorm and bias
-                "embeddings": {},  # lm_head, embed_tokens,
-                "no_weight_decay": {},
-            }
+            optimizer_grouped_parameters = [
+                {
+                    "params": [
+                        p
+                        for n, p in opt_model.named_parameters()
+                        if (n in decay_parameters and p.requires_grad)
+                    ],
+                    "weight_decay": self.args.weight_decay,
+                },
+                {
+                    "params": [
+                        p
+                        for n, p in opt_model.named_parameters()
+                        if (n not in decay_parameters and p.requires_grad)
+                    ],
+                    "weight_decay": 0.0,
+                },
+            ]
 
             optimizer_cls, optimizer_kwargs = Trainer.get_optimizer_cls_and_kwargs(
                 self.args,
                 opt_model,
             )
 
-            for name, param in opt_model.named_parameters():
-                if not param.requires_grad:
-                    continue
-                if name.endswith("modules_to_save.default.weight") or any(
-                    embed_name in name for embed_name in ["embed_tokens", "lm_head"]
-                ):
-                    params["embeddings"][name] = param
-                elif name in decay_parameters:
-                    params["to_weight_decay"][name] = param
-                else:
-                    params["no_weight_decay"][name] = param
-            optimizer_grouped_parameters = []
-            if params["to_weight_decay"]:
-                optimizer_grouped_parameters.append(
-                    {
-                        "params": list(params["to_weight_decay"].values()),
-                        "weight_decay": self.args.weight_decay,
-                        "lr": optimizer_kwargs["lr"],
-                    }
-                )
-            if params["embeddings"]:
-                lr = optimizer_kwargs["lr"]  # pylint: disable=invalid-name
-                if self.args.embedding_lr_scale:
-                    lr *= self.args.embedding_lr_scale  # pylint: disable=invalid-name
-                elif self.args.embedding_lr:
-                    lr = self.args.embedding_lr  # pylint: disable=invalid-name
-                optimizer_grouped_parameters.append(
-                    {
-                        "params": list(params["embeddings"].values()),
-                        "weight_decay": 0.0,
-                        "lr": lr,
-                    }
-                )
-            if params["no_weight_decay"]:
-                optimizer_grouped_parameters.append(
-                    {
-                        "params": list(params["no_weight_decay"].values()),
-                        "weight_decay": 0.0,
-                        "lr": optimizer_kwargs["lr"],
-                    }
-                )
-
             if self.args.loraplus_lr_ratio is not None:
                 loraplus_lr_ratio = getattr(self.args, "loraplus_lr_ratio", None)
                 loraplus_lr_embedding = getattr(
@@ -543,13 +479,6 @@ class AxolotlTrainer(SchedulerMixin, Trainer):
                     loraplus_lr_embedding=loraplus_lr_embedding,
                     **optimizer_kwargs,
                 )
-            elif (
-                self.args.embedding_lr_scale is not None
-                or self.args.embedding_lr is not None
-            ):
-                self.optimizer = (  # pylint: disable=attribute-defined-outside-init
-                    optimizer_cls(optimizer_grouped_parameters, **optimizer_kwargs)
-                )
             elif self.args.alternate_optimizer == "optimi_adamw":
                 from optimi import AdamW
 
@@ -576,16 +505,6 @@ class AxolotlTrainer(SchedulerMixin, Trainer):
                 self.optimizer = (  # pylint: disable=attribute-defined-outside-init
                     AdamWFp8(optimizer_grouped_parameters, **optimizer_kwargs)
                 )
-            elif self.args.alternate_optimizer == "adopt_adamw":
-                from axolotl.utils.optimizers.adopt import ADOPT
-
-                self.optimizer = (  # pylint: disable=attribute-defined-outside-init
-                    ADOPT(
-                        optimizer_grouped_parameters,
-                        decouple=True,
-                        **optimizer_kwargs,
-                    )
-                )
 
         if is_sagemaker_mp_enabled():
             self.optimizer = smp.DistributedOptimizer(  # pylint: disable=attribute-defined-outside-init
@@ -938,9 +857,6 @@ class AxolotlTrainer(SchedulerMixin, Trainer):
         Overwrite the `push_to_hub` method in order to force-add the tags when pushing the
         model on the Hub. Please refer to `~transformers.Trainer.push_to_hub` for more details.
         """
-        kwargs = _sanitize_kwargs_for_ds_tagging(
-            dataset_tags=self.dataset_tags, kwargs=kwargs
-        )
         kwargs = _sanitize_kwargs_for_tagging(tag_names=self.tag_names, kwargs=kwargs)
 
         return super().push_to_hub(*args, **kwargs)
@@ -958,15 +874,13 @@ class AxolotlTrainer(SchedulerMixin, Trainer):
 
         return res
 
-    def log(self, logs: Dict[str, float], start_time: Optional[float] = None) -> None:
+    def log(self, logs: Dict[str, float]) -> None:
         """
         Log `logs` on the various objects watching training, including stored metrics.
 
         Args:
             logs (`Dict[str, float]`):
                 The values to log.
-            start_time (`Optional[float]`):
-                The start of training.
         """
         # logs either has 'loss' or 'eval_loss'
         train_eval = "train" if "loss" in logs else "eval"
@@ -974,13 +888,7 @@ class AxolotlTrainer(SchedulerMixin, Trainer):
         for key, metrics in self._stored_metrics[train_eval].items():
             logs[key] = torch.tensor(metrics).mean().item()
         del self._stored_metrics[train_eval]
-
-        if version.parse(transformers.__version__) >= version.parse("4.47.0.dev0"):
-            try:
-                return super().log(logs, start_time)
-            except TypeError:
-                return super().log(logs)  # transformers<=4.46
-        return super().log(logs)  # transformers<=4.46
+        return super().log(logs)
 
     def store_metrics(
         self, metrics: Dict[str, float], train_eval: Literal["train", "eval"] = "train"
@@ -1072,9 +980,8 @@ class AxolotlDPOTrainer(SchedulerMixin, DPOTrainer):
 
     tag_names = ["axolotl", "dpo"]
 
-    def __init__(self, *args, dataset_tags=None, **kwargs):
+    def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
-        self.dataset_tags = dataset_tags
         self.optimizer = None
 
     def create_optimizer(self):
@@ -1113,44 +1020,28 @@ class AxolotlDPOTrainer(SchedulerMixin, DPOTrainer):
         Overwrite the `push_to_hub` method in order to force-add the tags when pushing the
         model on the Hub. Please refer to `~transformers.Trainer.push_to_hub` for more details.
         """
-        kwargs = _sanitize_kwargs_for_ds_tagging(
-            dataset_tags=self.dataset_tags, kwargs=kwargs
-        )
         kwargs = _sanitize_kwargs_for_tagging(tag_names=self.tag_names, kwargs=kwargs)
 
         return super().push_to_hub(*args, **kwargs)
 
-    @staticmethod
     def tokenize_row(
+        self,
         features,
         processing_class,
         max_prompt_length,
         max_completion_length,
         add_special_tokens,
     ) -> Dict:
-        res = DPOTrainer.tokenize_row(
+        res = super().tokenize_row(
             features,
             processing_class,
             max_prompt_length,
             max_completion_length,
             add_special_tokens,
         )
-        # fix when the tokenizer doesn't have a bos_token_id, e.g. Qwen
-        if processing_class.bos_token is None and res["prompt_input_ids"][0] is None:
+        if processing_class.bos_token_id is None and res["prompt_input_ids"][0] is None:
             for key in res.keys():
                 res[key] = res[key][1:]
-
-        if processing_class.bos_token and processing_class.bos_token_id is not None:
-            # dpo trainer may incorrectly prepend the bos_token_id to the dpo outputs
-            if res["chosen_input_ids"][0] == processing_class.bos_token_id:
-                res["chosen_input_ids"] = res["chosen_input_ids"][1:]
-                res["chosen_labels"] = res["chosen_labels"][1:]
-                res["chosen_attention_mask"] = res["chosen_attention_mask"][1:]
-            if res["rejected_input_ids"][0] == processing_class.bos_token_id:
-                res["rejected_input_ids"] = res["rejected_input_ids"][1:]
-                res["rejected_labels"] = res["rejected_labels"][1:]
-                res["rejected_attention_mask"] = res["rejected_attention_mask"][1:]
-
         return res
 
     def training_step(
@@ -1164,22 +1055,6 @@ class AxolotlDPOTrainer(SchedulerMixin, DPOTrainer):
         torch.cuda.empty_cache()
         return loss
 
-    def log(self, logs: Dict[str, float], start_time: Optional[float] = None) -> None:
-        # TODO remove once trl supports the updated to the Trainer.log method
-        # logs either has 'loss' or 'eval_loss'
-        train_eval = "train" if "loss" in logs else "eval"
-        # Add averaged stored metrics to logs
-        for key, metrics in self._stored_metrics[train_eval].items():
-            logs[key] = torch.tensor(metrics).mean().item()
-        del self._stored_metrics[train_eval]
-
-        if version.parse(transformers.__version__) >= version.parse("4.47.0.dev0"):
-            return super(DPOTrainer, self).log(  # pylint: disable=bad-super-call
-                logs, start_time
-            )
-        # transformers<=4.46
-        return super(DPOTrainer, self).log(logs)  # pylint: disable=bad-super-call
-
 
 class AxolotlORPOTrainer(SchedulerMixin, ORPOTrainer):
     """
@@ -1188,22 +1063,6 @@ class AxolotlORPOTrainer(SchedulerMixin, ORPOTrainer):
 
     tag_names = ["axolotl", "orpo"]
 
-    def log(self, logs: Dict[str, float], start_time: Optional[float] = None) -> None:
-        # TODO remove once trl supports the updated to the Trainer.log method
-        # logs either has 'loss' or 'eval_loss'
-        train_eval = "train" if "loss" in logs else "eval"
-        # Add averaged stored metrics to logs
-        for key, metrics in self._stored_metrics[train_eval].items():
-            logs[key] = torch.tensor(metrics).mean().item()
-        del self._stored_metrics[train_eval]
-
-        if version.parse(transformers.__version__) >= version.parse("4.47.0.dev0"):
-            return super(ORPOTrainer, self).log(  # pylint: disable=bad-super-call
-                logs, start_time
-            )
-        # transformers<=4.46
-        return super(ORPOTrainer, self).log(logs)  # pylint: disable=bad-super-call
-
 
 class AxolotlKTOTrainer(SchedulerMixin, KTOTrainer):
     """
@@ -1212,49 +1071,6 @@ class AxolotlKTOTrainer(SchedulerMixin, KTOTrainer):
 
     tag_names = ["axolotl", "kto"]
 
-    def log(self, logs: Dict[str, float], start_time: Optional[float] = None) -> None:
-        # TODO remove once trl supports the updated to the Trainer.log method
-        # logs either has 'loss' or 'eval_loss'
-        train_eval = "train" if "loss" in logs else "eval"
-        # train metrics should have no prefix, eval should have 'eval_'
-        prefix = "eval_" if train_eval == "eval" else ""
-        # accumulate average metrics from sums and lengths
-        for split in ["chosen", "rejected"]:
-            if f"count/{split}" in self._stored_metrics[train_eval]:
-                count_sum = (
-                    torch.Tensor(self._stored_metrics[train_eval][f"count/{split}"])
-                    .sum()
-                    .item()
-                )
-                for metric in ["rewards", "logps", "logits"]:
-                    logs[f"{prefix}{metric}/{split}"] = (
-                        torch.Tensor(
-                            self._stored_metrics[train_eval][f"{metric}/{split}_sum"]
-                        )
-                        .sum()
-                        .item()
-                        / count_sum
-                    )
-                    # delete obsolete metric
-                    del self._stored_metrics[train_eval][f"{metric}/{split}_sum"]
-                del self._stored_metrics[train_eval][f"count/{split}"]
-        # calculate reward margin
-        if f"{prefix}rewards/chosen" in logs and f"{prefix}rewards/rejected" in logs:
-            logs[f"{prefix}rewards/margins"] = (
-                logs[f"{prefix}rewards/chosen"] - logs[f"{prefix}rewards/rejected"]
-            )
-        # Add averaged stored metrics to logs
-        for key, metrics in self._stored_metrics[train_eval].items():
-            logs[f"{prefix}{key}"] = torch.Tensor(metrics).mean().item()
-        del self._stored_metrics[train_eval]
-
-        if version.parse(transformers.__version__) >= version.parse("4.47.0.dev0"):
-            return super(KTOTrainer, self).log(  # pylint: disable=bad-super-call
-                logs, start_time
-            )
-        # transformers<=4.46
-        return super(KTOTrainer, self).log(logs)  # pylint: disable=bad-super-call
-
 
 class AxolotlCPOTrainer(SchedulerMixin, CPOTrainer):
     """
@@ -1263,22 +1079,6 @@ class AxolotlCPOTrainer(SchedulerMixin, CPOTrainer):
 
     tag_names = ["axolotl", "cpo"]
 
-    def log(self, logs: Dict[str, float], start_time: Optional[float] = None) -> None:
-        # TODO remove once trl supports the updated to the Trainer.log method
-        # logs either has 'loss' or 'eval_loss'
-        train_eval = "train" if "loss" in logs else "eval"
-        # Add averaged stored metrics to logs
-        for key, metrics in self._stored_metrics[train_eval].items():
-            logs[key] = torch.tensor(metrics).mean().item()
-        del self._stored_metrics[train_eval]
-
-        if version.parse(transformers.__version__) >= version.parse("4.47.0.dev0"):
-            return super(CPOTrainer, self).log(  # pylint: disable=bad-super-call
-                logs, start_time
-            )
-        # transformers<=4.46
-        return super(CPOTrainer, self).log(logs)  # pylint: disable=bad-super-call
-
 
 class AxolotlRewardTrainer(SchedulerMixin, RewardTrainer):
     """
@@ -1287,15 +1087,6 @@ class AxolotlRewardTrainer(SchedulerMixin, RewardTrainer):
 
     tag_names = ["axolotl", "reward"]
 
-    def log(self, logs: Dict[str, float], start_time: Optional[float] = None) -> None:
-        # TODO remove once trl supports the updated to the Trainer.log method
-        if version.parse(transformers.__version__) >= version.parse("4.47.0.dev0"):
-            return super(RewardTrainer, self).log(  # pylint: disable=bad-super-call
-                logs, start_time
-            )
-        # transformers<=4.46
-        return super(RewardTrainer, self).log(logs)  # pylint: disable=bad-super-call
-
 
 class TrainerBuilderBase(abc.ABC):
     """
@@ -1368,6 +1159,8 @@ class TrainerBuilderBase(abc.ABC):
                 SaveAxolotlConfigtoWandBCallback(self.cfg.axolotl_config_path)
             )
         if self.cfg.use_mlflow and is_mlflow_available():
+            from transformers.integrations.integration_utils import MLflowCallback
+
             from axolotl.utils.callbacks.mlflow_ import (
                 SaveAxolotlConfigtoMlflowCallback,
             )
@@ -1375,6 +1168,7 @@ class TrainerBuilderBase(abc.ABC):
             callbacks.extend(
                 [
                     SaveAxolotlConfigtoMlflowCallback(self.cfg.axolotl_config_path),
+                    MLflowCallback,
                 ]
             )
         if self.cfg.use_comet and is_comet_available():
@@ -1391,17 +1185,11 @@ class TrainerBuilderBase(abc.ABC):
         Callbacks added after the trainer is created, usually b/c these need access to the trainer
         """
         callbacks = []
-        if self.cfg.plugins:
-            plugin_manager = PluginManager.get_instance()
-            callbacks.extend(
-                [
-                    cb
-                    for cb in plugin_manager.add_callbacks_post_trainer(
-                        self.cfg, trainer
-                    )
-                    if cb
-                ]
-            )
+
+        plugin_manager = PluginManager.get_instance()
+        callbacks.extend(
+            plugin_manager.add_callbacks_post_trainer(cfg=self.cfg, trainer=trainer)
+        )
         return callbacks
 
     def hook_pre_create_training_args(self, training_arguments_kwargs):
@@ -1448,7 +1236,7 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         return callbacks
 
     def get_post_trainer_create_callbacks(self, trainer):
-        callbacks = []
+        callbacks = super().get_post_trainer_create_callbacks(trainer=trainer)
         if self.cfg.use_wandb and self.cfg.eval_table_size > 0:
             LogPredictionCallback = log_prediction_callback_factory(
                 trainer, self.tokenizer, "wandb"
@@ -1485,8 +1273,6 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
 
         if self.cfg.lisa_step_interval and self.cfg.lisa_n_layers:
             callbacks.append(lisa_callback_factory(trainer))
-
-        callbacks.extend(super().get_post_trainer_create_callbacks(trainer=trainer))
         return callbacks
 
     def _get_trainer_cls(self):
@@ -1604,15 +1390,17 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
 
         if not self.cfg.test_datasets and self.cfg.val_set_size == 0:
             # no eval set, so don't eval
-            training_arguments_kwargs["eval_strategy"] = "no"
+            training_arguments_kwargs["evaluation_strategy"] = "no"
         elif self.cfg.eval_steps:
-            training_arguments_kwargs["eval_strategy"] = "steps"
+            training_arguments_kwargs["evaluation_strategy"] = "steps"
             training_arguments_kwargs["eval_steps"] = self.cfg.eval_steps
-        elif self.cfg.eval_strategy:
-            training_arguments_kwargs["eval_strategy"] = self.cfg.eval_strategy
+        elif self.cfg.evaluation_strategy:
+            training_arguments_kwargs[
+                "evaluation_strategy"
+            ] = self.cfg.evaluation_strategy
         else:
             # we have an eval set, but no steps defined, default to use epoch
-            training_arguments_kwargs["eval_strategy"] = "epoch"
+            training_arguments_kwargs["evaluation_strategy"] = "epoch"
 
         if self.cfg.save_steps:
             training_arguments_kwargs["save_strategy"] = "steps"
@@ -1750,9 +1538,6 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         training_arguments_kwargs[
             "loraplus_lr_embedding"
         ] = self.cfg.loraplus_lr_embedding
-        training_arguments_kwargs["embedding_lr"] = self.cfg.embedding_lr
-        training_arguments_kwargs["embedding_lr_scale"] = self.cfg.embedding_lr_scale
-
         if self.cfg.lr_scheduler in ["one_cycle", "log_sweep"]:
             training_arguments_kwargs["lr_scheduler_type"] = "cosine"
             training_arguments_kwargs[
@@ -1840,13 +1625,11 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         if self.cfg.reward_model:
             trainer_kwargs["max_length"] = self.cfg.sequence_len
 
-        # pylint: disable=duplicate-code
         if self.cfg.optimizer in [
             "optimi_adamw",
             "ao_adamw_4bit",
             "ao_adamw_8bit",
             "ao_adamw_fp8",
-            "adopt_adamw",
         ]:
             # Set default so transformers doesn't throw
             training_arguments_kwargs["optim"] = "adamw_hf"
@@ -1937,10 +1720,6 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         else:
             trainer_kwargs["tokenizer"] = self.tokenizer
 
-        if (trainer_cls is not AxolotlRewardTrainer) and self.cfg.datasets is not None:
-            trainer_kwargs["dataset_tags"] = [
-                d["path"] for d in self.cfg.datasets if not Path(d["path"]).is_dir()
-            ]
         trainer = trainer_cls(
             model=self.model,
             train_dataset=self.train_dataset,
@@ -2053,10 +1832,10 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
             training_args_kwargs["save_safetensors"] = self.cfg.save_safetensors
 
         if self.eval_dataset:
-            training_args_kwargs["eval_strategy"] = "steps"
+            training_args_kwargs["evaluation_strategy"] = "steps"
             training_args_kwargs["eval_steps"] = self.cfg.eval_steps
         else:
-            training_args_kwargs["eval_strategy"] = "no"
+            training_args_kwargs["evaluation_strategy"] = "no"
 
         if self.cfg.bf16 or self.cfg.bfloat16:
             training_args_kwargs["bf16"] = True
@@ -2111,18 +1890,17 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
             # default to saving each epoch if not defined
             training_args_kwargs["save_strategy"] = "epoch"
 
-        training_args_kwargs["dataset_num_proc"] = self.cfg.dataset_processes
-
         if self.cfg.rl_beta:
             training_args_kwargs["beta"] = self.cfg.rl_beta
         if self.cfg.orpo_alpha:
             # trl does some odd mapping of alpha to beta to reuse the beta parameter ???
             training_args_kwargs["beta"] = self.cfg.orpo_alpha
 
+        training_args_kwargs["dataset_num_proc"] = self.cfg.dataset_processes
+        training_args_cls = AxolotlDPOConfig
         if self.cfg.rpo_alpha is not None:
             training_args_kwargs["rpo_alpha"] = self.cfg.rpo_alpha
 
-        training_args_cls = None
         if self.cfg.rl == "simpo":
             training_args_cls = AxolotlCPOConfig
             training_args_kwargs["loss_type"] = "simpo"
@@ -2131,13 +1909,13 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
             if self.cfg.cpo_alpha is not None:
                 training_args_kwargs["cpo_alpha"] = self.cfg.cpo_alpha
 
-        elif self.cfg.rl == "orpo":
+        if self.cfg.rl == "orpo":
             training_args_cls = AxolotlORPOConfig
             training_args_kwargs["max_length"] = self.cfg.sequence_len
             if self.cfg.max_prompt_len:
                 training_args_kwargs["max_prompt_length"] = self.cfg.max_prompt_len
 
-        elif self.cfg.rl == "kto":
+        if self.cfg.rl == "kto":
             training_args_cls = AxolotlKTOConfig
 
             training_args_kwargs["desirable_weight"] = (
@@ -2152,17 +1930,6 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
             if self.cfg.max_prompt_len:
                 training_args_kwargs["max_prompt_length"] = self.cfg.max_prompt_len
 
-        else:
-            training_args_cls = AxolotlDPOConfig
-            if self.cfg.rl == "ipo":
-                training_args_kwargs["loss_type"] = "ipo"
-            training_args_kwargs["max_length"] = self.cfg.sequence_len
-            training_args_kwargs["max_completion_length"] = None
-            training_args_kwargs["max_prompt_length"] = self.cfg.sequence_len
-            training_args_kwargs["generate_during_eval"] = self.cfg.use_wandb
-            if self.cfg.dpo_use_weighting is not None:
-                training_args_kwargs["use_weighting"] = self.cfg.dpo_use_weighting
-
         training_args = training_args_cls(  # pylint: disable=unexpected-keyword-arg
             output_dir=self.cfg.output_dir,
             per_device_train_batch_size=self.cfg.micro_batch_size,
@@ -2183,6 +1950,7 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
         training_args = self.build_training_arguments(total_num_steps)
         dpo_trainer_kwargs = {}
         if self.cfg.rl == "ipo":
+            dpo_trainer_kwargs["loss_type"] = "ipo"
             if self.cfg.dpo_label_smoothing:
                 dpo_trainer_kwargs["label_smoothing"] = self.cfg.dpo_label_smoothing
         if self.eval_dataset:
@@ -2196,6 +1964,12 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
         if self.cfg.rl in ["dpo", "ipo"]:
             trainer_cls = AxolotlDPOTrainer
             trainer_cls_args = [self.model, self.model_ref]
+
+            # these aren't used for the ORPO trainer
+            dpo_trainer_kwargs["max_length"] = self.cfg.sequence_len
+            dpo_trainer_kwargs["max_target_length"] = None
+            dpo_trainer_kwargs["max_prompt_length"] = self.cfg.sequence_len
+            dpo_trainer_kwargs["generate_during_eval"] = self.cfg.use_wandb
         elif self.cfg.rl == "orpo":
             trainer_cls = AxolotlORPOTrainer
             trainer_cls_args = [self.model]
@@ -2214,10 +1988,6 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
         else:
             dpo_trainer_kwargs["tokenizer"] = self.tokenizer
 
-        if self.cfg.datasets is not None and (trainer_cls is AxolotlDPOTrainer):
-            dpo_trainer_kwargs["dataset_tags"] = [
-                d["path"] for d in self.cfg.datasets if not Path(d["path"]).is_dir()
-            ]
         dpo_trainer = trainer_cls(
             *trainer_cls_args,
             args=training_args,

src/axolotl/core/trainers/trl.py

@@ -40,7 +40,7 @@ class TRLPPOTrainer(PPOTrainer):
                 query_tensors,
                 return_prompt=False,
                 generate_ref_response=True,
-                **generation_kwargs,
+                **generation_kwargs
             )
             batch["response"] = self.tokenizer.batch_decode(response_tensors)
             batch["ref_response"] = self.tokenizer.batch_decode(ref_response_tensors)

src/axolotl/integrations/base.py

@@ -140,7 +140,7 @@ class BasePlugin:
 
     def add_callbacks_pre_trainer(self, cfg, model):  # pylint: disable=unused-argument
         """
-        setup callbacks before creating the trainer.
+        Adds callbacks to the trainer before training.
 
         Parameters:
         cfg (dict): The configuration for the plugin.
@@ -155,15 +155,14 @@ class BasePlugin:
         self, cfg, trainer
     ):  # pylint: disable=unused-argument
         """
-        Adds callbacks to the trainer after creating the trainer.
-        This is useful for callbacks that require access to the model or trainer.
+        Adds callbacks to the trainer after training.
 
         Parameters:
         cfg (dict): The configuration for the plugin.
         trainer (object): The trainer object for training.
 
         Returns:
-        List[callable]: A list of callback functions to be added
+        List[callable]: A list of callback functions to be added to the TrainingArgs
         """
         return []
 
@@ -394,9 +393,7 @@ class PluginManager:
         """
         callbacks = []
         for plugin in self.plugins.values():
-            plugin_callbacks = plugin.add_callbacks_pre_trainer(cfg, model)
-            if plugin_callbacks:  # if the plugin returned a list of callbacks
-                callbacks.extend(plugin_callbacks)
+            callbacks.extend(plugin.add_callbacks_pre_trainer(cfg, model))
         return callbacks
 
     def add_callbacks_post_trainer(self, cfg, trainer):
@@ -412,9 +409,7 @@ class PluginManager:
         """
         callbacks = []
         for plugin in self.plugins.values():
-            plugin_callbacks = plugin.add_callbacks_post_trainer(cfg, trainer)
-            if plugin_callbacks:
-                callbacks.extend(plugin_callbacks)
+            callbacks.extend(plugin.add_callbacks_post_trainer(cfg, trainer))
         return callbacks
 
     def post_train_unload(self, cfg):

src/axolotl/integrations/cut_cross_entropy/ACKNOWLEDGEMENTS.md

@@ -1,325 +0,0 @@
-Acknowledgements
-
-Portions of this Cut Cross Entropy Software may utilize the following copyrighted
-material, the use of which is hereby acknowledged.
-
-
-------
-
-
-PyTorch
-
-    From PyTorch:
-
-    Copyright (c) 2016-     Facebook, Inc            (Adam Paszke)
-    Copyright (c) 2014-     Facebook, Inc            (Soumith Chintala)
-    Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert)
-    Copyright (c) 2012-2014 Deepmind Technologies    (Koray Kavukcuoglu)
-    Copyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu)
-    Copyright (c) 2011-2013 NYU                      (Clement Farabet)
-    Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston)
-    Copyright (c) 2006      Idiap Research Institute (Samy Bengio)
-    Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz)
-
-    From Caffe2:
-
-    Copyright (c) 2016-present, Facebook Inc. All rights reserved.
-
-    All contributions by Facebook:
-    Copyright (c) 2016 Facebook Inc.
-
-    All contributions by Google:
-    Copyright (c) 2015 Google Inc.
-    All rights reserved.
-
-    All contributions by Yangqing Jia:
-    Copyright (c) 2015 Yangqing Jia
-    All rights reserved.
-
-    All contributions by Kakao Brain:
-    Copyright 2019-2020 Kakao Brain
-
-    All contributions by Cruise LLC:
-    Copyright (c) 2022 Cruise LLC.
-    All rights reserved.
-
-    All contributions by Arm:
-    Copyright (c) 2021, 2023-2024 Arm Limited and/or its affiliates
-
-    All contributions from Caffe:
-    Copyright(c) 2013, 2014, 2015, the respective contributors
-    All rights reserved.
-
-    All other contributions:
-    Copyright(c) 2015, 2016 the respective contributors
-    All rights reserved.
-
-    Caffe2 uses a copyright model similar to Caffe: each contributor holds
-    copyright over their contributions to Caffe2. The project versioning records
-    all such contribution and copyright details. If a contributor wants to further
-    mark their specific copyright on a particular contribution, they should
-    indicate their copyright solely in the commit message of the change when it is
-    committed.
-
-    All rights reserved.
-
-    Redistribution and use in source and binary forms, with or without
-    modification, are permitted provided that the following conditions are met:
-
-    1. Redistributions of source code must retain the above copyright
-    notice, this list of conditions and the following disclaimer.
-
-    2. Redistributions in binary form must reproduce the above copyright
-    notice, this list of conditions and the following disclaimer in the
-    documentation and/or other materials provided with the distribution.
-
-    3. Neither the names of Facebook, Deepmind Technologies, NYU, NEC Laboratories America
-    and IDIAP Research Institute nor the names of its contributors may be
-    used to endorse or promote products derived from this software without
-    specific prior written permission.
-
-    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-    AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-    IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
-    ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
-    LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
-    CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
-    SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
-    INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
-    CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
-    ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-    POSSIBILITY OF SUCH DAMAGE.
-
-
-Triton
-
-    /*
-    * Copyright 2018-2020 Philippe Tillet
-    * Copyright 2020-2022 OpenAI
-    *
-    * Permission is hereby granted, free of charge, to any person obtaining
-    * a copy of this software and associated documentation files
-    * (the "Software"), to deal in the Software without restriction,
-    * including without limitation the rights to use, copy, modify, merge,
-    * publish, distribute, sublicense, and/or sell copies of the Software,
-    * and to permit persons to whom the Software is furnished to do so,
-    * subject to the following conditions:
-    *
-    * The above copyright notice and this permission notice shall be
-    * included in all copies or substantial portions of the Software.
-    *
-    * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
-    * EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
-    * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
-    * IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY
-    * CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
-    * TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
-    * SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
-    */
-
-
-Transformers
-
-    Copyright 2018- The Hugging Face team. All rights reserved.
-
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-                            Version 2.0, January 2004
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src/axolotl/integrations/cut_cross_entropy/LICENSE

@@ -1,47 +0,0 @@
-Copyright (C) 2024 Apple Inc. All Rights Reserved.
-
-IMPORTANT:  This Apple software is supplied to you by Apple
-Inc. ("Apple") in consideration of your agreement to the following
-terms, and your use, installation, modification or redistribution of
-this Apple software constitutes acceptance of these terms.  If you do
-not agree with these terms, please do not use, install, modify or
-redistribute this Apple software.
-
-In consideration of your agreement to abide by the following terms, and
-subject to these terms, Apple grants you a personal, non-exclusive
-license, under Apple's copyrights in this original Apple software (the
-"Apple Software"), to use, reproduce, modify and redistribute the Apple
-Software, with or without modifications, in source and/or binary forms;
-provided that if you redistribute the Apple Software in its entirety and
-without modifications, you must retain this notice and the following
-text and disclaimers in all such redistributions of the Apple Software.
-Neither the name, trademarks, service marks or logos of Apple Inc. may
-be used to endorse or promote products derived from the Apple Software
-without specific prior written permission from Apple.  Except as
-expressly stated in this notice, no other rights or licenses, express or
-implied, are granted by Apple herein, including but not limited to any
-patent rights that may be infringed by your derivative works or by other
-works in which the Apple Software may be incorporated.
-
-The Apple Software is provided by Apple on an "AS IS" basis.  APPLE
-MAKES NO WARRANTIES, EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION
-THE IMPLIED WARRANTIES OF NON-INFRINGEMENT, MERCHANTABILITY AND FITNESS
-FOR A PARTICULAR PURPOSE, REGARDING THE APPLE SOFTWARE OR ITS USE AND
-OPERATION ALONE OR IN COMBINATION WITH YOUR PRODUCTS.
-
-IN NO EVENT SHALL APPLE BE LIABLE FOR ANY SPECIAL, INDIRECT, INCIDENTAL
-OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
-SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
-INTERRUPTION) ARISING IN ANY WAY OUT OF THE USE, REPRODUCTION,
-MODIFICATION AND/OR DISTRIBUTION OF THE APPLE SOFTWARE, HOWEVER CAUSED
-AND WHETHER UNDER THEORY OF CONTRACT, TORT (INCLUDING NEGLIGENCE),
-STRICT LIABILITY OR OTHERWISE, EVEN IF APPLE HAS BEEN ADVISED OF THE
-POSSIBILITY OF SUCH DAMAGE.
-
-
--------------------------------------------------------------------------------
-SOFTWARE DISTRIBUTED WITH CUT CROSS ENTROPY:
-
-The Cut Cross Entropy software includes a number of subcomponents with separate
-copyright notices and license terms - please see the file ACKNOWLEDGEMENTS.md.
--------------------------------------------------------------------------------

src/axolotl/integrations/cut_cross_entropy/README.md

@@ -1,10 +0,0 @@
-# Cut Cross Entropy
-
-### Usage
-
-```yaml
-plugins:
-  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
-
-cut_cross_entropy: true
-```

src/axolotl/integrations/cut_cross_entropy/__init__.py

@@ -1,83 +0,0 @@
-# Copyright 2024 Axolotl AI. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""
-Module for the Plugin for Cut Cross Entropy integration with Axolotl.
-
-Cut Cross Entropy is an optimized implementation of cross entropy loss
-from Apple's ML team.
-"""
-import importlib
-import logging
-
-import torch
-
-from axolotl.integrations.base import BasePlugin
-from axolotl.utils import get_pytorch_version
-
-from ...utils.distributed import zero_only
-from .args import CutCrossEntropyArgs  # pylint: disable=unused-import. # noqa: F401
-
-LOG = logging.getLogger("axolotl.integrations.cut_cross_entropy")
-
-_CCE_INSTALL_MESSAGE = (
-    "Please install cut_cross_entropy with transformers support using "
-    '`pip install "cut-cross-entropy[transformers]==24.11.4"`'
-)
-
-
-class CutCrossEntropyPlugin(BasePlugin):
-    """
-    Plugin for Cut Cross Entropy integration with Axolotl.
-    """
-
-    def get_input_args(self):
-        return "axolotl.integrations.cut_cross_entropy.CutCrossEntropyArgs"
-
-    def _check_requirements(self):
-        """Check if all requirements are met."""
-        # Check PyTorch version
-
-        major, minor, _ = get_pytorch_version()
-        if (major, minor) < (2, 4):
-            raise ImportError(
-                "Cut Cross Entropy requires PyTorch >= 2.4.0. "
-                f"Current version: {torch.__version__}"
-            )
-
-        # Check if cut_cross_entropy is installed
-        cce_spec = importlib.util.find_spec("cut_cross_entropy")
-        if cce_spec is None:
-            raise ImportError(_CCE_INSTALL_MESSAGE)
-
-        cce_spec_transformers = importlib.util.find_spec(
-            "cut_cross_entropy.transformers"
-        )
-        if cce_spec_transformers is None:
-            raise ImportError(_CCE_INSTALL_MESSAGE)
-
-    def pre_model_load(self, cfg):
-        """Apply cut cross entropy before model loading if enabled."""
-        if cfg.cut_cross_entropy:
-            self._check_requirements()
-
-            from cut_cross_entropy.transformers import cce_patch
-
-            with zero_only():
-                LOG.info(
-                    f"Applying Cut Cross Entropy to model type: {cfg.model_config_type}"
-                )
-
-            # The patch checks model_type internally
-            cce_patch(cfg.model_config_type)

src/axolotl/integrations/cut_cross_entropy/args.py

@@ -1,42 +0,0 @@
-# Copyright 2024 Axolotl AI. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""
-Module for handling Cut Cross Entropy input arguments.
-"""
-import logging
-from typing import Optional
-
-from pydantic import BaseModel, model_validator
-
-LOG = logging.getLogger("axolotl.integrations.cut_cross_entropy.args")
-
-
-class CutCrossEntropyArgs(BaseModel):
-    """
-    Input args for Cut Cross Entropy.
-    """
-
-    cut_cross_entropy: Optional[bool] = None
-
-    @model_validator(mode="before")
-    @classmethod
-    def check_dtype_is_half(cls, data):
-        if data.get("cut_cross_entropy") and not (data.get("bf16") or data.get("fp16")):
-            raise ValueError(
-                "Cut Cross Entropy requires fp16/bf16 training for backward pass. "
-                "Please set `bf16` or `fp16` to `True`."
-            )
-
-        return data

src/axolotl/integrations/grokfast/LICENSE

@@ -1,21 +0,0 @@
-MIT License
-
-Copyright (c) 2024 Jaerin Lee, Bong Gyun Kang, Kihoon Kim, Kyoung Mu Lee
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.

src/axolotl/integrations/grokfast/README.md

@@ -1,13 +0,0 @@
-# Grokfast Optimizer
-
-See https://github.com/ironjr/grokfast
-
-### Usage
-
-```yaml
-plugins:
-  - axolotl.integrations.grokfast.GrokfastPlugin
-
-grokfast_alpha: 2.0
-grokfast_lamb: 0.98
-```

src/axolotl/integrations/grokfast/__init__.py

@@ -1,50 +0,0 @@
-"""
-Grokfast plugin for Axolotl
-"""
-import logging
-
-from transformers.trainer_callback import TrainerCallback
-
-from ..base import BasePlugin
-from .args import GrokfastArgs  # pylint: disable=unused-import. # noqa: F401
-from .optimizer import gradfilter_ema
-
-LOG = logging.getLogger("axolotl.integrations.grokfast")
-
-
-class GrokfastCallbackHandler(TrainerCallback):
-    """
-    Transformer trainer callbacks for Grokfast
-    """
-
-    def __init__(self, *args_, alpha=0.98, lamb=2.0, **kwargs):
-        super().__init__(*args_, **kwargs)
-        self.grads = None
-        self.alpha = alpha
-        self.lamb = lamb
-
-    def on_train_begin(self, *args_, **kwargs):  # pylint: disable=unused-argument
-        self.grads = None
-
-    def on_pre_optimizer_step(
-        self, args_, state, control, **kwargs
-    ):  # pylint: disable=unused-argument
-        model = kwargs.pop("model")
-        self.grads = gradfilter_ema(model, self.grads, alpha=self.alpha, lamb=self.lamb)
-        return control
-
-
-class GrokfastPlugin(BasePlugin):
-    """
-    Plugin for Grokfast optimizer integraton with Axolotl.
-    """
-
-    def get_input_args(self):
-        return "axolotl.integrations.grokfast.GrokfastArgs"
-
-    def add_callbacks_post_trainer(self, cfg, trainer):
-        LOG.info("Adding Grokfast callback to the trainer")
-        callback = GrokfastCallbackHandler(
-            alpha=cfg.grokfast_alpha, lamb=cfg.grokfast_lamb
-        )
-        return [callback]

src/axolotl/integrations/grokfast/args.py

@@ -1,15 +0,0 @@
-"""
-config args for grokfast plugin
-"""
-from typing import Optional
-
-from pydantic import BaseModel
-
-
-class GrokfastArgs(BaseModel):
-    """
-    Input args for Grokfast optimizer.
-    """
-
-    grokfast_alpha: Optional[float] = 0.98
-    grokfast_lamb: Optional[float] = 2.0

src/axolotl/integrations/grokfast/optimizer.py

@@ -1,63 +0,0 @@
-# Copyright: MIT License (c) 2024 Jaerin Lee, Bong Gyun Kang, Kihoon Kim, Kyoung Mu Lee
-# Reference: https://github.com/ironjr/grokfast
-
-# pylint: skip-file
-from collections import deque
-from typing import Dict, Literal, Optional
-
-import torch
-import torch.nn as nn
-
-
-def gradfilter_ma(
-    m: nn.Module,
-    grads: Optional[Dict[str, deque]] = None,
-    window_size: int = 100,
-    lamb: float = 5.0,
-    filter_type: Literal["mean", "sum"] = "mean",
-    warmup: bool = True,
-    trigger: bool = False,  # For ablation study.
-) -> Dict[str, deque]:
-    if grads is None:
-        grads = {
-            n: deque(maxlen=window_size)
-            for n, p in m.named_parameters()
-            if p.requires_grad and p.grad is not None
-        }
-
-    for n, p in m.named_parameters():
-        if p.requires_grad and p.grad is not None:
-            grads[n].append(p.grad.data.detach())  # .cpu())
-
-            # Modify the gradients.
-            if not warmup or len(grads[n]) == window_size and not trigger:
-                if filter_type == "mean":
-                    avg = sum(grads[n]) / len(grads[n])
-                elif filter_type == "sum":
-                    avg = sum(grads[n])
-                else:
-                    raise ValueError(f"Unrecognized filter_type {filter_type}")
-                p.grad.data = p.grad.data + avg * lamb
-
-    return grads
-
-
-def gradfilter_ema(
-    m: nn.Module,
-    grads: Optional[Dict[str, torch.Tensor]] = None,
-    alpha: float = 0.98,
-    lamb: float = 2.0,
-) -> Dict[str, torch.Tensor]:
-    if grads is None:
-        grads = {
-            n: p.grad.data.detach()
-            for n, p in m.named_parameters()
-            if p.requires_grad and p.grad is not None
-        }
-
-    for n, p in m.named_parameters():
-        if p.requires_grad and p.grad is not None:
-            grads[n] = grads[n] * alpha + p.grad.data.detach() * (1 - alpha)
-            p.grad.data = p.grad.data + grads[n] * lamb
-
-    return grads

src/axolotl/integrations/liger/__init__.py

@@ -23,7 +23,6 @@ import logging
 import sys
 
 from liger_kernel.transformers.cross_entropy import LigerCrossEntropyLoss
-from liger_kernel.transformers.functional import liger_cross_entropy
 from liger_kernel.transformers.monkey_patch import MODEL_TYPE_TO_APPLY_LIGER_FN
 from liger_kernel.transformers.rms_norm import LigerRMSNorm
 from liger_kernel.transformers.rope import liger_rotary_pos_emb
@@ -83,9 +82,7 @@ class LigerPlugin(BasePlugin):
             if cfg.liger_glu_activation:
                 modeling_jamba.JambaMLP = LigerSwiGLUMLP
             if cfg.liger_cross_entropy:
-                from transformers.loss.loss_utils import nn
-
-                nn.functional.cross_entropy = liger_cross_entropy
+                modeling_jamba.CrossEntropyLoss = LigerCrossEntropyLoss
             if cfg.liger_fused_linear_cross_entropy:
                 modeling_jamba.JambaForCausalLM.forward = jamba_lce_forward
         elif cfg.model_config_type == "deepseek_v2":
@@ -109,8 +106,6 @@ class LigerPlugin(BasePlugin):
             if cfg.liger_glu_activation:
                 modeling_mod.DeepseekV2MLP.forward = LigerSwiGLUMLP.forward
             if cfg.liger_cross_entropy:
-                # We do not patch `nn.functional.cross_entropy` for DeepseekV2 as it still uses
-                # nn.CrossEntropyLoss in the forward method.
                 modeling_mod.CrossEntropyLoss = LigerCrossEntropyLoss
             if cfg.liger_fused_linear_cross_entropy:
                 modeling_mod.DeepseekV2ForCausalLM.forward = deepseekv2_lce_forward

src/axolotl/monkeypatch/fastchat_conversation_turns.py

@@ -0,0 +1,231 @@
+"""
+monkeypatch to add a get_turns method
+"""
+
+import logging
+from typing import Generator, Tuple
+
+from fastchat.conversation import SeparatorStyle
+
+LOG = logging.getLogger("axolotl.monkeypatch.fastchat_conversation_turns")
+
+
+def get_prompt(self) -> str:
+    ret = ""
+    for role, msg in self.get_turns():
+        ret += role + msg
+    return ret
+
+
+def get_turns(  # pylint: disable=too-many-return-statements
+    self,
+) -> Generator[Tuple[str, str], None, None]:
+    """Get the prompt for generation."""
+    system_prompt = self.system_template.format(system_message=self.system_message)
+    if self.sep_style == SeparatorStyle.ADD_COLON_SINGLE:
+        yield "", system_prompt + self.sep
+        for role, message in self.messages:
+            if message:
+                yield role + ": ", message + self.sep
+            else:
+                yield role + ":", ""
+        return
+    if self.sep_style == SeparatorStyle.ADD_COLON_TWO:
+        seps = [self.sep, self.sep2]
+        yield "", system_prompt + seps[0]
+        for i, (role, message) in enumerate(self.messages):
+            if message:
+                yield role + ": ", message + seps[i % 2]
+            else:
+                yield role + ":", ""
+        return
+    if self.sep_style == SeparatorStyle.ADD_COLON_SPACE_SINGLE:
+        yield "", system_prompt + self.sep
+        for role, message in self.messages:
+            if message:
+                yield role + ": ", message + self.sep
+            else:
+                yield role + ": ", ""  # must be end with a space
+        return
+    if self.sep_style == SeparatorStyle.ADD_NEW_LINE_SINGLE:
+        yield "", "" if system_prompt == "" else system_prompt + self.sep
+        for role, message in self.messages:
+            if message:
+                yield role + "\n", message + self.sep
+            else:
+                yield role + "\n", ""
+        return
+    if self.sep_style == SeparatorStyle.NO_COLON_SINGLE:
+        yield "", system_prompt
+        for role, message in self.messages:
+            if message:
+                yield role, message + self.sep
+            else:
+                yield role, ""
+        return
+    if self.sep_style == SeparatorStyle.NO_COLON_TWO:
+        seps = [self.sep, self.sep2]
+        yield "", system_prompt
+        for i, (role, message) in enumerate(self.messages):
+            if message:
+                yield role, message + seps[i % 2]
+            else:
+                yield role, ""
+        return
+    if self.sep_style == SeparatorStyle.RWKV:
+        yield "", system_prompt
+        for i, (role, message) in enumerate(self.messages):
+            if message:
+                yield role + ": ", message.replace("\r\n", "\n").replace(
+                    "\n\n", "\n"
+                ) + "\n\n"
+            else:
+                yield role + ":", ""
+        return
+    if self.sep_style == SeparatorStyle.LLAMA2 and self.name != "mistral":
+        if self.system_message:
+            if self.messages:
+                # For llama, the system message is incorporated into the first human instruction
+                first_role, first_msg = self.messages[0]
+                if first_role == self.roles[0]:
+                    system_prompt += first_msg
+                    self.messages.pop(0)
+            yield "", system_prompt
+        for i, (role, message) in enumerate(self.messages):
+            if message:
+                if (i % 2 == 0 and not self.system_message) or (
+                    i % 2 != 0 and self.system_message
+                ):
+                    role = "<s> " + role
+                yield role + " ", message
+            else:
+                yield role, ""
+        return
+    if self.sep_style == SeparatorStyle.LLAMA2 and self.name == "mistral":
+        contains_sys_msg = False
+        if self.system_message:
+            contains_sys_msg = True
+            if self.messages:
+                # There is no clear guidance on how to handle system messages in Mistral so we just prepend it to the first human instruction separated by a newline
+                first_role, first_msg = self.messages[0]
+                if first_role == self.roles[0]:
+                    system_prompt = self.system_template.format(
+                        system_message=" " + self.system_message
+                    )
+                    system_prompt += first_msg
+                    self.messages.pop(0)
+            yield "", system_prompt
+        for i, (role, message) in enumerate(self.messages):
+            if message and i == 0 and not contains_sys_msg:
+                yield "", system_prompt.strip() + " " + message  # if there is no system message, we need to make sure there is the a `<s> [INST]` at the beginning of the first instruction.
+            elif message:
+                yield role + " ", message
+            else:
+                yield role, ""
+        return
+    if self.sep_style == SeparatorStyle.LLAMA3:
+        if self.system_message:
+            # For llama3, the system message is NOT incorporated into the first human instruction
+            # All messages follow <|start_header_id|>' + role + '<|end_header_id|>\n\n'+ message + '<|eot_id|>
+            yield "", system_prompt
+        for i, (role, message) in enumerate(self.messages):
+            if message:
+                yield f"<|start_header_id|>{role}<|end_header_id|>\n\n", f"{message.strip()}<|eot_id|>"
+            else:
+                yield f"<|start_header_id|>{role}<|end_header_id|>\n\n", ""
+        return
+    if self.sep_style == SeparatorStyle.GEMMA:
+        if self.system_message:
+            raise ValueError("Gemma chat template does not support system messages")
+        for i, (role, message) in enumerate(self.messages):
+            prefix = "<bos>" if i == 0 else ""
+            message_str = message if message else ""
+            yield prefix + "<start_of_turn>" + role + "\n", message_str + "<end_of_turn>\n"
+        return
+    if self.sep_style == SeparatorStyle.CHATGLM:
+        # source: https://huggingface.co/THUDM/chatglm-6b/blob/1d240ba371910e9282298d4592532d7f0f3e9f3e/modeling_chatglm.py#L1302-L1308
+        # source2: https://huggingface.co/THUDM/chatglm2-6b/blob/e186c891cf64310ac66ef10a87e6635fa6c2a579/modeling_chatglm.py#L926
+        round_add_n = 1 if self.name == "chatglm2" else 0
+        if system_prompt:
+            yield "", system_prompt + self.sep
+
+        for i, (role, message) in enumerate(self.messages):
+            if i % 2 == 0:
+                yield "", f"[Round {i//2 + round_add_n}]{self.sep}"
+
+            if message:
+                yield f"{role}：", f"{message}{self.sep}"
+            else:
+                yield f"{role}：", ""
+        return
+    if self.sep_style == SeparatorStyle.CHATML:
+        yield "", "" if system_prompt == "" else system_prompt + self.sep + "\n"
+        for role, message in self.messages:
+            if message:
+                yield role + "\n", message + self.sep + "\n"
+            else:
+                yield role + "\n", ""
+        return
+    if self.sep_style == SeparatorStyle.CHATGLM3:
+        if self.system_message:
+            yield "", system_prompt
+        for role, message in self.messages:
+            if message:
+                yield role + "\n", " " + message
+            else:
+                yield role
+        return
+    if self.sep_style == SeparatorStyle.CHATINTERN:
+        # source: https://huggingface.co/internlm/internlm-chat-7b-8k/blob/bd546fa984b4b0b86958f56bf37f94aa75ab8831/modeling_internlm.py#L771
+        seps = [self.sep, self.sep2]
+        yield "", system_prompt
+        for i, (role, message) in enumerate(self.messages):
+            prefix = "<s>" if i % 2 == 0 else ""
+            if message:
+                yield prefix + role + ":", message + seps[i % 2] + "\n"
+            else:
+                yield role + ":", ""
+        return
+    if self.sep_style == SeparatorStyle.DOLLY:
+        seps = [self.sep, self.sep2]
+        yield "", system_prompt
+        for i, (role, message) in enumerate(self.messages):
+            if message:
+                suffix = "\n\n" if i % 2 == 1 else ""
+                yield role + ":\n", message + seps[i % 2] + suffix
+            else:
+                yield role + ":\n", ""
+        return
+    if self.sep_style == SeparatorStyle.PHOENIX:
+        yield "", system_prompt
+        for role, message in self.messages:
+            if message:
+                yield role + ": ", "<s>" + message + "</s>"
+            else:
+                yield role + ": " + "<s>", ""
+        return
+    if self.sep_style == SeparatorStyle.ROBIN:
+        yield "", system_prompt + self.sep
+        for role, message in self.messages:
+            if message:
+                yield role + ":\n", message + self.sep
+            else:
+                yield role + ":\n", ""
+        return
+    if self.sep_style == SeparatorStyle.FALCON_CHAT:
+        if self.system_message:
+            yield "", system_prompt + self.sep
+        for role, message in self.messages:
+            if message:
+                yield role + ": ", message + self.sep
+            else:
+                yield role + ":", ""
+    else:
+        raise ValueError(f"Invalid style: {self.sep_style}")
+
+
+def add_get_turns_to_conversation():
+    import fastchat.conversation
+
+    fastchat.conversation.Conversation.get_turns = get_turns
+    fastchat.conversation.Conversation.get_prompt = get_prompt

src/axolotl/monkeypatch/llama_attn_hijack_flash.py

@@ -4,6 +4,7 @@
 
 import logging
 import warnings
+from functools import partial
 from typing import List, Optional, Tuple, Union
 
 import torch
@@ -93,32 +94,13 @@ def replace_llama_qkv_with_fused(model):
             set_module_name(model, name, qkv)
 
 
-def patch_fa_llama_cross_entropy():
-    LOG.info(
-        "patching transformers.loss.loss_utils.fixed_cross_entropy with flash_attn.ops.triton.cross_entropy"
-    )
-    from flash_attn.ops.triton.cross_entropy import (
-        cross_entropy_loss as flash_attn_cross_entropy_loss,
-    )
+def patch_llama_cross_entropy():
+    from flash_attn.losses.cross_entropy import CrossEntropyLoss
 
-    def fa2_fixed_cross_entropy(
-        source,
-        target,
-        num_items_in_batch: int = None,
-        ignore_index: int = -100,
-        **kwargs,
-    ):  # pylint: disable=unused-argument
-        reduction = "sum" if num_items_in_batch is not None else "mean"
-        loss, _ = flash_attn_cross_entropy_loss(
-            source, target, ignore_index=ignore_index
-        )
-        if reduction == "sum":
-            loss = loss.sum() / num_items_in_batch
-        else:
-            loss = loss.sum() / (target != ignore_index).sum()
-        return loss
-
-    transformers.loss.loss_utils.fixed_cross_entropy = fa2_fixed_cross_entropy
+    LOG.info("patching with flash_attn.losses.cross_entropy")
+    transformers.models.llama.modeling_llama.CrossEntropyLoss = partial(
+        CrossEntropyLoss, inplace_backward=True
+    )
 
 
 def patch_llama_rms_norm():
@@ -165,7 +147,7 @@ def replace_llama_attn_with_flash_attn(
 
     # skip only if explicitly disabled
     if cross_entropy:
-        patch_fa_llama_cross_entropy()
+        patch_llama_cross_entropy()
 
     # skip only if explicitly disabled
     if rms_norm:

src/axolotl/monkeypatch/multipack.py

@@ -1,5 +1,4 @@
 """multipack patching for v2 of sample packing"""
-
 import importlib
 
 import transformers
@@ -28,28 +27,71 @@ SUPPORTED_MULTIPACK_MODEL_TYPES = [
 ]
 
 
-def patch_for_multipack(model_type, model_name=None, has_remote_code=False):
-    if has_remote_code:
-        patch_remote(model_name)
-    elif hasattr(transformers, "modeling_flash_attention_utils"):
+def patch_for_multipack(model_type, model_name=None, is_remote_code=False):
+    if model_type == "gemmoe":
+        patch_remote(model_name, ".configuration_gemmoe", ".modeling_gemmoe")
+    elif model_type == "deepseek_v2":
+        patch_remote(model_name, ".configuration_deepseek", ".modeling_deepseek")
+    elif hasattr(transformers, "modeling_flash_attention_utils") and not is_remote_code:
         transformers.modeling_flash_attention_utils._get_unpad_data = (  # pylint: disable=protected-access
             get_unpad_data
         )
+        if model_type == "mixtral" and is_deepspeed_zero3_enabled():
+            patch_mixtral_moe_forward_zero3()
+        return
 
-    if model_type == "mixtral" and is_deepspeed_zero3_enabled():
-        patch_mixtral_moe_forward_zero3()
+    # retain for legacy
+    if model_type == "mixtral":
+        transformers.models.mixtral.modeling_mixtral._get_unpad_data = (  # pylint: disable=protected-access
+            get_unpad_data
+        )
+        if is_deepspeed_zero3_enabled():
+            patch_mixtral_moe_forward_zero3()
+    elif model_type == "llama":
+        if hasattr(transformers.models.llama.modeling_llama, "_get_unpad_data"):
+            transformers.models.llama.modeling_llama._get_unpad_data = (  # pylint: disable=protected-access
+                get_unpad_data
+            )
+    elif model_type == "mistral":
+        if hasattr(transformers.models.mistral.modeling_mistral, "_get_unpad_data"):
+            transformers.models.llama.modeling_llama._get_unpad_data = (  # pylint: disable=protected-access
+                get_unpad_data
+            )
+    elif model_type == "qwen2":
+        transformers.models.qwen2.modeling_qwen2._get_unpad_data = (  # pylint: disable=protected-access
+            get_unpad_data
+        )
+    elif model_type == "qwen2_moe":
+        transformers.models.qwen2_moe.modeling_qwen2_moe._get_unpad_data = (  # pylint: disable=protected-access
+            get_unpad_data
+        )
+    elif model_type == "falcon":
+        transformers.models.falcon.modeling_falcon._get_unpad_data = (  # pylint: disable=protected-access
+            get_unpad_data
+        )
+    elif model_type == "phi":
+        transformers.models.phi.modeling_phi._get_unpad_data = (  # pylint: disable=protected-access
+            get_unpad_data
+        )
+    elif model_type == "gemma":
+        transformers.models.gemma.modeling_gemma._get_unpad_data = (  # pylint: disable=protected-access
+            get_unpad_data
+        )
+    elif model_type == "gemma2":
+        transformers.models.gemma2.modeling_gemma2._get_unpad_data = (  # pylint: disable=protected-access
+            get_unpad_data
+        )
+    elif model_type == "starcoder2":
+        transformers.models.starcoder2.modeling_starcoder2._get_unpad_data = (  # pylint: disable=protected-access
+            get_unpad_data
+        )
 
 
-def patch_remote(model_name):
+def patch_remote(model_name, config_name, modeling_name):
     model_config = AutoConfig.from_pretrained(model_name, trust_remote_code=True)
     # we need to load the model here in order for modeling_* to be available
     with init_empty_weights():
         AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True)
-    parts = model_config.__class__.__module__.split(".")
-    parts[-1] = parts[-1].replace("configuration_", "modeling_", 1)
-    module_name = ".".join(parts)
+    module_name = model_config.__class__.__module__.replace(config_name, modeling_name)
     modeling_arch = importlib.import_module(module_name)
-    if hasattr(modeling_arch, "_get_unpad_data"):
-        modeling_arch._get_unpad_data = (  # pylint: disable=protected-access
-            get_unpad_data
-        )
+    modeling_arch._get_unpad_data = get_unpad_data  # pylint: disable=protected-access

src/axolotl/monkeypatch/relora.py

@@ -46,10 +46,9 @@ def reset_optimizer(
     *,
     reset_params: List[str],  # where str is the key to a torch.nn.Parameter
     optimizer_state_keys: List[str],
-    optimizer_magnitude_pruning: float = 0.9,
+    prune_ratio: float = 0.9,
 ):
-    # pylint:disable=unused-argument
-    pruning_fn = partial(magnitude_pruning_, prune_ratio=optimizer_magnitude_pruning)
+    pruning_fn = partial(magnitude_pruning_, prune_ratio=prune_ratio)
     n_zeros = 0
     n_total = 0
 
@@ -57,22 +56,16 @@ def reset_optimizer(
     if isinstance(optimizer, ZeroRedundancyOptimizer):
         optimizer_state = optimizer.optim.state
 
-    for group in optimizer.param_groups:
-        for param in group["params"]:
-            state = optimizer_state[param]
-            for key, value in state.items():
-                if key not in optimizer_state_keys:
-                    continue
-                if torch.is_tensor(value):
-                    try:
-                        pruning_fn(value)
-                        n_total += value.numel()
-                        n_zeros += torch.sum(value == 0).item()
-                    except RuntimeError as exc:
-                        if "quantile() input tensor is too large" in str(exc):
-                            pass
-                        else:
-                            raise exc
+    for param in reset_params:
+        param_state = optimizer_state[param]
+        if len(param_state) == 0:  # no state for this param, happens for ZeRo optimizer
+            continue
+        for key in optimizer_state_keys:
+            pruning_fn(
+                param_state[key]
+            )  # pruning fn has to be inplace to keep the same keys in the dict
+            n_total += param_state[key].numel()
+            n_zeros += torch.sum(param_state[key] == 0).item()
 
     _zeroed = n_zeros / (1e-7 + n_total) * 100
     LOG.info(f"Percent of optimizer states zeroed: {_zeroed:.2f}")
@@ -136,9 +129,6 @@ class ReLoRACallback(TrainerCallback):
 
             if "adam" in args.optim.lower():
                 optimizer_state_keys = ["exp_avg", "exp_avg_sq"]
-                if "8bit" in args.optim.lower():
-                    optimizer_state_keys.append("state1")
-                    optimizer_state_keys.append("state2")
             else:
                 raise ValueError(f"Optimizer {args.optim} not supported with ReLoRA")
 
@@ -170,7 +160,7 @@ class ReLoRACallback(TrainerCallback):
                     optimizer,
                     reset_params=lora_params,
                     optimizer_state_keys=optimizer_state_keys,
-                    optimizer_magnitude_pruning=args.relora_prune_ratio,
+                    prune_ratio=args.relora_prune_ratio,
                 )
 
             if self.quantized:

src/axolotl/monkeypatch/trainer_fsdp_optim.py

@@ -1,80 +0,0 @@
-"""
-fix for FSDP optimizer save in trainer w 4.47.0
-"""
-import inspect
-import logging
-
-from transformers import Trainer
-
-from axolotl.monkeypatch.unsloth_ import detab_code
-
-LOG = logging.getLogger("axolotl.monkeypatch.trainer_fsdp_save")
-
-ORIGINAL_TRAINER_CODE = """
-
-    delay_optimizer_creation = is_sagemaker_mp_enabled() or self.is_fsdp_xla_enabled
-
-"""
-
-PATCHED_TRAINER_CODE = """
-
-    delay_optimizer_creation = is_sagemaker_mp_enabled() or self.is_fsdp_xla_enabled or self.is_fsdp_enabled
-
-"""
-
-
-def get_training_loop_code() -> str:
-    training_loop = inspect.getsource(
-        Trainer._inner_training_loop  # pylint: disable=protected-access
-    )
-    return training_loop
-
-
-def check_training_loop_is_patchable() -> bool:
-    training_loop = get_training_loop_code()
-    training_loop, _ = detab_code(training_loop)
-    return ORIGINAL_TRAINER_CODE in training_loop
-
-
-def patch_training_loop_for_fsdp():
-    """
-    monkeypatch for fixing the training loop for fsdp with optimizer save
-    """
-
-    try:
-        training_loop = get_training_loop_code()
-    except OSError:
-        return
-    Trainer._original_inner_training_loop = (  # pylint: disable=protected-access
-        training_loop
-    )
-    training_loop, _ = detab_code(training_loop)
-    if ORIGINAL_TRAINER_CODE not in training_loop:
-        return
-
-    training_loop = training_loop.replace(ORIGINAL_TRAINER_CODE, PATCHED_TRAINER_CODE)
-    training_loop = training_loop.replace(
-        "def _inner_training_loop(",
-        "def _fixed_inner_training_loop(",
-        1,
-    )
-
-    # load imports necessary
-    import transformers.trainer
-
-    items_to_import = []
-    for item in dir(transformers.trainer):
-        if item in training_loop:
-            items_to_import.append(item)
-
-    exec(  # pylint: disable=exec-used  # nosec B102
-        "from transformers.trainer import ("
-        + ", ".join(x for x in items_to_import)
-        + ")",
-        globals(),
-    )
-    exec(training_loop, globals())  # pylint: disable=exec-used  # nosec B102
-    LOG.info("patching _inner_training_loop for fsdp optimizer save")
-    Trainer._inner_training_loop = (  # pylint: disable=protected-access
-        _fixed_inner_training_loop  # pylint: disable=undefined-variable  # noqa: F821
-    )

src/axolotl/monkeypatch/trainer_grad_accum.py

@@ -1,290 +0,0 @@
-"""
-fix for FSDP gradient accumulation
-see https://github.com/huggingface/transformers/pull/35128
-"""
-import inspect
-import logging
-
-from transformers import LlamaForCausalLM, Trainer
-
-from axolotl.monkeypatch.unsloth_ import detab_code
-
-LOG = logging.getLogger("axolotl.monkeypatch.trainer_grad_accum")
-
-ORIGINAL_CONTEXT_CODE = """
-    with self.compute_loss_context_manager():
-        if self.model_accepts_loss_kwargs:
-            loss = self.compute_loss(model, inputs)
-        else:
-            loss = self.compute_loss(model, inputs, num_items_in_batch=num_items_in_batch)
-"""
-
-PATCHED_CONTEXT_CODE = """
-    with self.compute_loss_context_manager():
-        if self.model_accepts_loss_kwargs:
-            loss = self.compute_loss(model, inputs, num_items_in_batch=num_items_in_batch)
-        else:
-            loss = self.compute_loss(model, inputs)
-"""
-
-ORIGINAL_LLAMA_FCLM_CODE = """
-    output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-    output_hidden_states = (
-        output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-    )
-    return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-    # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
-    outputs = self.model(
-        input_ids=input_ids,
-        attention_mask=attention_mask,
-        position_ids=position_ids,
-        past_key_values=past_key_values,
-        inputs_embeds=inputs_embeds,
-        use_cache=use_cache,
-        output_attentions=output_attentions,
-        output_hidden_states=output_hidden_states,
-        return_dict=return_dict,
-        cache_position=cache_position,
-        **kwargs,
-    )
-
-    hidden_states = outputs[0]
-    # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
-    logits = self.lm_head(hidden_states[:, -num_logits_to_keep:, :])
-
-    loss = None
-    if labels is not None:
-        loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size, **kwargs)
-"""
-
-PATCHED_LLAMA_FCLM_CODE = """
-    output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-    output_hidden_states = (
-        output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-    )
-    return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-    # remove num_items_in_batch otherwise self.model attempts to pass it to flash_attention
-    num_items_in_batch = kwargs.pop("num_items_in_batch", None)
-
-    # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
-    outputs = self.model(
-        input_ids=input_ids,
-        attention_mask=attention_mask,
-        position_ids=position_ids,
-        past_key_values=past_key_values,
-        inputs_embeds=inputs_embeds,
-        use_cache=use_cache,
-        output_attentions=output_attentions,
-        output_hidden_states=output_hidden_states,
-        return_dict=return_dict,
-        cache_position=cache_position,
-        **kwargs,
-    )
-    hidden_states = outputs[0]
-    # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
-    logits = self.lm_head(hidden_states[:, -num_logits_to_keep:, :])
-
-    loss = None
-    if labels is not None:
-        loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size, num_items_in_batch=num_items_in_batch, **kwargs)
-"""
-
-
-def get_training_step_code() -> str:
-    training_step = inspect.getsource(
-        Trainer.training_step  # pylint: disable=protected-access
-    )
-    return training_step
-
-
-def check_training_step_is_patchable() -> bool:
-    training_step = get_training_step_code()
-    training_step, _ = detab_code(training_step)
-    return ORIGINAL_CONTEXT_CODE in training_step
-
-
-def patch_training_step_for_ga():
-    """
-    monkeypatch for fixing the training loop for gradient accumulation
-    """
-
-    try:
-        training_step = get_training_step_code()
-    except OSError:
-        return
-    Trainer._original_training_step = training_step  # pylint: disable=protected-access
-    training_step, _ = detab_code(training_step)
-    if ORIGINAL_CONTEXT_CODE not in training_step:
-        return
-    # assert (
-    #     ORIGINAL_CONTEXT_CODE in training_step
-    # ), "Original training_step code not found"
-
-    training_step = training_step.replace(ORIGINAL_CONTEXT_CODE, PATCHED_CONTEXT_CODE)
-    training_step = training_step.replace(
-        "def training_step(",
-        "def _fixed_training_step(",
-        1,
-    )
-
-    # load imports necessary
-    import transformers.trainer
-
-    items_to_import = []
-    for item in dir(transformers.trainer):
-        if item in training_step:
-            items_to_import.append(item)
-
-    exec(  # pylint: disable=exec-used  # nosec B102
-        "from transformers.trainer import ("
-        + ", ".join(x for x in items_to_import)
-        + ")",
-        globals(),
-    )
-    exec(training_step, globals())  # pylint: disable=exec-used  # nosec B102
-    LOG.info("patching training_step")
-    Trainer.training_step = (  # pylint: disable=protected-access
-        _fixed_training_step  # pylint: disable=undefined-variable  # noqa: F821
-    )
-
-
-def get_model_forward_code() -> str:
-    forward = inspect.getsource(
-        LlamaForCausalLM.forward  # pylint: disable=protected-access
-    )
-    return forward
-
-
-def check_forward_is_patchable() -> bool:
-    forward = get_model_forward_code()
-    forward, _ = detab_code(forward)
-    return ORIGINAL_LLAMA_FCLM_CODE in forward
-
-
-def patch_forward_for_ga():
-    """
-    monkeypatch for fixing the training loop for gradient accumulation
-    """
-
-    try:
-        forward = get_model_forward_code()
-    except OSError:
-        return
-    LlamaForCausalLM._original_forward = forward  # pylint: disable=protected-access
-    forward, _ = detab_code(forward)
-    if ORIGINAL_LLAMA_FCLM_CODE not in forward:
-        return
-    # assert ORIGINAL_LLAMA_FCLM_CODE in forward, "Original forward code not found"
-
-    forward = forward.replace(ORIGINAL_LLAMA_FCLM_CODE, PATCHED_LLAMA_FCLM_CODE)
-    forward = forward.replace(
-        "def forward(",
-        "def _fixed_forward(",
-        1,
-    )
-
-    # load imports necessary
-    import transformers.models.llama.modeling_llama
-
-    items_to_import = []
-    for item in dir(transformers.models.llama.modeling_llama):
-        if item in forward:
-            items_to_import.append(item)
-
-    exec(  # pylint: disable=exec-used  # nosec B102
-        "from transformers.models.llama.modeling_llama import ("
-        + ", ".join(x for x in items_to_import)
-        + ")",
-        globals(),
-    )
-    exec(forward, globals())  # pylint: disable=exec-used  # nosec B102
-    LOG.info("patching forward")
-    LlamaForCausalLM.forward = (  # pylint: disable=protected-access
-        _fixed_forward  # pylint: disable=undefined-variable  # noqa: F821
-    )
-
-
-ORIGINAL_TRAINER_CODE = """
-                context = (
-                    functools.partial(self.accelerator.no_sync, model=model)
-                    if i != len(batch_samples) - 1
-                    else contextlib.nullcontext
-                )
-                with context():
-                    tr_loss_step = self.training_step(model, inputs, num_items_in_batch)
-"""
-
-PATCHED_TRAINER_CODE = """
-                disable_deepspeed_no_sync = (
-                        self.accelerator.distributed_type == DistributedType.DEEPSPEED
-                        # and self.accelerator.deepspeed_engine_wrapped.engine.zero_optimization_partition_gradients()
-                )
-                context = (
-                    functools.partial(self.accelerator.no_sync, model=model)
-                    if i != len(batch_samples) - 1 and not disable_deepspeed_no_sync
-                    else contextlib.nullcontext
-                )
-                with context():
-                    tr_loss_step = self.training_step(model, inputs, num_items_in_batch)
-"""
-
-
-def get_training_loop_code() -> str:
-    training_loop = inspect.getsource(
-        Trainer._inner_training_loop  # pylint: disable=protected-access
-    )
-    return training_loop
-
-
-def check_training_loop_is_patchable() -> bool:
-    training_loop = get_training_loop_code()
-    training_loop, _ = detab_code(training_loop)
-    return ORIGINAL_TRAINER_CODE in training_loop
-
-
-def patch_training_loop_for_deepspeed_0_16_x():
-    """
-    monkeypatch for fixing the training loop for deepspeed GA
-
-    see https://github.com/huggingface/transformers/pull/35157
-    """
-
-    try:
-        training_loop = get_training_loop_code()
-    except OSError:
-        return
-    Trainer._original_inner_training_loop = (  # pylint: disable=protected-access
-        training_loop
-    )
-    training_loop, _ = detab_code(training_loop)
-    if ORIGINAL_TRAINER_CODE not in training_loop:
-        return
-
-    training_loop = training_loop.replace(ORIGINAL_TRAINER_CODE, PATCHED_TRAINER_CODE)
-    training_loop = training_loop.replace(
-        "def _inner_training_loop(",
-        "def _fixed_inner_training_loop(",
-        1,
-    )
-
-    # load imports necessary
-    import transformers.trainer
-
-    items_to_import = []
-    for item in dir(transformers.trainer):
-        if item in training_loop:
-            items_to_import.append(item)
-
-    exec(  # pylint: disable=exec-used  # nosec B102
-        "from transformers.trainer import ("
-        + ", ".join(x for x in items_to_import)
-        + ")",
-        globals(),
-    )
-    exec(training_loop, globals())  # pylint: disable=exec-used  # nosec B102
-    LOG.info("patching _inner_training_loop for fsdp optimizer save")
-    Trainer._inner_training_loop = (  # pylint: disable=protected-access
-        _fixed_inner_training_loop  # pylint: disable=undefined-variable  # noqa: F821
-    )

src/axolotl/monkeypatch/unsloth_.py

@@ -9,7 +9,10 @@ import torch
 from accelerate.logging import get_logger
 from peft import PeftModelForCausalLM
 from torch import nn
-from transformers.models.llama.modeling_llama import LlamaFlashAttention2
+from transformers.models.llama.modeling_llama import (
+    LlamaFlashAttention2,
+    LlamaForCausalLM,
+)
 
 LOG = get_logger("axolotl.monkeypatch.unsloth")
 
@@ -52,6 +55,11 @@ def original_apply_o(self, hidden_states):
     return attn_output
 
 
+def get_forward_code() -> str:
+    forward = inspect.getsource(LlamaForCausalLM.forward)
+    return forward
+
+
 def get_self_attn_code() -> str:
     forward = inspect.getsource(LlamaFlashAttention2.forward)
     return forward
@@ -94,22 +102,12 @@ def integrate_cross_entropy_loss_patch(model_type: str = "llama") -> None:
 
 
 def detab_code(code: str) -> Tuple[str, str]:
-    try:
-        spaces = re.match(r"([\s\t]{1,})", code).group(0)
-        code = re.sub(r"^" + spaces, "", code, flags=re.MULTILINE)
-    except AttributeError:
-        return code, ""
+    spaces = re.match(r"([\s\t]{1,})", code).group(0)
+    code = re.sub(r"^" + spaces, "", code, flags=re.MULTILINE)
     return code, spaces
 
 
-self_attn_lora_patched = False  # pylint: disable=invalid-name
-
-
 def patch_self_attn_lora():
-    global self_attn_lora_patched  # pylint: disable=global-statement
-    if self_attn_lora_patched:
-        # prevent patching multiple times
-        return
     self_attn_forward = get_self_attn_code()
     LlamaFlashAttention2._original_forward = (  # pylint: disable=protected-access
         self_attn_forward
@@ -141,7 +139,6 @@ def patch_self_attn_lora():
         globals(),
     )
     exec(self_attn_forward, globals())  # pylint: disable=exec-used  # nosec B102
-    self_attn_lora_patched = True
     LOG.info("patching unsloth attn lora", main_process_only=True)
     LlamaFlashAttention2.forward = (
         unsloth_attn_forward  # pylint: disable=undefined-variable  # noqa: F821
@@ -191,7 +188,7 @@ def integrate_lora_mlp_patch(peft_model: PeftModelForCausalLM):
             for module in layer_modules
         )
         mlp_not_dora = all(
-            len(getattr(module, "lora_magnitude_vector", []) or []) == 0
+            getattr(module, "lora_magnitude_vector", None) is None
             for module in layer_modules
         )
 
@@ -216,7 +213,7 @@ def integrate_lora_patch(peft_model: PeftModelForCausalLM, cfg):
                 for module in layer_modules
             )
             qkv_not_dora = all(
-                len(getattr(module, "lora_magnitude_vector", []) or []) == 0
+                getattr(module, "lora_magnitude_vector", None) is None
                 for module in layer_modules
             )
 
@@ -235,7 +232,7 @@ def integrate_lora_patch(peft_model: PeftModelForCausalLM, cfg):
                 for module in layer_modules
             )
             o_not_dora = all(
-                len(getattr(module, "lora_magnitude_vector", []) or []) == 0
+                getattr(module, "lora_magnitude_vector", None) is None
                 for module in layer_modules
             )
 

src/axolotl/prompt_strategies/bradley_terry/chat_template.py

@@ -28,8 +28,6 @@ class BTChatTemplateStrategy(ChatTemplateStrategy):
         :return:
         """
 
-        max_length = self.prompter.max_length
-
         self.messages = "chosen_messages"
         # pylint: disable=duplicate-code
         prompt[self.messages] = []
@@ -41,16 +39,6 @@ class BTChatTemplateStrategy(ChatTemplateStrategy):
         prompt[self.messages].append({"role": "assistant", "content": prompt["chosen"]})
         chosen_tokenized = super().tokenize_prompt(prompt)
 
-        if len(chosen_tokenized["input_ids"]) > max_length:
-            LOG.warning(
-                f"Chosen sequence exceeds max sequence length: {len(chosen_tokenized['input_ids'])}",
-            )
-
-            chosen_tokenized["input_ids"] = chosen_tokenized["input_ids"][:max_length]
-            chosen_tokenized["attention_mask"] = chosen_tokenized["attention_mask"][
-                :max_length
-            ]
-
         self.messages = "rejected_messages"
         # pylint: disable=duplicate-code
         prompt[self.messages] = []
@@ -64,18 +52,6 @@ class BTChatTemplateStrategy(ChatTemplateStrategy):
         )
         rejected_tokenized = super().tokenize_prompt(prompt)
 
-        if len(rejected_tokenized["input_ids"]) > max_length:
-            LOG.warning(
-                f"Rejected sequence exceeds max sequence length: {len(rejected_tokenized['input_ids'])}",
-            )
-
-            rejected_tokenized["input_ids"] = rejected_tokenized["input_ids"][
-                :max_length
-            ]
-            rejected_tokenized["attention_mask"] = rejected_tokenized["attention_mask"][
-                :max_length
-            ]
-
         return {
             "input_ids_chosen": chosen_tokenized["input_ids"],
             "attention_mask_chosen": chosen_tokenized["attention_mask"],
@@ -104,9 +80,9 @@ def load(tokenizer, cfg, ds_cfg: Optional[Dict[str, Any]] = None):
         "roles": ds_cfg.get("roles"),
         "drop_system_message": ds_cfg.get("drop_system_message", False),
         # we need to add one for detecting sequences with exceeding the `sequence_len` limit.
-        "max_length": (
-            cfg.sequence_len + 1 if not cfg.reward_model else cfg.sequence_len
-        ),
+        "max_length": cfg.sequence_len + 1
+        if not cfg.reward_model
+        else cfg.sequence_len,
     }
 
     strategy_params = {

src/axolotl/prompt_strategies/chat_template.py

@@ -42,7 +42,6 @@ class ChatTemplatePrompter(Prompter):
                 "gpt": "assistant",
                 "system": "system",
             }
-
         self.message_field_role = message_field_role
         self.message_field_content = message_field_content
         self.message_field_training = message_field_training
@@ -54,9 +53,21 @@ class ChatTemplatePrompter(Prompter):
         self.drop_system_message = drop_system_message
 
     def build_prompt(self, conversation, add_generation_prompt=False, images=None):
+        turns = [
+            {
+                "role": self.roles[t[self.message_field_role]],
+                "content": t[self.message_field_content],
+                "training": t.get(self.message_field_training, None),
+            }
+            for t in conversation
+        ]
+
+        if self.drop_system_message and turns[0]["role"] == "system":
+            turns = turns[1:]
+
         if self.processor:
             text = self.processor.apply_chat_template(
-                conversation,
+                turns,
                 chat_template=self.chat_template,
                 tokenize=False,
                 add_generation_prompt=add_generation_prompt,
@@ -65,6 +76,8 @@ class ChatTemplatePrompter(Prompter):
                 text=text,
                 images=images,
                 return_tensors="pt",
+                truncation=True,
+                max_length=self.max_length,
             )
             # workaround since processor works in batches instead of single examples
             for k, val in batch.items():
@@ -75,7 +88,9 @@ class ChatTemplatePrompter(Prompter):
             return batch
 
         return self.tokenizer.apply_chat_template(
-            conversation,
+            turns,
+            truncation=True,
+            max_length=self.max_length,
             add_generation_prompt=add_generation_prompt,
             chat_template=self.chat_template,
         )
@@ -200,14 +215,7 @@ class ChatTemplateStrategy(PromptTokenizingStrategy):
         train_on_eos=None,
     ):
         super().__init__(prompter, tokenizer, train_on_inputs, sequence_len)
-
-        self.roles_to_train = []
-        if roles_to_train:
-            # map roles if exist in prompter.roles else use the role as is
-            self.roles_to_train = [
-                prompter.roles.get(role, role) for role in roles_to_train
-            ]
-
+        self.roles_to_train = roles_to_train if roles_to_train is not None else []
         self.train_on_eos = train_on_eos
         self.images = "images"
 
@@ -254,28 +262,30 @@ class ChatTemplateStrategy(PromptTokenizingStrategy):
 
             return tokenized_prompt
 
-        turns = self.get_conversation_thread(prompt)
+        turns = prompt[self.messages]
         input_ids = self.prompter.build_prompt(turns)
         labels = [IGNORE_TOKEN_ID] * len(input_ids)
 
         last_eos_idx = -1
         for index, turn in enumerate(turns):
-            role = turn.get("role")
-            content = turn.get("content")
-            train_turn = turn.get("training")
-            train_detail = turn.get("training_detail")
+            role = turn.get(self.prompter.message_field_role)
+            content = turn.get(self.prompter.message_field_content)
+            train_turn = turn.get(self.prompter.message_field_training)
+            train_detail = turn.get(self.prompter.message_field_training_detail)
 
             LOG.debug(
                 f"Processing turn {index}: role={role}, content={content}, train_turn={train_turn}, train_detail={train_detail}"
             )
 
-            should_train = None
-            if train_turn is not None:
-                should_train = train_turn
-            elif train_detail is not None:
-                should_train = bool(train_detail)
-            else:
-                should_train = self.train_on_inputs or role in self.roles_to_train
+            should_train = (
+                train_turn
+                if train_turn is not None
+                else (
+                    bool(train_detail is not None)
+                    if train_detail is not None
+                    else self.train_on_inputs or role in self.roles_to_train
+                )
+            )
 
             LOG.debug(f"Should train: {should_train}")
 
@@ -283,9 +293,6 @@ class ChatTemplateStrategy(PromptTokenizingStrategy):
                 conversation_ids=input_ids, turn=index, turn_content=turn
             )
 
-            if turn_start_idx == -1 or turn_end_idx == -1:
-                LOG.warning(f"Failed to find boundaries for turn {index}")
-
             LOG.debug(f"Turn indices: start={turn_start_idx}, end={turn_end_idx}")
 
             if should_train and turn_start_idx != -1 and turn_end_idx != -1:
@@ -306,9 +313,7 @@ class ChatTemplateStrategy(PromptTokenizingStrategy):
                     labels[turn_start_idx:turn_end_idx] = input_ids[
                         turn_start_idx:turn_end_idx
                     ]
-                    LOG.debug(
-                        f"Set labels for training from {turn_start_idx} to {turn_end_idx}"
-                    )
+                    LOG.debug(f"Labels set for range {turn_start_idx}:{turn_end_idx}")
 
                 LOG.debug(f"Labels after processing turn {index}: {labels}")
 
@@ -346,73 +351,52 @@ class ChatTemplateStrategy(PromptTokenizingStrategy):
                 return i
         return -1
 
-    def find_turn(self, conversation_ids: list[int], turn: int, turn_content: dict):
+    def find_turn(self, conversation_ids, turn, turn_content):
         """
         Locate the starting and ending indices of the specified turn in a conversation.
+
+        Args:
+            conversation_ids (list[int]): Token IDs representing the conversation.
+            turn (int): The turn number to locate (based on EOS tokens).
+            turn_content (str): String containing the content of the turn.
+
+        Returns:
+            tuple: (start_idx, end_idx) indices of the start and end of the turn content.
+                   Returns (-1, -1) if the turn content is not found.
         """
-        content = turn_content.get("content")
+        content = turn_content.get(self.prompter.message_field_content, "")
         content_ids = self.tokenizer.encode(content, add_special_tokens=False)
 
-        LOG.debug(f"content_ids (length {len(content_ids)}): {content_ids}")
+        eos_token_id = self.tokenizer.eos_token_id
+        eos_count = 0
+        start_search_idx = 0
 
-        if not content_ids:
-            LOG.warning(f"Empty content for turn {turn}")
-            return -1, -1
+        # Locate the starting index after the specified number of EOS tokens
+        for i, token_id in enumerate(conversation_ids):
+            if token_id == eos_token_id:
+                eos_count += 1
+                if eos_count == turn:
+                    start_search_idx = (
+                        i + 1
+                    )  # Start searching after the specified turn's EOS token
+                    break
 
-        # For first turn, start from beginning
-        if turn == 0:
-            start_search_idx = 0
+        # Find the start index of the content within the conversation
+        start_idx = -1
+        for i in range(start_search_idx, len(conversation_ids) - len(content_ids) + 1):
+            if conversation_ids[i : i + len(content_ids)] == content_ids:
+                start_idx = i
+                break
+
+        if start_idx != -1:
+            end_idx = start_idx + len(content_ids)
         else:
-            # For subsequent turns, find the previous EOS token
-            eos_token_id = self.tokenizer.eos_token_id
-            eos_count = 0
-            start_search_idx = 0
+            end_idx = -1
 
-            for i, token_id in enumerate(conversation_ids):
-                if token_id == eos_token_id:
-                    eos_count += 1
-                    if eos_count == turn:  # Find the nth EOS token where n = turn
-                        start_search_idx = i + 1
-                        break
-
-        # we can optimize this to only search for a few tokens from start_search_idx
-        # but it would risk missing the content if it's not found within the first few tokens or
-        # if start_search_idx cannot be found above.
-        last_index = len(conversation_ids) - len(content_ids) + 1
-
-        if last_index < start_search_idx:
-            LOG.warning(
-                f"last_index to search is less than start_search_idx for turn {turn}"
-            )
-            return -1, -1
-
-        # Search for content starting from start_search_idx
-        first_elem = content_ids[0]
-        for i in range(start_search_idx, last_index):
-            # Quick check of first element before doing full comparison
-            if conversation_ids[i] == first_elem:
-                # Check if the rest of the content matches
-                if conversation_ids[i : i + len(content_ids)] == content_ids:
-                    LOG.debug(f"Found turn {turn} content at position {i}")
-                    return i, i + len(content_ids)
-
-        return -1, -1
+        return start_idx, end_idx
 
     def get_conversation_thread(self, prompt):
-        turns = [
-            {
-                "role": self.prompter.roles[t[self.prompter.message_field_role]],
-                "content": t[self.prompter.message_field_content],
-                "training": t.get(self.prompter.message_field_training),
-                "training_detail": t.get(self.prompter.message_field_training_detail),
-            }
-            for t in prompt[self.messages]
-        ]
-
-        if self.prompter.drop_system_message and turns[0]["role"] == "system":
-            turns = turns[1:]
-
-        return turns
+        return prompt[self.messages]
 
     def get_images(self, prompt):
         return prompt.get(self.images, None)

src/axolotl/prompt_strategies/instruct.py

@@ -0,0 +1,33 @@
+"""Module containing the InstructShareGPTPromptTokenizingStrategy class"""
+from typing import Any, Dict, Optional
+
+from axolotl.prompt_tokenizers import ShareGPTPromptTokenizingStrategy
+from axolotl.prompters import ShareGPTPrompterV2
+
+
+def load(tokenizer, cfg, ds_cfg: Optional[Dict[str, Any]] = None):
+    conversation = (
+        ds_cfg["conversation"] if ds_cfg and "conversation" in ds_cfg else None
+    )
+    strategy = InstructShareGPTPromptTokenizingStrategy(
+        # pylint: disable=duplicate-code
+        ShareGPTPrompterV2(
+            conversation=conversation,
+        ),
+        tokenizer,
+        cfg.train_on_inputs,
+        cfg.sequence_len,
+    )
+    return strategy
+
+
+class InstructShareGPTPromptTokenizingStrategy(ShareGPTPromptTokenizingStrategy):
+    """
+    basic sharegpt strategy to grab conversations from the sample row
+    """
+
+    def get_conversation_thread(self, prompt):
+        return [
+            {"from": "human", "value": prompt["instruction"]},
+            {"from": "gpt", "value": prompt["output"]},
+        ]

src/axolotl/prompt_strategies/llama2_chat.py

@@ -29,7 +29,7 @@ from dataclasses import dataclass, field
 from typing import Generator, List, Sequence
 
 from axolotl.prompt_tokenizers import PromptTokenizingStrategy
-from axolotl.prompters import ALTERNATING_ASSERTION_FAILED_ROLE, IGNORE_TOKEN_ID
+from axolotl.prompters import IGNORE_TOKEN_ID, SHAREGPT_ASSERTION_FAILED_ROLE
 
 
 @dataclass
@@ -75,7 +75,7 @@ class Llama2ChatConversation:
 
 class LLama2ChatTokenizingStrategy(PromptTokenizingStrategy):
     """
-    Tokenizing strategy for Llama2 prompts.
+    Tokenizing strategy for ShareGPT prompts.
     adapted from https://github.com/lm-sys/FastChat/blob/main/fastchat/train/train.py
     """
 
@@ -191,7 +191,7 @@ class Llama2ChatPrompter:  # pylint: disable=too-few-public-methods
         conv.messages = []  # pylint: disable=R0801
         for j, sentence in enumerate(source):
             role = roles[sentence["from"]]
-            assert role == conv.roles[j % 2], ALTERNATING_ASSERTION_FAILED_ROLE
+            assert role == conv.roles[j % 2], SHAREGPT_ASSERTION_FAILED_ROLE
             if sentence["value"]:
                 conv.append_message(role, sentence["value"])
         yield conv

src/axolotl/prompt_strategies/sharegpt.py

@@ -0,0 +1,223 @@
+"""Module containing the SimpleShareGPTPromptTokenizingStrategy class"""
+
+import logging
+from typing import Any, Dict, Optional, Type
+
+from fastchat.conversation import Conversation, SeparatorStyle, register_conv_template
+
+from axolotl.prompt_tokenizers import ShareGPTPromptTokenizingStrategy
+from axolotl.prompters import ShareGPTPrompterV2
+from axolotl.utils.tokenization import (
+    chatml_to_conversation,
+    merge_consecutive_messages,
+)
+
+LOG = logging.getLogger("axolotl")
+
+
+def register_chatml_template(system_message=None):
+    system_message = system_message or "You are a helpful assistant."
+    register_conv_template(
+        Conversation(
+            name="chatml",
+            system_template="<|im_start|>system\n{system_message}",
+            system_message=system_message,
+            roles=("<|im_start|>user", "<|im_start|>assistant"),
+            sep_style=SeparatorStyle.CHATML,
+            sep="<|im_end|>",
+        )
+    )
+    register_conv_template(
+        Conversation(
+            name="chatml_glaive",
+            system_template="<|im_start|>system\n{system_message}",
+            system_message=system_message,
+            roles=("<|im_start|>user", "<|im_start|>assistant", "<|im_start|>tool"),
+            sep_style=SeparatorStyle.CHATML,
+            sep="<|im_end|>",
+        )
+    )
+
+
+def register_llama3_template(system_message=None):
+    system_message = system_message or "You are a helpful assistant."
+    register_conv_template(
+        Conversation(
+            name="llama3",
+            system_template="<|start_header_id|>system<|end_header_id|>\n\n{system_message}<|eot_id|>",
+            system_message=system_message,
+            roles=("user", "assistant"),
+            sep_style=SeparatorStyle.LLAMA3,
+            sep="",
+            stop_str="<|eot_id|>",
+            stop_token_ids=[128001, 128009],
+        )
+    )
+
+
+def build_loader(
+    tokenization_strategy_cls: Type["ShareGPTPromptTokenizingStrategy"],
+    prompter_cls: Type["ShareGPTPrompterV2"],
+    default_conversation: Optional[str] = None,
+):
+    def _load(tokenizer, cfg, ds_cfg: Optional[Dict[str, Any]] = None):
+        LOG.warning(
+            "sharegpt type support will be deprecated in the next release of Axolotl. Please use chat_template instead. https://axolotl-ai-cloud.github.io/axolotl/docs/dataset-formats/conversation.html#chat_template",
+        )
+        conversation = (
+            ds_cfg["conversation"]
+            if ds_cfg and "conversation" in ds_cfg
+            else default_conversation
+        )
+        field_human = (
+            ds_cfg["field_human"] if ds_cfg and "field_human" in ds_cfg else None
+        )
+        field_model = (
+            ds_cfg["field_model"] if ds_cfg and "field_model" in ds_cfg else None
+        )
+        roles = ds_cfg["roles"].to_dict() if ds_cfg and "roles" in ds_cfg else None
+        strategy = tokenization_strategy_cls(
+            prompter_cls(
+                conversation=conversation,
+                role_key_model=field_model,
+                role_key_human=field_human,
+                roles=roles,
+            ),
+            tokenizer,
+            cfg.train_on_inputs,
+            cfg.sequence_len,
+        )
+        if ds_cfg and "strict" in ds_cfg and hasattr(strategy, "strict"):
+            strategy.strict = ds_cfg["strict"]
+        if ds_cfg and "field_messages" in ds_cfg and hasattr(strategy, "messages"):
+            strategy.messages = ds_cfg["field_messages"]
+        return strategy
+
+    return _load
+
+
+class SimpleShareGPTPromptTokenizingStrategy(ShareGPTPromptTokenizingStrategy):
+    """
+    basic sharegpt strategy to grab conversations from the sample row
+    """
+
+    _strict = False
+    _messages = "conversations"
+
+    @property
+    def strict(self):
+        return self._strict
+
+    @strict.setter
+    def strict(self, strict):
+        self._strict = strict
+
+    @property
+    def messages(self):
+        return self._messages
+
+    @messages.setter
+    def messages(self, messages):
+        self._messages = messages
+
+    def get_conversation_thread(self, prompt):
+        conversations = prompt[self.messages]
+        if self.strict:
+            return conversations
+        role_key = "from"
+        if "role" in conversations[0].keys():
+            role_key = "role"
+        value_key = "value"
+        if "text" in conversations[0].keys():
+            value_key = "text"
+        elif "content" in conversations[0].keys():
+            value_key = "content"
+        # remap roles - allow for assistant turn"
+        role_map = {
+            "user": "human",
+            "human": "human",
+            "assistant": "gpt",
+            "gpt": "gpt",
+            "system": "system",
+        }
+        turns = [
+            {
+                "from": (
+                    role_map[t[role_key]] if t[role_key] in role_map else t[role_key]
+                ),
+                "value": t[value_key],
+                "weight": 1
+                if "weight" not in t or t["weight"] is None
+                else t["weight"],
+            }
+            for t in conversations
+        ]
+        return turns
+
+
+class SimpleRoleShareGPTPromptTokenizingStrategy(
+    SimpleShareGPTPromptTokenizingStrategy
+):
+    """
+    basic sharegpt strategy to grab conversations from the sample row, but uses role instead of from
+    """
+
+    def get_conversation_thread(self, prompt):
+        conversations = prompt["conversations"]
+        # remap role: prompter/assistant, text: ... => from: human/gpt, value: ...
+        turns = [{"from": t["role"], "value": t["value"]} for t in conversations]
+        return turns
+
+
+class GuanacoShareGPTPromptTokenizingStrategy(ShareGPTPromptTokenizingStrategy):
+    """
+    sharegpt strategy that remaps oasst data to sharegpt format
+    """
+
+    def get_conversation_thread(self, prompt):
+        conversations = prompt["conversations"]
+        # remap role: prompter/assistant, text: ... => from: human/gpt, value: ...
+        role_map = {"prompter": "human", "assistant": "gpt"}
+        turns = [
+            {"from": role_map[t["role"]], "value": t["text"]} for t in conversations
+        ]
+        return turns
+
+
+class UltrachatShareGPTPromptTokenizingStrategy(SimpleShareGPTPromptTokenizingStrategy):
+    """
+    sharegpt strategy that remaps ultrachat data to sharegpt format
+    """
+
+    def get_conversation_thread(self, prompt):
+        conversations = prompt["messages"]
+        role_map = {"user": "human", "assistant": "gpt"}
+        turns = [
+            {"from": role_map[t["role"]], "value": t["content"]} for t in conversations
+        ]
+        return turns
+
+
+class GlaiveShareGPTPromptTokenizingStrategy(SimpleShareGPTPromptTokenizingStrategy):
+    """
+    sharegpt strategy that remaps glaive data to sharegpt format
+    """
+
+    def get_conversation_thread(self, prompt):
+        conversation = chatml_to_conversation(prompt)
+        conversation = merge_consecutive_messages(conversation)
+
+        return conversation
+
+
+load = build_loader(SimpleShareGPTPromptTokenizingStrategy, ShareGPTPrompterV2)
+load_role = build_loader(SimpleRoleShareGPTPromptTokenizingStrategy, ShareGPTPrompterV2)
+load_ultrachat = build_loader(
+    UltrachatShareGPTPromptTokenizingStrategy, ShareGPTPrompterV2
+)
+load_guanaco = build_loader(GuanacoShareGPTPromptTokenizingStrategy, ShareGPTPrompterV2)
+load_glaive = build_loader(
+    GlaiveShareGPTPromptTokenizingStrategy,
+    ShareGPTPrompterV2,
+    default_conversation="chatml_glaive",
+)

src/axolotl/prompt_strategies/sharegpt_jokes.py

@@ -0,0 +1,28 @@
+"""Module for Jokes prompts using sharegpt style """
+from axolotl.prompt_tokenizers import ShareGPTPromptTokenizingStrategy
+from axolotl.prompters import ShareGPTPrompterV2
+
+
+def load(tokenizer, cfg):
+    return SimpleJokesShareGPTPromptTokenizingStrategy(
+        ShareGPTPrompterV2(),
+        tokenizer,
+        cfg.train_on_inputs,
+        cfg.sequence_len,
+    )
+
+
+class SimpleJokesShareGPTPromptTokenizingStrategy(ShareGPTPromptTokenizingStrategy):
+    """
+    Tokenization strategy for asking bot to tell a joke and then explain why its funny
+    """
+
+    # title, text, explanation
+    def get_conversation_thread(self, prompt):
+        title = "" if not prompt["title"] else prompt["title"] + " "
+        return [
+            {"from": "human", "value": "Tell me a joke."},
+            {"from": "gpt", "value": title + prompt["text"]},
+            {"from": "human", "value": "Why is that joke funny?"},
+            {"from": "gpt", "value": prompt["explanation"]},
+        ]

src/axolotl/prompt_tokenizers.py

@@ -1,12 +1,17 @@
 """Module containing PromptTokenizingStrategy and Prompter classes"""
 
 import abc
+import copy
 import logging
 from typing import Dict, List, Tuple, Union
 
+from fastchat.conversation import Conversation
 from transformers import BatchEncoding, PreTrainedTokenizer
 
-from axolotl.prompters import Prompter
+from axolotl.monkeypatch.fastchat_conversation_turns import (
+    add_get_turns_to_conversation,
+)
+from axolotl.prompters import IGNORE_TOKEN_ID, Prompter
 
 LOG = logging.getLogger("axolotl")
 
@@ -16,6 +21,8 @@ LLAMA_DEFAULT_EOS_TOKEN = "</s>"  # nosec
 LLAMA_DEFAULT_BOS_TOKEN = "<s>"  # nosec
 LLAMA_DEFAULT_UNK_TOKEN = "<unk>"  # nosec
 
+add_get_turns_to_conversation()
+
 
 class InvalidDataException(Exception):
     """
@@ -324,6 +331,154 @@ class AlpacaReflectionPTStrategy(ReflectionPromptTokenizingStrategy):
         )
 
 
+class ShareGPTPromptTokenizingStrategy(PromptTokenizingStrategy):
+    """
+    Tokenizing strategy for ShareGPT prompts.
+    """
+
+    def get_conversation_thread(self, prompt):
+        return prompt["conversations"]
+
+    def tokenize_prompt(self, prompt):
+        # Initial values. We will append to these as we go through the conversation.
+        result, current_len = tokenize_prompt_default()
+        conversation: Conversation = (
+            self.prompter._conversation.copy()  # pylint: disable=protected-access
+        )
+
+        input_roles = {conversation.roles[0]}
+        output_roles = {conversation.roles[1]}
+
+        if len(conversation.roles) == 3:
+            tool_role_label = conversation.roles[2]
+            input_roles.add(tool_role_label)
+
+        # Add roles from the config
+        if self.prompter.roles:
+            if "input" in self.prompter.roles and self.prompter.roles["input"]:
+                for role in self.prompter.roles["input"]:
+                    input_roles.add(role)
+
+            if "output" in self.prompter.roles and self.prompter.roles["output"]:
+                for role in self.prompter.roles["output"]:
+                    output_roles.add(role)
+
+        # support for custom roles from the dataset, only useful for vicuna style prompts/roles
+        role_remap = []
+        if (
+            conversation.name == "vicuna_v1.1"
+            and "roles" in prompt
+            and len(prompt["roles"]) >= 2
+        ):
+            role_remap = [
+                {"from": conversation.roles[0], "to": prompt["roles"][0]},
+                {"from": conversation.roles[1], "to": prompt["roles"][1]},
+            ]
+
+        try:
+            for _, part in enumerate(
+                self.prompter.build_prompt(self.get_conversation_thread(prompt))
+            ):
+                if not isinstance(part, tuple):
+                    LOG.warning(f"expected tuple, got {part}")
+                    continue
+
+                if len(part) <= 2:
+                    role, content = part
+                    weight = 1
+                else:
+                    role, content, weight = part
+
+                # Uses "in" because role contains extra characters
+                input_turn = any(r.lower() in role.lower() for r in input_roles)
+                output_turn = any(r.lower() in role.lower() for r in output_roles)
+                empty_role = role.strip() == ""
+
+                if not any([input_turn, output_turn, empty_role]):
+                    LOG.warning(f"unhandled role: {role}")
+                    continue
+
+                if input_turn:
+                    role = (
+                        role.replace(role_remap[0]["from"], role_remap[0]["to"])
+                        if role_remap
+                        else role
+                    )
+                    turn = role + content
+                    # this is still the user query, we should
+                    if not content.strip():
+                        LOG.warning(f"user turn has empty text: {prompt}")
+                    res = self._tokenize(
+                        turn,
+                        add_eos_token=False,
+                        strip_bos_token=True,
+                    )
+                    if self.train_on_inputs and weight == 1:
+                        labels = copy.deepcopy(res["input_ids"])
+                    else:
+                        # everything from this is masked out from the labels
+                        labels = [IGNORE_TOKEN_ID] * len(res["input_ids"])
+                elif output_turn:
+                    role = (
+                        role.replace(role_remap[1]["from"], role_remap[1]["to"])
+                        if role_remap
+                        else role
+                    )
+                    turn = role + content
+                    # this should be the assistant response, should end with an eos token
+                    if not content.strip():
+                        LOG.warning(f"assistant turn has empty text: {prompt}")
+                    add_eos_token = not (
+                        conversation.name == "chatml"
+                        and conversation.sep == self.tokenizer.eos_token
+                    )
+                    res = self._tokenize(
+                        turn,
+                        add_eos_token=add_eos_token,
+                        strip_bos_token=True,
+                    )
+                    role_res = self._tokenize(
+                        role.rstrip(),
+                        add_eos_token=False,
+                        strip_bos_token=True,
+                    )
+                    labels = copy.deepcopy(res["input_ids"])
+                    if not self.train_on_inputs:
+                        # mask out role tokens from the labels
+                        len_role = len(role_res["input_ids"])
+                        labels[:len_role] = [IGNORE_TOKEN_ID] * min(
+                            len_role, len(labels)
+                        )
+                    if weight == 0:
+                        # everything from this is masked out from the labels
+                        # (role is masked out too because it makes no sense if contents is masked out)
+                        labels = [IGNORE_TOKEN_ID] * len(res["input_ids"])
+
+                elif empty_role:
+                    turn = content
+                    # this is only ever the first part, should include the bos token and the user query
+                    res = self._tokenize(
+                        turn, add_eos_token=False, strip_bos_token=False
+                    )
+                    if self.train_on_inputs and weight == 1:
+                        labels = copy.deepcopy(res["input_ids"])
+                    else:
+                        # everything from this is masked out from the labels
+                        labels = [IGNORE_TOKEN_ID] * len(res["input_ids"])
+
+                # pylint: disable=duplicate-code
+                result, current_len = parse_tokenized_to_result(
+                    result,
+                    current_len,
+                    res,
+                    labels,
+                    pad_token_id=self.tokenizer.pad_token_id,
+                )
+            return result
+        except (KeyError, AssertionError, IndexError) as err:
+            raise InvalidDataException(str(err)) from err
+
+
 def tokenize_prompt_default() -> Tuple[Dict[str, List[int]], int]:
     """
     Returns the default values for the tokenize prompt function

src/axolotl/prompters.py

@@ -5,6 +5,7 @@ from enum import Enum
 from typing import Generator, Optional, Union
 
 from colorama import Fore
+from fastchat.conversation import Conversation, get_conv_template
 
 LOG = logging.getLogger("axolotl")
 IGNORE_TOKEN_ID = -100
@@ -261,10 +262,166 @@ class ReflectAlpacaPrompter(Prompter):
         )
 
 
-ALTERNATING_ASSERTION_FAILED_ROLE = (
+SHAREGPT_ASSERTION_FAILED_ROLE = (
     "Role did not alternate between turns (gpt and human). Please check your data."
 )
 
+CONVERSATION_ROLE_FORMAT = {
+    "chatml": "<|im_start|>{ROLE}",
+    "zephyr": "<|{ROLE}|>",
+    "vicuna_v1.1": "{ROLE}",
+    "llama3": "<|start_header_id|>{ROLE}<|end_header_id|>",
+}
+
+
+class ShareGPTPrompter(Prompter):  # pylint: disable=too-few-public-methods
+    """
+    A prompter that generates prompts for the ShareGPT
+    """
+
+    role_key_human = "human"
+    role_key_model = "gpt"
+    # Optional, only used for tool usage datasets.
+    role_key_tool: Optional[str] = None
+    # Optional, role input/output mapping
+    roles: Optional[dict] = None
+
+    def __init__(
+        self,
+        prompt_style=None,  # pylint: disable=unused-argument
+        conversation: Optional[Union[str, Conversation]] = None,
+        role_key_human: Optional[str] = None,
+        role_key_model: Optional[str] = None,
+        role_key_tool: Optional[str] = None,
+        roles: Optional[dict] = None,
+    ):
+        if conversation:
+            if isinstance(conversation, Conversation):
+                self._conversation = conversation
+            else:
+                self._conversation = get_conv_template(conversation)
+        else:
+            self._conversation = get_conv_template("vicuna_v1.1")
+        if role_key_human:
+            self.role_key_human = role_key_human
+        if role_key_model:
+            self.role_key_model = role_key_model
+        if role_key_tool:
+            self.role_key_tool = role_key_tool
+        if roles:
+            self.roles = roles
+
+    def _build_result(self, source):
+        if len(source) < 2:
+            # If there isn't a back and forth conversation, ignore it
+            # also happens on the data splitting leaving empty conversations
+            raise IndexError(
+                f"A conversation entry has less than 2 messages :\n{source}"
+            )
+
+        conv = self._conversation.copy()
+
+        original_source = source.copy()
+        # Add the conversation system prompt if provided, otherwise use the default one
+        if source[0]["from"] == "system":
+            conv.set_system_message(source[0]["value"])
+            source.pop(0)
+
+        roles = {self.role_key_human: conv.roles[0], self.role_key_model: conv.roles[1]}
+        if self.role_key_tool:
+            roles[self.role_key_tool] = conv.roles[2]
+
+        try:
+            # Apply prompt templates
+            if source[0]["from"] not in roles:
+                # Skip the first one if it is not from human
+                source = source[1:]
+        except IndexError as err:
+            # sometimes there is a bing or system chat
+            raise err
+
+        conv.messages = []
+        for _, sentence in enumerate(source):
+            from_role = sentence["from"]
+            if from_role in roles:
+                role = roles[from_role]
+            else:
+                if self._conversation.name not in CONVERSATION_ROLE_FORMAT:
+                    raise NotImplementedError(
+                        f"Role ({role}) not in default roles, and {self._conversation.name} does not support role remapping yet."
+                        "Please help us by creating an Issue to add support for this conversation type."
+                    )
+
+                if self._conversation.name in ["llama3"]:
+                    role = from_role
+                else:
+                    role = CONVERSATION_ROLE_FORMAT[self._conversation.name].format(
+                        ROLE=from_role
+                    )
+
+            if len(conv.messages) > 0 and ((role == conv.messages[-1][0])):
+                if (
+                    role != "assistant"
+                ):  # back to back assistant calls may be okay for tool calls
+                    LOG.warning(f"{SHAREGPT_ASSERTION_FAILED_ROLE}: {sentence}")
+
+            conv.append_message(role, sentence["value"])
+        turns = list(conv.get_turns())
+        original_source_length = len(original_source)
+        assert len(turns) in [
+            original_source_length - 1,
+            original_source_length,
+            original_source_length + 1,
+        ]
+        if len(turns) == original_source_length + 1:
+            original_source = [{"weight": None}] + original_source
+        elif len(turns) == original_source_length - 1:
+            original_source = original_source[1:]
+        return [
+            (*turn, weight)
+            for turn, weight in zip(
+                turns,
+                [
+                    1 if "weight" not in e or e["weight"] is None else e["weight"]
+                    for e in original_source
+                ],
+            )
+        ]
+
+    def build_prompt(self, source) -> Generator[str, None, None]:
+        turns = self._build_result(source)
+
+        for part in turns:
+            if part[0] and not part[1]:
+                LOG.warning(f"role with empty message: {part[0]}")
+            yield part
+
+    def __repr__(self) -> str:
+        turns = self._build_result([{"from": "{from}", "value": "{value}"}])
+        return "\n".join([REPR_TEMPLATE.format(full_prompt=part) for part in turns])
+
+
+class ShareGPTPrompterV2(ShareGPTPrompter):
+    """
+    A V2 prompter that generates prompts for the ShareGPT
+    """
+
+    def __init__(
+        self,
+        conversation: Optional[Union[str, Conversation]] = None,
+        role_key_human: Optional[str] = None,
+        role_key_model: Optional[str] = None,
+        role_key_tool: Optional[str] = None,
+        roles: Optional[dict] = None,
+    ):
+        super().__init__(
+            conversation=conversation,
+            role_key_human=role_key_human,
+            role_key_model=role_key_model,
+            role_key_tool=role_key_tool,
+            roles=roles,
+        )
+
 
 class UnsupportedPrompter(Prompter):
     """

src/axolotl/train.py

@@ -260,28 +260,9 @@ def train(
 
     if not cfg.hub_model_id:
         try:
-            model_card_kwarg = {
-                "model_name": cfg.output_dir.lstrip("./")
-                .encode("utf-8")
-                .decode("utf-8")
-            }
-            if cfg.datasets is not None:
-                if cfg.rl is not None or cfg.reward_model:
-                    dataset_tags = [
-                        d["path"] for d in cfg.datasets if not Path(d["path"]).is_dir()
-                    ]
-                    if dataset_tags:
-                        # guard as create_model_card may fail if dataset_tags is empty list
-                        model_card_kwarg["dataset_name"] = dataset_tags
-                else:
-                    dataset_tags = [
-                        d["path"] for d in cfg.datasets if not Path(d["path"]).is_dir()
-                    ]
-                    if dataset_tags:
-                        # guard as create_model_card may fail if dataset_tags is empty list
-                        model_card_kwarg["dataset_tags"] = dataset_tags
-
-            trainer.create_model_card(**model_card_kwarg)
+            trainer.create_model_card(
+                model_name=cfg.output_dir.lstrip("./").encode("utf-8").decode("utf-8")
+            )
         except (AttributeError, UnicodeDecodeError):
             pass
     elif cfg.hub_model_id:

src/axolotl/utils/__init__.py

@@ -1,11 +1,7 @@
 """
 Basic utils for Axolotl
 """
-
 import importlib.util
-import re
-
-import torch
 
 
 def is_mlflow_available():
@@ -14,23 +10,3 @@ def is_mlflow_available():
 
 def is_comet_available():
     return importlib.util.find_spec("comet_ml") is not None
-
-
-# pylint: disable=duplicate-code
-def get_pytorch_version() -> tuple[int, int, int]:
-    """
-    Get Pytorch version as a tuple of (major, minor, patch).
-    """
-    torch_version = torch.__version__
-    version_match = re.match(r"^(\d+)\.(\d+)(?:\.(\d+))?", torch_version)
-
-    if not version_match:
-        raise ValueError("Invalid version format")
-
-    major, minor, patch = version_match.groups()
-    major, minor = int(major), int(minor)
-    patch = int(patch) if patch is not None else 0  # Default patch to 0 if not present
-    return major, minor, patch
-
-
-# pylint: enable=duplicate-code

src/axolotl/utils/bench.py

@@ -1,23 +1,9 @@
 """Benchmarking and measurement utilities"""
 import functools
 
+import pynvml
 import torch
-from transformers.utils.import_utils import is_torch_npu_available
-
-from axolotl.utils.distributed import get_device_type
-
-try:
-    from pynvml import (
-        NVMLError,
-        nvmlDeviceGetHandleByIndex,
-        nvmlDeviceGetMemoryInfo,
-        nvmlInit,
-    )
-except ImportError:
-    NVMLError = None
-    nvmlDeviceGetHandleByIndex = None
-    nvmlDeviceGetMemoryInfo = None
-    nvmlInit = None
+from pynvml.nvml import NVMLError
 
 
 def check_cuda_device(default_value):
@@ -67,35 +53,24 @@ def mps_memory_usage_all():
     return usage, reserved - usage, 0
 
 
-def npu_memory_usage_all(device=0):
-    usage = torch.npu.memory_allocated(device) / 1024.0**3
-    reserved = torch.npu.memory_reserved(device) / 1024.0**3
-    return usage, reserved - usage, 0
-
-
 @check_cuda_device(0.0)
 def gpu_memory_usage_smi(device=0):
     if isinstance(device, torch.device):
         device = device.index
     if isinstance(device, str) and device.startswith("cuda:"):
         device = int(device[5:])
-    if not nvmlInit:
-        return 0.0
     try:
-        nvmlInit()
-        handle = nvmlDeviceGetHandleByIndex(device)
-        info = nvmlDeviceGetMemoryInfo(handle)
+        pynvml.nvmlInit()
+        handle = pynvml.nvmlDeviceGetHandleByIndex(device)
+        info = pynvml.nvmlDeviceGetMemoryInfo(handle)
         return info.used / 1024.0**3
     except NVMLError:
         return 0.0
 
 
 def log_gpu_memory_usage(log, msg, device):
-    cur_device = get_device_type()
     if torch.backends.mps.is_available():
         usage, cache, misc = mps_memory_usage_all()
-    elif "npu" in str(cur_device) and is_torch_npu_available():
-        usage, cache, misc = npu_memory_usage_all(device)
     else:
         usage, cache, misc = gpu_memory_usage_all(device)
     extras = []
@@ -104,7 +79,6 @@ def log_gpu_memory_usage(log, msg, device):
     if misc > 0:
         extras.append(f"+{misc:.03f}GB misc")
     log.info(
-        f"{str(cur_device)} memory usage {msg}: {usage:.03f}GB ({', '.join(extras)})",
-        stacklevel=2,
+        f"GPU memory usage {msg}: {usage:.03f}GB ({', '.join(extras)})", stacklevel=2
     )
     return usage, cache, misc

src/axolotl/utils/callbacks/__init__.py

@@ -28,7 +28,6 @@ from transformers import (
     TrainingArguments,
 )
 from transformers.trainer_utils import PREFIX_CHECKPOINT_DIR, IntervalStrategy
-from trl.models import unwrap_model_for_generation
 
 from axolotl.utils import is_comet_available, is_mlflow_available
 from axolotl.utils.bench import log_gpu_memory_usage
@@ -47,7 +46,6 @@ from axolotl.utils.distributed import (
 if TYPE_CHECKING:
     from axolotl.core.trainer_builder import AxolotlTrainingArguments
 
-
 IGNORE_INDEX = -100
 LOG = logging.getLogger("axolotl.callbacks")
 
@@ -380,10 +378,7 @@ def causal_lm_bench_eval_callback_factory(trainer: Trainer, tokenizer):
             for metric in self.cfg.eval_causal_lm_metrics:
                 if metric == "perplexity":
                     max_seq_len = self.cfg.eval_max_new_tokens
-                    metrics[metric] = Perplexity(
-                        tokenizer=tokenizer,
-                        max_seq_len=max_seq_len,
-                    )
+                    metrics[metric] = Perplexity(trainer.model, tokenizer, max_seq_len)
                 else:
                     try:
                         metrics[metric] = evaluate.load(metric)
@@ -400,11 +395,8 @@ def causal_lm_bench_eval_callback_factory(trainer: Trainer, tokenizer):
             eval_dataloader,
             **kwargs,  # pylint: disable=unused-argument
         ):
-            trainer.model_wrapped.eval()
-
-            device = torch.device(
-                self.cfg.device
-            )  # Use this instead of trainer.model_wrapped.device as it may return cpu if fsdp offloaded
+            trainer.model.eval()
+            device = torch.device(self.cfg.device)
 
             # pylint: disable=duplicate-code
             generation_config = GenerationConfig(
@@ -441,10 +433,6 @@ def causal_lm_bench_eval_callback_factory(trainer: Trainer, tokenizer):
                         for k in metric._feature_names()  # pylint: disable=protected-access
                         if k in kwargs
                     }
-
-                    if isinstance(metric, Perplexity):
-                        metric_kwargs["model"] = trainer.model_wrapped
-
                     metric_score = metric.compute(**metric_kwargs)
                     return (
                         metric_score["score"]
@@ -480,97 +468,89 @@ def causal_lm_bench_eval_callback_factory(trainer: Trainer, tokenizer):
             def predict_with_generate():
                 eval_src, eval_pred, eval_ref = [], [], []
 
-                with unwrap_model_for_generation(
-                    trainer.model_wrapped, trainer.accelerator
-                ) as unwrapped_model:
-                    for batch in tqdm(eval_dataloader, disable=not is_main_process()):
-                        batch_labels = batch["labels"].to(device)
-                        batch_input_ids = batch["input_ids"].to(device)
+                for batch in tqdm(eval_dataloader):
+                    batch_labels = batch["labels"].to(device)
+                    batch_input_ids = batch["input_ids"].to(device)
 
-                        if "position_ids" in batch:
-                            batch_pos_ids = batch["position_ids"].tolist()
+                    if "position_ids" in batch:
+                        batch_pos_ids = batch["position_ids"].tolist()
+                    else:
+                        batch_pos_ids = [None] * len(batch["input_ids"])
+
+                    prompt_token_ids_list = []
+                    completion_token_ids_list = []
+
+                    for input_ids_all, labels_all, pos_ids in zip(
+                        batch_input_ids,
+                        batch_labels,
+                        batch_pos_ids,
+                    ):
+                        if pos_ids is None:
+                            pos_ranges = [(0, len(input_ids_all) - 1)]
                         else:
-                            batch_pos_ids = [None] * len(batch["input_ids"])
+                            pos_ranges = find_ranges(pos_ids)
 
-                        prompt_token_ids_list = []
-                        completion_token_ids_list = []
+                        for pos_range in pos_ranges:
+                            start, end = pos_range
+                            if start == end:
+                                continue
 
-                        for input_ids_all, labels_all, pos_ids in zip(
-                            batch_input_ids,
-                            batch_labels,
-                            batch_pos_ids,
-                        ):
-                            if pos_ids is None:
-                                pos_ranges = [(0, len(input_ids_all) - 1)]
-                            else:
-                                pos_ranges = find_ranges(pos_ids)
+                            input_ids = input_ids_all[start : end + 1]
+                            labels = labels_all[start : end + 1]
 
-                            for pos_range in pos_ranges:
-                                start, end = pos_range
-                                if start == end:
-                                    continue
-
-                                input_ids = input_ids_all[start : end + 1]
-                                labels = labels_all[start : end + 1]
-
-                                tokens_without_loss = labels == IGNORE_INDEX
-                                tokens_with_loss = labels != IGNORE_INDEX
-                                tokens_exclude_padding = (
-                                    input_ids != tokenizer.pad_token_id
-                                )
-                                prompt_token_includes = (
-                                    tokens_without_loss & tokens_exclude_padding
-                                )
-
-                                prompt_token_ids = input_ids[prompt_token_includes]
-                                prompt_token_ids_list.append(prompt_token_ids)
-
-                                completion_token_ids = input_ids[tokens_with_loss]
-                                completion_token_ids_list.append(completion_token_ids)
-
-                        prompt_texts = tokenizer.batch_decode(
-                            prompt_token_ids_list, skip_special_tokens=True
-                        )
-                        completion_texts = tokenizer.batch_decode(
-                            completion_token_ids_list, skip_special_tokens=True
-                        )
-
-                        with torch.no_grad():
-                            prompt_encoding = tokenizer(
-                                prompt_texts, padding=True, return_tensors="pt"
-                            ).to(device)
-
-                            predictions = unwrapped_model.generate(
-                                **prompt_encoding, generation_config=generation_config
+                            tokens_without_loss = labels == IGNORE_INDEX
+                            tokens_with_loss = labels != IGNORE_INDEX
+                            tokens_exclude_padding = input_ids != tokenizer.pad_token_id
+                            prompt_token_includes = (
+                                tokens_without_loss & tokens_exclude_padding
                             )
 
-                            del prompt_encoding
+                            prompt_token_ids = input_ids[prompt_token_includes]
+                            prompt_token_ids_list.append(prompt_token_ids)
 
-                        prediction_all_tokens = predictions["sequences"].cpu().tolist()
-                        prediction_without_prompt_tokens_list = []
-                        for prompt_token_ids, prediction_tokens in zip(
-                            prompt_token_ids_list, prediction_all_tokens
-                        ):
-                            prediction_without_prompt_tokens = prediction_tokens[
-                                len(prompt_token_ids) :
-                            ]
-                            prediction_without_prompt_tokens_list.append(
-                                prediction_without_prompt_tokens
-                            )
+                            completion_token_ids = input_ids[tokens_with_loss]
+                            completion_token_ids_list.append(completion_token_ids)
 
-                        predicted_texts = tokenizer.batch_decode(
-                            prediction_without_prompt_tokens_list,
-                            skip_special_tokens=True,
+                    prompt_texts = tokenizer.batch_decode(
+                        prompt_token_ids_list, skip_special_tokens=True
+                    )
+                    completion_texts = tokenizer.batch_decode(
+                        completion_token_ids_list, skip_special_tokens=True
+                    )
+
+                    with torch.no_grad():
+                        prompt_encoding = tokenizer(
+                            prompt_texts, padding=True, return_tensors="pt"
+                        ).to(self.cfg.device)
+                        predictions = trainer.model.generate(
+                            **prompt_encoding, generation_config=generation_config
                         )
 
-                        eval_src.extend(prompt_texts)
-                        eval_pred.extend(predicted_texts)
-                        eval_ref.extend(completion_texts)
+                    prediction_all_tokens = predictions["sequences"].cpu().tolist()
+                    prediction_without_prompt_tokens_list = []
+                    for prompt_token_ids, prediction_tokens in zip(
+                        prompt_token_ids_list, prediction_all_tokens
+                    ):
+                        prediction_without_prompt_tokens = prediction_tokens[
+                            len(prompt_token_ids) :
+                        ]
+                        prediction_without_prompt_tokens_list.append(
+                            prediction_without_prompt_tokens
+                        )
+
+                    predicted_texts = tokenizer.batch_decode(
+                        prediction_without_prompt_tokens_list, skip_special_tokens=True
+                    )
+
+                    eval_src.extend(prompt_texts)
+                    eval_pred.extend(predicted_texts)
+                    eval_ref.extend(completion_texts)
 
                 return eval_src, eval_pred, eval_ref
 
-            eval_preds = predict_with_generate()
-            trainer.log(evaluate_preds(*eval_preds))
+            if is_main_process():
+                eval_preds = predict_with_generate()
+                trainer.log(evaluate_preds(*eval_preds))
 
             return control
 

src/axolotl/utils/callbacks/perplexity.py

@@ -8,8 +8,6 @@ from transformers.modeling_outputs import CausalLMOutput
 from transformers.modeling_utils import PreTrainedModel
 from transformers.tokenization_utils import PreTrainedTokenizer
 
-from axolotl.utils.distributed import is_main_process
-
 
 class Perplexity:
     """
@@ -19,13 +17,16 @@ class Perplexity:
 
     def __init__(
         self,
+        model: PreTrainedModel,
         tokenizer: PreTrainedTokenizer,
         max_seq_len: int,
         stride: int = 512,
     ) -> None:
         self.max_seq_len = max_seq_len
         self.stride = stride
+        self.model = model
         self.tokenizer = tokenizer
+        self.device = model.device
         self.name = "perplexity"
 
     def _feature_names(self) -> List[str]:
@@ -33,7 +34,6 @@ class Perplexity:
 
     def compute(
         self,
-        model: PreTrainedModel,
         references: Optional[List[str]] = None,
     ) -> Dict[str, float]:
         """
@@ -41,21 +41,17 @@ class Perplexity:
         """
         assert references is not None, "Missing parameter: references"
 
-        model.eval()
-
         references_tokenized = self.tokenizer(
             references, return_tensors="pt", padding=True, truncation=True
         )
         input_ids: Tensor = references_tokenized["input_ids"]  # type: ignore
-        input_ids = input_ids.to(model.device)
+        input_ids = input_ids.to(self.device)
 
         sequence_length = input_ids.size(1)
 
         losses = []
         prev_end_loc = 0
-        for begin_loc in tqdm(
-            range(0, sequence_length, self.stride), disable=not is_main_process()
-        ):
+        for begin_loc in tqdm(range(0, sequence_length, self.stride)):
             end_loc = min(begin_loc + self.max_seq_len, sequence_length)
             trg_len = end_loc - prev_end_loc
             input_ids_slice = input_ids[:, begin_loc:end_loc]
@@ -63,7 +59,7 @@ class Perplexity:
             labels_slice[:, :-trg_len] = -100
 
             with torch.no_grad():
-                outputs: CausalLMOutput = model(
+                outputs: CausalLMOutput = self.model(
                     input_ids=input_ids_slice, labels=labels_slice
                 )
 

src/axolotl/utils/collators/mm_chat.py

@@ -1,10 +1,8 @@
 """
 Collators for multi-modal chat messages and packing
 """
-
-from copy import deepcopy
 from dataclasses import dataclass
-from typing import Any, Optional, Union
+from typing import Any, Dict, List, Optional, Union
 
 from PIL import Image
 from transformers import PreTrainedTokenizerBase, ProcessorMixin
@@ -32,8 +30,8 @@ class MultiModalChatDataCollator(DataCollatorMixin):
             raise ValueError("Packing is currently not supported.")
 
     def torch_call(
-        self, examples: list[Union[list[int], Any, dict[str, Any]]]
-    ) -> dict[str, Any]:
+        self, examples: List[Union[List[int], Any, Dict[str, Any]]]
+    ) -> Dict[str, Any]:
         # Handle dict or lists with proper padding and conversion to tensor.
 
         return self.__class__.process_rows(
@@ -48,120 +46,6 @@ class MultiModalChatDataCollator(DataCollatorMixin):
         # *** This is COPIED from the trl example sft_vlm.py code ***
         # use this as a starting point
 
-        def _preprocess(examples: list[dict]) -> list[dict]:
-            """
-            Preprocess conversation examples to ensure consistent format.
-
-            Converts different conversation formats to OpenAI format with 'messages'.
-            Supports two formats:
-            1. OpenAI format with 'messages'
-            2. Legacy format with 'conversations'
-
-            Args:
-                examples: list of conversation dictionaries
-
-            Returns:
-                dict in OpenAI format with 'messages' key
-
-            Raises:
-                ValueError: If the conversation format is not supported
-            """
-            role_mapping = {
-                "human": "user",
-                "gpt": "assistant",
-            }
-
-            def normalize_role(role: str) -> str:
-                """Normalize role names to OpenAI format. Default to original role if not found."""
-                return role_mapping.get(role, role)
-
-            def convert_legacy_format(example: dict) -> dict:
-                """Convert legacy 'conversations' format to OpenAI 'messages' format."""
-                messages = [
-                    {
-                        "role": normalize_role(convo["from"]),
-                        "content": convo["value"],
-                    }
-                    for convo in example["conversations"]
-                ]
-
-                # Create new dict without 'conversations' key
-                result = deepcopy(example)
-                result.pop("conversations")
-                return {"messages": messages, **result}
-
-            processed_examples = []
-            for example in examples:
-                # OpenAI format
-                if "messages" in example:
-                    processed_examples.append(example)
-
-                # Legacy format
-                elif "conversations" in example:
-                    processed_examples.append(convert_legacy_format(example))
-
-                else:
-                    raise ValueError(
-                        "Only `messages` and `conversations` message keys are currently supported."
-                    )
-
-            return processed_examples
-
-        def _process_images(examples, max_images):
-            """
-            Process images from examples, ensuring consistency in image presence and applying max_images limit.
-
-            Args:
-                examples: List of dictionaries that may contain 'images' key
-                max_images: Maximum number of images to keep per example (0 means no limit)
-
-            Returns:
-                Either None (if no images) or List[Image objects] (if all examples have images)
-
-            Raises:
-                ValueError: If there's a mix of None and non-None images
-            """
-
-            def get_image(example):
-                if "images" not in example:
-                    return None
-                images = example["images"]
-                if isinstance(images, str):
-                    return Image.open(images)
-                return images
-
-            images = [get_image(example) for example in examples]
-
-            # Count None and non-None images
-            none_count = sum(1 for img in images if img is None)
-
-            # All images are None
-            if none_count == len(images):
-                return None
-
-            # Mix of None and non-None images
-            if none_count > 0:
-                raise ValueError(
-                    "All images should be either None or not None. "
-                    "Please provide images for all examples or None."
-                )
-
-            # Apply max_images limit if specified
-            if max_images > 0:
-                images = [
-                    (
-                        img_batch[:max_images]
-                        if isinstance(img_batch, (list, tuple))
-                        else img_batch
-                    )
-                    for img_batch in images
-                ]
-
-            return images
-
-        # Preprocess the examples
-        examples = _preprocess(examples)
-
         # Get the texts and images, and apply the chat template
         texts = [
             processor.apply_chat_template(
@@ -169,8 +53,15 @@ class MultiModalChatDataCollator(DataCollatorMixin):
             )
             for example in examples
         ]
+        images = [
+            Image.open(example["images"])
+            if isinstance(example["images"], str)
+            else example["images"]
+            for example in examples
+        ]
 
-        images = _process_images(examples, max_images=max_images)
+        if max_images > 0:
+            images = [img_batch[:max_images] for img_batch in images]
 
         # Tokenize the texts and process the images
         batch = processor(text=texts, images=images, return_tensors="pt", padding=True)