add missing dunder-init for monkeypatches and add tests for install from sdist (#2085)

* add missing dunder-init for monkeypatches and add tests for install from sdist

* fix gha name

* reduce matrix for sdist test

.github/workflows/base.yml

@@ -27,7 +27,7 @@ jobs:
           - cuda: "124"
             cuda_version: 12.4.1
             cudnn_version: ""
-            python_version: "3.11"
+            python_version: "3.10"
             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"
@@ -36,21 +36,29 @@ jobs:
             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"
+            cuda_version: 12.4.1
+            cudnn_version: ""
+            python_version: "3.11"
+            pytorch: 2.5.1
+            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@v3
+        uses: actions/checkout@v4
       - name: Docker metadata
         id: metadata
-        uses: docker/metadata-action@v3
+        uses: docker/metadata-action@v5
         with:
-          images: winglian/axolotl-base
+          images: |
+            winglian/axolotl-base
+            axolotlai/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@v2
+        uses: docker/setup-buildx-action@v3
       - 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@v3
+          uses: actions/setup-python@v5
           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@v3
-      - uses: actions/setup-python@v4
+      - uses: actions/checkout@v4
+      - uses: actions/setup-python@v5
         with:
           python-version: "3.10"
           cache: 'pip' # caching pip dependencies
-      - uses: pre-commit/action@v3.0.0
+      - uses: pre-commit/action@v3.0.1

.github/workflows/main.yml

@@ -4,11 +4,13 @@ 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: ${{ ! contains(github.event.commits[0].message, '[skip docker]') && github.repository_owner == 'axolotl-ai-cloud' }}
     strategy:
       fail-fast: false
       matrix:
@@ -29,6 +31,11 @@ jobs:
             python_version: "3.11"
             pytorch: 2.4.1
             axolotl_extras:
+          - cuda: 124
+            cuda_version: 12.4.1
+            python_version: "3.11"
+            pytorch: 2.5.1
+            axolotl_extras:
     runs-on: axolotl-gpu-runner
     steps:
       - name: Checkout
@@ -37,7 +44,12 @@ jobs:
         id: metadata
         uses: docker/metadata-action@v5
         with:
-          images: winglian/axolotl
+          images: |
+            winglian/axolotl
+            axolotlai/axolotl
+          tags: |
+            type=ref,event=branch
+            type=semver,pattern={{version}}
       - name: Set up Docker Buildx
         uses: docker/setup-buildx-action@v3
       - name: Login to Docker Hub
@@ -51,7 +63,7 @@ jobs:
         with:
           context: .
           build-args: |
-            BASE_TAG=${{ github.ref_name }}-base-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}
+            BASE_TAG=${{ github.ref_type == 'tag' && 'main' || 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 }}
@@ -65,7 +77,7 @@ 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:
@@ -86,6 +98,11 @@ jobs:
             python_version: "3.11"
             pytorch: 2.4.1
             axolotl_extras:
+          - cuda: 124
+            cuda_version: 12.4.1
+            python_version: "3.11"
+            pytorch: 2.5.1
+            axolotl_extras:
     runs-on: axolotl-gpu-runner
     steps:
       - name: Checkout
@@ -94,20 +111,25 @@ jobs:
         id: metadata
         uses: docker/metadata-action@v5
         with:
-          images: winglian/axolotl-cloud
+          images: |
+            winglian/axolotl-cloud
+            axolotlai/axolotl-cloud
+          tags: |
+            type=ref,event=branch
+            type=semver,pattern={{version}}
       - name: Login to Docker Hub
         uses: docker/login-action@v3
         with:
           username: ${{ secrets.DOCKERHUB_USERNAME }}
           password: ${{ secrets.DOCKERHUB_TOKEN }}
       - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v2
+        uses: docker/setup-buildx-action@v3
       - name: Build
         uses: docker/build-push-action@v5
         with:
           context: .
           build-args: |
-            BASE_TAG=${{ github.ref_name }}-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
+            BASE_TAG=${{ github.ref_type == 'tag' && 'main' || github.ref_name }}-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
             CUDA=${{ matrix.cuda }}
           file: ./docker/Dockerfile-cloud
           push: ${{ github.event_name != 'pull_request' }}
@@ -118,7 +140,7 @@ 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:
@@ -136,20 +158,25 @@ jobs:
         id: metadata
         uses: docker/metadata-action@v5
         with:
-          images: winglian/axolotl-cloud-term
+          images: |
+            winglian/axolotl-cloud-term
+            axolotlai/axolotl-cloud-term
+          tags: |
+            type=ref,event=branch
+            type=semver,pattern={{version}}
       - name: Login to Docker Hub
         uses: docker/login-action@v3
         with:
           username: ${{ secrets.DOCKERHUB_USERNAME }}
           password: ${{ secrets.DOCKERHUB_TOKEN }}
       - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v2
+        uses: docker/setup-buildx-action@v3
       - name: Build
         uses: docker/build-push-action@v5
         with:
           context: .
           build-args: |
-            BASE_TAG=${{ github.ref_name }}-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
+            BASE_TAG=${{ github.ref_type == 'tag' && 'main' || github.ref_name }}-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
             CUDA=${{ matrix.cuda }}
           file: ./docker/Dockerfile-cloud-no-tmux
           push: ${{ github.event_name != 'pull_request' }}

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

@@ -8,9 +8,14 @@ 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 docker]]') && github.repository_owner == 'axolotl-ai-cloud' }}
+    if: ${{ ! contains(github.event.commits[0].message, '[skip e2e]') && github.repository_owner == 'axolotl-ai-cloud' }}
     strategy:
       fail-fast: false
       matrix:
@@ -21,10 +26,17 @@ jobs:
             pytorch: 2.3.1
             axolotl_extras:
             num_gpus: 2
-          - cuda: 121
-            cuda_version: 12.1.1
+          - cuda: 124
+            cuda_version: 12.4.1
             python_version: "3.11"
-            pytorch: 2.3.1
+            pytorch: 2.4.1
+            axolotl_extras:
+            num_gpus: 2
+            nightly_build: "true"
+          - cuda: 124
+            cuda_version: 12.4.1
+            python_version: "3.11"
+            pytorch: 2.5.1
             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:
@@ -28,6 +28,11 @@ jobs:
             python_version: "3.11"
             pytorch: 2.4.1
             axolotl_extras:
+          - cuda: 124
+            cuda_version: 12.4.1
+            python_version: "3.11"
+            pytorch: 2.5.1
+            axolotl_extras:
     runs-on: axolotl-gpu-runner
     steps:
       - name: Checkout
@@ -36,7 +41,9 @@ jobs:
         id: metadata
         uses: docker/metadata-action@v5
         with:
-          images: winglian/axolotl
+          images: |
+            winglian/axolotl
+            axolotlai/axolotl
           tags: |
             type=raw,value={{ branch }}-{{ date 'YYYYMMDD' }}
       - name: Set up Docker Buildx
@@ -64,7 +71,7 @@ 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:
@@ -85,6 +92,11 @@ jobs:
             python_version: "3.11"
             pytorch: 2.4.1
             axolotl_extras:
+          - cuda: 124
+            cuda_version: 12.4.1
+            python_version: "3.11"
+            pytorch: 2.5.1
+            axolotl_extras:
     runs-on: axolotl-gpu-runner
     steps:
       - name: Checkout
@@ -93,7 +105,9 @@ jobs:
         id: metadata
         uses: docker/metadata-action@v5
         with:
-          images: winglian/axolotl-cloud
+          images: |
+            winglian/axolotl-cloud
+            axolotlai/axolotl-cloud
           tags: |
             type=raw,value={{ branch }}-{{ date 'YYYYMMDD' }}
       - name: Login to Docker Hub
@@ -102,7 +116,7 @@ jobs:
           username: ${{ secrets.DOCKERHUB_USERNAME }}
           password: ${{ secrets.DOCKERHUB_TOKEN }}
       - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v2
+        uses: docker/setup-buildx-action@v3
       - name: Build
         uses: docker/build-push-action@v5
         with:

.github/workflows/pypi.yml

@@ -3,12 +3,33 @@ name: publish pypi
 on:
   push:
     tags:
-      - '*'
+      - 'v*'
+  workflow_dispatch:
 
 jobs:
+  setup_release:
+    name: Create Release
+    runs-on: ubuntu-latest
+    permissions:
+      contents: write
+    steps:
+      - name: Get the tag version
+        id: extract_branch
+        run: echo ::set-output name=branch::${GITHUB_REF#refs/tags/}
+        shell: bash
+
+      - name: Create Release
+        id: create_release
+        uses: actions/create-release@v1
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+        with:
+          tag_name: ${{ steps.extract_branch.outputs.branch }}
+          release_name: ${{ steps.extract_branch.outputs.branch }}
   pypi-publish:
     name: Upload release to PyPI
     runs-on: ubuntu-latest
+    needs: [setup_release]
     environment:
       name: pypi
       url: https://pypi.org/p/axolotl
@@ -16,10 +37,10 @@ jobs:
       id-token: write  # IMPORTANT: this permission is mandatory for trusted publishing
     steps:
       - name: Check out repository code
-        uses: actions/checkout@v3
+        uses: actions/checkout@v4
 
       - name: Setup Python
-        uses: actions/setup-python@v4
+        uses: actions/setup-python@v5
         with:
           python-version: "3.10"
 
@@ -27,7 +48,7 @@ jobs:
         run: |
           pip3 install wheel packaging
           pip3 install -e .
-          pip3 install -r requirements-tests.txt
+          pip3 install -r requirements-dev.txt -r requirements-tests.txt
 
       - name: Extract tag name
         id: tag
@@ -37,9 +58,9 @@ jobs:
         run: |
           sed -i -E 's/version="([0-9.]+)",/version="${{ steps.tag.outputs.TAG_NAME }}",/g' setup.py
 
-      - name: Build a binary wheel
+      - name: Build a source dist
         run: |
-          python setup.py sdist bdist_wheel
+          python setup.py sdist
 
       - 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@v3
-      - uses: actions/setup-python@v4
+      - uses: actions/checkout@v4
+      - uses: actions/setup-python@v5
         with:
           python-version: "3.10"
           cache: 'pip' # caching pip dependencies
-      - uses: pre-commit/action@v3.0.0
+      - uses: pre-commit/action@v3.0.1
         env:
           SKIP: no-commit-to-branch
 
@@ -25,15 +25,15 @@ jobs:
       fail-fast: false
       matrix:
         python_version: ["3.10", "3.11"]
-        pytorch_version: ["2.3.1", "2.4.1"]
+        pytorch_version: ["2.3.1", "2.4.1", "2.5.1"]
     timeout-minutes: 20
 
     steps:
       - name: Check out repository code
-        uses: actions/checkout@v3
+        uses: actions/checkout@v4
 
       - name: Setup Python
-        uses: actions/setup-python@v4
+        uses: actions/setup-python@v5
         with:
           python-version: ${{ matrix.python_version }}
           cache: 'pip' # caching pip dependencies
@@ -47,13 +47,15 @@ jobs:
           sed -i 's#^transformers.*#transformers @ git+https://github.com/huggingface/transformers.git@main#' requirements.txt
           sed -i 's#^peft.*#peft @ git+https://github.com/huggingface/peft.git@main#' requirements.txt
           sed -i 's#^accelerate.*#accelerate @ git+https://github.com/huggingface/accelerate.git@main#' requirements.txt
+          sed -i 's#^trl.*#trl @ git+https://github.com/huggingface/trl.git@main#' requirements.txt
+          sed -i 's#^datasets.*#datasets @ git+https://github.com/huggingface/datasets.git@main#' requirements.txt
 
       - name: Install dependencies
         run: |
           pip3 install --upgrade pip
           pip3 install --upgrade packaging
           pip3 install -U -e .
-          pip3 install -r requirements-tests.txt
+          pip3 install -r requirements-dev.txt -r requirements-tests.txt
 
       - name: Run tests
         run: |
@@ -81,13 +83,6 @@ jobs:
             num_gpus: 1
             axolotl_extras: mamba-ssm
             nightly_build: "true"
-          - cuda: 121
-            cuda_version: 12.1.1
-            python_version: "3.11"
-            pytorch: 2.3.1
-            num_gpus: 1
-            axolotl_extras: mamba-ssm
-            nightly_build: "true"
           - cuda: 124
             cuda_version: 12.4.1
             python_version: "3.11"
@@ -95,6 +90,13 @@ jobs:
             num_gpus: 1
             axolotl_extras:
             nightly_build: "true"
+          - cuda: 124
+            cuda_version: 12.4.1
+            python_version: "3.11"
+            pytorch: 2.5.1
+            num_gpus: 1
+            axolotl_extras:
+            nightly_build: "true"
     steps:
       - name: Checkout
         uses: actions/checkout@v4

.github/workflows/tests.yml

@@ -15,17 +15,22 @@ on:
        - '.github/workflows/*.yml'
   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@v3
-      - uses: actions/setup-python@v4
+      - uses: actions/checkout@v4
+      - uses: actions/setup-python@v5
         with:
           python-version: "3.10"
           cache: 'pip' # caching pip dependencies
-      - uses: pre-commit/action@v3.0.0
+      - uses: pre-commit/action@v3.0.1
         env:
           SKIP: no-commit-to-branch
 
@@ -36,61 +41,97 @@ jobs:
       fail-fast: false
       matrix:
         python_version: ["3.10", "3.11"]
-        pytorch_version: ["2.3.1", "2.4.1"]
+        pytorch_version: ["2.3.1", "2.4.1", "2.5.1"]
     timeout-minutes: 20
 
     steps:
       - name: Check out repository code
-        uses: actions/checkout@v3
+        uses: actions/checkout@v4
 
       - name: Setup Python
-        uses: actions/setup-python@v4
+        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==${{ matrix.pytorch_version }} --index-url https://download.pytorch.org/whl/cpu
+          pip3 install torch==${{ matrix.pytorch_version }}
 
       - name: Install dependencies
         run: |
-          pip3 install --upgrade pip
-          pip3 install --upgrade packaging
+          pip3 show torch
           pip3 install -U -e .
-          pip3 install -r requirements-tests.txt
+          pip3 install -r requirements-dev.txt -r requirements-tests.txt
 
       - name: Run tests
         run: |
-          pytest --ignore=tests/e2e/ tests/
+          pytest -n8 --ignore=tests/e2e/ tests/
 
       - name: cleanup pip cache
         run: |
           find "$(pip cache dir)/http-v2" -type f -mtime +14 -exec rm {} \;
 
-  docker-e2e-tests:
-    if: github.repository_owner == 'axolotl-ai-cloud'
+  pytest-sdist:
+    name: PyTest from Source Dist
+    runs-on: ubuntu-latest
+    strategy:
+      fail-fast: false
+      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
+          pip3 install -r requirements-dev.txt -r requirements-tests.txt
+
+      - name: Run tests
+        run: |
+          pytest -n8 --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' }}
     # this job needs to be run on self-hosted GPU runners...
     runs-on: [self-hosted, modal]
-    timeout-minutes: 60
-    needs: [pre-commit, pytest]
+    timeout-minutes: 90
+    needs: [pre-commit, pytest, pytest-sdist]
 
     strategy:
       fail-fast: false
       matrix:
         include:
-          - cuda: 121
-            cuda_version: 12.1.1
-            python_version: "3.10"
-            pytorch: 2.3.1
-            num_gpus: 1
-            axolotl_extras: mamba-ssm
-          - cuda: 121
-            cuda_version: 12.1.1
-            python_version: "3.11"
-            pytorch: 2.3.1
-            num_gpus: 1
-            axolotl_extras: mamba-ssm
           - cuda: 124
             cuda_version: 12.4.1
             python_version: "3.11"
@@ -119,3 +160,49 @@ jobs:
       - name: Run tests job on Modal
         run: |
           modal run cicd.tests
+
+  docker-e2e-tests:
+    if: github.repository_owner == 'axolotl-ai-cloud'
+    # this job needs to be run on self-hosted GPU runners...
+    runs-on: [self-hosted, modal]
+    timeout-minutes: 90
+    needs: [pre-commit, pytest, docker-e2e-tests-1st]
+
+    strategy:
+      fail-fast: false
+      matrix:
+        include:
+          - cuda: 121
+            cuda_version: 12.1.1
+            python_version: "3.10"
+            pytorch: 2.3.1
+            num_gpus: 1
+            axolotl_extras: mamba-ssm
+          - cuda: 124
+            cuda_version: 12.4.1
+            python_version: "3.11"
+            pytorch: 2.5.1
+            num_gpus: 1
+            axolotl_extras:
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+      - name: Install Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: "3.10"
+      - name: Install Modal
+        run: |
+          python -m pip install --upgrade pip
+          pip install modal==0.63.64 jinja2
+      - name: Update env vars
+        run: |
+          echo "BASE_TAG=main-base-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}" >> $GITHUB_ENV
+          echo "PYTORCH_VERSION=${{ matrix.pytorch}}" >> $GITHUB_ENV
+          echo "AXOLOTL_ARGS=${{ matrix.axolotl_args}}" >> $GITHUB_ENV
+          echo "AXOLOTL_EXTRAS=${{ matrix.axolotl_extras}}" >> $GITHUB_ENV
+          echo "CUDA=${{ matrix.cuda }}" >> $GITHUB_ENV
+          echo "N_GPUS=${{ matrix.num_gpus }}" >> $GITHUB_ENV
+      - name: Run tests job on Modal
+        run: |
+          modal run cicd.tests

MANIFEST.in

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

README.md

@@ -1,8 +1,21 @@
-# Axolotl
+<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>
 
-![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)
+<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>
+    <img src="https://img.shields.io/github/stars/axolotl-ai-cloud/axolotl" alt="GitHub Repo stars">
+</p>
+<p align="center">
+    <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>
 
 Axolotl is a tool designed to streamline the fine-tuning of various AI models, offering support for multiple configurations and architectures.
 
@@ -75,7 +88,7 @@ Features:
 <td>
 
 <div align="center">
-  <img src="image/axolotl.png" alt="axolotl" width="160">
+  <img src="image/axolotl_symbol_digital_white.svg" alt="axolotl" width="160">
   <div>
     <p>
       <b>Axolotl provides a unified repository for fine-tuning <br />a variety of AI models with ease</b>
@@ -121,7 +134,7 @@ Features:
 
 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**: Python >=3.10 and Pytorch >=2.1.1.
+**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
@@ -159,7 +172,7 @@ accelerate launch -m axolotl.cli.train https://raw.githubusercontent.com/axolotl
 #### Docker
 
   ```bash
-  docker run --gpus '"all"' --rm -it winglian/axolotl:main-latest
+  docker run --gpus '"all"' --rm -it axolotlai/axolotl:main-latest
   ```
 
   Or run on the current files for development:
@@ -178,7 +191,7 @@ accelerate launch -m axolotl.cli.train https://raw.githubusercontent.com/axolotl
   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 ${HOME}/.cache/huggingface:/root/.cache/huggingface winglian/axolotl:main-latest
+docker run --privileged --gpus '"all"' --shm-size 10g --rm -it --name axolotl --ipc=host --ulimit memlock=-1 --ulimit stack=67108864 --mount type=bind,src="${PWD}",target=/workspace/axolotl -v ${HOME}/.cache/huggingface:/root/.cache/huggingface axolotlai/axolotl:main-latest
   ```
 
   It additionally:
@@ -210,7 +223,7 @@ docker run --privileged --gpus '"all"' --shm-size 10g --rm -it --name axolotl --
 
 #### Cloud GPU
 
-For cloud GPU providers that support docker images, use [`winglian/axolotl-cloud:main-latest`](https://hub.docker.com/r/winglian/axolotl-cloud/tags)
+For cloud GPU providers that support docker images, use [`axolotlai/axolotl-cloud:main-latest`](https://hub.docker.com/r/axolotlai/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)
@@ -319,7 +332,7 @@ Write a job description in YAML as below:
 # dstack.yaml
 type: task
 
-image: winglian/axolotl-cloud:main-20240429-py3.11-cu121-2.2.2
+image: axolotlai/axolotl-cloud:main-latest
 
 env:
   - HUGGING_FACE_HUB_TOKEN
@@ -383,11 +396,10 @@ See [examples](examples) for quick start. It is recommended to duplicate and mod
         - typescript
       type: ... # unimplemented custom format
 
-      # fastchat conversation (deprecation soon, use chat_template)
-      # See 'conversation' options: https://github.com/lm-sys/FastChat/blob/main/fastchat/conversation.py
+      # chat_template https://axolotl-ai-cloud.github.io/axolotl/docs/dataset-formats/conversation.html#chat_template
     - path: ...
-      type: sharegpt
-      conversation: chatml # default: vicuna_v1.1
+      type: chat_template
+      chat_template: chatml # defaults to tokenizer's chat_template
 
       # local
     - path: data.jsonl # or json
@@ -562,7 +574,8 @@ plugins:
   - axolotl.integrations.liger.LigerPlugin
 liger_rope: true
 liger_rms_norm: true
-liger_swiglu: true
+liger_glu_activation: true
+liger_layer_norm: true
 liger_fused_linear_cross_entropy: true
 ```
 

cicd/Dockerfile.jinja

@@ -1,4 +1,4 @@
-FROM winglian/axolotl-base:{{ BASE_TAG }}
+FROM axolotlai/axolotl-base:{{ BASE_TAG }}
 
 ENV TORCH_CUDA_ARCH_LIST="7.0 7.5 8.0 8.6+PTX"
 ENV AXOLOTL_EXTRAS="{{ AXOLOTL_EXTRAS }}"
@@ -23,11 +23,12 @@ RUN git fetch origin +$GITHUB_REF && \
     git checkout FETCH_HEAD
 
 # If AXOLOTL_EXTRAS is set, append it in brackets
-RUN pip install causal_conv1d
 RUN if [ "$NIGHTLY_BUILD" = "true" ] ; then \
         sed -i 's#^transformers.*#transformers @ git+https://github.com/huggingface/transformers.git@main#' requirements.txt; \
         sed -i 's#^peft.*#peft @ git+https://github.com/huggingface/peft.git@main#' requirements.txt; \
         sed -i 's#^accelerate.*#accelerate @ git+https://github.com/huggingface/accelerate.git@main#' requirements.txt; \
+        sed -i 's#^trl.*#trl @ git+https://github.com/huggingface/trl.git@main#' requirements.txt; \
+        sed -i 's#^datasets.*#datasets @ git+https://github.com/huggingface/datasets.git@main#' requirements.txt; \
     fi
 
 RUN if [ "$AXOLOTL_EXTRAS" != "" ] ; then \
@@ -37,7 +38,7 @@ RUN if [ "$AXOLOTL_EXTRAS" != "" ] ; then \
     fi
 
 # So we can test the Docker image
-RUN pip install -r requirements-tests.txt
+RUN pip install -r requirements-dev.txt -r requirements-tests.txt
 
 # fix so that git fetch/pull from remote works
 RUN git config remote.origin.fetch "+refs/heads/*:refs/remotes/origin/*" && \

cicd/cicd.sh

@@ -1,6 +1,6 @@
 #!/bin/bash
 set -e
 
-pytest --ignore=tests/e2e/ /workspace/axolotl/tests/
+pytest -n8 --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 Image, Stub
+from modal import App, Image
 
 cicd_path = pathlib.Path(__file__).parent.resolve()
 
@@ -46,7 +46,7 @@ cicd_image = (
     .pip_install("fastapi==0.110.0", "pydantic==2.6.3")
 )
 
-stub = Stub("Axolotl CI/CD", secrets=[])
+app = App("Axolotl CI/CD", secrets=[])
 
 
 N_GPUS = int(os.environ.get("N_GPUS", 2))
@@ -61,10 +61,10 @@ def run_cmd(cmd: str, run_folder: str):
         exit(exit_code)  # pylint: disable=consider-using-sys-exit
 
 
-@stub.function(
+@app.function(
     image=cicd_image,
     gpu=GPU_CONFIG,
-    timeout=45 * 60,
+    timeout=60 * 60,
     cpu=8.0,
     memory=131072 * N_GPUS,
 )
@@ -72,6 +72,6 @@ def cicd_pytest():
     run_cmd("./cicd/multigpu.sh", "/workspace/axolotl")
 
 
-@stub.local_entrypoint()
+@app.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 -n1 /workspace/axolotl/tests/e2e/multigpu/
+pytest -v -n2 /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 Image, Stub
+from modal import App, Image
 
 cicd_path = pathlib.Path(__file__).parent.resolve()
 
@@ -47,7 +47,7 @@ cicd_image = (
     .pip_install("fastapi==0.110.0", "pydantic==2.6.3")
 )
 
-stub = Stub("Axolotl CI/CD", secrets=[])
+app = App("Axolotl CI/CD", secrets=[])
 
 
 N_GPUS = int(os.environ.get("N_GPUS", 1))
@@ -62,10 +62,10 @@ def run_cmd(cmd: str, run_folder: str):
         exit(exit_code)  # pylint: disable=consider-using-sys-exit
 
 
-@stub.function(
+@app.function(
     image=cicd_image,
     gpu=GPU_CONFIG,
-    timeout=45 * 60,
+    timeout=60 * 60,
     cpu=8.0,
     memory=131072,
 )
@@ -73,6 +73,6 @@ def cicd_pytest():
     run_cmd("./cicd/cicd.sh", "/workspace/axolotl")
 
 
-@stub.local_entrypoint()
+@app.local_entrypoint()
 def main():
     cicd_pytest.remote()

deepspeed_configs/zero3_bf16.json

@@ -14,15 +14,6 @@
   "bf16": {
     "enabled": true
   },
-  "fp16": {
-    "enabled": "auto",
-    "auto_cast": false,
-    "loss_scale": 0,
-    "initial_scale_power": 32,
-    "loss_scale_window": 1000,
-    "hysteresis": 2,
-    "min_loss_scale": 1
-  },
   "gradient_accumulation_steps": "auto",
   "gradient_clipping": "auto",
   "train_batch_size": "auto",

deepspeed_configs/zero3_bf16_cpuoffload_all.json

@@ -24,15 +24,6 @@
   "bf16": {
     "enabled": true
   },
-  "fp16": {
-    "enabled": "auto",
-    "auto_cast": false,
-    "loss_scale": 0,
-    "initial_scale_power": 32,
-    "loss_scale_window": 1000,
-    "hysteresis": 2,
-    "min_loss_scale": 1
-  },
   "gradient_accumulation_steps": "auto",
   "gradient_clipping": "auto",
   "train_batch_size": "auto",

deepspeed_configs/zero3_bf16_cpuoffload_params.json

@@ -20,15 +20,6 @@
   "bf16": {
     "enabled": true
   },
-  "fp16": {
-    "enabled": "auto",
-    "auto_cast": false,
-    "loss_scale": 0,
-    "initial_scale_power": 32,
-    "loss_scale_window": 1000,
-    "hysteresis": 2,
-    "min_loss_scale": 1
-  },
   "gradient_accumulation_steps": "auto",
   "gradient_clipping": "auto",
   "train_batch_size": "auto",

devtools/dev_chat_template.yml

@@ -1,4 +1,4 @@
-# Example config for debugging the sharegpt prompt format
+# Example config for debugging the chat_template prompt format
 base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
 model_type: LlamaForCausalLM
 tokenizer_type: LlamaTokenizer
@@ -7,8 +7,8 @@ load_in_8bit: true
 load_in_4bit: false
 
 datasets:
-  - path: philschmid/guanaco-sharegpt-style
-    type: sharegpt
+  - path: fozziethebeat/alpaca_messages_2k_test
+    type: chat_template
     shards: 10
 val_set_size: 0
 output_dir: temp_debug/axolotl_outputs/model

docker/Dockerfile

@@ -1,5 +1,5 @@
 ARG BASE_TAG=main-base
-FROM winglian/axolotl-base:$BASE_TAG
+FROM axolotlai/axolotl-base:$BASE_TAG
 
 ARG TORCH_CUDA_ARCH_LIST="7.0 7.5 8.0 8.6+PTX"
 ARG AXOLOTL_EXTRAS=""
@@ -20,7 +20,6 @@ RUN git clone --depth=1 https://github.com/axolotl-ai-cloud/axolotl.git
 WORKDIR /workspace/axolotl
 
 # If AXOLOTL_EXTRAS is set, append it in brackets
-RUN pip install causal_conv1d
 RUN if [ "$AXOLOTL_EXTRAS" != "" ] ; then \
         pip install -e .[deepspeed,flash-attn,optimizers,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \
     else \

docker/Dockerfile-cloud

@@ -1,5 +1,5 @@
 ARG BASE_TAG=main
-FROM winglian/axolotl:$BASE_TAG
+FROM axolotlai/axolotl:$BASE_TAG
 
 ENV HF_DATASETS_CACHE="/workspace/data/huggingface-cache/datasets"
 ENV HUGGINGFACE_HUB_CACHE="/workspace/data/huggingface-cache/hub"

docker/Dockerfile-cloud-no-tmux

@@ -1,5 +1,5 @@
 ARG BASE_TAG=main
-FROM winglian/axolotl:$BASE_TAG
+FROM axolotlai/axolotl:$BASE_TAG
 
 ENV HF_DATASETS_CACHE="/workspace/data/huggingface-cache/datasets"
 ENV HUGGINGFACE_HUB_CACHE="/workspace/data/huggingface-cache/hub"

docker/Dockerfile-tests

@@ -1,5 +1,5 @@
 ARG BASE_TAG=main-base
-FROM winglian/axolotl-base:$BASE_TAG
+FROM axolotlai/axolotl-base:$BASE_TAG
 
 ARG TORCH_CUDA_ARCH_LIST="7.0 7.5 8.0 8.6+PTX"
 ARG AXOLOTL_EXTRAS=""

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, sharegpt, gpteacher, oasst, reflection]
+    # The type of prompt to use for training. [alpaca, 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
@@ -91,15 +91,7 @@ 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.
-
-    # 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]].
+    trust_remote_code: # Optional[bool] Trust remote code for untrusted source
 
   # Custom user instruction prompt
   - path: repo
@@ -124,6 +116,48 @@ datasets:
       # For `completion` datsets only, uses the provided field instead of `text` column
       field:
 
+  # Using chat template
+  - path: ...
+    # Set type to `chat_template` to use this strategy
+    type: chat_template
+    # Specify the name of the chat template to use
+    # 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.
+    # - alpaca/inst/chatml/gemma/cohere/llama3/phi_3/deepseek_v2/jamba: These chat templates are available in the axolotl codebase at src/axolotl/utils/chat_templates.py
+    # - tokenizer_default_fallback_*: where * is the name of the chat template to fallback to if the tokenizer does not have a chat template else default to tokenizer. E.g. tokenizer_default_fallback_chatml.
+    # - jinja: Uses a custom jinja template for the chat template. The custom jinja template should be provided in the chat_template_jinja field.
+    chat_template: tokenizer_default
+    # Custom jinja template for chat template. This will be only used if `chat_template` is set to `jinja` or empty (in which case chat_template is automatically set to `jinja`).
+    chat_template_jinja:
+    # The key in the data example that contains the messages. Default is "messages".
+    field_messages: messages
+    # The key in the message turn that contains the role. Default is "role".
+    message_field_role: role
+    # The key in the message turn that contains the content. Default is "content".
+    message_field_content: content
+    # Optional[Dict[str, List]]. Roles mapping for the messages.
+    roles:
+      user: ["human", "user"]
+      assistant: ["gpt", "assistant", "ai"]
+      system: ["system"]
+
+    ## NOTE: Leaving the below empty will default to using the simple legacy tokenization strategy where only last message is trained on.
+
+    # Optional[List[str]]. Roles to train on. The tokens from these roles will be considered for the loss.
+    roles_to_train: ["gpt", "assistant"]
+    # Optional[str]. Which EOS tokens to train on in the conversation. Possible values are:
+    # - all: train on all EOS tokens
+    # - turn: train on the EOS token at the end of each trainable turn
+    # - last: train on the last EOS token in the conversation
+    train_on_eos: last
+    # The key in the message turn that indicates via boolean whether tokens of a turn should be considered for training. Useful to selectively train on certain turns besides the `roles_to_train`.
+    message_field_training: training
+    # The key in the message turn that contains the training details. Useful to selectively train on certain tokens in a turn.
+    # The value of the key is a List[Dict] containing `begin_offset` (start character index in content), `end_offset` (end character index in content), and `train` (boolean whether to train).
+    # See example at `docs/dataset-formats/conversation.qmd`
+    message_field_training_detail: train_detail
+
+
 # If false, the datasets will not be shuffled and will keep their original order in `datasets`.
 # The same applies to the `test_datasets` option and the `pretraining_dataset` option. Default is true.
 shuffle_merged_datasets: true
@@ -141,10 +175,19 @@ test_datasets:
 
 # use RL training: 'dpo', 'ipo', 'kto'
 rl:
+# whether to perform weighting if doing DPO training. Boolean.
+dpo_use_weighting:
 
-# Saves the desired chat template to the tokenizer_config.json for easier inferencing
-# Currently supports chatml and inst (mistral/mixtral)
-chat_template: chatml
+# 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.
+# - alpaca/inst/chatml/gemma/cohere/llama3/phi_3/deepseek_v2/jamba: These chat templates are available in the axolotl codebase at src/axolotl/utils/chat_templates.py
+# - tokenizer_default_fallback_*: where * is the name of the chat template to fallback to. E.g. tokenizer_default_fallback_chatml. This is useful when the chat template is not available in the tokenizer.
+# - jinja: Uses a custom jinja template for the chat template. The custom jinja template should be provided in the chat_template_jinja field.
+# The selected chat template will be saved to the tokenizer_config.json for easier inferencing
+# Note: It is recommended to set train_on_inputs to true when using a chat template that is different from the model's default chat template.
+chat_template: tokenizer_default
+# custom jinja template for chat template. This will be only used if chat_template is set to `jinja` or `null` (in which case chat_template is automatically set to `jinja`). Default is null.
+chat_template_jinja: null
 # Changes the default system message
 default_system_message: You are a helpful assistant. Please give a long and detailed answer. # Currently only supports chatml.
 # Axolotl attempts to save the dataset as an arrow after packing the data together so
@@ -363,6 +406,7 @@ lr_div_factor: # Learning rate div factor
 # - adamw_torch_fused
 # - adamw_torch_xla
 # - adamw_apex_fused
+# - adopt_adamw (only for torch version >= 2.5.1)
 # - adafactor
 # - adamw_anyprecision
 # - sgd

docs/dataset-formats/conversation.qmd

@@ -6,31 +6,8 @@ order: 3
 
 ## sharegpt
 
-conversations where `from` is `human`/`gpt`. (optional: first row with role `system` to override default system prompt)
+IMPORTANT: ShareGPT is deprecated!. Please see `chat_template` section below.
 
-```{.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
 
@@ -38,34 +15,137 @@ datasets:
 {"conversations": [{"role": "...", "value": "..."}]}
 ```
 
-## sharegpt.load_role
 
-conversations where `role` is used instead of `from`
+## chat_template
+
+Chat Template strategy uses a jinja2 template that converts a list of messages into a prompt. Support using tokenizer's template, a supported template, or custom jinja2.
 
 ```{.json filename="data.jsonl"}
-{"conversations": [{"role": "...", "value": "..."}]}
+{"conversations": [{"role": "...", "content": "..."}]}
 ```
 
-## sharegpt.load_guanaco
+See `config.qmd` for full configs and supported templates.
 
-conversations where `from` is `prompter` `assistant` instead of default sharegpt
+### Migrating from sharegpt
+
+Most configs can be adapted as follows:
+
+```yaml
+# old
+chat_template: chatml
+datasets:
+  - path: ...
+    type: sharegpt
+    conversation: chatml
+
+# new (if using tokenizer's chat_template)
+datasets:
+  - path: ...
+    type: chat_template
+
+    field_messages: conversations
+    message_field_role: from
+    message_field_content: value
+
+# new (if setting a new chat_template like chatml, gemma, etc)
+chat_template: chatml
+datasets:
+  - path: ...
+    type: chat_template
+
+    field_messages: conversations
+    message_field_role: from
+    message_field_content: value
+```
+
+We recommend checking the below examples for other usecases.
+
+### Examples
+
+1. Using the default chat template in the tokenizer_config.json on OpenAI messages format, training on only last message.
+
+```yaml
+datasets:
+  - path: ...
+    type: chat_template
+```
+
+2. Using the `gemma` chat template to override the tokenizer_config.json's chat template on OpenAI messages format, training on all assistant messages.
+
+```yaml
+chat_template: gemma # this overwrites the tokenizer's chat_template
+datasets:
+  - path: ...
+    type: chat_template
+    roles_to_train: ["assistant"]
+```
+
+3. Using the tokenizer_config.json's chat template or `chatml` as fallback if the former's chat template does not exist, on OpenAI messages format, training on all assistant messages.
+
+```yaml
+chat_template: tokenizer_default_fallback_chatml # this overwrites the tokenizer's chat_template
+datasets:
+  - path: ...
+    type: chat_template
+    roles_to_train: ["assistant"]
+```
+
+4. Using a custom jinja template on OpenAI messages format, training on all assistant messages.
+
+```yaml
+# chat_template: jinja # `jinja` will be implied if the `chat_template_jinja` is set and this field is empty
+chat_template_jinja: "{{ bos_token }}{% for message in messages %}{% if (message['role'] == 'system') %}{{'<|system|>' + '\n' + message['content'] + '<|end|>' + '\n'}}{% elif (message['role'] == 'user') %}{{'<|user|>' + '\n' + message['content'] + '<|end|>' + '\n' + '<|assistant|>' + '\n'}}{% elif message['role'] == 'assistant' %}{{message['content'] + '<|end|>' + '\n'}}{% endif %}{% endfor %}"
+
+datasets:
+  - path: ...
+    type: chat_template
+    roles_to_train: ["assistant"]
+```
+
+5. (Advanced) Using fine-grained control over tokens and turns to train in a conversation
+
+For a data sample that looks like:
 
 ```{.json filename="data.jsonl"}
-{"conversations": [{"from": "...", "value": "..."}]}
+{
+  "conversations": [
+    {"from": "system", "value": "You are an AI assistant.", "train": false},
+    {"from": "human", "value": "Hello", "train": false},
+    {"from": "assistant", "value": "Hello", "train": true},
+    {"from": "human", "value": "How are you?", "train": true},
+    {
+      "from": "assistant",
+      "value": "I'm doing very well, thank you!",
+      "train_detail": [
+        {"begin_offset": 0, "end_offset": 8, "train": false},
+        {"begin_offset": 9, "end_offset": 18, "train": true},
+        {"begin_offset": 19, "end_offset": 30, "train": false},
+      ],
+    },
+    {
+        "from": "human",
+        "value": "I'm doing very well, thank you!",
+        "train": true,
+    },
+    {"from": "assistant", "value": "Hi there!", "train": true}
+  ]
+}
 ```
 
-## sharegpt.load_ultrachat
+The configuration would look like:
 
-conversations where the turns field is 'messages', human is 'user' and gpt is 'assistant'.
-
-```{.json filename="data.jsonl"}
-{"messages": [{"user": "...", "assistant": "..."}]}
+```yaml
+datasets:
+  - path: ...
+    type: chat_template
+    chat_template: tokenizer_default
+    field_messages: conversations
+    message_field_role: from
+    message_field_content: value
+    roles_to_train: []
+    train_on_eos: turn
+    message_field_training: train
+    message_field_training_detail: train_detail
 ```
 
-## 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": "..."}]}
-```
+Tip: It is not necessary to use both `message_field_training` and `message_field_training_detail` at a time.

docs/debugging.qmd

@@ -51,12 +51,12 @@ While debugging it's helpful to simplify your test scenario as much as possible.
 
 ### Background
 
-The below example shows how to configure VSCode to debug data preprocessing of the `sharegpt` format.  This is the format used when you have the following in your axolotl config:
+The below example shows how to configure VSCode to debug data preprocessing of the `chat_template` format.  This is the format used when you have the following in your axolotl config:
 
 ```yaml
 datasets:
-  - path: <path to your sharegpt formatted dataset> # example on HF Hub: philschmid/guanaco-sharegpt-style
-    type: sharegpt
+  - path: <path to your chat_template formatted dataset> # example on HF Hub: fozziethebeat/alpaca_messages_2k_test
+    type: chat_template
 ```
 
 >[!Important]
@@ -83,7 +83,7 @@ If you developing on a remote host, you can easily use VSCode to debug remotely.
 
 The easiest way to get started is to modify the [.vscode/launch.json](../.vscode/launch.json) file in this project.  This is just an example configuration, so you may need to modify or copy it to suit your needs.
 
-For example, to mimic the command `cd devtools && CUDA_VISIBLE_DEVICES=0 accelerate launch -m axolotl.cli.train dev_sharegpt.yml`, you would use the below configuration[^1].  Note that we add additional flags that override the axolotl config and incorporate the tips above (see the comments). We also set the working directory to `devtools` and set the `env` variable `HF_HOME` to a temporary folder that is later partially deleted.  This is because we want to delete the HF dataset cache before each run in order to ensure that the data preprocessing code is run from scratch.
+For example, to mimic the command `cd devtools && CUDA_VISIBLE_DEVICES=0 accelerate launch -m axolotl.cli.train dev_chat_template.yml`, you would use the below configuration[^1].  Note that we add additional flags that override the axolotl config and incorporate the tips above (see the comments). We also set the working directory to `devtools` and set the `env` variable `HF_HOME` to a temporary folder that is later partially deleted.  This is because we want to delete the HF dataset cache before each run in order to ensure that the data preprocessing code is run from scratch.
 
 ```jsonc
 // .vscode/launch.json
@@ -91,12 +91,12 @@ For example, to mimic the command `cd devtools && CUDA_VISIBLE_DEVICES=0 acceler
     "version": "0.2.0",
     "configurations": [
         {
-            "name": "Debug axolotl prompt - sharegpt",
+            "name": "Debug axolotl prompt - chat_template",
             "type": "python",
             "module": "accelerate.commands.launch",
             "request": "launch",
             "args": [
-                "-m", "axolotl.cli.train", "dev_sharegpt.yml",
+                "-m", "axolotl.cli.train", "dev_chat_template.yml",
                 // The flags below simplify debugging by overriding the axolotl config
                 // with the debugging tips above.  Modify as needed.
                 "--dataset_processes=1",      // limits data preprocessing to one process
@@ -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/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.
+Using [official Axolotl Docker images](https://hub.docker.com/r/axolotlai/axolotl/tags) is a great way to debug your code, and is a very popular way to use Axolotl.  Attaching VSCode to Docker takes a few more steps.
 
 ### Setup
 
@@ -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 ${HOME}/.cache/huggingface:/root/.cache/huggingface winglian/axolotl:main-py3.10-cu118-2.0.1
+docker run --privileged --gpus '"all"' --shm-size 10g --rm -it --name axolotl --ipc=host --ulimit memlock=-1 --ulimit stack=67108864 --mount type=bind,src="${PWD}",target=/workspace/axolotl -v ${HOME}/.cache/huggingface:/root/.cache/huggingface axolotlai/axolotl:main-py3.10-cu118-2.0.1
 ```
 
 >[!Tip]
-> To understand which containers are available, see the [Docker section of the README](../README.md#docker) and the [DockerHub repo](https://hub.docker.com/r/winglian/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/axolotlai/axolotl/tags).  For details of how the Docker containers are built, see axolotl's [Docker CI builds](../.github/workflows/main.yml).
 
 You will now be in the container.  Next, perform an editable install of Axolotl:
 
@@ -240,6 +240,6 @@ style="border-radius: 10px; display: block; margin: auto;" width="560" height="3
 </div>
 <br>
 
-[^1]: The config actually mimics the command `CUDA_VISIBLE_DEVICES=0 python -m accelerate.commands.launch -m axolotl.cli.train devtools/sharegpt.yml`, but this is the same thing.
+[^1]: The config actually mimics the command `CUDA_VISIBLE_DEVICES=0 python -m accelerate.commands.launch -m axolotl.cli.train devtools/chat_template.yml`, but this is the same thing.
 
 [^2]: Many of the below flags are recommended best practices by Nvidia when using nvidia-container-toolkit.  You can read more about these flags [here](https://docs.nvidia.com/deeplearning/frameworks/user-guide/index.html).

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

@@ -44,7 +44,7 @@
    "outputs": [],
    "source": [
     "!pip install -e git+https://github.com/axolotl-ai-cloud/axolotl#egg=axolotl\n",
-    "!pip install flash-attn==\"2.5.0\"\n",
+    "!pip install flash-attn==\"2.7.0.post2\"\n",
     "!pip install deepspeed==\"0.13.1\"!pip install mlflow==\"2.13.0\""
    ]
   },

examples/deepseek-v2/qlora-fsdp-2_5.yaml

@@ -9,14 +9,17 @@ strict: false
 plugins:
   - axolotl.integrations.liger.LigerPlugin
 liger_rms_norm: true
-liger_swiglu: true
+liger_glu_activation: true
 liger_fused_linear_cross_entropy: true
 
 chat_template: deepseek_v2
 datasets:
   - path: mlabonne/FineTome-100k
     type: chat_template
-    split: train
+    split: train[:20%]
+    field_messages: conversations
+    message_field_role: from
+    message_field_content: value
 
 dataset_prepared_path: last_run_prepared
 val_set_size: 0.0

examples/gemma2/qlora.yml

@@ -11,8 +11,11 @@ chat_template: gemma
 datasets:
   - path: cgato/SlimOrcaDedupCleaned
     type: chat_template
-    chat_template: gemma
     drop_system_message: true
+    field_messages: conversations
+    message_field_role: from
+    message_field_content: value
+
 val_set_size: 0.0
 output_dir: ./outputs/out
 

examples/jamba/qlora_fsdp_large.yaml

@@ -4,11 +4,15 @@ tokenizer_type: AutoTokenizer
 load_in_4bit: true
 strict: false
 use_tensorboard: true
+chat_template: jamba
 datasets:
   - path: cgato/SlimOrcaDedupCleaned
     type: chat_template
-    chat_template: jamba
     drop_system_message: true
+    field_messages: conversations
+    message_field_role: from
+    message_field_content: value
+
 dataset_prepared_path: last_run_prepared
 val_set_size: 0.0
 output_dir: jamba-large-fsdp-qlora-ft

examples/llama-3/fft-8b-liger-fsdp.yaml

@@ -4,7 +4,7 @@ plugins:
   - axolotl.integrations.liger.LigerPlugin
 liger_rope: true
 liger_rms_norm: true
-liger_swiglu: true
+liger_glu_activation: true
 liger_fused_linear_cross_entropy: true
 
 strict: false
@@ -14,6 +14,10 @@ datasets:
   - path: mlabonne/FineTome-100k
     type: chat_template
     split: train[:20%]
+    field_messages: conversations
+    message_field_role: from
+    message_field_content: value
+
 dataset_prepared_path: last_run_prepared
 val_set_size: 0.02
 output_dir: ./outputs/out

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

@@ -0,0 +1,77 @@
+base_model: meta-llama/Llama-3.2-1B
+
+load_in_8bit: false
+load_in_4bit: true
+strict: false
+
+datasets:
+  - path: teknium/GPT4-LLM-Cleaned
+    type: alpaca
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.1
+output_dir: ./outputs/qlora-out
+
+adapter: qlora
+lora_model_dir:
+
+sequence_len: 2048
+sample_packing: true
+eval_sample_packing: true
+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
+  - 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: 4
+micro_batch_size: 2
+num_epochs: 1
+optimizer: adamw_bnb_8bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+train_on_inputs: false
+group_by_length: false
+bf16: auto
+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
+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/mistral/mistral-dpo-qlora.yml

@@ -0,0 +1,93 @@
+#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/phi/lora-3.5.yaml

@@ -10,7 +10,6 @@ chat_template: phi_3
 datasets:
   - path: fozziethebeat/alpaca_messages_2k_test
     type: chat_template
-    chat_template: phi_3
     field_messages: messages
     message_field_role: role
     message_field_content: content

examples/qwen2/dpo.yaml

@@ -0,0 +1,67 @@
+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

@@ -0,0 +1,19 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<svg xmlns="http://www.w3.org/2000/svg" version="1.1" viewBox="0 0 1113 283.5">
+    <path fill="#141310" d="M435,234.3l-12.1-48.8h-54.4l-12.1,48.8h-24.7l48.2-185.1h31.6l47.9,185.1h-24.5ZM417.7,164.9l-13.8-55.6c-2.7-10.7-4.8-19.7-6.3-26.9-.9-4.2-1.5-7.5-2-9.9-.5,2.5-1.2,5.8-2,9.9-1.5,7.1-3.6,16.1-6.3,26.7l-13.8,55.9h44.3Z"/>
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image/axolotl_symbol_digital_black.svg

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image/axolotl_wordmark_digital_black.svg

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+        <path class="cls-1" d="M547.3,200.1c-16.7,0-30.2-5-40.1-14.8-9.9-9.8-14.9-23.7-14.9-41.3v-31.7c0-17.7,5-31.7,14.8-41.4,9.8-9.7,23.4-14.7,40.3-14.7s30.4,4.9,40.3,14.7c9.8,9.7,14.8,23.7,14.8,41.4v31.7c0,17.6-5,31.5-14.9,41.3-9.9,9.8-23.4,14.8-40.1,14.8ZM547.3,77.8c-9.5,0-17.1,2.7-22.6,8.1-5.5,5.4-8.3,13.4-8.3,23.8v36.7c0,10.5,2.8,18.5,8.3,23.8,5.5,5.4,13.1,8.1,22.6,8.1s17.3-2.7,22.7-8.1c5.4-5.4,8.2-13.4,8.2-23.9v-36.7c0-10.5-2.8-18.5-8.2-23.9-5.4-5.4-13.1-8.1-22.7-8.1Z"/>
+        <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>

requirements-dev.txt

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

requirements-tests.txt

@@ -1,2 +1,3 @@
 pytest
 pytest-xdist
+pytest-retry

requirements.txt

@@ -1,22 +1,22 @@
 --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/
 packaging==23.2
 peft==0.13.2
-transformers==4.45.2
+transformers==4.46.3
 tokenizers>=0.20.1
 bitsandbytes==0.44.1
-accelerate==1.0.1
-datasets==3.0.1
-deepspeed==0.14.4
+accelerate==1.1.0
+datasets==3.1.0
+deepspeed==0.15.4
 pydantic==2.6.3
 addict
 fire
 PyYAML>=6.0
 requests
-flash-attn==2.6.3
+flash-attn==2.7.0.post2
 sentencepiece
 wandb
 einops
-xformers==0.0.28.post1
+xformers>=0.0.23.post1
 optimum==1.16.2
 hf_transfer
 colorama
@@ -28,13 +28,12 @@ scipy
 scikit-learn==1.4.2
 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
+autoawq==0.2.7.post2
 triton>=2.3.0
-liger-kernel==0.3.0
+liger-kernel==0.4.1
 
 mamba-ssm==1.2.0.post1
 
@@ -43,7 +42,7 @@ s3fs>=2024.5.0
 gcsfs>=2024.5.0
 # adlfs
 
-trl==0.9.6
+trl==0.12.0
 zstandard==0.22.0
 fastcore
 
@@ -54,3 +53,4 @@ 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 '^RUNPOD_|^PATH=|^_=' | sed 's/^\(.*\)=\(.*\)$/export \1="\2"/' >> /etc/rp_environment
+printenv | grep -E '^HF_|^BNB_|^CUDA_|^NCCL_|^NV|^RUNPOD_|^PATH=|^_=' | sed 's/^\([^=]*\)=\(.*\)$/export \1="\2"/' | grep -v 'printenv' >> /etc/rp_environment
 echo 'source /etc/rp_environment' >> ~/.bashrc
 
 add_keys_to_authorized() {

setup.py

@@ -31,13 +31,18 @@ def parse_requirements():
     try:
         xformers_version = [req for req in _install_requires if "xformers" in req][0]
         torchao_version = [req for req in _install_requires if "torchao" in req][0]
+        autoawq_version = [req for req in _install_requires if "autoawq" in req][0]
+
         if "Darwin" in platform.system():
             # don't install xformers on MacOS
             _install_requires.pop(_install_requires.index(xformers_version))
         else:
             # detect the version of torch already installed
             # and set it so dependencies don't clobber the torch version
-            torch_version = version("torch")
+            try:
+                torch_version = version("torch")
+            except PackageNotFoundError:
+                torch_version = "2.5.1"
             _install_requires.append(f"torch=={torch_version}")
 
             version_match = re.match(r"^(\d+)\.(\d+)(?:\.(\d+))?", torch_version)
@@ -50,10 +55,20 @@ def parse_requirements():
             else:
                 raise ValueError("Invalid version format")
 
-            if (major, minor) >= (2, 4):
+            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:
                     _install_requires.pop(_install_requires.index(xformers_version))
                     _install_requires.append("xformers>=0.0.27")
+                else:
+                    _install_requires.pop(_install_requires.index(xformers_version))
+                    _install_requires.append("xformers==0.0.28.post1")
             elif (major, minor) >= (2, 3):
                 _install_requires.pop(_install_requires.index(torchao_version))
                 if patch == 0:
@@ -73,7 +88,6 @@ def parse_requirements():
 
     except PackageNotFoundError:
         pass
-
     return _install_requires, _dependency_links
 
 
@@ -82,26 +96,24 @@ install_requires, dependency_links = parse_requirements()
 
 setup(
     name="axolotl",
-    version="0.4.1",
+    version="0.5.1",
     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(),
+    packages=find_packages("src"),
     install_requires=install_requires,
     dependency_links=dependency_links,
     extras_require={
         "flash-attn": [
-            "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",
+            "flash-attn==2.7.0.post2",
         ],
         "deepspeed": [
-            "deepspeed==0.14.4",
+            "deepspeed==0.15.4",
             "deepspeed-kernels",
         ],
         "mamba-ssm": [
             "mamba-ssm==1.2.0.post1",
+            "causal_conv1d",
         ],
         "auto-gptq": [
             "auto-gptq==0.5.1",

src/axolotl/cli/__init__.py

@@ -30,7 +30,7 @@ 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 chat_templates
+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,
@@ -190,18 +190,15 @@ 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)
 
     model = model.to(cfg.device, dtype=cfg.torch_dtype)
 
@@ -211,13 +208,31 @@ 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()
-        batch = tokenizer(prompt, return_tensors="pt", add_special_tokens=True)
+
+        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)
 
         print("=" * 40)
         model.eval()
@@ -257,13 +272,6 @@ 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
@@ -272,7 +280,7 @@ def do_inference_gradio(
             importlib.import_module("axolotl.prompters"), prompter
         )
     elif cfg.chat_template:
-        chat_template_str = chat_templates(cfg.chat_template)
+        chat_template_str = get_chat_template(cfg.chat_template, tokenizer=tokenizer)
 
     model = model.to(cfg.device, dtype=cfg.torch_dtype)
 
@@ -462,7 +470,12 @@ def load_datasets(
         processor=processor,
     )
 
-    if cli_args.debug or cfg.debug:
+    if (
+        cli_args.debug
+        or cfg.debug
+        or cli_args.debug_text_only
+        or int(cli_args.debug_num_examples) > 0
+    ):
         LOG.info("check_dataset_labels...")
         check_dataset_labels(
             train_dataset.select(

src/axolotl/cli/preprocess.py

@@ -23,10 +23,6 @@ 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")
@@ -44,23 +40,6 @@ 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

@@ -19,10 +19,6 @@ 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")
@@ -42,21 +38,6 @@ 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/common/cli.py

@@ -23,7 +23,7 @@ class TrainerCliArgs:
 
     debug: bool = field(default=False)
     debug_text_only: bool = field(default=False)
-    debug_num_examples: int = field(default=5)
+    debug_num_examples: int = field(default=0)
     inference: bool = field(default=False)
     merge_lora: bool = field(default=False)
     prompter: Optional[str] = field(default=None)

src/axolotl/core/trainer_builder.py

@@ -7,6 +7,7 @@ import abc
 import gc
 import importlib
 import importlib.util
+import inspect
 import logging
 import math
 import os
@@ -27,7 +28,6 @@ from torch.optim.lr_scheduler import OneCycleLR
 from torch.utils.data import BatchSampler, DataLoader, RandomSampler, SequentialSampler
 from transformers import (
     EarlyStoppingCallback,
-    PreTrainedModel,
     Trainer,
     TrainerCallback,
     TrainingArguments,
@@ -48,6 +48,7 @@ from trl import (
 )
 from trl.trainer.utils import RewardDataCollatorWithPadding, pad_to_length
 
+from axolotl.integrations.base import PluginManager
 from axolotl.monkeypatch.multipack import SUPPORTED_MULTIPACK_MODEL_TYPES
 from axolotl.monkeypatch.relora import ReLoRACallback, ReLoRAScheduler
 from axolotl.utils import is_comet_available, is_mlflow_available
@@ -63,7 +64,7 @@ from axolotl.utils.callbacks import (
     log_prediction_callback_factory,
 )
 from axolotl.utils.callbacks.lisa import lisa_callback_factory
-from axolotl.utils.chat_templates import chat_templates
+from axolotl.utils.chat_templates import get_chat_template
 from axolotl.utils.collators import (
     BatchSamplerDataCollatorForSeq2Seq,
     DataCollatorForSeq2Seq,
@@ -435,7 +436,13 @@ class AxolotlTrainer(SchedulerMixin, Trainer):
         if (
             self.args.loraplus_lr_ratio is None
             and self.args.alternate_optimizer
-            not in ["optimi_adamw", "ao_adamw_8bit", "ao_adamw_4bit", "ao_adamw_fp8"]
+            not in [
+                "optimi_adamw",
+                "ao_adamw_8bit",
+                "ao_adamw_4bit",
+                "ao_adamw_fp8",
+                "adopt_adamw",
+            ]
         ):
             return super().create_optimizer()
 
@@ -504,6 +511,14 @@ 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, decoupled=True, **optimizer_kwargs
+                    )
+                )
 
         if is_sagemaker_mp_enabled():
             self.optimizer = smp.DistributedOptimizer(  # pylint: disable=attribute-defined-outside-init
@@ -666,7 +681,9 @@ class AxolotlTrainer(SchedulerMixin, Trainer):
         return DataLoader(bench_dataset, **dataloader_params)
         # return self.accelerator.prepare(DataLoader(bench_dataset, **dataloader_params))
 
-    def compute_loss(self, model, inputs, return_outputs=False):
+    def compute_loss(
+        self, model, inputs, return_outputs=False, num_items_in_batch=None
+    ):
         # use one's weighted cross entropy loss calc
         # if self.args.sample_packing:
         #     labels = inputs.pop("labels")
@@ -674,8 +691,18 @@ class AxolotlTrainer(SchedulerMixin, Trainer):
         #     loss = trainer_weighted_loss(outputs, labels, shift_labels=True)
         #     return (loss, outputs) if return_outputs else loss
         if self.args.orpo_alpha:
-            return self.orpo_compute_loss(model, inputs, return_outputs=return_outputs)
-        return super().compute_loss(model, inputs, return_outputs=return_outputs)
+            return self.orpo_compute_loss(
+                model,
+                inputs,
+                return_outputs=return_outputs,
+                num_items_in_batch=num_items_in_batch,
+            )
+        return super().compute_loss(
+            model,
+            inputs,
+            return_outputs=return_outputs,
+            num_items_in_batch=num_items_in_batch,
+        )
 
     @staticmethod
     def orpo_concatenate_inputs(inputs, label_pad_token=-100, pad_token=0, device=None):
@@ -771,7 +798,13 @@ class AxolotlTrainer(SchedulerMixin, Trainer):
         ).squeeze(2)
         return torch.mul(per_token_logps, mask).sum(dim=1) / mask.sum(dim=1)
 
-    def orpo_compute_loss(self, model, inputs, return_outputs=False):
+    def orpo_compute_loss(
+        self,
+        model,
+        inputs,
+        return_outputs=False,
+        num_items_in_batch=None,  # pylint: disable=unused-argument
+    ):
         concat_inputs = AxolotlTrainer.orpo_concatenate_inputs(
             inputs,
             label_pad_token=-100,
@@ -877,13 +910,13 @@ class AxolotlTrainer(SchedulerMixin, Trainer):
         for key, value in metrics.items():
             self._stored_metrics[train_eval][key].append(value)
 
-    def _save_checkpoint(self, model, trial, metrics=None):
+    def _save_checkpoint(self, model, trial, **kwargs):
         # make sure the checkpoint dir exists, since trainer is flakey
         checkpoint_folder = f"{PREFIX_CHECKPOINT_DIR}-{self.state.global_step}"
         run_dir = self._get_output_dir(trial=trial)
         output_dir = os.path.join(run_dir, checkpoint_folder)
         os.makedirs(output_dir, exist_ok=True)
-        return super()._save_checkpoint(model, trial, metrics=metrics)
+        return super()._save_checkpoint(model, trial, **kwargs)
 
 
 class AxolotlMambaTrainer(AxolotlTrainer):
@@ -898,6 +931,7 @@ class AxolotlMambaTrainer(AxolotlTrainer):
         model,
         inputs,
         return_outputs=False,  # pylint: disable=unused-argument
+        num_items_in_batch=None,  # pylint: disable=unused-argument
     ):
         input_ids = inputs.pop("input_ids")
         lm_logits = model(input_ids).logits
@@ -1004,19 +1038,46 @@ class AxolotlDPOTrainer(SchedulerMixin, DPOTrainer):
 
         return super().push_to_hub(*args, **kwargs)
 
+    @staticmethod
     def tokenize_row(
-        self, feature, model: Optional[Union[PreTrainedModel, torch.nn.Module]] = None
+        features,
+        processing_class,
+        max_prompt_length,
+        max_completion_length,
+        add_special_tokens,
     ) -> Dict:
-        res = super().tokenize_row(feature, model=model)
-        if self.tokenizer.bos_token_id is None and res["prompt_input_ids"][0] is None:
+        res = DPOTrainer.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:
             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(
-        self, model: nn.Module, inputs: Dict[str, Union[torch.Tensor, Any]]
+        self,
+        model: nn.Module,
+        inputs: Dict[str, Union[torch.Tensor, Any]],
+        num_items_in_batch=None,
     ) -> torch.Tensor:
-        loss: torch.Tensor = super().training_step(model, inputs)
+        loss: torch.Tensor = super().training_step(model, inputs, num_items_in_batch)
         gc.collect()
         torch.cuda.empty_cache()
         return loss
@@ -1114,17 +1175,28 @@ class TrainerBuilderBase(abc.ABC):
 
     def get_callbacks(self) -> List[TrainerCallback]:
         callbacks = []
+
+        plugin_manager = PluginManager.get_instance()
+        callbacks.extend(
+            plugin_manager.add_callbacks_pre_trainer(cfg=self.cfg, model=self.model)
+        )
+
         if self.cfg.use_wandb:
             callbacks.append(
                 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,
             )
 
-            callbacks.append(
-                SaveAxolotlConfigtoMlflowCallback(self.cfg.axolotl_config_path)
+            callbacks.extend(
+                [
+                    SaveAxolotlConfigtoMlflowCallback(self.cfg.axolotl_config_path),
+                    MLflowCallback,
+                ]
             )
         if self.cfg.use_comet and is_comet_available():
             from axolotl.utils.callbacks.comet_ import SaveAxolotlConfigtoCometCallback
@@ -1135,11 +1207,17 @@ class TrainerBuilderBase(abc.ABC):
 
         return callbacks
 
-    @abstractmethod
     def get_post_trainer_create_callbacks(self, trainer):
         """
         Callbacks added after the trainer is created, usually b/c these need access to the trainer
         """
+        callbacks = []
+
+        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):
         # TODO
@@ -1185,7 +1263,7 @@ 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"
@@ -1222,6 +1300,18 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
 
         if self.cfg.lisa_step_interval and self.cfg.lisa_n_layers:
             callbacks.append(lisa_callback_factory(trainer))
+
+        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
+                ]
+            )
         return callbacks
 
     def _get_trainer_cls(self):
@@ -1339,17 +1429,15 @@ 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["evaluation_strategy"] = "no"
+            training_arguments_kwargs["eval_strategy"] = "no"
         elif self.cfg.eval_steps:
-            training_arguments_kwargs["evaluation_strategy"] = "steps"
+            training_arguments_kwargs["eval_strategy"] = "steps"
             training_arguments_kwargs["eval_steps"] = self.cfg.eval_steps
-        elif self.cfg.evaluation_strategy:
-            training_arguments_kwargs[
-                "evaluation_strategy"
-            ] = self.cfg.evaluation_strategy
+        elif self.cfg.eval_strategy:
+            training_arguments_kwargs["eval_strategy"] = self.cfg.eval_strategy
         else:
             # we have an eval set, but no steps defined, default to use epoch
-            training_arguments_kwargs["evaluation_strategy"] = "epoch"
+            training_arguments_kwargs["eval_strategy"] = "epoch"
 
         if self.cfg.save_steps:
             training_arguments_kwargs["save_strategy"] = "steps"
@@ -1556,8 +1644,9 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         training_arguments_kwargs["model_type"] = self.cfg.model_config_type
         training_arguments_kwargs["pretraining"] = bool(self.cfg.pretraining_dataset)
         if self.cfg.chat_template:
-            training_arguments_kwargs["chat_template"] = chat_templates(
-                self.cfg.chat_template
+            training_arguments_kwargs["chat_template"] = get_chat_template(
+                self.cfg.chat_template,
+                tokenizer=self.tokenizer,
             )
 
         if self.cfg.rl == "orpo":
@@ -1573,11 +1662,13 @@ 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"
@@ -1662,12 +1753,17 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
                 return_tensors="pt",
                 **data_collator_kwargs,
             )
+        sig = inspect.signature(trainer_cls)
+        if "processing_class" in sig.parameters.keys():
+            trainer_kwargs["processing_class"] = self.tokenizer
+        else:
+            trainer_kwargs["tokenizer"] = self.tokenizer
+
         trainer = trainer_cls(
             model=self.model,
             train_dataset=self.train_dataset,
             eval_dataset=self.eval_dataset,
             args=training_args,
-            tokenizer=self.tokenizer,
             data_collator=self.build_collator(training_args, **data_collator_kwargs),
             callbacks=self.get_callbacks(),
             **trainer_kwargs,
@@ -1708,6 +1804,8 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         ]
         if self.cfg.reward_model:
             collator = RewardDataCollatorWithPadding
+            if "max_length" in kwargs:
+                kwargs.pop("max_length")
         elif use_batch_sampler_collator:
             if self.cfg.model_config_type in SUPPORTED_MULTIPACK_MODEL_TYPES:
                 collator = V2BatchSamplerDataCollatorForSeq2Seq
@@ -1745,7 +1843,7 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
         return callbacks
 
     def get_post_trainer_create_callbacks(self, trainer):
-        callbacks = []
+        callbacks = super().get_post_trainer_create_callbacks(trainer=trainer)
         return callbacks
 
     def build_training_arguments(self, total_num_steps):
@@ -1773,10 +1871,10 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
             training_args_kwargs["save_safetensors"] = self.cfg.save_safetensors
 
         if self.eval_dataset:
-            training_args_kwargs["evaluation_strategy"] = "steps"
+            training_args_kwargs["eval_strategy"] = "steps"
             training_args_kwargs["eval_steps"] = self.cfg.eval_steps
         else:
-            training_args_kwargs["evaluation_strategy"] = "no"
+            training_args_kwargs["eval_strategy"] = "no"
 
         if self.cfg.bf16 or self.cfg.bfloat16:
             training_args_kwargs["bf16"] = True
@@ -1831,17 +1929,18 @@ 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"
@@ -1850,13 +1949,13 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
             if self.cfg.cpo_alpha is not None:
                 training_args_kwargs["cpo_alpha"] = self.cfg.cpo_alpha
 
-        if self.cfg.rl == "orpo":
+        elif 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
 
-        if self.cfg.rl == "kto":
+        elif self.cfg.rl == "kto":
             training_args_cls = AxolotlKTOConfig
 
             training_args_kwargs["desirable_weight"] = (
@@ -1871,6 +1970,17 @@ 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,
@@ -1891,7 +2001,6 @@ 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:
@@ -1905,12 +2014,6 @@ 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"] = True
         elif self.cfg.rl == "orpo":
             trainer_cls = AxolotlORPOTrainer
             trainer_cls_args = [self.model]
@@ -1922,11 +2025,17 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
             trainer_cls_args = [self.model]
         else:
             raise ValueError(f"Unsupported RL: {self.cfg.rl}")
+
+        sig = inspect.signature(trainer_cls)
+        if "processing_class" in sig.parameters.keys():
+            dpo_trainer_kwargs["processing_class"] = self.tokenizer
+        else:
+            dpo_trainer_kwargs["tokenizer"] = self.tokenizer
+
         dpo_trainer = trainer_cls(
             *trainer_cls_args,
             args=training_args,
             train_dataset=self.train_dataset,
-            tokenizer=self.tokenizer,
             callbacks=self.get_callbacks(),
             **dpo_trainer_kwargs,
         )
@@ -1948,11 +2057,11 @@ class HFPPOTrainerBuilder(TrainerBuilderBase):
     """
 
     def get_callbacks(self):
-        callbacks = []
+        callbacks = super().get_callbacks()
         return callbacks
 
     def get_post_trainer_create_callbacks(self, trainer):
-        callbacks = []
+        callbacks = super().get_post_trainer_create_callbacks(trainer=trainer)
         return callbacks
 
     def build(self, total_num_steps):

src/axolotl/integrations/base.py

@@ -18,9 +18,10 @@ Plugins can be used to integrate third-party models, modify the training process
 
 To create a new plugin, you need to inherit from the BasePlugin class and implement the required methods.
 """
+import collections
 import importlib
 import logging
-from typing import List
+from typing import OrderedDict
 
 
 class BasePlugin:
@@ -47,7 +48,7 @@ class BasePlugin:
         Initializes the BasePlugin.
         """
 
-    def register(self, cfg):
+    def register(self, cfg):  # pylint: disable=unused-argument
         """
         Registers the plugin with the given configuration.
 
@@ -63,7 +64,7 @@ class BasePlugin:
         Returns a pydantic model for the plugin's input arguments.
         """
 
-    def pre_model_load(self, cfg):
+    def pre_model_load(self, cfg):  # pylint: disable=unused-argument
         """
         Performs actions before the model is loaded.
 
@@ -74,7 +75,7 @@ class BasePlugin:
         None
         """
 
-    def post_model_load(self, cfg, model):
+    def post_model_load(self, cfg, model):  # pylint: disable=unused-argument
         """
         Performs actions after the model is loaded.
 
@@ -86,7 +87,7 @@ class BasePlugin:
         None
         """
 
-    def pre_lora_load(self, cfg, model):
+    def pre_lora_load(self, cfg, model):  # pylint: disable=unused-argument
         """
         Performs actions before LoRA weights are loaded.
 
@@ -98,7 +99,7 @@ class BasePlugin:
         None
         """
 
-    def post_lora_load(self, cfg, model):
+    def post_lora_load(self, cfg, model):  # pylint: disable=unused-argument
         """
         Performs actions after LoRA weights are loaded.
 
@@ -110,7 +111,7 @@ class BasePlugin:
         None
         """
 
-    def create_optimizer(self, cfg, trainer):
+    def create_optimizer(self, cfg, trainer):  # pylint: disable=unused-argument
         """
         Creates and returns an optimizer for training.
 
@@ -122,7 +123,9 @@ class BasePlugin:
         object: The created optimizer.
         """
 
-    def create_lr_scheduler(self, cfg, trainer, optimizer):
+    def create_lr_scheduler(
+        self, cfg, trainer, optimizer
+    ):  # pylint: disable=unused-argument
         """
         Creates and returns a learning rate scheduler.
 
@@ -135,9 +138,9 @@ class BasePlugin:
         object: The created learning rate scheduler.
         """
 
-    def add_callbacks_pre_trainer(self, cfg, model):
+    def add_callbacks_pre_trainer(self, cfg, model):  # pylint: disable=unused-argument
         """
-        Adds callbacks to the trainer before training.
+        setup callbacks before creating the trainer.
 
         Parameters:
         cfg (dict): The configuration for the plugin.
@@ -146,20 +149,25 @@ class BasePlugin:
         Returns:
         List[callable]: A list of callback functions to be added to the TrainingArgs
         """
+        return []
 
-    def add_callbacks_post_trainer(self, cfg, trainer):
+    def add_callbacks_post_trainer(
+        self, cfg, trainer
+    ):  # pylint: disable=unused-argument
         """
-        Adds callbacks to the trainer after training.
+        Adds callbacks to the trainer after creating the trainer.
+        This is useful for callbacks that require access to the model or trainer.
 
         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 to the TrainingArgs
+        List[callable]: A list of callback functions to be added
         """
+        return []
 
-    def post_train(self, cfg, model):
+    def post_train(self, cfg, model):  # pylint: disable=unused-argument
         """
         Performs actions after training is complete.
 
@@ -171,7 +179,7 @@ class BasePlugin:
         None
         """
 
-    def post_train_unload(self, cfg):
+    def post_train_unload(self, cfg):  # pylint: disable=unused-argument
         """
         Performs actions after training is complete and the model is unloaded.
 
@@ -227,7 +235,7 @@ class PluginManager:
     pre_model_load(cfg): Calls the pre_model_load method of all registered plugins.
     """
 
-    plugins: List[BasePlugin] = []
+    plugins: OrderedDict[str, BasePlugin] = collections.OrderedDict()
 
     _instance = None
 
@@ -237,7 +245,7 @@ class PluginManager:
         """
         if cls._instance is None:
             cls._instance = super(PluginManager, cls).__new__(cls)
-            cls._instance.plugins: List[BasePlugin] = []
+            cls._instance.plugins = collections.OrderedDict()
         return cls._instance
 
     @staticmethod
@@ -265,7 +273,7 @@ class PluginManager:
         """
         try:
             plugin = load_plugin(plugin_name)
-            self.plugins.append(plugin)
+            self.plugins[plugin_name] = plugin
         except ImportError:
             logging.error(f"Failed to load plugin: {plugin_name}")
 
@@ -277,7 +285,7 @@ class PluginManager:
         list[str]: A list of Pydantic classes for all registered plugins' input arguments.'
         """
         input_args = []
-        for plugin in self.plugins:
+        for plugin in self.plugins.values():
             input_args_from_plugin = plugin.get_input_args()
             if input_args_from_plugin is not None:
                 input_args.append(input_args_from_plugin)
@@ -293,7 +301,7 @@ class PluginManager:
         Returns:
         None
         """
-        for plugin in self.plugins:
+        for plugin in self.plugins.values():
             plugin.pre_model_load(cfg)
 
     def post_model_load(self, cfg, model):
@@ -307,7 +315,7 @@ class PluginManager:
         Returns:
         None
         """
-        for plugin in self.plugins:
+        for plugin in self.plugins.values():
             plugin.post_model_load(cfg, model)
 
     def pre_lora_load(self, cfg, model):
@@ -321,7 +329,7 @@ class PluginManager:
         Returns:
         None
         """
-        for plugin in self.plugins:
+        for plugin in self.plugins.values():
             plugin.pre_lora_load(cfg, model)
 
     def post_lora_load(self, cfg, model):
@@ -335,7 +343,7 @@ class PluginManager:
         Returns:
         None
         """
-        for plugin in self.plugins:
+        for plugin in self.plugins.values():
             plugin.post_lora_load(cfg, model)
 
     def create_optimizer(self, cfg, trainer):
@@ -349,7 +357,7 @@ class PluginManager:
         Returns:
         object: The created optimizer, or None if none was found.
         """
-        for plugin in self.plugins:
+        for plugin in self.plugins.values():
             optimizer = plugin.create_optimizer(cfg, trainer)
             if optimizer is not None:
                 return optimizer
@@ -367,7 +375,7 @@ class PluginManager:
         Returns:
         object: The created learning rate scheduler, or None if none was found.
         """
-        for plugin in self.plugins:
+        for plugin in self.plugins.values():
             scheduler = plugin.create_lr_scheduler(cfg, trainer, optimizer)
             if scheduler is not None:
                 return scheduler
@@ -385,8 +393,10 @@ class PluginManager:
         List[callable]: A list of callback functions to be added to the TrainingArgs.
         """
         callbacks = []
-        for plugin in self.plugins:
-            callbacks.extend(plugin.add_callbacks_pre_trainer(cfg, model))
+        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)
         return callbacks
 
     def add_callbacks_post_trainer(self, cfg, trainer):
@@ -401,8 +411,10 @@ class PluginManager:
         List[callable]: A list of callback functions to be added to the TrainingArgs.
         """
         callbacks = []
-        for plugin in self.plugins:
-            callbacks.extend(plugin.add_callbacks_post_trainer(cfg, trainer))
+        for plugin in self.plugins.values():
+            plugin_callbacks = plugin.add_callbacks_post_trainer(cfg, trainer)
+            if plugin_callbacks:
+                callbacks.extend(plugin_callbacks)
         return callbacks
 
     def post_train_unload(self, cfg):
@@ -416,5 +428,5 @@ class PluginManager:
         Returns:
         None
         """
-        for plugin in self.plugins:
+        for plugin in self.plugins.values():
             plugin.post_train_unload(cfg)

src/axolotl/integrations/grokfast/LICENSE

@@ -0,0 +1,21 @@
+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

@@ -0,0 +1,13 @@
+# 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

@@ -0,0 +1,50 @@
+"""
+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

@@ -0,0 +1,15 @@
+"""
+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

@@ -0,0 +1,63 @@
+# 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

@@ -18,20 +18,24 @@ Module for the Plugin for LIGER integraton with Axolotl.
 Liger Kernel is the collection of Triton-native kernels for LLM Training.
 It is designed to be performant, correct, and light-weight.
 """
+import inspect
 import logging
 import sys
-from functools import partial
 
 from liger_kernel.transformers.cross_entropy import LigerCrossEntropyLoss
-from liger_kernel.transformers.geglu import LigerGEGLUMLP
+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
 from liger_kernel.transformers.swiglu import LigerSwiGLUMLP
 
 from axolotl.integrations.base import BasePlugin
 
+from ...utils.distributed import zero_only
 from .args import LigerArgs  # pylint: disable=unused-import. # noqa: F401
 
+LOG = logging.getLogger("axolotl.integrations.liger")
+
 
 class LigerPlugin(BasePlugin):
     """
@@ -42,59 +46,31 @@ class LigerPlugin(BasePlugin):
         return "axolotl.integrations.liger.LigerArgs"
 
     def pre_model_load(self, cfg):
-        if cfg.model_config_type == "llama":
-            from liger_kernel.transformers.model.llama import (
-                lce_forward as llama_lce_forward,
-            )
-            from transformers.models.llama import modeling_llama
-
-            if cfg.liger_rope:
-                modeling_llama.apply_rotary_pos_emb = liger_rotary_pos_emb
-            if cfg.liger_rms_norm:
-                modeling_llama.LlamaRMSNorm = LigerRMSNorm
-            if cfg.liger_swiglu:
-                modeling_llama.LlamaMLP = LigerSwiGLUMLP
-            if cfg.liger_cross_entropy:
-                modeling_llama.CrossEntropyLoss = LigerCrossEntropyLoss
-            elif cfg.liger_fused_linear_cross_entropy:
-                modeling_llama.LlamaForCausalLM.forward = llama_lce_forward
-
-        elif cfg.model_config_type == "mistral":
-            from liger_kernel.transformers.model.mistral import (
-                lce_forward as mistral_lce_forward,
-            )
-            from transformers.models.mistral import modeling_mistral
-
-            if cfg.liger_rope:
-                modeling_mistral.apply_rotary_pos_emb = liger_rotary_pos_emb
-            if cfg.liger_rms_norm:
-                modeling_mistral.MistralRMSNorm = LigerRMSNorm
-            if cfg.liger_swiglu:
-                modeling_mistral.MistralMLP = LigerSwiGLUMLP
-            if cfg.liger_cross_entropy:
-                modeling_mistral.CrossEntropyLoss = LigerCrossEntropyLoss
-            if cfg.liger_fused_linear_cross_entropy:
-                modeling_mistral.MistralForCausalLM.forward = mistral_lce_forward
-
-        elif cfg.model_config_type == "gemma":
-            from liger_kernel.transformers.model.gemma import (
-                lce_forward as gemma_lce_forward,
-            )
-            from transformers.models.gemma import modeling_gemma
-
-            if cfg.liger_rope:
-                modeling_gemma.apply_rotary_pos_emb = liger_rotary_pos_emb
-            if cfg.liger_rms_norm:
-                modeling_gemma.GemmaRMSNorm = partial(
-                    LigerRMSNorm, offset=1.0, init_fn="zeros", casting_mode="gemma"
+        if cfg.model_config_type in MODEL_TYPE_TO_APPLY_LIGER_FN:
+            apply_liger_fn = MODEL_TYPE_TO_APPLY_LIGER_FN[cfg.model_config_type]
+            liger_fn_sig = inspect.signature(apply_liger_fn)
+            kwargs = {}
+            if "rope" in liger_fn_sig.parameters:
+                kwargs["rope"] = cfg.liger_rope
+            if "cross_entropy" in liger_fn_sig.parameters:
+                kwargs["cross_entropy"] = cfg.liger_cross_entropy
+            if "fused_linear_cross_entropy" in liger_fn_sig.parameters:
+                kwargs[
+                    "fused_linear_cross_entropy"
+                ] = cfg.liger_fused_linear_cross_entropy
+            if "rms_norm" in liger_fn_sig.parameters:
+                kwargs["rms_norm"] = cfg.liger_rms_norm
+            if "layer_norm" in liger_fn_sig.parameters:
+                kwargs["layer_norm"] = cfg.liger_layer_norm
+            if "geglu" in liger_fn_sig.parameters:
+                kwargs["geglu"] = cfg.liger_glu_activation
+            elif "swiglu" in liger_fn_sig.parameters:
+                kwargs["swiglu"] = cfg.liger_glu_activation
+            with zero_only():
+                LOG.info(
+                    f"Applying LIGER to {cfg.model_config_type} with kwargs: {kwargs}"
                 )
-            if cfg.liger_swiglu:
-                modeling_gemma.GemmaMLP = LigerGEGLUMLP
-            if cfg.liger_cross_entropy:
-                modeling_gemma.CrossEntropyLoss = LigerCrossEntropyLoss
-            if cfg.liger_fused_linear_cross_entropy:
-                modeling_gemma.GemmaForCausalLM.forward = gemma_lce_forward
-
+            apply_liger_fn(**kwargs)
         elif cfg.model_config_type == "jamba":
             from transformers.models.jamba import modeling_jamba
 
@@ -104,30 +80,14 @@ class LigerPlugin(BasePlugin):
                 modeling_jamba.apply_rotary_pos_emb = liger_rotary_pos_emb
             if cfg.liger_rms_norm:
                 modeling_jamba.JambaRMSNorm = LigerRMSNorm
-            if cfg.liger_swiglu:
+            if cfg.liger_glu_activation:
                 modeling_jamba.JambaMLP = LigerSwiGLUMLP
             if cfg.liger_cross_entropy:
-                modeling_jamba.CrossEntropyLoss = LigerCrossEntropyLoss
+                from transformers.loss.loss_utils import nn
+
+                nn.functional.cross_entropy = liger_cross_entropy
             if cfg.liger_fused_linear_cross_entropy:
                 modeling_jamba.JambaForCausalLM.forward = jamba_lce_forward
-
-        elif cfg.model_config_type == "qwen2":
-            from liger_kernel.transformers.model.qwen2 import (
-                lce_forward as qwen2_lce_forward,
-            )
-            from transformers.models.qwen2 import modeling_qwen2
-
-            if cfg.liger_rope:
-                modeling_qwen2.apply_rotary_pos_emb = liger_rotary_pos_emb
-            if cfg.liger_rms_norm:
-                modeling_qwen2.Qwen2RMSNorm = LigerRMSNorm
-            if cfg.liger_swiglu:
-                modeling_qwen2.Qwen2MLP = LigerSwiGLUMLP
-            if cfg.liger_cross_entropy:
-                modeling_qwen2.CrossEntropyLoss = LigerCrossEntropyLoss
-            if cfg.liger_fused_linear_cross_entropy:
-                modeling_qwen2.Qwen2ForCausalLM.forward = qwen2_lce_forward
-
         elif cfg.model_config_type == "deepseek_v2":
             from accelerate import init_empty_weights
             from transformers import AutoModelForCausalLM
@@ -146,44 +106,11 @@ class LigerPlugin(BasePlugin):
                 logging.warning("Fused liger_rope is not supported for DeepseekV2.")
             if cfg.liger_rms_norm:
                 modeling_mod.DeepseekV2RMSNorm = LigerRMSNorm
-            if cfg.liger_swiglu:
+            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
-
-        elif cfg.model_config_type == "gemma2":
-            from transformers.models.gemma2 import modeling_gemma2
-
-            if cfg.liger_rope:
-                modeling_gemma2.apply_rotary_pos_emb = liger_rotary_pos_emb
-            if cfg.liger_rms_norm:
-                modeling_gemma2.Gemma2RMSNorm = partial(
-                    LigerRMSNorm, offset=1.0, init_fn="zeros", casting_mode="gemma"
-                )
-            if cfg.liger_swiglu:
-                modeling_gemma2.Gemma2MLP = LigerGEGLUMLP
-            if cfg.liger_cross_entropy:
-                modeling_gemma2.CrossEntropyLoss = LigerCrossEntropyLoss
-            if cfg.liger_fused_linear_cross_entropy:
-                logging.warning(
-                    "Fused linear cross entropy is not supported for Gemma 2."
-                )
-
-        elif cfg.model_config_type == "phi3":
-            from liger_kernel.transformers.model.phi3 import (
-                lce_forward as phi3_lce_forward,
-            )
-            from transformers.models.phi3 import modeling_phi3
-
-            if cfg.liger_rope:
-                modeling_phi3.apply_rotary_pos_emb = liger_rotary_pos_emb
-            if cfg.liger_rms_norm:
-                modeling_phi3.Phi3RMSNorm = LigerRMSNorm
-            if cfg.liger_swiglu:
-                modeling_phi3.Phi3MLP = LigerSwiGLUMLP
-            if cfg.liger_cross_entropy:
-                modeling_phi3.CrossEntropyLoss = LigerCrossEntropyLoss
-            if cfg.liger_fused_linear_cross_entropy:
-                modeling_phi3.Phi3ForCausalLM.forward = phi3_lce_forward

src/axolotl/integrations/liger/args.py

@@ -15,9 +15,12 @@
 """
 Module for handling LIGER input arguments.
 """
+import logging
 from typing import Optional
 
-from pydantic import BaseModel
+from pydantic import BaseModel, model_validator
+
+LOG = logging.getLogger("axolotl.integrations.liger.args")
 
 
 class LigerArgs(BaseModel):
@@ -27,6 +30,24 @@ class LigerArgs(BaseModel):
 
     liger_rope: Optional[bool] = None
     liger_rms_norm: Optional[bool] = None
+    liger_layer_norm: Optional[bool] = None
     liger_swiglu: Optional[bool] = None
+    liger_glu_activation: Optional[bool] = None
     liger_cross_entropy: Optional[bool] = None
     liger_fused_linear_cross_entropy: Optional[bool] = None
+
+    @model_validator(mode="before")
+    @classmethod
+    def check_deprecated_swiglu(cls, data):
+        if data.get("liger_swiglu") is not None:
+            if data.get("liger_glu_activation") is not None:
+                raise ValueError(
+                    "You cannot have both `liger_swiglu` and `liger_glu_activation` set."
+                )
+
+            LOG.warning(
+                "The 'liger_swiglu' argument is deprecated and will be removed in a future release. "
+                "Please use 'liger_glu_activation' instead."
+            )
+            data["liger_glu_activation"] = data.pop("liger_swiglu")
+        return data

src/axolotl/monkeypatch/fastchat_conversation_turns.py

@@ -1,231 +0,0 @@
-"""
-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

@@ -22,7 +22,6 @@ from transformers.models.llama.modeling_llama import (
     apply_rotary_pos_emb,
     repeat_kv,
 )
-from xformers.ops import SwiGLU
 
 from axolotl.monkeypatch.utils import get_cu_seqlens_from_pos_ids, set_module_name
 
@@ -44,7 +43,19 @@ except ImportError:
 LOG = logging.getLogger("axolotl")
 
 
+def is_xformers_available() -> bool:
+    try:
+        import xformers  # pylint: disable=unused-import  # noqa: F401
+
+        return True
+    except ImportError:
+        return False
+
+
 def is_xformers_swiglu_available() -> bool:
+    if not is_xformers_available():
+        return False
+
     from xformers.ops.common import get_xformers_operator
 
     try:
@@ -57,6 +68,11 @@ def is_xformers_swiglu_available() -> bool:
 
 
 def replace_llama_mlp_with_swiglu(model):
+    if is_xformers_swiglu_available():
+        from axolotl.monkeypatch.xformers_ import FusedMLP
+    else:
+        raise RuntimeError("xformers SwiGLU not available for this environment")
+
     for name, module in model.named_modules():
         if isinstance(module, LlamaMLP):
             mlp = FusedMLP(
@@ -181,49 +197,6 @@ class FusedAttention(LlamaAttention):
         set_module_name(model, name, new_attn)
 
 
-class FusedMLP(torch.nn.Module):
-    """
-    Fused MLP layer for incrementally improved training efficiency
-    """
-
-    def __init__(
-        self,
-        config,
-        gate_proj: torch.nn.Linear,
-        up_proj: torch.nn.Linear,
-        down_proj: torch.nn.Linear,
-    ):
-        super().__init__()
-        self.config = config
-        self.swiglu = SwiGLU(
-            in_features=config.hidden_size,
-            hidden_features=config.intermediate_size,
-            bias=False,
-            _pack_weights=True,
-        )
-        # overwrite initialized weights with pretrained weights
-        self.swiglu.w12.weight.data = torch.cat(
-            (gate_proj.weight.data, up_proj.weight.data), dim=0
-        )
-        self.swiglu.w3.weight.data = down_proj.weight.data
-
-    def _post_training(self, model, name):
-        w1, w2 = torch.split(  # pylint: disable=invalid-name
-            self.swiglu.w12.weight.data, self.config.intermediate_size, dim=0
-        )
-
-        # Assign the split weights back to the original layers
-        new_mlp = LlamaMLP(self.config)
-        new_mlp.gate_proj.weight.data = w1
-        new_mlp.up_proj.weight.data = w2
-        new_mlp.down_proj.weight.data = self.swiglu.w3.weight.data
-
-        set_module_name(model, name, new_mlp)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:  # pylint: disable=invalid-name
-        return self.swiglu(x)
-
-
 # Disable the transformation of the attention mask in LlamaModel as the flash attention
 # requires the attention mask to be the same as the key_padding_mask
 def _prepare_decoder_attention_mask(

src/axolotl/monkeypatch/multipack.py

@@ -1,4 +1,5 @@
 """multipack patching for v2 of sample packing"""
+
 import importlib
 
 import transformers
@@ -27,71 +28,28 @@ SUPPORTED_MULTIPACK_MODEL_TYPES = [
 ]
 
 
-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:
+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"):
         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
 
-    # 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
-        )
+    if model_type == "mixtral" and is_deepspeed_zero3_enabled():
+        patch_mixtral_moe_forward_zero3()
 
 
-def patch_remote(model_name, config_name, modeling_name):
+def patch_remote(model_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)
-    module_name = model_config.__class__.__module__.replace(config_name, modeling_name)
+    parts = model_config.__class__.__module__.split(".")
+    parts[-1] = parts[-1].replace("configuration_", "modeling_", 1)
+    module_name = ".".join(parts)
     modeling_arch = importlib.import_module(module_name)
-    modeling_arch._get_unpad_data = get_unpad_data  # pylint: disable=protected-access
+    if hasattr(modeling_arch, "_get_unpad_data"):
+        modeling_arch._get_unpad_data = (  # pylint: disable=protected-access
+            get_unpad_data
+        )

src/axolotl/monkeypatch/unsloth_.py

@@ -16,26 +16,6 @@ from transformers.models.llama.modeling_llama import (
 
 LOG = get_logger("axolotl.monkeypatch.unsloth")
 
-ORIGINAL_CEL_CODE = """# Shift so that tokens < n predict n
-        shift_logits = logits[..., :-1, :].contiguous()
-        shift_labels = labels[..., 1:].contiguous()
-        # Flatten the tokens
-        loss_fct = CrossEntropyLoss()
-        shift_logits = shift_logits.view(-1, self.config.vocab_size)
-        shift_labels = shift_labels.view(-1)
-        # Enable model parallelism
-        shift_labels = shift_labels.to(shift_logits.device)
-        loss = loss_fct(shift_logits, shift_labels)
-"""
-
-PATCHED_CEL_CODE = """shift_logits = logits[..., :-1, :].contiguous()
-        shift_labels = labels[..., 1:].contiguous()
-        loss = fast_cross_entropy_loss(
-            logits = shift_logits,
-            labels = shift_labels,
-        )
-"""
-
 ORIGINAL_QKV_CODE = """
     query_states = self.q_proj(hidden_states)
     key_states = self.k_proj(hidden_states)
@@ -80,12 +60,6 @@ def get_forward_code() -> str:
     return forward
 
 
-def check_cel_is_patchable() -> bool:
-    forward = get_forward_code()
-    forward, _ = detab_code(forward)
-    return ORIGINAL_CEL_CODE in forward
-
-
 def get_self_attn_code() -> str:
     forward = inspect.getsource(LlamaFlashAttention2.forward)
     return forward
@@ -98,48 +72,31 @@ def check_self_attn_is_patchable() -> bool:
 
 
 def integrate_cross_entropy_loss_patch(model_type: str = "llama") -> None:
+    from unsloth.kernels.cross_entropy_loss import fast_cross_entropy_loss
+
+    def UnslothForCausalLMLoss(  # pylint: disable=invalid-name
+        logits,
+        labels,
+        vocab_size: int,  # pylint: disable=unused-argument
+        num_items_in_batch: int = None,
+        ignore_index: int = -100,  # pylint: disable=unused-argument
+        **kwargs,  # pylint: disable=unused-argument
+    ):
+        # Upcast to float if we need to compute the loss to avoid potential precision issues
+        logits = logits.float()
+        # Shift so that tokens < n predict n
+        shift_logits = logits[..., :-1, :].contiguous()
+        shift_labels = labels[..., 1:].contiguous()
+
+        loss = fast_cross_entropy_loss(
+            logits=shift_logits, labels=shift_labels, n_items=num_items_in_batch
+        )
+        return loss
+
     if model_type == "llama":
-        forward = get_forward_code()
-        LlamaForCausalLM._original_forward = forward  # pylint: disable=protected-access
-        forward, _ = detab_code(forward)
-        assert ORIGINAL_CEL_CODE in forward, "Original forward code not found"
+        from transformers.loss import loss_utils
 
-        forward = forward.replace(
-            "@add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)", ""
-        )
-        forward = forward.replace(
-            "@replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)",
-            "",
-        )
-        forward = forward.replace(ORIGINAL_CEL_CODE, PATCHED_CEL_CODE)
-        forward = forward.replace(
-            "def forward(",
-            "def fast_cross_entropy_loss_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 unsloth.kernels.cross_entropy_loss import fast_cross_entropy_loss",
-            globals(),
-        )
-
-        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 unsloth fast_cross_entropy_loss", main_process_only=True)
-        LlamaForCausalLM.forward = fast_cross_entropy_loss_forward  # pylint: disable=undefined-variable  # noqa: F821
+        loss_utils.ForCausalLMLoss = UnslothForCausalLMLoss  # type: ignore[assignment]
     else:
         raise ValueError("Unsupported model type")
 

src/axolotl/monkeypatch/xformers_/__init__.py

@@ -0,0 +1,51 @@
+"""
+Fused MLP layer for incrementally improved training efficiency
+"""
+import torch
+from transformers.models.llama.modeling_llama import LlamaMLP
+from xformers.ops import SwiGLU
+
+from axolotl.monkeypatch.utils import set_module_name
+
+
+class FusedMLP(torch.nn.Module):
+    """
+    Fused MLP layer for incrementally improved training efficiency
+    """
+
+    def __init__(
+        self,
+        config,
+        gate_proj: torch.nn.Linear,
+        up_proj: torch.nn.Linear,
+        down_proj: torch.nn.Linear,
+    ):
+        super().__init__()
+        self.config = config
+        self.swiglu = SwiGLU(
+            in_features=config.hidden_size,
+            hidden_features=config.intermediate_size,
+            bias=False,
+            _pack_weights=True,
+        )
+        # overwrite initialized weights with pretrained weights
+        self.swiglu.w12.weight.data = torch.cat(
+            (gate_proj.weight.data, up_proj.weight.data), dim=0
+        )
+        self.swiglu.w3.weight.data = down_proj.weight.data
+
+    def _post_training(self, model, name):
+        w1, w2 = torch.split(  # pylint: disable=invalid-name
+            self.swiglu.w12.weight.data, self.config.intermediate_size, dim=0
+        )
+
+        # Assign the split weights back to the original layers
+        new_mlp = LlamaMLP(self.config)
+        new_mlp.gate_proj.weight.data = w1
+        new_mlp.up_proj.weight.data = w2
+        new_mlp.down_proj.weight.data = self.swiglu.w3.weight.data
+
+        set_module_name(model, name, new_mlp)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:  # pylint: disable=invalid-name
+        return self.swiglu(x)

src/axolotl/prompt_strategies/bradley_terry/__init__.py

@@ -6,7 +6,7 @@ import logging
 
 from axolotl.prompt_strategies.user_defined import UserDefinedDatasetConfig
 
-LOG = logging.getLogger("axolotl.prompt_strategies")
+LOG = logging.getLogger("axolotl.prompt_strategies.bradley_terry")
 
 
 def load(strategy, tokenizer, cfg, ds_cfg):

src/axolotl/prompt_strategies/bradley_terry/chat_template.py

@@ -2,13 +2,18 @@
 Bradley-Terry model with chat template prompt strategy.
 """
 
+import logging
 from typing import Any, Dict, Optional
 
 from axolotl.prompt_strategies.chat_template import (
     ChatTemplatePrompter,
     ChatTemplateStrategy,
 )
-from axolotl.utils.chat_templates import chat_templates
+from axolotl.utils.chat_templates import get_chat_template_from_config
+
+# Configure the logger
+LOG = logging.getLogger("axolotl.prompt_strategies.bradley_terry.chat_template")
+LOG.setLevel(logging.INFO)
 
 
 class BTChatTemplateStrategy(ChatTemplateStrategy):
@@ -27,18 +32,24 @@ class BTChatTemplateStrategy(ChatTemplateStrategy):
         # pylint: disable=duplicate-code
         prompt[self.messages] = []
         if prompt["system"]:
-            prompt[self.messages].append({"from": "system", "value": prompt["system"]})
-        prompt[self.messages].append({"from": "user", "value": prompt["input"]})
-        prompt[self.messages].append({"from": "assistant", "value": prompt["chosen"]})
+            prompt[self.messages].append(
+                {"role": "system", "content": prompt["system"]}
+            )
+        prompt[self.messages].append({"role": "user", "content": prompt["input"]})
+        prompt[self.messages].append({"role": "assistant", "content": prompt["chosen"]})
         chosen_tokenized = super().tokenize_prompt(prompt)
 
         self.messages = "rejected_messages"
         # pylint: disable=duplicate-code
         prompt[self.messages] = []
         if prompt["system"]:
-            prompt[self.messages].append({"from": "system", "value": prompt["system"]})
-        prompt[self.messages].append({"from": "user", "value": prompt["input"]})
-        prompt[self.messages].append({"from": "assistant", "value": prompt["rejected"]})
+            prompt[self.messages].append(
+                {"role": "system", "content": prompt["system"]}
+            )
+        prompt[self.messages].append({"role": "user", "content": prompt["input"]})
+        prompt[self.messages].append(
+            {"role": "assistant", "content": prompt["rejected"]}
+        )
         rejected_tokenized = super().tokenize_prompt(prompt)
 
         return {
@@ -53,15 +64,18 @@ class BTChatTemplateStrategy(ChatTemplateStrategy):
 
 def load(tokenizer, cfg, ds_cfg: Optional[Dict[str, Any]] = None):
     ds_cfg = ds_cfg or {}
+    chat_template_string = get_chat_template_from_config(
+        cfg=cfg, ds_cfg=ds_cfg, tokenizer=tokenizer
+    )
 
     prompter_params = {
         "tokenizer": tokenizer,
-        "chat_template": chat_templates(ds_cfg.get("chat_template", "chatml")),
-        "message_field_role": ds_cfg.get("message_field_role", "from"),
-        "message_field_content": ds_cfg.get("message_field_content", "value"),
-        "message_field_training": ds_cfg.get("message_field_training", "training"),
+        "chat_template": chat_template_string,
+        "message_field_role": ds_cfg.get("message_field_role", "role"),
+        "message_field_content": ds_cfg.get("message_field_content", "content"),
+        "message_field_training": ds_cfg.get("message_field_training", None),
         "message_field_training_detail": ds_cfg.get(
-            "message_field_training_detail", "train_detail"
+            "message_field_training_detail", None
         ),
         "roles": ds_cfg.get("roles"),
         "drop_system_message": ds_cfg.get("drop_system_message", False),
@@ -74,8 +88,8 @@ def load(tokenizer, cfg, ds_cfg: Optional[Dict[str, Any]] = None):
     strategy_params = {
         "train_on_inputs": cfg.train_on_inputs,
         "sequence_len": cfg.sequence_len,
-        "roles_to_train": ds_cfg.get("roles_to_train", ["gpt", "assistant"]),
-        "train_on_eos": ds_cfg.get("train_on_eos", "turn"),
+        "roles_to_train": ds_cfg.get("roles_to_train", []),
+        "train_on_eos": ds_cfg.get("train_on_eos", None),
     }
 
     strategy = BTChatTemplateStrategy(

src/axolotl/prompt_strategies/chat_template.py

@@ -9,7 +9,7 @@ from transformers import ProcessorMixin
 
 from axolotl.prompt_tokenizers import PromptTokenizingStrategy
 from axolotl.prompters import IGNORE_TOKEN_ID, Prompter
-from axolotl.utils.chat_templates import chat_templates
+from axolotl.utils.chat_templates import get_chat_template_from_config
 
 # Configure the logger
 LOG = logging.getLogger("axolotl")
@@ -405,10 +405,14 @@ class ChatTemplateStrategy(PromptTokenizingStrategy):
 def load(tokenizer, cfg, ds_cfg: Optional[Dict[str, Any]] = None, processor=None):
     # pylint: disable=duplicate-code
     ds_cfg = ds_cfg or {}
+    chat_template_string = get_chat_template_from_config(
+        cfg=cfg, ds_cfg=ds_cfg, tokenizer=tokenizer
+    )
+    LOG.info(f"Using chat template:\n---\n{chat_template_string!s}\n---")
 
     prompter_params = {
         "tokenizer": tokenizer,
-        "chat_template": chat_templates(ds_cfg.get("chat_template", "chatml")),
+        "chat_template": chat_template_string,
         "message_field_role": ds_cfg.get("message_field_role", "role"),
         "message_field_content": ds_cfg.get("message_field_content", "content"),
         "message_field_training": ds_cfg.get("message_field_training", None),

src/axolotl/prompt_strategies/dpo/chat_template.py

@@ -2,15 +2,16 @@
 DPO prompt strategies for using tokenizer chat templates.
 """
 
-from axolotl.utils.chat_templates import chat_templates
+from axolotl.utils.chat_templates import extract_chat_template_args, get_chat_template
 
 
 def default(
     cfg, dataset_idx=0, **kwargs
 ):  # pylint: disable=possibly-unused-variable,unused-argument
     ds_cfg = cfg["datasets"][dataset_idx]
-    chat_template_str = chat_templates(cfg.chat_template)
-
+    chat_template_choice, chat_template_jinja = extract_chat_template_args(
+        cfg=cfg, ds_cfg=ds_cfg
+    )
     field_messages = ds_cfg.get("field_messages", "messages")
     field_chosen = ds_cfg.get("field_chosen", "chosen")
     field_rejected = ds_cfg.get("field_rejected", "rejected")
@@ -30,6 +31,12 @@ def default(
             role_map[source] = target
 
     def transform_fn(sample, tokenizer=None):
+        chat_template_string = get_chat_template(
+            user_choice=chat_template_choice,
+            jinja_template=chat_template_jinja,
+            tokenizer=tokenizer,
+        )
+
         messages = sample[field_messages]
         messages = [
             {
@@ -46,28 +53,29 @@ def default(
             "role": role_map[sample[field_rejected][field_message_role]],
             "content": sample[field_rejected][field_message_content],
         }
+        dummy_user_message = {"role": "user", "content": "[[dummy_message]]"}
 
         result = {}
         result["prompt"] = tokenizer.apply_chat_template(
             messages,
             add_generation_prompt=True,
-            chat_template=chat_template_str,
+            chat_template=chat_template_string,
             tokenize=False,
         )
 
         result["chosen"] = tokenizer.apply_chat_template(
-            [chosen],
+            [dummy_user_message, chosen],
             add_generation_prompt=False,
-            chat_template=chat_template_str,
+            chat_template=chat_template_string,
             tokenize=False,
         )
         chosen_strip_index = result["chosen"].find(chosen["content"])
         result["chosen"] = result["chosen"][chosen_strip_index:].rstrip()
 
         result["rejected"] = tokenizer.apply_chat_template(
-            [rejected],
+            [dummy_user_message, rejected],
             add_generation_prompt=False,
-            chat_template=chat_template_str,
+            chat_template=chat_template_string,
             tokenize=False,
         )
         rejected_strip_index = result["rejected"].find(rejected["content"])

src/axolotl/prompt_strategies/instruct.py

@@ -1,33 +0,0 @@
-"""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 IGNORE_TOKEN_ID, SHAREGPT_ASSERTION_FAILED_ROLE
+from axolotl.prompters import ALTERNATING_ASSERTION_FAILED_ROLE, IGNORE_TOKEN_ID
 
 
 @dataclass
@@ -75,7 +75,7 @@ class Llama2ChatConversation:
 
 class LLama2ChatTokenizingStrategy(PromptTokenizingStrategy):
     """
-    Tokenizing strategy for ShareGPT prompts.
+    Tokenizing strategy for Llama2 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], SHAREGPT_ASSERTION_FAILED_ROLE
+            assert role == conv.roles[j % 2], ALTERNATING_ASSERTION_FAILED_ROLE
             if sentence["value"]:
                 conv.append_message(role, sentence["value"])
         yield conv

src/axolotl/prompt_strategies/orpo/chat_template.py

@@ -5,7 +5,7 @@ from pydantic import BaseModel
 
 from axolotl.prompt_tokenizers import IGNORE_INDEX, PromptTokenizingStrategy
 from axolotl.prompters import Prompter
-from axolotl.utils.chat_templates import chat_templates
+from axolotl.utils.chat_templates import get_chat_template_from_config
 
 
 class Message(BaseModel):
@@ -28,18 +28,13 @@ def load(
     """
     chatml transforms for datasets with system, input, chosen, rejected
     """
-
-    chat_template = chat_templates("chatml")
-    if ds_cfg and "chat_template" in ds_cfg:
-        chat_template = ds_cfg["chat_template"]
-        try:
-            chat_template = chat_templates(chat_template)
-        except ValueError:
-            pass
-    tokenizer.chat_template = chat_template
+    chat_template_string = get_chat_template_from_config(
+        cfg=cfg, ds_cfg=ds_cfg, tokenizer=tokenizer
+    )
+    tokenizer.chat_template = chat_template_string
 
     return ORPOTokenizingStrategy(
-        ORPOPrompter(chat_template, tokenizer),
+        ORPOPrompter(chat_template_string, tokenizer),
         tokenizer,
         cfg.train_on_inputs,
         cfg.sequence_len,
@@ -248,28 +243,30 @@ class ORPOPrompter(Prompter):
 def argilla(cfg, **kwargs):  # pylint: disable=possibly-unused-variable,unused-argument
     dataset_parser = ORPODatasetParsingStrategy()
 
-    chat_template_str = chat_templates(cfg.chat_template)
-
     def transform_fn(sample, tokenizer=None):
         res = {}
 
+        chat_template_string = get_chat_template_from_config(
+            cfg=cfg, tokenizer=tokenizer
+        )
+
         res["prompt"] = tokenizer.apply_chat_template(
             [msg.model_dump() for msg in dataset_parser.get_prompt(sample).messages],
             add_generation_prompt=True,
-            chat_template=chat_template_str,
+            chat_template=chat_template_string,
             tokenize=False,
         )
         prompt_str_len = len(res["prompt"])
         res["chosen"] = tokenizer.apply_chat_template(
             [msg.model_dump() for msg in dataset_parser.get_chosen(sample).messages],
             add_generation_prompt=False,
-            chat_template=chat_template_str,
+            chat_template=chat_template_string,
             tokenize=False,
         )[prompt_str_len:]
         res["rejected"] = tokenizer.apply_chat_template(
             [msg.model_dump() for msg in dataset_parser.get_rejected(sample).messages],
             add_generation_prompt=False,
-            chat_template=chat_template_str,
+            chat_template=chat_template_string,
             tokenize=False,
         )[prompt_str_len:]
 

src/axolotl/prompt_strategies/sharegpt.py

@@ -1,223 +0,0 @@
-"""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.",
-        )
-        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

@@ -1,28 +0,0 @@
-"""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

@@ -1,17 +1,12 @@
 """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.monkeypatch.fastchat_conversation_turns import (
-    add_get_turns_to_conversation,
-)
-from axolotl.prompters import IGNORE_TOKEN_ID, Prompter
+from axolotl.prompters import Prompter
 
 LOG = logging.getLogger("axolotl")
 
@@ -21,8 +16,6 @@ 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):
     """
@@ -331,154 +324,6 @@ 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

@@ -5,7 +5,6 @@ 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
@@ -262,166 +261,10 @@ class ReflectAlpacaPrompter(Prompter):
         )
 
 
-SHAREGPT_ASSERTION_FAILED_ROLE = (
+ALTERNATING_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

@@ -260,8 +260,10 @@ def train(
 
     if not cfg.hub_model_id:
         try:
-            trainer.create_model_card(model_name=cfg.output_dir.lstrip("./"))
-        except AttributeError:
+            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:
         # defensively push to the hub to ensure the model card is updated

src/axolotl/utils/callbacks/__init__.py

@@ -64,10 +64,7 @@ class EvalFirstStepCallback(
         control: TrainerControl,
         **kwargs,
     ):
-        if (
-            args.evaluation_strategy == IntervalStrategy.STEPS
-            and state.global_step == 1
-        ):
+        if args.eval_strategy == IntervalStrategy.STEPS and state.global_step == 1:
             control.should_evaluate = True
         return control
 

src/axolotl/utils/config/__init__.py

@@ -1,8 +1,6 @@
 """Module for working with config dicts"""
-import json
 import logging
 import os
-from pathlib import Path
 from typing import Optional
 
 import torch
@@ -10,7 +8,6 @@ from transformers.utils import is_torch_bf16_gpu_available
 
 from axolotl.integrations.config import merge_input_args
 from axolotl.utils.bench import log_gpu_memory_usage
-from axolotl.utils.config.models.input.v0_4_1 import SUPPORTED_METRICS
 from axolotl.utils.config.models.input.v0_4_1 import (
     AxolotlConfigWCapabilities as AxolotlConfigWCapabilitiesBase,
 )
@@ -215,11 +212,6 @@ def normalize_cfg_datasets(cfg):
     if cfg.chat_template:
         if cfg.datasets:
             for idx, ds_cfg in enumerate(cfg.datasets):
-                if ds_cfg.type == "sharegpt" and not ds_cfg.conversation:
-                    LOG.info(
-                        f"updating dataset {ds_cfg.path} with `conversation: {cfg.chat_template}` to match your chat_template"
-                    )
-                    cfg.datasets[idx].conversation = cfg.chat_template
                 if (
                     ds_cfg.type in ["orpo.chat_template", "chat_template"]
                     and not ds_cfg.chat_template
@@ -228,6 +220,7 @@ def normalize_cfg_datasets(cfg):
                         f"updating dataset {ds_cfg.path} with `chat_template: {cfg.chat_template}` to match your chat_template"
                     )
                     cfg.datasets[idx].chat_template = cfg.chat_template
+                    cfg.datasets[idx].chat_template_jinja = cfg.chat_template_jinja
 
 
 def validate_config(cfg: DictDefault, capabilities: Optional[dict] = None):
@@ -251,391 +244,3 @@ def validate_config(cfg: DictDefault, capabilities: Optional[dict] = None):
     return DictDefault(
         dict(AxolotlInputConfig(**cfg.to_dict()).model_dump(exclude_none=True))
     )
-
-
-def legacy_validate_config(cfg):
-    """
-    This is a "pre-validation" step that handles the yaml configuration before we have any
-    information about the model architecture
-    """
-    if is_torch_bf16_gpu_available():
-        if not cfg.bf16 and not cfg.bfloat16:
-            LOG.info("bf16 support detected, but not enabled for this configuration.")
-    else:
-        if (
-            not cfg.merge_lora
-            and not cfg.is_preprocess
-            and (cfg.bf16 is True or cfg.bfloat16 is True)
-        ):
-            raise ValueError(
-                "bf16 requested, but AMP is not supported on this GPU. Requires Ampere series or above."
-            )
-    if (
-        # pylint: disable=too-many-boolean-expressions
-        not (cfg.bf16 or cfg.bfloat16)
-        and (cfg.fp16 or cfg.float16)
-        and not cfg.adapter
-        and not cfg.flash_attention
-        and cfg.sample_packing
-    ):
-        LOG.warning(
-            "Full fine tune w/o FA2 w/ sample packing and fp16/float16 is likely to raise errors. Try LoRA."
-        )
-        # ValueError: Attempting to unscale FP16 gradients.
-        # OR
-        # RuntimeError: expected mat1 and mat2 to have the same dtype, but got: float != c10::Half
-    if cfg.max_packed_sequence_len:
-        raise DeprecationWarning("`max_packed_sequence_len` is no longer supported")
-
-    if cfg.sample_packing and cfg.rl:
-        raise ValueError("`sample_packing: true` does not work with RLHF training")
-
-    if cfg.sample_packing and not cfg.pad_to_sequence_len:
-        LOG.warning(
-            "`pad_to_sequence_len: true` is recommended when using sample_packing"
-        )
-
-    if cfg.gradient_accumulation_steps and cfg.batch_size:
-        raise ValueError(
-            "please set only one of gradient_accumulation_steps or batch_size"
-        )
-    if cfg.batch_size:
-        LOG.warning(
-            "%s\n%s",
-            "batch_size is not recommended. Please use gradient_accumulation_steps instead.",
-            "To calculate the equivalent gradient_accumulation_steps, divide batch_size / micro_batch_size / number of gpus.",
-        )
-    if (
-        cfg.eval_batch_size
-        and cfg.micro_batch_size
-        and cfg.eval_batch_size != cfg.micro_batch_size
-    ):
-        LOG.warning(
-            "eval_batch_size != micro_batch_size. This can lead to VRAM instability."
-        )
-
-    if cfg.adapter == "qlora":
-        if cfg.merge_lora:
-            # can't merge qlora if loaded in 8bit or 4bit
-            if cfg.load_in_8bit:
-                raise ValueError("Can't merge qlora if loaded in 8bit")
-
-            if cfg.gptq:
-                raise ValueError("Can't merge qlora if gptq")
-
-            if cfg.load_in_4bit:
-                raise ValueError("Can't merge qlora if loaded in 4bit")
-
-        else:
-            if cfg.load_in_8bit:
-                raise ValueError("Can't load qlora in 8bit")
-
-            if cfg.gptq:
-                raise ValueError("Can't load qlora if gptq")
-
-            if not cfg.load_in_4bit:
-                raise ValueError("Require cfg.load_in_4bit to be True for qlora")
-
-        if cfg.flash_attn_fuse_qkv or cfg.flash_attn_fuse_mlp:
-            raise ValueError("Fused modules are not supported with QLoRA")
-
-    loftq = cfg.peft and cfg.peft.loftq_config and cfg.peft.loftq_config.loftq_bits
-    if not cfg.load_in_8bit and cfg.adapter == "lora" and not loftq:
-        LOG.warning("We recommend setting `load_in_8bit: true` for LORA finetuning")
-
-    if cfg.adapter == "lora" and (cfg.flash_attn_fuse_qkv or cfg.flash_attn_fuse_mlp):
-        raise ValueError("Fused modules are not supported with LoRA")
-
-    if cfg.adapter and cfg.peft_layers_to_transform and cfg.unfrozen_parameters:
-        raise ValueError(
-            "`unfrozen_parameters` used with `peft_layers_to_transform` can have unexpected behavior."
-        )
-
-    if cfg.relora_steps:
-        if cfg.adapter not in ("lora", "qlora"):
-            raise ValueError("cfg.adapter must be lora or qlora to use ReLoRA")
-
-        if cfg.fsdp:
-            raise ValueError("fsdp not supported with ReLoRA")
-
-        if cfg.deepspeed:
-            raise ValueError("deepspeed not supported with ReLoRA")
-
-        if cfg.lr_scheduler == "one_cycle":
-            raise ValueError("ReLoRA is not compatible with the one_cycle scheduler")
-
-        if cfg.flash_attn_fuse_qkv or cfg.flash_attn_fuse_mlp:
-            raise ValueError("Fused modules are not supported with ReLoRA")
-
-    if cfg.trust_remote_code:
-        LOG.warning(
-            "`trust_remote_code` is set to true. Please make sure that you reviewed the remote code/model."
-        )
-
-    if cfg.push_dataset_to_hub and cfg.hf_use_auth_token is not True:
-        raise ValueError(
-            "Require cfg.hf_use_auth_token to be True for push_dataset_to_hub"
-        )
-
-    if (cfg.base_model and "falcon" in cfg.base_model.lower()) and cfg.fsdp:
-        raise ValueError("FSDP is not supported for falcon models")
-
-    if (
-        cfg.base_model and "mpt" in cfg.base_model.lower()
-    ) and cfg.gradient_checkpointing:
-        raise ValueError("gradient_checkpointing is not supported for MPT models")
-
-    if cfg.flash_optimum is True:
-        if cfg.adapter:
-            LOG.warning("BetterTransformers probably doesn't work with PEFT adapters")
-        if cfg.fp16 or cfg.bf16:
-            raise ValueError("AMP is not supported with BetterTransformer")
-        if cfg.float16 is not True and cfg.bfloat16 is not True:
-            LOG.warning(
-                "You should probably set bfloat16 or float16 to true to "
-                "load the model in float16 for BetterTransformers"
-            )
-        if int(torch.__version__.split(".", maxsplit=1)[0]) < 2:
-            LOG.warning("torch>=2.0.0 required")
-            raise ValueError(
-                f"flash_optimum for BetterTransformers may not be used with {torch.__version__}"
-            )
-
-    if cfg.pretraining_dataset and cfg.group_by_length:
-        LOG.warning(
-            "You probably want to disable group_by_length as it will force a streamed dataset to download completely."
-        )
-    if cfg.pretraining_dataset and not cfg.max_steps:
-        raise ValueError(
-            "max_steps must be set when using iterable pretraining_dataset, Trainer can't infer length and schedule optimizer/learning rate without it!"
-        )
-
-    if any([cfg.adam_beta1, cfg.adam_beta2, cfg.adam_epsilon]) and (
-        not cfg.optimizer or "adamw" not in cfg.optimizer
-    ):
-        LOG.warning("adamw hyperparameters found, but no adamw optimizer set")
-
-    if cfg.push_to_hub_model_id:
-        raise ValueError(
-            "push_to_hub_model_id is deprecated. Please use hub_model_id instead."
-        )
-
-    if cfg.hub_model_id and cfg.save_strategy not in ["steps", "epoch", None]:
-        LOG.warning(
-            "hub_model_id is set without any models being saved. To save a model, set save_strategy to steps, epochs or leave empty."
-        )
-
-    if cfg.gptq and cfg.revision_of_model:
-        raise ValueError(
-            "revision_of_model is not supported for GPTQ models. "
-            + "Please download the model from HuggingFace Hub manually for correct branch, "
-            + "point to its path, and remove revision_of_model from the config."
-        )
-
-    # if cfg.sample_packing and cfg.sdp_attention:
-    #     # incompatible due to bug w/ accelerate causing 0.0 loss when using llama2
-    #     raise ValueError(
-    #         "sample_packing not compatible with sdp_attention. Use flash_attention"
-    #     )
-
-    if cfg.sample_packing and cfg.xformers_attention:
-        raise ValueError(
-            "sample_packing not compatible with xformers_attention. Use flash_attention"
-        )
-
-    if cfg.sample_packing and cfg.sdp_attention and (cfg.bfloat16 or cfg.bf16):
-        # https://github.com/pytorch/pytorch/blob/1b03423526536b5f3d35bdfa95ccc6197556cf9b/test/test_transformers.py#L2440-L2450
-        LOG.warning(
-            "sample_packing & torch sdpa with bf16 is unsupported may results in 0.0 loss. "
-            "This may work on H100s."
-        )
-
-    if cfg.early_stopping_patience:
-        if not cfg.save_steps or not cfg.eval_steps:
-            raise ValueError(
-                "`early_stopping_patience` requires save_steps and eval_steps to be set. eval_steps should evenly divide save_steps."
-            )
-        if cfg.save_steps % cfg.eval_steps != 0:
-            raise ValueError(
-                "`early_stopping_patience` requires that eval_steps should evenly divide save_steps."
-            )
-
-    if cfg.datasets:
-        for idx, ds_cfg in enumerate(cfg.datasets):
-            if not ds_cfg.type:
-                continue
-            if ds_cfg.type == "sharegpt:chat":
-                LOG.warning(
-                    PendingDeprecationWarning(
-                        "`type: sharegpt:chat` will soon be deprecated. simply use `type: sharegpt` instead."
-                    )
-                )
-                cfg.datasets[idx].type = "sharegpt"
-            if "sharegpt_simple" in ds_cfg.type:
-                LOG.warning(
-                    PendingDeprecationWarning(
-                        "`type: sharegpt_simple` will soon be deprecated. simply use `type: sharegpt` instead."
-                    )
-                )
-                cfg.datasets[idx].type = cfg.datasets[idx].type.replace(
-                    "sharegpt_simple", "sharegpt"
-                )
-
-    if cfg.saves_per_epoch and cfg.save_steps:
-        raise ValueError(
-            "save_steps and saves_per_epoch are mutually exclusive and cannot be used together."
-        )
-    if cfg.save_strategy and cfg.saves_per_epoch and cfg.save_strategy != "steps":
-        raise ValueError(
-            "save_strategy must be empty or set to `steps` when used with saves_per_epoch."
-        )
-    if cfg.save_strategy and cfg.save_steps and cfg.save_strategy != "steps":
-        raise ValueError(
-            "save_strategy and save_steps mismatch. Please set save_strategy to 'steps' or remove save_steps."
-        )
-    if cfg.evals_per_epoch and cfg.eval_steps:
-        raise ValueError(
-            "eval_steps and evals_per_epoch are mutually exclusive and cannot be used together."
-        )
-    if (
-        cfg.evals_per_epoch
-        and cfg.evaluation_strategy
-        and cfg.evaluation_strategy != "steps"
-    ):
-        raise ValueError(
-            "evaluation_strategy must be empty or set to `steps` when used with evals_per_epoch."
-        )
-    if (
-        cfg.evaluation_strategy
-        and cfg.eval_steps
-        and cfg.evaluation_strategy != "steps"
-    ):
-        raise ValueError(
-            "evaluation_strategy and eval_steps mismatch. Please set evaluation_strategy to 'steps' or remove eval_steps."
-        )
-
-    if (
-        cfg.val_set_size == 0
-        and (cfg.eval_steps or cfg.evaluation_strategy)
-        and not cfg.test_datasets
-    ):
-        raise ValueError(
-            "eval_steps and evaluation_strategy are not supported with val_set_size == 0"
-        )
-
-    if (
-        cfg.sample_packing
-        and cfg.eval_table_size
-        and cfg.eval_sample_packing is not False
-    ):
-        raise ValueError(
-            "eval_table_size and eval_sample_packing are not supported together with sample_packing. Please set 'eval_sample_packing' to false."
-        )
-
-    if not cfg.adapter and (cfg.load_in_8bit or cfg.load_in_4bit):
-        raise ValueError(
-            "load_in_8bit and load_in_4bit are not supported without setting an adapter."
-            "If you want to full finetune, please turn off load_in_8bit and load_in_4bit."
-        )
-
-    if cfg.rope_scaling:
-        LOG.warning("`rope_scaling` should now be be a key under `model_config`")
-
-    if cfg.wandb_run_id and not cfg.wandb_name:
-        cfg.wandb_name = cfg.wandb_run_id
-
-        LOG.warning(
-            "wandb_run_id sets the ID of the run. If you would like to set the name, please use wandb_name instead."
-        )
-
-    if cfg.noisy_embedding_alpha is not None:
-        # Deprecated, use neftune_noise_alpha
-        LOG.warning("noisy_embedding_alpha is deprecated, use neftune_noise_alpha")
-        if cfg.neftune_noise_alpha is None:
-            cfg.neftune_noise_alpha = cfg.noisy_embedding_alpha
-        else:
-            # User is providing both; bail and have them sort out their settings
-            raise ValueError(
-                "noisy_embedding_alpha is deprecated, use neftune_noise_alpha; both are set, please remove the deprecated noisy_embedding_alpha setting"
-            )
-
-    if cfg.neftune_noise_alpha is not None and cfg.neftune_noise_alpha <= 0.0:
-        raise ValueError("neftune_noise_alpha must be > 0.0")
-
-    if cfg.max_memory is not None and cfg.gpu_memory_limit is not None:
-        raise ValueError(
-            "max_memory and gpu_memory_limit are mutually exclusive and cannot be used together."
-        )
-
-    if (
-        cfg.unfrozen_parameters
-        and cfg.gradient_checkpointing_kwargs
-        and cfg.gradient_checkpointing_kwargs.use_reentrant is True
-    ):
-        # https://github.com/huggingface/transformers/issues/21381
-        raise ValueError(
-            "`use_reentrant` must be false when used with partially frozen model."
-        )
-
-    if cfg.deepspeed and Path(cfg.deepspeed).is_file():
-        with open(cfg.deepspeed, encoding="utf-8") as file:
-            contents = file.read()
-            deepspeed_cfg: DictDefault = DictDefault(json.loads(contents))
-            if cfg.flash_attention:
-                if (
-                    deepspeed_cfg.zero_optimization
-                    and deepspeed_cfg.zero_optimization.stage == 3
-                ):
-                    if not (
-                        (
-                            deepspeed_cfg.bf16
-                            and deepspeed_cfg.bf16.enabled  # pylint: disable=no-member
-                            is True
-                        )
-                        or (
-                            deepspeed_cfg.fp16
-                            and deepspeed_cfg.fp16.enabled  # pylint: disable=no-member
-                            is True
-                        )
-                    ):
-                        raise ValueError(
-                            "bf16.enabled or fp16.enabled must be set to true when using ZeRO-3 with flash-attention"
-                        )
-            if "8bit" in cfg.optimizer and deepspeed_cfg.optimizer:
-                LOG.warning(
-                    f"conflicting optimizer: {cfg.optimizer} used alongside deepspeed optimizer."
-                )
-
-    if cfg.test_datasets and cfg.val_set_size:
-        raise ValueError(
-            "non-zero val_set_size should not be used with test_datasets configuration"
-        )
-
-    if cfg.fsdp and "bnb" in cfg.optimizer:
-        raise ValueError(f"FSDP not compatible with {cfg.optimizer}")
-
-    if cfg.do_causal_lm_eval and cfg.eval_sample_packing:
-        raise ValueError(
-            "do_causal_lm_eval is enabled, eval_sample_packing must be set to False"
-        )
-
-    if cfg.eval_causal_lm_metrics:
-        if not isinstance(cfg.eval_causal_lm_metrics, list):
-            raise ValueError("eval_causal_lm_metrics must be a list")
-        # only ["sacrebleu", "comet", "ter", "chrf"] supported
-        if set(cfg.eval_causal_lm_metrics) - SUPPORTED_METRICS:
-            raise ValueError(
-                f"eval_causal_lm_metrics must be one of {SUPPORTED_METRICS}"
-            )
-
-    # TODO
-    # MPT 7b
-    # https://github.com/facebookresearch/bitsandbytes/issues/25
-    # no 8bit adaAmw w bf16
-
-    # GPT-NeoX
-    # evals broken when extending context len
-    # File "/root/miniconda3/envs/py3.9/lib/python3.9/site-packages/transformers/models/gpt_neox/modeling_gpt_neox.py", line 162, in forward                        attn_output, attn_weights = self._attn(query, key, value, attention_mask, head_mask)
-    # File "/root/miniconda3/envs/py3.9/lib/python3.9/site-packages/optimum/bettertransformer/models/attention.py", line 74, in gpt2_wrapped_scaled_dot_product
-    # attention_mask = causal_mask + attention_mask
-    # RuntimeError: The size of tensor a (2048) must match the size of tensor b (8132) at non-singleton dimension 3

src/axolotl/utils/config/models/input/v0_4_1/__init__.py

@@ -8,9 +8,16 @@ import logging
 import os
 from enum import Enum
 from importlib.metadata import version
-from typing import Any, Dict, List, Literal, Optional, Tuple, Union
+from typing import Annotated, Any, Dict, List, Literal, Optional, Tuple, Union
 
-from pydantic import BaseModel, Field, conlist, field_validator, model_validator
+from pydantic import (
+    BaseModel,
+    Field,
+    StringConstraints,
+    conlist,
+    field_validator,
+    model_validator,
+)
 from transformers import SchedulerType
 from transformers.training_args import OptimizerNames
 
@@ -21,6 +28,39 @@ LOG = logging.getLogger("axolotl.utils.config.models.input")
 SUPPORTED_METRICS = {"sacrebleu", "comet", "ter", "chrf", "perplexity"}
 
 
+class RLType(str, Enum):
+    """RL trainer type configuration subset"""
+
+    dpo = "dpo"  # pylint: disable=invalid-name
+    ipo = "ipo"  # pylint: disable=invalid-name
+    orpo = "orpo"  # pylint: disable=invalid-name
+    kto = "kto"  # pylint: disable=invalid-name
+    simpo = "simpo"  # pylint: disable=invalid-name
+
+
+class ChatTemplate(str, Enum):
+    """Chat templates configuration subset"""
+
+    alpaca = "alpaca"  # pylint: disable=invalid-name
+    chatml = "chatml"  # pylint: disable=invalid-name
+    mistral_v1 = "mistral_v1"  # pylint: disable=invalid-name
+    mistral_v2v3 = "mistral_v2v3"  # pylint: disable=invalid-name
+    mistral_v3_tekken = "mistral_v3_tekken"  # pylint: disable=invalid-name
+    gemma = "gemma"  # pylint: disable=invalid-name
+    cohere = "cohere"  # pylint: disable=invalid-name
+    llama3 = "llama3"  # pylint: disable=invalid-name
+    llama3_2_vision = "llama3_2_vision"  # pylint: disable=invalid-name
+    phi_3 = "phi_3"  # pylint: disable=invalid-name
+    phi_35 = "phi_35"  # pylint: disable=invalid-name
+    deepseek_v2 = "deepseek_v2"  # pylint: disable=invalid-name
+    jamba = "jamba"  # pylint: disable=invalid-name
+    jinja = "jinja"  # pylint: disable=invalid-name
+    qwen_25 = "qwen_25"  # pylint: disable=invalid-name
+    tokenizer_default = "tokenizer_default"  # pylint: disable=invalid-name
+    exaone = "exaone"  # pylint: disable=invalid-name
+    metharme = "metharme"  # pylint: disable=invalid-name
+
+
 class DeprecatedParameters(BaseModel):
     """configurations that are deprecated"""
 
@@ -28,6 +68,7 @@ class DeprecatedParameters(BaseModel):
     rope_scaling: Optional[Any] = None
     noisy_embedding_alpha: Optional[float] = None
     dpo_beta: Optional[float] = None
+    evaluation_strategy: Optional[str] = None
 
     @field_validator("max_packed_sequence_len")
     @classmethod
@@ -59,6 +100,13 @@ class DeprecatedParameters(BaseModel):
             LOG.warning("dpo_beta is deprecated, use rl_beta instead")
         return dpo_beta
 
+    @field_validator("evaluation_strategy")
+    @classmethod
+    def validate_evaluation_strategy(cls, evaluation_strategy):
+        if evaluation_strategy is not None:
+            LOG.warning("evaluation_strategy is deprecated, use eval_strategy instead")
+        return evaluation_strategy
+
 
 class RemappedParameters(BaseModel):
     """parameters that have been remapped to other names"""
@@ -105,13 +153,19 @@ class SFTDataset(BaseModel):
     input_transform: Optional[str] = None
     shards: Optional[int] = None
     conversation: Optional[str] = None
-    chat_template: Optional[str] = None
+    # Do not make this too strict or it will break the validator to choose different dataset class
+    chat_template: Optional[
+        Union[
+            ChatTemplate,
+            str,
+        ]
+    ] = None
+    chat_template_jinja: Optional[str] = None
     data_files: Optional[Union[str, List[str]]] = None
     input_format: Optional[str] = None
     name: Optional[str] = None
     ds_type: Optional[str] = None
     train_on_split: Optional[str] = None
-
     field: Optional[str] = None
     field_human: Optional[str] = None
     field_model: Optional[str] = None
@@ -122,13 +176,32 @@ class SFTDataset(BaseModel):
     message_field_training_detail: Optional[str] = None
     roles_to_train: Optional[List[str]] = None
     train_on_eos: Optional[str] = None
-
     roles: Optional[Dict[str, List[str]]] = None
     drop_system_message: Optional[bool] = None
-
     trust_remote_code: Optional[bool] = False
     revision: Optional[str] = None
 
+    @model_validator(mode="before")
+    @classmethod
+    def check_chat_template_config(cls, data):
+        # Set chat_template to tokenizer_default if not set
+        if data.get("type") == "chat_template" and not data.get("chat_template"):
+            data["chat_template"] = ChatTemplate.tokenizer_default
+
+        # if chat_template is set to jinja, chat_template_jinja is required
+        if data.get("chat_template") == ChatTemplate.jinja and not data.get(
+            "chat_template_jinja"
+        ):
+            raise ValueError(
+                "chat_template_jinja is required when chat_template is set to jinja"
+            )
+
+        # If chat_template_jinja is set, set chat_template to jinja
+        if data.get("chat_template_jinja") and not data.get("chat_template"):
+            data["chat_template"] = ChatTemplate.jinja
+
+        return data
+
 
 class UserDefinedDPOType(BaseModel):
     """User defined typing for DPO"""
@@ -174,40 +247,13 @@ class KTODataset(BaseModel):
     revision: Optional[str] = None
 
 
-class RLType(str, Enum):
-    """RL trainer type configuration subset"""
-
-    dpo = "dpo"  # pylint: disable=invalid-name
-    ipo = "ipo"  # pylint: disable=invalid-name
-    orpo = "orpo"  # pylint: disable=invalid-name
-    kto = "kto"  # pylint: disable=invalid-name
-    simpo = "simpo"  # pylint: disable=invalid-name
-
-
-class ChatTemplate(str, Enum):
-    """Chat templates configuration subset"""
-
-    alpaca = "alpaca"  # pylint: disable=invalid-name
-    chatml = "chatml"  # pylint: disable=invalid-name
-    mistral_v1 = "mistral_v1"  # pylint: disable=invalid-name
-    mistral_v2v3 = "mistral_v2v3"  # pylint: disable=invalid-name
-    mistral_v3_tekken = "mistral_v3_tekken"  # pylint: disable=invalid-name
-    gemma = "gemma"  # pylint: disable=invalid-name
-    cohere = "cohere"  # pylint: disable=invalid-name
-    llama3 = "llama3"  # pylint: disable=invalid-name
-    llama3_2_vision = "llama3_2_vision"  # pylint: disable=invalid-name
-    phi_3 = "phi_3"  # pylint: disable=invalid-name
-    phi_35 = "phi_35"  # pylint: disable=invalid-name
-    deepseek_v2 = "deepseek_v2"  # pylint: disable=invalid-name
-    jamba = "jamba"  # pylint: disable=invalid-name
-    qwen_25 = "qwen_25"  # pylint: disable=invalid-name
-
-
 class LoftQConfig(BaseModel):
     """LoftQ configuration subset"""
 
-    loftq_bits: int = Field(default=4, metadata={"help": "Quantization bits for LoftQ"})
-    # loftq_iter: int = Field(default=1, metadata={"help": "Alternating iterations for LoftQ"})
+    loftq_bits: int = Field(
+        default=4, json_schema_extra={"description": "Quantization bits for LoftQ"}
+    )
+    # loftq_iter: int = Field(default=1, json_schema_extra={"description": "Alternating iterations for LoftQ"})
 
 
 class PeftConfig(BaseModel):
@@ -250,8 +296,8 @@ class LoraConfig(BaseModel):
 
     qlora_sharded_model_loading: Optional[bool] = Field(
         default=False,
-        metadata={
-            "help": "load qlora model in sharded format for FSDP using answer.ai technique."
+        json_schema_extra={
+            "description": "load qlora model in sharded format for FSDP using answer.ai technique."
         },
     )
     lora_on_cpu: Optional[bool] = None
@@ -260,13 +306,15 @@ class LoraConfig(BaseModel):
 
     loraplus_lr_ratio: Optional[float] = Field(
         default=None,
-        metadata={
-            "help": "loraplus learning rate ratio lr_B / lr_A. Recommended value is 2^4."
+        json_schema_extra={
+            "description": "loraplus learning rate ratio lr_B / lr_A. Recommended value is 2^4."
         },
     )
     loraplus_lr_embedding: Optional[float] = Field(
         default=1e-6,
-        metadata={"help": "loraplus learning rate for lora embedding layers."},
+        json_schema_extra={
+            "description": "loraplus learning rate for lora embedding layers."
+        },
     )
 
     merge_lora: Optional[bool] = None
@@ -336,10 +384,10 @@ class ModelInputConfig(BaseModel):
     tokenizer_use_fast: Optional[bool] = None
     tokenizer_legacy: Optional[bool] = None
     tokenizer_type: Optional[str] = Field(
-        default=None, metadata={"help": "transformers tokenizer class"}
+        default=None, json_schema_extra={"description": "transformers tokenizer class"}
     )
     processor_type: Optional[str] = Field(
-        default=None, metadata={"help": "transformers processor class"}
+        default=None, json_schema_extra={"description": "transformers processor class"}
     )
     trust_remote_code: Optional[bool] = None
 
@@ -361,18 +409,18 @@ class HyperparametersConfig(BaseModel):
     gradient_accumulation_steps: Optional[int] = Field(default=1)
     micro_batch_size: Optional[int] = Field(
         default=1,
-        metadata={"help": "per gpu micro batch size for training"},
+        json_schema_extra={"description": "per gpu micro batch size for training"},
     )
     batch_size: Optional[int] = Field(
         default=None,
-        metadata={
-            "help": "Total batch size, we do not recommended setting this manually"
+        json_schema_extra={
+            "description": "Total batch size, we do not recommended setting this manually"
         },
     )
     eval_batch_size: Optional[int] = Field(
         default=None,
-        metadata={
-            "help": "per gpu micro batch size for evals, defaults to value of micro_batch_size"
+        json_schema_extra={
+            "description": "per gpu micro batch size for evals, defaults to value of micro_batch_size"
         },
     )
 
@@ -392,16 +440,18 @@ class HyperparametersConfig(BaseModel):
                 "ao_adamw_4bit",
                 "ao_adamw_8bit",
                 "ao_adamw_fp8",
+                "adopt_adamw",
             ],
         ]
     ] = OptimizerNames.ADAMW_HF.value
     optim_args: Optional[Union[str, Dict[str, Any]]] = Field(
-        default=None, metadata={"help": "Optional arguments to supply to optimizer."}
+        default=None,
+        json_schema_extra={"description": "Optional arguments to supply to optimizer."},
     )
     optim_target_modules: Optional[Union[List[str], Literal["all_linear"]]] = Field(
         default=None,
-        metadata={
-            "help": "The target modules to optimize, i.e. the module names that you would like to train."
+        json_schema_extra={
+            "description": "The target modules to optimize, i.e. the module names that you would like to train."
         },
     )
     torchdistx_path: Optional[str] = None
@@ -461,15 +511,15 @@ class LISAConfig(BaseModel):
 
     lisa_n_layers: Optional[int] = Field(
         default=None,
-        metadata={"help": "the number of activate layers in LISA"},
+        json_schema_extra={"description": "the number of activate layers in LISA"},
     )
     lisa_step_interval: Optional[int] = Field(
         default=None,
-        metadata={"help": "how often to switch layers in LISA"},
+        json_schema_extra={"description": "how often to switch layers in LISA"},
     )
     lisa_layers_attribute: Optional[str] = Field(
         default="model.layers",
-        metadata={"help": "path under the model to access the layers"},
+        json_schema_extra={"description": "path under the model to access the layers"},
     )
 
 
@@ -549,9 +599,13 @@ class AxolotlInputConfig(
     resume_from_checkpoint: Optional[str] = None
     auto_resume_from_checkpoints: Optional[bool] = None
     resize_token_embeddings_to_32x: Optional[bool] = None
+    mean_resizing_embeddings: Optional[bool] = False
 
     rl: Optional[RLType] = None
     reward_model: Optional[bool] = None
+    dpo_use_weighting: Optional[
+        bool
+    ] = None  # whether to use weighting in DPO trainer. If none, default is false in the trainer.
 
     datasets: Optional[conlist(Union[SFTDataset, DPODataset, KTODataset], min_length=1)] = None  # type: ignore
     test_datasets: Optional[conlist(Union[SFTDataset, DPODataset, KTODataset], min_length=1)] = None  # type: ignore
@@ -564,7 +618,8 @@ class AxolotlInputConfig(
     pretraining_dataset: Optional[  # type: ignore
         conlist(Union[PretrainingDataset, SFTDataset], min_length=1)
     ] = Field(
-        default=None, metadata={"help": {"streaming dataset to use for pretraining"}}
+        default=None,
+        json_schema_extra={"description": "streaming dataset to use for pretraining"},
     )
     dataset_processes: Optional[int] = Field(default=os.cpu_count())
     dataset_keep_in_memory: Optional[bool] = None
@@ -624,7 +679,8 @@ class AxolotlInputConfig(
     sequence_len: int = Field(default=512)
     min_sample_len: Optional[int] = None
     max_prompt_len: int = Field(
-        default=512, metadata={"help": "maximum prompt length for RL training"}
+        default=512,
+        json_schema_extra={"description": "maximum prompt length for RL training"},
     )
     sample_packing: Optional[bool] = None
     sample_packing_group_size: Optional[int] = 100_000
@@ -643,8 +699,8 @@ class AxolotlInputConfig(
     pretrain_multipack_buffer_size: Optional[int] = 10_000
     pretrain_multipack_attn: Optional[bool] = Field(
         default=True,
-        metadata={
-            "help": "whether to prevent cross attention for packed sequences during pretraining",
+        json_schema_extra={
+            "description": "whether to prevent cross attention for packed sequences during pretraining",
         },
     )
 
@@ -690,7 +746,7 @@ class AxolotlInputConfig(
     warmup_ratio: Optional[float] = None
     eval_steps: Optional[Union[int, float]] = None
     evals_per_epoch: Optional[Union[int]] = None
-    evaluation_strategy: Optional[str] = None
+    eval_strategy: Optional[str] = None
     save_steps: Optional[Union[int, float]] = None
     saves_per_epoch: Optional[int] = None
     save_strategy: Optional[str] = None
@@ -718,7 +774,13 @@ class AxolotlInputConfig(
     gpu_memory_limit: Optional[Union[int, str]] = None
     low_cpu_mem_usage: Optional[bool] = None
 
-    chat_template: Optional[ChatTemplate] = None
+    chat_template: Optional[
+        Union[
+            ChatTemplate,
+            Annotated[str, StringConstraints(pattern="^tokenizer_default_fallback_")],
+        ]
+    ] = None
+    chat_template_jinja: Optional[str] = None
     default_system_message: Optional[str] = None
 
     fix_untrained_tokens: Optional[bool] = None
@@ -736,28 +798,25 @@ class AxolotlInputConfig(
     is_mistral_derived_model: Optional[bool] = Field(default=None)
     is_qwen_derived_model: Optional[bool] = Field(default=None)
 
+    plugins: Optional[List[str]] = Field(default=None)
+
     @field_validator("datasets", mode="before")
     @classmethod
-    def fix_sharegpt_datasets(cls, datasets):
-        for idx, ds_cfg in enumerate(datasets):
-            if not ds_cfg["type"]:
+    def deprecate_sharegpt_datasets(cls, datasets):
+        for _, ds_cfg in enumerate(datasets):
+            if not ds_cfg.get("type"):
                 continue
-            if ds_cfg["type"] == "sharegpt:chat":
-                LOG.warning(
-                    PendingDeprecationWarning(
-                        "`type: sharegpt:chat` will soon be deprecated. simply use `type: sharegpt` instead."
-                    )
-                )
-                datasets[idx]["type"] = "sharegpt"
-            if "sharegpt_simple" in ds_cfg["type"]:
-                LOG.warning(
-                    PendingDeprecationWarning(
-                        "`type: sharegpt_simple` will soon be deprecated. simply use `type: sharegpt` instead."
-                    )
-                )
-                datasets[idx]["type"] = datasets[idx]["type"].replace(
-                    "sharegpt_simple", "sharegpt"
+
+            ds_type = ds_cfg["type"]
+            # skip if it's a dict (for custom user instruction prompt)
+            if isinstance(ds_type, dict):
+                continue
+
+            if isinstance(ds_type, str) and ds_type.startswith("sharegpt"):
+                raise ValueError(
+                    "`type: sharegpt.*` is deprecated. Please use `type: chat_template` instead."
                 )
+
         return datasets
 
     @model_validator(mode="before")
@@ -827,6 +886,23 @@ class AxolotlInputConfig(
 
         return data
 
+    @model_validator(mode="before")
+    @classmethod
+    def check_chat_template_config(cls, data):
+        # if chat_template is set to jinja, chat_template_jinja is required
+        if data.get("chat_template") == ChatTemplate.jinja and not data.get(
+            "chat_template_jinja"
+        ):
+            raise ValueError(
+                "chat_template_jinja is required when chat_template is set to jinja"
+            )
+
+        # If chat_template_jinja is set, set chat_template to jinja
+        if data.get("chat_template_jinja") and not data.get("chat_template"):
+            data["chat_template"] = ChatTemplate.jinja
+
+        return data
+
     @model_validator(mode="before")
     @classmethod
     def check_sample_packing_wo_flash(cls, data):
@@ -972,21 +1048,21 @@ class AxolotlInputConfig(
     @classmethod
     def check_evals(cls, data):
         if (
-            data.get("evaluation_strategy")
+            data.get("eval_strategy")
             and data.get("eval_steps")
-            and data.get("evaluation_strategy") != "steps"
+            and data.get("eval_strategy") != "steps"
         ):
             raise ValueError(
-                "evaluation_strategy and eval_steps mismatch. Please set evaluation_strategy to 'steps' or remove eval_steps."
+                "eval_strategy and eval_steps mismatch. Please set eval_strategy to 'steps' or remove eval_steps."
             )
 
         if (
             data.get("val_set_size") == 0
-            and (data.get("eval_steps") or data.get("evaluation_strategy"))
+            and (data.get("eval_steps") or data.get("eval_strategy"))
             and not data.get("test_datasets")
         ):
             raise ValueError(
-                "eval_steps and evaluation_strategy are not supported with val_set_size == 0"
+                "eval_steps and eval_strategy are not supported with val_set_size == 0"
             )
         if data.get("evals_per_epoch") and data.get("eval_steps"):
             raise ValueError(
@@ -994,11 +1070,11 @@ class AxolotlInputConfig(
             )
         if (
             data.get("evals_per_epoch")
-            and data.get("evaluation_strategy")
-            and data.get("evaluation_strategy") != "steps"
+            and data.get("eval_strategy")
+            and data.get("eval_strategy") != "steps"
         ):
             raise ValueError(
-                "evaluation_strategy must be empty or set to `steps` when used with evals_per_epoch."
+                "eval_strategy must be empty or set to `steps` when used with evals_per_epoch."
             )
 
         if data.get("do_bench_eval") and not (
@@ -1230,6 +1306,25 @@ class AxolotlInputConfig(
             )
         return data
 
+    @model_validator(mode="before")
+    @classmethod
+    def warn_qlora_zero3_w_use_reentrant(cls, data):
+        if (
+            data.get("adapter") == "qlora"
+            and data.get("gradient_checkpointing_kwargs", {})
+            and data.get("gradient_checkpointing_kwargs", {}).get("use_reentrant")
+            is False
+            and "zero3" in data.get("deepspeed", "")
+        ):
+            # may result in:
+            # torch.utils.checkpoint.CheckpointError: torch.utils.checkpoint:
+            # Recomputed values for the following tensors have different metadata
+            # than during the forward pass.
+            LOG.warning(
+                "qlora + zero3 with use_reentrant: false may result in a CheckpointError about recomputed values"
+            )
+        return data
+
     @model_validator(mode="before")
     @classmethod
     def check_val_w_test_datasets(cls, data):
@@ -1239,6 +1334,19 @@ class AxolotlInputConfig(
             )
         return data
 
+    @model_validator(mode="before")
+    @classmethod
+    def check_eval_strategy(cls, data):
+        if (
+            data.get("evaluation_strategy") is not None
+            and data.get("eval_strategy") is None
+        ):
+            LOG.info(
+                "explicitly setting `eval_strategy` from the `evaluation_strategy`"
+            )
+            data["eval_strategy"] = data.get("evaluation_strategy")
+        return data
+
     @model_validator(mode="before")
     @classmethod
     def check_fsdp_offload_w_8bit_optimizer(cls, data):

src/axolotl/utils/data/rl.py

@@ -64,15 +64,57 @@ def map_dataset(cfg, data_set, ds_transform_fn, tokenizer):
             tokenizer = load_tokenizer(cfg)
         ds_transform_fn = partial(ds_transform_fn, tokenizer=tokenizer)
 
+    if isinstance(data_set, DatasetDict):
+        data_set = data_set["train"]
+
     data_set = data_set.map(
         ds_transform_fn,
         desc="Mapping RL Dataset",
     )
-    if isinstance(data_set, DatasetDict):
-        data_set = data_set["train"]
+
     return data_set
 
 
+def drop_long_rl_seq(
+    sample, rl, tokenizer, sequence_len  # pylint: disable=invalid-name
+):
+    if rl in ("dpo", "ipo", "orpo", "simpo"):
+        if not (
+            sample.get("prompt") and sample.get("chosen") and sample.get("rejected")
+        ):
+            raise ValueError(
+                "Prompt, chosen and rejected keys are required for DPO/ORPO datasets"
+            )
+
+        prompt = sample["prompt"]
+        chosen = sample["chosen"]
+        rejected = sample["rejected"]
+
+        len_prompt = len(tokenizer(prompt, add_special_tokens=False)["input_ids"])
+        len_chosen = len(tokenizer(chosen, add_special_tokens=False)["input_ids"])
+        len_rejected = len(tokenizer(rejected, add_special_tokens=False)["input_ids"])
+
+        return (len_prompt + len_chosen) <= sequence_len and (
+            len_prompt + len_rejected
+        ) <= sequence_len
+
+    if rl == "kto":
+        if not (sample.get("prompt") and sample.get("completion")):
+            raise ValueError("Prompt and completion keys are required for KTO datasets")
+
+        prompt = sample["prompt"]
+        completion = sample["completion"]
+
+        len_prompt = len(tokenizer(prompt, add_special_tokens=False)["input_ids"])
+        len_completion = len(
+            tokenizer(completion, add_special_tokens=False)["input_ids"]
+        )
+
+        return (len_prompt + len_completion) <= sequence_len
+
+    raise ValueError("Unknown RL type")
+
+
 def load_prepare_dpo_datasets(cfg):
     def load_split(dataset_cfgs, _cfg):
         split_datasets: List[Any] = []
@@ -94,7 +136,7 @@ def load_prepare_dpo_datasets(cfg):
                 )
                 split_datasets.insert(i, ds)
 
-        tokenizer = None
+        tokenizer = load_tokenizer(cfg)
 
         for i, data_set in enumerate(split_datasets):
             _type = dataset_cfgs[i]["type"]
@@ -121,7 +163,28 @@ def load_prepare_dpo_datasets(cfg):
                 # "prompt", "chosen" and "rejected" already preprocessed
                 split_datasets[i] = data_set
 
-        return concatenate_datasets(split_datasets)
+            drop_long = partial(
+                drop_long_rl_seq,
+                rl=_cfg.rl,
+                tokenizer=tokenizer,
+                sequence_len=cfg.sequence_len,
+            )
+
+            prior_len = len(split_datasets[i])
+            split_datasets[i] = split_datasets[i].filter(
+                drop_long,
+                num_proc=cfg.dataset_processes,
+                load_from_cache_file=not cfg.is_preprocess,
+                desc="Dropping Long Sequences",
+            )
+            dropped = prior_len - len(split_datasets[i])
+            if dropped:
+                LOG.warning(f"Dropped {dropped} long samples from dataset index {i}")
+
+        combined_datasets = concatenate_datasets(split_datasets)
+        combined_datasets = combined_datasets.shuffle(seed=cfg.seed)
+
+        return combined_datasets
 
     with zero_first(is_main_process()):
         train_is_preprocessed = False

src/axolotl/utils/data/sft.py

@@ -2,9 +2,11 @@
 
 import functools
 import logging
+import time
 from pathlib import Path
 from typing import List, Optional, Tuple, Union
 
+import requests
 from datasets import (
     Dataset,
     DatasetDict,
@@ -53,6 +55,28 @@ from axolotl.utils.trainer import (
 LOG = logging.getLogger("axolotl")
 
 
+def retry_on_request_exceptions(max_retries=3, delay=1):
+    def decorator(func):
+        @functools.wraps(func)
+        def wrapper(*args, **kwargs):  # pylint: disable=inconsistent-return-statements
+            for attempt in range(max_retries):
+                try:
+                    return func(*args, **kwargs)
+                except (
+                    requests.exceptions.ReadTimeout,
+                    requests.exceptions.ConnectionError,
+                ) as exc:
+                    if attempt < max_retries - 1:
+                        time.sleep(delay)
+                    else:
+                        raise exc
+
+        return wrapper
+
+    return decorator
+
+
+@retry_on_request_exceptions(max_retries=3, delay=5)
 def prepare_dataset(cfg, tokenizer, processor=None):
     prompters = []
     if not cfg.pretraining_dataset:
@@ -236,6 +260,7 @@ def load_tokenized_prepared_datasets(
         for config_dataset in for_d_in_datasets(cfg_datasets):
             ds: Optional[Union[Dataset, DatasetDict]] = None
             ds_from_hub = False
+            ds_trust_remote_code = config_dataset.trust_remote_code
             try:
                 # this is just a basic check to see if the path is a
                 # valid HF dataset that's loadable
@@ -245,6 +270,7 @@ def load_tokenized_prepared_datasets(
                     streaming=True,
                     token=use_auth_token,
                     revision=config_dataset.revision,
+                    trust_remote_code=ds_trust_remote_code,
                 )
                 ds_from_hub = True
             except (FileNotFoundError, ConnectionError, HFValidationError, ValueError):
@@ -324,7 +350,15 @@ def load_tokenized_prepared_datasets(
                             split=None,
                         )
                     else:
-                        ds = load_from_disk(config_dataset.path)
+                        try:
+                            ds = load_from_disk(config_dataset.path)
+                        except FileNotFoundError:
+                            ds = load_dataset(
+                                config_dataset.path,
+                                name=config_dataset.name,
+                                streaming=False,
+                                split=None,
+                            )
                 elif local_path.is_file():
                     ds_type = get_ds_type(config_dataset)
 
@@ -342,7 +376,7 @@ def load_tokenized_prepared_datasets(
             elif ds_from_hub:
                 load_ds_kwargs = {}
                 if config_dataset.split:
-                    load_ds_kwargs = {"split": config_dataset.split}
+                    load_ds_kwargs["split"] = config_dataset.split
                 ds = load_dataset(
                     config_dataset.path,
                     name=config_dataset.name,
@@ -350,6 +384,7 @@ def load_tokenized_prepared_datasets(
                     data_files=config_dataset.data_files,
                     token=use_auth_token,
                     revision=config_dataset.revision,
+                    trust_remote_code=config_dataset.trust_remote_code,
                     **load_ds_kwargs,
                 )
             elif ds_from_cloud and remote_file_system:
@@ -367,6 +402,7 @@ def load_tokenized_prepared_datasets(
                         streaming=False,
                         split=None,
                         storage_options=storage_options,
+                        trust_remote_code=config_dataset.trust_remote_code,
                     )
             elif config_dataset.path.startswith("https://"):
                 ds_type = get_ds_type(config_dataset)
@@ -377,6 +413,7 @@ def load_tokenized_prepared_datasets(
                     streaming=False,
                     split=None,
                     storage_options=storage_options,
+                    trust_remote_code=config_dataset.trust_remote_code,
                 )
             else:
                 if isinstance(config_dataset.data_files, str):

src/axolotl/utils/environment.py

@@ -0,0 +1,25 @@
+"""
+utils to get GPU info for the current environment
+"""
+from accelerate.utils.environment import (
+    check_cuda_p2p_ib_support as accelerate_check_cuda_p2p_ib_support,
+)
+from accelerate.utils.environment import get_gpu_info
+
+
+def check_cuda_p2p_ib_support():
+    if not accelerate_check_cuda_p2p_ib_support():
+        return False
+    unsupported_devices = {"RTX 6000 Ada"}
+    try:
+        device_names, device_count = get_gpu_info()
+        if 1 < device_count < 8:
+            if any(
+                unsupported_device in device_name
+                for device_name in device_names
+                for unsupported_device in unsupported_devices
+            ):
+                return False
+    except Exception:  # pylint: disable=broad-except # nosec
+        pass
+    return True

src/axolotl/utils/gradient_checkpointing/unsloth.py

@@ -14,6 +14,16 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import torch
+from packaging import version
+
+torch_version = version.parse(torch.__version__)
+
+if torch_version < version.parse("2.4.0"):
+    torch_cuda_amp_custom_fwd = torch.cuda.amp.custom_fwd
+    torch_cuda_amp_custom_bwd = torch.cuda.amp.custom_bwd
+else:
+    torch_cuda_amp_custom_fwd = torch.amp.custom_fwd(device_type="cuda")
+    torch_cuda_amp_custom_bwd = torch.amp.custom_bwd(device_type="cuda")
 
 
 class Unsloth_Offloaded_Gradient_Checkpointer(  # pylint: disable=invalid-name
@@ -25,7 +35,7 @@ class Unsloth_Offloaded_Gradient_Checkpointer(  # pylint: disable=invalid-name
     """
 
     @staticmethod
-    @torch.cuda.amp.custom_fwd
+    @torch_cuda_amp_custom_fwd
     def forward(ctx, forward_function, hidden_states, *args):
         saved_hidden_states = hidden_states.to("cpu", non_blocking=True)
         with torch.no_grad():
@@ -36,7 +46,7 @@ class Unsloth_Offloaded_Gradient_Checkpointer(  # pylint: disable=invalid-name
         return output
 
     @staticmethod
-    @torch.cuda.amp.custom_bwd
+    @torch_cuda_amp_custom_bwd
     def backward(ctx, dY):
         (hidden_states,) = ctx.saved_tensors
         hidden_states = hidden_states.to("cuda", non_blocking=True).detach()

src/axolotl/utils/mlflow_.py

@@ -16,3 +16,7 @@ def setup_mlflow_env_vars(cfg: DictDefault):
     # Enable mlflow if experiment name is present
     if cfg.mlflow_experiment_name and len(cfg.mlflow_experiment_name) > 0:
         cfg.use_mlflow = True
+
+    # Enable logging hf artifacts in mlflow if value is truthy
+    if cfg.hf_mlflow_log_artifacts is True:
+        os.environ["HF_MLFLOW_LOG_ARTIFACTS"] = "true"

src/axolotl/utils/optimizers/adopt.py

@@ -0,0 +1,508 @@
+"""
+Copied from https://github.com/iShohei220/adopt
+
+ADOPT: Modified Adam Can Converge with Any β2 with the Optimal Rate (2024)
+Taniguchi, Shohei and Harada, Keno and Minegishi, Gouki and Oshima, Yuta and Jeong, Seong Cheol and Nagahara, Go and Iiyama, Tomoshi and Suzuki, Masahiro and Iwasawa, Yusuke and Matsuo, Yutaka
+"""
+# mypy: ignore-errors
+# pylint: skip-file
+# mypy: allow-untyped-decorators
+# mypy: allow-untyped-defs
+from typing import List, Optional, Tuple, Union, cast
+
+import torch
+from torch import Tensor
+from torch.optim.optimizer import (
+    Optimizer,
+    ParamsT,
+    _default_to_fused_or_foreach,
+    _device_dtype_check_for_fused,
+    _disable_dynamo_if_unsupported,
+    _get_capturable_supported_devices,
+    _get_scalar_dtype,
+    _get_value,
+    _use_grad_for_differentiable,
+    _view_as_real,
+)
+
+__all__ = ["ADOPT", "adopt"]
+
+
+class ADOPT(Optimizer):
+    def __init__(
+        self,
+        params: ParamsT,
+        lr: Union[float, Tensor] = 1e-3,
+        betas: Tuple[float, float] = (0.9, 0.9999),
+        eps: float = 1e-6,
+        weight_decay: float = 0.0,
+        decoupled: bool = False,
+        *,
+        foreach: Optional[bool] = None,
+        maximize: bool = False,
+        capturable: bool = False,
+        differentiable: bool = False,
+        fused: Optional[bool] = None,
+    ):
+        if isinstance(lr, Tensor):
+            if foreach and not capturable:
+                raise ValueError(
+                    "lr as a Tensor is not supported for capturable=False and foreach=True"
+                )
+            if lr.numel() != 1:
+                raise ValueError("Tensor lr must be 1-element")
+        if not 0.0 <= lr:
+            raise ValueError(f"Invalid learning rate: {lr}")
+        if not 0.0 <= eps:
+            raise ValueError(f"Invalid epsilon value: {eps}")
+        if not 0.0 <= betas[0] < 1.0:
+            raise ValueError(f"Invalid beta parameter at index 0: {betas[0]}")
+        if not 0.0 <= betas[1] < 1.0:
+            raise ValueError(f"Invalid beta parameter at index 1: {betas[1]}")
+        if not 0.0 <= weight_decay:
+            raise ValueError(f"Invalid weight_decay value: {weight_decay}")
+
+        defaults = dict(
+            lr=lr,
+            betas=betas,
+            eps=eps,
+            weight_decay=weight_decay,
+            decoupled=decoupled,
+            maximize=maximize,
+            foreach=foreach,
+            capturable=capturable,
+            differentiable=differentiable,
+            fused=fused,
+        )
+        super().__init__(params, defaults)
+
+        if fused:
+            # TODO: support fused
+            raise RuntimeError("`fused` is not currently supported")
+
+            if differentiable:
+                raise RuntimeError("`fused` does not support `differentiable`")
+            self._step_supports_amp_scaling = True
+            # TODO(crcrpar): [low prec params & their higher prec copy]
+            # Support AMP with FP16/BF16 model params which would need
+            # higher prec copy of params to do update math in higher prec to
+            # alleviate the loss of information.
+            if foreach:
+                raise RuntimeError("`fused` and `foreach` cannot be `True` together.")
+
+    def __setstate__(self, state):
+        super().__setstate__(state)
+        for group in self.param_groups:
+            group.setdefault("maximize", False)
+            group.setdefault("foreach", None)
+            group.setdefault("capturable", False)
+            group.setdefault("differentiable", False)
+            fused = group.setdefault("fused", None)
+            for p in group["params"]:
+                p_state = self.state.get(p, [])
+                if len(p_state) != 0 and not torch.is_tensor(p_state["step"]):
+                    step_val = float(p_state["step"])
+                    p_state["step"] = (
+                        torch.tensor(
+                            step_val,
+                            dtype=_get_scalar_dtype(is_fused=fused),
+                            device=p.device,
+                        )
+                        if group["capturable"] or group["fused"]
+                        else torch.tensor(step_val, dtype=_get_scalar_dtype())
+                    )
+
+    def _init_group(
+        self,
+        group,
+        params_with_grad,
+        grads,
+        exp_avgs,
+        exp_avg_sqs,
+        state_steps,
+    ):
+        has_complex = False
+        for p in group["params"]:
+            if p.grad is not None:
+                has_complex |= torch.is_complex(p)
+                params_with_grad.append(p)
+                if p.grad.is_sparse:
+                    raise RuntimeError("ADOPT does not support sparse gradients")
+                grads.append(p.grad)
+
+                state = self.state[p]
+                # Lazy state initialization
+                if len(state) == 0:
+                    if group["fused"]:
+                        _device_dtype_check_for_fused(p)
+                    # note(crcrpar): [special device hosting for step]
+                    # Deliberately host `step` on CPU if both capturable and fused are off.
+                    # This is because kernel launches are costly on CUDA and XLA.
+                    state["step"] = (
+                        torch.zeros(
+                            (),
+                            dtype=_get_scalar_dtype(is_fused=group["fused"]),
+                            device=p.device,
+                        )
+                        if group["capturable"] or group["fused"]
+                        else torch.tensor(0.0, dtype=_get_scalar_dtype())
+                    )
+                    # Exponential moving average of gradient values
+                    state["exp_avg"] = torch.zeros_like(
+                        p, memory_format=torch.preserve_format
+                    )
+                    # Exponential moving average of squared gradient values
+                    state["exp_avg_sq"] = torch.zeros_like(
+                        p, memory_format=torch.preserve_format
+                    )
+
+                exp_avgs.append(state["exp_avg"])
+                exp_avg_sqs.append(state["exp_avg_sq"])
+
+                if group["differentiable"] and state["step"].requires_grad:
+                    raise RuntimeError(
+                        "`requires_grad` is not supported for `step` in differentiable mode"
+                    )
+
+                # Foreach without capturable does not support a tensor lr
+                if (
+                    group["foreach"]
+                    and torch.is_tensor(group["lr"])
+                    and not group["capturable"]
+                ):
+                    raise RuntimeError(
+                        "lr as a Tensor is not supported for capturable=False and foreach=True"
+                    )
+
+                state_steps.append(state["step"])
+        return has_complex
+
+    @_use_grad_for_differentiable
+    def step(self, closure=None):
+        """Perform a single optimization step.
+
+        Args:
+            closure (Callable, optional): A closure that reevaluates the model
+                and returns the loss.
+        """
+        self._cuda_graph_capture_health_check()
+
+        loss = None
+        if closure is not None:
+            with torch.enable_grad():
+                loss = closure()
+
+        for group in self.param_groups:
+            params_with_grad: List[Tensor] = []
+            grads: List[Tensor] = []
+            exp_avgs: List[Tensor] = []
+            exp_avg_sqs: List[Tensor] = []
+            state_steps: List[Tensor] = []
+            beta1, beta2 = group["betas"]
+
+            has_complex = self._init_group(
+                group,
+                params_with_grad,
+                grads,
+                exp_avgs,
+                exp_avg_sqs,
+                state_steps,
+            )
+
+            adopt(
+                params_with_grad,
+                grads,
+                exp_avgs,
+                exp_avg_sqs,
+                state_steps,
+                has_complex=has_complex,
+                beta1=beta1,
+                beta2=beta2,
+                lr=group["lr"],
+                weight_decay=group["weight_decay"],
+                decoupled=group["decoupled"],
+                eps=group["eps"],
+                maximize=group["maximize"],
+                foreach=group["foreach"],
+                capturable=group["capturable"],
+                differentiable=group["differentiable"],
+                fused=group["fused"],
+                grad_scale=getattr(self, "grad_scale", None),
+                found_inf=getattr(self, "found_inf", None),
+            )
+
+        return loss
+
+
+def _single_tensor_adopt(
+    params: List[Tensor],
+    grads: List[Tensor],
+    exp_avgs: List[Tensor],
+    exp_avg_sqs: List[Tensor],
+    state_steps: List[Tensor],
+    grad_scale: Optional[Tensor],
+    found_inf: Optional[Tensor],
+    *,
+    has_complex: bool,
+    beta1: float,
+    beta2: float,
+    lr: Union[float, Tensor],
+    weight_decay: float,
+    decoupled: bool,
+    eps: float,
+    maximize: bool,
+    capturable: bool,
+    differentiable: bool,
+):
+    assert grad_scale is None and found_inf is None
+
+    if torch.jit.is_scripting():
+        # this assert is due to JIT being dumb and not realizing that the ops below
+        # have overloads to handle both float and Tensor lrs, so we just assert it's
+        # a float since most people using JIT are using floats
+        assert isinstance(lr, float)
+
+    for i, param in enumerate(params):
+        grad = grads[i] if not maximize else -grads[i]
+        exp_avg = exp_avgs[i]
+        exp_avg_sq = exp_avg_sqs[i]
+        step_t = state_steps[i]
+
+        # If compiling, the compiler will handle cudagraph checks, see note [torch.compile x capturable]
+        if not torch._utils.is_compiling() and capturable:
+            capturable_supported_devices = _get_capturable_supported_devices()
+            assert (
+                param.device.type == step_t.device.type
+                and param.device.type in capturable_supported_devices
+            ), f"If capturable=True, params and state_steps must be on supported devices: {capturable_supported_devices}."
+
+        # update step
+        step_t += 1
+
+        if weight_decay != 0:
+            if decoupled:
+                param.add_(param, alpha=-lr * weight_decay)
+            else:
+                grad = grad.add(param, alpha=weight_decay)
+
+        if torch.is_complex(param):
+            grad = torch.view_as_real(grad)
+            if exp_avg is not None:
+                exp_avg = torch.view_as_real(exp_avg)
+            if exp_avg_sq is not None:
+                exp_avg_sq = torch.view_as_real(exp_avg_sq)
+            param = torch.view_as_real(param)
+
+        step = step_t if capturable or differentiable else _get_value(step_t)
+        if step == 1:
+            exp_avg_sq.addcmul_(grad, grad.conj())
+            continue
+
+        denom = torch.clamp(exp_avg_sq.sqrt(), eps)
+        if step == 2:
+            exp_avg.addcdiv_(grad, denom)
+        else:
+            exp_avg.mul_(beta1).addcdiv_(grad, denom, value=1 - beta1)
+
+        param.add_(exp_avg, alpha=-lr)
+        exp_avg_sq.mul_(beta2).addcmul_(grad, grad.conj(), value=1 - beta2)
+
+
+def _multi_tensor_adopt(
+    params: List[Tensor],
+    grads: List[Tensor],
+    exp_avgs: List[Tensor],
+    exp_avg_sqs: List[Tensor],
+    state_steps: List[Tensor],
+    grad_scale: Optional[Tensor],
+    found_inf: Optional[Tensor],
+    *,
+    has_complex: bool,
+    beta1: float,
+    beta2: float,
+    lr: Union[float, Tensor],
+    weight_decay: float,
+    decoupled: bool,
+    eps: float,
+    maximize: bool,
+    capturable: bool,
+    differentiable: bool,
+):
+    if len(params) == 0:
+        return
+
+    if isinstance(lr, Tensor) and not capturable:
+        raise RuntimeError(
+            "lr as a Tensor is not supported for capturable=False and foreach=True"
+        )
+
+    # If compiling, the compiler will handle cudagraph checks, see note [torch.compile x capturable]
+    if not torch._utils.is_compiling() and capturable:
+        capturable_supported_devices = _get_capturable_supported_devices(
+            supports_xla=False
+        )
+        assert all(
+            p.device.type == step.device.type
+            and p.device.type in capturable_supported_devices
+            for p, step in zip(params, state_steps)
+        ), f"If capturable=True, params and state_steps must be on supported devices: {capturable_supported_devices}."
+
+    assert grad_scale is None and found_inf is None
+
+    assert not differentiable, "_foreach ops don't support autograd"
+
+    grouped_tensors = Optimizer._group_tensors_by_device_and_dtype(
+        [params, grads, exp_avgs, exp_avg_sqs, state_steps]  # type: ignore[list-item]
+    )
+    for (
+        device_params_,
+        device_grads_,
+        device_exp_avgs_,
+        device_exp_avg_sqs_,
+        device_state_steps_,
+    ), _ in grouped_tensors.values():
+        device_params = cast(List[Tensor], device_params_)
+        device_grads = cast(List[Tensor], device_grads_)
+        device_exp_avgs = cast(List[Tensor], device_exp_avgs_)
+        device_exp_avg_sqs = cast(List[Tensor], device_exp_avg_sqs_)
+        device_state_steps = cast(List[Tensor], device_state_steps_)
+
+        # Handle complex parameters
+        if has_complex:
+            _view_as_real(
+                device_params, device_grads, device_exp_avgs, device_exp_avg_sqs
+            )
+
+        if maximize:
+            device_grads = torch._foreach_neg(device_grads)  # type: ignore[assignment]
+
+        # Update steps
+        # If steps are on CPU, foreach will fall back to the slow path, which is a for-loop calling t.add(1) over
+        # and over. 1 will then be wrapped into a Tensor over and over again, which is slower than if we just
+        # wrapped it once now. The alpha is required to assure we go to the right overload.
+        if not torch._utils.is_compiling() and device_state_steps[0].is_cpu:
+            torch._foreach_add_(
+                device_state_steps, torch.tensor(1.0, device="cpu"), alpha=1.0
+            )
+        else:
+            torch._foreach_add_(device_state_steps, 1)
+
+        if weight_decay != 0:
+            if decoupled:
+                torch._foreach_add_(
+                    device_params, device_params, alpha=-lr * weight_decay
+                )
+            else:
+                # Re-use the intermediate memory (device_grads) already allocated for maximize
+                if maximize:
+                    torch._foreach_add_(device_grads, device_params, alpha=weight_decay)
+                else:
+                    device_grads = torch._foreach_add(  # type: ignore[assignment]
+                        device_grads, device_params, alpha=weight_decay
+                    )
+
+        if device_state_steps[0] == 1:
+            torch._foreach_addcmul_(device_exp_avg_sqs, device_grads, device_grads)
+            continue
+
+        exp_avg_sq_sqrt = torch._foreach_sqrt(device_exp_avg_sqs)
+        exp_avg_sq_sqrt = torch._foreach_maximum(exp_avg_sq_sqrt, eps)
+
+        if device_state_steps[0] == 2:
+            torch._foreach_addcdiv_(device_exp_avgs, device_grads, exp_avg_sq_sqrt)
+        else:
+            torch._foreach_mul_(device_exp_avgs, beta1)
+            torch._foreach_addcdiv_(
+                device_exp_avgs, device_grads, exp_avg_sq_sqrt, value=1 - beta1
+            )
+
+        torch._foreach_add_(device_params, device_exp_avgs, alpha=-lr)
+        torch._foreach_mul_(device_exp_avg_sqs, beta2)
+        torch._foreach_addcmul_(
+            device_exp_avg_sqs, device_grads, device_grads, value=1 - beta2
+        )
+
+
+@_disable_dynamo_if_unsupported(single_tensor_fn=_single_tensor_adopt)
+def adopt(
+    params: List[Tensor],
+    grads: List[Tensor],
+    exp_avgs: List[Tensor],
+    exp_avg_sqs: List[Tensor],
+    state_steps: List[Tensor],
+    # kwonly args with defaults are not supported by functions compiled with torchscript issue #70627
+    # setting this as kwarg for now as functional API is compiled by torch/distributed/optim
+    foreach: Optional[bool] = None,
+    capturable: bool = False,
+    differentiable: bool = False,
+    fused: Optional[bool] = None,
+    grad_scale: Optional[Tensor] = None,
+    found_inf: Optional[Tensor] = None,
+    has_complex: bool = False,
+    *,
+    beta1: float,
+    beta2: float,
+    lr: Union[float, Tensor],
+    weight_decay: float,
+    decoupled: bool,
+    eps: float,
+    maximize: bool,
+):
+    r"""Functional API that performs ADOPT algorithm computation."""
+    # Respect when the user inputs False/True for foreach or fused. We only want to change
+    # the default when neither have been user-specified. Note that we default to foreach
+    # and pass False to use_fused. This is not a mistake--we want to give the fused impl
+    # bake-in time before making it the default, even if it is typically faster.
+    if fused is None and foreach is None:
+        _, foreach = _default_to_fused_or_foreach(
+            params, differentiable, use_fused=False
+        )
+        # Do not flip on foreach for the unsupported case where lr is a Tensor and capturable=False.
+        if foreach and isinstance(lr, Tensor) and not capturable:
+            foreach = False
+    if fused is None:
+        fused = False
+    if foreach is None:
+        foreach = False
+
+    # this check is slow during compilation, so we skip it
+    # if it's strictly needed we can add this check back in dynamo
+    if not torch._utils.is_compiling() and not all(
+        isinstance(t, torch.Tensor) for t in state_steps
+    ):
+        raise RuntimeError(
+            "API has changed, `state_steps` argument must contain a list of singleton tensors"
+        )
+
+    if foreach and torch.jit.is_scripting():
+        raise RuntimeError("torch.jit.script not supported with foreach optimizers")
+    if fused and torch.jit.is_scripting():
+        raise RuntimeError("torch.jit.script not supported with fused optimizers")
+
+    # if fused and not torch.jit.is_scripting():
+    #     func = _fused_adopt
+    # elif foreach and not torch.jit.is_scripting():
+    if foreach and not torch.jit.is_scripting():
+        func = _multi_tensor_adopt
+    else:
+        func = _single_tensor_adopt
+
+    func(
+        params,
+        grads,
+        exp_avgs,
+        exp_avg_sqs,
+        state_steps,
+        has_complex=has_complex,
+        beta1=beta1,
+        beta2=beta2,
+        lr=lr,
+        weight_decay=weight_decay,
+        decoupled=decoupled,
+        eps=eps,
+        maximize=maximize,
+        capturable=capturable,
+        differentiable=differentiable,
+        grad_scale=grad_scale,
+        found_inf=found_inf,
+    )

src/axolotl/utils/samplers/multipack.py

@@ -133,6 +133,8 @@ class MultipackBatchSampler(BatchSampler):
         self.eff_total_used = 0
         self.eff_total_slots = 0
 
+        self.len_across_ranks = None
+
     def set_epoch(self, epoch: int):
         self.epoch = epoch
 
@@ -195,15 +197,14 @@ class MultipackBatchSampler(BatchSampler):
             LOG.info(f"gather_len_batches: {repr(estimates)}")
             return math.floor(0.998 * min(estimates))
 
-        min_len_batches = reduce_and_broadcast(
-            lambda: num,
-            calc_min_len,
-        )
+        min_len_batches = reduce_and_broadcast(lambda: num, calc_min_len)
         return min_len_batches
 
     def __len__(self):
-        len_batches = self.num_batches()
-        return self.gather_len_batches(len_batches)
+        if not self.len_across_ranks:
+            len_batches = self.num_batches()
+            self.len_across_ranks = self.gather_len_batches(len_batches)
+        return self.len_across_ranks
 
     def _len_est(self):
         efficiency = (

src/axolotl/utils/tokenization.py

@@ -1,8 +1,6 @@
 """Module for tokenization utilities"""
 
 import logging
-import re
-from typing import Dict, List
 
 from termcolor import colored
 
@@ -68,90 +66,47 @@ def process_tokens_for_rl_debug(tokens, color, tokenizer, text_only):
 
 
 def check_rl_example_labels(example, tokenizer, text_only=False):
-    field_prompt, field_chosen, field_rejected = "prompt", "chosen", "rejected"
+    field_prompt, field_chosen, field_rejected, field_completion = (
+        "prompt",
+        "chosen",
+        "rejected",
+        "completion",
+    )
 
     input_tokens = example[field_prompt]
-    labels_chosen, labels_rejected = example[field_chosen], example[field_rejected]
+
+    labels_chosen = example.get(field_chosen)
+    labels_rejected = example.get(field_rejected)
+    labels_completion = example.get(field_completion)
+
+    # Create a delimiter based on text_only flag
+    delimiter = "" if text_only else " "
 
     # Process and color each type of token
     colored_tokens = process_tokens_for_rl_debug(
         input_tokens, "yellow", tokenizer, text_only
     )
-    colored_chosens = process_tokens_for_rl_debug(
-        labels_chosen, "green", tokenizer, text_only
-    )
-    colored_rejecteds = process_tokens_for_rl_debug(
-        labels_rejected, "red", tokenizer, text_only
-    )
 
-    # Create a delimiter based on text_only flag
-    delimiter = "" if text_only else " "
+    # Process tokens
+    if labels_completion is None:
+        colored_chosens = process_tokens_for_rl_debug(
+            labels_chosen, "green", tokenizer, text_only
+        )
+        colored_rejecteds = process_tokens_for_rl_debug(
+            labels_rejected, "red", tokenizer, text_only
+        )
+    else:
+        colored_completion = process_tokens_for_rl_debug(
+            labels_completion, "green", tokenizer, text_only
+        )
 
     # Logging information
     LOG.info(f"INPUT PROMPT: {delimiter.join(colored_tokens)}\n\n")
-    LOG.info(f"CHOSEN RESPONSE: {delimiter.join(colored_chosens)}\n\n")
-    LOG.info(f"REJECTED RESPONSE: {delimiter.join(colored_rejecteds)}\n\n\n")
+
+    if labels_completion is None:
+        LOG.info(f"CHOSEN RESPONSE: {delimiter.join(colored_chosens)}\n\n")
+        LOG.info(f"REJECTED RESPONSE: {delimiter.join(colored_rejecteds)}\n\n\n")
+    else:
+        LOG.info(f"COMPLETION RESPONSE: {delimiter.join(colored_completion)}\n\n\n")
 
     return delimiter.join(colored_tokens)
-
-
-GLAIVE_ROLES = ["USER", "ASSISTANT", "FUNCTION RESPONSE"]
-GLAIVE_TO_SHAREGPT_ROLE = {
-    "SYSTEM": "system",
-    "USER": "human",
-    "ASSISTANT": "gpt",
-    "FUNCTION RESPONSE": "tool",
-}
-
-GLAIVE_MSG_REGEX = re.compile(rf"({'|'.join(GLAIVE_ROLES)}): ")
-
-
-def chatml_to_conversation(row: Dict[str, str]) -> List[Dict[str, str]]:
-    """
-    Converts a ChatML formatted row to a list of messages in ShareGPT format.
-    Initially based off https://github.com/lilacai/lilac/blob/main/notebooks/GlaiveToShareGPT.ipynb.
-    """
-
-    system_prompt = row.get("system")
-    if system_prompt:
-        system_prompt = system_prompt.removeprefix("SYSTEM: ")
-
-    chat_str = row["chat"]
-    chat_msgs = [s.strip() for s in GLAIVE_MSG_REGEX.split(chat_str) if s]
-
-    chat_msg_dicts = [
-        {"from": GLAIVE_TO_SHAREGPT_ROLE[role], "value": value}
-        for role, value in zip(chat_msgs[::2], chat_msgs[1::2])
-    ]
-
-    if system_prompt:
-        chat_msg_dicts = [
-            {"from": GLAIVE_TO_SHAREGPT_ROLE["SYSTEM"], "value": system_prompt}
-        ] + chat_msg_dicts
-
-    return chat_msg_dicts
-
-
-def merge_consecutive_messages(messages):
-    """
-    Merge consecutive messages from the same sender into a single message.
-    This can be useful with datasets that contain multiple consecutive tool calls.
-    """
-
-    merged_messages = []
-    current_from = None
-    current_message = ""
-
-    for msg in messages:
-        if current_from == msg["from"]:
-            current_message += msg["value"]
-        else:
-            if current_from is not None:
-                merged_messages.append({"from": current_from, "value": current_message})
-            current_from = msg["from"]
-            current_message = msg["value"]
-
-    if current_from is not None:
-        merged_messages.append({"from": current_from, "value": current_message})
-
-    return merged_messages

src/axolotl/utils/trainer.py

@@ -17,6 +17,7 @@ from transformers.utils import is_torch_bf16_gpu_available
 
 from axolotl.core.trainer_builder import HFCausalTrainerBuilder, HFRLTrainerBuilder
 from axolotl.utils.distributed import reduce_and_broadcast
+from axolotl.utils.environment import check_cuda_p2p_ib_support
 from axolotl.utils.samplers import MultipackBatchSampler, get_dataset_lengths
 
 LOG = get_logger("axolotl")
@@ -184,11 +185,10 @@ def process_datasets_for_packing(cfg, train_dataset, eval_dataset):
         min_sequence_len=cfg.min_sample_len or 2,
     )
 
-    if cfg.is_preprocess:
-        min_input_len = np.min(get_dataset_lengths(train_dataset))
-        LOG.debug(f"min_input_len: {min_input_len}", main_process_only=True)
-        max_input_len = np.max(get_dataset_lengths(train_dataset))
-        LOG.debug(f"max_input_len: {max_input_len}", main_process_only=True)
+    min_input_len = np.min(get_dataset_lengths(train_dataset))
+    LOG.debug(f"min_input_len: {min_input_len}", main_process_only=True)
+    max_input_len = np.max(get_dataset_lengths(train_dataset))
+    LOG.debug(f"max_input_len: {max_input_len}", main_process_only=True)
 
     if cfg.model_config_type == "mamba":
         LOG.info("dropping attention_mask column")
@@ -203,37 +203,59 @@ def process_datasets_for_packing(cfg, train_dataset, eval_dataset):
         if eval_dataset and "token_type_ids" in eval_dataset.column_names:
             eval_dataset = eval_dataset.remove_columns("token_type_ids")
 
+    prior_len = len(train_dataset)
     train_dataset = train_dataset.filter(
         drop_long,
         num_proc=cfg.dataset_processes,
         load_from_cache_file=not cfg.is_preprocess,
         desc="Dropping Long Sequences",
     )
+    dropped = prior_len - len(train_dataset)
+    if dropped:
+        LOG.warning(f"Dropped {dropped} long samples from train dataset")
+
     if eval_dataset:
+        prior_len = len(eval_dataset)
         eval_dataset = eval_dataset.filter(
             drop_long,
             num_proc=cfg.dataset_processes,
             load_from_cache_file=not cfg.is_preprocess,
             desc="Dropping Long Sequences",
         )
+        dropped = prior_len - len(eval_dataset)
+        if dropped:
+            LOG.warning(f"Dropped {dropped} long samples from eval dataset")
 
     # drop samples with where the number of elements with labels not equal to -100 is zero
     def drop_no_trainable_tokens(sample):
         return np.sum(np.array(sample["labels"]) != -100) > 0
 
+    prior_len = len(train_dataset)
     train_dataset = train_dataset.filter(
         drop_no_trainable_tokens,
         num_proc=cfg.dataset_processes,
         load_from_cache_file=not cfg.is_preprocess,
         desc="Drop Samples with Zero Trainable Tokens",
     )
+    dropped = prior_len - len(train_dataset)
+    if dropped:
+        LOG.warning(
+            f"Dropped {dropped} samples with no trainable tokens from train dataset"
+        )
+
     if eval_dataset:
+        prior_len = len(eval_dataset)
         eval_dataset = eval_dataset.filter(
             drop_no_trainable_tokens,
             num_proc=cfg.dataset_processes,
             load_from_cache_file=not cfg.is_preprocess,
             desc="Drop Samples with Zero Trainable Tokens",
         )
+        dropped = prior_len - len(eval_dataset)
+        if dropped:
+            LOG.warning(
+                f"Dropped {dropped} samples with no trainable tokens from eval dataset"
+            )
 
     if cfg.group_by_length:
         train_dataset = train_dataset.map(
@@ -461,6 +483,9 @@ def setup_fsdp_envs(cfg):
 
 
 def prepare_optim_env(cfg):
+    if not check_cuda_p2p_ib_support():
+        if os.getenv("NCCL_P2P_DISABLE") is None:
+            os.environ["NCCL_P2P_DISABLE"] = "1"
     if cfg.fsdp:
         setup_fsdp_envs(cfg)
     elif cfg.deepspeed:
@@ -490,7 +515,7 @@ def prepare_opinionated_env(cfg):
 def setup_trainer(
     cfg, train_dataset, eval_dataset, model, tokenizer, processor, total_num_steps
 ):
-    if cfg.rl in ["dpo", "ipo", "orpo", "kto", "simpo"]:
+    if cfg.rl in ("dpo", "ipo", "orpo", "kto", "simpo"):
         trainer_builder = HFRLTrainerBuilder(cfg, model[0], tokenizer, processor)
         trainer_builder.model_ref = model[1]
         trainer_builder.peft_config = model[2]

tests/e2e/conftest.py

@@ -0,0 +1,35 @@
+"""
+shared pytest fixtures
+"""
+import shutil
+import tempfile
+
+import pytest
+from huggingface_hub import snapshot_download
+
+
+@pytest.fixture(scope="session", autouse=True)
+def download_smollm2_135m_model():
+    # download the model
+    snapshot_download("HuggingFaceTB/SmolLM2-135M")
+
+
+@pytest.fixture(scope="session", autouse=True)
+def download_tatsu_lab_alpaca_dataset():
+    # download the model
+    snapshot_download("tatsu-lab/alpaca", repo_type="dataset")
+
+
+@pytest.fixture(scope="session", autouse=True)
+def download_mhenrichsen_alpaca_2k_dataset():
+    # download the model
+    snapshot_download("mhenrichsen/alpaca_2k_test", repo_type="dataset")
+
+
+@pytest.fixture
+def temp_dir():
+    # Create a temporary directory
+    _temp_dir = tempfile.mkdtemp()
+    yield _temp_dir
+    # Clean up the directory after the test
+    shutil.rmtree(_temp_dir)

tests/e2e/integrations/liger.py

@@ -1,7 +1,6 @@
 """
 Simple end-to-end test for Liger integration
 """
-
 import unittest
 from pathlib import Path
 

tests/e2e/multigpu/test_eval.py

@@ -0,0 +1,155 @@
+"""
+E2E tests for multigpu eval
+"""
+import logging
+import os
+from pathlib import Path
+
+import yaml
+from accelerate.test_utils import execute_subprocess_async
+from transformers.testing_utils import get_torch_dist_unique_port
+
+from axolotl.utils.dict import DictDefault
+
+LOG = logging.getLogger("axolotl.tests.e2e.multigpu")
+os.environ["WANDB_DISABLED"] = "true"
+
+AXOLOTL_ROOT = Path(__file__).parent.parent.parent.parent
+
+
+class TestMultiGPUEval:
+    """
+    Test case for MultiGPU Eval Sample Packing
+    """
+
+    def test_eval_sample_packing(self, temp_dir):
+        # pylint: disable=duplicate-code
+        cfg = DictDefault(
+            {
+                "base_model": "JackFram/llama-68m",
+                "load_in_8bit": False,
+                "load_in_4bit": True,
+                "strict": False,
+                "sequence_len": 2048,
+                "adapter": "qlora",
+                "sample_packing": True,
+                "eval_sample_packing": True,
+                "pad_to_sequence_len": True,
+                "lora_r": 8,
+                "lora_alpha": 16,
+                "lora_dropout": 0.05,
+                "lora_target_linear": True,
+                "lora_modules_to_save": ["embed_tokens", "lm_head"],
+                "val_set_size": 0.1,
+                "special_tokens": {"pad_token": "<|end_of_text|>"},
+                "datasets": [
+                    {
+                        "path": "teknium/GPT4-LLM-Cleaned",
+                        "type": "alpaca",
+                    },
+                ],
+                "num_epochs": 1,
+                "max_steps": 5,
+                "micro_batch_size": 2,
+                "gradient_accumulation_steps": 4,
+                "output_dir": temp_dir,
+                "learning_rate": 0.00001,
+                "optimizer": "adamw_8bit",
+                "lr_scheduler": "cosine",
+                "flash_attention": True,
+                "loss_watchdog_threshold": 5.0,
+                "loss_watchdog_patience": 3,
+                "bf16": "auto",
+                "warmup_steps": 1,
+                "evals_per_epoch": 2,
+                "eval_max_new_tokens": 128,
+                "saves_per_epoch": 1,
+                "logging_steps": 1,
+                "weight_decay": 0.0,
+            }
+        )
+
+        # write cfg to yaml file
+        Path(temp_dir).mkdir(parents=True, exist_ok=True)
+        with open(Path(temp_dir) / "config.yaml", "w", encoding="utf-8") as fout:
+            fout.write(yaml.dump(cfg.to_dict(), Dumper=yaml.Dumper))
+
+        execute_subprocess_async(
+            [
+                "accelerate",
+                "launch",
+                "--num-processes",
+                "2",
+                "--main_process_port",
+                f"{get_torch_dist_unique_port()}",
+                "-m",
+                "axolotl.cli.train",
+                str(Path(temp_dir) / "config.yaml"),
+            ]
+        )
+
+    def test_eval(self, temp_dir):
+        # pylint: disable=duplicate-code
+        cfg = DictDefault(
+            {
+                "base_model": "JackFram/llama-68m",
+                "load_in_8bit": False,
+                "load_in_4bit": True,
+                "strict": False,
+                "sequence_len": 2048,
+                "adapter": "qlora",
+                "sample_packing": True,
+                "eval_sample_packing": False,
+                "pad_to_sequence_len": True,
+                "lora_r": 8,
+                "lora_alpha": 16,
+                "lora_dropout": 0.05,
+                "lora_target_linear": True,
+                "lora_modules_to_save": ["embed_tokens", "lm_head"],
+                "val_set_size": 0.1,
+                "special_tokens": {"pad_token": "<|end_of_text|>"},
+                "datasets": [
+                    {
+                        "path": "teknium/GPT4-LLM-Cleaned",
+                        "type": "alpaca",
+                    },
+                ],
+                "num_epochs": 1,
+                "max_steps": 5,
+                "micro_batch_size": 2,
+                "gradient_accumulation_steps": 4,
+                "output_dir": temp_dir,
+                "learning_rate": 0.00001,
+                "optimizer": "adamw_8bit",
+                "lr_scheduler": "cosine",
+                "flash_attention": True,
+                "loss_watchdog_threshold": 5.0,
+                "loss_watchdog_patience": 3,
+                "bf16": "auto",
+                "warmup_steps": 1,
+                "evals_per_epoch": 2,
+                "eval_max_new_tokens": 128,
+                "saves_per_epoch": 1,
+                "logging_steps": 1,
+                "weight_decay": 0.0,
+            }
+        )
+
+        # write cfg to yaml file
+        Path(temp_dir).mkdir(parents=True, exist_ok=True)
+        with open(Path(temp_dir) / "config.yaml", "w", encoding="utf-8") as fout:
+            fout.write(yaml.dump(cfg.to_dict(), Dumper=yaml.Dumper))
+
+        execute_subprocess_async(
+            [
+                "accelerate",
+                "launch",
+                "--num-processes",
+                "2",
+                "--main_process_port",
+                f"{get_torch_dist_unique_port()}",
+                "-m",
+                "axolotl.cli.train",
+                str(Path(temp_dir) / "config.yaml"),
+            ]
+        )

tests/e2e/multigpu/test_llama.py

@@ -4,17 +4,17 @@ E2E tests for multigpu lora tinyllama
 
 import logging
 import os
-import unittest
 from pathlib import Path
 
 import pytest
 import yaml
 from accelerate.test_utils import execute_subprocess_async
 from huggingface_hub import snapshot_download
+from transformers.testing_utils import get_torch_dist_unique_port
 
 from axolotl.utils.dict import DictDefault
 
-from ..utils import with_temp_dir
+from ..utils import is_hopper
 
 LOG = logging.getLogger("axolotl.tests.e2e.multigpu")
 os.environ["WANDB_DISABLED"] = "true"
@@ -25,21 +25,19 @@ AXOLOTL_ROOT = Path(__file__).parent.parent.parent.parent
 @pytest.fixture(scope="session", autouse=True)
 def download_model():
     # download the model
-    snapshot_download("TinyLlama/TinyLlama_v1.1")
+    snapshot_download("HuggingFaceTB/SmolLM2-135M")
 
 
-class TestMultiGPULlama(unittest.TestCase):
+class TestMultiGPULlama:
     """
     Test case for Llama models using LoRA
     """
 
-    @with_temp_dir
     def test_lora_ddp(self, temp_dir):
         # pylint: disable=duplicate-code
         cfg = DictDefault(
             {
-                "base_model": "TinyLlama/TinyLlama_v1.1",
-                "tokenizer_type": "LlamaTokenizer",
+                "base_model": "HuggingFaceTB/SmolLM2-135M",
                 "sequence_len": 2048,
                 "adapter": "lora",
                 "lora_r": 8,
@@ -48,9 +46,7 @@ class TestMultiGPULlama(unittest.TestCase):
                 "lora_target_linear": True,
                 "val_set_size": 0.05,
                 "special_tokens": {
-                    "unk_token": "<unk>",
-                    "bos_token": "<s>",
-                    "eos_token": "</s>",
+                    "pad_token": "<|endoftext|>",
                 },
                 "datasets": [
                     {
@@ -59,7 +55,7 @@ class TestMultiGPULlama(unittest.TestCase):
                     },
                 ],
                 "num_epochs": 1,
-                "max_steps": 100,
+                "max_steps": 15,
                 "micro_batch_size": 4,
                 "gradient_accumulation_steps": 4,
                 "output_dir": temp_dir,
@@ -81,19 +77,23 @@ class TestMultiGPULlama(unittest.TestCase):
                 "launch",
                 "--num-processes",
                 "2",
+                "--main_process_port",
+                f"{get_torch_dist_unique_port()}",
                 "-m",
                 "axolotl.cli.train",
                 str(Path(temp_dir) / "config.yaml"),
             ]
         )
 
-    @with_temp_dir
-    def test_lora_ddp_packed(self, temp_dir):
+    @pytest.mark.parametrize(
+        "gradient_accumulation_steps",
+        [1, 4],
+    )
+    def test_lora_ddp_packed(self, temp_dir, gradient_accumulation_steps):
         # pylint: disable=duplicate-code
         cfg = DictDefault(
             {
-                "base_model": "TinyLlama/TinyLlama_v1.1",
-                "tokenizer_type": "LlamaTokenizer",
+                "base_model": "HuggingFaceTB/SmolLM2-135M",
                 "sequence_len": 2048,
                 "sample_packing": True,
                 "eval_sample_packing": False,
@@ -105,9 +105,7 @@ class TestMultiGPULlama(unittest.TestCase):
                 "lora_target_linear": True,
                 "val_set_size": 0.05,
                 "special_tokens": {
-                    "unk_token": "<unk>",
-                    "bos_token": "<s>",
-                    "eos_token": "</s>",
+                    "pad_token": "<|endoftext|>",
                 },
                 "datasets": [
                     {
@@ -116,9 +114,9 @@ class TestMultiGPULlama(unittest.TestCase):
                     },
                 ],
                 "num_epochs": 1,
-                "max_steps": 50,
+                "max_steps": 15,
                 "micro_batch_size": 4,
-                "gradient_accumulation_steps": 4,
+                "gradient_accumulation_steps": gradient_accumulation_steps,
                 "output_dir": temp_dir,
                 "learning_rate": 0.00001,
                 "optimizer": "adamw_8bit",
@@ -138,25 +136,166 @@ class TestMultiGPULlama(unittest.TestCase):
                 "launch",
                 "--num-processes",
                 "2",
+                "--main_process_port",
+                f"{get_torch_dist_unique_port()}",
                 "-m",
                 "axolotl.cli.train",
                 str(Path(temp_dir) / "config.yaml"),
             ]
         )
 
-    @with_temp_dir
-    def test_fsdp(self, temp_dir):
+    @pytest.mark.skipif(is_hopper(), reason="h100 doesn't support 8-bit lora")
+    def test_dpo_lora_ddp(self, temp_dir):
         # pylint: disable=duplicate-code
         cfg = DictDefault(
             {
-                "base_model": "TinyLlama/TinyLlama_v1.1",
-                "tokenizer_type": "LlamaTokenizer",
+                "base_model": "HuggingFaceTB/SmolLM2-135M",
                 "sequence_len": 2048,
+                "sample_packing": False,
+                "eval_sample_packing": False,
+                "pad_to_sequence_len": True,
+                "load_in_8bit": True,
+                "adapter": "lora",
+                "lora_r": 8,
+                "lora_alpha": 16,
+                "lora_dropout": 0.05,
+                "lora_target_linear": True,
                 "val_set_size": 0.05,
                 "special_tokens": {
-                    "unk_token": "<unk>",
-                    "bos_token": "<s>",
-                    "eos_token": "</s>",
+                    "pad_token": "<|endoftext|>",
+                },
+                "rl": "dpo",
+                "chat_template": "chatml",
+                "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"],
+                        },
+                    },
+                ],
+                "num_epochs": 1,
+                "max_steps": 15,
+                "micro_batch_size": 4,
+                "gradient_accumulation_steps": 4,
+                "output_dir": temp_dir,
+                "warmup_steps": 0,
+                "learning_rate": 0.00001,
+                "optimizer": "adamw_8bit",
+                "lr_scheduler": "cosine",
+                "flash_attention": True,
+            }
+        )
+
+        # write cfg to yaml file
+        Path(temp_dir).mkdir(parents=True, exist_ok=True)
+        with open(Path(temp_dir) / "config.yaml", "w", encoding="utf-8") as fout:
+            fout.write(yaml.dump(cfg.to_dict(), Dumper=yaml.Dumper))
+
+        execute_subprocess_async(
+            [
+                "accelerate",
+                "launch",
+                "--num-processes",
+                "2",
+                "--main_process_port",
+                f"{get_torch_dist_unique_port()}",
+                "-m",
+                "axolotl.cli.train",
+                str(Path(temp_dir) / "config.yaml"),
+            ]
+        )
+
+    def test_dpo_qlora_ddp(self, temp_dir):
+        # pylint: disable=duplicate-code
+        cfg = DictDefault(
+            {
+                "base_model": "HuggingFaceTB/SmolLM2-135M",
+                "sequence_len": 2048,
+                "sample_packing": False,
+                "eval_sample_packing": False,
+                "pad_to_sequence_len": True,
+                "load_in_4bit": True,
+                "adapter": "qlora",
+                "lora_r": 8,
+                "lora_alpha": 16,
+                "lora_dropout": 0.05,
+                "lora_target_linear": True,
+                "val_set_size": 0.05,
+                "special_tokens": {
+                    "pad_token": "<|endoftext|>",
+                },
+                "rl": "dpo",
+                "chat_template": "chatml",
+                "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"],
+                        },
+                    },
+                ],
+                "num_epochs": 1,
+                "max_steps": 15,
+                "micro_batch_size": 4,
+                "gradient_accumulation_steps": 4,
+                "output_dir": temp_dir,
+                "warmup_steps": 0,
+                "learning_rate": 0.00001,
+                "optimizer": "adamw_8bit",
+                "lr_scheduler": "cosine",
+                "flash_attention": True,
+            }
+        )
+
+        # write cfg to yaml file
+        Path(temp_dir).mkdir(parents=True, exist_ok=True)
+        with open(Path(temp_dir) / "config.yaml", "w", encoding="utf-8") as fout:
+            fout.write(yaml.dump(cfg.to_dict(), Dumper=yaml.Dumper))
+
+        execute_subprocess_async(
+            [
+                "accelerate",
+                "launch",
+                "--num-processes",
+                "2",
+                "--main_process_port",
+                f"{get_torch_dist_unique_port()}",
+                "-m",
+                "axolotl.cli.train",
+                str(Path(temp_dir) / "config.yaml"),
+            ]
+        )
+
+    @pytest.mark.parametrize(
+        "gradient_accumulation_steps",
+        [1, 4],
+    )
+    def test_fsdp(self, temp_dir, gradient_accumulation_steps):
+        # pylint: disable=duplicate-code
+        cfg = DictDefault(
+            {
+                "base_model": "HuggingFaceTB/SmolLM2-135M",
+                "sequence_len": 2048,
+                "val_set_size": 0.01,
+                "special_tokens": {
+                    "pad_token": "<|endoftext|>",
                 },
                 "datasets": [
                     {
@@ -165,9 +304,9 @@ class TestMultiGPULlama(unittest.TestCase):
                     },
                 ],
                 "num_epochs": 1,
-                "max_steps": 100,
+                "max_steps": 10,
                 "micro_batch_size": 4,
-                "gradient_accumulation_steps": 4,
+                "gradient_accumulation_steps": gradient_accumulation_steps,
                 "output_dir": temp_dir,
                 "learning_rate": 0.00001,
                 "optimizer": "adamw_torch",
@@ -184,7 +323,7 @@ class TestMultiGPULlama(unittest.TestCase):
                     "fsdp_use_orig_params": False,
                     "fsdp_cpu_ram_efficient_loading": False,
                     "fsdp_transformer_layer_cls_to_wrap": "LlamaDecoderLayer",
-                    "fsdp_state_dict_type": "SHARDED_STATE_DICT",
+                    "fsdp_state_dict_type": "FULL_STATE_DICT",
                     "fsdp_auto_wrap_policy": "TRANSFORMER_BASED_WRAP",
                 },
             }
@@ -201,28 +340,29 @@ class TestMultiGPULlama(unittest.TestCase):
                 "launch",
                 "--num-processes",
                 "2",
+                "--main_process_port",
+                f"{get_torch_dist_unique_port()}",
                 "-m",
                 "axolotl.cli.train",
                 str(Path(temp_dir) / "config.yaml"),
             ]
         )
 
-    @with_temp_dir
-    def test_fsdp_packed(self, temp_dir):
+    @pytest.mark.parametrize(
+        "fsdp_state_dict_type",
+        ["FULL_STATE_DICT", "SHARDED_STATE_DICT"],
+    )
+    def test_fsdp_packed(self, temp_dir, fsdp_state_dict_type):
         # pylint: disable=duplicate-code
         cfg = DictDefault(
             {
-                "base_model": "TinyLlama/TinyLlama_v1.1",
-                "tokenizer_type": "LlamaTokenizer",
+                "base_model": "HuggingFaceTB/SmolLM2-135M",
                 "sample_packing": True,
-                "eval_sample_packing": False,
                 "pad_to_sequence_len": True,
                 "sequence_len": 2048,
                 "val_set_size": 0.05,
                 "special_tokens": {
-                    "unk_token": "<unk>",
-                    "bos_token": "<s>",
-                    "eos_token": "</s>",
+                    "pad_token": "<|endoftext|>",
                 },
                 "datasets": [
                     {
@@ -231,7 +371,7 @@ class TestMultiGPULlama(unittest.TestCase):
                     },
                 ],
                 "num_epochs": 1,
-                "max_steps": 100,
+                "max_steps": 15,
                 "micro_batch_size": 4,
                 "gradient_accumulation_steps": 4,
                 "output_dir": temp_dir,
@@ -250,7 +390,7 @@ class TestMultiGPULlama(unittest.TestCase):
                     "fsdp_use_orig_params": False,
                     "fsdp_cpu_ram_efficient_loading": False,
                     "fsdp_transformer_layer_cls_to_wrap": "LlamaDecoderLayer",
-                    "fsdp_state_dict_type": "SHARDED_STATE_DICT",
+                    "fsdp_state_dict_type": fsdp_state_dict_type,
                     "fsdp_auto_wrap_policy": "TRANSFORMER_BASED_WRAP",
                 },
             }
@@ -267,37 +407,37 @@ class TestMultiGPULlama(unittest.TestCase):
                 "launch",
                 "--num-processes",
                 "2",
+                "--main_process_port",
+                f"{get_torch_dist_unique_port()}",
                 "-m",
                 "axolotl.cli.train",
                 str(Path(temp_dir) / "config.yaml"),
             ]
         )
 
-    @pytest.mark.skip("disabled due to upstream issue")
-    @with_temp_dir
     def test_fsdp_qlora_prequant_packed(self, temp_dir):
         # pylint: disable=duplicate-code
         cfg = DictDefault(
             {
-                "base_model": "axolotl-ai-co/TinyLlama_v1.1-bnb-nf4-bf16",
-                "tokenizer_type": "AutoTokenizer",
+                "base_model": "axolotl-ai-co/SmolLM2-135M-bnb-nf4-bf16",
                 "adapter": "qlora",
+                "mean_resizing_embeddings": True,
                 "load_in_4bit": True,
                 "lora_r": 8,
                 "lora_alpha": 16,
                 "lora_dropout": 0.05,
                 "lora_target_linear": True,
-                "lora_modules_to_save": [
-                    "embed_tokens",
-                    "lm_head",
-                ],
+                # "lora_modules_to_save": [
+                #     "embed_tokens",
+                #     "lm_head",
+                # ],
                 "sample_packing": True,
                 "eval_sample_packing": False,
                 "pad_to_sequence_len": True,
                 "sequence_len": 2048,
                 "val_set_size": 0.05,
                 "special_tokens": {
-                    "pad_token": "<|end_of_text|>",
+                    "pad_token": "<|endoftext|>",
                 },
                 "datasets": [
                     {
@@ -307,7 +447,7 @@ class TestMultiGPULlama(unittest.TestCase):
                     },
                 ],
                 "num_epochs": 1,
-                "max_steps": 100,
+                "max_steps": 15,
                 "micro_batch_size": 4,
                 "gradient_accumulation_steps": 4,
                 "output_dir": temp_dir,
@@ -343,28 +483,29 @@ class TestMultiGPULlama(unittest.TestCase):
                 "launch",
                 "--num-processes",
                 "2",
+                "--main_process_port",
+                f"{get_torch_dist_unique_port()}",
                 "-m",
                 "axolotl.cli.train",
                 str(Path(temp_dir) / "config.yaml"),
             ]
         )
 
-    @with_temp_dir
-    def test_ds_zero3_packed(self, temp_dir):
+    @pytest.mark.parametrize(
+        "gradient_accumulation_steps",
+        [1, 4],
+    )
+    def test_ds_zero3_packed(self, temp_dir, gradient_accumulation_steps):
         # pylint: disable=duplicate-code
         cfg = DictDefault(
             {
-                "base_model": "TinyLlama/TinyLlama_v1.1",
-                "tokenizer_type": "LlamaTokenizer",
+                "base_model": "HuggingFaceTB/SmolLM2-135M",
                 "sample_packing": True,
-                "eval_sample_packing": False,
                 "pad_to_sequence_len": True,
                 "sequence_len": 2048,
                 "val_set_size": 0.05,
                 "special_tokens": {
-                    "unk_token": "<unk>",
-                    "bos_token": "<s>",
-                    "eos_token": "</s>",
+                    "pad_token": "<|endoftext|>",
                 },
                 "datasets": [
                     {
@@ -373,9 +514,9 @@ class TestMultiGPULlama(unittest.TestCase):
                     },
                 ],
                 "num_epochs": 1,
-                "max_steps": 100,
+                "max_steps": 15,
                 "micro_batch_size": 4,
-                "gradient_accumulation_steps": 4,
+                "gradient_accumulation_steps": gradient_accumulation_steps,
                 "output_dir": temp_dir,
                 "learning_rate": 0.00001,
                 "optimizer": "adamw_torch",
@@ -396,19 +537,19 @@ class TestMultiGPULlama(unittest.TestCase):
                 "launch",
                 "--num-processes",
                 "2",
+                "--main_process_port",
+                f"{get_torch_dist_unique_port()}",
                 "-m",
                 "axolotl.cli.train",
                 str(Path(temp_dir) / "config.yaml"),
             ]
         )
 
-    @with_temp_dir
     def test_ds_zero3_qlora_packed(self, temp_dir):
         # pylint: disable=duplicate-code
         cfg = DictDefault(
             {
-                "base_model": "TinyLlama/TinyLlama_v1.1",
-                "tokenizer_type": "LlamaTokenizer",
+                "base_model": "HuggingFaceTB/SmolLM2-135M",
                 "load_in_4bit": True,
                 "adapter": "qlora",
                 "lora_r": 8,
@@ -421,9 +562,7 @@ class TestMultiGPULlama(unittest.TestCase):
                 "sequence_len": 2048,
                 "val_set_size": 0.05,
                 "special_tokens": {
-                    "unk_token": "<unk>",
-                    "bos_token": "<s>",
-                    "eos_token": "</s>",
+                    "pad_token": "<|endoftext|>",
                 },
                 "datasets": [
                     {
@@ -432,7 +571,7 @@ class TestMultiGPULlama(unittest.TestCase):
                     },
                 ],
                 "num_epochs": 1,
-                "max_steps": 100,
+                "max_steps": 15,
                 "micro_batch_size": 4,
                 "gradient_accumulation_steps": 4,
                 "output_dir": temp_dir,
@@ -455,6 +594,8 @@ class TestMultiGPULlama(unittest.TestCase):
                 "launch",
                 "--num-processes",
                 "2",
+                "--main_process_port",
+                f"{get_torch_dist_unique_port()}",
                 "-m",
                 "axolotl.cli.train",
                 str(Path(temp_dir) / "config.yaml"),

tests/e2e/multigpu/test_qwen2.py

@@ -4,31 +4,30 @@ E2E tests for multigpu qwen2
 
 import logging
 import os
-import unittest
 from pathlib import Path
 
+import pytest
 import yaml
 from accelerate.test_utils import execute_subprocess_async
+from transformers.testing_utils import get_torch_dist_unique_port
 
 from axolotl.utils.dict import DictDefault
 
-from ..utils import with_temp_dir
-
 LOG = logging.getLogger("axolotl.tests.e2e.multigpu")
 os.environ["WANDB_DISABLED"] = "true"
 
 
-class TestMultiGPUQwen2(unittest.TestCase):
+class TestMultiGPUQwen2:
     """
     Test case for Llama models using LoRA
     """
 
-    @with_temp_dir
-    def test_qlora_fsdp_dpo(self, temp_dir):
+    @pytest.mark.parametrize("base_model", ["Qwen/Qwen2-0.5B", "Qwen/Qwen2.5-0.5B"])
+    def test_qlora_fsdp_dpo(self, base_model, temp_dir):
         # pylint: disable=duplicate-code
         cfg = DictDefault(
             {
-                "base_model": "Qwen/Qwen2-1.5B",
+                "base_model": base_model,
                 "load_in_4bit": True,
                 "rl": "dpo",
                 "chat_template": "chatml",
@@ -47,9 +46,9 @@ class TestMultiGPUQwen2(unittest.TestCase):
                     },
                 ],
                 "num_epochs": 1,
-                "max_steps": 100,
+                "max_steps": 5,
                 "warmup_steps": 20,
-                "micro_batch_size": 4,
+                "micro_batch_size": 2,
                 "gradient_accumulation_steps": 2,
                 "output_dir": temp_dir,
                 "learning_rate": 0.00001,
@@ -91,6 +90,8 @@ class TestMultiGPUQwen2(unittest.TestCase):
                 "launch",
                 "--num-processes",
                 "2",
+                "--main_process_port",
+                f"{get_torch_dist_unique_port()}",
                 "-m",
                 "axolotl.cli.train",
                 str(Path(temp_dir) / "config.yaml"),