stuff

.github/workflows/base.yml

@@ -1,16 +1,6 @@
 name: ci-cd-base
 
 on:
-  push:
-    branches:
-      - "main"
-    paths:
-      - 'Dockerfile-base'
-      - '.github/workflows/base.yml'
-  pull_request:
-    paths:
-      - 'Dockerfile-base'
-      - '.github/workflows/base.yml'
   workflow_dispatch:
 
 jobs:
@@ -37,7 +27,7 @@ jobs:
           - cuda: "124"
             cuda_version: 12.4.1
             cudnn_version: ""
-            python_version: "3.10"
+            python_version: "3.11"
             pytorch: 2.4.1
             torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
           - cuda: "124"
@@ -50,25 +40,23 @@ jobs:
             cuda_version: 12.4.1
             cudnn_version: ""
             python_version: "3.11"
-            pytorch: 2.5.1
+            pytorch: 2.5.0
             torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
     steps:
       - name: Checkout
-        uses: actions/checkout@v4
+        uses: actions/checkout@v3
       - name: Docker metadata
         id: metadata
-        uses: docker/metadata-action@v5
+        uses: docker/metadata-action@v3
         with:
-          images: |
-            winglian/axolotl-base
-            axolotlai/axolotl-base
+          images: winglian/axolotl-base
       - name: Login to Docker Hub
         uses: docker/login-action@v2
         with:
           username: ${{ secrets.DOCKERHUB_USERNAME }}
           password: ${{ secrets.DOCKERHUB_TOKEN }}
       - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3
+        uses: docker/setup-buildx-action@v2
       - name: Build
         uses: docker/build-push-action@v4
         with:

.github/workflows/docs.yml

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

.github/workflows/lint.yml

@@ -15,9 +15,9 @@ jobs:
     name: pre-commit
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v4
-      - uses: actions/setup-python@v5
+      - uses: actions/checkout@v3
+      - uses: actions/setup-python@v4
         with:
           python-version: "3.10"
           cache: 'pip' # caching pip dependencies
-      - uses: pre-commit/action@v3.0.1
+      - uses: pre-commit/action@v3.0.0

.github/workflows/main.yml

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

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

@@ -8,14 +8,9 @@ on:
   schedule:
     - cron: '0 0 * * 1,4'  # Runs at 00:00 UTC every monday & thursday
 
-# Cancel jobs on the same ref if a new one is triggered
-concurrency:
-  group: ${{ github.workflow }}-${{ github.ref }}
-  cancel-in-progress: ${{ github.ref != 'refs/heads/main' }}
-
 jobs:
   test-axolotl-multigpu:
-    if: ${{ ! contains(github.event.commits[0].message, '[skip e2e]') && github.repository_owner == 'axolotl-ai-cloud' }}
+    if: ${{ ! contains(github.event.commits[0].message, '[skip docker]]') && github.repository_owner == 'axolotl-ai-cloud' }}
     strategy:
       fail-fast: false
       matrix:
@@ -36,7 +31,7 @@ jobs:
           - cuda: 124
             cuda_version: 12.4.1
             python_version: "3.11"
-            pytorch: 2.5.1
+            pytorch: 2.5.0
             axolotl_extras:
             num_gpus: 2
             nightly_build: "true"

.github/workflows/nightlies.yml

@@ -7,7 +7,7 @@ on:
 
 jobs:
   build-axolotl:
-    if: ${{ ! contains(github.event.commits[0].message, '[skip docker]') && github.repository_owner == 'axolotl-ai-cloud' }}
+    if: ${{ ! contains(github.event.commits[0].message, '[skip docker]]') && github.repository_owner == 'axolotl-ai-cloud' }}
     strategy:
       fail-fast: false
       matrix:
@@ -31,7 +31,7 @@ jobs:
           - cuda: 124
             cuda_version: 12.4.1
             python_version: "3.11"
-            pytorch: 2.5.1
+            pytorch: 2.5.0
             axolotl_extras:
     runs-on: axolotl-gpu-runner
     steps:
@@ -41,9 +41,7 @@ jobs:
         id: metadata
         uses: docker/metadata-action@v5
         with:
-          images: |
-            winglian/axolotl
-            axolotlai/axolotl
+          images: winglian/axolotl
           tags: |
             type=raw,value={{ branch }}-{{ date 'YYYYMMDD' }}
       - name: Set up Docker Buildx
@@ -71,7 +69,7 @@ jobs:
 
   build-axolotl-cloud:
     needs: build-axolotl
-    if: ${{ ! contains(github.event.commits[0].message, '[skip docker]') && github.repository_owner == 'axolotl-ai-cloud' }}
+    if: ${{ ! contains(github.event.commits[0].message, '[skip docker]]') && github.repository_owner == 'axolotl-ai-cloud' }}
     # this job needs to be run on self-hosted GPU runners...
     strategy:
       matrix:
@@ -95,7 +93,7 @@ jobs:
           - cuda: 124
             cuda_version: 12.4.1
             python_version: "3.11"
-            pytorch: 2.5.1
+            pytorch: 2.5.0
             axolotl_extras:
     runs-on: axolotl-gpu-runner
     steps:
@@ -105,9 +103,7 @@ jobs:
         id: metadata
         uses: docker/metadata-action@v5
         with:
-          images: |
-            winglian/axolotl-cloud
-            axolotlai/axolotl-cloud
+          images: winglian/axolotl-cloud
           tags: |
             type=raw,value={{ branch }}-{{ date 'YYYYMMDD' }}
       - name: Login to Docker Hub
@@ -116,7 +112,7 @@ jobs:
           username: ${{ secrets.DOCKERHUB_USERNAME }}
           password: ${{ secrets.DOCKERHUB_TOKEN }}
       - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3
+        uses: docker/setup-buildx-action@v2
       - name: Build
         uses: docker/build-push-action@v5
         with:

.github/workflows/pypi.yml

@@ -3,24 +3,12 @@ name: publish pypi
 on:
   push:
     tags:
-      - 'v*'
-  workflow_dispatch:
+      - '*'
 
 jobs:
-  setup_release:
-    name: Create Release
-    runs-on: ubuntu-latest
-    permissions:
-      contents: write
-    steps:
-      - name: Create release
-        env:
-          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-        run: gh release create "$GITHUB_REF_NAME" # GITHUB_REF_NAME is the tag name in `on.push.tags` workflows
   pypi-publish:
     name: Upload release to PyPI
     runs-on: ubuntu-latest
-    needs: [setup_release]
     environment:
       name: pypi
       url: https://pypi.org/p/axolotl
@@ -28,10 +16,10 @@ jobs:
       id-token: write  # IMPORTANT: this permission is mandatory for trusted publishing
     steps:
       - name: Check out repository code
-        uses: actions/checkout@v4
+        uses: actions/checkout@v3
 
       - name: Setup Python
-        uses: actions/setup-python@v5
+        uses: actions/setup-python@v4
         with:
           python-version: "3.10"
 
@@ -49,9 +37,9 @@ jobs:
         run: |
           sed -i -E 's/version="([0-9.]+)",/version="${{ steps.tag.outputs.TAG_NAME }}",/g' setup.py
 
-      - name: Build a source dist
+      - name: Build a binary wheel
         run: |
-          python setup.py sdist
+          python setup.py sdist bdist_wheel
 
       - name: Publish package distributions to PyPI
         uses: pypa/gh-action-pypi-publish@release/v1

.github/workflows/tests-nightly.yml

@@ -9,12 +9,12 @@ jobs:
     name: pre-commit
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v4
-      - uses: actions/setup-python@v5
+      - uses: actions/checkout@v3
+      - uses: actions/setup-python@v4
         with:
           python-version: "3.10"
           cache: 'pip' # caching pip dependencies
-      - uses: pre-commit/action@v3.0.1
+      - uses: pre-commit/action@v3.0.0
         env:
           SKIP: no-commit-to-branch
 
@@ -25,15 +25,15 @@ jobs:
       fail-fast: false
       matrix:
         python_version: ["3.10", "3.11"]
-        pytorch_version: ["2.3.1", "2.4.1", "2.5.1"]
+        pytorch_version: ["2.3.1", "2.4.1", "2.5.0"]
     timeout-minutes: 20
 
     steps:
       - name: Check out repository code
-        uses: actions/checkout@v4
+        uses: actions/checkout@v3
 
       - name: Setup Python
-        uses: actions/setup-python@v5
+        uses: actions/setup-python@v4
         with:
           python-version: ${{ matrix.python_version }}
           cache: 'pip' # caching pip dependencies
@@ -48,14 +48,12 @@ jobs:
           sed -i 's#^peft.*#peft @ git+https://github.com/huggingface/peft.git@main#' requirements.txt
           sed -i 's#^accelerate.*#accelerate @ git+https://github.com/huggingface/accelerate.git@main#' requirements.txt
           sed -i 's#^trl.*#trl @ git+https://github.com/huggingface/trl.git@main#' requirements.txt
-          sed -i 's#^datasets.*#datasets @ git+https://github.com/huggingface/datasets.git@main#' requirements.txt
 
       - name: Install dependencies
         run: |
           pip3 install --upgrade pip
           pip3 install --upgrade packaging
           pip3 install -U -e .
-          python scripts/cutcrossentropy_install.py | sh
           pip3 install -r requirements-dev.txt -r requirements-tests.txt
 
       - name: Run tests
@@ -84,6 +82,13 @@ 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"
@@ -94,7 +99,7 @@ jobs:
           - cuda: 124
             cuda_version: 12.4.1
             python_version: "3.11"
-            pytorch: 2.5.1
+            pytorch: 2.5.0
             num_gpus: 1
             axolotl_extras:
             nightly_build: "true"

.github/workflows/tests.yml

@@ -8,33 +8,24 @@ on:
       - '**.py'
       - 'requirements.txt'
       - '.github/workflows/*.yml'
-      - 'requirements-tests.txt'
-      - 'cicd/cicd.sh'
   pull_request:
       paths:
        - '**.py'
        - 'requirements.txt'
        - '.github/workflows/*.yml'
-       - 'requirements-tests.txt'
-       - 'cicd/cicd.sh'
   workflow_dispatch:
 
-# Cancel jobs on the same ref if a new one is triggered
-concurrency:
-  group: ${{ github.workflow }}-${{ github.ref }}
-  cancel-in-progress: ${{ github.ref != 'refs/heads/main' }}
-
 jobs:
   pre-commit:
     name: pre-commit
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v4
-      - uses: actions/setup-python@v5
+      - uses: actions/checkout@v3
+      - uses: actions/setup-python@v4
         with:
           python-version: "3.10"
           cache: 'pip' # caching pip dependencies
-      - uses: pre-commit/action@v3.0.1
+      - uses: pre-commit/action@v3.0.0
         env:
           SKIP: no-commit-to-branch
 
@@ -45,15 +36,15 @@ jobs:
       fail-fast: false
       matrix:
         python_version: ["3.10", "3.11"]
-        pytorch_version: ["2.3.1", "2.4.1", "2.5.1"]
+        pytorch_version: ["2.3.1", "2.4.1", "2.5.0"]
     timeout-minutes: 20
 
     steps:
       - name: Check out repository code
-        uses: actions/checkout@v4
+        uses: actions/checkout@v3
 
       - name: Setup Python
-        uses: actions/setup-python@v5
+        uses: actions/setup-python@v4
         with:
           python-version: ${{ matrix.python_version }}
           cache: 'pip' # caching pip dependencies
@@ -71,108 +62,22 @@ jobs:
         run: |
           pip3 show torch
           pip3 install -U -e .
-          python scripts/unsloth_install.py | sh
-          python scripts/cutcrossentropy_install.py | sh
           pip3 install -r requirements-dev.txt -r requirements-tests.txt
 
       - name: Run tests
         run: |
-          pytest -n8 --ignore=tests/e2e/ tests/
+          pytest --ignore=tests/e2e/ tests/
 
       - name: cleanup pip cache
         run: |
           find "$(pip cache dir)/http-v2" -type f -mtime +14 -exec rm {} \;
 
-  pytest-sdist:
-    name: PyTest from Source Dist
-    runs-on: ubuntu-latest
-    strategy:
-      fail-fast: false
-      matrix:
-        python_version: ["3.11"]
-        pytorch_version: ["2.4.1", "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: ${{ ! contains(github.event.commits[0].message, '[skip e2e]') && github.repository_owner == 'axolotl-ai-cloud' }}
-    # this job needs to be run on self-hosted GPU runners...
-    runs-on: [self-hosted, modal]
-    timeout-minutes: 90
-    needs: [pre-commit, pytest, pytest-sdist]
-
-    strategy:
-      fail-fast: false
-      matrix:
-        include:
-          - cuda: 124
-            cuda_version: 12.4.1
-            python_version: "3.11"
-            pytorch: 2.4.1
-            num_gpus: 1
-            axolotl_extras:
-    steps:
-      - name: Checkout
-        uses: actions/checkout@v4
-      - name: Install Python
-        uses: actions/setup-python@v5
-        with:
-          python-version: "3.10"
-      - name: Install Modal
-        run: |
-          python -m pip install --upgrade pip
-          pip install modal==0.63.64 jinja2
-      - name: Update env vars
-        run: |
-          echo "BASE_TAG=main-base-py${{ 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
-
   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]
+    needs: [pre-commit, pytest]
 
     strategy:
       fail-fast: false
@@ -184,10 +89,22 @@ jobs:
             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"
-            pytorch: 2.5.1
+            pytorch: 2.4.1
+            num_gpus: 1
+            axolotl_extras:
+          - cuda: 124
+            cuda_version: 12.4.1
+            python_version: "3.11"
+            pytorch: 2.5.0
             num_gpus: 1
             axolotl_extras:
     steps:

.gitignore

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

1991.yml

@@ -0,0 +1,295 @@
+base_model: Qwen/Qwen2.5-14B-Instruct
+model_type: AutoModelForCausalLM #nohup accelerate launch -m axolotl.cli.train /home/ubuntu/qwen2.5_14B.yml > training_output.log 2>&1 &
+tokenizer_type: AutoTokenizer
+trust_remote_code: true
+
+load_in_8bit: false
+load_in_4bit: false
+strict: false
+
+datasets:
+  - path: tatsu-lab/alpaca
+    type: alpaca
+
+chat_template: chatml
+dataset_prepared_path:
+val_set_size: 0
+output_dir: ./outputs/out
+
+sequence_len: 2048
+sample_packing: true
+eval_sample_packing: true
+pad_to_sequence_len: true
+
+unfrozen_parameters:
+- ^lm_head.weight$
+- ^model.embed_tokens.weight$
+# input_layernorm layers
+- model.layers.0.input_layernorm
+- model.layers.1.input_layernorm
+- model.layers.2.input_layernorm
+- model.layers.3.input_layernorm
+- model.layers.4.input_layernorm
+- model.layers.5.input_layernorm
+- model.layers.6.input_layernorm
+- model.layers.7.input_layernorm
+- model.layers.8.input_layernorm
+- model.layers.9.input_layernorm
+- model.layers.10.input_layernorm
+- model.layers.11.input_layernorm
+- model.layers.12.input_layernorm
+- model.layers.13.input_layernorm
+- model.layers.14.input_layernorm
+- model.layers.15.input_layernorm
+- model.layers.16.input_layernorm
+- model.layers.17.input_layernorm
+- model.layers.18.input_layernorm
+- model.layers.19.input_layernorm
+- model.layers.20.input_layernorm
+- model.layers.21.input_layernorm
+- model.layers.22.input_layernorm
+- model.layers.23.input_layernorm
+# lm_head layers
+# mlp.down_proj layers
+- model.layers.1.mlp.down_proj
+- model.layers.35.mlp.down_proj
+- model.layers.38.mlp.down_proj
+- model.layers.37.mlp.down_proj
+- model.layers.36.mlp.down_proj
+- model.layers.15.mlp.down_proj
+- model.layers.11.mlp.down_proj
+- model.layers.12.mlp.down_proj
+- model.layers.34.mlp.down_proj
+- model.layers.44.mlp.down_proj
+- model.layers.45.mlp.down_proj
+- model.layers.9.mlp.down_proj
+- model.layers.41.mlp.down_proj
+- model.layers.33.mlp.down_proj
+- model.layers.43.mlp.down_proj
+- model.layers.40.mlp.down_proj
+- model.layers.13.mlp.down_proj
+- model.layers.8.mlp.down_proj
+- model.layers.39.mlp.down_proj
+- model.layers.10.mlp.down_proj
+- model.layers.14.mlp.down_proj
+- model.layers.16.mlp.down_proj
+- model.layers.31.mlp.down_proj
+- model.layers.32.mlp.down_proj
+# mlp.gate_proj layers
+- model.layers.1.mlp.gate_proj
+- model.layers.44.mlp.gate_proj
+- model.layers.46.mlp.gate_proj
+- model.layers.45.mlp.gate_proj
+- model.layers.43.mlp.gate_proj
+- model.layers.47.mlp.gate_proj
+- model.layers.42.mlp.gate_proj
+- model.layers.32.mlp.gate_proj
+- model.layers.27.mlp.gate_proj
+- model.layers.33.mlp.gate_proj
+- model.layers.28.mlp.gate_proj
+- model.layers.39.mlp.gate_proj
+- model.layers.41.mlp.gate_proj
+- model.layers.40.mlp.gate_proj
+- model.layers.30.mlp.gate_proj
+- model.layers.29.mlp.gate_proj
+- model.layers.31.mlp.gate_proj
+- model.layers.26.mlp.gate_proj
+- model.layers.37.mlp.gate_proj
+- model.layers.10.mlp.gate_proj
+- model.layers.38.mlp.gate_proj
+- model.layers.12.mlp.gate_proj
+- model.layers.36.mlp.gate_proj
+- model.layers.13.mlp.gate_proj
+# mlp.up_proj layers
+- model.layers.1.mlp.up_proj
+- model.layers.13.mlp.up_proj
+- model.layers.11.mlp.up_proj
+- model.layers.14.mlp.up_proj
+- model.layers.15.mlp.up_proj
+- model.layers.12.mlp.up_proj
+- model.layers.8.mlp.up_proj
+- model.layers.16.mlp.up_proj
+- model.layers.9.mlp.up_proj
+- model.layers.19.mlp.up_proj
+- model.layers.10.mlp.up_proj
+- model.layers.7.mlp.up_proj
+- model.layers.17.mlp.up_proj
+- model.layers.20.mlp.up_proj
+- model.layers.21.mlp.up_proj
+- model.layers.18.mlp.up_proj
+- model.layers.38.mlp.up_proj
+- model.layers.37.mlp.up_proj
+- model.layers.39.mlp.up_proj
+- model.layers.42.mlp.up_proj
+- model.layers.41.mlp.up_proj
+- model.layers.27.mlp.up_proj
+- model.layers.28.mlp.up_proj
+- model.layers.34.mlp.up_proj
+# model.norm layers
+# post_attention_layernorm layers
+- model.layers.0.post_attention_layernorm
+- model.layers.1.post_attention_layernorm
+- model.layers.2.post_attention_layernorm
+- model.layers.3.post_attention_layernorm
+- model.layers.4.post_attention_layernorm
+- model.layers.5.post_attention_layernorm
+- model.layers.6.post_attention_layernorm
+- model.layers.7.post_attention_layernorm
+- model.layers.8.post_attention_layernorm
+- model.layers.9.post_attention_layernorm
+- model.layers.10.post_attention_layernorm
+- model.layers.11.post_attention_layernorm
+- model.layers.12.post_attention_layernorm
+- model.layers.13.post_attention_layernorm
+- model.layers.14.post_attention_layernorm
+- model.layers.15.post_attention_layernorm
+- model.layers.16.post_attention_layernorm
+- model.layers.17.post_attention_layernorm
+- model.layers.18.post_attention_layernorm
+- model.layers.19.post_attention_layernorm
+- model.layers.20.post_attention_layernorm
+- model.layers.21.post_attention_layernorm
+- model.layers.22.post_attention_layernorm
+- model.layers.23.post_attention_layernorm
+# self_attn.k_proj layers
+- model.layers.47.self_attn.k_proj
+- model.layers.39.self_attn.k_proj
+- model.layers.41.self_attn.k_proj
+- model.layers.37.self_attn.k_proj
+- model.layers.35.self_attn.k_proj
+- model.layers.44.self_attn.k_proj
+- model.layers.38.self_attn.k_proj
+- model.layers.14.self_attn.k_proj
+- model.layers.7.self_attn.k_proj
+- model.layers.12.self_attn.k_proj
+- model.layers.11.self_attn.k_proj
+- model.layers.32.self_attn.k_proj
+- model.layers.10.self_attn.k_proj
+- model.layers.8.self_attn.k_proj
+- model.layers.9.self_attn.k_proj
+- model.layers.6.self_attn.k_proj
+- model.layers.45.self_attn.k_proj
+- model.layers.42.self_attn.k_proj
+- model.layers.5.self_attn.k_proj
+- model.layers.40.self_attn.k_proj
+- model.layers.33.self_attn.k_proj
+- model.layers.0.self_attn.k_proj
+- model.layers.34.self_attn.k_proj
+- model.layers.13.self_attn.k_proj
+# self_attn.o_proj layers
+- model.layers.12.self_attn.o_proj
+- model.layers.5.self_attn.o_proj
+- model.layers.14.self_attn.o_proj
+- model.layers.16.self_attn.o_proj
+- model.layers.20.self_attn.o_proj
+- model.layers.13.self_attn.o_proj
+- model.layers.11.self_attn.o_proj
+- model.layers.4.self_attn.o_proj
+- model.layers.6.self_attn.o_proj
+- model.layers.19.self_attn.o_proj
+- model.layers.7.self_attn.o_proj
+- model.layers.18.self_attn.o_proj
+- model.layers.8.self_attn.o_proj
+- model.layers.38.self_attn.o_proj
+- model.layers.15.self_attn.o_proj
+- model.layers.17.self_attn.o_proj
+- model.layers.9.self_attn.o_proj
+- model.layers.10.self_attn.o_proj
+- model.layers.21.self_attn.o_proj
+- model.layers.28.self_attn.o_proj
+- model.layers.32.self_attn.o_proj
+- model.layers.35.self_attn.o_proj
+- model.layers.39.self_attn.o_proj
+- model.layers.3.self_attn.o_proj
+# self_attn.q_proj layers
+- model.layers.1.self_attn.q_proj
+- model.layers.2.self_attn.q_proj
+- model.layers.3.self_attn.q_proj
+- model.layers.44.self_attn.q_proj
+- model.layers.29.self_attn.q_proj
+- model.layers.45.self_attn.q_proj
+- model.layers.43.self_attn.q_proj
+- model.layers.32.self_attn.q_proj
+- model.layers.38.self_attn.q_proj
+- model.layers.19.self_attn.q_proj
+- model.layers.42.self_attn.q_proj
+- model.layers.34.self_attn.q_proj
+- model.layers.36.self_attn.q_proj
+- model.layers.40.self_attn.q_proj
+- model.layers.26.self_attn.q_proj
+- model.layers.20.self_attn.q_proj
+- model.layers.39.self_attn.q_proj
+- model.layers.28.self_attn.q_proj
+- model.layers.35.self_attn.q_proj
+- model.layers.41.self_attn.q_proj
+- model.layers.33.self_attn.q_proj
+- model.layers.25.self_attn.q_proj
+- model.layers.30.self_attn.q_proj
+- model.layers.27.self_attn.q_proj
+# self_attn.v_proj layers
+- model.layers.0.self_attn.v_proj
+- model.layers.7.self_attn.v_proj
+- model.layers.39.self_attn.v_proj
+- model.layers.31.self_attn.v_proj
+- model.layers.15.self_attn.v_proj
+- model.layers.10.self_attn.v_proj
+- model.layers.32.self_attn.v_proj
+- model.layers.41.self_attn.v_proj
+- model.layers.6.self_attn.v_proj
+- model.layers.33.self_attn.v_proj
+- model.layers.42.self_attn.v_proj
+- model.layers.29.self_attn.v_proj
+- model.layers.14.self_attn.v_proj
+- model.layers.9.self_attn.v_proj
+- model.layers.35.self_attn.v_proj
+- model.layers.38.self_attn.v_proj
+- model.layers.13.self_attn.v_proj
+- model.layers.30.self_attn.v_proj
+- model.layers.5.self_attn.v_proj
+- model.layers.34.self_attn.v_proj
+- model.layers.28.self_attn.v_proj
+- model.layers.37.self_attn.v_proj
+- model.layers.27.self_attn.v_proj
+- model.layers.11.self_attn.v_proj
+# model.embed_tokens layers
+
+
+gradient_accumulation_steps: 2
+micro_batch_size: 2
+num_epochs: 3
+optimizer: adamw_torch_fused
+lr_scheduler: linear
+learning_rate: 5e-6
+
+train_on_inputs: false
+group_by_length: false
+bf16: auto
+fp16:
+tf32: false
+
+plugins:
+  - axolotl.integrations.liger.LigerPlugin
+liger_rope: true
+liger_rms_norm: true
+liger_swiglu: true
+liger_fused_linear_cross_entropy: true
+
+gradient_checkpointing: unsloth
+gradient_checkpointing_kwargs:
+  use_reentrant: false
+early_stopping_patience:
+resume_from_checkpoint:
+local_rank:
+logging_steps: 1
+xformers_attention:
+flash_attention: true
+
+warmup_steps: 10
+evals_per_epoch: 2
+saves_per_epoch: 1
+save_total_limit: 4
+debug:
+deepspeed: deepspeed_configs/zero3_bf16.json
+weight_decay: 0.05
+special_tokens:
+  eos_token: <|im_end|>

MANIFEST.in

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

README.md

@@ -1,21 +1,8 @@
-<p align="center">
-    <picture>
-        <source media="(prefers-color-scheme: dark)" srcset="image/axolotl_logo_digital_white.svg">
-        <source media="(prefers-color-scheme: light)" srcset="image/axolotl_logo_digital_black.svg">
-        <img alt="Axolotl" src="image/axolotl_logo_digital_black.svg" width="400" height="104" style="max-width: 100%;">
-    </picture>
-</p>
+# Axolotl
 
-<p align="center">
-    <img src="https://img.shields.io/github/license/axolotl-ai-cloud/axolotl.svg?color=blue" alt="GitHub License">
-    <img src="https://github.com/axolotl-ai-cloud/axolotl/actions/workflows/tests.yml/badge.svg" alt="tests">
-    <a href="https://github.com/axolotl-ai-cloud/axolotl/releases"><img src="https://img.shields.io/github/release/axolotl-ai-cloud/axolotl.svg" alt="Releases"></a>
-    <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>
+![tests](https://github.com/axolotl-ai-cloud/axolotl/actions/workflows/tests.yml/badge.svg)
+![tests-nightly](https://github.com/axolotl-ai-cloud/axolotl/actions/workflows/tests-nightly.yml/badge.svg)
+![multigpu-semi-weekly tests](https://github.com/axolotl-ai-cloud/axolotl/actions/workflows/multi-gpu-e2e.yml/badge.svg)
 
 Axolotl is a tool designed to streamline the fine-tuning of various AI models, offering support for multiple configurations and architectures.
 
@@ -88,7 +75,7 @@ Features:
 <td>
 
 <div align="center">
-  <img src="image/axolotl_symbol_digital_white.svg" alt="axolotl" width="160">
+  <img src="image/axolotl.png" alt="axolotl" width="160">
   <div>
     <p>
       <b>Axolotl provides a unified repository for fine-tuning <br />a variety of AI models with ease</b>
@@ -147,7 +134,7 @@ pip3 install -e '.[flash-attn,deepspeed]'
 ### Usage
 ```bash
 # preprocess datasets - optional but recommended
-CUDA_VISIBLE_DEVICES="0" python -m axolotl.cli.preprocess examples/openllama-3b/lora.yml
+CUDA_VISIBLE_DEVICES="" python -m axolotl.cli.preprocess examples/openllama-3b/lora.yml
 
 # finetune lora
 accelerate launch -m axolotl.cli.train examples/openllama-3b/lora.yml
@@ -172,7 +159,7 @@ accelerate launch -m axolotl.cli.train https://raw.githubusercontent.com/axolotl
 #### Docker
 
   ```bash
-  docker run --gpus '"all"' --rm -it axolotlai/axolotl:main-latest
+  docker run --gpus '"all"' --rm -it winglian/axolotl:main-latest
   ```
 
   Or run on the current files for development:
@@ -191,7 +178,7 @@ 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 ${HOME}/.cache/huggingface:/root/.cache/huggingface axolotlai/axolotl:main-latest
+docker run --privileged --gpus '"all"' --shm-size 10g --rm -it --name axolotl --ipc=host --ulimit memlock=-1 --ulimit stack=67108864 --mount type=bind,src="${PWD}",target=/workspace/axolotl -v ${HOME}/.cache/huggingface:/root/.cache/huggingface winglian/axolotl:main-latest
   ```
 
   It additionally:
@@ -223,7 +210,7 @@ docker run --privileged --gpus '"all"' --shm-size 10g --rm -it --name axolotl --
 
 #### Cloud GPU
 
-For cloud GPU providers that support docker images, use [`axolotlai/axolotl-cloud:main-latest`](https://hub.docker.com/r/axolotlai/axolotl-cloud/tags)
+For cloud GPU providers that support docker images, use [`winglian/axolotl-cloud:main-latest`](https://hub.docker.com/r/winglian/axolotl-cloud/tags)
 
 - on Latitude.sh use this [direct link](https://latitude.sh/blueprint/989e0e79-3bf6-41ea-a46b-1f246e309d5c)
 - on JarvisLabs.ai use this [direct link](https://jarvislabs.ai/templates/axolotl)
@@ -332,7 +319,7 @@ Write a job description in YAML as below:
 # dstack.yaml
 type: task
 
-image: axolotlai/axolotl-cloud:main-latest
+image: winglian/axolotl-cloud:main-20240429-py3.11-cu121-2.2.2
 
 env:
   - HUGGING_FACE_HUB_TOKEN
@@ -396,10 +383,11 @@ See [examples](examples) for quick start. It is recommended to duplicate and mod
         - typescript
       type: ... # unimplemented custom format
 
-      # chat_template https://axolotl-ai-cloud.github.io/axolotl/docs/dataset-formats/conversation.html#chat_template
+      # fastchat conversation (deprecation soon, use chat_template https://axolotl-ai-cloud.github.io/axolotl/docs/dataset-formats/conversation.html#chat_template)
+      # See 'conversation' options: https://github.com/lm-sys/FastChat/blob/main/fastchat/conversation.py
     - path: ...
-      type: chat_template
-      chat_template: chatml # defaults to tokenizer's chat_template
+      type: sharegpt
+      conversation: chatml # default: vicuna_v1.1
 
       # local
     - path: data.jsonl # or json
@@ -574,8 +562,7 @@ plugins:
   - axolotl.integrations.liger.LigerPlugin
 liger_rope: true
 liger_rms_norm: true
-liger_glu_activation: true
-liger_layer_norm: true
+liger_swiglu: true
 liger_fused_linear_cross_entropy: true
 ```
 

cicd/Dockerfile.jinja

@@ -1,4 +1,4 @@
-FROM axolotlai/axolotl-base:{{ BASE_TAG }}
+FROM winglian/axolotl-base:{{ BASE_TAG }}
 
 ENV TORCH_CUDA_ARCH_LIST="7.0 7.5 8.0 8.6+PTX"
 ENV AXOLOTL_EXTRAS="{{ AXOLOTL_EXTRAS }}"
@@ -28,7 +28,6 @@ RUN if [ "$NIGHTLY_BUILD" = "true" ] ; then \
         sed -i 's#^peft.*#peft @ git+https://github.com/huggingface/peft.git@main#' requirements.txt; \
         sed -i 's#^accelerate.*#accelerate @ git+https://github.com/huggingface/accelerate.git@main#' requirements.txt; \
         sed -i 's#^trl.*#trl @ git+https://github.com/huggingface/trl.git@main#' requirements.txt; \
-        sed -i 's#^datasets.*#datasets @ git+https://github.com/huggingface/datasets.git@main#' requirements.txt; \
     fi
 
 RUN if [ "$AXOLOTL_EXTRAS" != "" ] ; then \
@@ -37,9 +36,6 @@ RUN if [ "$AXOLOTL_EXTRAS" != "" ] ; then \
         pip install -e .[deepspeed,flash-attn,optimizers] $AXOLOTL_ARGS; \
     fi
 
-RUN python scripts/unsloth_install.py | sh
-RUN python scripts/cutcrossentropy_install.py | sh
-
 # So we can test the Docker image
 RUN pip install -r requirements-dev.txt -r requirements-tests.txt
 

cicd/cicd.sh

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

cicd/multigpu.py

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

cicd/multigpu.sh

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

cicd/tests.py

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

devtools/dev_sharegpt.yml

@@ -1,4 +1,4 @@
-# Example config for debugging the chat_template prompt format
+# Example config for debugging the sharegpt prompt format
 base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
 model_type: LlamaForCausalLM
 tokenizer_type: LlamaTokenizer
@@ -7,8 +7,8 @@ load_in_8bit: true
 load_in_4bit: false
 
 datasets:
-  - path: fozziethebeat/alpaca_messages_2k_test
-    type: chat_template
+  - path: philschmid/guanaco-sharegpt-style
+    type: sharegpt
     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 axolotlai/axolotl-base:$BASE_TAG
+FROM winglian/axolotl-base:$BASE_TAG
 
 ARG TORCH_CUDA_ARCH_LIST="7.0 7.5 8.0 8.6+PTX"
 ARG AXOLOTL_EXTRAS=""
@@ -26,9 +26,6 @@ RUN if [ "$AXOLOTL_EXTRAS" != "" ] ; then \
         pip install -e .[deepspeed,flash-attn,optimizers] $AXOLOTL_ARGS; \
     fi
 
-RUN python scripts/unsloth_install.py | sh
-RUN python scripts/cutcrossentropy_install.py | sh
-
 # So we can test the Docker image
 RUN pip install pytest
 

docker/Dockerfile-base

@@ -29,9 +29,7 @@ ENV PATH="/root/miniconda3/envs/py${PYTHON_VERSION}/bin:${PATH}"
 WORKDIR /workspace
 
 RUN python3 -m pip install --upgrade pip && pip3 install packaging && \
-    python3 -m pip install --no-cache-dir -U torch==${PYTORCH_VERSION}+cu${CUDA} --extra-index-url https://download.pytorch.org/whl/cu$CUDA && \
-    python3 -m pip install --no-cache-dir "causal_conv1d @ git+https://github.com/Dao-AILab/causal-conv1d.git@main" && \
-    python3 -m pip install --no-cache-dir "mamba_ssm @ git+https://github.com/state-spaces/mamba.git@main"
+    python3 -m pip install --no-cache-dir -U torch==${PYTORCH_VERSION}+cu${CUDA} --extra-index-url https://download.pytorch.org/whl/cu$CUDA
 
 RUN git lfs install --skip-repo && \
     pip3 install awscli && \

docker/Dockerfile-cloud

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

docs/config.qmd

@@ -83,7 +83,7 @@ lora_on_cpu: true
 datasets:
   # HuggingFace dataset repo | s3://,gs:// path | "json" for local dataset, make sure to fill data_files
   - path: vicgalle/alpaca-gpt4
-    # The type of prompt to use for training. [alpaca, gpteacher, oasst, reflection]
+    # The type of prompt to use for training. [alpaca, sharegpt, gpteacher, oasst, reflection]
     type: alpaca # format | format:<prompt_style> (chat/instruct) | <prompt_strategies>.load_<load_fn>
     ds_type: # Optional[str] (json|arrow|parquet|text|csv) defines the datatype when path is a file
     data_files: # Optional[str] path to source data files
@@ -91,7 +91,15 @@ datasets:
     name: # Optional[str] name of dataset configuration to load
     train_on_split: train # Optional[str] name of dataset split to load from
     revision: # Optional[str] The specific revision of the dataset to use when loading from the Hugging Face Hub. This can be a commit hash, tag, or branch name. If not specified, the latest version will be used. This parameter is ignored for local datasets.
-    trust_remote_code: # Optional[bool] Trust remote code for untrusted source
+
+    # Optional[str] fastchat conversation type, only used with type: sharegpt
+    conversation: # Options (see Conversation 'name'): https://github.com/lm-sys/FastChat/blob/main/fastchat/conversation.py
+    field_human: # Optional[str]. Human key to use for conversation.
+    field_model: # Optional[str]. Assistant key to use for conversation.
+    # Add additional keys from your dataset as input or output roles
+    roles:
+      input: # Optional[List[str]]. These will be masked based on train_on_input
+      output: # Optional[List[str]].
 
   # Custom user instruction prompt
   - path: repo
@@ -162,9 +170,6 @@ datasets:
 # The same applies to the `test_datasets` option and the `pretraining_dataset` option. Default is true.
 shuffle_merged_datasets: true
 
-Deduplicates datasets and test_datasets with identical entries.
-dataset_exact_deduplication: true
-
 # A list of one or more datasets to eval the model with.
 # You can use either test_datasets, or val_set_size, but not both.
 test_datasets:
@@ -178,8 +183,6 @@ test_datasets:
 
 # use RL training: 'dpo', 'ipo', 'kto'
 rl:
-# whether to perform weighting if doing DPO training. Boolean.
-dpo_use_weighting:
 
 # The name of the chat template to use for training, following values are supported:
 # - tokenizer_default: Uses the chat template that is available in the tokenizer_config.json. If the chat template is not available in the tokenizer, it will raise an error. This is the default value.
@@ -409,7 +412,6 @@ lr_div_factor: # Learning rate div factor
 # - adamw_torch_fused
 # - adamw_torch_xla
 # - adamw_apex_fused
-# - adopt_adamw (an EXPERIMENTAL optimizer, only for torch version >= 2.5.1)
 # - adafactor
 # - adamw_anyprecision
 # - sgd

docs/dataset-formats/conversation.qmd

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

docs/debugging.qmd

@@ -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 `chat_template` 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 `sharegpt` format.  This is the format used when you have the following in your axolotl config:
 
 ```yaml
 datasets:
-  - path: <path to your chat_template formatted dataset> # example on HF Hub: fozziethebeat/alpaca_messages_2k_test
-    type: chat_template
+  - path: <path to your sharegpt formatted dataset> # example on HF Hub: philschmid/guanaco-sharegpt-style
+    type: sharegpt
 ```
 
 >[!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_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.
+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.
 
 ```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 - chat_template",
+            "name": "Debug axolotl prompt - sharegpt",
             "type": "python",
             "module": "accelerate.commands.launch",
             "request": "launch",
             "args": [
-                "-m", "axolotl.cli.train", "dev_chat_template.yml",
+                "-m", "axolotl.cli.train", "dev_sharegpt.yml",
                 // The flags below simplify debugging by overriding the axolotl config
                 // with the debugging tips above.  Modify as needed.
                 "--dataset_processes=1",      // limits data preprocessing to one process
@@ -185,7 +185,7 @@ style="border-radius: 10px; display: block; margin: auto;" width="560" height="3
 
 ## Debugging With Docker
 
-Using [official Axolotl Docker images](https://hub.docker.com/r/axolotlai/axolotl/tags) is a great way to debug your code, and is a very popular way to use Axolotl.  Attaching VSCode to Docker takes a few more steps.
+Using [official Axolotl Docker images](https://hub.docker.com/r/winglian/axolotl/tags) is a great way to debug your code, and is a very popular way to use Axolotl.  Attaching VSCode to Docker takes a few more steps.
 
 ### Setup
 
@@ -202,11 +202,11 @@ cd axolotl
 Next, run the desired docker image and mount the current directory. Below is a docker command you can run to do this:[^2]
 
 ```bash
-docker run --privileged --gpus '"all"' --shm-size 10g --rm -it --name axolotl --ipc=host --ulimit memlock=-1 --ulimit stack=67108864 --mount type=bind,src="${PWD}",target=/workspace/axolotl -v ${HOME}/.cache/huggingface:/root/.cache/huggingface axolotlai/axolotl:main-py3.10-cu118-2.0.1
+docker run --privileged --gpus '"all"' --shm-size 10g --rm -it --name axolotl --ipc=host --ulimit memlock=-1 --ulimit stack=67108864 --mount type=bind,src="${PWD}",target=/workspace/axolotl -v ${HOME}/.cache/huggingface:/root/.cache/huggingface winglian/axolotl:main-py3.10-cu118-2.0.1
 ```
 
 >[!Tip]
-> To understand which containers are available, see the [Docker section of the README](../README.md#docker) and the [DockerHub repo](https://hub.docker.com/r/axolotlai/axolotl/tags).  For details of how the Docker containers are built, see axolotl's [Docker CI builds](../.github/workflows/main.yml).
+> To understand which containers are available, see the [Docker section of the README](../README.md#docker) and the [DockerHub repo](https://hub.docker.com/r/winglian/axolotl/tags).  For details of how the Docker containers are built, see axolotl's [Docker CI builds](../.github/workflows/main.yml).
 
 You will now be in the container.  Next, perform an editable install of Axolotl:
 
@@ -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/chat_template.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/sharegpt.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).

docs/unsloth.qmd

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

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

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

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

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

examples/gemma2/qlora.yml

@@ -11,11 +11,8 @@ 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

@@ -4,15 +4,11 @@ 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_glu_activation: true
+liger_swiglu: true
 liger_fused_linear_cross_entropy: true
 
 strict: false
@@ -14,10 +14,6 @@ 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/lora-1b-deduplicate-dpo.yml

@@ -1,95 +0,0 @@
-base_model: meta-llama/Llama-3.2-1B
-model_type: LlamaForCausalLM
-tokenizer_type: AutoTokenizer
-
-load_in_8bit: true
-load_in_4bit: false
-strict: false
-
-chat_template: llama3
-rl: dpo
-datasets:
-  - path: fozziethebeat/alpaca_messages_2k_dpo_test
-    type: chat_template.default
-    field_messages: conversation
-    field_chosen: chosen
-    field_rejected: rejected
-    message_field_role: role
-    message_field_content: content
-    roles:
-      system:
-        - system
-      user:
-        - user
-      assistant:
-        - assistant
-  - path: fozziethebeat/alpaca_messages_2k_dpo_test
-    type: chat_template.default
-    field_messages: conversation
-    field_chosen: chosen
-    field_rejected: rejected
-    message_field_role: role
-    message_field_content: content
-    roles:
-      system:
-        - system
-      user:
-        - user
-      assistant:
-        - assistant
-
-dataset_exact_deduplication: true
-dataset_prepared_path:
-val_set_size: 0
-output_dir: ./outputs/lora-out
-
-sequence_len: 4096
-sample_packing: false
-pad_to_sequence_len: true
-
-adapter: lora
-lora_model_dir:
-lora_r: 32
-lora_alpha: 16
-lora_dropout: 0.05
-lora_target_linear: true
-lora_fan_in_fan_out:
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 4
-micro_batch_size: 2
-num_epochs: 4
-optimizer: adamw_bnb_8bit
-lr_scheduler: cosine
-learning_rate: 0.0002
-
-train_on_inputs: false
-group_by_length: false
-bf16: auto
-fp16:
-tf32: false
-
-gradient_checkpointing: true
-early_stopping_patience:
-resume_from_checkpoint:
-local_rank:
-logging_steps: 1
-xformers_attention:
-flash_attention: true
-s2_attention:
-
-warmup_steps: 10
-evals_per_epoch: 4
-eval_table_size:
-eval_max_new_tokens: 128
-saves_per_epoch: 1
-debug:
-deepspeed:
-weight_decay: 0.0
-fsdp:
-fsdp_config:

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

@@ -1,76 +0,0 @@
-base_model: meta-llama/Llama-3.2-1B
-model_type: LlamaForCausalLM
-tokenizer_type: AutoTokenizer
-
-load_in_8bit: true
-load_in_4bit: false
-strict: false
-
-datasets:
-  - path: mhenrichsen/alpaca_2k_test
-    type: alpaca
-  - path: mhenrichsen/alpaca_2k_test
-    type: alpaca
-dataset_prepared_path:
-val_set_size: 0.0
-output_dir: ./outputs/lora-out
-
-dataset_exact_deduplication: true
-test_value: true
-
-sequence_len: 4096
-sample_packing: true
-eval_sample_packing: false
-pad_to_sequence_len: true
-
-adapter: lora
-lora_model_dir:
-lora_r: 32
-lora_alpha: 16
-lora_dropout: 0.05
-lora_target_linear: true
-lora_fan_in_fan_out:
-lora_modules_to_save:
-  - embed_tokens
-  - lm_head
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 4
-micro_batch_size: 2
-num_epochs: 4
-optimizer: adamw_bnb_8bit
-lr_scheduler: cosine
-learning_rate: 0.0002
-
-train_on_inputs: false
-group_by_length: false
-bf16: auto
-fp16:
-tf32: false
-
-gradient_checkpointing: true
-early_stopping_patience:
-resume_from_checkpoint:
-local_rank:
-logging_steps: 1
-xformers_attention:
-flash_attention: true
-s2_attention:
-
-warmup_steps: 10
-evals_per_epoch: 4
-eval_table_size:
-eval_max_new_tokens: 128
-saves_per_epoch: 1
-debug:
-deepspeed:
-weight_decay: 0.0
-fsdp:
-fsdp_config:
-special_tokens:
-   pad_token: <|end_of_text|>

examples/llava/lora-7b.yaml

@@ -1,63 +0,0 @@
-base_model: llava-hf/llava-1.5-7b-hf
-processor_type: AutoProcessor
-strict: false
-
-# these 3 lines are needed for now to handle vision chat templates w images
-skip_prepare_dataset: true
-remove_unused_columns: false
-sample_packing: false
-
-chat_template: llava
-datasets:
-  - path: HuggingFaceH4/llava-instruct-mix-vsft
-    type: chat_template
-    split: train[:1%]
-    field_messages: messages
-dataset_prepared_path: last_run_prepared
-val_set_size: 0.0
-output_dir: ./outputs/out
-
-adapter: lora
-lora_model_dir:
-
-sequence_len: 8192
-pad_to_sequence_len: false
-
-lora_r: 32
-lora_alpha: 16
-lora_dropout: 0.05
-lora_target_modules: 'language_model.model.layers.[\d]+.(mlp|cross_attn|self_attn).(up|down|gate|q|k|v|o)_proj'
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 4
-micro_batch_size: 1
-num_epochs: 1
-optimizer: adamw_bnb_8bit
-lr_scheduler: cosine
-learning_rate: 0.0002
-
-train_on_inputs: false
-group_by_length: false
-bf16: true
-fp16:
-tf32: true
-
-gradient_checkpointing: true
-local_rank:
-logging_steps: 1
-flash_attention: true
-eager_attention:
-
-warmup_ratio: 0.1
-evals_per_epoch: 1
-saves_per_epoch: 1
-debug:
-deepspeed:
-weight_decay: 0.0
-fsdp:
-fsdp_config:

examples/mistral/mistral-dpo-qlora.yml

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

examples/phi/lora-3.5.yaml

@@ -10,6 +10,7 @@ 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/pixtral/lora-12b.yml

@@ -1,65 +0,0 @@
-base_model: mistral-community/pixtral-12b
-processor_type: AutoProcessor
-strict: false
-
-# these 3 lines are needed for now to handle vision chat templates w images
-skip_prepare_dataset: true
-remove_unused_columns: false
-sample_packing: false
-
-chat_template: pixtral
-datasets:
-  - path: HuggingFaceH4/llava-instruct-mix-vsft
-    type: chat_template
-    split: train[:1%]
-    field_messages: messages
-dataset_prepared_path: last_run_prepared
-val_set_size: 0.0
-output_dir: ./outputs/out
-
-adapter: lora
-lora_model_dir:
-
-sequence_len: 8192
-pad_to_sequence_len: false
-
-lora_r: 32
-lora_alpha: 16
-lora_dropout: 0.05
-lora_target_modules: 'language_model.model.layers.[\d]+.(mlp|cross_attn|self_attn).(up|down|gate|q|k|v|o)_proj'
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 4
-micro_batch_size: 1
-num_epochs: 1
-optimizer: adamw_bnb_8bit
-lr_scheduler: cosine
-learning_rate: 0.0002
-
-train_on_inputs: false
-group_by_length: false
-bf16: true
-fp16:
-tf32: true
-
-gradient_checkpointing: true
-local_rank:
-logging_steps: 1
-flash_attention: false # PixtralVisionModel does not support Flash Attention 2.0 yet
-eager_attention:
-
-warmup_ratio: 0.1
-evals_per_epoch: 1
-saves_per_epoch: 1
-debug:
-deepspeed:
-weight_decay: 0.0
-fsdp:
-fsdp_config:
-special_tokens:
-  pad_token: <|end_of_text|>

examples/qwen2-vl/lora-7b.yaml

@@ -1,63 +0,0 @@
-base_model: Qwen/Qwen2-VL-7B-Instruct
-processor_type: AutoProcessor
-strict: false
-
-# these 3 lines are needed for now to handle vision chat templates w images
-skip_prepare_dataset: true
-remove_unused_columns: false
-sample_packing: false
-
-chat_template: qwen2_vl
-datasets:
-  - path: HuggingFaceH4/llava-instruct-mix-vsft
-    type: chat_template
-    split: train[:1%]
-    field_messages: messages
-dataset_prepared_path: last_run_prepared
-val_set_size: 0.0
-output_dir: ./outputs/out
-
-adapter: lora
-lora_model_dir:
-
-sequence_len: 8192
-pad_to_sequence_len: false
-
-lora_r: 32
-lora_alpha: 16
-lora_dropout: 0.05
-lora_target_modules: 'model.layers.[\d]+.(mlp|cross_attn|self_attn).(up|down|gate|q|k|v|o)_proj'
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 4
-micro_batch_size: 1
-num_epochs: 1
-optimizer: adamw_bnb_8bit
-lr_scheduler: cosine
-learning_rate: 0.0002
-
-train_on_inputs: false
-group_by_length: false
-bf16: true
-fp16:
-tf32: true
-
-gradient_checkpointing: true
-local_rank:
-logging_steps: 1
-flash_attention: true
-eager_attention:
-
-warmup_ratio: 0.1
-evals_per_epoch: 1
-saves_per_epoch: 1
-debug:
-deepspeed:
-weight_decay: 0.0
-fsdp:
-fsdp_config:

examples/qwen2/dpo.yaml

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

image/axolotl_logo_digital_black.svg

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image/axolotl_logo_digital_white.svg

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image/axolotl_symbol_digital_black.svg

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image/axolotl_wordmark_digital_black.svg

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image/axolotl_wordmark_digital_white.svg

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requirements-dev.txt

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

requirements-tests.txt

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

requirements.txt

@@ -1,18 +1,18 @@
 --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/
 packaging==23.2
 peft==0.13.2
-transformers==4.46.3
+transformers==4.46.0
 tokenizers>=0.20.1
 bitsandbytes==0.44.1
-accelerate==1.1.0
-datasets==3.1.0
-deepspeed==0.15.4
+accelerate==1.0.1
+datasets==3.0.1
+deepspeed==0.15.3
 pydantic==2.6.3
 addict
 fire
 PyYAML>=6.0
 requests
-flash-attn==2.7.0.post2
+flash-attn==2.6.3
 sentencepiece
 wandb
 einops
@@ -26,14 +26,15 @@ numpy>=1.24.4,<=2.0.1
 evaluate==0.4.1
 scipy
 scikit-learn==1.4.2
-nvidia-ml-py==12.560.30
+pynvml
 art
+fschat @ git+https://github.com/lm-sys/FastChat.git@27a05b04a35510afb1d767ae7e5990cbd278f8fe
 gradio==3.50.2
 tensorboard
 python-dotenv==1.0.1
-autoawq==0.2.7.post2
+autoawq>=0.2.5
 triton>=2.3.0
-liger-kernel==0.4.2
+liger-kernel==0.3.0
 
 mamba-ssm==1.2.0.post1
 
@@ -42,7 +43,7 @@ s3fs>=2024.5.0
 gcsfs>=2024.5.0
 # adlfs
 
-trl==0.12.0
+trl @ git+https://github.com/huggingface/trl.git@31d02cfb795284591a084416b9dcb7bef5d08924
 zstandard==0.22.0
 fastcore
 
@@ -53,4 +54,3 @@ immutabledict==4.2.0
 antlr4-python3-runtime==4.13.2
 
 torchao==0.5.0
-schedulefree==1.3.0

scripts/cloud-entrypoint.sh

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

scripts/cutcrossentropy_install.py

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

scripts/unsloth_install.py

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

setup.py

@@ -39,10 +39,7 @@ def parse_requirements():
         else:
             # detect the version of torch already installed
             # and set it so dependencies don't clobber the torch version
-            try:
-                torch_version = version("torch")
-            except PackageNotFoundError:
-                torch_version = "2.5.1"
+            torch_version = version("torch")
             _install_requires.append(f"torch=={torch_version}")
 
             version_match = re.match(r"^(\d+)\.(\d+)(?:\.(\d+))?", torch_version)
@@ -57,10 +54,6 @@ def parse_requirements():
 
             if (major, minor) >= (2, 5):
                 _install_requires.pop(_install_requires.index(xformers_version))
-                if patch == 0:
-                    _install_requires.append("xformers==0.0.28.post2")
-                else:
-                    _install_requires.append("xformers==0.0.28.post3")
                 _install_requires.pop(_install_requires.index(autoawq_version))
             elif (major, minor) >= (2, 4):
                 if patch == 0:
@@ -96,19 +89,22 @@ install_requires, dependency_links = parse_requirements()
 
 setup(
     name="axolotl",
-    version="0.5.2",
+    version="0.4.1",
     description="LLM Trainer",
     long_description="Axolotl is a tool designed to streamline the fine-tuning of various AI models, offering support for multiple configurations and architectures.",
     package_dir={"": "src"},
-    packages=find_packages("src"),
+    packages=find_packages(),
     install_requires=install_requires,
     dependency_links=dependency_links,
     extras_require={
         "flash-attn": [
-            "flash-attn==2.7.0.post2",
+            "flash-attn==2.6.3",
+        ],
+        "fused-dense-lib": [
+            "fused-dense-lib  @ git+https://github.com/Dao-AILab/flash-attention@v2.6.2#subdirectory=csrc/fused_dense_lib",
         ],
         "deepspeed": [
-            "deepspeed==0.15.4",
+            "deepspeed==0.14.4",
             "deepspeed-kernels",
         ],
         "mamba-ssm": [

src/axolotl/cli/__init__.py

@@ -27,17 +27,14 @@ from transformers.utils import is_torch_bf16_gpu_available
 from transformers.utils.import_utils import _is_package_available
 
 from axolotl.common.cli import TrainerCliArgs, load_model_and_tokenizer
+from axolotl.integrations.base import PluginManager
 from axolotl.logging_config import configure_logging
 from axolotl.train import TrainDatasetMeta
-from axolotl.utils.chat_templates import (
-    get_chat_template,
-    get_chat_template_from_config,
-)
+from axolotl.utils.chat_templates import get_chat_template
 from axolotl.utils.comet_ import setup_comet_env_vars
 from axolotl.utils.config import (
     normalize_cfg_datasets,
     normalize_config,
-    prepare_plugins,
     validate_config,
 )
 from axolotl.utils.data import load_prepare_dpo_datasets, prepare_dataset
@@ -100,8 +97,8 @@ def print_dep_versions():
         print("*" * 40)
         print("**** Axolotl Dependency Versions *****")
         for pkg in packages:
-            pkg_version = _is_package_available(pkg, return_version=True)
-            print(f"{pkg: >{max_len}}: {pkg_version[1]: <15}")
+            version = _is_package_available(pkg, return_version=True)
+            print(f"{pkg: >{max_len}}: {version[1]: <15}")
         print("*" * 40)
 
 
@@ -139,7 +136,7 @@ def check_remote_config(config: Union[str, Path]):
         with open(output_path, "wb") as file:
             file.write(content)
         LOG.info(
-            f"Using the following config obtained from {config}: \n\n{content.decode('utf-8')}\n"
+            f"Using the following config obtained from {config}:\n\n{content.decode('utf-8')}\n"
         )
         return output_path
 
@@ -193,19 +190,18 @@ def do_inference(
 ):
     model, tokenizer = load_model_and_tokenizer(cfg=cfg, cli_args=cli_args)
     prompter = cli_args.prompter
+    default_tokens = {"unk_token": "<unk>", "bos_token": "<s>", "eos_token": "</s>"}
+
+    for token, symbol in default_tokens.items():
+        # If the token isn't already specified in the config, add it
+        if not (cfg.special_tokens and token in cfg.special_tokens):
+            tokenizer.add_special_tokens({token: symbol})
 
     prompter_module = None
-    chat_template_str = None
     if prompter:
         prompter_module = getattr(
             importlib.import_module("axolotl.prompters"), prompter
         )
-    elif cfg.chat_template:
-        chat_template_str = get_chat_template(cfg.chat_template)
-    elif cfg.datasets[0].type == "chat_template":
-        chat_template_str = get_chat_template_from_config(
-            cfg=cfg, ds_cfg=cfg.datasets[0], tokenizer=tokenizer
-        )
 
     model = model.to(cfg.device, dtype=cfg.torch_dtype)
 
@@ -215,31 +211,13 @@ def do_inference(
         instruction = get_multi_line_input()
         if not instruction:
             return
-
         if prompter_module:
             prompt: str = next(
                 prompter_module().build_prompt(instruction=instruction.strip("\n"))
             )
         else:
             prompt = instruction.strip()
-
-        if chat_template_str:
-            batch = tokenizer.apply_chat_template(
-                [
-                    {
-                        "role": "user",
-                        "content": prompt,
-                    }
-                ],
-                return_tensors="pt",
-                add_special_tokens=True,
-                add_generation_prompt=True,
-                chat_template=chat_template_str,
-                tokenize=True,
-                return_dict=True,
-            )
-        else:
-            batch = tokenizer(prompt, return_tensors="pt", add_special_tokens=True)
+        batch = tokenizer(prompt, return_tensors="pt", add_special_tokens=True)
 
         print("=" * 40)
         model.eval()
@@ -279,6 +257,13 @@ def do_inference_gradio(
 
     model, tokenizer = load_model_and_tokenizer(cfg=cfg, cli_args=cli_args)
     prompter = cli_args.prompter
+    # default_tokens = {"unk_token": "<unk>", "bos_token": "<s>", "eos_token": "</s>"}
+    default_tokens: Dict[str, str] = {}
+
+    for token, symbol in default_tokens.items():
+        # If the token isn't already specified in the config, add it
+        if not (cfg.special_tokens and token in cfg.special_tokens):
+            tokenizer.add_special_tokens({token: symbol})
 
     prompter_module = None
     chat_template_str = None
@@ -287,7 +272,7 @@ def do_inference_gradio(
             importlib.import_module("axolotl.prompters"), prompter
         )
     elif cfg.chat_template:
-        chat_template_str = get_chat_template(cfg.chat_template, tokenizer=tokenizer)
+        chat_template_str = get_chat_template(cfg.chat_template)
 
     model = model.to(cfg.device, dtype=cfg.torch_dtype)
 
@@ -426,6 +411,11 @@ def load_cfg(config: Union[str, Path] = Path("examples/"), **kwargs):
 
     cfg.axolotl_config_path = config
 
+    if cfg.get("plugins"):
+        plugin_manager = PluginManager.get_instance()
+        for plugin_name in cfg["plugins"]:
+            plugin_manager.register(plugin_name)
+
     try:
         device_props = torch.cuda.get_device_properties("cuda")
         gpu_version = "sm_" + str(device_props.major) + str(device_props.minor)
@@ -439,13 +429,8 @@ def load_cfg(config: Union[str, Path] = Path("examples/"), **kwargs):
             "n_gpu": int(os.environ.get("WORLD_SIZE", 1)),
             "compute_capability": gpu_version,
         },
-        env_capabilities={
-            "torch_version": str(torch.__version__).split("+", maxsplit=1)[0]
-        },
     )
 
-    prepare_plugins(cfg)
-
     prepare_optim_env(cfg)
 
     prepare_opinionated_env(cfg)

src/axolotl/cli/inference.py

@@ -19,7 +19,7 @@ from axolotl.common.cli import TrainerCliArgs
 def do_cli(config: Path = Path("examples/"), gradio=False, **kwargs):
     # pylint: disable=duplicate-code
     print_axolotl_text_art()
-    parsed_cfg = load_cfg(config, inference=True, **kwargs)
+    parsed_cfg = load_cfg(config, **kwargs)
     parsed_cfg.sample_packing = False
     parser = transformers.HfArgumentParser((TrainerCliArgs))
     parsed_cli_args, _ = parser.parse_args_into_dataclasses(

src/axolotl/cli/preprocess.py

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

src/axolotl/cli/train.py

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

src/axolotl/core/trainer_builder.py

@@ -48,7 +48,6 @@ 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
@@ -107,22 +106,6 @@ def _sanitize_kwargs_for_tagging(tag_names, kwargs=None):
     return kwargs
 
 
-def _sanitize_kwargs_for_ds_tagging(dataset_tags, kwargs=None):
-    if isinstance(dataset_tags, str):
-        dataset_tags = [dataset_tags]
-
-    if (dataset_tags is not None) and (kwargs is not None):
-        if "dataset_tags" not in kwargs:
-            kwargs["dataset_tags"] = dataset_tags
-        elif "dataset_tags" in kwargs and isinstance(kwargs["dataset_tags"], list):
-            kwargs["dataset_tags"].extend(dataset_tags)
-        elif "dataset_tags" in kwargs and isinstance(kwargs["dataset_tags"], str):
-            dataset_tags.append(kwargs["dataset_tags"])
-            kwargs["dataset_tags"] = dataset_tags
-
-    return kwargs
-
-
 @dataclass
 class AxolotlTrainingMixins:
     """
@@ -236,14 +219,6 @@ class AxolotlTrainingMixins:
         default=1e-6,
         metadata={"help": "loraplus learning rate for lora embedding layers."},
     )
-    embedding_lr_scale: Optional[float] = field(
-        default=None,
-        metadata={"help": "Scale the learning rate for the embedding layers."},
-    )
-    embedding_lr: Optional[float] = field(
-        default=None,
-        metadata={"help": "absolute learning rate for the embedding layers."},
-    )
     qlora: bool = field(
         default=False,
         metadata={"help": "whether this is a qlora training"},
@@ -410,7 +385,7 @@ class SchedulerMixin(Trainer):
                     min_lr_ratio=self.args.cosine_min_lr_ratio,
                 )
             else:
-                return super().create_scheduler(num_training_steps, optimizer=optimizer)
+                return super().create_scheduler(num_training_steps, optimizer)
         else:
             if use_cosine_quadratic:
                 LOG.warning("axolotl's cosine scheduler with quadratic warmup not used (e.g., because of deepspeed).")
@@ -434,12 +409,10 @@ class AxolotlTrainer(SchedulerMixin, Trainer):
         *_args,
         bench_data_collator=None,
         eval_data_collator=None,
-        dataset_tags=None,
         **kwargs,
     ):
         self.bench_data_collator = bench_data_collator
         self.eval_data_collator = eval_data_collator
-        self.dataset_tags = dataset_tags
         super().__init__(*_args, **kwargs)
         self.train_data_collator = self.data_collator
         self._stored_metrics = defaultdict(lambda: defaultdict(list))
@@ -461,75 +434,38 @@ class AxolotlTrainer(SchedulerMixin, Trainer):
     def create_optimizer(self):
         if (
             self.args.loraplus_lr_ratio is None
-            and self.args.embedding_lr_scale is None
-            and self.args.embedding_lr is None
             and self.args.alternate_optimizer
-            not in [
-                "optimi_adamw",
-                "ao_adamw_8bit",
-                "ao_adamw_4bit",
-                "ao_adamw_fp8",
-                "adopt_adamw",
-            ]
+            not in ["optimi_adamw", "ao_adamw_8bit", "ao_adamw_4bit", "ao_adamw_fp8"]
         ):
             return super().create_optimizer()
 
         opt_model = self.model_wrapped if is_sagemaker_mp_enabled() else self.model
         if self.optimizer is None:  # pylint: disable=access-member-before-definition
             decay_parameters = self.get_decay_parameter_names(opt_model)
-            params = {
-                "to_weight_decay": {},  # LayerNorm and bias
-                "embeddings": {},  # lm_head, embed_tokens,
-                "no_weight_decay": {},
-            }
+            optimizer_grouped_parameters = [
+                {
+                    "params": [
+                        p
+                        for n, p in opt_model.named_parameters()
+                        if (n in decay_parameters and p.requires_grad)
+                    ],
+                    "weight_decay": self.args.weight_decay,
+                },
+                {
+                    "params": [
+                        p
+                        for n, p in opt_model.named_parameters()
+                        if (n not in decay_parameters and p.requires_grad)
+                    ],
+                    "weight_decay": 0.0,
+                },
+            ]
 
             optimizer_cls, optimizer_kwargs = Trainer.get_optimizer_cls_and_kwargs(
                 self.args,
                 opt_model,
             )
 
-            for name, param in opt_model.named_parameters():
-                if not param.requires_grad:
-                    continue
-                if name.endswith("modules_to_save.default.weight") or any(
-                    embed_name in name for embed_name in ["embed_tokens", "lm_head"]
-                ):
-                    params["embeddings"][name] = param
-                elif name in decay_parameters:
-                    params["to_weight_decay"][name] = param
-                else:
-                    params["no_weight_decay"][name] = param
-            optimizer_grouped_parameters = []
-            if params["to_weight_decay"]:
-                optimizer_grouped_parameters.append(
-                    {
-                        "params": list(params["to_weight_decay"].values()),
-                        "weight_decay": self.args.weight_decay,
-                        "lr": optimizer_kwargs["lr"],
-                    }
-                )
-            if params["embeddings"]:
-                lr = optimizer_kwargs["lr"]  # pylint: disable=invalid-name
-                if self.args.embedding_lr_scale:
-                    lr *= self.args.embedding_lr_scale  # pylint: disable=invalid-name
-                elif self.args.embedding_lr:
-                    lr = self.args.embedding_lr  # pylint: disable=invalid-name
-                optimizer_grouped_parameters.append(
-                    {
-                        "params": list(params["embeddings"].values()),
-                        "weight_decay": 0.0,
-                        "lr": lr,
-                    }
-                )
-            if params["no_weight_decay"]:
-                optimizer_grouped_parameters.append(
-                    {
-                        "params": list(params["no_weight_decay"].values()),
-                        "weight_decay": 0.0,
-                        "lr": optimizer_kwargs["lr"],
-                    }
-                )
-
             if self.args.loraplus_lr_ratio is not None:
                 loraplus_lr_ratio = getattr(self.args, "loraplus_lr_ratio", None)
                 loraplus_lr_embedding = getattr(
@@ -542,13 +478,6 @@ class AxolotlTrainer(SchedulerMixin, Trainer):
                     loraplus_lr_embedding=loraplus_lr_embedding,
                     **optimizer_kwargs,
                 )
-            elif (
-                self.args.embedding_lr_scale is not None
-                or self.args.embedding_lr is not None
-            ):
-                self.optimizer = (  # pylint: disable=attribute-defined-outside-init
-                    optimizer_cls(optimizer_grouped_parameters, **optimizer_kwargs)
-                )
             elif self.args.alternate_optimizer == "optimi_adamw":
                 from optimi import AdamW
 
@@ -575,16 +504,6 @@ class AxolotlTrainer(SchedulerMixin, Trainer):
                 self.optimizer = (  # pylint: disable=attribute-defined-outside-init
                     AdamWFp8(optimizer_grouped_parameters, **optimizer_kwargs)
                 )
-            elif self.args.alternate_optimizer == "adopt_adamw":
-                from axolotl.utils.optimizers.adopt import ADOPT
-
-                self.optimizer = (  # pylint: disable=attribute-defined-outside-init
-                    ADOPT(
-                        optimizer_grouped_parameters,
-                        decouple=True,
-                        **optimizer_kwargs,
-                    )
-                )
 
         if is_sagemaker_mp_enabled():
             self.optimizer = smp.DistributedOptimizer(  # pylint: disable=attribute-defined-outside-init
@@ -937,9 +856,6 @@ class AxolotlTrainer(SchedulerMixin, Trainer):
         Overwrite the `push_to_hub` method in order to force-add the tags when pushing the
         model on the Hub. Please refer to `~transformers.Trainer.push_to_hub` for more details.
         """
-        kwargs = _sanitize_kwargs_for_ds_tagging(
-            dataset_tags=self.dataset_tags, kwargs=kwargs
-        )
         kwargs = _sanitize_kwargs_for_tagging(tag_names=self.tag_names, kwargs=kwargs)
 
         return super().push_to_hub(*args, **kwargs)
@@ -979,13 +895,13 @@ class AxolotlTrainer(SchedulerMixin, Trainer):
         for key, value in metrics.items():
             self._stored_metrics[train_eval][key].append(value)
 
-    def _save_checkpoint(self, model, trial, **kwargs):
+    def _save_checkpoint(self, model, trial):
         # 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, **kwargs)
+        return super()._save_checkpoint(model, trial)
 
 
 class AxolotlMambaTrainer(AxolotlTrainer):
@@ -1063,9 +979,8 @@ class AxolotlDPOTrainer(SchedulerMixin, DPOTrainer):
 
     tag_names = ["axolotl", "dpo"]
 
-    def __init__(self, *args, dataset_tags=None, **kwargs):
+    def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
-        self.dataset_tags = dataset_tags
         self.optimizer = None
 
     def create_optimizer(self):
@@ -1104,44 +1019,28 @@ class AxolotlDPOTrainer(SchedulerMixin, DPOTrainer):
         Overwrite the `push_to_hub` method in order to force-add the tags when pushing the
         model on the Hub. Please refer to `~transformers.Trainer.push_to_hub` for more details.
         """
-        kwargs = _sanitize_kwargs_for_ds_tagging(
-            dataset_tags=self.dataset_tags, kwargs=kwargs
-        )
         kwargs = _sanitize_kwargs_for_tagging(tag_names=self.tag_names, kwargs=kwargs)
 
         return super().push_to_hub(*args, **kwargs)
 
-    @staticmethod
     def tokenize_row(
+        self,
         features,
         processing_class,
         max_prompt_length,
         max_completion_length,
         add_special_tokens,
     ) -> Dict:
-        res = DPOTrainer.tokenize_row(
+        res = super().tokenize_row(
             features,
             processing_class,
             max_prompt_length,
             max_completion_length,
             add_special_tokens,
         )
-        # fix when the tokenizer doesn't have a bos_token_id, e.g. Qwen
-        if processing_class.bos_token is None and res["prompt_input_ids"][0] is None:
+        if processing_class.bos_token_id is None and res["prompt_input_ids"][0] is None:
             for key in res.keys():
                 res[key] = res[key][1:]
-
-        if processing_class.bos_token and processing_class.bos_token_id is not None:
-            # dpo trainer may incorrectly prepend the bos_token_id to the dpo outputs
-            if res["chosen_input_ids"][0] == processing_class.bos_token_id:
-                res["chosen_input_ids"] = res["chosen_input_ids"][1:]
-                res["chosen_labels"] = res["chosen_labels"][1:]
-                res["chosen_attention_mask"] = res["chosen_attention_mask"][1:]
-            if res["rejected_input_ids"][0] == processing_class.bos_token_id:
-                res["rejected_input_ids"] = res["rejected_input_ids"][1:]
-                res["rejected_labels"] = res["rejected_labels"][1:]
-                res["rejected_attention_mask"] = res["rejected_attention_mask"][1:]
-
         return res
 
     def training_step(
@@ -1248,12 +1147,6 @@ 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)
@@ -1280,23 +1173,11 @@ 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 = []
-        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 hook_pre_create_training_args(self, training_arguments_kwargs):
         # TODO
@@ -1379,8 +1260,6 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
 
         if self.cfg.lisa_step_interval and self.cfg.lisa_n_layers:
             callbacks.append(lisa_callback_factory(trainer))
-
-        callbacks.extend(super().get_post_trainer_create_callbacks(trainer=trainer))
         return callbacks
 
     def _get_trainer_cls(self):
@@ -1498,15 +1377,17 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
 
         if not self.cfg.test_datasets and self.cfg.val_set_size == 0:
             # no eval set, so don't eval
-            training_arguments_kwargs["eval_strategy"] = "no"
+            training_arguments_kwargs["evaluation_strategy"] = "no"
         elif self.cfg.eval_steps:
-            training_arguments_kwargs["eval_strategy"] = "steps"
+            training_arguments_kwargs["evaluation_strategy"] = "steps"
             training_arguments_kwargs["eval_steps"] = self.cfg.eval_steps
-        elif self.cfg.eval_strategy:
-            training_arguments_kwargs["eval_strategy"] = self.cfg.eval_strategy
+        elif self.cfg.evaluation_strategy:
+            training_arguments_kwargs[
+                "evaluation_strategy"
+            ] = self.cfg.evaluation_strategy
         else:
             # we have an eval set, but no steps defined, default to use epoch
-            training_arguments_kwargs["eval_strategy"] = "epoch"
+            training_arguments_kwargs["evaluation_strategy"] = "epoch"
 
         if self.cfg.save_steps:
             training_arguments_kwargs["save_strategy"] = "steps"
@@ -1644,9 +1525,6 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         training_arguments_kwargs[
             "loraplus_lr_embedding"
         ] = self.cfg.loraplus_lr_embedding
-        training_arguments_kwargs["embedding_lr"] = self.cfg.embedding_lr
-        training_arguments_kwargs["embedding_lr_scale"] = self.cfg.embedding_lr_scale
-
         if self.cfg.lr_scheduler in ["one_cycle", "log_sweep"]:
             training_arguments_kwargs["lr_scheduler_type"] = "cosine"
             training_arguments_kwargs[
@@ -1717,8 +1595,7 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         training_arguments_kwargs["pretraining"] = bool(self.cfg.pretraining_dataset)
         if self.cfg.chat_template:
             training_arguments_kwargs["chat_template"] = get_chat_template(
-                self.cfg.chat_template,
-                tokenizer=self.tokenizer,
+                self.cfg.chat_template
             )
 
         if self.cfg.rl == "orpo":
@@ -1734,13 +1611,11 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         if self.cfg.reward_model:
             trainer_kwargs["max_length"] = self.cfg.sequence_len
 
-        # pylint: disable=duplicate-code
         if self.cfg.optimizer in [
             "optimi_adamw",
             "ao_adamw_4bit",
             "ao_adamw_8bit",
             "ao_adamw_fp8",
-            "adopt_adamw",
         ]:
             # Set default so transformers doesn't throw
             training_arguments_kwargs["optim"] = "adamw_hf"
@@ -1831,10 +1706,6 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         else:
             trainer_kwargs["tokenizer"] = self.tokenizer
 
-        if (trainer_cls is not AxolotlRewardTrainer) and self.cfg.datasets is not None:
-            trainer_kwargs["dataset_tags"] = [
-                d["path"] for d in self.cfg.datasets if not Path(d["path"]).is_dir()
-            ]
         trainer = trainer_cls(
             model=self.model,
             train_dataset=self.train_dataset,
@@ -1897,7 +1768,6 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
                 collator = MultiModalChatDataCollator
                 kwargs["processor"] = self.processor
                 kwargs["chat_template"] = training_args.chat_template
-                kwargs["chat_template_type"] = self.cfg.chat_template
             else:
                 collator = DataCollatorForSeq2Seq
 
@@ -1920,7 +1790,7 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
         return callbacks
 
     def get_post_trainer_create_callbacks(self, trainer):
-        callbacks = super().get_post_trainer_create_callbacks(trainer=trainer)
+        callbacks = []
         return callbacks
 
     def build_training_arguments(self, total_num_steps):
@@ -1948,10 +1818,10 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
             training_args_kwargs["save_safetensors"] = self.cfg.save_safetensors
 
         if self.eval_dataset:
-            training_args_kwargs["eval_strategy"] = "steps"
+            training_args_kwargs["evaluation_strategy"] = "steps"
             training_args_kwargs["eval_steps"] = self.cfg.eval_steps
         else:
-            training_args_kwargs["eval_strategy"] = "no"
+            training_args_kwargs["evaluation_strategy"] = "no"
 
         if self.cfg.bf16 or self.cfg.bfloat16:
             training_args_kwargs["bf16"] = True
@@ -2006,18 +1876,17 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
             # default to saving each epoch if not defined
             training_args_kwargs["save_strategy"] = "epoch"
 
-        training_args_kwargs["dataset_num_proc"] = self.cfg.dataset_processes
-
         if self.cfg.rl_beta:
             training_args_kwargs["beta"] = self.cfg.rl_beta
         if self.cfg.orpo_alpha:
             # trl does some odd mapping of alpha to beta to reuse the beta parameter ???
             training_args_kwargs["beta"] = self.cfg.orpo_alpha
 
+        training_args_kwargs["dataset_num_proc"] = self.cfg.dataset_processes
+        training_args_cls = AxolotlDPOConfig
         if self.cfg.rpo_alpha is not None:
             training_args_kwargs["rpo_alpha"] = self.cfg.rpo_alpha
 
-        training_args_cls = None
         if self.cfg.rl == "simpo":
             training_args_cls = AxolotlCPOConfig
             training_args_kwargs["loss_type"] = "simpo"
@@ -2026,13 +1895,13 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
             if self.cfg.cpo_alpha is not None:
                 training_args_kwargs["cpo_alpha"] = self.cfg.cpo_alpha
 
-        elif self.cfg.rl == "orpo":
+        if self.cfg.rl == "orpo":
             training_args_cls = AxolotlORPOConfig
             training_args_kwargs["max_length"] = self.cfg.sequence_len
             if self.cfg.max_prompt_len:
                 training_args_kwargs["max_prompt_length"] = self.cfg.max_prompt_len
 
-        elif self.cfg.rl == "kto":
+        if self.cfg.rl == "kto":
             training_args_cls = AxolotlKTOConfig
 
             training_args_kwargs["desirable_weight"] = (
@@ -2047,17 +1916,6 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
             if self.cfg.max_prompt_len:
                 training_args_kwargs["max_prompt_length"] = self.cfg.max_prompt_len
 
-        else:
-            training_args_cls = AxolotlDPOConfig
-            if self.cfg.rl == "ipo":
-                training_args_kwargs["loss_type"] = "ipo"
-            training_args_kwargs["max_length"] = self.cfg.sequence_len
-            training_args_kwargs["max_completion_length"] = None
-            training_args_kwargs["max_prompt_length"] = self.cfg.sequence_len
-            training_args_kwargs["generate_during_eval"] = self.cfg.use_wandb
-            if self.cfg.dpo_use_weighting is not None:
-                training_args_kwargs["use_weighting"] = self.cfg.dpo_use_weighting
-
         training_args = training_args_cls(  # pylint: disable=unexpected-keyword-arg
             output_dir=self.cfg.output_dir,
             per_device_train_batch_size=self.cfg.micro_batch_size,
@@ -2078,6 +1936,7 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
         training_args = self.build_training_arguments(total_num_steps)
         dpo_trainer_kwargs = {}
         if self.cfg.rl == "ipo":
+            dpo_trainer_kwargs["loss_type"] = "ipo"
             if self.cfg.dpo_label_smoothing:
                 dpo_trainer_kwargs["label_smoothing"] = self.cfg.dpo_label_smoothing
         if self.eval_dataset:
@@ -2091,6 +1950,12 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
         if self.cfg.rl in ["dpo", "ipo"]:
             trainer_cls = AxolotlDPOTrainer
             trainer_cls_args = [self.model, self.model_ref]
+
+            # these aren't used for the ORPO trainer
+            dpo_trainer_kwargs["max_length"] = self.cfg.sequence_len
+            dpo_trainer_kwargs["max_target_length"] = None
+            dpo_trainer_kwargs["max_prompt_length"] = self.cfg.sequence_len
+            dpo_trainer_kwargs["generate_during_eval"] = self.cfg.use_wandb
         elif self.cfg.rl == "orpo":
             trainer_cls = AxolotlORPOTrainer
             trainer_cls_args = [self.model]
@@ -2109,10 +1974,6 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
         else:
             dpo_trainer_kwargs["tokenizer"] = self.tokenizer
 
-        if self.cfg.datasets is not None and (trainer_cls is AxolotlDPOTrainer):
-            dpo_trainer_kwargs["dataset_tags"] = [
-                d["path"] for d in self.cfg.datasets if not Path(d["path"]).is_dir()
-            ]
         dpo_trainer = trainer_cls(
             *trainer_cls_args,
             args=training_args,
@@ -2138,11 +1999,11 @@ class HFPPOTrainerBuilder(TrainerBuilderBase):
     """
 
     def get_callbacks(self):
-        callbacks = super().get_callbacks()
+        callbacks = []
         return callbacks
 
     def get_post_trainer_create_callbacks(self, trainer):
-        callbacks = super().get_post_trainer_create_callbacks(trainer=trainer)
+        callbacks = []
         return callbacks
 
     def build(self, total_num_steps):

src/axolotl/core/trainers/trl.py

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

src/axolotl/integrations/base.py

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

src/axolotl/integrations/cut_cross_entropy/ACKNOWLEDGEMENTS.md

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

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

src/axolotl/integrations/cut_cross_entropy/README.md

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

src/axolotl/integrations/cut_cross_entropy/__init__.py

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

src/axolotl/integrations/cut_cross_entropy/args.py

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

src/axolotl/integrations/grokfast/LICENSE

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

src/axolotl/integrations/grokfast/README.md

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

src/axolotl/integrations/grokfast/__init__.py

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

src/axolotl/integrations/grokfast/args.py

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

src/axolotl/integrations/grokfast/optimizer.py

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

src/axolotl/integrations/liger/__init__.py

@@ -18,24 +18,20 @@ 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.functional import liger_cross_entropy
-from liger_kernel.transformers.monkey_patch import MODEL_TYPE_TO_APPLY_LIGER_FN
+from liger_kernel.transformers.geglu import LigerGEGLUMLP
 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):
     """
@@ -46,31 +42,59 @@ class LigerPlugin(BasePlugin):
         return "axolotl.integrations.liger.LigerArgs"
 
     def pre_model_load(self, cfg):
-        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.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"
                 )
-            apply_liger_fn(**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
+
         elif cfg.model_config_type == "jamba":
             from transformers.models.jamba import modeling_jamba
 
@@ -80,14 +104,30 @@ 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_glu_activation:
+            if cfg.liger_swiglu:
                 modeling_jamba.JambaMLP = LigerSwiGLUMLP
             if cfg.liger_cross_entropy:
-                from transformers.loss.loss_utils import nn
-
-                nn.functional.cross_entropy = liger_cross_entropy
+                modeling_jamba.CrossEntropyLoss = LigerCrossEntropyLoss
             if cfg.liger_fused_linear_cross_entropy:
                 modeling_jamba.JambaForCausalLM.forward = jamba_lce_forward
+
+        elif cfg.model_config_type == "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
@@ -106,11 +146,44 @@ 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_glu_activation:
+            if cfg.liger_swiglu:
                 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

@@ -15,12 +15,9 @@
 """
 Module for handling LIGER input arguments.
 """
-import logging
 from typing import Optional
 
-from pydantic import BaseModel, model_validator
-
-LOG = logging.getLogger("axolotl.integrations.liger.args")
+from pydantic import BaseModel
 
 
 class LigerArgs(BaseModel):
@@ -30,24 +27,6 @@ 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

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

src/axolotl/monkeypatch/llama_attn_hijack_flash.py

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

src/axolotl/monkeypatch/multipack.py

@@ -1,5 +1,4 @@
 """multipack patching for v2 of sample packing"""
-
 import importlib
 
 import transformers
@@ -28,28 +27,74 @@ SUPPORTED_MULTIPACK_MODEL_TYPES = [
 ]
 
 
+# def patch_for_multipack(model_type, model_name=None, is_remote_code=False):
 def patch_for_multipack(model_type, model_name=None, has_remote_code=False):
-    if has_remote_code:
-        patch_remote(model_name)
+    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:
     elif hasattr(transformers, "modeling_flash_attention_utils"):
-        transformers.modeling_flash_attention_utils._get_unpad_data = (  # pylint: disable=protected-access
+        if not has_remote_code:
+            transformers.modeling_flash_attention_utils._get_unpad_data = (  # pylint: disable=protected-access
+                get_unpad_data
+            )
+        if model_type == "mixtral" and is_deepspeed_zero3_enabled():
+            patch_mixtral_moe_forward_zero3()
+        return
+
+    # 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):
+def patch_remote(model_name, config_name, modeling_name):
     model_config = AutoConfig.from_pretrained(model_name, trust_remote_code=True)
     # we need to load the model here in order for modeling_* to be available
     with init_empty_weights():
         AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True)
-    parts = model_config.__class__.__module__.split(".")
-    parts[-1] = parts[-1].replace("configuration_", "modeling_", 1)
-    module_name = ".".join(parts)
+    module_name = model_config.__class__.__module__.replace(config_name, modeling_name)
     modeling_arch = importlib.import_module(module_name)
-    if hasattr(modeling_arch, "_get_unpad_data"):
-        modeling_arch._get_unpad_data = (  # pylint: disable=protected-access
-            get_unpad_data
-        )
+    modeling_arch._get_unpad_data = get_unpad_data  # pylint: disable=protected-access

src/axolotl/monkeypatch/relora.py

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

src/axolotl/monkeypatch/unsloth_.py

@@ -188,7 +188,7 @@ def integrate_lora_mlp_patch(peft_model: PeftModelForCausalLM):
             for module in layer_modules
         )
         mlp_not_dora = all(
-            len(getattr(module, "lora_magnitude_vector", []) or []) == 0
+            getattr(module, "lora_magnitude_vector", None) is None
             for module in layer_modules
         )
 
@@ -213,7 +213,7 @@ def integrate_lora_patch(peft_model: PeftModelForCausalLM, cfg):
                 for module in layer_modules
             )
             qkv_not_dora = all(
-                len(getattr(module, "lora_magnitude_vector", []) or []) == 0
+                getattr(module, "lora_magnitude_vector", None) is None
                 for module in layer_modules
             )
 
@@ -232,7 +232,7 @@ def integrate_lora_patch(peft_model: PeftModelForCausalLM, cfg):
                 for module in layer_modules
             )
             o_not_dora = all(
-                len(getattr(module, "lora_magnitude_vector", []) or []) == 0
+                getattr(module, "lora_magnitude_vector", None) is None
                 for module in layer_modules
             )
 

src/axolotl/prompt_strategies/instruct.py

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

src/axolotl/prompt_strategies/llama2_chat.py

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

src/axolotl/prompt_strategies/sharegpt.py

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

src/axolotl/prompt_strategies/sharegpt_jokes.py

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

src/axolotl/prompt_tokenizers.py

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

src/axolotl/prompters.py

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

src/axolotl/train.py

@@ -259,31 +259,11 @@ def train(
         model.save_pretrained(cfg.output_dir, safe_serialization=safe_serialization)
 
     if not cfg.hub_model_id:
-        from huggingface_hub import HfApi
-        from huggingface_hub.utils import RepositoryNotFoundError
-
         try:
-            # Check to make sure the base model is from HuggingFace not a local directory
-            hf_api = HfApi()
-            hf_api.model_info(cfg.base_model)
-
-            model_card_kwarg = {
-                "model_name": cfg.output_dir.lstrip("./")
-                .encode("utf-8")
-                .decode("utf-8")
-            }
-            if cfg.datasets is not None:
-                if cfg.rl is not None or cfg.reward_model:
-                    model_card_kwarg["dataset_name"] = [
-                        d["path"] for d in cfg.datasets if not Path(d["path"]).is_dir()
-                    ]
-                else:
-                    model_card_kwarg["dataset_tags"] = [
-                        d["path"] for d in cfg.datasets if not Path(d["path"]).is_dir()
-                    ]
-
-            trainer.create_model_card(**model_card_kwarg)
-        except (AttributeError, UnicodeDecodeError, RepositoryNotFoundError):
+            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/__init__.py

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

src/axolotl/utils/bench.py

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

src/axolotl/utils/callbacks/__init__.py

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

src/axolotl/utils/callbacks/perplexity.py

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

src/axolotl/utils/collators/mm_chat.py

@@ -1,10 +1,8 @@
 """
 Collators for multi-modal chat messages and packing
 """
-
-from copy import deepcopy
 from dataclasses import dataclass
-from typing import Any, Optional, Union
+from typing import Any, Dict, List, Optional, Union
 
 from PIL import Image
 from transformers import PreTrainedTokenizerBase, ProcessorMixin
@@ -22,7 +20,6 @@ class MultiModalChatDataCollator(DataCollatorMixin):
     processor: ProcessorMixin
     return_tensors: str = "pt"
     chat_template: Optional[str] = None
-    chat_template_type: Optional[str] = None
     packing: bool = False
     max_images: int = -1
     padding: Union[bool, str, PaddingStrategy] = True
@@ -33,190 +30,38 @@ class MultiModalChatDataCollator(DataCollatorMixin):
             raise ValueError("Packing is currently not supported.")
 
     def torch_call(
-        self, examples: list[Union[list[int], Any, dict[str, Any]]]
-    ) -> dict[str, Any]:
+        self, examples: List[Union[List[int], Any, Dict[str, Any]]]
+    ) -> Dict[str, Any]:
         # Handle dict or lists with proper padding and conversion to tensor.
+
         return self.__class__.process_rows(
-            examples,
-            self.processor,
-            self.chat_template,
-            self.max_images,
-            chat_template_type=self.chat_template_type,
+            examples, self.processor, self.chat_template, self.max_images
         )
 
     @staticmethod
-    def preprocess(examples: list[dict]) -> list[dict]:
-        """
-        Preprocess conversation examples to ensure consistent format.
-        Converts different conversation formats to OpenAI format with 'messages'.
-        Supports two formats:
-        1. OpenAI format with 'messages'
-        2. Legacy format with 'conversations'
-
-        Args:
-        examples: list of conversation dictionaries
-        Returns:
-            dict in OpenAI format with 'messages' key
-
-        Raises:
-            ValueError: If the conversation format is not supported
-        """
-        role_mapping = {
-            "human": "user",
-            "gpt": "assistant",
-        }
-
-        def normalize_role(role: str) -> str:
-            """Normalize role names to OpenAI format. Default to original role if not found."""
-            return role_mapping.get(role, role)
-
-        def convert_legacy_format(example: dict) -> dict:
-            """Convert legacy 'conversations' format to OpenAI 'messages' format."""
-            messages = [
-                {
-                    "role": normalize_role(convo["from"]),
-                    "content": convo["value"],
-                }
-                for convo in example["conversations"]
-            ]
-
-            # Create new dict without 'conversations' key
-            result = deepcopy(example)
-            result.pop("conversations")
-            return {"messages": messages, **result}
-
-        processed_examples = []
-        for example in examples:
-            # OpenAI format
-            if "messages" in example:
-                processed_examples.append(example)
-
-            # Legacy format
-            elif "conversations" in example:
-                processed_examples.append(convert_legacy_format(example))
-
-            else:
-                raise ValueError(
-                    "Only `messages` and `conversations` message keys are currently supported."
-                )
-
-        return processed_examples
-
-    @staticmethod
-    def process_images(examples, max_images):
-        """
-        Process images from examples, ensuring consistency in image presence and applying max_images limit.
-
-        Args:
-            examples: List of dictionaries that may contain 'images' key
-            max_images: Maximum number of images to keep per example (0 means no limit)
-
-        Returns:
-            Either None (if no images) or List[Image objects] (if all examples have images)
-
-        Raises:
-            ValueError: If there's a mix of None and non-None images
-        """
-
-        def get_image(example):
-            if "images" not in example:
-                return None
-            images = example["images"]
-            if isinstance(images, str):
-                return Image.open(images)
-            return images
-
-        images = [get_image(example) for example in examples]
-
-        # Count None and non-None images
-        none_count = sum(1 for img in images if img is None)
-
-        # All images are None
-        if none_count == len(images):
-            return None
-
-        # Mix of None and non-None images
-        if none_count > 0:
-            raise ValueError(
-                "All images should be either None or not None. "
-                "Please provide images for all examples or None."
-            )
-
-        # Apply max_images limit if specified
-        if max_images > 0:
-            images = [
-                (
-                    img_batch[:max_images]
-                    if isinstance(img_batch, (list, tuple))
-                    else img_batch
-                )
-                for img_batch in images
-            ]
-
-        return images
-
-    @staticmethod
-    def pixtral_chat_conversion(messages):
-        is_single_message = not isinstance(messages, list)
-        if is_single_message:
-            messages = [messages]
-
-        for i, message in enumerate(messages):
-            if message["role"] == "user":
-                for j, content in enumerate(message["content"]):
-                    if "type" in content and content["type"] == "text":
-                        messages[i]["content"][j] = {
-                            "type": "text",
-                            "content": content["text"],
-                        }
-
-            if message["role"] == "assistant":
-                messages[i]["content"] = message["content"][0]["text"]
-
-        if is_single_message:
-            return messages[0]
-        return messages
-
-    @staticmethod
-    def process_rows(
-        examples,
-        processor,
-        chat_template,
-        max_images,
-        length_only=False,
-        chat_template_type=None,
-    ):
+    def process_rows(examples, processor, chat_template, max_images, length_only=False):
         # HINT: use `_torch_collate_batch` to stack and pad tensors
         # see also DataCollatorWithFlattening and DefaultDataCollator
 
         # *** This is COPIED from the trl example sft_vlm.py code ***
         # use this as a starting point
 
-        # Preprocess the examples
-        examples = __class__.preprocess(examples)
-
         # Get the texts and images, and apply the chat template
-        if chat_template_type == "pixtral":
-            texts = [
-                processor.apply_chat_template(
-                    __class__.pixtral_chat_conversion(example["messages"]),
-                    chat_template=chat_template,
-                    tokenize=False,
-                )
-                for example in examples
-            ]
-        else:
-            texts = [
-                processor.apply_chat_template(
-                    example["messages"], chat_template=chat_template, tokenize=False
-                )
-                for example in examples
-            ]
+        texts = [
+            processor.apply_chat_template(
+                example["messages"], chat_template=chat_template, tokenize=False
+            )
+            for example in examples
+        ]
+        images = [
+            Image.open(example["images"])
+            if isinstance(example["images"], str)
+            else example["images"]
+            for example in examples
+        ]
 
-        images = __class__.process_images(examples, max_images=max_images)
-        if chat_template_type == "llava":
-            # LLava1.5 does not support multiple images
-            images = [image[0] for image in images]
+        if max_images > 0:
+            images = [img_batch[:max_images] for img_batch in images]
 
         # Tokenize the texts and process the images
         batch = processor(text=texts, images=images, return_tensors="pt", padding=True)
@@ -225,12 +70,9 @@ class MultiModalChatDataCollator(DataCollatorMixin):
         labels = batch["input_ids"].clone()
         labels[labels == processor.tokenizer.pad_token_id] = -100  #
         # Ignore the image token index in the loss computation (model specific)
-        if chat_template_type == "qwen2_vl":
-            image_token_id = processor.tokenizer.convert_tokens_to_ids("<|image_pad|>")
-        else:
-            image_token_id = processor.tokenizer.convert_tokens_to_ids(
-                processor.image_token
-            )
+        image_token_id = processor.tokenizer.convert_tokens_to_ids(
+            processor.image_token
+        )
         labels[labels == image_token_id] = -100
         batch["labels"] = labels
 

src/axolotl/utils/config/__init__.py

@@ -1,15 +1,16 @@
 """Module for working with config dicts"""
+import json
 import logging
 import os
+from pathlib import Path
 from typing import Optional
 
 import torch
 from transformers.utils import is_torch_bf16_gpu_available
-from transformers.utils.import_utils import is_torch_npu_available
 
-from axolotl.integrations.base import PluginManager
 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,
 )
@@ -31,10 +32,7 @@ def choose_device(cfg):
             if torch.backends.mps.is_available():
                 return "mps"
 
-            if is_torch_npu_available():
-                return f"npu:{cfg.local_rank}"
-
-            raise SystemError("No CUDA/mps/npu device found")
+            raise SystemError("No CUDA/mps device found")
         except Exception:  # pylint: disable=broad-exception-caught
             return "cpu"
 
@@ -44,8 +42,6 @@ def choose_device(cfg):
     else:
         if cfg.device.startswith("cuda"):
             cfg.device_map = {"": torch.cuda.current_device()}
-        elif cfg.device.startswith("npu"):
-            cfg.device_map = {"npu": torch.npu.current_device()}
         else:
             cfg.device_map = {"": cfg.device}
 
@@ -132,7 +128,7 @@ def normalize_config(cfg):
 
     cfg.is_multimodal = (
         hasattr(model_config, "model_type")
-        and model_config.model_type in ["llava", "mllama", "qwen2_vl", "qwen2_5_vl"]
+        and model_config.model_type in ["llava", "mllama"]
         or any(
             multimodal_name in cfg.base_model.lower()
             for multimodal_name in [
@@ -145,12 +141,7 @@ def normalize_config(cfg):
         cfg.processor_config = (
             cfg.processor_config or cfg.base_model_config or cfg.base_model
         )
-
-        try:
-            model_config = model_config.text_config
-        except AttributeError:
-            # for qwen2_vl
-            model_config = model_config.get_text_config()
+        model_config = model_config.text_config
 
     cfg.model_config_type = model_config.model_type
 
@@ -224,6 +215,11 @@ 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
@@ -235,11 +231,7 @@ def normalize_cfg_datasets(cfg):
                     cfg.datasets[idx].chat_template_jinja = cfg.chat_template_jinja
 
 
-def validate_config(
-    cfg: DictDefault,
-    capabilities: Optional[dict] = None,
-    env_capabilities: Optional[dict] = None,
-):
+def validate_config(cfg: DictDefault, capabilities: Optional[dict] = None):
     AxolotlConfigWCapabilities = AxolotlConfigWCapabilitiesBase
     AxolotlInputConfig = AxolotlInputConfigBase
 
@@ -249,35 +241,402 @@ def validate_config(
             AxolotlInputConfig,  # pylint: disable=invalid-name
         ) = merge_input_args()
 
-    if capabilities or env_capabilities:
-        if (capabilities and not env_capabilities) or (
-            env_capabilities and not capabilities
-        ):
-            raise ValueError(
-                "Both capabilities and env_capabilities must be provided or not provided."
-            )
-
+    if capabilities:
         return DictDefault(
             dict(
                 AxolotlConfigWCapabilities(
-                    **cfg.to_dict(),
-                    capabilities=capabilities,
-                    env_capabilities=env_capabilities,
+                    **cfg.to_dict(), capabilities=capabilities
                 ).model_dump(exclude_none=True)
             )
         )
-
     return DictDefault(
         dict(AxolotlInputConfig(**cfg.to_dict()).model_dump(exclude_none=True))
     )
 
 
-def prepare_plugins(cfg):
+def legacy_validate_config(cfg):
     """
-    Prepare the plugins for the configuration
+    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.get("plugins"):
-        plugin_manager = PluginManager.get_instance()
-        for plugin_name in cfg["plugins"]:
-            plugin_manager.register(plugin_name)
+    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

@@ -7,9 +7,9 @@ Module for pydantic models for configuration
 import logging
 import os
 from enum import Enum
+from importlib.metadata import version
 from typing import Annotated, Any, Dict, List, Literal, Optional, Tuple, Union
 
-from packaging import version
 from pydantic import (
     BaseModel,
     Field,
@@ -20,9 +20,8 @@ from pydantic import (
 )
 from transformers import SchedulerType
 from transformers.training_args import OptimizerNames
-from transformers.utils.import_utils import is_torch_npu_available
 
-from axolotl.utils.config.models.internals import EnvCapabilities, GPUCapabilities
+from axolotl.utils.config.models.internals import GPUCapabilities
 
 LOG = logging.getLogger("axolotl.utils.config.models.input")
 
@@ -51,7 +50,6 @@ class ChatTemplate(str, Enum):
     cohere = "cohere"  # pylint: disable=invalid-name
     llama3 = "llama3"  # pylint: disable=invalid-name
     llama3_2_vision = "llama3_2_vision"  # pylint: disable=invalid-name
-    llava = "llava"  # 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
@@ -59,10 +57,6 @@ class ChatTemplate(str, Enum):
     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
-    pixtral = "pixtral"  # pylint: disable=invalid-name
-    qwen2_vl = "qwen2_vl"  # pylint: disable=invalid-name
 
 
 class DeprecatedParameters(BaseModel):
@@ -72,7 +66,6 @@ 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
@@ -104,13 +97,6 @@ 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"""
@@ -254,10 +240,8 @@ class KTODataset(BaseModel):
 class LoftQConfig(BaseModel):
     """LoftQ configuration subset"""
 
-    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"})
+    loftq_bits: int = Field(default=4, metadata={"help": "Quantization bits for LoftQ"})
+    # loftq_iter: int = Field(default=1, metadata={"help": "Alternating iterations for LoftQ"})
 
 
 class PeftConfig(BaseModel):
@@ -300,8 +284,8 @@ class LoraConfig(BaseModel):
 
     qlora_sharded_model_loading: Optional[bool] = Field(
         default=False,
-        json_schema_extra={
-            "description": "load qlora model in sharded format for FSDP using answer.ai technique."
+        metadata={
+            "help": "load qlora model in sharded format for FSDP using answer.ai technique."
         },
     )
     lora_on_cpu: Optional[bool] = None
@@ -310,15 +294,13 @@ class LoraConfig(BaseModel):
 
     loraplus_lr_ratio: Optional[float] = Field(
         default=None,
-        json_schema_extra={
-            "description": "loraplus learning rate ratio lr_B / lr_A. Recommended value is 2^4."
+        metadata={
+            "help": "loraplus learning rate ratio lr_B / lr_A. Recommended value is 2^4."
         },
     )
     loraplus_lr_embedding: Optional[float] = Field(
         default=1e-6,
-        json_schema_extra={
-            "description": "loraplus learning rate for lora embedding layers."
-        },
+        metadata={"help": "loraplus learning rate for lora embedding layers."},
     )
 
     merge_lora: Optional[bool] = None
@@ -326,13 +308,11 @@ class LoraConfig(BaseModel):
     @model_validator(mode="before")
     @classmethod
     def validate_adapter(cls, data):
-        if (
-            not data.get("adapter")
-            and not data.get("inference")
-            and (data.get("load_in_8bit") or data.get("load_in_4bit"))
+        if not data.get("adapter") and (
+            data.get("load_in_8bit") or data.get("load_in_4bit")
         ):
             raise ValueError(
-                "load_in_8bit and load_in_4bit are not supported without setting an adapter for training."
+                "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."
             )
         return data
@@ -390,10 +370,10 @@ class ModelInputConfig(BaseModel):
     tokenizer_use_fast: Optional[bool] = None
     tokenizer_legacy: Optional[bool] = None
     tokenizer_type: Optional[str] = Field(
-        default=None, json_schema_extra={"description": "transformers tokenizer class"}
+        default=None, metadata={"help": "transformers tokenizer class"}
     )
     processor_type: Optional[str] = Field(
-        default=None, json_schema_extra={"description": "transformers processor class"}
+        default=None, metadata={"help": "transformers processor class"}
     )
     trust_remote_code: Optional[bool] = None
 
@@ -415,18 +395,18 @@ class HyperparametersConfig(BaseModel):
     gradient_accumulation_steps: Optional[int] = Field(default=1)
     micro_batch_size: Optional[int] = Field(
         default=1,
-        json_schema_extra={"description": "per gpu micro batch size for training"},
+        metadata={"help": "per gpu micro batch size for training"},
     )
     batch_size: Optional[int] = Field(
         default=None,
-        json_schema_extra={
-            "description": "Total batch size, we do not recommended setting this manually"
+        metadata={
+            "help": "Total batch size, we do not recommended setting this manually"
         },
     )
     eval_batch_size: Optional[int] = Field(
         default=None,
-        json_schema_extra={
-            "description": "per gpu micro batch size for evals, defaults to value of micro_batch_size"
+        metadata={
+            "help": "per gpu micro batch size for evals, defaults to value of micro_batch_size"
         },
     )
 
@@ -436,8 +416,6 @@ class HyperparametersConfig(BaseModel):
     group_by_length: Optional[bool] = None
 
     learning_rate: Union[str, float]
-    embedding_lr: Optional[float] = None
-    embedding_lr_scale: Optional[float] = None
     weight_decay: Optional[float] = 0.0
     optimizer: Optional[
         Union[
@@ -448,18 +426,16 @@ 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,
-        json_schema_extra={"description": "Optional arguments to supply to optimizer."},
+        default=None, metadata={"help": "Optional arguments to supply to optimizer."}
     )
     optim_target_modules: Optional[Union[List[str], Literal["all_linear"]]] = Field(
         default=None,
-        json_schema_extra={
-            "description": "The target modules to optimize, i.e. the module names that you would like to train."
+        metadata={
+            "help": "The target modules to optimize, i.e. the module names that you would like to train."
         },
     )
     torchdistx_path: Optional[str] = None
@@ -519,15 +495,15 @@ class LISAConfig(BaseModel):
 
     lisa_n_layers: Optional[int] = Field(
         default=None,
-        json_schema_extra={"description": "the number of activate layers in LISA"},
+        metadata={"help": "the number of activate layers in LISA"},
     )
     lisa_step_interval: Optional[int] = Field(
         default=None,
-        json_schema_extra={"description": "how often to switch layers in LISA"},
+        metadata={"help": "how often to switch layers in LISA"},
     )
     lisa_layers_attribute: Optional[str] = Field(
         default="model.layers",
-        json_schema_extra={"description": "path under the model to access the layers"},
+        metadata={"help": "path under the model to access the layers"},
     )
 
 
@@ -611,9 +587,6 @@ class AxolotlInputConfig(
 
     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
@@ -626,11 +599,9 @@ class AxolotlInputConfig(
     pretraining_dataset: Optional[  # type: ignore
         conlist(Union[PretrainingDataset, SFTDataset], min_length=1)
     ] = Field(
-        default=None,
-        json_schema_extra={"description": "streaming dataset to use for pretraining"},
+        default=None, metadata={"help": {"streaming dataset to use for pretraining"}}
     )
     dataset_processes: Optional[int] = Field(default=os.cpu_count())
-    dataset_exact_deduplication: Optional[bool] = None
     dataset_keep_in_memory: Optional[bool] = None
     dataloader_pin_memory: Optional[bool] = None
     dataloader_num_workers: Optional[int] = None
@@ -688,8 +659,7 @@ class AxolotlInputConfig(
     sequence_len: int = Field(default=512)
     min_sample_len: Optional[int] = None
     max_prompt_len: int = Field(
-        default=512,
-        json_schema_extra={"description": "maximum prompt length for RL training"},
+        default=512, metadata={"help": "maximum prompt length for RL training"}
     )
     sample_packing: Optional[bool] = None
     sample_packing_group_size: Optional[int] = 100_000
@@ -708,8 +678,8 @@ class AxolotlInputConfig(
     pretrain_multipack_buffer_size: Optional[int] = 10_000
     pretrain_multipack_attn: Optional[bool] = Field(
         default=True,
-        json_schema_extra={
-            "description": "whether to prevent cross attention for packed sequences during pretraining",
+        metadata={
+            "help": "whether to prevent cross attention for packed sequences during pretraining",
         },
     )
 
@@ -755,7 +725,7 @@ class AxolotlInputConfig(
     warmup_ratio: Optional[float] = None
     eval_steps: Optional[Union[int, float]] = None
     evals_per_epoch: Optional[Union[int]] = None
-    eval_strategy: Optional[str] = None
+    evaluation_strategy: Optional[str] = None
     save_steps: Optional[Union[int, float]] = None
     saves_per_epoch: Optional[int] = None
     save_strategy: Optional[str] = None
@@ -807,25 +777,28 @@ 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 deprecate_sharegpt_datasets(cls, datasets):
-        for _, ds_cfg in enumerate(datasets):
-            if not ds_cfg.get("type"):
+    def fix_sharegpt_datasets(cls, datasets):
+        for idx, ds_cfg in enumerate(datasets):
+            if not ds_cfg["type"]:
                 continue
-
-            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."
+            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"
                 )
-
         return datasets
 
     @model_validator(mode="before")
@@ -1057,21 +1030,21 @@ class AxolotlInputConfig(
     @classmethod
     def check_evals(cls, data):
         if (
-            data.get("eval_strategy")
+            data.get("evaluation_strategy")
             and data.get("eval_steps")
-            and data.get("eval_strategy") != "steps"
+            and data.get("evaluation_strategy") != "steps"
         ):
             raise ValueError(
-                "eval_strategy and eval_steps mismatch. Please set eval_strategy to 'steps' or remove eval_steps."
+                "evaluation_strategy and eval_steps mismatch. Please set evaluation_strategy to 'steps' or remove eval_steps."
             )
 
         if (
             data.get("val_set_size") == 0
-            and (data.get("eval_steps") or data.get("eval_strategy"))
+            and (data.get("eval_steps") or data.get("evaluation_strategy"))
             and not data.get("test_datasets")
         ):
             raise ValueError(
-                "eval_steps and eval_strategy are not supported with val_set_size == 0"
+                "eval_steps and evaluation_strategy are not supported with val_set_size == 0"
             )
         if data.get("evals_per_epoch") and data.get("eval_steps"):
             raise ValueError(
@@ -1079,11 +1052,11 @@ class AxolotlInputConfig(
             )
         if (
             data.get("evals_per_epoch")
-            and data.get("eval_strategy")
-            and data.get("eval_strategy") != "steps"
+            and data.get("evaluation_strategy")
+            and data.get("evaluation_strategy") != "steps"
         ):
             raise ValueError(
-                "eval_strategy must be empty or set to `steps` when used with evals_per_epoch."
+                "evaluation_strategy must be empty or set to `steps` when used with evals_per_epoch."
             )
 
         if data.get("do_bench_eval") and not (
@@ -1315,26 +1288,6 @@ 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 data.get("deepspeed", "") is not None
-            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):
@@ -1344,19 +1297,6 @@ 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):
@@ -1435,6 +1375,21 @@ class AxolotlInputConfig(
                 )
         return data
 
+    @model_validator(mode="before")
+    @classmethod
+    def check_unsloth_xformers_version(cls, data):
+        if (
+            data.get("unsloth_lora_mlp")
+            or data.get("unsloth_lora_qkv")
+            or data.get("unsloth_lora_o")
+        ):
+            xformers_version = version("xformers")
+            if xformers_version == "0.0.27":
+                raise ValueError(
+                    "xformers version 0.0.27 is not supported with unsloth. Please downgrade to 0.0.26.post1"
+                )
+        return data
+
     @model_validator(mode="before")
     @classmethod
     def check_torch_compile_deepspeed(cls, data):
@@ -1444,46 +1399,11 @@ class AxolotlInputConfig(
             )
         return data
 
-    @model_validator(mode="before")
-    @classmethod
-    def check_npu_config(cls, data):
-        if is_torch_npu_available():
-            # check attention config
-            attn_list = ["flash_attention", "sdp_attention", "s2_attention"]
-            for attn in attn_list:
-                if data.get(attn):
-                    raise NotImplementedError(
-                        f"{attn} is currently not supported in Ascend npu, please disable this configuration."
-                    )
-
-            # check quant config
-            if data.get("optimizer") is not None and "bit" in data.get("optimizer"):
-                optimizer = data.get("optimizer")
-                raise NotImplementedError(
-                    f"{optimizer} is currently not supported in Ascend npu, choose another one please."
-                )
-
-            quant_list = ["load_in_8bit", "load_in_4bit"]
-            for quant in quant_list:
-                if data.get(quant):
-                    raise NotImplementedError(
-                        f"Quantification is currently not supported in Ascend npu, please disable {quant}."
-                    )
-
-            # check dtype config
-            if data.get("tf32"):
-                raise NotImplementedError(
-                    "tf32 dtype is currently not supported in Ascend npu, please disable this configuration"
-                )
-
-        return data
-
 
 class AxolotlConfigWCapabilities(AxolotlInputConfig):
     """wrapper to valdiate gpu capabilities with the configured options"""
 
     capabilities: GPUCapabilities
-    env_capabilities: EnvCapabilities
 
     @model_validator(mode="after")
     def check_bf16(self):
@@ -1558,21 +1478,3 @@ class AxolotlConfigWCapabilities(AxolotlInputConfig):
                     "unsloth_lora_mlp, unsloth_lora_qkv, and unsloth_lora_o are not compatible with multi-GPU training."
                 )
         return data
-
-    @model_validator(mode="before")
-    @classmethod
-    def check_adopt_torch_version(cls, data):
-        if (data.get("optimizer") is not None) and ("adopt" in data.get("optimizer")):
-            env_capabilities = data.get("env_capabilities", {})
-            torch_version = env_capabilities.get("torch_version")
-
-            if torch_version is None:
-                import torch
-
-                torch_version = str(torch.__version__).split("+", maxsplit=1)[0]
-
-            if version.parse(torch_version) < version.parse("2.5.1"):
-                raise ValueError(
-                    "ADOPT optimizer is incompatible with torch version < 2.5.1"
-                )
-        return data

src/axolotl/utils/config/models/internals/__init__.py

@@ -12,9 +12,3 @@ class GPUCapabilities(BaseModel):
     n_gpu: int = Field(default=1)
     n_node: int = Field(default=1)
     compute_capability: Optional[str] = Field(default=None)
-
-
-class EnvCapabilities(BaseModel):
-    """model to manage the environment capabilities statically"""
-
-    torch_version: Optional[str] = Field(default=None)

src/axolotl/utils/data/rl.py

@@ -13,7 +13,7 @@ from axolotl.common.const import DEFAULT_DATASET_PREPARED_PATH
 from axolotl.prompt_strategies.dpo import load as load_dpo
 from axolotl.prompt_strategies.kto import load as load_kto
 from axolotl.prompt_strategies.orpo import load as load_orpo
-from axolotl.utils.data.utils import deduplicate_and_log_datasets, md5
+from axolotl.utils.data.utils import md5
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.distributed import is_main_process, zero_first
 from axolotl.utils.models import load_tokenizer
@@ -64,57 +64,15 @@ 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] = []
@@ -136,7 +94,7 @@ def load_prepare_dpo_datasets(cfg):
                 )
                 split_datasets.insert(i, ds)
 
-        tokenizer = load_tokenizer(cfg)
+        tokenizer = None
 
         for i, data_set in enumerate(split_datasets):
             _type = dataset_cfgs[i]["type"]
@@ -163,28 +121,7 @@ def load_prepare_dpo_datasets(cfg):
                 # "prompt", "chosen" and "rejected" already preprocessed
                 split_datasets[i] = data_set
 
-            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
+        return concatenate_datasets(split_datasets)
 
     with zero_first(is_main_process()):
         train_is_preprocessed = False
@@ -208,9 +145,4 @@ def load_prepare_dpo_datasets(cfg):
         if eval_dataset and not eval_is_preprocessed:
             _save_preprocessed_ds(cfg, cfg.test_datasets, eval_dataset)
 
-    if cfg.dataset_exact_deduplication:
-        train_dataset, eval_dataset, _ = deduplicate_and_log_datasets(
-            train_dataset=train_dataset, eval_dataset=eval_dataset
-        )
-
     return train_dataset, eval_dataset

src/axolotl/utils/data/sft.py

@@ -2,11 +2,9 @@
 
 import functools
 import logging
-import time
 from pathlib import Path
 from typing import List, Optional, Tuple, Union
 
-import requests
 from datasets import (
     Dataset,
     DatasetDict,
@@ -44,7 +42,7 @@ from axolotl.prompters import (
     UnsupportedPrompter,
 )
 from axolotl.utils.data.pretraining import wrap_pretraining_dataset
-from axolotl.utils.data.utils import deduplicate_and_log_datasets, md5
+from axolotl.utils.data.utils import md5
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.distributed import is_local_main_process, zero_first
 from axolotl.utils.trainer import (
@@ -55,28 +53,6 @@ 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:
@@ -136,9 +112,8 @@ def prepare_dataset(cfg, tokenizer, processor=None):
         # https://discuss.huggingface.co/t/how-to-use-huggingface-trainer-streaming-datasets-without-wrapping-it-with-torchdatas-iterablewrapper/25230
         train_dataset = train_dataset.with_format("torch")
         eval_dataset = None
-        if cfg.dataset_exact_deduplication:
-            LOG.info("Deduplication not available for pretrained datasets")
         return train_dataset, eval_dataset, cfg.max_steps, prompters
+
     if eval_dataset and cfg.sample_packing and cfg.eval_sample_packing is not False:
         total_eval_steps = calculate_total_num_steps(cfg, eval_dataset, update=False)
         if total_eval_steps == 0:
@@ -261,7 +236,6 @@ 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
@@ -271,7 +245,6 @@ 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):
@@ -351,15 +324,7 @@ def load_tokenized_prepared_datasets(
                             split=None,
                         )
                     else:
-                        try:
-                            ds = load_from_disk(config_dataset.path)
-                        except FileNotFoundError:
-                            ds = load_dataset(
-                                config_dataset.path,
-                                name=config_dataset.name,
-                                streaming=False,
-                                split=None,
-                            )
+                        ds = load_from_disk(config_dataset.path)
                 elif local_path.is_file():
                     ds_type = get_ds_type(config_dataset)
 
@@ -377,7 +342,7 @@ 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,
@@ -385,7 +350,6 @@ 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:
@@ -403,7 +367,6 @@ 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)
@@ -414,7 +377,6 @@ 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):
@@ -585,8 +547,7 @@ def load_prepare_datasets(
         )
         train_fingerprint = md5(to_hash_train)
         test_fingerprint = md5(to_hash_test)
-        if cfg.dataset_exact_deduplication:
-            _, _, dataset = deduplicate_and_log_datasets(dataset=dataset)
+
         dataset = dataset.train_test_split(
             test_size=val_set_size,
             shuffle=False,
@@ -598,17 +559,12 @@ def load_prepare_datasets(
         train_dataset = dataset["train"]
         eval_dataset = dataset["test"]
     elif split == "test":
-        if cfg.dataset_exact_deduplication:
-            _, eval_dataset, _ = deduplicate_and_log_datasets(eval_dataset=dataset)
-        else:
-            eval_dataset = dataset
         train_dataset = None
+        eval_dataset = dataset
     else:
-        if cfg.dataset_exact_deduplication:
-            train_dataset, _, _ = deduplicate_and_log_datasets(train_dataset=dataset)
-        else:
-            train_dataset = dataset
+        train_dataset = dataset
         eval_dataset = None
+
     return train_dataset, eval_dataset, prompters