multipack support for phi moe

* Update `get_unpad_data` patching for multipack

* Update src/axolotl/utils/models.py

* Update src/axolotl/utils/models.py

* Add test case

---------

Co-authored-by: Wing Lian <wing.lian@gmail.com>
Co-authored-by: Wing Lian <wing@axolotl.ai>

.github/workflows/base.yml

@@ -24,27 +24,41 @@ jobs:
             python_version: "3.11"
             pytorch: 2.3.1
             torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
+          - cuda: "124"
+            cuda_version: 12.4.1
+            cudnn_version: ""
+            python_version: "3.10"
+            pytorch: 2.4.1
+            torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
           - cuda: "124"
             cuda_version: 12.4.1
             cudnn_version: ""
             python_version: "3.11"
-            pytorch: 2.4.0
+            pytorch: 2.4.1
+            torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
+          - cuda: "124"
+            cuda_version: 12.4.1
+            cudnn_version: ""
+            python_version: "3.11"
+            pytorch: 2.5.1
             torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
     steps:
       - name: Checkout
-        uses: actions/checkout@v3
+        uses: actions/checkout@v4
       - name: Docker metadata
         id: metadata
-        uses: docker/metadata-action@v3
+        uses: docker/metadata-action@v5
         with:
-          images: winglian/axolotl-base
+          images: |
+            winglian/axolotl-base
+            axolotlai/axolotl-base
       - name: Login to Docker Hub
         uses: docker/login-action@v2
         with:
           username: ${{ secrets.DOCKERHUB_USERNAME }}
           password: ${{ secrets.DOCKERHUB_TOKEN }}
       - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v2
+        uses: docker/setup-buildx-action@v3
       - name: Build
         uses: docker/build-push-action@v4
         with:

.github/workflows/docs.yml

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

.github/workflows/lint.yml

@@ -6,7 +6,7 @@ on:
        - '**.py'
        - 'requirements.txt'
        - '.github/workflows/*.yml'
-       - "*.md"
+       - "*.[q]md"
        - "examples/**/*.y[a]?ml"
   workflow_dispatch:
 
@@ -15,9 +15,9 @@ jobs:
     name: pre-commit
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v3
-      - uses: actions/setup-python@v4
+      - uses: actions/checkout@v4
+      - uses: actions/setup-python@v5
         with:
           python-version: "3.10"
           cache: 'pip' # caching pip dependencies
-      - uses: pre-commit/action@v3.0.0
+      - uses: pre-commit/action@v3.0.1

.github/workflows/main.yml

@@ -4,6 +4,8 @@ on:
   push:
     branches:
       - "main"
+    tags:
+      - "v*"
   workflow_dispatch:
 
 jobs:
@@ -27,7 +29,12 @@ jobs:
           - cuda: 124
             cuda_version: 12.4.1
             python_version: "3.11"
-            pytorch: 2.4.0
+            pytorch: 2.4.1
+            axolotl_extras:
+          - cuda: 124
+            cuda_version: 12.4.1
+            python_version: "3.11"
+            pytorch: 2.5.1
             axolotl_extras:
     runs-on: axolotl-gpu-runner
     steps:
@@ -37,7 +44,12 @@ jobs:
         id: metadata
         uses: docker/metadata-action@v5
         with:
-          images: winglian/axolotl
+          images: |
+            winglian/axolotl
+            axolotlai/axolotl
+          tags: |
+            type=ref,event=branch
+            type=semver,pattern={{version}}
       - name: Set up Docker Buildx
         uses: docker/setup-buildx-action@v3
       - name: Login to Docker Hub
@@ -51,7 +63,7 @@ jobs:
         with:
           context: .
           build-args: |
-            BASE_TAG=${{ github.ref_name }}-base-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}
+            BASE_TAG=${{ github.ref_type == 'tag' && 'main' || github.ref_name }}-base-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}
             CUDA=${{ matrix.cuda }}
             PYTORCH_VERSION=${{ matrix.pytorch }}
             AXOLOTL_ARGS=${{ matrix.axolotl_args }}
@@ -84,7 +96,12 @@ jobs:
           - cuda: 124
             cuda_version: 12.4.1
             python_version: "3.11"
-            pytorch: 2.4.0
+            pytorch: 2.4.1
+            axolotl_extras:
+          - cuda: 124
+            cuda_version: 12.4.1
+            python_version: "3.11"
+            pytorch: 2.5.1
             axolotl_extras:
     runs-on: axolotl-gpu-runner
     steps:
@@ -94,20 +111,25 @@ jobs:
         id: metadata
         uses: docker/metadata-action@v5
         with:
-          images: winglian/axolotl-cloud
+          images: |
+            winglian/axolotl-cloud
+            axolotlai/axolotl-cloud
+          tags: |
+            type=ref,event=branch
+            type=semver,pattern={{version}}
       - name: Login to Docker Hub
         uses: docker/login-action@v3
         with:
           username: ${{ secrets.DOCKERHUB_USERNAME }}
           password: ${{ secrets.DOCKERHUB_TOKEN }}
       - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v2
+        uses: docker/setup-buildx-action@v3
       - name: Build
         uses: docker/build-push-action@v5
         with:
           context: .
           build-args: |
-            BASE_TAG=${{ github.ref_name }}-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
+            BASE_TAG=${{ github.ref_type == 'tag' && 'main' || github.ref_name }}-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
             CUDA=${{ matrix.cuda }}
           file: ./docker/Dockerfile-cloud
           push: ${{ github.event_name != 'pull_request' }}
@@ -136,20 +158,25 @@ jobs:
         id: metadata
         uses: docker/metadata-action@v5
         with:
-          images: winglian/axolotl-cloud-term
+          images: |
+            winglian/axolotl-cloud-term
+            axolotlai/axolotl-cloud-term
+          tags: |
+            type=ref,event=branch
+            type=semver,pattern={{version}}
       - name: Login to Docker Hub
         uses: docker/login-action@v3
         with:
           username: ${{ secrets.DOCKERHUB_USERNAME }}
           password: ${{ secrets.DOCKERHUB_TOKEN }}
       - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v2
+        uses: docker/setup-buildx-action@v3
       - name: Build
         uses: docker/build-push-action@v5
         with:
           context: .
           build-args: |
-            BASE_TAG=${{ github.ref_name }}-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
+            BASE_TAG=${{ github.ref_type == 'tag' && 'main' || github.ref_name }}-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
             CUDA=${{ matrix.cuda }}
           file: ./docker/Dockerfile-cloud-no-tmux
           push: ${{ github.event_name != 'pull_request' }}

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

@@ -1,10 +1,18 @@
 name: docker-multigpu-tests-biweekly
 
 on:
+  pull_request:
+    paths:
+      - 'tests/e2e/multigpu/*.py'
   workflow_dispatch:
   schedule:
     - cron: '0 0 * * 1,4'  # Runs at 00:00 UTC every monday & thursday
 
+# Cancel jobs on the same ref if a new one is triggered
+concurrency:
+  group: ${{ github.workflow }}-${{ github.ref }}
+  cancel-in-progress: ${{ github.ref != 'refs/heads/main' }}
+
 jobs:
   test-axolotl-multigpu:
     if: ${{ ! contains(github.event.commits[0].message, '[skip docker]]') && github.repository_owner == 'axolotl-ai-cloud' }}
@@ -18,6 +26,20 @@ jobs:
             pytorch: 2.3.1
             axolotl_extras:
             num_gpus: 2
+          - cuda: 124
+            cuda_version: 12.4.1
+            python_version: "3.11"
+            pytorch: 2.4.1
+            axolotl_extras:
+            num_gpus: 2
+            nightly_build: "true"
+          - cuda: 124
+            cuda_version: 12.4.1
+            python_version: "3.11"
+            pytorch: 2.5.1
+            axolotl_extras:
+            num_gpus: 2
+            nightly_build: "true"
     runs-on: [self-hosted, modal]
     timeout-minutes: 120
     steps:
@@ -39,6 +61,7 @@ jobs:
           echo "AXOLOTL_EXTRAS=${{ matrix.axolotl_extras}}" >> $GITHUB_ENV
           echo "CUDA=${{ matrix.cuda }}" >> $GITHUB_ENV
           echo "N_GPUS=${{ matrix.num_gpus }}" >> $GITHUB_ENV
+          echo "NIGHTLY_BUILD=${{ matrix.nightly_build }}" >> $GITHUB_ENV
       - name: Run tests job on Modal
         run: |
           modal run cicd.multigpu

.github/workflows/nightlies.yml

@@ -26,7 +26,12 @@ jobs:
           - cuda: 124
             cuda_version: 12.4.1
             python_version: "3.11"
-            pytorch: 2.4.0
+            pytorch: 2.4.1
+            axolotl_extras:
+          - cuda: 124
+            cuda_version: 12.4.1
+            python_version: "3.11"
+            pytorch: 2.5.1
             axolotl_extras:
     runs-on: axolotl-gpu-runner
     steps:
@@ -36,7 +41,9 @@ jobs:
         id: metadata
         uses: docker/metadata-action@v5
         with:
-          images: winglian/axolotl
+          images: |
+            winglian/axolotl
+            axolotlai/axolotl
           tags: |
             type=raw,value={{ branch }}-{{ date 'YYYYMMDD' }}
       - name: Set up Docker Buildx
@@ -83,7 +90,12 @@ jobs:
           - cuda: 124
             cuda_version: 12.4.1
             python_version: "3.11"
-            pytorch: 2.4.0
+            pytorch: 2.4.1
+            axolotl_extras:
+          - cuda: 124
+            cuda_version: 12.4.1
+            python_version: "3.11"
+            pytorch: 2.5.1
             axolotl_extras:
     runs-on: axolotl-gpu-runner
     steps:
@@ -93,7 +105,9 @@ jobs:
         id: metadata
         uses: docker/metadata-action@v5
         with:
-          images: winglian/axolotl-cloud
+          images: |
+            winglian/axolotl-cloud
+            axolotlai/axolotl-cloud
           tags: |
             type=raw,value={{ branch }}-{{ date 'YYYYMMDD' }}
       - name: Login to Docker Hub
@@ -102,7 +116,7 @@ jobs:
           username: ${{ secrets.DOCKERHUB_USERNAME }}
           password: ${{ secrets.DOCKERHUB_TOKEN }}
       - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v2
+        uses: docker/setup-buildx-action@v3
       - name: Build
         uses: docker/build-push-action@v5
         with:

.github/workflows/pypi.yml

@@ -3,12 +3,31 @@ name: publish pypi
 on:
   push:
     tags:
-      - '*'
+      - 'v*'
+  workflow_dispatch:
 
 jobs:
+  setup_release:
+    name: Create Release
+    runs-on: ubuntu-latest
+    steps:
+      - name: Get the tag version
+        id: extract_branch
+        run: echo ::set-output name=branch::${GITHUB_REF#refs/tags/}
+        shell: bash
+
+      - name: Create Release
+        id: create_release
+        uses: actions/create-release@v1
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+        with:
+          tag_name: ${{ steps.extract_branch.outputs.branch }}
+          release_name: ${{ steps.extract_branch.outputs.branch }}
   pypi-publish:
     name: Upload release to PyPI
     runs-on: ubuntu-latest
+    needs: [setup_release]
     environment:
       name: pypi
       url: https://pypi.org/p/axolotl
@@ -16,10 +35,10 @@ jobs:
       id-token: write  # IMPORTANT: this permission is mandatory for trusted publishing
     steps:
       - name: Check out repository code
-        uses: actions/checkout@v3
+        uses: actions/checkout@v4
 
       - name: Setup Python
-        uses: actions/setup-python@v4
+        uses: actions/setup-python@v5
         with:
           python-version: "3.10"
 
@@ -27,7 +46,7 @@ jobs:
         run: |
           pip3 install wheel packaging
           pip3 install -e .
-          pip3 install -r requirements-tests.txt
+          pip3 install -r requirements-dev.txt -r requirements-tests.txt
 
       - name: Extract tag name
         id: tag

.github/workflows/tests-nightly.yml

@@ -0,0 +1,122 @@
+name: Tests Nightly against upstream main
+on:
+  workflow_dispatch:
+  schedule:
+    - cron: '0 0 * * *'  # Runs at 00:00 UTC every day
+
+jobs:
+  pre-commit:
+    name: pre-commit
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - uses: actions/setup-python@v5
+        with:
+          python-version: "3.10"
+          cache: 'pip' # caching pip dependencies
+      - uses: pre-commit/action@v3.0.1
+        env:
+          SKIP: no-commit-to-branch
+
+  pytest:
+    name: PyTest
+    runs-on: ubuntu-latest
+    strategy:
+      fail-fast: false
+      matrix:
+        python_version: ["3.10", "3.11"]
+        pytorch_version: ["2.3.1", "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: ${{ matrix.python_version }}
+          cache: 'pip' # caching pip dependencies
+
+      - name: Install PyTorch
+        run: |
+          pip3 install torch==${{ matrix.pytorch_version }} --index-url https://download.pytorch.org/whl/cpu
+
+      - name: Update requirements.txt
+        run: |
+          sed -i 's#^transformers.*#transformers @ git+https://github.com/huggingface/transformers.git@main#' requirements.txt
+          sed -i 's#^peft.*#peft @ git+https://github.com/huggingface/peft.git@main#' requirements.txt
+          sed -i 's#^accelerate.*#accelerate @ git+https://github.com/huggingface/accelerate.git@main#' requirements.txt
+          sed -i 's#^trl.*#trl @ git+https://github.com/huggingface/trl.git@main#' requirements.txt
+          sed -i 's#^datasets.*#datasets @ git+https://github.com/huggingface/datasets.git@main#' requirements.txt
+
+      - name: Install dependencies
+        run: |
+          pip3 install --upgrade pip
+          pip3 install --upgrade packaging
+          pip3 install -U -e .
+          pip3 install -r requirements-dev.txt -r requirements-tests.txt
+
+      - name: Run tests
+        run: |
+          pytest --ignore=tests/e2e/ tests/
+
+      - name: cleanup pip cache
+        run: |
+          find "$(pip cache dir)/http-v2" -type f -mtime +14 -exec rm {} \;
+
+  docker-e2e-tests:
+    if: github.repository_owner == 'axolotl-ai-cloud'
+    # this job needs to be run on self-hosted GPU runners...
+    runs-on: [self-hosted, modal]
+    timeout-minutes: 60
+    needs: [pre-commit, pytest]
+
+    strategy:
+      fail-fast: false
+      matrix:
+        include:
+          - cuda: 121
+            cuda_version: 12.1.1
+            python_version: "3.10"
+            pytorch: 2.3.1
+            num_gpus: 1
+            axolotl_extras: mamba-ssm
+            nightly_build: "true"
+          - cuda: 124
+            cuda_version: 12.4.1
+            python_version: "3.11"
+            pytorch: 2.4.1
+            num_gpus: 1
+            axolotl_extras:
+            nightly_build: "true"
+          - cuda: 124
+            cuda_version: 12.4.1
+            python_version: "3.11"
+            pytorch: 2.5.1
+            num_gpus: 1
+            axolotl_extras:
+            nightly_build: "true"
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+      - name: Install Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: "3.10"
+      - name: Install Modal
+        run: |
+          python -m pip install --upgrade pip
+          pip install modal==0.63.64 jinja2
+      - name: Update env vars
+        run: |
+          echo "BASE_TAG=main-base-py${{ 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 }}" >> $GITHUB_ENV
+          echo "NIGHTLY_BUILD=${{ matrix.nightly_build }}" >> $GITHUB_ENV
+      - name: Run tests job on Modal
+        run: |
+          modal run cicd.tests

.github/workflows/tests.yml

@@ -15,17 +15,22 @@ on:
        - '.github/workflows/*.yml'
   workflow_dispatch:
 
+# Cancel jobs on the same ref if a new one is triggered
+concurrency:
+  group: ${{ github.workflow }}-${{ github.ref }}
+  cancel-in-progress: ${{ github.ref != 'refs/heads/main' }}
+
 jobs:
   pre-commit:
     name: pre-commit
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v3
-      - uses: actions/setup-python@v4
+      - uses: actions/checkout@v4
+      - uses: actions/setup-python@v5
         with:
           python-version: "3.10"
           cache: 'pip' # caching pip dependencies
-      - uses: pre-commit/action@v3.0.0
+      - uses: pre-commit/action@v3.0.1
         env:
           SKIP: no-commit-to-branch
 
@@ -36,24 +41,33 @@ jobs:
       fail-fast: false
       matrix:
         python_version: ["3.10", "3.11"]
+        pytorch_version: ["2.3.1", "2.4.1", "2.5.1"]
     timeout-minutes: 20
 
     steps:
       - name: Check out repository code
-        uses: actions/checkout@v3
+        uses: actions/checkout@v4
 
       - name: Setup Python
-        uses: actions/setup-python@v4
+        uses: actions/setup-python@v5
         with:
           python-version: ${{ matrix.python_version }}
           cache: 'pip' # caching pip dependencies
 
-      - name: Install dependencies
+      - name: upgrade pip
         run: |
           pip3 install --upgrade pip
-          pip3 install --upgrade packaging
+          pip3 install --upgrade packaging setuptools wheel
+
+      - name: Install PyTorch
+        run: |
+          pip3 install torch==${{ matrix.pytorch_version }}
+
+      - name: Install dependencies
+        run: |
+          pip3 show torch
           pip3 install -U -e .
-          pip3 install -r requirements-tests.txt
+          pip3 install -r requirements-dev.txt -r requirements-tests.txt
 
       - name: Run tests
         run: |
@@ -63,33 +77,67 @@ jobs:
         run: |
           find "$(pip cache dir)/http-v2" -type f -mtime +14 -exec rm {} \;
 
-  docker-e2e-tests:
+  docker-e2e-tests-1st:
     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: 60
+    timeout-minutes: 90
     needs: [pre-commit, pytest]
 
     strategy:
       fail-fast: false
       matrix:
         include:
-          - cuda: 121
-            cuda_version: 12.1.1
-            python_version: "3.10"
-            pytorch: 2.3.1
-            num_gpus: 1
-            axolotl_extras: mamba-ssm
-          - cuda: 121
-            cuda_version: 12.1.1
-            python_version: "3.11"
-            pytorch: 2.3.1
-            num_gpus: 1
-            axolotl_extras: mamba-ssm
           - cuda: 124
             cuda_version: 12.4.1
             python_version: "3.11"
-            pytorch: 2.4.0
+            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]
+
+    strategy:
+      fail-fast: false
+      matrix:
+        include:
+          - cuda: 121
+            cuda_version: 12.1.1
+            python_version: "3.10"
+            pytorch: 2.3.1
+            num_gpus: 1
+            axolotl_extras: mamba-ssm
+          - cuda: 124
+            cuda_version: 12.4.1
+            python_version: "3.11"
+            pytorch: 2.5.1
             num_gpus: 1
             axolotl_extras:
     steps:

.isort.cfg

@@ -1,3 +1,3 @@
 [settings]
 profile=black
-known_third_party=wandb
+known_third_party=wandb,comet_ml

.mypy.ini

@@ -11,6 +11,9 @@ ignore_errors = True
 [mypy-axolotl.models.mixtral.*]
 ignore_errors = True
 
+[mypy-axolotl.integrations.liger.models.*]
+ignore_errors = True
+
 [mypy-axolotl.models.phi.*]
 ignore_errors = True
 

README.md

@@ -1,5 +1,9 @@
 # Axolotl
 
+![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.
 
 Features:
@@ -7,10 +11,10 @@ Features:
 - Supports fullfinetune, lora, qlora, relora, and gptq
 - Customize configurations using a simple yaml file or CLI overwrite
 - Load different dataset formats, use custom formats, or bring your own tokenized datasets
-- Integrated with xformer, flash attention, rope scaling, and multipacking
+- Integrated with xformer, flash attention, [liger kernel](https://github.com/linkedin/Liger-Kernel), rope scaling, and multipacking
 - Works with single GPU or multiple GPUs via FSDP or Deepspeed
 - Easily run with Docker locally or on the cloud
-- Log results and optionally checkpoints to wandb or mlflow
+- Log results and optionally checkpoints to wandb, mlflow or Comet
 - And more!
 
 <a href="https://www.phorm.ai/query?projectId=e315ba4a-4e14-421f-ab05-38a1f9076f25">
@@ -22,39 +26,50 @@ Features:
 <td>
 
 ## Table of Contents
-- [Introduction](#axolotl)
-- [Supported Features](#axolotl-supports)
-- [Quickstart](#quickstart-)
-- [Environment](#environment)
-  - [Docker](#docker)
-  - [Conda/Pip venv](#condapip-venv)
-  - [Cloud GPU](#cloud-gpu) - Latitude.sh, JarvisLabs, RunPod
-  - [Bare Metal Cloud GPU](#bare-metal-cloud-gpu)
-  - [Windows](#windows)
-  - [Mac](#mac)
-  - [Google Colab](#google-colab)
-  - [Launching on public clouds via SkyPilot](#launching-on-public-clouds-via-skypilot)
-  - [Launching on public clouds via dstack](#launching-on-public-clouds-via-dstack)
-- [Dataset](#dataset)
-- [Config](#config)
-  - [Train](#train)
-  - [Inference](#inference-playground)
-  - [Merge LORA to Base](#merge-lora-to-base)
-  - [Special Tokens](#special-tokens)
-  - [All Config Options](#all-config-options)
-- Advanced Topics
-  - [Multipack](./docs/multipack.qmd)<svg width="24" height="24" viewBox="0 0 24 24" xmlns="http://www.w3.org/2000/svg"><path d="M17 13.5v6H5v-12h6m3-3h6v6m0-6-9 9" class="icon_svg-stroke" stroke="#666" stroke-width="1.5" fill="none" fill-rule="evenodd" stroke-linecap="round" stroke-linejoin="round"></path></svg>
-  - [RLHF & DPO](./docs/rlhf.qmd)<svg width="24" height="24" viewBox="0 0 24 24" xmlns="http://www.w3.org/2000/svg"><path d="M17 13.5v6H5v-12h6m3-3h6v6m0-6-9 9" class="icon_svg-stroke" stroke="#666" stroke-width="1.5" fill="none" fill-rule="evenodd" stroke-linecap="round" stroke-linejoin="round"></path></svg>
-  - [Dataset Pre-Processing](./docs/dataset_preprocessing.qmd)<svg width="24" height="24" viewBox="0 0 24 24" xmlns="http://www.w3.org/2000/svg"><path d="M17 13.5v6H5v-12h6m3-3h6v6m0-6-9 9" class="icon_svg-stroke" stroke="#666" stroke-width="1.5" fill="none" fill-rule="evenodd" stroke-linecap="round" stroke-linejoin="round"></path></svg>
-  - [Unsloth](./docs/unsloth.qmd)<svg width="24" height="24" viewBox="0 0 24 24" xmlns="http://www.w3.org/2000/svg"><path d="M17 13.5v6H5v-12h6m3-3h6v6m0-6-9 9" class="icon_svg-stroke" stroke="#666" stroke-width="1.5" fill="none" fill-rule="evenodd" stroke-linecap="round" stroke-linejoin="round"></path></svg>
-- [Common Errors](#common-errors-)
-  - [Tokenization Mismatch b/w Training & Inference](#tokenization-mismatch-bw-inference--training)
-- [Debugging Axolotl](#debugging-axolotl)
-- [Need Help?](#need-help-)
-- [Badge](#badge-)
-- [Community Showcase](#community-showcase)
-- [Contributing](#contributing-)
-- [Sponsors](#sponsors-)
+- [Axolotl](#axolotl)
+  - [Table of Contents](#table-of-contents)
+  - [Axolotl supports](#axolotl-supports)
+  - [Quickstart ⚡](#quickstart-)
+    - [Usage](#usage)
+  - [Advanced Setup](#advanced-setup)
+    - [Environment](#environment)
+      - [Docker](#docker)
+      - [Conda/Pip venv](#condapip-venv)
+      - [Cloud GPU](#cloud-gpu)
+      - [Bare Metal Cloud GPU](#bare-metal-cloud-gpu)
+        - [LambdaLabs](#lambdalabs)
+        - [GCP](#gcp)
+      - [Windows](#windows)
+      - [Mac](#mac)
+      - [Google Colab](#google-colab)
+      - [Launching on public clouds via SkyPilot](#launching-on-public-clouds-via-skypilot)
+      - [Launching on public clouds via dstack](#launching-on-public-clouds-via-dstack)
+    - [Dataset](#dataset)
+    - [Config](#config)
+      - [All Config Options](#all-config-options)
+    - [Train](#train)
+      - [Preprocess dataset](#preprocess-dataset)
+      - [Multi-GPU](#multi-gpu)
+        - [DeepSpeed](#deepspeed)
+        - [FSDP](#fsdp)
+        - [FSDP + QLoRA](#fsdp--qlora)
+        - [Weights \& Biases Logging](#weights--biases-logging)
+        - [Special Tokens](#special-tokens)
+      - [Liger Kernel](#liger-kernel)
+    - [Inference Playground](#inference-playground)
+    - [Merge LORA to base](#merge-lora-to-base)
+  - [Common Errors 🧰](#common-errors-)
+    - [Tokenization Mismatch b/w Inference \& Training](#tokenization-mismatch-bw-inference--training)
+  - [Debugging Axolotl](#debugging-axolotl)
+  - [Need help? 🙋](#need-help-)
+  - [Badge ❤🏷️](#badge-️)
+  - [Community Showcase](#community-showcase)
+  - [Contributing 🤝](#contributing-)
+  - [Sponsors 🤝❤](#sponsors-)
+      - [💎 Diamond Sponsors - Contact directly](#-diamond-sponsors---contact-directly)
+      - [🥇 Gold Sponsors - $5000/mo](#-gold-sponsors---5000mo)
+      - [🥈 Silver Sponsors - $1000/mo](#-silver-sponsors---1000mo)
+      - [🥉 Bronze Sponsors - $500/mo](#-bronze-sponsors---500mo)
 
 </td>
 <td>
@@ -96,6 +111,7 @@ Features:
 | RWKV        | ✅         | ❓    | ❓     | ❓             | ❓                 | ❓          | ❓            |
 | Qwen        | ✅         | ✅    | ✅     | ❓             | ❓                 | ❓          | ❓            |
 | Gemma       | ✅         | ✅    | ✅     | ❓             | ❓                 | ✅          | ❓            |
+| Jamba       | ✅         | ✅    | ✅     | ❓             | ❓                 | ✅          | ❓            |
 
 ✅: supported
 ❌: not supported
@@ -105,7 +121,7 @@ Features:
 
 Get started with Axolotl in just a few steps! This quickstart guide will walk you through setting up and running a basic fine-tuning task.
 
-**Requirements**: Python >=3.10 and Pytorch >=2.1.1.
+**Requirements**: Nvidia GPU (Ampere architecture or newer for `bf16` and Flash Attention), Python >=3.10 and PyTorch >=2.3.1.
 
 ```bash
 git clone https://github.com/axolotl-ai-cloud/axolotl
@@ -143,7 +159,7 @@ accelerate launch -m axolotl.cli.train https://raw.githubusercontent.com/axolotl
 #### Docker
 
   ```bash
-  docker run --gpus '"all"' --rm -it winglian/axolotl:main-latest
+  docker run --gpus '"all"' --rm -it axolotlai/axolotl:main-latest
   ```
 
   Or run on the current files for development:
@@ -162,7 +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 ${HOME}/.cache/huggingface:/root/.cache/huggingface winglian/axolotl:main-latest
+docker run --privileged --gpus '"all"' --shm-size 10g --rm -it --name axolotl --ipc=host --ulimit memlock=-1 --ulimit stack=67108864 --mount type=bind,src="${PWD}",target=/workspace/axolotl -v ${HOME}/.cache/huggingface:/root/.cache/huggingface axolotlai/axolotl:main-latest
   ```
 
   It additionally:
@@ -194,7 +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 [`winglian/axolotl-cloud:main-latest`](https://hub.docker.com/r/winglian/axolotl-cloud/tags)
+For cloud GPU providers that support docker images, use [`axolotlai/axolotl-cloud:main-latest`](https://hub.docker.com/r/axolotlai/axolotl-cloud/tags)
 
 - on Latitude.sh use this [direct link](https://latitude.sh/blueprint/989e0e79-3bf6-41ea-a46b-1f246e309d5c)
 - on JarvisLabs.ai use this [direct link](https://jarvislabs.ai/templates/axolotl)
@@ -303,7 +319,7 @@ Write a job description in YAML as below:
 # dstack.yaml
 type: task
 
-image: winglian/axolotl-cloud:main-20240429-py3.11-cu121-2.2.2
+image: axolotlai/axolotl-cloud:main-latest
 
 env:
   - HUGGING_FACE_HUB_TOKEN
@@ -367,11 +383,10 @@ See [examples](examples) for quick start. It is recommended to duplicate and mod
         - typescript
       type: ... # unimplemented custom format
 
-      # fastchat conversation
-      # See 'conversation' options: https://github.com/lm-sys/FastChat/blob/main/fastchat/conversation.py
+      # chat_template https://axolotl-ai-cloud.github.io/axolotl/docs/dataset-formats/conversation.html#chat_template
     - path: ...
-      type: sharegpt
-      conversation: chatml # default: vicuna_v1.1
+      type: chat_template
+      chat_template: chatml # defaults to tokenizer's chat_template
 
       # local
     - path: data.jsonl # or json
@@ -499,6 +514,22 @@ wandb_name:
 wandb_log_model:
 ```
 
+##### Comet Logging
+
+Make sure your `COMET_API_KEY` environment variable is set (recommended) or you login to wandb with `comet login`.
+
+- wandb options
+```yaml
+use_comet:
+comet_api_key:
+comet_workspace:
+comet_project_name:
+comet_experiment_key:
+comet_mode:
+comet_online:
+comet_experiment_config:
+```
+
 ##### Special Tokens
 
 It is important to have special tokens like delimiters, end-of-sequence, beginning-of-sequence in your tokenizer's vocabulary.  This will help you avoid tokenization issues and help your model train better.  You can do this in axolotl like this:
@@ -515,6 +546,26 @@ tokens: # these are delimiters
 
 When you include these tokens in your axolotl config, axolotl adds these tokens to the tokenizer's vocabulary.
 
+##### Liger Kernel
+
+Liger Kernel: Efficient Triton Kernels for LLM Training
+
+https://github.com/linkedin/Liger-Kernel
+
+Liger (LinkedIn GPU Efficient Runtime) Kernel is a collection of Triton kernels designed specifically for LLM training.
+It can effectively increase multi-GPU training throughput by 20% and reduces memory usage by 60%. The Liger Kernel
+composes well and is compatible with both FSDP and Deepspeed.
+
+```yaml
+plugins:
+  - axolotl.integrations.liger.LigerPlugin
+liger_rope: true
+liger_rms_norm: true
+liger_glu_activation: true
+liger_layer_norm: true
+liger_fused_linear_cross_entropy: true
+```
+
 ### Inference Playground
 
 Axolotl allows you to load your model in an interactive terminal playground for quick experimentation.

_quarto.yml

@@ -37,6 +37,7 @@ website:
             - docs/mac.qmd
             - docs/multi-node.qmd
             - docs/unsloth.qmd
+            - docs/amd_hpc.qmd
         - section: "Dataset Formats"
           contents: docs/dataset-formats/*
         - section: "Reference"

cicd/Dockerfile.jinja

@@ -1,4 +1,4 @@
-FROM winglian/axolotl-base:{{ BASE_TAG }}
+FROM axolotlai/axolotl-base:{{ BASE_TAG }}
 
 ENV TORCH_CUDA_ARCH_LIST="7.0 7.5 8.0 8.6+PTX"
 ENV AXOLOTL_EXTRAS="{{ AXOLOTL_EXTRAS }}"
@@ -8,6 +8,7 @@ ENV BNB_CUDA_VERSION="{{ CUDA }}"
 ENV PYTORCH_VERSION="{{ PYTORCH_VERSION }}"
 ENV GITHUB_REF="{{ GITHUB_REF }}"
 ENV GITHUB_SHA="{{ GITHUB_SHA }}"
+ENV NIGHTLY_BUILD="{{ NIGHTLY_BUILD }}"
 
 RUN apt-get update && \
     apt-get install -y --allow-change-held-packages vim curl nano libnccl2 libnccl-dev
@@ -22,7 +23,14 @@ RUN git fetch origin +$GITHUB_REF && \
     git checkout FETCH_HEAD
 
 # If AXOLOTL_EXTRAS is set, append it in brackets
-RUN pip install causal_conv1d
+RUN if [ "$NIGHTLY_BUILD" = "true" ] ; then \
+        sed -i 's#^transformers.*#transformers @ git+https://github.com/huggingface/transformers.git@main#' requirements.txt; \
+        sed -i 's#^peft.*#peft @ git+https://github.com/huggingface/peft.git@main#' requirements.txt; \
+        sed -i 's#^accelerate.*#accelerate @ git+https://github.com/huggingface/accelerate.git@main#' requirements.txt; \
+        sed -i 's#^trl.*#trl @ git+https://github.com/huggingface/trl.git@main#' requirements.txt; \
+        sed -i 's#^datasets.*#datasets @ git+https://github.com/huggingface/datasets.git@main#' requirements.txt; \
+    fi
+
 RUN if [ "$AXOLOTL_EXTRAS" != "" ] ; then \
         pip install -e .[deepspeed,flash-attn,optimizers,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \
     else \
@@ -30,7 +38,7 @@ RUN if [ "$AXOLOTL_EXTRAS" != "" ] ; then \
     fi
 
 # So we can test the Docker image
-RUN pip install -r requirements-tests.txt
+RUN pip install -r requirements-dev.txt -r requirements-tests.txt
 
 # fix so that git fetch/pull from remote works
 RUN git config remote.origin.fetch "+refs/heads/*:refs/remotes/origin/*" && \

cicd/cicd.sh

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

cicd/multigpu.sh

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

cicd/tests.py

@@ -10,7 +10,7 @@ import tempfile
 import jinja2
 import modal
 from jinja2 import select_autoescape
-from modal import Image, Stub
+from modal import App, Image
 
 cicd_path = pathlib.Path(__file__).parent.resolve()
 
@@ -28,6 +28,7 @@ df_args = {
     "CUDA": os.environ.get("CUDA", "121"),
     "GITHUB_REF": os.environ.get("GITHUB_REF", "refs/heads/main"),
     "GITHUB_SHA": os.environ.get("GITHUB_SHA", ""),
+    "NIGHTLY_BUILD": os.environ.get("NIGHTLY_BUILD", ""),
 }
 
 dockerfile_contents = df_template.render(**df_args)
@@ -46,7 +47,7 @@ cicd_image = (
     .pip_install("fastapi==0.110.0", "pydantic==2.6.3")
 )
 
-stub = Stub("Axolotl CI/CD", secrets=[])
+app = App("Axolotl CI/CD", secrets=[])
 
 
 N_GPUS = int(os.environ.get("N_GPUS", 1))
@@ -61,10 +62,10 @@ def run_cmd(cmd: str, run_folder: str):
         exit(exit_code)  # pylint: disable=consider-using-sys-exit
 
 
-@stub.function(
+@app.function(
     image=cicd_image,
     gpu=GPU_CONFIG,
-    timeout=45 * 60,
+    timeout=60 * 60,
     cpu=8.0,
     memory=131072,
 )
@@ -72,6 +73,6 @@ def cicd_pytest():
     run_cmd("./cicd/cicd.sh", "/workspace/axolotl")
 
 
-@stub.local_entrypoint()
+@app.local_entrypoint()
 def main():
     cicd_pytest.remote()

deepspeed_configs/zero3_bf16.json

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

deepspeed_configs/zero3_bf16_cpuoffload_all.json

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

deepspeed_configs/zero3_bf16_cpuoffload_params.json

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

devtools/dev_chat_template.yml

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

docker/Dockerfile

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

docker/Dockerfile-cloud

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

docker/Dockerfile-cloud-no-tmux

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

docker/Dockerfile-tests

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

docs/amd_hpc.qmd

@@ -0,0 +1,108 @@
+---
+title: Training with AMD GPUs on HPC Systems
+description: A comprehensive guide for using Axolotl on distributed systems with AMD GPUs
+---
+
+This guide provides step-by-step instructions for installing and configuring Axolotl on a High-Performance Computing (HPC) environment equipped with AMD GPUs.
+
+## Setup
+
+### 1. Install Python
+
+We recommend using Miniforge, a minimal conda-based Python distribution:
+
+```bash
+curl -L -O "https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-$(uname)-$(uname -m).sh"
+bash Miniforge3-$(uname)-$(uname -m).sh
+```
+
+### 2. Configure Python Environment
+Add Python to your PATH and ensure it's available at login:
+
+```bash
+echo 'export PATH=~/miniforge3/bin:$PATH' >> ~/.bashrc
+echo 'if [ -f ~/.bashrc ]; then . ~/.bashrc; fi' >> ~/.bash_profile
+```
+
+### 3. Load AMD GPU Software
+
+Load the ROCm module:
+
+```bash
+module load rocm/5.7.1
+```
+
+Note: The specific module name and version may vary depending on your HPC system. Consult your system documentation for the correct module name.
+
+### 4. Install PyTorch
+
+Install PyTorch with ROCm support:
+
+```bash
+pip install -U torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm5.7 --force-reinstall
+```
+
+### 5. Install Flash Attention
+
+Clone and install the Flash Attention repository:
+
+```bash
+git clone --recursive https://github.com/ROCmSoftwarePlatform/flash-attention.git
+export GPU_ARCHS="gfx90a"
+cd flash-attention
+export PYTHON_SITE_PACKAGES=$(python -c 'import site; print(site.getsitepackages()[0])')
+patch "${PYTHON_SITE_PACKAGES}/torch/utils/hipify/hipify_python.py" hipify_patch.patch
+pip install .
+```
+
+### 6. Install Axolotl
+
+Clone and install Axolotl:
+
+```bash
+git clone https://github.com/axolotl-ai-cloud/axolotl
+cd axolotl
+pip install packaging ninja
+pip install -e .
+```
+
+### 7. Apply xformers Workaround
+
+xformers appears to be incompatible with ROCm. Apply the following workarounds:
+ - Edit $HOME/packages/axolotl/src/axolotl/monkeypatch/llama_attn_hijack_flash.py modifying the code to always return `False` for SwiGLU availability from xformers.
+ - Edit $HOME/miniforge3/lib/python3.10/site-packages/xformers/ops/swiglu_op.py replacing the "SwiGLU" function with a pass statement.
+
+### 8. Prepare Job Submission Script
+
+Create a script for job submission using your HPC's particular software (e.g. Slurm, PBS). Include necessary environment setup and the command to run Axolotl training. If the compute node(s) do(es) not have internet access, it is recommended to include
+
+```bash
+export TRANSFORMERS_OFFLINE=1
+export HF_DATASETS_OFFLINE=1
+```
+
+### 9. Download Base Model
+
+Download a base model using the Hugging Face CLI:
+
+```bash
+huggingface-cli download meta-llama/Meta-Llama-3.1-8B --local-dir ~/hfdata/llama3.1-8B
+```
+
+### 10. Create Axolotl Configuration
+
+Create an Axolotl configuration file (YAML format) tailored to your specific training requirements and dataset. Use FSDP for multi-node training.
+
+Note: Deepspeed did not work at the time of testing. However, if anyone managed to get it working, please let us know.
+
+### 11. Preprocess Data
+
+Run preprocessing on the login node:
+
+```bash
+CUDA_VISIBLE_DEVICES="" python -m axolotl.cli.preprocess /path/to/your/config.yaml
+```
+
+### 12. Train
+
+You are now ready to submit your previously prepared job script. 🚂

docs/config.qmd

@@ -83,22 +83,15 @@ lora_on_cpu: true
 datasets:
   # HuggingFace dataset repo | s3://,gs:// path | "json" for local dataset, make sure to fill data_files
   - path: vicgalle/alpaca-gpt4
-  # The type of prompt to use for training. [alpaca, sharegpt, gpteacher, oasst, reflection]
+    # The type of prompt to use for training. [alpaca, gpteacher, oasst, reflection]
     type: alpaca # format | format:<prompt_style> (chat/instruct) | <prompt_strategies>.load_<load_fn>
     ds_type: # Optional[str] (json|arrow|parquet|text|csv) defines the datatype when path is a file
     data_files: # Optional[str] path to source data files
     shards: # Optional[int] number of shards to split data into
     name: # Optional[str] name of dataset configuration to load
     train_on_split: train # Optional[str] name of dataset split to load from
-
-    # Optional[str] fastchat conversation type, only used with type: sharegpt
-    conversation: # Options (see Conversation 'name'): https://github.com/lm-sys/FastChat/blob/main/fastchat/conversation.py
-    field_human: # Optional[str]. Human key to use for conversation.
-    field_model: # Optional[str]. Assistant key to use for conversation.
-    # 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]].
+    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
 
   # Custom user instruction prompt
   - path: repo
@@ -123,6 +116,48 @@ datasets:
       # For `completion` datsets only, uses the provided field instead of `text` column
       field:
 
+  # Using chat template
+  - path: ...
+    # Set type to `chat_template` to use this strategy
+    type: chat_template
+    # Specify the name of the chat template to use
+    # The name of the chat template to use for training, following values are supported:
+    # - tokenizer_default: Uses the chat template that is available in the tokenizer_config.json. If the chat template is not available in the tokenizer, it will raise an error. This is the default.
+    # - alpaca/inst/chatml/gemma/cohere/llama3/phi_3/deepseek_v2/jamba: These chat templates are available in the axolotl codebase at src/axolotl/utils/chat_templates.py
+    # - tokenizer_default_fallback_*: where * is the name of the chat template to fallback to if the tokenizer does not have a chat template else default to tokenizer. E.g. tokenizer_default_fallback_chatml.
+    # - jinja: Uses a custom jinja template for the chat template. The custom jinja template should be provided in the chat_template_jinja field.
+    chat_template: tokenizer_default
+    # Custom jinja template for chat template. This will be only used if `chat_template` is set to `jinja` or empty (in which case chat_template is automatically set to `jinja`).
+    chat_template_jinja:
+    # The key in the data example that contains the messages. Default is "messages".
+    field_messages: messages
+    # The key in the message turn that contains the role. Default is "role".
+    message_field_role: role
+    # The key in the message turn that contains the content. Default is "content".
+    message_field_content: content
+    # Optional[Dict[str, List]]. Roles mapping for the messages.
+    roles:
+      user: ["human", "user"]
+      assistant: ["gpt", "assistant", "ai"]
+      system: ["system"]
+
+    ## NOTE: Leaving the below empty will default to using the simple legacy tokenization strategy where only last message is trained on.
+
+    # Optional[List[str]]. Roles to train on. The tokens from these roles will be considered for the loss.
+    roles_to_train: ["gpt", "assistant"]
+    # Optional[str]. Which EOS tokens to train on in the conversation. Possible values are:
+    # - all: train on all EOS tokens
+    # - turn: train on the EOS token at the end of each trainable turn
+    # - last: train on the last EOS token in the conversation
+    train_on_eos: last
+    # The key in the message turn that indicates via boolean whether tokens of a turn should be considered for training. Useful to selectively train on certain turns besides the `roles_to_train`.
+    message_field_training: training
+    # The key in the message turn that contains the training details. Useful to selectively train on certain tokens in a turn.
+    # The value of the key is a List[Dict] containing `begin_offset` (start character index in content), `end_offset` (end character index in content), and `train` (boolean whether to train).
+    # See example at `docs/dataset-formats/conversation.qmd`
+    message_field_training_detail: train_detail
+
+
 # If false, the datasets will not be shuffled and will keep their original order in `datasets`.
 # The same applies to the `test_datasets` option and the `pretraining_dataset` option. Default is true.
 shuffle_merged_datasets: true
@@ -140,10 +175,19 @@ test_datasets:
 
 # use RL training: 'dpo', 'ipo', 'kto'
 rl:
+# whether to perform weighting if doing DPO training. Boolean.
+dpo_use_weighting:
 
-# Saves the desired chat template to the tokenizer_config.json for easier inferencing
-# Currently supports chatml and inst (mistral/mixtral)
-chat_template: chatml
+# The name of the chat template to use for training, following values are supported:
+# - tokenizer_default: Uses the chat template that is available in the tokenizer_config.json. If the chat template is not available in the tokenizer, it will raise an error. This is the default value.
+# - alpaca/inst/chatml/gemma/cohere/llama3/phi_3/deepseek_v2/jamba: These chat templates are available in the axolotl codebase at src/axolotl/utils/chat_templates.py
+# - tokenizer_default_fallback_*: where * is the name of the chat template to fallback to. E.g. tokenizer_default_fallback_chatml. This is useful when the chat template is not available in the tokenizer.
+# - jinja: Uses a custom jinja template for the chat template. The custom jinja template should be provided in the chat_template_jinja field.
+# The selected chat template will be saved to the tokenizer_config.json for easier inferencing
+# Note: It is recommended to set train_on_inputs to true when using a chat template that is different from the model's default chat template.
+chat_template: tokenizer_default
+# custom jinja template for chat template. This will be only used if chat_template is set to `jinja` or `null` (in which case chat_template is automatically set to `jinja`). Default is null.
+chat_template_jinja: null
 # Changes the default system message
 default_system_message: You are a helpful assistant. Please give a long and detailed answer. # Currently only supports chatml.
 # Axolotl attempts to save the dataset as an arrow after packing the data together so
@@ -265,8 +309,21 @@ wandb_log_model: # "checkpoint" to log model to wandb Artifacts every `save_step
 # mlflow configuration if you're using it
 mlflow_tracking_uri: # URI to mlflow
 mlflow_experiment_name: # Your experiment name
+mlflow_run_name: # Your run name
 hf_mlflow_log_artifacts:  # set to true to copy each saved checkpoint on each save to mlflow artifact registry
 
+# Comet configuration if you're using it
+# Make sure your `COMET_API_KEY` environment variable is set (recommended) or you login to Comet with `comet login`.
+# Check out our documentation for more details https://www.comet.com/docs/v2/api-and-sdk/python-sdk/reference/Experiment-Creation/#comet_ml.start
+use_comet: # Enable or disable Comet integration.
+comet_api_key: # API key for Comet. Recommended to set via `comet login`.
+comet_workspace: # Workspace name in Comet. Defaults to the user's default workspace.
+comet_project_name: # Project name in Comet. Defaults to Uncategorized.
+comet_experiment_key: # Identifier for the experiment. Used to append data to an existing experiment or control the key of new experiments. Default to a random key.
+comet_mode: # Create a new experiment ("create") or log to an existing one ("get"). Default ("get_or_create") auto-selects based on configuration.
+comet_online: # Set to True to log data to Comet server, or False for offline storage. Default is True.
+comet_experiment_config: # Dictionary for additional configuration settings, see the doc for more details.
+
 # Where to save the full-finetuned model to
 output_dir: ./completed-model
 
@@ -301,7 +358,7 @@ max_steps:
 
 eval_table_size: # Approximate number of predictions sent to wandb depending on batch size. Enabled above 0. Default is 0
 eval_max_new_tokens: # Total number of tokens generated for predictions sent to wandb. Default is 128
-eval_causal_lm_metrics: # HF evaluate metrics used during evaluation. Default is ["sacrebleu", "comet", "ter", chrf]
+eval_causal_lm_metrics: # HF evaluate metrics used during evaluation. Default is ["sacrebleu", "comet", "ter", "chrf", "perplexity"]
 
 loss_watchdog_threshold: # High loss value, indicating the learning has broken down (a good estimate is ~2 times the loss at the start of training)
 loss_watchdog_patience: # Number of high-loss steps in a row before the trainer aborts (default: 3)
@@ -349,6 +406,7 @@ lr_div_factor: # Learning rate div factor
 # - adamw_torch_fused
 # - adamw_torch_xla
 # - adamw_apex_fused
+# - adopt_adamw (only for torch version >= 2.5.1)
 # - adafactor
 # - adamw_anyprecision
 # - sgd

docs/dataset-formats/conversation.qmd

@@ -6,31 +6,8 @@ order: 3
 
 ## sharegpt
 
-conversations where `from` is `human`/`gpt`. (optional: first row with role `system` to override default system prompt)
+IMPORTANT: ShareGPT is deprecated!. Please see `chat_template` section below.
 
-```{.json filename="data.jsonl"}
-{"conversations": [{"from": "...", "value": "..."}]}
-```
-
-Note: `type: sharegpt` opens special configs:
-- `conversation`: enables conversions to many Conversation types. Refer to the 'name' [here](https://github.com/lm-sys/FastChat/blob/main/fastchat/conversation.py) for options.
-- `roles`: allows you to specify the roles for input and output. This is useful for datasets with custom roles such as `tool` etc to support masking.
-- `field_human`: specify the key to use instead of `human` in the conversation.
-- `field_model`: specify the key to use instead of `gpt` in the conversation.
-
-```yaml
-datasets:
-    path: ...
-    type: sharegpt
-
-    conversation: # Options (see Conversation 'name'): https://github.com/lm-sys/FastChat/blob/main/fastchat/conversation.py
-    field_human: # Optional[str]. Human key to use for conversation.
-    field_model: # Optional[str]. Assistant key to use for conversation.
-    # Add additional keys from your dataset as input or output roles
-    roles:
-      input: # Optional[List[str]]. These will be masked based on train_on_input
-      output: # Optional[List[str]].
-```
 
 ## pygmalion
 
@@ -38,34 +15,137 @@ datasets:
 {"conversations": [{"role": "...", "value": "..."}]}
 ```
 
-## sharegpt.load_role
 
-conversations where `role` is used instead of `from`
+## chat_template
+
+Chat Template strategy uses a jinja2 template that converts a list of messages into a prompt. Support using tokenizer's template, a supported template, or custom jinja2.
 
 ```{.json filename="data.jsonl"}
-{"conversations": [{"role": "...", "value": "..."}]}
+{"conversations": [{"role": "...", "content": "..."}]}
 ```
 
-## sharegpt.load_guanaco
+See `config.qmd` for full configs and supported templates.
 
-conversations where `from` is `prompter` `assistant` instead of default sharegpt
+### Migrating from sharegpt
+
+Most configs can be adapted as follows:
+
+```yaml
+# old
+chat_template: chatml
+datasets:
+  - path: ...
+    type: sharegpt
+    conversation: chatml
+
+# new (if using tokenizer's chat_template)
+datasets:
+  - path: ...
+    type: chat_template
+
+    field_messages: conversations
+    message_field_role: from
+    message_field_content: value
+
+# new (if setting a new chat_template like chatml, gemma, etc)
+chat_template: chatml
+datasets:
+  - path: ...
+    type: chat_template
+
+    field_messages: conversations
+    message_field_role: from
+    message_field_content: value
+```
+
+We recommend checking the below examples for other usecases.
+
+### Examples
+
+1. Using the default chat template in the tokenizer_config.json on OpenAI messages format, training on only last message.
+
+```yaml
+datasets:
+  - path: ...
+    type: chat_template
+```
+
+2. Using the `gemma` chat template to override the tokenizer_config.json's chat template on OpenAI messages format, training on all assistant messages.
+
+```yaml
+chat_template: gemma # this overwrites the tokenizer's chat_template
+datasets:
+  - path: ...
+    type: chat_template
+    roles_to_train: ["assistant"]
+```
+
+3. Using the tokenizer_config.json's chat template or `chatml` as fallback if the former's chat template does not exist, on OpenAI messages format, training on all assistant messages.
+
+```yaml
+chat_template: tokenizer_default_fallback_chatml # this overwrites the tokenizer's chat_template
+datasets:
+  - path: ...
+    type: chat_template
+    roles_to_train: ["assistant"]
+```
+
+4. Using a custom jinja template on OpenAI messages format, training on all assistant messages.
+
+```yaml
+# chat_template: jinja # `jinja` will be implied if the `chat_template_jinja` is set and this field is empty
+chat_template_jinja: "{{ bos_token }}{% for message in messages %}{% if (message['role'] == 'system') %}{{'<|system|>' + '\n' + message['content'] + '<|end|>' + '\n'}}{% elif (message['role'] == 'user') %}{{'<|user|>' + '\n' + message['content'] + '<|end|>' + '\n' + '<|assistant|>' + '\n'}}{% elif message['role'] == 'assistant' %}{{message['content'] + '<|end|>' + '\n'}}{% endif %}{% endfor %}"
+
+datasets:
+  - path: ...
+    type: chat_template
+    roles_to_train: ["assistant"]
+```
+
+5. (Advanced) Using fine-grained control over tokens and turns to train in a conversation
+
+For a data sample that looks like:
 
 ```{.json filename="data.jsonl"}
-{"conversations": [{"from": "...", "value": "..."}]}
+{
+  "conversations": [
+    {"from": "system", "value": "You are an AI assistant.", "train": false},
+    {"from": "human", "value": "Hello", "train": false},
+    {"from": "assistant", "value": "Hello", "train": true},
+    {"from": "human", "value": "How are you?", "train": true},
+    {
+      "from": "assistant",
+      "value": "I'm doing very well, thank you!",
+      "train_detail": [
+        {"begin_offset": 0, "end_offset": 8, "train": false},
+        {"begin_offset": 9, "end_offset": 18, "train": true},
+        {"begin_offset": 19, "end_offset": 30, "train": false},
+      ],
+    },
+    {
+        "from": "human",
+        "value": "I'm doing very well, thank you!",
+        "train": true,
+    },
+    {"from": "assistant", "value": "Hi there!", "train": true}
+  ]
+}
 ```
 
-## sharegpt.load_ultrachat
+The configuration would look like:
 
-conversations where the turns field is 'messages', human is 'user' and gpt is 'assistant'.
-
-```{.json filename="data.jsonl"}
-{"messages": [{"user": "...", "assistant": "..."}]}
+```yaml
+datasets:
+  - path: ...
+    type: chat_template
+    chat_template: tokenizer_default
+    field_messages: conversations
+    message_field_role: from
+    message_field_content: value
+    roles_to_train: []
+    train_on_eos: turn
+    message_field_training: train
+    message_field_training_detail: train_detail
 ```
 
-## sharegpt_jokes
-
-creates a chat where bot is asked to tell a joke, then explain why the joke is funny
-
-```{.json filename="data.jsonl"}
-{"conversations": [{"title": "...", "text": "...", "explanation": "..."}]}
-```
+Tip: It is not necessary to use both `message_field_training` and `message_field_training_detail` at a time.

docs/dataset-formats/tokenized.qmd

@@ -7,7 +7,7 @@ order: 5
 - Pass an empty `type:` in your axolotl config.
 - Columns in Dataset must be exactly `input_ids`, `attention_mask`, `labels`
 - To indicate that a token should be ignored during training, set its corresponding label to `-100`.
-- Do not add BOS/EOS. Axolotl will add them for you based on the default tokenizer for the model you're using.
+- You must add BOS and EOS, and make sure that you are training on EOS by not setting its label to -100.
 - For pretraining, do not truncate/pad documents to the context window length.
 - For instruction training, documents must be truncated/padded as desired.
 

docs/debugging.qmd

@@ -51,12 +51,12 @@ While debugging it's helpful to simplify your test scenario as much as possible.
 
 ### Background
 
-The below example shows how to configure VSCode to debug data preprocessing of the `sharegpt` format.  This is the format used when you have the following in your axolotl config:
+The below example shows how to configure VSCode to debug data preprocessing of the `chat_template` format.  This is the format used when you have the following in your axolotl config:
 
 ```yaml
 datasets:
-  - path: <path to your sharegpt formatted dataset> # example on HF Hub: philschmid/guanaco-sharegpt-style
-    type: sharegpt
+  - path: <path to your chat_template formatted dataset> # example on HF Hub: fozziethebeat/alpaca_messages_2k_test
+    type: chat_template
 ```
 
 >[!Important]
@@ -83,7 +83,7 @@ If you developing on a remote host, you can easily use VSCode to debug remotely.
 
 The easiest way to get started is to modify the [.vscode/launch.json](../.vscode/launch.json) file in this project.  This is just an example configuration, so you may need to modify or copy it to suit your needs.
 
-For example, to mimic the command `cd devtools && CUDA_VISIBLE_DEVICES=0 accelerate launch -m axolotl.cli.train dev_sharegpt.yml`, you would use the below configuration[^1].  Note that we add additional flags that override the axolotl config and incorporate the tips above (see the comments). We also set the working directory to `devtools` and set the `env` variable `HF_HOME` to a temporary folder that is later partially deleted.  This is because we want to delete the HF dataset cache before each run in order to ensure that the data preprocessing code is run from scratch.
+For example, to mimic the command `cd devtools && CUDA_VISIBLE_DEVICES=0 accelerate launch -m axolotl.cli.train dev_chat_template.yml`, you would use the below configuration[^1].  Note that we add additional flags that override the axolotl config and incorporate the tips above (see the comments). We also set the working directory to `devtools` and set the `env` variable `HF_HOME` to a temporary folder that is later partially deleted.  This is because we want to delete the HF dataset cache before each run in order to ensure that the data preprocessing code is run from scratch.
 
 ```jsonc
 // .vscode/launch.json
@@ -91,12 +91,12 @@ For example, to mimic the command `cd devtools && CUDA_VISIBLE_DEVICES=0 acceler
     "version": "0.2.0",
     "configurations": [
         {
-            "name": "Debug axolotl prompt - sharegpt",
+            "name": "Debug axolotl prompt - chat_template",
             "type": "python",
             "module": "accelerate.commands.launch",
             "request": "launch",
             "args": [
-                "-m", "axolotl.cli.train", "dev_sharegpt.yml",
+                "-m", "axolotl.cli.train", "dev_chat_template.yml",
                 // The flags below simplify debugging by overriding the axolotl config
                 // with the debugging tips above.  Modify as needed.
                 "--dataset_processes=1",      // limits data preprocessing to one process
@@ -185,7 +185,7 @@ style="border-radius: 10px; display: block; margin: auto;" width="560" height="3
 
 ## Debugging With Docker
 
-Using [official Axolotl Docker images](https://hub.docker.com/r/winglian/axolotl/tags) is a great way to debug your code, and is a very popular way to use Axolotl.  Attaching VSCode to Docker takes a few more steps.
+Using [official Axolotl Docker images](https://hub.docker.com/r/axolotlai/axolotl/tags) is a great way to debug your code, and is a very popular way to use Axolotl.  Attaching VSCode to Docker takes a few more steps.
 
 ### Setup
 
@@ -202,11 +202,11 @@ cd axolotl
 Next, run the desired docker image and mount the current directory. Below is a docker command you can run to do this:[^2]
 
 ```bash
-docker run --privileged --gpus '"all"' --shm-size 10g --rm -it --name axolotl --ipc=host --ulimit memlock=-1 --ulimit stack=67108864 --mount type=bind,src="${PWD}",target=/workspace/axolotl -v ${HOME}/.cache/huggingface:/root/.cache/huggingface winglian/axolotl:main-py3.10-cu118-2.0.1
+docker run --privileged --gpus '"all"' --shm-size 10g --rm -it --name axolotl --ipc=host --ulimit memlock=-1 --ulimit stack=67108864 --mount type=bind,src="${PWD}",target=/workspace/axolotl -v ${HOME}/.cache/huggingface:/root/.cache/huggingface axolotlai/axolotl:main-py3.10-cu118-2.0.1
 ```
 
 >[!Tip]
-> To understand which containers are available, see the [Docker section of the README](../README.md#docker) and the [DockerHub repo](https://hub.docker.com/r/winglian/axolotl/tags).  For details of how the Docker containers are built, see axolotl's [Docker CI builds](../.github/workflows/main.yml).
+> To understand which containers are available, see the [Docker section of the README](../README.md#docker) and the [DockerHub repo](https://hub.docker.com/r/axolotlai/axolotl/tags).  For details of how the Docker containers are built, see axolotl's [Docker CI builds](../.github/workflows/main.yml).
 
 You will now be in the container.  Next, perform an editable install of Axolotl:
 
@@ -240,6 +240,6 @@ style="border-radius: 10px; display: block; margin: auto;" width="560" height="3
 </div>
 <br>
 
-[^1]: The config actually mimics the command `CUDA_VISIBLE_DEVICES=0 python -m accelerate.commands.launch -m axolotl.cli.train devtools/sharegpt.yml`, but this is the same thing.
+[^1]: The config actually mimics the command `CUDA_VISIBLE_DEVICES=0 python -m accelerate.commands.launch -m axolotl.cli.train devtools/chat_template.yml`, but this is the same thing.
 
 [^2]: Many of the below flags are recommended best practices by Nvidia when using nvidia-container-toolkit.  You can read more about these flags [here](https://docs.nvidia.com/deeplearning/frameworks/user-guide/index.html).

docs/input_output.qmd

@@ -205,7 +205,7 @@ ds = load_from_disk(f'last_run_prepared/{directory[0]}/')
     hi there!.  goodbye  farewell</s>
 ```
 
-We can check that the right tokens are ingored by comparing the labels
+We can check that the right tokens are ignored by comparing the labels
 to each token:
 
 ```python

docs/multimodal.qmd

@@ -0,0 +1,28 @@
+# MultiModal / Vision Language Models (BETA)
+
+### Supported Models
+
+- Mllama, i.e. llama with vision models
+
+### Usage
+
+Currently multimodal support is limited and doesn't have full feature parity. To finetune a multimodal Llama w/ LoRA,
+you'll need to use the following in YAML in combination with the rest of the required hyperparams.
+
+```yaml
+base_model: alpindale/Llama-3.2-11B-Vision-Instruct
+processor_type: AutoProcessor
+skip_prepare_dataset: true
+
+chat_template: llama3_2_vision
+datasets:
+  - path: HuggingFaceH4/llava-instruct-mix-vsft
+    type: chat_template
+    split: train[:1%]
+    field_messages: messages
+remove_unused_columns: false
+sample_packing: false
+
+# only finetune the Language model, leave the vision model and vision tower frozen
+lora_target_modules: 'language_model.model.layers.[\d]+.(mlp|cross_attn|self_attn).(up|down|gate|q|k|v|o)_proj'
+```

docs/unsloth.qmd

@@ -34,7 +34,7 @@ unsloth_lora_o: true
 ```
 
 These options are composable and can be used with multi-gpu finetuning
-```
+```yaml
 unsloth_cross_entropy_loss: true
 unsloth_rms_norm: true
 unsloth_rope: true

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

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

examples/deepseek-v2/fft-fsdp-16b.yaml

@@ -0,0 +1,67 @@
+base_model: deepseek-ai/DeepSeek-V2-Lite
+trust_remote_code: true
+
+load_in_8bit: false
+load_in_4bit: false
+strict: false
+
+datasets:
+  - path: tatsu-lab/alpaca
+    type: alpaca
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.0
+output_dir: ./outputs/out
+
+sequence_len: 2048
+sample_packing: true
+pad_to_sequence_len: true
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+gradient_accumulation_steps: 8
+micro_batch_size: 1
+num_epochs: 1
+optimizer: adamw_torch
+lr_scheduler: cosine
+learning_rate: 2e-5
+
+train_on_inputs: false
+group_by_length: false
+bf16: auto
+fp16:
+tf32: false
+
+gradient_checkpointing: true
+gradient_checkpointing_kwargs:
+  use_reentrant: false
+early_stopping_patience:
+resume_from_checkpoint:
+logging_steps: 1
+xformers_attention:
+flash_attention: true
+
+warmup_steps: 100
+evals_per_epoch: 2
+eval_table_size:
+saves_per_epoch: 1
+debug:
+deepspeed:
+weight_decay: 0.0
+special_tokens:
+fsdp:
+  - full_shard
+  - auto_wrap
+fsdp_config:
+  fsdp_limit_all_gathers: true
+  fsdp_sync_module_states: true
+  fsdp_offload_params: true
+  fsdp_use_orig_params: false
+  fsdp_cpu_ram_efficient_loading: true
+  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
+  fsdp_transformer_layer_cls_to_wrap: DeepseekV2DecoderLayer
+  fsdp_state_dict_type: FULL_STATE_DICT
+  fsdp_sharding_strategy: FULL_SHARD

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

@@ -0,0 +1,86 @@
+base_model: axolotl-quants/DeepSeek-V2.5-bnb-nf4-bf16
+trust_remote_code: true
+
+load_in_8bit: false
+load_in_4bit: true
+strict: false
+
+
+plugins:
+  - axolotl.integrations.liger.LigerPlugin
+liger_rms_norm: true
+liger_glu_activation: true
+liger_fused_linear_cross_entropy: true
+
+chat_template: deepseek_v2
+datasets:
+  - path: mlabonne/FineTome-100k
+    type: chat_template
+    split: train[:20%]
+    field_messages: conversations
+    message_field_role: from
+    message_field_content: value
+
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.0
+output_dir: ./outputs/out
+
+sequence_len: 4096
+sample_packing: true
+pad_to_sequence_len: true
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+adapter: qlora
+lora_r: 256
+lora_alpha: 256
+lora_target_linear: true
+peft_use_rslora: true
+
+gradient_accumulation_steps: 1
+micro_batch_size: 8
+num_epochs: 1
+optimizer: adamw_torch
+lr_scheduler: cosine
+learning_rate: 2e-5
+
+train_on_inputs: false
+group_by_length: false
+bf16: auto
+fp16:
+tf32: false
+
+gradient_checkpointing: true
+gradient_checkpointing_kwargs:
+  use_reentrant: false
+early_stopping_patience:
+resume_from_checkpoint:
+logging_steps: 1
+xformers_attention:
+flash_attention: true
+
+warmup_steps: 100
+evals_per_epoch: 2
+eval_table_size:
+saves_per_epoch: 1
+debug:
+deepspeed:
+weight_decay: 0.0
+special_tokens:
+fsdp:
+  - full_shard
+  - auto_wrap
+fsdp_config:
+  fsdp_limit_all_gathers: true
+  fsdp_sync_module_states: true
+  fsdp_offload_params: true
+  fsdp_use_orig_params: false
+  fsdp_cpu_ram_efficient_loading: true
+  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
+  fsdp_transformer_layer_cls_to_wrap: DeepseekV2DecoderLayer
+  fsdp_state_dict_type: FULL_STATE_DICT
+  fsdp_sharding_strategy: FULL_SHARD

examples/gemma2/qlora.yml

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

examples/gemma2/reward-model.yaml

@@ -0,0 +1,63 @@
+base_model: google/gemma-2-2b
+model_type: AutoModelForSequenceClassification
+tokenizer_type: AutoTokenizer
+
+load_in_8bit: false
+load_in_4bit: false
+strict: false
+
+reward_model: true
+chat_template: gemma
+datasets:
+  - path: argilla/distilabel-intel-orca-dpo-pairs
+    type: bradley_terry.chat_template
+val_set_size: 0.0
+output_dir: ./outputs/out
+remove_unused_columns: false
+
+sequence_len: 2048
+sample_packing: false
+eval_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: true
+fp16:
+tf32: true
+
+gradient_checkpointing: true
+gradient_checkpointing_kwargs:
+  use_reentrant: false
+early_stopping_patience:
+resume_from_checkpoint:
+local_rank:
+logging_steps: 1
+xformers_attention:
+flash_attention: true
+
+warmup_ratio: 0.1
+evals_per_epoch:
+eval_table_size:
+eval_max_new_tokens: 128
+saves_per_epoch: 1
+debug:
+deepspeed:
+weight_decay: 0.0
+fsdp:
+fsdp_config:
+special_tokens:

examples/jamba/README.md

@@ -6,5 +6,5 @@
 - ✅ qlora w/ deepspeed Zero-3 needs at least 2x GPUs and 67GiB VRAM (wtf?)
 - ✅ qlora single-gpu, ~51GiB VRAM
 - ✅ multipack
-- ❓ FSDP
+- ✅ FSDP
 - ❓ 8-bit LoRA

examples/jamba/qlora_fsdp_large.yaml

@@ -0,0 +1,65 @@
+base_model: ai21labs/AI21-Jamba-1.5-Large
+tokenizer_type: AutoTokenizer
+
+load_in_4bit: true
+strict: false
+use_tensorboard: true
+chat_template: jamba
+datasets:
+  - path: cgato/SlimOrcaDedupCleaned
+    type: chat_template
+    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
+save_safetensors: true
+adapter: qlora
+sequence_len: 2048
+sample_packing: true
+pad_to_sequence_len: true
+
+lora_r: 16
+lora_alpha: 16
+lora_dropout: 0.05
+lora_target_modules: [down_proj,gate_proj,in_proj,k_proj,o_proj,out_proj,q_proj,up_proj,v_proj,x_proj]
+lora_target_linear: false
+
+gradient_accumulation_steps: 4
+micro_batch_size: 1
+num_epochs: 2
+optimizer: adamw_torch
+lr_scheduler: cosine
+learning_rate: 0.00001
+
+train_on_inputs: false
+group_by_length: false
+bf16: true
+tf32: true
+
+gradient_checkpointing: true
+gradient_checkpointing_kwargs:
+  use_reentrant: true
+logging_steps: 1
+flash_attention: true
+
+warmup_steps: 10
+evals_per_epoch: 1
+saves_per_epoch: 1
+weight_decay: 0.0
+fsdp:
+  - full_shard
+  - auto_wrap
+fsdp_config:
+  fsdp_limit_all_gathers: true
+  fsdp_sync_module_states: true
+  fsdp_offload_params: false
+  fsdp_use_orig_params: false
+  fsdp_cpu_ram_efficient_loading: true
+  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
+  fsdp_transformer_layer_cls_to_wrap: JambaAttentionDecoderLayer,JambaMambaDecoderLayer
+  fsdp_state_dict_type: FULL_STATE_DICT
+  fsdp_sharding_strategy: FULL_SHARD

examples/llama-3-vision/lora-11b.yaml

@@ -0,0 +1,63 @@
+base_model: alpindale/Llama-3.2-11B-Vision-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: llama3_2_vision
+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/llama-3/fft-8b-liger-fsdp.yaml

@@ -0,0 +1,80 @@
+base_model: NousResearch/Meta-Llama-3.1-8B
+
+plugins:
+  - axolotl.integrations.liger.LigerPlugin
+liger_rope: true
+liger_rms_norm: true
+liger_glu_activation: true
+liger_fused_linear_cross_entropy: true
+
+strict: false
+
+chat_template: llama3
+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
+
+sequence_len: 4096
+sample_packing: true
+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: 1
+optimizer: adamw_torch
+lr_scheduler: cosine
+learning_rate: 2e-5
+
+train_on_inputs: false
+group_by_length: false
+bf16: auto
+fp16:
+tf32: false
+
+gradient_checkpointing: true
+gradient_checkpointing_kwargs:
+  use_reentrant: false
+early_stopping_patience:
+resume_from_checkpoint:
+logging_steps: 1
+xformers_attention:
+flash_attention: true
+
+warmup_steps: 100
+evals_per_epoch: 2
+eval_table_size:
+saves_per_epoch: 1
+debug:
+deepspeed:
+weight_decay: 0.0
+fsdp:
+  - full_shard
+  - auto_wrap
+fsdp_config:
+  fsdp_limit_all_gathers: true
+  fsdp_sync_module_states: true
+  fsdp_offload_params: true
+  fsdp_use_orig_params: false
+  fsdp_cpu_ram_efficient_loading: true
+  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
+  fsdp_transformer_layer_cls_to_wrap: LlamaDecoderLayer
+  fsdp_state_dict_type: FULL_STATE_DICT
+  fsdp_sharding_strategy: FULL_SHARD
+  fsdp_backward_prefetch: BACKWARD_PRE
+special_tokens:
+  pad_token: <|finetune_right_pad_id|>
+  eos_token: <|eot_id|>

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

@@ -1,6 +1,4 @@
-base_model: NousResearch/Meta-Llama-3-8B
-model_type: LlamaForCausalLM
-tokenizer_type: AutoTokenizer
+base_model: NousResearch/Meta-Llama-3.1-8B
 
 load_in_8bit: false
 load_in_4bit: false

examples/llama-3/instruct-dpo-lora-8b.yml

@@ -11,7 +11,6 @@ rl: dpo
 datasets:
   - path: fozziethebeat/alpaca_messages_2k_dpo_test
     type: chat_template.default
-    chat_template: llama3
     field_messages: conversation
     field_chosen: chosen
     field_rejected: rejected

examples/llama-3/instruct-lora-8b.yml

@@ -10,7 +10,6 @@ chat_template: llama3
 datasets:
   - path: fozziethebeat/alpaca_messages_2k_test
     type: chat_template
-    chat_template: llama3
     field_messages: messages
     message_field_role: role
     message_field_content: content

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

@@ -0,0 +1,77 @@
+base_model: meta-llama/Llama-3.2-1B
+
+load_in_8bit: false
+load_in_4bit: true
+strict: false
+
+datasets:
+  - path: teknium/GPT4-LLM-Cleaned
+    type: alpaca
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.1
+output_dir: ./outputs/qlora-out
+
+adapter: qlora
+lora_model_dir:
+
+sequence_len: 2048
+sample_packing: true
+eval_sample_packing: true
+pad_to_sequence_len: true
+
+lora_r: 32
+lora_alpha: 16
+lora_dropout: 0.05
+lora_target_linear: true
+lora_fan_in_fan_out:
+lora_target_modules:
+  - gate_proj
+  - down_proj
+  - up_proj
+  - q_proj
+  - v_proj
+  - k_proj
+  - o_proj
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+gradient_accumulation_steps: 4
+micro_batch_size: 2
+num_epochs: 1
+optimizer: adamw_bnb_8bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+train_on_inputs: false
+group_by_length: false
+bf16: auto
+fp16:
+tf32: false
+
+gradient_checkpointing: true
+early_stopping_patience:
+resume_from_checkpoint:
+local_rank:
+logging_steps: 1
+xformers_attention:
+flash_attention: true
+
+loss_watchdog_threshold: 5.0
+loss_watchdog_patience: 3
+
+warmup_steps: 10
+evals_per_epoch: 4
+eval_table_size:
+eval_max_new_tokens: 128
+saves_per_epoch: 1
+debug:
+deepspeed:
+weight_decay: 0.0
+fsdp:
+fsdp_config:
+special_tokens:
+  pad_token: "<|end_of_text|>"

examples/mistral/mistral-dpo-qlora.yml

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

examples/phi/lora-3.5.yaml

@@ -0,0 +1,75 @@
+base_model: microsoft/Phi-3.5-mini-instruct
+model_type: AutoModelForCausalLM
+tokenizer_type: AutoTokenizer
+
+load_in_8bit: true
+load_in_4bit: false
+strict: false
+
+chat_template: phi_3
+datasets:
+  - path: fozziethebeat/alpaca_messages_2k_test
+    type: chat_template
+    field_messages: messages
+    message_field_role: role
+    message_field_content: content
+    roles:
+      user:
+        - user
+      assistant:
+        - assistant
+
+dataset_prepared_path:
+val_set_size: 0.05
+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: 4
+num_epochs: 2
+optimizer: adamw_bnb_8bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+train_on_inputs: false
+group_by_length: false
+bfloat16: true
+bf16: true
+fp16:
+tf32: false
+
+gradient_checkpointing: true
+early_stopping_patience:
+resume_from_checkpoint:
+local_rank:
+logging_steps: 1
+xformers_attention:
+s2_attention:
+
+warmup_steps: 10
+evals_per_epoch: 4
+eval_table_size:
+eval_max_new_tokens: 128
+saves_per_epoch: 4
+debug:
+deepspeed:
+weight_decay: 0.0
+fsdp:
+fsdp_config:

examples/qwen2/dpo.yaml

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

examples/qwen2/qlora-fsdp.yaml

@@ -72,4 +72,5 @@ fsdp_config:
   fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
   fsdp_transformer_layer_cls_to_wrap: Qwen2DecoderLayer
   fsdp_state_dict_type: FULL_STATE_DICT
+  fsdp_sharding_strategy: FULL_SHARD
 special_tokens:

examples/tiny-llama/pretrain.yml

@@ -9,9 +9,9 @@ strict: false
 
 max_steps: 200
 pretraining_dataset:
-  path: c4
-  name: en
-  type: pretrain
+  - path: allenai/c4
+    name: en
+    type: pretrain
 dataset_prepared_path:
 val_set_size: 0.0
 output_dir: ./outputs/model-out

requirements-dev.txt

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

requirements-tests.txt

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

requirements.txt

@@ -1,38 +1,39 @@
 --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/
 packaging==23.2
-peft==0.12.0
-transformers==4.44.0
-tokenizers>=0.19.1
-bitsandbytes==0.43.3
-accelerate==0.33.0
-datasets==2.20.0
-deepspeed==0.14.4
+peft==0.13.2
+transformers==4.46.2
+tokenizers>=0.20.1
+bitsandbytes==0.44.1
+accelerate==1.1.0
+datasets==3.1.0
+deepspeed==0.15.3
 pydantic==2.6.3
 addict
 fire
 PyYAML>=6.0
 requests
-flash-attn==2.6.3
+flash-attn==2.7.0.post2
 sentencepiece
 wandb
 einops
-xformers==0.0.27
+xformers>=0.0.23.post1
 optimum==1.16.2
 hf_transfer
 colorama
 numba
-numpy>=1.24.4
+numpy>=1.24.4,<=2.0.1
 # qlora things
 evaluate==0.4.1
 scipy
-scikit-learn==1.2.2
+scikit-learn==1.4.2
 pynvml
 art
-fschat @ git+https://github.com/lm-sys/FastChat.git@27a05b04a35510afb1d767ae7e5990cbd278f8fe
 gradio==3.50.2
 tensorboard
 python-dotenv==1.0.1
 autoawq>=0.2.5
+triton>=2.3.0
+liger-kernel==0.4.1
 
 mamba-ssm==1.2.0.post1
 
@@ -41,6 +42,15 @@ s3fs>=2024.5.0
 gcsfs>=2024.5.0
 # adlfs
 
-trl==0.9.6
+trl==0.12.0
 zstandard==0.22.0
 fastcore
+
+# lm eval harness
+lm_eval==0.4.4
+langdetect==1.0.9
+immutabledict==4.2.0
+antlr4-python3-runtime==4.13.2
+
+torchao==0.5.0
+schedulefree==1.3.0

requirements_env.txt

@@ -0,0 +1,315 @@
+accelerate==0.34.1
+addict==2.4.0
+aiofiles==23.2.1
+aiohttp==3.9.0
+aiosignal==1.3.1
+aiostream==0.5.2
+alembic==1.13.1
+annotated-types==0.6.0
+annoy==1.17.3
+ansible==6.7.0
+ansible-core==2.13.13
+ansible-vault==2.1.0
+anyio==3.7.1
+appdirs==1.4.4
+art==6.0
+asgiref==3.7.2
+async-timeout==4.0.2
+attrdict==2.0.1
+attrs==22.2.0
+awscli==1.32.75
+-e git+ssh://git@github.com/OpenAccess-AI-Collective/axolotl.git@6e354682e3c1735d3f7fb9e362280c38e922260f#egg=axolotl
+backoff==2.2.1
+base58==2.1.1
+beartype==0.17.2
+bitnet==0.2.1
+bitsandbytes==0.42.0
+bittensor==6.7.0
+black==23.7.0
+blinker==1.7.0
+boto3==1.34.75
+botocore==1.34.75
+cachetools==5.3.3
+cachy==0.1.1
+certifi==2023.7.22
+cffi==1.16.0
+cfgv==3.3.1
+chai-guanaco==1.2.4
+charset-normalizer==3.2.0
+cleo==0.6.8
+click==8.1.7
+cloudpickle==2.0.0
+cohere==4.11.2
+colorama==0.4.4
+coloredlogs==15.0.1
+CoLT5-attention==0.10.20
+contextlib2==21.6.0
+contourpy==1.2.0
+cryptography==41.0.3
+cycler==0.12.1
+cytoolz==0.12.3
+databricks-cli==0.18.0
+dataclasses-json==0.5.7
+datasets==2.11.0
+ddt==1.6.0
+decorator==5.1.1
+deepspeed==0.15.0
+# Editable Git install with no remote (dialogpt==0.1)
+-e /Users/wing/Projects/ml/dialogpt/src
+dill==0.3.6
+distlib==0.3.6
+docker==7.0.0
+docker-pycreds==0.4.0
+docstring-parser==0.15
+docutils==0.16
+ecdsa==0.18.0
+einops==0.7.0
+einops-exts==0.0.4
+einx==0.1.3
+entrypoints==0.4
+eth-hash==0.6.0
+eth-keys==0.5.0
+eth-typing==4.0.0
+eth-utils==2.3.1
+evaluate==0.4.0
+exceptiongroup==1.1.1
+fastapi==0.109.2
+fastcore==1.5.29
+ffmpy==0.4.0
+filelock==3.12.2
+-e git+https://github.com/NousResearch/finetuning-subnet.git@24e9407d6b4430a7ca39d344692f89ce5a97d27e#egg=finetuning_subnet
+fire==0.5.0
+first==2.0.2
+flake8==7.0.0
+Flask==3.0.1
+fonttools==4.47.2
+frozendict==2.4.1
+frozenlist==1.3.3
+fschat @ git+https://github.com/lm-sys/FastChat.git@27a05b04a35510afb1d767ae7e5990cbd278f8fe
+fsspec==2023.6.0
+fuzzywuzzy==0.18.0
+gitdb==4.0.10
+GitPython==3.1.31
+google-pasta==0.2.0
+gradio==4.42.0
+gradio_client==1.3.0
+greenlet==2.0.2
+grpclib==0.4.7
+gunicorn==21.2.0
+h11==0.14.0
+h2==4.1.0
+hpack==4.0.0
+httpcore==0.17.3
+httpx==0.24.1
+huggingface-hub==0.23.4
+humanfriendly==10.0
+hyperframe==6.0.1
+identify==2.5.24
+idna==3.4
+immutables==0.20
+importlib-metadata==6.7.0
+importlib-resources==6.1.1
+inflection==0.5.1
+iniconfig==2.0.0
+itsdangerous==2.1.2
+Jinja2==3.1.2
+jmespath==1.0.1
+joblib==1.3.2
+jsonlines==3.1.0
+jsonschema==2.6.0
+kiwisolver==1.4.5
+langchain==0.0.144
+Levenshtein==0.24.0
+libcst==1.1.0
+liger-kernel==0.0.0
+lion-pytorch==0.1.2
+llama-cpp-python==0.1.36
+llvmlite==0.40.1
+local-attention==1.9.0
+loguru==0.7.0
+Mako==1.3.2
+Markdown==3.5.2
+markdown-it-py==3.0.0
+markdown2==2.4.10
+MarkupSafe==2.1.2
+marshmallow==3.19.0
+marshmallow-enum==1.5.1
+matplotlib==3.8.2
+mccabe==0.7.0
+mdurl==0.1.2
+MEGABYTE-pytorch==0.0.7
+-e git+https://github.com/cg123/mergekit.git@53c5f414774a0558b8d84858fb6374bc93a8f1c1#egg=mergekit
+mlflow==2.10.0
+modal==0.62.77
+more-itertools==10.2.0
+mpmath==1.2.1
+msgpack==1.0.7
+msgpack-numpy-opentensor==0.5.0
+multidict==6.0.4
+multiprocess==0.70.14
+munch==2.5.0
+mypy==1.3.0
+mypy-extensions==1.0.0
+nest-asyncio==1.6.0
+netaddr==0.10.1
+networkx==3.0rc1
+nh3==0.2.14
+nodeenv==1.8.0
+nomic==2.0.2
+numba==0.57.1
+numexpr==2.8.4
+numpy==1.24.4
+oauthlib==3.2.2
+openai==0.27.4
+openapi==1.1.0
+openapi-schema-pydantic==1.2.4
+optimum==1.8.6
+orjson==3.10.7
+packaging==23.1
+pandas==2.0.0
+parameterized==0.9.0
+password-strength==0.0.3.post2
+pastel==0.1.1
+pathos==0.3.0
+pathspec==0.11.1
+pathtools==0.1.2
+peft==0.11.1
+pendulum==3.0.0
+Pillow==9.5.0
+pip-tools==1.11.0
+platformdirs==3.2.0
+pluggy==1.4.0
+poetry==0.7.1
+pox==0.3.2
+ppft==1.7.6.6
+pre-commit==3.3.2
+prettytable==3.10.0
+prompt-toolkit==3.0.39
+protobuf==3.20.2
+protobuf3-to-dict==0.1.5
+psutil==5.9.5
+psycopg==3.1.18
+PuLP==2.8.0
+py==1.11.0
+py-bip39-bindings==0.1.11
+py-cpuinfo==9.0.0
+py-ed25519-zebra-bindings==1.0.1
+py-sr25519-bindings==0.2.0
+pyarrow==11.0.0
+pyasn1==0.6.0
+pycodestyle==2.11.1
+pycparser==2.21
+pycryptodome==3.20.0
+pydantic==2.5.3
+pydantic_core==2.14.6
+pydub==0.25.1
+pyfiglet==0.8.post1
+pyflakes==3.2.0
+Pygments==2.15.1
+PyJWT==2.8.0
+pylev==1.4.0
+PyNaCl==1.5.0
+pynvml==11.5.0
+pyparsing==2.4.7
+pyrsistent==0.14.11
+pytest==8.0.2
+pytest-asyncio==0.23.4
+python-dateutil==2.8.2
+python-dotenv==1.0.1
+python-Levenshtein==0.24.0
+python-multipart==0.0.9
+pytz==2023.3
+PyYAML==6.0.1
+querystring-parser==1.2.4
+rapidfuzz==3.6.1
+regex==2023.6.3
+requests==2.31.0
+requests-toolbelt==0.8.0
+resolvelib==0.8.1
+responses==0.18.0
+retry==0.9.2
+rich==13.7.0
+rsa==4.7.2
+ruff==0.6.3
+s3transfer==0.10.1
+safetensors==0.4.5
+sagemaker==2.148.0
+scalecodec==1.2.7
+schedulefree==1.2.1
+schema==0.7.5
+scikit-learn==1.4.0
+scipy==1.9.3
+seaborn==0.13.2
+semantic-version==2.10.0
+sentencepiece==0.2.0
+sentry-sdk==1.19.1
+setproctitle==1.3.2
+shellingham==1.5.4
+shortuuid==1.0.11
+shtab==1.6.5
+sigtools==4.0.1
+six==1.16.0
+skypilot==0.4.1
+smdebug-rulesconfig==1.0.1
+smmap==5.0.0
+sniffio==1.3.0
+SQLAlchemy==1.4.47
+sqlparse==0.4.4
+starlette==0.36.3
+substrate-interface==1.5.2
+svgwrite==1.4.3
+sympy==1.11.1
+synchronicity==0.6.7
+tabulate==0.9.0
+tblib==1.7.0
+tenacity==8.2.2
+tensor-parallel==2.0.0
+termcolor==2.2.0
+text2art==0.2.0
+threadpoolctl==3.2.0
+tiktoken==0.6.0
+time-machine==2.14.1
+timm==0.9.16
+tokenizers==0.19.1
+tokenmonster==1.1.12
+toml==0.9.6
+tomli==2.0.1
+tomlkit==0.12.0
+toolz==0.12.1
+torch==2.2.0
+torchdata==0.6.1
+torchdiffeq==0.2.3
+TorchFix==0.4.0
+torchtext==0.15.2
+torchvision==0.17.0
+tqdm==4.66.2
+transformers==4.44.2
+trl==0.9.6
+typer==0.12.5
+types-certifi==2021.10.8.3
+types-requests==2.31.0.20240125
+types-setuptools==69.0.0.20240125
+types-toml==0.10.8.7
+typing==3.7.4.3
+typing-inspect==0.8.0
+typing_extensions==4.9.0
+tyro==0.5.18
+tzdata==2023.3
+unique-names-generator==1.0.2
+urllib3==2.2.2
+uvicorn==0.22.0
+vector_quantize_pytorch==1.14.1
+virtualenv==20.23.0
+voyager==2.0.2
+wandb==0.16.2
+watchfiles==0.21.0
+wavedrom==2.0.3.post3
+wcwidth==0.2.6
+websocket-client==1.7.0
+websockets==12.0
+Werkzeug==3.0.1
+wonderwords==2.2.0
+xxhash==3.2.0
+yarl==1.8.2
+zetascale==2.2.7
+zipp==3.15.0

scripts/chat_datasets.py

@@ -0,0 +1,60 @@
+"""
+helper script to parse chat datasets into a usable yaml
+"""
+import click
+import yaml
+from datasets import load_dataset
+
+
+@click.command()
+@click.argument("dataset", type=str)
+@click.option("--split", type=str, default="train")
+def parse_dataset(dataset=None, split="train"):
+    ds_cfg = {}
+    ds_cfg["path"] = dataset
+    ds_cfg["split"] = split
+    ds_cfg["type"] = "chat_template"
+    ds_cfg["chat_template"] = "<<<Replace based on your model>>>"
+
+    dataset = load_dataset(dataset, split=split)
+    features = dataset.features
+    feature_keys = features.keys()
+    field_messages = None
+    for key in ["conversation", "conversations", "messages"]:
+        if key in feature_keys:
+            field_messages = key
+            break
+    if not field_messages:
+        raise ValueError(
+            f'No conversation field found in dataset: {", ".join(feature_keys)}'
+        )
+    ds_cfg["field_messages"] = field_messages
+
+    message_fields = features["conversations"][0].keys()
+    message_field_role = None
+    for key in ["from", "role"]:
+        if key in message_fields:
+            message_field_role = key
+            break
+    if not message_field_role:
+        raise ValueError(
+            f'No role field found in messages: {", ".join(message_fields)}'
+        )
+    ds_cfg["message_field_role"] = message_field_role
+
+    message_field_content = None
+    for key in ["content", "text", "value"]:
+        if key in message_fields:
+            message_field_content = key
+            break
+    if not message_field_content:
+        raise ValueError(
+            f'No content field found in messages: {", ".join(message_fields)}'
+        )
+    ds_cfg["message_field_content"] = message_field_content
+
+    print(yaml.dump({"datasets": [ds_cfg]}))
+
+
+if __name__ == "__main__":
+    parse_dataset()

scripts/cloud-entrypoint.sh

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

setup.py

@@ -30,13 +30,19 @@ def parse_requirements():
 
     try:
         xformers_version = [req for req in _install_requires if "xformers" in req][0]
+        torchao_version = [req for req in _install_requires if "torchao" in req][0]
+        autoawq_version = [req for req in _install_requires if "autoawq" in req][0]
+
         if "Darwin" in platform.system():
             # don't install xformers on MacOS
             _install_requires.pop(_install_requires.index(xformers_version))
         else:
             # detect the version of torch already installed
             # and set it so dependencies don't clobber the torch version
-            torch_version = version("torch")
+            try:
+                torch_version = version("torch")
+            except PackageNotFoundError:
+                torch_version = "2.5.1"
             _install_requires.append(f"torch=={torch_version}")
 
             version_match = re.match(r"^(\d+)\.(\d+)(?:\.(\d+))?", torch_version)
@@ -49,20 +55,39 @@ def parse_requirements():
             else:
                 raise ValueError("Invalid version format")
 
-            if (major, minor) >= (2, 3):
+            if (major, minor) >= (2, 5):
+                _install_requires.pop(_install_requires.index(xformers_version))
+                if patch == 0:
+                    _install_requires.append("xformers==0.0.28.post2")
+                else:
+                    _install_requires.append("xformers==0.0.28.post3")
+                _install_requires.pop(_install_requires.index(autoawq_version))
+            elif (major, minor) >= (2, 4):
+                if patch == 0:
+                    _install_requires.pop(_install_requires.index(xformers_version))
+                    _install_requires.append("xformers>=0.0.27")
+                else:
+                    _install_requires.pop(_install_requires.index(xformers_version))
+                    _install_requires.append("xformers==0.0.28.post1")
+            elif (major, minor) >= (2, 3):
+                _install_requires.pop(_install_requires.index(torchao_version))
                 if patch == 0:
                     _install_requires.pop(_install_requires.index(xformers_version))
                     _install_requires.append("xformers>=0.0.26.post1")
+                else:
+                    _install_requires.pop(_install_requires.index(xformers_version))
+                    _install_requires.append("xformers>=0.0.27")
             elif (major, minor) >= (2, 2):
+                _install_requires.pop(_install_requires.index(torchao_version))
                 _install_requires.pop(_install_requires.index(xformers_version))
                 _install_requires.append("xformers>=0.0.25.post1")
             else:
+                _install_requires.pop(_install_requires.index(torchao_version))
                 _install_requires.pop(_install_requires.index(xformers_version))
                 _install_requires.append("xformers>=0.0.23.post1")
 
     except PackageNotFoundError:
         pass
-
     return _install_requires, _dependency_links
 
 
@@ -71,7 +96,7 @@ install_requires, dependency_links = parse_requirements()
 
 setup(
     name="axolotl",
-    version="0.4.1",
+    version="0.5.0",
     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"},
@@ -80,10 +105,7 @@ setup(
     dependency_links=dependency_links,
     extras_require={
         "flash-attn": [
-            "flash-attn==2.6.2",
-        ],
-        "fused-dense-lib": [
-            "fused-dense-lib  @ git+https://github.com/Dao-AILab/flash-attention@v2.6.2#subdirectory=csrc/fused_dense_lib",
+            "flash-attn==2.7.0.post2",
         ],
         "deepspeed": [
             "deepspeed==0.14.4",
@@ -91,6 +113,7 @@ setup(
         ],
         "mamba-ssm": [
             "mamba-ssm==1.2.0.post1",
+            "causal_conv1d",
         ],
         "auto-gptq": [
             "auto-gptq==0.5.1",

src/axolotl/cli/__init__.py

@@ -27,8 +27,11 @@ 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
+from axolotl.utils.comet_ import setup_comet_env_vars
 from axolotl.utils.config import (
     normalize_cfg_datasets,
     normalize_config,
@@ -38,7 +41,7 @@ from axolotl.utils.data import load_prepare_dpo_datasets, prepare_dataset
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.distributed import is_main_process
 from axolotl.utils.mlflow_ import setup_mlflow_env_vars
-from axolotl.utils.models import load_tokenizer
+from axolotl.utils.models import load_processor, load_tokenizer
 from axolotl.utils.tokenization import check_dataset_labels
 from axolotl.utils.trainer import prepare_opinionated_env, prepare_optim_env
 from axolotl.utils.wandb_ import setup_wandb_env_vars
@@ -52,8 +55,22 @@ LOG = logging.getLogger("axolotl.scripts")
 
 os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"
 
+AXOLOTL_LOGO = """
+     #@@ #@@      @@# @@#
+    @@  @@          @@  @@           =@@#                               @@                 #@    =@@#.
+    @@    #@@@@@@@@@    @@           #@#@=                              @@                 #@     .=@@
+      #@@@@@@@@@@@@@@@@@            =@# @#     ##=     ##    =####=+    @@      =#####+  =#@@###.   @@
+    @@@@@@@@@@/  +@@/  +@@          #@  =@=     #@=   @@   =@#+  +#@#   @@    =@#+  +#@#   #@.      @@
+    @@@@@@@@@@  ##@@  ##@@         =@#   @#      =@# @#    @@      @@   @@    @@      #@   #@       @@
+     @@@@@@@@@@@@@@@@@@@@          #@=+++#@=      =@@#     @@      @@   @@    @@      #@   #@       @@
+                                  =@#=====@@     =@# @#    @@      @@   @@    @@      #@   #@       @@
+    @@@@@@@@@@@@@@@@  @@@@        #@      #@=   #@=  +@@   #@#    =@#   @@.   =@#    =@#   #@.      @@
+                                 =@#       @#  #@=     #@   =#@@@@#=    +#@@=  +#@@@@#=    .##@@+   @@
+    @@@@  @@@@@@@@@@@@@@@@
+"""
 
-def print_axolotl_text_art(suffix=None):
+
+def print_legacy_axolotl_text_art(suffix=None):
     font = "nancyj"
     ascii_text = "  axolotl"
     if suffix:
@@ -66,6 +83,13 @@ def print_axolotl_text_art(suffix=None):
     print_dep_versions()
 
 
+def print_axolotl_text_art(
+    **kwargs,  # pylint: disable=unused-argument
+):
+    if is_main_process():
+        print(AXOLOTL_LOGO)
+
+
 def print_dep_versions():
     packages = ["accelerate", "peft", "transformers", "trl", "torch", "bitsandbytes"]
     max_len = max(len(pkg) for pkg in packages)
@@ -166,18 +190,15 @@ def do_inference(
 ):
     model, tokenizer = load_model_and_tokenizer(cfg=cfg, cli_args=cli_args)
     prompter = cli_args.prompter
-    default_tokens = {"unk_token": "<unk>", "bos_token": "<s>", "eos_token": "</s>"}
-
-    for token, symbol in default_tokens.items():
-        # If the token isn't already specified in the config, add it
-        if not (cfg.special_tokens and token in cfg.special_tokens):
-            tokenizer.add_special_tokens({token: symbol})
 
     prompter_module = None
+    chat_template_str = None
     if prompter:
         prompter_module = getattr(
             importlib.import_module("axolotl.prompters"), prompter
         )
+    elif cfg.chat_template:
+        chat_template_str = get_chat_template(cfg.chat_template)
 
     model = model.to(cfg.device, dtype=cfg.torch_dtype)
 
@@ -187,13 +208,31 @@ def do_inference(
         instruction = get_multi_line_input()
         if not instruction:
             return
+
         if prompter_module:
             prompt: str = next(
                 prompter_module().build_prompt(instruction=instruction.strip("\n"))
             )
         else:
             prompt = instruction.strip()
-        batch = tokenizer(prompt, return_tensors="pt", add_special_tokens=True)
+
+        if chat_template_str:
+            batch = tokenizer.apply_chat_template(
+                [
+                    {
+                        "role": "user",
+                        "content": prompt,
+                    }
+                ],
+                return_tensors="pt",
+                add_special_tokens=True,
+                add_generation_prompt=True,
+                chat_template=chat_template_str,
+                tokenize=True,
+                return_dict=True,
+            )
+        else:
+            batch = tokenizer(prompt, return_tensors="pt", add_special_tokens=True)
 
         print("=" * 40)
         model.eval()
@@ -233,18 +272,15 @@ 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>"}
-
-    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, tokenizer=tokenizer)
 
     model = model.to(cfg.device, dtype=cfg.torch_dtype)
 
@@ -258,7 +294,24 @@ def do_inference_gradio(
             )
         else:
             prompt = instruction.strip()
-        batch = tokenizer(prompt, return_tensors="pt", add_special_tokens=True)
+
+        if chat_template_str:
+            batch = tokenizer.apply_chat_template(
+                [
+                    {
+                        "role": "user",
+                        "content": prompt,
+                    }
+                ],
+                return_tensors="pt",
+                add_special_tokens=True,
+                add_generation_prompt=True,
+                chat_template=chat_template_str,
+                tokenize=True,
+                return_dict=True,
+            )
+        else:
+            batch = tokenizer(prompt, return_tensors="pt", add_special_tokens=True)
 
         model.eval()
         with torch.no_grad():
@@ -281,6 +334,7 @@ def do_inference_gradio(
             streamer = TextIteratorStreamer(tokenizer)
             generation_kwargs = {
                 "inputs": batch["input_ids"].to(cfg.device),
+                "attention_mask": batch["attention_mask"].to(cfg.device),
                 "generation_config": generation_config,
                 "streamer": streamer,
             }
@@ -365,6 +419,11 @@ 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)
@@ -392,6 +451,8 @@ def load_cfg(config: Union[str, Path] = Path("examples/"), **kwargs):
 
     setup_mlflow_env_vars(cfg)
 
+    setup_comet_env_vars(cfg)
+
     return cfg
 
 
@@ -401,12 +462,20 @@ def load_datasets(
     cli_args: TrainerCliArgs,
 ) -> TrainDatasetMeta:
     tokenizer = load_tokenizer(cfg)
+    processor = load_processor(cfg, tokenizer=tokenizer) if cfg.processor_type else None
 
     train_dataset, eval_dataset, total_num_steps, prompters = prepare_dataset(
-        cfg, tokenizer
+        cfg,
+        tokenizer,
+        processor=processor,
     )
 
-    if cli_args.debug or cfg.debug:
+    if (
+        cli_args.debug
+        or cfg.debug
+        or cli_args.debug_text_only
+        or int(cli_args.debug_num_examples) > 0
+    ):
         LOG.info("check_dataset_labels...")
         check_dataset_labels(
             train_dataset.select(

src/axolotl/cli/merge_sharded_fsdp_weights.py

@@ -0,0 +1,204 @@
+"""
+This module provides a CLI to merge sharded FSDP model checkpoints into a single combined checkpoint
+"""
+import json
+import logging
+import os
+import shutil
+from pathlib import Path
+from typing import Dict, Union
+
+import fire
+import torch
+import torch.distributed.checkpoint as dist_cp
+import torch.distributed.checkpoint.format_utils as dist_cp_format_utils
+import transformers
+from accelerate.utils import (
+    SAFE_WEIGHTS_INDEX_NAME,
+    SAFE_WEIGHTS_NAME,
+    WEIGHTS_INDEX_NAME,
+    WEIGHTS_NAME,
+    is_torch_version,
+)
+from dotenv import load_dotenv
+from huggingface_hub import split_torch_state_dict_into_shards
+from safetensors.torch import save_file as safe_save_file
+from torch.distributed.checkpoint.format_utils import _EmptyStateDictLoadPlanner
+
+from axolotl.cli import load_cfg, print_axolotl_text_art
+from axolotl.common.cli import TrainerCliArgs
+
+LOG = logging.getLogger("axolotl.cli.merge_sharded_fsdp_weights")
+
+
+class BFloat16CastPlanner(_EmptyStateDictLoadPlanner):
+    """
+    A custom planner to cast tensors to bfloat16 on the fly during loading.
+    """
+
+    def commit_tensor(self, read_item, tensor):  # pylint: disable=unused-argument
+        tensor.copy_(tensor.to(torch.bfloat16))
+
+
+def _distributed_checkpoint_to_merged_weights(
+    checkpoint_dir: Union[str, Path],
+    save_path: str,
+    safe_serialization: bool = False,
+    max_shard_size: str = "5GB",
+):
+    """
+    Passthrough to `torch.distributed.checkpoint.format_utils.dcp_to_torch_save`
+
+    Will save under `save_path` as either `model.safetensors` or `pytorch_model.bin`.
+    """
+
+    state_dict: Dict = {}
+    save_path_ = Path(save_path)
+    save_path_.mkdir(exist_ok=True)
+    dist_cp_format_utils._load_state_dict(  # pylint: disable=protected-access
+        state_dict,
+        storage_reader=dist_cp.FileSystemReader(checkpoint_dir),
+        planner=BFloat16CastPlanner(),  # pylint: disable=protected-access
+        no_dist=True,
+    )
+
+    # To handle if state is a dict like {model: {...}}
+    if len(state_dict.keys()) == 1:
+        state_dict = state_dict[list(state_dict)[0]]
+
+    # Ensure all tensors are in bfloat16
+    for key, value in state_dict.items():
+        if isinstance(value, torch.Tensor) and value.dtype != torch.bfloat16:
+            state_dict[key] = value.to(torch.bfloat16)
+
+    weights_name = SAFE_WEIGHTS_NAME if safe_serialization else WEIGHTS_NAME
+
+    filename_pattern = weights_name.replace(".bin", "{suffix}.bin").replace(
+        ".safetensors", "{suffix}.safetensors"
+    )
+    state_dict_split = split_torch_state_dict_into_shards(
+        state_dict, filename_pattern=filename_pattern, max_shard_size=max_shard_size
+    )
+    # Save index if sharded
+    index = None
+    if state_dict_split.is_sharded:
+        index = {
+            "metadata": state_dict_split.metadata,
+            "weight_map": state_dict_split.tensor_to_filename,
+        }
+
+    # Save the model
+    filename_to_tensors = state_dict_split.filename_to_tensors.items()
+
+    for shard_file, tensors in filename_to_tensors:
+        shard = {tensor: state_dict[tensor] for tensor in tensors}
+
+        if safe_serialization:
+            safe_save_file(
+                shard, os.path.join(save_path_, shard_file), metadata={"format": "pt"}
+            )
+        else:
+            torch.save(shard, os.path.join(save_path_, shard_file))
+
+    if index is not None:
+        save_index_file = (
+            SAFE_WEIGHTS_INDEX_NAME if safe_serialization else WEIGHTS_INDEX_NAME
+        )
+        save_index_file = os.path.join(save_path_, save_index_file)
+        # Save the index as well
+        with open(save_index_file, "w", encoding="utf-8") as fout:
+            content = json.dumps(index, indent=2, sort_keys=True) + "\n"
+            fout.write(content)
+
+    return save_path_
+
+
+def merge_fsdp_weights(
+    checkpoint_dir: str,
+    output_path: str,
+    safe_serialization: bool = False,
+    remove_checkpoint_dir: bool = False,
+):
+    """
+    Merge the weights from sharded FSDP model checkpoints into a single combined checkpoint. Should be used if
+    `SHARDED_STATE_DICT` was used for the model. Weights will be saved to `{output_path}/model.safetensors` if
+    `safe_serialization` else `pytorch_model.bin`.
+
+    Note: this is a CPU-bound process.
+
+    Args:
+        checkpoint_dir (`str`):
+            The directory containing the FSDP checkpoints (can be either the model or optimizer).
+        output_path (`str`):
+            The path to save the merged checkpoint.
+        safe_serialization (`bool`, *optional*, defaults to `True`):
+            Whether to save the merged weights with safetensors (recommended).
+        remove_checkpoint_dir (`bool`, *optional*, defaults to `False`):
+            Whether to remove the checkpoint directory after merging.
+    """
+    checkpoint_dir_ = Path(checkpoint_dir)
+    from accelerate.state import PartialState
+
+    if not is_torch_version(">=", "2.3.0"):
+        raise ValueError("`merge_fsdp_weights` requires PyTorch >= 2.3.0`")
+
+    # Verify that the checkpoint directory exists
+    if not checkpoint_dir_.exists():
+        model_path_exists = (checkpoint_dir_ / "pytorch_model_fsdp_0").exists()
+        optimizer_path_exists = (checkpoint_dir_ / "optimizer_0").exists()
+        err = f"Tried to load from {checkpoint_dir_} but couldn't find a valid metadata file."
+        if model_path_exists and optimizer_path_exists:
+            err += (
+                " However, potential model and optimizer checkpoint directories exist."
+            )
+            err += f"Please pass in either {checkpoint_dir_}/pytorch_model_fsdp_0 or {checkpoint_dir_}/optimizer_0"
+            err += "instead."
+        elif model_path_exists:
+            err += " However, a potential model checkpoint directory exists."
+            err += (
+                f"Please try passing in {checkpoint_dir_}/pytorch_model_fsdp_0 instead."
+            )
+        elif optimizer_path_exists:
+            err += " However, a potential optimizer checkpoint directory exists."
+            err += f"Please try passing in {checkpoint_dir_}/optimizer_0 instead."
+        raise ValueError(err)
+
+    # To setup `save` to work
+    state = PartialState()
+    if state.is_main_process:
+        LOG.info(f"Merging FSDP weights from {checkpoint_dir_}")
+        save_path = _distributed_checkpoint_to_merged_weights(
+            checkpoint_dir_, output_path, safe_serialization
+        )
+        LOG.info(f"Successfully merged FSDP weights and saved to {save_path}")
+        if remove_checkpoint_dir:
+            LOG.info(f"Removing old checkpoint directory {checkpoint_dir_}")
+            shutil.rmtree(checkpoint_dir_)
+    state.wait_for_everyone()
+
+
+def do_cli(config: Path = Path("examples/"), **kwargs):
+    # pylint: disable=duplicate-code
+    print_axolotl_text_art()
+    parser = transformers.HfArgumentParser((TrainerCliArgs))
+    parsed_cli_args, _ = parser.parse_args_into_dataclasses(
+        return_remaining_strings=True
+    )
+    parsed_cli_args.merge_lora = True
+
+    parsed_cfg = load_cfg(
+        config,
+        **kwargs,
+    )
+
+    fsdp_dir = Path(parsed_cfg.output_dir) / "pytorch_model_fsdp_0"
+    merge_fsdp_weights(
+        checkpoint_dir=str(fsdp_dir),
+        output_path=str(Path(parsed_cfg.output_dir) / "merged"),
+        safe_serialization=True,
+    )
+
+
+if __name__ == "__main__":
+    load_dotenv()
+    fire.Fire(do_cli)

src/axolotl/cli/preprocess.py

@@ -23,10 +23,7 @@ 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")
 
@@ -43,23 +40,6 @@ def do_cli(config: Union[Path, str] = Path("examples/"), **kwargs):
         return_remaining_strings=True
     )
 
-    if parsed_cfg.chat_template == "chatml":
-        if parsed_cfg.default_system_message:
-            LOG.info(
-                f"ChatML set. Adding default system message: {parsed_cfg.default_system_message}"
-            )
-            register_chatml_template(parsed_cfg.default_system_message)
-        else:
-            register_chatml_template()
-    elif parsed_cfg.chat_template == "llama3":
-        if parsed_cfg.default_system_message:
-            LOG.info(
-                f"LLaMA-3 set. Adding default system message: {parsed_cfg.default_system_message}"
-            )
-            register_llama3_template(parsed_cfg.default_system_message)
-        else:
-            register_llama3_template()
-
     if not parsed_cfg.dataset_prepared_path:
         msg = (
             Fore.RED
@@ -70,10 +50,11 @@ def do_cli(config: Union[Path, str] = Path("examples/"), **kwargs):
         LOG.warning(msg)
         parsed_cfg.dataset_prepared_path = DEFAULT_DATASET_PREPARED_PATH
 
-    if parsed_cfg.rl:  # and parsed_cfg.rl != "orpo":
-        load_rl_datasets(cfg=parsed_cfg, cli_args=parsed_cli_args)
-    else:
-        load_datasets(cfg=parsed_cfg, cli_args=parsed_cli_args)
+    with disable_datasets_caching():
+        if parsed_cfg.rl:  # and parsed_cfg.rl != "orpo":
+            load_rl_datasets(cfg=parsed_cfg, cli_args=parsed_cli_args)
+        else:
+            load_datasets(cfg=parsed_cfg, cli_args=parsed_cli_args)
 
     if parsed_cli_args.download:
         model_name = parsed_cfg.base_model
@@ -82,7 +63,14 @@ def do_cli(config: Union[Path, str] = Path("examples/"), **kwargs):
             # "copying from a non-meta parameter in the checkpoint to a meta parameter in the current model"
             warnings.simplefilter("ignore")
             with init_empty_weights(include_buffers=True):
-                AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True)
+                # fmt: off
+                try:
+                    AutoModelForCausalLM.from_pretrained(
+                        model_name, trust_remote_code=True
+                    )
+                except Exception as exc:  # pylint: disable=broad-exception-caught,unused-variable  # nosec B110  # noqa F841
+                    pass
+                # fmt: on
 
     LOG.info(
         Fore.GREEN

src/axolotl/cli/train.py

@@ -3,13 +3,11 @@ CLI to run training on a model
 """
 import logging
 from pathlib import Path
-from typing import Tuple, Union
+from typing import Union
 
 import fire
 from dotenv import load_dotenv
 from transformers.hf_argparser import HfArgumentParser
-from transformers.modeling_utils import PreTrainedModel
-from transformers.tokenization_utils import PreTrainedTokenizer
 
 from axolotl.cli import (
     check_accelerate_default_config,
@@ -20,10 +18,7 @@ from axolotl.cli import (
     print_axolotl_text_art,
 )
 from axolotl.common.cli import TrainerCliArgs
-from axolotl.prompt_strategies.sharegpt import (
-    register_chatml_template,
-    register_llama3_template,
-)
+from axolotl.integrations.base import PluginManager
 from axolotl.train import train
 
 LOG = logging.getLogger("axolotl.cli.train")
@@ -39,32 +34,23 @@ def do_cli(config: Union[Path, str] = Path("examples/"), **kwargs):
     return do_train(parsed_cfg, parsed_cli_args)
 
 
-def do_train(cfg, cli_args) -> Tuple[PreTrainedModel, PreTrainedTokenizer]:
+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)
     else:
         dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
 
-    return train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
+    model, tokenizer = train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
+    plugin_manager = PluginManager.get_instance()
+
+    del model
+    del tokenizer
+
+    plugin_manager.post_train_unload(cfg)
 
 
 if __name__ == "__main__":

src/axolotl/common/architectures.py

@@ -10,6 +10,7 @@ MOE_ARCH_BLOCK = {
         "JetMoeMoE",
     ],
     "mixtral": "MixtralSparseMoeBlock",
+    "phimoe": "PhiMoESparseMoeBlock",
     "qwen2_moe": "Qwen2MoeSparseMoeBlock",
     "deepseek_v2": "DeepseekV2MoE",
 }

src/axolotl/common/cli.py

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

src/axolotl/core/chat/format/chatml.py

@@ -0,0 +1,34 @@
+"""
+ChatML transformation functions for MessageContents
+"""
+from typing import Optional
+
+from ..messages import MessageContents, Messages
+from .shared import wrap_tools
+
+
+def format_message(
+    message: Messages,
+    message_index: Optional[int] = None,  # pylint: disable=unused-argument
+) -> Messages:
+    if message.is_chat_formatted:
+        return message
+
+    # prepend the role prefix within a MessageContents to message.content
+    message.content.insert(
+        0,
+        MessageContents(
+            type="text",
+            value=f"<|im_start|>{message.role}\n",
+            weight=0,
+        ),
+    )
+    message.content.append(
+        MessageContents(type="text", value="<|im_end|>", weight=message.weight)
+    )
+    message.content.append(MessageContents(type="text", value="\n", weight=0))
+
+    message = wrap_tools(message)
+
+    message.is_chat_formatted = True
+    return message

src/axolotl/core/chat/format/llama3x.py

@@ -0,0 +1,45 @@
+"""
+Llama 3.x chat formatting functions for MessageContents
+"""
+from typing import Optional
+
+from ..messages import MessageContents, Messages
+from .shared import wrap_tools
+
+
+def format_message(message: Messages, message_index: Optional[int] = None) -> Messages:
+    if message.is_chat_formatted:
+        return message
+
+    message_role = message.role
+    if message.role == "tool":
+        message_role = "ipython"
+
+    # prepend the role prefix within a MessageContents to message.content
+    message.content.insert(
+        0,
+        MessageContents(
+            type="text",
+            value=f"<|start_header_id|>{message_role}<|end_header_id|>\n\n",
+            weight=0,
+        ),
+    )
+
+    message.content.append(
+        MessageContents(type="text", value="<|eot_id|>", weight=message.weight)
+    )
+
+    message = wrap_tools(message)
+
+    if message_index == 0:
+        message.content.insert(
+            0,
+            MessageContents(
+                type="text",
+                value="<|begin_of_text|>",
+                weight=0,
+            ),
+        )
+
+    message.is_chat_formatted = True
+    return message

src/axolotl/core/chat/format/shared.py

@@ -0,0 +1,47 @@
+"""
+shared functions for format transforms
+"""
+from axolotl.core.chat.messages import MessageContents, Messages
+
+
+def wrap_tools(message: Messages):
+    # loop over message.content by index to find tool calls, we need to wrap each with tags,
+    # so be wary of indexing issues when changing the list while iterating.
+    # iterate over the range in reverse order to avoid index shifting
+    for i in range(len(message.content) - 1, -1, -1):
+        if message.content[i].type == "tool_call":
+            # append a </tool_call> MessageContents text tag after
+            message.content.insert(
+                i + 1,
+                MessageContents(
+                    type="text", value="</tool_call>\n", weight=message.weight
+                ),
+            )
+            # make sure the actual tool call content ends with a newline
+            message.content[i].has_newline = True
+            # prepend a <tool_call> MessageContents text tag before
+            message.content.insert(
+                i,
+                MessageContents(
+                    type="text", value="<tool_call>\n", weight=message.weight
+                ),
+            )
+        elif message.content[i].type == "tool_response":
+            # append a </tool_call> MessageContents text tag after
+            message.content.insert(
+                i + 1,
+                MessageContents(
+                    type="text", value="</tool_response>\n", weight=message.weight
+                ),
+            )
+            # make sure the actual tool response content ends with a newline
+            message.content[i].has_newline = True
+            # prepend a <tool_call> MessageContents text tag before
+            message.content.insert(
+                i,
+                MessageContents(
+                    type="text", value="<tool_response>\n", weight=message.weight
+                ),
+            )
+
+    return message

src/axolotl/core/chat/messages.py

@@ -0,0 +1,230 @@
+"""
+internal message representations of chat messages
+"""
+import json
+from enum import Enum
+from typing import Any, Callable, List, Optional, Union
+
+from pydantic import BaseModel
+from transformers import PreTrainedTokenizer
+
+
+class MessageRoles(str, Enum):
+    """
+    Message roles for the system, user, assistant, and tools
+    """
+
+    system = "system"  # pylint: disable=invalid-name
+    user = "user"  # pylint: disable=invalid-name
+    assistant = "assistant"  # pylint: disable=invalid-name
+    tool = "tool"  # pylint: disable=invalid-name
+    ipython = (  # pylint: disable=invalid-name
+        # for responses from builtin tools
+        "ipython"
+    )
+
+
+class MessageContentTypes(str, Enum):
+    """
+    Message content types for text, image, audio, tool calls, and tool responses
+    """
+
+    special_token = "special_token"  # pylint: disable=invalid-name  # nosec B105
+    text = "text"  # pylint: disable=invalid-name
+    image = "image"  # pylint: disable=invalid-name
+    audio = "audio"  # pylint: disable=invalid-name
+    tool_call = "tool_call"  # pylint: disable=invalid-name  # to differentiate regular responses from tool calls from the assistant
+    tool_response = "tool_response"  # pylint: disable=invalid-name
+
+
+class SpecialToken(str, Enum):
+    """
+    Special tokens for beginning of string and end of string
+    """
+
+    bos_token = "bos_token"  # pylint: disable=invalid-name  # nosec B105
+    eos_token = "eos_token"  # pylint: disable=invalid-name  # nosec B105
+
+
+class ToolCallFunction(BaseModel):
+    """
+    Tool call function with name and arguments
+    """
+
+    name: str
+    arguments: dict[str, str]
+
+
+class Tool(BaseModel):
+    """
+    Tool with description, function, and parameters
+    """
+
+    description: str
+    function: ToolCallFunction
+    parameters: dict[str, str]  # .properties
+
+
+class ToolCallContents(BaseModel):
+    """
+    Tool call contents with name, arguments, and optional id
+    """
+
+    name: str
+    arguments: dict[str, Union[str, int]]
+    id: Optional[str] = None  # pylint: disable=invalid-name
+
+    def __str__(self) -> str:
+        data = {"name": self.name, "arguments": self.arguments}
+        if self.id is not None:
+            data["id"] = self.id
+        return json.dumps(data)
+
+
+class ToolResponseContents(BaseModel):
+    """
+    Tool response contents with name, content, and optional id
+    """
+
+    name: str
+    content: Union[str, dict[str, Union[str, int, float]]]
+    id: Optional[str] = None  # pylint: disable=invalid-name
+
+    def __str__(self) -> str:
+        data = {"name": self.name, "content": self.content}
+        if self.id is not None:
+            data["id"] = self.id
+        return json.dumps(data)
+
+
+class MessageContents(BaseModel):
+    """
+    Message contents with type, value, metadata, weight, newline, and end of contents
+    """
+
+    type: Union[str, MessageContentTypes]
+    value: Union[str, ToolCallContents, ToolResponseContents, SpecialToken]
+    meta: Optional[dict[str, Any]] = None  # support additional arbitrary metadata
+    weight: Optional[Union[int, float]] = None
+    has_newline: bool = False
+    eoc: bool = False  # end of contents
+
+    def __str__(self) -> str:
+        str_val = str(self.value)
+        if self.has_newline and not str_val.endswith("\n"):
+            str_val += "\n"
+        return str_val
+
+
+class Messages(BaseModel):
+    """
+    Messages with role, content, metadata, weight, and chat formatting
+    """
+
+    role: Union[MessageRoles, str]  # allows for arbitrary roles
+    content: List["MessageContents"]
+    meta: Optional[dict[str, Any]] = None  # support additional arbitrary metadata
+    weight: Optional[Union[int, float]] = None
+    is_chat_formatted: bool = False
+
+    def __str__(self) -> str:
+        return "".join(str(c) for c in self.content)
+
+    def tokenized(
+        self, tokenizer: PreTrainedTokenizer, ignore_index=-100
+    ) -> dict[str, List[int]]:
+        # iterate over the contents, tokenizing the concatenated string values up to the current MessageContents
+        # returns a dictionary mapping w input_ids, attention_mask, and labels
+        input_ids: List[int] = []
+        labels: List[int] = []
+        pending_input_ids: List[int] = []
+        pending_weight = self.weight
+        running_content = ""
+        for _, msg_content in enumerate(self.content):
+            # TODO also handle non-text content types
+            if msg_content.type in [
+                MessageContentTypes.text.value,
+                MessageContentTypes.tool_call.value,
+                MessageContentTypes.tool_response.value,
+            ]:
+                running_content += str(msg_content)
+                tok_results = tokenizer(running_content, add_special_tokens=False)
+                tok_input_ids = tok_results["input_ids"]
+                if pending_input_ids:
+                    new_pending_inputs = tok_input_ids[
+                        len(input_ids) : len(input_ids) + len(pending_input_ids)
+                    ]
+                    if new_pending_inputs != pending_input_ids:
+                        # logging.warning("tokenization mismatch from concatenation.")
+                        pending_input_ids = new_pending_inputs
+                    input_ids.extend(pending_input_ids)
+                    if pending_weight:
+                        labels.extend(pending_input_ids)
+                    else:
+                        labels.extend([ignore_index] * len(pending_input_ids))
+                pending_input_ids = tok_results["input_ids"][len(input_ids) :]
+                pending_weight = self.weight and msg_content.weight not in [0, 0.0]
+        input_ids.extend(pending_input_ids)
+        if pending_weight:
+            labels.extend(pending_input_ids)
+        else:
+            labels.extend([ignore_index] * len(pending_input_ids))
+        attention_mask = [1] * len(input_ids)
+        return {
+            "input_ids": input_ids,
+            "attention_mask": attention_mask,
+            "labels": labels,
+        }
+
+
+class Chats(BaseModel):
+    """
+    top level data structure for chat conversations
+    """
+
+    conversation: List[Messages]
+
+    def __str__(self) -> str:
+        return "".join(str(c) for c in self.conversation)
+
+    def tokenized(
+        self, tokenizer: Callable[[str], dict[str, List[int]]], ignore_index=-100
+    ) -> dict[str, List[int]]:
+        input_ids = []
+        attention_mask = []
+        labels = []
+        for msg in self.conversation:
+            msg_results = msg.tokenized(tokenizer, ignore_index)
+            input_ids.extend(msg_results["input_ids"])
+            attention_mask.extend(msg_results["attention_mask"])
+            labels.extend(msg_results["labels"])
+        return {
+            "input_ids": input_ids,
+            "attention_mask": attention_mask,
+            "labels": labels,
+        }
+
+
+class ChatFormattedChats(Chats):
+    """
+    Chat formatted chats with formatter and optional train on inputs
+    """
+
+    formatter: Callable  # [[Union[dict, Chats]], Chats]
+    train_on_inputs: bool = False
+
+    def model_post_init(self, __context):
+        for i, msg in enumerate(self.conversation):
+            self.conversation[i] = self.formatter(msg, message_index=i)
+            if self.train_on_inputs:
+                self.conversation[i].weight = 1
+
+
+class PreferenceChats(BaseModel):
+    """
+    representation for preference data for chat
+    """
+
+    prompt: List[Messages]
+    chosen: Messages
+    rejected: Messages

src/axolotl/core/datasets/chat.py

@@ -0,0 +1,55 @@
+"""
+chat dataset module
+"""
+import os
+from typing import Callable, Optional, Union
+
+from datasets import Dataset
+from transformers import PreTrainedTokenizer
+
+from axolotl.core.chat.messages import ChatFormattedChats
+
+
+class TokenizedChatDataset(Dataset):
+    """
+    Tokenized chat dataset
+    """
+
+    def __init__(
+        self,
+        data: Dataset,
+        model_transform: Union[PreTrainedTokenizer, Callable],
+        *args,
+        message_transform: Optional[Callable] = None,
+        formatter=None,
+        process_count: Optional[int] = None,
+        keep_in_memory: Optional[bool] = False,
+        **kwargs,
+    ):
+        def map_fn(ex):
+            if message_transform is not None:
+                ex = message_transform(ex)
+            if formatter is not None:
+                ex = ChatFormattedChats(
+                    formatter=formatter,
+                    **ex,
+                )
+            else:
+                ex = ChatFormattedChats(
+                    **ex,
+                )
+            return ex.tokenized(model_transform)
+
+        process_or_cpu_count: int = (
+            process_count or os.cpu_count()  # type: ignore[assignment]
+        )
+        num_proc = min(64, process_or_cpu_count)
+        features = data.features.keys()
+        tokenized_data = data.map(
+            map_fn,
+            num_proc=num_proc,
+            keep_in_memory=keep_in_memory,
+            remove_columns=features,
+            desc="Tokenizing Chats",
+        )
+        super().__init__(tokenized_data.data, *args, **kwargs)

src/axolotl/core/datasets/transforms/chat_builder.py

@@ -0,0 +1,150 @@
+"""
+This module contains a function that builds a transform that takes a row from the dataset and converts it to a Chat.
+"""
+from typing import Any, Mapping, Union
+
+
+def chat_message_transform_builder(  # pylint: disable=dangerous-default-value
+    train_on_inputs=False,
+    conversations_field: str = "conversations",
+    message_field_role: Union[str, list[str]] = ["role", "from"],  # commonly "role"
+    message_field_content: Union[str, list[str]] = [
+        "value",
+        "text",
+        "content",
+    ],  # commonly "content"
+    message_field_training: Union[str, list[str]] = [
+        "train",
+        "weight",
+    ],  # commonly "weight"
+):
+    """Builds a transform that takes a row from the dataset and converts it to a Chat
+
+    Args:
+        train_on_inputs (bool, optional):
+            If True, the transform will train on the inputs. If False, the transform will train on the targets.
+            Defaults to False.
+        conversations_field (str, optional):
+            The field name of the conversations. Defaults to "conversations".
+        message_field_role (str | list[str], optional):
+            The field name of the role. Defaults to "role".
+        message_field_content (str | list[str], optional):
+            The field name of the message content. Defaults to "content".
+        message_field_training (str | list[str], optional):
+            The field name of the train/weight. Defaults to "weight".
+
+    Returns:
+        Callable:
+            A function that takes a list of conversations and returns a list of messages.
+    """
+
+    message_field_role = (
+        [message_field_role]
+        if isinstance(message_field_role, str)
+        else message_field_role
+    )
+    message_field_content = (
+        [message_field_content]
+        if isinstance(message_field_content, str)
+        else message_field_content
+    )
+    message_weight_fields = (
+        [message_field_training]
+        if isinstance(message_field_training, str)
+        else message_field_training
+    )
+
+    role_value_mappings = {
+        "system": "system",
+        "user": "user",
+        "human": "user",
+        "assistant": "assistant",
+        "gpt": "assistant",
+        "tool": "tool",
+        "ipython": "ipython",
+    }
+    if train_on_inputs:
+        role_default_weights_mappings = {
+            "system": 1,
+            "user": 1,
+            "assistant": 1,
+            "tool": 1,
+            "ipython": 1,
+        }
+    else:
+        role_default_weights_mappings = {
+            "system": 0,
+            "user": 0,
+            "assistant": 1,
+            "tool": 0,
+            "ipython": 0,
+        }
+
+    def transform_builder(sample: Mapping[str, Any]):
+        if conversations_field not in sample:
+            raise ValueError(f"Field '{conversations_field}' not found in sample.")
+        # if none of the role fields are in the message, raise an error
+        if not any(
+            role in sample[conversations_field][0] for role in message_field_role
+        ):
+            raise ValueError("No role field found in message.")
+        role_field = next(
+            role
+            for role in message_field_role
+            if role in sample[conversations_field][0]
+        )
+        if not any(
+            field in sample[conversations_field][0] for field in message_field_content
+        ):
+            raise ValueError("No message_content field found in message.")
+        message_content_field = next(
+            field
+            for field in message_field_content
+            if field in sample[conversations_field][0]
+        )
+        if not any(
+            field in sample[conversations_field][0] for field in message_field_training
+        ):
+            message_weight_field = None
+        else:
+            message_weight_field = next(
+                field
+                for field in message_weight_fields
+                if field in sample[conversations_field][0]
+            )
+
+        messages = []
+        for message in sample[conversations_field]:
+            role = role_value_mappings[message[role_field]]
+            weight = (
+                int(message[message_weight_field])
+                if message_weight_field
+                else role_default_weights_mappings[role]
+            )
+
+            # TODO if "tool_calls" in message[message_content_field]: then convert tool call to ToolCallContents
+            if isinstance(message[message_content_field], str):
+                messages.append(
+                    {
+                        "role": role,
+                        "content": [
+                            {
+                                "type": "text",
+                                "value": message[message_content_field],
+                            }
+                        ],
+                        "weight": weight,
+                    }
+                )
+            else:
+                messages.append(
+                    {
+                        "role": role,
+                        "content": message[message_content_field],
+                        "weight": weight,
+                    }
+                )
+
+        return {"conversation": messages}
+
+    return transform_builder

src/axolotl/core/trainer_builder.py

@@ -4,8 +4,10 @@ Builder for the training args and trainer
 """
 
 import abc
+import gc
 import importlib
 import importlib.util
+import inspect
 import logging
 import math
 import os
@@ -15,16 +17,17 @@ from collections import defaultdict
 from dataclasses import dataclass, field
 from functools import wraps
 from pathlib import Path
-from typing import Dict, List, Literal, Optional, Type, Union
+from typing import Any, Dict, List, Literal, Optional, Type, Union
 
 import torch
 import transformers
 from datasets import Dataset
+from peft.optimizers import create_loraplus_optimizer
+from torch import nn
 from torch.optim.lr_scheduler import OneCycleLR
 from torch.utils.data import BatchSampler, DataLoader, RandomSampler, SequentialSampler
 from transformers import (
     EarlyStoppingCallback,
-    PreTrainedModel,
     Trainer,
     TrainerCallback,
     TrainingArguments,
@@ -40,13 +43,15 @@ from trl import (
     KTOTrainer,
     ORPOConfig,
     ORPOTrainer,
+    RewardConfig,
+    RewardTrainer,
 )
-from trl.trainer.utils import pad_to_length
+from trl.trainer.utils import RewardDataCollatorWithPadding, pad_to_length
 
-from axolotl.loraplus import create_loraplus_optimizer
+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_mlflow_available
+from axolotl.utils import is_comet_available, is_mlflow_available
 from axolotl.utils.callbacks import (
     EvalFirstStepCallback,
     GPUStatsCallback,
@@ -59,12 +64,14 @@ from axolotl.utils.callbacks import (
     log_prediction_callback_factory,
 )
 from axolotl.utils.callbacks.lisa import lisa_callback_factory
+from axolotl.utils.chat_templates import get_chat_template
 from axolotl.utils.collators import (
     BatchSamplerDataCollatorForSeq2Seq,
     DataCollatorForSeq2Seq,
     MambaDataCollator,
     V2BatchSamplerDataCollatorForSeq2Seq,
 )
+from axolotl.utils.collators.mm_chat import MultiModalChatDataCollator
 from axolotl.utils.models import ensure_dtype
 from axolotl.utils.samplers import MultipackBatchSampler, get_dataset_lengths
 from axolotl.utils.schedulers import (
@@ -248,6 +255,10 @@ class AxolotlTrainingMixins:
             "help": "workaround to pass an alternate lr scheduler to the HF trainer"
         },
     )
+    chat_template: Optional[str] = field(
+        default=None,
+        metadata={"help": "Chat template converting chat messages to text"},
+    )
 
 
 @dataclass
@@ -293,6 +304,13 @@ class AxolotlCPOConfig(AxolotlTrainingMixins, CPOConfig):
     )
 
 
+@dataclass
+class AxolotlRewardConfig(AxolotlTrainingMixins, RewardConfig):
+    """
+    Reward config for Reward training
+    """
+
+
 class SchedulerMixin(Trainer):
     """
     Mixin class for scheduler setup in CausalTrainer.
@@ -390,12 +408,10 @@ class AxolotlTrainer(SchedulerMixin, Trainer):
     def __init__(
         self,
         *_args,
-        num_epochs=1,
         bench_data_collator=None,
         eval_data_collator=None,
         **kwargs,
     ):
-        self.num_epochs = num_epochs
         self.bench_data_collator = bench_data_collator
         self.eval_data_collator = eval_data_collator
         super().__init__(*_args, **kwargs)
@@ -420,7 +436,13 @@ class AxolotlTrainer(SchedulerMixin, Trainer):
         if (
             self.args.loraplus_lr_ratio is None
             and self.args.alternate_optimizer
-            not in ["optimi_adamw", "ao_adamw_8bit", "ao_adamw_4bit", "ao_adamw_fp8"]
+            not in [
+                "optimi_adamw",
+                "ao_adamw_8bit",
+                "ao_adamw_4bit",
+                "ao_adamw_fp8",
+                "adopt_adamw",
+            ]
         ):
             return super().create_optimizer()
 
@@ -454,14 +476,14 @@ class AxolotlTrainer(SchedulerMixin, Trainer):
             if self.args.loraplus_lr_ratio is not None:
                 loraplus_lr_ratio = getattr(self.args, "loraplus_lr_ratio", None)
                 loraplus_lr_embedding = getattr(
-                    self.args, "loraplus_lr_embedding", None
+                    self.args, "loraplus_lr_embedding", 1e-6
                 )
                 self.optimizer = create_loraplus_optimizer(  # pylint: disable=attribute-defined-outside-init
                     opt_model,
                     optimizer_cls,
-                    optimizer_kwargs,
-                    loraplus_lr_ratio,
-                    loraplus_lr_embedding,
+                    loraplus_lr_ratio=loraplus_lr_ratio,
+                    loraplus_lr_embedding=loraplus_lr_embedding,
+                    **optimizer_kwargs,
                 )
             elif self.args.alternate_optimizer == "optimi_adamw":
                 from optimi import AdamW
@@ -489,6 +511,14 @@ class AxolotlTrainer(SchedulerMixin, Trainer):
                 self.optimizer = (  # pylint: disable=attribute-defined-outside-init
                     AdamWFp8(optimizer_grouped_parameters, **optimizer_kwargs)
                 )
+            elif self.args.alternate_optimizer == "adopt_adamw":
+                from axolotl.utils.optimizers.adopt import ADOPT
+
+                self.optimizer = (  # pylint: disable=attribute-defined-outside-init
+                    ADOPT(
+                        optimizer_grouped_parameters, decoupled=True, **optimizer_kwargs
+                    )
+                )
 
         if is_sagemaker_mp_enabled():
             self.optimizer = smp.DistributedOptimizer(  # pylint: disable=attribute-defined-outside-init
@@ -504,9 +534,10 @@ class AxolotlTrainer(SchedulerMixin, Trainer):
                 batch_max_len = self.args.max_seq_length
             else:
                 batch_size = 1
-                batch_max_len = (
-                    self.args.per_device_train_batch_size * self.args.max_seq_length
+                train_batch_size = (
+                    self.state.train_batch_size or self.args.per_device_train_batch_size
                 )
+                batch_max_len = train_batch_size * self.args.max_seq_length
             return MultipackBatchSampler(
                 RandomSampler(self.train_dataset),
                 lengths=get_dataset_lengths(self.train_dataset),
@@ -650,7 +681,9 @@ class AxolotlTrainer(SchedulerMixin, Trainer):
         return DataLoader(bench_dataset, **dataloader_params)
         # return self.accelerator.prepare(DataLoader(bench_dataset, **dataloader_params))
 
-    def compute_loss(self, model, inputs, return_outputs=False):
+    def compute_loss(
+        self, model, inputs, return_outputs=False, num_items_in_batch=None
+    ):
         # use one's weighted cross entropy loss calc
         # if self.args.sample_packing:
         #     labels = inputs.pop("labels")
@@ -658,8 +691,18 @@ class AxolotlTrainer(SchedulerMixin, Trainer):
         #     loss = trainer_weighted_loss(outputs, labels, shift_labels=True)
         #     return (loss, outputs) if return_outputs else loss
         if self.args.orpo_alpha:
-            return self.orpo_compute_loss(model, inputs, return_outputs=return_outputs)
-        return super().compute_loss(model, inputs, return_outputs=return_outputs)
+            return self.orpo_compute_loss(
+                model,
+                inputs,
+                return_outputs=return_outputs,
+                num_items_in_batch=num_items_in_batch,
+            )
+        return super().compute_loss(
+            model,
+            inputs,
+            return_outputs=return_outputs,
+            num_items_in_batch=num_items_in_batch,
+        )
 
     @staticmethod
     def orpo_concatenate_inputs(inputs, label_pad_token=-100, pad_token=0, device=None):
@@ -755,7 +798,13 @@ class AxolotlTrainer(SchedulerMixin, Trainer):
         ).squeeze(2)
         return torch.mul(per_token_logps, mask).sum(dim=1) / mask.sum(dim=1)
 
-    def orpo_compute_loss(self, model, inputs, return_outputs=False):
+    def orpo_compute_loss(
+        self,
+        model,
+        inputs,
+        return_outputs=False,
+        num_items_in_batch=None,  # pylint: disable=unused-argument
+    ):
         concat_inputs = AxolotlTrainer.orpo_concatenate_inputs(
             inputs,
             label_pad_token=-100,
@@ -861,13 +910,13 @@ class AxolotlTrainer(SchedulerMixin, Trainer):
         for key, value in metrics.items():
             self._stored_metrics[train_eval][key].append(value)
 
-    def _save_checkpoint(self, model, trial, metrics=None):
+    def _save_checkpoint(self, model, trial, **kwargs):
         # make sure the checkpoint dir exists, since trainer is flakey
         checkpoint_folder = f"{PREFIX_CHECKPOINT_DIR}-{self.state.global_step}"
         run_dir = self._get_output_dir(trial=trial)
         output_dir = os.path.join(run_dir, checkpoint_folder)
         os.makedirs(output_dir, exist_ok=True)
-        return super()._save_checkpoint(model, trial, metrics=metrics)
+        return super()._save_checkpoint(model, trial, **kwargs)
 
 
 class AxolotlMambaTrainer(AxolotlTrainer):
@@ -882,6 +931,7 @@ class AxolotlMambaTrainer(AxolotlTrainer):
         model,
         inputs,
         return_outputs=False,  # pylint: disable=unused-argument
+        num_items_in_batch=None,  # pylint: disable=unused-argument
     ):
         input_ids = inputs.pop("input_ids")
         lm_logits = model(input_ids).logits
@@ -966,9 +1016,9 @@ class AxolotlDPOTrainer(SchedulerMixin, DPOTrainer):
             self.optimizer = create_loraplus_optimizer(  # pylint: disable=attribute-defined-outside-init
                 opt_model,
                 optimizer_cls,
-                optimizer_kwargs,
-                loraplus_lr_ratio,
-                loraplus_lr_embedding,
+                loraplus_lr_ratio=loraplus_lr_ratio,
+                loraplus_lr_embedding=loraplus_lr_embedding,
+                **optimizer_kwargs,
             )
 
         if is_sagemaker_mp_enabled():
@@ -988,15 +1038,50 @@ class AxolotlDPOTrainer(SchedulerMixin, DPOTrainer):
 
         return super().push_to_hub(*args, **kwargs)
 
+    @staticmethod
     def tokenize_row(
-        self, feature, model: Optional[Union[PreTrainedModel, torch.nn.Module]] = None
+        features,
+        processing_class,
+        max_prompt_length,
+        max_completion_length,
+        add_special_tokens,
     ) -> Dict:
-        res = super().tokenize_row(feature, model=model)
-        if self.tokenizer.bos_token_id is None and res["prompt_input_ids"][0] is None:
+        res = DPOTrainer.tokenize_row(
+            features,
+            processing_class,
+            max_prompt_length,
+            max_completion_length,
+            add_special_tokens,
+        )
+        # fix when the tokenizer doesn't have a bos_token_id, e.g. Qwen
+        if processing_class.bos_token is None and res["prompt_input_ids"][0] is None:
             for key in res.keys():
                 res[key] = res[key][1:]
+
+        if processing_class.bos_token and processing_class.bos_token_id is not None:
+            # dpo trainer may incorrectly prepend the bos_token_id to the dpo outputs
+            if res["chosen_input_ids"][0] == processing_class.bos_token_id:
+                res["chosen_input_ids"] = res["chosen_input_ids"][1:]
+                res["chosen_labels"] = res["chosen_labels"][1:]
+                res["chosen_attention_mask"] = res["chosen_attention_mask"][1:]
+            if res["rejected_input_ids"][0] == processing_class.bos_token_id:
+                res["rejected_input_ids"] = res["rejected_input_ids"][1:]
+                res["rejected_labels"] = res["rejected_labels"][1:]
+                res["rejected_attention_mask"] = res["rejected_attention_mask"][1:]
+
         return res
 
+    def training_step(
+        self,
+        model: nn.Module,
+        inputs: Dict[str, Union[torch.Tensor, Any]],
+        num_items_in_batch=None,
+    ) -> torch.Tensor:
+        loss: torch.Tensor = super().training_step(model, inputs, num_items_in_batch)
+        gc.collect()
+        torch.cuda.empty_cache()
+        return loss
+
 
 class AxolotlORPOTrainer(SchedulerMixin, ORPOTrainer):
     """
@@ -1022,6 +1107,14 @@ class AxolotlCPOTrainer(SchedulerMixin, CPOTrainer):
     tag_names = ["axolotl", "cpo"]
 
 
+class AxolotlRewardTrainer(SchedulerMixin, RewardTrainer):
+    """
+    Extend the base RewardTrainer for axolotl helpers
+    """
+
+    tag_names = ["axolotl", "reward"]
+
+
 class TrainerBuilderBase(abc.ABC):
     """
     Base class for trainer builder
@@ -1032,10 +1125,11 @@ class TrainerBuilderBase(abc.ABC):
     _model_ref = None
     _peft_config = None
 
-    def __init__(self, cfg, model, tokenizer):
+    def __init__(self, cfg, model, tokenizer, processor=None):
         self.cfg = cfg
         self.model = model
         self.tokenizer = tokenizer
+        self.processor = processor
 
         # in case the model supports tagging, add the axolotl tag.
         # This makes sure the tag is correctly pushed even if a user calls
@@ -1081,26 +1175,49 @@ class TrainerBuilderBase(abc.ABC):
 
     def get_callbacks(self) -> List[TrainerCallback]:
         callbacks = []
+
+        plugin_manager = PluginManager.get_instance()
+        callbacks.extend(
+            plugin_manager.add_callbacks_pre_trainer(cfg=self.cfg, model=self.model)
+        )
+
         if self.cfg.use_wandb:
             callbacks.append(
                 SaveAxolotlConfigtoWandBCallback(self.cfg.axolotl_config_path)
             )
         if self.cfg.use_mlflow and is_mlflow_available():
+            from transformers.integrations.integration_utils import MLflowCallback
+
             from axolotl.utils.callbacks.mlflow_ import (
                 SaveAxolotlConfigtoMlflowCallback,
             )
 
+            callbacks.extend(
+                [
+                    SaveAxolotlConfigtoMlflowCallback(self.cfg.axolotl_config_path),
+                    MLflowCallback,
+                ]
+            )
+        if self.cfg.use_comet and is_comet_available():
+            from axolotl.utils.callbacks.comet_ import SaveAxolotlConfigtoCometCallback
+
             callbacks.append(
-                SaveAxolotlConfigtoMlflowCallback(self.cfg.axolotl_config_path)
+                SaveAxolotlConfigtoCometCallback(self.cfg.axolotl_config_path)
             )
 
         return callbacks
 
-    @abstractmethod
     def get_post_trainer_create_callbacks(self, trainer):
         """
         Callbacks added after the trainer is created, usually b/c these need access to the trainer
         """
+        callbacks = []
+
+        plugin_manager = PluginManager.get_instance()
+        callbacks.extend(
+            plugin_manager.add_callbacks_post_trainer(cfg=self.cfg, trainer=trainer)
+        )
+        return callbacks
 
     def hook_pre_create_training_args(self, training_arguments_kwargs):
         # TODO
@@ -1146,7 +1263,7 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         return callbacks
 
     def get_post_trainer_create_callbacks(self, trainer):
-        callbacks = []
+        callbacks = super().get_post_trainer_create_callbacks(trainer=trainer)
         if self.cfg.use_wandb and self.cfg.eval_table_size > 0:
             LogPredictionCallback = log_prediction_callback_factory(
                 trainer, self.tokenizer, "wandb"
@@ -1161,6 +1278,11 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
                 trainer, self.tokenizer, "mlflow"
             )
             callbacks.append(LogPredictionCallback(self.cfg))
+        if self.cfg.use_comet and is_comet_available() and self.cfg.eval_table_size > 0:
+            LogPredictionCallback = log_prediction_callback_factory(
+                trainer, self.tokenizer, "comet_ml"
+            )
+            callbacks.append(LogPredictionCallback(self.cfg))
 
         if self.cfg.do_bench_eval:
             callbacks.append(bench_eval_callback_factory(trainer, self.tokenizer))
@@ -1178,6 +1300,18 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
 
         if self.cfg.lisa_step_interval and self.cfg.lisa_n_layers:
             callbacks.append(lisa_callback_factory(trainer))
+
+        if self.cfg.plugins:
+            plugin_manager = PluginManager.get_instance()
+            callbacks.extend(
+                [
+                    cb
+                    for cb in plugin_manager.add_callbacks_post_trainer(
+                        self.cfg, trainer
+                    )
+                    if cb
+                ]
+            )
         return callbacks
 
     def _get_trainer_cls(self):
@@ -1185,6 +1319,8 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
             return ReLoRATrainer
         if self.cfg.model_config_type == "mamba":
             return AxolotlMambaTrainer
+        if self.cfg.reward_model:
+            return AxolotlRewardTrainer
         return AxolotlTrainer
 
     def build(self, total_num_steps):
@@ -1293,17 +1429,15 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
 
         if not self.cfg.test_datasets and self.cfg.val_set_size == 0:
             # no eval set, so don't eval
-            training_arguments_kwargs["evaluation_strategy"] = "no"
+            training_arguments_kwargs["eval_strategy"] = "no"
         elif self.cfg.eval_steps:
-            training_arguments_kwargs["evaluation_strategy"] = "steps"
+            training_arguments_kwargs["eval_strategy"] = "steps"
             training_arguments_kwargs["eval_steps"] = self.cfg.eval_steps
-        elif self.cfg.evaluation_strategy:
-            training_arguments_kwargs[
-                "evaluation_strategy"
-            ] = self.cfg.evaluation_strategy
+        elif self.cfg.eval_strategy:
+            training_arguments_kwargs["eval_strategy"] = self.cfg.eval_strategy
         else:
             # we have an eval set, but no steps defined, default to use epoch
-            training_arguments_kwargs["evaluation_strategy"] = "epoch"
+            training_arguments_kwargs["eval_strategy"] = "epoch"
 
         if self.cfg.save_steps:
             training_arguments_kwargs["save_strategy"] = "steps"
@@ -1369,6 +1503,10 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
             training_arguments_kwargs[
                 "per_device_eval_batch_size"
             ] = self.cfg.eval_batch_size
+        if self.cfg.auto_find_batch_size is not None:
+            training_arguments_kwargs[
+                "auto_find_batch_size"
+            ] = self.cfg.auto_find_batch_size
         training_arguments_kwargs[
             "gradient_accumulation_steps"
         ] = self.cfg.gradient_accumulation_steps
@@ -1402,15 +1540,22 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         report_to = []
         if self.cfg.use_wandb:
             report_to.append("wandb")
+            if self.cfg.wandb_name:
+                training_arguments_kwargs["run_name"] = self.cfg.wandb_name
         if self.cfg.use_mlflow:
             report_to.append("mlflow")
         if self.cfg.use_tensorboard:
             report_to.append("tensorboard")
+        if self.cfg.use_comet:
+            report_to.append("comet_ml")
 
         training_arguments_kwargs["report_to"] = report_to
-        training_arguments_kwargs["run_name"] = (
-            self.cfg.wandb_name if self.cfg.use_wandb else None
-        )
+        if self.cfg.use_wandb:
+            training_arguments_kwargs["run_name"] = self.cfg.wandb_name
+        elif self.cfg.use_mlflow:
+            training_arguments_kwargs["run_name"] = self.cfg.mlflow_run_name
+        else:
+            training_arguments_kwargs["run_name"] = None
         training_arguments_kwargs["optim"] = (
             self.cfg.optimizer if self.cfg.optimizer else "adamw_hf"
         )
@@ -1451,9 +1596,9 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         )
 
         training_arguments_kwargs["sample_packing"] = bool(self.cfg.sample_packing)
-        training_arguments_kwargs[
-            "multipack_real_batches"
-        ] = not self.cfg.flash_attention
+        training_arguments_kwargs["multipack_real_batches"] = (
+            not self.cfg.flash_attention or self.cfg.multipack_real_batches
+        )
         training_arguments_kwargs["eval_sample_packing"] = bool(
             self.cfg.eval_sample_packing
         )
@@ -1498,6 +1643,11 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         )
         training_arguments_kwargs["model_type"] = self.cfg.model_config_type
         training_arguments_kwargs["pretraining"] = bool(self.cfg.pretraining_dataset)
+        if self.cfg.chat_template:
+            training_arguments_kwargs["chat_template"] = get_chat_template(
+                self.cfg.chat_template,
+                tokenizer=self.tokenizer,
+            )
 
         if self.cfg.rl == "orpo":
             training_arguments_kwargs["orpo_alpha"] = self.cfg.orpo_alpha
@@ -1509,11 +1659,16 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
 
         trainer_kwargs = {}
 
+        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"
@@ -1552,13 +1707,22 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
                 "accelerator_config"
             ] = self.cfg.accelerator_config
 
-        training_args = (
-            AxolotlTrainingArguments(  # pylint: disable=unexpected-keyword-arg
-                **training_arguments_kwargs,
-            )
+        training_args_cls = (
+            AxolotlTrainingArguments
+            if not self.cfg.reward_model
+            else AxolotlRewardConfig
+        )
+        training_args = training_args_cls(  # pylint: disable=unexpected-keyword-arg
+            **training_arguments_kwargs,
         )
         training_args = self.hook_post_create_training_args(training_args)
 
+        # unset run_name so wandb sets up experiment names
+        if self.cfg.use_wandb and training_args.run_name == training_args.output_dir:
+            training_args.run_name = (  # pylint: disable=attribute-defined-outside-init
+                None
+            )
+
         data_collator_kwargs = {
             "padding": True,  # True/"longest" is the default
         }
@@ -1571,27 +1735,37 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
             # https://docs.nvidia.com/deeplearning/performance/dl-performance-matrix-multiplication/index.html
             data_collator_kwargs["pad_to_multiple_of"] = 64
 
+        if self.cfg.reward_model:
+            data_collator_kwargs["max_length"] = self.cfg.sequence_len
+
         trainer_cls = self._get_trainer_cls()
         trainer_kwargs, trainer_cls = self.hook_pre_create_trainer(
             trainer_kwargs, trainer_cls
         )
+        if eval_data_collator := self.build_collator(
+            training_args, is_eval=True, **data_collator_kwargs
+        ):
+            if not self.cfg.reward_model:
+                trainer_kwargs["eval_data_collator"] = eval_data_collator
+        if not self.cfg.reward_model:
+            trainer_kwargs["bench_data_collator"] = transformers.DataCollatorForSeq2Seq(
+                self.tokenizer,
+                return_tensors="pt",
+                **data_collator_kwargs,
+            )
+        sig = inspect.signature(trainer_cls)
+        if "processing_class" in sig.parameters.keys():
+            trainer_kwargs["processing_class"] = self.tokenizer
+        else:
+            trainer_kwargs["tokenizer"] = self.tokenizer
+
         trainer = trainer_cls(
             model=self.model,
             train_dataset=self.train_dataset,
             eval_dataset=self.eval_dataset,
             args=training_args,
-            tokenizer=self.tokenizer,
             data_collator=self.build_collator(training_args, **data_collator_kwargs),
-            eval_data_collator=self.build_collator(
-                training_args, is_eval=True, **data_collator_kwargs
-            ),
-            bench_data_collator=transformers.DataCollatorForSeq2Seq(
-                self.tokenizer,
-                return_tensors="pt",
-                **data_collator_kwargs,
-            ),
             callbacks=self.get_callbacks(),
-            num_epochs=self.cfg.num_epochs,
             **trainer_kwargs,
         )
         trainer = self.hook_post_create_trainer(trainer)
@@ -1625,9 +1799,14 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
                 V2BatchSamplerDataCollatorForSeq2Seq,
                 BatchSamplerDataCollatorForSeq2Seq,
                 DataCollatorForSeq2Seq,
+                RewardDataCollatorWithPadding,
             ]
         ]
-        if use_batch_sampler_collator:
+        if self.cfg.reward_model:
+            collator = RewardDataCollatorWithPadding
+            if "max_length" in kwargs:
+                kwargs.pop("max_length")
+        elif use_batch_sampler_collator:
             if self.cfg.model_config_type in SUPPORTED_MULTIPACK_MODEL_TYPES:
                 collator = V2BatchSamplerDataCollatorForSeq2Seq
             elif (
@@ -1638,7 +1817,12 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
             else:
                 collator = BatchSamplerDataCollatorForSeq2Seq
         else:
-            collator = DataCollatorForSeq2Seq
+            if self.cfg.processor_type and self.processor:
+                collator = MultiModalChatDataCollator
+                kwargs["processor"] = self.processor
+                kwargs["chat_template"] = training_args.chat_template
+            else:
+                collator = DataCollatorForSeq2Seq
 
         return collator(
             self.tokenizer,
@@ -1659,7 +1843,7 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
         return callbacks
 
     def get_post_trainer_create_callbacks(self, trainer):
-        callbacks = []
+        callbacks = super().get_post_trainer_create_callbacks(trainer=trainer)
         return callbacks
 
     def build_training_arguments(self, total_num_steps):
@@ -1687,10 +1871,10 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
             training_args_kwargs["save_safetensors"] = self.cfg.save_safetensors
 
         if self.eval_dataset:
-            training_args_kwargs["evaluation_strategy"] = "steps"
+            training_args_kwargs["eval_strategy"] = "steps"
             training_args_kwargs["eval_steps"] = self.cfg.eval_steps
         else:
-            training_args_kwargs["evaluation_strategy"] = "no"
+            training_args_kwargs["eval_strategy"] = "no"
 
         if self.cfg.bf16 or self.cfg.bfloat16:
             training_args_kwargs["bf16"] = True
@@ -1745,17 +1929,18 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
             # default to saving each epoch if not defined
             training_args_kwargs["save_strategy"] = "epoch"
 
+        training_args_kwargs["dataset_num_proc"] = self.cfg.dataset_processes
+
         if self.cfg.rl_beta:
             training_args_kwargs["beta"] = self.cfg.rl_beta
         if self.cfg.orpo_alpha:
             # trl does some odd mapping of alpha to beta to reuse the beta parameter ???
             training_args_kwargs["beta"] = self.cfg.orpo_alpha
 
-        training_args_kwargs["dataset_num_proc"] = self.cfg.dataset_processes
-        training_args_cls = AxolotlDPOConfig
         if self.cfg.rpo_alpha is not None:
             training_args_kwargs["rpo_alpha"] = self.cfg.rpo_alpha
 
+        training_args_cls = None
         if self.cfg.rl == "simpo":
             training_args_cls = AxolotlCPOConfig
             training_args_kwargs["loss_type"] = "simpo"
@@ -1764,13 +1949,13 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
             if self.cfg.cpo_alpha is not None:
                 training_args_kwargs["cpo_alpha"] = self.cfg.cpo_alpha
 
-        if self.cfg.rl == "orpo":
+        elif self.cfg.rl == "orpo":
             training_args_cls = AxolotlORPOConfig
             training_args_kwargs["max_length"] = self.cfg.sequence_len
             if self.cfg.max_prompt_len:
                 training_args_kwargs["max_prompt_length"] = self.cfg.max_prompt_len
 
-        if self.cfg.rl == "kto":
+        elif self.cfg.rl == "kto":
             training_args_cls = AxolotlKTOConfig
 
             training_args_kwargs["desirable_weight"] = (
@@ -1785,6 +1970,17 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
             if self.cfg.max_prompt_len:
                 training_args_kwargs["max_prompt_length"] = self.cfg.max_prompt_len
 
+        else:
+            training_args_cls = AxolotlDPOConfig
+            if self.cfg.rl == "ipo":
+                training_args_kwargs["loss_type"] = "ipo"
+            training_args_kwargs["max_length"] = self.cfg.sequence_len
+            training_args_kwargs["max_completion_length"] = None
+            training_args_kwargs["max_prompt_length"] = self.cfg.sequence_len
+            training_args_kwargs["generate_during_eval"] = self.cfg.use_wandb
+            if self.cfg.dpo_use_weighting is not None:
+                training_args_kwargs["use_weighting"] = self.cfg.dpo_use_weighting
+
         training_args = training_args_cls(  # pylint: disable=unexpected-keyword-arg
             output_dir=self.cfg.output_dir,
             per_device_train_batch_size=self.cfg.micro_batch_size,
@@ -1805,7 +2001,6 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
         training_args = self.build_training_arguments(total_num_steps)
         dpo_trainer_kwargs = {}
         if self.cfg.rl == "ipo":
-            dpo_trainer_kwargs["loss_type"] = "ipo"
             if self.cfg.dpo_label_smoothing:
                 dpo_trainer_kwargs["label_smoothing"] = self.cfg.dpo_label_smoothing
         if self.eval_dataset:
@@ -1819,12 +2014,6 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
         if self.cfg.rl in ["dpo", "ipo"]:
             trainer_cls = AxolotlDPOTrainer
             trainer_cls_args = [self.model, self.model_ref]
-
-            # these aren't used for the ORPO trainer
-            dpo_trainer_kwargs["max_length"] = self.cfg.sequence_len
-            dpo_trainer_kwargs["max_target_length"] = None
-            dpo_trainer_kwargs["max_prompt_length"] = self.cfg.sequence_len
-            dpo_trainer_kwargs["generate_during_eval"] = True
         elif self.cfg.rl == "orpo":
             trainer_cls = AxolotlORPOTrainer
             trainer_cls_args = [self.model]
@@ -1836,16 +2025,24 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
             trainer_cls_args = [self.model]
         else:
             raise ValueError(f"Unsupported RL: {self.cfg.rl}")
+
+        sig = inspect.signature(trainer_cls)
+        if "processing_class" in sig.parameters.keys():
+            dpo_trainer_kwargs["processing_class"] = self.tokenizer
+        else:
+            dpo_trainer_kwargs["tokenizer"] = self.tokenizer
+
         dpo_trainer = trainer_cls(
             *trainer_cls_args,
             args=training_args,
             train_dataset=self.train_dataset,
-            tokenizer=self.tokenizer,
             callbacks=self.get_callbacks(),
             **dpo_trainer_kwargs,
         )
         if self.cfg.fsdp:
             ensure_dtype(dpo_trainer.model, dtype=self.cfg.torch_dtype)
+            if self.cfg.rl in ["dpo", "ipo"] and dpo_trainer.ref_model:
+                ensure_dtype(dpo_trainer.ref_model, dtype=self.cfg.torch_dtype)
 
         dpo_trainer = self.hook_post_create_trainer(dpo_trainer)
         for callback in self.get_post_trainer_create_callbacks(dpo_trainer):
@@ -1860,11 +2057,11 @@ class HFPPOTrainerBuilder(TrainerBuilderBase):
     """
 
     def get_callbacks(self):
-        callbacks = []
+        callbacks = super().get_callbacks()
         return callbacks
 
     def get_post_trainer_create_callbacks(self, trainer):
-        callbacks = []
+        callbacks = super().get_post_trainer_create_callbacks(trainer=trainer)
         return callbacks
 
     def build(self, total_num_steps):

src/axolotl/integrations/LICENSE.md

@@ -0,0 +1,58 @@
+### AXOLOTL COMMUNITY LICENSE AGREEMENT
+
+This Axolotl Community License Agreement (“Agreement”) is entered into by and between Axolotl AI Corp. (“Axolotl”) and
+any individual or entity (“Licensee”) who wishes to use the Software (as defined below) in accordance with the terms
+and conditions set forth in this Agreement.
+
+1.  Definitions
+    1.1 “Licensee” refers to any individual or entity who has obtained a copy of the Software under this Agreement.
+    1.2 “Plugin Integration” means independent integration software modules which may or may not be offered by Axolotl,
+        which may be licensed separately by their respective  authors and/or licensors.
+    1.3 “Software” refers to the specific sub-directory of the Axolotl, Inc. software located at
+        https://github.com/axolotl-ai-cloud/axolotl/tree/main/src/axolotl/integrations and its subdirectories which
+        permits Plugin Integrations to integrate with the Axolotl service.
+2.  Grant of License
+    2.1	Axolotl hereby grants Licensee a worldwide, non-exclusive, royalty-free, license to use, copy, modify, merge,
+        publish, distribute, sublicense, and/or otherwise exploit the Software, subject to the following conditions:
+        - Licensee must comply with all the terms and conditions of this Agreement.
+        - Licensee must include the original copyright notice and disclaimer of warranty in all copies or substantial
+          portions of the Software.
+    2.2 Licensee may use the Software for any lawful purpose, except as restricted in Section 3.
+3.  Restrictions
+    3.1 Licensee shall not use the Software for any activity that constitutes a commercial activity of offering for
+        free or for sale any services, platform, or equivalent  to third parties for the purposes of allowing such
+        third parties to fine-tune artificial intelligence models.
+    3.2 Licensee shall not:
+        - Use the Software for any illegal or unauthorized purpose.
+        - Reverse engineer, decompile, or disassemble the Software.
+        - Remove or modify any copyright, trademark, or other proprietary notices contained in the Software.
+        - Use the Software in a way that could damage, disable, overburden, or impair the functionality of the
+          Software or interfere with any third-party use of the Software.
+    3.3 Axolotl reserves the right to restrict certain Plugin Integrations for use with the Software. To the extent Licensee integrates a permitted, applicable Plugin Integration with the Software, Licensee shall comply with any additional terms and conditions imposed by the licensors of such Plugin Integration for use of such Plugin Integrations. Licensee shall contact Axolotl if it has questions about whether its use of the Software falls beyond the scope of this Agreement.
+4.  Intellectual Property Rights
+    4.1 Axolotl and its contributors retain all intellectual property rights in and to the Software. Licensee
+        acknowledges that this Agreement does not transfer any ownership rights or intellectual property rights to
+        Licensee.
+5.  Disclaimer of Warranty
+    5.1 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 NON-INFRINGEMENT. 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.
+6.  Termination
+    6.1 Axolotl may terminate this Agreement at any time if Licensee fails to comply with any of the terms and
+        conditions set forth herein. Upon termination, Licensee shall cease all use of the Software and destroy any
+        copies in its possession.
+7.  Governing Law
+    7.1 This Agreement shall be governed by and construed in accordance with the laws of the State of California,
+        without regards to conflicts of laws provisions thereof.
+8.  Entire Agreement
+    8.1 This Agreement constitutes the entire agreement between Axolotl and Licensee with respect to the subject matter
+        hereof and supersedes all prior or contemporaneous understandings or agreements between the parties concerning
+        the Software, whether written or oral. Axolotl may update the terms of this Agreement from time to time, and
+        Licensee’s continued use of the Software after any such updates shall constitute acceptance of updated terms
+        on a go-forward basis.  Axolotl will use commercially reasonable efforts to provide Licensee notice of any
+        material updates. By using the Software, Licensee acknowledges that it has read, understood, and agrees to be
+        bound by the terms and conditions of this Agreement.
+
+This Agreement was last updated on August 23, 2024.

src/axolotl/integrations/base.py

@@ -0,0 +1,432 @@
+# Copyright 2024 Axolotl AI. All rights reserved.
+#
+# This software may be used and distributed according to
+# the terms of the Axolotl Community License Agreement (the "License");
+# you may not use this file except in compliance with the License.
+#
+# 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.
+
+"""
+Base class for all plugins.
+
+A plugin is a reusable, modular, and self-contained piece of code that extends the functionality of Axolotl.
+Plugins can be used to integrate third-party models, modify the training process, or add new features.
+
+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
+
+
+class BasePlugin:
+    """
+    Base class for all plugins. Defines the interface for plugin methods.
+
+    Attributes:
+    None
+
+    Methods:
+    register(cfg): Registers the plugin with the given configuration.
+    pre_model_load(cfg): Performs actions before the model is loaded.
+    post_model_load(cfg, model): Performs actions after the model is loaded.
+    pre_lora_load(cfg, model): Performs actions before LoRA weights are loaded.
+    post_lora_load(cfg, model): Performs actions after LoRA weights are loaded.
+    create_optimizer(cfg, trainer): Creates and returns an optimizer for training.
+    create_lr_scheduler(cfg, trainer, optimizer): Creates and returns a learning rate scheduler.
+    add_callbacks_pre_trainer(cfg, model): Adds callbacks to the trainer before training.
+    add_callbacks_post_trainer(cfg, trainer): Adds callbacks to the trainer after training.
+    """
+
+    def __init__(self):
+        """
+        Initializes the BasePlugin.
+        """
+
+    def register(self, cfg):  # pylint: disable=unused-argument
+        """
+        Registers the plugin with the given configuration.
+
+        Parameters:
+        cfg (dict): The configuration for the plugin.
+
+        Returns:
+        None
+        """
+
+    def get_input_args(self):
+        """
+        Returns a pydantic model for the plugin's input arguments.
+        """
+
+    def pre_model_load(self, cfg):  # pylint: disable=unused-argument
+        """
+        Performs actions before the model is loaded.
+
+        Parameters:
+        cfg (dict): The configuration for the plugin.
+
+        Returns:
+        None
+        """
+
+    def post_model_load(self, cfg, model):  # pylint: disable=unused-argument
+        """
+        Performs actions after the model is loaded.
+
+        Parameters:
+        cfg (dict): The configuration for the plugin.
+        model (object): The loaded model.
+
+        Returns:
+        None
+        """
+
+    def pre_lora_load(self, cfg, model):  # pylint: disable=unused-argument
+        """
+        Performs actions before LoRA weights are loaded.
+
+        Parameters:
+        cfg (dict): The configuration for the plugin.
+        model (object): The loaded model.
+
+        Returns:
+        None
+        """
+
+    def post_lora_load(self, cfg, model):  # pylint: disable=unused-argument
+        """
+        Performs actions after LoRA weights are loaded.
+
+        Parameters:
+        cfg (dict): The configuration for the plugin.
+        model (object): The loaded model.
+
+        Returns:
+        None
+        """
+
+    def create_optimizer(self, cfg, trainer):  # pylint: disable=unused-argument
+        """
+        Creates and returns an optimizer for training.
+
+        Parameters:
+        cfg (dict): The configuration for the plugin.
+        trainer (object): The trainer object for training.
+
+        Returns:
+        object: The created optimizer.
+        """
+
+    def create_lr_scheduler(
+        self, cfg, trainer, optimizer
+    ):  # pylint: disable=unused-argument
+        """
+        Creates and returns a learning rate scheduler.
+
+        Parameters:
+        cfg (dict): The configuration for the plugin.
+        trainer (object): The trainer object for training.
+        optimizer (object): The optimizer for training.
+
+        Returns:
+        object: The created learning rate scheduler.
+        """
+
+    def add_callbacks_pre_trainer(self, cfg, model):  # pylint: disable=unused-argument
+        """
+        setup callbacks before creating the trainer.
+
+        Parameters:
+        cfg (dict): The configuration for the plugin.
+        model (object): The loaded model.
+
+        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
+        """
+        Adds callbacks to the trainer after creating the trainer.
+        This is useful for callbacks that require access to the model or trainer.
+
+        Parameters:
+        cfg (dict): The configuration for the plugin.
+        trainer (object): The trainer object for training.
+
+        Returns:
+        List[callable]: A list of callback functions to be added
+        """
+        return []
+
+    def post_train(self, cfg, model):  # pylint: disable=unused-argument
+        """
+        Performs actions after training is complete.
+
+        Parameters:
+        cfg (dict): The axolotl configuration
+        model (object): The loaded model.
+
+        Returns:
+        None
+        """
+
+    def post_train_unload(self, cfg):  # pylint: disable=unused-argument
+        """
+        Performs actions after training is complete and the model is unloaded.
+
+        Parameters:
+        cfg (dict): The configuration for the plugin.
+
+        Returns:
+        None
+        """
+
+
+def load_plugin(plugin_name: str) -> BasePlugin:
+    """
+    Loads a plugin based on the given plugin name.
+
+    The plugin name should be in the format "module_name.class_name".
+    This function splits the plugin name into module and class, imports the module,
+    retrieves the class from the module, and creates an instance of the class.
+
+    Parameters:
+    plugin_name (str): The name of the plugin to be loaded. The name should be in the format "module_name.class_name".
+
+    Returns:
+    BasePlugin: An instance of the loaded plugin.
+
+    Raises:
+    ImportError: If the plugin module cannot be imported.
+    """
+    # split the plugin name into module and class
+    module_name, class_name = plugin_name.rsplit(".", 1)
+
+    # import the module
+    module = importlib.import_module(module_name)
+    # instantiate the class
+    plugin_class = getattr(module, class_name)
+    # create an instance of the class
+    plugin = plugin_class()
+
+    return plugin
+
+
+class PluginManager:
+    """
+    The PluginManager class is responsible for loading and managing plugins.
+    It should be a singleton so it can be accessed from anywhere in the codebase.
+
+    Attributes:
+    plugins (List[BasePlugin]): A list of loaded plugins.
+
+    Methods:
+    get_instance(): Static method to get the singleton instance of PluginManager.
+    register(plugin_name: str): Registers a new plugin by its name.
+    pre_model_load(cfg): Calls the pre_model_load method of all registered plugins.
+    """
+
+    plugins: OrderedDict[str, BasePlugin] = collections.OrderedDict()
+
+    _instance = None
+
+    def __new__(cls):
+        """
+        Creates a new instance of PluginManager if it doesn't exist yet.
+        """
+        if cls._instance is None:
+            cls._instance = super(PluginManager, cls).__new__(cls)
+            cls._instance.plugins = collections.OrderedDict()
+        return cls._instance
+
+    @staticmethod
+    def get_instance() -> "PluginManager":
+        """
+        Returns the singleton instance of PluginManager.
+        If the instance doesn't exist, it creates a new one.
+        """
+        if PluginManager._instance is None:
+            PluginManager()
+        return PluginManager._instance  # type: ignore
+
+    def register(self, plugin_name: str):
+        """
+        Registers a new plugin by its name.
+
+        Parameters:
+        plugin_name (str): The name of the plugin to be registered.
+
+        Returns:
+        None
+
+        Raises:
+        ImportError: If the plugin module cannot be imported.
+        """
+        try:
+            plugin = load_plugin(plugin_name)
+            self.plugins[plugin_name] = plugin
+        except ImportError:
+            logging.error(f"Failed to load plugin: {plugin_name}")
+
+    def get_input_args(self):
+        """
+        Returns a list of Pydantic classes for all registered plugins' input arguments.'
+
+        Returns:
+        list[str]: A list of Pydantic classes for all registered plugins' input arguments.'
+        """
+        input_args = []
+        for plugin in self.plugins.values():
+            input_args_from_plugin = plugin.get_input_args()
+            if input_args_from_plugin is not None:
+                input_args.append(input_args_from_plugin)
+        return input_args
+
+    def pre_model_load(self, cfg):
+        """
+        Calls the pre_model_load method of all registered plugins.
+
+        Parameters:
+        cfg (dict): The configuration for the plugins.
+
+        Returns:
+        None
+        """
+        for plugin in self.plugins.values():
+            plugin.pre_model_load(cfg)
+
+    def post_model_load(self, cfg, model):
+        """
+        Calls the post_model_load method of all registered plugins.
+
+        Parameters:
+        cfg (dict): The configuration for the plugins.
+        model (object): The loaded model.
+
+        Returns:
+        None
+        """
+        for plugin in self.plugins.values():
+            plugin.post_model_load(cfg, model)
+
+    def pre_lora_load(self, cfg, model):
+        """
+        Calls the pre_lora_load method of all registered plugins.
+
+        Parameters:
+        cfg (dict): The configuration for the plugins.
+        model (object): The loaded model.
+
+        Returns:
+        None
+        """
+        for plugin in self.plugins.values():
+            plugin.pre_lora_load(cfg, model)
+
+    def post_lora_load(self, cfg, model):
+        """
+        Calls the post_lora_load method of all registered plugins.
+
+        Parameters:
+        cfg (dict): The configuration for the plugins.
+        model (object): The loaded model.
+
+        Returns:
+        None
+        """
+        for plugin in self.plugins.values():
+            plugin.post_lora_load(cfg, model)
+
+    def create_optimizer(self, cfg, trainer):
+        """
+        Calls the create_optimizer method of all registered plugins and returns the first non-None optimizer.
+
+        Parameters:
+        cfg (dict): The configuration for the plugins.
+        trainer (object): The trainer object for training.
+
+        Returns:
+        object: The created optimizer, or None if none was found.
+        """
+        for plugin in self.plugins.values():
+            optimizer = plugin.create_optimizer(cfg, trainer)
+            if optimizer is not None:
+                return optimizer
+        return None
+
+    def create_lr_scheduler(self, cfg, trainer, optimizer):
+        """
+        Calls the create_lr_scheduler method of all registered plugins and returns the first non-None scheduler.
+
+        Parameters:
+        cfg (dict): The configuration for the plugins.
+        trainer (object): The trainer object for training.
+        optimizer (object): The optimizer for training.
+
+        Returns:
+        object: The created learning rate scheduler, or None if none was found.
+        """
+        for plugin in self.plugins.values():
+            scheduler = plugin.create_lr_scheduler(cfg, trainer, optimizer)
+            if scheduler is not None:
+                return scheduler
+        return None
+
+    def add_callbacks_pre_trainer(self, cfg, model):
+        """
+        Calls the add_callbacks_pre_trainer method of all registered plugins.
+
+        Parameters:
+        cfg (dict): The configuration for the plugins.
+        model (object): The loaded model.
+
+        Returns:
+        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)
+        return callbacks
+
+    def add_callbacks_post_trainer(self, cfg, trainer):
+        """
+        Calls the add_callbacks_post_trainer method of all registered plugins.
+
+        Parameters:
+        cfg (dict): The configuration for the plugins.
+        trainer (object): The trainer object for training.
+
+        Returns:
+        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)
+        return callbacks
+
+    def post_train_unload(self, cfg):
+        """
+        Calls the post_train_unload method of all registered plugins.
+
+        Parameters:
+        cfg (dict): The configuration for the plugins.
+        model (object): The loaded model.
+
+        Returns:
+        None
+        """
+        for plugin in self.plugins.values():
+            plugin.post_train_unload(cfg)

src/axolotl/integrations/config.py

@@ -0,0 +1,65 @@
+# Copyright 2024 Axolotl AI. All rights reserved.
+#
+# This software may be used and distributed according to
+# the terms of the Axolotl Community License Agreement (the "License");
+# you may not use this file except in compliance with the License.
+#
+# 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 to handle merging the plugins' input arguments with the base configurations.
+
+this was moved here to prevent circular imports
+"""
+
+from typing import Any, Dict, List
+
+from axolotl.utils.config.models.input.v0_4_1 import (
+    AxolotlConfigWCapabilities as AxolotlConfigWCapabilitiesBase,
+)
+from axolotl.utils.config.models.input.v0_4_1 import (
+    AxolotlInputConfig as AxolotlInputConfigBase,
+)
+
+
+def merge_input_args():
+    """
+    Merges input arguments from registered plugins with the base configurations.
+
+    This function retrieves the input arguments from registered plugins using the PluginManager.
+    It then dynamically creates new classes, AxolotlConfigWCapabilities and AxolotlInputConfig,
+    that inherit from the base configurations and include the input arguments from the plugins.
+
+    Returns:
+    tuple: A tuple containing the newly created classes, AxolotlConfigWCapabilities and AxolotlInputConfig.
+    """
+    from axolotl.integrations.base import PluginManager
+
+    plugin_manager = PluginManager.get_instance()
+    input_args: List[str] = plugin_manager.get_input_args()
+    plugin_classes = []
+    dynamic_input = ""
+    for plugin_args in input_args:
+        plugin_module, plugin_cls = plugin_args.rsplit(".", 1)
+        dynamic_input += f"from {plugin_module} import {plugin_cls}\n"
+        plugin_classes.append(plugin_cls)
+    if dynamic_input:
+        dynamic_input += f"class AxolotlConfigWCapabilities(AxolotlConfigWCapabilitiesBase, {', '.join(plugin_classes)}):\n    pass\n"
+        dynamic_input += f"class AxolotlInputConfig(AxolotlInputConfigBase, {', '.join(plugin_classes)}):\n    pass\n"
+
+        namespace: Dict[Any, Any] = {}
+        exec(  # pylint: disable=exec-used  # nosec B102
+            dynamic_input, globals(), namespace
+        )
+        AxolotlInputConfig = namespace[  # pylint: disable=invalid-name
+            "AxolotlInputConfig"
+        ]
+        AxolotlConfigWCapabilities = namespace[  # pylint: disable=invalid-name
+            "AxolotlConfigWCapabilities"
+        ]
+        return AxolotlConfigWCapabilities, AxolotlInputConfig
+    return AxolotlConfigWCapabilitiesBase, AxolotlInputConfigBase

src/axolotl/integrations/grokfast/LICENSE

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

src/axolotl/integrations/grokfast/README.md

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

src/axolotl/integrations/grokfast/__init__.py

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

src/axolotl/integrations/grokfast/args.py

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

src/axolotl/integrations/grokfast/optimizer.py

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

src/axolotl/integrations/liger/LICENSE

@@ -0,0 +1,202 @@
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src/axolotl/integrations/liger/__init__.py

@@ -0,0 +1,116 @@
+# 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 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 liger_kernel.transformers.cross_entropy import LigerCrossEntropyLoss
+from liger_kernel.transformers.functional import liger_cross_entropy
+from liger_kernel.transformers.monkey_patch import MODEL_TYPE_TO_APPLY_LIGER_FN
+from liger_kernel.transformers.rms_norm import LigerRMSNorm
+from liger_kernel.transformers.rope import liger_rotary_pos_emb
+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):
+    """
+    Plugin for LIGER integraton with Axolotl.
+    """
+
+    def get_input_args(self):
+        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}"
+                )
+            apply_liger_fn(**kwargs)
+        elif cfg.model_config_type == "jamba":
+            from transformers.models.jamba import modeling_jamba
+
+            from .models.jamba import lce_forward as jamba_lce_forward
+
+            if cfg.liger_rope:
+                modeling_jamba.apply_rotary_pos_emb = liger_rotary_pos_emb
+            if cfg.liger_rms_norm:
+                modeling_jamba.JambaRMSNorm = LigerRMSNorm
+            if cfg.liger_glu_activation:
+                modeling_jamba.JambaMLP = LigerSwiGLUMLP
+            if cfg.liger_cross_entropy:
+                from transformers.loss.loss_utils import nn
+
+                nn.functional.cross_entropy = liger_cross_entropy
+            if cfg.liger_fused_linear_cross_entropy:
+                modeling_jamba.JambaForCausalLM.forward = jamba_lce_forward
+        elif cfg.model_config_type == "deepseek_v2":
+            from accelerate import init_empty_weights
+            from transformers import AutoModelForCausalLM
+
+            with init_empty_weights():
+                model = AutoModelForCausalLM.from_pretrained(
+                    cfg.base_model, trust_remote_code=cfg.trust_remote_code or False
+                )
+                modeling_mod = sys.modules[model.__class__.__module__]
+
+            from .models.deepseekv2 import lce_forward as deepseekv2_lce_forward
+
+            if cfg.liger_rope:
+                # The DeepseekV2 version of RoPE is different than upstream LLaMA.
+                # See https://github.com/linkedin/Liger-Kernel/issues/129#issuecomment-2313763528
+                logging.warning("Fused liger_rope is not supported for DeepseekV2.")
+            if cfg.liger_rms_norm:
+                modeling_mod.DeepseekV2RMSNorm = LigerRMSNorm
+            if cfg.liger_glu_activation:
+                modeling_mod.DeepseekV2MLP.forward = LigerSwiGLUMLP.forward
+            if cfg.liger_cross_entropy:
+                # We do not patch `nn.functional.cross_entropy` for DeepseekV2 as it still uses
+                # nn.CrossEntropyLoss in the forward method.
+                modeling_mod.CrossEntropyLoss = LigerCrossEntropyLoss
+            if cfg.liger_fused_linear_cross_entropy:
+                modeling_mod.DeepseekV2ForCausalLM.forward = deepseekv2_lce_forward

src/axolotl/integrations/liger/args.py

@@ -0,0 +1,53 @@
+# 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 LIGER input arguments.
+"""
+import logging
+from typing import Optional
+
+from pydantic import BaseModel, model_validator
+
+LOG = logging.getLogger("axolotl.integrations.liger.args")
+
+
+class LigerArgs(BaseModel):
+    """
+    Input args for LIGER.
+    """
+
+    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/integrations/liger/models/deepseekv2.py

@@ -0,0 +1,127 @@
+"""
+DeepseekV2 model with LigerFusedLinearCrossEntropyLoss
+"""
+# pylint: disable=duplicate-code
+
+from typing import List, Optional, Tuple, Union
+
+import torch
+from liger_kernel.transformers.fused_linear_cross_entropy import (
+    LigerFusedLinearCrossEntropyLoss,
+)
+from torch.nn import CrossEntropyLoss
+from transformers.modeling_outputs import CausalLMOutputWithPast
+
+
+# @add_start_docstrings_to_model_forward(DeepseekV2_INPUTS_DOCSTRING)
+# @replace_return_docstrings(
+#    output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC
+# )
+def lce_forward(
+    self,
+    input_ids: torch.LongTensor = None,
+    attention_mask: Optional[torch.Tensor] = None,
+    position_ids: Optional[torch.LongTensor] = None,
+    past_key_values: Optional[List[torch.FloatTensor]] = None,
+    inputs_embeds: Optional[torch.FloatTensor] = None,
+    labels: Optional[torch.LongTensor] = None,
+    use_cache: Optional[bool] = None,
+    output_attentions: Optional[bool] = None,
+    output_hidden_states: Optional[bool] = None,
+    return_dict: Optional[bool] = None,
+) -> Union[Tuple, CausalLMOutputWithPast]:
+    r"""
+    Args:
+        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Labels for computing the masked language modeling loss. Indices should either be in `[0, transformers.,
+            config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+            (masked), the loss is only computed for the tokens with labels in `[0, transformers., config.vocab_size]`.
+
+    Returns:
+
+    Example:
+
+    ```python
+    >>> from transformers import AutoTokenizer, DeepseekV2ForCausalLM
+
+    >>> model = DeepseekV2ForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
+    >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
+
+    >>> prompt = "Hey, are you conscious? Can you talk to me?"
+    >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+    >>> # Generate
+    >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+    >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+    "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
+    ```"""
+    output_attentions = (
+        output_attentions
+        if output_attentions is not None
+        else self.config.output_attentions
+    )
+    output_hidden_states = (
+        output_hidden_states
+        if output_hidden_states is not None
+        else self.config.output_hidden_states
+    )
+    return_dict = (
+        return_dict if return_dict is not None else self.config.use_return_dict
+    )
+
+    # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+    outputs = self.model(
+        input_ids=input_ids,
+        attention_mask=attention_mask,
+        position_ids=position_ids,
+        past_key_values=past_key_values,
+        inputs_embeds=inputs_embeds,
+        use_cache=use_cache,
+        output_attentions=output_attentions,
+        output_hidden_states=output_hidden_states,
+        return_dict=return_dict,
+    )
+
+    hidden_states = outputs[0]
+
+    loss = None
+    logits = None
+
+    if self.training:
+        shift_hidden_states = hidden_states[..., :-1, :].contiguous()
+        shift_labels = labels[..., 1:].contiguous()
+
+        # flatten tokens
+        shift_hidden_states = shift_hidden_states.view(-1, self.config.hidden_size)
+        shift_labels = shift_labels.view(-1)
+
+        lce = LigerFusedLinearCrossEntropyLoss()
+        loss = lce(self.lm_head.weight, shift_hidden_states, shift_labels)
+    else:
+        logits = self.lm_head(hidden_states)
+        logits = logits.float()
+
+        loss = None
+        if labels is not None:
+            # Shift so that tokens < n predict n
+            shift_logits = logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            # Flatten the tokens
+            loss_fct = CrossEntropyLoss()
+            shift_logits = shift_logits.view(-1, self.config.vocab_size)
+            shift_labels = shift_labels.view(-1)
+            # Enable model parallelism
+            shift_labels = shift_labels.to(shift_logits.device)
+            loss = loss_fct(shift_logits, shift_labels)
+
+    if not return_dict:
+        output = (logits,) + outputs[1:]
+        return (loss,) + output if loss is not None else output
+
+    return CausalLMOutputWithPast(
+        loss=loss,
+        logits=logits,
+        past_key_values=outputs.past_key_values,
+        hidden_states=outputs.hidden_states,
+        attentions=outputs.attentions,
+    )

src/axolotl/integrations/liger/models/jamba.py

@@ -0,0 +1,173 @@
+"""
+Jamba model with LigerFusedLinearCrossEntropyLoss
+"""
+# pylint: disable=duplicate-code
+
+from typing import Optional, Tuple, Union
+
+import torch
+from liger_kernel.transformers.fused_linear_cross_entropy import (
+    LigerFusedLinearCrossEntropyLoss,
+)
+from torch.nn import CrossEntropyLoss
+from transformers.modeling_outputs import MoeCausalLMOutputWithPast
+from transformers.models.jamba.modeling_jamba import (
+    _CONFIG_FOR_DOC,
+    JAMBA_INPUTS_DOCSTRING,
+    HybridMambaAttentionDynamicCache,
+    load_balancing_loss_func,
+)
+from transformers.utils import (
+    add_start_docstrings_to_model_forward,
+    replace_return_docstrings,
+)
+
+
+@add_start_docstrings_to_model_forward(JAMBA_INPUTS_DOCSTRING)
+@replace_return_docstrings(
+    output_type=MoeCausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC
+)
+def lce_forward(
+    self,
+    input_ids: torch.LongTensor = None,
+    attention_mask: Optional[torch.Tensor] = None,
+    position_ids: Optional[torch.LongTensor] = None,
+    past_key_values: Optional[HybridMambaAttentionDynamicCache] = None,
+    inputs_embeds: Optional[torch.FloatTensor] = None,
+    labels: Optional[torch.LongTensor] = None,
+    use_cache: Optional[bool] = None,
+    output_attentions: Optional[bool] = None,
+    output_hidden_states: Optional[bool] = None,
+    output_router_logits: Optional[bool] = None,
+    return_dict: Optional[bool] = None,
+    cache_position: Optional[torch.LongTensor] = None,
+    num_logits_to_keep: Optional[Union[int, None]] = None,
+) -> Union[Tuple, MoeCausalLMOutputWithPast]:
+    r"""
+    Args:
+        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+            config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+            (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+        num_logits_to_keep (`int` or `None`, *optional*):
+            Calculate logits for the last `num_logits_to_keep` tokens. If `None`, calculate logits for all
+            `input_ids`. Only last token logits are needed for generation, and calculating them only for that token
+            can save memory, which becomes pretty significant for long sequences.
+
+    Returns:
+
+    Example:
+
+    ```python
+    >>> from transformers import AutoTokenizer, JambaForCausalLM
+
+    >>> model = JambaForCausalLM.from_pretrained("ai21labs/Jamba-v0.1")
+    >>> tokenizer = AutoTokenizer.from_pretrained("ai21labs/Jamba-v0.1")
+
+    >>> prompt = "Hey, are you conscious? Can you talk to me?"
+    >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+    >>> # Generate
+    >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+    >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+    "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
+    ```"""
+
+    output_attentions = (
+        output_attentions
+        if output_attentions is not None
+        else self.config.output_attentions
+    )
+    output_router_logits = (
+        output_router_logits
+        if output_router_logits is not None
+        else self.config.output_router_logits
+    )
+
+    output_hidden_states = (
+        output_hidden_states
+        if output_hidden_states is not None
+        else self.config.output_hidden_states
+    )
+    return_dict = (
+        return_dict if return_dict is not None else self.config.use_return_dict
+    )
+
+    # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+    outputs = self.model(
+        input_ids=input_ids,
+        attention_mask=attention_mask,
+        position_ids=position_ids,
+        past_key_values=past_key_values,
+        inputs_embeds=inputs_embeds,
+        use_cache=use_cache,
+        output_attentions=output_attentions,
+        output_hidden_states=output_hidden_states,
+        output_router_logits=output_router_logits,
+        cache_position=cache_position,
+        return_dict=return_dict,
+    )
+
+    hidden_states = outputs[0]
+
+    loss = None
+    logits = None
+
+    if self.training:
+        shift_hidden_states = hidden_states[..., :-1, :].contiguous()
+        shift_labels = labels[..., 1:].contiguous()
+
+        # flatten tokens
+        shift_hidden_states = shift_hidden_states.view(-1, self.config.hidden_size)
+        shift_labels = shift_labels.view(-1)
+
+        lce = LigerFusedLinearCrossEntropyLoss()
+        loss = lce(self.lm_head.weight, shift_hidden_states, shift_labels)
+    else:
+        if num_logits_to_keep is None:
+            logits = self.lm_head(hidden_states)
+        else:
+            logits = self.lm_head(hidden_states[..., -num_logits_to_keep:, :])
+        logits = logits.float()
+
+        if labels is not None:
+            # Shift so that tokens < n predict n
+            shift_logits = logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            # Flatten the tokens
+            loss_fct = CrossEntropyLoss()
+            shift_logits = shift_logits.view(-1, self.config.vocab_size)
+            shift_labels = shift_labels.view(-1)
+            # Enable model parallelism
+            shift_labels = shift_labels.to(shift_logits.device)
+            loss = loss_fct(shift_logits, shift_labels)
+
+    aux_loss = None
+    if output_router_logits:
+        aux_loss = load_balancing_loss_func(
+            outputs.router_logits if return_dict else outputs[-1],
+            self.num_experts,
+            self.num_experts_per_tok,
+            attention_mask,
+        )
+        if labels is not None:
+            loss += self.router_aux_loss_coef * aux_loss.to(
+                loss.device
+            )  # make sure to reside in the same device
+
+    if not return_dict:
+        output = (logits,) + outputs[1:]
+        if output_router_logits:
+            output = (aux_loss,) + output
+        return (loss,) + output if loss is not None else output
+
+    return MoeCausalLMOutputWithPast(
+        loss=loss,
+        aux_loss=aux_loss,
+        logits=logits,
+        past_key_values=outputs.past_key_values,
+        hidden_states=outputs.hidden_states,
+        attentions=outputs.attentions,
+        router_logits=outputs.router_logits,
+    )

src/axolotl/integrations/lm_eval/README.md

@@ -0,0 +1,13 @@
+# LM Eval Harness
+
+### Usage
+
+```yaml
+plugins:
+  - axolotl.integrations.lm_eval.LMEvalPlugin
+
+lm_eval_tasks:
+  - gsm8k
+  - hellaswag
+  - arc_easy
+```

src/axolotl/integrations/lm_eval/__init__.py

@@ -0,0 +1,42 @@
+"""
+Module for the Plugin for LM Eval Harness
+"""
+import subprocess  # nosec
+from datetime import datetime
+
+from axolotl.integrations.base import BasePlugin
+
+from .args import LMEvalArgs  # pylint: disable=unused-import. # noqa: F401
+
+
+class LMEvalPlugin(BasePlugin):
+    """
+    Plugin for LM Evaluation Harness integraton with Axolotl.
+    """
+
+    def get_input_args(self):
+        return "axolotl.integrations.lm_eval.LMEvalArgs"
+
+    def post_train_unload(self, cfg):
+        tasks = ",".join(cfg.lm_eval_tasks)
+        fa2 = ",attn_implementation=flash_attention_2" if cfg.flash_attention else ""
+        dtype = ",dtype=bfloat16" if cfg.bf16 else ",dtype=float16"
+        output_path = cfg.output_dir
+        output_path += "" if cfg.output_dir.endswith("/") else "/"
+        output_path += "lm_eval_results/" + datetime.now().strftime("%Y%m%d_%H%M%S")
+        subprocess.run(  # nosec
+            [
+                "lm_eval",
+                "--model",
+                "hf",
+                "--model_args",
+                f"pretrained={cfg.output_dir}{fa2}{dtype}",
+                "--tasks",
+                tasks,
+                "--batch_size",
+                str(cfg.lm_eval_batch_size),
+                "--output_path",
+                output_path,
+            ],
+            check=True,
+        )

src/axolotl/integrations/lm_eval/args.py

@@ -0,0 +1,15 @@
+"""
+Module for handling lm eval harness input arguments.
+"""
+from typing import List, Optional
+
+from pydantic import BaseModel
+
+
+class LMEvalArgs(BaseModel):
+    """
+    Input args for lm eval harness
+    """
+
+    lm_eval_tasks: List[str] = []
+    lm_eval_batch_size: Optional[int] = 8

src/axolotl/integrations/spectrum/LICENSE

@@ -0,0 +1,202 @@
+
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+
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src/axolotl/integrations/spectrum/README.md

@@ -0,0 +1,21 @@
+## Spectrum: Targeted Training on Signal to Noise Ratio
+
+by Eric Hartford, Lucas Atkins, Fernando Fernandes, David Golchinfar
+
+This plugin contains code to freeze the bottom fraction of modules in a model, based on the Signal-to-Noise Ratio (SNR).
+
+### Overview
+
+Spectrum is a tool for scanning and evaluating the Signal-to-Noise Ratio (SNR) of layers in large language models.
+By identifying the top n% of layers with the highest SNR, you can optimize training efficiency.
+
+### Usage
+
+```yaml
+plugins:
+  - axolotl.integrations.spectrum.SpectrumPlugin
+
+spectrum_top_fraction: 0.5
+# Optional if using a pre-scanned model as your base_model. Useful if using a model mirror
+spectrum_model_name: meta-llama/Meta-Llama-3.1-8B
+```

src/axolotl/integrations/spectrum/__init__.py

@@ -0,0 +1,102 @@
+# 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.
+
+"""
+Spectrum Plugin to automatically generate unfrozen parameters based on SNR data.
+"""
+
+import json
+import logging
+
+import requests
+
+from axolotl.integrations.base import BasePlugin
+
+from .args import SpectrumArgs  # pylint: disable=unused-import. # noqa: F401
+
+
+def _generate_unfrozen_params_yaml(snr_data, top_fraction=0.5):
+    unfrozen_parameters = {}
+    for layer_name, info in snr_data.items():
+        layer_type = info["type"]
+        if layer_type not in unfrozen_parameters:
+            unfrozen_parameters[layer_type] = []
+        unfrozen_parameters[layer_type].append((layer_name, info["snr"]))
+    top_layers_by_type = {}
+    for layer_type, layers in unfrozen_parameters.items():
+        layers_sorted = sorted(layers, key=lambda x: x[1], reverse=True)
+        num_top_layers = int(len(layers) * top_fraction)
+        top_layers_by_type[layer_type] = [
+            layer[0] for layer in layers_sorted[:num_top_layers]
+        ]
+    unfrozen_parameters = [
+        "^lm_head.weight$",
+        "^model.embed_tokens.weight$",
+    ]
+    for layer_type, layer_names in top_layers_by_type.items():
+        for layer_name in layer_names:
+            unfrozen_parameters.append(layer_name)
+    return unfrozen_parameters
+
+
+class SpectrumPlugin(BasePlugin):
+    """
+    Spectrum Plugin to automatically generate unfrozen parameters based on SNR data.
+    """
+
+    base_url = "https://raw.githubusercontent.com/cognitivecomputations/spectrum/main/model_snr_results/"
+    base_path = "./model_snr_results/"
+    snr_file_template = "snr_results_{model_name_slug}.json"
+
+    def get_input_args(self):
+        return "axolotl.integrations.spectrum.SpectrumArgs"
+
+    def pre_model_load(self, cfg):
+        if cfg.get("spectrum_model_name"):
+            model_name = cfg["spectrum_model_name"]
+        else:
+            model_name = cfg["base_model"]
+        top_fraction = cfg.get("spectrum_top_fraction", 50)
+        model_slug = model_name.replace("/", "-").replace("_", "-")
+        snr_url = self.base_url + self.snr_file_template.format(
+            model_name_slug=model_slug
+        )
+        snr_path = self.base_path + self.snr_file_template.format(
+            model_name_slug=model_slug
+        )
+        # first check if the files exist locally and read the json
+        snr_data = None
+        try:
+            with open(snr_path, "r", encoding="utf-8") as fin:
+                snr_data = json.load(fin)
+        except FileNotFoundError:
+            pass
+        except Exception as exc:  # pylint: disable=broad-exception-caught
+            logging.warning(f"Failed to read SNR data from {snr_path}: {exc}")
+
+        if not snr_data:
+            try:
+                snr_data = requests.get(snr_url, timeout=60).json()
+            except requests.exceptions.RequestException as exc:
+                logging.warning(f"Failed to fetch SNR data from {snr_url}: {exc}")
+                return
+            # also catch json parsing errors
+            except json.JSONDecodeError as exc:
+                logging.warning(f"Failed to parse SNR data from {snr_url}: {exc}")
+                return
+
+        unfrozen_parameters = _generate_unfrozen_params_yaml(
+            snr_data, top_fraction=top_fraction
+        )
+        cfg["unfrozen_parameters"] = unfrozen_parameters

src/axolotl/integrations/spectrum/args.py

@@ -0,0 +1,29 @@
+# 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 Spectrum input arguments.
+"""
+from typing import Optional
+
+from pydantic import BaseModel
+
+
+class SpectrumArgs(BaseModel):
+    """
+    Input args for Spectrum.
+    """
+
+    spectrum_top_fraction: Optional[float] = 0.5
+    spectrum_model_name: Optional[str] = None

src/axolotl/loraplus.py

@@ -1,133 +0,0 @@
-"""Module for LoRA+"""
-
-# MIT License
-#
-# Copyright (c) 2024 nikhil-ghosh-berkeley
-# https://github.com/nikhil-ghosh-berkeley/loraplus
-
-import logging
-from functools import reduce
-
-from peft.tuners import lora
-from torch import nn
-from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS
-from transformers.trainer_pt_utils import get_parameter_names
-
-LOG = logging.getLogger("axolotl.loraplus")
-
-
-def get_module(name, opt_model):
-    """
-    Retrieve a module from a model using its parameter name.
-    Args:
-        name (str): Full name of the parameter, typically including module path.
-        opt_model (torch.nn.Module): The model from which to retrieve the module.
-
-    Returns:
-        Module corresponding to the given name.
-    """
-    parent_idx = 2 if "lora" in name else 1
-    module_names = name.split(sep=".")[:-parent_idx]
-    module = reduce(getattr, module_names, opt_model)
-    return module
-
-
-def create_loraplus_optimizer(
-    opt_model,
-    optimizer_cls,
-    optimizer_kwargs,
-    loraplus_lr_ratio,
-    loraplus_lr_embedding=None,
-):
-    """
-    Creates an optimizer for the given model, applying LoRA-specific learning rate adjustments to different parameter groups.
-
-    Args:
-        opt_model (torch.nn.Module): The model for which the optimizer is being created.
-        optimizer_cls (class): The class of the optimizer to be used (e.g., torch.optim.Adam).
-        optimizer_kwargs (dict): A dictionary of keyword arguments for the optimizer's initialization.
-        loraplus_lr_ratio (float): The learning rate ratio to be applied to LoRA parameters.
-        loraplus_lr_embedding (float, optional): A specific learning rate for embedding parameters, with a default value if not provided.
-
-    Returns:
-        An instance of the specified optimizer class configured with the model's parameters organized into groups with custom learning rates.
-    """
-
-    assert loraplus_lr_ratio is not None, "loraplus_lr_ratio must be provided."
-
-    if loraplus_lr_embedding is None:
-        loraplus_lr_embedding = 1e-6
-
-    decay_parameters = get_parameter_names(opt_model, ALL_LAYERNORM_LAYERS)
-    decay_parameters = [name for name in decay_parameters if "bias" not in name]
-    param_groups = {
-        "groupA": {},
-        "groupB": {},
-        "groupB_no_decay": {},
-        "embedding": {},
-    }
-
-    for name, param in opt_model.named_parameters():
-        if not param.requires_grad:
-            continue
-
-        module = get_module(name, opt_model)
-        if isinstance(module, lora.Embedding):
-            param_groups["embedding"][name] = param
-        elif "lora_B" in name or param.ndim == 1:
-            if name in decay_parameters:
-                param_groups["groupB"][name] = param
-            else:
-                param_groups["groupB_no_decay"][name] = param
-        else:
-            param_groups["groupA"][name] = param
-
-    assigned_param_groups = ""
-    for group, group_params in param_groups.items():
-        assigned_param_groups += f"{group}\n {list(group_params.keys())}\n\n"
-    LOG.info(assigned_param_groups)
-
-    lr = optimizer_kwargs["lr"]  # pylint: disable=invalid-name
-    weight_decay = optimizer_kwargs.get("weight_decay", 0.0)
-
-    optimizer_grouped_parameters = [
-        {
-            "params": list(param_groups["groupA"].values()),
-            "weight_decay": weight_decay,
-            "lr": lr,
-        },
-        {
-            "params": list(param_groups["embedding"].values()),
-            "weight_decay": weight_decay,
-            "lr": loraplus_lr_embedding,
-        },
-        {
-            "params": list(param_groups["groupB"].values()),
-            "weight_decay": weight_decay,
-            "lr": lr * loraplus_lr_ratio,
-        },
-        {
-            "params": list(param_groups["groupB_no_decay"].values()),
-            "weight_decay": 0.0,
-            "lr": lr * loraplus_lr_ratio,
-        },
-    ]
-
-    optimizer = optimizer_cls(optimizer_grouped_parameters, **optimizer_kwargs)
-    if optimizer_cls.__name__ == "Adam8bit":
-        import bitsandbytes
-
-        manager = bitsandbytes.optim.GlobalOptimManager.get_instance()
-
-        skipped = 0
-        for module in opt_model.modules():
-            if isinstance(module, nn.Embedding):
-                skipped += sum(
-                    {p.data_ptr(): p.numel() for p in module.parameters()}.values()
-                )
-                LOG.info(f"skipped {module}: {skipped/2**20}M params")
-                manager.register_module_override(module, "weight", {"optim_bits": 32})
-                LOG.debug(f"bitsandbytes: will optimize {module} in fp32")
-        LOG.info(f"skipped: {skipped/2**20}M params")
-
-    return optimizer

src/axolotl/monkeypatch/attention/mllama.py

@@ -0,0 +1,229 @@
+"""
+Monkeypatch for Vision Llama for FA2 support
+"""
+# pylint: disable=duplicate-code
+
+from typing import Optional, Tuple
+
+import torch
+from flash_attn.flash_attn_interface import flash_attn_func
+from transformers.cache_utils import Cache
+from transformers.modeling_flash_attention_utils import _flash_attention_forward
+from transformers.models.mllama.configuration_mllama import MllamaTextConfig
+from transformers.models.mllama.modeling_mllama import (
+    MllamaTextCrossAttention,
+    MllamaTextSelfAttention,
+    apply_rotary_pos_emb,
+    repeat_kv,
+)
+from transformers.utils import is_flash_attn_greater_or_equal_2_10
+
+
+class MllamaTextCrossFlashAttention2(MllamaTextCrossAttention):
+    """
+    Mllama flash cross-attention module. This module inherits from `MllamaTextCrossAttention` and
+    implements the forward pass using Flash Attention for improved performance.
+    """
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+        # Check if flash attention version is greater or equal to 2.1
+        self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        cross_attention_states: Optional[torch.Tensor] = None,
+        past_key_value: Optional[Cache] = None,
+        attention_mask: Optional[  # pylint: disable=unused-argument
+            torch.Tensor
+        ] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,  # pylint: disable=unused-argument
+        cache_position: Optional[torch.LongTensor] = None,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        bsz, q_len, _ = hidden_states.size()
+
+        query_states = self.q_proj(hidden_states)
+        query_states = query_states.view(
+            bsz, q_len, self.num_heads, self.head_dim
+        ).transpose(1, 2)
+        query_states = self.q_norm(query_states)
+
+        if cross_attention_states is not None:
+            key_states = self.k_proj(cross_attention_states)
+            value_states = self.v_proj(cross_attention_states)
+            key_states = key_states.view(
+                bsz, -1, self.num_key_value_heads, self.head_dim
+            ).transpose(1, 2)
+            value_states = value_states.view(
+                bsz, -1, self.num_key_value_heads, self.head_dim
+            ).transpose(1, 2)
+            key_states = repeat_kv(key_states, self.num_key_value_groups)
+            value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+            key_states = self.k_norm(key_states)
+            if past_key_value is not None:
+                key_states, value_states = past_key_value.update(
+                    key_states,
+                    value_states,
+                    self.layer_idx,
+                    {"cache_position": cache_position},
+                )
+        elif cache_position[0] != 0:
+            key_states, value_states = (
+                past_key_value.key_cache[self.layer_idx],
+                past_key_value.value_cache[self.layer_idx],
+            )
+        else:
+            raise ValueError(
+                "Cross attention layer can't find neither `cross_attn_states` nor cached values for key/values!"
+            )
+
+        # Transpose to get the expected layout for flash attention
+        query_states = query_states.transpose(1, 2)
+        key_states = key_states.transpose(1, 2)
+        value_states = value_states.transpose(1, 2)
+
+        # Apply Flash Attention
+        dropout_rate = self.dropout if self.training else 0.0
+        output = flash_attn_func(
+            query_states,
+            key_states,
+            value_states,
+            dropout_p=dropout_rate,
+            softmax_scale=None,
+            causal=False,
+            return_attn_probs=output_attentions,
+        )
+
+        attn_output = output.contiguous().view(bsz, q_len, -1)
+        attn_output = self.o_proj(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+
+
+class MllamaTextSelfFlashAttention2(MllamaTextSelfAttention):
+    """
+    Mllama flash self-attention module. This module inherits from `MllamaTextSelfAttention` and
+    implements the forward pass using Flash Attention for improved performance.
+    """
+
+    def __init__(self, config: MllamaTextConfig, layer_idx: int, *args, **kwargs):
+        super().__init__(config, layer_idx, *args, **kwargs)
+
+        # Check if flash attention version is greater or equal to 2.1
+        self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,  # pylint: disable=unused-argument
+        past_key_value=None,
+        cache_position: Optional[torch.LongTensor] = None,
+        **kwargs,  # pylint: disable=unused-argument
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        output_attentions = False
+
+        bsz, q_len, _ = hidden_states.size()
+
+        query_states = self.q_proj(hidden_states)
+        key_states = self.k_proj(hidden_states)
+        value_states = self.v_proj(hidden_states)
+
+        # Flash attention requires the input to have the shape
+        # batch_size x seq_length x num_heads x head_dim
+        query_states = query_states.view(
+            bsz, q_len, self.num_heads, self.head_dim
+        ).transpose(1, 2)
+        key_states = key_states.view(
+            bsz, q_len, self.num_key_value_heads, self.head_dim
+        ).transpose(1, 2)
+        value_states = value_states.view(
+            bsz, q_len, self.num_key_value_heads, self.head_dim
+        ).transpose(1, 2)
+
+        cos, sin = position_embeddings
+        query_states, key_states = apply_rotary_pos_emb(
+            query_states, key_states, cos, sin
+        )
+
+        if past_key_value is not None:
+            # sin and cos are specific to RoPE models; cache_position needed for the static cache
+            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
+            key_states, value_states = past_key_value.update(
+                key_states, value_states, self.layer_idx, cache_kwargs
+            )
+
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+        # Transpose to get the expected layout for flash attention
+        query_states = query_states.transpose(1, 2)
+        key_states = key_states.transpose(1, 2)
+        value_states = value_states.transpose(1, 2)
+
+        dropout_rate = self.dropout if self.training else 0.0
+
+        # Handle potential silent casting to float32
+        input_dtype = query_states.dtype
+        if input_dtype == torch.float32:
+            if torch.is_autocast_enabled():
+                target_dtype = torch.get_autocast_gpu_dtype()
+            elif hasattr(self.config, "_pre_quantization_dtype"):
+                target_dtype = (
+                    self.config._pre_quantization_dtype  # pylint: disable=protected-access
+                )
+            else:
+                target_dtype = self.q_proj.weight.dtype
+
+            query_states = query_states.to(target_dtype)
+            key_states = key_states.to(target_dtype)
+            value_states = value_states.to(target_dtype)
+
+        attn_output = _flash_attention_forward(
+            query_states,
+            key_states,
+            value_states,
+            attention_mask,
+            q_len,
+            dropout=dropout_rate,
+            use_top_left_mask=self._flash_attn_uses_top_left_mask,
+            is_causal=True,
+        )
+
+        attn_output = attn_output.reshape(bsz, q_len, -1).contiguous()
+        attn_output = self.o_proj(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+
+
+def patch_mllama():
+    from transformers.models.mllama.modeling_mllama import (
+        MLLAMA_TEXT_ATTENTION_CLASSES,
+        MLLAMA_TEXT_CROSS_ATTENTION_CLASSES,
+        MLLAMA_VISION_ATTENTION_CLASSES,
+        MllamaPreTrainedModel,
+    )
+
+    MllamaPreTrainedModel._supports_flash_attn_2 = (  # pylint: disable=protected-access
+        True
+    )
+    MLLAMA_TEXT_ATTENTION_CLASSES["flash_attention_2"] = MllamaTextSelfFlashAttention2
+    MLLAMA_TEXT_CROSS_ATTENTION_CLASSES[
+        "flash_attention_2"
+    ] = MllamaTextCrossFlashAttention2
+    # fallback to SDPA
+    MLLAMA_VISION_ATTENTION_CLASSES[
+        "flash_attention_2"
+    ] = MLLAMA_VISION_ATTENTION_CLASSES["sdpa"]

src/axolotl/monkeypatch/fastchat_conversation_turns.py

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

src/axolotl/monkeypatch/llama_attn_hijack_flash.py

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