set manifest and fix for source dist (#2084)

* add optimizer step to prevent warning in tests

* add optimizer step to warmup as well

.github/workflows/base.yml

@@ -27,7 +27,7 @@ jobs:
           - cuda: "124"
             cuda_version: 12.4.1
             cudnn_version: ""
-            python_version: "3.11"
+            python_version: "3.10"
             pytorch: 2.4.1
             torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
           - cuda: "124"
@@ -44,19 +44,21 @@ jobs:
             torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
     steps:
       - name: Checkout
-        uses: actions/checkout@v3
+        uses: actions/checkout@v4
       - name: Docker metadata
         id: metadata
-        uses: docker/metadata-action@v3
+        uses: docker/metadata-action@v5
         with:
-          images: winglian/axolotl-base
+          images: |
+            winglian/axolotl-base
+            axolotlai/axolotl-base
       - name: Login to Docker Hub
         uses: docker/login-action@v2
         with:
           username: ${{ secrets.DOCKERHUB_USERNAME }}
           password: ${{ secrets.DOCKERHUB_TOKEN }}
       - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v2
+        uses: docker/setup-buildx-action@v3
       - name: Build
         uses: docker/build-push-action@v4
         with:

.github/workflows/docs.yml

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

.github/workflows/lint.yml

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

.github/workflows/main.yml

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

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

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

.github/workflows/nightlies.yml

@@ -7,7 +7,7 @@ on:
 
 jobs:
   build-axolotl:
-    if: ${{ ! contains(github.event.commits[0].message, '[skip docker]]') && github.repository_owner == 'axolotl-ai-cloud' }}
+    if: ${{ ! contains(github.event.commits[0].message, '[skip docker]') && github.repository_owner == 'axolotl-ai-cloud' }}
     strategy:
       fail-fast: false
       matrix:
@@ -41,7 +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
@@ -69,7 +71,7 @@ jobs:
 
   build-axolotl-cloud:
     needs: build-axolotl
-    if: ${{ ! contains(github.event.commits[0].message, '[skip docker]]') && github.repository_owner == 'axolotl-ai-cloud' }}
+    if: ${{ ! contains(github.event.commits[0].message, '[skip docker]') && github.repository_owner == 'axolotl-ai-cloud' }}
     # this job needs to be run on self-hosted GPU runners...
     strategy:
       matrix:
@@ -103,7 +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
@@ -112,7 +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

@@ -3,13 +3,33 @@ name: publish pypi
 on:
   push:
     tags:
-      - '*'
+      - 'v*'
   workflow_dispatch:
 
 jobs:
+  setup_release:
+    name: Create Release
+    runs-on: ubuntu-latest
+    permissions:
+      contents: write
+    steps:
+      - name: Get the tag version
+        id: extract_branch
+        run: echo ::set-output name=branch::${GITHUB_REF#refs/tags/}
+        shell: bash
+
+      - name: Create Release
+        id: create_release
+        uses: actions/create-release@v1
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+        with:
+          tag_name: ${{ steps.extract_branch.outputs.branch }}
+          release_name: ${{ steps.extract_branch.outputs.branch }}
   pypi-publish:
     name: Upload release to PyPI
     runs-on: ubuntu-latest
+    needs: [setup_release]
     environment:
       name: pypi
       url: https://pypi.org/p/axolotl
@@ -17,10 +37,10 @@ jobs:
       id-token: write  # IMPORTANT: this permission is mandatory for trusted publishing
     steps:
       - name: Check out repository code
-        uses: actions/checkout@v3
+        uses: actions/checkout@v4
 
       - name: Setup Python
-        uses: actions/setup-python@v4
+        uses: actions/setup-python@v5
         with:
           python-version: "3.10"
 
@@ -38,9 +58,9 @@ jobs:
         run: |
           sed -i -E 's/version="([0-9.]+)",/version="${{ steps.tag.outputs.TAG_NAME }}",/g' setup.py
 
-      - name: Build a binary wheel
+      - name: Build a source dist
         run: |
-          python setup.py sdist bdist_wheel
+          python setup.py sdist
 
       - name: Publish package distributions to PyPI
         uses: pypa/gh-action-pypi-publish@release/v1

.github/workflows/tests-nightly.yml

@@ -9,12 +9,12 @@ jobs:
     name: pre-commit
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v3
-      - uses: actions/setup-python@v4
+      - uses: actions/checkout@v4
+      - uses: actions/setup-python@v5
         with:
           python-version: "3.10"
           cache: 'pip' # caching pip dependencies
-      - uses: pre-commit/action@v3.0.0
+      - uses: pre-commit/action@v3.0.1
         env:
           SKIP: no-commit-to-branch
 
@@ -30,10 +30,10 @@ jobs:
 
     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
@@ -48,6 +48,7 @@ jobs:
           sed -i 's#^peft.*#peft @ git+https://github.com/huggingface/peft.git@main#' requirements.txt
           sed -i 's#^accelerate.*#accelerate @ git+https://github.com/huggingface/accelerate.git@main#' requirements.txt
           sed -i 's#^trl.*#trl @ git+https://github.com/huggingface/trl.git@main#' requirements.txt
+          sed -i 's#^datasets.*#datasets @ git+https://github.com/huggingface/datasets.git@main#' requirements.txt
 
       - name: Install dependencies
         run: |

.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
 
@@ -41,10 +46,10 @@ jobs:
 
     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
@@ -66,14 +71,14 @@ jobs:
 
       - name: Run tests
         run: |
-          pytest --ignore=tests/e2e/ tests/
+          pytest -n8 --ignore=tests/e2e/ tests/
 
       - name: cleanup pip cache
         run: |
           find "$(pip cache dir)/http-v2" -type f -mtime +14 -exec rm {} \;
 
   docker-e2e-tests-1st:
-    if: github.repository_owner == 'axolotl-ai-cloud'
+    if: ${{ ! contains(github.event.commits[0].message, '[skip e2e]') && github.repository_owner == 'axolotl-ai-cloud' }}
     # this job needs to be run on self-hosted GPU runners...
     runs-on: [self-hosted, modal]
     timeout-minutes: 90

MANIFEST.in

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

README.md

@@ -1,8 +1,21 @@
-# Axolotl
+<p align="center">
+    <picture>
+        <source media="(prefers-color-scheme: dark)" srcset="image/axolotl_logo_digital_white.svg">
+        <source media="(prefers-color-scheme: light)" srcset="image/axolotl_logo_digital_black.svg">
+        <img alt="Axolotl" src="image/axolotl_logo_digital_black.svg" width="400" height="104" style="max-width: 100%;">
+    </picture>
+</p>
 
-![tests](https://github.com/axolotl-ai-cloud/axolotl/actions/workflows/tests.yml/badge.svg)
-![tests-nightly](https://github.com/axolotl-ai-cloud/axolotl/actions/workflows/tests-nightly.yml/badge.svg)
-![multigpu-semi-weekly tests](https://github.com/axolotl-ai-cloud/axolotl/actions/workflows/multi-gpu-e2e.yml/badge.svg)
+<p align="center">
+    <img src="https://img.shields.io/github/license/axolotl-ai-cloud/axolotl.svg?color=blue" alt="GitHub License">
+    <img src="https://github.com/axolotl-ai-cloud/axolotl/actions/workflows/tests.yml/badge.svg" alt="tests">
+    <a href="https://github.com/axolotl-ai-cloud/axolotl/releases"><img src="https://img.shields.io/github/release/axolotl-ai-cloud/axolotl.svg" alt="Releases"></a>
+    <img src="https://img.shields.io/github/stars/axolotl-ai-cloud/axolotl" alt="GitHub Repo stars">
+</p>
+<p align="center">
+    <img src="https://github.com/axolotl-ai-cloud/axolotl/actions/workflows/tests-nightly.yml/badge.svg" alt="tests-nightly">
+    <img src="https://github.com/axolotl-ai-cloud/axolotl/actions/workflows/multi-gpu-e2e.yml/badge.svg" alt="multigpu-semi-weekly tests">
+</p>
 
 Axolotl is a tool designed to streamline the fine-tuning of various AI models, offering support for multiple configurations and architectures.
 
@@ -75,7 +88,7 @@ Features:
 <td>
 
 <div align="center">
-  <img src="image/axolotl.png" alt="axolotl" width="160">
+  <img src="image/axolotl_symbol_digital_white.svg" alt="axolotl" width="160">
   <div>
     <p>
       <b>Axolotl provides a unified repository for fine-tuning <br />a variety of AI models with ease</b>
@@ -159,7 +172,7 @@ accelerate launch -m axolotl.cli.train https://raw.githubusercontent.com/axolotl
 #### Docker
 
   ```bash
-  docker run --gpus '"all"' --rm -it winglian/axolotl:main-latest
+  docker run --gpus '"all"' --rm -it axolotlai/axolotl:main-latest
   ```
 
   Or run on the current files for development:
@@ -178,7 +191,7 @@ accelerate launch -m axolotl.cli.train https://raw.githubusercontent.com/axolotl
   A more powerful Docker command to run would be this:
 
   ```bash
-docker run --privileged --gpus '"all"' --shm-size 10g --rm -it --name axolotl --ipc=host --ulimit memlock=-1 --ulimit stack=67108864 --mount type=bind,src="${PWD}",target=/workspace/axolotl -v ${HOME}/.cache/huggingface:/root/.cache/huggingface winglian/axolotl:main-latest
+docker run --privileged --gpus '"all"' --shm-size 10g --rm -it --name axolotl --ipc=host --ulimit memlock=-1 --ulimit stack=67108864 --mount type=bind,src="${PWD}",target=/workspace/axolotl -v ${HOME}/.cache/huggingface:/root/.cache/huggingface axolotlai/axolotl:main-latest
   ```
 
   It additionally:
@@ -210,7 +223,7 @@ docker run --privileged --gpus '"all"' --shm-size 10g --rm -it --name axolotl --
 
 #### Cloud GPU
 
-For cloud GPU providers that support docker images, use [`winglian/axolotl-cloud:main-latest`](https://hub.docker.com/r/winglian/axolotl-cloud/tags)
+For cloud GPU providers that support docker images, use [`axolotlai/axolotl-cloud:main-latest`](https://hub.docker.com/r/axolotlai/axolotl-cloud/tags)
 
 - on Latitude.sh use this [direct link](https://latitude.sh/blueprint/989e0e79-3bf6-41ea-a46b-1f246e309d5c)
 - on JarvisLabs.ai use this [direct link](https://jarvislabs.ai/templates/axolotl)
@@ -319,7 +332,7 @@ Write a job description in YAML as below:
 # dstack.yaml
 type: task
 
-image: winglian/axolotl-cloud:main-20240429-py3.11-cu121-2.2.2
+image: axolotlai/axolotl-cloud:main-latest
 
 env:
   - HUGGING_FACE_HUB_TOKEN

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 }}"
@@ -28,6 +28,7 @@ RUN if [ "$NIGHTLY_BUILD" = "true" ] ; then \
         sed -i 's#^peft.*#peft @ git+https://github.com/huggingface/peft.git@main#' requirements.txt; \
         sed -i 's#^accelerate.*#accelerate @ git+https://github.com/huggingface/accelerate.git@main#' requirements.txt; \
         sed -i 's#^trl.*#trl @ git+https://github.com/huggingface/trl.git@main#' requirements.txt; \
+        sed -i 's#^datasets.*#datasets @ git+https://github.com/huggingface/datasets.git@main#' requirements.txt; \
     fi
 
 RUN if [ "$AXOLOTL_EXTRAS" != "" ] ; then \

cicd/cicd.sh

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

cicd/multigpu.py

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

cicd/multigpu.sh

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

cicd/tests.py

@@ -10,7 +10,7 @@ import tempfile
 import jinja2
 import modal
 from jinja2 import select_autoescape
-from modal import Image, Stub
+from modal import App, Image
 
 cicd_path = pathlib.Path(__file__).parent.resolve()
 
@@ -47,7 +47,7 @@ cicd_image = (
     .pip_install("fastapi==0.110.0", "pydantic==2.6.3")
 )
 
-stub = Stub("Axolotl CI/CD", secrets=[])
+app = App("Axolotl CI/CD", secrets=[])
 
 
 N_GPUS = int(os.environ.get("N_GPUS", 1))
@@ -62,7 +62,7 @@ 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=60 * 60,
@@ -73,6 +73,6 @@ def cicd_pytest():
     run_cmd("./cicd/cicd.sh", "/workspace/axolotl")
 
 
-@stub.local_entrypoint()
+@app.local_entrypoint()
 def main():
     cicd_pytest.remote()

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=""

docker/Dockerfile-base

@@ -35,7 +35,3 @@ RUN git lfs install --skip-repo && \
     pip3 install awscli && \
     # The base image ships with `pydantic==1.8.2` which is not working
     pip3 install -U --no-cache-dir pydantic==1.10.10
-
-RUN if [ "$PYTHON_VERSION" != "2.5.1" ] ; then \
-        pip3 install flash-attn==2.6.3; \
-    fi

docker/Dockerfile-cloud

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

docker/Dockerfile-cloud-no-tmux

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

docker/Dockerfile-tests

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

docs/config.qmd

@@ -91,6 +91,7 @@ datasets:
     name: # Optional[str] name of dataset configuration to load
     train_on_split: train # Optional[str] name of dataset split to load from
     revision: # Optional[str] The specific revision of the dataset to use when loading from the Hugging Face Hub. This can be a commit hash, tag, or branch name. If not specified, the latest version will be used. This parameter is ignored for local datasets.
+    trust_remote_code: # Optional[bool] Trust remote code for untrusted source
 
   # Custom user instruction prompt
   - path: repo
@@ -405,6 +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/debugging.qmd

@@ -185,7 +185,7 @@ style="border-radius: 10px; display: block; margin: auto;" width="560" height="3
 
 ## Debugging With Docker
 
-Using [official Axolotl Docker images](https://hub.docker.com/r/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:
 

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/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/qwen2/dpo.yaml

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

image/axolotl_logo_digital_black.svg

@@ -0,0 +1,19 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<svg xmlns="http://www.w3.org/2000/svg" version="1.1" viewBox="0 0 1113 283.5">
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+    <rect fill="#141310" x="22.5" y="193.3" width="164.9" height="20.6"/>
+</svg>

image/axolotl_logo_digital_white.svg

@@ -0,0 +1,11 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<svg xmlns="http://www.w3.org/2000/svg" version="1.1" viewBox="0 0 1113 283.5">
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image/axolotl_symbol_digital_black.svg

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image/axolotl_symbol_digital_white.svg

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+    </g>
+  </g>
+</svg>

image/axolotl_wordmark_digital_black.svg

@@ -0,0 +1,17 @@
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+        <path d="M722.8,198.1V45.2c0-4.7-3.8-8.5-8.5-8.5h-16.8V12.5h16.8c18,0,32.6,14.6,32.6,32.6v152.9h-24.1Z"/>
+        <path d="M665.2,198.1c-18,0-32.6-14.6-32.6-32.6v-85.1h-20.3v-22.1h20.3V12.9h24.1v45.3h30.2v22.1h-30.2v85.1c0,4.7,3.8,8.5,8.5,8.5h21.7v24.1h-21.7Z"/>
+      </g>
+    </g>
+  </g>
+</svg>

image/axolotl_wordmark_digital_white.svg

@@ -0,0 +1,24 @@
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+<svg xmlns="http://www.w3.org/2000/svg" version="1.1" viewBox="0 0 765.4 212.6">
+  <defs>
+    <style>
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+    </style>
+  </defs>
+  <!-- Generator: Adobe Illustrator 28.7.1, SVG Export Plug-In . SVG Version: 1.2.0 Build 142)  -->
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+    <g id="Layer_1">
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+        <path class="cls-1" d="M665.2,198.1c-18,0-32.6-14.6-32.6-32.6v-85.1h-20.3v-22.1h20.3V12.9h24.1v45.3h30.2v22.1h-30.2v85.1c0,4.7,3.8,8.5,8.5,8.5h21.7v24.1h-21.7Z"/>
+      </g>
+    </g>
+  </g>
+</svg>

requirements-tests.txt

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

requirements.txt

@@ -1,18 +1,18 @@
 --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/
 packaging==23.2
 peft==0.13.2
-transformers==4.46.1
+transformers==4.46.3
 tokenizers>=0.20.1
 bitsandbytes==0.44.1
 accelerate==1.1.0
-datasets==3.0.1
-deepspeed==0.15.3
+datasets==3.1.0
+deepspeed==0.15.4
 pydantic==2.6.3
 addict
 fire
 PyYAML>=6.0
 requests
-flash-attn==2.6.3
+flash-attn==2.7.0.post2
 sentencepiece
 wandb
 einops
@@ -31,9 +31,9 @@ art
 gradio==3.50.2
 tensorboard
 python-dotenv==1.0.1
-autoawq>=0.2.5
+autoawq==0.2.7.post2
 triton>=2.3.0
-liger-kernel==0.4.0
+liger-kernel==0.4.1
 
 mamba-ssm==1.2.0.post1
 
@@ -53,3 +53,4 @@ immutabledict==4.2.0
 antlr4-python3-runtime==4.13.2
 
 torchao==0.5.0
+schedulefree==1.3.0

scripts/cloud-entrypoint.sh

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

setup.py

@@ -39,7 +39,10 @@ def parse_requirements():
         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)
@@ -54,6 +57,10 @@ def parse_requirements():
 
             if (major, minor) >= (2, 5):
                 _install_requires.pop(_install_requires.index(xformers_version))
+                if patch == 0:
+                    _install_requires.append("xformers==0.0.28.post2")
+                else:
+                    _install_requires.append("xformers==0.0.28.post3")
                 _install_requires.pop(_install_requires.index(autoawq_version))
             elif (major, minor) >= (2, 4):
                 if patch == 0:
@@ -89,19 +96,19 @@ install_requires, dependency_links = parse_requirements()
 
 setup(
     name="axolotl",
-    version="0.5.0",
+    version="0.5.1",
     description="LLM Trainer",
     long_description="Axolotl is a tool designed to streamline the fine-tuning of various AI models, offering support for multiple configurations and architectures.",
     package_dir={"": "src"},
-    packages=find_packages(),
+    packages=find_packages("src"),
     install_requires=install_requires,
     dependency_links=dependency_links,
     extras_require={
         "flash-attn": [
-            "flash-attn==2.6.3",
+            "flash-attn==2.7.0.post2",
         ],
         "deepspeed": [
-            "deepspeed==0.14.4",
+            "deepspeed==0.15.4",
             "deepspeed-kernels",
         ],
         "mamba-ssm": [

src/axolotl/cli/__init__.py

@@ -190,18 +190,15 @@ def do_inference(
 ):
     model, tokenizer = load_model_and_tokenizer(cfg=cfg, cli_args=cli_args)
     prompter = cli_args.prompter
-    default_tokens = {"unk_token": "<unk>", "bos_token": "<s>", "eos_token": "</s>"}
-
-    for token, symbol in default_tokens.items():
-        # If the token isn't already specified in the config, add it
-        if not (cfg.special_tokens and token in cfg.special_tokens):
-            tokenizer.add_special_tokens({token: symbol})
 
     prompter_module = None
+    chat_template_str = None
     if prompter:
         prompter_module = getattr(
             importlib.import_module("axolotl.prompters"), prompter
         )
+    elif cfg.chat_template:
+        chat_template_str = get_chat_template(cfg.chat_template)
 
     model = model.to(cfg.device, dtype=cfg.torch_dtype)
 
@@ -211,13 +208,31 @@ def do_inference(
         instruction = get_multi_line_input()
         if not instruction:
             return
+
         if prompter_module:
             prompt: str = next(
                 prompter_module().build_prompt(instruction=instruction.strip("\n"))
             )
         else:
             prompt = instruction.strip()
-        batch = tokenizer(prompt, return_tensors="pt", add_special_tokens=True)
+
+        if chat_template_str:
+            batch = tokenizer.apply_chat_template(
+                [
+                    {
+                        "role": "user",
+                        "content": prompt,
+                    }
+                ],
+                return_tensors="pt",
+                add_special_tokens=True,
+                add_generation_prompt=True,
+                chat_template=chat_template_str,
+                tokenize=True,
+                return_dict=True,
+            )
+        else:
+            batch = tokenizer(prompt, return_tensors="pt", add_special_tokens=True)
 
         print("=" * 40)
         model.eval()
@@ -257,13 +272,6 @@ def do_inference_gradio(
 
     model, tokenizer = load_model_and_tokenizer(cfg=cfg, cli_args=cli_args)
     prompter = cli_args.prompter
-    # default_tokens = {"unk_token": "<unk>", "bos_token": "<s>", "eos_token": "</s>"}
-    default_tokens: Dict[str, str] = {}
-
-    for token, symbol in default_tokens.items():
-        # If the token isn't already specified in the config, add it
-        if not (cfg.special_tokens and token in cfg.special_tokens):
-            tokenizer.add_special_tokens({token: symbol})
 
     prompter_module = None
     chat_template_str = None

src/axolotl/core/trainer_builder.py

@@ -436,7 +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()
 
@@ -505,6 +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
@@ -1024,24 +1038,37 @@ class AxolotlDPOTrainer(SchedulerMixin, DPOTrainer):
 
         return super().push_to_hub(*args, **kwargs)
 
+    @staticmethod
     def tokenize_row(
-        self,
         features,
         processing_class,
         max_prompt_length,
         max_completion_length,
         add_special_tokens,
     ) -> Dict:
-        res = super().tokenize_row(
+        res = DPOTrainer.tokenize_row(
             features,
             processing_class,
             max_prompt_length,
             max_completion_length,
             add_special_tokens,
         )
-        if processing_class.bos_token_id is None and res["prompt_input_ids"][0] is None:
+        # 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(
@@ -1273,6 +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):
@@ -1390,17 +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"
@@ -1625,11 +1662,13 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         if self.cfg.reward_model:
             trainer_kwargs["max_length"] = self.cfg.sequence_len
 
+        # pylint: disable=duplicate-code
         if self.cfg.optimizer in [
             "optimi_adamw",
             "ao_adamw_4bit",
             "ao_adamw_8bit",
             "ao_adamw_fp8",
+            "adopt_adamw",
         ]:
             # Set default so transformers doesn't throw
             training_arguments_kwargs["optim"] = "adamw_hf"
@@ -1832,10 +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
@@ -1933,6 +1972,12 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
 
         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
 
@@ -1956,7 +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:
@@ -1970,12 +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"] = self.cfg.use_wandb
         elif self.cfg.rl == "orpo":
             trainer_cls = AxolotlORPOTrainer
             trainer_cls_args = [self.model]

src/axolotl/integrations/base.py

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

src/axolotl/integrations/grokfast/LICENSE

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

src/axolotl/integrations/grokfast/README.md

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

src/axolotl/integrations/grokfast/__init__.py

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

src/axolotl/integrations/grokfast/args.py

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

src/axolotl/integrations/grokfast/optimizer.py

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

src/axolotl/integrations/liger/__init__.py

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

src/axolotl/monkeypatch/multipack.py

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

src/axolotl/utils/callbacks/__init__.py

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

src/axolotl/utils/config/__init__.py

@@ -1,8 +1,6 @@
 """Module for working with config dicts"""
-import json
 import logging
 import os
-from pathlib import Path
 from typing import Optional
 
 import torch
@@ -10,7 +8,6 @@ from transformers.utils import is_torch_bf16_gpu_available
 
 from axolotl.integrations.config import merge_input_args
 from axolotl.utils.bench import log_gpu_memory_usage
-from axolotl.utils.config.models.input.v0_4_1 import SUPPORTED_METRICS
 from axolotl.utils.config.models.input.v0_4_1 import (
     AxolotlConfigWCapabilities as AxolotlConfigWCapabilitiesBase,
 )
@@ -247,370 +244,3 @@ def validate_config(cfg: DictDefault, capabilities: Optional[dict] = None):
     return DictDefault(
         dict(AxolotlInputConfig(**cfg.to_dict()).model_dump(exclude_none=True))
     )
-
-
-def legacy_validate_config(cfg):
-    """
-    This is a "pre-validation" step that handles the yaml configuration before we have any
-    information about the model architecture
-    """
-    if is_torch_bf16_gpu_available():
-        if not cfg.bf16 and not cfg.bfloat16:
-            LOG.info("bf16 support detected, but not enabled for this configuration.")
-    else:
-        if (
-            not cfg.merge_lora
-            and not cfg.is_preprocess
-            and (cfg.bf16 is True or cfg.bfloat16 is True)
-        ):
-            raise ValueError(
-                "bf16 requested, but AMP is not supported on this GPU. Requires Ampere series or above."
-            )
-    if (
-        # pylint: disable=too-many-boolean-expressions
-        not (cfg.bf16 or cfg.bfloat16)
-        and (cfg.fp16 or cfg.float16)
-        and not cfg.adapter
-        and not cfg.flash_attention
-        and cfg.sample_packing
-    ):
-        LOG.warning(
-            "Full fine tune w/o FA2 w/ sample packing and fp16/float16 is likely to raise errors. Try LoRA."
-        )
-        # ValueError: Attempting to unscale FP16 gradients.
-        # OR
-        # RuntimeError: expected mat1 and mat2 to have the same dtype, but got: float != c10::Half
-    if cfg.max_packed_sequence_len:
-        raise DeprecationWarning("`max_packed_sequence_len` is no longer supported")
-
-    if cfg.sample_packing and cfg.rl:
-        raise ValueError("`sample_packing: true` does not work with RLHF training")
-
-    if cfg.sample_packing and not cfg.pad_to_sequence_len:
-        LOG.warning(
-            "`pad_to_sequence_len: true` is recommended when using sample_packing"
-        )
-
-    if cfg.gradient_accumulation_steps and cfg.batch_size:
-        raise ValueError(
-            "please set only one of gradient_accumulation_steps or batch_size"
-        )
-    if cfg.batch_size:
-        LOG.warning(
-            "%s\n%s",
-            "batch_size is not recommended. Please use gradient_accumulation_steps instead.",
-            "To calculate the equivalent gradient_accumulation_steps, divide batch_size / micro_batch_size / number of gpus.",
-        )
-    if (
-        cfg.eval_batch_size
-        and cfg.micro_batch_size
-        and cfg.eval_batch_size != cfg.micro_batch_size
-    ):
-        LOG.warning(
-            "eval_batch_size != micro_batch_size. This can lead to VRAM instability."
-        )
-
-    if cfg.adapter == "qlora":
-        if cfg.merge_lora:
-            # can't merge qlora if loaded in 8bit or 4bit
-            if cfg.load_in_8bit:
-                raise ValueError("Can't merge qlora if loaded in 8bit")
-
-            if cfg.gptq:
-                raise ValueError("Can't merge qlora if gptq")
-
-            if cfg.load_in_4bit:
-                raise ValueError("Can't merge qlora if loaded in 4bit")
-
-        else:
-            if cfg.load_in_8bit:
-                raise ValueError("Can't load qlora in 8bit")
-
-            if cfg.gptq:
-                raise ValueError("Can't load qlora if gptq")
-
-            if not cfg.load_in_4bit:
-                raise ValueError("Require cfg.load_in_4bit to be True for qlora")
-
-        if cfg.flash_attn_fuse_qkv or cfg.flash_attn_fuse_mlp:
-            raise ValueError("Fused modules are not supported with QLoRA")
-
-    loftq = cfg.peft and cfg.peft.loftq_config and cfg.peft.loftq_config.loftq_bits
-    if not cfg.load_in_8bit and cfg.adapter == "lora" and not loftq:
-        LOG.warning("We recommend setting `load_in_8bit: true` for LORA finetuning")
-
-    if cfg.adapter == "lora" and (cfg.flash_attn_fuse_qkv or cfg.flash_attn_fuse_mlp):
-        raise ValueError("Fused modules are not supported with LoRA")
-
-    if cfg.adapter and cfg.peft_layers_to_transform and cfg.unfrozen_parameters:
-        raise ValueError(
-            "`unfrozen_parameters` used with `peft_layers_to_transform` can have unexpected behavior."
-        )
-
-    if cfg.relora_steps:
-        if cfg.adapter not in ("lora", "qlora"):
-            raise ValueError("cfg.adapter must be lora or qlora to use ReLoRA")
-
-        if cfg.fsdp:
-            raise ValueError("fsdp not supported with ReLoRA")
-
-        if cfg.deepspeed:
-            raise ValueError("deepspeed not supported with ReLoRA")
-
-        if cfg.lr_scheduler == "one_cycle":
-            raise ValueError("ReLoRA is not compatible with the one_cycle scheduler")
-
-        if cfg.flash_attn_fuse_qkv or cfg.flash_attn_fuse_mlp:
-            raise ValueError("Fused modules are not supported with ReLoRA")
-
-    if cfg.trust_remote_code:
-        LOG.warning(
-            "`trust_remote_code` is set to true. Please make sure that you reviewed the remote code/model."
-        )
-
-    if cfg.push_dataset_to_hub and cfg.hf_use_auth_token is not True:
-        raise ValueError(
-            "Require cfg.hf_use_auth_token to be True for push_dataset_to_hub"
-        )
-
-    if (cfg.base_model and "falcon" in cfg.base_model.lower()) and cfg.fsdp:
-        raise ValueError("FSDP is not supported for falcon models")
-
-    if (
-        cfg.base_model and "mpt" in cfg.base_model.lower()
-    ) and cfg.gradient_checkpointing:
-        raise ValueError("gradient_checkpointing is not supported for MPT models")
-
-    if cfg.flash_optimum is True:
-        if cfg.adapter:
-            LOG.warning("BetterTransformers probably doesn't work with PEFT adapters")
-        if cfg.fp16 or cfg.bf16:
-            raise ValueError("AMP is not supported with BetterTransformer")
-        if cfg.float16 is not True and cfg.bfloat16 is not True:
-            LOG.warning(
-                "You should probably set bfloat16 or float16 to true to "
-                "load the model in float16 for BetterTransformers"
-            )
-        if int(torch.__version__.split(".", maxsplit=1)[0]) < 2:
-            LOG.warning("torch>=2.0.0 required")
-            raise ValueError(
-                f"flash_optimum for BetterTransformers may not be used with {torch.__version__}"
-            )
-
-    if cfg.pretraining_dataset and cfg.group_by_length:
-        LOG.warning(
-            "You probably want to disable group_by_length as it will force a streamed dataset to download completely."
-        )
-    if cfg.pretraining_dataset and not cfg.max_steps:
-        raise ValueError(
-            "max_steps must be set when using iterable pretraining_dataset, Trainer can't infer length and schedule optimizer/learning rate without it!"
-        )
-
-    if any([cfg.adam_beta1, cfg.adam_beta2, cfg.adam_epsilon]) and (
-        not cfg.optimizer or "adamw" not in cfg.optimizer
-    ):
-        LOG.warning("adamw hyperparameters found, but no adamw optimizer set")
-
-    if cfg.push_to_hub_model_id:
-        raise ValueError(
-            "push_to_hub_model_id is deprecated. Please use hub_model_id instead."
-        )
-
-    if cfg.hub_model_id and cfg.save_strategy not in ["steps", "epoch", None]:
-        LOG.warning(
-            "hub_model_id is set without any models being saved. To save a model, set save_strategy to steps, epochs or leave empty."
-        )
-
-    if cfg.gptq and cfg.revision_of_model:
-        raise ValueError(
-            "revision_of_model is not supported for GPTQ models. "
-            + "Please download the model from HuggingFace Hub manually for correct branch, "
-            + "point to its path, and remove revision_of_model from the config."
-        )
-
-    # if cfg.sample_packing and cfg.sdp_attention:
-    #     # incompatible due to bug w/ accelerate causing 0.0 loss when using llama2
-    #     raise ValueError(
-    #         "sample_packing not compatible with sdp_attention. Use flash_attention"
-    #     )
-
-    if cfg.sample_packing and cfg.xformers_attention:
-        raise ValueError(
-            "sample_packing not compatible with xformers_attention. Use flash_attention"
-        )
-
-    if cfg.sample_packing and cfg.sdp_attention and (cfg.bfloat16 or cfg.bf16):
-        # https://github.com/pytorch/pytorch/blob/1b03423526536b5f3d35bdfa95ccc6197556cf9b/test/test_transformers.py#L2440-L2450
-        LOG.warning(
-            "sample_packing & torch sdpa with bf16 is unsupported may results in 0.0 loss. "
-            "This may work on H100s."
-        )
-
-    if cfg.early_stopping_patience:
-        if not cfg.save_steps or not cfg.eval_steps:
-            raise ValueError(
-                "`early_stopping_patience` requires save_steps and eval_steps to be set. eval_steps should evenly divide save_steps."
-            )
-        if cfg.save_steps % cfg.eval_steps != 0:
-            raise ValueError(
-                "`early_stopping_patience` requires that eval_steps should evenly divide save_steps."
-            )
-
-    if cfg.saves_per_epoch and cfg.save_steps:
-        raise ValueError(
-            "save_steps and saves_per_epoch are mutually exclusive and cannot be used together."
-        )
-    if cfg.save_strategy and cfg.saves_per_epoch and cfg.save_strategy != "steps":
-        raise ValueError(
-            "save_strategy must be empty or set to `steps` when used with saves_per_epoch."
-        )
-    if cfg.save_strategy and cfg.save_steps and cfg.save_strategy != "steps":
-        raise ValueError(
-            "save_strategy and save_steps mismatch. Please set save_strategy to 'steps' or remove save_steps."
-        )
-    if cfg.evals_per_epoch and cfg.eval_steps:
-        raise ValueError(
-            "eval_steps and evals_per_epoch are mutually exclusive and cannot be used together."
-        )
-    if (
-        cfg.evals_per_epoch
-        and cfg.evaluation_strategy
-        and cfg.evaluation_strategy != "steps"
-    ):
-        raise ValueError(
-            "evaluation_strategy must be empty or set to `steps` when used with evals_per_epoch."
-        )
-    if (
-        cfg.evaluation_strategy
-        and cfg.eval_steps
-        and cfg.evaluation_strategy != "steps"
-    ):
-        raise ValueError(
-            "evaluation_strategy and eval_steps mismatch. Please set evaluation_strategy to 'steps' or remove eval_steps."
-        )
-
-    if (
-        cfg.val_set_size == 0
-        and (cfg.eval_steps or cfg.evaluation_strategy)
-        and not cfg.test_datasets
-    ):
-        raise ValueError(
-            "eval_steps and evaluation_strategy are not supported with val_set_size == 0"
-        )
-
-    if (
-        cfg.sample_packing
-        and cfg.eval_table_size
-        and cfg.eval_sample_packing is not False
-    ):
-        raise ValueError(
-            "eval_table_size and eval_sample_packing are not supported together with sample_packing. Please set 'eval_sample_packing' to false."
-        )
-
-    if not cfg.adapter and (cfg.load_in_8bit or cfg.load_in_4bit):
-        raise ValueError(
-            "load_in_8bit and load_in_4bit are not supported without setting an adapter."
-            "If you want to full finetune, please turn off load_in_8bit and load_in_4bit."
-        )
-
-    if cfg.rope_scaling:
-        LOG.warning("`rope_scaling` should now be be a key under `model_config`")
-
-    if cfg.wandb_run_id and not cfg.wandb_name:
-        cfg.wandb_name = cfg.wandb_run_id
-
-        LOG.warning(
-            "wandb_run_id sets the ID of the run. If you would like to set the name, please use wandb_name instead."
-        )
-
-    if cfg.noisy_embedding_alpha is not None:
-        # Deprecated, use neftune_noise_alpha
-        LOG.warning("noisy_embedding_alpha is deprecated, use neftune_noise_alpha")
-        if cfg.neftune_noise_alpha is None:
-            cfg.neftune_noise_alpha = cfg.noisy_embedding_alpha
-        else:
-            # User is providing both; bail and have them sort out their settings
-            raise ValueError(
-                "noisy_embedding_alpha is deprecated, use neftune_noise_alpha; both are set, please remove the deprecated noisy_embedding_alpha setting"
-            )
-
-    if cfg.neftune_noise_alpha is not None and cfg.neftune_noise_alpha <= 0.0:
-        raise ValueError("neftune_noise_alpha must be > 0.0")
-
-    if cfg.max_memory is not None and cfg.gpu_memory_limit is not None:
-        raise ValueError(
-            "max_memory and gpu_memory_limit are mutually exclusive and cannot be used together."
-        )
-
-    if (
-        cfg.unfrozen_parameters
-        and cfg.gradient_checkpointing_kwargs
-        and cfg.gradient_checkpointing_kwargs.use_reentrant is True
-    ):
-        # https://github.com/huggingface/transformers/issues/21381
-        raise ValueError(
-            "`use_reentrant` must be false when used with partially frozen model."
-        )
-
-    if cfg.deepspeed and Path(cfg.deepspeed).is_file():
-        with open(cfg.deepspeed, encoding="utf-8") as file:
-            contents = file.read()
-            deepspeed_cfg: DictDefault = DictDefault(json.loads(contents))
-            if cfg.flash_attention:
-                if (
-                    deepspeed_cfg.zero_optimization
-                    and deepspeed_cfg.zero_optimization.stage == 3
-                ):
-                    if not (
-                        (
-                            deepspeed_cfg.bf16
-                            and deepspeed_cfg.bf16.enabled  # pylint: disable=no-member
-                            is True
-                        )
-                        or (
-                            deepspeed_cfg.fp16
-                            and deepspeed_cfg.fp16.enabled  # pylint: disable=no-member
-                            is True
-                        )
-                    ):
-                        raise ValueError(
-                            "bf16.enabled or fp16.enabled must be set to true when using ZeRO-3 with flash-attention"
-                        )
-            if "8bit" in cfg.optimizer and deepspeed_cfg.optimizer:
-                LOG.warning(
-                    f"conflicting optimizer: {cfg.optimizer} used alongside deepspeed optimizer."
-                )
-
-    if cfg.test_datasets and cfg.val_set_size:
-        raise ValueError(
-            "non-zero val_set_size should not be used with test_datasets configuration"
-        )
-
-    if cfg.fsdp and "bnb" in cfg.optimizer:
-        raise ValueError(f"FSDP not compatible with {cfg.optimizer}")
-
-    if cfg.do_causal_lm_eval and cfg.eval_sample_packing:
-        raise ValueError(
-            "do_causal_lm_eval is enabled, eval_sample_packing must be set to False"
-        )
-
-    if cfg.eval_causal_lm_metrics:
-        if not isinstance(cfg.eval_causal_lm_metrics, list):
-            raise ValueError("eval_causal_lm_metrics must be a list")
-        # only ["sacrebleu", "comet", "ter", "chrf"] supported
-        if set(cfg.eval_causal_lm_metrics) - SUPPORTED_METRICS:
-            raise ValueError(
-                f"eval_causal_lm_metrics must be one of {SUPPORTED_METRICS}"
-            )
-
-    # TODO
-    # MPT 7b
-    # https://github.com/facebookresearch/bitsandbytes/issues/25
-    # no 8bit adaAmw w bf16
-
-    # GPT-NeoX
-    # evals broken when extending context len
-    # File "/root/miniconda3/envs/py3.9/lib/python3.9/site-packages/transformers/models/gpt_neox/modeling_gpt_neox.py", line 162, in forward                        attn_output, attn_weights = self._attn(query, key, value, attention_mask, head_mask)
-    # File "/root/miniconda3/envs/py3.9/lib/python3.9/site-packages/optimum/bettertransformer/models/attention.py", line 74, in gpt2_wrapped_scaled_dot_product
-    # attention_mask = causal_mask + attention_mask
-    # RuntimeError: The size of tensor a (2048) must match the size of tensor b (8132) at non-singleton dimension 3

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

@@ -58,6 +58,7 @@ class ChatTemplate(str, Enum):
     qwen_25 = "qwen_25"  # pylint: disable=invalid-name
     tokenizer_default = "tokenizer_default"  # pylint: disable=invalid-name
     exaone = "exaone"  # pylint: disable=invalid-name
+    metharme = "metharme"  # pylint: disable=invalid-name
 
 
 class DeprecatedParameters(BaseModel):
@@ -67,6 +68,7 @@ class DeprecatedParameters(BaseModel):
     rope_scaling: Optional[Any] = None
     noisy_embedding_alpha: Optional[float] = None
     dpo_beta: Optional[float] = None
+    evaluation_strategy: Optional[str] = None
 
     @field_validator("max_packed_sequence_len")
     @classmethod
@@ -98,6 +100,13 @@ class DeprecatedParameters(BaseModel):
             LOG.warning("dpo_beta is deprecated, use rl_beta instead")
         return dpo_beta
 
+    @field_validator("evaluation_strategy")
+    @classmethod
+    def validate_evaluation_strategy(cls, evaluation_strategy):
+        if evaluation_strategy is not None:
+            LOG.warning("evaluation_strategy is deprecated, use eval_strategy instead")
+        return evaluation_strategy
+
 
 class RemappedParameters(BaseModel):
     """parameters that have been remapped to other names"""
@@ -241,8 +250,10 @@ class KTODataset(BaseModel):
 class LoftQConfig(BaseModel):
     """LoftQ configuration subset"""
 
-    loftq_bits: int = Field(default=4, metadata={"help": "Quantization bits for LoftQ"})
-    # loftq_iter: int = Field(default=1, metadata={"help": "Alternating iterations for LoftQ"})
+    loftq_bits: int = Field(
+        default=4, json_schema_extra={"description": "Quantization bits for LoftQ"}
+    )
+    # loftq_iter: int = Field(default=1, json_schema_extra={"description": "Alternating iterations for LoftQ"})
 
 
 class PeftConfig(BaseModel):
@@ -285,8 +296,8 @@ class LoraConfig(BaseModel):
 
     qlora_sharded_model_loading: Optional[bool] = Field(
         default=False,
-        metadata={
-            "help": "load qlora model in sharded format for FSDP using answer.ai technique."
+        json_schema_extra={
+            "description": "load qlora model in sharded format for FSDP using answer.ai technique."
         },
     )
     lora_on_cpu: Optional[bool] = None
@@ -295,13 +306,15 @@ class LoraConfig(BaseModel):
 
     loraplus_lr_ratio: Optional[float] = Field(
         default=None,
-        metadata={
-            "help": "loraplus learning rate ratio lr_B / lr_A. Recommended value is 2^4."
+        json_schema_extra={
+            "description": "loraplus learning rate ratio lr_B / lr_A. Recommended value is 2^4."
         },
     )
     loraplus_lr_embedding: Optional[float] = Field(
         default=1e-6,
-        metadata={"help": "loraplus learning rate for lora embedding layers."},
+        json_schema_extra={
+            "description": "loraplus learning rate for lora embedding layers."
+        },
     )
 
     merge_lora: Optional[bool] = None
@@ -371,10 +384,10 @@ class ModelInputConfig(BaseModel):
     tokenizer_use_fast: Optional[bool] = None
     tokenizer_legacy: Optional[bool] = None
     tokenizer_type: Optional[str] = Field(
-        default=None, metadata={"help": "transformers tokenizer class"}
+        default=None, json_schema_extra={"description": "transformers tokenizer class"}
     )
     processor_type: Optional[str] = Field(
-        default=None, metadata={"help": "transformers processor class"}
+        default=None, json_schema_extra={"description": "transformers processor class"}
     )
     trust_remote_code: Optional[bool] = None
 
@@ -396,18 +409,18 @@ class HyperparametersConfig(BaseModel):
     gradient_accumulation_steps: Optional[int] = Field(default=1)
     micro_batch_size: Optional[int] = Field(
         default=1,
-        metadata={"help": "per gpu micro batch size for training"},
+        json_schema_extra={"description": "per gpu micro batch size for training"},
     )
     batch_size: Optional[int] = Field(
         default=None,
-        metadata={
-            "help": "Total batch size, we do not recommended setting this manually"
+        json_schema_extra={
+            "description": "Total batch size, we do not recommended setting this manually"
         },
     )
     eval_batch_size: Optional[int] = Field(
         default=None,
-        metadata={
-            "help": "per gpu micro batch size for evals, defaults to value of micro_batch_size"
+        json_schema_extra={
+            "description": "per gpu micro batch size for evals, defaults to value of micro_batch_size"
         },
     )
 
@@ -427,16 +440,18 @@ class HyperparametersConfig(BaseModel):
                 "ao_adamw_4bit",
                 "ao_adamw_8bit",
                 "ao_adamw_fp8",
+                "adopt_adamw",
             ],
         ]
     ] = OptimizerNames.ADAMW_HF.value
     optim_args: Optional[Union[str, Dict[str, Any]]] = Field(
-        default=None, metadata={"help": "Optional arguments to supply to optimizer."}
+        default=None,
+        json_schema_extra={"description": "Optional arguments to supply to optimizer."},
     )
     optim_target_modules: Optional[Union[List[str], Literal["all_linear"]]] = Field(
         default=None,
-        metadata={
-            "help": "The target modules to optimize, i.e. the module names that you would like to train."
+        json_schema_extra={
+            "description": "The target modules to optimize, i.e. the module names that you would like to train."
         },
     )
     torchdistx_path: Optional[str] = None
@@ -496,15 +511,15 @@ class LISAConfig(BaseModel):
 
     lisa_n_layers: Optional[int] = Field(
         default=None,
-        metadata={"help": "the number of activate layers in LISA"},
+        json_schema_extra={"description": "the number of activate layers in LISA"},
     )
     lisa_step_interval: Optional[int] = Field(
         default=None,
-        metadata={"help": "how often to switch layers in LISA"},
+        json_schema_extra={"description": "how often to switch layers in LISA"},
     )
     lisa_layers_attribute: Optional[str] = Field(
         default="model.layers",
-        metadata={"help": "path under the model to access the layers"},
+        json_schema_extra={"description": "path under the model to access the layers"},
     )
 
 
@@ -603,7 +618,8 @@ class AxolotlInputConfig(
     pretraining_dataset: Optional[  # type: ignore
         conlist(Union[PretrainingDataset, SFTDataset], min_length=1)
     ] = Field(
-        default=None, metadata={"help": {"streaming dataset to use for pretraining"}}
+        default=None,
+        json_schema_extra={"description": "streaming dataset to use for pretraining"},
     )
     dataset_processes: Optional[int] = Field(default=os.cpu_count())
     dataset_keep_in_memory: Optional[bool] = None
@@ -663,7 +679,8 @@ class AxolotlInputConfig(
     sequence_len: int = Field(default=512)
     min_sample_len: Optional[int] = None
     max_prompt_len: int = Field(
-        default=512, metadata={"help": "maximum prompt length for RL training"}
+        default=512,
+        json_schema_extra={"description": "maximum prompt length for RL training"},
     )
     sample_packing: Optional[bool] = None
     sample_packing_group_size: Optional[int] = 100_000
@@ -682,8 +699,8 @@ class AxolotlInputConfig(
     pretrain_multipack_buffer_size: Optional[int] = 10_000
     pretrain_multipack_attn: Optional[bool] = Field(
         default=True,
-        metadata={
-            "help": "whether to prevent cross attention for packed sequences during pretraining",
+        json_schema_extra={
+            "description": "whether to prevent cross attention for packed sequences during pretraining",
         },
     )
 
@@ -729,7 +746,7 @@ class AxolotlInputConfig(
     warmup_ratio: Optional[float] = None
     eval_steps: Optional[Union[int, float]] = None
     evals_per_epoch: Optional[Union[int]] = None
-    evaluation_strategy: Optional[str] = None
+    eval_strategy: Optional[str] = None
     save_steps: Optional[Union[int, float]] = None
     saves_per_epoch: Optional[int] = None
     save_strategy: Optional[str] = None
@@ -781,6 +798,8 @@ class AxolotlInputConfig(
     is_mistral_derived_model: Optional[bool] = Field(default=None)
     is_qwen_derived_model: Optional[bool] = Field(default=None)
 
+    plugins: Optional[List[str]] = Field(default=None)
+
     @field_validator("datasets", mode="before")
     @classmethod
     def deprecate_sharegpt_datasets(cls, datasets):
@@ -788,7 +807,12 @@ class AxolotlInputConfig(
             if not ds_cfg.get("type"):
                 continue
 
-            if ds_cfg["type"].startswith("sharegpt"):
+            ds_type = ds_cfg["type"]
+            # skip if it's a dict (for custom user instruction prompt)
+            if isinstance(ds_type, dict):
+                continue
+
+            if isinstance(ds_type, str) and ds_type.startswith("sharegpt"):
                 raise ValueError(
                     "`type: sharegpt.*` is deprecated. Please use `type: chat_template` instead."
                 )
@@ -1024,21 +1048,21 @@ class AxolotlInputConfig(
     @classmethod
     def check_evals(cls, data):
         if (
-            data.get("evaluation_strategy")
+            data.get("eval_strategy")
             and data.get("eval_steps")
-            and data.get("evaluation_strategy") != "steps"
+            and data.get("eval_strategy") != "steps"
         ):
             raise ValueError(
-                "evaluation_strategy and eval_steps mismatch. Please set evaluation_strategy to 'steps' or remove eval_steps."
+                "eval_strategy and eval_steps mismatch. Please set eval_strategy to 'steps' or remove eval_steps."
             )
 
         if (
             data.get("val_set_size") == 0
-            and (data.get("eval_steps") or data.get("evaluation_strategy"))
+            and (data.get("eval_steps") or data.get("eval_strategy"))
             and not data.get("test_datasets")
         ):
             raise ValueError(
-                "eval_steps and evaluation_strategy are not supported with val_set_size == 0"
+                "eval_steps and eval_strategy are not supported with val_set_size == 0"
             )
         if data.get("evals_per_epoch") and data.get("eval_steps"):
             raise ValueError(
@@ -1046,11 +1070,11 @@ class AxolotlInputConfig(
             )
         if (
             data.get("evals_per_epoch")
-            and data.get("evaluation_strategy")
-            and data.get("evaluation_strategy") != "steps"
+            and data.get("eval_strategy")
+            and data.get("eval_strategy") != "steps"
         ):
             raise ValueError(
-                "evaluation_strategy must be empty or set to `steps` when used with evals_per_epoch."
+                "eval_strategy must be empty or set to `steps` when used with evals_per_epoch."
             )
 
         if data.get("do_bench_eval") and not (
@@ -1282,6 +1306,25 @@ class AxolotlInputConfig(
             )
         return data
 
+    @model_validator(mode="before")
+    @classmethod
+    def warn_qlora_zero3_w_use_reentrant(cls, data):
+        if (
+            data.get("adapter") == "qlora"
+            and data.get("gradient_checkpointing_kwargs", {})
+            and data.get("gradient_checkpointing_kwargs", {}).get("use_reentrant")
+            is False
+            and "zero3" in data.get("deepspeed", "")
+        ):
+            # may result in:
+            # torch.utils.checkpoint.CheckpointError: torch.utils.checkpoint:
+            # Recomputed values for the following tensors have different metadata
+            # than during the forward pass.
+            LOG.warning(
+                "qlora + zero3 with use_reentrant: false may result in a CheckpointError about recomputed values"
+            )
+        return data
+
     @model_validator(mode="before")
     @classmethod
     def check_val_w_test_datasets(cls, data):
@@ -1291,6 +1334,19 @@ class AxolotlInputConfig(
             )
         return data
 
+    @model_validator(mode="before")
+    @classmethod
+    def check_eval_strategy(cls, data):
+        if (
+            data.get("evaluation_strategy") is not None
+            and data.get("eval_strategy") is None
+        ):
+            LOG.info(
+                "explicitly setting `eval_strategy` from the `evaluation_strategy`"
+            )
+            data["eval_strategy"] = data.get("evaluation_strategy")
+        return data
+
     @model_validator(mode="before")
     @classmethod
     def check_fsdp_offload_w_8bit_optimizer(cls, data):

src/axolotl/utils/data/rl.py

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

src/axolotl/utils/data/sft.py

@@ -260,6 +260,7 @@ def load_tokenized_prepared_datasets(
         for config_dataset in for_d_in_datasets(cfg_datasets):
             ds: Optional[Union[Dataset, DatasetDict]] = None
             ds_from_hub = False
+            ds_trust_remote_code = config_dataset.trust_remote_code
             try:
                 # this is just a basic check to see if the path is a
                 # valid HF dataset that's loadable
@@ -269,6 +270,7 @@ def load_tokenized_prepared_datasets(
                     streaming=True,
                     token=use_auth_token,
                     revision=config_dataset.revision,
+                    trust_remote_code=ds_trust_remote_code,
                 )
                 ds_from_hub = True
             except (FileNotFoundError, ConnectionError, HFValidationError, ValueError):
@@ -348,7 +350,15 @@ def load_tokenized_prepared_datasets(
                             split=None,
                         )
                     else:
-                        ds = load_from_disk(config_dataset.path)
+                        try:
+                            ds = load_from_disk(config_dataset.path)
+                        except FileNotFoundError:
+                            ds = load_dataset(
+                                config_dataset.path,
+                                name=config_dataset.name,
+                                streaming=False,
+                                split=None,
+                            )
                 elif local_path.is_file():
                     ds_type = get_ds_type(config_dataset)
 
@@ -366,7 +376,7 @@ def load_tokenized_prepared_datasets(
             elif ds_from_hub:
                 load_ds_kwargs = {}
                 if config_dataset.split:
-                    load_ds_kwargs = {"split": config_dataset.split}
+                    load_ds_kwargs["split"] = config_dataset.split
                 ds = load_dataset(
                     config_dataset.path,
                     name=config_dataset.name,
@@ -374,6 +384,7 @@ def load_tokenized_prepared_datasets(
                     data_files=config_dataset.data_files,
                     token=use_auth_token,
                     revision=config_dataset.revision,
+                    trust_remote_code=config_dataset.trust_remote_code,
                     **load_ds_kwargs,
                 )
             elif ds_from_cloud and remote_file_system:
@@ -391,6 +402,7 @@ def load_tokenized_prepared_datasets(
                         streaming=False,
                         split=None,
                         storage_options=storage_options,
+                        trust_remote_code=config_dataset.trust_remote_code,
                     )
             elif config_dataset.path.startswith("https://"):
                 ds_type = get_ds_type(config_dataset)
@@ -401,6 +413,7 @@ def load_tokenized_prepared_datasets(
                     streaming=False,
                     split=None,
                     storage_options=storage_options,
+                    trust_remote_code=config_dataset.trust_remote_code,
                 )
             else:
                 if isinstance(config_dataset.data_files, str):

src/axolotl/utils/environment.py

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

src/axolotl/utils/gradient_checkpointing/unsloth.py

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

src/axolotl/utils/models.py

@@ -238,6 +238,7 @@ def load_tokenizer(cfg):
                         x in cfg.lora_modules_to_save for x in lora_modules_to_save
                     )
                 )
+                and k != "pad_token"
             ):
                 lora_modules_to_save = ", ".join(
                     [f"`{x}`" for x in lora_modules_to_save]
@@ -394,10 +395,17 @@ class ModelLoader:
             and self.cfg.flash_attention
             and self.cfg.sample_packing
         ):
+            has_remote_code = (
+                "auto_map" in self.model_config
+                and "AutoModelForCausalLM" in self.model_config["auto_map"]
+            )
+            if has_remote_code and self.cfg.trust_remote_code is False:
+                # if explicitly set in the YAML, we should prefer that, for example if explicitly disabled
+                has_remote_code = self.cfg.trust_remote_code
             patch_for_multipack(
                 self.cfg.model_config_type,
                 model_name=self.cfg.base_model,
-                is_remote_code=self.cfg.trust_remote_code,
+                has_remote_code=has_remote_code,
             )
 
             if self.cfg.is_llama_derived_model:

src/axolotl/utils/optimizers/adopt.py

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

src/axolotl/utils/tokenization.py

@@ -66,28 +66,47 @@ def process_tokens_for_rl_debug(tokens, color, tokenizer, text_only):
 
 
 def check_rl_example_labels(example, tokenizer, text_only=False):
-    field_prompt, field_chosen, field_rejected = "prompt", "chosen", "rejected"
+    field_prompt, field_chosen, field_rejected, field_completion = (
+        "prompt",
+        "chosen",
+        "rejected",
+        "completion",
+    )
 
     input_tokens = example[field_prompt]
-    labels_chosen, labels_rejected = example[field_chosen], example[field_rejected]
+
+    labels_chosen = example.get(field_chosen)
+    labels_rejected = example.get(field_rejected)
+    labels_completion = example.get(field_completion)
+
+    # Create a delimiter based on text_only flag
+    delimiter = "" if text_only else " "
 
     # Process and color each type of token
     colored_tokens = process_tokens_for_rl_debug(
         input_tokens, "yellow", tokenizer, text_only
     )
-    colored_chosens = process_tokens_for_rl_debug(
-        labels_chosen, "green", tokenizer, text_only
-    )
-    colored_rejecteds = process_tokens_for_rl_debug(
-        labels_rejected, "red", tokenizer, text_only
-    )
 
-    # Create a delimiter based on text_only flag
-    delimiter = "" if text_only else " "
+    # Process tokens
+    if labels_completion is None:
+        colored_chosens = process_tokens_for_rl_debug(
+            labels_chosen, "green", tokenizer, text_only
+        )
+        colored_rejecteds = process_tokens_for_rl_debug(
+            labels_rejected, "red", tokenizer, text_only
+        )
+    else:
+        colored_completion = process_tokens_for_rl_debug(
+            labels_completion, "green", tokenizer, text_only
+        )
 
     # Logging information
     LOG.info(f"INPUT PROMPT: {delimiter.join(colored_tokens)}\n\n")
-    LOG.info(f"CHOSEN RESPONSE: {delimiter.join(colored_chosens)}\n\n")
-    LOG.info(f"REJECTED RESPONSE: {delimiter.join(colored_rejecteds)}\n\n\n")
+
+    if labels_completion is None:
+        LOG.info(f"CHOSEN RESPONSE: {delimiter.join(colored_chosens)}\n\n")
+        LOG.info(f"REJECTED RESPONSE: {delimiter.join(colored_rejecteds)}\n\n\n")
+    else:
+        LOG.info(f"COMPLETION RESPONSE: {delimiter.join(colored_completion)}\n\n\n")
 
     return delimiter.join(colored_tokens)

src/axolotl/utils/trainer.py

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

tests/e2e/conftest.py

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

tests/e2e/multigpu/test_eval.py

@@ -3,28 +3,25 @@ E2E tests for multigpu eval
 """
 import logging
 import os
-import unittest
 from pathlib import Path
 
 import yaml
 from accelerate.test_utils import execute_subprocess_async
+from transformers.testing_utils import get_torch_dist_unique_port
 
 from axolotl.utils.dict import DictDefault
 
-from ..utils import with_temp_dir
-
 LOG = logging.getLogger("axolotl.tests.e2e.multigpu")
 os.environ["WANDB_DISABLED"] = "true"
 
 AXOLOTL_ROOT = Path(__file__).parent.parent.parent.parent
 
 
-class TestMultiGPUEval(unittest.TestCase):
+class TestMultiGPUEval:
     """
     Test case for MultiGPU Eval Sample Packing
     """
 
-    @with_temp_dir
     def test_eval_sample_packing(self, temp_dir):
         # pylint: disable=duplicate-code
         cfg = DictDefault(
@@ -83,13 +80,14 @@ class TestMultiGPUEval(unittest.TestCase):
                 "launch",
                 "--num-processes",
                 "2",
+                "--main_process_port",
+                f"{get_torch_dist_unique_port()}",
                 "-m",
                 "axolotl.cli.train",
                 str(Path(temp_dir) / "config.yaml"),
             ]
         )
 
-    @with_temp_dir
     def test_eval(self, temp_dir):
         # pylint: disable=duplicate-code
         cfg = DictDefault(
@@ -148,6 +146,8 @@ class TestMultiGPUEval(unittest.TestCase):
                 "launch",
                 "--num-processes",
                 "2",
+                "--main_process_port",
+                f"{get_torch_dist_unique_port()}",
                 "-m",
                 "axolotl.cli.train",
                 str(Path(temp_dir) / "config.yaml"),

tests/e2e/multigpu/test_llama.py

@@ -4,17 +4,17 @@ E2E tests for multigpu lora tinyllama
 
 import logging
 import os
-import unittest
 from pathlib import Path
 
 import pytest
 import yaml
 from accelerate.test_utils import execute_subprocess_async
 from huggingface_hub import snapshot_download
+from transformers.testing_utils import get_torch_dist_unique_port
 
 from axolotl.utils.dict import DictDefault
 
-from ..utils import is_hopper, with_temp_dir
+from ..utils import is_hopper
 
 LOG = logging.getLogger("axolotl.tests.e2e.multigpu")
 os.environ["WANDB_DISABLED"] = "true"
@@ -25,21 +25,19 @@ AXOLOTL_ROOT = Path(__file__).parent.parent.parent.parent
 @pytest.fixture(scope="session", autouse=True)
 def download_model():
     # download the model
-    snapshot_download("TinyLlama/TinyLlama_v1.1")
+    snapshot_download("HuggingFaceTB/SmolLM2-135M")
 
 
-class TestMultiGPULlama(unittest.TestCase):
+class TestMultiGPULlama:
     """
     Test case for Llama models using LoRA
     """
 
-    @with_temp_dir
     def test_lora_ddp(self, temp_dir):
         # pylint: disable=duplicate-code
         cfg = DictDefault(
             {
-                "base_model": "TinyLlama/TinyLlama_v1.1",
-                "tokenizer_type": "LlamaTokenizer",
+                "base_model": "HuggingFaceTB/SmolLM2-135M",
                 "sequence_len": 2048,
                 "adapter": "lora",
                 "lora_r": 8,
@@ -48,9 +46,7 @@ class TestMultiGPULlama(unittest.TestCase):
                 "lora_target_linear": True,
                 "val_set_size": 0.05,
                 "special_tokens": {
-                    "unk_token": "<unk>",
-                    "bos_token": "<s>",
-                    "eos_token": "</s>",
+                    "pad_token": "<|endoftext|>",
                 },
                 "datasets": [
                     {
@@ -81,19 +77,23 @@ class TestMultiGPULlama(unittest.TestCase):
                 "launch",
                 "--num-processes",
                 "2",
+                "--main_process_port",
+                f"{get_torch_dist_unique_port()}",
                 "-m",
                 "axolotl.cli.train",
                 str(Path(temp_dir) / "config.yaml"),
             ]
         )
 
-    @with_temp_dir
-    def test_lora_ddp_packed(self, temp_dir):
+    @pytest.mark.parametrize(
+        "gradient_accumulation_steps",
+        [1, 4],
+    )
+    def test_lora_ddp_packed(self, temp_dir, gradient_accumulation_steps):
         # pylint: disable=duplicate-code
         cfg = DictDefault(
             {
-                "base_model": "TinyLlama/TinyLlama_v1.1",
-                "tokenizer_type": "LlamaTokenizer",
+                "base_model": "HuggingFaceTB/SmolLM2-135M",
                 "sequence_len": 2048,
                 "sample_packing": True,
                 "eval_sample_packing": False,
@@ -105,9 +105,7 @@ class TestMultiGPULlama(unittest.TestCase):
                 "lora_target_linear": True,
                 "val_set_size": 0.05,
                 "special_tokens": {
-                    "unk_token": "<unk>",
-                    "bos_token": "<s>",
-                    "eos_token": "</s>",
+                    "pad_token": "<|endoftext|>",
                 },
                 "datasets": [
                     {
@@ -118,7 +116,7 @@ class TestMultiGPULlama(unittest.TestCase):
                 "num_epochs": 1,
                 "max_steps": 15,
                 "micro_batch_size": 4,
-                "gradient_accumulation_steps": 4,
+                "gradient_accumulation_steps": gradient_accumulation_steps,
                 "output_dir": temp_dir,
                 "learning_rate": 0.00001,
                 "optimizer": "adamw_8bit",
@@ -138,6 +136,8 @@ class TestMultiGPULlama(unittest.TestCase):
                 "launch",
                 "--num-processes",
                 "2",
+                "--main_process_port",
+                f"{get_torch_dist_unique_port()}",
                 "-m",
                 "axolotl.cli.train",
                 str(Path(temp_dir) / "config.yaml"),
@@ -145,13 +145,11 @@ class TestMultiGPULlama(unittest.TestCase):
         )
 
     @pytest.mark.skipif(is_hopper(), reason="h100 doesn't support 8-bit lora")
-    @with_temp_dir
     def test_dpo_lora_ddp(self, temp_dir):
         # pylint: disable=duplicate-code
         cfg = DictDefault(
             {
-                "base_model": "TinyLlama/TinyLlama_v1.1",
-                "tokenizer_type": "LlamaTokenizer",
+                "base_model": "HuggingFaceTB/SmolLM2-135M",
                 "sequence_len": 2048,
                 "sample_packing": False,
                 "eval_sample_packing": False,
@@ -164,12 +162,10 @@ class TestMultiGPULlama(unittest.TestCase):
                 "lora_target_linear": True,
                 "val_set_size": 0.05,
                 "special_tokens": {
-                    "unk_token": "<unk>",
-                    "bos_token": "<s>",
-                    "eos_token": "</s>",
+                    "pad_token": "<|endoftext|>",
                 },
                 "rl": "dpo",
-                "chat_template": "llama3",
+                "chat_template": "chatml",
                 "datasets": [
                     {
                         "path": "fozziethebeat/alpaca_messages_2k_dpo_test",
@@ -210,18 +206,19 @@ class TestMultiGPULlama(unittest.TestCase):
                 "launch",
                 "--num-processes",
                 "2",
+                "--main_process_port",
+                f"{get_torch_dist_unique_port()}",
                 "-m",
                 "axolotl.cli.train",
                 str(Path(temp_dir) / "config.yaml"),
             ]
         )
 
-    @with_temp_dir
     def test_dpo_qlora_ddp(self, temp_dir):
         # pylint: disable=duplicate-code
         cfg = DictDefault(
             {
-                "base_model": "HuggingFaceTB/SmolLM-135M",
+                "base_model": "HuggingFaceTB/SmolLM2-135M",
                 "sequence_len": 2048,
                 "sample_packing": False,
                 "eval_sample_packing": False,
@@ -278,25 +275,27 @@ class TestMultiGPULlama(unittest.TestCase):
                 "launch",
                 "--num-processes",
                 "2",
+                "--main_process_port",
+                f"{get_torch_dist_unique_port()}",
                 "-m",
                 "axolotl.cli.train",
                 str(Path(temp_dir) / "config.yaml"),
             ]
         )
 
-    @with_temp_dir
-    def test_fsdp(self, temp_dir):
+    @pytest.mark.parametrize(
+        "gradient_accumulation_steps",
+        [1, 4],
+    )
+    def test_fsdp(self, temp_dir, gradient_accumulation_steps):
         # pylint: disable=duplicate-code
         cfg = DictDefault(
             {
-                "base_model": "TinyLlama/TinyLlama_v1.1",
-                "tokenizer_type": "LlamaTokenizer",
+                "base_model": "HuggingFaceTB/SmolLM2-135M",
                 "sequence_len": 2048,
-                "val_set_size": 0.05,
+                "val_set_size": 0.01,
                 "special_tokens": {
-                    "unk_token": "<unk>",
-                    "bos_token": "<s>",
-                    "eos_token": "</s>",
+                    "pad_token": "<|endoftext|>",
                 },
                 "datasets": [
                     {
@@ -305,9 +304,9 @@ class TestMultiGPULlama(unittest.TestCase):
                     },
                 ],
                 "num_epochs": 1,
-                "max_steps": 15,
+                "max_steps": 10,
                 "micro_batch_size": 4,
-                "gradient_accumulation_steps": 4,
+                "gradient_accumulation_steps": gradient_accumulation_steps,
                 "output_dir": temp_dir,
                 "learning_rate": 0.00001,
                 "optimizer": "adamw_torch",
@@ -324,7 +323,7 @@ class TestMultiGPULlama(unittest.TestCase):
                     "fsdp_use_orig_params": False,
                     "fsdp_cpu_ram_efficient_loading": False,
                     "fsdp_transformer_layer_cls_to_wrap": "LlamaDecoderLayer",
-                    "fsdp_state_dict_type": "SHARDED_STATE_DICT",
+                    "fsdp_state_dict_type": "FULL_STATE_DICT",
                     "fsdp_auto_wrap_policy": "TRANSFORMER_BASED_WRAP",
                 },
             }
@@ -341,28 +340,29 @@ class TestMultiGPULlama(unittest.TestCase):
                 "launch",
                 "--num-processes",
                 "2",
+                "--main_process_port",
+                f"{get_torch_dist_unique_port()}",
                 "-m",
                 "axolotl.cli.train",
                 str(Path(temp_dir) / "config.yaml"),
             ]
         )
 
-    @with_temp_dir
-    def test_fsdp_packed(self, temp_dir):
+    @pytest.mark.parametrize(
+        "fsdp_state_dict_type",
+        ["FULL_STATE_DICT", "SHARDED_STATE_DICT"],
+    )
+    def test_fsdp_packed(self, temp_dir, fsdp_state_dict_type):
         # pylint: disable=duplicate-code
         cfg = DictDefault(
             {
-                "base_model": "TinyLlama/TinyLlama_v1.1",
-                "tokenizer_type": "LlamaTokenizer",
+                "base_model": "HuggingFaceTB/SmolLM2-135M",
                 "sample_packing": True,
-                "eval_sample_packing": False,
                 "pad_to_sequence_len": True,
                 "sequence_len": 2048,
                 "val_set_size": 0.05,
                 "special_tokens": {
-                    "unk_token": "<unk>",
-                    "bos_token": "<s>",
-                    "eos_token": "</s>",
+                    "pad_token": "<|endoftext|>",
                 },
                 "datasets": [
                     {
@@ -390,7 +390,7 @@ class TestMultiGPULlama(unittest.TestCase):
                     "fsdp_use_orig_params": False,
                     "fsdp_cpu_ram_efficient_loading": False,
                     "fsdp_transformer_layer_cls_to_wrap": "LlamaDecoderLayer",
-                    "fsdp_state_dict_type": "SHARDED_STATE_DICT",
+                    "fsdp_state_dict_type": fsdp_state_dict_type,
                     "fsdp_auto_wrap_policy": "TRANSFORMER_BASED_WRAP",
                 },
             }
@@ -407,19 +407,19 @@ class TestMultiGPULlama(unittest.TestCase):
                 "launch",
                 "--num-processes",
                 "2",
+                "--main_process_port",
+                f"{get_torch_dist_unique_port()}",
                 "-m",
                 "axolotl.cli.train",
                 str(Path(temp_dir) / "config.yaml"),
             ]
         )
 
-    @with_temp_dir
     def test_fsdp_qlora_prequant_packed(self, temp_dir):
         # pylint: disable=duplicate-code
         cfg = DictDefault(
             {
-                "base_model": "axolotl-ai-co/TinyLlama_v1.1-bnb-nf4-bf16",
-                "tokenizer_type": "AutoTokenizer",
+                "base_model": "axolotl-ai-co/SmolLM2-135M-bnb-nf4-bf16",
                 "adapter": "qlora",
                 "mean_resizing_embeddings": True,
                 "load_in_4bit": True,
@@ -427,17 +427,17 @@ class TestMultiGPULlama(unittest.TestCase):
                 "lora_alpha": 16,
                 "lora_dropout": 0.05,
                 "lora_target_linear": True,
-                "lora_modules_to_save": [
-                    "embed_tokens",
-                    "lm_head",
-                ],
+                # "lora_modules_to_save": [
+                #     "embed_tokens",
+                #     "lm_head",
+                # ],
                 "sample_packing": True,
                 "eval_sample_packing": False,
                 "pad_to_sequence_len": True,
                 "sequence_len": 2048,
                 "val_set_size": 0.05,
                 "special_tokens": {
-                    "pad_token": "</s>",
+                    "pad_token": "<|endoftext|>",
                 },
                 "datasets": [
                     {
@@ -483,28 +483,29 @@ class TestMultiGPULlama(unittest.TestCase):
                 "launch",
                 "--num-processes",
                 "2",
+                "--main_process_port",
+                f"{get_torch_dist_unique_port()}",
                 "-m",
                 "axolotl.cli.train",
                 str(Path(temp_dir) / "config.yaml"),
             ]
         )
 
-    @with_temp_dir
-    def test_ds_zero3_packed(self, temp_dir):
+    @pytest.mark.parametrize(
+        "gradient_accumulation_steps",
+        [1, 4],
+    )
+    def test_ds_zero3_packed(self, temp_dir, gradient_accumulation_steps):
         # pylint: disable=duplicate-code
         cfg = DictDefault(
             {
-                "base_model": "TinyLlama/TinyLlama_v1.1",
-                "tokenizer_type": "LlamaTokenizer",
+                "base_model": "HuggingFaceTB/SmolLM2-135M",
                 "sample_packing": True,
-                "eval_sample_packing": False,
                 "pad_to_sequence_len": True,
                 "sequence_len": 2048,
                 "val_set_size": 0.05,
                 "special_tokens": {
-                    "unk_token": "<unk>",
-                    "bos_token": "<s>",
-                    "eos_token": "</s>",
+                    "pad_token": "<|endoftext|>",
                 },
                 "datasets": [
                     {
@@ -515,7 +516,7 @@ class TestMultiGPULlama(unittest.TestCase):
                 "num_epochs": 1,
                 "max_steps": 15,
                 "micro_batch_size": 4,
-                "gradient_accumulation_steps": 4,
+                "gradient_accumulation_steps": gradient_accumulation_steps,
                 "output_dir": temp_dir,
                 "learning_rate": 0.00001,
                 "optimizer": "adamw_torch",
@@ -536,19 +537,19 @@ class TestMultiGPULlama(unittest.TestCase):
                 "launch",
                 "--num-processes",
                 "2",
+                "--main_process_port",
+                f"{get_torch_dist_unique_port()}",
                 "-m",
                 "axolotl.cli.train",
                 str(Path(temp_dir) / "config.yaml"),
             ]
         )
 
-    @with_temp_dir
     def test_ds_zero3_qlora_packed(self, temp_dir):
         # pylint: disable=duplicate-code
         cfg = DictDefault(
             {
-                "base_model": "TinyLlama/TinyLlama_v1.1",
-                "tokenizer_type": "LlamaTokenizer",
+                "base_model": "HuggingFaceTB/SmolLM2-135M",
                 "load_in_4bit": True,
                 "adapter": "qlora",
                 "lora_r": 8,
@@ -561,9 +562,7 @@ class TestMultiGPULlama(unittest.TestCase):
                 "sequence_len": 2048,
                 "val_set_size": 0.05,
                 "special_tokens": {
-                    "unk_token": "<unk>",
-                    "bos_token": "<s>",
-                    "eos_token": "</s>",
+                    "pad_token": "<|endoftext|>",
                 },
                 "datasets": [
                     {
@@ -595,6 +594,8 @@ class TestMultiGPULlama(unittest.TestCase):
                 "launch",
                 "--num-processes",
                 "2",
+                "--main_process_port",
+                f"{get_torch_dist_unique_port()}",
                 "-m",
                 "axolotl.cli.train",
                 str(Path(temp_dir) / "config.yaml"),

tests/e2e/multigpu/test_qwen2.py

@@ -4,31 +4,30 @@ E2E tests for multigpu qwen2
 
 import logging
 import os
-import unittest
 from pathlib import Path
 
+import pytest
 import yaml
 from accelerate.test_utils import execute_subprocess_async
+from transformers.testing_utils import get_torch_dist_unique_port
 
 from axolotl.utils.dict import DictDefault
 
-from ..utils import with_temp_dir
-
 LOG = logging.getLogger("axolotl.tests.e2e.multigpu")
 os.environ["WANDB_DISABLED"] = "true"
 
 
-class TestMultiGPUQwen2(unittest.TestCase):
+class TestMultiGPUQwen2:
     """
     Test case for Llama models using LoRA
     """
 
-    @with_temp_dir
-    def test_qlora_fsdp_dpo(self, temp_dir):
+    @pytest.mark.parametrize("base_model", ["Qwen/Qwen2-0.5B", "Qwen/Qwen2.5-0.5B"])
+    def test_qlora_fsdp_dpo(self, base_model, temp_dir):
         # pylint: disable=duplicate-code
         cfg = DictDefault(
             {
-                "base_model": "Qwen/Qwen2-1.5B",
+                "base_model": base_model,
                 "load_in_4bit": True,
                 "rl": "dpo",
                 "chat_template": "chatml",
@@ -47,9 +46,9 @@ class TestMultiGPUQwen2(unittest.TestCase):
                     },
                 ],
                 "num_epochs": 1,
-                "max_steps": 15,
+                "max_steps": 5,
                 "warmup_steps": 20,
-                "micro_batch_size": 4,
+                "micro_batch_size": 2,
                 "gradient_accumulation_steps": 2,
                 "output_dir": temp_dir,
                 "learning_rate": 0.00001,
@@ -91,6 +90,8 @@ class TestMultiGPUQwen2(unittest.TestCase):
                 "launch",
                 "--num-processes",
                 "2",
+                "--main_process_port",
+                f"{get_torch_dist_unique_port()}",
                 "-m",
                 "axolotl.cli.train",
                 str(Path(temp_dir) / "config.yaml"),

tests/e2e/patched/test_fa_xentropy.py

@@ -66,6 +66,8 @@ class TestFAXentropyLlama(unittest.TestCase):
                     },
                 ],
                 "num_epochs": 1,
+                "max_steps": 10,
+                "save_steps": 10,
                 "micro_batch_size": 8,
                 "gradient_accumulation_steps": 1,
                 "output_dir": temp_dir,

tests/e2e/patched/test_lora_llama_multipack.py

@@ -56,6 +56,8 @@ class TestLoraLlama(unittest.TestCase):
                     },
                 ],
                 "num_epochs": 2,
+                "max_steps": 20,
+                "save_steps": 10,
                 "micro_batch_size": 8,
                 "gradient_accumulation_steps": 1,
                 "output_dir": temp_dir,
@@ -109,6 +111,7 @@ class TestLoraLlama(unittest.TestCase):
                     },
                 ],
                 "num_epochs": 2,
+                "max_steps": 20,
                 "save_steps": 0.5,
                 "micro_batch_size": 8,
                 "gradient_accumulation_steps": 1,

tests/e2e/test_llama.py

@@ -0,0 +1,66 @@
+"""
+E2E tests for llama
+"""
+
+import logging
+import os
+import unittest
+from pathlib import Path
+
+from axolotl.cli import load_datasets
+from axolotl.common.cli import TrainerCliArgs
+from axolotl.train import train
+from axolotl.utils.config import normalize_config
+from axolotl.utils.dict import DictDefault
+
+from .utils import with_temp_dir
+
+LOG = logging.getLogger("axolotl.tests.e2e")
+os.environ["WANDB_DISABLED"] = "true"
+
+
+class TestLlama(unittest.TestCase):
+    """
+    Test case for Llama models
+    """
+
+    @with_temp_dir
+    def test_fft_trust_remote_code(self, temp_dir):
+        # pylint: disable=duplicate-code
+        cfg = DictDefault(
+            {
+                "base_model": "JackFram/llama-68m",
+                "tokenizer_type": "LlamaTokenizer",
+                "trust_remote_code": True,
+                "sequence_len": 512,
+                "val_set_size": 0.1,
+                "special_tokens": {
+                    "unk_token": "<unk>",
+                    "bos_token": "<s>",
+                    "eos_token": "</s>",
+                },
+                "datasets": [
+                    {
+                        "path": "mhenrichsen/alpaca_2k_test",
+                        "type": "alpaca",
+                    },
+                ],
+                "num_epochs": 1,
+                "micro_batch_size": 8,
+                "gradient_accumulation_steps": 1,
+                "output_dir": temp_dir,
+                "learning_rate": 0.00001,
+                "optimizer": "adamw_bnb_8bit",
+                "lr_scheduler": "cosine",
+                "flash_attention": True,
+                "sample_packing": True,
+                "bf16": True,
+                "save_safetensors": True,
+            }
+        )
+        normalize_config(cfg)
+        cli_args = TrainerCliArgs()
+        dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
+
+        train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
+        assert (Path(temp_dir) / "model.safetensors").exists()

tests/e2e/test_optimizers.py

@@ -13,7 +13,7 @@ from axolotl.train import train
 from axolotl.utils.config import normalize_config
 from axolotl.utils.dict import DictDefault
 
-from .utils import with_temp_dir
+from .utils import require_torch_2_5_1, with_temp_dir
 
 LOG = logging.getLogger("axolotl.tests.e2e")
 os.environ["WANDB_DISABLED"] = "true"
@@ -65,3 +65,80 @@ class TestCustomOptimizers(unittest.TestCase):
 
         train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
         assert (Path(temp_dir) / "adapter_model.bin").exists()
+
+    @with_temp_dir
+    @require_torch_2_5_1
+    def test_adopt_adamw(self, temp_dir):
+        # pylint: disable=duplicate-code
+        cfg = DictDefault(
+            {
+                "base_model": "JackFram/llama-68m",
+                "tokenizer_type": "LlamaTokenizer",
+                "sequence_len": 1024,
+                "load_in_8bit": True,
+                "adapter": "lora",
+                "lora_r": 8,
+                "lora_alpha": 16,
+                "lora_dropout": 0.05,
+                "lora_target_linear": True,
+                "val_set_size": 0.1,
+                "special_tokens": {
+                    "unk_token": "<unk>",
+                    "bos_token": "<s>",
+                    "eos_token": "</s>",
+                },
+                "datasets": [
+                    {
+                        "path": "mhenrichsen/alpaca_2k_test",
+                        "type": "alpaca",
+                    },
+                ],
+                "num_epochs": 1,
+                "micro_batch_size": 8,
+                "gradient_accumulation_steps": 1,
+                "output_dir": temp_dir,
+                "learning_rate": 0.00001,
+                "optimizer": "adopt_adamw",
+                "lr_scheduler": "cosine",
+            }
+        )
+        normalize_config(cfg)
+        cli_args = TrainerCliArgs()
+        dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
+
+        train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
+        assert (Path(temp_dir) / "adapter_model.bin").exists()
+
+    @with_temp_dir
+    def test_fft_schedule_free_adamw(self, temp_dir):
+        cfg = DictDefault(
+            {
+                "base_model": "HuggingFaceTB/SmolLM2-135M",
+                "sequence_len": 1024,
+                "val_set_size": 0.1,
+                "special_tokens": {
+                    "pad_token": "<|endoftext|>",
+                },
+                "datasets": [
+                    {
+                        "path": "mhenrichsen/alpaca_2k_test",
+                        "type": "alpaca",
+                    },
+                ],
+                "num_epochs": 1,
+                "micro_batch_size": 4,
+                "gradient_accumulation_steps": 2,
+                "output_dir": temp_dir,
+                "learning_rate": 0.00001,
+                "optimizer": "schedule_free_adamw",
+                "lr_scheduler": "constant",
+                "save_safetensors": True,
+            }
+        )
+        # pylint: disable=duplicate-code
+        normalize_config(cfg)
+        cli_args = TrainerCliArgs()
+        dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
+
+        train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
+        assert (Path(temp_dir) / "model.safetensors").exists()

tests/e2e/test_packing_loss.py

@@ -31,7 +31,7 @@ class TestPackedLlama(unittest.TestCase):
         # pylint: disable=duplicate-code
         cfg = DictDefault(
             {
-                "base_model": "HuggingFaceTB/SmolLM-135M",
+                "base_model": "HuggingFaceTB/SmolLM2-135M",
                 "sequence_len": 1024,
                 "sample_packing": True,
                 "flash_attention": True,

tests/e2e/test_qwen.py

@@ -0,0 +1,85 @@
+"""
+E2E tests for qwen
+"""
+
+import logging
+import os
+from pathlib import Path
+
+import pytest
+import yaml
+from accelerate.test_utils import execute_subprocess_async
+from transformers.testing_utils import get_torch_dist_unique_port
+
+from axolotl.utils.dict import DictDefault
+
+LOG = logging.getLogger("axolotl.tests.qwen")
+os.environ["WANDB_DISABLED"] = "true"
+
+
+class TestE2eQwen:
+    """
+    Test cases for qwen models
+    """
+
+    @pytest.mark.parametrize("base_model", ["Qwen/Qwen2-0.5B", "Qwen/Qwen2.5-0.5B"])
+    def test_dpo(self, base_model, temp_dir):
+        # pylint: disable=duplicate-code
+        cfg = DictDefault(
+            {
+                "base_model": base_model,
+                "rl": "dpo",
+                "chat_template": "qwen_25",
+                "sequence_len": 2048,
+                "val_set_size": 0.0,
+                "datasets": [
+                    {
+                        "path": "fozziethebeat/alpaca_messages_2k_dpo_test",
+                        "split": "train",
+                        "type": "chat_template.default",
+                        "field_messages": "conversation",
+                        "field_chosen": "chosen",
+                        "field_rejected": "rejected",
+                        "message_field_role": "role",
+                        "message_field_content": "content",
+                        "roles": {
+                            "system": ["system"],
+                            "user": ["user"],
+                            "assistant": ["assistant"],
+                        },
+                    },
+                ],
+                "num_epochs": 1,
+                "max_steps": 5,
+                "warmup_steps": 20,
+                "micro_batch_size": 2,
+                "gradient_accumulation_steps": 2,
+                "output_dir": temp_dir,
+                "learning_rate": 0.00001,
+                "optimizer": "adamw_bnb_8bit",
+                "lr_scheduler": "cosine",
+                "flash_attention": True,
+                "bf16": "auto",
+                "tf32": True,
+                "gradient_checkpointing": True,
+            }
+        )
+
+        # write cfg to yaml file
+        Path(temp_dir).mkdir(parents=True, exist_ok=True)
+        with open(Path(temp_dir) / "config.yaml", "w", encoding="utf-8") as fout:
+            fout.write(yaml.dump(cfg.to_dict(), Dumper=yaml.Dumper))
+
+        execute_subprocess_async(
+            [
+                "accelerate",
+                "launch",
+                "--num-processes",
+                "2",
+                "--main_process_port",
+                f"{get_torch_dist_unique_port()}",
+                "-m",
+                "axolotl.cli.train",
+                str(Path(temp_dir) / "config.yaml"),
+            ]
+        )

tests/e2e/utils.py

@@ -6,11 +6,13 @@ import shutil
 import tempfile
 import unittest
 from functools import wraps
-from importlib.metadata import version
 from pathlib import Path
 
 import torch
 
+# from importlib.metadata import version
+from packaging import version
+
 
 def with_temp_dir(test_func):
     @wraps(test_func)
@@ -43,12 +45,24 @@ def require_torch_2_3_1(test_case):
     """
 
     def is_min_2_3_1():
-        torch_version = version("torch")
-        return torch_version >= "2.3.1"
+        torch_version = version.parse(torch.__version__)
+        return torch_version >= version.parse("2.3.1")
 
     return unittest.skipUnless(is_min_2_3_1(), "test torch 2.3.1")(test_case)
 
 
+def require_torch_2_5_1(test_case):
+    """
+    Decorator marking a test that requires torch >= 2.3.1
+    """
+
+    def is_min_2_5_1():
+        torch_version = version.parse(torch.__version__)
+        return torch_version >= version.parse("2.5.1")
+
+    return unittest.skipUnless(is_min_2_5_1(), "test torch 2.5.1")(test_case)
+
+
 def is_hopper():
     compute_capability = torch.cuda.get_device_capability()
     return compute_capability == (9, 0)

tests/test_datasets.py

@@ -371,44 +371,79 @@ class TestDatasetPreparation(unittest.TestCase):
     def test_load_local_hub_with_revision(self):
         """Verify that a local copy of a hub dataset can be loaded with a specific revision"""
         with tempfile.TemporaryDirectory() as tmp_dir:
-            with tempfile.TemporaryDirectory() as tmp_dir2:
-                tmp_ds_path = Path(tmp_dir2) / "mhenrichsen/alpaca_2k_test"
-                tmp_ds_path.mkdir(parents=True, exist_ok=True)
-                snapshot_download(
-                    repo_id="mhenrichsen/alpaca_2k_test",
-                    repo_type="dataset",
-                    local_dir=tmp_ds_path,
-                    revision="d05c1cb",
-                )
+            tmp_ds_path = Path(tmp_dir) / "mhenrichsen/alpaca_2k_test"
+            tmp_ds_path.mkdir(parents=True, exist_ok=True)
+            snapshot_download(
+                repo_id="mhenrichsen/alpaca_2k_test",
+                repo_type="dataset",
+                local_dir=tmp_ds_path,
+                revision="d05c1cb",
+            )
 
-                prepared_path = Path(tmp_dir) / "prepared"
-                cfg = DictDefault(
-                    {
-                        "tokenizer_config": "huggyllama/llama-7b",
-                        "sequence_len": 1024,
-                        "datasets": [
-                            {
-                                "path": "mhenrichsen/alpaca_2k_test",
-                                "ds_type": "parquet",
-                                "type": "alpaca",
-                                "data_files": [
-                                    f"{tmp_ds_path}/alpaca_2000.parquet",
-                                ],
-                                "revision": "d05c1cb",
-                            },
-                        ],
-                    }
-                )
+            prepared_path = Path(tmp_dir) / "prepared"
+            cfg = DictDefault(
+                {
+                    "tokenizer_config": "huggyllama/llama-7b",
+                    "sequence_len": 1024,
+                    "datasets": [
+                        {
+                            "path": "mhenrichsen/alpaca_2k_test",
+                            "ds_type": "parquet",
+                            "type": "alpaca",
+                            "data_files": [
+                                f"{tmp_ds_path}/alpaca_2000.parquet",
+                            ],
+                            "revision": "d05c1cb",
+                        },
+                    ],
+                }
+            )
 
-                dataset, _ = load_tokenized_prepared_datasets(
-                    self.tokenizer, cfg, prepared_path
-                )
+            dataset, _ = load_tokenized_prepared_datasets(
+                self.tokenizer, cfg, prepared_path
+            )
 
-                assert len(dataset) == 2000
-                assert "input_ids" in dataset.features
-                assert "attention_mask" in dataset.features
-                assert "labels" in dataset.features
-                shutil.rmtree(tmp_ds_path)
+            assert len(dataset) == 2000
+            assert "input_ids" in dataset.features
+            assert "attention_mask" in dataset.features
+            assert "labels" in dataset.features
+            shutil.rmtree(tmp_ds_path)
+
+    def test_loading_local_dataset_folder(self):
+        """Verify that a dataset downloaded to a local folder can be loaded"""
+
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            tmp_ds_path = Path(tmp_dir) / "mhenrichsen/alpaca_2k_test"
+            tmp_ds_path.mkdir(parents=True, exist_ok=True)
+            snapshot_download(
+                repo_id="mhenrichsen/alpaca_2k_test",
+                repo_type="dataset",
+                local_dir=tmp_ds_path,
+            )
+
+            prepared_path = Path(tmp_dir) / "prepared"
+            cfg = DictDefault(
+                {
+                    "tokenizer_config": "huggyllama/llama-7b",
+                    "sequence_len": 1024,
+                    "datasets": [
+                        {
+                            "path": str(tmp_ds_path),
+                            "type": "alpaca",
+                        },
+                    ],
+                }
+            )
+
+            dataset, _ = load_tokenized_prepared_datasets(
+                self.tokenizer, cfg, prepared_path
+            )
+
+            assert len(dataset) == 2000
+            assert "input_ids" in dataset.features
+            assert "attention_mask" in dataset.features
+            assert "labels" in dataset.features
+            shutil.rmtree(tmp_ds_path)
 
 
 if __name__ == "__main__":

tests/test_packed_pretraining.py

@@ -2,6 +2,7 @@
 import functools
 import unittest
 
+import pytest
 import torch
 from datasets import load_dataset
 from torch.utils.data import DataLoader
@@ -21,6 +22,7 @@ class TestPretrainingPacking(unittest.TestCase):
         self.tokenizer = AutoTokenizer.from_pretrained("huggyllama/llama-7b")
         self.tokenizer.pad_token = "</s>"
 
+    @pytest.mark.flaky(retries=3, delay=5)
     def test_packing_stream_dataset(self):
         # pylint: disable=duplicate-code
         dataset = load_dataset(

tests/test_schedulers.py

@@ -32,16 +32,19 @@ class TestCosineConstantLr(unittest.TestCase):
     def test_schedulers(self):
         self.assertEqual(self.lr_scheduler.get_last_lr()[0], 0)
         for _ in range(self.warmup_steps):
+            self.optimizer.step()
             self.lr_scheduler.step()
         self.assertEqual(self.lr_scheduler.get_last_lr()[0], self._lr)
         constant_step = int(self.train_steps * self.constant_lr_ratio)
         remaining_step = self.train_steps - constant_step
         for _ in range(constant_step):
+            self.optimizer.step()
             self.lr_scheduler.step()
         self.assertEqual(
             self.lr_scheduler.get_last_lr()[0], self._lr * self.min_lr_ratio
         )
         for _ in range(remaining_step):
+            self.optimizer.step()
             self.lr_scheduler.step()
         self.assertEqual(
             self.lr_scheduler.get_last_lr()[0], self._lr * self.min_lr_ratio

tests/test_validation.py

@@ -726,7 +726,7 @@ class TestValidation(BaseValidation):
         cfg = (
             DictDefault(
                 {
-                    "evaluation_strategy": "epoch",
+                    "eval_strategy": "epoch",
                     "eval_steps": 10,
                 }
             )
@@ -734,14 +734,14 @@ class TestValidation(BaseValidation):
         )
 
         with pytest.raises(
-            ValueError, match=r".*evaluation_strategy and eval_steps mismatch.*"
+            ValueError, match=r".*eval_strategy and eval_steps mismatch.*"
         ):
             validate_config(cfg)
 
         cfg = (
             DictDefault(
                 {
-                    "evaluation_strategy": "no",
+                    "eval_strategy": "no",
                     "eval_steps": 10,
                 }
             )
@@ -749,14 +749,14 @@ class TestValidation(BaseValidation):
         )
 
         with pytest.raises(
-            ValueError, match=r".*evaluation_strategy and eval_steps mismatch.*"
+            ValueError, match=r".*eval_strategy and eval_steps mismatch.*"
         ):
             validate_config(cfg)
 
         cfg = (
             DictDefault(
                 {
-                    "evaluation_strategy": "steps",
+                    "eval_strategy": "steps",
                 }
             )
             | minimal_cfg
@@ -767,7 +767,7 @@ class TestValidation(BaseValidation):
         cfg = (
             DictDefault(
                 {
-                    "evaluation_strategy": "steps",
+                    "eval_strategy": "steps",
                     "eval_steps": 10,
                 }
             )
@@ -790,7 +790,7 @@ class TestValidation(BaseValidation):
         cfg = (
             DictDefault(
                 {
-                    "evaluation_strategy": "no",
+                    "eval_strategy": "no",
                 }
             )
             | minimal_cfg
@@ -801,7 +801,7 @@ class TestValidation(BaseValidation):
         cfg = (
             DictDefault(
                 {
-                    "evaluation_strategy": "epoch",
+                    "eval_strategy": "epoch",
                     "val_set_size": 0,
                 }
             )
@@ -810,7 +810,7 @@ class TestValidation(BaseValidation):
 
         with pytest.raises(
             ValueError,
-            match=r".*eval_steps and evaluation_strategy are not supported with val_set_size == 0.*",
+            match=r".*eval_steps and eval_strategy are not supported with val_set_size == 0.*",
         ):
             validate_config(cfg)
 
@@ -826,7 +826,7 @@ class TestValidation(BaseValidation):
 
         with pytest.raises(
             ValueError,
-            match=r".*eval_steps and evaluation_strategy are not supported with val_set_size == 0.*",
+            match=r".*eval_steps and eval_strategy are not supported with val_set_size == 0.*",
         ):
             validate_config(cfg)
 
@@ -856,7 +856,7 @@ class TestValidation(BaseValidation):
         cfg = (
             DictDefault(
                 {
-                    "evaluation_strategy": "epoch",
+                    "eval_strategy": "epoch",
                     "val_set_size": 0.01,
                 }
             )
@@ -1095,6 +1095,24 @@ class TestValidation(BaseValidation):
             assert new_cfg["dpo_beta"] is None
             assert len(self._caplog.records) == 1
 
+    def test_eval_strategy_remap(self, minimal_cfg):
+        cfg = (
+            DictDefault(
+                {
+                    "evaluation_strategy": "steps",
+                }
+            )
+            | minimal_cfg
+        )
+
+        with self._caplog.at_level(logging.WARNING):
+            new_cfg = validate_config(cfg)
+            assert new_cfg.eval_strategy == "steps"
+            assert (
+                "evaluation_strategy is deprecated, use eval_strategy instead"
+                in self._caplog.records[0].message
+            )
+
 
 class TestValidationCheckModelConfig(BaseValidation):
     """

tests/test_validation_dataset.py

@@ -234,3 +234,59 @@ class TestValidationCheckDatasetConfig(BaseValidation):
         )
 
         _check_config()
+
+    def test_dataset_sharegpt_deprecation(self, minimal_cfg):
+        cfg = DictDefault(
+            minimal_cfg
+            | {
+                "chat_template": "chatml",
+                "datasets": [
+                    {
+                        "path": "LDJnr/Puffin",
+                        "type": "sharegpt",
+                        "conversation": "chatml",
+                    }
+                ],
+            }
+        )
+
+        # Check sharegpt deprecation is raised
+        with pytest.raises(ValueError, match=r".*type: sharegpt.*` is deprecated.*"):
+            validate_config(cfg)
+
+        # Check that deprecation is not thrown for non-str type
+        cfg = DictDefault(
+            minimal_cfg
+            | {
+                "datasets": [
+                    {
+                        "path": "mhenrichsen/alpaca_2k_test",
+                        "type": {
+                            "field_instruction": "instruction",
+                            "field_output": "output",
+                            "field_system": "system",
+                            "format": "<|user|> {instruction} {input} <|model|>",
+                            "no_input_format": "<|user|> {instruction} <|model|>",
+                            "system_prompt": "",
+                        },
+                    }
+                ],
+            }
+        )
+
+        validate_config(cfg)
+
+        # Check that deprecation is not thrown for non-sharegpt type
+        cfg = DictDefault(
+            minimal_cfg
+            | {
+                "datasets": [
+                    {
+                        "path": "mhenrichsen/alpaca_2k_test",
+                        "type": "alpaca",
+                    }
+                ],
+            }
+        )
+
+        validate_config(cfg)