match protected method

.github/workflows/base.yml

@@ -51,30 +51,14 @@ jobs:
             torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
             dockerfile: "Dockerfile-base"
             platforms: "linux/amd64,linux/arm64"
-          - cuda: "128"
+          - cuda: "129"
-            cuda_version: 12.8.1
+            cuda_version: 12.9.1
-            cudnn_version: ""
-            python_version: "3.11"
-            pytorch: 2.10.0
-            torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
-            dockerfile: "Dockerfile-base"
-            platforms: "linux/amd64,linux/arm64"
-          - cuda: "128"
-            cuda_version: 12.8.1
             cudnn_version: ""
             python_version: "3.12"
-            pytorch: 2.10.0
+            pytorch: 2.9.1
             torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
             dockerfile: "Dockerfile-base"
             platforms: "linux/amd64,linux/arm64"
-#          - cuda: "129"
-#            cuda_version: 12.9.1
-#            cudnn_version: ""
-#            python_version: "3.12"
-#            pytorch: 2.9.1
-#            torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
-#            dockerfile: "Dockerfile-base"
-#            platforms: "linux/amd64,linux/arm64"
           - cuda: "130"
             cuda_version: 13.0.0
             cudnn_version: ""
@@ -91,14 +75,6 @@ jobs:
             torch_cuda_arch_list: "9.0+PTX"
             dockerfile: "Dockerfile-base"
             platforms: "linux/amd64,linux/arm64"
-          - cuda: "130"
-            cuda_version: 13.0.0
-            cudnn_version: ""
-            python_version: "3.12"
-            pytorch: 2.10.0
-            torch_cuda_arch_list: "9.0+PTX"
-            dockerfile: "Dockerfile-base"
-            platforms: "linux/amd64,linux/arm64"
 #          - cuda: "128"
 #            cuda_version: 12.8.1
 #            cudnn_version: ""
@@ -181,30 +157,14 @@ jobs:
             torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
             dockerfile: "Dockerfile-uv-base"
             platforms: "linux/amd64,linux/arm64"
-          - cuda: "128"
+          - cuda: "129"
-            cuda_version: 12.8.1
+            cuda_version: 12.9.1
-            cudnn_version: ""
-            python_version: "3.11"
-            pytorch: 2.10.0
-            torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
-            dockerfile: "Dockerfile-uv-base"
-            platforms: "linux/amd64,linux/arm64"
-          - cuda: "128"
-            cuda_version: 12.8.1
             cudnn_version: ""
             python_version: "3.12"
-            pytorch: 2.10.0
+            pytorch: 2.9.1
             torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
             dockerfile: "Dockerfile-uv-base"
             platforms: "linux/amd64,linux/arm64"
-#          - cuda: "129"
-#            cuda_version: 12.9.1
-#            cudnn_version: ""
-#            python_version: "3.12"
-#            pytorch: 2.9.1
-#            torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
-#            dockerfile: "Dockerfile-uv-base"
-#            platforms: "linux/amd64,linux/arm64"
           - cuda: "130"
             cuda_version: 13.0.0
             cudnn_version: ""
@@ -221,14 +181,6 @@ jobs:
             torch_cuda_arch_list: "9.0+PTX"
             dockerfile: "Dockerfile-uv-base"
             platforms: "linux/amd64,linux/arm64"
-          - cuda: "130"
-            cuda_version: 13.0.0
-            cudnn_version: ""
-            python_version: "3.12"
-            pytorch: 2.10.0
-            torch_cuda_arch_list: "9.0+PTX"
-            dockerfile: "Dockerfile-uv-base"
-            platforms: "linux/amd64,linux/arm64"
     steps:
       - name: Checkout
         uses: actions/checkout@v4

.github/workflows/main.yml

@@ -34,28 +34,16 @@ jobs:
             axolotl_extras:
             platforms: "linux/amd64,linux/arm64"
             is_latest: true
-          - cuda: 128
+          - cuda: 129
-            cuda_version: 12.8.1
+            cuda_version: 12.9.1
             python_version: "3.12"
-            pytorch: 2.10.0
-            axolotl_extras:
-            platforms: "linux/amd64,linux/arm64"
-#          - cuda: 129
-#            cuda_version: 12.9.1
-#            python_version: "3.12"
-#            pytorch: 2.9.1
-#            axolotl_extras:
-#            platforms: "linux/amd64,linux/arm64"
-          - cuda: 130
-            cuda_version: 13.0.0
-            python_version: "3.11"
             pytorch: 2.9.1
             axolotl_extras:
             platforms: "linux/amd64,linux/arm64"
           - cuda: 130
             cuda_version: 13.0.0
-            python_version: "3.12"
+            python_version: "3.11"
-            pytorch: 2.10.0
+            pytorch: 2.9.1
             axolotl_extras:
             platforms: "linux/amd64,linux/arm64"
     runs-on: axolotl-gpu-runner
@@ -98,77 +86,6 @@ jobs:
             ${{ (matrix.is_latest) && format('{0}-latest', steps.metadata.outputs.tags) || '' }}
           labels: ${{ steps.metadata.outputs.labels }}
 
-  build-axolotl-uv:
-    if: ${{ ! contains(github.event.commits[0].message, '[skip docker]') && github.repository_owner == 'axolotl-ai-cloud' }}
-    strategy:
-      fail-fast: false
-      matrix:
-        include:
-          - cuda: 128
-            cuda_version: 12.8.1
-            python_version: "3.11"
-            pytorch: 2.9.1
-            axolotl_extras:
-            platforms: "linux/amd64,linux/arm64"
-            is_latest: true
-          - cuda: 128
-            cuda_version: 12.8.1
-            python_version: "3.12"
-            pytorch: 2.10.0
-            axolotl_extras:
-            platforms: "linux/amd64,linux/arm64"
-          - cuda: 130
-            cuda_version: 13.0.0
-            python_version: "3.11"
-            pytorch: 2.9.1
-            axolotl_extras:
-            platforms: "linux/amd64,linux/arm64"
-          - cuda: 130
-            cuda_version: 13.0.0
-            python_version: "3.12"
-            pytorch: 2.10.0
-            axolotl_extras:
-            platforms: "linux/amd64,linux/arm64"
-    runs-on: axolotl-gpu-runner
-    steps:
-      - name: Checkout
-        uses: actions/checkout@v4
-      - name: Docker metadata
-        id: metadata
-        uses: docker/metadata-action@v5
-        with:
-          images: |
-            axolotlai/axolotl-uv
-          tags: |
-            type=ref,event=branch
-            type=pep440,pattern={{version}}
-      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3
-      - name: Login to Docker Hub
-        uses: docker/login-action@v3
-        with:
-          username: ${{ secrets.DOCKERHUB_USERNAME }}
-          password: ${{ secrets.DOCKERHUB_TOKEN }}
-      # guidance for testing before pushing: https://docs.docker.com/build/ci/github-actions/test-before-push/
-      - name: Build and export to Docker
-        uses: docker/build-push-action@v5
-        with:
-          context: .
-          platforms: ${{ matrix.platforms }}
-          build-args: |
-            BASE_TAG=${{ 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 }}
-            AXOLOTL_EXTRAS=${{ matrix.axolotl_extras}}
-          file: ./docker/Dockerfile-uv
-          push: ${{ github.event_name != 'pull_request' }}
-          tags: |
-            ${{ steps.metadata.outputs.tags }}-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
-            ${{ steps.metadata.outputs.tags }}-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}
-            ${{ (matrix.is_latest) && format('{0}-latest', steps.metadata.outputs.tags) || '' }}
-          labels: ${{ steps.metadata.outputs.labels }}
-
   build-axolotl-cloud:
     needs: build-axolotl
     if: ${{ ! contains(github.event.commits[0].message, '[skip docker]') && github.repository_owner == 'axolotl-ai-cloud' }}
@@ -195,28 +112,16 @@ jobs:
             axolotl_extras:
             is_latest: true
             platforms: "linux/amd64,linux/arm64"
-          - cuda: 128
+          - cuda: 129
-            cuda_version: 12.8.1
+            cuda_version: 12.9.1
             python_version: "3.12"
-            pytorch: 2.10.0
-            axolotl_extras:
-            platforms: "linux/amd64,linux/arm64"
-#          - cuda: 129
-#            cuda_version: 12.9.1
-#            python_version: "3.12"
-#            pytorch: 2.9.1
-#            axolotl_extras:
-#            platforms: "linux/amd64,linux/arm64"
-          - cuda: 130
-            cuda_version: 13.0.0
-            python_version: "3.11"
             pytorch: 2.9.1
             axolotl_extras:
             platforms: "linux/amd64,linux/arm64"
           - cuda: 130
             cuda_version: 13.0.0
-            python_version: "3.12"
+            python_version: "3.11"
-            pytorch: 2.10.0
+            pytorch: 2.9.1
             axolotl_extras:
             platforms: "linux/amd64,linux/arm64"
     runs-on: axolotl-gpu-runner
@@ -254,73 +159,6 @@ jobs:
              ${{ (matrix.is_latest) && format('{0}-latest', steps.metadata.outputs.tags) || '' }}
           labels: ${{ steps.metadata.outputs.labels }}
 
-  build-axolotl-cloud-uv:
-    needs: build-axolotl-uv
-    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:
-        include:
-          - cuda: 128
-            cuda_version: 12.8.1
-            python_version: "3.11"
-            pytorch: 2.9.1
-            axolotl_extras:
-            is_latest: true
-            platforms: "linux/amd64,linux/arm64"
-          - cuda: 128
-            cuda_version: 12.8.1
-            python_version: "3.12"
-            pytorch: 2.10.0
-            axolotl_extras:
-            platforms: "linux/amd64,linux/arm64"
-          - cuda: 130
-            cuda_version: 13.0.0
-            python_version: "3.11"
-            pytorch: 2.9.1
-            axolotl_extras:
-            platforms: "linux/amd64,linux/arm64"
-          - cuda: 130
-            cuda_version: 13.0.0
-            python_version: "3.12"
-            pytorch: 2.10.0
-            axolotl_extras:
-            platforms: "linux/amd64,linux/arm64"
-    runs-on: axolotl-gpu-runner
-    steps:
-      - name: Checkout
-        uses: actions/checkout@v4
-      - name: Docker metadata
-        id: metadata
-        uses: docker/metadata-action@v5
-        with:
-          images: |
-            axolotlai/axolotl-cloud-uv
-          tags: |
-            type=ref,event=branch
-            type=pep440,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@v3
-      - name: Build
-        uses: docker/build-push-action@v5
-        with:
-          context: .
-          platforms: ${{ matrix.platforms }}
-          build-args: |
-            BASE_TAG=${{ github.ref_type == 'tag' && 'main' || github.ref_name }}-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
-            CUDA=${{ matrix.cuda }}
-          file: ./docker/Dockerfile-cloud-uv
-          push: ${{ github.event_name != 'pull_request' }}
-          tags: |
-             ${{ steps.metadata.outputs.tags }}-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
-             ${{ (matrix.is_latest) && format('{0}-latest', steps.metadata.outputs.tags) || '' }}
-          labels: ${{ steps.metadata.outputs.labels }}
-
   build-axolotl-cloud-no-tmux:
     needs: build-axolotl
     if: ${{ ! contains(github.event.commits[0].message, '[skip docker]') && github.repository_owner == 'axolotl-ai-cloud' }}

.github/workflows/tests-nightly.yml

@@ -37,7 +37,7 @@ jobs:
         id: hf-cache-restore-s3
         run: |
           mkdir -p /home/runner/.cache/huggingface/hub
-          curl -L https://axolotl-ci.b-cdn.net/hf-cache.tar.zst | tar -xf - -C /home/runner/.cache/huggingface/hub/  --use-compress-program unzstd
+          curl -L https://d1dttdx32dkk5p.cloudfront.net/hf-cache.tar.zst | tar -xf - -C /home/runner/.cache/huggingface/hub/  --use-compress-program unzstd
 
       - name: Setup Python
         uses: actions/setup-python@v5

.github/workflows/tests.yml

@@ -54,13 +54,13 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python_version: ["3.12"]  # TODO include py3.14 once https://github.com/mistralai/mistral-common/pull/194 is merged
+        python_version: ["3.11", "3.12"]
-        pytorch_version: ["2.8.0", "2.9.1", "2.10.0"]
+        pytorch_version: ["2.8.0", "2.9.0", "2.9.1"]
-#        exclude:
+        exclude:
-#          - python_version: "3.14"
+          - python_version: "3.12"
-#            pytorch_version: "2.8.0"
+            pytorch_version: "2.8.0"
-#          - python_version: "3.14"
+          - python_version: "3.12"
-#            pytorch_version: "2.9.1"
+            pytorch_version: "2.9.0"
     timeout-minutes: 20
 
     steps:
@@ -75,7 +75,7 @@ jobs:
         id: hf-cache-restore-s3
         run: |
           mkdir -p ~/.cache/huggingface/hub
-          curl -L https://axolotl-ci.b-cdn.net/hf-cache.tar.zst | tar -xpf - -C ~/.cache/huggingface/hub/  --use-compress-program unzstd --strip-components=1
+          curl -L https://d1dttdx32dkk5p.cloudfront.net/hf-cache.tar.zst | tar -xpf - -C ~/.cache/huggingface/hub/  --use-compress-program unzstd --strip-components=1
           ls -ltr ~/.cache/huggingface/hub/
 
       - name: Setup Python
@@ -149,13 +149,13 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python_version: ["3.12"]  # TODO include py3.14 once https://github.com/mistralai/mistral-common/pull/194 is merged
+        python_version: ["3.11", "3.12"]
-        pytorch_version: ["2.8.0", "2.9.1", "2.10.0"]
+        pytorch_version: ["2.8.0", "2.9.0", "2.9.1"]
-#        exclude:
+        exclude:
-#          - python_version: "3.14"
+          - python_version: "3.12"
-#            pytorch_version: "2.8.0"
+            pytorch_version: "2.8.0"
-#          - python_version: "3.14"
+          - python_version: "3.12"
-#            pytorch_version: "2.9.1"
+            pytorch_version: "2.9.0"
     timeout-minutes: 20
 
     steps:
@@ -170,7 +170,7 @@ jobs:
         id: hf-cache-restore-s3
         run: |
           mkdir -p ~/.cache/huggingface/hub
-          curl -L https://axolotl-ci.b-cdn.net/hf-cache.tar.zst | tar -xpf - -C ~/.cache/huggingface/hub/  --use-compress-program unzstd --strip-components=1
+          curl -L https://d1dttdx32dkk5p.cloudfront.net/hf-cache.tar.zst | tar -xpf - -C ~/.cache/huggingface/hub/  --use-compress-program unzstd --strip-components=1
           ls -ltr ~/.cache/huggingface/hub/
 
       - name: Setup Python
@@ -264,8 +264,8 @@ jobs:
       fail-fast: false
       matrix:
         include:
-          - cuda: 130
+          - cuda: 129
-            cuda_version: 13.0.0
+            cuda_version: 12.9.1
             python_version: "3.12"
             pytorch: 2.9.1
             num_gpus: 1
@@ -326,12 +326,6 @@ jobs:
             pytorch: 2.9.1
             num_gpus: 1
             axolotl_extras:
-          - cuda: 128
-            cuda_version: 12.8.1
-            python_version: "3.11"
-            pytorch: 2.10.0
-            num_gpus: 1
-            axolotl_extras:
           - cuda: 130
             cuda_version: 13.0.0
             python_version: "3.11"
@@ -377,7 +371,7 @@ jobs:
         include:
           - cuda: 129
             cuda_version: 12.9.1
-            python_version: "3.11"
+            python_version: "3.12"
             pytorch: 2.9.1
             num_gpus: 1
             axolotl_extras:

docker/Dockerfile-base

@@ -59,18 +59,34 @@ RUN git lfs install --skip-repo && \
     pip3 install -U --no-cache-dir pydantic==1.10.10 && \
     pip3 cache purge
 
-# Map Python version (e.g., 3.12 -> cp312)
+RUN case "$PYTORCH_VERSION" in \
-RUN PYTHON_CP="cp$(echo $PYTHON_VERSION | tr -d '.')" && \
+        2.9.[0-9]*) \
-    # Map PyTorch version (e.g., 2.9.1 -> torch2.9, 2.10.0 -> torch2.10)
+            if [ "$CUDA" = "128" ]; then \
-    TORCH_TAG="torch$(echo $PYTORCH_VERSION | grep -oP '^\d+\.\d+')" && \
+                if [ "$TARGETARCH" = "amd64" ]; then \
-    # Map architecture
+                    WHL_FILE="flash_attn-2.8.3+cu128torch2.9-cp311-cp311-linux_x86_64.whl"; \
-    case "$TARGETARCH" in \
+                    WHL_VERSION="v0.5.4"; \
-        amd64) ARCH_TAG="x86_64" ;; \
+                elif [ "$TARGETARCH" = "arm64" ]; then \
-        arm64) ARCH_TAG="aarch64" ;; \
+                    WHL_FILE="flash_attn-2.8.3+cu128torch2.9-cp311-cp311-linux_aarch64.whl"; \
-        *) echo "Unsupported architecture: $TARGETARCH"; exit 1 ;; \
+                    WHL_VERSION="v0.6.4"; \
-    esac && \
+                else \
-    WHL_VERSION="v0.7.16" && \
+                    echo "Unsupported architecture: $TARGETARCH"; exit 1; \
-    WHL_FILE="flash_attn-2.8.3+cu${CUDA}${TORCH_TAG}-${PYTHON_CP}-${PYTHON_CP}-linux_${ARCH_TAG}.whl" && \
+                fi; \
-    wget -nv "https://github.com/mjun0812/flash-attention-prebuild-wheels/releases/download/${WHL_VERSION}/${WHL_FILE}" && \
+                wget -nv https://github.com/mjun0812/flash-attention-prebuild-wheels/releases/download/${WHL_VERSION}/${WHL_FILE}; \
-    pip3 install --no-cache-dir "${WHL_FILE}" && \
+                pip3 install --no-cache-dir ${WHL_FILE}; \
-    rm "${WHL_FILE}"
+                rm ${WHL_FILE}; \
+            elif [ "$CUDA" = "130" ]; then \
+                if [ "$TARGETARCH" = "amd64" ]; then \
+                    WHL_FILE="flash_attn-2.8.3+cu130torch2.9-cp311-cp311-linux_x86_64.whl"; \
+                    WHL_VERSION="v0.5.4"; \
+                elif [ "$TARGETARCH" = "arm64" ]; then \
+                    WHL_FILE="flash_attn-2.8.3+cu130torch2.9-cp311-cp311-linux_aarch64.whl"; \
+                    WHL_VERSION="v0.6.4"; \
+                else \
+                    echo "Unsupported architecture: $TARGETARCH"; exit 1; \
+                fi; \
+                wget -nv https://github.com/mjun0812/flash-attention-prebuild-wheels/releases/download/${WHL_VERSION}/${WHL_FILE}; \
+                pip3 install --no-cache-dir ${WHL_FILE}; \
+                rm ${WHL_FILE}; \
+            fi \
+            ;; \
+    esac

docker/Dockerfile-cloud-uv

@@ -1,30 +0,0 @@
-ARG BASE_TAG=main
-FROM axolotlai/axolotl-uv:$BASE_TAG
-
-ENV HF_DATASETS_CACHE="/workspace/data/huggingface-cache/datasets"
-ENV HF_HUB_CACHE="/workspace/data/huggingface-cache/hub"
-ENV HF_HOME="/workspace/data/huggingface-cache/hub"
-ENV HF_HUB_ENABLE_HF_TRANSFER="1"
-
-EXPOSE 8888
-EXPOSE 22
-
-COPY scripts/cloud-entrypoint.sh /root/cloud-entrypoint.sh
-COPY scripts/motd /etc/motd
-
-RUN uv pip install jupyterlab notebook ipywidgets && \
-    jupyter lab clean
-RUN apt update && \
-    apt install --yes --no-install-recommends openssh-server tmux iproute2 nvtop && \
-    rm -rf /var/cache/apt/archives && \
-    rm -rf /var/lib/apt/lists/* && \
-    mkdir -p ~/.ssh && \
-    chmod 700 ~/.ssh && \
-    printf "\n[[ -z \"\$TMUX\"  ]] && { tmux attach-session -t ssh_tmux || tmux new-session -s ssh_tmux; exit; }\n" >> ~/.bashrc && \
-    printf "[ ! -z \"\$TERM\" -a -r /etc/motd ] && cat /etc/motd\n" >> ~/.bashrc && \
-    chmod +x /workspace/axolotl/scripts/cloud-entrypoint.sh && \
-    chmod +x /root/cloud-entrypoint.sh && \
-    echo 'set-option -g history-limit 5000' >> ~/.tmux.conf
-
-ENTRYPOINT ["/root/cloud-entrypoint.sh"]
-CMD ["sleep", "infinity"]

docker/Dockerfile-uv

@@ -1,47 +0,0 @@
-ARG BASE_TAG=main-base
-FROM axolotlai/axolotl-base-uv:$BASE_TAG
-
-ARG TORCH_CUDA_ARCH_LIST="7.0 7.5 8.0 8.6+PTX"
-ARG AXOLOTL_EXTRAS=""
-ARG AXOLOTL_ARGS=""
-ARG CUDA="118"
-ARG PYTORCH_VERSION="2.1.2"
-ARG TARGETARCH
-
-ENV PYTORCH_VERSION=$PYTORCH_VERSION
-
-RUN apt-get update && \
-    apt-get install -y --allow-change-held-packages vim curl nano libnccl2 libnccl-dev rsync s3fs && \
-    rm -rf /var/cache/apt/archives && \
-    rm -rf /var/lib/apt/lists/*
-
-WORKDIR /workspace
-
-RUN git clone --depth=1 https://github.com/axolotl-ai-cloud/axolotl.git
-
-WORKDIR /workspace/axolotl
-
-# If AXOLOTL_EXTRAS is set, append it in brackets; don't install deepspeed with arm64
-RUN if [ "$TARGETARCH" = "arm64" ]; then \
-        BASE_EXTRAS="flash-attn,ring-flash-attn,optimizers,ray"; \
-    else \
-        BASE_EXTRAS="deepspeed,flash-attn,ring-flash-attn,optimizers,ray"; \
-    fi && \
-    if [ "$AXOLOTL_EXTRAS" != "" ]; then \
-        uv pip install --no-build-isolation -e .[$BASE_EXTRAS,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \
-    else \
-        uv pip install --no-build-isolation -e .[$BASE_EXTRAS] $AXOLOTL_ARGS; \
-    fi && \
-    python scripts/unsloth_install.py --uv | sh && \
-    python scripts/cutcrossentropy_install.py --uv | sh && \
-    uv pip install pytest && \
-    uv cache clean
-
-# fix so that git fetch/pull from remote works with shallow clone
-RUN git config remote.origin.fetch "+refs/heads/*:refs/remotes/origin/*" && \
-    git config --get remote.origin.fetch && \
-    git config --global credential.helper store
-
-COPY .axolotl-complete.bash /root/.axolotl-complete.bash
-RUN chmod +x /root/.axolotl-complete.bash && \
-    echo 'source /root/.axolotl-complete.bash' >> ~/.bashrc

docker/Dockerfile-uv-base

@@ -6,7 +6,6 @@ ARG TARGETARCH
 
 FROM nvidia/cuda:$CUDA_VERSION-cudnn$CUDNN_VERSION-devel-ubuntu$UBUNTU_VERSION AS base-builder
 
-ARG TARGETARCH
 ARG PYTHON_VERSION="3.11"
 ARG PYTORCH_VERSION="2.6.0"
 ARG CUDA="126"
@@ -40,18 +39,28 @@ RUN if [ "$TARGETARCH" = "amd64" ]; then \
         uv pip install "mamba_ssm @ git+https://github.com/state-spaces/mamba.git@main"; \
     fi
 
-# Map Python version (e.g., 3.12 -> cp312)
+RUN case "$PYTORCH_VERSION" in \
-RUN PYTHON_CP="cp$(echo $PYTHON_VERSION | tr -d '.')" && \
+        2.9.[0-9]*) \
-    # Map PyTorch version (e.g., 2.9.1 -> torch2.9, 2.10.0 -> torch2.10)
+            if [ "$TARGETARCH" = "amd64" ]; then \
-    TORCH_TAG="torch$(echo $PYTORCH_VERSION | grep -oP '^\d+\.\d+')" && \
+                if [ "$CUDA" = "128" ]; then \
-    # Map architecture
+                    wget -nv https://github.com/mjun0812/flash-attention-prebuild-wheels/releases/download/v0.5.4/flash_attn-2.8.3+cu128torch2.9-cp311-cp311-linux_x86_64.whl; \
-    case "$TARGETARCH" in \
+                    uv pip install --no-cache-dir flash_attn-2.8.3+cu128torch2.9-cp311-cp311-linux_x86_64.whl; \
-        amd64) ARCH_TAG="x86_64" ;; \
+                    rm flash_attn-2.8.3+cu128torch2.9-cp311-cp311-linux_x86_64.whl; \
-        arm64) ARCH_TAG="aarch64" ;; \
+                elif [ "$CUDA" = "130" ]; then \
-        *) echo "Unsupported architecture: $TARGETARCH"; exit 1 ;; \
+                    wget -nv https://github.com/mjun0812/flash-attention-prebuild-wheels/releases/download/v0.5.4/flash_attn-2.8.3+cu130torch2.9-cp311-cp311-linux_x86_64.whl; \
-    esac && \
+                    uv pip install --no-cache-dir flash_attn-2.8.3+cu130torch2.9-cp311-cp311-linux_x86_64.whl; \
-    WHL_VERSION="v0.7.16" && \
+                    rm flash_attn-2.8.3+cu130torch2.9-cp311-cp311-linux_x86_64.whl; \
-    WHL_FILE="flash_attn-2.8.3+cu${CUDA}${TORCH_TAG}-${PYTHON_CP}-${PYTHON_CP}-linux_${ARCH_TAG}.whl" && \
+                fi \
-    wget -nv "https://github.com/mjun0812/flash-attention-prebuild-wheels/releases/download/${WHL_VERSION}/${WHL_FILE}" && \
+            elif [ "$TARGETARCH" = "arm64" ]; then \
-    uv pip install --no-cache-dir "${WHL_FILE}" && \
+                if [ "$CUDA" = "128" ]; then \
-    rm "${WHL_FILE}"
+                    wget -nv https://github.com/mjun0812/flash-attention-prebuild-wheels/releases/download/v0.6.4/flash_attn-2.8.3+cu128torch2.9-cp311-cp311-linux_aarch64.whl; \
+                    uv pip install --no-cache-dir flash_attn-2.8.3+cu128torch2.9-cp311-cp311-linux_aarch64.whl; \
+                    rm flash_attn-2.8.3+cu128torch2.9-cp311-cp311-linux_aarch64.whl; \
+                elif [ "$CUDA" = "130" ]; then \
+                    wget -nv https://github.com/mjun0812/flash-attention-prebuild-wheels/releases/download/v0.6.4/flash_attn-2.8.3+cu130torch2.9-cp311-cp311-linux_aarch64.whl; \
+                    uv pip install --no-cache-dir flash_attn-2.8.3+cu130torch2.9-cp311-cp311-linux_aarch64.whl; \
+                    rm flash_attn-2.8.3+cu130torch2.9-cp311-cp311-linux_aarch64.whl; \
+                fi \
+            fi \
+            ;; \
+    esac

docs/cli.qmd

@@ -210,8 +210,6 @@ axolotl lm-eval config.yml
 Configuration options:
 
 ```yaml
-lm_eval_model: # model to evaluate (local or hf path)
-
 # List of tasks to evaluate
 lm_eval_tasks:
   - arc_challenge
@@ -220,7 +218,7 @@ lm_eval_batch_size: # Batch size for evaluation
 output_dir: # Directory to save evaluation results
 ```
 
-See [LM Eval Harness integration docs](https://docs.axolotl.ai/docs/custom_integrations.html#language-model-evaluation-harness-lm-eval) for full configuration details.
+See [LM Eval Harness](https://github.com/EleutherAI/lm-evaluation-harness) for more details.
 
 ### delinearize-llama4
 

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

@@ -40,7 +40,7 @@
     "%%capture\n",
     "# This step can take ~5-10 minutes to install dependencies\n",
     "!pip install --no-build-isolation axolotl[flash-attn]>=0.9.1\n",
-    "!pip install \"cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@a668583\""
+    "!pip install \"cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@0d4ce4b\""
    ]
   },
   {

examples/glm4.7-flash/README.md

@@ -1,77 +0,0 @@
-# Finetune Z.ai's GLM-4.7-Flash with Axolotl
-
-[GLM-4.7-Flash](https://huggingface.co/zai-org/GLM-4.7-Flash) is a 30B-A3B MoE model by Z.ai.
-
-This guide shows how to fine-tune it with Axolotl.
-
-## Getting started
-
-1. Install Axolotl following the [installation guide](https://docs.axolotl.ai/docs/installation.html).
-
-2. Install [Cut Cross Entropy](https://docs.axolotl.ai/docs/custom_integrations.html#cut-cross-entropy) to reduce training VRAM usage.
-
-3. Run the finetuning example:
-
-```bash
-# QLoRA
-# - no target experts (1x48GB @ ~24GiB/GPU)
-# - target experts (1x48GB @ ~34GiB/GPU)
-axolotl train examples/glm4.7-flash/qlora.yaml
-
-# QLoRA FSDP2 no target experts (2x48GB @ ~29GiB/GPU)
-axolotl train examples/glm4.7-flash/qlora_fsdp.yaml
-```
-
-```bash
-# LoRA
-# - no target experts (1x48GB @ ~35GiB/GPU)
-# - target experts (1x48GB @ OOM. Projected ~45-50GiB/GPU)
-axolotl train examples/glm4.7-flash/lora.yaml
-
-# LoRA FSDP2 no target experts (2x48GB @ ~43GiB/GPU)
-axolotl train examples/glm4.7-flash/lora_fsdp.yaml
-```
-
-### Expert LoRA
-
-To also apply LoRA adapters to expert weights, add `lora_target_parameters` to your config.
-
-Note: `lora_dropout` must be `0` when using `lora_target_parameters`.
-
-```yaml
-lora_target_parameters:
-  - mlp.experts.gate_up_proj
-  - mlp.experts.down_proj
-  # - mlp.gate.weight  # router, untested but should work, not normally targeted
-```
-
-## Limitations
-
-- **FSDP VRAM**: FSDP2 may use more VRAM per GPU than single GPU training. We suspect not all layers are properly sharded across ranks.
-- **FSDP initial spike**: FSDP LoRA (8-bit) may have a large initial VRAM spike at the first 1-2 steps that then drops. FSDP QLoRA (4-bit) does not exhibit this.
-- **cpu_ram_efficient_loading**: Must be set to `false` with FSDP2 — causes hang otherwise.
-- **lora_target_linear**: Incompatible for this model.
-- **LoRA kernels**: Incompatible with this model due to non-standard attention projections (DSA). Must be explicitly disabled (`lora_*_kernel: false`).
-
-
-### TIPS
-
-- For inference, the official Z.ai team recommends these default settings (most tasks):
-  - `temperature: 1.0`
-  - `top_p: 0.95`
-  - `max_new_tokens: 131072`
-- You can run a full finetuning by removing `adapter: qlora`, `load_in_4bit: true`, and `quantize_moe_experts: true` from the config. This is heavy, so we have not tested this.
-- Read more on how to load your own dataset at [docs](https://docs.axolotl.ai/docs/dataset_loading.html).
-
-## Optimization Guides
-
-Please check the [Optimizations doc](https://docs.axolotl.ai/docs/optimizations.html).
-
-## Related Resources
-
-- [GLM-4.7-Flash on HuggingFace](https://huggingface.co/zai-org/GLM-4.7-Flash)
-- [GLM-4.7 Blog](https://z.ai/blog/glm-4.7)
-- [Axolotl Docs](https://docs.axolotl.ai)
-- [Axolotl Website](https://axolotl.ai)
-- [Axolotl GitHub](https://github.com/axolotl-ai-cloud/axolotl)
-- [Axolotl Discord](https://discord.gg/7m9sfhzaf3)

examples/glm4.7-flash/lora.yaml

@@ -1,65 +0,0 @@
-base_model: zai-org/GLM-4.7-Flash
-
-plugins:
-  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
-
-load_in_8bit: true
-quantize_moe_experts: true
-
-datasets:
-  - path: fozziethebeat/alpaca_messages_2k_test
-    type: chat_template
-
-dataset_prepared_path: last_run_prepared
-val_set_size: 0.1
-output_dir: ./outputs/glm4.7-flash-lora-8bit-out
-
-adapter: lora
-lora_model_dir:
-
-sequence_len: 2048
-sample_packing: true
-
-lora_r: 32
-lora_alpha: 16
-lora_dropout: 0
-lora_target_modules:
-  - q_proj
-  - v_proj
-  - k_proj
-  - o_proj
-
-# Uncomment to also target MoE expert weights:
-# lora_target_parameters:
-#   - mlp.experts.gate_up_proj
-#   - mlp.experts.down_proj
-
-# LoRA kernels incompatible with DSA attention
-lora_mlp_kernel: false
-lora_qkv_kernel: false
-lora_o_kernel: false
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 4
-micro_batch_size: 2
-num_epochs: 1
-optimizer: adamw_torch_8bit
-lr_scheduler: cosine
-learning_rate: 0.0002
-
-bf16: auto
-tf32: false
-
-gradient_checkpointing: true
-resume_from_checkpoint:
-logging_steps: 1
-flash_attention: true
-
-warmup_ratio: 0.1
-evals_per_epoch: 1
-saves_per_epoch: 1

examples/glm4.7-flash/lora_fsdp.yaml

@@ -1,75 +0,0 @@
-base_model: zai-org/GLM-4.7-Flash
-
-plugins:
-  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
-
-load_in_8bit: true
-quantize_moe_experts: true
-
-datasets:
-  - path: fozziethebeat/alpaca_messages_2k_test
-    type: chat_template
-
-dataset_prepared_path: last_run_prepared
-val_set_size: 0.1
-output_dir: ./outputs/glm4.7-flash-lora-8bit-fsdp-out
-
-adapter: lora
-lora_model_dir:
-
-sequence_len: 2048
-sample_packing: true
-
-lora_r: 32
-lora_alpha: 16
-lora_dropout: 0
-lora_target_modules:
-  - q_proj
-  - v_proj
-  - k_proj
-  - o_proj
-
-# Uncomment to also target MoE expert weights:
-# lora_target_parameters:
-#   - mlp.experts.gate_up_proj
-#   - mlp.experts.down_proj
-
-# LoRA kernels incompatible with DSA attention
-lora_mlp_kernel: false
-lora_qkv_kernel: false
-lora_o_kernel: false
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 4
-micro_batch_size: 2
-num_epochs: 1
-optimizer: adamw_torch_8bit
-lr_scheduler: cosine
-learning_rate: 0.0002
-
-bf16: auto
-tf32: false
-
-resume_from_checkpoint:
-logging_steps: 1
-flash_attention: true
-
-warmup_ratio: 0.1
-evals_per_epoch: 1
-saves_per_epoch: 1
-
-fsdp_config:
-  fsdp_version: 2
-  offload_params: false
-  cpu_ram_efficient_loading: false
-  auto_wrap_policy: TRANSFORMER_BASED_WRAP
-  transformer_layer_cls_to_wrap: Glm4MoeLiteDecoderLayer
-  state_dict_type: FULL_STATE_DICT
-  sharding_strategy: FULL_SHARD
-  reshard_after_forward: true
-  activation_checkpointing: true

examples/glm4.7-flash/qlora.yaml

@@ -1,65 +0,0 @@
-base_model: zai-org/GLM-4.7-Flash
-
-plugins:
-  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
-
-load_in_4bit: true
-quantize_moe_experts: true
-
-datasets:
-  - path: fozziethebeat/alpaca_messages_2k_test
-    type: chat_template
-
-dataset_prepared_path: last_run_prepared
-val_set_size: 0.1
-output_dir: ./outputs/glm4.7-flash-qlora-out
-
-adapter: qlora
-lora_model_dir:
-
-sequence_len: 2048
-sample_packing: true
-
-lora_r: 32
-lora_alpha: 16
-lora_dropout: 0
-lora_target_modules:
-  - q_proj
-  - v_proj
-  - k_proj
-  - o_proj
-
-# Uncomment to also target MoE expert weights:
-# lora_target_parameters:
-#   - mlp.experts.gate_up_proj
-#   - mlp.experts.down_proj
-
-# LoRA kernels incompatible with DSA attention
-lora_mlp_kernel: false
-lora_qkv_kernel: false
-lora_o_kernel: false
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 4
-micro_batch_size: 2
-num_epochs: 1
-optimizer: adamw_torch_8bit
-lr_scheduler: cosine
-learning_rate: 0.0002
-
-bf16: auto
-tf32: false
-
-gradient_checkpointing: true
-resume_from_checkpoint:
-logging_steps: 1
-flash_attention: true
-
-warmup_ratio: 0.1
-evals_per_epoch: 1
-saves_per_epoch: 1

examples/glm4.7-flash/qlora_fsdp.yaml

@@ -1,75 +0,0 @@
-base_model: zai-org/GLM-4.7-Flash
-
-plugins:
-  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
-
-load_in_4bit: true
-quantize_moe_experts: true
-
-datasets:
-  - path: fozziethebeat/alpaca_messages_2k_test
-    type: chat_template
-
-dataset_prepared_path: last_run_prepared
-val_set_size: 0.1
-output_dir: ./outputs/glm4.7-flash-qlora-fsdp-out
-
-adapter: qlora
-lora_model_dir:
-
-sequence_len: 2048
-sample_packing: true
-
-lora_r: 32
-lora_alpha: 16
-lora_dropout: 0
-lora_target_modules:
-  - q_proj
-  - v_proj
-  - k_proj
-  - o_proj
-
-# Uncomment to also target MoE expert weights:
-# lora_target_parameters:
-#   - mlp.experts.gate_up_proj
-#   - mlp.experts.down_proj
-
-# LoRA kernels incompatible with DSA attention
-lora_mlp_kernel: false
-lora_qkv_kernel: false
-lora_o_kernel: false
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 4
-micro_batch_size: 2
-num_epochs: 1
-optimizer: adamw_torch_8bit
-lr_scheduler: cosine
-learning_rate: 0.0002
-
-bf16: auto
-tf32: false
-
-resume_from_checkpoint:
-logging_steps: 1
-flash_attention: true
-
-warmup_ratio: 0.1
-evals_per_epoch: 1
-saves_per_epoch: 1
-
-fsdp_config:
-  fsdp_version: 2
-  offload_params: false
-  cpu_ram_efficient_loading: false
-  auto_wrap_policy: TRANSFORMER_BASED_WRAP
-  transformer_layer_cls_to_wrap: Glm4MoeLiteDecoderLayer
-  state_dict_type: FULL_STATE_DICT
-  sharding_strategy: FULL_SHARD
-  reshard_after_forward: true
-  activation_checkpointing: true

examples/qwen3-next/README.md

@@ -6,13 +6,30 @@ This guide shows how to fine-tune it with Axolotl with multi-turn conversations
 
 ## Getting started
 
-1. Install Axolotl following the [installation guide](https://docs.axolotl.ai/docs/installation.html).
+1. Install Axolotl following the [installation guide](https://docs.axolotl.ai/docs/installation.html). You need to install from main as Qwen3-Next is only on nightly or use our latest [Docker images](https://docs.axolotl.ai/docs/docker.html).
 
-2. Install [Cut Cross Entropy](https://docs.axolotl.ai/docs/custom_integrations.html#cut-cross-entropy) to reduce training VRAM usage.
+    Here is an example of how to install from main for pip:
+
+```bash
+# Ensure you have Pytorch installed (Pytorch 2.6.0 min)
+git clone https://github.com/axolotl-ai-cloud/axolotl.git
+cd axolotl
+
+pip3 install packaging==26.0 setuptools==75.8.0 wheel ninja
+pip3 install --no-build-isolation -e '.[flash-attn]'
+
+# Install CCE https://docs.axolotl.ai/docs/custom_integrations.html#cut-cross-entropy
+python scripts/cutcrossentropy_install.py | sh
+```
+
+2. Install Qwen3-Next transformers commit
+```bash
+pip3 uninstall -y transformers && pip3 install "git+https://github.com/huggingface/transformers.git@b9282355bea846b54ed850a066901496b19da654"
+```
 
 3. Install FLA for improved performance
 ```bash
-pip3 uninstall -y causal-conv1d && pip3 install flash-linear-attention==0.4.1
+pip3 uninstall -y causal-conv1d && pip3 install flash-linear-attention==0.3.2
 ```
 
 4. Run the finetuning example:
@@ -21,7 +38,7 @@ pip3 uninstall -y causal-conv1d && pip3 install flash-linear-attention==0.4.1
 axolotl train examples/qwen3-next/qwen3-next-80b-a3b-qlora.yaml
 ```
 
-This config uses about ~47 GiB (no target experts) and ~71GiB (target experts) VRAM.
+This config uses about 45.62 GiB VRAM.
 
 Let us know how it goes. Happy finetuning! 🚀
 

examples/qwen3-next/qwen3-next-80b-a3b-qlora.yaml

@@ -9,8 +9,6 @@ plugins:
 load_in_8bit: false
 load_in_4bit: true
 
-quantize_moe_experts: true
-
 datasets:
   - path: fozziethebeat/alpaca_messages_2k_test
     type: chat_template
@@ -27,7 +25,7 @@ sample_packing: true
 
 lora_r: 16
 lora_alpha: 8
-lora_dropout: 0
+lora_dropout: 0.05
 lora_target_modules:
   - linear_attn.in_proj_ba
   - linear_attn.in_proj_qkvz
@@ -36,19 +34,12 @@ lora_target_modules:
   - shared_expert.down_proj
   - shared_expert.gate_proj
   - shared_expert_gate
+  - mlp.gate
   - q_proj
   - v_proj
   - k_proj
   - o_proj
 
-# lora_target_parameters:
-#   - mlp.experts.gate_up_proj
-#   - mlp.experts.down_proj
-
-lora_mlp_kernel: false
-lora_qkv_kernel: false
-lora_o_kernel: false
-
 wandb_project:
 wandb_entity:
 wandb_watch:

examples/trinity/README.md

@@ -8,15 +8,13 @@ This guide shows how to fine-tune it with Axolotl with multi-turn conversations
 
 1. Install Axolotl following the main from the [installation guide](https://docs.axolotl.ai/docs/installation.html#sec-edge-build).
 
-2. Install [Cut Cross Entropy](https://docs.axolotl.ai/docs/custom_integrations.html#cut-cross-entropy) to reduce training VRAM usage.
+2. Run the finetuning example:
-
-3. Run the finetuning example:
 
     ```bash
     axolotl train examples/trinity/trinity-nano-preview-qlora.yaml
     ```
 
-This config uses about 24.9 GiB VRAM (w/o CCE).
+This config uses about 24.9 GiB VRAM.
 
 Let us know how it goes. Happy finetuning! 🚀
 
@@ -31,6 +29,10 @@ Let us know how it goes. Happy finetuning! 🚀
 
 Please check the [Optimizations doc](https://docs.axolotl.ai/docs/optimizations.html).
 
+## Limitations
+
+**Cut Cross Entropy (CCE)**: Currently not supported. We plan to include CCE support for Trinity in the near future.
+
 ## Related Resources
 
 - [Trinity Blog](https://www.arcee.ai/blog/the-trinity-manifesto)

examples/trinity/trinity-nano-preview-qlora.yaml

@@ -1,4 +1,5 @@
 base_model: arcee-ai/Trinity-Nano-Preview
+trust_remote_code: true
 revision_of_model: 2ee94b0
 
 # Automatically upload checkpoint and final model to HF

pyproject.toml

@@ -63,5 +63,3 @@ docstring-code-format = false
 
 [tool.uv.extra-build-dependencies]
 axolotl = ["huggingface_hub"]
-flash-attn = [{ requirement = "torch", match-runtime = true }]
-deepspeed = [{ requirement = "torch", match-runtime = true }]

requirements.txt

@@ -2,25 +2,25 @@
 
 # START section of dependencies that don't install on Darwin/MacOS
 bitsandbytes==0.49.1
-triton>=3.4.0
+triton>=3.0.0
 mamba-ssm==1.2.0.post1
 xformers>=0.0.23.post1
-liger-kernel==0.7.0
+liger-kernel==0.6.4
 # END section
 
 packaging==26.0
 huggingface_hub>=1.1.7
 peft>=0.18.1
 tokenizers>=0.22.1
-transformers==5.2.0
+transformers==5.0.0
 accelerate==1.12.0
 datasets==4.5.0
 deepspeed>=0.18.3
-trl==0.28.0
+trl==0.27.1
 hf_xet==1.2.0
-kernels==0.12.1
+kernels==0.11.5
 
-trackio>=0.16.1
+trackio>=0.13.0
 typing-extensions>=4.15.0
 
 optimum==1.16.2
@@ -63,7 +63,7 @@ langdetect==1.0.9
 immutabledict==4.2.0
 antlr4-python3-runtime==4.13.2
 
-torchao==0.16.0
+torchao==0.13.0
 openenv-core==0.1.0
 schedulefree==1.4.1
 

scripts/cutcrossentropy_install.py

@@ -29,5 +29,5 @@ UV_PREFIX = "uv " if USE_UV else ""
 
 print(
     UNINSTALL_PREFIX
-    + f'{UV_PREFIX}pip install "cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@a668583"'
+    + f'{UV_PREFIX}pip install "cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@0d4ce4b"'
 )

setup.py

@@ -26,11 +26,6 @@ def parse_requirements(extras_require_map):
     try:
         xformers_version = [req for req in _install_requires if "xformers" in req][0]
         install_xformers = platform.machine() != "aarch64"
-        if platform.machine() == "aarch64":
-            # skip torchao on ARM64
-            _install_requires = [
-                req for req in _install_requires if "torchao" not in req
-            ]
         if "Darwin" in platform.system():
             # skip packages not compatible with OSX
             skip_packages = [

src/axolotl/cli/config.py

@@ -5,7 +5,7 @@ import os
 import tempfile
 from pathlib import Path
 from tempfile import NamedTemporaryFile
-from typing import Any, Optional, Union
+from typing import Union
 from urllib.parse import urlparse
 
 import requests
@@ -32,63 +32,6 @@ from axolotl.utils.wandb_ import setup_wandb_env_vars
 
 LOG = get_logger(__name__)
 
-
-def _coerce_value(value: Any, existing: Optional[Any] = None) -> Any:
-    """Coerce a string CLI value to its most likely Python type.
-
-    If an existing value is present in the config, its type is used to guide
-    casting.  Otherwise, YAML-style inference is applied: booleans, ints,
-    floats, and None literals are recognised automatically.
-
-    Args:
-        value: The raw value (typically a string from the CLI).
-        existing: An optional existing config value whose type guides coercion.
-
-    Returns:
-        The value cast to the inferred or expected type.
-    """
-    if not isinstance(value, str):
-        return value
-
-    # If the config already has a typed value, cast to match
-    if existing is not None:
-        if isinstance(existing, bool):
-            return value.lower() in ("true", "1", "yes")
-        if isinstance(existing, int):
-            try:
-                return int(value)
-            except (ValueError, TypeError):
-                return value
-        if isinstance(existing, float):
-            try:
-                return float(value)
-            except (ValueError, TypeError):
-                return value
-        # For other types (str, list, dict, etc.), return as-is
-        return value
-
-    # No existing value -- use YAML-style inference
-    lower = value.lower()
-    if lower in ("true", "yes"):
-        return True
-    if lower in ("false", "no"):
-        return False
-    if lower in ("null", "none", "~"):
-        return None
-
-    # Try int then float
-    try:
-        return int(value)
-    except ValueError:
-        pass
-    try:
-        return float(value)
-    except ValueError:
-        pass
-
-    return value
-
-
 API_KEY_FIELDS = {"comet_api_key"}
 
 TELEMETRY_MANAGER = TelemetryManager.get_instance()
@@ -265,37 +208,13 @@ def load_cfg(
     # If there are any options passed in the cli, if it is something that seems valid
     # from the yaml, then overwrite the value
     cfg_keys = cfg.keys()
-
-    # Separate nested (dot-notation) kwargs from flat kwargs
-    nested_kwargs: dict[str, dict[str, Any]] = {}
-    flat_kwargs: dict[str, Any] = {}
     for key, value in kwargs.items():
-        if "__" in key:
-            parent, child = key.split("__", 1)
-            nested_kwargs.setdefault(parent, {})[child] = value
-        else:
-            flat_kwargs[key] = value
-
-    # Apply flat kwargs
-    for key, value in flat_kwargs.items():
         # If not strict, allow writing to cfg even if it's not in the yml already
         if key in cfg_keys or not cfg.strict:
-            cfg[key] = _coerce_value(value, cfg.get(key))
+            if isinstance(cfg[key], bool):
-
+                cfg[key] = bool(value)
-    # Apply nested kwargs (e.g., trl__beta -> cfg.trl.beta)
+            else:
-    for parent, children in nested_kwargs.items():
+                cfg[key] = value
-        if parent not in cfg_keys and cfg.strict:
-            continue
-        if cfg[parent] is None:
-            cfg[parent] = {}
-        if not isinstance(cfg[parent], dict):
-            LOG.warning(
-                "Overwriting non-dict value for '%s' with nested CLI overrides", parent
-            )
-            cfg[parent] = {}
-        for child_key, child_value in children.items():
-            existing_child = cfg[parent].get(child_key)
-            cfg[parent][child_key] = _coerce_value(child_value, existing_child)
 
     try:
         device_props = torch.cuda.get_device_properties("cuda")

src/axolotl/cli/utils/args.py

@@ -2,7 +2,7 @@
 
 import dataclasses
 from functools import wraps
-from types import NoneType, UnionType
+from types import NoneType
 from typing import Any, Callable, Type, Union, get_args, get_origin
 
 import click
@@ -20,8 +20,7 @@ def _strip_optional_type(field_type: type | str | None):
         If the input type is `Union[T, None]` or `Optional[T]`, returns `T`. Otherwise
             returns the input type unchanged.
     """
-    is_union = get_origin(field_type) is Union or isinstance(field_type, UnionType)
+    if get_origin(field_type) is Union and type(None) in get_args(field_type):
-    if is_union and type(None) in get_args(field_type):
         field_type = next(
             t for t in get_args(field_type) if not isinstance(t, NoneType)
         )
@@ -88,70 +87,10 @@ def add_options_from_dataclass(config_class: Type[Any]) -> Callable:
     return decorator
 
 
-def _is_pydantic_model(field_type: type) -> bool:
-    """Check if a type is a Pydantic BaseModel subclass."""
-    try:
-        return isinstance(field_type, type) and issubclass(field_type, BaseModel)
-    except TypeError:
-        return False
-
-
-def _get_field_description(field) -> str | None:
-    """Get description from a Pydantic field, checking both .description and json_schema_extra."""
-    if field.description:
-        return field.description
-    if field.json_schema_extra and isinstance(field.json_schema_extra, dict):
-        return field.json_schema_extra.get("description")
-    return None
-
-
-def _add_nested_model_options(
-    function: Callable, parent_name: str, model_class: Type[BaseModel]
-) -> Callable:
-    """
-    Add Click options for all fields of a nested Pydantic model using dot-notation.
-
-    Note: Only single-level nesting is supported (e.g., ``--trl.beta``).
-    Deeper nesting (e.g., ``--trl.scheduler.warmup``) is not handled.
-
-    Args:
-        function: Click command function to add options to.
-        parent_name: Parent field name (e.g., "trl").
-        model_class: Nested Pydantic model class.
-
-    Returns:
-        Function with added Click options.
-    """
-    for sub_name, sub_field in reversed(model_class.model_fields.items()):
-        sub_type = _strip_optional_type(sub_field.annotation)
-        # Use dot notation: --parent.sub_field
-        cli_name = f"{parent_name}.{sub_name}".replace("_", "-")
-        # The kwarg name uses double-underscore as separator
-        param_name = f"{parent_name}__{sub_name}"
-        description = _get_field_description(sub_field)
-
-        if sub_type is bool:
-            option_name = f"--{cli_name}/--no-{cli_name}"
-            function = click.option(
-                option_name, param_name, default=None, help=description
-            )(function)
-        else:
-            option_name = f"--{cli_name}"
-            click_type = {str: str, int: int, float: float}.get(sub_type)
-            function = click.option(
-                option_name, param_name, default=None, type=click_type, help=description
-            )(function)
-
-    return function
-
-
 def add_options_from_config(config_class: Type[BaseModel]) -> Callable:
     """
     Create Click options from the fields of a Pydantic model.
 
-    For fields whose type is itself a Pydantic BaseModel, dot-notation CLI options are
-    generated for each sub-field (e.g., ``--trl.beta=0.1``).
-
     Args:
         config_class: PyDantic model with fields to parse from the CLI
 
@@ -164,11 +103,6 @@ def add_options_from_config(config_class: Type[BaseModel]) -> Callable:
         for name, field in reversed(config_class.model_fields.items()):
             field_type = _strip_optional_type(field.annotation)
 
-            # Handle nested Pydantic models with dot-notation options
-            if _is_pydantic_model(field_type):
-                function = _add_nested_model_options(function, name, field_type)
-                continue
-
             if field_type is bool:
                 field_name = name.replace("_", "-")
                 option_name = f"--{field_name}/--no-{field_name}"

src/axolotl/common/architectures.py

@@ -18,7 +18,4 @@ MOE_ARCH_BLOCK = {
     "gpt_oss": "GptOssDecoderLayer",
     "lfm2_moe": "Lfm2MoeSparseMoeBlock",
     "afmoe": "AfmoeMoE",
-    "glm4_moe": "Glm4MoeDecoderLayer",
-    "glm4_moe_lite": "Glm4MoeLiteDecoderLayer",
-    "glm_moe_dsa": "GlmMoeDsaDecoderLayer",
 }

src/axolotl/core/builders/base.py

@@ -258,6 +258,11 @@ class TrainerBuilderBase(abc.ABC):
             bf16 = bf16 if bf16 is not None else False
             training_args_kwargs["bf16"] = bf16
 
+        if self.cfg.fp8:
+            training_args_kwargs["fp8"] = True
+            if self.cfg.fp8_enable_fsdp_float8_all_gather:
+                training_args_kwargs["enable_fsdp_float8_all_gather:"] = True
+
     def _configure_scheduler(self, training_args_kwargs: dict):
         if self.cfg.lr_scheduler in ["one_cycle", "rex"]:
             training_args_kwargs["lr_scheduler_type"] = "cosine"

src/axolotl/core/builders/causal.py

@@ -122,12 +122,6 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
             ColabCallback = colab_inference_post_train_callback(trainer)
             callbacks.append(ColabCallback(self.cfg))
 
-        if getattr(self.cfg, "generate_samples", False):
-            from axolotl.utils.callbacks.generation import SFTGenerationCallback
-
-            callbacks.append(SFTGenerationCallback(trainer))
-            LOG.info("SFT sample generation enabled")
-
         callbacks.extend(super().get_post_trainer_create_callbacks(trainer=trainer))
         return callbacks
 
@@ -252,8 +246,7 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
             ddp_find_unused_parameters
         )
 
-        if self.cfg.group_by_length:
+        training_arguments_kwargs["group_by_length"] = self.cfg.group_by_length
-            training_arguments_kwargs["train_sampling_strategy"] = "group_by_length"
         training_arguments_kwargs["curriculum_sampling"] = self.cfg.curriculum_sampling
 
         training_arguments_kwargs["sample_packing"] = bool(self.cfg.sample_packing)

src/axolotl/core/builders/rl.py

@@ -11,6 +11,7 @@ from axolotl.core.trainers import (
 )
 from axolotl.core.trainers.dpo import DPOStrategy
 from axolotl.core.trainers.dpo.args import AxolotlDPOConfig
+from axolotl.core.trainers.grpo import GRPOStrategy
 from axolotl.integrations.base import PluginManager
 from axolotl.loaders.utils import ensure_dtype
 from axolotl.utils.callbacks.qat import QATCallback
@@ -52,8 +53,6 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
         trainer_cls_args = [self.model]
 
         if self.cfg.rl in {RLType.GRPO, RLType.GDPO}:
-            from axolotl.core.trainers.grpo import GRPOStrategy
-
             trainer_cls = GRPOStrategy.get_trainer_class(
                 sequence_parallel=self.cfg.context_parallel_size > 1
             )
@@ -134,17 +133,21 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
             if self.cfg.cpo_alpha is not None:
                 training_args_kwargs["cpo_alpha"] = self.cfg.cpo_alpha
 
-            blocklist_args_kwargs.append("max_prompt_length")
+            # Handle when max_prompt_length == max_length from defaults
+            # CPOTrainer requires strictly less than
+            if (
+                training_args_kwargs["max_prompt_length"]
+                == training_args_kwargs["max_length"]
+            ):
+                training_args_kwargs["max_prompt_length"] -= 1
 
         elif self.cfg.rl is RLType.ORPO:
             training_args_cls = AxolotlORPOConfig
 
-            blocklist_args_kwargs.append("max_prompt_length")
-
         elif self.cfg.rl is RLType.KTO:
             training_args_cls = AxolotlKTOConfig
             # KTOConfig in TRL >= 0.27.0 no longer accepts max_prompt_length
-            blocklist_args_kwargs.append("max_prompt_length")
+            blocklist_args_kwargs = ["max_prompt_length"]
 
             training_args_kwargs["desirable_weight"] = (
                 self.cfg.kto_desirable_weight or 1.0
@@ -154,8 +157,6 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
             )
 
         elif self.cfg.rl in {RLType.GRPO, RLType.GDPO}:
-            from axolotl.core.trainers.grpo import GRPOStrategy
-
             training_args_cls = GRPOStrategy.get_training_args_class()
             training_args_kwargs.update(GRPOStrategy.set_training_args_kwargs(self.cfg))
             blocklist_args_kwargs = GRPOStrategy.get_blocklist_args_kwargs()

src/axolotl/core/trainers/base.py

@@ -584,11 +584,9 @@ class AxolotlTrainer(
 
         super().create_accelerator_and_postprocess()
 
-    def additional_accelerator_args(
+    def build_fp8_accelerator_args(self) -> dict[str, Any]:
-        self, fp8: bool = False, enable_fsdp_float8_all_gather: bool = False, **kwargs
+        args = {}
-    ) -> dict[str, Any]:
+        if self.args.fp8:
-        ret_kwargs = {}
-        if fp8:
             from accelerate.utils import AORecipeKwargs
             from torchao.float8 import Float8LinearConfig
 
@@ -596,15 +594,22 @@ class AxolotlTrainer(
             # scaling strategy. See more details here:
             # https://github.com/pytorch/ao/tree/main/torchao/float8.
             config = Float8LinearConfig(
-                enable_fsdp_float8_all_gather=enable_fsdp_float8_all_gather,
+                enable_fsdp_float8_all_gather=self.args.enable_fsdp_float8_all_gather,
-                force_recompute_fp8_weight_in_bwd=enable_fsdp_float8_all_gather is True,
+                force_recompute_fp8_weight_in_bwd=self.args.enable_fsdp_float8_all_gather
+                is True,
             )
 
-            ret_kwargs["mixed_precision"] = "fp8"
+            args["mixed_precision"] = "fp8"
-            ret_kwargs["kwargs_handlers"] = [AORecipeKwargs(config=config)]  # type: ignore
+            args["kwargs_handlers"] = [AORecipeKwargs(config=config)]  # type: ignore
             os.environ["ACCELERATE_MIXED_PRECISION"] = "fp8"
 
-        return ret_kwargs
+        return args
+
+    def _build_accelerator_args(self, **kwargs) -> dict[str, Any]:
+        args = super().build_accelerator_args(**kwargs)
+        fp8_args = self.build_fp8_accelerator_args()
+        args.update(fp8_args)
+        return args
 
     def log(self, logs: dict[str, float], start_time: float | None = None) -> None:
         """
@@ -719,20 +724,13 @@ class AxolotlTrainer(
         output_dir = output_dir if output_dir is not None else self.args.output_dir
         os.makedirs(output_dir, exist_ok=True)
         LOG.info(f"Saving model checkpoint to {output_dir}")
-
+        if state_dict is None:
-        # fix for Context Parallel save: CP eval invalidates tensor storage
+            state_dict = self.accelerator.get_state_dict(self.model)
-        # pointers, so clone to CPU to get fresh valid storage for safetensors
+        if state_dict is not None:
-        if (
-            state_dict is not None
-            and self.axolotl_cfg
-            and self.axolotl_cfg.context_parallel_size
-            and self.axolotl_cfg.context_parallel_size > 1
-        ):
             state_dict = {
-                k: v.detach().cpu() if isinstance(v, torch.Tensor) else v
+                k: v.clone() if isinstance(v, torch.Tensor) else v
                 for k, v in state_dict.items()
             }
-
         supported_classes = (
             (PreTrainedModel,)
             if not is_peft_available()
@@ -743,7 +741,6 @@ class AxolotlTrainer(
         if not isinstance(self.model, supported_classes):
             if state_dict is None:
                 state_dict = self.model.state_dict()
-
             if isinstance(
                 self.accelerator.unwrap_model(self.model, keep_torch_compile=False),
                 supported_classes,
@@ -753,7 +750,6 @@ class AxolotlTrainer(
                 ).save_pretrained(
                     output_dir,
                     state_dict=state_dict,
-                    is_main_process=self.accelerator.is_main_process,
                 )
             else:
                 LOG.info(
@@ -781,7 +777,11 @@ class AxolotlTrainer(
             LOG.info(
                 "Saving Trainer.data_collator.tokenizer by default as Trainer.processing_class is `None`"
             )
-            self.data_collator.tokenizer.save_pretrained(output_dir)
+            save_jinja_files = True
-
+            if self.axolotl_cfg:
+                save_jinja_files = self.axolotl_cfg.tokenizer_save_jinja_files
+            self.data_collator.tokenizer.save_pretrained(
+                output_dir, save_jinja_files=save_jinja_files
+            )
         # Good practice: save your training arguments together with the trained model
         torch.save(self.args, os.path.join(output_dir, TRAINING_ARGS_NAME))

src/axolotl/core/trainers/dpo/trainer.py

@@ -57,18 +57,16 @@ class AxolotlDPOTrainer(
     def tokenize_row(
         features,
         processing_class,
-        max_prompt_length: int | None = None,
+        max_prompt_length,
-        max_completion_length: int | None = None,
+        max_completion_length,
-        add_special_tokens: bool = True,
+        add_special_tokens,
-        is_chat: bool = False,
     ) -> Dict:
         res = DPOTrainer.tokenize_row(
             features,
             processing_class,
-            max_prompt_length=max_prompt_length,
+            max_prompt_length,
-            max_completion_length=max_completion_length,
+            max_completion_length,
-            add_special_tokens=add_special_tokens,
+            add_special_tokens,
-            is_chat=is_chat,
         )
         # 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:

src/axolotl/core/trainers/mixins/scheduler.py

@@ -25,7 +25,7 @@ class SchedulerMixin(Trainer):
     args = None  # type: "AxolotlTrainingArguments"  # type: ignore[name-defined]
 
     def create_scheduler(
-        self, num_training_steps: int, optimizer: None | torch.optim.Optimizer = None
+        self, num_training_steps: int, optimizer: torch.optim.Optimizer = None
     ) -> LRScheduler:
         """
         Set up the scheduler. The optimizer of the trainer must have been set up either before this method is called or
@@ -45,13 +45,6 @@ class SchedulerMixin(Trainer):
             and self.args.cosine_min_lr_ratio is not None
         )
 
-        if optimizer is None:
-            if self.optimizer is None:
-                raise ValueError(
-                    "Optimizer must be set before calling create_scheduler or passed as an argument."
-                )
-            optimizer = self.optimizer
-
         # fmt: off
         if self.lr_scheduler is None:  # type: ignore
             # fmt: on

src/axolotl/core/training_args_base.py

@@ -263,3 +263,13 @@ class AxolotlTrainingMixins:
     dion_rank_multiple_of: int | None = field(
         default=None,
     )
+
+    fp8: bool | None = field(
+        default=None,
+        metadata={"help": "Whether to use FP8 precision for training"},
+    )
+
+    enable_fsdp_float8_all_gather: bool | None = field(
+        default=None,
+        metadata={"help": "Whether to use FSDP with FP8 precision for all_gather"},
+    )

src/axolotl/integrations/cut_cross_entropy/README.md

@@ -19,7 +19,7 @@ python scripts/cutcrossentropy_install.py | sh
 
 - If you are installing from pip
 ```bash
-pip3 uninstall -y cut-cross-entropy && pip3 install "cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@a668583"
+pip3 uninstall -y cut-cross-entropy && pip3 install "cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@0d4ce4b"
 ```
 
 ## Usage
@@ -31,7 +31,6 @@ plugins:
 
 ## Supported Models
 
-- afmoe
 - apertus
 - arcee
 - cohere
@@ -52,7 +51,6 @@ plugins:
 - glm4v
 - glm4v_moe
 - glm_image
-- glm_moe_dsa
 - gpt_oss
 - granite
 - granitemoe
@@ -78,19 +76,14 @@ plugins:
 - olmo
 - olmo2
 - olmo3
-- olmoe
 - phi
 - phi3
 - phi4_multimodal
 - qwen2
-- qwen2_5_vl
 - qwen2_moe
 - qwen2_vl
+- qwen2_5_vl
 - qwen3
-- qwen3_5
-- qwen3_5_text
-- qwen3_5_moe
-- qwen3_5_moe_text
 - qwen3_moe
 - qwen3_next
 - qwen3_vl

src/axolotl/integrations/cut_cross_entropy/__init__.py

@@ -35,7 +35,7 @@ LOG = get_logger(__name__)
 
 _CCE_INSTALL_MESSAGE = (
     "Please install Axolotl's fork of cut_cross_entropy with transformers support using "
-    '`pip install "cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@a668583"`'
+    '`pip install "cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@0d4ce4b"`'
 )
 
 

src/axolotl/integrations/kernels/README.md

@@ -1,46 +0,0 @@
-# Kernels Integration
-
-MoE (Mixture of Experts) kernels speed up training for MoE layers and reduce VRAM costs. In transformers v5, `batched_mm` and `grouped_mm` were integrated as built-in options via the `experts_implementation` config kwarg:
-
-```python
-class ExpertsInterface(GeneralInterface):
-    _global_mapping = {
-        "batched_mm": batched_mm_experts_forward,
-        "grouped_mm": grouped_mm_experts_forward,
-    }
-```
-
-In our custom integration, we add support for **ScatterMoE**, which is even more efficient and faster than `grouped_mm`.
-
-## Usage
-
-Add the following to your axolotl YAML config:
-
-```yaml
-plugins:
-  - axolotl.integrations.kernels.KernelsPlugin
-
-use_kernels: true
-use_scattermoe: true
-```
-
-**Important:** Setting `experts_implementation` is incompatible with `use_scattermoe`.
-
-## How It Works
-
-The `KernelsPlugin` runs before model loading and:
-
-1. Registers the ScatterMoE kernel from the [`axolotl-ai-co/scattermoe`](https://huggingface.co/axolotl-ai-co/scattermoe) Hub repo.
-2. Patches the model's `SparseMoeBlock` forward method with the optimized ScatterMoE implementation.
-
-This works for any MoE model in transformers that uses a `SparseMoeBlock` class (Mixtral, Qwen2-MoE, OLMoE, etc.).
-
-## Limitations
-
-ScatterMoE uses a softmax -> topk routing, so results may be different for some model arch as baseline (GPT-OSS, GLM_MOE_DSA).
-
-ScatterMoE does not work for GLM4.7 Flash (glm4_moe_lite) atm.
-
-## Note on MegaBlocks
-
-We tested [MegaBlocks](https://huggingface.co/kernels-community/megablocks) but were unable to ensure numerical accuracy, so we did not integrate it. It was also incompatible with many newer model architectures in transformers.

src/axolotl/integrations/kernels/args.py

@@ -33,16 +33,3 @@ class KernelsArgs(BaseModel):
             data["experts_implementation"] = "eager"
 
         return data
-
-    @model_validator(mode="before")
-    @classmethod
-    def disable_mlp_kernel_scattermoe(cls, data):
-        if data.get("use_scattermoe") is True:
-            if data.get("lora_mlp_kernel") is True:
-                LOG.warning(
-                    "Disabling lora_mlp_kernel when using scattermoe due to compatibility issues."
-                )
-                data["lora_mlp_kernel"] = False
-            data["mlp_kernel"] = False
-
-        return data

src/axolotl/integrations/kernels/libs/scattermoe_lora/__init__.py

@@ -1,18 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# Copyright (c) Axolotl AI
-# Licensed under the Apache License, Version 2.0
-
-from . import layers
-from .lora_ops import ParallelExperts
-from .parallel_experts import flatten_sort_count, parallel_linear
-from .parallel_linear_lora import ScatterMoELoRA, parallel_linear_lora
-
-__all__ = [
-    "layers",
-    "ParallelExperts",
-    "flatten_sort_count",
-    "parallel_linear",
-    "ScatterMoELoRA",
-    "parallel_linear_lora",
-    "lora_ops",
-]

src/axolotl/integrations/kernels/libs/scattermoe_lora/kernels/__init__.py

@@ -1,12 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-#
-# Original work Copyright (c) Shawn Tan and ScatterMoE Contributors
-# Adapted from https://github.com/shawntan/scattermoe
-# See https://github.com/shawntan/scattermoe/blob/main/LICENSE
-#
-# Modifications and LoRA adaptation Copyright (c) Axolotl AI
-# Licensed under the Apache License, Version 2.0
-
-from . import lora_ops, ops
-
-__all__ = ["ops", "lora_ops"]

src/axolotl/integrations/kernels/libs/scattermoe_lora/kernels/ops.py

@@ -1,645 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# Adapted from https://github.com/shawntan/scattermoe
-# Copyright (c) Shawn Tan and ScatterMoE Contributors
-# Licensed under the Apache License, Version 2.0
-# See https://github.com/shawntan/scattermoe/blob/main/LICENSE
-
-from typing import Optional
-
-import torch
-import triton
-import triton.language as tl
-
-BLOCK_M = 128
-ALLOW_TF32 = True
-
-
-@triton.jit
-def _compute_expert_block(
-    E_idx,
-    E_mask,
-    M_in_idx,
-    N_block,
-    N_mask,
-    X_ptr,
-    stride_xm,
-    stride_xk,
-    W_ptr,
-    stride_we,
-    stride_wk,
-    stride_wn,
-    K,
-    acc,
-    no_k_mask,
-    BLOCK_K,
-    allow_tf32=True,
-):
-    K_block = tl.arange(0, BLOCK_K)
-    X_blk_ptrs = X_ptr + M_in_idx[:, None] * stride_xm + K_block[None, :] * stride_xk
-    W_blk_ptrs = (
-        W_ptr
-        + K_block[:, None] * stride_wk
-        + N_block[None, :] * stride_wn
-        + E_idx * stride_we
-    )
-    iters = tl.cdiv(K, BLOCK_K)
-
-    for K_block_id in range(iters):
-        if no_k_mask:
-            x = tl.load(X_blk_ptrs, mask=E_mask[:, None])
-            w = tl.load(W_blk_ptrs, mask=N_mask[None, :])
-        else:
-            K_mask = (K_block_id * BLOCK_K + K_block) < K
-            x = tl.load(X_blk_ptrs, mask=E_mask[:, None] & K_mask[None, :])
-            w = tl.load(W_blk_ptrs, mask=K_mask[:, None] & N_mask[None, :])
-
-        X_blk_ptrs += BLOCK_K * stride_xk
-        W_blk_ptrs += BLOCK_K * stride_wk
-        acc = tl.dot(x, w, acc, allow_tf32=allow_tf32)
-    return acc
-
-
-def _scatter2scatter_configs():
-    return [
-        triton.Config({"BLOCK_N": 128, "BLOCK_K": 32}, num_stages=4, num_warps=4),
-    ]
-
-
-@triton.autotune(
-    configs=_scatter2scatter_configs(),
-    key=["M", "N", "K"],
-)
-@triton.heuristics(
-    {
-        "NO_K_MASK": lambda args: (args["K"] % args["BLOCK_K"]) == 0,
-        "NO_N_MASK": lambda args: (args["N"] % args["BLOCK_N"]) == 0,
-    }
-)
-@triton.jit
-def _scatter2scatter(
-    X_ptr,
-    stride_xm: tl.constexpr,
-    stride_xk: tl.constexpr,
-    W_ptr,
-    stride_we,
-    stride_wk: tl.constexpr,
-    stride_wn: tl.constexpr,
-    Y_ptr,
-    stride_ym: tl.constexpr,
-    stride_yn: tl.constexpr,
-    B_ptr,
-    stride_be: tl.constexpr,
-    stride_bn: tl.constexpr,
-    grouped_idx_ptr,
-    expert_idxs_ptr,
-    # block_start_idx_ptr,
-    FAN_OUT: tl.constexpr,
-    M,
-    K: tl.constexpr,
-    N: tl.constexpr,
-    E: tl.constexpr,
-    BLOCK_M: tl.constexpr,
-    BLOCK_N: tl.constexpr,
-    BLOCK_K: tl.constexpr,
-    ACC_TYPE: tl.constexpr,
-    # OUT_M,
-    allow_tf32: tl.constexpr,
-    x_grouped: tl.constexpr,
-    y_grouped: tl.constexpr,
-    NO_K_MASK: tl.constexpr,
-    NO_N_MASK: tl.constexpr,
-):
-    pid = tl.program_id(axis=0)
-
-    N_BLOCK_COUNT = tl.cdiv(N, BLOCK_N)
-    M_block_id = pid // N_BLOCK_COUNT
-    N_block_id = pid % N_BLOCK_COUNT
-
-    M_block = M_block_id * BLOCK_M + tl.arange(0, BLOCK_M)
-    N_block = N_block_id * BLOCK_N + tl.arange(0, BLOCK_N)
-    N_mask = N_block < N
-    M_boundary_mask = M_block < (FAN_OUT * M)
-    E_idxs = tl.load(expert_idxs_ptr + M_block, mask=M_boundary_mask, other=E)
-
-    no_k_mask = K % BLOCK_K == 0
-
-    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=ACC_TYPE)
-    E_first_idx = tl.min(E_idxs)
-    E_last_idx = tl.minimum(tl.max(E_idxs), E - 1)
-    M_idx = tl.load(grouped_idx_ptr + M_block, mask=M_boundary_mask).to(tl.int32)
-    for E_idx in range(E_first_idx, E_last_idx + 1):
-        E_mask = E_idxs == E_idx
-        E_M_idx = M_idx
-        if x_grouped:
-            M_in_idx = M_block
-        else:
-            M_in_idx = E_M_idx // FAN_OUT
-        acc = _compute_expert_block(
-            E_idx,
-            E_mask,
-            M_in_idx,
-            N_block,
-            N_mask,
-            X_ptr,
-            stride_xm,
-            stride_xk,
-            W_ptr,
-            stride_we,
-            stride_wk,
-            stride_wn,
-            K,
-            acc,
-            no_k_mask,
-            BLOCK_K,
-            allow_tf32=allow_tf32,
-        )
-
-    if B_ptr is not None:
-        B_blk_ptrs = B_ptr + E_idxs[:, None] * stride_be + N_block[None, :] * stride_bn
-        acc += tl.load(B_blk_ptrs, mask=M_boundary_mask[:, None] & N_mask[None, :])
-
-    if y_grouped:
-        M_out_idx = M_block
-    else:
-        M_out_idx = M_idx
-    Y_blk_ptrs = Y_ptr + (M_out_idx[:, None] * stride_ym + N_block[None, :] * stride_yn)
-    tl.store(Y_blk_ptrs, acc, mask=M_boundary_mask[:, None] & N_mask[None, :])
-
-
-def scatter2scatter(
-    X,
-    W,
-    sorted_expert_idxs,
-    sorted_scattered_idxs,
-    k,
-    b=None,
-    x_grouped=False,
-    y_grouped=False,
-    out=None,
-):
-    assert sorted_scattered_idxs.size(0) == sorted_expert_idxs.size(0)
-    assert sorted_scattered_idxs.size(0) == X.size(0) * k
-    # Pre-kernel setup
-    y_dim = W.size(-1)
-    L_scattered = sorted_expert_idxs.size(0)
-    if out is None:
-        output = torch.empty((L_scattered, y_dim), device=X.device, dtype=X.dtype)
-    else:
-        assert out.size(0) == L_scattered and out.size(1) == y_dim
-        output = out
-
-    scatter2scatter_compileable(
-        output,
-        W,
-        X,
-        k,
-        sorted_expert_idxs,
-        sorted_scattered_idxs,
-        b,
-        x_grouped,
-        y_grouped,
-    )
-    return output
-
-
-@torch.library.custom_op("scattermoe::scatter2scatter", mutates_args={"output"})
-def scatter2scatter_compileable(
-    output: torch.Tensor,
-    W: torch.Tensor,
-    X: torch.Tensor,
-    k: int,
-    sorted_expert_idxs: torch.Tensor,
-    sorted_scattered_idxs: torch.Tensor,
-    b: Optional[torch.Tensor],
-    x_grouped: bool,
-    y_grouped: bool,
-) -> None:
-    def grid(META):
-        grid_num = (
-            triton.cdiv(sorted_expert_idxs.size(0), META["BLOCK_M"])
-            * triton.cdiv(META["N"], META["BLOCK_N"]),
-        )
-        return grid_num
-
-    if b is None:
-        b = None
-        stride_be = stride_bn = 0
-    else:
-        stride_be, stride_bn = b.stride()
-
-    _scatter2scatter[grid](
-        # X_ptr, stride_xm, stride_xk,
-        X,
-        X.stride(0),
-        X.stride(1),
-        # W_ptr, stride_we, stride_wk, stride_wn,
-        W,
-        W.stride(0),
-        W.stride(1),
-        W.stride(2),
-        # Y_ptr, stride_ym, stride_yn,
-        output,
-        output.stride(0),
-        output.stride(1),
-        # B_ptr, stride_be, stride_bn
-        b,
-        stride_be,
-        stride_bn,
-        grouped_idx_ptr=sorted_scattered_idxs,
-        expert_idxs_ptr=sorted_expert_idxs,
-        # block_start_idx_ptr=padded_block_idxs,
-        FAN_OUT=k,
-        M=X.size(0),
-        K=X.size(1),
-        N=output.size(1),
-        E=W.size(0),
-        BLOCK_M=BLOCK_M,
-        ACC_TYPE=tl.float32,
-        allow_tf32=ALLOW_TF32,
-        x_grouped=x_grouped,
-        y_grouped=y_grouped,
-    )
-
-
-def _config_XtY():
-    return [
-        triton.Config(
-            {"BLOCK_N": 128, "BLOCK_K": 128, "BLOCK_M": 32}, num_stages=4, num_warps=4
-        ),
-    ]
-
-
-def group_bwd_W(DY, X, expert_offsets, E, has_bias=False):
-    DWt = torch.zeros((E, DY.size(-1), X.size(-1)), device=DY.device, dtype=DY.dtype)
-    DW = DWt.permute(0, 2, 1)
-    if has_bias:
-        Db = torch.zeros((E, DY.size(-1)), device=DY.device, dtype=DY.dtype)
-    else:
-        Db = None
-    groupXtY_compileable(E, DW, Db, DY, X, expert_offsets)
-    return DW, Db
-
-
-@torch.library.custom_op("scattermoe::groupXtY", mutates_args={"DW", "Db"})
-def groupXtY_compileable(
-    E: int,
-    DW: torch.Tensor,
-    Db: Optional[torch.Tensor],
-    DY: torch.Tensor,
-    X: torch.Tensor,
-    expert_offsets: torch.Tensor,
-) -> None:
-    def grid(META):
-        grid = (
-            E * triton.cdiv(META["K"], META["BLOCK_K"]),
-            triton.cdiv(META["N"], META["BLOCK_N"]),
-        )
-        return grid
-
-    if Db is None:
-        stride_dbe = 0
-        stride_dbn = 0
-    else:
-        stride_dbe, stride_dbn = Db.stride()
-
-    _groupXtY[grid](
-        # DY_ptr, stride_dym, stride_dyk,
-        DY,
-        DY.stride(0),
-        DY.stride(1),
-        # X_ptr, stride_xm, stride_xn,
-        X,
-        X.stride(0),
-        X.stride(1),
-        # DW_ptr, stride_dwe, stride_dwk, stride_dwn,
-        DW,
-        DW.stride(0),
-        DW.stride(1),
-        DW.stride(2),
-        # Db_ptr, stride_dwe, stride_dbn,
-        Db,
-        stride_dbe,
-        stride_dbn,
-        # expert_offsets_ptr,
-        expert_offsets,
-        # K: tl.constexpr, N: tl.constexpr,
-        M=DY.size(0),
-        N=DY.size(-1),
-        K=X.size(-1),
-        # ACC_TYPE: tl.constexpr,
-        ACC_TYPE=tl.float32,
-        allow_tf32=ALLOW_TF32,
-    )
-
-
-@triton.autotune(
-    configs=_config_XtY(),
-    key=["M", "N", "K"],
-)
-@triton.heuristics(
-    {
-        "NO_K_MASK": lambda args: (args["K"] % args["BLOCK_K"]) == 0,
-        "NO_N_MASK": lambda args: (args["N"] % args["BLOCK_N"]) == 0,
-    }
-)
-@triton.jit
-def _groupXtY(
-    DY_ptr,
-    stride_dym,
-    stride_dyk,
-    X_ptr,
-    stride_xm,
-    stride_xn,
-    DW_ptr,
-    stride_dwe,
-    stride_dwk,
-    stride_dwn,
-    Db_ptr,
-    stride_dbe,
-    stride_dbn,
-    expert_offsets_ptr,
-    M,
-    K: tl.constexpr,
-    N: tl.constexpr,
-    BLOCK_M: tl.constexpr,
-    BLOCK_N: tl.constexpr,
-    BLOCK_K: tl.constexpr,
-    ACC_TYPE: tl.constexpr,
-    allow_tf32: tl.constexpr,
-    NO_K_MASK: tl.constexpr,
-    NO_N_MASK: tl.constexpr,
-):
-    pid0 = tl.program_id(axis=0)
-    pid1 = tl.program_id(axis=1)
-    num0 = tl.num_programs(0)
-    num1 = tl.num_programs(1)
-    # pid1, pid0 = tl.swizzle2d(pid1, pid0, num1, num0, 128)
-    pid0, pid1 = tl.swizzle2d(pid0, pid1, num0, num1, 4)
-
-    K_BLOCK_COUNT = tl.cdiv(K, BLOCK_K)
-    E_idx = pid0 // K_BLOCK_COUNT
-    K_block_id = pid0 % K_BLOCK_COUNT
-    N_block_id = pid1
-
-    if E_idx == 0:
-        start_idx = 0
-    else:
-        start_idx = tl.load(expert_offsets_ptr + E_idx - 1).to(tl.int32)
-    end_idx = tl.load(expert_offsets_ptr + E_idx).to(tl.int32)
-
-    if end_idx > start_idx:
-        M_block = tl.max_contiguous(start_idx + tl.arange(0, BLOCK_M), BLOCK_M)
-
-        K_block = K_block_id * BLOCK_K + tl.arange(0, BLOCK_K)
-        K_mask = K_block < K
-        K_block = tl.max_contiguous(tl.multiple_of(K_block % K, BLOCK_K), BLOCK_K)
-
-        N_block = N_block_id * BLOCK_N + tl.arange(0, BLOCK_N)
-        N_mask = N_block < N
-        N_block = tl.max_contiguous(tl.multiple_of(N_block % N, BLOCK_N), BLOCK_N)
-
-        M_idxs = M_block
-        xt_blk_ptrs = X_ptr + K_block[:, None] * stride_xn + M_idxs[None, :] * stride_xm
-        dy_blk_ptrs = (
-            DY_ptr + M_idxs[:, None] * stride_dym + N_block[None, :] * stride_dyk
-        )
-        if (Db_ptr is not None) and (K_block_id == 0):
-            _xty_and_bias(
-                E_idx,
-                start_idx,
-                end_idx,
-                M_block,
-                K_block,
-                K_mask,
-                N_block,
-                N_mask,
-                dy_blk_ptrs,
-                stride_dym,
-                xt_blk_ptrs,
-                stride_xm,
-                DW_ptr,
-                stride_dwe,
-                stride_dwk,
-                stride_dwn,
-                Db_ptr,
-                stride_dbe,
-                stride_dbn,
-                BLOCK_M,
-                BLOCK_N,
-                BLOCK_K,
-                ACC_TYPE,
-                allow_tf32,
-                NO_K_MASK,
-                NO_N_MASK,
-                compute_bias=True,
-            )
-        else:
-            _xty_and_bias(
-                E_idx,
-                start_idx,
-                end_idx,
-                M_block,
-                K_block,
-                K_mask,
-                N_block,
-                N_mask,
-                dy_blk_ptrs,
-                stride_dym,
-                xt_blk_ptrs,
-                stride_xm,
-                DW_ptr,
-                stride_dwe,
-                stride_dwk,
-                stride_dwn,
-                Db_ptr,
-                stride_dbe,
-                stride_dbn,
-                BLOCK_M,
-                BLOCK_N,
-                BLOCK_K,
-                ACC_TYPE,
-                allow_tf32,
-                NO_K_MASK,
-                NO_N_MASK,
-                compute_bias=False,
-            )
-
-
-@triton.jit
-def _xty_and_bias(
-    E_idx,
-    start_idx,
-    end_idx,
-    M_block,
-    K_block,
-    K_mask,
-    N_block,
-    N_mask,
-    dy_blk_ptrs,
-    stride_dym,
-    xt_blk_ptrs,
-    stride_xm,
-    DW_ptr,
-    stride_dwe,
-    stride_dwk,
-    stride_dwn,
-    Db_ptr,
-    stride_dbe,
-    stride_dbn,
-    BLOCK_M,
-    BLOCK_N,
-    BLOCK_K,
-    ACC_TYPE,
-    allow_tf32,
-    NO_K_MASK,
-    NO_N_MASK,
-    compute_bias: tl.constexpr,
-):
-    if compute_bias:
-        db_acc = tl.zeros((BLOCK_N,), dtype=ACC_TYPE)
-    else:
-        db_acc = None
-
-    acc = tl.zeros((BLOCK_K, BLOCK_N), dtype=ACC_TYPE)
-    iters = tl.cdiv(end_idx - start_idx, BLOCK_M)
-    for i in range(0, iters):
-        M_mask = (i * BLOCK_M + M_block) < end_idx
-        if NO_K_MASK:
-            xt = tl.load(xt_blk_ptrs, mask=M_mask[None, :])
-        else:
-            xt = tl.load(xt_blk_ptrs, mask=K_mask[:, None] & M_mask[None, :])
-        if NO_N_MASK:
-            dy = tl.load(dy_blk_ptrs, mask=M_mask[:, None])
-        else:
-            dy = tl.load(dy_blk_ptrs, mask=M_mask[:, None] & N_mask[None, :])
-
-        acc += tl.dot(xt, dy, out_dtype=ACC_TYPE, allow_tf32=allow_tf32)
-
-        xt_blk_ptrs += BLOCK_M * stride_xm
-        dy_blk_ptrs += BLOCK_M * stride_dym
-
-        if compute_bias:
-            db_acc += tl.sum(dy, axis=0)
-
-    DW_blk_ptrs = (
-        DW_ptr
-        + E_idx * stride_dwe
-        + K_block[:, None] * stride_dwk
-        + N_block[None, :] * stride_dwn
-    )
-    acc = acc.to(DW_blk_ptrs.dtype.element_ty)
-    tl.store(DW_blk_ptrs, acc, mask=K_mask[:, None] & N_mask[None, :])
-    if compute_bias:
-        Db_blk_ptrs = Db_ptr + E_idx * stride_dbe + N_block * stride_dbn
-        tl.store(Db_blk_ptrs, db_acc, mask=N_mask)
-
-
-def _config_grouping():
-    return [
-        triton.Config({"BLOCK_N": 256, "BLOCK_K": 128}, num_stages=4, num_warps=4),
-        # triton.Config({'BLOCK_N': 128, 'BLOCK_K': 64}, num_stages=4, num_warps=4),
-        # triton.Config({'BLOCK_N': 64, 'BLOCK_K': 32}, num_stages=4, num_warps=4),
-    ]
-
-
-def group(A, sorted_expert_idxs, coeff=None, fan_out=1, out=None):
-    N = sorted_expert_idxs.size(0)
-    K = A.size(1)
-    assert A.size(0) * fan_out == N
-    if out is not None:
-        Y = out
-    else:
-        Y = torch.empty((N, K), dtype=A.dtype, device=A.device)
-    group_compileable(A, K, N, Y, coeff, coeff is not None, fan_out, sorted_expert_idxs)
-    return Y
-
-
-@torch.library.custom_op("scattermoe::group", mutates_args={"Y"})
-def group_compileable(
-    A: torch.Tensor,
-    K: int,
-    N: int,
-    Y: torch.Tensor,
-    coeff: Optional[torch.Tensor],
-    has_coeff: bool,
-    fan_out: int,
-    sorted_expert_idxs: torch.Tensor,
-) -> None:
-    def grid(META):
-        grid_num = (triton.cdiv(META["N"], META["BLOCK_N"]),)
-        return grid_num
-
-    _group[grid](
-        # A_ptr, stride_an, stride_ai,
-        A,
-        A.stride(0),
-        A.stride(1),
-        has_coeff,
-        coeff,
-        fan_out,
-        # Y_ptr, stride_yn, stride_yk,
-        Y,
-        Y.stride(0),
-        Y.stride(1),
-        # grouped_idx_ptr,
-        sorted_expert_idxs,
-        # N: tl.constexpr, K: tl.constexpr,
-        N,
-        K,
-    )
-
-
-@triton.autotune(configs=_config_grouping(), key=["K"])
-@triton.heuristics({"NO_K_MASK": lambda args: (args["K"] % args["BLOCK_K"]) == 0})
-@triton.jit
-def _group(
-    src_ptr,
-    stride_sn,
-    stride_sk,
-    has_coeff: tl.constexpr,
-    coeff_ptr,
-    FAN_OUT: tl.constexpr,
-    tgt_ptr,
-    stride_tn,
-    stride_ti,
-    grouped_idx_ptr,
-    N,
-    K: tl.constexpr,
-    BLOCK_N: tl.constexpr,
-    BLOCK_K: tl.constexpr,
-    NO_K_MASK: tl.constexpr,
-):
-    pid = tl.program_id(axis=0)
-
-    N_block_id = pid
-    N_blk = N_block_id * BLOCK_N + tl.arange(0, BLOCK_N)
-    N_mask = N_blk < N
-    N_blk = tl.max_contiguous(tl.multiple_of(N_blk % N, BLOCK_N), BLOCK_N)
-    N_idx = tl.load(grouped_idx_ptr + N_blk, mask=N_mask, other=0)
-
-    K_blk = tl.arange(0, BLOCK_K)
-    src_blk_ptrs = (
-        src_ptr + (N_idx // FAN_OUT)[:, None] * stride_sn + K_blk[None, :] * stride_sk
-    )
-    tgt_blk_ptrs = tgt_ptr + N_blk[:, None] * stride_tn + K_blk[None, :] * stride_ti
-
-    if has_coeff:
-        c = tl.load(coeff_ptr + N_idx, mask=N_mask)[:, None]
-
-    iters = tl.cdiv(K, BLOCK_K)
-    for i in range(0, iters):
-        if NO_K_MASK or i < iters - 1:
-            block = tl.load(src_blk_ptrs, mask=N_mask[:, None])
-            if has_coeff:
-                block *= c
-            tl.store(tgt_blk_ptrs, block, mask=N_mask[:, None])
-
-        else:
-            K_mask = (i * BLOCK_K + K_blk) < K
-            mask = N_mask[:, None] & K_mask[None, :]
-            block = tl.load(src_blk_ptrs, mask=mask)
-            if has_coeff:
-                block *= c
-            tl.store(tgt_blk_ptrs, block, mask=mask)
-        src_blk_ptrs += BLOCK_K * stride_sk
-        tgt_blk_ptrs += BLOCK_K * stride_ti

src/axolotl/integrations/kernels/libs/scattermoe_lora/kernels/single.py

@@ -1,98 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# Adapted from https://github.com/shawntan/scattermoe
-# Copyright (c) Shawn Tan and ScatterMoE Contributors
-# Licensed under the Apache License, Version 2.0
-# See https://github.com/shawntan/scattermoe/blob/main/LICENSE
-
-import torch
-import triton
-import triton.language as tl
-
-
-@triton.jit
-def _single2scatter(
-    X_ptr,
-    stride_xm,
-    stride_xk,
-    W_ptr,
-    stride_we,
-    stride_wk,
-    stride_wn,
-    Y_ptr,
-    stride_ym,
-    stride_yn,
-    expert_idxs_ptr,
-    FAN_OUT: tl.constexpr,
-    K: tl.constexpr,
-    N: tl.constexpr,
-    E: tl.constexpr,
-    BLOCK_N: tl.constexpr,
-    BLOCK_K: tl.constexpr,
-    ACC_TYPE: tl.constexpr,
-):
-    pid0 = tl.program_id(axis=0)
-    pid1 = tl.program_id(axis=1)
-
-    N_block_id = pid0
-    if FAN_OUT == 1:
-        in_idx = pid1
-    else:
-        in_idx = 0
-    out_idx = pid1
-
-    K_block = tl.arange(0, BLOCK_K)
-    N_block = tl.max_contiguous(
-        tl.multiple_of((N_block_id * BLOCK_N + tl.arange(0, BLOCK_N)) % N, BLOCK_N),
-        BLOCK_N,
-    )
-    E_idx = tl.load(expert_idxs_ptr + pid1)
-    X_blk_ptrs = X_ptr + in_idx * stride_xm + K_block[:, None] * stride_xk
-    W_blk_ptrs = (
-        W_ptr
-        + E_idx * stride_we
-        + K_block[:, None] * stride_wk
-        + N_block[None, :] * stride_wn
-    )
-    N_mask = N_block < N
-    acc = tl.zeros((1, BLOCK_N), dtype=ACC_TYPE)
-    for _K_block_id in range(0, tl.cdiv(K, BLOCK_K)):
-        K_mask = K_block < K
-        x = tl.load(X_blk_ptrs, mask=K_mask[:, None], other=0.0)
-        w = tl.load(W_blk_ptrs, mask=K_mask[:, None] & N_mask[None, :], other=0.0)
-        acc += tl.sum(x * w, axis=0)[None, :]
-        X_blk_ptrs += BLOCK_K * stride_xk
-        W_blk_ptrs += BLOCK_K * stride_wk
-        K_block += BLOCK_K
-    Y_blk_ptrs = Y_ptr + out_idx * stride_ym + N_block[None, :] * stride_yn
-    tl.store(Y_blk_ptrs, acc, mask=N_mask[None, :])
-
-
-def single2scatter(X, W, expert_idxs):
-    E, xdim, ydim = W.size()
-    k = expert_idxs.size(1)
-    assert X.size(0) == k or X.size(0) == 1
-    Y = torch.empty((k, ydim), device=X.device, dtype=X.dtype)
-    BLOCK_N = 128
-    BLOCK_K = 128
-    grid = triton.cdiv(ydim, BLOCK_N), k
-    _single2scatter[grid](
-        X,
-        X.stride(0),
-        X.stride(1),
-        W,
-        W.stride(0),
-        W.stride(1),
-        W.stride(2),
-        Y,
-        Y.stride(0),
-        Y.stride(1),
-        expert_idxs,
-        FAN_OUT=Y.size(0) // X.size(0),
-        K=xdim,
-        N=ydim,
-        E=E,
-        BLOCK_N=BLOCK_N,
-        BLOCK_K=BLOCK_K,
-        ACC_TYPE=tl.float32,
-    )
-    return Y

src/axolotl/integrations/kernels/libs/scattermoe_lora/layers.py

@@ -1,439 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-#
-# Original work Copyright (c) Shawn Tan and ScatterMoE Contributors
-# Adapted from https://github.com/shawntan/scattermoe
-# See https://github.com/shawntan/scattermoe/blob/main/LICENSE
-#
-# Modifications and LoRA adaptation Copyright (c) Axolotl AI
-# Licensed under the Apache License, Version 2.0
-
-"""
-ScatterMoE layer replacements for HuggingFace MoE architectures.
-
-Provides drop-in forward replacements that use ScatterMoE kernels for
-acceleration. When used via the HF ``kernels`` library
-(``replace_kernel_forward_from_hub``), these classes replace the forward
-method of the original MoE block.
-
-LoRA support
-------------
-When peft wraps parameters via ``target_parameters``, the ``self.experts``
-submodule becomes a chain of ``ParamWrapper`` objects and the ``self.gate``
-router may also become a ``ParamWrapper``.  The ``HFScatterMoEGatedMLP``
-forward detects this and automatically:
-
-1. Unwraps ``self.gate`` to the base router, applying gate LoRA delta
-2. Unwraps ``self.experts`` to the base ``OlmoeExperts`` module
-3. Extracts LoRA A/B weights and scaling from each wrapper
-4. Converts B layout from peft rank-major to scattermoe expert-major
-5. Routes to ``parallel_linear_lora`` for fused LoRA computation
-6. Passes through ``self.shared_expert`` / ``self.shared_expert_gate``
-   (peft wraps their linear layers with standard LoRA, no special handling)
-"""
-
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from .parallel_experts import flatten_sort_count, parallel_linear
-from .parallel_linear_lora import get_lora_params_from_wrapper, parallel_linear_lora
-
-# =============================================================================
-# LoRA layout conversion utilities (peft <-> scattermoe)
-# =============================================================================
-
-
-def peft_lora_B_to_scattermoe(peft_B, num_experts, rank):
-    """Convert peft rank-major lora_B ``[out, E*r]`` to scattermoe
-    expert-major ``[N, r*E]``.
-
-    peft reshapes B to ``[out, r, E]`` (rank-major).
-    scattermoe slices B as ``[:, e*r:(e+1)*r]`` (expert-major).
-    """
-    N = peft_B.shape[0]
-    return (
-        peft_B.reshape(N, rank, num_experts)
-        .permute(0, 2, 1)
-        .contiguous()
-        .reshape(N, num_experts * rank)
-    )
-
-
-def peft_lora_to_scattermoe(peft_A, peft_B, num_experts, rank):
-    """Convert peft LoRA weights to scattermoe layout (with A<->B swap).
-
-    peft operates on the parameter in its native storage layout ``[E, dim1, dim2]``
-    where ``in_features=dim1, out_features=dim2``.  ScatterMoE transposes the
-    parameter (``W = param.transpose(2, 1)``) giving ``[E, dim2, dim1]`` with
-    ``K=dim2, N=dim1``.  Because of this transposition, peft's A and B roles
-    are swapped relative to scattermoe's convention.
-
-    peft gives:
-        lora_A ``[r*E, dim1]``, lora_B ``[dim2, r*E]``
-
-    scattermoe needs:
-        lora_A ``[r*E, K=dim2]``, lora_B ``[N=dim1, r*E]``
-
-    This function swaps A<->B and converts B from rank-major to expert-major.
-    Uses vectorized tensor operations (no Python loop over experts).
-
-    Works for **both** gate_up_proj and down_proj since the transposition
-    issue is the same for any parameter.
-    """
-    peft_B_em = peft_lora_B_to_scattermoe(peft_B, num_experts, rank)
-
-    dim1 = peft_A.shape[1]  # peft in_features -> scattermoe N
-    dim2 = peft_B_em.shape[0]  # peft out_features -> scattermoe K
-
-    # smoe_A: per expert, transpose B_e [dim2, r] -> [r, dim2]
-    # [dim2, E*r] -> [dim2, E, r] -> [E, r, dim2] -> [E*r, dim2]
-    smoe_A = (
-        peft_B_em.reshape(dim2, num_experts, rank)
-        .permute(1, 2, 0)
-        .contiguous()
-        .reshape(rank * num_experts, dim2)
-    )
-
-    # smoe_B: per expert, transpose A_e [r, dim1] -> [dim1, r]
-    # [E*r, dim1] -> [E, r, dim1] -> [dim1, E, r] -> [dim1, E*r]
-    smoe_B = (
-        peft_A.reshape(num_experts, rank, dim1)
-        .permute(2, 0, 1)
-        .contiguous()
-        .reshape(dim1, num_experts * rank)
-    )
-
-    return smoe_A, smoe_B
-
-
-def peft_down_proj_lora_to_scattermoe(peft_A, peft_B, num_experts, rank):
-    """Deprecated alias for :func:`peft_lora_to_scattermoe`."""
-    return peft_lora_to_scattermoe(peft_A, peft_B, num_experts, rank)
-
-
-# =============================================================================
-# ParamWrapper unwrapping
-# =============================================================================
-
-
-def _unwrap_gate_lora(gate_module):
-    """Unwrap peft ``ParamWrapper`` on the router gate.
-
-    When peft targets ``gate.weight``, ``self.gate`` becomes::
-
-        ParamWrapper(weight)
-          -> base_layer: OlmoeTopKRouter (the real module)
-
-    This function detects the wrapping and returns the base router, its
-    weight tensor, and an optional LoRA delta tensor.
-
-    Returns:
-        (base_gate, gate_weight, gate_lora_delta_or_None)
-
-        ``base_gate`` is the original router module (with ``.top_k``,
-        ``.num_experts``, ``.norm_topk_prob``).
-        ``gate_weight`` is the base router weight (may be a DTensor under FSDP).
-        ``gate_lora_delta_or_None`` is the LoRA delta tensor if LoRA is active,
-        else ``None``.  Kept separate to avoid mixing DTensor + Tensor in an add.
-    """
-    if hasattr(gate_module, "base_layer") and hasattr(gate_module, "lora_A"):
-        base_gate = gate_module.base_layer
-        lora_A, lora_B, scaling = get_lora_params_from_wrapper(gate_module)
-        if lora_A is not None:
-            # gate weight: [num_experts, hidden_size]
-            # lora_A: [r, hidden_size], lora_B: [num_experts, r]
-            # delta = scaling * B @ A = [num_experts, hidden_size]
-            delta = scaling * (lora_B @ lora_A)
-            return base_gate, base_gate.weight, delta
-        else:
-            return base_gate, base_gate.weight, None
-    else:
-        # No wrapping — gate is the original module
-        return gate_module, gate_module.weight, None
-
-
-def _convert_smoe_lora(lora_A, lora_B, num_experts, rank, scaling):
-    """Convert peft LoRA weights to scattermoe layout."""
-    smoe_A, smoe_B = peft_lora_to_scattermoe(lora_A, lora_B, num_experts, rank)
-    return (smoe_A, smoe_B, scaling)
-
-
-def _unwrap_experts_lora(experts_module):
-    """Walk a peft ``ParamWrapper`` chain on ``self.experts``.
-
-    When peft targets ``experts.gate_up_proj`` and ``experts.down_proj`` via
-    ``target_parameters``, ``self.experts`` becomes a nested chain::
-
-        ParamWrapper(down_proj)
-          -> base_layer: ParamWrapper(gate_up_proj)
-              -> base_layer: OlmoeExperts (the real module)
-
-    This function walks the chain, collects LoRA params keyed by
-    ``parameter_name``, and returns the base experts module.
-
-    Returns:
-        (base_experts, gup_lora, down_lora)
-
-        Each ``*_lora`` is either ``(smoe_A, smoe_B, scaling)`` or ``None``.
-        A/B are already in scattermoe layout.
-    """
-    # Collect ParamWrapper layers by their parameter_name
-    wrappers = {}
-    module = experts_module
-    while hasattr(module, "base_layer") and hasattr(module, "lora_A"):
-        param_name = getattr(module, "parameter_name", None)
-        if param_name is not None:
-            wrappers[param_name] = module
-        module = module.base_layer
-
-    base_experts = module
-
-    if not wrappers:
-        return base_experts, None, None
-
-    # Determine num_experts from base module
-    num_experts = getattr(base_experts, "num_experts", None)
-    if num_experts is None:
-        # Fallback: infer from parameter shape
-        gup = getattr(base_experts, "gate_up_proj", None)
-        if gup is not None:
-            num_experts = gup.shape[0]
-
-    # Extract gate_up_proj LoRA (needs A<->B swap due to transposition)
-    gup_lora = None
-    gup_wrapper = wrappers.get("gate_up_proj")
-    if gup_wrapper is not None:
-        lora_A, lora_B, scaling = get_lora_params_from_wrapper(gup_wrapper)
-        if lora_A is not None:
-            rank = lora_A.shape[0] // num_experts
-            gup_lora = _convert_smoe_lora(lora_A, lora_B, num_experts, rank, scaling)
-
-    # Extract down_proj LoRA (needs A<->B swap due to transposition)
-    down_lora = None
-    down_wrapper = wrappers.get("down_proj")
-    if down_wrapper is not None:
-        lora_A, lora_B, scaling = get_lora_params_from_wrapper(down_wrapper)
-        if lora_A is not None:
-            rank = lora_A.shape[0] // num_experts
-            down_lora = _convert_smoe_lora(lora_A, lora_B, num_experts, rank, scaling)
-
-    return base_experts, gup_lora, down_lora
-
-
-# =============================================================================
-# Layer classes
-# =============================================================================
-
-
-class ScatterMoEGatedMLP(nn.Module):
-    def forward(self, layer_input):
-        """
-        Forward pass of the mixture of experts layer.
-
-        Args:
-            layer_input (Tensor):
-                Input tensor.
-
-        Returns:
-            Tensor:
-                Output tensor.
-        """
-        bsz, length, emb_size = layer_input.size()
-        layer_input = layer_input.reshape(-1, emb_size)
-        # compute the top_k routing decision
-        router_logits = self.router.layer(layer_input)
-        routing_weights = F.softmax(router_logits, dim=1, dtype=torch.float)
-        routing_weights, selected_experts = torch.topk(
-            routing_weights, self.router.top_k, dim=-1
-        )
-        routing_weights /= routing_weights.sum(dim=-1, keepdim=True)
-        routing_weights = routing_weights.to(layer_input.dtype)
-        sorted_expert_idxs, sorted_scattered_idxs, expert_offsets = flatten_sort_count(
-            selected_experts, num_experts=self.router.num_experts
-        )
-
-        # compute experts
-        gates, h = parallel_linear(
-            layer_input,
-            self.input_linear.weight.transpose(2, 1),
-            self.router.top_k,
-            sorted_expert_idxs,
-            sorted_scattered_idxs,
-            expert_offsets,
-            grouped_in=False,
-            grouped_out=True,
-        ).chunk(2, dim=-1)
-        h = self.activation(gates) * h
-        layer_output = parallel_linear(
-            h,
-            self.output_linear.weight.transpose(2, 1),
-            1,
-            sorted_expert_idxs,
-            sorted_scattered_idxs,
-            expert_offsets,
-            grouped_in=True,
-            grouped_out=False,
-            gates=routing_weights,
-        )
-        layer_output = layer_output.view(bsz, length, emb_size)
-        return layer_output
-
-
-class HFScatterMoEGatedMLP(nn.Module):
-    """
-    ScatterMoE-accelerated forward pass for HF MoEs (OLMoE / Qwen2MoE).
-
-    Used as a kernel layer via the HF ``kernels`` library.  The ``forward``
-    method replaces the original ``OlmoeSparseMoeBlock.forward``.
-
-    Supports both full-parameter training and LoRA fine-tuning:
-
-    * **Full-param**: uses ``parallel_linear`` (base ScatterMoE kernel)
-    * **LoRA**: detects peft ``ParamWrapper`` on ``self.experts``, extracts
-      adapter weights, and uses ``parallel_linear_lora`` (fused kernel)
-    """
-
-    @staticmethod
-    def forward(self: nn.Module, layer_input: torch.Tensor):
-        """
-        Forward pass using ScatterMoE kernels.
-
-        Args:
-            self: The MoeSparseMoeBlock module containing:
-                - self.gate: Router (or peft ParamWrapper wrapping it)
-                - self.experts: Experts module (or peft ParamWrapper chain)
-                - self.shared_expert: Optional shared expert (e.g. Qwen2MoE)
-                - self.shared_expert_gate: Optional shared expert gate
-            layer_input: Input tensor [batch_size, seq_len, hidden_size]
-
-        Returns:
-            Tensor: [batch_size, seq_len, hidden_size]
-        """
-        batch_size, sequence_length, hidden_dim = layer_input.shape
-        hidden_states_flat = layer_input.view(-1, hidden_dim)
-
-        # ====================================================================
-        # Shared Expert (if present, e.g. Qwen2MoE)
-        # ====================================================================
-        # peft wraps individual linear layers inside shared_expert with
-        # standard LoRA — calling forward() handles this transparently.
-        if hasattr(self, "shared_expert") and self.shared_expert is not None:
-            shared_expert_output = self.shared_expert(hidden_states_flat)
-            # shared_expert_gate may also be peft-wrapped (standard LoRA
-            # on nn.Linear), its forward() applies LoRA automatically.
-            shared_expert_gate_output = F.sigmoid(
-                self.shared_expert_gate(hidden_states_flat)
-            )
-            shared_expert_output = shared_expert_output * shared_expert_gate_output
-        else:
-            shared_expert_output = None
-
-        # ====================================================================
-        # Router Computation (with optional gate LoRA)
-        # ====================================================================
-        base_gate, gate_weight, gate_lora_delta = _unwrap_gate_lora(self.gate)
-        router_logits = F.linear(hidden_states_flat, gate_weight)
-        if gate_lora_delta is not None:
-            router_logits = router_logits + F.linear(
-                hidden_states_flat, gate_lora_delta
-            )
-        routing_weights = F.softmax(router_logits, dim=1, dtype=torch.float)
-
-        top_k = base_gate.top_k
-        num_experts = base_gate.num_experts
-        routing_weights, selected_experts = torch.topk(routing_weights, top_k, dim=-1)
-
-        if base_gate.norm_topk_prob:
-            routing_weights /= routing_weights.sum(dim=-1, keepdim=True)
-        routing_weights = routing_weights.to(hidden_states_flat.dtype)
-
-        sorted_expert_idxs, sorted_scattered_idxs, expert_offsets = flatten_sort_count(
-            selected_experts, num_experts=num_experts
-        )
-
-        # ====================================================================
-        # Detect LoRA (peft ParamWrapper) and extract adapter weights
-        # ====================================================================
-        experts, gup_lora, down_lora = _unwrap_experts_lora(self.experts)
-
-        # ====================================================================
-        # Gate + Up projection
-        # ====================================================================
-        gate_up_W = experts.gate_up_proj.transpose(2, 1)  # [E, hidden, 2*inter]
-
-        if gup_lora is not None:
-            gup_A, gup_B, gup_scaling = gup_lora
-            gup = parallel_linear_lora(
-                hidden_states_flat,
-                gate_up_W,
-                top_k,
-                sorted_expert_idxs,
-                sorted_scattered_idxs,
-                expert_offsets,
-                lora_A=gup_A,
-                lora_B=gup_B,
-                scaling=gup_scaling,
-                grouped_in=False,
-                grouped_out=True,
-                use_fused_dX=True,
-                use_fused_gather=True,
-            )
-        else:
-            gup = parallel_linear(
-                hidden_states_flat,
-                gate_up_W,
-                top_k,
-                sorted_expert_idxs,
-                sorted_scattered_idxs,
-                expert_offsets,
-                grouped_in=False,
-                grouped_out=True,
-            )
-
-        gates, h = gup.chunk(2, dim=-1)
-        h = experts.act_fn(gates) * h
-
-        # ====================================================================
-        # Down projection
-        # ====================================================================
-        down_W = experts.down_proj.transpose(2, 1)  # [E, inter, hidden]
-
-        if down_lora is not None:
-            down_A, down_B, down_scaling = down_lora
-            expert_output = parallel_linear_lora(
-                h,
-                down_W,
-                1,
-                sorted_expert_idxs,
-                sorted_scattered_idxs,
-                expert_offsets,
-                lora_A=down_A,
-                lora_B=down_B,
-                scaling=down_scaling,
-                gates=routing_weights,
-                grouped_in=True,
-                grouped_out=False,
-                use_fused_dX=True,
-                use_fused_gather=True,
-            )
-        else:
-            expert_output = parallel_linear(
-                h,
-                down_W,
-                1,
-                sorted_expert_idxs,
-                sorted_scattered_idxs,
-                expert_offsets,
-                grouped_in=True,
-                grouped_out=False,
-                gates=routing_weights,
-            )
-
-        # ====================================================================
-        # Combine with shared expert and reshape
-        # ====================================================================
-        if shared_expert_output is not None:
-            expert_output = expert_output + shared_expert_output
-
-        expert_output = expert_output.view(batch_size, sequence_length, hidden_dim)
-        return expert_output

src/axolotl/integrations/kernels/libs/scattermoe_lora/lora_ops.py

@@ -1,99 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# Copyright (c) Axolotl AI
-# Licensed under the Apache License, Version 2.0
-
-"""
-ParallelExperts module with LoRA support.
-
-Provides a drop-in replacement for ScatterMoE's ParallelExperts that
-uses the fused LoRA kernel when adapter weights are attached.
-"""
-
-from typing import Optional
-
-import torch
-import torch.nn as nn
-
-from .parallel_linear_lora import parallel_linear_lora
-
-
-class ParallelExperts(nn.Module):
-    """
-    Parallel Experts with fused LoRA support.
-
-    Drop-in replacement for the original ParallelExperts. When LoRA parameters
-    are attached via set_lora(), the forward pass uses a fused kernel:
-        Y = X @ W + scaling * (X @ A^T) @ B^T
-    """
-
-    def __init__(
-        self,
-        num_experts: int,
-        input_size: int,
-        output_size: int,
-        bias: bool = False,
-    ) -> None:
-        super().__init__()
-        self.weight = nn.Parameter(torch.empty(num_experts, output_size, input_size))
-        if bias:
-            self.bias = nn.Parameter(torch.empty(num_experts, output_size))
-        else:
-            self.bias = None
-        self.num_experts = num_experts
-        self.input_size = input_size
-        self.output_size = output_size
-        self._lora_A: torch.Tensor | None = None
-        self._lora_B: torch.Tensor | None = None
-        self._lora_scaling: float | None = None
-        self.reset_parameters()
-
-    def reset_parameters(self) -> None:
-        nn.init.normal_(self.weight, std=0.02)
-        if self.bias is not None:
-            nn.init.zeros_(self.bias)
-
-    def extra_repr(self) -> str:
-        return (
-            f"num_experts={self.num_experts}, "
-            f"input_size={self.input_size}, "
-            f"output_size={self.output_size}"
-        )
-
-    def set_lora(self, lora_A: torch.Tensor, lora_B: torch.Tensor, scaling: float):
-        """Attach LoRA parameters for fused computation."""
-        self._lora_A = lora_A
-        self._lora_B = lora_B
-        self._lora_scaling = scaling
-
-    def clear_lora(self):
-        """Remove LoRA parameters."""
-        self._lora_A = None
-        self._lora_B = None
-        self._lora_scaling = None
-
-    def forward(
-        self,
-        inputs: torch.Tensor,
-        k: int,
-        sorted_expert_idxs: torch.Tensor,
-        sorted_scattered_idxs: torch.Tensor,
-        expert_offsets: torch.Tensor,
-        gates: Optional[torch.Tensor] = None,
-        grouped_in: bool = False,
-        grouped_out: bool = False,
-    ) -> torch.Tensor:
-        return parallel_linear_lora(
-            inputs,
-            self.weight.permute(0, 2, 1),  # [E, input, output]
-            k,
-            sorted_expert_idxs,
-            sorted_scattered_idxs,
-            expert_offsets,
-            lora_A=self._lora_A,
-            lora_B=self._lora_B,
-            scaling=self._lora_scaling if self._lora_scaling is not None else 1.0,
-            expert_biases=self.bias,
-            gates=gates,
-            grouped_in=grouped_in,
-            grouped_out=grouped_out,
-        )

src/axolotl/integrations/kernels/libs/scattermoe_lora/parallel_experts.py

@@ -1,253 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# Adapted from https://github.com/shawntan/scattermoe
-# Copyright (c) Shawn Tan and ScatterMoE Contributors
-# Licensed under the Apache License, Version 2.0
-# See https://github.com/shawntan/scattermoe/blob/main/LICENSE
-
-from typing import Optional
-
-import torch
-import torch.nn as nn
-
-from . import kernels
-
-
-@torch.library.custom_op("scattermoe::bincount", mutates_args={})
-def compileable_bincount(x: torch.Tensor, minlength: int) -> torch.Tensor:
-    return x.bincount(minlength=minlength)
-
-
-@compileable_bincount.register_fake
-def _(x: torch.Tensor, minlength: int) -> torch.Tensor:
-    return torch.empty(minlength, dtype=torch.long, device=x.device)
-
-
-@torch.compile
-def flatten_sort_count(expert_idxs: torch.Tensor, num_experts: int):
-    with torch.no_grad():
-        flattened_expert_idxs = expert_idxs.flatten()
-        sorted_expert_idxs, sorted_scattered_idxs = torch.sort(flattened_expert_idxs)
-        expert_counts = compileable_bincount(
-            flattened_expert_idxs, minlength=num_experts
-        )
-        expert_offsets = expert_counts.cumsum(-1)
-        return sorted_expert_idxs, sorted_scattered_idxs, expert_offsets
-
-
-class ParallelLinear(torch.autograd.Function):
-    @staticmethod
-    def forward(
-        ctx,
-        x: torch.Tensor,
-        expert_weights: torch.Tensor,
-        k: int,
-        sorted_expert_idxs: torch.Tensor,
-        sorted_scattered_idxs: torch.Tensor,
-        expert_offsets: torch.Tensor,
-        expert_biases: Optional[torch.Tensor] = None,
-        gates: Optional[torch.Tensor] = None,
-        grouped_in: bool = False,
-        grouped_out: bool = False,
-    ):
-        with torch.device(x.device):
-            output = kernels.ops.scatter2scatter(
-                X=x,
-                W=expert_weights,
-                b=expert_biases,
-                k=k,
-                sorted_expert_idxs=sorted_expert_idxs,
-                sorted_scattered_idxs=sorted_scattered_idxs,
-                x_grouped=grouped_in,
-                y_grouped=grouped_out,
-            )
-            if gates is not None:
-                output_expanded = output.view(
-                    gates.size(0), gates.size(1), output.size(-1)
-                )
-                output = (gates.unsqueeze(1) @ output_expanded).squeeze(1)
-            else:
-                output_expanded = None
-
-            ctx.save_for_backward(
-                x,
-                expert_weights,
-                expert_biases,
-                sorted_expert_idxs,
-                sorted_scattered_idxs,
-                expert_offsets,
-                gates,
-                output_expanded,
-            )
-            ctx.grouped_in = grouped_in
-            ctx.grouped_out = grouped_out
-            ctx.k = k
-        return output
-
-    @staticmethod
-    def backward(ctx, grad_out: torch.Tensor):
-        with torch.device(grad_out.device):
-            (
-                x,
-                expert_weights,
-                expert_biases,
-                sorted_expert_idxs,
-                sorted_scattered_idxs,
-                expert_offsets,
-                gates,
-                output_expanded,
-            ) = ctx.saved_tensors
-            k = ctx.k
-            grouped_in = ctx.grouped_in
-            grouped_out = ctx.grouped_out
-
-            if gates is not None:
-                # calculate gates gradient
-                # d_gates = torch.bmm(output_expanded, grad_out[:, :, None]).squeeze(-1)
-                d_gates = (output_expanded @ grad_out.unsqueeze(-1)).squeeze(-1)
-                gates_flat = gates.flatten()
-                gate_fan = gates.size(1)
-                grouped_grad_out = output_expanded.flatten(
-                    0, 1
-                )  # reuse expanded buffer later
-            else:
-                d_gates = None
-                gates_flat = None
-                gate_fan = 1
-                grouped_grad_out = None
-
-            if grouped_out:
-                grouped_grad_out = grad_out
-            else:
-                grouped_grad_out = kernels.ops.group(
-                    grad_out,
-                    sorted_scattered_idxs,
-                    fan_out=gate_fan,
-                    coeff=gates_flat,
-                    out=grouped_grad_out,
-                )
-            if grouped_in:
-                grouped_x = x
-                d_expanded_input = None
-            else:
-                grouped_x = kernels.ops.group(x, sorted_scattered_idxs, fan_out=k)
-                d_expanded_input = grouped_x
-
-            d_weights, d_biases = kernels.ops.group_bwd_W(
-                DY=grouped_grad_out,
-                X=grouped_x,
-                expert_offsets=expert_offsets,
-                E=expert_weights.size(0),
-                has_bias=expert_biases is not None,
-            )
-
-            d_expanded_input = kernels.ops.scatter2scatter(
-                X=grouped_grad_out,
-                x_grouped=True,
-                W=expert_weights.permute(0, 2, 1),
-                sorted_expert_idxs=sorted_expert_idxs,
-                sorted_scattered_idxs=sorted_scattered_idxs,
-                k=1,
-                y_grouped=grouped_in,
-                out=d_expanded_input,  # Reuse grouped_x buffer
-            )
-
-            if k == 1:
-                d_input = d_expanded_input
-            else:
-                d_input = d_expanded_input.view(
-                    x.size(0), k, d_expanded_input.size(-1)
-                ).sum(-2)
-        return (
-            # x, expert_weights,
-            d_input,
-            d_weights,
-            # k, sorted_expert_idxs, sorted_scattered_idxs, expert_offsets,
-            None,
-            None,
-            None,
-            None,
-            # bias, gates
-            d_biases,
-            d_gates,
-            # grouped_in, grouped_out,
-            None,
-            None,
-        )
-
-
-def parallel_linear(
-    inputs,
-    expert_weights,
-    k,
-    sorted_expert_idxs,
-    sorted_scattered_idxs,
-    expert_offsets,
-    expert_biases=None,
-    gates=None,
-    grouped_in=False,
-    grouped_out=False,
-):
-    results = ParallelLinear.apply(
-        inputs,
-        expert_weights,
-        k,
-        sorted_expert_idxs,
-        sorted_scattered_idxs,
-        expert_offsets,
-        expert_biases,
-        gates,
-        grouped_in,
-        grouped_out,
-    )
-    return results
-
-
-class ParallelExperts(nn.Module):
-    def __init__(self, num_experts, input_size, output_size, bias=False) -> None:
-        super().__init__()
-        self.weight = nn.Parameter(torch.empty(num_experts, output_size, input_size))
-
-        if bias:
-            self.bias = nn.Parameter(torch.empty(num_experts, output_size))
-        else:
-            self.bias = None
-
-        self.num_experts = num_experts
-        self.input_size = input_size
-        self.output_size = output_size
-        self.reset_parameters()
-
-    def extra_repr(self):
-        return "num_experts={}, input_size={}, output_size={}".format(
-            self.num_experts, self.input_size, self.output_size
-        )
-
-    def reset_parameters(self) -> None:
-        nn.init.normal_(self.weight, std=0.02)
-        if self.bias is not None:
-            nn.init.zeros_(self.bias)
-
-    def forward(
-        self,
-        inputs,
-        k,
-        sorted_expert_idxs,
-        sorted_scattered_idxs,
-        expert_offsets,
-        gates=None,
-        grouped_in=False,
-        grouped_out=False,
-    ):
-        results = parallel_linear(
-            inputs,
-            self.weight.permute(0, 2, 1),
-            k,
-            sorted_expert_idxs,
-            sorted_scattered_idxs,
-            expert_offsets,
-            expert_biases=self.bias,
-            gates=gates,
-            grouped_in=grouped_in,
-            grouped_out=grouped_out,
-        )
-        return results

src/axolotl/integrations/kernels/libs/scattermoe_lora/parallel_linear_lora.py

@@ -1,480 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# Copyright (c) Axolotl AI
-# Licensed under the Apache License, Version 2.0
-
-"""
-ScatterMoE + LoRA Autograd Function
-====================================
-
-Provides the autograd function and Python interface for fused ScatterMoE + LoRA.
-
-Key design for LoRA training:
-  - Expert weights W are FROZEN (no gradient computed for W).
-  - Only LoRA adapter weights (A, B) receive gradients.
-  - The input gradient dX is still computed (needed for upstream layers).
-  - This avoids the expensive group_bwd_W computation entirely.
-
-Forward:
-  Y = X @ W + scaling * (X @ A^T) @ B^T
-
-Backward (W frozen):
-  dX = dY @ W^T + scaling * (dY @ B) @ A          (via scatter2scatter for base, separate for LoRA)
-  dA = scaling * (dY @ B)^T @ X                     (per-expert, on grouped data)
-  dB = scaling * dY^T @ (X @ A^T)                   (per-expert, on grouped data)
-"""
-
-from typing import Optional
-
-import torch
-
-from .kernels import ops as base_ops
-from .kernels.lora_ops import (
-    group_bwd_lora,
-    group_bwd_lora_fused,
-    scatter2scatter_lora,
-    scatter2scatter_lora_dX,
-)
-
-
-class ScatterMoELoRA(torch.autograd.Function):
-    """
-    Autograd function for fused ScatterMoE + LoRA with frozen expert weights.
-
-    This function is optimized for the LoRA fine-tuning scenario where:
-    - Expert weights W are frozen (requires_grad=False)
-    - Only LoRA A and B matrices receive gradients
-    - Input gradients are computed for upstream layer backprop
-    """
-
-    @staticmethod
-    def forward(
-        ctx,
-        x: torch.Tensor,
-        expert_weights: torch.Tensor,
-        k: int,
-        sorted_expert_idxs: torch.Tensor,
-        sorted_scattered_idxs: torch.Tensor,
-        expert_offsets: torch.Tensor,
-        lora_A: torch.Tensor,
-        lora_B: torch.Tensor,
-        scaling: float,
-        expert_biases: Optional[torch.Tensor] = None,
-        gates: Optional[torch.Tensor] = None,
-        grouped_in: bool = False,
-        grouped_out: bool = False,
-        use_fused_dX: bool = False,
-        use_fused_gather: bool = False,
-    ):
-        with torch.device(x.device):
-            # Fused forward: Y = X @ W + scaling * (X @ A^T) @ B^T
-            output = scatter2scatter_lora(
-                X=x,
-                W=expert_weights,
-                sorted_expert_idxs=sorted_expert_idxs,
-                sorted_scattered_idxs=sorted_scattered_idxs,
-                k=k,
-                lora_A=lora_A,
-                lora_B=lora_B,
-                scaling=scaling,
-                b=expert_biases,
-                x_grouped=grouped_in,
-                y_grouped=grouped_out,
-            )
-
-            # Handle gating (weighted combination of top-k expert outputs)
-            if gates is not None:
-                output_expanded = output.view(
-                    gates.size(0), gates.size(1), output.size(-1)
-                )
-                output = (gates.unsqueeze(1) @ output_expanded).squeeze(1)
-            else:
-                output_expanded = None
-
-            ctx.save_for_backward(
-                x,
-                lora_A,
-                lora_B,
-                sorted_expert_idxs,
-                sorted_scattered_idxs,
-                expert_offsets,
-                gates,
-                output_expanded,
-            )
-            # Store frozen weights as plain Python attributes instead of
-            # save_for_backward.  This avoids:
-            # 1. Version-check conflicts with FSDP unshard/reshard
-            # 2. Pinning all-gathered parameters via saved_tensors hooks
-            # 3. Interfering with activation offloading pack/unpack hooks
-            # Safe because expert_weights are frozen (requires_grad=False).
-            ctx.expert_weights = expert_weights
-            ctx.expert_biases = expert_biases
-            ctx.grouped_in = grouped_in
-            ctx.grouped_out = grouped_out
-            ctx.k = k
-            ctx.scaling = scaling
-            ctx.use_fused_dX = use_fused_dX
-            ctx.use_fused_gather = use_fused_gather
-
-        return output
-
-    @staticmethod
-    def backward(ctx, grad_out: torch.Tensor):
-        with torch.device(grad_out.device):
-            (
-                x,
-                lora_A,
-                lora_B,
-                sorted_expert_idxs,
-                sorted_scattered_idxs,
-                expert_offsets,
-                gates,
-                output_expanded,
-            ) = ctx.saved_tensors
-            expert_weights = ctx.expert_weights
-
-            k = ctx.k
-            scaling = ctx.scaling
-            grouped_in = ctx.grouped_in
-            grouped_out = ctx.grouped_out
-            E = expert_weights.size(0)
-
-            # ------------------------------------------------------------------
-            # Gate gradients (if using top-k gating with routing weights)
-            # ------------------------------------------------------------------
-            if gates is not None:
-                # d_gates[t, j] = output_expanded[t, j, :] . grad_out[t, :]
-                d_gates = (output_expanded @ grad_out.unsqueeze(-1)).squeeze(-1)
-                gates_flat = gates.flatten()
-                gate_fan = gates.size(1)
-                # Reuse output_expanded buffer for grouped_grad_out
-                grouped_grad_out = output_expanded.flatten(0, 1)
-            else:
-                d_gates = None
-                gates_flat = None
-                gate_fan = 1
-                grouped_grad_out = None
-
-            # ------------------------------------------------------------------
-            # LoRA gradients (dA, dB) and setup for dX
-            # ------------------------------------------------------------------
-            # Fused gather uses sorted_scattered_idxs for indirect X access
-            # in the Triton kernel, avoiding the group(x) allocation.
-            #
-            # can_fuse_gather: X is ungrouped and not too large for scatter loads
-            #   - When gates is None and grouped_out=False: both DY and X ungrouped
-            #   - When grouped_out=True (gate_up_proj): DY already grouped, X ungrouped
-            #     -> use dy_grouped=True in the fused kernel
-            M_total = sorted_scattered_idxs.size(0)
-            K_dim = x.size(-1)
-            N_dim = expert_weights.size(-1)
-            fuse_gather_workload = M_total * max(K_dim, N_dim)
-            _FUSE_GATHER_THRESHOLD = 2**24  # ~16M elements
-
-            can_fuse_gather = (
-                ctx.use_fused_gather
-                and not grouped_in  # X must be ungrouped for scatter access
-                and gates is None  # gate coeff requires multiplicative gather
-                and fuse_gather_workload < _FUSE_GATHER_THRESHOLD
-            )
-
-            if can_fuse_gather:
-                # ------------------------------------------------------------------
-                # Fused path: skip group(x) entirely
-                # ------------------------------------------------------------------
-                d_expanded_input = None
-
-                d_lora_A, d_lora_B = group_bwd_lora_fused(
-                    DY=grad_out,
-                    X=x,
-                    lora_A=lora_A,
-                    lora_B=lora_B,
-                    expert_offsets=expert_offsets,
-                    sorted_scattered_idxs=sorted_scattered_idxs,
-                    E=E,
-                    k=k,
-                    scaling=scaling,
-                    dy_grouped=grouped_out,
-                )
-
-                # Prepare grouped_grad_out for the dX path (needed by both
-                # the fused dX kernel when grouped_out=True, and the non-fused path)
-                if grouped_out:
-                    grouped_grad_out = grad_out
-                elif not ctx.use_fused_dX:
-                    grouped_grad_out = base_ops.group(
-                        grad_out,
-                        sorted_scattered_idxs,
-                        fan_out=gate_fan,
-                        coeff=gates_flat,
-                        out=grouped_grad_out,
-                    )
-            else:
-                # ------------------------------------------------------------------
-                # Original path: explicit group() calls
-                # ------------------------------------------------------------------
-                if grouped_out:
-                    grouped_grad_out = grad_out
-                else:
-                    grouped_grad_out = base_ops.group(
-                        grad_out,
-                        sorted_scattered_idxs,
-                        fan_out=gate_fan,
-                        coeff=gates_flat,
-                        out=grouped_grad_out,
-                    )
-
-                if grouped_in:
-                    grouped_x = x
-                    d_expanded_input = None
-                else:
-                    grouped_x = base_ops.group(x, sorted_scattered_idxs, fan_out=k)
-                    d_expanded_input = grouped_x  # Will be overwritten; reuse buffer
-
-                d_lora_A, d_lora_B = group_bwd_lora(
-                    DY=grouped_grad_out,
-                    X=grouped_x,
-                    lora_A=lora_A,
-                    lora_B=lora_B,
-                    expert_offsets=expert_offsets,
-                    E=E,
-                    scaling=scaling,
-                )
-
-            # ------------------------------------------------------------------
-            # Input gradient: dX = dY @ W^T + scaling * (dY @ B) @ A
-            # ------------------------------------------------------------------
-            if ctx.use_fused_dX:
-                if can_fuse_gather and not grouped_out:
-                    # Fully fused: read ungrouped DY via scatter pattern
-                    d_expanded_input = scatter2scatter_lora_dX(
-                        DY=grad_out,
-                        W=expert_weights,
-                        sorted_expert_idxs=sorted_expert_idxs,
-                        sorted_scattered_idxs=sorted_scattered_idxs,
-                        k=1,
-                        lora_A=lora_A,
-                        lora_B=lora_B,
-                        scaling=scaling,
-                        dy_grouped=False,
-                        dx_grouped=grouped_in,
-                        out=d_expanded_input,
-                    )
-                else:
-                    # Fused dX only: read from pre-grouped DY
-                    d_expanded_input = scatter2scatter_lora_dX(
-                        DY=grouped_grad_out,
-                        W=expert_weights,
-                        sorted_expert_idxs=sorted_expert_idxs,
-                        sorted_scattered_idxs=sorted_scattered_idxs,
-                        k=1,
-                        lora_A=lora_A,
-                        lora_B=lora_B,
-                        scaling=scaling,
-                        dy_grouped=True,
-                        dx_grouped=grouped_in,
-                        out=d_expanded_input,
-                    )
-            else:
-                # Original path: separate base scatter2scatter + LoRA Python loop
-                d_expanded_input = base_ops.scatter2scatter(
-                    X=grouped_grad_out,
-                    x_grouped=True,
-                    W=expert_weights.permute(0, 2, 1),  # [E, N, K]
-                    sorted_expert_idxs=sorted_expert_idxs,
-                    sorted_scattered_idxs=sorted_scattered_idxs,
-                    k=1,
-                    y_grouped=grouped_in,
-                    out=d_expanded_input,
-                )
-
-                # LoRA part: dX_lora = scaling * (dY @ B) @ A
-                if scaling != 0.0:
-                    d_input_lora_grouped = _compute_lora_input_grad(
-                        grouped_grad_out,
-                        lora_A,
-                        lora_B,
-                        expert_offsets,
-                        E,
-                        scaling,
-                    )
-                    if grouped_in:
-                        d_expanded_input.add_(d_input_lora_grouped)
-                    else:
-                        # Scatter-add LoRA gradient directly into d_expanded_input.
-                        # Avoids allocating a zeros_like + add result
-                        d_expanded_input[sorted_scattered_idxs] += d_input_lora_grouped
-
-            # Reduce over top-k if k > 1
-            if k == 1:
-                d_input = d_expanded_input
-            else:
-                d_input = d_expanded_input.view(
-                    x.size(0), k, d_expanded_input.size(-1)
-                ).sum(-2)
-
-            # W is frozen during LoRA training -- skip weight gradient
-            d_weights = (
-                torch.zeros_like(expert_weights)
-                if expert_weights.requires_grad
-                else None
-            )
-            d_biases = None
-
-        return (
-            d_input,
-            d_weights,
-            None,
-            None,
-            None,
-            None,  # k, sorted indices, offsets
-            d_lora_A,
-            d_lora_B,
-            None,  # lora_A, lora_B, scaling
-            d_biases,
-            d_gates,
-            None,
-            None,  # grouped_in, grouped_out
-            None,  # use_fused_dX
-            None,  # use_fused_gather
-        )
-
-
-def _compute_lora_input_grad(
-    grouped_grad_out: torch.Tensor,
-    lora_A: torch.Tensor,
-    lora_B: torch.Tensor,
-    expert_offsets: torch.Tensor,
-    E: int,
-    scaling: float,
-) -> torch.Tensor:
-    """
-    Compute the LoRA contribution to the input gradient:
-      dX_lora = scaling * (dY @ B) @ A
-
-    Uses PyTorch ops on expert-grouped data.
-    Each expert e: dX_e = scaling * (dY_e @ B_e) @ A_e
-    """
-    R = lora_A.size(0) // E
-    K = lora_A.size(1)
-    M_total = grouped_grad_out.size(0)
-
-    d_input_lora = torch.zeros(
-        (M_total, K), device=grouped_grad_out.device, dtype=grouped_grad_out.dtype
-    )
-
-    compute_dtype = grouped_grad_out.dtype
-
-    prev_offset = 0
-    for e in range(E):
-        curr_offset = expert_offsets[e].item()
-        if curr_offset > prev_offset:
-            dy_e = grouped_grad_out[prev_offset:curr_offset]  # [M_e, N]
-            a_e = lora_A[e * R : (e + 1) * R, :].to(compute_dtype)  # [r, K]
-            b_e = lora_B[:, e * R : (e + 1) * R].to(compute_dtype)  # [N, r]
-
-            # dX_e = scaling * (dY_e @ B_e) @ A_e
-            dy_b = dy_e @ b_e  # [M_e, r]
-            dx_e = scaling * (dy_b @ a_e)  # [M_e, K]
-            d_input_lora[prev_offset:curr_offset] = dx_e
-
-        prev_offset = curr_offset
-
-    return d_input_lora
-
-
-# =============================================================================
-# Helper: Extract LoRA params from PEFT ParamWrapper
-# =============================================================================
-
-
-def get_lora_params_from_wrapper(module) -> tuple:
-    """
-    Extract LoRA parameters from a PEFT ParamWrapper.
-
-    Returns:
-        (lora_A, lora_B, scaling) if LoRA is active, else (None, None, None)
-    """
-    if not hasattr(module, "lora_A") or not hasattr(module, "lora_B"):
-        return None, None, None
-
-    active_adapters = getattr(module, "active_adapters", ["default"])
-    if not active_adapters:
-        return None, None, None
-
-    adapter_name = active_adapters[0]
-
-    lora_A_dict = getattr(module, "lora_A", {})
-    lora_B_dict = getattr(module, "lora_B", {})
-    scaling_dict = getattr(module, "scaling", {})
-
-    if adapter_name not in lora_A_dict:
-        return None, None, None
-
-    lora_A = lora_A_dict[adapter_name].weight
-    lora_B = lora_B_dict[adapter_name].weight
-    scaling = scaling_dict[adapter_name]
-
-    return lora_A, lora_B, scaling
-
-
-# =============================================================================
-# Drop-in replacement for parallel_linear
-# =============================================================================
-
-
-def parallel_linear_lora(
-    inputs: torch.Tensor,
-    expert_weights: torch.Tensor,
-    k: int,
-    sorted_expert_idxs: torch.Tensor,
-    sorted_scattered_idxs: torch.Tensor,
-    expert_offsets: torch.Tensor,
-    lora_A: Optional[torch.Tensor] = None,
-    lora_B: Optional[torch.Tensor] = None,
-    scaling: float = 1.0,
-    expert_biases: Optional[torch.Tensor] = None,
-    gates: Optional[torch.Tensor] = None,
-    grouped_in: bool = False,
-    grouped_out: bool = False,
-    use_fused_dX: bool = False,
-    use_fused_gather: bool = False,
-):
-    """
-    Drop-in replacement for parallel_linear that supports LoRA.
-
-    If lora_A and lora_B are provided, uses fused LoRA kernel.
-    Otherwise falls back to standard scatter2scatter.
-    """
-    if lora_A is not None and lora_B is not None:
-        return ScatterMoELoRA.apply(
-            inputs,
-            expert_weights,
-            k,
-            sorted_expert_idxs,
-            sorted_scattered_idxs,
-            expert_offsets,
-            lora_A,
-            lora_B,
-            scaling,
-            expert_biases,
-            gates,
-            grouped_in,
-            grouped_out,
-            use_fused_dX,
-            use_fused_gather,
-        )
-    else:
-        from .parallel_experts import ParallelLinear
-
-        return ParallelLinear.apply(
-            inputs,
-            expert_weights,
-            k,
-            sorted_expert_idxs,
-            sorted_scattered_idxs,
-            expert_offsets,
-            expert_biases,
-            gates,
-            grouped_in,
-            grouped_out,
-        )

src/axolotl/integrations/kernels/plugin.py

@@ -1,7 +1,5 @@
-from pathlib import Path
-
 from kernels import (
-    LocalLayerRepository,
+    LayerRepository,
     Mode,
     register_kernel_mapping,
     replace_kernel_forward_from_hub,
@@ -21,19 +19,16 @@ class KernelsPlugin(BasePlugin):
             self._kernelize_model(cfg.model_config_type)
 
     def _register_kernels(self):
-        plugin_root = Path(__file__).parent
         register_kernel_mapping(
             {
                 "HFScatterMoEParallelExperts": {
                     "cuda": {
-                        Mode.TRAINING: LocalLayerRepository(
+                        Mode.TRAINING: LayerRepository(
-                            repo_path=plugin_root / "libs" / "scattermoe_lora",
+                            repo_id="axolotl-ai-co/scattermoe",
-                            package_name="scattermoe_lora",
                             layer_name="HFScatterMoEGatedMLP",
                         ),
-                        Mode.INFERENCE: LocalLayerRepository(
+                        Mode.INFERENCE: LayerRepository(
-                            repo_path=plugin_root / "libs" / "scattermoe_lora",
+                            repo_id="axolotl-ai-co/scattermoe",
-                            package_name="scattermoe_lora",
                             layer_name="HFScatterMoEGatedMLP",
                         ),
                     },

src/axolotl/integrations/lm_eval/README.md

@@ -6,12 +6,6 @@ See https://github.com/EleutherAI/lm-evaluation-harness
 
 ## Usage
 
-There are two ways to use the LM Eval integration:
-
-### 1. Post-Training Evaluation
-
-When training with the plugin enabled, evaluation runs automatically after training completes:
-
 ```yaml
 plugins:
   - axolotl.integrations.lm_eval.LMEvalPlugin
@@ -22,50 +16,9 @@ lm_eval_tasks:
   - arc_easy
 
 lm_eval_batch_size: # Batch size for evaluation
-
+output_dir: # Directory to save evaluation results
-# Directory to save evaluation results.
-# The final model is loaded from this directory
-# unless specified otherwise (see below)
-output_dir:
 ```
 
-Run training as usual:
-```bash
-axolotl train config.yml
-```
-
-### 2. Standalone CLI Evaluation
-
-Evaluate any model directly without training:
-
-```yaml
-lm_eval_model: meta-llama/Llama-2-7b-hf
-
-plugins:
-  - axolotl.integrations.lm_eval.LMEvalPlugin
-
-lm_eval_tasks:
-  - gsm8k
-  - hellaswag
-  - arc_easy
-
-lm_eval_batch_size: 8
-output_dir: ./outputs
-```
-
-Run evaluation:
-```bash
-axolotl lm-eval config.yml
-```
-
-## Model Selection Priority
-
-The model to evaluate is selected in the following priority order:
-
-1. **`lm_eval_model`** - Explicit model path or HuggingFace repo (highest priority)
-2. **`hub_model_id`** - Trained model pushed to HuggingFace Hub
-3. **`output_dir`** - Local checkpoint directory containing trained model weights
-
 ## Citation
 
 ```bib

src/axolotl/integrations/lm_eval/__init__.py

@@ -5,7 +5,7 @@ Module for the Plugin for LM Eval Harness
 import subprocess  # nosec
 
 from axolotl.integrations.base import BasePlugin
-from axolotl.integrations.lm_eval.cli import build_lm_eval_command, get_model_path
+from axolotl.integrations.lm_eval.cli import build_lm_eval_command
 
 from .args import LMEvalArgs as LMEvalArgs
 
@@ -29,7 +29,7 @@ class LMEvalPlugin(BasePlugin):
                 wandb_project=cfg.wandb_project,
                 wandb_entity=cfg.wandb_entity,
                 wandb_name=cfg.wandb_name,
-                model=get_model_path(cfg),
+                model=cfg.lm_eval_model or cfg.hub_model_id,
             ):
                 subprocess.run(  # nosec
                     lm_eval_args,

src/axolotl/integrations/lm_eval/cli.py

@@ -13,21 +13,6 @@ import yaml
 from axolotl.utils.dict import DictDefault
 
 
-def get_model_path(cfg: DictDefault) -> str | None:
-    """
-    Determine which model path to use for evaluation.
-
-    Priority order (highest to lowest):
-    1. lm_eval_model - Explicit model path override
-    2. hub_model_id - Model pushed to HuggingFace Hub
-    3. None - Falls back to output_dir in build_lm_eval_command
-
-    Returns:
-        Model path string or None to use output_dir fallback
-    """
-    return cfg.lm_eval_model or cfg.hub_model_id or None
-
-
 def build_lm_eval_command(
     tasks: list[str],
     bfloat16=True,
@@ -123,7 +108,7 @@ def lm_eval(config: str, cloud: Optional[str] = None):
             wandb_project=cfg.wandb_project,
             wandb_entity=cfg.wandb_entity,
             wandb_name=cfg.wandb_name,
-            model=get_model_path(cfg),
+            model=cfg.lm_eval_model or cfg.hub_model_id,
             revision=cfg.revision,
             apply_chat_template=cfg.apply_chat_template,
             fewshot_as_multiturn=cfg.fewshot_as_multiturn,

src/axolotl/loaders/adapter.py

@@ -34,7 +34,7 @@ def setup_quantized_meta_for_peft(model: torch.nn.Module):
         return self
 
     for param in model.parameters():
-        if isinstance(param, Params4bit) and param.quant_state is not None:
+        if isinstance(param, Params4bit):
             param.quant_state._orig_to = param.quant_state.to
             param.quant_state.to = types.MethodType(temp_to_method, param.quant_state)
 

src/axolotl/loaders/model.py

@@ -172,10 +172,7 @@ class ModelLoader:
         # Build the model
         PLUGIN_MANAGER.pre_model_load(self.cfg)
         self.patch_manager.apply_post_plugin_pre_model_load_patches()
-
         skip_move_to_device = self._build_model()
-        self.patch_manager.apply_post_model_build_patches(self.model)
-
         PLUGIN_MANAGER.post_model_build(self.cfg, self.model)
 
         # Post-build model configuration
@@ -863,10 +860,6 @@ class ModelLoader:
             # Make sure everything is in the same dtype
             skip_prepare_model_for_kbit_training = True
 
-        if getattr(self.model, "_moe_experts_quantized", False):
-            # Parametrized expert tensors dequantize on access — would OOM.
-            skip_prepare_model_for_kbit_training = True
-
         if (
             not skip_prepare_model_for_kbit_training
             and self.cfg.adapter in ["lora", "qlora"]

src/axolotl/loaders/patch_manager.py

@@ -10,7 +10,6 @@ from functools import cached_property
 import addict
 import transformers
 from transformers import PretrainedConfig, PreTrainedModel
-from transformers.modeling_flash_attention_utils import is_flash_attn_available
 
 from axolotl.integrations.base import PluginManager
 from axolotl.monkeypatch.multipack import (
@@ -101,7 +100,6 @@ class PatchManager:
         self._apply_fsdp_patches()
         self._apply_adapter_patches()
         self._apply_model_specific_patches()
-        self._apply_fp8_patches()
         self._apply_flash_attention_peft_patches()
         self._apply_gradient_checkpointing_patches()
         self._patch_attention()
@@ -118,7 +116,6 @@ class PatchManager:
     def apply_post_plugin_pre_model_load_patches(self):
         """Apply post plugin-pre_model_load load patches based on config."""
         self._apply_tiled_mlp(self.cfg.model_config_type)
-        self._apply_moe_expert_quantization_patch()
 
     def _apply_transformers_patches(self):
         from axolotl.monkeypatch.transformers.trainer_loss_calc import (
@@ -136,10 +133,6 @@ class PatchManager:
 
             patch_prepare_context_parallel_inputs()
 
-    def apply_post_model_build_patches(self, model: PreTrainedModel):
-        """Apply patches right after model build, before post-load setup."""
-        self._finalize_moe_expert_quantization(model)
-
     def apply_post_model_load_patches(self, model: PreTrainedModel):
         """Apply patches that require the model instance."""
         self._apply_llama_flash_attn_patches(model)
@@ -175,14 +168,9 @@ class PatchManager:
 
             patch_parallelism_config()
         if self.cfg.fsdp_config and str(self.cfg.fsdp_version) == "2":
-            from axolotl.monkeypatch.accelerate.fsdp2 import (
+            from axolotl.monkeypatch.accelerate.fsdp2 import patch_accelerate_fsdp2
-                patch_accelerate_fsdp2,
-                patch_tied_keys_for_meta_device,
-            )
 
             patch_accelerate_fsdp2()
-            if self.cfg.fsdp_config.cpu_ram_efficient_loading:
-                patch_tied_keys_for_meta_device()
             if self.cfg.rl:
                 from axolotl.monkeypatch.trainer.trl import patch_trl_prepare_fsdp2
 
@@ -246,17 +234,6 @@ class PatchManager:
 
             patch_kimi_model()
 
-    def _apply_fp8_patches(self):
-        """Apply patches for FP8 support."""
-        if self.cfg.fp8:
-            from axolotl.monkeypatch.trainer_accelerator_args import (
-                patch_create_accelerate_code_for_fp8,
-            )
-
-            patch_create_accelerate_code_for_fp8(
-                self.cfg.fp8_enable_fsdp_float8_all_gather
-            )
-
     def _apply_flash_attention_peft_patches(self):
         """Apply patches for Flash Attention with PEFT."""
         if self.cfg.adapter:
@@ -339,7 +316,7 @@ class PatchManager:
             else:
                 has_remote_code = False
 
-            if has_remote_code and self.cfg.trust_remote_code is not None:
+            if has_remote_code and self.cfg.trust_remote_code is False:
                 # If explicitly set in YAML, prefer that
                 has_remote_code = self.cfg.trust_remote_code
 
@@ -362,54 +339,15 @@ class PatchManager:
         if (
             self.cfg.fsdp_config
             and str(self.cfg.fsdp_version) == "2"
-            and (self.cfg.load_in_4bit or self.cfg.load_in_8bit)
+            and self.cfg.adapter == "qlora"
         ):
             from axolotl.monkeypatch.fsdp2_qlora import (
-                apply_init_dtype_attrs_patch,
                 apply_init_sharded_param_patch,
                 apply_init_unsharded_param_patch,
-                apply_linear8bitlt_save_patch,
             )
 
             apply_init_sharded_param_patch()
             apply_init_unsharded_param_patch()
-            apply_init_dtype_attrs_patch()
-            if self.cfg.load_in_8bit:
-                apply_linear8bitlt_save_patch()
-
-    def _apply_moe_expert_quantization_patch(self):
-        """Patch transformers weight loading to quantize MoE expert params on-the-fly."""
-        if not self.cfg.quantize_moe_experts:
-            return
-
-        from axolotl.monkeypatch.moe_quant import (
-            patch_moe_quantization_on_load,
-            patch_peft_target_parameters_matching,
-        )
-
-        patch_moe_quantization_on_load(self.cfg)
-        patch_peft_target_parameters_matching()
-
-    def _finalize_moe_expert_quantization(self, model: PreTrainedModel):
-        """Log quantization results and set model flag for downstream use."""
-        import torch
-
-        model._moe_experts_quantized = False
-        if self.cfg.quantize_moe_experts:
-            from axolotl.monkeypatch.moe_quant import get_moe_quantized_count
-
-            count = get_moe_quantized_count()
-            if count > 0:
-                import gc
-
-                model._moe_experts_quantized = True
-                LOG.info(
-                    "Quantized %d MoE expert parameter(s) to %s during model loading",
-                    count,
-                    "4-bit" if self.cfg.load_in_4bit else "8-bit",
-                )
-                gc.collect()
-                torch.cuda.empty_cache()
 
     def _apply_tiled_mlp(self, model_type: str):
         if self.cfg.tiled_mlp:
@@ -550,7 +488,6 @@ class PatchManager:
             and not self.cfg.trust_remote_code
             and not self.cfg.gptq
             and self.cfg.flash_attention
-            and is_flash_attn_available()
             and not self.inference
         ):
             # TODO(MengqingCao): split these patches separately

src/axolotl/loaders/processor.py

@@ -19,11 +19,6 @@ def load_processor(cfg: DictDefault, tokenizer: PreTrainedTokenizerBase):
     if cfg.processor_type:
         processor_cls = getattr(transformers, cfg.processor_type)
 
-    # Build common kwargs for processor loading
-    processor_kwargs = {}
-    if cfg.revision_of_model:
-        processor_kwargs["revision"] = cfg.revision_of_model
-
     if cfg.tokenizer_use_mistral_common:
 
         def _patch_mistralcommontokenizer():
@@ -45,7 +40,6 @@ def load_processor(cfg: DictDefault, tokenizer: PreTrainedTokenizerBase):
         if processor_cls == VoxtralProcessor:
             return VoxtralProcessor.from_pretrained(
                 cfg.processor_config,
-                **processor_kwargs,
             )
 
         from axolotl.utils.mistral import Mistral3Processor
@@ -54,12 +48,10 @@ def load_processor(cfg: DictDefault, tokenizer: PreTrainedTokenizerBase):
             tokenizer=tokenizer,
         )
 
-    processor_kwargs["trust_remote_code"] = cfg.trust_remote_code or False
-    processor_kwargs["tokenizer"] = tokenizer
-
     processor = processor_cls.from_pretrained(
         cfg.processor_config,
-        **processor_kwargs,
+        trust_remote_code=cfg.trust_remote_code or False,
+        tokenizer=tokenizer,
     )
 
     # Attempt to load image size from processor if available

src/axolotl/loaders/tokenizer.py

@@ -28,10 +28,7 @@ PLUGIN_MANAGER = PluginManager.get_instance()
 
 
 def modify_tokenizer_files(
-    tokenizer_path: str,
+    tokenizer_path: str, token_mappings: dict[int, str], output_dir: str
-    token_mappings: dict[int, str],
-    output_dir: str,
-    revision: str = "main",
 ) -> str:
     """
     Modify tokenizer files to replace added_tokens strings, save to output directory,
@@ -44,7 +41,6 @@ def modify_tokenizer_files(
         tokenizer_path: Path or name of the original tokenizer
         token_mappings: Dict mapping {token_id (int): new_token_string}
         output_dir: Directory to save the modified tokenizer
-        revision: Model revision/branch/tag/commit to load from (HF Hub)
 
     Returns:
         Path to the modified tokenizer directory
@@ -57,9 +53,7 @@ def modify_tokenizer_files(
 
     if is_local_main_process():
         # Load the tokenizer
-        temp_tokenizer = AutoTokenizer.from_pretrained(
+        temp_tokenizer = AutoTokenizer.from_pretrained(tokenizer_path, use_fast=True)
-            tokenizer_path, use_fast=True, revision=revision
-        )
 
         # Save the tokenizer to the output directory
         temp_tokenizer.save_pretrained(tokenizer_dir)
@@ -140,10 +134,7 @@ def load_tokenizer(cfg: DictDefault) -> PreTrainedTokenizer:
         from axolotl.utils.mistral import HFMistralTokenizer
 
         # Load the HF-compatible wrapper around MistralTokenizer
-        kwargs = {}
+        tokenizer = HFMistralTokenizer.from_pretrained(cfg.tokenizer_config)
-        if cfg.revision_of_model:
-            kwargs["revision"] = cfg.revision_of_model
-        tokenizer = HFMistralTokenizer.from_pretrained(cfg.tokenizer_config, **kwargs)
 
         return tokenizer
 
@@ -159,8 +150,6 @@ def load_tokenizer(cfg: DictDefault) -> PreTrainedTokenizer:
     if cfg.tokenizer_legacy is not None:
         # True is the default w/ https://github.com/huggingface/transformers/pull/25224
         tokenizer_kwargs["legacy"] = cfg.tokenizer_legacy
-    if cfg.revision_of_model:
-        tokenizer_kwargs["revision"] = cfg.revision_of_model
 
     tokenizer_cls = AutoTokenizer
     if cfg.tokenizer_type:
@@ -172,11 +161,8 @@ def load_tokenizer(cfg: DictDefault) -> PreTrainedTokenizer:
     # Apply token string overrides if specified
     if cfg.added_tokens_overrides:
         # Modify tokenizer files and get path to modified tokenizer
-        modify_kwargs = {"output_dir": cfg.output_dir}
-        if cfg.revision_of_model:
-            modify_kwargs["revision"] = cfg.revision_of_model
         tokenizer_path = modify_tokenizer_files(
-            tokenizer_path, cfg.added_tokens_overrides, **modify_kwargs
+            tokenizer_path, cfg.added_tokens_overrides, output_dir=cfg.output_dir
         )
 
     tokenizer = tokenizer_cls.from_pretrained(

src/axolotl/models/mamba/modeling_mamba.py

@@ -111,7 +111,6 @@ class MambaLMHeadModel(nn.Module, GenerationMixin):
         self,
         save_directory: Union[str, os.PathLike],
         state_dict: Optional[dict] = None,
-        **kwargs,
     ):
         if state_dict is None:
             state_dict = self.state_dict()

src/axolotl/monkeypatch/accelerate/fsdp2.py

@@ -150,17 +150,13 @@ def get_state_dict(self, model, unwrap=True):
             )
     elif self.is_fsdp2:
         # https://github.com/pytorch/torchtune/blob/main/torchtune/training/_distributed.py#L465
-        from torch.distributed.tensor import DTensor
-
         state_dict = {}
         sharded_state_dict = model.state_dict()
         for param_name, param in sharded_state_dict.items():
             if param.is_cpu:
                 param = param.to(torch.device("cuda"))
 
-            if isinstance(param, DTensor):
+            param = param.full_tensor()
-                param = param.full_tensor()
-
             if torch.distributed.get_rank() == 0:
                 state_dict[param_name] = param.cpu()
             torch.distributed.barrier()
@@ -186,56 +182,10 @@ def get_state_dict(self, model, unwrap=True):
     return state_dict
 
 
-def patch_peft_param_wrapper_for_fsdp2():
-    """Patch PEFT's _LoraParameterProxy.forward for FSDP2 DTensor compatibility.
-
-    PEFT's ParamWrapper applies LoRA via torch.nn.utils.parametrize, which adds
-    delta_weight to the base weight W inside _LoraParameterProxy.forward().
-    Under FSDP2, W may be a DTensor (from FSDP unshard) while delta_weight is a
-    regular Tensor (or vice versa), causing a RuntimeError on mixed types.
-
-    This patch promotes the non-DTensor operand to match the DTensor's spec
-    using DTensor.from_local(), which is free for Replicate placement (just
-    metadata wrapping, no communication).
-    """
-    from peft.tuners.lora.layer import _LoraParameterProxy
-
-    if getattr(_LoraParameterProxy, "_axolotl_fsdp2_patched", False):
-        return
-
-    _original_forward = _LoraParameterProxy.forward
-
-    # NOTE: Replaces (not wraps) forward; assumes original is just `W + self.delta_weight`.
-    def _patched_forward(self, W):
-        from torch.distributed.tensor import DTensor
-
-        delta = self.delta_weight
-        w_is_dt = isinstance(W, DTensor)
-        d_is_dt = isinstance(delta, DTensor)
-
-        with torch.nn.utils.parametrize.cached():
-            if w_is_dt == d_is_dt:
-                return W + delta
-            if w_is_dt:
-                return W + DTensor.from_local(delta, W.device_mesh, W.placements)
-            return DTensor.from_local(W, delta.device_mesh, delta.placements) + delta
-
-    _LoraParameterProxy.forward = _patched_forward
-    _LoraParameterProxy._axolotl_fsdp2_patched = True
-    LOG.info("Patched PEFT _LoraParameterProxy.forward for FSDP2 DTensor compatibility")
-
-
 def _process_lora_module_for_fsdp(module, fsdp2_kwargs):
     """Helper function to process LoRA modules for FSDP2."""
-    from peft.tuners.lora.layer import ParamWrapper
     from torch.distributed.fsdp import fully_shard
 
-    # Skip ParamWrapper — its lora_A/B must not be independently sharded.
-    # The parent decoder layer's FSDP wrapper handles unsharding them.
-    # TODO: review if we even need to shard them separately in first place.
-    if isinstance(module, ParamWrapper):
-        return False
-
     log_bias_dtype_mismatch = False
 
     # Linear4Bit will keep it's bias term in fp32. If the weight dtype is in bf16 we are not able to
@@ -377,14 +327,6 @@ def fsdp2_prepare_model(accelerator, model: torch.nn.Module) -> torch.nn.Module:
 
     is_peft_model = isinstance(model, PeftModel)
 
-    # Patch PEFT's _LoraParameterProxy for DTensor compatibility if any
-    # ParamWrapper modules exist (used for target_parameters / 3D expert params).
-    if is_peft_model:
-        from peft.tuners.lora.layer import ParamWrapper
-
-        if any(isinstance(m, ParamWrapper) for m in model.modules()):
-            patch_peft_param_wrapper_for_fsdp2()
-
     auto_wrap_policy = fsdp2_prepare_auto_wrap_policy(fsdp2_plugin, model)
     log_bias_dtype_mismatch = False
     if auto_wrap_policy is not None:
@@ -434,43 +376,6 @@ def fsdp2_prepare_model(accelerator, model: torch.nn.Module) -> torch.nn.Module:
     return model
 
 
-def patch_tied_keys_for_meta_device():
-    """Patch _adjust_tied_keys_with_tied_pointers to skip meta tensors.
-
-    Meta tensors all share data_ptr()==0, causing every parameter to be incorrectly
-    grouped as "tied". Skipping them is safe since they have no real storage.
-    """
-    from collections import defaultdict
-
-    from transformers import PreTrainedModel
-
-    def _patched_adjust_tied_keys_with_tied_pointers(self, missing_keys):
-        param_pointers = defaultdict(list)
-        for param_name, param_value in self.state_dict().items():
-            if param_value.is_meta:
-                continue
-            param_pointers[param_value.data_ptr()].append(param_name)
-
-        tied_param_names = [
-            names
-            for names in param_pointers.values()
-            if len(names) > 1
-            and not any(name in self.all_tied_weights_keys.keys() for name in names)
-            and not all(name in missing_keys for name in names)
-        ]
-
-        tied_weights_keys_by_pointers = {
-            param_name: group[0]
-            for group in tied_param_names
-            for param_name in group[1:]
-        }
-        self.all_tied_weights_keys.update(tied_weights_keys_by_pointers)
-
-    PreTrainedModel._adjust_tied_keys_with_tied_pointers = (
-        _patched_adjust_tied_keys_with_tied_pointers
-    )
-
-
 def patch_accelerate_fsdp2():
     import accelerate
 

src/axolotl/monkeypatch/fsdp2_qlora.py

@@ -1,10 +1,9 @@
 """
-Monkeypatch to add Params4bit and Int8Params support to FSDP2. This enables QLoRA + FSDP2
+Monkeypatch to add Params4bit support to FSDP2. This enables QLoRA + FSDP2, as well as
-and 8-bit LoRA + FSDP2, as well as our LoRA / QLoRA Triton kernels to work with FSDP2.
+our LoRA / QLoRA Triton kernels to work with FSDP2.
 
-This patch modifies the _init_sharded_param and init_unsharded_param methods in FSDPParam
+This patch modifies the _init_sharded_param method in FSDPParam to handle bitsandbytes
-to handle bitsandbytes Params4bit and Int8Params parameters, preserving their quantization
+Params4bit parameters.
-metadata through the FSDP2 shard/unshard cycle.
 """
 
 import importlib
@@ -18,8 +17,6 @@ LOG = get_logger(__name__)
 
 def apply_init_sharded_param_patch():
     """Apply patch to FSDPParam._init_sharded_param to support Params4bit."""
-    if getattr(apply_init_sharded_param_patch, "_axolotl_patched", False):
-        return
     from torch.distributed.fsdp._fully_shard._fsdp_param import FSDPParam
 
     # Get original source
@@ -44,20 +41,9 @@ def apply_init_sharded_param_patch():
             bnb_quantized=param.bnb_quantized,
         )
         self.sharded_param = self.to_sharded_dtensor(self.sharded_param)
-    elif isinstance(param, bnb.nn.modules.Int8Params):
-        self.sharded_param = bnb.nn.modules.Int8Params(
-            data=sharded_param,
-            requires_grad=param.requires_grad,
-            has_fp16_weights=param.has_fp16_weights,
-            CB=None,
-            SCB=param.SCB,
-        )
-        self.sharded_param = self.to_sharded_dtensor(self.sharded_param)
     else:
-        self.sharded_param = nn.Parameter(
+        self.sharded_param = nn.Parameter(self.to_sharded_dtensor(sharded_param))
-            self.to_sharded_dtensor(sharded_param),
+        self.sharded_param.requires_grad_(param.requires_grad)"""
-            requires_grad=param.requires_grad,
-        )"""
 
     # Apply the replacement
     if original_param_creation in original_source:
@@ -87,7 +73,6 @@ def apply_init_sharded_param_patch():
 
         # Replace the method
         FSDPParam._init_sharded_param = patched_init_sharded_param
-        apply_init_sharded_param_patch._axolotl_patched = True
         LOG.info("Successfully applied FSDP _init_sharded_param patch")
     else:
         LOG.warning("Could not find target code for _init_sharded_param patching")
@@ -95,8 +80,6 @@ def apply_init_sharded_param_patch():
 
 def apply_init_unsharded_param_patch():
     """Apply patch to FSDPParam.init_unsharded_param to support Params4bit."""
-    if getattr(apply_init_unsharded_param_patch, "_axolotl_patched", False):
-        return
     from torch.distributed.fsdp._fully_shard._fsdp_param import FSDPParam
 
     # Get original source
@@ -122,14 +105,6 @@ def apply_init_unsharded_param_patch():
                 module=local_tensor.module,
                 bnb_quantized=local_tensor.bnb_quantized,
             )
-        elif isinstance(local_tensor, bnb.nn.modules.Int8Params):
-            self._unsharded_param = bnb.nn.modules.Int8Params(
-                data=unsharded_param,
-                requires_grad=self.sharded_param.requires_grad,
-                has_fp16_weights=local_tensor.has_fp16_weights,
-                CB=unsharded_param,
-                SCB=local_tensor.SCB,
-            )
         else:
             self._unsharded_param = nn.Parameter(
                 unsharded_param, requires_grad=self.sharded_param.requires_grad
@@ -163,74 +138,6 @@ def apply_init_unsharded_param_patch():
 
         # Replace the method
         FSDPParam.init_unsharded_param = patched_init_unsharded_param
-        apply_init_unsharded_param_patch._axolotl_patched = True
         LOG.info("Successfully applied FSDP init_unsharded_param patch")
     else:
         LOG.warning("Could not find target code for patching")
-
-
-def apply_linear8bitlt_save_patch():
-    """Patch Linear8bitLt._save_to_state_dict to handle DTensor-wrapped Int8Params.
-
-    After FSDP2 sharding, Linear8bitLt.weight is a DTensor wrapping Int8Params.
-    BnB's _save_to_state_dict accesses self.weight.SCB directly, but DTensor
-    doesn't proxy custom attribute access to its _local_tensor. This patch
-    temporarily unwraps the DTensor during saving so BnB can find the SCB attribute.
-    """
-    if getattr(apply_linear8bitlt_save_patch, "_axolotl_patched", False):
-        return
-    import bitsandbytes as bnb
-    from torch.distributed.tensor import DTensor
-
-    original_save = bnb.nn.Linear8bitLt._save_to_state_dict
-
-    def _patched_save_to_state_dict(self, destination, prefix, keep_vars):
-        # Use _parameters dict directly to bypass nn.Module.__setattr__ type check.
-        weight = self._parameters["weight"]
-        unwrapped = False
-        if isinstance(weight, DTensor) and hasattr(weight, "_local_tensor"):
-            self._parameters["weight"] = weight._local_tensor
-            unwrapped = True
-        try:
-            original_save(self, destination, prefix, keep_vars)
-        finally:
-            if unwrapped:
-                self._parameters["weight"] = weight
-
-    bnb.nn.Linear8bitLt._save_to_state_dict = _patched_save_to_state_dict
-    apply_linear8bitlt_save_patch._axolotl_patched = True
-    LOG.info("Patched Linear8bitLt._save_to_state_dict for DTensor compatibility")
-
-
-def apply_init_dtype_attrs_patch():
-    """Prevent FSDP2 mixed precision from casting non-float quantized params.
-
-    When mixed precision is enabled (e.g., bf16), FSDP2's init_dtype_attrs sets
-    param_dtype=bf16 for ALL params. During all-gather, _to_dtype_if_needed casts
-    the sharded param to param_dtype. For non-float params (uint8 packed 4-bit,
-    int8 quantized) without FSDP2 extensions, this destroys the quantized data.
-
-    Params4bit handles this via fsdp_pre/post_all_gather extensions, but our
-    parametrize-based expert quantization uses plain nn.Parameter(uint8/int8)
-    without extensions.
-    """
-    if getattr(apply_init_dtype_attrs_patch, "_axolotl_patched", False):
-        return
-    from torch.distributed.fsdp._fully_shard._fsdp_param import FSDPParam
-
-    original_init_dtype_attrs = FSDPParam.init_dtype_attrs
-
-    def patched_init_dtype_attrs(self, mp_policy):
-        original_init_dtype_attrs(self, mp_policy)
-        # Skip casting non-float quantized params (uint8/int8) without FSDP2
-        # extensions — the parametrization chain handles dequantization.
-        if self.param_dtype is not None and not self.sharded_param.is_floating_point():
-            local = self.sharded_param
-            if hasattr(local, "_local_tensor"):
-                local = local._local_tensor
-            if not hasattr(local, "fsdp_pre_all_gather"):
-                self.param_dtype = None
-
-    FSDPParam.init_dtype_attrs = patched_init_dtype_attrs
-    apply_init_dtype_attrs_patch._axolotl_patched = True
-    LOG.info("Patched FSDPParam.init_dtype_attrs for non-float quantized params")

src/axolotl/monkeypatch/gradient_checkpointing/offload_cpu.py

@@ -59,12 +59,7 @@ class CPU_Offloaded_Gradient_Checkpointer(torch.autograd.Function):
         hidden_states = hidden_states.to("cuda", non_blocking=True).detach()
         hidden_states.requires_grad = True
         with torch.enable_grad():
-            output = ctx.forward_function(hidden_states, *ctx.args)
+            (output,) = ctx.forward_function(hidden_states, *ctx.args)
-            # Newer HF models (e.g. Qwen3MoE) using GradientCheckpointingLayer
-            # return a plain tensor, not a tuple.  Older models return tuples
-            # like (hidden_states, present_kv, ...).  Unwrap if needed.
-            if isinstance(output, (tuple, list)):
-                (output,) = output
         torch.autograd.backward(output, dY)
         return (
             None,

src/axolotl/monkeypatch/models/qwen3_next/modeling.py

@@ -9,11 +9,6 @@ from axolotl.utils.logging import get_logger
 
 LOG = get_logger(__name__)
 
-try:
-    from fla.modules.convolution import causal_conv1d as fla_causal_conv1d
-except ImportError:
-    fla_causal_conv1d = None
-
 
 def get_cu_seqlens(position_ids):
     """
@@ -142,11 +137,6 @@ def patch_qwen3_next_gateddelta_layer():
             and cache_position is not None
         )
 
-        # Compute cu_seqlens early for use by both causal_conv1d and chunk_gated_delta_rule
-        cu_seqlens = None
-        if not use_precomputed_states and position_ids is not None:
-            cu_seqlens = get_cu_seqlens(position_ids=position_ids)
-
         # getting projected states from cache if it exists
         if cache_params is not None:
             conv_state = cache_params.conv_states[self.layer_idx]
@@ -161,11 +151,12 @@ def patch_qwen3_next_gateddelta_layer():
             x.reshape(x.shape[0], x.shape[1], -1) for x in (query, key, value)
         )
 
-        mixed_qkv = torch.cat((query, key, value), dim=-1)  # [B, T, D]
+        mixed_qkv = torch.cat((query, key, value), dim=-1)
+        mixed_qkv = mixed_qkv.transpose(1, 2)
 
         if use_precomputed_states:
-            # Inference single-token path: causal_conv1d_update expects [B, D, T]
+            # 2. Convolution sequence transformation
-            mixed_qkv = mixed_qkv.transpose(1, 2)
+            # NOTE: the conv state is updated in `causal_conv1d_update`
             mixed_qkv = self.causal_conv1d_update(
                 mixed_qkv,
                 conv_state,
@@ -173,41 +164,24 @@ def patch_qwen3_next_gateddelta_layer():
                 self.conv1d.bias,
                 self.activation,
             )
-            mixed_qkv = mixed_qkv.transpose(1, 2)
         else:
             if cache_params is not None:
-                # Cache state expects [B, D, T] for the inference update path
-                mixed_qkv_t = mixed_qkv.transpose(1, 2)
                 conv_state = F.pad(
-                    mixed_qkv_t,
+                    mixed_qkv, (self.conv_kernel_size - mixed_qkv.shape[-1], 0)
-                    (self.conv_kernel_size - mixed_qkv_t.shape[-1], 0),
                 )
                 cache_params.conv_states[self.layer_idx] = conv_state
-
+            if self.causal_conv1d_fn is not None:
-            if fla_causal_conv1d is not None:
+                mixed_qkv = self.causal_conv1d_fn(
-                # FLA Triton causal_conv1d: [B, T, D] in/out, with cu_seqlens support
-                mixed_qkv, _ = fla_causal_conv1d(
                     x=mixed_qkv,
                     weight=self.conv1d.weight.squeeze(1),
                     bias=self.conv1d.bias,
                     activation=self.activation,
-                    cu_seqlens=cu_seqlens,
+                    seq_idx=None,
                 )
             else:
-                # PyTorch fallback (no cu_seqlens support)
-                if cu_seqlens is not None and cu_seqlens.shape[0] > batch_size + 1:
-                    raise RuntimeError(
-                        "Packed sequences require fla.modules.convolution.causal_conv1d "
-                        "(cu_seqlens support). Install flash-linear-attention or disable packing."
-                    )
-                LOG.warning_once(
-                    "FLA causal_conv1d not available. Falling back to PyTorch conv1d."
-                )
-                mixed_qkv = mixed_qkv.transpose(1, 2)
                 mixed_qkv = F.silu(self.conv1d(mixed_qkv)[:, :, :seq_len])
-                mixed_qkv = mixed_qkv.transpose(1, 2)
 
-        # mixed_qkv is [B, T, D] in all paths
+        mixed_qkv = mixed_qkv.transpose(1, 2)
         query, key, value = torch.split(
             mixed_qkv,
             [
@@ -229,6 +203,7 @@ def patch_qwen3_next_gateddelta_layer():
             key = key.repeat_interleave(self.num_v_heads // self.num_k_heads, dim=2)
 
         if not use_precomputed_states:
+            cu_seqlens = get_cu_seqlens(position_ids=position_ids)
             core_attn_out, last_recurrent_state = self.chunk_gated_delta_rule(
                 query,
                 key,

src/axolotl/monkeypatch/moe_quant.py

@@ -1,188 +0,0 @@
-"""
-Loading-time quantization for MoE expert weights stored as 3D nn.Parameter tensors.
-
-In transformers v5, MoE models store expert weights as fused 3D tensors that BnB
-skips (only targets nn.Linear). This module patches weight loading to quantize them
-on-the-fly (4-bit via bitsandbytes parametrize, 8-bit via custom int8 parametrization),
-reducing peak VRAM from "all experts in bf16" to "one expert at a time."
-"""
-
-import bitsandbytes as bnb
-import torch
-import torch.nn.utils.parametrize as P
-
-from axolotl.utils.logging import get_logger
-
-LOG = get_logger(__name__)
-
-# Module-level state for the loading-time quantization patch.
-_moe_load_state = {
-    "count": 0,
-    "mode": "4bit",
-    "quant_type": "nf4",
-    "compress_statistics": True,
-    "patched": False,
-}
-
-
-class Bnb8bitParametrization(torch.nn.Module):
-    """Parametrization that dequantizes int8 row-wise quantized data on access."""
-
-    def __init__(self, row_stats: torch.Tensor):
-        super().__init__()
-        self.register_buffer("row_stats", row_stats)
-
-    @torch.no_grad()
-    def forward(self, quantized_param: torch.Tensor) -> torch.Tensor:
-        # Flatten 3D+ to 2D for BnB's dequant, then reshape back.
-        orig_shape = quantized_param.shape
-        if quantized_param.ndim > 2:
-            quantized_param = quantized_param.reshape(-1, orig_shape[-1])
-        result = bnb.functional.int8_vectorwise_dequant(quantized_param, self.row_stats)
-        return result.reshape(orig_shape)
-
-
-def _enable_parametrization_cache(module, inputs):
-    P._cache_enabled += 1
-
-
-def _disable_parametrization_cache(module, inputs, output):
-    P._cache_enabled -= 1
-    if not P._cache_enabled:
-        P._cache = {}
-
-
-def replace_parameter_8bit(module, param_name):
-    """Replace a module parameter with an 8-bit quantized version using parametrization."""
-    original_param = getattr(module, param_name)
-    int8_data, row_stats, _ = bnb.functional.int8_vectorwise_quant(
-        original_param.data.to(torch.float16)
-    )
-
-    setattr(module, param_name, torch.nn.Parameter(int8_data, requires_grad=False))
-    del original_param
-
-    P.register_parametrization(
-        module, param_name, Bnb8bitParametrization(row_stats), unsafe=True
-    )
-
-    # Cache dequantized values during forward to avoid redundant dequantization.
-    if not getattr(module, "_axolotl_8bit_hooks_registered", False):
-        module.register_forward_pre_hook(_enable_parametrization_cache)
-        module.register_forward_hook(_disable_parametrization_cache)
-        module._axolotl_8bit_hooks_registered = True
-
-
-def patch_moe_quantization_on_load(cfg):
-    """Patch transformers' weight loading to quantize MoE expert params on-the-fly.
-
-    Wraps ``set_param_for_module`` so that 3D+ CUDA tensors with "expert" in their
-    name are quantized (4-bit or 8-bit) as they're loaded, keeping peak VRAM low.
-    """
-    mode = "8bit" if getattr(cfg, "load_in_8bit", False) else "4bit"
-    _moe_load_state["mode"] = mode
-    _moe_load_state["count"] = 0
-
-    if _moe_load_state["patched"]:
-        LOG.debug("MoE loading-time quantization patch already active")
-        return
-
-    import transformers.core_model_loading
-    import transformers.modeling_utils
-
-    if mode == "4bit":
-        from bitsandbytes.nn.parametrize import replace_parameter_4bit
-
-        quant_type = getattr(cfg, "bnb_4bit_quant_type", None) or "nf4"
-        compress_statistics = getattr(cfg, "bnb_4bit_use_double_quant", None)
-        if compress_statistics is None:
-            compress_statistics = True
-
-        _moe_load_state["quant_type"] = quant_type
-        _moe_load_state["compress_statistics"] = compress_statistics
-
-    # Disable caching_allocator_warmup — it pre-allocates a huge tensor at bf16
-    # size for all params, defeating our on-load quantization VRAM savings.
-    def _noop_warmup(*args, **kwargs):
-        pass
-
-    transformers.modeling_utils.caching_allocator_warmup = _noop_warmup
-
-    original_set_param = transformers.core_model_loading.set_param_for_module
-
-    def _patched_set_param_for_module(model, target_name, param_value, *args, **kwargs):
-        original_set_param(model, target_name, param_value, *args, **kwargs)
-
-        # Quantize 3D+ expert params that BnB skipped (only on CUDA).
-        if param_value.ndim >= 3 and param_value.is_cuda:
-            mod_path, _, pname = target_name.rpartition(".")
-            mod = model.get_submodule(mod_path) if mod_path else model
-            if not isinstance(mod, (bnb.nn.Linear4bit, bnb.nn.Linear8bitLt)):
-                if "expert" not in target_name.lower():
-                    LOG.debug(
-                        "Skipping non-expert 3D param: %s (shape=%s)",
-                        target_name,
-                        list(param_value.shape),
-                    )
-                    return
-
-                if _moe_load_state["mode"] == "4bit":
-                    replace_parameter_4bit(
-                        mod,
-                        pname,
-                        compress_statistics=_moe_load_state["compress_statistics"],
-                        quant_type=_moe_load_state["quant_type"],
-                    )
-                else:
-                    replace_parameter_8bit(mod, pname)
-                _moe_load_state["count"] += 1
-
-                # Release the bf16 tensor so CUDA memory is freed immediately.
-                param_value.data = torch.empty(0, device="cpu")
-                torch.cuda.empty_cache()
-
-    transformers.core_model_loading.set_param_for_module = _patched_set_param_for_module
-    _moe_load_state["patched"] = True
-
-
-def get_moe_quantized_count():
-    """Return the number of expert parameters quantized during loading."""
-    return _moe_load_state["count"]
-
-
-def patch_peft_target_parameters_matching():
-    """Fix PEFT's _inject_parameters to use suffix matching for parametrized modules."""
-    if getattr(patch_peft_target_parameters_matching, "_axolotl_patched", False):
-        return
-    from peft.tuners.tuners_utils import BaseTuner
-
-    original_inject = BaseTuner._inject_parameters
-
-    def _patched_inject_parameters(
-        self, peft_config, model, adapter_name, low_cpu_mem_usage
-    ):
-        # Patch target_parameters to use full paths for parametrized modules
-        original_targets = list(peft_config.target_parameters)
-        expanded = set(original_targets)
-
-        for module_name, module in model.named_modules():
-            if not hasattr(module, "parametrizations"):
-                continue
-            for target in original_targets:
-                mod_path, _, param_name = target.rpartition(".")
-                if (
-                    module_name == mod_path or module_name.endswith("." + mod_path)
-                ) and hasattr(module, param_name):
-                    expanded.add(f"{module_name}.{param_name}")
-
-        peft_config.target_parameters = sorted(expanded)
-        try:
-            return original_inject(
-                self, peft_config, model, adapter_name, low_cpu_mem_usage
-            )
-        finally:
-            peft_config.target_parameters = original_targets
-
-    BaseTuner._inject_parameters = _patched_inject_parameters
-    patch_peft_target_parameters_matching._axolotl_patched = True
-    LOG.info("Patched PEFT _inject_parameters for parametrized module suffix matching")

src/axolotl/monkeypatch/trainer_accelerator_args.py

@@ -1,83 +0,0 @@
-"""
-allow adding additional kwargs to Accelerator init
-"""
-
-import inspect
-
-from transformers import Trainer
-
-from axolotl.monkeypatch.utils import detab_code
-from axolotl.utils.logging import get_logger
-
-LOG = get_logger(__name__)
-
-ORIGINAL_TRAINER_CODE = """
-    # create accelerator object
-    self.accelerator = Accelerator(**args)
-"""
-
-PATCHED_TRAINER_CODE = """
-    if hasattr(self, "additional_accelerator_args"):
-        additional_args = self.additional_accelerator_args(fp8=True, enable_fsdp_float8_all_gather={enable_fsdp_float8_all_gather}, **args)
-        if additional_args:
-            args.update(additional_args)
-
-    # create accelerator object
-    self.accelerator = Accelerator(**args)
-"""
-
-
-def get_create_accelerate_code() -> str:
-    training_loop = inspect.getsource(Trainer.create_accelerator_and_postprocess)
-    return training_loop
-
-
-def check_create_accelerate_code_is_patchable() -> bool:
-    create_code = get_create_accelerate_code()
-    create_code, _ = detab_code(create_code)
-    return ORIGINAL_TRAINER_CODE in create_code
-
-
-def patch_create_accelerate_code_for_fp8(enable_fsdp_float8_all_gather: bool):
-    """
-    Monkeypatch create_accelerator_and_postprocess so it checks for additional kwargs.
-    """
-
-    try:
-        create_code = get_create_accelerate_code()
-    except OSError:
-        return
-    Trainer._original_create_accelerator_and_postprocess = create_code
-    create_code, _ = detab_code(create_code)
-    if ORIGINAL_TRAINER_CODE not in create_code:
-        return
-
-    patched_trainer_code = PATCHED_TRAINER_CODE.format(
-        enable_fsdp_float8_all_gather=enable_fsdp_float8_all_gather
-    )
-    create_code = create_code.replace(ORIGINAL_TRAINER_CODE, patched_trainer_code)
-    create_code = create_code.replace(
-        "def create_accelerator_and_postprocess(",
-        "def fixed_create_accelerator_and_postprocess(",
-        1,
-    )
-
-    # load imports necessary
-    import transformers.trainer
-
-    items_to_import = []
-    for item in dir(transformers.trainer):
-        if item in create_code:
-            items_to_import.append(item)
-
-    exec(
-        "from transformers.trainer import ("
-        + ", ".join(x for x in items_to_import)
-        + ")",
-        globals(),
-    )
-    exec(create_code, globals())
-    LOG.info("patching create_accelerator_and_postprocess to allow for overrides")
-    Trainer.create_accelerator_and_postprocess = (
-        fixed_create_accelerator_and_postprocess
-    )

src/axolotl/monkeypatch/transformers/trainer_loss_calc.py

@@ -28,12 +28,8 @@ PATCHED_EVAL_CODE = {
     "array": 'metrics[f"{metric_key_prefix}_loss"] = np.nanmean(all_losses).item()',
 }
 
-ORIGINAL_MAYBE_CODE = (
+ORIGINAL_MAYBE_CODE = "tr_loss_scalar = self._nested_gather(tr_loss).mean().item()"
-    "tr_loss_scalar = nested_gather(tr_loss, self.args.parallel_mode).mean().item()"
+PATCHED_MAYBE_CODE = "tr_loss_scalar = self._nested_gather(tr_loss).nanmean().item()"
-)
-PATCHED_MAYBE_CODE = (
-    "tr_loss_scalar = nested_gather(tr_loss, self.args.parallel_mode).nanmean().item()"
-)
 
 
 def check_evaluation_loop_is_patchable() -> bool:

src/axolotl/telemetry/manager.py

@@ -156,10 +156,6 @@ class TelemetryManager:
         Returns:
             Boolean denoting whether telemetry is enabled or not.
         """
-        # Only rank 0 will send telemetry
-        if not is_main_process():
-            return False
-
         # Parse relevant env vars
         axolotl_do_not_track = os.getenv("AXOLOTL_DO_NOT_TRACK")
         do_not_track = os.getenv("DO_NOT_TRACK")
@@ -173,6 +169,10 @@ class TelemetryManager:
         ):
             return True
 
+        # Only rank 0 will send telemetry
+        if not is_main_process():
+            return False
+
         if do_not_track is None:
             do_not_track = "0"
 

src/axolotl/utils/callbacks/generation.py

@@ -1,84 +0,0 @@
-"""Callback for generating samples during SFT/Pretrain training."""
-
-from transformers.trainer_callback import TrainerCallback, TrainerControl, TrainerState
-from transformers.training_args import TrainingArguments
-
-from axolotl.utils.generation.sft import generate_samples
-from axolotl.utils.logging import get_logger
-
-LOG = get_logger(__name__)
-
-
-class SFTGenerationCallback(TrainerCallback):
-    """Callback for generating samples during SFT/Pretrain training."""
-
-    def __init__(self, trainer):
-        self.trainer = trainer
-
-    def on_evaluate(
-        self,
-        args: TrainingArguments,
-        state: TrainerState,
-        control: TrainerControl,
-        **kwargs,
-    ):
-        """Generate samples at specified intervals."""
-        cfg = self.trainer.axolotl_cfg
-
-        if not getattr(cfg, "generate_samples", False):
-            return
-
-            dataloader = None
-            try:
-                if getattr(self.trainer, "eval_dataset", None) is not None:
-                    dataloader = self.trainer.get_eval_dataloader()
-                    LOG.info(
-                        f"Using eval dataloader for generation at step {state.global_step}"
-                    )
-            except Exception as e:
-                LOG.warning(f"Could not get eval dataloader: {e}")
-                dataloader = None
-
-            if dataloader is None:
-                dataloader = self.trainer.get_train_dataloader()
-                LOG.info(
-                    f"Using train dataloader for generation at step {state.global_step}"
-                )
-
-            samples = generate_samples(
-                model=self.trainer.model,
-                tokenizer=self.trainer.processing_class,
-                dataloader=dataloader,
-                num_generation_samples=getattr(cfg, "num_generation_samples", 3),
-                max_new_tokens=getattr(cfg, "generation_max_new_tokens", 50),
-                temperature=getattr(cfg, "generation_temperature", 0.7),
-                top_p=getattr(cfg, "generation_top_p", None),
-                top_k=getattr(cfg, "generation_top_k", None),
-                do_sample=getattr(cfg, "generation_do_sample", True),
-                prompt_ratio=getattr(cfg, "generation_prompt_ratio", 0.5),
-            )
-            self._log_samples(samples, state.global_step)
-
-    def _log_samples(self, samples: list, step: int):
-        """Log generated samples to console and W&B."""
-        from axolotl.utils.generation.sft import format_generation_for_logging
-
-        for i, sample in enumerate(samples):
-            console_text, wandb_text = format_generation_for_logging(sample, i, step)
-
-            LOG.info(console_text)
-
-            try:
-                import wandb
-
-                if wandb.run is not None:
-                    wandb.log(
-                        {
-                            f"samples/sample_{i + 1}": wandb.Html(
-                                f"<pre>{wandb_text}</pre>"
-                            )
-                        },
-                        step=step,
-                    )
-            except (ImportError, Exception):
-                pass

src/axolotl/utils/data/lock.py

@@ -54,19 +54,15 @@ class FileLockLoader:
 
     def cleanup(self):
         """Clean up ready flag when last process is done."""
-        try:
+        with FileLock(str(self.lock_file_path)):
-            with FileLock(str(self.lock_file_path)):
+            counter_content = self.counter_path.read_text().strip()
-                counter_content = self.counter_path.read_text().strip()
+            count = int(counter_content) if counter_content else 0
-                count = int(counter_content) if counter_content else 0
+            count -= 1
-                count -= 1
 
-                if count <= 0:
+            if count <= 0:
-                    # Last process cleans everything up
+                # Last process cleans everything up
-                    self.ready_flag_path.unlink(missing_ok=True)
+                self.ready_flag_path.unlink(missing_ok=True)
-                    self.counter_path.unlink(missing_ok=True)
+                self.counter_path.unlink(missing_ok=True)
-                else:
+            else:
-                    # Still have active processes
+                # Still have active processes
-                    self.counter_path.write_text(str(count))
+                self.counter_path.write_text(str(count))
-        except FileNotFoundError:
-            # Lock file might have already been deleted by another process
-            pass

src/axolotl/utils/data/rl.py

@@ -246,10 +246,6 @@ def _load_split(cfg: DictDefault, split: Literal["train", "test"]) -> Dataset:
     dataset = merge_datasets(split_datasets, cfg)
 
     if not cfg.skip_prepare_dataset:
-        # Deduplicate before saving so the saved dataset is already de-duplicated
-        if cfg.dataset_exact_deduplication:
-            dataset, _ = deduplicate_and_log_datasets(dataset=dataset)
-
         # Save preprocessed dataset
         dataset_hash = generate_dataset_hash_from_config(
             cfg, datasets_configs, tokenizer.name_or_path

src/axolotl/utils/data/sft.py

@@ -351,10 +351,6 @@ def _load_raw_datasets(
         if cfg.sample_packing:
             dataset, _ = process_datasets_for_packing(cfg, dataset, None)
 
-        # Deduplicate before saving so the saved dataset is already de-duplicated
-        if cfg.dataset_exact_deduplication:
-            dataset, _ = deduplicate_and_log_datasets(dataset=dataset)
-
         # Save the prepared dataset
         dataset_hash = generate_dataset_hash_from_config(
             cfg, datasets_configs, tokenizer.name_or_path
@@ -442,8 +438,25 @@ def _handle_train_dataset_split(
         )
         return train_dataset, eval_dataset
 
-    # No validation split - deduplication already applied during preprocessing
+    # No validation split - apply deduplication if needed and return as train dataset
-    return dataset, None
+    if cfg.dataset_exact_deduplication:
+        train_dataset, _ = deduplicate_and_log_datasets(dataset=dataset)
+    else:
+        train_dataset = dataset
+
+    return train_dataset, None
+
+
+def _handle_test_dataset_split(
+    dataset: Dataset, cfg: DictDefault
+) -> tuple[None, Dataset | None]:
+    """Handle processing for test split."""
+    if cfg.dataset_exact_deduplication:
+        eval_dataset, _ = deduplicate_and_log_datasets(dataset=dataset)
+    else:
+        eval_dataset = dataset
+
+    return None, eval_dataset
 
 
 def _apply_dataset_sharding(dataset: Dataset, cfg: DictDefault) -> Dataset:
@@ -502,7 +515,6 @@ def _load_and_prepare_datasets(
     if split == "train":
         train_dataset, eval_dataset = _handle_train_dataset_split(dataset, cfg)
     else:
-        # Deduplication already applied during preprocessing
+        train_dataset, eval_dataset = _handle_test_dataset_split(dataset, cfg)
-        train_dataset, eval_dataset = None, dataset
 
     return train_dataset, eval_dataset, prompters

src/axolotl/utils/data/shared.py

@@ -520,8 +520,7 @@ def generate_dataset_hash_from_config(
     """
     config_str = (
         f"{cfg.sequence_len}@{cfg.sample_packing}@{cfg.eval_sample_packing}@"
-        f"{cfg.group_by_length}@{cfg.kd_temperature or 1.0}@"
+        f"{cfg.group_by_length}@{cfg.kd_temperature or 1.0}|"
-        f"{cfg.dataset_exact_deduplication or False}|"
         f"{'|'.join(sorted([f'{d.path}:{d.type}:{d.shards}:{d.conversation}:{d.split}:{d.temperature or 1.0}' for d in cfg_datasets]))}"
         f"|{tokenizer_name}"
     )

src/axolotl/utils/data/utils.py

@@ -15,7 +15,7 @@ from datasets import Dataset, IterableDataset
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.logging import get_logger
 from axolotl.utils.samplers.utils import get_dataset_lengths
-from axolotl.utils.trainer import filter_sequences_by_length
+from axolotl.utils.trainer import drop_long_seq
 
 LOG = get_logger(__name__)
 
@@ -148,33 +148,22 @@ def deduplicate_and_log_datasets(
     return dataset, other_dataset
 
 
-def keep_min_len(sample, min_sequence_len=2):
+def truncate_long_seq(sample, sequence_len=2048, min_sequence_len=2):
     """
-    Batched filter function that keeps only samples with sequence length >= min_sequence_len.
+    Truncate samples whose sequence length is too long (> sequence_len)
-    Returns a list of booleans indicating which samples to keep.
+    or drop those too short (< min_sequence_len).
     """
     min_sequence_len = min_sequence_len or 2
 
     input_ids = sample["input_ids"]
-
-    # Batched (input_ids is a list of lists)
     results = []
-    for seq in input_ids:
-        results.append(len(seq) >= min_sequence_len)
-    return results
-
-
-def truncate_long_seq(sample, sequence_len=2048):
-    """
-    Truncate samples whose sequence length is too long (> sequence_len).
-    Modifies the sample in-place and returns the modified sample.
-    """
-    input_ids = sample["input_ids"]
 
     # Batched (input_ids is a list of lists)
     for i, seq in enumerate(input_ids):
         length = len(seq)
-        if length > sequence_len:
+        if length < min_sequence_len:
+            results.append(False)
+        elif length > sequence_len:
             sample["input_ids"][i] = seq[:sequence_len]
             if "attention_mask" in sample:
                 sample["attention_mask"][i] = sample["attention_mask"][i][:sequence_len]
@@ -182,133 +171,10 @@ def truncate_long_seq(sample, sequence_len=2048):
                 sample["labels"][i] = sample["labels"][i][:sequence_len]
             if "position_ids" in sample:
                 sample["position_ids"][i] = sample["position_ids"][i][:sequence_len]
-    return sample
+            results.append(True)
-
+        else:
-
+            results.append(True)
-def _should_skip_processing(dataset: Dataset) -> bool:
+    return results
-    """Check if dataset should skip long sequence handling."""
-    if (
-        hasattr(dataset, "column_names")
-        and dataset.column_names
-        and "input_ids" not in dataset.column_names
-    ):
-        LOG.warning(
-            "Dataset does not contain 'input_ids' column. Skip drop long seq. This is "
-            "expected for reward modeling."
-        )
-        return True
-    elif not hasattr(dataset, "column_names") or dataset.column_names is None:
-        LOG.info(
-            "Dataset is streaming (IterableDataset), skipping long sequence handling"
-        )
-        return True
-    return False
-
-
-def _log_dataset_stats(dataset: Dataset) -> None:
-    """Log min/max sequence lengths for debugging."""
-    with contextlib.suppress(AttributeError, ValueError):
-        ds_lengths = get_dataset_lengths(dataset, from_arrow=True)
-        LOG.info(f"min_input_len: {np.min(ds_lengths)}")
-        LOG.info(f"max_input_len: {np.max(ds_lengths)}")
-
-
-def _build_filter_kwargs(dataset: Dataset, cfg: DictDefault) -> dict:
-    """Build kwargs for dataset filter/map operations."""
-    kwargs = {}
-    if not isinstance(dataset, IterableDataset):
-        kwargs["num_proc"] = cfg.dataset_num_proc
-        kwargs["load_from_cache_file"] = not cfg.is_preprocess
-    return kwargs
-
-
-def _filter_short_sequences(
-    dataset: Dataset, min_len: int, filter_kwargs: dict
-) -> tuple[Dataset, int]:
-    """Filter out sequences shorter than min_len. Returns (dataset, num_dropped)."""
-    prior_len = len(dataset) if hasattr(dataset, "__len__") else None
-
-    desc_kwargs = {}
-    if filter_kwargs:
-        desc_kwargs["desc"] = f"Filtering Short Sequences (<{min_len})"
-
-    dataset = dataset.filter(
-        functools.partial(keep_min_len, min_sequence_len=min_len),
-        batched=True,
-        **filter_kwargs,
-        **desc_kwargs,
-    )
-
-    dropped = 0
-    if prior_len:
-        dropped = prior_len - len(dataset)
-        if dropped > 0:
-            LOG.info(f"Dropped {dropped} short sequences (<{min_len} tokens)")
-
-    return dataset, dropped
-
-
-def _truncate_long_sequences(
-    dataset: Dataset, max_len: int, map_kwargs: dict
-) -> Dataset:
-    """Truncate sequences longer than max_len."""
-    desc_kwargs = {}
-    if map_kwargs:
-        desc_kwargs["desc"] = f"Truncating Sequences (target_len={max_len})"
-
-    dataset = dataset.map(
-        functools.partial(truncate_long_seq, sequence_len=max_len),
-        batched=True,
-        **map_kwargs,
-        **desc_kwargs,
-    )
-    LOG.info(f"Truncated long sequences to max length {max_len}")
-    return dataset
-
-
-def _drop_outside_range(
-    dataset: Dataset,
-    max_len: int,
-    min_len: int,
-    raise_on_long: bool,
-    filter_kwargs: dict,
-) -> tuple[Dataset, int]:
-    """Drop sequences outside valid length range [min_len, max_len].
-
-    Returns (dataset, num_dropped)."""
-    prior_len = len(dataset) if hasattr(dataset, "__len__") else None
-
-    desc_kwargs = {}
-    if filter_kwargs:
-        action = (
-            "Checking Sequence Lengths"
-            if raise_on_long
-            else "Dropping Invalid Sequences"
-        )
-        desc_kwargs["desc"] = f"{action} (<{min_len} or >{max_len})"
-
-    dataset = dataset.filter(
-        functools.partial(
-            filter_sequences_by_length,
-            sequence_len=max_len,
-            min_sequence_len=min_len,
-            raise_on_drop=raise_on_long,
-        ),
-        batched=True,
-        **filter_kwargs,
-        **desc_kwargs,
-    )
-
-    dropped = 0
-    if not raise_on_long and prior_len:
-        dropped = prior_len - len(dataset)
-        if dropped > 0:
-            LOG.info(
-                f"Dropped {dropped} sequences outside valid range "
-                f"([{min_len}, {max_len}])"
-            )
-
-    return dataset, dropped
 
 
 def handle_long_seq_in_dataset(
@@ -327,25 +193,80 @@ def handle_long_seq_in_dataset(
             'truncate'          truncates them down to sequence_len
             'raise'             raises a ValueError if any sequence was found that was longer than sequence_len
     """
-    # Early returns for special cases
+    if (
-    if _should_skip_processing(dataset):
+        hasattr(dataset, "column_names")
+        and dataset.column_names
+        and "input_ids" not in dataset.column_names
+    ):
+        LOG.warning(
+            "Dataset does not contain 'input_ids' column. Skip drop long seq. This is "
+            "expected for reward modeling."
+        )
+        return dataset
+    elif not hasattr(dataset, "column_names") or dataset.column_names is None:
+        LOG.info(
+            "Dataset is streaming (IterableDataset), skipping long sequence handling"
+        )
         return dataset
 
     excess_length_strategy = (cfg.excess_length_strategy or "drop").lower()
 
-    _log_dataset_stats(dataset)
+    drop_long = functools.partial(
+        drop_long_seq,
+        sequence_len=sequence_len,
+        min_sequence_len=cfg.min_sample_len,
+        raise_on_drop=excess_length_strategy == "raise",
+    )
 
-    # Setup kwargs
+    with contextlib.suppress(AttributeError):
-    filter_kwargs = _build_filter_kwargs(dataset, cfg)
+        ds_lengths = get_dataset_lengths(dataset, from_arrow=True)
+        min_input_len = np.min(ds_lengths)
+        LOG.info(f"min_input_len: {min_input_len}")
+        max_input_len = np.max(ds_lengths)
+        LOG.info(f"max_input_len: {max_input_len}")
 
-    # Handle sequences based on strategy
+    prior_len = len(dataset) if hasattr(dataset, "__len__") else None
-    if excess_length_strategy == "truncate":
+
-        dataset, _ = _filter_short_sequences(dataset, cfg.min_sample_len, filter_kwargs)
+    filter_map_kwargs = {}
-        dataset = _truncate_long_sequences(dataset, sequence_len, filter_kwargs)
+    if not isinstance(dataset, IterableDataset):
-    else:
+        filter_map_kwargs["num_proc"] = cfg.dataset_num_proc
-        raise_on_long = excess_length_strategy == "raise"
+        filter_map_kwargs["load_from_cache_file"] = not cfg.is_preprocess
-        dataset, _ = _drop_outside_range(
+
-            dataset, sequence_len, cfg.min_sample_len, raise_on_long, filter_kwargs
+    drop_long_kwargs = {}
+    if filter_map_kwargs:
+        action = (
+            "Checking Sequence Lengths"
+            if excess_length_strategy == "raise"
+            else "Dropping Long Sequences"
         )
+        drop_long_kwargs["desc"] = f"{action} (>{sequence_len})"
+
+    if excess_length_strategy == "truncate":
+        process_fn = functools.partial(
+            truncate_long_seq,
+            sequence_len=sequence_len,
+            min_sequence_len=cfg.min_sample_len,
+        )
+        drop_long_kwargs["desc"] = (
+            f"Truncating/Filtering Sequences (target_len={sequence_len})"
+        )
+    else:
+        process_fn = drop_long
+
+    dataset = dataset.filter(
+        process_fn,
+        batched=True,
+        **filter_map_kwargs,
+        **drop_long_kwargs,
+    )
+    if prior_len:
+        dropped = prior_len - len(dataset)
+        if dropped:
+            action = (
+                "truncated/filtered"
+                if excess_length_strategy == "truncate"
+                else "dropped"
+            )
+            LOG.warning(f"{action.title()} {dropped} samples from dataset")
 
     return dataset

src/axolotl/utils/generation/__init__.py

@@ -1,5 +0,0 @@
-"""Generation utilities for monitoring during training."""
-
-from .sft import format_generation_for_logging, generate_samples
-
-__all__ = ["generate_samples", "format_generation_for_logging"]

src/axolotl/utils/generation/sft.py

@@ -1,174 +0,0 @@
-"""Sample generation utilities for SFT/Pretrain training."""
-
-from typing import Any, List, Optional
-
-import torch
-from accelerate.utils import extract_model_from_parallel
-from colorama import Fore, Style
-
-from axolotl.utils.logging import get_logger
-
-LOG = get_logger(__name__)
-
-
-def generate_samples(
-    model: torch.nn.Module,
-    tokenizer: Any,
-    dataloader: Any,
-    num_generation_samples: int = 3,
-    max_new_tokens: int = 50,
-    temperature: float = 0.7,
-    top_p: Optional[float] = None,
-    top_k: Optional[int] = None,
-    do_sample: bool = True,
-    prompt_ratio: float = 0.5,
-) -> List[dict]:
-    """
-    Generate samples from the model during training for monitoring.
-
-    Args:
-        model: The model to generate from
-        tokenizer: The tokenizer to use for encoding/decoding
-        dataloader: Dataloader to sample prompts from
-        num_generation_samples: Number of samples to generate
-        max_new_tokens: Maximum new tokens to generate
-        temperature: Sampling temperature (0.0 = greedy)
-        top_p: Nucleus sampling parameter
-        top_k: Top-k sampling parameter
-        do_sample: Whether to use sampling vs greedy decoding
-        prompt_ratio: Ratio of sequence to use as prompt (0.0-1.0)
-
-    Returns:
-        List of dicts with 'prompt', 'generated', and 'full_text' keys
-    """
-    unwrapped_model = extract_model_from_parallel(model)
-
-    training = unwrapped_model.training
-    unwrapped_model.eval()
-
-    device = next(unwrapped_model.parameters()).device
-
-    generations = []
-
-    try:
-        with torch.no_grad():
-            samples_collected = 0
-
-            for batch in dataloader:
-                if samples_collected >= num_generation_samples:
-                    break
-
-                input_ids = batch["input_ids"].to(device)
-                attention_mask = batch.get("attention_mask")
-                if attention_mask is not None:
-                    attention_mask = attention_mask.to(device)
-                batch_size = input_ids.shape[0]
-
-                indices = torch.randperm(batch_size)[
-                    : num_generation_samples - samples_collected
-                ]
-
-                for idx in indices:
-                    if samples_collected >= num_generation_samples:
-                        break
-
-                    sequence = input_ids[idx]
-
-                    if attention_mask is not None:
-                        seq_len = attention_mask[idx].sum().item()
-                    else:
-                        seq_len = sequence.shape[0]
-
-                    if seq_len < 5:
-                        continue
-
-                    prompt_len = max(1, int(seq_len * prompt_ratio))
-                    prompt_ids = sequence[:prompt_len].unsqueeze(0)
-
-                    try:
-                        generation_config = {
-                            "max_new_tokens": max_new_tokens,
-                            "do_sample": do_sample,
-                            "pad_token_id": tokenizer.pad_token_id
-                            if tokenizer.pad_token_id is not None
-                            else tokenizer.eos_token_id,
-                        }
-
-                        if do_sample:
-                            generation_config["temperature"] = temperature
-                            if top_p is not None:
-                                generation_config["top_p"] = top_p
-                            if top_k is not None:
-                                generation_config["top_k"] = top_k
-
-                        generated_ids = unwrapped_model.generate(
-                            prompt_ids, **generation_config
-                        )
-
-                        prompt_text = tokenizer.decode(
-                            prompt_ids[0], skip_special_tokens=True
-                        )
-                        generated_text = tokenizer.decode(
-                            generated_ids[0][prompt_len:], skip_special_tokens=True
-                        )
-                        full_text = tokenizer.decode(
-                            generated_ids[0], skip_special_tokens=True
-                        )
-
-                        generations.append(
-                            {
-                                "prompt": prompt_text,
-                                "generated": generated_text,
-                                "full_text": full_text,
-                            }
-                        )
-
-                        samples_collected += 1
-
-                    except Exception as e:
-                        LOG.warning(f"Failed to generate sample: {e}", exc_info=True)
-                        continue
-
-    except Exception as e:
-        LOG.warning(f"Error during sample generation: {e}", exc_info=True)
-
-    if training:
-        unwrapped_model.train()
-    else:
-        unwrapped_model.eval()
-
-    return generations
-
-
-def format_generation_for_logging(
-    sample: dict, sample_idx: int, step: int
-) -> tuple[str, str]:
-    """
-    Format a generation sample for pretty logging.
-
-    Args:
-        sample: Dict with 'prompt', 'generated', and 'full_text' keys
-        sample_idx: Index of the sample
-        step: Current training step
-
-    Returns:
-        Tuple of (console_text, wandb_text)
-    """
-    console_text = (
-        f"\n{Style.BRIGHT}{Fore.CYAN}{'=' * 80}{Style.RESET_ALL}\n"
-        f"{Style.BRIGHT}{Fore.GREEN}Sample {sample_idx + 1} (Step {step}){Style.RESET_ALL}\n"
-        f"{Style.BRIGHT}{Fore.CYAN}{'=' * 80}{Style.RESET_ALL}\n"
-        f"{Style.BRIGHT}{Fore.YELLOW}[PROMPT]{Style.RESET_ALL}\n{sample['prompt']}\n\n"
-        f"{Style.BRIGHT}{Fore.MAGENTA}[GENERATED]{Style.RESET_ALL}\n{sample['generated']}\n"
-        f"{Style.BRIGHT}{Fore.CYAN}{'=' * 80}{Style.RESET_ALL}\n"
-    )
-    wandb_text = (
-        f"\n{'=' * 80}\n"
-        f"Sample {sample_idx + 1} (Step {step})\n"
-        f"{'=' * 80}\n"
-        f"[PROMPT]\n{sample['prompt']}\n\n"
-        f"[GENERATED]\n{sample['generated']}\n"
-        f"{'=' * 80}\n"
-    )
-
-    return console_text, wandb_text

src/axolotl/utils/mistral/mistral3_processor.py

@@ -30,8 +30,18 @@ class Mistral3Processor(ProcessorMixin):
     Wraps HFMistralTokenizer and adds image processing capabilities.
     """
 
+    # TODO(nano): This should be removed in transformers V5
+    attributes = ["tokenizer"]
+    tokenizer_class = "HFMistralTokenizer"
+
     def __init__(self, tokenizer: HFMistralTokenizer):
-        super().__init__(tokenizer)
+        # Don't call super().__init__ to avoid the class validation issue
+        self.tokenizer = tokenizer
+
+    @property
+    def chat_template(self) -> None:
+        """Chat template is not supported. Dummy method to satisfy HuggingFace API."""
+        return None
 
     @property
     def audio_tokenizer(self) -> None:

src/axolotl/utils/schemas/config.py

@@ -338,6 +338,18 @@ class AxolotlInputConfig(
     )
     ddp_find_unused_parameters: bool | None = None
 
+    eval_table_size: int | None = Field(
+        default=None,
+        json_schema_extra={
+            "description": "Approximate number of predictions sent to wandb depending on batch size. Enabled above 0. Default is 0"
+        },
+    )
+    eval_max_new_tokens: int | None = Field(
+        default=None,
+        json_schema_extra={
+            "description": "Total number of tokens generated for predictions sent to wandb. Default is 128"
+        },
+    )
     do_causal_lm_eval: bool | None = Field(
         default=None,
         json_schema_extra={
@@ -434,16 +446,7 @@ class AxolotlInputConfig(
         },
     )
 
-    unfrozen_parameters: list[str] | None = Field(
+    unfrozen_parameters: list[str] | None = None
-        default=None,
-        json_schema_extra={
-            "description": "List of regex patterns for parameter names to keep unfrozen. "
-            "All other parameters will be frozen via requires_grad=False. "
-            "Note: range-based patterns (e.g. embed_tokens.weight$[:32000]) use gradient "
-            "zeroing rather than a true freeze, so weight decay will still apply to the "
-            "frozen portion and optimizer states are allocated for the full parameter."
-        },
-    )
 
     sequence_len: int = Field(
         default=512,
@@ -629,17 +632,6 @@ class AxolotlInputConfig(
         },
     )
 
-    quantize_moe_experts: bool = Field(
-        default=False,
-        json_schema_extra={
-            "description": "Quantize MoE expert weights on load to reduce VRAM. "
-            "Requires adapter (lora/qlora) with load_in_4bit or load_in_8bit. "
-            "Requires CUDA (not compatible with ROCm or other backends). "
-            "Note: total parameter count may be reported incorrectly when enabled "
-            "(trainable param count is correct)."
-        },
-    )
-
     scaling_softmax: bool | None = Field(
         default=None,
         json_schema_extra={
@@ -1105,46 +1097,6 @@ class AxolotlInputConfig(
             "description": "Add plugins to extend the pipeline. See `src/axolotl/integrations` for the available plugins or doc below for more details. https://docs.axolotl.ai/docs/custom_integrations.html"
         },
     )
-    generate_samples: bool | None = Field(
-        default=False,
-        json_schema_extra={
-            "description": "Enable sample generation during training for monitoring"
-        },
-    )
-    num_generation_samples: int | None = Field(
-        default=3,
-        json_schema_extra={
-            "description": "Number of samples to generate at each interval"
-        },
-    )
-    generation_max_new_tokens: int | None = Field(
-        default=50,
-        json_schema_extra={"description": "Maximum new tokens to generate per sample"},
-    )
-    generation_temperature: float | None = Field(
-        default=0.7,
-        json_schema_extra={
-            "description": "Temperature for sample generation (0.0 = greedy)"
-        },
-    )
-    generation_top_p: float | None = Field(
-        default=None,
-        json_schema_extra={"description": "Nucleus sampling parameter for generation"},
-    )
-    generation_top_k: int | None = Field(
-        default=None,
-        json_schema_extra={"description": "Top-k sampling parameter for generation"},
-    )
-    generation_prompt_ratio: float | None = Field(
-        default=0.5,
-        json_schema_extra={"description": "Ratio of input to use as prompt (0.0-1.0)"},
-    )
-    generation_do_sample: bool | None = Field(
-        default=True,
-        json_schema_extra={
-            "description": "Whether to use sampling (vs greedy decoding)"
-        },
-    )
 
     @field_serializer("datasets")
     def datasets_serializer(
@@ -1300,26 +1252,6 @@ class AxolotlConfigWCapabilities(AxolotlInputConfig):
                     )
         return data
 
-    @model_validator(mode="before")
-    @classmethod
-    def check_quantize_moe_experts(cls, data):
-        if data.get("quantize_moe_experts"):
-            if data.get("adapter") not in ("lora", "qlora"):
-                raise ValueError("quantize_moe_experts requires adapter: lora or qlora")
-            if not (data.get("load_in_4bit") or data.get("load_in_8bit")):
-                raise ValueError(
-                    "quantize_moe_experts requires load_in_4bit or load_in_8bit"
-                )
-            if (
-                data.get("capabilities")
-                and data["capabilities"].get("compute_capability")
-                and not data["capabilities"]["compute_capability"].startswith("sm_")
-            ):
-                raise ValueError(
-                    "quantize_moe_experts requires CUDA (not compatible with ROCm or other backends)"
-                )
-        return data
-
     @model_validator(mode="before")
     @classmethod
     def check_auto_enable_lora_kernels(cls, data):
@@ -1540,16 +1472,3 @@ class AxolotlConfigWCapabilities(AxolotlInputConfig):
                 "dataset_exact_deduplication is not available for streaming datasets. "
             )
         return data
-
-    @model_validator(mode="before")
-    @classmethod
-    def check_deduplication_with_skip_prepare(cls, data):
-        if data.get("dataset_exact_deduplication") and data.get("skip_prepare_dataset"):
-            raise ValueError(
-                "dataset_exact_deduplication=True has no effect when "
-                "skip_prepare_dataset=True. Deduplication runs as part of the "
-                "prepare pipeline, which is skipped. Either set "
-                "skip_prepare_dataset: false or disable "
-                "dataset_exact_deduplication."
-            )
-        return data

src/axolotl/utils/schemas/deprecated.py

@@ -17,8 +17,6 @@ class DeprecatedParameters(BaseModel):
     noisy_embedding_alpha: float | None = None
     dpo_beta: float | None = None
     evaluation_strategy: str | None = None
-    eval_table_size: int | None = None
-    eval_max_new_tokens: int | None = None
 
     @field_validator("max_packed_sequence_len")
     @classmethod
@@ -57,27 +55,6 @@ class DeprecatedParameters(BaseModel):
             LOG.warning("evaluation_strategy is deprecated, use eval_strategy instead")
         return evaluation_strategy
 
-    @field_validator("eval_table_size")
-    @classmethod
-    def validate_eval_table_size(cls, eval_table_size):
-        if eval_table_size is not None:
-            LOG.warning(
-                "eval_table_size is deprecated and superseded by generate_samples config. "
-                "Please use generate_samples: true and num_generation_samples instead. "
-                "The LogPredictionCallback is replaced by the new sample generation feature."
-            )
-        return eval_table_size
-
-    @field_validator("eval_max_new_tokens")
-    @classmethod
-    def validate_eval_max_new_tokens(cls, eval_max_new_tokens):
-        if eval_max_new_tokens is not None:
-            LOG.warning(
-                "eval_max_new_tokens is deprecated and superseded by generate_samples config. "
-                "Please use generation_max_new_tokens instead."
-            )
-        return eval_max_new_tokens
-
 
 class RemappedParameters(BaseModel):
     """Parameters that have been remapped to other names"""

src/axolotl/utils/schemas/peft.py

@@ -1,6 +1,6 @@
 """Pydantic models for PEFT-related configuration"""
 
-from typing import Any, Literal
+from typing import Any
 
 from pydantic import BaseModel, Field, field_validator, model_validator
 
@@ -38,10 +38,10 @@ class LoraConfig(BaseModel):
         default=False, json_schema_extra={"description": "Use bitsandbytes 4 bit"}
     )
 
-    adapter: Literal["lora", "qlora", "llama-adapter"] | None = Field(
+    adapter: str | None = Field(
         default=None,
         json_schema_extra={
-            "description": "If you want to use 'lora', 'qlora', or 'llama-adapter', or leave blank to train all parameters in original model"
+            "description": "If you want to use 'lora' or 'qlora' or leave blank to train all parameters in original model"
         },
     )
     lora_model_dir: str | None = Field(
@@ -209,19 +209,6 @@ class LoraConfig(BaseModel):
             data["lora_dropout"] = 0.0
         return data
 
-    @model_validator(mode="after")
-    def validate_lora_target_parameters_dropout(self):
-        if (
-            self.lora_target_parameters
-            and self.lora_dropout
-            and self.lora_dropout != 0.0
-        ):
-            raise ValueError(
-                "lora_dropout must be 0 when lora_target_parameters is set. "
-                "PEFT's ParamWrapper does not support lora_dropout != 0."
-            )
-        return self
-
 
 class ReLoRAConfig(BaseModel):
     """ReLoRA configuration subset"""

src/axolotl/utils/trainer.py

@@ -205,13 +205,10 @@ def add_length(sample):
     return sample
 
 
-def filter_sequences_by_length(
+def drop_long_seq(sample, sequence_len=2048, min_sequence_len=2, raise_on_drop=False):
-    sample, sequence_len=2048, min_sequence_len=2, raise_on_drop=False
-):
     """
-    Filter sequences outside valid length range [min_sequence_len, sequence_len].
+    Drop samples whose sequence length is either too long (> sequence_len)
-
+    or too short (< min_sequence_len).
-    Drops samples that are either too short (< min_sequence_len) or too long (> sequence_len).
 
     Works for both single-example (list[int]) or batched (list[list[int]]).
 
@@ -386,10 +383,10 @@ def process_datasets_for_packing(cfg, train_dataset, eval_dataset):
 def process_pretraining_datasets_for_packing(
     train_dataset, sequence_len, skip_position_ids=True, drop_attention_mask=False
 ):
-    drop_outside_range = partial(filter_sequences_by_length, sequence_len=sequence_len)
+    drop_long = partial(drop_long_seq, sequence_len=sequence_len)
 
     train_dataset = train_dataset.filter(
-        drop_outside_range,
+        drop_long,
         desc="Dropping Long Sequences",
         load_from_cache_file=False,
     )
@@ -483,7 +480,7 @@ def calculate_total_num_steps(cfg, train_dataset, update=True):
                 bin_size=cfg.sample_packing_bin_size,
                 sequential=cfg.sample_packing_sequentially,
                 drop_last=True,
-                num_processes=cfg.dataset_num_proc,
+                num_processes=cfg.dataset_prcoesses,
                 mp_start_method=cfg.sample_packing_mp_start_method or "fork",
             )
 

tests/cli/test_nested_options.py

@@ -1,227 +0,0 @@
-"""Tests for nested config option handling via CLI dot-notation."""
-
-import click
-from click.testing import CliRunner
-from pydantic import BaseModel, Field
-
-from axolotl.cli.utils.args import add_options_from_config, filter_none_kwargs
-
-
-class InnerConfig(BaseModel):
-    """A nested config model for testing."""
-
-    beta: float | None = Field(
-        default=None,
-        description="Beta parameter.",
-    )
-    host: str | None = Field(
-        default=None,
-        description="Server host.",
-    )
-    use_feature: bool = Field(
-        default=False,
-        description="Whether to use the feature.",
-    )
-
-
-class OuterConfig(BaseModel):
-    """A top-level config model for testing."""
-
-    learning_rate: float | None = Field(
-        default=None,
-        description="Learning rate.",
-    )
-    inner: InnerConfig | None = Field(
-        default=None,
-        description="Inner config.",
-    )
-    name: str | None = Field(
-        default=None,
-        description="Model name.",
-    )
-
-
-class TestAddOptionsFromConfigNested:
-    """Test that add_options_from_config handles nested BaseModel fields."""
-
-    def setup_method(self):
-        self.runner = CliRunner()
-
-    def test_nested_dot_notation_options_are_registered(self):
-        """Nested model fields should create --parent.child CLI options."""
-
-        @click.command()
-        @add_options_from_config(OuterConfig)
-        @filter_none_kwargs
-        def cmd(**kwargs):
-            for k, v in sorted(kwargs.items()):
-                click.echo(f"{k}={v}")
-
-        result = self.runner.invoke(cmd, ["--inner.beta=0.5", "--inner.host=localhost"])
-        assert result.exit_code == 0, result.output
-        assert "inner__beta=0.5" in result.output
-        assert "inner__host=localhost" in result.output
-
-    def test_nested_bool_option(self):
-        """Nested bool fields should support --parent.field/--no-parent.field."""
-
-        @click.command()
-        @add_options_from_config(OuterConfig)
-        @filter_none_kwargs
-        def cmd(**kwargs):
-            for k, v in sorted(kwargs.items()):
-                click.echo(f"{k}={v}")
-
-        result = self.runner.invoke(cmd, ["--inner.use-feature"])
-        assert result.exit_code == 0, result.output
-        assert "inner__use_feature=True" in result.output
-
-    def test_flat_and_nested_options_together(self):
-        """Flat and nested options should work together."""
-
-        @click.command()
-        @add_options_from_config(OuterConfig)
-        @filter_none_kwargs
-        def cmd(**kwargs):
-            for k, v in sorted(kwargs.items()):
-                click.echo(f"{k}={v}")
-
-        result = self.runner.invoke(
-            cmd, ["--learning-rate=0.001", "--inner.beta=0.1", "--name=test"]
-        )
-        assert result.exit_code == 0, result.output
-        assert "learning_rate=0.001" in result.output
-        assert "inner__beta=0.1" in result.output
-        assert "name=test" in result.output
-
-    def test_no_nested_options_passed(self):
-        """When no nested options are passed, they should not appear in kwargs."""
-
-        @click.command()
-        @add_options_from_config(OuterConfig)
-        @filter_none_kwargs
-        def cmd(**kwargs):
-            click.echo(f"keys={sorted(kwargs.keys())}")
-
-        result = self.runner.invoke(cmd, ["--learning-rate=0.01"])
-        assert result.exit_code == 0, result.output
-        assert "inner__" not in result.output
-
-
-class TestLoadCfgNestedKwargs:
-    """Test that load_cfg correctly applies nested (double-underscore) kwargs."""
-
-    @staticmethod
-    def _apply_nested_kwargs(cfg, kwargs):
-        """Helper that mirrors the nested kwargs handling from load_cfg,
-        including type coercion for string CLI values."""
-        from axolotl.cli.config import _coerce_value
-
-        nested_kwargs: dict = {}
-        flat_kwargs: dict = {}
-        for key, value in kwargs.items():
-            if "__" in key:
-                parent, child = key.split("__", 1)
-                nested_kwargs.setdefault(parent, {})[child] = value
-            else:
-                flat_kwargs[key] = value
-
-        cfg_keys = cfg.keys()
-        for key, value in flat_kwargs.items():
-            if key in cfg_keys:
-                cfg[key] = _coerce_value(value, cfg.get(key))
-
-        for parent, children in nested_kwargs.items():
-            if cfg[parent] is None:
-                cfg[parent] = {}
-            if not isinstance(cfg[parent], dict):
-                cfg[parent] = {}
-            for child_key, child_value in children.items():
-                existing = cfg[parent].get(child_key)
-                cfg[parent][child_key] = _coerce_value(child_value, existing)
-
-        return cfg
-
-    def test_nested_kwargs_applied_to_cfg(self, tmp_path):
-        """Double-underscore kwargs should set nested config values."""
-        from axolotl.utils.dict import DictDefault
-
-        cfg = DictDefault({"trl": {"beta": 0.1}, "learning_rate": 0.01})
-        # CLI passes strings, so simulate that
-        kwargs = {
-            "trl__beta": "0.5",
-            "trl__host": "192.168.1.1",
-            "learning_rate": "0.02",
-        }
-
-        cfg = self._apply_nested_kwargs(cfg, kwargs)
-
-        assert cfg["learning_rate"] == 0.02
-        assert isinstance(cfg["learning_rate"], float)
-        assert cfg["trl"]["beta"] == 0.5
-        assert isinstance(cfg["trl"]["beta"], float)
-        assert cfg["trl"]["host"] == "192.168.1.1"
-
-    def test_nested_kwargs_creates_parent_if_none(self):
-        """If the parent key is None, nested kwargs should create the dict."""
-        from axolotl.utils.dict import DictDefault
-
-        cfg = DictDefault({"trl": None, "learning_rate": 0.01})
-        cfg = self._apply_nested_kwargs(cfg, {"trl__beta": "0.5"})
-
-        # No existing value, YAML-style inference: "0.5" -> 0.5
-        assert cfg["trl"]["beta"] == 0.5
-        assert isinstance(cfg["trl"]["beta"], float)
-
-    def test_nested_kwargs_overwrites_string_parent(self):
-        """If the parent key is a string, it should be replaced with a dict."""
-        from axolotl.utils.dict import DictDefault
-
-        cfg = DictDefault({"trl": "some_string", "learning_rate": 0.01})
-        cfg = self._apply_nested_kwargs(cfg, {"trl__beta": "0.5"})
-
-        assert cfg["trl"]["beta"] == 0.5
-
-
-class TestCoerceValue:
-    """Test YAML-style type coercion for CLI string values."""
-
-    def test_coerce_with_existing_float(self):
-        from axolotl.cli.config import _coerce_value
-
-        assert _coerce_value("0.5", 0.1) == 0.5
-        assert isinstance(_coerce_value("0.5", 0.1), float)
-
-    def test_coerce_with_existing_int(self):
-        from axolotl.cli.config import _coerce_value
-
-        assert _coerce_value("42", 10) == 42
-        assert isinstance(_coerce_value("42", 10), int)
-
-    def test_coerce_with_existing_bool(self):
-        from axolotl.cli.config import _coerce_value
-
-        assert _coerce_value("true", False) is True
-        assert _coerce_value("false", True) is False
-        assert _coerce_value("1", False) is True
-        assert _coerce_value("0", True) is False
-
-    def test_coerce_yaml_inference_no_existing(self):
-        """Without an existing value, use YAML-style inference."""
-        from axolotl.cli.config import _coerce_value
-
-        assert _coerce_value("true", None) is True
-        assert _coerce_value("false", None) is False
-        assert _coerce_value("42", None) == 42
-        assert isinstance(_coerce_value("42", None), int)
-        assert _coerce_value("3.14", None) == 3.14
-        assert isinstance(_coerce_value("3.14", None), float)
-        assert _coerce_value("null", None) is None
-        assert _coerce_value("hello", None) == "hello"
-
-    def test_coerce_non_string_passthrough(self):
-        """Non-string values should pass through unchanged."""
-        from axolotl.cli.config import _coerce_value
-
-        assert _coerce_value(0.5, 0.1) == 0.5
-        assert _coerce_value(True, False) is True

tests/core/test_builders.py

@@ -300,6 +300,7 @@ class TestHFRLTrainerBuilder:
         self._test_common_training_arguments(training_arguments, rl=orpo_cfg.rl)
         # ORPO specific
         assert training_arguments.beta == 0.1  # maps from orpo_alpha
+        assert training_arguments.max_prompt_length == 512
 
     def test_kto_training_arguments(self, kto_cfg, model, tokenizer):
         builder = HFRLTrainerBuilder(kto_cfg, model, tokenizer)

tests/e2e/multigpu/test_fsdp1.py

@@ -186,7 +186,6 @@ class TestFSDP1:
 
         verify_training_success(temp_dir)
 
-    @pytest.mark.skip(reason="slow test, deprecate fsdp1 asap")
     def test_dpo_fft(self, temp_dir):
         cfg = DictDefault(
             {

tests/e2e/multigpu/test_fsdp2.py

@@ -365,7 +365,6 @@ class TestFSDP2:
 
         verify_training_success(temp_dir)
 
-    @pytest.mark.skip(reason="slow test w cu129 + torch 2.9.1 + py3.12")
     @require_torch_2_7_0
     def test_dpo_fft(self, temp_dir):
         cfg = DictDefault(
@@ -423,7 +422,6 @@ class TestFSDP2:
 
         verify_training_success(temp_dir)
 
-    @pytest.mark.skip(reason="slow test w cu129 + torch 2.9.1 + py3.12")
     @require_torch_2_7_0
     def test_dpo_lora(self, temp_dir):
         cfg = DictDefault(

tests/integrations/test_scattermoe_lora.py

@@ -1,323 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# Copyright (c) Axolotl AI
-# Licensed under the Apache License, Version 2.0
-
-"""
-Unit tests for scattermoe-lora code-review fixes.
-
-Tests cover:
-- KernelsArgs validator: disable_mlp_kernel_scattermoe
-- CPU_Offloaded_Gradient_Checkpointer: tuple vs plain tensor backward
-- ParallelExperts: scaling=0.0 not treated as falsy
-- single2scatter: non-aligned K/N dimensions
-- group_compileable: coeff=None accepted
-- HFScatterMoEGatedMLP / ScatterMoEGatedMLP: return value contract
-"""
-
-from unittest.mock import patch
-
-import pytest
-import torch
-
-# ============================================================================
-# 1. KernelsArgs: disable_mlp_kernel_scattermoe validator
-# ============================================================================
-
-
-class TestKernelsArgsValidator:
-    """Test that disable_mlp_kernel_scattermoe sets both flags correctly.
-
-    These tests call the validator classmethod directly on raw dicts,
-    since lora_mlp_kernel / mlp_kernel are not declared model fields.
-    """
-
-    def test_disables_lora_mlp_kernel_when_scattermoe(self):
-        """lora_mlp_kernel=True gets set to False when use_scattermoe=True."""
-        from axolotl.integrations.kernels.args import KernelsArgs
-
-        data = {
-            "use_kernels": True,
-            "use_scattermoe": True,
-            "lora_mlp_kernel": True,
-        }
-        result = KernelsArgs.disable_mlp_kernel_scattermoe(data)
-        assert result["lora_mlp_kernel"] is False
-        assert result["mlp_kernel"] is False
-
-    def test_mlp_kernel_disabled_without_lora(self):
-        """Even without lora_mlp_kernel, mlp_kernel should be disabled."""
-        from axolotl.integrations.kernels.args import KernelsArgs
-
-        data = {
-            "use_kernels": True,
-            "use_scattermoe": True,
-        }
-        result = KernelsArgs.disable_mlp_kernel_scattermoe(data)
-        assert result["mlp_kernel"] is False
-        # lora_mlp_kernel was not in data, should not be added
-        assert "lora_mlp_kernel" not in result
-
-    def test_lora_mlp_kernel_false_unchanged(self):
-        """lora_mlp_kernel=False should stay False (no warning, no change)."""
-        from axolotl.integrations.kernels.args import KernelsArgs
-
-        data = {
-            "use_kernels": True,
-            "use_scattermoe": True,
-            "lora_mlp_kernel": False,
-        }
-        result = KernelsArgs.disable_mlp_kernel_scattermoe(data)
-        assert result["lora_mlp_kernel"] is False
-
-    def test_no_change_when_scattermoe_disabled(self):
-        """When use_scattermoe is not True, nothing should be changed."""
-        from axolotl.integrations.kernels.args import KernelsArgs
-
-        data = {
-            "use_kernels": True,
-            "use_scattermoe": False,
-            "lora_mlp_kernel": True,
-        }
-        result = KernelsArgs.disable_mlp_kernel_scattermoe(data)
-        assert result["lora_mlp_kernel"] is True
-
-
-class TestParallelExpertsScaling:
-    """Test that scaling=0.0 is preserved and not overridden to 1.0."""
-
-    def test_scaling_zero_preserved(self):
-        """scaling=0.0 should be passed as 0.0, not replaced with 1.0."""
-        pytest.importorskip("triton")
-        from axolotl.integrations.kernels.libs.scattermoe_lora.lora_ops import (
-            ParallelExperts,
-        )
-
-        pe = ParallelExperts(num_experts=2, input_size=4, output_size=4)
-        pe.set_lora(
-            lora_A=torch.randn(4, 4),
-            lora_B=torch.randn(4, 4),
-            scaling=0.0,
-        )
-        assert pe._lora_scaling == 0.0
-
-        # Patch parallel_linear_lora to capture the scaling arg
-        with patch(
-            "axolotl.integrations.kernels.libs.scattermoe_lora.lora_ops.parallel_linear_lora"
-        ) as mock_pll:
-            mock_pll.return_value = torch.randn(4, 4)
-            # Create dummy routing tensors
-            pe.forward(
-                inputs=torch.randn(2, 4),
-                k=1,
-                sorted_expert_idxs=torch.tensor([0, 0, 1, 1]),
-                sorted_scattered_idxs=torch.tensor([0, 1, 0, 1]),
-                expert_offsets=torch.tensor([2, 4]),
-            )
-            # Check that scaling=0.0 was passed, not 1.0
-            call_kwargs = mock_pll.call_args
-            assert (
-                call_kwargs.kwargs.get("scaling") == 0.0
-                or call_kwargs[1].get("scaling") == 0.0
-            ), f"Expected scaling=0.0 but got {call_kwargs}"
-
-    def test_scaling_none_defaults_to_one(self):
-        """scaling=None (no LoRA attached) should default to 1.0."""
-        pytest.importorskip("triton")
-        from axolotl.integrations.kernels.libs.scattermoe_lora.lora_ops import (
-            ParallelExperts,
-        )
-
-        pe = ParallelExperts(num_experts=2, input_size=4, output_size=4)
-        # No set_lora called, so _lora_scaling is None
-
-        with patch(
-            "axolotl.integrations.kernels.libs.scattermoe_lora.lora_ops.parallel_linear_lora"
-        ) as mock_pll:
-            mock_pll.return_value = torch.randn(4, 4)
-            pe.forward(
-                inputs=torch.randn(2, 4),
-                k=1,
-                sorted_expert_idxs=torch.tensor([0, 0, 1, 1]),
-                sorted_scattered_idxs=torch.tensor([0, 1, 0, 1]),
-                expert_offsets=torch.tensor([2, 4]),
-            )
-            call_kwargs = mock_pll.call_args
-            scaling_val = call_kwargs.kwargs.get("scaling") or call_kwargs[1].get(
-                "scaling"
-            )
-            assert scaling_val == 1.0, (
-                f"Expected scaling=1.0 for None but got {scaling_val}"
-            )
-
-    def test_scaling_positive_preserved(self):
-        """Normal positive scaling should be preserved."""
-        pytest.importorskip("triton")
-        from axolotl.integrations.kernels.libs.scattermoe_lora.lora_ops import (
-            ParallelExperts,
-        )
-
-        pe = ParallelExperts(num_experts=2, input_size=4, output_size=4)
-        pe.set_lora(
-            lora_A=torch.randn(4, 4),
-            lora_B=torch.randn(4, 4),
-            scaling=0.5,
-        )
-
-        with patch(
-            "axolotl.integrations.kernels.libs.scattermoe_lora.lora_ops.parallel_linear_lora"
-        ) as mock_pll:
-            mock_pll.return_value = torch.randn(4, 4)
-            pe.forward(
-                inputs=torch.randn(2, 4),
-                k=1,
-                sorted_expert_idxs=torch.tensor([0, 0, 1, 1]),
-                sorted_scattered_idxs=torch.tensor([0, 1, 0, 1]),
-                expert_offsets=torch.tensor([2, 4]),
-            )
-            call_kwargs = mock_pll.call_args
-            scaling_val = call_kwargs.kwargs.get("scaling") or call_kwargs[1].get(
-                "scaling"
-            )
-            assert scaling_val == 0.5
-
-
-# ============================================================================
-# 4. single2scatter: non-aligned K/N dimensions (GPU only)
-# ============================================================================
-
-
-@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available")
-class TestSingle2ScatterBounds:
-    """Test single2scatter with non-aligned dimensions."""
-
-    def test_non_aligned_k(self):
-        """K not a multiple of BLOCK_K should produce correct results."""
-        from axolotl.integrations.kernels.libs.scattermoe_lora.kernels.single import (
-            single2scatter,
-        )
-
-        E, K, N = 2, 100, 128  # K=100 not a multiple of 128
-        W = torch.randn(E, K, N, device="cuda", dtype=torch.float32)
-        X = torch.randn(1, K, device="cuda", dtype=torch.float32)
-        expert_idxs = torch.tensor([[0, 1]], device="cuda", dtype=torch.long)
-
-        Y = single2scatter(X, W, expert_idxs)
-        assert Y.shape == (2, N)
-
-        # Verify against manual computation
-        Y_ref_0 = X[0] @ W[0]
-        Y_ref_1 = X[0] @ W[1]
-        torch.testing.assert_close(Y[0], Y_ref_0, atol=1e-2, rtol=1e-2)
-        torch.testing.assert_close(Y[1], Y_ref_1, atol=1e-2, rtol=1e-2)
-
-    def test_non_aligned_n(self):
-        """N not a multiple of BLOCK_N should produce correct results."""
-        from axolotl.integrations.kernels.libs.scattermoe_lora.kernels.single import (
-            single2scatter,
-        )
-
-        E, K, N = 2, 128, 100  # N=100 not a multiple of 128
-        W = torch.randn(E, K, N, device="cuda", dtype=torch.float32)
-        X = torch.randn(1, K, device="cuda", dtype=torch.float32)
-        expert_idxs = torch.tensor([[0, 1]], device="cuda", dtype=torch.long)
-
-        Y = single2scatter(X, W, expert_idxs)
-        assert Y.shape == (2, N)
-
-        Y_ref_0 = X[0] @ W[0]
-        Y_ref_1 = X[0] @ W[1]
-        torch.testing.assert_close(Y[0], Y_ref_0, atol=1e-2, rtol=1e-2)
-        torch.testing.assert_close(Y[1], Y_ref_1, atol=1e-2, rtol=1e-2)
-
-    def test_non_aligned_both(self):
-        """Both K and N not aligned should produce correct results."""
-        from axolotl.integrations.kernels.libs.scattermoe_lora.kernels.single import (
-            single2scatter,
-        )
-
-        E, K, N = 2, 100, 100  # Neither aligned to 128
-        W = torch.randn(E, K, N, device="cuda", dtype=torch.float32)
-        X = torch.randn(1, K, device="cuda", dtype=torch.float32)
-        expert_idxs = torch.tensor([[0, 1]], device="cuda", dtype=torch.long)
-
-        Y = single2scatter(X, W, expert_idxs)
-        assert Y.shape == (2, N)
-
-        Y_ref_0 = X[0] @ W[0]
-        Y_ref_1 = X[0] @ W[1]
-        torch.testing.assert_close(Y[0], Y_ref_0, atol=1e-2, rtol=1e-2)
-        torch.testing.assert_close(Y[1], Y_ref_1, atol=1e-2, rtol=1e-2)
-
-
-# ============================================================================
-# 5. group_compileable: coeff=None accepted
-# ============================================================================
-
-
-@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available")
-class TestGroupCoeffNone:
-    """Test that group() works with coeff=None."""
-
-    def test_group_with_none_coeff(self):
-        """group() should accept coeff=None without errors."""
-        from axolotl.integrations.kernels.libs.scattermoe_lora.kernels.ops import group
-
-        M, K = 4, 32
-        A = torch.randn(M, K, device="cuda", dtype=torch.float32)
-        sorted_expert_idxs = torch.tensor([0, 1, 2, 3], device="cuda", dtype=torch.long)
-
-        # This should not raise a TypeError
-        Y = group(A, sorted_expert_idxs, coeff=None, fan_out=1)
-        assert Y.shape == (M, K)
-
-    def test_group_with_coeff(self):
-        """group() should also work with actual coeff values."""
-        from axolotl.integrations.kernels.libs.scattermoe_lora.kernels.ops import group
-
-        M, K = 4, 32
-        A = torch.randn(M, K, device="cuda", dtype=torch.float32)
-        sorted_expert_idxs = torch.tensor([0, 1, 2, 3], device="cuda", dtype=torch.long)
-        coeff = torch.ones(M, device="cuda", dtype=torch.float32) * 0.5
-
-        Y = group(A, sorted_expert_idxs, coeff=coeff, fan_out=1)
-        assert Y.shape == (M, K)
-
-
-# ============================================================================
-# 6. Layer return value contracts
-# ============================================================================
-
-
-class TestLayerReturnValues:
-    """Test that layer forward methods return the correct types."""
-
-    def test_hf_scatter_moe_returns_single_tensor(self):
-        """HFScatterMoEGatedMLP.forward should return a single tensor, not a tuple."""
-        pytest.importorskip("triton")
-        # Verify the forward method signature and return annotation
-        import inspect
-
-        from axolotl.integrations.kernels.libs.scattermoe_lora.layers import (
-            HFScatterMoEGatedMLP,
-        )
-
-        sig = inspect.signature(HFScatterMoEGatedMLP.forward)
-        # It's a staticmethod taking (self, layer_input)
-        params = list(sig.parameters.keys())
-        assert "self" in params
-        assert "layer_input" in params
-
-    def test_scatter_moe_gated_mlp_docstring_no_router_logits(self):
-        """ScatterMoEGatedMLP.forward docstring should not mention router logits as return."""
-        pytest.importorskip("triton")
-        from axolotl.integrations.kernels.libs.scattermoe_lora.layers import (
-            ScatterMoEGatedMLP,
-        )
-
-        docstring = ScatterMoEGatedMLP.forward.__doc__
-        assert docstring is not None
-        # The docstring should mention output tensor but NOT router logits
-        assert "Output tensor" in docstring or "output tensor" in docstring.lower()
-        assert "Router logits" not in docstring, (
-            "Docstring should not mention 'Router logits' in Returns section"
-        )

tests/test_data.py

@@ -7,7 +7,7 @@ import unittest
 from transformers import LlamaTokenizer
 
 from axolotl.utils.data import encode_streaming, md5
-from axolotl.utils.trainer import filter_sequences_by_length
+from axolotl.utils.trainer import drop_long_seq
 
 from tests.hf_offline_utils import enable_hf_offline
 
@@ -70,19 +70,17 @@ class TestEncodePretraining(unittest.TestCase):
         # -- single sequence --
         # This should work
         data = {"input_ids": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]}
-        filter_sequences_by_length(data, 32, raise_on_drop=True)
+        drop_long_seq(data, 32, raise_on_drop=True)
 
         # This should return True, since data fits
-        dropped = filter_sequences_by_length(data, 32)
+        dropped = drop_long_seq(data, 32)
         self.assertTrue(dropped)
 
         # This should raise
-        self.assertRaises(
+        self.assertRaises(ValueError, drop_long_seq, data, 15, raise_on_drop=True)
-            ValueError, filter_sequences_by_length, data, 15, raise_on_drop=True
-        )
 
         # This should return False, since data doesn't fit
-        dropped = filter_sequences_by_length(data, 15)
+        dropped = drop_long_seq(data, 15)
         self.assertFalse(dropped)
 
         # -- batch sequence --
@@ -93,15 +91,13 @@ class TestEncodePretraining(unittest.TestCase):
                 [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16],
             ]
         }
-        filter_sequences_by_length(data, 32, raise_on_drop=True)
+        drop_long_seq(data, 32, raise_on_drop=True)
 
         # This should raise
-        self.assertRaises(
+        self.assertRaises(ValueError, drop_long_seq, data, 15, raise_on_drop=True)
-            ValueError, filter_sequences_by_length, data, 15, raise_on_drop=True
-        )
 
         # This should keep the first but drop the second entry
-        dropped = filter_sequences_by_length(data, 15)
+        dropped = drop_long_seq(data, 15)
         self.assertEqual(dropped, [True, False])
 
 

tests/test_revision_parameter.py

@@ -1,135 +0,0 @@
-"""Tests for revision_of_model being passed to tokenizer and processor loaders."""
-
-from unittest.mock import MagicMock, patch
-
-from transformers import PreTrainedTokenizerBase
-
-from axolotl.utils.dict import DictDefault
-
-
-class TestRevisionParameter:
-    """Tests for revision_of_model being passed to tokenizer and processor loaders."""
-
-    @patch("axolotl.loaders.tokenizer.load_model_config")
-    @patch("axolotl.loaders.tokenizer.AutoTokenizer")
-    @patch(
-        "axolotl.loaders.patch_manager.PatchManager.apply_pre_tokenizer_load_patches"
-    )
-    def test_load_tokenizer_passes_revision(
-        self, _mock_patches, mock_auto_tokenizer, _mock_load_config
-    ):
-        mock_tokenizer = MagicMock()
-        mock_tokenizer.__class__.__name__ = "MockTokenizer"
-        mock_auto_tokenizer.from_pretrained.return_value = mock_tokenizer
-
-        cfg = DictDefault(
-            {
-                "tokenizer_config": "some-model",
-                "revision_of_model": "abc123",
-            }
-        )
-        from axolotl.loaders.tokenizer import load_tokenizer
-
-        load_tokenizer(cfg)
-
-        call_kwargs = mock_auto_tokenizer.from_pretrained.call_args
-        assert call_kwargs.kwargs.get("revision") == "abc123"
-
-    @patch("axolotl.loaders.tokenizer.load_model_config")
-    @patch("axolotl.loaders.tokenizer.AutoTokenizer")
-    @patch(
-        "axolotl.loaders.patch_manager.PatchManager.apply_pre_tokenizer_load_patches"
-    )
-    def test_load_tokenizer_omits_revision_when_unset(
-        self, _mock_patches, mock_auto_tokenizer, _mock_load_config
-    ):
-        mock_tokenizer = MagicMock()
-        mock_tokenizer.__class__.__name__ = "MockTokenizer"
-        mock_auto_tokenizer.from_pretrained.return_value = mock_tokenizer
-
-        cfg = DictDefault(
-            {
-                "tokenizer_config": "some-model",
-            }
-        )
-        from axolotl.loaders.tokenizer import load_tokenizer
-
-        load_tokenizer(cfg)
-
-        call_kwargs = mock_auto_tokenizer.from_pretrained.call_args
-        assert "revision" not in call_kwargs.kwargs
-
-    @patch("axolotl.loaders.tokenizer.AutoTokenizer")
-    @patch("axolotl.loaders.tokenizer.is_local_main_process", return_value=True)
-    @patch("axolotl.loaders.tokenizer.barrier")
-    def test_modify_tokenizer_files_passes_revision(
-        self, _mock_barrier, _mock_main, mock_auto_tokenizer, temp_dir
-    ):
-        mock_tokenizer = MagicMock()
-        mock_auto_tokenizer.from_pretrained.return_value = mock_tokenizer
-
-        from axolotl.loaders.tokenizer import modify_tokenizer_files
-
-        modify_tokenizer_files("some-model", {}, output_dir=temp_dir, revision="abc123")
-
-        call_kwargs = mock_auto_tokenizer.from_pretrained.call_args
-        assert call_kwargs.kwargs.get("revision") == "abc123"
-
-    @patch("axolotl.loaders.tokenizer.AutoTokenizer")
-    @patch("axolotl.loaders.tokenizer.is_local_main_process", return_value=True)
-    @patch("axolotl.loaders.tokenizer.barrier")
-    def test_modify_tokenizer_files_defaults_revision_to_main(
-        self, _mock_barrier, _mock_main, mock_auto_tokenizer, temp_dir
-    ):
-        mock_tokenizer = MagicMock()
-        mock_auto_tokenizer.from_pretrained.return_value = mock_tokenizer
-
-        from axolotl.loaders.tokenizer import modify_tokenizer_files
-
-        modify_tokenizer_files("some-model", {}, output_dir=temp_dir)
-
-        call_kwargs = mock_auto_tokenizer.from_pretrained.call_args
-        assert call_kwargs.kwargs.get("revision") == "main"
-
-    @patch("axolotl.loaders.processor.AutoProcessor")
-    def test_load_processor_passes_revision(self, mock_auto_processor):
-        mock_processor = MagicMock()
-        mock_processor.size = {}
-        mock_auto_processor.from_pretrained.return_value = mock_processor
-
-        cfg = DictDefault(
-            {
-                "processor_config": "some-model",
-                "revision_of_model": "abc123",
-                "trust_remote_code": False,
-            }
-        )
-        tokenizer = MagicMock(spec=PreTrainedTokenizerBase)
-
-        from axolotl.loaders.processor import load_processor
-
-        load_processor(cfg, tokenizer)
-
-        call_kwargs = mock_auto_processor.from_pretrained.call_args
-        assert call_kwargs.kwargs.get("revision") == "abc123"
-
-    @patch("axolotl.loaders.processor.AutoProcessor")
-    def test_load_processor_omits_revision_when_unset(self, mock_auto_processor):
-        mock_processor = MagicMock()
-        mock_processor.size = {}
-        mock_auto_processor.from_pretrained.return_value = mock_processor
-
-        cfg = DictDefault(
-            {
-                "processor_config": "some-model",
-                "trust_remote_code": False,
-            }
-        )
-        tokenizer = MagicMock(spec=PreTrainedTokenizerBase)
-
-        from axolotl.loaders.processor import load_processor
-
-        load_processor(cfg, tokenizer)
-
-        call_kwargs = mock_auto_processor.from_pretrained.call_args
-        assert "revision" not in call_kwargs.kwargs

tests/test_save_deduplicated.py

@@ -1,210 +0,0 @@
-"""Tests to verify that deduplication runs before dataset saving during preprocessing.
-
-This addresses GitHub issue #2719: Save De-duplicated Set During Pre-processing.
-"""
-
-from unittest.mock import MagicMock, patch
-
-from datasets import Dataset
-
-from axolotl.utils.dict import DictDefault
-
-
-class TestSFTSaveDeduplicatedBeforeSave:
-    """Verify that in SFT data loading, deduplication occurs before saving."""
-
-    @patch("axolotl.utils.data.sft.save_preprocessed_dataset")
-    @patch("axolotl.utils.data.sft.generate_dataset_hash_from_config")
-    @patch("axolotl.utils.data.sft.deduplicate_and_log_datasets")
-    @patch("axolotl.utils.data.sft.merge_datasets")
-    @patch("axolotl.utils.data.sft._load_and_process_single_dataset")
-    @patch("axolotl.utils.data.sft.datasets_with_name_generator")
-    def test_dedup_called_before_save_sft(
-        self,
-        mock_datasets_gen,
-        mock_load_single,
-        mock_merge,
-        mock_dedup,
-        mock_gen_hash,
-        mock_save,
-    ):
-        """Deduplication should be called before save_preprocessed_dataset in SFT."""
-        from axolotl.utils.data.sft import _load_raw_datasets
-
-        # Set up mock data
-        dataset = Dataset.from_dict({"text": ["a", "b", "a"], "label": [1, 2, 1]})
-        deduped_dataset = Dataset.from_dict({"text": ["a", "b"], "label": [1, 2]})
-
-        mock_datasets_gen.return_value = [
-            DictDefault({"path": "test", "type": "alpaca"})
-        ]
-        mock_load_single.return_value = (dataset, None)
-        mock_merge.return_value = dataset
-        mock_dedup.return_value = (deduped_dataset, None)
-        mock_gen_hash.return_value = "testhash"
-
-        cfg = DictDefault(
-            {
-                "skip_prepare_dataset": False,
-                "dataset_exact_deduplication": True,
-                "sequence_len": 1024,
-                "eval_sequence_len": None,
-                "sample_packing": False,
-                "is_preprocess": False,
-                "seed": 42,
-                "datasets": [{"path": "test", "type": "alpaca"}],
-            }
-        )
-
-        tokenizer = MagicMock()
-        tokenizer.name_or_path = "test-tokenizer"
-
-        # Track call order
-        call_order = []
-        mock_dedup.side_effect = lambda **kwargs: (
-            call_order.append("dedup") or (deduped_dataset, None)
-        )
-        mock_save.side_effect = lambda *args, **kwargs: call_order.append("save")
-
-        _load_raw_datasets(
-            cfg=cfg,
-            datasets_configs=cfg.datasets,
-            tokenizer=tokenizer,
-            split="train",
-        )
-
-        # Verify dedup was called
-        assert "dedup" in call_order, "Deduplication should have been called"
-        # Verify save was called
-        assert "save" in call_order, "Save should have been called"
-        # Verify dedup happened before save
-        assert call_order.index("dedup") < call_order.index("save"), (
-            "Deduplication must occur before saving the dataset"
-        )
-
-    @patch("axolotl.utils.data.sft.save_preprocessed_dataset")
-    @patch("axolotl.utils.data.sft.generate_dataset_hash_from_config")
-    @patch("axolotl.utils.data.sft.merge_datasets")
-    @patch("axolotl.utils.data.sft._load_and_process_single_dataset")
-    @patch("axolotl.utils.data.sft.datasets_with_name_generator")
-    def test_no_dedup_when_disabled_sft(
-        self,
-        mock_datasets_gen,
-        mock_load_single,
-        mock_merge,
-        mock_gen_hash,
-        mock_save,
-    ):
-        """Deduplication should not be called when dataset_exact_deduplication is False."""
-        from axolotl.utils.data.sft import _load_raw_datasets
-
-        dataset = Dataset.from_dict({"text": ["a", "b", "a"], "label": [1, 2, 1]})
-
-        mock_datasets_gen.return_value = [
-            DictDefault({"path": "test", "type": "alpaca"})
-        ]
-        mock_load_single.return_value = (dataset, None)
-        mock_merge.return_value = dataset
-        mock_gen_hash.return_value = "testhash"
-
-        cfg = DictDefault(
-            {
-                "skip_prepare_dataset": False,
-                "dataset_exact_deduplication": False,
-                "sequence_len": 1024,
-                "eval_sequence_len": None,
-                "sample_packing": False,
-                "is_preprocess": False,
-                "seed": 42,
-                "datasets": [{"path": "test", "type": "alpaca"}],
-            }
-        )
-
-        tokenizer = MagicMock()
-        tokenizer.name_or_path = "test-tokenizer"
-
-        with patch("axolotl.utils.data.sft.deduplicate_and_log_datasets") as mock_dedup:
-            _load_raw_datasets(
-                cfg=cfg,
-                datasets_configs=cfg.datasets,
-                tokenizer=tokenizer,
-                split="train",
-            )
-            mock_dedup.assert_not_called()
-
-
-class TestRLSaveDeduplicatedBeforeSave:
-    """Verify that in RL data loading, deduplication occurs before saving."""
-
-    @patch.object(Dataset, "filter", lambda self, *args, **kwargs: self)
-    @patch("axolotl.utils.data.rl.save_preprocessed_dataset")
-    @patch("axolotl.utils.data.rl.generate_dataset_hash_from_config")
-    @patch("axolotl.utils.data.rl.deduplicate_and_log_datasets")
-    @patch("axolotl.utils.data.rl.merge_datasets")
-    @patch("axolotl.utils.data.rl.load_dataset_with_config")
-    @patch("axolotl.utils.data.rl.datasets_with_name_generator")
-    @patch("axolotl.utils.data.rl.load_tokenizer")
-    def test_dedup_called_before_save_rl(
-        self,
-        mock_load_tokenizer,
-        mock_datasets_gen,
-        mock_load_dataset,
-        mock_merge,
-        mock_dedup,
-        mock_gen_hash,
-        mock_save,
-    ):
-        """Deduplication should be called before save_preprocessed_dataset in RL."""
-        from axolotl.utils.data.rl import _load_split
-
-        dataset = Dataset.from_dict(
-            {
-                "prompt": ["hi", "bye", "hi"],
-                "chosen": ["a", "b", "a"],
-                "rejected": ["c", "d", "c"],
-            }
-        )
-        deduped_dataset = Dataset.from_dict(
-            {
-                "prompt": ["hi", "bye"],
-                "chosen": ["a", "b"],
-                "rejected": ["c", "d"],
-            }
-        )
-
-        mock_datasets_gen.return_value = [DictDefault({"path": "test", "type": None})]
-        mock_load_dataset.return_value = dataset
-        mock_merge.return_value = dataset
-        mock_dedup.return_value = (deduped_dataset, None)
-        mock_gen_hash.return_value = "testhash"
-
-        tokenizer = MagicMock()
-        tokenizer.name_or_path = "test-tokenizer"
-        mock_load_tokenizer.return_value = tokenizer
-
-        cfg = DictDefault(
-            {
-                "skip_prepare_dataset": False,
-                "dataset_exact_deduplication": True,
-                "sequence_len": 1024,
-                "rl": "dpo",
-                "datasets": [{"path": "test", "type": None}],
-                "hf_use_auth_token": False,
-                "dataset_num_proc": 1,
-                "is_preprocess": False,
-            }
-        )
-
-        call_order = []
-        mock_dedup.side_effect = lambda **kwargs: (
-            call_order.append("dedup") or (deduped_dataset, None)
-        )
-        mock_save.side_effect = lambda *args, **kwargs: call_order.append("save")
-
-        _load_split(cfg, split="train")
-
-        assert "dedup" in call_order, "Deduplication should have been called"
-        assert "save" in call_order, "Save should have been called"
-        assert call_order.index("dedup") < call_order.index("save"), (
-            "Deduplication must occur before saving the dataset"
-        )

tests/test_tokenizers.py

@@ -116,7 +116,6 @@ class TestTokenizers:
             tokenizer.decode([128041, 128042]) == "RANDOM_OVERRIDE_1RANDOM_OVERRIDE_2"
         )
 
-    @pytest.mark.skip("FIXME slow test sdist py3.11 + torch2.8.0")
     @enable_hf_offline
     def test_added_tokens_overrides_gemma3(self, temp_dir):
         cfg = DictDefault(

tests/utils/data/test_utils.py

@@ -1,545 +0,0 @@
-"""
-Unit tests for data utility functions
-"""
-
-import unittest
-from unittest.mock import MagicMock
-
-from datasets import Dataset
-
-from axolotl.utils.data.utils import handle_long_seq_in_dataset
-from axolotl.utils.dict import DictDefault
-
-
-class TestHandleLongSeqInDataset(unittest.TestCase):
-    """
-    Test class for handle_long_seq_in_dataset function
-    """
-
-    def test_drop_strategy_removes_long_sequences(self):
-        """Test that 'drop' strategy removes sequences longer than sequence_len"""
-        # Create dataset with mixed length sequences
-        dataset = Dataset.from_dict(
-            {
-                "input_ids": [
-                    [1, 2, 3],  # length 3 - keep
-                    [1, 2, 3, 4, 5],  # length 5 - keep
-                    [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11],  # length 11 - drop
-                    [1, 2],  # length 2 - keep
-                ]
-            }
-        )
-
-        cfg = DictDefault(
-            {
-                "excess_length_strategy": "drop",
-                "min_sample_len": 2,
-                "dataset_num_proc": None,
-                "is_preprocess": False,
-            }
-        )
-
-        result = handle_long_seq_in_dataset(dataset, sequence_len=10, cfg=cfg)
-
-        # Should have dropped the sequence with length 11
-        self.assertEqual(len(result), 3)
-        self.assertEqual(len(result[0]["input_ids"]), 3)
-        self.assertEqual(len(result[1]["input_ids"]), 5)
-        self.assertEqual(len(result[2]["input_ids"]), 2)
-
-    def test_drop_strategy_is_default(self):
-        """Test that 'drop' is the default strategy when not specified"""
-        dataset = Dataset.from_dict(
-            {
-                "input_ids": [
-                    [1, 2, 3],
-                    [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11],  # length 11 - should drop
-                ]
-            }
-        )
-
-        cfg = DictDefault(
-            {
-                "min_sample_len": 2,
-                "dataset_num_proc": None,
-                "is_preprocess": False,
-            }
-        )
-
-        result = handle_long_seq_in_dataset(dataset, sequence_len=10, cfg=cfg)
-
-        # Should have dropped the long sequence
-        self.assertEqual(len(result), 1)
-
-    def test_truncate_strategy_truncates_long_sequences(self):
-        """Test that 'truncate' strategy truncates sequences to sequence_len"""
-        dataset = Dataset.from_dict(
-            {
-                "input_ids": [
-                    [1, 2, 3],  # length 3 - keep as is
-                    [
-                        1,
-                        2,
-                        3,
-                        4,
-                        5,
-                        6,
-                        7,
-                        8,
-                        9,
-                        10,
-                        11,
-                        12,
-                    ],  # length 12 - truncate to 10
-                ]
-            }
-        )
-
-        cfg = DictDefault(
-            {
-                "excess_length_strategy": "truncate",
-                "min_sample_len": 2,
-                "dataset_num_proc": None,
-                "is_preprocess": False,
-            }
-        )
-
-        result = handle_long_seq_in_dataset(dataset, sequence_len=10, cfg=cfg)
-
-        # Should have 2 samples
-        self.assertEqual(len(result), 2)
-        # First sample unchanged
-        self.assertEqual(len(result[0]["input_ids"]), 3)
-        # Second sample truncated to 10
-        self.assertEqual(len(result[1]["input_ids"]), 10)
-        self.assertEqual(result[1]["input_ids"], [1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
-
-    def test_truncate_strategy_truncates_all_auxiliary_fields(self):
-        """Test that truncation applies to all auxiliary fields consistently"""
-        dataset = Dataset.from_dict(
-            {
-                "input_ids": [
-                    [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12],
-                ],
-                "attention_mask": [
-                    [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
-                ],
-                "labels": [
-                    [-100, -100, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12],
-                ],
-                "position_ids": [
-                    [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11],
-                ],
-            }
-        )
-
-        cfg = DictDefault(
-            {
-                "excess_length_strategy": "truncate",
-                "min_sample_len": 2,
-                "dataset_num_proc": None,
-                "is_preprocess": False,
-            }
-        )
-
-        result = handle_long_seq_in_dataset(dataset, sequence_len=10, cfg=cfg)
-
-        # All fields should be truncated to 10
-        self.assertEqual(len(result[0]["input_ids"]), 10)
-        self.assertEqual(len(result[0]["attention_mask"]), 10)
-        self.assertEqual(len(result[0]["labels"]), 10)
-        self.assertEqual(len(result[0]["position_ids"]), 10)
-
-        # Verify content is correct
-        self.assertEqual(result[0]["input_ids"], [1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
-        self.assertEqual(result[0]["attention_mask"], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1])
-        self.assertEqual(result[0]["labels"], [-100, -100, 3, 4, 5, 6, 7, 8, 9, 10])
-        self.assertEqual(result[0]["position_ids"], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
-
-    def test_raise_strategy_raises_on_long_sequences(self):
-        """Test that 'raise' strategy raises ValueError when encountering long sequences"""
-        dataset = Dataset.from_dict(
-            {
-                "input_ids": [
-                    [1, 2, 3],
-                    [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11],  # length 11 - should raise
-                ]
-            }
-        )
-
-        cfg = DictDefault(
-            {
-                "excess_length_strategy": "raise",
-                "min_sample_len": 2,
-                "dataset_num_proc": None,
-                "is_preprocess": False,
-            }
-        )
-
-        with self.assertRaises(ValueError):
-            handle_long_seq_in_dataset(dataset, sequence_len=10, cfg=cfg)
-
-    def test_min_sequence_len_filters_short_sequences(self):
-        """Test that sequences shorter than min_sample_len are filtered out"""
-        dataset = Dataset.from_dict(
-            {
-                "input_ids": [
-                    [1],  # length 1 - drop (< min_sample_len=3)
-                    [1, 2],  # length 2 - drop
-                    [1, 2, 3],  # length 3 - keep
-                    [1, 2, 3, 4, 5],  # length 5 - keep
-                ]
-            }
-        )
-
-        cfg = DictDefault(
-            {
-                "excess_length_strategy": "drop",
-                "min_sample_len": 3,
-                "dataset_num_proc": None,
-                "is_preprocess": False,
-            }
-        )
-
-        result = handle_long_seq_in_dataset(dataset, sequence_len=10, cfg=cfg)
-
-        # Should only keep sequences with length >= 3
-        self.assertEqual(len(result), 2)
-        self.assertEqual(len(result[0]["input_ids"]), 3)
-        self.assertEqual(len(result[1]["input_ids"]), 5)
-
-    def test_dataset_without_input_ids_column(self):
-        """Test that datasets without 'input_ids' column are returned unchanged"""
-        dataset = Dataset.from_dict(
-            {
-                "chosen": [1, 2, 3],
-                "rejected": [4, 5, 6],
-            }
-        )
-
-        cfg = DictDefault(
-            {
-                "excess_length_strategy": "drop",
-                "min_sample_len": 2,
-            }
-        )
-
-        result = handle_long_seq_in_dataset(dataset, sequence_len=10, cfg=cfg)
-
-        # Dataset should be unchanged
-        self.assertEqual(len(result), len(dataset))
-        self.assertListEqual(list(result.column_names), ["chosen", "rejected"])
-
-    def test_truncate_filters_short_before_truncating(self):
-        """Test that truncate strategy filters short sequences before truncating long ones
-
-        This is important for efficiency - we should not waste time truncating
-        sequences that will be filtered out anyway.
-        """
-        dataset = Dataset.from_dict(
-            {
-                "input_ids": [
-                    [1],  # length 1 - filter out first
-                    [1, 2, 3],  # length 3 - keep, no truncation needed
-                    [
-                        1,
-                        2,
-                        3,
-                        4,
-                        5,
-                        6,
-                        7,
-                        8,
-                        9,
-                        10,
-                        11,
-                        12,
-                    ],  # length 12 - keep and truncate
-                ]
-            }
-        )
-
-        cfg = DictDefault(
-            {
-                "excess_length_strategy": "truncate",
-                "min_sample_len": 2,
-                "dataset_num_proc": None,
-                "is_preprocess": False,
-            }
-        )
-
-        result = handle_long_seq_in_dataset(dataset, sequence_len=10, cfg=cfg)
-
-        # Should have filtered out the first (short) sequence
-        self.assertEqual(len(result), 2)
-        # Second sample unchanged
-        self.assertEqual(len(result[0]["input_ids"]), 3)
-        # Third sample truncated to 10
-        self.assertEqual(len(result[1]["input_ids"]), 10)
-
-    def test_case_insensitive_strategy(self):
-        """Test that excess_length_strategy is case-insensitive"""
-        dataset = Dataset.from_dict(
-            {
-                "input_ids": [
-                    [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12],
-                ]
-            }
-        )
-
-        cfg = DictDefault(
-            {
-                "excess_length_strategy": "TRUNCATE",  # uppercase
-                "min_sample_len": 2,
-                "dataset_num_proc": None,
-                "is_preprocess": False,
-            }
-        )
-
-        result = handle_long_seq_in_dataset(dataset, sequence_len=10, cfg=cfg)
-
-        # Should still truncate
-        self.assertEqual(len(result[0]["input_ids"]), 10)
-
-    def test_raise_strategy_silently_drops_short_sequences(self):
-        """Test that 'raise' strategy drops short sequences without raising"""
-        dataset = Dataset.from_dict(
-            {
-                "input_ids": [
-                    [1],  # length 1 - too short, should be dropped silently
-                    [1, 2, 3, 4, 5],  # length 5 - keep
-                ]
-            }
-        )
-
-        cfg = DictDefault(
-            {
-                "excess_length_strategy": "raise",
-                "min_sample_len": 3,
-                "dataset_num_proc": None,
-                "is_preprocess": False,
-            }
-        )
-
-        # Should NOT raise, just silently drop the short sequence
-        result = handle_long_seq_in_dataset(dataset, sequence_len=10, cfg=cfg)
-
-        self.assertEqual(len(result), 1)
-        self.assertEqual(len(result[0]["input_ids"]), 5)
-
-    def test_drop_boundary_sequence_equal_to_sequence_len(self):
-        """Test that drop strategy keeps sequences with length exactly equal to sequence_len"""
-        dataset = Dataset.from_dict(
-            {
-                "input_ids": [
-                    [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],  # length 10 == sequence_len
-                    [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11],  # length 11 > sequence_len
-                ]
-            }
-        )
-
-        cfg = DictDefault(
-            {
-                "excess_length_strategy": "drop",
-                "min_sample_len": 2,
-                "dataset_num_proc": None,
-                "is_preprocess": False,
-            }
-        )
-
-        result = handle_long_seq_in_dataset(dataset, sequence_len=10, cfg=cfg)
-
-        # Exactly equal should be kept, one over should be dropped
-        self.assertEqual(len(result), 1)
-        self.assertEqual(len(result[0]["input_ids"]), 10)
-
-    def test_truncate_boundary_sequence_equal_to_sequence_len(self):
-        """Test that truncate strategy leaves sequences with length exactly equal to sequence_len unchanged"""
-        dataset = Dataset.from_dict(
-            {
-                "input_ids": [
-                    [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],  # length 10 == sequence_len
-                ]
-            }
-        )
-
-        cfg = DictDefault(
-            {
-                "excess_length_strategy": "truncate",
-                "min_sample_len": 2,
-                "dataset_num_proc": None,
-                "is_preprocess": False,
-            }
-        )
-
-        result = handle_long_seq_in_dataset(dataset, sequence_len=10, cfg=cfg)
-
-        # Should be unchanged - not truncated
-        self.assertEqual(len(result), 1)
-        self.assertEqual(result[0]["input_ids"], [1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
-
-    def test_empty_dataset(self):
-        """Test that an empty dataset is handled gracefully"""
-        dataset = Dataset.from_dict({"input_ids": []})
-
-        cfg = DictDefault(
-            {
-                "excess_length_strategy": "drop",
-                "min_sample_len": 2,
-                "dataset_num_proc": None,
-                "is_preprocess": False,
-            }
-        )
-
-        result = handle_long_seq_in_dataset(dataset, sequence_len=10, cfg=cfg)
-
-        self.assertEqual(len(result), 0)
-
-    def test_all_sequences_dropped_returns_empty_dataset(self):
-        """Test that dropping all sequences results in an empty dataset"""
-        dataset = Dataset.from_dict(
-            {
-                "input_ids": [
-                    [1],  # too short
-                    [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11],  # too long
-                ]
-            }
-        )
-
-        cfg = DictDefault(
-            {
-                "excess_length_strategy": "drop",
-                "min_sample_len": 5,
-                "dataset_num_proc": None,
-                "is_preprocess": False,
-            }
-        )
-
-        result = handle_long_seq_in_dataset(dataset, sequence_len=10, cfg=cfg)
-
-        self.assertEqual(len(result), 0)
-
-    def test_iterable_dataset_skips_processing(self):
-        """Test that streaming datasets (column_names is None) are returned unchanged.
-
-        The skip check in _should_skip_processing triggers when column_names is
-        None, which happens with true streaming datasets loaded via
-        load_dataset(..., streaming=True).
-        """
-        mock_dataset = MagicMock()
-        mock_dataset.column_names = None
-
-        cfg = DictDefault(
-            {
-                "excess_length_strategy": "drop",
-                "min_sample_len": 2,
-                "dataset_num_proc": None,
-                "is_preprocess": False,
-            }
-        )
-
-        result = handle_long_seq_in_dataset(mock_dataset, sequence_len=10, cfg=cfg)
-
-        # Should be returned unchanged (same object)
-        self.assertIs(result, mock_dataset)
-
-    def test_truncate_with_partial_auxiliary_fields(self):
-        """Test truncation when only some auxiliary fields are present"""
-        dataset = Dataset.from_dict(
-            {
-                "input_ids": [
-                    [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12],
-                ],
-                "labels": [
-                    [-100, -100, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12],
-                ],
-                # No attention_mask or position_ids
-            }
-        )
-
-        cfg = DictDefault(
-            {
-                "excess_length_strategy": "truncate",
-                "min_sample_len": 2,
-                "dataset_num_proc": None,
-                "is_preprocess": False,
-            }
-        )
-
-        result = handle_long_seq_in_dataset(dataset, sequence_len=10, cfg=cfg)
-
-        self.assertEqual(len(result[0]["input_ids"]), 10)
-        self.assertEqual(len(result[0]["labels"]), 10)
-        self.assertEqual(result[0]["input_ids"], [1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
-        self.assertEqual(result[0]["labels"], [-100, -100, 3, 4, 5, 6, 7, 8, 9, 10])
-        # Confirm no extra columns were introduced
-        self.assertListEqual(sorted(result.column_names), ["input_ids", "labels"])
-
-    def test_min_sample_len_defaults_to_two_when_not_set(self):
-        """Test that min_sample_len defaults to 2 when not specified in config"""
-        dataset = Dataset.from_dict(
-            {
-                "input_ids": [
-                    [1],  # length 1 - should be dropped (< default 2)
-                    [1, 2],  # length 2 - should be kept (>= default 2)
-                    [1, 2, 3],  # length 3 - should be kept
-                ]
-            }
-        )
-
-        cfg = DictDefault(
-            {
-                "excess_length_strategy": "drop",
-                # min_sample_len not set
-                "dataset_num_proc": None,
-                "is_preprocess": False,
-            }
-        )
-
-        result = handle_long_seq_in_dataset(dataset, sequence_len=10, cfg=cfg)
-
-        self.assertEqual(len(result), 2)
-        self.assertEqual(len(result[0]["input_ids"]), 2)
-        self.assertEqual(len(result[1]["input_ids"]), 3)
-
-    def test_invalid_strategy_falls_through_to_drop(self):
-        """Test that an unrecognized strategy value falls through to drop behavior"""
-        dataset = Dataset.from_dict(
-            {
-                "input_ids": [
-                    [1, 2, 3],  # keep
-                    [
-                        1,
-                        2,
-                        3,
-                        4,
-                        5,
-                        6,
-                        7,
-                        8,
-                        9,
-                        10,
-                        11,
-                    ],  # length 11 - should be dropped
-                ]
-            }
-        )
-
-        cfg = DictDefault(
-            {
-                "excess_length_strategy": "not_a_real_strategy",
-                "min_sample_len": 2,
-                "dataset_num_proc": None,
-                "is_preprocess": False,
-            }
-        )
-
-        result = handle_long_seq_in_dataset(dataset, sequence_len=10, cfg=cfg)
-
-        # Should behave like 'drop'
-        self.assertEqual(len(result), 1)
-        self.assertEqual(len(result[0]["input_ids"]), 3)
-
-
-if __name__ == "__main__":
-    unittest.main()

tests/utils/schemas/validation/test_moe_quant.py

@@ -1,142 +0,0 @@
-"""Tests for MoE expert quantization config validation and PEFT patch idempotency."""
-
-import pytest
-
-from axolotl.utils.config import validate_config
-from axolotl.utils.dict import DictDefault
-
-
-@pytest.fixture()
-def gpu_caps():
-    return {"compute_capability": "sm_89", "bf16": True, "n_gpu": 1, "n_node": 1}
-
-
-@pytest.fixture()
-def env_caps():
-    return {"torch_version": "2.7.0"}
-
-
-class TestQuantizeMoeExpertsValidation:
-    """Test suite for quantize_moe_experts config validator."""
-
-    def test_requires_adapter(self, min_base_cfg, gpu_caps, env_caps):
-        """quantize_moe_experts without adapter should fail."""
-        cfg = (
-            DictDefault(
-                quantize_moe_experts=True,
-            )
-            | min_base_cfg
-        )
-        with pytest.raises(ValueError, match="requires adapter"):
-            validate_config(cfg, capabilities=gpu_caps, env_capabilities=env_caps)
-
-    def test_requires_quantization(self, min_base_cfg, gpu_caps, env_caps):
-        """quantize_moe_experts without load_in_4bit/8bit should fail."""
-        cfg = (
-            DictDefault(
-                quantize_moe_experts=True,
-                adapter="lora",
-            )
-            | min_base_cfg
-        )
-        with pytest.raises(ValueError, match="requires load_in_4bit or load_in_8bit"):
-            validate_config(cfg, capabilities=gpu_caps, env_capabilities=env_caps)
-
-    def test_valid_qlora_4bit(self, min_base_cfg, gpu_caps, env_caps):
-        """quantize_moe_experts with qlora + 4bit should pass."""
-        cfg = (
-            DictDefault(
-                quantize_moe_experts=True,
-                adapter="qlora",
-                load_in_4bit=True,
-            )
-            | min_base_cfg
-        )
-        result = validate_config(cfg, capabilities=gpu_caps, env_capabilities=env_caps)
-        assert result["quantize_moe_experts"] is True
-
-    def test_valid_lora_8bit(self, min_base_cfg, gpu_caps, env_caps):
-        """quantize_moe_experts with lora + 8bit should pass."""
-        cfg = (
-            DictDefault(
-                quantize_moe_experts=True,
-                adapter="lora",
-                load_in_8bit=True,
-            )
-            | min_base_cfg
-        )
-        result = validate_config(cfg, capabilities=gpu_caps, env_capabilities=env_caps)
-        assert result["quantize_moe_experts"] is True
-
-    def test_false_skips_validation(self, min_base_cfg, gpu_caps, env_caps):
-        """quantize_moe_experts=false should not check adapter/quantization."""
-        cfg = (
-            DictDefault(
-                quantize_moe_experts=False,
-            )
-            | min_base_cfg
-        )
-        result = validate_config(cfg, capabilities=gpu_caps, env_capabilities=env_caps)
-        assert result["quantize_moe_experts"] is False
-
-    def test_default_is_false(self, min_base_cfg, gpu_caps, env_caps):
-        """quantize_moe_experts should default to false."""
-        cfg = DictDefault({}) | min_base_cfg
-        result = validate_config(cfg, capabilities=gpu_caps, env_capabilities=env_caps)
-        assert result["quantize_moe_experts"] is False
-
-
-class TestLoraTargetParametersDropout:
-    """Test that lora_dropout must be 0 when lora_target_parameters is set."""
-
-    def test_rejects_nonzero_dropout(self, min_base_cfg):
-        """lora_dropout > 0 with lora_target_parameters should fail."""
-        cfg = (
-            DictDefault(
-                adapter="lora",
-                lora_target_parameters=["mlp.experts.gate_up_proj"],
-                lora_dropout=0.1,
-                load_in_8bit=True,
-            )
-            | min_base_cfg
-        )
-        with pytest.raises(ValueError, match="lora_dropout must be 0"):
-            validate_config(cfg)
-
-    def test_zero_dropout_passes(self, min_base_cfg):
-        """lora_dropout=0 with lora_target_parameters should pass."""
-        cfg = (
-            DictDefault(
-                adapter="lora",
-                lora_target_parameters=["mlp.experts.gate_up_proj"],
-                lora_dropout=0.0,
-                load_in_8bit=True,
-            )
-            | min_base_cfg
-        )
-        result = validate_config(cfg)
-        assert result["lora_dropout"] == 0.0
-
-
-class TestPeftPatchIdempotency:
-    """Test that patch_peft_target_parameters_matching is idempotent."""
-
-    def test_double_call_does_not_stack_wrappers(self):
-        """Calling patch twice should not double-wrap _inject_parameters."""
-        from peft.tuners.tuners_utils import BaseTuner
-
-        from axolotl.monkeypatch.moe_quant import (
-            patch_peft_target_parameters_matching,
-        )
-
-        original = BaseTuner._inject_parameters
-        try:
-            patch_peft_target_parameters_matching()
-            first_patched = BaseTuner._inject_parameters
-            patch_peft_target_parameters_matching()
-            second_patched = BaseTuner._inject_parameters
-            # Should be same function, not double-wrapped
-            assert first_patched is second_patched
-        finally:
-            BaseTuner._inject_parameters = original
-            patch_peft_target_parameters_matching._axolotl_patched = False

tests/utils/test_mistral3_processor.py

@@ -1,149 +0,0 @@
-"""Tests for Mistral3Processor with transformers v5 ProcessorMixin integration"""
-
-from unittest.mock import MagicMock
-
-import pytest
-import torch
-from transformers.feature_extraction_utils import BatchFeature
-
-from axolotl.utils.mistral.mistral3_processor import Mistral3Processor
-from axolotl.utils.mistral.mistral_tokenizer import HFMistralTokenizer
-
-
-@pytest.fixture()
-def mock_tokenizer():
-    """Create a mock HFMistralTokenizer that passes v5 ProcessorMixin isinstance checks."""
-    return MagicMock(spec=HFMistralTokenizer)
-
-
-@pytest.fixture()
-def processor(mock_tokenizer):
-    return Mistral3Processor(tokenizer=mock_tokenizer)
-
-
-class TestMistral3ProcessorInit:
-    def test_tokenizer_is_set(self, processor, mock_tokenizer):
-        assert processor.tokenizer is mock_tokenizer
-
-    def test_chat_template_is_none(self, processor):
-        assert processor.chat_template is None
-
-    def test_audio_tokenizer_is_none(self, processor):
-        assert processor.audio_tokenizer is None
-
-
-class TestApplyChatTemplateTokenized:
-    """Test apply_chat_template with tokenize=True, return_dict=True"""
-
-    @pytest.fixture()
-    def batched_conversations(self):
-        return [
-            [
-                {"role": "user", "content": "Describe this image."},
-                {"role": "assistant", "content": "It is red."},
-            ],
-            [
-                {"role": "user", "content": "What is this?"},
-                {"role": "assistant", "content": "A cat."},
-            ],
-        ]
-
-    def test_returns_batch_feature_with_pixel_values(
-        self, processor, mock_tokenizer, batched_conversations
-    ):
-        pixel_values = torch.randn(2, 3, 224, 224, dtype=torch.float64)
-        mock_tokenizer.apply_chat_template.return_value = {
-            "input_ids": torch.tensor([[1, 2, 3], [4, 5, 6]]),
-            "attention_mask": torch.tensor([[1, 1, 1], [1, 1, 1]]),
-            "pixel_values": pixel_values,
-        }
-
-        result = processor.apply_chat_template(
-            batched_conversations, tokenize=True, return_dict=True
-        )
-
-        assert isinstance(result, BatchFeature)
-        assert "pixel_values" in result
-        assert "image_sizes" in result
-        assert result["pixel_values"].dtype == torch.float32
-        assert result["image_sizes"].shape == (2, 2)
-        assert result["image_sizes"][0].tolist() == [224, 224]
-
-    def test_returns_batch_feature_without_pixel_values(
-        self, processor, mock_tokenizer, batched_conversations
-    ):
-        mock_tokenizer.apply_chat_template.return_value = {
-            "input_ids": torch.tensor([[1, 2, 3], [4, 5, 6]]),
-            "attention_mask": torch.tensor([[1, 1, 1], [1, 1, 1]]),
-        }
-
-        result = processor.apply_chat_template(
-            batched_conversations, tokenize=True, return_dict=True
-        )
-
-        assert isinstance(result, BatchFeature)
-        assert "input_ids" in result
-        assert "image_sizes" not in result
-
-
-class TestApplyChatTemplateNotTokenized:
-    def test_single_conversation_returns_unwrapped(self, processor, mock_tokenizer):
-        """Single conversation (not batched) should return unwrapped result."""
-        single_conversation = [
-            {"role": "user", "content": "Hello"},
-            {"role": "assistant", "content": "Hi"},
-        ]
-        mock_tokenizer.apply_chat_template.return_value = [
-            "<s>[INST]Hello[/INST]Hi</s>"
-        ]
-
-        result = processor.apply_chat_template(
-            single_conversation, tokenize=False, return_dict=False
-        )
-
-        assert result == "<s>[INST]Hello[/INST]Hi</s>"
-
-    def test_batched_conversations_returns_list(self, processor, mock_tokenizer):
-        batched = [
-            [
-                {"role": "user", "content": "Hello"},
-                {"role": "assistant", "content": "Hi"},
-            ],
-            [
-                {"role": "user", "content": "Bye"},
-                {"role": "assistant", "content": "Bye"},
-            ],
-        ]
-        mock_tokenizer.apply_chat_template.return_value = ["text1", "text2"]
-
-        result = processor.apply_chat_template(
-            batched, tokenize=False, return_dict=False
-        )
-
-        assert result == ["text1", "text2"]
-
-
-class TestCall:
-    def test_delegates_to_tokenizer(self, processor, mock_tokenizer):
-        mock_tokenizer.return_value = {
-            "input_ids": [1, 2, 3],
-            "attention_mask": [1, 1, 1],
-        }
-
-        result = processor("Hello world")
-
-        mock_tokenizer.assert_called_once()
-        assert isinstance(result, BatchFeature)
-
-
-class TestReturnTensorsValidation:
-    def test_rejects_non_pt_return_tensors(self, processor):
-        conversation = [
-            {"role": "user", "content": "Hello"},
-            {"role": "assistant", "content": "Hi"},
-        ]
-
-        with pytest.raises(ValueError, match=r"only supports.*return_tensors='pt'"):
-            processor.apply_chat_template(
-                conversation, tokenize=True, return_dict=True, return_tensors="np"
-            )