version tag 0.13.2

.github/workflows/base.yml

@@ -51,22 +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_version: 12.8.1
+          - cuda: "129"
+            cuda_version: 12.9.1
             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: ""
@@ -83,14 +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: ""
@@ -173,22 +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_version: 12.8.1
+          - cuda: "129"
+            cuda_version: 12.9.1
             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: ""
@@ -205,14 +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

@@ -34,30 +34,18 @@ jobs:
             axolotl_extras:
             platforms: "linux/amd64,linux/arm64"
             is_latest: true
-          - cuda: 128
-            cuda_version: 12.8.1
+          - cuda: 129
+            cuda_version: 12.9.1
             python_version: "3.12"
-            pytorch: 2.10.0
-            axolotl_extras:
+            pytorch: 2.9.1
+            axolotl_extras: vllm
             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"
-            pytorch: 2.10.0
-            axolotl_extras:
-            platforms: "linux/amd64,linux/arm64"
     runs-on: axolotl-gpu-runner
     steps:
       - name: Checkout
@@ -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_version: 12.8.1
+          - cuda: 129
+            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"
-            pytorch: 2.10.0
+            python_version: "3.11"
+            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/multi-gpu-e2e.yml

@@ -45,7 +45,7 @@ jobs:
             cuda_version: 12.9.1
             python_version: "3.12"
             pytorch: 2.9.1
-            axolotl_extras: "fbgemm-gpu"
+            axolotl_extras: "fbgemm-gpu,vllm"
             num_gpus: 2
             dockerfile: "Dockerfile-uv.jinja"
           - cuda: 130

.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

@@ -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
@@ -115,10 +115,10 @@ jobs:
 
       - name: Pre-Download dataset fixture
         run: |
-          hf download --repo-type=dataset axolotl-ai-internal/axolotl-oss-dataset-fixtures
+          huggingface-cli download --repo-type=dataset axolotl-ai-internal/axolotl-oss-dataset-fixtures
 
       - name: Show HF cache
-        run: hf cache ls
+        run: hf cache scan
 
       - name: Run tests
         run: |
@@ -132,7 +132,7 @@ jobs:
           pytest -v --durations=10 tests/cli/ --cov=axolotl --cov-append --cov-report=xml
 
       - name: Show HF cache
-        run: hf cache ls
+        run: hf cache scan
 
       - name: Upload coverage to Codecov
         uses: codecov/codecov-action@v5
@@ -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
@@ -210,7 +210,7 @@ jobs:
           axolotl --help
 
       - name: Show HF cache
-        run: hf cache ls
+        run: hf cache scan
 
       - name: Run tests
         run: |
@@ -219,10 +219,10 @@ jobs:
           pytest -v --durations=10 tests/cli/
 
       - name: Show HF cache
-        run: hf cache ls
+        run: hf cache scan
 
   gate-skip-e2e:
-    needs: [pre-commit]
+    needs: [pre-commit, pytest, pytest-sdist]
     runs-on: ubuntu-latest
     outputs:
       skip: ${{ steps.compute.outputs.skip }}
@@ -258,18 +258,18 @@ jobs:
     # this job needs to be run on self-hosted GPU runners...
     runs-on: [self-hosted, modal]
     timeout-minutes: 120
-    needs: [pre-commit, pytest]
+    needs: [pre-commit, pytest, pytest-sdist, gate-skip-e2e]
 
     strategy:
       fail-fast: false
       matrix:
         include:
-          - cuda: 130
-            cuda_version: 13.0.0
+          - cuda: 129
+            cuda_version: 12.9.1
             python_version: "3.12"
             pytorch: 2.9.1
             num_gpus: 1
-            axolotl_extras:
+            axolotl_extras: vllm
             dockerfile: "Dockerfile-uv.jinja"
     steps:
       - name: Checkout

.runpod/README.md

@@ -123,7 +123,7 @@ datasets:
 | --------------------------------- | -------------------------- | ----------------------------------- |
 | `dataset_prepared_path`           | `"data/last_run_prepared"` | Path for prepared dataset           |
 | `push_dataset_to_hub`             | `""`                       | Push dataset to HF hub              |
-| `dataset_num_proc`                | `4`                        | Number of preprocessing processes   |
+| `dataset_processes`               | `4`                        | Number of preprocessing processes   |
 | `dataset_keep_in_memory`          | `false`                    | Keep dataset in memory              |
 | `shuffle_merged_datasets`         | `true`                     | Shuffle merged datasets             |
 | `shuffle_before_merging_datasets` | `false`                    | Shuffle each dataset before merging |

.runpod/src/config/config.yaml

@@ -39,6 +39,7 @@
 #     type: # linear | dynamic
 #     factor: # float
 
+
 # # Whether you are training a 4-bit GPTQ quantized model
 # gptq: true
 # gptq_groupsize: 128 # group size
@@ -106,7 +107,7 @@
 # push_dataset_to_hub: # repo path
 # # The maximum number of processes to use while preprocessing your input dataset. This defaults to `os.cpu_count()`
 # # if not set.
-# dataset_num_proc: # defaults to os.cpu_count() if not set
+# dataset_processes: # defaults to os.cpu_count() if not set
 # # push checkpoints to hub
 # hub_model_id: # repo path to push finetuned model
 # # how to push checkpoints to hub
@@ -223,6 +224,9 @@
 # eval_table_size: # Approximate number of predictions sent to wandb depending on batch size. Enabled above 0. Default is 0
 # eval_table_max_new_tokens: # Total number of tokens generated for predictions sent to wandb. Default is 128
 
+# # Save model as safetensors (require safetensors package)
+# save_safetensors:
+
 # # Whether to mask out or include the human's prompt from the training labels
 # train_on_inputs: false
 # # Group similarly sized data to minimize padding.
@@ -348,6 +352,8 @@
 # # Allow overwrite yml config using from cli
 # strict:
 
+
+
 base_model: ${BASE_MODEL}
 base_model_ignore_patterns: ${BASE_MODEL_IGNORE_PATTERNS}
 base_model_config: ${BASE_MODEL_CONFIG}
@@ -406,7 +412,7 @@ chat_template_jinja: ${CHAT_TEMPLATE_JINJA}
 default_system_message: ${DEFAULT_SYSTEM_MESSAGE}
 dataset_prepared_path: ${DATASET_PREPARED_PATH}
 push_dataset_to_hub: ${PUSH_DATASET_TO_HUB}
-dataset_num_proc: ${DATASET_NUM_PROC}
+dataset_processes: ${DATASET_PROCESSES}
 dataset_keep_in_memory: ${DATASET_KEEP_IN_MEMORY}
 hub_model_id: ${HUB_MODEL_ID}
 hub_strategy: ${HUB_STRATEGY}
@@ -506,6 +512,7 @@ profiler_steps: ${PROFILER_STEPS}
 loss_watchdog_threshold: ${LOSS_WATCHDOG_THRESHOLD}
 loss_watchdog_patience: ${LOSS_WATCHDOG_PATIENCE}
 
+save_safetensors: ${SAVE_SAFETENSORS}
 train_on_inputs: ${TRAIN_ON_INPUTS}
 group_by_length: ${GROUP_BY_LENGTH}
 gradient_checkpointing: ${GRADIENT_CHECKPOINTING}

VERSION

@@ -1 +1 @@
-0.15.0.dev0
+0.13.2

_quarto.yml

@@ -251,6 +251,7 @@ website:
                 - docs/models/olmo3.qmd
                 - docs/models/trinity.qmd
                 - docs/models/arcee.qmd
+                - docs/models/mistral.qmd
                 - section: "Ministral3"
                   contents:
                     - docs/models/ministral3.qmd
@@ -265,7 +266,6 @@ website:
                 - docs/models/mistral-small.qmd
                 - docs/models/voxtral.qmd
                 - docs/models/devstral.qmd
-                - docs/models/mistral.qmd
                 - docs/models/llama-4.qmd
                 - docs/models/llama-2.qmd
                 - docs/models/qwen3-next.qmd
@@ -320,7 +320,6 @@ website:
             - docs/multipack.qmd
             - docs/mixed_precision.qmd
             - docs/optimizers.qmd
-            - docs/attention.qmd
 
         - section: "Advanced Features"
           contents:

cicd/multigpu.sh

@@ -2,7 +2,7 @@
 set -e
 
 # Only run two tests at a time to avoid OOM on GPU (with coverage collection)
-pytest -v --durations=10 -n2 --maxfail=3 \
+pytest -v --durations=10 -n2 --maxfail=4 \
   --ignore=/workspace/axolotl/tests/e2e/multigpu/solo/ \
   --ignore=/workspace/axolotl/tests/e2e/multigpu/patched/ \
   /workspace/axolotl/tests/e2e/multigpu/ \

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

@@ -86,7 +86,7 @@ export HF_DATASETS_OFFLINE=1
 Download a base model using the Hugging Face CLI:
 
 ```bash
-hf download meta-llama/Meta-Llama-3.1-8B --local-dir ~/hfdata/llama3.1-8B
+huggingface-cli download meta-llama/Meta-Llama-3.1-8B --local-dir ~/hfdata/llama3.1-8B
 ```
 
 ### 10. Create Axolotl Configuration

docs/attention.qmd

@@ -1,140 +0,0 @@
----
-title: Attention
-description: Supported attention modules in Axolotl
----
-
-## SDP Attention
-
-This is the default built-in attention in PyTorch.
-
-```yaml
-sdp_attention: true
-```
-
-For more details: [PyTorch docs](https://docs.pytorch.org/docs/stable/generated/torch.nn.functional.scaled_dot_product_attention.html)
-
-## Flash Attention 2
-
-Uses efficient kernels to compute attention.
-
-```yaml
-flash_attention: true
-```
-
-For more details: [Flash Attention](https://github.com/Dao-AILab/flash-attention/)
-
-### Nvidia
-
-Requirements: Ampere, Ada, or Hopper GPUs
-
-Note: For Turing GPUs or lower, please use other attention methods.
-
-```bash
-pip install flash-attn --no-build-isolation
-```
-
-::: {.callout-tip}
-
-If you get `undefined symbol` while training, ensure you installed PyTorch prior to Axolotl. Alternatively, try reinstall or downgrade a version.
-
-:::
-
-#### Flash Attention 3
-
-Requirements: Hopper only and CUDA 12.8 (recommended)
-
-```bash
-git clone https://github.com/Dao-AILab/flash-attention.git
-cd flash-attention/hopper
-
-python setup.py install
-```
-
-### AMD
-
-Requirements: ROCm 6.0 and above.
-
-See [Flash Attention AMD docs](https://github.com/Dao-AILab/flash-attention/tree/main?tab=readme-ov-file#amd-rocm-support).
-
-## Flex Attention
-
-A flexible PyTorch API for attention used in combination with `torch.compile`.
-
-```yaml
-flex_attention: true
-
-# recommended
-torch_compile: true
-```
-
-::: {.callout-note}
-
-We recommend using latest stable version of PyTorch for best performance.
-
-:::
-
-For more details: [PyTorch docs](https://pytorch.org/blog/flexattention/)
-
-## SageAttention
-
-Attention kernels with QK Int8 and PV FP16 accumulator.
-
-```yaml
-sage_attention: true
-```
-
-Requirements: Ampere, Ada, or Hopper GPUs
-
-```bash
-pip install sageattention==2.2.0 --no-build-isolation
-```
-
-::: {.callout-warning}
-
-Only LoRA/QLoRA recommended at the moment. We found loss drop to 0 for full finetuning. See [GitHub Issue](https://github.com/thu-ml/SageAttention/issues/198).
-
-:::
-
-For more details: [Sage Attention](https://github.com/thu-ml/SageAttention)
-
-::: {.callout-note}
-
-We do not support SageAttention 3 at the moment. If you are interested on adding this or improving SageAttention implementation, please make an Issue.
-
-:::
-
-
-## xFormers
-
-```yaml
-xformers_attention: true
-```
-
-::: {.callout-tip}
-
-We recommend using with Turing GPUs or below (such as on Colab).
-
-:::
-
-For more details: [xFormers](https://github.com/facebookresearch/xformers)
-
-## Shifted Sparse Attention
-
-::: {.callout-warning}
-
-We plan to deprecate this! If you use this feature, we recommend switching to methods above.
-
-:::
-
-Requirements: LLaMA model architecture
-
-```yaml
-flash_attention: true
-s2_attention: true
-```
-
-::: {.callout-tip}
-
-No sample packing support!
-
-:::

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
 

docs/installation.qmd

@@ -165,7 +165,7 @@ We recommend using WSL2 (Windows Subsystem for Linux) or Docker.
    ```
 4. (Optional) Login to Hugging Face:
    ```{.bash}
-   hf auth login
+   huggingface-cli login
    ```
 
 ## Troubleshooting {#sec-troubleshooting}

docs/lora_optims.qmd

@@ -89,10 +89,6 @@ lora_o_kernel: true
 Currently, LoRA kernels are not supported for RLHF training, only SFT.
 :::
 
-::: {.callout-warning}
-LoRA kernels do not support remote modeling code.
-:::
-
 ## Requirements
 
 - One or more NVIDIA or AMD GPUs (in order to use the Triton kernels)

docs/multimodal.qmd

@@ -19,7 +19,6 @@ format:
 - [Gemma-3n](#sec-gemma-3n)
 - [Qwen2-VL](#sec-qwen2-vl)
 - [Qwen2.5-VL](#sec-qwen25-vl)
-- [GLM-4.6V](#sec-glm-4-6v)
 - [SmolVLM2](#sec-smolvlm2)
 - [LFM2-VL](#sec-lfm2-vl)
 - [Intern-VL](#sec-intern-vl)
@@ -184,18 +183,6 @@ base_model: Qwen/Qwen3-VL-4B-Instruct
 chat_template: qwen2_vl  # same as qwen2-vl
 ```
 
-### GLM-4.6V {#sec-glm-4-6v}
-
-Both GLM-4.6V (106B MoE) and GLM-4.6V-Flash (9B) are supported.
-
-```yaml
-# GLM-4.6V (106B MoE version)
-base_model: zai-org/GLM-4.6V
-
-# OR GLM-4.6V-Flash (9B version)
-base_model: zai-org/GLM-4.6V-Flash
-```
-
 ### SmolVLM2 {#sec-smolvlm2}
 
 ::: {.callout-tip}

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@58d6572\""
+    "!pip install \"cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@318b7e2\""
    ]
   },
   {

examples/eaft/eaft-example.yml

@@ -1,77 +0,0 @@
-base_model: google/gemma-3-1b-it
-
-model_type: Gemma3ForCausalLM
-cls_model_config: Gemma3TextConfig
-
-# gemma3 doesn't seem to play nice with ddp
-ddp_find_unused_parameters: true
-
-chat_template: gemma3
-eot_tokens:
-  - <end_of_turn>
-
-load_in_8bit: false
-load_in_4bit: false
-strict: false
-
-datasets:
-  - path: cgato/SlimOrcaDedupCleaned
-    type: chat_template
-    field_messages: conversations
-    message_property_mappings:
-      role: from
-      content: value
-
-dataset_prepared_path:
-val_set_size: 0
-output_dir: ./outputs/eaft-gemma-3-1b
-
-use_eaft: true
-eaft_alpha: 1.0
-eaft_k: 20
-
-sequence_len: 1024
-sample_packing: false
-
-adapter:
-lora_model_dir:
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 4
-micro_batch_size: 1
-eval_batch_size: 1
-max_steps: 1000
-evaluation_strategy: "no"
-optimizer: adamw_torch_fused
-lr_scheduler: cosine
-learning_rate: 5e-5
-
-train_on_inputs: false
-group_by_length: false
-bf16: auto
-fp16:
-tf32: true
-
-gradient_checkpointing: true
-gradient_checkpointing_kwargs:
-  use_reentrant: false
-
-early_stopping_patience:
-resume_from_checkpoint:
-local_rank:
-logging_steps: 1
-xformers_attention:
-flash_attention: true
-
-warmup_ratio: 0.1
-weight_decay: 0.0
-debug:
-deepspeed:
-fsdp:
-fsdp_config:
-special_tokens:

examples/glm46v/README.md

@@ -1,44 +0,0 @@
-# Finetune GLM-4.6V with Axolotl
-
-GLM-4.6V is a family of vision-language models from ZhipuAI found on [HuggingFace](https://huggingface.co/zai-org/GLM-4.6V). This guide shows how to fine-tune it with Axolotl for vision-language tasks.
-
-
-
-## Getting started
-
-1. Install Axolotl from source following 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.
-
-
-3. Run the fine-tuning:
-
-    glm-4-6v-flash(9B)
-    ```bash
-    axolotl train examples/glm46v/glm-4-6v-flash-qlora.yaml
-    ```
-
-Let us know how it goes. Happy finetuning! 🚀
-
-## Tips
-
-- Vision datasets should follow the format described in the [multimodal docs](https://docs.axolotl.ai/docs/multimodal.html#dataset-format)
-- You can run a **full finetuning** by removing the `adapter: qlora` and `load_in_4bit: true` from the config.
-- Read more on how to load your own dataset in the [dataset loading docs](https://docs.axolotl.ai/docs/dataset_loading.html).
-
-## Supported Models
-
-- **GLM-4.6V**: Full vision-language model (`zai-org/GLM-4.6V`)
-- **GLM-4.6V-Flash**: Faster variant (`zai-org/GLM-4.6V-Flash`)
-
-## Optimization Guides
-
-Please check the [Optimizations doc](https://docs.axolotl.ai/docs/optimizations.html).
-
-## Related Resources
-
-- [ZhipuAI GLM-4.6V](https://huggingface.co/zai-org/GLM-4.6V)
-- [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/glm46v/glm-4-6v-flash-ddp.yaml

@@ -1,53 +0,0 @@
-base_model: zai-org/GLM-4.6V-Flash
-trust_remote_code: true
-
-processor_type: AutoProcessor
-load_in_4bit: true
-
-# these 3 lines are needed for now to handle vision chat templates w images
-skip_prepare_dataset: true
-remove_unused_columns: false
-sample_packing: false
-ddp_find_unused_parameters: true
-
-output_dir: ./outputs/glm-4-6v-flash-qlora
-datasets:
-  - path: HuggingFaceH4/llava-instruct-mix-vsft
-    type: chat_template
-    split: train[:1%]
-
-adapter: qlora
-lora_r: 16
-lora_alpha: 32
-lora_dropout: 0.05
-lora_target_modules:
-  - gate_proj
-  - down_proj
-  - up_proj
-  - q_proj
-  - v_proj
-  - k_proj
-  - o_proj
-
-sequence_len: 2048
-
-gradient_accumulation_steps: 4
-micro_batch_size: 1
-num_epochs: 1
-optimizer: adamw_8bit
-lr_scheduler: cosine
-learning_rate: 0.0002
-
-bf16: auto
-tf32: false
-
-gradient_checkpointing: true
-gradient_checkpointing_kwargs:
-  use_reentrant: false
-logging_steps: 1
-sdp_attention: true
-
-warmup_ratio: 0.1
-evals_per_epoch: 0
-saves_per_epoch: 1
-weight_decay: 0.0

examples/glm46v/glm-4-6v-flash-qlora.yaml

@@ -1,50 +0,0 @@
-base_model: zai-org/GLM-4.6V-Flash
-trust_remote_code: true
-
-processor_type: AutoProcessor
-load_in_4bit: true
-
-# these 3 lines are needed for now to handle vision chat templates w images
-skip_prepare_dataset: true
-remove_unused_columns: false
-sample_packing: false
-
-output_dir: ./outputs/glm-4-6v-flash-qlora
-datasets:
-  - path: HuggingFaceH4/llava-instruct-mix-vsft
-    type: chat_template
-    split: train[:1%]
-
-adapter: qlora
-lora_r: 16
-lora_alpha: 32
-lora_dropout: 0.05
-lora_target_modules:
-  - gate_proj
-  - down_proj
-  - up_proj
-  - q_proj
-  - v_proj
-  - k_proj
-  - o_proj
-
-sequence_len: 2048
-
-gradient_accumulation_steps: 4
-micro_batch_size: 1
-num_epochs: 1
-optimizer: adamw_8bit
-lr_scheduler: cosine
-learning_rate: 0.0002
-
-bf16: auto
-tf32: false
-
-gradient_checkpointing: true
-logging_steps: 1
-sdp_attention: true
-
-warmup_ratio: 0.1
-evals_per_epoch: 0
-saves_per_epoch: 1
-weight_decay: 0.0

examples/jamba/qlora_fsdp_large.yaml

@@ -19,6 +19,7 @@ datasets:
 dataset_prepared_path: last_run_prepared
 val_set_size: 0.0
 output_dir: jamba-large-fsdp-qlora-ft
+save_safetensors: true
 adapter: qlora
 sequence_len: 2048
 sample_packing: true

examples/llama-3/qlora-fsdp-405b.yaml

@@ -12,6 +12,7 @@ datasets:
 dataset_prepared_path: last_run_prepared
 val_set_size: 0.0
 output_dir: ./outputs/out/qlora-llama3_1-405b
+save_safetensors: true
 
 adapter: qlora
 

examples/mamba/config.yml

@@ -47,5 +47,6 @@ saves_per_epoch: 1
 weight_decay: 0.0
 special_tokens:
 tokens:
+save_safetensors: False
 
 # save_first_step: true  # uncomment this to validate checkpoint saving works with your config

pyproject.toml

@@ -60,6 +60,3 @@ indent-style = "space"
 skip-magic-trailing-comma = false
 line-ending = "auto"
 docstring-code-format = false
-
-[tool.uv.extra-build-dependencies]
-axolotl = ["huggingface_hub"]

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
+
+huggingface_hub>=0.36.0
 peft>=0.18.1
 tokenizers>=0.22.1
-transformers==5.2.0
+transformers==4.57.6
 accelerate==1.12.0
 datasets==4.5.0
 deepspeed>=0.18.3
-trl==0.28.0
+trl==0.27.0
 hf_xet==1.2.0
-kernels==0.12.1
-
-trackio>=0.16.1
+kernels==0.11.5
+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@58d6572"'
+    + f'{UV_PREFIX}pip install "cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@318b7e2"'
 )

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/__init__.py

@@ -5,6 +5,6 @@ import os
 from axolotl.logging_config import configure_logging
 
 os.environ.setdefault("TOKENIZERS_PARALLELISM", "false")
-os.environ.setdefault("HF_XET_HIGH_PERFORMANCE", "1")
+os.environ.setdefault("HF_HUB_ENABLE_HF_TRANSFER", "1")
 
 configure_logging()

src/axolotl/cli/checks.py

@@ -44,7 +44,7 @@ def check_user_token() -> bool:
         return bool(user_info)
     except LocalTokenNotFoundError:
         LOG.warning(
-            "Error verifying HuggingFace token. Remember to log in using `hf auth login` and get your access token from https://huggingface.co/settings/tokens if you want to use gated models or datasets."
+            "Error verifying HuggingFace token. Remember to log in using `huggingface-cli login` and get your access token from https://huggingface.co/settings/tokens if you want to use gated models or datasets."
         )
         return False
     except HTTPError:

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))
-
-    # Apply nested kwargs (e.g., trl__beta -> cfg.trl.beta)
-    for parent, children in nested_kwargs.items():
-        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)
+            if isinstance(cfg[key], bool):
+                cfg[key] = bool(value)
+            else:
+                cfg[key] = value
 
     try:
         device_props = torch.cuda.get_device_properties("cuda")

src/axolotl/cli/merge_lora.py

@@ -24,6 +24,7 @@ def do_merge_lora(*, cfg: DictDefault) -> None:
         cfg: Dictionary mapping `axolotl` config keys to values.
     """
     model, tokenizer, processor = load_model_and_tokenizer(cfg=cfg)
+    safe_serialization = cfg.save_safetensors is True
 
     LOG.info("Running merge of LoRA with base model...")
     model = model.merge_and_unload(progressbar=True)
@@ -41,6 +42,7 @@ def do_merge_lora(*, cfg: DictDefault) -> None:
         LOG.info(f"Saving merged model to: {str(Path(cfg.output_dir) / 'merged')}...")
         model.save_pretrained(
             str(Path(cfg.output_dir) / "merged"),
+            safe_serialization=safe_serialization,
             progressbar=True,
         )
         tokenizer.save_pretrained(

src/axolotl/cli/merge_sharded_fsdp_weights.py

@@ -14,6 +14,8 @@ from accelerate import PartialState
 from accelerate.utils import (
     SAFE_WEIGHTS_INDEX_NAME,
     SAFE_WEIGHTS_NAME,
+    WEIGHTS_INDEX_NAME,
+    WEIGHTS_NAME,
     is_torch_version,
 )
 from huggingface_hub import split_torch_state_dict_into_shards
@@ -38,15 +40,17 @@ class BFloat16CastPlanner(_EmptyStateDictLoadPlanner):
 def _distributed_checkpoint_to_merged_weights(
     checkpoint_dir: Union[str, Path],
     save_path: str,
+    safe_serialization: bool = False,
     max_shard_size: str = "5GB",
 ) -> Path:
     """
     Passthrough to `torch.distributed.checkpoint.format_utils.dcp_to_torch_save`. Will
-    save under `save_path` as `model.safetensors`.
+    save under `save_path` as either `model.safetensors` or `pytorch_model.bin`.
 
     Args:
         checkpoint_dir: Directory where distributed checkpoint is saved.
         save_path: Path to save model to.
+        safe_serialization: Whether to save in safetensors format.
         max_shard_size: Max size of model shards to save.
 
     Returns:
@@ -72,7 +76,11 @@ def _distributed_checkpoint_to_merged_weights(
         if isinstance(value, torch.Tensor) and value.dtype != torch.bfloat16:
             state_dict[key] = value.to(torch.bfloat16)
 
-    filename_pattern = SAFE_WEIGHTS_NAME.replace(".safetensors", "{suffix}.safetensors")
+    weights_name = SAFE_WEIGHTS_NAME if safe_serialization else WEIGHTS_NAME
+
+    filename_pattern = weights_name.replace(".bin", "{suffix}.bin").replace(
+        ".safetensors", "{suffix}.safetensors"
+    )
     state_dict_split = split_torch_state_dict_into_shards(
         state_dict, filename_pattern=filename_pattern, max_shard_size=max_shard_size
     )
@@ -90,12 +98,19 @@ def _distributed_checkpoint_to_merged_weights(
 
     for shard_file, tensors in filename_to_tensors:
         shard = {tensor: state_dict[tensor] for tensor in tensors}
-        safe_save_file(
-            shard, os.path.join(save_path_, shard_file), metadata={"format": "pt"}
-        )
+
+        if safe_serialization:
+            safe_save_file(
+                shard, os.path.join(save_path_, shard_file), metadata={"format": "pt"}
+            )
+        else:
+            torch.save(shard, os.path.join(save_path_, shard_file))
 
     if index is not None:
-        save_index_file = os.path.join(save_path_, SAFE_WEIGHTS_INDEX_NAME)
+        save_index_file = (
+            SAFE_WEIGHTS_INDEX_NAME if safe_serialization else WEIGHTS_INDEX_NAME
+        )
+        save_index_file = os.path.join(save_path_, save_index_file)
         # Save the index as well
         with open(save_index_file, "w", encoding="utf-8") as fout:
             content = json.dumps(index, indent=2, sort_keys=True) + "\n"
@@ -108,11 +123,13 @@ def _distributed_checkpoint_to_merged_weights(
 def merge_fsdp_weights(
     checkpoint_dir: str,
     output_path: str,
+    safe_serialization: bool = False,
     remove_checkpoint_dir: bool = False,
 ):
     """
     Merge the weights from sharded FSDP model checkpoints into a single combined checkpoint. Should be used if
-    `SHARDED_STATE_DICT` was used for the model. Weights will be saved to `{output_path}/model.safetensors`.
+    `SHARDED_STATE_DICT` was used for the model. Weights will be saved to `{output_path}/model.safetensors` if
+    `safe_serialization` else `pytorch_model.bin`.
 
     Note: this is a CPU-bound process.
 
@@ -121,6 +138,8 @@ def merge_fsdp_weights(
             The directory containing the FSDP checkpoints (can be either the model or optimizer).
         output_path (`str`):
             The path to save the merged checkpoint.
+        safe_serialization (`bool`, *optional*, defaults to `True`):
+            Whether to save the merged weights with safetensors (recommended).
         remove_checkpoint_dir (`bool`, *optional*, defaults to `False`):
             Whether to remove the checkpoint directory after merging.
 
@@ -158,7 +177,7 @@ def merge_fsdp_weights(
     if state.is_main_process:
         LOG.info(f"Merging FSDP weights from {checkpoint_dir_}")
         save_path = _distributed_checkpoint_to_merged_weights(
-            checkpoint_dir_, output_path
+            checkpoint_dir_, output_path, safe_serialization
         )
         LOG.info(f"Successfully merged FSDP weights and saved to {save_path}")
         if remove_checkpoint_dir:
@@ -191,6 +210,7 @@ def do_cli(config: Union[Path, str] = Path("examples/"), **kwargs):
     merge_fsdp_weights(
         checkpoint_dir=str(fsdp_dir),
         output_path=output_path,
+        safe_serialization=True,
     )
     state = PartialState()
     state.wait_for_everyone()

src/axolotl/cli/quantize.py

@@ -102,10 +102,12 @@ def do_quantize(
     LOG.info(f"Saving quantized model to: {str(Path(output_dir) / 'quantized')}.")
     model.save_pretrained(
         str(Path(output_dir) / "quantized"),
+        safe_serialization=False,
         progressbar=True,
     )
     tokenizer.save_pretrained(
         str(Path(output_dir) / "quantized"),
+        safe_serialization=False,
         progressbar=True,
         save_jinja_files=cfg.tokenizer_save_jinja_files,
     )
@@ -119,7 +121,7 @@ def do_quantize(
             hub_model_id.rstrip("-")
             + f"-{quantization_config_to_str[type(quantization_config)]}"
         )
-        model.push_to_hub(hub_model_id)
+        model.push_to_hub(hub_model_id, safe_serialization=False)
         tokenizer.push_to_hub(hub_model_id)
         if processor:
             processor.push_to_hub(hub_model_id)

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 is_union and type(None) in get_args(field_type):
+    if get_origin(field_type) 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/core/builders/base.py

@@ -216,7 +216,7 @@ class TrainerBuilderBase(abc.ABC):
     def _configure_warmup_and_logging(
         self, total_num_steps: int, training_args_kwargs: dict
     ):
-        warmup_steps: int | float = 0
+        warmup_steps = 0
         warmup_ratio = 0.0
         if self.cfg.warmup_steps is not None:
             warmup_steps = self.cfg.warmup_steps
@@ -230,10 +230,6 @@ class TrainerBuilderBase(abc.ABC):
         else:
             warmup_ratio = 0.03
 
-        # transformers v5
-        if warmup_ratio > 0.0 and warmup_steps == 0:
-            warmup_steps = warmup_ratio
-
         if warmup_steps == 1:
             warmup_steps = 2
 
@@ -246,6 +242,7 @@ class TrainerBuilderBase(abc.ABC):
                 else max(min(int(0.005 * total_num_steps), 10), 1)
             )
 
+        training_args_kwargs["warmup_ratio"] = warmup_ratio
         training_args_kwargs["warmup_steps"] = warmup_steps
 
     def _configure_precision_settings(self, training_args_kwargs: dict):
@@ -409,9 +406,6 @@ class TrainerBuilderBase(abc.ABC):
             if self.cfg.hub_strategy:
                 training_args_kwargs["hub_strategy"] = self.cfg.hub_strategy
 
-            if self.cfg.hub_revision:
-                training_args_kwargs["hub_revision"] = self.cfg.hub_revision
-
     def _configure_save_and_eval_strategy(self, training_args_kwargs: dict):
         # save_strategy and save_steps
         if self.cfg.save_steps:
@@ -536,7 +530,9 @@ class TrainerBuilderBase(abc.ABC):
             "loraplus_lr_ratio",
             "loraplus_lr_embedding",
             "output_dir",
+            "save_safetensors",
             "save_only_model",
+            "include_tokens_per_second",
             "weight_decay",
             "seed",
             "dion_momentum",
@@ -549,7 +545,6 @@ class TrainerBuilderBase(abc.ABC):
 
         arg_map = {
             "dion_learning_rate": "dion_lr",
-            "include_num_input_tokens_seen": "include_tokens_per_second",
         }
         for kwarg, cfg_arg in arg_map.items():
             if hasattr(self.cfg, cfg_arg) and getattr(self.cfg, cfg_arg) is not None:

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["train_sampling_strategy"] = "group_by_length"
+        training_arguments_kwargs["group_by_length"] = self.cfg.group_by_length
         training_arguments_kwargs["curriculum_sampling"] = self.cfg.curriculum_sampling
 
         training_arguments_kwargs["sample_packing"] = bool(self.cfg.sample_packing)
@@ -380,18 +373,6 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
             # https://docs.nvidia.com/deeplearning/performance/dl-performance-matrix-multiplication/index.html
             data_collator_kwargs["pad_to_multiple_of"] = multiple
 
-        if self.cfg.use_eaft:
-            from functools import partial
-
-            from axolotl.monkeypatch.loss.eaft import eaft_loss
-
-            configured_eaft_loss = partial(
-                eaft_loss,
-                alpha=self.cfg.eaft_alpha if self.cfg.eaft_alpha is not None else 1.0,
-                k=self.cfg.eaft_k if self.cfg.eaft_k is not None else 20,
-            )
-            trainer_kwargs["compute_loss_func"] = configured_eaft_loss
-
         trainer_cls = self._get_trainer_cls()
 
         trainer_kwargs, trainer_cls = self.hook_pre_create_trainer(
@@ -456,9 +437,7 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
                 or self.cfg.micro_batch_size > 1
             ):
                 return DataCollatorForSeq2Seq(self.tokenizer, **kwargs)
-            if not (self.cfg.sample_packing and self.cfg.pretrain_multipack_attn) or (
-                self.cfg.micro_batch_size == 1 and is_eval is False
-            ):
+            if not (self.cfg.sample_packing and self.cfg.pretrain_multipack_attn):
                 return None
 
         if self.cfg.model_config_type == "mamba":

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

@@ -25,7 +25,7 @@ from torch.utils.data import (
 from transformers import PreTrainedModel, Trainer
 from transformers.trainer import TRAINING_ARGS_NAME
 from transformers.trainer_utils import PREFIX_CHECKPOINT_DIR, has_length, seed_worker
-from transformers.utils import SAFE_WEIGHTS_NAME, is_peft_available
+from transformers.utils import SAFE_WEIGHTS_NAME, WEIGHTS_NAME, is_peft_available
 from trl.trainer.utils import pad_to_length
 from typing_extensions import override
 
@@ -719,16 +719,6 @@ 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}")
-
-        # fix for Context Parallel save
-        if state_dict is None:
-            state_dict = self.accelerator.get_state_dict(self.model)
-        if state_dict is not None:
-            state_dict = {
-                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()
@@ -739,7 +729,6 @@ 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,
@@ -749,31 +738,43 @@ class AxolotlTrainer(
                 ).save_pretrained(
                     output_dir,
                     state_dict=state_dict,
-                    is_main_process=self.accelerator.is_main_process,
+                    safe_serialization=self.args.save_safetensors,
                 )
             else:
                 LOG.info(
                     "Trainer.model is not a `PreTrainedModel`, only saving its state dict."
                 )
-                safetensors.torch.save_file(
-                    state_dict,
-                    os.path.join(output_dir, SAFE_WEIGHTS_NAME),
-                    metadata={"format": "pt"},
-                )
+                if self.args.save_safetensors:
+                    safetensors.torch.save_file(
+                        state_dict,
+                        os.path.join(output_dir, SAFE_WEIGHTS_NAME),
+                        metadata={"format": "pt"},
+                    )
+                else:
+                    torch.save(state_dict, os.path.join(output_dir, WEIGHTS_NAME))
         else:
-            self.model.save_pretrained(output_dir, state_dict=state_dict)
-
-        if self.processing_class is not None:
-            self.processing_class.save_pretrained(output_dir)
-        elif (
-            self.data_collator is not None
-            and hasattr(self.data_collator, "tokenizer")
-            and self.data_collator.tokenizer is not None
-        ):
-            LOG.info(
-                "Saving Trainer.data_collator.tokenizer by default as Trainer.processing_class is `None`"
+            self.model.save_pretrained(
+                output_dir,
+                state_dict=state_dict,
+                safe_serialization=self.args.save_safetensors,
+                is_main_process=self.accelerator.is_main_process,
             )
-            self.data_collator.tokenizer.save_pretrained(output_dir)
 
-        # Good practice: save your training arguments together with the trained model
-        torch.save(self.args, os.path.join(output_dir, TRAINING_ARGS_NAME))
+            if self.processing_class is not None:
+                self.processing_class.save_pretrained(output_dir)
+            elif (
+                self.data_collator is not None
+                and hasattr(self.data_collator, "tokenizer")
+                and self.data_collator.tokenizer is not None
+            ):
+                LOG.info(
+                    "Saving Trainer.data_collator.tokenizer by default as Trainer.processing_class is `None`"
+                )
+                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_completion_length: int | None = None,
-        add_special_tokens: bool = True,
-        is_chat: bool = False,
+        max_prompt_length,
+        max_completion_length,
+        add_special_tokens,
     ) -> Dict:
         res = DPOTrainer.tokenize_row(
             features,
             processing_class,
-            max_prompt_length=max_prompt_length,
-            max_completion_length=max_completion_length,
-            add_special_tokens=add_special_tokens,
-            is_chat=is_chat,
+            max_prompt_length,
+            max_completion_length,
+            add_special_tokens,
         )
         # fix when the tokenizer doesn't have a bos_token_id, e.g. Qwen
         if processing_class.bos_token is None and res["prompt_input_ids"][0] is None:

src/axolotl/core/trainers/grpo/__init__.py

@@ -126,6 +126,9 @@ class GRPOStrategy:
         if trl.use_liger_loss is not None:
             grpo_args_kwargs["use_liger_loss"] = trl.use_liger_loss
 
+        if trl.rollout_func:
+            grpo_args_kwargs["rollout_func"] = cls.get_rollout_func(trl.rollout_func)
+
         if trl.multi_objective_aggregation is not None:
             grpo_args_kwargs["multi_objective_aggregation"] = (
                 trl.multi_objective_aggregation
@@ -151,8 +154,6 @@ class GRPOStrategy:
             trainer_kwargs["reward_processing_classes"] = (
                 cfg.trl.reward_processing_classes
             )
-        if cfg.trl and cfg.trl.rollout_func:
-            trainer_kwargs["rollout_func"] = cls.get_rollout_func(cfg.trl.rollout_func)
 
         return trainer_kwargs
 
@@ -163,12 +164,7 @@ class GRPOStrategy:
 
     @classmethod
     def get_blocklist_args_kwargs(cls) -> list[str]:
-        return [
-            "dataset_num_proc",
-            "max_length",
-            "include_tokens_per_second",
-            "max_prompt_length",
-        ]
+        return ["dataset_num_proc", "max_length", "include_tokens_per_second"]
 
     @classmethod
     def get_reward_func(cls, reward_func_fqn: str) -> RewardFunc:

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/trainers/trl.py

@@ -1,10 +1,12 @@
 """Module for TRL RL trainers"""
 
-from trl import RewardTrainer
-from trl.experimental.cpo import CPOTrainer
-from trl.experimental.kto import KTOTrainer
-from trl.experimental.orpo import ORPOTrainer
-from trl.experimental.prm import PRMTrainer
+from trl import (
+    CPOTrainer,
+    KTOTrainer,
+    ORPOTrainer,
+    PRMTrainer,
+    RewardTrainer,
+)
 
 from axolotl.core.trainers.mixins import DistributedParallelMixin, RngLoaderMixin
 from axolotl.core.trainers.mixins.optimizer import OptimizerInitMixin, OptimizerMixin

src/axolotl/core/training_args.py

@@ -8,11 +8,7 @@ from dataclasses import dataclass, field
 from typing import Optional, Type
 
 from transformers import TrainingArguments
-from trl import RewardConfig
-from trl.experimental.cpo import CPOConfig
-from trl.experimental.kto import KTOConfig
-from trl.experimental.orpo import ORPOConfig
-from trl.experimental.prm import PRMConfig
+from trl import CPOConfig, KTOConfig, ORPOConfig, PRMConfig, RewardConfig
 
 from axolotl.integrations.config import merge_training_args
 

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@58d6572"
+pip3 uninstall -y cut-cross-entropy && pip3 install "cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@318b7e2"
 ```
 
 ## Usage
@@ -31,13 +31,11 @@ plugins:
 
 ## Supported Models
 
-- afmoe
 - apertus
 - arcee
 - cohere
 - cohere2
 - deepseek_v3
-- exaone4
 - gemma
 - gemma2
 - gemma3
@@ -47,17 +45,13 @@ plugins:
 - glm
 - glm4
 - glm4_moe
-- glm4_moe_lite
-- glm46v
 - glm4v
 - glm4v_moe
-- glm_image
-- glm_moe_dsa
 - gpt_oss
 - granite
 - granitemoe
-- granitemoehybrid
 - granitemoeshared
+- granitemoehybrid
 - hunyuan_v1_dense
 - hunyuan_v1_moe
 - internvl
@@ -78,26 +72,20 @@ plugins:
 - olmo
 - olmo2
 - olmo3
-- olmoe
 - phi
 - phi3
 - phi4_multimodal
 - qwen2
-- qwen2_5_vl
-- qwen2_moe
 - qwen2_vl
+- qwen2_moe
+- qwen2_5_vl
 - qwen3
-- qwen3_5
-- qwen3_5_moe
-- qwen3_5_moe_vl
-- qwen3_5_vl
 - qwen3_moe
-- qwen3_next
 - qwen3_vl
 - qwen3_vl_moe
-- seed_oss
+- qwen3_next
 - smollm3
-- step3p5
+- seed_oss
 - voxtral
 
 ## Citation

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@58d6572"`'
+    '`pip install "cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@318b7e2"`'
 )
 
 
@@ -104,7 +104,7 @@ class CutCrossEntropyPlugin(BasePlugin):
 
     def patch_llama_like(
         self,
-        model_type_to_patch: str,
+        model_type: str,
     ) -> None:
         """
         Generic patch for model architectures with causal lm similar to llama
@@ -112,10 +112,7 @@ class CutCrossEntropyPlugin(BasePlugin):
         from cut_cross_entropy.transformers.patch import PATCH_FNS
 
         def patch_generic(
-            maybe_model,
-            patch_options,
-            remote_model_id: str | None,
-            model_type: str,
+            maybe_model, patch_options, model_type: str, remote_model_id: str | None
         ):
             import cut_cross_entropy.transformers.llama
             from cut_cross_entropy.transformers.llama import cce_forward
@@ -139,13 +136,11 @@ class CutCrossEntropyPlugin(BasePlugin):
                     f"Error: {str(e)}"
                 ) from e
 
-        if model_type_to_patch not in PATCH_FNS:
+        if model_type not in PATCH_FNS:
             LOG.warning_once(
-                "Setting up generic cce patch for model type: %s", model_type_to_patch
+                "Setting up generic cce patch for model type: %s", model_type
             )
             LOG.warning_once(
-                f"Generic Cut Cross Entropy + {model_type_to_patch} support is experimental and may not work as expected."
-            )
-            PATCH_FNS[model_type_to_patch] = partial(
-                patch_generic, model_type=model_type_to_patch
+                f"Generic Cut Cross Entropy + {model_type} support is experimental and may not work as expected."
             )
+            PATCH_FNS[model_type] = partial(patch_generic, model_type=model_type)

src/axolotl/integrations/kernels/README.md

@@ -1,44 +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).
-
-## 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/__init__.py

@@ -1,7 +0,0 @@
-from .args import KernelsArgs
-from .plugin import KernelsPlugin
-
-__all__ = [
-    "KernelsArgs",
-    "KernelsPlugin",
-]

src/axolotl/integrations/kernels/args.py

@@ -1,48 +0,0 @@
-from pydantic import BaseModel, model_validator
-
-from axolotl.utils.logging import get_logger
-
-LOG = get_logger(__name__)
-
-
-class KernelsArgs(BaseModel):
-    use_scattermoe: bool | None = True
-
-    @model_validator(mode="before")
-    @classmethod
-    def check_use_kernels(cls, data):
-        if data.get("use_kernels") is not True:
-            LOG.warning(
-                "`use_kernels` must be set to True to use this. Automatically setting it to True."
-            )
-            data["use_kernels"] = True
-
-        return data
-
-    @model_validator(mode="before")
-    @classmethod
-    def check_experts_implementation(cls, data):
-        experts_implementation = data.get("experts_implementation")
-        if experts_implementation is None:
-            # transformers may default to batched_mm when unset
-            data["experts_implementation"] = "eager"
-        elif experts_implementation != "eager":
-            LOG.warning(
-                "`experts_implementation` must be set to 'eager' to use this. Automatically setting it to 'eager'."
-            )
-            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

@@ -1,66 +0,0 @@
-from pathlib import Path
-
-from kernels import (
-    LocalLayerRepository,
-    Mode,
-    register_kernel_mapping,
-    replace_kernel_forward_from_hub,
-)
-
-from axolotl.integrations.base import BasePlugin
-from axolotl.utils.callbacks.models import get_causal_lm_model_cls_prefix
-
-
-class KernelsPlugin(BasePlugin):
-    def get_input_args(self):
-        return "axolotl.integrations.kernels.KernelsArgs"
-
-    def pre_model_load(self, cfg):
-        if cfg.use_scattermoe:
-            self._register_kernels()
-            self._kernelize_model(cfg.model_config_type)
-
-    def _register_kernels(self):
-        plugin_root = Path(__file__).parent
-        register_kernel_mapping(
-            {
-                "HFScatterMoEParallelExperts": {
-                    "cuda": {
-                        Mode.TRAINING: LocalLayerRepository(
-                            repo_path=plugin_root / "libs" / "scattermoe_lora",
-                            package_name="scattermoe_lora",
-                            layer_name="HFScatterMoEGatedMLP",
-                        ),
-                        Mode.INFERENCE: LocalLayerRepository(
-                            repo_path=plugin_root / "libs" / "scattermoe_lora",
-                            package_name="scattermoe_lora",
-                            layer_name="HFScatterMoEGatedMLP",
-                        ),
-                    },
-                }
-            }
-        )
-
-    def _kernelize_model(self, model_type: str):
-        if model_type == "olmoe":
-            from transformers.models.olmoe.modeling_olmoe import OlmoeSparseMoeBlock
-
-            replace_kernel_forward_from_hub(
-                OlmoeSparseMoeBlock, "HFScatterMoEParallelExperts"
-            )
-        else:
-            try:
-                model_moe_cls = get_model_moe_block(model_type)
-                replace_kernel_forward_from_hub(
-                    model_moe_cls, "HFScatterMoEParallelExperts"
-                )
-            except Exception as err:
-                raise ValueError(f"Unsupported model type: {model_type}") from err
-
-
-def get_model_moe_block(model_type: str):
-    module_path = f"transformers.models.{model_type}.modeling_{model_type}"
-    model_cls_prefix, _ = get_causal_lm_model_cls_prefix(model_type)
-    module = __import__(module_path, fromlist=[f"{model_cls_prefix}SparseMoeBlock"])
-    model_cls = getattr(module, f"{model_cls_prefix}SparseMoeBlock")
-    return model_cls

src/axolotl/integrations/llm_compressor/utils.py

@@ -12,6 +12,7 @@ def save_compressed_model(
     model: PreTrainedModel,
     output_dir: Union[str, bytes],
     trainer: Trainer,
+    safe_serialization: bool = False,
     save_compressed: bool = False,
 ) -> None:
     """
@@ -21,6 +22,7 @@ def save_compressed_model(
         model (PreTrainedModel): The model to be saved.
         output_dir (str or bytes): Path where the model files will be written.
         trainer (Trainer): Hugging Face Trainer for process synchronization.
+        safe_serialization (bool): Use safe serialization if True.
         save_compressed (bool): Write compressed tensors if True.
     """
     trainer.accelerator.wait_for_everyone()
@@ -32,6 +34,7 @@ def save_compressed_model(
     modify_save_pretrained(model)
     model.save_pretrained(
         output_dir,
+        safe_serialization=safe_serialization,
         save_compressed=save_compressed,
         skip_sparsity_compression_stats=not save_compressed,
     )

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
-
-# Directory to save evaluation results.
-# The final model is loaded from this directory
-# unless specified otherwise (see below)
-output_dir:
+output_dir: # Directory to save evaluation results
 ```
 
-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/model.py

@@ -26,6 +26,7 @@ from torch.distributed import DeviceMesh
 from transformers import (
     AutoModelForCausalLM,
     AutoModelForImageTextToText,
+    AutoModelForVision2Seq,
     AwqConfig,
     BitsAndBytesConfig,
     GPTQConfig,
@@ -225,7 +226,6 @@ class ModelLoader:
         ):
             self.model = self.model.merge_and_unload()
 
-        self._configure_experts_implementation()
         self._apply_activation_checkpointing()
         self._resize_token_embeddings()
         self._adjust_model_config()
@@ -233,10 +233,6 @@ class ModelLoader:
         self._configure_qat()
         log_gpu_memory_usage(LOG, "Memory usage after model load", 0)
 
-    def _configure_experts_implementation(self):
-        if self.cfg.experts_implementation is not None:
-            self.model.set_experts_implementation(self.cfg.experts_implementation)
-
     def _apply_activation_checkpointing(self):
         if self.cfg.activation_offloading is True:
             from axolotl.core.trainers.mixins.activation_checkpointing import (
@@ -338,12 +334,7 @@ class ModelLoader:
             # LlamaRMSNorm layers are in fp32 after kbit_training or full finetune, so
             # we need to convert them back to fp16/bf16 for flash-attn compatibility.
             (
-                (
-                    needs_fa2_dtype
-                    or self.cfg.flash_attention
-                    or self.cfg.flex_attention
-                    or self.cfg.sage_attention
-                )
+                (needs_fa2_dtype or self.cfg.flash_attention or self.cfg.flex_attention)
                 and not self.is_qlora_and_fsdp_enabled
             )
             or (
@@ -443,7 +434,7 @@ class ModelLoader:
         """
         if self.cfg.is_multimodal:
             self.auto_model_loader = MULTIMODAL_AUTO_MODEL_MAPPING.get(
-                self.model_config.model_type, AutoModelForImageTextToText
+                self.model_config.model_type, AutoModelForVision2Seq
             )
             if isinstance(self.auto_model_loader, str):
                 self.auto_model_loader = AutoModelForImageTextToText
@@ -485,7 +476,6 @@ class ModelLoader:
             max_memory = None
 
         self.model_kwargs["torch_dtype"] = self.cfg.torch_dtype
-        self.model_kwargs["dtype"] = self.cfg.torch_dtype
 
         is_ds_zero3 = is_deepspeed_zero3_enabled()
 
@@ -617,10 +607,6 @@ class ModelLoader:
         elif self.cfg.sdp_attention:
             self.model_kwargs["attn_implementation"] = "sdpa"
             self.model_config._attn_implementation = "sdpa"
-        elif self.cfg.sage_attention:
-            # sets FA2 attention to re-use same internal handling like masking
-            self.model_kwargs["attn_implementation"] = "flash_attention_2"
-            self.model_config._attn_implementation = "flash_attention_2"
         elif self.cfg.eager_attention:
             self.model_kwargs["attn_implementation"] = "eager"
             self.model_config._attn_implementation = "eager"
@@ -684,7 +670,7 @@ class ModelLoader:
         Uses the selected loader when provided; otherwise falls back to the auto loader.
         """
         loader = model_loader_class or self.auto_model_loader
-        if loader in [AutoModelForCausalLM, AutoModelForImageTextToText]:
+        if loader in [AutoModelForCausalLM, AutoModelForVision2Seq]:
             model = loader.from_config(
                 config=self.model_config,
                 trust_remote_code=self.cfg.trust_remote_code or False,
@@ -802,7 +788,6 @@ class ModelLoader:
                 # Use auto model loader (handles gptq and default cases)
                 model_loader_class = self.auto_model_loader
 
-            self.model_kwargs["dtype"] = self.model_kwargs["torch_dtype"]
             if self.cfg.reinit_weights:
                 self.model = self._load_model_from_config(model_loader_class)
             else:

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 (
@@ -97,7 +96,6 @@ class PatchManager:
         # self._apply_flex_attention_patches()
         self._apply_flash_attention_patches()
         self._apply_chunked_cross_entropy_patch()
-        self._apply_sageattn_patches()
         self._apply_fsdp_patches()
         self._apply_adapter_patches()
         self._apply_model_specific_patches()
@@ -203,13 +201,6 @@ class PatchManager:
             flex_attn_compile_kwargs = self.cfg.flex_attn_compile_kwargs or {}
             patch_flex_wrapper(**flex_attn_compile_kwargs)
 
-    def _apply_sageattn_patches(self):
-        """Apply patches for SageAttention."""
-        if self.cfg.sage_attention:
-            from axolotl.monkeypatch.attention.sage_attn import patch_sageattn
-
-            patch_sageattn()
-
     def _apply_model_specific_patches(self):
         """Apply patches specific to model architectures."""
         if (
@@ -229,6 +220,13 @@ class PatchManager:
 
             patch_qwen3_next_modeling_packing()
 
+        if self.cfg.model_config_type == "mistral3" and self.cfg.processor_type:
+            from axolotl.monkeypatch.models.mistral3.mistral_common_tokenizer import (
+                apply_mistral_tokenizer_image_patch,
+            )
+
+            apply_mistral_tokenizer_image_patch()
+
         if self.cfg.model_config_type == "kimi_linear":
             from axolotl.monkeypatch.models.kimi_linear.patch_kimi_linear import (
                 patch_kimi_model,
@@ -329,7 +327,7 @@ 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
 
@@ -501,7 +499,6 @@ 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():
@@ -36,7 +31,7 @@ def load_processor(cfg: DictDefault, tokenizer: PreTrainedTokenizerBase):
 
             from axolotl.utils.mistral import HFMistralTokenizer
 
-            tokenization_mistral_common.MistralCommonBackend = HFMistralTokenizer
+            tokenization_mistral_common.MistralCommonTokenizer = HFMistralTokenizer
 
         _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,
-    token_mappings: dict[int, str],
-    output_dir: str,
-    revision: str = "main",
+    tokenizer_path: str, token_mappings: dict[int, str], output_dir: str
 ) -> 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(
-            tokenizer_path, use_fast=True, revision=revision
-        )
+        temp_tokenizer = AutoTokenizer.from_pretrained(tokenizer_path, use_fast=True)
 
         # 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 = {}
-        if cfg.revision_of_model:
-            kwargs["revision"] = cfg.revision_of_model
-        tokenizer = HFMistralTokenizer.from_pretrained(cfg.tokenizer_config, **kwargs)
+        tokenizer = HFMistralTokenizer.from_pretrained(cfg.tokenizer_config)
 
         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

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

src/axolotl/monkeypatch/attention/sage_attn.py

@@ -1,211 +0,0 @@
-"""
-Monkeypatch for SageAttention for use with transformers.
-
-https://github.com/thu-ml/SageAttention/
-"""
-
-import torch
-from transformers.integrations.sdpa_attention import repeat_kv
-
-from axolotl.utils.logging import get_logger
-
-LOG = get_logger(__name__)
-
-sageattn = None  # pylint: disable=invalid-name
-sageattn_varlen = None  # pylint: disable=invalid-name
-
-
-def _is_sageattn_available():
-    """Determine if SageAttention is available"""
-    try:
-        import sageattention  # noqa: F401 # pylint: disable=unused-import
-
-        return True
-    except ImportError:
-        return False
-
-
-if _is_sageattn_available():
-    # import sageattn here if available
-    from sageattention import sageattn, sageattn_varlen
-
-
-def _check_sageattn_imported():
-    """Check if SageAttention is imported. Raises an ImportError if not."""
-    if sageattn is None:
-        raise ImportError(
-            "SageAttention is not installed. Please install it from source: "
-            "`pip install git+https://github.com/thu-ml/SageAttention.git@1718ddc06dbc694bcf3c6b49ac28c1921aa2d8bd`"
-        )
-
-
-def sage_attention_forward(
-    module: torch.nn.Module,
-    query: torch.Tensor,
-    key: torch.Tensor,
-    value: torch.Tensor,
-    attention_mask: torch.Tensor | None = None,
-    dropout: float = 0.0,
-    scaling: float | None = None,
-    is_causal: bool | None = None,
-    **kwargs,
-) -> tuple[torch.Tensor, None]:
-    """
-    Forward pass for SageAttention compatible with transformers attention interfaces.
-
-    https://github.com/thu-ml/SageAttention/
-    """
-
-    _check_sageattn_imported()
-
-    if kwargs.get("output_attentions", False) or kwargs.get("head_mask") is not None:
-        raise NotImplementedError(
-            "SageAttention does not support `output_attentions=True` or `head_mask`."
-        )
-
-    # The base sageattn API does not support dropout.
-    if dropout > 0.0:
-        raise NotImplementedError("SageAttention does not support dropout.")
-
-    # Handle Grouped-Query Attention (GQA) and Multi-Query Attention (MQA)
-    if hasattr(module, "num_key_value_groups"):
-        key = repeat_kv(key, module.num_key_value_groups)
-        value = repeat_kv(value, module.num_key_value_groups)
-
-    # Calculate is_causal following transformers
-    assert is_causal is not False, "is_causal must be True or None"
-    is_causal = True
-
-    position_ids = kwargs.get("position_ids", None)
-    query_length = query.shape[2]
-
-    cu_seqlens_q = kwargs.get("cu_seqlens_q", None)
-    cu_seqlens_k = kwargs.get("cu_seqlens_k", None)
-    max_length_q = kwargs.get("max_length_q", None)
-    max_length_k = kwargs.get("max_length_k", None)
-
-    # Sample packing uses position_ids, so we check for it first
-    if position_ids is not None and (
-        max_length_q is not None
-        or (query_length != 1 and not (torch.diff(position_ids, dim=-1) >= 0).all())
-    ):
-        # transpose inputs to NHD layout for use with FA2 utils
-        query = query.transpose(1, 2)
-        key = key.transpose(1, 2)
-        value = value.transpose(1, 2)
-
-        batch_size = query.size(0)
-
-        from transformers.modeling_flash_attention_utils import (
-            prepare_fa2_from_position_ids,
-        )
-
-        if cu_seqlens_q is None or cu_seqlens_k is None:
-            query, key, value, indices_q, cu_seq_lens, max_seq_lens = (
-                prepare_fa2_from_position_ids(query, key, value, position_ids)
-            )
-
-            cu_seqlens_q, cu_seqlens_k = cu_seq_lens
-            max_length_q, max_length_k = max_seq_lens
-
-        else:
-            query = query.reshape(-1, query.size(-2), query.size(-1))
-            key = key.reshape(-1, key.size(-2), key.size(-1))
-            value = value.reshape(-1, value.size(-2), value.size(-1))
-
-        attn_output_unpad = sageattn_varlen(
-            q=query,
-            k=key,
-            v=value,
-            cu_seqlens_q=cu_seqlens_q,
-            cu_seqlens_k=cu_seqlens_k,
-            max_seqlen_q=max_length_q,
-            max_seqlen_k=max_length_k,
-            is_causal=is_causal,
-            sm_scale=scaling,
-            smooth_k=False,  # reduces loss 0 / nan grad norms
-            tensor_layout="NHD",
-        )
-
-        attn_output = attn_output_unpad.view(
-            batch_size, -1, attn_output_unpad.size(-2), attn_output_unpad.size(-1)
-        )
-
-    elif attention_mask is not None:
-        # NOTE: When used without `pad_to_sequence_len`, the loss becomes unstable after a few steps.
-
-        assert attention_mask.ndim == 2, "Attention mask must be 2D"
-
-        from transformers.modeling_flash_attention_utils import (
-            _upad_input,
-        )
-
-        # transpose inputs to NHD layout for use with FA2 utils
-        query = query.transpose(1, 2)
-        key = key.transpose(1, 2)
-        value = value.transpose(1, 2)
-
-        batch_size = query.shape[0]
-
-        query, key, value, indices_q, cu_seq_lens, max_seq_lens = _upad_input(
-            query, key, value, attention_mask, query_length
-        )
-        cu_seqlens_q, cu_seqlens_k = cu_seq_lens
-        max_seqlen_q, max_seqlen_k = max_seq_lens
-
-        attn_output_unpad = sageattn_varlen(
-            q=query,
-            k=key,
-            v=value,
-            cu_seqlens_q=cu_seqlens_q,
-            cu_seqlens_k=cu_seqlens_k,
-            max_seqlen_q=max_seqlen_q,
-            max_seqlen_k=max_seqlen_k,
-            is_causal=is_causal,
-            sm_scale=scaling,
-            tensor_layout="NHD",
-        )
-
-        from flash_attn.bert_padding import pad_input
-
-        attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
-    else:
-        # Use standard sageattn
-        # The input layout for transformers models is (batch_size, num_heads, seq_len, head_dim),
-        # which corresponds to SageAttention's "HND" layout.
-        attn_output = sageattn(
-            q=query,
-            k=key,
-            v=value,
-            tensor_layout="HND",
-            is_causal=is_causal,
-            sm_scale=scaling,
-        )
-
-        # SageAttention with "HND" returns (batch, heads, seq_len, head_dim)
-        # Transformers expects (batch, seq_len, heads, head_dim) for the output
-        # So we need to transpose dimensions 1 and 2
-        attn_output = attn_output.transpose(1, 2).contiguous()
-
-    return attn_output, None
-
-
-def patch_sageattn():
-    """Patch SageAttention for use with transformers."""
-
-    _check_sageattn_imported()
-
-    from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS
-
-    # Replace flash attention with sage attention
-    ALL_ATTENTION_FUNCTIONS.register("flash_attention_2", sage_attention_forward)
-
-    # Note: New method after transformers refactor to use ALL_MASK_ATTENTION_FUNCTIONS
-    # Register sage_attention with the global attention interface
-    # ALL_ATTENTION_FUNCTIONS.register("sage_attention", sage_attention_forward)
-
-    # from transformers.masking_utils import ALL_MASK_ATTENTION_FUNCTIONS, flash_attention_mask
-
-    # ALL_MASK_ATTENTION_FUNCTIONS.register("sage_attention", flash_attention_mask)
-
-    LOG.info("SageAttention patched successfully")

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)
-            # 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
+            (output,) = ctx.forward_function(hidden_states, *ctx.args)
         torch.autograd.backward(output, dY)
         return (
             None,

src/axolotl/monkeypatch/lora_kernels.py

@@ -169,8 +169,7 @@ def get_attention_cls_from_config(cfg: DictDefault) -> Type[nn.Module]:
         return attention_cls
     except (ImportError, AttributeError) as e:
         raise ValueError(
-            f"Axolotl could not import attention class for model_type: {model_type}. "
-            "Please raise an Issue and turn off lora kernels to continue training. "
+            f"Could not import attention class for model_type: {model_type}. "
             f"Error: {str(e)}"
         ) from e
 

src/axolotl/monkeypatch/loss/eaft.py

@@ -1,51 +0,0 @@
-"""
-eaft (entropy-aware focal training) loss implementation
-weights examples by entropy approximation from top-k logits
-
-Reference: https://github.com/ymxyll/LlamaFactory-EAFT/blob/e2ce19e8efcc226450ee8f2b81dfe4e69f1f945d/src/llamafactory/train/trainer_utils.py
-"""
-
-import torch
-import torch.nn.functional as F
-
-
-def eaft_loss(outputs, labels, num_items_in_batch=None, alpha=1.0, k=20):
-    """
-    compute eaft loss with entropy weighting
-
-    args:
-        outputs: model outputs containing logits
-        labels: target labels for computing loss
-        num_items_in_batch: for sample packing support
-        alpha: exponent for entropy weighting (default 1.0)
-        k: number of top logits for entropy approximation (default 20)
-    """
-    logits = outputs.logits
-
-    shift_logits = logits[..., :-1, :].contiguous()
-    shift_labels = labels[..., 1:].contiguous()
-
-    vocab_size = shift_logits.size(-1)
-    shift_logits_view = shift_logits.view(-1, vocab_size)
-    shift_labels_view = shift_labels.view(-1)
-
-    mask = shift_labels_view != -100
-
-    with torch.no_grad():
-        top_k_logits, _ = torch.topk(
-            shift_logits_view[mask].float(), k=min(k, vocab_size), dim=-1
-        )
-        top_k_probs = F.softmax(top_k_logits, dim=-1)
-        entropy = -(top_k_probs * torch.log(top_k_probs + 1e-10)).sum(dim=-1)
-        weights = torch.pow(entropy, alpha)
-
-    loss_fct = torch.nn.CrossEntropyLoss(reduction="none")
-    per_token_loss = loss_fct(shift_logits_view[mask], shift_labels_view[mask])
-    weighted_loss = per_token_loss * weights
-
-    if num_items_in_batch is not None:
-        loss = weighted_loss.sum() / num_items_in_batch
-    else:
-        loss = weighted_loss.mean()
-
-    return loss

src/axolotl/monkeypatch/models/mistral3/mistral_common_tokenizer.py

@@ -1,5 +1,5 @@
 """
-Monkeypatch to fix inefficient tensor conversion in MistralCommonBackend.apply_chat_template
+Monkeypatch to fix inefficient tensor conversion in MistralCommonTokenizer.apply_chat_template
 """
 
 import importlib
@@ -12,11 +12,11 @@ LOG = get_logger(__name__)
 
 
 def apply_mistral_tokenizer_image_patch():
-    """Apply patch to MistralCommonBackend.apply_chat_template to fix image tensor conversion."""
-    from transformers.tokenization_mistral_common import MistralCommonBackend
+    """Apply patch to MistralCommonTokenizer.apply_chat_template to fix image tensor conversion."""
+    from transformers.tokenization_mistral_common import MistralCommonTokenizer
 
     # Get original source
-    original_source = inspect.getsource(MistralCommonBackend.apply_chat_template)
+    original_source = inspect.getsource(MistralCommonTokenizer.apply_chat_template)
     original_source, _ = detab_code(original_source)
 
     # Define the replacement
@@ -41,7 +41,7 @@ def apply_mistral_tokenizer_image_patch():
         )
 
         # Load necessary imports from the module
-        module_name = MistralCommonBackend.__module__
+        module_name = MistralCommonTokenizer.__module__
         module = importlib.import_module(module_name)
 
         # Detect what needs to be imported
@@ -79,7 +79,7 @@ def apply_mistral_tokenizer_image_patch():
         exec(patched_source, globals())  # nosec B102
 
         # Replace the method
-        MistralCommonBackend.apply_chat_template = patched_apply_chat_template
-        LOG.info("Successfully applied MistralCommonBackend tensor conversion patch")
+        MistralCommonTokenizer.apply_chat_template = patched_apply_chat_template
+        LOG.info("Successfully applied MistralCommonTokenizer tensor conversion patch")
     else:
-        LOG.warning("Could not find target code for MistralCommonBackend patching")
+        LOG.warning("Could not find target code for MistralCommonTokenizer patching")

src/axolotl/monkeypatch/relora.py

@@ -155,6 +155,7 @@ class ReLoRACallback(TrainerCallback):
                     f"{PREFIX_CHECKPOINT_DIR}-{state.global_step}",
                     "adapter",
                 ),
+                safe_serialization=True,
             )
             with torch.no_grad():
                 merge_and_save(
@@ -213,7 +214,7 @@ class ReLoRACallback(TrainerCallback):
 
                     self.last_full_model = checkpoint_folder
             else:
-                model.model.save_pretrained(checkpoint_folder)
+                model.model.save_pretrained(checkpoint_folder, safe_serialization=True)
 
         return control
 

src/axolotl/monkeypatch/transformers/trainer_context_parallel.py

@@ -52,15 +52,9 @@ def patch_prepare_context_parallel_inputs() -> None:
         if item in patched_source:
             items_to_import.append(item)
 
-    # Use a separate namespace to capture the exec'd function
-    namespace = {}
-    exec(f"from {module_name} import ({', '.join(items_to_import)})", namespace)
-    exec(patched_source, namespace)
+    exec(f"from {module_name} import ({', '.join(items_to_import)})", globals())
+    exec(patched_source, globals())
 
-    # Explicitly get the function from the namespace
-    axolotl_prepare_context_parallel_inputs = namespace[
-        "axolotl_prepare_context_parallel_inputs"
-    ]
     Trainer._original_prepare_context_parallel_inputs = (
         Trainer._prepare_context_parallel_inputs
     )

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 = (
-    "tr_loss_scalar = nested_gather(tr_loss, self.args.parallel_mode).mean().item()"
-)
-PATCHED_MAYBE_CODE = (
-    "tr_loss_scalar = nested_gather(tr_loss, self.args.parallel_mode).nanmean().item()"
-)
+ORIGINAL_MAYBE_CODE = "tr_loss_scalar = self._nested_gather(tr_loss).mean().item()"
+PATCHED_MAYBE_CODE = "tr_loss_scalar = self._nested_gather(tr_loss).nanmean().item()"
 
 
 def check_evaluation_loop_is_patchable() -> bool:

src/axolotl/processing_strategies.py

@@ -14,6 +14,7 @@ from transformers.models.voxtral import VoxtralProcessor
 
 from axolotl.utils.dict import remove_none_values
 from axolotl.utils.logging import get_logger
+from axolotl.utils.mistral.mistral3_processor import Mistral3Processor
 
 LOG = get_logger(__name__)
 
@@ -429,7 +430,7 @@ class Mistral3ProcessingStrategy(ProcessingStrategy):
 
     def __init__(
         self,
-        processor,
+        processor: Mistral3Processor,
         chat_template: Optional[str] = None,
         image_size: int | tuple[int, int] | None = None,
         image_resize_algorithm: Resampling | None = None,
@@ -485,58 +486,6 @@ class InternVLProcessingStrategy(ProcessingStrategy):
         return labels
 
 
-class Glm4vProcessingStrategy(ProcessingStrategy):
-    """Processing Strategy class for GLM4V and GLM4V-MoE vision models."""
-
-    def __init__(
-        self,
-        processor: ProcessorMixin,
-        chat_template: Optional[str] = None,
-        image_size: int | tuple[int, int] | None = None,
-        image_resize_algorithm: Resampling | None = None,
-    ):
-        super().__init__(processor, chat_template, image_size, image_resize_algorithm)
-
-        self.tokenizer = getattr(processor, "tokenizer", processor)
-
-        self.image_token = "<|image|>"  # nosec
-        self.begin_image_token = "<|begin_of_image|>"  # nosec
-        self.end_image_token = "<|end_of_image|>"  # nosec
-        self.video_token = "<|video|>"  # nosec
-        self.begin_video_token = "<|begin_of_video|>"  # nosec
-        self.end_video_token = "<|end_of_video|>"  # nosec
-
-        self.image_token_id = self.tokenizer.convert_tokens_to_ids(self.image_token)
-        self.begin_image_token_id = self.tokenizer.convert_tokens_to_ids(
-            self.begin_image_token
-        )
-        self.end_image_token_id = self.tokenizer.convert_tokens_to_ids(
-            self.end_image_token
-        )
-        self.video_token_id = self.tokenizer.convert_tokens_to_ids(self.video_token)
-        self.begin_video_token_id = self.tokenizer.convert_tokens_to_ids(
-            self.begin_video_token
-        )
-        self.end_video_token_id = self.tokenizer.convert_tokens_to_ids(
-            self.end_video_token
-        )
-
-    def process_labels(self, input_ids):
-        labels = input_ids.clone()
-
-        labels[labels == self.tokenizer.pad_token_id] = -100
-
-        labels[labels == self.image_token_id] = -100
-        labels[labels == self.begin_image_token_id] = -100
-        labels[labels == self.end_image_token_id] = -100
-
-        labels[labels == self.video_token_id] = -100
-        labels[labels == self.begin_video_token_id] = -100
-        labels[labels == self.end_video_token_id] = -100
-
-        return labels
-
-
 def get_processing_strategy(
     processor: ProcessorMixin,
     chat_template,
@@ -544,8 +493,6 @@ def get_processing_strategy(
     image_size: int | tuple[int, int] | None = None,
     image_resize_algorithm: Resampling | None = None,
 ):
-    from axolotl.utils.mistral.mistral3_processor import Mistral3Processor
-
     processing_kwargs = {
         "processor": processor,
         "chat_template": chat_template,
@@ -553,10 +500,10 @@ def get_processing_strategy(
         "image_resize_algorithm": image_resize_algorithm,
     }
 
-    if chat_template_type in [None, "tokenizer_default"]:
-        tokenizer = getattr(processor, "tokenizer", processor)
-        if hasattr(tokenizer, "chat_template"):
-            processing_kwargs["chat_template"] = tokenizer.chat_template
+    if chat_template_type in [None, "tokenizer_default"] and hasattr(
+        processor.tokenizer, "chat_template"
+    ):
+        processing_kwargs["chat_template"] = processor.tokenizer.chat_template
 
     if chat_template_type == "qwen2_vl":
         return Qwen2VLProcessingStrategy(
@@ -585,15 +532,6 @@ def get_processing_strategy(
         return Mistral3ProcessingStrategy(
             **processing_kwargs,
         )
-    try:
-        from transformers.models.glm46v.processing_glm46v import Glm46VProcessor
-
-        if isinstance(processor, Glm46VProcessor):
-            return Glm4vProcessingStrategy(
-                **processing_kwargs,
-            )
-    except ImportError:
-        pass
 
     if isinstance(processor, InternVLProcessor):
         return InternVLProcessingStrategy(

src/axolotl/prompt_strategies/chat_template.py

@@ -48,9 +48,9 @@ class ChatTemplatePrompter(Prompter):
     ):
         # check if message_property_mappings is None or empty dict
         if message_property_mappings is None or (not message_property_mappings):
-            default_message_property_mappings_keys = ["role", "content", "tool"]
             message_property_mappings = {
-                prop: prop for prop in default_message_property_mappings_keys
+                "role": "role",
+                "content": "content",
             }
             if template_thinking_key and field_thinking:
                 message_property_mappings[template_thinking_key] = field_thinking
@@ -150,8 +150,6 @@ class ChatTemplatePrompter(Prompter):
 
         return self.tokenizer.apply_chat_template(
             conversation,
-            tokenize=True,
-            return_dict=False,
             **chat_template_kwargs,
         )
 

src/axolotl/telemetry/callbacks.py

@@ -153,27 +153,13 @@ class TelemetryCallback(TrainerCallback):
                 self.last_report_step = step
 
     def _extract_last_metrics(self, state: TrainerState) -> dict:
-        """Extract last loss, learning_rate, grad_norm, and token metrics from log history."""
+        """Extract last loss, learning_rate, and grad_norm from log history."""
         if not state.log_history:
-            return {
-                "loss": 0,
-                "ppl": 0,
-                "learning_rate": 0,
-                "grad_norm": 0,
-                "tokens/total": 0,
-                "tokens/trainable": 0,
-                "tokens/train_per_sec_per_gpu": 0,
-            }
+            return {"loss": 0, "learning_rate": 0, "grad_norm": 0}
 
         last_log = state.log_history[-1]
         return {
             "loss": last_log.get("loss", 0),
-            "ppl": last_log.get("ppl", 0),
             "learning_rate": last_log.get("learning_rate", 0),
             "grad_norm": last_log.get("grad_norm", 0),
-            "tokens/total": last_log.get("tokens/total", 0),
-            "tokens/trainable": last_log.get("tokens/trainable", 0),
-            "tokens/train_per_sec_per_gpu": last_log.get(
-                "tokens/train_per_sec_per_gpu", 0
-            ),
         }

src/axolotl/telemetry/errors.py

@@ -155,10 +155,6 @@ def send_errors(func: Callable) -> Callable:
                     },
                 )
 
-                LOG.error(
-                    f"Error captured in telemetry. Run ID: {telemetry_manager.run_id}"
-                )
-
             raise
 
     return wrapper

src/axolotl/telemetry/manager.py

@@ -5,6 +5,7 @@ import importlib
 import logging
 import os
 import platform
+import time
 import uuid
 from pathlib import Path
 from typing import Any
@@ -19,6 +20,21 @@ LOG = logging.getLogger(__name__)
 POSTHOG_HOST = "https://app.posthog.com"
 POSTHOG_WRITE_KEY = "phc_1kUR0o04oJKKTTeSsIz2Mfm5mpiVsQEf2WOlzljMD7y"
 
+OPT_OUT_WARNING_SLEEP_SECONDS = 10
+OPT_OUT_WARNING = (
+    "\nTelemetry is now enabled by default to help improve Axolotl. "
+    "If you'd like to disable it, set AXOLOTL_DO_NOT_TRACK=1 in your environment.\n\n"
+    "Telemetry data helps us understand:\n"
+    "- Which features are most used\n"
+    "- What hardware configurations to prioritize\n"
+    "- Where users encounter errors\n\n"
+    "Personally identifiable information (PII) is not collected.\n\n"
+    "To remove this warning, explicitly set AXOLOTL_DO_NOT_TRACK=0 (enable telemetry) "
+    "or AXOLOTL_DO_NOT_TRACK=1 (disable telemetry).\n\n"
+    "For details, see: https://docs.axolotl.ai/docs/telemetry.html\n\n"
+    f"Sleeping for {OPT_OUT_WARNING_SLEEP_SECONDS}s..."
+)
+
 WHITELIST_PATH = str(Path(__file__).parent / "whitelist.yaml")
 
 # NOTE: Need to keep these up to date with any config schema changes
@@ -30,8 +46,8 @@ FIELDS_TO_REDACT = {
     "resume_from_checkpoint",
     "hub_model_id",
 }
-PREFIXES_TO_REDACT = {"wandb_", "comet_", "mlflow_", "gradio_", "trackio_", "swanlab_"}
-PATH_INDICATORS = {"path", "dir", "data_files"}
+PREFIXES_TO_REDACT = {"wandb_", "comet_", "mlflow_", "gradio_"}
+PATH_INDICATORS = {"path", "dir"}
 
 # pylint: disable=duplicate-code
 RELEVANT_PACKAGES = {
@@ -156,10 +172,6 @@ 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")
@@ -171,8 +183,17 @@ class TelemetryManager:
             "false",
             "true",
         ):
+            # Print opt-out info message for main process only
+            if is_main_process():
+                LOG.warning(OPT_OUT_WARNING)
+            time.sleep(OPT_OUT_WARNING_SLEEP_SECONDS)
+
             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/telemetry/whitelist.yaml

@@ -31,10 +31,3 @@ organizations:
   - "mistral-community"
   - "llava-hf"
   - "ByteDance-Seed"
-  - "ACE-Step"
-  - "openbmb"
-  - "MiniMaxAI"
-  - "stepfun-ai"
-  - "internlm"
-  - "katanemo"
-  - "XiaomiMiMo"

src/axolotl/train.py

@@ -135,13 +135,16 @@ def setup_reference_model(
     return model_ref
 
 
-def setup_signal_handler(cfg: DictDefault, model: PreTrainedModel):
+def setup_signal_handler(
+    cfg: DictDefault, model: PreTrainedModel, safe_serialization: bool
+):
     """
     Set up signal handler for graceful termination.
 
     Args:
         cfg: Dictionary mapping `axolotl` config keys to values.
         model: The model to save on termination
+        safe_serialization: Whether to use safe serialization when saving
     """
     # ray workers don't have access to this signal
     if cfg.local_rank == 0 and not cfg.use_ray:
@@ -149,7 +152,9 @@ def setup_signal_handler(cfg: DictDefault, model: PreTrainedModel):
         def terminate_handler(_, __, model_weakref):
             if model_weakref() is not None:
                 _model = model_weakref()
-                _model.save_pretrained(cfg.output_dir)
+                _model.save_pretrained(
+                    cfg.output_dir, safe_serialization=safe_serialization
+                )
 
             cleanup_distributed()
             sys.exit(0)
@@ -214,6 +219,7 @@ def save_trained_model(
     cfg: DictDefault,
     trainer: Any,
     model: PreTrainedModel,
+    safe_serialization: bool,
 ):
     """
     Save the trained model according to configuration and training setup.
@@ -222,6 +228,7 @@ def save_trained_model(
         cfg: Dictionary mapping `axolotl` config keys to values.
         trainer: The trainer object.
         model: The trained model to save.
+        safe_serialization: Whether to use safe serialization.
     """
     LOG.info(f"Training completed! Saving trained model to {cfg.output_dir}.")
 
@@ -276,6 +283,7 @@ def save_trained_model(
                 merge_fsdp_weights(
                     checkpoint_dir=str(fsdp_dir),
                     output_path=merged_path,
+                    safe_serialization=True,
                 )
                 trainer.accelerator.wait_for_everyone()
                 if trainer.accelerator.is_main_process:
@@ -322,9 +330,11 @@ def save_trained_model(
                 pass
     elif cfg.local_rank == 0:
         if cfg.rl and cfg.adapter and not cfg.rl_adapter_ref_model:
-            trainer.model.save_pretrained(cfg.output_dir)
+            trainer.model.save_pretrained(
+                cfg.output_dir, safe_serialization=safe_serialization
+            )
 
-        model.save_pretrained(cfg.output_dir)
+        model.save_pretrained(cfg.output_dir, safe_serialization=safe_serialization)
 
     if hasattr(cfg, "llmcompressor") and cfg.llmcompressor:
         # TODO: add integration support so this can be implemented completely within the plugin
@@ -334,6 +344,7 @@ def save_trained_model(
             model=model,
             output_dir=cfg.output_dir,
             trainer=trainer,
+            safe_serialization=safe_serialization,
             save_compressed=cfg.llmcompressor.save_compressed,
         )
 
@@ -438,6 +449,7 @@ def handle_untrained_tokens_fix(
     model: PreTrainedModel,
     tokenizer: PreTrainedTokenizer,
     train_dataset: Dataset,
+    safe_serialization: bool,
 ):
     """
     Apply fixes for untrained tokens if configured.
@@ -447,6 +459,7 @@ def handle_untrained_tokens_fix(
         model: The model to apply fixes to.
         tokenizer: The tokenizer for token identification.
         train_dataset: The training dataset to use.
+        safe_serialization: Whether to use safe serialization when saving.
     """
     if not cfg.fix_untrained_tokens:
         return
@@ -470,7 +483,9 @@ def handle_untrained_tokens_fix(
     fix_untrained_tokens(model, tokenizer, train_dataset, **fix_kwargs)
 
     if cfg.local_rank == 0:
-        model.save_pretrained(str(Path(cfg.output_dir)))
+        model.save_pretrained(
+            str(Path(cfg.output_dir)), safe_serialization=safe_serialization
+        )
 
 
 def setup_model_and_trainer(
@@ -567,12 +582,15 @@ def train(
     ) = setup_model_and_trainer(cfg, dataset_meta)
 
     # Handle untrained tokens if configured
+    safe_serialization = cfg.save_safetensors is True
     train_dataset = dataset_meta.train_dataset
-    handle_untrained_tokens_fix(cfg, model, tokenizer, train_dataset)
+    handle_untrained_tokens_fix(
+        cfg, model, tokenizer, train_dataset, safe_serialization
+    )
 
     # Additional setup
     save_initial_configs(cfg, tokenizer, model, peft_config, processor)
-    setup_signal_handler(cfg, model)
+    setup_signal_handler(cfg, model, safe_serialization)
     setup_model_card(cfg)
 
     # Execute the training
@@ -584,7 +602,7 @@ def train(
         torch.cuda.empty_cache()
 
     # Save the trained model and cleanup
-    save_trained_model(cfg, trainer, model)
+    save_trained_model(cfg, trainer, model, safe_serialization)
     tokenizer.save_pretrained(
         str(Path(cfg.output_dir)), save_jinja_files=cfg.tokenizer_save_jinja_files
     )

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/callbacks/perplexity.py

@@ -7,11 +7,7 @@ from torch import Tensor
 from tqdm import tqdm
 from transformers.modeling_outputs import CausalLMOutput
 from transformers.modeling_utils import PreTrainedModel
-
-try:
-    from transformers.tokenization_python import PreTrainedTokenizer
-except ImportError:
-    from transformers.tokenization_utils import PreTrainedTokenizer
+from transformers.tokenization_utils import PreTrainedTokenizer
 
 from axolotl.utils.distributed import is_main_process
 

src/axolotl/utils/callbacks/tokens_per_second.py

@@ -78,19 +78,12 @@ class TokensPerSecondCallback(TrainerCallback):
         **kwargs,
     ):  # pylint: disable=unused-argument
         tokens = getattr(state, "tokens", None)
-        if not (tokens and "trainable_tokens" in tokens):
-            return
-        step_time = time.perf_counter() - self.start_time
-        if step_time <= 0:
-            return
-
-        num_tokens = tokens["trainable_tokens"].clone() / self.non_data_parallel_size
-        if torch.distributed.is_initialized():
-            dp_size = max(
-                1, torch.distributed.get_world_size() // self.non_data_parallel_size
-            )
-            num_tokens = num_tokens / dp_size
-        state.last_tokens_per_second = num_tokens / step_time
+        if tokens and "trainable_tokens" in tokens:
+            step_time = time.perf_counter() - self.start_time
+            num_tokens_per_device = tokens["trainable_tokens"].clone()
+            # non data parallel groups have duplicated tokens, so we avoid double-counting
+            num_tokens_per_device = num_tokens_per_device / self.non_data_parallel_size
+            state.last_tokens_per_second = num_tokens_per_device / step_time
 
     def on_log(
         self,

src/axolotl/utils/ctx_managers/sequence_parallel.py

@@ -218,9 +218,6 @@ class SequenceParallelContextManager:
         self.original_seq_len = 0
         self.pad_len = 0
 
-        # Track local valid token count for eval loss correction across CP ranks
-        self._local_valid_tokens: torch.Tensor | None = None
-
         # Create a partially applied version of the apply_sequence_parallelism function
         self.apply_sequence_parallelism = functools.partial(
             apply_sequence_parallelism,
@@ -273,18 +270,6 @@ class SequenceParallelContextManager:
                 self.apply_sequence_parallelism(updated_kwargs)
             )
 
-            # Track local valid tokens for eval loss correction
-            if "labels" in updated_kwargs and not self.models[0].training:
-                self._local_valid_tokens = (
-                    (updated_kwargs["labels"] != -100).sum().float()
-                )
-                # Strip num_items_in_batch during eval so the model uses
-                # reduction='mean', allowing the post-hook weighted all-reduce
-                # formula (loss * local_valid) to correctly recover the loss sum
-                updated_kwargs.pop("num_items_in_batch", None)
-            else:
-                self._local_valid_tokens = None
-
             return remaining_args, updated_kwargs
 
         # Forward post-hook to gather outputs
@@ -302,44 +287,6 @@ class SequenceParallelContextManager:
 
             return output
 
-        # Post-hook to correct eval loss via weighted all-reduce across CP ranks
-        def eval_loss_correction_post_hook(_, __, output: ModelOutput) -> ModelOutput:
-            if self._local_valid_tokens is None:
-                return output
-            if not hasattr(output, "loss") or output.loss is None:
-                return output
-
-            local_valid = self._local_valid_tokens.to(output.loss.device)
-            loss = output.loss.detach().clone()
-
-            # Handle rank with zero valid tokens (loss is NaN)
-            if local_valid.item() == 0:
-                weighted_loss = torch.zeros(1, device=loss.device, dtype=loss.dtype)
-            else:
-                weighted_loss = loss * local_valid
-
-            total_valid = local_valid.clone()
-            dist.all_reduce(
-                weighted_loss,
-                op=dist.ReduceOp.SUM,
-                group=self.process_group,
-            )
-            dist.all_reduce(
-                total_valid,
-                op=dist.ReduceOp.SUM,
-                group=self.process_group,
-            )
-
-            if total_valid.item() > 0:
-                output["loss"] = (weighted_loss / total_valid).squeeze()
-            else:
-                output["loss"] = torch.tensor(
-                    float("nan"), device=loss.device, dtype=loss.dtype
-                )
-
-            self._local_valid_tokens = None
-            return output
-
         # Register hooks
         for model in self.models:
             self.hook_handles.append(
@@ -351,10 +298,6 @@ class SequenceParallelContextManager:
                 self.hook_handles.append(
                     model.register_forward_hook(sequence_parallel_post_hook)
                 )
-            # Always register eval loss correction hook
-            self.hook_handles.append(
-                model.register_forward_hook(eval_loss_correction_post_hook)
-            )
 
     def _gather_outputs(self, output: CausalLMOutputWithPast) -> CausalLMOutputWithPast:
         """Gather sharded outputs from all ranks and reconstruct the full tensor."""

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)):
-                counter_content = self.counter_path.read_text().strip()
-                count = int(counter_content) if counter_content else 0
-                count -= 1
+        with FileLock(str(self.lock_file_path)):
+            counter_content = self.counter_path.read_text().strip()
+            count = int(counter_content) if counter_content else 0
+            count -= 1
 
-                if count <= 0:
-                    # Last process cleans everything up
-                    self.ready_flag_path.unlink(missing_ok=True)
-                    self.counter_path.unlink(missing_ok=True)
-                else:
-                    # Still have active processes
-                    self.counter_path.write_text(str(count))
-        except FileNotFoundError:
-            # Lock file might have already been deleted by another process
-            pass
+            if count <= 0:
+                # Last process cleans everything up
+                self.ready_flag_path.unlink(missing_ok=True)
+                self.counter_path.unlink(missing_ok=True)
+            else:
+                # Still have active processes
+                self.counter_path.write_text(str(count))

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
-    return dataset, None
+    # No validation split - apply deduplication if needed and return as train dataset
+    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 = None, dataset
+        train_dataset, eval_dataset = _handle_test_dataset_split(dataset, cfg)
 
     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.dataset_exact_deduplication or False}|"
+        f"{cfg.group_by_length}@{cfg.kd_temperature or 1.0}|"
         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.
-    Returns a list of booleans indicating which samples to keep.
+    Truncate samples whose sequence length is too long (> sequence_len)
+    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
-
-
-def _should_skip_processing(dataset: Dataset) -> bool:
-    """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
+            results.append(True)
+        else:
+            results.append(True)
+    return results
 
 
 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 _should_skip_processing(dataset):
+    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 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
-    filter_kwargs = _build_filter_kwargs(dataset, cfg)
+    with contextlib.suppress(AttributeError):
+        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
-    if excess_length_strategy == "truncate":
-        dataset, _ = _filter_short_sequences(dataset, cfg.min_sample_len, filter_kwargs)
-        dataset = _truncate_long_sequences(dataset, sequence_len, filter_kwargs)
-    else:
-        raise_on_long = excess_length_strategy == "raise"
-        dataset, _ = _drop_outside_range(
-            dataset, sequence_len, cfg.min_sample_len, raise_on_long, filter_kwargs
+    prior_len = len(dataset) if hasattr(dataset, "__len__") else None
+
+    filter_map_kwargs = {}
+    if not isinstance(dataset, IterableDataset):
+        filter_map_kwargs["num_proc"] = cfg.dataset_num_proc
+        filter_map_kwargs["load_from_cache_file"] = not cfg.is_preprocess
+
+    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/datasets.py

@@ -2,19 +2,11 @@
 
 import os
 
-from axolotl.utils.logging import get_logger
-
-LOG = get_logger(__name__)
-
 
 def get_default_process_count():
     if axolotl_dataset_num_proc := os.environ.get("AXOLOTL_DATASET_NUM_PROC"):
         return int(axolotl_dataset_num_proc)
     if axolotl_dataset_processes := os.environ.get("AXOLOTL_DATASET_PROCESSES"):
-        LOG.warning(
-            "AXOLOTL_DATASET_PROCESSES and `dataset_processes` are deprecated and will be "
-            "removed in a future version. Please use `dataset_num_proc` instead."
-        )
         return int(axolotl_dataset_processes)
     if runpod_cpu_count := os.environ.get("RUNPOD_CPU_COUNT"):
         return int(runpod_cpu_count)

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