wip ao upgrade

.github/PULL_REQUEST_TEMPLATE.md

@@ -15,11 +15,6 @@
 <!--- Include details of your testing environment, tests ran to see how -->
 <!--- your change affects other areas of the code, etc. -->
 
-## AI Usage Disclaimer
-
-<!--- Was AI (e.g., ChatGPT, Claude, Copilot) used to generate or assist with this PR? -->
-<!--- Please indicate: No / Yes (specify which tool and to what extent) -->
-
 ## Screenshots (if appropriate)
 
 ## Types of changes

.github/workflows/base.yml

@@ -21,8 +21,6 @@ jobs:
     timeout-minutes: 480
     # this job needs to be run on self-hosted GPU runners...
     runs-on: ubuntu-latest-m
-    env:
-      HAS_DOCKERHUB_CREDS: ${{ secrets.DOCKERHUB_USERNAME != '' && secrets.DOCKERHUB_TOKEN != '' }}
     strategy:
       fail-fast: false
       matrix:
@@ -34,7 +32,6 @@ jobs:
             pytorch: 2.8.0
             torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
             dockerfile: "Dockerfile-base"
-            platforms: "linux/amd64"
           - cuda: "128"
             cuda_version: 12.8.1
             cudnn_version: ""
@@ -42,7 +39,6 @@ jobs:
             pytorch: 2.9.0
             torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
             dockerfile: "Dockerfile-base"
-            platforms: "linux/amd64,linux/arm64"
           - cuda: "128"
             cuda_version: 12.8.1
             cudnn_version: ""
@@ -50,7 +46,6 @@ jobs:
             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: ""
@@ -58,7 +53,6 @@ jobs:
             pytorch: 2.9.1
             torch_cuda_arch_list: "9.0+PTX"
             dockerfile: "Dockerfile-base"
-            platforms: "linux/amd64,linux/arm64"
 #          - cuda: "128"
 #            cuda_version: 12.8.1
 #            cudnn_version: ""
@@ -85,7 +79,6 @@ jobs:
             axolotlai/axolotl-base
       - name: Login to Docker Hub
         uses: docker/login-action@v2
-        if: ${{ github.event_name != 'pull_request' && env.HAS_DOCKERHUB_CREDS == 'true' }}
         with:
           username: ${{ secrets.DOCKERHUB_USERNAME }}
           password: ${{ secrets.DOCKERHUB_TOKEN }}
@@ -96,7 +89,6 @@ jobs:
         with:
           context: .
           file: ./docker/${{ matrix.dockerfile }}
-          platforms: ${{ matrix.platforms }}
           push: ${{ github.event_name != 'pull_request' }}
           tags: ${{ steps.metadata.outputs.tags }}-base-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
           labels: ${{ steps.metadata.outputs.labels }}
@@ -111,8 +103,6 @@ jobs:
     if: ${{ github.repository_owner == 'axolotl-ai-cloud' && (github.event_name != 'pull_request' || !github.event.pull_request.draft) }}
     timeout-minutes: 480
     runs-on: ubuntu-latest-m
-    env:
-      HAS_DOCKERHUB_CREDS: ${{ secrets.DOCKERHUB_USERNAME != '' && secrets.DOCKERHUB_TOKEN != '' }}
     strategy:
       fail-fast: false
       matrix:
@@ -124,7 +114,6 @@ jobs:
             pytorch: 2.8.0
             torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
             dockerfile: "Dockerfile-uv-base"
-            platforms: "linux/amd64"
           - cuda: "128"
             cuda_version: 12.8.1
             cudnn_version: ""
@@ -132,7 +121,6 @@ jobs:
             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: "128"
             cuda_version: 12.8.1
             cudnn_version: ""
@@ -140,7 +128,6 @@ jobs:
             pytorch: 2.9.0
             torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
             dockerfile: "Dockerfile-uv-base"
-            platforms: "linux/amd64,linux/arm64"
           - cuda: "130"
             cuda_version: 13.0.0
             cudnn_version: ""
@@ -148,7 +135,6 @@ jobs:
             pytorch: 2.9.1
             torch_cuda_arch_list: "9.0+PTX"
             dockerfile: "Dockerfile-uv-base"
-            platforms: "linux/amd64,linux/arm64"
     steps:
       - name: Checkout
         uses: actions/checkout@v4
@@ -160,7 +146,6 @@ jobs:
             axolotlai/axolotl-base-uv
       - name: Login to Docker Hub
         uses: docker/login-action@v2
-        if: ${{ github.event_name != 'pull_request' && env.HAS_DOCKERHUB_CREDS == 'true' }}
         with:
           username: ${{ secrets.DOCKERHUB_USERNAME }}
           password: ${{ secrets.DOCKERHUB_TOKEN }}
@@ -171,7 +156,6 @@ jobs:
         with:
           context: .
           file: ./docker/${{ matrix.dockerfile }}
-          platforms: ${{ matrix.platforms }}
           push: ${{ github.event_name != 'pull_request' }}
           tags: ${{ steps.metadata.outputs.tags }}-base-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
           labels: ${{ steps.metadata.outputs.labels }}

.github/workflows/main.yml

@@ -20,26 +20,22 @@ jobs:
             python_version: "3.11"
             pytorch: 2.8.0
             axolotl_extras:
-            platforms: "linux/amd64"
+            is_latest: true
           - cuda: 128
             cuda_version: 12.8.1
             python_version: "3.11"
             pytorch: 2.9.0
             axolotl_extras:
-            platforms: "linux/amd64,linux/arm64"
           - 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: 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: "3.11"
+#            pytorch: 2.9.1
+#            axolotl_extras:
     runs-on: axolotl-gpu-runner
     steps:
       - name: Checkout
@@ -65,7 +61,6 @@ jobs:
         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 }}
@@ -92,26 +87,22 @@ jobs:
             python_version: "3.11"
             pytorch: 2.8.0
             axolotl_extras:
-            platforms: "linux/amd64"
+            is_latest: true
           - cuda: 128
             cuda_version: 12.8.1
             python_version: "3.11"
             pytorch: 2.9.0
             axolotl_extras:
-            platforms: "linux/amd64,linux/arm64"
           - 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: 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: "3.11"
+#            pytorch: 2.9.1
+#            axolotl_extras:
     runs-on: axolotl-gpu-runner
     steps:
       - name: Checkout
@@ -136,7 +127,6 @@ jobs:
         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 }}
@@ -157,11 +147,11 @@ jobs:
           - cuda: 128
             cuda_version: 12.8.1
             python_version: "3.11"
-            pytorch: 2.9.1
+            pytorch: 2.8.0
             axolotl_extras:
-            is_latest: true
-          - cuda: 130
-            cuda_version: 13.0.0
+            is_latest:
+          - cuda: 128
+            cuda_version: 12.8.1
             python_version: "3.11"
             pytorch: 2.9.1
             axolotl_extras:
@@ -190,7 +180,6 @@ jobs:
         uses: docker/build-push-action@v5
         with:
           context: .
-          platforms: linux/amd64,linux/arm64
           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 }}

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

@@ -43,13 +43,6 @@ jobs:
             axolotl_extras: fbgemm-gpu
             num_gpus: 2
             nightly_build: "true"
-          - cuda: 130
-            cuda_version: 13.0.0
-            python_version: "3.11"
-            pytorch: 2.9.1
-            axolotl_extras: fbgemm-gpu
-            num_gpus: 2
-            nightly_build: "true"
     runs-on: [self-hosted, modal]
     timeout-minutes: 120
     steps:

.github/workflows/tests.yml

@@ -316,12 +316,6 @@ jobs:
             pytorch: 2.9.1
             num_gpus: 1
             axolotl_extras:
-          - cuda: 130
-            cuda_version: 13.0.0
-            python_version: "3.11"
-            pytorch: 2.9.1
-            num_gpus: 1
-            axolotl_extras:
     steps:
       - name: Checkout
         uses: actions/checkout@v4

docker/Dockerfile

@@ -6,7 +6,6 @@ ARG AXOLOTL_EXTRAS=""
 ARG AXOLOTL_ARGS=""
 ARG CUDA="118"
 ARG PYTORCH_VERSION="2.1.2"
-ARG TARGETARCH
 
 ENV PYTORCH_VERSION=$PYTORCH_VERSION
 
@@ -21,17 +20,13 @@ 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 [ "$TARGETARCH" = "arm64" ]; then \
-        BASE_EXTRAS="flash-attn,ring-flash-attn,optimizers,ray"; \
+# If AXOLOTL_EXTRAS is set, append it in brackets
+RUN if [ "$AXOLOTL_EXTRAS" != "" ] ; then \
+        pip install --no-build-isolation -e .[deepspeed,flash-attn,ring-flash-attn,optimizers,ray,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \
     else \
-        BASE_EXTRAS="deepspeed,flash-attn,ring-flash-attn,optimizers,ray"; \
+        pip install --no-build-isolation -e .[deepspeed,flash-attn,ring-flash-attn,optimizers,ray] $AXOLOTL_ARGS; \
     fi && \
-    if [ "$AXOLOTL_EXTRAS" != "" ]; then \
-        pip install --no-build-isolation -e .[$BASE_EXTRAS,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \
-    else \
-        pip install --no-build-isolation -e .[$BASE_EXTRAS] $AXOLOTL_ARGS; \
-    fi && \    python scripts/unsloth_install.py | sh && \
+    python scripts/unsloth_install.py | sh && \
     python scripts/cutcrossentropy_install.py | sh && \
     pip install pytest && \
     pip cache purge

docker/Dockerfile-base

@@ -2,16 +2,14 @@ ARG CUDA_VERSION="11.8.0"
 ARG CUDNN_VERSION="8"
 ARG UBUNTU_VERSION="22.04"
 ARG MAX_JOBS=4
-ARG TARGETARCH
 
 FROM nvidia/cuda:$CUDA_VERSION-cudnn$CUDNN_VERSION-devel-ubuntu$UBUNTU_VERSION AS base-builder
 
 ENV PATH="/root/miniconda3/bin:${PATH}"
 
-ARG TARGETARCH
-ARG PYTHON_VERSION="3.11"
+ARG PYTHON_VERSION="3.10"
 ARG PYTORCH_VERSION="2.1.2"
-ARG CUDA="128"
+ARG CUDA="118"
 ARG TORCH_CUDA_ARCH_LIST="7.0 7.5 8.0 8.6 9.0+PTX"
 
 ENV PYTHON_VERSION=$PYTHON_VERSION
@@ -24,17 +22,11 @@ RUN apt-get update \
         librdmacm-dev librdmacm1 rdmacm-utils slurm-wlm \
     && rm -rf /var/cache/apt/archives \
     && rm -rf /var/lib/apt/lists/* \
-    && if [ "$TARGETARCH" = "amd64" ]; then \
-        MINICONDA_ARCH="x86_64"; \
-    elif [ "$TARGETARCH" = "arm64" ]; then \
-        MINICONDA_ARCH="aarch64"; \
-    else \
-        echo "Unsupported architecture: $TARGETARCH"; exit 1; \
-    fi \
-    && wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-${MINICONDA_ARCH}.sh \
+    && wget \
+    https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh \
     && mkdir /root/.conda \
-    && bash Miniconda3-latest-Linux-${MINICONDA_ARCH}.sh -b \
-    && rm -f Miniconda3-latest-Linux-${MINICONDA_ARCH}.sh \
+    && bash Miniconda3-latest-Linux-x86_64.sh -b \
+    && rm -f Miniconda3-latest-Linux-x86_64.sh \
     && conda tos accept --override-channels --channel https://repo.anaconda.com/pkgs/main \
     && conda tos accept --override-channels --channel https://repo.anaconda.com/pkgs/r \
     && conda create -n "py${PYTHON_VERSION}" python="${PYTHON_VERSION}"
@@ -59,34 +51,8 @@ RUN git lfs install --skip-repo && \
     pip3 install -U --no-cache-dir pydantic==1.10.10 && \
     pip3 cache purge
 
-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 ${WHL_FILE}; \
-            fi \
-            ;; \
-    esac
+RUN if [ "$PYTORCH_VERSION" =~ ^2\.9\.[0-9]+$ ] && [ "$CUDA" = "128" ] ; then \
+        wget https://github.com/mjun0812/flash-attention-prebuild-wheels/releases/download/v0.4.17/flash_attn-2.8.3+cu128torch2.9-cp311-cp311-linux_x86_64.whl; \
+        pip3 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; \
+    fi

docker/Dockerfile-uv-base

@@ -2,7 +2,6 @@ ARG CUDA_VERSION="12.6.3"
 ARG CUDNN_VERSION=""
 ARG UBUNTU_VERSION="22.04"
 ARG MAX_JOBS=4
-ARG TARGETARCH
 
 FROM nvidia/cuda:$CUDA_VERSION-cudnn$CUDNN_VERSION-devel-ubuntu$UBUNTU_VERSION AS base-builder
 
@@ -32,35 +31,12 @@ ENV PATH="/workspace/axolotl-venv/bin:${PATH}"
 
 RUN uv pip install packaging setuptools wheel psutil \
     && uv pip install torch==${PYTORCH_VERSION} torchvision \
+    && uv pip install --no-build-isolation "causal_conv1d @ git+https://github.com/Dao-AILab/causal-conv1d.git@main" \
+    && uv pip install "mamba_ssm @ git+https://github.com/state-spaces/mamba.git@main" \
     && uv pip install awscli pydantic
 
-RUN if [ "$TARGETARCH" = "amd64" ]; then \
-        uv pip install --no-build-isolation "causal_conv1d @ git+https://github.com/Dao-AILab/causal-conv1d.git@main"; \
-        uv pip install "mamba_ssm @ git+https://github.com/state-spaces/mamba.git@main"; \
+RUN if [ "$PYTORCH_VERSION" = "2.9.0" ] && [ "$CUDA" = "128" ] ; then \
+        wget https://github.com/mjun0812/flash-attention-prebuild-wheels/releases/download/v0.4.17/flash_attn-2.8.3+cu128torch2.9-cp311-cp311-linux_x86_64.whl; \
+        uv pip install --no-cache-dir flash_attn-2.8.3+cu128torch2.9-cp311-cp311-linux_x86_64.whl; \
+        rm flash_attn-2.8.3+cu128torch2.9-cp311-cp311-linux_x86_64.whl; \
     fi
-
-RUN case "$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

examples/devstral/devstral-small-qlora.yml

@@ -52,7 +52,6 @@ gradient_checkpointing: true
 resume_from_checkpoint:
 logging_steps: 1
 flash_attention: true
-scaling_softmax: true
 
 loss_watchdog_threshold: 5.0
 loss_watchdog_patience: 3

examples/gemma3/gemma-3-1b-fft-dft.yml

@@ -1,53 +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>
-datasets:
-  - path: cgato/SlimOrcaDedupCleaned
-    type: chat_template
-    field_messages: conversations
-    message_property_mappings:
-      role: from
-      content: value
-
-val_set_size: 0.05
-output_dir: ./outputs/gemma-3-1b-fft-dft
-
-sequence_len: 2048
-
-use_dynamic_finetuning: true
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 4
-micro_batch_size: 2
-num_epochs: 1
-optimizer: adamw_torch_fused
-lr_scheduler: cosine
-learning_rate: 5e-5
-
-bf16: auto
-tf32: true
-
-gradient_checkpointing: true
-gradient_checkpointing_kwargs:
-  use_reentrant: false
-resume_from_checkpoint:
-logging_steps: 1
-flash_attention: true
-
-warmup_ratio: 0.1
-evals_per_epoch: 2
-saves_per_epoch: 1
-weight_decay: 0.0

examples/gemma3/gemma-3-1b-qlora.yml

@@ -1,7 +1,6 @@
 base_model: google/gemma-3-1b-it
 
 model_type: Gemma3ForCausalLM
-cls_model_config: Gemma3TextConfig
 
 # Automatically upload checkpoint and final model to HF
 # hub_model_id: username/custom_model_name
@@ -30,7 +29,7 @@ output_dir: ./outputs/out
 adapter: qlora
 lora_r: 32
 lora_alpha: 16
-lora_dropout: 0
+lora_dropout: 0.05
 lora_target_linear: true
 
 sequence_len: 2048

examples/gemma3/gemma-3-270m-qlora.yml

@@ -1,7 +1,6 @@
 base_model: google/gemma-3-270m-it
 
 model_type: Gemma3ForCausalLM
-cls_model_config: Gemma3TextConfig
 
 # Automatically upload checkpoint and final model to HF
 # hub_model_id: username/custom_model_name
@@ -30,7 +29,7 @@ output_dir: ./outputs/out
 adapter: qlora
 lora_r: 32
 lora_alpha: 16
-lora_dropout: 0
+lora_dropout: 0.05
 lora_target_linear: true
 
 sequence_len: 2048

examples/gemma3/gemma-3-4b-qlora.yml

@@ -2,7 +2,6 @@ base_model: google/gemma-3-4b-it
 
 # Need to set else transformers tries to load vision too
 model_type: Gemma3ForCausalLM
-cls_model_config: Gemma3TextConfig
 
 load_in_4bit: true
 
@@ -33,8 +32,8 @@ sample_packing: true
 
 lora_r: 32
 lora_alpha: 16
-lora_dropout: 0
-lora_target_linear: true
+lora_dropout: 0.05
+lora_target_modules: 'model.language_model.layers.[\d]+.(mlp|cross_attn|self_attn).(up|down|gate|q|k|v|o)_proj'
 
 wandb_project:
 wandb_entity:

examples/gemma3/gemma-3-4b-vision-qlora.yml

@@ -31,7 +31,7 @@ pad_to_sequence_len: false
 
 lora_r: 32
 lora_alpha: 16
-lora_dropout: 0
+lora_dropout: 0.05
 lora_target_modules: 'model.language_model.layers.[\d]+.(mlp|cross_attn|self_attn).(up|down|gate|q|k|v|o)_proj'
 
 wandb_project:

examples/ministral3/ministral3-3b-qlora.yaml

@@ -59,7 +59,6 @@ gradient_checkpointing: true
 resume_from_checkpoint:
 logging_steps: 1
 flash_attention: true
-scaling_softmax: true
 
 warmup_ratio: 0.1
 evals_per_epoch: 1

examples/swanlab/README.md

@@ -1,285 +0,0 @@
-# SwanLab Integration Examples
-
-This directory contains example configurations demonstrating SwanLab integration with Axolotl.
-
-## Examples Overview
-
-### 1. DPO with Completion Logging
-**File**: `dpo-swanlab-completions.yml`
-
-Demonstrates DPO (Direct Preference Optimization) training with RLHF completion table logging.
-
-**Features**:
-- Basic SwanLab experiment tracking
-- Completion table logging (prompts, chosen/rejected responses, rewards)
-- Memory-bounded buffer for long training runs
-- Cloud sync configuration
-
-**Best for**: RLHF practitioners who want to analyze model outputs qualitatively
-
-**Quick start**:
-```bash
-export SWANLAB_API_KEY=your-api-key
-accelerate launch -m axolotl.cli.train examples/swanlab/dpo-swanlab-completions.yml
-```
-
----
-
-### 2. LoRA with Performance Profiling
-**File**: `lora-swanlab-profiling.yml`
-
-Demonstrates standard LoRA fine-tuning with performance profiling enabled.
-
-**Features**:
-- SwanLab experiment tracking
-- Automatic profiling of trainer methods
-- Profiling metrics visualization
-- Performance optimization guidance
-
-**Best for**: Engineers optimizing training performance and comparing different configurations
-
-**Quick start**:
-```bash
-export SWANLAB_API_KEY=your-api-key
-accelerate launch -m axolotl.cli.train examples/swanlab/lora-swanlab-profiling.yml
-```
-
----
-
-### 3. Full-Featured DPO Production Setup
-**File**: `dpo-swanlab-full-featured.yml`
-
-Comprehensive production-ready configuration with ALL SwanLab features enabled.
-
-**Features**:
-- Experiment tracking with team workspace
-- RLHF completion logging
-- Performance profiling
-- Lark (Feishu) team notifications
-- Private deployment support
-- Production checklist and troubleshooting
-
-**Best for**: Production RLHF training with team collaboration
-
-**Quick start**:
-```bash
-export SWANLAB_API_KEY=your-api-key
-export SWANLAB_LARK_WEBHOOK_URL=https://open.feishu.cn/...
-export SWANLAB_LARK_SECRET=your-webhook-secret
-accelerate launch -m axolotl.cli.train examples/swanlab/dpo-swanlab-full-featured.yml
-```
-
----
-
-### 4. Custom Trainer Profiling (Python)
-**File**: `custom_trainer_profiling.py`
-
-Python code examples showing how to add SwanLab profiling to custom trainers.
-
-**Features**:
-- `@swanlab_profile` decorator examples
-- Context manager profiling for fine-grained timing
-- `ProfilingConfig` for advanced filtering and throttling
-- Multiple profiling patterns and best practices
-
-**Best for**: Advanced users creating custom trainers
-
-**Usage**:
-```python
-from custom_trainer_profiling import CustomTrainerWithProfiling
-# See file for detailed examples and patterns
-```
-
----
-
-## Feature Matrix
-
-| Example | Tracking | Completion Logging | Profiling | Lark Notifications | Team Workspace |
-|---------|----------|-------------------|-----------|-------------------|----------------|
-| dpo-swanlab-completions.yml | ✅ | ✅ | ✅ (auto) | ➖ (commented) | ➖ (commented) |
-| lora-swanlab-profiling.yml | ✅ | ➖ (disabled) | ✅ (auto) | ➖ (commented) | ➖ (commented) |
-| dpo-swanlab-full-featured.yml | ✅ | ✅ | ✅ (auto) | ✅ | ✅ |
-| custom_trainer_profiling.py | N/A | N/A | ✅ (manual) | N/A | N/A |
-
----
-
-## Configuration Quick Reference
-
-### Basic SwanLab Setup
-```yaml
-plugins:
-  - axolotl.integrations.swanlab.SwanLabPlugin
-
-use_swanlab: true
-swanlab_project: my-project
-swanlab_experiment_name: my-experiment
-swanlab_mode: cloud  # cloud, local, offline, disabled
-```
-
-### RLHF Completion Logging
-```yaml
-swanlab_log_completions: true
-swanlab_completion_log_interval: 100  # Log every 100 steps
-swanlab_completion_max_buffer: 128    # Memory-bounded buffer
-```
-
-### Lark Team Notifications
-```yaml
-swanlab_lark_webhook_url: https://open.feishu.cn/...
-swanlab_lark_secret: your-webhook-secret  # Required for production
-```
-
-### Team Workspace
-```yaml
-swanlab_workspace: my-research-team
-```
-
-### Private Deployment
-```yaml
-swanlab_web_host: https://swanlab.yourcompany.com
-swanlab_api_host: https://api.swanlab.yourcompany.com
-```
-
----
-
-## Authentication
-
-### Recommended: Environment Variable
-```bash
-export SWANLAB_API_KEY=your-api-key
-export SWANLAB_LARK_WEBHOOK_URL=https://open.feishu.cn/...
-export SWANLAB_LARK_SECRET=your-webhook-secret
-```
-
-### Alternative: Config File (less secure)
-```yaml
-swanlab_api_key: your-api-key
-swanlab_lark_webhook_url: https://open.feishu.cn/...
-swanlab_lark_secret: your-webhook-secret
-```
-
----
-
-## Common Use Cases
-
-### Use Case 1: Migrate from WandB to SwanLab
-Start with `lora-swanlab-profiling.yml`, add your model/dataset config, disable WandB:
-```yaml
-use_swanlab: true
-use_wandb: false
-```
-
-### Use Case 2: Analyze DPO Model Outputs
-Use `dpo-swanlab-completions.yml`, adjust completion logging interval based on your training length:
-```yaml
-swanlab_completion_log_interval: 50   # More frequent for short training
-swanlab_completion_log_interval: 200  # Less frequent for long training
-```
-
-### Use Case 3: Optimize Training Performance
-Use `lora-swanlab-profiling.yml`, run multiple experiments with different optimizations:
-- Baseline: `flash_attention: false, gradient_checkpointing: false`
-- Flash Attention: `flash_attention: true`
-- Gradient Checkpointing: `gradient_checkpointing: true`
-- Both: `flash_attention: true, gradient_checkpointing: true`
-
-Compare profiling metrics in SwanLab dashboard.
-
-### Use Case 4: Production RLHF with Team Collaboration
-Use `dpo-swanlab-full-featured.yml`, set up team workspace and Lark notifications:
-```yaml
-swanlab_workspace: ml-team
-swanlab_lark_webhook_url: ...
-swanlab_lark_secret: ...
-```
-
----
-
-## Viewing Your Experiments
-
-### Cloud Mode
-Visit [https://swanlab.cn](https://swanlab.cn) and navigate to your project.
-
-**Dashboard sections**:
-- **Metrics**: Training loss, learning rate, profiling metrics
-- **Tables**: RLHF completions (for DPO/KTO/ORPO/GRPO)
-- **Config**: Hyperparameters and configuration
-- **System**: Resource usage (GPU, memory, CPU)
-- **Files**: Logged artifacts
-
-### Local Mode
-```bash
-swanlab watch ./swanlog
-# Open browser to http://localhost:5092
-```
-
----
-
-## Troubleshooting
-
-### SwanLab not initializing
-```bash
-# Check API key
-echo $SWANLAB_API_KEY
-
-# Verify SwanLab is installed
-pip show swanlab
-
-# Check config
-grep -A 5 "use_swanlab" your-config.yml
-```
-
-### Completions not appearing
-- Verify you're using an RLHF trainer (DPO/KTO/ORPO/GRPO)
-- Check `swanlab_log_completions: true`
-- Wait for `swanlab_completion_log_interval` steps
-- Look for "Registered SwanLab RLHF completion logging" in logs
-
-### Lark notifications not working
-- Test webhook manually: `curl -X POST "$SWANLAB_LARK_WEBHOOK_URL" ...`
-- Verify `SWANLAB_LARK_SECRET` is set correctly
-- Check bot is added to Lark group chat
-- Look for "Registered Lark notification callback" in logs
-
-### Profiling metrics not appearing
-- Verify `use_swanlab: true`
-- Check SwanLab is initialized (look for init log message)
-- Profiling metrics are under "profiling/" namespace
-- Profiling auto-enabled when SwanLab is enabled
-
----
-
-## Performance Notes
-
-### Overhead Comparison
-
-| Feature | Overhead per Step | Memory Usage |
-|---------|------------------|--------------|
-| Basic tracking | < 0.1% | ~10 MB |
-| Completion logging | < 0.5% | ~64 KB (buffer=128) |
-| Profiling | < 0.1% | ~1 KB |
-| **Total** | **< 0.7%** | **~10 MB** |
-
-### Best Practices
-1. Use ONE logging tool in production (disable WandB/MLflow when using SwanLab)
-2. Adjust completion log interval based on training length (100-200 steps)
-3. Keep completion buffer size reasonable (128-512)
-4. Profile critical path methods first (training_step, compute_loss)
-5. Use ProfilingConfig to throttle high-frequency operations
-
----
-
-## Further Reading
-
-- **Full Documentation**: [src/axolotl/integrations/swanlab/README.md](../../src/axolotl/integrations/swanlab/README.md)
-- **SwanLab Docs**: [https://docs.swanlab.cn](https://docs.swanlab.cn)
-- **Axolotl Docs**: [https://axolotl-ai-cloud.github.io/axolotl/](https://axolotl-ai-cloud.github.io/axolotl/)
-- **DPO Paper**: [Direct Preference Optimization](https://arxiv.org/abs/2305.18290)
-
----
-
-## Contributing
-
-Found an issue or have an improvement? Please submit a PR or open an issue:
-- [Axolotl Issues](https://github.com/axolotl-ai-cloud/axolotl/issues)
-- [SwanLab Issues](https://github.com/SwanHubX/SwanLab/issues)

examples/swanlab/custom_trainer_profiling.py

@@ -1,299 +0,0 @@
-"""Example: Custom Trainer with SwanLab Profiling
-
-This example demonstrates how to add SwanLab profiling to your custom trainer.
-
-Features:
-- @swanlab_profile decorator for automatic profiling
-- swanlab_profiling_context for fine-grained profiling
-- ProfilingConfig for advanced filtering and throttling
-
-Usage:
-    1. Create your custom trainer extending AxolotlTrainer
-    2. Add @swanlab_profile decorators to methods you want to profile
-    3. Use swanlab_profiling_context for fine-grained profiling within methods
-    4. Enable SwanLab in your config (use_swanlab: true)
-
-See also:
-    - examples/swanlab/lora-swanlab-profiling.yml for config
-    - src/axolotl/integrations/swanlab/profiling.py for implementation
-"""
-
-from axolotl.core.trainers.base import AxolotlTrainer
-from axolotl.integrations.swanlab.profiling import (
-    ProfilingConfig,
-    swanlab_profile,
-    swanlab_profiling_context,
-    swanlab_profiling_context_advanced,
-)
-
-
-class CustomTrainerWithProfiling(AxolotlTrainer):
-    """Custom trainer with SwanLab profiling enabled.
-
-    This trainer demonstrates three profiling patterns:
-    1. Decorator-based profiling (@swanlab_profile)
-    2. Context manager profiling (swanlab_profiling_context)
-    3. Advanced profiling with filtering (ProfilingConfig)
-    """
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-
-        # Create custom profiling config for high-frequency operations
-        self.fast_op_config = ProfilingConfig(
-            enabled=True,
-            min_duration_ms=0.5,  # Only log if duration > 0.5ms
-            log_interval=50,  # Log every 50th call
-        )
-
-    # ========================================================================
-    # Pattern 1: Decorator-based Profiling
-    # ========================================================================
-    # Best for: Methods you always want to profile
-    # Overhead: ~2-5 microseconds per call (negligible)
-
-    @swanlab_profile
-    def training_step(self, model, inputs):
-        """Main training step - always profile.
-
-        Profiling metric: profiling/Time taken: CustomTrainerWithProfiling.training_step
-        """
-        return super().training_step(model, inputs)
-
-    @swanlab_profile
-    def compute_loss(self, model, inputs, return_outputs=False):
-        """Loss computation - always profile.
-
-        Profiling metric: profiling/Time taken: CustomTrainerWithProfiling.compute_loss
-        """
-        return super().compute_loss(model, inputs, return_outputs)
-
-    @swanlab_profile
-    def prediction_step(self, model, inputs, prediction_loss_only, ignore_keys=None):
-        """Prediction step - always profile.
-
-        Profiling metric: profiling/Time taken: CustomTrainerWithProfiling.prediction_step
-        """
-        return super().prediction_step(model, inputs, prediction_loss_only, ignore_keys)
-
-    # ========================================================================
-    # Pattern 2: Fine-grained Context Manager Profiling
-    # ========================================================================
-    # Best for: Profiling specific code blocks within a method
-    # Use case: When you want to profile forward vs backward separately
-
-    def complex_training_step(self, model, inputs):
-        """Training step with fine-grained profiling.
-
-        Profiling metrics:
-        - profiling/Time taken: CustomTrainerWithProfiling.forward_pass
-        - profiling/Time taken: CustomTrainerWithProfiling.backward_pass
-        - profiling/Time taken: CustomTrainerWithProfiling.optimizer_step
-        """
-        # Profile just the forward pass
-        with swanlab_profiling_context(self, "forward_pass"):
-            outputs = model(**inputs)
-            loss = outputs.loss
-
-        # Profile just the backward pass
-        with swanlab_profiling_context(self, "backward_pass"):
-            loss.backward()
-
-        # Profile optimizer step
-        with swanlab_profiling_context(self, "optimizer_step"):
-            self.optimizer.step()
-            self.optimizer.zero_grad()
-
-        return outputs
-
-    # ========================================================================
-    # Pattern 3: Advanced Profiling with Filtering
-    # ========================================================================
-    # Best for: High-frequency operations where you want to throttle logging
-    # Use case: Methods called 100+ times per step
-
-    def _prepare_inputs(self, inputs):
-        """Prepare inputs - throttled profiling.
-
-        This method is called frequently (once per batch), so we throttle
-        profiling to reduce overhead:
-        - Only log if duration > 0.5ms (skip very fast operations)
-        - Only log every 50th call (reduce logging frequency)
-
-        Profiling metric: profiling/Time taken: CustomTrainerWithProfiling.prepare_inputs
-        """
-        with swanlab_profiling_context_advanced(
-            self, "prepare_inputs", config=self.fast_op_config
-        ):
-            return super()._prepare_inputs(inputs)
-
-    def _prepare_input_for_model(self, input_ids):
-        """Another high-frequency operation - throttled profiling.
-
-        Profiling metric: profiling/Time taken: CustomTrainerWithProfiling.prepare_input_for_model
-        """
-        with swanlab_profiling_context_advanced(
-            self, "prepare_input_for_model", config=self.fast_op_config
-        ):
-            # Your custom input preparation logic
-            return input_ids
-
-    # ========================================================================
-    # Pattern 4: Exception-safe Profiling
-    # ========================================================================
-    # Profiling is exception-safe: duration is logged even if method raises
-
-    @swanlab_profile
-    def potentially_failing_method(self):
-        """This method may raise an exception.
-
-        SwanLab profiling will still log the duration before re-raising.
-        Profiling metric: profiling/Time taken: CustomTrainerWithProfiling.potentially_failing_method
-        """
-        # Do some work
-        result = self._do_risky_computation()
-
-        # If this raises, profiling duration is still logged
-        if result < 0:
-            raise ValueError("Invalid result")
-
-        return result
-
-    def _do_risky_computation(self):
-        """Placeholder for risky computation."""
-        return 42
-
-
-# ============================================================================
-# Advanced Example: Custom ProfilingConfig Per Method
-# ============================================================================
-
-
-class AdvancedProfilingTrainer(AxolotlTrainer):
-    """Trainer with method-specific profiling configurations."""
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-
-        # Different profiling configs for different method types
-        self.critical_path_config = ProfilingConfig(
-            enabled=True,
-            min_duration_ms=0.0,  # Log everything on critical path
-            log_interval=1,  # Log every call
-        )
-
-        self.fast_path_config = ProfilingConfig(
-            enabled=True,
-            min_duration_ms=1.0,  # Only log if > 1ms
-            log_interval=100,  # Log every 100th call
-        )
-
-        self.debug_config = ProfilingConfig(
-            enabled=True,
-            min_duration_ms=0.0,  # Log everything
-            log_interval=1,  # Log every call
-        )
-
-    def training_step(self, model, inputs):
-        """Critical path - log everything."""
-        with swanlab_profiling_context_advanced(
-            self, "training_step", config=self.critical_path_config
-        ):
-            return super().training_step(model, inputs)
-
-    def _prepare_inputs(self, inputs):
-        """Fast path - throttle logging."""
-        with swanlab_profiling_context_advanced(
-            self, "prepare_inputs", config=self.fast_path_config
-        ):
-            return super()._prepare_inputs(inputs)
-
-    def _debug_method(self, data):
-        """Debug-only method - verbose logging."""
-        with swanlab_profiling_context_advanced(
-            self, "debug_method", config=self.debug_config
-        ):
-            # Your debug logic
-            pass
-
-
-# ============================================================================
-# How to Use This Custom Trainer
-# ============================================================================
-
-"""
-To use this custom trainer:
-
-1. Save this file to your project (e.g., my_custom_trainer.py)
-
-2. Create a config file that uses your custom trainer:
-
-    # config.yml
-    base_model: NousResearch/Llama-3.2-1B
-
-    # ... other config ...
-
-    plugins:
-      - axolotl.integrations.swanlab.SwanLabPlugin
-
-    use_swanlab: true
-    swanlab_project: my-profiling-experiment
-
-    # Optional: Specify custom trainer
-    # (Or modify axolotl to use your custom trainer class)
-
-3. Run training:
-
-    export SWANLAB_API_KEY=your-api-key
-    accelerate launch -m axolotl.cli.train config.yml
-
-4. View profiling metrics in SwanLab dashboard:
-   - profiling/Time taken: CustomTrainerWithProfiling.training_step
-   - profiling/Time taken: CustomTrainerWithProfiling.forward_pass
-   - profiling/Time taken: CustomTrainerWithProfiling.backward_pass
-   - etc.
-
-5. Compare profiling metrics across runs:
-   - Run baseline without optimizations
-   - Run with flash_attention enabled
-   - Run with gradient_checkpointing enabled
-   - Compare profiling metrics to see performance impact
-"""
-
-# ============================================================================
-# Tips for Effective Profiling
-# ============================================================================
-
-"""
-1. Profile the critical path first:
-   - training_step, compute_loss, prediction_step
-   - These methods are called most frequently and have biggest impact
-
-2. Use throttling for high-frequency operations:
-   - Methods called 100+ times per step
-   - Use log_interval=50 or log_interval=100
-   - Reduces profiling overhead and dashboard clutter
-
-3. Filter noise with min_duration_ms:
-   - Set min_duration_ms=1.0 to skip very fast operations
-   - Focus on operations that actually take time
-
-4. Compare across runs:
-   - Run same config multiple times to check consistency
-   - Compare different optimization strategies
-   - Track profiling trends over time
-
-5. Monitor distributed training:
-   - Check for per-rank timing differences
-   - Look for stragglers (slower ranks)
-   - Identify synchronization bottlenecks
-
-6. Disable profiling in production:
-   - from axolotl.integrations.swanlab.profiling import DEFAULT_PROFILING_CONFIG
-   - DEFAULT_PROFILING_CONFIG.enabled = False
-
-7. Exception handling:
-   - Profiling is exception-safe
-   - Duration logged even if method raises
-   - Useful for debugging methods that fail intermittently
-"""

examples/swanlab/dpo-swanlab-completions.yml

@@ -1,168 +0,0 @@
-# SwanLab DPO Training Example with Completion Logging
-#
-# This example demonstrates DPO (Direct Preference Optimization) training
-# with SwanLab integration for experiment tracking and completion table logging.
-#
-# Features enabled:
-# - SwanLab experiment tracking
-# - RLHF completion table logging (prompts, chosen/rejected responses, rewards)
-# - Lark (Feishu) team notifications (optional)
-#
-# To run:
-#   export SWANLAB_API_KEY=your-api-key
-#   accelerate launch -m axolotl.cli.train examples/swanlab/dpo-swanlab-completions.yml
-
-# Model Configuration
-base_model: meta-llama/Meta-Llama-3-8B-Instruct
-model_type: LlamaForCausalLM
-tokenizer_type: AutoTokenizer
-
-special_tokens:
-  pad_token: <|finetune_right_pad_id|>
-  eos_token: <|eot_id|>
-
-# Quantization
-load_in_8bit: true
-load_in_4bit: false
-
-# LoRA Configuration
-adapter: lora
-lora_r: 32
-lora_alpha: 16
-lora_dropout: 0.05
-lora_target_linear: true
-
-# DPO Configuration
-chat_template: llama3
-rl: dpo
-
-datasets:
-  - path: fozziethebeat/alpaca_messages_2k_dpo_test
-    type: chat_template.default
-    field_messages: conversation
-    field_chosen: chosen
-    field_rejected: rejected
-    message_property_mappings:
-      role: role
-      content: content
-    roles:
-      system:
-        - system
-      user:
-        - user
-      assistant:
-        - assistant
-
-# Dataset and Output
-dataset_prepared_path:
-val_set_size: 0.05
-output_dir: ./outputs/dpo-swanlab-out
-
-# Training Configuration
-sequence_len: 4096
-sample_packing: false
-micro_batch_size: 2
-gradient_accumulation_steps: 4
-num_epochs: 4
-
-# Optimization
-optimizer: adamw_bnb_8bit
-lr_scheduler: cosine
-learning_rate: 0.0002
-warmup_ratio: 0.1
-weight_decay: 0.0
-
-# Precision
-bf16: auto
-tf32: false
-
-# Performance
-gradient_checkpointing: true
-flash_attention: true
-
-# Checkpointing and Logging
-logging_steps: 1
-evals_per_epoch: 4
-saves_per_epoch: 1
-
-# ============================================================================
-# SwanLab Integration
-# ============================================================================
-
-plugins:
-  - axolotl.integrations.swanlab.SwanLabPlugin
-
-# Basic SwanLab Configuration
-use_swanlab: true
-swanlab_project: dpo-training
-swanlab_experiment_name: llama-3-dpo-completions-demo
-swanlab_description: "DPO training with completion table logging"
-swanlab_mode: cloud  # Options: cloud, local, offline, disabled
-
-# SwanLab Authentication
-# Recommended: Set via environment variable
-#   export SWANLAB_API_KEY=your-api-key
-# Or set in config (less secure):
-# swanlab_api_key: your-api-key
-
-# Optional: Team workspace
-# swanlab_workspace: my-research-team
-
-# ============================================================================
-# RLHF Completion Table Logging
-# ============================================================================
-#
-# Automatically logs model completions to SwanLab for qualitative analysis:
-# - Prompts from your DPO dataset
-# - Chosen responses (preferred)
-# - Rejected responses (non-preferred)
-# - Reward differences
-#
-# View the table in SwanLab dashboard under "rlhf_completions"
-
-swanlab_log_completions: true
-swanlab_completion_log_interval: 100  # Log every 100 training steps
-swanlab_completion_max_buffer: 128    # Keep last 128 completions in memory
-
-# Memory Usage Notes:
-# - Buffer size 128: ~64 KB (default, recommended)
-# - Buffer size 512: ~256 KB (for more historical completions)
-# - Buffer size 1024: ~512 KB (maximum for very long training runs)
-
-# Performance Notes:
-# - Completion logging overhead: < 0.5% per training step
-# - Only logs every N steps to minimize impact
-# - Memory-bounded buffer prevents memory leaks
-
-# ============================================================================
-# Optional: Lark (Feishu) Team Notifications
-# ============================================================================
-#
-# Get real-time training notifications in your team chat
-# Uncomment to enable:
-
-# swanlab_lark_webhook_url: https://open.feishu.cn/open-apis/bot/v2/hook/xxxxxxxxxx
-# swanlab_lark_secret: your-webhook-secret  # Recommended for production
-
-# Notifications sent for:
-# - Training start
-# - Training completion
-# - Training errors
-# - Metric milestones (if configured)
-
-# ============================================================================
-# Optional: Private SwanLab Deployment
-# ============================================================================
-#
-# For enterprise users with private SwanLab deployment:
-
-# swanlab_web_host: https://swanlab.yourcompany.com
-# swanlab_api_host: https://api.swanlab.yourcompany.com
-
-# ============================================================================
-# Disable WandB if you're migrating from it
-# ============================================================================
-
-# wandb_project:
-# wandb_entity:
-# use_wandb: false

examples/swanlab/dpo-swanlab-full-featured.yml

@@ -1,329 +0,0 @@
-# SwanLab Full-Featured DPO Training Example
-#
-# This example demonstrates ALL SwanLab integration features:
-# - Experiment tracking with cloud sync
-# - RLHF completion table logging
-# - Performance profiling
-# - Lark (Feishu) team notifications
-# - Team workspace collaboration
-#
-# Use this as a reference for production RLHF training setups.
-#
-# To run:
-#   export SWANLAB_API_KEY=your-api-key
-#   export SWANLAB_LARK_WEBHOOK_URL=https://open.feishu.cn/...
-#   export SWANLAB_LARK_SECRET=your-webhook-secret
-#   accelerate launch -m axolotl.cli.train examples/swanlab/dpo-swanlab-full-featured.yml
-
-# ============================================================================
-# Model Configuration
-# ============================================================================
-
-base_model: meta-llama/Meta-Llama-3-8B-Instruct
-model_type: LlamaForCausalLM
-tokenizer_type: AutoTokenizer
-
-special_tokens:
-  pad_token: <|finetune_right_pad_id|>
-  eos_token: <|eot_id|>
-
-# Quantization for efficient training
-load_in_8bit: true
-load_in_4bit: false
-
-# ============================================================================
-# LoRA Configuration
-# ============================================================================
-
-adapter: lora
-lora_r: 32
-lora_alpha: 16
-lora_dropout: 0.05
-lora_target_linear: true  # Target all linear layers
-
-# ============================================================================
-# DPO (Direct Preference Optimization) Configuration
-# ============================================================================
-
-chat_template: llama3
-rl: dpo  # Enable DPO trainer
-
-datasets:
-  - path: fozziethebeat/alpaca_messages_2k_dpo_test
-    type: chat_template.default
-    field_messages: conversation
-    field_chosen: chosen
-    field_rejected: rejected
-    message_property_mappings:
-      role: role
-      content: content
-    roles:
-      system:
-        - system
-      user:
-        - user
-      assistant:
-        - assistant
-
-# ============================================================================
-# Dataset and Output Configuration
-# ============================================================================
-
-dataset_prepared_path:
-val_set_size: 0.05
-output_dir: ./outputs/dpo-swanlab-full-featured-out
-
-# ============================================================================
-# Training Configuration
-# ============================================================================
-
-sequence_len: 4096
-sample_packing: false
-
-micro_batch_size: 2
-gradient_accumulation_steps: 4
-num_epochs: 4
-
-# ============================================================================
-# Optimization
-# ============================================================================
-
-optimizer: adamw_bnb_8bit
-lr_scheduler: cosine
-learning_rate: 0.0002
-warmup_ratio: 0.1
-weight_decay: 0.0
-
-# ============================================================================
-# Precision and Performance
-# ============================================================================
-
-bf16: auto
-tf32: false
-
-gradient_checkpointing: true
-flash_attention: true
-
-# ============================================================================
-# Checkpointing and Logging
-# ============================================================================
-
-logging_steps: 1
-evals_per_epoch: 4
-saves_per_epoch: 1
-
-# ============================================================================
-# SwanLab Integration - Full Configuration
-# ============================================================================
-
-plugins:
-  - axolotl.integrations.swanlab.SwanLabPlugin
-
-# ------------------------------------------------------------------------------
-# Basic SwanLab Configuration
-# ------------------------------------------------------------------------------
-
-use_swanlab: true
-swanlab_project: dpo-production
-swanlab_experiment_name: llama-3-dpo-full-featured-v1
-swanlab_description: |
-  Production DPO training with all SwanLab features enabled:
-  - Completion table logging for qualitative analysis
-  - Performance profiling for optimization
-  - Lark notifications for team collaboration
-
-swanlab_mode: cloud  # Options: cloud, local, offline, disabled
-
-# ------------------------------------------------------------------------------
-# Team Collaboration
-# ------------------------------------------------------------------------------
-
-# Workspace for team collaboration (shared experiments)
-swanlab_workspace: ml-research-team
-
-# Authentication (recommended: use environment variable)
-#   export SWANLAB_API_KEY=your-api-key
-# Or set in config (less secure):
-# swanlab_api_key: your-api-key
-
-# ------------------------------------------------------------------------------
-# RLHF Completion Table Logging
-# ------------------------------------------------------------------------------
-# Automatically logs model completions for qualitative analysis:
-# - Prompts from your DPO dataset
-# - Chosen responses (preferred)
-# - Rejected responses (non-preferred)
-# - Reward differences
-#
-# View in SwanLab dashboard under "rlhf_completions" table
-
-swanlab_log_completions: true
-swanlab_completion_log_interval: 100  # Log every 100 steps
-swanlab_completion_max_buffer: 256    # Larger buffer for long training runs
-
-# Buffer size recommendations:
-# - 128: Default, ~64 KB memory (recommended for most cases)
-# - 256: ~128 KB memory (this config, good for longer training)
-# - 512: ~256 KB memory (maximum for very long runs)
-
-# ------------------------------------------------------------------------------
-# Lark (Feishu) Team Notifications
-# ------------------------------------------------------------------------------
-# Get real-time training notifications in your team chat
-#
-# Notifications sent for:
-# - Training start
-# - Training completion
-# - Training errors
-# - Metric milestones (if configured)
-
-# Recommended: Set via environment variables
-#   export SWANLAB_LARK_WEBHOOK_URL=https://open.feishu.cn/...
-#   export SWANLAB_LARK_SECRET=your-webhook-secret
-
-# Or set in config (less secure):
-# swanlab_lark_webhook_url: https://open.feishu.cn/open-apis/bot/v2/hook/xxxxxxxxxx
-# swanlab_lark_secret: your-webhook-secret  # REQUIRED for production
-
-# Security note: ALWAYS use swanlab_lark_secret in production to prevent
-# unauthorized parties from sending fake notifications to your team chat.
-
-# ------------------------------------------------------------------------------
-# Performance Profiling
-# ------------------------------------------------------------------------------
-# Profiling is automatically enabled when SwanLab is enabled.
-# Metrics logged to SwanLab under "profiling/" namespace:
-#   profiling/Time taken: AxolotlTrainer.training_step
-#   profiling/Time taken: AxolotlTrainer.compute_loss
-#   profiling/Time taken: AxolotlTrainer.prediction_step
-#
-# Use these metrics to:
-# - Identify bottlenecks in training loop
-# - Compare performance across different configurations
-# - Monitor performance regressions over time
-# - Debug unexpected slowdowns
-
-# For custom profiling in your own trainer, see:
-#   examples/swanlab/custom_trainer_profiling.py
-
-# ------------------------------------------------------------------------------
-# Optional: Private SwanLab Deployment
-# ------------------------------------------------------------------------------
-# For enterprise users with private SwanLab deployment:
-
-# swanlab_web_host: https://swanlab.yourcompany.com
-# swanlab_api_host: https://api.swanlab.yourcompany.com
-
-# ------------------------------------------------------------------------------
-# Optional: Model Checkpointing to SwanLab
-# ------------------------------------------------------------------------------
-# Log model checkpoints to SwanLab (coming soon)
-
-swanlab_log_model: false
-
-# ============================================================================
-# Disable Other Logging Tools (Recommended)
-# ============================================================================
-# Using multiple logging tools simultaneously can impact performance:
-# - Expected overhead: ~1-2% per logger
-# - Potential config/callback conflicts
-#
-# For production training, use ONLY SwanLab:
-
-# wandb_project:
-# use_wandb: false
-#
-# use_mlflow: false
-#
-# use_comet: false
-
-# ============================================================================
-# Expected Training Behavior
-# ============================================================================
-
-# With this configuration, you should see:
-#
-# 1. SwanLab Initialization (rank 0 only):
-#    INFO: SwanLab initialized for project: dpo-production
-#    INFO: SwanLab experiment: llama-3-dpo-full-featured-v1
-#    INFO: SwanLab mode: cloud
-#    INFO: SwanLab workspace: ml-research-team
-#
-# 2. Completion Logging (rank 0 only):
-#    INFO: Registered SwanLab RLHF completion logging callback for DPOTrainer
-#          (log_interval=100, max_buffer=256)
-#
-# 3. Lark Notifications (rank 0 only):
-#    INFO: Registered Lark notification callback with HMAC authentication
-#
-# 4. Distributed Training Detection (if multi-GPU):
-#    INFO: Distributed training detected (world_size=N)
-#    INFO: Only rank 0 will initialize SwanLab
-#    INFO: Other ranks will skip SwanLab to avoid conflicts
-#
-# 5. Training Start Notification (Lark):
-#    Your team chat receives: "Training started: llama-3-dpo-full-featured-v1"
-#
-# 6. Periodic Completion Logging:
-#    Every 100 steps, completion table is updated in SwanLab dashboard
-#
-# 7. Training Complete Notification (Lark):
-#    Your team chat receives: "Training completed: llama-3-dpo-full-featured-v1"
-#    With link to SwanLab dashboard and final metrics
-#
-# 8. SwanLab Dashboard Shows:
-#    - Training metrics (loss, learning rate, etc.)
-#    - Completion table (rlhf_completions)
-#    - Profiling metrics (profiling/Time taken: ...)
-#    - Hyperparameters and configuration
-#    - System resource usage
-
-# ============================================================================
-# Production Checklist
-# ============================================================================
-
-# Before deploying to production, verify:
-# ✅ SwanLab API key is set via environment variable (not in config)
-# ✅ Lark webhook secret is set (required for HMAC authentication)
-# ✅ Workspace is set to your team's workspace
-# ✅ Experiment name is descriptive and unique
-# ✅ Only SwanLab is enabled (other loggers disabled)
-# ✅ Completion logging buffer size is appropriate for your training duration
-# ✅ Private deployment hosts are set (if using enterprise SwanLab)
-# ✅ Test run completes successfully and shows up in SwanLab dashboard
-# ✅ Lark notifications are received in team chat
-# ✅ Profiling metrics are logged correctly
-
-# ============================================================================
-# Troubleshooting
-# ============================================================================
-
-# If SwanLab initialization fails:
-# 1. Check SWANLAB_API_KEY environment variable is set
-# 2. Verify swanlab_project is set in config
-# 3. Check swanlab_mode is valid (cloud/local/offline/disabled)
-# 4. Verify internet connectivity (for cloud mode)
-
-# If Lark notifications not received:
-# 1. Check SWANLAB_LARK_WEBHOOK_URL is set correctly
-# 2. Verify SWANLAB_LARK_SECRET matches your Lark bot settings
-# 3. Test webhook manually: curl -X POST "$SWANLAB_LARK_WEBHOOK_URL" ...
-# 4. Check training logs for "Registered Lark notification callback"
-# 5. Verify bot is added to the target Lark group chat
-
-# If completions not appearing in SwanLab:
-# 1. Verify you're using an RLHF trainer (DPO/KTO/ORPO/GRPO)
-# 2. Check swanlab_log_completions is true
-# 3. Wait for log_interval steps (default: 100)
-# 4. Check training logs for "Registered SwanLab RLHF completion logging"
-
-# If profiling metrics not appearing:
-# 1. Verify use_swanlab is true
-# 2. Check SwanLab is initialized (check logs)
-# 3. Look under "profiling/" namespace in dashboard
-# 4. Profiling may be disabled if DEFAULT_PROFILING_CONFIG.enabled = False
-
-# For more help:
-# - SwanLab docs: https://docs.swanlab.cn
-# - Axolotl SwanLab integration: src/axolotl/integrations/swanlab/README.md
-# - GitHub issues: https://github.com/axolotl-ai-cloud/axolotl/issues

examples/swanlab/lora-swanlab-profiling.yml

@@ -1,178 +0,0 @@
-# SwanLab LoRA Training Example with Performance Profiling
-#
-# This example demonstrates standard LoRA fine-tuning with SwanLab integration
-# for performance profiling and optimization.
-#
-# Features enabled:
-# - SwanLab experiment tracking
-# - Performance profiling (training step, forward/backward pass timing)
-# - Real-time metrics visualization
-#
-# To run:
-#   export SWANLAB_API_KEY=your-api-key
-#   accelerate launch -m axolotl.cli.train examples/swanlab/lora-swanlab-profiling.yml
-
-# Model Configuration
-base_model: NousResearch/Llama-3.2-1B
-
-# Dataset Configuration
-datasets:
-  - path: teknium/GPT4-LLM-Cleaned
-    type: alpaca
-
-val_set_size: 0.1
-output_dir: ./outputs/lora-swanlab-profiling-out
-
-# LoRA Configuration
-adapter: lora
-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
-
-# Training Configuration
-sequence_len: 2048
-sample_packing: true
-eval_sample_packing: true
-
-micro_batch_size: 2
-gradient_accumulation_steps: 2
-num_epochs: 1
-
-# Optimization
-optimizer: adamw_8bit
-lr_scheduler: cosine
-learning_rate: 0.0002
-warmup_ratio: 0.1
-weight_decay: 0.0
-
-# Precision
-bf16: auto
-tf32: false
-
-# Performance
-gradient_checkpointing: true
-flash_attention: true
-
-# Checkpointing and Logging
-logging_steps: 1
-evals_per_epoch: 4
-saves_per_epoch: 1
-
-# Loss Monitoring
-loss_watchdog_threshold: 5.0
-loss_watchdog_patience: 3
-
-special_tokens:
-  pad_token: "<|end_of_text|>"
-
-# ============================================================================
-# SwanLab Integration
-# ============================================================================
-
-plugins:
-  - axolotl.integrations.swanlab.SwanLabPlugin
-
-# Basic SwanLab Configuration
-use_swanlab: true
-swanlab_project: lora-profiling
-swanlab_experiment_name: llama-3.2-1b-profiling-demo
-swanlab_description: "LoRA fine-tuning with performance profiling"
-swanlab_mode: cloud  # Options: cloud, local, offline, disabled
-
-# SwanLab Authentication
-# Recommended: Set via environment variable
-#   export SWANLAB_API_KEY=your-api-key
-# Or set in config (less secure):
-# swanlab_api_key: your-api-key
-
-# Optional: Team workspace
-# swanlab_workspace: my-ml-team
-
-# ============================================================================
-# Performance Profiling
-# ============================================================================
-#
-# SwanLab automatically profiles trainer methods when enabled.
-# Profiling metrics appear in SwanLab dashboard under "profiling/" namespace.
-#
-# Built-in profiling:
-# - Minimal overhead (< 0.1% per step)
-# - High-precision timing (microsecond accuracy)
-# - Exception-safe (logs duration even if method fails)
-#
-# View profiling metrics in SwanLab dashboard:
-#   profiling/Time taken: AxolotlTrainer.training_step
-#   profiling/Time taken: AxolotlTrainer.compute_loss
-#   profiling/Time taken: AxolotlTrainer.prediction_step
-#
-# For custom profiling in your own trainer, see:
-#   examples/swanlab/custom_trainer_profiling.py
-
-# Completion logging is disabled for non-RLHF trainers
-swanlab_log_completions: false  # Only works with DPO/KTO/ORPO/GRPO
-
-# ============================================================================
-# Optional: Compare with Multiple Runs
-# ============================================================================
-#
-# To compare profiling metrics across different configurations:
-#
-# 1. Run baseline without flash attention:
-#    swanlab_experiment_name: llama-3.2-1b-no-flash-attn
-#    flash_attention: false
-#
-# 2. Run with gradient checkpointing:
-#    swanlab_experiment_name: llama-3.2-1b-grad-checkpoint
-#    gradient_checkpointing: true
-#
-# 3. Run with both:
-#    swanlab_experiment_name: llama-3.2-1b-optimized
-#    flash_attention: true
-#    gradient_checkpointing: true
-#
-# Then compare profiling metrics in SwanLab dashboard to see performance impact
-
-# ============================================================================
-# Optional: Lark (Feishu) Team Notifications
-# ============================================================================
-#
-# Get notified when profiling experiments complete:
-
-# swanlab_lark_webhook_url: https://open.feishu.cn/open-apis/bot/v2/hook/xxxxxxxxxx
-# swanlab_lark_secret: your-webhook-secret
-
-# ============================================================================
-# Profiling Best Practices
-# ============================================================================
-#
-# 1. Run multiple epochs to see profiling trends over time
-# 2. Ignore first ~10 steps (warmup period, slower)
-# 3. Look for outliers (steps that take significantly longer)
-# 4. Compare profiling metrics before/after optimization changes
-# 5. Monitor per-rank profiling in distributed training
-#
-# Common bottlenecks to profile:
-# - training_step: Overall step time (should be consistent)
-# - compute_loss: Loss computation (scales with sequence length)
-# - prediction_step: Evaluation time (can be slow for large val sets)
-#
-# If you see inconsistent timing:
-# - Check for data loading bottlenecks
-# - Monitor GPU utilization (may be CPU-bound)
-# - Check for gradient accumulation effects
-# - Verify CUDA kernel synchronization
-
-# ============================================================================
-# Disable WandB if you're migrating from it
-# ============================================================================
-
-# wandb_project:
-# use_wandb: false

requirements.txt

@@ -1,7 +1,7 @@
 --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/
 
 # START section of dependencies that don't install on Darwin/MacOS
-bitsandbytes==0.49.1
+bitsandbytes==0.48.2
 triton>=3.0.0
 mamba-ssm==1.2.0.post1
 xformers>=0.0.23.post1
@@ -63,7 +63,7 @@ langdetect==1.0.9
 immutabledict==4.2.0
 antlr4-python3-runtime==4.13.2
 
-torchao==0.13.0
+torchao==0.15.0
 openenv-core==0.1.0
 schedulefree==1.4.1
 

src/axolotl/core/builders/causal.py

@@ -373,11 +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_dynamic_finetuning:
-            from axolotl.monkeypatch.loss.dft import dft_loss
-
-            trainer_kwargs["compute_loss_func"] = dft_loss
-
         trainer_cls = self._get_trainer_cls()
 
         trainer_kwargs, trainer_cls = self.hook_pre_create_trainer(

src/axolotl/core/trainers/base.py

@@ -660,10 +660,11 @@ class AxolotlTrainer(
             logs["tokens/train_per_sec_per_gpu"] = round(
                 self.state.last_tokens_per_second.item() / self.args.logging_steps, 2
             )
-            if "total" in self.state.tokens:
-                logs["tokens/total"] = int(self.state.tokens["total"].item())
-            if "trainable" in self.state.tokens:
-                logs["tokens/trainable"] = int(self.state.tokens["trainable"].item())
+            if (
+                hasattr(self.state, "total_tokens")
+                and self.state.total_tokens is not None
+            ):
+                logs["total_tokens"] = int(self.state.total_tokens.item())
 
         del self._stored_metrics[train_eval]
 

src/axolotl/integrations/swanlab/__init__.py

@@ -1,6 +0,0 @@
-"""SwanLab integration plugin for Axolotl"""
-
-from axolotl.integrations.swanlab.args import SwanLabConfig
-from axolotl.integrations.swanlab.plugins import SwanLabPlugin
-
-__all__ = ["SwanLabConfig", "SwanLabPlugin"]

src/axolotl/integrations/swanlab/args.py

@@ -1,140 +0,0 @@
-"""SwanLab configuration arguments"""
-
-from pydantic import BaseModel, Field, field_validator, model_validator
-
-
-class SwanLabConfig(BaseModel):
-    """SwanLab configuration subset"""
-
-    use_swanlab: bool | None = Field(
-        default=True,
-        json_schema_extra={
-            "description": "Enable SwanLab experiment tracking and visualization"
-        },
-    )
-    swanlab_project: str | None = Field(
-        default=None,
-        json_schema_extra={"description": "Your SwanLab project name"},
-    )
-    swanlab_experiment_name: str | None = Field(
-        default=None,
-        json_schema_extra={"description": "Set the name of your SwanLab experiment"},
-    )
-    swanlab_description: str | None = Field(
-        default=None,
-        json_schema_extra={"description": "Description for your SwanLab experiment"},
-    )
-    swanlab_mode: str | None = Field(
-        default=None,
-        json_schema_extra={
-            "description": '"cloud" to sync to SwanLab cloud, "local" for local only, "offline" to save metadata locally, "disabled" to turn off SwanLab'
-        },
-    )
-    swanlab_workspace: str | None = Field(
-        default=None,
-        json_schema_extra={
-            "description": "SwanLab workspace name (organization or username)"
-        },
-    )
-    swanlab_api_key: str | None = Field(
-        default=None,
-        json_schema_extra={
-            "description": "SwanLab API key for authentication. Can also be set via SWANLAB_API_KEY environment variable"
-        },
-    )
-    swanlab_log_model: bool | None = Field(
-        default=False,
-        json_schema_extra={
-            "description": "Whether to log model checkpoints to SwanLab (feature coming soon)"
-        },
-    )
-    swanlab_web_host: str | None = Field(
-        default=None,
-        json_schema_extra={
-            "description": "Web address for SwanLab cloud environment (for private deployment)"
-        },
-    )
-    swanlab_api_host: str | None = Field(
-        default=None,
-        json_schema_extra={
-            "description": "API address for SwanLab cloud environment (for private deployment)"
-        },
-    )
-    swanlab_lark_webhook_url: str | None = Field(
-        default=None,
-        json_schema_extra={
-            "description": "Lark (Feishu) webhook URL for sending training notifications to team chat"
-        },
-    )
-    swanlab_lark_secret: str | None = Field(
-        default=None,
-        json_schema_extra={
-            "description": "Secret for Lark webhook HMAC signature authentication (optional)"
-        },
-    )
-    swanlab_log_completions: bool | None = Field(
-        default=True,
-        json_schema_extra={
-            "description": "Enable logging RLHF completions to SwanLab for qualitative analysis (DPO/KTO/ORPO/GRPO)"
-        },
-    )
-    swanlab_completion_log_interval: int | None = Field(
-        default=100,
-        json_schema_extra={
-            "description": "Number of training steps between completion table logging to SwanLab"
-        },
-    )
-    swanlab_completion_max_buffer: int | None = Field(
-        default=128,
-        json_schema_extra={
-            "description": "Maximum number of completions to buffer before logging (prevents memory leaks)"
-        },
-    )
-
-    @field_validator("swanlab_mode")
-    @classmethod
-    def validate_swanlab_mode(cls, v):
-        """Validate swanlab_mode is one of the allowed values."""
-        if v is None:
-            return v
-
-        valid_modes = ["cloud", "local", "offline", "disabled"]
-        if v not in valid_modes:
-            raise ValueError(
-                f"Invalid swanlab_mode: '{v}'.\n\n"
-                f"Valid options: {', '.join(valid_modes)}\n\n"
-                f"Examples:\n"
-                f"  swanlab_mode: cloud     # Sync to SwanLab cloud\n"
-                f"  swanlab_mode: local     # Local only, no cloud sync\n"
-                f"  swanlab_mode: offline   # Save metadata locally\n"
-                f"  swanlab_mode: disabled  # Turn off SwanLab\n"
-            )
-        return v
-
-    @field_validator("swanlab_project")
-    @classmethod
-    def validate_swanlab_project(cls, v):
-        """Validate swanlab_project is non-empty when provided."""
-        if v is not None and isinstance(v, str) and len(v.strip()) == 0:
-            raise ValueError(
-                "swanlab_project cannot be an empty string.\n\n"
-                "Either:\n"
-                "  1. Provide a valid project name: swanlab_project: my-project\n"
-                "  2. Remove the swanlab_project field entirely\n"
-            )
-        return v
-
-    @model_validator(mode="after")
-    def validate_swanlab_enabled_requires_project(self):
-        """Validate that if use_swanlab is True, swanlab_project must be set."""
-        if self.use_swanlab is True and not self.swanlab_project:
-            raise ValueError(
-                "SwanLab enabled (use_swanlab: true) but 'swanlab_project' is not set.\n\n"
-                "Solutions:\n"
-                "  1. Add 'swanlab_project: your-project-name' to your config\n"
-                "  2. Set 'use_swanlab: false' to disable SwanLab\n\n"
-                "Example:\n"
-                "  use_swanlab: true\n"
-                "  swanlab_project: my-llm-training\n"
-            )
-        return self

src/axolotl/integrations/swanlab/callbacks.py

@@ -1,179 +0,0 @@
-"""SwanLab callbacks for Axolotl trainers.
-
-This module provides HuggingFace Trainer callbacks for logging
-RLHF completions to SwanLab.
-"""
-
-from transformers import (
-    TrainerCallback,
-    TrainerControl,
-    TrainerState,
-    TrainingArguments,
-)
-
-from axolotl.integrations.swanlab.completion_logger import CompletionLogger
-from axolotl.utils.logging import get_logger
-
-LOG = get_logger(__name__)
-
-
-class SwanLabRLHFCompletionCallback(TrainerCallback):
-    """Callback for logging RLHF completions to SwanLab.
-
-    This callback periodically logs model completions (prompts, chosen/rejected
-    responses, rewards) to SwanLab during RLHF training for qualitative analysis.
-
-    Supports DPO, KTO, ORPO, and GRPO trainers.
-
-    Example usage:
-        >>> callback = SwanLabRLHFCompletionCallback(
-        ...     log_interval=100,  # Log every 100 steps
-        ...     max_completions=128,  # Keep last 128 completions
-        ... )
-        >>> trainer.add_callback(callback)
-
-    Attributes:
-        logger: CompletionLogger instance
-        log_interval: Number of steps between SwanLab logging
-        trainer_type: Auto-detected trainer type (dpo/kto/orpo/grpo)
-    """
-
-    def __init__(
-        self,
-        log_interval: int = 100,
-        max_completions: int = 128,
-        table_name: str = "rlhf_completions",
-    ):
-        """Initialize SwanLab RLHF completion callback.
-
-        Args:
-            log_interval: Log to SwanLab every N steps. Default: 100
-            max_completions: Maximum completions to buffer. Default: 128
-            table_name: SwanLab table name. Default: "rlhf_completions"
-        """
-        super().__init__()
-        self.logger = CompletionLogger(maxlen=max_completions)
-        self.log_interval = log_interval
-        self.table_name = table_name
-        self.trainer_type: str | None = None  # Auto-detected
-        self._last_logged_step = 0
-
-    def on_init_end(
-        self,
-        args: TrainingArguments,
-        state: TrainerState,
-        control: TrainerControl,
-        **kwargs,
-    ):
-        """Detect trainer type on initialization."""
-        trainer = kwargs.get("trainer")
-        if trainer is not None:
-            trainer_name = trainer.__class__.__name__
-            if "DPO" in trainer_name:
-                self.trainer_type = "dpo"
-            elif "KTO" in trainer_name:
-                self.trainer_type = "kto"
-            elif "ORPO" in trainer_name:
-                self.trainer_type = "orpo"
-            elif "GRPO" in trainer_name:
-                self.trainer_type = "grpo"
-            else:
-                self.trainer_type = "unknown"
-
-            LOG.info(
-                f"SwanLab RLHF completion logging enabled for {trainer_name} "
-                f"(type: {self.trainer_type})"
-            )
-
-    def on_log(
-        self,
-        args: TrainingArguments,
-        state: TrainerState,
-        control: TrainerControl,
-        logs: dict | None = None,
-        **kwargs,
-    ):
-        """Capture completions from logs and buffer them.
-
-        Different trainers log completions in different formats:
-        - DPO: logs['dpo/chosen'], logs['dpo/rejected'], logs['dpo/reward_diff']
-        - KTO: logs['kto/completion'], logs['kto/label'], logs['kto/reward']
-        - ORPO: logs['orpo/chosen'], logs['orpo/rejected']
-        - GRPO: logs['grpo/completion'], logs['grpo/reward']
-
-        Note: This is a placeholder implementation. Actual log keys depend
-        on the TRL trainer implementation. You may need to patch the trainers
-        to expose completion data in logs.
-        """
-        if logs is None or self.trainer_type is None:
-            return
-
-        step = state.global_step
-
-        # DPO completions
-        if self.trainer_type == "dpo":
-            if all(key in logs for key in ["dpo/prompt", "dpo/chosen", "dpo/rejected"]):
-                self.logger.add_dpo_completion(
-                    step=step,
-                    prompt=logs.get("dpo/prompt", ""),
-                    chosen=logs.get("dpo/chosen", ""),
-                    rejected=logs.get("dpo/rejected", ""),
-                    reward_diff=logs.get("dpo/reward_diff"),
-                )
-
-        # KTO completions
-        elif self.trainer_type == "kto":
-            if all(key in logs for key in ["kto/prompt", "kto/completion"]):
-                self.logger.add_kto_completion(
-                    step=step,
-                    prompt=logs.get("kto/prompt", ""),
-                    completion=logs.get("kto/completion", ""),
-                    label=logs.get("kto/label", False),
-                    reward=logs.get("kto/reward"),
-                )
-
-        # ORPO completions
-        elif self.trainer_type == "orpo":
-            if all(
-                key in logs for key in ["orpo/prompt", "orpo/chosen", "orpo/rejected"]
-            ):
-                self.logger.add_orpo_completion(
-                    step=step,
-                    prompt=logs.get("orpo/prompt", ""),
-                    chosen=logs.get("orpo/chosen", ""),
-                    rejected=logs.get("orpo/rejected", ""),
-                    log_odds_ratio=logs.get("orpo/log_odds_ratio"),
-                )
-
-        # GRPO completions
-        elif self.trainer_type == "grpo":
-            if all(key in logs for key in ["grpo/prompt", "grpo/completion"]):
-                self.logger.add_grpo_completion(
-                    step=step,
-                    prompt=logs.get("grpo/prompt", ""),
-                    completion=logs.get("grpo/completion", ""),
-                    reward=logs.get("grpo/reward"),
-                    advantage=logs.get("grpo/advantage"),
-                )
-
-        # Periodically log to SwanLab
-        if step - self._last_logged_step >= self.log_interval:
-            if len(self.logger) > 0:
-                self.logger.log_to_swanlab(table_name=self.table_name)
-                self.logger.clear()
-                self._last_logged_step = step
-
-    def on_train_end(
-        self,
-        args: TrainingArguments,
-        state: TrainerState,
-        control: TrainerControl,
-        **kwargs,
-    ):
-        """Log remaining completions at end of training."""
-        if len(self.logger) > 0:
-            LOG.info(
-                f"Training complete, logging final {len(self.logger)} completions to SwanLab"
-            )
-            self.logger.log_to_swanlab(table_name=self.table_name)
-            self._last_logged_step = state.global_step

src/axolotl/integrations/swanlab/completion_logger.py

@@ -1,228 +0,0 @@
-"""SwanLab completion logger for RLHF/DPO/KTO/ORPO/GRPO training.
-
-This module provides utilities for logging model completions during
-preference training to SwanLab for qualitative analysis.
-"""
-
-from collections import deque
-from collections.abc import Mapping
-from typing import Any
-
-from axolotl.utils.logging import get_logger
-
-LOG = get_logger(__name__)
-
-
-class CompletionLogger:
-    """Memory-bounded logger for RLHF completions.
-
-    Stores prompts, completions, and rewards in fixed-size deques to prevent
-    memory leaks during long training runs. Logs completion tables to SwanLab
-    for qualitative analysis of model outputs.
-
-    Example usage:
-        >>> logger = CompletionLogger(maxlen=128)
-        >>> logger.add_dpo_completion(
-        ...     step=0,
-        ...     prompt="What is AI?",
-        ...     chosen="Artificial Intelligence is...",
-        ...     rejected="AI means...",
-        ...     reward_diff=0.5
-        ... )
-        >>> logger.log_to_swanlab()
-
-    Attributes:
-        maxlen: Maximum number of completions to store (older ones are dropped)
-        data: Deque storing completion dictionaries
-    """
-
-    def __init__(self, maxlen: int = 128):
-        """Initialize completion logger with bounded buffer.
-
-        Args:
-            maxlen: Maximum number of completions to store. When the buffer
-                is full, oldest completions are automatically discarded.
-                Default: 128 (sufficient for most RLHF runs without memory issues)
-        """
-        self.maxlen = maxlen
-        self.data: deque[Mapping[str, Any]] = deque(maxlen=maxlen)
-
-    def add_dpo_completion(
-        self,
-        step: int,
-        prompt: str,
-        chosen: str,
-        rejected: str,
-        reward_diff: float | None = None,
-    ) -> None:
-        """Add a DPO completion to the buffer.
-
-        Args:
-            step: Training step number
-            prompt: Input prompt
-            chosen: Chosen (preferred) completion
-            rejected: Rejected (non-preferred) completion
-            reward_diff: Reward difference (chosen - rejected), if available
-        """
-        entry = {
-            "step": step,
-            "prompt": prompt,
-            "chosen": chosen,
-            "rejected": rejected,
-        }
-        if reward_diff is not None:
-            entry["reward_diff"] = reward_diff
-
-        self.data.append(entry)
-
-    def add_kto_completion(
-        self,
-        step: int,
-        prompt: str,
-        completion: str,
-        label: bool,
-        reward: float | None = None,
-    ) -> None:
-        """Add a KTO completion to the buffer.
-
-        Args:
-            step: Training step number
-            prompt: Input prompt
-            completion: Model-generated completion
-            label: True if desirable, False if undesirable
-            reward: Reward score, if available
-        """
-        entry = {
-            "step": step,
-            "prompt": prompt,
-            "completion": completion,
-            "label": "desirable" if label else "undesirable",
-        }
-        if reward is not None:
-            entry["reward"] = reward
-
-        self.data.append(entry)
-
-    def add_orpo_completion(
-        self,
-        step: int,
-        prompt: str,
-        chosen: str,
-        rejected: str,
-        log_odds_ratio: float | None = None,
-    ) -> None:
-        """Add an ORPO completion to the buffer.
-
-        Args:
-            step: Training step number
-            prompt: Input prompt
-            chosen: Chosen (preferred) completion
-            rejected: Rejected (non-preferred) completion
-            log_odds_ratio: Log odds ratio between chosen and rejected
-        """
-        entry = {
-            "step": step,
-            "prompt": prompt,
-            "chosen": chosen,
-            "rejected": rejected,
-        }
-        if log_odds_ratio is not None:
-            entry["log_odds_ratio"] = log_odds_ratio
-
-        self.data.append(entry)
-
-    def add_grpo_completion(
-        self,
-        step: int,
-        prompt: str,
-        completion: str,
-        reward: float | None = None,
-        advantage: float | None = None,
-    ) -> None:
-        """Add a GRPO completion to the buffer.
-
-        Args:
-            step: Training step number
-            prompt: Input prompt
-            completion: Model-generated completion
-            reward: Reward score from reward model
-            advantage: Advantage estimate (reward - baseline)
-        """
-        entry = {
-            "step": step,
-            "prompt": prompt,
-            "completion": completion,
-        }
-        if reward is not None:
-            entry["reward"] = reward
-        if advantage is not None:
-            entry["advantage"] = advantage
-
-        self.data.append(entry)
-
-    def log_to_swanlab(self, table_name: str = "completions") -> bool:
-        """Log buffered completions to SwanLab as a table.
-
-        Creates a SwanLab echarts Table with all buffered completions.
-        Only logs if SwanLab is initialized and data is available.
-
-        Args:
-            table_name: Name of the table in SwanLab dashboard.
-                Default: "completions"
-
-        Returns:
-            True if logging succeeded, False otherwise
-        """
-        if not self.data:
-            LOG.debug("No completions to log to SwanLab")
-            return False
-
-        try:
-            import swanlab
-
-            if swanlab.get_run() is None:
-                LOG.debug("SwanLab not initialized, skipping completion logging")
-                return False
-
-            # Convert deque to list of dicts
-            completions = list(self.data)
-
-            # Extract headers from first entry (all entries should have same structure)
-            headers = list(completions[0].keys())
-
-            # Build rows: each completion becomes one row
-            rows = []
-            for completion in completions:
-                row = [completion.get(header, "") for header in headers]
-                rows.append(row)
-
-            # Log to SwanLab as echarts Table
-            swanlab.log({table_name: swanlab.echarts.Table().add(headers, rows)})
-
-            LOG.info(f"Logged {len(rows)} completions to SwanLab table '{table_name}'")
-            return True
-
-        except ImportError:
-            LOG.warning(
-                "SwanLab not installed, cannot log completions. "
-                "Install with: pip install swanlab"
-            )
-            return False
-        except Exception as err:  # pylint: disable=broad-except
-            LOG.exception("Failed to log completions to SwanLab: %s", err)
-            return False
-
-    def clear(self) -> None:
-        """Clear all buffered completions."""
-        self.data.clear()
-
-    def __len__(self) -> int:
-        """Return number of buffered completions."""
-        return len(self.data)
-
-    def __repr__(self) -> str:
-        """String representation showing buffer status."""
-        return (
-            f"CompletionLogger(maxlen={self.maxlen}, "
-            f"buffered={len(self.data)}/{self.maxlen})"
-        )

src/axolotl/integrations/swanlab/plugins.py

@@ -1,554 +0,0 @@
-"""SwanLab Plugin for Axolotl"""
-
-from __future__ import annotations
-
-from typing import TYPE_CHECKING
-
-from axolotl.integrations.base import BasePlugin
-from axolotl.utils.logging import get_logger
-
-if TYPE_CHECKING:
-    from transformers import TrainerCallback
-
-    from axolotl.utils.dict import DictDefault
-
-LOG = get_logger(__name__)
-
-
-class SwanLabPlugin(BasePlugin):
-    """
-    SwanLab integration plugin for Axolotl.
-
-    Provides experiment tracking, visualization, and logging capabilities
-    using SwanLab (https://swanlab.cn).
-
-    Usage in config.yaml:
-        plugins:
-          - axolotl.integrations.swanlab.SwanLabPlugin
-
-        use_swanlab: true
-        swanlab_project: my-project
-        swanlab_experiment_name: my-experiment
-        swanlab_mode: cloud  # or 'local', 'offline', 'disabled'
-    """
-
-    def __init__(self):
-        super().__init__()
-        self.swanlab_initialized = False
-        LOG.info("SwanLab plugin initialized")
-
-    def get_input_args(self) -> str:
-        """Returns the configuration model for SwanLab integration."""
-        return "axolotl.integrations.swanlab.SwanLabConfig"
-
-    def register(self, cfg: dict):
-        """Register SwanLab plugin with configuration and conflict detection."""
-        LOG.info("Registering SwanLab plugin")
-
-        # === Conflict Detection: Required Fields ===
-
-        # Check if SwanLab is enabled
-        if cfg.get("use_swanlab"):
-            # 1. Validate project name is set
-            if not cfg.get("swanlab_project"):
-                raise ValueError(
-                    "SwanLab enabled but 'swanlab_project' is not set.\n\n"
-                    "Solutions:\n"
-                    "  1. Add 'swanlab_project: your-project-name' to your config\n"
-                    "  2. Set 'use_swanlab: false' to disable SwanLab\n\n"
-                    "See: src/axolotl/integrations/swanlab/README.md for examples"
-                )
-
-            # 2. Validate swanlab_mode value
-            valid_modes = ["cloud", "local", "offline", "disabled"]
-            mode = cfg.get("swanlab_mode")
-            if mode and mode not in valid_modes:
-                raise ValueError(
-                    f"Invalid swanlab_mode: '{mode}'.\n\n"
-                    f"Valid options: {', '.join(valid_modes)}\n\n"
-                    f"Example:\n"
-                    f"  swanlab_mode: cloud  # Sync to SwanLab cloud\n"
-                    f"  swanlab_mode: local  # Local only, no cloud sync\n"
-                )
-
-            # 3. Check API key for cloud mode
-            import os
-
-            mode = cfg.get("swanlab_mode", "cloud")  # Default is cloud
-            if mode == "cloud":
-                api_key = cfg.get("swanlab_api_key") or os.environ.get(
-                    "SWANLAB_API_KEY"
-                )
-                if not api_key:
-                    LOG.warning(
-                        "SwanLab cloud mode enabled but no API key found.\n"
-                        "SwanLab may fail to initialize during training.\n\n"
-                        "Solutions:\n"
-                        "  1. Set SWANLAB_API_KEY environment variable:\n"
-                        "     export SWANLAB_API_KEY=your-api-key\n"
-                        "  2. Add 'swanlab_api_key: your-api-key' to config (less secure)\n"
-                        "  3. Run 'swanlab login' before training\n"
-                        "  4. Use 'swanlab_mode: local' for offline tracking\n"
-                    )
-
-        # === Conflict Detection: Multi-Logger Performance Warning ===
-
-        # Detect all active logging tools
-        active_loggers = []
-        if cfg.get("use_wandb"):
-            active_loggers.append("WandB")
-        if cfg.get("use_mlflow"):
-            active_loggers.append("MLflow")
-        if cfg.get("comet_api_key") or cfg.get("comet_project_name"):
-            active_loggers.append("Comet")
-        if cfg.get("use_swanlab"):
-            active_loggers.append("SwanLab")
-
-        if len(active_loggers) > 1:
-            LOG.warning(
-                f"\n{'=' * 70}\n"
-                f"Multiple logging tools enabled: {', '.join(active_loggers)}\n"
-                f"{'=' * 70}\n"
-                f"This may cause:\n"
-                f"  - Performance overhead (~1-2% per logger, cumulative)\n"
-                f"  - Increased memory usage\n"
-                f"  - Longer training time per step\n"
-                f"  - Potential config/callback conflicts\n\n"
-                f"Recommendations:\n"
-                f"  - Choose ONE primary logging tool for production training\n"
-                f"  - Use multiple loggers only for:\n"
-                f"    * Migration period (transitioning between tools)\n"
-                f"    * Short comparison runs\n"
-                f"    * Debugging specific tool issues\n"
-                f"  - Monitor system resources (CPU, memory) during training\n"
-                f"{'=' * 70}\n"
-            )
-
-            if len(active_loggers) >= 3:
-                LOG.error(
-                    f"\n{'!' * 70}\n"
-                    f"WARNING: {len(active_loggers)} logging tools enabled simultaneously!\n"
-                    f"{'!' * 70}\n"
-                    f"This is likely unintentional and WILL significantly impact performance.\n"
-                    f"Expected overhead: ~{len(active_loggers) * 1.5:.1f}% per training step.\n\n"
-                    f"STRONGLY RECOMMEND:\n"
-                    f"  - Disable all but ONE logging tool\n"
-                    f"  - Use config inheritance to manage multiple configs\n"
-                    f"{'!' * 70}\n"
-                )
-
-        # === Auto-Enable Logic ===
-
-        # Enable SwanLab if project is specified
-        if cfg.get("swanlab_project") and not cfg.get("use_swanlab"):
-            cfg["use_swanlab"] = True
-            LOG.info("Automatically enabled use_swanlab because swanlab_project is set")
-
-    def pre_model_load(self, cfg: DictDefault):
-        """Initialize SwanLab before model loading with runtime checks."""
-        if not cfg.use_swanlab:
-            return
-
-        # === Runtime Check: Import Availability ===
-        try:
-            import swanlab
-        except ImportError as err:
-            raise ImportError(
-                "SwanLab is not installed.\n\n"
-                "Install with:\n"
-                "  pip install swanlab\n\n"
-                "Or add to requirements:\n"
-                "  swanlab>=0.3.0\n\n"
-                f"Original error: {err}"
-            ) from err
-
-        # Log SwanLab version
-        try:
-            swanlab_version = swanlab.__version__
-            LOG.info(f"SwanLab version: {swanlab_version}")
-        except AttributeError:
-            LOG.warning("Could not determine SwanLab version")
-
-        # === Runtime Check: Distributed Training Setup ===
-        from axolotl.utils.distributed import get_world_size, is_main_process
-
-        world_size = get_world_size()
-        if world_size > 1:
-            mode = getattr(cfg, "swanlab_mode", "cloud")
-            LOG.info(
-                f"\n{'=' * 70}\n"
-                f"Distributed training detected (world_size={world_size})\n"
-                f"SwanLab mode: {mode}\n"
-                f"{'=' * 70}\n"
-                f"Behavior:\n"
-                f"  - Only rank 0 will initialize SwanLab\n"
-                f"  - Other ranks will skip SwanLab to avoid conflicts\n"
-            )
-
-            if mode == "cloud":
-                LOG.info(
-                    f"  - Only rank 0 will upload to SwanLab cloud\n"
-                    f"  - Other ranks run without SwanLab overhead\n"
-                    f"{'=' * 70}\n"
-                )
-
-        # Only initialize SwanLab on the main process (rank 0)
-        # to avoid creating multiple runs in distributed training
-        if not is_main_process():
-            LOG.debug("Skipping SwanLab initialization on non-main process")
-            return
-
-        # Initialize SwanLab run (passing all params directly to init)
-        try:
-            init_kwargs = self._get_swanlab_init_kwargs(cfg)
-            swanlab.init(**init_kwargs)
-            self.swanlab_initialized = True
-            LOG.info(f"SwanLab initialized with project: {cfg.swanlab_project}")
-
-            # Register Lark notification callback (if configured)
-            self._register_lark_callback(cfg)
-
-            # Log configuration (with error handling)
-            try:
-                config_dict = self._prepare_config_for_logging(cfg)
-                swanlab.config.update(config_dict)
-                LOG.debug("Successfully logged config to SwanLab")
-            except Exception as config_err:  # pylint: disable=broad-except
-                LOG.warning(
-                    f"Failed to log config to SwanLab: {config_err}. Continuing anyway."
-                )
-
-        except Exception as err:  # pylint: disable=broad-except
-            LOG.exception("Failed to initialize SwanLab: %s", err)
-            self.swanlab_initialized = False
-
-    def add_callbacks_pre_trainer(self, cfg: DictDefault, model):
-        """Add SwanLab callbacks before trainer creation."""
-        callbacks: list[TrainerCallback] = []
-
-        if not cfg.use_swanlab:
-            return callbacks
-
-        if not self.swanlab_initialized:
-            LOG.warning("SwanLab not initialized, skipping callback registration")
-            return callbacks
-
-        try:
-            from axolotl.utils.callbacks.swanlab import (
-                CustomSwanLabCallback,
-                SaveAxolotlConfigtoSwanLabCallback,
-            )
-
-            # Add our custom lightweight SwanLabCallback
-            # (avoids omegaconf/antlr4 version conflicts)
-            swanlab_callback = CustomSwanLabCallback()
-            callbacks.append(swanlab_callback)
-            LOG.info("Added CustomSwanLabCallback for metrics logging")
-
-            # Add Axolotl config logging callback
-            if cfg.axolotl_config_path:
-                config_callback = SaveAxolotlConfigtoSwanLabCallback(
-                    cfg.axolotl_config_path
-                )
-                callbacks.append(config_callback)
-                LOG.info("Added SaveAxolotlConfigtoSwanLabCallback")
-
-        except ImportError as err:
-            LOG.exception("Failed to import SwanLab callbacks: %s", err)
-
-        return callbacks
-
-    def post_trainer_create(self, cfg: DictDefault, trainer):
-        """Post-trainer creation hook."""
-        if cfg.use_swanlab and self.swanlab_initialized:
-            try:
-                import swanlab
-
-                # Log additional trainer information (with safe conversion)
-                trainer_config = {
-                    "total_steps": int(trainer.state.max_steps)
-                    if trainer.state.max_steps
-                    else None,
-                    "num_train_epochs": float(trainer.args.num_train_epochs)
-                    if trainer.args.num_train_epochs
-                    else None,
-                    "train_batch_size": int(trainer.args.train_batch_size)
-                    if hasattr(trainer.args, "train_batch_size")
-                    else None,
-                    "gradient_accumulation_steps": int(
-                        trainer.args.gradient_accumulation_steps
-                    )
-                    if trainer.args.gradient_accumulation_steps
-                    else None,
-                }
-                # Remove None values
-                trainer_config = {
-                    k: v for k, v in trainer_config.items() if v is not None
-                }
-
-                if trainer_config:
-                    swanlab.config.update(trainer_config)
-                    LOG.info("Logged trainer configuration to SwanLab")
-            except Exception as err:  # pylint: disable=broad-except
-                LOG.debug(f"Failed to log trainer config to SwanLab: {err}")
-
-            # Register RLHF completion logging callback if enabled
-            self._register_completion_callback(cfg, trainer)
-
-    def _get_swanlab_init_kwargs(self, cfg: DictDefault) -> dict:
-        """Prepare kwargs for swanlab.init().
-
-        Passes all configuration parameters directly to swanlab.init()
-        instead of using environment variables as an intermediate layer.
-
-        Returns:
-            dict: Keyword arguments for swanlab.init()
-        """
-        init_kwargs = {}
-
-        # Project name (required)
-        if cfg.swanlab_project:
-            init_kwargs["project"] = cfg.swanlab_project
-
-        # Experiment name
-        if cfg.swanlab_experiment_name:
-            init_kwargs["experiment_name"] = cfg.swanlab_experiment_name
-
-        # Description
-        if cfg.swanlab_description:
-            init_kwargs["description"] = cfg.swanlab_description
-
-        # Workspace (organization)
-        if cfg.swanlab_workspace:
-            init_kwargs["workspace"] = cfg.swanlab_workspace
-
-        # Mode: cloud, local, offline, disabled
-        if cfg.swanlab_mode:
-            init_kwargs["mode"] = cfg.swanlab_mode
-
-        # API key (pass directly instead of via env var)
-        if cfg.swanlab_api_key:
-            init_kwargs["api_key"] = cfg.swanlab_api_key
-
-        # Private deployment hosts (pass directly instead of via env var)
-        if cfg.swanlab_web_host:
-            init_kwargs["web_host"] = cfg.swanlab_web_host
-
-        if cfg.swanlab_api_host:
-            init_kwargs["api_host"] = cfg.swanlab_api_host
-
-        # Log model checkpoints (coming soon in SwanLab)
-        if cfg.swanlab_log_model:
-            init_kwargs["log_model"] = cfg.swanlab_log_model
-
-        # Custom branding - adds Axolotl identifier to SwanLab UI
-        # This helps identify runs from Axolotl vs other frameworks
-        init_kwargs["config"] = {"UPPERFRAME": "🦎 Axolotl"}
-
-        return init_kwargs
-
-    def _prepare_config_for_logging(self, cfg: DictDefault) -> dict:
-        """Prepare configuration dict for logging to SwanLab."""
-
-        def safe_convert(value):
-            """Convert value to JSON-serializable type."""
-            if value is None:
-                return None
-            if isinstance(value, (int, float, bool)):
-                return value
-            if isinstance(value, str):
-                return value
-            # Convert everything else to string
-            return str(value)
-
-        try:
-            # Extract important training parameters with safe conversion
-            config_dict = {
-                "base_model": safe_convert(getattr(cfg, "base_model", "")),
-                "model_type": safe_convert(getattr(cfg, "model_type", "")),
-                "sequence_len": safe_convert(getattr(cfg, "sequence_len", None)),
-                "micro_batch_size": safe_convert(
-                    getattr(cfg, "micro_batch_size", None)
-                ),
-                "gradient_accumulation_steps": safe_convert(
-                    getattr(cfg, "gradient_accumulation_steps", None)
-                ),
-                "num_epochs": safe_convert(getattr(cfg, "num_epochs", None)),
-                "max_steps": safe_convert(getattr(cfg, "max_steps", None)),
-                "learning_rate": safe_convert(getattr(cfg, "learning_rate", None)),
-                "lr_scheduler": safe_convert(getattr(cfg, "lr_scheduler", "")),
-                "optimizer": safe_convert(getattr(cfg, "optimizer", "")),
-                "warmup_ratio": safe_convert(getattr(cfg, "warmup_ratio", None)),
-                "weight_decay": safe_convert(getattr(cfg, "weight_decay", None)),
-                "seed": safe_convert(getattr(cfg, "seed", None)),
-                "bf16": safe_convert(getattr(cfg, "bf16", None)),
-                "tf32": safe_convert(getattr(cfg, "tf32", None)),
-                "flash_attention": safe_convert(getattr(cfg, "flash_attention", None)),
-                "sample_packing": safe_convert(getattr(cfg, "sample_packing", None)),
-            }
-
-            # Add FSDP/parallel config - only boolean flags
-            if hasattr(cfg, "fsdp_config") and cfg.fsdp_config:
-                config_dict["fsdp_enabled"] = True
-                config_dict["fsdp_version"] = safe_convert(
-                    getattr(cfg, "fsdp_version", None)
-                )
-
-            if hasattr(cfg, "deepspeed") and cfg.deepspeed:
-                config_dict["deepspeed_enabled"] = True
-
-            # Add context parallel info
-            if hasattr(cfg, "context_parallel_size"):
-                config_dict["context_parallel_size"] = safe_convert(
-                    getattr(cfg, "context_parallel_size", None)
-                )
-            if hasattr(cfg, "tensor_parallel_size"):
-                config_dict["tensor_parallel_size"] = safe_convert(
-                    getattr(cfg, "tensor_parallel_size", None)
-                )
-            if hasattr(cfg, "dp_shard_size"):
-                config_dict["dp_shard_size"] = safe_convert(
-                    getattr(cfg, "dp_shard_size", None)
-                )
-
-            # Remove None values and empty strings
-            config_dict = {
-                k: v
-                for k, v in config_dict.items()
-                if v is not None and v != "" and v != "None"
-            }
-
-            return config_dict
-        except Exception as err:  # pylint: disable=broad-except
-            LOG.warning(f"Failed to prepare config for logging: {err}")
-            # Return minimal config
-            try:
-                lr = getattr(cfg, "learning_rate", None)
-                lr_value = float(lr) if lr is not None else None
-            except (TypeError, ValueError):
-                lr_value = None
-            return {
-                "base_model": str(getattr(cfg, "base_model", "unknown")),
-                "learning_rate": lr_value,
-            }
-
-    def _register_lark_callback(self, cfg: DictDefault):
-        """Register Lark (Feishu) notification callback if configured.
-
-        Lark notifications enable sending training updates to team chat channels,
-        useful for production monitoring and team collaboration.
-
-        Args:
-            cfg: Configuration object with Lark webhook settings
-        """
-        # Check if Lark webhook URL is configured
-        lark_webhook_url = getattr(cfg, "swanlab_lark_webhook_url", None)
-        if not lark_webhook_url:
-            return  # Lark not configured, skip
-
-        try:
-            import swanlab
-            from swanlab.plugin.notification import LarkCallback
-
-            # Get optional secret for HMAC signature authentication
-            lark_secret = getattr(cfg, "swanlab_lark_secret", None)
-
-            # Create Lark callback with webhook URL and optional secret
-            lark_callback = LarkCallback(
-                webhook_url=lark_webhook_url,
-                secret=lark_secret,
-            )
-
-            # Register callback with SwanLab
-            swanlab.register_callbacks([lark_callback])
-
-            if lark_secret:
-                LOG.info(
-                    "Registered Lark notification callback with HMAC authentication"
-                )
-            else:
-                LOG.info("Registered Lark notification callback (no HMAC secret)")
-                LOG.warning(
-                    "Lark webhook has no secret configured. "
-                    "For production use, set 'swanlab_lark_secret' to enable HMAC signature verification."
-                )
-
-        except ImportError as err:
-            LOG.warning(
-                f"Failed to import SwanLab Lark plugin: {err}\n\n"
-                "Lark notifications require SwanLab >= 0.3.0 with plugin support.\n"
-                "Install with: pip install 'swanlab>=0.3.0'\n\n"
-                "Continuing without Lark notifications..."
-            )
-        except Exception as err:  # pylint: disable=broad-except
-            LOG.exception(
-                "Failed to register Lark callback: %s\n\n"
-                "Check your Lark webhook URL and secret configuration.\n"
-                "Continuing without Lark notifications...",
-                err,
-            )
-
-    def _register_completion_callback(self, cfg: DictDefault, trainer):
-        """Register RLHF completion logging callback if enabled and applicable.
-
-        This callback logs model completions (prompts, chosen/rejected responses,
-        rewards) to SwanLab during RLHF training for qualitative analysis.
-
-        Args:
-            cfg: Configuration object with completion logging settings
-            trainer: The trainer instance to add callback to
-        """
-        # Check if completion logging is enabled
-        log_completions = getattr(cfg, "swanlab_log_completions", True)
-        if not log_completions:
-            LOG.debug("SwanLab completion logging disabled by config")
-            return
-
-        # Check if trainer is an RLHF trainer
-        trainer_name = trainer.__class__.__name__
-        rlhf_trainers = ["DPO", "KTO", "ORPO", "GRPO", "CPO"]
-        is_rlhf_trainer = any(name in trainer_name for name in rlhf_trainers)
-
-        if not is_rlhf_trainer:
-            LOG.debug(
-                f"Trainer {trainer_name} is not an RLHF trainer, "
-                "skipping completion logging callback"
-            )
-            return
-
-        try:
-            from axolotl.integrations.swanlab.callbacks import (
-                SwanLabRLHFCompletionCallback,
-            )
-
-            # Get configuration parameters
-            log_interval = getattr(cfg, "swanlab_completion_log_interval", 100)
-            max_buffer = getattr(cfg, "swanlab_completion_max_buffer", 128)
-
-            # Create and register callback
-            completion_callback = SwanLabRLHFCompletionCallback(
-                log_interval=log_interval,
-                max_completions=max_buffer,
-                table_name="rlhf_completions",
-            )
-
-            trainer.add_callback(completion_callback)
-
-            LOG.info(
-                f"Registered SwanLab RLHF completion logging callback for {trainer_name} "
-                f"(log_interval={log_interval}, max_buffer={max_buffer})"
-            )
-
-        except ImportError as err:
-            LOG.warning(
-                f"Failed to import SwanLab completion callback: {err}\n\n"
-                "This is a bug - the callback should be available.\n"
-                "Please report this issue.\n\n"
-                "Continuing without completion logging..."
-            )
-        except Exception as err:  # pylint: disable=broad-except
-            LOG.exception(
-                "Failed to register SwanLab completion callback: %s\n\n"
-                "Continuing without completion logging...",
-                err,
-            )

src/axolotl/integrations/swanlab/profiling.py

@@ -1,203 +0,0 @@
-"""SwanLab profiling utilities for Axolotl trainers.
-
-This module provides decorators and context managers for profiling
-trainer methods and logging execution times to SwanLab.
-"""
-
-import time
-from contextlib import contextmanager
-from functools import wraps
-from typing import Any, Callable
-
-from axolotl.utils.logging import get_logger
-
-LOG = get_logger(__name__)
-
-
-@contextmanager
-def swanlab_profiling_context(trainer: Any, func_name: str):
-    """Context manager for profiling trainer methods.
-
-    Measures execution time and logs to SwanLab if enabled.
-
-    Example usage:
-        >>> with swanlab_profiling_context(self, "training_step"):
-        ...     result = do_expensive_computation()
-
-    Args:
-        trainer: Trainer instance (must have cfg attribute with use_swanlab flag)
-        func_name: Name of the function being profiled
-
-    Yields:
-        None
-    """
-    start_time = time.perf_counter()
-    try:
-        yield
-    finally:
-        duration = time.perf_counter() - start_time
-
-        # Check if SwanLab is enabled and initialized
-        use_swanlab = getattr(getattr(trainer, "cfg", None), "use_swanlab", False)
-        if use_swanlab:
-            try:
-                import swanlab
-
-                if swanlab.get_run() is not None:
-                    # Log profiling metric
-                    trainer_class = trainer.__class__.__name__
-                    metric_name = f"profiling/Time taken: {trainer_class}.{func_name}"
-
-                    swanlab.log({metric_name: duration})
-
-            except ImportError:
-                # SwanLab not installed, silently skip
-                pass
-            except Exception as err:  # pylint: disable=broad-except
-                # Log error but don't fail training
-                LOG.debug(f"Failed to log profiling metric for {func_name}: {err}")
-
-
-def swanlab_profile(func: Callable) -> Callable:
-    """Decorator to profile and log function execution time to SwanLab.
-
-    Automatically measures execution time of trainer methods and logs
-    to SwanLab as profiling metrics.
-
-    Example usage:
-        >>> class MyTrainer:
-        ...     @swanlab_profile
-        ...     def training_step(self, model, inputs):
-        ...         return super().training_step(model, inputs)
-
-    Args:
-        func: Function to profile (must be a method of a trainer instance)
-
-    Returns:
-        Wrapped function with profiling
-    """
-
-    @wraps(func)
-    def wrapper(self, *args, **kwargs):
-        with swanlab_profiling_context(self, func.__name__):
-            return func(self, *args, **kwargs)
-
-    return wrapper
-
-
-class ProfilingConfig:
-    """Configuration for SwanLab profiling.
-
-    This class provides a centralized way to control profiling behavior.
-
-    Attributes:
-        enabled: Whether profiling is enabled globally
-        min_duration_ms: Minimum duration (in ms) to log (filters out very fast ops)
-        log_interval: Log every N function calls (to reduce overhead)
-    """
-
-    def __init__(
-        self,
-        enabled: bool = True,
-        min_duration_ms: float = 0.1,
-        log_interval: int = 1,
-    ):
-        """Initialize profiling configuration.
-
-        Args:
-            enabled: Enable profiling. Default: True
-            min_duration_ms: Minimum duration to log (ms). Default: 0.1
-            log_interval: Log every N calls. Default: 1 (log all)
-        """
-        self.enabled = enabled
-        self.min_duration_ms = min_duration_ms
-        self.log_interval = log_interval
-        self._call_counts: dict[str, int] = {}
-
-    def should_log(self, func_name: str, duration_seconds: float) -> bool:
-        """Check if a profiling measurement should be logged.
-
-        Args:
-            func_name: Name of the profiled function
-            duration_seconds: Execution duration in seconds
-
-        Returns:
-            True if should log, False otherwise
-        """
-        if not self.enabled:
-            return False
-
-        # Check minimum duration threshold
-        duration_ms = duration_seconds * 1000
-        if duration_ms < self.min_duration_ms:
-            return False
-
-        # Check log interval
-        self._call_counts.setdefault(func_name, 0)
-        self._call_counts[func_name] += 1
-
-        # Always log on first call OR at intervals
-        count = self._call_counts[func_name]
-        if count == 1 or count % self.log_interval == 0:
-            return True
-
-        return False
-
-
-# Global profiling config (can be modified by users)
-DEFAULT_PROFILING_CONFIG = ProfilingConfig()
-
-
-@contextmanager
-def swanlab_profiling_context_advanced(
-    trainer: Any,
-    func_name: str,
-    config: ProfilingConfig | None = None,
-):
-    """Advanced profiling context with configurable behavior.
-
-    Similar to swanlab_profiling_context but with additional configuration
-    options for filtering and throttling profiling logs.
-
-    Example usage:
-        >>> config = ProfilingConfig(min_duration_ms=1.0, log_interval=10)
-        >>> with swanlab_profiling_context_advanced(self, "forward", config):
-        ...     output = model(inputs)
-
-    Args:
-        trainer: Trainer instance
-        func_name: Function name
-        config: Profiling configuration. If None, uses DEFAULT_PROFILING_CONFIG
-
-    Yields:
-        None
-    """
-    if config is None:
-        config = DEFAULT_PROFILING_CONFIG
-
-    start_time = time.perf_counter()
-    try:
-        yield
-    finally:
-        duration = time.perf_counter() - start_time
-
-        # Check if should log based on config
-        if config.should_log(func_name, duration):
-            # Check if SwanLab is enabled
-            use_swanlab = getattr(getattr(trainer, "cfg", None), "use_swanlab", False)
-            if use_swanlab:
-                try:
-                    import swanlab
-
-                    if swanlab.get_run() is not None:
-                        trainer_class = trainer.__class__.__name__
-                        metric_name = (
-                            f"profiling/Time taken: {trainer_class}.{func_name}"
-                        )
-
-                        swanlab.log({metric_name: duration})
-
-                except ImportError:
-                    pass
-                except Exception as err:  # pylint: disable=broad-except
-                    LOG.debug(f"Failed to log profiling metric for {func_name}: {err}")

src/axolotl/loaders/patch_manager.py

@@ -138,7 +138,6 @@ class PatchManager:
         self._apply_llama_flash_attn_patches(model)
         self._apply_unsloth_patches(model)
         self._apply_lora_kernel_patch(model)
-        self._apply_scaling_softmax_patch(model)
 
     def _apply_flash_attention_patches(self):
         """Apply patches related to Flash Attention."""
@@ -561,16 +560,3 @@ class PatchManager:
             )
 
             patch_apertus_xielu_activation()
-
-    def _apply_scaling_softmax_patch(self, model: PreTrainedModel):
-        """Apply Scaling Softmax (SSMax) patch.  Ref: https://arxiv.org/abs/2501.19399"""
-        if self.cfg.scaling_softmax:
-            from axolotl.monkeypatch.scaled_softmax_attn import (
-                patch_scaled_softmax_attention,
-            )
-
-            patch_scaled_softmax_attention(
-                scaling_factor_init=self.cfg.scaling_softmax_factor or 0.43,
-                bias=self.cfg.scaling_softmax_bias or 0.0,
-                model=model,
-            )

src/axolotl/loaders/utils.py

@@ -5,7 +5,6 @@ from typing import Type
 
 import addict
 import torch
-import transformers
 from transformers import AutoConfig, PretrainedConfig, PreTrainedModel
 
 from axolotl.utils.dict import DictDefault
@@ -154,9 +153,6 @@ def load_model_config(cfg: DictDefault) -> PretrainedConfig | addict.Dict:
     This function determines the appropriate model config source, loads it, applies any
     necessary overrides, and validates it for compatibility with the `axolotl` config.
 
-    If `cfg.cls_model_config` is set, a custom config class from transformers will be
-    used instead of `AutoConfig` (e.g., 'LlamaConfig', 'MistralConfig').
-
     Args:
         cfg: Dictionary mapping `axolotl` config keys to values.
 
@@ -178,13 +174,8 @@ def load_model_config(cfg: DictDefault) -> PretrainedConfig | addict.Dict:
     if cfg.num_labels:
         # num_labels is used to initialize classifier models
         config_kwargs["num_labels"] = cfg.num_labels
-
-    config_cls = AutoConfig
-    if cfg.cls_model_config:
-        config_cls = getattr(transformers, cfg.cls_model_config)
-
     try:
-        model_config = config_cls.from_pretrained(
+        model_config = AutoConfig.from_pretrained(
             model_config_name,
             trust_remote_code=trust_remote_code,
             **config_kwargs,

src/axolotl/monkeypatch/loss/dft.py

@@ -1,98 +0,0 @@
-"""Dynamic Fine-Tuning (DFT) loss implementation"""
-
-from typing import Optional
-
-import torch
-import torch.nn.functional as F
-
-
-def selective_log_softmax(logits, index):
-    """Memory-efficient log_softmax -> gather"""
-    if logits.dtype in [torch.float32, torch.float64]:
-        selected_logits = torch.gather(
-            logits, dim=-1, index=index.unsqueeze(-1)
-        ).squeeze(-1)
-        logsumexp_values = torch.stack([torch.logsumexp(lg, dim=-1) for lg in logits])
-        per_token_logps = selected_logits - logsumexp_values
-    else:
-        per_token_logps = []
-        for row_logits, row_labels in zip(logits, index, strict=True):
-            row_logps = F.log_softmax(row_logits, dim=-1)
-            row_per_token_logps = row_logps.gather(
-                dim=-1, index=row_labels.unsqueeze(-1)
-            ).squeeze(-1)
-            per_token_logps.append(row_per_token_logps)
-        per_token_logps = torch.stack(per_token_logps)
-    return per_token_logps
-
-
-def get_dft_loss(ignore_index: int = -100):
-    """Creates DFT loss function"""
-
-    def for_causal_lm_dft_loss(
-        logits,
-        labels,
-        vocab_size: int = None,
-        num_items_in_batch: Optional[int] = None,
-        ignore_index: int = -100,
-        shift_labels: Optional[torch.Tensor] = None,
-        **kwargs,
-    ) -> torch.Tensor:
-        """DFT loss: -exp(logprobs).detach() * logprobs"""
-        if shift_labels is None:
-            # Shift so that tokens < n predict n
-            labels = F.pad(labels, (0, 1), value=ignore_index)
-            shift_labels = labels[..., 1:].contiguous()
-
-        shift_labels = shift_labels.to(logits.device)
-
-        # Create loss mask
-        loss_mask = shift_labels != ignore_index
-        shift_labels_masked = shift_labels.clone()
-        shift_labels_masked[~loss_mask] = 0
-
-        # Compute log probabilities
-        logprobs = selective_log_softmax(logits, shift_labels_masked)
-
-        # DFT loss: -exp(logprobs).detach() * logprobs
-        per_token_loss = -logprobs.exp().detach() * logprobs
-
-        # Sum over valid tokens and normalize
-        if num_items_in_batch is None:
-            num_items_in_batch = loss_mask.sum()
-
-        loss = (per_token_loss * loss_mask).sum() / num_items_in_batch
-        return loss
-
-    return for_causal_lm_dft_loss
-
-
-def dft_loss(outputs, labels, num_items_in_batch=None):
-    """DFT loss compatible with Trainer.compute_loss_func signature.
-
-    This function is designed to be passed to Trainer's compute_loss_func parameter.
-    """
-    ignore_index = -100
-
-    # Shift labels for causal LM
-    labels = F.pad(labels, (0, 1), value=ignore_index)
-    shift_labels = labels[..., 1:].contiguous()
-    shift_labels = shift_labels.to(outputs.logits.device)
-
-    # Create loss mask
-    loss_mask = shift_labels != ignore_index
-    shift_labels_masked = shift_labels.clone()
-    shift_labels_masked[~loss_mask] = 0
-
-    # Compute log probabilities
-    logprobs = selective_log_softmax(outputs.logits, shift_labels_masked)
-
-    # DFT loss: -exp(logprobs).detach() * logprobs
-    per_token_loss = -logprobs.exp().detach() * logprobs
-
-    # Sum over valid tokens and normalize
-    if num_items_in_batch is None:
-        num_items_in_batch = loss_mask.sum()
-
-    loss = (per_token_loss * loss_mask).sum() / num_items_in_batch
-    return loss

src/axolotl/monkeypatch/scaled_softmax_attn.py

@@ -1,141 +0,0 @@
-"""
-Scaled Softmax (SSMax) attention patch using FlexAttention.
-SSMax:  softmax(scores * s * log(n) + b) where n is the position index
-Ref: https://arxiv.org/abs/2501.19399
-"""
-
-import torch
-from transformers import PreTrainedModel
-
-from axolotl.utils.logging import get_logger
-
-LOG = get_logger(__name__)
-
-try:
-    from torch.nn.attention.flex_attention import BlockMask
-    from transformers.integrations.flex_attention import (
-        compile_friendly_flex_attention,
-        repeat_kv,
-    )
-
-    FLEX_ATTENTION_AVAILABLE = True
-except ImportError:
-    FLEX_ATTENTION_AVAILABLE = False
-    BlockMask = None
-
-_ssmax_config = {}
-
-
-def patch_scaled_softmax_attention(
-    scaling_factor_init: float = 0.43, bias: float = 0.0, model: PreTrainedModel = None
-):
-    """Patch attention to apply SSMax via FlexAttention score_mod."""
-    global _ssmax_config
-
-    if not FLEX_ATTENTION_AVAILABLE:
-        raise RuntimeError("SSMax requires FlexAttention.")
-
-    _ssmax_config["ssmax_s"] = scaling_factor_init
-    _ssmax_config["ssmax_b"] = bias
-
-    from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS
-
-    if "flex_attention" in ALL_ATTENTION_FUNCTIONS:
-        _ssmax_config["original_flex_fn"] = ALL_ATTENTION_FUNCTIONS["flex_attention"]
-        ALL_ATTENTION_FUNCTIONS["flex_attention"] = ssmax_flex_attention_forward
-        LOG.info(
-            f"Patched flex_attention with SSMax (s={scaling_factor_init}, b={bias})"
-        )
-    else:
-        LOG.warning("flex_attention not found.  Ensure flex_attention:  true is set.")
-
-
-def ssmax_flex_attention_forward(
-    module: torch.nn.Module,
-    query: torch.Tensor,
-    key: torch.Tensor,
-    value: torch.Tensor,
-    attention_mask,
-    scaling: float | None = None,
-    softcap: float | None = None,
-    **kwargs,
-) -> tuple[torch.Tensor, torch.Tensor | None]:
-    """FlexAttention forward with SSMax:  score * (s * log(n) + b)."""
-
-    if kwargs.get("dropout", 0.0) > 0:
-        raise ValueError("flex_attention does not support dropout")
-
-    ssmax_s = _ssmax_config.get("ssmax_s", 0.43)
-    ssmax_b = _ssmax_config.get("ssmax_b", 0.0)
-
-    position_ids = kwargs.get("position_ids", None)
-    position_ids_flat = position_ids.view(-1) if position_ids is not None else None
-
-    block_mask = attention_mask if isinstance(attention_mask, BlockMask) else None
-    score_mask = None if block_mask else attention_mask
-
-    if score_mask is not None:
-        score_mask = score_mask[:, :, :, : key.shape[-2]]
-
-    def score_mod(score, batch_idx, head_idx, q_idx, kv_idx):
-        """
-        Apply SSMax scaling:  score * (s * log(n) + b)
-        where n is the relative position within each packed sequence.
-        """
-        if position_ids_flat is not None:
-            relative_pos = position_ids_flat[q_idx]
-            n = (relative_pos + 1).float()
-        else:
-            n = (q_idx + 1).float()
-
-        n = torch.clamp(n, min=2.0)
-
-        ssmax_scale = ssmax_s * torch.log(n) + ssmax_b
-        score = score * ssmax_scale
-
-        if softcap is not None:
-            score = softcap * torch.tanh(score / softcap)
-
-        if score_mask is not None:
-            score = score + score_mask[batch_idx][0][q_idx][kv_idx]
-
-        return score
-
-    enable_gqa = True
-    if (query.shape[1] & (query.shape[1] - 1)) != 0:
-        key = repeat_kv(key, query.shape[1] // key.shape[1])
-        value = repeat_kv(value, query.shape[1] // value.shape[1])
-        enable_gqa = False
-
-    return_lse = query.device.type != "cpu"
-    flex_output = compile_friendly_flex_attention(
-        query,
-        key,
-        value,
-        score_mod=score_mod,
-        block_mask=block_mask,
-        enable_gqa=enable_gqa,
-        scale=scaling,
-        kernel_options=kwargs.get("kernel_options"),
-        return_lse=return_lse,
-        training=module.training,
-    )
-
-    if return_lse:
-        attention_output, lse = flex_output
-        lse = lse.to(value.dtype)
-    else:
-        attention_output, lse = flex_output, None
-
-    return attention_output.transpose(1, 2).contiguous(), lse
-
-
-def unpatch_scaled_softmax_attention():
-    """Restore the original FlexAttention function."""
-    global _ssmax_config
-    from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS
-
-    if "original_flex_fn" in _ssmax_config:
-        ALL_ATTENTION_FUNCTIONS["flex_attention"] = _ssmax_config["original_flex_fn"]
-        _ssmax_config.clear()
-        LOG.info("Unpatched flex_attention, restored original")

src/axolotl/utils/callbacks/swanlab.py

@@ -1,248 +0,0 @@
-"""Callbacks for SwanLab integration"""
-
-from __future__ import annotations
-
-import json
-import os
-from shutil import copyfile
-from tempfile import NamedTemporaryFile
-from typing import TYPE_CHECKING
-
-from transformers import (
-    TrainerCallback,
-    TrainerControl,
-    TrainerState,
-    TrainingArguments,
-)
-
-from axolotl.utils.logging import get_logger
-
-if TYPE_CHECKING:
-    from axolotl.core.training_args import AxolotlTrainingArguments
-
-LOG = get_logger(__name__)
-
-
-class CustomSwanLabCallback(TrainerCallback):
-    """
-    Lightweight SwanLab callback that directly logs metrics without using
-    SwanLab's transformers integration (which requires omegaconf).
-
-    This avoids the antlr4 version conflict between omegaconf and axolotl.
-    """
-
-    def __init__(self):
-        self._initialized = False
-        self.swanlab = None
-
-    def setup(self):
-        """Lazy initialization of SwanLab"""
-        if self._initialized:
-            return
-
-        try:
-            import swanlab
-
-            self.swanlab = swanlab
-
-            # Check if SwanLab run is initialized
-            if swanlab.get_run() is None:
-                LOG.warning("SwanLab run is not initialized")
-                return
-
-            self._initialized = True
-            LOG.info("CustomSwanLabCallback initialized successfully")
-        except ImportError:
-            LOG.error("SwanLab is not installed")
-
-    def on_train_begin(
-        self,
-        args: TrainingArguments,
-        state: TrainerState,
-        control: TrainerControl,
-        **kwargs,
-    ):
-        """Called at the beginning of training"""
-        if not state.is_world_process_zero:
-            return control
-
-        self.setup()
-
-        if not self._initialized:
-            return control
-
-        # Log training configuration
-        try:
-            self.swanlab.config.update(
-                {
-                    "train_batch_size": args.per_device_train_batch_size,
-                    "eval_batch_size": args.per_device_eval_batch_size,
-                    "learning_rate": args.learning_rate,
-                    "num_train_epochs": args.num_train_epochs,
-                    "max_steps": args.max_steps,
-                    "warmup_steps": args.warmup_steps,
-                    "logging_steps": args.logging_steps,
-                    "save_steps": args.save_steps,
-                    "gradient_accumulation_steps": args.gradient_accumulation_steps,
-                }
-            )
-            LOG.debug("Training configuration logged to SwanLab")
-        except Exception as err:
-            LOG.warning(f"Failed to log training config: {err}")
-
-        return control
-
-    def on_log(
-        self,
-        args: TrainingArguments,
-        state: TrainerState,
-        control: TrainerControl,
-        logs=None,
-        **kwargs,
-    ):
-        """Called when logging metrics"""
-        if not state.is_world_process_zero:
-            return control
-
-        if not self._initialized:
-            self.setup()
-
-        if not self._initialized or logs is None:
-            return control
-
-        # Log metrics to SwanLab
-        try:
-            # Filter out non-numeric values and prepare for logging
-            metrics = {}
-            for key, value in logs.items():
-                if isinstance(value, (int, float)):
-                    # Use step from state
-                    metrics[key] = value
-
-            if metrics and state.global_step is not None:
-                self.swanlab.log(metrics, step=state.global_step)
-        except Exception as err:
-            LOG.warning(f"Failed to log metrics to SwanLab: {err}")
-
-        return control
-
-    def on_train_end(
-        self,
-        args: TrainingArguments,
-        state: TrainerState,
-        control: TrainerControl,
-        **kwargs,
-    ):
-        """Called at the end of training"""
-        if not state.is_world_process_zero:
-            return control
-
-        if self._initialized:
-            LOG.info("Training completed. SwanLab logs are available.")
-
-        return control
-
-
-class SaveAxolotlConfigtoSwanLabCallback(TrainerCallback):
-    """Callback to save axolotl config to SwanLab"""
-
-    def __init__(self, axolotl_config_path):
-        self.axolotl_config_path = axolotl_config_path
-
-    def on_train_begin(
-        self,
-        args: AxolotlTrainingArguments,
-        state: TrainerState,
-        control: TrainerControl,
-        **kwargs,
-    ):
-        if state.is_world_process_zero:
-            try:
-                import swanlab
-
-                # Check if SwanLab is initialized
-                if swanlab.get_run() is None:
-                    LOG.warning(
-                        "SwanLab run is not initialized. Please initialize SwanLab before training."
-                    )
-                    return control
-
-                # Log Axolotl config as artifact
-                with NamedTemporaryFile(
-                    mode="w", delete=False, suffix=".yml", prefix="axolotl_config_"
-                ) as temp_file:
-                    copyfile(self.axolotl_config_path, temp_file.name)
-
-                    # Log config file to SwanLab
-                    with open(temp_file.name, "r", encoding="utf-8") as config_file:
-                        swanlab.log(
-                            {
-                                "axolotl_config": swanlab.Text(
-                                    config_file.read(), caption="Axolotl Config"
-                                )
-                            }
-                        )
-
-                    LOG.info(
-                        "The Axolotl config has been saved to the SwanLab run under logs."
-                    )
-
-                    # Clean up temp file
-                    os.unlink(temp_file.name)
-
-            except ImportError:
-                LOG.warning(
-                    "SwanLab is not installed. Install it with: pip install swanlab"
-                )
-            except (FileNotFoundError, ConnectionError) as err:
-                LOG.warning(f"Error while saving Axolotl config to SwanLab: {err}")
-
-            # Log DeepSpeed config if available
-            if args.deepspeed:
-                try:
-                    import swanlab
-
-                    with NamedTemporaryFile(
-                        mode="w",
-                        delete=False,
-                        suffix=".json",
-                        prefix="deepspeed_config_",
-                    ) as temp_file:
-                        skip_upload = False
-                        if isinstance(args.deepspeed, dict):
-                            json.dump(args.deepspeed, temp_file, indent=4)
-                        elif isinstance(args.deepspeed, str) and os.path.exists(
-                            args.deepspeed
-                        ):
-                            copyfile(args.deepspeed, temp_file.name)
-                        else:
-                            skip_upload = True
-
-                        if not skip_upload:
-                            temp_file.flush()
-                            with open(
-                                temp_file.name, "r", encoding="utf-8"
-                            ) as ds_config_file:
-                                swanlab.log(
-                                    {
-                                        "deepspeed_config": swanlab.Text(
-                                            ds_config_file.read(),
-                                            caption="DeepSpeed Config",
-                                        )
-                                    }
-                                )
-                            LOG.info(
-                                "The DeepSpeed config has been saved to the SwanLab run under logs."
-                            )
-
-                        # Clean up temp file
-                        os.unlink(temp_file.name)
-
-                except (FileNotFoundError, ConnectionError) as err:
-                    LOG.warning(
-                        f"Error while saving DeepSpeed config to SwanLab: {err}"
-                    )
-                except ImportError:
-                    pass
-
-        return control

src/axolotl/utils/callbacks/tokens_per_second.py

@@ -101,3 +101,9 @@ class TokensPerSecondCallback(TrainerCallback):
         # Clear per-step tokens after logging
         if tokens and "trainable_tokens" in tokens:
             tokens["trainable_tokens"] = torch.zeros_like(tokens["trainable_tokens"])
+
+        if tokens and "total" in tokens:
+            logs["tokens/total"] = tokens["total"].item()
+
+        if tokens and "trainable" in tokens:
+            logs["tokens/trainable"] = tokens["trainable"].item()

src/axolotl/utils/quantization.py

@@ -9,6 +9,10 @@ from torchao.quantization import quantize_
 from torchao.quantization.qat import (
     QATConfig,
 )
+from torchao.quantization.qat import fake_quantizer
+from torchao.quantization.qat.fake_quantizer import (
+    Int4WeightFakeQuantizer as AoInt4WeightFakeQuantizer,
+)
 from torchao.quantization.quant_api import (
     Float8DynamicActivationFloat8WeightConfig,
     Float8DynamicActivationInt4WeightConfig,
@@ -17,6 +21,27 @@ from torchao.quantization.quant_api import (
 
 from axolotl.utils.schemas.enums import TorchAOQuantDType
 
+
+class Int4WeightFakeQuantizer(AoInt4WeightFakeQuantizer):
+    """
+    Adds 'enabled' attribute to Int4WeightFakeQuantizer (removed in torchao 0.15).
+    Allows toggling fake quantization on/off for fake_quant_after_n_steps.
+    """
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.enabled = True
+
+    def forward(self, w: torch.Tensor) -> torch.Tensor:
+        if not self.enabled:
+            return w
+        return super().forward(w)
+
+
+# Replace the original Int4WeightFakeQuantizer in the fake_quantizer module
+# so that torchao's quantize_() function will use our version
+fake_quantizer.Int4WeightFakeQuantizer = Int4WeightFakeQuantizer
+
 quantization_config_to_str = {
     Int8DynamicActivationInt4WeightConfig: "int8int4",
     Float8DynamicActivationFloat8WeightConfig: "fp8fp8",

src/axolotl/utils/schemas/config.py

@@ -619,25 +619,6 @@ class AxolotlInputConfig(
         },
     )
 
-    scaling_softmax: bool | None = Field(
-        default=None,
-        json_schema_extra={
-            "description": "Whether to use Scaled Softmax (SSMax) attention. Ref: https://arxiv.org/abs/2501.19399"
-        },
-    )
-    scaling_softmax_factor: float | None = Field(
-        default=None,
-        json_schema_extra={
-            "description": "Scaling factor for SSMax attention. Default is 0.43"
-        },
-    )
-    scaling_softmax_bias: float | None = Field(
-        default=None,
-        json_schema_extra={
-            "description": "Bias for SSMax attention. Default is 0.0. Note: The paper recommends bias=0 for better length generalization."
-        },
-    )
-
     unsloth_cross_entropy_loss: bool | None = None
     unsloth_lora_mlp: bool | None = None
     unsloth_lora_qkv: bool | None = None
@@ -676,10 +657,6 @@ class AxolotlInputConfig(
             "description": "Number of chunks to use for chunked cross entropy loss"
         },
     )
-    use_dynamic_finetuning: bool | None = Field(
-        default=None,
-        json_schema_extra={"description": "Enable Dynamic Fine-Tuning loss (DFT)"},
-    )
 
     tiled_mlp: bool | None = Field(
         default=None,

src/axolotl/utils/schemas/model.py

@@ -25,12 +25,7 @@ class ModelInputConfig(BaseModel):
             "description": "If the base_model repo on hf hub doesn't include configuration .json files, You can set that here, or leave this empty to default to base_model"
         },
     )
-    cls_model_config: str | None = Field(
-        default=None,
-        json_schema_extra={
-            "description": "transformers config class (e.g., 'LlamaConfig', 'MistralConfig'). Defaults to AutoConfig."
-        },
-    )
+    cls_model_config: str | None = None
     tokenizer_config: str | None = Field(
         default=None,
         json_schema_extra={

src/axolotl/utils/schemas/validation.py

@@ -201,16 +201,6 @@ class AttentionValidationMixin:
             )
         return data
 
-    @model_validator(mode="before")
-    @classmethod
-    def check_scaling_softmax_requires_flex(cls, data):
-        if data.get("scaling_softmax") and not data.get("flex_attention"):
-            raise ValueError(
-                "scaling_softmax requires flex_attention: true\n"
-                "Add 'flex_attention: true' to your config file.\n"
-            )
-        return data
-
 
 class TrainingValidationMixin:
     """Validation methods related to training configuration."""

tests/utils/lora/test_config_validation_lora.py

@@ -1,92 +0,0 @@
-import pytest
-
-from axolotl.utils.config import validate_config
-from axolotl.utils.dict import DictDefault
-
-
-class TestLoRAConfigValidation:
-    """Test suite for LoRA/QLoRA configuration validation"""
-
-    def test_basic_configuration_validation(self):
-        """Test basic LoRA configuration validation"""
-
-        valid_config = DictDefault(
-            {
-                "adapter": "lora",
-                "lora_r": 8,
-                "lora_alpha": 16,
-                "lora_dropout": 0.1,
-                "lora_target_modules": ["q_proj", "v_proj"],
-                "datasets": [{"path": "dummy_dataset", "type": "alpaca"}],
-                "micro_batch_size": 1,
-                "gradient_accumulation_steps": 1,
-                "learning_rate": 1e-5,
-                "base_model": "dummy_model",
-            }
-        )
-
-        result = validate_config(valid_config)
-        assert result["adapter"] == "lora"
-
-        with pytest.raises(ValueError, match="not compatible with DoRA"):
-            invalid_config = DictDefault(
-                {
-                    "adapter": "lora",
-                    "lora_mlp_kernel": True,
-                    "peft_use_dora": True,
-                    "datasets": [{"path": "dummy_dataset", "type": "alpaca"}],
-                    "micro_batch_size": 1,
-                    "gradient_accumulation_steps": 1,
-                    "learning_rate": 1e-5,
-                    "base_model": "dummy_model",
-                }
-            )
-            validate_config(invalid_config)
-
-    def test_qlora_4bit_validation(self):
-        """Test QLoRA 4-bit configuration validation"""
-        valid_config = DictDefault(
-            {
-                "adapter": "qlora",
-                "load_in_4bit": True,
-                "bnb_4bit_compute_dtype": "float16",
-                "datasets": [{"path": "dummy_dataset", "type": "alpaca"}],
-                "micro_batch_size": 1,
-                "gradient_accumulation_steps": 1,
-                "learning_rate": 1e-5,
-                "base_model": "dummy_model",
-            }
-        )
-        result = validate_config(valid_config)
-        assert result["adapter"] == "qlora"
-        assert result["load_in_4bit"] is True
-
-        # Test QLoRA without 4-bit (should fail via PEFT validation)
-        with pytest.raises(ValueError, match=r"Require cfg\.load_in_4bit"):
-            invalid_config = DictDefault(
-                {
-                    "adapter": "qlora",
-                    "load_in_4bit": False,
-                    "datasets": [{"path": "dummy_dataset", "type": "alpaca"}],
-                    "micro_batch_size": 1,
-                    "gradient_accumulation_steps": 1,
-                    "learning_rate": 1e-5,
-                    "base_model": "dummy_model",
-                }
-            )
-            validate_config(invalid_config)
-
-        # Test QLoRA with 8-bit (incompatible)
-        with pytest.raises(ValueError, match="Can't load qlora in 8bit"):
-            invalid_config = DictDefault(
-                {
-                    "adapter": "qlora",
-                    "load_in_8bit": True,
-                    "datasets": [{"path": "dummy_dataset", "type": "alpaca"}],
-                    "micro_batch_size": 1,
-                    "gradient_accumulation_steps": 1,
-                    "learning_rate": 1e-5,
-                    "base_model": "dummy_model",
-                }
-            )
-            validate_config(invalid_config)

tests/utils/lora/test_freeze_lora.py

@@ -1,261 +0,0 @@
-import importlib.util
-from unittest.mock import Mock
-
-import pytest
-import torch
-import torch.nn as nn
-
-from axolotl.kernels.lora import get_lora_parameters
-
-PEFT_AVAILABLE = importlib.util.find_spec("peft") is not None
-
-
-class TestLoRAParameterFreezing:
-    """Test suite for LoRA parameter freezing validation."""
-
-    def setup_method(self):
-        self.dtype = torch.float32
-
-    def create_mock_lora_layer(
-        self, has_adapters=True, adapters_disabled=False, merged=False
-    ):
-        """Create a mock LoRA layer for testing."""
-        mock_layer = Mock()
-
-        base_layer = Mock()
-        base_layer.weight = torch.randn(512, 256, dtype=self.dtype)
-        base_layer.bias = torch.randn(512, dtype=self.dtype)
-
-        if has_adapters:
-            mock_layer.base_layer = base_layer
-            mock_layer.disable_adapters = adapters_disabled
-            mock_layer.merged = merged
-
-            mock_layer.active_adapters = ["default"]
-            mock_layer.lora_A = {"default": Mock()}
-            mock_layer.lora_B = {"default": Mock()}
-            mock_layer.scaling = {"default": 0.1}
-
-            mock_layer.lora_A["default"].weight = torch.randn(16, 256, dtype=self.dtype)
-            mock_layer.lora_B["default"].weight = torch.randn(512, 16, dtype=self.dtype)
-        else:
-            mock_layer.weight = base_layer.weight
-            mock_layer.bias = base_layer.bias
-
-        return mock_layer
-
-    def test_parameter_freezing_adapters_disabled(self):
-        """Test that LoRA parameters are None when adapters are disabled."""
-        layer = self.create_mock_lora_layer(has_adapters=True, adapters_disabled=True)
-
-        W, b, quant_state, A, B, s = get_lora_parameters(layer)
-
-        # Base parameters should be returned
-        assert W is not None
-        assert b is not None
-        # LoRA parameters should be None (frozen)
-        assert A is None
-        assert B is None
-        assert s is None
-
-    def test_parameter_freezing_adapters_merged(self):
-        """Test that LoRA parameters are None when adapters are merged."""
-        layer = self.create_mock_lora_layer(has_adapters=True, merged=True)
-
-        W, b, quant_state, A, B, s = get_lora_parameters(layer)
-
-        # Base parameters should be returned
-        assert W is not None
-        assert b is not None
-
-        # LoRA parameters should be None (frozen)
-        assert A is None
-        assert B is None
-        assert s is None
-
-    def test_parameter_freezing_no_adapters(self):
-        """Test parameter behavior when no adapters are present."""
-        layer = self.create_mock_lora_layer(has_adapters=False)
-
-        W, b, quant_state, A, B, s = get_lora_parameters(layer)
-
-        # Base parameters should be returned
-        assert W is not None
-        assert b is not None
-
-        # LoRA parameters should be None (frozen)
-        assert A is None
-        assert B is None
-        assert s is None
-
-    def test_parameter_active_adapters_enabled(self):
-        """Test that LoRA parameters are returned when adapters are active."""
-        layer = self.create_mock_lora_layer(
-            has_adapters=True, adapters_disabled=False, merged=False
-        )
-
-        W, b, quant_state, A, B, s = get_lora_parameters(layer)
-
-        # All parameters should be returned
-        assert W is not None
-        assert b is not None
-        assert A is not None
-        assert B is not None
-        assert s is not None
-        assert s == 0.1
-
-    def test_parameter_shapes_consistency(self):
-        """Test that parameter shapes are consistent when active."""
-        layer = self.create_mock_lora_layer(
-            has_adapters=True, adapters_disabled=False, merged=False
-        )
-
-        W, b, quant_state, A, B, s = get_lora_parameters(layer)
-
-        # Check shape consistency
-        assert W.shape == (512, 256)
-        assert b.shape == (512,)
-        assert A.shape == (16, 256)
-        assert B.shape == (512, 16)
-
-    def test_parameter_dtypes_consistency(self):
-        """Test that parameter dtypes are consistent."""
-        layer = self.create_mock_lora_layer(
-            has_adapters=True, adapters_disabled=False, merged=False
-        )
-
-        W, b, quant_state, A, B, s = get_lora_parameters(layer)
-
-        assert W.dtype == self.dtype
-        assert b.dtype == self.dtype
-        assert A.dtype == self.dtype
-        assert B.dtype == self.dtype
-
-    def test_quantization_state_handling(self):
-        """Test that quantization state is properly handled."""
-        layer = self.create_mock_lora_layer(has_adapters=True)
-
-        quant_state_mock = Mock()
-        layer.base_layer.weight.quant_state = quant_state_mock
-
-        W, b, quant_state, A, B, s = get_lora_parameters(layer)
-
-        assert quant_state == quant_state_mock
-
-    def test_multiple_adapters_active_adapter_selection(self):
-        """Test that the correct adapter is selected when multiple adapters exist."""
-        layer = self.create_mock_lora_layer(
-            has_adapters=True, adapters_disabled=False, merged=False
-        )
-
-        layer.lora_A["adapter2"] = Mock()
-        layer.lora_B["adapter2"] = Mock()
-        layer.scaling["adapter2"] = 0.2
-
-        layer.lora_A["adapter2"].weight = torch.randn(16, 256, dtype=self.dtype)
-        layer.lora_B["adapter2"].weight = torch.randn(512, 16, dtype=self.dtype)
-
-        layer.active_adapters = ["adapter2"]
-
-        W, b, quant_state, A, B, s = get_lora_parameters(layer)
-
-        assert s == 0.2
-        assert torch.equal(A, layer.lora_A["adapter2"].weight)
-        assert torch.equal(B, layer.lora_B["adapter2"].weight)
-
-
-class TestLoRAParameterFreezingIntegration:
-    """Integration tests for parameter freezing with actual LoRA layers."""
-
-    @pytest.mark.skipif(
-        not PEFT_AVAILABLE, reason="PEFT not available for integration tests"
-    )
-    def test_parameter_freezing_with_real_lora_layer(self):
-        """Test parameter freezing with actual PEFT LoRA layer."""
-        from peft import LoraConfig, get_peft_model
-
-        class SimpleModel(nn.Module):
-            def __init__(self):
-                super().__init__()
-                self.linear = nn.Linear(256, 512)
-
-            def forward(self, x):
-                return self.linear(x)
-
-        base_model = SimpleModel()
-        lora_config = LoraConfig(
-            r=16,
-            lora_alpha=32,
-            target_modules=["linear"],
-            lora_dropout=0.1,
-        )
-        model = get_peft_model(base_model, lora_config)
-        lora_layer = model.base_model.model.linear
-        # Test with adapters enabled
-        W, b, quant_state, A, B, s = get_lora_parameters(lora_layer)
-        assert A is not None
-        assert B is not None
-        assert s is not None
-        # Test with adapters disabled
-        model.disable_adapter_layers()
-        W, b, quant_state, A, B, s = get_lora_parameters(lora_layer)
-        assert A is None
-        assert B is None
-        assert s is None
-
-    @pytest.mark.skipif(
-        not PEFT_AVAILABLE, reason="PEFT not available for integration tests"
-    )
-    def test_parameter_freezing_gradient_behavior(self):
-        """Test that frozen parameters don't receive gradients."""
-        from peft import LoraConfig, get_peft_model
-
-        class SimpleModel(nn.Module):
-            def __init__(self):
-                super().__init__()
-                self.linear = nn.Linear(256, 512)
-
-            def forward(self, x):
-                return self.linear(x)
-
-        base_model = SimpleModel()
-        lora_config = LoraConfig(
-            r=16,
-            lora_alpha=32,
-            target_modules=["linear"],
-            lora_dropout=0.1,
-        )
-        model = get_peft_model(base_model, lora_config)
-        x = torch.randn(1, 256)
-        target = torch.randn(1, 512)
-        model.enable_adapter_layers()
-        output = model(x)
-        loss = nn.MSELoss()(output, target)
-        loss.backward()
-        lora_layer = model.base_model.model.linear
-        has_lora_grads = any(
-            param.grad is not None
-            for name, param in lora_layer.named_parameters()
-            if "lora_" in name
-        )
-        assert has_lora_grads, (
-            "LoRA parameters should have gradients when adapters are enabled"
-        )
-        model.zero_grad()
-        model.disable_adapter_layers()
-        output = model(x)
-        loss = nn.MSELoss()(output, target)
-        any_requires_grad = any(param.requires_grad for param in model.parameters())
-        if any_requires_grad:
-            loss.backward()
-        has_lora_grads_disabled = any(
-            param.grad is not None
-            for name, param in lora_layer.named_parameters()
-            if "lora_" in name
-        )
-        assert not has_lora_grads_disabled, (
-            "LoRA parameters should not have gradients when adapters are disabled"
-        )
-        model.zero_grad()
-        del model, base_model, lora_layer, x, target, output, loss
-        torch.cuda.empty_cache() if torch.cuda.is_available() else None

tests/utils/lora/test_merge_lora.py

@@ -1,181 +0,0 @@
-from unittest.mock import Mock, patch
-
-import torch
-
-from axolotl.cli.merge_lora import do_merge_lora
-from axolotl.utils.dict import DictDefault
-
-
-class TestAdapterMergeUnmerge:
-    """Test suite for LoRA adapter merging/unmerging functionality"""
-
-    def setup_method(self):
-        self.dtype = torch.float32
-        self.device = torch.device("cpu")
-
-    def create_mock_base_model(self, vocab_size=1000, hidden_size=256):
-        """Create a mock base model with linear layers"""
-        mock_model = Mock()
-
-        mock_model.config = Mock()
-        mock_model.config.vocab_size = vocab_size
-        mock_model.config.hidden_size = hidden_size
-
-        mock_model.q_proj = Mock()
-        mock_model.q_proj.weight = torch.randn(
-            hidden_size, hidden_size, dtype=self.dtype
-        )
-        mock_model.q_proj.bias = torch.randn(hidden_size, dtype=self.dtype)
-
-        mock_model.v_proj = Mock()
-        mock_model.v_proj.weight = torch.randn(
-            hidden_size, hidden_size, dtype=self.dtype
-        )
-        mock_model.v_proj.bias = torch.randn(hidden_size, dtype=self.dtype)
-
-        return mock_model
-
-    def create_mock_lora_model(self, base_model, r=8, alpha=16):
-        """Create a mock LoRA model wrapping the base model"""
-        mock_lora_model = Mock()
-        mock_lora_model.base_model = base_model
-
-        mock_lora_model.merge_and_unload = None
-        mock_lora_model.to = Mock(return_value=mock_lora_model)
-
-        mock_lora_model.generation_config = Mock()
-        mock_lora_model.config = Mock()
-
-        self.original_q_weight = base_model.q_proj.weight.clone()
-        self.original_v_weight = base_model.v_proj.weight.clone()
-
-        mock_lora_model.peft_config = {"default": Mock()}
-        mock_lora_model.peft_config["default"].r = r
-        mock_lora_model.peft_config["default"].lora_alpha = alpha
-
-        self.lora_A_q = torch.randn(
-            r, base_model.q_proj.weight.shape[1], dtype=self.dtype
-        )
-        self.lora_B_q = torch.randn(
-            base_model.q_proj.weight.shape[0], r, dtype=self.dtype
-        )
-
-        self.lora_A_v = torch.randn(
-            r, base_model.v_proj.weight.shape[1], dtype=self.dtype
-        )
-        self.lora_B_v = torch.randn(
-            base_model.v_proj.weight.shape[0], r, dtype=self.dtype
-        )
-
-        self.scaling = alpha / r
-
-        def mock_merge_and_unload(progressbar=False):
-            """Simulate the actual merge operation"""
-            # Apply LoRA delta to base weights: W_new = W_base + (B @ A) * scaling
-            delta_q = (self.lora_B_q @ self.lora_A_q) * self.scaling
-            delta_v = (self.lora_B_v @ self.lora_A_v) * self.scaling
-
-            base_model.q_proj.weight = self.original_q_weight + delta_q
-            base_model.v_proj.weight = self.original_v_weight + delta_v
-
-            return base_model
-
-        mock_lora_model.merge_and_unload = mock_merge_and_unload
-        return mock_lora_model
-
-    def test_basic_lora_merge_unmerge_cycle(self):
-        """Test: original_weights -> merge -> unmerge -> should equal original_weights"""
-
-        base_model = self.create_mock_base_model()
-        lora_model = self.create_mock_lora_model(base_model)
-
-        original_q_weight = self.original_q_weight.clone()
-        original_v_weight = self.original_v_weight.clone()
-
-        merged_model = lora_model.merge_and_unload()
-
-        assert not torch.equal(merged_model.q_proj.weight, original_q_weight)
-        assert not torch.equal(merged_model.v_proj.weight, original_v_weight)
-
-        delta_q = (self.lora_B_q @ self.lora_A_q) * self.scaling
-        delta_v = (self.lora_B_v @ self.lora_A_v) * self.scaling
-
-        unmerged_q_weight = merged_model.q_proj.weight - delta_q
-        unmerged_v_weight = merged_model.v_proj.weight - delta_v
-
-        assert torch.allclose(unmerged_q_weight, original_q_weight, atol=1e-6)
-        assert torch.allclose(unmerged_v_weight, original_v_weight, atol=1e-6)
-
-    def test_merge_weight_calculation_accuracy(self):
-        """Test: merged_weight = base_weight + (lora_B @ lora_A * scaling)"""
-        base_model = self.create_mock_base_model()
-        lora_model = self.create_mock_lora_model(base_model, r=16, alpha=32)
-
-        expected_delta_q = (self.lora_B_q @ self.lora_A_q) * self.scaling
-        expected_merged_q = self.original_q_weight + expected_delta_q
-        merged_model = lora_model.merge_and_unload()
-
-        assert torch.allclose(merged_model.q_proj.weight, expected_merged_q, atol=1e-6)
-
-    @patch("axolotl.cli.merge_lora.load_model_and_tokenizer")
-    def test_cli_do_merge_functionality(self, mock_load_model, tmp_path):
-        base_model = self.create_mock_base_model()
-        lora_model = self.create_mock_lora_model(base_model)
-        tokenizer = Mock()
-        processor = None
-
-        mock_load_model.return_value = (lora_model, tokenizer, processor)
-
-        cfg = DictDefault(
-            {
-                "save_safetensors": True,
-                "torch_dtype": torch.float32,
-                "local_rank": 0,
-                "output_dir": str(tmp_path),
-            }
-        )
-
-        with (
-            patch("pathlib.Path.mkdir"),
-            patch.object(base_model, "save_pretrained") as mock_save_model,
-            patch.object(tokenizer, "save_pretrained") as mock_save_tokenizer,
-        ):
-            do_merge_lora(cfg=cfg)
-
-        mock_save_model.assert_called_once()
-        mock_save_tokenizer.assert_called_once()
-
-    def test_quantized_model_merge_compatibility(self):
-        """Test 4-bit/8-bit model merging scenarios"""
-        base_model = self.create_mock_base_model()
-
-        # Mock quantized weights
-        base_model.q_proj.weight.quant_state = Mock()
-        base_model.q_proj.weight.quant_state.dtype = torch.uint8
-
-        lora_model = self.create_mock_lora_model(base_model)
-
-        merged_model = lora_model.merge_and_unload()
-        assert merged_model is not None
-
-    @patch.dict("os.environ", {"CUDA_VISIBLE_DEVICES": ""})
-    def test_memory_efficient_merge_with_cpu_offload(self, tmp_path):
-        """Test lora_on_cpu configuration during merge"""
-        cfg = DictDefault(
-            {
-                "lora_on_cpu": True,
-                "save_safetensors": True,
-                "output_dir": str(tmp_path),
-                "local_rank": 0,
-            }
-        )
-
-        with patch("axolotl.cli.merge_lora.load_model_and_tokenizer") as mock_load:
-            base_model = self.create_mock_base_model()
-            lora_model = self.create_mock_lora_model(base_model)
-            mock_load.return_value = (lora_model, Mock(), None)
-
-            with patch("pathlib.Path.mkdir"), patch("torch.save"):
-                do_merge_lora(cfg=cfg)
-
-            assert mock_load.called