precommit

* quartodoc integration

* quartodoc progress

* deletions

* Update docs/.gitignore to exclude auto-generated API documentation files

* Fix

* more autodoc progress

* moving reference up near the top of the sidebar

* fix broken link

* update to reflect recent changes

* pydantic models refactor + add to autodoc + fixes

* fix

* shrinking header sizes

* fix accidental change

* include quartodoc build step

* update pre-commit version

* update pylint

* pre-commit

---------

Co-authored-by: Dan Saunders <dan@axolotl.ai>

.github/workflows/base.yml

@@ -40,6 +40,12 @@ jobs:
             python_version: "3.11"
             pytorch: 2.6.0
             torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
+          - cuda: "128"
+            cuda_version: 12.8.1
+            cudnn_version: ""
+            python_version: "3.11"
+            pytorch: nightly
+            torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
     steps:
       - name: Checkout
         uses: actions/checkout@v4
@@ -61,7 +67,7 @@ jobs:
         uses: docker/build-push-action@v4
         with:
           context: .
-          file: ./docker/Dockerfile-base
+          file: ${{ matrix.pytorch == 'nightly' && './docker/Dockerfile-base-nightly' || './docker/Dockerfile-base' }}
           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/docs.yml

@@ -20,9 +20,11 @@ jobs:
           uses: actions/setup-python@v5
           with:
             python-version: '3.11'
-        - name: install dependencies
+        - name: Install dependencies
           run: |
-            python3 -m pip install jupyter
+            python3 -m pip install jupyter quartodoc
+        - name: Build autodoc
+          run: quartodoc build
         - name: Publish to GitHub Pages (and render)
           uses: quarto-dev/quarto-actions/publish@v2
           with:

.github/workflows/precommit-autoupdate.yml

@@ -0,0 +1,49 @@
+name: Pre-commit auto-update
+
+on:
+  schedule:
+    - cron: '0 0 * * 0'  # Run weekly
+  workflow_dispatch:  # Manual kickoff
+
+jobs:
+  auto-update:
+    runs-on: ubuntu-latest
+    permissions:
+      contents: write
+      pull-requests: write
+    steps:
+      - uses: actions/checkout@v4
+
+      - uses: actions/setup-python@v5
+        with:
+          python-version: '3.11'
+
+      - name: Update pre-commit hooks
+        id: update
+        run: |
+          pip install pre-commit
+          pre-commit autoupdate
+          if [[ -n $(git status --porcelain) ]]; then
+            echo "changes=true" >> $GITHUB_OUTPUT
+            git diff .pre-commit-config.yaml > pre-commit-update.diff
+          fi
+
+      - name: Create Pull Request
+        if: steps.update.outputs.changes == 'true'
+        uses: peter-evans/create-pull-request@v6
+        with:
+          token: ${{ secrets.GITHUB_TOKEN }}
+          branch: update/pre-commit-hooks
+          delete-branch: true
+          title: "chore: update pre-commit hooks"
+          commit-message: "chore: update pre-commit hooks"
+          body: |
+            Automated PR to update pre-commit hooks to their latest versions.
+
+            <details>
+            <summary>Changes:</summary>
+
+            ```diff
+            ${{ steps.update.outputs.diff }}
+            ```
+            </details>

.github/workflows/pypi.yml

@@ -40,7 +40,7 @@ jobs:
 
       - name: Install dependencies
         run: |
-          pip3 install wheel packaging
+          pip3 install wheel packaging==23.2
           pip3 install --no-build-isolation -e .
           pip3 install -r requirements-dev.txt -r requirements-tests.txt
 

.github/workflows/tests-nightly.yml

@@ -42,7 +42,7 @@ jobs:
       - name: upgrade pip
         run: |
           pip3 install --upgrade pip
-          pip3 install --upgrade packaging setuptools wheel
+          pip3 install --upgrade packaging==23.2 setuptools==75.8.0 wheel
 
       - name: Install PyTorch
         run: |
@@ -59,7 +59,7 @@ jobs:
       - name: Install dependencies
         run: |
           pip3 install --upgrade pip
-          pip3 install --upgrade packaging
+          pip3 install --upgrade packaging==23.2
           pip3 install --no-build-isolation -U -e .
           python scripts/unsloth_install.py | sh
           python scripts/cutcrossentropy_install.py | sh

.github/workflows/tests.yml

@@ -74,7 +74,7 @@ jobs:
       - name: upgrade pip
         run: |
           pip3 install --upgrade pip
-          pip3 install --upgrade packaging setuptools wheel
+          pip3 install --upgrade packaging==23.2 setuptools==75.8.0 wheel
 
       - name: Install PyTorch
         run: |
@@ -98,8 +98,9 @@ jobs:
 
       - name: Run tests
         run: |
-          pytest -v -n8 --dist loadfile --ignore=tests/e2e/ --ignore=tests/patched/ tests/
+          pytest -v -n8 --dist loadfile --ignore=tests/e2e/ --ignore=tests/patched/ --ignore=tests/cli/ tests/
           pytest -v tests/patched/
+          pytest -v tests/cli/
 
       - name: cleanup pip cache
         run: |
@@ -147,7 +148,7 @@ jobs:
       - name: upgrade pip
         run: |
           pip3 install --upgrade pip
-          pip3 install --upgrade packaging setuptools setuptools_scm build wheel
+          pip3 install --upgrade packaging==23.2 setuptools==75.8.0 setuptools_scm build wheel
 
       - name: Install PyTorch
         run: |
@@ -172,8 +173,9 @@ jobs:
 
       - name: Run tests
         run: |
-          pytest -v -n8 --dist loadfile --ignore=tests/e2e/ --ignore=tests/patched/ tests/
+          pytest -v -n8 --dist loadfile --ignore=tests/e2e/ --ignore=tests/patched/ --ignore=tests/cli/ tests/
           pytest -v tests/patched/
+          pytest -v tests/cli/
 
       - name: cleanup pip cache
         run: |

.gitignore

@@ -181,6 +181,10 @@ prepared-datasets/
 submit.sh
 *.out*
 
+# Quartodoc generated files
+objects.json
+site_libs/
+
 typings/
 out/
 

.pre-commit-config.yaml

@@ -3,7 +3,7 @@ default_language_version:
 
 repos:
 -   repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.4.0
+    rev: v5.0.0
     hooks:
     -   id: check-yaml
     -   id: end-of-file-fixer
@@ -11,23 +11,23 @@ repos:
     -   id: no-commit-to-branch
         args: ['--branch', 'main']
 -   repo: https://github.com/psf/black
-    rev: 23.3.0
+    rev: 25.1.0
     hooks:
     -   id: black
 -   repo: https://github.com/pycqa/isort
-    rev: 5.12.0
+    rev: 6.0.1
     hooks:
       - id: isort
 -   repo: https://github.com/PyCQA/flake8
-    rev: 6.1.0
+    rev: 7.1.2
     hooks:
     - id: flake8
--   repo: https://github.com/PyCQA/pylint
-    rev: v3.3.0
+-   repo: https://github.com/pylint-dev/pylint
+    rev: v3.3.6
     hooks:
     - id: pylint
 -   repo: https://github.com/pre-commit/mirrors-mypy
-    rev: v1.3.0
+    rev: v1.15.0
     hooks:
     - id: mypy
       additional_dependencies:
@@ -36,7 +36,7 @@ repos:
             'pydantic>=2.5.3',
         ]
 -   repo: https://github.com/PyCQA/bandit
-    rev: 1.7.5
+    rev: 1.8.3
     hooks:
     -   id: bandit
         args: [

README.md

@@ -55,6 +55,7 @@ Features:
 ### Installation
 
 ```bash
+pip3 install -U packaging==23.2 setuptools==75.8.0 wheel ninja
 pip3 install --no-build-isolation axolotl[flash-attn,deepspeed]
 
 # Download example axolotl configs, deepspeed configs
@@ -96,6 +97,7 @@ That's it! Check out our [Getting Started Guide](https://axolotl-ai-cloud.github
 - [Multi-GPU Training](https://axolotl-ai-cloud.github.io/axolotl/docs/multi-gpu.html)
 - [Multi-Node Training](https://axolotl-ai-cloud.github.io/axolotl/docs/multi-node.html)
 - [Multipacking](https://axolotl-ai-cloud.github.io/axolotl/docs/multipack.html)
+- [API Reference](https://axolotl-ai-cloud.github.io/axolotl/docs/api/) - Auto-generated code documentation
 - [FAQ](https://axolotl-ai-cloud.github.io/axolotl/docs/faq.html) - Frequently asked questions
 
 ## 🤝 Getting Help

_quarto.yml

@@ -1,6 +1,178 @@
 project:
   type: website
 
+quartodoc:
+  dir: docs/api
+  package: axolotl
+  title: API Reference
+  parser: google
+
+  sections:
+    - title: Core
+      desc: Core functionality for training
+      contents:
+        - train
+        - evaluate
+        - datasets
+        - convert
+        - prompt_tokenizers
+        - logging_config
+        - core.trainer_builder
+        - core.training_args
+        - core.chat.messages
+        - core.chat.format.chatml
+        - core.chat.format.llama3x
+        - core.chat.format.shared
+        - core.datasets.chat
+        - core.datasets.transforms.chat_builder
+    - title: CLI
+      desc: Command-line interface
+      contents:
+        - cli.main
+        - cli.train
+        - cli.evaluate
+        - cli.args
+        - cli.checks
+        - cli.config
+        - cli.inference
+        - cli.merge_lora
+        - cli.merge_sharded_fsdp_weights
+        - cli.preprocess
+        - cli.sweeps
+        - cli.utils
+        - cli.cloud.base
+        - cli.cloud.modal_
+    - title: Trainers
+      desc: Training implementations
+      contents:
+        - core.trainers.base
+        - core.trainers.trl
+        - core.trainers.dpo.trainer
+        - core.trainers.grpo.trainer
+    - title: Prompt Strategies
+      desc: Prompt formatting strategies
+      contents:
+        - prompt_strategies.base
+        - prompt_strategies.chat_template
+        - prompt_strategies.alpaca_chat
+        - prompt_strategies.alpaca_instruct
+        - prompt_strategies.alpaca_w_system
+        - prompt_strategies.user_defined
+        - prompt_strategies.llama2_chat
+        - prompt_strategies.completion
+        - prompt_strategies.input_output
+        - prompt_strategies.stepwise_supervised
+        - prompt_strategies.metharme
+        - prompt_strategies.orcamini
+        - prompt_strategies.pygmalion
+        - prompt_strategies.messages.chat
+        - prompt_strategies.dpo.chat_template
+        - prompt_strategies.dpo.llama3
+        - prompt_strategies.dpo.chatml
+        - prompt_strategies.dpo.zephyr
+        - prompt_strategies.dpo.user_defined
+        - prompt_strategies.dpo.passthrough
+        - prompt_strategies.kto.llama3
+        - prompt_strategies.kto.chatml
+        - prompt_strategies.kto.user_defined
+        - prompt_strategies.orpo.chat_template
+        - prompt_strategies.bradley_terry.llama3
+    - title: Kernels
+      desc: Low-level performance optimizations
+      contents:
+        - kernels.lora
+        - kernels.geglu
+        - kernels.swiglu
+        - kernels.quantize
+        - kernels.utils
+    - title: MonkeyPatches
+      desc: Runtime patches for model optimizations
+      contents:
+        - monkeypatch.llama_attn_hijack_flash
+        - monkeypatch.llama_attn_hijack_xformers
+        - monkeypatch.mistral_attn_hijack_flash
+        - monkeypatch.multipack
+        - monkeypatch.relora
+        - monkeypatch.llama_expand_mask
+        - monkeypatch.lora_kernels
+        - monkeypatch.utils
+        - monkeypatch.btlm_attn_hijack_flash
+        - monkeypatch.llama_patch_multipack
+        - monkeypatch.stablelm_attn_hijack_flash
+        - monkeypatch.trainer_fsdp_optim
+        - monkeypatch.transformers_fa_utils
+        - monkeypatch.unsloth_
+        - monkeypatch.attention.mllama
+        - monkeypatch.data.batch_dataset_fetcher
+        - monkeypatch.mixtral
+    - title: Utils
+      desc: Utility functions
+      contents:
+        - utils.models
+        - utils.tokenization
+        - utils.chat_templates
+        - utils.lora
+        - utils.lora_embeddings
+        - utils.model_shard_quant
+        - utils.bench
+        - utils.freeze
+        - utils.trainer
+        - utils.schedulers
+        - utils.distributed
+        - utils.dict
+        - utils.optimizers.adopt
+        - utils.data.pretraining
+        - utils.data.sft
+        - utils.gradient_checkpointing.unsloth
+    - title: Schemas
+      desc: Pydantic data models for Axolotl config
+      contents:
+        - utils.schemas.config
+        - utils.schemas.model
+        - utils.schemas.training
+        - utils.schemas.datasets
+        - utils.schemas.peft
+        - utils.schemas.trl
+        - utils.schemas.integrations
+        - utils.schemas.enums
+        - utils.schemas.utils
+    - title: Integrations
+      desc: Third-party integrations and extensions
+      contents:
+        - integrations.base
+        - integrations.cut_cross_entropy.args
+        - integrations.grokfast.optimizer
+        - integrations.kd.trainer
+        - integrations.liger.args
+        - integrations.lm_eval.args
+        - integrations.spectrum.args
+    - title: Common
+      desc: Common utilities and shared functionality
+      contents:
+        - common.architectures
+        - common.const
+        - common.datasets
+    - title: Models
+      desc: Custom model implementations
+      contents:
+        - models.mamba.modeling_mamba
+    - title: Data Processing
+      desc: Data processing utilities
+      contents:
+        - utils.collators.core
+        - utils.collators.batching
+        - utils.collators.mamba
+        - utils.collators.mm_chat
+        - utils.samplers.multipack
+    - title: Callbacks
+      desc: Training callbacks
+      contents:
+        - utils.callbacks.perplexity
+        - utils.callbacks.profiler
+        - utils.callbacks.lisa
+        - utils.callbacks.mlflow_
+        - utils.callbacks.comet_
+
 website:
   title: "Axolotl"
   description: "We make fine-tuning accessible, scalable, and fun"
@@ -32,8 +204,11 @@ website:
           contents:
             - docs/getting-started.qmd
             - docs/installation.qmd
-            - docs/cli.qmd
             - docs/inference.qmd
+            - docs/cli.qmd
+            - docs/config.qmd
+            - text: "API Reference"
+              href: docs/api
 
         - section: "Dataset Formats"
           contents: docs/dataset-formats/*
@@ -74,12 +249,27 @@ website:
             - docs/debugging.qmd
             - docs/nccl.qmd
 
-        - section: "Reference"
-          contents:
-            - docs/config.qmd
-
 format:
   html:
     theme: darkly
     css: styles.css
     toc: true
+    # Enable better handling of line breaks in markdown
+    preserve-tabs: true
+    html-math-method: mathjax
+    # Improved markdown processing options
+    md-extensions:
+      - markdown_it
+      - def_list
+      - attr_list
+      - fenced_divs
+      - tables
+      - html_admonition
+      - lineblocks
+      - fancy_lists
+    # Control whitespace handling
+    whitespace: preserve
+    # Process newlines in paragraphs
+    wrap: preserve
+    # Better line break handling
+    preserve-linebreaks: true

cicd/Dockerfile.jinja

@@ -31,10 +31,11 @@ RUN if [ "$NIGHTLY_BUILD" = "true" ] ; then \
         sed -i 's#^datasets.*#datasets @ git+https://github.com/huggingface/datasets.git@main#' requirements.txt; \
     fi
 
+RUN pip install packaging==23.2 setuptools==75.8.0
 RUN if [ "$AXOLOTL_EXTRAS" != "" ] ; then \
-        pip install --no-build-isolation -e .[deepspeed,flash-attn,optimizers,ray,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \
+        pip install --no-build-isolation -e .[deepspeed,flash-attn,ring-flash-attn,optimizers,ray,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \
     else \
-        pip install --no-build-isolation -e .[deepspeed,flash-attn,optimizers,ray] $AXOLOTL_ARGS; \
+        pip install --no-build-isolation -e .[deepspeed,flash-attn,ring-flash-attn,optimizers,ray] $AXOLOTL_ARGS; \
     fi
 
 RUN python scripts/unsloth_install.py | sh

cicd/cicd.sh

@@ -3,9 +3,10 @@ set -e
 
 python -c "import torch; assert '$PYTORCH_VERSION' in torch.__version__"
 
-pytest -v --durations=10 -n8 --ignore=tests/e2e/ --ignore=tests/patched/ /workspace/axolotl/tests/
+pytest -v --durations=10 -n8 --ignore=tests/e2e/ --ignore=tests/patched/ --ignore=tests/cli /workspace/axolotl/tests/
 pytest -v --durations=10 /workspace/axolotl/tests/e2e/patched/lora_kernels  # running these with the other patches causes a failure
 pytest -v --durations=10 --ignore=tests/e2e/patched/lora_kernels /workspace/axolotl/tests/e2e/patched
 pytest -v --durations=10 -n1 /workspace/axolotl/tests/e2e/solo/
 pytest -v --durations=10 /workspace/axolotl/tests/e2e/integrations/
-pytest -v --durations=10 --ignore=tests/e2e/solo/ --ignore=tests/e2e/patched/ --ignore=tests/e2e/multigpu/ --ignore=tests/e2e/integrations/ /workspace/axolotl/tests/e2e/
+pytest -v --durations=10 /workspace/axolotl/tests/cli
+pytest -v --durations=10 --ignore=tests/e2e/solo/ --ignore=tests/e2e/patched/ --ignore=tests/e2e/multigpu/ --ignore=tests/e2e/integrations/ --ignore=tests/cli /workspace/axolotl/tests/e2e/

cicd/multigpu.py

@@ -1,6 +1,7 @@
 """
- modal application to run axolotl gpu tests in Modal
- """
+modal application to run axolotl gpu tests in Modal
+"""
+
 # pylint: disable=duplicate-code
 
 import os

cicd/tests.py

@@ -1,4 +1,5 @@
 """Modal app to run axolotl GPU tests"""
+
 # pylint: disable=duplicate-code
 
 import os

docker/Dockerfile-base

@@ -28,7 +28,7 @@ ENV PATH="/root/miniconda3/envs/py${PYTHON_VERSION}/bin:${PATH}"
 
 WORKDIR /workspace
 
-RUN python3 -m pip install --upgrade pip && pip3 install packaging && \
+RUN python3 -m pip install --upgrade pip && pip3 install -U packaging==23.2 setuptools==75.8.0 wheel && \
     python3 -m pip install --no-cache-dir -U torch==${PYTORCH_VERSION}+cu${CUDA} --extra-index-url https://download.pytorch.org/whl/cu$CUDA && \
     python3 -m pip install --no-cache-dir "causal_conv1d @ git+https://github.com/Dao-AILab/causal-conv1d.git@main" && \
     python3 -m pip install --no-cache-dir "mamba_ssm @ git+https://github.com/state-spaces/mamba.git@main"

docker/Dockerfile-base-nightly

@@ -0,0 +1,39 @@
+ARG CUDA_VERSION="12.8.1"
+ARG CUDNN_VERSION="8"
+ARG UBUNTU_VERSION="22.04"
+ARG MAX_JOBS=4
+
+FROM nvidia/cuda:$CUDA_VERSION-cudnn$CUDNN_VERSION-devel-ubuntu$UBUNTU_VERSION AS base-builder
+
+ENV PATH="/root/miniconda3/bin:${PATH}"
+
+ARG PYTHON_VERSION="3.11"
+ARG PYTORCH_VERSION="nightly"
+ARG CUDA="128"
+ARG TORCH_CUDA_ARCH_LIST="7.0 7.5 8.0 8.6 9.0+PTX"
+
+ENV PYTHON_VERSION=$PYTHON_VERSION
+ENV TORCH_CUDA_ARCH_LIST=$TORCH_CUDA_ARCH_LIST
+
+RUN apt-get update \
+    && apt-get install -y wget git build-essential ninja-build git-lfs libaio-dev pkg-config && rm -rf /var/lib/apt/lists/* \
+    && wget \
+    https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh \
+    && mkdir /root/.conda \
+    && bash Miniconda3-latest-Linux-x86_64.sh -b \
+    && rm -f Miniconda3-latest-Linux-x86_64.sh \
+    && conda create -n "py${PYTHON_VERSION}" python="${PYTHON_VERSION}"
+
+ENV PATH="/root/miniconda3/envs/py${PYTHON_VERSION}/bin:${PATH}"
+
+WORKDIR /workspace
+
+RUN python3 -m pip install --upgrade pip && pip3 install packaging && \
+    python3 -m pip install --no-cache-dir -U torch --extra-index-url https://download.pytorch.org/whl/nightly/cu$CUDA && \
+    python3 -m pip install --no-cache-dir "causal_conv1d @ git+https://github.com/Dao-AILab/causal-conv1d.git@main" && \
+    python3 -m pip install --no-cache-dir "mamba_ssm @ git+https://github.com/state-spaces/mamba.git@main"
+
+RUN git lfs install --skip-repo && \
+    pip3 install awscli && \
+    # The base image ships with `pydantic==1.8.2` which is not working
+    pip3 install -U --no-cache-dir pydantic==1.10.10

docs/.gitignore

@@ -1,2 +1,4 @@
 /.quarto/
 _site/
+/api/*.qmd
+/api/*.html

docs/cli.qmd

@@ -1,5 +1,5 @@
 ---
-title: "CLI Reference"
+title: "Command Line Interface (CLI)"
 format:
   html:
     toc: true

docs/config.qmd

@@ -1,5 +1,5 @@
 ---
-title: Config options
+title: Config Reference
 description: A complete list of all configuration options.
 ---
 
@@ -30,6 +30,11 @@ tokenizer_legacy:
 # Resize the model embeddings when new tokens are added to multiples of 32
 # This is reported to improve training speed on some models
 resize_token_embeddings_to_32x:
+# Optional[bool] Whether to shrink the embeddings to len(tokenizer). By default, we won't shrink.
+shrink_embeddings:
+# Whether to load the model with randomly initialized weights. Useful for
+# pre-training a model from scratch or debugging purposes.
+random_init_weights:
 
 # (Internal use only)
 # Used to identify which the model is based on
@@ -83,6 +88,12 @@ gpu_memory_limit: 20GiB
 # Do the LoRA/PEFT loading on CPU -- this is required if the base model is so large it takes up most or all of the available GPU VRAM, e.g. during a model and LoRA merge
 lora_on_cpu: true
 
+# List[str]. Add plugins to extend the pipeline.
+# See `src/axolotl/integrations` for the available plugins or doc below for more details.
+# https://axolotl-ai-cloud.github.io/axolotl/docs/custom_integrations.html
+plugins:
+  # - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
+
 # A list of one or more datasets to finetune the model with
 datasets:
   # HuggingFace dataset repo | s3://,gs:// path | "json" for local dataset, make sure to fill data_files
@@ -205,10 +216,46 @@ test_datasets:
     data_files:
       - /workspace/data/eval.jsonl
 
-# use RL training: 'dpo', 'ipo', 'kto'
+# use RL training: 'dpo', 'ipo', 'kto', 'simpo', 'orpo', 'grpo'
 rl:
-# whether to perform weighting if doing DPO training. Boolean.
-dpo_use_weighting:
+rl_beta:  # Optional[float]. The beta parameter for the RL training.
+
+# dpo
+dpo_use_weighting:  # Optional[bool]. Whether to perform weighting.
+rpo_alpha: # Optional[float]. Weighting of NLL term in loss from RPO paper.
+
+# orpo
+orpo_alpha: 0.1  # Parameter controlling the relative ratio loss weight in the ORPO loss. Passed to `beta` in `ORPOConfig` due to trl mapping.
+
+# kto
+kto_desirable_weight: # Optional[float]. Factor for desirable loss term in KTO loss.
+kto_undesirable_weight: # Optional[float]. Factor for undesirable loss term in KTO loss.
+
+# simpo
+cpo_alpha: 1.0  # Weight of the BC regularizer
+simpo_gamma: 0.5  # Target reward margin for the SimPO loss
+
+# grpo
+trl:
+  use_vllm: # Optional[bool]. Whether to use VLLM for RL training.
+  vllm_device: # Optional[str]. Device to use for VLLM.
+  vllm_gpu_memory_utilization: # Optional[float]. GPU memory utilization for VLLM.
+  vllm_max_model_len: # Optional[int]. Maximum length of the model for VLLM.
+  vllm_dtype: # Optional[str]. Data type for VLLM.
+
+  beta: # Optional[float]. Beta parameter for the RL training. Same as `rl_beta`. Use
+  max_completion_length: # Optional[int]. Maximum length of the completion for RL training.
+
+  reward_funcs: # Optional[list[str]]. List of reward functions to load. Paths must be importable from current dir.
+  reward_weights: # Optional[list[float]]. List of reward weights for the reward functions.
+
+  num_generations: # Optional[int]. Number of generations to sample.
+  log_completions: # Optional[bool]. Whether to log completions.
+
+  sync_ref_model: # Optional[bool]. Whether to sync the reference model.
+  ref_model_mixup_alpha: # Optional[float]. Mixup alpha for the reference model.
+  ref_model_sync_steps: # Optional[int]. Sync steps for the reference model.
+
 
 # reward modelling: `True` or `False`
 reward_model:
@@ -232,7 +279,7 @@ default_system_message: You are a helpful assistant. Please give a long and deta
 # subsequent training attempts load faster, relative path
 dataset_prepared_path: data/last_run_prepared
 # Push prepared dataset to hub
-push_dataset_to_hub: # repo path
+push_dataset_to_hub: # Optional[str] repo_org/repo_name
 # The maximum number of processes to use while preprocessing your input dataset. This defaults to `os.cpu_count()`
 # if not set.
 dataset_processes: # defaults to os.cpu_count() if not set
@@ -573,6 +620,14 @@ ddp_timeout:
 ddp_bucket_cap_mb:
 ddp_broadcast_buffers:
 
+# Sequence parallelism
+# Set to a divisor of the number of GPUs available to split sequences into chunks of equal size.
+# Use in long context training to prevent OOM when sequences cannot fit into a single GPU's VRAM.
+# E.g., if 4 GPUs are available, set this value to 2 to split each sequence into two equal-sized
+# subsequences, or set to 4 to split into four equal-sized subsequences.
+# See https://axolotl-ai-cloud.github.io/axolotl/docs/sequence_parallelism.html for more details.
+sequence_parallel_degree:
+
 # Path to torch distx for optim 'adamw_anyprecision'
 torchdistx_path:
 

docs/custom_integrations.qmd

@@ -55,3 +55,47 @@ sections = [
 for section_name, folder_name in sections:
     print(print_section(section_name, folder_name))
 ```
+
+## Adding a new integration
+
+Plugins can be used to customize the behavior of the training pipeline through [hooks](https://en.wikipedia.org/wiki/Hooking). See [`axolotl.integrations.BasePlugin`](https://github.com/axolotl-ai-cloud/axolotl/blob/main/src/axolotl/integrations/base.py) for the possible hooks.
+
+To add a new integration, please follow these steps:
+
+1. Create a new folder in the `src/axolotl/integrations` directory.
+2. Add any relevant files (`LICENSE`, `README.md`, `ACKNOWLEDGEMENTS.md`, etc.) to the new folder.
+3. Add `__init__.py` and `args.py` files to the new folder.
+  - `__init__.py` should import the integration and hook into the appropriate functions.
+  - `args.py` should define the arguments for the integration.
+4. (If applicable) Add CPU tests under `tests/integrations` or GPU tests under `tests/e2e/integrations`.
+
+::: {.callout-tip}
+
+See [src/axolotl/integrations/cut_cross_entropy](https://github.com/axolotl-ai-cloud/axolotl/tree/main/src/axolotl/integrations/cut_cross_entropy) for a minimal integration example.
+
+:::
+
+::: {.callout-warning}
+
+If you could not load your integration, please ensure you are pip installing in editable mode.
+
+```bash
+pip install -e .
+```
+
+and correctly spelled the integration name in the config file.
+
+```yaml
+plugins:
+  - axolotl.integrations.your_integration_name.YourIntegrationPlugin
+```
+
+:::
+
+::: {.callout-note}
+
+It is not necessary to place your integration in the `integrations` folder. It can be in any location, so long as it's installed in a package in your python env.
+
+See this repo for an example: [https://github.com/axolotl-ai-cloud/diff-transformer](https://github.com/axolotl-ai-cloud/diff-transformer)
+
+:::

docs/dataset_preprocessing.qmd

@@ -6,7 +6,7 @@ description: How datasets are processed
 ## Overview
 
 Dataset pre-processing is the step where Axolotl takes each dataset you've configured alongside
-the [dataset format](docs/dataset-formats) and prompt strategies to:
+the [dataset format](dataset-formats) and prompt strategies to:
 
  - parse the dataset based on the *dataset format*
  - transform the dataset to how you would interact with the model based on the *prompt strategy*

docs/faq.qmd

@@ -27,6 +27,20 @@ description: Frequently asked questions
 
 > A: This is usually an issue with the GPU. This can be resolved through setting the os environment variable `CUDA_VISIBLE_DEVICES=0`. If you are on runpod, this is usually a pod issue. Starting a new pod should take care of it.
 
+**Q: Received mismatch error on merge adapters / loading adapters between torch.Size of checkpoint and model.**
+
+> A: This is likely due to vocab size mismatch. By default, Axolotl expands the model's embeddings if the tokenizer has more tokens than the model. Please use the `axolotl merge-lora` command to merge the adapters instead of using your own scripts.
+
+> On the other hand, if the model has more tokens than the tokenizer, Axolotl does not shrink the model's embeddings unless `shrink_embeddings: true` is set in the config.
+
+**Q: How to call Axolotl via custom python scripts?**
+
+> A: Yes, since Axolotl is just Python, please see `src/axolotl/cli/main.py` on how each command is called.
+
+**Q: How to know the value to use for `fsdp_transformer_layer_cls_to_wrap`?**
+
+> A: This is the class name of the transformer layer to wrap with FSDP. For example, for `LlamaForCausalLM`, the value is `LlamaDecoderLayer`. To find this for a specific model, check the model's `PreTrainedModel` definition and look for `_no_split_modules` variable in the `modeling_<model_name>.py` file within `transformers` library.
+
 ### Chat templates
 
 **Q: `jinja2.exceptions.UndefinedError: 'dict object' has no attribute 'content' / 'role' / ____`**

docs/getting-started.qmd

@@ -36,7 +36,9 @@ The YAML configuration file controls everything about your training. Here's what
 
 ```yaml
 base_model: NousResearch/Llama-3.2-1B
-# hub_model_id: username/custom_model_name
+
+load_in_8bit: true
+adapter: lora
 
 datasets:
   - path: teknium/GPT4-LLM-Cleaned
@@ -44,11 +46,15 @@ datasets:
 dataset_prepared_path: last_run_prepared
 val_set_size: 0.1
 output_dir: ./outputs/lora-out
-
-adapter: lora
-lora_model_dir:
 ```
 
+::: {.callout-tip}
+`load_in_8bit: true` and `adapter: lora` enables LoRA adapter finetuning.
+
+- To perform Full finetuning, remove these two lines.
+- To perform QLoRA finetuning, replace with `load_in_4bit: true` and `adapter: qlora`.
+:::
+
 See our [Config options](config.qmd) for more details.
 
 ### Training {#sec-training}
@@ -56,7 +62,7 @@ See our [Config options](config.qmd) for more details.
 When you run `axolotl train`, Axolotl:
 
 1. Downloads the base model
-2. (If specified) applies LoRA adapter layers
+2. (If specified) applies QLoRA/LoRA adapter layers
 3. Loads and processes the dataset
 4. Runs the training loop
 5. Saves the trained model and / or LoRA weights
@@ -69,6 +75,8 @@ Let's modify the example for your own data:
 
 ```yaml
 base_model: NousResearch/Nous-Hermes-llama-1b-v1
+
+load_in_8bit: true
 adapter: lora
 
 # Training settings
@@ -104,8 +112,6 @@ format):
 {"instruction": "Classify this text", "input": "Not good at all", "output": "negative"}
 ```
 
-Please consult the supported [Dataset Formats](dataset-formats/) for more details.
-
 3. Run the training:
 
 ```bash

docs/inference.qmd

@@ -1,5 +1,5 @@
 ---
-title: "Inference"
+title: "Inference and Merging"
 format:
   html:
     toc: true
@@ -9,10 +9,14 @@ execute:
   enabled: false
 ---
 
-This guide covers how to use your trained models for inference, including model loading, interactive testing, and common troubleshooting steps.
+This guide covers how to use your trained models for inference, including model loading, interactive testing, merging adapters, and common troubleshooting steps.
 
 ## Quick Start {#sec-quickstart}
 
+::: {.callout-tip}
+Use the same config used for training on inference/merging.
+:::
+
 ### Basic Inference {#sec-basic}
 
 ::: {.panel-tabset}

docs/installation.qmd

@@ -22,6 +22,7 @@ This guide covers all the ways you can install and set up Axolotl for your envir
 ### PyPI Installation (Recommended) {#sec-pypi}
 
 ```{.bash}
+pip3 install -U packaging setuptools wheel ninja
 pip3 install --no-build-isolation axolotl[flash-attn,deepspeed]
 ```
 
@@ -37,7 +38,7 @@ For the latest features between releases:
 ```{.bash}
 git clone https://github.com/axolotl-ai-cloud/axolotl.git
 cd axolotl
-pip3 install packaging ninja
+pip3 install -U packaging setuptools wheel ninja
 pip3 install --no-build-isolation -e '.[flash-attn,deepspeed]'
 ```
 
@@ -78,6 +79,7 @@ For providers supporting Docker:
   - [Latitude.sh](https://latitude.sh/blueprint/989e0e79-3bf6-41ea-a46b-1f246e309d5c)
   - [JarvisLabs.ai](https://jarvislabs.ai/templates/axolotl)
   - [RunPod](https://runpod.io/gsc?template=v2ickqhz9s&ref=6i7fkpdz)
+  - [Novita](https://novita.ai/gpus-console?templateId=311)
 
 ### Google Colab {#sec-colab}
 
@@ -107,7 +109,7 @@ We recommend using WSL2 (Windows Subsystem for Linux) or Docker.
 2. Install PyTorch: https://pytorch.org/get-started/locally/
 3. Install Axolotl:
    ```{.bash}
-   pip3 install packaging
+   pip3 install -U packaging setuptools wheel ninja
    pip3 install --no-build-isolation -e '.[flash-attn,deepspeed]'
    ```
 4. (Optional) Login to Hugging Face:

docs/lora_optims.qmd

@@ -66,6 +66,10 @@ logic to be compatible with more of them.
 
 </details>
 
+::: {.callout-tip}
+Check out our [LoRA optimizations blog](https://axolotlai.substack.com/p/accelerating-lora-fine-tuning-with).
+:::
+
 ## Usage
 
 These optimizations can be enabled in your Axolotl config YAML file. The

docs/reward_modelling.qmd

@@ -41,6 +41,10 @@ Bradley-Terry chat templates expect single-turn conversations in the following f
 
 ### Process Reward Models (PRM)
 
+::: {.callout-tip}
+Check out our [PRM blog](https://axolotlai.substack.com/p/process-reward-models).
+:::
+
 Process reward models are trained using data which contains preference annotations for each step in a series of interactions. Typically, PRMs are trained to provide reward signals over each step of a reasoning trace and are used for downstream reinforcement learning.
 ```yaml
 base_model: Qwen/Qwen2.5-3B

docs/rlhf.qmd

@@ -298,7 +298,7 @@ The input format is a simple JSON input with customizable fields based on the ab
 
 ### IPO
 
-As IPO is just DPO with a different loss function, all supported options for DPO works here.
+As IPO is just DPO with a different loss function, all supported dataset formats for [DPO](#dpo) are also supported for IPO.
 
 ```yaml
 rl: ipo
@@ -344,8 +344,9 @@ ORPO supports the following types with the following dataset format:
 
 ```yaml
 rl: kto
-rl_beta: 0.5
-kto_desirable_weight: 0.2
+rl_beta: 0.1  # default
+kto_desirable_weight: 1.0  # default
+kto_undesirable_weight: 1.0  # default
 
 remove_unused_columns: false
 
@@ -497,6 +498,10 @@ The input format is a simple JSON input with customizable fields based on the ab
 
 ### GRPO
 
+::: {.callout-tip}
+Check out our [GRPO cookbook](https://github.com/axolotl-ai-cloud/axolotl-cookbook/tree/main/grpo#training-an-r1-style-large-language-model-using-grpo).
+:::
+
 GRPO uses custom reward functions and transformations. Please have them ready locally.
 
 For ex, to load OpenAI's GSM8K and use a random reward for completions:
@@ -540,6 +545,19 @@ To see other examples of custom reward functions, please see [TRL GRPO Docs](htt
 
 To see description of the configs, please see [TRLConfig](https://github.com/axolotl-ai-cloud/axolotl/blob/main/src/axolotl/utils/config/models/input/v0_4_1/trl.py).
 
+### SimPO
+
+SimPO uses [CPOTrainer](https://huggingface.co/docs/trl/main/en/cpo_trainer) but with alternative loss function.
+
+```yaml
+rl: simpo
+rl_beta: 0.1  # default in CPOTrainer
+cpo_alpha: 1.0  # default in CPOTrainer
+simpo_gamma: 0.5  # default in CPOTrainer
+```
+
+This method uses the same dataset format as [DPO](#dpo).
+
 ### Using local dataset files
 
 ```yaml

docs/sequence_parallelism.qmd

@@ -0,0 +1,90 @@
+---
+title: Sequence Parallelism
+description: Train with long sequences split across multiple GPUs.
+---
+
+# Sequence Parallelism
+
+Sequence parallelism is a technique that splits sequences across multiple GPUs,
+allowing you to train with very long sequences that wouldn't fit on a single GPU. Each
+GPU processes a different portion of the sequence, and the results are aggregated
+through a ring communication pattern.
+
+## When to Use Sequence Parallelism
+
+Use sequence parallelism when:
+
+- You need to train with sequence lengths that don't fit into a single GPU's memory
+- You have multiple GPUs available
+- You're experiencing OOM (Out Of Memory) errors with long sequences
+
+## Configuration
+
+To enable sequence parallelism, add the following to your configuration file:
+
+```yaml
+# Set to a divisor (> 1) of the number of GPUs available
+sequence_parallel_degree: 4  # Split sequences across 4 GPUs
+```
+
+The `sequence_parallel_degree` should be a divisor of the total number of GPUs. For example:
+
+- With 8 GPUs, valid values would be 2, 4, or 8
+- With 4 GPUs, valid values would be 2 or 4
+
+## Implementation Details
+
+When sequence parallelism is enabled:
+
+1. Each sequence is divided into equal chunks across the GPUs in a sequence parallel group
+2. The data collator handles the chunking of input_ids, attention_mask, labels, and position_ids
+3. Position IDs are adjusted to maintain proper relative positions, especially for packed sequences
+4. The trainer uses special ring communication patterns for attention operations
+
+## Requirements
+
+To use sequence parallelism, you need:
+
+- Multiple GPUs (at least 2)
+- The `ring-flash-attn` package. Install with:
+  - `pip install axolotl[ring-flash-attn]` (preferred)
+  - `pip install ring-flash-attn>=0.1.4`
+
+## Limitations
+
+- Flash attention must be enabled for this to work (`flash_attention: true` in config YAML)
+- May have a small performance overhead due to communication between GPUs
+
+## Example
+
+```yaml
+# Example config with sequence parallelism
+base_model: meta-llama/Llama-3-8B-Instruct
+sequence_len: 8192
+sequence_parallel_degree: 2  # Split each sequence into 4 parts
+flash_attention: true  # Required with sequence parallelism
+...
+```
+
+This will train the Llama 3 8B model with 8K context length, with each sequence split
+into 2 subsequences of length 4096 across 2 GPUs.
+
+## Sample Packing with Sequence Parallelism
+
+Sequence parallelism is compatible with Axolotl's sample packing functionality. When using both features together:
+
+1. Samples are first packed together
+2. The packed sequences are then divided across GPUs in the sequence parallel group
+3. Position IDs are automatically adjusted to maintain proper relative positions
+
+## Effect on Batch Size
+
+When using sequence parallelism, your effective global batch size is **divided** by the `sequence_parallel_degree`. This happens because:
+
+- Each group of `sequence_parallel_degree` GPUs works on the same batch (just different parts of each sequence)
+- The number of batches processed per step decreases
+
+For example:
+- With 8 GPUs and no sequence parallelism: 8 different batches processed per step
+- With 8 GPUs and `sequence_parallel_degree=4`: Only 2 different batches processed per step (each split across 4 GPUs)
+- If your per-GPU `micro_batch_size` is 2, the global batch size decreases from 16 to 4

examples/llama-3/qlora-1b-kto.yaml

@@ -55,7 +55,7 @@ tf32: true
 
 gradient_checkpointing: true
 gradient_checkpointing_kwargs:
-  use_reentrant: true
+  use_reentrant: false
 early_stopping_patience:
 resume_from_checkpoint:
 local_rank:

pyproject.toml

@@ -1,5 +1,5 @@
 [build-system]
-requires = ["setuptools>=64", "wheel", "setuptools_scm>=8"]
+requires = ["setuptools>=64", "wheel", "setuptools_scm>=8", "packaging==23.2"]
 build-backend = "setuptools.build_meta"
 
 [project]
@@ -8,6 +8,7 @@ dynamic = ["version", "dependencies", "optional-dependencies"]
 description = "LLM Trainer"
 readme = "README.md"
 requires-python = ">=3.10"
+# license = "Apache-2.0"
 
 [project.scripts]
 axolotl = "axolotl.cli.main:main"

requirements-dev.txt

@@ -2,3 +2,5 @@ pre-commit
 black
 mypy
 types-requests
+quartodoc
+jupyter

requirements.txt

@@ -1,10 +1,9 @@
 --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/
 
 # START section of dependencies that don't install on Darwin/MacOS
-bitsandbytes==0.45.2
+bitsandbytes==0.45.3
 triton>=3.0.0
 mamba-ssm==1.2.0.post1
-flash-attn==2.7.4.post1
 xformers>=0.0.23.post1
 autoawq==0.2.7.post3
 liger-kernel==0.5.3
@@ -12,12 +11,12 @@ liger-kernel==0.5.3
 
 packaging==23.2
 
-peft==0.14.0
+peft==0.15.0
 transformers==4.49.0
-tokenizers>=0.21.0
-accelerate==1.3.0
-datasets==3.2.0
-deepspeed==0.16.1
+tokenizers>=0.21.1
+accelerate==1.5.2
+datasets==3.4.1
+deepspeed==0.16.4
 trl==0.15.1
 
 optimum==1.16.2
@@ -36,6 +35,7 @@ einops
 colorama
 numba
 numpy>=1.24.4,<=2.0.1
+
 # qlora things
 evaluate==0.4.1
 scipy

scripts/chat_datasets.py

@@ -1,6 +1,7 @@
 """
 helper script to parse chat datasets into a usable yaml
 """
+
 import click
 import yaml
 from datasets import load_dataset

scripts/cutcrossentropy_install.py

@@ -1,4 +1,5 @@
 """Script to output the correct installation command for cut-cross-entropy."""
+
 import importlib.util
 import sys
 

setup.py

@@ -17,11 +17,7 @@ def parse_requirements():
         lines = [r.strip() for r in requirements_file.readlines()]
         for line in lines:
             is_extras = (
-                "flash-attn" in line
-                or "flash-attention" in line
-                or "deepspeed" in line
-                or "mamba-ssm" in line
-                or "lion-pytorch" in line
+                "deepspeed" in line or "mamba-ssm" in line or "lion-pytorch" in line
             )
             if line.startswith("--extra-index-url"):
                 # Handle custom index URLs
@@ -39,7 +35,6 @@ def parse_requirements():
                 "bitsandbytes",
                 "triton",
                 "mamba-ssm",
-                "flash-attn",
                 "xformers",
                 "autoawq",
                 "liger-kernel",
@@ -124,11 +119,10 @@ setup(
         ],
     },
     extras_require={
-        "flash-attn": [
-            "flash-attn==2.7.4.post1",
-        ],
+        "flash-attn": ["flash-attn==2.7.4.post1"],
+        "ring-flash-attn": ["ring-flash-attn>=0.1.4", "yunchang==0.6.0"],
         "deepspeed": [
-            "deepspeed==0.16.1",
+            "deepspeed==0.16.4",
             "deepspeed-kernels",
         ],
         "mamba-ssm": [

src/axolotl/cli/cloud/__init__.py

@@ -1,6 +1,7 @@
 """
 launch axolotl in supported cloud platforms
 """
+
 from pathlib import Path
 from typing import Union
 

src/axolotl/cli/cloud/base.py

@@ -1,6 +1,7 @@
 """
 base class for cloud platforms from cli
 """
+
 from abc import ABC, abstractmethod
 
 

src/axolotl/cli/cloud/modal_.py

@@ -1,6 +1,7 @@
 """
 Modal Cloud support from CLI
 """
+
 import copy
 import json
 import os

src/axolotl/cli/main.py

@@ -1,4 +1,5 @@
 """Click CLI definitions for various axolotl commands."""
+
 # pylint: disable=redefined-outer-name
 
 import logging
@@ -24,7 +25,7 @@ from axolotl.cli.utils import (
 )
 from axolotl.integrations.lm_eval.cli import lm_eval
 from axolotl.utils import set_pytorch_cuda_alloc_conf
-from axolotl.utils.config.models.input.v0_4_1 import AxolotlInputConfig
+from axolotl.utils.schemas.config import AxolotlInputConfig
 
 
 @click.group()

src/axolotl/cli/train.py

@@ -23,7 +23,7 @@ from axolotl.utils.dict import DictDefault
 LOG = logging.getLogger(__name__)
 
 
-def do_train(cfg: DictDefault, cli_args: TrainerCliArgs) -> None:
+def do_train(cfg: DictDefault, cli_args: TrainerCliArgs):
     """
     Trains a `transformers` model by first loading the dataset(s) specified in the
     `axolotl` config, and then calling `axolotl.train.train`. Also runs the plugin
@@ -44,16 +44,13 @@ def do_train(cfg: DictDefault, cli_args: TrainerCliArgs) -> None:
         dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
 
     model, tokenizer, trainer = train(cfg=cfg, dataset_meta=dataset_meta)
+    del model, tokenizer, trainer
+
     plugin_manager = PluginManager.get_instance()
-
-    del model
-    del tokenizer
-    del trainer
-
     plugin_manager.post_train_unload(cfg)
 
 
-def do_cli(config: Union[Path, str] = Path("examples/"), **kwargs) -> None:
+def do_cli(config: Union[Path, str] = Path("examples/"), **kwargs):
     """
     Parses `axolotl` config, CLI args, and calls `do_train`.
 

src/axolotl/cli/utils.py

@@ -5,7 +5,6 @@ import dataclasses
 import hashlib
 import json
 import logging
-import typing
 from functools import wraps
 from pathlib import Path
 from types import NoneType
@@ -24,7 +23,7 @@ configure_logging()
 LOG = logging.getLogger(__name__)
 
 
-def strip_optional_type(field_type: type | typing._SpecialForm | None):
+def strip_optional_type(field_type: type | str | None):
     """
     Extracts the non-`None` type from an `Optional` / `Union` type.
 

src/axolotl/convert.py

@@ -1,6 +1,5 @@
 """Module containing File Reader, File Writer, Json Parser, and Jsonl Serializer classes"""
 
-
 import json
 import sys
 

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

@@ -1,6 +1,7 @@
 """
 ChatML transformation functions for MessageContents
 """
+
 from typing import Optional
 
 from ..messages import MessageContents, Messages

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

@@ -1,6 +1,7 @@
 """
 Llama 3.x chat formatting functions for MessageContents
 """
+
 from typing import Optional
 
 from ..messages import MessageContents, Messages

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

@@ -1,6 +1,7 @@
 """
 shared functions for format transforms
 """
+
 from axolotl.core.chat.messages import MessageContents, Messages
 
 

src/axolotl/core/chat/messages.py

@@ -1,6 +1,7 @@
 """
 internal message representations of chat messages
 """
+
 import json
 from enum import Enum
 from typing import Any, Callable, List, Optional, Union

src/axolotl/core/datasets/chat.py

@@ -1,6 +1,7 @@
 """
 chat dataset module
 """
+
 import os
 from typing import Callable, Optional, Union
 

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

@@ -1,6 +1,7 @@
 """
 This module contains a function that builds a transform that takes a row from the dataset and converts it to a Chat.
 """
+
 from typing import Any, Mapping, Union
 
 

src/axolotl/core/trainer_builder.py

@@ -13,9 +13,7 @@
 # limitations under the License.
 
 # pylint: disable=too-many-lines
-"""
-Builder for the training args and trainer
-"""
+"""Builder for the training args and trainer"""
 
 import abc
 import importlib
@@ -38,8 +36,9 @@ from transformers import (
 from transformers.training_args import OptimizerNames
 from trl.trainer.utils import RewardDataCollatorWithPadding
 
-from axolotl.core.trainers.base import (
+from axolotl.core.trainers import (
     AxolotlCPOTrainer,
+    AxolotlKTOTrainer,
     AxolotlMambaTrainer,
     AxolotlORPOTrainer,
     AxolotlPRMTrainer,
@@ -50,7 +49,6 @@ from axolotl.core.trainers.base import (
 from axolotl.core.trainers.dpo import DPOStrategy
 from axolotl.core.trainers.dpo.args import AxolotlDPOConfig
 from axolotl.core.trainers.grpo import GRPOStrategy
-from axolotl.core.trainers.kto import AxolotlKTOTrainer
 from axolotl.core.training_args import (
     AxolotlCPOConfig,
     AxolotlKTOConfig,
@@ -85,8 +83,8 @@ from axolotl.utils.collators import (
     V2BatchSamplerDataCollatorForSeq2Seq,
 )
 from axolotl.utils.collators.mm_chat import MultiModalChatDataCollator
-from axolotl.utils.config.models.input.v0_4_1 import CustomSupportedOptimizers
 from axolotl.utils.models import ensure_dtype
+from axolotl.utils.schemas.enums import CustomSupportedOptimizers
 
 try:
     import torch._dynamo  # pylint: disable=ungrouped-imports
@@ -332,9 +330,9 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         training_arguments_kwargs = {}
 
         if self.cfg.include_tokens_per_second is not None:
-            training_arguments_kwargs[
-                "include_tokens_per_second"
-            ] = self.cfg.include_tokens_per_second
+            training_arguments_kwargs["include_tokens_per_second"] = (
+                self.cfg.include_tokens_per_second
+            )
 
         if self.cfg.bf16 == "full":
             training_arguments_kwargs["bf16_full_eval"] = True
@@ -351,13 +349,13 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
             training_arguments_kwargs["seed"] = self.cfg.seed
 
         if self.cfg.gradient_checkpointing:
-            training_arguments_kwargs[
-                "gradient_checkpointing"
-            ] = self.cfg.gradient_checkpointing
+            training_arguments_kwargs["gradient_checkpointing"] = (
+                self.cfg.gradient_checkpointing
+            )
             if self.cfg.gradient_checkpointing_kwargs is not None:
-                training_arguments_kwargs[
-                    "gradient_checkpointing_kwargs"
-                ] = self.cfg.gradient_checkpointing_kwargs
+                training_arguments_kwargs["gradient_checkpointing_kwargs"] = (
+                    self.cfg.gradient_checkpointing_kwargs
+                )
         if self.cfg.fsdp:
             training_arguments_kwargs["fsdp"] = self.cfg.fsdp
             if self.cfg.fsdp_config:
@@ -373,9 +371,9 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
             training_arguments_kwargs["deepspeed"] = self.cfg.deepspeed
 
         if self.cfg.lr_quadratic_warmup is not None:
-            training_arguments_kwargs[
-                "lr_quadratic_warmup"
-            ] = self.cfg.lr_quadratic_warmup
+            training_arguments_kwargs["lr_quadratic_warmup"] = (
+                self.cfg.lr_quadratic_warmup
+            )
 
         if self.cfg.adam_beta1:
             training_arguments_kwargs["adam_beta1"] = self.cfg.adam_beta1
@@ -399,28 +397,28 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
             training_arguments_kwargs["save_safetensors"] = self.cfg.save_safetensors
 
         if self.cfg.dataloader_pin_memory is not None:
-            training_arguments_kwargs[
-                "dataloader_pin_memory"
-            ] = self.cfg.dataloader_pin_memory
+            training_arguments_kwargs["dataloader_pin_memory"] = (
+                self.cfg.dataloader_pin_memory
+            )
         if self.cfg.dataloader_num_workers is not None:
-            training_arguments_kwargs[
-                "dataloader_num_workers"
-            ] = self.cfg.dataloader_num_workers
+            training_arguments_kwargs["dataloader_num_workers"] = (
+                self.cfg.dataloader_num_workers
+            )
         if self.cfg.dataloader_prefetch_factor is not None:
-            training_arguments_kwargs[
-                "dataloader_prefetch_factor"
-            ] = self.cfg.dataloader_prefetch_factor
+            training_arguments_kwargs["dataloader_prefetch_factor"] = (
+                self.cfg.dataloader_prefetch_factor
+            )
         if self.cfg.dataloader_drop_last is not None:
-            training_arguments_kwargs[
-                "dataloader_drop_last"
-            ] = self.cfg.dataloader_drop_last
+            training_arguments_kwargs["dataloader_drop_last"] = (
+                self.cfg.dataloader_drop_last
+            )
         elif self.cfg.sample_packing and self.cfg.eval_sample_packing is False:
             training_arguments_kwargs["dataloader_drop_last"] = True
 
         if self.cfg.remove_unused_columns is not None:
-            training_arguments_kwargs[
-                "remove_unused_columns"
-            ] = self.cfg.remove_unused_columns
+            training_arguments_kwargs["remove_unused_columns"] = (
+                self.cfg.remove_unused_columns
+            )
 
         if not self.cfg.test_datasets and self.cfg.val_set_size == 0:
             # no eval set, so don't eval
@@ -452,9 +450,9 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         if self.cfg.do_causal_lm_eval:
             training_arguments_kwargs["do_causal_lm_eval"] = self.cfg.do_causal_lm_eval
         if self.cfg.metric_for_best_model:
-            training_arguments_kwargs[
-                "metric_for_best_model"
-            ] = self.cfg.metric_for_best_model
+            training_arguments_kwargs["metric_for_best_model"] = (
+                self.cfg.metric_for_best_model
+            )
         if self.cfg.greater_is_better:
             training_arguments_kwargs["greater_is_better"] = self.cfg.greater_is_better
 
@@ -467,13 +465,13 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
                 )
                 training_arguments_kwargs["torch_compile"] = self.cfg.torch_compile
                 if self.cfg.torch_compile_backend:
-                    training_arguments_kwargs[
-                        "torch_compile_backend"
-                    ] = self.cfg.torch_compile_backend
+                    training_arguments_kwargs["torch_compile_backend"] = (
+                        self.cfg.torch_compile_backend
+                    )
                 if self.cfg.torch_compile_mode:
-                    training_arguments_kwargs[
-                        "torch_compile_mode"
-                    ] = self.cfg.torch_compile_mode
+                    training_arguments_kwargs["torch_compile_mode"] = (
+                        self.cfg.torch_compile_mode
+                    )
 
         # DDP Config
         if self.cfg.ddp_timeout:
@@ -482,32 +480,32 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         if self.cfg.ddp_bucket_cap_mb:
             training_arguments_kwargs["ddp_bucket_cap_mb"] = self.cfg.ddp_bucket_cap_mb
         if self.cfg.ddp_broadcast_buffers is not None:
-            training_arguments_kwargs[
-                "ddp_broadcast_buffers"
-            ] = self.cfg.ddp_broadcast_buffers
+            training_arguments_kwargs["ddp_broadcast_buffers"] = (
+                self.cfg.ddp_broadcast_buffers
+            )
 
         # these are all the "standard" kwargs that are def used
         training_arguments_kwargs["max_steps"] = (
             total_num_steps if self.cfg.max_steps else -1
         )
         training_arguments_kwargs["max_seq_length"] = self.cfg.sequence_len
-        training_arguments_kwargs[
-            "per_device_train_batch_size"
-        ] = self.cfg.micro_batch_size
+        training_arguments_kwargs["per_device_train_batch_size"] = (
+            self.cfg.micro_batch_size
+        )
         if self.cfg.eval_batch_size:
-            training_arguments_kwargs[
-                "per_device_eval_batch_size"
-            ] = self.cfg.eval_batch_size
+            training_arguments_kwargs["per_device_eval_batch_size"] = (
+                self.cfg.eval_batch_size
+            )
         if self.cfg.auto_find_batch_size is not None:
-            training_arguments_kwargs[
-                "auto_find_batch_size"
-            ] = self.cfg.auto_find_batch_size
-        training_arguments_kwargs[
-            "gradient_accumulation_steps"
-        ] = self.cfg.gradient_accumulation_steps
-        training_arguments_kwargs[
-            "eval_accumulation_steps"
-        ] = self.cfg.gradient_accumulation_steps
+            training_arguments_kwargs["auto_find_batch_size"] = (
+                self.cfg.auto_find_batch_size
+            )
+        training_arguments_kwargs["gradient_accumulation_steps"] = (
+            self.cfg.gradient_accumulation_steps
+        )
+        training_arguments_kwargs["eval_accumulation_steps"] = (
+            self.cfg.gradient_accumulation_steps
+        )
         training_arguments_kwargs["num_train_epochs"] = self.cfg.num_epochs
         training_arguments_kwargs["learning_rate"] = self.cfg.learning_rate
         training_arguments_kwargs["output_dir"] = self.cfg.output_dir
@@ -554,9 +552,9 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
 
         if self.cfg.lr_scheduler in ["one_cycle", "rex", "log_sweep"]:
             training_arguments_kwargs["lr_scheduler_type"] = "cosine"
-            training_arguments_kwargs[
-                "alternate_lr_scheduler_type"
-            ] = self.cfg.lr_scheduler
+            training_arguments_kwargs["alternate_lr_scheduler_type"] = (
+                self.cfg.lr_scheduler
+            )
         else:
             training_arguments_kwargs["lr_scheduler_type"] = (
                 self.cfg.lr_scheduler if self.cfg.lr_scheduler else "cosine"
@@ -565,9 +563,9 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
             self.cfg.lr_scheduler_kwargs if self.cfg.lr_scheduler_kwargs else {}
         )
         training_arguments_kwargs["cosine_min_lr_ratio"] = self.cfg.cosine_min_lr_ratio
-        training_arguments_kwargs[
-            "cosine_constant_lr_ratio"
-        ] = self.cfg.cosine_constant_lr_ratio
+        training_arguments_kwargs["cosine_constant_lr_ratio"] = (
+            self.cfg.cosine_constant_lr_ratio
+        )
         training_arguments_kwargs["weight_decay"] = (
             self.cfg.weight_decay if self.cfg.weight_decay is not None else 0.0
         )
@@ -580,40 +578,40 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
             self.cfg.eval_sample_packing
         )
         if self.cfg.sample_packing_bin_size is not None:
-            training_arguments_kwargs[
-                "sample_packing_bin_size"
-            ] = self.cfg.sample_packing_bin_size
+            training_arguments_kwargs["sample_packing_bin_size"] = (
+                self.cfg.sample_packing_bin_size
+            )
         if self.cfg.sample_packing_group_size is not None:
-            training_arguments_kwargs[
-                "sample_packing_group_size"
-            ] = self.cfg.sample_packing_group_size
+            training_arguments_kwargs["sample_packing_group_size"] = (
+                self.cfg.sample_packing_group_size
+            )
         if self.cfg.sample_packing_eff_est:
-            training_arguments_kwargs[
-                "sample_packing_efficiency"
-            ] = self.cfg.sample_packing_eff_est
+            training_arguments_kwargs["sample_packing_efficiency"] = (
+                self.cfg.sample_packing_eff_est
+            )
 
         if self.cfg.relora_steps:
             training_arguments_kwargs["relora_steps"] = self.cfg.relora_steps
-            training_arguments_kwargs[
-                "relora_warmup_steps"
-            ] = self.cfg.relora_warmup_steps
+            training_arguments_kwargs["relora_warmup_steps"] = (
+                self.cfg.relora_warmup_steps
+            )
             if self.cfg.relora_anneal_steps:
-                training_arguments_kwargs[
-                    "relora_anneal_steps"
-                ] = self.cfg.relora_anneal_steps
+                training_arguments_kwargs["relora_anneal_steps"] = (
+                    self.cfg.relora_anneal_steps
+                )
             if self.cfg.relora_prune_ratio:
-                training_arguments_kwargs[
-                    "relora_prune_ratio"
-                ] = self.cfg.relora_prune_ratio
+                training_arguments_kwargs["relora_prune_ratio"] = (
+                    self.cfg.relora_prune_ratio
+                )
 
         if self.cfg.lisa_step_interval and self.cfg.lisa_n_layers:
             training_arguments_kwargs["lisa_n_layers"] = self.cfg.lisa_n_layers
-            training_arguments_kwargs[
-                "lisa_step_interval"
-            ] = self.cfg.lisa_step_interval
-            training_arguments_kwargs[
-                "lisa_layers_attribute"
-            ] = self.cfg.lisa_layers_attribute
+            training_arguments_kwargs["lisa_step_interval"] = (
+                self.cfg.lisa_step_interval
+            )
+            training_arguments_kwargs["lisa_layers_attribute"] = (
+                self.cfg.lisa_layers_attribute
+            )
 
         training_arguments_kwargs = self.hook_pre_create_training_args(
             training_arguments_kwargs
@@ -627,9 +625,9 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
             )
 
         if self.cfg.neftune_noise_alpha is not None:
-            training_arguments_kwargs[
-                "neftune_noise_alpha"
-            ] = self.cfg.neftune_noise_alpha
+            training_arguments_kwargs["neftune_noise_alpha"] = (
+                self.cfg.neftune_noise_alpha
+            )
 
         trainer_kwargs = {}
 
@@ -731,23 +729,23 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
                 importlib.import_module("torchdistx")
 
         if self.cfg.optim_target_modules:
-            training_arguments_kwargs[
-                "optim_target_modules"
-            ] = self.cfg.optim_target_modules
+            training_arguments_kwargs["optim_target_modules"] = (
+                self.cfg.optim_target_modules
+            )
 
         training_arguments_kwargs["embedding_lr"] = self.cfg.embedding_lr
         training_arguments_kwargs["embedding_lr_scale"] = self.cfg.embedding_lr_scale
 
         training_arguments_kwargs["loraplus_lr_ratio"] = self.cfg.loraplus_lr_ratio
-        training_arguments_kwargs[
-            "loraplus_lr_embedding"
-        ] = self.cfg.loraplus_lr_embedding
+        training_arguments_kwargs["loraplus_lr_embedding"] = (
+            self.cfg.loraplus_lr_embedding
+        )
         training_arguments_kwargs["lr_groups"] = self.cfg.lr_groups
 
         if self.cfg.accelerator_config:
-            training_arguments_kwargs[
-                "accelerator_config"
-            ] = self.cfg.accelerator_config
+            training_arguments_kwargs["accelerator_config"] = (
+                self.cfg.accelerator_config
+            )
 
         if self.cfg.kd_ce_alpha is not None:
             training_arguments_kwargs["kd_ce_alpha"] = self.cfg.kd_ce_alpha
@@ -756,13 +754,17 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         if self.cfg.kd_temperature is not None:
             training_arguments_kwargs["kd_temperature"] = self.cfg.kd_temperature
         if self.cfg.kd_zscore_base_temp is not None:
-            training_arguments_kwargs[
-                "kd_zscore_base_temp"
-            ] = self.cfg.kd_zscore_base_temp
+            training_arguments_kwargs["kd_zscore_base_temp"] = (
+                self.cfg.kd_zscore_base_temp
+            )
         if self.cfg.kd_top_k_before_softmax is not None:
-            training_arguments_kwargs[
-                "kd_top_k_before_softmax"
-            ] = self.cfg.kd_top_k_before_softmax
+            training_arguments_kwargs["kd_top_k_before_softmax"] = (
+                self.cfg.kd_top_k_before_softmax
+            )
+
+        training_arguments_kwargs["sequence_parallel_degree"] = (
+            self.cfg.sequence_parallel_degree
+        )
 
         if self.cfg.reward_model:
             training_args_cls = AxolotlRewardConfig
@@ -847,9 +849,10 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         self, training_args: AxolotlTrainingArguments, is_eval=False, **kwargs
     ):
         if training_args.pretraining:
-            if self.cfg.pretraining_sample_concatenation is False:
-                return DataCollatorForSeq2Seq(self.tokenizer, **kwargs)
-            if self.cfg.micro_batch_size > 1:
+            if (
+                self.cfg.pretraining_sample_concatenation is False
+                or self.cfg.micro_batch_size > 1
+            ):
                 return DataCollatorForSeq2Seq(self.tokenizer, **kwargs)
             return None
 
@@ -877,9 +880,7 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
             if "max_length" in kwargs:
                 kwargs.pop("max_length")
         elif use_batch_sampler_collator:
-            if self.cfg.model_config_type in SUPPORTED_MULTIPACK_MODEL_TYPES:
-                collator = V2BatchSamplerDataCollatorForSeq2Seq
-            elif (
+            if self.cfg.model_config_type in SUPPORTED_MULTIPACK_MODEL_TYPES or (
                 self.cfg.model_config_type in ["llama"]
                 and self.cfg.flash_attention is not True
             ):
@@ -910,6 +911,8 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
                 collator = DataCollatorForSeq2Seq
 
         kwargs["return_tensors"] = "pt"
+        if issubclass(collator, DataCollatorForSeq2Seq):
+            kwargs["sequence_parallel_degree"] = training_args.sequence_parallel_degree
 
         return collator(
             *collator_args,
@@ -972,32 +975,32 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
             self.cfg.lr_scheduler_kwargs if self.cfg.lr_scheduler_kwargs else {}
         )
         if self.cfg.remove_unused_columns is not None:
-            training_args_kwargs[
-                "remove_unused_columns"
-            ] = self.cfg.remove_unused_columns
+            training_args_kwargs["remove_unused_columns"] = (
+                self.cfg.remove_unused_columns
+            )
         else:
             training_args_kwargs["remove_unused_columns"] = False
 
         if self.cfg.dataloader_pin_memory is not None:
-            training_args_kwargs[
-                "dataloader_pin_memory"
-            ] = self.cfg.dataloader_pin_memory
+            training_args_kwargs["dataloader_pin_memory"] = (
+                self.cfg.dataloader_pin_memory
+            )
         if self.cfg.dataloader_num_workers is not None:
-            training_args_kwargs[
-                "dataloader_num_workers"
-            ] = self.cfg.dataloader_num_workers
+            training_args_kwargs["dataloader_num_workers"] = (
+                self.cfg.dataloader_num_workers
+            )
         if self.cfg.dataloader_prefetch_factor is not None:
-            training_args_kwargs[
-                "dataloader_prefetch_factor"
-            ] = self.cfg.dataloader_prefetch_factor
+            training_args_kwargs["dataloader_prefetch_factor"] = (
+                self.cfg.dataloader_prefetch_factor
+            )
         if self.cfg.gradient_checkpointing:
-            training_args_kwargs[
-                "gradient_checkpointing"
-            ] = self.cfg.gradient_checkpointing
+            training_args_kwargs["gradient_checkpointing"] = (
+                self.cfg.gradient_checkpointing
+            )
             if self.cfg.gradient_checkpointing_kwargs is not None:
-                training_args_kwargs[
-                    "gradient_checkpointing_kwargs"
-                ] = self.cfg.gradient_checkpointing_kwargs
+                training_args_kwargs["gradient_checkpointing_kwargs"] = (
+                    self.cfg.gradient_checkpointing_kwargs
+                )
             else:
                 training_args_kwargs["gradient_checkpointing_kwargs"] = {
                     "use_reentrant": False
@@ -1071,9 +1074,9 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
             if self.cfg.dpo_use_weighting is not None:
                 training_args_kwargs["use_weighting"] = self.cfg.dpo_use_weighting
             if self.cfg.dpo_use_logits_to_keep is not None:
-                training_args_kwargs[
-                    "use_logits_to_keep"
-                ] = self.cfg.dpo_use_logits_to_keep
+                training_args_kwargs["use_logits_to_keep"] = (
+                    self.cfg.dpo_use_logits_to_keep
+                )
 
         for blocklist_key in blocklist_args_kwargs:
             if blocklist_key in training_args_kwargs:
@@ -1108,9 +1111,9 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
         if self.cfg.adapter and self.peft_config:
             dpo_trainer_kwargs["peft_config"] = self.peft_config
         if self.cfg.precompute_ref_log_probs is not None:
-            dpo_trainer_kwargs[
-                "precompute_ref_log_probs"
-            ] = self.cfg.precompute_ref_log_probs
+            dpo_trainer_kwargs["precompute_ref_log_probs"] = (
+                self.cfg.precompute_ref_log_probs
+            )
         if self.cfg.rl == "grpo":
             trainer_cls = GRPOStrategy.get_trainer_class()
             trainer_cls_args = [self.model]

src/axolotl/core/trainers/__init__.py

@@ -0,0 +1,18 @@
+"""Init for axolotl.core.trainers"""
+
+# pylint: disable=unused-import
+# flake8: noqa
+
+from .base import AxolotlTrainer
+from .dpo.trainer import AxolotlDPOTrainer
+from .grpo.trainer import AxolotlGRPOTrainer
+from .mamba import AxolotlMambaTrainer
+from .relora import ReLoRATrainer
+from .trl import (
+    AxolotlCPOTrainer,
+    AxolotlKTOTrainer,
+    AxolotlORPOTrainer,
+    AxolotlPRMTrainer,
+    AxolotlRewardTrainer,
+    TRLPPOTrainer,
+)

src/axolotl/core/trainers/base.py

@@ -1,366 +1,47 @@
-"""
-module for customized trainers
-"""
+"""Module for customized trainers"""
+
+# pylint: disable=too-many-lines
 
 from __future__ import annotations
 
-# pylint: disable=too-many-lines
 import logging
 import os
 from collections import defaultdict
 from functools import wraps
-from typing import Dict, Literal, Optional
+from typing import Any, Literal
 
+import datasets
 import torch
 from datasets import Dataset
-from peft.optimizers import create_loraplus_optimizer
 from torch import nn
-from torch.optim.lr_scheduler import OneCycleLR
-from torch.utils.data import BatchSampler, DataLoader, RandomSampler, SequentialSampler
+from torch.utils.data import (
+    BatchSampler,
+    DataLoader,
+    RandomSampler,
+    Sampler,
+    SequentialSampler,
+)
 from transformers import Trainer
 from transformers.trainer_utils import PREFIX_CHECKPOINT_DIR, seed_worker
-from transformers.utils import is_sagemaker_mp_enabled
-from trl import CPOTrainer, ORPOTrainer, PRMTrainer, RewardTrainer
 from trl.trainer.utils import pad_to_length
+from typing_extensions import override
 
-from axolotl.core.trainers.kto import AxolotlKTOTrainer
-from axolotl.integrations.base import BaseOptimizerFactory
-from axolotl.monkeypatch.relora import ReLoRAScheduler
-from axolotl.utils.samplers import MultipackBatchSampler, get_dataset_lengths
-from axolotl.utils.schedulers import (
-    RexLR,
-    get_cosine_schedule_with_min_lr,
-    get_cosine_schedule_with_quadratic_warmup,
-    get_cosine_schedule_with_warmup_decay_constant,
+from axolotl.core.trainers.mixins import (
+    OptimizerMixin,
+    SchedulerMixin,
+    SequenceParallelMixin,
 )
+from axolotl.core.trainers.utils import (
+    sanitize_kwargs_for_ds_tagging,
+    sanitize_kwargs_for_tagging,
+)
+from axolotl.utils.samplers import MultipackBatchSampler, get_dataset_lengths
 
-if is_sagemaker_mp_enabled():
-    import smdistributed.modelparallel.torch as smp
-
-LOG = logging.getLogger("axolotl.core.trainer_builder")
+LOG = logging.getLogger(__name__)
 
 
-def _sanitize_kwargs_for_tagging(tag_names, kwargs=None):
-    if isinstance(tag_names, str):
-        tag_names = [tag_names]
-
-    if kwargs is not None:
-        if "tags" not in kwargs:
-            kwargs["tags"] = tag_names
-        elif "tags" in kwargs and isinstance(kwargs["tags"], list):
-            kwargs["tags"].extend(tag_names)
-        elif "tags" in kwargs and isinstance(kwargs["tags"], str):
-            tag_names.append(kwargs["tags"])
-            kwargs["tags"] = tag_names
-
-    return kwargs
-
-
-def _sanitize_kwargs_for_ds_tagging(dataset_tags, kwargs=None):
-    if isinstance(dataset_tags, str):
-        dataset_tags = [dataset_tags]
-
-    if (dataset_tags is not None) and (kwargs is not None):
-        if "dataset_tags" not in kwargs:
-            kwargs["dataset_tags"] = dataset_tags
-        elif "dataset_tags" in kwargs and isinstance(kwargs["dataset_tags"], list):
-            kwargs["dataset_tags"].extend(dataset_tags)
-        elif "dataset_tags" in kwargs and isinstance(kwargs["dataset_tags"], str):
-            dataset_tags.append(kwargs["dataset_tags"])
-            kwargs["dataset_tags"] = dataset_tags
-
-    return kwargs
-
-
-class SchedulerMixin(Trainer):
-    """
-    Mixin class for scheduler setup in CausalTrainer.
-    """
-
-    args = None  # type: "AxolotlTrainingArguments"  # type: ignore[name-defined]
-
-    def create_scheduler(
-        self, num_training_steps: int, optimizer: torch.optim.Optimizer = None
-    ):
-        """
-        Setup the scheduler. The optimizer of the trainer must have been set up either before this method is called or
-        passed as an argument.
-
-        Args:
-            num_training_steps (int): The number of training steps to do.
-            optimizer (torch.optim.Optimizer): The training optimizer
-        """
-        use_cosine_quadratic = (
-            self.args.lr_scheduler_type == "cosine"
-            and self.args.lr_quadratic_warmup is True
-        )
-
-        use_cosine_min_lr = (
-            self.args.lr_scheduler_type == "cosine"
-            and self.args.cosine_min_lr_ratio is not None
-        )
-
-        # fmt: off
-        if self.lr_scheduler is None:  # type: ignore  # pylint: disable=access-member-before-definition
-            # fmt: on
-            if self.args.alternate_lr_scheduler_type == "one_cycle":
-                num_warmup_steps = self.args.get_warmup_steps(num_training_steps)
-                pct_start = num_warmup_steps / num_training_steps
-                extra_lr_kwargs = {}
-                if "pct_start" not in self.args.lr_scheduler_kwargs:
-                    extra_lr_kwargs["pct_start"] = pct_start
-                if "anneal_strategy" not in self.args.lr_scheduler_kwargs:
-                    extra_lr_kwargs["anneal_strategy"] = "cos"
-
-                self.lr_scheduler = OneCycleLR(
-                    optimizer,
-                    max_lr=self.args.learning_rate,
-                    total_steps=num_training_steps,
-                    **extra_lr_kwargs,
-                    **self.args.lr_scheduler_kwargs,
-                )
-            elif self.args.alternate_lr_scheduler_type == "rex":
-                if use_cosine_min_lr:
-                    assert 0 <= self.args.cosine_min_lr_ratio <= 1.0, "cosine_min_lr_ratio must be between 0.0 and 1.0"
-
-                self.lr_scheduler = RexLR(
-                    optimizer=optimizer,
-                    max_lr=self.args.learning_rate,
-                    min_lr=0 if not use_cosine_min_lr else (self.args.learning_rate * self.args.cosine_min_lr_ratio),
-                    total_steps=num_training_steps,
-                    num_warmup_steps=self.args.get_warmup_steps(num_training_steps),
-                )
-            elif use_cosine_quadratic:
-                if use_cosine_min_lr:
-                    LOG.warning("Both cosine quadratic warmup and min lr detected. Using quadratic warmup.")
-
-                self.lr_scheduler = get_cosine_schedule_with_quadratic_warmup(  # pylint: disable=attribute-defined-outside-init
-                    optimizer,
-                    num_warmup_steps=self.args.get_warmup_steps(num_training_steps),
-                    num_training_steps=num_training_steps,
-                )
-            elif self.args.cosine_min_lr_ratio and self.args.cosine_constant_lr_ratio and use_cosine_min_lr:
-                assert 0 <= self.args.cosine_min_lr_ratio <= 1.0, "cosine_min_lr_ratio must be between 0.0 and 1.0"
-                assert 0 <= self.args.cosine_constant_lr_ratio <= 1.0, "cosine_constant_lr_ratio must be between 0.0 and 1.0"
-                self.lr_scheduler = get_cosine_schedule_with_warmup_decay_constant(  # pylint: disable=attribute-defined-outside-init
-                    optimizer,
-                    num_warmup_steps=self.args.get_warmup_steps(num_training_steps),
-                    num_training_steps=num_training_steps,
-                    min_lr_ratio=self.args.cosine_min_lr_ratio,
-                    constant_lr_ratio=self.args.cosine_constant_lr_ratio,
-                )
-            elif self.args.cosine_min_lr_ratio and use_cosine_min_lr:
-                assert 0 <= self.args.cosine_min_lr_ratio <= 1.0, "cosine_min_lr_ratio must be between 0.0 and 1.0"
-                self.lr_scheduler = get_cosine_schedule_with_min_lr(  # pylint: disable=attribute-defined-outside-init
-                    optimizer,
-                    num_warmup_steps=self.args.get_warmup_steps(num_training_steps),
-                    num_training_steps=num_training_steps,
-                    min_lr_ratio=self.args.cosine_min_lr_ratio,
-                )
-            else:
-                return super().create_scheduler(num_training_steps, optimizer=optimizer)
-        else:
-            if use_cosine_quadratic:
-                LOG.warning("axolotl's cosine scheduler with quadratic warmup not used (e.g., because of deepspeed).")
-
-            if use_cosine_min_lr:
-                LOG.warning("axolotl's cosine scheduler with min lr not used (e.g., because of deepspeed).")
-
-        return self.lr_scheduler
-
-
-class OptimizerMixin(Trainer):
-    """
-    Mixin class for shared handling of building custom optimizers
-    """
-
-    args = None  # type: "AxolotlTrainingArguments"  # type: ignore[name-defined]
-
-    def create_optimizer_grouped_parameters(
-        self, opt_model, optimizer_kwargs
-    ) -> list[dict]:
-        decay_parameters = self.get_decay_parameter_names(opt_model)
-        params: dict = {
-            "to_weight_decay": {},  # LayerNorm and bias
-            "embeddings": {},  # lm_head, embed_tokens,
-            "no_weight_decay": {},
-        }
-        lr_groups_lookup = {}
-        lr_groups_learning_rates = {}
-        if self.args.lr_groups:
-            for lr_group in self.args.lr_groups:
-                group_name = lr_group["name"]
-                group_modules = lr_group["modules"]
-                for module in group_modules:
-                    lr_groups_lookup[module] = group_name
-                lr_groups_learning_rates[group_name] = lr_group["lr"]
-                params[f"to_weight_decay_{group_name}"] = {}
-
-        for name, param in opt_model.named_parameters():
-            if not param.requires_grad:
-                continue
-            if name.endswith("modules_to_save.default.weight") or any(
-                embed_name in name for embed_name in ["embed_tokens", "lm_head"]
-            ):
-                params["embeddings"][name] = param
-            elif name in decay_parameters:
-                lr_group_modules = [
-                    group_modules
-                    for group_modules in lr_groups_lookup
-                    if group_modules in name
-                ]
-                if lr_groups_lookup and any(lr_group_modules):
-                    lr_group_module = lr_group_modules[0]
-                    group_name = lr_groups_lookup[lr_group_module]
-                    params[f"to_weight_decay_{group_name}"][name] = param
-                else:
-                    params["to_weight_decay"][name] = param
-            else:
-                params["no_weight_decay"][name] = param
-        optimizer_grouped_parameters = []
-        if params["to_weight_decay"]:
-            optimizer_grouped_parameters.append(
-                {
-                    "params": list(params["to_weight_decay"].values()),
-                    "weight_decay": self.args.weight_decay,
-                    "lr": optimizer_kwargs["lr"],
-                }
-            )
-        if params["embeddings"]:
-            lr = optimizer_kwargs["lr"]  # pylint: disable=invalid-name
-            if self.args.embedding_lr_scale:
-                lr *= self.args.embedding_lr_scale  # pylint: disable=invalid-name
-            elif self.args.embedding_lr:
-                lr = self.args.embedding_lr  # pylint: disable=invalid-name
-            optimizer_grouped_parameters.append(
-                {
-                    "params": list(params["embeddings"].values()),
-                    "weight_decay": 0.0,
-                    "lr": lr,
-                }
-            )
-        if params["no_weight_decay"]:
-            optimizer_grouped_parameters.append(
-                {
-                    "params": list(params["no_weight_decay"].values()),
-                    "weight_decay": 0.0,
-                    "lr": optimizer_kwargs["lr"],
-                }
-            )
-        for group_name, group_lr in lr_groups_learning_rates.items():
-            if params[f"to_weight_decay_{group_name}"]:
-                optimizer_grouped_parameters.append(
-                    {
-                        "params": list(
-                            params[f"to_weight_decay_{group_name}"].values()
-                        ),
-                        "weight_decay": self.args.weight_decay,
-                        "lr": group_lr,
-                    }
-                )
-
-        return optimizer_grouped_parameters
-
-    def create_optimizer(self):
-        if (
-            self.args.loraplus_lr_ratio is None
-            and self.args.embedding_lr_scale is None
-            and self.args.embedding_lr is None
-            and self.args.lr_groups is None
-            and self.optimizer_cls_and_kwargs is None
-        ):
-            return super().create_optimizer()
-
-        opt_model = self.model_wrapped if is_sagemaker_mp_enabled() else self.model
-
-        if (
-            not self.optimizer
-            and self.optimizer_cls_and_kwargs is not None
-            and issubclass(self.optimizer_cls_and_kwargs[0], BaseOptimizerFactory)
-        ):
-            optimizer_factory_cls, optimizer_kwargs = self.optimizer_cls_and_kwargs
-            self.optimizer = optimizer_factory_cls()(
-                opt_model, self.args, **optimizer_kwargs
-            )
-
-        if not self.optimizer:
-            if self.optimizer_cls_and_kwargs is not None:
-                optimizer_cls, optimizer_kwargs = self.optimizer_cls_and_kwargs
-            else:
-                optimizer_cls, optimizer_kwargs = self.get_optimizer_cls_and_kwargs(
-                    self.args, opt_model
-                )
-
-            optimizer_grouped_parameters = self.create_optimizer_grouped_parameters(
-                opt_model, optimizer_kwargs
-            )
-
-            if self.args.loraplus_lr_ratio is not None:
-                loraplus_lr_ratio = getattr(self.args, "loraplus_lr_ratio", None)
-                loraplus_lr_embedding = getattr(
-                    self.args, "loraplus_lr_embedding", 1e-6
-                )
-                self.optimizer = create_loraplus_optimizer(  # pylint: disable=attribute-defined-outside-init
-                    opt_model,
-                    optimizer_cls,
-                    loraplus_lr_ratio=loraplus_lr_ratio,
-                    loraplus_lr_embedding=loraplus_lr_embedding,
-                    **optimizer_kwargs,
-                )
-            else:
-                # Overwrite `params` in case it's created by `get_optimizer_cls_and_kwargs`
-                # e.g. for GaLore optimizer.
-                if "params" in optimizer_kwargs:
-                    optimizer_grouped_parameters = optimizer_kwargs.pop("params")
-
-                # Overwrite `model` in case it's created by `get_optimizer_cls_and_kwargs`
-                # e.g. for LOMO optimizer.
-                if "model" in optimizer_kwargs:
-                    optimizer_grouped_parameters = optimizer_kwargs.pop("model")
-
-                # For layer-wise dummy optimizers we overwrite optimizer_grouped_parameters with `optimizer_dict`
-                # to avoid arguments conflicts.
-                if "optimizer_dict" in optimizer_kwargs:
-                    optimizer_grouped_parameters = optimizer_kwargs.pop(
-                        "optimizer_dict"
-                    )
-
-                self.optimizer = optimizer_cls(
-                    optimizer_grouped_parameters, **optimizer_kwargs
-                )
-
-            if optimizer_cls.__name__ == "Adam8bit":
-                import bitsandbytes
-
-                manager = bitsandbytes.optim.GlobalOptimManager.get_instance()
-
-                skipped = 0
-                for module in opt_model.modules():
-                    if isinstance(module, nn.Embedding):
-                        skipped += sum(
-                            {
-                                p.data_ptr(): p.numel() for p in module.parameters()
-                            }.values()
-                        )
-                        LOG.info(f"skipped {module}: {skipped/2**20}M params")
-                        manager.register_module_override(
-                            module, "weight", {"optim_bits": 32}
-                        )
-                        LOG.debug(f"bitsandbytes: will optimize {module} in fp32")
-                LOG.info(f"skipped: {skipped/2**20}M params")
-
-        if is_sagemaker_mp_enabled():
-            self.optimizer = smp.DistributedOptimizer(  # pylint: disable=attribute-defined-outside-init
-                self.optimizer
-            )
-
-        return self.optimizer
-
-
-class AxolotlTrainer(SchedulerMixin, OptimizerMixin, Trainer):
-    """
-    Extend the base Trainer for axolotl helpers
-    """
+class AxolotlTrainer(SchedulerMixin, OptimizerMixin, SequenceParallelMixin, Trainer):
+    """Extend the base Trainer for axolotl helpers"""
 
     args = None  # type: "AxolotlTrainingArguments"  # type: ignore[name-defined]
     tag_names = ["axolotl"]
@@ -377,12 +58,18 @@ class AxolotlTrainer(SchedulerMixin, OptimizerMixin, Trainer):
         self.eval_data_collator = eval_data_collator
         self.dataset_tags = dataset_tags
         self._signature_columns = None  # workaround for pylint
+
         super().__init__(*_args, **kwargs)
+
         self.train_data_collator = self.data_collator
         self._stored_metrics = defaultdict(lambda: defaultdict(list))
         if self.args.orpo_alpha:
             self.loss_fct = torch.nn.CrossEntropyLoss(reduction="none")
 
+        # Initialize sequence parallelism if enabled
+        if self.args.sequence_parallel_degree > 1:
+            self._setup_sequence_parallel()
+
     def _wrap_model(self, model, training=True, dataloader=None):
         if self.args.torch_compile:
             torch._dynamo.config.accumulated_cache_size_limit = (  # pylint: disable=protected-access
@@ -395,142 +82,247 @@ class AxolotlTrainer(SchedulerMixin, OptimizerMixin, Trainer):
             )
         return super()._wrap_model(model, training=training, dataloader=dataloader)
 
-    def _get_train_sampler(self) -> Optional[torch.utils.data.Sampler]:
-        if self.args.sample_packing and not self.args.pretraining:
-            if self.args.multipack_real_batches:
-                batch_size = self.args.per_device_train_batch_size
-                batch_max_len = self.args.max_seq_length
-            else:
-                batch_size = 1
-                train_batch_size = (
-                    self.state.train_batch_size or self.args.per_device_train_batch_size
-                )
-                batch_max_len = train_batch_size * self.args.max_seq_length
+    def _create_multipack_sampler(
+        self, base_sampler: Sampler, dataset: Dataset
+    ) -> MultipackBatchSampler:
+        """
+        Helper method to create a `MultipackBatchSampler` for multipacking sequences
+        for training.
 
-            if self.args.curriculum_sampling:
-                sampler = SequentialSampler(self.train_dataset)
-            else:
-                sampler = RandomSampler(self.train_dataset)
+        Args:
+            base_sampler: Sampler to wrap with `MultipackBatchSampler`.
+            dataset: Dataset to sample from.
 
-            return MultipackBatchSampler(
-                sampler,
-                lengths=get_dataset_lengths(self.train_dataset),
-                packing_efficiency_estimate=self.args.sample_packing_efficiency,
-                batch_max_len=batch_max_len,
-                batch_size=batch_size,
-                group_size=self.args.sample_packing_group_size,
-                bin_size=self.args.sample_packing_bin_size,
-                drop_last=True,
+        Returns:
+            Multipack (sample packing) batch sampler.
+        """
+        if self.args.multipack_real_batches:
+            batch_size = self.args.per_device_train_batch_size
+            batch_max_len = self.args.max_seq_length
+        else:
+            batch_size = 1
+            train_batch_size = (
+                self.state.train_batch_size or self.args.per_device_train_batch_size
             )
-        if self.args.curriculum_sampling:
-            return SequentialSampler(self.train_dataset)
-        return super()._get_train_sampler()
+            batch_max_len = train_batch_size * self.args.max_seq_length
 
-    def _get_eval_sampler(
-        self, eval_dataset: Dataset
-    ) -> Optional[torch.utils.data.Sampler]:
-        if self.args.sample_packing and self.args.eval_sample_packing is not False:
-            if self.args.multipack_real_batches:
-                batch_size = self.args.per_device_eval_batch_size
-                batch_max_len = self.args.max_seq_length
-            else:
-                batch_size = 1
-                batch_max_len = (
-                    self.args.per_device_eval_batch_size * self.args.max_seq_length
-                )
-            return MultipackBatchSampler(
-                SequentialSampler(eval_dataset),
-                lengths=get_dataset_lengths(self.eval_dataset),
-                packing_efficiency_estimate=self.args.sample_packing_efficiency,
-                batch_max_len=batch_max_len,
-                batch_size=batch_size,
-                group_size=self.args.sample_packing_group_size,
-                bin_size=self.args.sample_packing_bin_size,
-                drop_last=True,
+        return MultipackBatchSampler(
+            base_sampler,
+            lengths=get_dataset_lengths(dataset),
+            packing_efficiency_estimate=self.args.sample_packing_efficiency,
+            batch_max_len=batch_max_len,
+            batch_size=batch_size,
+            drop_last=True,
+        )
+
+    def _get_train_sampler(self) -> Sampler | None:
+        """
+        Helper method to get the sampler for training. Handles cases for sequence
+        parallelism, sample packing, and curriculum sampling (sequential).
+
+        Returns:
+            If the dataset is non-empty, a sampler is returned, the type of which
+                depends on the passed training args.
+        """
+        use_sample_packing = self.args.sample_packing and not self.args.pretraining
+
+        # Determine the base sampler first
+        if self.args.sequence_parallel_degree > 1:
+            base_sampler = self._sp_get_train_sampler(self.train_dataset)
+        elif self.args.curriculum_sampling:
+            base_sampler = SequentialSampler(self.train_dataset)
+        elif use_sample_packing:
+            base_sampler = RandomSampler(self.train_dataset)
+        else:
+            # Default to parent class implementation for standard random sampling
+            return super()._get_train_sampler()
+
+        # Apply multipack wrapper if needed
+        if use_sample_packing:
+            return self._create_multipack_sampler(
+                base_sampler=base_sampler,
+                dataset=self.train_dataset,
             )
-        return super()._get_eval_sampler(eval_dataset)
 
-    def get_train_dataloader(self) -> DataLoader:
-        if self.args.sample_packing and not self.args.pretraining:
-            train_dataset = self.train_dataset
-            if "length" in train_dataset.features.keys():
-                train_dataset = train_dataset.remove_columns(["length"])
-            data_collator = self.data_collator
-            dataloader_params = {
-                "batch_size": self._train_batch_size,
-                "collate_fn": data_collator,
-                "num_workers": self.args.dataloader_num_workers,
-                "pin_memory": self.args.dataloader_pin_memory,
-            }
-            if self.args.dataloader_prefetch_factor:
-                dataloader_params[
-                    "prefetch_factor"
-                ] = self.args.dataloader_prefetch_factor
+        return base_sampler
 
-            sampler = self._get_train_sampler()
+    def _get_eval_sampler(self, eval_dataset: Dataset | None = None) -> Sampler | None:
+        """
+        Helper method to get the sampler for evaluation. Handles sequence parallelism
+        and sample packing cases.
+
+        Returns:
+            If the dataset is non-empty, a sampler is returned, the type of which
+                depends on the passed training args.
+        """
+        eval_dataset = eval_dataset if eval_dataset is not None else self.eval_dataset
+
+        # Multipacking enabled if training is enabled and eval is not explicitly disabled
+        use_multipack = (
+            self.args.sample_packing and self.args.eval_sample_packing is not False
+        )
+
+        # Determine the base sampler
+        if self.args.sequence_parallel_degree > 1:
+            base_sampler = self._sp_get_eval_sampler(eval_dataset)
+        elif use_multipack:
+            base_sampler = SequentialSampler(eval_dataset)
+        else:
+            return super()._get_eval_sampler(eval_dataset)
+
+        # Apply multipack wrapper if needed
+        if use_multipack:
+            return self._create_multipack_sampler(
+                base_sampler=base_sampler,
+                dataset=eval_dataset,
+            )
+
+        return base_sampler
+
+    def _create_dataloader_params(self, is_eval=False, custom_batch_size=None):
+        """Create common dataloader parameters for train or eval."""
+        batch_size = custom_batch_size or (
+            self.args.eval_batch_size if is_eval else self._train_batch_size
+        )
+
+        params = {
+            "batch_size": batch_size,
+            "collate_fn": self.data_collator,
+            "num_workers": self.args.dataloader_num_workers,
+            "pin_memory": self.args.dataloader_pin_memory,
+        }
+
+        # Add persistent workers only for training
+        if not is_eval and hasattr(self.args, "dataloader_persistent_workers"):
+            params["persistent_workers"] = self.args.dataloader_persistent_workers
+
+        # Add prefetch factor if specified
+        if self.args.dataloader_prefetch_factor:
+            params["prefetch_factor"] = self.args.dataloader_prefetch_factor
+
+        return params
+
+    def _prepare_dataloader(
+        self, dataset, sampler, is_eval=False, custom_batch_size=None
+    ):
+        """Prepare a dataloader with the given dataset and sampler."""
+        # Get base parameters
+        dataloader_params = self._create_dataloader_params(is_eval, custom_batch_size)
+
+        # Add sampler configuration
+        if not isinstance(dataset, torch.utils.data.IterableDataset):
             if isinstance(sampler, BatchSampler):
+                # batch_size and batch_sampler are mutually exclusive
                 dataloader_params["batch_sampler"] = sampler
                 del dataloader_params["batch_size"]
             else:
                 dataloader_params["sampler"] = sampler
                 dataloader_params["drop_last"] = self.args.dataloader_drop_last
-            dataloader_params["worker_init_fn"] = seed_worker
 
+            if not is_eval:
+                dataloader_params["worker_init_fn"] = seed_worker
+
+        # Create the dataloader
+        dataloader = DataLoader(dataset, **dataloader_params)
+
+        if self.args.sample_packing and (
+            (not is_eval and not self.args.pretraining)
+            or (is_eval and self.args.eval_sample_packing is not False)
+        ):
             self.accelerator.even_batches = False
-            return self.accelerator.prepare_data_loader(
-                DataLoader(train_dataset, **dataloader_params)
-            )
-        return super().get_train_dataloader()
 
-    def get_eval_dataloader(self, eval_dataset: Optional[Dataset] = None) -> DataLoader:
+        # Return unprepared dataloader if using sequence parallelism
+        if self.args.sequence_parallel_degree > 1:
+            return dataloader
+
+        # Otherwise prepare with accelerator
+        return self.accelerator.prepare_data_loader(dataloader)
+
+    def get_train_dataloader(self) -> DataLoader:
+        """Get dataloader for training"""
+        train_dataset = self.train_dataset
+        data_collator = self.data_collator  # type: ignore
+
+        # Handle dataset preprocessing
+        if isinstance(train_dataset, datasets.Dataset):
+            if self.args.sample_packing and not self.args.pretraining:
+                train_dataset = train_dataset.remove_columns(["length"])
+            if not self.args.sample_packing or self.args.pretraining:
+                train_dataset = self._remove_unused_columns(
+                    train_dataset, description="training"
+                )
+        else:
+            self.data_collator = self._get_collator_with_removed_columns(  # pylint: disable=attribute-defined-outside-init
+                data_collator,
+                description="training",
+            )
+
+        # Get sampler and create dataloader
+        sampler = self._get_train_sampler()
+        return self._prepare_dataloader(train_dataset, sampler, is_eval=False)
+
+    def get_eval_dataloader(self, eval_dataset: Dataset | None = None) -> DataLoader:
+        """Get dataloader for evaluation"""
+        eval_dataset = eval_dataset if eval_dataset is not None else self.eval_dataset
+
+        # Handle special case: sample packing is enabled but eval_sample_packing is False
         if self.args.sample_packing and self.args.eval_sample_packing is False:
             self.data_collator = (  # pylint: disable=attribute-defined-outside-init
                 self.eval_data_collator
             )
-            if eval_dataset:
+            if "length" in eval_dataset.column_names:
                 eval_dataset = eval_dataset.remove_columns(["length"])
             dataloader = super().get_eval_dataloader(eval_dataset)
             self.data_collator = (  # pylint: disable=attribute-defined-outside-init
                 self.train_data_collator
             )
+
             return dataloader
 
-        if self.args.sample_packing and self.args.eval_sample_packing is not False:
-            eval_dataset = (
-                eval_dataset if eval_dataset is not None else self.eval_dataset
+        # Handle sample packing or sequence parallelism
+        if (
+            self.args.sample_packing
+            and self.args.eval_sample_packing is not False
+            or self.args.sequence_parallel_degree > 1
+        ):
+            # Get appropriate data collator
+            self.data_collator = (  # pylint: disable=attribute-defined-outside-init
+                self.eval_data_collator
+                if hasattr(self, "eval_data_collator") and self.eval_data_collator
+                else self.data_collator
+            )
+            if "length" in eval_dataset.column_names:
+                eval_dataset = eval_dataset.remove_columns(["length"])
+
+            # Handle dataset preprocessing for SP
+            if self.args.sequence_parallel_degree > 1:
+                if isinstance(eval_dataset, datasets.Dataset):
+                    eval_dataset = self._remove_unused_columns(
+                        eval_dataset, description="evaluation"
+                    )
+                else:
+                    self.data_collator = self._get_collator_with_removed_columns(  # pylint: disable=attribute-defined-outside-init
+                        self.data_collator, description="evaluation"
+                    )
+
+            # Use eval_batch_size for sample packing, per_device_eval_batch_size otherwise
+            batch_size = (
+                self.args.eval_batch_size
+                if self.args.sample_packing
+                else self.args.per_device_eval_batch_size
+            )
+            sampler = self._get_eval_sampler(eval_dataset)
+            dataloader = self._prepare_dataloader(
+                eval_dataset, sampler, is_eval=True, custom_batch_size=batch_size
             )
 
-            eval_sampler = self._get_eval_sampler(eval_dataset)
-            eval_dataset = eval_dataset.remove_columns(["length"])
-            data_collator = self.data_collator
-            dataloader_params = {
-                "batch_size": self.args.eval_batch_size,
-                "collate_fn": data_collator,
-                "num_workers": self.args.dataloader_num_workers,
-                "pin_memory": self.args.dataloader_pin_memory,
-            }
-            if self.args.dataloader_prefetch_factor:
-                dataloader_params[
-                    "prefetch_factor"
-                ] = self.args.dataloader_prefetch_factor
-
-            if isinstance(eval_sampler, BatchSampler):
-                dataloader_params["batch_sampler"] = eval_sampler
-                del dataloader_params["batch_size"]
-            else:
-                dataloader_params["sampler"] = eval_sampler
-                dataloader_params["drop_last"] = self.args.dataloader_drop_last
-
-            self.accelerator.even_batches = False
-            return self.accelerator.prepare_data_loader(
-                DataLoader(eval_dataset, **dataloader_params)
-            )
+            return dataloader
 
         return super().get_eval_dataloader(eval_dataset)
 
     def _get_bench_sampler(
         self, bench_dataset: Dataset
-    ) -> Optional[torch.utils.data.Sampler]:
+    ) -> torch.utils.data.Sampler | None:
         if self.args.world_size <= 1:
             return SequentialSampler(bench_dataset)
         return None
@@ -555,6 +347,7 @@ class AxolotlTrainer(SchedulerMixin, OptimizerMixin, Trainer):
         return DataLoader(bench_dataset, **dataloader_params)
         # return self.accelerator.prepare(DataLoader(bench_dataset, **dataloader_params))
 
+    @override
     def compute_loss(
         self, model, inputs, return_outputs=False, num_items_in_batch=None
     ):
@@ -571,6 +364,7 @@ class AxolotlTrainer(SchedulerMixin, OptimizerMixin, Trainer):
                 return_outputs=return_outputs,
                 num_items_in_batch=num_items_in_batch,
             )
+
         return super().compute_loss(
             model,
             inputs,
@@ -745,10 +539,10 @@ class AxolotlTrainer(SchedulerMixin, OptimizerMixin, Trainer):
         Overwrite the `push_to_hub` method in order to force-add the tags when pushing the
         model on the Hub. Please refer to `~transformers.Trainer.push_to_hub` for more details.
         """
-        kwargs = _sanitize_kwargs_for_ds_tagging(
+        kwargs = sanitize_kwargs_for_ds_tagging(
             dataset_tags=self.dataset_tags, kwargs=kwargs
         )
-        kwargs = _sanitize_kwargs_for_tagging(tag_names=self.tag_names, kwargs=kwargs)
+        kwargs = sanitize_kwargs_for_tagging(tag_names=self.tag_names, kwargs=kwargs)
 
         return super().push_to_hub(*args, **kwargs)
 
@@ -765,15 +559,13 @@ class AxolotlTrainer(SchedulerMixin, OptimizerMixin, Trainer):
 
         return res
 
-    def log(self, logs: Dict[str, float], start_time: Optional[float] = None) -> None:
+    def log(self, logs: dict[str, float], start_time: float | None = None) -> None:
         """
         Log `logs` on the various objects watching training, including stored metrics.
 
         Args:
-            logs (`Dict[str, float]`):
-                The values to log.
-            start_time (`Optional[float]`):
-                The start of training.
+            logs: The values to log.
+            start_time: The start of training.
         """
         # logs either has 'loss' or 'eval_loss'
         train_eval = "train" if "loss" in logs else "eval"
@@ -785,7 +577,7 @@ class AxolotlTrainer(SchedulerMixin, OptimizerMixin, Trainer):
         return super().log(logs, start_time)
 
     def store_metrics(
-        self, metrics: Dict[str, float], train_eval: Literal["train", "eval"] = "train"
+        self, metrics: dict[str, float], train_eval: Literal["train", "eval"] = "train"
     ) -> None:
         for key, value in metrics.items():
             self._stored_metrics[train_eval][key].append(value)
@@ -798,102 +590,26 @@ class AxolotlTrainer(SchedulerMixin, OptimizerMixin, Trainer):
         os.makedirs(output_dir, exist_ok=True)
         return super()._save_checkpoint(model, trial, **kwargs)
 
-
-class AxolotlMambaTrainer(AxolotlTrainer):
-    """
-    Mamba specific trainer to handle loss calculation
-    """
-
-    tag_names = ["axolotl", "mamba"]
-
-    def compute_loss(
+    def training_step(
         self,
-        model,
-        inputs,
-        return_outputs=False,  # pylint: disable=unused-argument
-        num_items_in_batch=None,  # pylint: disable=unused-argument
-    ):
-        input_ids = inputs.pop("input_ids")
-        lm_logits = model(input_ids).logits
+        model: nn.Module,
+        inputs: dict[str, torch.Tensor | Any],
+        num_items_in_batch: int | None = None,
+    ) -> torch.Tensor:
+        """
+        Perform a training step on a batch of inputs. Overrides the
+        `transformers.trainer.Trainer` method to handle sequence parallelism if
+        enabled.
 
-        labels = input_ids.to(lm_logits.device)
-        shift_logits = lm_logits[:, :-1, :].contiguous()
-        labels = labels[:, 1:].contiguous()
+        Args:
+            model: Model to perform training step for.
+            inputs: Dictionary mapping.
+        """
+        # Set up sequence parallelism for this step if enabled
+        if self.args.sequence_parallel_degree > 1:
+            self._update_ring_flash_attn_params(inputs)
 
-        loss_fct = torch.nn.CrossEntropyLoss()
-        lm_loss = loss_fct(
-            shift_logits.view(-1, shift_logits.size(-1)), labels.view(-1)
-        )
+        # Proceed with normal training step
+        loss = super().training_step(model, inputs, num_items_in_batch)
 
-        return lm_loss
-
-
-class ReLoRATrainer(AxolotlTrainer):
-    """
-    Trainer subclass that uses the OneCycleLR scheduler
-    """
-
-    tag_names = ["axolotl", "relora"]
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.lr_scheduler = None
-
-    def create_scheduler(
-        self,
-        num_training_steps: int,
-        optimizer: Optional[torch.optim.Optimizer] = None,
-    ):
-        optimizer = self.optimizer if optimizer is None else optimizer
-        lr_scheduler = super().create_scheduler(num_training_steps, optimizer)
-
-        if self.args.relora_steps:
-            warmup_steps = (
-                self.args.relora_warmup_steps if self.args.relora_warmup_steps else 10
-            )
-            anneal_steps = (
-                self.args.relora_anneal_steps if self.args.relora_anneal_steps else 1
-            )
-            self.lr_scheduler = ReLoRAScheduler(
-                optimizer,
-                lr_scheduler,
-                self.args.relora_steps,
-                anneal_steps,
-                warmup_steps,
-            )
-        else:
-            self.lr_scheduler = lr_scheduler
-
-        return self.lr_scheduler
-
-
-class AxolotlORPOTrainer(SchedulerMixin, ORPOTrainer):
-    """
-    Extend the base ORPOTrainer for axolotl helpers
-    """
-
-    tag_names = ["axolotl", "orpo"]
-
-
-class AxolotlCPOTrainer(SchedulerMixin, CPOTrainer):
-    """
-    Extend the base CPOTrainer for axolotl helpers
-    """
-
-    tag_names = ["axolotl", "cpo"]
-
-
-class AxolotlRewardTrainer(SchedulerMixin, RewardTrainer):
-    """
-    Extend the base RewardTrainer for axolotl helpers
-    """
-
-    tag_names = ["axolotl", "reward"]
-
-
-class AxolotlPRMTrainer(SchedulerMixin, PRMTrainer):
-    """
-    Extend the base trl.PRMTrainer for axolotl helpers
-    """
-
-    tag_names = ["axolotl", "prm"]
+        return loss

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

@@ -1,6 +1,7 @@
 """
 DPO Specific Strategy for training
 """
+
 from axolotl.core.trainers.dpo.trainer import AxolotlDPOTrainer
 
 

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

@@ -1,6 +1,7 @@
 """
 Axolotl specific DPO args
 """
+
 from dataclasses import dataclass
 
 from trl import DPOConfig

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

@@ -1,6 +1,7 @@
 """
 DPO trainer for axolotl
 """
+
 import gc
 from functools import wraps
 from typing import Any, Dict, Union
@@ -12,10 +13,10 @@ from transformers import Trainer
 from transformers.utils import is_sagemaker_mp_enabled
 from trl import DPOTrainer
 
-from axolotl.core.trainers.base import (
-    SchedulerMixin,
-    _sanitize_kwargs_for_ds_tagging,
-    _sanitize_kwargs_for_tagging,
+from axolotl.core.trainers.mixins import SchedulerMixin
+from axolotl.core.trainers.utils import (
+    sanitize_kwargs_for_ds_tagging,
+    sanitize_kwargs_for_tagging,
 )
 
 if is_sagemaker_mp_enabled():
@@ -73,10 +74,10 @@ class AxolotlDPOTrainer(SchedulerMixin, DPOTrainer):
         Overwrite the `push_to_hub` method in order to force-add the tags when pushing the
         model on the Hub. Please refer to `~transformers.Trainer.push_to_hub` for more details.
         """
-        kwargs = _sanitize_kwargs_for_ds_tagging(
+        kwargs = sanitize_kwargs_for_ds_tagging(
             dataset_tags=self.dataset_tags, kwargs=kwargs
         )
-        kwargs = _sanitize_kwargs_for_tagging(tag_names=self.tag_names, kwargs=kwargs)
+        kwargs = sanitize_kwargs_for_tagging(tag_names=self.tag_names, kwargs=kwargs)
 
         return super().push_to_hub(*args, **kwargs)
 

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

@@ -9,7 +9,7 @@ import logging
 from trl.trainer.grpo_trainer import RewardFunc
 
 from axolotl.core.trainers.grpo.trainer import AxolotlGRPOTrainer
-from axolotl.utils.config.models.input.v0_4_1.trl import TRLConfig
+from axolotl.utils.schemas.trl import TRLConfig
 
 LOG = logging.getLogger("axolotl")
 
@@ -45,9 +45,9 @@ class GRPOStrategy:
             )
 
             if trl.vllm_gpu_memory_utilization:
-                grpo_args_kwargs[
-                    "vllm_gpu_memory_utilization"
-                ] = trl.vllm_gpu_memory_utilization
+                grpo_args_kwargs["vllm_gpu_memory_utilization"] = (
+                    trl.vllm_gpu_memory_utilization
+                )
 
             if trl.vllm_max_model_len:
                 grpo_args_kwargs["vllm_max_model_len"] = trl.vllm_max_model_len
@@ -86,9 +86,9 @@ class GRPOStrategy:
     def set_trainer_kwargs(cls, cfg):
         trainer_kwargs = {}
         if cfg.trl and cfg.trl.reward_processing_classes:
-            trainer_kwargs[
-                "reward_processing_classes"
-            ] = cfg.trl.reward_processing_classes
+            trainer_kwargs["reward_processing_classes"] = (
+                cfg.trl.reward_processing_classes
+            )
         return trainer_kwargs
 
     @classmethod

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

@@ -1,6 +1,7 @@
 """
 Axolotl Specific Training Args
 """
+
 from dataclasses import dataclass
 
 from trl import GRPOConfig

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

@@ -1,6 +1,7 @@
 """
 Axolotl GRPO trainer
 """
+
 from accelerate.utils import is_peft_model
 from accelerate.utils.other import is_compiled_module
 from transformers import PreTrainedModel

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

@@ -1,7 +0,0 @@
-"""
-KTO package initialization.
-"""
-
-from axolotl.core.trainers.kto.trainer import AxolotlKTOTrainer
-
-__all__ = ["AxolotlKTOTrainer"]

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

@@ -1,512 +0,0 @@
-"""
-KTO trainer implementation for Axolotl.
-"""
-
-from __future__ import annotations
-
-import inspect
-import logging
-import warnings
-from contextlib import nullcontext
-from typing import Any, Callable, Literal, Optional, Union
-
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from datasets import Dataset
-from torch.utils.data import DataLoader, SequentialSampler
-from transformers import (
-    AutoModelForCausalLM,
-    BaseImageProcessor,
-    DataCollator,
-    FeatureExtractionMixin,
-    PreTrainedModel,
-    PreTrainedTokenizerBase,
-    ProcessorMixin,
-    Trainer,
-    TrainerCallback,
-    TrainingArguments,
-)
-from transformers.trainer_utils import EvalLoopOutput
-from trl import KTOTrainer
-from trl.trainer.kto_config import KTOConfig
-from trl.trainer.utils import KTODataCollatorWithPadding, pad_to_length
-
-from axolotl.core.trainers.base import SchedulerMixin
-
-# Check if PEFT is available
-try:
-    from peft import PeftModel, get_peft_model, prepare_model_for_kbit_training, peft_module_casting_to_bf16
-    is_peft_available = True
-except ImportError:
-    is_peft_available = False
-
-LOG = logging.getLogger("axolotl.core.trainers.kto")
-
-
-class AxolotlKTOTrainer(SchedulerMixin, Trainer):
-    """
-    Extend the base KTOTrainer for axolotl helpers
-    """
-
-    tag_names = ["axolotl", "kto"]
-
-    def __init__(
-        self,
-        model: Union[PreTrainedModel, nn.Module, str] = None,
-        args: KTOConfig = None,
-        train_dataset: Optional[Dataset] = None,
-        eval_dataset: Optional[Union[Dataset, dict[str, Dataset]]] = None,
-        processing_class: Optional[
-            Union[PreTrainedTokenizerBase, BaseImageProcessor, FeatureExtractionMixin, ProcessorMixin]
-        ] = None,
-        data_collator: Optional[DataCollator] = None,
-        model_init: Optional[Callable[[], PreTrainedModel]] = None,
-        callbacks: Optional[list[TrainerCallback]] = None,
-        optimizers: tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR] = (None, None),
-        preprocess_logits_for_metrics: Optional[Callable[[torch.Tensor, torch.Tensor], torch.Tensor]] = None,
-        peft_config: Optional[dict] = None,
-        compute_metrics: Optional[Callable[[EvalLoopOutput], dict]] = None,
-        dataset_tags=None,
-        model_adapter_name: Optional[str] = None,
-        ref_adapter_name: Optional[str] = None,
-    ):
-        self.dataset_tags = dataset_tags
-        self._tag_names = ["trl", "kto"]
-        if hasattr(self, "tag_names"):
-            self._tag_names.extend(self.tag_names)
-
-        if type(args) is TrainingArguments:
-            raise ValueError("Please use `KTOConfig` instead TrainingArguments.")
-
-        if args.model_init_kwargs is None:
-            model_init_kwargs = {}
-        elif not isinstance(model, str):
-            raise ValueError("You passed model_kwargs to the KTOTrainer. But your model is already instantiated.")
-        else:
-            model_init_kwargs = args.model_init_kwargs
-            torch_dtype = model_init_kwargs.get("torch_dtype")
-            if torch_dtype is not None:
-                # Convert to `torch.dtype` if an str is passed
-                if isinstance(torch_dtype, str) and torch_dtype != "auto":
-                    torch_dtype = getattr(torch, torch_dtype)
-                if torch_dtype != "auto" and not isinstance(torch_dtype, torch.dtype):
-                    raise ValueError(
-                        f"Invalid `torch_dtype` passed to the KTOConfig. Expected a string with either `torch.dtype` or 'auto', but got {torch_dtype}."
-                    )
-                model_init_kwargs["torch_dtype"] = torch_dtype
-
-        if args.ref_model_init_kwargs is None:
-            ref_model_init_kwargs = {}
-        elif not isinstance(ref_model, str):
-            raise ValueError(
-                "You passed ref_model_kwargs to the KTOTrainer. But your ref_model is already instantiated."
-            )
-        else:
-            ref_model_init_kwargs = args.ref_model_init_kwargs
-            torch_dtype = ref_model_init_kwargs.get("torch_dtype")
-            if torch_dtype is not None:
-                # Convert to `torch.dtype` if an str is passed
-                if isinstance(torch_dtype, str) and torch_dtype != "auto":
-                    torch_dtype = getattr(torch, torch_dtype)
-                if torch_dtype != "auto" and not isinstance(torch_dtype, torch.dtype):
-                    raise ValueError(
-                        f"Invalid `torch_dtype` passed to the KTOConfig. Expected a string with either `torch.dtype` or 'auto', but got {torch_dtype}."
-                    )
-                ref_model_init_kwargs["torch_dtype"] = torch_dtype
-
-        if isinstance(model, str):
-            model = AutoModelForCausalLM.from_pretrained(model, **model_init_kwargs)
-
-        if isinstance(ref_model, str):
-            ref_model = AutoModelForCausalLM.from_pretrained(ref_model, **ref_model_init_kwargs)
-
-        # Initialize this variable to False. This helps tracking the case when `peft_module_casting_to_bf16`
-        # has been called in order to properly call autocast if needed.
-        self._peft_has_been_casted_to_bf16 = False
-
-        if not is_peft_available() and peft_config is not None:
-            raise ValueError(
-                "PEFT is not installed and you passed a `peft_config` in the trainer's kwargs, please install it with `pip install peft` to use the PEFT models"
-            )
-        elif is_peft_available() and peft_config is not None:
-            # if model is a peft model and we have a peft_config, we merge and unload it first
-            if isinstance(model, PeftModel):
-                model = model.merge_and_unload()
-
-            if getattr(model, "is_loaded_in_8bit", False) or getattr(model, "is_loaded_in_4bit", False):
-                _support_gc_kwargs = hasattr(
-                    args, "gradient_checkpointing_kwargs"
-                ) and "gradient_checkpointing_kwargs" in list(
-                    inspect.signature(prepare_model_for_kbit_training).parameters
-                )
-
-                prepare_model_kwargs = {"use_gradient_checkpointing": args.gradient_checkpointing}
-
-                if _support_gc_kwargs:
-                    prepare_model_kwargs["gradient_checkpointing_kwargs"] = args.gradient_checkpointing_kwargs
-
-                model = prepare_model_for_kbit_training(model, **prepare_model_kwargs)
-            elif getattr(args, "gradient_checkpointing", False):
-                # For backward compatibility with older versions of transformers
-                if hasattr(model, "enable_input_require_grads"):
-                    model.enable_input_require_grads()
-                else:
-
-                    def make_inputs_require_grad(module, input, output):
-                        output.requires_grad_(True)
-
-                    model.get_input_embeddings().register_forward_hook(make_inputs_require_grad)
-
-            # get peft model with the given config
-            model = get_peft_model(model, peft_config)
-            if args.bf16 and getattr(model, "is_loaded_in_4bit", False):
-                peft_module_casting_to_bf16(model)
-                # If args.bf16 we need to explicitly call `generate` with torch amp autocast context manager
-                self._peft_has_been_casted_to_bf16 = True
-
-        # For models that use gradient_checkpointing, we need to attach a hook that enables input
-        # to explicitly have `requires_grad=True`, otherwise training will either silently
-        # fail or completely fail.
-        elif getattr(args, "gradient_checkpointing", False):
-            # For backward compatibility with older versions of transformers
-            if hasattr(model, "enable_input_require_grads"):
-                model.enable_input_require_grads()
-            else:
-
-                def make_inputs_require_grad(module, input, output):
-                    output.requires_grad_(True)
-
-                model.get_input_embeddings().register_forward_hook(make_inputs_require_grad)
-
-        if args.generate_during_eval and not (is_wandb_available() or is_comet_available()):
-            raise ValueError(
-                "`generate_during_eval=True` requires Weights and Biases or Comet to be installed."
-                " Please install `wandb` or `comet-ml` to resolve."
-            )
-
-        if model is not None:
-            self.is_encoder_decoder = model.config.is_encoder_decoder
-        elif args.is_encoder_decoder is None:
-            raise ValueError("When no model is provided, you need to pass the parameter is_encoder_decoder.")
-        else:
-            self.is_encoder_decoder = args.is_encoder_decoder
-
-        self.is_peft_model = is_peft_available() and isinstance(model, PeftModel)
-        self.model_adapter_name = model_adapter_name
-        self.ref_adapter_name = ref_adapter_name
-
-        if ref_model:
-            self.ref_model = ref_model
-        elif self.is_peft_model or args.precompute_ref_log_probs:
-            # The `model` with adapters turned off will be used as the reference model
-            self.ref_model = None
-        else:
-            self.ref_model = create_reference_model(model)
-
-        if processing_class is None:
-            raise ValueError(
-                "max_length or a processing_class must be specified when using the default DPODataCollatorWithPadding"
-            )
-        if args.max_length is None:
-            warnings.warn(
-                "When using DPODataCollatorWithPadding, you should set `max_length` in the KTOTrainer's init"
-                " it will be set to `512` by default, but you should do it yourself in the future.",
-                UserWarning,
-            )
-            max_length = 512
-        if args.max_length is not None:
-            max_length = args.max_length
-
-        if args.max_prompt_length is None:
-            warnings.warn(
-                "When using DPODataCollatorWithPadding, you should set `max_prompt_length` in the KTOTrainer's init"
-                " it will be set to `128` by default, but you should do it yourself in the future.",
-                UserWarning,
-            )
-            max_prompt_length = 128
-        if args.max_prompt_length is not None:
-            max_prompt_length = args.max_prompt_length
-
-        max_completion_length = None
-        if args.max_completion_length is None and self.is_encoder_decoder:
-            warnings.warn(
-                "When using DPODataCollatorWithPadding with an encoder decoder architecture, you should set `max_completion_length` in the KTOTrainer's init"
-                " it will be set to `128` by default, but you should do it yourself in the future.",
-                UserWarning,
-            )
-            max_completion_length = 128
-        if args.max_completion_length is not None and self.is_encoder_decoder:
-            max_completion_length = args.max_completion_length
-
-        if data_collator is None:
-            data_collator = DPODataCollatorWithPadding(
-                pad_token_id=processing_class.pad_token_id,
-                label_pad_token_id=args.label_pad_token_id,
-                is_encoder_decoder=self.is_encoder_decoder,
-            )
-
-            if args.remove_unused_columns:
-                args.remove_unused_columns = False
-                # warn users
-                warnings.warn(
-                    "When using DPODataCollatorWithPadding, you should set `remove_unused_columns=False` in your KTOConfig"
-                    " we have set it for you, but you should do it yourself in the future.",
-                    UserWarning,
-                )
-
-            self.use_dpo_data_collator = True
-        else:
-            self.use_dpo_data_collator = False
-
-        # Disable dropout in the model and reference model
-        if args.disable_dropout:
-            disable_dropout_in_model(model)
-            if self.ref_model is not None:
-                disable_dropout_in_model(self.ref_model)
-
-        self.loss_type = args.loss_type
-        self.max_length = max_length
-        self.generate_during_eval = args.generate_during_eval
-        self.label_pad_token_id = args.label_pad_token_id
-        self.padding_value = args.padding_value if args.padding_value is not None else processing_class.pad_token_id
-        self.max_prompt_length = max_prompt_length
-        self.truncation_mode = args.truncation_mode
-        self.max_completion_length = max_completion_length
-        self.processing_class = processing_class
-        self.precompute_ref_log_probs = args.precompute_ref_log_probs
-
-        # Not all losses require a KL calculation
-        self.calculate_KL = True
-        if self.loss_type in ["apo_zero_unpaired"]:
-            self.calculate_KL = False
-
-        # Since ref_logs are precomputed on the first call to get_train/eval_dataloader
-        # keep track of first called to avoid computation of future calls
-        self._precomputed_train_ref_log_probs = False
-        self._precomputed_eval_ref_log_probs = False
-
-        # metric
-        self._stored_metrics = defaultdict(lambda: defaultdict(list))
-
-        # KTO parameter
-        self.beta = args.beta
-        self.desirable_weight = args.desirable_weight
-        self.undesirable_weight = args.undesirable_weight
-        self.aux_loss_enabled = getattr(model.config, "output_router_logits", False)
-        self.aux_loss_coef = getattr(model.config, "router_aux_loss_coef", 0.0)
-        if self.aux_loss_enabled and self.aux_loss_coef == 0.0:
-            warnings.warn(
-                "You set `output_router_logits` to `True` in the model config, but `router_aux_loss_coef` is set to "
-                "`0.0`, meaning the auxiliary loss will not be used. Either set `router_aux_loss_coef` to a value "
-                "greater than `0.0`, or set `output_router_logits` to `False` if you don't want to use the auxiliary "
-                "loss.",
-                UserWarning,
-            )
-
-        # The trainer estimates the number of FLOPs (floating-point operations) using the number of elements in the
-        # input tensor associated with the key "input_ids". However, in KTO, the sampled data does not include the
-        # "input_ids" key. Instead, the available keys are "prompt_input_ids" and "completion_input_ids". As a result,
-        # the trainer issues the warning: "Could not estimate the number of tokens of the input, floating-point
-        # operations will not be computed." To suppress this warning, we set the "estimate_tokens" key in the model's
-        # "warnings_issued" dictionary to True. This acts as a flag to indicate that the warning has already been
-        # issued.
-        model.warnings_issued["estimate_tokens"] = True
-
-        # Compute that only on the main process for faster data processing.
-        # see: https://github.com/huggingface/trl/pull/1255
-        with PartialState().local_main_process_first():
-            # Extract the prompt if needed
-            train_dataset = train_dataset.map(
-                maybe_extract_prompt, num_proc=args.dataset_num_proc, desc="Extracting prompt from train dataset"
-            )
-            # Unpair the dataset if needed
-            train_dataset = maybe_unpair_preference_dataset(
-                train_dataset, args.dataset_num_proc, desc="Unpairing train dataset"
-            )
-            # Apply the chat template if needed
-            train_dataset = train_dataset.map(
-                maybe_apply_chat_template,
-                fn_kwargs={"tokenizer": processing_class},
-                num_proc=args.dataset_num_proc,
-                desc="Applying chat template to train dataset",
-            )
-            if eval_dataset is not None:
-                eval_dataset = eval_dataset.map(
-                    maybe_extract_prompt, num_proc=args.dataset_num_proc, desc="Extracting prompt from eval dataset"
-                )
-                eval_dataset = maybe_unpair_preference_dataset(
-                    eval_dataset, args.dataset_num_proc, desc="Unpairing eval dataset"
-                )
-                eval_dataset = eval_dataset.map(
-                    maybe_apply_chat_template,
-                    fn_kwargs={"tokenizer": processing_class},
-                    num_proc=args.dataset_num_proc,
-                    desc="Applying chat template to eval dataset",
-                )
-
-            # Tokenize and prepare the training datasets
-            train_dataset = train_dataset.map(
-                _tokenize,
-                batched=True,
-                fn_kwargs={"tokenizer": self.processing_class},
-                num_proc=args.dataset_num_proc,
-                desc="Tokenizing train dataset",
-            )
-
-            fn_kwargs = {
-                "prefix": "",
-                "is_encoder_decoder": self.is_encoder_decoder,
-                "tokenizer": self.processing_class,
-                "max_length": self.max_length,
-                "truncation_mode": self.truncation_mode,
-                "label_pad_token_id": self.label_pad_token_id,
-                "max_prompt_length": self.max_prompt_length,
-                "max_completion_length": self.max_completion_length,
-            }
-
-            train_dataset = train_dataset.map(
-                _process_tokens,
-                fn_kwargs=fn_kwargs,
-                num_proc=args.dataset_num_proc,
-                desc="Processing tokenized train dataset",
-            )
-
-            # Tokenize and prepare the eval datasets
-            if eval_dataset is not None:
-                eval_dataset = eval_dataset.map(
-                    _tokenize,
-                    fn_kwargs={"tokenizer": self.processing_class},
-                    batched=True,
-                    num_proc=args.dataset_num_proc,
-                    desc="Tokenizing eval dataset",
-                )
-
-                eval_dataset = eval_dataset.map(
-                    _process_tokens,
-                    fn_kwargs=fn_kwargs,
-                    num_proc=args.dataset_num_proc,
-                    desc="Processing tokenized eval dataset",
-                )
-
-            # Get KL datasets if needed
-            if self.calculate_KL:
-                if args.per_device_train_batch_size <= 1:
-                    raise ValueError(
-                        "Actual (not effective) batch size must be > 1. KTO will not work properly because the KL term will be equivalent to the implied reward."
-                    )
-
-                # create pairs for estimating the KL term by flipping the matched pairs in each batch of size total_batch_size
-                # i.e., (x_1, y_1), ..., (x_n, y_n) --> (x_1, y_n), ..., (x_n, y_1) = (x'_1, y'_1), ..., (x'_n, y'_n)
-                train_kl_dataset = train_dataset.map(
-                    _get_kl_dataset,
-                    batched=True,
-                    batch_size=args.per_device_train_batch_size,
-                    num_proc=args.dataset_num_proc,
-                    desc="Extracting KL train dataset",
-                )
-
-                fn_kwargs["prefix"] = "KL_"
-                train_kl_dataset = train_kl_dataset.map(
-                    _process_tokens,
-                    fn_kwargs=fn_kwargs,
-                    num_proc=args.dataset_num_proc,
-                    remove_columns=[c for c in train_kl_dataset.column_names if c in train_dataset.column_names],
-                    desc="Processing tokenized train KL dataset",
-                )
-
-                # merge the datasets
-                train_dataset = concatenate_datasets([train_dataset, train_kl_dataset], axis=1)
-
-                if eval_dataset is not None:
-                    # Get KL dataset
-                    eval_kl_dataset = eval_dataset.map(
-                        _get_kl_dataset,
-                        batched=True,
-                        batch_size=args.per_device_train_batch_size,
-                        num_proc=args.dataset_num_proc,
-                        desc="Extracting eval KL dataset",
-                    )
-
-                    eval_kl_dataset = eval_kl_dataset.map(
-                        _process_tokens,
-                        fn_kwargs=fn_kwargs,
-                        num_proc=args.dataset_num_proc,
-                        remove_columns=[c for c in eval_kl_dataset.column_names if c in eval_dataset.column_names],
-                        desc="Processing tokenized eval KL dataset",
-                    )
-
-                    # merge the datasets
-                    eval_dataset = concatenate_datasets([eval_dataset, eval_kl_dataset], axis=1)
-
-            # calculate dataset desirability balance
-            num_desirable = max(sum(train_dataset["label"]), 1)
-            num_undesirable = max(len(train_dataset["label"]) - num_desirable, 1)  # "label" is binary
-
-            if num_desirable != num_undesirable:
-                # The lower and upper bounds come from Eq. (8) of https://huggingface.co/papers/2402.01306
-                des_weight_lower_bound = round((num_undesirable * self.undesirable_weight / num_desirable) * 1, 2)
-                des_weight_upper_bound = round((num_undesirable * self.undesirable_weight / num_desirable) * 1.33, 2)
-                und_weight_lower_bound = round((num_desirable * self.desirable_weight / num_undesirable) / 1.33, 2)
-                und_weight_upper_bound = round((num_desirable * self.desirable_weight / num_undesirable) / 1, 2)
-
-                des_weight_in_range = des_weight_lower_bound <= self.desirable_weight <= des_weight_upper_bound
-                und_weight_in_range = und_weight_lower_bound <= self.undesirable_weight <= und_weight_upper_bound
-
-                if not (des_weight_in_range or und_weight_in_range):
-                    warnings.warn(
-                        "You have different amounts of desirable/positive and undesirable/negative examples but the "
-                        "weights on the desirable and undesirable losses don't seem to be in an ideal range. Based "
-                        f"on your data, we recommend EITHER "
-                        f"desirable_weight in [{des_weight_lower_bound}, {des_weight_upper_bound}] or "
-                        f"undesirable_weight in [{und_weight_lower_bound}, {und_weight_upper_bound}] (but NOT BOTH). "
-                        "See the documentation on how to optimally set these weights.",
-                        UserWarning,
-                    )
-
-        super().__init__(
-            model=model,
-            args=args,
-            data_collator=data_collator,
-            train_dataset=train_dataset,
-            eval_dataset=eval_dataset,
-            processing_class=processing_class,
-            model_init=model_init,
-            compute_metrics=compute_metrics,
-            callbacks=callbacks,
-            optimizers=optimizers,
-            preprocess_logits_for_metrics=preprocess_logits_for_metrics,
-        )
-
-        # Gradient accumulation requires scaled loss. Normally, loss scaling in the parent class depends on whether the
-        # model accepts loss-related kwargs. Since we compute our own loss, this check is irrelevant. We set
-        # self.model_accepts_loss_kwargs to False to enable scaling.
-        self.model_accepts_loss_kwargs = False
-
-        # Add tags for models that have been loaded with the correct transformers version
-        if hasattr(self.model, "add_model_tags"):
-            self.model.add_model_tags(self._tag_names)
-
-        if not hasattr(self, "accelerator"):
-            raise AttributeError(
-                "Your `Trainer` does not have an `accelerator` object. Consider upgrading `transformers`."
-            )
-
-        # Deepspeed Zero-3 does not support precompute_ref_log_probs
-        if self.is_deepspeed_enabled:
-            if self.accelerator.state.deepspeed_plugin.zero_stage == 3 and self.precompute_ref_log_probs:
-                raise ValueError(
-                    "You cannot use `precompute_ref_log_probs=True` with Deepspeed ZeRO-3. Please set `precompute_ref_log_probs=False`."
-                )
-
-        if self.ref_model is None:
-            if not (self.is_peft_model or self.precompute_ref_log_probs):
-                raise ValueError(
-                    "No reference model and model is not a Peft model. Try setting `precompute_ref_log_probs=True`"
-                )
-        else:
-            if self.is_deepspeed_enabled:
-                self.ref_model = self._prepare_deepspeed(self.ref_model)
-            else:
-                self.ref_model = self.accelerator.prepare_model(self.ref_model, evaluation_mode=True)

src/axolotl/core/trainers/mamba.py

@@ -0,0 +1,32 @@
+"""Module for mamba trainer"""
+
+import torch
+
+from axolotl.core.trainers.base import AxolotlTrainer
+
+
+class AxolotlMambaTrainer(AxolotlTrainer):
+    """Mamba specific trainer to handle loss calculation"""
+
+    tag_names = ["axolotl", "mamba"]
+
+    def compute_loss(
+        self,
+        model,
+        inputs,
+        return_outputs=False,  # pylint: disable=unused-argument
+        num_items_in_batch=None,  # pylint: disable=unused-argument
+    ):
+        input_ids = inputs.pop("input_ids")
+        lm_logits = model(input_ids).logits
+
+        labels = input_ids.to(lm_logits.device)
+        shift_logits = lm_logits[:, :-1, :].contiguous()
+        labels = labels[:, 1:].contiguous()
+
+        loss_fct = torch.nn.CrossEntropyLoss()
+        lm_loss = loss_fct(
+            shift_logits.view(-1, shift_logits.size(-1)), labels.view(-1)
+        )
+
+        return lm_loss

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

@@ -0,0 +1,8 @@
+"""Init for axolotl.core.trainers.mixins"""
+
+# pylint: disable=unused-import
+# flake8: noqa
+
+from .optimizer import OptimizerMixin
+from .scheduler import SchedulerMixin
+from .sequence_parallel import SequenceParallelMixin

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

@@ -0,0 +1,201 @@
+"""Module for Axolotl trainer optimizer mixin"""
+
+import logging
+
+from peft.optimizers import create_loraplus_optimizer
+from torch import nn
+from transformers.trainer import Trainer
+from transformers.utils import is_sagemaker_mp_enabled
+
+from axolotl.integrations.base import BaseOptimizerFactory
+
+if is_sagemaker_mp_enabled():
+    import smdistributed.modelparallel.torch as smp
+
+LOG = logging.getLogger(__name__)
+
+
+class OptimizerMixin(Trainer):
+    """Mixin class for shared handling of building custom optimizers"""
+
+    args = None  # type: "AxolotlTrainingArguments"  # type: ignore[name-defined]
+
+    def create_optimizer_grouped_parameters(
+        self, opt_model, optimizer_kwargs
+    ) -> list[dict]:
+        decay_parameters = self.get_decay_parameter_names(opt_model)
+        params: dict = {
+            "to_weight_decay": {},  # LayerNorm and bias
+            "embeddings": {},  # lm_head, embed_tokens,
+            "no_weight_decay": {},
+        }
+        lr_groups_lookup = {}
+        lr_groups_learning_rates = {}
+        if self.args.lr_groups:
+            for lr_group in self.args.lr_groups:
+                group_name = lr_group["name"]
+                group_modules = lr_group["modules"]
+                for module in group_modules:
+                    lr_groups_lookup[module] = group_name
+                lr_groups_learning_rates[group_name] = lr_group["lr"]
+                params[f"to_weight_decay_{group_name}"] = {}
+
+        for name, param in opt_model.named_parameters():
+            if not param.requires_grad:
+                continue
+            if name.endswith("modules_to_save.default.weight") or any(
+                embed_name in name for embed_name in ["embed_tokens", "lm_head"]
+            ):
+                params["embeddings"][name] = param
+            elif name in decay_parameters:
+                lr_group_modules = [
+                    group_modules
+                    for group_modules in lr_groups_lookup
+                    if group_modules in name
+                ]
+                if lr_groups_lookup and any(lr_group_modules):
+                    lr_group_module = lr_group_modules[0]
+                    group_name = lr_groups_lookup[lr_group_module]
+                    params[f"to_weight_decay_{group_name}"][name] = param
+                else:
+                    params["to_weight_decay"][name] = param
+            else:
+                params["no_weight_decay"][name] = param
+        optimizer_grouped_parameters = []
+        if params["to_weight_decay"]:
+            optimizer_grouped_parameters.append(
+                {
+                    "params": list(params["to_weight_decay"].values()),
+                    "weight_decay": self.args.weight_decay,
+                    "lr": optimizer_kwargs["lr"],
+                }
+            )
+        if params["embeddings"]:
+            lr = optimizer_kwargs["lr"]  # pylint: disable=invalid-name
+            if self.args.embedding_lr_scale:
+                lr *= self.args.embedding_lr_scale  # pylint: disable=invalid-name
+            elif self.args.embedding_lr:
+                lr = self.args.embedding_lr  # pylint: disable=invalid-name
+            optimizer_grouped_parameters.append(
+                {
+                    "params": list(params["embeddings"].values()),
+                    "weight_decay": 0.0,
+                    "lr": lr,
+                }
+            )
+        if params["no_weight_decay"]:
+            optimizer_grouped_parameters.append(
+                {
+                    "params": list(params["no_weight_decay"].values()),
+                    "weight_decay": 0.0,
+                    "lr": optimizer_kwargs["lr"],
+                }
+            )
+        for group_name, group_lr in lr_groups_learning_rates.items():
+            if params[f"to_weight_decay_{group_name}"]:
+                optimizer_grouped_parameters.append(
+                    {
+                        "params": list(
+                            params[f"to_weight_decay_{group_name}"].values()
+                        ),
+                        "weight_decay": self.args.weight_decay,
+                        "lr": group_lr,
+                    }
+                )
+
+        return optimizer_grouped_parameters
+
+    def create_optimizer(self):
+        if (
+            self.args.loraplus_lr_ratio is None
+            and self.args.embedding_lr_scale is None
+            and self.args.embedding_lr is None
+            and self.args.lr_groups is None
+            and self.optimizer_cls_and_kwargs is None
+        ):
+            return super().create_optimizer()
+
+        opt_model = self.model_wrapped if is_sagemaker_mp_enabled() else self.model
+
+        if (
+            not self.optimizer
+            and self.optimizer_cls_and_kwargs is not None
+            and issubclass(self.optimizer_cls_and_kwargs[0], BaseOptimizerFactory)
+        ):
+            optimizer_factory_cls, optimizer_kwargs = self.optimizer_cls_and_kwargs
+            self.optimizer = optimizer_factory_cls()(
+                opt_model, self.args, **optimizer_kwargs
+            )
+
+        if not self.optimizer:
+            if self.optimizer_cls_and_kwargs is not None:
+                optimizer_cls, optimizer_kwargs = self.optimizer_cls_and_kwargs
+            else:
+                optimizer_cls, optimizer_kwargs = self.get_optimizer_cls_and_kwargs(
+                    self.args, opt_model
+                )
+
+            optimizer_grouped_parameters = self.create_optimizer_grouped_parameters(
+                opt_model, optimizer_kwargs
+            )
+
+            if self.args.loraplus_lr_ratio is not None:
+                loraplus_lr_ratio = getattr(self.args, "loraplus_lr_ratio", None)
+                loraplus_lr_embedding = getattr(
+                    self.args, "loraplus_lr_embedding", 1e-6
+                )
+                self.optimizer = create_loraplus_optimizer(  # pylint: disable=attribute-defined-outside-init
+                    opt_model,
+                    optimizer_cls,
+                    loraplus_lr_ratio=loraplus_lr_ratio,
+                    loraplus_lr_embedding=loraplus_lr_embedding,
+                    **optimizer_kwargs,
+                )
+            else:
+                # Overwrite `params` in case it's created by `get_optimizer_cls_and_kwargs`
+                # e.g. for GaLore optimizer.
+                if "params" in optimizer_kwargs:
+                    optimizer_grouped_parameters = optimizer_kwargs.pop("params")
+
+                # Overwrite `model` in case it's created by `get_optimizer_cls_and_kwargs`
+                # e.g. for LOMO optimizer.
+                if "model" in optimizer_kwargs:
+                    optimizer_grouped_parameters = optimizer_kwargs.pop("model")
+
+                # For layer-wise dummy optimizers we overwrite optimizer_grouped_parameters with `optimizer_dict`
+                # to avoid arguments conflicts.
+                if "optimizer_dict" in optimizer_kwargs:
+                    optimizer_grouped_parameters = optimizer_kwargs.pop(
+                        "optimizer_dict"
+                    )
+
+                self.optimizer = optimizer_cls(
+                    optimizer_grouped_parameters, **optimizer_kwargs
+                )
+
+            if optimizer_cls.__name__ == "Adam8bit":
+                import bitsandbytes
+
+                manager = bitsandbytes.optim.GlobalOptimManager.get_instance()
+
+                skipped = 0
+                for module in opt_model.modules():
+                    if isinstance(module, nn.Embedding):
+                        skipped += sum(
+                            {
+                                p.data_ptr(): p.numel() for p in module.parameters()
+                            }.values()
+                        )
+                        LOG.info(f"skipped {module}: {skipped/2**20}M params")
+                        manager.register_module_override(
+                            module, "weight", {"optim_bits": 32}
+                        )
+                        LOG.debug(f"bitsandbytes: will optimize {module} in fp32")
+                LOG.info(f"skipped: {skipped/2**20}M params")
+
+        if is_sagemaker_mp_enabled():
+            self.optimizer = smp.DistributedOptimizer(  # pylint: disable=attribute-defined-outside-init
+                self.optimizer
+            )
+
+        return self.optimizer

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

@@ -0,0 +1,113 @@
+"""Module for Axolotl trainer scheduler mixin"""
+
+import logging
+
+import torch
+from torch.optim.lr_scheduler import OneCycleLR
+from transformers.trainer import Trainer
+
+from axolotl.utils.schedulers import (
+    RexLR,
+    get_cosine_schedule_with_min_lr,
+    get_cosine_schedule_with_quadratic_warmup,
+    get_cosine_schedule_with_warmup_decay_constant,
+)
+
+LOG = logging.getLogger(__name__)
+
+
+class SchedulerMixin(Trainer):
+    """
+    Mixin class for scheduler setup in CausalTrainer.
+    """
+
+    args = None  # type: "AxolotlTrainingArguments"  # type: ignore[name-defined]
+
+    def create_scheduler(
+        self, num_training_steps: int, optimizer: torch.optim.Optimizer = None
+    ):
+        """
+        Setup the scheduler. The optimizer of the trainer must have been set up either before this method is called or
+        passed as an argument.
+
+        Args:
+            num_training_steps (int): The number of training steps to do.
+            optimizer (torch.optim.Optimizer): The training optimizer
+        """
+        use_cosine_quadratic = (
+            self.args.lr_scheduler_type == "cosine"
+            and self.args.lr_quadratic_warmup is True
+        )
+
+        use_cosine_min_lr = (
+            self.args.lr_scheduler_type == "cosine"
+            and self.args.cosine_min_lr_ratio is not None
+        )
+
+        # fmt: off
+        if self.lr_scheduler is None:  # type: ignore  # pylint: disable=access-member-before-definition
+            # fmt: on
+            if self.args.alternate_lr_scheduler_type == "one_cycle":
+                num_warmup_steps = self.args.get_warmup_steps(num_training_steps)
+                pct_start = num_warmup_steps / num_training_steps
+                extra_lr_kwargs = {}
+                if "pct_start" not in self.args.lr_scheduler_kwargs:
+                    extra_lr_kwargs["pct_start"] = pct_start
+                if "anneal_strategy" not in self.args.lr_scheduler_kwargs:
+                    extra_lr_kwargs["anneal_strategy"] = "cos"
+
+                self.lr_scheduler = OneCycleLR(
+                    optimizer,
+                    max_lr=self.args.learning_rate,
+                    total_steps=num_training_steps,
+                    **extra_lr_kwargs,
+                    **self.args.lr_scheduler_kwargs,
+                )
+            elif self.args.alternate_lr_scheduler_type == "rex":
+                if use_cosine_min_lr:
+                    assert 0 <= self.args.cosine_min_lr_ratio <= 1.0, "cosine_min_lr_ratio must be between 0.0 and 1.0"
+
+                self.lr_scheduler = RexLR(
+                    optimizer=optimizer,
+                    max_lr=self.args.learning_rate,
+                    min_lr=0 if not use_cosine_min_lr else (self.args.learning_rate * self.args.cosine_min_lr_ratio),
+                    total_steps=num_training_steps,
+                    num_warmup_steps=self.args.get_warmup_steps(num_training_steps),
+                )
+            elif use_cosine_quadratic:
+                if use_cosine_min_lr:
+                    LOG.warning("Both cosine quadratic warmup and min lr detected. Using quadratic warmup.")
+
+                self.lr_scheduler = get_cosine_schedule_with_quadratic_warmup(  # pylint: disable=attribute-defined-outside-init
+                    optimizer,
+                    num_warmup_steps=self.args.get_warmup_steps(num_training_steps),
+                    num_training_steps=num_training_steps,
+                )
+            elif self.args.cosine_min_lr_ratio and self.args.cosine_constant_lr_ratio and use_cosine_min_lr:
+                assert 0 <= self.args.cosine_min_lr_ratio <= 1.0, "cosine_min_lr_ratio must be between 0.0 and 1.0"
+                assert 0 <= self.args.cosine_constant_lr_ratio <= 1.0, "cosine_constant_lr_ratio must be between 0.0 and 1.0"
+                self.lr_scheduler = get_cosine_schedule_with_warmup_decay_constant(  # pylint: disable=attribute-defined-outside-init
+                    optimizer,
+                    num_warmup_steps=self.args.get_warmup_steps(num_training_steps),
+                    num_training_steps=num_training_steps,
+                    min_lr_ratio=self.args.cosine_min_lr_ratio,
+                    constant_lr_ratio=self.args.cosine_constant_lr_ratio,
+                )
+            elif self.args.cosine_min_lr_ratio and use_cosine_min_lr:
+                assert 0 <= self.args.cosine_min_lr_ratio <= 1.0, "cosine_min_lr_ratio must be between 0.0 and 1.0"
+                self.lr_scheduler = get_cosine_schedule_with_min_lr(  # pylint: disable=attribute-defined-outside-init
+                    optimizer,
+                    num_warmup_steps=self.args.get_warmup_steps(num_training_steps),
+                    num_training_steps=num_training_steps,
+                    min_lr_ratio=self.args.cosine_min_lr_ratio,
+                )
+            else:
+                return super().create_scheduler(num_training_steps, optimizer=optimizer)
+        else:
+            if use_cosine_quadratic:
+                LOG.warning("axolotl's cosine scheduler with quadratic warmup not used (e.g., because of deepspeed).")
+
+            if use_cosine_min_lr:
+                LOG.warning("axolotl's cosine scheduler with min lr not used (e.g., because of deepspeed).")
+
+        return self.lr_scheduler

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

@@ -0,0 +1,131 @@
+"""Module for Axolotl trainer sequence parallelism mixin"""
+
+import logging
+from typing import Any
+
+import torch
+import torch.distributed as dist
+import torch.nn.functional as F
+from datasets import Dataset
+from torch.utils.data import DistributedSampler, Sampler
+
+from axolotl.monkeypatch.attention.ring_attn import get_ring_attn_group
+
+LOG = logging.getLogger(__name__)
+
+try:
+    from ring_flash_attn import update_ring_flash_attn_params
+except ImportError:
+    # We pass silently here, but raise an ImportError in our Axolotl config validation
+    # if cfg.sequence_parallel_degree > 1 and `ring-flash-attn` is not installed.
+    pass
+
+
+class SequenceParallelMixin:
+    """
+    Mixin class for sequence parallelism support in trainers.
+
+    This mixin provides functionality for handling sequence parallelism,
+    including creating appropriate samplers, managing data partitioning,
+    and updating ring flash attention parameters during training.
+    """
+
+    args = None  # type: "AxolotlTrainingArguments"  # type: ignore[name-defined]
+
+    def _setup_sequence_parallel(self):
+        """Set up sequence parallelism environment."""
+        self.ring_attn_group = get_ring_attn_group()
+
+    def _create_sequence_parallel_sampler(
+        self,
+        dataset: Dataset,
+        shuffle: bool = True,
+        is_eval: bool = False,
+    ) -> DistributedSampler:
+        """
+        Helper method to create sampler for sequence parallelism (SP).
+
+        We create a distributed sampler with rank equal to the SP group ID, which
+        means that all ranks in the SP group receive the same sample / set of samples
+        per training step. We also set the number of replicas equal to the number of
+        SP groups, which is a bit of a hack / unintended use, but works!
+
+        Args:
+            dataset: Dataset to sample from.
+            shuffle: Whether to shuffle the dataset.
+            is_eval: Whether we are creating a sampler for evaluation or training.
+
+        Returns:
+            Distributed sampler.
+        """
+        num_sp_groups = self.args.world_size // self.args.sequence_parallel_degree
+        sp_group_id = dist.get_rank() // self.args.sequence_parallel_degree
+
+        return DistributedSampler(
+            dataset,
+            num_replicas=num_sp_groups,
+            rank=sp_group_id,
+            seed=self.args.seed if shuffle else None,
+            shuffle=shuffle,
+            drop_last=not is_eval,
+        )
+
+    def _sp_get_train_sampler(self, dataset) -> Sampler | None:
+        """
+        Get a training sampler configured for sequence parallelism.
+
+        Args:
+            dataset: The training dataset
+
+        Returns:
+            Configured sequence parallel sampler.
+        """
+        return self._create_sequence_parallel_sampler(
+            dataset,
+            shuffle=not self.args.curriculum_sampling,
+        )
+
+    def _sp_get_eval_sampler(self, eval_dataset) -> Sampler | None:
+        """
+        Get an evaluation sampler configured for sequence parallelism.
+
+        Args:
+            eval_dataset: The evaluation dataset.
+
+        Returns:
+            Configured sequence parallel sampler.
+        """
+        return self._create_sequence_parallel_sampler(
+            eval_dataset, shuffle=False, is_eval=True
+        )
+
+    def _update_ring_flash_attn_params(self, inputs: dict[str, torch.Tensor | Any]):
+        """
+        Calculate the cu_seqlens for the current forward pass and pass the value to
+        the substituted ring_flash_attn. This is accomplished by using the passed
+        `input_ids`.
+
+        Args:
+            inputs: Current batch of inputs.
+        """
+        # At this point, inputs should already be partitioned by the sequence
+        # parallel data collator
+        batch_size = inputs["input_ids"].shape[0]
+        seq_len = inputs["input_ids"].shape[1]
+        packed_seq_lens = [seq_len] * batch_size
+
+        # Calculate the full sequence length across all GPUs in this SP group
+        total_seq_len = seq_len * self.args.sequence_parallel_degree
+
+        cu_seqlens = torch.cumsum(
+            torch.tensor(
+                packed_seq_lens, device=torch.cuda.current_device(), dtype=torch.int32
+            ),
+            dim=-1,
+            dtype=torch.int32,
+        )
+        cu_seqlens = F.pad(
+            F.pad(cu_seqlens, (1, 0), value=0), (0, 1), value=total_seq_len
+        )
+
+        update_ring_flash_attn_params(cu_seqlens, self.ring_attn_group)

src/axolotl/core/trainers/relora.py

@@ -0,0 +1,43 @@
+"""Module for ReLoRA trainer"""
+
+import torch
+
+from axolotl.core.trainers.base import AxolotlTrainer
+from axolotl.monkeypatch.relora import ReLoRAScheduler
+
+
+class ReLoRATrainer(AxolotlTrainer):
+    """Trainer subclass that uses the `OneCycleLR` scheduler"""
+
+    tag_names = ["axolotl", "relora"]
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.lr_scheduler = None
+
+    def create_scheduler(
+        self,
+        num_training_steps: int,
+        optimizer: torch.optim.Optimizer | None = None,
+    ):
+        optimizer = self.optimizer if optimizer is None else optimizer
+        lr_scheduler = super().create_scheduler(num_training_steps, optimizer)
+
+        if self.args.relora_steps:
+            warmup_steps = (
+                self.args.relora_warmup_steps if self.args.relora_warmup_steps else 10
+            )
+            anneal_steps = (
+                self.args.relora_anneal_steps if self.args.relora_anneal_steps else 1
+            )
+            self.lr_scheduler = ReLoRAScheduler(
+                optimizer,
+                lr_scheduler,
+                self.args.relora_steps,
+                anneal_steps,
+                warmup_steps,
+            )
+        else:
+            self.lr_scheduler = lr_scheduler
+
+        return self.lr_scheduler

src/axolotl/core/trainers/trl.py

@@ -1,15 +1,23 @@
-"""
-module for TRL PPO training
-"""
+"""Module for TRL PPO trainer"""
+
 import torch
 from tqdm import tqdm
-from trl import PPOTrainer
+from trl import (
+    CPOTrainer,
+    KTOTrainer,
+    ORPOTrainer,
+    PPOTrainer,
+    PRMTrainer,
+    RewardTrainer,
+)
+
+from axolotl.core.trainers.mixins.scheduler import SchedulerMixin
 
 
 class TRLPPOTrainer(PPOTrainer):
-    """
-    wrapper for ppo trainer to handle customizations
-    """
+    """Wrapper for TRL PPO trainer to handle customizations"""
+
+    tag_names = ["axolotl", "ppo"]
 
     def train(
         self,
@@ -30,9 +38,7 @@ class TRLPPOTrainer(PPOTrainer):
             "batch_size": 16,
         }
 
-        for epoch, batch in tqdm(  # pylint: disable=unused-variable
-            enumerate(self.dataloader)
-        ):
+        for _, batch in tqdm(enumerate(self.dataloader)):
             query_tensors = batch["input_ids"]
 
             # generate model response
@@ -64,3 +70,43 @@ class TRLPPOTrainer(PPOTrainer):
                 rewards,
                 columns_to_log=["query", "response", "ref_response", "ref_rewards"],
             )
+
+
+class AxolotlORPOTrainer(SchedulerMixin, ORPOTrainer):
+    """
+    Extend the base ORPOTrainer for axolotl helpers
+    """
+
+    tag_names = ["axolotl", "orpo"]
+
+
+class AxolotlKTOTrainer(SchedulerMixin, KTOTrainer):
+    """
+    Extend the base KTOTrainer for axolotl helpers
+    """
+
+    tag_names = ["axolotl", "kto"]
+
+
+class AxolotlCPOTrainer(SchedulerMixin, CPOTrainer):
+    """
+    Extend the base CPOTrainer for axolotl helpers
+    """
+
+    tag_names = ["axolotl", "cpo"]
+
+
+class AxolotlRewardTrainer(SchedulerMixin, RewardTrainer):
+    """
+    Extend the base RewardTrainer for axolotl helpers
+    """
+
+    tag_names = ["axolotl", "reward"]
+
+
+class AxolotlPRMTrainer(SchedulerMixin, PRMTrainer):
+    """
+    Extend the base trl.PRMTrainer for axolotl helpers
+    """
+
+    tag_names = ["axolotl", "prm"]

src/axolotl/core/trainers/utils.py

@@ -0,0 +1,33 @@
+"""Utils for Axolotl trainers"""
+
+
+def sanitize_kwargs_for_tagging(tag_names, kwargs=None):
+    if isinstance(tag_names, str):
+        tag_names = [tag_names]
+
+    if kwargs is not None:
+        if "tags" not in kwargs:
+            kwargs["tags"] = tag_names
+        elif "tags" in kwargs and isinstance(kwargs["tags"], list):
+            kwargs["tags"].extend(tag_names)
+        elif "tags" in kwargs and isinstance(kwargs["tags"], str):
+            tag_names.append(kwargs["tags"])
+            kwargs["tags"] = tag_names
+
+    return kwargs
+
+
+def sanitize_kwargs_for_ds_tagging(dataset_tags, kwargs=None):
+    if isinstance(dataset_tags, str):
+        dataset_tags = [dataset_tags]
+
+    if (dataset_tags is not None) and (kwargs is not None):
+        if "dataset_tags" not in kwargs:
+            kwargs["dataset_tags"] = dataset_tags
+        elif "dataset_tags" in kwargs and isinstance(kwargs["dataset_tags"], list):
+            kwargs["dataset_tags"].extend(dataset_tags)
+        elif "dataset_tags" in kwargs and isinstance(kwargs["dataset_tags"], str):
+            dataset_tags.append(kwargs["dataset_tags"])
+            kwargs["dataset_tags"] = dataset_tags
+
+    return kwargs

src/axolotl/core/training_args.py

@@ -1,6 +1,7 @@
 """
 extra axolotl specific training args
 """
+
 from dataclasses import dataclass, field
 from typing import Optional
 
@@ -206,14 +207,19 @@ class AxolotlTrainingMixins:
         },
     )
 
+    sequence_parallel_degree: Optional[int] = field(
+        default=1,
+        metadata={"help": "The number of workers to use in sequence parallelism"},
+    )
+
 
 @dataclass
 class AxolotlTrainingArguments(AxolotlTrainingMixins, TrainingArguments):
     """
     Training arguments for Causal trainer
 
-    This code is duplicated due to HF TrainingArguments not setting output_dir with a defaujlt value
-    so it can't be used as a mixin.
+    This code is duplicated due to HF TrainingArguments not setting output_dir with a
+    default value so it can't be used as a mixin.
     """
 
 

src/axolotl/evaluate.py

@@ -8,6 +8,8 @@ from typing import Dict, Optional
 
 import torch
 from accelerate.logging import get_logger
+from datasets import Dataset
+from transformers.trainer import Trainer
 
 from axolotl.logging_config import configure_logging
 from axolotl.train import TrainDatasetMeta
@@ -25,18 +27,18 @@ LOG = get_logger("axolotl.evaluate")
 
 
 def evaluate_dataset(
-    trainer, dataset, dataset_type: str, flash_optimum: bool = False
+    trainer: Trainer, dataset: Dataset, dataset_type: str, flash_optimum: bool = False
 ) -> Optional[Dict[str, float]]:
-    """Helper function to evaluate a single dataset safely.
+    """Helper function to evaluate a single dataset.
 
     Args:
-        trainer: The trainer instance
-        dataset: Dataset to evaluate
-        dataset_type: Type of dataset ('train' or 'eval')
-        flash_optimum: Whether to use flash optimum
+        trainer: The trainer instance.
+        dataset: Dataset to evaluate.
+        dataset_type: Type of dataset ('train' or 'eval').
+        flash_optimum: Whether to use flash optimum.
 
     Returns:
-        Dictionary of metrics or None if dataset is None
+        Dictionary of metrics or None if dataset is None.
     """
     if dataset is None:
         return None
@@ -63,17 +65,14 @@ def evaluate_dataset(
 
 def evaluate(*, cfg: DictDefault, dataset_meta: TrainDatasetMeta) -> Dict[str, float]:
     """
-    Evaluate a model on training and validation datasets
+    Evaluate a model on training and validation datasets.
 
     Args:
         cfg: Dictionary mapping `axolotl` config keys to values.
         dataset_meta: Dataset metadata containing training and evaluation datasets.
 
     Returns:
-        Tuple containing:
-        - The model (either PeftModel or PreTrainedModel)
-        - The tokenizer
-        - Dictionary of evaluation metrics
+        Dictionary mapping metric names to their values.
     """
     # pylint: disable=duplicate-code
     # Enable expandable segments for cuda allocation to improve VRAM usage

src/axolotl/integrations/config.py

@@ -11,19 +11,17 @@
 # the License.
 
 """
-module to handle merging the plugins' input arguments with the base configurations.
+Module to handle merging the plugins' input arguments with the base configurations.
 
-this was moved here to prevent circular imports
+This was moved here to prevent circular imports.
 """
 
 from typing import Any, Dict, List
 
-from axolotl.utils.config.models.input.v0_4_1 import (
+from axolotl.utils.schemas.config import (
     AxolotlConfigWCapabilities as AxolotlConfigWCapabilitiesBase,
 )
-from axolotl.utils.config.models.input.v0_4_1 import (
-    AxolotlInputConfig as AxolotlInputConfigBase,
-)
+from axolotl.utils.schemas.config import AxolotlInputConfig as AxolotlInputConfigBase
 
 
 def merge_input_args():

src/axolotl/integrations/grokfast/__init__.py

@@ -1,6 +1,7 @@
 """
 Grokfast plugin for Axolotl
 """
+
 import logging
 
 from transformers.trainer_callback import TrainerCallback

src/axolotl/integrations/grokfast/args.py

@@ -1,6 +1,7 @@
 """
 config args for grokfast plugin
 """
+
 from typing import Optional
 
 from pydantic import BaseModel

src/axolotl/integrations/kd/args.py

@@ -26,12 +26,12 @@ class KDArgs(BaseModel):
     """
 
     kd_trainer: Optional[bool] = None  # whether to use KD trainer
-    kd_ce_alpha: Optional[
-        float
-    ] = None  # loss coefficient for cross-entropy loss during KD
+    kd_ce_alpha: Optional[float] = (
+        None  # loss coefficient for cross-entropy loss during KD
+    )
     kd_alpha: Optional[float] = None  # loss coefficient for KD loss
     kd_temperature: Optional[float] = None  # temperature for sampling during KD
     kd_zscore_base_temp: Optional[float] = None  # base temperature for zscore scaling
-    kd_top_k_before_softmax: Optional[
-        bool
-    ] = None  # whether to sample top k before softmax during KD
+    kd_top_k_before_softmax: Optional[bool] = (
+        None  # whether to sample top k before softmax during KD
+    )

src/axolotl/integrations/liger/__init__.py

@@ -55,9 +55,9 @@ class LigerPlugin(BasePlugin):
             if "cross_entropy" in liger_fn_sig.parameters:
                 kwargs["cross_entropy"] = cfg.liger_cross_entropy
             if "fused_linear_cross_entropy" in liger_fn_sig.parameters:
-                kwargs[
-                    "fused_linear_cross_entropy"
-                ] = cfg.liger_fused_linear_cross_entropy
+                kwargs["fused_linear_cross_entropy"] = (
+                    cfg.liger_fused_linear_cross_entropy
+                )
             if "rms_norm" in liger_fn_sig.parameters:
                 kwargs["rms_norm"] = cfg.liger_rms_norm
             if "layer_norm" in liger_fn_sig.parameters:

src/axolotl/integrations/liger/models/deepseekv2.py

@@ -1,6 +1,7 @@
 """
 DeepseekV2 model with LigerFusedLinearCrossEntropyLoss
 """
+
 # pylint: disable=duplicate-code
 
 from typing import List, Optional, Tuple, Union

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

@@ -1,6 +1,7 @@
 """
 Jamba model with LigerFusedLinearCrossEntropyLoss
 """
+
 # pylint: disable=duplicate-code
 
 from typing import Optional, Tuple, Union

src/axolotl/integrations/lm_eval/__init__.py

@@ -1,6 +1,7 @@
 """
 Module for the Plugin for LM Eval Harness
 """
+
 import subprocess  # nosec
 
 from axolotl.integrations.base import BasePlugin

src/axolotl/integrations/lm_eval/args.py

@@ -1,6 +1,7 @@
 """
 Module for handling lm eval harness input arguments.
 """
+
 from typing import List, Optional
 
 from pydantic import BaseModel

src/axolotl/integrations/lm_eval/cli.py

@@ -1,6 +1,7 @@
 """
 axolotl CLI for running lm_eval tasks
 """
+
 import subprocess  # nosec
 from collections import defaultdict
 from datetime import datetime

src/axolotl/kernels/geglu.py

@@ -5,6 +5,7 @@ See "GLU Variants Improve Transformer" (https://arxiv.org/abs/2002.05202).
 
 Credit to `unsloth` (https://unsloth.ai/) for inspiration for this implementation.
 """
+
 # pylint: disable=invalid-name,unnecessary-lambda-assignment,duplicate-code
 
 import torch

src/axolotl/kernels/lora.py

@@ -6,6 +6,7 @@ See "LoRA: Low-Rank Adaptation of Large Language Models"
 
 Credit to `unsloth` (https://unsloth.ai/) for inspiration for this implementation.
 """
+
 # pylint: disable=invalid-name
 
 from typing import Callable

src/axolotl/kernels/quantize.py

@@ -1,4 +1,5 @@
 """Dequantization utilities for `bitsandbytes` integration."""
+
 # pylint: disable=invalid-name,global-statement
 
 import ctypes

src/axolotl/kernels/swiglu.py

@@ -5,6 +5,7 @@ See "GLU Variants Improve Transformer" (https://arxiv.org/abs/2002.05202).
 
 Credit to `unsloth` (https://unsloth.ai/) for inspiration for this implementation.
 """
+
 import torch
 import triton
 import triton.language as tl

src/axolotl/models/mamba/configuration_mamba.py

@@ -1,6 +1,7 @@
 """
 HF Transformers MambaConfig
 """
+
 from transformers import PretrainedConfig
 
 

src/axolotl/monkeypatch/attention/mllama.py

@@ -1,6 +1,7 @@
 """
 Monkeypatch for Vision Llama for FA2 support
 """
+
 # pylint: disable=duplicate-code
 
 from typing import Optional, Tuple
@@ -220,10 +221,10 @@ def patch_mllama():
         True
     )
     MLLAMA_TEXT_ATTENTION_CLASSES["flash_attention_2"] = MllamaTextSelfFlashAttention2
-    MLLAMA_TEXT_CROSS_ATTENTION_CLASSES[
-        "flash_attention_2"
-    ] = MllamaTextCrossFlashAttention2
+    MLLAMA_TEXT_CROSS_ATTENTION_CLASSES["flash_attention_2"] = (
+        MllamaTextCrossFlashAttention2
+    )
     # fallback to SDPA
-    MLLAMA_VISION_ATTENTION_CLASSES[
-        "flash_attention_2"
-    ] = MLLAMA_VISION_ATTENTION_CLASSES["sdpa"]
+    MLLAMA_VISION_ATTENTION_CLASSES["flash_attention_2"] = (
+        MLLAMA_VISION_ATTENTION_CLASSES["sdpa"]
+    )

src/axolotl/monkeypatch/attention/ring_attn.py

@@ -0,0 +1,89 @@
+"""
+Ring attention group registration and flash attention patching.
+
+Make use of the `ring-flash-attn` (https://github.com/zhuzilin/ring-flash-attention)
+package, specifically the `hf_adapter.substitute_hf_flash_attn` function to patch in
+their sequence parallel version of Flash Attention 2.
+"""
+
+import torch.distributed as dist
+from accelerate.logging import get_logger
+
+from axolotl.logging_config import configure_logging
+
+configure_logging()
+LOG = get_logger(__name__)
+
+RING_ATTN_GROUP = None
+
+
+def get_ring_attn_group() -> dist.ProcessGroup:
+    """
+    Getter for ring attention group on this rank.
+
+    Returns:
+        The process group for ring attention for this rank.
+    """
+    return RING_ATTN_GROUP
+
+
+def set_ring_attn_group(ring_attn_group: dist.ProcessGroup):
+    """
+    Setter for ring attention group on this rank.
+
+    Args:
+        Process group for ring attention.
+    """
+    global RING_ATTN_GROUP  # pylint: disable=global-statement
+    RING_ATTN_GROUP = ring_attn_group
+
+
+def register_ring_attn(sequence_parallel_degree: int):
+    """
+    Create ring attention group and substitute flash attn with ring flash attn.
+
+    Args:
+        sequence_parallel_degree: Sequence parallelism factor.
+    """
+    LOG.info(
+        "Enabling ring attention sequence parallelism: "
+        f"each sequence will be processed across {sequence_parallel_degree} GPUs"
+    )
+
+    world_size = dist.get_world_size()
+    assert sequence_parallel_degree <= world_size, (
+        f"sequence_parallel_degree ({sequence_parallel_degree}) "
+        f"must be less than or equal to world_size ({world_size})"
+    )
+    assert world_size % sequence_parallel_degree == 0, (
+        f"sequence_parallel_degree ({sequence_parallel_degree}) "
+        f"must evenly divide world_size ({world_size})"
+    )
+
+    # Detailed logging of group formation
+    rank = dist.get_rank()
+    group_assignments = {}
+
+    for i in range(world_size // sequence_parallel_degree):
+        ring_attn_ranks = list(
+            range(
+                i * sequence_parallel_degree,
+                (i + 1) * sequence_parallel_degree,
+            )
+        )
+        group = dist.new_group(ranks=ring_attn_ranks, backend="nccl")
+
+        # Track which GPUs are in which groups
+        for r in ring_attn_ranks:
+            group_assignments[r] = i
+
+        if rank in ring_attn_ranks:
+            set_ring_attn_group(group)
+
+    # Log the GPU group assignments
+    if rank == 0:
+        LOG.info(f"Sequence parallel group assignments: {group_assignments}")
+
+    from ring_flash_attn import substitute_hf_flash_attn
+
+    substitute_hf_flash_attn(get_ring_attn_group(), sequence_parallel_degree)

src/axolotl/monkeypatch/data/batch_dataset_fetcher.py

@@ -1,4 +1,5 @@
 """monkey patches for the dataset fetcher to handle batches of packed indexes"""
+
 # pylint: disable=protected-access
 
 import torch

src/axolotl/monkeypatch/llama_attn_hijack_flash.py

@@ -12,7 +12,9 @@ import transformers
 from einops import rearrange
 from flash_attn.bert_padding import pad_input, unpad_input
 from transformers.modeling_outputs import BaseModelOutputWithPast
-from transformers.models.llama.modeling_llama import LlamaAttention
+from transformers.models.llama.modeling_llama import (
+    LlamaAttention,
+)
 from transformers.models.llama.modeling_llama import (
     LlamaDecoderLayer as OriginalLlamaDecoderLayer,
 )
@@ -490,9 +492,11 @@ def flashattn_forward(
             # We have disabled _prepare_decoder_attention_mask in LlamaModel
             # the attention_mask should be the same as the key_padding_mask
             key_padding_mask=attention_mask,
-            query_padding_mask=attention_mask[:, -query_states.size(1) :]
-            if attention_mask is not None
-            else None,
+            query_padding_mask=(
+                attention_mask[:, -query_states.size(1) :]
+                if attention_mask is not None
+                else None
+            ),
         )
         output_unpad = flash_attn_varlen_qkvpacked_func(
             qkv_unpad,
@@ -531,9 +535,11 @@ def flashattn_forward(
                 value_states,
                 kvpacked=True,
                 key_padding_mask=attention_mask,
-                query_padding_mask=attention_mask[:, -query_states.size(1) :]
-                if attention_mask is not None
-                else None,
+                query_padding_mask=(
+                    attention_mask[:, -query_states.size(1) :]
+                    if attention_mask is not None
+                    else None
+                ),
             )
             if q_unpad.dtype != kv_unpad.dtype:
                 kv_unpad = kv_unpad.to(q_unpad.dtype)

src/axolotl/monkeypatch/llama_expand_mask.py

@@ -1,6 +1,7 @@
 """
 expands the binary attention mask per 3.2.2 of https://arxiv.org/pdf/2107.02027.pdf
 """
+
 from typing import Optional
 
 import torch

src/axolotl/monkeypatch/mistral_attn_hijack_flash.py

@@ -1,4 +1,5 @@
 """Flash attention monkey patch for mistral model"""
+
 # pylint: disable=duplicate-code
 
 import logging
@@ -21,7 +22,10 @@ from transformers.models.mistral.modeling_mistral import (
 from transformers.models.mistral.modeling_mistral import (
     MistralDecoderLayer as OriginalMistralDecoderLayer,
 )
-from transformers.models.mistral.modeling_mistral import apply_rotary_pos_emb, repeat_kv
+from transformers.models.mistral.modeling_mistral import (
+    apply_rotary_pos_emb,
+    repeat_kv,
+)
 
 from axolotl.monkeypatch.utils import get_cu_seqlens_from_pos_ids
 
@@ -243,9 +247,11 @@ def flashattn_forward(
             # We have disabled _prepare_decoder_attention_mask in LlamaModel
             # the attention_mask should be the same as the key_padding_mask
             key_padding_mask=attention_mask,
-            query_padding_mask=attention_mask[:, -query_states.size(1) :]
-            if attention_mask is not None
-            else None,
+            query_padding_mask=(
+                attention_mask[:, -query_states.size(1) :]
+                if attention_mask is not None
+                else None
+            ),
         )
         output_unpad = flash_attn_varlen_qkvpacked_func(
             qkv_unpad,
@@ -286,9 +292,11 @@ def flashattn_forward(
                 value_states,
                 kvpacked=True,
                 key_padding_mask=attention_mask,
-                query_padding_mask=attention_mask[:, -query_states.size(1) :]
-                if attention_mask is not None
-                else None,
+                query_padding_mask=(
+                    attention_mask[:, -query_states.size(1) :]
+                    if attention_mask is not None
+                    else None
+                ),
             )
             if q_unpad.dtype != kv_unpad.dtype:
                 kv_unpad = kv_unpad.to(q_unpad.dtype)

src/axolotl/monkeypatch/mixtral/__init__.py

@@ -1,6 +1,7 @@
 """
 Patches to support multipack for mixtral
 """
+
 import torch
 
 

src/axolotl/monkeypatch/relora.py

@@ -1,4 +1,5 @@
 """Implements the ReLoRA training procedure from https://arxiv.org/abs/2307.05695, minus the initial full fine-tune."""
+
 import glob
 import json
 import logging
@@ -411,7 +412,10 @@ def merge_and_save(
         if shard_path.endswith(".safetensors"):
             in_tensors = st.load_file(str(Path(model_src) / shard_path))
         else:
-            in_tensors = torch.load(Path(model_src) / shard_path)
+            in_tensors = torch.load(
+                Path(model_src) / shard_path,
+                weights_only=True,  # to prevent arbitrary code execution
+            )
             if "state_dict" in in_tensors:
                 in_tensors = in_tensors["state_dict"]
 

src/axolotl/monkeypatch/stablelm_attn_hijack_flash.py

@@ -17,7 +17,7 @@
 # https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/modeling_llama.py
 # https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt_neox/modeling_gpt_neox.py
 # pylint: disable=duplicate-code
-""" PyTorch StableLM Epoch model. """
+"""PyTorch StableLM Epoch model."""
 import importlib
 import math
 from typing import Optional, Tuple, Union

src/axolotl/monkeypatch/trainer_fsdp_optim.py

@@ -1,6 +1,7 @@
 """
 fix for FSDP optimizer save in trainer w 4.47.0
 """
+
 import inspect
 import logging
 

src/axolotl/monkeypatch/utils.py

@@ -1,6 +1,7 @@
 """
 Shared utils for the monkeypatches
 """
+
 import re
 from typing import Optional, Tuple
 

src/axolotl/monkeypatch/xformers_/__init__.py

@@ -1,6 +1,7 @@
 """
 Fused MLP layer for incrementally improved training efficiency
 """
+
 import torch
 from transformers.models.llama.modeling_llama import LlamaMLP
 from xformers.ops import SwiGLU

src/axolotl/prompt_strategies/alpaca_w_system.py

@@ -1,6 +1,7 @@
 """
 Prompt strategies loader for alpaca instruction datasets with system prompts
 """
+
 from typing import Generator, Tuple, Union
 
 from axolotl.prompt_tokenizers import PromptTokenizingStrategy

src/axolotl/prompt_strategies/chat_template.py

@@ -13,7 +13,7 @@ from axolotl.prompt_strategies.jinja_template_analyzer import JinjaTemplateAnaly
 from axolotl.prompt_tokenizers import PromptTokenizingStrategy
 from axolotl.prompters import IGNORE_TOKEN_ID, Prompter
 from axolotl.utils.chat_templates import get_chat_template_from_config
-from axolotl.utils.config.models.input.v0_4_1 import DatasetConfig
+from axolotl.utils.schemas.datasets import DatasetConfig
 
 # Configure the logger
 LOG = logging.getLogger("axolotl")

src/axolotl/prompt_strategies/completion.py

@@ -1,6 +1,7 @@
 """
 Basic completion text
 """
+
 from collections import defaultdict
 from typing import Any, Dict, Generator, Optional, Tuple