don't use zero first context for loading datasets

* don't set peft_config on grpo to prevent double peft wrap

* remove overrides needed to support bug

* fix grpo tests

* require more CPU for multigpu to help with torch compile for vllm

.github/workflows/main.yml

@@ -31,6 +31,11 @@ jobs:
             python_version: "3.11"
             pytorch: 2.7.0
             axolotl_extras:
+          - cuda: 128
+            cuda_version: 12.8.1
+            python_version: "3.11"
+            pytorch: 2.7.0
+            axolotl_extras:
     runs-on: axolotl-gpu-runner
     steps:
       - name: Checkout
@@ -94,6 +99,11 @@ jobs:
             python_version: "3.11"
             pytorch: 2.7.0
             axolotl_extras:
+          - cuda: 128
+            cuda_version: 12.8.1
+            python_version: "3.11"
+            pytorch: 2.7.0
+            axolotl_extras:
     runs-on: axolotl-gpu-runner
     steps:
       - name: Checkout

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

@@ -3,7 +3,7 @@ name: docker-multigpu-tests-biweekly
 on:
   pull_request:
     paths:
-      - 'tests/e2e/multigpu/*.py'
+      - 'tests/e2e/multigpu/**.py'
       - 'requirements.txt'
       - 'setup.py'
       - 'pyproject.toml'

.github/workflows/tests-nightly.yml

@@ -18,9 +18,96 @@ jobs:
         env:
           SKIP: no-commit-to-branch
 
+  preload-cache:
+    name: Preload HF cache
+    runs-on: ubuntu-latest
+    strategy:
+      fail-fast: false
+      matrix:
+        python_version: ["3.11"]
+        pytorch_version: ["2.6.0"]
+    timeout-minutes: 20
+
+    env:
+      AXOLOTL_IS_CI_CACHE_PRELOAD: "1"
+
+    steps:
+      - name: Check out repository code
+        uses: actions/checkout@v4
+
+      - name: Restore HF cache
+        id: hf-cache-restore
+        uses: actions/cache/restore@v4
+        with:
+          path: |
+            /home/runner/.cache/huggingface/hub/datasets--*
+            /home/runner/.cache/huggingface/hub/models--*
+          key: ${{ runner.os }}-hf-hub-cache-v2
+
+      - name: Setup Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: ${{ matrix.python_version }}
+          cache: 'pip' # caching pip dependencies
+
+      - name: upgrade pip
+        run: |
+          pip3 install --upgrade pip
+          pip3 install --upgrade packaging==23.2 setuptools==75.8.0 wheel
+
+      - name: Install PyTorch
+        run: |
+          pip3 install torch==${{ matrix.pytorch_version }}
+
+      - name: Install dependencies
+        run: |
+          pip3 show torch
+          pip3 install --no-build-isolation -U -e .
+          python scripts/unsloth_install.py | sh
+          python scripts/cutcrossentropy_install.py | sh
+          pip3 install -r requirements-dev.txt -r requirements-tests.txt
+
+      - name: Make sure PyTorch version wasn't clobbered
+        run: |
+          python -c "import torch; assert '${{ matrix.pytorch_version }}' in torch.__version__"
+
+      - name: Ensure axolotl CLI was installed
+        run: |
+          axolotl --help
+
+      - name: Pre-Download dataset fixture
+        run: |
+          huggingface-cli download --repo-type=dataset axolotl-ai-internal/axolotl-oss-dataset-fixtures
+
+      - name: Run tests
+        run: |
+          pytest -v tests/conftest.py
+
+      - name: Upload coverage to Codecov
+        uses: codecov/codecov-action@v5
+        with:
+          token: ${{ secrets.CODECOV_TOKEN }}
+          files: ./coverage.xml
+          flags: unittests,pytorch-${{ matrix.pytorch_version }}
+          fail_ci_if_error: false
+
+      - name: cleanup pip cache
+        run: |
+          find "$(pip cache dir)/http-v2" -type f -mtime +14 -exec rm {} \;
+
+      - name: Save HF cache
+        id: hf-cache
+        uses: actions/cache/save@v4
+        with:
+          path: |
+            /home/runner/.cache/huggingface/hub/datasets--*
+            /home/runner/.cache/huggingface/hub/models--*
+          key: ${{ steps.hf-cache-restore.outputs.cache-primary-key }}
+
   pytest:
     name: PyTest
     runs-on: ubuntu-latest
+    needs: [preload-cache]
     strategy:
       fail-fast: false
       max-parallel: 2

.github/workflows/tests.yml

@@ -44,96 +44,102 @@ jobs:
         env:
           SKIP: no-commit-to-branch
 
-  preload-cache:
-    name: Preload HF cache
-    runs-on: ubuntu-latest
-    strategy:
-      fail-fast: false
-      matrix:
-        python_version: ["3.11"]
-        pytorch_version: ["2.6.0"]
-    timeout-minutes: 20
-
-    env:
-      AXOLOTL_IS_CI_CACHE_PRELOAD: "1"
-
-    steps:
-      - name: Check out repository code
-        uses: actions/checkout@v4
-
-      - name: Restore HF cache
-        id: hf-cache-restore
-        uses: actions/cache/restore@v4
-        with:
-          path: |
-            /home/runner/.cache/huggingface/hub/datasets--*
-            /home/runner/.cache/huggingface/hub/models--*
-          key: ${{ runner.os }}-hf-hub-cache-v2
-
-      - name: Setup Python
-        uses: actions/setup-python@v5
-        with:
-          python-version: ${{ matrix.python_version }}
-          cache: 'pip' # caching pip dependencies
-
-      - name: upgrade pip
-        run: |
-          pip3 install --upgrade pip
-          pip3 install --upgrade packaging==23.2 setuptools==75.8.0 wheel
-
-      - name: Install PyTorch
-        run: |
-          pip3 install torch==${{ matrix.pytorch_version }}
-
-      - name: Install dependencies
-        run: |
-          pip3 show torch
-          pip3 install --no-build-isolation -U -e .
-          python scripts/unsloth_install.py | sh
-          python scripts/cutcrossentropy_install.py | sh
-          pip3 install -r requirements-dev.txt -r requirements-tests.txt
-
-      - name: Make sure PyTorch version wasn't clobbered
-        run: |
-          python -c "import torch; assert '${{ matrix.pytorch_version }}' in torch.__version__"
-
-      - name: Ensure axolotl CLI was installed
-        run: |
-          axolotl --help
-
-      - name: Pre-Download dataset fixture
-        run: |
-          huggingface-cli download --repo-type=dataset axolotl-ai-internal/axolotl-oss-dataset-fixtures
-
-      - name: Run tests
-        run: |
-          pytest -v tests/conftest.py
-
-      - name: Upload coverage to Codecov
-        uses: codecov/codecov-action@v5
-        with:
-          token: ${{ secrets.CODECOV_TOKEN }}
-          files: ./coverage.xml
-          flags: unittests,pytorch-${{ matrix.pytorch_version }}
-          fail_ci_if_error: false
-
-      - name: cleanup pip cache
-        run: |
-          find "$(pip cache dir)/http-v2" -type f -mtime +14 -exec rm {} \;
-
-      - name: Save HF cache
-        id: hf-cache
-        uses: actions/cache/save@v4
-        with:
-          path: |
-            /home/runner/.cache/huggingface/hub/datasets--*
-            /home/runner/.cache/huggingface/hub/models--*
-          key: ${{ steps.hf-cache-restore.outputs.cache-primary-key }}
+#  preload-cache:
+#    name: Preload HF cache
+#    runs-on: ubuntu-latest
+#    strategy:
+#      fail-fast: false
+#      matrix:
+#        python_version: ["3.11"]
+#        pytorch_version: ["2.6.0"]
+#    timeout-minutes: 20
+#
+#    env:
+#      AXOLOTL_IS_CI_CACHE_PRELOAD: "1"
+#
+#    steps:
+#      - name: Check out repository code
+#        uses: actions/checkout@v4
+#
+#      - name: Restore HF cache
+#        id: hf-cache-restore
+#        uses: actions/cache/restore@v4
+#        with:
+#          path: |
+#            /home/runner/.cache/huggingface/hub/datasets--*
+#            /home/runner/.cache/huggingface/hub/models--*
+#          key: ${{ runner.os }}-hf-hub-cache-v2
+#
+#      - name: Restore Cache from S3
+#        id: hf-cache-restore-s3
+#        run: |
+#          mkdir -p /home/runner/.cache/huggingface/hub
+#          curl -L https://d1dttdx32dkk5p.cloudfront.net/hf-cache.tar.zst | tar -xf - -C /home/runner/.cache/huggingface/hub/  --use-compress-program unzstd
+#
+#      - name: Setup Python
+#        uses: actions/setup-python@v5
+#        with:
+#          python-version: ${{ matrix.python_version }}
+#          cache: 'pip' # caching pip dependencies
+#
+#      - name: upgrade pip
+#        run: |
+#          pip3 install --upgrade pip
+#          pip3 install --upgrade packaging==23.2 setuptools==75.8.0 wheel
+#
+#      - name: Install PyTorch
+#        run: |
+#          pip3 install torch==${{ matrix.pytorch_version }}
+#
+#      - name: Install dependencies
+#        run: |
+#          pip3 show torch
+#          pip3 install --no-build-isolation -U -e .
+#          python scripts/unsloth_install.py | sh
+#          python scripts/cutcrossentropy_install.py | sh
+#          pip3 install -r requirements-dev.txt -r requirements-tests.txt
+#
+#      - name: Make sure PyTorch version wasn't clobbered
+#        run: |
+#          python -c "import torch; assert '${{ matrix.pytorch_version }}' in torch.__version__"
+#
+#      - name: Ensure axolotl CLI was installed
+#        run: |
+#          axolotl --help
+#
+#      - name: Pre-Download dataset fixture
+#        run: |
+#          huggingface-cli download --repo-type=dataset axolotl-ai-internal/axolotl-oss-dataset-fixtures
+#
+#      - name: Run tests
+#        run: |
+#          pytest -v tests/conftest.py
+#
+#      - name: Upload coverage to Codecov
+#        uses: codecov/codecov-action@v5
+#        with:
+#          token: ${{ secrets.CODECOV_TOKEN }}
+#          files: ./coverage.xml
+#          flags: unittests,pytorch-${{ matrix.pytorch_version }}
+#          fail_ci_if_error: false
+#
+#      - name: cleanup pip cache
+#        run: |
+#          find "$(pip cache dir)/http-v2" -type f -mtime +14 -exec rm {} \;
+#
+#      - name: Save HF cache
+#        id: hf-cache
+#        uses: actions/cache/save@v4
+#        with:
+#          path: |
+#            /home/runner/.cache/huggingface/hub/datasets--*
+#            /home/runner/.cache/huggingface/hub/models--*
+#          key: ${{ steps.hf-cache-restore.outputs.cache-primary-key }}
 
   pytest:
     name: PyTest
     runs-on: ubuntu-latest
-    needs: [preload-cache]
+#    needs: [preload-cache]
     strategy:
       fail-fast: false
       matrix:
@@ -145,14 +151,20 @@ jobs:
       - name: Check out repository code
         uses: actions/checkout@v4
 
-      - name: Restore HF cache
-        id: hf-cache-restore
-        uses: actions/cache/restore@v4
-        with:
-          path: |
-            /home/runner/.cache/huggingface/hub/datasets--*
-            /home/runner/.cache/huggingface/hub/models--*
-          key: ${{ runner.os }}-hf-hub-cache-v2
+#      - name: Restore HF cache
+#        id: hf-cache-restore
+#        uses: actions/cache/restore@v4
+#        with:
+#          path: |
+#            /home/runner/.cache/huggingface/hub/datasets--*
+#            /home/runner/.cache/huggingface/hub/models--*
+#          key: ${{ runner.os }}-hf-hub-cache-v2
+
+      - name: Restore Cache from S3
+        id: hf-cache-restore-s3
+        run: |
+          mkdir -p /home/runner/.cache/huggingface/hub
+          curl -L https://d1dttdx32dkk5p.cloudfront.net/hf-cache.tar.zst | tar -xf - -C /home/runner/.cache/huggingface/hub/  --use-compress-program unzstd
 
       - name: Setup Python
         uses: actions/setup-python@v5
@@ -210,7 +222,7 @@ jobs:
   pytest-sdist:
     name: PyTest from Source Dist
     runs-on: ubuntu-latest
-    needs: [preload-cache]
+#    needs: [preload-cache]
     strategy:
       fail-fast: false
       matrix:
@@ -222,14 +234,20 @@ jobs:
       - name: Check out repository code
         uses: actions/checkout@v4
 
-      - name: Restore HF cache
-        id: hf-cache-restore
-        uses: actions/cache/restore@v4
-        with:
-          path: |
-            /home/runner/.cache/huggingface/hub/datasets--*
-            /home/runner/.cache/huggingface/hub/models--*
-          key: ${{ runner.os }}-hf-hub-cache-v2
+#      - name: Restore HF cache
+#        id: hf-cache-restore
+#        uses: actions/cache/restore@v4
+#        with:
+#          path: |
+#            /home/runner/.cache/huggingface/hub/datasets--*
+#            /home/runner/.cache/huggingface/hub/models--*
+#          key: ${{ runner.os }}-hf-hub-cache-v2
+
+      - name: Restore Cache from S3
+        id: hf-cache-restore-s3
+        run: |
+          mkdir -p /home/runner/.cache/huggingface/hub
+          curl -L https://d1dttdx32dkk5p.cloudfront.net/hf-cache.tar.zst | tar -xf - -C /home/runner/.cache/huggingface/hub/  --use-compress-program unzstd
 
       - name: Setup Python
         uses: actions/setup-python@v5
@@ -277,6 +295,7 @@ jobs:
           find "$(pip cache dir)/http-v2" -type f -mtime +14 -exec rm {} \;
 
   docker-e2e-tests-1st:
+    # Run this job first as a gate for running the remainder of the test matrix
     if: ${{ ! contains(github.event.commits[0].message, '[skip e2e]') && github.repository_owner == 'axolotl-ai-cloud' }}
     # this job needs to be run on self-hosted GPU runners...
     runs-on: [self-hosted, modal]
@@ -323,6 +342,8 @@ jobs:
     # this job needs to be run on self-hosted GPU runners...
     runs-on: [self-hosted, modal]
     timeout-minutes: 90
+    # Only run the remainder of the matrix if the first e2e check passed;
+    # this is to save on wasted compute costs for known failures that get caught in the first run
     needs: [pre-commit, pytest, docker-e2e-tests-1st]
 
     strategy:
@@ -335,12 +356,6 @@ jobs:
             pytorch: 2.6.0
             num_gpus: 1
             axolotl_extras: llmcompressor
-          - cuda: 124
-            cuda_version: 12.4.1
-            python_version: "3.11"
-            pytorch: 2.4.1
-            num_gpus: 1
-            axolotl_extras:
           - cuda: 124
             cuda_version: 12.4.1
             python_version: "3.11"
@@ -353,6 +368,12 @@ jobs:
             pytorch: 2.7.0
             num_gpus: 1
             axolotl_extras:
+          - cuda: 128
+            cuda_version: 12.8.1
+            python_version: "3.11"
+            pytorch: 2.7.0
+            num_gpus: 1
+            axolotl_extras:
     steps:
       - name: Checkout
         uses: actions/checkout@v4
@@ -377,3 +398,43 @@ jobs:
       - name: Run tests job on Modal
         run: |
           modal run cicd.e2e_tests
+
+  docker-e2e-cleanup:
+    runs-on: [self-hosted, modal]
+    timeout-minutes: 90
+    needs: [docker-e2e-tests]
+
+    strategy:
+      fail-fast: false
+      matrix:
+        include:
+          - cuda: 124
+            cuda_version: 12.4.1
+            python_version: "3.11"
+            pytorch: 2.6.0
+            num_gpus: 1
+            axolotl_extras: vllm
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+      - name: Install Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: "3.11"
+      - name: Install Modal
+        run: |
+          python -m pip install --upgrade pip
+          pip install modal==0.71.8 jinja2
+      - name: Update env vars
+        run: |
+          echo "BASE_TAG=main-base-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}" >> $GITHUB_ENV
+          echo "PYTORCH_VERSION=${{ matrix.pytorch}}" >> $GITHUB_ENV
+          echo "AXOLOTL_ARGS=${{ matrix.axolotl_args}}" >> $GITHUB_ENV
+          echo "AXOLOTL_EXTRAS=${{ matrix.axolotl_extras}}" >> $GITHUB_ENV
+          echo "CUDA=${{ matrix.cuda }}" >> $GITHUB_ENV
+          echo "MODAL_IMAGE_BUILDER_VERSION=2024.10" >> $GITHUB_ENV
+          echo "N_GPUS=${{ matrix.num_gpus }}" >> $GITHUB_ENV
+          echo "CODECOV_TOKEN=${{ secrets.CODECOV_TOKEN }}" >> $GITHUB_ENV
+      - name: Run tests job on Modal
+        run: |
+          modal run cicd.cleanup

.runpod/src/handler.py

@@ -57,8 +57,10 @@ async def handler(job):
     logger.info("Training Complete.")
 
     # Cleanup
-    del os.environ["WANDB_API_KEY"]
-    del os.environ["HF_TOKEN"]
+    if "WANDB_API_KEY" in os.environ:
+        del os.environ["WANDB_API_KEY"]
+    if "HF_TOKEN" in os.environ:
+        del os.environ["HF_TOKEN"]
 
 
 runpod.serverless.start({"handler": handler, "return_aggregate_stream": True})

_quarto.yml

@@ -48,8 +48,23 @@ quartodoc:
       contents:
         - core.trainers.base
         - core.trainers.trl
+        - core.trainers.mamba
+        - core.trainers.relora
         - core.trainers.dpo.trainer
         - core.trainers.grpo.trainer
+        - core.trainers.grpo.sampler
+        - core.trainers.utils
+    - title: Mixins
+      desc: Mixin classes for augmenting trainers
+      contents:
+        - core.trainers.mixins.optimizer
+        - core.trainers.mixins.rng_state_loader
+        - core.trainers.mixins.scheduler
+        - core.trainers.mixins.sequence_parallel
+    - title: Context Managers
+      desc: Context managers for altering trainer behaviors
+      contents:
+        - utils.ctx_managers.sequence_parallel
     - title: Prompt Strategies
       desc: Prompt formatting strategies
       contents:
@@ -86,7 +101,7 @@ quartodoc:
         - kernels.swiglu
         - kernels.quantize
         - kernels.utils
-    - title: MonkeyPatches
+    - title: Monkey Patches
       desc: Runtime patches for model optimizations
       contents:
         - monkeypatch.llama_attn_hijack_flash
@@ -124,7 +139,8 @@ quartodoc:
         - utils.optimizers.adopt
         - utils.data.pretraining
         - utils.data.sft
-        - utils.gradient_checkpointing.unsloth
+        - utils.gradient_checkpointing.offload_cpu
+        - utils.gradient_checkpointing.offload_disk
     - title: Schemas
       desc: Pydantic data models for Axolotl config
       contents:

cicd/cicd.sh

@@ -18,7 +18,7 @@ pytest -v --durations=10 \
   --cov-append
 
 # Run patched tests excluding lora kernels with coverage append
-pytest -v --durations=10 \
+pytest --full-trace -vvv --durations=10 \
   --ignore=tests/e2e/patched/lora_kernels \
   /workspace/axolotl/tests/e2e/patched \
   --cov=axolotl \

cicd/cleanup.py

@@ -0,0 +1,19 @@
+"""Modal app to run axolotl GPU cleanup"""
+
+from .single_gpu import VOLUME_CONFIG, app, cicd_image, run_cmd
+
+
+@app.function(
+    image=cicd_image,
+    timeout=60 * 60,
+    cpu=8.0,
+    memory=131072,
+    volumes=VOLUME_CONFIG,
+)
+def cleanup():
+    run_cmd("./cicd/cleanup.sh", "/workspace/axolotl")
+
+
+@app.local_entrypoint()
+def main():
+    cleanup.remote()

cicd/cleanup.sh

@@ -0,0 +1,6 @@
+#!/bin/bash
+set -e
+
+# cleanup old cache files for datasets processing and intermediate mappings
+find /workspace/data/huggingface-cache/hub/datasets -name "cache-*" -type f -mtime +1 -exec rm {} \;
+find /workspace/data/huggingface-cache/hub/datasets -name "*.lock" -type f -mtime +1 -exec rm {} \;

cicd/e2e_tests.py

@@ -1,75 +1,12 @@
 """Modal app to run axolotl GPU tests"""
 
-# pylint: disable=duplicate-code
-
-import os
-import pathlib
-import tempfile
-
-import jinja2
-import modal
-from jinja2 import select_autoescape
-from modal import App, Image
-
-cicd_path = pathlib.Path(__file__).parent.resolve()
-
-template_loader = jinja2.FileSystemLoader(searchpath=cicd_path)
-template_env = jinja2.Environment(
-    loader=template_loader, autoescape=select_autoescape()
-)
-df_template = template_env.get_template("Dockerfile.jinja")
-
-df_args = {
-    "AXOLOTL_EXTRAS": os.environ.get("AXOLOTL_EXTRAS", ""),
-    "AXOLOTL_ARGS": os.environ.get("AXOLOTL_ARGS", ""),
-    "PYTORCH_VERSION": os.environ.get("PYTORCH_VERSION", "2.4.1"),
-    "BASE_TAG": os.environ.get("BASE_TAG", "main-base-py3.11-cu121-2.4.1"),
-    "CUDA": os.environ.get("CUDA", "121"),
-    "GITHUB_REF": os.environ.get("GITHUB_REF", "refs/heads/main"),
-    "GITHUB_SHA": os.environ.get("GITHUB_SHA", ""),
-    "NIGHTLY_BUILD": os.environ.get("NIGHTLY_BUILD", ""),
-    "CODECOV_TOKEN": os.environ.get("CODECOV_TOKEN", ""),
-    "HF_HOME": "/workspace/data/huggingface-cache/hub",
-}
-
-dockerfile_contents = df_template.render(**df_args)
-
-temp_dir = tempfile.mkdtemp()
-with open(pathlib.Path(temp_dir) / "Dockerfile", "w", encoding="utf-8") as f:
-    f.write(dockerfile_contents)
-
-cicd_image = Image.from_dockerfile(
-    pathlib.Path(temp_dir) / "Dockerfile",
-    context_mount=None,
-    force_build=True,
-    gpu="A10G",
-).env(df_args)
-
-app = App("Axolotl CI/CD", secrets=[])
-
-hf_cache_volume = modal.Volume.from_name(
-    "axolotl-ci-hf-hub-cache", create_if_missing=True
-)
-VOLUME_CONFIG = {
-    "/workspace/data/huggingface-cache/hub": hf_cache_volume,
-}
-
-N_GPUS = int(os.environ.get("N_GPUS", 1))
-GPU_CONFIG = modal.gpu.L40S(count=N_GPUS)
-
-
-def run_cmd(cmd: str, run_folder: str):
-    import subprocess  # nosec
-
-    # Propagate errors from subprocess.
-    if exit_code := subprocess.call(cmd.split(), cwd=run_folder):  # nosec
-        exit(exit_code)  # pylint: disable=consider-using-sys-exit
+from .single_gpu import GPU_CONFIG, VOLUME_CONFIG, app, cicd_image, run_cmd
 
 
 @app.function(
     image=cicd_image,
     gpu=GPU_CONFIG,
-    timeout=60 * 60,
+    timeout=90 * 60,  # 90 min
     cpu=8.0,
     memory=131072,
     volumes=VOLUME_CONFIG,

cicd/multigpu.py

@@ -70,7 +70,7 @@ def run_cmd(cmd: str, run_folder: str):
     image=cicd_image,
     gpu=GPU_CONFIG,
     timeout=90 * 60,
-    cpu=8.0,
+    cpu=16.0,
     memory=131072 * N_GPUS,
     volumes=VOLUME_CONFIG,
 )

cicd/single_gpu.py

@@ -0,0 +1,66 @@
+"""Modal app to run axolotl GPU tests"""
+
+# pylint: disable=duplicate-code
+
+import os
+import pathlib
+import tempfile
+
+import jinja2
+import modal
+from jinja2 import select_autoescape
+from modal import App, Image
+
+cicd_path = pathlib.Path(__file__).parent.resolve()
+
+template_loader = jinja2.FileSystemLoader(searchpath=cicd_path)
+template_env = jinja2.Environment(
+    loader=template_loader, autoescape=select_autoescape()
+)
+df_template = template_env.get_template("Dockerfile.jinja")
+
+df_args = {
+    "AXOLOTL_EXTRAS": os.environ.get("AXOLOTL_EXTRAS", ""),
+    "AXOLOTL_ARGS": os.environ.get("AXOLOTL_ARGS", ""),
+    "PYTORCH_VERSION": os.environ.get("PYTORCH_VERSION", "2.4.1"),
+    "BASE_TAG": os.environ.get("BASE_TAG", "main-base-py3.11-cu121-2.4.1"),
+    "CUDA": os.environ.get("CUDA", "121"),
+    "GITHUB_REF": os.environ.get("GITHUB_REF", "refs/heads/main"),
+    "GITHUB_SHA": os.environ.get("GITHUB_SHA", ""),
+    "NIGHTLY_BUILD": os.environ.get("NIGHTLY_BUILD", ""),
+    "CODECOV_TOKEN": os.environ.get("CODECOV_TOKEN", ""),
+    "HF_HOME": "/workspace/data/huggingface-cache/hub",
+}
+
+dockerfile_contents = df_template.render(**df_args)
+
+temp_dir = tempfile.mkdtemp()
+with open(pathlib.Path(temp_dir) / "Dockerfile", "w", encoding="utf-8") as f:
+    f.write(dockerfile_contents)
+
+cicd_image = Image.from_dockerfile(
+    pathlib.Path(temp_dir) / "Dockerfile",
+    context_mount=None,
+    force_build=True,
+    gpu="A10G",
+).env(df_args)
+
+app = App("Axolotl CI/CD", secrets=[])
+
+hf_cache_volume = modal.Volume.from_name(
+    "axolotl-ci-hf-hub-cache", create_if_missing=True
+)
+VOLUME_CONFIG = {
+    "/workspace/data/huggingface-cache/hub": hf_cache_volume,
+}
+
+N_GPUS = int(os.environ.get("N_GPUS", 1))
+GPU_CONFIG = modal.gpu.L40S(count=N_GPUS)
+
+
+def run_cmd(cmd: str, run_folder: str):
+    import subprocess  # nosec
+
+    # Propagate errors from subprocess.
+    if exit_code := subprocess.call(cmd.split(), cwd=run_folder):  # nosec
+        exit(exit_code)  # pylint: disable=consider-using-sys-exit

codecov.yml

@@ -19,7 +19,7 @@ coverage:
         if_no_uploads: error
         if_not_found: success
         if_ci_failed: error
-        only_pulls: false
+        only_pulls: true
         flags: null
         paths: null
     patch:

docs/config.qmd

@@ -505,6 +505,7 @@ save_strategy: # Set to `"no"` to skip checkpoint saves, `"epoch"` at end of eac
 save_steps: # Leave empty to save at each epoch, integer for every N steps. float for fraction of total steps
 saves_per_epoch: # number of times per epoch to save a checkpoint, mutually exclusive with save_steps
 save_total_limit: # Checkpoints saved at a time
+save_only_model: # Save only the model weights, skipping the optimizer. Using this means you can't resume from checkpoints.
 # Maximum number of iterations to train for. It precedes num_epochs which means that
 # if both are set, num_epochs will not be guaranteed.
 # e.g., when 1 epoch is 1000 steps => `num_epochs: 2` and `max_steps: 100` will train for 100 steps
@@ -538,7 +539,7 @@ train_on_inputs: false
 # Note that training loss may have an oscillating pattern with this enabled.
 group_by_length: false
 
-# Whether to use gradient checkpointing. Available options are: true, false, "offload".
+# Whether to use gradient checkpointing. Available options are: true, false, "offload", "offload_disk".
 # https://huggingface.co/docs/transformers/v4.18.0/en/performance#gradient-checkpointing
 gradient_checkpointing: false
 # additional kwargs to pass to the trainer for gradient checkpointing
@@ -612,6 +613,7 @@ lr_div_factor: # Learning rate div factor
 # - optimi_adamw
 # - ao_adamw_8bit
 # - ao_adamw_fp8
+# - came_pytorch
 optimizer:
 # Dictionary of arguments to pass to the optimizer
 optim_args:
@@ -631,7 +633,9 @@ weight_decay:
 # adamw hyperparams
 adam_beta1:
 adam_beta2:
+adam_beta3:  # only used for CAME Optimizer
 adam_epsilon:
+adam_epsilon2:  # only used for CAME Optimizer
 # Gradient clipping max norm
 max_grad_norm:
 

docs/getting-started.qmd

@@ -104,7 +104,7 @@ the `alpaca` dataset format, which has the following format:
 Please see our [Dataset Formats](dataset-formats) for more dataset formats and how to
 format them.
 
-2. Prepare your JSONL data in the specified format (in this case, the expected `alpaca
+2. Prepare your JSONL data in the specified format (in this case, the expected `alpaca`
 format):
 
 ```json
@@ -120,6 +120,12 @@ axolotl train my_training.yml
 
 ## Common Tasks {#sec-common-tasks}
 
+::: {.callout-tip}
+
+The same yaml file is used for training, inference, and merging.
+
+:::
+
 ### Testing Your Model {#sec-testing}
 
 After training, test your model:
@@ -128,6 +134,16 @@ After training, test your model:
 axolotl inference my_training.yml --lora-model-dir="./outputs/lora-out"
 ```
 
+More details can be found in [Inference](inference.qmd).
+
+### Using a UI {#sec-ui}
+
+Launch a Gradio interface:
+
+```bash
+axolotl inference my_training.yml --lora-model-dir="./outputs/lora-out" --gradio
+```
+
 ### Preprocessing Data {#sec-preprocessing}
 
 For large datasets, preprocess first:
@@ -136,14 +152,22 @@ For large datasets, preprocess first:
 axolotl preprocess my_training.yml
 ```
 
-### Using a UI {#sec-ui}
+Please make sure to set `dataset_prepared_path: ` in your config to set the path to save the prepared dataset.
 
-Launch a Gradio interface:
+More details can be found in [Dataset Preprocessing](dataset_preprocessing.qmd).
+
+### Merging LoRA weights {#sec-merging-lora}
+
+To merge the LoRA weights back into the base model, run:
 
 ```bash
-axolotl inference my_training.yml --lora-model-dir="./outputs/lora-out" --gradio
+axolotl merge-lora my_training.yml --lora-model-dir="./outputs/lora-out"
 ```
 
+The merged model will be saved in the `{output_dir}/merged` directory.
+
+More details can be found in [Merging LoRA weights](inference.qmd#sec-merging).
+
 ## Next Steps {#sec-next-steps}
 
 Now that you have the basics, you might want to:
@@ -156,6 +180,7 @@ Now that you have the basics, you might want to:
 Check our other guides for details on these topics:
 
 - [Configuration Guide](config.qmd) - Full configuration options
+- [Dataset Loading](dataset-loading.qmd) - Loading datasets from various sources
 - [Dataset Formats](dataset-formats) - Working with different data formats
 - [Multi-GPU Training](multi-gpu.qmd)
 - [Multi-Node Training](multi-node.qmd)

docs/sequence_parallelism.qmd

@@ -3,8 +3,6 @@ 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
@@ -27,7 +25,7 @@ To enable sequence parallelism, add the following to your configuration file:
 sequence_parallel_degree: 4  # Split sequences across 4 GPUs
 # Optional; strides across the key dimension. Larger values use more memory but should make training faster.
 heads_k_stride: 1
-# Optional; one of "varlen_llama3", "batch_ring", "batch_zigzag", "batch_stripe". Defaults to
+# Optional; one of "varlen_llama3" or "batch_ring". Defaults to
 # "varlen_llama3" when `sample_packing: true`, and "batch_ring" otherwise.
 ring_attn_func:
 ```

examples/llama-4/README.md

@@ -34,3 +34,5 @@ We provide a script to delinearize Llama 4 linearized models into regular Huggin
 ```bash
 axolotl delinearize-llama4 --model path/to/model_dir --output path/to/output_dir
 ```
+
+Note: This only works with the non-quantized linearized model. If you have an adapter, merge it with the *non-quantized linearized* model before delinearizing.

requirements.txt

@@ -11,6 +11,7 @@ liger-kernel==0.5.9
 
 packaging==23.2
 
+huggingface_hub==0.31.0
 peft==0.15.2
 transformers==4.51.3
 tokenizers>=0.21.1

setup.py

@@ -142,6 +142,7 @@ extras_require = {
         "apollo-torch",
         "lomo-optim==0.1.1",
         "torch-optimi==0.2.1",
+        "came_pytorch==0.1.3",
     ],
     "ray": [
         "ray[train]",

src/axolotl/cli/args.py

@@ -82,6 +82,12 @@ class VllmServeCliArgs:
             "hardware support this feature."
         },
     )
+    serve_module: Optional[str] = field(
+        default=None,
+        metadata={
+            "help": "Module to serve. If not set, the default module will be used."
+        },
+    )
 
 
 @dataclass

src/axolotl/cli/vllm_serve.py

@@ -6,7 +6,6 @@ from pathlib import Path
 from typing import Union
 
 from trl.scripts.vllm_serve import ScriptArguments
-from trl.scripts.vllm_serve import main as vllm_serve_main
 
 from axolotl.cli.config import load_cfg
 
@@ -28,6 +27,9 @@ def do_vllm_serve(
     cfg = load_cfg(config)
     model = cfg.base_model
 
+    serve_module = cli_args.get("serve_module", "trl.scripts.vllm_serve")
+    vllm_serve_main = getattr(__import__(serve_module, fromlist=["main"]), "main")
+
     tensor_parallel_size = (
         cli_args.get("tensor_parallel_size") or cfg.vllm.tensor_parallel_size
     )

src/axolotl/common/datasets.py

@@ -14,6 +14,7 @@ from axolotl.utils.data import prepare_dataset
 from axolotl.utils.data.rl import load_prepare_preference_datasets
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.models import load_processor, load_tokenizer
+from axolotl.utils.schemas.enums import RLType
 from axolotl.utils.tokenization import check_dataset_labels
 
 LOG = logging.getLogger(__name__)
@@ -133,7 +134,7 @@ def load_preference_datasets(
     total_num_steps: Optional[int] = int(
         math.ceil(len(train_dataset) * cfg.num_epochs / cfg.batch_size)
     )
-    if cfg.rl == "grpo":
+    if cfg.rl is RLType.GRPO:
         total_num_steps = None
 
     if cli_args.debug or cfg.debug:

src/axolotl/core/trainer_builder.py

@@ -87,7 +87,7 @@ from axolotl.utils.collators import (
 )
 from axolotl.utils.collators.mm_chat import MultiModalChatDataCollator
 from axolotl.utils.models import ensure_dtype
-from axolotl.utils.schemas.enums import CustomSupportedOptimizers
+from axolotl.utils.schemas.enums import CustomSupportedOptimizers, RLType
 
 try:
     import torch._dynamo  # pylint: disable=ungrouped-imports
@@ -353,7 +353,7 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         training_arguments_kwargs["warmup_steps"] = warmup_steps
         training_arguments_kwargs["logging_steps"] = logging_steps
 
-        if self.cfg.seed:
+        if self.cfg.seed is not None:
             training_arguments_kwargs["seed"] = self.cfg.seed
 
         if self.cfg.gradient_checkpointing:
@@ -387,8 +387,12 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
             training_arguments_kwargs["adam_beta1"] = self.cfg.adam_beta1
         if self.cfg.adam_beta2:
             training_arguments_kwargs["adam_beta2"] = self.cfg.adam_beta2
+        if self.cfg.adam_beta3:
+            training_arguments_kwargs["adam_beta3"] = self.cfg.adam_beta3
         if self.cfg.adam_epsilon:
             training_arguments_kwargs["adam_epsilon"] = self.cfg.adam_epsilon
+        if self.cfg.adam_epsilon2:
+            training_arguments_kwargs["adam_epsilon2"] = self.cfg.adam_epsilon2
         if self.cfg.max_grad_norm:
             training_arguments_kwargs["max_grad_norm"] = self.cfg.max_grad_norm
 
@@ -547,8 +551,6 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         report_to = []
         if self.cfg.use_wandb:
             report_to.append("wandb")
-            if self.cfg.wandb_name:
-                training_arguments_kwargs["run_name"] = self.cfg.wandb_name
         if self.cfg.use_mlflow:
             report_to.append("mlflow")
         if self.cfg.use_tensorboard:
@@ -708,6 +710,20 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
                 optimizer_cls = ADOPT
                 adam_kwargs["decouple"] = True
                 optimizer_kwargs.update(adam_kwargs)
+            elif self.cfg.optimizer == "came_pytorch":
+                from came_pytorch import CAME
+
+                optimizer_cls = CAME
+
+                beta1 = training_arguments_kwargs.get("adam_beta1", 0.9)
+                beta2 = training_arguments_kwargs.get("adam_beta2", 0.999)
+                beta3 = training_arguments_kwargs.get("adam_beta3", 0.9999)
+                eps1 = training_arguments_kwargs.get("adam_epsilon", 1e-30)
+                eps2 = training_arguments_kwargs.get("adam_epsilon2", 1e-16)
+                adam_kwargs["betas"] = (beta1, beta2, beta3)
+                adam_kwargs["eps"] = (eps1, eps2)
+
+                optimizer_kwargs.update(adam_kwargs)
 
             # Parse any additional optimizer args from config
             if self.cfg.optim_args:
@@ -807,14 +823,15 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         data_collator_kwargs = {
             "padding": True,  # True/"longest" is the default
         }
+        multiple = 64
         if self.cfg.pad_to_sequence_len:
-            data_collator_kwargs["pad_to_multiple_of"] = 64 * math.ceil(
-                self.cfg.sequence_len / 64
+            data_collator_kwargs["pad_to_multiple_of"] = multiple * math.ceil(
+                self.cfg.sequence_len / multiple
             )
         else:
             # A100 is best at 64, while others at 8. Let's use the larger so we don't have to check
             # https://docs.nvidia.com/deeplearning/performance/dl-performance-matrix-multiplication/index.html
-            data_collator_kwargs["pad_to_multiple_of"] = 64
+            data_collator_kwargs["pad_to_multiple_of"] = multiple
 
         if self.cfg.reward_model:
             data_collator_kwargs["max_length"] = self.cfg.sequence_len
@@ -1020,6 +1037,10 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
             training_args_kwargs["dataloader_prefetch_factor"] = (
                 self.cfg.dataloader_prefetch_factor
             )
+
+        if self.cfg.seed is not None:
+            training_args_kwargs["seed"] = self.cfg.seed
+
         if self.cfg.gradient_checkpointing:
             training_args_kwargs["gradient_checkpointing"] = (
                 self.cfg.gradient_checkpointing
@@ -1043,6 +1064,8 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
             # default to saving each epoch if not defined
             training_args_kwargs["save_strategy"] = "epoch"
 
+        training_args_kwargs["save_only_model"] = self.cfg.save_only_model
+
         if self.cfg.dataset_processes:
             training_args_kwargs["dataset_num_proc"] = self.cfg.dataset_processes
 
@@ -1060,9 +1083,13 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
         if self.cfg.use_wandb:
             training_args_kwargs["run_name"] = self.cfg.wandb_name
 
+        training_args_kwargs["sequence_parallel_degree"] = (
+            self.cfg.sequence_parallel_degree
+        )
+
         training_args_cls = None
         blocklist_args_kwargs = []
-        if self.cfg.rl == "simpo":
+        if self.cfg.rl is RLType.SIMPO:
             training_args_cls = AxolotlCPOConfig
             training_args_kwargs["loss_type"] = "simpo"
             training_args_kwargs["max_length"] = self.cfg.sequence_len
@@ -1070,13 +1097,13 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
             if self.cfg.cpo_alpha is not None:
                 training_args_kwargs["cpo_alpha"] = self.cfg.cpo_alpha
 
-        elif self.cfg.rl == "orpo":
+        elif self.cfg.rl is RLType.ORPO:
             training_args_cls = AxolotlORPOConfig
             training_args_kwargs["max_length"] = self.cfg.sequence_len
             if self.cfg.max_prompt_len:
                 training_args_kwargs["max_prompt_length"] = self.cfg.max_prompt_len
 
-        elif self.cfg.rl == "kto":
+        elif self.cfg.rl is RLType.KTO:
             training_args_cls = AxolotlKTOConfig
 
             training_args_kwargs["desirable_weight"] = (
@@ -1090,14 +1117,14 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
             if self.cfg.max_prompt_len:
                 training_args_kwargs["max_prompt_length"] = self.cfg.max_prompt_len
 
-        elif self.cfg.rl == "grpo":
+        elif self.cfg.rl is RLType.GRPO:
             training_args_cls = GRPOStrategy.get_training_args_class()
             training_args_kwargs.update(GRPOStrategy.set_training_args_kwargs(self.cfg))
             blocklist_args_kwargs = GRPOStrategy.get_blocklist_args_kwargs()
 
         else:
             training_args_cls = AxolotlDPOConfig
-            if self.cfg.rl == "ipo":
+            if self.cfg.rl is RLType.IPO:
                 training_args_kwargs["loss_type"] = "ipo"
             training_args_kwargs["max_length"] = self.cfg.sequence_len
             training_args_kwargs["max_completion_length"] = None
@@ -1140,67 +1167,74 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
 
     def build(self, total_num_steps):
         training_args = self.build_training_arguments(total_num_steps)
-        dpo_trainer_kwargs = {}
-        if self.cfg.rl == "ipo":
+        trainer_kwargs = {}
+        if self.cfg.rl is RLType.IPO:
             if self.cfg.dpo_label_smoothing:
-                dpo_trainer_kwargs["label_smoothing"] = self.cfg.dpo_label_smoothing
+                trainer_kwargs["label_smoothing"] = self.cfg.dpo_label_smoothing
         if self.eval_dataset:
-            dpo_trainer_kwargs["eval_dataset"] = self.eval_dataset
+            trainer_kwargs["eval_dataset"] = self.eval_dataset
         if self.cfg.adapter and self.peft_config:
-            dpo_trainer_kwargs["peft_config"] = self.peft_config
+            if self.cfg.rl is not RLType.GRPO:
+                trainer_kwargs["peft_config"] = self.peft_config
         if self.cfg.precompute_ref_log_probs is not None:
-            dpo_trainer_kwargs["precompute_ref_log_probs"] = (
+            trainer_kwargs["precompute_ref_log_probs"] = (
                 self.cfg.precompute_ref_log_probs
             )
-        if self.cfg.rl == "grpo":
-            trainer_cls = GRPOStrategy.get_trainer_class()
+        if self.cfg.rl is RLType.GRPO:
+            trainer_cls = GRPOStrategy.get_trainer_class(
+                sequence_parallel=self.cfg.sequence_parallel_degree > 1
+            )
             trainer_cls_args = [self.model]
             trainer_cls_args.extend(GRPOStrategy.set_trainer_args(self.cfg))
-            dpo_trainer_kwargs.update(GRPOStrategy.set_trainer_kwargs(self.cfg))
-        elif self.cfg.rl in ["dpo", "ipo"]:
+            trainer_kwargs.update(GRPOStrategy.set_trainer_kwargs(self.cfg))
+        elif self.cfg.rl in [RLType.DPO, RLType.IPO]:
             trainer_cls = DPOStrategy.get_trainer_class()
             trainer_cls_args = [self.model, self.model_ref]
-        elif self.cfg.rl == "orpo":
+        elif self.cfg.rl is RLType.ORPO:
             trainer_cls = AxolotlORPOTrainer
             trainer_cls_args = [self.model]
-        elif self.cfg.rl in ["kto"]:
+        elif self.cfg.rl is RLType.KTO:
             trainer_cls = AxolotlKTOTrainer
             trainer_cls_args = [self.model]
-        elif self.cfg.rl in ["simpo"]:
+        elif self.cfg.rl is RLType.SIMPO:
             trainer_cls = AxolotlCPOTrainer
             trainer_cls_args = [self.model]
         else:
             raise ValueError(f"Unsupported RL: {self.cfg.rl}")
 
+        if self.cfg.plugins:
+            plugin_manager = PluginManager.get_instance()
+            trainer_cls = plugin_manager.get_trainer_cls(self.cfg)
+
         sig = inspect.signature(trainer_cls)
         if "tokenizer" in sig.parameters.keys():
-            dpo_trainer_kwargs["tokenizer"] = self.tokenizer
+            trainer_kwargs["tokenizer"] = self.tokenizer
         else:
-            dpo_trainer_kwargs["processing_class"] = self.tokenizer
+            trainer_kwargs["processing_class"] = self.tokenizer
 
         if self.cfg.datasets is not None and (
             trainer_cls is DPOStrategy.get_trainer_class()
         ):
-            dpo_trainer_kwargs["dataset_tags"] = [
+            trainer_kwargs["dataset_tags"] = [
                 d["path"] for d in self.cfg.datasets if not Path(d["path"]).is_dir()
             ]
-        dpo_trainer = trainer_cls(
+        trainer = trainer_cls(
             *trainer_cls_args,
             args=training_args,
             train_dataset=self.train_dataset,
             callbacks=self.get_callbacks(),
-            **dpo_trainer_kwargs,
+            **trainer_kwargs,
         )
         if self.cfg.fsdp:
-            ensure_dtype(dpo_trainer.model, dtype=self.cfg.torch_dtype)
-            if self.cfg.rl in ["dpo", "ipo"] and dpo_trainer.ref_model:
-                ensure_dtype(dpo_trainer.ref_model, dtype=self.cfg.torch_dtype)
+            ensure_dtype(trainer.model, dtype=self.cfg.torch_dtype)
+            if self.cfg.rl in [RLType.DPO, RLType.IPO] and trainer.ref_model:
+                ensure_dtype(trainer.ref_model, dtype=self.cfg.torch_dtype)
 
-        dpo_trainer = self.hook_post_create_trainer(dpo_trainer)
-        for callback in self.get_post_trainer_create_callbacks(dpo_trainer):
-            dpo_trainer.add_callback(callback)
+        trainer = self.hook_post_create_trainer(trainer)
+        for callback in self.get_post_trainer_create_callbacks(trainer):
+            trainer.add_callback(callback)
 
-        return dpo_trainer
+        return trainer
 
 
 class HFPPOTrainerBuilder(TrainerBuilderBase):

src/axolotl/core/trainers/__init__.py

@@ -5,7 +5,7 @@
 
 from .base import AxolotlTrainer
 from .dpo.trainer import AxolotlDPOTrainer
-from .grpo.trainer import AxolotlGRPOTrainer
+from .grpo.trainer import AxolotlGRPOSequenceParallelTrainer, AxolotlGRPOTrainer
 from .mamba import AxolotlMambaTrainer
 from .relora import ReLoRATrainer
 from .trl import (

src/axolotl/core/trainers/base.py

@@ -373,15 +373,13 @@ class AxolotlTrainer(
                 num_items_in_batch=num_items_in_batch,
             )
 
-        loss = super().compute_loss(
+        return super().compute_loss(
             model,
             inputs,
             return_outputs=return_outputs,
             num_items_in_batch=num_items_in_batch,
         )
 
-        return loss
-
     @staticmethod
     def orpo_concatenate_inputs(inputs, label_pad_token=-100, pad_token=0, device=None):
         concatenated_batch = {}

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

@@ -1,14 +1,11 @@
-"""
-DPO Specific Strategy for training
-"""
+"""DPO Specific Strategy for training"""
 
 from axolotl.core.trainers.dpo.trainer import AxolotlDPOTrainer
+from axolotl.utils.schemas.enums import RLType
 
 
 class DPOStrategy:
-    """
-    Strategy for DPO training
-    """
+    """Strategy for DPO training"""
 
     @classmethod
     def get_trainer_class(cls):
@@ -23,7 +20,7 @@ class DPOStrategy:
     @classmethod
     def set_training_args_kwargs(cls, cfg):
         training_args_kwargs = {}
-        if cfg.rl == "ipo":
+        if cfg.rl is RLType.IPO:
             training_args_kwargs["loss_type"] = "ipo"
         training_args_kwargs["max_length"] = cfg.sequence_len
         training_args_kwargs["max_completion_length"] = None

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

@@ -1,37 +1,41 @@
-"""
-GRPO Specific Strategy for training
-"""
+"""GRPO Specific Strategy for training"""
 
 import importlib
 import inspect
 import logging
+from typing import Any
 
 from trl.trainer.grpo_trainer import RewardFunc
 
-from axolotl.core.trainers.grpo.trainer import AxolotlGRPOTrainer
+from axolotl.core.trainers.grpo.args import AxolotlGRPOConfig
+from axolotl.core.trainers.grpo.trainer import (
+    AxolotlGRPOSequenceParallelTrainer,
+    AxolotlGRPOTrainer,
+)
+from axolotl.utils.dict import DictDefault
 from axolotl.utils.schemas.trl import TRLConfig
 
-LOG = logging.getLogger("axolotl")
+LOG = logging.getLogger(__name__)
 
 
 class GRPOStrategy:
-    """
-    Strategy for GRPO training
-    """
+    """Strategy for GRPO training"""
 
     @classmethod
-    def get_trainer_class(cls):
+    def get_trainer_class(
+        cls, sequence_parallel: bool
+    ) -> type[AxolotlGRPOTrainer] | type[AxolotlGRPOSequenceParallelTrainer]:
+        if sequence_parallel:
+            return AxolotlGRPOSequenceParallelTrainer
         return AxolotlGRPOTrainer
 
     @classmethod
-    def get_training_args_class(cls):
-        from axolotl.core.trainers.grpo.args import AxolotlGRPOConfig
-
+    def get_training_args_class(cls) -> type[AxolotlGRPOConfig]:
         return AxolotlGRPOConfig
 
     @classmethod
-    def set_training_args_kwargs(cls, cfg):
-        grpo_args_kwargs = {}
+    def set_training_args_kwargs(cls, cfg: DictDefault) -> dict[str, Any]:
+        grpo_args_kwargs: dict[str, Any] = {}
 
         if not hasattr(cfg, "trl") or not cfg.trl:
             return grpo_args_kwargs
@@ -40,8 +44,8 @@ class GRPOStrategy:
 
         if trl.use_vllm:
             grpo_args_kwargs["use_vllm"] = trl.use_vllm
-            grpo_args_kwargs["vllm_server_host"] = trl.vllm_server_host or trl.vllm.host
-            grpo_args_kwargs["vllm_server_port"] = trl.vllm_server_port or trl.vllm.port
+            grpo_args_kwargs["vllm_server_host"] = trl.vllm_server_host or trl.vllm.host  # type: ignore[attr-defined]
+            grpo_args_kwargs["vllm_server_port"] = trl.vllm_server_port or trl.vllm.port  # type: ignore[attr-defined]
             if trl.vllm_server_timeout:
                 grpo_args_kwargs["vllm_server_timeout"] = trl.vllm_server_timeout
             if trl.vllm_guided_decoding_regex:
@@ -102,17 +106,18 @@ class GRPOStrategy:
         return grpo_args_kwargs
 
     @classmethod
-    def set_trainer_args(cls, cfg):
+    def set_trainer_args(cls, cfg: DictDefault) -> list[Any]:
         trainer_args = []
         if cfg.trl and cfg.trl.reward_funcs:
             reward_funcs = []
             for reward_func_fqn in cfg.trl.reward_funcs:
                 reward_funcs.append(cls.get_reward_func(reward_func_fqn))
             trainer_args.append(reward_funcs)
+
         return trainer_args
 
     @classmethod
-    def set_trainer_kwargs(cls, cfg):
+    def set_trainer_kwargs(cls, cfg: DictDefault) -> dict[str, Any]:
         trainer_kwargs = {}
         if cfg.trl and cfg.trl.reward_processing_classes:
             trainer_kwargs["reward_processing_classes"] = (
@@ -126,7 +131,7 @@ class GRPOStrategy:
         return None
 
     @classmethod
-    def get_blocklist_args_kwargs(cls):
+    def get_blocklist_args_kwargs(cls) -> list[str]:
         return ["dataset_num_proc"]
 
     @classmethod
@@ -137,13 +142,13 @@ class GRPOStrategy:
         Args:
             reward_func_fqn (str): Fully qualified name of the reward function (e.g. r1_grpo.gsm8k_transform),
                 or a HF hub path to the reward model.
-        Raises:
-            ValueError: If the reward function does not accept at least two arguments.
 
         Returns:
             RewardFunc: A callable that accepts prompts and completions and returns rewards,
                 or a path to a reward model.
 
+        Raises:
+            ValueError: If the reward function does not accept at least two arguments.
         """
         try:
             # use importlib to dynamically load the reward function from the module

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

@@ -11,6 +11,4 @@ from axolotl.core.training_args import AxolotlTrainingMixins
 
 @dataclass
 class AxolotlGRPOConfig(AxolotlTrainingMixins, GRPOConfig):
-    """
-    Axolotl GRPO Config for GRPO training
-    """
+    """Axolotl GRPO Config for GRPO training"""

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

@@ -0,0 +1,172 @@
+"""Repeat random sampler (similar to the one implemented in
+https://github.com/huggingface/trl/blob/main/trl/trainer/grpo_trainer.py) that adds
+sequence parallelism functionality; i.e., duplicating data across ranks in the same
+sequence parallel group.
+"""
+
+from typing import Iterator, Sized
+
+import torch
+from torch.utils.data import Sampler
+
+
+class SequenceParallelRepeatRandomSampler(Sampler):
+    """Sampler for GRPO training with sequence parallelism.
+
+    This sampler ensures:
+    - Ranks in the same sequence parallel (SP) group receive identical data.
+    - Each index is repeated multiple times for sampling different completions.
+    - Entire batches are repeated for reuse in multiple updates.
+    - Data is properly distributed across SP groups.
+
+    In the table below, the values represent dataset indices. Each SP group has
+    `sequence_parallel_degree = 2` GPUs working together on the same data. There are 2
+    SP groups (SP0 and SP1), with `world_size = 4` total GPUs.
+
+                                               Sequence Parallel Groups
+                                        |       SP0        |       SP1        |
+                                        |  GPU 0  |  GPU 1 |  GPU 2  |  GPU 3 |
+                    global_step  step    <---> mini_repeat_count=3
+                                            <----------> batch_size=2 per SP group
+    grad_accum=2   ▲  ▲  0       0         [0 0 0  1 1 1]     [2 2 2  3 3 3]   <- SP groups get different data
+                   ▼  |  0       1         [0 0 0  1 1 1]     [2 2 2  3 3 3]   <- Same data for each SP group GPU
+                      |
+                      |  1       2         [0 0 0  1 1 1]     [2 2 2  3 3 3]   <- Repeat same indices for iterations
+    num_iterations=2  ▼  1       3         [0 0 0  1 1 1]     [2 2 2  3 3 3]   <- When using gradient accumulation
+
+                         2       4         [4 4 4  5 5 5]     [6 6 6  7 7 7]   <- New batch of data indices
+                         2       5         [4 4 4  5 5 5]     [6 6 6  7 7 7]
+                                            ...
+
+    Args:
+        dataset: Dataset to sample from.
+        mini_repeat_count: How many times to repeat each sample immediately.
+        world_size: Total number of processes.
+        rank: Rank of current process.
+        batch_size: Number of samples per batch.
+        repeat_count: How many times to repeat the full sampling process.
+        sequence_parallel_degree: Number of ranks in a sequence parallel group.
+        shuffle: Whether to shuffle the dataset.
+        seed: Random seed for shuffling.
+        drop_last: Whether to drop the last incomplete batch.
+    """
+
+    def __init__(
+        self,
+        dataset: Sized,
+        mini_repeat_count: int,
+        world_size: int,
+        rank: int,
+        batch_size: int = 1,
+        repeat_count: int = 1,
+        sequence_parallel_degree: int = 1,
+        shuffle: bool = True,
+        seed: int = 0,
+        drop_last: bool = False,
+    ):
+        self.dataset = dataset
+        self.mini_repeat_count = mini_repeat_count
+        self.batch_size = batch_size
+        self.repeat_count = repeat_count
+        self.shuffle = shuffle
+        self.seed = seed
+        self.drop_last = drop_last
+        self.epoch = 0
+
+        self.world_size = world_size
+        self.rank = rank
+
+        # Sequence parallelism parameters
+        self.sequence_parallel_degree = sequence_parallel_degree
+        self.num_sp_groups = world_size // sequence_parallel_degree
+        self.sp_group_id = rank // sequence_parallel_degree
+
+        # Adjust dataset size for distributed sampling
+        self.num_samples = len(self.dataset)
+        self.total_size = self.num_samples
+
+        # Calculate effective number of samples per SP group
+        if (
+            self.drop_last
+            and self.total_size % (self.num_sp_groups * self.batch_size) != 0
+        ):
+            # Drop last incomplete batch if drop_last is True
+            self.num_samples_per_sp_group = (
+                self.total_size // self.batch_size // self.num_sp_groups
+            ) * self.batch_size
+        else:
+            # Round up to include last batch if drop_last is False
+            self.num_samples_per_sp_group = (
+                (self.total_size + self.batch_size * self.num_sp_groups - 1)
+                // (self.batch_size * self.num_sp_groups)
+                * self.batch_size
+            )
+
+        if shuffle:
+            self.generator = torch.Generator()
+            self.generator.manual_seed(seed)
+
+    def __iter__(self) -> Iterator[int]:
+        """Creates iterator over dataset indices.
+
+        Returns:
+            Iterator that yields indices into the dataset.
+        """
+        # Deterministically shuffle based on epoch and seed
+        if self.shuffle:
+            indices = torch.randperm(
+                self.num_samples, generator=self.generator
+            ).tolist()
+        else:
+            indices = list(range(self.num_samples))
+
+        # Add extra samples to make it evenly divisible by batch_size
+        if len(indices) % self.batch_size != 0:
+            padding = indices[: self.batch_size - len(indices) % self.batch_size]
+            indices += padding
+
+        # Subsample based on SP group ID
+        # Each SP group gets distinct batches of data
+        batch_indices = []
+        for i in range(0, len(indices), self.batch_size * self.num_sp_groups):
+            start_idx = i + self.sp_group_id * self.batch_size
+            end_idx = min(start_idx + self.batch_size, len(indices))
+            if start_idx < len(indices):
+                for j in range(self.batch_size):
+                    if start_idx + j < end_idx:
+                        batch_indices.append(indices[start_idx + j])
+
+        # Make sure batch_indices is exactly batch_size * num_batches_per_sp_group
+        if self.drop_last:
+            num_batches_per_sp_group = self.num_samples_per_sp_group // self.batch_size
+            target_len = self.batch_size * num_batches_per_sp_group
+            if len(batch_indices) > target_len:
+                batch_indices = batch_indices[:target_len]
+
+        # Apply the GRPO repeat pattern
+        final_indices = []
+        for _ in range(self.repeat_count):
+            for idx in batch_indices:
+                for _ in range(self.mini_repeat_count):
+                    final_indices.append(idx)
+
+        return iter(final_indices)
+
+    def __len__(self) -> int:
+        """Returns the total length of the iterable including repetitions.
+
+        Returns:
+            Total number of samples.
+        """
+        # Total length including all repetitions
+        return (
+            self.num_samples_per_sp_group * self.mini_repeat_count * self.repeat_count
+        )
+
+    def set_epoch(self, epoch: int) -> None:
+        """Sets the epoch for this sampler.
+
+        Args:
+            epoch: Epoch number to use for shuffling.
+        """
+        self.epoch = epoch

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

@@ -1,69 +1,653 @@
-"""
-Axolotl GRPO trainer
-"""
+"""Axolotl GRPO trainers (with and without sequence parallelism handling)"""
 
-from contextlib import nullcontext
+# pylint: disable=too-many-lines,duplicate-code,protected-access,no-member
 
-from accelerate.utils import is_deepspeed_available, is_peft_model
+import warnings
+from typing import Any
+
+import datasets
+import torch
+import torch.distributed as dist
+import torch.utils.data
+from accelerate.utils import (
+    broadcast_object_list,
+    gather,
+    gather_object,
+    is_peft_available,
+)
+from datasets import Dataset, IterableDataset
+from torch import nn
+from torch.utils.data import (
+    BatchSampler,
+    DataLoader,
+    Sampler,
+)
+from transformers import (
+    PreTrainedModel,
+    PreTrainedTokenizerBase,
+    Trainer,
+    TrainerCallback,
+)
+from transformers.trainer_utils import seed_worker
 from trl import GRPOTrainer
-from trl.extras.profiling import profiling_decorator
+from trl.data_utils import (
+    apply_chat_template,
+    is_conversational,
+    maybe_apply_chat_template,
+)
+from trl.extras.profiling import profiling_context
+from trl.models import unwrap_model_for_generation
+from trl.trainer.grpo_config import GRPOConfig
+from trl.trainer.grpo_trainer import RewardFunc, nanstd
+from trl.trainer.utils import pad
 
+from axolotl.core.trainers.grpo.sampler import SequenceParallelRepeatRandomSampler
 from axolotl.core.trainers.mixins import RngLoaderMixin, SchedulerMixin
+from axolotl.monkeypatch.attention.ring_attn.patch import get_ring_attn_group
 
-if is_deepspeed_available():
-    import deepspeed
+if is_peft_available():
+    # pylint: disable=unused-import
+    from peft import PeftConfig
 
 
 class AxolotlGRPOTrainer(RngLoaderMixin, SchedulerMixin, GRPOTrainer):
-    """
-    Extend the base GRPOTrainer for axolotl helpers
-    """
+    """Extend the base GRPOTrainer for axolotl helpers"""
 
     _tag_names = ["trl", "grpo", "axolotl"]
 
-    @profiling_decorator
-    def _move_model_to_vllm(self):
-        # For DeepSpeed ZeRO-3, we need to gather all parameters before operations
-        deepspeed_plugin = self.accelerator.state.deepspeed_plugin
-        zero_stage_3 = deepspeed_plugin is not None and deepspeed_plugin.zero_stage == 3
-        gather_if_zero3 = (
-            deepspeed.zero.GatheredParameters if zero_stage_3 else nullcontext
+
+class AxolotlGRPOSequenceParallelTrainer(AxolotlGRPOTrainer):
+    """Extend the base GRPOTrainer for sequence parallelism handling"""
+
+    def __init__(
+        self,
+        model: str | PreTrainedModel,
+        reward_funcs: RewardFunc | list[RewardFunc],
+        args: GRPOConfig | None = None,
+        train_dataset: Dataset | IterableDataset | None = None,
+        eval_dataset: (
+            Dataset | IterableDataset | dict[str, Dataset | IterableDataset] | None
+        ) = None,
+        processing_class: PreTrainedTokenizerBase | None = None,
+        reward_processing_classes: (
+            PreTrainedTokenizerBase | list[PreTrainedTokenizerBase] | None
+        ) = None,
+        callbacks: list[TrainerCallback] | None = None,
+        optimizers: tuple[
+            torch.optim.Optimizer | None, torch.optim.lr_scheduler.LambdaLR | None
+        ] = (None, None),
+        peft_config: "PeftConfig | None" = None,
+    ):
+        # First call the superclass constructor with all arguments
+        super().__init__(
+            model=model,
+            reward_funcs=reward_funcs,
+            args=args,
+            train_dataset=train_dataset,
+            eval_dataset=eval_dataset,
+            processing_class=processing_class,
+            reward_processing_classes=reward_processing_classes,
+            callbacks=callbacks,
+            optimizers=optimizers,
+            peft_config=peft_config,
         )
 
-        if is_peft_model(self.model):
-            # With PEFT and DeepSpeed ZeRO Stage 3, we must gather the full model at once before merging, as merging
-            # adapters in a sharded manner is not supported.
-            with gather_if_zero3(list(self.model.parameters())):
-                self.model.merge_adapter()
+        # Get number of SP groups (number of processes divided by SP degree)
+        num_processes = self.accelerator.num_processes
+        num_sp_groups = num_processes // self.args.sequence_parallel_degree
 
-                # Update vLLM weights while parameters are gathered
-                for name, param in self.model.named_parameters():
-                    # When using PEFT, we need to recover the original parameter name and discard some parameters
-                    name = (
-                        name.removeprefix("base_model.model.")
-                        .removeprefix("base_model.model.")
-                        .replace(".base_layer", "")
-                    )
-                    if self.model.prefix in name:
-                        continue
-                    # When module to save, remove its prefix and discard the original module
-                    if "original_module" in name:
-                        continue
-                    name = name.replace("modules_to_save.default.", "")
+        # Calculate batch size per SP group (not per process)
+        sp_group_batch_size = self.args.per_device_train_batch_size * num_sp_groups
+        possible_values = [
+            n_gen
+            for n_gen in range(2, sp_group_batch_size + 1)
+            if (sp_group_batch_size) % n_gen == 0
+        ]
 
-                    if self.accelerator.is_main_process:
-                        self.vllm_client.update_named_param(name, param.data)
+        if self.num_generations not in possible_values:
+            raise ValueError(
+                f"The batch size per SP group ({num_sp_groups} x "
+                f"{self.args.per_device_train_batch_size}) must be evenly divisible by "
+                f"the number of generations per prompt ({self.num_generations}). Given "
+                "the current configuration, the valid values for the number of "
+                f"generations are: {possible_values}."
+            )
 
-                # Unmerge adapters while parameters are still gathered
-                self.model.unmerge_adapter()
-                # Parameters will automatically be repartitioned when exiting the context
+        if self.args.eval_strategy != "no":
+            # If sequence parallelism is enabled, calculate batch size per SP group
+            sp_group_eval_batch_size = args.per_device_eval_batch_size * num_sp_groups  # type: ignore[union-attr]
+            possible_values = [
+                n_gen
+                for n_gen in range(2, sp_group_eval_batch_size + 1)
+                if (sp_group_eval_batch_size) % n_gen == 0
+            ]
+
+            if self.num_generations not in possible_values:
+                raise ValueError(
+                    f"With sequence parallelism (degree {self.args.sequence_parallel_degree}), "
+                    f"the eval batch size per SP group ({num_sp_groups} x {self.args.per_device_eval_batch_size}) "
+                    f"must be evenly divisible by the number of generations per prompt "
+                    f"({self.num_generations}). Given the current eval batch size, "
+                    f"the valid values for the number of generations are: {possible_values}."
+                )
+
+        # Initialize the SP group
+        self.sp_group = get_ring_attn_group()
+        self.rank = dist.get_rank()
+        self.world_size = dist.get_world_size()
+        self.local_rank = dist.get_rank(group=self.sp_group)
+        self.local_world_size = dist.get_world_size(group=self.sp_group)
+
+    def _get_train_sampler(self) -> Sampler:
+        effective_batch_size = (
+            self.args.per_device_train_batch_size
+            * self.world_size
+            * self.args.gradient_accumulation_steps
+        )
+
+        return SequenceParallelRepeatRandomSampler(
+            dataset=self.train_dataset,
+            mini_repeat_count=self.num_generations,
+            world_size=self.world_size,
+            rank=self.rank,
+            batch_size=effective_batch_size
+            // self.num_generations
+            // self.args.sequence_parallel_degree,
+            repeat_count=self.num_iterations * self.args.gradient_accumulation_steps,
+            sequence_parallel_degree=self.args.sequence_parallel_degree,
+            shuffle=True,
+            seed=self.args.seed,
+            drop_last=True,
+        )
+
+    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
+
+            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 unprepared dataloader if using sequence parallelism
+        # TODO(djsaunde): We might be able to use `accelerate`'s dataloader preparation
+        # if we use `dispatch_batches` and `slice_fn_for_dispatch` properly (i.e.,
+        # slice each batch along the sequence dimension).
+        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
+        # pylint: disable=access-member-before-definition
+        data_collator = self.data_collator  # type: ignore
+
+        # Handle dataset preprocessing
+        if isinstance(train_dataset, datasets.Dataset):
+            # Add debug print before any modifications
+            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:
-            # For non-PEFT models, simply gather and update each parameter individually.
-            for name, param in self.model.named_parameters():
-                with gather_if_zero3([param]):
-                    if self.accelerator.is_main_process:
-                        self.vllm_client.update_named_param(name, param.data)
+            self.data_collator = self._get_collator_with_removed_columns(  # pylint: disable=attribute-defined-outside-init
+                data_collator,
+                description="training",
+            )
 
-        # Reset cache on main process
-        if self.accelerator.is_main_process:
-            self.vllm_client.reset_prefix_cache()
+        # Get sampler and create dataloader
+        sampler = self._get_train_sampler()
+        dataloader = self._prepare_dataloader(train_dataset, sampler, is_eval=False)
+
+        return dataloader
+
+    def _generate_and_score_completions(
+        self, inputs: list[dict[str, torch.Tensor | Any]]
+    ) -> dict[str, torch.Tensor | Any]:
+        device = self.accelerator.device
+        mode = "eval" if self.control.should_evaluate else "train"
+
+        prompts = [x["prompt"] for x in inputs]
+        prompts_text = [
+            maybe_apply_chat_template(example, self.processing_class)["prompt"]
+            for example in inputs
+        ]
+        prompt_inputs = self.processing_class(
+            text=prompts_text,
+            return_tensors="pt",
+            padding=True,
+            padding_side="left",
+            add_special_tokens=False,
+        )
+        prompt_inputs = Trainer._prepare_inputs(self, prompt_inputs)
+        prompt_ids, prompt_mask = (
+            prompt_inputs["input_ids"],
+            prompt_inputs["attention_mask"],
+        )
+
+        if self.max_prompt_length is not None:
+            prompt_ids = prompt_ids[:, -self.max_prompt_length :]
+            prompt_mask = prompt_mask[:, -self.max_prompt_length :]
+
+        # Generate completions using either vLLM or regular generation
+        if self.args.use_vllm:
+            # First, have main process load weights if needed
+            # pylint: disable=access-member-before-definition
+            if self.state.global_step != self._last_loaded_step:  # type: ignore[has-type]
+                self._move_model_to_vllm()
+                # pylint: disable=attribute-defined-outside-init
+                self._last_loaded_step = self.state.global_step
+
+            # Generate completions using vLLM: gather all prompts and use them in a single call in the main process
+            all_prompts_text = gather_object(prompts_text)
+            if self.accelerator.is_main_process:
+                if self.args.sequence_parallel_degree > 1:
+                    # Calculate sequence parallel group information
+                    world_size = self.accelerator.num_processes
+                    sequence_parallel_degree = self.args.sequence_parallel_degree
+                    num_sp_groups = world_size // sequence_parallel_degree
+
+                    # Since processes in the same SP group have the same prompts, we need to ensure
+                    # we only take one copy of each prompt from each SP group
+                    ordered_set_of_prompts = []
+                    for sp_group_id in range(num_sp_groups):
+                        # Get the first process from each SP group (typically the group leader)
+                        group_leader_rank = sp_group_id * sequence_parallel_degree
+
+                        # Extract prompts from this SP group, accounting for num_generations duplicates
+                        # We only need prompts from one rank in each SP group
+                        group_prompts = all_prompts_text[
+                            group_leader_rank
+                            * len(prompts_text) : (group_leader_rank + 1)
+                            * len(prompts_text) : self.num_generations
+                        ]
+
+                        ordered_set_of_prompts.extend(group_prompts)
+                else:
+                    # Since 'prompts' contains 'num_generations' duplicates, we first take unique prompts, and generate
+                    # num_generations outputs for each one. This is faster than generating outputs for each duplicate
+                    # prompt individually.
+                    ordered_set_of_prompts = all_prompts_text[
+                        :: self.num_generations * self.args.sequence_parallel_degree
+                    ]
+
+                with profiling_context(self, "vLLM.generate"):
+                    completion_ids = self.vllm_client.generate(
+                        prompts=ordered_set_of_prompts,
+                        n=self.num_generations,
+                        repetition_penalty=self.repetition_penalty,
+                        temperature=self.temperature,
+                        top_p=self.top_p,
+                        top_k=-1 if self.top_k is None else self.top_k,
+                        min_p=0.0 if self.min_p is None else self.min_p,
+                        max_tokens=self.max_completion_length,
+                        guided_decoding_regex=self.guided_decoding_regex,
+                    )
+            else:
+                completion_ids = [None] * (
+                    len(all_prompts_text) // self.args.sequence_parallel_degree
+                )
+
+            # Broadcast the completions from the main process to all processes
+            completion_ids = broadcast_object_list(completion_ids, from_process=0)
+
+            # Determine the appropriate slice based on sequence parallelism
+            if self.args.sequence_parallel_degree > 1:
+                # Calculate SP group ID (which group of ranks this rank belongs to)
+                sp_group_id = self.accelerator.process_index // self.local_world_size
+
+                # Calculate the start index for this SP group
+                sp_group_start = sp_group_id * len(prompts) * self.local_world_size
+
+                # All ranks in the same SP group get the same data slice
+                process_slice = slice(
+                    sp_group_start,
+                    sp_group_start + len(prompts),
+                )
+                completion_ids = completion_ids[process_slice]
+            else:
+                # Original behavior for non-sequence parallel case
+                process_slice = slice(
+                    self.accelerator.process_index * len(prompts),
+                    (self.accelerator.process_index + 1) * len(prompts),
+                )
+                completion_ids = completion_ids[process_slice]
+
+            # Pad the completions, and concatenate them with the prompts
+            completion_ids = [
+                torch.tensor(ids, device=device) for ids in completion_ids
+            ]
+            completion_ids = pad(
+                completion_ids, padding_value=self.processing_class.pad_token_id
+            )
+            prompt_completion_ids = torch.cat([prompt_ids, completion_ids], dim=1)
+        else:
+            # Regular generation path
+            with unwrap_model_for_generation(
+                self.model_wrapped,
+                self.accelerator,
+                gather_deepspeed3_params=self.args.ds3_gather_for_generation,
+            ) as unwrapped_model:
+                prompt_completion_ids = unwrapped_model.generate(
+                    prompt_ids,
+                    attention_mask=prompt_mask,
+                    generation_config=self.generation_config,
+                )
+
+            # Compute prompt length and extract completion ids
+            prompt_length = prompt_ids.size(1)
+            prompt_ids = prompt_completion_ids[:, :prompt_length]
+            completion_ids = prompt_completion_ids[:, prompt_length:]
+
+        # Mask everything after the first EOS token
+        is_eos = completion_ids == self.processing_class.eos_token_id
+        eos_idx = torch.full(
+            (is_eos.size(0),), is_eos.size(1), dtype=torch.long, device=device
+        )
+        eos_idx[is_eos.any(dim=1)] = is_eos.int().argmax(dim=1)[is_eos.any(dim=1)]
+        sequence_indices = torch.arange(is_eos.size(1), device=device).expand(
+            is_eos.size(0), -1
+        )
+        completion_mask = (sequence_indices <= eos_idx.unsqueeze(1)).int()
+
+        # If mask_truncated_completions is enabled, zero out truncated completions in completion_mask
+        if self.args.mask_truncated_completions:
+            truncated_completions = ~is_eos.any(dim=1)
+            completion_mask = (
+                completion_mask * (~truncated_completions).unsqueeze(1).int()
+            )
+
+        # Concatenate prompt_mask with completion_mask for logit computation
+        attention_mask = torch.cat([prompt_mask, completion_mask], dim=1)  # (B, P+C)
+
+        logits_to_keep = completion_ids.size(
+            1
+        )  # we only need to compute the logits for the completion tokens
+        batch_size = (
+            self.args.per_device_train_batch_size
+            if mode == "train"
+            else self.args.per_device_eval_batch_size
+        )
+
+        with torch.no_grad():
+            # When using num_iterations == 1, old_per_token_logps == per_token_logps, so we can skip it's
+            # computation here, and use per_token_logps.detach() instead.
+            if self.num_iterations > 1:
+                old_per_token_logps = self._get_per_token_logps(
+                    self.model,
+                    prompt_completion_ids,
+                    attention_mask,
+                    logits_to_keep,
+                    batch_size,
+                )
+            else:
+                old_per_token_logps = None
+
+            if self.beta == 0.0:
+                ref_per_token_logps = None
+            elif self.ref_model is not None:
+                ref_per_token_logps = self._get_per_token_logps(
+                    self.ref_model,
+                    prompt_completion_ids,
+                    attention_mask,
+                    logits_to_keep,
+                    batch_size,
+                )
+            else:
+                with self.accelerator.unwrap_model(self.model).disable_adapter():
+                    ref_per_token_logps = self._get_per_token_logps(
+                        self.model,
+                        prompt_completion_ids,
+                        attention_mask,
+                        logits_to_keep,
+                        batch_size,
+                    )
+
+        # Decode the generated completions
+        completions_text = self.processing_class.batch_decode(
+            completion_ids, skip_special_tokens=True
+        )
+        if is_conversational(inputs[0]):
+            completions = []
+            for prompt, completion in zip(prompts, completions_text):
+                bootstrap = (
+                    prompt.pop()["content"] if prompt[-1]["role"] == "assistant" else ""
+                )
+                completions.append(
+                    [{"role": "assistant", "content": bootstrap + completion}]
+                )
+        else:
+            completions = completions_text
+
+        rewards_per_func = torch.zeros(
+            len(prompts), len(self.reward_funcs), device=device
+        )
+        for i, (reward_func, reward_processing_class, reward_func_name) in enumerate(
+            zip(
+                self.reward_funcs,
+                self.reward_processing_classes,
+                self.reward_func_names,
+            )
+        ):
+            with profiling_context(self, reward_func_name):
+                if isinstance(
+                    reward_func, nn.Module
+                ):  # Module instead of PretrainedModel for compat with compiled models
+                    if is_conversational(inputs[0]):
+                        messages = [
+                            {"messages": p + c} for p, c in zip(prompts, completions)
+                        ]
+                        texts = [
+                            apply_chat_template(x, reward_processing_class)["text"]
+                            for x in messages
+                        ]
+                    else:
+                        texts = [p + c for p, c in zip(prompts, completions)]
+                    reward_inputs = reward_processing_class(
+                        text=texts,
+                        return_tensors="pt",
+                        padding=True,
+                        padding_side="right",
+                        add_special_tokens=False,
+                    )
+                    reward_inputs = Trainer._prepare_inputs(self, reward_inputs)
+                    with torch.inference_mode():
+                        rewards_per_func[:, i] = reward_func(**reward_inputs).logits[
+                            :, 0
+                        ]  # Shape (B*G,)
+                else:
+                    # Repeat all input columns (but "prompt" and "completion") to match the number of generations
+                    keys = [
+                        key for key in inputs[0] if key not in ["prompt", "completion"]
+                    ]
+                    reward_kwargs = {
+                        key: [example[key] for example in inputs] for key in keys
+                    }
+                    output_reward_func = reward_func(
+                        prompts=prompts, completions=completions, **reward_kwargs
+                    )
+                    # Convert None values to NaN
+                    output_reward_func = [
+                        reward if reward is not None else torch.nan
+                        for reward in output_reward_func
+                    ]
+
+                    rewards_per_func[:, i] = torch.tensor(
+                        output_reward_func, dtype=torch.float32, device=device
+                    )
+
+        # If all reward functions return None for a given row, issue a detailed warning
+        if torch.isnan(rewards_per_func).all(dim=1).any():
+            nan_row_idx = (
+                torch.isnan(rewards_per_func).all(dim=1).nonzero(as_tuple=True)[0][0]
+            )
+            row_reward_kwargs = {
+                key: value[nan_row_idx] for key, value in reward_kwargs.items()
+            }
+            row_reward_kwargs["prompt"] = prompts[nan_row_idx]
+            row_reward_kwargs["completion"] = completions[nan_row_idx]
+            warnings.warn(
+                f"All reward functions returned None for the following kwargs: {row_reward_kwargs}. "
+                "Please ensure that at least one reward function returns a valid reward."
+            )
+
+        # Gather the reward per function: this part is crucial, because the rewards are normalized per group and the
+        # completions may be distributed across processes
+        rewards_per_func = gather(rewards_per_func)
+
+        # Apply weights to each reward function's output and sum
+        rewards = (
+            rewards_per_func * self.reward_weights.to(device).unsqueeze(0)
+        ).nansum(dim=1)
+
+        # Compute grouped-wise rewards
+        mean_grouped_rewards = rewards.view(-1, self.num_generations).mean(dim=1)
+        std_grouped_rewards = rewards.view(-1, self.num_generations).std(dim=1)
+
+        # Normalize the rewards to compute the advantages
+        mean_grouped_rewards = mean_grouped_rewards.repeat_interleave(
+            self.num_generations, dim=0
+        )
+        std_grouped_rewards = std_grouped_rewards.repeat_interleave(
+            self.num_generations, dim=0
+        )
+        advantages = rewards - mean_grouped_rewards
+        if self.args.scale_rewards:
+            advantages = advantages / (std_grouped_rewards + 1e-4)
+
+        # Slice to keep only the local part of the data
+        if self.args.sequence_parallel_degree > 1:
+            # Calculate SP group ID (which group of ranks this rank belongs to)
+            sp_group_id = self.accelerator.process_index // self.local_world_size
+
+            # Calculate the start index for this SP group
+            sp_group_start = sp_group_id * len(prompts) * self.local_world_size
+
+            # All ranks in the same SP group get the same data slice
+            process_slice = slice(
+                sp_group_start,
+                sp_group_start + len(prompts),
+            )
+        else:
+            # Original behavior for non-sequence parallel case
+            process_slice = slice(
+                self.accelerator.process_index * len(prompts),
+                (self.accelerator.process_index + 1) * len(prompts),
+            )
+        advantages = advantages[process_slice]
+
+        # Log the metrics
+        if mode == "train":
+            self._total_train_tokens += (
+                self.accelerator.gather_for_metrics(attention_mask.sum()).sum().item()
+            )
+        self._metrics[mode]["num_tokens"] = [self._total_train_tokens]
+
+        # log completion lengths, mean, min, max
+        agg_completion_mask = self.accelerator.gather_for_metrics(
+            completion_mask.sum(1)
+        )
+        self._metrics[mode]["completions/mean_length"].append(
+            agg_completion_mask.float().mean().item()
+        )
+        self._metrics[mode]["completions/min_length"].append(
+            agg_completion_mask.float().min().item()
+        )
+        self._metrics[mode]["completions/max_length"].append(
+            agg_completion_mask.float().max().item()
+        )
+
+        # identify sequences that terminated with EOS and log their lengths
+        agg_terminated_with_eos = self.accelerator.gather_for_metrics(is_eos.any(dim=1))
+        term_completion_mask = agg_completion_mask[agg_terminated_with_eos]
+        clipped_completions_ratio = 1 - len(term_completion_mask) / len(
+            agg_completion_mask
+        )
+        self._metrics[mode]["completions/clipped_ratio"].append(
+            clipped_completions_ratio
+        )
+        if len(term_completion_mask) == 0:
+            # edge case where no completed sequences are found
+            term_completion_mask = torch.zeros(1, device=device)
+        self._metrics[mode]["completions/mean_terminated_length"].append(
+            term_completion_mask.float().mean().item()
+        )
+        self._metrics[mode]["completions/min_terminated_length"].append(
+            term_completion_mask.float().min().item()
+        )
+        self._metrics[mode]["completions/max_terminated_length"].append(
+            term_completion_mask.float().max().item()
+        )
+
+        # Calculate mean reward per function, but only for samples where the function was applied (non-NaN values)
+        for i, reward_func_name in enumerate(self.reward_func_names):
+            mean_rewards = torch.nanmean(rewards_per_func[:, i]).item()
+            self._metrics[mode][f"rewards/{reward_func_name}/mean"].append(mean_rewards)
+            std_rewards = nanstd(rewards_per_func[:, i]).item()
+            self._metrics[mode][f"rewards/{reward_func_name}/std"].append(std_rewards)
+        self._metrics[mode]["reward"].append(mean_grouped_rewards.mean().item())
+        self._metrics[mode]["reward_std"].append(std_grouped_rewards.mean().item())
+
+        # Log prompt and completion texts
+        self._textual_logs["prompt"].extend(gather_object(prompts_text))
+        self._textual_logs["completion"].extend(gather_object(completions_text))
+        for i, name in enumerate(self.reward_func_names):
+            self._textual_logs["rewards"][name].extend(rewards_per_func[:, i].tolist())
+
+        return {
+            "prompt_ids": prompt_ids,
+            "prompt_mask": prompt_mask,
+            "completion_ids": completion_ids,
+            "completion_mask": completion_mask,
+            "advantages": advantages,
+            "old_per_token_logps": old_per_token_logps,
+            "ref_per_token_logps": ref_per_token_logps,
+        }

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

@@ -6,4 +6,4 @@
 from .optimizer import OptimizerMixin
 from .rng_state_loader import RngLoaderMixin
 from .scheduler import SchedulerMixin
-from .sequence_parallel import SequenceParallelContextManager, SequenceParallelMixin
+from .sequence_parallel import SequenceParallelMixin

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

@@ -1,85 +1,13 @@
-"""
-Module for Axolotl trainer sequence parallelism mixin and training context manager
-"""
+"""Module for Axolotl trainer sequence parallelism mixin"""
 
-import functools
-import logging
-
-import torch
 import torch.distributed as dist
 from datasets import Dataset
-from torch import nn
 from torch.utils.data import DistributedSampler, Sampler
-from torch.utils.hooks import RemovableHandle
 
 from axolotl.monkeypatch.attention.ring_attn import (
-    RingAttnFunc,
     get_ring_attn_group,
-    update_ring_attn_params,
 )
 
-LOG = logging.getLogger(__name__)
-
-
-def apply_sequence_parallelism(
-    batch: dict[str, torch.Tensor],
-    local_rank: int,
-    local_world_size: int,
-    ring_attn_func: RingAttnFunc,
-) -> dict[str, torch.Tensor]:
-    """
-    Apply sequence parallelism slicing to a batch.
-
-    Args:
-        batch: Batch dictionary (e.g., input_ids, attention_mask, etc.)
-        local_rank: Local rank in the sequence parallel group
-        local_world_size: World size of the sequence parallel group
-        ring_attn_func: The ring attention function to use
-
-    Returns:
-        Sliced batch dictionary.
-    """
-    # Update ring attention params if needed
-    if batch.get("position_ids") is not None:
-        update_ring_attn_params(position_ids=batch["position_ids"])
-
-    # Slice batch for sequence parallel processing
-    total_seq_len = batch["input_ids"].size(1)
-    for key in batch:
-        if (
-            key in batch
-            and isinstance(batch[key], torch.Tensor)
-            and batch[key].dim() > 1
-            and batch[key].size(1) == total_seq_len
-        ):
-
-            if ring_attn_func in [
-                RingAttnFunc.VARLEN_LLAMA3,
-                RingAttnFunc.BATCH_RING,
-            ]:
-                # Split in sequential fashion and grab this rank's chunk
-                batch[key] = (
-                    batch[key].chunk(local_world_size, dim=1)[local_rank].contiguous()
-                )
-            elif ring_attn_func is RingAttnFunc.BATCH_ZIGZAG:
-                chunks = batch[key].chunk(2 * local_world_size, dim=1)
-
-                # Take rank's chunk and opposing chunk for zigzag pattern
-                selected_chunks = [
-                    chunks[local_rank],
-                    chunks[2 * local_world_size - local_rank - 1],
-                ]
-                batch[key] = torch.cat(selected_chunks, dim=1).contiguous()
-            elif ring_attn_func is RingAttnFunc.BATCH_STRIPE:
-                # Split into striped data and stack
-                tensor = torch.stack(
-                    batch[key].split(local_world_size, dim=1),
-                    dim=1,
-                ).transpose(1, 2)
-                batch[key] = tensor[:, local_rank].contiguous()
-
-    return batch
-
 
 class SequenceParallelMixin:
     """
@@ -157,157 +85,3 @@ class SequenceParallelMixin:
         return self._create_sequence_parallel_sampler(
             eval_dataset, shuffle=False, is_eval=True
         )
-
-
-class SequenceParallelContextManager:
-    """
-    Context manager for sequence parallelism operations.
-
-    This class provides a context that will automatically apply sequence parallelism
-    during model forward passes using a pre-forward hook, and gather outputs from
-    across the sequence parallelism group using a post-forward hook.
-    """
-
-    def __init__(
-        self,
-        model: nn.Module,
-        sequence_parallel_degree: int,
-        ring_attn_func: RingAttnFunc,
-    ):
-        self.model = model
-        self.sequence_parallel_degree = sequence_parallel_degree
-        self.ring_attn_func = ring_attn_func
-        self.process_group = get_ring_attn_group()
-
-        # Initialize sequence parallel group details
-        self.local_rank = dist.get_rank(self.process_group)
-        self.local_world_size = dist.get_world_size(self.process_group)
-
-        # Will store hook handles for removal
-        self.hook_handles: list[RemovableHandle] = []
-
-        # Create a partially applied version of the apply_sequence_parallelism function
-        # with pre-configured params
-        self.apply_sequence_parallelism = functools.partial(
-            apply_sequence_parallelism,
-            local_rank=self.local_rank,
-            local_world_size=self.local_world_size,
-            ring_attn_func=self.ring_attn_func,
-        )
-
-    def __enter__(self):
-        # Forward pre-hook to apply sequence parallelism
-        def sequence_parallel_pre_hook(_, args, kwargs):
-            # Apply sequence parallelism to kwargs
-            kwargs = self.apply_sequence_parallelism(batch=kwargs)
-            return args, kwargs
-
-        # Forward post-hook to gather outputs
-        def sequence_parallel_post_hook(_, __, output):
-            # Gather the sharded outputs
-            return self.gather_outputs(output)
-
-        # Register both hooks
-        self.hook_handles.append(
-            self.model.register_forward_pre_hook(
-                sequence_parallel_pre_hook, with_kwargs=True
-            )
-        )
-        self.hook_handles.append(
-            self.model.register_forward_hook(sequence_parallel_post_hook)
-        )
-
-        return self
-
-    def __exit__(self, exc_type, exc_val, exc_tb):
-        # Remove all hooks
-        for handle in self.hook_handles:
-            handle.remove()
-        self.hook_handles = []
-
-    def gather_outputs(self, output):
-        """Gather sharded outputs from all ranks and reconstruct the full tensor."""
-        # Handle different output formats (dict, tensor, etc.)
-        if isinstance(output, dict):
-            gathered_output = {}
-            for key, value in output.items():
-                if isinstance(value, torch.Tensor) and value.dim() > 1:
-                    # Gather logits or other sequence-sharded tensors
-                    gathered_value = self.gather_tensor(value)
-                    gathered_output[key] = gathered_value
-                else:
-                    gathered_value = value.clone()
-                    dist.all_reduce(
-                        gathered_value, op=dist.ReduceOp.SUM, group=self.process_group
-                    )
-                    gathered_output[key] = gathered_value
-            return gathered_output
-        if isinstance(output, torch.Tensor):
-            return self.gather_tensor(output)
-
-        return output
-
-    def gather_tensor(self, tensor):
-        """Gather a sharded tensor from all ranks."""
-        # Prepare tensors for all_gather
-        world_size = self.local_world_size
-
-        # Create list to store tensors from all ranks
-        gathered_tensors = [torch.zeros_like(tensor) for _ in range(world_size)]
-
-        # All-gather operation
-        dist.all_gather(gathered_tensors, tensor, group=self.process_group)
-
-        # Concatenate along sequence dimension (typically dim=1)
-        if self.ring_attn_func in [RingAttnFunc.VARLEN_LLAMA3, RingAttnFunc.BATCH_RING]:
-            # Simple concatenation for standard sharding
-            return torch.cat(gathered_tensors, dim=1)
-
-        if self.ring_attn_func is RingAttnFunc.BATCH_ZIGZAG:
-            # Each rank has a pattern of (rank, world_size*2-rank-1)
-            reconstituted_tensors = [None] * (world_size * 2)
-
-            # First, split each gathered tensor into its two chunks
-            for rank, gathered_tensor in enumerate(gathered_tensors):
-                # Each tensor contains two chunks in the sequence dimension
-                chunk_size = gathered_tensor.size(1) // 2
-                chunk1, chunk2 = gathered_tensor.split(chunk_size, dim=1)
-
-                # Place chunks in their original positions
-                reconstituted_tensors[rank] = chunk1
-                reconstituted_tensors[world_size * 2 - rank - 1] = chunk2
-
-            # Concatenate the reconstituted tensors in the correct order
-            return torch.cat(reconstituted_tensors, dim=1)
-
-        # Otherwise, RingAttnFunc.BATCH_STRIPE
-        # In striping, each rank has every world_size-th slice
-        batch_size = tensor.size(0)
-        hidden_dim = tensor.size(-1)
-
-        # First, determine the full sequence length
-        total_seq_len = 0
-        for t in gathered_tensors:
-            total_seq_len += t.size(1)
-
-        # Create a tensor to hold the unstriped result
-        result = torch.zeros(
-            batch_size,
-            total_seq_len,
-            hidden_dim,
-            dtype=tensor.dtype,
-            device=tensor.device,
-        )
-
-        # For each rank's tensor, distribute its slices to the correct positions
-        for rank, gathered_tensor in enumerate(gathered_tensors):
-            # The rank's tensor contains every world_size-th slice
-            # starting from its rank position
-            seq_len = gathered_tensor.size(1)
-            for i in range(seq_len):
-                # Calculate the position in the full tensor
-                pos = i * world_size + rank
-                if pos < total_seq_len:
-                    result[:, pos] = gathered_tensor[:, i]
-
-        return result

src/axolotl/core/training_args.py

@@ -9,7 +9,7 @@ from PIL.Image import Resampling
 from transformers import TrainingArguments
 from trl import CPOConfig, KTOConfig, ORPOConfig, PRMConfig, RewardConfig
 
-from axolotl.monkeypatch.attention.ring_attn.patch import RingAttnFunc
+from axolotl.utils.schemas.enums import RingAttnFunc
 
 
 @dataclass
@@ -227,6 +227,19 @@ class AxolotlTrainingMixins:
         },
     )
 
+    adam_beta3: Optional[float] = field(
+        default=None,
+        metadata={
+            "help": "The beta3 hyperparameter used in some optimizers such as CAME"
+        },
+    )
+    adam_epsilon2: Optional[float] = field(
+        default=None,
+        metadata={
+            "help": "The epsilon2 hyperparameter used in some optimizers such as CAME"
+        },
+    )
+
     # multi-modal section
 
     image_size: int | tuple[int, int] | None = field(

src/axolotl/integrations/cut_cross_entropy/monkeypatch/cohere.py

@@ -20,25 +20,15 @@ from cut_cross_entropy.transformers.utils import (
 from transformers.cache_utils import Cache
 from transformers.modeling_outputs import CausalLMOutputWithPast
 from transformers.models.cohere.modeling_cohere import (
-    _CONFIG_FOR_DOC,
-    COHERE_INPUTS_DOCSTRING,
     KwargsForCausalLM,
 )
 from transformers.processing_utils import Unpack
-from transformers.utils import (
-    add_start_docstrings_to_model_forward,
-    replace_return_docstrings,
-)
 from transformers.utils.deprecation import deprecate_kwarg
 
 _PATCH_OPTS: PatchOptions | None = None
 
 
 @deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep")
-@add_start_docstrings_to_model_forward(COHERE_INPUTS_DOCSTRING)
-@replace_return_docstrings(
-    output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC
-)
 def cce_forward(
     self,
     input_ids: torch.LongTensor | None = None,

src/axolotl/integrations/cut_cross_entropy/monkeypatch/gemma.py

@@ -17,25 +17,15 @@ from cut_cross_entropy.transformers.utils import (
 from transformers.cache_utils import Cache
 from transformers.modeling_outputs import CausalLMOutputWithPast
 from transformers.models.gemma.modeling_gemma import (
-    _CONFIG_FOR_DOC,
-    GEMMA_INPUTS_DOCSTRING,
     KwargsForCausalLM,
 )
 from transformers.processing_utils import Unpack
-from transformers.utils import (
-    add_start_docstrings_to_model_forward,
-    replace_return_docstrings,
-)
 from transformers.utils.deprecation import deprecate_kwarg
 
 _PATCH_OPTS: PatchOptions | None = None
 
 
 @deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep")
-@add_start_docstrings_to_model_forward(GEMMA_INPUTS_DOCSTRING)
-@replace_return_docstrings(
-    output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC
-)
 def cce_forward(
     self,
     input_ids: torch.LongTensor | None = None,

src/axolotl/integrations/cut_cross_entropy/monkeypatch/gemma3.py

@@ -20,15 +20,11 @@ from torch import nn
 from transformers.cache_utils import Cache, HybridCache
 from transformers.modeling_outputs import CausalLMOutputWithPast
 from transformers.models.gemma3.modeling_gemma3 import (
-    _CONFIG_FOR_DOC,
-    GEMMA3_INPUTS_DOCSTRING,
     Gemma3CausalLMOutputWithPast,
     logger,
 )
 from transformers.utils import (
-    add_start_docstrings_to_model_forward,
     is_torchdynamo_compiling,
-    replace_return_docstrings,
 )
 from transformers.utils.deprecation import deprecate_kwarg
 
@@ -38,10 +34,6 @@ _PATCH_OPTS: PatchOptions | None = None
 
 
 @deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep")
-@add_start_docstrings_to_model_forward(GEMMA3_INPUTS_DOCSTRING)
-@replace_return_docstrings(
-    output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC
-)
 def cce_forward(
     self,
     input_ids: torch.LongTensor | None = None,
@@ -170,10 +162,6 @@ def cce_forward(
 
 
 @deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep")
-@add_start_docstrings_to_model_forward(GEMMA3_INPUTS_DOCSTRING)
-@replace_return_docstrings(
-    output_type=Gemma3CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC
-)
 def cce_forward_multimodal(
     self,
     input_ids: torch.LongTensor | None = None,

src/axolotl/integrations/cut_cross_entropy/monkeypatch/llama.py

@@ -19,15 +19,9 @@ from transformers.modeling_outputs import (
     CausalLMOutputWithPast,
 )
 from transformers.models.llama.modeling_llama import (
-    _CONFIG_FOR_DOC,
-    LLAMA_INPUTS_DOCSTRING,
     KwargsForCausalLM,
 )
 from transformers.processing_utils import Unpack
-from transformers.utils import (
-    add_start_docstrings_to_model_forward,
-    replace_return_docstrings,
-)
 from transformers.utils.deprecation import deprecate_kwarg
 from transformers.utils.generic import can_return_tuple
 
@@ -36,10 +30,6 @@ _PATCH_OPTS: PatchOptions | None = None
 
 @can_return_tuple
 @deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep")
-@add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
-@replace_return_docstrings(
-    output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC
-)
 def cce_forward(
     self,
     input_ids: Optional[torch.LongTensor] = None,

src/axolotl/integrations/cut_cross_entropy/monkeypatch/llama4.py

@@ -16,22 +16,12 @@ from torch import nn
 from transformers.cache_utils import Cache
 from transformers.modeling_outputs import CausalLMOutputWithPast
 from transformers.models.llama4.modeling_llama4 import (
-    _CONFIG_FOR_DOC,
-    LLAMA4_INPUTS_DOCSTRING,
     Llama4CausalLMOutputWithPast,
 )
-from transformers.utils import (
-    add_start_docstrings_to_model_forward,
-    replace_return_docstrings,
-)
 
 _PATCH_OPTS: PatchOptions | None = None
 
 
-@add_start_docstrings_to_model_forward(LLAMA4_INPUTS_DOCSTRING)
-@replace_return_docstrings(
-    output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC
-)
 def cce_forward(
     self,
     input_ids: torch.LongTensor | None = None,
@@ -160,9 +150,6 @@ def cce_forward(
     )
 
 
-@replace_return_docstrings(
-    output_type=Llama4CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC
-)
 def cce_forward_multimodal(
     self,
     input_ids: torch.LongTensor | None = None,  # type: ignore

src/axolotl/integrations/cut_cross_entropy/monkeypatch/mistral3.py

@@ -19,15 +19,11 @@ from transformers.models.mistral3.modeling_mistral3 import (
     Mistral3CausalLMOutputWithPast,
 )
 from transformers.models.mistral.modeling_mistral import (
-    _CONFIG_FOR_DOC,
-    MISTRAL_INPUTS_DOCSTRING,
     KwargsForCausalLM,
 )
 from transformers.processing_utils import Unpack
 from transformers.utils import (
-    add_start_docstrings_to_model_forward,
     is_torchdynamo_compiling,
-    replace_return_docstrings,
 )
 from transformers.utils.deprecation import deprecate_kwarg
 
@@ -35,10 +31,6 @@ _PATCH_OPTS: PatchOptions | None = None
 
 
 @deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep")
-@add_start_docstrings_to_model_forward(MISTRAL_INPUTS_DOCSTRING)
-@replace_return_docstrings(
-    output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC
-)
 def cce_forward(
     self,
     input_ids: torch.LongTensor | None = None,

src/axolotl/integrations/cut_cross_entropy/monkeypatch/qwen2_moe.py

@@ -13,16 +13,10 @@ from cut_cross_entropy.transformers.utils import (
     apply_lce,
 )
 from transformers.models.qwen2_moe.modeling_qwen2_moe import (
-    _CONFIG_FOR_DOC,
-    QWEN2MOE_INPUTS_DOCSTRING,
     MoeCausalLMOutputWithPast,
     MoeModelOutputWithPast,
     load_balancing_loss_func,
 )
-from transformers.utils import (
-    add_start_docstrings_to_model_forward,
-    replace_return_docstrings,
-)
 from transformers.utils.deprecation import deprecate_kwarg
 from transformers.utils.generic import can_return_tuple
 
@@ -31,10 +25,6 @@ _PATCH_OPTS: PatchOptions | None = None
 
 @can_return_tuple
 @deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep")
-@add_start_docstrings_to_model_forward(QWEN2MOE_INPUTS_DOCSTRING)
-@replace_return_docstrings(
-    output_type=MoeCausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC
-)
 def forward(
     self,
     input_ids: Optional[torch.LongTensor] = None,

src/axolotl/integrations/cut_cross_entropy/monkeypatch/qwen2_vl.py

@@ -14,22 +14,12 @@ from cut_cross_entropy.transformers.utils import (
 )
 from torch.nn import CrossEntropyLoss
 from transformers.models.qwen2_vl.modeling_qwen2_vl import (
-    _CONFIG_FOR_DOC,
-    QWEN2_VL_INPUTS_DOCSTRING,
     Qwen2VLCausalLMOutputWithPast,
 )
-from transformers.utils import (
-    add_start_docstrings_to_model_forward,
-    replace_return_docstrings,
-)
 
 _PATCH_OPTS: PatchOptions | None = None
 
 
-@add_start_docstrings_to_model_forward(QWEN2_VL_INPUTS_DOCSTRING)
-@replace_return_docstrings(
-    output_type=Qwen2VLCausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC
-)
 def cce_forward_multimodal(
     self,
     input_ids: Optional[torch.LongTensor] = None,

src/axolotl/integrations/cut_cross_entropy/monkeypatch/qwen3_moe.py

@@ -12,20 +12,13 @@ from cut_cross_entropy.transformers.utils import (
     TransformersModelT,
     apply_lce,
 )
-from transformers.modeling_outputs import CausalLMOutputWithPast
 from transformers.models.qwen3_moe.modeling_qwen3_moe import (
-    _CONFIG_FOR_DOC,
-    QWEN3_MOE_INPUTS_DOCSTRING,
     KwargsForCausalLM,
     MoeCausalLMOutputWithPast,
     MoeModelOutputWithPast,
     load_balancing_loss_func,
 )
 from transformers.processing_utils import Unpack
-from transformers.utils import (
-    add_start_docstrings_to_model_forward,
-    replace_return_docstrings,
-)
 from transformers.utils.deprecation import deprecate_kwarg
 from transformers.utils.generic import can_return_tuple
 
@@ -34,10 +27,6 @@ _PATCH_OPTS: PatchOptions | None = None
 
 @can_return_tuple
 @deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep")
-@add_start_docstrings_to_model_forward(QWEN3_MOE_INPUTS_DOCSTRING)
-@replace_return_docstrings(
-    output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC
-)
 def forward(
     self,
     input_ids: Optional[torch.LongTensor] = None,

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

@@ -14,10 +14,6 @@ from torch.nn import CrossEntropyLoss
 from transformers.modeling_outputs import CausalLMOutputWithPast
 
 
-# @add_start_docstrings_to_model_forward(DeepseekV2_INPUTS_DOCSTRING)
-# @replace_return_docstrings(
-#    output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC
-# )
 def lce_forward(
     self,
     input_ids: torch.LongTensor = None,

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

@@ -13,21 +13,11 @@ from liger_kernel.transformers.fused_linear_cross_entropy import (
 from torch.nn import CrossEntropyLoss
 from transformers.modeling_outputs import MoeCausalLMOutputWithPast
 from transformers.models.jamba.modeling_jamba import (
-    _CONFIG_FOR_DOC,
-    JAMBA_INPUTS_DOCSTRING,
     HybridMambaAttentionDynamicCache,
     load_balancing_loss_func,
 )
-from transformers.utils import (
-    add_start_docstrings_to_model_forward,
-    replace_return_docstrings,
-)
 
 
-@add_start_docstrings_to_model_forward(JAMBA_INPUTS_DOCSTRING)
-@replace_return_docstrings(
-    output_type=MoeCausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC
-)
 def lce_forward(
     self,
     input_ids: torch.LongTensor = None,

src/axolotl/monkeypatch/attention/ring_attn/__init__.py

@@ -4,7 +4,6 @@
 # flake8: noqa
 
 from .patch import (
-    RingAttnFunc,
     get_ring_attn_group,
     register_ring_attn,
     set_ring_attn_group,

src/axolotl/monkeypatch/attention/ring_attn/adapters/batch.py

@@ -16,11 +16,7 @@ import torch
 import torch.distributed as dist
 import transformers
 import transformers.modeling_flash_attention_utils
-from ring_flash_attn import (
-    ring_flash_attn_func,
-    stripe_flash_attn_func,
-    zigzag_ring_flash_attn_func,
-)
+from ring_flash_attn import ring_flash_attn_func
 from ring_flash_attn.adapters.hf_adapter import check_params
 from transformers.modeling_flash_attention_utils import (
     _flash_supports_window_size,
@@ -28,12 +24,12 @@ from transformers.modeling_flash_attention_utils import (
 )
 from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS
 
-from axolotl.monkeypatch.attention.ring_attn.patch import RingAttnFunc
+from axolotl.utils.schemas.enums import RingAttnFunc
 
 RING_ATTN_FUNC_MAPPING = {
-    RingAttnFunc.BATCH_RING: ring_flash_attn_func,
-    RingAttnFunc.BATCH_ZIGZAG: zigzag_ring_flash_attn_func,
-    RingAttnFunc.BATCH_STRIPE: stripe_flash_attn_func,
+    RingAttnFunc.BATCH_RING: torch.compile(ring_flash_attn_func),
+    # RingAttnFunc.BATCH_ZIGZAG: torch.compile(zigzag_ring_flash_attn_func),
+    # RingAttnFunc.BATCH_STRIPE: torch.compile(stripe_flash_attn_func),
 }
 
 

src/axolotl/monkeypatch/attention/ring_attn/patch.py

@@ -6,13 +6,12 @@ package, specifically the `hf_adapter.substitute_hf_flash_attn` function to patc
 their sequence parallel version of Flash Attention 2.
 """
 
-from enum import Enum
-
 import torch
 import torch.distributed as dist
 from accelerate.logging import get_logger
 
 from axolotl.monkeypatch.utils import get_cu_seqlens_from_pos_ids
+from axolotl.utils.schemas.enums import RingAttnFunc
 
 LOG = get_logger(__name__)
 
@@ -41,17 +40,6 @@ def set_ring_attn_group(ring_attn_group: dist.ProcessGroup | None):
     RING_ATTN_GROUP = ring_attn_group
 
 
-class RingAttnFunc(str, Enum):
-    """Enum class for supported `ring-flash-attn` implementations"""
-
-    # VARLEN_RING = "varlen_ring"
-    # VARLEN_ZIGZAG = "varlen_zigzag"
-    VARLEN_LLAMA3 = "varlen_llama3"
-    BATCH_RING = "batch_ring"
-    BATCH_ZIGZAG = "batch_zigzag"
-    BATCH_STRIPE = "batch_stripe"
-
-
 def register_ring_attn(
     sequence_parallel_degree: int,
     heads_k_stride: int | None,
@@ -117,11 +105,7 @@ def register_ring_attn(
         substitute_hf_flash_attn(
             process_group=get_ring_attn_group(), heads_k_stride=heads_k_stride or 1
         )
-    elif ring_attn_func in [
-        RingAttnFunc.BATCH_RING,
-        RingAttnFunc.BATCH_ZIGZAG,
-        RingAttnFunc.BATCH_STRIPE,
-    ]:
+    elif ring_attn_func is RingAttnFunc.BATCH_RING:
         from axolotl.monkeypatch.attention.ring_attn.adapters.batch import (
             substitute_hf_flash_attn,
         )

src/axolotl/monkeypatch/gemma3.py

@@ -7,24 +7,16 @@ from typing import Optional, Tuple, Union
 import torch
 from transformers.cache_utils import Cache
 from transformers.models.gemma3.modeling_gemma3 import (
-    _CONFIG_FOR_DOC,
-    GEMMA3_INPUTS_DOCSTRING,
     Gemma3CausalLMOutputWithPast,
     logger,
 )
 from transformers.utils import (
-    add_start_docstrings_to_model_forward,
     is_torchdynamo_compiling,
-    replace_return_docstrings,
 )
 from transformers.utils.deprecation import deprecate_kwarg
 
 
 @deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep")
-@add_start_docstrings_to_model_forward(GEMMA3_INPUTS_DOCSTRING)
-@replace_return_docstrings(
-    output_type=Gemma3CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC
-)
 def new_forward(
     self,
     input_ids: torch.LongTensor = None,

src/axolotl/train.py

@@ -2,17 +2,17 @@
 
 import importlib
 import inspect
+import logging
 import os
 import signal
 import sys
 import weakref
-from contextlib import nullcontext
+from contextlib import ExitStack
 from pathlib import Path
 from typing import Any, Dict
 
 import torch
 import transformers.modelcard
-from accelerate.logging import get_logger
 from accelerate.utils import save_fsdp_model
 from datasets import Dataset
 from huggingface_hub.errors import OfflineModeIsEnabled
@@ -27,14 +27,13 @@ from axolotl.contribs.lgpl import (  # pylint: disable = no-name-in-module
     fix_untrained_tokens,
 )
 from axolotl.core.trainer_builder import HFCausalTrainerBuilder, HFRLTrainerBuilder
-from axolotl.core.trainers.mixins.sequence_parallel import (
-    SequenceParallelContextManager,
-)
 from axolotl.integrations.base import PluginManager
+from axolotl.utils.ctx_managers.sequence_parallel import SequenceParallelContextManager
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.distributed import cleanup_distributed
 from axolotl.utils.freeze import freeze_layers_except
 from axolotl.utils.models import load_model, load_processor, load_tokenizer
+from axolotl.utils.schemas.enums import RLType
 from axolotl.utils.trainer import setup_trainer
 
 try:
@@ -42,7 +41,7 @@ try:
 except ImportError:
     BetterTransformer = None
 
-LOG = get_logger(__name__)
+LOG = logging.getLogger(__name__)
 
 
 def setup_model_and_tokenizer(
@@ -63,7 +62,6 @@ def setup_model_and_tokenizer(
     # Load tokenizer
     LOG.debug(
         f"loading tokenizer... {cfg.tokenizer_config or cfg.base_model_config}",
-        main_process_only=True,
     )
     tokenizer = load_tokenizer(cfg)
 
@@ -108,7 +106,7 @@ def setup_reference_model(
         Reference model if needed for RL training, `None` otherwise.
     """
     model_ref = None
-    if cfg.rl and cfg.rl != "orpo":
+    if cfg.rl and cfg.rl != RLType.ORPO:
         if cfg.adapter and not cfg.rl_adapter_ref_model:
             # use built-in trl autounwrap
             LOG.debug("Passing model_ref: None to RL trainer")
@@ -189,28 +187,32 @@ def execute_training(
         trainer: The configured trainer object.
         resume_from_checkpoint: Path to checkpoint to resume from, if applicable.
     """
-    # Define the context managers to use
-    flash_context = (
-        torch.backends.cuda.sdp_kernel(
-            enable_flash=True,
-            enable_math=True,
-            enable_mem_efficient=True,
-        )
-        if cfg.flash_optimum
-        else nullcontext()
-    )
-    sequence_parallel_context = (
-        SequenceParallelContextManager(
-            model=trainer.model,
-            sequence_parallel_degree=cfg.sequence_parallel_degree,
-            ring_attn_func=cfg.ring_attn_func,
-        )
-        if cfg.sequence_parallel_degree > 1
-        else nullcontext()
-    )
+    with ExitStack() as stack:
+        # Define the context managers to use
+        if cfg.flash_optimum:
+            stack.enter_context(
+                torch.backends.cuda.sdp_kernel(
+                    enable_flash=True,
+                    enable_math=True,
+                    enable_mem_efficient=True,
+                )
+            )
 
-    LOG.info("Starting trainer...")
-    with flash_context, sequence_parallel_context:
+        if cfg.sequence_parallel_degree > 1:
+            models = [trainer.model]
+            if hasattr(trainer, "ref_model"):
+                models.append(trainer.ref_model)
+
+            stack.enter_context(
+                SequenceParallelContextManager(
+                    models=models,
+                    sequence_parallel_degree=cfg.sequence_parallel_degree,
+                    gradient_accumulation_steps=cfg.gradient_accumulation_steps,
+                    ring_attn_func=cfg.ring_attn_func,
+                )
+            )
+
+        LOG.info("Starting trainer...")
         trainer.train(resume_from_checkpoint=resume_from_checkpoint)
 
 

src/axolotl/utils/callbacks/mlflow_.py

@@ -1,6 +1,7 @@
 """MLFlow module for trainer callbacks"""
 
 import logging
+import os
 from shutil import copyfile
 from tempfile import NamedTemporaryFile
 from typing import TYPE_CHECKING
@@ -16,6 +17,11 @@ if TYPE_CHECKING:
 LOG = logging.getLogger("axolotl.callbacks")
 
 
+def should_log_artifacts() -> bool:
+    truths = ["TRUE", "1", "YES"]
+    return os.getenv("HF_MLFLOW_LOG_ARTIFACTS", "FALSE").upper() in truths
+
+
 class SaveAxolotlConfigtoMlflowCallback(TrainerCallback):
     # pylint: disable=duplicate-code
     """Callback to save axolotl config to mlflow"""
@@ -32,13 +38,18 @@ class SaveAxolotlConfigtoMlflowCallback(TrainerCallback):
     ):
         if is_main_process():
             try:
-                with NamedTemporaryFile(
-                    mode="w", delete=False, suffix=".yml", prefix="axolotl_config_"
-                ) as temp_file:
-                    copyfile(self.axolotl_config_path, temp_file.name)
-                    mlflow.log_artifact(temp_file.name, artifact_path="")
+                if should_log_artifacts():
+                    with NamedTemporaryFile(
+                        mode="w", delete=False, suffix=".yml", prefix="axolotl_config_"
+                    ) as temp_file:
+                        copyfile(self.axolotl_config_path, temp_file.name)
+                        mlflow.log_artifact(temp_file.name, artifact_path="")
+                        LOG.info(
+                            "The Axolotl config has been saved to the MLflow artifacts."
+                        )
+                else:
                     LOG.info(
-                        "The Axolotl config has been saved to the MLflow artifacts."
+                        "Skipping logging artifacts to MLflow (hf_mlflow_log_artifacts is false)"
                     )
             except (FileNotFoundError, ConnectionError) as err:
                 LOG.warning(f"Error while saving Axolotl config to MLflow: {err}")

src/axolotl/utils/ctx_managers/__init__.py

@@ -0,0 +1,6 @@
+"""Init for context manager submodule"""
+
+# pylint: disable=unused-import
+# flake8: noqa
+
+from .sequence_parallel import SequenceParallelContextManager

src/axolotl/utils/ctx_managers/sequence_parallel.py

@@ -0,0 +1,335 @@
+"""Module for Axolotl trainer sequence parallelism manager and utilities"""
+
+import functools
+
+import torch
+import torch.distributed as dist
+from torch import nn
+from torch.utils.hooks import RemovableHandle
+from transformers.modeling_outputs import CausalLMOutputWithPast
+from transformers.utils import ModelOutput
+
+from axolotl.monkeypatch.attention.ring_attn.patch import (
+    get_ring_attn_group,
+    update_ring_attn_params,
+)
+from axolotl.utils.schemas.enums import RingAttnFunc
+
+
+# TODO(djsaunde): implement zigzag, stripe patterns here (and elsewhere) in this
+# module. Currently, we just focus on batch ring and varlen llama3 for simplicity.
+def apply_sequence_parallelism(
+    batch: dict[str, torch.Tensor],
+    local_rank: int,
+    local_world_size: int,
+    gradient_accumulation_steps: int,
+    ring_attn_func: RingAttnFunc,  # pylint: disable=unused-argument
+) -> tuple[dict[str, torch.Tensor], int, int]:
+    """
+    Apply sequence parallelism slicing to a batch.
+
+    Special handling is implemented for integer logits_to_keep, which indicates
+    to only keep the last N tokens in the sequence during generation.
+
+    Args:
+        batch: Batch dictionary (e.g., input_ids, attention_mask, etc.).
+        local_rank: Local rank in the sequence parallel group.
+        local_world_size: World size of the sequence parallel group.
+        gradient_accumulation_steps: Number of steps to accumulate gradients over.
+        ring_attn_func: Which ring attention function to use. Currently unused, but
+            related to above TODO.
+
+    Returns:
+        tuple of:
+            - Batch dictionary with sliced tensors.
+            - The original sequence length before padding.
+            - The number of padding tokens added.
+    """
+    original_seq_len = batch["input_ids"].size(1)
+
+    # Update ring attention params if needed
+    if batch.get("position_ids") is not None:
+        update_ring_attn_params(position_ids=batch["position_ids"])
+    else:
+        # If position_ids aren't already in the batch, create them
+        batch["position_ids"] = torch.arange(
+            0,
+            original_seq_len,
+            dtype=torch.long,
+            device=batch["input_ids"].device,
+        ).expand(batch["input_ids"].size(0), -1)
+
+    if "logits_to_keep" in batch and isinstance(batch["logits_to_keep"], int):
+        logits_to_keep = batch["logits_to_keep"]
+
+        # Calculate which positions in the full sequence contain the last N tokens
+        start_position = max(0, original_seq_len - logits_to_keep)
+        chunk_size = original_seq_len // local_world_size
+        rank_start = local_rank * chunk_size
+        rank_end = rank_start + chunk_size
+
+        # Create a boolean mask tensor for this rank's chunk
+        mask = torch.zeros(
+            chunk_size,
+            dtype=torch.bool,
+            device=batch["input_ids"].device,
+        )
+
+        if rank_end > start_position:
+            # Calculate how many of the last N tokens fall within this rank's range
+            tokens_in_rank = min(rank_end, original_seq_len) - max(
+                rank_start, start_position
+            )
+
+            # Calculate where these tokens start in the local chunk
+            local_start_idx = max(0, start_position - rank_start)
+
+            # Set the appropriate positions in the mask to True
+            mask[local_start_idx : local_start_idx + tokens_in_rank] = True
+
+        # Replace the integer with the boolean mask
+        batch["logits_to_keep"] = mask
+
+    # Add padding to make sequence length divisible by local_world_size
+    total_seq_len = original_seq_len
+    pad_len = 0
+    divisor = min(local_world_size, 64)
+    if total_seq_len % divisor != 0:
+        pad_len = divisor - (total_seq_len % divisor)
+
+        # Apply padding to all relevant tensors
+        for key in batch:
+            if (
+                isinstance(batch[key], torch.Tensor)
+                and batch[key].dim() > 1
+                and batch[key].size(1) == total_seq_len
+            ):
+                # Create padding tensor
+                pad_value = -100 if key == "labels" else 0
+                padding = torch.full(
+                    (batch[key].size(0), pad_len, *batch[key].shape[2:]),
+                    pad_value,
+                    dtype=batch[key].dtype,
+                    device=batch[key].device,
+                )
+
+                # Concatenate padding to the right side of the tensor
+                batch[key] = torch.cat([batch[key], padding], dim=1)
+            if key == "logits_to_keep":
+                # Create padding tensor
+                padding = torch.ones(
+                    1,
+                    dtype=batch[key].dtype,
+                    device=batch[key].device,
+                )
+
+                # Concatenate padding to the right side of the tensor
+                batch[key] = torch.cat([batch[key], padding], dim=0)
+
+        # Update the total sequence length after padding
+        total_seq_len = batch["input_ids"].size(1)
+
+    # Slice batch for sequence parallel
+    for key in batch:
+        if not isinstance(batch[key], torch.Tensor) or batch[key].dim() <= 1:
+            continue
+
+        # Split in sequential fashion and grab this rank's chunk
+        if batch[key].size(1) == total_seq_len:
+            batch[key] = (
+                batch[key].chunk(local_world_size, dim=1)[local_rank].contiguous()
+            )
+        elif key == "logits_to_keep":
+            batch[key] = (
+                batch[key].chunk(local_world_size, dim=0)[local_rank].contiguous()
+            )
+
+        # Handle num_items_in_batch
+        if "num_items_in_batch" in batch:
+            # Approximation; this needed since num_items_in_batch may be counted across
+            # all samples in a gradient accumulated batch, not on a per-step basis.
+            batch["num_items_in_batch"] = (
+                batch["labels"] != -100
+            ).sum() * gradient_accumulation_steps
+
+    return batch, original_seq_len, pad_len
+
+
+class SequenceParallelContextManager:
+    """Context manager for sequence parallelism operations.
+
+    This class provides a context that will automatically apply sequence parallelism
+    during model forward passes using a pre-forward hook, and gather outputs from
+    across the sequence parallelism group using a post-forward hook.
+
+    Args:
+        models: List of models to apply sequence parallelism to pre- and post- forward
+            hooks.
+        sequence_parallel_degree: Number of processes to split sequences over.
+        gradient_accumulation_steps: Number of steps to accumulate gradients over.
+        ring_attn_func: Which ring attention function to use. Currently unused.
+    """
+
+    def __init__(
+        self,
+        models: list[nn.Module],
+        sequence_parallel_degree: int,
+        gradient_accumulation_steps: int,
+        ring_attn_func: RingAttnFunc,
+    ):
+        self.models = models
+        self.sequence_parallel_degree = sequence_parallel_degree
+        self.gradient_accumulation_steps = gradient_accumulation_steps
+        self.ring_attn_func = ring_attn_func
+        self.process_group = get_ring_attn_group()
+
+        # Initialize sequence parallel group details
+        self.local_rank = dist.get_rank(self.process_group)
+        self.local_world_size = dist.get_world_size(self.process_group)
+
+        # Will store hook handles for removal
+        self.hook_handles: list[RemovableHandle] = []
+
+        # Store original sequence length and padding information
+        self.original_seq_len = 0
+        self.pad_len = 0
+
+        # Create a partially applied version of the apply_sequence_parallelism function
+        self.apply_sequence_parallelism = functools.partial(
+            apply_sequence_parallelism,
+            local_rank=self.local_rank,
+            local_world_size=self.local_world_size,
+            gradient_accumulation_steps=self.gradient_accumulation_steps,
+            ring_attn_func=self.ring_attn_func,
+        )
+
+    def __enter__(self):
+        # Forward pre-hook to apply sequence parallelism
+        def sequence_parallel_pre_hook(_, args, kwargs):
+            # Apply sequence parallelism to kwargs and get original sequence length and padding info
+            kwargs, self.original_seq_len, self.pad_len = (
+                self.apply_sequence_parallelism(batch=kwargs)
+            )
+
+            return args, kwargs
+
+        # Forward post-hook to gather outputs
+        def sequence_parallel_post_hook(_, __, output: ModelOutput) -> ModelOutput:
+            # Gather the sharded outputs
+            output = self.gather_outputs(output)
+
+            # Remove padding if it was added
+            if self.pad_len > 0:
+                for key, value in output.items():
+                    if isinstance(value, torch.Tensor) and value.dim() > 1:
+                        if value.size(1) == self.original_seq_len + self.pad_len:
+                            # Slice to remove padding
+                            output[key] = value[:, : self.original_seq_len].contiguous()
+
+            return output
+
+        # Register both hooks
+        for model in self.models:
+            self.hook_handles.append(
+                model.register_forward_pre_hook(
+                    sequence_parallel_pre_hook, with_kwargs=True
+                )
+            )
+            self.hook_handles.append(
+                model.register_forward_hook(sequence_parallel_post_hook)
+            )
+
+        return self
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        # Remove all hooks
+        for handle in self.hook_handles:
+            handle.remove()
+        self.hook_handles = []
+
+    def gather_outputs(self, output: CausalLMOutputWithPast) -> CausalLMOutputWithPast:
+        """Gather sharded outputs from all ranks and reconstruct the full tensor."""
+        for key, value in output.items():
+            if isinstance(value, torch.Tensor) and value.dim() > 1:
+                output[key] = AllGatherWithGrad.apply(value, self.process_group)
+
+        return output
+
+
+class AllGatherWithGrad(torch.autograd.Function):
+    """Custom autograd function for all-gather to preserve gradients."""
+
+    @staticmethod
+    def forward(
+        ctx: torch.autograd.function.FunctionCtx,
+        input_tensor: torch.Tensor,
+        group: dist.ProcessGroup,
+    ) -> torch.Tensor:
+        """
+        Forward pass of all-gather of data with sequence dimension.
+
+        Args:
+            ctx: `torch.autograd` function context.
+            input_tensor: Tensor from model output with sequence dimension.
+            group: `torch.distributed` process group.
+
+        Returns:
+            Tensor from gathering the `input_tensor` from across the process group and
+                concatenating along the sequence dimension.
+        """
+        ctx.group = group
+        ctx.rank = dist.get_rank(group)
+        world_size = dist.get_world_size(group)
+
+        # Gather shape metadata
+        local_shape = torch.tensor(list(input_tensor.shape), device=input_tensor.device)
+        all_shapes = [torch.zeros_like(local_shape) for _ in range(world_size)]
+        dist.all_gather(all_shapes, local_shape, group=group)
+
+        # Store sequence lengths for backward pass
+        seq_lens = [int(shape[1].item()) for shape in all_shapes]
+        ctx.seq_lens = seq_lens
+
+        # Perform all_gather operation
+        gathered = [
+            torch.zeros(
+                tuple(shape.tolist()),
+                dtype=input_tensor.dtype,
+                device=input_tensor.device,
+            )
+            for shape in all_shapes
+        ]
+        dist.all_gather(gathered, input_tensor, group=group)
+
+        # Concatenate tensors along sequence dimension
+        result = torch.cat(gathered, dim=1)
+
+        return result
+
+    @staticmethod
+    def backward(
+        ctx: torch.autograd.function.FunctionCtx, grad_output: torch.Tensor
+    ) -> tuple[torch.Tensor, None]:
+        """
+        Backward pass for all-gather operation.
+
+        Extracts the gradient slice corresponding to this rank's original input
+        from the full gradient tensor.
+
+        Args:
+            ctx: `torch.autograd` function context.
+            grad_output: Gradient from subsequent layers with respect to the
+                concatenated output tensor.
+
+        Returns:
+            Tuple containing the gradient slice for this rank's input tensor and `None`
+                for the process group parameter which doesn't require gradients.
+        """
+        rank = ctx.rank
+        seq_lens = ctx.seq_lens
+
+        # Extract gradient for this rank's chunk
+        offset = sum(seq_lens[:rank])
+        grad_slice = grad_output[:, offset : offset + seq_lens[rank]].contiguous()
+
+        return grad_slice, None

src/axolotl/utils/data/rl.py

@@ -18,8 +18,9 @@ from axolotl.utils.data.utils import deduplicate_and_log_datasets, md5
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.distributed import is_main_process, zero_first
 from axolotl.utils.models import load_tokenizer
+from axolotl.utils.schemas.enums import RLType
 
-LOG = logging.getLogger("axolotl")
+LOG = logging.getLogger(__name__)
 
 
 def _get_path(ds_hash, cfg):
@@ -71,6 +72,7 @@ def map_dataset(cfg, data_set, ds_transform_fn, tokenizer, **map_kwargs):
     data_set = data_set.map(
         ds_transform_fn,
         desc="Mapping RL Dataset",
+        num_proc=cfg.dataset_processes,
         **map_kwargs,
     )
 
@@ -80,7 +82,7 @@ def map_dataset(cfg, data_set, ds_transform_fn, tokenizer, **map_kwargs):
 def drop_long_rl_seq(
     sample, rl, tokenizer, sequence_len  # pylint: disable=invalid-name
 ):
-    if rl in ("dpo", "ipo", "orpo", "simpo"):
+    if rl in (RLType.DPO, RLType.IPO, RLType.ORPO, RLType.SIMPO):
         if not (
             sample.get("prompt") and sample.get("chosen") and sample.get("rejected")
         ):
@@ -100,7 +102,7 @@ def drop_long_rl_seq(
             len_prompt + len_rejected
         ) <= sequence_len
 
-    if rl == "kto":
+    if rl is RLType.KTO:
         if not (sample.get("prompt") and sample.get("completion")):
             raise ValueError("Prompt and completion keys are required for KTO datasets")
 
@@ -114,7 +116,7 @@ def drop_long_rl_seq(
 
         return (len_prompt + len_completion) <= sequence_len
 
-    if rl == "grpo":
+    if rl is RLType.GRPO:
         return True
 
     raise ValueError("Unknown RL type")
@@ -137,9 +139,9 @@ def load_prepare_preference_datasets(cfg):
             if _type:
                 if isinstance(_type, DictDefault):
                     _type = "user_defined.default"
-                if _cfg.rl == "orpo":
+                if _cfg.rl is RLType.ORPO:
                     ds_transform_fn = load_orpo(_type, _cfg, dataset_idx=i)
-                elif _cfg.rl == "kto":
+                elif _cfg.rl is RLType.KTO:
                     ds_transform_fn = load_kto(_type, _cfg, dataset_idx=i)
                 else:
                     ds_transform_fn = load_dpo(_type, _cfg, dataset_idx=i)
@@ -150,7 +152,7 @@ def load_prepare_preference_datasets(cfg):
                 split_datasets[i] = map_dataset(
                     cfg, data_set, ds_transform_fn, tokenizer, **map_kwargs
                 )
-            elif _cfg.rl == "kto":
+            elif _cfg.rl is RLType.KTO:
                 ds_transform_fn = load_kto(_type, _cfg, dataset_idx=i)
                 map_kwargs = {}
                 if isinstance(ds_transform_fn, tuple):
@@ -185,7 +187,7 @@ def load_prepare_preference_datasets(cfg):
                     )
 
         combined_datasets = concatenate_datasets(split_datasets)
-        combined_datasets = combined_datasets.shuffle(seed=cfg.seed)
+        combined_datasets = combined_datasets.shuffle(seed=cfg.seed or 42)
 
         return combined_datasets
 
@@ -205,6 +207,8 @@ def load_prepare_preference_datasets(cfg):
                 eval_dataset = load_split(cfg.test_datasets, cfg)
         if not eval_dataset:
             if cfg.val_set_size:
+                seed = cfg.seed if cfg.seed is not None else 42
+
                 # ensure we end up with the same fingerprint by doing rank0 first and being able to cache
                 to_hash_train = (
                     train_dataset._fingerprint  # pylint: disable=protected-access
@@ -213,7 +217,7 @@ def load_prepare_preference_datasets(cfg):
                     + "|"
                     + "train"
                     + "|"
-                    + str(cfg.seed or 42)
+                    + str(seed)
                 )
                 to_hash_test = (
                     train_dataset._fingerprint  # pylint: disable=protected-access
@@ -222,13 +226,13 @@ def load_prepare_preference_datasets(cfg):
                     + "|"
                     + "test"
                     + "|"
-                    + str(cfg.seed or 42)
+                    + str(seed)
                 )
                 train_fingerprint = md5(to_hash_train)
                 test_fingerprint = md5(to_hash_test)
                 ds_w_test_split = train_dataset.train_test_split(
                     test_size=cfg.val_set_size,
-                    seed=cfg.seed,
+                    seed=seed,
                     shuffle=False,
                     train_new_fingerprint=train_fingerprint,
                     test_new_fingerprint=test_fingerprint,

src/axolotl/utils/data/sft.py

@@ -53,7 +53,7 @@ from axolotl.utils.data.utils import (
     retry_on_request_exceptions,
 )
 from axolotl.utils.dict import DictDefault
-from axolotl.utils.distributed import is_local_main_process, zero_first
+from axolotl.utils.distributed import is_local_main_process
 from axolotl.utils.trainer import (
     calculate_total_num_steps,
     process_datasets_for_packing,
@@ -66,32 +66,31 @@ LOG = logging.getLogger(__name__)
 def prepare_dataset(cfg, tokenizer, processor=None, preprocess_iterable=None):
     prompters = []
     if not cfg.pretraining_dataset:
-        with zero_first(is_local_main_process()):
-            if cfg.test_datasets:
-                train_dataset, _, prompters = load_prepare_datasets(
-                    tokenizer,
-                    cfg,
-                    DEFAULT_DATASET_PREPARED_PATH,
-                    split="train",
-                    processor=processor,
-                    preprocess_iterable=preprocess_iterable,
-                )
-                _, eval_dataset, _ = load_prepare_datasets(
-                    tokenizer,
-                    cfg,
-                    DEFAULT_DATASET_PREPARED_PATH,
-                    split="test",
-                    processor=processor,
-                    preprocess_iterable=preprocess_iterable,
-                )
-            else:
-                train_dataset, eval_dataset, prompters = load_prepare_datasets(
-                    tokenizer,
-                    cfg,
-                    DEFAULT_DATASET_PREPARED_PATH,
-                    processor=processor,
-                    preprocess_iterable=preprocess_iterable,
-                )
+        if cfg.test_datasets:
+            train_dataset, _, prompters = load_prepare_datasets(
+                tokenizer,
+                cfg,
+                DEFAULT_DATASET_PREPARED_PATH,
+                split="train",
+                processor=processor,
+                preprocess_iterable=preprocess_iterable,
+            )
+            _, eval_dataset, _ = load_prepare_datasets(
+                tokenizer,
+                cfg,
+                DEFAULT_DATASET_PREPARED_PATH,
+                split="test",
+                processor=processor,
+                preprocess_iterable=preprocess_iterable,
+            )
+        else:
+            train_dataset, eval_dataset, prompters = load_prepare_datasets(
+                tokenizer,
+                cfg,
+                DEFAULT_DATASET_PREPARED_PATH,
+                processor=processor,
+                preprocess_iterable=preprocess_iterable,
+            )
     else:
         # Load streaming dataset if pretraining_dataset is given
         path = cfg.pretraining_dataset
@@ -148,7 +147,7 @@ def prepare_dataset(cfg, tokenizer, processor=None, preprocess_iterable=None):
             ds_wrapper_partial,
             max_tokens=cfg.sequence_len,
             batch_size=cfg.micro_batch_size,
-            seed=cfg.seed or 42,
+            seed=cfg.seed if cfg.seed is not None else 42,
             buffer_size=cfg.pretrain_multipack_buffer_size or 10_000,
         )
         # https://discuss.huggingface.co/t/how-to-use-huggingface-trainer-streaming-datasets-without-wrapping-it-with-torchdatas-iterablewrapper/25230
@@ -272,7 +271,7 @@ def load_tokenized_prepared_datasets(
         LOG.info("Loading raw datasets...")
         if not cfg.is_preprocess:
             LOG.warning(
-                "Processing datasets during training can lead to VRAM instability. Please pre-process your dataset."
+                "Processing datasets during training can lead to VRAM instability. Please use `axolotl preprocess` to prepare your dataset."
             )
 
         if cfg.seed:
@@ -416,6 +415,8 @@ def load_prepare_datasets(
     )
 
     if split == "train" and val_set_size:
+        seed = cfg.seed if cfg.seed is not None else 42
+
         # ensure we end up with the same fingerprint by doing rank0 first and being able to cache
         to_hash_train = (
             dataset._fingerprint  # pylint: disable=protected-access
@@ -424,7 +425,7 @@ def load_prepare_datasets(
             + "|"
             + "train"
             + "|"
-            + str(cfg.seed or 42)
+            + str(seed)
         )
         to_hash_test = (
             dataset._fingerprint  # pylint: disable=protected-access
@@ -433,7 +434,7 @@ def load_prepare_datasets(
             + "|"
             + "test"
             + "|"
-            + str(cfg.seed or 42)
+            + str(seed)
         )
         train_fingerprint = md5(to_hash_train)
         test_fingerprint = md5(to_hash_test)
@@ -442,7 +443,7 @@ def load_prepare_datasets(
         dataset = dataset.train_test_split(
             test_size=val_set_size,
             shuffle=False,
-            seed=cfg.seed or 42,
+            seed=seed,
             train_new_fingerprint=train_fingerprint,
             test_new_fingerprint=test_fingerprint,
         )
@@ -482,7 +483,7 @@ def get_dataset_wrapper(
     }
 
     LOG.info(
-        f"Loading dataset with base_type: {d_base_type} and prompt_style: {d_prompt_style}"
+        f"Loading dataset: {config_dataset['path']} with base_type: {d_base_type} and prompt_style: {d_prompt_style}"
     )
 
     if (

src/axolotl/utils/data/shared.py

@@ -281,6 +281,10 @@ def load_dataset_w_config(
             **load_ds_kwargs,
         )
     if not ds:
-        raise ValueError("unhandled dataset load")
+        raise ValueError(
+            "The dataset could not be loaded. This could be due to a misconfigured dataset path "
+            f"({config_dataset.path}). Try double-check your path / name / data_files. "
+            "This is not caused by the dataset type."
+        )
 
     return ds

src/axolotl/utils/gradient_checkpointing/__init__.py

@@ -1,16 +1,59 @@
 """custom checkpointing utils"""
 
+import importlib
 from functools import partial
 
-from axolotl.utils.gradient_checkpointing.unsloth import (
-    Unsloth_Offloaded_Gradient_Checkpointer,
+from packaging import version
+
+from axolotl.utils.gradient_checkpointing.offload_cpu import (
+    CPU_Offloaded_Gradient_Checkpointer,
 )
+from axolotl.utils.gradient_checkpointing.offload_disk import (
+    Disco,
+)
+
+transformers_version = version.parse(importlib.metadata.version("transformers"))
+if transformers_version > version.parse("4.51.3"):
+    from transformers.modeling_layers import GradientCheckpointingLayer
+
+    def uses_gc_layers(decoder_layer):
+        return isinstance(decoder_layer.func.__self__, GradientCheckpointingLayer)
+
+else:
+
+    def uses_gc_layers(_):
+        return False
 
 
 def hf_grad_checkpoint_offload_wrapper(
     decoder_layer, *args, use_reentrant=None
 ):  # pylint: disable=unused-argument
-    return Unsloth_Offloaded_Gradient_Checkpointer.apply(
+    if uses_gc_layers(decoder_layer):
+        return CPU_Offloaded_Gradient_Checkpointer.apply(
+            decoder_layer,
+            *args,
+        )
+
+    return CPU_Offloaded_Gradient_Checkpointer.apply(
+        (
+            decoder_layer.func.__self__
+            if isinstance(decoder_layer, partial)
+            else decoder_layer.__self__
+        ),
+        *args,
+    )
+
+
+def hf_grad_checkpoint_disk_offload_wrapper(
+    decoder_layer, *args, use_reentrant=None
+):  # pylint: disable=unused-argument
+    if uses_gc_layers(decoder_layer):
+        return Disco.apply(
+            decoder_layer,
+            *args,
+        )
+
+    return Disco.apply(
         (
             decoder_layer.func.__self__
             if isinstance(decoder_layer, partial)

src/axolotl/utils/gradient_checkpointing/offload_cpu.py

@@ -1,4 +1,4 @@
-"""Unsloth checkpointing"""
+"""CPU offloaded checkpointing"""
 
 # Copyright 2023-present Daniel Han-Chen & the Unsloth team. All rights reserved.
 #
@@ -26,7 +26,7 @@ else:
     torch_cuda_amp_custom_bwd = torch.amp.custom_bwd(device_type="cuda")
 
 
-class Unsloth_Offloaded_Gradient_Checkpointer(  # pylint: disable=invalid-name
+class CPU_Offloaded_Gradient_Checkpointer(  # pylint: disable=invalid-name
     torch.autograd.Function
 ):
     """

src/axolotl/utils/gradient_checkpointing/offload_disk.py

@@ -0,0 +1,531 @@
+"""
+DISCO - DIsk-based Storage and Checkpointing with Optimized prefetching
+"""
+
+# Copyright 2025 Axolotl AI. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import atexit
+import concurrent.futures
+import logging
+import os
+import queue
+import shutil
+import tempfile
+import threading
+import time
+import uuid
+from collections import deque
+from concurrent.futures import Future
+from typing import Dict
+
+import torch
+
+torch_cuda_amp_custom_fwd = torch.amp.custom_fwd(device_type="cuda")
+torch_cuda_amp_custom_bwd = torch.amp.custom_bwd(device_type="cuda")
+
+# Setup logger
+logger = logging.getLogger(__name__)
+
+
+class DiskOffloadManager:
+    """
+    Manages offloaded tensors and handles prefetching in a separate thread.
+    Includes synchronization to prevent race conditions.
+    """
+
+    def __init__(
+        self,
+        prefetch_size: int = 3,
+        prefetch_to_gpu: bool = True,
+        save_workers: int = 4,
+    ):
+        """
+        Args:
+            prefetch_size: Maximum number of tensors to prefetch in the background.
+            prefetch_to_gpu: Whether to prefetch tensors directly to GPU memory.
+            save_workers: Maximum number of concurrent save operations.
+        """
+        self.temp_dir = tempfile.mkdtemp(prefix="disco_")
+
+        # Track tensor paths and their status
+        self.tensor_paths: deque = deque()  # Ordered history of tensor paths (LIFO)
+        self.file_locks: Dict[str, threading.Lock] = (
+            {}
+        )  # Maps file_path -> threading.Lock()
+        # Maps file_path -> status ("saving", "ready", "prefetching", "loaded", "deleted")
+        self.file_status: Dict[str, str] = {}
+
+        self.max_prefetch = prefetch_size
+        self.prefetch_to_gpu = prefetch_to_gpu
+
+        # Thread synchronization
+        self.manager_lock = threading.RLock()  # Used for thread-safe operations
+
+        # Prefetch queue and cache
+        self.prefetch_queue: queue.Queue = queue.Queue()
+        self.prefetch_cache: Dict[str, torch.Tensor] = {}  # Maps file_path -> tensor
+
+        # Save queue and thread pool
+        self.save_queue: queue.Queue = queue.Queue()
+        self.save_pool = concurrent.futures.ThreadPoolExecutor(max_workers=save_workers)
+        self.save_futures: Dict[str, Future] = {}
+        self.save_semaphore = threading.Semaphore(
+            save_workers * 2
+        )  # Limit concurrent save operations
+
+        # Start prefetch worker thread
+        self.stop_event = threading.Event()
+        # start multiple threads for prefetching
+        self.prefetch_worker_count = 2
+        self.prefetch_workers = []
+        for _ in range(self.prefetch_worker_count):
+            worker = threading.Thread(target=self._prefetch_worker, daemon=True)
+            worker.start()
+            self.prefetch_workers.append(worker)
+
+        # Start save worker thread
+        self.save_worker = threading.Thread(target=self._save_worker, daemon=True)
+        self.save_worker.start()
+        self.idx = 0
+
+        atexit.register(self.cleanup)
+
+    def _save_worker(self):
+        """Background thread that processes the save queue"""
+        while not self.stop_event.is_set():
+            try:
+                save_item = self.save_queue.get(timeout=0.5)
+                if save_item is None:
+                    continue
+
+                tensor, file_path = save_item
+
+                # Submit the save task to the thread pool
+                future = self.save_pool.submit(
+                    self._save_tensor_to_disk, tensor, file_path
+                )
+                with self.manager_lock:
+                    self.save_futures[file_path] = future
+
+                self.save_queue.task_done()
+
+            except queue.Empty:
+                time.sleep(0.01)  # Small sleep to prevent CPU spinning
+                continue
+
+    def _save_tensor_to_disk(self, tensor: torch.Tensor, file_path: str):
+        """Actually save the tensor to disk"""
+        try:
+            # Save tensor to disk
+            cpu_tensor = tensor.detach().cpu()
+            torch.save(cpu_tensor, file_path)
+            del cpu_tensor
+
+            with self.manager_lock:
+                # Mark file as ready
+                self.file_status[file_path] = "ready"
+
+            # Release semaphore
+            self.save_semaphore.release()
+
+            return True
+        except FileNotFoundError as e:
+            logger.error(f"Error saving tensor to {file_path}: {e}")
+            with self.manager_lock:
+                self.file_status[file_path] = "error"
+
+            # Release semaphore
+            self.save_semaphore.release()
+
+            return False
+
+    def _prefetch_worker(self):
+        """Background thread that loads tensors from disk ahead of time"""
+        while not self.stop_event.is_set():
+            try:
+                file_path = self.prefetch_queue.get(timeout=0.5)
+                if file_path is None:
+                    continue
+
+                # Check if file is available and not already in cache
+                with self.manager_lock:
+                    if (
+                        file_path not in self.file_status
+                        or self.file_status[file_path] == "deleted"
+                    ):
+                        self.prefetch_queue.task_done()
+                    if file_path in self.prefetch_cache:
+                        self.prefetch_queue.task_done()
+                        continue
+
+                    # If file is still being saved, wait for it
+                    if (
+                        self.file_status[file_path] == "saving"
+                        and file_path in self.save_futures
+                    ):
+                        # Re-queue this prefetch request with a little delay
+                        self.prefetch_queue.task_done()
+                        time.sleep(0.1)
+                        self.prefetch_queue.put(file_path)
+                        continue
+
+                    # Mark file as being prefetched
+                    self.file_status[file_path] = "prefetching"
+
+                # Load tensor from disk and store in cache
+                try:
+                    if os.path.exists(file_path):
+                        if self.prefetch_to_gpu:
+                            tensor = torch.load(
+                                file_path,
+                                map_location=torch.device("cuda"),
+                                weights_only=True,
+                            )
+                        else:
+                            tensor = torch.load(file_path, weights_only=True)
+
+                        with self.manager_lock:
+                            self.prefetch_cache[file_path] = tensor
+                            self.file_status[file_path] = "ready"
+                    else:
+                        with self.manager_lock:
+                            if self.file_status.get(file_path) != "deleted":
+                                logger.warning(
+                                    f"Prefetch error: File not found {file_path}"
+                                )
+                                self.file_status[file_path] = "missing"
+
+                except FileNotFoundError as e:
+                    with self.manager_lock:
+                        if self.file_status.get(file_path) != "deleted":
+                            logger.warning(f"Prefetch error for {file_path}: {e}")
+                            self.file_status[file_path] = "error"
+
+                self.prefetch_queue.task_done()
+
+            except queue.Empty:
+                time.sleep(0.01)  # Small sleep to prevent CPU spinning
+                continue
+
+    def save_tensor(self, tensor: torch.Tensor):
+        """Save tensor to disk asynchronously and return file path with thread-safe operations"""
+        # Generate unique file path
+        self.idx += 1
+        file_path: str = os.path.join(
+            self.temp_dir, f"{self.idx:06d}-{uuid.uuid4()}.pt"
+        )
+
+        with self.manager_lock:
+            # Mark file as being saved
+            self.file_locks[file_path] = threading.Lock()
+            self.file_status[file_path] = "saving"
+            # Add to history
+            self.tensor_paths.append(file_path)
+
+        # Acquire semaphore to limit concurrent save operations
+        self.save_semaphore.acquire()  # pylint: disable=consider-using-with
+        # Queue tensor for saving in background
+        self.save_queue.put((tensor.detach(), file_path))
+
+        return file_path
+
+    def wait_for_save(self, file_path, timeout=None) -> None:
+        """Wait for a tensor to be saved to disk"""
+        start_time = time.time()
+        while timeout is None or time.time() - start_time < timeout:
+            with self.manager_lock:
+                if self.file_status.get(file_path) == "ready":
+                    return
+                if self.file_status.get(file_path) in ["error", "missing", "deleted"]:
+                    return
+
+                if file_path in self.save_futures:
+                    future = self.save_futures[file_path]
+                    if future.done():
+                        return
+
+            # Small sleep to prevent CPU spinning
+            time.sleep(0.01)
+
+        # Timeout
+        logger.warning(f"Timeout waiting for tensor to be saved: {file_path}")
+        return
+
+    def load_tensor(self, file_path, target_device="cuda"):
+        """Load tensor from disk or prefetch cache with proper synchronization"""
+        # Wait for tensor to be saved if it's still in progress
+        self.wait_for_save(file_path)
+
+        tensor = None
+
+        # Try to get from cache first
+        with self.manager_lock:
+            # Check if tensor is already in cache
+            if file_path in self.prefetch_cache:
+                tensor = self.prefetch_cache[file_path]
+                del self.prefetch_cache[file_path]
+                self.file_status[file_path] = "loaded"
+
+        if tensor is not None:
+            # Ensure tensor is on correct device
+            if target_device != "cpu" and tensor.device.type == "cpu":
+                tensor = tensor.to(target_device, non_blocking=True)
+            return tensor
+
+        # If not in cache, load directly from disk
+        try:
+            if not os.path.exists(file_path):
+                logger.error(f"File not found for loading: {file_path}")
+                raise FileNotFoundError(f"File not found: {file_path}")
+
+            tensor = torch.load(file_path, weights_only=True)
+
+            with self.manager_lock:
+                self.file_status[file_path] = "loaded"
+
+            if target_device != "cpu":
+                tensor = tensor.to(target_device, non_blocking=True)
+
+            return tensor
+
+        except Exception as e:
+            logger.error(f"Error loading tensor from {file_path}: {e}")
+            raise
+
+    def _safe_delete_file(self, file_path):
+        """Safely delete a file with proper synchronization"""
+        with self.manager_lock:
+            # Make sure any save operation is completed
+            if file_path in self.save_futures:
+                future = self.save_futures[file_path]
+                try:
+                    if not future.done():
+                        future.cancel()
+                    del self.save_futures[file_path]
+                except FileNotFoundError as e:
+                    logger.warning(
+                        f"Error canceling save operation for {file_path}: {e}"
+                    )
+
+            # Only delete if file exists and is not being prefetched
+            status = self.file_status.get(file_path)
+            if status in ["ready", "loaded", "error", "missing"]:
+                try:
+                    if os.path.exists(file_path):
+                        os.remove(file_path)
+                    self.file_status[file_path] = "deleted"
+                    return True
+                except FileNotFoundError as e:
+                    logger.warning(f"Error deleting file {file_path}: {e}")
+            return False
+
+    def trigger_prefetch(self, n=None):
+        """Trigger prefetching of the next N tensors with proper synchronization"""
+        if n is None:
+            n = self.max_prefetch
+
+        prefetch_paths = []
+        with self.manager_lock:
+            # Find files that are ready to be prefetched (not already in cache or being prefetched)
+            for path in reversed(self.tensor_paths):
+                if (
+                    path not in self.prefetch_cache
+                    and self.file_status.get(path) == "ready"
+                ):
+                    prefetch_paths.append(path)
+                    if len(prefetch_paths) >= n:
+                        break
+
+        # Queue files for prefetching
+        for path in prefetch_paths:
+            self.prefetch_queue.put(path)
+
+    def cleanup_tensor(self, file_path: str):
+        """Clean up a specific tensor file after it's been used"""
+        with self.manager_lock:
+            if file_path in self.tensor_paths:
+                self.tensor_paths.remove(file_path)
+
+            # Remove from prefetch cache if present
+            if file_path in self.prefetch_cache:
+                del self.prefetch_cache[file_path]
+
+            # Remove from save futures if present
+            if file_path in self.save_futures:
+                future = self.save_futures[file_path]
+                if not future.done():
+                    future.cancel()
+                del self.save_futures[file_path]
+
+        # Try to delete the file
+        self._safe_delete_file(file_path)
+
+    def cleanup(self):
+        """Clean up all temp files and stop prefetch thread with proper synchronization"""
+        self.stop_event.set()
+
+        # Cancel all pending save operations
+        with self.manager_lock:
+            for _, future in self.save_futures.items():
+                if not future.done():
+                    future.cancel()
+            self.save_futures.clear()
+
+        # Drain the save queue
+        while not self.save_queue.empty():
+            try:
+                self.save_queue.get_nowait()
+                self.save_queue.task_done()
+            except queue.Empty:
+                break
+
+        # Shutdown the save pool
+        self.save_pool.shutdown(wait=False)
+
+        # Join the save worker thread
+        if self.save_worker.is_alive():
+            self.save_worker.join(timeout=2.0)
+
+        # Join the prefetch worker threads
+        for thread in self.prefetch_workers:
+            if thread.is_alive():
+                thread.join(timeout=2.0)
+
+        # Clear cache and remove all temporary files
+        with self.manager_lock:
+            self.prefetch_cache.clear()
+            paths_to_delete = list(self.tensor_paths)
+            self.tensor_paths.clear()
+
+        # Delete all temporary files
+        for path in paths_to_delete:
+            self._safe_delete_file(path)
+
+        # Remove temp directory
+        try:
+            if os.path.exists(self.temp_dir):
+                shutil.rmtree(self.temp_dir, ignore_errors=True)
+        except FileNotFoundError as e:
+            logger.warning(f"Error removing temporary directory {self.temp_dir}: {e}")
+
+
+class Disco(torch.autograd.Function):
+    """
+    Disco: DIsk-based Storage and Checkpointing with Optimized prefetching
+    Advanced disk-based gradient checkpointer with prefetching.
+    """
+
+    # Shared manager instance across all checkpointing operations
+    _manager = None
+
+    @staticmethod
+    def get_instance(prefetch_size=1, prefetch_to_gpu=True, save_workers=4):
+        """Get or create the offload manager"""
+        if Disco._manager is None:
+            Disco._manager = DiskOffloadManager(
+                prefetch_size=prefetch_size,
+                prefetch_to_gpu=prefetch_to_gpu,
+                save_workers=save_workers,
+            )
+        return Disco._manager
+
+    @staticmethod
+    @torch_cuda_amp_custom_fwd
+    def forward(
+        ctx,
+        forward_function,
+        hidden_states,
+        *args,
+        prefetch_size=1,
+        prefetch_to_gpu=True,
+        save_workers=4,
+    ):
+        """Forward pass that offloads activations to disk asynchronously"""
+        # Get or create the manager
+        manager = Disco.get_instance(
+            prefetch_size=prefetch_size,
+            prefetch_to_gpu=prefetch_to_gpu,
+            save_workers=save_workers,
+        )
+
+        # Save tensor to disk asynchronously
+        file_path = manager.save_tensor(hidden_states)
+
+        # Run forward pass immediately without waiting for save to complete
+        with torch.no_grad():
+            output = forward_function(hidden_states, *args)
+
+        # Store what we need for backward
+        ctx.save_for_backward(torch.tensor([0]))  # Dummy tensor
+        ctx.file_path = file_path
+        ctx.forward_function = forward_function
+        ctx.args = args
+
+        return output
+
+    @staticmethod
+    @torch_cuda_amp_custom_bwd
+    def backward(ctx, *grad_outputs):
+        """Backward pass that loads activations from disk with prefetching"""
+        # Get the manager
+        manager = Disco._manager
+
+        # Trigger prefetching for future tensors
+        # This happens at the start of backward, so should have time to complete
+        manager.trigger_prefetch()
+
+        # Load hidden states from disk or prefetch cache
+        file_path = ctx.file_path
+        try:
+            # Ensure the file is saved before we try to load it
+            manager.wait_for_save(file_path)
+
+            hidden_states = manager.load_tensor(file_path)
+            hidden_states.requires_grad = True
+
+            # Compute gradients
+            with torch.enable_grad():
+                output = ctx.forward_function(hidden_states, *ctx.args)
+
+                # Handle tuple outputs properly
+                if isinstance(output, tuple):
+                    if len(grad_outputs) == len(output):
+                        torch.autograd.backward(output, grad_outputs)
+                    else:
+                        torch.autograd.backward(output, grad_outputs[0])
+                else:
+                    torch.autograd.backward(output, grad_outputs[0])
+
+            # Clean up the file after we're done with it
+            manager.cleanup_tensor(file_path)
+
+            return (
+                (
+                    None,  # forward_function
+                    hidden_states.grad,  # hidden_states grad
+                )
+                + (None,) * len(ctx.args)  # for each arg
+                + (
+                    None,  # prefetch_size
+                    None,  # prefetch_to_gpu
+                    None,  # save_workers
+                )
+            )
+
+        except Exception as e:
+            logger.error(f"Error in backward pass: {e}")
+            # Clean up the file even on error
+            manager.cleanup_tensor(file_path)
+            raise

src/axolotl/utils/models.py

@@ -70,9 +70,13 @@ from axolotl.utils.distributed import (
     is_local_main_process,
     is_main_process,
 )
-from axolotl.utils.gradient_checkpointing import hf_grad_checkpoint_offload_wrapper
+from axolotl.utils.gradient_checkpointing import (
+    hf_grad_checkpoint_disk_offload_wrapper,
+    hf_grad_checkpoint_offload_wrapper,
+)
 from axolotl.utils.lora_embeddings import get_linear_embedding_layers
 from axolotl.utils.model_shard_quant import load_sharded_model, load_sharded_model_quant
+from axolotl.utils.schemas.enums import RLType
 
 LOG = logging.getLogger(__name__)
 PLUGIN_MANAGER = PluginManager.get_instance()
@@ -619,6 +623,10 @@ class ModelLoader:
 
         if self.cfg.gradient_checkpointing in ["unsloth", "offload"]:
             transformers.modeling_utils.checkpoint = hf_grad_checkpoint_offload_wrapper
+        if self.cfg.gradient_checkpointing == "offload_disk":
+            transformers.modeling_utils.checkpoint = (
+                hf_grad_checkpoint_disk_offload_wrapper
+            )
 
         if self.cfg.flash_attention:
             self.patch_attention()
@@ -1372,7 +1380,7 @@ class ModelLoader:
             # then the dpo trainer doesn't want the peft model loaded over it, it just wants the lora/peft config
             if (
                 self.cfg.adapter
-                and self.cfg.rl in ["dpo", "ipo", "kto"]
+                and self.cfg.rl in [RLType.DPO, RLType.IPO, RLType.KTO]
                 and not self.cfg.merge_lora
             ):
                 _, lora_config = load_lora(

src/axolotl/utils/samplers/multipack.py

@@ -6,7 +6,7 @@ into fixed-capacity batches to optimize memory usage and training throughput.
 import logging
 import math
 from concurrent.futures import ProcessPoolExecutor
-from multiprocessing import cpu_count
+from multiprocessing import cpu_count, get_context
 from typing import Iterable, Union
 
 import numba
@@ -78,15 +78,11 @@ def pack_group(
     Returns:
         List of bins, where each bin contains indices of sequences assigned to it
     """
-    # Get sorting indices and sort lengths in descending order
-    indices = np.argsort(sequence_lengths)[::-1]
-    sorted_lengths = sequence_lengths[indices]
-
     bins_remaining_space: list = []  # Tracks remaining capacity in each bin
     bins_assigned_sequences: list = []  # Tracks sequence indices assigned to each bin
 
-    for seq_id, size in enumerate(sorted_lengths):
-        global_idx = indices[seq_id] + group_offset
+    for seq_id, size in enumerate(sequence_lengths):
+        global_idx = seq_id + group_offset
 
         # Try to place sequence in existing bins
         add_new_bin = True
@@ -130,6 +126,7 @@ def pack_parallel(
     bin_size: int,
     num_processes: int | None = None,
     safe_mode: bool = True,
+    mp_start_method: str | None = "spawn",
 ):
     """
     Pack sequences into bins using parallel processing
@@ -141,7 +138,9 @@ def pack_parallel(
         bin_size: Maximum number of bins to use
         num_processes: Number of parallel processes to use
         safe_mode: If True, use a more conservative packing approach
-
+        mp_start_method: Multiprocessing start method ('fork', 'spawn', 'forkserver').
+                         'spawn' is often safer with Numba/PyTorch.
+                         Set to None to use system default.
     Returns:
         List of bins, where each bin contains indices of sequences assigned to it
     """
@@ -158,9 +157,33 @@ def pack_parallel(
 
     # Process groups in parallel
     all_bins = []
-    with ProcessPoolExecutor(max_workers=num_processes) as executor:
-        for group_bins in executor.map(_process_group, tasks):
+
+    mp_ctx = None
+    if mp_start_method:
+        try:
+            mp_ctx = get_context(mp_start_method)
+        except ValueError:
+            LOG.warning(
+                f"Failed to get multiprocessing context '{mp_start_method}'. "
+                f"Falling back to default. Available: {get_context().get_all_start_methods()}"
+            )
+            mp_ctx = (
+                None  # Fallback to default context if specified one is not available
+            )
+
+    if num_processes == 1:
+        LOG.debug("Using single process for pack_parallel, running sequentially.")
+        for task_args in tasks:
+            group_bins = _process_group(task_args)
             all_bins.extend(group_bins)
+    else:
+        # Use ProcessPoolExecutor only if num_processes > 1
+        # Pass mp_context if available
+        with ProcessPoolExecutor(
+            max_workers=num_processes, mp_context=mp_ctx
+        ) as executor:
+            for group_bins in executor.map(_process_group, tasks):
+                all_bins.extend(group_bins)
 
     return all_bins
 

src/axolotl/utils/schemas/config.py

@@ -27,7 +27,7 @@ from axolotl.utils.schemas.datasets import (
     StepwiseSupervisedDataset,
 )
 from axolotl.utils.schemas.deprecated import DeprecatedParameters, RemappedParameters
-from axolotl.utils.schemas.enums import ChatTemplate, RLType
+from axolotl.utils.schemas.enums import ChatTemplate, RingAttnFunc, RLType
 from axolotl.utils.schemas.integrations import (
     CometConfig,
     GradioConfig,
@@ -178,7 +178,7 @@ class AxolotlInputConfig(
 
     # torch_dtype: torch.dtype | None
 
-    gradient_checkpointing: Literal["unsloth", "offload"] | bool | None = Field(
+    gradient_checkpointing: Literal["offload", "offload_disk"] | bool | None = Field(
         default=False
     )
     gradient_checkpointing_kwargs: dict[str, Any] | None = None
@@ -260,7 +260,7 @@ class AxolotlInputConfig(
 
     sequence_parallel_degree: int | None = None
     heads_k_stride: int | None = None
-    ring_attn_func: str | None = None
+    ring_attn_func: RingAttnFunc | None = None
 
     special_tokens: SpecialTokensConfig | None = None
     tokens: list[str] | None = None
@@ -782,7 +782,7 @@ class AxolotlInputConfig(
 
     @model_validator(mode="after")
     def check_simpo_warmup(self):
-        if self.rl == "simpo" and self.warmup_ratio:
+        if self.rl is RLType.SIMPO and self.warmup_ratio:
             raise ValueError(
                 "warmup_ratio is not supported with the simpo trainer. Please use `warmup_steps` instead"
             )
@@ -1149,16 +1149,28 @@ class AxolotlInputConfig(
 
         return data
 
+    # @model_validator(mode="before")
+    # @classmethod
+    # def check_grpo_peft_liger(cls, data):
+    #     if (
+    #         data.get("rl") == "grpo"
+    #         and data.get("trl", {})
+    #         and data.get("trl").get("use_liger_loss")
+    #         and data.get("adapter")
+    #     ):
+    #         raise ValueError("PEFT + GRPO + Liger is not yet supported")
+    #     return data
+    #
     @model_validator(mode="before")
     @classmethod
-    def check_grpo_peft_liger(cls, data):
+    def check_grpo_liger_sequence_parallel(cls, data):
         if (
             data.get("rl") == "grpo"
             and data.get("trl", {})
             and data.get("trl").get("use_liger_loss")
-            and data.get("adapter")
+            and data.get("sequence_parallel_degree", 1) > 1
         ):
-            raise ValueError("PEFT + GRPO + Liger is not yet supported")
+            raise ValueError("GRPO + SP + Liger not currently supported")
         return data
 
     @model_validator(mode="after")
@@ -1173,7 +1185,7 @@ class AxolotlInputConfig(
 
             if self.sample_packing and self.micro_batch_size > 1:
                 raise ValueError(
-                    "micro_batch_size must be set to 1 when sample_packing is enabled"
+                    "micro_batch_size must be set to 1 when sample_packing is enabled "
                     "due to a `ring-flash-attn` requirement"
                 )
 
@@ -1205,16 +1217,8 @@ class AxolotlInputConfig(
         if getattr(self, "sequence_parallel_degree", 1) == 1:
             return self
 
-        from axolotl.monkeypatch.attention.ring_attn.patch import RingAttnFunc
-
         if self.ring_attn_func is not None:
-            valid_funcs = list(RingAttnFunc)
-            if self.ring_attn_func in valid_funcs:
-                self.ring_attn_func = RingAttnFunc(self.ring_attn_func)
-            else:
-                raise ValueError(
-                    f"ring_attn_func: {self.ring_attn_func} must be in {valid_funcs}"
-                )
+            self.ring_attn_func = RingAttnFunc(self.ring_attn_func)
         else:
             # Default ring attention function selection
             sample_packing = getattr(self, "sample_packing", False)
@@ -1345,6 +1349,10 @@ class AxolotlConfigWCapabilities(AxolotlInputConfig):
             ):
                 return data
 
+            # Skip if dropout is not 0, as auto enabling it would just disable it during runtime patch checks
+            if data.get("lora_dropout") != 0:
+                return data
+
             # Check multi-GPU compatibility
             capabilities = data.get("capabilities")
             is_multi_gpu = capabilities and capabilities.get("n_gpu", 0) > 1

src/axolotl/utils/schemas/enums.py

@@ -6,12 +6,12 @@ from enum import Enum
 class RLType(str, Enum):
     """RL trainer type configuration subset"""
 
-    dpo = "dpo"  # pylint: disable=invalid-name
-    grpo = "grpo"  # pylint: disable=invalid-name
-    ipo = "ipo"  # pylint: disable=invalid-name
-    orpo = "orpo"  # pylint: disable=invalid-name
-    kto = "kto"  # pylint: disable=invalid-name
-    simpo = "simpo"  # pylint: disable=invalid-name
+    DPO = "dpo"  # pylint: disable=invalid-name
+    GRPO = "grpo"  # pylint: disable=invalid-name
+    IPO = "ipo"  # pylint: disable=invalid-name
+    ORPO = "orpo"  # pylint: disable=invalid-name
+    KTO = "kto"  # pylint: disable=invalid-name
+    SIMPO = "simpo"  # pylint: disable=invalid-name
 
 
 class ChatTemplate(str, Enum):
@@ -53,4 +53,16 @@ class CustomSupportedOptimizers(str, Enum):
     ao_adamw_8bit = "ao_adamw_8bit"  # pylint: disable=invalid-name
     ao_adamw_fp8 = "ao_adamw_fp8"  # pylint: disable=invalid-name
     adopt_adamw = "adopt_adamw"  # pylint: disable=invalid-name
+    came_pytorch = "came_pytorch"  # pylint: disable=invalid-name
     muon = "muon"  # pylint: disable=invalid-name
+
+
+class RingAttnFunc(str, Enum):
+    """Enum class for supported `ring-flash-attn` implementations"""
+
+    # VARLEN_RING = "varlen_ring"
+    # VARLEN_ZIGZAG = "varlen_zigzag"
+    VARLEN_LLAMA3 = "varlen_llama3"
+    BATCH_RING = "batch_ring"
+    # BATCH_ZIGZAG = "batch_zigzag"
+    # BATCH_STRIPE = "batch_stripe"

src/axolotl/utils/schemas/training.py

@@ -75,8 +75,10 @@ class HyperparametersConfig(BaseModel):
     lr_groups: list[LrGroup] | None = None
 
     adam_epsilon: float | None = None
+    adam_epsilon2: float | None = None
     adam_beta1: float | None = None
     adam_beta2: float | None = None
+    adam_beta3: float | None = None
     max_grad_norm: float | None = None
     num_epochs: float = Field(default=1.0)
 

tests/e2e/integrations/test_kd.py

@@ -90,7 +90,7 @@ class TestKnowledgeDistillation:
         train(cfg=cfg, dataset_meta=dataset_meta)
         assert (Path(temp_dir) / "model.safetensors").exists()
         check_tensorboard(
-            temp_dir + "/runs", "train/loss", 1.0, "Train Loss is too high"
+            temp_dir + "/runs", "train/loss", 1.2, "Train Loss (%s) is too high"
         )
 
     @pytest.mark.parametrize(
@@ -121,5 +121,5 @@ class TestKnowledgeDistillation:
         train(cfg=cfg, dataset_meta=dataset_meta)
         assert (Path(temp_dir) / "adapter_model.safetensors").exists()
         check_tensorboard(
-            temp_dir + "/runs", "train/loss", 1.0, "Train Loss is too high"
+            temp_dir + "/runs", "train/loss", 1.2, "Train Loss (%s) is too high"
         )

tests/e2e/multigpu/patched/test_sp.py

@@ -25,6 +25,7 @@ class TestSequenceParallelism:
         micro_batch_size=1,
         pad_to_sequence_len=True,
         ring_attn_func=None,
+        threshold=2.0,
     ):
         """Helper method to run sequence parallel tests with different configurations"""
         cfg = DictDefault(
@@ -93,22 +94,22 @@ class TestSequenceParallelism:
         )
 
         check_tensorboard(
-            temp_dir + "/runs", "train/train_loss", 2.6, "Train Loss is too high"
+            temp_dir + "/runs", "train/train_loss", threshold, "Train Loss is too high"
         )
 
     @pytest.mark.parametrize(
-        "sample_packing, micro_batch_size, pad_to_sequence_len, ring_attn_func",
+        "sample_packing, micro_batch_size, pad_to_sequence_len, ring_attn_func, threshold",
         [
-            (True, 1, True, None),  # defaults to varlen_llama3 ring_attn_func
-            (False, 2, True, None),  # defaults to batch_ring ring_attn_func
-            (False, 2, True, "batch_zigzag"),
-            # (False, 2, False),  # not yet working
+            (True, 1, True, None, 2.5),  # defaults to varlen_llama3 ring_attn_func
+            (False, 2, True, None, 2.5),  # defaults to batch_ring ring_attn_func
+            # (False, 2, True, "batch_zigzag", 2.5),
+            (False, 2, False, None, 2.5),  # defaults to batch_ring ring_attn_func
         ],
         ids=[
             "sample_packing, varlen_llama3 ring_attn_func",
+            "no sample_packing, pad_to_sequence_len, batch_ring ring_attn_func",
+            # "no sample_packing, no pad_to_sequence_len, batch_zigzag ring_attn_func",
             "no sample_packing, no pad_to_sequence_len, batch_ring ring_attn_func",
-            "no sample_packing, no pad_to_sequence_len, batch_zigzag ring_attn_func",
-            # "no sample_packing, pad_to_sequence_len",  # not yet working
         ],
     )
     def test_sequence_parallel_training(
@@ -118,6 +119,7 @@ class TestSequenceParallelism:
         micro_batch_size,
         pad_to_sequence_len,
         ring_attn_func,
+        threshold,
     ):
         """Test sequence parallel training with different configurations"""
         self._run_sequence_parallel_test(
@@ -126,4 +128,5 @@ class TestSequenceParallelism:
             micro_batch_size=micro_batch_size,
             pad_to_sequence_len=pad_to_sequence_len,
             ring_attn_func=ring_attn_func,
+            threshold=threshold,
         )

tests/e2e/multigpu/solo/test_grpo.py

@@ -227,11 +227,9 @@ def oai_gsm8k_transform(cfg, *args, **kwargs):
 
         current_env = os.environ.copy()
         env = {
-            "NCCL_P2P_LEVEL": "NVL",
+            "NCCL_P2P_LEVEL": "LOC",
             **current_env,
             "CUDA_VISIBLE_DEVICES": "1",
-            "VLLM_DISABLE_COMPILE_CACHE": "1",
-            # "VLLM_USE_V1": "0",
         }
         vllm_process = start_vllm(
             cfg.base_model,
@@ -257,7 +255,7 @@ def oai_gsm8k_transform(cfg, *args, **kwargs):
                     f"{get_torch_dist_unique_port()}",
                 ],
                 env={
-                    "NCCL_P2P_LEVEL": "NVL",
+                    "NCCL_P2P_LEVEL": "LOC",
                     "NCCL_DEBUG": "INFO",
                     **current_env,
                 },
@@ -320,11 +318,9 @@ def oai_gsm8k_transform(cfg, *args, **kwargs):
 
         current_env = os.environ.copy()
         env = {
-            "NCCL_P2P_LEVEL": "NVL",  # nccl can be brittle, assume P2P isn't reliable
+            "NCCL_P2P_LEVEL": "LOC",  # nccl can be brittle, assume P2P isn't reliable
             **current_env,
             "CUDA_VISIBLE_DEVICES": "1",
-            "VLLM_DISABLE_COMPILE_CACHE": "1",
-            # "VLLM_USE_V1": "0",
         }
         vllm_process = start_vllm(
             cfg.base_model,
@@ -350,7 +346,7 @@ def oai_gsm8k_transform(cfg, *args, **kwargs):
                     f"{get_torch_dist_unique_port()}",
                 ],
                 env={
-                    "NCCL_P2P_LEVEL": "NVL",
+                    "NCCL_P2P_LEVEL": "LOC",
                     "NCCL_DEBUG": "INFO",
                     **current_env,
                 },

tests/e2e/multigpu/test_llama.py

@@ -479,7 +479,7 @@ class TestMultiGPULlama:
                 "sample_packing": True,
                 "pad_to_sequence_len": True,
                 "sequence_len": 2048,
-                "val_set_size": 0.05,
+                "val_set_size": 0.1,
                 "special_tokens": {
                     "pad_token": "<|endoftext|>",
                 },

tests/e2e/patched/lora_kernels/test_lora_kernel_patching.py

@@ -29,12 +29,12 @@ from axolotl.utils.dict import DictDefault
 
 MODEL_CONFIGS = [
     {
-        "name": "openaccess-ai-collective/tiny-mistral",
+        "name": "trl-internal-testing/tiny-MistralForCausalLM-0.2",
         "expected_activation": apply_lora_mlp_swiglu,
         "dtype": torch.float16,
     },
     {
-        "name": "Qwen/Qwen2-7B",
+        "name": "trl-internal-testing/tiny-Qwen2ForCausalLM-2.5",
         "expected_activation": apply_lora_mlp_swiglu,
         "dtype": torch.float16,
     },
@@ -44,7 +44,7 @@ MODEL_CONFIGS = [
         "dtype": torch.float32,
     },
     {
-        "name": "mhenrichsen/gemma-2b",
+        "name": "trl-internal-testing/tiny-Gemma2ForCausalLM",
         "expected_activation": apply_lora_mlp_geglu,
         "dtype": torch.float16,
     },
@@ -156,7 +156,9 @@ def test_swiglu_mlp_integration(small_llama_model):
 def test_geglu_model_integration():
     """Test GeGLU activation with Gemma model."""
     model = AutoModelForCausalLM.from_pretrained(
-        "mhenrichsen/gemma-2b", torch_dtype=torch.float16, device_map="cuda:0"
+        "trl-internal-testing/tiny-Gemma2ForCausalLM",
+        torch_dtype=torch.float16,
+        device_map="cuda:0",
     )
     peft_config = get_peft_config(
         {

tests/e2e/patched/test_4d_multipack_llama.py

@@ -57,9 +57,9 @@ class Test4dMultipackLlama(unittest.TestCase):
                 "learning_rate": 0.00001,
                 "optimizer": "adamw_torch_fused",
                 "lr_scheduler": "cosine",
-                "max_steps": 20,
-                "save_steps": 10,
-                "eval_steps": 10,
+                "max_steps": 5,
+                "save_steps": 3,
+                "eval_steps": 4,
                 "fp16": True,
             }
         )
@@ -105,9 +105,9 @@ class Test4dMultipackLlama(unittest.TestCase):
                 "learning_rate": 0.00001,
                 "optimizer": "adamw_torch_fused",
                 "lr_scheduler": "cosine",
-                "max_steps": 20,
-                "save_steps": 10,
-                "eval_steps": 10,
+                "max_steps": 5,
+                "save_steps": 3,
+                "eval_steps": 4,
                 "fp16": True,
             }
         )

tests/e2e/patched/test_activation_checkpointing.py

@@ -26,10 +26,15 @@ class TestActivationCheckpointing:
     E2E tests for activation checkpointing
     """
 
+    @pytest.mark.parametrize(
+        "gradient_checkpointing",
+        ["offload", "offload_disk"],
+    )
     def test_activation_checkpointing_offload(
         self,
         temp_dir,
         fix_checkpoint_after_test,  # pylint: disable=unused-argument,redefined-outer-name
+        gradient_checkpointing,
     ):
         # pylint: disable=duplicate-code
         cfg = DictDefault(
@@ -64,7 +69,7 @@ class TestActivationCheckpointing:
                 "sample_packing": True,
                 "bf16": True,
                 "save_safetensors": True,
-                "gradient_checkpointing": "offload",
+                "gradient_checkpointing": gradient_checkpointing,
             }
         )
 

tests/e2e/patched/test_falcon_samplepack.py

@@ -6,6 +6,8 @@ import logging
 import os
 import unittest
 
+import pytest
+
 from axolotl.cli.args import TrainerCliArgs
 from axolotl.common.datasets import load_datasets
 from axolotl.train import train
@@ -23,6 +25,7 @@ class TestFalconPatched(unittest.TestCase):
     Test case for Falcon models
     """
 
+    @pytest.mark.skip(reason="no tiny models for testing with safetensors")
     @with_temp_dir
     def test_qlora(self, temp_dir):
         # pylint: disable=duplicate-code
@@ -71,6 +74,7 @@ class TestFalconPatched(unittest.TestCase):
         train(cfg=cfg, dataset_meta=dataset_meta)
         check_model_output_exists(temp_dir, cfg)
 
+    @pytest.mark.skip(reason="no tiny models for testing with safetensors")
     @with_temp_dir
     def test_ft(self, temp_dir):
         # pylint: disable=duplicate-code

tests/e2e/patched/test_mistral_samplepack.py

@@ -28,7 +28,7 @@ class TestMistral(unittest.TestCase):
         # pylint: disable=duplicate-code
         cfg = DictDefault(
             {
-                "base_model": "openaccess-ai-collective/tiny-mistral",
+                "base_model": "trl-internal-testing/tiny-MistralForCausalLM-0.2",
                 "flash_attention": True,
                 "sample_packing": True,
                 "sequence_len": 1024,
@@ -57,9 +57,9 @@ class TestMistral(unittest.TestCase):
                 "learning_rate": 0.00001,
                 "optimizer": "adamw_torch_fused",
                 "lr_scheduler": "cosine",
-                "max_steps": 20,
-                "save_steps": 10,
-                "eval_steps": 10,
+                "max_steps": 5,
+                "save_steps": 3,
+                "eval_steps": 4,
                 "bf16": "auto",
             }
         )
@@ -76,7 +76,7 @@ class TestMistral(unittest.TestCase):
         # pylint: disable=duplicate-code
         cfg = DictDefault(
             {
-                "base_model": "openaccess-ai-collective/tiny-mistral",
+                "base_model": "trl-internal-testing/tiny-MistralForCausalLM-0.2",
                 "flash_attention": True,
                 "sample_packing": True,
                 "sequence_len": 1024,
@@ -99,9 +99,9 @@ class TestMistral(unittest.TestCase):
                 "learning_rate": 0.00001,
                 "optimizer": "adamw_torch_fused",
                 "lr_scheduler": "cosine",
-                "max_steps": 20,
-                "save_steps": 10,
-                "eval_steps": 10,
+                "max_steps": 5,
+                "save_steps": 3,
+                "eval_steps": 4,
                 "bf16": "auto",
             }
         )

tests/e2e/patched/test_mixtral_samplepack.py

@@ -54,9 +54,9 @@ class TestMixtral(unittest.TestCase):
                 "learning_rate": 0.00001,
                 "optimizer": "adamw_bnb_8bit",
                 "lr_scheduler": "cosine",
-                "max_steps": 20,
-                "save_steps": 10,
-                "eval_steps": 10,
+                "max_steps": 5,
+                "save_steps": 3,
+                "eval_steps": 4,
                 "bf16": "auto",
             }
         )
@@ -93,9 +93,9 @@ class TestMixtral(unittest.TestCase):
                 "learning_rate": 0.00001,
                 "optimizer": "adamw_bnb_8bit",
                 "lr_scheduler": "cosine",
-                "max_steps": 20,
-                "save_steps": 10,
-                "eval_steps": 10,
+                "max_steps": 5,
+                "save_steps": 3,
+                "eval_steps": 4,
                 "bf16": "auto",
             }
         )

tests/e2e/patched/test_model_patches.py

@@ -56,7 +56,7 @@ class TestModelPatches(unittest.TestCase):
     def test_mistral_multipack(self, temp_dir):
         cfg = DictDefault(
             {
-                "base_model": "openaccess-ai-collective/tiny-mistral",
+                "base_model": "trl-internal-testing/tiny-MistralForCausalLM-0.2",
                 "flash_attention": True,
                 "sample_packing": True,
                 "sequence_len": 2048,

tests/e2e/patched/test_phi_multipack.py

@@ -56,9 +56,9 @@ class TestPhiMultipack(unittest.TestCase):
                 "learning_rate": 0.00001,
                 "optimizer": "adamw_bnb_8bit",
                 "lr_scheduler": "cosine",
-                "max_steps": 20,
-                "eval_steps": 10,
-                "save_steps": 10,
+                "max_steps": 5,
+                "eval_steps": 3,
+                "save_steps": 4,
                 "bf16": "auto",
             }
         )
@@ -108,9 +108,9 @@ class TestPhiMultipack(unittest.TestCase):
                 "learning_rate": 0.00001,
                 "optimizer": "adamw_bnb_8bit",
                 "lr_scheduler": "cosine",
-                "max_steps": 20,
-                "eval_steps": 10,
-                "save_steps": 10,
+                "max_steps": 5,
+                "eval_steps": 3,
+                "save_steps": 4,
                 "bf16": "auto",
             }
         )

tests/e2e/patched/test_resume.py

@@ -15,7 +15,7 @@ from axolotl.train import train
 from axolotl.utils.config import normalize_config, validate_config
 from axolotl.utils.dict import DictDefault
 
-from ..utils import check_model_output_exists, most_recent_subdir
+from ..utils import check_model_output_exists, most_recent_subdir, require_torch_2_6_0
 
 LOG = logging.getLogger("axolotl.tests.e2e")
 os.environ["WANDB_DISABLED"] = "true"
@@ -26,6 +26,7 @@ class TestResumeLlama:
     Test case for resuming training of llama models
     """
 
+    @require_torch_2_6_0
     def test_resume_lora_packed(self, temp_dir):
         # pylint: disable=duplicate-code
         cfg = DictDefault(
@@ -62,6 +63,7 @@ class TestResumeLlama:
                 "save_total_limit": 5,
                 "max_steps": 15,
                 "use_tensorboard": True,
+                "save_safetensors": True,
             }
         )
         if is_torch_bf16_gpu_available():

tests/e2e/patched/test_sp.py

@@ -10,14 +10,15 @@ import pytest
 import torch
 from accelerate.state import PartialState
 
-from axolotl.core.trainers.mixins.sequence_parallel import apply_sequence_parallelism
 from axolotl.monkeypatch.attention.ring_attn import (
-    RingAttnFunc,
     get_ring_attn_group,
     register_ring_attn,
     set_ring_attn_group,
 )
+from axolotl.utils.ctx_managers.sequence_parallel import apply_sequence_parallelism
 from axolotl.utils.dict import DictDefault
+from axolotl.utils.schemas.enums import RingAttnFunc
+from axolotl.utils.schemas.trl import TRLConfig
 
 
 @pytest.fixture
@@ -62,12 +63,14 @@ def sequence_parallel_batch():
     input_ids = torch.arange(batch_size * seq_len).reshape(batch_size, seq_len)
     attention_mask = torch.ones(batch_size, seq_len)
     position_ids = torch.arange(seq_len).expand(batch_size, seq_len)
+    labels = input_ids.clone()
 
     # Create test batch
     batch = {
         "input_ids": input_ids,
         "attention_mask": attention_mask,
         "position_ids": position_ids,
+        "labels": labels,
     }
 
     return batch
@@ -179,12 +182,44 @@ class TestConfigValidation:
                 False,
                 "micro_batch_size must be set to 1",
             ),
+            # Valid: Basic GRPO config
+            (
+                {
+                    "sequence_parallel_degree": 2,
+                    "flash_attention": True,
+                    "micro_batch_size": 2,
+                    "trl": {"use_liger_loss": True},
+                },
+                {
+                    "sequence_parallel_degree": 2,
+                    "flash_attention": True,
+                    "micro_batch_size": 2,
+                    "trl": TRLConfig(use_liger_loss=True),
+                },
+                True,
+                "GRPO + SP + Liger not currently supported",
+            ),
+            # Invalid: GRPO config with Liger loss
+            (
+                {
+                    "rl": "grpo",
+                    "sequence_parallel_degree": 2,
+                    "flash_attention": True,
+                    "micro_batch_size": 2,
+                    "trl": {"use_liger_loss": True},
+                },
+                None,
+                False,
+                "GRPO + SP + Liger not currently supported",
+            ),
         ],
         ids=[
             "valid_config",
             "default_sp_degree",
             "without_flash_attention",
             "sample_packing_with_large_batch",
+            "valid_grpo",
+            "grpo_with_liger_loss",
         ],
     )
     def test_sequence_parallel_config_validation(
@@ -256,7 +291,7 @@ class TestConfigValidation:
             AxolotlInputConfig(**cfg)
 
         # Verify error message
-        assert "ring_attn_func: INVALID_FUNC must be in" in str(excinfo.value)
+        assert "Input should be 'varlen_llama3' or 'batch_ring'" in str(excinfo.value)
 
 
 class TestApplySequenceParallelism:
@@ -290,10 +325,11 @@ class TestApplySequenceParallelism:
 
     def test_world_size_one(self, sequence_parallel_batch):
         """Test that function returns original batch when world size is 1."""
-        result = apply_sequence_parallelism(
+        result, _, _ = apply_sequence_parallelism(
             batch=sequence_parallel_batch,
             local_rank=0,
             local_world_size=1,
+            gradient_accumulation_steps=1,
             ring_attn_func=RingAttnFunc.BATCH_RING,
         )
 
@@ -305,10 +341,11 @@ class TestApplySequenceParallelism:
         batch = sequence_parallel_batch
         seq_len = batch["input_ids"].size(1)
 
-        result = apply_sequence_parallelism(
+        result, _, _ = apply_sequence_parallelism(
             batch=batch,
             local_rank=0,
             local_world_size=2,
+            gradient_accumulation_steps=1,
             ring_attn_func=RingAttnFunc.BATCH_RING,
         )
 
@@ -328,57 +365,59 @@ class TestApplySequenceParallelism:
         seq_len = batch["input_ids"].size(1)
         original_input_ids = batch["input_ids"].clone()
 
-        result = apply_sequence_parallelism(
+        result, _, _ = apply_sequence_parallelism(
             batch=batch,
             local_rank=1,
             local_world_size=2,
+            gradient_accumulation_steps=1,
             ring_attn_func=RingAttnFunc.BATCH_RING,
         )
 
         # Verify content: rank 1 should get the second half of the sequence
         assert torch.equal(result["input_ids"], original_input_ids[:, seq_len // 2 :])
 
-    def test_batch_zigzag(self, sequence_parallel_batch):
-        """Test BATCH_ZIGZAG sharding pattern."""
-        batch = sequence_parallel_batch
-        original_input_ids = batch["input_ids"].clone()
-        seq_len = batch["input_ids"].size(1)
+    # TODO(djsaunde): add back once implemented.
+    # def test_batch_zigzag(self, sequence_parallel_batch):
+    #     """Test BATCH_ZIGZAG sharding pattern."""
+    #     batch = sequence_parallel_batch
+    #     original_input_ids = batch["input_ids"].clone()
+    #     seq_len = batch["input_ids"].size(1)
 
-        # Test rank 0
-        result_rank0 = apply_sequence_parallelism(
-            batch={k: v.clone() for k, v in batch.items()},
-            local_rank=0,
-            local_world_size=2,
-            ring_attn_func=RingAttnFunc.BATCH_ZIGZAG,
-        )
+    #     # Test rank 0
+    #     result_rank0 = apply_sequence_parallelism(
+    #         batch={k: v.clone() for k, v in batch.items()},
+    #         local_rank=0,
+    #         local_world_size=2,
+    #         ring_attn_func=RingAttnFunc.BATCH_ZIGZAG,
+    #     )
 
-        # Test rank 1
-        result_rank1 = apply_sequence_parallelism(
-            batch={k: v.clone() for k, v in batch.items()},
-            local_rank=1,
-            local_world_size=2,
-            ring_attn_func=RingAttnFunc.BATCH_ZIGZAG,
-        )
+    #     # Test rank 1
+    #     result_rank1 = apply_sequence_parallelism(
+    #         batch={k: v.clone() for k, v in batch.items()},
+    #         local_rank=1,
+    #         local_world_size=2,
+    #         ring_attn_func=RingAttnFunc.BATCH_ZIGZAG,
+    #     )
 
-        # Checks for both ranks
-        assert result_rank0["input_ids"].shape[1] == seq_len // 2
-        assert result_rank1["input_ids"].shape[1] == seq_len // 2
+    #     # Checks for both ranks
+    #     assert result_rank0["input_ids"].shape[1] == seq_len // 2
+    #     assert result_rank1["input_ids"].shape[1] == seq_len // 2
 
-        # For a 2-rank system with 8 tokens, check specific zigzag pattern
-        # Rank 0 should get chunks [0, 1] and [6, 7]
-        # Rank 1 should get chunks [2, 3] and [4, 5]
-        if seq_len == 8:
-            # Create expected tensors for comparison
-            rank0_expected = torch.cat(
-                [original_input_ids[:, :2], original_input_ids[:, 6:8]], dim=1
-            )
+    #     # For a 2-rank system with 8 tokens, check specific zigzag pattern
+    #     # Rank 0 should get chunks [0, 1] and [6, 7]
+    #     # Rank 1 should get chunks [2, 3] and [4, 5]
+    #     if seq_len == 8:
+    #         # Create expected tensors for comparison
+    #         rank0_expected = torch.cat(
+    #             [original_input_ids[:, :2], original_input_ids[:, 6:8]], dim=1
+    #         )
 
-            rank1_expected = torch.cat(
-                [original_input_ids[:, 2:4], original_input_ids[:, 4:6]], dim=1
-            )
+    #         rank1_expected = torch.cat(
+    #             [original_input_ids[:, 2:4], original_input_ids[:, 4:6]], dim=1
+    #         )
 
-            assert torch.equal(result_rank0["input_ids"], rank0_expected)
-            assert torch.equal(result_rank1["input_ids"], rank1_expected)
+    #         assert torch.equal(result_rank0["input_ids"], rank0_expected)
+    #         assert torch.equal(result_rank1["input_ids"], rank1_expected)
 
     def test_partial_application(self, sequence_parallel_batch):
         """Test that we can create a partially applied version of the function."""
@@ -390,11 +429,12 @@ class TestApplySequenceParallelism:
             apply_sequence_parallelism,
             local_rank=0,
             local_world_size=2,
+            gradient_accumulation_steps=1,
             ring_attn_func=RingAttnFunc.BATCH_RING,
         )
 
         # Use the partially applied function
-        result = rank0_ring_parallel(batch=batch)
+        result, _, _ = rank0_ring_parallel(batch=batch)
 
         # Verify it works as expected
         assert result["input_ids"].shape[1] == original_input_ids.shape[1] // 2
@@ -412,13 +452,15 @@ class TestApplySequenceParallelism:
         original_input_ids = batch["input_ids"].clone()
 
         # This should run without error even though position_ids is missing
-        result = apply_sequence_parallelism(
+        result, _, _ = apply_sequence_parallelism(
             batch=batch,
             local_rank=0,
             local_world_size=2,
+            gradient_accumulation_steps=1,
             ring_attn_func=RingAttnFunc.BATCH_RING,
         )
 
         # Verification should pass
-        assert "position_ids" not in result
+        assert "position_ids" in result
+        assert result["input_ids"].shape[1] == result["position_ids"].shape[1]
         assert result["input_ids"].shape[1] == original_input_ids.shape[1] // 2

tests/e2e/test_evaluate.py

@@ -19,14 +19,11 @@ class TestE2eEvaluate:
         # pylint: disable=duplicate-code
         cfg = DictDefault(
             {
-                "base_model": "JackFram/llama-68m",
-                "tokenizer_type": "LlamaTokenizer",
+                "base_model": "HuggingFaceTB/SmolLM2-135M",
                 "sequence_len": 1024,
                 "val_set_size": 0.02,
                 "special_tokens": {
-                    "unk_token": "<unk>",
-                    "bos_token": "<s>",
-                    "eos_token": "</s>",
+                    "pad_token": "<|endoftext|>",
                 },
                 "datasets": [
                     {

tests/e2e/test_falcon.py

@@ -6,6 +6,8 @@ import logging
 import os
 import unittest
 
+import pytest
+
 from axolotl.cli.args import TrainerCliArgs
 from axolotl.common.datasets import load_datasets
 from axolotl.train import train
@@ -23,6 +25,7 @@ class TestFalcon(unittest.TestCase):
     Test case for falcon
     """
 
+    @pytest.mark.skip(reason="no tiny models for testing with safetensors")
     @with_temp_dir
     def test_lora(self, temp_dir):
         # pylint: disable=duplicate-code
@@ -74,6 +77,7 @@ class TestFalcon(unittest.TestCase):
         train(cfg=cfg, dataset_meta=dataset_meta)
         check_model_output_exists(temp_dir, cfg)
 
+    @pytest.mark.skip(reason="no tiny models for testing with safetensors")
     @with_temp_dir
     def test_lora_added_vocab(self, temp_dir):
         # pylint: disable=duplicate-code
@@ -129,6 +133,7 @@ class TestFalcon(unittest.TestCase):
         train(cfg=cfg, dataset_meta=dataset_meta)
         check_model_output_exists(temp_dir, cfg)
 
+    @pytest.mark.skip(reason="no tiny models for testing with safetensors")
     @with_temp_dir
     def test_ft(self, temp_dir):
         # pylint: disable=duplicate-code

tests/e2e/test_mistral.py

@@ -30,7 +30,7 @@ class TestMistral(unittest.TestCase):
         # pylint: disable=duplicate-code
         cfg = DictDefault(
             {
-                "base_model": "openaccess-ai-collective/tiny-mistral",
+                "base_model": "trl-internal-testing/tiny-MistralForCausalLM-0.2",
                 "flash_attention": True,
                 "sequence_len": 1024,
                 "load_in_8bit": True,
@@ -77,7 +77,7 @@ class TestMistral(unittest.TestCase):
         # pylint: disable=duplicate-code
         cfg = DictDefault(
             {
-                "base_model": "openaccess-ai-collective/tiny-mistral",
+                "base_model": "trl-internal-testing/tiny-MistralForCausalLM-0.2",
                 "flash_attention": True,
                 "sequence_len": 1024,
                 "val_set_size": 0.02,

tests/e2e/test_optimizers.py

@@ -199,3 +199,50 @@ class TestCustomOptimizers(unittest.TestCase):
 
         train(cfg=cfg, dataset_meta=dataset_meta)
         check_model_output_exists(temp_dir, cfg)
+
+    @with_temp_dir
+    def test_came_pytorch(self, temp_dir):
+        # pylint: disable=duplicate-code
+        cfg = DictDefault(
+            {
+                "base_model": "JackFram/llama-68m",
+                "tokenizer_type": "LlamaTokenizer",
+                "sequence_len": 1024,
+                "load_in_8bit": True,
+                "adapter": "lora",
+                "lora_r": 8,
+                "lora_alpha": 16,
+                "lora_dropout": 0.05,
+                "lora_target_linear": True,
+                "val_set_size": 0.1,
+                "special_tokens": {
+                    "unk_token": "<unk>",
+                    "bos_token": "<s>",
+                    "eos_token": "</s>",
+                },
+                "datasets": [
+                    {
+                        "path": "mhenrichsen/alpaca_2k_test",
+                        "type": "alpaca",
+                    },
+                ],
+                "num_epochs": 1,
+                "micro_batch_size": 8,
+                "gradient_accumulation_steps": 1,
+                "output_dir": temp_dir,
+                "learning_rate": 0.00001,
+                "optimizer": "came_pytorch",
+                "adam_beta3": 0.9999,
+                "adam_epsilon2": 1e-16,
+                "max_steps": 5,
+                "lr_scheduler": "cosine",
+            }
+        )
+
+        cfg = validate_config(cfg)
+        normalize_config(cfg)
+        cli_args = TrainerCliArgs()
+        dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
+
+        train(cfg=cfg, dataset_meta=dataset_meta)
+        check_model_output_exists(temp_dir, cfg)

tests/test_datasets.py

@@ -414,7 +414,6 @@ class TestDatasetPreparation:
             snapshot_path = snapshot_download(
                 repo_id="mhenrichsen/alpaca_2k_test",
                 repo_type="dataset",
-                local_dir=tmp_ds_path,
             )
             shutil.copytree(snapshot_path, tmp_ds_path, dirs_exist_ok=True)
 

tests/test_packed_batch_sampler.py

@@ -106,3 +106,4 @@ class TestBatchedSamplerPacking:
 
         original_idxs = set(range(len(train_dataset)))
         assert original_idxs == set(batch_idxs)
+        assert len(batch_idxs) == len(set(batch_idxs))