set v0.9.2 version for tag

* offload activations to disk instead of CPU RAM

* add prefetch

* Disco :dance:

* include offload_disk in e2e test for AC

* document and make sure to cleanup

* fix annotation to match docs

* fix docs build

* address PR feedback

.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.yml

@@ -44,96 +44,102 @@ jobs:
         env:
           SKIP: no-commit-to-branch
 
-  preload-cache:
+#  preload-cache:
-    name: Preload HF cache
+#    name: Preload HF cache
-    runs-on: ubuntu-latest
+#    runs-on: ubuntu-latest
-    strategy:
+#    strategy:
-      fail-fast: false
+#      fail-fast: false
-      matrix:
+#      matrix:
-        python_version: ["3.11"]
+#        python_version: ["3.11"]
-        pytorch_version: ["2.6.0"]
+#        pytorch_version: ["2.6.0"]
-    timeout-minutes: 20
+#    timeout-minutes: 20
-
+#
-    env:
+#    env:
-      AXOLOTL_IS_CI_CACHE_PRELOAD: "1"
+#      AXOLOTL_IS_CI_CACHE_PRELOAD: "1"
-
+#
-    steps:
+#    steps:
-      - name: Check out repository code
+#      - name: Check out repository code
-        uses: actions/checkout@v4
+#        uses: actions/checkout@v4
-
+#
-      - name: Restore HF cache
+#      - name: Restore HF cache
-        id: hf-cache-restore
+#        id: hf-cache-restore
-        uses: actions/cache/restore@v4
+#        uses: actions/cache/restore@v4
-        with:
+#        with:
-          path: |
+#          path: |
-            /home/runner/.cache/huggingface/hub/datasets--*
+#            /home/runner/.cache/huggingface/hub/datasets--*
-            /home/runner/.cache/huggingface/hub/models--*
+#            /home/runner/.cache/huggingface/hub/models--*
-          key: ${{ runner.os }}-hf-hub-cache-v2
+#          key: ${{ runner.os }}-hf-hub-cache-v2
-
+#
-      - name: Setup Python
+#      - name: Restore Cache from S3
-        uses: actions/setup-python@v5
+#        id: hf-cache-restore-s3
-        with:
+#        run: |
-          python-version: ${{ matrix.python_version }}
+#          mkdir -p /home/runner/.cache/huggingface/hub
-          cache: 'pip' # caching pip dependencies
+#          curl -L https://d1dttdx32dkk5p.cloudfront.net/hf-cache.tar.zst | tar -xf - -C /home/runner/.cache/huggingface/hub/  --use-compress-program unzstd
-
+#
-      - name: upgrade pip
+#      - name: Setup Python
-        run: |
+#        uses: actions/setup-python@v5
-          pip3 install --upgrade pip
+#        with:
-          pip3 install --upgrade packaging==23.2 setuptools==75.8.0 wheel
+#          python-version: ${{ matrix.python_version }}
-
+#          cache: 'pip' # caching pip dependencies
-      - name: Install PyTorch
+#
-        run: |
+#      - name: upgrade pip
-          pip3 install torch==${{ matrix.pytorch_version }}
+#        run: |
-
+#          pip3 install --upgrade pip
-      - name: Install dependencies
+#          pip3 install --upgrade packaging==23.2 setuptools==75.8.0 wheel
-        run: |
+#
-          pip3 show torch
+#      - name: Install PyTorch
-          pip3 install --no-build-isolation -U -e .
+#        run: |
-          python scripts/unsloth_install.py | sh
+#          pip3 install torch==${{ matrix.pytorch_version }}
-          python scripts/cutcrossentropy_install.py | sh
+#
-          pip3 install -r requirements-dev.txt -r requirements-tests.txt
+#      - name: Install dependencies
-
+#        run: |
-      - name: Make sure PyTorch version wasn't clobbered
+#          pip3 show torch
-        run: |
+#          pip3 install --no-build-isolation -U -e .
-          python -c "import torch; assert '${{ matrix.pytorch_version }}' in torch.__version__"
+#          python scripts/unsloth_install.py | sh
-
+#          python scripts/cutcrossentropy_install.py | sh
-      - name: Ensure axolotl CLI was installed
+#          pip3 install -r requirements-dev.txt -r requirements-tests.txt
-        run: |
+#
-          axolotl --help
+#      - name: Make sure PyTorch version wasn't clobbered
-
+#        run: |
-      - name: Pre-Download dataset fixture
+#          python -c "import torch; assert '${{ matrix.pytorch_version }}' in torch.__version__"
-        run: |
+#
-          huggingface-cli download --repo-type=dataset axolotl-ai-internal/axolotl-oss-dataset-fixtures
+#      - name: Ensure axolotl CLI was installed
-
+#        run: |
-      - name: Run tests
+#          axolotl --help
-        run: |
+#
-          pytest -v tests/conftest.py
+#      - name: Pre-Download dataset fixture
-
+#        run: |
-      - name: Upload coverage to Codecov
+#          huggingface-cli download --repo-type=dataset axolotl-ai-internal/axolotl-oss-dataset-fixtures
-        uses: codecov/codecov-action@v5
+#
-        with:
+#      - name: Run tests
-          token: ${{ secrets.CODECOV_TOKEN }}
+#        run: |
-          files: ./coverage.xml
+#          pytest -v tests/conftest.py
-          flags: unittests,pytorch-${{ matrix.pytorch_version }}
+#
-          fail_ci_if_error: false
+#      - name: Upload coverage to Codecov
-
+#        uses: codecov/codecov-action@v5
-      - name: cleanup pip cache
+#        with:
-        run: |
+#          token: ${{ secrets.CODECOV_TOKEN }}
-          find "$(pip cache dir)/http-v2" -type f -mtime +14 -exec rm {} \;
+#          files: ./coverage.xml
-
+#          flags: unittests,pytorch-${{ matrix.pytorch_version }}
-      - name: Save HF cache
+#          fail_ci_if_error: false
-        id: hf-cache
+#
-        uses: actions/cache/save@v4
+#      - name: cleanup pip cache
-        with:
+#        run: |
-          path: |
+#          find "$(pip cache dir)/http-v2" -type f -mtime +14 -exec rm {} \;
-            /home/runner/.cache/huggingface/hub/datasets--*
+#
-            /home/runner/.cache/huggingface/hub/models--*
+#      - name: Save HF cache
-          key: ${{ steps.hf-cache-restore.outputs.cache-primary-key }}
+#        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
+#      - name: Restore HF cache
-        id: hf-cache-restore
+#        id: hf-cache-restore
-        uses: actions/cache/restore@v4
+#        uses: actions/cache/restore@v4
-        with:
+#        with:
-          path: |
+#          path: |
-            /home/runner/.cache/huggingface/hub/datasets--*
+#            /home/runner/.cache/huggingface/hub/datasets--*
-            /home/runner/.cache/huggingface/hub/models--*
+#            /home/runner/.cache/huggingface/hub/models--*
-          key: ${{ runner.os }}-hf-hub-cache-v2
+#          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
+#      - name: Restore HF cache
-        id: hf-cache-restore
+#        id: hf-cache-restore
-        uses: actions/cache/restore@v4
+#        uses: actions/cache/restore@v4
-        with:
+#        with:
-          path: |
+#          path: |
-            /home/runner/.cache/huggingface/hub/datasets--*
+#            /home/runner/.cache/huggingface/hub/datasets--*
-            /home/runner/.cache/huggingface/hub/models--*
+#            /home/runner/.cache/huggingface/hub/models--*
-          key: ${{ runner.os }}-hf-hub-cache-v2
+#          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
@@ -365,3 +383,43 @@ 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"]
+    if "WANDB_API_KEY" in os.environ:
-    del os.environ["HF_TOKEN"]
+        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

@@ -124,7 +124,8 @@ 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
+from .single_gpu import GPU_CONFIG, VOLUME_CONFIG, app, cicd_image, run_cmd
-
-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
 
 
 @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/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

src/axolotl/__init__.py

@@ -4,4 +4,4 @@ import pkgutil
 
 __path__ = pkgutil.extend_path(__path__, __name__)  # Make this a namespace package
 
-__version__ = "0.9.1"
+__version__ = "0.9.2"

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/core/trainer_builder.py

@@ -1057,6 +1057,8 @@ 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
 
@@ -1186,6 +1188,10 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
         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

src/axolotl/utils/gradient_checkpointing/__init__.py

@@ -5,8 +5,11 @@ from functools import partial
 
 from packaging import version
 
-from axolotl.utils.gradient_checkpointing.unsloth import (
+from axolotl.utils.gradient_checkpointing.offload_cpu import (
-    Unsloth_Offloaded_Gradient_Checkpointer,
+    CPU_Offloaded_Gradient_Checkpointer,
+)
+from axolotl.utils.gradient_checkpointing.offload_disk import (
+    Disco,
 )
 
 transformers_version = version.parse(importlib.metadata.version("transformers"))
@@ -26,12 +29,31 @@ def hf_grad_checkpoint_offload_wrapper(
     decoder_layer, *args, use_reentrant=None
 ):  # pylint: disable=unused-argument
     if uses_gc_layers(decoder_layer):
-        return Unsloth_Offloaded_Gradient_Checkpointer.apply(
+        return CPU_Offloaded_Gradient_Checkpointer.apply(
             decoder_layer,
             *args,
         )
 
-    return Unsloth_Offloaded_Gradient_Checkpointer.apply(
+    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

@@ -70,7 +70,10 @@ 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
 
@@ -603,6 +606,10 @@ 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()

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):
+    for seq_id, size in enumerate(sequence_lengths):
-        global_idx = indices[seq_id] + group_offset
+        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

@@ -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
@@ -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")
@@ -1345,6 +1357,10 @@ 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

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/solo/test_grpo.py

@@ -166,6 +166,7 @@ def oai_gsm8k_transform(cfg, *args, **kwargs):
 """
             )
 
+    @pytest.mark.skip(reason="flaky test")
     @pytest.mark.parametrize(
         "num_gpus",
         [1, 2],
@@ -227,7 +228,7 @@ 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",
@@ -257,7 +258,7 @@ 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,
                 },
@@ -265,6 +266,7 @@ def oai_gsm8k_transform(cfg, *args, **kwargs):
         finally:
             recursive_kill(vllm_process)
 
+    @pytest.mark.skip(reason="flaky test")
     @pytest.mark.parametrize(
         "num_gpus",
         [1, 2],
@@ -320,7 +322,7 @@ 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",
@@ -350,7 +352,7 @@ 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/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,
+                "max_steps": 5,
-                "save_steps": 10,
+                "save_steps": 3,
-                "eval_steps": 10,
+                "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,
+                "max_steps": 5,
-                "save_steps": 10,
+                "save_steps": 3,
-                "eval_steps": 10,
+                "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_mistral_samplepack.py

@@ -57,9 +57,9 @@ class TestMistral(unittest.TestCase):
                 "learning_rate": 0.00001,
                 "optimizer": "adamw_torch_fused",
                 "lr_scheduler": "cosine",
-                "max_steps": 20,
+                "max_steps": 5,
-                "save_steps": 10,
+                "save_steps": 3,
-                "eval_steps": 10,
+                "eval_steps": 4,
                 "bf16": "auto",
             }
         )
@@ -99,9 +99,9 @@ class TestMistral(unittest.TestCase):
                 "learning_rate": 0.00001,
                 "optimizer": "adamw_torch_fused",
                 "lr_scheduler": "cosine",
-                "max_steps": 20,
+                "max_steps": 5,
-                "save_steps": 10,
+                "save_steps": 3,
-                "eval_steps": 10,
+                "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,
+                "max_steps": 5,
-                "save_steps": 10,
+                "save_steps": 3,
-                "eval_steps": 10,
+                "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,
+                "max_steps": 5,
-                "save_steps": 10,
+                "save_steps": 3,
-                "eval_steps": 10,
+                "eval_steps": 4,
                 "bf16": "auto",
             }
         )

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,
+                "max_steps": 5,
-                "eval_steps": 10,
+                "eval_steps": 3,
-                "save_steps": 10,
+                "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,
+                "max_steps": 5,
-                "eval_steps": 10,
+                "eval_steps": 3,
-                "save_steps": 10,
+                "save_steps": 4,
                 "bf16": "auto",
             }
         )

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))