feat: add dpo liger

* fix build w pyproject to respect insalled torch version

* include in manifest

* disable duplicate code check for now

* move parser so it can be found

* add checks for correct pytorch version so this doesn't slip by again

.github/workflows/pypi.yml

@@ -13,10 +13,13 @@ jobs:
     permissions:
       contents: write
     steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+
       - name: Create release
         env:
           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-        run: gh release create "$GITHUB_REF_NAME" # GITHUB_REF_NAME is the tag name in `on.push.tags` workflows
+        run: gh release create "$GITHUB_REF_NAME" --generate-notes
   pypi-publish:
     name: Upload release to PyPI
     runs-on: ubuntu-latest
@@ -38,7 +41,7 @@ jobs:
       - name: Install dependencies
         run: |
           pip3 install wheel packaging
-          pip3 install -e .
+          pip3 install --no-build-isolation -e .
           pip3 install -r requirements-dev.txt -r requirements-tests.txt
 
       - name: Extract tag name

.github/workflows/tests-nightly.yml

@@ -44,6 +44,11 @@ jobs:
           python-version: ${{ matrix.python_version }}
           cache: 'pip' # caching pip dependencies
 
+      - name: upgrade pip
+        run: |
+          pip3 install --upgrade pip
+          pip3 install --upgrade packaging setuptools wheel
+
       - name: Install PyTorch
         run: |
           pip3 install torch==${{ matrix.pytorch_version }} --index-url https://download.pytorch.org/whl/cpu
@@ -60,11 +65,15 @@ jobs:
         run: |
           pip3 install --upgrade pip
           pip3 install --upgrade packaging
-          pip3 install -U -e .
+          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

.github/workflows/tests.yml

@@ -78,19 +78,23 @@ jobs:
       - name: Install dependencies
         run: |
           pip3 show torch
-          pip3 install -U -e .
+          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: Run tests
         run: |
-          pytest -n8 --dist loadfile --ignore=tests/e2e/ --ignore=tests/patched/ tests/
-          pytest tests/patched/
+          pytest -v -n8 --dist loadfile --ignore=tests/e2e/ --ignore=tests/patched/ tests/
+          pytest -v tests/patched/
 
       - name: cleanup pip cache
         run: |
@@ -120,7 +124,7 @@ jobs:
       - name: upgrade pip
         run: |
           pip3 install --upgrade pip
-          pip3 install --upgrade packaging setuptools wheel
+          pip3 install --upgrade packaging setuptools setuptools_scm build wheel
 
       - name: Install PyTorch
         run: |
@@ -129,20 +133,24 @@ jobs:
       - name: Install dependencies
         run: |
           pip3 show torch
-          python3 setup.py sdist
-          pip3 install dist/axolotl*.tar.gz
+          python -m build --no-isolation --sdist
+          pip3 install --no-build-isolation dist/axolotl*.tar.gz
           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: Run tests
         run: |
-          pytest -n8 --dist loadfile --ignore=tests/e2e/ --ignore=tests/patched/ tests/
-          pytest tests/patched/
+          pytest -v -n8 --dist loadfile --ignore=tests/e2e/ --ignore=tests/patched/ tests/
+          pytest -v tests/patched/
 
       - name: cleanup pip cache
         run: |

MANIFEST.in

@@ -1,4 +1,5 @@
 include requirements.txt
 include README.md
 include LICENSE
+include src/setuptools_axolotl_dynamic_dependencies.py
 recursive-include axolotl *.py

README.md

@@ -10,9 +10,13 @@
     <img src="https://img.shields.io/github/license/axolotl-ai-cloud/axolotl.svg?color=blue" alt="GitHub License">
     <img src="https://github.com/axolotl-ai-cloud/axolotl/actions/workflows/tests.yml/badge.svg" alt="tests">
     <a href="https://github.com/axolotl-ai-cloud/axolotl/releases"><img src="https://img.shields.io/github/release/axolotl-ai-cloud/axolotl.svg" alt="Releases"></a>
+    <br/>
+    <a href="https://github.com/axolotl-ai-cloud/axolotl/graphs/contributors"><img src="https://img.shields.io/github/contributors-anon/axolotl-ai-cloud/axolotl?color=yellow&style=flat-square" alt="contributors" style="height: 20px;"></a>
     <img src="https://img.shields.io/github/stars/axolotl-ai-cloud/axolotl" alt="GitHub Repo stars">
-</p>
-<p align="center">
+    <br/>
+    <a href="https://discord.com/invite/HhrNrHJPRb"><img src="https://img.shields.io/badge/discord-7289da.svg?style=flat-square&logo=discord" alt="discord" style="height: 20px;"></a>
+    <a href="https://twitter.com/axolotl_ai"><img src="https://img.shields.io/twitter/follow/axolotl_ai?style=social" alt="twitter" style="height: 20px;"></a>
+    <br/>
     <img src="https://github.com/axolotl-ai-cloud/axolotl/actions/workflows/tests-nightly.yml/badge.svg" alt="tests-nightly">
     <img src="https://github.com/axolotl-ai-cloud/axolotl/actions/workflows/multi-gpu-e2e.yml/badge.svg" alt="multigpu-semi-weekly tests">
 </p>
@@ -42,7 +46,8 @@ Features:
 - [Axolotl](#axolotl)
   - [Table of Contents](#table-of-contents)
   - [Quickstart ⚡](#quickstart-)
-    - [Usage](#usage)
+    - [Edge Builds](#edge-builds-)
+    - [Axolotl CLI Usage](#axolotl-cli-usage)
   - [Badge ❤🏷️](#badge-️)
   - [Contributing 🤝](#contributing-)
   - [Sponsors 🤝❤](#sponsors-)
@@ -107,58 +112,49 @@ Get started with Axolotl in just a few steps! This quickstart guide will walk yo
 **Requirements**: *Nvidia* GPU (Ampere architecture or newer for `bf16` and Flash Attention) or *AMD* GPU, Python >=3.10 and PyTorch >=2.3.1.
 
 ```bash
-git clone https://github.com/axolotl-ai-cloud/axolotl
+pip3 install --no-build-isolation axolotl[flash-attn,deepspeed]
+
+# download examples and optionally deepspeed configs to the local path
+axolotl fetch examples
+axolotl fetch deepspeed_configs  # OPTIONAL
+
+# finetune using lora
+axolotl train examples/llama-3/lora-1b.yml
+```
+
+### Edge Builds 🏎️
+
+If you're looking for the latest features and updates between releases, you'll need to install
+from source.
+
+```bash
+git clone https://github.com/axolotl-ai-cloud/axolotl.git
 cd axolotl
-
 pip3 install packaging ninja
-pip3 install -e '.[flash-attn,deepspeed]'
+pip3 install --no-build-isolation -e '.[flash-attn,deepspeed]'
 ```
 
-### Usage
-```bash
-# preprocess datasets - optional but recommended
-CUDA_VISIBLE_DEVICES="0" python -m axolotl.cli.preprocess examples/openllama-3b/lora.yml
-
-# finetune lora
-accelerate launch -m axolotl.cli.train examples/openllama-3b/lora.yml
-
-# inference
-accelerate launch -m axolotl.cli.inference examples/openllama-3b/lora.yml \
-    --lora_model_dir="./outputs/lora-out"
-
-# gradio
-accelerate launch -m axolotl.cli.inference examples/openllama-3b/lora.yml \
-    --lora_model_dir="./outputs/lora-out" --gradio
-
-# remote yaml files - the yaml config can be hosted on a public URL
-# Note: the yaml config must directly link to the **raw** yaml
-accelerate launch -m axolotl.cli.train https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/examples/openllama-3b/lora.yml
-```
-
-### Axolotl CLI
-
-If you've installed this package using `pip` from source, we now support a new, more
-streamlined CLI using [click](https://click.palletsprojects.com/en/stable/). Rewriting
-the above commands:
+### Axolotl CLI Usage
+We now support a new, more streamlined CLI using [click](https://click.palletsprojects.com/en/stable/).
 
 ```bash
 # preprocess datasets - optional but recommended
-CUDA_VISIBLE_DEVICES="0" axolotl preprocess examples/openllama-3b/lora.yml
+CUDA_VISIBLE_DEVICES="0" axolotl preprocess examples/llama-3/lora-1b.yml
 
 # finetune lora
-axolotl train examples/openllama-3b/lora.yml
+axolotl train examples/llama-3/lora-1b.yml
 
 # inference
-axolotl inference examples/openllama-3b/lora.yml \
+axolotl inference examples/llama-3/lora-1b.yml \
     --lora-model-dir="./outputs/lora-out"
 
 # gradio
-axolotl inference examples/openllama-3b/lora.yml \
+axolotl inference examples/llama-3/lora-1b.yml \
     --lora-model-dir="./outputs/lora-out" --gradio
 
 # remote yaml files - the yaml config can be hosted on a public URL
 # Note: the yaml config must directly link to the **raw** yaml
-axolotl train https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/examples/openllama-3b/lora.yml
+axolotl train https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/examples/llama-3/lora-1b.yml
 ```
 
 We've also added a new command for fetching `examples` and `deepspeed_configs` to your
@@ -175,6 +171,36 @@ axolotl fetch deepspeed_configs
 axolotl fetch examples --dest path/to/folder
 ```
 
+### Legacy Usage
+<details>
+
+<summary>Click to Expand</summary>
+
+While the Axolotl CLI is the preferred method for interacting with axolotl, we
+still support the legacy `-m axolotl.cli.*` usage.
+
+```bash
+# preprocess datasets - optional but recommended
+CUDA_VISIBLE_DEVICES="0" python -m axolotl.cli.preprocess examples/llama-3/lora-1b.yml
+
+# finetune lora
+accelerate launch -m axolotl.cli.train examples/llama-3/lora-1b.yml
+
+# inference
+accelerate launch -m axolotl.cli.inference examples/llama-3/lora-1b.yml \
+    --lora_model_dir="./outputs/lora-out"
+
+# gradio
+accelerate launch -m axolotl.cli.inference examples/llama-3/lora-1b.yml \
+    --lora_model_dir="./outputs/lora-out" --gradio
+
+# remote yaml files - the yaml config can be hosted on a public URL
+# Note: the yaml config must directly link to the **raw** yaml
+accelerate launch -m axolotl.cli.train https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/examples/llama-3/lora-1b.yml
+```
+
+</details>
+
 ## Badge ❤🏷️
 
 Building something cool with Axolotl? Consider adding a badge to your model card.
@@ -294,7 +320,7 @@ docker run --privileged --gpus '"all"' --shm-size 10g --rm -it --name axolotl --
   3. Install Axolotl along with python dependencies
         ```bash
         pip3 install packaging
-        pip3 install -e '.[flash-attn,deepspeed]'
+        pip3 install --no-build-isolation -e '.[flash-attn,deepspeed]'
         ```
   4. (Optional) Login to Huggingface to use gated models/datasets.
         ```bash
@@ -373,7 +399,7 @@ Please use WSL or Docker!
 
 Use the below instead of the install method in QuickStart.
 ```
-pip3 install -e '.'
+pip3 install --no-build-isolation -e '.'
 ```
 More info: [mac.md](/docs/mac.qmd)
 

cicd/Dockerfile.jinja

@@ -31,9 +31,9 @@ RUN if [ "$NIGHTLY_BUILD" = "true" ] ; then \
     fi
 
 RUN if [ "$AXOLOTL_EXTRAS" != "" ] ; then \
-        pip install -e .[deepspeed,flash-attn,optimizers,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \
+        pip install --no-build-isolation -e .[deepspeed,flash-attn,optimizers,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \
     else \
-        pip install -e .[deepspeed,flash-attn,optimizers] $AXOLOTL_ARGS; \
+        pip install --no-build-isolation -e .[deepspeed,flash-attn,optimizers] $AXOLOTL_ARGS; \
     fi
 
 RUN python scripts/unsloth_install.py | sh

cicd/cicd.sh

@@ -1,7 +1,10 @@
 #!/bin/bash
 set -e
 
+python -c "import torch; assert '$PYTORCH_VERSION' in torch.__version__"
+
 pytest -v --durations=10 -n8 --ignore=tests/e2e/ --ignore=tests/patched/ /workspace/axolotl/tests/
-pytest -v --durations=10 -n8 --dist loadfile /workspace/axolotl/tests/patched/
-pytest -v --durations=10 -n1 --dist loadfile /workspace/axolotl/tests/e2e/patched/ /workspace/axolotl/tests/e2e/integrations/
+# pytest -v --durations=10 -n8 --dist loadfile /workspace/axolotl/tests/patched/
+pytest -v --durations=10 -n1 --dist loadfile /workspace/axolotl/tests/e2e/patched/
+pytest -v --durations=10 -n1 --dist loadfile /workspace/axolotl/tests/e2e/integrations/
 pytest -v --durations=10 --ignore=tests/e2e/patched/ --ignore=tests/e2e/multigpu/ --ignore=tests/e2e/integrations/ /workspace/axolotl/tests/e2e/

docker/Dockerfile

@@ -20,9 +20,9 @@ WORKDIR /workspace/axolotl
 
 # If AXOLOTL_EXTRAS is set, append it in brackets
 RUN if [ "$AXOLOTL_EXTRAS" != "" ] ; then \
-        pip install -e .[deepspeed,flash-attn,optimizers,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \
+        pip install --no-build-isolation -e .[deepspeed,flash-attn,optimizers,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \
     else \
-        pip install -e .[deepspeed,flash-attn,optimizers] $AXOLOTL_ARGS; \
+        pip install --no-build-isolation -e .[deepspeed,flash-attn,optimizers] $AXOLOTL_ARGS; \
     fi
 
 RUN python scripts/unsloth_install.py | sh

docker/Dockerfile-tests

@@ -24,9 +24,9 @@ RUN git fetch origin +$GITHUB_REF && \
 
 # If AXOLOTL_EXTRAS is set, append it in brackets
 RUN if [ "$AXOLOTL_EXTRAS" != "" ] ; then \
-        pip install -e .[deepspeed,flash-attn,mamba-ssm,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \
+        pip install --no-build-isolation -e .[deepspeed,flash-attn,mamba-ssm,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \
     else \
-        pip install -e .[deepspeed,flash-attn,mamba-ssm] $AXOLOTL_ARGS; \
+        pip install --no-build-isolation -e .[deepspeed,flash-attn,mamba-ssm] $AXOLOTL_ARGS; \
     fi
 
 # So we can test the Docker image

docs/amd_hpc.qmd

@@ -52,7 +52,7 @@ export GPU_ARCHS="gfx90a"
 cd flash-attention
 export PYTHON_SITE_PACKAGES=$(python -c 'import site; print(site.getsitepackages()[0])')
 patch "${PYTHON_SITE_PACKAGES}/torch/utils/hipify/hipify_python.py" hipify_patch.patch
-pip install .
+pip install --no-build-isolation .
 ```
 
 ### 6. Install Axolotl
@@ -63,7 +63,7 @@ Clone and install Axolotl:
 git clone https://github.com/axolotl-ai-cloud/axolotl
 cd axolotl
 pip install packaging ninja
-pip install -e .
+pip install --no-build-isolation -e .
 ```
 
 ### 7. Apply xformers Workaround

docs/debugging.qmd

@@ -71,7 +71,7 @@ Make sure you have an [editable install](https://setuptools.pypa.io/en/latest/us
 
 ```bash
 pip3 install packaging
-pip3 install -e '.[flash-attn,deepspeed]'
+pip3 install --no-build-isolation -e '.[flash-attn,deepspeed]'
 ```
 
 #### Remote Hosts
@@ -212,7 +212,7 @@ You will now be in the container.  Next, perform an editable install of Axolotl:
 
 ```bash
 pip3 install packaging
-pip3 install -e '.[flash-attn,deepspeed]'
+pip3 install --no-build-isolation -e '.[flash-attn,deepspeed]'
 ```
 
 ### Attach To Container

docs/rlhf.qmd

@@ -52,6 +52,26 @@ datasets:
     type: chat_template.argilla
 ```
 
+
+#### KTO
+
+```yaml
+rl: kto
+rl_beta: 0.5
+kto_desirable_weight: 0.2
+
+remove_unused_columns: false
+
+datasets:
+  - path: argilla/ultrafeedback-binarized-preferences-cleaned-kto
+    type: llama3.ultra
+    split: train
+
+gradient_checkpointing: true
+gradient_checkpointing_kwargs:
+  use_reentrant: true
+```
+
 #### Using local dataset files
 ```yaml
 datasets:

examples/colab-notebooks/colab-axolotl-example.ipynb

@@ -24,7 +24,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pip install axolotl[deepspeed]"
+    "!pip install --no-build-isolation axolotl[deepspeed]"
    ]
   },
   {

examples/llama-3/lora-1b.yml

@@ -0,0 +1,74 @@
+base_model: NousResearch/Llama-3.2-1B
+
+load_in_8bit: false
+load_in_4bit: false
+strict: false
+
+datasets:
+  - path: teknium/GPT4-LLM-Cleaned
+    type: alpaca
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.1
+output_dir: ./outputs/lora-out
+
+adapter: lora
+lora_model_dir:
+
+sequence_len: 2048
+sample_packing: true
+eval_sample_packing: true
+pad_to_sequence_len: true
+
+lora_r: 16
+lora_alpha: 32
+lora_dropout: 0.05
+lora_fan_in_fan_out:
+lora_target_modules:
+  - gate_proj
+  - down_proj
+  - up_proj
+  - q_proj
+  - v_proj
+  - k_proj
+  - o_proj
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+gradient_accumulation_steps: 2
+micro_batch_size: 2
+num_epochs: 1
+optimizer: adamw_8bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+train_on_inputs: false
+group_by_length: false
+bf16: auto
+fp16:
+tf32: false
+
+gradient_checkpointing: true
+early_stopping_patience:
+resume_from_checkpoint:
+local_rank:
+logging_steps: 1
+xformers_attention:
+flash_attention: true
+
+loss_watchdog_threshold: 5.0
+loss_watchdog_patience: 3
+
+warmup_steps: 10
+evals_per_epoch: 4
+saves_per_epoch: 1
+debug:
+deepspeed:
+weight_decay: 0.0
+fsdp:
+fsdp_config:
+special_tokens:
+  pad_token: "<|end_of_text|>"

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

@@ -0,0 +1,75 @@
+base_model: meta-llama/Llama-3.2-1B
+
+load_in_8bit: false
+load_in_4bit: true
+strict: false
+
+rl: kto
+rl_beta: 0.5
+kto_desirable_weight: 0.2
+
+datasets:
+  - path: argilla/ultrafeedback-binarized-preferences-cleaned-kto
+    type: llama3.ultra
+    split: train
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.0
+output_dir: ./outputs/qlora-out
+
+remove_unused_columns: false
+
+adapter: qlora
+lora_model_dir:
+
+sequence_len: 2048
+sample_packing: false  # not supported with kto
+eval_sample_packing: false
+pad_to_sequence_len: false
+
+lora_r: 32
+lora_alpha: 64
+lora_dropout: 0.05
+lora_target_linear: true
+lora_fan_in_fan_out:
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+gradient_accumulation_steps: 1
+micro_batch_size: 2
+num_epochs: 1
+optimizer: adamw_8bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+train_on_inputs: false
+group_by_length: false
+bf16: auto
+fp16:
+tf32: true
+
+gradient_checkpointing: true
+gradient_checkpointing_kwargs:
+  use_reentrant: true
+early_stopping_patience:
+resume_from_checkpoint:
+local_rank:
+logging_steps: 1
+xformers_attention:
+flash_attention: true
+
+warmup_steps: 20
+evals_per_epoch: 4
+eval_table_size:
+eval_max_new_tokens: 128
+saves_per_epoch: 1
+debug:
+deepspeed:
+weight_decay: 0.0
+fsdp:
+fsdp_config:
+special_tokens:
+  pad_token: "<|end_of_text|>"

examples/llama-3/qlora-1b.yml

@@ -1,4 +1,4 @@
-base_model: meta-llama/Llama-3.2-1B
+base_model: NousResearch/Llama-3.2-1B
 
 load_in_8bit: false
 load_in_4bit: true
@@ -22,7 +22,6 @@ pad_to_sequence_len: true
 lora_r: 32
 lora_alpha: 16
 lora_dropout: 0.05
-lora_target_linear: true
 lora_fan_in_fan_out:
 lora_target_modules:
   - gate_proj

pyproject.toml

@@ -17,3 +17,10 @@ Homepage = "https://axolotl-ai-cloud.github.io/axolotl/"
 Repository = "https://github.com/axolotl-ai-cloud/axolotl.git"
 
 [tool.setuptools_scm]
+
+[tool.setuptools]
+py-modules = ["setuptools_axolotl_dynamic_dependencies"]
+include-package-data = true
+
+[tool.setuptools.cmdclass]
+build_py = "setuptools_axolotl_dynamic_dependencies.BuildPyCommand"

requirements.txt

@@ -1,22 +1,30 @@
 --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/
+
+# START section of dependencies that don't install on Darwin/MacOS
+bitsandbytes==0.45.0
+triton>=2.3.0
+mamba-ssm==1.2.0.post1
+flash-attn==2.7.0.post2
+xformers>=0.0.23.post1
+autoawq==0.2.7.post3
+liger-kernel==0.4.2
+# END section
+
 packaging==23.2
 peft==0.14.0
 transformers>=4.46.3
 tokenizers>=0.20.1
-bitsandbytes==0.45.0
 accelerate==1.2.0
 datasets==3.1.0
-deepspeed==0.15.4
+deepspeed==0.16.1
 pydantic==2.6.3
 addict
 fire
 PyYAML>=6.0
 requests
-flash-attn==2.7.0.post2
 sentencepiece
 wandb
 einops
-xformers>=0.0.23.post1
 optimum==1.16.2
 hf_transfer
 colorama
@@ -31,11 +39,6 @@ art
 gradio==3.50.2
 tensorboard
 python-dotenv==1.0.1
-autoawq==0.2.7.post3
-triton>=2.3.0
-liger-kernel==0.4.2
-
-mamba-ssm==1.2.0.post1
 
 # remote filesystems
 s3fs>=2024.5.0

scripts/motd

@@ -13,5 +13,5 @@ cd /workspace
 rm -rf /workspace/axolotl
 git clone https://github.com/axolotl-ai-cloud/axolotl.git
 cd axolotl
-pip install --no-deps -e .
+pip install --no-build-isolation --no-deps -e .
 ```

setup.py

@@ -1,7 +1,10 @@
 """setup.py for axolotl"""
+import ast
+import os
 import platform
 import re
 from importlib.metadata import PackageNotFoundError, version
+from pathlib import Path
 
 from setuptools import find_packages, setup
 
@@ -90,9 +93,24 @@ def parse_requirements():
     return _install_requires, _dependency_links
 
 
+def get_package_version():
+    with open(
+        Path(os.path.dirname(os.path.abspath(__file__)))
+        / "src"
+        / "axolotl"
+        / "__init__.py",
+        "r",
+        encoding="utf-8",
+    ) as fin:
+        version_match = re.search(r"^__version__\s*=\s*(.*)$", fin.read(), re.MULTILINE)
+    version_ = ast.literal_eval(version_match.group(1))
+    return version_
+
+
 install_requires, dependency_links = parse_requirements()
 
 setup(
+    version=get_package_version(),
     package_dir={"": "src"},
     packages=find_packages("src"),
     install_requires=install_requires,
@@ -107,7 +125,7 @@ setup(
             "flash-attn==2.7.0.post2",
         ],
         "deepspeed": [
-            "deepspeed==0.15.4",
+            "deepspeed==0.16.1",
             "deepspeed-kernels",
         ],
         "mamba-ssm": [

src/axolotl/__init__.py

@@ -1,8 +1,3 @@
 """Axolotl - Train and fine-tune large language models"""
 
-try:
-    from importlib.metadata import version
-
-    __version__ = version("axolotl")
-except ImportError:
-    __version__ = "unknown"
+__version__ = "0.6.0"

src/axolotl/core/trainer_builder.py

@@ -14,17 +14,22 @@ import os
 import sys
 from abc import abstractmethod
 from collections import defaultdict
+from contextlib import nullcontext
 from dataclasses import dataclass, field
 from functools import wraps
 from pathlib import Path
 from typing import Any, Dict, List, Literal, Optional, Type, Union
 
 import torch
+import torch.nn.functional as F
 import transformers
 from datasets import Dataset
+from liger_kernel.chunked_loss.fused_linear_preference import (
+    LigerFusedLinearPreferenceBase,
+)
 from packaging import version
 from peft.optimizers import create_loraplus_optimizer
-from torch import nn
+from torch import amp, nn
 from torch.optim.lr_scheduler import OneCycleLR
 from torch.utils.data import BatchSampler, DataLoader, RandomSampler, SequentialSampler
 from transformers import (
@@ -1077,6 +1082,15 @@ class AxolotlDPOTrainer(SchedulerMixin, DPOTrainer):
         self.dataset_tags = dataset_tags
         self.optimizer = None
 
+        from liger_kernel.chunked_loss import LigerFusedLinearDPOLoss
+
+        self.liger_loss = LigerFusedLinearDPOLoss(
+            ignore_index=self.label_pad_token_id,
+            beta=self.beta,
+            compute_nll_loss=True,  # not same as rpo_alpha hasattr(self.args, "rpo_alpha") and self.args.rpo_alpha is not None,
+            use_ref_model=not self.reference_free,
+        )
+
     def create_optimizer(self):
         if self.args.loraplus_lr_ratio is None:
             return super().create_optimizer()
@@ -1180,6 +1194,309 @@ class AxolotlDPOTrainer(SchedulerMixin, DPOTrainer):
         # transformers<=4.46
         return super(DPOTrainer, self).log(logs)  # pylint: disable=bad-super-call
 
+    def get_batch_loss_metrics(
+        self,
+        model,
+        batch: dict[str, Union[list, torch.LongTensor]],
+        train_eval: Literal["train", "eval"] = "train",
+    ):
+        """Compute the DPO loss and other metrics using Liger kernel."""
+        # return super().get_batch_loss_metrics(model, batch, train_eval)
+        if not self.liger_loss:
+            raise ValueError("Liger loss not initialized")
+
+        metrics = {}
+
+        model_output = self.concatenated_forward(model, batch)
+
+        # Get the lm_head weights and bias
+        lin_weight = model.lm_head.weight
+        lin_bias = getattr(model.lm_head, "bias", None)
+
+        hidden_states = model_output["hidden_states"]
+        labels = model_output["labels"]
+
+        if not self.reference_free:
+            # Adapted from DPO's compute_ref_log_probs
+            compte_ref_context_manager = (
+                amp.autocast("cuda")
+                if self._peft_has_been_casted_to_bf16
+                else nullcontext()
+            )
+            with torch.no_grad(), compte_ref_context_manager:  # type: ignore
+                if self.ref_model is None:
+                    with self.null_ref_context():
+                        ref_model_output = self.concatenated_forward(self.model, batch)
+                        ref_weight = self.model.lm_head.weight
+                        ref_bias = getattr(self.model.lm_head, "bias", None)
+
+                        ref_hidden_states = ref_model_output["hidden_states"]
+
+                else:
+                    ref_model_output = self.concatenated_forward(self.ref_model, batch)
+                    ref_weight = self.ref_model.lm_head.weight
+                    ref_bias = getattr(self.ref_model.lm_head, "bias", None)
+
+                ref_hidden_states = ref_model_output["hidden_states"]
+                (
+                    ref_chosen_logps,
+                    ref_rejected_logps,
+                    _ref_chosen_logits,
+                    _ref_rejected_logits,
+                    _ref_chosen_nll_loss,
+                ) = LigerFusedLinearPreferenceBase.chunk_forward(
+                    input_chunk=ref_hidden_states,
+                    weight=ref_weight,
+                    target_chunk=labels,
+                    bias=ref_bias,
+                    # ignore_index=ignore_index,
+                    compute_nll_loss=False,
+                )
+
+        else:
+            ref_hidden_states = None
+            ref_weight = None
+            ref_bias = None
+
+        # Compute loss using Liger kernel
+        loss, return_vars = self.liger_loss(
+            lin_weight=lin_weight,
+            _input=hidden_states,
+            target=labels,
+            bias=lin_bias,  # TODO: check whether to pass bias as FCLE doesn't
+            ref_input=ref_hidden_states,
+            ref_weight=ref_weight,
+            ref_bias=ref_bias,
+        )
+
+        (
+            policy_chosen_logps,
+            policy_rejected_logps,
+            policy_chosen_logits_mean,
+            policy_rejected_logits_mean,
+            policy_nll_loss,
+        ) = return_vars
+
+        # Calculate rewards
+        if not self.reference_free:
+            chosen_rewards = (
+                self.beta * (policy_chosen_logps - (ref_chosen_logps)).detach()
+            )
+            rejected_rewards = (
+                self.beta * (policy_rejected_logps - (ref_rejected_logps)).detach()
+            )
+
+        else:
+            chosen_rewards = self.beta * policy_chosen_logps
+            rejected_rewards = self.beta * policy_rejected_logps
+
+        reward_accuracies = (chosen_rewards > rejected_rewards).float()
+
+        prefix = "eval_" if train_eval == "eval" else ""
+        metrics.update(
+            {
+                f"{prefix}rewards/chosen": chosen_rewards.mean().cpu(),
+                f"{prefix}rewards/rejected": rejected_rewards.mean().cpu(),
+                f"{prefix}rewards/accuracies": reward_accuracies.mean().cpu(),
+                f"{prefix}rewards/margins": (chosen_rewards - rejected_rewards)
+                .mean()
+                .cpu(),
+                f"{prefix}logps/chosen": policy_chosen_logps.mean().cpu(),
+                f"{prefix}logps/rejected": policy_rejected_logps.mean().cpu(),
+                f"{prefix}logits/chosen": policy_chosen_logits_mean.cpu(),
+                f"{prefix}logits/rejected": policy_rejected_logits_mean.cpu(),
+            }
+        )
+
+        if hasattr(self.args, "rpo_alpha") and self.args.rpo_alpha is not None:
+            metrics[f"{prefix}nll_loss"] = policy_nll_loss.cpu()
+
+        # TODO: Handle use_weighting, aux_loss_enabled as in upstream
+
+        return loss, metrics
+
+    def concatenated_forward(
+        self, model: nn.Module, batch: dict[str, Union[list, torch.LongTensor]]
+    ):
+        """Run the given model on the given batch of inputs, concatenating the chosen and rejected inputs together.
+
+        We do this to avoid doing two forward passes, because it's faster for FSDP.
+
+        Overridden base function to return the hidden states and labels for the loss calculation.
+        """
+        num_examples = batch["prompt_input_ids"].shape[0]  # type: ignore
+
+        concatenated_batch = self.concatenated_inputs(
+            batch, padding_value=self.padding_value
+        )
+
+        model_kwargs = {}
+        if self.aux_loss_enabled:
+            model_kwargs["output_router_logits"] = True
+
+        # Add to get the hidden states for the loss
+        model_kwargs["output_hidden_states"] = True
+
+        # Add the pixel values and attention masks for vision models
+        if "pixel_values" in concatenated_batch:
+            model_kwargs["pixel_values"] = concatenated_batch["pixel_values"]
+        if "pixel_attention_mask" in concatenated_batch:
+            model_kwargs["pixel_attention_mask"] = concatenated_batch[
+                "pixel_attention_mask"
+            ]
+        if "image_sizes" in concatenated_batch:
+            model_kwargs["image_sizes"] = concatenated_batch["image_sizes"]
+
+        prompt_input_ids = concatenated_batch["prompt_input_ids"]
+        prompt_attention_mask = concatenated_batch["prompt_attention_mask"]
+        completion_input_ids = concatenated_batch["completion_input_ids"]
+        completion_attention_mask = concatenated_batch["completion_attention_mask"]
+        if self.is_encoder_decoder:
+            labels = completion_input_ids
+            labels[completion_attention_mask == 0] = self.label_pad_token_id
+            outputs = model(
+                input_ids=prompt_input_ids,
+                attention_mask=prompt_attention_mask,
+                labels=labels,  # we need the labels for the logits to be returned
+                **model_kwargs,
+            )
+            logits = outputs.logits
+            hidden_states = outputs.decoder_hidden_states[-1]
+            loss_mask = completion_attention_mask.bool()
+        else:
+            # Concatenate the prompt and completion inputs
+            input_ids = torch.cat((prompt_input_ids, completion_input_ids), dim=1)
+            attention_mask = torch.cat(
+                (prompt_attention_mask, completion_attention_mask), dim=1
+            )
+            # Mask the prompt but not the completion for the loss
+            loss_mask = torch.cat(
+                (torch.zeros_like(prompt_attention_mask), completion_attention_mask),
+                dim=1,
+            )
+
+            # Flush left to reduce the memory usage
+            # [[0, 0, x, x, x, x],  ->  [[x, x, x, x],
+            #  [0, x, x, x, 0, 0]]       [x, x, x, 0]]
+            for i in range(attention_mask.size(0)):
+                first_one_idx = torch.nonzero(attention_mask[i])[0].item()
+                input_ids[i] = torch.roll(input_ids[i], shifts=-first_one_idx)  # type: ignore
+                attention_mask[i] = torch.roll(attention_mask[i], shifts=-first_one_idx)  # type: ignore
+                loss_mask[i] = torch.roll(loss_mask[i], shifts=-first_one_idx)  # type: ignore
+
+            # Get the first column idx that is all zeros and remove every column after that
+            empty_cols = torch.sum(attention_mask, dim=0) == 0
+            first_empty_col = (
+                torch.nonzero(empty_cols)[0].item()
+                if empty_cols.any()
+                else attention_mask.size(1)
+            )
+            input_ids = input_ids[:, :first_empty_col]  # type: ignore
+            attention_mask = attention_mask[:, :first_empty_col]  # type: ignore
+            loss_mask = loss_mask[:, :first_empty_col]  # type: ignore
+
+            # Truncate right
+            if self.args.max_length is not None:
+                input_ids = input_ids[:, : self.args.max_length]
+                attention_mask = attention_mask[:, : self.args.max_length]
+                loss_mask = loss_mask[:, : self.args.max_length]
+
+            # if self.use_num_logits_to_keep:
+            #     # Compute num_logits_to_keep based on loss_mask pattern:
+            #     # [[0, 0, 0, x, x, x, x],
+            #     #  [0, 0, 0, x, x, x, 0]]
+            #     #         ^ start computing logits from here ([:, -(7-3+1):])
+            #     first_compute_index = loss_mask.nonzero(as_tuple=True)[1].min()
+            #     num_logits_to_keep = loss_mask.shape[1] - first_compute_index
+            #     model_kwargs["num_logits_to_keep"] = num_logits_to_keep.item() + 1  # +1 for the first label
+
+            outputs = model(
+                input_ids=input_ids, attention_mask=attention_mask, **model_kwargs
+            )
+
+            # Offset the logits by one to align with the labels
+            logits = outputs.logits[:, :-1, :]
+            hidden_states = outputs.hidden_states[-1][:, :-1, :]
+            labels = input_ids[:, 1:].clone()
+            loss_mask = loss_mask[:, 1:].bool()
+
+            # if self.use_num_logits_to_keep:
+            #     # Align labels with logits
+            #     # logits:    -,  -, [x2, x3, x4, x5, x6]
+            #     #                     ^ --------- ^       after logits[:, :-1, :]
+            #     # labels:   [y0, y1, y2, y3, y4, y5, y6]
+            #     #                         ^ --------- ^   with num_logits_to_keep=4, [:, -4:]
+            #     # loss_mask: [0,  0,  0,  1,  1,  1,  1]
+            #     labels = labels[:, -num_logits_to_keep:]
+            #     loss_mask = loss_mask[:, -num_logits_to_keep:]
+            #     hidden_states = hidden_states[:, -num_logits_to_keep:, :]
+
+        if logits.shape[:2] != labels.shape[:2]:
+            # for llava, the returned logits include the image tokens (placed before the text tokens)
+            seq_len = labels.shape[1]
+            logits = logits[:, -seq_len:]
+            hidden_states = hidden_states[:, -seq_len:]
+
+        # Compute the log probabilities of the labels
+        labels[
+            ~loss_mask
+        ] = 0  # dummy token; we'll ignore the losses on these tokens later
+        per_token_logps = torch.gather(
+            logits.log_softmax(-1), dim=2, index=labels.unsqueeze(2)
+        ).squeeze(2)
+        per_token_logps[~loss_mask] = 0
+        all_logps = per_token_logps.sum(-1)
+
+        output = {}
+
+        if self.use_weighting:
+            with torch.no_grad():
+                # Eq (2) of the WPO paper: https://huggingface.co/papers/2406.11827
+                logprobs = F.log_softmax(logits, dim=-1)
+                weights_adjustment_factor = torch.logsumexp(
+                    2 * logprobs, dim=-1
+                )  # same as sum(probs**2) in log space
+                per_token_logps_adjusted = per_token_logps - weights_adjustment_factor
+                all_weights = (per_token_logps_adjusted * loss_mask).sum(
+                    -1
+                ) / loss_mask.sum(-1)
+                chosen_weights = all_weights[:num_examples]
+                rejected_weights = all_weights[num_examples:]
+                output["policy_weights"] = torch.clamp(
+                    torch.exp(chosen_weights + rejected_weights), max=1
+                )
+
+        if self.args.rpo_alpha is not None:
+            # Only use the chosen logits for the RPO loss
+            chosen_logits = logits[:num_examples]
+            chosen_labels = labels[:num_examples]
+
+            # Compute the log probabilities of the labels
+            output["nll_loss"] = F.cross_entropy(
+                torch.flatten(chosen_logits, end_dim=1),
+                torch.flatten(chosen_labels, end_dim=1),
+                ignore_index=0,
+            )
+
+        if self.loss_type == "ipo":
+            all_logps = all_logps / loss_mask.sum(-1)
+
+        output["chosen_logps"] = all_logps[:num_examples]
+        output["rejected_logps"] = all_logps[num_examples:]
+        output["mean_chosen_logits"] = logits[:num_examples][
+            loss_mask[:num_examples]
+        ].mean()
+        output["mean_rejected_logits"] = logits[num_examples:][
+            loss_mask[num_examples:]
+        ].mean()
+        output["hidden_states"] = hidden_states
+        output["labels"] = labels
+
+        if self.aux_loss_enabled:
+            output["aux_loss"] = outputs.aux_loss
+
+        return output
+
 
 class AxolotlORPOTrainer(SchedulerMixin, ORPOTrainer):
     """
@@ -1368,8 +1685,6 @@ class TrainerBuilderBase(abc.ABC):
                 SaveAxolotlConfigtoWandBCallback(self.cfg.axolotl_config_path)
             )
         if self.cfg.use_mlflow and is_mlflow_available():
-            from transformers.integrations.integration_utils import MLflowCallback
-
             from axolotl.utils.callbacks.mlflow_ import (
                 SaveAxolotlConfigtoMlflowCallback,
             )
@@ -1377,7 +1692,6 @@ class TrainerBuilderBase(abc.ABC):
             callbacks.extend(
                 [
                     SaveAxolotlConfigtoMlflowCallback(self.cfg.axolotl_config_path),
-                    MLflowCallback,
                 ]
             )
         if self.cfg.use_comet and is_comet_available():
@@ -2166,6 +2480,14 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
             if self.cfg.dpo_use_weighting is not None:
                 training_args_kwargs["use_weighting"] = self.cfg.dpo_use_weighting
 
+        report_to = []
+        if self.cfg.use_wandb:
+            report_to.append("wandb")
+            if self.cfg.wandb_name:
+                training_args_kwargs["run_name"] = self.cfg.wandb_name
+
+        training_args_kwargs["report_to"] = report_to
+
         training_args = training_args_cls(  # pylint: disable=unexpected-keyword-arg
             output_dir=self.cfg.output_dir,
             per_device_train_batch_size=self.cfg.micro_batch_size,

src/axolotl/monkeypatch/trainer_grad_accum.py

@@ -204,3 +204,87 @@ def patch_forward_for_ga():
     LlamaForCausalLM.forward = (  # pylint: disable=protected-access
         _fixed_forward  # pylint: disable=undefined-variable  # noqa: F821
     )
+
+
+ORIGINAL_TRAINER_CODE = """
+                context = (
+                    functools.partial(self.accelerator.no_sync, model=model)
+                    if i != len(batch_samples) - 1
+                    else contextlib.nullcontext
+                )
+                with context():
+                    tr_loss_step = self.training_step(model, inputs, num_items_in_batch)
+"""
+
+PATCHED_TRAINER_CODE = """
+                disable_deepspeed_no_sync = (
+                        self.accelerator.distributed_type == DistributedType.DEEPSPEED
+                        # and self.accelerator.deepspeed_engine_wrapped.engine.zero_optimization_partition_gradients()
+                )
+                context = (
+                    functools.partial(self.accelerator.no_sync, model=model)
+                    if i != len(batch_samples) - 1 and not disable_deepspeed_no_sync
+                    else contextlib.nullcontext
+                )
+                with context():
+                    tr_loss_step = self.training_step(model, inputs, num_items_in_batch)
+"""
+
+
+def get_training_loop_code() -> str:
+    training_loop = inspect.getsource(
+        Trainer._inner_training_loop  # pylint: disable=protected-access
+    )
+    return training_loop
+
+
+def check_training_loop_is_patchable() -> bool:
+    training_loop = get_training_loop_code()
+    training_loop, _ = detab_code(training_loop)
+    return ORIGINAL_TRAINER_CODE in training_loop
+
+
+def patch_training_loop_for_deepspeed_0_16_x():
+    """
+    monkeypatch for fixing the training loop for deepspeed GA
+
+    see https://github.com/huggingface/transformers/pull/35157
+    """
+
+    try:
+        training_loop = get_training_loop_code()
+    except OSError:
+        return
+    Trainer._original_inner_training_loop = (  # pylint: disable=protected-access
+        training_loop
+    )
+    training_loop, _ = detab_code(training_loop)
+    if ORIGINAL_TRAINER_CODE not in training_loop:
+        return
+
+    training_loop = training_loop.replace(ORIGINAL_TRAINER_CODE, PATCHED_TRAINER_CODE)
+    training_loop = training_loop.replace(
+        "def _inner_training_loop(",
+        "def _fixed_inner_training_loop(",
+        1,
+    )
+
+    # load imports necessary
+    import transformers.trainer
+
+    items_to_import = []
+    for item in dir(transformers.trainer):
+        if item in training_loop:
+            items_to_import.append(item)
+
+    exec(  # pylint: disable=exec-used  # nosec B102
+        "from transformers.trainer import ("
+        + ", ".join(x for x in items_to_import)
+        + ")",
+        globals(),
+    )
+    exec(training_loop, globals())  # pylint: disable=exec-used  # nosec B102
+    LOG.info("patching _inner_training_loop for fsdp optimizer save")
+    Trainer._inner_training_loop = (  # pylint: disable=protected-access
+        _fixed_inner_training_loop  # pylint: disable=undefined-variable  # noqa: F821
+    )

src/axolotl/prompt_strategies/bradley_terry/chat_template.py

@@ -28,6 +28,8 @@ class BTChatTemplateStrategy(ChatTemplateStrategy):
         :return:
         """
 
+        max_length = self.prompter.max_length
+
         self.messages = "chosen_messages"
         # pylint: disable=duplicate-code
         prompt[self.messages] = []
@@ -39,6 +41,16 @@ class BTChatTemplateStrategy(ChatTemplateStrategy):
         prompt[self.messages].append({"role": "assistant", "content": prompt["chosen"]})
         chosen_tokenized = super().tokenize_prompt(prompt)
 
+        if len(chosen_tokenized["input_ids"]) > max_length:
+            LOG.warning(
+                f"Chosen sequence exceeds max sequence length: {len(chosen_tokenized['input_ids'])}",
+            )
+
+            chosen_tokenized["input_ids"] = chosen_tokenized["input_ids"][:max_length]
+            chosen_tokenized["attention_mask"] = chosen_tokenized["attention_mask"][
+                :max_length
+            ]
+
         self.messages = "rejected_messages"
         # pylint: disable=duplicate-code
         prompt[self.messages] = []
@@ -52,6 +64,18 @@ class BTChatTemplateStrategy(ChatTemplateStrategy):
         )
         rejected_tokenized = super().tokenize_prompt(prompt)
 
+        if len(rejected_tokenized["input_ids"]) > max_length:
+            LOG.warning(
+                f"Rejected sequence exceeds max sequence length: {len(rejected_tokenized['input_ids'])}",
+            )
+
+            rejected_tokenized["input_ids"] = rejected_tokenized["input_ids"][
+                :max_length
+            ]
+            rejected_tokenized["attention_mask"] = rejected_tokenized["attention_mask"][
+                :max_length
+            ]
+
         return {
             "input_ids_chosen": chosen_tokenized["input_ids"],
             "attention_mask_chosen": chosen_tokenized["attention_mask"],
@@ -80,9 +104,9 @@ def load(tokenizer, cfg, ds_cfg: Optional[Dict[str, Any]] = None):
         "roles": ds_cfg.get("roles"),
         "drop_system_message": ds_cfg.get("drop_system_message", False),
         # we need to add one for detecting sequences with exceeding the `sequence_len` limit.
-        "max_length": cfg.sequence_len + 1
-        if not cfg.reward_model
-        else cfg.sequence_len,
+        "max_length": (
+            cfg.sequence_len + 1 if not cfg.reward_model else cfg.sequence_len
+        ),
     }
 
     strategy_params = {

src/axolotl/prompt_strategies/chat_template.py

@@ -42,6 +42,7 @@ class ChatTemplatePrompter(Prompter):
                 "gpt": "assistant",
                 "system": "system",
             }
+
         self.message_field_role = message_field_role
         self.message_field_content = message_field_content
         self.message_field_training = message_field_training
@@ -53,21 +54,9 @@ class ChatTemplatePrompter(Prompter):
         self.drop_system_message = drop_system_message
 
     def build_prompt(self, conversation, add_generation_prompt=False, images=None):
-        turns = [
-            {
-                "role": self.roles[t[self.message_field_role]],
-                "content": t[self.message_field_content],
-                "training": t.get(self.message_field_training, None),
-            }
-            for t in conversation
-        ]
-
-        if self.drop_system_message and turns[0]["role"] == "system":
-            turns = turns[1:]
-
         if self.processor:
             text = self.processor.apply_chat_template(
-                turns,
+                conversation,
                 chat_template=self.chat_template,
                 tokenize=False,
                 add_generation_prompt=add_generation_prompt,
@@ -76,8 +65,6 @@ class ChatTemplatePrompter(Prompter):
                 text=text,
                 images=images,
                 return_tensors="pt",
-                truncation=True,
-                max_length=self.max_length,
             )
             # workaround since processor works in batches instead of single examples
             for k, val in batch.items():
@@ -88,9 +75,7 @@ class ChatTemplatePrompter(Prompter):
             return batch
 
         return self.tokenizer.apply_chat_template(
-            turns,
-            truncation=True,
-            max_length=self.max_length,
+            conversation,
             add_generation_prompt=add_generation_prompt,
             chat_template=self.chat_template,
         )
@@ -215,7 +200,14 @@ class ChatTemplateStrategy(PromptTokenizingStrategy):
         train_on_eos=None,
     ):
         super().__init__(prompter, tokenizer, train_on_inputs, sequence_len)
-        self.roles_to_train = roles_to_train if roles_to_train is not None else []
+
+        self.roles_to_train = []
+        if roles_to_train:
+            # map roles if exist in prompter.roles else use the role as is
+            self.roles_to_train = [
+                prompter.roles.get(role, role) for role in roles_to_train
+            ]
+
         self.train_on_eos = train_on_eos
         self.images = "images"
 
@@ -262,30 +254,28 @@ class ChatTemplateStrategy(PromptTokenizingStrategy):
 
             return tokenized_prompt
 
-        turns = prompt[self.messages]
+        turns = self.get_conversation_thread(prompt)
         input_ids = self.prompter.build_prompt(turns)
         labels = [IGNORE_TOKEN_ID] * len(input_ids)
 
         last_eos_idx = -1
         for index, turn in enumerate(turns):
-            role = turn.get(self.prompter.message_field_role)
-            content = turn.get(self.prompter.message_field_content)
-            train_turn = turn.get(self.prompter.message_field_training)
-            train_detail = turn.get(self.prompter.message_field_training_detail)
+            role = turn.get("role")
+            content = turn.get("content")
+            train_turn = turn.get("training")
+            train_detail = turn.get("training_detail")
 
             LOG.debug(
                 f"Processing turn {index}: role={role}, content={content}, train_turn={train_turn}, train_detail={train_detail}"
             )
 
-            should_train = (
-                train_turn
-                if train_turn is not None
-                else (
-                    bool(train_detail is not None)
-                    if train_detail is not None
-                    else self.train_on_inputs or role in self.roles_to_train
-                )
-            )
+            should_train = None
+            if train_turn is not None:
+                should_train = train_turn
+            elif train_detail is not None:
+                should_train = bool(train_detail)
+            else:
+                should_train = self.train_on_inputs or role in self.roles_to_train
 
             LOG.debug(f"Should train: {should_train}")
 
@@ -293,6 +283,9 @@ class ChatTemplateStrategy(PromptTokenizingStrategy):
                 conversation_ids=input_ids, turn=index, turn_content=turn
             )
 
+            if turn_start_idx == -1 or turn_end_idx == -1:
+                LOG.warning(f"Failed to find boundaries for turn {index}")
+
             LOG.debug(f"Turn indices: start={turn_start_idx}, end={turn_end_idx}")
 
             if should_train and turn_start_idx != -1 and turn_end_idx != -1:
@@ -313,7 +306,9 @@ class ChatTemplateStrategy(PromptTokenizingStrategy):
                     labels[turn_start_idx:turn_end_idx] = input_ids[
                         turn_start_idx:turn_end_idx
                     ]
-                    LOG.debug(f"Labels set for range {turn_start_idx}:{turn_end_idx}")
+                    LOG.debug(
+                        f"Set labels for training from {turn_start_idx} to {turn_end_idx}"
+                    )
 
                 LOG.debug(f"Labels after processing turn {index}: {labels}")
 
@@ -351,52 +346,73 @@ class ChatTemplateStrategy(PromptTokenizingStrategy):
                 return i
         return -1
 
-    def find_turn(self, conversation_ids, turn, turn_content):
+    def find_turn(self, conversation_ids: list[int], turn: int, turn_content: dict):
         """
         Locate the starting and ending indices of the specified turn in a conversation.
-
-        Args:
-            conversation_ids (list[int]): Token IDs representing the conversation.
-            turn (int): The turn number to locate (based on EOS tokens).
-            turn_content (str): String containing the content of the turn.
-
-        Returns:
-            tuple: (start_idx, end_idx) indices of the start and end of the turn content.
-                   Returns (-1, -1) if the turn content is not found.
         """
-        content = turn_content.get(self.prompter.message_field_content, "")
+        content = turn_content.get("content")
         content_ids = self.tokenizer.encode(content, add_special_tokens=False)
 
-        eos_token_id = self.tokenizer.eos_token_id
-        eos_count = 0
-        start_search_idx = 0
+        LOG.debug(f"content_ids (length {len(content_ids)}): {content_ids}")
 
-        # Locate the starting index after the specified number of EOS tokens
-        for i, token_id in enumerate(conversation_ids):
-            if token_id == eos_token_id:
-                eos_count += 1
-                if eos_count == turn:
-                    start_search_idx = (
-                        i + 1
-                    )  # Start searching after the specified turn's EOS token
-                    break
+        if not content_ids:
+            LOG.warning(f"Empty content for turn {turn}")
+            return -1, -1
 
-        # Find the start index of the content within the conversation
-        start_idx = -1
-        for i in range(start_search_idx, len(conversation_ids) - len(content_ids) + 1):
-            if conversation_ids[i : i + len(content_ids)] == content_ids:
-                start_idx = i
-                break
-
-        if start_idx != -1:
-            end_idx = start_idx + len(content_ids)
+        # For first turn, start from beginning
+        if turn == 0:
+            start_search_idx = 0
         else:
-            end_idx = -1
+            # For subsequent turns, find the previous EOS token
+            eos_token_id = self.tokenizer.eos_token_id
+            eos_count = 0
+            start_search_idx = 0
 
-        return start_idx, end_idx
+            for i, token_id in enumerate(conversation_ids):
+                if token_id == eos_token_id:
+                    eos_count += 1
+                    if eos_count == turn:  # Find the nth EOS token where n = turn
+                        start_search_idx = i + 1
+                        break
+
+        # we can optimize this to only search for a few tokens from start_search_idx
+        # but it would risk missing the content if it's not found within the first few tokens or
+        # if start_search_idx cannot be found above.
+        last_index = len(conversation_ids) - len(content_ids) + 1
+
+        if last_index < start_search_idx:
+            LOG.warning(
+                f"last_index to search is less than start_search_idx for turn {turn}"
+            )
+            return -1, -1
+
+        # Search for content starting from start_search_idx
+        first_elem = content_ids[0]
+        for i in range(start_search_idx, last_index):
+            # Quick check of first element before doing full comparison
+            if conversation_ids[i] == first_elem:
+                # Check if the rest of the content matches
+                if conversation_ids[i : i + len(content_ids)] == content_ids:
+                    LOG.debug(f"Found turn {turn} content at position {i}")
+                    return i, i + len(content_ids)
+
+        return -1, -1
 
     def get_conversation_thread(self, prompt):
-        return prompt[self.messages]
+        turns = [
+            {
+                "role": self.prompter.roles[t[self.prompter.message_field_role]],
+                "content": t[self.prompter.message_field_content],
+                "training": t.get(self.prompter.message_field_training),
+                "training_detail": t.get(self.prompter.message_field_training_detail),
+            }
+            for t in prompt[self.messages]
+        ]
+
+        if self.prompter.drop_system_message and turns[0]["role"] == "system":
+            turns = turns[1:]
+
+        return turns
 
     def get_images(self, prompt):
         return prompt.get(self.images, None)

src/axolotl/train.py

@@ -259,14 +259,7 @@ def train(
         model.save_pretrained(cfg.output_dir, safe_serialization=safe_serialization)
 
     if not cfg.hub_model_id:
-        from huggingface_hub import HfApi
-        from huggingface_hub.utils import RepositoryNotFoundError
-
         try:
-            # Check to make sure the base model is from HuggingFace not a local directory
-            hf_api = HfApi()
-            hf_api.model_info(cfg.base_model)
-
             model_card_kwarg = {
                 "model_name": cfg.output_dir.lstrip("./")
                 .encode("utf-8")
@@ -274,16 +267,22 @@ def train(
             }
             if cfg.datasets is not None:
                 if cfg.rl is not None or cfg.reward_model:
-                    model_card_kwarg["dataset_name"] = [
+                    dataset_tags = [
                         d["path"] for d in cfg.datasets if not Path(d["path"]).is_dir()
                     ]
+                    if dataset_tags:
+                        # guard as create_model_card may fail if dataset_tags is empty list
+                        model_card_kwarg["dataset_name"] = dataset_tags
                 else:
-                    model_card_kwarg["dataset_tags"] = [
+                    dataset_tags = [
                         d["path"] for d in cfg.datasets if not Path(d["path"]).is_dir()
                     ]
+                    if dataset_tags:
+                        # guard as create_model_card may fail if dataset_tags is empty list
+                        model_card_kwarg["dataset_tags"] = dataset_tags
 
             trainer.create_model_card(**model_card_kwarg)
-        except (AttributeError, UnicodeDecodeError, RepositoryNotFoundError):
+        except (AttributeError, UnicodeDecodeError):
             pass
     elif cfg.hub_model_id:
         # defensively push to the hub to ensure the model card is updated

src/axolotl/utils/callbacks/__init__.py

@@ -66,10 +66,7 @@ class EvalFirstStepCallback(
         control: TrainerControl,
         **kwargs,
     ):
-        if (
-            args.evaluation_strategy == IntervalStrategy.STEPS
-            and state.global_step == 1
-        ):
+        if args.eval_strategy == IntervalStrategy.STEPS and state.global_step == 1:
             control.should_evaluate = True
         return control
 

src/axolotl/utils/config/models/input/v0_4_1/__init__.py

@@ -1475,6 +1475,27 @@ class AxolotlInputConfig(
 
         return data
 
+    @model_validator(mode="before")
+    @classmethod
+    def check_kto_config(cls, data):
+        if data.get("rl") == "kto":
+            if data.get("sample_packing") or data.get("eval_sample_packing"):
+                raise ValueError("sample_packing is not supported with kto")
+
+            if data.get("remove_unused_columns") is not False:
+                raise ValueError("Set `remove_unused_columns: False` when using kto")
+
+            if data.get("gradient_checkpointing") and not (
+                data.get("gradient_checkpointing_kwargs")
+                and isinstance(data.get("gradient_checkpointing_kwargs"), dict)
+                and data["gradient_checkpointing_kwargs"].get("use_reentrant")
+            ):
+                raise ValueError(
+                    "Set `gradient_checkpointing_kwargs: {use_reentrant: true}` for when kto is enabled"
+                )
+
+        return data
+
 
 class AxolotlConfigWCapabilities(AxolotlInputConfig):
     """wrapper to valdiate gpu capabilities with the configured options"""

src/axolotl/utils/models.py

@@ -386,6 +386,12 @@ class ModelLoader:
             )
 
             patch_training_loop_for_fsdp()
+        elif self.cfg.deepspeed and self.cfg.gradient_accumulation_steps > 1:
+            from axolotl.monkeypatch.trainer_grad_accum import (
+                patch_training_loop_for_deepspeed_0_16_x,
+            )
+
+            patch_training_loop_for_deepspeed_0_16_x()
 
         if self.cfg.gradient_checkpointing == "unsloth":
             transformers.modeling_utils.checkpoint = hf_grad_checkpoint_unsloth_wrapper

src/setuptools_axolotl_dynamic_dependencies.py

@@ -0,0 +1,104 @@
+"""
+dynamic requirements for axolotl
+"""
+import platform
+import re
+from importlib.metadata import PackageNotFoundError, version
+
+from setuptools.command.build_py import build_py as _build_py
+
+
+# pylint: disable=duplicate-code
+def parse_requirements():
+    _install_requires = []
+    _dependency_links = []
+    with open("./requirements.txt", encoding="utf-8") as requirements_file:
+        lines = [r.strip() for r in requirements_file.readlines()]
+        for line in lines:
+            is_extras = (
+                "flash-attn" in line
+                or "flash-attention" in line
+                or "deepspeed" in line
+                or "mamba-ssm" in line
+                or "lion-pytorch" in line
+            )
+            if line.startswith("--extra-index-url"):
+                # Handle custom index URLs
+                _, url = line.split()
+                _dependency_links.append(url)
+            elif not is_extras and line and line[0] != "#":
+                # Handle standard packages
+                _install_requires.append(line)
+
+    try:
+        xformers_version = [req for req in _install_requires if "xformers" in req][0]
+        torchao_version = [req for req in _install_requires if "torchao" in req][0]
+        autoawq_version = [req for req in _install_requires if "autoawq" in req][0]
+
+        if "Darwin" in platform.system():
+            # don't install xformers on MacOS
+            _install_requires.pop(_install_requires.index(xformers_version))
+        else:
+            # detect the version of torch already installed
+            # and set it so dependencies don't clobber the torch version
+            try:
+                torch_version = version("torch")
+            except PackageNotFoundError:
+                torch_version = "2.5.1"
+            _install_requires.append(f"torch=={torch_version}")
+
+            version_match = re.match(r"^(\d+)\.(\d+)(?:\.(\d+))?", torch_version)
+            if version_match:
+                major, minor, patch = version_match.groups()
+                major, minor = int(major), int(minor)
+                patch = (
+                    int(patch) if patch is not None else 0
+                )  # Default patch to 0 if not present
+            else:
+                raise ValueError("Invalid version format")
+
+            if (major, minor) >= (2, 5):
+                _install_requires.pop(_install_requires.index(xformers_version))
+                if patch == 0:
+                    _install_requires.append("xformers==0.0.28.post2")
+                else:
+                    _install_requires.append("xformers==0.0.28.post3")
+                _install_requires.pop(_install_requires.index(autoawq_version))
+            elif (major, minor) >= (2, 4):
+                if patch == 0:
+                    _install_requires.pop(_install_requires.index(xformers_version))
+                    _install_requires.append("xformers>=0.0.27")
+                else:
+                    _install_requires.pop(_install_requires.index(xformers_version))
+                    _install_requires.append("xformers==0.0.28.post1")
+            elif (major, minor) >= (2, 3):
+                _install_requires.pop(_install_requires.index(torchao_version))
+                if patch == 0:
+                    _install_requires.pop(_install_requires.index(xformers_version))
+                    _install_requires.append("xformers>=0.0.26.post1")
+                else:
+                    _install_requires.pop(_install_requires.index(xformers_version))
+                    _install_requires.append("xformers>=0.0.27")
+            elif (major, minor) >= (2, 2):
+                _install_requires.pop(_install_requires.index(torchao_version))
+                _install_requires.pop(_install_requires.index(xformers_version))
+                _install_requires.append("xformers>=0.0.25.post1")
+            else:
+                _install_requires.pop(_install_requires.index(torchao_version))
+                _install_requires.pop(_install_requires.index(xformers_version))
+                _install_requires.append("xformers>=0.0.23.post1")
+
+    except PackageNotFoundError:
+        pass
+    return _install_requires, _dependency_links
+
+
+class BuildPyCommand(_build_py):
+    """
+    custom build_py command to parse dynamic requirements
+    """
+
+    def finalize_options(self):
+        super().finalize_options()
+        install_requires, _ = parse_requirements()
+        self.distribution.install_requires = install_requires

tests/cli/test_cli_version.py

@@ -0,0 +1,10 @@
+"""pytest tests for axolotl CLI --version"""
+from axolotl.cli.main import cli
+
+
+def test_print_version(cli_runner):
+    """Test that version is printed when --version is used."""
+
+    result = cli_runner.invoke(cli, ["--version"])
+    assert result.exit_code == 0
+    assert "axolotl, version " in result.output

tests/conftest.py

@@ -120,9 +120,15 @@ def temp_dir():
 @pytest.fixture(scope="function", autouse=True)
 def cleanup_monkeypatches():
     from transformers import Trainer
-    from transformers.models.llama.modeling_llama import LlamaFlashAttention2
+    from transformers.models.llama.modeling_llama import (
+        LlamaAttention,
+        LlamaFlashAttention2,
+        LlamaForCausalLM,
+    )
 
     original_fa2_forward = LlamaFlashAttention2.forward
+    original_llama_attn_forward = LlamaAttention.forward
+    original_llama_forward = LlamaForCausalLM.forward
     original_trainer_inner_training_loop = (
         Trainer._inner_training_loop  # pylint: disable=protected-access
     )
@@ -131,6 +137,8 @@ def cleanup_monkeypatches():
     yield
     # Reset LlamaFlashAttention2 forward
     LlamaFlashAttention2.forward = original_fa2_forward
+    LlamaAttention.forward = original_llama_attn_forward
+    LlamaForCausalLM.forward = original_llama_forward
     Trainer._inner_training_loop = (  # pylint: disable=protected-access
         original_trainer_inner_training_loop
     )
@@ -138,16 +146,25 @@ def cleanup_monkeypatches():
 
     # Reset other known monkeypatches
     modules_to_reset: list[tuple[str, list[str]]] = [
-        ("transformers",),
-        ("transformers.models.llama.modeling_llama", ["LlamaFlashAttention2"]),
-        ("transformers.trainer", ["Trainer"]),
+        ("transformers.models.llama",),
+        (
+            "transformers.models.llama.modeling_llama",
+            ["LlamaFlashAttention2", "LlamaAttention"],
+        ),
+        ("transformers.trainer",),
+        ("transformers", ["Trainer"]),
         ("transformers.loss.loss_utils",),
     ]
     for module_name_tuple in modules_to_reset:
         module_name = module_name_tuple[0]
-        module = importlib.import_module(module_name)
-        sys.modules[module_name] = module
-        importlib.reload(sys.modules[module_name])
+
+        spec = importlib.util.spec_from_file_location(
+            module_name, sys.modules[module_name].__file__
+        )
+        sys.modules[module_name] = importlib.util.module_from_spec(spec)
+        spec.loader.exec_module(sys.modules[module_name])
+
+        sys.modules[module_name] = importlib.reload(sys.modules[module_name])
         if len(module_name_tuple) > 1:
             module_globals = module_name_tuple[1]
             for module_global in module_globals:

tests/e2e/integrations/test_cut_cross_entropy.py

@@ -71,7 +71,11 @@ class TestCutCrossEntropyIntegration:
 
     @pytest.mark.parametrize(
         "attention_type",
-        ["flash_attention", "sdp_attention", "xformers_attention"],
+        [
+            "flash_attention",
+            "sdp_attention",
+            # "xformers_attention",
+        ],
     )
     def test_llama_w_cce_and_attention(self, min_cfg, temp_dir, attention_type):
         cfg = DictDefault(

tests/e2e/multigpu/test_llama.py

@@ -9,6 +9,7 @@ from pathlib import Path
 import pytest
 import yaml
 from accelerate.test_utils import execute_subprocess_async
+from e2e.utils import check_tensorboard
 from huggingface_hub import snapshot_download
 from transformers.testing_utils import get_torch_dist_unique_port
 
@@ -53,7 +54,7 @@ class TestMultiGPULlama:
                     },
                 ],
                 "num_epochs": 1,
-                "max_steps": 15,
+                "max_steps": 2,
                 "micro_batch_size": 4,
                 "gradient_accumulation_steps": 4,
                 "output_dir": temp_dir,
@@ -61,6 +62,7 @@ class TestMultiGPULlama:
                 "optimizer": "adamw_8bit",
                 "lr_scheduler": "cosine",
                 "flash_attention": True,
+                "use_tensorboard": True,
             }
         )
 
@@ -83,9 +85,13 @@ class TestMultiGPULlama:
             ]
         )
 
+        check_tensorboard(
+            temp_dir + "/runs", "train/train_loss", 2.3, "Train Loss is too high"
+        )
+
     @pytest.mark.parametrize(
         "gradient_accumulation_steps",
-        [1, 4],
+        [1, 2],
     )
     def test_lora_ddp_packed(self, temp_dir, gradient_accumulation_steps):
         # pylint: disable=duplicate-code
@@ -112,14 +118,15 @@ class TestMultiGPULlama:
                     },
                 ],
                 "num_epochs": 1,
-                "max_steps": 15,
-                "micro_batch_size": 4,
+                "max_steps": 2,
+                "micro_batch_size": 1,
                 "gradient_accumulation_steps": gradient_accumulation_steps,
                 "output_dir": temp_dir,
                 "learning_rate": 0.00001,
                 "optimizer": "adamw_8bit",
                 "lr_scheduler": "cosine",
                 "flash_attention": True,
+                "use_tensorboard": True,
             }
         )
 
@@ -142,6 +149,10 @@ class TestMultiGPULlama:
             ]
         )
 
+        check_tensorboard(
+            temp_dir + "/runs", "train/train_loss", 2.3, "Train Loss is too high"
+        )
+
     def test_dpo_lora_ddp(self, temp_dir):
         # pylint: disable=duplicate-code
         cfg = DictDefault(
@@ -180,7 +191,7 @@ class TestMultiGPULlama:
                     },
                 ],
                 "num_epochs": 1,
-                "max_steps": 15,
+                "max_steps": 2,
                 "micro_batch_size": 4,
                 "gradient_accumulation_steps": 4,
                 "output_dir": temp_dir,
@@ -189,6 +200,7 @@ class TestMultiGPULlama:
                 "optimizer": "adamw_8bit",
                 "lr_scheduler": "cosine",
                 "flash_attention": True,
+                "use_tensorboard": True,
             }
         )
 
@@ -211,6 +223,10 @@ class TestMultiGPULlama:
             ]
         )
 
+        check_tensorboard(
+            temp_dir + "/runs", "train/train_loss", 2.3, "Train Loss is too high"
+        )
+
     def test_dpo_qlora_ddp(self, temp_dir):
         # pylint: disable=duplicate-code
         cfg = DictDefault(
@@ -249,8 +265,8 @@ class TestMultiGPULlama:
                     },
                 ],
                 "num_epochs": 1,
-                "max_steps": 15,
-                "micro_batch_size": 4,
+                "max_steps": 2,
+                "micro_batch_size": 2,
                 "gradient_accumulation_steps": 4,
                 "output_dir": temp_dir,
                 "warmup_steps": 0,
@@ -258,6 +274,7 @@ class TestMultiGPULlama:
                 "optimizer": "adamw_8bit",
                 "lr_scheduler": "cosine",
                 "flash_attention": True,
+                "use_tensorboard": True,
             }
         )
 
@@ -280,9 +297,13 @@ class TestMultiGPULlama:
             ]
         )
 
+        check_tensorboard(
+            temp_dir + "/runs", "train/train_loss", 2.3, "Train Loss is too high"
+        )
+
     @pytest.mark.parametrize(
         "gradient_accumulation_steps",
-        [1, 4],
+        [1, 2],
     )
     def test_fsdp(self, temp_dir, gradient_accumulation_steps):
         # pylint: disable=duplicate-code
@@ -301,8 +322,8 @@ class TestMultiGPULlama:
                     },
                 ],
                 "num_epochs": 1,
-                "max_steps": 10,
-                "micro_batch_size": 4,
+                "max_steps": 2,
+                "micro_batch_size": 2,
                 "gradient_accumulation_steps": gradient_accumulation_steps,
                 "output_dir": temp_dir,
                 "learning_rate": 0.00001,
@@ -323,6 +344,7 @@ class TestMultiGPULlama:
                     "fsdp_state_dict_type": "FULL_STATE_DICT",
                     "fsdp_auto_wrap_policy": "TRANSFORMER_BASED_WRAP",
                 },
+                "use_tensorboard": True,
             }
         )
 
@@ -345,6 +367,10 @@ class TestMultiGPULlama:
             ]
         )
 
+        check_tensorboard(
+            temp_dir + "/runs", "train/train_loss", 2.3, "Train Loss is too high"
+        )
+
     @pytest.mark.parametrize(
         "fsdp_state_dict_type",
         ["FULL_STATE_DICT", "SHARDED_STATE_DICT"],
@@ -368,7 +394,7 @@ class TestMultiGPULlama:
                     },
                 ],
                 "num_epochs": 1,
-                "max_steps": 15,
+                "max_steps": 2,
                 "micro_batch_size": 4,
                 "gradient_accumulation_steps": 4,
                 "output_dir": temp_dir,
@@ -390,6 +416,7 @@ class TestMultiGPULlama:
                     "fsdp_state_dict_type": fsdp_state_dict_type,
                     "fsdp_auto_wrap_policy": "TRANSFORMER_BASED_WRAP",
                 },
+                "use_tensorboard": True,
             }
         )
 
@@ -412,6 +439,10 @@ class TestMultiGPULlama:
             ]
         )
 
+        check_tensorboard(
+            temp_dir + "/runs", "train/train_loss", 2.3, "Train Loss is too high"
+        )
+
     def test_fsdp_qlora_prequant_packed(self, temp_dir):
         # pylint: disable=duplicate-code
         cfg = DictDefault(
@@ -444,7 +475,7 @@ class TestMultiGPULlama:
                     },
                 ],
                 "num_epochs": 1,
-                "max_steps": 15,
+                "max_steps": 2,
                 "micro_batch_size": 4,
                 "gradient_accumulation_steps": 4,
                 "output_dir": temp_dir,
@@ -466,6 +497,7 @@ class TestMultiGPULlama:
                     "fsdp_state_dict_type": "SHARDED_STATE_DICT",
                     "fsdp_auto_wrap_policy": "TRANSFORMER_BASED_WRAP",
                 },
+                "use_tensorboard": True,
             }
         )
 
@@ -488,12 +520,41 @@ class TestMultiGPULlama:
             ]
         )
 
+        check_tensorboard(
+            temp_dir + "/runs", "train/train_loss", 2.3, "Train Loss is too high"
+        )
+
     @pytest.mark.parametrize(
         "gradient_accumulation_steps",
-        [1, 4],
+        [1, 2],
     )
-    def test_ds_zero3_packed(self, temp_dir, gradient_accumulation_steps):
+    @pytest.mark.parametrize(
+        "deepspeed",
+        [
+            "deepspeed_configs/zero3_bf16.json",
+            "deepspeed_configs/zero3_bf16_cpuoffload_all.json",
+            # "deepspeed_configs/zero3_bf16_cpuoffload_params.json",
+        ],
+    )
+    @pytest.mark.parametrize(
+        "qlora",
+        [True, False],
+    )
+    def test_ds_zero3_packed(
+        self, temp_dir, gradient_accumulation_steps, deepspeed, qlora
+    ):
         # pylint: disable=duplicate-code
+        if qlora:
+            adapter = {
+                "adapter": "qlora",
+                "lora_r": 8,
+                "lora_alpha": 16,
+                "lora_dropout": 0.05,
+                "lora_target_linear": True,
+                "load_in_4bit": True,
+            }
+        else:
+            adapter = {}
         cfg = DictDefault(
             {
                 "base_model": "HuggingFaceTB/SmolLM2-135M",
@@ -511,15 +572,17 @@ class TestMultiGPULlama:
                     },
                 ],
                 "num_epochs": 1,
-                "max_steps": 15,
-                "micro_batch_size": 4,
+                "max_steps": 2,
+                "micro_batch_size": 1,
                 "gradient_accumulation_steps": gradient_accumulation_steps,
                 "output_dir": temp_dir,
                 "learning_rate": 0.00001,
                 "optimizer": "adamw_torch",
                 "lr_scheduler": "cosine",
                 "flash_attention": True,
-                "deepspeed": str(AXOLOTL_ROOT / "deepspeed_configs/zero3_bf16.json"),
+                "deepspeed": str(AXOLOTL_ROOT / deepspeed),
+                "use_tensorboard": True,
+                **adapter,
             }
         )
 
@@ -542,19 +605,35 @@ class TestMultiGPULlama:
             ]
         )
 
-    def test_ds_zero3_qlora_packed(self, temp_dir):
+        check_tensorboard(
+            temp_dir + "/runs", "train/train_loss", 2.3, "Train Loss is too high"
+        )
+
+    @pytest.mark.parametrize(
+        "gradient_accumulation_steps",
+        [1, 2],
+    )
+    @pytest.mark.parametrize(
+        "qlora",
+        [True, False],
+    )
+    def test_ds_zero2_packed(self, temp_dir, gradient_accumulation_steps, qlora):
         # pylint: disable=duplicate-code
-        cfg = DictDefault(
-            {
-                "base_model": "HuggingFaceTB/SmolLM2-135M",
-                "load_in_4bit": True,
+        if qlora:
+            adapter = {
                 "adapter": "qlora",
                 "lora_r": 8,
                 "lora_alpha": 16,
                 "lora_dropout": 0.05,
                 "lora_target_linear": True,
+                "load_in_4bit": True,
+            }
+        else:
+            adapter = {}
+        cfg = DictDefault(
+            {
+                "base_model": "HuggingFaceTB/SmolLM2-135M",
                 "sample_packing": True,
-                "eval_sample_packing": False,
                 "pad_to_sequence_len": True,
                 "sequence_len": 2048,
                 "val_set_size": 0.05,
@@ -568,15 +647,17 @@ class TestMultiGPULlama:
                     },
                 ],
                 "num_epochs": 1,
-                "max_steps": 15,
-                "micro_batch_size": 4,
-                "gradient_accumulation_steps": 4,
+                "max_steps": 2,
+                "micro_batch_size": 1,
+                "gradient_accumulation_steps": gradient_accumulation_steps,
                 "output_dir": temp_dir,
-                "learning_rate": 0.0001,
+                "learning_rate": 0.00001,
                 "optimizer": "adamw_torch",
                 "lr_scheduler": "cosine",
                 "flash_attention": True,
-                "deepspeed": str(AXOLOTL_ROOT / "deepspeed_configs/zero3_bf16.json"),
+                "deepspeed": str(AXOLOTL_ROOT / "deepspeed_configs/zero2.json"),
+                "use_tensorboard": True,
+                **adapter,
             }
         )
 
@@ -598,3 +679,82 @@ class TestMultiGPULlama:
                 str(Path(temp_dir) / "config.yaml"),
             ]
         )
+
+        check_tensorboard(
+            temp_dir + "/runs", "train/train_loss", 2.3, "Train Loss is too high"
+        )
+
+    @pytest.mark.parametrize(
+        "gradient_accumulation_steps",
+        [1, 2],
+    )
+    @pytest.mark.parametrize(
+        "qlora",
+        [True, False],
+    )
+    def test_ds_zero1_packed(self, temp_dir, gradient_accumulation_steps, qlora):
+        # pylint: disable=duplicate-code
+        if qlora:
+            adapter = {
+                "adapter": "qlora",
+                "lora_r": 8,
+                "lora_alpha": 16,
+                "lora_dropout": 0.05,
+                "lora_target_linear": True,
+                "load_in_4bit": True,
+            }
+        else:
+            adapter = {}
+        cfg = DictDefault(
+            {
+                "base_model": "HuggingFaceTB/SmolLM2-135M",
+                "sample_packing": True,
+                "pad_to_sequence_len": True,
+                "sequence_len": 2048,
+                "val_set_size": 0.05,
+                "special_tokens": {
+                    "pad_token": "<|endoftext|>",
+                },
+                "datasets": [
+                    {
+                        "path": "tatsu-lab/alpaca",
+                        "type": "alpaca",
+                    },
+                ],
+                "num_epochs": 1,
+                "max_steps": 2,
+                "micro_batch_size": 1,
+                "gradient_accumulation_steps": gradient_accumulation_steps,
+                "output_dir": temp_dir,
+                "learning_rate": 0.00001,
+                "optimizer": "adamw_torch",
+                "lr_scheduler": "cosine",
+                "flash_attention": True,
+                "deepspeed": str(AXOLOTL_ROOT / "deepspeed_configs/zero1.json"),
+                "use_tensorboard": True,
+                **adapter,
+            }
+        )
+
+        # write cfg to yaml file
+        Path(temp_dir).mkdir(parents=True, exist_ok=True)
+        with open(Path(temp_dir) / "config.yaml", "w", encoding="utf-8") as fout:
+            fout.write(yaml.dump(cfg.to_dict(), Dumper=yaml.Dumper))
+
+        execute_subprocess_async(
+            [
+                "accelerate",
+                "launch",
+                "--num-processes",
+                "2",
+                "--main_process_port",
+                f"{get_torch_dist_unique_port()}",
+                "-m",
+                "axolotl.cli.train",
+                str(Path(temp_dir) / "config.yaml"),
+            ]
+        )
+
+        check_tensorboard(
+            temp_dir + "/runs", "train/train_loss", 2.3, "Train Loss is too high"
+        )

tests/e2e/patched/test_fa_xentropy.py

@@ -4,7 +4,6 @@ E2E tests for lora llama
 
 import logging
 import os
-from importlib import reload
 from pathlib import Path
 
 import pytest
@@ -22,14 +21,6 @@ LOG = logging.getLogger("axolotl.tests.e2e")
 os.environ["WANDB_DISABLED"] = "true"
 
 
-@pytest.fixture(autouse=True)
-def reload_transformers():
-    import transformers.models.llama.modeling_llama
-
-    yield
-    reload(transformers.models.llama.modeling_llama)
-
-
 class TestFAXentropyLlama:
     """
     Test case for Llama models using LoRA w multipack

tests/e2e/patched/test_fused_llama.py

@@ -7,6 +7,7 @@ import os
 import unittest
 from pathlib import Path
 
+import pytest
 from transformers.utils import is_torch_bf16_gpu_available
 
 from axolotl.cli import load_datasets
@@ -21,6 +22,7 @@ LOG = logging.getLogger("axolotl.tests.e2e")
 os.environ["WANDB_DISABLED"] = "true"
 
 
+@pytest.mark.skip("FIXME, mostly underused functionality")
 class TestFusedLlama(unittest.TestCase):
     """
     Test case for Llama models using Fused layers