add transformers module to cleanup

.github/workflows/pypi.yml

@@ -13,13 +13,10 @@ 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" --generate-notes
+        run: gh release create "$GITHUB_REF_NAME" # GITHUB_REF_NAME is the tag name in `on.push.tags` workflows
   pypi-publish:
     name: Upload release to PyPI
     runs-on: ubuntu-latest
@@ -41,7 +38,7 @@ jobs:
       - name: Install dependencies
         run: |
           pip3 install wheel packaging
-          pip3 install --no-build-isolation -e .
+          pip3 install -e .
           pip3 install -r requirements-dev.txt -r requirements-tests.txt
 
       - name: Extract tag name

.github/workflows/tests-nightly.yml

@@ -44,11 +44,6 @@ 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
@@ -65,15 +60,11 @@ jobs:
         run: |
           pip3 install --upgrade pip
           pip3 install --upgrade packaging
-          pip3 install --no-build-isolation -U -e .
+          pip3 install -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

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

MANIFEST.in

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

README.md

@@ -10,13 +10,9 @@
     <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">
-    <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/>
+</p>
+<p align="center">
     <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>
@@ -46,8 +42,7 @@ Features:
 - [Axolotl](#axolotl)
   - [Table of Contents](#table-of-contents)
   - [Quickstart ⚡](#quickstart-)
-    - [Edge Builds](#edge-builds-)
-    - [Axolotl CLI Usage](#axolotl-cli-usage)
+    - [Usage](#usage)
   - [Badge ❤🏷️](#badge-️)
   - [Contributing 🤝](#contributing-)
   - [Sponsors 🤝❤](#sponsors-)
@@ -112,49 +107,58 @@ 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
-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
+git clone https://github.com/axolotl-ai-cloud/axolotl
 cd axolotl
+
 pip3 install packaging ninja
-pip3 install --no-build-isolation -e '.[flash-attn,deepspeed]'
+pip3 install -e '.[flash-attn,deepspeed]'
 ```
 
-### Axolotl CLI Usage
-We now support a new, more streamlined CLI using [click](https://click.palletsprojects.com/en/stable/).
+### 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:
 
 ```bash
 # preprocess datasets - optional but recommended
-CUDA_VISIBLE_DEVICES="0" axolotl preprocess examples/llama-3/lora-1b.yml
+CUDA_VISIBLE_DEVICES="0" axolotl preprocess examples/openllama-3b/lora.yml
 
 # finetune lora
-axolotl train examples/llama-3/lora-1b.yml
+axolotl train examples/openllama-3b/lora.yml
 
 # inference
-axolotl inference examples/llama-3/lora-1b.yml \
+axolotl inference examples/openllama-3b/lora.yml \
     --lora-model-dir="./outputs/lora-out"
 
 # gradio
-axolotl inference examples/llama-3/lora-1b.yml \
+axolotl 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
-axolotl train https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/examples/llama-3/lora-1b.yml
+axolotl train https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/examples/openllama-3b/lora.yml
 ```
 
 We've also added a new command for fetching `examples` and `deepspeed_configs` to your
@@ -171,36 +175,6 @@ 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.
@@ -320,7 +294,7 @@ 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 --no-build-isolation -e '.[flash-attn,deepspeed]'
+        pip3 install -e '.[flash-attn,deepspeed]'
         ```
   4. (Optional) Login to Huggingface to use gated models/datasets.
         ```bash
@@ -399,7 +373,7 @@ Please use WSL or Docker!
 
 Use the below instead of the install method in QuickStart.
 ```
-pip3 install --no-build-isolation -e '.'
+pip3 install -e '.'
 ```
 More info: [mac.md](/docs/mac.qmd)
 

cicd/Dockerfile.jinja

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

cicd/cicd.sh

@@ -1,10 +1,7 @@
 #!/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/
-pytest -v --durations=10 -n1 --dist loadfile /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/ /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 [ "$AXOLOTL_EXTRAS" != "" ] ; then \
-        pip install --no-build-isolation -e .[deepspeed,flash-attn,optimizers,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \
+        pip install -e .[deepspeed,flash-attn,optimizers,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \
     else \
-        pip install --no-build-isolation -e .[deepspeed,flash-attn,optimizers] $AXOLOTL_ARGS; \
+        pip install -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 [ "$AXOLOTL_EXTRAS" != "" ] ; then \
-        pip install --no-build-isolation -e .[deepspeed,flash-attn,mamba-ssm,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \
+        pip install -e .[deepspeed,flash-attn,mamba-ssm,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \
     else \
-        pip install --no-build-isolation -e .[deepspeed,flash-attn,mamba-ssm] $AXOLOTL_ARGS; \
+        pip install -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 --no-build-isolation .
+pip install .
 ```
 
 ### 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 --no-build-isolation -e .
+pip install -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 --no-build-isolation -e '.[flash-attn,deepspeed]'
+pip3 install -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 --no-build-isolation -e '.[flash-attn,deepspeed]'
+pip3 install -e '.[flash-attn,deepspeed]'
 ```
 
 ### Attach To Container

docs/rlhf.qmd

@@ -52,26 +52,6 @@ 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 --no-build-isolation axolotl[deepspeed]"
+    "!pip install axolotl[deepspeed]"
    ]
   },
   {

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

@@ -1,74 +0,0 @@
-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

@@ -1,75 +0,0 @@
-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: NousResearch/Llama-3.2-1B
+base_model: meta-llama/Llama-3.2-1B
 
 load_in_8bit: false
 load_in_4bit: true
@@ -22,6 +22,7 @@ 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

@@ -17,10 +17,3 @@ 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

@@ -1,30 +1,22 @@
 --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==4.47.0
+transformers>=4.46.3
 tokenizers>=0.20.1
+bitsandbytes==0.45.0
 accelerate==1.2.0
 datasets==3.1.0
-deepspeed==0.16.1
+deepspeed==0.15.4
 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
@@ -39,6 +31,11 @@ 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-build-isolation --no-deps -e .
+pip install --no-deps -e .
 ```

setup.py

@@ -1,10 +1,7 @@
 """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
 
@@ -93,24 +90,9 @@ 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,
@@ -125,7 +107,7 @@ setup(
             "flash-attn==2.7.0.post2",
         ],
         "deepspeed": [
-            "deepspeed==0.16.1",
+            "deepspeed==0.15.4",
             "deepspeed-kernels",
         ],
         "mamba-ssm": [

src/axolotl/__init__.py

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

src/axolotl/core/training_args.py

@@ -1,220 +0,0 @@
-"""
-extra axolotl specific training args
-"""
-from dataclasses import dataclass, field
-from typing import Optional
-
-from transformers import TrainingArguments
-from trl import CPOConfig, DPOConfig, KTOConfig, ORPOConfig, RewardConfig
-
-
-@dataclass
-class AxolotlTrainingMixins:
-    """
-    Mixin class for the Axolotl training args.
-    """
-
-    # pylint: disable=duplicate-code
-    model_type: Optional[str] = field(
-        default=None, metadata={"help": "HF model configuration model_type."}
-    )
-    lr_quadratic_warmup: bool = field(
-        default=False,
-        metadata={"help": "Use quadratic warmup for cosine scheduling."},
-    )
-    pretraining: bool = field(
-        default=False,
-        metadata={
-            "help": "Indicates to trainer whether we are doing continued pretraining."
-        },
-    )
-    sample_packing: bool = field(
-        default=False,
-        metadata={"help": "Use sample packing for efficient training."},
-    )
-    multipack_real_batches: bool = field(
-        default=False,
-        metadata={"help": "Use real batches for efficient training."},
-    )
-    eval_sample_packing: Optional[bool] = field(
-        default=None,
-        metadata={"help": "Use sample packing for efficient evals."},
-    )
-    sample_packing_efficiency: float = field(
-        default=1.0,
-        metadata={"help": "Sample packing efficiency for calculating batch length."},
-    )
-    sample_packing_bin_size: int = field(
-        default=200,
-        metadata={
-            "help": "The max number of samples that packed sample can contain after packing. Increase for better packing."
-        },
-    )
-    sample_packing_group_size: int = field(
-        default=100000,
-        metadata={
-            "help": "The number of samples to group together for packing. Increase for better packing."
-        },
-    )
-    max_seq_length: int = field(
-        default=2048,
-        metadata={"help": "The maximum sequence length the model can handle"},
-    )
-    relora_steps: Optional[int] = field(
-        default=None,
-        metadata={"help": "how often to reset for ReLoRA"},
-    )
-    relora_warmup_steps: Optional[int] = field(
-        default=None,
-        metadata={"help": "how many warmup steps to take after reset for ReLoRA"},
-    )
-    relora_anneal_steps: Optional[int] = field(
-        default=None,
-        metadata={"help": "how many warmup steps to take after reset for ReLoRA"},
-    )
-    relora_prune_ratio: Optional[float] = field(
-        default=0.9,
-        metadata={"help": "prune ratio for magnitude pruning of the optimizer"},
-    )
-    bench_split: Optional[str] = field(
-        default="eval", metadata={"help": "The benchmark split to run on"}
-    )
-    bench_dataset: Optional[str] = field(
-        default="pharaouk/dharma-1/dharma_1_mini.json",
-        metadata={
-            "help": "Benchmark dataset to use: options are `mmlu-zs`, `mmlu-fs`, or the full path to the dataset file"
-        },
-    )
-    do_bench_eval: Optional[bool] = field(
-        default=False, metadata={"help": "Whether to run the Benchmark evaluation."}
-    )
-    do_causal_lm_eval: Optional[bool] = field(
-        default=False, metadata={"help": "Whether to run the Causal LM evaluation."}
-    )
-    max_bench_samples: Optional[int] = field(
-        default=None,
-        metadata={
-            "help": "If set, only evaluates on `max_bench_samples` of the benchmark dataset."
-        },
-    )
-    bench_source_max_len: int = field(
-        default=2048, metadata={"help": "Maximum source sequence length for bench."}
-    )
-    dataloader_prefetch_factor: Optional[int] = field(
-        default=None,
-        metadata={"help": "prefetch_factor argument to the dataloader"},
-    )
-    cosine_min_lr_ratio: Optional[float] = field(
-        default=None,
-        metadata={"help": "Minimum learning rate is min_lr_ratio * learning_rate"},
-    )
-    cosine_constant_lr_ratio: Optional[float] = field(
-        default=None,
-        metadata={
-            "help": "Starting constant learning rate step is cosine_constant_lr_ratio * max_steps"
-        },
-    )
-    loraplus_lr_ratio: Optional[float] = field(
-        default=None, metadata={"help": "loraplus learning rate ratio lr_B / lr_A."}
-    )
-    loraplus_lr_embedding: Optional[float] = field(
-        default=1e-6,
-        metadata={"help": "loraplus learning rate for lora embedding layers."},
-    )
-    embedding_lr_scale: Optional[float] = field(
-        default=None,
-        metadata={"help": "Scale the learning rate for the embedding layers."},
-    )
-    embedding_lr: Optional[float] = field(
-        default=None,
-        metadata={"help": "absolute learning rate for the embedding layers."},
-    )
-    qlora: bool = field(
-        default=False,
-        metadata={"help": "whether this is a qlora training"},
-    )
-    orpo_alpha: Optional[float] = field(
-        default=None,
-    )
-    lisa_n_layers: Optional[int] = field(
-        default=None,
-        metadata={"help": "the number of activate layers in LISA"},
-    )
-    lisa_step_interval: Optional[int] = field(
-        default=None,
-        metadata={"help": "how often to switch layers in LISA"},
-    )
-    lisa_layers_attribute: Optional[str] = field(
-        default=None,
-        metadata={"help": "path under the model to access the layers"},
-    )
-    curriculum_sampling: Optional[bool] = field(
-        default=None,
-        metadata={"help": "whether to use sequential sampling for curriculum learning"},
-    )
-    alternate_optimizer: Optional[str] = field(
-        default=None,
-        metadata={
-            "help": "workaround to pass an alternate optimizer to the HF trainer"
-        },
-    )
-    alternate_lr_scheduler_type: Optional[str] = field(
-        default=None,
-        metadata={
-            "help": "workaround to pass an alternate lr scheduler to the HF trainer"
-        },
-    )
-    chat_template: Optional[str] = field(
-        default=None,
-        metadata={"help": "Chat template converting chat messages to text"},
-    )
-
-
-@dataclass
-class AxolotlTrainingArguments(AxolotlTrainingMixins, TrainingArguments):
-    """
-    Training arguments for Causal trainer
-
-    This code is duplicated due to HF TrainingArguments not setting output_dir with a defaujlt value
-    so it can't be used as a mixin.
-    """
-
-
-@dataclass
-class AxolotlDPOConfig(AxolotlTrainingMixins, DPOConfig):
-    """
-    DPO config for DPO training
-    """
-
-
-@dataclass
-class AxolotlORPOConfig(AxolotlTrainingMixins, ORPOConfig):
-    """
-    ORPO config for ORPO training
-    """
-
-
-@dataclass
-class AxolotlKTOConfig(AxolotlTrainingMixins, KTOConfig):
-    """
-    KTO config for KTO training
-    """
-
-
-@dataclass
-class AxolotlCPOConfig(AxolotlTrainingMixins, CPOConfig):
-    """
-    CPO config for CPO training
-    """
-
-    simpo_gamma: Optional[float] = field(
-        default=None,
-        metadata={"help": "simpo gamma parameter"},
-    )
-
-
-@dataclass
-class AxolotlRewardConfig(AxolotlTrainingMixins, RewardConfig):
-    """
-    Reward config for Reward training
-    """

src/axolotl/integrations/liger/args.py

@@ -36,8 +36,6 @@ class LigerArgs(BaseModel):
     liger_cross_entropy: Optional[bool] = None
     liger_fused_linear_cross_entropy: Optional[bool] = None
 
-    liger_pref_rl: Optional[bool] = None
-
     @model_validator(mode="before")
     @classmethod
     def check_deprecated_swiglu(cls, data):

src/axolotl/integrations/liger/trainer/dpo_trainer.py

@@ -1,253 +0,0 @@
-"""
-integration of liger dpo kernels with dpotrainer
-"""
-from typing import Dict, List, Literal, Union
-
-import torch
-from liger_kernel.chunked_loss import LigerFusedLinearDPOLoss
-from liger_kernel.transformers.trainer.orpo_trainer import _FSDPForwardRedirection
-from torch import nn
-from torch.distributed.fsdp import FullyShardedDataParallel
-
-from axolotl.core.trainers.base import AxolotlDPOTrainer
-
-
-class AxolotlLigerDPOTrainer(AxolotlDPOTrainer):
-    """
-    Extend the DPO Trainer to use LIGER kernels for DPO
-    """
-
-    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,
-        and compute the DPO loss using Liger's fused kernel.
-
-        This method replaces the original `concatenated_forward` implementation to use Liger.
-        """
-
-        # Prepare concatenated inputs
-        concatenated_batch = self.concatenated_inputs(batch, self.padding_value)
-
-        # Extract concatenated inputs
-        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"]
-
-        # For encoder-decoder models, you'd need to construct decoder_input_ids, etc.
-        # This example assumes a causal decoder-only model.
-        input_ids = torch.cat((prompt_input_ids, completion_input_ids), dim=1)
-        attention_mask = torch.cat(
-            (prompt_attention_mask, completion_attention_mask), dim=1
-        )
-
-        # Align inputs by removing leading padding
-        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)
-            attention_mask[i] = torch.roll(attention_mask[i], shifts=-first_one_idx)
-
-        # Remove trailing empty columns
-        empty_cols = torch.sum(attention_mask, dim=0) == 0
-        if empty_cols.any():
-            first_empty_col = torch.nonzero(empty_cols)[0].item()
-            input_ids = input_ids[:, :first_empty_col]
-            attention_mask = attention_mask[:, :first_empty_col]
-
-        if self.args.max_length is not None:
-            input_ids = input_ids[:, : self.args.max_length]
-            attention_mask = attention_mask[:, : self.args.max_length]
-
-        # Labels are completion_input_ids shifted by one token right
-        # For causal LM, labels are the completion part only
-        labels = torch.cat(
-            (torch.zeros_like(prompt_input_ids), completion_input_ids), dim=1
-        )
-        labels = labels[:, 1:]  # shift left by one
-        attention_mask = attention_mask[:, 1:]
-        labels = labels[:, : attention_mask.size(1)]
-
-        # Mask out the prompt portion from loss
-        labels[~attention_mask.bool()] = self.label_pad_token_id
-
-        # Prepare reference model hidden states if ref_model exists
-        use_ref_model = self.ref_model is not None and not self.reference_free
-
-        # Run main model forward to get hidden states
-        # If using FSDP, redirect forward calls
-        if isinstance(model, FullyShardedDataParallel):
-            outputs = _FSDPForwardRedirection()(
-                model,
-                model._fsdp_wrapped_module.model,  # pylint: disable=protected-access
-                input_ids,
-                attention_mask=attention_mask,
-                use_cache=False,
-            )
-        else:
-            # If model is a DataParallel, unwrap
-            if isinstance(model, torch.nn.DataParallel):
-                model = model.module
-            outputs = model.model(
-                input_ids, attention_mask=attention_mask, use_cache=False
-            )
-
-        last_hidden_state = outputs.last_hidden_state
-
-        ref_last_hidden_state = None
-        if use_ref_model:
-            ref_model = self.ref_model
-            if isinstance(ref_model, FullyShardedDataParallel):
-                with torch.no_grad():
-                    ref_outputs = _FSDPForwardRedirection()(
-                        ref_model,
-                        ref_model._fsdp_wrapped_module.model,  # pylint: disable=protected-accessåå
-                        input_ids,
-                        attention_mask=attention_mask,
-                        use_cache=False,
-                    )
-            else:
-                if isinstance(ref_model, torch.nn.DataParallel):
-                    ref_model = ref_model.module
-                with torch.no_grad():
-                    ref_outputs = ref_model.model(
-                        input_ids, attention_mask=attention_mask, use_cache=False
-                    )
-            ref_last_hidden_state = ref_outputs.last_hidden_state
-
-        # Retrieve lm_head parameters
-        lm_head = model.lm_head
-        ref_lm_head = (
-            self.ref_model.lm_head
-            if (use_ref_model and self.ref_model is not None)
-            else None
-        )
-
-        # Use Liger fused DPO loss
-        dpo_loss_fn = LigerFusedLinearDPOLoss(
-            ignore_index=self.label_pad_token_id,
-            beta=self.beta,
-            compute_nll_loss=False,
-            compiled=True,
-            use_ref_model=use_ref_model,
-        )
-
-        # call fused Liger DPO
-        if use_ref_model:
-            loss_acc, aux_outputs = dpo_loss_fn(
-                lm_head.weight,  # lin_weight
-                last_hidden_state,  # _input
-                labels,  # target
-                bias=lm_head.bias,
-                ref_input=ref_last_hidden_state,
-                ref_weight=ref_lm_head.weight,
-                ref_bias=ref_lm_head.bias,
-            )
-
-            (
-                policy_chosen_logps,
-                policy_rejected_logps,
-                policy_chosen_logits_mean,
-                policy_rejected_logits_mean,
-                policy_nll_loss,
-            ) = aux_outputs[:5]
-
-        else:
-            # No reference model scenario: Liger kernel treats ref_logps as 0
-            loss_acc, aux_outputs = dpo_loss_fn(
-                lm_head.weight,
-                last_hidden_state,
-                labels,
-                bias=lm_head.bias,
-            )
-            (
-                policy_chosen_logps,
-                policy_rejected_logps,
-                policy_chosen_logits_mean,
-                policy_rejected_logits_mean,
-                policy_nll_loss,
-            ) = aux_outputs[:5]
-
-        # Add aux loss if enabled
-        if self.aux_loss_enabled and hasattr(outputs, "aux_loss"):
-            loss_acc = loss_acc + self.aux_loss_coef * outputs.aux_loss
-
-        # Add RPO loss if requested (RPO is a variant that adds NLL loss)
-        if self.args.rpo_alpha is not None:
-            # policy_nll_loss: average negative log-likelihood of chosen completions
-            loss_acc = loss_acc + self.args.rpo_alpha * policy_nll_loss.mean()
-
-        return (
-            loss_acc,
-            policy_chosen_logps,
-            policy_rejected_logps,
-            policy_chosen_logits_mean,
-            policy_rejected_logits_mean,
-            policy_nll_loss,
-        )
-
-    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 for a given batch using the Liger fused kernel.
-        """
-        metrics = {}
-
-        (
-            loss,
-            policy_chosen_logps,
-            policy_rejected_logps,
-            policy_chosen_logits_mean,
-            policy_rejected_logits_mean,
-            policy_nll_loss,
-        ) = self.concatenated_forward(model, batch)
-
-        # For metrics, we approximate chosen/rejected rewards as beta * (log π(y) - log π_ref(y)) if ref model used.
-        # If no ref model is used, we can't compute reward_accuracies meaningfully. For simplicity, we assume ref_model presence.
-        if self.ref_model is not None and not self.reference_free:
-            # If you want full parity with original DPOTrainer metrics (like chosen_rewards, rejected_rewards),
-            # you'd need to run reference forward or store reference log ps. The Liger kernel currently doesn't
-            # return ref_chosen_logps/ref_rejected_logps explicitly. By design, Liger directly computes DPO.
-            #
-            # Here we approximate chosen_rewards and rejected_rewards from the difference in chosen/rejected logps.
-            # Since Liger DPO does not output ref logps separately, you may need to modify the Liger kernel to
-            # also output them if you need all the metrics. For now, we'll skip them or provide a placeholder.
-
-            # Placeholder: chosen/rejected "rewards" can't be retrieved directly from Liger as-is.
-            # If needed, integrate ref_chosen_logps/ref_rejected_logps into Liger kernel returns.
-            chosen_rewards = policy_chosen_logps * self.beta  # approximation
-            rejected_rewards = policy_rejected_logps * self.beta  # approximation
-            reward_accuracies = (chosen_rewards > rejected_rewards).float()
-            metrics[f"{train_eval}_rewards/chosen"] = chosen_rewards.mean().cpu().item()
-            metrics[f"{train_eval}_rewards/rejected"] = (
-                rejected_rewards.mean().cpu().item()
-            )
-            metrics[f"{train_eval}_rewards/accuracies"] = (
-                reward_accuracies.mean().cpu().item()
-            )
-            metrics[f"{train_eval}_rewards/margins"] = (
-                (chosen_rewards - rejected_rewards).mean().cpu().item()
-            )
-
-        metrics[f"{train_eval}_logps/chosen"] = policy_chosen_logps.mean().cpu().item()
-        metrics[f"{train_eval}_logps/rejected"] = (
-            policy_rejected_logps.mean().cpu().item()
-        )
-        metrics[f"{train_eval}_logits/chosen"] = (
-            policy_chosen_logits_mean.detach().cpu().item()
-        )
-        metrics[f"{train_eval}_logits/rejected"] = (
-            policy_rejected_logits_mean.detach().cpu().item()
-        )
-
-        if self.args.rpo_alpha is not None:
-            metrics[f"{train_eval}_nll_loss"] = (
-                policy_nll_loss.mean().detach().cpu().item()
-            )
-
-        return loss.mean(), metrics

src/axolotl/monkeypatch/trainer_grad_accum.py

@@ -204,87 +204,3 @@ 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

@@ -28,8 +28,6 @@ class BTChatTemplateStrategy(ChatTemplateStrategy):
         :return:
         """
 
-        max_length = self.prompter.max_length
-
         self.messages = "chosen_messages"
         # pylint: disable=duplicate-code
         prompt[self.messages] = []
@@ -41,16 +39,6 @@ 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] = []
@@ -64,18 +52,6 @@ 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"],
@@ -104,9 +80,9 @@ 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

@@ -42,7 +42,6 @@ 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
@@ -54,9 +53,21 @@ 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(
-                conversation,
+                turns,
                 chat_template=self.chat_template,
                 tokenize=False,
                 add_generation_prompt=add_generation_prompt,
@@ -65,6 +76,8 @@ 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():
@@ -75,7 +88,9 @@ class ChatTemplatePrompter(Prompter):
             return batch
 
         return self.tokenizer.apply_chat_template(
-            conversation,
+            turns,
+            truncation=True,
+            max_length=self.max_length,
             add_generation_prompt=add_generation_prompt,
             chat_template=self.chat_template,
         )
@@ -200,14 +215,7 @@ class ChatTemplateStrategy(PromptTokenizingStrategy):
         train_on_eos=None,
     ):
         super().__init__(prompter, tokenizer, train_on_inputs, sequence_len)
-
-        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.roles_to_train = roles_to_train if roles_to_train is not None else []
         self.train_on_eos = train_on_eos
         self.images = "images"
 
@@ -254,28 +262,30 @@ class ChatTemplateStrategy(PromptTokenizingStrategy):
 
             return tokenized_prompt
 
-        turns = self.get_conversation_thread(prompt)
+        turns = prompt[self.messages]
         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("role")
-            content = turn.get("content")
-            train_turn = turn.get("training")
-            train_detail = turn.get("training_detail")
+            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)
 
             LOG.debug(
                 f"Processing turn {index}: role={role}, content={content}, train_turn={train_turn}, train_detail={train_detail}"
             )
 
-            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
+            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
+                )
+            )
 
             LOG.debug(f"Should train: {should_train}")
 
@@ -283,9 +293,6 @@ 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:
@@ -306,9 +313,7 @@ class ChatTemplateStrategy(PromptTokenizingStrategy):
                     labels[turn_start_idx:turn_end_idx] = input_ids[
                         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 set for range {turn_start_idx}:{turn_end_idx}")
 
                 LOG.debug(f"Labels after processing turn {index}: {labels}")
 
@@ -346,73 +351,52 @@ class ChatTemplateStrategy(PromptTokenizingStrategy):
                 return i
         return -1
 
-    def find_turn(self, conversation_ids: list[int], turn: int, turn_content: dict):
+    def find_turn(self, conversation_ids, turn, turn_content):
         """
         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("content")
+        content = turn_content.get(self.prompter.message_field_content, "")
         content_ids = self.tokenizer.encode(content, add_special_tokens=False)
 
-        LOG.debug(f"content_ids (length {len(content_ids)}): {content_ids}")
+        eos_token_id = self.tokenizer.eos_token_id
+        eos_count = 0
+        start_search_idx = 0
 
-        if not content_ids:
-            LOG.warning(f"Empty content for turn {turn}")
-            return -1, -1
+        # 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
 
-        # For first turn, start from beginning
-        if turn == 0:
-            start_search_idx = 0
+        # 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)
         else:
-            # For subsequent turns, find the previous EOS token
-            eos_token_id = self.tokenizer.eos_token_id
-            eos_count = 0
-            start_search_idx = 0
+            end_idx = -1
 
-            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
+        return start_idx, end_idx
 
     def get_conversation_thread(self, prompt):
-        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
+        return prompt[self.messages]
 
     def get_images(self, prompt):
         return prompt.get(self.images, None)

src/axolotl/prompt_strategies/dpo/chatml.py

@@ -8,36 +8,17 @@ def argilla(
     **kwargs,
 ):  # pylint: disable=possibly-unused-variable,unused-argument
     def transform_fn(sample):
-        if "prompt" in sample.keys():
-            prompt_key = "prompt"
-        elif "input" in sample.keys():
-            prompt_key = "input"
-        elif "question" in sample.keys():
-            prompt_key = "question"
-        else:
-            prompt_key = "instruction"
-
-        if "chosen" in sample.keys():
-            chosen_key = "chosen"
-        else:
-            chosen_key = "chosen_response"
-
-        if "rejected" in sample.keys():
-            rejected_key = "rejected"
-        else:
-            rejected_key = "rejected_response"
-
         if "system" in sample and sample["system"]:
             sample["prompt"] = (
                 f"<|im_start|>system\n{sample['system']}<|im_end|>\n"
-                f"<|im_start|>user\n{sample[prompt_key]}<|im_end|>\n<|im_start|>assistant\n"
+                f"<|im_start|>user\n{sample['instruction']}<|im_end|>\n<|im_start|>assistant\n"
             )
         else:
             sample[
                 "prompt"
-            ] = f"<|im_start|>user\n{sample[prompt_key]}<|im_end|>\n<|im_start|>assistant\n"
-        sample["chosen"] = f"{sample[chosen_key]}<|im_end|>"
-        sample["rejected"] = f"{sample[rejected_key]}<|im_end|>"
+            ] = f"<|im_start|>user\n{sample['instruction']}<|im_end|>\n<|im_start|>assistant\n"
+        sample["chosen"] = f"{sample['chosen_response']}<|im_end|>"
+        sample["rejected"] = f"{sample['rejected_response']}<|im_end|>"
         return sample
 
     return transform_fn

src/axolotl/prompt_strategies/dpo/llama3.py

@@ -8,37 +8,17 @@ def argilla(
     **kwargs,
 ):  # pylint: disable=possibly-unused-variable,unused-argument
     def transform_fn(sample):
-        # pylint: disable=duplicate-code
-        if "prompt" in sample.keys():
-            prompt_key = "prompt"
-        elif "input" in sample.keys():
-            prompt_key = "input"
-        elif "question" in sample.keys():
-            prompt_key = "question"
-        else:
-            prompt_key = "instruction"
-
-        if "chosen" in sample.keys():
-            chosen_key = "chosen"
-        else:
-            chosen_key = "chosen_response"
-
-        if "rejected" in sample.keys():
-            rejected_key = "rejected"
-        else:
-            rejected_key = "rejected_response"
-
         if "system" in sample and sample["system"]:
             sample["prompt"] = (
                 f"<|start_header_id|>system<|end_header_id|>\n\n{sample['system']}<|eot_id|>"
-                f"<|start_header_id|>user<|end_header_id|>\n\n{sample[prompt_key]}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
+                f"<|start_header_id|>user<|end_header_id|>\n\n{sample['instruction']}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
             )
         else:
             sample[
                 "prompt"
-            ] = f"<|start_header_id|>user<|end_header_id|>\n\n{sample[prompt_key]}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
-        sample["chosen"] = f"{sample[chosen_key]}<|eot_id|>"
-        sample["rejected"] = f"{sample[rejected_key]}<|eot_id|>"
+            ] = f"<|start_header_id|>user<|end_header_id|>\n\n{sample['instruction']}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
+        sample["chosen"] = f"{sample['chosen_response']}<|eot_id|>"
+        sample["rejected"] = f"{sample['rejected_response']}<|eot_id|>"
         return sample
 
     return transform_fn

src/axolotl/train.py

@@ -259,7 +259,14 @@ 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")
@@ -267,22 +274,16 @@ def train(
             }
             if cfg.datasets is not None:
                 if cfg.rl is not None or cfg.reward_model:
-                    dataset_tags = [
+                    model_card_kwarg["dataset_name"] = [
                         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:
-                    dataset_tags = [
+                    model_card_kwarg["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):
+        except (AttributeError, UnicodeDecodeError, RepositoryNotFoundError):
             pass
     elif cfg.hub_model_id:
         # defensively push to the hub to ensure the model card is updated

src/axolotl/utils/callbacks/__init__.py

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

@@ -1475,27 +1475,6 @@ 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

@@ -386,12 +386,6 @@ 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

@@ -1,104 +0,0 @@
-"""
-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

@@ -1,10 +0,0 @@
-"""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

@@ -120,15 +120,9 @@ def temp_dir():
 @pytest.fixture(scope="function", autouse=True)
 def cleanup_monkeypatches():
     from transformers import Trainer
-    from transformers.models.llama.modeling_llama import (
-        LlamaAttention,
-        LlamaFlashAttention2,
-        LlamaForCausalLM,
-    )
+    from transformers.models.llama.modeling_llama import LlamaFlashAttention2
 
     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
     )
@@ -137,8 +131,6 @@ 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
     )
@@ -146,25 +138,16 @@ def cleanup_monkeypatches():
 
     # Reset other known monkeypatches
     modules_to_reset: list[tuple[str, list[str]]] = [
-        ("transformers.models.llama",),
-        (
-            "transformers.models.llama.modeling_llama",
-            ["LlamaFlashAttention2", "LlamaAttention"],
-        ),
-        ("transformers.trainer",),
-        ("transformers", ["Trainer"]),
+        ("transformers",),
+        ("transformers.models.llama.modeling_llama", ["LlamaFlashAttention2"]),
+        ("transformers.trainer", ["Trainer"]),
         ("transformers.loss.loss_utils",),
     ]
     for module_name_tuple in modules_to_reset:
         module_name = module_name_tuple[0]
-
-        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])
+        module = importlib.import_module(module_name)
+        sys.modules[module_name] = module
+        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

@@ -71,11 +71,7 @@ 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

@@ -9,7 +9,6 @@ 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
 
@@ -54,7 +53,7 @@ class TestMultiGPULlama:
                     },
                 ],
                 "num_epochs": 1,
-                "max_steps": 2,
+                "max_steps": 15,
                 "micro_batch_size": 4,
                 "gradient_accumulation_steps": 4,
                 "output_dir": temp_dir,
@@ -62,7 +61,6 @@ class TestMultiGPULlama:
                 "optimizer": "adamw_8bit",
                 "lr_scheduler": "cosine",
                 "flash_attention": True,
-                "use_tensorboard": True,
             }
         )
 
@@ -85,13 +83,9 @@ class TestMultiGPULlama:
             ]
         )
 
-        check_tensorboard(
-            temp_dir + "/runs", "train/train_loss", 2.3, "Train Loss is too high"
-        )
-
     @pytest.mark.parametrize(
         "gradient_accumulation_steps",
-        [1, 2],
+        [1, 4],
     )
     def test_lora_ddp_packed(self, temp_dir, gradient_accumulation_steps):
         # pylint: disable=duplicate-code
@@ -118,15 +112,14 @@ class TestMultiGPULlama:
                     },
                 ],
                 "num_epochs": 1,
-                "max_steps": 2,
-                "micro_batch_size": 1,
+                "max_steps": 15,
+                "micro_batch_size": 4,
                 "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,
             }
         )
 
@@ -149,10 +142,6 @@ 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(
@@ -191,7 +180,7 @@ class TestMultiGPULlama:
                     },
                 ],
                 "num_epochs": 1,
-                "max_steps": 2,
+                "max_steps": 15,
                 "micro_batch_size": 4,
                 "gradient_accumulation_steps": 4,
                 "output_dir": temp_dir,
@@ -200,7 +189,6 @@ class TestMultiGPULlama:
                 "optimizer": "adamw_8bit",
                 "lr_scheduler": "cosine",
                 "flash_attention": True,
-                "use_tensorboard": True,
             }
         )
 
@@ -223,10 +211,6 @@ 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(
@@ -265,8 +249,8 @@ class TestMultiGPULlama:
                     },
                 ],
                 "num_epochs": 1,
-                "max_steps": 2,
-                "micro_batch_size": 2,
+                "max_steps": 15,
+                "micro_batch_size": 4,
                 "gradient_accumulation_steps": 4,
                 "output_dir": temp_dir,
                 "warmup_steps": 0,
@@ -274,7 +258,6 @@ class TestMultiGPULlama:
                 "optimizer": "adamw_8bit",
                 "lr_scheduler": "cosine",
                 "flash_attention": True,
-                "use_tensorboard": True,
             }
         )
 
@@ -297,13 +280,9 @@ class TestMultiGPULlama:
             ]
         )
 
-        check_tensorboard(
-            temp_dir + "/runs", "train/train_loss", 2.3, "Train Loss is too high"
-        )
-
     @pytest.mark.parametrize(
         "gradient_accumulation_steps",
-        [1, 2],
+        [1, 4],
     )
     def test_fsdp(self, temp_dir, gradient_accumulation_steps):
         # pylint: disable=duplicate-code
@@ -322,8 +301,8 @@ class TestMultiGPULlama:
                     },
                 ],
                 "num_epochs": 1,
-                "max_steps": 2,
-                "micro_batch_size": 2,
+                "max_steps": 10,
+                "micro_batch_size": 4,
                 "gradient_accumulation_steps": gradient_accumulation_steps,
                 "output_dir": temp_dir,
                 "learning_rate": 0.00001,
@@ -344,7 +323,6 @@ class TestMultiGPULlama:
                     "fsdp_state_dict_type": "FULL_STATE_DICT",
                     "fsdp_auto_wrap_policy": "TRANSFORMER_BASED_WRAP",
                 },
-                "use_tensorboard": True,
             }
         )
 
@@ -367,10 +345,6 @@ 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"],
@@ -394,7 +368,7 @@ class TestMultiGPULlama:
                     },
                 ],
                 "num_epochs": 1,
-                "max_steps": 2,
+                "max_steps": 15,
                 "micro_batch_size": 4,
                 "gradient_accumulation_steps": 4,
                 "output_dir": temp_dir,
@@ -416,7 +390,6 @@ class TestMultiGPULlama:
                     "fsdp_state_dict_type": fsdp_state_dict_type,
                     "fsdp_auto_wrap_policy": "TRANSFORMER_BASED_WRAP",
                 },
-                "use_tensorboard": True,
             }
         )
 
@@ -439,10 +412,6 @@ 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(
@@ -475,7 +444,7 @@ class TestMultiGPULlama:
                     },
                 ],
                 "num_epochs": 1,
-                "max_steps": 2,
+                "max_steps": 15,
                 "micro_batch_size": 4,
                 "gradient_accumulation_steps": 4,
                 "output_dir": temp_dir,
@@ -497,7 +466,6 @@ class TestMultiGPULlama:
                     "fsdp_state_dict_type": "SHARDED_STATE_DICT",
                     "fsdp_auto_wrap_policy": "TRANSFORMER_BASED_WRAP",
                 },
-                "use_tensorboard": True,
             }
         )
 
@@ -520,41 +488,12 @@ class TestMultiGPULlama:
             ]
         )
 
-        check_tensorboard(
-            temp_dir + "/runs", "train/train_loss", 2.3, "Train Loss is too high"
-        )
-
     @pytest.mark.parametrize(
         "gradient_accumulation_steps",
-        [1, 2],
+        [1, 4],
     )
-    @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
-    ):
+    def test_ds_zero3_packed(self, temp_dir, gradient_accumulation_steps):
         # 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",
@@ -572,17 +511,15 @@ class TestMultiGPULlama:
                     },
                 ],
                 "num_epochs": 1,
-                "max_steps": 2,
-                "micro_batch_size": 1,
+                "max_steps": 15,
+                "micro_batch_size": 4,
                 "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),
-                "use_tensorboard": True,
-                **adapter,
+                "deepspeed": str(AXOLOTL_ROOT / "deepspeed_configs/zero3_bf16.json"),
             }
         )
 
@@ -605,35 +542,19 @@ class TestMultiGPULlama:
             ]
         )
 
-        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):
+    def test_ds_zero3_qlora_packed(self, temp_dir):
         # pylint: disable=duplicate-code
-        if qlora:
-            adapter = {
+        cfg = DictDefault(
+            {
+                "base_model": "HuggingFaceTB/SmolLM2-135M",
+                "load_in_4bit": True,
                 "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,
@@ -647,17 +568,15 @@ class TestMultiGPULlama:
                     },
                 ],
                 "num_epochs": 1,
-                "max_steps": 2,
-                "micro_batch_size": 1,
-                "gradient_accumulation_steps": gradient_accumulation_steps,
+                "max_steps": 15,
+                "micro_batch_size": 4,
+                "gradient_accumulation_steps": 4,
                 "output_dir": temp_dir,
-                "learning_rate": 0.00001,
+                "learning_rate": 0.0001,
                 "optimizer": "adamw_torch",
                 "lr_scheduler": "cosine",
                 "flash_attention": True,
-                "deepspeed": str(AXOLOTL_ROOT / "deepspeed_configs/zero2.json"),
-                "use_tensorboard": True,
-                **adapter,
+                "deepspeed": str(AXOLOTL_ROOT / "deepspeed_configs/zero3_bf16.json"),
             }
         )
 
@@ -679,82 +598,3 @@ 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

@@ -4,6 +4,7 @@ E2E tests for lora llama
 
 import logging
 import os
+from importlib import reload
 from pathlib import Path
 
 import pytest
@@ -21,6 +22,14 @@ 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

@@ -7,7 +7,6 @@ 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
@@ -22,7 +21,6 @@ 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