fix: update bradleyterry to use new chat_template

* add ds zero3 to multigpu biweekly tests

* fix for upstream api change

* use updated accelerate and fix deepspeed tests

* stringify the Path, and run multigpu tests if the multigpu tests change for a PR

* use correct json rather than yaml

* revert accelerate for deepspeed

.github/workflows/base.yml

@@ -12,36 +12,30 @@ jobs:
       fail-fast: false
       matrix:
         include:
-          - cuda: "118"
-            cuda_version: 11.8.0
+          - cuda: "121"
+            cuda_version: 12.1.1
+            cudnn_version: 8
             python_version: "3.10"
-            pytorch: 2.1.2
+            pytorch: 2.3.1
             torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
           - cuda: "121"
-            cuda_version: 12.1.0
-            python_version: "3.10"
-            pytorch: 2.1.2
-            torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
-          - cuda: "121"
-            cuda_version: 12.1.0
-            python_version: "3.11"
-            pytorch: 2.1.2
-            torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
-          - cuda: "121"
-            cuda_version: 12.1.0
-            python_version: "3.11"
-            pytorch: 2.2.2
-            torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
-          - cuda: "121"
-            cuda_version: 12.1.0
-            python_version: "3.11"
-            pytorch: 2.3.0
-            torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
-          - cuda: "121"
-            cuda_version: 12.1.0
+            cuda_version: 12.1.1
+            cudnn_version: 8
             python_version: "3.11"
             pytorch: 2.3.1
             torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
+          - cuda: "124"
+            cuda_version: 12.4.1
+            cudnn_version: ""
+            python_version: "3.11"
+            pytorch: 2.4.1
+            torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
+          - cuda: "124"
+            cuda_version: 12.4.1
+            cudnn_version: ""
+            python_version: "3.11"
+            pytorch: 2.4.1
+            torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
     steps:
       - name: Checkout
         uses: actions/checkout@v3
@@ -67,6 +61,7 @@ jobs:
           labels: ${{ steps.metadata.outputs.labels }}
           build-args: |
             CUDA_VERSION=${{ matrix.cuda_version }}
+            CUDNN_VERSION=${{ matrix.cudnn_version }}
             CUDA=${{ matrix.cuda }}
             PYTHON_VERSION=${{ matrix.python_version }}
             PYTORCH_VERSION=${{ matrix.pytorch }}

.github/workflows/lint.yml

@@ -6,7 +6,7 @@ on:
        - '**.py'
        - 'requirements.txt'
        - '.github/workflows/*.yml'
-       - "*.md"
+       - "*.[q]md"
        - "examples/**/*.y[a]?ml"
   workflow_dispatch:
 

.github/workflows/main.yml

@@ -13,28 +13,22 @@ jobs:
       fail-fast: false
       matrix:
         include:
-          - cuda: 118
-            cuda_version: 11.8.0
+          - cuda: 121
+            cuda_version: 12.1.1
             python_version: "3.10"
-            pytorch: 2.1.2
-            axolotl_extras:
-            axolotl_args: "--extra-index-url https://download.pytorch.org/whl/cu118"
+            pytorch: 2.3.1
+            axolotl_extras: mamba-ssm
           - cuda: 121
-            cuda_version: 12.1.0
-            python_version: "3.10"
-            pytorch: 2.1.2
-            axolotl_extras:
-          - cuda: 121
-            cuda_version: 12.1.0
-            python_version: "3.11"
-            pytorch: 2.2.2
-            axolotl_extras:
-          - cuda: 121
-            cuda_version: 12.1.0
+            cuda_version: 12.1.1
             python_version: "3.11"
             pytorch: 2.3.1
-            axolotl_extras:
+            axolotl_extras: mamba-ssm
             is_latest: true
+          - cuda: 124
+            cuda_version: 12.4.1
+            python_version: "3.11"
+            pytorch: 2.4.1
+            axolotl_extras:
     runs-on: axolotl-gpu-runner
     steps:
       - name: Checkout
@@ -65,6 +59,7 @@ jobs:
           push: ${{ github.event_name != 'pull_request' }}
           tags: |
             ${{ steps.metadata.outputs.tags }}-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
+            ${{ steps.metadata.outputs.tags }}-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}
             ${{ (matrix.is_latest) && format('{0}-latest', steps.metadata.outputs.tags) || '' }}
           labels: ${{ steps.metadata.outputs.labels }}
 
@@ -75,27 +70,22 @@ jobs:
     strategy:
       matrix:
         include:
-          - cuda: 118
-            cuda_version: 11.8.0
+          - cuda: 121
+            cuda_version: 12.1.1
             python_version: "3.10"
-            pytorch: 2.1.2
+            pytorch: 2.3.1
             axolotl_extras:
           - cuda: 121
-            cuda_version: 12.1.0
-            python_version: "3.10"
-            pytorch: 2.1.2
-            axolotl_extras:
-          - cuda: 121
-            cuda_version: 12.1.0
-            python_version: "3.11"
-            pytorch: 2.2.2
-            axolotl_extras:
-          - cuda: 121
-            cuda_version: 12.1.0
+            cuda_version: 12.1.1
             python_version: "3.11"
             pytorch: 2.3.1
             axolotl_extras:
             is_latest: true
+          - cuda: 124
+            cuda_version: 12.4.1
+            python_version: "3.11"
+            pytorch: 2.4.1
+            axolotl_extras:
     runs-on: axolotl-gpu-runner
     steps:
       - name: Checkout
@@ -134,7 +124,7 @@ jobs:
       matrix:
         include:
           - cuda: 121
-            cuda_version: 12.1.0
+            cuda_version: 12.1.1
             python_version: "3.11"
             pytorch: 2.3.1
             axolotl_extras:

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

@@ -0,0 +1,55 @@
+name: docker-multigpu-tests-biweekly
+
+on:
+  pull_request:
+    paths:
+      - 'tests/e2e/multigpu/*.py'
+  workflow_dispatch:
+  schedule:
+    - cron: '0 0 * * 1,4'  # Runs at 00:00 UTC every monday & thursday
+
+jobs:
+  test-axolotl-multigpu:
+    if: ${{ ! contains(github.event.commits[0].message, '[skip docker]]') && github.repository_owner == 'axolotl-ai-cloud' }}
+    strategy:
+      fail-fast: false
+      matrix:
+        include:
+          - cuda: 121
+            cuda_version: 12.1.1
+            python_version: "3.11"
+            pytorch: 2.3.1
+            axolotl_extras:
+            num_gpus: 2
+          - cuda: 121
+            cuda_version: 12.1.1
+            python_version: "3.11"
+            pytorch: 2.3.1
+            axolotl_extras:
+            num_gpus: 2
+            nightly_build: "true"
+    runs-on: [self-hosted, modal]
+    timeout-minutes: 120
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+      - name: Install Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: "3.10"
+      - name: Install Modal
+        run: |
+          python -m pip install --upgrade pip
+          pip install modal==0.63.64 jinja2
+      - name: Update env vars
+        run: |
+          echo "BASE_TAG=main-base-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}" >> $GITHUB_ENV
+          echo "PYTORCH_VERSION=${{ matrix.pytorch}}" >> $GITHUB_ENV
+          echo "AXOLOTL_ARGS=${{ matrix.axolotl_args}}" >> $GITHUB_ENV
+          echo "AXOLOTL_EXTRAS=${{ matrix.axolotl_extras}}" >> $GITHUB_ENV
+          echo "CUDA=${{ matrix.cuda }}" >> $GITHUB_ENV
+          echo "N_GPUS=${{ matrix.num_gpus }}" >> $GITHUB_ENV
+          echo "NIGHTLY_BUILD=${{ matrix.nightly_build }}" >> $GITHUB_ENV
+      - name: Run tests job on Modal
+        run: |
+          modal run cicd.multigpu

.github/workflows/nightlies.yml

@@ -12,28 +12,22 @@ jobs:
       fail-fast: false
       matrix:
         include:
-          - cuda: 118
-            cuda_version: 11.8.0
+          - cuda: 121
+            cuda_version: 12.1.1
             python_version: "3.10"
-            pytorch: 2.1.2
-            axolotl_extras:
-            axolotl_args: "--extra-index-url https://download.pytorch.org/whl/cu118"
-          - cuda: 121
-            cuda_version: 12.1.0
-            python_version: "3.10"
-            pytorch: 2.1.2
+            pytorch: 2.3.1
             axolotl_extras:
           - cuda: 121
-            cuda_version: 12.1.0
-            python_version: "3.11"
-            pytorch: 2.2.2
-            axolotl_extras:
-          - cuda: 121
-            cuda_version: 12.1.0
+            cuda_version: 12.1.1
             python_version: "3.11"
             pytorch: 2.3.1
             axolotl_extras:
             is_latest: true
+          - cuda: 124
+            cuda_version: 12.4.1
+            python_version: "3.11"
+            pytorch: 2.4.1
+            axolotl_extras:
     runs-on: axolotl-gpu-runner
     steps:
       - name: Checkout
@@ -75,27 +69,22 @@ jobs:
     strategy:
       matrix:
         include:
-          - cuda: 118
-            cuda_version: 11.8.0
+          - cuda: 121
+            cuda_version: 12.1.1
             python_version: "3.10"
-            pytorch: 2.1.2
+            pytorch: 2.3.1
             axolotl_extras:
           - cuda: 121
-            cuda_version: 12.1.0
-            python_version: "3.10"
-            pytorch: 2.1.2
-            axolotl_extras:
-          - cuda: 121
-            cuda_version: 12.1.0
-            python_version: "3.11"
-            pytorch: 2.2.2
-            axolotl_extras:
-          - cuda: 121
-            cuda_version: 12.1.0
+            cuda_version: 12.1.1
             python_version: "3.11"
             pytorch: 2.3.1
             axolotl_extras:
             is_latest: true
+          - cuda: 124
+            cuda_version: 12.4.1
+            python_version: "3.11"
+            pytorch: 2.4.1
+            axolotl_extras:
     runs-on: axolotl-gpu-runner
     steps:
       - name: Checkout

.github/workflows/tests-nightly.yml

@@ -0,0 +1,120 @@
+name: Tests Nightly against upstream main
+on:
+  workflow_dispatch:
+  schedule:
+    - cron: '0 0 * * *'  # Runs at 00:00 UTC every day
+
+jobs:
+  pre-commit:
+    name: pre-commit
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+      - uses: actions/setup-python@v4
+        with:
+          python-version: "3.10"
+          cache: 'pip' # caching pip dependencies
+      - uses: pre-commit/action@v3.0.0
+        env:
+          SKIP: no-commit-to-branch
+
+  pytest:
+    name: PyTest
+    runs-on: ubuntu-latest
+    strategy:
+      fail-fast: false
+      matrix:
+        python_version: ["3.10", "3.11"]
+        pytorch_version: ["2.3.1", "2.4.1"]
+    timeout-minutes: 20
+
+    steps:
+      - name: Check out repository code
+        uses: actions/checkout@v3
+
+      - name: Setup Python
+        uses: actions/setup-python@v4
+        with:
+          python-version: ${{ matrix.python_version }}
+          cache: 'pip' # caching pip dependencies
+
+      - name: Install PyTorch
+        run: |
+          pip3 install torch==${{ matrix.pytorch_version }} --index-url https://download.pytorch.org/whl/cpu
+
+      - name: Update requirements.txt
+        run: |
+          sed -i 's#^transformers.*#transformers @ git+https://github.com/huggingface/transformers.git@main#' requirements.txt
+          sed -i 's#^peft.*#peft @ git+https://github.com/huggingface/peft.git@main#' requirements.txt
+          sed -i 's#^accelerate.*#accelerate @ git+https://github.com/huggingface/accelerate.git@main#' requirements.txt
+
+      - name: Install dependencies
+        run: |
+          pip3 install --upgrade pip
+          pip3 install --upgrade packaging
+          pip3 install -U -e .
+          pip3 install -r requirements-tests.txt
+
+      - name: Run tests
+        run: |
+          pytest --ignore=tests/e2e/ tests/
+
+      - name: cleanup pip cache
+        run: |
+          find "$(pip cache dir)/http-v2" -type f -mtime +14 -exec rm {} \;
+
+  docker-e2e-tests:
+    if: github.repository_owner == 'axolotl-ai-cloud'
+    # this job needs to be run on self-hosted GPU runners...
+    runs-on: [self-hosted, modal]
+    timeout-minutes: 60
+    needs: [pre-commit, pytest]
+
+    strategy:
+      fail-fast: false
+      matrix:
+        include:
+          - cuda: 121
+            cuda_version: 12.1.1
+            python_version: "3.10"
+            pytorch: 2.3.1
+            num_gpus: 1
+            axolotl_extras: mamba-ssm
+            nightly_build: "true"
+          - cuda: 121
+            cuda_version: 12.1.1
+            python_version: "3.11"
+            pytorch: 2.3.1
+            num_gpus: 1
+            axolotl_extras: mamba-ssm
+            nightly_build: "true"
+          - cuda: 124
+            cuda_version: 12.4.1
+            python_version: "3.11"
+            pytorch: 2.4.1
+            num_gpus: 1
+            axolotl_extras:
+            nightly_build: "true"
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+      - name: Install Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: "3.10"
+      - name: Install Modal
+        run: |
+          python -m pip install --upgrade pip
+          pip install modal==0.63.64 jinja2
+      - name: Update env vars
+        run: |
+          echo "BASE_TAG=main-base-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}" >> $GITHUB_ENV
+          echo "PYTORCH_VERSION=${{ matrix.pytorch}}" >> $GITHUB_ENV
+          echo "AXOLOTL_ARGS=${{ matrix.axolotl_args}}" >> $GITHUB_ENV
+          echo "AXOLOTL_EXTRAS=${{ matrix.axolotl_extras}}" >> $GITHUB_ENV
+          echo "CUDA=${{ matrix.cuda }}" >> $GITHUB_ENV
+          echo "N_GPUS=${{ matrix.num_gpus }}" >> $GITHUB_ENV
+          echo "NIGHTLY_BUILD=${{ matrix.nightly_build }}" >> $GITHUB_ENV
+      - name: Run tests job on Modal
+        run: |
+          modal run cicd.tests

.github/workflows/tests.yml

@@ -26,6 +26,8 @@ jobs:
           python-version: "3.10"
           cache: 'pip' # caching pip dependencies
       - uses: pre-commit/action@v3.0.0
+        env:
+          SKIP: no-commit-to-branch
 
   pytest:
     name: PyTest
@@ -34,6 +36,7 @@ jobs:
       fail-fast: false
       matrix:
         python_version: ["3.10", "3.11"]
+        pytorch_version: ["2.3.1", "2.4.1"]
     timeout-minutes: 20
 
     steps:
@@ -46,6 +49,10 @@ jobs:
           python-version: ${{ matrix.python_version }}
           cache: 'pip' # caching pip dependencies
 
+      - name: Install PyTorch
+        run: |
+          pip3 install torch==${{ matrix.pytorch_version }} --index-url https://download.pytorch.org/whl/cpu
+
       - name: Install dependencies
         run: |
           pip3 install --upgrade pip
@@ -57,6 +64,10 @@ jobs:
         run: |
           pytest --ignore=tests/e2e/ tests/
 
+      - name: cleanup pip cache
+        run: |
+          find "$(pip cache dir)/http-v2" -type f -mtime +14 -exec rm {} \;
+
   docker-e2e-tests:
     if: github.repository_owner == 'axolotl-ai-cloud'
     # this job needs to be run on self-hosted GPU runners...
@@ -68,27 +79,24 @@ jobs:
       fail-fast: false
       matrix:
         include:
-          - cuda: 118
-            cuda_version: 11.8.0
+          - cuda: 121
+            cuda_version: 12.1.1
             python_version: "3.10"
-            pytorch: 2.1.2
-            axolotl_args: "--extra-index-url https://download.pytorch.org/whl/cu118"
+            pytorch: 2.3.1
             num_gpus: 1
+            axolotl_extras: mamba-ssm
           - cuda: 121
-            cuda_version: 12.1.0
-            python_version: "3.10"
-            pytorch: 2.1.2
-            num_gpus: 1
-          - cuda: 121
-            cuda_version: 12.1.0
-            python_version: "3.11"
-            pytorch: 2.2.2
-            num_gpus: 1
-          - cuda: 121
-            cuda_version: 12.1.0
+            cuda_version: 12.1.1
             python_version: "3.11"
             pytorch: 2.3.1
             num_gpus: 1
+            axolotl_extras: mamba-ssm
+          - cuda: 124
+            cuda_version: 12.4.1
+            python_version: "3.11"
+            pytorch: 2.4.1
+            num_gpus: 1
+            axolotl_extras:
     steps:
       - name: Checkout
         uses: actions/checkout@v4
@@ -99,12 +107,13 @@ jobs:
       - name: Install Modal
         run: |
           python -m pip install --upgrade pip
-          pip install modal jinja2
+          pip install modal==0.63.64 jinja2
       - name: Update env vars
         run: |
           echo "BASE_TAG=main-base-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}" >> $GITHUB_ENV
           echo "PYTORCH_VERSION=${{ matrix.pytorch}}" >> $GITHUB_ENV
           echo "AXOLOTL_ARGS=${{ matrix.axolotl_args}}" >> $GITHUB_ENV
+          echo "AXOLOTL_EXTRAS=${{ matrix.axolotl_extras}}" >> $GITHUB_ENV
           echo "CUDA=${{ matrix.cuda }}" >> $GITHUB_ENV
           echo "N_GPUS=${{ matrix.num_gpus }}" >> $GITHUB_ENV
       - name: Run tests job on Modal

.isort.cfg

@@ -1,3 +1,3 @@
 [settings]
 profile=black
-known_third_party=wandb
+known_third_party=wandb,comet_ml

.mypy.ini

@@ -11,6 +11,9 @@ ignore_errors = True
 [mypy-axolotl.models.mixtral.*]
 ignore_errors = True
 
+[mypy-axolotl.integrations.liger.models.*]
+ignore_errors = True
+
 [mypy-axolotl.models.phi.*]
 ignore_errors = True
 

.pre-commit-config.yaml

@@ -8,6 +8,8 @@ repos:
     -   id: check-yaml
     -   id: end-of-file-fixer
     -   id: trailing-whitespace
+    -   id: no-commit-to-branch
+        args: ['--branch', 'main']
 -   repo: https://github.com/psf/black
     rev: 23.3.0
     hooks:

README.md

@@ -1,5 +1,9 @@
 # Axolotl
 
+![tests](https://github.com/axolotl-ai-cloud/axolotl/actions/workflows/tests.yml/badge.svg)
+![tests-nightly](https://github.com/axolotl-ai-cloud/axolotl/actions/workflows/tests-nightly.yml/badge.svg)
+![multigpu-semi-weekly tests](https://github.com/axolotl-ai-cloud/axolotl/actions/workflows/multi-gpu-e2e.yml/badge.svg)
+
 Axolotl is a tool designed to streamline the fine-tuning of various AI models, offering support for multiple configurations and architectures.
 
 Features:
@@ -7,10 +11,10 @@ Features:
 - Supports fullfinetune, lora, qlora, relora, and gptq
 - Customize configurations using a simple yaml file or CLI overwrite
 - Load different dataset formats, use custom formats, or bring your own tokenized datasets
-- Integrated with xformer, flash attention, rope scaling, and multipacking
+- Integrated with xformer, flash attention, [liger kernel](https://github.com/linkedin/Liger-Kernel), rope scaling, and multipacking
 - Works with single GPU or multiple GPUs via FSDP or Deepspeed
 - Easily run with Docker locally or on the cloud
-- Log results and optionally checkpoints to wandb or mlflow
+- Log results and optionally checkpoints to wandb, mlflow or Comet
 - And more!
 
 <a href="https://www.phorm.ai/query?projectId=e315ba4a-4e14-421f-ab05-38a1f9076f25">
@@ -22,38 +26,50 @@ Features:
 <td>
 
 ## Table of Contents
-- [Introduction](#axolotl)
-- [Supported Features](#axolotl-supports)
-- [Quickstart](#quickstart-)
-- [Environment](#environment)
-  - [Docker](#docker)
-  - [Conda/Pip venv](#condapip-venv)
-  - [Cloud GPU](#cloud-gpu) - Latitude.sh, JarvisLabs, RunPod
-  - [Bare Metal Cloud GPU](#bare-metal-cloud-gpu)
-  - [Windows](#windows)
-  - [Mac](#mac)
-  - [Google Colab](#google-colab)
-  - [Launching on public clouds via SkyPilot](#launching-on-public-clouds-via-skypilot)
-  - [Launching on public clouds via dstack](#launching-on-public-clouds-via-dstack)
-- [Dataset](#dataset)
-- [Config](#config)
-  - [Train](#train)
-  - [Inference](#inference-playground)
-  - [Merge LORA to Base](#merge-lora-to-base)
-  - [Special Tokens](#special-tokens)
-  - [All Config Options](#all-config-options)
-- Advanced Topics
-  - [Multipack](./docs/multipack.qmd)<svg width="24" height="24" viewBox="0 0 24 24" xmlns="http://www.w3.org/2000/svg"><path d="M17 13.5v6H5v-12h6m3-3h6v6m0-6-9 9" class="icon_svg-stroke" stroke="#666" stroke-width="1.5" fill="none" fill-rule="evenodd" stroke-linecap="round" stroke-linejoin="round"></path></svg>
-  - [RLHF & DPO](./docs/rlhf.qmd)<svg width="24" height="24" viewBox="0 0 24 24" xmlns="http://www.w3.org/2000/svg"><path d="M17 13.5v6H5v-12h6m3-3h6v6m0-6-9 9" class="icon_svg-stroke" stroke="#666" stroke-width="1.5" fill="none" fill-rule="evenodd" stroke-linecap="round" stroke-linejoin="round"></path></svg>
-  - [Dataset Pre-Processing](./docs/dataset_preprocessing.qmd)<svg width="24" height="24" viewBox="0 0 24 24" xmlns="http://www.w3.org/2000/svg"><path d="M17 13.5v6H5v-12h6m3-3h6v6m0-6-9 9" class="icon_svg-stroke" stroke="#666" stroke-width="1.5" fill="none" fill-rule="evenodd" stroke-linecap="round" stroke-linejoin="round"></path></svg>
-- [Common Errors](#common-errors-)
-  - [Tokenization Mismatch b/w Training & Inference](#tokenization-mismatch-bw-inference--training)
-- [Debugging Axolotl](#debugging-axolotl)
-- [Need Help?](#need-help-)
-- [Badge](#badge-)
-- [Community Showcase](#community-showcase)
-- [Contributing](#contributing-)
-- [Sponsors](#sponsors-)
+- [Axolotl](#axolotl)
+  - [Table of Contents](#table-of-contents)
+  - [Axolotl supports](#axolotl-supports)
+  - [Quickstart ⚡](#quickstart-)
+    - [Usage](#usage)
+  - [Advanced Setup](#advanced-setup)
+    - [Environment](#environment)
+      - [Docker](#docker)
+      - [Conda/Pip venv](#condapip-venv)
+      - [Cloud GPU](#cloud-gpu)
+      - [Bare Metal Cloud GPU](#bare-metal-cloud-gpu)
+        - [LambdaLabs](#lambdalabs)
+        - [GCP](#gcp)
+      - [Windows](#windows)
+      - [Mac](#mac)
+      - [Google Colab](#google-colab)
+      - [Launching on public clouds via SkyPilot](#launching-on-public-clouds-via-skypilot)
+      - [Launching on public clouds via dstack](#launching-on-public-clouds-via-dstack)
+    - [Dataset](#dataset)
+    - [Config](#config)
+      - [All Config Options](#all-config-options)
+    - [Train](#train)
+      - [Preprocess dataset](#preprocess-dataset)
+      - [Multi-GPU](#multi-gpu)
+        - [DeepSpeed](#deepspeed)
+        - [FSDP](#fsdp)
+        - [FSDP + QLoRA](#fsdp--qlora)
+        - [Weights \& Biases Logging](#weights--biases-logging)
+        - [Special Tokens](#special-tokens)
+      - [Liger Kernel](#liger-kernel)
+    - [Inference Playground](#inference-playground)
+    - [Merge LORA to base](#merge-lora-to-base)
+  - [Common Errors 🧰](#common-errors-)
+    - [Tokenization Mismatch b/w Inference \& Training](#tokenization-mismatch-bw-inference--training)
+  - [Debugging Axolotl](#debugging-axolotl)
+  - [Need help? 🙋](#need-help-)
+  - [Badge ❤🏷️](#badge-️)
+  - [Community Showcase](#community-showcase)
+  - [Contributing 🤝](#contributing-)
+  - [Sponsors 🤝❤](#sponsors-)
+      - [💎 Diamond Sponsors - Contact directly](#-diamond-sponsors---contact-directly)
+      - [🥇 Gold Sponsors - $5000/mo](#-gold-sponsors---5000mo)
+      - [🥈 Silver Sponsors - $1000/mo](#-silver-sponsors---1000mo)
+      - [🥉 Bronze Sponsors - $500/mo](#-bronze-sponsors---500mo)
 
 </td>
 <td>
@@ -95,6 +111,7 @@ Features:
 | RWKV        | ✅         | ❓    | ❓     | ❓             | ❓                 | ❓          | ❓            |
 | Qwen        | ✅         | ✅    | ✅     | ❓             | ❓                 | ❓          | ❓            |
 | Gemma       | ✅         | ✅    | ✅     | ❓             | ❓                 | ✅          | ❓            |
+| Jamba       | ✅         | ✅    | ✅     | ❓             | ❓                 | ✅          | ❓            |
 
 ✅: supported
 ❌: not supported
@@ -333,7 +350,7 @@ For further and fine-grained use cases, please refer to the official [dstack doc
 
 Axolotl supports a variety of dataset formats.  It is recommended to use a JSONL.  The schema of the JSONL depends upon the task and the prompt template you wish to use.  Instead of a JSONL, you can also use a HuggingFace dataset with columns for each JSONL field.
 
-See [these docs](https://axolotl-ai-cloud.github.io/axolotl/docs/dataset-formats/) for more information on how to use different dataset formats.
+See [the documentation](https://axolotl-ai-cloud.github.io/axolotl/docs/dataset-formats/) for more information on how to use different dataset formats.
 
 ### Config
 
@@ -366,7 +383,7 @@ See [examples](examples) for quick start. It is recommended to duplicate and mod
         - typescript
       type: ... # unimplemented custom format
 
-      # fastchat conversation
+      # fastchat conversation (deprecation soon, use chat_template https://axolotl-ai-cloud.github.io/axolotl/docs/dataset-formats/conversation.html#chat_template)
       # See 'conversation' options: https://github.com/lm-sys/FastChat/blob/main/fastchat/conversation.py
     - path: ...
       type: sharegpt
@@ -498,6 +515,22 @@ wandb_name:
 wandb_log_model:
 ```
 
+##### Comet Logging
+
+Make sure your `COMET_API_KEY` environment variable is set (recommended) or you login to wandb with `comet login`.
+
+- wandb options
+```yaml
+use_comet:
+comet_api_key:
+comet_workspace:
+comet_project_name:
+comet_experiment_key:
+comet_mode:
+comet_online:
+comet_experiment_config:
+```
+
 ##### Special Tokens
 
 It is important to have special tokens like delimiters, end-of-sequence, beginning-of-sequence in your tokenizer's vocabulary.  This will help you avoid tokenization issues and help your model train better.  You can do this in axolotl like this:
@@ -514,6 +547,25 @@ tokens: # these are delimiters
 
 When you include these tokens in your axolotl config, axolotl adds these tokens to the tokenizer's vocabulary.
 
+##### Liger Kernel
+
+Liger Kernel: Efficient Triton Kernels for LLM Training
+
+https://github.com/linkedin/Liger-Kernel
+
+Liger (LinkedIn GPU Efficient Runtime) Kernel is a collection of Triton kernels designed specifically for LLM training.
+It can effectively increase multi-GPU training throughput by 20% and reduces memory usage by 60%. The Liger Kernel
+composes well and is compatible with both FSDP and Deepspeed.
+
+```yaml
+plugins:
+  - axolotl.integrations.liger.LigerPlugin
+liger_rope: true
+liger_rms_norm: true
+liger_swiglu: true
+liger_fused_linear_cross_entropy: true
+```
+
 ### Inference Playground
 
 Axolotl allows you to load your model in an interactive terminal playground for quick experimentation.

_quarto.yml

@@ -36,6 +36,8 @@ website:
             - docs/nccl.qmd
             - docs/mac.qmd
             - docs/multi-node.qmd
+            - docs/unsloth.qmd
+            - docs/amd_hpc.qmd
         - section: "Dataset Formats"
           contents: docs/dataset-formats/*
         - section: "Reference"

cicd/Dockerfile.jinja

@@ -8,6 +8,7 @@ ENV BNB_CUDA_VERSION="{{ CUDA }}"
 ENV PYTORCH_VERSION="{{ PYTORCH_VERSION }}"
 ENV GITHUB_REF="{{ GITHUB_REF }}"
 ENV GITHUB_SHA="{{ GITHUB_SHA }}"
+ENV NIGHTLY_BUILD="{{ NIGHTLY_BUILD }}"
 
 RUN apt-get update && \
     apt-get install -y --allow-change-held-packages vim curl nano libnccl2 libnccl-dev
@@ -23,10 +24,16 @@ RUN git fetch origin +$GITHUB_REF && \
 
 # If AXOLOTL_EXTRAS is set, append it in brackets
 RUN pip install causal_conv1d
+RUN if [ "$NIGHTLY_BUILD" = "true" ] ; then \
+        sed -i 's#^transformers.*#transformers @ git+https://github.com/huggingface/transformers.git@main#' requirements.txt; \
+        sed -i 's#^peft.*#peft @ git+https://github.com/huggingface/peft.git@main#' requirements.txt; \
+        sed -i 's#^accelerate.*#accelerate @ git+https://github.com/huggingface/accelerate.git@main#' requirements.txt; \
+    fi
+
 RUN if [ "$AXOLOTL_EXTRAS" != "" ] ; then \
-        pip install -e .[deepspeed,flash-attn,mamba-ssm,optimizers,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \
+        pip install -e .[deepspeed,flash-attn,optimizers,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \
     else \
-        pip install -e .[deepspeed,flash-attn,mamba-ssm,optimizers] $AXOLOTL_ARGS; \
+        pip install -e .[deepspeed,flash-attn,optimizers] $AXOLOTL_ARGS; \
     fi
 
 # So we can test the Docker image

cicd/cicd.sh

@@ -2,5 +2,5 @@
 set -e
 
 pytest --ignore=tests/e2e/ /workspace/axolotl/tests/
-pytest /workspace/axolotl/tests/e2e/patched/
-pytest --ignore=tests/e2e/patched/ /workspace/axolotl/tests/e2e/
+pytest -n1 --dist loadfile -v /workspace/axolotl/tests/e2e/patched/ /workspace/axolotl/tests/e2e/integrations/
+pytest --ignore=tests/e2e/patched/ --ignore=tests/e2e/multigpu/ --ignore=tests/e2e/integrations/ /workspace/axolotl/tests/e2e/

cicd/multigpu.py

@@ -0,0 +1,77 @@
+"""
+ modal application to run axolotl gpu tests in Modal
+ """
+# pylint: disable=duplicate-code
+
+import os
+import pathlib
+import tempfile
+
+import jinja2
+import modal
+from jinja2 import select_autoescape
+from modal import Image, Stub
+
+cicd_path = pathlib.Path(__file__).parent.resolve()
+
+template_loader = jinja2.FileSystemLoader(searchpath=cicd_path)
+template_env = jinja2.Environment(
+    loader=template_loader, autoescape=select_autoescape()
+)
+df_template = template_env.get_template("Dockerfile.jinja")
+
+df_args = {
+    "AXOLOTL_EXTRAS": os.environ.get("AXOLOTL_EXTRAS", ""),
+    "AXOLOTL_ARGS": os.environ.get("AXOLOTL_ARGS", ""),
+    "PYTORCH_VERSION": os.environ.get("PYTORCH_VERSION", "2.3.1"),
+    "BASE_TAG": os.environ.get("BASE_TAG", "main-base-py3.11-cu121-2.3.1"),
+    "CUDA": os.environ.get("CUDA", "121"),
+    "GITHUB_REF": os.environ.get("GITHUB_REF", "refs/heads/main"),
+    "GITHUB_SHA": os.environ.get("GITHUB_SHA", ""),
+}
+
+dockerfile_contents = df_template.render(**df_args)
+
+temp_dir = tempfile.mkdtemp()
+with open(pathlib.Path(temp_dir) / "Dockerfile", "w", encoding="utf-8") as f:
+    f.write(dockerfile_contents)
+
+cicd_image = (
+    Image.from_dockerfile(
+        pathlib.Path(temp_dir) / "Dockerfile",
+        force_build=True,
+        gpu="A10G",
+    )
+    .env(df_args)
+    .pip_install("fastapi==0.110.0", "pydantic==2.6.3")
+)
+
+stub = Stub("Axolotl CI/CD", secrets=[])
+
+
+N_GPUS = int(os.environ.get("N_GPUS", 2))
+GPU_CONFIG = modal.gpu.H100(count=N_GPUS)
+
+
+def run_cmd(cmd: str, run_folder: str):
+    import subprocess  # nosec
+
+    # Propagate errors from subprocess.
+    if exit_code := subprocess.call(cmd.split(), cwd=run_folder):  # nosec
+        exit(exit_code)  # pylint: disable=consider-using-sys-exit
+
+
+@stub.function(
+    image=cicd_image,
+    gpu=GPU_CONFIG,
+    timeout=45 * 60,
+    cpu=8.0,
+    memory=131072 * N_GPUS,
+)
+def cicd_pytest():
+    run_cmd("./cicd/multigpu.sh", "/workspace/axolotl")
+
+
+@stub.local_entrypoint()
+def main():
+    cicd_pytest.remote()

cicd/multigpu.sh

@@ -0,0 +1,5 @@
+#!/bin/bash
+set -e
+
+# only run one test at a time so as not to OOM the GPU
+pytest -n1 /workspace/axolotl/tests/e2e/multigpu/

cicd/tests.py

@@ -1,6 +1,8 @@
 """
  modal application to run axolotl gpu tests in Modal
  """
+# pylint: disable=duplicate-code
+
 import os
 import pathlib
 import tempfile
@@ -21,11 +23,12 @@ df_template = template_env.get_template("Dockerfile.jinja")
 df_args = {
     "AXOLOTL_EXTRAS": os.environ.get("AXOLOTL_EXTRAS", ""),
     "AXOLOTL_ARGS": os.environ.get("AXOLOTL_ARGS", ""),
-    "PYTORCH_VERSION": os.environ.get("PYTORCH_VERSION", "2.0.1"),
-    "BASE_TAG": os.environ.get("BASE_TAG", "main-base-py3.10-cu118-2.0.1"),
-    "CUDA": os.environ.get("CUDA", "118"),
+    "PYTORCH_VERSION": os.environ.get("PYTORCH_VERSION", "2.3.1"),
+    "BASE_TAG": os.environ.get("BASE_TAG", "main-base-py3.11-cu121-2.3.1"),
+    "CUDA": os.environ.get("CUDA", "121"),
     "GITHUB_REF": os.environ.get("GITHUB_REF", "refs/heads/main"),
     "GITHUB_SHA": os.environ.get("GITHUB_SHA", ""),
+    "NIGHTLY_BUILD": os.environ.get("NIGHTLY_BUILD", ""),
 }
 
 dockerfile_contents = df_template.render(**df_args)

docker/Dockerfile

@@ -22,9 +22,9 @@ WORKDIR /workspace/axolotl
 # If AXOLOTL_EXTRAS is set, append it in brackets
 RUN pip install causal_conv1d
 RUN if [ "$AXOLOTL_EXTRAS" != "" ] ; then \
-        pip install -e .[deepspeed,flash-attn,mamba-ssm,optimizers,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \
+        pip install -e .[deepspeed,flash-attn,optimizers,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \
     else \
-        pip install -e .[deepspeed,flash-attn,mamba-ssm,optimizers] $AXOLOTL_ARGS; \
+        pip install -e .[deepspeed,flash-attn,optimizers] $AXOLOTL_ARGS; \
     fi
 
 # So we can test the Docker image

docker/Dockerfile-base

@@ -3,7 +3,7 @@ ARG CUDNN_VERSION="8"
 ARG UBUNTU_VERSION="22.04"
 ARG MAX_JOBS=4
 
-FROM nvidia/cuda:$CUDA_VERSION-cudnn$CUDNN_VERSION-devel-ubuntu$UBUNTU_VERSION as base-builder
+FROM nvidia/cuda:$CUDA_VERSION-cudnn$CUDNN_VERSION-devel-ubuntu$UBUNTU_VERSION AS base-builder
 
 ENV PATH="/root/miniconda3/bin:${PATH}"
 

docker/Dockerfile-cloud

@@ -3,7 +3,6 @@ FROM winglian/axolotl:$BASE_TAG
 
 ENV HF_DATASETS_CACHE="/workspace/data/huggingface-cache/datasets"
 ENV HUGGINGFACE_HUB_CACHE="/workspace/data/huggingface-cache/hub"
-ENV TRANSFORMERS_CACHE="/workspace/data/huggingface-cache/hub"
 ENV HF_HOME="/workspace/data/huggingface-cache/hub"
 ENV HF_HUB_ENABLE_HF_TRANSFER="1"
 

docker/Dockerfile-cloud-no-tmux

@@ -3,7 +3,6 @@ FROM winglian/axolotl:$BASE_TAG
 
 ENV HF_DATASETS_CACHE="/workspace/data/huggingface-cache/datasets"
 ENV HUGGINGFACE_HUB_CACHE="/workspace/data/huggingface-cache/hub"
-ENV TRANSFORMERS_CACHE="/workspace/data/huggingface-cache/hub"
 ENV HF_HOME="/workspace/data/huggingface-cache/hub"
 ENV HF_HUB_ENABLE_HF_TRANSFER="1"
 

docs/amd_hpc.qmd

@@ -0,0 +1,108 @@
+---
+title: Training with AMD GPUs on HPC Systems
+description: A comprehensive guide for using Axolotl on distributed systems with AMD GPUs
+---
+
+This guide provides step-by-step instructions for installing and configuring Axolotl on a High-Performance Computing (HPC) environment equipped with AMD GPUs.
+
+## Setup
+
+### 1. Install Python
+
+We recommend using Miniforge, a minimal conda-based Python distribution:
+
+```bash
+curl -L -O "https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-$(uname)-$(uname -m).sh"
+bash Miniforge3-$(uname)-$(uname -m).sh
+```
+
+### 2. Configure Python Environment
+Add Python to your PATH and ensure it's available at login:
+
+```bash
+echo 'export PATH=~/miniforge3/bin:$PATH' >> ~/.bashrc
+echo 'if [ -f ~/.bashrc ]; then . ~/.bashrc; fi' >> ~/.bash_profile
+```
+
+### 3. Load AMD GPU Software
+
+Load the ROCm module:
+
+```bash
+module load rocm/5.7.1
+```
+
+Note: The specific module name and version may vary depending on your HPC system. Consult your system documentation for the correct module name.
+
+### 4. Install PyTorch
+
+Install PyTorch with ROCm support:
+
+```bash
+pip install -U torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm5.7 --force-reinstall
+```
+
+### 5. Install Flash Attention
+
+Clone and install the Flash Attention repository:
+
+```bash
+git clone --recursive https://github.com/ROCmSoftwarePlatform/flash-attention.git
+export GPU_ARCHS="gfx90a"
+cd flash-attention
+export PYTHON_SITE_PACKAGES=$(python -c 'import site; print(site.getsitepackages()[0])')
+patch "${PYTHON_SITE_PACKAGES}/torch/utils/hipify/hipify_python.py" hipify_patch.patch
+pip install .
+```
+
+### 6. Install Axolotl
+
+Clone and install Axolotl:
+
+```bash
+git clone https://github.com/axolotl-ai-cloud/axolotl
+cd axolotl
+pip install packaging ninja
+pip install -e .
+```
+
+### 7. Apply xformers Workaround
+
+xformers appears to be incompatible with ROCm. Apply the following workarounds:
+ - Edit $HOME/packages/axolotl/src/axolotl/monkeypatch/llama_attn_hijack_flash.py modifying the code to always return `False` for SwiGLU availability from xformers.
+ - Edit $HOME/miniforge3/lib/python3.10/site-packages/xformers/ops/swiglu_op.py replacing the "SwiGLU" function with a pass statement.
+
+### 8. Prepare Job Submission Script
+
+Create a script for job submission using your HPC's particular software (e.g. Slurm, PBS). Include necessary environment setup and the command to run Axolotl training. If the compute node(s) do(es) not have internet access, it is recommended to include
+
+```bash
+export TRANSFORMERS_OFFLINE=1
+export HF_DATASETS_OFFLINE=1
+```
+
+### 9. Download Base Model
+
+Download a base model using the Hugging Face CLI:
+
+```bash
+huggingface-cli download meta-llama/Meta-Llama-3.1-8B --local-dir ~/hfdata/llama3.1-8B
+```
+
+### 10. Create Axolotl Configuration
+
+Create an Axolotl configuration file (YAML format) tailored to your specific training requirements and dataset. Use FSDP for multi-node training.
+
+Note: Deepspeed did not work at the time of testing. However, if anyone managed to get it working, please let us know.
+
+### 11. Preprocess Data
+
+Run preprocessing on the login node:
+
+```bash
+CUDA_VISIBLE_DEVICES="" python -m axolotl.cli.preprocess /path/to/your/config.yaml
+```
+
+### 12. Train
+
+You are now ready to submit your previously prepared job script. 🚂

docs/config.qmd

@@ -83,13 +83,14 @@ lora_on_cpu: true
 datasets:
   # HuggingFace dataset repo | s3://,gs:// path | "json" for local dataset, make sure to fill data_files
   - path: vicgalle/alpaca-gpt4
-  # The type of prompt to use for training. [alpaca, sharegpt, gpteacher, oasst, reflection]
+    # The type of prompt to use for training. [alpaca, sharegpt, gpteacher, oasst, reflection]
     type: alpaca # format | format:<prompt_style> (chat/instruct) | <prompt_strategies>.load_<load_fn>
     ds_type: # Optional[str] (json|arrow|parquet|text|csv) defines the datatype when path is a file
     data_files: # Optional[str] path to source data files
     shards: # Optional[int] number of shards to split data into
     name: # Optional[str] name of dataset configuration to load
     train_on_split: train # Optional[str] name of dataset split to load from
+    revision: # Optional[str] The specific revision of the dataset to use when loading from the Hugging Face Hub. This can be a commit hash, tag, or branch name. If not specified, the latest version will be used. This parameter is ignored for local datasets.
 
     # Optional[str] fastchat conversation type, only used with type: sharegpt
     conversation: # Options (see Conversation 'name'): https://github.com/lm-sys/FastChat/blob/main/fastchat/conversation.py
@@ -123,6 +124,48 @@ datasets:
       # For `completion` datsets only, uses the provided field instead of `text` column
       field:
 
+  # Using chat template
+  - path: ...
+    # Set type to `chat_template` to use this strategy
+    type: chat_template
+    # Specify the name of the chat template to use
+    # The name of the chat template to use for training, following values are supported:
+    # - tokenizer_default: Uses the chat template that is available in the tokenizer_config.json. If the chat template is not available in the tokenizer, it will raise an error. This is the default.
+    # - alpaca/inst/chatml/gemma/cohere/llama3/phi_3/deepseek_v2/jamba: These chat templates are available in the axolotl codebase at src/axolotl/utils/chat_templates.py
+    # - tokenizer_default_fallback_*: where * is the name of the chat template to fallback to if the tokenizer does not have a chat template else default to tokenizer. E.g. tokenizer_default_fallback_chatml.
+    # - jinja: Uses a custom jinja template for the chat template. The custom jinja template should be provided in the chat_template_jinja field.
+    chat_template: tokenizer_default
+    # Custom jinja template for chat template. This will be only used if `chat_template` is set to `jinja` or empty (in which case chat_template is automatically set to `jinja`).
+    chat_template_jinja:
+    # The key in the data example that contains the messages. Default is "messages".
+    field_messages: messages
+    # The key in the message turn that contains the role. Default is "role".
+    message_field_role: role
+    # The key in the message turn that contains the content. Default is "content".
+    message_field_content: content
+    # Optional[Dict[str, List]]. Roles mapping for the messages.
+    roles:
+      user: ["human", "user"]
+      assistant: ["gpt", "assistant", "ai"]
+      system: ["system"]
+
+    ## NOTE: Leaving the below empty will default to using the simple legacy tokenization strategy where only last message is trained on.
+
+    # Optional[List[str]]. Roles to train on. The tokens from these roles will be considered for the loss.
+    roles_to_train: ["gpt", "assistant"]
+    # Optional[str]. Which EOS tokens to train on in the conversation. Possible values are:
+    # - all: train on all EOS tokens
+    # - turn: train on the EOS token at the end of each trainable turn
+    # - last: train on the last EOS token in the conversation
+    train_on_eos: last
+    # The key in the message turn that indicates via boolean whether tokens of a turn should be considered for training. Useful to selectively train on certain turns besides the `roles_to_train`.
+    message_field_training: training
+    # The key in the message turn that contains the training details. Useful to selectively train on certain tokens in a turn.
+    # The value of the key is a List[Dict] containing `begin_offset` (start character index in content), `end_offset` (end character index in content), and `train` (boolean whether to train).
+    # See example at `docs/dataset-formats/conversation.qmd`
+    message_field_training_detail: train_detail
+
+
 # If false, the datasets will not be shuffled and will keep their original order in `datasets`.
 # The same applies to the `test_datasets` option and the `pretraining_dataset` option. Default is true.
 shuffle_merged_datasets: true
@@ -141,9 +184,16 @@ test_datasets:
 # use RL training: 'dpo', 'ipo', 'kto'
 rl:
 
-# Saves the desired chat template to the tokenizer_config.json for easier inferencing
-# Currently supports chatml and inst (mistral/mixtral)
-chat_template: chatml
+# The name of the chat template to use for training, following values are supported:
+# - tokenizer_default: Uses the chat template that is available in the tokenizer_config.json. If the chat template is not available in the tokenizer, it will raise an error. This is the default value.
+# - alpaca/inst/chatml/gemma/cohere/llama3/phi_3/deepseek_v2/jamba: These chat templates are available in the axolotl codebase at src/axolotl/utils/chat_templates.py
+# - tokenizer_default_fallback_*: where * is the name of the chat template to fallback to. E.g. tokenizer_default_fallback_chatml. This is useful when the chat template is not available in the tokenizer.
+# - jinja: Uses a custom jinja template for the chat template. The custom jinja template should be provided in the chat_template_jinja field.
+# The selected chat template will be saved to the tokenizer_config.json for easier inferencing
+# Note: It is recommended to set train_on_inputs to true when using a chat template that is different from the model's default chat template.
+chat_template: tokenizer_default
+# custom jinja template for chat template. This will be only used if chat_template is set to `jinja` or `null` (in which case chat_template is automatically set to `jinja`). Default is null.
+chat_template_jinja: null
 # Changes the default system message
 default_system_message: You are a helpful assistant. Please give a long and detailed answer. # Currently only supports chatml.
 # Axolotl attempts to save the dataset as an arrow after packing the data together so
@@ -265,8 +315,21 @@ wandb_log_model: # "checkpoint" to log model to wandb Artifacts every `save_step
 # mlflow configuration if you're using it
 mlflow_tracking_uri: # URI to mlflow
 mlflow_experiment_name: # Your experiment name
+mlflow_run_name: # Your run name
 hf_mlflow_log_artifacts:  # set to true to copy each saved checkpoint on each save to mlflow artifact registry
 
+# Comet configuration if you're using it
+# Make sure your `COMET_API_KEY` environment variable is set (recommended) or you login to Comet with `comet login`.
+# Check out our documentation for more details https://www.comet.com/docs/v2/api-and-sdk/python-sdk/reference/Experiment-Creation/#comet_ml.start
+use_comet: # Enable or disable Comet integration.
+comet_api_key: # API key for Comet. Recommended to set via `comet login`.
+comet_workspace: # Workspace name in Comet. Defaults to the user's default workspace.
+comet_project_name: # Project name in Comet. Defaults to Uncategorized.
+comet_experiment_key: # Identifier for the experiment. Used to append data to an existing experiment or control the key of new experiments. Default to a random key.
+comet_mode: # Create a new experiment ("create") or log to an existing one ("get"). Default ("get_or_create") auto-selects based on configuration.
+comet_online: # Set to True to log data to Comet server, or False for offline storage. Default is True.
+comet_experiment_config: # Dictionary for additional configuration settings, see the doc for more details.
+
 # Where to save the full-finetuned model to
 output_dir: ./completed-model
 
@@ -301,7 +364,7 @@ max_steps:
 
 eval_table_size: # Approximate number of predictions sent to wandb depending on batch size. Enabled above 0. Default is 0
 eval_max_new_tokens: # Total number of tokens generated for predictions sent to wandb. Default is 128
-eval_causal_lm_metrics: # HF evaluate metrics used during evaluation. Default is ["sacrebleu", "comet", "ter", chrf]
+eval_causal_lm_metrics: # HF evaluate metrics used during evaluation. Default is ["sacrebleu", "comet", "ter", "chrf", "perplexity"]
 
 loss_watchdog_threshold: # High loss value, indicating the learning has broken down (a good estimate is ~2 times the loss at the start of training)
 loss_watchdog_patience: # Number of high-loss steps in a row before the trainer aborts (default: 3)

docs/dataset-formats/conversation.qmd

@@ -6,6 +6,8 @@ order: 3
 
 ## sharegpt
 
+UPDATE: ShareGPT is being deprecated in the next release. Please see `chat_template` section below.
+
 conversations where `from` is `human`/`gpt`. (optional: first row with role `system` to override default system prompt)
 
 ```{.json filename="data.jsonl"}
@@ -54,6 +56,14 @@ conversations where `from` is `prompter` `assistant` instead of default sharegpt
 {"conversations": [{"from": "...", "value": "..."}]}
 ```
 
+## sharegpt.load_ultrachat
+
+conversations where the turns field is 'messages', human is 'user' and gpt is 'assistant'.
+
+```{.json filename="data.jsonl"}
+{"messages": [{"user": "...", "assistant": "..."}]}
+```
+
 ## sharegpt_jokes
 
 creates a chat where bot is asked to tell a joke, then explain why the joke is funny
@@ -61,3 +71,138 @@ creates a chat where bot is asked to tell a joke, then explain why the joke is f
 ```{.json filename="data.jsonl"}
 {"conversations": [{"title": "...", "text": "...", "explanation": "..."}]}
 ```
+
+
+## chat_template
+
+Chat Template strategy uses a jinja2 template that converts a list of messages into a prompt. Support using tokenizer's template, a supported template, or custom jinja2.
+
+```{.json filename="data.jsonl"}
+{"conversations": [{"role": "...", "content": "..."}]}
+```
+
+See `config.qmd` for full configs and supported templates.
+
+### Migrating from sharegpt
+
+Most configs can be adapted as follows:
+
+```yaml
+# old
+chat_template: chatml
+datasets:
+  - path: ...
+    type: sharegpt
+    conversation: chatml
+
+# new (if using tokenizer's chat_template)
+datasets:
+  - path: ...
+    type: chat_template
+
+    field_messages: conversations
+    message_field_role: from
+    message_field_content: value
+
+# new (if setting a new chat_template like chatml, gemma, etc)
+chat_template: chatml
+datasets:
+  - path: ...
+    type: chat_template
+
+    field_messages: conversations
+    message_field_role: from
+    message_field_content: value
+```
+
+We recommend checking the below examples for other usecases.
+
+### Examples
+
+1. Using the default chat template in the tokenizer_config.json on OpenAI messages format, training on only last message.
+
+```yaml
+datasets:
+  - path: ...
+    type: chat_template
+```
+
+2. Using the `gemma` chat template to override the tokenizer_config.json's chat template on OpenAI messages format, training on all assistant messages.
+
+```yaml
+chat_template: gemma # this overwrites the tokenizer's chat_template
+datasets:
+  - path: ...
+    type: chat_template
+    roles_to_train: ["assistant"]
+```
+
+3. Using the tokenizer_config.json's chat template or `chatml` as fallback if the former's chat template does not exist, on OpenAI messages format, training on all assistant messages.
+
+```yaml
+chat_template: tokenizer_default_fallback_chatml # this overwrites the tokenizer's chat_template
+datasets:
+  - path: ...
+    type: chat_template
+    roles_to_train: ["assistant"]
+```
+
+4. Using a custom jinja template on OpenAI messages format, training on all assistant messages.
+
+```yaml
+# chat_template: jinja # `jinja` will be implied if the `chat_template_jinja` is set and this field is empty
+chat_template_jinja: "{{ bos_token }}{% for message in messages %}{% if (message['role'] == 'system') %}{{'<|system|>' + '\n' + message['content'] + '<|end|>' + '\n'}}{% elif (message['role'] == 'user') %}{{'<|user|>' + '\n' + message['content'] + '<|end|>' + '\n' + '<|assistant|>' + '\n'}}{% elif message['role'] == 'assistant' %}{{message['content'] + '<|end|>' + '\n'}}{% endif %}{% endfor %}"
+
+datasets:
+  - path: ...
+    type: chat_template
+    roles_to_train: ["assistant"]
+```
+
+5. (Advanced) Using fine-grained control over tokens and turns to train in a conversation
+
+For a data sample that looks like:
+
+```{.json filename="data.jsonl"}
+{
+  "conversations": [
+    {"from": "system", "value": "You are an AI assistant.", "train": false},
+    {"from": "human", "value": "Hello", "train": false},
+    {"from": "assistant", "value": "Hello", "train": true},
+    {"from": "human", "value": "How are you?", "train": true},
+    {
+      "from": "assistant",
+      "value": "I'm doing very well, thank you!",
+      "train_detail": [
+        {"begin_offset": 0, "end_offset": 8, "train": false},
+        {"begin_offset": 9, "end_offset": 18, "train": true},
+        {"begin_offset": 19, "end_offset": 30, "train": false},
+      ],
+    },
+    {
+        "from": "human",
+        "value": "I'm doing very well, thank you!",
+        "train": true,
+    },
+    {"from": "assistant", "value": "Hi there!", "train": true}
+  ]
+}
+```
+
+The configuration would look like:
+
+```yaml
+datasets:
+  - path: ...
+    type: chat_template
+    chat_template: tokenizer_default
+    field_messages: conversations
+    message_field_role: from
+    message_field_content: value
+    roles_to_train: []
+    train_on_eos: turn
+    message_field_training: train
+    message_field_training_detail: train_detail
+```
+
+Tip: It is not necessary to use both `message_field_training` and `message_field_training_detail` at a time.

docs/dataset-formats/tokenized.qmd

@@ -7,7 +7,7 @@ order: 5
 - Pass an empty `type:` in your axolotl config.
 - Columns in Dataset must be exactly `input_ids`, `attention_mask`, `labels`
 - To indicate that a token should be ignored during training, set its corresponding label to `-100`.
-- Do not add BOS/EOS. Axolotl will add them for you based on the default tokenizer for the model you're using.
+- You must add BOS and EOS, and make sure that you are training on EOS by not setting its label to -100.
 - For pretraining, do not truncate/pad documents to the context window length.
 - For instruction training, documents must be truncated/padded as desired.
 

docs/input_output.qmd

@@ -205,7 +205,7 @@ ds = load_from_disk(f'last_run_prepared/{directory[0]}/')
     hi there!.  goodbye  farewell</s>
 ```
 
-We can check that the right tokens are ingored by comparing the labels
+We can check that the right tokens are ignored by comparing the labels
 to each token:
 
 ```python

docs/multimodal.qmd

@@ -0,0 +1,28 @@
+# MultiModal / Vision Language Models (BETA)
+
+### Supported Models
+
+- Mllama, i.e. llama with vision models
+
+### Usage
+
+Currently multimodal support is limited and doesn't have full feature parity. To finetune a multimodal Llama w/ LoRA,
+you'll need to use the following in YAML in combination with the rest of the required hyperparams.
+
+```yaml
+base_model: alpindale/Llama-3.2-11B-Vision-Instruct
+processor_type: AutoProcessor
+skip_prepare_dataset: true
+
+chat_template: llama3_2_vision
+datasets:
+  - path: HuggingFaceH4/llava-instruct-mix-vsft
+    type: chat_template
+    split: train[:1%]
+    field_messages: messages
+remove_unused_columns: false
+sample_packing: false
+
+# only finetune the Language model, leave the vision model and vision tower frozen
+lora_target_modules: 'language_model.model.layers.[\d]+.(mlp|cross_attn|self_attn).(up|down|gate|q|k|v|o)_proj'
+```

docs/torchao.qmd

@@ -0,0 +1,19 @@
+---
+title: "PyTorch ao"
+description: "Custom data types and layouts for training and inference"
+---
+
+### Installation
+
+Stable Release from the PyTorch index
+
+```bash
+pip install torchao --extra-index-url https://download.pytorch.org/whl/cu121 # full options are cpu/cu118/cu121/cu124
+```
+
+
+Nightly release
+
+```bash
+pip install --pre torchao-nightly --index-url https://download.pytorch.org/whl/nightly/cu121 # full options are cpu/cu118/cu121/cu124
+```

docs/unsloth.qmd

@@ -0,0 +1,49 @@
+---
+title: "Unsloth"
+description: "Hyper-optimized QLoRA finetuning for single GPUs"
+---
+
+### Overview
+
+Unsloth provides hand-written optimized kernels for LLM finetuning that slightly improve speed and VRAM over
+standard industry baselines.
+
+
+### Installation
+
+The following will install unsloth from source and downgrade xformers as unsloth is incompatible with the most up
+to date libraries.
+
+```bash
+pip install --no-deps "unsloth @ git+https://github.com/unslothai/unsloth.git"
+pip install --no-deps --force-reinstall xformers==0.0.26.post1
+```
+
+### Using unsloth w Axolotl
+
+Axolotl exposes a few configuration options to try out unsloth and get most of the performance gains.
+
+Our unsloth integration is currently limited to the following model architectures:
+ - llama
+
+These options are specific to LoRA finetuning and cannot be used for multi-GPU finetuning
+```yaml
+unsloth_lora_mlp: true
+unsloth_lora_qkv: true
+unsloth_lora_o: true
+```
+
+These options are composable and can be used with multi-gpu finetuning
+```yaml
+unsloth_cross_entropy_loss: true
+unsloth_rms_norm: true
+unsloth_rope: true
+```
+
+### Limitations
+
+- Single GPU only; e.g. no multi-gpu support
+- No deepspeed or FSDP support (requires multi-gpu)
+- LoRA + QLoRA support only. No full fine tunes or fp8 support.
+- Limited model architecture support. Llama, Phi, Gemma, Mistral only
+- No MoE support.

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

@@ -43,7 +43,6 @@
    },
    "outputs": [],
    "source": [
-    "!pip install torch==\"2.1.2\"\n",
     "!pip install -e git+https://github.com/axolotl-ai-cloud/axolotl#egg=axolotl\n",
     "!pip install flash-attn==\"2.5.0\"\n",
     "!pip install deepspeed==\"0.13.1\"!pip install mlflow==\"2.13.0\""

examples/deepseek-v2/fft-fsdp-16b.yaml

@@ -0,0 +1,67 @@
+base_model: deepseek-ai/DeepSeek-V2-Lite
+trust_remote_code: true
+
+load_in_8bit: false
+load_in_4bit: false
+strict: false
+
+datasets:
+  - path: tatsu-lab/alpaca
+    type: alpaca
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.0
+output_dir: ./outputs/out
+
+sequence_len: 2048
+sample_packing: true
+pad_to_sequence_len: true
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+gradient_accumulation_steps: 8
+micro_batch_size: 1
+num_epochs: 1
+optimizer: adamw_torch
+lr_scheduler: cosine
+learning_rate: 2e-5
+
+train_on_inputs: false
+group_by_length: false
+bf16: auto
+fp16:
+tf32: false
+
+gradient_checkpointing: true
+gradient_checkpointing_kwargs:
+  use_reentrant: false
+early_stopping_patience:
+resume_from_checkpoint:
+logging_steps: 1
+xformers_attention:
+flash_attention: true
+
+warmup_steps: 100
+evals_per_epoch: 2
+eval_table_size:
+saves_per_epoch: 1
+debug:
+deepspeed:
+weight_decay: 0.0
+special_tokens:
+fsdp:
+  - full_shard
+  - auto_wrap
+fsdp_config:
+  fsdp_limit_all_gathers: true
+  fsdp_sync_module_states: true
+  fsdp_offload_params: true
+  fsdp_use_orig_params: false
+  fsdp_cpu_ram_efficient_loading: true
+  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
+  fsdp_transformer_layer_cls_to_wrap: DeepseekV2DecoderLayer
+  fsdp_state_dict_type: FULL_STATE_DICT
+  fsdp_sharding_strategy: FULL_SHARD

examples/deepseek-v2/qlora-fsdp-2_5.yaml

@@ -0,0 +1,83 @@
+base_model: axolotl-quants/DeepSeek-V2.5-bnb-nf4-bf16
+trust_remote_code: true
+
+load_in_8bit: false
+load_in_4bit: true
+strict: false
+
+
+plugins:
+  - axolotl.integrations.liger.LigerPlugin
+liger_rms_norm: true
+liger_swiglu: true
+liger_fused_linear_cross_entropy: true
+
+chat_template: deepseek_v2
+datasets:
+  - path: mlabonne/FineTome-100k
+    type: chat_template
+    split: train
+
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.0
+output_dir: ./outputs/out
+
+sequence_len: 4096
+sample_packing: true
+pad_to_sequence_len: true
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+adapter: qlora
+lora_r: 256
+lora_alpha: 256
+lora_target_linear: true
+peft_use_rslora: true
+
+gradient_accumulation_steps: 1
+micro_batch_size: 8
+num_epochs: 1
+optimizer: adamw_torch
+lr_scheduler: cosine
+learning_rate: 2e-5
+
+train_on_inputs: false
+group_by_length: false
+bf16: auto
+fp16:
+tf32: false
+
+gradient_checkpointing: true
+gradient_checkpointing_kwargs:
+  use_reentrant: false
+early_stopping_patience:
+resume_from_checkpoint:
+logging_steps: 1
+xformers_attention:
+flash_attention: true
+
+warmup_steps: 100
+evals_per_epoch: 2
+eval_table_size:
+saves_per_epoch: 1
+debug:
+deepspeed:
+weight_decay: 0.0
+special_tokens:
+fsdp:
+  - full_shard
+  - auto_wrap
+fsdp_config:
+  fsdp_limit_all_gathers: true
+  fsdp_sync_module_states: true
+  fsdp_offload_params: true
+  fsdp_use_orig_params: false
+  fsdp_cpu_ram_efficient_loading: true
+  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
+  fsdp_transformer_layer_cls_to_wrap: DeepseekV2DecoderLayer
+  fsdp_state_dict_type: FULL_STATE_DICT
+  fsdp_sharding_strategy: FULL_SHARD

examples/gemma2/reward-model.yaml

@@ -0,0 +1,63 @@
+base_model: google/gemma-2-2b
+model_type: AutoModelForSequenceClassification
+tokenizer_type: AutoTokenizer
+
+load_in_8bit: false
+load_in_4bit: false
+strict: false
+
+reward_model: true
+chat_template: gemma
+datasets:
+  - path: argilla/distilabel-intel-orca-dpo-pairs
+    type: bradley_terry.chat_template
+val_set_size: 0.0
+output_dir: ./outputs/out
+remove_unused_columns: false
+
+sequence_len: 2048
+sample_packing: false
+eval_sample_packing: false
+pad_to_sequence_len: true
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+
+gradient_accumulation_steps: 4
+micro_batch_size: 2
+num_epochs: 4
+optimizer: adamw_bnb_8bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+train_on_inputs: false
+group_by_length: false
+bf16: true
+fp16:
+tf32: true
+
+gradient_checkpointing: true
+gradient_checkpointing_kwargs:
+  use_reentrant: false
+early_stopping_patience:
+resume_from_checkpoint:
+local_rank:
+logging_steps: 1
+xformers_attention:
+flash_attention: true
+
+warmup_ratio: 0.1
+evals_per_epoch:
+eval_table_size:
+eval_max_new_tokens: 128
+saves_per_epoch: 1
+debug:
+deepspeed:
+weight_decay: 0.0
+fsdp:
+fsdp_config:
+special_tokens:

examples/jamba/README.md

@@ -6,5 +6,5 @@
 - ✅ qlora w/ deepspeed Zero-3 needs at least 2x GPUs and 67GiB VRAM (wtf?)
 - ✅ qlora single-gpu, ~51GiB VRAM
 - ✅ multipack
-- ❓ FSDP
+- ✅ FSDP
 - ❓ 8-bit LoRA

examples/jamba/qlora_fsdp_large.yaml

@@ -0,0 +1,61 @@
+base_model: ai21labs/AI21-Jamba-1.5-Large
+tokenizer_type: AutoTokenizer
+
+load_in_4bit: true
+strict: false
+use_tensorboard: true
+datasets:
+  - path: cgato/SlimOrcaDedupCleaned
+    type: chat_template
+    chat_template: jamba
+    drop_system_message: true
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.0
+output_dir: jamba-large-fsdp-qlora-ft
+save_safetensors: true
+adapter: qlora
+sequence_len: 2048
+sample_packing: true
+pad_to_sequence_len: true
+
+lora_r: 16
+lora_alpha: 16
+lora_dropout: 0.05
+lora_target_modules: [down_proj,gate_proj,in_proj,k_proj,o_proj,out_proj,q_proj,up_proj,v_proj,x_proj]
+lora_target_linear: false
+
+gradient_accumulation_steps: 4
+micro_batch_size: 1
+num_epochs: 2
+optimizer: adamw_torch
+lr_scheduler: cosine
+learning_rate: 0.00001
+
+train_on_inputs: false
+group_by_length: false
+bf16: true
+tf32: true
+
+gradient_checkpointing: true
+gradient_checkpointing_kwargs:
+  use_reentrant: true
+logging_steps: 1
+flash_attention: true
+
+warmup_steps: 10
+evals_per_epoch: 1
+saves_per_epoch: 1
+weight_decay: 0.0
+fsdp:
+  - full_shard
+  - auto_wrap
+fsdp_config:
+  fsdp_limit_all_gathers: true
+  fsdp_sync_module_states: true
+  fsdp_offload_params: false
+  fsdp_use_orig_params: false
+  fsdp_cpu_ram_efficient_loading: true
+  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
+  fsdp_transformer_layer_cls_to_wrap: JambaAttentionDecoderLayer,JambaMambaDecoderLayer
+  fsdp_state_dict_type: FULL_STATE_DICT
+  fsdp_sharding_strategy: FULL_SHARD

examples/llama-3-vision/lora-11b.yaml

@@ -0,0 +1,63 @@
+base_model: alpindale/Llama-3.2-11B-Vision-Instruct
+processor_type: AutoProcessor
+strict: false
+
+# these 3 lines are needed for now to handle vision chat templates w images
+skip_prepare_dataset: true
+remove_unused_columns: false
+sample_packing: false
+
+chat_template: llama3_2_vision
+datasets:
+  - path: HuggingFaceH4/llava-instruct-mix-vsft
+    type: chat_template
+    split: train[:1%]
+    field_messages: messages
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.0
+output_dir: ./outputs/out
+
+adapter: lora
+lora_model_dir:
+
+sequence_len: 8192
+pad_to_sequence_len: false
+
+lora_r: 32
+lora_alpha: 16
+lora_dropout: 0.05
+lora_target_modules: 'language_model.model.layers.[\d]+.(mlp|cross_attn|self_attn).(up|down|gate|q|k|v|o)_proj'
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+gradient_accumulation_steps: 4
+micro_batch_size: 1
+num_epochs: 1
+optimizer: adamw_bnb_8bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+train_on_inputs: false
+group_by_length: false
+bf16: true
+fp16:
+tf32: true
+
+gradient_checkpointing: true
+local_rank:
+logging_steps: 1
+flash_attention: true
+eager_attention:
+
+warmup_ratio: 0.1
+evals_per_epoch: 1
+saves_per_epoch: 1
+debug:
+deepspeed:
+weight_decay: 0.0
+fsdp:
+fsdp_config:

examples/llama-3/fft-8b-liger-fsdp.yaml

@@ -0,0 +1,76 @@
+base_model: NousResearch/Meta-Llama-3.1-8B
+
+plugins:
+  - axolotl.integrations.liger.LigerPlugin
+liger_rope: true
+liger_rms_norm: true
+liger_swiglu: true
+liger_fused_linear_cross_entropy: true
+
+strict: false
+
+chat_template: llama3
+datasets:
+  - path: mlabonne/FineTome-100k
+    type: chat_template
+    split: train[:20%]
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.02
+output_dir: ./outputs/out
+
+sequence_len: 4096
+sample_packing: true
+pad_to_sequence_len: true
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+gradient_accumulation_steps: 4
+micro_batch_size: 2
+num_epochs: 1
+optimizer: adamw_torch
+lr_scheduler: cosine
+learning_rate: 2e-5
+
+train_on_inputs: false
+group_by_length: false
+bf16: auto
+fp16:
+tf32: false
+
+gradient_checkpointing: true
+gradient_checkpointing_kwargs:
+  use_reentrant: false
+early_stopping_patience:
+resume_from_checkpoint:
+logging_steps: 1
+xformers_attention:
+flash_attention: true
+
+warmup_steps: 100
+evals_per_epoch: 2
+eval_table_size:
+saves_per_epoch: 1
+debug:
+deepspeed:
+weight_decay: 0.0
+fsdp:
+  - full_shard
+  - auto_wrap
+fsdp_config:
+  fsdp_limit_all_gathers: true
+  fsdp_sync_module_states: true
+  fsdp_offload_params: true
+  fsdp_use_orig_params: false
+  fsdp_cpu_ram_efficient_loading: true
+  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
+  fsdp_transformer_layer_cls_to_wrap: LlamaDecoderLayer
+  fsdp_state_dict_type: FULL_STATE_DICT
+  fsdp_sharding_strategy: FULL_SHARD
+  fsdp_backward_prefetch: BACKWARD_PRE
+special_tokens:
+  pad_token: <|finetune_right_pad_id|>
+  eos_token: <|eot_id|>

examples/llama-3/fft-8b.yaml

@@ -1,6 +1,4 @@
-base_model: meta-llama/Meta-Llama-3-8B
-model_type: LlamaForCausalLM
-tokenizer_type: AutoTokenizer
+base_model: NousResearch/Meta-Llama-3.1-8B
 
 load_in_8bit: false
 load_in_4bit: false

examples/llama-3/instruct-dpo-lora-8b.yml

@@ -0,0 +1,81 @@
+base_model: meta-llama/Meta-Llama-3-8B-Instruct
+model_type: LlamaForCausalLM
+tokenizer_type: AutoTokenizer
+
+load_in_8bit: true
+load_in_4bit: false
+strict: false
+
+chat_template: llama3
+rl: dpo
+datasets:
+  - path: fozziethebeat/alpaca_messages_2k_dpo_test
+    type: chat_template.default
+    chat_template: llama3
+    field_messages: conversation
+    field_chosen: chosen
+    field_rejected: rejected
+    message_field_role: role
+    message_field_content: content
+    roles:
+      system:
+        - system
+      user:
+        - user
+      assistant:
+        - assistant
+
+dataset_prepared_path:
+val_set_size: 0.05
+output_dir: ./outputs/lora-out
+
+sequence_len: 4096
+sample_packing: false
+pad_to_sequence_len: true
+
+adapter: lora
+lora_model_dir:
+lora_r: 32
+lora_alpha: 16
+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: 4
+micro_batch_size: 2
+num_epochs: 4
+optimizer: adamw_bnb_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
+s2_attention:
+
+warmup_steps: 10
+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:

examples/llama-3/instruct-lora-8b.yml

@@ -1,4 +1,4 @@
-base_model: meta-llama/Meta-Llama-3-8B-Instruct
+base_model: NousResearch/Meta-Llama-3-8B-Instruct
 model_type: LlamaForCausalLM
 tokenizer_type: AutoTokenizer
 
@@ -74,3 +74,5 @@ deepspeed:
 weight_decay: 0.0
 fsdp:
 fsdp_config:
+special_tokens:
+   pad_token: <|end_of_text|>

examples/llama-3/lora-8b.yml

@@ -1,4 +1,4 @@
-base_model: meta-llama/Meta-Llama-3-8B
+base_model: NousResearch/Meta-Llama-3-8B
 model_type: LlamaForCausalLM
 tokenizer_type: AutoTokenizer
 

examples/llama-3/qlora-fsdp-405b.yaml

@@ -0,0 +1,63 @@
+base_model: hugging-quants/Meta-Llama-3.1-405B-BNB-NF4-BF16
+tokenizer_type: AutoTokenizer
+
+load_in_4bit: true
+strict: false
+
+datasets:
+  - path: tatsu-lab/alpaca
+    type: alpaca
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.0
+output_dir: ./outputs/out/qlora-llama3_1-405b
+save_safetensors: true
+
+adapter: qlora
+
+sequence_len: 2048
+sample_packing: true
+pad_to_sequence_len: true
+
+lora_r: 16
+lora_alpha: 16
+lora_dropout: 0.05
+lora_target_modules:
+lora_target_linear: true
+
+gradient_accumulation_steps: 4
+micro_batch_size: 1
+num_epochs: 2
+optimizer: adamw_torch
+lr_scheduler: cosine
+learning_rate: 0.00001
+
+train_on_inputs: false
+group_by_length: false
+bf16: true
+tf32: true
+
+gradient_checkpointing: true
+gradient_checkpointing_kwargs:
+  use_reentrant: true
+logging_steps: 1
+flash_attention: true
+
+warmup_steps: 10
+evals_per_epoch: 4
+saves_per_epoch: 1
+weight_decay: 0.0
+fsdp:
+  - full_shard
+  - auto_wrap
+fsdp_config:
+  fsdp_limit_all_gathers: true
+  fsdp_sync_module_states: true
+  fsdp_offload_params: true
+  fsdp_use_orig_params: false
+  fsdp_cpu_ram_efficient_loading: true
+  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
+  fsdp_transformer_layer_cls_to_wrap: LlamaDecoderLayer
+  fsdp_state_dict_type: FULL_STATE_DICT
+  fsdp_sharding_strategy: FULL_SHARD
+special_tokens:
+  pad_token: <|finetune_right_pad_id|>

examples/llama-3/qlora.yml

@@ -1,4 +1,4 @@
-base_model: meta-llama/Meta-Llama-3-8B
+base_model: NousResearch/Meta-Llama-3-8B
 model_type: AutoModelForCausalLM
 tokenizer_type: AutoTokenizer
 

examples/phi/lora-3.5.yaml

@@ -0,0 +1,76 @@
+base_model: microsoft/Phi-3.5-mini-instruct
+model_type: AutoModelForCausalLM
+tokenizer_type: AutoTokenizer
+
+load_in_8bit: true
+load_in_4bit: false
+strict: false
+
+chat_template: phi_3
+datasets:
+  - path: fozziethebeat/alpaca_messages_2k_test
+    type: chat_template
+    chat_template: phi_3
+    field_messages: messages
+    message_field_role: role
+    message_field_content: content
+    roles:
+      user:
+        - user
+      assistant:
+        - assistant
+
+dataset_prepared_path:
+val_set_size: 0.05
+output_dir: ./outputs/lora-out
+
+sequence_len: 4096
+sample_packing: false
+pad_to_sequence_len: true
+
+adapter: lora
+lora_model_dir:
+lora_r: 32
+lora_alpha: 16
+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: 4
+micro_batch_size: 4
+num_epochs: 2
+optimizer: adamw_bnb_8bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+train_on_inputs: false
+group_by_length: false
+bfloat16: true
+bf16: true
+fp16:
+tf32: false
+
+gradient_checkpointing: true
+early_stopping_patience:
+resume_from_checkpoint:
+local_rank:
+logging_steps: 1
+xformers_attention:
+s2_attention:
+
+warmup_steps: 10
+evals_per_epoch: 4
+eval_table_size:
+eval_max_new_tokens: 128
+saves_per_epoch: 4
+debug:
+deepspeed:
+weight_decay: 0.0
+fsdp:
+fsdp_config:

examples/qwen2/qlora-fsdp.yaml

@@ -72,4 +72,5 @@ fsdp_config:
   fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
   fsdp_transformer_layer_cls_to_wrap: Qwen2DecoderLayer
   fsdp_state_dict_type: FULL_STATE_DICT
+  fsdp_sharding_strategy: FULL_SHARD
 special_tokens:

examples/tiny-llama/lora-mps.yml

@@ -1,4 +1,4 @@
-base_model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
+base_model: TinyLlama/TinyLlama_v1.1
 model_type: LlamaForCausalLM
 tokenizer_type: LlamaTokenizer
 

examples/tiny-llama/lora.yml

@@ -1,6 +1,5 @@
-base_model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
-model_type: LlamaForCausalLM
-tokenizer_type: LlamaTokenizer
+base_model: TinyLlama/TinyLlama_v1.1
+tokenizer_type: AutoTokenizer
 
 load_in_8bit: true
 load_in_4bit: false

examples/tiny-llama/pretrain.yml

@@ -9,9 +9,9 @@ strict: false
 
 max_steps: 200
 pretraining_dataset:
-  path: c4
-  name: en
-  type: pretrain
+  - path: allenai/c4
+    name: en
+    type: pretrain
 dataset_prepared_path:
 val_set_size: 0.0
 output_dir: ./outputs/model-out

examples/tiny-llama/qlora.yml

@@ -1,4 +1,4 @@
-base_model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
+base_model: TinyLlama/TinyLlama_v1.1
 model_type: LlamaForCausalLM
 tokenizer_type: LlamaTokenizer
 

requirements.txt

@@ -1,45 +1,56 @@
 --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/
 packaging==23.2
-peft==0.11.1
-transformers==4.42.3
-tokenizers==0.19.1
-bitsandbytes==0.43.1
-accelerate==0.32.0
-deepspeed @ git+https://github.com/microsoft/DeepSpeed.git@bc48371c5e1fb8fd70fc79285e66201dbb65679b
+peft==0.13.2
+transformers==4.45.2
+tokenizers>=0.20.1
+bitsandbytes==0.44.1
+accelerate==1.0.1
+datasets==3.0.1
+deepspeed==0.14.4
 pydantic==2.6.3
 addict
 fire
 PyYAML>=6.0
 requests
-datasets==2.19.1
-flash-attn==2.6.1
+flash-attn==2.6.3
 sentencepiece
 wandb
 einops
-xformers==0.0.27
+xformers==0.0.28.post1
 optimum==1.16.2
 hf_transfer
 colorama
 numba
-numpy>=1.24.4
+numpy>=1.24.4,<=2.0.1
 # qlora things
 evaluate==0.4.1
 scipy
-scikit-learn==1.2.2
+scikit-learn==1.4.2
 pynvml
 art
 fschat @ git+https://github.com/lm-sys/FastChat.git@27a05b04a35510afb1d767ae7e5990cbd278f8fe
 gradio==3.50.2
 tensorboard
 python-dotenv==1.0.1
+autoawq>=0.2.5
+triton>=2.3.0
+liger-kernel==0.3.0
 
 mamba-ssm==1.2.0.post1
 
 # remote filesystems
-s3fs
-gcsfs
+s3fs>=2024.5.0
+gcsfs>=2024.5.0
 # adlfs
 
 trl==0.9.6
 zstandard==0.22.0
 fastcore
+
+# lm eval harness
+lm_eval==0.4.4
+langdetect==1.0.9
+immutabledict==4.2.0
+antlr4-python3-runtime==4.13.2
+
+torchao==0.5.0

requirements_env.txt

@@ -0,0 +1,315 @@
+accelerate==0.34.1
+addict==2.4.0
+aiofiles==23.2.1
+aiohttp==3.9.0
+aiosignal==1.3.1
+aiostream==0.5.2
+alembic==1.13.1
+annotated-types==0.6.0
+annoy==1.17.3
+ansible==6.7.0
+ansible-core==2.13.13
+ansible-vault==2.1.0
+anyio==3.7.1
+appdirs==1.4.4
+art==6.0
+asgiref==3.7.2
+async-timeout==4.0.2
+attrdict==2.0.1
+attrs==22.2.0
+awscli==1.32.75
+-e git+ssh://git@github.com/OpenAccess-AI-Collective/axolotl.git@6e354682e3c1735d3f7fb9e362280c38e922260f#egg=axolotl
+backoff==2.2.1
+base58==2.1.1
+beartype==0.17.2
+bitnet==0.2.1
+bitsandbytes==0.42.0
+bittensor==6.7.0
+black==23.7.0
+blinker==1.7.0
+boto3==1.34.75
+botocore==1.34.75
+cachetools==5.3.3
+cachy==0.1.1
+certifi==2023.7.22
+cffi==1.16.0
+cfgv==3.3.1
+chai-guanaco==1.2.4
+charset-normalizer==3.2.0
+cleo==0.6.8
+click==8.1.7
+cloudpickle==2.0.0
+cohere==4.11.2
+colorama==0.4.4
+coloredlogs==15.0.1
+CoLT5-attention==0.10.20
+contextlib2==21.6.0
+contourpy==1.2.0
+cryptography==41.0.3
+cycler==0.12.1
+cytoolz==0.12.3
+databricks-cli==0.18.0
+dataclasses-json==0.5.7
+datasets==2.11.0
+ddt==1.6.0
+decorator==5.1.1
+deepspeed==0.15.0
+# Editable Git install with no remote (dialogpt==0.1)
+-e /Users/wing/Projects/ml/dialogpt/src
+dill==0.3.6
+distlib==0.3.6
+docker==7.0.0
+docker-pycreds==0.4.0
+docstring-parser==0.15
+docutils==0.16
+ecdsa==0.18.0
+einops==0.7.0
+einops-exts==0.0.4
+einx==0.1.3
+entrypoints==0.4
+eth-hash==0.6.0
+eth-keys==0.5.0
+eth-typing==4.0.0
+eth-utils==2.3.1
+evaluate==0.4.0
+exceptiongroup==1.1.1
+fastapi==0.109.2
+fastcore==1.5.29
+ffmpy==0.4.0
+filelock==3.12.2
+-e git+https://github.com/NousResearch/finetuning-subnet.git@24e9407d6b4430a7ca39d344692f89ce5a97d27e#egg=finetuning_subnet
+fire==0.5.0
+first==2.0.2
+flake8==7.0.0
+Flask==3.0.1
+fonttools==4.47.2
+frozendict==2.4.1
+frozenlist==1.3.3
+fschat @ git+https://github.com/lm-sys/FastChat.git@27a05b04a35510afb1d767ae7e5990cbd278f8fe
+fsspec==2023.6.0
+fuzzywuzzy==0.18.0
+gitdb==4.0.10
+GitPython==3.1.31
+google-pasta==0.2.0
+gradio==4.42.0
+gradio_client==1.3.0
+greenlet==2.0.2
+grpclib==0.4.7
+gunicorn==21.2.0
+h11==0.14.0
+h2==4.1.0
+hpack==4.0.0
+httpcore==0.17.3
+httpx==0.24.1
+huggingface-hub==0.23.4
+humanfriendly==10.0
+hyperframe==6.0.1
+identify==2.5.24
+idna==3.4
+immutables==0.20
+importlib-metadata==6.7.0
+importlib-resources==6.1.1
+inflection==0.5.1
+iniconfig==2.0.0
+itsdangerous==2.1.2
+Jinja2==3.1.2
+jmespath==1.0.1
+joblib==1.3.2
+jsonlines==3.1.0
+jsonschema==2.6.0
+kiwisolver==1.4.5
+langchain==0.0.144
+Levenshtein==0.24.0
+libcst==1.1.0
+liger-kernel==0.0.0
+lion-pytorch==0.1.2
+llama-cpp-python==0.1.36
+llvmlite==0.40.1
+local-attention==1.9.0
+loguru==0.7.0
+Mako==1.3.2
+Markdown==3.5.2
+markdown-it-py==3.0.0
+markdown2==2.4.10
+MarkupSafe==2.1.2
+marshmallow==3.19.0
+marshmallow-enum==1.5.1
+matplotlib==3.8.2
+mccabe==0.7.0
+mdurl==0.1.2
+MEGABYTE-pytorch==0.0.7
+-e git+https://github.com/cg123/mergekit.git@53c5f414774a0558b8d84858fb6374bc93a8f1c1#egg=mergekit
+mlflow==2.10.0
+modal==0.62.77
+more-itertools==10.2.0
+mpmath==1.2.1
+msgpack==1.0.7
+msgpack-numpy-opentensor==0.5.0
+multidict==6.0.4
+multiprocess==0.70.14
+munch==2.5.0
+mypy==1.3.0
+mypy-extensions==1.0.0
+nest-asyncio==1.6.0
+netaddr==0.10.1
+networkx==3.0rc1
+nh3==0.2.14
+nodeenv==1.8.0
+nomic==2.0.2
+numba==0.57.1
+numexpr==2.8.4
+numpy==1.24.4
+oauthlib==3.2.2
+openai==0.27.4
+openapi==1.1.0
+openapi-schema-pydantic==1.2.4
+optimum==1.8.6
+orjson==3.10.7
+packaging==23.1
+pandas==2.0.0
+parameterized==0.9.0
+password-strength==0.0.3.post2
+pastel==0.1.1
+pathos==0.3.0
+pathspec==0.11.1
+pathtools==0.1.2
+peft==0.11.1
+pendulum==3.0.0
+Pillow==9.5.0
+pip-tools==1.11.0
+platformdirs==3.2.0
+pluggy==1.4.0
+poetry==0.7.1
+pox==0.3.2
+ppft==1.7.6.6
+pre-commit==3.3.2
+prettytable==3.10.0
+prompt-toolkit==3.0.39
+protobuf==3.20.2
+protobuf3-to-dict==0.1.5
+psutil==5.9.5
+psycopg==3.1.18
+PuLP==2.8.0
+py==1.11.0
+py-bip39-bindings==0.1.11
+py-cpuinfo==9.0.0
+py-ed25519-zebra-bindings==1.0.1
+py-sr25519-bindings==0.2.0
+pyarrow==11.0.0
+pyasn1==0.6.0
+pycodestyle==2.11.1
+pycparser==2.21
+pycryptodome==3.20.0
+pydantic==2.5.3
+pydantic_core==2.14.6
+pydub==0.25.1
+pyfiglet==0.8.post1
+pyflakes==3.2.0
+Pygments==2.15.1
+PyJWT==2.8.0
+pylev==1.4.0
+PyNaCl==1.5.0
+pynvml==11.5.0
+pyparsing==2.4.7
+pyrsistent==0.14.11
+pytest==8.0.2
+pytest-asyncio==0.23.4
+python-dateutil==2.8.2
+python-dotenv==1.0.1
+python-Levenshtein==0.24.0
+python-multipart==0.0.9
+pytz==2023.3
+PyYAML==6.0.1
+querystring-parser==1.2.4
+rapidfuzz==3.6.1
+regex==2023.6.3
+requests==2.31.0
+requests-toolbelt==0.8.0
+resolvelib==0.8.1
+responses==0.18.0
+retry==0.9.2
+rich==13.7.0
+rsa==4.7.2
+ruff==0.6.3
+s3transfer==0.10.1
+safetensors==0.4.5
+sagemaker==2.148.0
+scalecodec==1.2.7
+schedulefree==1.2.1
+schema==0.7.5
+scikit-learn==1.4.0
+scipy==1.9.3
+seaborn==0.13.2
+semantic-version==2.10.0
+sentencepiece==0.2.0
+sentry-sdk==1.19.1
+setproctitle==1.3.2
+shellingham==1.5.4
+shortuuid==1.0.11
+shtab==1.6.5
+sigtools==4.0.1
+six==1.16.0
+skypilot==0.4.1
+smdebug-rulesconfig==1.0.1
+smmap==5.0.0
+sniffio==1.3.0
+SQLAlchemy==1.4.47
+sqlparse==0.4.4
+starlette==0.36.3
+substrate-interface==1.5.2
+svgwrite==1.4.3
+sympy==1.11.1
+synchronicity==0.6.7
+tabulate==0.9.0
+tblib==1.7.0
+tenacity==8.2.2
+tensor-parallel==2.0.0
+termcolor==2.2.0
+text2art==0.2.0
+threadpoolctl==3.2.0
+tiktoken==0.6.0
+time-machine==2.14.1
+timm==0.9.16
+tokenizers==0.19.1
+tokenmonster==1.1.12
+toml==0.9.6
+tomli==2.0.1
+tomlkit==0.12.0
+toolz==0.12.1
+torch==2.2.0
+torchdata==0.6.1
+torchdiffeq==0.2.3
+TorchFix==0.4.0
+torchtext==0.15.2
+torchvision==0.17.0
+tqdm==4.66.2
+transformers==4.44.2
+trl==0.9.6
+typer==0.12.5
+types-certifi==2021.10.8.3
+types-requests==2.31.0.20240125
+types-setuptools==69.0.0.20240125
+types-toml==0.10.8.7
+typing==3.7.4.3
+typing-inspect==0.8.0
+typing_extensions==4.9.0
+tyro==0.5.18
+tzdata==2023.3
+unique-names-generator==1.0.2
+urllib3==2.2.2
+uvicorn==0.22.0
+vector_quantize_pytorch==1.14.1
+virtualenv==20.23.0
+voyager==2.0.2
+wandb==0.16.2
+watchfiles==0.21.0
+wavedrom==2.0.3.post3
+wcwidth==0.2.6
+websocket-client==1.7.0
+websockets==12.0
+Werkzeug==3.0.1
+wonderwords==2.2.0
+xxhash==3.2.0
+yarl==1.8.2
+zetascale==2.2.7
+zipp==3.15.0

scripts/chat_datasets.py

@@ -0,0 +1,60 @@
+"""
+helper script to parse chat datasets into a usable yaml
+"""
+import click
+import yaml
+from datasets import load_dataset
+
+
+@click.command()
+@click.argument("dataset", type=str)
+@click.option("--split", type=str, default="train")
+def parse_dataset(dataset=None, split="train"):
+    ds_cfg = {}
+    ds_cfg["path"] = dataset
+    ds_cfg["split"] = split
+    ds_cfg["type"] = "chat_template"
+    ds_cfg["chat_template"] = "<<<Replace based on your model>>>"
+
+    dataset = load_dataset(dataset, split=split)
+    features = dataset.features
+    feature_keys = features.keys()
+    field_messages = None
+    for key in ["conversation", "conversations", "messages"]:
+        if key in feature_keys:
+            field_messages = key
+            break
+    if not field_messages:
+        raise ValueError(
+            f'No conversation field found in dataset: {", ".join(feature_keys)}'
+        )
+    ds_cfg["field_messages"] = field_messages
+
+    message_fields = features["conversations"][0].keys()
+    message_field_role = None
+    for key in ["from", "role"]:
+        if key in message_fields:
+            message_field_role = key
+            break
+    if not message_field_role:
+        raise ValueError(
+            f'No role field found in messages: {", ".join(message_fields)}'
+        )
+    ds_cfg["message_field_role"] = message_field_role
+
+    message_field_content = None
+    for key in ["content", "text", "value"]:
+        if key in message_fields:
+            message_field_content = key
+            break
+    if not message_field_content:
+        raise ValueError(
+            f'No content field found in messages: {", ".join(message_fields)}'
+        )
+    ds_cfg["message_field_content"] = message_field_content
+
+    print(yaml.dump({"datasets": [ds_cfg]}))
+
+
+if __name__ == "__main__":
+    parse_dataset()

setup.py

@@ -30,6 +30,7 @@ def parse_requirements():
 
     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]
         if "Darwin" in platform.system():
             # don't install xformers on MacOS
             _install_requires.pop(_install_requires.index(xformers_version))
@@ -49,14 +50,24 @@ def parse_requirements():
             else:
                 raise ValueError("Invalid version format")
 
-            if (major, minor) >= (2, 3):
+            if (major, minor) >= (2, 4):
+                if patch == 0:
+                    _install_requires.pop(_install_requires.index(xformers_version))
+                    _install_requires.append("xformers>=0.0.27")
+            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")
 
@@ -80,13 +91,13 @@ setup(
     dependency_links=dependency_links,
     extras_require={
         "flash-attn": [
-            "flash-attn==2.6.1",
+            "flash-attn==2.6.3",
         ],
         "fused-dense-lib": [
-            "fused-dense-lib  @ git+https://github.com/Dao-AILab/flash-attention@v2.6.1#subdirectory=csrc/fused_dense_lib",
+            "fused-dense-lib  @ git+https://github.com/Dao-AILab/flash-attention@v2.6.2#subdirectory=csrc/fused_dense_lib",
         ],
         "deepspeed": [
-            "deepspeed @ git+https://github.com/microsoft/DeepSpeed.git@bc48371c5e1fb8fd70fc79285e66201dbb65679b",
+            "deepspeed==0.14.4",
             "deepspeed-kernels",
         ],
         "mamba-ssm": [

src/axolotl/cli/__init__.py

@@ -27,8 +27,11 @@ from transformers.utils import is_torch_bf16_gpu_available
 from transformers.utils.import_utils import _is_package_available
 
 from axolotl.common.cli import TrainerCliArgs, load_model_and_tokenizer
+from axolotl.integrations.base import PluginManager
 from axolotl.logging_config import configure_logging
 from axolotl.train import TrainDatasetMeta
+from axolotl.utils.chat_templates import get_chat_template
+from axolotl.utils.comet_ import setup_comet_env_vars
 from axolotl.utils.config import (
     normalize_cfg_datasets,
     normalize_config,
@@ -38,9 +41,9 @@ from axolotl.utils.data import load_prepare_dpo_datasets, prepare_dataset
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.distributed import is_main_process
 from axolotl.utils.mlflow_ import setup_mlflow_env_vars
-from axolotl.utils.models import load_tokenizer
+from axolotl.utils.models import load_processor, load_tokenizer
 from axolotl.utils.tokenization import check_dataset_labels
-from axolotl.utils.trainer import prepare_optim_env
+from axolotl.utils.trainer import prepare_opinionated_env, prepare_optim_env
 from axolotl.utils.wandb_ import setup_wandb_env_vars
 
 project_root = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
@@ -52,8 +55,22 @@ LOG = logging.getLogger("axolotl.scripts")
 
 os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"
 
+AXOLOTL_LOGO = """
+     #@@ #@@      @@# @@#
+    @@  @@          @@  @@           =@@#                               @@                 #@    =@@#.
+    @@    #@@@@@@@@@    @@           #@#@=                              @@                 #@     .=@@
+      #@@@@@@@@@@@@@@@@@            =@# @#     ##=     ##    =####=+    @@      =#####+  =#@@###.   @@
+    @@@@@@@@@@/  +@@/  +@@          #@  =@=     #@=   @@   =@#+  +#@#   @@    =@#+  +#@#   #@.      @@
+    @@@@@@@@@@  ##@@  ##@@         =@#   @#      =@# @#    @@      @@   @@    @@      #@   #@       @@
+     @@@@@@@@@@@@@@@@@@@@          #@=+++#@=      =@@#     @@      @@   @@    @@      #@   #@       @@
+                                  =@#=====@@     =@# @#    @@      @@   @@    @@      #@   #@       @@
+    @@@@@@@@@@@@@@@@  @@@@        #@      #@=   #@=  +@@   #@#    =@#   @@.   =@#    =@#   #@.      @@
+                                 =@#       @#  #@=     #@   =#@@@@#=    +#@@=  +#@@@@#=    .##@@+   @@
+    @@@@  @@@@@@@@@@@@@@@@
+"""
 
-def print_axolotl_text_art(suffix=None):
+
+def print_legacy_axolotl_text_art(suffix=None):
     font = "nancyj"
     ascii_text = "  axolotl"
     if suffix:
@@ -66,6 +83,13 @@ def print_axolotl_text_art(suffix=None):
     print_dep_versions()
 
 
+def print_axolotl_text_art(
+    **kwargs,  # pylint: disable=unused-argument
+):
+    if is_main_process():
+        print(AXOLOTL_LOGO)
+
+
 def print_dep_versions():
     packages = ["accelerate", "peft", "transformers", "trl", "torch", "bitsandbytes"]
     max_len = max(len(pkg) for pkg in packages)
@@ -233,7 +257,8 @@ def do_inference_gradio(
 
     model, tokenizer = load_model_and_tokenizer(cfg=cfg, cli_args=cli_args)
     prompter = cli_args.prompter
-    default_tokens = {"unk_token": "<unk>", "bos_token": "<s>", "eos_token": "</s>"}
+    # default_tokens = {"unk_token": "<unk>", "bos_token": "<s>", "eos_token": "</s>"}
+    default_tokens: Dict[str, str] = {}
 
     for token, symbol in default_tokens.items():
         # If the token isn't already specified in the config, add it
@@ -241,10 +266,13 @@ def do_inference_gradio(
             tokenizer.add_special_tokens({token: symbol})
 
     prompter_module = None
+    chat_template_str = None
     if prompter:
         prompter_module = getattr(
             importlib.import_module("axolotl.prompters"), prompter
         )
+    elif cfg.chat_template:
+        chat_template_str = get_chat_template(cfg.chat_template)
 
     model = model.to(cfg.device, dtype=cfg.torch_dtype)
 
@@ -258,7 +286,24 @@ def do_inference_gradio(
             )
         else:
             prompt = instruction.strip()
-        batch = tokenizer(prompt, return_tensors="pt", add_special_tokens=True)
+
+        if chat_template_str:
+            batch = tokenizer.apply_chat_template(
+                [
+                    {
+                        "role": "user",
+                        "content": prompt,
+                    }
+                ],
+                return_tensors="pt",
+                add_special_tokens=True,
+                add_generation_prompt=True,
+                chat_template=chat_template_str,
+                tokenize=True,
+                return_dict=True,
+            )
+        else:
+            batch = tokenizer(prompt, return_tensors="pt", add_special_tokens=True)
 
         model.eval()
         with torch.no_grad():
@@ -281,6 +326,7 @@ def do_inference_gradio(
             streamer = TextIteratorStreamer(tokenizer)
             generation_kwargs = {
                 "inputs": batch["input_ids"].to(cfg.device),
+                "attention_mask": batch["attention_mask"].to(cfg.device),
                 "generation_config": generation_config,
                 "streamer": streamer,
             }
@@ -365,6 +411,11 @@ def load_cfg(config: Union[str, Path] = Path("examples/"), **kwargs):
 
     cfg.axolotl_config_path = config
 
+    if cfg.get("plugins"):
+        plugin_manager = PluginManager.get_instance()
+        for plugin_name in cfg["plugins"]:
+            plugin_manager.register(plugin_name)
+
     try:
         device_props = torch.cuda.get_device_properties("cuda")
         gpu_version = "sm_" + str(device_props.major) + str(device_props.minor)
@@ -375,13 +426,15 @@ def load_cfg(config: Union[str, Path] = Path("examples/"), **kwargs):
         cfg,
         capabilities={
             "bf16": is_torch_bf16_gpu_available(),
-            "n_gpu": os.environ.get("WORLD_SIZE", 1),
+            "n_gpu": int(os.environ.get("WORLD_SIZE", 1)),
             "compute_capability": gpu_version,
         },
     )
 
     prepare_optim_env(cfg)
 
+    prepare_opinionated_env(cfg)
+
     normalize_config(cfg)
 
     normalize_cfg_datasets(cfg)
@@ -390,6 +443,8 @@ def load_cfg(config: Union[str, Path] = Path("examples/"), **kwargs):
 
     setup_mlflow_env_vars(cfg)
 
+    setup_comet_env_vars(cfg)
+
     return cfg
 
 
@@ -399,9 +454,12 @@ def load_datasets(
     cli_args: TrainerCliArgs,
 ) -> TrainDatasetMeta:
     tokenizer = load_tokenizer(cfg)
+    processor = load_processor(cfg, tokenizer=tokenizer) if cfg.processor_type else None
 
     train_dataset, eval_dataset, total_num_steps, prompters = prepare_dataset(
-        cfg, tokenizer
+        cfg,
+        tokenizer,
+        processor=processor,
     )
 
     if cli_args.debug or cfg.debug:

src/axolotl/cli/merge_sharded_fsdp_weights.py

@@ -0,0 +1,204 @@
+"""
+This module provides a CLI to merge sharded FSDP model checkpoints into a single combined checkpoint
+"""
+import json
+import logging
+import os
+import shutil
+from pathlib import Path
+from typing import Dict, Union
+
+import fire
+import torch
+import torch.distributed.checkpoint as dist_cp
+import torch.distributed.checkpoint.format_utils as dist_cp_format_utils
+import transformers
+from accelerate.utils import (
+    SAFE_WEIGHTS_INDEX_NAME,
+    SAFE_WEIGHTS_NAME,
+    WEIGHTS_INDEX_NAME,
+    WEIGHTS_NAME,
+    is_torch_version,
+)
+from dotenv import load_dotenv
+from huggingface_hub import split_torch_state_dict_into_shards
+from safetensors.torch import save_file as safe_save_file
+from torch.distributed.checkpoint.format_utils import _EmptyStateDictLoadPlanner
+
+from axolotl.cli import load_cfg, print_axolotl_text_art
+from axolotl.common.cli import TrainerCliArgs
+
+LOG = logging.getLogger("axolotl.cli.merge_sharded_fsdp_weights")
+
+
+class BFloat16CastPlanner(_EmptyStateDictLoadPlanner):
+    """
+    A custom planner to cast tensors to bfloat16 on the fly during loading.
+    """
+
+    def commit_tensor(self, read_item, tensor):  # pylint: disable=unused-argument
+        tensor.copy_(tensor.to(torch.bfloat16))
+
+
+def _distributed_checkpoint_to_merged_weights(
+    checkpoint_dir: Union[str, Path],
+    save_path: str,
+    safe_serialization: bool = False,
+    max_shard_size: str = "5GB",
+):
+    """
+    Passthrough to `torch.distributed.checkpoint.format_utils.dcp_to_torch_save`
+
+    Will save under `save_path` as either `model.safetensors` or `pytorch_model.bin`.
+    """
+
+    state_dict: Dict = {}
+    save_path_ = Path(save_path)
+    save_path_.mkdir(exist_ok=True)
+    dist_cp_format_utils._load_state_dict(  # pylint: disable=protected-access
+        state_dict,
+        storage_reader=dist_cp.FileSystemReader(checkpoint_dir),
+        planner=BFloat16CastPlanner(),  # pylint: disable=protected-access
+        no_dist=True,
+    )
+
+    # To handle if state is a dict like {model: {...}}
+    if len(state_dict.keys()) == 1:
+        state_dict = state_dict[list(state_dict)[0]]
+
+    # Ensure all tensors are in bfloat16
+    for key, value in state_dict.items():
+        if isinstance(value, torch.Tensor) and value.dtype != torch.bfloat16:
+            state_dict[key] = value.to(torch.bfloat16)
+
+    weights_name = SAFE_WEIGHTS_NAME if safe_serialization else WEIGHTS_NAME
+
+    filename_pattern = weights_name.replace(".bin", "{suffix}.bin").replace(
+        ".safetensors", "{suffix}.safetensors"
+    )
+    state_dict_split = split_torch_state_dict_into_shards(
+        state_dict, filename_pattern=filename_pattern, max_shard_size=max_shard_size
+    )
+    # Save index if sharded
+    index = None
+    if state_dict_split.is_sharded:
+        index = {
+            "metadata": state_dict_split.metadata,
+            "weight_map": state_dict_split.tensor_to_filename,
+        }
+
+    # Save the model
+    filename_to_tensors = state_dict_split.filename_to_tensors.items()
+
+    for shard_file, tensors in filename_to_tensors:
+        shard = {tensor: state_dict[tensor] for tensor in tensors}
+
+        if safe_serialization:
+            safe_save_file(
+                shard, os.path.join(save_path_, shard_file), metadata={"format": "pt"}
+            )
+        else:
+            torch.save(shard, os.path.join(save_path_, shard_file))
+
+    if index is not None:
+        save_index_file = (
+            SAFE_WEIGHTS_INDEX_NAME if safe_serialization else WEIGHTS_INDEX_NAME
+        )
+        save_index_file = os.path.join(save_path_, save_index_file)
+        # Save the index as well
+        with open(save_index_file, "w", encoding="utf-8") as fout:
+            content = json.dumps(index, indent=2, sort_keys=True) + "\n"
+            fout.write(content)
+
+    return save_path_
+
+
+def merge_fsdp_weights(
+    checkpoint_dir: str,
+    output_path: str,
+    safe_serialization: bool = False,
+    remove_checkpoint_dir: bool = False,
+):
+    """
+    Merge the weights from sharded FSDP model checkpoints into a single combined checkpoint. Should be used if
+    `SHARDED_STATE_DICT` was used for the model. Weights will be saved to `{output_path}/model.safetensors` if
+    `safe_serialization` else `pytorch_model.bin`.
+
+    Note: this is a CPU-bound process.
+
+    Args:
+        checkpoint_dir (`str`):
+            The directory containing the FSDP checkpoints (can be either the model or optimizer).
+        output_path (`str`):
+            The path to save the merged checkpoint.
+        safe_serialization (`bool`, *optional*, defaults to `True`):
+            Whether to save the merged weights with safetensors (recommended).
+        remove_checkpoint_dir (`bool`, *optional*, defaults to `False`):
+            Whether to remove the checkpoint directory after merging.
+    """
+    checkpoint_dir_ = Path(checkpoint_dir)
+    from accelerate.state import PartialState
+
+    if not is_torch_version(">=", "2.3.0"):
+        raise ValueError("`merge_fsdp_weights` requires PyTorch >= 2.3.0`")
+
+    # Verify that the checkpoint directory exists
+    if not checkpoint_dir_.exists():
+        model_path_exists = (checkpoint_dir_ / "pytorch_model_fsdp_0").exists()
+        optimizer_path_exists = (checkpoint_dir_ / "optimizer_0").exists()
+        err = f"Tried to load from {checkpoint_dir_} but couldn't find a valid metadata file."
+        if model_path_exists and optimizer_path_exists:
+            err += (
+                " However, potential model and optimizer checkpoint directories exist."
+            )
+            err += f"Please pass in either {checkpoint_dir_}/pytorch_model_fsdp_0 or {checkpoint_dir_}/optimizer_0"
+            err += "instead."
+        elif model_path_exists:
+            err += " However, a potential model checkpoint directory exists."
+            err += (
+                f"Please try passing in {checkpoint_dir_}/pytorch_model_fsdp_0 instead."
+            )
+        elif optimizer_path_exists:
+            err += " However, a potential optimizer checkpoint directory exists."
+            err += f"Please try passing in {checkpoint_dir_}/optimizer_0 instead."
+        raise ValueError(err)
+
+    # To setup `save` to work
+    state = PartialState()
+    if state.is_main_process:
+        LOG.info(f"Merging FSDP weights from {checkpoint_dir_}")
+        save_path = _distributed_checkpoint_to_merged_weights(
+            checkpoint_dir_, output_path, safe_serialization
+        )
+        LOG.info(f"Successfully merged FSDP weights and saved to {save_path}")
+        if remove_checkpoint_dir:
+            LOG.info(f"Removing old checkpoint directory {checkpoint_dir_}")
+            shutil.rmtree(checkpoint_dir_)
+    state.wait_for_everyone()
+
+
+def do_cli(config: Path = Path("examples/"), **kwargs):
+    # pylint: disable=duplicate-code
+    print_axolotl_text_art()
+    parser = transformers.HfArgumentParser((TrainerCliArgs))
+    parsed_cli_args, _ = parser.parse_args_into_dataclasses(
+        return_remaining_strings=True
+    )
+    parsed_cli_args.merge_lora = True
+
+    parsed_cfg = load_cfg(
+        config,
+        **kwargs,
+    )
+
+    fsdp_dir = Path(parsed_cfg.output_dir) / "pytorch_model_fsdp_0"
+    merge_fsdp_weights(
+        checkpoint_dir=str(fsdp_dir),
+        output_path=str(Path(parsed_cfg.output_dir) / "merged"),
+        safe_serialization=True,
+    )
+
+
+if __name__ == "__main__":
+    load_dotenv()
+    fire.Fire(do_cli)

src/axolotl/cli/preprocess.py

@@ -2,6 +2,7 @@
 CLI to run training on a model
 """
 import logging
+import warnings
 from pathlib import Path
 from typing import Union
 
@@ -26,6 +27,7 @@ from axolotl.prompt_strategies.sharegpt import (
     register_chatml_template,
     register_llama3_template,
 )
+from axolotl.utils.trainer import disable_datasets_caching
 
 LOG = logging.getLogger("axolotl.cli.preprocess")
 
@@ -69,15 +71,27 @@ def do_cli(config: Union[Path, str] = Path("examples/"), **kwargs):
         LOG.warning(msg)
         parsed_cfg.dataset_prepared_path = DEFAULT_DATASET_PREPARED_PATH
 
-    if parsed_cfg.rl:  # and parsed_cfg.rl != "orpo":
-        load_rl_datasets(cfg=parsed_cfg, cli_args=parsed_cli_args)
-    else:
-        load_datasets(cfg=parsed_cfg, cli_args=parsed_cli_args)
+    with disable_datasets_caching():
+        if parsed_cfg.rl:  # and parsed_cfg.rl != "orpo":
+            load_rl_datasets(cfg=parsed_cfg, cli_args=parsed_cli_args)
+        else:
+            load_datasets(cfg=parsed_cfg, cli_args=parsed_cli_args)
 
     if parsed_cli_args.download:
         model_name = parsed_cfg.base_model
-        with init_empty_weights():
-            AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True)
+        with warnings.catch_warnings():
+            # there are a bunch of useless UserWarnings about
+            # "copying from a non-meta parameter in the checkpoint to a meta parameter in the current model"
+            warnings.simplefilter("ignore")
+            with init_empty_weights(include_buffers=True):
+                # fmt: off
+                try:
+                    AutoModelForCausalLM.from_pretrained(
+                        model_name, trust_remote_code=True
+                    )
+                except Exception as exc:  # pylint: disable=broad-exception-caught,unused-variable  # nosec B110  # noqa F841
+                    pass
+                # fmt: on
 
     LOG.info(
         Fore.GREEN

src/axolotl/cli/train.py

@@ -3,13 +3,11 @@ CLI to run training on a model
 """
 import logging
 from pathlib import Path
-from typing import Tuple, Union
+from typing import Union
 
 import fire
 from dotenv import load_dotenv
 from transformers.hf_argparser import HfArgumentParser
-from transformers.modeling_utils import PreTrainedModel
-from transformers.tokenization_utils import PreTrainedTokenizer
 
 from axolotl.cli import (
     check_accelerate_default_config,
@@ -20,6 +18,7 @@ from axolotl.cli import (
     print_axolotl_text_art,
 )
 from axolotl.common.cli import TrainerCliArgs
+from axolotl.integrations.base import PluginManager
 from axolotl.prompt_strategies.sharegpt import (
     register_chatml_template,
     register_llama3_template,
@@ -39,7 +38,7 @@ def do_cli(config: Union[Path, str] = Path("examples/"), **kwargs):
     return do_train(parsed_cfg, parsed_cli_args)
 
 
-def do_train(cfg, cli_args) -> Tuple[PreTrainedModel, PreTrainedTokenizer]:
+def do_train(cfg, cli_args) -> None:
     print_axolotl_text_art()
     check_accelerate_default_config()
     check_user_token()
@@ -64,7 +63,13 @@ def do_train(cfg, cli_args) -> Tuple[PreTrainedModel, PreTrainedTokenizer]:
     else:
         dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
 
-    return train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
+    model, tokenizer = train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
+    plugin_manager = PluginManager.get_instance()
+
+    del model
+    del tokenizer
+
+    plugin_manager.post_train_unload(cfg)
 
 
 if __name__ == "__main__":

src/axolotl/common/architectures.py

@@ -0,0 +1,15 @@
+"""
+Common architecture specific constants
+"""
+
+MOE_ARCH_BLOCK = {
+    "dbrx": "DbrxFFN",
+    "jamba": "JambaSparseMoeBlock",
+    "jetmoe": [
+        "JetMoeMoA",
+        "JetMoeMoE",
+    ],
+    "mixtral": "MixtralSparseMoeBlock",
+    "qwen2_moe": "Qwen2MoeSparseMoeBlock",
+    "deepseek_v2": "DeepseekV2MoE",
+}

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

@@ -0,0 +1,34 @@
+"""
+ChatML transformation functions for MessageContents
+"""
+from typing import Optional
+
+from ..messages import MessageContents, Messages
+from .shared import wrap_tools
+
+
+def format_message(
+    message: Messages,
+    message_index: Optional[int] = None,  # pylint: disable=unused-argument
+) -> Messages:
+    if message.is_chat_formatted:
+        return message
+
+    # prepend the role prefix within a MessageContents to message.content
+    message.content.insert(
+        0,
+        MessageContents(
+            type="text",
+            value=f"<|im_start|>{message.role}\n",
+            weight=0,
+        ),
+    )
+    message.content.append(
+        MessageContents(type="text", value="<|im_end|>", weight=message.weight)
+    )
+    message.content.append(MessageContents(type="text", value="\n", weight=0))
+
+    message = wrap_tools(message)
+
+    message.is_chat_formatted = True
+    return message

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

@@ -0,0 +1,45 @@
+"""
+Llama 3.x chat formatting functions for MessageContents
+"""
+from typing import Optional
+
+from ..messages import MessageContents, Messages
+from .shared import wrap_tools
+
+
+def format_message(message: Messages, message_index: Optional[int] = None) -> Messages:
+    if message.is_chat_formatted:
+        return message
+
+    message_role = message.role
+    if message.role == "tool":
+        message_role = "ipython"
+
+    # prepend the role prefix within a MessageContents to message.content
+    message.content.insert(
+        0,
+        MessageContents(
+            type="text",
+            value=f"<|start_header_id|>{message_role}<|end_header_id|>\n\n",
+            weight=0,
+        ),
+    )
+
+    message.content.append(
+        MessageContents(type="text", value="<|eot_id|>", weight=message.weight)
+    )
+
+    message = wrap_tools(message)
+
+    if message_index == 0:
+        message.content.insert(
+            0,
+            MessageContents(
+                type="text",
+                value="<|begin_of_text|>",
+                weight=0,
+            ),
+        )
+
+    message.is_chat_formatted = True
+    return message

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

@@ -0,0 +1,47 @@
+"""
+shared functions for format transforms
+"""
+from axolotl.core.chat.messages import MessageContents, Messages
+
+
+def wrap_tools(message: Messages):
+    # loop over message.content by index to find tool calls, we need to wrap each with tags,
+    # so be wary of indexing issues when changing the list while iterating.
+    # iterate over the range in reverse order to avoid index shifting
+    for i in range(len(message.content) - 1, -1, -1):
+        if message.content[i].type == "tool_call":
+            # append a </tool_call> MessageContents text tag after
+            message.content.insert(
+                i + 1,
+                MessageContents(
+                    type="text", value="</tool_call>\n", weight=message.weight
+                ),
+            )
+            # make sure the actual tool call content ends with a newline
+            message.content[i].has_newline = True
+            # prepend a <tool_call> MessageContents text tag before
+            message.content.insert(
+                i,
+                MessageContents(
+                    type="text", value="<tool_call>\n", weight=message.weight
+                ),
+            )
+        elif message.content[i].type == "tool_response":
+            # append a </tool_call> MessageContents text tag after
+            message.content.insert(
+                i + 1,
+                MessageContents(
+                    type="text", value="</tool_response>\n", weight=message.weight
+                ),
+            )
+            # make sure the actual tool response content ends with a newline
+            message.content[i].has_newline = True
+            # prepend a <tool_call> MessageContents text tag before
+            message.content.insert(
+                i,
+                MessageContents(
+                    type="text", value="<tool_response>\n", weight=message.weight
+                ),
+            )
+
+    return message

src/axolotl/core/chat/messages.py

@@ -0,0 +1,230 @@
+"""
+internal message representations of chat messages
+"""
+import json
+from enum import Enum
+from typing import Any, Callable, List, Optional, Union
+
+from pydantic import BaseModel
+from transformers import PreTrainedTokenizer
+
+
+class MessageRoles(str, Enum):
+    """
+    Message roles for the system, user, assistant, and tools
+    """
+
+    system = "system"  # pylint: disable=invalid-name
+    user = "user"  # pylint: disable=invalid-name
+    assistant = "assistant"  # pylint: disable=invalid-name
+    tool = "tool"  # pylint: disable=invalid-name
+    ipython = (  # pylint: disable=invalid-name
+        # for responses from builtin tools
+        "ipython"
+    )
+
+
+class MessageContentTypes(str, Enum):
+    """
+    Message content types for text, image, audio, tool calls, and tool responses
+    """
+
+    special_token = "special_token"  # pylint: disable=invalid-name  # nosec B105
+    text = "text"  # pylint: disable=invalid-name
+    image = "image"  # pylint: disable=invalid-name
+    audio = "audio"  # pylint: disable=invalid-name
+    tool_call = "tool_call"  # pylint: disable=invalid-name  # to differentiate regular responses from tool calls from the assistant
+    tool_response = "tool_response"  # pylint: disable=invalid-name
+
+
+class SpecialToken(str, Enum):
+    """
+    Special tokens for beginning of string and end of string
+    """
+
+    bos_token = "bos_token"  # pylint: disable=invalid-name  # nosec B105
+    eos_token = "eos_token"  # pylint: disable=invalid-name  # nosec B105
+
+
+class ToolCallFunction(BaseModel):
+    """
+    Tool call function with name and arguments
+    """
+
+    name: str
+    arguments: dict[str, str]
+
+
+class Tool(BaseModel):
+    """
+    Tool with description, function, and parameters
+    """
+
+    description: str
+    function: ToolCallFunction
+    parameters: dict[str, str]  # .properties
+
+
+class ToolCallContents(BaseModel):
+    """
+    Tool call contents with name, arguments, and optional id
+    """
+
+    name: str
+    arguments: dict[str, Union[str, int]]
+    id: Optional[str] = None  # pylint: disable=invalid-name
+
+    def __str__(self) -> str:
+        data = {"name": self.name, "arguments": self.arguments}
+        if self.id is not None:
+            data["id"] = self.id
+        return json.dumps(data)
+
+
+class ToolResponseContents(BaseModel):
+    """
+    Tool response contents with name, content, and optional id
+    """
+
+    name: str
+    content: Union[str, dict[str, Union[str, int, float]]]
+    id: Optional[str] = None  # pylint: disable=invalid-name
+
+    def __str__(self) -> str:
+        data = {"name": self.name, "content": self.content}
+        if self.id is not None:
+            data["id"] = self.id
+        return json.dumps(data)
+
+
+class MessageContents(BaseModel):
+    """
+    Message contents with type, value, metadata, weight, newline, and end of contents
+    """
+
+    type: Union[str, MessageContentTypes]
+    value: Union[str, ToolCallContents, ToolResponseContents, SpecialToken]
+    meta: Optional[dict[str, Any]] = None  # support additional arbitrary metadata
+    weight: Optional[Union[int, float]] = None
+    has_newline: bool = False
+    eoc: bool = False  # end of contents
+
+    def __str__(self) -> str:
+        str_val = str(self.value)
+        if self.has_newline and not str_val.endswith("\n"):
+            str_val += "\n"
+        return str_val
+
+
+class Messages(BaseModel):
+    """
+    Messages with role, content, metadata, weight, and chat formatting
+    """
+
+    role: Union[MessageRoles, str]  # allows for arbitrary roles
+    content: List["MessageContents"]
+    meta: Optional[dict[str, Any]] = None  # support additional arbitrary metadata
+    weight: Optional[Union[int, float]] = None
+    is_chat_formatted: bool = False
+
+    def __str__(self) -> str:
+        return "".join(str(c) for c in self.content)
+
+    def tokenized(
+        self, tokenizer: PreTrainedTokenizer, ignore_index=-100
+    ) -> dict[str, List[int]]:
+        # iterate over the contents, tokenizing the concatenated string values up to the current MessageContents
+        # returns a dictionary mapping w input_ids, attention_mask, and labels
+        input_ids: List[int] = []
+        labels: List[int] = []
+        pending_input_ids: List[int] = []
+        pending_weight = self.weight
+        running_content = ""
+        for _, msg_content in enumerate(self.content):
+            # TODO also handle non-text content types
+            if msg_content.type in [
+                MessageContentTypes.text.value,
+                MessageContentTypes.tool_call.value,
+                MessageContentTypes.tool_response.value,
+            ]:
+                running_content += str(msg_content)
+                tok_results = tokenizer(running_content, add_special_tokens=False)
+                tok_input_ids = tok_results["input_ids"]
+                if pending_input_ids:
+                    new_pending_inputs = tok_input_ids[
+                        len(input_ids) : len(input_ids) + len(pending_input_ids)
+                    ]
+                    if new_pending_inputs != pending_input_ids:
+                        # logging.warning("tokenization mismatch from concatenation.")
+                        pending_input_ids = new_pending_inputs
+                    input_ids.extend(pending_input_ids)
+                    if pending_weight:
+                        labels.extend(pending_input_ids)
+                    else:
+                        labels.extend([ignore_index] * len(pending_input_ids))
+                pending_input_ids = tok_results["input_ids"][len(input_ids) :]
+                pending_weight = self.weight and msg_content.weight not in [0, 0.0]
+        input_ids.extend(pending_input_ids)
+        if pending_weight:
+            labels.extend(pending_input_ids)
+        else:
+            labels.extend([ignore_index] * len(pending_input_ids))
+        attention_mask = [1] * len(input_ids)
+        return {
+            "input_ids": input_ids,
+            "attention_mask": attention_mask,
+            "labels": labels,
+        }
+
+
+class Chats(BaseModel):
+    """
+    top level data structure for chat conversations
+    """
+
+    conversation: List[Messages]
+
+    def __str__(self) -> str:
+        return "".join(str(c) for c in self.conversation)
+
+    def tokenized(
+        self, tokenizer: Callable[[str], dict[str, List[int]]], ignore_index=-100
+    ) -> dict[str, List[int]]:
+        input_ids = []
+        attention_mask = []
+        labels = []
+        for msg in self.conversation:
+            msg_results = msg.tokenized(tokenizer, ignore_index)
+            input_ids.extend(msg_results["input_ids"])
+            attention_mask.extend(msg_results["attention_mask"])
+            labels.extend(msg_results["labels"])
+        return {
+            "input_ids": input_ids,
+            "attention_mask": attention_mask,
+            "labels": labels,
+        }
+
+
+class ChatFormattedChats(Chats):
+    """
+    Chat formatted chats with formatter and optional train on inputs
+    """
+
+    formatter: Callable  # [[Union[dict, Chats]], Chats]
+    train_on_inputs: bool = False
+
+    def model_post_init(self, __context):
+        for i, msg in enumerate(self.conversation):
+            self.conversation[i] = self.formatter(msg, message_index=i)
+            if self.train_on_inputs:
+                self.conversation[i].weight = 1
+
+
+class PreferenceChats(BaseModel):
+    """
+    representation for preference data for chat
+    """
+
+    prompt: List[Messages]
+    chosen: Messages
+    rejected: Messages

src/axolotl/core/datasets/chat.py

@@ -0,0 +1,55 @@
+"""
+chat dataset module
+"""
+import os
+from typing import Callable, Optional, Union
+
+from datasets import Dataset
+from transformers import PreTrainedTokenizer
+
+from axolotl.core.chat.messages import ChatFormattedChats
+
+
+class TokenizedChatDataset(Dataset):
+    """
+    Tokenized chat dataset
+    """
+
+    def __init__(
+        self,
+        data: Dataset,
+        model_transform: Union[PreTrainedTokenizer, Callable],
+        *args,
+        message_transform: Optional[Callable] = None,
+        formatter=None,
+        process_count: Optional[int] = None,
+        keep_in_memory: Optional[bool] = False,
+        **kwargs,
+    ):
+        def map_fn(ex):
+            if message_transform is not None:
+                ex = message_transform(ex)
+            if formatter is not None:
+                ex = ChatFormattedChats(
+                    formatter=formatter,
+                    **ex,
+                )
+            else:
+                ex = ChatFormattedChats(
+                    **ex,
+                )
+            return ex.tokenized(model_transform)
+
+        process_or_cpu_count: int = (
+            process_count or os.cpu_count()  # type: ignore[assignment]
+        )
+        num_proc = min(64, process_or_cpu_count)
+        features = data.features.keys()
+        tokenized_data = data.map(
+            map_fn,
+            num_proc=num_proc,
+            keep_in_memory=keep_in_memory,
+            remove_columns=features,
+            desc="Tokenizing Chats",
+        )
+        super().__init__(tokenized_data.data, *args, **kwargs)

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

@@ -0,0 +1,150 @@
+"""
+This module contains a function that builds a transform that takes a row from the dataset and converts it to a Chat.
+"""
+from typing import Any, Mapping, Union
+
+
+def chat_message_transform_builder(  # pylint: disable=dangerous-default-value
+    train_on_inputs=False,
+    conversations_field: str = "conversations",
+    message_field_role: Union[str, list[str]] = ["role", "from"],  # commonly "role"
+    message_field_content: Union[str, list[str]] = [
+        "value",
+        "text",
+        "content",
+    ],  # commonly "content"
+    message_field_training: Union[str, list[str]] = [
+        "train",
+        "weight",
+    ],  # commonly "weight"
+):
+    """Builds a transform that takes a row from the dataset and converts it to a Chat
+
+    Args:
+        train_on_inputs (bool, optional):
+            If True, the transform will train on the inputs. If False, the transform will train on the targets.
+            Defaults to False.
+        conversations_field (str, optional):
+            The field name of the conversations. Defaults to "conversations".
+        message_field_role (str | list[str], optional):
+            The field name of the role. Defaults to "role".
+        message_field_content (str | list[str], optional):
+            The field name of the message content. Defaults to "content".
+        message_field_training (str | list[str], optional):
+            The field name of the train/weight. Defaults to "weight".
+
+    Returns:
+        Callable:
+            A function that takes a list of conversations and returns a list of messages.
+    """
+
+    message_field_role = (
+        [message_field_role]
+        if isinstance(message_field_role, str)
+        else message_field_role
+    )
+    message_field_content = (
+        [message_field_content]
+        if isinstance(message_field_content, str)
+        else message_field_content
+    )
+    message_weight_fields = (
+        [message_field_training]
+        if isinstance(message_field_training, str)
+        else message_field_training
+    )
+
+    role_value_mappings = {
+        "system": "system",
+        "user": "user",
+        "human": "user",
+        "assistant": "assistant",
+        "gpt": "assistant",
+        "tool": "tool",
+        "ipython": "ipython",
+    }
+    if train_on_inputs:
+        role_default_weights_mappings = {
+            "system": 1,
+            "user": 1,
+            "assistant": 1,
+            "tool": 1,
+            "ipython": 1,
+        }
+    else:
+        role_default_weights_mappings = {
+            "system": 0,
+            "user": 0,
+            "assistant": 1,
+            "tool": 0,
+            "ipython": 0,
+        }
+
+    def transform_builder(sample: Mapping[str, Any]):
+        if conversations_field not in sample:
+            raise ValueError(f"Field '{conversations_field}' not found in sample.")
+        # if none of the role fields are in the message, raise an error
+        if not any(
+            role in sample[conversations_field][0] for role in message_field_role
+        ):
+            raise ValueError("No role field found in message.")
+        role_field = next(
+            role
+            for role in message_field_role
+            if role in sample[conversations_field][0]
+        )
+        if not any(
+            field in sample[conversations_field][0] for field in message_field_content
+        ):
+            raise ValueError("No message_content field found in message.")
+        message_content_field = next(
+            field
+            for field in message_field_content
+            if field in sample[conversations_field][0]
+        )
+        if not any(
+            field in sample[conversations_field][0] for field in message_field_training
+        ):
+            message_weight_field = None
+        else:
+            message_weight_field = next(
+                field
+                for field in message_weight_fields
+                if field in sample[conversations_field][0]
+            )
+
+        messages = []
+        for message in sample[conversations_field]:
+            role = role_value_mappings[message[role_field]]
+            weight = (
+                int(message[message_weight_field])
+                if message_weight_field
+                else role_default_weights_mappings[role]
+            )
+
+            # TODO if "tool_calls" in message[message_content_field]: then convert tool call to ToolCallContents
+            if isinstance(message[message_content_field], str):
+                messages.append(
+                    {
+                        "role": role,
+                        "content": [
+                            {
+                                "type": "text",
+                                "value": message[message_content_field],
+                            }
+                        ],
+                        "weight": weight,
+                    }
+                )
+            else:
+                messages.append(
+                    {
+                        "role": role,
+                        "content": message[message_content_field],
+                        "weight": weight,
+                    }
+                )
+
+        return {"conversation": messages}
+
+    return transform_builder

src/axolotl/core/tokenizer_utils.py

@@ -0,0 +1,150 @@
+"""
+helper functions for fixing the embeddings/tokenizer
+"""
+
+# Copyright 2023-present Daniel Han-Chen & the Unsloth team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import gc
+import itertools
+
+import numpy as np
+import torch
+
+
+@torch.inference_mode
+def fix_untrained_tokens(model, tokenizer, train_dataset, eps=1e-16):
+    """
+    Many of the newer models have reserved tokens that are not trained.
+    """
+    embedding_matrix = model.get_input_embeddings().weight
+    lm_head_matrix = model.get_output_embeddings().weight
+
+    # Get untrained tokens
+    indicator_untrained = torch.amax(embedding_matrix, axis=1) <= eps
+    where_untrained = torch.where(indicator_untrained)[0]
+    n_untrained = where_untrained.shape[0]
+    n_trained = embedding_matrix.shape[0] - n_untrained
+
+    # Get set and actual tokens
+    where_untrained = where_untrained.tolist()
+    if len(where_untrained) == 0:
+        return False
+
+    # Remove untrained indices where it's longer
+
+    where_untrained_set = frozenset(where_untrained)
+    actual_bad_tokens = tokenizer.convert_ids_to_tokens(where_untrained)
+    # Remove None items in actual_bad_tokens
+    actual_bad_tokens = [x for x in actual_bad_tokens if x is not None]
+
+    # Check if tokenizer and training datasets have bad tokens
+    if_bad_first = False
+    if_bad_second = False
+    # Check tokenizer's chat template for any untrained tokens
+    chat_template = getattr(tokenizer, "chat_template", None)
+    if chat_template is not None:
+        if_bad_first = any(x in chat_template for x in actual_bad_tokens)
+
+    # Check the first 250, last 250 input_ids
+    size_dataset = len(train_dataset)
+    size = min(size_dataset, 250)
+    for j in range(size):
+        input_ids = train_dataset[j]
+        if "input_ids" in input_ids:
+            input_ids = input_ids["input_ids"]
+            if_bad = any(item in where_untrained_set for item in input_ids)
+            if if_bad:
+                if_bad_second = True
+                break
+
+    # Check last 250
+    if not if_bad_second:
+        left = max(size_dataset - 250, 0)
+        for j in range(left, size_dataset):
+            input_ids = train_dataset[j]
+            if "input_ids" in input_ids:
+                input_ids = input_ids["input_ids"]
+                if_bad = any(item in where_untrained_set for item in input_ids)
+                if if_bad:
+                    if_bad_second = True
+                    break
+
+    # Check if bad tokens exists!
+    if not if_bad_first and not if_bad_second:
+        return False
+
+    # Count all the possible bad tokens
+    final_counts = np.zeros(
+        max(len(tokenizer), embedding_matrix.shape[0]), dtype=np.int64
+    )
+
+    def mapping(examples):
+        input_ids = examples["input_ids"]
+        counter = np.fromiter(itertools.chain.from_iterable(input_ids), dtype=np.int32)
+        np.add.at(final_counts, counter, 1)
+
+    train_dataset.map(mapping, batched=True, desc="Counting untrained tokens")
+
+    # Get sum of all items
+    sum_embedding = torch.sum(embedding_matrix, dtype=torch.float32, axis=0)
+    sum_lm_head = torch.sum(lm_head_matrix, dtype=torch.float32, axis=0)
+
+    # Remove bad tokens
+    sum_embedding -= torch.sum(
+        embedding_matrix[where_untrained], dtype=torch.float32, axis=0
+    )
+    sum_lm_head -= torch.sum(
+        lm_head_matrix[where_untrained], dtype=torch.float32, axis=0
+    )
+
+    # Find correct average by dividing by sum of trained tokens
+    mean_embedding = sum_embedding / n_trained
+    mean_lm_head = sum_lm_head / n_trained
+
+    # Scale each to be equal to 1/max_frequency. Also set some to 0 if none seen
+    scaling = final_counts[where_untrained] / max(final_counts.max(), 1)
+    scaling = torch.tensor(scaling, device=mean_embedding.device).unsqueeze(1)
+    mean_embedding = (
+        mean_embedding.repeat(
+            (
+                n_untrained,
+                1,
+            )
+        )
+        * scaling
+    )
+    mean_lm_head = (
+        mean_lm_head.repeat(
+            (
+                n_untrained,
+                1,
+            )
+        )
+        * scaling
+    )
+    where_null = scaling.ravel() == 0
+    mean_embedding[where_null] = 0
+    mean_lm_head[where_null] = 0
+
+    # Set them to the mean
+    embedding_matrix[where_untrained] = mean_embedding.to(embedding_matrix.dtype)
+    lm_head_matrix[where_untrained] = mean_lm_head.to(lm_head_matrix.dtype)
+
+    # Clean up
+    for _ in range(3):
+        gc.collect()
+        torch.cuda.empty_cache()
+
+    return True

src/axolotl/core/trainer_builder.py

@@ -4,21 +4,25 @@ Builder for the training args and trainer
 """
 
 import abc
+import gc
 import importlib
 import importlib.util
 import logging
 import math
+import os
 import sys
 from abc import abstractmethod
 from collections import defaultdict
 from dataclasses import dataclass, field
 from functools import wraps
 from pathlib import Path
-from typing import Dict, List, Literal, Optional, Type, Union
+from typing import Any, Dict, List, Literal, Optional, Type, Union
 
 import torch
 import transformers
 from datasets import Dataset
+from peft.optimizers import create_loraplus_optimizer
+from torch import nn
 from torch.optim.lr_scheduler import OneCycleLR
 from torch.utils.data import BatchSampler, DataLoader, RandomSampler, SequentialSampler
 from transformers import (
@@ -28,15 +32,25 @@ from transformers import (
     TrainerCallback,
     TrainingArguments,
 )
-from transformers.trainer_utils import seed_worker
+from transformers.trainer_utils import PREFIX_CHECKPOINT_DIR, seed_worker
 from transformers.utils import is_sagemaker_mp_enabled
-from trl import DPOConfig, DPOTrainer, KTOConfig, KTOTrainer, ORPOConfig, ORPOTrainer
-from trl.trainer.utils import pad_to_length
+from trl import (
+    CPOConfig,
+    CPOTrainer,
+    DPOConfig,
+    DPOTrainer,
+    KTOConfig,
+    KTOTrainer,
+    ORPOConfig,
+    ORPOTrainer,
+    RewardConfig,
+    RewardTrainer,
+)
+from trl.trainer.utils import RewardDataCollatorWithPadding, pad_to_length
 
-from axolotl.loraplus import create_loraplus_optimizer
 from axolotl.monkeypatch.multipack import SUPPORTED_MULTIPACK_MODEL_TYPES
 from axolotl.monkeypatch.relora import ReLoRACallback, ReLoRAScheduler
-from axolotl.utils import is_mlflow_available
+from axolotl.utils import is_comet_available, is_mlflow_available
 from axolotl.utils.callbacks import (
     EvalFirstStepCallback,
     GPUStatsCallback,
@@ -49,12 +63,14 @@ from axolotl.utils.callbacks import (
     log_prediction_callback_factory,
 )
 from axolotl.utils.callbacks.lisa import lisa_callback_factory
+from axolotl.utils.chat_templates import get_chat_template
 from axolotl.utils.collators import (
     BatchSamplerDataCollatorForSeq2Seq,
     DataCollatorForSeq2Seq,
     MambaDataCollator,
     V2BatchSamplerDataCollatorForSeq2Seq,
 )
+from axolotl.utils.collators.mm_chat import MultiModalChatDataCollator
 from axolotl.utils.models import ensure_dtype
 from axolotl.utils.samplers import MultipackBatchSampler, get_dataset_lengths
 from axolotl.utils.schedulers import (
@@ -232,6 +248,16 @@ class AxolotlTrainingMixins:
             "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
@@ -265,92 +291,31 @@ class AxolotlKTOConfig(AxolotlTrainingMixins, KTOConfig):
     """
 
 
-class AxolotlTrainer(Trainer):
+@dataclass
+class AxolotlCPOConfig(AxolotlTrainingMixins, CPOConfig):
     """
-    Extend the base Trainer for axolotl helpers
+    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
+    """
+
+
+class SchedulerMixin(Trainer):
+    """
+    Mixin class for scheduler setup in CausalTrainer.
     """
 
     args = None  # type: AxolotlTrainingArguments
-    tag_names = ["axolotl"]
-
-    def __init__(
-        self,
-        *_args,
-        num_epochs=1,
-        bench_data_collator=None,
-        eval_data_collator=None,
-        **kwargs,
-    ):
-        self.num_epochs = num_epochs
-        self.bench_data_collator = bench_data_collator
-        self.eval_data_collator = eval_data_collator
-        super().__init__(*_args, **kwargs)
-        self.train_data_collator = self.data_collator
-        self._stored_metrics = defaultdict(lambda: defaultdict(list))
-        if self.args.orpo_alpha:
-            self.loss_fct = torch.nn.CrossEntropyLoss(reduction="none")
-
-    def create_optimizer(self):
-        if (
-            self.args.loraplus_lr_ratio is None
-            and self.args.alternate_optimizer != "optimi_adamw"
-        ):
-            return super().create_optimizer()
-
-        opt_model = self.model_wrapped if is_sagemaker_mp_enabled() else self.model
-        if self.optimizer is None:  # pylint: disable=access-member-before-definition
-            decay_parameters = self.get_decay_parameter_names(opt_model)
-            optimizer_grouped_parameters = [
-                {
-                    "params": [
-                        p
-                        for n, p in opt_model.named_parameters()
-                        if (n in decay_parameters and p.requires_grad)
-                    ],
-                    "weight_decay": self.args.weight_decay,
-                },
-                {
-                    "params": [
-                        p
-                        for n, p in opt_model.named_parameters()
-                        if (n not in decay_parameters and p.requires_grad)
-                    ],
-                    "weight_decay": 0.0,
-                },
-            ]
-
-            optimizer_cls, optimizer_kwargs = Trainer.get_optimizer_cls_and_kwargs(
-                self.args,
-                opt_model,
-            )
-
-            if self.args.loraplus_lr_ratio is not None:
-                loraplus_lr_ratio = getattr(self.args, "loraplus_lr_ratio", None)
-                loraplus_lr_embedding = getattr(
-                    self.args, "loraplus_lr_embedding", None
-                )
-                self.optimizer = create_loraplus_optimizer(  # pylint: disable=attribute-defined-outside-init
-                    opt_model,
-                    optimizer_cls,
-                    optimizer_kwargs,
-                    loraplus_lr_ratio,
-                    loraplus_lr_embedding,
-                )
-            elif self.args.alternate_optimizer == "optimi_adamw":
-                from optimi import AdamW
-
-                self.optimizer = (  # pylint: disable=attribute-defined-outside-init
-                    AdamW(
-                        optimizer_grouped_parameters, foreach=False, **optimizer_kwargs
-                    )
-                )
-
-        if is_sagemaker_mp_enabled():
-            self.optimizer = smp.DistributedOptimizer(  # pylint: disable=attribute-defined-outside-init
-                self.optimizer
-            )
-
-        return self.optimizer
 
     def create_scheduler(
         self, num_training_steps: int, optimizer: torch.optim.Optimizer = None
@@ -376,7 +341,23 @@ class AxolotlTrainer(Trainer):
         # fmt: off
         if self.lr_scheduler is None:  # type: ignore  # pylint: disable=access-member-before-definition
             # fmt: on
-            if use_cosine_quadratic:
+            if self.args.alternate_lr_scheduler_type == "one_cycle":
+                num_warmup_steps = self.args.get_warmup_steps(num_training_steps)
+                pct_start = num_warmup_steps / num_training_steps
+                extra_lr_kwargs = {}
+                if "pct_start" not in self.args.lr_scheduler_kwargs:
+                    extra_lr_kwargs["pct_start"] = pct_start
+                if "anneal_strategy" not in self.args.lr_scheduler_kwargs:
+                    extra_lr_kwargs["anneal_strategy"] = "cos"
+
+                self.lr_scheduler = OneCycleLR(
+                    optimizer,
+                    max_lr=self.args.learning_rate,
+                    total_steps=num_training_steps,
+                    **extra_lr_kwargs,
+                    **self.args.lr_scheduler_kwargs,
+                )
+            elif use_cosine_quadratic:
                 if use_cosine_min_lr:
                     LOG.warning("Both cosine quadratic warmup and min lr detected. Using quadratic warmup.")
 
@@ -414,6 +395,123 @@ class AxolotlTrainer(Trainer):
 
         return self.lr_scheduler
 
+
+class AxolotlTrainer(SchedulerMixin, Trainer):
+    """
+    Extend the base Trainer for axolotl helpers
+    """
+
+    args = None  # type: AxolotlTrainingArguments
+    tag_names = ["axolotl"]
+
+    def __init__(
+        self,
+        *_args,
+        bench_data_collator=None,
+        eval_data_collator=None,
+        **kwargs,
+    ):
+        self.bench_data_collator = bench_data_collator
+        self.eval_data_collator = eval_data_collator
+        super().__init__(*_args, **kwargs)
+        self.train_data_collator = self.data_collator
+        self._stored_metrics = defaultdict(lambda: defaultdict(list))
+        if self.args.orpo_alpha:
+            self.loss_fct = torch.nn.CrossEntropyLoss(reduction="none")
+
+    def _wrap_model(self, model, training=True, dataloader=None):
+        if self.args.torch_compile:
+            torch._dynamo.config.accumulated_cache_size_limit = (  # pylint: disable=protected-access
+                256
+            )
+            model = torch.compile(
+                model,
+                backend=self.args.torch_compile_backend,
+                mode=self.args.torch_compile_mode,
+            )
+        return super()._wrap_model(model, training=training, dataloader=dataloader)
+
+    def create_optimizer(self):
+        if (
+            self.args.loraplus_lr_ratio is None
+            and self.args.alternate_optimizer
+            not in ["optimi_adamw", "ao_adamw_8bit", "ao_adamw_4bit", "ao_adamw_fp8"]
+        ):
+            return super().create_optimizer()
+
+        opt_model = self.model_wrapped if is_sagemaker_mp_enabled() else self.model
+        if self.optimizer is None:  # pylint: disable=access-member-before-definition
+            decay_parameters = self.get_decay_parameter_names(opt_model)
+            optimizer_grouped_parameters = [
+                {
+                    "params": [
+                        p
+                        for n, p in opt_model.named_parameters()
+                        if (n in decay_parameters and p.requires_grad)
+                    ],
+                    "weight_decay": self.args.weight_decay,
+                },
+                {
+                    "params": [
+                        p
+                        for n, p in opt_model.named_parameters()
+                        if (n not in decay_parameters and p.requires_grad)
+                    ],
+                    "weight_decay": 0.0,
+                },
+            ]
+
+            optimizer_cls, optimizer_kwargs = Trainer.get_optimizer_cls_and_kwargs(
+                self.args,
+                opt_model,
+            )
+
+            if self.args.loraplus_lr_ratio is not None:
+                loraplus_lr_ratio = getattr(self.args, "loraplus_lr_ratio", None)
+                loraplus_lr_embedding = getattr(
+                    self.args, "loraplus_lr_embedding", 1e-6
+                )
+                self.optimizer = create_loraplus_optimizer(  # pylint: disable=attribute-defined-outside-init
+                    opt_model,
+                    optimizer_cls,
+                    loraplus_lr_ratio=loraplus_lr_ratio,
+                    loraplus_lr_embedding=loraplus_lr_embedding,
+                    **optimizer_kwargs,
+                )
+            elif self.args.alternate_optimizer == "optimi_adamw":
+                from optimi import AdamW
+
+                self.optimizer = (  # pylint: disable=attribute-defined-outside-init
+                    AdamW(
+                        optimizer_grouped_parameters, foreach=False, **optimizer_kwargs
+                    )
+                )
+            elif self.args.alternate_optimizer == "ao_adamw_4bit":
+                from torchao.prototype.low_bit_optim import AdamW4bit
+
+                self.optimizer = (  # pylint: disable=attribute-defined-outside-init
+                    AdamW4bit(optimizer_grouped_parameters, **optimizer_kwargs)
+                )
+            elif self.args.alternate_optimizer == "ao_adamw_8bit":
+                from torchao.prototype.low_bit_optim import AdamW8bit
+
+                self.optimizer = (  # pylint: disable=attribute-defined-outside-init
+                    AdamW8bit(optimizer_grouped_parameters, **optimizer_kwargs)
+                )
+            elif self.args.alternate_optimizer == "ao_adamw_fp8":
+                from torchao.prototype.low_bit_optim import AdamWFp8
+
+                self.optimizer = (  # pylint: disable=attribute-defined-outside-init
+                    AdamWFp8(optimizer_grouped_parameters, **optimizer_kwargs)
+                )
+
+        if is_sagemaker_mp_enabled():
+            self.optimizer = smp.DistributedOptimizer(  # pylint: disable=attribute-defined-outside-init
+                self.optimizer
+            )
+
+        return self.optimizer
+
     def _get_train_sampler(self) -> Optional[torch.utils.data.Sampler]:
         if self.args.sample_packing and not self.args.pretraining:
             if self.args.multipack_real_batches:
@@ -421,9 +519,10 @@ class AxolotlTrainer(Trainer):
                 batch_max_len = self.args.max_seq_length
             else:
                 batch_size = 1
-                batch_max_len = (
-                    self.args.per_device_train_batch_size * self.args.max_seq_length
+                train_batch_size = (
+                    self.state.train_batch_size or self.args.per_device_train_batch_size
                 )
+                batch_max_len = train_batch_size * self.args.max_seq_length
             return MultipackBatchSampler(
                 RandomSampler(self.train_dataset),
                 lengths=get_dataset_lengths(self.train_dataset),
@@ -778,6 +877,14 @@ class AxolotlTrainer(Trainer):
         for key, value in metrics.items():
             self._stored_metrics[train_eval][key].append(value)
 
+    def _save_checkpoint(self, model, trial, metrics=None):
+        # make sure the checkpoint dir exists, since trainer is flakey
+        checkpoint_folder = f"{PREFIX_CHECKPOINT_DIR}-{self.state.global_step}"
+        run_dir = self._get_output_dir(trial=trial)
+        output_dir = os.path.join(run_dir, checkpoint_folder)
+        os.makedirs(output_dir, exist_ok=True)
+        return super()._save_checkpoint(model, trial, metrics=metrics)
+
 
 class AxolotlMambaTrainer(AxolotlTrainer):
     """
@@ -807,37 +914,6 @@ class AxolotlMambaTrainer(AxolotlTrainer):
         return lm_loss
 
 
-class OneCycleLRSchedulerTrainer(AxolotlTrainer):
-    """
-    Trainer subclass that uses the OneCycleLR scheduler
-    """
-
-    tag_names = ["axolotl", "onecycle"]
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.lr_scheduler = None
-
-    def create_scheduler(
-        self,
-        num_training_steps: int,
-        optimizer: Optional[torch.optim.Optimizer] = None,
-    ):
-        optimizer = self.optimizer if optimizer is None else optimizer
-        num_warmup_steps = self.args.get_warmup_steps(num_training_steps)
-        pct_start = num_warmup_steps / num_training_steps
-
-        self.lr_scheduler = OneCycleLR(
-            optimizer,
-            max_lr=self.args.learning_rate,
-            total_steps=num_training_steps,
-            pct_start=pct_start,
-            div_factor=6,
-        )
-
-        return self.lr_scheduler
-
-
 class ReLoRATrainer(AxolotlTrainer):
     """
     Trainer subclass that uses the OneCycleLR scheduler
@@ -877,7 +953,7 @@ class ReLoRATrainer(AxolotlTrainer):
         return self.lr_scheduler
 
 
-class AxolotlDPOTrainer(DPOTrainer):
+class AxolotlDPOTrainer(SchedulerMixin, DPOTrainer):
     """
     Extend the base DPOTrainer for axolotl helpers
     """
@@ -906,9 +982,9 @@ class AxolotlDPOTrainer(DPOTrainer):
             self.optimizer = create_loraplus_optimizer(  # pylint: disable=attribute-defined-outside-init
                 opt_model,
                 optimizer_cls,
-                optimizer_kwargs,
-                loraplus_lr_ratio,
-                loraplus_lr_embedding,
+                loraplus_lr_ratio=loraplus_lr_ratio,
+                loraplus_lr_embedding=loraplus_lr_embedding,
+                **optimizer_kwargs,
             )
 
         if is_sagemaker_mp_enabled():
@@ -937,8 +1013,16 @@ class AxolotlDPOTrainer(DPOTrainer):
                 res[key] = res[key][1:]
         return res
 
+    def training_step(
+        self, model: nn.Module, inputs: Dict[str, Union[torch.Tensor, Any]]
+    ) -> torch.Tensor:
+        loss: torch.Tensor = super().training_step(model, inputs)
+        gc.collect()
+        torch.cuda.empty_cache()
+        return loss
 
-class AxolotlORPOTrainer(ORPOTrainer):
+
+class AxolotlORPOTrainer(SchedulerMixin, ORPOTrainer):
     """
     Extend the base ORPOTrainer for axolotl helpers
     """
@@ -946,7 +1030,7 @@ class AxolotlORPOTrainer(ORPOTrainer):
     tag_names = ["axolotl", "orpo"]
 
 
-class AxolotlKTOTrainer(KTOTrainer):
+class AxolotlKTOTrainer(SchedulerMixin, KTOTrainer):
     """
     Extend the base KTOTrainer for axolotl helpers
     """
@@ -954,6 +1038,22 @@ class AxolotlKTOTrainer(KTOTrainer):
     tag_names = ["axolotl", "kto"]
 
 
+class AxolotlCPOTrainer(SchedulerMixin, CPOTrainer):
+    """
+    Extend the base CPOTrainer for axolotl helpers
+    """
+
+    tag_names = ["axolotl", "cpo"]
+
+
+class AxolotlRewardTrainer(SchedulerMixin, RewardTrainer):
+    """
+    Extend the base RewardTrainer for axolotl helpers
+    """
+
+    tag_names = ["axolotl", "reward"]
+
+
 class TrainerBuilderBase(abc.ABC):
     """
     Base class for trainer builder
@@ -964,10 +1064,11 @@ class TrainerBuilderBase(abc.ABC):
     _model_ref = None
     _peft_config = None
 
-    def __init__(self, cfg, model, tokenizer):
+    def __init__(self, cfg, model, tokenizer, processor=None):
         self.cfg = cfg
         self.model = model
         self.tokenizer = tokenizer
+        self.processor = processor
 
         # in case the model supports tagging, add the axolotl tag.
         # This makes sure the tag is correctly pushed even if a user calls
@@ -1025,6 +1126,12 @@ class TrainerBuilderBase(abc.ABC):
             callbacks.append(
                 SaveAxolotlConfigtoMlflowCallback(self.cfg.axolotl_config_path)
             )
+        if self.cfg.use_comet and is_comet_available():
+            from axolotl.utils.callbacks.comet_ import SaveAxolotlConfigtoCometCallback
+
+            callbacks.append(
+                SaveAxolotlConfigtoCometCallback(self.cfg.axolotl_config_path)
+            )
 
         return callbacks
 
@@ -1093,6 +1200,11 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
                 trainer, self.tokenizer, "mlflow"
             )
             callbacks.append(LogPredictionCallback(self.cfg))
+        if self.cfg.use_comet and is_comet_available() and self.cfg.eval_table_size > 0:
+            LogPredictionCallback = log_prediction_callback_factory(
+                trainer, self.tokenizer, "comet_ml"
+            )
+            callbacks.append(LogPredictionCallback(self.cfg))
 
         if self.cfg.do_bench_eval:
             callbacks.append(bench_eval_callback_factory(trainer, self.tokenizer))
@@ -1113,14 +1225,12 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         return callbacks
 
     def _get_trainer_cls(self):
-        if self.cfg.lr_scheduler == "one_cycle" and (
-            self.cfg.fsdp or self.cfg.adapter == "qlora"
-        ):
-            return OneCycleLRSchedulerTrainer
         if self.cfg.relora_steps:
             return ReLoRATrainer
         if self.cfg.model_config_type == "mamba":
             return AxolotlMambaTrainer
+        if self.cfg.reward_model:
+            return AxolotlRewardTrainer
         return AxolotlTrainer
 
     def build(self, total_num_steps):
@@ -1166,7 +1276,9 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         if self.cfg.fsdp:
             training_arguments_kwargs["fsdp"] = self.cfg.fsdp
             if self.cfg.fsdp_config:
-                training_arguments_kwargs["fsdp_config"] = dict(self.cfg.fsdp_config)
+                training_arguments_kwargs["fsdp_config"] = {
+                    k.lstrip("fsdp_"): v for k, v in dict(self.cfg.fsdp_config).items()
+                }
 
         if self.cfg.adapter == "qlora":
             training_arguments_kwargs["qlora"] = True
@@ -1275,6 +1387,10 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
                     training_arguments_kwargs[
                         "torch_compile_backend"
                     ] = self.cfg.torch_compile_backend
+                if self.cfg.torch_compile_mode:
+                    training_arguments_kwargs[
+                        "torch_compile_mode"
+                    ] = self.cfg.torch_compile_mode
 
         # DDP Config
         if self.cfg.ddp_timeout:
@@ -1299,6 +1415,10 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
             training_arguments_kwargs[
                 "per_device_eval_batch_size"
             ] = self.cfg.eval_batch_size
+        if self.cfg.auto_find_batch_size is not None:
+            training_arguments_kwargs[
+                "auto_find_batch_size"
+            ] = self.cfg.auto_find_batch_size
         training_arguments_kwargs[
             "gradient_accumulation_steps"
         ] = self.cfg.gradient_accumulation_steps
@@ -1332,15 +1452,22 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         report_to = []
         if self.cfg.use_wandb:
             report_to.append("wandb")
+            if self.cfg.wandb_name:
+                training_arguments_kwargs["run_name"] = self.cfg.wandb_name
         if self.cfg.use_mlflow:
             report_to.append("mlflow")
         if self.cfg.use_tensorboard:
             report_to.append("tensorboard")
+        if self.cfg.use_comet:
+            report_to.append("comet_ml")
 
         training_arguments_kwargs["report_to"] = report_to
-        training_arguments_kwargs["run_name"] = (
-            self.cfg.wandb_name if self.cfg.use_wandb else None
-        )
+        if self.cfg.use_wandb:
+            training_arguments_kwargs["run_name"] = self.cfg.wandb_name
+        elif self.cfg.use_mlflow:
+            training_arguments_kwargs["run_name"] = self.cfg.mlflow_run_name
+        else:
+            training_arguments_kwargs["run_name"] = None
         training_arguments_kwargs["optim"] = (
             self.cfg.optimizer if self.cfg.optimizer else "adamw_hf"
         )
@@ -1360,12 +1487,15 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         training_arguments_kwargs[
             "loraplus_lr_embedding"
         ] = self.cfg.loraplus_lr_embedding
-        training_arguments_kwargs["lr_scheduler_type"] = (
-            self.cfg.lr_scheduler
-            if self.cfg.lr_scheduler
-            and self.cfg.lr_scheduler not in ("one_cycle", "log_sweep")
-            else "cosine"
-        )
+        if self.cfg.lr_scheduler in ["one_cycle", "log_sweep"]:
+            training_arguments_kwargs["lr_scheduler_type"] = "cosine"
+            training_arguments_kwargs[
+                "alternate_lr_scheduler_type"
+            ] = self.cfg.lr_scheduler
+        else:
+            training_arguments_kwargs["lr_scheduler_type"] = (
+                self.cfg.lr_scheduler if self.cfg.lr_scheduler else "cosine"
+            )
         training_arguments_kwargs["lr_scheduler_kwargs"] = (
             self.cfg.lr_scheduler_kwargs if self.cfg.lr_scheduler_kwargs else {}
         )
@@ -1378,9 +1508,9 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         )
 
         training_arguments_kwargs["sample_packing"] = bool(self.cfg.sample_packing)
-        training_arguments_kwargs[
-            "multipack_real_batches"
-        ] = not self.cfg.flash_attention
+        training_arguments_kwargs["multipack_real_batches"] = (
+            not self.cfg.flash_attention or self.cfg.multipack_real_batches
+        )
         training_arguments_kwargs["eval_sample_packing"] = bool(
             self.cfg.eval_sample_packing
         )
@@ -1425,6 +1555,10 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         )
         training_arguments_kwargs["model_type"] = self.cfg.model_config_type
         training_arguments_kwargs["pretraining"] = bool(self.cfg.pretraining_dataset)
+        if self.cfg.chat_template:
+            training_arguments_kwargs["chat_template"] = get_chat_template(
+                self.cfg.chat_template
+            )
 
         if self.cfg.rl == "orpo":
             training_arguments_kwargs["orpo_alpha"] = self.cfg.orpo_alpha
@@ -1436,7 +1570,15 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
 
         trainer_kwargs = {}
 
-        if self.cfg.optimizer == "optimi_adamw":
+        if self.cfg.reward_model:
+            trainer_kwargs["max_length"] = self.cfg.sequence_len
+
+        if self.cfg.optimizer in [
+            "optimi_adamw",
+            "ao_adamw_4bit",
+            "ao_adamw_8bit",
+            "ao_adamw_fp8",
+        ]:
             # Set default so transformers doesn't throw
             training_arguments_kwargs["optim"] = "adamw_hf"
             training_arguments_kwargs["alternate_optimizer"] = self.cfg.optimizer
@@ -1469,13 +1611,27 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
                 sys.path.append(self.cfg.torchdistx_path)
                 importlib.import_module("torchdistx")
 
-        training_args = (
-            AxolotlTrainingArguments(  # pylint: disable=unexpected-keyword-arg
-                **training_arguments_kwargs,
-            )
+        if self.cfg.accelerator_config:
+            training_arguments_kwargs[
+                "accelerator_config"
+            ] = self.cfg.accelerator_config
+
+        training_args_cls = (
+            AxolotlTrainingArguments
+            if not self.cfg.reward_model
+            else AxolotlRewardConfig
+        )
+        training_args = training_args_cls(  # pylint: disable=unexpected-keyword-arg
+            **training_arguments_kwargs,
         )
         training_args = self.hook_post_create_training_args(training_args)
 
+        # unset run_name so wandb sets up experiment names
+        if self.cfg.use_wandb and training_args.run_name == training_args.output_dir:
+            training_args.run_name = (  # pylint: disable=attribute-defined-outside-init
+                None
+            )
+
         data_collator_kwargs = {
             "padding": True,  # True/"longest" is the default
         }
@@ -1488,10 +1644,24 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
             # https://docs.nvidia.com/deeplearning/performance/dl-performance-matrix-multiplication/index.html
             data_collator_kwargs["pad_to_multiple_of"] = 64
 
+        if self.cfg.reward_model:
+            data_collator_kwargs["max_length"] = self.cfg.sequence_len
+
         trainer_cls = self._get_trainer_cls()
         trainer_kwargs, trainer_cls = self.hook_pre_create_trainer(
             trainer_kwargs, trainer_cls
         )
+        if eval_data_collator := self.build_collator(
+            training_args, is_eval=True, **data_collator_kwargs
+        ):
+            if not self.cfg.reward_model:
+                trainer_kwargs["eval_data_collator"] = eval_data_collator
+        if not self.cfg.reward_model:
+            trainer_kwargs["bench_data_collator"] = transformers.DataCollatorForSeq2Seq(
+                self.tokenizer,
+                return_tensors="pt",
+                **data_collator_kwargs,
+            )
         trainer = trainer_cls(
             model=self.model,
             train_dataset=self.train_dataset,
@@ -1499,16 +1669,7 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
             args=training_args,
             tokenizer=self.tokenizer,
             data_collator=self.build_collator(training_args, **data_collator_kwargs),
-            eval_data_collator=self.build_collator(
-                training_args, is_eval=True, **data_collator_kwargs
-            ),
-            bench_data_collator=transformers.DataCollatorForSeq2Seq(
-                self.tokenizer,
-                return_tensors="pt",
-                **data_collator_kwargs,
-            ),
             callbacks=self.get_callbacks(),
-            num_epochs=self.cfg.num_epochs,
             **trainer_kwargs,
         )
         trainer = self.hook_post_create_trainer(trainer)
@@ -1542,9 +1703,12 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
                 V2BatchSamplerDataCollatorForSeq2Seq,
                 BatchSamplerDataCollatorForSeq2Seq,
                 DataCollatorForSeq2Seq,
+                RewardDataCollatorWithPadding,
             ]
         ]
-        if use_batch_sampler_collator:
+        if self.cfg.reward_model:
+            collator = RewardDataCollatorWithPadding
+        elif use_batch_sampler_collator:
             if self.cfg.model_config_type in SUPPORTED_MULTIPACK_MODEL_TYPES:
                 collator = V2BatchSamplerDataCollatorForSeq2Seq
             elif (
@@ -1555,7 +1719,12 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
             else:
                 collator = BatchSamplerDataCollatorForSeq2Seq
         else:
-            collator = DataCollatorForSeq2Seq
+            if self.cfg.processor_type and self.processor:
+                collator = MultiModalChatDataCollator
+                kwargs["processor"] = self.processor
+                kwargs["chat_template"] = training_args.chat_template
+            else:
+                collator = DataCollatorForSeq2Seq
 
         return collator(
             self.tokenizer,
@@ -1662,16 +1831,27 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
             # default to saving each epoch if not defined
             training_args_kwargs["save_strategy"] = "epoch"
 
+        if self.cfg.rl_beta:
+            training_args_kwargs["beta"] = self.cfg.rl_beta
         if self.cfg.orpo_alpha:
             # trl does some odd mapping of alpha to beta to reuse the beta parameter ???
             training_args_kwargs["beta"] = self.cfg.orpo_alpha
 
+        training_args_kwargs["dataset_num_proc"] = self.cfg.dataset_processes
         training_args_cls = AxolotlDPOConfig
         if self.cfg.rpo_alpha is not None:
             training_args_kwargs["rpo_alpha"] = self.cfg.rpo_alpha
+
+        if self.cfg.rl == "simpo":
+            training_args_cls = AxolotlCPOConfig
+            training_args_kwargs["loss_type"] = "simpo"
+            training_args_kwargs["max_length"] = self.cfg.sequence_len
+            training_args_kwargs["simpo_gamma"] = self.cfg.simpo_gamma
+            if self.cfg.cpo_alpha is not None:
+                training_args_kwargs["cpo_alpha"] = self.cfg.cpo_alpha
+
         if self.cfg.rl == "orpo":
             training_args_cls = AxolotlORPOConfig
-            training_args_kwargs["dataset_num_proc"] = self.cfg.dataset_processes
             training_args_kwargs["max_length"] = self.cfg.sequence_len
             if self.cfg.max_prompt_len:
                 training_args_kwargs["max_prompt_length"] = self.cfg.max_prompt_len
@@ -1679,7 +1859,6 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
         if self.cfg.rl == "kto":
             training_args_cls = AxolotlKTOConfig
 
-            training_args_kwargs["beta"] = self.cfg.rl_beta or 0.1
             training_args_kwargs["desirable_weight"] = (
                 self.cfg.kto_desirable_weight or 1.0
             )
@@ -1725,7 +1904,6 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
             ] = self.cfg.precompute_ref_log_probs
         if self.cfg.rl in ["dpo", "ipo"]:
             trainer_cls = AxolotlDPOTrainer
-            dpo_trainer_kwargs["beta"] = self.cfg.rl_beta or 0.1
             trainer_cls_args = [self.model, self.model_ref]
 
             # these aren't used for the ORPO trainer
@@ -1733,14 +1911,15 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
             dpo_trainer_kwargs["max_target_length"] = None
             dpo_trainer_kwargs["max_prompt_length"] = self.cfg.sequence_len
             dpo_trainer_kwargs["generate_during_eval"] = True
-            if self.cfg.rl == "dpo":
-                dpo_trainer_kwargs["dataset_num_proc"] = self.cfg.dataset_processes
         elif self.cfg.rl == "orpo":
             trainer_cls = AxolotlORPOTrainer
             trainer_cls_args = [self.model]
         elif self.cfg.rl in ["kto"]:
             trainer_cls = AxolotlKTOTrainer
             trainer_cls_args = [self.model]
+        elif self.cfg.rl in ["simpo"]:
+            trainer_cls = AxolotlCPOTrainer
+            trainer_cls_args = [self.model]
         else:
             raise ValueError(f"Unsupported RL: {self.cfg.rl}")
         dpo_trainer = trainer_cls(
@@ -1753,6 +1932,8 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
         )
         if self.cfg.fsdp:
             ensure_dtype(dpo_trainer.model, dtype=self.cfg.torch_dtype)
+            if self.cfg.rl in ["dpo", "ipo"] and dpo_trainer.ref_model:
+                ensure_dtype(dpo_trainer.ref_model, dtype=self.cfg.torch_dtype)
 
         dpo_trainer = self.hook_post_create_trainer(dpo_trainer)
         for callback in self.get_post_trainer_create_callbacks(dpo_trainer):

src/axolotl/integrations/LICENSE.md

@@ -0,0 +1,58 @@
+### AXOLOTL COMMUNITY LICENSE AGREEMENT
+
+This Axolotl Community License Agreement (“Agreement”) is entered into by and between Axolotl AI Corp. (“Axolotl”) and
+any individual or entity (“Licensee”) who wishes to use the Software (as defined below) in accordance with the terms
+and conditions set forth in this Agreement.
+
+1.  Definitions
+    1.1 “Licensee” refers to any individual or entity who has obtained a copy of the Software under this Agreement.
+    1.2 “Plugin Integration” means independent integration software modules which may or may not be offered by Axolotl,
+        which may be licensed separately by their respective  authors and/or licensors.
+    1.3 “Software” refers to the specific sub-directory of the Axolotl, Inc. software located at
+        https://github.com/axolotl-ai-cloud/axolotl/tree/main/src/axolotl/integrations and its subdirectories which
+        permits Plugin Integrations to integrate with the Axolotl service.
+2.  Grant of License
+    2.1	Axolotl hereby grants Licensee a worldwide, non-exclusive, royalty-free, license to use, copy, modify, merge,
+        publish, distribute, sublicense, and/or otherwise exploit the Software, subject to the following conditions:
+        - Licensee must comply with all the terms and conditions of this Agreement.
+        - Licensee must include the original copyright notice and disclaimer of warranty in all copies or substantial
+          portions of the Software.
+    2.2 Licensee may use the Software for any lawful purpose, except as restricted in Section 3.
+3.  Restrictions
+    3.1 Licensee shall not use the Software for any activity that constitutes a commercial activity of offering for
+        free or for sale any services, platform, or equivalent  to third parties for the purposes of allowing such
+        third parties to fine-tune artificial intelligence models.
+    3.2 Licensee shall not:
+        - Use the Software for any illegal or unauthorized purpose.
+        - Reverse engineer, decompile, or disassemble the Software.
+        - Remove or modify any copyright, trademark, or other proprietary notices contained in the Software.
+        - Use the Software in a way that could damage, disable, overburden, or impair the functionality of the
+          Software or interfere with any third-party use of the Software.
+    3.3 Axolotl reserves the right to restrict certain Plugin Integrations for use with the Software. To the extent Licensee integrates a permitted, applicable Plugin Integration with the Software, Licensee shall comply with any additional terms and conditions imposed by the licensors of such Plugin Integration for use of such Plugin Integrations. Licensee shall contact Axolotl if it has questions about whether its use of the Software falls beyond the scope of this Agreement.
+4.  Intellectual Property Rights
+    4.1 Axolotl and its contributors retain all intellectual property rights in and to the Software. Licensee
+        acknowledges that this Agreement does not transfer any ownership rights or intellectual property rights to
+        Licensee.
+5.  Disclaimer of Warranty
+    5.1 THE SOFTWARE IS PROVIDED “AS IS,” WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED
+        TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, AND NON-INFRINGEMENT. IN NO EVENT SHALL
+        THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES, OR OTHER LIABILITY, WHETHER IN AN ACTION OF
+        CONTRACT, TORT, OR OTHERWISE, ARISING FROM, OUT OF, OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
+        DEALINGS IN THE SOFTWARE.
+6.  Termination
+    6.1 Axolotl may terminate this Agreement at any time if Licensee fails to comply with any of the terms and
+        conditions set forth herein. Upon termination, Licensee shall cease all use of the Software and destroy any
+        copies in its possession.
+7.  Governing Law
+    7.1 This Agreement shall be governed by and construed in accordance with the laws of the State of California,
+        without regards to conflicts of laws provisions thereof.
+8.  Entire Agreement
+    8.1 This Agreement constitutes the entire agreement between Axolotl and Licensee with respect to the subject matter
+        hereof and supersedes all prior or contemporaneous understandings or agreements between the parties concerning
+        the Software, whether written or oral. Axolotl may update the terms of this Agreement from time to time, and
+        Licensee’s continued use of the Software after any such updates shall constitute acceptance of updated terms
+        on a go-forward basis.  Axolotl will use commercially reasonable efforts to provide Licensee notice of any
+        material updates. By using the Software, Licensee acknowledges that it has read, understood, and agrees to be
+        bound by the terms and conditions of this Agreement.
+
+This Agreement was last updated on August 23, 2024.

src/axolotl/integrations/base.py

@@ -0,0 +1,420 @@
+# Copyright 2024 Axolotl AI. All rights reserved.
+#
+# This software may be used and distributed according to
+# the terms of the Axolotl Community License Agreement (the "License");
+# you may not use this file except in compliance with the License.
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+# License for the specific language governing permissions and limitations under
+# the License.
+
+"""
+Base class for all plugins.
+
+A plugin is a reusable, modular, and self-contained piece of code that extends the functionality of Axolotl.
+Plugins can be used to integrate third-party models, modify the training process, or add new features.
+
+To create a new plugin, you need to inherit from the BasePlugin class and implement the required methods.
+"""
+import importlib
+import logging
+from typing import List
+
+
+class BasePlugin:
+    """
+    Base class for all plugins. Defines the interface for plugin methods.
+
+    Attributes:
+    None
+
+    Methods:
+    register(cfg): Registers the plugin with the given configuration.
+    pre_model_load(cfg): Performs actions before the model is loaded.
+    post_model_load(cfg, model): Performs actions after the model is loaded.
+    pre_lora_load(cfg, model): Performs actions before LoRA weights are loaded.
+    post_lora_load(cfg, model): Performs actions after LoRA weights are loaded.
+    create_optimizer(cfg, trainer): Creates and returns an optimizer for training.
+    create_lr_scheduler(cfg, trainer, optimizer): Creates and returns a learning rate scheduler.
+    add_callbacks_pre_trainer(cfg, model): Adds callbacks to the trainer before training.
+    add_callbacks_post_trainer(cfg, trainer): Adds callbacks to the trainer after training.
+    """
+
+    def __init__(self):
+        """
+        Initializes the BasePlugin.
+        """
+
+    def register(self, cfg):
+        """
+        Registers the plugin with the given configuration.
+
+        Parameters:
+        cfg (dict): The configuration for the plugin.
+
+        Returns:
+        None
+        """
+
+    def get_input_args(self):
+        """
+        Returns a pydantic model for the plugin's input arguments.
+        """
+
+    def pre_model_load(self, cfg):
+        """
+        Performs actions before the model is loaded.
+
+        Parameters:
+        cfg (dict): The configuration for the plugin.
+
+        Returns:
+        None
+        """
+
+    def post_model_load(self, cfg, model):
+        """
+        Performs actions after the model is loaded.
+
+        Parameters:
+        cfg (dict): The configuration for the plugin.
+        model (object): The loaded model.
+
+        Returns:
+        None
+        """
+
+    def pre_lora_load(self, cfg, model):
+        """
+        Performs actions before LoRA weights are loaded.
+
+        Parameters:
+        cfg (dict): The configuration for the plugin.
+        model (object): The loaded model.
+
+        Returns:
+        None
+        """
+
+    def post_lora_load(self, cfg, model):
+        """
+        Performs actions after LoRA weights are loaded.
+
+        Parameters:
+        cfg (dict): The configuration for the plugin.
+        model (object): The loaded model.
+
+        Returns:
+        None
+        """
+
+    def create_optimizer(self, cfg, trainer):
+        """
+        Creates and returns an optimizer for training.
+
+        Parameters:
+        cfg (dict): The configuration for the plugin.
+        trainer (object): The trainer object for training.
+
+        Returns:
+        object: The created optimizer.
+        """
+
+    def create_lr_scheduler(self, cfg, trainer, optimizer):
+        """
+        Creates and returns a learning rate scheduler.
+
+        Parameters:
+        cfg (dict): The configuration for the plugin.
+        trainer (object): The trainer object for training.
+        optimizer (object): The optimizer for training.
+
+        Returns:
+        object: The created learning rate scheduler.
+        """
+
+    def add_callbacks_pre_trainer(self, cfg, model):
+        """
+        Adds callbacks to the trainer before training.
+
+        Parameters:
+        cfg (dict): The configuration for the plugin.
+        model (object): The loaded model.
+
+        Returns:
+        List[callable]: A list of callback functions to be added to the TrainingArgs
+        """
+
+    def add_callbacks_post_trainer(self, cfg, trainer):
+        """
+        Adds callbacks to the trainer after training.
+
+        Parameters:
+        cfg (dict): The configuration for the plugin.
+        trainer (object): The trainer object for training.
+
+        Returns:
+        List[callable]: A list of callback functions to be added to the TrainingArgs
+        """
+
+    def post_train(self, cfg, model):
+        """
+        Performs actions after training is complete.
+
+        Parameters:
+        cfg (dict): The axolotl configuration
+        model (object): The loaded model.
+
+        Returns:
+        None
+        """
+
+    def post_train_unload(self, cfg):
+        """
+        Performs actions after training is complete and the model is unloaded.
+
+        Parameters:
+        cfg (dict): The configuration for the plugin.
+
+        Returns:
+        None
+        """
+
+
+def load_plugin(plugin_name: str) -> BasePlugin:
+    """
+    Loads a plugin based on the given plugin name.
+
+    The plugin name should be in the format "module_name.class_name".
+    This function splits the plugin name into module and class, imports the module,
+    retrieves the class from the module, and creates an instance of the class.
+
+    Parameters:
+    plugin_name (str): The name of the plugin to be loaded. The name should be in the format "module_name.class_name".
+
+    Returns:
+    BasePlugin: An instance of the loaded plugin.
+
+    Raises:
+    ImportError: If the plugin module cannot be imported.
+    """
+    # split the plugin name into module and class
+    module_name, class_name = plugin_name.rsplit(".", 1)
+
+    # import the module
+    module = importlib.import_module(module_name)
+    # instantiate the class
+    plugin_class = getattr(module, class_name)
+    # create an instance of the class
+    plugin = plugin_class()
+
+    return plugin
+
+
+class PluginManager:
+    """
+    The PluginManager class is responsible for loading and managing plugins.
+    It should be a singleton so it can be accessed from anywhere in the codebase.
+
+    Attributes:
+    plugins (List[BasePlugin]): A list of loaded plugins.
+
+    Methods:
+    get_instance(): Static method to get the singleton instance of PluginManager.
+    register(plugin_name: str): Registers a new plugin by its name.
+    pre_model_load(cfg): Calls the pre_model_load method of all registered plugins.
+    """
+
+    plugins: List[BasePlugin] = []
+
+    _instance = None
+
+    def __new__(cls):
+        """
+        Creates a new instance of PluginManager if it doesn't exist yet.
+        """
+        if cls._instance is None:
+            cls._instance = super(PluginManager, cls).__new__(cls)
+            cls._instance.plugins: List[BasePlugin] = []
+        return cls._instance
+
+    @staticmethod
+    def get_instance() -> "PluginManager":
+        """
+        Returns the singleton instance of PluginManager.
+        If the instance doesn't exist, it creates a new one.
+        """
+        if PluginManager._instance is None:
+            PluginManager()
+        return PluginManager._instance  # type: ignore
+
+    def register(self, plugin_name: str):
+        """
+        Registers a new plugin by its name.
+
+        Parameters:
+        plugin_name (str): The name of the plugin to be registered.
+
+        Returns:
+        None
+
+        Raises:
+        ImportError: If the plugin module cannot be imported.
+        """
+        try:
+            plugin = load_plugin(plugin_name)
+            self.plugins.append(plugin)
+        except ImportError:
+            logging.error(f"Failed to load plugin: {plugin_name}")
+
+    def get_input_args(self):
+        """
+        Returns a list of Pydantic classes for all registered plugins' input arguments.'
+
+        Returns:
+        list[str]: A list of Pydantic classes for all registered plugins' input arguments.'
+        """
+        input_args = []
+        for plugin in self.plugins:
+            input_args_from_plugin = plugin.get_input_args()
+            if input_args_from_plugin is not None:
+                input_args.append(input_args_from_plugin)
+        return input_args
+
+    def pre_model_load(self, cfg):
+        """
+        Calls the pre_model_load method of all registered plugins.
+
+        Parameters:
+        cfg (dict): The configuration for the plugins.
+
+        Returns:
+        None
+        """
+        for plugin in self.plugins:
+            plugin.pre_model_load(cfg)
+
+    def post_model_load(self, cfg, model):
+        """
+        Calls the post_model_load method of all registered plugins.
+
+        Parameters:
+        cfg (dict): The configuration for the plugins.
+        model (object): The loaded model.
+
+        Returns:
+        None
+        """
+        for plugin in self.plugins:
+            plugin.post_model_load(cfg, model)
+
+    def pre_lora_load(self, cfg, model):
+        """
+        Calls the pre_lora_load method of all registered plugins.
+
+        Parameters:
+        cfg (dict): The configuration for the plugins.
+        model (object): The loaded model.
+
+        Returns:
+        None
+        """
+        for plugin in self.plugins:
+            plugin.pre_lora_load(cfg, model)
+
+    def post_lora_load(self, cfg, model):
+        """
+        Calls the post_lora_load method of all registered plugins.
+
+        Parameters:
+        cfg (dict): The configuration for the plugins.
+        model (object): The loaded model.
+
+        Returns:
+        None
+        """
+        for plugin in self.plugins:
+            plugin.post_lora_load(cfg, model)
+
+    def create_optimizer(self, cfg, trainer):
+        """
+        Calls the create_optimizer method of all registered plugins and returns the first non-None optimizer.
+
+        Parameters:
+        cfg (dict): The configuration for the plugins.
+        trainer (object): The trainer object for training.
+
+        Returns:
+        object: The created optimizer, or None if none was found.
+        """
+        for plugin in self.plugins:
+            optimizer = plugin.create_optimizer(cfg, trainer)
+            if optimizer is not None:
+                return optimizer
+        return None
+
+    def create_lr_scheduler(self, cfg, trainer, optimizer):
+        """
+        Calls the create_lr_scheduler method of all registered plugins and returns the first non-None scheduler.
+
+        Parameters:
+        cfg (dict): The configuration for the plugins.
+        trainer (object): The trainer object for training.
+        optimizer (object): The optimizer for training.
+
+        Returns:
+        object: The created learning rate scheduler, or None if none was found.
+        """
+        for plugin in self.plugins:
+            scheduler = plugin.create_lr_scheduler(cfg, trainer, optimizer)
+            if scheduler is not None:
+                return scheduler
+        return None
+
+    def add_callbacks_pre_trainer(self, cfg, model):
+        """
+        Calls the add_callbacks_pre_trainer method of all registered plugins.
+
+        Parameters:
+        cfg (dict): The configuration for the plugins.
+        model (object): The loaded model.
+
+        Returns:
+        List[callable]: A list of callback functions to be added to the TrainingArgs.
+        """
+        callbacks = []
+        for plugin in self.plugins:
+            callbacks.extend(plugin.add_callbacks_pre_trainer(cfg, model))
+        return callbacks
+
+    def add_callbacks_post_trainer(self, cfg, trainer):
+        """
+        Calls the add_callbacks_post_trainer method of all registered plugins.
+
+        Parameters:
+        cfg (dict): The configuration for the plugins.
+        trainer (object): The trainer object for training.
+
+        Returns:
+        List[callable]: A list of callback functions to be added to the TrainingArgs.
+        """
+        callbacks = []
+        for plugin in self.plugins:
+            callbacks.extend(plugin.add_callbacks_post_trainer(cfg, trainer))
+        return callbacks
+
+    def post_train_unload(self, cfg):
+        """
+        Calls the post_train_unload method of all registered plugins.
+
+        Parameters:
+        cfg (dict): The configuration for the plugins.
+        model (object): The loaded model.
+
+        Returns:
+        None
+        """
+        for plugin in self.plugins:
+            plugin.post_train_unload(cfg)

src/axolotl/integrations/config.py

@@ -0,0 +1,65 @@
+# Copyright 2024 Axolotl AI. All rights reserved.
+#
+# This software may be used and distributed according to
+# the terms of the Axolotl Community License Agreement (the "License");
+# you may not use this file except in compliance with the License.
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+# License for the specific language governing permissions and limitations under
+# the License.
+
+"""
+module to handle merging the plugins' input arguments with the base configurations.
+
+this was moved here to prevent circular imports
+"""
+
+from typing import Any, Dict, List
+
+from axolotl.utils.config.models.input.v0_4_1 import (
+    AxolotlConfigWCapabilities as AxolotlConfigWCapabilitiesBase,
+)
+from axolotl.utils.config.models.input.v0_4_1 import (
+    AxolotlInputConfig as AxolotlInputConfigBase,
+)
+
+
+def merge_input_args():
+    """
+    Merges input arguments from registered plugins with the base configurations.
+
+    This function retrieves the input arguments from registered plugins using the PluginManager.
+    It then dynamically creates new classes, AxolotlConfigWCapabilities and AxolotlInputConfig,
+    that inherit from the base configurations and include the input arguments from the plugins.
+
+    Returns:
+    tuple: A tuple containing the newly created classes, AxolotlConfigWCapabilities and AxolotlInputConfig.
+    """
+    from axolotl.integrations.base import PluginManager
+
+    plugin_manager = PluginManager.get_instance()
+    input_args: List[str] = plugin_manager.get_input_args()
+    plugin_classes = []
+    dynamic_input = ""
+    for plugin_args in input_args:
+        plugin_module, plugin_cls = plugin_args.rsplit(".", 1)
+        dynamic_input += f"from {plugin_module} import {plugin_cls}\n"
+        plugin_classes.append(plugin_cls)
+    if dynamic_input:
+        dynamic_input += f"class AxolotlConfigWCapabilities(AxolotlConfigWCapabilitiesBase, {', '.join(plugin_classes)}):\n    pass\n"
+        dynamic_input += f"class AxolotlInputConfig(AxolotlInputConfigBase, {', '.join(plugin_classes)}):\n    pass\n"
+
+        namespace: Dict[Any, Any] = {}
+        exec(  # pylint: disable=exec-used  # nosec B102
+            dynamic_input, globals(), namespace
+        )
+        AxolotlInputConfig = namespace[  # pylint: disable=invalid-name
+            "AxolotlInputConfig"
+        ]
+        AxolotlConfigWCapabilities = namespace[  # pylint: disable=invalid-name
+            "AxolotlConfigWCapabilities"
+        ]
+        return AxolotlConfigWCapabilities, AxolotlInputConfig
+    return AxolotlConfigWCapabilitiesBase, AxolotlInputConfigBase

src/axolotl/integrations/liger/LICENSE

@@ -0,0 +1,202 @@
+
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src/axolotl/integrations/liger/__init__.py

@@ -0,0 +1,189 @@
+# Copyright 2024 Axolotl AI. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+Module for the Plugin for LIGER integraton with Axolotl.
+
+Liger Kernel is the collection of Triton-native kernels for LLM Training.
+It is designed to be performant, correct, and light-weight.
+"""
+import logging
+import sys
+from functools import partial
+
+from liger_kernel.transformers.cross_entropy import LigerCrossEntropyLoss
+from liger_kernel.transformers.geglu import LigerGEGLUMLP
+from liger_kernel.transformers.rms_norm import LigerRMSNorm
+from liger_kernel.transformers.rope import liger_rotary_pos_emb
+from liger_kernel.transformers.swiglu import LigerSwiGLUMLP
+
+from axolotl.integrations.base import BasePlugin
+
+from .args import LigerArgs  # pylint: disable=unused-import. # noqa: F401
+
+
+class LigerPlugin(BasePlugin):
+    """
+    Plugin for LIGER integraton with Axolotl.
+    """
+
+    def get_input_args(self):
+        return "axolotl.integrations.liger.LigerArgs"
+
+    def pre_model_load(self, cfg):
+        if cfg.model_config_type == "llama":
+            from liger_kernel.transformers.model.llama import (
+                lce_forward as llama_lce_forward,
+            )
+            from transformers.models.llama import modeling_llama
+
+            if cfg.liger_rope:
+                modeling_llama.apply_rotary_pos_emb = liger_rotary_pos_emb
+            if cfg.liger_rms_norm:
+                modeling_llama.LlamaRMSNorm = LigerRMSNorm
+            if cfg.liger_swiglu:
+                modeling_llama.LlamaMLP = LigerSwiGLUMLP
+            if cfg.liger_cross_entropy:
+                modeling_llama.CrossEntropyLoss = LigerCrossEntropyLoss
+            elif cfg.liger_fused_linear_cross_entropy:
+                modeling_llama.LlamaForCausalLM.forward = llama_lce_forward
+
+        elif cfg.model_config_type == "mistral":
+            from liger_kernel.transformers.model.mistral import (
+                lce_forward as mistral_lce_forward,
+            )
+            from transformers.models.mistral import modeling_mistral
+
+            if cfg.liger_rope:
+                modeling_mistral.apply_rotary_pos_emb = liger_rotary_pos_emb
+            if cfg.liger_rms_norm:
+                modeling_mistral.MistralRMSNorm = LigerRMSNorm
+            if cfg.liger_swiglu:
+                modeling_mistral.MistralMLP = LigerSwiGLUMLP
+            if cfg.liger_cross_entropy:
+                modeling_mistral.CrossEntropyLoss = LigerCrossEntropyLoss
+            if cfg.liger_fused_linear_cross_entropy:
+                modeling_mistral.MistralForCausalLM.forward = mistral_lce_forward
+
+        elif cfg.model_config_type == "gemma":
+            from liger_kernel.transformers.model.gemma import (
+                lce_forward as gemma_lce_forward,
+            )
+            from transformers.models.gemma import modeling_gemma
+
+            if cfg.liger_rope:
+                modeling_gemma.apply_rotary_pos_emb = liger_rotary_pos_emb
+            if cfg.liger_rms_norm:
+                modeling_gemma.GemmaRMSNorm = partial(
+                    LigerRMSNorm, offset=1.0, init_fn="zeros", casting_mode="gemma"
+                )
+            if cfg.liger_swiglu:
+                modeling_gemma.GemmaMLP = LigerGEGLUMLP
+            if cfg.liger_cross_entropy:
+                modeling_gemma.CrossEntropyLoss = LigerCrossEntropyLoss
+            if cfg.liger_fused_linear_cross_entropy:
+                modeling_gemma.GemmaForCausalLM.forward = gemma_lce_forward
+
+        elif cfg.model_config_type == "jamba":
+            from transformers.models.jamba import modeling_jamba
+
+            from .models.jamba import lce_forward as jamba_lce_forward
+
+            if cfg.liger_rope:
+                modeling_jamba.apply_rotary_pos_emb = liger_rotary_pos_emb
+            if cfg.liger_rms_norm:
+                modeling_jamba.JambaRMSNorm = LigerRMSNorm
+            if cfg.liger_swiglu:
+                modeling_jamba.JambaMLP = LigerSwiGLUMLP
+            if cfg.liger_cross_entropy:
+                modeling_jamba.CrossEntropyLoss = LigerCrossEntropyLoss
+            if cfg.liger_fused_linear_cross_entropy:
+                modeling_jamba.JambaForCausalLM.forward = jamba_lce_forward
+
+        elif cfg.model_config_type == "qwen2":
+            from liger_kernel.transformers.model.qwen2 import (
+                lce_forward as qwen2_lce_forward,
+            )
+            from transformers.models.qwen2 import modeling_qwen2
+
+            if cfg.liger_rope:
+                modeling_qwen2.apply_rotary_pos_emb = liger_rotary_pos_emb
+            if cfg.liger_rms_norm:
+                modeling_qwen2.Qwen2RMSNorm = LigerRMSNorm
+            if cfg.liger_swiglu:
+                modeling_qwen2.Qwen2MLP = LigerSwiGLUMLP
+            if cfg.liger_cross_entropy:
+                modeling_qwen2.CrossEntropyLoss = LigerCrossEntropyLoss
+            if cfg.liger_fused_linear_cross_entropy:
+                modeling_qwen2.Qwen2ForCausalLM.forward = qwen2_lce_forward
+
+        elif cfg.model_config_type == "deepseek_v2":
+            from accelerate import init_empty_weights
+            from transformers import AutoModelForCausalLM
+
+            with init_empty_weights():
+                model = AutoModelForCausalLM.from_pretrained(
+                    cfg.base_model, trust_remote_code=cfg.trust_remote_code or False
+                )
+                modeling_mod = sys.modules[model.__class__.__module__]
+
+            from .models.deepseekv2 import lce_forward as deepseekv2_lce_forward
+
+            if cfg.liger_rope:
+                # The DeepseekV2 version of RoPE is different than upstream LLaMA.
+                # See https://github.com/linkedin/Liger-Kernel/issues/129#issuecomment-2313763528
+                logging.warning("Fused liger_rope is not supported for DeepseekV2.")
+            if cfg.liger_rms_norm:
+                modeling_mod.DeepseekV2RMSNorm = LigerRMSNorm
+            if cfg.liger_swiglu:
+                modeling_mod.DeepseekV2MLP.forward = LigerSwiGLUMLP.forward
+            if cfg.liger_cross_entropy:
+                modeling_mod.CrossEntropyLoss = LigerCrossEntropyLoss
+            if cfg.liger_fused_linear_cross_entropy:
+                modeling_mod.DeepseekV2ForCausalLM.forward = deepseekv2_lce_forward
+
+        elif cfg.model_config_type == "gemma2":
+            from transformers.models.gemma2 import modeling_gemma2
+
+            if cfg.liger_rope:
+                modeling_gemma2.apply_rotary_pos_emb = liger_rotary_pos_emb
+            if cfg.liger_rms_norm:
+                modeling_gemma2.Gemma2RMSNorm = partial(
+                    LigerRMSNorm, offset=1.0, init_fn="zeros", casting_mode="gemma"
+                )
+            if cfg.liger_swiglu:
+                modeling_gemma2.Gemma2MLP = LigerGEGLUMLP
+            if cfg.liger_cross_entropy:
+                modeling_gemma2.CrossEntropyLoss = LigerCrossEntropyLoss
+            if cfg.liger_fused_linear_cross_entropy:
+                logging.warning(
+                    "Fused linear cross entropy is not supported for Gemma 2."
+                )
+
+        elif cfg.model_config_type == "phi3":
+            from liger_kernel.transformers.model.phi3 import (
+                lce_forward as phi3_lce_forward,
+            )
+            from transformers.models.phi3 import modeling_phi3
+
+            if cfg.liger_rope:
+                modeling_phi3.apply_rotary_pos_emb = liger_rotary_pos_emb
+            if cfg.liger_rms_norm:
+                modeling_phi3.Phi3RMSNorm = LigerRMSNorm
+            if cfg.liger_swiglu:
+                modeling_phi3.Phi3MLP = LigerSwiGLUMLP
+            if cfg.liger_cross_entropy:
+                modeling_phi3.CrossEntropyLoss = LigerCrossEntropyLoss
+            if cfg.liger_fused_linear_cross_entropy:
+                modeling_phi3.Phi3ForCausalLM.forward = phi3_lce_forward

src/axolotl/integrations/liger/args.py

@@ -0,0 +1,32 @@
+# Copyright 2024 Axolotl AI. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+Module for handling LIGER input arguments.
+"""
+from typing import Optional
+
+from pydantic import BaseModel
+
+
+class LigerArgs(BaseModel):
+    """
+    Input args for LIGER.
+    """
+
+    liger_rope: Optional[bool] = None
+    liger_rms_norm: Optional[bool] = None
+    liger_swiglu: Optional[bool] = None
+    liger_cross_entropy: Optional[bool] = None
+    liger_fused_linear_cross_entropy: Optional[bool] = None

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

@@ -0,0 +1,127 @@
+"""
+DeepseekV2 model with LigerFusedLinearCrossEntropyLoss
+"""
+# pylint: disable=duplicate-code
+
+from typing import List, Optional, Tuple, Union
+
+import torch
+from liger_kernel.transformers.fused_linear_cross_entropy import (
+    LigerFusedLinearCrossEntropyLoss,
+)
+from torch.nn import CrossEntropyLoss
+from transformers.modeling_outputs import CausalLMOutputWithPast
+
+
+# @add_start_docstrings_to_model_forward(DeepseekV2_INPUTS_DOCSTRING)
+# @replace_return_docstrings(
+#    output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC
+# )
+def lce_forward(
+    self,
+    input_ids: torch.LongTensor = None,
+    attention_mask: Optional[torch.Tensor] = None,
+    position_ids: Optional[torch.LongTensor] = None,
+    past_key_values: Optional[List[torch.FloatTensor]] = None,
+    inputs_embeds: Optional[torch.FloatTensor] = None,
+    labels: Optional[torch.LongTensor] = None,
+    use_cache: Optional[bool] = None,
+    output_attentions: Optional[bool] = None,
+    output_hidden_states: Optional[bool] = None,
+    return_dict: Optional[bool] = None,
+) -> Union[Tuple, CausalLMOutputWithPast]:
+    r"""
+    Args:
+        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Labels for computing the masked language modeling loss. Indices should either be in `[0, transformers.,
+            config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+            (masked), the loss is only computed for the tokens with labels in `[0, transformers., config.vocab_size]`.
+
+    Returns:
+
+    Example:
+
+    ```python
+    >>> from transformers import AutoTokenizer, DeepseekV2ForCausalLM
+
+    >>> model = DeepseekV2ForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
+    >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
+
+    >>> prompt = "Hey, are you conscious? Can you talk to me?"
+    >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+    >>> # Generate
+    >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+    >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+    "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
+    ```"""
+    output_attentions = (
+        output_attentions
+        if output_attentions is not None
+        else self.config.output_attentions
+    )
+    output_hidden_states = (
+        output_hidden_states
+        if output_hidden_states is not None
+        else self.config.output_hidden_states
+    )
+    return_dict = (
+        return_dict if return_dict is not None else self.config.use_return_dict
+    )
+
+    # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+    outputs = self.model(
+        input_ids=input_ids,
+        attention_mask=attention_mask,
+        position_ids=position_ids,
+        past_key_values=past_key_values,
+        inputs_embeds=inputs_embeds,
+        use_cache=use_cache,
+        output_attentions=output_attentions,
+        output_hidden_states=output_hidden_states,
+        return_dict=return_dict,
+    )
+
+    hidden_states = outputs[0]
+
+    loss = None
+    logits = None
+
+    if self.training:
+        shift_hidden_states = hidden_states[..., :-1, :].contiguous()
+        shift_labels = labels[..., 1:].contiguous()
+
+        # flatten tokens
+        shift_hidden_states = shift_hidden_states.view(-1, self.config.hidden_size)
+        shift_labels = shift_labels.view(-1)
+
+        lce = LigerFusedLinearCrossEntropyLoss()
+        loss = lce(self.lm_head.weight, shift_hidden_states, shift_labels)
+    else:
+        logits = self.lm_head(hidden_states)
+        logits = logits.float()
+
+        loss = None
+        if labels is not None:
+            # Shift so that tokens < n predict n
+            shift_logits = logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            # Flatten the tokens
+            loss_fct = CrossEntropyLoss()
+            shift_logits = shift_logits.view(-1, self.config.vocab_size)
+            shift_labels = shift_labels.view(-1)
+            # Enable model parallelism
+            shift_labels = shift_labels.to(shift_logits.device)
+            loss = loss_fct(shift_logits, shift_labels)
+
+    if not return_dict:
+        output = (logits,) + outputs[1:]
+        return (loss,) + output if loss is not None else output
+
+    return CausalLMOutputWithPast(
+        loss=loss,
+        logits=logits,
+        past_key_values=outputs.past_key_values,
+        hidden_states=outputs.hidden_states,
+        attentions=outputs.attentions,
+    )

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

@@ -0,0 +1,173 @@
+"""
+Jamba model with LigerFusedLinearCrossEntropyLoss
+"""
+# pylint: disable=duplicate-code
+
+from typing import Optional, Tuple, Union
+
+import torch
+from liger_kernel.transformers.fused_linear_cross_entropy import (
+    LigerFusedLinearCrossEntropyLoss,
+)
+from torch.nn import CrossEntropyLoss
+from transformers.modeling_outputs import MoeCausalLMOutputWithPast
+from transformers.models.jamba.modeling_jamba import (
+    _CONFIG_FOR_DOC,
+    JAMBA_INPUTS_DOCSTRING,
+    HybridMambaAttentionDynamicCache,
+    load_balancing_loss_func,
+)
+from transformers.utils import (
+    add_start_docstrings_to_model_forward,
+    replace_return_docstrings,
+)
+
+
+@add_start_docstrings_to_model_forward(JAMBA_INPUTS_DOCSTRING)
+@replace_return_docstrings(
+    output_type=MoeCausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC
+)
+def lce_forward(
+    self,
+    input_ids: torch.LongTensor = None,
+    attention_mask: Optional[torch.Tensor] = None,
+    position_ids: Optional[torch.LongTensor] = None,
+    past_key_values: Optional[HybridMambaAttentionDynamicCache] = None,
+    inputs_embeds: Optional[torch.FloatTensor] = None,
+    labels: Optional[torch.LongTensor] = None,
+    use_cache: Optional[bool] = None,
+    output_attentions: Optional[bool] = None,
+    output_hidden_states: Optional[bool] = None,
+    output_router_logits: Optional[bool] = None,
+    return_dict: Optional[bool] = None,
+    cache_position: Optional[torch.LongTensor] = None,
+    num_logits_to_keep: Optional[Union[int, None]] = None,
+) -> Union[Tuple, MoeCausalLMOutputWithPast]:
+    r"""
+    Args:
+        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+            config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+            (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+        num_logits_to_keep (`int` or `None`, *optional*):
+            Calculate logits for the last `num_logits_to_keep` tokens. If `None`, calculate logits for all
+            `input_ids`. Only last token logits are needed for generation, and calculating them only for that token
+            can save memory, which becomes pretty significant for long sequences.
+
+    Returns:
+
+    Example:
+
+    ```python
+    >>> from transformers import AutoTokenizer, JambaForCausalLM
+
+    >>> model = JambaForCausalLM.from_pretrained("ai21labs/Jamba-v0.1")
+    >>> tokenizer = AutoTokenizer.from_pretrained("ai21labs/Jamba-v0.1")
+
+    >>> prompt = "Hey, are you conscious? Can you talk to me?"
+    >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+    >>> # Generate
+    >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+    >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+    "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
+    ```"""
+
+    output_attentions = (
+        output_attentions
+        if output_attentions is not None
+        else self.config.output_attentions
+    )
+    output_router_logits = (
+        output_router_logits
+        if output_router_logits is not None
+        else self.config.output_router_logits
+    )
+
+    output_hidden_states = (
+        output_hidden_states
+        if output_hidden_states is not None
+        else self.config.output_hidden_states
+    )
+    return_dict = (
+        return_dict if return_dict is not None else self.config.use_return_dict
+    )
+
+    # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+    outputs = self.model(
+        input_ids=input_ids,
+        attention_mask=attention_mask,
+        position_ids=position_ids,
+        past_key_values=past_key_values,
+        inputs_embeds=inputs_embeds,
+        use_cache=use_cache,
+        output_attentions=output_attentions,
+        output_hidden_states=output_hidden_states,
+        output_router_logits=output_router_logits,
+        cache_position=cache_position,
+        return_dict=return_dict,
+    )
+
+    hidden_states = outputs[0]
+
+    loss = None
+    logits = None
+
+    if self.training:
+        shift_hidden_states = hidden_states[..., :-1, :].contiguous()
+        shift_labels = labels[..., 1:].contiguous()
+
+        # flatten tokens
+        shift_hidden_states = shift_hidden_states.view(-1, self.config.hidden_size)
+        shift_labels = shift_labels.view(-1)
+
+        lce = LigerFusedLinearCrossEntropyLoss()
+        loss = lce(self.lm_head.weight, shift_hidden_states, shift_labels)
+    else:
+        if num_logits_to_keep is None:
+            logits = self.lm_head(hidden_states)
+        else:
+            logits = self.lm_head(hidden_states[..., -num_logits_to_keep:, :])
+        logits = logits.float()
+
+        if labels is not None:
+            # Shift so that tokens < n predict n
+            shift_logits = logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            # Flatten the tokens
+            loss_fct = CrossEntropyLoss()
+            shift_logits = shift_logits.view(-1, self.config.vocab_size)
+            shift_labels = shift_labels.view(-1)
+            # Enable model parallelism
+            shift_labels = shift_labels.to(shift_logits.device)
+            loss = loss_fct(shift_logits, shift_labels)
+
+    aux_loss = None
+    if output_router_logits:
+        aux_loss = load_balancing_loss_func(
+            outputs.router_logits if return_dict else outputs[-1],
+            self.num_experts,
+            self.num_experts_per_tok,
+            attention_mask,
+        )
+        if labels is not None:
+            loss += self.router_aux_loss_coef * aux_loss.to(
+                loss.device
+            )  # make sure to reside in the same device
+
+    if not return_dict:
+        output = (logits,) + outputs[1:]
+        if output_router_logits:
+            output = (aux_loss,) + output
+        return (loss,) + output if loss is not None else output
+
+    return MoeCausalLMOutputWithPast(
+        loss=loss,
+        aux_loss=aux_loss,
+        logits=logits,
+        past_key_values=outputs.past_key_values,
+        hidden_states=outputs.hidden_states,
+        attentions=outputs.attentions,
+        router_logits=outputs.router_logits,
+    )

src/axolotl/integrations/lm_eval/README.md

@@ -0,0 +1,13 @@
+# LM Eval Harness
+
+### Usage
+
+```yaml
+plugins:
+  - axolotl.integrations.lm_eval.LMEvalPlugin
+
+lm_eval_tasks:
+  - gsm8k
+  - hellaswag
+  - arc_easy
+```

src/axolotl/integrations/lm_eval/__init__.py

@@ -0,0 +1,42 @@
+"""
+Module for the Plugin for LM Eval Harness
+"""
+import subprocess  # nosec
+from datetime import datetime
+
+from axolotl.integrations.base import BasePlugin
+
+from .args import LMEvalArgs  # pylint: disable=unused-import. # noqa: F401
+
+
+class LMEvalPlugin(BasePlugin):
+    """
+    Plugin for LM Evaluation Harness integraton with Axolotl.
+    """
+
+    def get_input_args(self):
+        return "axolotl.integrations.lm_eval.LMEvalArgs"
+
+    def post_train_unload(self, cfg):
+        tasks = ",".join(cfg.lm_eval_tasks)
+        fa2 = ",attn_implementation=flash_attention_2" if cfg.flash_attention else ""
+        dtype = ",dtype=bfloat16" if cfg.bf16 else ",dtype=float16"
+        output_path = cfg.output_dir
+        output_path += "" if cfg.output_dir.endswith("/") else "/"
+        output_path += "lm_eval_results/" + datetime.now().strftime("%Y%m%d_%H%M%S")
+        subprocess.run(  # nosec
+            [
+                "lm_eval",
+                "--model",
+                "hf",
+                "--model_args",
+                f"pretrained={cfg.output_dir}{fa2}{dtype}",
+                "--tasks",
+                tasks,
+                "--batch_size",
+                str(cfg.lm_eval_batch_size),
+                "--output_path",
+                output_path,
+            ],
+            check=True,
+        )

src/axolotl/integrations/lm_eval/args.py

@@ -0,0 +1,15 @@
+"""
+Module for handling lm eval harness input arguments.
+"""
+from typing import List, Optional
+
+from pydantic import BaseModel
+
+
+class LMEvalArgs(BaseModel):
+    """
+    Input args for lm eval harness
+    """
+
+    lm_eval_tasks: List[str] = []
+    lm_eval_batch_size: Optional[int] = 8

src/axolotl/integrations/spectrum/LICENSE

@@ -0,0 +1,202 @@
+
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src/axolotl/integrations/spectrum/README.md

@@ -0,0 +1,21 @@
+## Spectrum: Targeted Training on Signal to Noise Ratio
+
+by Eric Hartford, Lucas Atkins, Fernando Fernandes, David Golchinfar
+
+This plugin contains code to freeze the bottom fraction of modules in a model, based on the Signal-to-Noise Ratio (SNR).
+
+### Overview
+
+Spectrum is a tool for scanning and evaluating the Signal-to-Noise Ratio (SNR) of layers in large language models.
+By identifying the top n% of layers with the highest SNR, you can optimize training efficiency.
+
+### Usage
+
+```yaml
+plugins:
+  - axolotl.integrations.spectrum.SpectrumPlugin
+
+spectrum_top_fraction: 0.5
+# Optional if using a pre-scanned model as your base_model. Useful if using a model mirror
+spectrum_model_name: meta-llama/Meta-Llama-3.1-8B
+```

src/axolotl/integrations/spectrum/__init__.py

@@ -0,0 +1,102 @@
+# Copyright 2024 Axolotl AI. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+Spectrum Plugin to automatically generate unfrozen parameters based on SNR data.
+"""
+
+import json
+import logging
+
+import requests
+
+from axolotl.integrations.base import BasePlugin
+
+from .args import SpectrumArgs  # pylint: disable=unused-import. # noqa: F401
+
+
+def _generate_unfrozen_params_yaml(snr_data, top_fraction=0.5):
+    unfrozen_parameters = {}
+    for layer_name, info in snr_data.items():
+        layer_type = info["type"]
+        if layer_type not in unfrozen_parameters:
+            unfrozen_parameters[layer_type] = []
+        unfrozen_parameters[layer_type].append((layer_name, info["snr"]))
+    top_layers_by_type = {}
+    for layer_type, layers in unfrozen_parameters.items():
+        layers_sorted = sorted(layers, key=lambda x: x[1], reverse=True)
+        num_top_layers = int(len(layers) * top_fraction)
+        top_layers_by_type[layer_type] = [
+            layer[0] for layer in layers_sorted[:num_top_layers]
+        ]
+    unfrozen_parameters = [
+        "^lm_head.weight$",
+        "^model.embed_tokens.weight$",
+    ]
+    for layer_type, layer_names in top_layers_by_type.items():
+        for layer_name in layer_names:
+            unfrozen_parameters.append(layer_name)
+    return unfrozen_parameters
+
+
+class SpectrumPlugin(BasePlugin):
+    """
+    Spectrum Plugin to automatically generate unfrozen parameters based on SNR data.
+    """
+
+    base_url = "https://raw.githubusercontent.com/cognitivecomputations/spectrum/main/model_snr_results/"
+    base_path = "./model_snr_results/"
+    snr_file_template = "snr_results_{model_name_slug}.json"
+
+    def get_input_args(self):
+        return "axolotl.integrations.spectrum.SpectrumArgs"
+
+    def pre_model_load(self, cfg):
+        if cfg.get("spectrum_model_name"):
+            model_name = cfg["spectrum_model_name"]
+        else:
+            model_name = cfg["base_model"]
+        top_fraction = cfg.get("spectrum_top_fraction", 50)
+        model_slug = model_name.replace("/", "-").replace("_", "-")
+        snr_url = self.base_url + self.snr_file_template.format(
+            model_name_slug=model_slug
+        )
+        snr_path = self.base_path + self.snr_file_template.format(
+            model_name_slug=model_slug
+        )
+        # first check if the files exist locally and read the json
+        snr_data = None
+        try:
+            with open(snr_path, "r", encoding="utf-8") as fin:
+                snr_data = json.load(fin)
+        except FileNotFoundError:
+            pass
+        except Exception as exc:  # pylint: disable=broad-exception-caught
+            logging.warning(f"Failed to read SNR data from {snr_path}: {exc}")
+
+        if not snr_data:
+            try:
+                snr_data = requests.get(snr_url, timeout=60).json()
+            except requests.exceptions.RequestException as exc:
+                logging.warning(f"Failed to fetch SNR data from {snr_url}: {exc}")
+                return
+            # also catch json parsing errors
+            except json.JSONDecodeError as exc:
+                logging.warning(f"Failed to parse SNR data from {snr_url}: {exc}")
+                return
+
+        unfrozen_parameters = _generate_unfrozen_params_yaml(
+            snr_data, top_fraction=top_fraction
+        )
+        cfg["unfrozen_parameters"] = unfrozen_parameters

src/axolotl/integrations/spectrum/args.py

@@ -0,0 +1,29 @@
+# Copyright 2024 Axolotl AI. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+Module for handling Spectrum input arguments.
+"""
+from typing import Optional
+
+from pydantic import BaseModel
+
+
+class SpectrumArgs(BaseModel):
+    """
+    Input args for Spectrum.
+    """
+
+    spectrum_top_fraction: Optional[float] = 0.5
+    spectrum_model_name: Optional[str] = None

src/axolotl/loraplus.py

@@ -1,133 +0,0 @@
-"""Module for LoRA+"""
-
-# MIT License
-#
-# Copyright (c) 2024 nikhil-ghosh-berkeley
-# https://github.com/nikhil-ghosh-berkeley/loraplus
-
-import logging
-from functools import reduce
-
-from peft.tuners import lora
-from torch import nn
-from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS
-from transformers.trainer_pt_utils import get_parameter_names
-
-LOG = logging.getLogger("axolotl.loraplus")
-
-
-def get_module(name, opt_model):
-    """
-    Retrieve a module from a model using its parameter name.
-    Args:
-        name (str): Full name of the parameter, typically including module path.
-        opt_model (torch.nn.Module): The model from which to retrieve the module.
-
-    Returns:
-        Module corresponding to the given name.
-    """
-    parent_idx = 2 if "lora" in name else 1
-    module_names = name.split(sep=".")[:-parent_idx]
-    module = reduce(getattr, module_names, opt_model)
-    return module
-
-
-def create_loraplus_optimizer(
-    opt_model,
-    optimizer_cls,
-    optimizer_kwargs,
-    loraplus_lr_ratio,
-    loraplus_lr_embedding=None,
-):
-    """
-    Creates an optimizer for the given model, applying LoRA-specific learning rate adjustments to different parameter groups.
-
-    Args:
-        opt_model (torch.nn.Module): The model for which the optimizer is being created.
-        optimizer_cls (class): The class of the optimizer to be used (e.g., torch.optim.Adam).
-        optimizer_kwargs (dict): A dictionary of keyword arguments for the optimizer's initialization.
-        loraplus_lr_ratio (float): The learning rate ratio to be applied to LoRA parameters.
-        loraplus_lr_embedding (float, optional): A specific learning rate for embedding parameters, with a default value if not provided.
-
-    Returns:
-        An instance of the specified optimizer class configured with the model's parameters organized into groups with custom learning rates.
-    """
-
-    assert loraplus_lr_ratio is not None, "loraplus_lr_ratio must be provided."
-
-    if loraplus_lr_embedding is None:
-        loraplus_lr_embedding = 1e-6
-
-    decay_parameters = get_parameter_names(opt_model, ALL_LAYERNORM_LAYERS)
-    decay_parameters = [name for name in decay_parameters if "bias" not in name]
-    param_groups = {
-        "groupA": {},
-        "groupB": {},
-        "groupB_no_decay": {},
-        "embedding": {},
-    }
-
-    for name, param in opt_model.named_parameters():
-        if not param.requires_grad:
-            continue
-
-        module = get_module(name, opt_model)
-        if isinstance(module, lora.Embedding):
-            param_groups["embedding"][name] = param
-        elif "lora_B" in name or param.ndim == 1:
-            if name in decay_parameters:
-                param_groups["groupB"][name] = param
-            else:
-                param_groups["groupB_no_decay"][name] = param
-        else:
-            param_groups["groupA"][name] = param
-
-    assigned_param_groups = ""
-    for group, group_params in param_groups.items():
-        assigned_param_groups += f"{group}\n {list(group_params.keys())}\n\n"
-    LOG.info(assigned_param_groups)
-
-    lr = optimizer_kwargs["lr"]  # pylint: disable=invalid-name
-    weight_decay = optimizer_kwargs.get("weight_decay", 0.0)
-
-    optimizer_grouped_parameters = [
-        {
-            "params": list(param_groups["groupA"].values()),
-            "weight_decay": weight_decay,
-            "lr": lr,
-        },
-        {
-            "params": list(param_groups["embedding"].values()),
-            "weight_decay": weight_decay,
-            "lr": loraplus_lr_embedding,
-        },
-        {
-            "params": list(param_groups["groupB"].values()),
-            "weight_decay": weight_decay,
-            "lr": lr * loraplus_lr_ratio,
-        },
-        {
-            "params": list(param_groups["groupB_no_decay"].values()),
-            "weight_decay": 0.0,
-            "lr": lr * loraplus_lr_ratio,
-        },
-    ]
-
-    optimizer = optimizer_cls(optimizer_grouped_parameters, **optimizer_kwargs)
-    if optimizer_cls.__name__ == "Adam8bit":
-        import bitsandbytes
-
-        manager = bitsandbytes.optim.GlobalOptimManager.get_instance()
-
-        skipped = 0
-        for module in opt_model.modules():
-            if isinstance(module, nn.Embedding):
-                skipped += sum(
-                    {p.data_ptr(): p.numel() for p in module.parameters()}.values()
-                )
-                LOG.info(f"skipped {module}: {skipped/2**20}M params")
-                manager.register_module_override(module, "weight", {"optim_bits": 32})
-                LOG.debug(f"bitsandbytes: will optimize {module} in fp32")
-        LOG.info(f"skipped: {skipped/2**20}M params")
-
-    return optimizer

src/axolotl/monkeypatch/attention/mllama.py

@@ -0,0 +1,229 @@
+"""
+Monkeypatch for Vision Llama for FA2 support
+"""
+# pylint: disable=duplicate-code
+
+from typing import Optional, Tuple
+
+import torch
+from flash_attn.flash_attn_interface import flash_attn_func
+from transformers.cache_utils import Cache
+from transformers.modeling_flash_attention_utils import _flash_attention_forward
+from transformers.models.mllama.configuration_mllama import MllamaTextConfig
+from transformers.models.mllama.modeling_mllama import (
+    MllamaTextCrossAttention,
+    MllamaTextSelfAttention,
+    apply_rotary_pos_emb,
+    repeat_kv,
+)
+from transformers.utils import is_flash_attn_greater_or_equal_2_10
+
+
+class MllamaTextCrossFlashAttention2(MllamaTextCrossAttention):
+    """
+    Mllama flash cross-attention module. This module inherits from `MllamaTextCrossAttention` and
+    implements the forward pass using Flash Attention for improved performance.
+    """
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+        # Check if flash attention version is greater or equal to 2.1
+        self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        cross_attention_states: Optional[torch.Tensor] = None,
+        past_key_value: Optional[Cache] = None,
+        attention_mask: Optional[  # pylint: disable=unused-argument
+            torch.Tensor
+        ] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,  # pylint: disable=unused-argument
+        cache_position: Optional[torch.LongTensor] = None,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        bsz, q_len, _ = hidden_states.size()
+
+        query_states = self.q_proj(hidden_states)
+        query_states = query_states.view(
+            bsz, q_len, self.num_heads, self.head_dim
+        ).transpose(1, 2)
+        query_states = self.q_norm(query_states)
+
+        if cross_attention_states is not None:
+            key_states = self.k_proj(cross_attention_states)
+            value_states = self.v_proj(cross_attention_states)
+            key_states = key_states.view(
+                bsz, -1, self.num_key_value_heads, self.head_dim
+            ).transpose(1, 2)
+            value_states = value_states.view(
+                bsz, -1, self.num_key_value_heads, self.head_dim
+            ).transpose(1, 2)
+            key_states = repeat_kv(key_states, self.num_key_value_groups)
+            value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+            key_states = self.k_norm(key_states)
+            if past_key_value is not None:
+                key_states, value_states = past_key_value.update(
+                    key_states,
+                    value_states,
+                    self.layer_idx,
+                    {"cache_position": cache_position},
+                )
+        elif cache_position[0] != 0:
+            key_states, value_states = (
+                past_key_value.key_cache[self.layer_idx],
+                past_key_value.value_cache[self.layer_idx],
+            )
+        else:
+            raise ValueError(
+                "Cross attention layer can't find neither `cross_attn_states` nor cached values for key/values!"
+            )
+
+        # Transpose to get the expected layout for flash attention
+        query_states = query_states.transpose(1, 2)
+        key_states = key_states.transpose(1, 2)
+        value_states = value_states.transpose(1, 2)
+
+        # Apply Flash Attention
+        dropout_rate = self.dropout if self.training else 0.0
+        output = flash_attn_func(
+            query_states,
+            key_states,
+            value_states,
+            dropout_p=dropout_rate,
+            softmax_scale=None,
+            causal=False,
+            return_attn_probs=output_attentions,
+        )
+
+        attn_output = output.contiguous().view(bsz, q_len, -1)
+        attn_output = self.o_proj(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+
+
+class MllamaTextSelfFlashAttention2(MllamaTextSelfAttention):
+    """
+    Mllama flash self-attention module. This module inherits from `MllamaTextSelfAttention` and
+    implements the forward pass using Flash Attention for improved performance.
+    """
+
+    def __init__(self, config: MllamaTextConfig, layer_idx: int, *args, **kwargs):
+        super().__init__(config, layer_idx, *args, **kwargs)
+
+        # Check if flash attention version is greater or equal to 2.1
+        self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,  # pylint: disable=unused-argument
+        past_key_value=None,
+        cache_position: Optional[torch.LongTensor] = None,
+        **kwargs,  # pylint: disable=unused-argument
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        output_attentions = False
+
+        bsz, q_len, _ = hidden_states.size()
+
+        query_states = self.q_proj(hidden_states)
+        key_states = self.k_proj(hidden_states)
+        value_states = self.v_proj(hidden_states)
+
+        # Flash attention requires the input to have the shape
+        # batch_size x seq_length x num_heads x head_dim
+        query_states = query_states.view(
+            bsz, q_len, self.num_heads, self.head_dim
+        ).transpose(1, 2)
+        key_states = key_states.view(
+            bsz, q_len, self.num_key_value_heads, self.head_dim
+        ).transpose(1, 2)
+        value_states = value_states.view(
+            bsz, q_len, self.num_key_value_heads, self.head_dim
+        ).transpose(1, 2)
+
+        cos, sin = position_embeddings
+        query_states, key_states = apply_rotary_pos_emb(
+            query_states, key_states, cos, sin
+        )
+
+        if past_key_value is not None:
+            # sin and cos are specific to RoPE models; cache_position needed for the static cache
+            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
+            key_states, value_states = past_key_value.update(
+                key_states, value_states, self.layer_idx, cache_kwargs
+            )
+
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+        # Transpose to get the expected layout for flash attention
+        query_states = query_states.transpose(1, 2)
+        key_states = key_states.transpose(1, 2)
+        value_states = value_states.transpose(1, 2)
+
+        dropout_rate = self.dropout if self.training else 0.0
+
+        # Handle potential silent casting to float32
+        input_dtype = query_states.dtype
+        if input_dtype == torch.float32:
+            if torch.is_autocast_enabled():
+                target_dtype = torch.get_autocast_gpu_dtype()
+            elif hasattr(self.config, "_pre_quantization_dtype"):
+                target_dtype = (
+                    self.config._pre_quantization_dtype  # pylint: disable=protected-access
+                )
+            else:
+                target_dtype = self.q_proj.weight.dtype
+
+            query_states = query_states.to(target_dtype)
+            key_states = key_states.to(target_dtype)
+            value_states = value_states.to(target_dtype)
+
+        attn_output = _flash_attention_forward(
+            query_states,
+            key_states,
+            value_states,
+            attention_mask,
+            q_len,
+            dropout=dropout_rate,
+            use_top_left_mask=self._flash_attn_uses_top_left_mask,
+            is_causal=True,
+        )
+
+        attn_output = attn_output.reshape(bsz, q_len, -1).contiguous()
+        attn_output = self.o_proj(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+
+
+def patch_mllama():
+    from transformers.models.mllama.modeling_mllama import (
+        MLLAMA_TEXT_ATTENTION_CLASSES,
+        MLLAMA_TEXT_CROSS_ATTENTION_CLASSES,
+        MLLAMA_VISION_ATTENTION_CLASSES,
+        MllamaPreTrainedModel,
+    )
+
+    MllamaPreTrainedModel._supports_flash_attn_2 = (  # pylint: disable=protected-access
+        True
+    )
+    MLLAMA_TEXT_ATTENTION_CLASSES["flash_attention_2"] = MllamaTextSelfFlashAttention2
+    MLLAMA_TEXT_CROSS_ATTENTION_CLASSES[
+        "flash_attention_2"
+    ] = MllamaTextCrossFlashAttention2
+    # fallback to SDPA
+    MLLAMA_VISION_ATTENTION_CLASSES[
+        "flash_attention_2"
+    ] = MLLAMA_VISION_ATTENTION_CLASSES["sdpa"]

src/axolotl/monkeypatch/llama_attn_hijack_flash.py

@@ -78,6 +78,33 @@ def replace_llama_qkv_with_fused(model):
             set_module_name(model, name, qkv)
 
 
+def patch_llama_cross_entropy():
+    from flash_attn.losses.cross_entropy import CrossEntropyLoss
+
+    LOG.info("patching with flash_attn.losses.cross_entropy")
+    transformers.models.llama.modeling_llama.CrossEntropyLoss = partial(
+        CrossEntropyLoss, inplace_backward=True
+    )
+
+
+def patch_llama_rms_norm():
+    try:
+        from flash_attn.ops.rms_norm import RMSNorm
+
+        class LlamaRMSNorm(RMSNorm):
+            """Patched LLamaRMSNorm"""
+
+            def __init__(self, hidden_size, eps=1e-6):
+                super().__init__(hidden_size, eps=eps)
+
+        LOG.info("patching with flash_attn.ops.rms_norm")
+        transformers.models.llama.modeling_llama.LlamaRMSNorm = LlamaRMSNorm
+    except ImportError:
+        LOG.warning(
+            "optimized flash-attention RMSNorm not found (run `pip install 'git+https://github.com/Dao-AILab/flash-attention.git#egg=dropout_layer_norm&subdirectory=csrc/layer_norm'`)"
+        )
+
+
 def replace_llama_attn_with_flash_attn(
     packed: Optional[bool] = False,
     cross_entropy: Optional[bool] = False,
@@ -104,30 +131,11 @@ def replace_llama_attn_with_flash_attn(
 
     # skip only if explicitly disabled
     if cross_entropy:
-        from flash_attn.losses.cross_entropy import CrossEntropyLoss
-
-        LOG.info("patching with flash_attn.losses.cross_entropy")
-        transformers.models.llama.modeling_llama.CrossEntropyLoss = partial(
-            CrossEntropyLoss, inplace_backward=True
-        )
+        patch_llama_cross_entropy()
 
     # skip only if explicitly disabled
     if rms_norm:
-        try:
-            from flash_attn.ops.rms_norm import RMSNorm
-
-            class LlamaRMSNorm(RMSNorm):
-                """Patched LLamaRMSNorm"""
-
-                def __init__(self, hidden_size, eps=1e-6):
-                    super().__init__(hidden_size, eps=eps)
-
-            LOG.info("patching with flash_attn.ops.rms_norm")
-            transformers.models.llama.modeling_llama.LlamaRMSNorm = LlamaRMSNorm
-        except ImportError:
-            LOG.warning(
-                "optimized flash-attention RMSNorm not found (run `pip install 'git+https://github.com/Dao-AILab/flash-attention.git#egg=dropout_layer_norm&subdirectory=csrc/layer_norm'`)"
-            )
+        patch_llama_rms_norm()
 
 
 class FusedAttention(LlamaAttention):

src/axolotl/monkeypatch/llama_patch_multipack.py

@@ -9,18 +9,18 @@ from axolotl.monkeypatch.utils import (
 
 
 def hijack_llama_prepare_4d_mask():
-    import transformers.modeling_attn_mask_utils
-    import transformers.models.llama.modeling_llama
+    from transformers import modeling_attn_mask_utils
+    from transformers.models.llama import modeling_llama
 
-    transformers.models.llama.modeling_llama._prepare_4d_causal_attention_mask_for_sdpa = (  # pylint: disable=protected-access
+    modeling_llama._prepare_4d_causal_attention_mask_for_sdpa = (  # pylint: disable=protected-access
         patched_prepare_4d_causal_attention_mask_for_sdpa
     )
-    transformers.modeling_attn_mask_utils._prepare_4d_causal_attention_mask_for_sdpa = (  # pylint: disable=protected-access
+    modeling_attn_mask_utils._prepare_4d_causal_attention_mask_for_sdpa = (  # pylint: disable=protected-access
         patched_prepare_4d_causal_attention_mask_for_sdpa
     )
-    transformers.models.llama.modeling_llama._prepare_4d_causal_attention_mask = (  # pylint: disable=protected-access
+    modeling_llama._prepare_4d_causal_attention_mask = (  # pylint: disable=protected-access
         patched_prepare_4d_causal_attention_mask
     )
-    transformers.modeling_attn_mask_utils._prepare_4d_causal_attention_mask = (  # pylint: disable=protected-access
+    modeling_attn_mask_utils._prepare_4d_causal_attention_mask = (  # pylint: disable=protected-access
         patched_prepare_4d_causal_attention_mask
     )

src/axolotl/monkeypatch/mistral_attn_hijack_flash.py

@@ -2,6 +2,7 @@
 # pylint: disable=duplicate-code
 
 import logging
+from functools import partial
 from typing import List, Optional, Tuple, Union
 
 import torch
@@ -45,6 +46,15 @@ def replace_mistral_attn_with_flash_attn(
         )
 
 
+def patch_mistral_cross_entropy():
+    from flash_attn.losses.cross_entropy import CrossEntropyLoss
+
+    LOG.info("patching with flash_attn.losses.cross_entropy")
+    transformers.models.mistral.modeling_mistral.CrossEntropyLoss = partial(
+        CrossEntropyLoss, inplace_backward=True
+    )
+
+
 @torch.jit.script
 def _make_sliding_window_causal_mask(
     bsz: int,

src/axolotl/monkeypatch/multipack.py

@@ -10,11 +10,15 @@ from axolotl.monkeypatch.mixtral import patch_mixtral_moe_forward_zero3
 from axolotl.monkeypatch.utils import get_unpad_data
 
 SUPPORTED_MULTIPACK_MODEL_TYPES = [
+    "mllama_text_model",
+    "llama",
+    "mistral",
     "mixtral",
     "qwen2",
     "qwen2_moe",
     "falcon",
     "phi",
+    "phi3",
     "gemma",
     "gemma2",
     "gemmoe",
@@ -23,13 +27,36 @@ SUPPORTED_MULTIPACK_MODEL_TYPES = [
 ]
 
 
-def patch_for_multipack(model_type, model_name=None):
+def patch_for_multipack(model_type, model_name=None, is_remote_code=False):
+    if model_type == "gemmoe":
+        patch_remote(model_name, ".configuration_gemmoe", ".modeling_gemmoe")
+    elif model_type == "deepseek_v2":
+        patch_remote(model_name, ".configuration_deepseek", ".modeling_deepseek")
+    elif hasattr(transformers, "modeling_flash_attention_utils") and not is_remote_code:
+        transformers.modeling_flash_attention_utils._get_unpad_data = (  # pylint: disable=protected-access
+            get_unpad_data
+        )
+        if model_type == "mixtral" and is_deepspeed_zero3_enabled():
+            patch_mixtral_moe_forward_zero3()
+        return
+
+    # retain for legacy
     if model_type == "mixtral":
         transformers.models.mixtral.modeling_mixtral._get_unpad_data = (  # pylint: disable=protected-access
             get_unpad_data
         )
         if is_deepspeed_zero3_enabled():
             patch_mixtral_moe_forward_zero3()
+    elif model_type == "llama":
+        if hasattr(transformers.models.llama.modeling_llama, "_get_unpad_data"):
+            transformers.models.llama.modeling_llama._get_unpad_data = (  # pylint: disable=protected-access
+                get_unpad_data
+            )
+    elif model_type == "mistral":
+        if hasattr(transformers.models.mistral.modeling_mistral, "_get_unpad_data"):
+            transformers.models.llama.modeling_llama._get_unpad_data = (  # pylint: disable=protected-access
+                get_unpad_data
+            )
     elif model_type == "qwen2":
         transformers.models.qwen2.modeling_qwen2._get_unpad_data = (  # pylint: disable=protected-access
             get_unpad_data
@@ -58,12 +85,6 @@ def patch_for_multipack(model_type, model_name=None):
         transformers.models.starcoder2.modeling_starcoder2._get_unpad_data = (  # pylint: disable=protected-access
             get_unpad_data
         )
-    elif model_type == "gemmoe":
-        patch_remote(model_name, ".configuration_gemmoe", ".modeling_gemmoe")
-    elif model_type == "jamba":
-        patch_remote(model_name, ".configuration_jamba", ".modeling_jamba")
-    elif model_type == "deepseek_v2":
-        patch_remote(model_name, ".configuration_deepseek", ".modeling_deepseek")
 
 
 def patch_remote(model_name, config_name, modeling_name):

src/axolotl/monkeypatch/relora.py

@@ -44,8 +44,8 @@ def magnitude_pruning_(tensor, prune_ratio):
 def reset_optimizer(
     optimizer: torch.optim.Optimizer,
     *,
-    reset_params: list[str],  # where str is the key to a torch.nn.Parameter
-    optimizer_state_keys: list[str],
+    reset_params: List[str],  # where str is the key to a torch.nn.Parameter
+    optimizer_state_keys: List[str],
     prune_ratio: float = 0.9,
 ):
     pruning_fn = partial(magnitude_pruning_, prune_ratio=prune_ratio)

src/axolotl/monkeypatch/stablelm_attn_hijack_flash.py

@@ -16,6 +16,7 @@
 # This code is based off the following work:
 # https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/modeling_llama.py
 # https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt_neox/modeling_gpt_neox.py
+# pylint: disable=duplicate-code
 """ PyTorch StableLM Epoch model. """
 import importlib
 import math

src/axolotl/monkeypatch/unsloth_.py

@@ -1,21 +1,22 @@
 """module for patching with unsloth optimizations"""
 
 import inspect
-import logging
 import re
 import types
 from typing import Tuple
 
+import torch
+from accelerate.logging import get_logger
 from peft import PeftModelForCausalLM
+from torch import nn
 from transformers.models.llama.modeling_llama import (
     LlamaFlashAttention2,
     LlamaForCausalLM,
 )
 
-LOG = logging.getLogger("axolotl.monkeypatch.unsloth")
+LOG = get_logger("axolotl.monkeypatch.unsloth")
 
-ORIGINAL_CEL_CODE = """    if labels is not None:
-        # Shift so that tokens < n predict n
+ORIGINAL_CEL_CODE = """# Shift so that tokens < n predict n
         shift_logits = logits[..., :-1, :].contiguous()
         shift_labels = labels[..., 1:].contiguous()
         # Flatten the tokens
@@ -27,8 +28,7 @@ ORIGINAL_CEL_CODE = """    if labels is not None:
         loss = loss_fct(shift_logits, shift_labels)
 """
 
-PATCHED_CEL_CODE = """    if labels is not None:
-        shift_logits = logits[..., :-1, :].contiguous()
+PATCHED_CEL_CODE = """shift_logits = logits[..., :-1, :].contiguous()
         shift_labels = labels[..., 1:].contiguous()
         loss = fast_cross_entropy_loss(
             logits = shift_logits,
@@ -97,48 +97,51 @@ def check_self_attn_is_patchable() -> bool:
     return ORIGINAL_QKV_CODE in qkv and ORIGINAL_O_CODE in qkv
 
 
-def integrate_cross_entropy_loss_patch():
-    forward = get_forward_code()
-    LlamaForCausalLM._original_forward = forward  # pylint: disable=protected-access
-    forward, _ = detab_code(forward)
-    assert ORIGINAL_CEL_CODE in forward, "Original forward code not found"
+def integrate_cross_entropy_loss_patch(model_type: str = "llama") -> None:
+    if model_type == "llama":
+        forward = get_forward_code()
+        LlamaForCausalLM._original_forward = forward  # pylint: disable=protected-access
+        forward, _ = detab_code(forward)
+        assert ORIGINAL_CEL_CODE in forward, "Original forward code not found"
 
-    forward = forward.replace(
-        "@add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)", ""
-    )
-    forward = forward.replace(
-        "@replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)",
-        "",
-    )
-    forward = forward.replace(ORIGINAL_CEL_CODE, PATCHED_CEL_CODE)
-    forward = forward.replace(
-        "def forward(",
-        "def fast_cross_entropy_loss_forward(",
-        1,
-    )
+        forward = forward.replace(
+            "@add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)", ""
+        )
+        forward = forward.replace(
+            "@replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)",
+            "",
+        )
+        forward = forward.replace(ORIGINAL_CEL_CODE, PATCHED_CEL_CODE)
+        forward = forward.replace(
+            "def forward(",
+            "def fast_cross_entropy_loss_forward(",
+            1,
+        )
 
-    # load imports necessary
-    import transformers.models.llama.modeling_llama
+        # load imports necessary
+        import transformers.models.llama.modeling_llama
 
-    items_to_import = []
-    for item in dir(transformers.models.llama.modeling_llama):
-        if item in forward:
-            items_to_import.append(item)
+        items_to_import = []
+        for item in dir(transformers.models.llama.modeling_llama):
+            if item in forward:
+                items_to_import.append(item)
 
-    exec(  # pylint: disable=exec-used  # nosec B102
-        "from unsloth.kernels.cross_entropy_loss import fast_cross_entropy_loss",
-        globals(),
-    )
+        exec(  # pylint: disable=exec-used  # nosec B102
+            "from unsloth.kernels.cross_entropy_loss import fast_cross_entropy_loss",
+            globals(),
+        )
 
-    exec(  # pylint: disable=exec-used  # nosec B102
-        "from transformers.models.llama.modeling_llama import ("
-        + ", ".join(x for x in items_to_import)
-        + ")",
-        globals(),
-    )
-    exec(forward, globals())  # pylint: disable=exec-used  # nosec B102
-    print("patching unsloth fast_cross_entropy_loss")
-    LlamaForCausalLM.forward = fast_cross_entropy_loss_forward  # pylint: disable=undefined-variable  # noqa: F821
+        exec(  # pylint: disable=exec-used  # nosec B102
+            "from transformers.models.llama.modeling_llama import ("
+            + ", ".join(x for x in items_to_import)
+            + ")",
+            globals(),
+        )
+        exec(forward, globals())  # pylint: disable=exec-used  # nosec B102
+        LOG.info("patching unsloth fast_cross_entropy_loss", main_process_only=True)
+        LlamaForCausalLM.forward = fast_cross_entropy_loss_forward  # pylint: disable=undefined-variable  # noqa: F821
+    else:
+        raise ValueError("Unsupported model type")
 
 
 def detab_code(code: str) -> Tuple[str, str]:
@@ -179,12 +182,30 @@ def patch_self_attn_lora():
         globals(),
     )
     exec(self_attn_forward, globals())  # pylint: disable=exec-used  # nosec B102
-    print("patching unsloth attn lora")
+    LOG.info("patching unsloth attn lora", main_process_only=True)
     LlamaFlashAttention2.forward = (
         unsloth_attn_forward  # pylint: disable=undefined-variable  # noqa: F821
     )
 
 
+def integrate_rope_embeddings():
+    import transformers.models.llama.modeling_llama
+    from unsloth.kernels.rope_embedding import fast_rope_embedding
+
+    def apply_rotary_pos_emb(  # pylint: disable=unused-argument
+        q,  # pylint: disable=invalid-name
+        k,  # pylint: disable=invalid-name
+        cos,
+        sin,
+        position_ids=None,
+        unsqueeze_dim=1,
+    ):
+        return fast_rope_embedding(q, k, cos, sin)
+
+    LOG.info("patching unsloth RoPE embeddings", main_process_only=True)
+    transformers.models.llama.modeling_llama.apply_rotary_pos_emb = apply_rotary_pos_emb
+
+
 def integrate_lora_mlp_patch(peft_model: PeftModelForCausalLM):
     if peft_model.base_model.config.model_type in ["llama", "mistral"]:
         from unsloth.kernels import apply_lora_mlp_swiglu
@@ -217,7 +238,7 @@ def integrate_lora_mlp_patch(peft_model: PeftModelForCausalLM):
         if is_mlp_lora and mlp_no_bias and mlp_not_dora:
             layer.mlp.forward = types.MethodType(apply_lora_mlp, layer.mlp)
         else:
-            logging.warning("unable to apply unsloth lora mlp patch to layer %d", idx)
+            LOG.warning("unable to apply unsloth lora mlp patch to layer %d", idx)
 
 
 def integrate_lora_patch(peft_model: PeftModelForCausalLM, cfg):
@@ -243,9 +264,7 @@ def integrate_lora_patch(peft_model: PeftModelForCausalLM, cfg):
                 layer.self_attn.apply_qkv = apply_lora_qkv
             else:
                 layer.self_attn.apply_qkv = original_apply_qkv
-                logging.warning(
-                    "unable to apply unsloth lora qkv patch to layer %d", idx
-                )
+                LOG.warning("unable to apply unsloth lora qkv patch to layer %d", idx)
         if cfg.unsloth_lora_o:
             layer_modules = [
                 getattr(layer.self_attn, linear_proj) for linear_proj in ["o_proj"]
@@ -264,6 +283,33 @@ def integrate_lora_patch(peft_model: PeftModelForCausalLM, cfg):
                 layer.self_attn.apply_o = apply_lora_o
             else:
                 layer.self_attn.apply_o = original_apply_o
-                logging.warning(
+                LOG.warning(
                     "unable to apply unsloth lora o_proj patch to layer %d", idx
                 )
+
+
+def patch_unsloth_layernorm():
+    try:
+        import transformers.models.llama.modeling_llama
+        from unsloth.kernels.rms_layernorm import Fast_RMS_Layernorm
+
+        class LlamaRMSNorm(nn.Module):
+            """LlamaRMSNorm"""
+
+            def __init__(self, hidden_size, eps=1e-6):
+                """
+                LlamaRMSNorm is equivalent to T5LayerNorm
+                """
+                super().__init__()
+                self.weight = nn.Parameter(torch.ones(hidden_size))
+                self.variance_epsilon = eps
+
+            def forward(self, hidden_states):
+                return Fast_RMS_Layernorm.apply(
+                    hidden_states, self.weight, self.variance_epsilon, False
+                )
+
+        LOG.info("patching with unsloth.kernels.rms_layernorm")
+        transformers.models.llama.modeling_llama.LlamaRMSNorm = LlamaRMSNorm
+    except ImportError:
+        LOG.warning("missing unsloth library")

src/axolotl/monkeypatch/utils.py

@@ -17,11 +17,9 @@ def get_max_seqlen_in_batch(attention_mask: torch.Tensor) -> torch.Tensor:
     max_num = int(torch.max(attention_mask).item())
     batch_size, _ = attention_mask.shape
     counts = torch.zeros((batch_size, max_num), dtype=torch.int32)
-
     for i in range(1, max_num + 1):
         mask = attention_mask == i
         counts[:, i - 1] = torch.sum(mask, dim=-1).to(dtype=torch.int32)
-
     result = counts.flatten()
     nonzero_indices = torch.nonzero(result).squeeze(-1)
     return result[nonzero_indices]

src/axolotl/prompt_strategies/__init__.py

@@ -9,8 +9,12 @@ from axolotl.prompt_strategies.user_defined import UserDefinedDatasetConfig
 LOG = logging.getLogger("axolotl.prompt_strategies")
 
 
-def load(strategy, tokenizer, cfg, ds_cfg):
+def load(strategy, tokenizer, cfg, ds_cfg, processor=None):
     try:
+        if strategy == "messages":
+            from .messages import load as messages_load
+
+            return messages_load(tokenizer, cfg, ds_cfg, processor=processor)
         load_fn = "load"
         if strategy.split(".")[-1].startswith("load_"):
             load_fn = strategy.split(".")[-1]
@@ -24,9 +28,12 @@ def load(strategy, tokenizer, cfg, ds_cfg):
             sig = inspect.signature(func)
             if "ds_cfg" in sig.parameters:
                 load_kwargs["ds_cfg"] = ds_cfg
+            if "processor" in sig.parameters:
+                load_kwargs["processor"] = processor
         return func(tokenizer, cfg, **load_kwargs)
     except ModuleNotFoundError:
         return None
     except Exception as exc:  # pylint: disable=broad-exception-caught
         LOG.error(f"Failed to load prompt strategy `{strategy}`: {str(exc)}")
-        return None
+        raise exc
+    return None

src/axolotl/prompt_strategies/bradley_terry/README.md

@@ -0,0 +1,10 @@
+### example yaml
+
+```yaml
+chat_template: gemma
+datasets:
+  - path: argilla/distilabel-intel-orca-dpo-pairs
+    type: bradley_terry.chat_template
+val_set_size: 0.0
+output_dir: ./outputs/out
+```

src/axolotl/prompt_strategies/bradley_terry/__init__.py

@@ -0,0 +1,35 @@
+"""Module to load prompt strategies."""
+
+import importlib
+import inspect
+import logging
+
+from axolotl.prompt_strategies.user_defined import UserDefinedDatasetConfig
+
+LOG = logging.getLogger("axolotl.prompt_strategies.bradley_terry")
+
+
+def load(strategy, tokenizer, cfg, ds_cfg):
+    # pylint: disable=duplicate-code
+    try:
+        load_fn = "load"
+        if strategy.split(".")[-1].startswith("load_"):
+            load_fn = strategy.split(".")[-1]
+            strategy = ".".join(strategy.split(".")[:-1])
+        mod = importlib.import_module(
+            f".{strategy}", "axolotl.prompt_strategies.bradley_terry"
+        )
+        func = getattr(mod, load_fn)
+        load_kwargs = {}
+        if strategy == "user_defined":
+            load_kwargs["ds_cfg"] = UserDefinedDatasetConfig(**ds_cfg)
+        else:
+            sig = inspect.signature(func)
+            if "ds_cfg" in sig.parameters:
+                load_kwargs["ds_cfg"] = ds_cfg
+        return func(tokenizer, cfg, **load_kwargs)
+    except ModuleNotFoundError:
+        return None
+    except Exception as exc:  # pylint: disable=broad-exception-caught
+        LOG.error(f"Failed to load prompt strategy `{strategy}`: {str(exc)}")
+        return None

src/axolotl/prompt_strategies/bradley_terry/chat_template.py

@@ -0,0 +1,102 @@
+"""
+Bradley-Terry model with chat template prompt strategy.
+"""
+
+import logging
+from typing import Any, Dict, Optional
+
+from axolotl.prompt_strategies.chat_template import (
+    ChatTemplatePrompter,
+    ChatTemplateStrategy,
+)
+from axolotl.utils.chat_templates import get_chat_template_from_config
+
+# Configure the logger
+LOG = logging.getLogger("axolotl.prompt_strategies.bradley_terry.chat_template")
+LOG.setLevel(logging.INFO)
+
+
+class BTChatTemplateStrategy(ChatTemplateStrategy):
+    """
+    Bradley-Terry reward model pairwise chat template prompt strategy.
+    """
+
+    def tokenize_prompt(self, prompt):
+        """
+
+        :param prompt: the actual row of data from the underlying dataset
+        :return:
+        """
+
+        self.messages = "chosen_messages"
+        # pylint: disable=duplicate-code
+        prompt[self.messages] = []
+        if prompt["system"]:
+            prompt[self.messages].append(
+                {"role": "system", "content": prompt["system"]}
+            )
+        prompt[self.messages].append({"role": "user", "content": prompt["input"]})
+        prompt[self.messages].append({"role": "assistant", "content": prompt["chosen"]})
+        chosen_tokenized = super().tokenize_prompt(prompt)
+
+        self.messages = "rejected_messages"
+        # pylint: disable=duplicate-code
+        prompt[self.messages] = []
+        if prompt["system"]:
+            prompt[self.messages].append(
+                {"role": "system", "content": prompt["system"]}
+            )
+        prompt[self.messages].append({"role": "user", "content": prompt["input"]})
+        prompt[self.messages].append(
+            {"role": "assistant", "content": prompt["rejected"]}
+        )
+        rejected_tokenized = super().tokenize_prompt(prompt)
+
+        return {
+            "input_ids_chosen": chosen_tokenized["input_ids"],
+            "attention_mask_chosen": chosen_tokenized["attention_mask"],
+            "labels_chosen": 1.0,
+            "input_ids_rejected": rejected_tokenized["input_ids"],
+            "attention_mask_rejected": rejected_tokenized["attention_mask"],
+            "labels_rejected": 0.0,
+        }
+
+
+def load(tokenizer, cfg, ds_cfg: Optional[Dict[str, Any]] = None):
+    ds_cfg = ds_cfg or {}
+    chat_template_string = get_chat_template_from_config(
+        cfg=cfg, ds_cfg=ds_cfg, tokenizer=tokenizer
+    )
+
+    prompter_params = {
+        "tokenizer": tokenizer,
+        "chat_template": chat_template_string,
+        "message_field_role": ds_cfg.get("message_field_role", "role"),
+        "message_field_content": ds_cfg.get("message_field_content", "content"),
+        "message_field_training": ds_cfg.get("message_field_training", None),
+        "message_field_training_detail": ds_cfg.get(
+            "message_field_training_detail", 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,
+    }
+
+    strategy_params = {
+        "train_on_inputs": cfg.train_on_inputs,
+        "sequence_len": cfg.sequence_len,
+        "roles_to_train": ds_cfg.get("roles_to_train", []),
+        "train_on_eos": ds_cfg.get("train_on_eos", None),
+    }
+
+    strategy = BTChatTemplateStrategy(
+        ChatTemplatePrompter(**prompter_params), tokenizer=tokenizer, **strategy_params
+    )
+
+    if "field_messages" in ds_cfg and hasattr(strategy, "messages"):
+        strategy.messages = ds_cfg["field_messages"]
+
+    return strategy

src/axolotl/prompt_strategies/bradley_terry/llama3.py

@@ -0,0 +1,27 @@
+"""
+chatml transforms for datasets with system, input, chosen, rejected to match llama3 chat template
+"""
+
+
+def icr(
+    cfg,
+    **kwargs,
+):  # pylint: disable=possibly-unused-variable,unused-argument
+    """
+    chatml transforms for datasets with system, input, chosen, rejected
+    ex. https://huggingface.co/datasets/argilla/distilabel-intel-orca-dpo-pairs
+    """
+
+    def transform_fn(sample):
+        if "system" in sample and sample["system"]:
+            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['input']}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
+            )
+        else:
+            prompt = f"<|start_header_id|>user<|end_header_id|>\n\n{sample['input']}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
+        sample["chosen"] = prompt + f"{sample['chosen']}<|eot_id|>"
+        sample["rejected"] = prompt + f"{sample['rejected']}<|eot_id|>"
+        return sample
+
+    return transform_fn