fix the context manager call

* fix: bad dtype for full finetune

* Update src/axolotl/utils/models.py

Co-authored-by: Wing Lian <wing.lian@gmail.com>

* Update models.py

---------

Co-authored-by: Wing Lian <wing.lian@gmail.com>

.github/CODE_OF_CONDUCT.md

@@ -0,0 +1,129 @@
+# Contributor Covenant Code of Conduct
+
+## Our Pledge
+
+We as members, contributors, and leaders pledge to make participation in our
+community a harassment-free experience for everyone, regardless of age, body
+size, visible or invisible disability, ethnicity, sex characteristics, gender
+identity and expression, level of experience, education, socio-economic status,
+nationality, personal appearance, race, religion, or sexual identity
+and orientation.
+
+We pledge to act and interact in ways that contribute to an open, welcoming,
+diverse, inclusive, and healthy community.
+
+## Our Standards
+
+Examples of behavior that contributes to a positive environment for our
+community include:
+
+* Demonstrating empathy and kindness toward other people
+* Being respectful of differing opinions, viewpoints, and experiences
+* Giving and gracefully accepting constructive feedback
+* Accepting responsibility and apologizing to those affected by our mistakes,
+  and learning from the experience
+* Focusing on what is best not just for us as individuals, but for the
+  overall community
+
+Examples of unacceptable behavior include:
+
+* The use of sexualized language or imagery, and sexual attention or
+  advances of any kind
+* Trolling, insulting or derogatory comments, and personal or political attacks
+* Public or private harassment
+* Publishing others' private information, such as a physical or email
+  address, without their explicit permission
+* Other conduct which could reasonably be considered inappropriate in a
+  professional setting
+
+## Enforcement Responsibilities
+
+Community leaders are responsible for clarifying and enforcing our standards of
+acceptable behavior and will take appropriate and fair corrective action in
+response to any behavior that they deem inappropriate, threatening, offensive,
+or harmful.
+
+Community leaders have the right and responsibility to remove, edit, or reject
+comments, commits, code, wiki edits, issues, and other contributions that are
+not aligned to this Code of Conduct, and will communicate reasons for moderation
+decisions when appropriate.
+
+## Scope
+
+This Code of Conduct applies within all community spaces, and also applies when
+an individual is officially representing the community in public spaces.
+Examples of representing our community include using an official e-mail address,
+posting via an official social media account, or acting as an appointed
+representative at an online or offline event.
+
+## Enforcement
+
+Instances of abusive, harassing, or otherwise unacceptable behavior may be
+reported to the community leaders responsible for enforcement on Discord
+at https://discord.gg/QYF8QrtEUm
+
+All complaints will be reviewed and investigated promptly and fairly.
+
+All community leaders are obligated to respect the privacy and security of the
+reporter of any incident.
+
+## Enforcement Guidelines
+
+Community leaders will follow these Community Impact Guidelines in determining
+the consequences for any action they deem in violation of this Code of Conduct:
+
+### 1. Correction
+
+**Community Impact**: Use of inappropriate language or other behavior deemed
+unprofessional or unwelcome in the community.
+
+**Consequence**: A private, written warning from community leaders, providing
+clarity around the nature of the violation and an explanation of why the
+behavior was inappropriate. A public apology may be requested.
+
+### 2. Warning
+
+**Community Impact**: A violation through a single incident or series
+of actions.
+
+**Consequence**: A warning with consequences for continued behavior. No
+interaction with the people involved, including unsolicited interaction with
+those enforcing the Code of Conduct, for a specified period of time. This
+includes avoiding interactions in community spaces as well as external channels
+like social media. Violating these terms may lead to a temporary or
+permanent ban.
+
+### 3. Temporary Ban
+
+**Community Impact**: A serious violation of community standards, including
+sustained inappropriate behavior.
+
+**Consequence**: A temporary ban from any sort of interaction or public
+communication with the community for a specified period of time. No public or
+private interaction with the people involved, including unsolicited interaction
+with those enforcing the Code of Conduct, is allowed during this period.
+Violating these terms may lead to a permanent ban.
+
+### 4. Permanent Ban
+
+**Community Impact**: Demonstrating a pattern of violation of community
+standards, including sustained inappropriate behavior,  harassment of an
+individual, or aggression toward or disparagement of classes of individuals.
+
+**Consequence**: A permanent ban from any sort of public interaction within
+the community.
+
+## Attribution
+
+This Code of Conduct is adapted from the [Contributor Covenant][homepage],
+version 2.0, available at
+https://www.contributor-covenant.org/version/2/0/code_of_conduct.html.
+
+Community Impact Guidelines were inspired by [Mozilla's code of conduct
+enforcement ladder](https://github.com/mozilla/diversity).
+
+[homepage]: https://www.contributor-covenant.org
+
+For answers to common questions about this code of conduct, see the FAQ at
+https://www.contributor-covenant.org/faq. Translations are available at
+https://www.contributor-covenant.org/translations.

.github/CONTRIBUTING.md

@@ -0,0 +1,76 @@
+# Contributing to axolotl
+
+First of all, thank you for your interest in contributing to axolotl! We appreciate the time and effort you're willing to invest in making our project better. This document provides guidelines and information to make the contribution process as smooth as possible.
+
+## Table of Contents
+
+- [Code of Conduct](#code-of-conduct)
+- [Getting Started](#getting-started)
+- [How to Contribute](#how-to-contribute)
+  - [Reporting Bugs](#reporting-bugs)
+  - [Suggesting Enhancements](#suggesting-enhancements)
+  - [Submitting Pull Requests](#submitting-pull-requests)
+- [Style Guidelines](#style-guidelines)
+  - [Code Style](#code-style)
+  - [Commit Messages](#commit-messages)
+- [Additional Resources](#additional-resources)
+
+## Code of Conductcode
+
+All contributors are expected to adhere to our [Code of Conduct](CODE_OF_CONDUCT.md). Please read it before participating in the axolotl community.
+
+## Getting Started
+
+Bugs? Please check for open issue else create a new [Issue](https://github.com/OpenAccess-AI-Collective/axolotl/issues/new).
+
+PRs are **greatly welcome**!
+
+1. Fork the repository and clone it to your local machine.
+2. Set up the development environment by following the instructions in the [README.md](https://github.com/OpenAccess-AI-Collective/axolotl/tree/main/README.md) file.
+3. Explore the codebase, run tests, and verify that everything works as expected.
+
+Please run below to setup env
+```bash
+pip3 install -r requirements-dev.txt -r requirements-tests.txt
+pre-commit install
+
+# test
+pytest tests/
+```
+
+## How to Contribute
+
+### Reporting Bugs
+
+If you encounter a bug or issue while using axolotl, please open a new issue on the [GitHub Issues](https://github.com/OpenAccess-AI-Collective/axolotl/issues) page. Provide a clear and concise description of the problem, steps to reproduce it, and any relevant error messages or logs.
+
+### Suggesting Enhancements
+
+We welcome ideas for improvements and new features. To suggest an enhancement, open a new issue on the [GitHub Issues](https://github.com/OpenAccess-AI-Collective/axolotl/issues) page. Describe the enhancement in detail, explain the use case, and outline the benefits it would bring to the project.
+
+### Submitting Pull Requests
+
+1. Create a new branch for your feature or bugfix. Use a descriptive name like `feature/your-feature-name` or `fix/your-bugfix-name`.
+2. Make your changes, following the [Style Guidelines](#style-guidelines) below.
+3. Test your changes and ensure that they don't introduce new issues or break existing functionality.
+4. Commit your changes, following the [commit message guidelines](#commit-messages).
+5. Push your branch to your fork on GitHub.
+6. Open a new pull request against the `main` branch of the axolotl repository. Include a clear and concise description of your changes, referencing any related issues.
+
+## Style Guidelines
+
+### Code Style
+
+axolotl uses [{codestyle}]({URLofCodestyle}) as its code style guide. Please ensure that your code follows these guidelines.
+
+### Commit Messages
+
+Write clear and concise commit messages that briefly describe the changes made in each commit. Use the imperative mood and start with a capitalized verb, e.g., "Add new feature" or "Fix bug in function".
+
+## Additional Resources
+
+- [GitHub Help](https://help.github.com/)
+- [GitHub Pull Request Documentation](https://docs.github.com/en/github/collaborating-with-issues-and-pull-requests)
+- [{codestyle}]({URLofCodestyle})
+
+Thank you once again for your interest in contributing to axolotl. We look forward to collaborating with you and creating an even better project together!

.github/FUNDING.yml

@@ -0,0 +1,13 @@
+# These are supported funding model platforms
+
+github: OpenAccess-AI-Collective # Replace with up to 4 GitHub Sponsors-enabled usernames e.g., [user1, user2]
+patreon: # Replace with a single Patreon username
+open_collective: # Replace with a single Open Collective username
+ko_fi: # Replace with a single Ko-fi username
+tidelift: # Replace with a single Tidelift platform-name/package-name e.g., npm/babel
+community_bridge: # Replace with a single Community Bridge project-name e.g., cloud-foundry
+liberapay: # Replace with a single Liberapay username
+issuehunt: # Replace with a single IssueHunt username
+otechie: # Replace with a single Otechie username
+lfx_crowdfunding: # Replace with a single LFX Crowdfunding project-name e.g., cloud-foundry
+custom: # Replace with up to 4 custom sponsorship URLs e.g., ['link1', 'link2']

.github/ISSUE_TEMPLATE/bug-report.yaml

@@ -0,0 +1,105 @@
+name: Bug Report
+description: File a bug report
+labels: ["bug", "needs triage"]
+body:
+  - type: markdown
+    attributes:
+      value: |
+        ## Before you start
+        Please **make sure you are on the latest version.**
+        If you encountered the issue after you installed, updated, or reloaded, **please try restarting before reporting the bug**.
+
+  - type: checkboxes
+    id: no-duplicate-issues
+    attributes:
+      label: "Please check that this issue hasn't been reported before."
+      description: "The **Label filters** may help make your search more focussed."
+      options:
+        - label: "I searched previous [Bug Reports](https://github.com/OpenAccess-AI-Collective/axolotl/labels/bug) didn't find any similar reports."
+          required: true
+
+  - type: textarea
+    id: expected
+    attributes:
+      label: Expected Behavior
+      description: Tell us what **should** happen.
+    validations:
+      required: true
+
+  - type: textarea
+    id: what-happened
+    attributes:
+      label: Current behaviour
+      description: |
+        Tell us what happens instead of the expected behavior.
+        Provide stacktrace and/or screenshots.
+    validations:
+      required: true
+
+  - type: textarea
+    id: reproduce
+    attributes:
+      label: Steps to reproduce
+      description: |
+        Which exact steps can a developer take to reproduce the issue?
+        The more detail you provide, the easier it will be to narrow down and fix the bug.
+        Please paste in tasks and/or queries **as text, not screenshots**.
+      placeholder: |
+        Example of the level of detail needed to reproduce any bugs efficiently and reliably.
+        1. Go to the '...' page.
+        2. Click on the '...' button.
+        3. Scroll down to '...'.
+        4. Observe the error.
+    validations:
+      required: true
+
+  - type: textarea
+    id: possible-solution
+    attributes:
+      label: Possible solution
+      description: |
+        Not obligatory, but please suggest a fix or reason for the bug, if you have an idea.
+
+
+  - type: checkboxes
+    id: operating-systems
+    attributes:
+      label: Which Operating Systems are you using?
+      description: You may select more than one.
+      options:
+        - label: Linux
+        - label: macOS
+        - label: Windows
+
+  - type: input
+    id: Python-version
+    attributes:
+      label: Python Version
+      description: Which {Programming} version are you using?
+      placeholder: 3.10 / please change accordingly
+    validations:
+      required: true
+
+  - type: input
+    id: axolotl-branch-commit
+    attributes:
+      label: axolotl branch-commit
+      description: On which branch/commit are you?
+      placeholder: main/4d6490b
+    validations:
+      required: true
+
+  - type: checkboxes
+    id: acknowledgements
+    attributes:
+      label: 'Acknowledgements'
+      description: 'Please confirm the following:'
+      options:
+        - label: 'My issue title is concise, descriptive, and in title casing.'
+          required: true
+        - label: 'I have searched the existing issues to make sure this bug has not been reported yet.'
+          required: true
+        - label: 'I am using the latest version of axolotl.'
+          required: true
+        - label: 'I have provided enough information for the maintainers to reproduce and diagnose the issue.'
+          required: true

.github/ISSUE_TEMPLATE/config.yml

@@ -0,0 +1,7 @@
+blank_issues_enabled: false
+contact_links:
+  - name: Ask a question
+    url: https://github.com/OpenAccess-AI-Collective/axolotl/discussions/categories/q-a
+    about: Ask questions and discuss with other community members
+  - name: Discuss the Project in Discord
+    url: https://discord.gg/HhrNrHJPRb

.github/ISSUE_TEMPLATE/docs.yml

@@ -0,0 +1,46 @@
+name: Documentation Improvement / Clarity
+description: Make a suggestion to improve the project documentation.
+labels: ['needs triage', 'docs']
+body:
+  - type: markdown
+    attributes:
+      value: '## :book: Documentation :book:'
+  - type: markdown
+    attributes:
+      value: |
+        * Ask questions in [Discord](https://discord.gg/HhrNrHJPRb).
+        * Before you file an issue read the [Contributing guide](./CONTRIBUTING.md).
+        * Check to make sure someone hasn't already opened a [similar issue](https://github.com/OpenAccess-AI-Collective/axolotl/issues).
+  - type: textarea
+    attributes:
+      label: What piece of documentation is affected?
+      description: Please link to the article you'd like to see updated.
+    validations:
+      required: true
+  - type: textarea
+    attributes:
+      label: What part(s) of the article would you like to see updated?
+      description: |
+        - Give as much detail as you can to help us understand the change you want to see.
+        - Why should the docs be changed? What use cases does it support?
+        - What is the expected outcome?
+    validations:
+      required: true
+  - type: textarea
+    attributes:
+      label: Additional Information
+      description: Add any other context or screenshots about the feature request here.
+    validations:
+      required: false
+  - type: checkboxes
+    id: acknowledgements
+    attributes:
+      label: 'Acknowledgements'
+      description: 'Please confirm the following:'
+      options:
+        - label: 'My issue title is concise, descriptive, and in title casing.'
+          required: true
+        - label: 'I have searched the existing issues to make sure this feature has not been requested yet.'
+          required: true
+        - label: 'I have provided enough information for the maintainers to understand and evaluate this request.'
+          required: true

.github/ISSUE_TEMPLATE/feature-request.yaml

@@ -0,0 +1,63 @@
+name: Feature Request / Enhancement
+description: Suggest a new feature or feature enhancement for the project
+labels: ["enhancement", "needs triage"]
+body:
+  - type: checkboxes
+    id: no-duplicate-issues
+    attributes:
+      label: "⚠️ Please check that this feature request hasn't been suggested before."
+      description: "There are two locations for previous feature requests. Please search in both. Thank you. The **Label filters** may help make your search more focussed."
+      options:
+        - label: "I searched previous [Ideas in Discussions](https://github.com/OpenAccess-AI-Collective/axolotl/discussions/categories/ideas) didn't find any similar feature requests."
+          required: true
+        - label: "I searched previous [Issues](https://github.com/OpenAccess-AI-Collective/axolotl/labels/enhancement) didn't find any similar feature requests."
+          required: true
+
+  - type: textarea
+    id: feature-description
+    validations:
+      required: true
+    attributes:
+      label: "🔖 Feature description"
+      description: "A clear and concise description of what the feature request is."
+      placeholder: "You should add ..."
+
+  - type: textarea
+    id: solution
+    validations:
+      required: true
+    attributes:
+      label: "✔️ Solution"
+      description: "A clear and concise description of what you want to happen, and why."
+      placeholder: "In my use-case, ..."
+
+  - type: textarea
+    id: alternatives
+    validations:
+      required: false
+    attributes:
+      label: "❓ Alternatives"
+      description: "A clear and concise description of any alternative solutions or features you've considered."
+      placeholder: "I have considered ..."
+
+  - type: textarea
+    id: additional-context
+    validations:
+      required: false
+    attributes:
+      label: "📝 Additional Context"
+      description: "Add any other context or screenshots about the feature request here."
+      placeholder: "..."
+
+  - type: checkboxes
+    id: acknowledgements
+    attributes:
+      label: 'Acknowledgements'
+      description: 'Please confirm the following:'
+      options:
+        - label: 'My issue title is concise, descriptive, and in title casing.'
+          required: true
+        - label: 'I have searched the existing issues to make sure this feature has not been requested yet.'
+          required: true
+        - label: 'I have provided enough information for the maintainers to understand and evaluate this request.'
+          required: true

.github/PULL_REQUEST_TEMPLATE/pull_request_template_simple.md

@@ -0,0 +1,22 @@
+<!--- Provide a general summary of your changes in the Title above -->
+
+# Description
+
+<!--- Describe your changes in detail -->
+
+## Motivation and Context
+
+<!--- Why is this change required? What problem does it solve? -->
+<!--- If it fixes an open issue, please link to the issue here. -->
+
+## How has this been tested?
+
+<!--- Please describe in detail how you tested your changes. -->
+<!--- Include details of your testing environment, tests ran to see how -->
+<!--- your change affects other areas of the code, etc. -->
+
+## Screenshots (if appropriate)
+
+## Types of changes
+
+<!--- What types of changes does your code introduce? Put an `x` in all the boxes that apply: -->

.github/SECURITY.md

@@ -0,0 +1,9 @@
+# Security Policy
+
+## Supported Versions
+
+Due to the nature of the fast development that is happening in this project, only the latest released version can be supported.
+
+## Reporting a Vulnerability
+
+If you find a vulnerability, please contact us on  [Discord](https://discord.gg/xcu3ECkH9a) rather than creating a GitHub issue to allow us some time to fix it before it is a known vulnerability to others.

.github/SUPPORT.md

@@ -0,0 +1,10 @@
+# Support
+
+If you need help with this project or have questions, please:
+
+1. Check the documentation.
+2. Search the existing issues and pull requests.
+3. Create a new issue if your question is not answered or your problem is not solved.
+4. Have a look in the [Discord server](https://discord.gg/HhrNrHJPRb)
+
+Please note that this project is maintained by volunteers who have limited availability. We'll do our best to address your questions and concerns in a timely manner.

.github/workflows/base.yml

@@ -12,19 +12,19 @@ jobs:
     # this job needs to be run on self-hosted GPU runners...
     runs-on: self-hosted
     strategy:
+      fail-fast: false
       matrix:
         include:
           - cuda: "118"
             cuda_version: 11.8.0
-            axolotl_extras:
-          - cuda: "117"
-            cuda_version: 11.7.0
-            pytorch: 1.13.1
-            axolotl_extras:
+            python_version: "3.9"
+            pytorch: 2.0.1
+            torch_cuda_arch_list: "7.0 7.5 8.0 8.6 9.0+PTX"
           - cuda: "118"
             cuda_version: 11.8.0
-            pytorch: 2.0.0
-            axolotl_extras: gptq
+            python_version: "3.10"
+            pytorch: 2.0.1
+            torch_cuda_arch_list: "7.0 7.5 8.0 8.6 9.0+PTX"
     steps:
       - name: Checkout
         uses: actions/checkout@v3
@@ -46,12 +46,11 @@ jobs:
           context: .
           file: ./docker/Dockerfile-base
           push: ${{ github.event_name != 'pull_request' }}
-          tags: ${{ steps.metadata.outputs.tags }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
+          tags: ${{ steps.metadata.outputs.tags }}-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
           labels: ${{ steps.metadata.outputs.labels }}
-          cache-from: type=gha
-          cache-to: type=gha,mode=max
           build-args: |
             CUDA_VERSION=${{ matrix.cuda_version }}
             CUDA=${{ matrix.cuda }}
+            PYTHON_VERSION=${{ matrix.python_version }}
             PYTORCH_VERSION=${{ matrix.pytorch }}
-            AXOLOTL_EXTRAS=${{ matrix.axolotl_extras }}
+            TORCH_CUDA_ARCH_LIST=${{ matrix.torch_cuda_arch_list }}

.github/workflows/main.yml

@@ -4,27 +4,30 @@ on:
   push:
     branches:
       - "main"
-      - "dev"
 
 jobs:
   build-axolotl:
     if: github.repository_owner == 'OpenAccess-AI-Collective'
     # this job needs to be run on self-hosted GPU runners...
     strategy:
+      fail-fast: false
       matrix:
         include:
-          - cuda: cu118
+          - cuda: 118
             cuda_version: 11.8.0
-            pytorch: 2.0.0
+            python_version: "3.9"
+            pytorch: 2.0.1
             axolotl_extras:
-          - cuda: cu118
+          - cuda: 118
             cuda_version: 11.8.0
-            pytorch: 2.0.0
+            python_version: "3.10"
+            pytorch: 2.0.1
+            axolotl_extras:
+          - cuda: 118
+            cuda_version: 11.8.0
+            python_version: "3.9"
+            pytorch: 2.0.1
             axolotl_extras: gptq
-          - cuda: cu117
-            cuda_version: 11.7.0
-            pytorch: 1.13.1
-            axolotl_extras:
     runs-on: self-hosted
     steps:
       - name: Checkout
@@ -46,13 +49,12 @@ jobs:
         with:
           context: .
           build-args: |
-            BASE_TAG=${{ github.ref_name }}-base-${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
+            BASE_TAG=${{ github.ref_name }}-base-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}
+            CUDA=${{ matrix.cuda }}
           file: ./docker/Dockerfile
           push: ${{ github.event_name != 'pull_request' }}
-          tags: ${{ steps.metadata.outputs.tags }}-${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
+          tags: ${{ steps.metadata.outputs.tags }}-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
           labels: ${{ steps.metadata.outputs.labels }}
-          cache-from: type=gha
-          cache-to: type=gha,mode=max
   build-axolotl-runpod:
     needs: build-axolotl
     if: github.repository_owner == 'OpenAccess-AI-Collective'
@@ -60,18 +62,22 @@ jobs:
     strategy:
       matrix:
         include:
-          - cuda: cu118
+          - cuda: 118
             cuda_version: 11.8.0
-            pytorch: 2.0.0
+            python_version: "3.9"
+            pytorch: 2.0.1
             axolotl_extras:
-          - cuda: cu118
+          - cuda: 118
             cuda_version: 11.8.0
-            pytorch: 2.0.0
+            python_version: "3.10"
+            pytorch: 2.0.1
+            axolotl_extras:
+            is_latest: true
+          - cuda: 118
+            cuda_version: 11.8.0
+            python_version: "3.9"
+            pytorch: 2.0.1
             axolotl_extras: gptq
-          - cuda: cu117
-            cuda_version: 11.7.0
-            pytorch: 1.13.1
-            axolotl_extras:
     runs-on: self-hosted
     steps:
       - name: Checkout
@@ -93,10 +99,11 @@ jobs:
         with:
           context: .
           build-args: |
-            BASE_TAG=${{ github.ref_name }}-${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
+            BASE_TAG=${{ github.ref_name }}-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
+            CUDA=${{ matrix.cuda }}
           file: ./docker/Dockerfile-runpod
           push: ${{ github.event_name != 'pull_request' }}
-          tags: ${{ steps.metadata.outputs.tags }}-${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
+          tags: |
+             ${{ steps.metadata.outputs.tags }}-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
+             ${{ (matrix.is_latest) && format('{0}-latest', steps.metadata.outputs.tags) || '' }}
           labels: ${{ steps.metadata.outputs.labels }}
-          cache-from: type=gha
-          cache-to: type=gha,mode=max

.github/workflows/tests.yml

@@ -7,6 +7,7 @@ jobs:
   test:
     runs-on: ubuntu-latest
     strategy:
+      fail-fast: false
       matrix:
         python_version: ["3.9", "3.10"]
     timeout-minutes: 10
@@ -23,7 +24,7 @@ jobs:
 
       - name: Install dependencies
         run: |
-          pip install -e .
+          pip install -e .[peft]
           pip install -r requirements-tests.txt
 
       - name: Run tests

.mypy.ini

@@ -5,6 +5,9 @@ exclude = venv
 [mypy-alpaca_lora_4bit.*]
 ignore_missing_imports = True
 
+[mypy-axolotl.monkeypatch.*]
+ignore_errors = True
+
 [mypy-flash_attn.*]
 ignore_missing_imports = True
 
@@ -31,3 +34,6 @@ ignore_missing_imports = True
 
 [mypy-addict]
 ignore_missing_imports = True
+
+[mypy-xformers.*]
+ignore_missing_imports = True

.pre-commit-config.yaml

@@ -1,5 +1,5 @@
 default_language_version:
-    python: python3.9
+    python: python3
 
 repos:
 -   repo: https://github.com/pre-commit/pre-commit-hooks

FAQS.md

@@ -2,3 +2,6 @@
 
 - Can you train StableLM with this? Yes, but only with a single GPU atm. Multi GPU support is coming soon! Just waiting on this [PR](https://github.com/huggingface/transformers/pull/22874)
 - Will this work with Deepspeed? That's still a WIP, but setting `export ACCELERATE_USE_DEEPSPEED=true` should work in some cases
+- `Error invalid argument at line 359 in file /workspace/bitsandbytes/csrc/pythonInterface.c`
+`/arrow/cpp/src/arrow/filesystem/s3fs.cc:2598:  arrow::fs::FinalizeS3 was not called even though S3 was initialized.`
+This could lead to a segmentation fault at exit. Try reinstalling bitsandbytes and transformers from source.

LICENSE

@@ -0,0 +1,202 @@
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README.md

@@ -1,10 +1,40 @@
 # Axolotl
 
+Axolotl is a tool designed to streamline the fine-tuning of various AI models, offering support for multiple configurations and architectures.
+
+<table>
+<tr>
+<td>
+
+## Table of Contents
+- [Introduction](#axolotl)
+- [Supported Features](#axolotl-supports)
+- [Quickstart](#quickstart-)
+- [Installation](#installation)
+  - [Docker Installation](#environment)
+  - [Conda/Pip venv Installation](#condapip-venv)
+  - [LambdaLabs Installation](#lambdalabs)
+- [Dataset](#dataset)
+  - [How to Add Custom Prompts](#how-to-add-custom-prompts)
+  - [How to Use Custom Pretokenized Dataset](#how-to-use-your-custom-pretokenized-dataset)
+- [Config](#config)
+  - [Train](#train)
+  - [Inference](#inference)
+  - [Merge LORA to Base](#merge-lora-to-base)
+- [Common Errors](#common-errors-)
+- [Need Help?](#need-help-)
+- [Badge](#badge-)
+- [Community Showcase](#community-showcase)
+- [Contributing](#contributing-)
+
+</td>
+<td>
+
 <div align="center">
   <img src="image/axolotl.png" alt="axolotl" width="160">
   <div>
     <p>
-      <b>One repo to finetune them all! </b>
+      <b>Axolotl provides a unified repository for fine-tuning <br />a variety of AI models with ease</b>
     </p>
     <p>
       Go ahead and axolotl questions!!
@@ -14,33 +44,41 @@
   </div>
 </div>
 
+</td>
+</tr>
+</table>
+
 ## Axolotl supports
 
-|         | fp16/fp32 | fp16/fp32 w/ lora | qlora | 4bit-quant | 4bit-quant w/flash attention | flash attention | xformers attention |
-|---------|:----------|:------------------|------|------------|------------------------------|-----------------|--------------------|
-| llama   | ✅         | ✅                 | ✅  | ✅          | ✅                            | ✅               | ✅                  |
-| Pythia  | ✅         | ✅                 | ❓  | ❌          | ❌                            | ❌               | ❓                  |
-| cerebras | ✅         | ✅                 | ❓  | ❌          | ❌                            | ❌               | ❓                  |
-| mpt     | ✅         | ❌                 | ❓  | ❌          | ❌                            | ❌               | ❓                  |
-| falcon  | ✅         | ❌                 | ❌  | ❌          | ❌                            | ❌               | ❓                  |
+|          | fp16/fp32 | lora | qlora | gptq | gptq w/flash attn | flash attn | xformers attn |
+|----------|:----------|:-----|-------|------|-------------------|------------|---------------|
+| llama    | ✅         | ✅    | ✅     | ✅             | ✅                 | ✅          | ✅             |
+| Pythia   | ✅         | ✅    | ✅     | ❌             | ❌                 | ❌          | ❓             |
+| cerebras | ✅         | ✅    | ✅     | ❌             | ❌                 | ❌          | ❓             |
+| mpt      | ✅         | ❌    | ❓     | ❌             | ❌                 | ❌          | ❓             |
+| falcon   | ✅         | ✅    | ✅     | ❌             | ❌                 | ❌          | ❓             |
+| gpt-j    | ✅         | ✅    | ✅     | ❌             | ❌                 | ❓          | ❓             |
+| XGen     | ✅         | ❓    | ✅     | ❓             | ❓                 | ❓          | ✅             |
 
 
 ## Quickstart ⚡
 
-**Requirements**: Python 3.9.
+Get started with Axolotl in just a few steps! This quickstart guide will walk you through setting up and running a basic fine-tuning task.
+
+**Requirements**: Python >=3.9 and Pytorch >=2.0.
 
 ```bash
 git clone https://github.com/OpenAccess-AI-Collective/axolotl
+cd axolotl
 
-pip3 install -e .
-
-accelerate config
+pip3 install -e .[flash-attn]
+pip3 install -U git+https://github.com/huggingface/peft.git
 
 # finetune lora
-accelerate launch scripts/finetune.py examples/lora-openllama-3b/config.yml
+accelerate launch scripts/finetune.py examples/openllama-3b/lora.yml
 
 # inference
-accelerate launch scripts/finetune.py examples/lora-openllama-3b/config.yml \
+accelerate launch scripts/finetune.py examples/openllama-3b/lora.yml \
     --inference --lora_model_dir="./lora-out"
 ```
 
@@ -50,28 +88,93 @@ accelerate launch scripts/finetune.py examples/lora-openllama-3b/config.yml \
 
 - Docker
   ```bash
-  docker run --gpus '"all"' --rm -it winglian/axolotl:main
+  docker run --gpus '"all"' --rm -it winglian/axolotl:main-py3.10-cu118-2.0.1
+  ```
+  - `winglian/axolotl-runpod:main-py3.10-cu118-2.0.1`: for runpod
+  - `winglian/axolotl-runpod:main-py3.9-cu118-2.0.1-gptq`: for gptq
+
+  Or run on the current files for development:
+
+  ```sh
+  docker compose up -d
   ```
-  - `winglian/axolotl:dev`: dev branch
-  - `winglian/axolotl-runpod:main`: for runpod
 
 - Conda/Pip venv
-  1. Install python **3.9**
+  1. Install python >=**3.9**
 
-  2. Install python dependencies with ONE of the following:
-      - `pip3 install -e .` (recommended, supports QLoRA, no gptq/int4 support)
-      - `pip3 install -e .[gptq]` (next best if you don't need QLoRA, but want to use gptq)
-      - `pip3 install -e .[gptq_triton]`
+  2. Install pytorch stable https://pytorch.org/get-started/locally/
+
+  3. Install python dependencies with ONE of the following:
+      - Recommended, supports QLoRA, NO gptq/int4 support
+        ```bash
+        pip3 install -e .
+        pip3 install -U git+https://github.com/huggingface/peft.git
+        ```
+      - gptq/int4 support, NO QLoRA
+        ```bash
+        pip3 install -e .[gptq]
+        ```
+      - same as above but not recommended
+        ```bash
+        pip3 install -e .[gptq_triton]
+        ```
+
+- LambdaLabs
+  <details>
+
+  <summary>Click to Expand</summary>
+
+  1. Install python
+  ```bash
+  sudo apt update
+  sudo apt install -y python3.9
+
+  sudo update-alternatives --install /usr/bin/python python /usr/bin/python3.9 1
+  sudo update-alternatives --config python # pick 3.9 if given option
+  python -V # should be 3.9
+
+  ```
+
+  2. Install pip
+  ```bash
+  wget https://bootstrap.pypa.io/get-pip.py
+  python get-pip.py
+  ```
+
+  3. Install torch
+  ```bash
+  pip3 install -U torch --index-url https://download.pytorch.org/whl/cu118
+  ```
+
+  4. Axolotl
+  ```bash
+  git clone https://github.com/OpenAccess-AI-Collective/axolotl
+  cd axolotl
+
+  pip3 install -e . # change depend on needs
+  pip3 install protobuf==3.20.3
+  pip3 install -U --ignore-installed requests Pillow psutil scipy
+  pip3 install git+https://github.com/huggingface/peft.git # not for gptq
+  ```
+
+  5. Set path
+  ```bash
+  export LD_LIBRARY_PATH=/usr/lib/x86_64-linux-gnu:$LD_LIBRARY_PATH
+  ```
+  </details>
+
+- Windows: Please use WSL or Docker!
 
 ### Dataset
 
+Axolotl supports a variety of dataset formats. Below are some of the formats you can use.
 Have dataset(s) in one of the following format (JSONL recommended):
 
 - `alpaca`: instruction; input(optional)
   ```json
   {"instruction": "...", "input": "...", "output": "..."}
   ```
-- `sharegpt`: conversations
+- `sharegpt:chat`: conversations where `from` is `human`/`gpt`
   ```json
   {"conversations": [{"from": "...", "value": "..."}]}
   ```
@@ -112,16 +215,99 @@ Have dataset(s) in one of the following format (JSONL recommended):
   ```json
   {"article": "...", "summary": "..."}
   ```
-
-> Have some new format to propose? Check if it's already defined in [data.py](src/axolotl/utils/data.py) in `dev` branch!
+- `alpaca_chat`: basic instruct for alpaca chat
+  ```json
+  {"instruction": "...", "input": "...", "response": "..."}
+  ```
+- `alpaca_chat.load_qa`: question and answer for alpaca chat
+  ```json
+  {"question": "...", "answer": "..."}
+  ```
+- `alpaca_chat.load_concise`: question and answer for alpaca chat, for concise answers
+  ```json
+  {"instruction": "...", "input": "...", "response": "..."}
+  ```
+- `alpaca_chat.load_camel_ai`: question and answer for alpaca chat, for load_camel_ai
+  ```json
+  {"message_1": "...", "message_2": "..."}
+  ```
+- `alpaca_w_system.load_open_orca`: support for open orca datasets with included system prompts, instruct
+  ```json
+  {"system_prompt": "...", "question": "...", "response": "..."}
+  ```
+- `context_qa`: in context question answering from an article
+  ```json
+  {"article": "...", "question": "...", "answer": "..."}
+  ```
+- `context_qa.load_404`: in context question answering from an article, with default response for no answer from context
+  ```json
+  {"article": "...", "unanswerable_question": "..."}
+  ```
+- `creative_acr.load_answer`: instruction and revision
+  ```json
+  {"instruction": "...", "revision": "..."}
+  ```
+- `creative_acr.load_critique`: critique
+  ```json
+  {"scores": "...", "critiques": "...", "instruction": "...", "answer": "..."}
+  ```
+- `creative_acr.load_revise`: critique and revise
+  ```json
+  {"scores": "...", "critiques": "...", "instruction": "...", "answer": "...", "revision": "..."}
+  ```
+- `pygmalion`: pygmalion
+  ```json
+  {"conversations": [{"role": "...", "value": "..."}]}
+  ```
+- `metharme`: instruction, adds additional eos tokens
+  ```json
+  {"prompt": "...", "generation": "..."}
+  ```
+- `sharegpt_simple.load_role`: conversations where `role` is used instead of `from`
+  ```json
+  {"conversations": [{"role": "...", "value": "..."}]}
+  ```
+- `sharegpt_simple.load_guanaco`: conversations where `from` is `prompter`/`assistant` instead of default sharegpt
+  ```json
+  {"conversations": [{"from": "...", "value": "..."}]}
+  ```
+- `sharegpt_jokes`: creates a chat where bot is asked to tell a joke, then explain why the joke is funny
+  ```json
+  {"conversations": [{"title": "...", "text": "...", "explanation": "..."}]}
+  ```
 
 </details>
 
-Optionally, download some datasets, see [data/README.md](data/README.md)
+#### How to add custom prompts
+
+Using yaml. Example:
+```yaml
+datasets:
+  - path: repo
+    type:
+      system_prompt: ""
+      no_input_format: |-
+        User: {instruction}<|end_of_turn|>
+        Assistant:
+      format: |-
+        User: {instruction}
+        {input}<|end_of_turn|>
+        Assistant:
+```
+
+Using file:
+1. Add your method to a file in [prompt_strategies](src/axolotl/prompt_strategies). Please see other files as example.
+2. Use your custom file name as the dataset type `<prompt_strategies_file>.load_<load_fn>`.
+
+#### How to use your custom pretokenized dataset
+
+- Do not pass a `type:`
+- Dataset must contain `input_ids`, `attention_mask`, `labels` in columns
+
 
 ### Config
 
-See sample configs in [configs](configs) folder or [examples](examples) for quick start. It is recommended to duplicate and modify to your needs. The most important options are:
+See [examples](examples) for quick start. It is recommended to duplicate and modify to your needs. The most important options are:
 
 - model
   ```yaml
@@ -131,10 +317,33 @@ See sample configs in [configs](configs) folder or [examples](examples) for quic
 
 - dataset
   ```yaml
+  sequence_len: 2048 # max token length for prompt
+
+  # huggingface repo
   datasets:
-    - path: vicgalle/alpaca-gpt4 # local or huggingface repo
+    - path: vicgalle/alpaca-gpt4
       type: alpaca # format from earlier
-  sequence_len: 2048 # max token length / prompt
+
+  # huggingface repo with specific configuration/subset
+  datasets:
+    - path: EleutherAI/pile
+      name: enron_emails
+      type: completion # format from earlier
+
+  # huggingface repo with multiple named configurations/subsets
+  datasets:
+    - path: bigcode/commitpackft
+      name:
+        - ruby
+        - python
+        - typescript
+      type: ... # unimplemented custom format
+
+  # local
+  datasets:
+    - path: data.jsonl # or json
+      ds_type: json # see other options below
+      type: alpaca
   ```
 
 - loading
@@ -144,6 +353,8 @@ See sample configs in [configs](configs) folder or [examples](examples) for quic
   bf16: true # require >=ampere
   fp16: true
   tf32: true # require >=ampere
+  bfloat16: true # require >=ampere, use instead of bf16 when you don't want AMP (automatic mixed precision)
+  float16: true # use instead of fp16 when you don't want AMP
   ```
   Note: Repo does not do 4-bit quantization.
 
@@ -171,12 +382,24 @@ base_model_ignore_patterns:
 # if the base_model repo on hf hub doesn't include configuration .json files,
 # you can set that here, or leave this empty to default to base_model
 base_model_config: ./llama-7b-hf
+# you can specify to choose a specific model revision from huggingface hub
+model_revision:
+# Optional tokenizer configuration override in case you want to use a different tokenizer
+# than the one defined in the base model
+tokenizer_config:
 # If you want to specify the type of model to load, AutoModelForCausalLM is a good choice too
 model_type: AutoModelForCausalLM
 # Corresponding tokenizer for the model AutoTokenizer is a good choice
 tokenizer_type: AutoTokenizer
 # Trust remote code for untrusted source
 trust_remote_code:
+# use_fast option for tokenizer loading from_pretrained, default to True
+tokenizer_use_fast:
+# Whether to use the legacy tokenizer setting, defaults to True
+tokenizer_legacy:
+# resize the model embeddings when new tokens are added to multiples of 32
+# this is reported to improve training speed on some models
+resize_token_embeddings_to_32x:
 
 # whether you are training a 4-bit GPTQ quantized model
 gptq: true
@@ -195,24 +418,53 @@ fp16: true
 # Use CUDA tf32
 tf32: true # require >=ampere
 
+# No AMP (automatic mixed precision)
+bfloat16: true # require >=ampere
+float16: true
+
 # a list of one or more datasets to finetune the model with
 datasets:
-  # this can be either a hf dataset, or relative path
+  # hf dataset repo | "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]
-    type: alpaca # format OR format:prompt_style (chat/instruct)
+    type: alpaca # format | format:<prompt_style> (chat/instruct) | <prompt_strategies>.load_<load_fn>
+    ds_type: # Optional[str] (json|arrow|parquet) defines the datatype when path is a file
     data_files: # path to source data files
     shards: # number of shards to split data into
+    name: # name of dataset configuration to load
+
+  # custom user prompt
+  - path: repo
+    type:
+      # the below are defaults. only set what's needed.
+      system_prompt: ""
+      field_system: system
+      field_instruction: instruction
+      field_output: input
+
+      # customizable to be single line or multi-line
+      system_format: "{system}"
+      # 'format' can include {input}
+      format: |-
+        User: {instruction} {input}
+        Assistant:
+      # 'no_input_format' cannot include {input}
+      no_input_format: "{instruction} "
 
 # axolotl attempts to save the dataset as an arrow after packing the data together so
 # subsequent training attempts load faster, relative path
 dataset_prepared_path: data/last_run_prepared
 # push prepared dataset to hub
 push_dataset_to_hub: # repo path
+# push checkpoints to hub
+hub_model_id: # repo path to push finetuned model
+# how to push checkpoints to hub
+# https://huggingface.co/docs/transformers/v4.31.0/en/main_classes/trainer#transformers.TrainingArguments.hub_strategy
+hub_strategy:
 # whether to use hf `use_auth_token` for loading datasets. Useful for fetching private datasets
 # required to be true when used in combination with `push_dataset_to_hub`
 hf_use_auth_token: # boolean
-# How much of the dataset to set aside as evaluation. 1 = 100%, 0.50 = 50%, etc
+# How much of the dataset to set aside as evaluation. 1 = 100%, 0.50 = 50%, etc. 0 for no eval.
 val_set_size: 0.04
 # Num shards for whole dataset
 dataset_shard_num:
@@ -222,9 +474,19 @@ dataset_shard_idx:
 # the maximum length of an input to train with, this should typically be less than 2048
 # as most models have a token/context limit of 2048
 sequence_len: 2048
+# pad inputs so each step uses constant sized buffers
+# this will reduce memory fragmentation and may prevent OOMs, by re-using memory more efficiently
+pad_to_sequence_len:
 # max sequence length to concatenate training samples together up to
 # inspired by StackLLaMA. see https://huggingface.co/blog/stackllama#supervised-fine-tuning
+# FutureWarning: This will soon be DEPRECATED
 max_packed_sequence_len: 1024
+# use efficient multi-packing with block diagonal attention and per sequence position_ids. Recommend set to 'true'
+sample_packing:
+# you can set these packing optimizations AFTER starting a training at least once.
+# The trainer will provide recommended values for these values.
+sample_packing_eff_est:
+total_num_tokens:
 
 # if you want to use 'lora' or 'qlora' or leave blank to train all parameters in original model
 adapter: lora
@@ -249,31 +511,49 @@ lora_modules_to_save:
 lora_out_dir:
 lora_fan_in_fan_out: false
 
+# ReLoRA configuration
+# must use either 'lora' or 'qlora' adapter, and does not support fsdp or deepspeed
+relora_steps: # number of steps per ReLoRA restart
+relora_warmup_steps: # number of per-restart warmup steps
+relora_cpu_offload: # true to perform lora weight merges on cpu during restarts, for modest gpu memory savings
+
 # wandb configuration if you're using it
-wandb_mode:
-wandb_project:
+wandb_mode: # "offline" to save run metadata locally and not sync to the server, "disabled" to turn off wandb
+wandb_project: # your wandb project name
+wandb_entity: # a wandb Team name if using a Team
 wandb_watch:
-wandb_run_id:
-wandb_log_model: # 'checkpoint'
+wandb_run_id: # set the name of your wandb run
+wandb_log_model: # "checkpoint" to log model to wandb Artifacts every `save_steps` or "end" to log only at the end of training
 
 # where to save the finished model to
 output_dir: ./completed-model
 
 # training hyperparameters
-batch_size: 8
+gradient_accumulation_steps: 1
 micro_batch_size: 2
 eval_batch_size: 2
 num_epochs: 3
 warmup_steps: 100
 learning_rate: 0.00003
+lr_quadratic_warmup:
 logging_steps:
+save_strategy: # set to `no` to skip checkpoint saves
+save_steps: # leave empty to save at each epoch
+eval_steps: # leave empty to eval at each epoch
+save_total_limit: # checkpoints saved at a time
+max_steps:
+
+# save model as safetensors (require safetensors package)
+save_safetensors:
 
 # whether to mask out or include the human's prompt from the training labels
 train_on_inputs: false
-# don't use this, leads to wonky training (according to someone on the internet)
+# group similarly sized data to minimize padding
+# may be slower to start, as it must download and sort the entire dataset
+# note that training loss may have an oscillating pattern with this enabled
 group_by_length: false
 
-# does not work with current implementation of 4-bit LoRA
+# Whether to use gradient checkpointing https://huggingface.co/docs/transformers/v4.18.0/en/performance#gradient-checkpointing
 gradient_checkpointing: false
 
 # stop training after this many evaluation losses have increased in a row
@@ -295,11 +575,31 @@ log_sweep_max_lr:
 optimizer:
 # specify weight decay
 weight_decay:
+# adamw hyperparams
+adam_beta1:
+adam_beta2:
+adam_epsilon:
+# Gradient clipping max norm
+max_grad_norm:
 
+# whether to bettertransformers
+flash_optimum:
 # whether to use xformers attention patch https://github.com/facebookresearch/xformers:
 xformers_attention:
-# whether to use flash attention patch https://github.com/HazyResearch/flash-attention:
-flash_attention:  # require a100 for llama
+# whether to use flash attention patch https://github.com/Dao-AILab/flash-attention:
+flash_attention:
+# whether to use scaled-dot-product attention
+# https://pytorch.org/docs/stable/generated/torch.nn.functional.scaled_dot_product_attention.html
+sdp_attention:
+# Landmark attention (only llama)
+landmark_attention:
+# xpos RoPE see https://github.com/kaiokendev/cutoff-len-is-context-len/blob/main/util/xpos_rope_llama_monkey_patch.py
+# llama only
+xpos_rope:
+# RoPE Scaling https://github.com/huggingface/transformers/pull/24653
+rope_scaling:
+  type: # linear | dynamic
+  factor: # float
 
 # resume from a specific checkpoint dir
 resume_from_checkpoint:
@@ -322,14 +622,19 @@ tokens:
 fsdp:
 fsdp_config:
 
-# Deepspeed
+# Deepspeed config path
 deepspeed:
 
+# Advanced DDP Arguments
+ddp_timeout:
+ddp_bucket_cap_mb:
+ddp_broadcast_buffers:
+
 # Path to torch distx for optim 'adamw_anyprecision'
 torchdistx_path:
 
-# Set padding for data collator to 'longest'
-collator_pad_to_longest:
+# Set to HF dataset for type: 'completion' for streaming instead of pre-tokenize
+pretraining_dataset:
 
 # Debug mode
 debug:
@@ -343,22 +648,48 @@ strict:
 
 </details>
 
-### Accelerate
-
-Configure accelerate
-
-```bash
-accelerate config
-
-# Edit manually
-# nano ~/.cache/huggingface/accelerate/default_config.yaml
-```
-
 ### Train
 
 Run
 ```bash
-accelerate launch scripts/finetune.py configs/your_config.yml
+accelerate launch scripts/finetune.py your_config.yml
+```
+
+#### Multi-GPU
+
+You can optionally pre-tokenize dataset with the following before finetuning:
+```bash
+CUDA_VISIBLE_DEVICES="" accelerate ... --prepare_ds_only
+```
+
+##### Config
+
+- llama FSDP
+```yaml
+fsdp:
+  - full_shard
+  - auto_wrap
+fsdp_config:
+  fsdp_offload_params: true
+  fsdp_state_dict_type: FULL_STATE_DICT
+  fsdp_transformer_layer_cls_to_wrap: LlamaDecoderLayer
+```
+
+- llama Deepspeed
+```yaml
+deepspeed: deepspeed/zero3.json
+```
+
+##### Weights & Biases Logging
+
+- wandb options
+```yaml
+wandb_mode:
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_run_id:
+wandb_log_model:
 ```
 
 ### Inference
@@ -367,11 +698,16 @@ Pass the appropriate flag to the train command:
 
 - Pretrained LORA:
   ```bash
-  --inference --lora_model_dir ./completed-model
+  --inference --lora_model_dir="./lora-output-dir"
   ```
 - Full weights finetune:
   ```bash
-  --inference --base_model ./completed-model
+  --inference --base_model="./completed-model"
+  ```
+- Full weights finetune w/ a prompt from a text file:
+  ```bash
+  cat /tmp/prompt.txt | python scripts/finetune.py configs/your_config.yml \
+    --base_model="./completed-model" --inference --prompter=None --load_in_8bit=True
   ```
 
 ### Merge LORA to base
@@ -382,15 +718,28 @@ Add below flag to train command above
 --merge_lora --lora_model_dir="./completed-model" --load_in_8bit=False --load_in_4bit=False
 ```
 
+If you run out of CUDA memory, you can try to merge in system RAM with
+
+```bash
+CUDA_VISIBLE_DEVICES="" python3 scripts/finetune.py ...
+```
+
 ## Common Errors 🧰
 
-> Cuda out of memory
+> If you encounter a 'Cuda out of memory' error, it means your GPU ran out of memory during the training process. Here's how to resolve it:
 
 Please reduce any below
   - `micro_batch_size`
   - `eval_batch_size`
+  - `gradient_accumulation_steps`
   - `sequence_len`
 
+> `failed (exitcode: -9)`
+
+Usually means your system has run out of system memory.
+Similarly, you should consider reducing the same settings as when you run out of VRAM.
+Additionally, look into upgrading your system RAM which should be simpler than GPU upgrades.
+
 > RuntimeError: expected scalar type Float but found Half
 
 Try set `fp16: true`
@@ -399,13 +748,41 @@ Try set `fp16: true`
 
 Try to turn off xformers.
 
-## Need help? 🙋‍♂️
+> accelerate config missing
+
+It's safe to ignore it.
+
+## Need help? 🙋♂️
 
 Join our [Discord server](https://discord.gg/HhrNrHJPRb) where we can help you
 
+## Badge ❤🏷️
+
+Building something cool with Axolotl? Consider adding a badge to your model card.
+
+```markdown
+[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
+```
+
+[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
+
+## Community Showcase
+
+Check out some of the projects and models that have been built using Axolotl! Have a model you'd like to add to our Community Showcase? Open a PR with your model.
+
+Open Access AI Collective
+- [Minotaur 13b](https://huggingface.co/openaccess-ai-collective/minotaur-13b)
+- [Manticore 13b](https://huggingface.co/openaccess-ai-collective/manticore-13b)
+- [Hippogriff 30b](https://huggingface.co/openaccess-ai-collective/hippogriff-30b-chat)
+
+PocketDoc Labs
+- [Dan's PersonalityEngine 13b LoRA](https://huggingface.co/PocketDoc/Dans-PersonalityEngine-13b-LoRA)
+
 ## Contributing 🤝
 
-Bugs? Please check for open issue else create a new [Issue](https://github.com/OpenAccess-AI-Collective/axolotl/issues/new).
+Please read the [contributing guide](./.github/CONTRIBUTING.md)
+
+Bugs? Please check the [open issues](https://github.com/OpenAccess-AI-Collective/axolotl/issues/bug) else create a new Issue.
 
 PRs are **greatly welcome**!
 

configs/accelerate/default_config.yaml

@@ -1,15 +0,0 @@
-compute_environment: LOCAL_MACHINE
-distributed_type: 'NO'
-downcast_bf16: 'no'
-gpu_ids: all
-machine_rank: 0
-main_training_function: main
-mixed_precision: bf16
-num_machines: 1
-num_processes: 1
-rdzv_backend: static
-same_network: true
-tpu_env: []
-tpu_use_cluster: false
-tpu_use_sudo: false
-use_cpu: false

configs/cerebras_1_3B_alpaca.yml

@@ -1,40 +0,0 @@
-base_model: cerebras/Cerebras-GPT-1.3B
-model_type: AutoModelForCausalLM
-tokenizer_type: AutoTokenizer
-load_in_8bit: true
-datasets:
-  - path: data/alpaca_data_gpt4.jsonl
-    type: alpaca
-  - path: data/vicuna_cleaned.jsonl
-    type: sharegpt
-  - path: data/gpt4-instruct-similarity-0.6-dataset.jsonl
-    type: gpteacher
-  - path: data/roleplay-similarity_0.6-instruct-dataset.jsonl
-    type: gpteacher
-dataset_prepared_path: last_run_prepared
-val_set_size: 0.05
-adapter: lora
-sequence_len: 2048
-lora_r: 8
-lora_alpha: 16
-lora_dropout: 0.05
-lora_target_modules:
-  - c_attn
-lora_fan_in_fan_out: false
-wandb_project: pythia-1.4b-lora
-wandb_watch:
-wandb_run_id:
-wandb_log_model:
-output_dir: ./lora-alpaca
-batch_size: 32
-micro_batch_size: 4
-num_epochs: 5
-learning_rate: 0.0003
-train_on_inputs: false
-group_by_length: false
-bf16: True
-tf32: True
-gradient_checkpointing:
-early_stopping_patience:
-resume_from_checkpoint:
-local_rank:

configs/galactica_1_3B.yml

@@ -1,41 +0,0 @@
-base_model: facebook/galactica-1.3b
-model_type: AutoModelForCausalLM
-tokenizer_type: AutoTokenizer
-load_in_8bit: false
-datasets:
-  - path: tatsu-lab/alpaca
-    type: alpaca
-dataset_prepared_path: last_run_prepared
-val_set_size: 0.1
-adapter:
-lora_model_dir:
-sequence_len: 1024
-max_packed_sequence_len: 1024
-lora_r: 8
-lora_alpha: 16
-lora_dropout: 0.05
-lora_target_modules:
-  - q_proj
-  - v_proj
-lora_fan_in_fan_out: false
-wandb_project:
-wandb_watch:
-wandb_run_id:
-wandb_log_model:
-output_dir: ./lora-llama-alpaca
-batch_size: 32
-micro_batch_size: 16
-num_epochs: 3
-learning_rate: 0.00003
-train_on_inputs: false
-group_by_length: false
-bf16: false
-tf32: false
-early_stopping_patience:
-resume_from_checkpoint:
-local_rank:
-tokens:
-  pad_token: "[PAD]"
-  bos_token: "<s>"
-  eos_token: "</s>"
-  unk_token: "<unk>"

configs/llama_13B_alpaca.yml

@@ -1,39 +0,0 @@
-base_model: huggyllama/llama-13b
-model_type: LlamaForCausalLM
-tokenizer_type: LlamaTokenizer
-load_in_8bit: true
-datasets:
-  - path: anon8231489123/ShareGPT_Vicuna_unfiltered
-    data_files: ShareGPT_V3_unfiltered_cleaned_split_no_imsorry.json
-    type: sharegpt
-dataset_prepared_path: last_run_prepared
-val_set_size: 0.002
-adapter:
-lora_model_dir:
-sequence_len: 2048
-lora_r: 8
-lora_alpha: 16
-lora_dropout: 0.05
-lora_target_modules:
-  - q_proj
-  - v_proj
-lora_fan_in_fan_out: false
-wandb_project:
-wandb_watch:
-wandb_run_id:
-wandb_log_model:
-output_dir: ./llama-13b-sharegpt
-batch_size: 64
-micro_batch_size: 2
-warmup_steps: 1000
-save_steps:
-eval_steps:
-num_epochs: 5
-learning_rate: 0.00003
-train_on_inputs: false
-group_by_length: false
-bf16: true
-tf32: true
-early_stopping_patience: 5
-resume_from_checkpoint:
-local_rank:

configs/llama_65B_alpaca.yml

@@ -1,44 +0,0 @@
-base_model: huggyllama/llama-65b
-model_type: LlamaForCausalLM
-tokenizer_type: LlamaTokenizer
-load_in_8bit: true
-datasets:
-  - path: data/alpaca_data_gpt4.jsonl
-    type: alpaca
-  - path: anon8231489123/ShareGPT_Vicuna_unfiltered
-    data_files: ShareGPT_V3_unfiltered_cleaned_split_no_imsorry.json
-    type: sharegpt
-  - path: data/gpt4-instruct-similarity-0.6-dataset.jsonl
-    type: gpteacher
-  - path: data/roleplay-similarity_0.6-instruct-dataset.jsonl
-    type: gpteacher
-dataset_prepared_path: last_run_prepared
-val_set_size: 0.04
-adapter: lora
-lora_model_dir:
-sequence_len: 2048
-lora_r: 8
-lora_alpha: 16
-lora_dropout: 0.05
-lora_target_modules:
-  - q_proj
-  - v_proj
-lora_fan_in_fan_out: false
-wandb_project: llama-65b-lora
-wandb_watch:
-wandb_run_id:
-wandb_log_model:
-output_dir: ./lora-llama-alpaca
-batch_size: 128
-micro_batch_size: 16
-warmup_steps: 1000
-save_steps:
-num_epochs: 5
-learning_rate: 0.00003
-train_on_inputs: false
-group_by_length: false
-bf16: true
-tf32: true
-early_stopping_patience:
-resume_from_checkpoint:
-local_rank:

configs/llama_7B_4bit.yml

@@ -1,45 +0,0 @@
-base_model: decapoda-research/llama-7b-hf-int4
-base_model_config: decapoda-research/llama-7b-hf
-model_type: LlamaForCausalLM
-tokenizer_type: LlamaTokenizer
-load_in_8bit: true
-datasets:
-  - path: tatsu-lab/alpaca  # original alpaca dataset
-    type: alpaca
-dataset_prepared_path: data/last_run_prepared
-val_set_size: 0.04
-adapter: lora
-lora_model_dir:
-sequence_len: 2048
-max_packed_sequence_len: 1024
-lora_r: 8
-lora_alpha: 16
-lora_dropout: 0.05
-lora_target_modules:
-  - q_proj
-  - v_proj
-#  - k_proj
-#  - o_proj
-lora_fan_in_fan_out: false
-wandb_project:
-wandb_watch:
-wandb_run_id:
-wandb_log_model:
-output_dir: ./lora-test
-batch_size: 8
-micro_batch_size: 2
-num_epochs: 3
-warmup_steps: 100
-learning_rate: 0.00003
-train_on_inputs: false
-group_by_length: false
-bf16: true
-tf32: true
-gradient_checkpointing: false
-early_stopping_patience: 3
-resume_from_checkpoint:
-auto_resume_from_checkpoints: true
-local_rank:
-load_4bit: true
-xformers_attention: true
-flash_attention:

configs/llama_7B_alpaca.yml

@@ -1,41 +0,0 @@
-base_model: huggyllama/llama-7b
-model_type: LlamaForCausalLM
-tokenizer_type: LlamaTokenizer
-load_in_8bit: true
-datasets:
-  - path: data/alpaca_data_gpt4.jsonl
-    type: alpaca
-  - path: data/vicuna_cleaned.jsonl
-    type: sharegpt
-  - path: data/gpt4-instruct-similarity-0.6-dataset.jsonl
-    type: gpteacher
-  - path: data/roleplay-similarity_0.6-instruct-dataset.jsonl
-    type: gpteacher
-dataset_prepared_path: last_run_prepared
-val_set_size: 0.04
-adapter: lora
-lora_model_dir:
-sequence_len: 2048
-lora_r: 8
-lora_alpha: 16
-lora_dropout: 0.05
-lora_target_modules:
-  - q_proj
-  - v_proj
-lora_fan_in_fan_out: false
-wandb_project: llama-7b-lora
-wandb_watch:
-wandb_run_id:
-wandb_log_model:
-output_dir: ./lora-llama-alpaca
-batch_size: 128
-micro_batch_size: 16
-num_epochs: 5
-learning_rate: 0.00003
-train_on_inputs: false
-group_by_length: false
-bf16: true
-tf32: true
-early_stopping_patience:
-resume_from_checkpoint:
-local_rank:

configs/quickstart.yml

@@ -1,45 +0,0 @@
-base_model: decapoda-research/llama-7b-hf-int4
-base_model_config: decapoda-research/llama-7b-hf
-model_type: LlamaForCausalLM
-tokenizer_type: LlamaTokenizer
-load_in_8bit: true
-datasets:
-  - path: tatsu-lab/alpaca  # original alpaca dataset
-    type: alpaca
-dataset_prepared_path: data/last_run_prepared
-val_set_size: 0.04
-adapter: lora
-lora_model_dir:
-sequence_len: 1024
-max_packed_sequence_len: 1024
-lora_r: 8
-lora_alpha: 16
-lora_dropout: 0.05
-lora_target_modules:
-  - q_proj
-  - v_proj
-#  - k_proj
-#  - o_proj
-lora_fan_in_fan_out: false
-wandb_project:
-wandb_watch:
-wandb_run_id:
-wandb_log_model:
-output_dir: ./lora-test
-batch_size: 4
-micro_batch_size: 1
-num_epochs: 3
-warmup_steps: 100
-learning_rate: 0.00003
-train_on_inputs: false
-group_by_length: false
-bf16: true
-tf32: true
-gradient_checkpointing: false
-early_stopping_patience: 3
-resume_from_checkpoint:
-auto_resume_from_checkpoints: true
-local_rank:
-gptq: true
-xformers_attention: true
-flash_attention:

configs/sample.yml

@@ -1,86 +0,0 @@
-# this is the huggingface model that contains *.pt, *.safetensors, or *.bin files
-# this can also be a relative path to a model on disk
-base_model: decapoda-research/llama-7b-hf-int4
-# you can specify an ignore pattern if the model repo contains more than 1 model type (*.pt, etc)
-base_model_ignore_patterns:
-# if the base_model repo on hf hub doesn't include configuration .json files,
-# you can set that here, or leave this empty to default to base_model
-base_model_config: decapoda-research/llama-7b-hf
-# If you want to specify the type of model to load, AutoModelForCausalLM is a good choice too
-model_type: AutoModelForCausalLM
-# Corresponding tokenizer for the model AutoTokenizer is a good choice
-tokenizer_type: AutoTokenizer
-# whether you are training a 4-bit quantized model
-load_4bit: true
-# this will attempt to quantize the model down to 8 bits and use adam 8 bit optimizer
-load_in_8bit: true
-# a list of one or more datasets to finetune the model with
-datasets:
-  # this can be either a hf dataset, or relative path
-  - path: vicgalle/alpaca-gpt4
-  # The type of prompt to use for training. [alpaca, sharegpt, gpteacher, oasst, reflection]
-    type: alpaca
-# axolotl attempts to save the dataset as an arrow after packing the data together so
-# subsequent training attempts load faster, relative path
-dataset_prepared_path: data/last_run_prepared
-# How much of the dataset to set aside as evaluation. 1 = 100%, 0.50 = 50%, etc
-val_set_size: 0.04
-# if you want to use lora, leave blank to train all parameters in original model
-adapter: lora
-# if you already have a lora model trained that you want to load, put that here
-lora_model_dir:
-# the maximum length of an input to train with, this should typically be less than 2048
-# as most models have a token/context limit of 2048
-sequence_len: 2048
-# max sequence length to concatenate training samples together up to
-# inspired by StackLLaMA. see https://huggingface.co/blog/stackllama#supervised-fine-tuning
-max_packed_sequence_len: 1024
-# lora hyperparameters
-lora_r: 8
-lora_alpha: 16
-lora_dropout: 0.05
-lora_target_modules:
-  - q_proj
-  - v_proj
-#  - k_proj
-#  - o_proj
-lora_fan_in_fan_out: false
-# wandb configuration if your're using it
-wandb_project:
-wandb_watch:
-wandb_run_id:
-wandb_log_model:
-# where to save the finsihed model to
-output_dir: ./completed-model
-# training hyperparameters
-batch_size: 8
-micro_batch_size: 2
-num_epochs: 3
-warmup_steps: 100
-learning_rate: 0.00003
-# whether to mask out or include the human's prompt from the training labels
-train_on_inputs: false
-# don't use this, leads to wonky training (according to someone on the internet)
-group_by_length: false
-# Use CUDA bf16
-bf16: true
-# Use CUDA tf32
-tf32: true
-# does not work with current implementation of 4-bit LoRA
-gradient_checkpointing: false
-# stop training after this many evaluation losses have increased in a row
-# https://huggingface.co/transformers/v4.2.2/_modules/transformers/trainer_callback.html#EarlyStoppingCallback
-early_stopping_patience: 3
-# specify a scheduler to use with the optimizer. only one_cycle is supported currently
-lr_scheduler:
-# whether to use xformers attention patch https://github.com/facebookresearch/xformers:
-xformers_attention:
-# whether to use flash attention patch https://github.com/HazyResearch/flash-attention:
-flash_attention:
-# resume from a specific checkpoint dir
-resume_from_checkpoint:
-# if resume_from_checkpoint isn't set and you simply want it to start where it left off
-# be careful with this being turned on between different models
-auto_resume_from_checkpoints: false
-# don't mess with this, it's here for accelerate and torchrun
-local_rank:

configs/stability_3b.yml

@@ -1,56 +0,0 @@
-base_model: stabilityai/stablelm-base-alpha-3b
-base_model_config: stabilityai/stablelm-base-alpha-3b
-load_in_8bit: false
-datasets:
-  - path: vicgalle/alpaca-gpt4
-    type: alpaca
-dataset_prepared_path: last_run_prepared
-val_set_size: 0.04
-adapter:
-lora_model_dir:
-sequence_len: 4096
-max_packed_sequence_len: 4096
-lora_r: 8
-lora_alpha: 16
-lora_dropout: 0.05
-lora_target_modules:
-  - q_proj
-  - v_proj
-lora_fan_in_fan_out: false
-wandb_project: stable-alpaca-3b
-wandb_watch:
-wandb_run_id:
-wandb_log_model:
-output_dir: ./stable-alpaca-3b
-batch_size: 2
-micro_batch_size: 1
-num_epochs: 1
-optimizer: adamw_bnb_8bit
-torchdistx_path:
-lr_scheduler: cosine
-learning_rate: 0.0000002
-train_on_inputs: false
-group_by_length: false
-bf16: true
-tf32: true
-early_stopping_patience:
-resume_from_checkpoint:
-local_rank:
-logging_steps: 1
-xformers_attention: true
-flash_attention:
-gptq_groupsize:
-gptq_model_v1:
-warmup_steps: 100
-eval_steps: 50
-save_steps: 200
-debug:
-deepspeed:
-weight_decay: 0.01
-fsdp:
-fsdp_config:
-#tokens:
-#  pad_token: "[PAD]"
-#  bos_token: "<s>"
-#  eos_token: "</s>"
-#  unk_token: "<unk>"

configs/vicuna_13B_4bit_reflect.yml

@@ -1,45 +0,0 @@
-base_model: anon8231489123/vicuna-13b-GPTQ-4bit-128g
-base_model_config: anon8231489123/vicuna-13b-GPTQ-4bit-128g
-model_type: LlamaForCausalLM
-tokenizer_type: LlamaTokenizer
-load_in_8bit: false
-load_4bit: true
-gptq_groupsize: 128
-gptq_model_v1: false
-datasets:
-# https://github.com/vaguenebula/AlpacaDataReflect/blob/main/alpaca_reflect_pruned.json
-  - path: data/alpaca_reflect_pruned.jsonl
-    type: reflection
-dataset_prepared_path: data/last_run_prepared
-val_set_size: 0.04
-adapter: lora
-lora_model_dir:
-sequence_len: 2048
-max_packed_sequence_len: 2048
-lora_r: 8
-lora_alpha: 16
-lora_dropout: 0.05
-lora_target_modules:
-  - q_proj
-  - v_proj
-#  - k_proj
-#  - o_proj
-lora_fan_in_fan_out: false
-wandb_project:
-wandb_watch:
-wandb_run_id:
-wandb_log_model:
-output_dir: ./lora-reflect
-batch_size: 8
-micro_batch_size: 2
-num_epochs: 3
-learning_rate: 0.00003
-train_on_inputs: false
-group_by_length: false
-bf16: true
-tf32: true
-gradient_checkpointing: false
-early_stopping_patience: 3
-resume_from_checkpoint:
-local_rank:
-flash_attention: true

data/README.md

@@ -1,24 +0,0 @@
-
-## Download some datasets
-```shell
-curl https://raw.githubusercontent.com/tloen/alpaca-lora/main/alpaca_data_gpt4.json -o data/raw/alpaca_data_gpt4.json
-curl https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json -L -o data/raw/vicuna_cleaned.json
-curl https://github.com/teknium1/GPTeacher/blob/main/Instruct/gpt4-instruct-similarity-0.6-dataset.json?raw=true -L -o data/raw/gpt4-instruct-similarity-0.6-dataset.json
-curl https://github.com/teknium1/GPTeacher/blob/main/Roleplay/roleplay-similarity_0.6-instruct-dataset.json?raw=true -L -o data/raw/roleplay-similarity_0.6-instruct-dataset.json
-```
-
-## Convert the JSON data files to JSONL.
-
-```shell
-python3 ./scripts/alpaca_json_to_jsonl.py --input data/alpaca_data_gpt4.json > data/alpaca_data_gpt4.jsonl
-python3 ./scripts/alpaca_json_to_jsonl.py --input data/raw/vicuna_cleaned.json > data/vicuna_cleaned.jsonl
-python3 ./scripts/alpaca_json_to_jsonl.py --input data/raw/roleplay-similarity_0.6-instruct-dataset.json > data/roleplay-similarity_0.6-instruct-dataset.jsonl
-python3 ./scripts/alpaca_json_to_jsonl.py --input data/raw/gpt4-instruct-similarity-0.6-dataset.json > data/gpt4-instruct-similarity-0.6-dataset.jsonl
-```
----
-
-Using JSONL makes it easier to subset the data if you want a smaller training set, i.e get 2000 random examples.
-
-```shell
-shuf -n2000 data/vicuna_cleaned.jsonl > data/vicuna_cleaned.subset0.jsonl
-```

data/raw/.gitignore

@@ -1 +0,0 @@
-**

deepspeed/zero2.json

@@ -0,0 +1,46 @@
+{
+    "zero_optimization": {
+      "stage": 2,
+      "offload_optimizer": {
+        "device": "cpu"
+      },
+      "contiguous_gradients": true,
+      "overlap_comm": true
+    },
+    "bf16": {
+      "enabled": "auto"
+    },
+    "fp16": {
+      "enabled": "auto",
+      "auto_cast": false,
+      "loss_scale": 0,
+      "initial_scale_power": 32,
+      "loss_scale_window": 1000,
+      "hysteresis": 2,
+      "min_loss_scale": 1
+    },
+    "optimizer": {
+      "type": "AdamW",
+      "params": {
+        "lr": "auto",
+        "betas": [
+          0.9,
+          0.999
+        ],
+        "eps": 1e-8,
+        "weight_decay": "auto"
+      }
+    },
+    "scheduler": {
+      "type": "WarmupDecayLR",
+      "params": {
+        "warmup_min_lr": "auto",
+        "warmup_max_lr": "auto",
+        "warmup_num_steps": "auto",
+        "total_num_steps": "auto"
+      }
+    },
+    "train_batch_size": "auto",
+    "train_micro_batch_size_per_gpu": "auto",
+    "wall_clock_breakdown": false
+}

deepspeed/zero3.json

@@ -35,20 +35,17 @@
     "type": "AdamW",
     "params": {
       "lr": "auto",
-      "betas": [
-        0.9,
-        0.999
-      ],
+      "betas": "auto",
       "eps": 1e-8,
       "weight_decay": "auto"
     }
   },
   "scheduler": {
-    "type": "OneCycle",
+    "type": "WarmupLR",
     "params": {
-      "cycle_min_lr": 0.00001,
-      "cycle_max_lr": 0.00003,
-      "cycle_first_step_size": 120
+      "warmup_min_lr": "auto",
+      "warmup_max_lr": "auto",
+      "warmup_num_steps": "auto"
     }
   },
   "train_batch_size": "auto",

docker-compose.yaml

@@ -0,0 +1,20 @@
+# version: '3.8'
+services:
+  axolotl:
+    build:
+      context: .
+      dockerfile: ./docker/Dockerfile
+    volumes:
+      - .:/workspace/axolotl
+      - ~/.cache/huggingface/:/root/.cache/huggingface/
+    # set environment variables
+    environment:
+      - WANDB_API_KEY=${WANDB_API_KEY}
+    deploy:
+      resources:
+        reservations:
+          devices:
+            - driver: nvidia
+              # count: 1
+              capabilities: [gpu]
+    command: tail -f /dev/null

docker/Dockerfile

@@ -3,25 +3,28 @@ FROM winglian/axolotl-base:$BASE_TAG
 
 ARG TORCH_CUDA_ARCH_LIST="7.0 7.5 8.0 8.6+PTX"
 ARG AXOLOTL_EXTRAS=""
+ARG CUDA="118"
+ENV BNB_CUDA_VERSION=$CUDA
 
 RUN apt-get update && \
     apt-get install -y vim curl
 
 WORKDIR /workspace
 
-RUN pip3 install --force-reinstall "peft @ git+https://github.com/huggingface/peft.git@main" \
-            "accelerate @ git+https://github.com/huggingface/accelerate.git@main" \
-            "transformers @ git+https://github.com/huggingface/transformers.git@main"
-
-RUN mkdir axolotl
-COPY . axolotl/
+RUN pip3 install "peft @ git+https://github.com/huggingface/peft.git@main"
+RUN git clone --depth=1 https://github.com/OpenAccess-AI-Collective/axolotl.git
 # If AXOLOTL_EXTRAS is set, append it in brackets
 RUN cd axolotl && \
     if [ "$AXOLOTL_EXTRAS" != "" ] ; then \
-        pip install -e .[$AXOLOTL_EXTRAS]; \
+        pip install -e .[flash-attn,$AXOLOTL_EXTRAS]; \
     else \
-        pip install -e .; \
+        pip install -e .[flash-attn]; \
     fi
 
+# fix so that git fetch/pull from remote works
+RUN cd axolotl && \
+    git config remote.origin.fetch "+refs/heads/*:refs/remotes/origin/*" && \
+    git config --get remote.origin.fetch
+
 # helper for huggingface-login cli
 RUN git config --global credential.helper store

docker/Dockerfile-base

@@ -8,7 +8,7 @@ FROM nvidia/cuda:$CUDA_VERSION-cudnn$CUDNN_VERSION-devel-ubuntu$UBUNTU_VERSION a
 ENV PATH="/root/miniconda3/bin:${PATH}"
 
 ARG PYTHON_VERSION="3.9"
-ARG PYTORCH="2.0.0"
+ARG PYTORCH_VERSION="2.0.1"
 ARG CUDA="118"
 
 ENV PYTHON_VERSION=$PYTHON_VERSION
@@ -29,29 +29,12 @@ ENV PATH="/root/miniconda3/envs/py${PYTHON_VERSION}/bin:${PATH}"
 WORKDIR /workspace
 
 RUN python3 -m pip install --upgrade pip && pip3 install packaging && \
-    python3 -m pip install --no-cache-dir -U torch==${PYTORCH} torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu$CUDA
-
-
-FROM base-builder AS flash-attn-builder
-
-WORKDIR /workspace
-
-ARG TORCH_CUDA_ARCH_LIST="7.0 7.5 8.0 8.6+PTX"
-
-RUN git clone https://github.com/HazyResearch/flash-attention.git && \
-    cd flash-attention && \
-    python3 setup.py bdist_wheel && \
-    cd csrc/fused_dense_lib && \
-    python3 setup.py bdist_wheel && \
-    cd ../xentropy && \
-    python3 setup.py bdist_wheel && \
-    cd ../rotary && \
-    python3 setup.py bdist_wheel && \
-    cd ../layer_norm && \
-    python3 setup.py bdist_wheel
+    python3 -m pip install --no-cache-dir -U torch==${PYTORCH_VERSION}+cu${CUDA} --extra-index-url https://download.pytorch.org/whl/cu$CUDA
 
 FROM base-builder AS deepspeed-builder
 
+ARG TORCH_CUDA_ARCH_LIST="7.0 7.5 8.0 8.6 9.0+PTX"
+
 WORKDIR /workspace
 
 RUN git clone https://github.com/microsoft/DeepSpeed.git && \
@@ -71,11 +54,14 @@ RUN git clone https://github.com/TimDettmers/bitsandbytes.git && \
 
 FROM base-builder
 
+ARG TORCH_CUDA_ARCH_LIST="7.0 7.5 8.0 8.6 9.0+PTX"
+ENV TORCH_CUDA_ARCH_LIST=$TORCH_CUDA_ARCH_LIST
+
 # recompile apex
 RUN python3 -m pip uninstall -y apex
 RUN git clone https://github.com/NVIDIA/apex
 #  `MAX_JOBS=1` disables parallel building to avoid cpu memory OOM when building image on GitHub Action (standard) runners
-RUN cd apex && MAX_JOBS=1 python3 -m pip install --global-option="--cpp_ext" --global-option="--cuda_ext" --no-cache -v --disable-pip-version-check .
+RUN cd apex && MAX_JOBS=1 python3 -m pip install -v --disable-pip-version-check --no-cache-dir --no-build-isolation --config-settings "--build-option=--cpp_ext" --config-settings "--build-option=--cuda_ext" ./
 
 RUN mkdir -p /workspace/builds
 COPY --from=bnb-builder /workspace/bitsandbytes /workspace/builds/bitsandbytes
@@ -84,15 +70,10 @@ RUN mkdir -p /workspace/wheels/bitsandbytes
 COPY --from=deepspeed-builder /workspace/DeepSpeed/dist/deepspeed-*.whl wheels
 COPY --from=bnb-builder /workspace/bitsandbytes/dist/bitsandbytes-*.whl wheels
 COPY --from=bnb-builder /workspace/bitsandbytes/bitsandbytes/libbitsandbytes*.so wheels/bitsandbytes
-COPY --from=flash-attn-builder /workspace/flash-attention/dist/flash_attn-*.whl wheels
-COPY --from=flash-attn-builder /workspace/flash-attention/csrc/fused_dense_lib/dist/fused_dense_lib-*.whl wheels
-COPY --from=flash-attn-builder /workspace/flash-attention/csrc/xentropy/dist/xentropy_cuda_lib-*.whl wheels
-COPY --from=flash-attn-builder /workspace/flash-attention/csrc/rotary/dist/rotary_emb-*.whl wheels
-COPY --from=flash-attn-builder /workspace/flash-attention/csrc/layer_norm/dist/dropout_layer_norm-*.whl wheels
 
-RUN pip3 install wheels/deepspeed-*.whl wheels/flash_attn-*.whl wheels/fused_dense_lib-*.whl wheels/xentropy_cuda_lib-*.whl wheels/rotary_emb-*.whl wheels/dropout_layer_norm-*.whl
+RUN pip3 install wheels/deepspeed-*.whl
 RUN cd /workspace/builds/bitsandbytes && python3 setup.py install
 RUN git lfs install --skip-repo
 RUN pip3 install awscli && \
     # The base image ships with `pydantic==1.8.2` which is not working
-    pip3 install -U --no-cache-dir pydantic
+    pip3 install -U --no-cache-dir pydantic==1.10.10

docker/Dockerfile-runpod

@@ -1,6 +1,10 @@
 ARG BASE_TAG=main
 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"
+
 COPY scripts/runpod-entrypoint.sh /root/runpod-entrypoint.sh
 
 RUN apt install --yes --no-install-recommends openssh-server tmux && \

examples/cerebras/qlora.yml

@@ -0,0 +1,61 @@
+base_model: cerebras/Cerebras-GPT-1.3B
+base_model_config: cerebras/Cerebras-GPT-1.3B
+load_in_8bit: false
+load_in_4bit: true
+strict: false
+push_dataset_to_hub:
+datasets:
+  - path: teknium/GPT4-LLM-Cleaned
+    type: alpaca
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.01
+adapter: qlora
+lora_model_dir:
+sequence_len: 2048
+max_packed_sequence_len: 2048
+lora_r: 16
+lora_alpha: 32
+lora_dropout: 0.05
+lora_target_modules:
+  - c_fc
+  - c_attn
+  - c_proj
+lora_target_linear:
+lora_fan_in_fan_out:
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_run_id:
+wandb_log_model:
+output_dir: ./qlora-out
+batch_size: 4
+micro_batch_size: 4
+num_epochs: 2
+optimizer: paged_adamw_8bit
+torchdistx_path:
+lr_scheduler: cosine
+learning_rate: 0.0002
+train_on_inputs: false
+group_by_length: false
+bf16: true
+fp16: false
+tf32: true
+gradient_checkpointing: true
+early_stopping_patience:
+resume_from_checkpoint:
+local_rank:
+logging_steps: 1
+xformers_attention: true
+flash_attention:
+gptq_groupsize:
+gptq_model_v1:
+warmup_steps: 10
+eval_steps: 20
+save_steps:
+debug:
+deepspeed:
+weight_decay: 0.1
+fsdp:
+fsdp_config:
+special_tokens:
+  pad_token: "<|endoftext|>"

examples/code-llama/13b/lora.yml

@@ -0,0 +1,67 @@
+base_model: codellama/CodeLlama-13b-hf
+base_model_config: codellama/CodeLlama-13b-hf
+model_type: LlamaForCausalLM
+tokenizer_type: CodeLlamaTokenizer
+is_llama_derived_model: true
+
+load_in_8bit: true
+load_in_4bit: false
+strict: false
+
+datasets:
+  - path: mhenrichsen/alpaca_2k_test
+    type: alpaca
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.01
+output_dir: ./lora-out
+
+sequence_len: 100000
+sample_packing: 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_run_id:
+wandb_log_model:
+
+gradient_accumulation_steps: 4
+micro_batch_size: 2
+num_epochs: 3
+optimizer: adamw_bnb_8bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+train_on_inputs: false
+group_by_length: false
+bf16: true
+fp16: false
+tf32: false
+
+gradient_checkpointing: true
+early_stopping_patience:
+resume_from_checkpoint:
+local_rank:
+logging_steps: 1
+xformers_attention:
+flash_attention: true
+
+warmup_steps: 10
+eval_steps: 20
+save_steps:
+debug:
+deepspeed:
+weight_decay: 0.0
+fsdp:
+fsdp_config:
+special_tokens:
+  bos_token: "<s>"
+  eos_token: "</s>"
+  unk_token: "<unk>"

examples/code-llama/13b/qlora.yml

@@ -0,0 +1,69 @@
+base_model: codellama/CodeLlama-13b-hf
+base_model_config: codellama/CodeLlama-13b-hf
+model_type: LlamaForCausalLM
+tokenizer_type: CodeLlamaTokenizer
+is_llama_derived_model: true
+
+load_in_8bit: false
+load_in_4bit: true
+strict: false
+
+datasets:
+  - path: mhenrichsen/alpaca_2k_test
+    type: alpaca
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.01
+output_dir: ./qlora-out
+
+adapter: qlora
+lora_model_dir:
+
+sequence_len: 100000
+sample_packing: true
+
+lora_r: 32
+lora_alpha: 16
+lora_dropout: 0.05
+lora_target_modules:
+lora_target_linear: true
+lora_fan_in_fan_out:
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_run_id:
+wandb_log_model:
+
+gradient_accumulation_steps: 4
+micro_batch_size: 2
+num_epochs: 3
+optimizer: paged_adamw_32bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+train_on_inputs: false
+group_by_length: false
+bf16: true
+fp16: false
+tf32: false
+
+gradient_checkpointing: true
+early_stopping_patience:
+resume_from_checkpoint:
+local_rank:
+logging_steps: 1
+xformers_attention:
+flash_attention: true
+
+warmup_steps: 10
+eval_steps: 20
+save_steps:
+debug:
+deepspeed:
+weight_decay: 0.0
+fsdp:
+fsdp_config:
+special_tokens:
+  bos_token: "<s>"
+  eos_token: "</s>"
+  unk_token: "<unk>"

examples/code-llama/34b/lora.yml

@@ -0,0 +1,67 @@
+base_model: codellama/CodeLlama-34b-hf
+base_model_config: codellama/CodeLlama-34b-hf
+model_type: LlamaForCausalLM
+tokenizer_type: CodeLlamaTokenizer
+is_llama_derived_model: true
+
+load_in_8bit: true
+load_in_4bit: false
+strict: false
+
+datasets:
+  - path: mhenrichsen/alpaca_2k_test
+    type: alpaca
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.01
+output_dir: ./lora-out
+
+sequence_len: 100000
+sample_packing: 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_run_id:
+wandb_log_model:
+
+gradient_accumulation_steps: 4
+micro_batch_size: 2
+num_epochs: 3
+optimizer: adamw_bnb_8bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+train_on_inputs: false
+group_by_length: false
+bf16: true
+fp16: false
+tf32: false
+
+gradient_checkpointing: true
+early_stopping_patience:
+resume_from_checkpoint:
+local_rank:
+logging_steps: 1
+xformers_attention:
+flash_attention: true
+
+warmup_steps: 10
+eval_steps: 20
+save_steps:
+debug:
+deepspeed:
+weight_decay: 0.0
+fsdp:
+fsdp_config:
+special_tokens:
+  bos_token: "<s>"
+  eos_token: "</s>"
+  unk_token: "<unk>"

examples/code-llama/34b/qlora.yml

@@ -0,0 +1,69 @@
+base_model: codellama/CodeLlama-34b-hf
+base_model_config: codellama/CodeLlama-34b-hf
+model_type: LlamaForCausalLM
+tokenizer_type: CodeLlamaTokenizer
+is_llama_derived_model: true
+
+load_in_8bit: false
+load_in_4bit: true
+strict: false
+
+datasets:
+  - path: mhenrichsen/alpaca_2k_test
+    type: alpaca
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.01
+output_dir: ./qlora-out
+
+adapter: qlora
+lora_model_dir:
+
+sequence_len: 100000
+sample_packing: true
+
+lora_r: 32
+lora_alpha: 16
+lora_dropout: 0.05
+lora_target_modules:
+lora_target_linear: true
+lora_fan_in_fan_out:
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_run_id:
+wandb_log_model:
+
+gradient_accumulation_steps: 4
+micro_batch_size: 2
+num_epochs: 3
+optimizer: paged_adamw_32bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+train_on_inputs: false
+group_by_length: false
+bf16: true
+fp16: false
+tf32: false
+
+gradient_checkpointing: true
+early_stopping_patience:
+resume_from_checkpoint:
+local_rank:
+logging_steps: 1
+xformers_attention:
+flash_attention: true
+
+warmup_steps: 10
+eval_steps: 20
+save_steps:
+debug:
+deepspeed:
+weight_decay: 0.0
+fsdp:
+fsdp_config:
+special_tokens:
+  bos_token: "<s>"
+  eos_token: "</s>"
+  unk_token: "<unk>"

examples/code-llama/7b/lora.yml

@@ -0,0 +1,67 @@
+base_model: codellama/CodeLlama-7b-hf
+base_model_config: codellama/CodeLlama-7b-hf
+model_type: LlamaForCausalLM
+tokenizer_type: CodeLlamaTokenizer
+is_llama_derived_model: true
+
+load_in_8bit: true
+load_in_4bit: false
+strict: false
+
+datasets:
+  - path: mhenrichsen/alpaca_2k_test
+    type: alpaca
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.01
+output_dir: ./lora-out
+
+sequence_len: 100000
+sample_packing: 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_run_id:
+wandb_log_model:
+
+gradient_accumulation_steps: 4
+micro_batch_size: 2
+num_epochs: 3
+optimizer: adamw_bnb_8bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+train_on_inputs: false
+group_by_length: false
+bf16: true
+fp16: false
+tf32: false
+
+gradient_checkpointing: true
+early_stopping_patience:
+resume_from_checkpoint:
+local_rank:
+logging_steps: 1
+xformers_attention:
+flash_attention: true
+
+warmup_steps: 10
+eval_steps: 20
+save_steps:
+debug:
+deepspeed:
+weight_decay: 0.0
+fsdp:
+fsdp_config:
+special_tokens:
+  bos_token: "<s>"
+  eos_token: "</s>"
+  unk_token: "<unk>"

examples/code-llama/7b/qlora.yml

@@ -0,0 +1,69 @@
+base_model: codellama/CodeLlama-7b-hf
+base_model_config: codellama/CodeLlama-7b-hf
+model_type: LlamaForCausalLM
+tokenizer_type: CodeLlamaTokenizer
+is_llama_derived_model: true
+
+load_in_8bit: false
+load_in_4bit: true
+strict: false
+
+datasets:
+  - path: mhenrichsen/alpaca_2k_test
+    type: alpaca
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.01
+output_dir: ./qlora-out
+
+adapter: qlora
+lora_model_dir:
+
+sequence_len: 100000
+sample_packing: true
+
+lora_r: 32
+lora_alpha: 16
+lora_dropout: 0.05
+lora_target_modules:
+lora_target_linear: true
+lora_fan_in_fan_out:
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_run_id:
+wandb_log_model:
+
+gradient_accumulation_steps: 4
+micro_batch_size: 2
+num_epochs: 3
+optimizer: paged_adamw_32bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+train_on_inputs: false
+group_by_length: false
+bf16: true
+fp16: false
+tf32: false
+
+gradient_checkpointing: true
+early_stopping_patience:
+resume_from_checkpoint:
+local_rank:
+logging_steps: 1
+xformers_attention:
+flash_attention: true
+
+warmup_steps: 10
+eval_steps: 20
+save_steps:
+debug:
+deepspeed:
+weight_decay: 0.0
+fsdp:
+fsdp_config:
+special_tokens:
+  bos_token: "<s>"
+  eos_token: "</s>"
+  unk_token: "<unk>"

examples/code-llama/README.md

@@ -0,0 +1,22 @@
+# Overview
+
+This is an example of CodeLLaMA configuration for 7b, 13b and 34b.
+
+The 7b variant fits on any 24GB VRAM GPU and will take up about 17 GB of VRAM during training if using qlora and 20 GB if using lora. On a RTX 4090 it trains 3 epochs of the default dataset in about 15 minutes.
+
+The 13b variant will fit if you change these settings to these values:
+gradient_accumulation_steps: 2
+micro_batch_size: 1
+
+The 34b variant does not fit on 24GB of VRAM - you will need something with +40 gb VRAM that also supports flash attention v2 - A6000 or A100 are good choices.
+
+```shell
+accelerate launch scripts/finetune.py examples/code-llama/[MODEL_SIZE]/qlora.yml
+
+```
+or
+
+```shell
+accelerate launch scripts/finetune.py examples/code-llama/[MODEL_SIZE]/lora.yml
+
+```

examples/falcon/config-7b-lora.yml

@@ -23,7 +23,8 @@ lora_dropout: 0.0
 lora_target_modules:
 lora_target_linear: true
 lora_fan_in_fan_out:
-wandb_project: falcon-7b
+wandb_project:
+wandb_entity:
 wandb_watch:
 wandb_run_id:
 wandb_log_model:

examples/falcon/config-7b-qlora.yml

@@ -0,0 +1,93 @@
+# 1b: tiiuae/falcon-rw-1b
+# 40b: tiiuae/falcon-40b
+base_model: tiiuae/falcon-7b
+base_model_config: tiiuae/falcon-7b
+# required by falcon custom model code: https://huggingface.co/tiiuae/falcon-7b/tree/main
+trust_remote_code: true
+model_type: AutoModelForCausalLM
+tokenizer_type: AutoTokenizer
+load_in_8bit: false
+# enable 4bit for QLoRA
+load_in_4bit: true
+gptq: false
+strict: false
+push_dataset_to_hub:
+datasets:
+  - path: QingyiSi/Alpaca-CoT
+    data_files:
+      - Chain-of-Thought/formatted_cot_data/gsm8k_train.json
+    type: "alpaca:chat"
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.01
+# enable QLoRA
+adapter: qlora
+lora_model_dir:
+sequence_len: 2048
+max_packed_sequence_len:
+
+# hyperparameters from QLoRA paper Appendix B.2
+# "We find hyperparameters to be largely robust across datasets"
+lora_r: 64
+lora_alpha: 16
+# 0.1 for models up to 13B
+# 0.05 for 33B and 65B models
+lora_dropout: 0.05
+# add LoRA modules on all linear layers of the base model
+lora_target_modules:
+lora_target_linear: true
+lora_fan_in_fan_out:
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_run_id:
+wandb_log_model:
+output_dir: ./qlora-out
+
+# QLoRA paper Table 9
+# - 16 for 7b & 13b
+# - 32 for 33b, 64 for 64b
+# Max size tested on A6000
+# - 7b: 40
+# - 40b: 4
+# decrease if OOM, increase for max VRAM utilization
+micro_batch_size: 1
+gradient_accumulation_steps: 2
+num_epochs: 3
+# Optimizer for QLoRA
+optimizer: paged_adamw_32bit
+torchdistx_path:
+lr_scheduler: cosine
+# QLoRA paper Table 9
+# - 2e-4 for 7b & 13b
+# - 1e-4 for 33b & 64b
+learning_rate: 0.0002
+train_on_inputs: false
+group_by_length: false
+bf16: true
+fp16: false
+tf32: true
+gradient_checkpointing: true
+# stop training after this many evaluation losses have increased in a row
+# https://huggingface.co/transformers/v4.2.2/_modules/transformers/trainer_callback.html#EarlyStoppingCallback
+early_stopping_patience: 3
+resume_from_checkpoint:
+auto_resume_from_checkpoints: true
+local_rank:
+logging_steps: 1
+xformers_attention: true
+flash_attention:
+gptq_groupsize:
+gptq_model_v1:
+warmup_steps: 10
+eval_steps: 5
+save_steps: 10
+debug:
+deepspeed:
+weight_decay: 0.000001
+fsdp:
+fsdp_config:
+special_tokens:
+  pad_token: "<|endoftext|>"
+  bos_token: ">>ABSTRACT<<"
+  eos_token: "<|endoftext|>"

examples/falcon/config-7b.yml

@@ -23,7 +23,8 @@ lora_dropout: 0.0
 lora_target_modules:
 lora_target_linear: true
 lora_fan_in_fan_out:
-wandb_project: falcon-7b
+wandb_project:
+wandb_entity:
 wandb_watch:
 wandb_run_id:
 wandb_log_model:

examples/gptj/qlora.yml

@@ -0,0 +1,58 @@
+base_model: EleutherAI/gpt-j-6b
+base_model_config: EleutherAI/gpt-j-6b
+load_in_8bit: false
+load_in_4bit: true
+strict: false
+push_dataset_to_hub:
+datasets:
+  - path: teknium/GPT4-LLM-Cleaned
+    type: alpaca
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.01
+adapter: qlora
+lora_model_dir:
+sequence_len: 2048
+max_packed_sequence_len:
+lora_r: 8
+lora_alpha: 32
+lora_dropout: 0.05
+lora_target_modules:
+lora_target_linear: true
+lora_fan_in_fan_out:
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_run_id:
+wandb_log_model:
+output_dir: ./qlora-out
+gradient_accumulation_steps: 2
+micro_batch_size: 2
+num_epochs: 2
+optimizer: paged_adamw_8bit
+torchdistx_path:
+lr_scheduler: cosine
+learning_rate: 0.0001
+train_on_inputs: false
+group_by_length: false
+bf16: true
+fp16: false
+tf32: true
+gradient_checkpointing: true
+early_stopping_patience:
+resume_from_checkpoint:
+local_rank:
+logging_steps: 1
+xformers_attention: true
+flash_attention:
+gptq_groupsize:
+gptq_model_v1:
+warmup_steps: 10
+eval_steps: 20
+save_steps:
+debug:
+deepspeed:
+weight_decay: 0.1
+fsdp:
+fsdp_config:
+special_tokens:
+  pad_token: "<|endoftext|>"

examples/gptq-lora-7b/README.md

@@ -3,6 +3,6 @@
 This is a good place to start for beginners. This will run on an NVIDIA RTX4090 with no other changes needed.
 
 ```shell
-accelerate launch scripts/finetune.py examples/4bit-lora-7b/config.yml
+accelerate launch scripts/finetune.py examples/gptq-lora-7b/config.yml
 
 ```

examples/gptq-lora-7b/config.yml

@@ -22,11 +22,12 @@ lora_target_modules:
   - v_proj
 lora_fan_in_fan_out: false
 wandb_project: llama-7b-lora-int4
+wandb_entity:
 wandb_watch:
 wandb_run_id:
 wandb_log_model:
 output_dir: ./llama-7b-lora-int4
-batch_size: 1
+gradient_accumulation_steps: 1
 micro_batch_size: 1
 num_epochs: 3
 optimizer: adamw_bnb_8bit
@@ -56,7 +57,7 @@ weight_decay: 0.0001
 fsdp:
 fsdp_config:
 tokens:
-  pad_token: "[PAD]"
+  pad_token: "<pad>"
   bos_token: "<s>"
   eos_token: "</s>"
   unk_token: "<unk>"

examples/jeopardy-bot/config.yml

@@ -7,30 +7,29 @@ datasets:
   - path: openaccess-ai-collective/jeopardy
     type: jeopardy
 dataset_prepared_path: last_run_prepared
-val_set_size: 0.01
+val_set_size: 0.02
 adapter:
 lora_model_dir:
-sequence_len: 2048
-max_packed_sequence_len: 2048
-lora_r: 8
-lora_alpha: 16
-lora_dropout: 0.05
+sequence_len: 512
+max_packed_sequence_len:
+lora_r:
+lora_alpha:
+lora_dropout:
 lora_target_modules:
-  - q_proj
-  - v_proj
 lora_fan_in_fan_out: false
-wandb_project: jeopardy-bot-7b
+wandb_project:
+wandb_entity:
 wandb_watch:
 wandb_run_id:
 wandb_log_model:
 output_dir: ./jeopardy-bot-7b
-batch_size: 4
+gradient_accumulation_steps: 1
 micro_batch_size: 1
-num_epochs: 2
+num_epochs: 3
 optimizer: adamw_bnb_8bit
 torchdistx_path:
 lr_scheduler: cosine
-learning_rate: 0.0000002
+learning_rate: 0.00003
 train_on_inputs: false
 group_by_length: false
 bf16: true
@@ -48,11 +47,10 @@ eval_steps: 110
 save_steps: 660
 debug:
 deepspeed:
-weight_decay: 0.0001
+weight_decay: 0.1
 fsdp:
 fsdp_config:
 tokens:
-  pad_token: "[PAD]"
   bos_token: "<s>"
   eos_token: "</s>"
   unk_token: "<unk>"

examples/llama-2/README.md

@@ -0,0 +1,20 @@
+# Overview
+
+This is an example of a llama-2 configuration for 7b and 13b. The yaml file contains configuration for the 7b variant, but you can just aswell use the same settings for 13b.
+
+The 7b variant fits on any 24GB VRAM GPU and will take up about 17 GB of VRAM during training if using qlora and 20 GB if using lora. On a RTX 4090 it trains 3 epochs of the default dataset in about 15 minutes.
+
+The 13b variant will fit if you change these settings to these values:
+gradient_accumulation_steps: 2
+micro_batch_size: 1
+
+```shell
+accelerate launch scripts/finetune.py examples/llama-2/qlora.yml
+
+```
+or
+
+```shell
+accelerate launch scripts/finetune.py examples/llama-2/lora.yml
+
+```

examples/llama-2/lora.yml

@@ -0,0 +1,67 @@
+base_model: meta-llama/Llama-2-7b-hf
+base_model_config: meta-llama/Llama-2-7b-hf
+model_type: LlamaForCausalLM
+tokenizer_type: LlamaTokenizer
+is_llama_derived_model: true
+
+load_in_8bit: true
+load_in_4bit: false
+strict: false
+
+datasets:
+  - path: mhenrichsen/alpaca_2k_test
+    type: alpaca
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.01
+output_dir: ./lora-out
+
+sequence_len: 4096
+sample_packing: 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_run_id:
+wandb_log_model:
+
+gradient_accumulation_steps: 4
+micro_batch_size: 2
+num_epochs: 3
+optimizer: adamw_bnb_8bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+train_on_inputs: false
+group_by_length: false
+bf16: true
+fp16: false
+tf32: false
+
+gradient_checkpointing: true
+early_stopping_patience:
+resume_from_checkpoint:
+local_rank:
+logging_steps: 1
+xformers_attention:
+flash_attention: true
+
+warmup_steps: 10
+eval_steps: 20
+save_steps:
+debug:
+deepspeed:
+weight_decay: 0.0
+fsdp:
+fsdp_config:
+special_tokens:
+  bos_token: "<s>"
+  eos_token: "</s>"
+  unk_token: "<unk>"

examples/llama-2/qlora.yml

@@ -0,0 +1,69 @@
+base_model: meta-llama/Llama-2-7b-hf
+base_model_config: meta-llama/Llama-2-7b-hf
+model_type: LlamaForCausalLM
+tokenizer_type: LlamaTokenizer
+is_llama_derived_model: true
+
+load_in_8bit: false
+load_in_4bit: true
+strict: false
+
+datasets:
+  - path: mhenrichsen/alpaca_2k_test
+    type: alpaca
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.01
+output_dir: ./qlora-out
+
+adapter: qlora
+lora_model_dir:
+
+sequence_len: 4096
+sample_packing: true
+
+lora_r: 32
+lora_alpha: 16
+lora_dropout: 0.05
+lora_target_modules:
+lora_target_linear: true
+lora_fan_in_fan_out:
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_run_id:
+wandb_log_model:
+
+gradient_accumulation_steps: 4
+micro_batch_size: 2
+num_epochs: 3
+optimizer: paged_adamw_32bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+train_on_inputs: false
+group_by_length: false
+bf16: true
+fp16: false
+tf32: false
+
+gradient_checkpointing: true
+early_stopping_patience:
+resume_from_checkpoint:
+local_rank:
+logging_steps: 1
+xformers_attention:
+flash_attention: true
+
+warmup_steps: 10
+eval_steps: 20
+save_steps:
+debug:
+deepspeed:
+weight_decay: 0.0
+fsdp:
+fsdp_config:
+special_tokens:
+  bos_token: "<s>"
+  eos_token: "</s>"
+  unk_token: "<unk>"

examples/llama-2/relora.yml

@@ -0,0 +1,73 @@
+base_model: meta-llama/Llama-2-7b-hf
+base_model_config: meta-llama/Llama-2-7b-hf
+model_type: LlamaForCausalLM
+tokenizer_type: LlamaTokenizer
+is_llama_derived_model: true
+
+load_in_8bit: false
+load_in_4bit: true
+strict: false
+
+datasets:
+  - path: teknium/GPT4-LLM-Cleaned
+    type: alpaca
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.01
+output_dir: ./relora-out
+
+adapter: qlora
+lora_model_dir:
+
+sequence_len: 4096
+sample_packing: true
+
+lora_r: 8
+lora_alpha: 16
+lora_dropout: 0.05
+lora_target_modules:
+lora_target_linear: true
+lora_fan_in_fan_out:
+
+relora_steps: 150
+relora_warmup_steps: 10
+relora_cpu_offload: false
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_run_id:
+wandb_log_model:
+
+gradient_accumulation_steps: 4
+micro_batch_size: 4
+num_epochs: 3
+optimizer: adamw_bnb_8bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+train_on_inputs: false
+group_by_length: false
+bf16: true
+fp16: false
+tf32: false
+
+gradient_checkpointing: true
+early_stopping_patience:
+resume_from_checkpoint:
+local_rank:
+logging_steps: 1
+xformers_attention:
+flash_attention: true
+
+warmup_steps: 10
+eval_steps: 20
+save_steps: 50
+debug:
+deepspeed:
+weight_decay: 0.0
+fsdp:
+fsdp_config:
+special_tokens:
+  bos_token: "<s>"
+  eos_token: "</s>"
+  unk_token: "<unk>"

examples/mpt-7b/config.yml

@@ -20,11 +20,12 @@ lora_target_modules:
   - v_proj
 lora_fan_in_fan_out: false
 wandb_project: mpt-alpaca-7b
+wandb_entity:
 wandb_watch:
 wandb_run_id:
 wandb_log_model:
 output_dir: ./mpt-alpaca-7b
-batch_size: 1
+gradient_accumulation_steps: 1
 micro_batch_size: 1
 num_epochs: 3
 optimizer: adamw_bnb_8bit

examples/openllama-3b/README.md

@@ -0,0 +1,16 @@
+# openllama-3b
+
+Basic full tune
+```shell
+accelerate launch scripts/finetune.py examples/openllama-3b/config.yml
+```
+
+LoRA
+```shell
+accelerate launch scripts/finetune.py examples/openllama-3b/lora.yml
+```
+
+QLoRA
+```shell
+accelerate launch scripts/finetune.py examples/openllama-3b/qlora.yml
+```

examples/openllama-3b/config.yml

@@ -0,0 +1,63 @@
+base_model: openlm-research/open_llama_3b
+base_model_config: openlm-research/open_llama_3b
+model_type: LlamaForCausalLM
+tokenizer_type: LlamaTokenizer
+load_in_8bit: false
+load_in_4bit: false
+strict: false
+push_dataset_to_hub:
+datasets:
+  - path: teknium/GPT4-LLM-Cleaned
+    type: alpaca
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.02
+adapter:
+lora_model_dir:
+sequence_len: 256
+max_packed_sequence_len:
+lora_r:
+lora_alpha:
+lora_dropout:
+lora_target_modules:
+lora_target_linear:
+lora_fan_in_fan_out:
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_run_id:
+wandb_log_model:
+output_dir: ./openllama-out
+gradient_accumulation_steps: 1
+micro_batch_size: 1
+num_epochs: 3
+optimizer: adamw_bnb_8bit
+torchdistx_path:
+lr_scheduler: cosine
+learning_rate: 0.00001
+train_on_inputs: false
+group_by_length: false
+float16: true
+bf16: false
+fp16: false
+tf32: false
+gradient_checkpointing: true
+early_stopping_patience:
+resume_from_checkpoint:
+local_rank:
+logging_steps: 1
+xformers_attention: true
+flash_attention:
+gptq_groupsize:
+gptq_model_v1:
+warmup_steps: 10
+eval_steps: 50
+save_steps:
+debug:
+deepspeed:
+weight_decay: 0.1
+fsdp:
+fsdp_config:
+special_tokens:
+  bos_token: "<s>"
+  eos_token: "</s>"
+  unk_token: "<unk>"

examples/openllama-3b/lora.yml

@@ -1,5 +1,5 @@
-base_model: openlm-research/open_llama_3b_600bt_preview
-base_model_config: openlm-research/open_llama_3b_600bt_preview
+base_model: openlm-research/open_llama_3b
+base_model_config: openlm-research/open_llama_3b
 model_type: LlamaForCausalLM
 tokenizer_type: LlamaTokenizer
 load_in_8bit: true
@@ -28,6 +28,7 @@ lora_target_modules:
   - o_proj
 lora_fan_in_fan_out:
 wandb_project:
+wandb_entity:
 wandb_watch:
 wandb_run_id:
 wandb_log_model:
@@ -49,7 +50,7 @@ early_stopping_patience:
 resume_from_checkpoint:
 local_rank:
 logging_steps: 1
-xformers_attention:
+xformers_attention: true
 flash_attention:
 gptq_groupsize:
 gptq_model_v1:

examples/openllama-3b/qlora.yml

@@ -1,5 +1,5 @@
-base_model: openlm-research/open_llama_3b_600bt_preview
-base_model_config: openlm-research/open_llama_3b_600bt_preview
+base_model: openlm-research/open_llama_3b
+base_model_config: openlm-research/open_llama_3b
 model_type: LlamaForCausalLM
 tokenizer_type: LlamaTokenizer
 load_in_8bit: false
@@ -22,6 +22,7 @@ lora_target_modules:
 lora_target_linear: true
 lora_fan_in_fan_out:
 wandb_project:
+wandb_entity:
 wandb_watch:
 wandb_run_id:
 wandb_log_model:
@@ -34,7 +35,7 @@ torchdistx_path:
 lr_scheduler: cosine
 learning_rate: 0.0002
 train_on_inputs: false
-group_by_length: true
+group_by_length: false
 bf16: true
 fp16: false
 tf32: true

examples/pythia-12b/README.md

@@ -0,0 +1,9 @@
+# Pythia 12B
+
+- Single-GPU A100 only (?)
+
+```shell
+python scripts/finetune.py examples/pythia-12b/config.yml
+```
+
+⚠️ Multiple-GPU A100 - Doesn't seem to work with multi-gpu without causing OOM! ⚠️

examples/pythia-12b/config.yml

@@ -1,39 +1,49 @@
-base_model: EleutherAI/gpt-neox-20b
+base_model: EleutherAI/pythia-12b-deduped
+base_model_config: EleutherAI/pythia-12b-deduped
 base_model_ignore_patterns: pytorch*  # prefer safetensors
 model_type: GPTNeoXForCausalLM
 tokenizer_type: AutoTokenizer
-load_in_8bit: true
+load_in_8bit: false
+load_in_4bit: false
+gptq: false
+device_map: auto
 datasets:
-  - path: nomic-ai/gpt4all-j-prompt-generations
+  - path: vicgalle/alpaca-gpt4
     type: alpaca
-    shards: 4
-    shards_index: 0
 dataset_prepared_path: last_run_prepared
 val_set_size: 0.05
-adapter: lora
+adapter:
 lora_model_dir:
 sequence_len: 2048
 max_packed_sequence_len: 2048
-lora_r: 8
+lora_r: 64
 lora_alpha: 32
-lora_dropout: 0.05
+lora_dropout: 0.0
 lora_target_modules:
-  - query_key_value
+lora_target_linear: true
 lora_fan_in_fan_out: true  # pythia/GPTNeoX lora specific
-wandb_project: gpt4all-neox-20b
+wandb_project:
+wandb_entity:
 wandb_watch:
 wandb_run_id:
 wandb_log_model:
-output_dir: ./gpt4all-neox-20b
-batch_size: 48
-micro_batch_size: 4
+output_dir: ./pythia-12b
+gradient_accumulation_steps: 1
+micro_batch_size: 1
 num_epochs: 5
 learning_rate: 0.00003
-lr_scheduler: one_cycle
+optimizer: adamw_bnb_8bit
+lr_scheduler: cosine
 train_on_inputs: false
 group_by_length: false
-bf16: True
-tf32: True
+bf16: false
+fp16: false
+float16: true
+tf32: true
+flash_optimum: true
 early_stopping_patience:
 resume_from_checkpoint:
 local_rank:
+gradient_checkpointing: true
+fsdp:
+fsdp_config:

examples/pythia/lora.yml

@@ -1,36 +1,30 @@
 base_model: EleutherAI/pythia-1.4b-deduped
-model_type: GPTNeoXForCausalLM
-tokenizer_type: AutoTokenizer
+base_model_config: EleutherAI/pythia-1.4b-deduped
 load_in_8bit: true
 datasets:
-  - path: data/alpaca_data_gpt4.jsonl
+  - path: teknium/GPT4-LLM-Cleaned
     type: alpaca
-  - path: data/vicuna_cleaned.jsonl
-    type: sharegpt
-  - path: data/gpt4-instruct-similarity-0.6-dataset.jsonl
-    type: gpteacher
-  - path: data/roleplay-similarity_0.6-instruct-dataset.jsonl
-    type: gpteacher
 dataset_prepared_path: last_run_prepared
 val_set_size: 0.05
 adapter: lora
 lora_model_dir:
-sequence_len: 2048
-lora_r: 8
+sequence_len: 512
+lora_r: 16
 lora_alpha: 32
 lora_dropout: 0.05
 lora_target_modules:
   - query_key_value
-#  - xxx
+lora_target_linear:
 lora_fan_in_fan_out: true  # pythia/GPTNeoX lora specific
-wandb_project: pythia-1.4b-lora
+wandb_project:
+wandb_entity:
 wandb_watch:
 wandb_run_id:
 wandb_log_model:
-output_dir: ./lora-alpaca
-batch_size: 48
+output_dir: ./lora-alpaca-pythia
+gradient_accumulation_steps: 1
 micro_batch_size: 4
-num_epochs: 5
+num_epochs: 3
 learning_rate: 0.00001
 train_on_inputs: false
 group_by_length: false
@@ -39,3 +33,6 @@ tf32: True
 early_stopping_patience:
 resume_from_checkpoint:
 local_rank:
+weight_decay: 0.1
+eval_steps: 20
+logging_steps: 1

examples/qlora-openllama-3b/README.md

@@ -1,6 +0,0 @@
-# qlora-openllama-3b
-
-```shell
-accelerate launch scripts/finetune.py examples/qlora-openllama-3b/config.yml
-
-```

examples/redpajama/config-3b.yml

@@ -1,7 +1,7 @@
 base_model: togethercomputer/RedPajama-INCITE-Chat-3B-v1
 base_model_config: togethercomputer/RedPajama-INCITE-Chat-3B-v1
 model_type: GPTNeoXForCausalLM
-tokenizer_type: GPTNeoXTokenizer
+tokenizer_type: AutoTokenizer
 trust_remote_code:
 load_in_8bit: false
 datasets:
@@ -21,6 +21,7 @@ lora_target_modules:
   - v_proj
 lora_fan_in_fan_out: false
 wandb_project: redpajama-alpaca-3b
+wandb_entity:
 wandb_watch:
 wandb_run_id:
 wandb_log_model:

examples/replit-3b/config-lora.yml

@@ -20,6 +20,7 @@ lora_target_modules:
   - mlp_down
 lora_fan_in_fan_out:
 wandb_project: lora-replit
+wandb_entity:
 wandb_watch:
 wandb_run_id:
 wandb_log_model:

examples/xgen-7b/xgen-7b-8k-qlora.yml

@@ -0,0 +1,91 @@
+# An example finetuning Saleforce's XGen-7b model with 8k context using qlora
+# on Tim Dettmer's Guanaco dataset.
+base_model: Salesforce/xgen-7b-8k-base
+base_model_config: Salesforce/xgen-7b-8k-base
+trust_remote_code: true
+model_type: AutoModelForCausalLM
+tokenizer_type: AutoTokenizer
+load_in_8bit: false
+# enable 4bit for QLoRA
+load_in_4bit: true
+gptq: false
+strict: false
+push_dataset_to_hub:
+datasets:
+  - path: timdettmers/openassistant-guanaco
+    data_files:
+      - openassistant_best_replies_train.jsonl
+    type: "completion"
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.01
+# enable QLoRA
+adapter: qlora
+lora_model_dir:
+sequence_len: 8192
+max_packed_sequence_len:
+
+# hyperparameters from QLoRA paper Appendix B.2
+# "We find hyperparameters to be largely robust across datasets"
+lora_r: 64
+lora_alpha: 16
+# 0.1 for models up to 13B
+# 0.05 for 33B and 65B models
+lora_dropout: 0.05
+# add LoRA modules on all linear layers of the base model
+lora_target_modules:
+lora_target_linear: true
+lora_fan_in_fan_out:
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_run_id:
+wandb_log_model:
+output_dir: ./qlora-out
+
+# QLoRA paper Table 9
+# - 16 for 7b & 13b
+# - 32 for 33b, 64 for 64b
+# Max size tested on A6000
+# - 7b: 40
+# - 40b: 4
+# decrease if OOM, increase for max VRAM utilization
+micro_batch_size: 1
+gradient_accumulation_steps: 1
+num_epochs: 3
+# Optimizer for QLoRA
+optimizer: paged_adamw_32bit
+torchdistx_path:
+lr_scheduler: cosine
+# QLoRA paper Table 9
+# - 2e-4 for 7b & 13b
+# - 1e-4 for 33b & 64b
+learning_rate: 0.00002
+train_on_inputs: false
+group_by_length: false
+bf16: true
+fp16: false
+tf32: false
+gradient_checkpointing: true
+# stop training after this many evaluation losses have increased in a row
+# https://huggingface.co/transformers/v4.2.2/_modules/transformers/trainer_callback.html#EarlyStoppingCallback
+early_stopping_patience: 3
+resume_from_checkpoint:
+auto_resume_from_checkpoints: true
+local_rank:
+logging_steps: 1
+xformers_attention: true
+flash_attention:
+gptq_groupsize:
+gptq_model_v1:
+warmup_steps: 10
+eval_steps: 50
+save_steps: 50
+debug:
+deepspeed:
+weight_decay: 0.0
+special_tokens:
+  eos_token: "<|endoftext|>"
+  bos_token: "<|endoftext|>"
+  unk_token: "<|endoftext|>"
+  pad_token: "<|endoftext|>"

requirements.txt

@@ -1,18 +1,28 @@
+packaging
 peft @ git+https://github.com/huggingface/peft.git
 transformers @ git+https://github.com/huggingface/transformers.git
-bitsandbytes>=0.39.0
+bitsandbytes>=0.41.1
+accelerate @ git+https://github.com/huggingface/accelerate@2a289f6108e77a77a4efffb3f6316bc98538413b
 addict
+evaluate
 fire
-PyYAML==6.0
+PyYAML>=6.0
 datasets
-accelerate>=0.19.0
+flash-attn>=2.0.8
 sentencepiece
 wandb
 einops
 xformers
+optimum
+hf_transfer
+colorama
+numba
+numpy>=1.24.4
 # qlora things
 bert-score==0.3.13
 evaluate==0.4.0
 rouge-score==0.1.2
 scipy
 scikit-learn==1.2.2
+pynvml
+art

scripts/alpaca_json_to_jsonl.py

@@ -1,49 +0,0 @@
-"""Module to convert json file to jsonl"""
-
-import os
-import sys
-from pathlib import Path
-from typing import Optional, Union
-
-import fire
-
-from axolotl.convert import (
-    FileReader,
-    FileWriter,
-    JsonlSerializer,
-    JsonParser,
-    JsonToJsonlConverter,
-    StdoutWriter,
-)
-
-# add src to the pythonpath so we don't need to pip install this
-project_root = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
-src_dir = os.path.join(project_root, "src")
-sys.path.insert(0, src_dir)
-
-
-def main(
-    file: Path,
-    output: Optional[Path] = None,
-    to_stdout: Optional[bool] = False,
-):
-    """
-    Convert a json file to jsonl
-    """
-
-    file_reader = FileReader()
-    writer: Union[StdoutWriter, FileWriter]
-    if to_stdout or output is None:
-        writer = StdoutWriter()
-    else:
-        writer = FileWriter(output)
-    json_parser = JsonParser()
-    jsonl_serializer = JsonlSerializer()
-
-    converter = JsonToJsonlConverter(file_reader, writer, json_parser, jsonl_serializer)
-
-    converter.convert(file, output)
-
-
-if __name__ == "__main__":
-    fire.Fire(main)

scripts/finetune.py

@@ -4,52 +4,49 @@ import importlib
 import logging
 import os
 import random
-import signal
 import sys
 from pathlib import Path
 from typing import Any, Dict, List, Optional, Union
 
 import fire
 import torch
+import transformers
 import yaml
 
-from axolotl.utils.data import load_prepare_datasets
-from axolotl.utils.dict import DictDefault
-from axolotl.utils.models import load_model, load_tokenizer
-
 # add src to the pythonpath so we don't need to pip install this
+from art import text2art
+from transformers import GenerationConfig, TextStreamer
+
+from axolotl.common.cli import TrainerCliArgs, load_model_and_tokenizer
+from axolotl.logging_config import configure_logging
+from axolotl.train import TrainDatasetMeta, train
+from axolotl.utils.config import normalize_config, validate_config
+from axolotl.utils.data import prepare_dataset
+from axolotl.utils.dict import DictDefault
+from axolotl.utils.distributed import is_main_process
+from axolotl.utils.models import load_model_config, load_tokenizer
 from axolotl.utils.tokenization import check_dataset_labels
-from axolotl.utils.trainer import setup_trainer
-from axolotl.utils.validation import validate_config
 from axolotl.utils.wandb import setup_wandb_env_vars
 
 project_root = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
 src_dir = os.path.join(project_root, "src")
 sys.path.insert(0, src_dir)
 
+configure_logging()
+LOG = logging.getLogger("axolotl.scripts")
 
-logging.basicConfig(level=os.getenv("LOG_LEVEL", "INFO"))
-DEFAULT_DATASET_PREPARED_PATH = "last_run_prepared"
+os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"
 
 
-def choose_device(cfg):
-    def get_device():
-        try:
-            if torch.cuda.is_available():
-                return f"cuda:{cfg.local_rank}"
+def print_axolotl_text_art(suffix=None):
+    font = "nancyj"
+    ascii_text = "  axolotl"
+    if suffix:
+        ascii_text += f"  x  {suffix}"
+    ascii_art = text2art(" axolotl", font=font)
 
-            if torch.backends.mps.is_available():
-                return "mps"
-
-            raise SystemError("No CUDA/mps device found")
-        except Exception:  # pylint: disable=broad-exception-caught
-            return "cpu"
-
-    cfg.device = get_device()
-    if cfg.device == "cuda":
-        cfg.device_map = {"": cfg.local_rank}
-    else:
-        cfg.device_map = {"": cfg.device}
+    if is_main_process():
+        print(ascii_art)
 
 
 def get_multi_line_input() -> Optional[str]:
@@ -61,38 +58,108 @@ def get_multi_line_input() -> Optional[str]:
     return instruction
 
 
-def do_inference(cfg, model, tokenizer, prompter="AlpacaPrompter"):
-    tokenizer.add_special_tokens({"unk_token": "<unk>"})
-    tokenizer.add_special_tokens({"bos_token": "<s>"})
-    tokenizer.add_special_tokens({"eos_token": "</s>"})
+def do_merge_lora(
+    *,
+    cfg: DictDefault,
+    cli_args: TrainerCliArgs,
+):
+    model, tokenizer = load_model_and_tokenizer(cfg=cfg, cli_args=cli_args)
+    safe_serialization = cfg.save_safetensors is True
 
-    prompter_module = getattr(importlib.import_module("axolotl.prompters"), prompter)
+    LOG.info("running merge of LoRA with base model")
+    model = model.merge_and_unload()
+    model.to(dtype=torch.float16)
+
+    if cfg.local_rank == 0:
+        LOG.info("saving merged model")
+        model.save_pretrained(
+            str(Path(cfg.output_dir) / "merged"),
+            safe_serialization=safe_serialization,
+        )
+        tokenizer.save_pretrained(str(Path(cfg.output_dir) / "merged"))
+
+
+def shard(
+    *,
+    cfg: DictDefault,
+    cli_args: TrainerCliArgs,
+):
+    model, _ = load_model_and_tokenizer(cfg=cfg, cli_args=cli_args)
+    safe_serialization = cfg.save_safetensors is True
+    LOG.debug("Re-saving model w/ sharding")
+    model.save_pretrained(cfg.output_dir, safe_serialization=safe_serialization)
+
+
+def do_inference(
+    *,
+    cfg: DictDefault,
+    cli_args: TrainerCliArgs,
+):
+    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>"}
+
+    for token, symbol in default_tokens.items():
+        # If the token isn't already specified in the config, add it
+        if not (cfg.special_tokens and token in cfg.special_tokens):
+            tokenizer.add_special_tokens({token: symbol})
+
+    prompter_module = None
+    if prompter:
+        prompter_module = getattr(
+            importlib.import_module("axolotl.prompters"), prompter
+        )
+
+    if cfg.landmark_attention:
+        from axolotl.monkeypatch.llama_landmark_attn import set_model_mem_id
+
+        set_model_mem_id(model, tokenizer)
+        model.set_mem_cache_args(
+            max_seq_len=255, mem_freq=50, top_k=5, max_cache_size=None
+        )
+
+    model = model.to(cfg.device)
 
     while True:
+        print("=" * 80)
         # support for multiline inputs
         instruction = get_multi_line_input()
         if not instruction:
             return
-        prompt: str = next(prompter_module().build_prompt(instruction=instruction))
+        if prompter_module:
+            prompt: str = next(
+                prompter_module().build_prompt(instruction=instruction.strip("\n"))
+            )
+        else:
+            prompt = instruction.strip()
         batch = tokenizer(prompt, return_tensors="pt", add_special_tokens=True)
 
+        print("=" * 40)
         model.eval()
         with torch.no_grad():
-            # gc = GenerationConfig()  # TODO swap out and use this
-            generated = model.generate(
-                inputs=batch["input_ids"].to(cfg.device),
-                do_sample=True,
-                use_cache=True,
+            generation_config = GenerationConfig(
                 repetition_penalty=1.1,
-                max_new_tokens=100,
+                max_new_tokens=1024,
                 temperature=0.9,
                 top_p=0.95,
                 top_k=40,
+                bos_token_id=tokenizer.bos_token_id,
+                eos_token_id=tokenizer.eos_token_id,
+                pad_token_id=tokenizer.pad_token_id,
+                do_sample=True,
+                use_cache=True,
                 return_dict_in_generate=True,
                 output_attentions=False,
                 output_hidden_states=False,
                 output_scores=False,
             )
+            streamer = TextStreamer(tokenizer)
+            generated = model.generate(
+                inputs=batch["input_ids"].to(cfg.device),
+                generation_config=generation_config,
+                streamer=streamer,
+            )
+        print("=" * 40)
         print(tokenizer.decode(generated["sequences"].cpu().tolist()[0]))
 
 
@@ -104,6 +171,10 @@ def choose_config(path: Path):
             "No YAML config files found in the specified directory. Are you using a .yml extension?"
         )
 
+    if len(yaml_files) == 1:
+        print(f"Using default YAML file '{yaml_files[0]}'")
+        return yaml_files[0]
+
     print("Choose a YAML file:")
     for idx, file in enumerate(yaml_files):
         print(f"{idx + 1}. {file}")
@@ -126,11 +197,7 @@ def check_not_in(list1: List[str], list2: Union[Dict[str, Any], List[str]]) -> b
     return not any(el in list2 for el in list1)
 
 
-def train(
-    config: Path = Path("configs/"),
-    prepare_ds_only: bool = False,
-    **kwargs,
-):
+def load_cfg(config: Path = Path("examples/"), **kwargs):
     if Path(config).is_dir():
         config = choose_config(config)
 
@@ -142,140 +209,79 @@ def train(
     cfg_keys = cfg.keys()
     for k, _ in kwargs.items():
         # if not strict, allow writing to cfg even if it's not in the yml already
-        if k in cfg_keys or cfg.strict is False:
+        if k in cfg_keys or not cfg.strict:
             # handle booleans
             if isinstance(cfg[k], bool):
                 cfg[k] = bool(kwargs[k])
             else:
                 cfg[k] = kwargs[k]
 
-    # setup some derived config / hyperparams
-    cfg.gradient_accumulation_steps = cfg.batch_size // cfg.micro_batch_size
-    cfg.world_size = int(os.environ.get("WORLD_SIZE", 1))
-    cfg.local_rank = int(os.environ.get("LOCAL_RANK", 0))
-    choose_device(cfg)
-    cfg.ddp = cfg.ddp if cfg.ddp is not None else cfg.world_size != 1
-    if cfg.ddp:
-        cfg.device_map = {"": int(os.environ.get("LOCAL_RANK", 0))}
-        cfg.gradient_accumulation_steps = (
-            cfg.gradient_accumulation_steps // cfg.world_size
-        )
-    setup_wandb_env_vars(cfg)
-    if cfg.device == "mps":
-        cfg.load_in_8bit = False
-        cfg.tf32 = False
-        if cfg.bf16:
-            cfg.fp16 = True
-        cfg.bf16 = False
+    model_config = load_model_config(cfg)
 
+    # figure out if the model is llama
+    cfg.is_llama_derived_model = (
+        (hasattr(model_config, "model_type") and model_config.model_type == "llama")
+        or cfg.is_llama_derived_model
+        or "llama" in cfg.base_model
+        or (cfg.model_type and "llama" in cfg.model_type.lower())
+    )
     validate_config(cfg)
 
-    # load the tokenizer first
-    logging.info("loading tokenizer...")
-    tokenizer = load_tokenizer(cfg.base_model_config, cfg.tokenizer_type, cfg)
+    normalize_config(cfg)
 
-    if check_not_in(
-        ["inference", "shard", "merge_lora"], kwargs
-    ):  # don't need to load dataset for these
-        train_dataset, eval_dataset = load_prepare_datasets(
-            tokenizer, cfg, DEFAULT_DATASET_PREPARED_PATH
-        )
+    setup_wandb_env_vars(cfg)
+    return cfg
 
-    if cfg.debug or "debug" in kwargs:
-        logging.info("check_dataset_labels...")
+
+def load_datasets(
+    *,
+    cfg: DictDefault,
+    cli_args: TrainerCliArgs,
+) -> TrainDatasetMeta:
+    tokenizer = load_tokenizer(cfg)
+
+    train_dataset, eval_dataset, total_num_steps = prepare_dataset(cfg, tokenizer)
+
+    if cli_args.debug or cfg.debug:
+        LOG.info("check_dataset_labels...")
         check_dataset_labels(
             train_dataset.select(
-                [random.randrange(0, len(train_dataset) - 1) for _ in range(5)]  # nosec
+                [
+                    random.randrange(0, len(train_dataset) - 1)  # nosec
+                    for _ in range(cli_args.debug_num_examples)
+                ]
             ),
             tokenizer,
+            num_examples=cli_args.debug_num_examples,
+            text_only=cli_args.debug_text_only,
         )
 
-    if prepare_ds_only:
-        logging.info("Finished preparing dataset. Exiting...")
-        return
-
-    # Load the model and tokenizer
-    logging.info("loading model and peft_config...")
-    model, peft_config = load_model(
-        cfg.base_model,
-        cfg.base_model_config,
-        cfg.model_type,
-        tokenizer,
-        cfg,
-        adapter=cfg.adapter,
-        inference=("inference" in kwargs),
+    return TrainDatasetMeta(
+        train_dataset=train_dataset,
+        eval_dataset=eval_dataset,
+        total_num_steps=total_num_steps,
     )
 
-    if "merge_lora" in kwargs and cfg.adapter is not None:
-        logging.info("running merge of LoRA with base model")
-        model = model.merge_and_unload()
-        model.to(dtype=torch.float16)
 
-        if cfg.local_rank == 0:
-            logging.info("saving merged model")
-            model.save_pretrained(str(Path(cfg.output_dir) / "merged"))
-        return
-
-    if "inference" in kwargs:
-        logging.info("calling do_inference function")
-        do_inference(cfg, model, tokenizer)
-        return
-
-    if "shard" in kwargs:
-        model.save_pretrained(cfg.output_dir)
-        return
-
-    trainer = setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer)
-
-    model.config.use_cache = False
-
-    if torch.__version__ >= "2" and sys.platform != "win32":
-        logging.info("Compiling torch model")
-        model = torch.compile(model)
-
-    # go ahead and presave, so we have the adapter config available to inspect
-    if peft_config:
-        logging.info(f"Pre-saving adapter config to {cfg.output_dir}")
-        peft_config.save_pretrained(cfg.output_dir)
-
-    # In case we want to stop early with ctrl+c, this is a nice to have to save the pretrained model
-    if cfg.local_rank == 0:
-        signal.signal(
-            signal.SIGINT,
-            lambda signal, frame: (
-                model.save_pretrained(cfg.output_dir),
-                sys.exit(0),
-            ),
-        )
-
-    logging.info("Starting trainer...")
-    if cfg.group_by_length:
-        logging.info("hang tight... sorting dataset for group_by_length")
-    resume_from_checkpoint = cfg.resume_from_checkpoint
-    if cfg.resume_from_checkpoint is None and cfg.auto_resume_from_checkpoints:
-        possible_checkpoints = [
-            str(cp) for cp in Path(cfg.output_dir).glob("checkpoint-*")
-        ]
-        if len(possible_checkpoints) > 0:
-            sorted_paths = sorted(
-                possible_checkpoints,
-                key=lambda path: int(path.split("-")[-1]),
-            )
-            resume_from_checkpoint = sorted_paths[-1]
-            logging.info(
-                f"Using Auto-resume functionality to start with checkpoint at {resume_from_checkpoint}"
-            )
-    trainer.train(resume_from_checkpoint=resume_from_checkpoint)
-
-    logging.info(f"Training Completed!!! Saving pre-trained model to {cfg.output_dir}")
-
-    # TODO do we need this fix? https://huggingface.co/docs/accelerate/usage_guides/fsdp#saving-and-loading
-    # only save on rank 0, otherwise it corrupts output on multi-GPU when multiple processes attempt to write the same file
-    if cfg.local_rank == 0:
-        model.save_pretrained(cfg.output_dir)
-
-    # trainer.save_model(cfg.output_dir)  # TODO this may be needed for deepspeed to work? need to review another time
+def do_cli(config: Path = Path("examples/"), **kwargs):
+    print_axolotl_text_art()
+    parsed_cfg = load_cfg(config, **kwargs)
+    parser = transformers.HfArgumentParser((TrainerCliArgs))
+    parsed_cli_args, _ = parser.parse_args_into_dataclasses(
+        return_remaining_strings=True
+    )
+    if parsed_cli_args.inference:
+        do_inference(cfg=parsed_cfg, cli_args=parsed_cli_args)
+    elif parsed_cli_args.merge_lora:
+        do_merge_lora(cfg=parsed_cfg, cli_args=parsed_cli_args)
+    elif parsed_cli_args.shard:
+        shard(cfg=parsed_cfg, cli_args=parsed_cli_args)
+    else:
+        dataset_meta = load_datasets(cfg=parsed_cfg, cli_args=parsed_cli_args)
+        if parsed_cli_args.prepare_ds_only:
+            return
+        train(cfg=parsed_cfg, cli_args=parsed_cli_args, dataset_meta=dataset_meta)
 
 
 if __name__ == "__main__":
-    fire.Fire(train)
+    fire.Fire(do_cli)

scripts/runpod-entrypoint.sh

@@ -1,10 +1,21 @@
 #!/bin/bash
 
-echo $PUBLIC_KEY >> ~/.ssh/authorized_keys
-chmod 700 -R ~/.ssh
+# Export specific ENV variables to /etc/rp_environment
+echo "Exporting environment variables..."
+printenv | grep -E '^RUNPOD_|^PATH=|^_=' | sed 's/^\(.*\)=\(.*\)$/export \1="\2"/' >> /etc/rp_environment
+echo 'source /etc/rp_environment' >> ~/.bashrc
 
-# Start the SSH service in the background
-service ssh start
+if [[ $PUBLIC_KEY ]]
+then
+    mkdir -p ~/.ssh
+    chmod 700 ~/.ssh
+    echo $PUBLIC_KEY >> ~/.ssh/authorized_keys
+    chmod 700 -R ~/.ssh
+    # Start the SSH service in the background
+    service ssh start
+else
+    echo "No PUBLIC_KEY ENV variable provided, not starting openSSH daemon"
+fi
 
 # Execute the passed arguments (CMD)
 exec "$@"

setup.py

@@ -7,6 +7,7 @@ with open("./requirements.txt", encoding="utf-8") as requirements_file:
     # don't include peft yet until we check the int4
     # need to manually install peft for now...
     reqs = [r.strip() for r in requirements_file.readlines() if "peft" not in r]
+    reqs = [r for r in reqs if "flash-attn" not in r]
     reqs = [r for r in reqs if r and r[0] != "#"]
     for r in reqs:
         install_requires.append(r)
@@ -25,9 +26,14 @@ setup(
         "gptq_triton": [
             "alpaca_lora_4bit[triton] @ git+https://github.com/winglian/alpaca_lora_4bit.git@setup_pip",
         ],
+        "flash-attn": [
+            "flash-attn==2.0.8",
+        ],
         "extras": [
-            "flash-attn",
             "deepspeed",
         ],
+        "peft": [
+            "peft @ git+https://github.com/huggingface/peft.git",
+        ],
     },
 )

src/axolotl/common/cli.py

@@ -0,0 +1,43 @@
+"""
+shared module for cli specific things
+"""
+
+import logging
+from dataclasses import dataclass, field
+from typing import Optional
+
+from axolotl.logging_config import configure_logging
+from axolotl.utils.dict import DictDefault
+from axolotl.utils.models import load_model, load_tokenizer
+
+configure_logging()
+LOG = logging.getLogger("axolotl.common.cli")
+
+
+@dataclass
+class TrainerCliArgs:
+    """
+    dataclass representing the various non-training arguments
+    """
+
+    debug: bool = field(default=False)
+    debug_text_only: bool = field(default=False)
+    debug_num_examples: int = field(default=5)
+    inference: bool = field(default=False)
+    merge_lora: bool = field(default=False)
+    prepare_ds_only: bool = field(default=False)
+    prompter: Optional[str] = field(default=None)
+    shard: bool = field(default=False)
+
+
+def load_model_and_tokenizer(
+    *,
+    cfg: DictDefault,
+    cli_args: TrainerCliArgs,
+):
+    LOG.info(f"loading tokenizer... {cfg.tokenizer_config or cfg.base_model_config}")
+    tokenizer = load_tokenizer(cfg)
+    LOG.info("loading model and (optionally) peft_config...")
+    model, _ = load_model(cfg, tokenizer, inference=cli_args.inference)
+
+    return model, tokenizer

src/axolotl/datasets.py

@@ -1,12 +1,13 @@
 """Module containing Dataset functionality"""
 
 import logging
+import os
 from typing import List
 
 import torch
-from datasets import IterableDataset
+from datasets import Dataset, IterableDataset
 
-from .prompt_tokenizers import InvalidDataException, PromptTokenizingStrategy
+from .prompt_tokenizers import PromptTokenizingStrategy
 
 # We want this to be a wrapper for an existing dataset that we have loaded
 # lets use the concept of middlewares to wrap each dataset, for example
@@ -14,10 +15,12 @@ from .prompt_tokenizers import InvalidDataException, PromptTokenizingStrategy
 # let's check to ensure we don't truncate an item in the middle, we'll use
 # the collators later on to pad the datasets
 
+LOG = logging.getLogger("axolotl")
 
-class TokenizedPromptDataset(IterableDataset):
+
+class TokenizedPromptDataset(Dataset):
     """
-    Iterable dataset that returns tokenized prompts from a stream of text files.
+    Dataset that returns tokenized prompts from a stream of text files.
         Args:
             prompt_tokenizer (PromptTokenizingStrategy): The prompt tokenizing method for proccessing the data.
             dataset (dataset.Dataset): Dataset with text files.
@@ -27,18 +30,19 @@ class TokenizedPromptDataset(IterableDataset):
         self,
         prompt_tokenizer: PromptTokenizingStrategy,
         dataset: IterableDataset,
+        **kwargs,
     ):
         self.prompt_tokenizer = prompt_tokenizer
-        self.dataset = dataset
+        super().__init__(self.process(dataset).data, **kwargs)
 
-    def __iter__(self):
-        iterator = iter(self.dataset)
-        # Loop through the entire dataset
-        for example in iterator:
-            try:
-                yield self.prompt_tokenizer.tokenize_prompt(example)
-            except InvalidDataException:
-                pass
+    def process(self, dataset):
+        features = dataset.features.keys()
+        num_proc = min(64, os.cpu_count())
+        return dataset.map(
+            self.prompt_tokenizer.tokenize_prompt,
+            num_proc=num_proc,
+            remove_columns=features,
+        )
 
 
 # TODO this isn't the best since it can't interleave datasets
@@ -72,14 +76,21 @@ class ConstantLengthDataset(IterableDataset):
             self.tokens_dtype = torch.int64
 
     def __iter__(self):
-        buffer = {"input_ids": [], "attention_mask": [], "labels": []}
+        buffer = {
+            "input_ids": [],
+            "attention_mask": [],
+            "labels": [],
+            "position_ids": [],
+        }
         buffer_len = 0
         for dataset in self.datasets:
+            idx = 0
             iterator = iter(dataset)
             more_examples = True
             while more_examples:
                 try:
                     example = next(iterator)
+                    idx += 1
                 except StopIteration:
                     more_examples = False
                     example = None
@@ -101,6 +112,9 @@ class ConstantLengthDataset(IterableDataset):
                         attention_mask = torch.cat(buffer["attention_mask"], dim=-1)[
                             : self.seq_length
                         ]
+                        position_ids = torch.cat(buffer["position_ids"], dim=-1)[
+                            : self.seq_length
+                        ]
                         labels = torch.cat(buffer["labels"], dim=-1)[: self.seq_length]
                         if labels.size() == input_ids.size() and (
                             attention_mask.size() == input_ids.size()
@@ -109,19 +123,23 @@ class ConstantLengthDataset(IterableDataset):
                                 "input_ids": input_ids,
                                 "labels": labels,
                                 "attention_mask": attention_mask,
+                                "position_ids": position_ids,
                             }
                         else:
-                            logging.warning(
+                            LOG.warning(
                                 f"dropping batch due to tensor size mismatch input_ids: {input_ids.size()}, labels: {labels.size()}, attention_mask: {attention_mask.size()}"
                             )
                     buffer = {
                         "input_ids": [],
                         "attention_mask": [],
                         "labels": [],
+                        "position_ids": [],
                     }
                     buffer_len = 0
+                    idx = 1
 
                 if example:
+                    # FIXME
                     # just going to drop data points that are too long
                     if len(example["input_ids"]) <= self.seq_length:
                         input_ids = example["input_ids"]
@@ -137,13 +155,17 @@ class ConstantLengthDataset(IterableDataset):
                             input_ids, dtype=self.tokens_dtype
                         )
                         attention_mask_with_concat = torch.tensor(
-                            attention_mask, dtype=self.tokens_dtype
+                            [idx * m for m in attention_mask], dtype=torch.int16
                         )
                         labels_with_concat = torch.tensor(
                             labels, dtype=self.tokens_dtype
                         )
+                        position_ids = torch.arange(
+                            len(input_ids), dtype=self.tokens_dtype
+                        )
 
                         buffer["input_ids"].append(input_ids_with_concat)
                         buffer["attention_mask"].append(attention_mask_with_concat)
                         buffer["labels"].append(labels_with_concat)
+                        buffer["position_ids"].append(position_ids)
                         buffer_len += len(input_ids)

src/axolotl/flash_attn.py

@@ -1,139 +0,0 @@
-"""Flash attention monkey patch for llama model"""
-
-# copied from https://github.com/lm-sys/FastChat/blob/main/fastchat/train/llama_flash_attn_monkey_patch.py
-
-from typing import Optional, Tuple
-
-import torch
-import transformers
-from einops import rearrange
-from flash_attn.bert_padding import pad_input, unpad_input
-from flash_attn.flash_attn_interface import flash_attn_unpadded_qkvpacked_func
-from transformers.models.llama.modeling_llama import apply_rotary_pos_emb
-
-
-def forward(
-    self,
-    hidden_states: torch.Tensor,
-    attention_mask: Optional[torch.Tensor] = None,
-    position_ids: Optional[torch.Tensor] = None,
-    past_key_value: Optional[Tuple[torch.Tensor]] = None,
-    output_attentions: bool = False,
-    use_cache: bool = False,
-) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
-    """Input shape: Batch x Time x Channel
-
-    attention_mask: [bsz, q_len]
-    """
-    bsz, q_len, _ = hidden_states.size()
-
-    query_states = (
-        self.q_proj(hidden_states)
-        .view(bsz, q_len, self.num_heads, self.head_dim)
-        .transpose(1, 2)
-    )
-    key_states = (
-        self.k_proj(hidden_states)
-        .view(bsz, q_len, self.num_heads, self.head_dim)
-        .transpose(1, 2)
-    )
-    value_states = (
-        self.v_proj(hidden_states)
-        .view(bsz, q_len, self.num_heads, self.head_dim)
-        .transpose(1, 2)
-    )
-    # [bsz, q_len, nh, hd]
-    # [bsz, nh, q_len, hd]
-
-    kv_seq_len = key_states.shape[-2]
-    assert past_key_value is None, "past_key_value is not supported"
-
-    cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
-    query_states, key_states = apply_rotary_pos_emb(
-        query_states, key_states, cos, sin, position_ids
-    )
-    # [bsz, nh, t, hd]
-    assert not output_attentions, "output_attentions is not supported"
-    assert not use_cache, "use_cache is not supported"
-
-    # Flash attention codes from
-    # https://github.com/HazyResearch/flash-attention/blob/main/flash_attn/flash_attention.py
-
-    # transform the data into the format required by flash attention
-    qkv = torch.stack(
-        [query_states, key_states, value_states], dim=2
-    )  # [bsz, nh, 3, q_len, hd]
-    qkv = qkv.transpose(1, 3)  # [bsz, q_len, 3, nh, hd]
-    # We have disabled _prepare_decoder_attention_mask in LlamaModel
-    # the attention_mask should be the same as the key_padding_mask
-    key_padding_mask = attention_mask
-
-    if key_padding_mask is None:
-        qkv = rearrange(qkv, "b s ... -> (b s) ...")
-        max_s = q_len
-        cu_q_lens = torch.arange(
-            0,
-            (bsz + 1) * q_len,
-            step=q_len,
-            dtype=torch.int32,
-            device=qkv.device,
-        )
-        output = flash_attn_unpadded_qkvpacked_func(
-            qkv, cu_q_lens, max_s, 0.0, softmax_scale=None, causal=True
-        )
-        output = rearrange(output, "(b s) ... -> b s ...", b=bsz)
-    else:
-        nheads = qkv.shape[-2]
-
-        # pylint: disable=invalid-name
-        x = rearrange(qkv, "b s three h d -> b s (three h d)")
-        x_unpad, indices, cu_q_lens, max_s = unpad_input(x, key_padding_mask)
-        x_unpad = rearrange(
-            x_unpad,
-            "nnz (three h d) -> nnz three h d",
-            three=3,
-            h=nheads,
-        )
-        output_unpad = flash_attn_unpadded_qkvpacked_func(
-            x_unpad,
-            cu_q_lens,
-            max_s,
-            0.0,
-            softmax_scale=None,
-            causal=True,
-        )
-        output = rearrange(
-            pad_input(
-                rearrange(output_unpad, "nnz h d -> nnz (h d)"),
-                indices,
-                bsz,
-                q_len,
-            ),
-            "b s (h d) -> b s h d",
-            h=nheads,
-        )
-    return (
-        self.o_proj(rearrange(output, "b s h d -> b s (h d)")),
-        None,
-        None,
-    )
-
-
-# Disable the transformation of the attention mask in LlamaModel as the flash attention
-# requires the attention mask to be the same as the key_padding_mask
-def _prepare_decoder_attention_mask(
-    self,
-    attention_mask,
-    input_shape,
-    inputs_embeds,
-    past_key_values_length,
-):  # pylint: disable=unused-argument
-    # [bsz, seq_len]
-    return attention_mask
-
-
-def replace_llama_attn_with_flash_attn():
-    transformers.models.llama.modeling_llama.LlamaModel._prepare_decoder_attention_mask = (  # pylint: disable=protected-access
-        _prepare_decoder_attention_mask
-    )
-    transformers.models.llama.modeling_llama.LlamaAttention.forward = forward

src/axolotl/logging_config.py

@@ -0,0 +1,70 @@
+"""
+Common logging module for axolotl
+"""
+
+import os
+import sys
+from logging import Formatter
+from logging.config import dictConfig
+from typing import Any, Dict
+
+from colorama import Fore, Style, init
+
+
+class ColorfulFormatter(Formatter):
+    """
+    Formatter to add coloring to log messages by log type
+    """
+
+    COLORS = {
+        "WARNING": Fore.YELLOW,
+        "ERROR": Fore.RED,
+        "CRITICAL": Fore.RED + Style.BRIGHT,
+    }
+
+    def format(self, record):
+        log_message = super().format(record)
+        return self.COLORS.get(record.levelname, "") + log_message + Fore.RESET
+
+
+DEFAULT_LOGGING_CONFIG: Dict[str, Any] = {
+    "version": 1,
+    "formatters": {
+        "simple": {
+            "format": "[%(asctime)s] [%(levelname)s] [%(name)s.%(funcName)s:%(lineno)d] [PID:%(process)d] %(message)s",
+        },
+        "colorful": {
+            "()": ColorfulFormatter,
+            "format": "[%(asctime)s] [%(levelname)s] [%(name)s.%(funcName)s:%(lineno)d] [PID:%(process)d] %(message)s",
+        },
+    },
+    "filters": {},
+    "handlers": {
+        "console": {
+            "class": "logging.StreamHandler",
+            "formatter": "simple",
+            "filters": [],
+            "stream": sys.stdout,
+        },
+        "color_console": {
+            "class": "logging.StreamHandler",
+            "formatter": "colorful",
+            "filters": [],
+            "stream": sys.stdout,
+        },
+    },
+    "root": {"handlers": ["console"], "level": os.getenv("LOG_LEVEL", "INFO")},
+    "loggers": {
+        "axolotl": {
+            "handlers": ["color_console"],
+            "level": "DEBUG",
+            "propagate": False,
+        },
+    },
+}
+
+
+def configure_logging():
+    """Configure with default logging"""
+    init()  # Initialize colorama
+    dictConfig(DEFAULT_LOGGING_CONFIG)

src/axolotl/monkeypatch/llama_attn_hijack_flash.py

@@ -0,0 +1,598 @@
+"""Flash attention monkey patch for llama model"""
+
+# copied from https://github.com/lm-sys/FastChat/blob/main/fastchat/train/llama_flash_attn_monkey_patch.py
+
+import warnings
+from typing import List, Optional, Tuple, Union
+
+import torch
+import torch.nn.functional as F
+import transformers
+from einops import rearrange
+from flash_attn.bert_padding import pad_input, unpad_input
+from transformers.modeling_outputs import BaseModelOutputWithPast
+from transformers.models.llama.modeling_llama import (
+    LlamaDecoderLayer as OriginalLlamaDecoderLayer,
+)
+from transformers.models.llama.modeling_llama import apply_rotary_pos_emb, repeat_kv
+
+from axolotl.monkeypatch.utils import get_cu_seqlens_from_pos_ids
+
+try:
+    from flash_attn.flash_attn_interface import (  # pylint: disable=ungrouped-imports
+        flash_attn_kvpacked_func,
+        flash_attn_varlen_kvpacked_func,
+        flash_attn_varlen_qkvpacked_func,
+    )
+except ImportError:
+    from flash_attn.flash_attn_interface import (
+        flash_attn_unpadded_kvpacked_func as flash_attn_varlen_kvpacked_func,
+    )
+    from flash_attn.flash_attn_interface import (
+        flash_attn_unpadded_qkvpacked_func as flash_attn_varlen_qkvpacked_func,
+    )
+
+
+def replace_llama_attn_with_flash_attn(packed: Optional[bool] = False):
+    transformers.models.llama.modeling_llama.LlamaModel._prepare_decoder_attention_mask = (  # pylint: disable=protected-access
+        _prepare_decoder_attention_mask
+    )
+    transformers.models.llama.modeling_llama.LlamaAttention.forward = flashattn_forward
+    if packed:
+        transformers.models.llama.modeling_llama.LlamaDecoderLayer = LlamaDecoderLayer
+        transformers.models.llama.modeling_llama.LlamaModel.forward = (
+            llama_model_forward
+        )
+
+
+# Disable the transformation of the attention mask in LlamaModel as the flash attention
+# requires the attention mask to be the same as the key_padding_mask
+def _prepare_decoder_attention_mask(
+    self,
+    attention_mask,
+    input_shape,
+    inputs_embeds,
+    past_key_values_length,
+):  # pylint: disable=unused-argument
+    # [bsz, seq_len]
+    return attention_mask
+
+
+def flashattn_forward(
+    self,
+    hidden_states: torch.Tensor,
+    attention_mask: Optional[torch.Tensor] = None,
+    position_ids: Optional[torch.Tensor] = None,
+    past_key_value: Optional[Tuple[torch.Tensor]] = None,
+    output_attentions: bool = False,
+    use_cache: bool = False,
+    cu_seqlens: Optional[torch.Tensor] = None,
+    max_seqlen: Optional[torch.Tensor] = None,
+) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+    """Input shape: Batch x Time x Channel
+
+    attention_mask: [bsz, q_len]
+    """
+    # pylint: disable=duplicate-code
+    bsz, q_len, _ = hidden_states.size()
+
+    if not hasattr(self, "pretraining_tp"):
+        self.pretraining_tp = 1
+
+    if self.pretraining_tp > 1:
+        key_value_slicing = (
+            self.num_key_value_heads * self.head_dim
+        ) // self.pretraining_tp
+        query_slices = self.q_proj.weight.split(
+            (self.num_heads * self.head_dim) // self.pretraining_tp, dim=0
+        )
+        key_slices = self.k_proj.weight.split(key_value_slicing, dim=0)
+        value_slices = self.v_proj.weight.split(key_value_slicing, dim=0)
+
+        query_states = [
+            F.linear(hidden_states, query_slices[i]) for i in range(self.pretraining_tp)
+        ]
+        query_states = torch.cat(query_states, dim=-1)
+
+        key_states = [
+            F.linear(hidden_states, key_slices[i]) for i in range(self.pretraining_tp)
+        ]
+        key_states = torch.cat(key_states, dim=-1)
+
+        value_states = [
+            F.linear(hidden_states, value_slices[i]) for i in range(self.pretraining_tp)
+        ]
+        value_states = torch.cat(value_states, dim=-1)
+
+    else:
+        query_states = self.q_proj(hidden_states)
+        key_states = self.k_proj(hidden_states)
+        value_states = self.v_proj(hidden_states)
+
+    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)
+    # [bsz, q_len, nh, hd]
+    # [bsz, nh, q_len, hd]
+
+    kv_seq_len = key_states.shape[-2]
+    if past_key_value is not None:
+        kv_seq_len += past_key_value[0].shape[-2]
+
+    cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+    query_states, key_states = apply_rotary_pos_emb(
+        query_states, key_states, cos, sin, position_ids
+    )
+    # [bsz, nh, t, hd]
+
+    if past_key_value is not None:
+        # reuse k, v, self_attention
+        key_states = torch.cat([past_key_value[0], key_states], dim=2)
+        value_states = torch.cat([past_key_value[1], value_states], dim=2)
+
+    past_key_value = (key_states, value_states) if use_cache else None
+
+    # repeat k/v heads if n_kv_heads < n_heads
+    key_states = repeat_kv(key_states, self.num_key_value_groups)
+    value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+    if output_attentions:
+        warnings.warn(
+            "Output attentions is not supported for patched `LlamaAttention`, returning `None` instead."
+        )
+
+    #
+    # flash-attn v2 start
+    #
+
+    if self.training:
+        # during training q,k,v always have same seqlen
+        assert key_states.shape == query_states.shape
+        is_causal = True
+    else:
+        # turn off FA causal mask after first inference autoregressive iteration
+        # only on first autoregressive step q,k,v have same seqlen
+        is_causal = key_states.shape == query_states.shape
+
+    if cu_seqlens is not None and max_seqlen is not None:
+        # special handling using sample packing
+        qkv = torch.stack(
+            [query_states, key_states, value_states], dim=2
+        )  # [bsz, nh, 3, q_len, hd]
+        qkv = qkv.transpose(1, 3)  # [bsz, q_len, 3, nh, hd]
+        qkv = rearrange(qkv, "b s ... -> (b s) ...")
+
+        output = flash_attn_varlen_qkvpacked_func(
+            qkv, cu_seqlens, max_seqlen, 0.0, softmax_scale=None, causal=True
+        )
+        output = rearrange(output, "(b s) ... -> b s ...", b=bsz)
+    elif query_states.shape == key_states.shape:
+        query_states = query_states.transpose(1, 2)
+        key_states = key_states.transpose(1, 2)
+        value_states = value_states.transpose(1, 2)
+        qkv_unpad, cu_seqlens_q, max_seqlen_q, _, output_pad_fn = generate_qkv(
+            query_states,
+            key_states,
+            value_states,
+            qkvpacked=True,
+            # We have disabled _prepare_decoder_attention_mask in LlamaModel
+            # the attention_mask should be the same as the key_padding_mask
+            key_padding_mask=attention_mask,
+            query_padding_mask=attention_mask[:, -query_states.size(1) :]
+            if attention_mask is not None
+            else None,
+        )
+        output_unpad = flash_attn_varlen_qkvpacked_func(
+            qkv_unpad,
+            cu_seqlens_q,
+            max_seqlen_q,
+            0.0,
+            softmax_scale=None,
+            causal=is_causal,
+        )
+        output = output_pad_fn(output_unpad)
+    else:
+        query_states = query_states.transpose(1, 2)
+        key_states = key_states.transpose(1, 2)
+        value_states = value_states.transpose(1, 2)
+        if attention_mask is None or attention_mask.all().item():
+            output = flash_attn_kvpacked_func(
+                query_states,
+                torch.stack([key_states, value_states], 2),
+                causal=is_causal,
+            )
+        else:
+            (  # pylint: disable=unbalanced-tuple-unpacking
+                q_unpad,
+                kv_unpad,
+                cu_seqlens_q,
+                cu_seqlens_k,
+                max_seqlen_q,
+                max_seqlen_k,
+                _,
+                _,
+                output_pad_fn,
+            ) = generate_qkv(
+                query_states,
+                key_states,
+                value_states,
+                kvpacked=True,
+                key_padding_mask=attention_mask,
+                query_padding_mask=attention_mask[:, -query_states.size(1) :]
+                if attention_mask is not None
+                else None,
+            )
+            output_unpad = flash_attn_varlen_kvpacked_func(
+                q_unpad,
+                kv_unpad,
+                cu_seqlens_q,
+                cu_seqlens_k,
+                max_seqlen_q,
+                max_seqlen_k,
+                0.0,
+                softmax_scale=None,
+                causal=is_causal,
+            )
+            output = output_pad_fn(output_unpad)
+
+    attn_output = output
+    if attn_output.size() != (bsz, q_len, self.num_heads, self.head_dim):
+        raise ValueError(
+            f"`attn_output` should be of size {(bsz, q_len, self.num_heads, self.head_dim)}, but is"
+            f" {attn_output.size()}"
+        )
+    attn_output = rearrange(attn_output, "b s h d -> b s (h d)")
+
+    #
+    # flash-attn v2 end
+    #
+
+    if self.pretraining_tp > 1:
+        attn_output = attn_output.split(self.hidden_size // self.pretraining_tp, dim=2)
+        o_proj_slices = self.o_proj.weight.split(
+            self.hidden_size // self.pretraining_tp, dim=1
+        )
+        attn_output = sum(
+            F.linear(attn_output[i], o_proj_slices[i])
+            for i in range(self.pretraining_tp)
+        )
+    else:
+        attn_output = self.o_proj(attn_output)
+
+    return attn_output, None, past_key_value
+
+
+# based on https://github.com/Dao-AILab/flash-attention/blob/364a5b/tests/test_flash_attn.py#L38
+def generate_qkv(
+    q,
+    k,
+    v,
+    query_padding_mask=None,
+    key_padding_mask=None,
+    kvpacked=False,
+    qkvpacked=False,
+):  # pylint: disable=invalid-name,unnecessary-lambda-assignment
+    """
+    Arguments:
+        q: (batch_size, seqlen_q, nheads, d)
+        k: (batch_size, seqlen_k, nheads_k, d)
+        v: (batch_size, seqlen_k, nheads_k, d)
+        query_padding_mask: (batch_size, seqlen), bool
+        key_padding_mask: (batch_size, seqlen), bool
+    """
+    assert not (kvpacked and qkvpacked)
+    batch_size, seqlen_q, nheads, d = q.shape
+    _, seqlen_k, nheads_k, _ = k.shape
+    assert k.shape == (batch_size, seqlen_k, nheads_k, d)
+    assert v.shape == (batch_size, seqlen_k, nheads_k, d)
+
+    if query_padding_mask is not None:
+        q_unpad, indices_q, cu_seqlens_q, max_seqlen_q = unpad_input(
+            q, query_padding_mask
+        )
+
+        output_pad_fn = lambda output_unpad: pad_input(  # noqa: E731
+            output_unpad, indices_q, batch_size, seqlen_q
+        )
+
+    else:
+        q_unpad = rearrange(q, "b s h d -> (b s) h d")
+        cu_seqlens_q = torch.arange(
+            0,
+            (batch_size + 1) * seqlen_q,
+            step=seqlen_q,
+            dtype=torch.int32,
+            device=q_unpad.device,
+        )
+        max_seqlen_q = seqlen_q
+
+        output_pad_fn = lambda output_unpad: rearrange(  # noqa: E731
+            output_unpad, "(b s) h d -> b s h d", b=batch_size
+        )
+
+    if key_padding_mask is not None:
+        k_unpad, _, cu_seqlens_k, max_seqlen_k = unpad_input(k, key_padding_mask)
+        v_unpad, _, _, _ = unpad_input(v, key_padding_mask)
+    else:
+        k_unpad = rearrange(k, "b s h d -> (b s) h d")
+        v_unpad = rearrange(v, "b s h d -> (b s) h d")
+        cu_seqlens_k = torch.arange(
+            0,
+            (batch_size + 1) * seqlen_k,
+            step=seqlen_k,
+            dtype=torch.int32,
+            device=k_unpad.device,
+        )
+        max_seqlen_k = seqlen_k
+
+    if qkvpacked:
+        assert nheads == nheads_k
+        qkv_unpad = torch.stack([q_unpad, k_unpad, v_unpad], dim=1)
+        qkv = torch.stack([q, k, v], dim=2)
+        return (qkv_unpad, cu_seqlens_q, max_seqlen_q, qkv, output_pad_fn)
+
+    if kvpacked:
+        kv_unpad = torch.stack([k_unpad, v_unpad], dim=1)
+        kv = torch.stack([k, v], dim=2)
+        return (
+            q_unpad,
+            kv_unpad,
+            cu_seqlens_q,
+            cu_seqlens_k,
+            max_seqlen_q,
+            max_seqlen_k,
+            q,
+            kv,
+            output_pad_fn,
+        )
+
+    return (
+        q_unpad,
+        k_unpad,
+        v_unpad,
+        cu_seqlens_q,
+        cu_seqlens_k,
+        max_seqlen_q,
+        max_seqlen_k,
+        q,
+        k,
+        v,
+        output_pad_fn,
+    )
+
+
+def llama_model_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,
+    use_cache: Optional[bool] = None,
+    output_attentions: Optional[bool] = None,
+    output_hidden_states: Optional[bool] = None,
+    return_dict: Optional[bool] = None,
+) -> Union[Tuple, BaseModelOutputWithPast]:
+    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
+    )
+    use_cache = use_cache if use_cache is not None else self.config.use_cache
+
+    return_dict = (
+        return_dict if return_dict is not None else self.config.use_return_dict
+    )
+
+    # retrieve input_ids and inputs_embeds
+    if input_ids is not None and inputs_embeds is not None:
+        raise ValueError(
+            "You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time"
+        )
+    if input_ids is not None:
+        batch_size, seq_length = input_ids.shape
+    elif inputs_embeds is not None:
+        batch_size, seq_length, _ = inputs_embeds.shape
+    else:
+        raise ValueError(
+            "You have to specify either decoder_input_ids or decoder_inputs_embeds"
+        )
+
+    seq_length_with_past = seq_length
+    past_key_values_length = 0
+
+    if past_key_values is not None:
+        past_key_values_length = past_key_values[0][0].shape[2]
+        seq_length_with_past = seq_length_with_past + past_key_values_length
+
+    cu_seqlens = None
+    max_seqlen = None
+    if position_ids is None:
+        device = input_ids.device if input_ids is not None else inputs_embeds.device
+        position_ids = torch.arange(
+            past_key_values_length,
+            seq_length + past_key_values_length,
+            dtype=torch.long,
+            device=device,
+        )
+        position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
+    else:
+        position_ids = position_ids.view(-1, seq_length).long()
+        cu_seqlens, max_seqlen = get_cu_seqlens_from_pos_ids(position_ids)
+        cu_seqlens = cu_seqlens.squeeze()
+
+    if inputs_embeds is None:
+        inputs_embeds = self.embed_tokens(input_ids)
+    # embed positions
+    if attention_mask is None:
+        attention_mask = torch.ones(
+            (batch_size, seq_length_with_past),
+            dtype=torch.bool,
+            device=inputs_embeds.device,
+        )
+    attention_mask = (
+        self._prepare_decoder_attention_mask(  # pylint: disable=protected-access
+            attention_mask,
+            (batch_size, seq_length),
+            inputs_embeds,
+            past_key_values_length,
+        )
+    )
+
+    hidden_states = inputs_embeds
+
+    if self.gradient_checkpointing and self.training:
+        if use_cache:
+            transformers.logger.warning_once(
+                "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
+            )
+            use_cache = False
+
+    # decoder layers
+    all_hidden_states = () if output_hidden_states else None
+    all_self_attns = () if output_attentions else None
+    next_decoder_cache = () if use_cache else None
+
+    for idx, decoder_layer in enumerate(self.layers):
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+
+        past_key_value = past_key_values[idx] if past_key_values is not None else None
+
+        if self.gradient_checkpointing and self.training:
+
+            def create_custom_forward(module):
+                def custom_forward(*inputs):
+                    # None for past_key_value
+                    return module(*inputs)
+
+                return custom_forward
+
+            layer_outputs = torch.utils.checkpoint.checkpoint(
+                create_custom_forward(decoder_layer),
+                hidden_states,
+                attention_mask,
+                position_ids,
+                None,
+                output_attentions,
+                None,
+                cu_seqlens,
+                max_seqlen,
+            )
+        else:
+            layer_outputs = decoder_layer(
+                hidden_states,
+                attention_mask=attention_mask,
+                position_ids=position_ids,
+                past_key_value=past_key_value,
+                output_attentions=output_attentions,
+                use_cache=use_cache,
+                cu_seqlens=cu_seqlens,
+                max_seqlen=max_seqlen,
+            )
+
+        hidden_states = layer_outputs[0]
+
+        if use_cache:
+            next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
+
+        if output_attentions:
+            all_self_attns += (layer_outputs[1],)
+
+    hidden_states = self.norm(hidden_states)
+
+    # add hidden states from the last decoder layer
+    if output_hidden_states:
+        all_hidden_states += (hidden_states,)
+
+    next_cache = next_decoder_cache if use_cache else None
+    if not return_dict:
+        return tuple(
+            v
+            for v in [hidden_states, next_cache, all_hidden_states, all_self_attns]
+            if v is not None
+        )
+    return BaseModelOutputWithPast(
+        last_hidden_state=hidden_states,
+        past_key_values=next_cache,
+        hidden_states=all_hidden_states,
+        attentions=all_self_attns,
+    )
+
+
+class LlamaDecoderLayer(OriginalLlamaDecoderLayer):
+    """
+    patched version of LlamaDecoderLayer to pass through the precalculated cu_seqlens
+    """
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Tuple[torch.Tensor]] = None,
+        output_attentions: Optional[bool] = False,
+        use_cache: Optional[bool] = False,
+        cu_seqlens: Optional[torch.Tensor] = None,
+        max_seqlen: Optional[torch.Tensor] = None,
+    ) -> Tuple[
+        torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]
+    ]:
+        """
+        Args:
+            hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
+            attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
+                `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
+            output_attentions (`bool`, *optional*):
+                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+                returned tensors for more detail.
+            use_cache (`bool`, *optional*):
+                If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
+                (see `past_key_values`).
+            past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
+            cu_seqlens (`torch.Tensor`, *optional*) cumulative sequence len when packing
+        """
+
+        residual = hidden_states
+
+        hidden_states = self.input_layernorm(hidden_states)
+
+        # Self Attention
+        hidden_states, self_attn_weights, present_key_value = self.self_attn(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_value=past_key_value,
+            output_attentions=output_attentions,
+            use_cache=use_cache,
+            cu_seqlens=cu_seqlens,
+            max_seqlen=max_seqlen,
+        )
+        hidden_states = residual + hidden_states
+
+        # Fully Connected
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+
+        outputs = (hidden_states,)
+
+        if output_attentions:
+            outputs += (self_attn_weights,)
+
+        if use_cache:
+            outputs += (present_key_value,)
+
+        return outputs

src/axolotl/monkeypatch/llama_attn_hijack_sdp.py

@@ -0,0 +1,140 @@
+"""
+Patched LlamaAttention to use torch.nn.functional.scaled_dot_product_attention
+"""
+
+import warnings
+from typing import Optional, Tuple
+
+import torch
+import torch.nn.functional as F
+import transformers.models.llama.modeling_llama
+from transformers.models.llama.modeling_llama import apply_rotary_pos_emb, repeat_kv
+
+
+def hijack_llama_sdp_attention():
+    transformers.models.llama.modeling_llama.LlamaAttention.forward = (
+        sdp_attention_forward
+    )
+
+
+def sdp_attention_forward(
+    self,
+    hidden_states: torch.Tensor,
+    attention_mask: Optional[torch.Tensor] = None,
+    position_ids: Optional[torch.LongTensor] = None,
+    past_key_value: Optional[Tuple[torch.Tensor]] = None,
+    output_attentions: bool = False,
+    use_cache: bool = False,
+) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+    # pylint: disable=duplicate-code
+    bsz, q_len, _ = hidden_states.size()
+
+    if not hasattr(self, "pretraining_tp"):
+        self.pretraining_tp = 1
+
+    if self.pretraining_tp > 1:
+        key_value_slicing = (
+            self.num_key_value_heads * self.head_dim
+        ) // self.pretraining_tp
+        query_slices = self.q_proj.weight.split(
+            (self.num_heads * self.head_dim) // self.pretraining_tp, dim=0
+        )
+        key_slices = self.k_proj.weight.split(key_value_slicing, dim=0)
+        value_slices = self.v_proj.weight.split(key_value_slicing, dim=0)
+
+        query_states = [
+            F.linear(hidden_states, query_slices[i]) for i in range(self.pretraining_tp)
+        ]
+        query_states = torch.cat(query_states, dim=-1)
+
+        key_states = [
+            F.linear(hidden_states, key_slices[i]) for i in range(self.pretraining_tp)
+        ]
+        key_states = torch.cat(key_states, dim=-1)
+
+        value_states = [
+            F.linear(hidden_states, value_slices[i]) for i in range(self.pretraining_tp)
+        ]
+        value_states = torch.cat(value_states, dim=-1)
+
+    else:
+        query_states = self.q_proj(hidden_states)
+        key_states = self.k_proj(hidden_states)
+        value_states = self.v_proj(hidden_states)
+
+    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)
+    # [bsz, q_len, nh, hd]
+    # [bsz, nh, q_len, hd]
+
+    kv_seq_len = key_states.shape[-2]
+    if past_key_value is not None:
+        kv_seq_len += past_key_value[0].shape[-2]
+
+    cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+    query_states, key_states = apply_rotary_pos_emb(
+        query_states, key_states, cos, sin, position_ids
+    )
+    # [bsz, nh, t, hd]
+
+    if past_key_value is not None:
+        # reuse k, v, self_attention
+        key_states = torch.cat([past_key_value[0], key_states], dim=2)
+        value_states = torch.cat([past_key_value[1], value_states], dim=2)
+
+    past_key_value = (key_states, value_states) if use_cache else None
+
+    # repeat k/v heads if n_kv_heads < n_heads
+    key_states = repeat_kv(key_states, self.num_key_value_groups)
+    value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+    if output_attentions:
+        warnings.warn(
+            "Output attentions is not supported for patched `LlamaAttention`, returning `None` instead."
+        )
+
+    #
+    # sdp-attn start
+    #
+
+    with torch.backends.cuda.sdp_kernel():
+        attn_output = torch.nn.functional.scaled_dot_product_attention(
+            query_states,
+            key_states,
+            value_states,
+            attn_mask=attention_mask,
+            is_causal=False,
+        )
+
+    if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
+        raise ValueError(
+            f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
+            f" {attn_output.size()}"
+        )
+    attn_output = attn_output.transpose(1, 2)
+    attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
+
+    #
+    # sdp-attn end
+    #
+
+    if self.pretraining_tp > 1:
+        attn_output = attn_output.split(self.hidden_size // self.pretraining_tp, dim=2)
+        o_proj_slices = self.o_proj.weight.split(
+            self.hidden_size // self.pretraining_tp, dim=1
+        )
+        attn_output = sum(
+            F.linear(attn_output[i], o_proj_slices[i])
+            for i in range(self.pretraining_tp)
+        )
+    else:
+        attn_output = self.o_proj(attn_output)
+
+    return attn_output, None, past_key_value

src/axolotl/monkeypatch/llama_attn_hijack_xformers.py

@@ -0,0 +1,155 @@
+"""
+Directly copied the code from https://raw.githubusercontent.com/oobabooga/text-generation-webui/main/modules/llama_attn_hijack.py and made some adjustments
+"""
+
+import logging
+import warnings
+from typing import Optional, Tuple
+
+import torch
+import torch.nn.functional as F
+import transformers.models.llama.modeling_llama
+from transformers.models.llama.modeling_llama import apply_rotary_pos_emb, repeat_kv
+
+try:
+    import xformers.ops
+except ImportError:
+    logging.error("xformers not found! Please install it before trying to use it.")
+
+
+def hijack_llama_attention():
+    transformers.models.llama.modeling_llama.LlamaAttention.forward = xformers_forward
+
+
+def xformers_forward(
+    self,
+    hidden_states: torch.Tensor,
+    attention_mask: Optional[torch.Tensor] = None,
+    position_ids: Optional[torch.LongTensor] = None,
+    past_key_value: Optional[Tuple[torch.Tensor]] = None,
+    output_attentions: bool = False,
+    use_cache: bool = False,
+) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+    # pylint: disable=duplicate-code
+    bsz, q_len, _ = hidden_states.size()
+
+    if not hasattr(self, "pretraining_tp"):
+        self.pretraining_tp = 1
+
+    if self.pretraining_tp > 1:
+        key_value_slicing = (
+            self.num_key_value_heads * self.head_dim
+        ) // self.pretraining_tp
+        query_slices = self.q_proj.weight.split(
+            (self.num_heads * self.head_dim) // self.pretraining_tp, dim=0
+        )
+        key_slices = self.k_proj.weight.split(key_value_slicing, dim=0)
+        value_slices = self.v_proj.weight.split(key_value_slicing, dim=0)
+
+        query_states = [
+            F.linear(hidden_states, query_slices[i]) for i in range(self.pretraining_tp)
+        ]
+        query_states = torch.cat(query_states, dim=-1)
+
+        key_states = [
+            F.linear(hidden_states, key_slices[i]) for i in range(self.pretraining_tp)
+        ]
+        key_states = torch.cat(key_states, dim=-1)
+
+        value_states = [
+            F.linear(hidden_states, value_slices[i]) for i in range(self.pretraining_tp)
+        ]
+        value_states = torch.cat(value_states, dim=-1)
+
+    else:
+        query_states = self.q_proj(hidden_states)
+        key_states = self.k_proj(hidden_states)
+        value_states = self.v_proj(hidden_states)
+
+    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)
+    # [bsz, q_len, nh, hd]
+    # [bsz, nh, q_len, hd]
+
+    kv_seq_len = key_states.shape[-2]
+    if past_key_value is not None:
+        kv_seq_len += past_key_value[0].shape[-2]
+
+    cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+    query_states, key_states = apply_rotary_pos_emb(
+        query_states, key_states, cos, sin, position_ids
+    )
+    # [bsz, nh, t, hd]
+
+    if past_key_value is not None:
+        # reuse k, v, self_attention
+        key_states = torch.cat([past_key_value[0], key_states], dim=2)
+        value_states = torch.cat([past_key_value[1], value_states], dim=2)
+
+    past_key_value = (key_states, value_states) if use_cache else None
+
+    # repeat k/v heads if n_kv_heads < n_heads
+    key_states = repeat_kv(key_states, self.num_key_value_groups)
+    value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+    if output_attentions:
+        warnings.warn(
+            "Output attentions is not supported for patched `LlamaAttention`, returning `None` instead."
+        )
+
+    #
+    # xformers-attn start
+    #
+
+    query_states = query_states.transpose(1, 2)
+    key_states = key_states.transpose(1, 2)
+    value_states = value_states.transpose(1, 2)
+
+    # This is a nasty hack. We know attention_mask in transformers is either LowerTriangular or all Zeros.
+    # We therefore check if one element in the upper triangular portion is zero. If it is, then the mask is all zeros.
+    if attention_mask is None or attention_mask[0, 0, 0, 1] == 0:
+        # input and output should be of form (bsz, q_len, num_heads, head_dim)
+        attn_output = xformers.ops.memory_efficient_attention(
+            query_states, key_states, value_states, attn_bias=None
+        )
+    else:
+        # input and output should be of form (bsz, q_len, num_heads, head_dim)
+        attn_output = xformers.ops.memory_efficient_attention(
+            query_states,
+            key_states,
+            value_states,
+            # attn_bias=attention_mask,
+            attn_bias=xformers.ops.LowerTriangularMask(),
+        )
+
+    if attn_output.size() != (bsz, q_len, self.num_heads, self.head_dim):
+        raise ValueError(
+            f"`attn_output` should be of size {(bsz, q_len, self.num_heads, self.head_dim)}, but is"
+            f" {attn_output.size()}"
+        )
+    attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
+
+    #
+    # xformers-attn end
+    #
+
+    if self.pretraining_tp > 1:
+        attn_output = attn_output.split(self.hidden_size // self.pretraining_tp, dim=2)
+        o_proj_slices = self.o_proj.weight.split(
+            self.hidden_size // self.pretraining_tp, dim=1
+        )
+        attn_output = sum(
+            F.linear(attn_output[i], o_proj_slices[i])
+            for i in range(self.pretraining_tp)
+        )
+    else:
+        attn_output = self.o_proj(attn_output)
+
+    return attn_output, None, past_key_value

src/axolotl/monkeypatch/llama_expand_mask.py

@@ -0,0 +1,52 @@
+"""
+expands the binary attention mask per 3.2.2 of https://arxiv.org/pdf/2107.02027.pdf
+"""
+from typing import Optional
+
+import torch
+
+
+def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
+    """
+    Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
+    This expansion handles packed sequences so that sequences share the same attention mask integer value
+    when they attend to each other within that sequence.
+    This expansion transforms the mask to lower triangular form to prevent future peeking.
+    """
+    bsz, src_len = mask.size()
+    tgt_len = tgt_len if tgt_len is not None else src_len
+
+    mask = mask.unsqueeze(1).unsqueeze(2)
+    mask = mask.expand(bsz, 1, tgt_len, src_len)
+
+    # Create a binary mask from the original mask where zeros remain zeros and all other values are set to one
+    binary_mask = torch.where(
+        mask != 0,
+        torch.tensor(1).to(dtype),
+        torch.tensor(0).to(dtype),
+    )
+
+    # Create a block-diagonal mask.
+    # we multiply by the binary mask so that 0's in the original mask are correctly excluded
+    zero_one_mask = torch.eq(mask, mask.transpose(-1, -2)).int() * binary_mask
+
+    # Now let's create a lower triangular mask of ones that will zero out the upper triangular part
+    lower_triangular_ones = torch.tril(torch.ones((tgt_len, src_len), dtype=dtype)).to(
+        mask.device
+    )
+
+    # Use the lower triangular mask to zero out the upper triangular part of the zero_one_mask
+    masked_zero_one_mask = zero_one_mask * lower_triangular_ones
+    inverted_mask = 1.0 - masked_zero_one_mask
+
+    return inverted_mask.masked_fill(
+        inverted_mask.to(torch.bool), torch.finfo(dtype).min
+    )
+
+
+def hijack_expand_mask():
+    import transformers
+
+    transformers.models.llama.modeling_llama._expand_mask = (  # pylint: disable=protected-access
+        _expand_mask
+    )

src/axolotl/monkeypatch/relora.py

@@ -0,0 +1,393 @@
+"""Implements the ReLoRA training procedure from https://arxiv.org/abs/2307.05695, minus the initial full fine-tune."""
+import glob
+import json
+import logging
+import os.path
+import shutil
+from pathlib import Path
+from typing import Dict, List, Sequence
+
+import bitsandbytes as bnb
+import peft
+import safetensors.torch as st
+import torch
+from huggingface_hub import snapshot_download
+from torch.optim.lr_scheduler import LRScheduler
+from torch.optim.optimizer import Optimizer
+from transformers import (
+    TrainerCallback,
+    TrainerControl,
+    TrainerState,
+    TrainingArguments,
+)
+from transformers.trainer_utils import PREFIX_CHECKPOINT_DIR
+
+from axolotl.utils.dict import DictDefault
+from axolotl.utils.distributed import is_main_process
+
+LOG = logging.getLogger("axolotl.relora")
+
+
+def reset_optimizer(optimizer: torch.optim.Optimizer):
+    for group in optimizer.param_groups:
+        for param in group["params"]:
+            param_state = optimizer.state[param]
+            for key in param_state:
+                if "qmap" in key:
+                    continue
+
+                if key == "step" and isinstance(param_state[key], int):
+                    param_state[key] = 0
+                else:
+                    param_state[key] = torch.zeros_like(param_state[key])
+
+
+class ReLoRACallback(TrainerCallback):
+    """Callback to merge LoRA weights into the base model and save full-weight checkpoints"""
+
+    def __init__(self, cfg: DictDefault):
+        self.relora_steps = cfg.relora_steps
+        self.cpu_offload = cfg.relora_cpu_offload
+        self.quantized = cfg.load_in_4bit or cfg.load_in_8bit
+        self.last_full_model = cfg.base_model
+        self.resume_from_checkpoint = cfg.resume_from_checkpoint
+
+        if not os.path.exists(self.last_full_model):
+            self.last_full_model = str(Path(snapshot_download(cfg.base_model)))
+
+        assert os.path.exists(
+            self.last_full_model
+        ), "for ReLORA base_model must be a local path"
+
+        self.num_lora_restarts = 0
+        self.need_full_save = False
+
+    def on_train_begin(
+        self,
+        _args: TrainingArguments,
+        _state: TrainerState,
+        control: TrainerControl,
+        model: peft.LoraModel,
+        **_kwargs,
+    ):
+        if self.resume_from_checkpoint:
+            weight_path = os.path.join(self.resume_from_checkpoint, "relora")
+            if not os.path.exists(weight_path):
+                LOG.warning(
+                    "Resuming ReLoRA from checkpoint, but no full-weight save found"
+                )
+            else:
+                LOG.info(f"Loading adjusted base weights from {weight_path}")
+                load_weight_checkpoint(model, weight_path)
+        return control
+
+    def on_step_begin(
+        self,
+        args: TrainingArguments,
+        state: TrainerState,
+        control: TrainerControl,
+        model: peft.LoraModel,
+        optimizer: torch.optim.Optimizer,
+        **_kwargs,
+    ):
+        if state.global_step > 0 and state.global_step % self.relora_steps == 0:
+            checkpoint_folder = os.path.join(
+                args.output_dir,
+                f"{PREFIX_CHECKPOINT_DIR}-{state.global_step}",
+                "relora",
+            )
+
+            with torch.no_grad():
+                merge_and_save(
+                    model,
+                    self.last_full_model,
+                    checkpoint_folder,
+                    reinit=True,
+                    quantized=self.quantized,
+                    actually_save=is_main_process(),
+                    cpu_offload=self.cpu_offload,
+                )
+                reset_optimizer(optimizer)
+
+            if self.quantized:
+                self.last_full_model = checkpoint_folder
+            self.num_lora_restarts += 1
+
+        return control
+
+    def on_save(
+        self,
+        args: TrainingArguments,
+        state: TrainerState,
+        control: TrainerControl,
+        model: peft.LoraModel,
+        **_kwargs,
+    ):
+        checkpoint_folder = os.path.join(
+            args.output_dir, f"{PREFIX_CHECKPOINT_DIR}-{state.global_step}", "relora"
+        )
+        if (
+            state.global_step >= self.relora_steps
+            and state.global_step % self.relora_steps != 0
+        ):
+            if self.quantized:
+                if is_main_process() and self.last_full_model != checkpoint_folder:
+                    # ensure the latest full parameter save is in the latest checkpoint
+                    # folder, so that automatic pruning of checkpoints does not remove it
+                    LOG.info(f"moving last full parameter save to {checkpoint_folder}")
+                    os.makedirs(checkpoint_folder, exist_ok=True)
+                    chunks = glob.glob(
+                        f"{self.last_full_model}/model*.safetensors"
+                    ) + glob.glob(f"{self.last_full_model}/model*.index.json")
+                    for path in chunks:
+                        new_path = os.path.abspath(shutil.move(path, checkpoint_folder))
+                        try:
+                            os.symlink(new_path, path)
+                        except OSError:
+                            # probably on windows without permission to symlink
+                            pass
+
+                    self.last_full_model = checkpoint_folder
+            else:
+                model.model.save_pretrained(checkpoint_folder, safe_serialization=True)
+
+        return control
+
+    def on_log(
+        self,
+        _args: TrainingArguments,
+        _state: TrainerState,
+        control: TrainerControl,
+        logs: Dict[str, float],
+        **_kwargs,
+    ):
+        logs["num_lora_restarts"] = self.num_lora_restarts
+        return control
+
+    def on_train_end(
+        self,
+        args: TrainingArguments,
+        _state: TrainerState,
+        control: TrainerControl,
+        model: peft.LoraModel,
+        **_kwargs,
+    ):
+        if self.quantized:
+            # perform final merge and save
+            with torch.no_grad():
+                merge_and_save(
+                    model,
+                    self.last_full_model,
+                    args.output_dir,
+                    reinit=False,
+                    quantized=self.quantized,
+                    actually_save=is_main_process(),
+                    cpu_offload=self.cpu_offload,
+                )
+        # no need to save if unquantized, as finetune.py will call merge_and_unload()
+        return control
+
+
+class ReLoRAScheduler(LRScheduler):
+    """Wraps another scheduler to apply per-lora-restart learning rate warmups."""
+
+    def __init__(
+        self,
+        optimizer: Optimizer,
+        inner_schedule: LRScheduler,
+        relora_steps: int,
+        warmup_steps: int,
+        min_lr_scale: float = 0.001,
+    ) -> None:
+        self.inner_schedule = inner_schedule
+        self.relora_steps = relora_steps
+        self.warmup_steps = warmup_steps
+        self.min_lr_scale = min_lr_scale
+        super().__init__(optimizer, inner_schedule.last_epoch, inner_schedule.verbose)
+
+    def get_lr(self) -> float:
+        self.inner_schedule.last_epoch = self.last_epoch
+
+        original = self.inner_schedule.get_lr()
+        step = self.last_epoch
+        if step < self.relora_steps:
+            scale = 1
+        else:
+            cycle_t = min(1.0, (step % self.relora_steps) / self.warmup_steps)
+            scale = cycle_t * (1 - self.min_lr_scale) + self.min_lr_scale
+
+        if isinstance(original, Sequence):
+            return [lr * scale for lr in original]
+        return original * scale
+
+
+def sharded_paths(path: str, module_names: List[str]) -> Dict[str, str]:
+    model_name = "model.safetensors"
+    if not os.path.exists(str(Path(path) / model_name)) and not os.path.exists(
+        str(Path(path) / f"{model_name}.index.json")
+    ):
+        model_name = "pytorch_model.bin"
+
+    index_path = str(Path(path) / f"{model_name}.index.json")
+    if os.path.exists(index_path):
+        with open(index_path, "r", encoding="utf-8") as file:
+            data = json.load(file)
+        return data["weight_map"]
+    return {(module_name + ".weight"): model_name for module_name in module_names}
+
+
+def lora_delta_weight(layer: peft.tuners.lora.LoraLayer, device) -> torch.Tensor:
+    if isinstance(layer, (peft.tuners.lora.Linear8bitLt, peft.tuners.lora.Linear4bit)):
+        adapter = layer.active_adapter
+        return (
+            peft.utils.transpose(
+                layer.lora_B[adapter].weight.detach().to(device)
+                @ layer.lora_A[adapter].weight.detach().to(device),
+                getattr(layer, "fan_in_fan_out", False),
+            )
+            * layer.scaling[adapter]
+        )
+
+    return layer.get_delta_weight().to(device)
+
+
+def find_lora_modules(model: peft.LoraModel) -> Dict[str, peft.tuners.lora.LoraLayer]:
+    modules: Dict[str, peft.tuners.lora.LoraLayer] = {}
+
+    key_list = [key for key, _ in model.model.named_modules() if "lora" not in key]
+    for key in key_list:
+        try:
+            # pylint: disable=protected-access
+            _parent, target, _target_name = peft.utils._get_submodules(model.model, key)
+        except AttributeError:
+            continue
+
+        if isinstance(target, peft.tuners.lora.LoraLayer):
+            modules[key] = target
+
+    return modules
+
+
+def update_weights(
+    target: peft.tuners.lora.LoraLayer, new_weight: torch.Tensor, reinit: bool, device
+):
+    if reinit:
+        for adapter_name in target.lora_A:
+            target.reset_lora_parameters(adapter_name)
+        for adapter_name in target.lora_embedding_A:
+            target.reset_lora_parameters(adapter_name)
+
+    if isinstance(target, peft.tuners.lora.Linear4bit):
+        # This could be faster, but the quantization of Linear4bit weights occurs
+        # when the module is moved from cpu to gpu. Without meddling *too* deeply in
+        # PEFT's innards or maintaining a duplicate of that codepath, this is good
+        # enough for now.
+        target.weight.quant_state = None
+        target.weight.data = new_weight.cpu()
+        target.to(device)
+    elif isinstance(target, peft.tuners.lora.Linear8bitLt):
+        target.weight = bnb.nn.Int8Params(new_weight, requires_grad=False).to(device)
+    else:
+        target.weight.data = new_weight.to(device)
+
+
+def merge_and_save(
+    model: peft.LoraModel,
+    model_src: str,
+    model_dst: str,
+    reinit: bool = False,
+    quantized: bool = False,
+    cpu_offload: bool = False,
+    actually_save: bool = True,
+):
+    modules = find_lora_modules(model)
+
+    if not quantized:
+        for module_name, target in modules.items():
+            update = target.get_delta_weight(target.active_adapter).detach()
+            target.weight.data += update
+
+            if reinit:
+                for adapter_name in target.lora_A:
+                    target.reset_lora_parameters(adapter_name)
+                for adapter_name in target.lora_embedding_A:
+                    target.reset_lora_parameters(adapter_name)
+        return
+
+    os.makedirs(model_dst, exist_ok=True)
+    shard_paths = sharded_paths(model_src, modules.keys())
+    out_shard_paths = {}
+
+    unique_shards = list(set(shard_paths.values()))
+    for shard_path in unique_shards:
+        out_tensors = {}
+        if shard_path.endswith(".safetensors"):
+            in_tensors = st.load_file(str(Path(model_src) / shard_path))
+        else:
+            in_tensors = torch.load(Path(model_src) / shard_path)
+            if "state_dict" in in_tensors:
+                in_tensors = in_tensors["state_dict"]
+
+        for module_name, target in modules.items():
+            key = module_name + ".weight"
+            if key not in shard_paths or shard_paths[key] != shard_path:
+                continue
+
+            orig_weight = in_tensors[key]
+            old_dev = target.weight.device
+            math_dev = "cpu" if cpu_offload else old_dev
+
+            delta_weight = lora_delta_weight(target, math_dev)
+            new_weight = orig_weight.to(math_dev) + delta_weight
+            del delta_weight
+
+            if actually_save:
+                out_tensors[key] = new_weight.half().cpu()
+
+            update_weights(target, new_weight, reinit=reinit, device=old_dev)
+
+        if actually_save:
+            out_shard_name = shard_path
+            if out_shard_name.startswith("pytorch_model"):
+                out_shard_name = (
+                    out_shard_name.replace("pytorch_model", "model").rstrip(".bin")
+                    + ".safetensors"
+                )
+
+            for module_name in in_tensors:
+                if module_name not in out_tensors:
+                    out_tensors[module_name] = in_tensors[module_name].half()
+                out_shard_paths[module_name] = out_shard_name
+
+            shard_fn = str(Path(model_dst) / out_shard_name)
+            LOG.info(f"saving tensors to {shard_fn}")
+            st.save_file(out_tensors, shard_fn, metadata={"format": "pt"})
+
+        del in_tensors
+        del out_tensors
+        torch.cuda.empty_cache()
+
+    if actually_save and len(unique_shards) > 1:
+        with open(
+            str(Path(model_dst, "model.safetensors.index.json")), "w", encoding="utf-8"
+        ) as file:
+            json.dump({"metadata": {}, "weight_map": out_shard_paths}, file)
+
+
+def load_weight_checkpoint(model: peft.LoraModel, checkpoint_path: str):
+    modules = find_lora_modules(model)
+    shard_paths = sharded_paths(checkpoint_path, modules.keys())
+    unique_shards = list(set(shard_paths.values()))
+
+    for shard_path in unique_shards:
+        tensors = st.load_file(os.path.join(checkpoint_path, shard_path))
+
+        for module_name, target in modules.items():
+            key = module_name + ".weight"
+            if key not in shard_paths or shard_paths[key] != shard_path:
+                continue
+
+            new_weight = tensors[key]
+            update_weights(
+                target, new_weight, reinit=False, device=target.weight.device
+            )

src/axolotl/monkeypatch/utils.py

@@ -0,0 +1,103 @@
+"""
+Shared utils for the monkeypatches
+"""
+import torch
+
+
+def get_cu_seqlens(attn_mask):
+    """generate a cumulative sequence length mask for flash attention using attn mask"""
+    if len(attn_mask.shape) == 1:
+        attn_mask = attn_mask.unsqueeze(0)
+
+    device = attn_mask.device
+    results = []
+    max_seq_lens = []
+
+    for row in attn_mask:
+        # Exclude zeros to avoid adding their positions to the mask
+        t_non_zeros = row[row != 0]
+        # Find where the sequence number changes (including the first position)
+        seq_change = torch.cat(
+            [
+                torch.tensor([1], dtype=torch.int32, device=device),
+                t_non_zeros[1:] != t_non_zeros[:-1],
+            ]
+        )
+        # Get the indices where the sequence changes
+        change_indices = torch.cat(
+            [
+                (seq_change == 1).nonzero(as_tuple=True)[0],
+                torch.tensor([len(t_non_zeros)], dtype=torch.int32, device=device),
+            ]
+        )
+        # Calculate the sequence lengths
+        seq_lengths = change_indices[1:] - change_indices[:-1]
+        # Calculate the length of the final sequence or padding
+        final_seq_length = len(row) - change_indices[-1]
+        # Append the length of the final sequence or padding to seq_lengths
+        if final_seq_length.item():
+            seq_lengths = torch.cat(
+                [
+                    seq_lengths,
+                    torch.tensor(
+                        [final_seq_length.item()], dtype=torch.int32, device=device
+                    ),
+                ]
+            )
+        # Calculate the cumulative sequence lengths
+        cu_seqlens = torch.cat(
+            [torch.tensor([0], dtype=torch.int32, device=device), seq_lengths.cumsum(0)]
+        )
+        max_seq_len = (cu_seqlens[1:] - cu_seqlens[:-1]).max()
+        results.append(cu_seqlens)
+        max_seq_lens.append(max_seq_len)
+
+    return torch.stack(results).to(dtype=torch.int32), torch.stack(max_seq_lens)
+
+
+def get_cu_seqlens_from_pos_ids(position_ids):
+    """generate a cumulative sequence length mask for flash attention using pos ids"""
+    if len(position_ids.shape) == 1:
+        position_ids = position_ids.unsqueeze(0)
+
+    device = position_ids.device
+    results = []
+    max_seq_lens = []
+
+    for row in position_ids:
+        # Count the number of consecutive zeros from the right side
+        padding_length = (row == 0).int().flip(dims=[0]).cumprod(dim=0).sum().item()
+
+        # Adjust the row to exclude padding
+        adjusted_row = row[:-padding_length] if padding_length else row.clone()
+
+        # Find where the position resets to 0 (indicating a new sequence)
+        seq_starts = torch.cat(
+            [
+                torch.tensor([True], dtype=torch.bool, device=device),
+                adjusted_row[1:] == 0,
+            ]
+        )
+        # Get the indices where the sequence starts
+        start_indices = torch.cat(
+            [
+                (seq_starts).nonzero(as_tuple=True)[0],
+                torch.tensor([len(adjusted_row)], dtype=torch.int32, device=device),
+            ]
+        )
+        # Calculate the sequence lengths
+        seq_lengths = start_indices[1:] - start_indices[:-1]
+        # Calculate the cumulative sequence lengths
+        cu_seqlens = torch.cat(
+            [torch.tensor([0], dtype=torch.int32, device=device), seq_lengths.cumsum(0)]
+        )
+        # Append the padding length to the cumulative sequence lengths
+        if padding_length:
+            cu_seqlens = torch.cat(
+                [cu_seqlens, torch.tensor([len(row)], dtype=torch.int32, device=device)]
+            )
+        max_seq_len = (cu_seqlens[1:] - cu_seqlens[:-1]).max()
+        results.append(cu_seqlens)
+        max_seq_lens.append(max_seq_len)
+
+    return torch.stack(results).to(dtype=torch.int32), torch.stack(max_seq_lens)

src/axolotl/monkeypatch/xpos_rope_llama_monkey_patch.py

@@ -0,0 +1,94 @@
+# pylint: skip-file
+"""
+Copied from https://github.com/kaiokendev/cutoff-len-is-context-len/blob/main/util/xpos_rope_llama_monkey_patch.py
+"""
+import torch
+import transformers
+import transformers.models.llama.modeling_llama
+from einops import rearrange
+
+
+class XposRotaryEmbedding(torch.nn.Module):
+    def __init__(
+        self,
+        dim,
+        max_position_embeddings=2048,
+        base=10000,
+        device=None,
+        scale_base=2048,
+        use_xpos=True,
+    ):
+        super().__init__()
+        self.max_seq_len_cached = max_position_embeddings
+        self.scale_base = scale_base
+
+        inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float() / dim))
+        t = torch.arange(self.max_seq_len_cached, device=device).type_as(inv_freq)
+        freqs = torch.einsum("i , j -> i j", t, inv_freq)
+        freqs = torch.cat((freqs, freqs), dim=-1)
+
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+        self.register_buffer("freqs_cached", freqs, persistent=False)
+
+        if not use_xpos:
+            self.register_buffer("scale", None)
+            self.register_buffer("scale_cached", torch.ones(1))
+            return
+
+        scale = (torch.arange(0, dim, 2) + 0.4 * dim) / (1.4 * dim)
+        power = (t - (self.max_seq_len_cached // 2)) / self.scale_base
+        scale_cached = scale ** rearrange(power, "n -> n 1")
+        scale_cached = torch.cat((scale_cached, scale_cached), dim=-1)
+
+        self.register_buffer("scale", scale, persistent=False)
+        self.register_buffer("scale_cached", scale_cached, persistent=False)
+
+    def forward(
+        self,
+        x,
+        seq_len,
+    ):
+        if seq_len > self.max_seq_len_cached:
+            self.max_seq_len_cached = seq_len
+            t = torch.arange(self.max_seq_len_cached, device=x.device).type_as(
+                self.inv_freq
+            )
+            freqs = torch.einsum("i , j -> i j", t, self.inv_freq)
+            freqs = torch.cat((freqs, freqs), dim=-1).to(dtype=x.dtype)
+
+            self.register_buffer("freqs_cached", freqs)
+
+            if self.scale is None:
+                self.register_buffer(
+                    "scale_cached", torch.ones(1, device=x.device).to(dtype=x.dtype)
+                )
+
+                return self.freqs_cached.to(dtype=x.dtype), self.scale_cached
+
+            power = (t - (seq_len // 2)) / self.scale_base
+            scale = self.scale ** rearrange(power, "n -> n 1")
+            scale = torch.cat((scale, scale), dim=-1).to(dtype=x.dtype)
+            self.register_buffer("scale_cached", scale)
+
+        return self.freqs_cached.to(dtype=x.dtype), self.scale_cached.to(dtype=x.dtype)
+
+
+def rotate_half(x):
+    x1, x2 = x.chunk(2, dim=-1)
+    return torch.cat((-x2, x1), dim=-1)
+
+
+def apply_rotary_pos_emb(q, k, freqs, scale=1, position_ids=None):
+    freqs = freqs[position_ids, :]
+    if scale.shape[-1] != 1:
+        scale = scale[position_ids, :]
+
+    q_embed = (q * freqs.cos() * scale) + (rotate_half(q) * freqs.sin() * scale)
+    k_embed = (k * freqs.cos() * 1 / scale) + (rotate_half(k) * freqs.sin() * 1 / scale)
+
+    return q_embed, k_embed
+
+
+def replace_llama_rope_with_xpos_rope():
+    transformers.models.llama.modeling_llama.LlamaRotaryEmbedding = XposRotaryEmbedding
+    transformers.models.llama.modeling_llama.apply_rotary_pos_emb = apply_rotary_pos_emb

src/axolotl/prompt_strategies/__init__.py

@@ -2,8 +2,10 @@
 
 import importlib
 
+from axolotl.prompt_strategies.user_defined import UserDefinedDatasetConfig
 
-def load(strategy, tokenizer, cfg):
+
+def load(strategy, tokenizer, cfg, ds_cfg):
     try:
         load_fn = "load"
         if strategy.split(".")[-1].startswith("load_"):
@@ -11,6 +13,9 @@ def load(strategy, tokenizer, cfg):
             strategy = ".".join(strategy.split(".")[:-1])
         mod = importlib.import_module(f".{strategy}", "axolotl.prompt_strategies")
         func = getattr(mod, load_fn)
-        return func(tokenizer, cfg)
+        load_kwargs = {}
+        if strategy == "user_defined":
+            load_kwargs["ds_cfg"] = UserDefinedDatasetConfig(**ds_cfg)
+        return func(tokenizer, cfg, **load_kwargs)
     except Exception:  # pylint: disable=broad-exception-caught
         return None

src/axolotl/prompt_strategies/alpaca_chat.py

@@ -6,7 +6,7 @@ from axolotl.prompt_tokenizers import (
     AlpacaPromptTokenizingStrategy,
     InstructionPromptTokenizingStrategy,
 )
-from axolotl.prompters import AlpacaPrompter, PromptStyle
+from axolotl.prompters import AlpacaPrompter, PromptStyle, UnpromptedPrompter
 
 
 def load(tokenizer, cfg):
@@ -18,6 +18,42 @@ def load(tokenizer, cfg):
     )
 
 
+class AlpacaConcisePrompter(AlpacaPrompter):
+    """
+    Alpaca Prompter extending the system prompt to ask for concise chat-instruct answers
+    """
+
+    system_prompt = "Below is an instruction from a USER that describes a task, paired with an input that provides further context. The ASSISTANT writes a response that concisely and appropriately completes the request.\n\n"
+    system_no_input_prompt = "Below is an instruction from a USER that describes a task. The ASSISTANT writes a response that appropriately and concisely completes the request.\n\n"
+
+
+class AlpacaChatPrompter(AlpacaPrompter):
+    """
+    Alpaca Chat Prompter extending the system prompt to for chat-instruct answers
+    """
+
+    system_prompt = "Below is an instruction from a USER that describes a task, paired with an input that provides further context. The ASSISTANT writes a response that concisely and appropriately completes the request.\n\n"
+    system_no_input_prompt = "Below is an instruction from a USER that describes a task. The ASSISTANT writes a response that appropriately and concisely completes the request.\n\n"
+
+    def __init__(self):  # pylint: disable=super-init-not-called
+        self.prompt_style = PromptStyle.CHAT.value
+        self.match_prompt_style()
+
+
+class NoSystemPrompter(AlpacaPrompter):
+    """
+    Null Prompter with no system prompts
+    """
+
+    system_prompt = ""
+    system_no_input_prompt = ""
+    turn_format = "{instruction} {input} "
+    turn_no_input_format = "{instruction} "
+
+    def __init__(self):  # pylint: disable=super-init-not-called
+        pass
+
+
 class AlpacaQAPromptTokenizingStrategy(InstructionPromptTokenizingStrategy):
     """
     Tokenizing strategy for AlpacaQA
@@ -31,9 +67,49 @@ class AlpacaQAPromptTokenizingStrategy(InstructionPromptTokenizingStrategy):
         )
 
 
-def load_qa(tokenizer, cfg):
-    return AlpacaQAPromptTokenizingStrategy(
-        AlpacaPrompter(PromptStyle.CHAT.value),
+class CamelAIPromptTokenizingStrategy(InstructionPromptTokenizingStrategy):
+    """
+    Tokenizing strategy for CamelAI datasets
+    """
+
+    def parse_instruction_fields(self, prompt) -> Tuple[str, str, str]:
+        return (
+            prompt["message_1"],
+            "",
+            prompt["message_2"],
+        )
+
+
+def load_concise(tokenizer, cfg):
+    return AlpacaPromptTokenizingStrategy(
+        AlpacaConcisePrompter(PromptStyle.CHAT.value),
+        tokenizer,
+        cfg.train_on_inputs,
+        cfg.sequence_len,
+    )
+
+
+def load_qa(tokenizer, cfg):
+    return AlpacaQAPromptTokenizingStrategy(
+        AlpacaChatPrompter(),
+        tokenizer,
+        cfg.train_on_inputs,
+        cfg.sequence_len,
+    )
+
+
+def load_camel_ai(tokenizer, cfg):
+    return CamelAIPromptTokenizingStrategy(
+        AlpacaChatPrompter(),
+        tokenizer,
+        cfg.train_on_inputs,
+        cfg.sequence_len,
+    )
+
+
+def load_no_prompt(tokenizer, cfg):
+    return AlpacaPromptTokenizingStrategy(
+        UnpromptedPrompter(PromptStyle.CHAT.value),
         tokenizer,
         cfg.train_on_inputs,
         cfg.sequence_len,

src/axolotl/prompt_strategies/alpaca_instruct.py

@@ -1,7 +1,7 @@
 """Module loading the AlpacaInstructPromptTokenizingStrategy class"""
 
 from axolotl.prompt_tokenizers import AlpacaPromptTokenizingStrategy
-from axolotl.prompters import AlpacaPrompter, PromptStyle
+from axolotl.prompters import AlpacaPrompter, PromptStyle, UnpromptedPrompter
 
 
 def load(tokenizer, cfg):
@@ -11,3 +11,12 @@ def load(tokenizer, cfg):
         cfg.train_on_inputs,
         cfg.sequence_len,
     )
+
+
+def load_no_prompt(tokenizer, cfg):
+    return AlpacaPromptTokenizingStrategy(
+        UnpromptedPrompter(PromptStyle.INSTRUCT.value),
+        tokenizer,
+        cfg.train_on_inputs,
+        cfg.sequence_len,
+    )

src/axolotl/prompt_strategies/alpaca_w_system.py

@@ -0,0 +1,163 @@
+"""
+Prompt strategies loader for alpaca instruction datasets with system prompts
+"""
+from typing import Generator, Tuple, Union
+
+from axolotl.prompt_tokenizers import PromptTokenizingStrategy
+from axolotl.prompters import AlpacaPrompter, PromptStyle
+
+
+class InstructionWSystemPromptTokenizingStrategy(PromptTokenizingStrategy):
+    """
+    Tokenizing strategy for instruction-based prompts.
+    """
+
+    def parse_instruction_fields(self, prompt) -> Tuple[str, str, str, str]:
+        return (
+            prompt["instruction"],
+            prompt["input"] if "input" in prompt else "",
+            prompt["output"],
+            prompt["system"],
+        )
+
+    def tokenize_prompt(self, prompt):
+        # pylint: disable=duplicate-code
+        (
+            instruction,
+            input,  # pylint: disable=redefined-builtin
+            response,
+            system,
+        ) = self.parse_instruction_fields(prompt)
+        user_prompt = next(
+            iter(
+                self.prompter.build_prompt_w_system(
+                    system,
+                    instruction,
+                    input,
+                )
+            )
+        )
+        tokenized_prompt = self._tokenize(user_prompt, add_eos_token=False)
+        if not self.train_on_inputs:
+            user_prompt_len = len(tokenized_prompt["input_ids"])
+            # TODO this could be sped up using numpy array slicing
+            tokenized_prompt["labels"] = [-100] * user_prompt_len
+        tokenized_res_prompt = self._tokenize(
+            response, strip_bos_token=True, add_eos_token=True
+        )
+        tokenized_prompt["input_ids"] += tokenized_res_prompt["input_ids"]
+        tokenized_prompt["attention_mask"] += tokenized_res_prompt["attention_mask"]
+        tokenized_prompt["labels"] += tokenized_res_prompt["input_ids"]
+
+        return tokenized_prompt
+
+
+class SystemDataPrompter(AlpacaPrompter):
+    """
+    Alpaca Style Prompter that uses system prompts from the dataset
+    """
+
+    system_format: str = "### System:\n{system}\n\n"
+
+    def build_prompt_w_system(
+        self,
+        system: str,
+        instruction: str,
+        input: Union[None, str] = None,  # pylint: disable=redefined-builtin
+        output: Union[None, str] = None,
+    ) -> Generator[str, None, None]:
+        # returns the full prompt from instruction and optional input
+        # if a label (=response, =output) is provided, it's also appended.
+        formatted_sys_prompt = (
+            self.system_format.format(system=system)
+            if system and self.system_format
+            else ""
+        )
+        if input:
+            res = formatted_sys_prompt + self.turn_format.format(
+                instruction=instruction, input=input
+            )
+        else:
+            res = formatted_sys_prompt + self.turn_no_input_format.format(
+                instruction=instruction
+            )
+        if output:
+            res = f"{res}{output}"
+        yield res
+
+
+class OpenOrcaSystemDataPrompter(SystemDataPrompter):
+    """
+    Alpaca Style Prompter that uses system prompts from the dataset, with OpenOrca prompts
+    """
+
+    def match_prompt_style(self):
+        # pylint: disable=duplicate-code
+        if self.prompt_style == PromptStyle.INSTRUCT.value:
+            self.turn_format = "### Human:\n{instruction}\n### Additional Context:\n{input}\n### Assistant:\n"
+            self.turn_no_input_format = "### Human:\n{instruction}\n### Assistant:\n"
+            self.system_format = "### System:\n{system}\n"
+        if self.prompt_style == PromptStyle.CHAT.value:
+            self.turn_format = "USER: {instruction}\n{input}\nASSISTANT:"
+            self.turn_no_input_format = "USER: {instruction}\nASSISTANT:"
+            self.system_format = "SYSTEM: {system}\n"
+        if self.prompt_style == PromptStyle.CHATML.value:
+            self.turn_format = "<|im_start|>user\n{instruction}\n{input}<|im_end|>\n<|im_start|>assistant\n"
+            self.turn_no_input_format = (
+                "<|im_start|>user\n{instruction}<|im_end|>\n<|im_start|>assistant\n"
+            )
+            self.system_format = "<|im_start|>system\n{system}<|im_end|>\n"
+
+
+class OpenOrcaPromptTokenizingStrategy(InstructionWSystemPromptTokenizingStrategy):
+    """
+    Tokenizing strategy for OpenOrca datasets
+    """
+
+    def parse_instruction_fields(self, prompt) -> Tuple[str, str, str, str]:
+        return (
+            prompt["question"],
+            "",
+            prompt["response"],
+            prompt["system_prompt"],
+        )
+
+
+def load(tokenizer, cfg):
+    return load_chat(tokenizer, cfg)
+
+
+def load_instruct(tokenizer, cfg):
+    return InstructionWSystemPromptTokenizingStrategy(
+        SystemDataPrompter(PromptStyle.INSTRUCT.value),
+        tokenizer,
+        cfg.train_on_inputs,
+        cfg.sequence_len,
+    )
+
+
+def load_chat(tokenizer, cfg):
+    return InstructionWSystemPromptTokenizingStrategy(
+        SystemDataPrompter(PromptStyle.CHAT.value),
+        tokenizer,
+        cfg.train_on_inputs,
+        cfg.sequence_len,
+    )
+
+
+def load_open_orca(tokenizer, cfg):
+    return OpenOrcaPromptTokenizingStrategy(
+        OpenOrcaSystemDataPrompter(PromptStyle.INSTRUCT.value),
+        tokenizer,
+        cfg.train_on_inputs,
+        cfg.sequence_len,
+    )
+
+
+def load_open_orca_chatml(tokenizer, cfg):
+    return OpenOrcaPromptTokenizingStrategy(
+        OpenOrcaSystemDataPrompter(PromptStyle.CHATML.value),
+        tokenizer,
+        cfg.train_on_inputs,
+        cfg.sequence_len,
+    )

src/axolotl/prompt_strategies/context_qa.py

@@ -0,0 +1,67 @@
+"""Module containing the classes for Context QA Prompt Tokenization Strategies"""
+from typing import Tuple
+
+from axolotl.prompt_tokenizers import InstructionPromptTokenizingStrategy
+from axolotl.prompters import AlpacaPrompter, PromptStyle
+
+
+# article, unanswerable_question, question, answer
+def load_404(tokenizer, cfg):
+    return AlpacaMissingInfoContextPromptTokenizingStrategy(
+        AlpacaContextPrompter(PromptStyle.CHAT.value),
+        tokenizer,
+        cfg.train_on_inputs,
+        cfg.sequence_len,
+    )
+
+
+def load(tokenizer, cfg):
+    return AlpacaContextPromptTokenizingStrategy(
+        AlpacaContextPrompter(PromptStyle.CHAT.value),
+        tokenizer,
+        cfg.train_on_inputs,
+        cfg.sequence_len,
+    )
+
+
+class AlpacaContextPrompter(AlpacaPrompter):
+    """
+    Customized system prompted for concise QA
+    """
+
+    system_prompt = (
+        "Use the following contextual information to concisely answer the question.\n"
+    )
+    system_no_input_prompt = (
+        "Use the following contextual information to concisely answer the question.\n"
+    )
+
+
+class AlpacaContextPromptTokenizingStrategy(InstructionPromptTokenizingStrategy):
+    """
+    Tokenization Strategy to combine in-context article with a question and answer
+    """
+
+    def parse_instruction_fields(self, prompt) -> Tuple[str, str, str]:
+        return (
+            prompt["article"] + "\n===\n" + prompt["question"],
+            "",
+            prompt["answer"],
+        )
+
+
+class AlpacaMissingInfoContextPromptTokenizingStrategy(
+    InstructionPromptTokenizingStrategy
+):
+    """
+    Tokenization Strategy to combine in-context article with a question that can't be answered
+    from the context and a default response to that effect
+    """
+
+    def parse_instruction_fields(self, prompt) -> Tuple[str, str, str]:
+        return (
+            prompt["article"] + "\n===\n" + prompt["unanswerable_question"],
+            "",
+            "The context provided does not contain any information about your inquiry. "
+            "Therefore, I'm unable to answer your question based on the given context.",
+        )

src/axolotl/prompt_strategies/llama2_chat.py

@@ -0,0 +1,205 @@
+"""
+Prompt Strategy for finetuning Llama2 chat models
+see also https://github.com/facebookresearch/llama/blob/6c7fe276574e78057f917549435a2554000a876d/llama/generation.py#L213 for ma reference implementation.
+
+This implementation is based on the Vicuna PR and the fastchat repo, see also:
+https://github.com/lm-sys/FastChat/blob/cdd7730686cb1bf9ae2b768ee171bdf7d1ff04f3/fastchat/conversation.py#L847
+
+Use dataset type: "llama2_chat" in conig.yml to use this prompt style.
+
+E.g. in the config.yml:
+```
+datasets:
+  - path: llama_finetune_train.jsonl
+    type: llama2_chat
+```
+
+The dataset itself should look like this:
+```
+{'conversations':[{"from": "human", "value": "Who are you?"}, {"from": "gpt", "value": "I am Vicuna"},...]}
+```
+in a jsonl file. The first message should be from the human, the second from gpt.
+For a custom system message, the first "from" can be "system" (followed by alternating "human" and "gpt" turns).
+
+Important: Don't use "special_tokens:" in your config.yml if you are not sure what you are doing!
+"""
+
+import logging
+from dataclasses import dataclass, field
+from typing import Generator, List, Sequence
+
+from axolotl.prompt_tokenizers import PromptTokenizingStrategy
+from axolotl.prompters import IGNORE_TOKEN_ID, SHAREGPT_ASSERTION_FAILED_ROLE
+
+
+@dataclass
+class Llama2ChatConversation:
+    """A class that manages prompt templates and keeps all conversation history.
+    copied from https://github.com/lm-sys/FastChat/blob/main/fastchat/conversation.py"""
+
+    name: str = "llama2"
+    # The system prompt
+    system: str = (
+        "[INST] <<SYS>>\nYou are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. "
+        "Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. "
+        "Please ensure that your responses are socially unbiased and positive in nature.\n\n"
+        "If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. "
+        "If you don't know the answer to a question, please don't share false information.\n<</SYS>>\n\n"
+    )
+    roles: Sequence[str] = ("[INST]", "[/INST]")
+    messages: List[List[str]] = field(default_factory=list)
+    offset: int = 0
+    sep = " "
+    sep2 = " </s><s>"
+    stop_token_ids = [2]
+
+    def get_prompt(self) -> str:
+        """Get the prompt for generation."""
+        seps = [self.sep, self.sep2]
+        ret = ""
+        for i, (role, message) in enumerate(self.messages):
+            if (i == len(self.messages) - 1) and (role == self.roles[0]):
+                # last message is from user (due to length),
+                #  return prompt without it for training
+                return ret
+            if i == 0:
+                ret += self.system + message.strip()
+            else:
+                ret += role + " " + message.strip() + seps[i % 2]
+        return ret
+
+    def append_message(self, role: str, message: str):
+        """Append a new message."""
+        self.messages.append([role, message])
+
+
+class LLama2ChatTokenizingStrategy(PromptTokenizingStrategy):
+    """
+    Tokenizing strategy for ShareGPT prompts.
+    adapted from https://github.com/lm-sys/FastChat/blob/main/fastchat/train/train.py
+    """
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.sequence_len = 4096
+        self.tokenizer.add_special_tokens({"pad_token": "<pad>"})
+        # https://huggingface.co/meta-llama/Llama-2-7b-chat-hf/blob/main/added_tokens.json
+
+    def tokenize_prompt(self, prompt):
+        conv = next(self.prompter.build_prompt(prompt))
+        conversation_str = conv.get_prompt()
+
+        # Tokenize conversations
+        input_ids = self.tokenizer(
+            conversation_str,
+            return_tensors="pt",
+            padding="max_length",
+            max_length=self.sequence_len,
+            truncation=True,
+        ).input_ids[0]
+        target = input_ids.clone()
+
+        # Mask targets. Only compute loss on the assistant outputs.
+        sep = conv.roles[1]
+
+        total_len = int(target.ne(self.tokenizer.pad_token_id).sum())
+
+        turns = conversation_str.split(conv.sep2)
+        cur_len = 1
+        target[:cur_len] = IGNORE_TOKEN_ID
+        for turn in turns:
+            if turn == "":
+                break
+            turn_len = len(self.tokenizer(turn).input_ids)
+
+            parts = turn.split(sep)
+            if len(parts) != 2:
+                break
+            parts[0] += sep
+            # "-1" is hardcoded for the LLaMA tokenizer to make the offset correct.
+            instruction_len = len(self.tokenizer(parts[0]).input_ids) - 1
+
+            # Ignore the user instructions
+            target[cur_len - 1 : cur_len + instruction_len] = IGNORE_TOKEN_ID
+            cur_len += turn_len + 2  # due to length of role token
+
+        target[cur_len:] = IGNORE_TOKEN_ID
+
+        if cur_len < self.sequence_len:
+            if cur_len != total_len:
+                target[:] = IGNORE_TOKEN_ID
+                logging.warning(
+                    f"WARNING: tokenization mismatch: {cur_len} vs. {total_len}."
+                    f" (ignored)"
+                )
+
+        attention_mask = input_ids.ne(self.tokenizer.pad_token_id).tolist()
+        input_ids = input_ids.tolist()
+        target = target.tolist()
+        # this is a fix for the tokenizer which tokenizes [ differently with eos tokens and
+        # follows the original llama implementation
+        for i in range(2, total_len - 2):
+            if input_ids[i] == 29961:
+                input_ids[i] = 518
+            if target[i] == 29961:
+                target[i] = 518
+        return {
+            "input_ids": input_ids,
+            "labels": target,
+            "attention_mask": attention_mask,
+        }
+
+
+class Llama2ChatPrompter:  # pylint: disable=too-few-public-methods
+    """
+    A prompter that generates prompts for Llama2 models.
+    """
+
+    system_prompt = (
+        "[INST] <<SYS>>\nYou are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. "
+        "Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. "
+        "Please ensure that your responses are socially unbiased and positive in nature.\n\n"
+        "If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. "
+        "If you don't know the answer to a question, please don't share false information.\n<</SYS>>\n\n"
+    )
+
+    def build_prompt(self, source) -> Generator[Llama2ChatConversation, None, None]:
+        # see https://github.com/lm-sys/FastChat/blob/da0641e567cf93756b0978ab5a6b092e96f06240/fastchat/train/train.py#L78
+        source = source["conversations"]  # fix data structure for datasets
+
+        # if system prompt provided, use it
+        if source[0]["from"] == "system":
+            system = f"[INST] <<SYS>>\n{source[0]['value']}\n<</SYS>>\n\n"
+            source = source[1:]
+        else:
+            system = self.system_prompt
+
+        conv = Llama2ChatConversation(system=system)
+
+        if len(source) < 2:
+            # If there isn't a back and forth conversation, ignore it
+            # also happens on the data splitting leaving empty conversations
+            raise IndexError
+
+        roles = {"human": conv.roles[0], "gpt": conv.roles[1]}
+
+        if roles[source[0]["from"]] != conv.roles[0]:
+            # Skip the first one if it is not from human
+            source = source[1:]
+
+        conv.messages = []  # pylint: disable=R0801
+        for j, sentence in enumerate(source):
+            role = roles[sentence["from"]]
+            assert role == conv.roles[j % 2], SHAREGPT_ASSERTION_FAILED_ROLE
+            if sentence["value"]:
+                conv.append_message(role, sentence["value"])
+        yield conv
+
+
+def load(tokenizer, cfg) -> LLama2ChatTokenizingStrategy:
+    return LLama2ChatTokenizingStrategy(
+        Llama2ChatPrompter(),
+        tokenizer,
+        cfg.train_on_inputs,
+        cfg.sequence_len,
+    )

src/axolotl/prompt_strategies/metharme.py

@@ -0,0 +1,76 @@
+"""Module containing the MetharmenPromptTokenizingStrategy and MetharmePrompter class"""
+
+import logging
+from typing import Tuple
+
+from axolotl.prompt_tokenizers import InstructionPromptTokenizingStrategy
+from axolotl.prompters import AlpacaPrompter
+
+LOG = logging.getLogger("axolotl")
+
+IGNORE_TOKEN_ID = -100
+
+# pylint: disable=duplicate-code
+
+
+class MetharmePromptTokenizingStrategy(InstructionPromptTokenizingStrategy):
+    """
+    Tokenizing strategy for the Metharme models
+    """
+
+    def parse_instruction_fields(self, prompt) -> Tuple[str, str, str]:
+        return (prompt["prompt"], "", prompt["generation"])
+
+    def _tokenize(
+        self,
+        prompt: str,
+        add_eos_token: bool = True,
+        strip_bos_token: bool = False,
+        num_eos_tokens: int = 3,
+    ):
+        result = self.tokenizer(
+            prompt,
+            truncation=True,
+            max_length=self.sequence_len,
+            padding=False,
+            return_tensors=None,
+        )
+        if len(result["input_ids"]) == 0:
+            LOG.warning("Tokenizer result is empty. You may want to audit your dataset")
+        # If there's already an EOS token there, subtract from the number added
+        if result["input_ids"][-1] == self.tokenizer.eos_token_id:
+            num_eos_tokens -= 1
+
+        if num_eos_tokens > 0 and add_eos_token and len(result["input_ids"]) > 0:
+            for _ in range(num_eos_tokens):
+                if len(result["input_ids"]) < self.sequence_len:
+                    result["input_ids"].append(self.tokenizer.eos_token_id)
+                    result["attention_mask"].append(1)
+
+        if result["input_ids"][0] == self.tokenizer.bos_token_id and strip_bos_token:
+            result["input_ids"] = result["input_ids"][1:]
+            result["attention_mask"] = result["attention_mask"][1:]
+
+        result["labels"] = result["input_ids"].copy()
+        return result
+
+
+class MetharmePrompter(AlpacaPrompter):
+    """
+    Prompter for the Metharme models.
+    """
+
+    system_prompt = ""
+    system_no_input_prompt = ""
+    system_format = ""
+    turn_format = "{instruction}"
+    turn_no_input_format = "{instruction}"
+
+    def __init__(self, *args, **kwargs):  # pylint: disable=super-init-not-called
+        pass
+
+
+def load(tokenizer, cfg):
+    return MetharmePromptTokenizingStrategy(
+        MetharmePrompter(), tokenizer, cfg.train_on_inputs, cfg.sequence_len
+    )

src/axolotl/prompt_strategies/orcamini.py

@@ -0,0 +1,46 @@
+"""
+Prompt Strategy for finetuning Orca Mini (v2) models
+see also https://huggingface.co/psmathur/orca_mini_v2_7b for more information
+
+Use dataset type: orcamini in conig.yml to use this prompt style.
+
+Compared to the alpaca_w_system.open_orca dataset type,
+this one specifies the system prompt with "### System:".
+
+Not suited/tested for multiple-turn conversations without further adjustments.
+"""
+from typing import Generator, Union
+
+from axolotl.prompt_strategies.alpaca_w_system import OpenOrcaPromptTokenizingStrategy
+from axolotl.prompters import AlpacaPrompter
+
+
+class OrcaMiniPrompter(AlpacaPrompter):
+    """Adjusted Prompter for Orca Mini (v2) datasets"""
+
+    def match_prompt_style(self):
+        self.turn_no_input_format = (
+            "### System:\n{system}\n\n### User:\n{instruction}\n\n### Response:\n"
+        )
+
+    def build_prompt_w_system(
+        self,
+        system: str,
+        instruction: str,
+        output: Union[None, str] = None,
+    ) -> Generator[str, None, None]:
+        # returns the full prompt from instruction and optional input
+        # if a label (=response, =output) is provided, it's also appended.
+        res = self.turn_no_input_format.format(system=system, instruction=instruction)
+        if output:
+            res = f"{res}{output}"
+        yield res
+
+
+def load(tokenizer, cfg):
+    return OpenOrcaPromptTokenizingStrategy(
+        OrcaMiniPrompter(),
+        tokenizer,
+        cfg.train_on_inputs,
+        cfg.sequence_len,
+    )

src/axolotl/prompt_strategies/pygmalion.py

@@ -11,6 +11,8 @@ from axolotl.prompt_tokenizers import (
     tokenize_prompt_default,
 )
 
+LOG = logging.getLogger("axolotl")
+
 IGNORE_TOKEN_ID = -100
 
 
@@ -64,7 +66,7 @@ class PygmalionPromptTokenizingStrategy(PromptTokenizingStrategy):
                     *copy.deepcopy(res["input_ids"])
                 ][len(self.bot_prefix_token_ids) :]
             else:
-                logging.warning(f"unknown role in conversation: {role}")
+                LOG.warning(f"unknown role in conversation: {role}")
                 res = defaultdict(lambda: [])
 
             # pylint: disable=duplicate-code

src/axolotl/prompt_strategies/sharegpt_jokes.py

@@ -0,0 +1,28 @@
+"""Module for Jokes prompts using sharegpt style """
+from axolotl.prompt_tokenizers import ShareGPTPromptTokenizingStrategy
+from axolotl.prompters import PromptStyle, ShareGPTPrompter
+
+
+def load(tokenizer, cfg):
+    return SimpleJokesShareGPTPromptTokenizingStrategy(
+        ShareGPTPrompter(PromptStyle.CHAT.value),
+        tokenizer,
+        cfg.train_on_inputs,
+        cfg.sequence_len,
+    )
+
+
+class SimpleJokesShareGPTPromptTokenizingStrategy(ShareGPTPromptTokenizingStrategy):
+    """
+    Tokenization strategy for asking bot to tell a joke and then explain why its funny
+    """
+
+    # title, text, explanation
+    def get_conversation_thread(self, prompt):
+        title = "" if not prompt["title"] else prompt["title"] + " "
+        return [
+            {"from": "human", "value": "Tell me a joke."},
+            {"from": "gpt", "value": title + prompt["text"]},
+            {"from": "human", "value": "Why is that joke funny?"},
+            {"from": "gpt", "value": prompt["explanation"]},
+        ]

src/axolotl/prompt_strategies/sharegpt_simple.py

@@ -0,0 +1,67 @@
+"""Module containing the SimpleShareGPTPromptTokenizingStrategy class"""
+
+from axolotl.prompt_tokenizers import ShareGPTPromptTokenizingStrategy
+from axolotl.prompters import PromptStyle, ShareGPTPrompter
+
+
+def load(tokenizer, cfg):
+    return SimpleShareGPTPromptTokenizingStrategy(
+        ShareGPTPrompter(PromptStyle.CHAT.value),
+        tokenizer,
+        cfg.train_on_inputs,
+        cfg.sequence_len,
+    )
+
+
+def load_role(tokenizer, cfg):
+    return SimpleRoleShareGPTPromptTokenizingStrategy(
+        ShareGPTPrompter(PromptStyle.CHAT.value),
+        tokenizer,
+        cfg.train_on_inputs,
+        cfg.sequence_len,
+    )
+
+
+def load_guanaco(tokenizer, cfg):
+    return GuanacoShareGPTPromptTokenizingStrategy(
+        ShareGPTPrompter(PromptStyle.CHAT.value),
+        tokenizer,
+        cfg.train_on_inputs,
+        cfg.sequence_len,
+    )
+
+
+class SimpleShareGPTPromptTokenizingStrategy(ShareGPTPromptTokenizingStrategy):
+    """
+    basic sharegpt strategy to grab conversations from the sample row
+    """
+
+    def get_conversation_thread(self, prompt):
+        return prompt["conversations"]
+
+
+class SimpleRoleShareGPTPromptTokenizingStrategy(ShareGPTPromptTokenizingStrategy):
+    """
+    basic sharegpt strategy to grab conversations from the sample row, but uses role instead of from
+    """
+
+    def get_conversation_thread(self, prompt):
+        conversations = prompt["conversations"]
+        # remap role: prompter/assistant, text: ... => from: human/gpt, value: ...
+        turns = [{"from": t["role"], "value": t["value"]} for t in conversations]
+        return turns
+
+
+class GuanacoShareGPTPromptTokenizingStrategy(ShareGPTPromptTokenizingStrategy):
+    """
+    sharegpt strategy that remaps oasst data to sharegpt format
+    """
+
+    def get_conversation_thread(self, prompt):
+        conversations = prompt["conversations"]
+        # remap role: prompter/assistant, text: ... => from: human/gpt, value: ...
+        role_map = {"prompter": "human", "assistant": "gpt"}
+        turns = [
+            {"from": role_map[t["role"]], "value": t["text"]} for t in conversations
+        ]
+        return turns

src/axolotl/prompt_strategies/user_defined.py

@@ -0,0 +1,98 @@
+"""
+User Defined prompts with configuration from the YML config
+"""
+
+from dataclasses import dataclass
+from functools import partial
+from typing import Optional, Tuple
+
+from axolotl.prompt_strategies.alpaca_w_system import (
+    InstructionWSystemPromptTokenizingStrategy,
+    SystemDataPrompter,
+)
+
+
+@dataclass
+class UserDefinedDatasetConfig:
+    """
+    dataclass configuration representing a userdefined dataset type
+    """
+
+    system_prompt: str = ""
+    field_system: str = "system"
+    field_instruction: str = "instruction"
+    field_input: str = "input"
+    field_output: str = "output"
+    format: str = "{instruction} {input} "
+    no_input_format: str = "{instruction} "
+    system_format: str = "{system}"
+
+    def __getitem__(self, item):
+        return getattr(self, item)
+
+
+class UserDefinedPromptTokenizationStrategy(InstructionWSystemPromptTokenizingStrategy):
+    """
+    Prompt Tokenization Strategy for user defined prompts
+    """
+
+
+def load(tokenizer, cfg, ds_cfg: Optional[UserDefinedDatasetConfig] = None):
+    if not ds_cfg:
+        raise ValueError("Missing dataset prompt configuration")
+
+    system_prompt = ""
+    if ds_cfg.system_prompt:
+        system_prompt = ds_cfg.system_prompt
+
+    def parse_instruction_fields(
+        field_instruction,
+        field_input,
+        field_output,
+        field_system,
+        system_prompt,
+        prompt,
+    ) -> Tuple[str, str, str, str]:
+        return (
+            prompt[field_instruction],
+            prompt[field_input] if field_input in prompt else "",
+            prompt[field_output] if field_output in prompt else "",
+            prompt[field_system] if field_system in prompt else system_prompt,
+        )
+
+    turn_format = ds_cfg.format
+    turn_no_input_format = ds_cfg.no_input_format
+    system_format = ds_cfg.system_format
+
+    class UserDefinedPrompter(SystemDataPrompter):
+        """
+        Prompter for user defined prompts
+        """
+
+        def match_prompt_style(self):
+            self.turn_format = turn_format
+            self.turn_no_input_format = turn_no_input_format
+            self.system_format = system_format
+
+    prompter = UserDefinedPrompter()
+
+    strat = UserDefinedPromptTokenizationStrategy(
+        prompter,
+        tokenizer,
+        cfg.train_on_inputs,
+        cfg.sequence_len,
+    )
+
+    setattr(
+        strat,
+        "parse_instruction_fields",
+        partial(
+            parse_instruction_fields,
+            ds_cfg.field_instruction,
+            ds_cfg.field_input,
+            ds_cfg.field_output,
+            ds_cfg.field_system,
+            system_prompt,
+        ),
+    )
+    return strat

src/axolotl/prompt_tokenizers.py

@@ -10,8 +10,10 @@ from transformers import PreTrainedTokenizer
 
 from axolotl.prompters import IGNORE_TOKEN_ID
 
+LOG = logging.getLogger("axolotl")
+
 IGNORE_INDEX = -100
-LLAMA_DEFAULT_PAD_TOKEN = "[PAD]"  # nosec
+LLAMA_DEFAULT_PAD_TOKEN = "<pad>"  # nosec
 LLAMA_DEFAULT_EOS_TOKEN = "</s>"  # nosec
 LLAMA_DEFAULT_BOS_TOKEN = "<s>"  # nosec
 LLAMA_DEFAULT_UNK_TOKEN = "<unk>"  # nosec
@@ -46,16 +48,22 @@ class PromptTokenizingStrategy(abc.ABC):
 
     @functools.lru_cache(maxsize=128)
     def _get_user_token(self):
-        id_or_ids = self.tokenizer.convert_tokens_to_ids("<|USER|>")
-        if isinstance(id_or_ids, (int,)):
-            return id_or_ids
+        try:
+            id_or_ids = self.tokenizer.convert_tokens_to_ids("<|USER|>")
+            if isinstance(id_or_ids, (int,)):
+                return id_or_ids
+        except KeyError:
+            pass
         return False
 
     @functools.lru_cache(maxsize=128)
     def _get_assistant_token(self):
-        id_or_ids = self.tokenizer.convert_tokens_to_ids("<|ASSISTANT|>")
-        if isinstance(id_or_ids, (int,)):
-            return id_or_ids
+        try:
+            id_or_ids = self.tokenizer.convert_tokens_to_ids("<|ASSISTANT|>")
+            if isinstance(id_or_ids, (int,)):
+                return id_or_ids
+        except KeyError:
+            pass
         return False
 
     def _tokenize(self, prompt: str, add_eos_token=True, strip_bos_token=False):
@@ -66,15 +74,22 @@ class PromptTokenizingStrategy(abc.ABC):
             padding=False,
             return_tensors=None,
         )
+        if len(result["input_ids"]) == 0:
+            LOG.warning("Tokenizer result is empty. You may want to audit your dataset")
         if (
-            result["input_ids"][-1] != self.tokenizer.eos_token_id
+            len(result["input_ids"]) > 0
+            and result["input_ids"][-1] != self.tokenizer.eos_token_id
             and len(result["input_ids"]) < self.sequence_len
             and add_eos_token
         ):
             result["input_ids"].append(self.tokenizer.eos_token_id)
             result["attention_mask"].append(1)
 
-        if result["input_ids"][0] == self.tokenizer.bos_token_id and strip_bos_token:
+        if (
+            len(result["input_ids"]) > 0
+            and result["input_ids"][0] == self.tokenizer.bos_token_id
+            and strip_bos_token
+        ):
             result["input_ids"] = result["input_ids"][1:]
             result["attention_mask"] = result["attention_mask"][1:]
 
@@ -87,7 +102,9 @@ class InstructionPromptTokenizingStrategy(PromptTokenizingStrategy):
     Tokenizing strategy for instruction-based prompts.
     """
 
-    def parse_instruction_fields(self, prompt) -> Tuple[str, str, str]:
+    def parse_instruction_fields(
+        self, prompt
+    ) -> Union[Tuple[str, str, str], Tuple[str, str, str, str]]:
         raise NotImplementedError
 
     def tokenize_prompt(self, prompt):
@@ -96,25 +113,27 @@ class InstructionPromptTokenizingStrategy(PromptTokenizingStrategy):
             input,  # pylint: disable=redefined-builtin
             response,
         ) = self.parse_instruction_fields(prompt)
-        full_prompt = self._build_full_prompt(instruction, input, response)
-        tokenized_full_prompt = self._tokenize(full_prompt)
-        if not self.train_on_inputs:
-            user_prompt = next(
-                iter(
-                    self.prompter.build_prompt(
-                        instruction,
-                        input,
-                    )
+        user_prompt = next(
+            iter(
+                self.prompter.build_prompt(
+                    instruction,
+                    input,
                 )
             )
-            tokenized_user_prompt = self._tokenize(user_prompt, add_eos_token=False)
-            user_prompt_len = len(tokenized_user_prompt["input_ids"])
+        )
+        tokenized_prompt = self._tokenize(user_prompt, add_eos_token=False)
+        if not self.train_on_inputs:
+            user_prompt_len = len(tokenized_prompt["input_ids"])
             # TODO this could be sped up using numpy array slicing
-            tokenized_full_prompt["labels"] = [
-                -100
-            ] * user_prompt_len + tokenized_full_prompt["labels"][user_prompt_len:]
+            tokenized_prompt["labels"] = [-100] * user_prompt_len
+        tokenized_res_prompt = self._tokenize(
+            response, strip_bos_token=True, add_eos_token=True
+        )
+        tokenized_prompt["input_ids"] += tokenized_res_prompt["input_ids"]
+        tokenized_prompt["attention_mask"] += tokenized_res_prompt["attention_mask"]
+        tokenized_prompt["labels"] += tokenized_res_prompt["input_ids"]
 
-        return tokenized_full_prompt
+        return tokenized_prompt
 
     def _build_full_prompt(
         self, instruction, input, response  # pylint: disable=redefined-builtin
@@ -380,7 +399,7 @@ class ShareGPTPromptTokenizingStrategy(PromptTokenizingStrategy):
                         # everything from this is masked out from the labels
                         labels = [IGNORE_TOKEN_ID] * len(res["input_ids"])
                     else:
-                        logging.warning(f"unhandled role: {part[0]}")
+                        LOG.warning(f"unhandled role: {part[0]}")
 
                 # pylint: disable=duplicate-code
                 result, current_len = parse_tokenized_to_result(
@@ -436,7 +455,7 @@ def parse_tokenized_to_result(
     result: Dict[str, List[int]],
     current_len: int,
     res: Dict[str, List[int]],
-    labels: list[int],
+    labels: List[int],
     pad_token_id: Union[int, None] = None,
 ) -> Tuple[Dict[str, List[int]], int]:
     """