remove fastapi and pydantic extras

.github/workflows/base.yml

@@ -22,6 +22,12 @@ jobs:
       fail-fast: false
       matrix:
         include:
+          - cuda: "124"
+            cuda_version: 12.4.1
+            cudnn_version: ""
+            python_version: "3.10"
+            pytorch: 2.4.1
+            torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
           - cuda: "124"
             cuda_version: 12.4.1
             cudnn_version: ""
@@ -34,12 +40,6 @@ jobs:
             python_version: "3.11"
             pytorch: 2.5.1
             torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
-          - cuda: "124"
-            cuda_version: 12.4.1
-            cudnn_version: ""
-            python_version: "3.11"
-            pytorch: 2.6.0
-            torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
     steps:
       - name: Checkout
         uses: actions/checkout@v4

.github/workflows/docs.yml

@@ -19,7 +19,7 @@ jobs:
         - name: Setup Python
           uses: actions/setup-python@v5
           with:
-            python-version: '3.11'
+            python-version: '3.10'
         - name: install dependencies
           run: |
             python3 -m pip install jupyter

.github/workflows/lint.yml

@@ -19,6 +19,6 @@ jobs:
       - uses: actions/checkout@v4
       - uses: actions/setup-python@v5
         with:
-          python-version: "3.11"
+          python-version: "3.10"
           cache: 'pip' # caching pip dependencies
       - uses: pre-commit/action@v3.0.1

.github/workflows/main.yml

@@ -24,13 +24,8 @@ jobs:
             cuda_version: 12.4.1
             python_version: "3.11"
             pytorch: 2.5.1
-            axolotl_extras: vllm
-            is_latest: true
-          - cuda: 124
-            cuda_version: 12.4.1
-            python_version: "3.11"
-            pytorch: 2.6.0
             axolotl_extras:
+            is_latest: true
     runs-on: axolotl-gpu-runner
     steps:
       - name: Checkout

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

@@ -4,10 +4,6 @@ on:
   pull_request:
     paths:
       - 'tests/e2e/multigpu/*.py'
-      - 'requirements.txt'
-      - 'setup.py'
-      - 'pyproject.toml'
-      - '.github/workflows/multi-gpu-e2e.yml'
   workflow_dispatch:
   schedule:
     - cron: '0 0 * * 1,4'  # Runs at 00:00 UTC every monday & thursday
@@ -28,21 +24,13 @@ jobs:
             cuda_version: 12.4.1
             python_version: "3.11"
             pytorch: 2.4.1
-            axolotl_extras:  # no vllm support for 2.4.1
+            axolotl_extras:
             num_gpus: 2
             nightly_build: "true"
           - cuda: 124
             cuda_version: 12.4.1
             python_version: "3.11"
             pytorch: 2.5.1
-            axolotl_extras: vllm
-            num_gpus: 2
-            nightly_build: "true"
-          - cuda: 124
-            cuda_version: 12.4.1
-            python_version: "3.11"
-            pytorch: 2.6.0
-            # awaiting vllm#12721
             axolotl_extras:
             num_gpus: 2
             nightly_build: "true"
@@ -54,7 +42,7 @@ jobs:
       - name: Install Python
         uses: actions/setup-python@v5
         with:
-          python-version: "3.11"
+          python-version: "3.10"
       - name: Install Modal
         run: |
           python -m pip install --upgrade pip

.github/workflows/nightlies.yml

@@ -22,11 +22,6 @@ jobs:
             python_version: "3.11"
             pytorch: 2.5.1
             axolotl_extras:
-          - cuda: 124
-            cuda_version: 12.4.1
-            python_version: "3.11"
-            pytorch: 2.6.0
-            axolotl_extras:
     runs-on: axolotl-gpu-runner
     steps:
       - name: Checkout

.github/workflows/pypi.yml

@@ -36,7 +36,7 @@ jobs:
       - name: Setup Python
         uses: actions/setup-python@v5
         with:
-          python-version: "3.11"
+          python-version: "3.10"
 
       - name: Install dependencies
         run: |

.github/workflows/tests-nightly.yml

@@ -12,7 +12,7 @@ jobs:
       - uses: actions/checkout@v4
       - uses: actions/setup-python@v5
         with:
-          python-version: "3.11"
+          python-version: "3.10"
           cache: 'pip' # caching pip dependencies
       - uses: pre-commit/action@v3.0.1
         env:
@@ -25,8 +25,13 @@ jobs:
       fail-fast: false
       max-parallel: 2
       matrix:
-        python_version: ["3.11"]
-        pytorch_version: ["2.4.1", "2.5.1", "2.6.0"]
+        python_version: ["3.10", "3.11"]
+        pytorch_version: ["2.4.1", "2.5.1"]
+        exclude:
+          - python_version: "3.10"
+            pytorch_version: "2.4.1"
+          - python_version: "3.10"
+            pytorch_version: "2.5.1"
     timeout-minutes: 20
 
     steps:
@@ -107,20 +112,13 @@ jobs:
             num_gpus: 1
             axolotl_extras:
             nightly_build: "true"
-          - cuda: 124
-            cuda_version: 12.4.1
-            python_version: "3.11"
-            pytorch: 2.6.0
-            num_gpus: 1
-            axolotl_extras:
-            nightly_build: "true"
     steps:
       - name: Checkout
         uses: actions/checkout@v4
       - name: Install Python
         uses: actions/setup-python@v5
         with:
-          python-version: "3.11"
+          python-version: "3.10"
       - name: Install Modal
         run: |
           python -m pip install --upgrade pip

.github/workflows/tests.yml

@@ -35,7 +35,7 @@ jobs:
       - uses: actions/checkout@v4
       - uses: actions/setup-python@v5
         with:
-          python-version: "3.11"
+          python-version: "3.10"
           cache: 'pip' # caching pip dependencies
       - uses: pre-commit/action@v3.0.1
         env:
@@ -48,8 +48,13 @@ jobs:
       fail-fast: false
       max-parallel: 2
       matrix:
-        python_version: ["3.11"]
-        pytorch_version: ["2.4.1", "2.5.1", "2.6.0"]
+        python_version: ["3.10", "3.11"]
+        pytorch_version: ["2.4.1", "2.5.1"]
+        exclude:
+          - python_version: "3.10"
+            pytorch_version: "2.4.1"
+          - python_version: "3.10"
+            pytorch_version: "2.5.1"
     timeout-minutes: 20
 
     steps:
@@ -122,7 +127,7 @@ jobs:
       max-parallel: 1
       matrix:
         python_version: ["3.11"]
-        pytorch_version: ["2.4.1", "2.5.1", "2.6.0"]
+        pytorch_version: ["2.4.1", "2.5.1"]
     timeout-minutes: 20
 
     steps:
@@ -202,16 +207,16 @@ jobs:
           - cuda: 124
             cuda_version: 12.4.1
             python_version: "3.11"
-            pytorch: 2.5.1
+            pytorch: 2.4.1
             num_gpus: 1
-            axolotl_extras: vllm
+            axolotl_extras:
     steps:
       - name: Checkout
         uses: actions/checkout@v4
       - name: Install Python
         uses: actions/setup-python@v5
         with:
-          python-version: "3.11"
+          python-version: "3.10"
       - name: Install Modal
         run: |
           python -m pip install --upgrade pip
@@ -243,13 +248,7 @@ jobs:
           - cuda: 124
             cuda_version: 12.4.1
             python_version: "3.11"
-            pytorch: 2.4.1
-            num_gpus: 1
-            axolotl_extras:
-          - cuda: 124
-            cuda_version: 12.4.1
-            python_version: "3.11"
-            pytorch: 2.6.0
+            pytorch: 2.5.1
             num_gpus: 1
             axolotl_extras:
     steps:
@@ -258,7 +257,7 @@ jobs:
       - name: Install Python
         uses: actions/setup-python@v5
         with:
-          python-version: "3.11"
+          python-version: "3.10"
       - name: Install Modal
         run: |
           python -m pip install --upgrade pip

README.md

@@ -50,14 +50,13 @@ Features:
 ## 🚀 Quick Start
 
 **Requirements**:
-
 - NVIDIA GPU (Ampere or newer for `bf16` and Flash Attention) or AMD GPU
-- Python 3.11
+- Python ≥3.10
 - PyTorch ≥2.4.1
 
 ### Installation
 
-```bash
+```shell
 pip3 install --no-build-isolation axolotl[flash-attn,deepspeed]
 
 # Download example axolotl configs, deepspeed configs
@@ -69,7 +68,7 @@ Other installation approaches are described [here](https://axolotl-ai-cloud.gith
 
 ### Your First Fine-tune
 
-```bash
+```shell
 # Fetch axolotl examples
 axolotl fetch examples
 

_quarto.yml

@@ -3,12 +3,10 @@ project:
 
 website:
   title: "Axolotl"
-  description: "We make fine-tuning accessible, scalable, and fun"
+  description: "Fine-tuning"
   favicon: favicon.jpg
-
   navbar:
-    logo: image/axolotl_logo_digital_white.svg
-    title: false
+    title: Axolotl
     background: dark
     pinned: false
     collapse: false
@@ -27,58 +25,33 @@ website:
       contents:
         - text: Home
           href: index.qmd
-
-        - section: "Getting Started"
+        - section: "How-To Guides"
           contents:
+          # TODO Edit folder structure after we have more docs.
             - docs/getting-started.qmd
             - docs/installation.qmd
-            - docs/cli.qmd
+            - docs/debugging.qmd
             - docs/inference.qmd
-
-        - section: "Dataset Formats"
-          contents: docs/dataset-formats/*
-
-        - section: "Deployments"
-          contents:
+            - docs/multipack.qmd
+            - docs/fsdp_qlora.qmd
+            - docs/input_output.qmd
+            - docs/rlhf.qmd
+            - docs/nccl.qmd
+            - docs/mac.qmd
             - docs/multi-gpu.qmd
             - docs/multi-node.qmd
-            - docs/ray-integration.qmd
-            - docs/amd_hpc.qmd
-            - docs/mac.qmd
-
-        - section: "How To Guides"
-          contents:
-            - docs/multimodal.qmd
-            - docs/rlhf.qmd
-            - docs/reward_modelling.qmd
-            - docs/lr_groups.qmd
-            - docs/lora_optims.qmd
-
-        - section: "Core Concepts"
-          contents:
-            - docs/batch_vs_grad.qmd
-            - docs/dataset_preprocessing.qmd
-            - docs/multipack.qmd
-
-        - section: "Advanced Features"
-          contents:
-            - docs/fsdp_qlora.qmd
             - docs/unsloth.qmd
-            - docs/torchao.qmd
-            - docs/custom_integrations.qmd
-
-        - section: "Troubleshooting"
-          contents:
-            - docs/faq.qmd
-            - docs/debugging.qmd
-            - docs/nccl.qmd
-
+            - docs/amd_hpc.qmd
+            - docs/ray-integration.qmd
+        - section: "Dataset Formats"
+          contents: docs/dataset-formats/*
         - section: "Reference"
           contents:
             - docs/config.qmd
+        - docs/faq.qmd
 
 format:
   html:
-    theme: darkly
+    theme: materia
     css: styles.css
     toc: true

cicd/cicd.sh

@@ -4,8 +4,8 @@ set -e
 python -c "import torch; assert '$PYTORCH_VERSION' in torch.__version__"
 
 pytest -v --durations=10 -n8 --ignore=tests/e2e/ --ignore=tests/patched/ /workspace/axolotl/tests/
-pytest -v --durations=10 /workspace/axolotl/tests/e2e/patched/lora_kernels  # running these with the other patches causes a failure
-pytest -v --durations=10 --ignore=tests/e2e/patched/lora_kernels /workspace/axolotl/tests/e2e/patched
+# pytest -v --durations=10 -n8 --dist loadfile /workspace/axolotl/tests/patched/
+pytest -v --durations=10 /workspace/axolotl/tests/e2e/patched/
 pytest -v --durations=10 -n1 /workspace/axolotl/tests/e2e/solo/
 pytest -v --durations=10 /workspace/axolotl/tests/e2e/integrations/
 pytest -v --durations=10 --ignore=tests/e2e/solo/ --ignore=tests/e2e/patched/ --ignore=tests/e2e/multigpu/ --ignore=tests/e2e/integrations/ /workspace/axolotl/tests/e2e/

cicd/multigpu.py

@@ -37,11 +37,15 @@ temp_dir = tempfile.mkdtemp()
 with open(pathlib.Path(temp_dir) / "Dockerfile", "w", encoding="utf-8") as f:
     f.write(dockerfile_contents)
 
-cicd_image = Image.from_dockerfile(
-    pathlib.Path(temp_dir) / "Dockerfile",
-    force_build=True,
-    gpu="A10G",
-).env(df_args)
+cicd_image = (
+    Image.from_dockerfile(
+        pathlib.Path(temp_dir) / "Dockerfile",
+        force_build=True,
+        gpu="A10G",
+    )
+    .env(df_args)
+    .pip_install("fastapi==0.110.0", "pydantic==2.6.3")
+)
 
 app = App("Axolotl CI/CD", secrets=[])
 

cicd/tests.py

@@ -1,4 +1,6 @@
-"""Modal app to run axolotl GPU tests"""
+"""
+ modal application to run axolotl gpu tests in Modal
+ """
 # pylint: disable=duplicate-code
 
 import os
@@ -53,7 +55,7 @@ VOLUME_CONFIG = {
 }
 
 N_GPUS = int(os.environ.get("N_GPUS", 1))
-GPU_CONFIG = modal.gpu.L40S(count=N_GPUS)
+GPU_CONFIG = modal.gpu.A10G(count=N_GPUS)
 
 
 def run_cmd(cmd: str, run_folder: str):

docs/amd_hpc.qmd

@@ -1,5 +1,5 @@
 ---
-title: AMD GPUs on HPC Systems
+title: Training with AMD GPUs on HPC Systems
 description: A comprehensive guide for using Axolotl on distributed systems with AMD GPUs
 ---
 

docs/cli.qmd

@@ -1,19 +1,28 @@
----
-title: "CLI Reference"
-format:
-  html:
-    toc: true
-    toc-expand: 2
-    toc-depth: 3
-execute:
-  enabled: false
----
+# Axolotl CLI Documentation
 
 The Axolotl CLI provides a streamlined interface for training and fine-tuning large language models. This guide covers
 the CLI commands, their usage, and common examples.
 
+### Table of Contents
 
-## Basic Commands
+- Basic Commands
+- Command Reference
+  - fetch
+  - preprocess
+  - train
+  - inference
+  - merge-lora
+  - merge-sharded-fsdp-weights
+  - evaluate
+  - lm-eval
+- Legacy CLI Usage
+- Remote Compute with Modal Cloud
+  - Cloud Configuration
+  - Running on Modal Cloud
+  - Cloud Configuration Options
+
+
+### Basic Commands
 
 All Axolotl commands follow this general structure:
 
@@ -23,9 +32,9 @@ axolotl <command> [config.yml] [options]
 
 The config file can be local or a URL to a raw YAML file.
 
-## Command Reference
+### Command Reference
 
-### fetch
+#### fetch
 
 Downloads example configurations and deepspeed configs to your local machine.
 
@@ -40,7 +49,7 @@ axolotl fetch deepspeed_configs
 axolotl fetch examples --dest path/to/folder
 ```
 
-### preprocess
+#### preprocess
 
 Preprocesses and tokenizes your dataset before training. This is recommended for large datasets.
 
@@ -65,7 +74,7 @@ dataset_prepared_path: Local folder for saving preprocessed data
 push_dataset_to_hub: HuggingFace repo to push preprocessed data (optional)
 ```
 
-### train
+#### train
 
 Trains or fine-tunes a model using the configuration specified in your YAML file.
 
@@ -86,38 +95,7 @@ axolotl train config.yml --no-accelerate
 axolotl train config.yml --resume-from-checkpoint path/to/checkpoint
 ```
 
-It is possible to run sweeps over multiple hyperparameters by passing in a sweeps config.
-
-```bash
-# Basic training with sweeps
-axolotl train config.yml --sweep path/to/sweep.yaml
-```
-
-Example sweep config:
-```yaml
-_:
-  # This section is for dependent variables we need to fix
-  - load_in_8bit: false
-    load_in_4bit: false
-    adapter: lora
-  - load_in_8bit: true
-    load_in_4bit: false
-    adapter: lora
-
-# These are independent variables
-learning_rate: [0.0003, 0.0006]
-lora_r:
-  - 16
-  - 32
-lora_alpha:
-  - 16
-  - 32
-  - 64
-```
-
-
-
-### inference
+#### inference
 
 Runs inference using your trained model in either CLI or Gradio interface mode.
 
@@ -137,7 +115,7 @@ cat prompt.txt | axolotl inference config.yml \
     --base-model="./completed-model"
 ```
 
-### merge-lora
+#### merge-lora
 
 Merges trained LoRA adapters into the base model.
 
@@ -159,7 +137,7 @@ gpu_memory_limit: Limit GPU memory usage
 lora_on_cpu: Load LoRA weights on CPU
 ```
 
-### merge-sharded-fsdp-weights
+#### merge-sharded-fsdp-weights
 
 Merges sharded FSDP model checkpoints into a single combined checkpoint.
 
@@ -168,7 +146,7 @@ Merges sharded FSDP model checkpoints into a single combined checkpoint.
 axolotl merge-sharded-fsdp-weights config.yml
 ```
 
-### evaluate
+#### evaluate
 
 Evaluates a model's performance using metrics specified in the config.
 
@@ -177,27 +155,27 @@ Evaluates a model's performance using metrics specified in the config.
 axolotl evaluate config.yml
 ```
 
-### lm-eval
+#### lm-eval
 
 Runs LM Evaluation Harness on your model.
 
 ```bash
 # Basic evaluation
 axolotl lm-eval config.yml
+
+# Evaluate specific tasks
+axolotl lm-eval config.yml --tasks arc_challenge,hellaswag
 ```
 
 Configuration options:
 
 ```yaml
-# List of tasks to evaluate
-lm_eval_tasks:
-  - arc_challenge
-  - hellaswag
-lm_eval_batch_size: # Batch size for evaluation
-output_dir: # Directory to save evaluation results
+lm_eval_tasks: List of tasks to evaluate
+lm_eval_batch_size: Batch size for evaluation
+output_dir: Directory to save evaluation results
 ```
 
-## Legacy CLI Usage
+### Legacy CLI Usage
 
 While the new Click-based CLI is preferred, Axolotl still supports the legacy module-based CLI:
 
@@ -217,18 +195,12 @@ accelerate launch -m axolotl.cli.inference config.yml \
     --lora_model_dir="./outputs/lora-out" --gradio
 ```
 
-::: {.callout-important}
-When overriding CLI parameters in the legacy CLI, use same notation as in yaml file (e.g., `--lora_model_dir`).
-
-**Note:** This differs from the new Click-based CLI, which uses dash notation (e.g., `--lora-model-dir`). Keep this in mind if you're referencing newer documentation or switching between CLI versions.
-:::
-
-## Remote Compute with Modal Cloud
+### Remote Compute with Modal Cloud
 
 Axolotl supports running training and inference workloads on Modal cloud infrastructure. This is configured using a
 cloud YAML file alongside your regular Axolotl config.
 
-### Cloud Configuration
+#### Cloud Configuration
 
 Create a cloud config YAML with your Modal settings:
 
@@ -243,17 +215,13 @@ branch: main    # Git branch to use (optional)
 volumes:        # Persistent storage volumes
   - name: axolotl-cache
     mount: /workspace/cache
-  - name: axolotl-data
-    mount: /workspace/data
-  - name: axolotl-artifacts
-    mount: /workspace/artifacts
 
 env:            # Environment variables
   - WANDB_API_KEY
   - HF_TOKEN
 ```
 
-### Running on Modal Cloud
+#### Running on Modal Cloud
 
 Commands that support the --cloud flag:
 
@@ -271,18 +239,18 @@ axolotl train config.yml --cloud cloud_config.yml --no-accelerate
 axolotl lm-eval config.yml --cloud cloud_config.yml
 ```
 
-### Cloud Configuration Options
+#### Cloud Configuration Options
 
 ```yaml
-provider: # compute provider, currently only `modal` is supported
-gpu: # GPU type to use
-gpu_count: # Number of GPUs (default: 1)
-memory: # RAM in GB (default: 128)
-timeout: # Maximum runtime in seconds
-timeout_preprocess: # Preprocessing timeout
-branch: # Git branch to use
-docker_tag: # Custom Docker image tag
-volumes: # List of persistent storage volumes
-env: # Environment variables to pass
-secrets: # Secrets to inject
+provider: compute provider, currently only `modal` is supported
+gpu: GPU type to use
+gpu_count: Number of GPUs (default: 1)
+memory: RAM in GB (default: 128)
+timeout: Maximum runtime in seconds
+timeout_preprocess: Preprocessing timeout
+branch: Git branch to use
+docker_tag: Custom Docker image tag
+volumes: List of persistent storage volumes
+env: Environment variables to pass
+secrets: Secrets to inject
 ```

docs/config.qmd

@@ -46,10 +46,6 @@ overrides_of_model_config:
     type: # linear | dynamic
     factor: # float
 
-# optional overrides the base model loading from_pretrained
-overrides_of_model_kwargs:
-  # use_cache: False
-
 # optional overrides to the bnb 4bit quantization configuration
 # https://huggingface.co/docs/transformers/main/main_classes/quantization#transformers.BitsAndBytesConfig
 bnb_config_kwargs:
@@ -91,12 +87,7 @@ datasets:
     type: alpaca # format | format:<prompt_style> (chat/instruct) | <prompt_strategies>.load_<load_fn>
     ds_type: # Optional[str] (json|arrow|parquet|text|csv) defines the datatype when path is a file
     data_files: # Optional[str] path to source data files
-
-    shards: # Optional[int] split dataset into N pieces (use with shards_idx)
-    shards_idx: # Optional[int] = 0 the index of sharded dataset to use
-
-    preprocess_shards: # Optional[int] process dataset in N sequential chunks for memory efficiency (exclusive with `shards`)
-
+    shards: # Optional[int] number of shards to split data into
     name: # Optional[str] name of dataset configuration to load
     train_on_split: train # Optional[str] name of dataset split to load from
     revision: # Optional[str] The specific revision of the dataset to use when loading from the Hugging Face Hub. This can be a commit hash, tag, or branch name. If not specified, the latest version will be used. This parameter is ignored for local datasets.
@@ -142,19 +133,10 @@ datasets:
 
     # Key containing the messages (default: "messages")
     field_messages: messages
-
-    # Mapping of properties from the input dataset to the chat template.
-    # (default: message_property_mappings={'role':'role', 'content':'content'})
-    # If a property exists in the template but not in this mapping, the system will attempt
-    # to load it directly from the message using the property name as the key.
-    # Example: In the mapping below, 'from' is loaded from input dataset and used as 'role',
-    # while 'value' is loaded and used as 'content' in the chat template.
-    message_property_mappings:
-      role: from
-      content: value
-      # ...
-
-    message_property_mappings:
+    # Key for role in each message (default: "role")
+    message_field_role: role
+    # Key for content in each message (default:  "content")
+    message_field_content: content
 
     # Optional[Dict[str, List]]. Roles mapping in the messages. The default is:
     roles:
@@ -166,7 +148,7 @@ datasets:
     # IMPORTANT: The following fields determine which parts of the conversation to train on.
     # Priority order: message_field_training > message_field_training_detail > train_on_inputs or role in roles_to_train
     # See examples at `docs/dataset-formats/conversation.qmd`
-    # Note: If the below 4 fields are set to empty, defaults to training only on the last message.
+    # Note: If the below 4 fields are empty, defaults to training only on the last message.
 
     # Optional[List[str]]. Roles to train on. The tokens from these roles will be considered for the loss.
     roles_to_train: ["assistant"]  # default
@@ -174,7 +156,6 @@ datasets:
     # - all: train on all EOS tokens
     # - turn (default): train on the EOS token at the end of each trainable turn
     # - last: train on the last EOS token in the conversation
-    # TIP: Please make sure that your `tokenizer.eos_token` is same as EOS/EOT token in template. Otherwise, set `eos_token` under `special_tokens`.
     train_on_eos: last
     # The key in the message turn that indicates via boolean whether tokens of a turn should be considered for training. Useful to selectively train on certain turns besides the `roles_to_train`.
     message_field_training: training
@@ -315,13 +296,6 @@ lora_modules_to_save:
 
 lora_fan_in_fan_out: false
 
-# Apply custom LoRA autograd functions and activation function Triton kernels for
-# speed and memory savings
-# See: https://axolotl-ai-cloud.github.io/axolotl/docs/lora_optims.html
-lora_mlp_kernel: true
-lora_qkv_kernel: true
-lora_o_kernel: true
-
 # LoRA+ hyperparameters
 # For more details about the following options, see:
 # https://arxiv.org/abs/2402.12354  and `src/axolotl/core/train_builder.py`
@@ -370,9 +344,6 @@ comet_mode: # Create a new experiment ("create") or log to an existing one ("get
 comet_online: # Set to True to log data to Comet server, or False for offline storage. Default is True.
 comet_experiment_config: # Dictionary for additional configuration settings, see the doc for more details.
 
-# Tensorboard
-use_tensorboard: # Optional[bool]
-
 # Where to save the full-finetuned model to
 output_dir: ./completed-model
 
@@ -407,12 +378,6 @@ save_total_limit: # Checkpoints saved at a time
 # e.g., when 1 epoch is 1000 steps => `num_epochs: 2` and `max_steps: 100` will train for 100 steps
 max_steps:
 
-# bool of whether to include tokens trainer per second in the training metrics. This iterates over the entire dataset once, so it takes some time.
-include_tokens_per_second: # Optional[bool]
-
-# whether to find batch size that fits in memory. Passed to underlying transformers Trainer
-auto_find_batch_size: # Optional[bool]
-
 eval_table_size: # Approximate number of predictions sent to wandb depending on batch size. Enabled above 0. Default is 0
 eval_max_new_tokens: # Total number of tokens generated for predictions sent to wandb. Default is 128
 eval_causal_lm_metrics: # HF evaluate metrics used during evaluation. Default is ["sacrebleu", "comet", "ter", "chrf", "perplexity"]

docs/custom_integrations.qmd

@@ -1,57 +0,0 @@
----
-title: Custom Integrations
-toc: true
-toc-depth: 3
----
-
-```{python}
-#| echo: false
-
-import re
-
-def process_readme(integration_name):
-    try:
-        path = f'../src/axolotl/integrations/{integration_name}/README.md'
-        with open(path, 'r') as f:
-            txt = f.read()
-            # Remove h1 headings
-            txt = re.sub(r'^# .*\n?', '', txt, flags=re.MULTILINE)
-            # Convert h2 to h3
-            txt = re.sub(r'^## ', '### ', txt, flags=re.MULTILINE)
-            return txt
-    except FileNotFoundError:
-        return None
-
-def print_section(name, folder_name):
-    output = f"\n## {name}\n"
-    content = process_readme(folder_name)
-    if content:
-        output += content
-    output += f"\nPlease see reference [here](https://github.com/axolotl-ai-cloud/axolotl/tree/main/src/axolotl/integrations/{folder_name})\n"
-    return output
-```
-
-```{python}
-#| output: asis
-#| echo: false
-
-# Introduction text
-print("""
-Axolotl adds custom features through `integrations`. They are located within the `src/axolotl/integrations` directory.
-
-To enable them, please check the respective documentations.
-""")
-
-# Sections
-sections = [
-    ("Cut Cross Entropy", "cut_cross_entropy"),
-    ("Grokfast", "grokfast"),
-    ("Knowledge Distillation (KD)", "kd"),
-    ("Liger Kernels", "liger"),
-    ("Language Model Evaluation Harness (LM Eval)", "lm_eval"),
-    ("Spectrum", "spectrum")
-]
-
-for section_name, folder_name in sections:
-    print(print_section(section_name, folder_name))
-```

docs/dataset-formats/conversation.qmd

@@ -6,9 +6,7 @@ order: 3
 
 ## sharegpt
 
-::: {.callout-important}
-ShareGPT is deprecated!. Please see [chat_template](#chat_template) section below.
-:::
+IMPORTANT: ShareGPT is deprecated!. Please see `chat_template` section below.
 
 ## pygmalion
 
@@ -24,7 +22,7 @@ Chat Template strategy uses a jinja2 template that converts a list of messages i
 {"conversations": [{"role": "...", "content": "..."}]}
 ```
 
-See [configs](../config.qmd) for full configs and supported templates.
+See `config.qmd` for full configs and supported templates.
 
 ### Migrating from sharegpt
 
@@ -44,9 +42,8 @@ datasets:
     type: chat_template
 
     field_messages: conversations
-    message_property_mappings:
-      role: from
-      content: value
+    message_field_role: from
+    message_field_content: value
 
 # new (if setting a new chat_template like chatml, gemma, etc)
 chat_template: chatml
@@ -55,9 +52,8 @@ datasets:
     type: chat_template
 
     field_messages: conversations
-    message_property_mappings:
-      role: from
-      content: value
+    message_field_role: from
+    message_field_content: value
 ```
 
 We recommend checking the below examples for other usecases.
@@ -104,10 +100,6 @@ datasets:
     type: chat_template
 ```
 
-::: {.callout-important}
-Please make sure that your `tokenizer.eos_token` is same as EOS/EOT token in template. Otherwise, set `eos_token` under `special_tokens`.
-:::
-
 5. (Advanced) Using fine-grained control over tokens and turns to train in a conversation
 
 For a data sample that looks like:
@@ -146,15 +138,12 @@ datasets:
     type: chat_template
     chat_template: tokenizer_default
     field_messages: conversations
-    message_property_mappings:
-      role: from
-      content: value
+    message_field_role: from
+    message_field_content: value
     roles_to_train: []
     train_on_eos: turn
     message_field_training: train
     message_field_training_detail: train_detail
 ```
 
-::: {.callout-tip}
-It is not necessary to set both `message_field_training` and `message_field_training_detail` at once.
-:::
+Tip: It is not necessary to use both `message_field_training` and `message_field_training_detail` at a time.

docs/dataset-formats/index.qmd

@@ -1,491 +1,14 @@
 ---
 title: Dataset Formats
-description: Guide to Dataset Formats in Axolotl
-back-to-top-navigation: true
-toc: true
-toc-depth: 5
+description: Supported dataset formats.
+listing:
+  fields: [title, description]
+  type: table
+  sort-ui: false
+  filter-ui: false
+  max-description-length: 250
 ---
 
+Axolotl supports a variety of dataset formats.  It is recommended to use a JSONL format.  The schema of the JSONL depends upon the task and the prompt template you wish to use. Instead of a JSONL, you can also use a HuggingFace dataset with columns for each JSONL field.
 
-Axolotl is a training framework that aims to make the process convenient yet flexible to users by simply passing a config yaml file.
-
-As there are a lot of available options in Axolotl, this guide aims to provide an simplify the user experience to choosing the proper choice.
-
-Axolotl supports 3 kinds of training methods: pre-training, supervised fine-tuning, and preference-based post-training (e.g. DPO, ORPO, PRMs). Each method has their own dataset format which are described below.
-
-## Pre-training
-
-When aiming to train on large corpora of text datasets, pre-training is your go-to choice. Due to the size of these datasets, downloading the entire-datasets before beginning training would be prohibitively time-consuming. Axolotl supports [streaming](https://huggingface.co/docs/datasets/en/stream) to only load batches into memory at a time.
-
-A sample format for a pre-training dataset is as follows:
-
-```json
-{"text": "first row"}
-{"text": "second row"}
-...
-```
-
-It is typically recommended to save your dataset as `.jsonl` due to its flexibility and simplicity.
-
-Axolotl supports loading from a Hugging Face hub repo or from local files.
-
-::: {.callout-important}
-For pre-training only, Axolotl would split texts if it exceeds the context length into multiple smaller prompts.
-:::
-
-### Pre-training from Hugging Face hub datasets
-
-As an example, to train using a Hugging Face dataset `hf_org/name`, you can pass the following config:
-
-```yaml
-pretraining_dataset: hf_org/name
-```
-
-### Pre-training from local dataset files
-
-Given a few corpus files: `A.jsonl`, `B.jsonl`, and `C.jsonl`, your config will look like the below:
-
-```yaml
-pretraining_dataset:
-  - path: json
-    data_files:
-      - A.jsonl
-      - B.jsonl
-      - C.jsonl
-```
-
-While we recommend `.jsonl`, you can also use the other formats (`csv`, `parquet`, `arrow`, `SQL`, `Webdataset`) that are supported by [`Dataset.load_dataset`](https://huggingface.co/docs/datasets/loading#local-and-remote-files)
-
-### Pre-training without streaming
-
-On the rare case that the dataset is small and can be loaded entirely into memory, another approach to running pre-training is to use the `completion` format. This would mean that the entire dataset is pre-tokenized instead of on-demand in streaming.
-
-One benefit of this is that the tokenization can be performed separately on a CPU-only machine, and then transferred to a GPU machine for training to save costs.
-
-From Hugging Face:
-
-```yaml
-datasets:
-  - path: hf_org/name
-    type: completion
-```
-
-From local files (either example works):
-
-```yaml
-datasets:
-  - path: A.jsonl
-    type: completion
-
-  - path: json
-    data_files: ["A.jsonl", "B.jsonl", "C.jsonl"]
-    type: completion
-```
-
-### Pre-training dataset configuration tips
-
-#### Setting max_steps
-
-When using streaming for large datasets, Axolotl does not know in advance how large the dataset is and does not know when to stop.
-
-Therefore, it is necessary to set `max_steps: int` in your config for pre-training to run, so that Axolotl knows when to stop training.
-
-One step is equal to `sequence_len * micro_batch_size * gradient_accumulation_steps * total_num_gpus` tokens.
-
-#### Group_by_length
-
-It is recommended to leave this off if downloading from Hugging Face hub as it would download the entire dataset which can be very large.
-
-### Reference
-
-Please see docs [here](pretraining.qmd).
-
-## Supervised fine-tuning (SFT)
-
-Supervised fine-tuning is the process of training models to respond to an instruction or chat input.
-
-As there are a wide variety of dataset formats, Axolotl tries to support a majority of the formats available in public datasets.
-
-Axolotl provides four approaches for loading datasets, however, it's easier to work backwards from the dataset you have available to figure out which approach to use.
-
-A flow chart is as follows:
-
-1. Do you already have the dataset tokenized? If yes, check [Pre-Tokenized Dataset](#pre-tokenized-dataset).
-
-2. Do you want to format the dataset yourself and manually choose each section to mask? If yes, check [Template Free Dataset](#template-free-dataset)
-
-3. Is your dataset in a "conversation" format, containing a `list[messages]`? If yes, check [Conversation Dataset](#conversation-dataset)
-
-4. Is your dataset in an "instruct" format, containing `{ instruction, response }`? If yes, check [Instruction Dataset](#instruction-dataset)
-
-If you went through the flow chart and did not find one that matches, it is recommended to preprocess your dataset into one of the above or create a thread on Github Discussion.
-
-::: {.callout-tip}
-You can mix and match within each approach or across approaches to train a model on a variety of datasets.
-:::
-
-### Pre-Tokenized Dataset
-
-We suggest this approach when you want to bring your own tokenized dataset.
-
-Axolotl expects the dataset to have three keys:
-- `input_ids`: from tokenizing formatted prompt
-- `attention_mask`: for masking padding. If you don't add padding, it would be equal to `len(input_ids) * [1]`
-- `labels`: this is the same as `input_ids`, however, if you want to mask certain tokens, you would set those indices to `-100`.
-
-::: {.callout-tip}
-Make sure to add BOS/EOS tokens to your prompt and mask it appropriately.
-:::
-
-A config for this would look like:
-
-```yaml
-datasets:
-  - path: A.jsonl
-    type:
-```
-
-::: {.callout-note}
-`type: ` is empty!
-:::
-
-Reference: [Pre-Tokenized Dataset Documentation](tokenized.qmd).
-
-### Template Free Dataset
-
-We reccomend this approach when you want granular control over the prompt formatting, special tokens, and masking, whilst letting Axolotl handle the tokenization. This is very useful if your dataset has unique prompts that differ across samples and where one single general template wouldn't suffice.
-
-In the example below, you could see that there is no proper structure. At the same time, it's very flexible as there are no constraints on how your prompt can look.
-
-```json
-{
-    "segments": [
-        {
-            "label": true,
-            "text": "<s>Hello\n"
-        },
-        {
-            "label": true,
-            "text": "hi there!. "
-        },
-        {
-            "label": false,
-            "text": "goodbye "
-        },
-        {
-            "label": true,
-            "text": "farewell</s>"
-        }
-    ]
-}
-```
-
-Each prompt must be have a key called `segments` which is a list of `{ text, label }`.
-
-```yaml
-datasets:
-  - path: A.jsonl
-    type: input_output
-```
-
-Reference: [Template Free Documentation](template_free.qmd).
-
-### Conversation Dataset
-
-`conversation` messages are a list of messages which usually contain a `role` and `content` key.
-
-::: {.callout-tip}
-Fun fact: Axolotl synonymously refers to "chat" messages as `conversation` messages due to how FastChat initially used this term to build a widely used [fastchat conversation](https://github.com/lm-sys/FastChat/blob/main/fastchat/conversation.py) method for formatting chat messages prior to the creation of `chat_templates`.
-:::
-
-#### What are `chat_templates`?
-
-The current most popular and convenient method for inference is to use `chat_templates` for formatting prompts. Axolotl supports using `chat_templates` for training to ensure that the model performs in the same environment as in inference.
-
-Here's a quick rundown on `chat_template`: A `chat_template` is a Jinja2 template which formats a list of messages into a prompt.
-
-An example of a prompt formatted into a popular template called ChatML can be seen below:
-
-Single prompt (pretty-printed):
-```json
-{
-    "messages": [
-        {
-            "role": "user",
-            "content": "Hi"
-        },
-        {
-            "role": "assistant",
-            "content": "How can I help you?"
-        },
-        {
-            "role": "user",
-            "content": "Can you add 3+5?"
-        },
-        {
-            "role": "assistant",
-            "content": "The answer is 8."
-        }
-    ]
-}
-```
-
-The ChatML template is as follows:
-```jinja2
-{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}
-```
-
-The above prompt formatted into this template will result in:
-
-```
-<|im_start|>user
-Hi<|im_end|>
-<|im_start|>assistant
-How can I help you?<|im_end|>
-<|im_start|>user
-Can you add 3+5?<|im_end|>
-<|im_start|>assistant
-The answer is 8.<|im_end|>
-```
-
-By using delimiters (`<|im_start|>` and `<|im_end|>`), a prompt separates different speakers which helps the model identify which portion belongs to whom.
-
-#### Common Conversation Dataset formats
-
-Older conversation datasets with the following format are colloquially called `sharegpt` datasets.
-
-```json
-{"conversations": [{"from": "...", "value": "..."}]}
-```
-
-Newer conversation datasets usually follow the OpenAI format.
-
-```json
-{"messages": [{"role": "...", "content": "..."}]}
-```
-
-Axolotl supports both as well as allowing customization of any kind of key.
-
-#### Chat Template Usage
-
-To properly use this method, it is important to identify three things:
-
-1. Which `chat_template` would you use?
-
-2. What are the keys in your dataset, and what are the possible roles? For example, in OpenAI format, the keys would be `messages`, `role`, and `content`, respectively, whereas the possible roles are `system`, `user`, and `assistant`.
-
-3. What do you want to mask? For instance, only assistant messages, only last message, or nothing.
-
-##### Choosing a `chat_template`
-
-There are a lot of `chat_templates` out there. Axolotl supports the common ones: [supported chat templates](https://github.com/axolotl-ai-cloud/axolotl/blob/860609392184cf62a7e0ca676658b170e059ce6c/src/axolotl/utils/chat_templates.py#L17). For example, to use ChatML, it would be `chat_template: chatml`.
-
-However, it is also possible to use the already configured template within the tokenizer by specifying `chat_template: tokenizer_default`. If you want a fallback (in case some tokenizer does not have it pre-configured), you can do `chat_template: tokenizer_default_fallback_chatml` to fallback to the ChatML template if a tokenizer template was not found.
-
-One last but powerful approach is to bring your own template. This can be set via:
-
-```yaml
-chat_template_jinja: # your template
-```
-
-##### Setting `chat_template` dataset keys
-
-We currently default to OpenAI format for dataset keys, so if that's your current dataset format, there's nothing to do here.
-
-If your dataset format is different, here are the keys you should check (with their defaults):
-
-```yaml
-datasets:
-    ...
-    field_messages: messages  # this should point to the key containing the list of conversations
-    message_property_mappings:  # this is a mapping from keys in your dataset to keys in chat_template
-      role: role
-      content: content
-```
-
-In some `chat_templates` (e.g. [Gemma](https://huggingface.co/google/gemma-2b-it/blob/main/tokenizer_config.json#L1507)), the roles are hardcoded to `user` and `assistant`. Consequently, you may find it necessary to map the roles in your dataset to these above. We currently have some defaults that should work for common datasets, but if you get a `KeyError`, it would be necessary to add mapping for your roles. Here is an example of how it would look like:
-
-```yaml
-datasets:
-    ...
-    roles:
-      assistant:
-        - gpt
-        - model
-      user:
-        - human
-```
-
-In the example above, all `gpt` and `model` values are converted to `assistant`. All `human` values are converted to `user.`
-
-##### Handling masking
-
-The common use case for `chat_template` is for chat messages, therefore, it is common to mask all non-assistant messages. Assistant messages refer to the bot messages that you want the model to learn on.
-
-To train on all `assistant` messages, you would set the following configs.
-
-```yaml
-datasets:
-    ...
-    roles_to_train: ["assistant"]
-    train_on_eos: "turn"
-```
-
-The `train_on_eos` config means that it would mask all EOS tokens for turns that aren't assistant-turns. The other options are: `all` and `last` to choose which EOS to train on.
-
-Perhaps, you want to train on `assistant` and `narrator` roles, you can simply add `narrator` to the list of `roles_to_train`. You would also need to add it to the mapping of `roles` above.
-
-```yaml
-datasets:
-    ...
-    roles_to_train: ["assistant", "narrator"]
-    roles:
-      assistant:
-        - gpt
-        - model
-      user:
-        - human
-      narrator: ["narrator"]
-```
-
-::: {.callout-tip}
-As chat_templates may use hardcoded EOS/EOT tokens that are different from the tokenizer's EOS, it is highly recommended to set them. For example, `ChatML` uses `<|im_end|>` to end turns.
-
-```yaml
-special_tokens:
-  eos_token: <|im_end|>
-```
-
-:::
-
-##### Applying `chat_template`
-
-Once all the above steps are completed, you could combine all these configs together to form a bespoke configuration for your custom dataset.
-
-```yaml
-datasets:
-  - path: A.jsonl
-    type: chat_template
-
-    # step 1
-    chat_template: chatml
-
-    # step 2
-    field_messages: messages
-    message_property_mappings:
-      role: role
-      content: content
-
-    roles:
-      assistant:
-        - gpt
-        - model
-        - assistant
-      user:
-        - human
-        - user
-
-    # step 3
-    roles_to_train: ["assistant"]
-    train_on_eos: "turn"
-
-special_tokens:
-  eos_token: <|im_end|>
-```
-
-If this config were to be applied to the sample dataset above, the output would look as such (which can be retrieved via `axolotl preprocess config.yaml --debug`):
-
-```
-<|im_start|>(-100, 128256) user(-100, 882)
-(-100, 198) Hi(-100, 13347) <|im_end|>(-100, 128257)
-(-100, 198) <|im_start|>(-100, 128256) assistant(-100, 78191)
-(-100, 198) How(4438, 4438)  can(649, 649)  I(358, 358)  help(1520, 1520)  you(499, 499) ?(30, 30) <|im_end|>(128257, 128257)
-(-100, 198) <|im_start|>(-100, 128256) user(-100, 882)
-(-100, 198) Can(-100, 6854)  you(-100, 499)  add(-100, 923)  (-100, 220) 3(-100, 18) +(-100, 10) 5(-100, 20) ?(-100, 30) <|im_end|>(-100, 128257)
-(-100, 198) <|im_start|>(-100, 128256) assistant(-100, 78191)
-(-100, 198) The(791, 791)  answer(4320, 4320)  is(374, 374)  (220, 220) 8(23, 23) .(13, 13) <|im_end|>(128257, 128257)
-(-100, 198)
-```
-
-The first number refers to the label, the second refers to the `token_id`. For example, `-100` labels appear on non-assistant portions, meaning that they are masked during. For assistant portions, the label is the same as the `token_id`.
-
-::: {.callout-note}
-
-If during `preprocess`, there are a lot of warnings of `Could not find content __ boundary`, please check the FAQ section for [chat_templates](../faq.qmd#chat-templates).
-
-:::
-
-#### Reference
-
-Please see docs [here](conversation.qmd).
-
-### Instruction Dataset
-
-Instruction datasets are used to train instruction-following models and comprise a prompt, containing an instruction, and a single response. In contrast to chat datasets which may be multi-turn, instruct datasets are typically single-turn.
-
-An example is of a common format called Alpaca:
-```json
-{"instruction": "...", "input": "...", "output": "..."}
-```
-
-Using those keys, a prompt can be built based on it.
-```
-Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
-
-### Instruction:
-{instruction}
-
-### Input:
-{input}
-
-### Response:
-{output}
-```
-
-This can be configured as such:
-```yaml
-datasets:
-  - path: A.jsonl
-    type: alpaca
-```
-
-Axolotl supports many kinds of instruction dataset. All of them can be found here (https://axolotl-ai-cloud.github.io/axolotl/docs/dataset-formats/inst_tune.html) with their respective type and sample row format.
-
-
-Reference: [Instruction Dataset Documentation](inst_tune.qmd).
-
-#### Custom Instruct Prompt Format
-
-Due to the myriad possibilities of instruction formats, Axolotl allows customizing your own instruction format without having to dive into the code directly.
-
-In the example below, a sample row is used to output in `mistral_v1` format.
-```json
-{"input": "...", "output": "..."}
-```
-
-```yaml
-datasets:
-  - path: repo
-    type:
-      system_prompt: ""
-
-      field_system:
-      field_instruction: input
-      field_input:
-      field_output: output
-
-      # multi-line example with input
-      format: |-
-        [INST] {instruction} {input} [/INST]
-
-      # single-line example without input
-      no_input_format: "[INST] {instruction} [/INST]"
-```
-
-The config sets that the `field_instruction` is actually named `input`, and the `field_input` is empty as we don't have an `input` in this sample. Generally, `instruction` can be thought as the question to the model, and `input` as the additional information with `output` being the response. It is not necessary to have an `input` nor `system`. In the end, the most important part is to understand what format you want it to look like and how you can customize this to your use case.
-
-Reference: [Custom Instruct Prompt Format Documentation](inst_tune.qmd#how-to-add-custom-prompt-format).
-
-## Reinforcement Learning from Human Feedback (RLHF)
-
-As there are multiple RLHF methods with their own dataset requirements. Please see [RLHF documentation](../rlhf.qmd) for more detail.
+Below are these various formats organized by task:

docs/dataset-formats/pretraining.qmd

@@ -27,6 +27,7 @@ pretraining_dataset:
     type: pretrain
     trust_remote_code:
     skip: # number of rows of data to skip over from the beginning
+...
 ```
 
 :::

docs/dataset-formats/template_free.qmd

@@ -1,239 +1,7 @@
 ---
 title: Template-Free
 description: Construct prompts without a template.
-toc: true
-toc-depth: 3
 order: 4
 ---
 
-## Background {#sec-background}
-
-### Masking Inputs {#masking-inputs}
-
-One of the most popular features of
-[axolotl](https://github.com/axolotl-ai-cloud/axolotl) is
-setting the following configuration value:
-
-
-```yaml
-train_on_inputs: false
-```
-
-If you declare a [dataset formats](https://github.com/axolotl-ai-cloud/axolotl?tab=readme-ov-file#dataset)
-such as `alpaca` or `chatml`, axolotl knows what is an input
-(i.e. human) vs. an output (i.e. the assistant) and masks the input
-labels so that your model can focus on predicting the outputs only.
-
-### You may not want prompt templates {#sec-you-may-not-want-prompt-templates}
-
-However, there are many situations where you don't want to use one of
-these formats or templates. This is because they can:
-
--   Add unnecessary boilerplate to your prompts.
--   Create artifacts like special delimiters `<|im_start|>` that can
-    quickly become footguns if you don't include them correctly at
-    inference time.
--   Enforce a *chat* interface when you do not want one. Sometimes you
-    just want to fine-tune a model to a very specific task and do NOT
-    want multi-turn conversations, roles, etc.
--   Limit you to only certain roles that the template allows.
-
-### The `input_output` format {#sec-the-inputoutput-format}
-
-You can construct your prompts without a template by using the
-`input_output` format, by setting `type: input_output` in your
-configuration file like this:
-
-**config.yml**
-
-```yaml
-train_on_inputs: false # Mask segments of your data
-datasets:
-  - path: output.jsonl
-    type: input_output  # use template free prompt construction
-```
-
-Unlike `type: completion`, which is also template-free,
-`type: input_output` allows you to mask segments of your text. More
-details on how this works are described below.
-
-## Usage {#sec-usage}
-
-This is how you can use the `input_output` format:
-
-### 1. Prepare Data {#sec-1-prepare-data}
-
-To use the `input_output` format, collect your data in the following
-format into a jsonl file (below is the first row from the file
-`output`.jsonl` pretty printed):
-
-```bash
-$ head -n1 output.jsonl | python -m json.tool
-```
-
-:::{.cell-output .cell-output-stdout}
-    {
-        "segments": [
-            {
-                "label": true,
-                "text": "<s>Hello\n"
-            },
-            {
-                "label": true,
-                "text": "hi there!. "
-            },
-            {
-                "label": false,
-                "text": "goodbye "
-            },
-            {
-                "label": true,
-                "text": "farewell</s>"
-            }
-        ]
-    }
-:::
-
-Set `label:false` when you want to mask a segment of text so that the
-model isn't trained on it. Some things to keep in mind:
-
-> [!IMPORTANT]
-> 1.  **EOS, BOS, spaces, newlines etc. are entirely up to you. Axolotl
-    concatenates all the segments as-is.** The tokenizer doesn't add
-    anything additional. Notice how I added spaces, newlines, `<s>`
-    (BOS), and `</s>` (EOS) myself.
-> 2.  Make sure you check the materialized output to validate that the
-    prompt is getting assembled how you like.
-
-### 2. Use `type: input_output` {#sec-2-use-type-inputoutput}
-
-Let's materialize data with our `output.jsonl` file by setting
-`type: input_output` in our axolotl config:
-
-```yaml
-# training_config.yaml
-base_model: mistralai/Mistral-7B-v0.1
-data_seed: 49
-seed: 49
-
-datasets:
-  - path: output.jsonl
-    type: input_output
-val_set_size: 0.1
-
-sequence_len: 896
-sample_packing: false
-
-micro_batch_size: 2
-gradient_accumulation_steps: 3
-eval_batch_size: 2
-num_epochs: 1
-learning_rate: 0.0002
-
-train_on_inputs: false
-special_tokens:
-  bos_token: "<s>"
-  eos_token: "</s>"
-  unk_token: "<unk>"
-```
-
-You can use the following command to materialize your data. The
-`--debug` flag will print the tokens, along with the labels so you can
-verify that the correct items are being ignored:
-
-```bash
-axolotl preprocess training_config.yaml --debug
-
-...
-[2024-03-05 23:36:46,969] [INFO] [axolotl.check_example_labels:35] [PID:607731] [RANK:0] <s>(1, 1) Hello(22557, 22557)
-(13, 13) hi(12014, 12014) there(736, 736) !(28808, 28808) .(28723, 28723) (28705, 28705) good(-100, 1179) bye(-100, 17664) (-100, 28705) fare(19111, 19111) well(5458, 5458) </s>(2, 2)
-
-```
-
-The format is `decoded_token`(`label`, `token_id`), for example,
-`<s>(1, 1)` means that the token is `<s>`, the label is `1` and the
-token_id is `1`. When the label is `-100` then that token is ignored for
-training.
-
-### 3. Check the prompts {#sec-3-check-the-prompts}
-
-Here is another way to check the materialized output:
-
-```python
-from transformers import AutoTokenizer
-from datasets import load_from_disk
-import yaml
-
-directory = !ls last_run_prepared/
-with open('training_config.yaml', 'r') as f:
-    cfg = yaml.safe_load(f)
-model_id = cfg['base_model']
-tok = AutoTokenizer.from_pretrained(model_id)
-ds = load_from_disk(f'last_run_prepared/{directory[0]}/')
-```
-
-```python
->>> row = ds[0]
->>> print(tok.decode(row['input_ids']))
-<s> Hello
-    hi there!.  goodbye  farewell</s>
-```
-
-We can check that the right tokens are ignored by comparing the labels
-to each token:
-
-```python
-import pandas as pd
-pd.DataFrame([{'token': tok.decode(i), 'label': l, 'id':i} for i,l in
-              zip(row['input_ids'], row['labels'])])
-```
-
-| token | label | id    |
-|-------|-------|-------|
-| 0     | \<s\> | 1     |
-| 1     | Hello | 22557 |
-| 2     | \\n   | 13    |
-| 3     | hi    | 12014 |
-| 4     | there | 736   |
-| 5     | !     | 28808 |
-| 6     | .     | 28723 |
-| 7     |       | 28705 |
-| 8     | good  | -100  |
-| 9     | bye   | -100  |
-| 10    |       | -100  |
-| 11    | fare  | 19111 |
-| 12    | well  | 5458  |
-| 13    | \</s\>| 2     |
-
-
-
-If we look at the input data, the above table seems correct! (The jsonl
-version is repeated below for reference):
-
-
-```bash
-$ head -n1 output.jsonl | python -m json.tool
-```
-
-:::{.cell-output .cell-output-stdout}
-    {
-        "segments": [
-            {
-                "label": true,
-                "text": "<s>Hello\n"
-            },
-            {
-                "label": true,
-                "text": "hi there!. "
-            },
-            {
-                "label": false,
-                "text": "goodbye "
-            },
-            {
-                "label": true,
-                "text": "farewell</s>"
-            }
-        ]
-    }
-:::
+See [these docs](../input_output.qmd).

docs/dataset_preprocessing.qmd

@@ -3,11 +3,8 @@ title: Dataset Preprocessing
 description: How datasets are processed
 ---
 
-## Overview
-
 Dataset pre-processing is the step where Axolotl takes each dataset you've configured alongside
-the [dataset format](docs/dataset-formats) and prompt strategies to:
-
+the (dataset format)[../dataset-formats/] and prompt strategies to:
  - parse the dataset based on the *dataset format*
  - transform the dataset to how you would interact with the model based on the *prompt strategy*
  - tokenize the dataset based on the configured model & tokenizer
@@ -15,12 +12,10 @@ the [dataset format](docs/dataset-formats) and prompt strategies to:
 
 The processing of the datasets can happen one of two ways:
 
-1. Before kicking off training by calling `axolotl preprocess config.yaml --debug`
+1. Before kicking off training by calling `python -m axolotl.cli.preprocess /path/to/your.yaml --debug`
 2. When training is started
 
-### What are the benefits of pre-processing?
-
-When training interactively or for sweeps
+What are the benefits of pre-processing? When training interactively or for sweeps
 (e.g. you are restarting the trainer often), processing the datasets can oftentimes be frustratingly
 slow. Pre-processing will cache the tokenized/formatted datasets according to a hash of dependent
 training parameters so that it will intelligently pull from its cache when possible.
@@ -33,12 +28,8 @@ default path of `./last_run_prepared/`, but will ignore anything already cached
 setting `dataset_prepared_path: ./last_run_prepared`, the trainer will use whatever pre-processed
 data is in the cache.
 
-### What are the edge cases?
-
-Let's say you are writing a custom prompt strategy or using a user-defined
+What are the edge cases? Let's say you are writing a custom prompt strategy or using a user-defined
 prompt template. Because the trainer cannot readily detect these changes, we cannot change the
-calculated hash value for the pre-processed dataset.
-
-If you have `dataset_prepared_path: ...` set
+calculated hash value for the pre-processed dataset. If you have `dataset_prepared_path: ...` set
 and change your prompt templating logic, it may not pick up the changes you made and you will be
 training over the old prompt.

docs/debugging.qmd

@@ -31,13 +31,11 @@ While debugging it's helpful to simplify your test scenario as much as possible.
     - Set `CUDA_VISIBLE_DEVICES` to a single GPU, ex: `export CUDA_VISIBLE_DEVICES=0`.
     - Set `dataset_processes: 1` in your axolotl config or run the training command with `--dataset_processes=1`.
 2. **Use a small dataset**: Construct or use a small dataset from HF Hub. When using a small dataset, you will often have to make sure `sample_packing: False` and `eval_sample_packing: False` to avoid errors.  If you are in a pinch and don't have time to construct a small dataset but want to use from the HF Hub, you can shard the data (this will still tokenize the entire dataset, but will only use a fraction of the data for training.  For example, to shard the dataset into 20 pieces, add the following to your axolotl config):
-
     ```yaml
-    datasets:
+    dataset:
         ...
         shards: 20
     ```
-
 3. **Use a small model**: A good example of a small model is [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0).
 4. **Minimize iteration time**: Make sure the training loop finishes as fast as possible, with these settings.
     - `micro_batch_size: 1`
@@ -87,7 +85,7 @@ The easiest way to get started is to modify the [.vscode/launch.json](../.vscode
 
 For example, to mimic the command `cd devtools && CUDA_VISIBLE_DEVICES=0 accelerate launch -m axolotl.cli.train dev_chat_template.yml`, you would use the below configuration[^1].  Note that we add additional flags that override the axolotl config and incorporate the tips above (see the comments). We also set the working directory to `devtools` and set the `env` variable `HF_HOME` to a temporary folder that is later partially deleted.  This is because we want to delete the HF dataset cache before each run in order to ensure that the data preprocessing code is run from scratch.
 
-```json
+```jsonc
 // .vscode/launch.json
 {
     "version": "0.2.0",
@@ -134,7 +132,7 @@ For example, to mimic the command `cd devtools && CUDA_VISIBLE_DEVICES=0 acceler
 
 Below is the [./vscode/tasks.json](../.vscode/tasks.json) file that defines the `cleanup-for-dataprep` task.  This task is run before each debugging session when you use the above configuration.  Note how there are two tasks that delete the two folders mentioned above.  The third task `cleanup-for-dataprep` is a composite task that combines the two tasks.  A composite task is necessary because VSCode does not allow you to specify multiple tasks in the `preLaunchTask` argument of the `launch.json` file.
 
-```json
+```jsonc
 // .vscode/tasks.json
 // this file is used by launch.json
 {

docs/faq.qmd

@@ -3,7 +3,6 @@ title: FAQ
 description: Frequently asked questions
 ---
 
-### General
 
 **Q: The trainer stopped and hasn't progressed in several minutes.**
 
@@ -20,33 +19,3 @@ description: Frequently asked questions
 **Q: AttributeError: 'DummyOptim' object has no attribute 'step'**
 
 > A: You may be using deepspeed with single gpu. Please don't set `deepspeed:` in yaml or cli.
-
-**Q: The codes is stuck on saving preprocessed datasets.**
-
-> A: This is usually an issue with the GPU. This can be resolved through setting the os environment variable `CUDA_VISIBLE_DEVICES=0`. If you are on runpod, this is usually a pod issue. Starting a new pod should take care of it.
-
-### Chat templates
-
-**Q: `jinja2.exceptions.UndefinedError: 'dict object' has no attribute 'content' / 'role' / ____`**
-
-> A: This means that the property mapping for the stated attribute does not exist when building `chat_template` prompt. For example, if `no attribute 'content'`, please check you have added the correct mapping for `content` under `message_property_mappings`.
-
-**Q: `Empty template generated for turn ___`**
-
-> A: The `content` is empty for that turn.
-
-**Q: `Could not find content start/end boundary for turn __`**
-
-> A: The specific turn's start/end could not be detected. Please ensure you have set the `eos_token` following your `chat_template`. Otherwise, this could be a `chat_template` which doesn't use proper boundaries for each turn (like system). On the rare occurrence, make sure your content is not `[[dummy_message]]`. Please let us know about this.
-
-**Q: `Content end boundary is before start boundary for turn ___`**
-
-> A: This is an edge case which should not occur. Please create an Issue if this happens.
-
-**Q: `Content end boundary is the same as start boundary for turn ___. This is likely an empty turn.`**
-
-> A: This is likely an empty turn.
-
-**Q: The EOS/EOT token is incorrectly being masked or not being masked.**
-
-> A: This is because of the mismatch between `tokenizer.eos_token` and EOS/EOT token in template. Please make sure to set `eos_token` under `special_tokens` to the same EOS/EOT token as in template.

docs/getting-started.qmd

@@ -1,5 +1,5 @@
 ---
-title: "Quickstart"
+title: "Getting Started with Axolotl"
 format:
   html:
     toc: true
@@ -17,12 +17,12 @@ Let's start by fine-tuning a small language model using LoRA. This example uses
 Assuming `axolotl` is installed (if not, see our [Installation Guide](installation.qmd))
 
 1. Download example configs:
-```bash
+```shell
 axolotl fetch examples
 ```
 
 2. Run the training:
-```bash
+```shell
 axolotl train examples/llama-3/lora-1b.yml
 ```
 
@@ -108,7 +108,7 @@ Please consult the supported [Dataset Formats](dataset-formats/) for more detail
 
 3. Run the training:
 
-```bash
+```shell
 axolotl train my_training.yml
 ```
 
@@ -118,7 +118,7 @@ axolotl train my_training.yml
 
 After training, test your model:
 
-```bash
+```shell
 axolotl inference my_training.yml --lora-model-dir="./outputs/lora-out"
 ```
 
@@ -126,7 +126,7 @@ axolotl inference my_training.yml --lora-model-dir="./outputs/lora-out"
 
 For large datasets, preprocess first:
 
-```bash
+```shell
 axolotl preprocess my_training.yml
 ```
 
@@ -134,7 +134,7 @@ axolotl preprocess my_training.yml
 
 Launch a Gradio interface:
 
-```bash
+```shell
 axolotl inference my_training.yml --lora-model-dir="./outputs/lora-out" --gradio
 ```
 

docs/inference.qmd

@@ -1,10 +1,11 @@
 ---
-title: "Inference"
+title: "Inference Guide"
 format:
   html:
     toc: true
     toc-depth: 3
     number-sections: true
+    code-tools: true
 execute:
   enabled: false
 ---

docs/input_output.qmd

@@ -3,4 +3,263 @@ title: Template-free prompt construction
 description: "Template-free prompt construction with the `input_output` format"
 ---
 
-The documentation moved to [here](dataset-formats/template_free.qmd).
+<!-- TOC -->
+
+- [Background](#background)
+    - [Masking Inputs](#masking-inputs)
+    - [You may not want prompt templates](#you-may-not-want-prompt-templates)
+    - [The `input_output` format](#the-input_output-format)
+- [Usage](#usage)
+    - [1. Prepare Data](#1-prepare-data)
+    - [2. Use `type: input_output`](#2-use-type-input_output)
+    - [3. Check the prompts](#3-check-the-prompts)
+
+<!-- /TOC -->
+
+<a id="markdown-background" name="background"></a>
+
+## Background
+
+<a id="markdown-masking-inputs" name="masking-inputs"></a>
+
+### Masking Inputs
+
+One of the most popular features of
+[axolotl](https://github.com/axolotl-ai-cloud/axolotl) is
+setting the following configuration value:
+
+
+```yaml
+train_on_inputs: false
+```
+
+If you declare a [dataset formats](https://github.com/axolotl-ai-cloud/axolotl?tab=readme-ov-file#dataset)
+such as `alpaca` or `chatml`, axolotl knows what is an input
+(i.e. human) vs. an output (i.e. the assistant) and masks the input
+labels so that your model can focus on predicting the outputs only.
+
+<a id="markdown-you-may-not-want-prompt-templates" name="you-may-not-want-prompt-templates"></a>
+
+### You may not want prompt templates
+
+However, there are many situations where you don't want to use one of
+these formats or templates. This is because they can:
+
+-   Add unnecessary boilerplate to your prompts.
+-   Create artifacts like special delimiters `<|im_start|>` that can
+    quickly become footguns if you don't include them correctly at
+    inference time.
+-   Enforce a *chat* interface when you do not want one. Sometimes you
+    just want to fine-tune a model to a very specific task and do NOT
+    want multi-turn conversations, roles, etc.
+-   Limit you to only certain roles that the template allows.
+
+<a id="markdown-the-inputoutput-format" name="the-inputoutput-format"></a>
+
+### The `input_output` format
+
+You can construct your prompts without a template by using the
+`input_output` format, by setting `type: input_output` in your
+configuration file like this:
+
+**config.yml**
+
+```yaml
+train_on_inputs: false # Mask segments of your data
+datasets:
+  - path: output.jsonl
+    type: input_output  # use template free prompt construction
+```
+
+Unlike `type: completion`, which is also template-free,
+`type: input_output` allows you to mask segments of your text. More
+details on how this works are described below.
+
+<a id="markdown-usage" name="usage"></a>
+
+## Usage
+
+This is how you can use the `input_output` format:
+
+<a id="markdown-1-prepare-data" name="1-prepare-data"></a>
+
+### 1. Prepare Data
+
+To use the `input_output` format, collect your data in the following
+format into a jsonl file (below is the first row from the file
+`output`.jsonl` pretty printed):
+
+```bash
+$ head -n1 output.jsonl | python -m json.tool
+```
+
+:::{.cell-output .cell-output-stdout}
+    {
+        "segments": [
+            {
+                "label": true,
+                "text": "<s>Hello\n"
+            },
+            {
+                "label": true,
+                "text": "hi there!. "
+            },
+            {
+                "label": false,
+                "text": "goodbye "
+            },
+            {
+                "label": true,
+                "text": "farewell</s>"
+            }
+        ]
+    }
+:::
+
+Set `label:false` when you want to mask a segment of text so that the
+model isn't trained on it. Some things to keep in mind:
+
+> [!IMPORTANT]
+> 1.  **EOS, BOS, spaces, newlines etc. are entirely up to you. Axolotl
+    concatenates all the segments as-is.** The tokenizer doesn't add
+    anything additional. Notice how I added spaces, newlines, `<s>`
+    (BOS), and `</s>` (EOS) myself.
+> 2.  Make sure you check the materialized output to validate that the
+    prompt is getting assembled how you like.
+
+<a id="markdown-2-use-type-inputoutput" name="2-use-type-inputoutput"></a>
+
+### 2. Use `type: input_output`
+
+Let's materialize data with our `output.jsonl` file by setting
+`type: input_output` in our axolotl config:
+
+```yaml
+# training_config.yaml
+base_model: mistralai/Mistral-7B-v0.1
+data_seed: 49
+seed: 49
+
+datasets:
+  - path: output.jsonl
+    type: input_output
+val_set_size: 0.1
+
+sequence_len: 896
+sample_packing: false
+
+micro_batch_size: 2
+gradient_accumulation_steps: 3
+eval_batch_size: 2
+num_epochs: 1
+learning_rate: 0.0002
+
+train_on_inputs: false
+special_tokens:
+  bos_token: "<s>"
+  eos_token: "</s>"
+  unk_token: "<unk>"
+```
+
+You can use the following command to materialize your data. The
+`--debug` flag will print the tokens, along with the labels so you can
+verify that the correct items are being ignored:
+
+```bash
+$ python -m axolotl.cli.preprocess training_config.yaml --debug
+
+...
+[2024-03-05 23:36:46,969] [INFO] [axolotl.check_example_labels:35] [PID:607731] [RANK:0] <s>(1, 1) Hello(22557, 22557)
+(13, 13) hi(12014, 12014) there(736, 736) !(28808, 28808) .(28723, 28723) (28705, 28705) good(-100, 1179) bye(-100, 17664) (-100, 28705) fare(19111, 19111) well(5458, 5458) </s>(2, 2)
+
+```
+
+The format is `decoded_token`(`label`, `token_id`), for example,
+`<s>(1, 1)` means that the token is `<s>`, the label is `1` and the
+token_id is `1`. When the label is `-100` then that token is ignored for
+training.
+
+<a id="markdown-3-check-the-prompts" name="3-check-the-prompts"></a>
+
+### 3. Check the prompts
+
+Here is another way to check the materialized output:
+
+```python
+from transformers import AutoTokenizer
+from datasets import load_from_disk
+import yaml
+
+directory = !ls last_run_prepared/
+with open('training_config.yaml', 'r') as f:
+    cfg = yaml.safe_load(f)
+model_id = cfg['base_model']
+tok = AutoTokenizer.from_pretrained(model_id)
+ds = load_from_disk(f'last_run_prepared/{directory[0]}/')
+```
+
+```python
+>>> row = ds[0]
+>>> print(tok.decode(row['input_ids']))
+<s> Hello
+    hi there!.  goodbye  farewell</s>
+```
+
+We can check that the right tokens are ignored by comparing the labels
+to each token:
+
+```python
+import pandas as pd
+pd.DataFrame([{'token': tok.decode(i), 'label': l, 'id':i} for i,l in
+              zip(row['input_ids'], row['labels'])])
+```
+
+| token | label | id    |
+|-------|-------|-------|
+| 0     | \<s\> | 1     |
+| 1     | Hello | 22557 |
+| 2     | \\n   | 13    |
+| 3     | hi    | 12014 |
+| 4     | there | 736   |
+| 5     | !     | 28808 |
+| 6     | .     | 28723 |
+| 7     |       | 28705 |
+| 8     | good  | -100  |
+| 9     | bye   | -100  |
+| 10    |       | -100  |
+| 11    | fare  | 19111 |
+| 12    | well  | 5458  |
+| 13    | \</s\>| 2     |
+
+
+
+If we look at the input data, the above table seems correct! (The jsonl
+version is repeated below for reference):
+
+
+```bash
+$ head -n1 output.jsonl | python -m json.tool
+```
+
+:::{.cell-output .cell-output-stdout}
+    {
+        "segments": [
+            {
+                "label": true,
+                "text": "<s>Hello\n"
+            },
+            {
+                "label": true,
+                "text": "hi there!. "
+            },
+            {
+                "label": false,
+                "text": "goodbye "
+            },
+            {
+                "label": true,
+                "text": "farewell</s>"
+            }
+        ]
+    }
+:::

docs/installation.qmd

@@ -1,10 +1,11 @@
 ---
-title: "Installation"
+title: "Installation Guide"
 format:
   html:
     toc: true
     toc-depth: 3
     number-sections: true
+    code-tools: true
 execute:
   enabled: false
 ---

docs/lora_optims.qmd

@@ -1,127 +0,0 @@
----
-title: "LoRA Optimizations"
-description: "Custom autograd functions and Triton kernels in Axolotl for optimized LoRA fine-tuning"
----
-
-Inspired by [Unsloth](https://github.com/unslothai/unsloth), we've implemented two
-optimizations for LoRA and QLoRA fine-tuning, supporting both single GPU and multi-GPU
-(in the DDP and DeepSpeed settings) training. These include (1) SwiGLU and GEGLU activation function
-Triton kernels, and (2) LoRA MLP and attention custom autograd functions. Our goal was
-to leverage operator fusion and tensor re-use in order to improve speed and reduce
-memory usage during the forward and backward passes of these calculations.
-
-We currently support several common model architectures, including (but not limited to):
-
-- `llama`
-- `mistral`
-- `qwen2`
-- `gemma`
-- `gemma2`
-
-<details>
-
-The set of models we support is currently limited by our attention patching strategy,
-which assumes (and replaces) specific code blocks for query / key / value and output
-projections:
-
-```python
-ORIGINAL_QKV_CODE = """
-    query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2)
-    key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2)
-    value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)
-""".lstrip(
-    "\n"
-)
-
-ORIGINAL_O_CODE = """
-    attn_output = self.o_proj(attn_output)
-""".lstrip(
-    "\n"
-)
-```
-
-Is replaced with:
-
-```python
-PATCHED_QKV_CODE = """
-    query_states, key_states, value_states = self.apply_qkv(hidden_states)
-    query_states = query_states.view(hidden_shape).transpose(1, 2)
-    key_states = key_states.view(hidden_shape).transpose(1, 2)
-    value_states = value_states.view(hidden_shape).transpose(1, 2)
-""".lstrip(
-    "\n"
-)
-
-PATCHED_O_CODE = """
-    attn_output = self.apply_o(attn_output)
-""".lstrip(
-    "\n"
-)
-```
-
-Where `apply_qkv` and `apply_o` are defined in the `axolotl.kernels.lora` module.
-
-We welcome testing of other model architectures and / or PRs to expand our patching
-logic to be compatible with more of them.
-
-</details>
-
-## Usage
-
-These optimizations can be enabled in your Axolotl config YAML file. The
-`lora_mlp_kernel` option enables the optimized MLP path, while `lora_qkv_kernel` and
-`lora_o_kernel` enable the fused query-key-value projection and optimized output
-projection, respectively.
-
-```yaml
-lora_mlp_kernel: true
-lora_qkv_kernel: true
-lora_o_kernel: true
-```
-
-## Requirements
-
-- One or more NVIDIA or AMD GPUs (in order to use the Triton kernels)
-    - Note: Set `TORCH_ROCM_AOTRITON_ENABLE_EXPERIMENTAL=1` to enable [memory-efficient attention on AMD GPUs](https://github.com/ROCm/aotriton/issues/16#issuecomment-2346675491)
-- Targeted LoRA adapters cannot use Dropout
-    - This may limit model expressivity / cause overfitting
-- Targeted LoRA adapters cannot have bias terms
-    - This may limit model expressivity
-
-Models with pre-existing LoRA adapters that use Dropout or have bias terms may need to
-be re-finetuned without these features in order to be useful.
-
-## Implementation details
-
-### Custom autograd functions
-
-The LoRA MLP autograd function optimizes the entire MLP computation path. It fuses the
-LoRA and base weight computations together and provides a single, efficient backward
-pass for the entire MLP block.
-
-For attention components, similar optimizations are provided through a function that
-handles the query, key, and value projections, and a function that handles the output
-projection. They are designed to work with the existing `transformers` attention
-implementation via some monkey-patching logic.
-
-### Triton kernels
-
-Two activation functions (SwiGLU and GeGLU) are implemented with Triton kernels for
-improved speed and memory performance. These kernels handle both the forward and
-backward passes.
-
-### Integration
-
-The custom autograd functions and Triton kernels are designed to work together. The
-autograd function manages the high-level computation flow and gradient tracking, while
-calling the Triton kernels for the activation function computation. During the backward
-pass, the kernel computes both the activation output and the required gradients, which
-the autograd function then uses to compute the final gradients for the entire
-computation path.
-
-## Future Work
-
-- Support for additional model architectures
-- Support for the FSDP setting
-- Support for dropout and bias
-- Additional operator fusions

docs/mac.qmd

@@ -19,5 +19,4 @@ Current support:
 - [ ] DeepSpeed
 
 Untested:
-
 - FSDP

docs/multi-gpu.qmd

@@ -1,5 +1,5 @@
 ---
-title: "Multi-GPU"
+title: "Multi-GPU Training Guide"
 format:
   html:
     toc: true
@@ -35,11 +35,7 @@ deepspeed: deepspeed_configs/zero1.json
 ### Usage {#sec-deepspeed-usage}
 
 ```{.bash}
-# Passing arg via config
-axolotl train config.yml
-
-# Passing arg via cli
-axolotl train config.yml --deepspeed deepspeed_configs/zero1.json
+accelerate launch -m axolotl.cli.train examples/llama-2/config.yml --deepspeed deepspeed_configs/zero1.json
 ```
 
 ### ZeRO Stages {#sec-zero-stages}
@@ -74,7 +70,25 @@ For combining FSDP with QLoRA, see our [dedicated guide](fsdp_qlora.qmd).
 
 ### Liger Kernel Integration {#sec-liger}
 
-Please see [docs](custom_integrations.qmd#liger) for more info.
+::: {.callout-note}
+Liger Kernel provides efficient Triton kernels for LLM training, offering:
+
+- 20% increase in multi-GPU training throughput
+- 60% reduction in memory usage
+- Compatibility with both FSDP and DeepSpeed
+:::
+
+Configuration:
+
+```{.yaml}
+plugins:
+  - axolotl.integrations.liger.LigerPlugin
+liger_rope: true
+liger_rms_norm: true
+liger_glu_activation: true
+liger_layer_norm: true
+liger_fused_linear_cross_entropy: true
+```
 
 ## Troubleshooting {#sec-troubleshooting}
 

docs/multi-node.qmd

@@ -3,18 +3,6 @@ title: Multi Node
 description: How to use Axolotl on multiple machines
 ---
 
-The below are three ways to train multi-node in Axolotl.
-
-::: {.callout-important}
-Each machine needs a copy of Axolotl, we suggest using the same commit to ensure compatibility.
-
-You will also need to have the same configuration file for your model on each machine.
-
-Make sure the main machine is reachable by other machines.
-:::
-
-## Accelerate
-
 You will need to create a configuration for accelerate, either by using `accelerate config` and follow the instructions or you can use one of the preset below:
 
 ~/.cache/huggingface/accelerate/default_config.yaml
@@ -38,7 +26,7 @@ tpu_use_sudo: false
 use_cpu: false
 ```
 
-Configure your model to use FSDP in the Axolotl yaml. For example:
+Configure your model to use FSDP with for example:
 ```yaml
 fsdp:
   - full_shard
@@ -49,40 +37,12 @@ fsdp_config:
   fsdp_transformer_layer_cls_to_wrap: LlamaDecoderLayer
 ```
 
+## Machine configuration
+
+On each machine you need a copy of Axolotl, we suggest using the same commit to ensure compatibility.
+
+You will also need to have the same configuration file for your model on each machine.
+
+On the main machine only, make sure the port you set as `main_process_port` is open in TCP and reachable by other machines.
+
 All you have to do now is launch using accelerate as you would usually do on each machine and voila, the processes will start once you have launched accelerate on every machine.
-
-## Raytrain
-
-Please see ray train doc [here](ray-integration.qmd).
-
-## Torchrun
-
-If you are using Infiniband, we recommend torchrun to utilize the full bandwidth.
-
-Set the following env (change buffersize/socketname depending on your system):
-
-```bash
-export NCCL_IB_DISABLE=0
-export NCCL_SOCKET_IFNAME="eth0,en,eth,em,bond"
-export NCCL_BUFFSIZE=2097152
-```
-
-Run the following on each node:
-
-```bash
-torchrun --nnodes $num_nodes --nproc_per_node $gpu_per_node --rdzv_id $rdzv_id --rdzv_backend c10d --rdzv_endpoint "$head_node_ip:$head_node_port" -m axolotl.cli.train config.yaml
-```
-
-Please make sure to substitute the placeholder variables.
-
-- `num_nodes`: Number of nodes (containing GPUs)
-- `gpu_per_node`: Number of gpus per node
-- `head_node_ip`: IP of the head node (make sure other machines can connect to this)
-- `head_node_port`: Port of the head node (make sure other machines can connect to this. Default 29400)
-- `rdzv_id`: A unique job ID that is used by the job across nodes.
-
-::: {.callout-note}
-You need to call `axolotl.cli.train` instead of `axolotl train` as the latter calls accelerate under the hood
-:::
-
-More info on the available configs can be found on the Pytorch docs [here](https://pytorch.org/docs/stable/elastic/run.html)

docs/nccl.qmd

@@ -13,13 +13,13 @@ Often, this timeout will happen after 30 minutes (the default setting) and is ac
 
 Forcing cross-GPU communication via [NVLink](https://en.wikipedia.org/wiki/NVLink) may help without increasing timeouts. To verify that your configuration is leveraging NVLink run the following command:
 
-```bash
+```shell
 nvidia-smi nvlink --status
 ```
 
 To force NCCL to use NVLink, simply set this in the environment:
 
-```bash
+```shell
 export NCCL_P2P_LEVEL=NVL
 ```
 
@@ -33,13 +33,13 @@ If NVLink is not available in your environment there are other options for ``NCC
 
 To validate that acceptable data transfer speeds exist for your training job, running [NCCL Tests](https://github.com/NVIDIA/nccl-tests/blob/master/README.md) can help pinpoint bottlenecks, for example:
 
-```bash
+```shell
 ./build/all_reduce_perf -b 8 -e 128M -f 2 -g 3
 ```
 
 It can be useful when debugging NCCL communication timeouts to activate additional logging in both PyTorch and NCCL:
 
-```bash
+```shell
 export NCCL_DEBUG=INFO
 export NCCL_DEBUG_SUBSYS=ALL
 export TORCH_DISTRIBUTED_DEBUG=INFO

docs/ray-integration.qmd

@@ -1,5 +1,5 @@
 ---
-title: Ray Train
+title: Ray Train integration
 description: How to use Axolotl with Ray Train
 ---
 
@@ -9,7 +9,7 @@ With the `--use-ray` CLI flag, Axolotl will use Ray Train's [`TorchTrainer`](htt
 
 ## Ray cluster setup
 
-A prerequisite using the Ray Train integration is to setup a Ray cluster on your desired node(s). For a detailed guide on how you can get started with ray clusters, check the official Ray docs [here](https://docs.ray.io/en/latest/cluster/getting-started.html).
+A prerequisite using the Ray Train integration is to setup a Ray cluster on your desired node(s). For a detailed guide on how you can get started with ray clusters, check the official Ray docs here: https://docs.ray.io/en/latest/cluster/getting-started.html
 
 Every Ray cluster has one _head_ node and a set of worker nodes. The head node is just like any other worker node, but it also runs certain special processes related to scheduling and orchestration. Ray-enabled scripts are run on the head node and depending on the resources (number of CPUs, GPUs, etc) they request, will be scheduled to run certain tasks on the worker nodes. For more on key concepts behind a Ray cluster, you can refer this [doc](https://docs.ray.io/en/latest/cluster/key-concepts.html#cluster-key-concepts).
 
@@ -58,11 +58,13 @@ You can find an example configuration at `configs/llama-3/lora-1b-ray.yaml`.
 The key parameters to note here are:
 
 ```yaml
+...
 use_ray: true
 ray_num_workers: 4
 # optional
 resources_per_worker:
     GPU: 1
+...
 ```
 
 - `use_ray`: This is the flag that enables the Ray Train integration. You can either use the corresponding `--use-ray` flag in the CLI or set `use_ray` in the config file.

docs/rlhf.qmd

@@ -1,39 +1,26 @@
 ---
 title: "RLHF (Beta)"
 description: "Reinforcement Learning from Human Feedback is a method whereby a language model is optimized from data using human feedback."
-back-to-top-navigation: true
-toc: true
-toc-depth: 4
 ---
 
-## Overview
+### Overview
 
 Reinforcement Learning from Human Feedback is a method whereby a language model is optimized from data using human
 feedback. Various methods include, but not limited to:
 
-- [Direct Preference Optimization (DPO)](#dpo)
-- [Identity Preference Optimization (IPO)](#ipo)
-- [Kahneman-Tversky Optimization (KTO)](#kto)
-- [Odds Ratio Preference Optimization (ORPO)](#orpo)
 - Proximal Policy Optimization (PPO) (not yet supported in axolotl)
+- Direct Preference Optimization (DPO)
+- Identity Preference Optimization (IPO)
 
 
-## RLHF using Axolotl
+### RLHF using Axolotl
 
-::: {.callout-important}
-This is a BETA feature and many features are not fully implemented. You are encouraged to open new PRs to improve the integration and functionality.
-:::
+>[!IMPORTANT]
+>This is a BETA feature and many features are not fully implemented. You are encouraged to open new PRs to improve the integration and functionality.
 
-We rely on the [TRL](https://github.com/huggingface/trl) library for implementations of various RL training methods, which we wrap around to expose in axolotl. Each method has their own supported ways of loading datasets and prompt formats.
-
-::: {.callout-tip}
-You can find what each method supports by going into `src/axolotl/prompt_strategies/{method}` where `{method}` is one of our supported methods. The `type: ` can be retrieved from `{method}.{function_name}`.
-:::
-
-### DPO
-
-Example config:
+The various RL training methods are implemented in trl and wrapped via axolotl. Below are various examples with how you can use various preference datasets to train models that use ChatML
 
+#### DPO
 ```yaml
 rl: dpo
 datasets:
@@ -42,268 +29,15 @@ datasets:
     type: chatml.intel
   - path: argilla/ultrafeedback-binarized-preferences
     split: train
-    type: chatml
+    type: chatml.argilla
 ```
 
-DPO supports the following types with the following dataset format:
-
-#### chatml.argilla
-
-```json
-{
-    "system": "...", // optional
-    "instruction": "...",
-    "chosen_response": "...",
-    "rejected_response": "..."
-}
-```
-
-#### chatml.argilla_chat
-
-```json
-{
-    "chosen": [
-        {"role": "user", "content": "..."},
-        {"role": "assistant", "content": "..."}
-    ],
-    "rejected": [
-        {"role": "user", "content": "..."},
-        {"role": "assistant", "content": "..."}
-    ]
-}
-```
-
-#### chatml.icr
-
-```json
-{
-    "system": "...", // optional
-    "input": "...",
-    "chosen": "...",
-    "rejected": "..."
-}
-```
-
-#### chatml.intel
-
-```json
-{
-    "system": "...", // optional
-    "question": "...",
-    "chosen": "...",
-    "rejected": "..."
-}
-```
-
-#### chatml.prompt_pairs
-
-```json
-{
-    "system": "...", // optional
-    "prompt": "...",
-    "chosen": "...",
-    "rejected": "..."
-}
-```
-
-#### chatml.ultra
-
-```json
-{
-    "system": "...", // optional
-    "prompt": "...",
-    "chosen": [
-        {"role": "user", "content": "..."},
-        {"role": "assistant", "content": "..."}
-    ],
-    "rejected": [
-        {"role": "user", "content": "..."},
-        {"role": "assistant", "content": "..."}
-    ]
-}
-```
-
-#### llama3.argilla
-
-```json
-{
-    "system": "...", // optional
-    "instruction": "...",
-    "chosen_response": "...",
-    "rejected_response": "..."
-}
-```
-
-#### llama3.argilla_chat
-
-```json
-{
-    "chosen": [
-        {"role": "user", "content": "..."},
-        {"role": "assistant", "content": "..."}
-    ],
-    "rejected": [
-        {"role": "user", "content": "..."},
-        {"role": "assistant", "content": "..."}
-    ]
-}
-```
-
-#### llama3.icr
-
-```json
-{
-    "system": "...", // optional
-    "input": "...",
-    "chosen": "...",
-    "rejected": "..."
-}
-```
-
-#### llama3.intel
-
-```json
-{
-    "system": "...", // optional
-    "question": "...",
-    "chosen": "...",
-    "rejected": "..."
-}
-```
-
-#### llama3.prompt_pairs
-
-```json
-{
-    "system": "...", // optional
-    "prompt": "...",
-    "chosen": "...",
-    "rejected": "..."
-}
-```
-
-#### llama3.ultra
-
-```json
-{
-    "system": "...", // optional
-    "prompt": "...",
-    "chosen": [
-        {"role": "user", "content": "..."},
-        {"role": "assistant", "content": "..."}
-    ],
-    "rejected": [
-        {"role": "user", "content": "..."},
-        {"role": "assistant", "content": "..."}
-    ]
-}
-```
-
-#### zephyr.nectar
-
-```json
-{
-    "prompt": "...",
-    "answers": [
-        {
-            "answer": "...",
-            "rank": 1
-        },
-        {
-            "answer": "...",
-            "rank": 2
-        }
-        // ... more answers with ranks
-    ]
-}
-```
-
-#### chat_template.default
-
-```yaml
-rl: dpo
-datasets:
-  - path: ...
-    split: train
-    type: chat_template.default
-    field_messages: "messages"
-    field_chosen: "chosen"
-    field_rejected: "rejected"
-    message_property_mappings:
-      role: role
-      content: content
-    roles:
-      user: ["user"]
-      assistant: ["assistant"]
-      system: ["system"]
-```
-
-Sample input format:
-
-```json
-{
-    "messages": [
-        {
-            "role": "system",
-            "content": "..."
-        },
-        {
-            "role": "user",
-            "content": "..."
-        },
-        // ... more messages
-    ],
-    "chosen": {
-        "role": "assistant",
-        "content": "..."
-    },
-    "rejected": {
-        "role": "assistant",
-        "content": "..."
-    }
-}
-```
-
-#### user_defined.default
-
-For custom behaviors,
-
-```yaml
-rl: dpo
-datasets:
-  - path: ...
-    split: train
-    type: user_defined.default
-
-    field_prompt: "prompt"
-    field_system: "system"
-    field_chosen: "chosen"
-    field_rejected: "rejected"
-    prompt_format: "{prompt}"
-    chosen_format: "{chosen}"
-    rejected_format: "{rejected}"
-```
-
-The input format is a simple JSON input with customizable fields based on the above config.
-
-```json
-{
-    "system": "...",  // optional
-    "prompt": "...",
-    "chosen": "...",
-    "rejected": "..."
-}
-```
-
-### IPO
-
-As IPO is just DPO with a different loss function, all supported options for DPO works here.
-
+#### IPO
 ```yaml
 rl: ipo
 ```
 
-### ORPO
+#### ORPO
 
 Paper: https://arxiv.org/abs/2403.07691
 
@@ -318,28 +52,8 @@ datasets:
     type: chat_template.argilla
 ```
 
-ORPO supports the following types with the following dataset format:
 
-#### chat_template.argilla
-
-```json
-{
-    "system": "...",  // optional
-    "prompt": "...",  // if available, will be taken as user message for single-turn instead of from list below
-
-    // chosen/rejected should be same till last content and only even-number of alternating user/assistant turns
-    "chosen": [
-        {"role": "user", "content": "..."},
-        {"role": "assistant", "content": "..."}
-    ],
-    "rejected": [
-        {"role": "user", "content": "..."},
-        {"role": "assistant", "content": "..."}
-    ]
-}
-```
-
-### KTO
+#### KTO
 
 ```yaml
 rl: kto
@@ -358,186 +72,7 @@ gradient_checkpointing_kwargs:
   use_reentrant: true
 ```
 
-KTO supports the following types with the following dataset format:
-
-#### chatml.argilla
-
-```json
-{
-    "system": "...", // optional
-    "instruction": "...",
-    "completion": "..."
-}
-```
-
-#### chatml.argilla_chat
-
-```json
-{
-    "chosen": [
-        {"role": "user", "content": "..."}
-    ],
-    "completion": [
-        {"role": "assistant", "content": "..."}
-    ]
-}
-```
-
-#### chatml.intel
-
-```json
-{
-    "system": "...", // optional
-    "question": "...",
-    "completion": "..."
-}
-```
-
-#### chatml.prompt_pairs
-
-```json
-{
-    "system": "...", // optional
-    "prompt": "...",
-    "completion": "..."
-}
-```
-
-#### chatml.ultra
-
-```json
-{
-    "system": "...", // optional
-    "prompt": "...",
-    "completion": "..."
-}
-```
-
-#### llama3.argilla
-
-```json
-{
-    "system": "...", // optional
-    "instruction": "...",
-    "completion": "..."
-}
-```
-
-#### llama3.argilla_chat
-
-```json
-{
-    "completion": [
-        {"role": "user", "content": "..."},
-        {"role": "assistant", "content": "..."}
-    ]
-}
-```
-
-#### llama3.intel
-
-```json
-{
-    "system": "...", // optional
-    "question": "...",
-    "completion": "..."
-}
-```
-
-#### llama3.prompt_pairs
-
-```json
-{
-    "system": "...", // optional
-    "prompt": "...",
-    "completion": "..."
-}
-```
-
-#### llama3.ultra
-
-```json
-{
-    "system": "...", // optional
-    "prompt": "...",
-    "completion": "..."
-}
-```
-
-#### user_defined.default
-
-For custom behaviors,
-
-```yaml
-rl: kto
-datasets:
-  - path: ...
-    split: train
-    type: user_defined.default
-
-    field_prompt: "prompt"
-    field_system: "system"
-    field_completion: "completion"
-    field_label: "label"
-    prompt_format: "{prompt}"
-    completion_format: "{completion}"
-```
-
-The input format is a simple JSON input with customizable fields based on the above config.
-
-```json
-{
-    "system": "...",  // optional
-    "prompt": "...",
-    "completion": "...",
-    "label": "..."
-}
-```
-
-### GRPO
-
-GRPO uses custom reward functions and transformations. Please have them ready locally.
-
-For ex, to load OpenAI's GSM8K and use a random reward for completions:
-
-```python
-# rewards.py
-import random
-
-def rand_reward_func(completions, **kwargs) -> list[float]:
-    return [random.uniform(0, 1) for _ in completions]
-
-def oai_gsm8k_transform(cfg, *args, **kwargs):
-    def transform_fn(example, tokenizer=None):
-        label = example["answer"].split("####")[-1].strip().replace(",", "")
-        return {
-            "prompt": [{"role": "user", "content": example["question"]},],
-            "answer": label,
-        }
-    return transform_fn, {"remove_columns": ["question"]}
-```
-
-```yaml
-rl: grpo
-
-trl:
-    beta: 0.001
-    max_completion_length: 256
-    use_vllm: True
-    vllm_device: auto
-    vllm_gpu_memory_utilization: 0.15
-    num_generations: 4
-    reward_funcs: ["rewards.rand_reward_func"]    # format: '{file_name}.{fn_name}'
-datasets:
-  - path: openai/gsm8k
-    name: main
-    type: rewards.oai_gsm8k_transform  # format: '{file_name}.{fn_name}'
-```
-
-To see other examples of custom reward functions, please see [TRL GRPO Docs](https://github.com/huggingface/trl/blob/main/docs/source/grpo_trainer.md#using-a-custom-reward-function).
-
-### Using local dataset files
-
+#### Using local dataset files
 ```yaml
 datasets:
   - ds_type: json
@@ -547,9 +82,9 @@ datasets:
     type: chatml.intel
 ```
 
-### TRL auto-unwrapping for PEFT
+#### Trl autounwrap for peft
 
-TRL supports auto-unwrapping PEFT models for RL training paradigms which rely on a reference model. This significantly reduces memory pressure as an additional refreference model does not need to be loaded, and reference model log-probabilities can be obtained by disabling PEFT adapters. This is enabled by default. To turn it off, pass the following config:
+Trl supports autounwrapping peft models, so that a ref model does not need to be additionally loaded, leading to less VRAM needed. This is on by default. To turn it off, pass the following config.
 
 ```yaml
 # load ref model when adapter training.

docs/torchao.qmd

@@ -3,12 +3,6 @@ title: "PyTorch ao"
 description: "Custom data types and layouts for training and inference"
 ---
 
-To use experimental optimizers (`AdamWFp8`, `AdamW4bit`, `AdamW8bit`) from Pytorch Ao, please install the package as shown below.
-
-::: {.callout-tip}
-Some experimental optimizers are already present in regular Pytorch, so please re-check if you actually need this package!
-:::
-
 ### Installation
 
 Stable Release from the PyTorch index

docs/unsloth.qmd

@@ -8,12 +8,6 @@ description: "Hyper-optimized QLoRA finetuning for single GPUs"
 Unsloth provides hand-written optimized kernels for LLM finetuning that slightly improve speed and VRAM over
 standard industry baselines.
 
-::: {.callout-important}
-Due to breaking changes in transformers `v4.48.0`, users will need to downgrade to `<=v4.47.1` to use this patch.
-
-This will later be deprecated in favor of [LoRA Optimizations](lora_optims.qmd).
-:::
-
 
 ### Installation
 
@@ -23,7 +17,7 @@ The following will install the correct unsloth and extras from source.
 python scripts/unsloth_install.py | sh
 ```
 
-### Usage
+### Using unsloth w Axolotl
 
 Axolotl exposes a few configuration options to try out unsloth and get most of the performance gains.
 

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

@@ -21,9 +21,8 @@ datasets:
     type: chat_template
     split: train[:20%]
     field_messages: conversations
-    message_property_mappings:
-      role: from
-      content: value
+    message_field_role: from
+    message_field_content: value
 
 dataset_prepared_path: last_run_prepared
 val_set_size: 0.0

examples/gemma2/qlora.yml

@@ -16,9 +16,8 @@ datasets:
     type: chat_template
     drop_system_message: true
     field_messages: conversations
-    message_property_mappings:
-      role: from
-      content: value
+    message_field_role: from
+    message_field_content: value
 
 val_set_size: 0.0
 output_dir: ./outputs/out

examples/jamba/qlora_fsdp_large.yaml

@@ -13,9 +13,8 @@ datasets:
     type: chat_template
     drop_system_message: true
     field_messages: conversations
-    message_property_mappings:
-      role: from
-      content: value
+    message_field_role: from
+    message_field_content: value
 
 dataset_prepared_path: last_run_prepared
 val_set_size: 0.0

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

@@ -17,9 +17,8 @@ datasets:
     type: chat_template
     split: train[:20%]
     field_messages: conversations
-    message_property_mappings:
-      role: from
-      content: value
+    message_field_role: from
+    message_field_content: value
 
 dataset_prepared_path: last_run_prepared
 val_set_size: 0.02

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

@@ -17,9 +17,8 @@ datasets:
     field_messages: conversation
     field_chosen: chosen
     field_rejected: rejected
-    message_property_mappings:
-      role: role
-      content: content
+    message_field_role: role
+    message_field_content: content
     roles:
       system:
         - system

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

@@ -14,9 +14,8 @@ datasets:
   - path: fozziethebeat/alpaca_messages_2k_test
     type: chat_template
     field_messages: messages
-    message_property_mappings:
-      role: role
-      content: content
+    message_field_role: role
+    message_field_content: content
     roles:
       user:
         - user

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

@@ -17,9 +17,8 @@ datasets:
     field_messages: conversation
     field_chosen: chosen
     field_rejected: rejected
-    message_property_mappings:
-      role: role
-      content: content
+    message_field_role: role
+    message_field_content: content
     roles:
       system:
         - system
@@ -32,9 +31,8 @@ datasets:
     field_messages: conversation
     field_chosen: chosen
     field_rejected: rejected
-    message_property_mappings:
-      role: role
-      content: content
+    message_field_role: role
+    message_field_content: content
     roles:
       system:
         - system

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

@@ -1,82 +0,0 @@
-base_model: NousResearch/Llama-3.2-1B
-# Automatically upload checkpoint and final model to HF
-# hub_model_id: username/custom_model_name
-
-load_in_8bit: false
-load_in_4bit: false
-strict: false
-
-datasets:
-  - path: teknium/GPT4-LLM-Cleaned
-    type: alpaca
-dataset_prepared_path: last_run_prepared
-val_set_size: 0.1
-output_dir: ./outputs/lora-out
-
-adapter: lora
-lora_model_dir:
-
-sequence_len: 2048
-sample_packing: true
-pad_to_sequence_len: true
-
-lora_r: 16
-lora_alpha: 32
-# Currently, we don't support dropout with our custom Triton kernels
-# lora_dropout: 0.05
-lora_fan_in_fan_out:
-lora_target_modules:
-  - gate_proj
-  - down_proj
-  - up_proj
-  - q_proj
-  - v_proj
-  - k_proj
-  - o_proj
-
-# These options enable our custom Triton kernels / autograd
-# functions for MLP and attention calculations
-lora_mlp_kernel: true
-lora_qkv_kernel: true
-lora_o_kernel: true
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 2
-micro_batch_size: 2
-num_epochs: 1
-optimizer: adamw_8bit
-lr_scheduler: cosine
-learning_rate: 0.0002
-
-train_on_inputs: false
-group_by_length: false
-bf16: auto
-fp16:
-tf32: false
-
-gradient_checkpointing: true
-early_stopping_patience:
-resume_from_checkpoint:
-local_rank:
-logging_steps: 1
-xformers_attention:
-flash_attention: true
-
-loss_watchdog_threshold: 5.0
-loss_watchdog_patience: 3
-
-warmup_steps: 10
-evals_per_epoch: 4
-saves_per_epoch: 1
-debug:
-deepspeed:
-weight_decay: 0.0
-fsdp:
-fsdp_config:
-special_tokens:
-  pad_token: "<|end_of_text|>"

examples/mistral/mistral-dpo-qlora.yml

@@ -22,9 +22,8 @@ datasets:
     field_messages: conversation
     field_chosen: chosen
     field_rejected: rejected
-    message_property_mappings:
-      role: role
-      content: content
+    message_field_role: role
+    message_field_content: content
 
 dataset_prepared_path:
 val_set_size: 0.05

examples/phi/lora-3.5.yaml

@@ -14,9 +14,8 @@ datasets:
   - path: fozziethebeat/alpaca_messages_2k_test
     type: chat_template
     field_messages: messages
-    message_property_mappings:
-      role: role
-      content: content
+    message_field_role: role
+    message_field_content: content
     roles:
       user:
         - user

examples/qwen2/dpo.yaml

@@ -12,9 +12,8 @@ datasets:
     field_messages: conversation
     field_chosen: chosen
     field_rejected: rejected
-    message_property_mappings:
-      role: role
-      content: content
+    message_field_role: role
+    message_field_content: content
     roles:
       system:
         - system

index.qmd

@@ -1,7 +1,7 @@
 ---
-# toc-location: right-body
-# toc-title: Table Of Contents
-# toc-expand: 2
+toc-location: right-body
+toc-title: Table Of Contents
+toc-expand: 2
 ---
 
 ```{python}

requirements.txt

@@ -1,24 +1,24 @@
 --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/
 
 # START section of dependencies that don't install on Darwin/MacOS
-bitsandbytes==0.45.2
+bitsandbytes==0.45.1
 triton>=3.0.0
 mamba-ssm==1.2.0.post1
-flash-attn==2.7.4.post1
+flash-attn==2.7.0.post2
 xformers>=0.0.23.post1
 autoawq==0.2.7.post3
-liger-kernel==0.5.3
+liger-kernel==0.5.2
 # END section
 
 packaging==23.2
 
 peft==0.14.0
-transformers==4.49.0
+transformers==4.48.1
 tokenizers>=0.21.0
 accelerate==1.3.0
 datasets==3.2.0
 deepspeed==0.16.1
-trl==0.15.1
+trl==0.13.0
 
 optimum==1.16.2
 hf_transfer
@@ -26,7 +26,7 @@ sentencepiece
 gradio==3.50.2
 
 modal==0.70.5
-pydantic==2.10.6
+pydantic==2.6.3
 addict
 fire
 PyYAML>=6.0

scripts/chat_datasets.py

@@ -31,26 +31,27 @@ def parse_dataset(dataset=None, split="train"):
     ds_cfg["field_messages"] = field_messages
 
     message_fields = features[field_messages][0].keys()
-
-    message_property_mappings = {"role": None, "content": None}
+    message_field_role = None
     for key in ["from", "role"]:
         if key in message_fields:
-            message_property_mappings["role"] = key
+            message_field_role = key
             break
-    if not message_property_mappings["role"]:
+    if not message_field_role:
         raise ValueError(
             f'No role field found in messages: {", ".join(message_fields)}'
         )
+    ds_cfg["message_field_role"] = message_field_role
 
+    message_field_content = None
     for key in ["content", "text", "value"]:
         if key in message_fields:
-            message_property_mappings["content"] = key
+            message_field_content = key
             break
-    if not message_property_mappings["content"]:
+    if not message_field_content:
         raise ValueError(
             f'No content field found in messages: {", ".join(message_fields)}'
         )
-    ds_cfg["message_property_mappings"] = message_property_mappings
+    ds_cfg["message_field_content"] = message_field_content
 
     print(yaml.dump({"datasets": [ds_cfg]}))
 

setup.py

@@ -71,15 +71,12 @@ def parse_requirements():
             else:
                 raise ValueError("Invalid version format")
 
-            if (major, minor) >= (2, 6):
-                _install_requires.pop(_install_requires.index(xformers_version))
-                _install_requires.append("xformers==0.0.29.post2")
-            elif (major, minor) >= (2, 5):
+            if (major, minor) >= (2, 5):
                 _install_requires.pop(_install_requires.index(xformers_version))
                 if patch == 0:
                     _install_requires.append("xformers==0.0.28.post2")
                 else:
-                    _install_requires.append("xformers>=0.0.28.post3")
+                    _install_requires.append("xformers==0.0.28.post3")
                 _install_requires.pop(_install_requires.index(autoawq_version))
             elif (major, minor) >= (2, 4):
                 if patch == 0:
@@ -125,7 +122,7 @@ setup(
     },
     extras_require={
         "flash-attn": [
-            "flash-attn==2.7.4.post1",
+            "flash-attn==2.7.0.post2",
         ],
         "deepspeed": [
             "deepspeed==0.16.1",
@@ -156,8 +153,5 @@ setup(
         "ray": [
             "ray[train]",
         ],
-        "vllm": [
-            "vllm==0.7.2",
-        ],
     },
 )

src/axolotl/__init__.py

@@ -4,4 +4,4 @@ import pkgutil
 
 __path__ = pkgutil.extend_path(__path__, __name__)  # Make this a namespace package
 
-__version__ = "0.8.0.dev0"
+__version__ = "0.6.0"

src/axolotl/cli/args.py

@@ -13,12 +13,6 @@ class PreprocessCliArgs:
     debug_num_examples: int = field(default=1)
     prompter: Optional[str] = field(default=None)
     download: Optional[bool] = field(default=True)
-    iterable: Optional[bool] = field(
-        default=None,
-        metadata={
-            "help": "Use IterableDataset for streaming processing of large datasets"
-        },
-    )
 
 
 @dataclass

src/axolotl/cli/cloud/__init__.py

@@ -35,18 +35,13 @@ def do_cli_train(
     cloud_config: Union[Path, str],
     config: Union[Path, str],
     accelerate: bool = True,
-    cwd=None,
-    **kwargs,
 ) -> None:
     print_axolotl_text_art()
     cloud_cfg = load_cloud_cfg(cloud_config)
     cloud = ModalCloud(cloud_cfg)
     with open(config, "r", encoding="utf-8") as file:
         config_yaml = file.read()
-    local_dirs = {}
-    if cwd and not Path(cwd).joinpath("src", "axolotl").exists():
-        local_dirs = {"/workspace/mounts": cwd}
-    cloud.train(config_yaml, accelerate=accelerate, local_dirs=local_dirs, **kwargs)
+    cloud.train(config_yaml, accelerate=accelerate)
 
 
 def do_cli_lm_eval(

src/axolotl/cli/cloud/modal_.py

@@ -7,7 +7,6 @@ import os
 import subprocess  # nosec B404
 from pathlib import Path
 from random import randint
-from typing import Optional
 
 import modal
 
@@ -23,18 +22,8 @@ def run_cmd(cmd: str, run_folder: str, volumes=None):
 
     # modal workaround so it doesn't use the automounted axolotl
     new_env = copy.deepcopy(os.environ)
-
     if "PYTHONPATH" in new_env:
-        paths = ["/workspace/mounts"]
-        for sub_python_path_str in new_env["PYTHONPATH"].split(":"):
-            sub_python_path = Path(sub_python_path_str)
-            if not sub_python_path.joinpath("src", "axolotl").exists():
-                # we don't want to use the automounted axolotl or unexpected behavior happens
-                paths.append(str(sub_python_path))
-        if paths:
-            new_env["PYTHONPATH"] = ":".join(paths)
-        else:
-            del new_env["PYTHONPATH"]
+        del new_env["PYTHONPATH"]
 
     # Propagate errors from subprocess.
     if exit_code := subprocess.call(  # nosec B603
@@ -123,6 +112,8 @@ class ModalCloud(Cloud):
         if env := self.get_env():
             image = image.env(env)
 
+        image = image.pip_install("fastapi==0.110.0", "pydantic==2.6.3")
+
         return image
 
     def get_secrets(self):
@@ -212,12 +203,9 @@ class ModalCloud(Cloud):
             memory = int(self.config.memory)
         return 1024 * memory
 
-    def get_train_env(self, local_dirs=None):
-        image = self.get_image()
-        for mount, local_dir in (local_dirs or {}).items():
-            image = image.add_local_dir(local_dir, mount)
+    def get_train_env(self):
         return self.app.function(
-            image=image,
+            image=self.get_image(),
             volumes={k: v[0] for k, v in self.volumes.items()},
             cpu=16.0,
             gpu=self.get_train_gpu(),
@@ -226,21 +214,14 @@ class ModalCloud(Cloud):
             secrets=self.get_secrets(),
         )
 
-    def train(
-        self,
-        config_yaml: str,
-        accelerate: bool = True,
-        local_dirs: Optional[dict[str, str]] = None,
-        **kwargs,
-    ):
-        modal_fn = self.get_train_env(local_dirs)(_train)
+    def train(self, config_yaml: str, accelerate: bool = True):
+        modal_fn = self.get_train_env()(_train)
         with modal.enable_output():
             with self.app.run(detach=True):
                 modal_fn.remote(
                     config_yaml,
                     accelerate=accelerate,
                     volumes={k: v[0] for k, v in self.volumes.items()},
-                    **kwargs,
                 )
 
     def lm_eval(self, config_yaml: str):
@@ -258,41 +239,44 @@ class ModalCloud(Cloud):
 
 
 def _preprocess(config_yaml: str, volumes=None):
-    Path("/workspace/mounts").mkdir(parents=True, exist_ok=True)
-    with open("/workspace/mounts/config.yaml", "w", encoding="utf-8") as f_out:
+    Path("/workspace/artifacts/axolotl").mkdir(parents=True, exist_ok=True)
+    with open(
+        "/workspace/artifacts/axolotl/config.yaml", "w", encoding="utf-8"
+    ) as f_out:
         f_out.write(config_yaml)
-    run_folder = "/workspace/mounts"
+    run_folder = "/workspace/artifacts/axolotl"
     run_cmd(
-        "axolotl preprocess /workspace/mounts/config.yaml --dataset-processes=8",
+        "axolotl preprocess /workspace/artifacts/axolotl/config.yaml --dataset-processes=8",
         run_folder,
         volumes,
     )
 
 
-def _train(config_yaml: str, accelerate: bool = True, volumes=None, **kwargs):
-    with open("/workspace/mounts/config.yaml", "w", encoding="utf-8") as f_out:
+def _train(config_yaml: str, accelerate: bool = True, volumes=None):
+    with open(
+        "/workspace/artifacts/axolotl/config.yaml", "w", encoding="utf-8"
+    ) as f_out:
         f_out.write(config_yaml)
-    run_folder = "/workspace/mounts"
+    run_folder = "/workspace/artifacts/axolotl"
     if accelerate:
         accelerate_args = "--accelerate"
     else:
         accelerate_args = "--no-accelerate"
-    num_processes_args = ""
-    if num_processes := kwargs.pop("num_processes", None):
-        num_processes_args = f"--num-processes {num_processes}"
     run_cmd(
-        f"axolotl train {accelerate_args} {num_processes_args} /workspace/mounts/config.yaml",
+        f"axolotl train {accelerate_args} /workspace/artifacts/axolotl/config.yaml",
         run_folder,
         volumes,
     )
 
 
 def _lm_eval(config_yaml: str, volumes=None):
-    with open("/workspace/mounts/config.yaml", "w", encoding="utf-8") as f_out:
+    with open(
+        "/workspace/artifacts/axolotl/config.yaml", "w", encoding="utf-8"
+    ) as f_out:
         f_out.write(config_yaml)
-    run_folder = "/workspace/mounts"
+    run_folder = "/workspace/artifacts/axolotl"
     run_cmd(
-        "axolotl lm-eval /workspace/mounts/config.yaml",
+        "axolotl lm-eval /workspace/artifacts/axolotl/config.yaml",
         run_folder,
         volumes,
     )

src/axolotl/cli/main.py

@@ -1,20 +1,13 @@
 """Click CLI definitions for various axolotl commands."""
 # pylint: disable=redefined-outer-name
 
-import logging
-import os
 import subprocess  # nosec B404
-import tempfile
-from pathlib import Path
 from typing import Optional
 
 import click
-import yaml
-from dotenv import load_dotenv
 
 import axolotl
 from axolotl.cli.args import EvaluateCliArgs, PreprocessCliArgs, TrainerCliArgs
-from axolotl.cli.sweeps import generate_sweep_configs
 from axolotl.cli.utils import (
     add_options_from_config,
     add_options_from_dataclass,
@@ -67,21 +60,10 @@ def preprocess(config: str, cloud: Optional[str] = None, **kwargs) -> None:
     help="Use accelerate launch for multi-GPU training",
 )
 @click.option("--cloud", default=None, type=click.Path(exists=True, path_type=str))
-@click.option(
-    "--sweep",
-    type=click.Path(exists=True, path_type=str),
-    help="YAML config for sweeping hyperparameters",
-)
 @add_options_from_dataclass(TrainerCliArgs)
 @add_options_from_config(AxolotlInputConfig)
 @filter_none_kwargs
-def train(
-    config: str,
-    accelerate: bool,
-    cloud: Optional[str] = None,
-    sweep: Optional[str] = None,
-    **kwargs,
-) -> None:
+def train(config: str, accelerate: bool, cloud: Optional[str] = None, **kwargs) -> None:
     """
     Train or fine-tune a model.
 
@@ -89,88 +71,44 @@ def train(
         config: Path to `axolotl` config YAML file.
         accelerate: Whether to use `accelerate` launcher.
         cloud: Path to a cloud accelerator configuration file
-        sweep: Path to YAML config for sweeping hyperparameters.
         kwargs: Additional keyword arguments which correspond to CLI args or `axolotl`
             config options.
     """
     # Enable expandable segments for cuda allocation to improve VRAM usage
     set_pytorch_cuda_alloc_conf()
+    from axolotl.cli.cloud import do_cli_train
 
     if "use_ray" in kwargs and kwargs["use_ray"]:
         accelerate = False
-    if sweep:
-        # load the sweep configuration yaml file
-        with open(sweep, "r", encoding="utf-8") as fin:
-            sweep_config: dict[str, list] = yaml.safe_load(fin)
-        with open(config, "r", encoding="utf-8") as fin:
-            base_config: dict[str, list] = yaml.safe_load(fin)
 
-        # generate all possible configurations
-        permutations = generate_sweep_configs(base_config, sweep_config)
-
-        def iter_configs():
-            for perm in permutations:
-                # open temp directory for temporary configurations
-                with tempfile.TemporaryDirectory() as temp_dir:
-                    with open(
-                        Path(temp_dir) / "config.yaml", "w", encoding="utf-8"
-                    ) as fout:
-                        yaml.dump(perm, fout)
-                    yield str(Path(temp_dir) / "config.yaml")
+    if accelerate:
+        if cloud:
+            do_cli_train(cloud_config=cloud, config=config, accelerate=True)
+        else:
+            accelerate_args = []
+            if "main_process_port" in kwargs:
+                main_process_port = kwargs.pop("main_process_port", None)
+                accelerate_args.append("--main_process_port")
+                accelerate_args.append(str(main_process_port))
+            if "num_processes" in kwargs:
+                num_processes = kwargs.pop("num_processes", None)
+                accelerate_args.append("--num-processes")
+                accelerate_args.append(str(num_processes))
 
+            base_cmd = ["accelerate", "launch"]
+            base_cmd.extend(accelerate_args)
+            base_cmd.extend(["-m", "axolotl.cli.train"])
+            if config:
+                base_cmd.append(config)
+            cmd = build_command(base_cmd, kwargs)
+            subprocess.run(cmd, check=True)  # nosec B603
     else:
+        if cloud:
+            do_cli_train(cloud_config=cloud, config=config, accelerate=False)
+        else:
+            from axolotl.cli.train import do_cli
 
-        def iter_configs():
-            yield config
-
-    for cfg_file in iter_configs():
-        # handle errors from subprocess so we can continue rest of sweeps
-        try:
-            if accelerate:
-                if cloud:
-                    from axolotl.cli.cloud import do_cli_train
-
-                    cwd = os.getcwd()
-                    do_cli_train(
-                        cloud_config=cloud,
-                        config=config,
-                        accelerate=True,
-                        cwd=cwd,
-                        **kwargs,
-                    )
-                else:
-                    accelerate_args = []
-                    if "main_process_port" in kwargs:
-                        main_process_port = kwargs.pop("main_process_port", None)
-                        accelerate_args.append("--main_process_port")
-                        accelerate_args.append(str(main_process_port))
-                    if "num_processes" in kwargs:
-                        num_processes = kwargs.pop("num_processes", None)
-                        accelerate_args.append("--num_processes")
-                        accelerate_args.append(str(num_processes))
-
-                    base_cmd = ["accelerate", "launch"]
-                    base_cmd.extend(accelerate_args)
-                    base_cmd.extend(["-m", "axolotl.cli.train"])
-                    if cfg_file:
-                        base_cmd.append(cfg_file)
-                    cmd = build_command(base_cmd, kwargs)
-                    subprocess.run(cmd, check=True)  # nosec B603
-            else:
-                if cloud:
-                    from axolotl.cli.cloud import do_cli_train
-
-                    do_cli_train(
-                        cloud_config=cloud, config=config, accelerate=False, **kwargs
-                    )
-                else:
-                    from axolotl.cli.train import do_cli
-
-                    do_cli(config=cfg_file, **kwargs)
-        except subprocess.CalledProcessError as exc:
-            logging.error(f"Failed to train/fine-tune config '{cfg_file}': {exc}")
-            if not sweep:
-                raise exc
+            do_cli(config=config, **kwargs)
 
 
 @cli.command()
@@ -323,5 +261,4 @@ def main():
 
 
 if __name__ == "__main__":
-    load_dotenv()
     main()

src/axolotl/cli/preprocess.py

@@ -75,10 +75,7 @@ def do_preprocess(cfg: DictDefault, cli_args: PreprocessCliArgs) -> None:
     )
 
 
-def do_cli(
-    config: Union[Path, str] = Path("examples/"),
-    **kwargs,
-) -> None:
+def do_cli(config: Union[Path, str] = Path("examples/"), **kwargs) -> None:
     """
     Parses `axolotl` config, CLI args, and calls `do_preprocess`.
 

src/axolotl/cli/sweeps.py

@@ -1,77 +0,0 @@
-"""Utilities for handling sweeps over configs for axolotl train CLI command"""
-
-import random
-from copy import deepcopy
-from itertools import product
-
-
-def generate_sweep_configs(
-    base_config: dict[str, list], sweeps_config: dict[str, list]
-) -> list[dict[str, list]]:
-    """
-    Recursively generates all possible configurations by applying sweeps to the base config.
-
-    Args:
-        base_config (dict): The original configuration dictionary
-        sweeps_config (dict): Dictionary where keys are parameters and values are either:
-            - lists of values to sweep independently
-            - or for paired values, a list of dicts under the '_' key
-
-    Returns:
-        list: List of all possible configuration dictionaries
-
-    Example:
-        sweeps_config = {
-            'learning_rate': [0.1, 0.01],
-            '_': [
-                {'load_in_8bit': True, 'adapter': 'lora'},
-                {'load_in_4bit': True, 'adapter': 'qlora'}
-            ]
-        }
-    """
-    # Separate paired values from regular sweeps
-    paired_values = sweeps_config.get("_", [])
-    regular_sweeps = {k: v for k, v in sweeps_config.items() if k != "_"}
-
-    # Process regular sweeps
-    param_names = list(regular_sweeps.keys())
-    param_values = list(regular_sweeps.values())
-
-    # Generate combinations for regular sweeps
-    regular_combinations = list(product(*param_values)) if param_values else [()]
-
-    # Combine regular sweeps with paired values
-    all_combinations = []
-    for reg_combo in regular_combinations:
-        if paired_values:
-            for paired_set in paired_values:
-                new_config = {}
-                # new_config = deepcopy(base_config)
-                # Combine regular parameters with paired parameters
-                full_combo = {**dict(zip(param_names, reg_combo)), **paired_set}
-                for param_name, param_value in full_combo.items():
-                    new_config[param_name] = param_value
-                print(new_config)
-                all_combinations.append(new_config)
-        else:
-            # If no paired values, just use regular combinations
-            # new_config = deepcopy(base_config)
-            new_config = {}
-            for param_name, param_value in zip(param_names, reg_combo):
-                new_config[param_name] = param_value
-            print(new_config)
-            all_combinations.append(new_config)
-
-    # randomize the order of trials
-    random.seed(42)
-    random.shuffle(all_combinations)
-
-    # Generate a new config for each combination
-    result_configs = []
-    for combination in all_combinations:
-        new_config = deepcopy(base_config)
-        for param_name, param_value in combination.items():
-            new_config[param_name] = param_value
-        result_configs.append(new_config)
-
-    return result_configs

src/axolotl/common/datasets.py

@@ -24,8 +24,8 @@ class TrainDatasetMeta:
     """Dataclass with fields for training and validation datasets and metadata."""
 
     train_dataset: Dataset
-    eval_dataset: Dataset | None = None
-    total_num_steps: int | None = None
+    eval_dataset: Optional[Dataset] = None
+    total_num_steps: Optional[int] = None
 
 
 def sample_dataset(dataset: Dataset, num_samples: int) -> Dataset:
@@ -63,17 +63,11 @@ def load_datasets(
     """
     tokenizer = load_tokenizer(cfg)
     processor = load_processor(cfg, tokenizer=tokenizer) if cfg.processor_type else None
-    preprocess_iterable = (
-        hasattr(cli_args, "iterable")
-        and cli_args.iterable is not None
-        and cli_args.iterable
-    )
 
     train_dataset, eval_dataset, total_num_steps, prompters = prepare_dataset(
         cfg,
         tokenizer,
         processor=processor,
-        preprocess_iterable=preprocess_iterable,
     )
 
     if (
@@ -122,11 +116,9 @@ def load_preference_datasets(
         `total_num_steps`.
     """
     train_dataset, eval_dataset = load_prepare_preference_datasets(cfg)
-    total_num_steps: Optional[int] = int(
+    total_num_steps = int(
         math.ceil(len(train_dataset) * cfg.num_epochs / cfg.batch_size)
     )
-    if cfg.rl == "grpo":
-        total_num_steps = None
 
     if cli_args.debug or cfg.debug:
         LOG.info("check_dataset_labels...")

src/axolotl/core/trainers/base.py

@@ -1,878 +0,0 @@
-"""
-module for customized trainers
-"""
-
-from __future__ import annotations
-
-# pylint: disable=too-many-lines
-import logging
-import os
-from collections import defaultdict
-from functools import wraps
-from typing import Dict, Literal, Optional
-
-import torch
-from datasets import Dataset
-from peft.optimizers import create_loraplus_optimizer
-from torch.optim.lr_scheduler import OneCycleLR
-from torch.utils.data import BatchSampler, DataLoader, RandomSampler, SequentialSampler
-from transformers import Trainer
-from transformers.trainer_utils import PREFIX_CHECKPOINT_DIR, seed_worker
-from transformers.utils import is_sagemaker_mp_enabled
-from trl import CPOTrainer, KTOTrainer, ORPOTrainer, PRMTrainer, RewardTrainer
-from trl.trainer.utils import pad_to_length
-
-from axolotl.monkeypatch.relora import ReLoRAScheduler
-from axolotl.utils.samplers import MultipackBatchSampler, get_dataset_lengths
-from axolotl.utils.schedulers import (
-    get_cosine_schedule_with_min_lr,
-    get_cosine_schedule_with_quadratic_warmup,
-    get_cosine_schedule_with_warmup_decay_constant,
-)
-
-if is_sagemaker_mp_enabled():
-    import smdistributed.modelparallel.torch as smp
-
-LOG = logging.getLogger("axolotl.core.trainer_builder")
-
-
-def _sanitize_kwargs_for_tagging(tag_names, kwargs=None):
-    if isinstance(tag_names, str):
-        tag_names = [tag_names]
-
-    if kwargs is not None:
-        if "tags" not in kwargs:
-            kwargs["tags"] = tag_names
-        elif "tags" in kwargs and isinstance(kwargs["tags"], list):
-            kwargs["tags"].extend(tag_names)
-        elif "tags" in kwargs and isinstance(kwargs["tags"], str):
-            tag_names.append(kwargs["tags"])
-            kwargs["tags"] = tag_names
-
-    return kwargs
-
-
-def _sanitize_kwargs_for_ds_tagging(dataset_tags, kwargs=None):
-    if isinstance(dataset_tags, str):
-        dataset_tags = [dataset_tags]
-
-    if (dataset_tags is not None) and (kwargs is not None):
-        if "dataset_tags" not in kwargs:
-            kwargs["dataset_tags"] = dataset_tags
-        elif "dataset_tags" in kwargs and isinstance(kwargs["dataset_tags"], list):
-            kwargs["dataset_tags"].extend(dataset_tags)
-        elif "dataset_tags" in kwargs and isinstance(kwargs["dataset_tags"], str):
-            dataset_tags.append(kwargs["dataset_tags"])
-            kwargs["dataset_tags"] = dataset_tags
-
-    return kwargs
-
-
-class SchedulerMixin(Trainer):
-    """
-    Mixin class for scheduler setup in CausalTrainer.
-    """
-
-    args = None  # type: "AxolotlTrainingArguments"  # type: ignore[name-defined]
-
-    def create_scheduler(
-        self, num_training_steps: int, optimizer: torch.optim.Optimizer = None
-    ):
-        """
-        Setup the scheduler. The optimizer of the trainer must have been set up either before this method is called or
-        passed as an argument.
-
-        Args:
-            num_training_steps (int): The number of training steps to do.
-            optimizer (torch.optim.Optimizer): The training optimizer
-        """
-        use_cosine_quadratic = (
-            self.args.lr_scheduler_type == "cosine"
-            and self.args.lr_quadratic_warmup is True
-        )
-
-        use_cosine_min_lr = (
-            self.args.lr_scheduler_type == "cosine"
-            and self.args.cosine_min_lr_ratio is not None
-        )
-
-        # fmt: off
-        if self.lr_scheduler is None:  # type: ignore  # pylint: disable=access-member-before-definition
-            # fmt: on
-            if self.args.alternate_lr_scheduler_type == "one_cycle":
-                num_warmup_steps = self.args.get_warmup_steps(num_training_steps)
-                pct_start = num_warmup_steps / num_training_steps
-                extra_lr_kwargs = {}
-                if "pct_start" not in self.args.lr_scheduler_kwargs:
-                    extra_lr_kwargs["pct_start"] = pct_start
-                if "anneal_strategy" not in self.args.lr_scheduler_kwargs:
-                    extra_lr_kwargs["anneal_strategy"] = "cos"
-
-                self.lr_scheduler = OneCycleLR(
-                    optimizer,
-                    max_lr=self.args.learning_rate,
-                    total_steps=num_training_steps,
-                    **extra_lr_kwargs,
-                    **self.args.lr_scheduler_kwargs,
-                )
-            elif use_cosine_quadratic:
-                if use_cosine_min_lr:
-                    LOG.warning("Both cosine quadratic warmup and min lr detected. Using quadratic warmup.")
-
-                self.lr_scheduler = get_cosine_schedule_with_quadratic_warmup(  # pylint: disable=attribute-defined-outside-init
-                    optimizer,
-                    num_warmup_steps=self.args.get_warmup_steps(num_training_steps),
-                    num_training_steps=num_training_steps,
-                )
-            elif self.args.cosine_min_lr_ratio and self.args.cosine_constant_lr_ratio and use_cosine_min_lr:
-                assert 0 <= self.args.cosine_min_lr_ratio <= 1.0, "cosine_min_lr_ratio must be between 0.0 and 1.0"
-                assert 0 <= self.args.cosine_constant_lr_ratio <= 1.0, "cosine_constant_lr_ratio must be between 0.0 and 1.0"
-                self.lr_scheduler = get_cosine_schedule_with_warmup_decay_constant(  # pylint: disable=attribute-defined-outside-init
-                    optimizer,
-                    num_warmup_steps=self.args.get_warmup_steps(num_training_steps),
-                    num_training_steps=num_training_steps,
-                    min_lr_ratio=self.args.cosine_min_lr_ratio,
-                    constant_lr_ratio=self.args.cosine_constant_lr_ratio,
-                )
-            elif self.args.cosine_min_lr_ratio and use_cosine_min_lr:
-                assert 0 <= self.args.cosine_min_lr_ratio <= 1.0, "cosine_min_lr_ratio must be between 0.0 and 1.0"
-                self.lr_scheduler = get_cosine_schedule_with_min_lr(  # pylint: disable=attribute-defined-outside-init
-                    optimizer,
-                    num_warmup_steps=self.args.get_warmup_steps(num_training_steps),
-                    num_training_steps=num_training_steps,
-                    min_lr_ratio=self.args.cosine_min_lr_ratio,
-                )
-            else:
-                return super().create_scheduler(num_training_steps, optimizer=optimizer)
-        else:
-            if use_cosine_quadratic:
-                LOG.warning("axolotl's cosine scheduler with quadratic warmup not used (e.g., because of deepspeed).")
-
-            if use_cosine_min_lr:
-                LOG.warning("axolotl's cosine scheduler with min lr not used (e.g., because of deepspeed).")
-
-        return self.lr_scheduler
-
-
-class AxolotlTrainer(SchedulerMixin, Trainer):
-    """
-    Extend the base Trainer for axolotl helpers
-    """
-
-    args = None  # type: "AxolotlTrainingArguments"  # type: ignore[name-defined]
-    tag_names = ["axolotl"]
-
-    def __init__(
-        self,
-        *_args,
-        bench_data_collator=None,
-        eval_data_collator=None,
-        dataset_tags=None,
-        **kwargs,
-    ):
-        self.bench_data_collator = bench_data_collator
-        self.eval_data_collator = eval_data_collator
-        self.dataset_tags = dataset_tags
-        self._signature_columns = None  # workaround for pylint
-        super().__init__(*_args, **kwargs)
-        self.train_data_collator = self.data_collator
-        self._stored_metrics = defaultdict(lambda: defaultdict(list))
-        if self.args.orpo_alpha:
-            self.loss_fct = torch.nn.CrossEntropyLoss(reduction="none")
-
-    def _wrap_model(self, model, training=True, dataloader=None):
-        if self.args.torch_compile:
-            torch._dynamo.config.accumulated_cache_size_limit = (  # pylint: disable=protected-access
-                256
-            )
-            model = torch.compile(
-                model,
-                backend=self.args.torch_compile_backend,
-                mode=self.args.torch_compile_mode,
-            )
-        return super()._wrap_model(model, training=training, dataloader=dataloader)
-
-    def create_optimizer_grouped_parameters(self, opt_model, optimizer_kwargs):
-        decay_parameters = self.get_decay_parameter_names(opt_model)
-        params = {
-            "to_weight_decay": {},  # LayerNorm and bias
-            "embeddings": {},  # lm_head, embed_tokens,
-            "no_weight_decay": {},
-        }
-        lr_groups_lookup = {}
-        lr_groups_learning_rates = {}
-        if self.args.lr_groups:
-            for lr_group in self.args.lr_groups:
-                group_name = lr_group["name"]
-                group_modules = lr_group["modules"]
-                for module in group_modules:
-                    lr_groups_lookup[module] = group_name
-                lr_groups_learning_rates[group_name] = lr_group["lr"]
-                params[f"to_weight_decay_{group_name}"] = {}
-
-        for name, param in opt_model.named_parameters():
-            if not param.requires_grad:
-                continue
-            if name.endswith("modules_to_save.default.weight") or any(
-                embed_name in name for embed_name in ["embed_tokens", "lm_head"]
-            ):
-                params["embeddings"][name] = param
-            elif name in decay_parameters:
-                lr_group_modules = [
-                    group_modules
-                    for group_modules in lr_groups_lookup
-                    if group_modules in name
-                ]
-                if lr_groups_lookup and any(lr_group_modules):
-                    lr_group_module = lr_group_modules[0]
-                    group_name = lr_groups_lookup[lr_group_module]
-                    params[f"to_weight_decay_{group_name}"][name] = param
-                else:
-                    params["to_weight_decay"][name] = param
-            else:
-                params["no_weight_decay"][name] = param
-        optimizer_grouped_parameters = []
-        if params["to_weight_decay"]:
-            optimizer_grouped_parameters.append(
-                {
-                    "params": list(params["to_weight_decay"].values()),
-                    "weight_decay": self.args.weight_decay,
-                    "lr": optimizer_kwargs["lr"],
-                }
-            )
-        if params["embeddings"]:
-            lr = optimizer_kwargs["lr"]  # pylint: disable=invalid-name
-            if self.args.embedding_lr_scale:
-                lr *= self.args.embedding_lr_scale  # pylint: disable=invalid-name
-            elif self.args.embedding_lr:
-                lr = self.args.embedding_lr  # pylint: disable=invalid-name
-            optimizer_grouped_parameters.append(
-                {
-                    "params": list(params["embeddings"].values()),
-                    "weight_decay": 0.0,
-                    "lr": lr,
-                }
-            )
-        if params["no_weight_decay"]:
-            optimizer_grouped_parameters.append(
-                {
-                    "params": list(params["no_weight_decay"].values()),
-                    "weight_decay": 0.0,
-                    "lr": optimizer_kwargs["lr"],
-                }
-            )
-        for group_name, group_lr in lr_groups_learning_rates.items():
-            if params[f"to_weight_decay_{group_name}"]:
-                optimizer_grouped_parameters.append(
-                    {
-                        "params": list(
-                            params[f"to_weight_decay_{group_name}"].values()
-                        ),
-                        "weight_decay": self.args.weight_decay,
-                        "lr": group_lr,
-                    }
-                )
-
-        return optimizer_grouped_parameters
-
-    def create_optimizer(self):
-        if (
-            self.args.loraplus_lr_ratio is None
-            and self.args.embedding_lr_scale is None
-            and self.args.embedding_lr is None
-            and self.args.lr_groups is None
-            and self.args.alternate_optimizer
-            not in [
-                "optimi_adamw",
-                "ao_adamw_8bit",
-                "ao_adamw_4bit",
-                "ao_adamw_fp8",
-                "adopt_adamw",
-            ]
-        ):
-            return super().create_optimizer()
-
-        opt_model = self.model_wrapped if is_sagemaker_mp_enabled() else self.model
-        if self.optimizer is None:  # pylint: disable=access-member-before-definition
-            optimizer_cls, optimizer_kwargs = Trainer.get_optimizer_cls_and_kwargs(
-                self.args,
-                opt_model,
-            )
-            optimizer_grouped_parameters = self.create_optimizer_grouped_parameters(
-                opt_model, optimizer_kwargs
-            )
-
-            if self.args.loraplus_lr_ratio is not None:
-                loraplus_lr_ratio = getattr(self.args, "loraplus_lr_ratio", None)
-                loraplus_lr_embedding = getattr(
-                    self.args, "loraplus_lr_embedding", 1e-6
-                )
-                self.optimizer = create_loraplus_optimizer(  # pylint: disable=attribute-defined-outside-init
-                    opt_model,
-                    optimizer_cls,
-                    loraplus_lr_ratio=loraplus_lr_ratio,
-                    loraplus_lr_embedding=loraplus_lr_embedding,
-                    **optimizer_kwargs,
-                )
-            elif (
-                self.args.embedding_lr_scale is not None
-                or self.args.embedding_lr is not None
-                or self.args.lr_groups is not None
-            ):
-                self.optimizer = (  # pylint: disable=attribute-defined-outside-init
-                    optimizer_cls(optimizer_grouped_parameters, **optimizer_kwargs)
-                )
-            elif self.args.alternate_optimizer == "optimi_adamw":
-                from optimi import AdamW
-
-                self.optimizer = (  # pylint: disable=attribute-defined-outside-init
-                    AdamW(
-                        optimizer_grouped_parameters, foreach=False, **optimizer_kwargs
-                    )
-                )
-            elif self.args.alternate_optimizer == "ao_adamw_4bit":
-                from torchao.prototype.low_bit_optim import AdamW4bit
-
-                self.optimizer = (  # pylint: disable=attribute-defined-outside-init
-                    AdamW4bit(optimizer_grouped_parameters, **optimizer_kwargs)
-                )
-            elif self.args.alternate_optimizer == "ao_adamw_8bit":
-                from torchao.prototype.low_bit_optim import AdamW8bit
-
-                self.optimizer = (  # pylint: disable=attribute-defined-outside-init
-                    AdamW8bit(optimizer_grouped_parameters, **optimizer_kwargs)
-                )
-            elif self.args.alternate_optimizer == "ao_adamw_fp8":
-                from torchao.prototype.low_bit_optim import AdamWFp8
-
-                self.optimizer = (  # pylint: disable=attribute-defined-outside-init
-                    AdamWFp8(optimizer_grouped_parameters, **optimizer_kwargs)
-                )
-            elif self.args.alternate_optimizer == "adopt_adamw":
-                from axolotl.utils.optimizers.adopt import ADOPT
-
-                self.optimizer = (  # pylint: disable=attribute-defined-outside-init
-                    ADOPT(
-                        optimizer_grouped_parameters,
-                        decouple=True,
-                        **optimizer_kwargs,
-                    )
-                )
-
-        if is_sagemaker_mp_enabled():
-            self.optimizer = smp.DistributedOptimizer(  # pylint: disable=attribute-defined-outside-init
-                self.optimizer
-            )
-
-        return self.optimizer
-
-    def _get_train_sampler(self) -> Optional[torch.utils.data.Sampler]:
-        if self.args.sample_packing and not self.args.pretraining:
-            if self.args.multipack_real_batches:
-                batch_size = self.args.per_device_train_batch_size
-                batch_max_len = self.args.max_seq_length
-            else:
-                batch_size = 1
-                train_batch_size = (
-                    self.state.train_batch_size or self.args.per_device_train_batch_size
-                )
-                batch_max_len = train_batch_size * self.args.max_seq_length
-
-            if self.args.curriculum_sampling:
-                sampler = SequentialSampler(self.train_dataset)
-            else:
-                sampler = RandomSampler(self.train_dataset)
-
-            return MultipackBatchSampler(
-                sampler,
-                lengths=get_dataset_lengths(self.train_dataset),
-                packing_efficiency_estimate=self.args.sample_packing_efficiency,
-                batch_max_len=batch_max_len,
-                batch_size=batch_size,
-                group_size=self.args.sample_packing_group_size,
-                bin_size=self.args.sample_packing_bin_size,
-                drop_last=True,
-            )
-        if self.args.curriculum_sampling:
-            return SequentialSampler(self.train_dataset)
-        return super()._get_train_sampler()
-
-    def _get_eval_sampler(
-        self, eval_dataset: Dataset
-    ) -> Optional[torch.utils.data.Sampler]:
-        if self.args.sample_packing and self.args.eval_sample_packing is not False:
-            if self.args.multipack_real_batches:
-                batch_size = self.args.per_device_eval_batch_size
-                batch_max_len = self.args.max_seq_length
-            else:
-                batch_size = 1
-                batch_max_len = (
-                    self.args.per_device_eval_batch_size * self.args.max_seq_length
-                )
-            return MultipackBatchSampler(
-                SequentialSampler(eval_dataset),
-                lengths=get_dataset_lengths(self.eval_dataset),
-                packing_efficiency_estimate=self.args.sample_packing_efficiency,
-                batch_max_len=batch_max_len,
-                batch_size=batch_size,
-                group_size=self.args.sample_packing_group_size,
-                bin_size=self.args.sample_packing_bin_size,
-                drop_last=True,
-            )
-        return super()._get_eval_sampler(eval_dataset)
-
-    def get_train_dataloader(self) -> DataLoader:
-        if self.args.sample_packing and not self.args.pretraining:
-            train_dataset = self.train_dataset
-            if "length" in train_dataset.features.keys():
-                train_dataset = train_dataset.remove_columns(["length"])
-            data_collator = self.data_collator
-            dataloader_params = {
-                "batch_size": self._train_batch_size,
-                "collate_fn": data_collator,
-                "num_workers": self.args.dataloader_num_workers,
-                "pin_memory": self.args.dataloader_pin_memory,
-            }
-            if self.args.dataloader_prefetch_factor:
-                dataloader_params[
-                    "prefetch_factor"
-                ] = self.args.dataloader_prefetch_factor
-
-            sampler = self._get_train_sampler()
-            if isinstance(sampler, BatchSampler):
-                dataloader_params["batch_sampler"] = sampler
-                del dataloader_params["batch_size"]
-            else:
-                dataloader_params["sampler"] = sampler
-                dataloader_params["drop_last"] = self.args.dataloader_drop_last
-            dataloader_params["worker_init_fn"] = seed_worker
-
-            self.accelerator.even_batches = False
-            return self.accelerator.prepare_data_loader(
-                DataLoader(train_dataset, **dataloader_params)
-            )
-        return super().get_train_dataloader()
-
-    def get_eval_dataloader(self, eval_dataset: Optional[Dataset] = None) -> DataLoader:
-        if self.args.sample_packing and self.args.eval_sample_packing is False:
-            self.data_collator = (  # pylint: disable=attribute-defined-outside-init
-                self.eval_data_collator
-            )
-            if eval_dataset:
-                eval_dataset = eval_dataset.remove_columns(["length"])
-            dataloader = super().get_eval_dataloader(eval_dataset)
-            self.data_collator = (  # pylint: disable=attribute-defined-outside-init
-                self.train_data_collator
-            )
-            return dataloader
-
-        if self.args.sample_packing and self.args.eval_sample_packing is not False:
-            eval_dataset = (
-                eval_dataset if eval_dataset is not None else self.eval_dataset
-            )
-
-            eval_sampler = self._get_eval_sampler(eval_dataset)
-            eval_dataset = eval_dataset.remove_columns(["length"])
-            data_collator = self.data_collator
-            dataloader_params = {
-                "batch_size": self.args.eval_batch_size,
-                "collate_fn": data_collator,
-                "num_workers": self.args.dataloader_num_workers,
-                "pin_memory": self.args.dataloader_pin_memory,
-            }
-            if self.args.dataloader_prefetch_factor:
-                dataloader_params[
-                    "prefetch_factor"
-                ] = self.args.dataloader_prefetch_factor
-
-            if isinstance(eval_sampler, BatchSampler):
-                dataloader_params["batch_sampler"] = eval_sampler
-                del dataloader_params["batch_size"]
-            else:
-                dataloader_params["sampler"] = eval_sampler
-                dataloader_params["drop_last"] = self.args.dataloader_drop_last
-
-            self.accelerator.even_batches = False
-            return self.accelerator.prepare_data_loader(
-                DataLoader(eval_dataset, **dataloader_params)
-            )
-
-        return super().get_eval_dataloader(eval_dataset)
-
-    def _get_bench_sampler(
-        self, bench_dataset: Dataset
-    ) -> Optional[torch.utils.data.Sampler]:
-        if self.args.world_size <= 1:
-            return SequentialSampler(bench_dataset)
-        return None
-
-    def get_bench_dataloader(
-        self,
-        bench_dataset: Dataset,
-    ) -> DataLoader:
-        dataloader_params = {
-            "batch_size": self.args.eval_batch_size,
-            "collate_fn": self.bench_data_collator,
-            "num_workers": self.args.dataloader_num_workers,
-            "pin_memory": self.args.dataloader_pin_memory,
-        }
-        if self.args.dataloader_prefetch_factor:
-            dataloader_params["prefetch_factor"] = self.args.dataloader_prefetch_factor
-
-        if not isinstance(bench_dataset, torch.utils.data.IterableDataset):
-            dataloader_params["sampler"] = self._get_bench_sampler(bench_dataset)
-            dataloader_params["drop_last"] = self.args.dataloader_drop_last
-
-        return DataLoader(bench_dataset, **dataloader_params)
-        # return self.accelerator.prepare(DataLoader(bench_dataset, **dataloader_params))
-
-    def compute_loss(
-        self, model, inputs, return_outputs=False, num_items_in_batch=None
-    ):
-        # use one's weighted cross entropy loss calc
-        # if self.args.sample_packing:
-        #     labels = inputs.pop("labels")
-        #     outputs = model(**inputs)
-        #     loss = trainer_weighted_loss(outputs, labels, shift_labels=True)
-        #     return (loss, outputs) if return_outputs else loss
-        if self.args.orpo_alpha:
-            return self.orpo_compute_loss(
-                model,
-                inputs,
-                return_outputs=return_outputs,
-                num_items_in_batch=num_items_in_batch,
-            )
-        return super().compute_loss(
-            model,
-            inputs,
-            return_outputs=return_outputs,
-            num_items_in_batch=num_items_in_batch,
-        )
-
-    @staticmethod
-    def orpo_concatenate_inputs(inputs, label_pad_token=-100, pad_token=0, device=None):
-        concatenated_batch = {}
-
-        max_length = max(
-            inputs["input_ids"].shape[1], inputs["rejected_input_ids"].shape[1]
-        )
-        # Concatenate positive and negative inputs
-        concatenated_batch["input_ids"] = pad_to_length(
-            inputs["input_ids"], max_length, pad_token
-        )
-        concatenated_batch["rejected_input_ids"] = pad_to_length(
-            inputs["rejected_input_ids"], max_length, pad_token
-        )
-        concatenated_batch["labels"] = pad_to_length(
-            inputs["labels"], max_length, label_pad_token
-        )
-        concatenated_batch["rejected_labels"] = pad_to_length(
-            inputs["rejected_labels"], max_length, label_pad_token
-        )
-        concatenated_batch["attention_mask"] = pad_to_length(
-            inputs["attention_mask"], max_length, 0
-        )
-        concatenated_batch["rejected_attention_mask"] = pad_to_length(
-            inputs["rejected_attention_mask"], max_length, 0
-        )
-        concatenated_batch["prompt_attention_mask"] = pad_to_length(
-            inputs["prompt_attention_mask"], max_length, 0
-        ).to(device=device)
-
-        input_ids = torch.cat(
-            [concatenated_batch["input_ids"], concatenated_batch["rejected_input_ids"]],
-            dim=0,
-        ).to(device=device)
-        attention_mask = torch.cat(
-            [
-                concatenated_batch["attention_mask"],
-                concatenated_batch["rejected_attention_mask"],
-            ],
-            dim=0,
-        ).to(device=device)
-        labels = torch.cat(
-            [concatenated_batch["labels"], concatenated_batch["rejected_labels"]], dim=0
-        ).to(device=device)
-
-        return {
-            "input_ids": input_ids,
-            "labels": labels,
-            "attention_mask": attention_mask,
-            "prompt_attention_mask": concatenated_batch["prompt_attention_mask"],
-        }
-
-    def orpo_compute_custom_loss(self, logits, labels):
-        logits = logits.contiguous()
-        loss = 0.0
-
-        if labels is not None:
-            # move labels to correct device to enable model parallelism
-            labels = labels.to(logits.device)
-            # Shift so that tokens < n predict n
-            shift_logits = logits[..., :-1, :].contiguous()
-            shift_labels = labels[..., 1:].contiguous()
-
-            # Flatten the tokens
-            loss = self.loss_fct(shift_logits.transpose(2, 1), shift_labels).mean(
-                dim=-1
-            )
-
-        return loss
-
-    def orpo_compute_logps(
-        self, prompt_attention_mask, chosen_inputs, chosen_attention_mask, logits
-    ):
-        # Get the shape of chosen_attention_mask[:, :-1]
-        chosen_shape = chosen_attention_mask[:, :-1].shape
-
-        # Calculate the padding size
-        pad_length = chosen_shape[1] - (prompt_attention_mask.shape[1] - 1)
-
-        # Pad prompt_attention_mask with zeros to match the desired shape
-        prompt_attention_mask_padded = torch.nn.functional.pad(
-            prompt_attention_mask[:, 1:], (0, pad_length), mode="constant", value=0
-        )
-
-        # Perform the subtraction operation
-        mask = chosen_attention_mask[:, :-1] > prompt_attention_mask_padded
-
-        per_token_logps = torch.gather(
-            logits[:, :-1, :].log_softmax(-1),
-            dim=2,
-            index=(mask * chosen_inputs[:, 1:]).unsqueeze(2),
-        ).squeeze(2)
-        return torch.mul(per_token_logps, mask).sum(dim=1) / mask.sum(dim=1)
-
-    def orpo_compute_loss(
-        self,
-        model,
-        inputs,
-        return_outputs=False,
-        num_items_in_batch=None,  # pylint: disable=unused-argument
-    ):
-        concat_inputs = AxolotlTrainer.orpo_concatenate_inputs(
-            inputs,
-            label_pad_token=-100,
-            pad_token=self.tokenizer.pad_token_id,
-            device=self.accelerator.device,
-        )
-
-        # Perform a single forward pass
-        outputs = model(
-            **{
-                "input_ids": concat_inputs["input_ids"],
-                "attention_mask": concat_inputs["attention_mask"],
-                "labels": concat_inputs["labels"],
-            },
-            output_hidden_states=True,
-        )
-
-        # Split the outputs for positive and negative examples
-        outputs_pos, outputs_neg = outputs.logits.chunk(2)
-
-        # Calculate NLL loss
-        pos_loss = self.orpo_compute_custom_loss(
-            logits=outputs_pos, labels=concat_inputs["input_ids"].chunk(2)[0]
-        )
-
-        # Calculate Log Probability
-        pos_prob = self.orpo_compute_logps(
-            prompt_attention_mask=concat_inputs["prompt_attention_mask"],
-            chosen_inputs=concat_inputs["input_ids"].chunk(2)[0],
-            chosen_attention_mask=concat_inputs["attention_mask"].chunk(2)[0],
-            logits=outputs_pos,
-        )
-        neg_prob = self.orpo_compute_logps(
-            prompt_attention_mask=concat_inputs["prompt_attention_mask"],
-            chosen_inputs=concat_inputs["input_ids"].chunk(2)[1],
-            chosen_attention_mask=concat_inputs["attention_mask"].chunk(2)[1],
-            logits=outputs_neg,
-        )
-
-        # Calculate log odds
-        log_odds = (pos_prob - neg_prob) - (
-            torch.log(1 - torch.exp(pos_prob)) - torch.log(1 - torch.exp(neg_prob))
-        )
-        sig_ratio = torch.nn.functional.sigmoid(log_odds)
-        ratio = torch.log(sig_ratio)
-
-        # Calculate the Final Loss
-        loss = torch.mean(pos_loss - self.args.orpo_alpha * ratio).to(
-            dtype=torch.bfloat16
-        )
-
-        metrics = {}
-        metrics["chosen_geometric_mean"] = torch.mean(pos_prob).cpu().item()
-        metrics["rejected_geometric_mean"] = torch.mean(neg_prob).cpu().item()
-        metrics["log_odds_ratio"] = torch.mean(ratio).cpu().item()
-        metrics["log_odds"] = torch.mean(log_odds).cpu().item()
-        self.store_metrics(metrics, train_eval="train")
-
-        return (loss, outputs_pos) if return_outputs else loss
-
-    @wraps(Trainer.push_to_hub)
-    def push_to_hub(self, *args, **kwargs) -> str:
-        """
-        Overwrite the `push_to_hub` method in order to force-add the tags when pushing the
-        model on the Hub. Please refer to `~transformers.Trainer.push_to_hub` for more details.
-        """
-        kwargs = _sanitize_kwargs_for_ds_tagging(
-            dataset_tags=self.dataset_tags, kwargs=kwargs
-        )
-        kwargs = _sanitize_kwargs_for_tagging(tag_names=self.tag_names, kwargs=kwargs)
-
-        return super().push_to_hub(*args, **kwargs)
-
-    @wraps(Trainer.create_accelerator_and_postprocess)
-    def create_accelerator_and_postprocess(self):
-        res = super().create_accelerator_and_postprocess()
-
-        if self.is_fsdp_enabled:
-            if (
-                "limit_all_gathers" in self.args.fsdp_config
-                and self.args.fsdp_config["limit_all_gathers"]
-            ):
-                self.accelerator.state.fsdp_plugin.limit_all_gathers = True
-
-        return res
-
-    def log(self, logs: Dict[str, float], start_time: Optional[float] = None) -> None:
-        """
-        Log `logs` on the various objects watching training, including stored metrics.
-
-        Args:
-            logs (`Dict[str, float]`):
-                The values to log.
-            start_time (`Optional[float]`):
-                The start of training.
-        """
-        # logs either has 'loss' or 'eval_loss'
-        train_eval = "train" if "loss" in logs else "eval"
-        # Add averaged stored metrics to logs
-        for key, metrics in self._stored_metrics[train_eval].items():
-            logs[key] = torch.tensor(metrics).mean().item()
-        del self._stored_metrics[train_eval]
-
-        return super().log(logs, start_time)
-
-    def store_metrics(
-        self, metrics: Dict[str, float], train_eval: Literal["train", "eval"] = "train"
-    ) -> None:
-        for key, value in metrics.items():
-            self._stored_metrics[train_eval][key].append(value)
-
-    def _save_checkpoint(self, model, trial, **kwargs):
-        # make sure the checkpoint dir exists, since trainer is flakey
-        checkpoint_folder = f"{PREFIX_CHECKPOINT_DIR}-{self.state.global_step}"
-        run_dir = self._get_output_dir(trial=trial)
-        output_dir = os.path.join(run_dir, checkpoint_folder)
-        os.makedirs(output_dir, exist_ok=True)
-        return super()._save_checkpoint(model, trial, **kwargs)
-
-
-class AxolotlMambaTrainer(AxolotlTrainer):
-    """
-    Mamba specific trainer to handle loss calculation
-    """
-
-    tag_names = ["axolotl", "mamba"]
-
-    def compute_loss(
-        self,
-        model,
-        inputs,
-        return_outputs=False,  # pylint: disable=unused-argument
-        num_items_in_batch=None,  # pylint: disable=unused-argument
-    ):
-        input_ids = inputs.pop("input_ids")
-        lm_logits = model(input_ids).logits
-
-        labels = input_ids.to(lm_logits.device)
-        shift_logits = lm_logits[:, :-1, :].contiguous()
-        labels = labels[:, 1:].contiguous()
-
-        loss_fct = torch.nn.CrossEntropyLoss()
-        lm_loss = loss_fct(
-            shift_logits.view(-1, shift_logits.size(-1)), labels.view(-1)
-        )
-
-        return lm_loss
-
-
-class ReLoRATrainer(AxolotlTrainer):
-    """
-    Trainer subclass that uses the OneCycleLR scheduler
-    """
-
-    tag_names = ["axolotl", "relora"]
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.lr_scheduler = None
-
-    def create_scheduler(
-        self,
-        num_training_steps: int,
-        optimizer: Optional[torch.optim.Optimizer] = None,
-    ):
-        optimizer = self.optimizer if optimizer is None else optimizer
-        lr_scheduler = super().create_scheduler(num_training_steps, optimizer)
-
-        if self.args.relora_steps:
-            warmup_steps = (
-                self.args.relora_warmup_steps if self.args.relora_warmup_steps else 10
-            )
-            anneal_steps = (
-                self.args.relora_anneal_steps if self.args.relora_anneal_steps else 1
-            )
-            self.lr_scheduler = ReLoRAScheduler(
-                optimizer,
-                lr_scheduler,
-                self.args.relora_steps,
-                anneal_steps,
-                warmup_steps,
-            )
-        else:
-            self.lr_scheduler = lr_scheduler
-
-        return self.lr_scheduler
-
-
-class AxolotlORPOTrainer(SchedulerMixin, ORPOTrainer):
-    """
-    Extend the base ORPOTrainer for axolotl helpers
-    """
-
-    tag_names = ["axolotl", "orpo"]
-
-
-class AxolotlKTOTrainer(SchedulerMixin, KTOTrainer):
-    """
-    Extend the base KTOTrainer for axolotl helpers
-    """
-
-    tag_names = ["axolotl", "kto"]
-
-
-class AxolotlCPOTrainer(SchedulerMixin, CPOTrainer):
-    """
-    Extend the base CPOTrainer for axolotl helpers
-    """
-
-    tag_names = ["axolotl", "cpo"]
-
-
-class AxolotlRewardTrainer(SchedulerMixin, RewardTrainer):
-    """
-    Extend the base RewardTrainer for axolotl helpers
-    """
-
-    tag_names = ["axolotl", "reward"]
-
-
-class AxolotlPRMTrainer(SchedulerMixin, PRMTrainer):
-    """
-    Extend the base trl.PRMTrainer for axolotl helpers
-    """
-
-    tag_names = ["axolotl", "prm"]

src/axolotl/core/trainers/dpo/__init__.py

@@ -1,33 +0,0 @@
-"""
-DPO Specific Strategy for training
-"""
-from axolotl.core.trainers.dpo.trainer import AxolotlDPOTrainer
-
-
-class DPOStrategy:
-    """
-    Strategy for DPO training
-    """
-
-    @classmethod
-    def get_trainer_class(cls):
-        return AxolotlDPOTrainer
-
-    @classmethod
-    def get_training_args_class(cls):
-        from axolotl.core.trainers.dpo.args import AxolotlDPOConfig
-
-        return AxolotlDPOConfig
-
-    @classmethod
-    def set_training_args_kwargs(cls, cfg):
-        training_args_kwargs = {}
-        if cfg.rl == "ipo":
-            training_args_kwargs["loss_type"] = "ipo"
-        training_args_kwargs["max_length"] = cfg.sequence_len
-        training_args_kwargs["max_completion_length"] = None
-        training_args_kwargs["max_prompt_length"] = cfg.sequence_len
-        training_args_kwargs["generate_during_eval"] = cfg.use_wandb
-        if cfg.dpo_use_weighting is not None:
-            training_args_kwargs["use_weighting"] = cfg.dpo_use_weighting
-        return training_args_kwargs

src/axolotl/core/trainers/dpo/args.py

@@ -1,15 +0,0 @@
-"""
-Axolotl specific DPO args
-"""
-from dataclasses import dataclass
-
-from trl import DPOConfig
-
-from axolotl.core.training_args import AxolotlTrainingMixins
-
-
-@dataclass
-class AxolotlDPOConfig(AxolotlTrainingMixins, DPOConfig):
-    """
-    DPO config for DPO training
-    """

src/axolotl/core/trainers/dpo/trainer.py

@@ -1,125 +0,0 @@
-"""
-DPO trainer for axolotl
-"""
-import gc
-from functools import wraps
-from typing import Any, Dict, Union
-
-import torch
-from peft.optimizers import create_loraplus_optimizer
-from torch import nn
-from transformers import Trainer
-from transformers.utils import is_sagemaker_mp_enabled
-from trl import DPOTrainer
-
-from axolotl.core.trainers.base import (
-    SchedulerMixin,
-    _sanitize_kwargs_for_ds_tagging,
-    _sanitize_kwargs_for_tagging,
-)
-
-if is_sagemaker_mp_enabled():
-    import smdistributed.modelparallel.torch as smp
-
-
-class AxolotlDPOTrainer(SchedulerMixin, DPOTrainer):
-    """
-    Extend the base DPOTrainer for axolotl helpers
-    """
-
-    tag_names = ["axolotl", "dpo"]
-
-    def __init__(self, *args, dataset_tags=None, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.dataset_tags = dataset_tags
-        self.optimizer = None
-        self.model_accepts_loss_kwargs = False
-
-    def create_optimizer(self):
-        # pylint: disable=duplicate-code
-        if self.args.loraplus_lr_ratio is None:
-            return super().create_optimizer()
-
-        opt_model = self.model_wrapped if is_sagemaker_mp_enabled() else self.model
-        if self.optimizer is None:  # pylint: disable=access-member-before-definition
-            optimizer_cls, optimizer_kwargs = Trainer.get_optimizer_cls_and_kwargs(
-                self.args,
-                opt_model,
-            )
-
-            loraplus_lr_ratio = getattr(self.args, "loraplus_lr_ratio", None)
-            if loraplus_lr_ratio:
-                print("Using lora+")
-            loraplus_lr_embedding = getattr(self.args, "loraplus_lr_embedding", None)
-            # pylint: disable=duplicate-code
-            self.optimizer = create_loraplus_optimizer(  # pylint: disable=attribute-defined-outside-init
-                opt_model,
-                optimizer_cls,
-                loraplus_lr_ratio=loraplus_lr_ratio,
-                loraplus_lr_embedding=loraplus_lr_embedding,
-                **optimizer_kwargs,
-            )
-
-        if is_sagemaker_mp_enabled():
-            self.optimizer = smp.DistributedOptimizer(  # pylint: disable=attribute-defined-outside-init
-                self.optimizer
-            )
-
-        return self.optimizer
-
-    @wraps(DPOTrainer.push_to_hub)
-    def push_to_hub(self, *args, **kwargs) -> str:
-        """
-        Overwrite the `push_to_hub` method in order to force-add the tags when pushing the
-        model on the Hub. Please refer to `~transformers.Trainer.push_to_hub` for more details.
-        """
-        kwargs = _sanitize_kwargs_for_ds_tagging(
-            dataset_tags=self.dataset_tags, kwargs=kwargs
-        )
-        kwargs = _sanitize_kwargs_for_tagging(tag_names=self.tag_names, kwargs=kwargs)
-
-        return super().push_to_hub(*args, **kwargs)
-
-    @staticmethod
-    def tokenize_row(
-        features,
-        processing_class,
-        max_prompt_length,
-        max_completion_length,
-        add_special_tokens,
-    ) -> Dict:
-        res = DPOTrainer.tokenize_row(
-            features,
-            processing_class,
-            max_prompt_length,
-            max_completion_length,
-            add_special_tokens,
-        )
-        # fix when the tokenizer doesn't have a bos_token_id, e.g. Qwen
-        if processing_class.bos_token is None and res["prompt_input_ids"][0] is None:
-            for key in res.keys():
-                res[key] = res[key][1:]
-
-        if processing_class.bos_token and processing_class.bos_token_id is not None:
-            # dpo trainer may incorrectly prepend the bos_token_id to the dpo outputs
-            if res["chosen_input_ids"][0] == processing_class.bos_token_id:
-                res["chosen_input_ids"] = res["chosen_input_ids"][1:]
-                res["chosen_labels"] = res["chosen_labels"][1:]
-                res["chosen_attention_mask"] = res["chosen_attention_mask"][1:]
-            if res["rejected_input_ids"][0] == processing_class.bos_token_id:
-                res["rejected_input_ids"] = res["rejected_input_ids"][1:]
-                res["rejected_labels"] = res["rejected_labels"][1:]
-                res["rejected_attention_mask"] = res["rejected_attention_mask"][1:]
-
-        return res
-
-    def training_step(
-        self,
-        model: nn.Module,
-        inputs: Dict[str, Union[torch.Tensor, Any]],
-        num_items_in_batch=None,
-    ) -> torch.Tensor:
-        loss: torch.Tensor = super().training_step(model, inputs, num_items_in_batch)
-        gc.collect()
-        torch.cuda.empty_cache()
-        return loss

src/axolotl/core/trainers/grpo/__init__.py

@@ -1,119 +0,0 @@
-"""
-GRPO Specific Strategy for training
-"""
-
-import importlib
-import inspect
-import logging
-
-from trl.trainer.grpo_trainer import RewardFunc
-
-from axolotl.core.trainers.grpo.trainer import AxolotlGRPOTrainer
-
-LOG = logging.getLogger("axolotl")
-
-
-class GRPOStrategy:
-    """
-    Strategy for GRPO training
-    """
-
-    @classmethod
-    def get_trainer_class(cls):
-        return AxolotlGRPOTrainer
-
-    @classmethod
-    def get_training_args_class(cls):
-        from axolotl.core.trainers.grpo.args import AxolotlGRPOConfig
-
-        return AxolotlGRPOConfig
-
-    @classmethod
-    def set_training_args_kwargs(cls, cfg):
-        grpo_args_kwargs = {}
-        if cfg.trl and cfg.trl.use_vllm:
-            grpo_args_kwargs["use_vllm"] = cfg.trl.use_vllm
-            if cfg.trl and cfg.trl.vllm_device:
-                grpo_args_kwargs["vllm_device"] = cfg.trl.vllm_device
-            else:
-                grpo_args_kwargs["vllm_device"] = "auto"
-            if cfg.trl and cfg.trl.vllm_gpu_memory_utilization:
-                grpo_args_kwargs[
-                    "vllm_gpu_memory_utilization"
-                ] = cfg.trl.vllm_gpu_memory_utilization
-            if cfg.trl and cfg.trl.vllm_max_model_len:
-                grpo_args_kwargs["vllm_max_model_len"] = cfg.trl.vllm_max_model_len
-        if cfg.trl and cfg.trl.num_generations:
-            grpo_args_kwargs["num_generations"] = cfg.trl.num_generations
-        if cfg.trl and cfg.trl.sync_ref_model:
-            grpo_args_kwargs["sync_ref_model"] = cfg.trl.sync_ref_model
-            if cfg.trl and cfg.trl.ref_model_mixup_alpha:
-                grpo_args_kwargs[
-                    "ref_model_mixup_alpha"
-                ] = cfg.trl.ref_model_mixup_alpha
-            if cfg.trl and cfg.trl.ref_model_sync_steps:
-                grpo_args_kwargs["ref_model_sync_steps"] = cfg.trl.ref_model_sync_steps
-        grpo_args_kwargs["max_completion_length"] = cfg.trl.max_completion_length
-        grpo_args_kwargs["log_completions"] = cfg.trl.log_completions
-        return grpo_args_kwargs
-
-    @classmethod
-    def set_trainer_args(cls, cfg):
-        trainer_args = []
-        if cfg.trl and cfg.trl.reward_funcs:
-            reward_funcs = []
-            for reward_func_fqn in cfg.trl.reward_funcs:
-                reward_funcs.append(cls.get_reward_func(reward_func_fqn))
-            trainer_args.append(reward_funcs)
-        return trainer_args
-
-    @classmethod
-    def set_trainer_kwargs(cls, cfg):
-        trainer_kwargs = {}
-        if cfg.trl and cfg.trl.reward_processing_classes:
-            trainer_kwargs[
-                "reward_processing_classes"
-            ] = cfg.trl.reward_processing_classes
-        return trainer_kwargs
-
-    @classmethod
-    def get_collator(cls, *args, **kwargs):  # pylint: disable=unused-argument
-        # No data collation is needed in GRPO, handled by trl's trainer __init__
-        return None
-
-    @classmethod
-    def get_blocklist_args_kwargs(cls):
-        return ["dataset_num_proc"]
-
-    @classmethod
-    def get_reward_func(cls, reward_func_fqn: str) -> RewardFunc:
-        """
-        Returns the reward function from the given fully qualified name, or the path to the reward function model.
-
-        Args:
-            reward_func_fqn (str): Fully qualified name of the reward function (e.g. r1_grpo.gsm8k_transform),
-                or a HF hub path to the reward model.
-        Raises:
-            ValueError: If the reward function does not accept at least two arguments.
-
-        Returns:
-            RewardFunc: A callable that accepts prompts and completions and returns rewards,
-                or a path to a reward model.
-
-        """
-        try:
-            # use importlib to dynamically load the reward function from the module
-            reward_func_module_name = reward_func_fqn.split(".")[-1]
-            reward_func_module = importlib.import_module(reward_func_fqn.split(".")[-2])
-            reward_func = getattr(reward_func_module, reward_func_module_name)
-            if not len(inspect.signature(reward_func).parameters) >= 2:
-                raise ValueError(
-                    "Reward function must accept at least two arguments: prompts: list and completions: list"
-                )
-            return reward_func
-        except ModuleNotFoundError:
-            # the user has passed a string (ideally indicating the path of a reward model)
-            LOG.info(
-                f"Reward function {reward_func_fqn} is a pre-trained model path - if this is unexpected, please check the reward function path."
-            )
-            return reward_func

src/axolotl/core/trainers/grpo/args.py

@@ -1,15 +0,0 @@
-"""
-Axolotl Specific Training Args
-"""
-from dataclasses import dataclass
-
-from trl import GRPOConfig
-
-from axolotl.core.training_args import AxolotlTrainingMixins
-
-
-@dataclass
-class AxolotlGRPOConfig(AxolotlTrainingMixins, GRPOConfig):
-    """
-    Axolotl GRPO Config for GRPO training
-    """

src/axolotl/core/trainers/grpo/trainer.py

@@ -1,108 +0,0 @@
-"""
-Axolotl GRPO trainer
-"""
-from accelerate.utils import is_peft_model
-from accelerate.utils.other import is_compiled_module
-from transformers import PreTrainedModel
-from trl import GRPOConfig, GRPOTrainer
-from trl.models import unwrap_model_for_generation
-
-from axolotl.core.trainers.base import SchedulerMixin
-
-
-# mypy: ignore-errors
-class AxolotlGRPOTrainer(SchedulerMixin, GRPOTrainer):
-    """
-    Extend the base GRPOTrainer for axolotl helpers
-    """
-
-    _tag_names = ["trl", "grpo", "axolotl"]
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-
-        # pylint: disable=access-member-before-definition
-        # Enable gradient checkpointing if requested
-        if kwargs["args"].gradient_checkpointing:
-            # Ensure use_cache is disabled
-            if hasattr(self.model, "config"):
-                self.model.config.use_cache = False
-
-            # Enable gradient checkpointing on the base model for PEFT
-            if is_peft_model(self.model) and hasattr(
-                self.model.base_model, "gradient_checkpointing_enable"
-            ):
-                self.model.base_model.gradient_checkpointing_enable()
-            # Enable gradient checkpointing for non-PEFT models
-            elif hasattr(self.model, "gradient_checkpointing_enable"):
-                self.model.gradient_checkpointing_enable()
-            self.model = self._enable_gradient_checkpointing(self.model, kwargs["args"])
-        # pylint: enable=access-member-before-definition
-
-    def _enable_gradient_checkpointing(
-        self, model: PreTrainedModel, args: GRPOConfig
-    ) -> PreTrainedModel:
-        """Enables gradient checkpointing for the model."""
-        # pylint: disable=unused-argument,redefined-builtin
-        gradient_checkpointing_kwargs = args.gradient_checkpointing_kwargs or {}
-        use_reentrant = (
-            "use_reentrant" not in gradient_checkpointing_kwargs
-            or gradient_checkpointing_kwargs["use_reentrant"]
-        )
-
-        if use_reentrant:
-            if hasattr(model, "enable_input_require_grads"):
-                model.enable_input_require_grads()
-            else:
-
-                def make_inputs_require_grad(module, input, output):
-                    output.requires_grad_(True)
-
-                model.get_input_embeddings().register_forward_hook(
-                    make_inputs_require_grad
-                )
-
-        return model
-        # pylint: enable=unused-argument,redefined-builtin
-
-    def _move_model_to_vllm(self):
-        with unwrap_model_for_generation(
-            self.model,
-            self.accelerator,
-            gather_deepspeed3_params=self.args.ds3_gather_for_generation,
-        ) as unwrapped_model:
-            if is_compiled_module(unwrapped_model):
-                unwrapped_model = (
-                    unwrapped_model._orig_mod  # pylint: disable=protected-access
-                )
-            if is_peft_model(unwrapped_model):
-                unwrapped_model.merge_adapter()
-                state_dict = unwrapped_model.state_dict()
-                # Remove base_model and base_layer prefixes
-                state_dict = {
-                    k.removeprefix("base_model.model.")
-                    .removeprefix("base_model.model.")
-                    .replace(".base_layer", ""): v
-                    for k, v in state_dict.items()
-                }
-                # Remove values with adapter prefix (example: "_lora")
-                state_dict = {
-                    k: v
-                    for k, v in state_dict.items()
-                    if unwrapped_model.prefix not in k
-                }
-                # When module to save, remove its prefix and discard the original module
-                state_dict = {
-                    k.replace("modules_to_save.default.", ""): v
-                    for k, v in state_dict.items()
-                    if "original_module" not in k
-                }
-            else:
-                state_dict = unwrapped_model.state_dict()
-            if self.accelerator.is_main_process:
-                llm_model = (
-                    self.llm.llm_engine.model_executor.driver_worker.model_runner.model
-                )
-                llm_model.load_weights(state_dict.items())
-            if is_peft_model(unwrapped_model):
-                unwrapped_model.unmerge_adapter()

src/axolotl/core/training_args.py

@@ -1,257 +0,0 @@
-"""
-extra axolotl specific training args
-"""
-from dataclasses import dataclass, field
-from typing import Optional
-
-from transformers import TrainingArguments
-from trl import CPOConfig, KTOConfig, ORPOConfig, PRMConfig, RewardConfig
-
-
-@dataclass
-class AxolotlTrainingMixins:
-    """
-    Mixin class for the Axolotl training args.
-    """
-
-    # pylint: disable=duplicate-code
-    model_type: Optional[str] = field(
-        default=None, metadata={"help": "HF model configuration model_type."}
-    )
-    lr_quadratic_warmup: bool = field(
-        default=False,
-        metadata={"help": "Use quadratic warmup for cosine scheduling."},
-    )
-    pretraining: bool = field(
-        default=False,
-        metadata={
-            "help": "Indicates to trainer whether we are doing continued pretraining."
-        },
-    )
-    sample_packing: bool = field(
-        default=False,
-        metadata={"help": "Use sample packing for efficient training."},
-    )
-    multipack_real_batches: bool = field(
-        default=False,
-        metadata={"help": "Use real batches for efficient training."},
-    )
-    eval_sample_packing: Optional[bool] = field(
-        default=None,
-        metadata={"help": "Use sample packing for efficient evals."},
-    )
-    sample_packing_efficiency: float = field(
-        default=1.0,
-        metadata={"help": "Sample packing efficiency for calculating batch length."},
-    )
-    sample_packing_bin_size: int = field(
-        default=200,
-        metadata={
-            "help": "The max number of samples that packed sample can contain after packing. Increase for better packing."
-        },
-    )
-    sample_packing_group_size: int = field(
-        default=100000,
-        metadata={
-            "help": "The number of samples to group together for packing. Increase for better packing."
-        },
-    )
-    max_seq_length: int = field(
-        default=2048,
-        metadata={"help": "The maximum sequence length the model can handle"},
-    )
-    relora_steps: Optional[int] = field(
-        default=None,
-        metadata={"help": "how often to reset for ReLoRA"},
-    )
-    relora_warmup_steps: Optional[int] = field(
-        default=None,
-        metadata={"help": "how many warmup steps to take after reset for ReLoRA"},
-    )
-    relora_anneal_steps: Optional[int] = field(
-        default=None,
-        metadata={"help": "how many warmup steps to take after reset for ReLoRA"},
-    )
-    relora_prune_ratio: Optional[float] = field(
-        default=0.9,
-        metadata={"help": "prune ratio for magnitude pruning of the optimizer"},
-    )
-    bench_split: Optional[str] = field(
-        default="eval", metadata={"help": "The benchmark split to run on"}
-    )
-    bench_dataset: Optional[str] = field(
-        default="pharaouk/dharma-1/dharma_1_mini.json",
-        metadata={
-            "help": "Benchmark dataset to use: options are `mmlu-zs`, `mmlu-fs`, or the full path to the dataset file"
-        },
-    )
-    do_bench_eval: Optional[bool] = field(
-        default=False, metadata={"help": "Whether to run the Benchmark evaluation."}
-    )
-    do_causal_lm_eval: Optional[bool] = field(
-        default=False, metadata={"help": "Whether to run the Causal LM evaluation."}
-    )
-    max_bench_samples: Optional[int] = field(
-        default=None,
-        metadata={
-            "help": "If set, only evaluates on `max_bench_samples` of the benchmark dataset."
-        },
-    )
-    bench_source_max_len: int = field(
-        default=2048, metadata={"help": "Maximum source sequence length for bench."}
-    )
-    dataloader_prefetch_factor: Optional[int] = field(
-        default=None,
-        metadata={"help": "prefetch_factor argument to the dataloader"},
-    )
-    cosine_min_lr_ratio: Optional[float] = field(
-        default=None,
-        metadata={"help": "Minimum learning rate is min_lr_ratio * learning_rate"},
-    )
-    cosine_constant_lr_ratio: Optional[float] = field(
-        default=None,
-        metadata={
-            "help": "Starting constant learning rate step is cosine_constant_lr_ratio * max_steps"
-        },
-    )
-    loraplus_lr_ratio: Optional[float] = field(
-        default=None, metadata={"help": "loraplus learning rate ratio lr_B / lr_A."}
-    )
-    loraplus_lr_embedding: Optional[float] = field(
-        default=1e-6,
-        metadata={"help": "loraplus learning rate for lora embedding layers."},
-    )
-    embedding_lr_scale: Optional[float] = field(
-        default=None,
-        metadata={"help": "Scale the learning rate for the embedding layers."},
-    )
-    lr_groups: Optional[list[dict]] = field(
-        default=None,
-        metadata={"help": "Specify learning rate groups for with different LRs."},
-    )
-    embedding_lr: Optional[float] = field(
-        default=None,
-        metadata={"help": "absolute learning rate for the embedding layers."},
-    )
-    qlora: bool = field(
-        default=False,
-        metadata={"help": "whether this is a qlora training"},
-    )
-    orpo_alpha: Optional[float] = field(
-        default=None,
-    )
-    lisa_n_layers: Optional[int] = field(
-        default=None,
-        metadata={"help": "the number of activate layers in LISA"},
-    )
-    lisa_step_interval: Optional[int] = field(
-        default=None,
-        metadata={"help": "how often to switch layers in LISA"},
-    )
-    lisa_layers_attribute: Optional[str] = field(
-        default=None,
-        metadata={"help": "path under the model to access the layers"},
-    )
-    curriculum_sampling: Optional[bool] = field(
-        default=None,
-        metadata={"help": "whether to use sequential sampling for curriculum learning"},
-    )
-    alternate_optimizer: Optional[str] = field(
-        default=None,
-        metadata={
-            "help": "workaround to pass an alternate optimizer to the HF trainer"
-        },
-    )
-    alternate_lr_scheduler_type: Optional[str] = field(
-        default=None,
-        metadata={
-            "help": "workaround to pass an alternate lr scheduler to the HF trainer"
-        },
-    )
-    chat_template: Optional[str] = field(
-        default=None,
-        metadata={"help": "Chat template converting chat messages to text"},
-    )
-
-    kd_ce_alpha: Optional[float] = field(
-        default=None,
-        metadata={
-            "help": "The alpha scaling parameter for SFT cross entropy loss when using KD"
-        },
-    )
-
-    kd_alpha: Optional[float] = field(
-        default=1.0,
-        metadata={"help": "The alpha scaling parameter for KD loss"},
-    )
-
-    kd_temperature: Optional[float] = field(
-        default=1.0,
-        metadata={
-            "help": "the temperature parameter for KL divergence loss when using KD"
-        },
-    )
-
-    kd_zscore_base_temp: Optional[float] = field(
-        default=None,
-        metadata={
-            "help": "the base temperature parameter for KL divergence with z-score when using KD"
-        },
-    )
-
-    kd_top_k_before_softmax: Optional[bool] = field(
-        default=None,
-        metadata={
-            "help": "Whether to apply top_k_before_softmax to the logits when using KD"
-        },
-    )
-
-
-@dataclass
-class AxolotlTrainingArguments(AxolotlTrainingMixins, TrainingArguments):
-    """
-    Training arguments for Causal trainer
-
-    This code is duplicated due to HF TrainingArguments not setting output_dir with a defaujlt value
-    so it can't be used as a mixin.
-    """
-
-
-@dataclass
-class AxolotlORPOConfig(AxolotlTrainingMixins, ORPOConfig):
-    """
-    ORPO config for ORPO training
-    """
-
-
-@dataclass
-class AxolotlKTOConfig(AxolotlTrainingMixins, KTOConfig):
-    """
-    KTO config for KTO training
-    """
-
-
-@dataclass
-class AxolotlCPOConfig(AxolotlTrainingMixins, CPOConfig):
-    """
-    CPO config for CPO training
-    """
-
-    simpo_gamma: Optional[float] = field(
-        default=None,
-        metadata={"help": "simpo gamma parameter"},
-    )
-
-
-@dataclass
-class AxolotlRewardConfig(AxolotlTrainingMixins, RewardConfig):
-    """
-    Reward config for Reward training
-    """
-
-
-@dataclass
-class AxolotlPRMConfig(AxolotlTrainingMixins, PRMConfig):
-    """
-    PRM config for PRM training
-    """

src/axolotl/datasets.py

@@ -2,7 +2,7 @@
 
 import logging
 import os
-from typing import List, Optional, Union
+from typing import List, Optional
 
 import torch
 from datasets import Dataset, IterableDataset
@@ -51,17 +51,7 @@ class TokenizedPromptDataset(Dataset):
         map_kwargs = {}
         if self.prompt_tokenizer.supports_batched:
             map_kwargs["batched"] = True
-            map_kwargs["batch_size"] = 1_000
-
-        if (
-            hasattr(self.prompt_tokenizer, "filter_rows")
-            and self.prompt_tokenizer.filter_rows
-        ):
-            dataset = dataset.filter(
-                self.prompt_tokenizer.filter_rows,
-                num_proc=num_proc,
-                desc="Strategy Filtering Rows",
-            )
+            map_kwargs["batch_size"] = 100
 
         return dataset.map(
             self.prompt_tokenizer.tokenize_prompt,
@@ -73,24 +63,6 @@ class TokenizedPromptDataset(Dataset):
         )
 
 
-def wrap_dataset_for_tokenized_prompt(
-    prompt_tokenizer: PromptTokenizingStrategy,
-    dataset: Union[Dataset, IterableDataset],
-    **kwargs,
-):
-    if isinstance(dataset, IterableDataset):
-        map_kwargs = {}
-        if prompt_tokenizer.supports_batched:
-            map_kwargs["batched"] = True
-        features = dataset.features.keys()
-        return dataset.map(
-            prompt_tokenizer.tokenize_prompt,
-            remove_columns=features,
-            **map_kwargs,
-        )
-    return TokenizedPromptDataset(prompt_tokenizer, dataset, **kwargs)
-
-
 # TODO this isn't the best since it can't interleave datasets
 class ConstantLengthDataset(IterableDataset):
     """

src/axolotl/integrations/base.py

@@ -111,17 +111,6 @@ class BasePlugin:
         None
         """
 
-    def get_trainer_cls(self, cfg):  # pylint: disable=unused-argument):
-        """
-        Returns a custom class for the trainer.
-
-        Parameters:
-        cfg (dict): The global axolotl configuration.
-
-        Returns:
-        class: The class for the trainer.
-        """
-
     def create_optimizer(self, cfg, trainer):  # pylint: disable=unused-argument
         """
         Creates and returns an optimizer for training.
@@ -223,17 +212,7 @@ def load_plugin(plugin_name: str) -> BasePlugin:
     module_name, class_name = plugin_name.rsplit(".", 1)
 
     # import the module
-    try:
-        module = importlib.import_module(module_name)
-    except ModuleNotFoundError as orig_exc:
-        try:
-            if not module_name.startswith("axolotl.integrations."):
-                module = importlib.import_module("axolotl.integrations." + module_name)
-            else:
-                raise orig_exc
-        except ModuleNotFoundError as exc:
-            raise orig_exc from exc
-
+    module = importlib.import_module(module_name)
     # instantiate the class
     plugin_class = getattr(module, class_name)
     # create an instance of the class
@@ -293,10 +272,8 @@ class PluginManager:
         ImportError: If the plugin module cannot be imported.
         """
         try:
-            logging.info(f"Attempting to load plugin: {plugin_name}")
             plugin = load_plugin(plugin_name)
             self.plugins[plugin_name] = plugin
-            logging.info(f"Plugin loaded successfully: {plugin_name}")
         except ImportError:
             logging.error(f"Failed to load plugin: {plugin_name}")
 
@@ -369,22 +346,6 @@ class PluginManager:
         for plugin in self.plugins.values():
             plugin.post_lora_load(cfg, model)
 
-    def get_trainer_cls(self, cfg):
-        """
-        Calls the get_trainer_cls method of all registered plugins and returns the first non-None trainer class.
-
-        Parameters:
-        cfg (dict): The configuration for the plugins.
-
-        Returns:
-        object: The trainer class, or None if none was found.
-        """
-        for plugin in self.plugins.values():
-            trainer_cls = plugin.get_trainer_cls(cfg)
-            if trainer_cls is not None:
-                return trainer_cls
-        return None
-
     def create_optimizer(self, cfg, trainer):
         """
         Calls the create_optimizer method of all registered plugins and returns the first non-None optimizer.

src/axolotl/integrations/cut_cross_entropy/README.md

@@ -1,10 +1,6 @@
 # Cut Cross Entropy
 
-Cut Cross Entropy reduces VRAM usage through optimization on the cross-entropy operation during loss calculation.
-
-See https://github.com/apple/ml-cross-entropy
-
-## Usage
+### Usage
 
 ```yaml
 plugins:
@@ -12,19 +8,3 @@ plugins:
 
 cut_cross_entropy: true
 ```
-
-## Citation
-
-```bib
-@article{wijmans2024cut,
-  author       = {Erik Wijmans and
-                  Brody Huval and
-                  Alexander Hertzberg and
-                  Vladlen Koltun and
-                  Philipp Kr\"ahenb\"uhl},
-  title        = {Cut Your Losses in Large-Vocabulary Language Models},
-  journal      = {arXiv},
-  year         = {2024},
-  url          = {https://arxiv.org/abs/2411.09009},
-}
-```

src/axolotl/integrations/grokfast/README.md

@@ -2,7 +2,7 @@
 
 See https://github.com/ironjr/grokfast
 
-## Usage
+### Usage
 
 ```yaml
 plugins:
@@ -11,14 +11,3 @@ plugins:
 grokfast_alpha: 2.0
 grokfast_lamb: 0.98
 ```
-
-## Citation
-
-```bib
-@article{lee2024grokfast,
-    title={{Grokfast}: Accelerated Grokking by Amplifying Slow Gradients},
-    author={Lee, Jaerin and Kang, Bong Gyun and Kim, Kihoon and Lee, Kyoung Mu},
-    journal={arXiv preprint arXiv:2405.20233},
-    year={2024}
-}
-```

src/axolotl/integrations/kd/README.md

@@ -1,23 +0,0 @@
-# Knowledge Distillation
-
-## Usage
-
-```yaml
-plugins:
-  - "axolotl.integrations.kd.KDPlugin"
-
-kd_trainer: True
-kd_ce_alpha: 0.1
-kd_alpha: 0.9
-kd_temperature: 1.0
-
-torch_compile: True  # torch>=2.5.1, recommended to reduce vram
-
-datasets:
-  - path: ...
-    type: "axolotl.integrations.kd.chat_template"
-    field_messages: "messages_combined"
-    logprobs_field: "llm_text_generation_vllm_logprobs"  # for kd only, field of logprobs
-```
-
-An example dataset can be found at [`axolotl-ai-co/evolkit-logprobs-pipeline-75k-v2-sample`](https://huggingface.co/datasets/axolotl-ai-co/evolkit-logprobs-pipeline-75k-v2-sample)

src/axolotl/integrations/kd/__init__.py

@@ -1,36 +0,0 @@
-# Copyright 2024 Axolotl AI. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""
-Plugin init to add KD support to Axolotl.
-"""
-from axolotl.integrations.base import BasePlugin
-
-from .args import KDArgs  # pylint: disable=unused-import. # noqa: F401
-
-
-class KDPlugin(BasePlugin):
-    """
-    Plugin for KD support in Axolotl.
-    """
-
-    def get_input_args(self):
-        return "axolotl.integrations.kd.KDArgs"
-
-    def get_trainer_cls(self, cfg):
-        if cfg.kd_trainer:
-            from .trainer import AxolotlKDTrainer
-
-            return AxolotlKDTrainer
-        return None

src/axolotl/integrations/kd/args.py

@@ -1,37 +0,0 @@
-# Copyright 2024 Axolotl AI. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""
-Plugin args for KD support.
-"""
-from typing import Optional
-
-from pydantic import BaseModel
-
-
-class KDArgs(BaseModel):
-    """
-    Input args for knowledge distillation.
-    """
-
-    kd_trainer: Optional[bool] = None  # whether to use KD trainer
-    kd_ce_alpha: Optional[
-        float
-    ] = None  # loss coefficient for cross-entropy loss during KD
-    kd_alpha: Optional[float] = None  # loss coefficient for KD loss
-    kd_temperature: Optional[float] = None  # temperature for sampling during KD
-    kd_zscore_base_temp: Optional[float] = None  # base temperature for zscore scaling
-    kd_top_k_before_softmax: Optional[
-        bool
-    ] = None  # whether to sample top k before softmax during KD

src/axolotl/integrations/kd/chat_template.py

@@ -1,201 +0,0 @@
-# Copyright 2024 Axolotl AI. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""
-Chat template prompt strategy loader with KD support
-"""
-from typing import Any, Dict
-
-import torch
-
-from axolotl.prompt_strategies.chat_template import ChatTemplateStrategy, StrategyLoader
-
-
-class ChatTemplateStrategyWithKD(ChatTemplateStrategy):
-    """
-    Handle fields for logprob KD
-    """
-
-    def __init__(
-        self,
-        prompter,
-        tokenizer,
-        train_on_inputs,
-        sequence_len,
-        roles_to_train=None,
-        train_on_eos=None,
-        logprobs_field="logprobs",
-        gen_temperature=1.0,
-        kd_temperature=1.0,
-    ):
-        self.logprobs_field = logprobs_field
-        self.gen_temperature = gen_temperature
-        self.kd_temperature = kd_temperature
-
-        super().__init__(
-            prompter,
-            tokenizer,
-            train_on_inputs,
-            sequence_len,
-            roles_to_train=roles_to_train,
-            train_on_eos=train_on_eos,
-        )
-
-    @property
-    def supports_batched(self) -> bool:
-        # batching doesn't work well for logprob data
-        return False
-
-    def transform_logprobs(self, sample):
-        """
-        Transform logprobs to target format for KD training
-        """
-
-        logprobs = sample.pop(self.logprobs_field)
-        target_seq_len = len(logprobs)
-        input_seq_len = len(sample["input_ids"])
-        input_padding_len = input_seq_len - target_seq_len
-        # get non-zero top-k (prune None logprobs from vllm data step)
-        top_k_vals = [
-            len(logprobs[i])
-            for i in range(len(logprobs))
-            if logprobs[i] is not None and len(logprobs[i])
-        ]
-        max_top_k = max(set(top_k_vals), key=top_k_vals.count)
-        min_top_k = min(set(top_k_vals), key=top_k_vals.count)
-        top_k = min(max_top_k, min_top_k)
-        if top_k == 0:
-            raise ValueError("No non-zero top-k logprobs found.")
-
-        target_logprobs = []
-        target_token_ids = []
-        target_mask = []
-
-        if input_padding_len < 0:
-            # logprobs is longer than target_seq_len,
-            # so we need to slice from the left/beginning of logprobs
-            logprobs = logprobs[:-input_seq_len]
-            input_padding_len = 0
-            # target_seq_len = input_seq_len
-
-        # truncate the second dimension of the logprobs to top_k
-        logprobs = [row[:top_k] for row in logprobs]
-
-        # fill with -inf for padding_len tokens for top_k tokens
-        # extend target_logprobs with a padding_len x top_k 2D list filled with -inf
-
-        # for causal models, if we start the range at 1, then we don't need to shift in the trainer
-        # otherwise, we need to shift in the trainer
-        shift = 0
-        for _ in range(shift, input_padding_len):
-            target_logprobs.append([-float("inf")] * top_k)
-            target_token_ids.append(list(range(top_k)))
-            target_mask.append([0] * top_k)
-
-        for position in range(input_padding_len, input_seq_len):
-            if sample["labels"][position] == -100:
-                target_mask.append([0] * top_k)
-            else:
-                target_mask.append([1] * top_k)
-
-        for _, token_pos_logprobs in enumerate(logprobs):
-            # Initialize collections for logprobs and token_ids
-            position_logprobs = []
-            position_token_ids = []
-
-            # Process each token probability entry
-            for entry in token_pos_logprobs:
-                # Extract logprob value
-                logprob = entry["logprob"]
-
-                # Parse token_id from the "token_id:###" format
-                token_id = int(entry["token"].split(":")[1])
-
-                # Append to our collections
-                position_logprobs.append(logprob)
-                position_token_ids.append(token_id)
-
-            # Convert to a tensor for easier manipulation
-            position_logprobs_tensor = torch.tensor(
-                position_logprobs, dtype=torch.float
-            )
-
-            # Now we have distribution at T1 in log form, i.e. log p_{T1}(k).
-            # Next, re-scale to T2 = self.kd_temperature via exponent-based trick
-            # p_{T2}(k) = [p_{T1}(k)]^(T1 / T2) / Z
-            #
-            # Convert from log to probability
-            teacher_probs_t1 = position_logprobs_tensor.exp()
-            if self.kd_temperature != self.gen_temperature:
-                # Exponentiate by factor (T1 / T2)
-                exponent = self.gen_temperature / self.kd_temperature
-                teacher_probs_t2 = teacher_probs_t1**exponent
-            else:
-                teacher_probs_t2 = teacher_probs_t1
-            # Re-normalize
-            teacher_probs_t2 = teacher_probs_t2 / teacher_probs_t2.sum(
-                dim=0, keepdim=True
-            )
-            # Convert back to log
-            position_logprobs_tensor = torch.log(teacher_probs_t2)
-
-            # Now we have log p_{teacher, T2}(k) stored in position_logprobs_tensor
-            position_logprobs_scaled = position_logprobs_tensor.tolist()
-
-            target_logprobs.append(position_logprobs_scaled)
-            target_token_ids.append(position_token_ids)
-
-        if shift == 1:
-            # since we started at index 1 for causal, we need one more padding token
-            target_logprobs.append([-float("inf")] * top_k)
-            target_token_ids.append(list(range(top_k)))
-            target_mask.append([0] * top_k)
-
-        # Update sample with transformed logprobs
-        sample["target_logprobs"] = target_logprobs
-        sample["target_token_ids"] = target_token_ids
-        sample["target_mask"] = target_mask
-
-        return sample
-
-    def _tokenize_single_prompt(self, prompt):
-        logprobs = prompt.pop(self.logprobs_field)
-        tokenized_prompt = super()._tokenize_single_prompt(prompt)
-        tokenized_prompt[self.logprobs_field] = logprobs
-        tokenized_prompt = self.transform_logprobs(tokenized_prompt)
-
-        return tokenized_prompt
-
-
-class KDStrategyLoader(StrategyLoader):
-    """
-    Load ChatTemplateStrategy with KD support using StrategyLoader.
-    """
-
-    def _get_strategy_cls(self):
-        return ChatTemplateStrategyWithKD
-
-    def _get_strategy_params(self, cfg, ds_cfg: Dict[str, Any]):
-        strategy_params = super()._get_strategy_params(cfg, ds_cfg)
-        if logprobs_field := ds_cfg.get("logprobs_field"):
-            strategy_params["logprobs_field"] = logprobs_field
-        if gen_temperature := ds_cfg.get("temperature"):
-            strategy_params["gen_temperature"] = gen_temperature
-        if kd_temperature := cfg.get("kd_temperature"):
-            strategy_params["kd_temperature"] = kd_temperature
-
-        return strategy_params
-
-
-load = KDStrategyLoader()

src/axolotl/integrations/kd/collator.py

@@ -1,255 +0,0 @@
-# Copyright 2024 Axolotl AI. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""
-DataCollator for axolotl to handle KD fields without using -inf for padding,
-and with a teacher_mask to identify padded positions.
-"""
-
-from dataclasses import dataclass
-from typing import Any, Optional, Union
-
-import numpy as np
-import torch
-from transformers import PreTrainedTokenizerBase
-from transformers.utils import PaddingStrategy
-
-from axolotl.utils.collators.batching import DataCollatorForSeq2Seq
-
-
-@dataclass
-class DataCollatorForKD(DataCollatorForSeq2Seq):
-    """
-    Data collator for KD, including handling KD-specific fields.
-
-    This version avoids using -inf and instead uses a large negative value for padding
-    target_logprobs. It also creates a teacher_mask to indicate which entries are valid.
-    """
-
-    # pylint: disable=duplicate-code
-    tokenizer: PreTrainedTokenizerBase
-    model: Optional[Any] = None
-    padding: Union[bool, str, PaddingStrategy] = True
-    max_length: Optional[int] = None
-    pad_to_multiple_of: Optional[int] = None
-    label_pad_token_id: int = -100
-    position_pad_token_id: int = 0
-    return_tensors: str = "pt"
-
-    def __call__(self, features, return_tensors=None):
-        if return_tensors is None:
-            return_tensors = self.return_tensors
-
-        padding_side = self.tokenizer.padding_side
-
-        # Pad labels and position_ids first
-        for feature_name, pad_token_id in [
-            ("labels", self.label_pad_token_id),
-            ("position_ids", self.position_pad_token_id),
-        ]:
-            if feature_name in features[0]:
-                feat = [f[feature_name] for f in features]
-                max_len = max(len(x) for x in feat)
-                if self.pad_to_multiple_of is not None:
-                    max_len = (
-                        (max_len + self.pad_to_multiple_of - 1)
-                        // self.pad_to_multiple_of
-                    ) * self.pad_to_multiple_of
-
-                for f in features:  # pylint: disable=invalid-name
-                    remainder = [pad_token_id] * (max_len - len(f[feature_name]))
-                    if isinstance(f[feature_name], list):
-                        f[feature_name] = (
-                            f[feature_name] + remainder
-                            if padding_side == "right"
-                            else remainder + f[feature_name]
-                        )
-                    else:
-                        # If they are numpy arrays
-                        if padding_side == "right":
-                            f[feature_name] = np.concatenate(
-                                [f[feature_name], remainder]
-                            ).astype(np.int64)
-                        else:
-                            f[feature_name] = np.concatenate(
-                                [remainder, f[feature_name]]
-                            ).astype(np.int64)
-
-        # Handle target_logprobs and target_token_ids manually
-        target_logprobs_list = []
-        target_token_ids_list = []
-        target_mask_list = []
-        has_teacher_data = ("target_logprobs" in features[0]) and (
-            "target_token_ids" in features[0]
-        )
-
-        if has_teacher_data:
-            # Extract and remove from features
-            for f in features:  # pylint: disable=invalid-name
-                target_logprobs_list.append(f.pop("target_logprobs"))
-                target_token_ids_list.append(f.pop("target_token_ids"))
-                target_mask_list.append(f.pop("target_mask"))
-
-            # Determine max lengths
-            max_teacher_seq_len = max(len(seq) for seq in target_logprobs_list)
-            max_k = max(len(seq_k) for seq in target_logprobs_list for seq_k in seq)
-
-            padded_target_logprobs = []
-            padded_target_token_ids = []
-            padded_teacher_mask_list = []
-
-            for t_logprobs, t_ids, t_mask in zip(
-                target_logprobs_list, target_token_ids_list, target_mask_list
-            ):
-                t_logprobs_padded = []
-                t_ids_padded = []
-                t_mask_padded = []
-
-                for lp, ids, mask in zip(  # pylint: disable=invalid-name
-                    t_logprobs, t_ids, t_mask
-                ):
-                    lp_len = len(lp)
-                    if lp_len < max_k:
-                        # Use -1e9 for padding logprobs and 0 for token_ids
-                        pad_len = max_k - lp_len
-                        lp = lp + [-1e9] * pad_len  # pylint: disable=invalid-name
-                        ids = ids + [0] * pad_len
-                        mask = mask + [0] * pad_len
-                    else:
-                        lp = lp[:max_k]  # pylint: disable=invalid-name
-                        ids = ids[:max_k]
-                        mask = mask[:max_k]
-
-                    t_logprobs_padded.append(lp)
-                    t_ids_padded.append(ids)
-                    t_mask_padded.append(mask)
-
-                seq_len_diff = max_teacher_seq_len - len(t_logprobs_padded)
-                if seq_len_diff > 0:
-                    # Pad sequences fully if needed
-                    t_logprobs_padded.extend(
-                        [[-1e9] * max_k for _ in range(seq_len_diff)]
-                    )
-                    t_ids_padded.extend([[0] * max_k for _ in range(seq_len_diff)])
-                    t_mask_padded.extend([[0] * max_k for _ in range(seq_len_diff)])
-
-                padded_target_logprobs.append(t_logprobs_padded)
-                padded_target_token_ids.append(t_ids_padded)
-                padded_teacher_mask_list.append(t_mask_padded)
-
-            # Convert to tensors
-            padded_target_logprobs = torch.tensor(
-                padded_target_logprobs, dtype=torch.float
-            )
-            padded_target_token_ids = torch.tensor(
-                padded_target_token_ids, dtype=torch.long
-            )
-            padded_teacher_mask_list = torch.tensor(
-                padded_teacher_mask_list, dtype=torch.int
-            )
-
-        # Pad using tokenizer for regular fields
-        features = self.tokenizer.pad(
-            features,
-            padding=self.padding,
-            max_length=self.max_length,
-            pad_to_multiple_of=self.pad_to_multiple_of,
-            return_tensors=return_tensors,
-        )
-
-        # Add back teacher data if present
-        if has_teacher_data:
-            features["target_logprobs"] = padded_target_logprobs
-            features["target_token_ids"] = padded_target_token_ids
-            features["target_mask"] = padded_teacher_mask_list
-
-        # Prepare decoder_input_ids if the model supports it
-        if (
-            "labels" in features
-            and self.model is not None
-            and hasattr(self.model, "prepare_decoder_input_ids_from_labels")
-        ):
-            decoder_input_ids = self.model.prepare_decoder_input_ids_from_labels(
-                labels=features["labels"]
-            )
-            features["decoder_input_ids"] = decoder_input_ids
-
-        return features
-
-
-class KDBatchSamplerDataCollatorForSeq2Seq(DataCollatorForKD):
-    """
-    Collator for multipack (batch of sub-batches) specifically for KD.
-    Adapts DataCollatorForKD so it can pack multiple sequences in a single batch item.
-    """
-
-    def __call__(self, features, return_tensors=None):
-        """
-        Expects that `features` could be either:
-          - a single list of dicts, OR
-          - a list of lists of dicts (the "sub-batches" to be packed).
-        """
-        # 1) If we are *not* dealing with multiple sequences per batch element,
-        #    just pass straight to parent.
-        if not isinstance(features[0], list):
-            return super().__call__(features, return_tensors=return_tensors)
-
-        # 2) Otherwise, we *are* dealing with multiple sequences in each batch item.
-        #    We want to produce a single "merged" feature dict for each sub-batch.
-        out_features = [{} for _ in features]
-
-        for i, sub_features in enumerate(features):
-            # sub_features is a list of dicts, each dict = one sequence’s features
-            # We'll merge them into out_features[i].
-            #
-            # NOTE: You can customize how you combine fields as needed (e.g. summation
-            # or offset for attention_mask). Below is a straightforward concatenation/extension.
-
-            for field_name in sub_features[0].keys():
-                # Some fields you might want to skip or treat specially:
-                if field_name == "length":
-                    continue
-
-                # If it’s a KD field that’s a list-of-lists (e.g. target_logprobs),
-                # you typically just want to flatten them by extending.
-                if field_name in ["target_logprobs", "target_token_ids", "target_mask"]:
-                    combined = []
-                    for feat in sub_features:
-                        combined.extend(feat[field_name])
-                    out_features[i][field_name] = combined
-
-                elif field_name == "attention_mask":
-                    # Here we apply the (j+1) factor to differentiate each sub-sample
-                    # within this merged batch item.
-                    arrays = []
-                    for j, feat in enumerate(sub_features):
-                        if field_name in feat:
-                            arrays.append((j + 1) * np.array(feat[field_name]))
-                    out_features[i][field_name] = np.concatenate(arrays)
-                else:
-                    # By default, just concatenate them if they are arrays
-                    # or extend them if they are lists.
-                    # For example, input_ids or labels are often arrays.
-                    arrays = []
-                    for feat in sub_features:
-                        if field_name in feat:
-                            arr = np.array(feat[field_name])
-                            arrays.append(arr)
-                    out_features[i][field_name] = np.concatenate(arrays)
-
-        # 3) Now call the parent collator, which will do:
-        #    - padding of labels/position_ids
-        #    - KD-specific padding for target_logprobs, target_token_ids, etc.
-        #    - final conversion to return_tensors
-        return super().__call__(out_features, return_tensors=return_tensors)

src/axolotl/integrations/kd/topk_logprob/forward_kl.py

@@ -1,237 +0,0 @@
-# Copyright 2024 Axolotl AI. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""
-loss for top_k KL divergence
-"""
-import torch
-
-
-def zscore_standardize(
-    logits: torch.Tensor,
-    mask: torch.Tensor = None,
-    base_temperature: float = 1.0,
-    eps: float = 1e-9,
-):
-    """
-    Z-score standardize along the last dimension of `logits`.
-    i.e., for each [B, seq_len] row, across K entries:
-        z = (logits - mean) / std,
-    then scale by 1 / base_temperature if desired.
-
-    mask can be broadcastable or None. If None, we standardize all elements.
-    """
-    if mask is None:
-        # shape: [B, seq_len, K]
-        # Mean and std over dim=-1
-        mean = logits.mean(dim=-1, keepdim=True)
-        var = logits.var(dim=-1, unbiased=False, keepdim=True)
-    else:
-        # If you have to exclude some tokens, multiply by mask, etc.
-        float_mask = mask.to(logits.dtype)
-        count = float_mask.sum(dim=-1, keepdim=True).clamp_min(1.0)
-        mean = (logits * float_mask).sum(dim=-1, keepdim=True) / count
-        var = (float_mask * (logits - mean) ** 2).sum(dim=-1, keepdim=True) / count
-
-    std = torch.sqrt(var.clamp_min(eps))
-    z = (logits - mean) / std
-
-    # Scale by 1 / base_temperature
-    z = z / base_temperature
-    return z
-
-
-@torch.jit.script
-def loss(
-    student_logits: torch.Tensor,
-    target_token_ids: torch.Tensor,
-    target_logprobs: torch.Tensor,
-    target_mask: torch.Tensor,
-    num_items_in_batch: int = -1,  # Use -1 to indicate "None"
-    kd_temperature: float = 1.0,
-    top_k_before_softmax: int = 0,
-) -> torch.Tensor:
-    """
-    A KD loss function that is TorchScript-friendly.
-
-    Arguments:
-        student_logits (torch.Tensor): The logits of the student model.
-            Shape: [B, student_seq_len, vocab_size]
-        target_token_ids (torch.Tensor): The top-k teacher/target token IDs
-            Shape: [B, teacher_seq_len, top_k]
-        target_logprobs (torch.Tensor): The top-k teacher/target logprobs, these should already be re-normalized.
-            Shape: [B, teacher_seq_len, top_k]
-        target_mask (torch.Tensor): The mask for valid tokens.
-            Shape: [B, teacher_seq_len, top_k]
-        num_items_in_batch (int, optional): The number of items in the batch.
-        kd_temperature (float, optional): The temperature for KD.
-            Default: 1.0
-        top_k_before_softmax (int, optional): Flag of whether to apply softmax before gathering student top-k logits
-            Default: 0
-    """
-
-    target_logprobs = target_logprobs.float()
-
-    # Determine the teacher sequence length
-    # target_token_ids shape: [B, teacher_seq_len, K]
-    # student_logits shape:   [B, student_seq_len, vocab_size]
-    teacher_seq_len = target_token_ids.shape[1]
-
-    if top_k_before_softmax:
-        # Slice student logits to match teacher-provided sequence length
-        student_logits_for_kd = student_logits[
-            :, :teacher_seq_len, :
-        ]  # [B, teacher_seq_len, vocab_size]
-
-        # Gather student logits for teacher's top-K tokens
-        student_logits_topk = torch.gather(
-            student_logits_for_kd, dim=-1, index=target_token_ids
-        )  # [B, teacher_seq_len, K]
-
-        student_logits_topk = student_logits_topk.float()
-
-        # Apply KD temperature to student’s logits
-        if kd_temperature != 1.0:
-            student_logits_topk = student_logits_topk / kd_temperature
-
-        # Convert student top-k logits to logprobs
-        student_logprobs_topk = student_logits_topk - torch.logsumexp(
-            student_logits_topk, dim=-1, keepdim=True
-        )  # [B, teacher_seq_len, K]
-    else:
-        # Slice student logits to match teacher-provided sequence length
-        student_logits_for_kd = (
-            student_logits[:, :teacher_seq_len, :] / kd_temperature
-        )  # [B, teacher_seq_len, vocab_size]
-
-        # keep in full precision for numerical stability of loss
-        student_logits_for_kd = student_logits_for_kd.float()
-
-        # Gather student logits for teacher's top-K tokens
-        student_logits_topk = torch.gather(
-            student_logits_for_kd, dim=-1, index=target_token_ids
-        )  # [B, teacher_seq_len, K]
-
-        # Compute logsumexp across full vocabulary
-        student_lse = torch.logsumexp(student_logits_for_kd, dim=-1, keepdim=True)
-
-        #  Convert just the top-k logits to logprobs
-        student_logprobs_topk = student_logits_topk - student_lse
-
-    # Convert teacher_mask to boolean for indexing
-    # In TorchScript, .bool() is sometimes unsupported, so we do:
-    valid_mask = target_mask.to(torch.bool)
-
-    # Prune tensors to only keep valid tokens
-    student_logprobs_topk = student_logprobs_topk[valid_mask]
-    target_logprobs = target_logprobs[valid_mask]
-
-    # Convert teacher logprobs to probabilities
-    teacher_probs = target_logprobs.exp()
-
-    # Compute forward KL
-    kd_loss_per_token = teacher_probs * (target_logprobs - student_logprobs_topk)
-    kd_loss = kd_loss_per_token.sum()
-
-    # Multiply by T^2 (classical KD scaling)
-    if kd_temperature != 1.0:
-        kd_loss = kd_loss * (kd_temperature**2)
-
-    # Normalize by number of items (if provided) or by valid tokens
-    if num_items_in_batch > 0:
-        kd_loss = kd_loss / float(num_items_in_batch)
-    else:
-        # Fall back to average over valid tokens
-        kd_loss = kd_loss / float(kd_loss_per_token.size(0))
-
-    return kd_loss
-
-
-def topk_kd_loss_with_zscore(
-    student_logits: torch.Tensor,  # [B, seq_len, vocab_size]
-    target_token_ids: torch.Tensor,  # [B, seq_len, K]
-    target_logprobs: torch.Tensor,  # [B, seq_len, K], sums to 1.0 in prob space
-    target_mask: torch.Tensor,  # [B, seq_len, K] or [B, seq_len]
-    kd_temperature: float = 1.0,  # classic KD temperature
-    zscore_base_temp: float = 1.0,  # from the paper
-    num_items_in_batch: int = -1,
-):
-    """
-    A variant of top_k KL divergence with Z-score scaling
-    from "Logit Standardization in Knowledge Distillation".
-    """
-
-    target_logprobs = target_logprobs.float()
-
-    B, teacher_seq_len, K = target_logprobs.shape  # pylint: disable=invalid-name
-    # 1) Gather the student's top-k logits to match teacher
-    student_logits_for_kd = student_logits[
-        :, :teacher_seq_len, :
-    ]  # [B, seq_len, vocab]
-    student_topk_logits = torch.gather(
-        student_logits_for_kd, dim=-1, index=target_token_ids
-    )  # [B, seq_len, K]
-
-    student_topk_logits = student_topk_logits.float()
-
-    # 2) If you want to keep the "classical" T scaling, apply it first
-    if kd_temperature != 1.0:
-        student_topk_logits = student_topk_logits / kd_temperature
-
-    # 3) Convert teacher logprobs -> treat them as “logits” for z-score
-    #    (They differ by +some_constant from real logits, but in z-score
-    #     that constant is subtracted out anyway.)
-    teacher_logits_for_zscore = target_logprobs  # rename variable for clarity
-
-    # 4) Z-score teacher and student
-    #    If target_mask is 2D, expand to 3D for the K dimension
-    if target_mask.dim() == 2 and target_mask.shape[:2] == (B, teacher_seq_len):
-        target_mask = target_mask.unsqueeze(-1).expand(-1, -1, K)
-
-    teacher_z = zscore_standardize(
-        teacher_logits_for_zscore, mask=target_mask, base_temperature=zscore_base_temp
-    )
-    student_z = zscore_standardize(
-        student_topk_logits, mask=target_mask, base_temperature=zscore_base_temp
-    )
-
-    # 5) Convert to log-probs for KL
-    teacher_logprobs_z = teacher_z - torch.logsumexp(teacher_z, dim=-1, keepdim=True)
-    student_logprobs_z = student_z - torch.logsumexp(student_z, dim=-1, keepdim=True)
-
-    # 6) Restrict to valid tokens if needed
-    valid_mask = target_mask.bool()  # shape [B, seq_len, K]
-    teacher_probs_z = teacher_logprobs_z.exp()
-    teacher_probs_z = teacher_probs_z[valid_mask]
-    teacher_logprobs_z = teacher_logprobs_z[valid_mask]
-    student_logprobs_z = student_logprobs_z[valid_mask]
-
-    # 7) forward KL:  sum( p_teacher * [log(p_teacher) - log(p_student)] )
-    kd_loss_per_token = teacher_probs_z * (teacher_logprobs_z - student_logprobs_z)
-    kd_loss = kd_loss_per_token.sum()
-
-    # 8) If using classical KD scaling by T^2
-    if kd_temperature != 1.0:
-        kd_loss = kd_loss * (kd_temperature**2)
-
-    # Optionally scale by zscore_base_temp**2 if you want (paper might differ).
-    # kd_loss = kd_loss * (zscore_base_temp**2)
-
-    # 9) Normalize
-    if num_items_in_batch is not None and num_items_in_batch > 0:
-        kd_loss = kd_loss / float(num_items_in_batch)
-    else:
-        kd_loss = kd_loss / float(kd_loss_per_token.size(0))
-
-    return kd_loss

src/axolotl/integrations/kd/trainer.py

@@ -1,113 +0,0 @@
-# Copyright 2024 Axolotl AI. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""
-KD trainer
-"""
-
-from axolotl.core.trainers.base import AxolotlTrainer
-
-from .topk_logprob.forward_kl import loss as topk_kd_loss
-from .topk_logprob.forward_kl import topk_kd_loss_with_zscore
-
-
-class AxolotlKDTrainer(AxolotlTrainer):
-    """
-    Custom trainer subclass for Knowledge Distillation (KD)
-    """
-
-    def _set_signature_columns_if_needed(self):
-        super()._set_signature_columns_if_needed()
-        columns_to_add = []
-        if self._signature_columns:
-            if "target_logprobs" not in self._signature_columns:
-                columns_to_add.append("target_logprobs")
-            if "target_token_ids" not in self._signature_columns:
-                columns_to_add.append("target_token_ids")
-            if "target_mask" not in self._signature_columns:
-                columns_to_add.append("target_mask")
-            if columns_to_add:
-                self._signature_columns += columns_to_add
-
-    def compute_loss(
-        self,
-        model,
-        inputs,
-        return_outputs=False,
-        num_items_in_batch=None,
-    ):
-        """
-        How the loss is computed by Trainer. By default, all models return the loss in the first element.
-
-        Subclass and override for custom behavior.
-        """
-
-        target_logprobs = inputs.pop("target_logprobs")
-        target_token_ids = inputs.pop("target_token_ids")
-        target_mask = inputs.pop("target_mask")
-
-        seq_len = target_token_ids.shape[1]
-
-        if self.model_accepts_loss_kwargs:
-            loss_kwargs = {}
-            if num_items_in_batch is not None:
-                loss_kwargs["num_items_in_batch"] = num_items_in_batch
-            inputs = {**inputs, **loss_kwargs}
-        outputs = model(**inputs)
-
-        # FIXME: account for tokenizer.padding_side
-        student_logits = outputs["logits"][:, : seq_len - 1, :].contiguous()
-
-        shift_logits = student_logits.contiguous()
-        target_logprobs_for_loss = target_logprobs[..., 1:, :].contiguous()
-        target_token_ids_for_loss = target_token_ids[..., 1:, :].contiguous()
-        target_mask_for_loss = target_mask[..., 1:, :].contiguous()
-
-        if self.args.kd_zscore_base_temp:
-            loss_kd = topk_kd_loss_with_zscore(
-                shift_logits,
-                target_token_ids_for_loss,
-                target_logprobs_for_loss,
-                target_mask_for_loss,
-                kd_temperature=self.args.kd_temperature,
-                zscore_base_temp=self.args.kd_zscore_base_temp,
-                num_items_in_batch=num_items_in_batch,
-            )
-        else:
-            loss_kd = topk_kd_loss(
-                shift_logits,
-                target_token_ids_for_loss,
-                target_logprobs_for_loss,
-                target_mask_for_loss,
-                num_items_in_batch=num_items_in_batch,
-                kd_temperature=self.args.kd_temperature,
-                top_k_before_softmax=1 if self.args.kd_top_k_before_softmax else 0,
-            )
-
-        if self.args.kd_ce_alpha > 0:
-            kd_alpha = self.args.kd_alpha
-            loss = self.args.kd_ce_alpha * outputs["loss"] + kd_alpha * loss_kd
-        else:
-            loss = loss_kd
-        # Save past state if it exists
-        # TODO: this needs to be fixed and made cleaner later.
-        if self.args.past_index >= 0:
-            self._past = outputs[  # pylint: disable=attribute-defined-outside-init
-                self.args.past_index
-            ]
-
-        if self.args.average_tokens_across_devices and self.model_accepts_loss_kwargs:
-            loss *= self.accelerator.num_processes
-
-        return (loss, outputs) if return_outputs else loss

src/axolotl/integrations/liger/README.md

@@ -1,36 +0,0 @@
-# Liger Kernel Integration
-
-Liger Kernel provides efficient Triton kernels for LLM training, offering:
-
-- 20% increase in multi-GPU training throughput
-- 60% reduction in memory usage
-- Compatibility with both FSDP and DeepSpeed
-
-See https://github.com/linkedin/Liger-Kernel
-
-## Usage
-
-```yaml
-plugins:
-  - axolotl.integrations.liger.LigerPlugin
-liger_rope: true
-liger_rms_norm: true
-liger_glu_activation: true
-liger_layer_norm: true
-liger_fused_linear_cross_entropy: true
-```
-
-## Citation
-
-```bib
-@article{hsu2024ligerkernelefficienttriton,
-      title={Liger Kernel: Efficient Triton Kernels for LLM Training},
-      author={Pin-Lun Hsu and Yun Dai and Vignesh Kothapalli and Qingquan Song and Shao Tang and Siyu Zhu and Steven Shimizu and Shivam Sahni and Haowen Ning and Yanning Chen},
-      year={2024},
-      eprint={2410.10989},
-      archivePrefix={arXiv},
-      primaryClass={cs.LG},
-      url={https://arxiv.org/abs/2410.10989},
-      journal={arXiv preprint arXiv:2410.10989},
-}
-```

src/axolotl/integrations/lm_eval/README.md

@@ -1,10 +1,6 @@
 # LM Eval Harness
 
-Run evaluation on model using the popular lm-evaluation-harness library.
-
-See https://github.com/EleutherAI/lm-evaluation-harness
-
-## Usage
+### Usage
 
 ```yaml
 plugins:
@@ -14,22 +10,4 @@ lm_eval_tasks:
   - gsm8k
   - hellaswag
   - arc_easy
-
-lm_eval_batch_size: # Batch size for evaluation
-output_dir: # Directory to save evaluation results
-```
-
-## Citation
-
-```bib
-@misc{eval-harness,
-  author       = {Gao, Leo and Tow, Jonathan and Abbasi, Baber and Biderman, Stella and Black, Sid and DiPofi, Anthony and Foster, Charles and Golding, Laurence and Hsu, Jeffrey and Le Noac'h, Alain and Li, Haonan and McDonell, Kyle and Muennighoff, Niklas and Ociepa, Chris and Phang, Jason and Reynolds, Laria and Schoelkopf, Hailey and Skowron, Aviya and Sutawika, Lintang and Tang, Eric and Thite, Anish and Wang, Ben and Wang, Kevin and Zou, Andy},
-  title        = {A framework for few-shot language model evaluation},
-  month        = 07,
-  year         = 2024,
-  publisher    = {Zenodo},
-  version      = {v0.4.3},
-  doi          = {10.5281/zenodo.12608602},
-  url          = {https://zenodo.org/records/12608602}
-}
 ```

src/axolotl/integrations/spectrum/README.md

@@ -1,17 +1,15 @@
-# Spectrum: Targeted Training on Signal to Noise Ratio
+## Spectrum: Targeted Training on Signal to Noise Ratio
 
 by Eric Hartford, Lucas Atkins, Fernando Fernandes, David Golchinfar
 
 This plugin contains code to freeze the bottom fraction of modules in a model, based on the Signal-to-Noise Ratio (SNR).
 
-See https://github.com/cognitivecomputations/spectrum
-
-## Overview
+### Overview
 
 Spectrum is a tool for scanning and evaluating the Signal-to-Noise Ratio (SNR) of layers in large language models.
 By identifying the top n% of layers with the highest SNR, you can optimize training efficiency.
 
-## Usage
+### Usage
 
 ```yaml
 plugins:
@@ -21,17 +19,3 @@ spectrum_top_fraction: 0.5
 # Optional if using a pre-scanned model as your base_model. Useful if using a model mirror
 spectrum_model_name: meta-llama/Meta-Llama-3.1-8B
 ```
-
-## Citation
-
-```bib
-@misc{hartford2024spectrumtargetedtrainingsignal,
-      title={Spectrum: Targeted Training on Signal to Noise Ratio},
-      author={Eric Hartford and Lucas Atkins and Fernando Fernandes Neto and David Golchinfar},
-      year={2024},
-      eprint={2406.06623},
-      archivePrefix={arXiv},
-      primaryClass={cs.LG},
-      url={https://arxiv.org/abs/2406.06623},
-}
-```

src/axolotl/integrations/spectrum/model_snr_results/snr_results_meta-llama-Llama-3.2-1B-Instruct.json

@@ -1,590 +0,0 @@
-{
-    "model.layers.0.input_layernorm": {
-        "snr": Infinity,
-        "type": "input_layernorm"
-    },
-    "model.layers.1.input_layernorm": {
-        "snr": Infinity,
-        "type": "input_layernorm"
-    },
-    "model.layers.2.input_layernorm": {
-        "snr": Infinity,
-        "type": "input_layernorm"
-    },
-    "model.layers.3.input_layernorm": {
-        "snr": Infinity,
-        "type": "input_layernorm"
-    },
-    "model.layers.4.input_layernorm": {
-        "snr": Infinity,
-        "type": "input_layernorm"
-    },
-    "model.layers.5.input_layernorm": {
-        "snr": Infinity,
-        "type": "input_layernorm"
-    },
-    "model.layers.6.input_layernorm": {
-        "snr": Infinity,
-        "type": "input_layernorm"
-    },
-    "model.layers.7.input_layernorm": {
-        "snr": Infinity,
-        "type": "input_layernorm"
-    },
-    "model.layers.8.input_layernorm": {
-        "snr": Infinity,
-        "type": "input_layernorm"
-    },
-    "model.layers.9.input_layernorm": {
-        "snr": Infinity,
-        "type": "input_layernorm"
-    },
-    "model.layers.10.input_layernorm": {
-        "snr": Infinity,
-        "type": "input_layernorm"
-    },
-    "model.layers.11.input_layernorm": {
-        "snr": Infinity,
-        "type": "input_layernorm"
-    },
-    "model.layers.12.input_layernorm": {
-        "snr": Infinity,
-        "type": "input_layernorm"
-    },
-    "model.layers.13.input_layernorm": {
-        "snr": Infinity,
-        "type": "input_layernorm"
-    },
-    "model.layers.14.input_layernorm": {
-        "snr": Infinity,
-        "type": "input_layernorm"
-    },
-    "model.layers.15.input_layernorm": {
-        "snr": Infinity,
-        "type": "input_layernorm"
-    },
-    "lm_head": {
-        "snr": Infinity,
-        "type": "lm_head"
-    },
-    "model.layers.0.mlp.down_proj": {
-        "snr": 70.0594253540039,
-        "type": "mlp.down_proj"
-    },
-    "model.layers.1.mlp.down_proj": {
-        "snr": 11.135851860046387,
-        "type": "mlp.down_proj"
-    },
-    "model.layers.2.mlp.down_proj": {
-        "snr": 7.035482883453369,
-        "type": "mlp.down_proj"
-    },
-    "model.layers.3.mlp.down_proj": {
-        "snr": 6.422532081604004,
-        "type": "mlp.down_proj"
-    },
-    "model.layers.4.mlp.down_proj": {
-        "snr": 5.748020172119141,
-        "type": "mlp.down_proj"
-    },
-    "model.layers.5.mlp.down_proj": {
-        "snr": 3.885556697845459,
-        "type": "mlp.down_proj"
-    },
-    "model.layers.6.mlp.down_proj": {
-        "snr": 3.4336745738983154,
-        "type": "mlp.down_proj"
-    },
-    "model.layers.7.mlp.down_proj": {
-        "snr": 2.791595935821533,
-        "type": "mlp.down_proj"
-    },
-    "model.layers.8.mlp.down_proj": {
-        "snr": 5.36277961730957,
-        "type": "mlp.down_proj"
-    },
-    "model.layers.9.mlp.down_proj": {
-        "snr": 4.459208011627197,
-        "type": "mlp.down_proj"
-    },
-    "model.layers.10.mlp.down_proj": {
-        "snr": 6.272170066833496,
-        "type": "mlp.down_proj"
-    },
-    "model.layers.11.mlp.down_proj": {
-        "snr": 5.264761447906494,
-        "type": "mlp.down_proj"
-    },
-    "model.layers.12.mlp.down_proj": {
-        "snr": 4.324735641479492,
-        "type": "mlp.down_proj"
-    },
-    "model.layers.13.mlp.down_proj": {
-        "snr": 3.878648042678833,
-        "type": "mlp.down_proj"
-    },
-    "model.layers.14.mlp.down_proj": {
-        "snr": 2.9773054122924805,
-        "type": "mlp.down_proj"
-    },
-    "model.layers.15.mlp.down_proj": {
-        "snr": 4.471445560455322,
-        "type": "mlp.down_proj"
-    },
-    "model.layers.0.mlp.gate_proj": {
-        "snr": 25.227100372314453,
-        "type": "mlp.gate_proj"
-    },
-    "model.layers.1.mlp.gate_proj": {
-        "snr": 6.58299446105957,
-        "type": "mlp.gate_proj"
-    },
-    "model.layers.2.mlp.gate_proj": {
-        "snr": 3.4688243865966797,
-        "type": "mlp.gate_proj"
-    },
-    "model.layers.3.mlp.gate_proj": {
-        "snr": 1.555246114730835,
-        "type": "mlp.gate_proj"
-    },
-    "model.layers.4.mlp.gate_proj": {
-        "snr": 0.7770601511001587,
-        "type": "mlp.gate_proj"
-    },
-    "model.layers.5.mlp.gate_proj": {
-        "snr": 0.6239906549453735,
-        "type": "mlp.gate_proj"
-    },
-    "model.layers.6.mlp.gate_proj": {
-        "snr": 0.6440379023551941,
-        "type": "mlp.gate_proj"
-    },
-    "model.layers.7.mlp.gate_proj": {
-        "snr": 0.5120116472244263,
-        "type": "mlp.gate_proj"
-    },
-    "model.layers.8.mlp.gate_proj": {
-        "snr": 0.6544050574302673,
-        "type": "mlp.gate_proj"
-    },
-    "model.layers.9.mlp.gate_proj": {
-        "snr": 0.5381016731262207,
-        "type": "mlp.gate_proj"
-    },
-    "model.layers.10.mlp.gate_proj": {
-        "snr": 0.622873842716217,
-        "type": "mlp.gate_proj"
-    },
-    "model.layers.11.mlp.gate_proj": {
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src/axolotl/integrations/spectrum/model_snr_results/snr_results_meta-llama-Llama-3.2-1B.json

@@ -1,590 +0,0 @@
-{
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src/axolotl/kernels/geglu.py

@@ -1,159 +0,0 @@
-"""
-Module for definition of GEGLU Triton kernels.
-
-See "GLU Variants Improve Transformer" (https://arxiv.org/abs/2002.05202).
-
-Credit to `unsloth` (https://unsloth.ai/) for inspiration for this implementation.
-"""
-# pylint: disable=invalid-name,unnecessary-lambda-assignment,duplicate-code
-
-import torch
-import triton
-import triton.language as tl
-
-SQRT_2_PI: tl.constexpr = 0.7978845608028654  # sqrt(2/π)
-
-
-@triton.jit
-def _geglu_fwd_kernel(
-    gate_ptr,
-    up_ptr,
-    out_ptr,
-    n_elements,
-    BLOCK_SIZE: tl.constexpr,
-):
-    """GEGLU forward kernel.
-
-    Args:
-        gate_ptr: Pointer to gate tensor [*, hidden_dim].
-        up_ptr: Pointer to up-projection tensor [*, hidden_dim].
-        out_ptr: Pointer to output tensor [*, hidden_dim].
-        n_elements: Total number of elements in the input tensors.
-        BLOCK_SIZE: Size of thread blocks for parallel computation.
-    """
-    block_idx = tl.program_id(0)
-    offsets = block_idx * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)
-    mask = offsets < n_elements
-
-    gate = tl.load(gate_ptr + offsets, mask=mask, other=0).to(tl.float32)
-    up = tl.load(up_ptr + offsets, mask=mask, other=0)
-
-    # Compute activation in fp32 then convert back
-    gelu_gate = 0.5 * gate * (tl.math.erf(tl.math.rsqrt(2.0) * gate) + 1.0)
-    gelu_gate = gelu_gate.to(up.dtype)
-    result = gelu_gate * up
-
-    tl.store(out_ptr + offsets, result, mask=mask)
-
-
-def geglu_forward(gate: torch.Tensor, up: torch.Tensor) -> torch.Tensor:
-    """GEGLU forward pass.
-
-    Args:
-        gate: Input gate tensor of shape [batch, seq_len, hidden_dim].
-        up: Up-projection tensor of shape [batch, seq_len, hidden_dim].
-
-    Returns:
-        torch.Tensor: Output tensor of shape [batch, seq_len, hidden_dim].
-    """
-    batch, seq_len, hidden_dim = gate.shape
-    n_elements = gate.numel()
-    out = torch.empty((batch, seq_len, hidden_dim), dtype=gate.dtype, device="cuda")
-
-    grid = lambda meta: (triton.cdiv(n_elements, meta["BLOCK_SIZE"]),)  # noqa: E731
-    _geglu_fwd_kernel[grid](
-        gate_ptr=gate,
-        up_ptr=up,
-        out_ptr=out,
-        n_elements=n_elements,
-        BLOCK_SIZE=1024,
-    )
-    return out
-
-
-@triton.jit
-def _geglu_bwd_kernel(
-    grad_out_ptr,
-    gate_ptr,
-    up_ptr,
-    n_elements,
-    BLOCK_SIZE: tl.constexpr,
-):
-    """GEGLU backward kernel. Stores gradient results in-place.
-
-    Args:
-        grad_out_ptr: Pointer to gradient output tensor [*, hidden_dim].
-        gate_ptr: Pointer to gate tensor [*, hidden_dim].
-        up_ptr: Pointer to up-projection tensor [*, hidden_dim].
-        n_elements: Total number of elements in the input tensors.
-        BLOCK_SIZE: Size of thread blocks for parallel computation.
-
-    Note:
-        After kernel execution, tensors are modified in-place:
-        - `grad_out_ptr` contains GEGLU activation output (`h`)
-        - `gate_ptr` contains gradient w.r.t gate (`grad_gate`)
-        - `up_ptr` contains gradient w.r.t up (`grad_up`)
-    """
-    block_idx = tl.program_id(0)
-    offsets = block_idx * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)
-    mask = offsets < n_elements
-
-    grad_out = tl.load(grad_out_ptr + offsets, mask=mask, other=0)
-    gate = tl.load(gate_ptr + offsets, mask=mask, other=0).to(tl.float32)
-    up = tl.load(up_ptr + offsets, mask=mask, other=0)
-
-    # Forward pass
-    gelu_partial = 0.5 * (tl.math.erf(tl.math.rsqrt(2.0) * gate) + 1.0)
-    gelu_gate = gelu_partial * gate
-    gelu_gate = gelu_gate.to(grad_out.dtype)
-
-    # Forward output
-    h = gelu_gate * up
-
-    # Compute gradients
-    grad_up = grad_out * gelu_gate
-
-    # Compute gate gradient using GELU derivative
-    temp = grad_out * up
-    t = 0.3989422804014327  # 1/sqrt(2*pi)
-    dgelu_dgate = gelu_partial + t * gate * tl.exp(-0.5 * gate * gate)
-    grad_gate = temp.to(tl.float32) * dgelu_dgate
-    grad_gate = grad_gate.to(grad_out.dtype)
-
-    # Store results
-    tl.store(grad_out_ptr + offsets, h, mask=mask)
-    tl.store(gate_ptr + offsets, grad_gate, mask=mask)
-    tl.store(up_ptr + offsets, grad_up, mask=mask)
-
-
-def geglu_backward(
-    grad_output: torch.Tensor, gate: torch.Tensor, up: torch.Tensor
-) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
-    """GEGLU backward pass using in-place operations.
-
-    Args:
-        grad_output: Gradient of loss with respect to output, shape `[batch, seq_len, hidden_dim]`.
-        gate: Gate tensor from forward pass, shape `[batch, seq_len, hidden_dim]`.
-        up: Up-projection tensor from forward pass, shape `[batch, seq_len, hidden_dim]`.
-
-    Returns:
-        Tuple containing:
-            - GEGLU activation output (`h`)
-            - Gradient with respect to gate (`grad_gate`)
-            - Gradient with respect to up (`grad_up`)
-
-    Note:
-        This function modifies its input tensors in-place to store results.
-    """
-    n_elements = grad_output.numel()
-
-    grid = lambda meta: (triton.cdiv(n_elements, meta["BLOCK_SIZE"]),)  # noqa: E731
-    _geglu_bwd_kernel[grid](
-        grad_out_ptr=grad_output,
-        gate_ptr=gate,
-        up_ptr=up,
-        n_elements=n_elements,
-        BLOCK_SIZE=1024,
-    )
-
-    return grad_output, gate, up

src/axolotl/kernels/lora.py

@@ -1,779 +0,0 @@
-"""
-Module for definition of Low-Rank Adaptation (LoRA) Triton kernels.
-
-See "LoRA: Low-Rank Adaptation of Large Language Models"
-(https://arxiv.org/abs/2106.09685).
-
-Credit to `unsloth` (https://unsloth.ai/) for inspiration for this implementation.
-"""
-# pylint: disable=invalid-name
-
-from typing import Callable
-
-import torch
-from bitsandbytes.functional import QuantState
-from torch import nn
-
-from .geglu import geglu_backward, geglu_forward
-from .quantize import dequantize
-from .swiglu import swiglu_backward, swiglu_forward
-from .utils import torch_amp_custom_bwd, torch_amp_custom_fwd
-
-
-def get_lora_parameters(
-    proj: nn.Module,
-) -> tuple[
-    torch.Tensor,
-    QuantState | None,
-    torch.Tensor | None,
-    torch.Tensor | None,
-    float | None,
-]:
-    """
-    Gets LoRA parameters from a projection module.
-
-    Args:
-        proj: The projection module to extract parameters from.
-
-    Returns:
-        A tuple containing the base weight matrix, quantization state, LoRA A matrix,
-        LoRA B matrix, and scaling factor. States and matrices may be None if not
-        available.
-    """
-    # For DPO or disabled adapters
-    base_layer = proj.base_layer if hasattr(proj, "base_layer") else proj
-    W = base_layer.weight
-
-    if not hasattr(proj, "disable_adapters") or proj.disable_adapters or proj.merged:
-        quant_state = getattr(W, "quant_state", None)
-        return W, quant_state, None, None, None
-
-    active_adapter = (
-        proj.active_adapters[0]
-        if hasattr(proj, "active_adapters")
-        else proj.active_adapter
-    )
-    A = proj.lora_A[active_adapter].weight
-    B = proj.lora_B[active_adapter].weight
-    s = proj.scaling[active_adapter]
-
-    quant_state = getattr(W, "quant_state", None)
-
-    return W, quant_state, A, B, s
-
-
-def matmul_lora(
-    X: torch.Tensor,
-    W: torch.Tensor,
-    W_quant: QuantState,
-    A: torch.Tensor,
-    B: torch.Tensor,
-    s: float,
-    out: torch.Tensor | None = None,
-) -> torch.Tensor:
-    """
-    Efficient fused matmul + LoRA computation.
-
-    Args:
-        X: Input tensor [*, in_features]
-        W: Base weight matrix [out_features, in_features]
-        W_quant: Quantization state for W
-        A: LoRA A matrix [rank, in_features]
-        B: LoRA B matrix [out_features, rank]
-        s: LoRA scaling factor
-        out: Optional output tensor for inplace operations
-
-    Returns:
-        Result of X @ W + X @ A @ B
-    """
-    dtype = X.dtype
-    W = dequantize(W.t(), W_quant)
-
-    if X.dim() == 3:
-        batch, seq_len, _ = X.shape
-        X = X.view(-1, X.shape[-1])
-        reshape = True
-    else:
-        reshape = False
-
-    out = torch.matmul(X, W, out=out)
-    if W_quant is not None:
-        del W
-
-    if A is not None:
-        A, B = A.t(), B.t()
-        out += (X @ A.to(dtype)) @ (s * B.to(dtype))
-
-    return out.view(batch, seq_len, -1) if reshape else out
-
-
-class LoRA_MLP(torch.autograd.Function):
-    """Optimized LoRA MLP implementation."""
-
-    @staticmethod
-    @torch_amp_custom_fwd
-    def forward(
-        ctx,
-        X: torch.Tensor,
-        gate_weight: torch.Tensor,
-        gate_quant: object | None,
-        gate_A: torch.Tensor | None,
-        gate_B: torch.Tensor | None,
-        gate_scale: float,
-        up_weight: torch.Tensor,
-        up_quant: object | None,
-        up_A: torch.Tensor | None,
-        up_B: torch.Tensor | None,
-        up_scale: float,
-        down_weight: torch.Tensor,
-        down_quant: object | None,
-        down_A: torch.Tensor | None,
-        down_B: torch.Tensor | None,
-        down_scale: float,
-        activation_fn: Callable,
-        activation_fn_backward: Callable,
-        inplace: bool | None = True,
-    ) -> torch.Tensor:
-        """
-        Forward pass for LoRA MLP.
-
-        Args:
-            ctx: Autograd context
-            X: Input features
-            gate_weight: Gate projection weight
-            gate_quant: Gate quantization state
-            gate_A: Gate LoRA A matrix
-            gate_B: Gate LoRA B matrix
-            gate_scale: Gate LoRA scale
-            up_weight: Up-projection weight
-            up_quant: Up-projection quantization state
-            up_A: Up-projection LoRA A matrix
-            up_B: Up-projection LoRA B matrix
-            up_scale: Up-projection LoRA scale
-            down_weight: Down-projection weight
-            down_quant: Down-projection quantization state
-            down_A: Down-projection LoRA A matrix
-            down_B: Down-projection LoRA B matrix
-            down_scale: Down-projection LoRA scale
-            activation_fn: Forward activation function
-            activation_fn_backward: Backward activation function
-            inplace: Whether to perform operations in-place
-
-        Returns:
-            Output transformed by multi-layer perceptron and activation function
-        """
-        # Compute projections
-        gate = matmul_lora(X, gate_weight, gate_quant, gate_A, gate_B, gate_scale)
-        up = matmul_lora(X, up_weight, up_quant, up_A, up_B, up_scale)
-
-        # Activation
-        hidden = activation_fn(gate, up)
-
-        # Down projection
-        output = matmul_lora(
-            hidden, down_weight, down_quant, down_A, down_B, down_scale
-        )
-
-        # Save for backward
-        ctx.save_for_backward(X, gate, up, gate_A, gate_B, up_A, up_B, down_A, down_B)
-        ctx.scales = (gate_scale, up_scale, down_scale)
-        ctx.quants = (gate_quant, up_quant, down_quant)
-        ctx.weights = (gate_weight, up_weight, down_weight)
-        ctx.activation_fn = activation_fn
-        ctx.activation_fn_backward = activation_fn_backward
-        ctx.inplace = inplace
-
-        return output
-
-    @staticmethod
-    @torch_amp_custom_bwd
-    def backward(
-        ctx: torch.autograd.function.FunctionCtx,
-        grad_output: torch.Tensor,
-    ) -> tuple[
-        torch.Tensor | None,
-        None,
-        None,
-        torch.Tensor | None,
-        torch.Tensor | None,
-        None,
-        None,
-        None,
-        torch.Tensor | None,
-        torch.Tensor | None,
-        None,
-        None,
-        None,
-        torch.Tensor | None,
-        torch.Tensor | None,
-        None,
-        None,
-        None,
-        None,
-    ]:
-        """
-        Performs backward pass computation for LoRA MLP.
-
-        Args:
-            ctx: Context object storing tensors saved during forward pass
-            grad_output: Gradient of loss with respect to layer output
-
-        Returns:
-            Tuple containing gradients for all inputs from forward pass:
-            - Input gradient tensor (or `None`)
-            - `None` for weights/quantization states
-            - LoRA A/B matrix gradients (or `None`)
-            - `None` for scaling factors
-            - `None` for activation functions and flags
-        """
-        (
-            X,
-            gate,
-            up,
-            gate_A,
-            gate_B,
-            up_A,
-            up_B,
-            down_A,
-            down_B,
-        ) = ctx.saved_tensors
-        gate_scale, up_scale, down_scale = ctx.scales
-        gate_quant, up_quant, down_quant = ctx.quants
-        gate_weight, up_weight, down_weight = ctx.weights
-
-        # Transpose all LoRA matrices
-        gate_A, gate_B = (
-            gate_A.t() if gate_A is not None else None,
-            gate_B.t() if gate_B is not None else None,
-        )
-        up_A, up_B = (
-            up_A.t() if up_A is not None else None,
-            up_B.t() if up_B is not None else None,
-        )
-        down_A, down_B = (
-            down_A.t() if down_A is not None else None,
-            down_B.t() if down_B is not None else None,
-        )
-
-        # Reshape inputs
-        batch, seq_len, hd = X.shape
-        grad_output = grad_output.view(-1, grad_output.shape[-1])
-        X = X.view(-1, X.shape[-1])
-        gate = gate.view(-1, gate.shape[-1])
-        up = up.view(-1, up.shape[-1])
-        dtype = X.dtype
-
-        # Down projection
-        DW = matmul_lora(
-            grad_output,
-            down_weight.t(),
-            down_quant,
-            down_B,
-            down_A,
-            down_scale,
-        )
-
-        # Activation backward
-        h, grad_gate, grad_up = ctx.activation_fn_backward(DW, gate, up)
-
-        # Initialize and compute LoRA gradients
-        d_down_A = d_down_B = d_up_A = d_up_B = d_gate_A = d_gate_B = None
-
-        if down_A is not None:
-            d_down_A = h.t() @ (grad_output @ down_B.t())
-            d_down_B = (down_A.t() @ h.t()) @ grad_output
-            d_down_A *= down_scale
-            d_down_B *= down_scale
-
-        if up_A is not None:
-            d_up_A = X.t() @ (grad_up @ up_B.t())
-            d_up_B = (up_A.t() @ X.t()) @ grad_up
-            d_up_A *= up_scale
-            d_up_B *= up_scale
-
-        if gate_A is not None:
-            d_gate_A = X.t() @ (grad_gate @ gate_B.t())
-            d_gate_B = (gate_A.t() @ X.t()) @ grad_gate
-            d_gate_A *= gate_scale
-            d_gate_B *= gate_scale
-
-        # Compute input gradients
-        dX = torch.zeros_like(X) if ctx.needs_input_grad[0] else None
-
-        if dX is not None:
-            # Up projection gradients
-            up_weight = dequantize(up_weight.t(), up_quant)
-            if ctx.inplace:
-                dX = torch.matmul(grad_up, up_weight.t(), out=X)
-            else:
-                dX = torch.matmul(grad_up, up_weight.t())
-            del up_weight
-
-            # Note the .to(dtype) only where mixing LoRA with base weights
-            if up_A is not None:
-                dX += grad_up @ up_B.to(dtype).t() @ (up_scale * up_A.to(dtype).t())
-
-            # Gate projection gradients
-            gate_weight = dequantize(gate_weight.t(), gate_quant)
-            dX += grad_gate @ gate_weight.t()
-            del gate_weight
-
-            if gate_A is not None:
-                dX += (
-                    grad_gate
-                    @ gate_B.to(dtype).t()
-                    @ (gate_scale * gate_A.to(dtype).t())
-                )
-
-            # Reshape back
-            dX = dX.view(batch, seq_len, hd)
-
-        # Return gradients in correct order matching forward inputs
-        return (
-            dX,
-            None,
-            None,
-            d_gate_A.t() if d_gate_A is not None else None,
-            d_gate_B.t() if d_gate_B is not None else None,
-            None,
-            None,
-            None,
-            d_up_A.t() if d_up_A is not None else None,
-            d_up_B.t() if d_up_B is not None else None,
-            None,
-            None,
-            None,
-            d_down_A.t() if d_down_A is not None else None,
-            d_down_B.t() if d_down_B is not None else None,
-            None,
-            None,
-            None,
-            None,
-        )
-
-
-def apply_lora_mlp_swiglu(self, X: torch.Tensor, inplace: bool = True) -> torch.Tensor:
-    """
-    Applies LoRA to MLP layer with SwiGLU activation.
-
-    Args:
-        X: Input tensor for the MLP layer
-        inplace: Whether to perform operations in-place to save memory
-
-    Returns:
-        Output tensor after applying LoRA-adapted MLP with SwiGLU activation
-    """
-    gateW, gateW_quant, gateA, gateB, gateS = get_lora_parameters(self.gate_proj)
-    upW, upW_quant, upA, upB, upS = get_lora_parameters(self.up_proj)
-    downW, downW_quant, downA, downB, downS = get_lora_parameters(self.down_proj)
-
-    out = LoRA_MLP.apply(
-        X,
-        gateW,
-        gateW_quant,
-        gateA,
-        gateB,
-        gateS,
-        upW,
-        upW_quant,
-        upA,
-        upB,
-        upS,
-        downW,
-        downW_quant,
-        downA,
-        downB,
-        downS,
-        swiglu_forward,
-        swiglu_backward,
-        inplace,
-    )
-
-    return out
-
-
-def apply_lora_mlp_geglu(self, X: torch.Tensor, inplace: bool = True) -> torch.Tensor:
-    """
-    Applies LoRA to MLP layer with GEGLU activation.
-
-    Args:
-        X: Input tensor for the MLP layer
-        inplace: Whether to perform operations in-place to save memory
-
-    Returns:
-        Output tensor after applying LoRA-adapted MLP with GEGLU activation
-    """
-    gateW, gateW_quant, gateA, gateB, gateS = get_lora_parameters(self.gate_proj)
-    upW, upW_quant, upA, upB, upS = get_lora_parameters(self.up_proj)
-    downW, downW_quant, downA, downB, downS = get_lora_parameters(self.down_proj)
-    out = LoRA_MLP.apply(
-        X,
-        gateW,
-        gateW_quant,
-        gateA,
-        gateB,
-        gateS,
-        upW,
-        upW_quant,
-        upA,
-        upB,
-        upS,
-        downW,
-        downW_quant,
-        downA,
-        downB,
-        downS,
-        geglu_forward,
-        geglu_backward,
-        inplace,
-    )
-
-    return out
-
-
-class LoRA_QKV(torch.autograd.Function):
-    """
-    Optimized LoRA QKV implementation with quantization support.
-
-    Implements efficient computation of query, key, value projections with LoRA,
-    supporting quantization and memory optimization.
-    """
-
-    @staticmethod
-    @torch_amp_custom_fwd
-    def forward(
-        ctx: torch.autograd.function.FunctionCtx,
-        X: torch.Tensor,
-        q_weight: torch.Tensor,
-        q_quant: QuantState | None,
-        q_A: torch.Tensor | None,
-        q_B: torch.Tensor | None,
-        q_scale: float,
-        k_weight: torch.Tensor,
-        k_quant: QuantState | None,
-        k_A: torch.Tensor | None,
-        k_B: torch.Tensor | None,
-        k_scale: float,
-        v_weight: torch.Tensor,
-        v_quant: QuantState | None,
-        v_A: torch.Tensor | None,
-        v_B: torch.Tensor | None,
-        v_scale: float,
-        inplace: bool = True,
-    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
-        """
-        Forward pass computing Q, K, V projections with LoRA.
-
-        Args:
-            ctx: Autograd context
-            X: Input tensor
-            q_weight: Query projection weight
-            q_quant: Query quantization state
-            q_A: Query LoRA A matrix
-            q_B: Query LoRA B matrix
-            q_scale: Query LoRA scale
-            k_weight: Key projection weight
-            k_quant: Key quantization state
-            k_A: Key LoRA A matrix
-            k_B: Key LoRA B matrix
-            k_scale: Key LoRA scale
-            v_weight: Value projection weight
-            v_quant: Value quantization state
-            v_A: Value LoRA A matrix
-            v_B: Value LoRA B matrix
-            v_scale: Value LoRA scale
-            inplace: Whether to perform operations in-place
-
-        Returns:
-            Tuple of (Query, Key, Value) projection tensors
-        """
-        Q = matmul_lora(X, q_weight, q_quant, q_A, q_B, q_scale)
-        K = matmul_lora(X, k_weight, k_quant, k_A, k_B, k_scale)
-        V = matmul_lora(X, v_weight, v_quant, v_A, v_B, v_scale)
-
-        ctx.save_for_backward(X, q_A, q_B, k_A, k_B, v_A, v_B)
-        ctx.scales = (q_scale, k_scale, v_scale)
-        ctx.quants = (q_quant, k_quant, v_quant)
-        ctx.weights = (q_weight, k_weight, v_weight)
-        ctx.inplace = inplace
-
-        return Q, K, V
-
-    @staticmethod
-    @torch_amp_custom_fwd
-    def backward(
-        ctx: torch.autograd.function.FunctionCtx,
-        q_grad: torch.Tensor,
-        k_grad: torch.Tensor,
-        v_grad: torch.Tensor,
-    ) -> tuple[
-        torch.Tensor,
-        None,
-        None,
-        torch.Tensor | None,
-        torch.Tensor | None,
-        None,
-        None,
-        None,
-        torch.Tensor | None,
-        torch.Tensor | None,
-        None,
-        None,
-        None,
-        torch.Tensor | None,
-        torch.Tensor | None,
-        None,
-        None,
-    ]:
-        """
-        Backward pass computing gradients for LoRA QKV.
-
-        Args:
-            ctx: Autograd context
-            q_grad: Gradient for query projection
-            k_grad: Gradient for key projection
-            v_grad: Gradient for value projection
-
-        Returns:
-            Tuple containing gradients for all forward inputs
-        """
-        X, A_q, B_q, A_k, B_k, A_v, B_v = ctx.saved_tensors
-        q_weight, k_weight, v_weight = ctx.weights
-        q_quant, k_quant, v_quant = ctx.quants
-        q_scale, k_scale, v_scale = ctx.scales
-        dtype = X.dtype
-
-        # Reshape gradients
-        batch, seq_len = X.shape[:2]
-        q_grad = q_grad.view(-1, q_grad.shape[-1])
-        k_grad = k_grad.reshape(-1, k_grad.shape[-1])
-        v_grad = v_grad.view(-1, v_grad.shape[-1])
-        X = X.view(-1, X.shape[-1])
-
-        # Pre-transpose X once
-        X_t = X.t()
-
-        # Initialize LoRA gradients as None
-        d_A_q = d_B_q = d_A_k = d_B_k = d_A_v = d_B_v = None
-
-        # Compute q path LoRA gradients if adapters exist
-        if A_q is not None and B_q is not None:
-            A_q_scaled = (q_scale * A_q).to(dtype)
-            B_q_scaled = B_q.to(dtype)
-            d_A_q = torch.mm(X_t, torch.mm(q_grad, B_q_scaled))
-            d_B_q = torch.mm(torch.mm(A_q_scaled, X_t), q_grad)
-
-        # Compute k path LoRA gradients if adapters exist
-        if A_k is not None and B_k is not None:
-            A_k_scaled = (k_scale * A_k).to(dtype)
-            B_k_scaled = B_k.to(dtype)
-            d_A_k = torch.mm(X_t, torch.mm(k_grad, B_k_scaled))
-            d_B_k = torch.mm(torch.mm(A_k_scaled, X_t), k_grad)
-
-        # Compute v path LoRA gradients if adapters exist
-        if A_v is not None and B_v is not None:
-            A_v_scaled = (v_scale * A_v).to(dtype)
-            B_v_scaled = B_v.to(dtype)
-            d_A_v = torch.mm(X_t, torch.mm(v_grad, B_v_scaled))
-            d_B_v = torch.mm(torch.mm(A_v_scaled, X_t), v_grad)
-
-        # Compute input gradient, reusing X memory if possible
-        out_buffer = X if ctx.inplace else None
-
-        # Q path
-        q_weight_t = dequantize(q_weight, q_quant)
-        grad_X = torch.mm(q_grad, q_weight_t, out=out_buffer)
-        del q_weight
-        del q_weight_t
-        if A_q is not None and B_q is not None:
-            grad_X.addmm_(q_grad, torch.mm(B_q_scaled, A_q_scaled))
-
-        # K path
-        k_weight_t = dequantize(k_weight, k_quant)
-        grad_X.addmm_(k_grad, k_weight_t)
-        del k_weight
-        del k_weight_t
-        if A_k is not None and B_k is not None:
-            grad_X.addmm_(k_grad, torch.mm(B_k_scaled, A_k_scaled))
-
-        # V path
-        v_weight_t = dequantize(v_weight, v_quant)
-        grad_X.addmm_(v_grad, v_weight_t)
-        del v_weight
-        del v_weight_t
-        if A_v is not None and B_v is not None:
-            grad_X.addmm_(v_grad, torch.mm(B_v_scaled, A_v_scaled))
-
-        # Transpose gradients if needed
-        if d_A_q is not None:
-            d_A_q = d_A_q.t()
-        if d_B_q is not None:
-            d_B_q = d_B_q.t()
-        if d_A_k is not None:
-            d_A_k = d_A_k.t()
-        if d_B_k is not None:
-            d_B_k = d_B_k.t()
-        if d_A_v is not None:
-            d_A_v = d_A_v.t()
-        if d_B_v is not None:
-            d_B_v = d_B_v.t()
-
-        return (
-            grad_X.view(batch, seq_len, -1),
-            None,
-            None,
-            d_A_q,
-            d_B_q,
-            None,
-            None,
-            None,
-            d_A_k,
-            d_B_k,
-            None,
-            None,
-            None,
-            d_A_v,
-            d_B_v,
-            None,
-            None,
-        )
-
-
-def apply_lora_qkv(
-    self, X: torch.Tensor, inplace: bool = True
-) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
-    """
-    Applies LoRA to compute Query, Key, Value projections.
-
-    Args:
-        X: Input tensor
-        inplace: Whether to perform operations in-place
-
-    Returns:
-        Tuple of (Query, Key, Value) projection tensors
-    """
-    QW, QW_quant, QA, QB, QS = get_lora_parameters(self.q_proj)
-    KW, KW_quant, KA, KB, KS = get_lora_parameters(self.k_proj)
-    VW, VW_quant, VA, VB, VS = get_lora_parameters(self.v_proj)
-    Q, K, V = LoRA_QKV.apply(
-        X,
-        QW,
-        QW_quant,
-        QA,
-        QB,
-        QS,
-        KW,
-        KW_quant,
-        KA,
-        KB,
-        KS,
-        VW,
-        VW_quant,
-        VA,
-        VB,
-        VS,
-        inplace,
-    )
-
-    return Q, K, V
-
-
-class LoRA_O(torch.autograd.Function):
-    """Optimized LoRA implementation for output projection."""
-
-    @staticmethod
-    @torch_amp_custom_fwd
-    def forward(
-        ctx: torch.autograd.function.FunctionCtx,
-        X: torch.Tensor,
-        W: torch.Tensor,
-        W_quant: QuantState | None,
-        A: torch.Tensor | None,
-        B: torch.Tensor | None,
-        S: float,
-    ) -> torch.Tensor:
-        """
-        Forward pass for output projection with LoRA.
-
-        Args:
-            ctx: Autograd context
-            X: Input tensor
-            W: Output projection weight
-            W_quant: Weight quantization state
-            A: LoRA A matrix
-            B: LoRA B matrix
-            S: LoRA scaling factor
-
-        Returns:
-            Output projection tensor
-        """
-        XW = matmul_lora(X, W, W_quant, A, B, S)
-        ctx.custom_saved_tensors = (
-            W,
-            W_quant,
-            S,
-        )
-        ctx.save_for_backward(A, B, X)
-
-        return XW
-
-    @staticmethod
-    @torch_amp_custom_bwd
-    def backward(
-        ctx: torch.autograd.function.FunctionCtx,
-        dY: torch.Tensor,
-    ) -> tuple[
-        torch.Tensor,
-        None,
-        None,
-        torch.Tensor | None,
-        torch.Tensor | None,
-        None,
-    ]:
-        """
-        Backward pass computing gradients for LoRA output projection.
-
-        Args:
-            ctx: Autograd context
-            dY: Gradient of loss with respect to output
-
-        Returns:
-            Tuple containing gradients for all forward inputs
-        """
-        W, W_quant, S = ctx.custom_saved_tensors
-        A, B, X = ctx.saved_tensors
-
-        batch, seq_len, hd = X.shape
-        dY = dY.reshape(-1, dY.shape[-1])
-        X = X.reshape(-1, X.shape[-1])
-        dtype = X.dtype
-
-        # Weight projection
-        dY_X = X.t() @ dY
-        d_A = S * dY_X @ B
-        d_B = S * A @ dY_X
-
-        # Get derivative for dX
-        W = dequantize(W.t(), W_quant)
-        dX = dY @ W.t()
-        del W
-        dX += dY @ B.to(dtype) @ (S * A.to(dtype))
-
-        # W, W_quant, A, B, S
-        return dX.view(batch, seq_len, hd), None, None, d_A.t(), d_B.t(), None
-
-
-def apply_lora_o(self, X: torch.Tensor) -> torch.Tensor:
-    """
-    Applies LoRA to output projection layer.
-
-    Args:
-        X: Input tensor
-
-    Returns:
-        Transformed output tensor
-    """
-    OW, OW_quant, OA, OB, OS = get_lora_parameters(self.o_proj)
-    output = LoRA_O.apply(X, OW, OW_quant, OA, OB, OS)
-
-    return output