wip

* bumping TRL version

* apply upstream fixes to our custom fix

---------

Co-authored-by: Wing Lian <wing@axolotl.ai>

.github/workflows/base.yml

@@ -22,12 +22,6 @@ 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: ""
@@ -40,6 +34,12 @@ 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: '3.10'
+            python-version: '3.11'
         - 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: "3.10"
+          python-version: "3.11"
           cache: 'pip' # caching pip dependencies
       - uses: pre-commit/action@v3.0.1

.github/workflows/main.yml

@@ -24,8 +24,13 @@ jobs:
             cuda_version: 12.4.1
             python_version: "3.11"
             pytorch: 2.5.1
-            axolotl_extras:
+            axolotl_extras: vllm
             is_latest: true
+          - 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/multi-gpu-e2e.yml

@@ -4,6 +4,10 @@ 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
@@ -24,13 +28,21 @@ jobs:
             cuda_version: 12.4.1
             python_version: "3.11"
             pytorch: 2.4.1
-            axolotl_extras:
+            axolotl_extras:  # no vllm support for 2.4.1
             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"
@@ -42,7 +54,7 @@ jobs:
       - name: Install Python
         uses: actions/setup-python@v5
         with:
-          python-version: "3.10"
+          python-version: "3.11"
       - name: Install Modal
         run: |
           python -m pip install --upgrade pip

.github/workflows/nightlies.yml

@@ -22,6 +22,11 @@ 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: "3.10"
+          python-version: "3.11"
 
       - 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: "3.10"
+          python-version: "3.11"
           cache: 'pip' # caching pip dependencies
       - uses: pre-commit/action@v3.0.1
         env:
@@ -25,13 +25,8 @@ jobs:
       fail-fast: false
       max-parallel: 2
       matrix:
-        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"
+        python_version: ["3.11"]
+        pytorch_version: ["2.4.1", "2.5.1", "2.6.0"]
     timeout-minutes: 20
 
     steps:
@@ -112,13 +107,20 @@ 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: "3.10"
+          python-version: "3.11"
       - 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: "3.10"
+          python-version: "3.11"
           cache: 'pip' # caching pip dependencies
       - uses: pre-commit/action@v3.0.1
         env:
@@ -48,13 +48,8 @@ jobs:
       fail-fast: false
       max-parallel: 2
       matrix:
-        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"
+        python_version: ["3.11"]
+        pytorch_version: ["2.4.1", "2.5.1", "2.6.0"]
     timeout-minutes: 20
 
     steps:
@@ -127,7 +122,7 @@ jobs:
       max-parallel: 1
       matrix:
         python_version: ["3.11"]
-        pytorch_version: ["2.4.1", "2.5.1"]
+        pytorch_version: ["2.4.1", "2.5.1", "2.6.0"]
     timeout-minutes: 20
 
     steps:
@@ -207,16 +202,16 @@ jobs:
           - cuda: 124
             cuda_version: 12.4.1
             python_version: "3.11"
-            pytorch: 2.4.1
+            pytorch: 2.5.1
             num_gpus: 1
-            axolotl_extras:
+            axolotl_extras: vllm
     steps:
       - name: Checkout
         uses: actions/checkout@v4
       - name: Install Python
         uses: actions/setup-python@v5
         with:
-          python-version: "3.10"
+          python-version: "3.11"
       - name: Install Modal
         run: |
           python -m pip install --upgrade pip
@@ -228,6 +223,7 @@ jobs:
           echo "AXOLOTL_ARGS=${{ matrix.axolotl_args}}" >> $GITHUB_ENV
           echo "AXOLOTL_EXTRAS=${{ matrix.axolotl_extras}}" >> $GITHUB_ENV
           echo "CUDA=${{ matrix.cuda }}" >> $GITHUB_ENV
+          echo "MODAL_IMAGE_BUILDER_VERSION=2024.10" >> $GITHUB_ENV
           echo "N_GPUS=${{ matrix.num_gpus }}" >> $GITHUB_ENV
       - name: Run tests job on Modal
         run: |
@@ -247,7 +243,13 @@ jobs:
           - cuda: 124
             cuda_version: 12.4.1
             python_version: "3.11"
-            pytorch: 2.5.1
+            pytorch: 2.4.1
+            num_gpus: 1
+            axolotl_extras:
+          - cuda: 124
+            cuda_version: 12.4.1
+            python_version: "3.11"
+            pytorch: 2.6.0
             num_gpus: 1
             axolotl_extras:
     steps:
@@ -256,7 +258,7 @@ jobs:
       - name: Install Python
         uses: actions/setup-python@v5
         with:
-          python-version: "3.10"
+          python-version: "3.11"
       - name: Install Modal
         run: |
           python -m pip install --upgrade pip
@@ -268,6 +270,7 @@ jobs:
           echo "AXOLOTL_ARGS=${{ matrix.axolotl_args}}" >> $GITHUB_ENV
           echo "AXOLOTL_EXTRAS=${{ matrix.axolotl_extras}}" >> $GITHUB_ENV
           echo "CUDA=${{ matrix.cuda }}" >> $GITHUB_ENV
+          echo "MODAL_IMAGE_BUILDER_VERSION=2024.10" >> $GITHUB_ENV
           echo "N_GPUS=${{ matrix.num_gpus }}" >> $GITHUB_ENV
       - name: Run tests job on Modal
         run: |

README.md

@@ -51,7 +51,7 @@ Features:
 
 **Requirements**:
 - NVIDIA GPU (Ampere or newer for `bf16` and Flash Attention) or AMD GPU
-- Python ≥3.10
+- Python 3.11
 - PyTorch ≥2.4.1
 
 ### Installation

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 -n8 --dist loadfile /workspace/axolotl/tests/patched/
-pytest -v --durations=10 /workspace/axolotl/tests/e2e/patched/
+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 -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

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

cicd/tests.py

@@ -1,6 +1,4 @@
-"""
- modal application to run axolotl gpu tests in Modal
- """
+"""Modal app to run axolotl GPU tests"""
 # pylint: disable=duplicate-code
 
 import os
@@ -38,16 +36,12 @@ 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",
-        context_mount=None,
-        force_build=True,
-        gpu="A10G",
-    )
-    .env(df_args)
-    .pip_install("fastapi==0.110.0", "pydantic==2.6.3")
-)
+cicd_image = Image.from_dockerfile(
+    pathlib.Path(temp_dir) / "Dockerfile",
+    context_mount=None,
+    force_build=True,
+    gpu="A10G",
+).env(df_args)
 
 app = App("Axolotl CI/CD", secrets=[])
 
@@ -59,7 +53,7 @@ VOLUME_CONFIG = {
 }
 
 N_GPUS = int(os.environ.get("N_GPUS", 1))
-GPU_CONFIG = modal.gpu.A10G(count=N_GPUS)
+GPU_CONFIG = modal.gpu.L40S(count=N_GPUS)
 
 
 def run_cmd(cmd: str, run_folder: str):

docs/config.qmd

@@ -46,6 +46,10 @@ 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:
@@ -87,7 +91,12 @@ 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] number of shards to split data into
+
+    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`)
+
     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.
@@ -133,10 +142,19 @@ datasets:
 
     # Key containing the messages (default: "messages")
     field_messages: messages
-    # Key for role in each message (default: "role")
-    message_field_role: role
-    # Key for content in each message (default:  "content")
-    message_field_content: content
+
+    # 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:
 
     # Optional[Dict[str, List]]. Roles mapping in the messages. The default is:
     roles:
@@ -296,6 +314,13 @@ 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`
@@ -344,6 +369,9 @@ 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
 
@@ -378,6 +406,12 @@ 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/dataset-formats/conversation.qmd

@@ -6,7 +6,7 @@ order: 3
 
 ## sharegpt
 
-IMPORTANT: ShareGPT is deprecated!. Please see `chat_template` section below.
+IMPORTANT: ShareGPT is deprecated!. Please see [chat_template](#chat_template) section below.
 
 ## pygmalion
 
@@ -22,7 +22,7 @@ Chat Template strategy uses a jinja2 template that converts a list of messages i
 {"conversations": [{"role": "...", "content": "..."}]}
 ```
 
-See `config.qmd` for full configs and supported templates.
+See [configs](../config.qmd) for full configs and supported templates.
 
 ### Migrating from sharegpt
 
@@ -42,8 +42,9 @@ datasets:
     type: chat_template
 
     field_messages: conversations
-    message_field_role: from
-    message_field_content: value
+    message_property_mappings:
+      role: from
+      content: value
 
 # new (if setting a new chat_template like chatml, gemma, etc)
 chat_template: chatml
@@ -52,8 +53,9 @@ datasets:
     type: chat_template
 
     field_messages: conversations
-    message_field_role: from
-    message_field_content: value
+    message_property_mappings:
+      role: from
+      content: value
 ```
 
 We recommend checking the below examples for other usecases.
@@ -138,8 +140,9 @@ datasets:
     type: chat_template
     chat_template: tokenizer_default
     field_messages: conversations
-    message_field_role: from
-    message_field_content: value
+    message_property_mappings:
+      role: from
+      content: value
     roles_to_train: []
     train_on_eos: turn
     message_field_training: train

docs/dataset-formats/index.qmd

@@ -1,14 +1,458 @@
 ---
 title: Dataset Formats
-description: Supported dataset formats.
-listing:
-  fields: [title, description]
-  type: table
-  sort-ui: false
-  filter-ui: false
-  max-description-length: 250
+description: Guide to Dataset Formats in Axolotl
+back-to-top-navigation: true
+toc: true
+toc-depth: 5
 ---
 
-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.
 
-Below are these various formats organized by task:
+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](pretraining.qmd)
+
+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.
+
+## 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](tokenized.qmd)
+
+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!
+:::
+
+### [Template Free Dataset](template_free.qmd)
+
+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
+```
+
+### [Conversation Dataset](conversation.qmd)
+
+`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](conversation.qmd#chat_template)
+
+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"]
+```
+
+#### 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. The final step would be to correctly set the EOS token in your config:
+
+```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`.
+
+### [Instruction Dataset](inst_tune.qmd)
+
+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.
+
+#### 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.
+
+## Reinforcement Learning from Human Feedback (RLHF)
+
+As there are multiple RLHF methods with their own dataset requirements. Please see [RLHF datasets](../rlhf.qmd) documentation for more detail.

docs/dataset-formats/stepwise_supervised.qmd

@@ -23,4 +23,4 @@ Here's a simple example of a stepwise supervised dataset entry:
   ],
   "labels": [true, false]
 }
-```
+```

docs/faq.qmd

@@ -19,3 +19,11 @@ 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.
+
+**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`.

docs/lora_optims.qmd

@@ -0,0 +1,128 @@
+---
+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/multi-node.qmd

@@ -3,6 +3,18 @@ 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
@@ -26,7 +38,7 @@ tpu_use_sudo: false
 use_cpu: false
 ```
 
-Configure your model to use FSDP with for example:
+Configure your model to use FSDP in the Axolotl yaml. For example:
 ```yaml
 fsdp:
   - full_shard
@@ -37,12 +49,40 @@ 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):
+
+```yaml
+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/rlhf.qmd

@@ -1,26 +1,39 @@
 ---
 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: 3
 ---
 
-### 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:
 
 - Proximal Policy Optimization (PPO) (not yet supported in axolotl)
-- Direct Preference Optimization (DPO)
-- Identity Preference Optimization (IPO)
+- [Direct Preference Optimization (DPO)](#dpo)
+- [Identity Preference Optimization (IPO)](#ipo)
+- [Kahneman-Tversky Optimization (KTO)](#kto)
+- [Odds Ratio Preference Optimization (ORPO)](#orpo)
 
 
-### RLHF using Axolotl
+# RLHF using Axolotl
 
->[!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.
+::: {.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.
+:::
 
-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
+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:
 
-#### DPO
 ```yaml
 rl: dpo
 datasets:
@@ -29,15 +42,268 @@ datasets:
     type: chatml.intel
   - path: argilla/ultrafeedback-binarized-preferences
     split: train
-    type: chatml.argilla
+    type: chatml
 ```
 
-#### IPO
+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.
+
 ```yaml
 rl: ipo
 ```
 
-#### ORPO
+## ORPO
 
 Paper: https://arxiv.org/abs/2403.07691
 
@@ -52,8 +318,28 @@ datasets:
     type: chat_template.argilla
 ```
 
+ORPO supports the following types with the following dataset format:
 
-#### KTO
+### 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
 
 ```yaml
 rl: kto
@@ -72,7 +358,144 @@ gradient_checkpointing_kwargs:
   use_reentrant: true
 ```
 
-#### Using local dataset files
+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": "..."
+}
+```
+
+## Using local dataset files
+
 ```yaml
 datasets:
   - ds_type: json
@@ -82,9 +505,9 @@ datasets:
     type: chatml.intel
 ```
 
-#### Trl autounwrap for peft
+## TRL auto-unwrapping for PEFT
 
-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.
+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:
 
 ```yaml
 # load ref model when adapter training.

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

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

examples/gemma2/qlora.yml

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

examples/jamba/qlora_fsdp_large.yaml

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

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

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

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

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

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

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

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

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

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

@@ -0,0 +1,82 @@
+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

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

examples/phi/lora-3.5.yaml

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

examples/qwen2/dpo.yaml

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

requirements.txt

@@ -1,10 +1,10 @@
 --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/
 
 # START section of dependencies that don't install on Darwin/MacOS
-bitsandbytes==0.45.1
+bitsandbytes==0.45.2
 triton>=3.0.0
 mamba-ssm==1.2.0.post1
-flash-attn==2.7.0.post2
+flash-attn==2.7.4.post1
 xformers>=0.0.23.post1
 autoawq==0.2.7.post3
 liger-kernel==0.5.2
@@ -13,12 +13,12 @@ liger-kernel==0.5.2
 packaging==23.2
 
 peft==0.14.0
-transformers==4.48.1
+transformers==4.49.0
 tokenizers>=0.21.0
 accelerate==1.3.0
 datasets==3.2.0
 deepspeed==0.16.1
-trl==0.13.0
+trl==0.15.1
 
 optimum==1.16.2
 hf_transfer
@@ -26,7 +26,7 @@ sentencepiece
 gradio==3.50.2
 
 modal==0.70.5
-pydantic==2.6.3
+pydantic==2.10.6
 addict
 fire
 PyYAML>=6.0

scripts/chat_datasets.py

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

setup.py

@@ -71,12 +71,15 @@ def parse_requirements():
             else:
                 raise ValueError("Invalid version format")
 
-            if (major, minor) >= (2, 5):
+            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):
                 _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:
@@ -122,7 +125,7 @@ setup(
     },
     extras_require={
         "flash-attn": [
-            "flash-attn==2.7.0.post2",
+            "flash-attn==2.7.4.post1",
         ],
         "deepspeed": [
             "deepspeed==0.16.1",
@@ -153,5 +156,8 @@ 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__ = "0.6.0"
+__version__ = "0.8.0.dev0"

src/axolotl/cli/args.py

@@ -13,6 +13,12 @@ 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

@@ -35,13 +35,18 @@ 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()
-    cloud.train(config_yaml, accelerate=accelerate)
+    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)
 
 
 def do_cli_lm_eval(

src/axolotl/cli/cloud/modal_.py

@@ -7,6 +7,7 @@ import os
 import subprocess  # nosec B404
 from pathlib import Path
 from random import randint
+from typing import Optional
 
 import modal
 
@@ -22,8 +23,18 @@ 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:
-        del new_env["PYTHONPATH"]
+        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"]
 
     # Propagate errors from subprocess.
     if exit_code := subprocess.call(  # nosec B603
@@ -112,8 +123,6 @@ 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):
@@ -203,9 +212,12 @@ class ModalCloud(Cloud):
             memory = int(self.config.memory)
         return 1024 * memory
 
-    def get_train_env(self):
+    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)
         return self.app.function(
-            image=self.get_image(),
+            image=image,
             volumes={k: v[0] for k, v in self.volumes.items()},
             cpu=16.0,
             gpu=self.get_train_gpu(),
@@ -214,14 +226,21 @@ class ModalCloud(Cloud):
             secrets=self.get_secrets(),
         )
 
-    def train(self, config_yaml: str, accelerate: bool = True):
-        modal_fn = self.get_train_env()(_train)
+    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)
         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):
@@ -252,7 +271,7 @@ def _preprocess(config_yaml: str, volumes=None):
     )
 
 
-def _train(config_yaml: str, accelerate: bool = True, volumes=None):
+def _train(config_yaml: str, accelerate: bool = True, volumes=None, **kwargs):
     with open(
         "/workspace/artifacts/axolotl/config.yaml", "w", encoding="utf-8"
     ) as f_out:
@@ -262,8 +281,11 @@ def _train(config_yaml: str, accelerate: bool = True, volumes=None):
         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} /workspace/artifacts/axolotl/config.yaml",
+        f"axolotl train {accelerate_args} {num_processes_args} /workspace/artifacts/axolotl/config.yaml",
         run_folder,
         volumes,
     )

src/axolotl/cli/main.py

@@ -1,13 +1,20 @@
 """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,
@@ -60,10 +67,21 @@ 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, **kwargs) -> None:
+def train(
+    config: str,
+    accelerate: bool,
+    cloud: Optional[str] = None,
+    sweep: Optional[str] = None,
+    **kwargs,
+) -> None:
     """
     Train or fine-tune a model.
 
@@ -71,44 +89,88 @@ def train(config: str, accelerate: bool, cloud: Optional[str] = None, **kwargs)
         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)
 
-    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))
+        # 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")
 
-            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
 
-            do_cli(config=config, **kwargs)
+        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
 
 
 @cli.command()
@@ -261,4 +323,5 @@ def main():
 
 
 if __name__ == "__main__":
+    load_dotenv()
     main()

src/axolotl/cli/preprocess.py

@@ -75,7 +75,10 @@ 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

@@ -0,0 +1,77 @@
+"""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

@@ -63,11 +63,17 @@ 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 (
@@ -116,9 +122,11 @@ def load_preference_datasets(
         `total_num_steps`.
     """
     train_dataset, eval_dataset = load_prepare_preference_datasets(cfg)
-    total_num_steps = int(
+    total_num_steps: Optional[int] = 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

@@ -0,0 +1,878 @@
+"""
+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

@@ -0,0 +1,33 @@
+"""
+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

@@ -0,0 +1,15 @@
+"""
+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

@@ -0,0 +1,125 @@
+"""
+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

@@ -0,0 +1,119 @@
+"""
+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

@@ -0,0 +1,15 @@
+"""
+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

@@ -0,0 +1,108 @@
+"""
+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

@@ -0,0 +1,267 @@
+"""
+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"
+        },
+    )
+
+    sp_ulysses_degree: Optional[int] = field(
+        default=None,
+        metadata={"help": "Ulysses parallelism for hybrid sequence parallel long context attn"},
+    )
+
+    sp_ring_degree: Optional[int] = field(
+        default=None,
+        metadata={"help": "Ring attention parallelism for sequence parallel long context attn"},
+    )
+
+
+@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
+from typing import List, Optional, Union
 
 import torch
 from datasets import Dataset, IterableDataset
@@ -51,7 +51,17 @@ class TokenizedPromptDataset(Dataset):
         map_kwargs = {}
         if self.prompt_tokenizer.supports_batched:
             map_kwargs["batched"] = True
-            map_kwargs["batch_size"] = 100
+            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",
+            )
 
         return dataset.map(
             self.prompt_tokenizer.tokenize_prompt,
@@ -63,6 +73,24 @@ 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

@@ -111,6 +111,17 @@ 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.
@@ -212,7 +223,17 @@ def load_plugin(plugin_name: str) -> BasePlugin:
     module_name, class_name = plugin_name.rsplit(".", 1)
 
     # import the module
-    module = importlib.import_module(module_name)
+    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
+
     # instantiate the class
     plugin_class = getattr(module, class_name)
     # create an instance of the class
@@ -272,8 +293,10 @@ 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}")
 
@@ -346,6 +369,22 @@ 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/kd/__init__.py

@@ -0,0 +1,36 @@
+# 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

@@ -0,0 +1,37 @@
+# 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

@@ -0,0 +1,201 @@
+# 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

@@ -0,0 +1,255 @@
+# 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/LICENSE.md

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

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

@@ -0,0 +1,235 @@
+# Copyright 2024 Axolotl AI. All rights reserved.
+#
+# This software may be used and distributed according to
+# the terms of the Axolotl Community License Agreement (the "License");
+# you may not use this file except in compliance with the License.
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+# License for the specific language governing permissions and limitations under
+# the License.
+
+"""
+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

@@ -0,0 +1,113 @@
+# 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/spectrum/model_snr_results/snr_results_meta-llama-Llama-3.2-1B-Instruct.json

@@ -0,0 +1,590 @@
+{
+    "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,
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src/axolotl/integrations/spectrum/model_snr_results/snr_results_meta-llama-Llama-3.2-1B.json

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

@@ -0,0 +1,159 @@
+"""
+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

@@ -0,0 +1,779 @@
+"""
+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

src/axolotl/kernels/quantize.py

@@ -0,0 +1,149 @@
+"""Dequantization utilities for `bitsandbytes` integration."""
+# pylint: disable=invalid-name,global-statement
+
+import ctypes
+
+import bitsandbytes as bnb
+import torch
+from bitsandbytes.functional import QuantState, get_ptr
+from packaging.version import Version
+
+cdequantize_blockwise_fp32 = bnb.functional.lib.cdequantize_blockwise_fp32
+cdequantize_blockwise_fp16_nf4 = bnb.functional.lib.cdequantize_blockwise_fp16_nf4
+cdequantize_blockwise_bf16_nf4 = bnb.functional.lib.cdequantize_blockwise_bf16_nf4
+
+CUDA_STREAM: torch.cuda.Stream | None = None
+HAS_CUDA_STREAM: bool = Version(bnb.__version__) > Version("0.43.3")
+
+
+def dequantize(
+    W: torch.Tensor,
+    quant_state: QuantState | list | None = None,
+    out: torch.Tensor | None = None,
+) -> torch.Tensor:
+    """
+    Fast NF4 dequantization using `bitsandbytes` CUDA kernels.
+
+    Performs efficient dequantization of weights from NF4 format using `bitsandbytes`'
+    optimized CUDA implementations. Supports both legacy list and new `QuantState`
+    formats.
+
+    Args:
+        W: Quantized weight tensor to dequantize
+        quant_state: Quantization state containing metadata needed for
+            dequantization. Can be either a `QuantState` object or legacy list format.
+            If None, returns `W` unchanged.
+        out: Optional output tensor for storing dequantized results. Must match
+            expected shape and dtype if provided.
+
+    Returns:
+        Dequantized tensor in the specified dtype (fp16 or bf16). Will be transposed if
+        input `W` was transposed.
+
+    Raises:
+        AssertionError: If provided output tensor doesn't match expected shape / dtype.
+
+    Note:
+        Uses CUDA streams for better performance when available in newer `bitsandbytes`
+        versions (>0.43.3).
+    """
+    if quant_state is None:
+        return W
+
+    # Get the target device from input tensor W
+    target_device = W.device
+
+    # Extract quantization state
+    if not isinstance(quant_state, list):
+        # New style quant_state class
+        absmax = quant_state.absmax.to(target_device)
+        shape = quant_state.shape
+        dtype = quant_state.dtype
+        blocksize = quant_state.blocksize
+        offset = quant_state.offset.to(target_device)
+        state2 = quant_state.state2
+        absmax2 = state2.absmax.to(target_device)
+        code2 = state2.code.to(target_device)
+        blocksize2 = state2.blocksize
+    else:
+        # Legacy list format
+        absmax, shape, dtype, blocksize, compressed_stats, _, _ = quant_state
+        absmax = absmax.to(target_device)
+        offset, state2 = compressed_stats
+        offset = offset.to(target_device)
+        absmax2, code2, blocksize2, _, _, _, _ = state2
+        absmax2 = absmax2.to(target_device)
+        code2 = code2.to(target_device)
+
+    # Setup output tensor on the same device as input
+    if out is None:
+        out = torch.empty(shape, dtype=dtype, device=target_device)
+    else:
+        assert out.shape == shape and out.dtype == dtype
+        out = out.to(target_device)
+
+    # Dequantize statistics on the target device
+    n_elements_absmax: int = absmax.numel()
+    out_absmax: torch.Tensor = torch.empty(
+        n_elements_absmax, dtype=torch.float32, device=target_device
+    )
+    ptr_out_absmax: int = get_ptr(out_absmax)
+
+    # Use CUDA stream if available
+    if HAS_CUDA_STREAM:
+        global CUDA_STREAM
+        if CUDA_STREAM is None:
+            CUDA_STREAM = torch.cuda.current_stream(target_device)
+
+        cdequantize_blockwise_fp32(
+            get_ptr(code2),
+            get_ptr(absmax),
+            get_ptr(absmax2),
+            ptr_out_absmax,
+            ctypes.c_int(blocksize2),
+            ctypes.c_int(n_elements_absmax),
+            CUDA_STREAM,
+        )
+    else:
+        cdequantize_blockwise_fp32(
+            get_ptr(code2),
+            get_ptr(absmax),
+            get_ptr(absmax2),
+            ptr_out_absmax,
+            ctypes.c_int(blocksize2),
+            ctypes.c_int(n_elements_absmax),
+        )
+
+    out_absmax += offset
+
+    # Choose appropriate dequantization function
+    fx = (
+        cdequantize_blockwise_fp16_nf4
+        if dtype == torch.float16
+        else cdequantize_blockwise_bf16_nf4
+    )
+
+    # Dequantize weights
+    if HAS_CUDA_STREAM:
+        fx(
+            get_ptr(None),
+            get_ptr(W),
+            ptr_out_absmax,
+            get_ptr(out),
+            ctypes.c_int(blocksize),
+            ctypes.c_int(out.numel()),
+            CUDA_STREAM,
+        )
+    else:
+        fx(
+            get_ptr(None),
+            get_ptr(W),
+            ptr_out_absmax,
+            get_ptr(out),
+            ctypes.c_int(blocksize),
+            ctypes.c_int(out.numel()),
+        )
+
+    # Handle transposed data
+    is_transposed: bool = W.shape[0] == 1
+    return out.t() if is_transposed else out

src/axolotl/kernels/swiglu.py

@@ -0,0 +1,163 @@
+"""
+Module for definition of SwiGLU Triton kernels.
+
+See "GLU Variants Improve Transformer" (https://arxiv.org/abs/2002.05202).
+
+Credit to `unsloth` (https://unsloth.ai/) for inspiration for this implementation.
+"""
+import torch
+import triton
+import triton.language as tl
+
+
+@triton.jit
+def _swiglu_fwd_kernel(
+    gate_ptr,
+    up_ptr,
+    out_ptr,
+    n_elements,
+    block_size: tl.constexpr,
+):
+    """
+    SwiGLU forward kernel. The kernel computes activation in fp32 precision for better
+    numerical stability, then converts back to original dtype for the final result.
+
+    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
+
+    # Load gate in fp32, keep up in original dtype
+    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
+    f = gate * tl.sigmoid(gate)
+    f = f.to(up.dtype)
+    result = f * up
+
+    tl.store(out_ptr + offsets, result, mask=mask)
+
+
+@triton.jit
+def _swiglu_bwd_kernel(
+    grad_out_ptr,
+    gate_ptr,
+    up_ptr,
+    n_elements,
+    block_size: tl.constexpr,
+):
+    """
+    SwiGLU 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 forward 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
+
+    # Load values - only convert gate to fp32
+    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)
+
+    # Compute SiLU and forward output
+    sigmoid_gate = tl.sigmoid(gate)
+    silu_gate = sigmoid_gate * gate
+    silu_gate = silu_gate.to(grad_out.dtype)
+    h = silu_gate * up
+
+    # Compute gradients
+    grad_up = grad_out * silu_gate  # gradient for up is grad_out * SiLU(gate)
+
+    # Compute gate gradient
+    temp = grad_out * up
+    grad_gate = temp.to(tl.float32) * sigmoid_gate * (1.0 + gate * (1.0 - sigmoid_gate))
+    grad_gate = grad_gate.to(grad_out.dtype)
+
+    # Store results with correct gradient ordering
+    tl.store(grad_out_ptr + offsets, h, mask=mask)
+    tl.store(gate_ptr + offsets, grad_gate, mask=mask)  # grad wrt gate
+    tl.store(up_ptr + offsets, grad_up, mask=mask)  # grad wrt up
+
+
+# pylint: disable=unnecessary-lambda-assignment
+def swiglu_forward(gate: torch.Tensor, up: torch.Tensor) -> torch.Tensor:
+    """
+    SwiGLU forward pass. Computes SwiGLU activation: `x * sigmoid(x) * up`, where
+    `x` is the gate tensor.
+
+    Args:
+        gate: Input gate tensor of shape `[batch, seq_len, hidden_dim]`.
+        up: Up-projection tensor of shape `[batch, seq_len, hidden_dim]`.
+
+    Returns:
+        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
+    _swiglu_fwd_kernel[grid](
+        gate_ptr=gate,
+        up_ptr=up,
+        out_ptr=out,
+        n_elements=n_elements,
+        block_size=1024,
+    )
+
+    return out
+
+
+# pylint: disable=unnecessary-lambda-assignment
+def swiglu_backward(
+    grad_output: torch.Tensor, gate: torch.Tensor, up: torch.Tensor
+) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+    """
+    SwiGLU 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:
+            - Forward pass output (`h`)
+            - Gradient with respect to gate (`df`)
+            - Gradient with respect to up-projection (`de`)
+    """
+    n_elements = grad_output.numel()
+
+    grid = lambda meta: (triton.cdiv(n_elements, meta["block_size"]),)  # noqa: E731
+    _swiglu_bwd_kernel[grid](
+        grad_out_ptr=grad_output,
+        gate_ptr=gate,
+        up_ptr=up,
+        n_elements=n_elements,
+        block_size=1024,
+    )
+
+    # After kernel execution, tensors contain:
+    # grad_output: h (forward output)
+    # gate: grad_gate (grad wrt gate)
+    # up: grad_up (grad wrt up)
+    return grad_output, gate, up

src/axolotl/kernels/utils.py

@@ -0,0 +1,11 @@
+"""Utilities for `axolotl.kernels` submodules."""
+
+import torch
+from packaging.version import Version
+
+if Version(torch.__version__) < Version("2.4.0"):
+    torch_amp_custom_fwd = torch.cuda.amp.custom_fwd
+    torch_amp_custom_bwd = torch.cuda.amp.custom_bwd
+else:
+    torch_amp_custom_fwd = torch.amp.custom_fwd(device_type="cuda")
+    torch_amp_custom_bwd = torch.amp.custom_bwd(device_type="cuda")

src/axolotl/monkeypatch/attention/sequence_parallel/__init__.py

@@ -0,0 +1,45 @@
+from enum import Enum
+from functools import partial
+
+from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS
+from yunchang import set_seq_parallel_pg, EXTRACT_FUNC_DICT
+
+from axolotl.utils.distributed import get_world_size, get_rank
+
+
+class USPRingAttnType(Enum):
+    BASIC = "basic"
+    ZIGZAG = "zigzag"
+    STRIPE = "stripe"
+
+def apply_usp_attn_patch(ring_impl_type: USPRingAttnType):
+    from axolotl.monkeypatch.attention.sequence_parallel.usp import build_usp_fa_forward
+
+    fa_forward = build_usp_fa_forward(ring_impl_type)
+    ALL_ATTENTION_FUNCTIONS["flash_attention_2"] = fa_forward
+
+def get_extract_fn(ring_impl_type: USPRingAttnType, sp_ulysses_degree: int):
+    fn = EXTRACT_FUNC_DICT["basic"]
+    if ring_impl_type.value in EXTRACT_FUNC_DICT.keys():
+        fn = EXTRACT_FUNC_DICT[ring_impl_type.value]
+
+    # map bad key upstream
+    elif ring_impl_type == USPRingAttnType.STRIPE:
+        fn = EXTRACT_FUNC_DICT["strip"]
+
+    world_size = get_world_size()
+    rd = world_size // sp_ulysses_degree
+
+    return partial(fn, rank=get_rank(), world_size=world_size, rd=rd, ud=sp_ulysses_degree)
+
+def set_usp_parallel_group(sp_ulysses_degree):
+    """
+    setup distributed parallel group for USP attention
+    make sure this gets called before building any USP attention modules
+    :param sp_ulysses_degree:
+    :return:
+    """
+    world_size = get_world_size()
+    rank = get_rank()
+    sp_ring_degree = world_size // sp_ulysses_degree
+    set_seq_parallel_pg(sp_ulysses_degree, sp_ring_degree, rank, world_size)

src/axolotl/monkeypatch/attention/sequence_parallel/usp.py

@@ -0,0 +1,36 @@
+from enum import Enum
+from typing import Optional, Tuple, Callable
+
+import torch
+from yunchang import LongContextAttention
+
+from axolotl.monkeypatch.attention.sequence_parallel import USPRingAttnType
+
+
+def build_usp_fa_forward(ring_impl_type: USPRingAttnType) -> Callable:
+    usp_attn = LongContextAttention(ring_impl_type.value)
+
+    def flash_attention_forward(
+            module: torch.nn.Module,  # pylint: disable=unused-argument
+            query: torch.Tensor,
+            key: torch.Tensor,
+            value: torch.Tensor,
+            attention_mask: Optional[torch.Tensor],  # pylint: disable=unused-argument
+            dropout: float = 0.0,
+            scaling: Optional[float] = None,
+            sliding_window: Optional[int] = None,  # pylint: disable=unused-argument
+            softcap: Optional[float] = None,
+            **kwargs,  # pylint: disable=unused-argument
+    ) -> Tuple[torch.Tensor, None]:
+        attn_output = usp_attn(
+            query,
+            key,
+            value,
+            dropout_p=dropout,
+            softmax_scale=scaling,
+            causal=True,
+            softcap=softcap,
+        )
+        return attn_output, None
+
+    return flash_attention_forward

src/axolotl/monkeypatch/lora_kernels.py

@@ -0,0 +1,333 @@
+"""Module for patching custom LoRA Triton kernels and `torch.autograd` functions."""
+
+import importlib
+import inspect
+import logging
+import types
+from typing import Type
+
+import torch
+from accelerate.logging import get_logger
+from peft import PeftModelForCausalLM
+from torch import nn
+from transformers import AutoConfig
+
+from axolotl.kernels.lora import (
+    apply_lora_mlp_geglu,
+    apply_lora_mlp_swiglu,
+    apply_lora_o,
+    apply_lora_qkv,
+)
+from axolotl.monkeypatch.utils import detab_code
+from axolotl.utils.dict import DictDefault
+
+LOG = get_logger(__name__)
+
+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"
+)
+
+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"
+)
+
+ORIGINAL_O_CODE = """
+    attn_output = self.o_proj(attn_output)
+""".lstrip(
+    "\n"
+)
+
+PATCHED_O_CODE = """
+    attn_output = self.apply_o(attn_output)
+""".lstrip(
+    "\n"
+)
+
+SUPPORTED_ACTIVATIONS = ["silu", "gelu"]
+APPLY_FN_MAPPING = {
+    "silu": apply_lora_mlp_swiglu,
+    "gelu": apply_lora_mlp_geglu,
+}
+
+
+def original_apply_qkv(
+    self: nn.Module, hidden_states: torch.Tensor
+) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+    """
+    Original implementation of QKV projection without optimizations.
+
+    Args:
+        self: The attention module instance.
+        hidden_states: Input tensor of shape [batch_size, seq_len, hidden_dim].
+
+    Returns:
+        A tuple `(query_states, key_states, value_states)` containing the projected
+            states for query, key, and value.
+    """
+    query_states = self.q_proj(hidden_states)
+    key_states = self.k_proj(hidden_states)
+    value_states = self.v_proj(hidden_states)
+
+    return query_states, key_states, value_states
+
+
+def original_apply_o(self: nn.Module, hidden_states: torch.Tensor) -> torch.Tensor:
+    """
+    Original implementation of output projection without optimizations.
+
+    Args:
+        self: The attention module instance.
+        hidden_states: Input tensor of shape `[`batch_size, seq_len, hidden_dim]`.
+
+    Returns:
+        The output projection result.
+    """
+    attn_output = self.o_proj(hidden_states)
+
+    return attn_output
+
+
+def get_attention_cls_from_config(cfg: DictDefault) -> Type[nn.Module]:
+    """
+    Get the appropriate attention class by inspecting the model config.
+    Uses dynamic import to support any model architecture that follows
+    the standard transformers naming convention.
+
+    Args:
+        cfg: Dictionary mapping `axolotl` config keys to values.
+
+    Returns:
+        The appropriate attention class for the model.
+
+    Raises:
+        ValueError: If `base_model` not specified or attention class cannot be imported
+        ImportError: If the model module or attention class doesn't exist
+    """
+    if "base_model" not in cfg:
+        raise ValueError("base_model must be specified in config")
+
+    # Get model config without loading the model
+    model_config = AutoConfig.from_pretrained(cfg["base_model"])
+    model_type = model_config.model_type
+
+    # Special case for model_type = "qwen2"
+    if model_type == "qwen2":
+        from transformers.models.qwen2.modeling_qwen2 import Qwen2Attention
+
+        return Qwen2Attention
+
+    try:
+        # Dynamically import the module and attention class
+        module_path = f"transformers.models.{model_type}.modeling_{model_type}"
+        module = __import__(
+            module_path, fromlist=[f"{model_type.capitalize()}Attention"]
+        )
+        attention_cls = getattr(module, f"{model_type.capitalize()}Attention")
+
+        return attention_cls
+    except (ImportError, AttributeError) as e:
+        raise ValueError(
+            f"Could not import attention class for model_type: {model_type}. "
+            f"Error: {str(e)}"
+        ) from e
+
+
+# pylint: disable=protected-access
+def patch_self_attn_lora(cfg: DictDefault):
+    """
+    Given an `axolotl` config, this method patches the inferred attention class forward
+    pass with optimized LoRA implementations.
+
+    It modifies the attention class to use optimized QKV and output projections. The
+    original implementation is preserved and can be restored if needed.
+
+    Args:
+        cfg: Dictionary mapping `axolotl` config keys to values.
+
+    Raises:
+        AssertionError: If the required code blocks are not found in the attention
+            implementation.
+    """
+    attention_cls = get_attention_cls_from_config(cfg)
+
+    # Check if already patched
+    if hasattr(attention_cls, "_original_forward"):
+        LOG.info(f"{attention_cls.__name__} already patched")
+        return
+
+    self_attn_forward = inspect.getsource(attention_cls.forward)
+    attention_cls._original_forward = self_attn_forward
+    self_attn_forward, _ = detab_code(self_attn_forward)
+
+    assert ORIGINAL_QKV_CODE in self_attn_forward, "Original QKV code not found"
+    assert ORIGINAL_O_CODE in self_attn_forward, "Original O code not found"
+
+    self_attn_forward = self_attn_forward.replace(ORIGINAL_QKV_CODE, PATCHED_QKV_CODE)
+    self_attn_forward = self_attn_forward.replace(ORIGINAL_O_CODE, PATCHED_O_CODE)
+    self_attn_forward = self_attn_forward.replace(
+        "def forward(",
+        "def axolotl_attn_forward(",
+        1,
+    )
+
+    # Load necessary imports
+    module_name = attention_cls.__module__
+    module = importlib.import_module(module_name)
+
+    items_to_import = []
+    for item in dir(module):
+        if item in self_attn_forward:
+            items_to_import.append(item)
+
+    exec(  # pylint: disable=exec-used  # nosec B102
+        f"from {module_name} import ({', '.join(items_to_import)})",
+        globals(),
+    )
+    exec(self_attn_forward, globals())  # pylint: disable=exec-used  # nosec B102
+
+    LOG.info(f"Patched attention class with LoRA optims: {attention_cls.__name__}")
+    attention_cls.forward = (
+        axolotl_attn_forward  # pylint: disable=undefined-variable  # noqa: F821
+    )
+
+
+def apply_lora_kernel_patches(
+    model: PeftModelForCausalLM, cfg: DictDefault
+) -> PeftModelForCausalLM:
+    """
+    Applies optimized Triton kernel patches to a PEFT model.
+
+    Patches a PEFT model with optimized implementations for MLP and attention
+    computations. The optimizations include custom Triton kernels for activation
+    functions and specialized autograd functions for LoRA computations.
+
+    Args:
+        model: A PEFT model to be patched with optimized kernels.
+        cfg: Dictionary mapping `axolotl` config keys to values.
+
+    Returns:
+        PeftModelForCausalLM: The patched model with optimized kernels.
+
+    Raises:
+        TypeError: If the provided model is not a `PeftModelForCausalLM`.
+        NotImplementedError: If the model type is not supported.
+        AssertionError: If multiple adapters are active (currently unsupported).
+
+    Note:
+        The optimizations require LoRA adapters with no dropout and no bias terms. The
+            function will skip patching if these conditions aren't met.
+    """
+    if not isinstance(model, PeftModelForCausalLM):
+        raise TypeError("Model must be a PeftModelForCausalLM")
+
+    # Get active LoRA adapter config
+    if hasattr(model, "active_adapters"):
+        assert (
+            len(model.active_adapters) == 1
+        ), "Axolotl currently does not support LoRA Triton kernels for multiple adapters"
+        active_adapter = model.active_adapters[0]
+    else:
+        active_adapter = model.active_adapter
+    lora_config = model.model.peft_config[active_adapter]
+
+    # Only patch if conditions are met
+    can_patch = lora_config.lora_dropout == 0 and lora_config.bias == "none"
+
+    if not can_patch:
+        LOG.warning("Cannot patch layers - requires no dropout and no bias")
+        LOG.warning("Please specify `lora_dropout: 0` in your axolotl config file")
+        return model
+
+    # This needs to be reset after patching
+    original_level = LOG.getEffectiveLevel()
+    LOG.setLevel(logging.INFO)
+
+    # Choose activation based on model type
+    activation = model.config.hidden_act
+    if activation not in SUPPORTED_ACTIVATIONS:
+        raise NotImplementedError(f"Activation {activation} is not supported")
+
+    # Patch each layer
+    for layer in model.model.model.layers:
+        # Add QKV, O fallback implementations to start
+        # These will be overwritten later (if some conditions apply)
+        layer.self_attn.apply_qkv = types.MethodType(
+            original_apply_qkv, layer.self_attn
+        )
+        layer.self_attn.apply_o = types.MethodType(original_apply_o, layer.self_attn)
+
+        if cfg.lora_mlp_kernel:
+            # MLP patching
+            gate_proj = layer.mlp.gate_proj
+            up_proj = layer.mlp.up_proj
+            down_proj = layer.mlp.down_proj
+
+            can_patch_mlp = all(
+                hasattr(proj, "lora_A")
+                and getattr(proj, "base_layer", proj).bias is None
+                and len(getattr(proj, "lora_magnitude_vector", []) or []) == 0
+                for proj in (gate_proj, up_proj, down_proj)
+            )
+
+            if can_patch_mlp:
+                apply_fn = APPLY_FN_MAPPING[activation]
+                layer.mlp.forward = types.MethodType(apply_fn, layer.mlp)
+            else:
+                LOG.warning_once(
+                    "Cannot patch some MLP layers - requires LoRA adapters with no bias"
+                )
+        if cfg.lora_qkv_kernel:
+            # Query, key, value patching
+            layer_modules = [
+                getattr(layer.self_attn, linear_proj)
+                for linear_proj in ["q_proj", "k_proj", "v_proj"]
+            ]
+            can_patch_qkv = all(
+                hasattr(module, "lora_A")
+                and getattr(module, "base_layer", module).bias is None
+                and len(getattr(module, "lora_magnitude_vector", []) or []) == 0
+                for module in layer_modules
+            )
+
+            if can_patch_qkv:
+                # Add optimized implementation
+                layer.self_attn.apply_qkv = types.MethodType(
+                    apply_lora_qkv, layer.self_attn
+                )
+            else:
+                LOG.warning_once(
+                    "Cannot patch some attention QKV projections - requires LoRA adapters with no bias"
+                )
+        if cfg.lora_o_kernel:
+            # Output patching
+            layer_modules = [
+                getattr(layer.self_attn, linear_proj) for linear_proj in ["o_proj"]
+            ]
+            can_patch_o = all(
+                hasattr(module, "lora_A")
+                and getattr(module, "base_layer", module).bias is None
+                and len(getattr(module, "lora_magnitude_vector", []) or []) == 0
+                for module in layer_modules
+            )
+
+            if can_patch_o:
+                layer.self_attn.apply_o = types.MethodType(
+                    apply_lora_o, layer.self_attn
+                )
+            else:
+                LOG.warning_once(
+                    "Cannot patch some attention output projection - requires LoRA adapters with no bias"
+                )
+
+    LOG.setLevel(original_level)
+
+    return model

src/axolotl/monkeypatch/relora.py

@@ -127,6 +127,8 @@ class ReLoRACallback(TrainerCallback):
         optimizer: torch.optim.Optimizer,
         **_kwargs,
     ):
+        if not optimizer:
+            optimizer = state.optimizer
         if state.global_step > 0 and state.global_step % self.relora_steps == 0:
             checkpoint_folder = os.path.join(
                 args.output_dir,

src/axolotl/prompt_strategies/__init__.py

@@ -16,10 +16,21 @@ def load(strategy, tokenizer, cfg, ds_cfg, processor=None):
 
             return messages_load(tokenizer, cfg, ds_cfg, processor=processor)
         load_fn = "load"
+        package = "axolotl.prompt_strategies"
         if strategy.split(".")[-1].startswith("load_"):
             load_fn = strategy.split(".")[-1]
             strategy = ".".join(strategy.split(".")[:-1])
-        mod = importlib.import_module(f".{strategy}", "axolotl.prompt_strategies")
+        elif len(strategy.split(".")) > 1:
+            try:
+                importlib.import_module(
+                    "." + strategy.split(".")[-1],
+                    ".".join(strategy.split(".")[:-1]),
+                )
+                package = ".".join(strategy.split(".")[:-1])
+                strategy = strategy.split(".")[-1]
+            except ModuleNotFoundError:
+                pass
+        mod = importlib.import_module(f".{strategy}", package)
         func = getattr(mod, load_fn)
         load_kwargs = {}
         if strategy == "user_defined":
@@ -30,10 +41,10 @@ def load(strategy, tokenizer, cfg, ds_cfg, processor=None):
                 load_kwargs["ds_cfg"] = ds_cfg
             if "processor" in sig.parameters:
                 load_kwargs["processor"] = processor
+
         return func(tokenizer, cfg, **load_kwargs)
     except ModuleNotFoundError:
         return None
     except Exception as exc:  # pylint: disable=broad-exception-caught
         LOG.error(f"Failed to load prompt strategy `{strategy}`: {str(exc)}")
         raise exc
-    return None

src/axolotl/prompt_strategies/base.py

@@ -10,9 +10,22 @@ LOG = logging.getLogger("axolotl")
 
 def load(strategy, cfg, module_base=None, **kwargs):
     try:
+        if len(strategy.split(".")) == 1:
+            strategy = strategy + ".default"
         load_fn = strategy.split(".")[-1]
-        strategy = ".".join(strategy.split(".")[:-1])
-        mod = importlib.import_module(f".{strategy}", module_base)
+        if len(strategy.split(".")) > 1:
+            try:
+                importlib.import_module(
+                    strategy.split(".")[-2],
+                    ".".join(strategy.split(".")[:-2]),
+                )
+                module_base = ".".join(strategy.split(".")[:-2])
+                strategy = strategy.split(".")[-2]
+            except ModuleNotFoundError:
+                strategy = "." + ".".join(strategy.split(".")[:-1])
+        else:
+            strategy = "." + ".".join(strategy.split(".")[:-1])
+        mod = importlib.import_module(strategy, module_base)
         func = getattr(mod, load_fn)
         return func(cfg, **kwargs)
     except Exception:  # pylint: disable=broad-exception-caught

src/axolotl/prompt_strategies/bradley_terry/chat_template.py

@@ -21,7 +21,11 @@ class BTChatTemplateStrategy(ChatTemplateStrategy):
     Bradley-Terry reward model pairwise chat template prompt strategy.
     """
 
-    def tokenize_prompt(self, prompt):
+    @property
+    def supports_batched(self) -> bool:
+        return False
+
+    def _tokenize_single_prompt(self, prompt):
         """
 
         :param prompt: the actual row of data from the underlying dataset
@@ -30,20 +34,17 @@ class BTChatTemplateStrategy(ChatTemplateStrategy):
 
         max_length = self.prompter.max_length
 
-        self.messages = "chosen_messages"
         # pylint: disable=duplicate-code
-        prompt[self.messages] = []
+        prompt["messages"] = []
         if prompt["system"]:
-            prompt[self.messages].append(
-                {"role": "system", "content": prompt["system"]}
-            )
-        prompt[self.messages].append({"role": "user", "content": prompt["input"]})
-        prompt[self.messages].append({"role": "assistant", "content": prompt["chosen"]})
-        chosen_tokenized = super().tokenize_prompt(prompt)
+            prompt["messages"].append({"role": "system", "content": prompt["system"]})
+        prompt["messages"].append({"role": "user", "content": prompt["input"]})
+        prompt["messages"].append({"role": "assistant", "content": prompt["chosen"]})
+        chosen_tokenized = super()._tokenize_single_prompt(prompt)
 
         if len(chosen_tokenized["input_ids"]) > max_length:
             LOG.warning(
-                f"Chosen sequence exceeds max sequence length: {len(chosen_tokenized['input_ids'])}",
+                f"To-be-trimmed chosen sequence exceeds max sequence length: {len(chosen_tokenized['input_ids'])}",
             )
 
             chosen_tokenized["input_ids"] = chosen_tokenized["input_ids"][:max_length]
@@ -51,22 +52,17 @@ class BTChatTemplateStrategy(ChatTemplateStrategy):
                 :max_length
             ]
 
-        self.messages = "rejected_messages"
         # pylint: disable=duplicate-code
-        prompt[self.messages] = []
+        prompt["messages"] = []
         if prompt["system"]:
-            prompt[self.messages].append(
-                {"role": "system", "content": prompt["system"]}
-            )
-        prompt[self.messages].append({"role": "user", "content": prompt["input"]})
-        prompt[self.messages].append(
-            {"role": "assistant", "content": prompt["rejected"]}
-        )
-        rejected_tokenized = super().tokenize_prompt(prompt)
+            prompt["messages"].append({"role": "system", "content": prompt["system"]})
+        prompt["messages"].append({"role": "user", "content": prompt["input"]})
+        prompt["messages"].append({"role": "assistant", "content": prompt["rejected"]})
+        rejected_tokenized = super()._tokenize_single_prompt(prompt)
 
         if len(rejected_tokenized["input_ids"]) > max_length:
             LOG.warning(
-                f"Rejected sequence exceeds max sequence length: {len(rejected_tokenized['input_ids'])}",
+                f"To-be-trimmed rejected sequence exceeds max sequence length: {len(rejected_tokenized['input_ids'])}",
             )
 
             rejected_tokenized["input_ids"] = rejected_tokenized["input_ids"][
@@ -95,8 +91,13 @@ def load(tokenizer, cfg, ds_cfg: Optional[Dict[str, Any]] = None):
     prompter_params = {
         "tokenizer": tokenizer,
         "chat_template": chat_template_string,
-        "message_field_role": ds_cfg.get("message_field_role", "role"),
-        "message_field_content": ds_cfg.get("message_field_content", "content"),
+        "message_property_mappings": ds_cfg.get(
+            "message_property_mappings",
+            {
+                "role": "role",
+                "content": "content",
+            },
+        ),
         "message_field_training": ds_cfg.get("message_field_training", None),
         "message_field_training_detail": ds_cfg.get(
             "message_field_training_detail", None
@@ -120,7 +121,4 @@ def load(tokenizer, cfg, ds_cfg: Optional[Dict[str, Any]] = None):
         ChatTemplatePrompter(**prompter_params), tokenizer=tokenizer, **strategy_params
     )
 
-    if "field_messages" in ds_cfg and hasattr(strategy, "messages"):
-        strategy.messages = ds_cfg["field_messages"]
-
     return strategy

src/axolotl/prompt_strategies/chat_template.py

@@ -3,13 +3,17 @@ HF Chat Templates prompt strategy
 """
 
 import logging
-from typing import Any, Dict, List, Optional
+from collections import defaultdict
+from typing import Any, Dict, List, Optional, Set, Union
 
+from pydantic import BaseModel
 from transformers import ProcessorMixin
 
+from axolotl.prompt_strategies.jinja_template_analyzer import JinjaTemplateAnalyzer
 from axolotl.prompt_tokenizers import PromptTokenizingStrategy
 from axolotl.prompters import IGNORE_TOKEN_ID, Prompter
 from axolotl.utils.chat_templates import get_chat_template_from_config
+from axolotl.utils.config.models.input.v0_4_1 import DatasetConfig
 
 # Configure the logger
 LOG = logging.getLogger("axolotl")
@@ -22,16 +26,23 @@ class ChatTemplatePrompter(Prompter):
     def __init__(
         self,
         tokenizer,
+        chat_template: str,
         processor=None,
-        chat_template=None,
         max_length=2048,
-        message_field_role: str = "role",
-        message_field_content: str = "content",
+        message_property_mappings: Optional[Dict[str, str]] = None,
         message_field_training: Optional[str] = None,
         message_field_training_detail: Optional[str] = None,
+        field_messages: str = "messages",
         roles: Optional[Dict[str, List[str]]] = None,
         drop_system_message: bool = False,
     ):
+        # check if message_property_mappings is None or empty dict
+        if message_property_mappings is None or (not message_property_mappings):
+            message_property_mappings = {
+                "role": "role",
+                "content": "content",
+            }
+
         if roles:
             self.roles = {s: t for t, sources in roles.items() for s in sources}
         else:
@@ -44,18 +55,28 @@ class ChatTemplatePrompter(Prompter):
                 "tool": "tool",
             }
 
-        self.message_field_role = message_field_role
-        self.message_field_content = message_field_content
+        self._chat_template_msg_variables = self.get_chat_template_msg_variables(
+            chat_template, field_messages
+        )
+        self.message_property_mappings = message_property_mappings
         self.message_field_training = message_field_training
         self.message_field_training_detail = message_field_training_detail
+        self.field_messages = field_messages
         self.tokenizer = tokenizer
-        self.processor: ProcessorMixin = processor
+        self.processor: Optional[ProcessorMixin] = processor
         self.chat_template = chat_template
         self.max_length = max_length
         self.drop_system_message = drop_system_message
 
+    @property
+    def chat_template_msg_variables(self) -> Set[str]:
+        return self._chat_template_msg_variables
+
     def build_prompt(self, conversation, add_generation_prompt=False, images=None):
         if self.processor:
+            if not callable(self.processor):
+                raise TypeError("Processor must be callable")
+
             text = self.processor.apply_chat_template(
                 conversation,
                 chat_template=self.chat_template,
@@ -183,17 +204,21 @@ class ChatTemplatePrompter(Prompter):
 
         return adjusted_details
 
+    def get_chat_template_msg_variables(
+        self, chat_template: str, field_messages: str
+    ) -> Set[str]:
+        template_analyzer = JinjaTemplateAnalyzer(chat_template)
+        return template_analyzer.get_message_vars(field_messages)
+
 
 class ChatTemplateStrategy(PromptTokenizingStrategy):
     """
     Tokenizing strategy for instruction-based prompts.
     """
 
-    _messages = "messages"
-
     def __init__(
         self,
-        prompter,
+        prompter: "ChatTemplatePrompter",
         tokenizer,
         train_on_inputs,
         sequence_len,
@@ -201,6 +226,7 @@ class ChatTemplateStrategy(PromptTokenizingStrategy):
         train_on_eos=None,
     ):
         super().__init__(prompter, tokenizer, train_on_inputs, sequence_len)
+        self.prompter: ChatTemplatePrompter = prompter
 
         self.roles_to_train = []
         if roles_to_train:
@@ -212,30 +238,64 @@ class ChatTemplateStrategy(PromptTokenizingStrategy):
         self.train_on_eos = train_on_eos
         self.images = "images"
 
+        LOG.debug(
+            f"The chat template uses the following properites on the message: {self.prompter.chat_template_msg_variables}"
+        )
+
     @property
-    def messages(self):
-        return self._messages
+    def supports_batched(self) -> bool:
+        # Let calling code know we can handle lists of examples
+        return True
 
-    @messages.setter
-    def messages(self, messages):
-        self._messages = messages
+    def is_prompt_batched(self, prompt: dict[str, Any]) -> bool:
+        try:
+            return all(isinstance(v, list) for v in prompt.values()) and all(
+                isinstance(v, list) for v in prompt[self.prompter.field_messages]
+            )
+        except KeyError:
+            return False
 
-    def tokenize_prompt(self, prompt):
+    def tokenize_prompt(self, prompt: dict[str, Any]):
+        """
+        Public method that can handle either a single prompt or a batch of prompts.
+        """
+
+        if not self.is_prompt_batched(prompt) or not self.supports_batched:
+            return self._tokenize_single_prompt(prompt)
+
+        res = defaultdict(lambda: [])
+        feature_names = list(prompt.keys())
+
+        # Process each prompt individually
+        for row in zip(*prompt.values()):
+            tokenized_prompt = self._tokenize_single_prompt(
+                dict(zip(feature_names, row))
+            )
+            for key, val in tokenized_prompt.items():
+                res[key].append(val)
+
+        # If there are no examples left, return an empty dictionary
+        if not res:
+            return {}
+
+        return dict(res)
+
+    def _tokenize_single_prompt(self, prompt: dict) -> Dict[str, List[int]]:
         # Old simple legacy behavior that works reliably.
         if (
             not self.roles_to_train
             and not self.train_on_eos
-            and not self.prompter.message_field_training
-            and not self.prompter.message_field_training_detail
+            and not self.prompter.message_field_training  # type: ignore
+            and not self.prompter.message_field_training_detail  # type: ignore
         ):
             turns = self.get_conversation_thread(prompt)
             images = self.get_images(prompt)
-            prompt_ids = self.prompter.build_prompt(
+            prompt_ids = self.prompter.build_prompt(  # type: ignore
                 turns[:-1],
                 add_generation_prompt=True,
                 images=images,
             )
-            tokenized_res = self.prompter.build_prompt(turns, images=images)
+            tokenized_res = self.prompter.build_prompt(turns, images=images)  # type: ignore
             tokenized_prompt = {}
             if isinstance(tokenized_res, list):
                 input_ids = prompt_ids + tokenized_res[len(prompt_ids) :]
@@ -256,7 +316,7 @@ class ChatTemplateStrategy(PromptTokenizingStrategy):
             return tokenized_prompt
 
         turns = self.get_conversation_thread(prompt)
-        input_ids = self.prompter.build_prompt(turns)
+        input_ids = self.prompter.build_prompt(turns)  # type: ignore
         labels = [IGNORE_TOKEN_ID] * len(input_ids)
 
         last_eos_idx = -1
@@ -286,7 +346,7 @@ class ChatTemplateStrategy(PromptTokenizingStrategy):
 
             if should_train and turn_start_idx != -1 and turn_end_idx != -1:
                 if train_detail:
-                    token_offsets = self.prompter.get_offsets_for_train_detail(
+                    token_offsets = self.prompter.get_offsets_for_train_detail(  # type: ignore
                         content, train_detail
                     )
                     LOG.debug(f"Token offsets: {token_offsets}")
@@ -424,30 +484,17 @@ class ChatTemplateStrategy(PromptTokenizingStrategy):
 
     def get_conversation_thread(self, prompt):
         turns = []
-        optional_keys = [
-            "tool_calls",  # tool that 'assistant' calls
-            "name",  # name of tool given by 'tool'
-            "tool_call_id",  # mistral/mixtral requires this
-        ]
-        for message in prompt[self.messages]:
+        for message in prompt[self.prompter.field_messages]:
+            transformed_message = self.transform_message(message)
+
             turn = {
-                "role": self.prompter.roles[message[self.prompter.message_field_role]],
+                **transformed_message,
                 "training": message.get(self.prompter.message_field_training),
                 "training_detail": message.get(
                     self.prompter.message_field_training_detail
                 ),
             }
 
-            # do not add content if None as it may conflict with some templates due to tools
-            content = message.get(self.prompter.message_field_content, None)
-            if content is not None:
-                turn["content"] = content
-
-            for key in optional_keys:
-                value = message.get(key, None)
-                if value is not None:
-                    turn[key] = value
-
             turns.append(turn)
 
         if self.prompter.drop_system_message and turns[0]["role"] == "system":
@@ -455,47 +502,107 @@ class ChatTemplateStrategy(PromptTokenizingStrategy):
 
         return turns
 
+    def transform_message(self, message):
+        # Build the initial transformed message from the mappings
+        transformed_message = {}
+        for key, value in self.prompter.message_property_mappings.items():
+            if message.get(value) is not None:
+                transformed_message[key] = message[value]
+            else:
+                LOG.debug(
+                    f"Could not find value for property {value} in message: {message}"
+                )
+
+        # Map the role if necessary
+        if "role" in transformed_message:
+            transformed_message["role"] = self.prompter.roles.get(
+                transformed_message["role"], transformed_message["role"]
+            )
+
+        # Determine which keys in the original message were not mapped
+        mapped_values = set(self.prompter.message_property_mappings.values())
+        remaining_keys = set(message) - mapped_values
+
+        # Keep only the properties defined in the chat template
+        # and not already mapped
+        for key in self.prompter.chat_template_msg_variables:
+            if key in remaining_keys:
+                val = message.get(key)
+                if val is not None:
+                    transformed_message[key] = val
+
+        return transformed_message
+
     def get_images(self, prompt):
         return prompt.get(self.images, None)
 
 
-def load(tokenizer, cfg, ds_cfg: Optional[Dict[str, Any]] = None, processor=None):
-    # pylint: disable=duplicate-code
-    ds_cfg = ds_cfg or {}
-    chat_template_string = get_chat_template_from_config(
-        cfg=cfg, ds_cfg=ds_cfg, tokenizer=tokenizer
-    )
-    LOG.info(f"Using chat template:\n---\n{chat_template_string!s}\n---")
+class StrategyLoader:
+    """
+    Load chat template strategy based on configuration.
+    """
 
-    prompter_params = {
-        "tokenizer": tokenizer,
-        "chat_template": chat_template_string,
-        "message_field_role": ds_cfg.get("message_field_role", "role"),
-        "message_field_content": ds_cfg.get("message_field_content", "content"),
-        "message_field_training": ds_cfg.get("message_field_training", None),
-        "message_field_training_detail": ds_cfg.get(
-            "message_field_training_detail",
-            None,
-        ),
-        "roles": ds_cfg.get("roles"),
-        "drop_system_message": ds_cfg.get("drop_system_message", False),
-        # we need to add one for detecting sequences with exceeding the `sequence_len` limit.
-        "max_length": cfg.sequence_len + 1,
-        "processor": processor,
-    }
+    def _get_strategy_cls(self):
+        return ChatTemplateStrategy
 
-    strategy_params = {
-        "train_on_inputs": cfg.train_on_inputs,
-        "sequence_len": cfg.sequence_len,
-        "roles_to_train": ds_cfg.get("roles_to_train", ["assistant"]),
-        "train_on_eos": ds_cfg.get("train_on_eos", "turn"),
-    }
+    def _get_strategy_params(self, cfg, ds_cfg: Dict[str, Any]):
+        return {
+            "train_on_inputs": cfg.train_on_inputs,
+            "sequence_len": cfg.sequence_len,
+            "roles_to_train": ds_cfg.get("roles_to_train", ["assistant"]),
+            "train_on_eos": ds_cfg.get("train_on_eos", "turn"),
+        }
 
-    strategy = ChatTemplateStrategy(
-        ChatTemplatePrompter(**prompter_params), tokenizer=tokenizer, **strategy_params
-    )
+    def __call__(
+        self,
+        tokenizer,
+        cfg,
+        ds_cfg: Optional[Union[Dict[str, Any], DatasetConfig]] = None,
+        processor=None,
+    ):
+        if ds_cfg is None:
+            dataset_config = {}
+        elif isinstance(ds_cfg, BaseModel):
+            dataset_config = ds_cfg.model_dump()
+        else:
+            dataset_config = ds_cfg
 
-    if "field_messages" in ds_cfg and hasattr(strategy, "messages"):
-        strategy.messages = ds_cfg["field_messages"]
+        chat_template_string = get_chat_template_from_config(
+            cfg=cfg, ds_cfg=dataset_config, tokenizer=tokenizer
+        )
+        LOG.info(f"Using chat template:\n---\n{chat_template_string!s}\n---")
 
-    return strategy
+        prompter_params = {
+            "tokenizer": tokenizer,
+            "chat_template": chat_template_string,
+            "message_property_mappings": dataset_config.get(
+                "message_property_mappings", {}
+            ),
+            "message_field_training": dataset_config.get(
+                "message_field_training", None
+            ),
+            "message_field_training_detail": dataset_config.get(
+                "message_field_training_detail",
+                None,
+            ),
+            "field_messages": dataset_config.get("field_messages", "messages"),
+            "roles": dataset_config.get("roles"),
+            "drop_system_message": dataset_config.get("drop_system_message", False),
+            # we need to add one for detecting sequences with exceeding the `sequence_len` limit.
+            "max_length": cfg.sequence_len + 1,
+            "processor": processor,
+        }
+
+        strategy_params = self._get_strategy_params(cfg, dataset_config)
+        strategy_cls = self._get_strategy_cls()
+
+        strategy = strategy_cls(
+            ChatTemplatePrompter(**prompter_params),
+            tokenizer=tokenizer,
+            **strategy_params,
+        )
+
+        return strategy
+
+
+load = StrategyLoader()

src/axolotl/prompt_strategies/dpo/chat_template.py

@@ -3,20 +3,28 @@ DPO prompt strategies for using tokenizer chat templates.
 """
 
 from axolotl.utils.chat_templates import extract_chat_template_args, get_chat_template
+from axolotl.utils.config.models.input.v0_4_1 import handle_legacy_message_fields_logic
 
 
 def default(
     cfg, dataset_idx=0, **kwargs
 ):  # pylint: disable=possibly-unused-variable,unused-argument
     ds_cfg = cfg["datasets"][dataset_idx]
+    ds_cfg = handle_legacy_message_fields_logic(ds_cfg)
+
     chat_template_choice, chat_template_jinja = extract_chat_template_args(
         cfg=cfg, ds_cfg=ds_cfg
     )
     field_messages = ds_cfg.get("field_messages", "messages")
     field_chosen = ds_cfg.get("field_chosen", "chosen")
     field_rejected = ds_cfg.get("field_rejected", "rejected")
-    field_message_role = ds_cfg.get("message_field_role", "role")
-    field_message_content = ds_cfg.get("message_field_content", "content")
+    message_property_mappings = ds_cfg.get(
+        "message_property_mappings",
+        {
+            "role": "role",
+            "content": "content",
+        },
+    )
     role_map_inv = ds_cfg.get(
         "roles",
         {
@@ -40,18 +48,18 @@ def default(
         messages = sample[field_messages]
         messages = [
             {
-                "role": role_map[m[field_message_role]],
-                "content": m[field_message_content],
+                "role": role_map[m[message_property_mappings["role"]]],
+                "content": m[message_property_mappings["content"]],
             }
             for m in messages
         ]
         chosen = {
-            "role": role_map[sample[field_chosen][field_message_role]],
-            "content": sample[field_chosen][field_message_content],
+            "role": role_map[sample[field_chosen][message_property_mappings["role"]]],
+            "content": sample[field_chosen][message_property_mappings["content"]],
         }
         rejected = {
-            "role": role_map[sample[field_rejected][field_message_role]],
-            "content": sample[field_rejected][field_message_content],
+            "role": role_map[sample[field_rejected][message_property_mappings["role"]]],
+            "content": sample[field_rejected][message_property_mappings["content"]],
         }
         dummy_user_message = {"role": "user", "content": "[[dummy_message]]"}
 

src/axolotl/prompt_strategies/dpo/chatml.py

@@ -3,22 +3,41 @@ DPO strategies for chatml
 """
 
 
-def argilla(
+def default(
     cfg,
     **kwargs,
 ):  # pylint: disable=possibly-unused-variable,unused-argument
     def transform_fn(sample):
+        if "prompt" in sample.keys():
+            prompt_key = "prompt"
+        elif "input" in sample.keys():
+            prompt_key = "input"
+        elif "question" in sample.keys():
+            prompt_key = "question"
+        else:
+            prompt_key = "instruction"
+
+        if "chosen" in sample.keys():
+            chosen_key = "chosen"
+        else:
+            chosen_key = "chosen_response"
+
+        if "rejected" in sample.keys():
+            rejected_key = "rejected"
+        else:
+            rejected_key = "rejected_response"
+
         if "system" in sample and sample["system"]:
             sample["prompt"] = (
                 f"<|im_start|>system\n{sample['system']}<|im_end|>\n"
-                f"<|im_start|>user\n{sample['instruction']}<|im_end|>\n<|im_start|>assistant\n"
+                f"<|im_start|>user\n{sample[prompt_key]}<|im_end|>\n<|im_start|>assistant\n"
             )
         else:
             sample[
                 "prompt"
-            ] = f"<|im_start|>user\n{sample['instruction']}<|im_end|>\n<|im_start|>assistant\n"
-        sample["chosen"] = f"{sample['chosen_response']}<|im_end|>"
-        sample["rejected"] = f"{sample['rejected_response']}<|im_end|>"
+            ] = f"<|im_start|>user\n{sample[prompt_key]}<|im_end|>\n<|im_start|>assistant\n"
+        sample["chosen"] = f"{sample[chosen_key]}<|im_end|>"
+        sample["rejected"] = f"{sample[rejected_key]}<|im_end|>"
         return sample
 
     return transform_fn

src/axolotl/prompt_strategies/dpo/llama3.py

@@ -3,22 +3,42 @@ DPO strategies for llama-3 chat template
 """
 
 
-def argilla(
+def default(
     cfg,
     **kwargs,
 ):  # pylint: disable=possibly-unused-variable,unused-argument
     def transform_fn(sample):
+        # pylint: disable=duplicate-code
+        if "prompt" in sample.keys():
+            prompt_key = "prompt"
+        elif "input" in sample.keys():
+            prompt_key = "input"
+        elif "question" in sample.keys():
+            prompt_key = "question"
+        else:
+            prompt_key = "instruction"
+
+        if "chosen" in sample.keys():
+            chosen_key = "chosen"
+        else:
+            chosen_key = "chosen_response"
+
+        if "rejected" in sample.keys():
+            rejected_key = "rejected"
+        else:
+            rejected_key = "rejected_response"
+
         if "system" in sample and sample["system"]:
             sample["prompt"] = (
                 f"<|start_header_id|>system<|end_header_id|>\n\n{sample['system']}<|eot_id|>"
-                f"<|start_header_id|>user<|end_header_id|>\n\n{sample['instruction']}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
+                f"<|start_header_id|>user<|end_header_id|>\n\n{sample[prompt_key]}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
             )
         else:
             sample[
                 "prompt"
-            ] = f"<|start_header_id|>user<|end_header_id|>\n\n{sample['instruction']}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
-        sample["chosen"] = f"{sample['chosen_response']}<|eot_id|>"
-        sample["rejected"] = f"{sample['rejected_response']}<|eot_id|>"
+            ] = f"<|start_header_id|>user<|end_header_id|>\n\n{sample[prompt_key]}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
+        sample["chosen"] = f"{sample[chosen_key]}<|eot_id|>"
+        sample["rejected"] = f"{sample[rejected_key]}<|eot_id|>"
         return sample
 
     return transform_fn

src/axolotl/prompt_strategies/dpo/passthrough.py

@@ -0,0 +1,14 @@
+"""
+DPO prompt strategies passthrough/zero-processing strategy
+"""
+
+
+def default(
+    cfg, dataset_idx=0, **kwargs
+):  # pylint: disable=possibly-unused-variable,unused-argument
+    def transform_fn(
+        sample, tokenizer=None
+    ):  # pylint: disable=possibly-unused-variable,unused-argument
+        return sample
+
+    return transform_fn

src/axolotl/prompt_strategies/jinja_template_analyzer.py

@@ -0,0 +1,318 @@
+"""Module for inspect jinja templates for the variables they use"""
+from typing import Dict, Optional, Set, TypedDict, Union
+
+from jinja2 import Environment, meta, nodes
+
+
+class JinjaTemplateAnalysis(TypedDict):
+    """
+    Represents the detailed analysis of a Jinja template variable.
+
+    Attributes:
+        accessed_properties (Set[str]): A set of properties accessed from the variable
+            (e.g., `foo.bar` results in 'bar' being accessed for 'foo').
+        accessed_indices (Set[Union[int, float]]): A set of indices accessed from the variable.
+        is_iterated (bool): Indicates if the variable is used as an iteration source in a `for` loop.
+        is_conditional (bool): Indicates if the variable is referenced within a conditional statement (e.g., an `if` block).
+        iteration_source (Optional[str]): The name of the variable being iterated over, if applicable.
+        iteration_target (Optional[Union[str, list[str]]]): The loop target(s) assigned in the iteration.
+    """
+
+    accessed_properties: Set[str]
+    accessed_indices: Set[Union[int, float]]
+    is_iterated: bool
+    is_conditional: bool
+    iteration_source: Optional[str]
+    iteration_target: Optional[Union[str, list[str]]]
+
+
+class JinjaTemplateAnalyzer:
+    """
+    Analyzes Jinja templates to extract information about variable usage,
+    including accessed properties, iteration, and conditional references.
+
+    Attributes:
+        env (jinja2.Environment): The Jinja2 environment used for parsing templates.
+        property_access (Dict[str, Set[str]]): Tracks accessed properties for variables.
+        iteration_targets (Dict[str, str]): Maps iteration target variables to their sources.
+
+    Methods:
+        get_template_variables(template: str) -> Dict[str, Set[str]]:
+            Parse a Jinja template and return a mapping of variables to their accessed properties.
+
+        analyze_template(template: str) -> Dict[str, JinjaTemplateAnalysis]:
+            Perform a detailed analysis of the template, including variable usage,
+            iteration, and conditional references.
+
+    Private Methods:
+        _visit_node(node) -> None:
+            Recursively visit AST nodes to detect attribute access and iteration targets.
+
+        _get_base_name(node) -> Optional[str]:
+            Extract the base variable name from a node.
+
+        _get_target_name(node) -> Optional[Union[str, list[str]]]:
+            Extract the target name(s) from a `For` node.
+    """
+
+    def __init__(self, template: str):
+        self.env: Environment = Environment(autoescape=True)
+        self.property_access: Dict[str, Set[str]] = {}
+        self.iteration_targets: Dict[str, Union[str, list[str]]] = {}
+        self.index_access: Dict[str, Set[Union[int, float]]] = {}
+        self.ast: nodes.Node = self.env.parse(template)
+        self.template: str = template
+        self.variable_assignments: Dict[str, str] = {}
+
+    def _visit_node(self, node) -> None:
+        """Recursively visit AST nodes to find attribute access."""
+        # Handle attribute access (dot notation)
+        if isinstance(node, nodes.Getattr):
+            base_name = self._get_base_name(node.node)
+            if base_name:
+                self.property_access.setdefault(base_name, set()).add(node.attr)
+
+        # Handle dictionary access (subscript notation)
+        elif isinstance(node, nodes.Getitem):
+            base_name = self._get_base_name(node.node)
+            if base_name and isinstance(node.arg, nodes.Const):
+                value = node.arg.value
+                if isinstance(value, (int, float)):
+                    self.index_access.setdefault(base_name, set()).add(value)
+                else:
+                    self.property_access.setdefault(base_name, set()).add(value)
+
+        elif isinstance(node, nodes.Test) and node.name == "defined":
+            base_name = self._get_base_name(node.node)
+            if base_name:
+                if isinstance(node.node, nodes.Getattr):
+                    self.property_access.setdefault(base_name, set()).add(
+                        node.node.attr
+                    )
+
+        # Handle loop variables
+        elif isinstance(node, nodes.For):
+            iter_name = self._get_base_name(node.iter)
+            target_name = self._get_target_name(node.target)
+            if iter_name and target_name:
+                self.iteration_targets[target_name] = iter_name
+                self.property_access.setdefault(iter_name, set())
+
+        elif isinstance(node, nodes.Assign):
+            target_name = self._get_target_name(node.target)
+            source_name = self._get_base_name(node.node)
+            if target_name and source_name:
+                self.variable_assignments[target_name] = source_name
+
+        elif isinstance(node, nodes.Filter):
+            if node.name == "selectattr":
+                target = self._get_base_name(node.node)
+                if target:
+                    self.variable_assignments[f"filtered_{target}"] = target
+
+        for child in node.iter_child_nodes():
+            self._visit_node(child)
+
+    def _get_target_name(self, node) -> Optional[str]:
+        """Get the target variable name from a For node.
+
+        Args:
+            node: A Jinja AST node representing either a Name or Tuple node
+
+        Returns:
+            - str: For simple variable targets (e.g., "item" in "for item in items")
+            - None: If the node type is not recognized or is a tuple
+        """
+        if isinstance(node, nodes.Name):
+            return node.name
+        return None
+
+    def _get_target_names(self, node) -> list[str]:
+        """Get all target variable names from a For node, including tuple unpacking.
+
+        Args:
+            node: A Jinja AST node representing either a Name or Tuple node
+
+        Returns:
+            List of target variable names
+        """
+        if isinstance(node, nodes.Name):
+            return [node.name]
+
+        if isinstance(node, nodes.Tuple):
+            names = []
+            for n in node.items:
+                if isinstance(n, nodes.Name):
+                    names.append(n.name)
+            return names
+
+        return []
+
+    def _get_base_name(self, node) -> Optional[str]:
+        """Get the base variable name from a node."""
+        if isinstance(node, nodes.Name):
+            return node.name
+
+        if isinstance(node, nodes.Getattr):
+            return self._get_base_name(node.node)
+
+        if isinstance(node, nodes.Getitem):
+            return self._get_base_name(node.node)
+
+        return None
+
+    def get_template_variables(self) -> Dict[str, Set[str]]:
+        """
+        Parse a Jinja template and return both variables and their accessed properties.
+
+        Args:
+            template (str): The Jinja template string
+
+        Returns:
+            Dict[str, Set[str]]: Dictionary mapping variable names to sets of accessed properties
+        """
+        # Parse the template
+        ast = self.env.parse(self.template)
+
+        # Get all undeclared variables
+        variables = meta.find_undeclared_variables(ast)
+
+        # Reset property access tracking
+        self.property_access = {}
+
+        # Visit all nodes to find property access
+        self._visit_node(ast)
+
+        # Create result dictionary
+        result: Dict[str, Set[str]] = {var: set() for var in variables}
+        # Merge in any discovered sub-properties
+        for var, props in self.property_access.items():
+            if var not in result:
+                result[var] = set()
+            result[var].update(props)
+
+        return result
+
+    def analyze_template(self) -> Dict[str, JinjaTemplateAnalysis]:
+        """
+        Provide a detailed analysis of template variables and their usage.
+        """
+        variables = self.get_template_variables()
+        self.iteration_targets = {}
+
+        analysis: Dict[str, JinjaTemplateAnalysis] = {
+            var: JinjaTemplateAnalysis(
+                accessed_properties=props,
+                accessed_indices=set(),
+                is_iterated=False,
+                is_conditional=False,
+                iteration_source=None,
+                iteration_target=None,
+            )
+            for var, props in variables.items()
+        }
+
+        for var, indices in self.index_access.items():
+            if var in analysis:
+                analysis[var]["accessed_indices"] = indices
+
+        def visit_node(node):
+            if isinstance(node, nodes.If):
+
+                def find_test_vars(test_node):
+                    if isinstance(test_node, nodes.Name):
+                        if test_node.name in analysis:
+                            analysis[test_node.name]["is_conditional"] = True
+                    for child in test_node.iter_child_nodes():
+                        find_test_vars(child)
+
+                find_test_vars(node.test)
+
+            if isinstance(node, nodes.For):
+                iter_target = self._get_base_name(node.iter)
+                target_name = self._get_target_name(node.target)
+                if iter_target in analysis:
+                    analysis[iter_target]["is_iterated"] = True
+                    if target_name:
+                        analysis[iter_target]["iteration_target"] = target_name
+                        if isinstance(target_name, str) and target_name not in analysis:
+                            analysis[target_name] = {
+                                "accessed_properties": set(),
+                                "is_iterated": False,
+                                "is_conditional": False,
+                                "iteration_source": iter_target,
+                                "iteration_target": None,
+                            }
+
+            for child in node.iter_child_nodes():
+                visit_node(child)
+
+        visit_node(self.ast)
+        return analysis
+
+    def get_downstream_properties(self, start_var: str) -> Dict[str, Set[str]]:
+        """
+        Get all properties accessed on a variable and its downstream assignments.
+
+        Args:
+            start_var: The starting variable to trace
+
+        Returns:
+            Dict mapping variable names to their accessed properties
+        """
+        visited = set()
+        properties = {}
+
+        def trace_variable(var_name: str):
+            if var_name in visited:
+                return
+            visited.add(var_name)
+
+            # Get direct properties
+            if var_name in self.property_access:
+                properties[var_name] = self.property_access[var_name]
+
+            # Get properties from iteration targets
+            if var_name in self.iteration_targets:
+                target = self.iteration_targets[var_name]
+                if isinstance(target, str):
+                    trace_variable(target)
+                elif isinstance(target, list):
+                    for t in target:
+                        trace_variable(t)
+
+            # Follow assignments
+            for target, source in self.variable_assignments.items():
+                if source == var_name:
+                    trace_variable(target)
+
+            # Check for array slicing
+            analysis = self.analyze_template()
+            if var_name in analysis:
+                var_info = analysis[var_name]
+                if var_info["accessed_indices"]:
+                    # If this variable is sliced, follow the resulting assignment
+                    slice_result = f"{var_name}_slice"
+                    if slice_result in self.property_access:
+                        trace_variable(slice_result)
+
+        trace_variable(start_var)
+        return properties
+
+    def get_message_vars(self, field_messages: str = "messages") -> Set[str]:
+        """
+        Get all properties accessed on messages and derived variables.
+        """
+        all_properties = self.get_downstream_properties(field_messages)
+
+        # Combine all properties from all related variables
+        combined_properties = set()
+        for properties in all_properties.values():
+            combined_properties.update(properties)
+
+        # Also include properties from the message iteration variable
+        analysis = self.analyze_template()
+        if "message" in analysis:
+            combined_properties.update(analysis["message"]["accessed_properties"])
+
+        return combined_properties

src/axolotl/prompt_strategies/messages/chat.py

@@ -51,8 +51,13 @@ def load(tokenizer, cfg, ds_cfg: Optional[Dict[str, Any]] = None):
     ds_cfg = ds_cfg or {}
 
     field_messages = ds_cfg.get("field_messages")
-    message_field_role = ds_cfg.get("message_field_role")
-    message_field_content = ds_cfg.get("message_field_content")
+    message_property_mappings = ds_cfg.get("message_property_mappings")
+    message_field_role = (
+        message_property_mappings.get("role") if message_property_mappings else None
+    )
+    message_field_content = (
+        message_property_mappings.get("content") if message_property_mappings else None
+    )
     message_field_training = ds_cfg.get("message_field_training")
 
     builder_kwargs = {}

src/axolotl/prompt_tokenizers.py

@@ -2,7 +2,7 @@
 
 import abc
 import logging
-from typing import Dict, List, Tuple, Union
+from typing import Callable, Dict, List, Optional, Tuple, Union
 
 from transformers import BatchEncoding, PreTrainedTokenizer
 
@@ -34,6 +34,8 @@ class PromptTokenizingStrategy(abc.ABC):
     Abstract class for tokenizing strategies
     """
 
+    filter_rows: Optional[Callable] = None
+
     def __init__(
         self,
         prompter: Prompter,

src/axolotl/train.py

@@ -175,6 +175,7 @@ def train(
         LOG.info("hang tight... sorting dataset for group_by_length")
 
     pretrain_hooks(cfg, trainer)
+
     if cfg.flash_optimum:
         with torch.backends.cuda.sdp_kernel(
             # TODO configure these from the YAML w/ sdp_kernel_kwargs: ...
@@ -185,6 +186,7 @@ def train(
             trainer.train(resume_from_checkpoint=resume_from_checkpoint)
     else:
         trainer.train(resume_from_checkpoint=resume_from_checkpoint)
+
     post_train_hooks(cfg, trainer)
 
     LOG.info(f"Training Completed!!! Saving pre-trained model to {cfg.output_dir}")

src/axolotl/utils/callbacks/__init__.py

@@ -846,6 +846,12 @@ class GCCallback(TrainerCallback):
     def on_step_end(
         self, args, state, control, **kwargs  # pylint: disable=unused-argument
     ):
-        if state.global_step % self.gc_steps == 0:
+        if self.gc_steps > 0 and state.global_step % self.gc_steps == 0:
             torch.cuda.empty_cache()
             gc.collect()
+
+    def on_epoch_end(
+        self, args, state, control, **kwargs  # pylint: disable=unused-argument
+    ):
+        torch.cuda.empty_cache()
+        gc.collect()

src/axolotl/utils/chat_templates.py

@@ -15,7 +15,7 @@ _DEFAULT_TEMPLATE_CHOICE = "tokenizer_default"
 _DEFAULT_FALLBACK_CHATML_TEMPLATE_CHOICE_PREFIX = "tokenizer_default_fallback_"
 
 _CHAT_TEMPLATES = {
-    "alpaca": "{% for message in messages %}{% if message['role'] == 'user' %}{{ '### Instruction: ' + message['content'] + '\n\n' }}{% elif message['role'] == 'assistant' %}{{ '### Response: ' + message['content'] + eos_token}}{% endif %}{% endfor %}",
+    "alpaca": "{{ bos_token }}{% for message in messages %}{% if message['role'] == 'system' and loop.first %}{{ message['content'] }}{% elif message['role'] == 'user' %}{{ '### Instruction:\n' + message['content'] }}{% elif message['role'] == 'assistant' %}{{ '### Response:\n' + message['content'] + eos_token }}{% endif %}{% if not loop.last %}{{ '\n\n' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '\n\n### Response:\n' }}{% endif %}",
     "mistral_v1": "{{ bos_token }}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if message['role'] == 'user' %}{{ ' [INST] ' + message['content'] + ' [/INST]' }}{% elif message['role'] == 'assistant' %}{{ ' ' + message['content'] + eos_token}}{% else %}{{ raise_exception('Only user and assistant roles are supported!') }}{% endif %}{% endfor %}",  # Mistral 7B V1, Mistral 7B V2, Mixtral 8x7B V1...
     "mistral_v2v3": "{{ bos_token }}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if message['role'] == 'user' %}{{ '[INST] ' + message['content'] + '[/INST]' }}{% elif message['role'] == 'assistant' %}{{ ' ' + message['content'] + eos_token}}{% else %}{{ raise_exception('Only user and assistant roles are supported!') }}{% endif %}{% endfor %}",  # V3: Mistral 7B V3, Small, Large...
     "mistral_v3_tekken": "{{ bos_token }}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if message['role'] == 'user' %}{{ '[INST]' + message['content'] + '[/INST]' }}{% elif message['role'] == 'assistant' %}{{ message['content'] + eos_token}}{% else %}{{ raise_exception('Only user and assistant roles are supported!') }}{% endif %}{% endfor %}",  # V3-Tekken: Nemo, Pixtral...
@@ -38,7 +38,7 @@ def get_chat_template(
     user_choice: str,
     jinja_template: Optional[str] = None,
     tokenizer: Optional["PreTrainedTokenizerBase"] = None,
-):
+) -> str:
     """
     Finds the correct chat_template based on the user's choice, jinja_template, and tokenizer.
 
@@ -70,7 +70,7 @@ def get_chat_template(
                 f"`chat_template choice is {_DEFAULT_TEMPLATE_CHOICE} but tokenizer's chat_template is null. "
                 f"Please add a chat_template in tokenizer config"
             )
-        return tokenizer.chat_template
+        return tokenizer.chat_template  # type: ignore
 
     if user_choice.startswith(_DEFAULT_FALLBACK_CHATML_TEMPLATE_CHOICE_PREFIX):
         if not tokenizer:
@@ -78,7 +78,7 @@ def get_chat_template(
                 f"`tokenizer` cannot be None when chat_template choice starts with {_DEFAULT_FALLBACK_CHATML_TEMPLATE_CHOICE_PREFIX}"
             )
         if tokenizer.chat_template:
-            return tokenizer.chat_template
+            return tokenizer.chat_template  # type: ignore
 
         user_choice = user_choice[
             len(_DEFAULT_FALLBACK_CHATML_TEMPLATE_CHOICE_PREFIX) :

src/axolotl/utils/collators/batching.py

@@ -3,7 +3,7 @@ DataCollator for axolotl to pad labels and position_ids for packed sequences
 """
 
 from dataclasses import dataclass
-from typing import Any, Optional, Union
+from typing import Any, Optional, Union, Callable
 
 import numpy as np
 from transformers import PreTrainedTokenizerBase
@@ -53,6 +53,7 @@ class DataCollatorForSeq2Seq:
     label_pad_token_id: int = -100
     position_pad_token_id: int = 0
     return_tensors: str = "pt"
+    sp_extract_fn: Optional[Callable] = None
 
     def __call__(self, features, return_tensors=None):
         labels = None
@@ -121,6 +122,10 @@ class DataCollatorForSeq2Seq:
 
         return features
 
+    def seq_parallel_split(self, features):
+        if self.sp_extract_fn:
+            pass
+        return features
 
 @dataclass
 class BatchSamplerDataCollatorForSeq2Seq(DataCollatorForSeq2Seq):

src/axolotl/utils/config/__init__.py

@@ -1,4 +1,5 @@
 """Module for working with config dicts"""
+
 import json
 import logging
 import os
@@ -17,6 +18,7 @@ from axolotl.utils.config.models.input.v0_4_1 import (
 from axolotl.utils.config.models.input.v0_4_1 import (
     AxolotlInputConfig as AxolotlInputConfigBase,
 )
+from axolotl.utils.config.models.input.v0_4_1 import DPODataset, KTODataset, SFTDataset
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.models import load_model_config
 
@@ -129,10 +131,18 @@ def normalize_config(cfg):
         save_steps = 1.0 / (cfg.saves_per_epoch * cfg.num_epochs)
         if save_steps < 1.0:  # prevent saves on every step
             cfg.save_steps = save_steps
+        elif save_steps > 1:
+            LOG.warning(
+                f"Invalid value for save_steps ({save_steps}) from saves_per_epoch and/or num_epochs. Saving at training end only."
+            )
     if (cfg.val_set_size or cfg.test_datasets) and cfg.evals_per_epoch:
         eval_steps = 1.0 / (cfg.evals_per_epoch * cfg.num_epochs)
         if eval_steps < 1.0:  # prevent evals on every step
             cfg.eval_steps = eval_steps
+        elif eval_steps > 1:
+            LOG.warning(
+                f"Invalid value for eval_steps ({eval_steps}) from evals_per_epoch and/or num_epochs. Skipping evaluations."
+            )
 
     cfg.dataset_processes = cfg.dataset_processes or os.cpu_count()
 
@@ -249,7 +259,7 @@ def validate_config(
     cfg: DictDefault,
     capabilities: Optional[dict] = None,
     env_capabilities: Optional[dict] = None,
-):
+) -> DictDefault:
     AxolotlConfigWCapabilities = AxolotlConfigWCapabilitiesBase
     AxolotlInputConfig = AxolotlInputConfigBase
 
@@ -259,6 +269,16 @@ def validate_config(
             AxolotlInputConfig,  # pylint: disable=invalid-name
         ) = merge_input_args()
 
+    # Convert datasets to proper format if needed
+    if cfg.get("datasets"):
+        for idx, ds_cfg in enumerate(cfg["datasets"]):
+            if cfg.get("rl") == "dpo" and not isinstance(ds_cfg, DPODataset):
+                cfg["datasets"][idx] = DPODataset(**ds_cfg)
+            elif cfg.get("rl") == "kto" and not isinstance(ds_cfg, KTODataset):
+                cfg["datasets"][idx] = KTODataset(**dict(ds_cfg))
+            elif not isinstance(ds_cfg, SFTDataset):
+                cfg["datasets"][idx] = SFTDataset(**dict(ds_cfg))
+
     if capabilities or env_capabilities:
         if (capabilities and env_capabilities is None) or (
             env_capabilities and capabilities is None