[docs] Custom components page

docs/source/basics/custom_components.rst

@@ -0,0 +1,171 @@
+.. _custom_components_label:
+
+=============================
+Custom Components and Recipes
+=============================
+
+torchtune lets you launch fine-tuning jobs directly from the command-line using both library and custom datasets,
+models, recipes, and configs. This is done with the ``tune run`` command (see :ref:`cli_label`), which can also be
+used from your project folder.
+
+Setting up your torchtune project
+---------------------------------
+First, ensure that you have torchtune installed, see :ref:`install_label`. This will also install the ``tune`` command
+to your environment.
+
+You can launch with ``tune run`` from any directory. Let's create a new project directory and ensure we can launch a built-in
+library recipe with a library config from that folder.
+
+.. code-block:: bash
+
+    mkdir ~/my_project
+    cd ~/my_project
+    # This downloads the Llama 3.2 1B Instruct model
+    tune download meta-llama/Llama-3.2-1B-Instruct --output-dir /tmp/Llama-3.2-1B-Instruct --ignore-patterns "original/consolidated.00.pth"
+    # This launches a lora finetuning run with the default single device config
+    tune run lora_finetune_single_device --config llama3_2/1B_lora_single_device
+
+Launching custom configs
+------------------------
+Often, you'll want to start with one of our default configs for a particular model and adjust a few training hyperparameters.
+You can use the ``tune cp`` command to create a copy of a default config in your project directory so you can make modifications.
+
+.. code-block:: bash
+
+    # Show all the default model configs for each recipe
+    tune ls
+    # This makes a copy of a Qwen2 full finetune config
+    tune cp qwen2/0.5B_full_single_device ~/my_project/config/qwen_config.yaml
+
+Now, you can make modifications to the config directly in your project folder and launch the custom config. Make sure you are using
+the correct recipe associated with the config and that you've downloaded the model. Even if you didn't start with copying a library
+recipe, you can launch a completely custom config using the same command. Note that for custom configs, you must specify the file extension.
+
+.. code-block:: bash
+
+    mkdir ~/my_project/config
+    tune run full_finetune_single_device --config ~/my_project/config/qwen_config.yaml
+    # Or launch directly from the project directory with a relative path
+    tune run full_finetune_single_device --config config/qwen_config.yaml
+
+For a more detailed discussion on downloading models and modifying library configs, see :ref:`finetune_llama_label`.
+
+Launching custom recipes
+------------------------
+torchtune's built-in recipes provide starting points for your fine-tuning workflows, but you can write your own training loop
+with customized logic for your use case and launch training with ``tune run``. Similar to modifying library configs, you can
+also copy one of our recipes as a starting point and modify, or write one completely from scratch. Note that for launching
+custom recipes, you must specify the file extension.
+
+.. code-block:: bash
+
+    mkdir ~/my_project/recipes
+    # Show all the default recipes
+    tune ls
+    # This makes a copy of the full finetune single device recipe locally
+    tune cp full_finetune_single_device ~/my_project/recipes/single_device.py
+    # Launch custom recipe with custom config from project directory
+    tune run recipes/single_device.py --config config/qwen_config.yaml
+
+If you are writing a new recipe from scratch, we recommend following the Python convention of defining a ``main()`` function
+in your script and decorating it with the :func:`~torchtune.config.parse` decorator. This will enable you to launch the recipe
+with ``tune run`` and pass in a yaml file for the ``--config`` argument.
+
+.. code-block:: python
+
+    from torchtune import config
+    from omegaconf import DictConfig
+
+    @config.parse
+    def main(cfg: DictConfig):
+        # Add all your recipe logic here, access config fields as attributes
+
+    if __name__ == "__main__":
+        # Config will be parsed from CLI, don't need to pass in here
+        main()
+
+Launching with custom components
+--------------------------------
+torchtune supports full experimentation with custom models, datasets, optimizers, or any fine-tuning component. You can define
+these locally in your repo and use them in your recipes and configs that you can launch with ``tune run``.
+
+We recommend following the "builder" pattern when making your components. This means creating "builder" functions that set up
+the classes you need with a few high level parameters that can be modified easily from the config. For example, we can define custom
+model and dataset builders in our project directory:
+
+.. code-block:: python
+
+    #
+    # In models/custom_decoder.py
+    #
+    class CustomTransformerDecoder(nn.Module):
+        # A custom architecture not present in torchtune
+
+    # Builder function for the custom model
+    def custom_model(num_layers: int, classification_head: bool = False):
+        # Any setup for defining the class
+        ...
+        # Return the module you want to train
+        return CustomTransformerDecoder(...)
+
+.. code-block:: python
+
+    #
+    # In datasets/custom_dataset.py
+    #
+    from torchtune.datasets import SFTDataset, PackedDataset
+    from torchtune.data import InputOutputToMessages
+    from torchtune.modules.tokenizers import ModelTokenizer
+
+    # Example builder function for a custom code instruct dataset not in torchtune, but using
+    # different dataset building blocks from torchtune
+    def tiny_codes(tokenizer: ModelTokenizer, packed: bool = True):
+        """
+        Python subset of nampdn-ai/tiny-codes. Instruct and code response pairs.
+        """
+        ds = SFTDataset(
+            model_transform=tokenizer,
+            source="nampdn-ai/tiny-codes",
+            message_transform=InputOutputToMessages(
+                column_map={"input": "prompt", "output": "response"},
+            ),
+            filter_fn=lambda x: x["language"] == "python",
+            split="train",
+        )
+        if packed:
+            return PackedDataset(ds, max_seq_len=tokenizer.max_seq_len, split_across_pack=False)
+        else:
+            return ds
+
+.. note::
+
+    If you are using a default torchtune recipe with a custom dataset, you must define the first
+    positional argument to be the tokenizer or model transform. These are automatically passed into
+    dataset instantiation and are defined separately in the config, not under the dataset field.
+
+You can define the custom model and custom dataset in the config using the relative import path from where
+you are launching with ``tune run``. It is best to define the path relative to your project root directory
+and launch from there.
+
+.. code-block:: yaml
+
+    # In YAML file config/custom_finetune.yaml
+    model:
+      _component_: models.custom_decoder.custom_model
+      num_layers: 32
+
+    dataset:
+      _component_: datasets.custom_dataset.tiny_codes
+      packed: True
+
+.. code-block:: bash
+
+    cd ~/my_project/
+    tune run recipes/single_device.py --config config/custom_finetune.yaml
+
+If your custom components are not being found or imported correctly, you can try to launch with ``tune run`` after
+modifying the ``PYTHONPATH`` to ensure the files in your project directory are importable.
+
+.. code-block:: bash
+
+    PYTHONPATH=${pwd}:PYTHONPATH tune run recipes/single_device.py --config config/custom_finetune.yaml

docs/source/index.rst

@@ -131,6 +131,7 @@ torchtune tutorials.
    basics/message_transforms
    basics/tokenizers
    basics/prompt_templates
+   basics/custom_components
    basics/packing
 
 .. toctree::