docs: update docstrings and readme (#5)

* docs: create docstrings for all public code

* docs: update README.md

* docs: update README.md

README.md

@@ -1 +1,157 @@
-# x-mlps
+# X-MLPs
+
+An MLP model that provides a flexible foundation to implement, mix-and-match, and test various state-of-the-art MLP building blocks and architectures.
+Built on Jax and Haiku.
+
+## Installation
+
+```sh
+pip install x-mlps
+```
+
+**Note**: X-MLPs will not install Jax for you (see [here](https://github.com/google/jax#installation) for install instructions).
+
+## Getting Started
+
+The `XMLP` class provides the foundation from which all MLP architectures are built on, and is the primary class you use.
+Additionally, X-MLPs relies heavily on factory functions to customize and instantiate the building blocks that make up a particular `XMLP` instance.
+Fortunately, this library provides several SOTA MLP blocks out-of-the-box as factory functions.
+For example, to implement the ResMLP architecture, you can implement the follow model function:
+
+```python
+import haiku as hk
+import jax
+from einops import rearrange
+from x_mlps import XMLP, Affine, resmlp_block_factory
+
+def create_model(patch_size: int, dim: int, depth: int, num_classes: int = 10):
+    # NOTE: Operating directly on batched data is supported as well.
+    @hk.vmap
+    def model_fn(x: jnp.ndarray) -> jnp.ndarray:
+        # Reformat input image into a sequence of patches
+        x = rearrange(x, "(h p1) (w p2) c -> (h w) (p1 p2 c)", p1=patch_size, p2=patch_size)
+        return XMLP(
+            num_patches=x.shape[-2],
+            dim=dim,
+            depth=depth,
+            block=resmlp_block_factory,
+            normalization=lambda num_patches, dim, depth, **kwargs: Affine(dim, **kwargs),
+            num_classes=num_classes,
+        )(x)
+
+    return model_fn
+
+model = create_model(patch_size=4, dim=384, depth=12)
+model_fn = hk.transform(model)
+model_fn = hk.without_apply_rng(model_fn)
+
+rng = jax.random.PRNGKey(0)
+params = model_fn.init(rng, jnp.ones((1, 32, 32, 3)))
+```
+
+It's important to note the `XMLP` module _does not_ reformat input data to the form appropriate for whatever block you make use of (e.g., a sequence of patches).
+As such, you must reformat data manually before feeding it to an `XMLP` module.
+The [einops](https://github.com/arogozhnikov/einops) library (which is installed by X-MLPs) provides functions (e.g., `rearrange`) that can help here.
+
+**Note**: Like the core Haiku modules, all modules implemented in X-MLPs support both batched and vectorized data.
+
+## X-MLPs Architecture Details
+
+X-MLPs uses a layered approach to construct arbitrary MLP networks. There are three core modules used to create a network's structure:
+
+1. `XSublayer` - bottom level module which wraps arbitrary feedforward functionality.
+2. `XBlock` - mid level module consisting of one or more `XSublayer` modules.
+3. `XMLP` - top level module which represents a generic MLP network, and is composed of a stack of repeated `XBlock` modules.
+
+To support user-defined modules, each of the above modules support passing arbitrary keyword arguments to child modules.
+This is accomplished by prepending arguments with one or more predefined prefixes (including user defined prefixes).
+Built-in prefixes include:
+
+1. "block\_" - arguments fed directly to the `XBlock` module.
+2. "sublayers\_" - arguments fed to all `XSublayer`s in each `XBlock`.
+3. "sublayers{i}\_" - arguments fed to the i-th `XSublayer` in each `XBlock` (where 1 <= i <= # of sublayers).
+4. "ff\_" - arguments fed to the feedforward module in a `XSublayer`.
+
+This must be combined in order when passing them to the `XMLP` module (e.g., "block_sublayer1_ff\_<argument name>").
+
+### XSublayer
+
+The `XSublayer` module is a flexible sublayer wrapper module providing skip connections and pre/post-normalization to an arbitrary child module (specifically, arbitrary feedforward modules e.g., `XChannelFeedForward`).
+Child module instances are not passed directly, rather a factory function which creates the child module is instead.
+This ensures that individual sublayers can be configured automatically based on depth.
+
+### XBlock
+
+The `XBlock` module is a generic MLP block. It is composed of one or more `XSublayer` modules, passed as factory functions.
+
+### Top Layer - XMLP
+
+At the top level is the `XMLP` module, which represents a generic MLP network.
+N `XBlock` modules are stacked together to form a network, created via a common factory function.
+
+## Built-in MLP Architectures
+
+The following architectures have been implemented in the form of `XBlock`s and have corresponding factory functions.
+
+- ResMLP - `resmlp_block_factory`
+- MLP-Mixer - `mlpmixer_block_factory`
+- gMLP - `gmlp_block_factory`
+- S²-MLP `s2mlp_block_factory`
+
+See their respective docstrings for more information.
+
+## LICENSE
+
+See [LICENSE](LICENSE).
+
+## Citations
+
+```bibtex
+@article{Touvron2021ResMLPFN,
+  title={ResMLP: Feedforward networks for image classification with data-efficient training},
+  author={Hugo Touvron and Piotr Bojanowski and Mathilde Caron and Matthieu Cord and Alaaeldin El-Nouby and Edouard Grave and Gautier Izacard and Armand Joulin and Gabriel Synnaeve and Jakob Verbeek and Herv'e J'egou},
+  journal={ArXiv},
+  year={2021},
+  volume={abs/2105.03404}
+}
+```
+
+```bibtex
+@article{Tolstikhin2021MLPMixerAA,
+  title={MLP-Mixer: An all-MLP Architecture for Vision},
+  author={Ilya O. Tolstikhin and Neil Houlsby and Alexander Kolesnikov and Lucas Beyer and Xiaohua Zhai and Thomas Unterthiner and Jessica Yung and Daniel Keysers and Jakob Uszkoreit and Mario Lucic and Alexey Dosovitskiy},
+  journal={ArXiv},
+  year={2021},
+  volume={abs/2105.01601}
+}
+```
+
+```bibtex
+@article{Liu2021PayAT,
+  title={Pay Attention to MLPs},
+  author={Hanxiao Liu and Zihang Dai and David R. So and Quoc V. Le},
+  journal={ArXiv},
+  year={2021},
+  volume={abs/2105.08050}
+}
+```
+
+```bibtex
+@article{Yu2021S2MLPSM,
+  title={S2-MLP: Spatial-Shift MLP Architecture for Vision},
+  author={Tan Yu and Xu Li and Yunfeng Cai and Mingming Sun and Ping Li},
+  journal={ArXiv},
+  year={2021},
+  volume={abs/2106.07477}
+}
+```
+
+```bibtex
+@article{Touvron2021GoingDW,
+  title={Going deeper with Image Transformers},
+  author={Hugo Touvron and Matthieu Cord and Alexandre Sablayrolles and Gabriel Synnaeve and Herv'e J'egou},
+  journal={ArXiv},
+  year={2021},
+  volume={abs/2103.17239}
+}
+```