api docs

Author: jjcmoon

docs/api.rst

@@ -0,0 +1,7 @@
+API
+===
+
+
+.. autoclass:: klay.Circuit()
+    :members:
+    :undoc-members:

docs/circuit_creation.rst

@@ -12,16 +12,16 @@ An SDD can be loaded from a file as follows.
 
 .. code-block:: Python
 
-   import klaycircuits
+   from klaycircuits import Circuit
 
-   circuit = klaycircuits.Circuit()
+   circuit = Circuit()
    circuit.add_SDD_from_file("path/to/my.sdd")
 
 Similarly, for d4 we can use
 
 .. code-block:: Python
 
-   circuit = klaycircuits.Circuit()
+   circuit = Circuit()
    circuit.add_D4_from_file("path/to/my.nnf")
 
 SDDs can also be loaded directly from a PySDD :code:`SddNode` object.
@@ -33,7 +33,7 @@ SDDs can also be loaded directly from a PySDD :code:`SddNode` object.
    manager = SddManager(var_count = 2)
    sdd_node = manager.literal(1) & manager.literal(2)
 
-   circuit = klaycircuits.Circuit()
+   circuit = Circuit()
    circuit.add_sdd(sdd_node)
 
 
@@ -44,7 +44,7 @@ If you want to evaluate multiple circuits in parallel, you can merge them into a
 
 .. code-block:: Python
 
-   circuit = klaycircuits.Circuit()
+   circuit = Circuit()
    circuit.add_sdd(first_sdd)
    circuit.add_sdd(second_sdd)
 
@@ -60,7 +60,7 @@ We start by defining some literals.
 
 .. code-block:: Python
 
-    circuit = klaycircuits.Circuit()
+    circuit = Circuit()
     a = circuit.literal_node(1)
     b = circuit.literal_node(-2)
 

docs/circuit_eval.rst

@@ -0,0 +1,16 @@
+.. _circuit_eval:
+
+Circuit Evaluation
+==================
+
+Once you created a circuit, you probably want to evaluate. KLay supports multiple backends to support this evaluation.
+
+PyTorch
+*******
+
+TODO.
+
+Jax
+***
+
+TODO.

docs/conf.py

@@ -14,7 +14,13 @@
 # -- General configuration ---------------------------------------------------
 # https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration
 
-extensions = []
+extensions = [
+    "sphinx.ext.duration",
+    "sphinx.ext.doctest",
+    "sphinx.ext.autodoc",
+    "sphinx.ext.autosummary",
+    "sphinx.ext.intersphinx",
+]
 
 templates_path = ['_templates']
 exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store']

docs/index.rst

@@ -9,6 +9,7 @@ KLay Documentation
 KLay is a Python library for accelerating inference on sparse arithmetic circuits.
 
 At its core, KLay transforms sparse directed acyclic graphs into layers that can be executed in parallel.
+The design of KLay is described in our paper `KLay: Accelerating Arithmetic Circuits for Neurosymbolic AI <https://arxiv.org/pdf/2410.11415>`_, published at ICLR 2025.
 
 .. image:: _static/scatter_reduce.png
   :width: 400
@@ -23,4 +24,6 @@ Contents
    Home <self>
    quickstart
    circuit_creation
+   circuit_eval
+   api
 

docs/quickstart.rst

@@ -22,9 +22,9 @@ For more information, check out the :ref:`circuit_construction` guide.
 
 .. code-block:: Python
 
-   import klaycircuits
+   from klaycircuits import Circuit
 
-   circuit = klaycircuits.Circuit()
+   circuit = Circuit()
    circuit.add_sdd(sdd_node)
 
 Now that we have the circuit, we can evaluate it. To do this, we first turn the circuit into a PyTorch module.
@@ -36,18 +36,13 @@ Now that we have the circuit, we can evaluate it. To do this, we first turn the
    module = circuit.to_torch_module()
    module = module.to("cuda:0")
 
-We can use our circuit as any other PyTorch module. The input should be a tensor with the weights for each literal, and the output is the result of evaluating circuit.
+We can use our circuit as any other PyTorch module.
+The input should be a tensor with the weights for each literal, and the output is the result of evaluating circuit.
+For more details, see the :ref:`circuit_eval` guide.
 
 .. code-block:: Python
 
    weights = torch.tensor([...], device="cuda:0")
    result = module(weights)
    result.backward()
 
-Backends
-********
-
-KLay needs a backend to evaluate the circuit in. So far, we implemented two different backends.
-
-- PyTorch: :code:`torch_module = circuit.to_torch_module()`
-- Jax: :code:`jax_function = circuit.to_jax_function()`