Update documentation text; fix spelling error in docstring

Author: jmitrevs

docs/details.rst

@@ -1,12 +1,14 @@
-======================
-Backends and I/O Types
-======================
+================
+Software Details
+================
 
-Backends
---------
+Frontends and Backends
+----------------------
 
-``hls4ml`` supports the concept of a *backend* that determines the target HLS language.
-Currently, Vivado HLS, Intel HLS, and Vitis HLS (experimental) are supported.
+In ``hls4ml`` there is a a concept of a *frontend* to parse the input NN into an internal model graph, and a *backend* that controls
+what type of output is produced from the graph. Frontends and backends can be independently chosen. Examples of frontends are the
+parsers for Keras or ONNX, and examples of backends are Vivado HLS, Intel HLS, and Vitis HLS. See :ref:`Status and Features` for the
+currently supported frontends and backends.
 
 I/O Types
 ---------

docs/flows.rst

@@ -2,6 +2,9 @@
 Optimizer Passes and Flows
 ==========================
 
+Internal Structure
+------------------
+
 The ``hls4ml`` library will parse models from Keras, PyTorch or ONNX into an internal execution graph. This model graph is represented with the
 :py:class:`~hls4ml.model.graph.ModelGraph` class. The nodes in this graph, corresponding to the layer and operations of the input model are represented
 by classes derived from the :py:class:`~hls4ml.model.layers.Layer` base class.
@@ -13,15 +16,17 @@ Layers can define expected attributes and can be verified for correctness, or to
 Optimizer passes
 ----------------
 
-To reach a state from which the code can be generated, internal model graph will undergo a series of optimizations (transformations), dubbed *optimization passes*.
-All transformations of the model and any modification to any layer's attributes must be implemented through an optimization pass. All optimizer passes derive from
-the :py:class:`~hls4ml.model.optimizer.optimizer.OptimizerPass` class. Optimizer passes are applied at the level of nodes/layers, however a special class
-:py:class:`~hls4ml.model.optimizer.optimizer.ModelOptimizerPass` exists that is applied on the full model. Subclasses of
-:py:class:`~hls4ml.model.optimizer.optimizer.OptimizerPass` must provide a criteria in ``match`` function that, if satisfied, will perform the transformation from
-``transform`` function. The boolean return value of ``transform`` indicates if the optimizer pass made changes to the model graph, requiring running the optimizers again.
-Example of an optimizer pass that runs on the full model, is :py:class:`~hls4ml.model.optimizer.passes.stamp.MakeStamp`, while an example of the layer optimizer is
-:py:class:`~hls4ml.model.optimizer.passes.fuse_biasadd` class that adds a bias to a :py:class:`~hls4ml.model.layers.Dense`,
-:py:class:`~hls4ml.model.layers.Conv1D`, or :py:class:`~hls4ml.model.layers.Conv2D` layer.
+To reach a state from which the code can be generated, the internal model graph undergoes a series of optimizations (transformations), dubbed
+*optimization passes*. All transformations of the model and any modification to any layer's attributes must be implemented through an optimization
+pass. All optimizer passes derive from the :py:class:`~hls4ml.model.optimizer.optimizer.OptimizerPass` class. Optimizer passes are usually applied to
+nodes/layers; however, a special class :py:class:`~hls4ml.model.optimizer.optimizer.ModelOptimizerPass` exists that is applied on the full model. An
+example of a layer optimizer is :py:class:`~hls4ml.model.optimizer.passes.fuse_biasadd`, which adds a bias to a
+:py:class:`~hls4ml.model.layers.Dense`, :py:class:`~hls4ml.model.layers.Conv1D`, or :py:class:`~hls4ml.model.layers.Conv2D` layer, while an example of
+an optimizer pass that runs on the full model is :py:class:`~hls4ml.model.optimizer.passes.stamp.MakeStamp`, which creates a unique number (stamp).
+
+Subclasses of :py:class:`~hls4ml.model.optimizer.optimizer.OptimizerPass` must provide a criteria in ``match`` function that, if satisfied, will
+perform the transformation from ``transform`` function. The boolean return value of ``transform`` indicates if the optimizer pass made changes to the
+model graph that may require running the optimizers again. In that case, optimizers in a flow are run again.
 
 Optimizers can be general, independent of the backend, in which case they are located in :py:mod:`hls4ml.model.optimizer.passes`, or they may be backend-specific,
 in which case they are located in a folder dependent on the backend, e.g., :py:mod:`hls4ml.backends.vivado.passes` or
@@ -44,10 +49,11 @@ New optimizers can be registered with the :py:func:`~hls4ml.model.optimizer.opti
 
 Flows
 -----
-A :py:class:`~hls4ml.model.flow.flow.Flow` is an ordered set of optimizers that represent a single stage in the conversion process. The optimizers from a flow are applied
-until they no longer make changes to the model graph after which the next flow (stage) can start. Flows may depend on other flows being applied before them,
-ensuring the model graph is in a desired state before a flow starts. The function :py:func:`~hls4ml.model.flow.flow.register_flow` is used to register a new flow. Flows
-are applied on a model graph with :py:func:`~hls4ml.model.graph.ModelGraph.apply_flow`.
+A :py:class:`~hls4ml.model.flow.flow.Flow` is an ordered set of optimizers that represent a single stage in the conversion process. The optimizers
+from a flow are applied in sequence until they no longer make changes to the model graph (controlled by the ``transform`` return value), after which
+the next flow (stage) can start. Flows may require that other flows are applied before them, ensuring the model graph is in a desired state before a
+flow starts. The function :py:func:`~hls4ml.model.flow.flow.register_flow` is used to register a new flow. Flows are applied on a model graph with
+:py:func:`~hls4ml.model.graph.ModelGraph.apply_flow`.
 
 There are common model-level flows that can run regardless of the backend, and there are backend-specific flows.
 The `convert and optimize <https://github.com/fastmachinelearning/hls4ml/blob/7c0a065935904f50bd7e4c547f85354b36276092/hls4ml/model/optimizer/__init__.py#L14-L20>`_

docs/index.rst

@@ -6,9 +6,9 @@
     status
     setup
     release_notes
-    command
     details
     flows
+    command
     reference
 
 .. toctree::

docs/status.rst

@@ -3,7 +3,7 @@ Status and Features
 ===================
 
 Status
-========
+======
 
 The latest version (built from ``main``) is |version|.
 The stable version (released on PyPI) is |release|.
@@ -49,14 +49,14 @@ A summary of the on-going status of the ``hls4ml`` tool is in the table below.
    * - MLP
      - ``supported``
      - ``limited``
-     - ``supported``
+     - ``in development``
      - ``supported``
      - ``supported``
      - ``experimental``
    * - CNN
      - ``supported``
      - ``limited``
-     - ``supported``
+     - ``in development``
      - ``supported``
      - ``supported``
      - ``experimental``

hls4ml/model/optimizer/optimizer.py

@@ -301,7 +301,7 @@ def optimize_model(model, passes):
         passes (list): List of passes to apply.
 
     Returns:
-        set: The set of applied passes (the pases that matched the predicate).
+        set: The set of applied passes (the passes that matched the predicate).
     """
     optimizers = {opt_pass: get_optimizer(opt_pass) for opt_pass in passes}
     applied_passes = set()