Use google style docstrings everywhere

docs/flows.rst

@@ -49,7 +49,7 @@ 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
+A :py:class:`~hls4ml.model.flow.flow.Flow` is an ordered set of optimizers that represents 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

hls4ml/backends/vivado_accelerator/vivado_accelerator_backend.py

@@ -58,13 +58,13 @@ def build(
         return parse_vivado_report(model.config.get_output_dir())
 
     def make_xclbin(self, model, platform='xilinx_u250_xdma_201830_2'):
-        """
+        """Create the xclbin for the given model and target platform.
 
-        Parameters
-        ----------
-        - model : compiled and built hls_model.
-        - platform : development Target Platform, must be installed first. On the host machine is required only the
-                     deployment target platform, both can be found on the Getting Started section of the Alveo card.
+        Args:
+            model (ModelGraph): Compiled and build model.
+            platform (str, optional): Development/Deployment target platform, must be installed first.
+                The host machine only requires the deployment target platform. Refer to the Getting Started section of
+                the Alveo guide. Defaults to 'xilinx_u250_xdma_201830_2'.
         """
         curr_dir = os.getcwd()
         abs_path_dir = os.path.abspath(model.config.get_output_dir())

hls4ml/converters/__init__.py

@@ -194,7 +194,7 @@ def convert_from_keras_model(
     hls_config=None,
     **kwargs,
 ):
-    """Convert to hls4ml model based on the provided configuration.
+    """Convert Keras model to hls4ml model based on the provided configuration.
 
     Args:
         model: Keras model to convert
@@ -221,7 +221,7 @@ def convert_from_keras_model(
         kwargs** (dict, optional): Additional parameters that will be used to create the config of the specified backend
 
     Raises:
-        Exception: If precision and reuse factor are not present in 'hls_config'
+        Exception: If precision and reuse factor are not present in 'hls_config'.
 
     Returns:
         ModelGraph: hls4ml model.
@@ -256,54 +256,35 @@ def convert_from_pytorch_model(
     hls_config=None,
     **kwargs,
 ):
-    """
+    """Convert PyTorch model to hls4ml model based on the provided configuration.
+
+    Args:
+        model: PyTorch model to conert.
+        input_shape (list): The shape of the input tensor.
+        output_dir (str, optional): Output directory of the generated HLS project. Defaults to 'my-hls-test'.
+        project_name (str, optional): Name of the HLS project. Defaults to 'myproject'.
+        input_data_tb (str, optional): String representing the path of input data in .npy or .dat format that will be
+            used during csim and cosim. Defaults to None.
+        output_data_tb (str, optional): String representing the path of output data in .npy or .dat format that will be
+            used during csim and cosim. Defaults to None.
+        backend (str, optional): Name of the backend to use, e.g., 'Vivado' or 'Quartus'. Defaults to 'Vivado'.
+        board (str, optional): One of target boards specified in `supported_board.json` file. If set to `None` a default
+            device of a backend will be used. See documentation of the backend used.
+        part (str, optional): The FPGA part. If set to `None` a default part of a backend will be used.
+            See documentation of the backend used. Note that if `board` is specified, the part associated to that board
+            will overwrite any part passed as a parameter.
+        clock_period (int, optional): Clock period of the design.
+            Defaults to 5.
+        io_type (str, optional): Type of implementation used. One of
+            'io_parallel' or 'io_stream'. Defaults to 'io_parallel'.
+        hls_config (dict, optional): The HLS config.
+        kwargs** (dict, optional): Additional parameters that will be used to create the config of the specified backend.
+
+    Raises:
+        Exception: If precision and reuse factor are not present in 'hls_config'.
 
-    Convert a Pytorch model to a hls model.
-
-    Parameters
-    ----------
-    model : Pytorch model object.
-        Model to be converted to hls model object.
-    input_shape : @todo: to be filled
-    output_dir (str, optional): Output directory of the generated HLS
-        project. Defaults to 'my-hls-test'.
-    project_name (str, optional): Name of the HLS project.
-        Defaults to 'myproject'.
-    input_data_tb (str, optional): String representing the path of input data in .npy or .dat format that will be
-        used during csim and cosim.
-    output_data_tb (str, optional): String representing the path of output data in .npy or .dat format that will be
-        used during csim and cosim.
-    backend (str, optional): Name of the backend to use, e.g., 'Vivado'
-        or 'Quartus'.
-    board (str, optional): One of target boards specified in `supported_board.json` file. If set to `None` a default
-        device of a backend will be used. See documentation of the backend used.
-    part (str, optional): The FPGA part. If set to `None` a default part of a backend will be used.
-        See documentation of the backend used. Note that if `board` is specified, the part associated to that board
-        will overwrite any part passed as a parameter.
-    clock_period (int, optional): Clock period of the design.
-        Defaults to 5.
-    io_type (str, optional): Type of implementation used. One of
-        'io_parallel' or 'io_stream'. Defaults to 'io_parallel'.
-    hls_config (dict, optional): The HLS config.
-    kwargs** (dict, optional): Additional parameters that will be used to create the config of the specified backend
-
-    Returns
-    -------
-    ModelGraph : hls4ml model object.
-
-    See Also
-    --------
-    hls4ml.convert_from_keras_model, hls4ml.convert_from_onnx_model
-
-    Examples
-    --------
-    >>> import hls4ml
-    >>> config = hls4ml.utils.config_from_pytorch_model(model, granularity='model')
-    >>> hls_model = hls4ml.converters.convert_from_pytorch_model(model, hls_config=config)
-
-    Notes
-    -----
-    Only sequential Pytorch models are supported for now.
+    Returns:
+        ModelGraph: hls4ml model.
     """
 
     config = create_config(output_dir=output_dir, project_name=project_name, backend=backend, **kwargs)
@@ -335,49 +316,37 @@ def convert_from_onnx_model(
     hls_config=None,
     **kwargs,
 ):
-    """
+    """Convert Keras model to hls4ml model based on the provided configuration.
+
+    Args:
+        model: ONNX model to convert.
+        output_dir (str, optional): Output directory of the generated HLS
+            project. Defaults to 'my-hls-test'.
+        project_name (str, optional): Name of the HLS project.
+            Defaults to 'myproject'.
+        input_data_tb (str, optional): String representing the path of input data in .npy or .dat format that will be
+            used during csim and cosim.
+        output_data_tb (str, optional): String representing the path of output data in .npy or .dat format that will be
+            used during csim and cosim.
+        backend (str, optional): Name of the backend to use, e.g., 'Vivado'
+            or 'Quartus'.
+        board (str, optional): One of target boards specified in `supported_board.json` file. If set to `None` a default
+            device of a backend will be used. See documentation of the backend used.
+        part (str, optional): The FPGA part. If set to `None` a default part of a backend will be used.
+            See documentation of the backend used. Note that if `board` is specified, the part associated to that board
+            will overwrite any part passed as a parameter.
+        clock_period (int, optional): Clock period of the design.
+            Defaults to 5.
+        io_type (str, optional): Type of implementation used. One of
+            'io_parallel' or 'io_stream'. Defaults to 'io_parallel'.
+        hls_config (dict, optional): The HLS config.
+        kwargs** (dict, optional): Additional parameters that will be used to create the config of the specified backend
+
+    Raises:
+        Exception: If precision and reuse factor are not present in 'hls_config'.
 
-    Convert an ONNX model to a hls model.
-
-    Parameters
-    ----------
-    model : ONNX model object.
-        Model to be converted to hls model object.
-    output_dir (str, optional): Output directory of the generated HLS
-        project. Defaults to 'my-hls-test'.
-    project_name (str, optional): Name of the HLS project.
-        Defaults to 'myproject'.
-    input_data_tb (str, optional): String representing the path of input data in .npy or .dat format that will be
-        used during csim and cosim.
-    output_data_tb (str, optional): String representing the path of output data in .npy or .dat format that will be
-        used during csim and cosim.
-    backend (str, optional): Name of the backend to use, e.g., 'Vivado'
-        or 'Quartus'.
-    board (str, optional): One of target boards specified in `supported_board.json` file. If set to `None` a default
-        device of a backend will be used. See documentation of the backend used.
-    part (str, optional): The FPGA part. If set to `None` a default part of a backend will be used.
-        See documentation of the backend used. Note that if `board` is specified, the part associated to that board
-        will overwrite any part passed as a parameter.
-    clock_period (int, optional): Clock period of the design.
-        Defaults to 5.
-    io_type (str, optional): Type of implementation used. One of
-        'io_parallel' or 'io_stream'. Defaults to 'io_parallel'.
-    hls_config (dict, optional): The HLS config.
-    kwargs** (dict, optional): Additional parameters that will be used to create the config of the specified backend
-
-    Returns
-    -------
-    ModelGraph : hls4ml model object.
-
-    See Also
-    --------
-    hls4ml.convert_from_keras_model, hls4ml.convert_from_pytorch_model
-
-    Examples
-    --------
-    >>> import hls4ml
-    >>> config = hls4ml.utils.config_from_onnx_model(model, granularity='model')
-    >>> hls_model = hls4ml.converters.convert_from_onnx_model(model, hls_config=config)
+    Returns:
+        ModelGraph: hls4ml model.
     """
 
     config = create_config(output_dir=output_dir, project_name=project_name, backend=backend, **kwargs)

hls4ml/converters/onnx_to_hls.py

@@ -29,18 +29,12 @@ def __init__(self, model):
     def get_weights_data(self, layer_name, var_name):
         """Extract weights data from ONNX model.
 
-        Parameters
-        ----------
-        layer_name : string
-            layer's name in the ONNX model
-        var_name : string
-            variable to be extracted
-
-        Returns
-        -------
-        data : numpy array
-            extracted weights data
+        Args:
+            layer_name (str): Layer's name in the ONNX model.
+            var_name (str): Variable to be extracted.
 
+        Returns:
+            ndarray: Extracted weights data.
         """
         # Get the node associated with the layer name
         node = next(node for node in self.model.graph.node if node.name == layer_name)
@@ -218,17 +212,15 @@ def get_out_layer_name(graph):
 def onnx_to_hls(config):
     """Convert onnx model to hls model from configuration.
 
-    Parameters
-    ----------
-    config: dict
-        onnx configuration from yaml file or passed through API.
+    Args:
+        config (dict): ONNX configuration from yaml file or passed through API.
 
-    Returns
-    -------
-    ModelGraph : hls4ml model object
+    Raises:
+        Exception: Raised if an unsupported operation is found in the ONNX model.
 
+    Returns:
+        ModelGraph: hls4ml model object
     """
-
     # This is a list of dictionaries to hold all the layer info we need to generate HLS
     layer_list = []
 

hls4ml/model/profiling.py

@@ -422,31 +422,22 @@ def activations_torch(model, X, fmt='longform', plot='boxplot'):
 
 
 def numerical(model=None, hls_model=None, X=None, plot='boxplot'):
-    """
-    Perform numerical profiling of a model
-
-    Parameters
-    ----------
-    model : keras or pytorch model
-        The model to profile
-    hls_model : ModelGraph
-        The ModelGraph to profile
-    X : array-like, optional
-        Test data on which to evaluate the model to profile activations
-        Must be formatted suitably for the ``model.predict(X)`` method
-    plot : str, optional
-        The type of plot to produce.
-        Options are: 'boxplot' (default), 'violinplot', 'histogram',
-        'FacetGrid'
-
-    Returns
-    -------
-    tuple
-        The quadruple of produced figures. First weights and biases
-        for the pre- and post-optimization models respectively,
-        then activations for the pre- and post-optimization models
-        respectively. (Optimizations are applied to an ModelGraph by hls4ml,
-        a post-optimization ModelGraph is a final model)
+    """Perform numerical profiling of a model.
+
+    Args:
+        model (optional): Keras of PyTorch model. Defaults to None.
+        hls_model (ModelGraph, optional): The ModelGraph to profile. Defaults to None.
+        X (ndarray, optional): Test data on which to evaluate the model to profile activations.
+            Must be formatted suitably for the ``model.predict(X)``. Defaults to None.
+        plot (str, optional): The type of plot to produce. Options are: 'boxplot' (default), 'violinplot', 'histogram',
+            'FacetGrid'. Defaults to 'boxplot'.
+
+    Returns:
+        tuple: The quadruple of produced figures. First weights and biases
+            for the pre- and post-optimization models respectively,
+            then activations for the pre- and post-optimization models
+            respectively. (Optimizations are applied to an ModelGraph by hls4ml,
+            a post-optimization ModelGraph is a final model).
     """
     wp, wph, ap, aph = None, None, None, None
 
@@ -554,21 +545,15 @@ def _get_output(layer, X, model_input):
 
 
 def get_ymodel_keras(keras_model, X):
-    """
-    Calculate each layer's ouput and put them into a dictionary
-
-    Parameters
-    ----------
-    keras_model :
-        a keras model
-    X : array-like
-        Test data on which to evaluate the model to profile activations.
-        Must be formatted suitably for the ``model.predict(X)`` method.
-
-    Returns
-    -------
-    dictionary
-        A dictionary in the form {"layer_name": ouput array of layer}
+    """Calculate each layer's ouput and put them into a dictionary.
+
+    Args:
+        keras_model (_type_): A keras Model
+        X (ndarray): Test data on which to evaluate the model to profile activations.
+            Must be formatted suitably for the ``model.predict(X)``.
+
+    Returns:
+        dict: A dictionary in the form {"layer_name": ouput array of layer}.
     """
 
     ymodel = {}
@@ -668,30 +653,20 @@ def _dist_diff(ymodel, ysim):
 
 
 def compare(keras_model, hls_model, X, plot_type="dist_diff"):
-    """
-    Compare each layer's output in keras and hls model. Note that the hls_model should not be compiled before using this.
-
-    Parameters
-    ----------
-    keras_model :
-        original keras model
-    hls_model :
-        converted ModelGraph, with "Trace:True" in the configuration file.
-    X : array-like
-        Input for the model.
-    plot_type : string
-        different methods to visualize the y_model and y_sim differences.
-        Possible options include:
-
-        - 'norm_diff' : square root of the sum of the squares of the differences
-          between each output vectors
-        - 'dist_diff' : The normalized distribution of the differences of the elements
-          between two output vectors
-
-    Returns
-    -------
-    matplotlib figure
-        plot object of the histogram depicting the difference in each layer's output
+    """Compare each layer's output in keras and hls model. Note that the hls_model should not be compiled before using this.
+
+    Args:
+        keras_model: Original keras model.
+        hls_model (ModelGraph): Converted ModelGraph, with "Trace:True" in the configuration file.
+        X (ndarray): Input tensor for the model.
+        plot_type (str, optional): Different methods to visualize the y_model and y_sim differences.
+            Possible options include:
+            - 'norm_diff':: square root of the sum of the squares of the differences between each output vectors.
+            - 'dist_diff':: The normalized distribution of the differences of the elements between two output vectors.
+            Defaults to "dist_diff".
+
+    Returns:
+        matplotlib figure: Plot object of the histogram depicting the difference in each layer's output.
     """
 
     # Take in output from both models