Merge pull request fastmachinelearning#796 from fastmachinelearning/p…

…re-commit-ci-update-config [pre-commit.ci] pre-commit autoupdate
calad0i · May 16, 2023 · 487d269 · 487d269
2 parents 08e309f + 05866f7
commit 487d269
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diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
@@ -2,7 +2,7 @@ exclude: (^hls4ml\/templates\/(vivado|quartus)\/(ap_types|ac_types)\/|^test/pyte
 
 repos:
 - repo: https://github.com/psf/black
-  rev: 22.12.0
+  rev: 23.3.0
   hooks:
   - id: black
     language_version: python3
@@ -30,7 +30,7 @@ repos:
     args: ["--profile", "black", --line-length=125]
 
 - repo: https://github.com/asottile/pyupgrade
-  rev: v3.3.1
+  rev: v3.4.0
   hooks:
   - id: pyupgrade
     args: ["--py36-plus"]
@@ -41,7 +41,7 @@ repos:
   - id: setup-cfg-fmt
 
 - repo: https://github.com/pycqa/flake8
-  rev: 5.0.4
+  rev: 6.0.0
   hooks:
   - id: flake8
     exclude: docs/conf.py

diff --git a/contrib/kl_layer/kl_layer.py b/contrib/kl_layer/kl_layer.py
@@ -34,7 +34,6 @@ def build(self, input_shape):
         super().build(input_shape)
 
     def _merge_function(self, inputs):
-
         mean = inputs[0]
         log_var = inputs[1]
 

diff --git a/hls4ml/backends/fpga/fpga_backend.py b/hls4ml/backends/fpga/fpga_backend.py
@@ -391,7 +391,6 @@ def product_type(self, data_T, weight_T):
         return product
 
     def compute_conv1d_instructions(self, in_W, in_C, kernel_size=3, stride=1, pad=0):
-
         # Current limitations
         assert pad == 0
 
@@ -427,7 +426,6 @@ def compute_conv1d_instructions(self, in_W, in_C, kernel_size=3, stride=1, pad=0
         return (min_W, windows_int)
 
     def compute_conv2d_instructions(self, in_H, in_W, in_C, kernel_size=3, stride=1, pad=0):
-
         if isinstance(kernel_size, Iterable):
             kernel_height = kernel_size[0]
             kernel_width = kernel_size[1]

diff --git a/hls4ml/backends/quartus/passes/convolution_winograd.py b/hls4ml/backends/quartus/passes/convolution_winograd.py
@@ -82,7 +82,6 @@ def match(self, node):
     def transform(self, model, node):
         if isinstance(node, Conv1D):
             if node.get_attr('filt_width', 3) == 3:
-
                 # First, transpose to a format suitable for the Winograd algorithm (F, C, W)
                 # Note, this assumes a format post-resource strategy optimizer, that is (F, W, C)
                 # Therefore, (F, W, C) => (F, C, W)
@@ -127,7 +126,6 @@ def transform(self, model, node):
 
         elif isinstance(node, Conv2D):
             if node.get_attr('filt_height', 3) == 3 and node.get_attr('filt_width', 3) == 3:
-
                 # First, transpose to a format suitable for the Winograd algorithm (F, C, H, W)
                 # Note, this assumes a format post-resource strategy optimizer, that is (F, H, W, C)
                 # Therefore, (F, H, W, C) => (F, C, H, W)

diff --git a/hls4ml/backends/quartus/quartus_backend.py b/hls4ml/backends/quartus/quartus_backend.py
@@ -133,7 +133,6 @@ def create_initial_config(self, part='Arria10', clock_period=5, io_type='io_para
         return config
 
     def build(self, model, synth=True, fpgasynth=False, log_level=1, cont_if_large_area=False):
-
         """
         Builds the project using Intel HLS compiler.
 

diff --git a/hls4ml/backends/vivado/passes/recurrent_templates.py b/hls4ml/backends/vivado/passes/recurrent_templates.py
@@ -79,7 +79,6 @@ def __init__(self):
         self.mult2_template = recr_mult_config_template
 
     def format(self, node):
-
         params = self._default_config_params(node)
 
         params['n_in'] = node.get_input_variable().dim_names[1]

diff --git a/hls4ml/backends/vivado/passes/resource_strategy.py b/hls4ml/backends/vivado/passes/resource_strategy.py
@@ -8,7 +8,6 @@ class ApplyResourceStrategy(OptimizerPass):
     '''Transposes the weights to use the dense_resource matrix multiply routine'''
 
     def match(self, node):
-
         node_matches = isinstance(node, (Dense, Conv1D, SeparableConv1D, Conv2D, SeparableConv2D, LSTM, GRU))
         is_resource_strategy = node.get_attr('strategy', '').lower() == 'resource'
         already_transformed = node.get_attr('_weights_transposed', False) is True

diff --git a/hls4ml/converters/__init__.py b/hls4ml/converters/__init__.py
@@ -49,7 +49,6 @@
                 # and is defined in this module (i.e., not imported)
                 if callable(func) and hasattr(func, 'handles') and func.__module__ == lib.__name__:
                     for layer in func.handles:
-
                         if model_type == 'keras':
                             register_keras_layer_handler(layer, func)
                         elif model_type == 'pytorch':

diff --git a/hls4ml/converters/keras/qkeras.py b/hls4ml/converters/keras/qkeras.py
@@ -20,7 +20,6 @@ def get_quantizer_from_config(keras_layer, quantizer_var):
 
 @keras_handler('QDense')
 def parse_qdense_layer(keras_layer, input_names, input_shapes, data_reader):
-
     layer, output_shape = parse_dense_layer(keras_layer, input_names, input_shapes, data_reader)
 
     layer['weight_quantizer'] = get_quantizer_from_config(keras_layer, 'kernel')
@@ -124,7 +123,6 @@ def parse_qactivation_layer(keras_layer, input_names, input_shapes, data_reader)
 
 @keras_handler('QBatchNormalization')
 def parse_qbatchnorm_layer(keras_layer, input_names, input_shapes, data_reader):
-
     layer, output_shape = parse_batchnorm_layer(keras_layer, input_names, input_shapes, data_reader)
 
     layer['mean_quantizer'] = get_quantizer_from_config(keras_layer, 'mean')

diff --git a/hls4ml/converters/keras_to_hls.py b/hls4ml/converters/keras_to_hls.py
@@ -191,7 +191,6 @@ def get_model_arch(config):
 
 
 def parse_keras_model(model_arch, reader):
-
     # This is a list of dictionaries to hold all the layer info we need to generate HLS
     layer_list = []
 

diff --git a/hls4ml/converters/onnx/convolution.py b/hls4ml/converters/onnx/convolution.py
@@ -10,7 +10,6 @@
 
 @onnx_handler('Conv')
 def parse_conv_layer(reader, node, inputs_map, input_shapes, graph, config):
-
     layer = {}
     layer['name'] = node.name
     layer['data_format'] = 'channels_first'  # ONNX's default is channel first
@@ -45,7 +44,6 @@ def parse_conv_layer(reader, node, inputs_map, input_shapes, graph, config):
         output_shape = [input_shapes[0][0], layer['n_filt'], layer['out_width']]
 
     elif len(input_shapes[0]) == 4:  # Conv2D
-
         layer['class_name'] = 'Conv2D'
 
         layer['in_height'] = input_shapes[0][2]

diff --git a/hls4ml/converters/onnx/core.py b/hls4ml/converters/onnx/core.py
@@ -3,7 +3,6 @@
 
 @onnx_handler(*['Gemm', 'MatMul'])
 def parse_gemm_layer(reader, node, inputs_map, input_shapes, graph, config):
-
     layer = {}
 
     layer['class_name'] = 'Dense'
@@ -61,7 +60,6 @@ def parse_gemm_layer(reader, node, inputs_map, input_shapes, graph, config):
 
 @onnx_handler(*activation_layers)
 def parse_activation_layer(reader, node, inputs_map, input_shapes, graph, config):
-
     layer = {}
 
     layer['name'] = node.name
@@ -70,7 +68,6 @@ def parse_activation_layer(reader, node, inputs_map, input_shapes, graph, config
     layer['inputs'] = get_onnx_input_name(node, graph)
 
     if layer['class_name'] != 'Activation':
-
         if layer['class_name'] == 'Softmax':
             layer['activation'] = 'softmax'
 
@@ -79,7 +76,6 @@ def parse_activation_layer(reader, node, inputs_map, input_shapes, graph, config
             layer['activ_param'] = get_onnx_attribute(node, 'alpha', 0.01)
 
         elif layer['class_name'] == 'Clip':
-
             clip_min_node = [x for x in graph.initializer if x.name in node.input]
             clip_min = clip_min_node[0].float_data[0]
 
@@ -99,7 +95,6 @@ def parse_activation_layer(reader, node, inputs_map, input_shapes, graph, config
 
 @onnx_handler('BatchNormalization')
 def parse_batchnorm_layer(reader, node, inputs_map, input_shapes, graph, config):
-
     layer = {}
 
     layer['class_name'] = 'BatchNormalization'

diff --git a/hls4ml/converters/onnx/merge.py b/hls4ml/converters/onnx/merge.py
@@ -5,7 +5,6 @@
 
 @onnx_handler(*merge_layers)
 def parse_merge_layer(reader, node, inputs_map, input_shapes, graph, config):
-
     layer = {}
     layer['class_name'] = node.op_type
     layer['name'] = node.name

diff --git a/hls4ml/converters/onnx/pooling.py b/hls4ml/converters/onnx/pooling.py
@@ -12,7 +12,6 @@
 
 @onnx_handler(*pool_operations)
 def parse_pool_layer(reader, node, inputs_map, input_shapes, graph, config):
-
     layer = {}
     layer['name'] = node.name
     layer['inputs'] = get_onnx_input_name(node, graph)
@@ -91,7 +90,6 @@ def parse_pool_layer(reader, node, inputs_map, input_shapes, graph, config):
 
 @onnx_handler(*global_pooling_layers)
 def parse_global_pooling_layer(reader, node, inputs_map, input_shapes, graph, config):
-
     layer = {}
     layer['name'] = node.name
     layer['inputs'] = get_onnx_input_name(node, graph)

diff --git a/hls4ml/converters/onnx/reshape.py b/hls4ml/converters/onnx/reshape.py
@@ -5,7 +5,6 @@
 
 @onnx_handler('Transpose')
 def parse_transpose_layer(reader, node, inputs_map, input_shapes, graph, config):
-
     layer = {}
     layer['name'] = node.name
     layer['class_name'] = 'Transpose'
@@ -21,7 +20,6 @@ def parse_transpose_layer(reader, node, inputs_map, input_shapes, graph, config)
 
 @onnx_handler('Reshape')
 def parse_reshape_layer(reader, node, inputs_map, input_shapes, graph, config):
-
     layer = {}
     layer['name'] = node.name
     layer['class_name'] = 'Reshape'

diff --git a/hls4ml/converters/onnx_to_hls.py b/hls4ml/converters/onnx_to_hls.py
@@ -53,7 +53,6 @@ def get_weights_data(self, layer_name, var_name):
         tensor = next((x for x in self.model.graph.initializer if x.name == inputs['inputs'][inp_idx]), None)
 
         if tensor is not None:
-
             data = numpy_helper.to_array(tensor)
 
             if inputs['transpose']:
@@ -278,7 +277,6 @@ def onnx_to_hls(config):
 
     print('Topology:')
     for node in graph.node:
-
         if node.op_type not in supported_layers:
             raise Exception(f'ERROR: Unsupported operation type: {node.op_type}')
 

diff --git a/hls4ml/converters/pytorch/core.py b/hls4ml/converters/pytorch/core.py
@@ -35,7 +35,6 @@ def parse_linear_layer(pytorch_layer, layer_name, input_shapes, data_reader, con
 
 @pytorch_handler(*activation_layers)
 def parse_activation_layer(pytorch_layer, layer_name, input_shapes, data_reader, config):
-
     layer = {}
 
     layer['class_name'] = pytorch_layer.__class__.__name__

diff --git a/hls4ml/converters/pytorch_to_hls.py b/hls4ml/converters/pytorch_to_hls.py
@@ -159,7 +159,6 @@ def pytorch_to_hls(config):
     print("Input Shape: ", input_shapes)
 
     for layer_name, pytorch_layer in model.named_modules():
-
         pytorch_class = pytorch_layer.__class__.__name__
 
         # First module is the whole model's class

diff --git a/hls4ml/model/graph.py b/hls4ml/model/graph.py
@@ -647,7 +647,6 @@ def compile(self):
 
         lib_name = self.config.backend.compile(self)
         if self._top_function_lib is not None:
-
             if platform.system() == "Linux":
                 libdl_libs = ['libdl.so', 'libdl.so.2']
                 for libdl in libdl_libs:

diff --git a/hls4ml/model/profiling.py b/hls4ml/model/profiling.py
@@ -567,7 +567,6 @@ def get_ymodel_keras(keras_model, X):
                 isinstance(layer, keras.layers.Activation) or isinstance(layer, qkeras.qlayers.QActivation)
             ):
                 if layer.activation:
-
                     if layer.activation.__class__.__name__ == "linear":
                         ymodel[layer.name] = _get_output(layer, X, keras_model.input)
 

diff --git a/hls4ml/model/types.py b/hls4ml/model/types.py
@@ -408,7 +408,6 @@ class ExponentType(NamedType):
     """
 
     def __init__(self, name, precision, **kwargs):
-
         if not name.startswith('exponent_'):
             name = 'exponent_' + name
         super().__init__(name, precision, **kwargs)

diff --git a/hls4ml/templates/vivado_accelerator/alveo/python_drivers/axi_stream_driver.py b/hls4ml/templates/vivado_accelerator/alveo/python_drivers/axi_stream_driver.py
@@ -6,7 +6,6 @@
 
 class NeuralNetworkOverlay(Overlay):
     def __init__(self, xclbin_name, dtbo=None, download=True, ignore_version=False, device=None):
-
         super().__init__(xclbin_name, dtbo=dtbo, download=download, ignore_version=ignore_version, device=device)
         self.input_buffer = None
         self.output_buffer = None

diff --git a/hls4ml/utils/config.py b/hls4ml/utils/config.py
@@ -6,7 +6,6 @@
 
 
 def create_config(output_dir='my-hls-test', project_name='myproject', backend='Vivado', **kwargs):
-
     backend_list = hls4ml.backends.get_available_backends()
     if backend.lower() not in backend_list:
         raise Exception(f'Unknown backend: {backend}')
@@ -25,7 +24,6 @@ def create_config(output_dir='my-hls-test', project_name='myproject', backend='V
 
 
 def _get_precision_from_quantizer(quantizer):
-
     if isinstance(quantizer, str):
         quantizer_obj = qkeras.get_quantizer(quantizer)
         quantizer = {}

diff --git a/hls4ml/utils/example_models.py b/hls4ml/utils/example_models.py
@@ -27,21 +27,18 @@ def _load_data_config_avai(model_name):
 
 
 def _data_is_available(model_name):
-
     data = _load_data_config_avai(model_name)
 
     return data['example_data']
 
 
 def _config_is_available(model_name):
-
     data = _load_data_config_avai(model_name)
 
     return data['example_config']
 
 
 def _create_default_config(model_name, model_config, backend):
-
     # Initiate the configuration file
     config = create_config(backend=backend)
 
@@ -69,7 +66,6 @@ def _filter_name(model_name):
 
 
 def _load_example_data(model_name):
-
     print("Downloading input & output example files ...")
 
     filtered_name = _filter_name(model_name)
@@ -89,7 +85,6 @@ def _load_example_data(model_name):
 
 
 def _load_example_config(model_name):
-
     print("Downloading configuration files ...")
 
     filtered_name = _filter_name(model_name)
@@ -179,7 +174,6 @@ def fetch_example_model(model_name, backend='Vivado'):
 
 
 def fetch_example_list():
-
     link_to_list = 'https://raw.githubusercontent.com/hls-fpga-machine-learning/example-models/master/available_models.json'
 
     temp_file, _ = urlretrieve(link_to_list)

diff --git a/hls4ml/writer/quartus_writer.py b/hls4ml/writer/quartus_writer.py
@@ -405,7 +405,6 @@ def write_defines(self, model):
         fout = open(f'{model.config.get_output_dir()}/firmware/defines.h', 'w')
 
         for line in f.readlines():
-
             # Insert numbers
             if '// hls-fpga-machine-learning insert numbers' in line:
                 newline = line
@@ -450,7 +449,6 @@ def write_parameters(self, model):
         fout = open(f'{model.config.get_output_dir()}/firmware/parameters.h', 'w')
 
         for line in f.readlines():
-
             if '// hls-fpga-machine-learning insert includes' in line:
                 newline = line
                 for include in sorted(set(sum((layer.get_attr('include_header', []) for layer in model.get_layers()), []))):
@@ -760,7 +758,6 @@ def write_bridge(self, model):
         indent = '    '
 
         for line in f.readlines():
-
             if 'MYPROJECT' in line:
                 newline = line.replace('MYPROJECT', format(model.config.get_project_name().upper()))
 
@@ -882,7 +879,6 @@ def write_build_script(self, model):
         fout = open(f'{model.config.get_output_dir()}/Makefile', 'w')
 
         for line in f.readlines():
-
             line = line.replace('myproject', model.config.get_project_name())
 
             if 'DEVICE   :=' in line:
@@ -1054,7 +1050,6 @@ def __write_softsign_table(self, model, path):
 
         sep = ''
         for i in range(table_size):
-
             in_val = (
                 i * (MAX_VALUE - MIN_VALUE) / float(table_size)
                 + (MAX_VALUE - MIN_VALUE) / (float(table_size) * 2)

diff --git a/hls4ml/writer/vivado_accelerator_writer.py b/hls4ml/writer/vivado_accelerator_writer.py
@@ -279,7 +279,6 @@ def modify_build_script(self, model):
         fout.close()
 
     def write_wrapper_test(self, model):
-
         ###################
         # write myproject_test_wrapper.cpp
         ###################