cosmatic

Author: calad0i

hls4ml/backends/fpga/passes/clone.py

@@ -1,4 +1,4 @@
-import numpy as np
+from math import prod
 
 from hls4ml.backends.template import FunctionCallTemplate
 from hls4ml.model.layers import Layer, register_layer
@@ -80,11 +80,12 @@ def transform(self, model, node):
             if n_outputs == 1:
                 continue
             if n_outputs > 3:
-                msg = f'ERROR: Cloning output {output} of {node.__class__.__name__} ({node.name}) more than 3 times not currently supported'  # noqa: E501
+                msg = f'ERROR: Cloning output {output} of {node.class_name}\
+                      ({node.name}) more than 3 times not currently supported'
                 raise ValueError(msg)
 
             out_var = node.get_output_variable(output)
-            attrs = {'size': np.prod(out_var.shape)}
+            attrs = {'size': prod(out_var.shape)}
 
             init_stream_idx = 1
             if in_output:

hls4ml/backends/fpga/passes/inplace_parallel_reshape.py

@@ -12,7 +12,7 @@ class InplaceParallelReshape(OptimizerPass):
 
     def match(self, node):
         if not isinstance(node, Reshape):
-            return
+            return False
         return node.model.config.get_config_value('IOType') == 'io_parallel'
 
     def transform(self, model, node):
@@ -24,6 +24,8 @@ def transform(self, model, node):
             prev_node = node.get_input_node()
             assert (
                 prev_node.name not in model.outputs
-            ), f"Cannot output node {prev_node.name}: reshape is a no-op in io_parallel. As a result, the previous node {prev_node.name}'s output will be used as the output. However, this node is already an output."  # noqa: E501
+            ), f"Cannot output node {prev_node.name}: reshape is a no-op in io_parallel.\
+            As a result, the previous node {prev_node.name}'s output will be used as the\
+            output. However, this node is already an output."
             model.outputs = [name if name != node.name else prev_node.name for name in model.outputs]
         return False

hls4ml/backends/fpga/passes/inplace_stream_flatten.py

@@ -15,8 +15,7 @@ def match(self, node):
         if not (isinstance(node, Reshape) and len(node.get_output_variable().shape)) == 1:
             # Reshape with multiple outputs will be kept as is, or repack cannot handle different shapes
             return False
-        io_type = node.model.config.get_config_value('IOType')
-        return io_type == 'io_stream'
+        return node.model.config.get_config_value('IOType') == 'io_stream'
 
     def transform(self, model, node):
         outvar = node.get_output_variable()

hls4ml/model/graph.py

@@ -524,14 +524,16 @@ def remove_node(self, node, rewire=True):
         node to the input of next one. If the removed node has multiple
         inputs/outputs tensors, an exception is raised.
 
-        Args:
-            node (Layer): The node to remove rewire (bool, optional):
-            Deprecated, no effect
+        :param node: The node to remove.
+        :type node: Layer
+        :param rewire: Deprecated, no effect.
+        :type rewire: bool, optional
 
-        Raises:
-            Exception: If an attempt is made to rewire a node with
-            multiple inputs/outputs.
-        """
+        :raises Exception: If an attempt is made to rewire a node with
+        multiple inputs/outputs.
+
+        .. deprecated:: 1.0
+            The `rewire` parameter is deprecated and has no effect."""
 
         inputs = [inp for inp in node.inputs if inp]
         outputs = [outp for outp in node.outputs if outp]

test/pytest/test_multiout_network.py

@@ -20,7 +20,7 @@ def model():
 
 
 @pytest.fixture(scope='module')
-def model2():
+def model_corner_cases():
     in1 = keras.layers.Input(shape=(24, 8))
     in2 = keras.layers.Input(shape=(16))
     out1 = keras.layers.Conv1D(1, 3)(in1)
@@ -40,7 +40,7 @@ def data():
 
 
 @pytest.fixture(scope='module')
-def data2():
+def data_corner_cases():
     X1 = np.random.normal(0, 1, (1000, 24, 8))
     X2 = np.random.normal(0, 1, (1000, 16))
     X1 = np.clip(X1, -16, 15)
@@ -70,18 +70,24 @@ def test_multi_output_nn(model, data, backend: str, io_type: str):
 @pytest.mark.parametrize('backend', ['Vivado', 'Quartus', 'Vitis', 'Catapult', 'OneAPI'])
 @pytest.mark.parametrize('io_type', ['io_parallel', 'io_stream'])
 @pytest.mark.parametrize('strategy', ['latency', 'resource'])
-def test_multi_output_nn_2(model2, data2, backend: str, io_type: str, strategy: str):
-    """Cover corner case where a flatten layer is cloned multiple times, and used as model output"""
+def test_multi_output_nn_corner_cases(model_corner_cases, data_corner_cases, backend: str, io_type: str, strategy: str):
+    """Cover corner cases, when:
+    - a layer outputs both to the next layer(s) and to the model output
+       - when an node removal/insertion is triggered internally
+    - a reshape in io_parallel, or flatten in io_stream layer's output is used multiple times
+       - and as layer output
+       - and  by layer taking multiple inputs
+    """
     output_dir = str(test_root_path / f'hls4mlprj_multiout_network_2_{backend}_{io_type}_{strategy}')
     hls_config = {'Model': {'Precision': 'fixed<32,5>', 'ReuseFactor': 1}, 'Strategy': strategy}
 
     model_hls = convert_from_keras_model(
-        model2, backend=backend, output_dir=output_dir, hls_config=hls_config, io_type=io_type
+        model_corner_cases, backend=backend, output_dir=output_dir, hls_config=hls_config, io_type=io_type
     )
 
     model_hls.compile()
-    r_hls = model_hls.predict(data2)
-    r_keras = model2.predict(data2, verbose=0, batch_size=1000)
+    r_hls = model_hls.predict(data_corner_cases)
+    r_keras = model_corner_cases.predict(data_corner_cases, verbose=0, batch_size=1000)
 
     assert np.allclose(r_hls[0], r_keras[0], atol=1e-5, rtol=0)
     assert np.allclose(r_hls[1], r_keras[1], atol=1e-5, rtol=0)