Quartus multi out with stream fix

hls4ml/backends/fpga/passes/clone.py

@@ -20,21 +20,19 @@ def initialize(self):
 class CloneFunctionTemplate(FunctionCallTemplate):
     def __init__(self):
         super().__init__(Clone, include_header=clone_include_list)
-        self.template = None  # to be filled once number of clones known
 
     def format(self, node):
         params = self._default_function_params(node)
         for i, _output in enumerate(node.outputs):
             params['output' + str(i + 1)] = node.variables[node.outputs[i]].name
 
-        if self.template is None:
-            self.template = (
-                'nnet::clone_stream<{input_t}, {output_t}, {size}>({input}, '
-                + ', '.join(['{output' + str(i + 1) + '}' for i in range(len(node.outputs))])
-                + ');'
-            )
+        template = (
+            'nnet::clone_stream<{input_t}, {output_t}, {size}>({input}, '
+            + ', '.join(['{output' + str(i + 1) + '}' for i in range(len(node.outputs))])
+            + ');'
+        )
 
-        return self.template.format(**params)
+        return template.format(**params)
 
 
 def register_clone(backend):

hls4ml/model/graph.py

@@ -471,7 +471,7 @@ def make_node(self, kind, name, attributes, inputs, outputs=None):
         node = layer_cls(self, name, attributes, inputs, outputs)
         for o in node.outputs:
             out_var = node.get_output_variable(output_name=o)
-            if o in self.outputs:
+            if len(self.outputs) == 1 and o in self.outputs:
                 out_var.type.name = 'result_t'
             self.output_vars[o] = out_var
         return node
@@ -608,7 +608,7 @@ def get_input_variables(self):
         return variables
 
     def register_output_variable(self, out_name, variable):
-        if out_name in self.outputs:
+        if len(self.outputs) == 1 and out_name in self.outputs:
             variable.type.name = 'result_t'
         self.output_vars[out_name] = variable
 

hls4ml/writer/quartus_writer.py

@@ -172,7 +172,8 @@ def write_project_cpp(self, model):
                     for inp in model_inputs:
                         newline += indent + f'stream_in<{inp.type.name}> &{inp.name}_stream,\n'
                     for out in model_outputs:
-                        newline += indent + f'stream_out<{out.type.name}> &{out.name}_stream'
+                        newline += indent + f'stream_out<{out.type.name}> &{out.name}_stream,\n'
+                    newline = newline[:-2]  # Remove the tailing ',\n'
                     if model_brams:
                         newline += ',\n' + brams_str
                     newline += '\n) {\n'
@@ -191,7 +192,8 @@ def write_project_cpp(self, model):
                     for inp in model_inputs:
                         newline += indent + f'stream_in<{inp.type.name}> &{inp.name}_stream,\n'
                     for out in model_outputs:
-                        newline += indent + f'stream_out<{out.type.name}> &{out.name}_stream'
+                        newline += indent + f'stream_out<{out.type.name}> &{out.name}_stream,\n'
+                    newline = newline[:-2]  # Remove the tailing ',\n'\
                     if model_brams:
                         newline += ',\n' + brams_str
                     newline += '\n) {\n'
@@ -277,7 +279,7 @@ def write_project_cpp(self, model):
                         newline += indent + f'  {out.type.name} tmp = {out.name}.read();\n'
                         newline += indent + f'  {out.name}_stream.write(tmp);\n'
                         newline += indent + '}\n'
-                        newline += '}\n'
+                    newline += '}\n'
                 else:
                     newline = line
                     newline += indent + 'return outputs;\n'
@@ -330,7 +332,8 @@ def write_project_header(self, model):
                     for inp in model_inputs:
                         newline += indent + f'stream_in<{inp.type.name}> &{inp.name}_stream,\n'
                     for out in model_outputs:
-                        newline += indent + f'stream_out<{out.type.name}> &{out.name}_stream'
+                        newline += indent + f'stream_out<{out.type.name}> &{out.name}_stream,\n'
+                    newline = newline[:-2]  # Remove the tailing ',\n'
                     if model_brams:
                         newline += ',\n' + brams_str
                     newline += '\n);\n'
@@ -350,7 +353,8 @@ def write_project_header(self, model):
                     for inp in model_inputs:
                         newline += indent + f'stream_in<{inp.type.name}> &{inp.name}_stream,\n'
                     for out in model_outputs:
-                        newline += indent + f'stream_out<{out.type.name}> &{out.name}_stream'
+                        newline += indent + f'stream_out<{out.type.name}> &{out.name}_stream,\n'
+                    newline = newline[:-2]  # Remove the tailing ',\n'
                     if model_brams:
                         newline += ',\n' + brams_str
                     newline += '\n);\n'

test/pytest/test_multiout_network.py

@@ -0,0 +1,52 @@
+from pathlib import Path
+
+import numpy as np
+import pytest
+from keras.layers import Dense
+from tensorflow import keras
+
+from hls4ml.converters import convert_from_keras_model
+
+test_root_path = Path(__file__).parent
+
+
+@pytest.fixture(scope='module')
+def model():
+    inp = keras.Input(shape=(10,))
+    x = Dense(10, name='dense1')(inp)
+    y = Dense(10, name='dense2')(inp)
+    model = keras.Model(inp, [x, y])
+    return model
+
+
+@pytest.fixture(scope='module')
+def data():
+    X = np.random.normal(0, 1, (1000, 10))
+    X = np.clip(X, -16, 15)
+    return X
+
+
+@pytest.mark.parametrize('backend', ['Vivado', 'Quartus', 'Vitis'])
+@pytest.mark.parametrize('io_type', ['io_parallel', 'io_stream'])
+def test_multi_clone(model, data, backend: str, io_type: str):
+    output_dir = str(test_root_path / f'hls4mlprj_multiout_network_{backend}_{io_type}')
+    hls_config = {'Model': {'Precision': 'fixed<32,5>', 'ReuseFactor': 1}}
+    layer_config = {
+        'dense1': {'Precision': {'result': 'fixed<35,5>'}},
+        'dense2': {'Precision': {'result': 'fixed<40,5>'}},
+        'dense1_linear': {'Precision': {'result': 'fixed<35,5>'}},
+        'dense2_linear': {'Precision': {'result': 'fixed<40,5>'}},
+    }
+    hls_config['LayerName'] = layer_config
+    model_hls = convert_from_keras_model(
+        model, backend=backend, output_dir=output_dir, hls_config=hls_config, io_type=io_type
+    )
+
+    assert model_hls.graph['dense1'].attributes['result_t'] != model_hls.graph['dense2'].attributes['result_t']
+
+    model_hls.compile()
+    r_hls = model_hls.predict(data)
+    r_keras = [x.numpy() for x in model(data)]
+
+    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)

test/pytest/test_stream_multi_clone.py

@@ -0,0 +1,48 @@
+from pathlib import Path
+
+import numpy as np
+import pytest
+from keras.layers import Add, Dense
+from tensorflow import keras
+
+from hls4ml.converters import convert_from_keras_model
+
+test_root_path = Path(__file__).parent
+
+
+@pytest.fixture(scope='module')
+def model():
+    inp = keras.Input(shape=(10,))
+    x = Dense(10)(inp)
+    y = Dense(10)(inp)
+    z = Dense(10)(inp)
+    xy = Add()([x, y])  # 5
+    xy = Add()([xy, y])  # 5
+    out = Add()([xy, z])  # 5
+    model = keras.Model(inp, out)
+    return model
+
+
+@pytest.fixture(scope='module')
+def data():
+    X = np.random.normal(0, 1, (1000, 10))
+    X = np.clip(X, -16, 15)
+    return X
+
+
+@pytest.mark.parametrize('backend', ['Vivado', 'Quartus', 'Vitis'])
+def test_multi_clone(model, data, backend: str):
+    output_dir = str(test_root_path / f'hls4mlprj_stream_multi_clone_{backend}')
+    hls_config = {'Model': {'Precision': 'fixed<32,5>', 'ReuseFactor': 1}}
+    model_hls = convert_from_keras_model(
+        model,
+        backend=backend,
+        output_dir=output_dir,
+        hls_config=hls_config,
+        io_type='io_stream',  # clone only happens with stream io.
+    )
+    model_hls.compile()
+    r_hls = model_hls.predict(data)
+    r_keras = model(data).numpy()
+
+    assert np.allclose(r_hls, r_keras, atol=1e-5, rtol=0)