Register ApplyAlpha layer templates

hls4ml/backends/vivado/passes/quantization_templates.py

@@ -0,0 +1,29 @@
+from hls4ml.backends.backend import get_backend
+from hls4ml.model.optimizer.passes.qkeras import ApplyAlpha
+from hls4ml.backends.template import LayerConfigTemplate, FunctionCallTemplate
+from hls4ml.backends.vivado.passes.core_templates import batchnorm_config_template, batchnorm_function_template, batchnorm_include_list
+
+class ApplyAlphaConfigTemplate(LayerConfigTemplate):
+    def __init__(self):
+        super().__init__(ApplyAlpha)
+        self.template = batchnorm_config_template
+
+    def format(self, node):
+        params = self._default_config_params(node)
+        params['n_in'] = node.get_input_variable().size_cpp()
+        params['product_type'] = get_backend('vivado').product_type(node.get_input_variable().type.precision, node.get_weights('scale').type.precision)
+
+        return self.template.format(**params)
+
+class ApplyAlphaFunctionTemplate(FunctionCallTemplate):
+    def __init__(self):
+        super().__init__(ApplyAlpha, include_header=batchnorm_include_list)
+        self.template = batchnorm_function_template
+
+    def format(self, node):
+        params = self._default_function_params(node)
+        params['scale'] = node.get_weights('scale').name
+        params['bias'] = node.get_weights('bias').name
+
+        return self.template.format(**params)
+

test/pytest/test_qkeras.py

@@ -110,16 +110,16 @@ def randX_100_16():
 # Note 4-bit test can still fail sometimes depending on random seed
 # https://github.com/fastmachinelearning/hls4ml/issues/381
 #@pytest.mark.parametrize('bits', [4, 6, 8])
-@pytest.mark.parametrize('bits', [4])
-def test_single_dense_activation_exact(randX_100_16, bits):
+@pytest.mark.parametrize('bits,alpha', [(4, 1), (4, 'auto_po2')])
+def test_single_dense_activation_exact(randX_100_16, bits, alpha):
   '''
   Test a single Dense -> Activation layer topology for
   bit exactness with number of bits parameter
   '''
   X = randX_100_16
   model = Sequential()
   model.add(QDense(16, input_shape=(16,), name='fc1',
-                  kernel_quantizer=quantized_bits(bits,0,alpha=1), bias_quantizer=quantized_bits(bits,0,alpha=1),
+                  kernel_quantizer=quantized_bits(bits,0,alpha=alpha), bias_quantizer=quantized_bits(bits,0,alpha=1),
                   kernel_initializer='lecun_uniform'))
   model.add(QActivation(activation=quantized_relu(bits,0), name='relu1'))
   model.compile()
@@ -128,7 +128,7 @@ def test_single_dense_activation_exact(randX_100_16, bits):
   config = hls4ml.utils.config_from_keras_model(model, granularity='name')
   hls_model = hls4ml.converters.convert_from_keras_model(model,
                                                        hls_config=config,
-                                                       output_dir=str(test_root_path / 'hls4mlprj_qkeras_single_dense_activation_exact_{}'.format(bits)),
+                                                       output_dir=str(test_root_path / 'hls4mlprj_qkeras_single_dense_activation_exact_{}_{}'.format(bits, alpha)),
                                                        part='xcu250-figd2104-2L-e')
   hls4ml.model.optimizer.get_optimizer('output_rounding_saturation_mode').configure(layers=[])
   hls_model.compile()