Support negative_slope in quantized_relu

hls4ml/converters/keras/qkeras.py

@@ -166,6 +166,10 @@ def parse_qactivation_layer(keras_layer, input_names, input_shapes, data_reader)
         layer['slope_prec'] = FixedPrecisionType(width=2, integer=0, signed=False)
         layer['shift_prec'] = FixedPrecisionType(width=2, integer=0, signed=False)
         layer['activation'] = activation_config['class_name'].replace('quantized_', 'hard_')
+    elif activation_config['class_name'] == 'quantized_relu' and activation_config['config']['negative_slope'] != 0:
+        layer['class_name'] = 'LeakyReLU'
+        layer['activation'] = activation_config['class_name'].replace('quantized_', 'leaky_')
+        layer['activ_param'] = activation_config['config']['negative_slope']
     else:
         layer['class_name'] = 'Activation'
         layer['activation'] = activation_config['class_name'].replace('quantized_', '')

hls4ml/model/profiling.py

@@ -588,10 +588,10 @@ def get_ymodel_keras(keras_model, X):
         # Note that if the layer is a standalone activation layer then skip this
         name = layer.name
         if (
-            hasattr(layer, "activation")
-            and hasattr(layer.activation, "__name__")
-            and layer.activation.__name__ != "linear"
+            hasattr(layer, 'activation')
+            and layer.activation is not None
             and not isinstance(layer, (keras.layers.Activation, qkeras.qlayers.QActivation))
+            and layer.activation.__name__ != 'linear'
         ):
             tmp_activation = layer.activation
             layer.activation = None

hls4ml/utils/config.py

@@ -79,8 +79,11 @@ def _get_precision_from_quantizer(quantizer):
         rnd = "AP_RND_CONV"
         overflow = "AP_SAT"
         if quantizer['class_name'] in ('quantized_relu', 'quantized_relu_po2'):
-            signed = False
-            integer -= 1
+            if quantizer['config']['negative_slope'] != 0.0:
+                signed = True
+            else:
+                signed = False
+                integer -= 1
         elif quantizer['class_name'] == 'quantized_tanh':
             overflow = "AP_SAT_SYM" if quantizer['config']['symmetric'] else "AP_SAT"
             integer = 1

test/pytest/test_qkeras.py

@@ -307,6 +307,44 @@ def test_quantizer(randX_1000_1, quantizer, backend, io_type):
     np.testing.assert_array_equal(y_qkeras, y_hls4ml)
 
 
+@pytest.mark.parametrize(
+    'quantizer',
+    [
+        (quantized_relu(4, negative_slope=0.5)),
+        (quantized_relu(8, 4, negative_slope=1.0)),
+        (quantized_relu(10, 2, negative_slope=0.25)),
+    ],
+)
+@pytest.mark.parametrize('backend', ['Vivado', 'Vitis', 'Quartus'])
+@pytest.mark.parametrize('io_type', ['io_parallel', 'io_stream'])
+def test_relu_negative_slope(randX_1000_1, quantizer, backend, io_type):
+    '''
+    Test a a transformation of quantized_relu with negative_slope to leaky_relu activation layer.
+    '''
+    X = randX_1000_1
+    X = -X  # Make it negative so leaky relu does something
+    X = np.round(X * 2**10) * 2**-10  # make it an exact ap_fixed<16,6>
+    model = Sequential()
+    model.add(QActivation(input_shape=(1,), activation=quantizer, name='quantizer'))
+    model.compile()
+
+    config = hls4ml.utils.config_from_keras_model(model, granularity='name')
+    output_dir = str(
+        test_root_path
+        / 'hls4mlprj_qkeras_leaky_relu_{}_{}_neg_slope_{}_{}_{}'.format(
+            quantizer.bits, quantizer.integer, quantizer.negative_slope, backend, io_type
+        )
+    )
+    hls_model = hls4ml.converters.convert_from_keras_model(
+        model, hls_config=config, output_dir=output_dir, backend=backend, io_type=io_type
+    )
+    hls_model.compile()
+
+    y_qkeras = model.predict(X)
+    y_hls4ml = hls_model.predict(X)
+    np.testing.assert_allclose(y_hls4ml, y_qkeras, rtol=1e-5, atol=0)
+
+
 @pytest.mark.parametrize(
     'weight_quantizer,activation_quantizer,',
     [