Fixes: quantized_relu & unsigned profiling

hls4ml/model/profiling.py

@@ -159,49 +159,48 @@ def types_histogram(data, fmt='longform'):
 types_plots = {'boxplot' : types_boxplot,
                'histogram' : types_histogram}
 
-def ap_fixed_WIF(dtype):
+def ap_fixed_WIFS(dtype):
     from hls4ml.templates.vivado_template import VivadoBackend
     dtype = VivadoBackend.convert_precision_string(None, dtype) 
-    W, I, F = dtype.width, dtype.integer, dtype.fractional
-    return W, I, F
+    W, I, F, S = dtype.width, dtype.integer, dtype.fractional, dtype.signed
+    return W, I, F, S
 
 def types_hlsmodel(model):
     suffix = ['w', 'b']
     data = {'layer' : [], 'low' : [], 'high' : []}
     # Plot the default precision
     default_precision = model.config.model_precision['default']
-    # assumes ap_fixed
-    W, I, F = ap_fixed_WIF(default_precision)
+    W, I, F, S = ap_fixed_WIFS(default_precision)
     data['layer'].append('model')
     data['low'].append(-F)
-    data['high'].append(I-1)
+    data['high'].append(I-1 if S else I)
 
     for layer in model.get_layers():
         for iw, weight in enumerate(layer.get_weights()):
             wname = '{}/{}'.format(layer.name, suffix[iw])
             T = weight.type
             if T.name != 'model':
-                W, I, F = ap_fixed_WIF(T.precision)
+                W, I, F, S = ap_fixed_WIFS(T.precision)
                 data['layer'].append(wname)
                 data['low'].append(-F)
-                data['high'].append(I-1)
+                data['high'].append(I-1 if S else I)
     data = pandas.DataFrame(data)
     return data
 
 def activation_types_hlsmodel(model):
     data = {'layer' : [], 'low' : [], 'high' : []}
     # Get the default precision
     default_precision = model.config.model_precision['default']
-    W, I, F = ap_fixed_WIF(default_precision)
+    W, I, F, S = ap_fixed_WIFS(default_precision)
     data['layer'].append('model')
     data['low'].append(-F)
-    data['high'].append(I-1)
+    data['high'].append(I-1 if S else I)
     for layer in model.get_layers():
         T = layer.get_output_variable().type.precision
-        W, I, F = ap_fixed_WIF(T)
+        W, I, F, S = ap_fixed_WIFS(T)
         data['layer'].append(layer.name)
         data['low'].append(-F)
-        data['high'].append(I-1)
+        data['high'].append(I-1 if S else I)
     data = pandas.DataFrame(data)
     return data
 

hls4ml/utils/config.py

@@ -37,11 +37,14 @@ def _get_precision_from_quantizer(quantizer):
             quantizer['class_name'] = quantizer_obj.__name__
 
     supported_quantizers = ['quantized_bits', 'quantized_relu', 'quantized_tanh', 'quantized_po2', 'quantized_relu_po2']
+    signed = True
     if quantizer['class_name'] in supported_quantizers:
         bits = int(quantizer['config']['bits'])
         # if integer isn't specified, it should be the same as bits
         integer = int(quantizer['config'].get('integer', bits-1)) + 1
-
+        if quantizer['class_name'] == 'quantized_relu':
+            signed = False
+            integer -= 1
     elif quantizer['class_name'] in ['binary', 'stochastic_binary', 'binary_tanh']:
         bits = 2
         integer = 2
@@ -53,10 +56,11 @@ def _get_precision_from_quantizer(quantizer):
         raise Exception('ERROR: Unsupported quantizer: {}'.format(quantizer['class_name']))
 
     decimal = bits - integer
+    signed = '' if signed else 'u'
     if decimal > 0:
-        return 'ap_fixed<{},{}>'.format(bits, integer)
+        return 'ap_{}fixed<{},{}>'.format(signed, bits, integer)
     else:
-        return 'ap_int<{}>'.format(bits)
+        return 'ap_{}int<{}>'.format(signed, bits)
 
 def config_from_keras_model(model, granularity='model', default_precision='ap_fixed<16,6>', default_reuse_factor=1):
     """Create an HLS conversion config given the Keras model.

test/pytest/test_qkeras.py

@@ -186,9 +186,13 @@ def randX_1000_1():
                                        (quantized_bits(8,4)),
                                        (quantized_bits(4,2)),
                                        (quantized_bits(4,0)),
-                                       (quantized_bits(10,0)),])
-                                       #(quantized_relu(4)),
-                                       #(quantized_relu(10))])
+                                       (quantized_bits(10,0)),
+                                       (quantized_relu(4)),
+                                       (quantized_relu(4,2)),
+                                       (quantized_relu(8)),
+                                       (quantized_relu(8,4)),
+                                       (quantized_relu(10)),
+                                       (quantized_relu(10,5))])
 def test_quantizer(randX_1000_1, quantizer):
   '''
   Test a single quantizer as an Activation function.