Merge branch 'pytorch_auto' of https://github.com/JanFSchulte/hls4ml into pytorch_auto

Author: JanFSchulte

hls4ml/model/layers.py

@@ -357,7 +357,7 @@ def initialize(self):
 
 
 class Constant(Layer):
-    # one could consider making this a weight attribute, but given it's transient nature, I am not sure it helps
+    # one could consider making this a weight attribute, but given its transient nature, I am not sure it helps
     _expected_attributes = [
         Attribute('value', value_type=np.ndarray),
     ]
@@ -371,6 +371,10 @@ def initialize(self):
         dims = [f'{self.name}_{i}' for i in range(len(shape))]
         quantizer = self.get_attr('quantizer')
 
+        # the graph._make_graph function sets the input node to the previous node
+        # if it is not set. That is incorrect for Constant nodes, so remove the input node
+        self.inputs = []
+
         # Should the else clause below be None or UnspecifiedPrecisionType
         precision = quantizer.hls_type if quantizer is not None else UnspecifiedPrecisionType()
 

hls4ml/utils/config.py

@@ -422,7 +422,7 @@ def make_layer_config(layer):
 
 
 def config_from_onnx_model(
-    model, granularity='name', backend=None, default_precision='ap_fixed<16,6>', default_reuse_factor=1
+    model, granularity='name', backend=None, default_precision='fixed<16,6>', default_reuse_factor=1, max_precision=None
 ):
     """Create an HLS conversion config given the ONNX model.
 
@@ -444,6 +444,8 @@ def config_from_onnx_model(
         backend(str, optional): Name of the backend to use
         default_precision (str, optional): Default precision to use. Defaults to 'fixed<16,6>'.
         default_reuse_factor (int, optional): Default reuse factor. Defaults to 1.
+        max_precision (str or None, optional): Maximum width precision to use. Defaults to None, meaning no maximum.
+            Note:  Only integer and fixed precisions are supported
 
     Raises:
         Exception: If ONNX model has layers not supported by hls4ml.
@@ -465,9 +467,14 @@ def config_from_onnx_model(
     config = {}
 
     model_config = {}
-    model_config['Precision'] = default_precision
+    model_config['Precision'] = {}
+    model_config['Precision']['default'] = default_precision
+    if max_precision is not None:
+        model_config['Precision']['maximum'] = max_precision
     model_config['ReuseFactor'] = default_reuse_factor
     model_config['Strategy'] = 'Latency'
+    model_config['BramFactor'] = 1_000_000_000
+    model_config['TraceOutput'] = False
 
     config['Model'] = model_config