Make auto default precision for pytorch parser

hls4ml/utils/config.py

@@ -284,6 +284,7 @@ def config_from_pytorch_model(
     default_reuse_factor=1,
     channels_last_conversion='full',
     transpose_outputs=True,
+    max_precision=None,
 ):
     """Create an HLS conversion config given the PyTorch model.
 
@@ -304,7 +305,8 @@ def config_from_pytorch_model(
             will generate config keys for every layer separately, allowing for highly specific
             configuration tweaks.
         backend(str, optional): Name of the backend to use
-        default_precision (str, optional): Default precision to use. Defaults to 'fixed<16,6>'.
+        default_precision (str, optional): Default precision to use. Defaults to 'fixed<16,6>'. Note, this must
+            be an explicit precision: 'auto' is not allowed.
         default_reuse_factor (int, optional): Default reuse factor. Defaults to 1.
         channels_last_conversion (string, optional): Configures the conversion of pytorch layers to
         'channels_last' dataformate. Can be set to 'full', 'internal', or 'off'. If 'full', both the inputs
@@ -313,6 +315,8 @@ def config_from_pytorch_model(
         transpose_outputs (bool, optional): Set to 'False' if the output should not be transposed from
             channels_last into channels_first data format. Defaults to 'False'. If False, outputs needs
             to be transposed manually.
+        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 PyTorch model has layers not supported by hls4ml.
@@ -324,11 +328,16 @@ def config_from_pytorch_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['ChannelsLastConversion'] = channels_last_conversion
     model_config['TransposeOutputs'] = transpose_outputs
     model_config['Strategy'] = 'Latency'
+    model_config['BramFactor'] = 1_000_000_000
+    model_config['TraceOutput'] = False
 
     config['Model'] = model_config
     config['PytorchModel'] = model
@@ -372,7 +381,7 @@ def make_layer_config(layer):
                 if name.endswith('_t'):
                     name = name[:-2]
                 if attr.default is None:
-                    precision_cfg[name] = default_precision
+                    precision_cfg[name] = 'auto'
                 else:
                     precision_cfg[name] = str(attr.default)
             elif attr.name == 'reuse_factor':