Some bug fix needed during model with branches development

Author: nghielme

hls4ml/backends/fpga/passes/clone.py

@@ -87,7 +87,7 @@ def transform(self, model, node):
                 )
                 for i in range(len(output_map[output])):
                     key = output + '_cpy' + str(i + 1)
-                    clone_layer.attributes[key].type = node.attributes['result_t']
+                    clone_layer.attributes[key].type = node.get_output_variable().type
                 model.insert_node(clone_layer)
                 transformed = True
 

hls4ml/converters/onnx_to_hls.py

@@ -63,6 +63,8 @@ def get_input_shape(graph, node):
     """
     rv = []
     for inp in node.input:
+        if inp == '':
+            continue
         try:
             value_info_idx = next((i for i, x in enumerate(graph.value_info) if x.name == inp))
             dim = list(d.dim_value for d in graph.value_info[value_info_idx].type.tensor_type.shape.dim)

hls4ml/model/optimizer/__init__.py

@@ -34,11 +34,12 @@
     'parse_qonnx',
     [
         'reshape_constant',
+        'resize_constant',
         'quant_constant_parameters',
         'quant_to_activation',
         'fuse_quant_with_constant',
-        'quant_to_alpha_activation_alpha',
         'const_quant_to_const_alpha',
+        'quant_to_alpha_activation_alpha',
         'batch_norm_onnx_constant_parameters',
         'constant_batch_norm_fusion',
         'merge_two_constants',
@@ -51,42 +52,39 @@
         'merge_to_apply_alpha_div',
         'matmul_const_to_dense',
         'conv_to_conv_x_d',
+        'conv_to_depthwise_conv_x_d',
     ],
 )
 
 register_flow(
     'convert',
     [
-        'fuse_consecutive_batch_normalization',
+        'channels_last_converter',
         'merge_linear_activation',
-        'fuse_batch_normalization',
-        'eliminate_linear_activation',
-        'qkeras_factorize_alpha',
-        'extract_ternary_threshold',
-        'replace_multidimensional_dense_with_conv',
         'seperable_to_depthwise_and_conv',
-        # The ones above here need to be before infer_precision_types
-        'infer_precision_types',
-        'channels_last_converter',
         'remove_transpose_before_flatten',
         'remove_nop_transpose',
         'remove_single_channel_transpose',
         'fuse_bias_add',
         'expand_layer_group',
         'output_rounding_saturation_mode',
+        'qkeras_factorize_alpha',
+        'extract_ternary_threshold',
         'fuse_consecutive_batch_normalization',
+        'fuse_batch_normalization',
+        'replace_multidimensional_dense_with_conv',
         'enforce_proxy_model_embedded_config',
+        'eliminate_linear_activation',
+        # many of the above optimzers need to be done before this
+        'infer_precision_types',
     ],
     requires=['parse_qonnx'],
 )  # TODO Maybe not all QKeras optmizers belong here?
 
 register_flow(
     'optimize',
     [
-        'eliminate_linear_activation',
         'remove_nop_batch_normalization',
-        'infer_precision_types',
-        'set_precision_concat',
     ],
     requires=['convert'],
 )

hls4ml/model/optimizer/passes/batchnorm_opt.py

@@ -98,7 +98,7 @@ def transform(self, model, node):
 
         const_prec = const_node.get_output_variable().type.precision
 
-        new_val = const_node.value * node.weights['scale'].data_unquantized + node.weights['bias'].data_unquantized
+        new_val = const_node.get_attr('value') * node.weights['scale'].data_unquantized + node.weights['bias'].data_unquantized
 
         const_node.set_attr('value', new_val)
         const_node.set_attr('quantizer', node.get_attr('quantizer'))  # None if not defined

hls4ml/model/optimizer/passes/linear.py

@@ -1,4 +1,4 @@
-from hls4ml.model.layers import Activation, BatchNormalization, Conv1D, Conv2D, Dense
+from hls4ml.model.layers import Activation, BatchNormalization, Concatenate, Conv1D, Conv2D, Dense, DepthwiseConv1D, DepthwiseConv2D, Input, Pooling1D, Pooling2D, Resize
 from hls4ml.model.optimizer import OptimizerPass
 from hls4ml.model.types import UnspecifiedPrecisionType
 
@@ -15,7 +15,7 @@ def transform(self, model, node):
         return True
 
 
-_safe_parents = (Dense, Conv1D, Conv2D, BatchNormalization, Activation)
+_safe_parents = (Input, Dense, Conv1D, Conv2D, DepthwiseConv1D, DepthwiseConv2D, BatchNormalization, Activation, Pooling1D, Pooling2D, Resize, Concatenate)
 
 
 class MergeLinearActivation(OptimizerPass):

hls4ml/model/optimizer/passes/quant_opt.py

@@ -187,7 +187,7 @@ def transform(self, model, node):
         integer = bitwidth
         scale = node.get_attr('scale')
         if _ALSO_MATCH_PO2 and not (scale == np.ones_like(scale)).all():
-            _, exp = np.frexp(np.squeeze(scale))
+            _, exp = np.frexp(np.unique(scale.ravel()).item())
             integer = bitwidth + exp - 1
 
         precision, quantizer = _calculate_precision_quantizer(bitwidth, integer, signed, narrow, rounding_mode)
@@ -332,7 +332,7 @@ def transform(self, model, node):
         const_node.types['result_t'].precision = precision
         const_node.get_output_variable().type.precision = precision
 
-        attributes_rescale = {}
+        attributes_rescale = {'quantizer': quantizer}
 
         rescale_config = copy.deepcopy(model.config.get_layer_config(node))
         rescale_name = f'{node.name}_rescale'