Merge pull request #1142 from fastmachinelearning/qonnx_warnings

Qonnx warnings
fastmachinelearning · Dec 5, 2024 · c8e1857 · c8e1857
2 parents ce7f1f1 + 915d2e1
commit c8e1857
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Showing 3 changed files with 33 additions and 3 deletions.
diff --git a/hls4ml/model/optimizer/passes/batchnorm_opt.py b/hls4ml/model/optimizer/passes/batchnorm_opt.py
@@ -166,7 +166,7 @@ class FuseConsecutiveBatchNormalization(OptimizerPass):
     """
 
     def match(self, node):
-        prev_node = node.get_input_node(node.inputs[0])
+        prev_node = node.get_input_node()
         basic_match = (
             isinstance(node, BatchNormalization)
             and isinstance(prev_node, BatchNormalization)
@@ -194,7 +194,7 @@ def match(self, node):
             return False
 
     def transform(self, model, node):
-        prev_node = node.get_input_node(node.inputs[0])
+        prev_node = node.get_input_node()
 
         prev_map = prev_node.get_output_use_map()
         if len(prev_map[prev_node.outputs[0]]) > 1:

diff --git a/hls4ml/model/optimizer/passes/bn_fuse.py b/hls4ml/model/optimizer/passes/bn_fuse.py
@@ -18,7 +18,7 @@ class FuseBatchNormalization(OptimizerPass):
     """
 
     def match(self, node):
-        prev_node = node.get_input_node(node.inputs[0])
+        prev_node = node.get_input_node()
         basic_match = (
             isinstance(node, BatchNormalization)
             and isinstance(prev_node, (Dense, Conv1D, Conv2D))

diff --git a/hls4ml/model/optimizer/passes/move_scales.py b/hls4ml/model/optimizer/passes/move_scales.py
@@ -5,6 +5,8 @@
 
 '''
 
+import warnings
+
 import numpy as np
 
 from hls4ml.model.layers import ApplyAlpha, Constant, Conv, MatMul, Merge
@@ -85,6 +87,9 @@ def transform(self, model, node):
                 can_propagate = False
 
         if not can_propagate:
+            warnings.warn(
+                'Failed to propagate quantization scales down MatMul node; model probably not suppored.', stacklevel=1
+            )
             return False
 
         model.remove_node(apply_alpha)
@@ -124,6 +129,9 @@ def transform(self, model, node):
         try:
             bias = bias0 + bias1
         except ValueError:
+            warnings.warn(
+                'Failed to propagate quantization scales down Add node; model probably not suppored.', stacklevel=1
+            )
             return False
 
         model.remove_node(in0)
@@ -169,6 +177,7 @@ def transform(self, model, node):
             model.insert_node(new_node)
             return True
         else:
+            warnings.warn('Failed to propagate quantization bias down Add node; model probably not suppored.', stacklevel=1)
             return False
 
 
@@ -243,6 +252,9 @@ def transform(self, model, node):
                     except ValueError:
                         can_propagate = False
             if not can_propagate:
+                warnings.warn(
+                    'Failed to propagate quantization scales down Conv node; model probably not suppored.', stacklevel=1
+                )
                 return False
 
             # to remove warning, since these get set again
@@ -287,6 +299,9 @@ def transform(self, model, node):
                     except ValueError:
                         can_propagate = False
             if not can_propagate:
+                warnings.warn(
+                    'Failed to propagate quantization scales down Conv node; model probably not suppored.', stacklevel=1
+                )
                 return False
 
             # to remove warning, since these get set again
@@ -308,6 +323,9 @@ def transform(self, model, node):
                     can_propagate = False
 
             if not can_propagate:
+                warnings.warn(
+                    'Failed to propagate quantization scales down Conv node; model probably not suppored.', stacklevel=1
+                )
                 return False
 
             # to remove warning, since these get set again
@@ -367,6 +385,9 @@ def transform(self, model, node):
                     except ValueError:
                         can_propagate = False
             if not can_propagate:
+                warnings.warn(
+                    'Failed to propagate quantization scales down Conv node; model probably not suppored.', stacklevel=1
+                )
                 return False
 
             # to remove warning, since these get set again
@@ -388,6 +409,9 @@ def transform(self, model, node):
                 except ValueError:
                     can_propagate = False
             if not can_propagate:
+                warnings.warn(
+                    'Failed to propagate quantization scales down Conv node; model probably not suppored.', stacklevel=1
+                )
                 return False
 
             # to remove warning, since these get set again
@@ -412,6 +436,9 @@ def transform(self, model, node):
                     except ValueError:
                         can_propagate = False
             if not can_propagate:
+                warnings.warn(
+                    'Failed to propagate quantization scales down Conv node; model probably not suppored.', stacklevel=1
+                )
                 return False
 
             # to remove warning, since these get set again
@@ -445,6 +472,9 @@ def transform(self, model, node):
                     except ValueError:
                         can_propagate = False
             if not can_propagate:
+                warnings.warn(
+                    'Failed to propagate quantization scales down Conv node; model probably not suppored.', stacklevel=1
+                )
                 return False
 
             # to remove warning, since these get set again