[Transform] Introduce ComposedTransformation

.isort.cfg

@@ -9,3 +9,7 @@ sections=FUTURE,STDLIB,TEST,THIRDPARTY,FIRSTPARTY,LOCALFOLDER
 default_section=THIRDPARTY
 multi_line_output=3
 profile=black
+ignore_comments=true
+ignore_whitespace=true
+honor_noqa=true
+use_parentheses=true

src/qonnx/transformation/composed.py

@@ -0,0 +1,70 @@
+# Copies (deep-copies) python objects
+import copy
+
+# QONNX wrapper of ONNX model graphs
+from qonnx.core.modelwrapper import ModelWrapper
+
+# QONNX graph transformations for annotating the graph with datatype and shape
+# information
+from qonnx.transformation.infer_datatypes import InferDataTypes
+from qonnx.transformation.infer_shapes import InferShapes
+
+# Cleanup transformations removing identities like multiplication by one or
+# addition of zero
+from qonnx.transformation.remove import RemoveIdentityOps
+
+# Base class for all QONNX graph transformations and some basic cleanup
+# transformations
+# fmt: off
+from qonnx.transformation.general import (  # isort: skip
+    GiveReadableTensorNames, GiveUniqueNodeNames, Transformation
+)
+
+
+# fmt: on
+
+
+# Composes graph transformations such that each individual transformation as
+# well as the whole sequence is applied exhaustively
+class ComposedTransformation(Transformation):
+    # Initializes the transformation given a list of transformations
+    def __init__(self, transformations: list[Transformation]):
+        # Initialize the transformation base class
+        super().__init__()
+        # Register the list of transformations to be applied in apply()
+        self.transformations = transformations
+
+    # Applies the transform to a whole model graph
+    def apply(self, model: ModelWrapper):  # noqa
+        # Keep track of whether the graph has been modified
+        graph_modified = False
+        # Iterate all transformations to be applied
+        for transformation in self.transformations:
+            # Start each transformation on a deep copy of the model to mimic the
+            # behavior of ModelWrapper.transform()
+            model = copy.deepcopy(model)
+            # Exhaustively apply the transformation until it no longer modifies
+            # the graph
+            while True:
+                # Apply the transformation once, reporting back whether any node
+                # or pattern has been modified
+                model, _graph_modified = transformation.apply(model)
+                # Keep track whether the graph has been modified at least once
+                graph_modified = graph_modified or _graph_modified
+                # Break the loop if this transformation did not change anything
+                if not _graph_modified:
+                    break
+            # Apply the cleanup transformations of the ModelWrapper
+            model.cleanup()
+            # Apply some further cleanup transformations to the model graph
+            # removing some clutter and keeping all names readable and ordered
+            # at any time
+            model = model.transform(RemoveIdentityOps())
+            model = model.transform(GiveUniqueNodeNames())
+            model = model.transform(GiveReadableTensorNames())
+            model = model.transform(InferShapes())
+            model = model.transform(InferDataTypes())
+        # Return the transformed model and indicate whether the graph actually
+        # has been transformed by at least one transformation so the whole
+        # sequence of transformations will be reapplied
+        return model, graph_modified