Add deduplication pass for initializer tensors (#66)

Author: AbhishekHerbertSamuel

src/onnx_ir/passes/common/deduplicate_initializers.py

@@ -0,0 +1,49 @@
+from onnx_ir import ir
+from onnx_ir.passes.base import GraphTransformPass
+
+
+class DeduplicateInitializersPass(GraphTransformPass):
+    """
+    This pass removes duplicate initializer tensors from the graph.
+
+    It identifies duplicates based on a content-based fingerprint consisting of:
+    - Tensor byte content (`tobytes()`)
+    - Data type (`dtype`)
+    - Shape
+
+    All duplicates are replaced with the first (canonical) occurrence, and node
+    inputs referring to redundant initializers are updated accordingly.
+    """
+
+    def apply(self, graph: ir.Graph) -> ir.Graph:
+        seen = {}      # Maps (tobytes, dtype, shape) -> canonical initializer name
+        name_map = {}  # Maps duplicate initializer name -> canonical name
+
+        # Iterate over all initializers in the graph
+        for initializer in list(graph.initializers.values()):
+            key = (
+                initializer.const_value.tobytes(),              # Content fingerprint
+                initializer.const_value.dtype,                  # Data type
+                tuple(initializer.const_value.shape),           # Shape tuple
+            )
+
+            if key in seen:
+                # Found a duplicate: store the name mapping and remove it from graph
+                canonical_name = seen[key]
+                name_map[initializer.name] = canonical_name
+                graph.initializers.pop(initializer.name)
+            else:
+                # First time seeing this tensor → keep it
+                seen[key] = initializer.name
+
+        # Update node inputs to use the canonical initializer names
+        for node in graph:
+            for i, input_value in enumerate(node.inputs):
+                if input_value is not None and input_value.name in name_map:
+                    # Replace input with the deduplicated initializer
+                    new_name = name_map[input_value.name]
+                    replacement = graph.initializers[new_name]
+                    node.replace_input_with(i, replacement)
+
+        return graph
+