Update Tests to use right version of CCT

Author: federicobrancasi

.gitignore

@@ -25,7 +25,6 @@ dist/
 *.gz
 *-ubyte
 *.pt
-.c*
 *.onnx
 *.npz
 onnx/*

DeepQuant/Utils/FixCTT2Graph.py

@@ -0,0 +1,147 @@
+# Copyright 2025 ETH Zurich and University of Bologna.
+# Licensed under the Apache License, Version 2.0, see LICENSE for details.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Federico Brancasi <[email protected]>
+
+"""
+Script to fix the CCTTrueQuantized ONNX model by duplicating shared constants.
+This resolves the issue where a single Floor constant (onnx::Floor_772) is shared
+across multiple bias quantization operations.
+"""
+
+import argparse
+import os
+
+import numpy as np
+import onnx
+from onnx import helper
+
+
+def fix_shared_constants(model_path, output_path):
+    """Fix shared constants in ONNX model by creating unique copies."""
+    print(f"Loading ONNX model from: {model_path}")
+    model = onnx.load(model_path)
+
+    graph = model.graph
+
+    shared_floor_tensor = None
+    for initializer in graph.initializer:
+        if initializer.name == "onnx::Floor_772":
+            shared_floor_tensor = initializer
+            break
+
+    if shared_floor_tensor is None:
+        print("No shared Floor constant found. Model may already be fixed.")
+        return False
+
+    print(f"Found shared Floor constant: {shared_floor_tensor.name}")
+    print(f"Tensor shape: {shared_floor_tensor.dims}")
+
+    floor_nodes = []
+    for node in graph.node:
+        if node.op_type == "Floor":
+            for input_name in node.input:
+                if input_name == shared_floor_tensor.name:
+                    floor_nodes.append(node)
+                    break
+
+    print(f"Found {len(floor_nodes)} Floor nodes sharing the constant:")
+    for node in floor_nodes:
+        print(f"  - {node.name}")
+
+    new_initializers = []
+    for i, node in enumerate(floor_nodes):
+        unique_name = f"Floor_772_unique_{i}_{node.name.replace('/', '_')}"
+
+        new_tensor = helper.make_tensor(
+            name=unique_name,
+            data_type=shared_floor_tensor.data_type,
+            dims=shared_floor_tensor.dims,
+            vals=(
+                shared_floor_tensor.float_data
+                if shared_floor_tensor.float_data
+                else np.frombuffer(
+                    shared_floor_tensor.raw_data, dtype=np.float32
+                ).tolist()
+            ),
+        )
+
+        new_initializers.append(new_tensor)
+
+        for j, input_name in enumerate(node.input):
+            if input_name == shared_floor_tensor.name:
+                node.input[j] = unique_name
+                break
+
+        print(f"  Created unique constant: {unique_name} for node: {node.name}")
+
+    graph.initializer.remove(shared_floor_tensor)
+
+    for new_tensor in new_initializers:
+        graph.initializer.append(new_tensor)
+
+    inputs_to_remove = []
+    for input_tensor in graph.input:
+        if input_tensor.name == shared_floor_tensor.name:
+            inputs_to_remove.append(input_tensor)
+
+    for input_tensor in inputs_to_remove:
+        graph.input.remove(input_tensor)
+
+    try:
+        onnx.checker.check_model(model)
+        print("Model validation passed!")
+    except Exception as e:
+        print(f"Model validation failed: {e}")
+        return False
+
+    print(f"Saving fixed model to: {output_path}")
+    onnx.save(model, output_path)
+
+    return True
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Fix shared constants in CCTTrueQuantized ONNX model"
+    )
+    parser.add_argument(
+        "--input",
+        required=True,
+        help="Path to input ONNX model",
+    )
+    parser.add_argument(
+        "--output",
+        required=True,
+        help="Path to output fixed ONNX model",
+    )
+
+    args = parser.parse_args()
+
+    if not os.path.exists(args.input):
+        print(f"Error: Input file does not exist: {args.input}")
+        return 1
+
+    success = fix_shared_constants(args.input, args.output)
+
+    if success:
+        print("Successfully fixed the ONNX model!")
+        print(f"Original model: {args.input}")
+        print(f"Fixed model: {args.output}")
+
+        # FBRANCASI: Replace the original model with the fixed one
+        backup_path = args.input + ".backup"
+        print(f"Creating backup: {backup_path}")
+        os.rename(args.input, backup_path)
+        os.rename(args.output, args.input)
+        print("Replaced original model with fixed version")
+
+        return 0
+    else:
+        print("Failed to fix the ONNX model")
+        return 1
+
+
+if __name__ == "__main__":
+    exit(main())