Add a script for randomizing onnx weights (#10551)

* Add a script for randomizing onnx weights

Required by customer that when sharing an onnx model for 3rd party debugging, a tool is needed to randomize all the weights in the model.

* Update onnx_randomizer.py

more comments

onnxruntime/python/tools/onnx_randomizer.py

@@ -0,0 +1,90 @@
+#-------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation.  All rights reserved.
+# Licensed under the MIT License.
+#--------------------------------------------------------------------------
+
+# An offline standalone script to declassify an ONNX model by randomizing the tensor data in initializers.
+# The ORT Performance may change especially on generative models.
+
+import argparse
+import numpy as np
+from onnx import onnx_pb, numpy_helper, save_model, load_model
+from pathlib import Path
+
+# An experimental small value for differentiating shape data and weights.
+# The tensor data with larger size can't be shape data.
+# User may adjust this value as needed.
+SIZE_THRESHOLD = 10
+
+def graph_iterator(model, func):
+    graph_queue = [model.graph]
+    while graph_queue:
+        graph = graph_queue.pop(0)
+        func(graph)
+        for node in graph.node:
+            for attr in node.attribute:
+                if attr.type == onnx_pb.AttributeProto.AttributeType.GRAPH:
+                    assert (isinstance(attr.g, onnx_pb.GraphProto))
+                    graph_queue.append(attr.g)
+                if attr.type == onnx_pb.AttributeProto.AttributeType.GRAPHS:
+                    for g in attr.graphs:
+                        assert (isinstance(g, onnx_pb.GraphProto))
+                        graph_queue.append(g)
+
+
+def randomize_graph_initializer(graph):
+    for i_tensor in graph.initializer:
+        array = numpy_helper.to_array(i_tensor)
+        # TODO: need to find a better way to differentiate shape data and weights.
+        if array.size > SIZE_THRESHOLD:
+            random_array = np.random.uniform(array.min(),
+                                             array.max(),
+                                             size=array.shape).astype(
+                                                 array.dtype)
+            o_tensor = numpy_helper.from_array(random_array, i_tensor.name)
+            i_tensor.CopyFrom(o_tensor)
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description='Randomize the weights of an ONNX model')
+    parser.add_argument('-m',
+                        type=str,
+                        required=True,
+                        help='input onnx model path')
+    parser.add_argument('-o',
+                        type=str,
+                        required=True,
+                        help='output onnx model path')
+    parser.add_argument("--use_external_data_format",
+                        required=False,
+                        action="store_true",
+                        help="Store or Save in external data format")
+    parser.add_argument("--all_tensors_to_one_file",
+                        required=False,
+                        action="store_true",
+                        help="Save all tensors to one file")
+    args = parser.parse_args()
+
+    data_path = None
+    if args.use_external_data_format:
+        if Path(args.m).parent == Path(args.o).parent:
+            raise RuntimeError(
+                "Please specify output directory with different parent path to input directory."
+            )
+        if args.all_tensors_to_one_file:
+            data_path = Path(args.o).name + ".data"
+
+    Path(args.o).parent.mkdir(parents=True, exist_ok=True)
+    onnx_model = load_model(args.m,
+                            load_external_data=args.use_external_data_format)
+    graph_iterator(onnx_model, randomize_graph_initializer)
+    save_model(onnx_model,
+               args.o,
+               save_as_external_data=args.use_external_data_format,
+               all_tensors_to_one_file=args.all_tensors_to_one_file,
+               location=data_path)
+
+
+if __name__ == '__main__':
+    main()