add model generation with onnxscript; add model and test data

Author: fengyuentau

testdata/dnn/onnx/data/input_gather_shared_indices.npy

@@ -0,0 +1,50 @@
+import os
+import numpy as np
+import onnx
+import onnxscript as ost
+from onnxscript import opset19 as op # opset19 is the lastest by 202309
+
+def make_model_and_data(model, *args, **kwargs):
+    name = model._name
+
+    # TODO: support multiple outputs
+    output = model(*args, **kwargs) # eager mode
+
+    # Save model
+    model_proto = model.to_model_proto()
+    try:
+        onnx.checker.check_model(model_proto)
+    except onnx.checker.ValidationError as e:
+        print(f"Model {name} is invalid: {e}. Skipping ...")
+        return False
+    else:
+        save_path = "./models/{}.onnx".format(name)
+        print(f"Model {name} is valid! Saved to {save_path}")
+        model_proto_ = onnx.shape_inference.infer_shapes(model_proto)
+        onnx.save(model_proto_, save_path)
+
+    # Save inputs and output
+    if len(args) == 1:
+        input_file = os.path.join("data", "input_" + name)
+        np.save(input_file, args[0])
+    else:
+        for idx, input in enumerate(args, start=0):
+            input_files = os.path.join("data", "input_" + name + "_" + str(index))
+            np.save(input_files, input)
+    output_files = os.path.join("data", "output_" + name)
+    np.save(output_files, output)
+
+'''
+    It builds a model with two Gather ops sharing a single same indices:
+
+    [Input] -> Gather(indices=0) -> Gather(indices=0) -> [Output]
+
+    , where the two indices constants have the same name.
+'''
+@ost.script()
+def gather_shared_indices(x: ost.FLOAT[2, 1, 3, 4]) -> ost.FLOAT[3, 4]:
+    indices = op.Constant(value=onnx.helper.make_tensor("", onnx.TensorProto.INT64, [], np.array([0], dtype=np.int64)))
+    y0 = op.Gather(x, indices, axis=0)
+    y1 = op.Gather(y0, indices, axis=0)
+    return y1
+make_model_and_data(gather_shared_indices, np.random.rand(2, 1, 3, 4).astype(np.float32))