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#!/usr/bin/env -S uv run
# /// script
# dependencies = [
# "onnx>=1.15.0",
# "numpy>=1.24.0",
# ]
# ///
"""
Generate ONNX model with Shape type in broadcasting context.
Tests:
- Broadcasting with Shape argument type
- Edge case #30: Shape type in broadcasting context
"""
import onnx
from onnx import helper, TensorProto
def create_shape_broadcasting_model():
"""Create model where Shape output is used in broadcasting operation."""
# Input
input_tensor = helper.make_tensor_value_info("input", TensorProto.FLOAT, [2, 3, 4])
# Output
output = helper.make_tensor_value_info("output", TensorProto.INT64, [3])
# Get shape, then use it in operations
nodes = [
# Extract shape as int64 tensor [2, 3, 4]
helper.make_node("Shape", ["input"], ["shape_tensor"], name="shape"),
# Use shape in Add operation (shape + shape, element-wise)
# This tests Shape type in broadcasting context
helper.make_node(
"Add", ["shape_tensor", "shape_tensor"], ["output"], name="add_shapes"
),
]
# Create the graph
graph = helper.make_graph(
nodes,
"shape_broadcasting_model",
[input_tensor],
[output],
)
# Create the model
model = helper.make_model(
graph, producer_name="onnx-ir-test", opset_imports=[helper.make_opsetid("", 16)]
)
# Check the model
onnx.checker.check_model(model)
return model
def main():
"""Generate and save the ONNX model."""
model = create_shape_broadcasting_model()
# Save the model
output_path = "../fixtures/shape_broadcasting.onnx"
onnx.save(model, output_path)
print(f"Model saved to {output_path}")
print(f"\nModel info:")
print(f" Shape operation produces shape tensor")
print(f" Shape tensor used in Add (broadcasting context)")
print(f" Tests ArgType::Shape in broadcasting operations")
if __name__ == "__main__":
main()