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#!/usr/bin/env -S uv run
# /// script
# dependencies = [
# "onnx>=1.15.0",
# "numpy>=1.24.0",
# ]
# ///
"""
Generate ONNX model where ONE operation has multiple input slots referencing THE SAME constant.
Tests:
- Constant lifting when same constant is referenced multiple times in one operation
- Reference counting for single operation with repeated constant inputs
- Edge case: Multiple inputs of one operation → same constant
"""
import onnx
from onnx import helper, TensorProto
import numpy as np
def create_same_constant_multiple_inputs_model():
"""Create model where one operation uses the same constant multiple times."""
# Input
input_tensor = helper.make_tensor_value_info("input", TensorProto.FLOAT, [2, 3])
# Output
output = helper.make_tensor_value_info("output", TensorProto.FLOAT, [2, 3])
# A single constant that will be used multiple times BY THE SAME OPERATION
shared_const = helper.make_tensor(
name="shared_constant",
data_type=TensorProto.FLOAT,
dims=[2, 3],
vals=np.ones((2, 3), dtype=np.float32).flatten().tobytes(),
raw=True,
)
# Boolean condition for Where operation
bool_condition = helper.make_tensor(
name="condition",
data_type=TensorProto.BOOL,
dims=[2, 3],
vals=np.ones((2, 3), dtype=bool).flatten().tolist(),
)
# Operations where the SAME constant appears in multiple input slots
nodes = [
# Where: Uses the same constant for BOTH true_value and false_value
# This is the key test: ONE operation with multiple inputs pointing to SAME constant
helper.make_node(
"Where",
[
"condition",
"shared_constant",
"shared_constant",
], # condition, true_val, false_val
["output"],
name="where_same_const",
),
]
# Create the graph
graph = helper.make_graph(
nodes,
"same_constant_multiple_inputs_model",
[input_tensor],
[output],
initializer=[shared_const, bool_condition],
)
# 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_same_constant_multiple_inputs_model()
# Save the model
output_path = "../fixtures/same_constant_multiple_inputs.onnx"
onnx.save(model, output_path)
print(f"Model saved to {output_path}")
print(f"\nModel info:")
print(
f" Where operation uses 'shared_constant' for BOTH true_value and false_value"
)
print(f" Tests: ONE operation, MULTIPLE input slots, SAME constant")
print(f" Critical edge case for constant lifting and reference counting")
if __name__ == "__main__":
main()