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#!/usr/bin/env -S uv run
# /// script
# dependencies = [
# "onnx>=1.15.0",
# "numpy>=1.24.0",
# ]
# ///
"""
Generate ONNX model with passthrough (input directly to output via Identity).
Tests:
- Graph where input flows directly to output
- Identity elimination edge case
- Minimal graph structure
"""
import onnx
from onnx import helper, TensorProto
def create_passthrough_model():
"""Create model where input passes through to output."""
# Input and output with same shape
input_tensor = helper.make_tensor_value_info("input", TensorProto.FLOAT, [1, 4])
output_tensor = helper.make_tensor_value_info("output", TensorProto.FLOAT, [1, 4])
# Single Identity node
nodes = [
helper.make_node("Identity", ["input"], ["output"], name="passthrough"),
]
# Create the graph
graph = helper.make_graph(
nodes,
"passthrough_model",
[input_tensor],
[output_tensor],
)
# 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_passthrough_model()
# Save the model
output_path = "../fixtures/passthrough.onnx"
onnx.save(model, output_path)
print(f"Model saved to {output_path}")
print(f"\nModel info:")
print(f" Single Identity node connecting input to output")
print(f" Should be optimized away in Phase 4")
if __name__ == "__main__":
main()