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#!/usr/bin/env -S uv run
# /// script
# dependencies = [
# "onnx>=1.15.0",
# "numpy>=1.24.0",
# ]
# ///
"""
Generate ONNX model with diamond pattern (split then merge).
Tests:
- Type inference with split and merge paths
- Convergence when multiple paths reconverge
- Edge case #9: Diamond pattern (split then merge)
"""
import onnx
from onnx import helper, TensorProto
def create_diamond_pattern_model():
"""Create model where computation splits and then merges back."""
# Input
input_tensor = helper.make_tensor_value_info("input", TensorProto.FLOAT, [1, 4])
# Output
output = helper.make_tensor_value_info("output", TensorProto.FLOAT, [1, 4])
# Diamond pattern:
# input
# / \
# Relu Abs
# \ /
# Add -> output
nodes = [
# Split: input feeds two different operations
helper.make_node("Relu", ["input"], ["path1"], name="relu_path"),
helper.make_node("Abs", ["input"], ["path2"], name="abs_path"),
# Merge: both paths combine
helper.make_node("Add", ["path1", "path2"], ["output"], name="merge"),
]
# Create the graph
graph = helper.make_graph(
nodes,
"diamond_pattern_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_diamond_pattern_model()
# Save the model
output_path = "../fixtures/diamond_pattern.onnx"
onnx.save(model, output_path)
print(f"Model saved to {output_path}")
print(f"\nModel info:")
print(f" Diamond pattern: input → (Relu, Abs) → Add → output")
print(f" Tests split and merge convergence")
if __name__ == "__main__":
main()