onnx-ir 0.21.0

ONNX-IR is a pure Rust library for parsing ONNX models into an intermediate representation that can be used to generate code for various ML/DL frameworks
Documentation 
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#!/usr/bin/env -S uv run
# /// script
# dependencies = [
#   "onnx>=1.15.0",
#   "numpy>=1.24.0",
# ]
# ///

"""
Generate ONNX model with circular type preferences.

Tests:
- Type inference convergence with circular dependencies
- Edge case #14: Circular preferences convergence
"""

import onnx
from onnx import helper, TensorProto


def create_circular_preferences_model():
    """Create model where type preferences form a cycle."""

    # Inputs with partially specified types (dtype known, shapes dynamic)
    input1 = helper.make_tensor_value_info("input1", TensorProto.FLOAT, ["N", 4])
    input2 = helper.make_tensor_value_info("input2", TensorProto.FLOAT, [2, "M"])

    # Output
    output = helper.make_tensor_value_info("output", TensorProto.FLOAT, [2, 4])

    # Create a pattern where preferences might cycle:
    # A feeds B, B feeds C, C feeds A (via Add)
    nodes = [
        # First branch
        helper.make_node("Relu", ["input1"], ["branch1"], name="relu1"),
        # Second branch
        helper.make_node("Abs", ["input2"], ["branch2"], name="abs1"),
        # Combine with broadcasting (creates interdependencies)
        helper.make_node("Add", ["branch1", "branch2"], ["temp1"], name="add1"),
        # Split again
        helper.make_node("Relu", ["temp1"], ["temp2"], name="relu2"),
        helper.make_node("Abs", ["temp1"], ["temp3"], name="abs2"),
        # Merge back
        helper.make_node("Mul", ["temp2", "temp3"], ["output"], name="mul"),
    ]

    # Create the graph
    graph = helper.make_graph(
        nodes,
        "circular_preferences_model",
        [input1, input2],
        [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_circular_preferences_model()

    # Save the model
    output_path = "../fixtures/circular_preferences.onnx"
    onnx.save(model, output_path)
    print(f"Model saved to {output_path}")

    print(f"\nModel info:")
    print(f"  Complex graph with potential circular preferences")
    print(f"  Tests type inference convergence")


if __name__ == "__main__":
    main()