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 a constant referenced multiple times.

Tests:
- Reference counting for constants used multiple times
- Edge case #20: Constant referenced multiple times
"""

import onnx
from onnx import helper, TensorProto
import numpy as np


def create_constant_multiple_refs_model():
    """Create model where one constant is used by multiple operations."""

    # Input
    input_tensor = helper.make_tensor_value_info("input", TensorProto.FLOAT, [2, 3])

    # Outputs
    output1 = helper.make_tensor_value_info("output1", TensorProto.FLOAT, [2, 3])
    output2 = helper.make_tensor_value_info("output2", TensorProto.FLOAT, [2, 3])
    output3 = helper.make_tensor_value_info("output3", TensorProto.FLOAT, [2, 3])

    # A single constant used multiple times
    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,
    )

    # Multiple operations using the same constant
    nodes = [
        # All three operations use the same constant
        helper.make_node("Add", ["input", "shared_constant"], ["output1"], name="add1"),
        helper.make_node("Mul", ["input", "shared_constant"], ["output2"], name="mul1"),
        helper.make_node("Sub", ["shared_constant", "input"], ["output3"], name="sub1"),
    ]

    # Create the graph
    graph = helper.make_graph(
        nodes,
        "constant_multiple_refs_model",
        [input_tensor],
        [output1, output2, output3],
        initializer=[shared_const],
    )

    # 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_constant_multiple_refs_model()

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

    print(f"\nModel info:")
    print(f"  One constant used by 3 different operations")
    print(f"  Tests reference counting with multiple consumers")


if __name__ == "__main__":
    main()