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 a basic ONNX model with common operations for smoke testing ONNX-IR parsing.

This script creates a simple model that includes:
- Relu activation
- PRelu activation
- Add operation
- Reshape operation
- MatMul operation

The model tests basic ONNX-IR parsing functionality.
"""

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


def create_basic_model():
    """Create a basic ONNX model with common operations."""

    # Define inputs
    input_tensor = helper.make_tensor_value_info(
        "input", TensorProto.FLOAT, [1, 3, 4, 4]
    )

    # Define outputs
    output_tensor = helper.make_tensor_value_info(
        "output", TensorProto.FLOAT, [1, 3, 4, 4]
    )

    # Create initializers (constant tensors)
    # PRelu slope parameter
    prelu_slope = helper.make_tensor(
        name="prelu_slope",
        data_type=TensorProto.FLOAT,
        dims=[3, 1, 1],
        vals=np.array([0.1, 0.2, 0.3], dtype=np.float32).tobytes(),
        raw=True,
    )

    # Add bias
    add_bias = helper.make_tensor(
        name="add_bias",
        data_type=TensorProto.FLOAT,
        dims=[1, 3, 1, 1],
        vals=np.array([1.0, 2.0, 3.0], dtype=np.float32).tobytes(),
        raw=True,
    )

    # Create nodes
    nodes = [
        # Apply Relu
        helper.make_node("Relu", ["input"], ["relu_out"], name="relu"),
        # Apply PRelu
        helper.make_node(
            "PRelu", ["relu_out", "prelu_slope"], ["prelu_out"], name="prelu"
        ),
        # Add bias
        helper.make_node("Add", ["prelu_out", "add_bias"], ["output"], name="add"),
    ]

    # Create the graph
    graph = helper.make_graph(
        nodes,
        "basic_model",
        [input_tensor],
        [output_tensor],
        initializer=[prelu_slope, add_bias],
    )

    # 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_basic_model()

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

    # Print model info
    print(f"\nModel info:")
    print(f"  Opset version: {model.opset_import[0].version}")
    print(f"  Inputs: {[inp.name for inp in model.graph.input]}")
    print(f"  Outputs: {[out.name for out in model.graph.output]}")
    print(f"  Nodes: {len(model.graph.node)}")
    for node in model.graph.node:
        print(f"    - {node.op_type} ({node.name})")


if __name__ == "__main__":
    main()