onnx-ir 0.19.1

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 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113

use crate::ir::{ArgType, Argument, Data, ElementType, Node, TensorData, TensorType};

/// Configuration for the Range operation.
#[derive(Debug, Clone)]
pub struct RangeConfig {
    pub start: RangeInput,
    pub limit: RangeInput,
    pub delta: RangeInput,
}

/// Represents either a static value or a runtime argument for range parameters.
#[derive(Debug, Clone)]
pub enum RangeInput {
    /// Static value known at compile time.
    Static(i64),
    /// Runtime argument determined during execution.
    Runtime(Argument),
}

/// Extract range configuration from the node.
pub fn range_config(node: &Node) -> RangeConfig {
    fn get_range_input(node: &Node, index: usize, param_name: &str) -> RangeInput {
        let input = node
            .inputs
            .get(index)
            .unwrap_or_else(|| panic!("Range: {} parameter is required", param_name));

        match &input.value {
            None => RangeInput::Runtime(input.clone()),
            Some(TensorData {
                data: Data::Int64s(values),
                ..
            }) if values.len() == 1 => RangeInput::Static(values[0]),
            Some(TensorData {
                data: Data::Int32s(values),
                ..
            }) if values.len() == 1 => RangeInput::Static(values[0] as i64),
            Some(v) => panic!(
                "Range {} must be a scalar int value, got {:?}",
                param_name, v
            ),
        }
    }

    let start = get_range_input(node, 0, "start");
    let limit = get_range_input(node, 1, "limit");
    let delta = get_range_input(node, 2, "delta");

    RangeConfig {
        start,
        limit,
        delta,
    }
}

/// Update output rank for Range (always rank 1).
pub fn range_update_outputs(node: &mut Node) {
    log::debug!("Range rank inference for node {}", node.name);

    if node.inputs.len() != 3 {
        panic!("Range: expected 3 inputs, found {}", node.inputs.len());
    }
    log::debug!(
        "Range operation always produces rank 1 tensor for {}",
        node.name
    );

    node.outputs[0].ty = ArgType::Tensor(TensorType {
        elem_type: ElementType::Int64,
        rank: 1,
        static_shape: None,
    });

    log::debug!("Range output rank for {}: 1", node.name);
}

#[cfg(test)]
mod tests {
    use super::*;
    use crate::ir::NodeType;
    use crate::node::test_utils::NodeBuilder;

    fn create_test_node() -> Node {
        NodeBuilder::new(NodeType::Range, "test_range")
            .input_scalar_i64("start")
            .input_scalar_i64("limit")
            .input_scalar_i64("delta")
            .output_tensor_i64("output", 0, None) // Rank 0 will be updated
            .build()
    }

    #[test]
    fn test_range_output() {
        let mut node = create_test_node();
        range_update_outputs(&mut node);

        match &node.outputs[0].ty {
            ArgType::Tensor(tensor) => {
                assert_eq!(tensor.elem_type, ElementType::Int64);
                assert_eq!(tensor.rank, 1);
            }
            _ => panic!("Expected tensor output"),
        }
    }

    #[test]
    #[should_panic(expected = "Range: expected 3 inputs, found 2")]
    fn test_range_missing_inputs() {
        let mut node = create_test_node();
        node.inputs.pop();
        range_update_outputs(&mut node);
    }
}