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
//! # MaxPool (3D)
//!
//! 3D max pooling operation.
//!
//! **ONNX Spec**: <https://onnx.ai/onnx/operators/onnx__MaxPool.html>
//!
//! ## Opset Versions
//! - **Opset 1**: Initial MaxPool operator
//! - **Opset 8**: Added storage_order attribute
//! - **Opset 10**: Added ceil_mode attribute
//! - **Opset 11**: Added dilations attribute support
//! - **Opset 12**: Added int8/uint8 dtype support
use derive_new::new;
use onnx_ir_derive::NodeBuilder;

use crate::ir::Argument;

use crate::ir::{Node, RawNode};
use crate::node::padding::{AutoPad, PaddingConfig3d, padding_config_3d};
use crate::processor::{
    InputSpec, NodeProcessor, NodeSpec, OutputPreferences, OutputSpec, ProcessError,
};

/// Configuration for MaxPool3d operations
#[derive(Debug, Clone, new)]
pub struct MaxPool3dConfig {
    /// Kernel size [depth, height, width]
    pub kernel_size: [usize; 3],
    /// Stride [depth, height, width]
    pub strides: [usize; 3],
    /// Padding configuration
    pub padding: PaddingConfig3d,
    /// Dilation [depth, height, width]
    pub dilation: [usize; 3],
    /// Whether to use ceil mode for output size calculation (opset 10+)
    pub ceil_mode: bool,
    /// Auto padding mode
    pub auto_pad: AutoPad,
}

/// Node representation for MaxPool3d operation
#[derive(Debug, Clone, NodeBuilder)]
pub struct MaxPool3dNode {
    pub name: String,
    pub inputs: Vec<Argument>,
    pub outputs: Vec<Argument>,
    pub config: MaxPool3dConfig,
}

pub(crate) struct MaxPool3dProcessor;

impl NodeProcessor for MaxPool3dProcessor {
    type Config = MaxPool3dConfig;

    fn spec(&self) -> NodeSpec {
        NodeSpec {
            min_opset: 1,
            max_opset: None,
            inputs: InputSpec::AtLeast(1),
            outputs: OutputSpec::Range(1, 2),
        }
    }

    fn infer_types(
        &self,
        node: &mut RawNode,
        opset: usize,
        _output_preferences: &OutputPreferences,
    ) -> Result<(), ProcessError> {
        for (key, value) in node.attrs.iter() {
            match key.as_str() {
                "kernel_shape" | "strides" | "pads" => {}
                "storage_order" => {}
                "dilations" => {
                    let dilations = value.clone().into_i64s();
                    if dilations.iter().any(|&d| d != 1) && opset < 11 {
                        return Err(ProcessError::Custom(format!(
                            "MaxPool: dilation requires opset 11+, got opset {}",
                            opset
                        )));
                    }
                }
                "auto_pad" => {
                    AutoPad::parse(&value.clone().into_string())?;
                }
                "ceil_mode" => {
                    let ceil_mode = value.clone().into_i64();
                    if ceil_mode != 0 && opset < 10 {
                        return Err(ProcessError::Custom(format!(
                            "MaxPool: ceil_mode requires opset 10+, got opset {}",
                            opset
                        )));
                    }
                }
                _ => {
                    return Err(ProcessError::InvalidAttribute {
                        name: key.clone(),
                        reason: format!("Unexpected attribute for MaxPool3d: {key}"),
                    });
                }
            }
        }

        let tensor = match &node.inputs[0].ty {
            crate::ir::ArgType::Tensor(tensor) => tensor.clone(),
            _ => {
                return Err(ProcessError::TypeMismatch {
                    expected: "Tensor".to_string(),
                    actual: format!("{:?}", node.inputs[0].ty),
                });
            }
        };

        if tensor.rank != 5 {
            return Err(ProcessError::Custom(format!(
                "MaxPool3d expects input tensor of rank 5 (N x C x D x H x W), got rank {}",
                tensor.rank
            )));
        }

        crate::processor::same_as_input(node);

        Ok(())
    }

    fn extract_config(&self, node: &RawNode, _opset: usize) -> Result<Self::Config, ProcessError> {
        let mut kernel_shape = Vec::new();
        let mut strides = vec![1, 1, 1];
        let mut pads = vec![0, 0, 0, 0, 0, 0];
        let mut dilations = vec![1, 1, 1];
        let mut ceil_mode: i64 = 0;
        let mut auto_pad = AutoPad::NotSet;

        for (key, value) in node.attrs.iter() {
            match key.as_str() {
                "kernel_shape" => kernel_shape = value.clone().into_i64s(),
                "strides" => strides = value.clone().into_i64s(),
                "pads" => pads = value.clone().into_i64s(),
                "dilations" => dilations = value.clone().into_i64s(),
                "ceil_mode" => ceil_mode = value.clone().into_i64(),
                "auto_pad" => auto_pad = AutoPad::parse(&value.clone().into_string())?,
                "storage_order" => {}
                _ => {}
            }
        }

        let padding = padding_config_3d(&pads);

        let config = MaxPool3dConfig::new(
            [
                kernel_shape[0] as usize,
                kernel_shape[1] as usize,
                kernel_shape[2] as usize,
            ],
            [
                strides[0] as usize,
                strides[1] as usize,
                strides[2] as usize,
            ],
            padding,
            [
                dilations[0] as usize,
                dilations[1] as usize,
                dilations[2] as usize,
            ],
            ceil_mode == 1,
            auto_pad,
        );

        Ok(config)
    }

    fn build_node(&self, builder: RawNode, opset: usize) -> Node {
        let config = self
            .extract_config(&builder, opset)
            .expect("Config extraction failed");

        Node::MaxPool3d(MaxPool3dNode {
            name: builder.name,
            inputs: builder.inputs,
            outputs: builder.outputs,
            config,
        })
    }
}

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

    fn create_test_node(
        kernel_shape: Vec<i64>,
        strides: Vec<i64>,
        pads: Vec<i64>,
        dilations: Vec<i64>,
        ceil_mode: i64,
        auto_pad: Option<&str>,
    ) -> RawNode {
        let mut builder = TestNodeBuilder::new(NodeType::MaxPool3d, "test_maxpool3d")
            .input_tensor_f32("data", 5, None)
            .output_tensor_f32("output", 5, None)
            .attr_ints("kernel_shape", kernel_shape)
            .attr_ints("strides", strides)
            .attr_ints("pads", pads)
            .attr_int("ceil_mode", ceil_mode)
            .attr_ints("dilations", dilations);
        if let Some(auto_pad) = auto_pad {
            builder = builder.attr_string("auto_pad", auto_pad);
        }
        builder.build()
    }

    #[test]
    fn test_max_pool3d_config_basic() {
        let mut node = create_test_node(
            vec![3, 3, 3],
            vec![1, 1, 1],
            vec![0, 0, 0, 0, 0, 0],
            vec![1, 1, 1],
            0,
            None,
        );
        let processor = MaxPool3dProcessor;
        let prefs = OutputPreferences::new();
        let config = processor.extract_config(&node, 16).unwrap();
        processor.infer_types(&mut node, 16, &prefs).unwrap();

        assert_eq!(config.kernel_size, [3, 3, 3]);
        assert_eq!(config.strides, [1, 1, 1]);
        assert_eq!(config.dilation, [1, 1, 1]);
        assert!(!config.ceil_mode);
        assert!(matches!(config.padding, PaddingConfig3d::Valid));
    }

    #[test]
    fn test_max_pool3d_config_with_padding() {
        let mut node = create_test_node(
            vec![2, 2, 2],
            vec![2, 2, 2],
            vec![1, 1, 1, 1, 1, 1],
            vec![1, 1, 1],
            0,
            None,
        );
        let processor = MaxPool3dProcessor;
        let prefs = OutputPreferences::new();
        let config = processor.extract_config(&node, 16).unwrap();
        processor.infer_types(&mut node, 16, &prefs).unwrap();

        assert!(matches!(
            config.padding,
            PaddingConfig3d::Explicit(1, 1, 1, 1, 1, 1)
        ));
    }

    #[test]
    fn test_max_pool3d_dilation_opset_validation() {
        let mut node = create_test_node(
            vec![3, 3, 3],
            vec![1, 1, 1],
            vec![0, 0, 0, 0, 0, 0],
            vec![2, 2, 2],
            0,
            None,
        );
        let processor = MaxPool3dProcessor;
        let prefs = OutputPreferences::new();
        let result = processor.infer_types(&mut node, 10, &prefs);
        assert!(matches!(result, Err(ProcessError::Custom(_))));
    }

    #[test]
    fn test_max_pool3d_ceil_mode_opset_validation() {
        let mut node = create_test_node(
            vec![3, 3, 3],
            vec![1, 1, 1],
            vec![0, 0, 0, 0, 0, 0],
            vec![1, 1, 1],
            1,
            None,
        );
        let processor = MaxPool3dProcessor;
        let prefs = OutputPreferences::new();
        let result = processor.infer_types(&mut node, 9, &prefs);
        assert!(matches!(result, Err(ProcessError::Custom(_))));
    }

    #[test]
    fn test_max_pool3d_rejects_non_5d_input() {
        let mut node = TestNodeBuilder::new(NodeType::MaxPool3d, "test_maxpool3d_bad")
            .input_tensor_f32("data", 4, None)
            .output_tensor_f32("output", 4, None)
            .attr_ints("kernel_shape", vec![3, 3, 3])
            .attr_ints("strides", vec![1, 1, 1])
            .attr_ints("pads", vec![0, 0, 0, 0, 0, 0])
            .attr_ints("dilations", vec![1, 1, 1])
            .attr_int("ceil_mode", 0)
            .build();
        let processor = MaxPool3dProcessor;
        let prefs = OutputPreferences::new();
        let result = processor.infer_types(&mut node, 16, &prefs);
        assert!(matches!(result, Err(ProcessError::Custom(_))));
    }
}