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use crate::PaddingConfig1d;
use ruda_model::config::Config;
use ruda_model::module::Module;
use ruda_model::module::{Content, DisplaySettings, ModuleDisplay};
use ruda_model::tensor::Tensor;
use ruda_model::tensor::backend::Backend;
use ruda_model::tensor::ops::PadMode;
use ruda_model::tensor::module::max_pool1d;
/// Configuration to create a [1D max pooling](MaxPool1d) layer using the [init function](MaxPool1dConfig::init).
#[derive(Config, Debug)]
pub struct MaxPool1dConfig {
/// The size of the kernel.
pub kernel_size: usize,
/// The stride.
#[config(default = "kernel_size")]
pub stride: usize,
/// The padding configuration.
///
/// Supports symmetric and asymmetric padding. `Same` padding with even kernel sizes
/// will automatically use asymmetric padding to preserve input dimensions.
#[config(default = "PaddingConfig1d::Valid")]
pub padding: PaddingConfig1d,
/// The dilation.
#[config(default = "1")]
pub dilation: usize,
/// If true, use ceiling instead of floor for output size calculation.
#[config(default = "false")]
pub ceil_mode: bool,
}
/// Applies a 1D max pooling over input tensors.
///
/// Should be created with [MaxPool1dConfig](MaxPool1dConfig).
#[derive(Module, Clone, Debug)]
#[module(custom_display)]
pub struct MaxPool1d {
/// The stride.
pub stride: usize,
/// The size of the kernel.
pub kernel_size: usize,
/// The padding configuration.
pub padding: PaddingConfig1d,
/// The dilation.
pub dilation: usize,
/// If true, use ceiling instead of floor for output size calculation.
pub ceil_mode: bool,
}
impl ModuleDisplay for MaxPool1d {
fn custom_settings(&self) -> Option<DisplaySettings> {
DisplaySettings::new()
.with_new_line_after_attribute(false)
.optional()
}
fn custom_content(&self, content: Content) -> Option<Content> {
content
.add("kernel_size", &self.kernel_size)
.add("stride", &self.stride)
.add_debug_attribute("padding", &self.padding)
.add("dilation", &self.dilation)
.add("ceil_mode", &self.ceil_mode)
.optional()
}
}
impl MaxPool1dConfig {
/// Initialize a new [max pool 1d](MaxPool1d) module.
pub fn init(&self) -> MaxPool1d {
MaxPool1d {
stride: self.stride,
kernel_size: self.kernel_size,
padding: self.padding.clone(),
dilation: self.dilation,
ceil_mode: self.ceil_mode,
}
}
}
impl MaxPool1d {
/// Applies the forward pass on the input tensor.
///
/// See [max_pool1d](ruda_tensor::api::module::max_pool1d) for more information.
///
/// # Shapes
///
/// - input: `[batch_size, channels, length_in]`
/// - output: `[batch_size, channels, length_out]`
pub fn forward<B: Backend>(&self, input: Tensor<B, 3>) -> Tensor<B, 3> {
let [_batch_size, _channels, length] = input.dims();
// Calculate padding as pair - handles Same, Valid, and Explicit uniformly
let (left, right) =
self.padding
.calculate_padding_1d_pair(length, self.kernel_size, self.stride);
// TODO: Move asymmetric padding to functional level via PoolOptions
// See: https://github.com/shuqi2077/RUDA/blob/main/THIRD_PARTY_NOTICES.md
// Handle asymmetric padding by applying explicit pad operation first
if left != right {
// For 1D (NCL format), pad the length dimension with (left, right)
// and no padding for channel dimension (top=0, bottom=0)
// Use -inf for max pooling so padded values don't affect the max
let padded = input.pad((left, right, 0, 0), PadMode::Constant(f32::NEG_INFINITY));
// Use zero padding for the pool operation since we already padded
max_pool1d(
padded,
self.kernel_size,
self.stride,
0,
self.dilation,
self.ceil_mode,
)
} else {
// Symmetric padding
max_pool1d(
input,
self.kernel_size,
self.stride,
left,
self.dilation,
self.ceil_mode,
)
}
}
}
#[cfg(test)]
mod tests {
use super::*;
use crate::TestBackend;
use rstest::rstest;
#[test]
fn same_with_even_kernel_uses_asymmetric_padding() {
let device = Default::default();
let config = MaxPool1dConfig::new(2)
.with_stride(1)
.with_padding(PaddingConfig1d::Same);
let pool = config.init();
// Input: [batch=1, channels=2, length=5]
let input = Tensor::<TestBackend, 3>::ones([1, 2, 5], &device);
let output = pool.forward(input);
// Same padding should preserve spatial dimensions
assert_eq!(output.dims(), [1, 2, 5]);
}
#[test]
fn display() {
let config = MaxPool1dConfig::new(3);
let layer = config.init();
assert_eq!(
alloc::format!("{layer}"),
"MaxPool1d {kernel_size: 3, stride: 3, padding: Valid, dilation: 1, ceil_mode: false}"
);
}
#[rstest]
#[case(1)]
#[case(2)]
fn default_strides_match_kernel_size(#[case] kernel_size: usize) {
let config = MaxPool1dConfig::new(kernel_size);
assert_eq!(
config.stride, kernel_size,
"Expected stride ({:?}) to match kernel size ({:?}) in default MaxPool1dConfig::new constructor",
config.stride, config.kernel_size
);
}
#[test]
fn asymmetric_padding_forward() {
let device = Default::default();
// Create max pool with asymmetric padding: left=1, right=2
let config = MaxPool1dConfig::new(3)
.with_stride(1)
.with_padding(PaddingConfig1d::Explicit(1, 2));
let pool = config.init();
// Input: [batch=1, channels=2, length=4]
let input = Tensor::<TestBackend, 3>::ones([1, 2, 4], &device);
let output = pool.forward(input);
// With asymmetric padding (1, 2), input length 4 becomes 4+1+2=7
// Output length = (7 - 3) / 1 + 1 = 5
assert_eq!(output.dims(), [1, 2, 5]);
}
#[test]
fn symmetric_explicit_padding_forward() {
let device = Default::default();
// Create max pool with symmetric explicit padding: left=2, right=2
let config = MaxPool1dConfig::new(3)
.with_stride(1)
.with_padding(PaddingConfig1d::Explicit(2, 2));
let pool = config.init();
// Input: [batch=1, channels=2, length=4]
let input = Tensor::<TestBackend, 3>::ones([1, 2, 4], &device);
let output = pool.forward(input);
// With symmetric padding (2, 2), input length 4 becomes 4+2+2=8
// Output length = (8 - 3) / 1 + 1 = 6
assert_eq!(output.dims(), [1, 2, 6]);
}
}