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Implement 3D pooling operations: avg_pool3d, max_pool3d, and adaptive_avg_pool3d #4231

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Implement 3D pooling operations: avg_pool3d, max_pool3d, and adaptive_avg_pool3d #4231

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097285a
Implement 3D pooling operations: avg_pool3d, max_pool3d, and adaptive…
Dec 19, 2025
a735801
Merge branch 'main' into three_d_pooling
MathijsdeBoer Dec 19, 2025
3addfaa
Fix cubecl changes
laggui Dec 19, 2025
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294 changes: 294 additions & 0 deletions crates/burn-autodiff/src/ops/module.rs
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Original file line number Diff line number Diff line change
Expand Up @@ -1684,6 +1684,258 @@ impl<B: Backend, C: CheckpointStrategy> ModuleOps<Autodiff<B, C>> for Autodiff<B
panic!("Can't differentiate adaptive avg pool2d backward.");
}

fn avg_pool3d(
x: AutodiffTensor<B>,
kernel_size: [usize; 3],
stride: [usize; 3],
padding: [usize; 3],
count_include_pad: bool,
ceil_mode: bool,
) -> AutodiffTensor<B> {
#[derive(Debug)]
struct AvgPool3D;

impl<B: Backend> Backward<B, 1> for AvgPool3D {
type State = (NodeId, [usize; 3], [usize; 3], [usize; 3], bool, bool);

fn backward(
self,
ops: Ops<Self::State, 1>,
grads: &mut Gradients,
checkpointer: &mut Checkpointer,
) {
let [node_parent] = ops.parents;
let grad = grads.consume::<B>(&ops.node);
let (x_state, kernel_size, stride, padding, count_include_pad, ceil_mode) =
ops.state;
let x = checkpointer.retrieve_node_output(x_state);

if let Some(node) = node_parent {
let grad = B::avg_pool3d_backward(
x,
grad,
kernel_size,
stride,
padding,
count_include_pad,
ceil_mode,
);
grads.register::<B>(node.id, grad);
}
}
}

match AvgPool3D
.prepare::<C>([x.node.clone()])
.compute_bound()
.stateful()
{
OpsKind::Tracked(mut prep) => {
let x_state = prep.checkpoint(&x);
prep.finish(
(
x_state,
kernel_size,
stride,
padding,
count_include_pad,
ceil_mode,
),
B::avg_pool3d(
x.primitive.clone(),
kernel_size,
stride,
padding,
count_include_pad,
ceil_mode,
),
)
}
OpsKind::UnTracked(prep) => prep.finish(B::avg_pool3d(
x.primitive,
kernel_size,
stride,
padding,
count_include_pad,
ceil_mode,
)),
}
}

fn avg_pool3d_backward(
_x: AutodiffTensor<B>,
_grad: AutodiffTensor<B>,
_kernel_size: [usize; 3],
_stride: [usize; 3],
_padding: [usize; 3],
_count_include_pad: bool,
_ceil_mode: bool,
) -> AutodiffTensor<B> {
panic!("Can't differentiate avg pool 3d backward.");
}

fn max_pool3d(
x: AutodiffTensor<B>,
kernel_size: [usize; 3],
stride: [usize; 3],
padding: [usize; 3],
dilation: [usize; 3],
ceil_mode: bool,
) -> AutodiffTensor<B> {
match MaxPool3D
.prepare::<C>([x.node.clone()])
.compute_bound()
.stateful()
{
OpsKind::Tracked(mut prep) => {
let x_state = prep.checkpoint(&x);
let output = B::max_pool3d_with_indices(
x.primitive,
kernel_size,
stride,
padding,
dilation,
ceil_mode,
);
prep.finish(
(
x_state,
output.indices,
kernel_size,
stride,
padding,
dilation,
ceil_mode,
),
output.output,
)
}
OpsKind::UnTracked(prep) => prep.finish(B::max_pool3d(
x.primitive,
kernel_size,
stride,
padding,
dilation,
ceil_mode,
)),
}
}

fn max_pool3d_with_indices(
x: AutodiffTensor<B>,
kernel_size: [usize; 3],
stride: [usize; 3],
padding: [usize; 3],
dilation: [usize; 3],
ceil_mode: bool,
) -> MaxPool3dWithIndices<Self> {
match MaxPool3D
.prepare::<C>([x.node.clone()])
.compute_bound()
.stateful()
{
OpsKind::Tracked(mut prep) => {
let x_state = prep.checkpoint(&x);

let output = B::max_pool3d_with_indices(
x.primitive,
kernel_size,
stride,
padding,
dilation,
ceil_mode,
);

let output_tensor = prep.finish(
(
x_state,
output.indices.clone(),
kernel_size,
stride,
padding,
dilation,
ceil_mode,
),
output.output,
);

MaxPool3dWithIndices::new(output_tensor, output.indices)
}
OpsKind::UnTracked(prep) => {
let output = B::max_pool3d_with_indices(
x.primitive,
kernel_size,
stride,
padding,
dilation,
ceil_mode,
);
let output_tensor = prep.finish(output.output);

MaxPool3dWithIndices::new(output_tensor, output.indices)
}
}
}

fn max_pool3d_with_indices_backward(
_x: AutodiffTensor<B>,
_kernel_size: [usize; 3],
_stride: [usize; 3],
_padding: [usize; 3],
_dilation: [usize; 3],
_ceil_mode: bool,
_output_grad: AutodiffTensor<B>,
_indices: IntTensor<B>,
) -> MaxPool3dBackward<Self> {
panic!("Can't differentiate max pool3d with indices backward.");
}

fn adaptive_avg_pool3d(x: AutodiffTensor<B>, output_size: [usize; 3]) -> AutodiffTensor<B> {
#[derive(Debug)]
struct AdaptiveAvgPool3D;

impl<B: Backend> Backward<B, 1> for AdaptiveAvgPool3D {
type State = NodeId;

fn backward(
self,
ops: Ops<Self::State, 1>,
grads: &mut Gradients,
checkpointer: &mut Checkpointer,
) {
let [node_parent] = ops.parents;
let grad = grads.consume::<B>(&ops.node);
let state = checkpointer.retrieve_node_output(ops.state);

if let Some(node) = node_parent {
let grad = B::adaptive_avg_pool3d_backward(state, grad);
grads.register::<B>(node.id, grad);
}
}
}

match AdaptiveAvgPool3D
.prepare::<C>([x.node.clone()])
.compute_bound()
.stateful()
{
OpsKind::Tracked(mut prep) => {
let x_state = prep.checkpoint(&x);
prep.finish(x_state, B::adaptive_avg_pool3d(x.primitive, output_size))
}
OpsKind::UnTracked(prep) => {
prep.finish(B::adaptive_avg_pool3d(x.primitive, output_size))
}
}
}

fn adaptive_avg_pool3d_backward(
_x: AutodiffTensor<B>,
_grad: AutodiffTensor<B>,
) -> <Autodiff<B> as Backend>::FloatTensorPrimitive {
panic!("Can't differentiate adaptive avg pool3d backward.");
}

fn interpolate(
x: AutodiffTensor<B>,
output_size: [usize; 2],
Expand Down Expand Up @@ -1814,3 +2066,45 @@ impl<B: Backend> Backward<B, 1> for MaxPool2D {
}
}
}

#[derive(Debug)]
struct MaxPool3D;

impl<B: Backend> Backward<B, 1> for MaxPool3D {
type State = (
NodeId,
IntTensor<B>,
[usize; 3],
[usize; 3],
[usize; 3],
[usize; 3],
bool,
);

fn backward(
self,
ops: Ops<Self::State, 1>,
grads: &mut Gradients,
checkpointer: &mut Checkpointer,
) {
let [node_parent] = ops.parents;
let grad = grads.consume::<B>(&ops.node);
let (x_state, indices, kernel_size, stride, padding, dilation, ceil_mode) = ops.state;
let x = checkpointer.retrieve_node_output(x_state);

if let Some(node) = node_parent {
let grad = B::max_pool3d_with_indices_backward(
x,
kernel_size,
stride,
padding,
dilation,
ceil_mode,
grad,
indices,
);

grads.register::<B>(node.id, grad.x_grad);
}
}
}
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