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//! Convolution operations: conv2d, conv_transpose2d.
use bon::bon;
use svod_ir::SInt;
use crate::Tensor;
use crate::reduce::AxisSpec;
type Result<T> = crate::Result<T>;
#[bon]
impl Tensor {
/// N-d convolution. Input `(N, Cin, *spatial)`, Weight `(Cout, Cin/groups, *kernel)`.
///
/// Computes cross-correlation (conv without kernel flip) by extracting sliding
/// windows via [`pool`](Tensor::pool), then contracting against the weight tensor.
/// Supports grouped convolution, strided/dilated kernels, and asymmetric padding.
///
/// # Examples
///
/// Basic 2D convolution with uniform data:
///
/// ```
/// # use svod_tensor::Tensor;
/// # use ndarray::Array4;
/// let x = Tensor::from_ndarray(&Array4::from_elem((1, 1, 5, 5), 1.0f32));
/// let w = Tensor::from_ndarray(&Array4::from_elem((1, 1, 3, 3), 1.0f32));
/// let mut y = x.conv2d().weight(&w).call().unwrap();
/// y.realize().unwrap();
/// // 3x3 kernel of ones on input of ones => each output element is 9.0
/// assert_eq!(y.as_vec::<f32>().unwrap(), vec![9.0; 9]);
/// ```
///
/// With stride:
///
/// ```
/// # use svod_tensor::Tensor;
/// # use ndarray::Array4;
/// let x = Tensor::from_ndarray(&Array4::from_elem((1, 1, 5, 5), 1.0f32));
/// let w = Tensor::from_ndarray(&Array4::from_elem((1, 1, 3, 3), 1.0f32));
/// let mut y = x.conv2d().weight(&w).stride(&[2, 2]).call().unwrap();
/// y.realize().unwrap();
/// let shape: Vec<_> = y.shape().unwrap().iter().map(|d| d.as_const().unwrap()).collect();
/// assert_eq!(shape, vec![1, 1, 2, 2]);
/// assert_eq!(y.as_vec::<f32>().unwrap(), vec![9.0; 4]);
/// ```
///
/// With padding:
///
/// ```
/// # use svod_tensor::Tensor;
/// # use ndarray::Array4;
/// let x = Tensor::from_ndarray(&Array4::from_elem((1, 1, 3, 3), 1.0f32));
/// let w = Tensor::from_ndarray(&Array4::from_elem((1, 1, 3, 3), 1.0f32));
/// // padding=1 on each side: output matches input spatial dims
/// let mut y = x.conv2d().weight(&w).padding(&[(1, 1), (1, 1)]).call().unwrap();
/// y.realize().unwrap();
/// let vals = y.as_vec::<f32>().unwrap();
/// assert_eq!(vals.len(), 9); // 3x3 output
/// // Center element sees full 3x3 window of ones = 9.0
/// assert_eq!(vals[4], 9.0);
/// // Corner element sees 2x2 window = 4.0
/// assert_eq!(vals[0], 4.0);
/// ```
///
/// With bias:
///
/// ```
/// # use svod_tensor::Tensor;
/// # use ndarray::Array4;
/// let x = Tensor::from_ndarray(&Array4::from_elem((1, 1, 3, 3), 1.0f32));
/// let w = Tensor::from_ndarray(&Array4::from_elem((1, 1, 3, 3), 1.0f32));
/// let b = Tensor::from_slice([10.0f32]);
/// let mut y = x.conv2d().weight(&w).bias(&b).call().unwrap();
/// y.realize().unwrap();
/// // Each output element: 9.0 + 10.0 = 19.0
/// assert_eq!(y.as_vec::<f32>().unwrap(), vec![19.0]);
/// ```
#[builder]
pub fn conv2d(
&self,
weight: &Tensor,
bias: Option<&Tensor>,
#[builder(default = 1)] groups: usize,
stride: Option<&[usize]>,
dilation: Option<&[usize]>,
padding: Option<&[(isize, isize)]>,
acc_dtype: Option<svod_dtype::DType>,
) -> Result<Tensor> {
let x_shape = self.shape()?;
let w_shape = weight.shape()?;
let bs = x_shape[0].clone(); // SInt — concrete or symbolic (Variable batch)
let cin_ = x_shape[1].as_const().expect("channel dim must be concrete");
let cout = w_shape[0].as_const().expect("cout must be concrete");
let cin = w_shape[1].as_const().expect("cin/g must be concrete");
let hw: Vec<usize> = w_shape[2..].iter().map(|s| s.as_const().expect("kernel dim must be concrete")).collect();
let n_spatial = hw.len();
if x_shape.len() != w_shape.len() {
return Err(crate::error::Error::IrConstruction {
details: format!("input and weight must have same ndim, got {} and {}", x_shape.len(), w_shape.len()),
});
}
if groups * cin != cin_ {
return Err(crate::error::Error::IrConstruction {
details: format!("groups*cin/g ({}) != input channels ({cin_})", groups * cin),
});
}
let default_ones: Vec<usize> = vec![1; n_spatial];
let stride = stride.unwrap_or(&default_ones);
let dilation = dilation.unwrap_or(&default_ones);
let no_padding: Vec<(isize, isize)> = vec![(0, 0); n_spatial];
let padding = padding.unwrap_or(&no_padding);
let mut x = self.clone();
if padding.iter().any(|&(b, e)| b != 0 || e != 0) {
let mut full_pad: Vec<(isize, isize)> = vec![(0, 0); 2];
full_pad.extend_from_slice(padding);
x = x.try_pad(&full_pad)?;
}
x = x.pool(&hw, stride, dilation)?;
let oyx: Vec<SInt> = {
let xs = x.shape()?;
xs[2..2 + n_spatial].to_vec()
};
let rcout = cout / groups;
// Reshape: (bs, groups, cin, 1, *oyx, *hw)
let mut reshape_dims: Vec<SInt> = vec![bs.clone(), groups.into(), cin.into(), 1usize.into()];
reshape_dims.extend(oyx.iter().cloned());
reshape_dims.extend(hw.iter().map(|&k| SInt::from(k)));
x = x.try_reshape(&reshape_dims)?;
// Expand: (bs, groups, cin, rcout, *oyx, *hw)
let mut expand_dims: Vec<SInt> = vec![bs.clone(), groups.into(), cin.into(), rcout.into()];
expand_dims.extend(oyx.iter().cloned());
expand_dims.extend(hw.iter().map(|&k| SInt::from(k)));
x = x.try_expand(&expand_dims)?;
// Permute: (bs, groups, rcout, *oyx, cin, *hw)
let mut perm: Vec<isize> = vec![0, 1, 3];
for j in 0..n_spatial {
perm.push(4 + j as isize);
}
perm.push(2);
for j in 0..n_spatial {
perm.push((4 + n_spatial + j) as isize);
}
x = x.try_permute(&perm)?;
// Reshape weight: (1, groups, rcout, *[1]*n_spatial, cin, *hw)
let mut w_reshape: Vec<isize> = vec![1, groups as isize, rcout as isize];
w_reshape.extend(std::iter::repeat_n(1isize, n_spatial));
w_reshape.push(cin as isize);
w_reshape.extend(hw.iter().map(|&k| k as isize));
let w = weight.try_reshape(&w_reshape)?;
x = x.try_mul(&w)?;
// Sum over last (1 + n_spatial) dims
let total_dims = x.ndim()?;
let reduce_axes: Vec<isize> = (0..(1 + n_spatial)).map(|i| (total_dims - 1 - i) as isize).collect();
x = x.sum_with().axes(AxisSpec::Multiple(reduce_axes)).keepdim(true).maybe_dtype(acc_dtype).call()?;
// Reshape to (bs, cout, *oyx)
let mut final_shape: Vec<SInt> = vec![bs.clone(), cout.into()];
final_shape.extend(oyx.iter().cloned());
x = x.try_reshape(&final_shape)?;
if let Some(bias) = bias {
let mut bias_shape: Vec<isize> = vec![1, cout as isize];
bias_shape.extend(std::iter::repeat_n(1isize, n_spatial));
let bias = bias.try_reshape(&bias_shape)?;
x = x.try_add(&bias)?;
}
Ok(x)
}
/// Transposed convolution (fractionally-strided convolution).
///
/// Computes the gradient of a forward convolution, commonly used for upsampling.
/// Internally flips the kernel, interleaves zeros for stride > 1, computes
/// transposed padding, then delegates to [`conv2d`](Tensor::conv2d).
///
/// Input `(N, Cin, *spatial)`, Weight `(Cin, Cout/groups, *kernel)`.
///
/// # Examples
///
/// Basic transposed convolution (upsampling):
///
/// ```
/// # use svod_tensor::Tensor;
/// # use ndarray::Array4;
/// let x = Tensor::from_ndarray(&Array4::from_elem((1, 1, 2, 2), 1.0f32));
/// let w = Tensor::from_ndarray(&Array4::from_elem((1, 1, 3, 3), 1.0f32));
/// let mut y = x.conv_transpose2d().weight(&w).call().unwrap();
/// y.realize().unwrap();
/// let vals = y.as_vec::<f32>().unwrap();
/// assert_eq!(vals.len(), 16); // 4x4 output
/// // Center elements see full overlap of both input positions
/// assert_eq!(vals[5], 4.0);
/// ```
///
/// With stride (stronger upsampling):
///
/// ```
/// # use svod_tensor::Tensor;
/// # use ndarray::Array4;
/// let x = Tensor::from_ndarray(&Array4::from_elem((1, 1, 2, 2), 1.0f32));
/// let w = Tensor::from_ndarray(&Array4::from_elem((1, 1, 3, 3), 1.0f32));
/// let mut y = x.conv_transpose2d().weight(&w).stride(&[2, 2]).call().unwrap();
/// y.realize().unwrap();
/// let vals = y.as_vec::<f32>().unwrap();
/// assert_eq!(vals.len(), 25); // 5x5 output
/// ```
///
/// With padding and output padding:
///
/// ```
/// # use svod_tensor::Tensor;
/// # use ndarray::Array4;
/// let x = Tensor::from_ndarray(&Array4::from_elem((1, 1, 2, 2), 1.0f32));
/// let w = Tensor::from_ndarray(&Array4::from_elem((1, 1, 3, 3), 1.0f32));
/// let mut y = x.conv_transpose2d()
/// .weight(&w)
/// .stride(&[2, 2])
/// .padding(&[(1, 1), (1, 1)])
/// .output_padding(&[1, 1])
/// .call()
/// .unwrap();
/// y.realize().unwrap();
/// let vals = y.as_vec::<f32>().unwrap();
/// assert_eq!(vals.len(), 16); // 4x4 output
/// ```
#[builder]
pub fn conv_transpose2d(
&self,
weight: &Tensor,
bias: Option<&Tensor>,
#[builder(default = 1)] groups: usize,
stride: Option<&[usize]>,
dilation: Option<&[usize]>,
padding: Option<&[(isize, isize)]>,
output_padding: Option<&[usize]>,
) -> Result<Tensor> {
let w_shape = weight.shape()?;
let hw: Vec<usize> = w_shape[2..].iter().map(|s| s.as_const().expect("kernel dim must be concrete")).collect();
let n_spatial = hw.len();
let default_ones: Vec<usize> = vec![1; n_spatial];
let default_zeros: Vec<usize> = vec![0; n_spatial];
let default_no_pad: Vec<(isize, isize)> = vec![(0, 0); n_spatial];
let stride = stride.unwrap_or(&default_ones);
let dilation = dilation.unwrap_or(&default_ones);
let padding = padding.unwrap_or(&default_no_pad);
let output_padding = output_padding.unwrap_or(&default_zeros);
let cout_in = w_shape[0].as_const().unwrap();
let cin_g = w_shape[1].as_const().unwrap();
let rcout = cout_in / groups;
// Reshape to (groups, rcout, cin_g, *HW)
let mut unflatten_shape: Vec<isize> = vec![groups as isize, rcout as isize, cin_g as isize];
unflatten_shape.extend(hw.iter().map(|&k| k as isize));
let mut w = weight.try_reshape(&unflatten_shape)?;
// Transpose dim 1 and 2: (groups, cin_g, rcout, *HW)
w = w.try_transpose(1, 2)?;
// Flip kernel dims
let flip_axes: Vec<isize> = (3..(3 + n_spatial) as isize).collect();
w = w.flip(&flip_axes)?;
// Flatten back: (groups * cin_g, rcout, *HW)
let mut flat_shape: Vec<isize> = vec![(groups * cin_g) as isize, rcout as isize];
flat_shape.extend(hw.iter().map(|&k| k as isize));
w = w.try_reshape(&flat_shape)?;
// Handle stride > 1: interleave zeros across all spatial dims at once.
// Matches Tinygrad: (k) -> reshape (k,1) -> pad (k,s) -> reshape (k*s) -> shrink (k-(s-1))
// All spatial dims are processed in a single reshape/pad/reshape/shrink sequence
// to avoid cascading PAD operations that create exponential boolean condition trees.
let mut x = self.clone();
if stride.iter().any(|&s| s > 1) {
let x_shape = x.shape()?;
let spatial: Vec<usize> = x_shape[2..].iter().map(|s| s.as_const().unwrap()).collect();
// Step 1: reshape (N,C,h,w) -> (N,C,h,1,w,1)
let mut rshape: Vec<SInt> = vec![x_shape[0].clone(), x_shape[1].clone()];
for &k in &spatial {
rshape.push(k.into());
rshape.push(1usize.into());
}
x = x.try_reshape(&rshape)?;
// Step 2: pad inserted dims by (0, s-1): (N,C,h,s,w,s)
let mut pad_spec: Vec<(isize, isize)> = vec![(0, 0); 2];
for &s in stride.iter() {
pad_spec.push((0, 0));
pad_spec.push((0, (s - 1) as isize));
}
x = x.try_pad(&pad_spec)?;
// Step 3: reshape to merge pairs: (N,C,h*s,w*s)
let x_shape = x.shape()?;
let mut rshape: Vec<SInt> = vec![x_shape[0].clone(), x_shape[1].clone()];
for j in 0..n_spatial {
let a = x_shape[2 + j * 2].as_const().unwrap();
let b = x_shape[2 + j * 2 + 1].as_const().unwrap();
rshape.push((a * b).into());
}
x = x.try_reshape(&rshape)?;
// Step 4: shrink to remove trailing stride-1
// Use None for batch/channel dims (pass through).
let mut ranges: Vec<Option<(isize, isize)>> = vec![None, None];
for j in 0..n_spatial {
let new_size = spatial[j] * stride[j] - (stride[j] - 1);
ranges.push(Some((0, new_size as isize)));
}
x = x.try_shrink(&ranges)?;
}
// Compute transposed padding
let conv_padding: Vec<(isize, isize)> = (0..n_spatial)
.map(|j| {
let pb = padding[j].0;
let pa = padding[j].1;
let begin = (hw[j] as isize - 1) * dilation[j] as isize - pb;
let end = (hw[j] as isize - 1) * dilation[j] as isize - pa + output_padding[j] as isize;
(begin, end)
})
.collect();
x.conv2d().weight(&w).groups(groups).maybe_bias(bias).dilation(dilation).padding(&conv_padding).call()
}
}