convolution.py

# coding=utf-8

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import abc
from typing import List, Tuple, Union, Optional

import math

import torch
import torch.nn as nn

from functools import partial

from torch_harmonics.quadrature import _precompute_grid, _precompute_latitudes
from torch_harmonics._disco_convolution import (
    _disco_s2_contraction_torch,
    _disco_s2_transpose_contraction_torch,
    _disco_s2_contraction_triton,
    _disco_s2_transpose_contraction_triton,
)


def _compute_support_vals_isotropic(r: torch.Tensor, phi: torch.Tensor, nr: int, r_cutoff: float, norm: str = "s2"):
    """
    Computes the index set that falls into the isotropic kernel's support and returns both indices and values.
    """

    # compute the support
    dr = (r_cutoff - 0.0) / nr
    ikernel = torch.arange(nr).reshape(-1, 1, 1)
    ir = ikernel * dr

    if norm == "none":
        norm_factor = 1.0
    elif norm == "2d":
        norm_factor = math.pi * (r_cutoff * nr / (nr + 1))**2 + math.pi * r_cutoff**2 * (2 * nr / (nr + 1) + 1) / (nr + 1) / 3
    elif norm == "s2":
        norm_factor = 2 * math.pi * (1 - math.cos(r_cutoff - dr) + math.cos(r_cutoff - dr) + (math.sin(r_cutoff - dr) - math.sin(r_cutoff)) / dr)
    else:
        raise ValueError(f"Unknown normalization mode {norm}.")

    # find the indices where the rotated position falls into the support of the kernel
    iidx = torch.argwhere(((r - ir).abs() <= dr) & (r <= r_cutoff))
    vals = (1 - (r[iidx[:, 1], iidx[:, 2]] - ir[iidx[:, 0], 0, 0]).abs() / dr) / norm_factor
    return iidx, vals


def _compute_support_vals_anisotropic(r: torch.Tensor, phi: torch.Tensor, nr: int, nphi: int, r_cutoff: float, norm: str = "s2"):
    """
    Computes the index set that falls into the anisotropic kernel's support and returns both indices and values.
    """

    # compute the support
    dr = (r_cutoff - 0.0) / nr
    dphi = 2.0 * math.pi / nphi
    kernel_size = (nr - 1) * nphi + 1
    ikernel = torch.arange(kernel_size).reshape(-1, 1, 1)
    ir = ((ikernel - 1) // nphi + 1) * dr
    iphi = ((ikernel - 1) % nphi) * dphi

    if norm == "none":
        norm_factor = 1.0
    elif norm == "2d":
        norm_factor = math.pi * (r_cutoff * nr / (nr + 1))**2 + math.pi * r_cutoff**2 * (2 * nr / (nr + 1) + 1) / (nr + 1) / 3
    elif norm == "s2":
        norm_factor = 2 * math.pi * (1 - math.cos(r_cutoff - dr) + math.cos(r_cutoff - dr) + (math.sin(r_cutoff - dr) - math.sin(r_cutoff)) / dr)
    else:
        raise ValueError(f"Unknown normalization mode {norm}.")

    # find the indices where the rotated position falls into the support of the kernel
    cond_r = ((r - ir).abs() <= dr) & (r <= r_cutoff)
    cond_phi = (ikernel == 0) | ((phi - iphi).abs() <= dphi) | ((2 * math.pi - (phi - iphi).abs()) <= dphi)
    iidx = torch.argwhere(cond_r & cond_phi)
    vals = (1 - (r[iidx[:, 1], iidx[:, 2]] - ir[iidx[:, 0], 0, 0]).abs() / dr) / norm_factor
    vals *= torch.where(
        iidx[:, 0] > 0,
        (1 - torch.minimum((phi[iidx[:, 1], iidx[:, 2]] - iphi[iidx[:, 0], 0, 0]).abs(), (2 * math.pi - (phi[iidx[:, 1], iidx[:, 2]] - iphi[iidx[:, 0], 0, 0]).abs())) / dphi),
        1.0,
    )
    return iidx, vals


def _precompute_convolution_tensor_s2(in_shape, out_shape, kernel_shape, grid_in="equiangular", grid_out="equiangular", theta_cutoff=0.01 * math.pi):
    """
    Precomputes the rotated filters at positions $R^{-1}_j \omega_i = R^{-1}_j R_i \nu = Y(-\theta_j)Z(\phi_i - \phi_j)Y(\theta_j)\nu$.
    Assumes a tensorized grid on the sphere with an equidistant sampling in longitude as described in Ocampo et al.
    The output tensor has shape kernel_shape x nlat_out x (nlat_in * nlon_in).

    The rotation of the Euler angles uses the YZY convention, which applied to the northpole $(0,0,1)^T$ yields
    $$
    Y(\alpha) Z(\beta) Y(\gamma) n =
        {\begin{bmatrix} 
            \cos(\gamma)\sin(\alpha) + \cos(\alpha)\cos(\beta)\sin(\gamma) \\
            \sin(\beta)\sin(\gamma) \\
            \cos(\alpha)\cos(\gamma)-\cos(\beta)\sin(\alpha)\sin(\gamma)
        \end{bmatrix}}
    $$
    """

    assert len(in_shape) == 2
    assert len(out_shape) == 2

    if len(kernel_shape) == 1:
        kernel_handle = partial(_compute_support_vals_isotropic, nr=kernel_shape[0], r_cutoff=theta_cutoff, norm="s2")
    elif len(kernel_shape) == 2:
        kernel_handle = partial(_compute_support_vals_anisotropic, nr=kernel_shape[0], nphi=kernel_shape[1], r_cutoff=theta_cutoff, norm="s2")
    else:
        raise ValueError("kernel_shape should be either one- or two-dimensional.")

    nlat_in, nlon_in = in_shape
    nlat_out, nlon_out = out_shape

    lats_in, _ = _precompute_latitudes(nlat_in, grid=grid_in)
    lats_in = torch.from_numpy(lats_in).float()
    lats_out, _ = _precompute_latitudes(nlat_out, grid=grid_out)
    lats_out = torch.from_numpy(lats_out).float()

    # compute the phi differences
    # It's imporatant to not include the 2 pi point in the longitudes, as it is equivalent to lon=0
    lons_in = torch.linspace(0, 2 * math.pi, nlon_in + 1)[:-1]

    out_idx = []
    out_vals = []
    for t in range(nlat_out):
        # the last angle has a negative sign as it is a passive rotation, which rotates the filter around the y-axis
        alpha = -lats_out[t]
        beta = lons_in
        gamma = lats_in.reshape(-1, 1)

        # compute cartesian coordinates of the rotated position
        # This uses the YZY convention of Euler angles, where the last angle (alpha) is a passive rotation,
        # and therefore applied with a negative sign
        z = -torch.cos(beta) * torch.sin(alpha) * torch.sin(gamma) + torch.cos(alpha) * torch.cos(gamma)
        x = torch.cos(alpha) * torch.cos(beta) * torch.sin(gamma) + torch.cos(gamma) * torch.sin(alpha)
        y = torch.sin(beta) * torch.sin(gamma)

        # normalization is emportant to avoid NaNs when arccos and atan are applied
        # this can otherwise lead to spurious artifacts in the solution
        norm = torch.sqrt(x * x + y * y + z * z)
        x = x / norm
        y = y / norm
        z = z / norm

        # compute spherical coordinates, where phi needs to fall into the [0, 2pi) range
        theta = torch.arccos(z)
        phi = torch.arctan2(y, x) + torch.pi

        # find the indices where the rotated position falls into the support of the kernel
        iidx, vals = kernel_handle(theta, phi)

        # add the output latitude and reshape such that psi has dimensions kernel_shape x nlat_out x (nlat_in*nlon_in)
        idx = torch.stack([iidx[:, 0], t * torch.ones_like(iidx[:, 0]), iidx[:, 1] * nlon_in + iidx[:, 2]], dim=0)

        # append indices and values to the COO datastructure
        out_idx.append(idx)
        out_vals.append(vals)

    # concatenate the indices and values
    out_idx = torch.cat(out_idx, dim=-1).to(torch.long).contiguous()
    out_vals = torch.cat(out_vals, dim=-1).to(torch.float32).contiguous()

    return out_idx, out_vals


def _precompute_convolution_tensor_2d(grid_in, grid_out, kernel_shape, radius_cutoff=0.01, periodic=False):
    """
    Precomputes the translated filters at positions $T^{-1}_j \omega_i = T^{-1}_j T_i \nu$. Similar to the S2 routine,
    only that it assumes a non-periodic subset of the euclidean plane
    """

    # check that input arrays are valid point clouds in 2D
    assert len(grid_in) == 2
    assert len(grid_out) == 2
    assert grid_in.shape[0] == 2
    assert grid_out.shape[0] == 2

    n_in = grid_in.shape[-1]
    n_out = grid_out.shape[-1]

    if len(kernel_shape) == 1:
        kernel_handle = partial(_compute_support_vals_isotropic, nr=kernel_shape[0], r_cutoff=radius_cutoff, norm="2d")
    elif len(kernel_shape) == 2:
        kernel_handle = partial(_compute_support_vals_anisotropic, nr=kernel_shape[0], nphi=kernel_shape[1], r_cutoff=radius_cutoff, norm="2d")
    else:
        raise ValueError("kernel_shape should be either one- or two-dimensional.")

    grid_in = grid_in.reshape(2, 1, n_in)
    grid_out = grid_out.reshape(2, n_out, 1)

    diffs = grid_in - grid_out
    if periodic:
        periodic_diffs = torch.where(diffs > 0.0, diffs-1, diffs+1)
        diffs = torch.where(diffs.abs() < periodic_diffs.abs(), diffs, periodic_diffs)


    r = torch.sqrt(diffs[0] ** 2 + diffs[1] ** 2)
    phi = torch.arctan2(diffs[1], diffs[0]) + torch.pi

    idx, vals = kernel_handle(r, phi)
    idx = idx.permute(1, 0)

    return idx, vals


class DiscreteContinuousConv(nn.Module, metaclass=abc.ABCMeta):
    """
    Abstract base class for DISCO convolutions
    """

    def __init__(
        self,
        in_channels: int,
        out_channels: int,
        kernel_shape: Union[int, List[int]],
        groups: Optional[int] = 1,
        bias: Optional[bool] = True,
    ):
        super().__init__()

        if isinstance(kernel_shape, int):
            self.kernel_shape = [kernel_shape]
        else:
            self.kernel_shape = kernel_shape

        if len(self.kernel_shape) == 1:
            self.kernel_size = self.kernel_shape[0]
        elif len(self.kernel_shape) == 2:
            self.kernel_size = (self.kernel_shape[0] - 1) * self.kernel_shape[1] + 1
        else:
            raise ValueError("kernel_shape should be either one- or two-dimensional.")

        # groups
        self.groups = groups

        # weight tensor
        if in_channels % self.groups != 0:
            raise ValueError("Error, the number of input channels has to be an integer multiple of the group size")
        if out_channels % self.groups != 0:
            raise ValueError("Error, the number of output channels has to be an integer multiple of the group size")
        self.groupsize = in_channels // self.groups
        scale = math.sqrt(1.0 / self.groupsize)
        self.weight = nn.Parameter(scale * torch.randn(out_channels, self.groupsize, self.kernel_size))

        if bias:
            self.bias = nn.Parameter(torch.zeros(out_channels))
        else:
            self.bias = None

    @abc.abstractmethod
    def forward(self, x: torch.Tensor):
        raise NotImplementedError


class DiscreteContinuousConvS2(DiscreteContinuousConv):
    """
    Discrete-continuous convolutions (DISCO) on the 2-Sphere as described in [1].

    [1] Ocampo, Price, McEwen, Scalable and equivariant spherical CNNs by discrete-continuous (DISCO) convolutions, ICLR (2023), arXiv:2209.13603
    """

    def __init__(
        self,
        in_channels: int,
        out_channels: int,
        in_shape: Tuple[int],
        out_shape: Tuple[int],
        kernel_shape: Union[int, List[int]],
        groups: Optional[int] = 1,
        grid_in: Optional[str] = "equiangular",
        grid_out: Optional[str] = "equiangular",
        bias: Optional[bool] = True,
        theta_cutoff: Optional[float] = None,
    ):
        super().__init__(in_channels, out_channels, kernel_shape, groups, bias)

        self.nlat_in, self.nlon_in = in_shape
        self.nlat_out, self.nlon_out = out_shape

        # compute theta cutoff based on the bandlimit of the input field
        if theta_cutoff is None:
            theta_cutoff = (self.kernel_shape[0] + 1) * torch.pi / float(self.nlat_in - 1)

        if theta_cutoff <= 0.0:
            raise ValueError("Error, theta_cutoff has to be positive.")

        # integration weights
        _, wgl = _precompute_latitudes(self.nlat_in, grid=grid_in)
        quad_weights = 2.0 * torch.pi * torch.from_numpy(wgl).float().reshape(-1, 1) / self.nlon_in
        self.register_buffer("quad_weights", quad_weights, persistent=False)

        idx, vals = _precompute_convolution_tensor_s2(in_shape, out_shape, self.kernel_shape, grid_in=grid_in, grid_out=grid_out, theta_cutoff=theta_cutoff)

        self.register_buffer("psi_idx", idx, persistent=False)
        self.register_buffer("psi_vals", vals, persistent=False)

    def get_psi(self):
        psi = torch.sparse_coo_tensor(self.psi_idx, self.psi_vals, size=(self.kernel_size, self.nlat_out, self.nlat_in * self.nlon_in)).coalesce()
        return psi

    def forward(self, x: torch.Tensor, use_triton_kernel: bool = True) -> torch.Tensor:
        # pre-multiply x with the quadrature weights
        x = self.quad_weights * x

        psi = self.get_psi()
        
        if x.is_cuda and use_triton_kernel:
            x = _disco_s2_contraction_triton(x, psi, self.nlon_out)
        else:
            x = _disco_s2_contraction_torch(x, psi, self.nlon_out)

        # extract shape
        B, C, K, H, W = x.shape
        x = x.reshape(B, self.groups, self.groupsize, K, H, W)

        # do weight multiplication
        out = torch.einsum("bgckxy,gock->bgoxy", x, self.weight.reshape(self.groups, -1, self.weight.shape[1], self.weight.shape[2])).contiguous()
        out = out.reshape(out.shape[0], -1, out.shape[-2], out.shape[-1])

        if self.bias is not None:
            out = out + self.bias.reshape(1, -1, 1, 1)

        return out


class DiscreteContinuousConvTransposeS2(DiscreteContinuousConv):
    """
    Discrete-continuous transpose convolutions (DISCO) on the 2-Sphere as described in [1].

    [1] Ocampo, Price, McEwen, Scalable and equivariant spherical CNNs by discrete-continuous (DISCO) convolutions, ICLR (2023), arXiv:2209.13603
    """

    def __init__(
        self,
        in_channels: int,
        out_channels: int,
        in_shape: Tuple[int],
        out_shape: Tuple[int],
        kernel_shape: Union[int, List[int]],
        groups: Optional[int] = 1,
        grid_in: Optional[str] = "equiangular",
        grid_out: Optional[str] = "equiangular",
        bias: Optional[bool] = True,
        theta_cutoff: Optional[float] = None,
    ):
        super().__init__(in_channels, out_channels, kernel_shape, groups, bias)

        self.nlat_in, self.nlon_in = in_shape
        self.nlat_out, self.nlon_out = out_shape

        # bandlimit
        if theta_cutoff is None:
            theta_cutoff = (self.kernel_shape[0] + 1) * torch.pi / float(self.nlat_in - 1)

        if theta_cutoff <= 0.0:
            raise ValueError("Error, theta_cutoff has to be positive.")

        # integration weights
        _, wgl = _precompute_latitudes(self.nlat_in, grid=grid_in)
        quad_weights = 2.0 * torch.pi * torch.from_numpy(wgl).float().reshape(-1, 1) / self.nlon_in
        self.register_buffer("quad_weights", quad_weights, persistent=False)

        # switch in_shape and out_shape since we want transpose conv
        idx, vals = _precompute_convolution_tensor_s2(out_shape, in_shape, self.kernel_shape, grid_in=grid_out, grid_out=grid_in, theta_cutoff=theta_cutoff)

        self.register_buffer("psi_idx", idx, persistent=False)
        self.register_buffer("psi_vals", vals, persistent=False)

    def get_psi(self, use_triton_kernel=True):
        if not use_triton_kernel:
            # we do a semi-transposition to faciliate the computation
            tout = self.psi_idx[2] // self.nlon_out
            pout = self.psi_idx[2] % self.nlon_out
            # flip the axis of longitudes
            pout = self.nlon_out - 1 - pout
            tin = self.psi_idx[1]
            idx = torch.stack([self.psi_idx[0], tout, tin*self.nlon_out + pout], dim=0)
            psi = torch.sparse_coo_tensor(idx, self.psi_vals, size=(self.kernel_size, self.nlat_out, self.nlat_in * self.nlon_out)).coalesce()
        else:
            psi = torch.sparse_coo_tensor(self.psi_idx, self.psi_vals, size=(self.kernel_size, self.nlat_in, self.nlat_out * self.nlon_out)).coalesce()
        return psi

    def forward(self, x: torch.Tensor, use_triton_kernel: bool = True) -> torch.Tensor:
        # extract shape
        B, C, H, W = x.shape
        x = x.reshape(B, self.groups, self.groupsize, H, W)

        # do weight multiplication
        x = torch.einsum("bgcxy,gock->bgokxy", x, self.weight.reshape(self.groups, -1, self.weight.shape[1], self.weight.shape[2])).contiguous()
        x = x.reshape(x.shape[0], -1, x.shape[-3], x.shape[-2], x.shape[-1])

        # pre-multiply x with the quadrature weights
        x = self.quad_weights * x

        psi = self.get_psi(x.is_cuda and use_triton_kernel)

        if x.is_cuda and use_triton_kernel:
            out = _disco_s2_transpose_contraction_triton(x, psi, self.nlon_out)
        else:
            out = _disco_s2_transpose_contraction_torch(x, psi, self.nlon_out)

        if self.bias is not None:
            out = out + self.bias.reshape(1, -1, 1, 1)

        return out