_disco_convolution.py

# coding=utf-8

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from typing import Optional
import math

import torch
from torch.amp import custom_fwd, custom_bwd

try:
    import disco_cuda_extension
except ImportError as err:
    disco_cuda_extension = None

# some helper functions
def _get_psi(kernel_size: int, psi_idx: torch.Tensor, psi_vals: torch.Tensor, nlat_in: int, nlon_in: int, nlat_out: int, nlon_out: int, nlat_in_local: Optional[int] = None, nlat_out_local: Optional[int] = None, semi_transposed: Optional[bool] = False):

    nlat_in_local = nlat_in_local if nlat_in_local is not None else nlat_in
    nlat_out_local = nlat_out_local if nlat_out_local is not None else nlat_out
    
    if semi_transposed:
        # do partial transpose
        # we do a semi-transposition to faciliate the computation
        tout = psi_idx[2] // nlon_out
        pout = psi_idx[2] % nlon_out
        # flip the axis of longitudes
        pout = nlon_out - 1 - pout
        tin = psi_idx[1]
        idx = torch.stack([psi_idx[0], tout, tin * nlon_out + pout], dim=0)
        psi = torch.sparse_coo_tensor(idx, psi_vals, size=(kernel_size, nlat_out_local, nlat_in_local * nlon_out)).coalesce()
    else:
        psi = torch.sparse_coo_tensor(psi_idx, psi_vals, size=(kernel_size, nlat_out_local, nlat_in_local * nlon_in)).coalesce()
    return psi


class _DiscoS2ContractionCuda(torch.autograd.Function):

    @staticmethod
    @custom_fwd(device_type="cuda")
    def forward(ctx, x: torch.Tensor, roff_idx: torch.Tensor, ker_idx: torch.Tensor,
                row_idx: torch.Tensor, col_idx: torch.Tensor, vals: torch.Tensor,
                kernel_size: int, nlat_out: int, nlon_out: int):
        
        ctx.save_for_backward(roff_idx, ker_idx, row_idx, col_idx, vals)
        ctx.kernel_size = kernel_size
        ctx.nlat_in = x.shape[-2]
        ctx.nlon_in = x.shape[-1]
        xtype = x.dtype
        x = x.to(torch.float32).contiguous()
        output = disco_cuda_extension.forward(x, roff_idx, ker_idx, row_idx, col_idx, vals, kernel_size, nlat_out, nlon_out)
        output = output.to(xtype)

        return output

    @staticmethod
    @custom_bwd(device_type="cuda")
    def backward(ctx, grad_output):

        roff_idx, ker_idx, row_idx, col_idx, vals = ctx.saved_tensors
        gtype =	grad_output.dtype
        grad_output = grad_output.to(torch.float32).contiguous()
        grad_input = disco_cuda_extension.backward(grad_output, roff_idx, ker_idx, row_idx, col_idx, vals,
                                         ctx.kernel_size, ctx.nlat_in, ctx.nlon_in)
        grad_input = grad_input.to(gtype)

        return grad_input, None, None, None, None, None, None, None, None


class _DiscoS2TransposeContractionCuda(torch.autograd.Function):

    @staticmethod
    @custom_fwd(device_type="cuda")
    def forward(ctx, x: torch.Tensor, roff_idx: torch.Tensor, ker_idx: torch.Tensor,
                row_idx: torch.Tensor, col_idx: torch.Tensor, vals: torch.Tensor,
                kernel_size: int, nlat_out: int, nlon_out: int):
        
        ctx.save_for_backward(roff_idx, ker_idx, row_idx, col_idx, vals)
        ctx.kernel_size = kernel_size
        ctx.nlat_in = x.shape[-2]
        ctx.nlon_in = x.shape[-1]
        xtype =	x.dtype
        x = x.to(torch.float32).contiguous()
        output = disco_cuda_extension.backward(x, roff_idx, ker_idx, row_idx, col_idx, vals, kernel_size, nlat_out, nlon_out)
        output = output.to(xtype)

        return output

    @staticmethod
    @custom_bwd(device_type="cuda")
    def backward(ctx, grad_output):
       
        roff_idx, ker_idx, row_idx, col_idx, vals = ctx.saved_tensors
        gtype = grad_output.dtype
        grad_output = grad_output.to(torch.float32).contiguous()
        grad_input = disco_cuda_extension.forward(grad_output, roff_idx, ker_idx, row_idx, col_idx, vals,
                                        ctx.kernel_size, ctx.nlat_in, ctx.nlon_in)
        grad_input = grad_input.to(gtype)

        return grad_input, None, None, None, None, None, None, None, None

# CUDA
def _disco_s2_contraction_cuda(x: torch.Tensor, roff_idx: torch.Tensor, ker_idx: torch.Tensor,
                               row_idx: torch.Tensor, col_idx: torch.Tensor, vals: torch.Tensor,
                               kernel_size: int, nlat_out: int, nlon_out: int) -> torch.Tensor:
    return _DiscoS2ContractionCuda.apply(x, roff_idx, ker_idx, row_idx, col_idx, vals,
                                         kernel_size, nlat_out, nlon_out)

def _disco_s2_transpose_contraction_cuda(x: torch.Tensor, roff_idx: torch.Tensor, ker_idx: torch.Tensor,
                                         row_idx: torch.Tensor, col_idx: torch.Tensor, vals: torch.Tensor,
                                         kernel_size: int, nlat_out: int, nlon_out: int) -> torch.Tensor:
    return _DiscoS2TransposeContractionCuda.apply(x, roff_idx, ker_idx, row_idx, col_idx, vals,
                                                  kernel_size, nlat_out, nlon_out)


def _disco_s2_contraction_torch(x: torch.Tensor, psi: torch.Tensor, nlon_out: int):
    """
    Reference implementation of the custom contraction as described in [1]. This requires repeated
    shifting of the input tensor, which can potentially be costly. For an efficient implementation
    on GPU, make sure to use the custom kernel written in CUDA.

    Parameters
    -----------
    x: torch.Tensor
        Input tensor
    psi: torch.Tensor
        Kernel tensor
    nlon_out: int   
        Number of output longitude points

    Returns
    --------
    y: torch.Tensor
        Output tensor
    """
    assert len(psi.shape) == 3
    assert len(x.shape) == 4
    psi = psi.to(x.device)

    batch_size, n_chans, nlat_in, nlon_in = x.shape
    kernel_size, nlat_out, _ = psi.shape

    assert psi.shape[-1] == nlat_in * nlon_in
    assert nlon_in % nlon_out == 0
    assert nlon_in >= nlat_out
    pscale = nlon_in // nlon_out

    # add a dummy dimension for nkernel and move the batch and channel dims to the end
    x = x.reshape(1, batch_size * n_chans, nlat_in, nlon_in).permute(0, 2, 3, 1)
    x = x.expand(kernel_size, -1, -1, -1)

    y = torch.zeros(nlon_out, kernel_size, nlat_out, batch_size * n_chans, device=x.device, dtype=x.dtype)

    for pout in range(nlon_out):
        # sparse contraction with psi
        y[pout] = torch.bmm(psi, x.reshape(kernel_size, nlat_in * nlon_in, -1))
        # we need to repeatedly roll the input tensor to faciliate the shifted multiplication
        x = torch.roll(x, -pscale, dims=2)

    # reshape y back to expose the correct dimensions
    y = y.permute(3, 1, 2, 0).reshape(batch_size, n_chans, kernel_size, nlat_out, nlon_out)

    return y


def _disco_s2_transpose_contraction_torch(x: torch.Tensor, psi: torch.Tensor, nlon_out: int):
    """
    Reference implementation of the custom contraction as described in [1]. This requires repeated
    shifting of the input tensor, which can potentially be costly. For an efficient implementation
    on GPU, make sure to use the custom kernel written in CUDA.

    Parameters
    -----------
    x: torch.Tensor
        Input tensor
    psi: torch.Tensor
        Kernel tensor   
    nlon_out: int
        Number of output longitude points

    Returns
    --------
    y: torch.Tensor
        Output tensor
    """
    assert len(psi.shape) == 3
    assert len(x.shape) == 5
    psi = psi.to(x.device)

    batch_size, n_chans, kernel_size, nlat_in, nlon_in = x.shape
    kernel_size, nlat_out, n_out = psi.shape

    assert n_out % nlon_out == 0
    assert nlon_out >= nlon_in
    pscale = nlon_out // nlon_in

    # interleave zeros along the longitude dimension to allow for fractional offsets to be considered
    x_ext = torch.zeros(kernel_size, nlat_in, nlon_out, batch_size * n_chans, device=x.device, dtype=x.dtype)
    x = x.reshape(batch_size * n_chans, kernel_size, nlat_in, nlon_in).permute(1, 2, 3, 0)

    # x has shape kernel_size x nlat_in x nlon_in x batch_size * n_chans
    # we only need to apoply the nlon stride here, since nlat stride is taken care of by the kernel
    x_ext[:, :, ::pscale, :] = x[...]

    # create output tensor
    y = torch.zeros(kernel_size, nlon_out, nlat_out, batch_size * n_chans, device=x.device, dtype=x.dtype)

    for pout in range(nlon_out):
        # we need to repeatedly roll the input tensor to faciliate the shifted multiplication
        # TODO: double-check why this has to happen first
        x_ext = torch.roll(x_ext, -1, dims=2)
        # sparse contraction with the modified psi
        y[:, pout, :, :] = torch.bmm(psi, x_ext.reshape(kernel_size, nlat_in * nlon_out, -1))

    # sum over the kernel dimension and reshape to the correct output size
    y = y.sum(dim=0).permute(2, 1, 0).reshape(batch_size, n_chans, nlat_out, nlon_out).contiguous()

    return y