_disco_convolution.py

# coding=utf-8

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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


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.
    """
    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.
    """
    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