File size: 9,744 Bytes
73d9e73 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 | # coding=utf-8
# SPDX-FileCopyrightText: Copyright (c) 2022 The torch-harmonics Authors. All rights reserved.
# SPDX-License-Identifier: BSD-3-Clause
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#
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):
"""Creates a sparse tensor for spherical harmonic convolution operations."""
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.
"""
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):
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
|