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eca4864 | 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 | # SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES.
# SPDX-FileCopyrightText: All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
# Helper routines for FNOs
@torch.jit.script
def compl_contract2d_fwd(a: torch.Tensor, b: torch.Tensor) -> torch.Tensor:
tmp = torch.einsum("bixys,kixyr->srbkxy", a, b)
res = torch.stack(
[tmp[0, 0, ...] - tmp[1, 1, ...], tmp[1, 0, ...] + tmp[0, 1, ...]], dim=-1
)
return res
@torch.jit.script
def compl_contract2d_fwd_c(a: torch.Tensor, b: torch.Tensor) -> torch.Tensor:
ac = torch.view_as_complex(a)
bc = torch.view_as_complex(b)
res = torch.einsum("bixy,kixy->bkxy", ac, bc)
return torch.view_as_real(res)
@torch.jit.script
def compl_contract_fwd(a: torch.Tensor, b: torch.Tensor) -> torch.Tensor:
tmp = torch.einsum("bins,kinr->srbkn", a, b)
res = torch.stack(
[tmp[0, 0, ...] - tmp[1, 1, ...], tmp[1, 0, ...] + tmp[0, 1, ...]], dim=-1
)
return res
@torch.jit.script
def compl_contract_fwd_c(a: torch.Tensor, b: torch.Tensor) -> torch.Tensor:
ac = torch.view_as_complex(a)
bc = torch.view_as_complex(b)
res = torch.einsum("bin,kin->bkn", ac, bc)
return torch.view_as_real(res)
@torch.jit.script
def compl_ttc1_c_fwd(a: torch.Tensor, b: torch.Tensor) -> torch.Tensor:
ac = torch.view_as_complex(a)
bc = torch.view_as_complex(b)
res = torch.einsum("jt,bct->jbct", ac, bc)
return torch.view_as_real(res)
@torch.jit.script
def compl_ttc2_c_fwd(a: torch.Tensor, b: torch.Tensor, c: torch.Tensor) -> torch.Tensor:
ac = torch.view_as_complex(a)
bc = torch.view_as_complex(b)
cc = torch.view_as_complex(c)
res = torch.einsum("oi,icj,jbct->bot", ac, bc, cc)
return torch.view_as_real(res)
def contract_tt(x, w):
y = compl_ttc1_c_fwd(w[2], x)
return compl_ttc2_c_fwd(w[0], w[1], y)
# Helper routines for spherical MLPs
@torch.jit.script
def compl_mul1d_fwd(a: torch.Tensor, b: torch.Tensor) -> torch.Tensor:
tmp = torch.einsum("bixs,ior->srbox", a, b)
res = torch.stack(
[tmp[0, 0, ...] - tmp[1, 1, ...], tmp[1, 0, ...] + tmp[0, 1, ...]], dim=-1
)
return res
@torch.jit.script
def compl_mul1d_fwd_c(a: torch.Tensor, b: torch.Tensor) -> torch.Tensor:
ac = torch.view_as_complex(a)
bc = torch.view_as_complex(b)
resc = torch.einsum("bix,io->box", ac, bc)
res = torch.view_as_real(resc)
return res
@torch.jit.script
def compl_muladd1d_fwd(
a: torch.Tensor, b: torch.Tensor, c: torch.Tensor
) -> torch.Tensor:
res = compl_mul1d_fwd(a, b) + c
return res
@torch.jit.script
def compl_muladd1d_fwd_c(
a: torch.Tensor, b: torch.Tensor, c: torch.Tensor
) -> torch.Tensor:
tmpcc = torch.view_as_complex(compl_mul1d_fwd_c(a, b))
cc = torch.view_as_complex(c)
return torch.view_as_real(tmpcc + cc)
# for the real-valued case:
@torch.jit.script
def compl_mul1d_fwd_r(a: torch.Tensor, b: torch.Tensor) -> torch.Tensor:
res = torch.einsum("bix,io->box", a, b)
return res
@torch.jit.script
def compl_muladd1d_fwd_r(
a: torch.Tensor, b: torch.Tensor, c: torch.Tensor
) -> torch.Tensor:
tmp = compl_mul1d_fwd_r(a, b)
return tmp + c
# Helper routines for FFT MLPs
@torch.jit.script
def compl_mul2d_fwd(a: torch.Tensor, b: torch.Tensor) -> torch.Tensor:
tmp = torch.einsum("bixys,ior->srboxy", a, b)
res = torch.stack(
[tmp[0, 0, ...] - tmp[1, 1, ...], tmp[1, 0, ...] + tmp[0, 1, ...]], dim=-1
)
return res
@torch.jit.script
def compl_mul2d_fwd_c(a: torch.Tensor, b: torch.Tensor) -> torch.Tensor:
ac = torch.view_as_complex(a)
bc = torch.view_as_complex(b)
resc = torch.einsum("bixy,io->boxy", ac, bc)
res = torch.view_as_real(resc)
return res
@torch.jit.script
def compl_muladd2d_fwd(
a: torch.Tensor, b: torch.Tensor, c: torch.Tensor
) -> torch.Tensor:
res = compl_mul2d_fwd(a, b) + c
return res
@torch.jit.script
def compl_muladd2d_fwd_c(
a: torch.Tensor, b: torch.Tensor, c: torch.Tensor
) -> torch.Tensor:
tmpcc = torch.view_as_complex(compl_mul2d_fwd_c(a, b))
cc = torch.view_as_complex(c)
return torch.view_as_real(tmpcc + cc)
# for the real-valued case:
@torch.jit.script
def compl_mul2d_fwd_r(a: torch.Tensor, b: torch.Tensor) -> torch.Tensor:
res = torch.einsum("bixy,io->boxy", a, b)
return res
@torch.jit.script
def compl_muladd2d_fwd_r(
a: torch.Tensor, b: torch.Tensor, c: torch.Tensor
) -> torch.Tensor:
tmp = compl_mul2d_fwd_c(a, b)
return torch.view_as_real(tmp + c)
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