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80cf062 | 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 | from __future__ import annotations
import torch
from torch import nn
from torch.nn import functional as F
class Mlp(nn.Module):
def __init__(
self,
in_features: int,
hidden_features: int | None = None,
out_features: int | None = None,
act_layer: type[nn.Module] = nn.GELU,
drop: float = 0.0,
) -> None:
super().__init__()
hidden_features = hidden_features or in_features
out_features = out_features or in_features
self.fc1 = nn.Linear(in_features, hidden_features)
self.act = act_layer()
self.fc2 = nn.Linear(hidden_features, out_features)
self.drop = nn.Dropout(drop)
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = self.fc1(x)
x = self.act(x)
x = self.drop(x)
x = self.fc2(x)
return self.drop(x)
class AFNO2D(nn.Module):
"""Adaptive Fourier mixing over a channel-last 2D patch grid."""
def __init__(
self,
hidden_size: int,
num_blocks: int = 8,
sparsity_threshold: float = 0.01,
hard_thresholding_fraction: float = 1.0,
hidden_size_factor: int = 1,
) -> None:
super().__init__()
if hidden_size % num_blocks != 0:
raise ValueError(f"hidden_size={hidden_size} must be divisible by num_blocks={num_blocks}.")
if not 0.0 < hard_thresholding_fraction <= 1.0:
raise ValueError("hard_thresholding_fraction must be in (0, 1].")
self.hidden_size = hidden_size
self.sparsity_threshold = sparsity_threshold
self.num_blocks = num_blocks
self.block_size = hidden_size // num_blocks
self.hard_thresholding_fraction = hard_thresholding_fraction
self.hidden_size_factor = hidden_size_factor
scale = 0.02
self.w1 = nn.Parameter(
scale * torch.randn(2, num_blocks, self.block_size, self.block_size * hidden_size_factor)
)
self.b1 = nn.Parameter(scale * torch.randn(2, num_blocks, self.block_size * hidden_size_factor))
self.w2 = nn.Parameter(
scale * torch.randn(2, num_blocks, self.block_size * hidden_size_factor, self.block_size)
)
self.b2 = nn.Parameter(scale * torch.randn(2, num_blocks, self.block_size))
def forward(self, x: torch.Tensor) -> torch.Tensor:
if x.ndim != 4 or x.shape[-1] != self.hidden_size:
raise ValueError(f"AFNO2D expects [B,H,W,{self.hidden_size}], got {tuple(x.shape)}.")
bias = x
dtype = x.dtype
x = torch.fft.rfft2(x.float(), dim=(1, 2), norm="ortho")
batch, height, freq_width, _ = x.shape
x = x.reshape(batch, height, freq_width, self.num_blocks, self.block_size)
hidden_block = self.block_size * self.hidden_size_factor
o1_real = torch.zeros(
batch, height, freq_width, self.num_blocks, hidden_block, device=x.device, dtype=x.real.dtype
)
o1_imag = torch.zeros_like(o1_real)
o2_real = torch.zeros_like(x.real)
o2_imag = torch.zeros_like(x.real)
total_modes = height // 2 + 1
kept_modes = max(1, int(total_modes * self.hard_thresholding_fraction))
row_slice = slice(total_modes - kept_modes, total_modes + kept_modes)
col_slice = slice(0, min(kept_modes, freq_width))
selected = x[:, row_slice, col_slice]
o1_real[:, row_slice, col_slice] = F.relu(
torch.einsum("...bi,bio->...bo", selected.real, self.w1[0])
- torch.einsum("...bi,bio->...bo", selected.imag, self.w1[1])
+ self.b1[0]
)
o1_imag[:, row_slice, col_slice] = F.relu(
torch.einsum("...bi,bio->...bo", selected.imag, self.w1[0])
+ torch.einsum("...bi,bio->...bo", selected.real, self.w1[1])
+ self.b1[1]
)
hidden_real = o1_real[:, row_slice, col_slice]
hidden_imag = o1_imag[:, row_slice, col_slice]
o2_real[:, row_slice, col_slice] = (
torch.einsum("...bi,bio->...bo", hidden_real, self.w2[0])
- torch.einsum("...bi,bio->...bo", hidden_imag, self.w2[1])
+ self.b2[0]
)
o2_imag[:, row_slice, col_slice] = (
torch.einsum("...bi,bio->...bo", hidden_imag, self.w2[0])
+ torch.einsum("...bi,bio->...bo", hidden_real, self.w2[1])
+ self.b2[1]
)
x = torch.stack((o2_real, o2_imag), dim=-1)
x = F.softshrink(x, lambd=self.sparsity_threshold)
x = torch.view_as_complex(x)
x = x.reshape(batch, height, freq_width, self.hidden_size)
x = torch.fft.irfft2(x, s=bias.shape[1:3], dim=(1, 2), norm="ortho")
return x.to(dtype=dtype) + bias
class Block(nn.Module):
def __init__(
self,
dim: int,
mlp_ratio: float = 4.0,
drop: float = 0.0,
act_layer: type[nn.Module] = nn.GELU,
norm_layer: type[nn.Module] = nn.LayerNorm,
double_skip: bool = True,
num_blocks: int = 8,
sparsity_threshold: float = 0.01,
hard_thresholding_fraction: float = 1.0,
) -> None:
super().__init__()
self.norm1 = norm_layer(dim)
self.filter = AFNO2D(dim, num_blocks, sparsity_threshold, hard_thresholding_fraction)
self.drop_path = nn.Identity()
self.norm2 = norm_layer(dim)
self.mlp = Mlp(dim, int(dim * mlp_ratio), act_layer=act_layer, drop=drop)
self.double_skip = double_skip
def forward(self, x: torch.Tensor) -> torch.Tensor:
residual = x
x = self.filter(self.norm1(x))
if self.double_skip:
x = x + residual
residual = x
x = self.drop_path(self.mlp(self.norm2(x)))
return x + residual
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