File size: 2,514 Bytes
3e77c56 | 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 | """Building blocks: MLP and the pre-norm Transolver block.
Faithful to Transolver ``model/Transolver_Irregular_Mesh.py`` (MIT). The only structural
change is that the attention module is injected (so Stage 2 can swap Physics-Attention for
LinearNO without touching anything else).
"""
import torch.nn as nn
ACTIVATION = {
"gelu": nn.GELU,
"tanh": nn.Tanh,
"sigmoid": nn.Sigmoid,
"relu": nn.ReLU,
"leaky_relu": lambda: nn.LeakyReLU(0.1),
"softplus": nn.Softplus,
"ELU": nn.ELU,
"silu": nn.SiLU,
}
class MLP(nn.Module):
"""Transolver MLP: Linear->act (->[Linear->act]^n_layers) ->Linear, optional residual."""
def __init__(self, n_input, n_hidden, n_output, n_layers=1, act="gelu", res=True):
super().__init__()
if act not in ACTIVATION:
raise NotImplementedError(act)
act_cls = ACTIVATION[act]
self.n_layers = n_layers
self.res = res
self.linear_pre = nn.Sequential(nn.Linear(n_input, n_hidden), act_cls())
self.linear_post = nn.Linear(n_hidden, n_output)
self.linears = nn.ModuleList(
[nn.Sequential(nn.Linear(n_hidden, n_hidden), act_cls()) for _ in range(n_layers)]
)
def forward(self, x):
x = self.linear_pre(x)
for layer in self.linears:
x = layer(x) + x if self.res else layer(x)
return self.linear_post(x)
class TransolverBlock(nn.Module):
"""Pre-norm transformer block; the last block also carries the decoder head.
fx = fx + Attn(LayerNorm(fx))
fx = fx + MLP(LayerNorm(fx))
(last block) return Linear(LayerNorm(fx)) -> out_dim
"""
def __init__(
self,
attention: nn.Module,
hidden_dim: int,
dropout: float = 0.0,
act: str = "gelu",
mlp_ratio: int = 1,
last_layer: bool = False,
out_dim: int = 1,
):
super().__init__()
self.last_layer = last_layer
self.ln_1 = nn.LayerNorm(hidden_dim)
self.Attn = attention
self.ln_2 = nn.LayerNorm(hidden_dim)
self.mlp = MLP(hidden_dim, hidden_dim * mlp_ratio, hidden_dim, n_layers=0, res=False, act=act)
if last_layer:
self.ln_3 = nn.LayerNorm(hidden_dim)
self.mlp2 = nn.Linear(hidden_dim, out_dim)
def forward(self, fx):
fx = self.Attn(self.ln_1(fx)) + fx
fx = self.mlp(self.ln_2(fx)) + fx
if self.last_layer:
return self.mlp2(self.ln_3(fx))
return fx
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