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3e77c56 2c93889 3e77c56 2c93889 3e77c56 2c93889 3e77c56 2c93889 3e77c56 2c93889 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 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 | """Assemble the stress operator: encoder -> N pre-norm blocks -> decoder head.
Faithful reproduction of Transolver ``model/Transolver_Irregular_Mesh.py::Model`` (MIT). The
attention type is configurable (``physics`` for Stage 1, ``linearno`` for Stage 2) and is the
*only* thing that changes between gates — a controlled comparison.
Reproduction note: ``initialize_weights()`` runs after the blocks are built, so the global
``trunc_normal_(std=0.02)`` init is applied to every Linear, **overwriting** the orthogonal
init of each attention's ``in_project_slice``. This matches the upstream assembly order.
"""
from __future__ import annotations
import numpy as np
import torch
import torch.nn as nn
from .blocks import MLP, TransolverBlock
from .physics_attention import Physics_Attention_Irregular_Mesh
def make_attention(
kind: str,
dim,
heads,
dim_head,
dropout,
slice_num,
linearno_variant: str = "shared_qk",
linearno_project_out: bool = False,
linearno_temperature: bool = False,
) -> nn.Module:
if kind == "physics":
return Physics_Attention_Irregular_Mesh(
dim, heads=heads, dim_head=dim_head, dropout=dropout, slice_num=slice_num
)
if kind == "linearno":
from .linear_no import LinearNO # lazy: only needed at Stage 2
return LinearNO(
dim,
heads=heads,
dim_head=dim_head,
slice_num=slice_num,
dropout=dropout,
variant=linearno_variant,
project_out=linearno_project_out,
temperature=linearno_temperature,
)
raise ValueError(f"unknown attention kind {kind!r} (expected 'physics' or 'linearno')")
class StressOperator(nn.Module):
def __init__(
self,
attention: str = "physics",
space_dim: int = 2,
n_layers: int = 8,
n_hidden: int = 128,
dropout: float = 0.0,
n_heads: int = 8,
dim_head: int | None = None,
mlp_ratio: int = 1,
fun_dim: int = 0,
out_dim: int = 1,
slice_num: int = 64,
unified_pos: bool = False,
ref: int = 8,
act: str = "gelu",
linearno_variant: str = "shared_qk",
linearno_project_out: bool = False,
linearno_temperature: bool = False,
):
super().__init__()
if dim_head is None:
dim_head = n_hidden // n_heads # = 16 for the Elasticity config (repo value)
self.attention_kind = attention
self.unified_pos = unified_pos
self.ref = ref
self.n_hidden = n_hidden
in_dim = (fun_dim + ref * ref) if unified_pos else (fun_dim + space_dim)
self.preprocess = MLP(in_dim, n_hidden * 2, n_hidden, n_layers=0, res=False, act=act)
self.blocks = nn.ModuleList(
[
TransolverBlock(
attention=make_attention(
attention, n_hidden, n_heads, dim_head, dropout, slice_num,
linearno_variant=linearno_variant,
linearno_project_out=linearno_project_out,
linearno_temperature=linearno_temperature,
),
hidden_dim=n_hidden,
dropout=dropout,
act=act,
mlp_ratio=mlp_ratio,
last_layer=(i == n_layers - 1),
out_dim=out_dim,
)
for i in range(n_layers)
]
)
self.initialize_weights()
self.placeholder = nn.Parameter((1 / n_hidden) * torch.rand(n_hidden, dtype=torch.float))
def initialize_weights(self):
self.apply(self._init_weights)
@staticmethod
def _init_weights(m):
if isinstance(m, nn.Linear):
nn.init.trunc_normal_(m.weight, std=0.02)
if m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, (nn.LayerNorm, nn.BatchNorm1d)):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
def get_grid(self, x):
"""Unified positional grid (only used when ``unified_pos`` is True). Device-agnostic."""
b = x.shape[0]
device = x.device
gx = torch.linspace(0, 1, self.ref, device=device).reshape(1, self.ref, 1, 1).repeat(b, 1, self.ref, 1)
gy = torch.linspace(0, 1, self.ref, device=device).reshape(1, 1, self.ref, 1).repeat(b, self.ref, 1, 1)
grid_ref = torch.cat((gx, gy), dim=-1).reshape(b, self.ref * self.ref, 2)
pos = torch.sqrt(((x[:, :, None, :] - grid_ref[:, None, :, :]) ** 2).sum(-1))
return pos.reshape(b, x.shape[1], self.ref * self.ref).contiguous()
def forward(self, x, fx=None):
# x: (B, N, space_dim) node coordinates; fx: optional extra input function
if self.unified_pos:
x = self.get_grid(x)
fx = self.preprocess(x if fx is None else torch.cat((x, fx), dim=-1))
fx = fx + self.placeholder[None, None, :]
for block in self.blocks:
fx = block(fx)
return fx # (B, N, out_dim)
def build_model(model_cfg: dict) -> StressOperator:
"""Instantiate :class:`StressOperator` from a config dict (configs/*.yaml ``model`` block)."""
return StressOperator(
attention=model_cfg.get("attention", "physics"),
space_dim=model_cfg.get("space_dim", 2),
n_layers=model_cfg.get("n_layers", 8),
n_hidden=model_cfg.get("n_hidden", 128),
dropout=model_cfg.get("dropout", 0.0),
n_heads=model_cfg.get("n_heads", 8),
dim_head=model_cfg.get("dim_head", None),
mlp_ratio=model_cfg.get("mlp_ratio", 1),
fun_dim=model_cfg.get("fun_dim", 0),
out_dim=model_cfg.get("out_dim", 1),
slice_num=model_cfg.get("slice_num", 64),
unified_pos=model_cfg.get("unified_pos", False),
ref=model_cfg.get("ref", 8),
act=model_cfg.get("act", "gelu"),
linearno_variant=model_cfg.get("linearno_variant", "shared_qk"),
linearno_project_out=model_cfg.get("linearno_project_out", False),
linearno_temperature=model_cfg.get("linearno_temperature", False),
)
def count_parameters(model: nn.Module) -> int:
return sum(p.numel() for p in model.parameters() if p.requires_grad)
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