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# import torch.nn as nn
# import torch.nn.functional as F
# import numpy as np
# from timm.layers import trunc_normal_
# from onescience.modules.layer.layers.Basic import MLP
# from onescience.modules.attention.linearattention import LinearAttention
# from onescience.modules.embedding import timestep_embedding, unified_pos_embedding
# from einops import rearrange, repeat
# from einops.layers.torch import Rearrange
# class Galerkin_Transformer_block(nn.Module):
# """Transformer encoder block."""
# def __init__(
# self,
# num_heads: int,
# hidden_dim: int,
# dropout: float,
# act="gelu",
# mlp_ratio=4,
# last_layer=False,
# out_dim=1,
# ):
# super().__init__()
# self.last_layer = last_layer
# self.ln_1 = nn.LayerNorm(hidden_dim)
# self.ln_1a = nn.LayerNorm(hidden_dim)
# self.Attn = LinearAttention(
# hidden_dim,
# heads=num_heads,
# dim_head=hidden_dim // num_heads,
# dropout=dropout,
# attn_type="galerkin",
# )
# 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 self.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), self.ln_1a(fx)) + fx
# fx = self.mlp(self.ln_2(fx)) + fx
# if self.last_layer:
# return self.mlp2(self.ln_3(fx))
# else:
# return fx
# class Model(nn.Module):
# ## Galerkin_Transformer
# def __init__(self, args, device):
# super(Model, self).__init__()
# self.__name__ = "Galerkin_Transformer"
# self.args = args
# ## embedding
# if (
# args.unified_pos and args.geotype != "unstructured"
# ): # only for structured mesh
# self.pos = unified_pos_embedding(args.shapelist, args.ref, device=device)
# self.preprocess = MLP(
# args.fun_dim + args.ref ** len(args.shapelist),
# args.n_hidden * 2,
# args.n_hidden,
# n_layers=0,
# res=False,
# act=args.act,
# )
# else:
# self.preprocess = MLP(
# args.fun_dim + args.space_dim,
# args.n_hidden * 2,
# args.n_hidden,
# n_layers=0,
# res=False,
# act=args.act,
# )
# if args.time_input:
# self.time_fc = nn.Sequential(
# nn.Linear(args.n_hidden, args.n_hidden),
# nn.SiLU(),
# nn.Linear(args.n_hidden, args.n_hidden),
# )
# ## models
# self.blocks = nn.ModuleList(
# [
# Galerkin_Transformer_block(
# num_heads=args.n_heads,
# hidden_dim=args.n_hidden,
# dropout=args.dropout,
# act=args.act,
# mlp_ratio=args.mlp_ratio,
# out_dim=args.out_dim,
# last_layer=(_ == args.n_layers - 1),
# )
# for _ in range(args.n_layers)
# ]
# )
# self.placeholder = nn.Parameter(
# (1 / (args.n_hidden)) * torch.rand(args.n_hidden, dtype=torch.float)
# )
# self.initialize_weights()
# def initialize_weights(self):
# self.apply(self._init_weights)
# def _init_weights(self, m):
# if isinstance(m, nn.Linear):
# trunc_normal_(m.weight, std=0.02)
# if isinstance(m, nn.Linear) and 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 forward(self, x, fx, T=None, geo=None):
# if self.args.unified_pos:
# x = self.pos.repeat(x.shape[0], 1, 1)
# if fx is not None:
# fx = torch.cat((x, fx), -1)
# fx = self.preprocess(fx)
# else:
# fx = self.preprocess(x)
# fx = fx + self.placeholder[None, None, :]
# if T is not None:
# Time_emb = timestep_embedding(T, self.args.n_hidden) # (B, C)
# Time_emb = self.time_fc(Time_emb) # (B, C)
# if Time_emb.ndim == 2:
# Time_emb = Time_emb.unsqueeze(1) # (B, 1, C)
# fx = fx + Time_emb # Broadcasting: (B, N, C) + (B, 1, C) -> (B, N, C)
# for block in self.blocks:
# fx = block(fx)
# return fx
import torch
import torch.nn as nn
from timm.layers import trunc_normal_
# --- 引入模块工厂 ---
from onescience.modules.mlp.MLP import StandardMLP
from onescience.modules.transformer.galerkin_transformer_block import Galerkin_Transformer_block
from onescience.modules.embedding import timestep_embedding, unified_pos_embedding
class Model(nn.Module):
"""
Galerkin Transformer 模型。
使用 Galerkin 线性注意力机制处理物理场数据的 Transformer 架构。
"""
def __init__(self, args, device):
super(Model, self).__init__()
self.__name__ = "Galerkin_Transformer"
self.args = args
# 1. Embedding & Preprocessing
# -----------------------------------------------------------
input_dim = args.fun_dim
if args.unified_pos and args.geotype != "unstructured":
self.pos = unified_pos_embedding(args.shapelist, args.ref, device=device)
input_dim += args.ref ** len(args.shapelist)
else:
input_dim += args.space_dim
# 对应原代码: MLP(input_dim, hidden*2, hidden, n_layers=0)
self.preprocess = StandardMLP(
input_dim=input_dim,
output_dim=args.n_hidden,
hidden_dims=[args.n_hidden * 2], # 中间层
activation=args.act,
use_bias=True
)
if args.time_input:
self.time_fc = nn.Sequential(
nn.Linear(args.n_hidden, args.n_hidden),
nn.SiLU(),
nn.Linear(args.n_hidden, args.n_hidden),
)
# 2. Transformer Blocks
# -----------------------------------------------------------
# 使用工厂实例化 Galerkin_Transformer_block
self.blocks = nn.ModuleList([
Galerkin_Transformer_block(
num_heads=args.n_heads,
hidden_dim=args.n_hidden,
dropout=args.dropout,
act=args.act,
mlp_ratio=args.mlp_ratio,
out_dim=args.out_dim,
last_layer=(_ == args.n_layers - 1)
)
for _ in range(args.n_layers)
])
self.placeholder = nn.Parameter(
(1 / (args.n_hidden)) * torch.rand(args.n_hidden, dtype=torch.float)
)
self.initialize_weights()
def initialize_weights(self):
self.apply(self._init_weights)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=0.02)
if isinstance(m, nn.Linear) and 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 forward(self, x, fx, T=None, geo=None):
if self.args.unified_pos:
x = self.pos.repeat(x.shape[0], 1, 1)
if fx is not None:
fx = torch.cat((x, fx), -1)
fx = self.preprocess(fx)
else:
fx = self.preprocess(x)
fx = fx + self.placeholder[None, None, :]
if T is not None:
Time_emb = timestep_embedding(T, self.args.n_hidden) # (B, C)
Time_emb = self.time_fc(Time_emb) # (B, C)
if Time_emb.ndim == 2:
Time_emb = Time_emb.unsqueeze(1) # (B, 1, C)
fx = fx + Time_emb # Broadcasting: (B, N, C) + (B, 1, C) -> (B, N, C)
for block in self.blocks:
fx = block(fx)
return fx
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