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添加STGODE_LLM和STGODE_LLM_GPT2模型实现，更新配置文件和README

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czzhangheng 2025-09-04 11:12:11 +08:00
parent f0d3460c89
commit e9e3da03d3
10 changed files with 448 additions and 8 deletions
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12
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@ -5,14 +5,10 @@
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@ -66,7 +62,7 @@
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"git-widget-placeholder": "STGODE",
"git-widget-placeholder": "main",
"last_opened_file_path": "/home/czzhangheng/code/Project-I/main.py",
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8
README.md
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@ -1,3 +1,9 @@
# Project-I
Secret Projct
Secret Projct
mkdir -p models/gpt2
Download config.json & pytorch_model.bin from https://huggingface.co/openai-community/gpt2/tree/main
Use pytorch >= 2.6 to load model.

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configs/STGODE_LLM/PEMS08.yaml Normal file
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basic:
device: cuda:0
dataset: PEMS08
model: STGODE-LLM
mode: test
seed: 2025
data:
dataset_dir: data/PEMS08
val_batch_size: 32
graph_pkl_filename: data/PEMS08/PEMS08_spatial_distance.npy
num_nodes: 170
batch_size: 64
input_dim: 1
lag: 12
horizon: 12
val_ratio: 0.2
test_ratio: 0.2
tod: False
normalizer: std
column_wise: False
default_graph: True
add_time_in_day: True
add_day_in_week: True
steps_per_day: 24
days_per_week: 7
model:
input_dim: 1
output_dim: 1
history: 12
horizon: 12
num_features: 1
rnn_units: 64
sigma1: 0.1
sigma2: 10
thres1: 0.6
thres2: 0.5
# LLM backbone settings
llm_hidden: 128
llm_layers: 4
llm_heads: 4
llm_pretrained: True
train:
loss: mae
batch_size: 64
epochs: 100
lr_init: 0.003
mape_thresh: 0.001
mae_thresh: None
debug: False
output_dim: 1
weight_decay: 0
lr_decay: False
lr_decay_rate: 0.3
lr_decay_step: "5,20,40,70"
early_stop: True
early_stop_patience: 15
grad_norm: False
max_grad_norm: 5
real_value: True
log_step: 3000

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configs/STGODE_LLM_GPT2/PEMS08.yaml Normal file
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basic:
device: cuda:0
dataset: PEMS08
model: STGODE-LLM-GPT2
mode: train
seed: 2025
data:
dataset_dir: data/PEMS08
val_batch_size: 16
graph_pkl_filename: data/PEMS08/PEMS08_spatial_distance.npy
num_nodes: 170
batch_size: 32
input_dim: 1
lag: 12
horizon: 12
val_ratio: 0.2
test_ratio: 0.2
tod: False
normalizer: std
column_wise: False
default_graph: True
add_time_in_day: True
add_day_in_week: True
steps_per_day: 24
days_per_week: 7
model:
input_dim: 1
output_dim: 1
history: 12
horizon: 12
num_features: 1
rnn_units: 64
sigma1: 0.1
sigma2: 10
thres1: 0.6
thres2: 0.5
# HF GPT-2 settings
gpt2_name: gpt2
gpt2_grad_ckpt: True
gpt2_freeze: True
gpt2_local_dir: ./models/gpt2
train:
loss: mae
batch_size: 32
epochs: 100
lr_init: 0.0003
mape_thresh: 0.001
mae_thresh: None
debug: False
output_dim: 1
weight_decay: 0
lr_decay: False
lr_decay_rate: 0.3
lr_decay_step: "10,30,60,90"
early_stop: True
early_stop_patience: 15
grad_norm: False
max_grad_norm: 5
real_value: True
log_step: 3000

152
models/STGODE_LLM/STGODE_LLM.py Normal file
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import torch
import math
import torch.nn as nn
import torch.nn.functional as F
from models.STGODE.odegcn import ODEG
from models.STGODE.adj import get_A_hat
class Chomp1d(nn.Module):
def __init__(self, chomp_size):
super(Chomp1d, self).__init__()
self.chomp_size = chomp_size
def forward(self, x):
return x[:, :, :, :-self.chomp_size].contiguous()
class TemporalConvNet(nn.Module):
def __init__(self, num_inputs, num_channels, kernel_size=2, dropout=0.2):
super(TemporalConvNet, self).__init__()
layers = []
num_levels = len(num_channels)
for i in range(num_levels):
dilation_size = 2 ** i
in_channels = num_inputs if i == 0 else num_channels[i - 1]
out_channels = num_channels[i]
padding = (kernel_size - 1) * dilation_size
self.conv = nn.Conv2d(in_channels, out_channels, (1, kernel_size), dilation=(1, dilation_size),
padding=(0, padding))
self.conv.weight.data.normal_(0, 0.01)
self.chomp = Chomp1d(padding)
self.relu = nn.ReLU()
self.dropout = nn.Dropout(dropout)
layers += [nn.Sequential(self.conv, self.chomp, self.relu, self.dropout)]
self.network = nn.Sequential(*layers)
self.downsample = nn.Conv2d(num_inputs, num_channels[-1], (1, 1)) if num_inputs != num_channels[-1] else None
if self.downsample:
self.downsample.weight.data.normal_(0, 0.01)
def forward(self, x):
y = x.permute(0, 3, 1, 2)
y = F.relu(self.network(y) + self.downsample(y) if self.downsample else y)
y = y.permute(0, 2, 3, 1)
return y
class STGCNBlock(nn.Module):
def __init__(self, in_channels, out_channels, num_nodes, A_hat):
super(STGCNBlock, self).__init__()
self.A_hat = A_hat
self.temporal1 = TemporalConvNet(num_inputs=in_channels, num_channels=out_channels)
self.odeg = ODEG(out_channels[-1], 12, A_hat, time=6)
self.temporal2 = TemporalConvNet(num_inputs=out_channels[-1], num_channels=out_channels)
self.batch_norm = nn.BatchNorm2d(num_nodes)
def forward(self, X):
t = self.temporal1(X)
t = self.odeg(t)
t = self.temporal2(F.relu(t))
return self.batch_norm(t)
class GPT2Backbone(nn.Module):
def __init__(self, hidden_size: int, n_layer: int = 4, n_head: int = 4, n_embd: int | None = None, use_pretrained: bool = True):
super().__init__()
self.hidden_size = hidden_size
self.use_transformers = False
self.model = None
if n_embd is None:
n_embd = hidden_size
if use_pretrained:
try:
from transformers import GPT2Model, GPT2Config
config = GPT2Config(n_embd=n_embd, n_layer=n_layer, n_head=n_head, n_positions=1024, n_ctx=1024, vocab_size=1)
self.model = GPT2Model(config)
self.use_transformers = True
except Exception:
self.use_transformers = False
if not self.use_transformers:
encoder_layer = nn.TransformerEncoderLayer(d_model=hidden_size, nhead=n_head, batch_first=True)
self.model = nn.TransformerEncoder(encoder_layer, num_layers=n_layer)
def forward(self, inputs_embeds: torch.Tensor) -> torch.Tensor:
if self.use_transformers:
outputs = self.model(inputs_embeds=inputs_embeds)
return outputs.last_hidden_state
else:
return self.model(inputs_embeds)
class ODEGCN_LLM(nn.Module):
def __init__(self, config):
super(ODEGCN_LLM, self).__init__()
args = config['model']
num_nodes = config['data']['num_nodes']
num_features = args['num_features']
num_timesteps_input = args['history']
num_timesteps_output = args['horizon']
A_sp_hat, A_se_hat = get_A_hat(config)
self.sp_blocks = nn.ModuleList(
[nn.Sequential(
STGCNBlock(in_channels=num_features, out_channels=[64, 32, 64], num_nodes=num_nodes, A_hat=A_sp_hat),
STGCNBlock(in_channels=64, out_channels=[64, 32, 64], num_nodes=num_nodes, A_hat=A_sp_hat)) for _ in range(3)
])
self.se_blocks = nn.ModuleList(
[nn.Sequential(
STGCNBlock(in_channels=num_features, out_channels=[64, 32, 64], num_nodes=num_nodes, A_hat=A_se_hat),
STGCNBlock(in_channels=64, out_channels=[64, 32, 64], num_nodes=num_nodes, A_hat=A_se_hat)) for _ in range(3)
])
self.history = num_timesteps_input
self.horizon = num_timesteps_output
hidden_size = int(args.get('llm_hidden', 128))
llm_layers = int(args.get('llm_layers', 4))
llm_heads = int(args.get('llm_heads', 4))
use_pretrained = bool(args.get('llm_pretrained', True))
self.to_llm_embed = nn.Linear(64, hidden_size)
self.gpt2 = GPT2Backbone(hidden_size=hidden_size, n_layer=llm_layers, n_head=llm_heads, use_pretrained=use_pretrained)
self.proj_head = nn.Sequential(
nn.Linear(hidden_size, hidden_size),
nn.ReLU(),
nn.Linear(hidden_size, self.horizon)
)
def forward(self, x):
x = x[..., 0:1].permute(0, 2, 1, 3)
outs = []
for blk in self.sp_blocks:
outs.append(blk(x))
for blk in self.se_blocks:
outs.append(blk(x))
outs = torch.stack(outs)
x = torch.max(outs, dim=0)[0]
# x: (B, N, T, 64) physical quantities after ODE-based transform
B, N, T, C = x.shape
x = self.to_llm_embed(x) # (B, N, T, H)
x = x.permute(0, 1, 2, 3).contiguous().view(B * N, T, -1) # (B*N, T, H)
llm_hidden = self.gpt2(inputs_embeds=x) # (B*N, T, H)
last_state = llm_hidden[:, -1, :] # (B*N, H)
y = self.proj_head(last_state) # (B*N, horizon)
y = y.view(B, N, self.horizon).permute(0, 2, 1).unsqueeze(-1) # (B, horizon, N, 1)
return y

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models/STGODE_LLM/__init__.py Normal file
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from .STGODE_LLM import ODEGCN_LLM

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models/STGODE_LLM_GPT2/STGODE_LLM_GPT2.py Normal file
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import torch
import torch.nn as nn
import torch.nn.functional as F
from models.STGODE.odegcn import ODEG
from models.STGODE.adj import get_A_hat
class Chomp1d(nn.Module):
def __init__(self, chomp_size):
super(Chomp1d, self).__init__()
self.chomp_size = chomp_size
def forward(self, x):
return x[:, :, :, :-self.chomp_size].contiguous()
class TemporalConvNet(nn.Module):
def __init__(self, num_inputs, num_channels, kernel_size=2, dropout=0.2):
super(TemporalConvNet, self).__init__()
layers = []
num_levels = len(num_channels)
for i in range(num_levels):
dilation_size = 2 ** i
in_channels = num_inputs if i == 0 else num_channels[i - 1]
out_channels = num_channels[i]
padding = (kernel_size - 1) * dilation_size
self.conv = nn.Conv2d(in_channels, out_channels, (1, kernel_size), dilation=(1, dilation_size),
padding=(0, padding))
self.conv.weight.data.normal_(0, 0.01)
self.chomp = Chomp1d(padding)
self.relu = nn.ReLU()
self.dropout = nn.Dropout(dropout)
layers += [nn.Sequential(self.conv, self.chomp, self.relu, self.dropout)]
self.network = nn.Sequential(*layers)
self.downsample = nn.Conv2d(num_inputs, num_channels[-1], (1, 1)) if num_inputs != num_channels[-1] else None
if self.downsample:
self.downsample.weight.data.normal_(0, 0.01)
def forward(self, x):
y = x.permute(0, 3, 1, 2)
y = F.relu(self.network(y) + self.downsample(y) if self.downsample else y)
y = y.permute(0, 2, 3, 1)
return y
class STGCNBlock(nn.Module):
def __init__(self, in_channels, out_channels, num_nodes, A_hat):
super(STGCNBlock, self).__init__()
self.A_hat = A_hat
self.temporal1 = TemporalConvNet(num_inputs=in_channels, num_channels=out_channels)
self.odeg = ODEG(out_channels[-1], 12, A_hat, time=6)
self.temporal2 = TemporalConvNet(num_inputs=out_channels[-1], num_channels=out_channels)
self.batch_norm = nn.BatchNorm2d(num_nodes)
def forward(self, X):
t = self.temporal1(X)
t = self.odeg(t)
t = self.temporal2(F.relu(t))
return self.batch_norm(t)
class GPT2BackboneHF(nn.Module):
def __init__(self, model_name: str | None = None, gradient_checkpointing: bool = False, freeze: bool = False, local_dir: str | None = None):
super().__init__()
from transformers import GPT2Model
if local_dir is not None and len(local_dir) > 0:
self.model = GPT2Model.from_pretrained(local_dir, local_files_only=True)
else:
if model_name is None:
model_name = 'gpt2'
self.model = GPT2Model.from_pretrained(model_name)
if gradient_checkpointing:
self.model.gradient_checkpointing_enable()
self.hidden_size = self.model.config.hidden_size
if freeze:
for p in self.model.parameters():
p.requires_grad = False
def forward(self, inputs_embeds: torch.Tensor) -> torch.Tensor:
outputs = self.model(inputs_embeds=inputs_embeds)
return outputs.last_hidden_state
class ODEGCN_LLM_GPT2(nn.Module):
def __init__(self, config):
super(ODEGCN_LLM_GPT2, self).__init__()
args = config['model']
num_nodes = config['data']['num_nodes']
num_features = args['num_features']
self.history = args['history']
self.horizon = args['horizon']
A_sp_hat, A_se_hat = get_A_hat(config)
self.sp_blocks = nn.ModuleList(
[nn.Sequential(
STGCNBlock(in_channels=num_features, out_channels=[64, 32, 64], num_nodes=num_nodes, A_hat=A_sp_hat),
STGCNBlock(in_channels=64, out_channels=[64, 32, 64], num_nodes=num_nodes, A_hat=A_sp_hat)) for _ in range(3)
])
self.se_blocks = nn.ModuleList(
[nn.Sequential(
STGCNBlock(in_channels=num_features, out_channels=[64, 32, 64], num_nodes=num_nodes, A_hat=A_se_hat),
STGCNBlock(in_channels=64, out_channels=[64, 32, 64], num_nodes=num_nodes, A_hat=A_se_hat)) for _ in range(3)
])
# HF GPT-2
gpt2_name = args.get('gpt2_name', 'gpt2')
grad_ckpt = bool(args.get('gpt2_grad_ckpt', False))
gpt2_freeze = bool(args.get('gpt2_freeze', False))
gpt2_local_dir = args.get('gpt2_local_dir', None)
self.gpt2 = GPT2BackboneHF(gpt2_name, gradient_checkpointing=grad_ckpt, freeze=gpt2_freeze, local_dir=gpt2_local_dir)
# Project ODE features to GPT-2 hidden size
self.to_llm_embed = nn.Linear(64, self.gpt2.hidden_size)
# Prediction head
self.proj_head = nn.Sequential(
nn.Linear(self.gpt2.hidden_size, self.gpt2.hidden_size),
nn.ReLU(),
nn.Linear(self.gpt2.hidden_size, self.horizon)
)
def forward(self, x):
x = x[..., 0:1].permute(0, 2, 1, 3)
outs = []
for blk in self.sp_blocks:
outs.append(blk(x))
for blk in self.se_blocks:
outs.append(blk(x))
outs = torch.stack(outs)
x = torch.max(outs, dim=0)[0] # (B, N, T, 64)
B, N, T, C = x.shape
x = self.to_llm_embed(x).view(B * N, T, -1)
llm_hidden = self.gpt2(inputs_embeds=x)
last_state = llm_hidden[:, -1, :]
y = self.proj_head(last_state)
y = y.view(B, N, self.horizon).permute(0, 2, 1).unsqueeze(-1)
return y

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models/STGODE_LLM_GPT2/__init__.py Normal file
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from .STGODE_LLM_GPT2 import ODEGCN_LLM_GPT2

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