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{
"cells": [
{
"metadata": {
"ExecuteTime": {
"end_time": "2024-12-12T16:11:02.453452Z",
"start_time": "2024-12-12T16:10:59.783010Z"
}
},
"cell_type": "code",
"source": [
"import os\n",
"import time\n",
"from rdkit import Chem\n",
"from rdkit import RDLogger;\n",
"from torch.utils.data import Dataset\n",
"import torch.nn.functional as F\n",
"RDLogger.DisableLog('rdApp.*')\n",
"import torch\n",
"import torch.nn as nn\n",
"import torch.optim as optim\n",
"import pickle\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import math\n",
"import dgl\n",
"import networkx as nx"
],
"id": "1517383df6eb646",
"outputs": [],
"execution_count": 1
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2024-12-12T16:11:02.468893Z",
"start_time": "2024-12-12T16:11:02.454576Z"
}
},
"cell_type": "code",
"source": [
"atom_number_index_dict ={\n",
" 1:0,\n",
" 6:1,\n",
" 7:2,\n",
" 8:3,\n",
" 9:4\n",
"}\n",
"atom_index_number_dict ={\n",
" 0:1,\n",
" 1:6,\n",
" 2:7,\n",
" 3:8,\n",
" 4:9\n",
"}\n",
"def atom_number2index(atom_number):\n",
" return atom_number_index_dict[atom_number]\n",
"def atom_index2number(atom_index):\n",
" return atom_index_number_dict[atom_index]"
],
"id": "697783252f244e50",
"outputs": [],
"execution_count": 2
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2024-12-12T16:11:03.301916Z",
"start_time": "2024-12-12T16:11:03.243204Z"
}
},
"cell_type": "code",
"source": [
"from dgl.data import QM9Dataset\n",
"from torch.utils.data import SubsetRandomSampler\n",
"from dgl.dataloading import GraphDataLoader\n",
"\n",
"dataset = QM9Dataset(label_keys=['mu', 'gap'], cutoff=5.0)\n",
"dataset_length = len(dataset)\n",
"train_idx = torch.arange(dataset_length)\n",
"train_sampler = SubsetRandomSampler(train_idx)\n",
"def collate_fn(batch):\n",
" graphs, labels = map(list, zip(*batch))\n",
" for g in graphs:\n",
" # g.ndata[\"R\"]->the coordinates of each atom[num_nodes,3], g.ndata[\"Z\"]->the atomic number(H:1,C:6) [num_nodes]\n",
" g.ndata[\"Z_index\"] = torch.tensor([atom_number2index(z.item()) for z in g.ndata[\"Z\"]])\n",
" batched_graph = dgl.batch(graphs)\n",
" return batched_graph, torch.stack(labels)\n",
"myGLoader = GraphDataLoader(dataset,collate_fn=collate_fn,batch_size=5, pin_memory=True)"
],
"id": "7074f5a11a15ebc6",
"outputs": [],
"execution_count": 3
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2024-12-12T15:59:44.314049Z",
"start_time": "2024-12-12T15:59:44.299072Z"
}
},
"cell_type": "code",
"source": [
"# atom_numbers = []\n",
"# for g,_ in dataset:\n",
"# for atom_z in g.ndata[\"Z\"]:\n",
"# if atom_z not in atom_numbers:\n",
"# atom_numbers.append(atom_z)\n",
"# print(atom_numbers)"
],
"id": "5758841a1f281514",
"outputs": [],
"execution_count": 12
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2024-12-12T16:14:46.301537Z",
"start_time": "2024-12-12T16:11:12.606641Z"
}
},
"cell_type": "code",
"source": [
"device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
"for batch in myGLoader:\n",
" batch_g,label = batch\n",
" batch_g.to(device)\n",
" "
],
"id": "13bcff8166b0e35b",
"outputs": [],
"execution_count": 5
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2024-12-12T16:07:15.834856Z",
"start_time": "2024-12-12T16:07:15.822783Z"
}
},
"cell_type": "code",
"source": [
"from functools import partial\n",
"import sys\n",
"sys.path.append(\"lib\")\n",
"from lib.metrics import sce_loss\n",
"\n",
"class GMae(nn.Module):\n",
" def __init__(self, encoder,decoder,\n",
" in_dim,hidden_dim,out_dim,mask_rate=0.3,replace_rate=0.1,alpha_l=2,\n",
" embedding_layer_classes=4,embedding_layer_dim=4):\n",
" super(GMae, self).__init__()\n",
" self.Z_embedding = nn.Embedding(embedding_layer_classes,embedding_layer_dim)\n",
" self.encoder = encoder\n",
" self.decoder = decoder\n",
" self.mask_rate = mask_rate\n",
" self.replace_rate = replace_rate\n",
" self.alpha_l = alpha_l\n",
" self.in_dim = in_dim\n",
" self.hidden_dim = hidden_dim\n",
" self.out_dim = out_dim\n",
" self.embedding_layer_classes = embedding_layer_classes\n",
" self.embedding_layer_dim = embedding_layer_dim\n",
" self.enc_mask_token = nn.Parameter(torch.zeros(1,in_dim))\n",
" self.criterion = partial(sce_loss, alpha=alpha_l)\n",
" self.encoder_to_decoder = nn.Linear(hidden_dim, hidden_dim, bias=False)\n",
" def encode_atom_index(self,Z_index):\n",
" return self.Z_embedding(Z_index)\n",
" def encoding_mask_noise(self, g, x, mask_rate=0.3):\n",
" num_nodes = g.num_nodes()\n",
" perm = torch.randperm(num_nodes, device=x.device)\n",
" # random masking\n",
" num_mask_nodes = int(mask_rate * num_nodes)\n",
" mask_nodes = perm[: num_mask_nodes]\n",
" keep_nodes = perm[num_mask_nodes: ]\n",
"\n",
" if self.replace_rate > 0:\n",
" num_noise_nodes = int(self.replace_rate * num_mask_nodes)\n",
" perm_mask = torch.randperm(num_mask_nodes, device=x.device)\n",
" token_nodes = mask_nodes[perm_mask[: int((1-self.replace_rate) * num_mask_nodes)]]\n",
" noise_nodes = mask_nodes[perm_mask[-int(self.replace_rate * num_mask_nodes):]]\n",
" noise_to_be_chosen = torch.randperm(num_nodes, device=x.device)[:num_noise_nodes]\n",
" out_x = x.clone()\n",
" out_x[token_nodes] = 0.0\n",
" out_x[noise_nodes] = x[noise_to_be_chosen]\n",
" else:\n",
" out_x = x.clone()\n",
" token_nodes = mask_nodes\n",
" out_x[mask_nodes] = 0.0\n",
"\n",
" out_x[token_nodes] += self.enc_mask_token\n",
" use_g = g.clone()\n",
"\n",
" return use_g, out_x, (mask_nodes, keep_nodes) \n",
" def mask_attr_prediction(self, g, x):\n",
" use_g, use_x, (mask_nodes, keep_nodes) = self.encoding_mask_noise(g, x, self.mask_rate)\n",
" enc_rep = self.encoder(use_g, use_x)\n",
" # ---- attribute reconstruction ----\n",
" rep = self.encoder_to_decoder(enc_rep)\n",
" recon = self.decoder(use_g, rep)\n",
" x_init = x[mask_nodes]\n",
" x_rec = recon[mask_nodes]\n",
" loss = self.criterion(x_rec, x_init)\n",
" return loss\n",
"\n",
" def embed(self, g, x):\n",
" rep = self.encoder(g, x)\n",
" return rep\n",
" "
],
"id": "1a5caea191a642bc",
"outputs": [],
"execution_count": 5
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2024-12-12T16:07:18.136174Z",
"start_time": "2024-12-12T16:07:18.122456Z"
}
},
"cell_type": "code",
"source": [
"import dgl.nn as dglnn\n",
"import torch.nn as nn\n",
"import torch.nn.functional as F\n",
"class SimpleGNN(nn.Module):\n",
" def __init__(self, in_feats, hid_feats, out_feats):\n",
" super().__init__()\n",
" self.conv1 = dglnn.SAGEConv(\n",
" in_feats=in_feats, out_feats=hid_feats,aggregator_type=\"mean\")\n",
" self.conv2 = dglnn.SAGEConv(\n",
" in_feats=hid_feats, out_feats=out_feats,aggregator_type=\"mean\")\n",
"\n",
" def forward(self, graph, inputs):\n",
" # 输入是节点的特征\n",
" h = self.conv1(graph, inputs)\n",
" h = F.relu(h)\n",
" h = self.conv2(graph, h)\n",
" return h"
],
"id": "c99cb509ac0f1054",
"outputs": [],
"execution_count": 6
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2024-12-12T16:07:21.516476Z",
"start_time": "2024-12-12T16:07:21.118135Z"
}
},
"cell_type": "code",
"source": [
"sage_enc = SimpleGNN(in_feats=7,hid_feats=4,out_feats=4)\n",
"sage_dec = SimpleGNN(in_feats=4,hid_feats=4,out_feats=7)\n",
"gmae = GMae(sage_enc,sage_dec,7,4,7,replace_rate=0)\n",
"epoches = 20\n",
"optimizer = optim.Adam(gmae.parameters(), lr=1e-3)\n",
"device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')"
],
"id": "5a8a4e4dd753b642",
"outputs": [],
"execution_count": 7
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2024-12-12T16:10:52.354863Z",
"start_time": "2024-12-12T16:10:52.311090Z"
}
},
"cell_type": "code",
"source": [
"print(f\"epoch {0} started!\")\n",
"gmae.train()\n",
"gmae.encoder.train()\n",
"gmae.decoder.train()\n",
"gmae.to(device)\n",
"loss_epoch = 0\n",
"for batch in myGLoader:\n",
" optimizer.zero_grad()\n",
" batch_g, _ = batch\n",
" R = batch_g.ndata[\"R\"].to(device)\n",
" Z_index = batch_g.ndata[\"Z_index\"].to(device)\n",
" Z_emb = gmae.encode_atom_index(Z_index)\n",
" feat = torch.cat([R,Z_emb],dim=1)\n",
" batch_g = batch_g.to(device)\n",
" # loss = gmae.mask_attr_prediction(batch_g, feat)\n",
" # loss.backward()\n",
" # optimizer.step()\n",
" # loss_epoch+=loss.item()"
],
"id": "224529a988b81ef5",
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"epoch 0 started!\n"
]
},
{
"ename": "RuntimeError",
"evalue": "CUDA error: device-side assert triggered\nCUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.\nFor debugging consider passing CUDA_LAUNCH_BLOCKING=1.\nCompile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.\n",
"output_type": "error",
"traceback": [
"\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
"\u001B[1;31mRuntimeError\u001B[0m Traceback (most recent call last)",
"Cell \u001B[1;32mIn[12], line 10\u001B[0m\n\u001B[0;32m 8\u001B[0m optimizer\u001B[38;5;241m.\u001B[39mzero_grad()\n\u001B[0;32m 9\u001B[0m batch_g, _ \u001B[38;5;241m=\u001B[39m batch\n\u001B[1;32m---> 10\u001B[0m R \u001B[38;5;241m=\u001B[39m \u001B[43mbatch_g\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mndata\u001B[49m\u001B[43m[\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mR\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m]\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mto\u001B[49m\u001B[43m(\u001B[49m\u001B[43mdevice\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 11\u001B[0m Z_index \u001B[38;5;241m=\u001B[39m batch_g\u001B[38;5;241m.\u001B[39mndata[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mZ_index\u001B[39m\u001B[38;5;124m\"\u001B[39m]\u001B[38;5;241m.\u001B[39mto(device)\n\u001B[0;32m 12\u001B[0m Z_emb \u001B[38;5;241m=\u001B[39m gmae\u001B[38;5;241m.\u001B[39mencode_atom_index(Z_index)\n",
"\u001B[1;31mRuntimeError\u001B[0m: CUDA error: device-side assert triggered\nCUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.\nFor debugging consider passing CUDA_LAUNCH_BLOCKING=1.\nCompile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.\n"
]
}
],
"execution_count": 12
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2024-12-12T15:59:46.502050Z",
"start_time": "2024-12-12T15:59:44.442506Z"
}
},
"cell_type": "code",
"source": [
"from datetime import datetime\n",
"\n",
"current_time = datetime.now().strftime(\"%m-%d@%H_%M\")\n",
"best_loss = 10000\n",
"for epoch in range(epoches):\n",
" print(f\"epoch {epoch} started!\")\n",
" gmae.train()\n",
" gmae.encoder.train()\n",
" gmae.decoder.train()\n",
" gmae.to(device)\n",
" loss_epoch = 0\n",
" for batch in myGLoader:\n",
" optimizer.zero_grad()\n",
" batch_g, _ = batch\n",
" R = batch_g.ndata[\"R\"].to(device)\n",
" Z_index = batch_g.ndata[\"Z_index\"].to(device)\n",
" Z_emb = gmae.encode_atom_index(Z_index)\n",
" feat = torch.cat([R,Z_emb],dim=1)\n",
" batch_g = batch_g.to(device)\n",
" loss = gmae.mask_attr_prediction(batch_g, feat)\n",
" loss.backward()\n",
" optimizer.step()\n",
" loss_epoch+=loss.item()\n",
" if loss_epoch < best_loss:\n",
" formatted_loss_epoch = f\"{loss_epoch:.3f}\"\n",
" save_path = f\"./experiments/consumption/gmae/{current_time}/gmae_epoch-{epoch}-{formatted_loss_epoch}.pt\"\n",
" save_dir = os.path.dirname(save_path)\n",
" if not os.path.exists(save_dir):\n",
" os.makedirs(save_dir,exist_ok=True)\n",
" torch.save(gmae.state_dict(), save_path)\n",
" best_loss = loss_epoch\n",
" print(f\"best model saved-loss:{formatted_loss_epoch}-save_path:{save_path}\")\n",
" print(f\"epoch {epoch}: loss {loss_epoch}\")"
],
"id": "a22599c4e591125b",
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"epoch 0 started!\n"
]
},
{
"ename": "RuntimeError",
"evalue": "CUDA error: device-side assert triggered\nCUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.\nFor debugging consider passing CUDA_LAUNCH_BLOCKING=1.\nCompile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.\n",
"output_type": "error",
"traceback": [
"\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
"\u001B[1;31mRuntimeError\u001B[0m Traceback (most recent call last)",
"Cell \u001B[1;32mIn[18], line 19\u001B[0m\n\u001B[0;32m 17\u001B[0m Z_emb \u001B[38;5;241m=\u001B[39m gmae\u001B[38;5;241m.\u001B[39mencode_atom_index(Z_index)\n\u001B[0;32m 18\u001B[0m feat \u001B[38;5;241m=\u001B[39m torch\u001B[38;5;241m.\u001B[39mcat([R,Z_emb],dim\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m1\u001B[39m)\n\u001B[1;32m---> 19\u001B[0m batch_g \u001B[38;5;241m=\u001B[39m \u001B[43mbatch_g\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mto\u001B[49m\u001B[43m(\u001B[49m\u001B[43mdevice\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 20\u001B[0m loss \u001B[38;5;241m=\u001B[39m gmae\u001B[38;5;241m.\u001B[39mmask_attr_prediction(batch_g, feat)\n\u001B[0;32m 21\u001B[0m loss\u001B[38;5;241m.\u001B[39mbackward()\n",
"File \u001B[1;32mE:\\Anaconda\\envs\\gnn_course\\lib\\site-packages\\dgl\\heterograph.py:5730\u001B[0m, in \u001B[0;36mDGLGraph.to\u001B[1;34m(self, device, **kwargs)\u001B[0m\n\u001B[0;32m 5728\u001B[0m \u001B[38;5;66;03m# 2. Copy misc info\u001B[39;00m\n\u001B[0;32m 5729\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_batch_num_nodes \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[1;32m-> 5730\u001B[0m new_bnn \u001B[38;5;241m=\u001B[39m {\n\u001B[0;32m 5731\u001B[0m k: F\u001B[38;5;241m.\u001B[39mcopy_to(num, device, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n\u001B[0;32m 5732\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m k, num \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_batch_num_nodes\u001B[38;5;241m.\u001B[39mitems()\n\u001B[0;32m 5733\u001B[0m }\n\u001B[0;32m 5734\u001B[0m ret\u001B[38;5;241m.\u001B[39m_batch_num_nodes \u001B[38;5;241m=\u001B[39m new_bnn\n\u001B[0;32m 5735\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_batch_num_edges \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n",
"File \u001B[1;32mE:\\Anaconda\\envs\\gnn_course\\lib\\site-packages\\dgl\\heterograph.py:5731\u001B[0m, in \u001B[0;36m<dictcomp>\u001B[1;34m(.0)\u001B[0m\n\u001B[0;32m 5728\u001B[0m \u001B[38;5;66;03m# 2. Copy misc info\u001B[39;00m\n\u001B[0;32m 5729\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_batch_num_nodes \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[0;32m 5730\u001B[0m new_bnn \u001B[38;5;241m=\u001B[39m {\n\u001B[1;32m-> 5731\u001B[0m k: \u001B[43mF\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcopy_to\u001B[49m\u001B[43m(\u001B[49m\u001B[43mnum\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mdevice\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 5732\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m k, num \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_batch_num_nodes\u001B[38;5;241m.\u001B[39mitems()\n\u001B[0;32m 5733\u001B[0m }\n\u001B[0;32m 5734\u001B[0m ret\u001B[38;5;241m.\u001B[39m_batch_num_nodes \u001B[38;5;241m=\u001B[39m new_bnn\n\u001B[0;32m 5735\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_batch_num_edges \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n",
"File \u001B[1;32mE:\\Anaconda\\envs\\gnn_course\\lib\\site-packages\\dgl\\backend\\pytorch\\tensor.py:143\u001B[0m, in \u001B[0;36mcopy_to\u001B[1;34m(input, ctx, **kwargs)\u001B[0m\n\u001B[0;32m 141\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m ctx\u001B[38;5;241m.\u001B[39mindex \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[0;32m 142\u001B[0m th\u001B[38;5;241m.\u001B[39mcuda\u001B[38;5;241m.\u001B[39mset_device(ctx\u001B[38;5;241m.\u001B[39mindex)\n\u001B[1;32m--> 143\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43minput\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcuda\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 144\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[0;32m 145\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mRuntimeError\u001B[39;00m(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mInvalid context\u001B[39m\u001B[38;5;124m\"\u001B[39m, ctx)\n",
"\u001B[1;31mRuntimeError\u001B[0m: CUDA error: device-side assert triggered\nCUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.\nFor debugging consider passing CUDA_LAUNCH_BLOCKING=1.\nCompile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.\n"
]
}
],
"execution_count": 18
}
],
"metadata": {
"kernelspec": {
"name": "gnn_course",
"language": "python",
"display_name": "gnn_course"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|