{ "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\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 }