[DGL-Go] Graph Property Prediction Pipeline (#3927)

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Co-authored-by: Minjie Wang <wmjlyjemaine@gmail.com>

dglgo/README.md

@@ -112,6 +112,9 @@ will list the available recipes:
 ===============================================================================
 | Filename                       |  Pipeline           | Dataset              |
 ===============================================================================
+| graphpred_pcba_gin.yaml        |  graphpred          | ogbg-molpcba         |
+| graphpred_hiv_pna.yaml         |  graphpred          | ogbg-molhiv          |
+| graphpred_hiv_gin.yaml         |  graphpred          | ogbg-molhiv          |
 | linkpred_citation2_sage.yaml   |  linkpred           | ogbl-citation2       |
 | linkpred_collab_sage.yaml      |  linkpred           | ogbl-collab          |
 | nodepred_citeseer_sage.yaml    |  nodepred           | citeseer             |
@@ -382,12 +385,11 @@ help message; use `dgl configure --help` for the configuration options; use
 `dgl configure nodepred --help` for the configuration options of node prediction pipeline.
 
 **Q: What exactly is nodepred/linkpred? How many are they?**
-A: They are called DGl-Go pipelines. A pipeline represents the training methodology for
+A: They are called DGL-Go pipelines. A pipeline represents the training methodology for
 a certain task. Therefore, its naming convention is *<task_name>[-<method_name>]*. For example,
 `nodepred` trains the selected GNN model for node classification using full-graph training method;
 while `nodepred-ns` trains the model for node classifiation but using neighbor sampling.
-The first release included three training pipelines (`nodepred`, `nodepred-ns` and `linkpred`)
-but you can expect more will be coming in the future. Use `dgl configure --help` to see
+Currently DGL-Go provides four training pipelines (`nodepred`, `nodepred-ns`, `linkpred`, and `graphpred`). Use `dgl configure --help` to see
 all the available pipelines.
 
 **Q: How to add my model to the official model recipe zoo?**

dglgo/dglgo/model/__init__.py

 from .node_encoder import *
 from .edge_encoder import *
+from .graph_encoder import *
\ No newline at end of file

dglgo/dglgo/model/graph_encoder/__init__.py

+from ...utils.factory import GraphModelFactory
+from .gin_ogbg import OGBGGIN
+from .pna import PNA
+
+GraphModelFactory.register("gin")(OGBGGIN)
+GraphModelFactory.register("pna")(PNA)
\ No newline at end of file

dglgo/dglgo/model/graph_encoder/gin_ogbg.py

+import dgl
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from dgl.nn import GINEConv, AvgPooling, SumPooling
+from ogb.graphproppred.mol_encoder import AtomEncoder, BondEncoder
+
+class MLP(nn.Module):
+    def __init__(self,
+                 feat_size: int):
+        """Multilayer Perceptron (MLP)"""
+        super(MLP, self).__init__()
+        self.mlp = nn.Sequential(
+            nn.Linear(feat_size, 2 * feat_size),
+            nn.BatchNorm1d(2 * feat_size),
+            nn.ReLU(),
+            nn.Linear(2 * feat_size, feat_size),
+            nn.BatchNorm1d(feat_size)
+        )
+
+    def forward(self, h):
+        return self.mlp(h)
+
+class OGBGGIN(nn.Module):
+    def __init__(self,
+                 data_info: dict,
+                 embed_size: int = 300,
+                 num_layers: int = 5,
+                 dropout: float = 0.5,
+                 virtual_node : bool = False):
+        """Graph Isomorphism Network (GIN) variant introduced in baselines
+        for OGB graph property prediction datasets
+
+        Parameters
+        ----------
+        data_info : dict
+            The information about the input dataset.
+        embed_size : int
+            Embedding size.
+        num_layers : int
+            Number of layers.
+        dropout : float
+            Dropout rate.
+        virtual_node : bool
+            Whether to use virtual node.
+        """
+        super(OGBGGIN, self).__init__()
+        self.data_info = data_info
+        self.embed_size = embed_size
+        self.num_layers = num_layers
+        self.virtual_node = virtual_node
+
+        if data_info['name'] in ['ogbg-molhiv', 'ogbg-molpcba']:
+            self.node_encoder = AtomEncoder(embed_size)
+            self.edge_encoders = nn.ModuleList([
+                BondEncoder(embed_size) for _ in range(num_layers)])
+        else:
+            # Handle other datasets
+            self.node_encoder = nn.Linear(data_info['node_feat_size'], embed_size)
+            self.edge_encoders = nn.ModuleList([nn.Linear(data_info['edge_feat_size'], embed_size)
+                                                for _ in range(num_layers)])
+
+        self.conv_layers = nn.ModuleList([GINEConv(MLP(embed_size)) for _ in range(num_layers)])
+
+        self.dropout = nn.Dropout(dropout)
+        self.pool = AvgPooling()
+        self.pred = nn.Linear(embed_size, data_info['out_size'])
+
+        if virtual_node:
+            self.virtual_emb = nn.Embedding(1, embed_size)
+            nn.init.constant_(self.virtual_emb.weight.data, 0)
+            self.mlp_virtual = nn.ModuleList()
+            for _ in range(num_layers - 1):
+                self.mlp_virtual.append(MLP(embed_size))
+            self.virtual_pool = SumPooling()
+
+    def forward(self, graph, node_feat, edge_feat):
+        if self.virtual_node:
+            virtual_emb = self.virtual_emb.weight.expand(graph.batch_size, -1)
+
+        hn = self.node_encoder(node_feat)
+
+        for layer in range(self.num_layers):
+
+            if self.virtual_node:
+                # messages from virtual nodes to graph nodes
+                virtual_hn = dgl.broadcast_nodes(graph, virtual_emb)
+                hn = hn + virtual_hn
+
+            he = self.edge_encoders[layer](edge_feat)
+            hn = self.conv_layers[layer](graph, hn, he)
+            if layer != self.num_layers - 1:
+                hn = F.relu(hn)
+            hn = self.dropout(hn)
+
+            if self.virtual_node and layer != self.num_layers - 1:
+                # messages from graph nodes to virtual nodes
+                virtual_emb_tmp = self.virtual_pool(graph, hn) + virtual_emb
+                virtual_emb = self.mlp_virtual[layer](virtual_emb_tmp)
+                virtual_emb = self.dropout(F.relu(virtual_emb))
+
+        hg = self.pool(graph, hn)
+
+        return self.pred(hg)

dglgo/dglgo/model/graph_encoder/pna.py

+from typing import List
+import dgl.function as fn
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from dgl.nn import SumPooling, AvgPooling
+from ogb.graphproppred.mol_encoder import AtomEncoder
+
+def aggregate_mean(h):
+    """mean aggregation"""
+    return torch.mean(h, dim=1)
+
+def aggregate_max(h):
+    """max aggregation"""
+    return torch.max(h, dim=1)[0]
+
+def aggregate_min(h):
+    """min aggregation"""
+    return torch.min(h, dim=1)[0]
+
+def aggregate_sum(h):
+    """sum aggregation"""
+    return torch.sum(h, dim=1)
+
+def aggregate_var(h):
+    """variance aggregation"""
+    h_mean_squares = torch.mean(h * h, dim=1)
+    h_mean = torch.mean(h, dim=1)
+    var = torch.relu(h_mean_squares - h_mean * h_mean)
+    return var
+
+def aggregate_std(h):
+    """standard deviation aggregation"""
+    return torch.sqrt(aggregate_var(h) + 1e-5)
+
+AGGREGATORS = {'mean': aggregate_mean, 'sum': aggregate_sum, 'max': aggregate_max,
+               'min': aggregate_min, 'std': aggregate_std, 'var': aggregate_var}
+
+def scale_identity(h, D, delta):
+    """identity scaling (no scaling operation)"""
+    return h
+
+def scale_amplification(h, D, delta):
+    """amplification scaling"""
+    return h * (np.log(D + 1) / delta)
+
+def scale_attenuation(h, D, delta):
+    """attenuation scaling"""
+    return h * (delta / np.log(D + 1))
+
+SCALERS = {
+    'identity': scale_identity,
+    'amplification': scale_amplification,
+    'attenuation': scale_attenuation
+}
+
+class MLP(nn.Module):
+    def __init__(self,
+                 in_feat_size: int,
+                 out_feat_size: int,
+                 num_layers: int=3,
+                 decreasing_hidden_size=False):
+        """Multilayer Perceptron (MLP)"""
+        super(MLP, self).__init__()
+
+        self.layers = nn.ModuleList()
+        if decreasing_hidden_size:
+            for i in range(num_layers - 1):
+                self.layers.append(nn.Linear(in_feat_size // 2 ** i,
+                                             in_feat_size // 2 ** (i + 1)))
+            self.layers.append(nn.Linear(in_feat_size // 2 ** (num_layers - 1),
+                                         out_feat_size))
+        else:
+            self.layers.append(nn.Linear(in_feat_size, out_feat_size))
+            for _ in range(num_layers - 1):
+                self.layers.append(nn.Linear(out_feat_size, out_feat_size))
+        self.num_layers = num_layers
+
+    def forward(self, h):
+        for i, layer in enumerate(self.layers):
+            h = layer(h)
+            if i != self.num_layers - 1:
+                h = F.relu(h)
+        return h
+
+class SimplePNAConv(nn.Module):
+    r"""A simplified PNAConv variant used in OGB submissions"""
+    def __init__(self,
+                 feat_size: int,
+                 aggregators: List[str],
+                 scalers: List[str],
+                 delta: float,
+                 dropout: float,
+                 batch_norm: bool,
+                 residual: bool,
+                 num_mlp_layers: int):
+        super(SimplePNAConv, self).__init__()
+
+        self.aggregators = [AGGREGATORS[aggr] for aggr in aggregators]
+        self.scalers = [SCALERS[scale] for scale in scalers]
+        self.delta = delta
+        self.mlp = MLP(in_feat_size=(len(aggregators) * len(scalers)) * feat_size,
+                       out_feat_size=feat_size, num_layers=num_mlp_layers)
+        self.dropout = nn.Dropout(dropout)
+        self.residual = residual
+
+        if batch_norm:
+            self.bn = nn.BatchNorm1d(feat_size)
+        else:
+            self.bn = None
+
+    def reduce(self, nodes):
+        h = nodes.mailbox['m']
+        D = h.shape[-2]
+        h = torch.cat([aggregate(h) for aggregate in self.aggregators], dim=1)
+        h = torch.cat([scale(h, D=D, delta=self.delta) for scale in self.scalers], dim=1)
+        return {'h': h}
+
+    def forward(self, g, h):
+        with g.local_scope():
+            g.ndata['h'] = h
+            g.update_all(fn.copy_u('h', 'm'), self.reduce)
+            h_new = g.ndata['h']
+        h_new = self.mlp(h_new)
+
+        if self.bn is not None:
+            h_new = self.bn(h_new)
+        h_new = F.relu(h_new)
+
+        if self.residual:
+            h_new = h_new + h
+        h_new = self.dropout(h_new)
+
+        return h_new
+
+class PNA(nn.Module):
+    def __init__(self,
+                 data_info: dict,
+                 embed_size: int = 80,
+                 aggregators: str = 'mean max min std',
+                 scalers: str = 'identity amplification attenuation',
+                 dropout: float = 0.3,
+                 batch_norm: bool = True,
+                 residual: bool = True,
+                 num_mlp_layers: int = 1,
+                 num_layers: int = 4,
+                 readout: str = 'mean'):
+        """Principal Neighbourhood Aggregation
+
+        Parameters
+        ----------
+        data_info : dict
+            The information about the input dataset.
+        embed_size : int
+            Embedding size.
+        aggregators : str
+            Aggregation function names separated by space, can include mean, max, min, std, sum
+        scalers : str
+            Scaler function names separated by space, can include identity, amplification, and attenuation
+        dropout : float
+            Dropout rate.
+        batch_norm : bool
+            Whether to use batch normalization.
+        residual : bool
+            Whether to use residual connection.
+        num_mlp_layers : int
+            Number of MLP layers to use after message aggregation in each PNA layer.
+        num_layers : int
+            Number of PNA layers.
+        readout : str
+            Readout for computing graph-level representations, can be 'sum' or 'mean'.
+        """
+        super(PNA, self).__init__()
+        self.data_info = data_info
+        self.embed_size = embed_size
+        self.dropout = dropout
+        self.batch_norm = batch_norm
+        self.residual = residual
+        self.num_mlp_layers = num_mlp_layers
+        self.num_layers = num_layers
+        self.readout = readout
+
+        if aggregators is None:
+            aggregators = ['mean', 'max', 'min', 'std']
+        else:
+            aggregators = [agg.strip() for agg in aggregators.split(' ')]
+            assert set(aggregators).issubset({'mean', 'max', 'min', 'std', 'sum'}), \
+                "Expect aggregators to be a subset of ['mean', 'max', 'min', 'std', 'sum'], \
+                    got {}".format(aggregators)
+        if scalers is None:
+            scalers = ['identity', 'amplification', 'attenuation']
+        else:
+            scalers = [scl.strip() for scl in scalers.split(' ')]
+            assert set(scalers).issubset({'identity', 'amplification', 'attenuation'}), \
+                "Expect scalers to be a subset of ['identity', 'amplification', 'attenuation'], \
+                    got {}".format(scalers)
+        self.aggregators = aggregators
+        self.scalers = scalers
+
+        if data_info['name'] in ['ogbg-molhiv', 'ogbg-molpcba']:
+            self.node_encoder = AtomEncoder(embed_size)
+        else:
+            # Handle other datasets
+            self.node_encoder = nn.Linear(data_info['node_feat_size'], embed_size)
+        self.conv_layers = nn.ModuleList([SimplePNAConv(feat_size=embed_size,
+                                                        aggregators=aggregators,
+                                                        scalers=scalers,
+                                                        delta=data_info['delta'],
+                                                        dropout=dropout,
+                                                        batch_norm=batch_norm,
+                                                        residual=residual,
+                                                        num_mlp_layers=num_mlp_layers)
+                                          for _ in range(num_layers)])
+
+        if readout == 'sum':
+            self.pool = SumPooling()
+        elif readout == 'mean':
+            self.pool = AvgPooling()
+        else:
+            raise ValueError("Expect readout to be 'sum' or 'mean', got {}".format(readout))
+        self.pred = MLP(embed_size, data_info['out_size'], decreasing_hidden_size=True)
+
+    def forward(self, graph, node_feat, edge_feat=None):
+        hn = self.node_encoder(node_feat)
+        for conv in self.conv_layers:
+            hn = conv(graph, hn)
+        hg = self.pool(graph, hn)
+
+        return self.pred(hg)

dglgo/dglgo/pipeline/__init__.py

 from .nodepred import NodepredPipeline
 from .nodepred_sample import NodepredNsPipeline
 from .linkpred import LinkpredPipeline
+from .graphpred import GraphpredPipeline
\ No newline at end of file

dglgo/dglgo/pipeline/graphpred/__init__.py

+from .gen import *
\ No newline at end of file

dglgo/dglgo/pipeline/graphpred/gen.py

+from pathlib import Path
+from jinja2 import Template
+import copy
+import typer
+from pydantic import BaseModel, Field
+from typing import Optional
+from ...utils.factory import PipelineFactory, GraphModelFactory, PipelineBase, DataFactory
+from ...utils.yaml_dump import deep_convert_dict, merge_comment
+import ruamel.yaml
+
+pipeline_comments = {
+    "num_runs": "Number of experiments to run",
+    "train_batch_size": "Graph batch size when training",
+    "eval_batch_size": "Graph batch size when evaluating",
+    "num_workers": "Number of workers for data loading",
+    "num_epochs": "Number of training epochs",
+    "save_path": "Path to save the model"
+}
+
+class GraphpredPipelineCfg(BaseModel):
+    num_runs: int = 1
+    train_batch_size: int = 32
+    eval_batch_size: int = 32
+    num_workers: int = 4
+    optimizer: dict = {"name": "Adam", "lr": 0.001, "weight_decay": 0}
+    # Default to no lr decay
+    lr_scheduler: dict = {"name": "StepLR", "step_size": 100, "gamma": 1}
+    loss: str = "BCEWithLogitsLoss"
+    metric: str = "roc_auc_score"
+    num_epochs: int = 100
+    save_path: str = "model.pth"
+
+@PipelineFactory.register("graphpred")
+class GraphpredPipeline(PipelineBase):
+    def __init__(self):
+        self.pipeline_name = "graphpred"
+
+    @classmethod
+    def setup_user_cfg_cls(cls):
+        from ...utils.enter_config import UserConfig
+        class GraphPredUserConfig(UserConfig):
+            data: DataFactory.filter("graphpred").get_pydantic_config() = Field(..., discriminator="name")
+            model: GraphModelFactory.get_pydantic_model_config() = Field(..., discriminator="name")
+            general_pipeline: GraphpredPipelineCfg = GraphpredPipelineCfg()
+
+        cls.user_cfg_cls = GraphPredUserConfig
+
+    @property
+    def user_cfg_cls(self):
+        return self.__class__.user_cfg_cls
+
+    def get_cfg_func(self):
+        def config(
+            data: DataFactory.filter("graphpred").get_dataset_enum() = typer.Option(..., help="input data name"),
+            cfg: Optional[str] = typer.Option(
+                None, help="output configuration path"),
+            model: GraphModelFactory.get_model_enum() = typer.Option(..., help="Model name"),
+        ):
+            self.__class__.setup_user_cfg_cls()
+            generated_cfg = {
+                "pipeline_name": self.pipeline_name,
+                "device": "cpu",
+                "data": {"name": data.name},
+                "model": {"name": model.value},
+                "general_pipeline": {}
+            }
+            output_cfg = self.user_cfg_cls(**generated_cfg).dict()
+            output_cfg = deep_convert_dict(output_cfg)
+            comment_dict = {
+                "device": "Torch device name, e.g., cpu or cuda or cuda:0",
+                "data": {
+                    "split_ratio": 'Ratio to generate data split, for example set to [0.8, 0.1, 0.1] for 80% train/10% val/10% test. Leave blank to use builtin split in original dataset'
+                },
+                "general_pipeline": pipeline_comments,
+                "model": GraphModelFactory.get_constructor_doc_dict(model.value)
+            }
+            comment_dict = merge_comment(output_cfg, comment_dict)
+
+            yaml = ruamel.yaml.YAML()
+            if cfg is None:
+                cfg = "_".join(["graphpred", data.value, model.value]) + ".yaml"
+            yaml.dump(comment_dict, Path(cfg).open("w"))
+            print("Configuration file is generated at {}".format(Path(cfg).absolute()))
+
+        return config
+
+    @classmethod
+    def gen_script(cls, user_cfg_dict):
+        cls.setup_user_cfg_cls()
+        file_current_dir = Path(__file__).resolve().parent
+        with open(file_current_dir / "graphpred.jinja-py", "r") as f:
+            template = Template(f.read())
+
+        render_cfg = copy.deepcopy(user_cfg_dict)
+        model_code = GraphModelFactory.get_source_code(
+            user_cfg_dict["model"]["name"])
+        render_cfg["model_code"] = model_code
+        render_cfg["model_class_name"] = GraphModelFactory.get_model_class_name(
+            user_cfg_dict["model"]["name"])
+        render_cfg.update(DataFactory.get_generated_code_dict(user_cfg_dict["data"]["name"], '**cfg["data"]'))
+
+        generated_user_cfg = copy.deepcopy(user_cfg_dict)
+        if "split_ratio" in generated_user_cfg["data"]:
+            generated_user_cfg["data"].pop("split_ratio")
+        generated_user_cfg["data_name"] = generated_user_cfg["data"].pop("name")
+        generated_user_cfg.pop("pipeline_name")
+        generated_user_cfg["model_name"] = generated_user_cfg["model"].pop("name")
+        generated_user_cfg["general_pipeline"]["optimizer"].pop("name")
+        generated_user_cfg["general_pipeline"]["lr_scheduler"].pop("name")
+
+        generated_train_cfg = copy.deepcopy(user_cfg_dict["general_pipeline"])
+        generated_train_cfg["optimizer"].pop("name")
+        generated_train_cfg["lr_scheduler"].pop("name")
+
+        if user_cfg_dict["data"].get("split_ratio", None) is not None:
+            render_cfg["data_initialize_code"] = "{}, split_ratio={}".format(render_cfg["data_initialize_code"], user_cfg_dict["data"]["split_ratio"])
+        render_cfg["user_cfg_str"] = f"cfg = {str(generated_user_cfg)}"
+        render_cfg["user_cfg"] = user_cfg_dict
+        return template.render(**render_cfg)
+
+    @staticmethod
+    def get_description() -> str:
+        return "Graph property prediction pipeline"

dglgo/dglgo/pipeline/graphpred/graphpred.jinja-py

+import numpy as np
+import sklearn
+import torch
+import torch.nn as nn
+from dgl.data import AsGraphPredDataset
+from dgl.dataloading import GraphDataLoader
+from torch.optim.lr_scheduler import ReduceLROnPlateau
+from tqdm import tqdm
+{{ data_import_code }}
+
+{{ model_code }}
+
+def train(device, loader, model, criterion, optimizer):
+    model.train()
+
+    for _, (g, labels) in enumerate(tqdm(loader, desc="Iteration")):
+        g = g.to(device)
+        labels = labels.to(device)
+        node_feat = g.ndata['feat']
+        edge_feat = g.edata['feat']
+
+        pred = model(g, node_feat, edge_feat)
+        optimizer.zero_grad()
+        # ignore nan targets (unlabeled) when computing training loss
+        is_labeled = labels == labels
+        loss = criterion(pred.float()[is_labeled], labels.float()[is_labeled])
+        loss.backward()
+        optimizer.step()
+
+def calc_metric(y_true, y_pred):
+    task_metric_list = []
+    for i in range(y_true.shape[1]):
+        # AUC is only defined when there is at least one positive and negative datapoint.
+        if np.sum(y_true[:, i] == 1) > 0 and np.sum(y_true[:, i] == 0) > 0:
+            # ignore nan values
+            is_labeled = y_true[:,i] == y_true[:,i]
+            task_metric = sklearn.metrics.{{ user_cfg.general_pipeline.metric }}(
+                y_true[is_labeled, i], y_pred[is_labeled, i])
+            task_metric_list.append(task_metric)
+
+    return sum(task_metric_list) / len(task_metric_list)
+
+def evaluate(device, loader, model):
+    model.eval()
+    y_true = []
+    y_pred = []
+
+    for _, (g, labels) in enumerate(tqdm(loader, desc="Iteration")):
+        g = g.to(device)
+        labels = labels.to(device)
+        node_feat = g.ndata['feat']
+        edge_feat = g.edata['feat']
+
+        with torch.no_grad():
+            pred = model(g, node_feat, edge_feat)
+        y_true.append(labels.view(pred.shape).detach().cpu())
+        y_pred.append(pred.detach().cpu())
+
+    y_true = torch.cat(y_true, dim=0).numpy()
+    y_pred = torch.cat(y_pred, dim=0).numpy()
+
+    return calc_metric(y_true, y_pred)
+
+def main(run):
+    {{ user_cfg_str }}
+    device = cfg['device']
+    if not torch.cuda.is_available():
+        device = 'cpu'
+    pipeline_cfg = cfg['general_pipeline']
+    save_path = pipeline_cfg['save_path']
+
+    # load data
+    data = AsGraphPredDataset({{data_initialize_code}})
+    train_loader = GraphDataLoader(data[data.train_idx], batch_size=pipeline_cfg['train_batch_size'],
+                                   shuffle=True, num_workers=pipeline_cfg['num_workers'])
+    val_loader = GraphDataLoader(data[data.val_idx], batch_size=pipeline_cfg['eval_batch_size'],
+                                 shuffle=False, num_workers=pipeline_cfg['num_workers'])
+    test_loader = GraphDataLoader(data[data.test_idx], batch_size=pipeline_cfg['eval_batch_size'],
+                                  shuffle=False, num_workers=pipeline_cfg['num_workers'])
+
+    # create model
+    model_cfg = cfg["model"]
+    # data[0] is a tuple (g, label)
+    cfg["model"]["data_info"] = {
+        "name": cfg["data_name"],
+        "node_feat_size": data.node_feat_size,
+        "edge_feat_size": data.edge_feat_size,
+        "out_size": data.num_tasks
+    }
+    if cfg["model_name"] == 'pna':
+        in_deg = torch.cat([g.in_degrees() for (g, _) in data[data.train_idx]])
+        cfg["model"]["data_info"]["delta"] = torch.mean(torch.log(in_deg + 1))
+    model = {{ model_class_name }}(**cfg["model"])
+    model = model.to(device)
+
+    criterion = nn.{{ user_cfg.general_pipeline.loss }}()
+    optimizer = torch.optim.{{ user_cfg.general_pipeline.optimizer.name }}(
+        model.parameters(), **pipeline_cfg["optimizer"])
+    lr_scheduler = torch.optim.lr_scheduler.{{ user_cfg.general_pipeline.lr_scheduler.name }}(
+        optimizer, **pipeline_cfg["lr_scheduler"])
+    best_val_metric = 0.
+
+    for epoch in range(pipeline_cfg['num_epochs']):
+        train(device, train_loader, model, criterion, optimizer)
+        val_metric = evaluate(device, val_loader, model)
+        if val_metric >= best_val_metric:
+            best_val_metric = val_metric
+            torch.save(model.state_dict(), save_path)
+        print('Run {:d} | Epoch {:d} | Val Metric {:.4f} | Best Val Metric {:.4f}'.format(
+              run, epoch, val_metric, best_val_metric))
+
+        if isinstance(lr_scheduler, ReduceLROnPlateau):
+            lr_scheduler.step(val_metric)
+        else:
+            lr_scheduler.step()
+
+    model.load_state_dict(torch.load(save_path))
+    test_metric = evaluate(device, test_loader, model)
+    print('Test Metric: {:.4f}'.format(test_metric))
+
+    return test_metric
+
+if __name__ == '__main__':
+    all_run_metrics = []
+    num_runs = {{ user_cfg.general_pipeline.num_runs }}
+    for run in range(num_runs):
+        print('Run experiment {:d}'.format(run))
+        test_metric = main(run)
+        all_run_metrics.append(test_metric)
+    avg_metric = np.round(np.mean(all_run_metrics), 6)
+    std_metric = np.round(np.std(all_run_metrics), 6)
+    print('Test Metric across {:d} runs: {:.6f} ± {:.6f}'.format(
+        num_runs, avg_metric, std_metric))

dglgo/dglgo/pipeline/linkpred/gen.py

@@ -89,7 +89,7 @@ class LinkpredPipeline(PipelineBase):
             output_cfg = self.user_cfg_cls(**generated_cfg).dict()
             output_cfg = deep_convert_dict(output_cfg)
             comment_dict = {
-                "device": "Torch device name, e.q. cpu or cuda or cuda:0",
+                "device": "Torch device name, e.g., cpu or cuda or cuda:0",
                 "general_pipeline": pipeline_comments,
                 "node_model": NodeModelFactory.get_constructor_doc_dict(node_model.value),
                 "edge_model": EdgeModelFactory.get_constructor_doc_dict(edge_model.value),

dglgo/dglgo/pipeline/nodepred/gen.py

@@ -71,7 +71,7 @@ class NodepredPipeline(PipelineBase):
             output_cfg = self.user_cfg_cls(**generated_cfg).dict()
             output_cfg = deep_convert_dict(output_cfg)
             comment_dict = {
-                "device": "Torch device name, e.q. cpu or cuda or cuda:0",
+                "device": "Torch device name, e.g., cpu or cuda or cuda:0",
                 "data": {
                     "split_ratio": 'Ratio to generate split masks, for example set to [0.8, 0.1, 0.1] for 80% train/10% val/10% test. Leave blank to use builtin split in original dataset'
                 },

dglgo/dglgo/pipeline/nodepred_sample/gen.py

@@ -96,7 +96,7 @@ class NodepredNsPipeline(PipelineBase):
             output_cfg = self.user_cfg_cls(**generated_cfg).dict()
             output_cfg = deep_convert_dict(output_cfg)
             comment_dict = {
-                "device": "Torch device name, e.q. cpu or cuda or cuda:0",
+                "device": "Torch device name, e.g., cpu or cuda or cuda:0",
                 "data": {
                     "split_ratio": 'Ratio to generate split masks, for example set to [0.8, 0.1, 0.1] for 80% train/10% val/10% test. Leave blank to use builtin split in original dataset'
                 },

dglgo/dglgo/utils/factory.py

@@ -12,7 +12,7 @@ import inspect
 from numpydoc import docscrape
 logger = logging.getLogger(__name__)
 
-ALL_PIPELINE = ["nodepred", "nodepred-ns", "linkpred"]
+ALL_PIPELINE = ["nodepred", "nodepred-ns", "linkpred", "graphpred"]
 
 class PipelineBase(ABC):
 
@@ -164,7 +164,7 @@ DataFactory.register(
     import_code="from dgl.data import CSVDataset",
     extra_args={"data_path": "./"},
     class_name="CSVDataset({})",
-    allowed_pipeline=["nodepred", "nodepred-ns", "linkpred"])
+    allowed_pipeline=["nodepred", "nodepred-ns", "linkpred", "graphpred"])
 
 DataFactory.register(
     "reddit",
@@ -206,6 +206,20 @@ DataFactory.register(
     class_name="DglLinkPropPredDataset('ogbl-citation2')",
     allowed_pipeline=["linkpred"])
 
+DataFactory.register(
+    "ogbg-molhiv",
+    import_code="from ogb.graphproppred import DglGraphPropPredDataset",
+    extra_args={},
+    class_name="DglGraphPropPredDataset(name='ogbg-molhiv')",
+    allowed_pipeline=["graphpred"])
+
+DataFactory.register(
+    "ogbg-molpcba",
+    import_code="from ogb.graphproppred import DglGraphPropPredDataset",
+    extra_args={},
+    class_name="DglGraphPropPredDataset(name='ogbg-molpcba')",
+    allowed_pipeline=["graphpred"])
+
 class PipelineFactory:
     """ The factory class for creating executors"""
 
@@ -424,3 +438,4 @@ NegativeSamplerFactory.register("persource")(PerSourceUniform)
 
 NodeModelFactory = ModelFactory()
 EdgeModelFactory = ModelFactory()
+GraphModelFactory = ModelFactory()

dglgo/recipes/graphpred_hiv_gin.yaml

+version: 0.0.1
+pipeline_name: graphpred
+device: cuda:0                # Torch device name, e.q. cpu or cuda or cuda:0
+data:
+  name: ogbg-molhiv
+  split_ratio:                # Ratio to generate data split, for example set to [0.8, 0.1, 0.1] for 80% train/10% val/10% test. Leave blank to use builtin split in original dataset
+model:
+  name: gin
+  embed_size: 300             # Embedding size.
+  num_layers: 5               # Number of layers.
+  dropout: 0.5                # Dropout rate.
+  virtual_node: true          # Whether to use virtual node.
+general_pipeline:
+  num_runs: 10                # Number of experiments to run
+  train_batch_size: 32        # Graph batch size when training
+  eval_batch_size: 32         # Graph batch size when evaluating
+  num_workers: 4              # Number of workers for data loading
+  optimizer:
+    name: Adam
+    lr: 0.001
+    weight_decay: 0
+  lr_scheduler:
+    name: StepLR
+    step_size: 100
+    gamma: 1
+  loss: BCEWithLogitsLoss
+  metric: roc_auc_score
+  num_epochs: 100             # Number of training epochs
+  save_path: model.pth        # Path to save the model

dglgo/recipes/graphpred_hiv_pna.yaml

+version: 0.0.1
+pipeline_name: graphpred
+device: cuda:0                # Torch device name, e.q. cpu or cuda or cuda:0
+data:
+  name: ogbg-molhiv
+  split_ratio:                # Ratio to generate data split, for example set to [0.8, 0.1, 0.1] for 80% train/10% val/10% test. Leave blank to use builtin split in original dataset
+model:
+  name: pna
+  embed_size: 80              # Embedding size.
+  aggregators: mean max min std # Aggregation function names separated by space, can include mean, max, min, std, sum
+  scalers: identity amplification attenuation # Scaler function names separated by space, can include identity, amplification, and attenuation
+  dropout: 0.3                # Dropout rate.
+  batch_norm: true            # Whether to use batch normalization.
+  residual: true              # Whether to use residual connection.
+  num_mlp_layers: 1           # Number of MLP layers to use after message aggregation in each PNA layer.
+  num_layers: 4               # Number of PNA layers.
+  readout: mean               # Readout for computing graph-level representations, can be 'sum' or 'mean'.
+general_pipeline:
+  num_runs: 10                # Number of experiments to run
+  train_batch_size: 128       # Graph batch size when training
+  eval_batch_size: 128        # Graph batch size when evaluating
+  num_workers: 4              # Number of workers for data loading
+  optimizer:
+    name: Adam
+    lr: 0.01
+    weight_decay: 0.000003
+  lr_scheduler:
+    name: ReduceLROnPlateau
+    mode: max
+    factor: 0.5
+    patience: 20
+    verbose: true
+  loss: BCEWithLogitsLoss
+  metric: roc_auc_score
+  num_epochs: 200             # Number of training epochs
+  save_path: model.pth        # Path to save the model

dglgo/recipes/graphpred_pcba_gin.yaml

+version: 0.0.1
+pipeline_name: graphpred
+device: cuda:0                # Torch device name, e.q. cpu or cuda or cuda:0
+data:
+  name: ogbg-molpcba
+  split_ratio:                # Ratio to generate data split, for example set to [0.8, 0.1, 0.1] for 80% train/10% val/10% test. Leave blank to use builtin split in original dataset
+model:
+  name: gin
+  embed_size: 300             # Embedding size.
+  num_layers: 5               # Number of layers.
+  dropout: 0.5                # Dropout rate.
+  virtual_node: true          # Whether to use virtual node.
+general_pipeline:
+  num_runs: 10                # Number of experiments to run
+  train_batch_size: 32        # Graph batch size when training
+  eval_batch_size: 32         # Graph batch size when evaluating
+  num_workers: 4              # Number of workers for data loading
+  optimizer:
+    name: Adam
+    lr: 0.001
+    weight_decay: 0
+  lr_scheduler:
+    name: StepLR
+    step_size: 100
+    gamma: 1
+  loss: BCEWithLogitsLoss
+  metric: average_precision_score
+  num_epochs: 100             # Number of training epochs
+  save_path: model.pth        # Path to save the model

dglgo/setup.py

@@ -16,7 +16,10 @@ setup(name='dglgo',
           'numpydoc>=1.1.0',
           "pydantic>=1.9.0",
           "ruamel.yaml>=0.17.20",
-          "PyYAML>=5.1"
+          "PyYAML>=5.1",
+          "ogb>=1.3.3",
+          "rdkit-pypi",
+          "scikit-learn>=0.20.0"
       ],
       package_data={"": ["./*"]},
       include_package_data=True,

tests/go/test_model.py

+import dgl
 import pytest
 import torch
 from dglgo.model import *
@@ -171,3 +172,69 @@ def test_ele():
     h_src = torch.randn(num_pairs, data_info['in_size'])
     h_dst = torch.randn(num_pairs, data_info['in_size'])
     model(h_src, h_dst)
+
+@pytest.mark.parametrize('virtual_node', [True, False])
+def test_ogbg_gin(virtual_node):
+    # Test for ogbg-mol datasets
+    data_info = {
+        'name': 'ogbg-molhiv',
+        'out_size': 1
+    }
+    model = OGBGGIN(data_info,
+                    embed_size=10,
+                    num_layers=2,
+                    virtual_node=virtual_node)
+    num_nodes = 5
+    num_edges = 15
+    g1 = dgl.rand_graph(num_nodes, num_edges)
+    g2 = dgl.rand_graph(num_nodes, num_edges)
+    g = dgl.batch([g1, g2])
+    num_nodes = g.num_nodes()
+    num_edges = g.num_edges()
+    nfeat = torch.zeros(num_nodes, 9).long()
+    efeat = torch.zeros(num_edges, 3).long()
+    model(g, nfeat, efeat)
+
+    # Test for non-ogbg-mol datasets
+    data_info = {
+        'name': 'a_dataset',
+        'out_size': 1,
+        'node_feat_size': 15,
+        'edge_feat_size': 5
+    }
+    model = OGBGGIN(data_info,
+                    embed_size=10,
+                    num_layers=2,
+                    virtual_node=virtual_node)
+    nfeat = torch.randn(num_nodes, data_info['node_feat_size'])
+    efeat = torch.randn(num_edges, data_info['edge_feat_size'])
+    model(g, nfeat, efeat)
+
+def test_pna():
+    # Test for ogbg-mol datasets
+    data_info = {
+        'name': 'ogbg-molhiv',
+        'delta': 1,
+        'out_size': 1
+    }
+    model = PNA(data_info,
+                embed_size=10,
+                num_layers=2)
+    num_nodes = 5
+    num_edges = 15
+    g = dgl.rand_graph(num_nodes, num_edges)
+    nfeat = torch.zeros(num_nodes, 9).long()
+    model(g, nfeat)
+
+    # Test for non-ogbg-mol datasets
+    data_info = {
+        'name': 'a_dataset',
+        'node_feat_size': 15,
+        'delta': 1,
+        'out_size': 1
+    }
+    model = PNA(data_info,
+                embed_size=10,
+                num_layers=2)
+    nfeat = torch.randn(num_nodes, data_info['node_feat_size'])
+    model(g, nfeat)

tests/go/test_pipeline.py

@@ -60,8 +60,26 @@ def test_linkpred_default_neg_sampler(data, node_model, edge_model):
         data, node_model, edge_model, custom_config_file))
     assert os.path.exists(custom_config_file)
 
+@pytest.mark.parametrize('data', ['csv', 'ogbg-molhiv', 'ogbg-molpcba'])
+@pytest.mark.parametrize('model', ['gin', 'pna'])
+def test_graphpred(data, model):
+    os.system('dgl configure graphpred --data {} --model {}'.format(data, model))
+    assert os.path.exists('graphpred_{}_{}.yaml'.format(data, model))
+
+    custom_config_file = 'custom_{}_{}.yaml'.format(data, model)
+    os.system('dgl configure graphpred --data {} --model {} --cfg {}'.format(data, model,
+                                                                             custom_config_file))
+    assert os.path.exists(custom_config_file)
+
+    custom_script = '_'.join([data, model]) + '.py'
+    os.system('dgl export --cfg {} --output {}'.format(custom_config_file, custom_script))
+    assert os.path.exists(custom_script)
+
 @pytest.mark.parametrize('recipe',
-                         ['linkpred_cora_sage.yaml',
+                         ['graphpred_hiv_gin.yaml',
+                          'graphpred_hiv_pna.yaml',
+                          'graphpred_pcba_gin.yaml',
+                          'linkpred_cora_sage.yaml',
                           'linkpred_citation2_sage.yaml',
                           'linkpred_collab_sage.yaml',
                           'nodepred_citeseer_gat.yaml',