[Feature] Build pretrain scheme for self-supervised learning

cogdl/tasks/unsupervised_node_classification.py

@@ -40,6 +40,7 @@ def __init__(self, args, dataset=None, model=None):
         super(UnsupervisedNodeClassification, self).__init__(args)
         dataset = build_dataset(args) if dataset is None else dataset
 
+        self.dataset = dataset
         self.data = dataset[0]
 
         self.num_nodes = self.data.y.shape[0]
@@ -103,7 +104,7 @@ def save_emb(self, embs):
 
     def train(self):
         if self.trainer is not None:
-            return self.trainer.fit(self.model, self.data)
+            return self.trainer.fit(self.model, self.dataset)
         if self.load_emb_path is None:
             if "gcc" in self.model_name:
                 features_matrix = self.model.train(self.data)

cogdl/trainers/__init__.py

@@ -54,6 +54,7 @@ def build_trainer(args):
     "neighborsampler": "cogdl.trainers.sampled_trainer",
     "clustergcn": "cogdl.trainers.sampled_trainer",
     "random_cluster": "cogdl.trainers.sampled_trainer",
+    "self_supervised": "cogdl.trainers.self_supervised_trainer",
     "self_auxiliary_task_pretrain": "cogdl.trainers.self_auxiliary_task_trainer",
     "self_auxiliary_task_joint": "cogdl.trainers.self_auxiliary_task_trainer",
     "m3s": "cogdl.trainers.m3s_trainer",

cogdl/trainers/self_supervised_trainer.py

@@ -1,12 +1,15 @@
 import os
+import argparse
 from tqdm import tqdm
 import numpy as np
 
 import torch
 from torch import nn
 from .base_trainer import BaseTrainer
+from . import register_trainer
 
 
+@register_trainer("self_supervised")
 class SelfSupervisedTrainer(BaseTrainer):
     def __init__(self, args):
         super(SelfSupervisedTrainer, self).__init__()
@@ -18,21 +21,31 @@ def __init__(self, args):
         self.save_dir = args.save_dir
         self.load_emb_path = args.load_emb_path
         self.lr = args.lr
+        self.sampling = args.sampling
+        self.sample_size = args.sample_size
+
+    @staticmethod
+    def add_args(parser: argparse.ArgumentParser):
+        """Add trainer-specific arguments to the parser."""
+        # fmt: off
+        parser.add_argument("--sampling", action="store_true")
+        parser.add_argument("--sample-size", type=int, default=20000)
+        # fmt: on
 
     @classmethod
     def build_trainer_from_args(cls, args):
         return cls(args)
 
-    def fit(self, model, data):
+    def fit(self, model, dataset):
+        data = dataset.data
+        data.add_remaining_self_loops()
         self.data = data
 
         if self.load_emb_path is not None:
             embeds = np.load(self.load_emb_path)
             embeds = torch.from_numpy(embeds).to(self.device)
             return self.evaluate(embeds)
 
-        self.data.to(self.device)
-
         best = 1e9
         cnt_wait = 0
         optimizer = torch.optim.Adam(model.parameters(), lr=self.lr, weight_decay=0.0)
@@ -42,39 +55,47 @@ def fit(self, model, data):
 
         model.train()
         for epoch in epoch_iter:
-            optimizer.zero_grad()
+            with self.data.local_graph():
+                if self.sampling:
+                    idx = np.random.choice(np.arange(self.data.num_nodes), self.sample_size, replace=False)
+                    self.data = data.subgraph(idx)
 
-            loss = model.node_classification_loss(data)
-            epoch_iter.set_description(f"Epoch: {epoch:03d}, Loss: {loss.item(): .4f}")
+                self.data.to(self.device)
+                optimizer.zero_grad()
 
-            if loss < best:
-                best = loss
-                cnt_wait = 0
-            else:
-                cnt_wait += 1
+                loss = model.node_classification_loss(self.data)
+                epoch_iter.set_description(f"Epoch: {epoch:03d}, Loss: {loss.item(): .4f}")
 
-            if cnt_wait == self.patience:
-                print("Early stopping!")
-                break
+                if loss < best:
+                    best = loss
+                    cnt_wait = 0
+                else:
+                    cnt_wait += 1
 
-            loss.backward()
-            optimizer.step()
+                if cnt_wait == self.patience:
+                    print("Early stopping!")
+                    break
 
+                loss.backward()
+                optimizer.step()
+
+        self.data = data
+        self.data.to(self.device)
         with torch.no_grad():
-            embeds = model.embed(data)
+            embeds = model.embed(self.data)
         self.save_embed(embeds)
 
-        return self.evaluate(embeds)
+        return self.evaluate(embeds, dataset.get_loss_fn(), dataset.get_evaluator())
 
-    def evaluate(self, embeds):
+    def evaluate(self, embeds, loss_fn=None, evaluator=None):
         nclass = int(torch.max(self.data.y) + 1)
         opt = {
             "idx_train": self.data.train_mask.to(self.device),
             "idx_val": self.data.val_mask.to(self.device),
             "idx_test": self.data.test_mask.to(self.device),
             "num_classes": nclass,
         }
-        result = LogRegTrainer().train(embeds, self.data.y.to(self.device), opt)
+        result = LogRegTrainer().train(embeds, self.data.y.to(self.device), opt, loss_fn, evaluator)
         print(f"TestAcc: {result: .4f}")
         return dict(Acc=result)
 
@@ -105,7 +126,7 @@ def forward(self, seq):
 
 
 class LogRegTrainer(object):
-    def train(self, data, labels, opt):
+    def train(self, data, labels, opt, loss_fn=None, evaluator=None):
         device = data.device
         idx_train = opt["idx_train"].to(device)
         idx_test = opt["idx_test"].to(device)
@@ -120,7 +141,7 @@ def train(self, data, labels, opt):
         test_lbls = labels[idx_test]
         tot = 0
 
-        xent = nn.CrossEntropyLoss()
+        xent = nn.CrossEntropyLoss() if loss_fn is None else loss_fn
 
         for _ in range(50):
             log = LogReg(nhid, nclass).to(device)
@@ -138,7 +159,10 @@ def train(self, data, labels, opt):
                 optimizer.step()
 
             logits = log(test_embs)
-            preds = torch.argmax(logits, dim=1)
-            acc = torch.sum(preds == test_lbls).float() / test_lbls.shape[0]
-            tot += acc.item()
+            if evaluator is None:
+                preds = torch.argmax(logits, dim=1)
+                acc = torch.sum(preds == test_lbls).float() / test_lbls.shape[0]
+            else:
+                acc = evaluator(logits, test_lbls)
+            tot += acc
         return tot / 50

tests/models/ssl/test_contrastive_models.py

@@ -19,6 +19,8 @@ def get_default_args():
         "training_percents": [0.1],
         "activation": "relu",
         "residual": False,
+        "sampling": False,
+        "sample_size": 20,
         "norm": None,
     }
     return build_args_from_dict(default_dict)
@@ -43,6 +45,8 @@ def get_unsupervised_nn_args():
         "task": "unsupervised_node_classification",
         "checkpoint": False,
         "load_emb_path": None,
+        "sampling": False,
+        "sample_size": 20,
         "training_percents": [0.1],
     }
     return build_args_from_dict(default_dict)

tests/models/ssl/test_generative_models.py

@@ -31,6 +31,8 @@ def get_default_args():
         "checkpoint": False,
         "num_layers": 2,
         "activation": "relu",
+        "dropedge_rate": 0,
+        "agc_eval": False,
         "residual": False,
         "norm": None,
     }
@@ -188,7 +190,7 @@ def test_m3s():
 
 
 if __name__ == "__main__":
-    test_supergat()
+    # test_supergat()
     test_m3s()
     test_edgemask()
     test_edgemask_pt_ft()

tests/tasks/test_encode_paper.py

@@ -2,24 +2,30 @@
 
 
 def test_encode_paper():
-    tokenizer, model = oagbert("oagbert-v2")
-    title = 'BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding'
-    abstract = 'We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation...'
-    authors = ['Jacob Devlin', 'Ming-Wei Chang', 'Kenton Lee', 'Kristina Toutanova']
-    venue = 'north american chapter of the association for computational linguistics'
-    affiliations = ['Google']
-    concepts = ['language model', 'natural language inference', 'question answering']
+    tokenizer, model = oagbert("oagbert-v2-test")
+    title = "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding"
+    abstract = "We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation..."
+    authors = ["Jacob Devlin", "Ming-Wei Chang", "Kenton Lee", "Kristina Toutanova"]
+    venue = "north american chapter of the association for computational linguistics"
+    affiliations = ["Google"]
+    concepts = ["language model", "natural language inference", "question answering"]
     # encode paper
     paper_info = model.encode_paper(
-        title=title, abstract=abstract, venue=venue, authors=authors, concepts=concepts, affiliations=affiliations, reduction="max"
+        title=title,
+        abstract=abstract,
+        venue=venue,
+        authors=authors,
+        concepts=concepts,
+        affiliations=affiliations,
+        reduction="max",
     )
 
     assert len(paper_info) == 5
-    assert paper_info['text'][0]['type'] == 'TEXT'
-    assert len(paper_info['authors']) == 4
-    assert len(paper_info['venue'][0]['token_ids']) == 9
-    assert tuple(paper_info['text'][0]['sequence_output'].shape) == (43, 768)
-    assert len(paper_info['text'][0]['pooled_output']) == 768
+    assert paper_info["text"][0]["type"] == "TEXT"
+    assert len(paper_info["authors"]) == 4
+    assert len(paper_info["venue"][0]["token_ids"]) == 9
+    assert tuple(paper_info["text"][0]["sequence_output"].shape) == (43, 768)
+    assert len(paper_info["text"][0]["pooled_output"]) == 768
 
 
 if __name__ == "__main__":