Add WebQSPDataset (#9481)

1. #9462
2. #9480
3. **->** #9481
4. #9167

---

Breaking down PR
#9167 further

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Akihiro Nitta <[email protected]>
Co-authored-by: rusty1s <[email protected]>

Author: 4 people

CHANGELOG.md

@@ -7,6 +7,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 ### Added
 
+- Added the `WebQSPDataset` dataset ([#9481](https://github.com/pyg-team/pytorch_geometric/pull/9481))
 - Added the `GRetriever` model ([#9480](https://github.com/pyg-team/pytorch_geometric/pull/9480))
 - Added the `ClusterPooling` layer ([#9627](https://github.com/pyg-team/pytorch_geometric/pull/9627))
 - Added the `LinkPredMRR` metric ([#9632](https://github.com/pyg-team/pytorch_geometric/pull/9632))

test/nn/nlp/test_sentence_transformer.py

@@ -23,9 +23,13 @@ def test_sentence_transformer(batch_size, pooling_strategy, device):
     ]
 
     out = model.encode(text, batch_size=batch_size)
+    assert out.device == device
+    assert out.size() == (2, 128)
+
+    out = model.encode(text, batch_size=batch_size, output_device='cpu')
     assert out.is_cpu
     assert out.size() == (2, 128)
 
-    out = model.encode(text, batch_size=batch_size, output_device=device)
+    out = model.encode([], batch_size=batch_size)
     assert out.device == device
-    assert out.size() == (2, 128)
+    assert out.size() == (0, 128)

torch_geometric/datasets/__init__.py

@@ -61,7 +61,6 @@
 from .twitch import Twitch
 from .airports import Airports
 from .lrgb import LRGBDataset
-from .neurograph import NeuroGraphDataset
 from .malnet_tiny import MalNetTiny
 from .omdb import OMDB
 from .polblogs import PolBlogs
@@ -76,6 +75,8 @@
 from .wikidata import Wikidata5M
 from .myket import MyketDataset
 from .brca_tgca import BrcaTcga
+from .neurograph import NeuroGraphDataset
+from .web_qsp_dataset import WebQSPDataset
 
 from .dbp15k import DBP15K
 from .aminer import AMiner
@@ -188,6 +189,7 @@
     'MyketDataset',
     'BrcaTcga',
     'NeuroGraphDataset',
+    'WebQSPDataset',
 ]
 
 hetero_datasets = [

torch_geometric/datasets/web_qsp_dataset.py

@@ -0,0 +1,239 @@
+# Code adapted from the G-Retriever paper: https://arxiv.org/abs/2402.07630
+from typing import Any, Dict, List, Tuple, no_type_check
+
+import numpy as np
+import torch
+from torch import Tensor
+from tqdm import tqdm
+
+from torch_geometric.data import Data, InMemoryDataset
+from torch_geometric.nn.nlp import SentenceTransformer
+
+
+@no_type_check
+def retrieval_via_pcst(
+    data: Data,
+    q_emb: Tensor,
+    textual_nodes: Any,
+    textual_edges: Any,
+    topk: int = 3,
+    topk_e: int = 3,
+    cost_e: float = 0.5,
+) -> Tuple[Data, str]:
+    c = 0.01
+    if len(textual_nodes) == 0 or len(textual_edges) == 0:
+        desc = textual_nodes.to_csv(index=False) + "\n" + textual_edges.to_csv(
+            index=False,
+            columns=["src", "edge_attr", "dst"],
+        )
+        return data, desc
+
+    from pcst_fast import pcst_fast
+
+    root = -1
+    num_clusters = 1
+    pruning = 'gw'
+    verbosity_level = 0
+    if topk > 0:
+        n_prizes = torch.nn.CosineSimilarity(dim=-1)(q_emb, data.x)
+        topk = min(topk, data.num_nodes)
+        _, topk_n_indices = torch.topk(n_prizes, topk, largest=True)
+
+        n_prizes = torch.zeros_like(n_prizes)
+        n_prizes[topk_n_indices] = torch.arange(topk, 0, -1).float()
+    else:
+        n_prizes = torch.zeros(data.num_nodes)
+
+    if topk_e > 0:
+        e_prizes = torch.nn.CosineSimilarity(dim=-1)(q_emb, data.edge_attr)
+        topk_e = min(topk_e, e_prizes.unique().size(0))
+
+        topk_e_values, _ = torch.topk(e_prizes.unique(), topk_e, largest=True)
+        e_prizes[e_prizes < topk_e_values[-1]] = 0.0
+        last_topk_e_value = topk_e
+        for k in range(topk_e):
+            indices = e_prizes == topk_e_values[k]
+            value = min((topk_e - k) / sum(indices), last_topk_e_value - c)
+            e_prizes[indices] = value
+            last_topk_e_value = value * (1 - c)
+        # reduce the cost of the edges such that at least one edge is selected
+        cost_e = min(cost_e, e_prizes.max().item() * (1 - c / 2))
+    else:
+        e_prizes = torch.zeros(data.num_edges)
+
+    costs = []
+    edges = []
+    virtual_n_prizes = []
+    virtual_edges = []
+    virtual_costs = []
+    mapping_n = {}
+    mapping_e = {}
+    for i, (src, dst) in enumerate(data.edge_index.t().numpy()):
+        prize_e = e_prizes[i]
+        if prize_e <= cost_e:
+            mapping_e[len(edges)] = i
+            edges.append((src, dst))
+            costs.append(cost_e - prize_e)
+        else:
+            virtual_node_id = data.num_nodes + len(virtual_n_prizes)
+            mapping_n[virtual_node_id] = i
+            virtual_edges.append((src, virtual_node_id))
+            virtual_edges.append((virtual_node_id, dst))
+            virtual_costs.append(0)
+            virtual_costs.append(0)
+            virtual_n_prizes.append(prize_e - cost_e)
+
+    prizes = np.concatenate([n_prizes, np.array(virtual_n_prizes)])
+    num_edges = len(edges)
+    if len(virtual_costs) > 0:
+        costs = np.array(costs + virtual_costs)
+        edges = np.array(edges + virtual_edges)
+
+    vertices, edges = pcst_fast(edges, prizes, costs, root, num_clusters,
+                                pruning, verbosity_level)
+
+    selected_nodes = vertices[vertices < data.num_nodes]
+    selected_edges = [mapping_e[e] for e in edges if e < num_edges]
+    virtual_vertices = vertices[vertices >= data.num_nodes]
+    if len(virtual_vertices) > 0:
+        virtual_vertices = vertices[vertices >= data.num_nodes]
+        virtual_edges = [mapping_n[i] for i in virtual_vertices]
+        selected_edges = np.array(selected_edges + virtual_edges)
+
+    edge_index = data.edge_index[:, selected_edges]
+    selected_nodes = np.unique(
+        np.concatenate(
+            [selected_nodes, edge_index[0].numpy(), edge_index[1].numpy()]))
+
+    n = textual_nodes.iloc[selected_nodes]
+    e = textual_edges.iloc[selected_edges]
+    desc = n.to_csv(index=False) + '\n' + e.to_csv(
+        index=False, columns=['src', 'edge_attr', 'dst'])
+
+    mapping = {n: i for i, n in enumerate(selected_nodes.tolist())}
+    src = [mapping[i] for i in edge_index[0].tolist()]
+    dst = [mapping[i] for i in edge_index[1].tolist()]
+
+    data = Data(
+        x=data.x[selected_nodes],
+        edge_index=torch.tensor([src, dst]),
+        edge_attr=data.edge_attr[selected_edges],
+    )
+
+    return data, desc
+
+
+class WebQSPDataset(InMemoryDataset):
+    r"""The WebQuestionsSP dataset of the `"The Value of Semantic Parse
+    Labeling for Knowledge Base Question Answering"
+    <https://aclanthology.org/P16-2033/>`_ paper.
+
+    Args:
+        root (str): Root directory where the dataset should be saved.
+        split (str, optional): If :obj:`"train"`, loads the training dataset.
+            If :obj:`"val"`, loads the validation dataset.
+            If :obj:`"test"`, loads the test dataset. (default: :obj:`"train"`)
+        force_reload (bool, optional): Whether to re-process the dataset.
+            (default: :obj:`False`)
+    """
+    def __init__(
+        self,
+        root: str,
+        split: str = "train",
+        force_reload: bool = False,
+    ) -> None:
+        super().__init__(root, force_reload=force_reload)
+
+        if split not in {'train', 'val', 'test'}:
+            raise ValueError(f"Invalid 'split' argument (got {split})")
+
+        path = self.processed_paths[['train', 'val', 'test'].index(split)]
+        self.load(path)
+
+    @property
+    def processed_file_names(self) -> List[str]:
+        return ['train_data.pt', 'val_data.pt', 'test_data.pt']
+
+    def process(self) -> None:
+        import datasets
+        import pandas as pd
+
+        datasets = datasets.load_dataset('rmanluo/RoG-webqsp')
+
+        device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+        model_name = 'sentence-transformers/all-roberta-large-v1'
+        model = SentenceTransformer(model_name).to(device)
+        model.eval()
+
+        for dataset, path in zip(
+            [datasets['train'], datasets['validation'], datasets['test']],
+                self.processed_paths,
+        ):
+            questions = [example["question"] for example in dataset]
+            question_embs = model.encode(
+                questions,
+                batch_size=256,
+                output_device='cpu',
+            )
+
+            data_list = []
+            for i, example in enumerate(tqdm(dataset)):
+                raw_nodes: Dict[str, int] = {}
+                raw_edges = []
+                for tri in example["graph"]:
+                    h, r, t = tri
+                    h = h.lower()
+                    t = t.lower()
+                    if h not in raw_nodes:
+                        raw_nodes[h] = len(raw_nodes)
+                    if t not in raw_nodes:
+                        raw_nodes[t] = len(raw_nodes)
+                    raw_edges.append({
+                        "src": raw_nodes[h],
+                        "edge_attr": r,
+                        "dst": raw_nodes[t]
+                    })
+                nodes = pd.DataFrame([{
+                    "node_id": v,
+                    "node_attr": k,
+                } for k, v in raw_nodes.items()])
+                edges = pd.DataFrame(raw_edges)
+
+                nodes.node_attr = nodes.node_attr.fillna("")
+                x = model.encode(
+                    nodes.node_attr.tolist(),
+                    batch_size=256,
+                    output_device='cpu',
+                )
+                edge_attr = model.encode(
+                    edges.edge_attr.tolist(),
+                    batch_size=256,
+                    output_device='cpu',
+                )
+                edge_index = torch.tensor([
+                    edges.src.tolist(),
+                    edges.dst.tolist(),
+                ])
+
+                question = f"Question: {example['question']}\nAnswer: "
+                label = ('|').join(example['answer']).lower()
+                data = Data(
+                    x=x,
+                    edge_index=edge_index,
+                    edge_attr=edge_attr,
+                )
+                data, desc = retrieval_via_pcst(
+                    data,
+                    question_embs[i],
+                    nodes,
+                    edges,
+                    topk=3,
+                    topk_e=5,
+                    cost_e=0.5,
+                )
+                data.question = question
+                data.label = label
+                data.desc = desc
+                data_list.append(data)
+
+            self.save(data_list, path)

torch_geometric/nn/nlp/sentence_transformer.py

@@ -54,8 +54,11 @@ def encode(
         self,
         text: List[str],
         batch_size: Optional[int] = None,
-        output_device: Optional[torch.device] = None,
+        output_device: Optional[Union[torch.device, str]] = None,
     ) -> Tensor:
+        is_empty = len(text) == 0
+        text = ['dummy'] if is_empty else text
+
         batch_size = len(text) if batch_size is None else batch_size
 
         embs: List[Tensor] = []
@@ -70,11 +73,13 @@ def encode(
             emb = self(
                 input_ids=token.input_ids.to(self.device),
                 attention_mask=token.attention_mask.to(self.device),
-            ).to(output_device or 'cpu')
+            ).to(output_device)
 
             embs.append(emb)
 
-        return torch.cat(embs, dim=0) if len(embs) > 1 else embs[0]
+        out = torch.cat(embs, dim=0) if len(embs) > 1 else embs[0]
+        out = out[:0] if is_empty else out
+        return out
 
     def __repr__(self) -> str:
         return f'{self.__class__.__name__}(model_name={self.model_name})'