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[GraphBolt][PyG] Link prediction example. #7752

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merged 7 commits into from
Aug 27, 2024
Merged

[GraphBolt][PyG] Link prediction example. #7752

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be3ec77
[GraphBolt][PyG] Link prediction example.
mfbalin Aug 27, 2024
1e4bbf3
linting
mfbalin Aug 27, 2024
222942d
make more similar to DGL example.
mfbalin Aug 27, 2024
fde88d4
make pinned-pinned-cuda default and fix bugs.
mfbalin Aug 27, 2024
46f2308
make script docstring short.
mfbalin Aug 27, 2024
526aeda
Merge branch 'master' into gb_pyg_link_prediction_example
mfbalin Aug 27, 2024
da77b93
fix comments.
mfbalin Aug 27, 2024
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linting
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@mfbalin
mfbalin committed Aug 27, 2024
commit 1e4bbf3bed68746caa4d15e0fdc563bda4cba46d
19 changes: 10 additions & 9 deletions examples/graphbolt/pyg/link_prediction.py
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Original file line number Diff line number Diff line change
Expand Up @@ -81,8 +81,8 @@
└───> Validation and test set evaluation
"""
import argparse
from functools import partial
import time
from functools import partial

import dgl.graphbolt as gb
import torch
Expand All @@ -95,8 +95,8 @@

import torch.nn.functional as F
from torch_geometric.nn import SAGEConv
from tqdm import tqdm
from torchmetrics.retrieval import RetrievalMRR
from tqdm import tqdm


class GraphSAGE(torch.nn.Module):
Expand Down Expand Up @@ -232,14 +232,16 @@ def create_dataloader(
# Create and return a DataLoader to handle data loading.
return gb.DataLoader(datapipe, num_workers=args.num_workers)


@torch.compile
def predictions_step(model, h_src, h_dst):
return model.predictor(h_src * h_dst).squeeze()


def compute_predictions(model, node_emb, seeds, device):
"""Compute the predictions for given source and destination nodes.

This function computes the predictions for a set of node pairs, dividing the
This function computes the predictions for a set of node pairs, dividing the
task into batches to handle potentially large graphs.
"""

Expand All @@ -260,6 +262,7 @@ def compute_predictions(model, node_emb, seeds, device):
preds[start:end] = predictions_step(model, h_src, h_dst)
return preds


@torch.no_grad()
def evaluate(model, graph, features, all_nodes_set, valid_set, test_set):
"""Evaluate the model on validation and test sets."""
Expand Down Expand Up @@ -314,9 +317,7 @@ def train_helper(dataloader, model, optimizer, device):
total_samples = 0 # Accumulator for the total number of samples processed
start = time.time()
for step, minibatch in tqdm(enumerate(dataloader), "Training"):
loss, num_samples = train_step(
minibatch, optimizer, model
)
loss, num_samples = train_step(minibatch, optimizer, model)
total_loss += loss * num_samples
total_samples += num_samples
if step + 1 == args.early_stop:
Expand Down Expand Up @@ -486,9 +487,9 @@ def main():

in_channels = features.size("node", None, "feat")[0]
hidden_channels = 256
model = GraphSAGE(
in_channels, hidden_channels, len(args.fanout)
).to(args.device)
model = GraphSAGE(in_channels, hidden_channels, len(args.fanout)).to(
args.device
)
assert len(args.fanout) == len(model.layers)

train(train_dataloader, model, args.device)
Expand Down
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