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# Final validation accuracy: ~95%
import argparse
import torch
import torch.nn.functional as F
from torch import Tensor
from torch_geometric.data import Data, InMemoryDataset
from torch_geometric.loader import DataLoader
from torch_geometric.nn import LCMAggregation
from torch_geometric.transforms import BaseTransform
parser = argparse.ArgumentParser()
parser.add_argument('--num_bits', type=int, default=8)
args = parser.parse_args()
class RandomPermutation(BaseTransform):
def forward(self, data: Data) -> Data:
data.x = torch.x[torch.randperm(data.x.size(0))]
return data
class Random2ndMinimumDataset(InMemoryDataset):
r""""A labeled dataset, where each sample is a multiset of integers
encoded as bit-vectors, and the label is the second smallest integer
in the multiset.
"""
def __init__(
self,
num_examples: int,
num_bits: int,
min_num_elems: int,
max_num_elems: int,
):
super().__init__(transform=RandomPermutation())
self.data, self.slices = self.collate([
self.get_data(num_bits, min_num_elems, max_num_elems)
for _ in range(num_examples)
])
def get_data(
self,
num_bits: int,
min_num_elems: int,
max_num_elems: int,
) -> Data:
num_elems = int(torch.randint(min_num_elems, max_num_elems + 1, (1, )))
x = torch.randint(0, 2, (num_elems, num_bits))
power = torch.pow(2, torch.arange(num_bits)).flip([0])
ints = (x * power.view(1, -1)).sum(dim=-1)
y = x[ints.topk(k=2, largest=False).indices[-1:]].to(torch.float)
return Data(x=x, y=y)
train_dataset = Random2ndMinimumDataset(
num_examples=2**16, # 65,536
num_bits=args.num_bits,
min_num_elems=2,
max_num_elems=16,
)
# Validate on multi sets of size 32, larger than observed during training:
val_dataset = Random2ndMinimumDataset(
num_examples=2**10, # 1024
num_bits=args.num_bits,
min_num_elems=32,
max_num_elems=32,
)
train_loader = DataLoader(train_dataset, batch_size=128, shuffle=True)
val_loader = DataLoader(val_dataset, batch_size=128)
class BitwiseEmbedding(torch.nn.Module):
def __init__(self, emb_dim: int):
super().__init__()
self.embs = torch.nn.ModuleList(
[torch.nn.Embedding(2, emb_dim) for _ in range(args.num_bits)])
def forward(self, x: Tensor) -> Tensor:
xs = [emb(b) for emb, b in zip(self.embs, x.t())]
return torch.stack(xs, dim=0).sum(0)
class LCM(torch.nn.Module):
def __init__(self, emb_dim: int, dropout: float = 0.25):
super().__init__()
self.encoder = torch.nn.Sequential(
BitwiseEmbedding(emb_dim),
torch.nn.Linear(emb_dim, emb_dim),
torch.nn.Dropout(),
torch.nn.GELU(),
)
self.aggr = LCMAggregation(emb_dim, emb_dim, project=False)
self.decoder = torch.nn.Sequential(
torch.nn.Linear(emb_dim, emb_dim),
torch.nn.Dropout(dropout),
torch.nn.GELU(),
torch.nn.Linear(emb_dim, args.num_bits),
)
def forward(self, x: Tensor, batch: Tensor) -> Tensor:
x = self.encoder(x)
x = self.aggr(x, batch)
x = self.decoder(x)
return x
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = LCM(emb_dim=128).to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=0.0001)
def train():
total_loss = total_examples = 0
for batch in train_loader:
batch = batch.to(device)
optimizer.zero_grad()
out = model(batch.x, batch.batch)
loss = F.binary_cross_entropy_with_logits(out, batch.y)
loss.backward()
optimizer.step()
total_loss += batch.num_graphs * float(loss)
total_examples += batch.num_graphs
return total_loss / total_examples
@torch.no_grad()
def test(loader):
total_correct = total_examples = 0
for batch in loader:
batch = batch.to(device)
pred = model(batch.x, batch.batch).sigmoid().round()
num_mistakes = (pred != batch.y).sum(dim=-1)
total_correct += int((num_mistakes == 0).sum())
total_examples += batch.num_graphs
return total_correct / total_examples
for epoch in range(1, 1001):
loss = train()
val_acc = test(val_loader)
print(f'Epoch: {epoch:04d}, Loss: {loss:.4f}, Val Acc: {val_acc:.4f}')
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