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Nan error while using GraphAF to optimize QED #235

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LuckyLittleMonster opened this issue Nov 15, 2023 · 0 comments
Open

Nan error while using GraphAF to optimize QED #235

LuckyLittleMonster opened this issue Nov 15, 2023 · 0 comments

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@LuckyLittleMonster
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I got an error while using GraphAF to optimize QED.
I followed the instruction in https://torchdrug.ai/docs/tutorials/generation.html
But the QEDs are Nan after 2 epochs. The task.generate() could not generate any molecules.
I also test the plogp rewards for GraphAF and observed similar results.
My pytorch and torchdrug version are :
torch 2.1.0
torch-cluster 1.6.2+pt21cu121
torch-scatter 2.1.2+pt21cu121
torchaudio 2.1.0
torchdrug 0.2.1
torchvision 0.16.0

The terminal output:
image

My code:
import torch
from torchdrug import core, datasets, models, tasks
from torchdrug.layers import distribution
from torch import nn, optim
from collections import defaultdict
import pickle

with open("zinc250k.pkl", "rb") as fin:
dataset = pickle.load(fin)

model = models.RGCN(input_dim=dataset.num_atom_type,
num_relation=dataset.num_bond_type,
hidden_dims=[256, 256, 256], batch_norm=True)

num_atom_type = dataset.num_atom_type
num_bond_type = dataset.num_bond_type + 1

node_prior = distribution.IndependentGaussian(torch.zeros(num_atom_type),
torch.ones(num_atom_type))
edge_prior = distribution.IndependentGaussian(torch.zeros(num_bond_type),
torch.ones(num_bond_type))
node_flow = models.GraphAF(model, node_prior, num_layer=12)
edge_flow = models.GraphAF(model, edge_prior, use_edge=True, num_layer=12)

task = tasks.AutoregressiveGeneration(node_flow, edge_flow,
max_node=38, max_edge_unroll=12,
task="qed", criterion="ppo",
reward_temperature=20, baseline_momentum=0.9,
agent_update_interval=5, gamma=0.9)

optimizer = optim.Adam(task.parameters(), lr=1e-5)
solver = core.Engine(task, dataset, None, None, optimizer,
gpus=(0,), batch_size=64, log_interval=10)

solver.load("graphaf_zinc250k_10epoch.pkl",
load_optimizer=False)

for i in range(10):
solver.train(num_epoch=1)
solver.save(f"graphaf_zinc250k_{i+1}epoch_QED_finetune.pkl")
results = task.generate(num_sample=1024)
with open(f"./GraphAF_1024_QED_{i+1}epoch.txt", "w") as f:
for s in results.to_smiles():
f.write(f'{s}\n')

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