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train_reinvent_prior_agent.py
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import os
import sys
import wandb
import hydra
import torch
import random
import numpy as np
import selfies as sf
from omegaconf import DictConfig
from optimizer import BaseOptimizer
path_here = os.path.dirname(os.path.realpath(__file__))
from models.reinvent import RnnPolicy
from data import smiles_vocabulary, selfies_vocabulary
def set_seed(seed):
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
def get_params(model):
return (p for p in model.parameters() if p.requires_grad)
def masked_mean(values, mask, axis=None):
"""Compute mean of tensor with a masked values."""
if axis is not None:
return (values * mask).sum(axis=axis) / mask.sum(axis=axis)
else:
return (values * mask).sum() / mask.sum()
class reinvent_optimizer(BaseOptimizer):
def __init__(self, cfg=None):
super().__init__(cfg)
self.agent_name = cfg.agent_name
def _init(self, cfg):
if cfg.dataset == 'zinc250k':
saved_path = 'saved/' + cfg.dataset + '/' + cfg.model_name + '_' + cfg.rep + '/' + cfg.saved_name
vocab_path = 'data/zinc250k/zinc_' + cfg.rep + '_vocab.txt'
max_dataset_len = 73
if cfg.max_len > max_dataset_len:
cfg.max_len = max_dataset_len
print('*** Changing the maximum length of sampled molecules because it was set to be greater than the maximum length seen during training ***')
else:
raise NotImplementedError
#get data
if cfg.rep == 'smiles':
self.vocab = smiles_vocabulary(vocab_path=os.path.join(path_here, vocab_path))
elif cfg.rep == 'selfies':
self.vocab = selfies_vocabulary(vocab_path=os.path.join(path_here, vocab_path))
else:
raise NotImplementedError
assert cfg.model_name == 'char_rnn'
#get pretrained weights
prior_saved_dict = torch.load(os.path.join(path_here, saved_path))
# get agent
self.agent = RnnPolicy(self.vocab, cfg.embedding_size, cfg.hidden_size, cfg.num_layers).to(self.device)
self.agent.load_save_dict(prior_saved_dict)
# get prior
self.prior = RnnPolicy(self.vocab, cfg.embedding_size, cfg.hidden_size, cfg.num_layers).to(self.device)
self.prior.load_save_dict(prior_saved_dict)
for param in self.prior.parameters():
param.requires_grad = False
# get optimizers
self.optimizer = torch.optim.Adam(get_params(self.agent), lr=cfg['learning_rate'])
def update(self, obs, rewards, old_logprobs, prior_logprobs, nonterms, episode_lens, cfg, metrics, log):
with torch.no_grad():
ir_logratio = prior_logprobs - old_logprobs
rewards = rewards - cfg.kl_coef * (ir_logratio ** 2)
rev_returns = torch.cumsum(rewards, dim=0)
advantages = rewards - rev_returns + rev_returns[-1:]
logprobs = self.agent.get_likelihood(obs, episode_lens, nonterms)
loss_pg = -advantages * logprobs
loss_pg = loss_pg.sum(0, keepdim=True).mean()
loss_p = - (1 / logprobs.sum(0, keepdim=True)).mean()
loss = loss_pg + cfg.lp_coef * loss_p
# Calculate gradients and make an update to the network weights
self.optimizer.zero_grad()
loss.backward()
grad_norm = torch.nn.utils.clip_grad_norm_(self.agent.parameters(), 0.5)
self.optimizer.step()
if log:
metrics['pg_loss'] = loss_pg.item()
metrics['agent_likelihood'] = logprobs.sum(0).mean().item()
metrics['grad_norm'] = grad_norm.item()
metrics['smiles_len'] = episode_lens.float().mean().item()
metrics['loss_p'] = loss_p.item()
print('logging!')
wandb.log(metrics)
def optimize(self, cfg):
if cfg.wandb_log:
self.define_wandb_metrics()
#set device
self.device = torch.device(cfg.device)
self._init(cfg)
train_steps = 0
eval_strings = 0
metrics = dict()
while eval_strings < cfg.max_strings:
with torch.no_grad():
# sample experience
# obs, rewards, nonterms, episode_lens = self.agent.get_data(cfg.batch_size, cfg.max_len, self.device)
obs, rewards, nonterms, old_logprobs, prior_logprobs, episode_lens = self.agent.get_data_with_prior(self.prior, cfg.batch_size, cfg.max_len, self.device)
if cfg.rep == 'selfies':
smiles_list = []
for en_sms in obs.cpu().numpy().T:
sms = self.vocab.decode_padded(en_sms)
smiles_list.append(sms)
score = np.array(self.predict(smiles_list))
scores = torch.tensor(score, dtype=torch.float32, device=self.device).unsqueeze(0)
else:
smiles_list = []
for en_sms in obs.cpu().numpy().T:
sms = self.vocab.decode_padded(en_sms)
smiles_list.append(sms)
score = np.array(self.predict(smiles_list))
scores = torch.tensor(score, dtype=torch.float32, device=self.device).unsqueeze(0)
if self.finish:
print('max oracle hit')
wandb.finish()
sys.exit(0)
train_steps += 1
eval_strings += cfg.batch_size
log = False
if cfg.wandb_log and train_steps % cfg.train_log_interval == 0:
log = True
metrics = dict()
metrics['eval_strings'] = eval_strings
metrics['mean_score'] = np.mean(score)
metrics['max_score'] = np.max(score)
metrics['min_score'] = np.min(score)
metrics['mean_episode_lens'] = np.mean(episode_lens.tolist())
metrics['max_episode_lens'] = np.max(episode_lens.tolist())
metrics['min_episode_lens'] = np.min(episode_lens.tolist())
wandb.log(metrics)
rewards = rewards * scores
self.update(obs, rewards, old_logprobs, prior_logprobs, nonterms, episode_lens, cfg, metrics, log)
print('max training string hit')
wandb.finish()
sys.exit(0)
@hydra.main(config_path='cfgs', config_name='reinvent_rnn', version_base=None)
def main(cfg: DictConfig):
hydra_cfg = hydra.core.hydra_config.HydraConfig.get()
if cfg.wandb_log:
project_name = cfg.task + '_' + cfg.target
if cfg.wandb_dir is not None:
cfg.wandb_dir = path_here
else:
cfg.wandb_dir = hydra_cfg['runtime']['output_dir']
wandb.init(project=project_name, entity=cfg.wandb_entity, config=dict(cfg), dir=cfg.wandb_dir)
wandb.run.name = cfg.wandb_run_name
set_seed(cfg.seed)
cfg.output_dir = hydra_cfg['runtime']['output_dir']
optimizer = reinvent_optimizer(cfg)
optimizer.optimize(cfg)
sys.exit(0)
if __name__ == '__main__':
main()
exit()