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validate_rexyz_ddp.py
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import datasets as datasets
import torch.utils.data
from config import config
import models as models
import torch_utils
import torch.optim
import timm.scheduler
import torch.nn as nn
from tqdm import tqdm
import numpy as np
from torch.utils.data.distributed import DistributedSampler
import torch.multiprocessing as mp
from torch.nn.parallel import DistributedDataParallel as DDP
import os
import torch.distributed as dist
import sys
import torch.cuda
import torch
run_id = 'dropout0.01_decay1_0.97_h32s32_hidden256_prednorm_l2loss_valid-train'
def setup(rank, world_size):
os.environ['MASTER_ADDR'] = 'localhost'
os.environ['MASTER_PORT'] = '12355'
# initialize the process group
dist.init_process_group('nccl', rank=rank, world_size=world_size)
pass
def main(rank, num_processes):
setup(rank, num_processes)
dataset_train = datasets.SimplePCQM4MDataset(
path=config['middle_data_path'], split_name='valid-train', rotate=True, path_atom_map=None, data_path_name='data2')
dataset_test = datasets.SimplePCQM4MDataset(
path=config['middle_data_path'], split_name='valid-test', rotate=False, path_atom_map=None, data_path_name='data2')
if rank == 0:
print(f'num train: {len(dataset_train)}')
print(f'num test: {len(dataset_test)}')
pass
sampler_train = DistributedSampler(dataset_train, shuffle=True)
loader_train = torch.utils.data.DataLoader(
dataset_train,
batch_size=config['batch_size'],
num_workers=config['num_data_workers'],
collate_fn=datasets.collate_fn,
sampler=sampler_train
)
loader_test = torch.utils.data.DataLoader(
dataset_test,
batch_size=config['batch_size'],
num_workers=config['num_data_workers'],
collate_fn=datasets.collate_fn,
sampler=DistributedSampler(dataset_test, shuffle=False)
)
torch.cuda.set_device(rank)
torch.cuda.empty_cache()
device = f'cuda:{rank}'
model = models.MoleculePairDistPredictor(config)
if rank == 0:
print('num of parameters: {0}'.format(np.sum([p.numel() for p in model.parameters()])))
pass
model.to(device)
ddp_model = DDP(model, device_ids=[rank], find_unused_parameters=True)
if len(sys.argv) > 1:
ddp_model.load_state_dict(torch.load(sys.argv[1]))
pass
optimizer = torch.optim.AdamW(torch_utils.get_optimizer_params(ddp_model, config['learning_rate'], config['weight_decay']))
scheduler = timm.scheduler.StepLRScheduler(
optimizer, decay_t=1, decay_rate=config['learning_rate_decay_rate'],
warmup_t=config['warmup_epochs'], warmup_lr_init=1e-6)
model_save_path = os.path.join('models_valid', run_id)
if rank == 0:
if os.path.exists(model_save_path):
raise RuntimeError('model_save_path already exists')
pass
os.makedirs(model_save_path, exist_ok=True)
pass
for iepoch in range(config['num_epochs']):
sampler_train.set_epoch(iepoch)
scheduler.step(iepoch)
ddp_model.train()
if rank == 0:
pbar = tqdm(loader_train)
running_loss = None
pass
for ibatch, batch in enumerate(loader_train):
graph, y = batch
# print(type(graph['num_atom']))
graph = torch_utils.batch_to_device(graph, device)
y = y.to(device)
# print(graph.keys())
scores = ddp_model(
graph['atom_feat_cate'],
graph['atom_feat_float'],
graph['atom_mask'],
graph['bond_index'],
graph['bond_feat_cate'],
graph['bond_feat_float'],
graph['bond_mask'],
graph['structure_feat_cate'],
graph['structure_feat_float'],
graph['triplet_feat_cate']
# graph
)
xyz = graph['xyz']
dist_gt = torch.cdist(xyz, xyz, p=2)
# loss = nn.functional.l1_loss(scores, y)
loss = torch_utils.dist_loss(scores, dist_gt, graph['atom_mask'], p=2)
loss_ = torch_utils.dist_loss(scores, dist_gt, graph['atom_mask'], p=1)
optimizer.zero_grad()
loss.backward()
optimizer.step()
loss = loss.item()
if rank == 0:
if running_loss is None:
running_loss = loss
else:
running_loss = 0.99 * running_loss + 0.01 * loss
pass
pbar.set_postfix(loss=running_loss, loss_=loss_.item(), lr=optimizer.param_groups[0]['lr'])
pbar.update(1)
pass
pass
ddp_model.eval()
if rank == 0:
pbar.close()
losses = []
pass
for batch in loader_test:
graph, y = batch
graph = torch_utils.batch_to_device(graph, device)
y = y.to(device)
with torch.no_grad():
scores = ddp_model(
graph['atom_feat_cate'],
graph['atom_feat_float'],
graph['atom_mask'],
graph['bond_index'],
graph['bond_feat_cate'],
graph['bond_feat_float'],
graph['bond_mask'],
graph['structure_feat_cate'],
graph['structure_feat_float'],
graph['triplet_feat_cate']
# graph
)
xyz = graph['xyz']
dist_gt = torch.cdist(xyz, xyz, p=2)
# loss = nn.functional.l1_loss(scores, y)
loss = torch_utils.dist_loss(scores, dist_gt, graph['atom_mask'], p=2)
pass
dist.reduce(loss, 0, op=dist.ReduceOp.SUM)
if rank == 0:
losses.append(loss.item() / num_processes)
pass
pass
if rank == 0:
mean_loss = np.mean(losses)
print(f'epoch: {iepoch}, loss: {mean_loss}')
torch.save(
ddp_model.state_dict(),
os.path.join(model_save_path, f'epoch_{iepoch:03d}.pt'))
with open(os.path.join(model_save_path, 'result.txt'), 'a') as f:
f.write(f'epoch: {iepoch}, loss: {mean_loss}\n')
pass
pass
pass
pass
if __name__ == '__main__':
num_gpus = torch.cuda.device_count()
mp.spawn(main, nprocs=num_gpus, args=(num_gpus, ), join=True,)
pass