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from scipy.stats import pearsonr, spearmanr
import os
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import transformers
import tqdm
import numpy as np
import random
from torch.utils import data
import wandb
from model import mRNA2vec, T5_encoder
from LoadData import DataLoad
os.environ['CUDA_VISIBLE_DEVICES'] = '0,1'
os.environ['OMP_NUM_THREADS'] = '1'
def setup_seed(seed):
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
np.random.seed(seed)
random.seed(seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = True
local_rank = int(os.environ.get('LOCAL_RANK', 0))
setup_seed(36)
if __name__ == '__main__':
# torch.cuda.set_device(local_rank)
# torch.distributed.init_process_group(backend='nccl')
# device = 'cuda'
device = 'cpu'
if local_rank == 0:
wandb.init(
project="mRNA_data2vec",
dir = './',
name = 'data2vec_mfe_ss_model',
)
encoder = T5_encoder(
hidden_size=256,
num_attention_heads = 4,
num_hidden_layers= 4,
)
print('loading encoder........')
#ckpt = torch.load('/home/honggen.zhang/data2vec-pytorch/models/model_mfe_mse.pt', map_location='cpu')
#encoder_state_dict = {k[8:]: v for k, v in ckpt['encoder'].items() if k.startswith('encoder.')}
#encoder.load_state_dict(ckpt['encoder'], strict=True)
model = mRNA2vec(encoder=encoder)
model.to(device)
model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[local_rank], )
optimizer = torch.optim.AdamW(model.parameters(), lr=1e-3, weight_decay=0.001)
criterion = nn.MSELoss()
criterion_binary = nn.BCEWithLogitsLoss()
criterion_cross = nn.CrossEntropyLoss(label_smoothing=0.2)
criterion.to(device)
criterion_binary.to(device)
criterion_cross.to(device)
print('loading model optimizer........')
# Datasets & Data Loaders
train_db = DataLoad(mask_ratio=0.15,
mode='train')
val_db = DataLoad(mask_ratio=0.15,
mode='valid')
bs = 256 #batch size
epoch = 10
param_mfe = 0.01
param_ss = 0.001
scaler = torch.cuda.amp.GradScaler()
train_sampler = torch.utils.data.distributed.DistributedSampler(train_db, shuffle=True, drop_last=False)
train_loader = torch.utils.data.DataLoader(train_db,
batch_size=bs,
num_workers=8,
drop_last=True,
sampler=train_sampler,
pin_memory=False,
)
val_loader = torch.utils.data.DataLoader(val_db,
batch_size=bs,
num_workers=8,
drop_last=True,
shuffle=False,
pin_memory=False, )
lr_decay = transformers.get_wsd_schedule(optimizer=optimizer,
num_warmup_steps = len(train_loader) * 5,
num_stable_steps = len(train_loader) * 0,
num_decay_steps = len(train_loader) * 5,
)
i = 0
best_loss = 100
targets = torch.tensor(range(bs))
for e in range(0, epoch):
train_sampler.set_epoch(e)
if local_rank == 0:
loop = tqdm.tqdm(enumerate(train_loader), total=len(train_loader), position=0)
else:
loop = enumerate(train_loader)
for no, batch in loop:
optimizer.zero_grad()
with torch.cuda.amp.autocast():
#src, mask, trg,label_mask = batch
rna_masked, mfe_value, ss_label, attention_mask, rna_unmasked,label_mask = batch
mask_ss = ss_label > 0
ss_label = ss_label[mask_ss]
a,b = rna_masked.shape
mfe_value,ss_label = mfe_value.to(device), ss_label.to(device)
mfe_value.add_(100.0).div_(100.0)
src, trg, attention_mask,label_mask = rna_masked.to(device), rna_unmasked.to(device), attention_mask.to(device),label_mask.to(device)
x, y,logit, ss_pred = model(src,trg,attention_mask,label_mask)
loss_lm = criterion(x.float(), y.float()).mean()
loss_ss = criterion_cross(ss_pred,ss_label)
targets = torch.tensor(range(a))
logit = -1*(logit.view(bs,1)-mfe_value.to(device))**2
loss_mfe = criterion_cross(logit, targets.to(device))
if e<2:
loss = loss_lm + 0*loss_mfe + 0*loss_ss
else:
loss = loss_lm + param_mfe*loss_mfe + param_ss*loss_ss
scaler.scale(loss).backward()
scaler.step(optimizer)
scaler.update()
lr_decay.step()
model.module.ema_step()
if local_rank == 0:
loop.set_postfix(Epoch=e,
loss=loss.item(),
)
current_lr = optimizer.param_groups[0]['lr']
i=i+1
if i%300 == 0:
print(loss.item())
wandb.log({"train_loss": loss.item(),
'lr':current_lr,
"loss_b":loss_mfe.item(),
"loss_ss":loss_ss.item(),
"loss_lm":loss_lm.item()
})
model.eval()
if local_rank == 0:
label_lst = []
res_lst = []
valid_loss_list = []
valid_loss_b_list = []
valid_loss_lm_list = []
valid_loss_ss_list = []
spearman_corr_list = []
for batch in val_loader:
with torch.no_grad():
with torch.cuda.amp.autocast():
src,mfe_,ss_, attention_mask, trg,label_mask = batch
mask_ss = ss_ > 0
ss_ = ss_[mask_ss]
mfe_ = mfe_.to(device)
mfe_.add_(100).div_(100)
ss_ = ss_.to(device)
a,b = src.shape
src, trg, attention_mask,label_mask = src.to(device), trg.to(device), attention_mask.to(device),label_mask.to(device)
x, y,logit,ss_pred = model(src,trg,attention_mask,label_mask)
#x, y = model(src,trg,mask,label_mask)
loss_ss = criterion_cross(ss_pred,ss_)
spearman_corr = spearmanr(logit.squeeze().cpu().numpy(), mfe_.cpu().numpy())[0].item()
loss_lm = criterion(x.float(), y.float()).mean()
targets = torch.tensor(range(a))
logit = -1*(logit.view(bs,1)-mfe_.to(device))**2
loss_b = criterion_cross(logit, targets.to(device))
valid_loss_lm_list.append(loss_lm.item())
valid_loss_b_list.append(loss_b.item())
valid_loss_list.append(loss_lm.item()+loss_b.item())
spearman_corr_list.append(spearman_corr)
valid_loss_ss_list.append(loss_ss.item())
wandb.log({"spearman_corr": np.mean(spearman_corr_list),
"valid_loss_b": np.mean(valid_loss_b_list),
"valid_lm_loss": np.mean(valid_loss_lm_list),
"valid_ss_loss": np.mean(valid_loss_ss_list)
})
if np.mean(valid_loss_lm_list)<best_loss:
best_loss = np.mean(valid_loss_lm_list)
checkpoint = {'encoder': model.module.ema.state_dict(),}
torch.save(checkpoint, f'./checkpoint/model_d2v_mfe{param_mfe}_ss{param_ss}_warmup.pt')
model.train()
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