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train_multitask.py
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train_multitask.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader
import numpy as np
import tqdm, random, string, os, time, math
from pdb import set_trace as stop
from collections import OrderedDict
from utils.metrics import evaluate
from utils.optim_schedule import WarmupLinearSchedule
class BERTMultitaskTrainer:
"""BERT Multitaks Trainer class, used to train the model
"""
def __init__(self,args,model_and_data):
"""Constructor
"""
task = args.task
self.device = args.device
self.grad_ac_steps=args.grad_ac_steps
self.train_data = None
self.valid_data = None
self.test_data = None
for model_dict in model_and_data:
if args.optimizer == 'adam':
optim = torch.optim.Adam(model_dict['model'].parameters(),lr=args.lr,weight_decay=args.adam_weight_decay)
else:
optim = torch.optim.SGD(model_dict['model'].parameters(),lr=args.lr,momentum=0.9)
model_dict['optim'] = optim
model_dict['scheduler_warmup'] = WarmupLinearSchedule(optim, args.warmup_steps, 1000000)
self.model_and_data = model_and_data
self.model_name = args.model_name
self.update_steps = 0
# print("Total Parameters:", sum([p.nelement() for p in self.model.parameters()]))
def train(self, epoch, max_batches=-1, evalu=False):
"""Trains the model
:param epoch: Number of epochs to train the model for
:param max_batches: Number of batches to train the model for
"""
return self.iteration(epoch, self.train_data,train=True,split_name='train', evalu=evalu)
def test(self, epoch, max_batches=-1, evalu=False):
"""Tests the model
:param epoch: Number of epochs to test the model for
:param max_batches: Number of batches to test the model for
"""
return self.iteration(epoch, self.test_data,train=False,split_name='test', evalu=evalu)
def valid(self, epoch, max_batches=-1, evalu=False):
"""Validates the model
:param epoch: Number of epochs to validate the model for
:param max_batches: Number of batches to validate the model for
"""
return self.iteration(epoch, self.valid_data,train=False,split_name='valid', evalu=evalu)
def iteration(self, epoch, data_loader, train=True,split_name='',evalu=False):
"""Runs each iteration of the model
:param epoch: Number of epochs to train the model for
:param data_loader: Data that has been loaded by the model
"""
results_dict = {}
task_idxs = []
task_lens = []
for task_idx,md in enumerate(self.model_and_data):
task = md['data']['task']
if train:
md['model'].train()
else:
md['model'].eval()
task_idxs += [task_idx]*len(md['data'][split_name])
task_lens += [len(md['data'][split_name])]
results_dict[task] = {}
results_dict[task]['total_loss'] = 0
results_dict[task]['total_preds'] = 0
results_dict[task]['total_correct'] = 0
results_dict[task]['all_preds'] = []
results_dict[task]['all_targets'] = []
results_dict[task]['all_seq_lens'] = []
total_batches = len(task_idxs)
task_lens = np.power(np.array(task_lens),0)
task_ratios = task_lens/task_lens.sum()
data_iter = tqdm.tqdm(task_idxs,desc="%s" % (split_name),total=total_batches,bar_format="{l_bar}{r_bar}")
for batch_idx,task_idx in enumerate(data_iter):
if train:
# task_idx = batch_idx % len(self.model_and_data) # sample uniformly
np.random.choice(np.arange(0,len(task_ratios)), p=task_ratios) # sample randomly
md = self.model_and_data[task_idx]
batch = next(iter(md['data'][split_name]))
task=md['data']['task']
task_inputs = batch["bert_input"].to(self.device)
evo = batch['bert_evo'].float().to(self.device)
target = batch['bert_label'].to(self.device)
sequence_lengths = batch['line_len'].to(self.device)
# stop()
if not train:
with torch.no_grad():
prediction = md['model'].forward(task_inputs, target)
else:
prediction = md['model'].forward(task_inputs, target)
if task in ['fluorescence','stability','covid']:
loss = md['criterion'](prediction,target.float())
results_dict[task]['total_loss'] += loss.item()
results_dict[task]['total_preds'] += len(target)
else:
loss = md['criterion'](prediction.view(-1,prediction.size(-1)),target.view(-1))
sum_loss_fun = nn.CrossEntropyLoss(ignore_index=-1,reduction='sum')
batch_sum_loss = sum_loss_fun(prediction.view(-1,prediction.size(-1)),target.view(-1))
results_dict[task]['total_loss'] += batch_sum_loss.item()
nonzero_targets = len(target[target != -1])
results_dict[task]['total_preds'] += nonzero_targets
if train:
loss.backward()
# if ((i+1)%self.grad_ac_steps == 0):
md['optim'].step()
md['optim'].zero_grad()
md['scheduler_warmup'].step(self.update_steps)
self.update_steps+=1
if task in ['secondary','homology','4prot','solubility','localization','transmembrane']:
_,pred_max = prediction.view(-1,prediction.size(-1)).detach().cpu().max(1)
target_out = target.view(-1).detach().cpu()[target.view(-1)>-1]
pred_max = pred_max[target.detach().cpu().view(-1)>-1]
results_dict[task]['all_preds'] += pred_max.tolist()
results_dict[task]['all_targets']+= target_out.tolist()
elif task in ['fluorescence','stability','covid']:
prediction = prediction.view(-1).detach().cpu()
target_out = target.view(-1).detach().cpu()
results_dict[task]['all_preds'] += prediction.tolist()
results_dict[task]['all_targets'] += target_out.tolist()
elif task == 'contact':
if not train:
results_dict[task]['all_seq_lens'] += sequence_lengths.tolist()
results_dict[task]['all_preds'].append(prediction.detach().cpu().numpy())
results_dict[task]['all_targets'].append(target.detach().cpu().numpy())
task_metrics = {}
for md in self.model_and_data:
task = md['data']['task']
metrics = evaluate(task,
results_dict[task]['all_preds'],
results_dict[task]['all_targets'],
results_dict[task]['total_loss'],
results_dict[task]['total_correct'],
results_dict[task]['total_preds'],
all_seq_lens=results_dict[task]['all_seq_lens'],
train=train)
task_metrics[task] = metrics
return task_metrics