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train_albert.py
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train_albert.py
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from config import *
from dataload import *
from utils import *
#from schedulers import *
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.optim import AdamW # or import it from
from transformers import AlbertConfig, AlbertForPreTraining, get_cosine_schedule_with_warmup, get_linear_schedule_with_warmup
from munch import Munch
import wandb
import os
import random
import numpy as np
from fire import Fire
def accuracy(soplogits, soplabels):
return (soplogits.argmax(dim=1) == soplabels).float().sum().item() / len(soplabels)
def evaldev(expconf, model, devloader, ep):
model.eval()
L = len(devloader)
bsz= len(devloader[0])
lossmlm = 0
losspp = 0
acc = 0
for i, (b, l, datasetids) in enumerate(tqdm(devloader, desc="eval iter progress")):
outputs = model(**b, sentence_order_label=l, return_dict=True)
vsz= outputs.prediction_logits.shape[-1]
lossmlm += F.cross_entropy(outputs.prediction_logits.detach().view(-1,vsz).contiguous(), b['labels'].view(-1)).item()
losspp += F.cross_entropy(outputs.sop_logits, l).item()
acc += accuracy(outputs.sop_logits, l)
lossmlm /= L
losspp /= L
acc /= L
wandb.log(
{
'step': (i + ep*L)*bsz if expconf.see_bsz_effect else ep,
'dev/mlm_loss': lossmlm,
'dev/pp_loss': losspp,
'dev/pp_acc': acc,
} )
return lossmlm, losspp, acc
def savemodel(expconf, model, vocab, ep, mlm=0, pp=0, acc=0):
d_expconf = expconf.toDict()
saveroot = Path(expconf.modelsaveroot)
todaydir = saveroot / get_date()
if not todaydir.is_dir():
Path.mkdir(todaydir, parents=True)
savename = f"devpp_acc{acc:.3f}.m{expconf.masking}_{get_time()}_ep{ep}.lr{expconf.lr}.w{expconf.warmups}.sch{expconf.scheduler}.bsz{expconf.bsz}.pth"
saved = dict()
saved = {
'expconf': d_expconf,
'model': model.state_dict(),
'vocab': vocab
}
savepath = todaydir/savename
print(f"saving {savename}\n\tat {str(todaydir)}")
torch.save(saved, savepath)
def main():
# my dice shows 777 only. period.
random.seed(EXPCONF.seed)
np.random.seed(EXPCONF.seed)
torch.manual_seed(EXPCONF.seed)
torch.cuda.manual_seed_all(EXPCONF.seed)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
trainloader, vocab, _trainds = get_loader(EXPCONF, getdev=False)
devloader, _, _devds = get_loader(EXPCONF, getdev=True)
assert len(trainloader)>0, f"trainloader is empty!"
assert len(devloader)>0, f"devloader is empty!"
# this is disgraceful.... but just specify things below
albertconf = AlbertConfig.from_pretrained(f'albert-{EXPCONF.albert_scale}-v2')
if EXPCONF.smaller: #originally used 4H for FFN but for memory issue, use 1H for FFN
albertconf.hidden_size = EXPCONF.hidden_size
albertconf.num_hidden_layers = EXPCONF.num_hidden_layers
albertconf.num_attention_heads = EXPCONF.num_attention_heads
albertconf.intermediate_size = albertconf.hidden_size
albertconf.vocab_size = len(vocab.itos)
albertconf.bos_token_id = vocab.stoi['BOS']
albertconf.eos_token_id = vocab.stoi['EOS']
albertconf.pad_token_id = vocab.stoi['PAD']
albertconf.max_position_embeddings = 40
model = AlbertForPreTraining(albertconf).to(device)
# huggingface example is doing this for language modeling...
# https://github.com/huggingface/transformers/blob/v2.6.0/examples/run_language_modeling.py
no_decay = ['bias', "LayerNorm.weight"]
grouped_parameters = [
{
"params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],
"weight_decay": EXPCONF.weight_decay,
},
{"params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], "weight_decay": 0.0},
]
optimizer = AdamW( grouped_parameters,
lr = EXPCONF.lr ) # otherwise, use default
getsch = get_cosine_schedule_with_warmup if EXPCONF.scheduler =='cosine' else get_linear_schedule_with_warmup
scheduler = getsch(optimizer, EXPCONF.warmups, EXPCONF.numep*len(trainloader))
global_step = 0
L = len(trainloader)
bsz = len(trainloader[0])
for ep in tqdm(range(1, EXPCONF.numep+1), desc="epoch progress"):
lossep_mlm = 0
lossep_pp = 0
accep_pp = 0
model.train()
for i, (b,l,datasetids) in enumerate(tqdm(trainloader, desc="iterations progress"),1):
'''
b.input_ids/token_type_ids/attention_mask .shape == (bsz, seqmaxlen,)
b.l.shape == (bsz,)
## bert families, when they do MLM with NSP (or other similar sentence based tasks,)
## they just uses masked input for their sentence representation encoding, not the unmasked ones
## it could be considered as some kind of dropout but at first it looked quite irregular to me.
## --> referred to transformers/examples/run_language_modeling.py (v2.1.0)
## --> modeling_albert.py ( class AlbertModel.forward() )
'''
outputs = model(**b, sentence_order_label=l, return_dict=True )
global_step+=1
vsz=outputs.prediction_logits.shape[-1]
lossmlm = F.cross_entropy(outputs.prediction_logits.view(-1,vsz).contiguous(), b['labels'].view(-1))
losspp = F.cross_entropy(outputs.sop_logits, l)
lossppval = losspp.item()
acc = accuracy(outputs.sop_logits.clone().detach(), l)
if EXPCONF.alpha_pp== 1 and not EXPCONF.alpha_warmup:
outputs.loss.backward()
else:
del outputs.loss
torch.cuda.empty_cache()
losspp *= EXPCONF.alpha_pp
if EXPCONF.alpha_warmup:
grow = min(global_step / EXPCONF.warmups, 1.0)
losspp *= grow
loss = lossmlm + losspp
loss.backward()
wandb.log(
{
'step': (i + ep*L)*bsz if EXPCONF.see_bsz_effect else global_step,
'train_step/learning_rate': get_lr_from_optim(optimizer),
'train_step/alpha_pp': EXPCONF.alpha_pp * (grow if EXPCONF.alpha_warmup else 1),
'train_step/mlm_loss': lossmlm.item(),
'train_step/pp_loss': lossppval,
'train_step/pp_acc': acc,
}
)
optimizer.step()
scheduler.step()
model.zero_grad()
lossep_mlm += lossmlm.item()
lossep_pp += lossppval
accep_pp += acc
lossep_mlm/=L
lossep_pp/=L
accep_pp/=L
wandb.log(
{
'step': ep,
'train_ep/mlm_loss': lossep_mlm,
'train_ep/pp_loss': lossep_pp,
'train_ep/pp_acc': accep_pp,
}
)
print(f"ep:{ep}: losspp = {lossep_pp}, lossmlm={lossep_mlm}")
devmlm_loss, devpp_loss, devpp_acc = evaldev(EXPCONF, model, devloader, ep)
if devpp_acc > EXPCONF.savethld:
savemodel(EXPCONF, model, vocab, ep, mlm=devmlm_loss, pp=devpp_loss, acc=devpp_acc)
return None
def get_arguments_from_cmd(**kwargs):
for k,v in kwargs.items():
EXPCONF[k] = v
if __name__ == '__main__':
#os.environ["WANDB_MODE"] = 'dryrun'
#os.environ["PYTHONIOENCODING"] = 'utf8'
Fire(get_arguments_from_cmd)
if EXPCONF.debug: ## made debug.jsonl by $ head -20 train.jsonl > debugtrain.jsonl etc.
EXPCONF.bsz = 6
EXPCONF.numep = 2
EXPCONF.warmups = 3
EXPCONF.alpha_warmup = True
print(EXPCONF)
wandb.init(project = "scatterlab")
wandb.config.update(EXPCONF)
with log_time():
print("start training")
main()