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train.py
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train.py
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# coding=utf-8
import torch
import torch.distributed as dist
import torch.nn as nn
import torch.nn.functional as F
import torch.multiprocessing as mp
from biglm import BIGLM
from data import Vocab, DataLoader, s2xy
from adam import AdamWeightDecayOptimizer
from optim import Optim
import argparse, os
import random
def parse_config():
parser = argparse.ArgumentParser()
parser.add_argument('--embed_dim', type=int)
parser.add_argument('--ff_embed_dim', type=int)
parser.add_argument('--num_heads', type=int)
parser.add_argument('--layers', type=int)
parser.add_argument('--dropout', type=float)
parser.add_argument('--train_data', type=str)
parser.add_argument('--dev_data', type=str)
parser.add_argument('--vocab', type=str)
parser.add_argument('--min_occur_cnt', type=int)
parser.add_argument('--batch_size', type=int)
parser.add_argument('--warmup_steps', type=int)
parser.add_argument('--lr', type=float)
parser.add_argument('--smoothing', type=float)
parser.add_argument('--weight_decay', type=float)
parser.add_argument('--max_len', type=int)
parser.add_argument('--min_len', type=int)
parser.add_argument('--print_every', type=int)
parser.add_argument('--save_every', type=int)
parser.add_argument('--epoch', type=int)
parser.add_argument('--start_from', type=str, default=None)
parser.add_argument('--save_dir', type=str)
parser.add_argument('--approx', type=str, default='none')
parser.add_argument('--fp16', action='store_true')
parser.add_argument('--world_size', type=int)
parser.add_argument('--gpus', type=int)
parser.add_argument('--MASTER_ADDR', type=str)
parser.add_argument('--MASTER_PORT', type=str)
parser.add_argument('--start_rank', type=int)
parser.add_argument('--backend', type=str)
return parser.parse_args()
def update_lr(optimizer, lr):
for param_group in optimizer.param_groups:
param_group['lr'] = lr
def average_gradients(model):
""" Gradient averaging. """
normal = True
size = float(dist.get_world_size())
for param in model.parameters():
if param.grad is not None:
dist.all_reduce(param.grad.data, op=dist.ReduceOp.SUM)
param.grad.data /= size
else:
normal = False
break
return normal
def eval_epoch(lm_args, model, lm_vocab, local_rank, label, batch_acm):
ds = []
with open(lm_args.dev_data, "r") as f:
for line in f:
line = line.strip()
if line:
ds.append(line)
batch_size = 10
batches = round(len(ds) / batch_size)
idx = 0
avg_nll = 0.
avg_ppl = 0.
avg_acc = 0.
count_bsz = 0.
count_tok = 0.
while idx < len(ds):
cplb = ds[idx:idx + batch_size]
ys_truth, ys_inp, msk = s2xy(cplb, lm_vocab, lm_args.max_len, lm_args.min_len)
ys_truth = ys_truth.cuda(local_rank)
ys_inp = ys_inp.cuda(local_rank)
msk = msk.cuda(local_rank)
acc, nll, ppl, toks, bsz = model.ppl(ys_truth, ys_inp, msk)
avg_acc += acc
avg_nll += nll
avg_ppl += ppl
count_bsz += bsz
count_tok += toks
idx += batch_size
print ('validating: label %s, batch_acm %d, acc %.6f, nll %.6f, ppl %.6f'\
%(label, batch_acm, avg_acc/count_tok, avg_nll/count_bsz, avg_ppl/count_bsz), flush=True)
def run(args, local_rank):
""" Distributed Synchronous """
torch.manual_seed(1234)
vocab = Vocab(args.vocab, min_occur_cnt=args.min_occur_cnt, specials=[])
if (args.world_size == 1 or dist.get_rank() == 0):
print ("vocab.size = %d"%vocab.size, flush=True)
model = BIGLM(local_rank, vocab, args.embed_dim, args.ff_embed_dim,\
args.num_heads, args.dropout, args.layers, args.smoothing, args.approx)
if args.start_from is not None:
ckpt = torch.load(args.start_from, map_location='cpu')
model.load_state_dict(ckpt['model'])
model = model.cuda(local_rank)
if args.world_size > 1:
torch.manual_seed(1234 + dist.get_rank())
random.seed(5678 + dist.get_rank())
optimizer = Optim(model.embed_dim, args.lr, args.warmup_steps, torch.optim.Adam(model.parameters(), lr=0, betas=(0.9, 0.998), eps=1e-9))
if args.start_from is not None:
optimizer.load_state_dict(ckpt['optimizer'])
#train_data = DataLoader(vocab, args.train_data+"0"+str(local_rank), args.batch_size, args.max_len, args.min_len)
train_data = DataLoader(vocab, args.train_data, args.batch_size, args.max_len, args.min_len)
batch_acm = 0
acc_acm, nll_acm, ppl_acm, ntokens_acm, nxs, npairs_acm, loss_acm = 0., 0., 0., 0., 0., 0., 0.
while True:
if train_data.epoch_id > args.epoch:
break
model.train()
for truth, inp, msk in train_data:
batch_acm += 1
truth = truth.cuda(local_rank)
inp = inp.cuda(local_rank)
msk = msk.cuda(local_rank)
model.zero_grad()
res, loss, acc, nll, ppl, ntokens, npairs = model(truth, inp, msk)
loss_acm += loss.item()
acc_acm += acc
nll_acm += nll
ppl_acm += ppl
ntokens_acm += ntokens
npairs_acm += npairs
nxs += npairs
loss.backward()
if args.world_size > 1:
average_gradients(model)
torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
optimizer.step()
if (args.world_size==1 or dist.get_rank() ==0) and batch_acm%args.print_every == -1%args.print_every:
print ('batch_acm %d, loss %.3f, acc %.3f, nll %.3f, ppl %.3f, x_acm %d, lr %.6f'\
%(batch_acm, loss_acm/args.print_every, acc_acm/ntokens_acm, \
nll_acm/nxs, ppl_acm/nxs, npairs_acm, optimizer._rate), flush=True)
acc_acm, nll_acm, ppl_acm, ntokens_acm, loss_acm, nxs = 0., 0., 0., 0., 0., 0.
if (args.world_size==1 or dist.get_rank() ==0) and batch_acm%args.save_every == -1%args.save_every:
if not os.path.exists(args.save_dir):
os.mkdir(args.save_dir)
torch.save({'args':args, 'model':model.state_dict(), 'optimizer':optimizer.state_dict()}, '%s/epoch%d_batch_%d'%(args.save_dir, train_data.epoch_id, batch_acm))
model.eval()
eval_epoch(args, model, vocab, local_rank, "epoch-" + str(train_data.epoch_id) + "-acm-" + str(batch_acm), batch_acm)
model.train()
def init_processes(args, local_rank, fn, backend='nccl'):
""" Initialize the distributed environment. """
os.environ['MASTER_ADDR'] = args.MASTER_ADDR
os.environ['MASTER_PORT'] = args.MASTER_PORT
dist.init_process_group(backend, rank=args.start_rank + local_rank, world_size=args.world_size)
fn(args, local_rank)
if __name__ == "__main__":
mp.set_start_method('spawn')
args = parse_config()
if args.world_size == 1:
run(args, 0)
exit(0)
processes = []
for rank in range(args.gpus):
p = mp.Process(target=init_processes, args=(args, rank, run, args.backend))
p.start()
processes.append(p)
for p in processes:
p.join()