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import former
from former import util
from former.util import d, here
import torch
from torch import nn
from torch.autograd import Variable
import torch.nn.functional as F
# from torchtext import data, datasets, vocab
from torchtext.legacy import data, datasets, vocab
import numpy as np
from argparse import ArgumentParser
from torch.utils.tensorboard import SummaryWriter
import random, tqdm, sys, math, gzip
# Used for converting between nats and bits
LOG2E = math.log2(math.e)
TEXT = data.Field(lower=True, include_lengths=True, batch_first=True)
LABEL = data.Field(sequential=False)
NUM_CLS = 2
def go(arg):
"""
Creates and trains a basic transformer for the IMDB sentiment classification task.
"""
tbw = SummaryWriter(log_dir=arg.tb_dir) # Tensorboard logging
# load the IMDB data
if arg.final:
train, test = datasets.IMDB.splits(TEXT, LABEL)
TEXT.build_vocab(train, max_size=arg.vocab_size - 2)
LABEL.build_vocab(train)
train_iter, test_iter = data.BucketIterator.splits((train, test), batch_size=arg.batch_size, device=util.d())
else:
tdata, _ = datasets.IMDB.splits(TEXT, LABEL)
train, test = tdata.split(split_ratio=0.8)
TEXT.build_vocab(train, max_size=arg.vocab_size - 2) # - 2 to make space for <unk> and <pad>
LABEL.build_vocab(train)
train_iter, test_iter = data.BucketIterator.splits((train, test), batch_size=arg.batch_size, device=util.d())
print(f'- nr. of training examples {len(train_iter)}')
print(f'- nr. of {"test" if arg.final else "validation"} examples {len(test_iter)}')
if arg.max_length < 0:
mx = max([input.text[0].size(1) for input in train_iter])
mx = mx * 2
print(f'- maximum sequence length: {mx}')
else:
mx = arg.max_length
# create the model
model = former.CTransformer(emb=arg.embedding_size, heads=arg.num_heads, depth=arg.depth, seq_length=mx, num_tokens=arg.vocab_size, num_classes=NUM_CLS, max_pool=arg.max_pool)
if torch.cuda.is_available():
model.cuda()
opt = torch.optim.Adam(lr=arg.lr, params=model.parameters())
sch = torch.optim.lr_scheduler.LambdaLR(opt, lambda i: min(i / (arg.lr_warmup / arg.batch_size), 1.0))
# training loop
seen = 0
for e in range(arg.num_epochs):
print(f'\n epoch {e}')
model.train(True)
for batch in tqdm.tqdm(train_iter):
opt.zero_grad()
input = batch.text[0]
label = batch.label - 1
if input.size(1) > mx:
input = input[:, :mx]
out = model(input)
loss = F.nll_loss(out, label)
loss.backward()
# clip gradients
# - If the total gradient vector has a length > 1, we clip it back down to 1.
if arg.gradient_clipping > 0.0:
nn.utils.clip_grad_norm_(model.parameters(), arg.gradient_clipping)
opt.step()
sch.step()
seen += input.size(0)
tbw.add_scalar('classification/train-loss', float(loss.item()), seen)
with torch.no_grad():
model.train(False)
tot, cor= 0.0, 0.0
for batch in test_iter:
input = batch.text[0]
label = batch.label - 1
if input.size(1) > mx:
input = input[:, :mx]
out = model(input).argmax(dim=1)
tot += float(input.size(0))
cor += float((label == out).sum().item())
acc = cor / tot
print(f'-- {"test" if arg.final else "validation"} accuracy {acc:.3}')
tbw.add_scalar('classification/test-loss', float(loss.item()), e)
if __name__ == "__main__":
parser = ArgumentParser()
parser.add_argument("-e", "--num-epochs",
dest="num_epochs",
help="Number of epochs.",
default=80, type=int)
parser.add_argument("-b", "--batch-size",
dest="batch_size",
help="The batch size.",
default=4, type=int)
parser.add_argument("-l", "--learn-rate",
dest="lr",
help="Learning rate",
default=0.0001, type=float)
parser.add_argument("-T", "--tb_dir", dest="tb_dir",
help="Tensorboard logging directory",
default='./runs')
parser.add_argument("-f", "--final", dest="final",
help="Whether to run on the real test set (if not included, the validation set is used).",
action="store_true")
parser.add_argument("--max-pool", dest="max_pool",
help="Use max pooling in the final classification layer.",
action="store_true")
parser.add_argument("-E", "--embedding", dest="embedding_size",
help="Size of the character embeddings.",
default=128, type=int)
parser.add_argument("-V", "--vocab-size", dest="vocab_size",
help="Number of words in the vocabulary.",
default=50_000, type=int)
parser.add_argument("-M", "--max", dest="max_length",
help="Max sequence length. Longer sequences are clipped (-1 for no limit).",
default=512, type=int)
parser.add_argument("-H", "--heads", dest="num_heads",
help="Number of attention heads.",
default=8, type=int)
parser.add_argument("-d", "--depth", dest="depth",
help="Depth of the network (nr. of self-attention layers)",
default=6, type=int)
parser.add_argument("-r", "--random-seed",
dest="seed",
help="RNG seed. Negative for random",
default=1, type=int)
parser.add_argument("--lr-warmup",
dest="lr_warmup",
help="Learning rate warmup.",
default=10_000, type=int)
parser.add_argument("--gradient-clipping",
dest="gradient_clipping",
help="Gradient clipping.",
default=1.0, type=float)
options = parser.parse_args()
print('OPTIONS ', options)
go(options)