def validate_lm(self):
if not self.exp.subword and self.exp.max_vocab is None:
raise NotImplementedError("figure out how to validate and save results")
# only if we use the unpreprocessed version and the full vocabulary
# are the perplexity results comparable to previous work
print(f"Validating model performance with test tokens from: {trn_path}")
tst_tok = read_whitespace_file(trn_path)
tst_ids = np.array([([stoi.get(w, stoi[UNK]) for w in s]) for s in tst_tok])
logloss, perplexity = validate(learn.model, tst_ids, self.exp.bptt)
print('Test logloss:', logloss.item(), 'perplexity:', perplexity.item())
def pretrain_lm(dir_path,
lang='en',
cuda_id=0,
qrnn=True,
subword=False,
max_vocab=60000,
bs=70,
bptt=70,
name='wt-103',
num_epochs=10,
bidir=False,
ds_pct=1.0):
"""
:param dir_path: The path to the directory of the file.
:param lang: the language unicode
:param cuda_id: The id of the GPU. Uses GPU 0 by default or no GPU when
run on CPU.
:param qrnn: Use a QRNN. Requires installing cupy.
:param subword: Use sub-word tokenization on the cleaned data.
:param max_vocab: The maximum size of the vocabulary.
:param bs: The batch size.
:param bptt: The back-propagation-through-time sequence length.
:param name: The name used for both the model and the vocabulary.
:param model_dir: The path to the directory where the models should be saved
:param bidir: whether the language model is bidirectional
"""
results = {}
if not torch.cuda.is_available():
print('CUDA not available. Setting device=-1.')
cuda_id = -1
torch.cuda.set_device(cuda_id)
dir_path = Path(dir_path)
assert dir_path.exists()
model_dir = dir_path / 'models' # removed from params, as it is absolute models location in train_clas and here it is relative
model_dir.mkdir(exist_ok=True)
print('Batch size:', bs)
print('Max vocab:', max_vocab)
model_name = 'qrnn' if qrnn else 'lstm'
if qrnn:
print('Using QRNNs...')
trn_path = dir_path / f'{lang}.wiki.train.tokens'
val_path = dir_path / f'{lang}.wiki.valid.tokens'
tst_path = dir_path / f'{lang}.wiki.test.tokens'
for path_ in [trn_path, val_path, tst_path]:
assert path_.exists(), f'Error: {path_} does not exist.'
if subword:
# apply sentencepiece tokenization
trn_path = dir_path / f'{lang}.wiki.train.tokens'
val_path = dir_path / f'{lang}.wiki.valid.tokens'
read_file(trn_path, 'train')
read_file(val_path, 'valid')
sp = get_sentencepiece(dir_path, trn_path, name, vocab_size=max_vocab)
lm_type = contrib_data.LanguageModelType.BiLM if bidir else contrib_data.LanguageModelType.FwdLM
data_lm = TextLMDataBunch.from_csv(dir_path,
'train.csv',
**sp,
bs=bs,
bptt=bptt,
lm_type=lm_type)
itos = data_lm.train_ds.vocab.itos
stoi = data_lm.train_ds.vocab.stoi
else:
# read the already whitespace separated data without any preprocessing
trn_tok = read_whitespace_file(trn_path)
val_tok = read_whitespace_file(val_path)
if ds_pct < 1.0:
trn_tok = trn_tok[:max(20, int(len(trn_tok) * ds_pct))]
val_tok = val_tok[:max(20, int(len(val_tok) * ds_pct))]
print(
f"Limiting data sets to {ds_pct*100}%, trn {len(trn_tok)}, val: {len(val_tok)}"
)
itos_fname = model_dir / f'itos_{name}.pkl'
if not itos_fname.exists():
# create the vocabulary
cnt = Counter(word for sent in trn_tok for word in sent)
itos = [o for o, c in cnt.most_common(n=max_vocab)]
itos.insert(1,
PAD) # set pad id to 1 to conform to fast.ai standard
assert UNK in itos, f'Unknown words are expected to have been replaced with {UNK} in the data.'
# save vocabulary
print(f"Saving vocabulary as {itos_fname}")
results['itos_fname'] = itos_fname
with open(itos_fname, 'wb') as f:
pickle.dump(itos, f)
else:
print("Loading itos:", itos_fname)
itos = np.load(itos_fname)
vocab = Vocab(itos)
stoi = vocab.stoi
trn_ids = np.array([([stoi.get(w, stoi[UNK]) for w in s])
for s in trn_tok])
val_ids = np.array([([stoi.get(w, stoi[UNK]) for w in s])
for s in val_tok])
lm_type = contrib_data.LanguageModelType.BiLM if bidir else contrib_data.LanguageModelType.FwdLM
# data_lm = TextLMDataBunch.from_ids(dir_path, trn_ids, [], val_ids, [], len(itos))
data_lm = TextLMDataBunch.from_ids(path=dir_path,
vocab=vocab,
train_ids=trn_ids,
valid_ids=val_ids,
bs=bs,
bptt=bptt,
lm_type=lm_type)
print('Size of vocabulary:', len(itos))
print('First 10 words in vocab:', ', '.join([itos[i] for i in range(10)]))
# these hyperparameters are for training on ~100M tokens (e.g. WikiText-103)
# for training on smaller datasets, more dropout is necessary
if qrnn:
emb_sz, nh, nl = 400, 1550, 3
#dps = np.array([0.0, 0.0, 0.0, 0.0, 0.0])
dps = np.array([0.25, 0.1, 0.2, 0.02, 0.15])
drop_mult = 0.1
else:
emb_sz, nh, nl = 400, 1150, 3
# emb_sz, nh, nl = 400, 1150, 3
dps = np.array([0.25, 0.1, 0.2, 0.02, 0.15])
drop_mult = 0.1
fastai.text.learner.default_dropout['language'] = dps
lm_learner = bilm_learner if bidir else language_model_learner
learn = lm_learner(data_lm,
bptt=bptt,
emb_sz=emb_sz,
nh=nh,
nl=nl,
pad_token=1,
drop_mult=drop_mult,
tie_weights=True,
model_dir=model_dir.name,
bias=True,
qrnn=qrnn,
clip=0.12)
# compared to standard Adam, we set beta_1 to 0.8
learn.opt_fn = partial(optim.Adam, betas=(0.8, 0.99))
learn.true_wd = False
print("true_wd: ", learn.true_wd)
if bidir:
learn.metrics = [accuracy_fwd, accuracy_bwd]
else:
learn.metrics = [accuracy]
try:
learn.load(f'{model_name}_{name}')
print("Weights loaded")
except FileNotFoundError:
print("Starting from random weights")
pass
learn.fit_one_cycle(num_epochs, 5e-3, (0.8, 0.7), wd=1e-7)
if not subword and max_vocab is None:
# only if we use the unpreprocessed version and the full vocabulary
# are the perplexity results comparable to previous work
print(
f"Validating model performance with test tokens from: {trn_path}")
tst_tok = read_whitespace_file(trn_path)
tst_ids = np.array([([stoi.get(w, stoi[UNK]) for w in s])
for s in tst_tok])
logloss, perplexity = validate(learn.model, tst_ids, bptt)
print('Test logloss:', logloss.item(), 'perplexity:',
perplexity.item())
print(f"Saving models at {learn.path / learn.model_dir}")
learn.save(f'{model_name}_{name}')
opt_state_path = learn.path / learn.model_dir / f'{model_name}3_{name}_state.pth'
print(f"Saving optimiser state at {opt_state_path}")
torch.save(learn.opt.opt.state_dict(), opt_state_path)
results['accuracy'] = learn.validate()[1]
return results