def train(dataset, model, sample, callback, staged):
if not isinstance(model, nn.Module):
return
opt = optim.Adam(model.parameters(), lr=FLAGS.lr)
#sched = opt_sched.CosineAnnealingLR(opt, T_max=FLAGS.n_epochs)
if FLAGS.sched_factor < 1:
sched = opt_sched.ReduceLROnPlateau(opt,
mode='max',
factor=FLAGS.sched_factor,
verbose=True)
for i_epoch in hlog.loop("%05d", range(FLAGS.n_epochs)):
model.train()
epoch_loss = 0
for i_batch in range(FLAGS.n_epoch_batches):
#sched.step()
opt.zero_grad()
datum = make_batch([sample() for _ in range(FLAGS.n_batch)],
dataset.vocab, staged)
loss = model(datum.inp_data, datum.out_data, datum.direct_out_data,
datum.copy_out_data, *datum.extra)
loss.backward()
clip_grad_norm_(model.parameters(), FLAGS.clip)
opt.step()
epoch_loss += loss.item()
epoch_loss /= FLAGS.n_epoch_batches
hlog.value("loss", epoch_loss)
val_score = callback(i_epoch)
if FLAGS.sched_factor < 1:
sched.step(val_score)
def enumerate_templates():
for i, utt in enumerate(utts):
inp, out = utt
seq = inp + (sep,) + out
if FLAGS.max_comp_len is not None and len(seq) >= FLAGS.max_comp_len:
continue
if i % 1000 == 0:
hlog.value("template_utt", "%d/%d" % (i, len(utts)))
for generic in self._make_generic(seq, keep_args):
yield generic, utt
def evaluate(score_utts, data, dataset):
_, utts = zip(*data)
baseline_nll = score_utts(utts, baseline=True)
nll = score_utts(utts)
tval, pval = scipy.stats.ttest_rel(nll, baseline_nll)
n_toks = sum(len(utt) - 1 for utt in utts)
nll_norm = nll.sum() / n_toks
ppl = np.exp(nll_norm)
hlog.value("ppl", ppl)
hlog.value("t/p", str(tval) + " " + str(pval))
return -ppl
def main():
factory = GrammarFactory()
vocab = factory.vocab()
model = InductorModel(vocab).to(DEVICE)
opt = optim.RMSprop(model.parameters(), lr=0.0003)
with hlog.task("train"):
for i_epoch in hlog.loop("%05d", range(1000), timer=False):
epoch_loss = 0
for i_iter in range(10):
opt.zero_grad()
loss = 0
for i_batch_part in range(n_batch):
ctx, inp, out = sample_batch(factory, vocab)
loss += model(ctx, inp, out)
loss.backward()
clip_grad_norm_(model.parameters(), .1)
opt.step()
epoch_loss += loss.item() / n_batch
hlog.value("loss", epoch_loss)
def __init__(
self,
train_utts,
val_utts,
test_utts,
aug_data=(),
invert=False,
):
vocab = Vocab()
for i in range(FLAGS.wug_count):
vocab.add(wug_template % i)
vocab.add(sep)
for utts in (train_utts, val_utts, test_utts):
for inp, out in utts:
for seq in (inp, out):
for word in seq:
vocab.add(word)
aug_utts = [(tuple(d["inp"]), tuple(d["out"])) for d in aug_data]
if FLAGS.dedup:
train_utts = [(tuple(i), tuple(o)) for i, o in train_utts]
train_utts = sorted(list(set(train_utts)))
hlog.value("train", len(train_utts))
hlog.value("aug", len(aug_utts))
if invert:
train_utts = [(o, i) for i, o in train_utts]
aug_utts = [(o, i) for i, o in aug_utts]
val_utts = [(o, i) for i, o in val_utts]
test_utts = [(o, i) for i, o in test_utts]
self.vocab = vocab
self.sep = sep
self.train_utts = train_utts
self.aug_utts = aug_utts
self.val_utts = val_utts
self.test_utts = test_utts
if FLAGS.compute_adjacency:
self._compute_adjacency(train_utts)
def visualize(datum, vocab, model):
for (inp, out) in zip(*datum.inp):
hlog.value("inp", " ".join(vocab.decode(inp + out)))
for (inp, out) in zip(*datum.out):
hlog.value("out", " ".join(vocab.decode(inp + out)))
samples, _ = model.sample(datum.inp_data, greedy=True)
for (inp, out) in zip(*samples):
hlog.value("???", " ".join(vocab.decode(inp + out)))
print()
def evaluate(model, data, dataset, vis=False, beam=False):
correct = 0
total = 0
for i in range(0, len(data), FLAGS.n_batch):
batch = make_batch(data[i:i + FLAGS.n_batch],
model.vocab,
staged=False)
preds, _ = model.sample(batch.inp_data, greedy=True, beam=beam)
for j in range(len(preds)):
score_here = dataset.score(preds[j], batch.out[j], batch.inp[j])
if vis:
with hlog.task(str(total)):
hlog.value("input",
" ".join(model.vocab.decode(batch.inp[j])))
hlog.value("pred", " ".join(model.vocab.decode(preds[j])))
hlog.value("gold",
" ".join(model.vocab.decode(batch.out[j])))
hlog.value("corr", score_here)
hlog.log("")
total += 1
correct += score_here
acc = 1. * correct / total
hlog.value("acc", acc)
return acc
def main(argv):
torch.manual_seed(FLAGS.seed)
np.random.seed(FLAGS.seed)
hlog.flags()
dataset = get_dataset()
model = pick_model(dataset)
model.prepare(dataset)
if isinstance(model, nn.Module):
path = os.path.join(FLAGS.model_dir, FLAGS.model)
checkpoint = torch.load(path)
model.load_state_dict(checkpoint)
realized = set()
examples = pick_examples(dataset)
while len(realized) < FLAGS.n_sample:
try:
templ, names = next(examples)
except StopIteration:
break
datum = make_batch([(templ, templ) for _ in range(10)],
dataset.vocab,
staged=True)
(inps, outs), scores = model.sample(datum.inp_data, datum.out_data)
keep = []
for inp, out, score in zip(inps, outs, scores):
inp_realized, inp_used = dataset.realize(inp, names)
out_realized, out_used = dataset.realize(out, names)
if ((not FLAGS.output_only)
and len(inp_used) == 0) or len(out_used) == 0:
continue
if len(inp_used | out_used) != len(names):
continue
if not ((FLAGS.output_only or dataset.novel(inp=inp_realized))
and dataset.novel(out=out_realized)):
continue
if (inp_realized, out_realized) in realized:
continue
keep.append(((inp_realized, out_realized), score))
for (inp_realized, out_realized), score in keep:
with hlog.task(str(len(realized))):
hlog.value("inp", " ".join(dataset.vocab.decode(templ[0])))
hlog.value("out", " ".join(dataset.vocab.decode(templ[1])))
hlog.value("var", names)
hlog.value("score", score)
with hlog.task("realized"):
hlog.value("inp", " ".join(inp_realized))
hlog.value("out", " ".join(out_realized))
realized.add((inp_realized, out_realized))
data = [{"inp": inp, "out": out} for inp, out in realized]
with open(FLAGS.write, "w") as fh:
json.dump(data, fh, indent=2)
def _compute_adjacency(self, utts):
counts = Counter()
for utt in utts:
inp, out = utt
for seq in (inp, out):
enc = self.vocab.encode(seq)[1:-1]
for span in range(1, FLAGS.wug_size+1):
for i in range(len(enc)+1-span):
counts[tuple(enc[i:i+span])] += 1
if FLAGS.wug_limit is None:
keep_args = set(counts.keys())
else:
keep_args = set([c for c, n in counts.items() if n <= FLAGS.wug_limit])
def make_store(initializer):
if FLAGS.use_trie:
return DefaultTrie(initializer)
else:
return defaultdict(initializer)
def compute_templ_sim(templates):
wugs = {self.vocab[w] for w in _wugs()}
size = FLAGS.sim_window_size
def wug_indices(templ):
return tuple(i for i, t in enumerate(templ) if t in wugs)
templ_to_sig = make_store(set)
sig_to_templ = make_store(set)
for templ in templates:
indices = wug_indices(templ)
sig = tuple(templ[i-size:i+size+1] for i in indices)
templ_to_sig[templ].add(sig)
sig_to_templ[sig].add(templ)
templ_sim = make_store(set)
for templ1 in templates:
for sig in templ_to_sig[templ1]:
for templ2 in sig_to_templ[sig]:
templ_sim[templ1].add(templ2)
return templ_sim
def enumerate_templates():
for i, utt in enumerate(utts):
inp, out = utt
seq = inp + (sep,) + out
if FLAGS.max_comp_len is not None and len(seq) >= FLAGS.max_comp_len:
continue
if i % 1000 == 0:
hlog.value("template_utt", "%d/%d" % (i, len(utts)))
for generic in self._make_generic(seq, keep_args):
yield generic, utt
arg_to_templ = make_store(set)
templ_to_arg = make_store(set)
templ_to_templ = make_store(set)
#sim_templ = FuzzyIndex(tfidf=True)
#templ_to_orig = defaultdict(set)
for (templ, args), orig in enumerate_templates():
arg_to_templ[args].add(templ)
templ_to_arg[templ].add(args)
#sim_templ.put(templ, args)
#templ_to_orig[templ].add(orig)
if FLAGS.template_sim == "window":
templ_sim = compute_templ_sim(templ_to_arg.keys())
else:
templ_sim = {t: set([t]) for t in templ_to_arg.keys()}
multiplicity = make_store(lambda: 0)
for i_arg, args1 in enumerate(arg_to_templ.keys()):
if i_arg % 10000 == 0:
hlog.value("template_arg", "%d/%d" % (i_arg, len(arg_to_templ)))
for templ1 in arg_to_templ[args1]:
multiplicity[templ1] += 1
c = 0
for templ2_pre in arg_to_templ[args1]:
for templ2 in templ_sim[templ2_pre]:
if templ1 == templ2:
continue
#if (templ1, templ2) in templ_to_templ:
# continue
templ_to_templ[templ2].add(templ1)
c += 1
if (
FLAGS.max_adjacencies is not None
and c >= FLAGS.max_adjacencies
):
break
self.templ_to_arg = templ_to_arg
#self.arg_to_templ = arg_to_templ
self.templ_to_templ = templ_to_templ
self.multiplicity = multiplicity
comp_pairs = []
for templ1 in self.templ_to_templ:
if self.multiplicity[templ1] <= 1:
continue
for templ2 in self.templ_to_templ[templ1]:
comp_pairs.append((templ1, templ2))
self.comp_pairs = sorted(comp_pairs)
self.templates = sorted(self.templ_to_arg.keys())
