def test_dropout_WITH_PROB_ZERO(self):
rnn = DecoderRNN(self.vocab_size, 50, 16, 0, 1, dropout_p=0)
for param in rnn.parameters():
param.data.uniform_(-1, 1)
output1, _, _ = rnn()
output2, _, _ = rnn()
for prob1, prob2 in zip(output1, output2):
self.assertTrue(torch.equal(prob1.data, prob2.data))
def test_dropout_WITH_NON_ZERO_PROB(self):
rnn = DecoderRNN(self.vocab_size, 50, 16, 0, 1, n_layers=2, dropout_p=0.5)
for param in rnn.parameters():
param.data.uniform_(-1, 1)
equal = True
for _ in range(50):
output1, _, _ = rnn()
output2, _, _ = rnn()
if not torch.equal(output1[0].data, output2[0].data):
equal = False
break
self.assertFalse(equal)
def test_k_1(self):
""" When k=1, the output of topk decoder should be the same as a normal decoder. """
batch_size = 1
eos = 1
for _ in range(10):
# Repeat the randomized test multiple times
decoder = DecoderRNN(self.vocab_size, 50, 16, 0, eos)
for param in decoder.parameters():
param.data.uniform_(-1, 1)
topk_decoder = TopKDecoder(decoder, 1)
output, _, other = decoder()
output_topk, _, other_topk = topk_decoder()
self.assertEqual(len(output), len(output_topk))
finished = [False] * batch_size
seq_scores = [0] * batch_size
for t_step, t_output in enumerate(output):
score, _ = t_output.topk(1)
symbols = other['sequence'][t_step]
for b in range(batch_size):
seq_scores[b] += score[b].data[0]
symbol = symbols[b].data[0]
if not finished[b] and symbol == eos:
finished[b] = True
self.assertEqual(other_topk['length'][b], t_step + 1)
self.assertTrue(
np.isclose(seq_scores[b],
other_topk['score'][b][0]))
if not finished[b]:
symbol_topk = other_topk['topk_sequence'][t_step][
b].data[0][0]
self.assertEqual(symbol, symbol_topk)
self.assertTrue(
torch.equal(t_output.data,
output_topk[t_step].data))
if sum(finished) == batch_size:
break
def test_k_greater_then_1(self):
""" Implement beam search manually and compare results from topk decoder. """
max_len = 50
beam_size = 3
batch_size = 1
hidden_size = 8
sos = 0
eos = 1
for _ in range(10):
decoder = DecoderRNN(self.vocab_size, max_len, hidden_size, sos,
eos)
for param in decoder.parameters():
param.data.uniform_(-1, 1)
topk_decoder = TopKDecoder(decoder, beam_size)
encoder_hidden = torch.autograd.Variable(
torch.randn(1, batch_size, hidden_size))
_, _, other_topk = topk_decoder(encoder_hidden=encoder_hidden)
# Queue state:
# 1. time step
# 2. symbol
# 3. hidden state
# 4. accumulated log likelihood
# 5. beam number
batch_queue = [[(-1, sos, encoder_hidden[:, b, :].unsqueeze(1), 0,
None)] for b in range(batch_size)]
time_batch_queue = [batch_queue]
batch_finished_seqs = [list() for _ in range(batch_size)]
for t in range(max_len):
new_batch_queue = []
for b in range(batch_size):
new_queue = []
for k in range(min(len(time_batch_queue[t][b]),
beam_size)):
_, inputs, hidden, seq_score, _ = time_batch_queue[t][
b][k]
if inputs == eos:
batch_finished_seqs[b].append(
time_batch_queue[t][b][k])
continue
inputs = torch.autograd.Variable(
torch.LongTensor([[inputs]]))
decoder_outputs, hidden, _ = decoder.forward_step(
inputs, hidden, None, F.log_softmax)
topk_score, topk = decoder_outputs[0].data.topk(
beam_size)
for score, sym in zip(topk_score.tolist()[0],
topk.tolist()[0]):
new_queue.append(
(t, sym, hidden, score + seq_score, k))
new_queue = sorted(new_queue,
key=lambda x: x[3],
reverse=True)[:beam_size]
new_batch_queue.append(new_queue)
time_batch_queue.append(new_batch_queue)
# finished beams
finalist = [l[:beam_size] for l in batch_finished_seqs]
# unfinished beams
for b in range(batch_size):
if len(finalist[b]) < beam_size:
last_step = sorted(time_batch_queue[-1][b],
key=lambda x: x[3],
reverse=True)
finalist[b] += last_step[:beam_size - len(finalist[b])]
# back track
topk = []
for b in range(batch_size):
batch_topk = []
for k in range(beam_size):
seq = [finalist[b][k]]
prev_k = seq[-1][4]
prev_t = seq[-1][0]
while prev_k is not None:
seq.append(time_batch_queue[prev_t][b][prev_k])
prev_k = seq[-1][4]
prev_t = seq[-1][0]
batch_topk.append([s for s in reversed(seq)])
topk.append(batch_topk)
for b in range(batch_size):
topk[b] = sorted(topk[b], key=lambda s: s[-1][3], reverse=True)
topk_scores = other_topk['score']
topk_lengths = other_topk['topk_length']
topk_pred_symbols = other_topk['topk_sequence']
for b in range(batch_size):
precision_error = False
for k in range(beam_size - 1):
if np.isclose(topk_scores[b][k], topk_scores[b][k + 1]):
precision_error = True
break
if precision_error:
break
for k in range(beam_size):
self.assertEqual(topk_lengths[b][k], len(topk[b][k]) - 1)
self.assertTrue(
np.isclose(topk_scores[b][k], topk[b][k][-1][3]))
total_steps = topk_lengths[b][k]
for t in range(total_steps):
self.assertEqual(topk_pred_symbols[t][b, k].data[0],
topk[b][k][t +
1][1]) # topk includes SOS