def testBeamSearchDecodeTgtPrefix(self, dtype=tf.float32):
with self.session(use_gpu=True) as sess, self.SetEval(True):
tf.random.set_seed(_TF_RANDOM_SEED)
src_batch = 2
p = self._DecoderParams(dtype=dtype)
p.init_step_ids = True # initializes beam search with predefined ids.
p.beam_search.num_hyps_per_beam = 2
p.rnn_cell_dim = 32
dec = decoder.MTDecoderV1(p)
encoder_outputs, _ = self._Inputs(dtype=dtype, init_step_ids=True)
decode = dec.BeamSearchDecode(encoder_outputs)
# topk_decoded is None in MT decoder, set it to a fake tensor to pass
# sess.run(decode).
decode = decode._replace(topk_decoded=tf.constant(0, tf.float32))
tf.global_variables_initializer().run()
actual_decode = sess.run(decode)
num_hyps = src_batch * p.beam_search.num_hyps_per_beam
self.assertTupleEqual((p.target_seq_len, num_hyps),
actual_decode.done_hyps.shape)
self.assertTupleEqual((src_batch, p.beam_search.num_hyps_per_beam),
actual_decode.topk_hyps.shape)
self.assertTupleEqual((num_hyps, p.target_seq_len),
actual_decode.topk_ids.shape)
self.assertTupleEqual((num_hyps,), actual_decode.topk_lens.shape)
self.assertTupleEqual((src_batch, p.beam_search.num_hyps_per_beam),
actual_decode.topk_scores.shape)
expected_topk_ids = [[2, 0, 0, 0, 0], [13, 2, 0, 0, 0], [0, 0, 0, 0, 0],
[0, 0, 0, 0, 0]]
expected_topk_lens = [1, 2, 0, 0]
expected_topk_scores = [[-3.783162, -5.767723], [0., 0.]]
self.assertAllEqual(expected_topk_ids, actual_decode.topk_ids)
self.assertAllEqual(expected_topk_lens, actual_decode.topk_lens)
self.assertAllClose(expected_topk_scores, actual_decode.topk_scores)
def testDecoderFPropFixedAttentionSeed(self, dtype=tf.float64):
with self.session(use_gpu=True):
tf.set_random_seed(_TF_RANDOM_SEED)
p = self._DecoderParams(dtype=dtype)
p.feed_attention_context_vec_to_softmax = False
p.attention.params_init = py_utils.WeightInit.Gaussian(0.1, 12345)
dec = decoder.MTDecoderV1(p)
encoder_outputs, targets = self._Inputs(dtype=dtype)
loss, _ = dec.FPropDefaultTheta(encoder_outputs, targets)['loss']
tf.global_variables_initializer().run()
actual_loss = loss.eval()
print('actual loss = ', actual_loss)
CompareToGoldenSingleFloat(self, 7.624183, actual_loss)
def testBeamSearchDecodeFeedingAttContext(self, dtype=tf.float32):
tf.set_random_seed(_TF_RANDOM_SEED)
src_batch = 2
p = self._DecoderParams(dtype=dtype)
p.is_eval = True
src_time = p.target_seq_len
p.beam_search.num_hyps_per_beam = 2
p.rnn_cell_dim = 32
p.feed_attention_context_vec_to_softmax = True
dec = decoder.MTDecoderV1(p)
encoder_outputs, _ = self._Inputs(dtype=dtype)
decode = dec.BeamSearchDecode(encoder_outputs.encoded,
encoder_outputs.padding)
# topk_decoded is None in MT decoder, set it to a fake tensor to pass
# sess.run(decode).
decode = decode._replace(topk_decoded=tf.constant(0, tf.float32))
with self.session(use_gpu=True) as sess:
tf.global_variables_initializer().run()
actual_decode_feeding_att_context = sess.run(decode)
self.assertTupleEqual(
(src_time, src_batch * p.beam_search.num_hyps_per_beam),
actual_decode_feeding_att_context.done_hyps.shape)
self.assertTupleEqual(
(src_batch, p.beam_search.num_hyps_per_beam),
actual_decode_feeding_att_context.topk_hyps.shape)
self.assertTupleEqual(
(src_batch * p.beam_search.num_hyps_per_beam, src_time),
actual_decode_feeding_att_context.topk_ids.shape)
self.assertTupleEqual(
(src_batch * p.beam_search.num_hyps_per_beam, ),
actual_decode_feeding_att_context.topk_lens.shape)
self.assertTupleEqual(
(src_batch, p.beam_search.num_hyps_per_beam),
actual_decode_feeding_att_context.topk_scores.shape)
expected_topk_ids = [[2, 0, 0, 0, 0], [12, 2, 0, 0, 0],
[0, 0, 0, 0, 0], [0, 0, 0, 0, 0]]
expected_topk_lens = [1, 2, 0, 0]
expected_topk_scores = [[-3.7437, -5.654146], [0., 0.]]
self.assertAllEqual(expected_topk_ids,
actual_decode_feeding_att_context.topk_ids)
self.assertAllEqual(expected_topk_lens,
actual_decode_feeding_att_context.topk_lens)
self.assertAllClose(expected_topk_scores,
actual_decode_feeding_att_context.topk_scores)
def testBeamSearchDecode(self, dtype=tf.float32):
tf.set_random_seed(_TF_RANDOM_SEED)
src_batch = 2
p = self._DecoderParams(dtype=dtype)
p.is_eval = True
src_time = p.target_seq_len
p.beam_search.num_hyps_per_beam = 2
p.rnn_cell_dim = 32
dec = decoder.MTDecoderV1(p)
src_enc, src_enc_padding, _ = self._testInputs(dtype=dtype)
decode = dec.BeamSearchDecode(src_enc, src_enc_padding)
# topk_decoded is None in MT decoder, set it to a fake tensor to pass
# sess.run(decode).
decode = decode._replace(topk_decoded=tf.constant(0, tf.float32))
with self.session(use_gpu=True) as sess:
tf.global_variables_initializer().run()
actual_decode = sess.run(decode)
self.assertTupleEqual(
(src_time, src_batch * p.beam_search.num_hyps_per_beam),
actual_decode.done_hyps.shape)
self.assertTupleEqual(
(src_batch, p.beam_search.num_hyps_per_beam),
actual_decode.topk_hyps.shape)
self.assertTupleEqual(
(src_batch * p.beam_search.num_hyps_per_beam, src_time),
actual_decode.topk_ids.shape)
self.assertTupleEqual(
(src_batch * p.beam_search.num_hyps_per_beam,),
actual_decode.topk_lens.shape)
self.assertTupleEqual(
(src_batch, p.beam_search.num_hyps_per_beam),
actual_decode.topk_scores.shape)
expected_topk_ids = [[2, 0, 0, 0, 0], [11, 2, 0, 0, 0], [2, 0, 0, 0, 0],
[6, 2, 0, 0, 0]]
expected_topk_lens = [1, 2, 1, 2]
expected_topk_scores = [[-3.781308, -5.741293], [-3.332158, -5.597181]]
self.assertAllEqual(expected_topk_ids, actual_decode.topk_ids)
self.assertAllEqual(expected_topk_lens, actual_decode.topk_lens)
self.assertAllClose(expected_topk_scores, actual_decode.topk_scores)
def testBeamSearchDecodeUseZeroAttenState(self, dtype=tf.float32):
with self.session(use_gpu=True) as sess, self.SetEval(True):
tf.set_random_seed(_TF_RANDOM_SEED)
src_batch = 2
p = self._DecoderParams(dtype=dtype)
src_time = p.target_seq_len
p.beam_search.num_hyps_per_beam = 2
p.use_zero_atten_state = True
p.rnn_cell_dim = 32
dec = decoder.MTDecoderV1(p)
encoder_outputs, _ = self._Inputs(dtype=dtype)
decode = dec.BeamSearchDecode(encoder_outputs)
# topk_decoded is None in MT decoder, set it to a fake tensor to pass
# sess.run(decode).
decode = decode._replace(topk_decoded=tf.constant(0, tf.float32))
tf.global_variables_initializer().run()
actual_decode = sess.run(decode)
self.assertTupleEqual(
(src_time, src_batch * p.beam_search.num_hyps_per_beam),
actual_decode.done_hyps.shape)
self.assertTupleEqual((src_batch, p.beam_search.num_hyps_per_beam),
actual_decode.topk_hyps.shape)
self.assertTupleEqual(
(src_batch * p.beam_search.num_hyps_per_beam, src_time),
actual_decode.topk_ids.shape)
self.assertTupleEqual((src_batch * p.beam_search.num_hyps_per_beam, ),
actual_decode.topk_lens.shape)
self.assertTupleEqual((src_batch, p.beam_search.num_hyps_per_beam),
actual_decode.topk_scores.shape)
expected_topk_ids = [[2, 0, 0, 0, 0], [13, 2, 0, 0, 0],
[0, 0, 0, 0, 0], [0, 0, 0, 0, 0]]
expected_topk_lens = [1, 2, 0, 0]
expected_topk_scores = [[-3.783176, -5.767704], [0., 0.]]
self.assertAllEqual(expected_topk_ids, actual_decode.topk_ids)
self.assertAllEqual(expected_topk_lens, actual_decode.topk_lens)
self.assertAllClose(expected_topk_scores, actual_decode.topk_scores)
def testDecoderConstruction(self):
p = self._DecoderParams()
_ = decoder.MTDecoderV1(p)
