def testTopKTerminatedHypsOp(self):
with self.session(use_gpu=False):
b_size = 8
num_beams = 2
num_hyps_per_beam = b_size / num_beams
seq_len = 6
scores = tf.random.uniform([b_size, 5], seed=12345)
atten_probs = tf.random.uniform([b_size, 3], seed=12345)
src_seq_lengths = [3, 3]
best_scores = tf.zeros([num_beams])
cumulative_scores = tf.zeros([b_size])
in_scores = tf.zeros([seq_len, b_size])
in_hyps = tf.zeros([seq_len, b_size], dtype=tf.int32)
in_prev_hyps = tf.zeros([seq_len, b_size], dtype=tf.int32)
in_done_hyps = tf.as_string(
tf.zeros([seq_len, b_size], dtype=tf.int32))
in_atten_probs = tf.zeros([seq_len, b_size, 3])
(out_best_scores_0, out_cumulative_scores_0, out_scores_0,
out_hyps_0, out_prev_hyps_0, out_done_hyps_0, out_atten_probs_0,
_) = ops.beam_search_step(scores,
atten_probs,
best_scores,
cumulative_scores,
in_scores,
in_hyps,
in_prev_hyps,
in_done_hyps,
in_atten_probs, [],
0,
eos_id=2,
beam_size=3.0,
num_hyps_per_beam=num_hyps_per_beam)
outputs = ops.beam_search_step(scores,
atten_probs,
out_best_scores_0,
out_cumulative_scores_0,
out_scores_0,
out_hyps_0,
out_prev_hyps_0,
out_done_hyps_0,
out_atten_probs_0, [],
1,
eos_id=2,
beam_size=3.0,
num_hyps_per_beam=num_hyps_per_beam)
# Get the topk terminated hyps.
in_done_hyps = outputs[5]
topk_hyps = ops.top_k_terminated_hyps(
in_done_hyps,
src_seq_lengths,
k=2,
num_hyps_per_beam=num_hyps_per_beam,
length_normalization=0.2,
coverage_penalty=0.2,
target_seq_length_ratio=1.0)
seq_ids, seq_lens, seq_scores = ops.unpack_hyp(tf.reshape(
topk_hyps, [-1]),
max_seq_length=5)
k1, k2, k3, k4 = self.evaluate(
[topk_hyps, seq_ids, seq_lens, seq_scores])
print(np.array_repr(k1))
assert k1.size == 4
expected_top1_for_beam_0 = """
beam_id: 0
ids: 1
ids: 2
scores: 0.86230338
scores: 0.65504861
atten_vecs {
prob: 0.45372832
prob: 0.86230338
prob: 0.65504861
}
atten_vecs {
prob: 0.45372832
prob: 0.86230338
prob: 0.65504861
}
normalized_score: 1.002714
"""
expected_top2_for_beam_1 = """
beam_id: 1
ids: 3
ids: 2
scores: 0.38127339
scores: 0.57700801
atten_vecs {
prob: 0.38612545
prob: 0.42067075
prob: 0.84442794
}
atten_vecs {
prob: 0.18693292
prob: 0.17821217
prob: 0.66380036
}
normalized_score: 0.480028
"""
self._SameHyp(expected_top1_for_beam_0, k1[0, 0])
self._SameHyp(expected_top2_for_beam_1, k1[1, 1])
self.assertAllClose(k2, [[1, 2, 0, 0, 0], [4, 2, 0, 0, 0],
[4, 2, 0, 0, 0], [3, 2, 0, 0, 0]])
self.assertAllClose(k3, [2, 2, 2, 2])
self.assertAllClose(k4, [1.002714, 0.684296, 0.522484, 0.480028])
def BeamSearchDecode(self,
theta,
encoder_outputs,
num_hyps_per_beam_override=0,
init_beam_search_state=None,
pre_beam_search_step_callback=None,
post_beam_search_step_callback=None,
max_steps=None):
"""Performs beam-search based decoding.
Args:
theta: A NestedMap object containing weights' values of the decoder layer
and its children layers.
encoder_outputs: A NestedMap containing encoder outputs to be passed to
the callbacks.
num_hyps_per_beam_override: If set to a value <= 0, this parameter is
ignored. If set to a value > 0, then this value will be used to override
`p.num_hyps_per_beam`.
init_beam_search_state: The `InitBeamSearchState` callback. Please refer
to the class header comments for more details.
pre_beam_search_step_callback: The `PreBeamSearchStepCallback` callback.
Please refer to the class header comments for more details.
post_beam_search_step_callback: The `PostBeamSearchStepCallback` callback.
Please refer to the class header comments for more details.
max_steps: maximum beam search steps. If None, use
self.params.target_seq_len.
Returns:
A `BeamSearchDecodeOutput`.
"""
p = self.params
num_hyps_per_beam = p.num_hyps_per_beam
if num_hyps_per_beam_override > 0:
num_hyps_per_beam = num_hyps_per_beam_override
if max_steps is None:
max_steps = p.target_seq_len
initial_results, other_states = init_beam_search_state(
theta, encoder_outputs, num_hyps_per_beam)
num_hyps = tf.shape(initial_results.log_probs)[0]
num_beams = num_hyps // num_hyps_per_beam
if 'step_ids' in initial_results:
# [num_hyps, 1]
step_ids = tf.ensure_shape(initial_results.step_ids, [None, 1])
else:
step_ids = tf.fill([num_hyps, 1],
tf.constant(p.target_sos_id, dtype=tf.int32))
min_score = -1e36
best_scores = (tf.zeros(shape=[num_beams], dtype=p.dtype) + min_score)
cumulative_scores = tf.zeros(shape=[num_hyps], dtype=p.dtype)
in_scores = tf.zeros([max_steps, num_hyps], dtype=p.dtype)
in_hyps = tf.zeros([max_steps, num_hyps], dtype=tf.int32)
in_prev_hyps = tf.zeros([max_steps, num_hyps], dtype=tf.int32)
in_done_hyps = tf.zeros([max_steps, num_hyps], dtype=tf.string)
bs_atten_probs = tf.zeros(
[max_steps, num_hyps,
tf.shape(initial_results.atten_probs)[1]],
dtype=p.dtype)
cur_step = tf.constant(0, dtype=tf.int32)
all_done = tf.constant(False, dtype=tf.bool)
core_bs_states = (best_scores, cumulative_scores, in_scores, in_hyps,
in_prev_hyps, in_done_hyps, bs_atten_probs)
def LoopContinue(cur_step, all_done, unused_step_ids, unused_core_bs_states,
unused_other_states_list):
return tf.logical_and(cur_step < max_steps, tf.logical_not(all_done))
def LoopBody(cur_step, unused_all_done, step_ids, core_bs_states,
other_states_list):
(cur_step, all_done, new_step_ids, new_bs_states,
new_other_states) = self._BeamSearchStep(
theta, encoder_outputs, cur_step, step_ids, core_bs_states,
other_states.Pack(other_states_list), num_hyps_per_beam,
pre_beam_search_step_callback, post_beam_search_step_callback)
return (cur_step, all_done, new_step_ids, new_bs_states,
new_other_states.Flatten())
flat_other_states = other_states.Flatten()
_, _, _, final_bs_states, flat_final_other_states = tf.while_loop(
LoopContinue,
LoopBody,
loop_vars=(cur_step, all_done, step_ids, core_bs_states,
flat_other_states),
parallel_iterations=10,
back_prop=False,
swap_memory=False,
shape_invariants=(tf.TensorShape(cur_step.get_shape()),
tf.TensorShape(all_done.get_shape()),
tf.TensorShape(step_ids.get_shape()),
_GetShapes(core_bs_states),
_GetShapes(flat_other_states, none_shapes=True)))
# [target_seq_len, num_beams * num_hyps_per_beam].
final_done_hyps = final_bs_states[5]
final_other_states = other_states.Pack(flat_final_other_states)
# TODO(rpang): avoid inspecting 'encoder_outputs'.
source_paddings = encoder_outputs.padding
if isinstance(source_paddings, py_utils.NestedMap):
source_seq_lengths = tf.cast(
tf.round(
tf.reduce_sum(1.0 - tf.transpose(source_paddings.Flatten()[0]),
1)), tf.int32)
else:
source_seq_lengths = tf.cast(
tf.round(tf.reduce_sum(1.0 - tf.transpose(source_paddings), 1)),
tf.int32)
# [num_beams, num_hyps_per_beam].
topk_hyps = ops.top_k_terminated_hyps(
final_done_hyps,
source_seq_lengths,
k=num_hyps_per_beam,
num_hyps_per_beam=num_hyps_per_beam,
length_normalization=p.length_normalization,
coverage_penalty=p.coverage_penalty,
target_seq_length_ratio=p.target_seq_length_ratio,
eoc_id=p.target_eoc_id,
merge_paths=p.merge_paths)
# [num_beams * num_hyps_per_beam, ...].
max_seq_length = 0 if isinstance(max_steps, tf.Tensor) else max_steps
topk_ids, topk_lens, topk_scores = ops.unpack_hyp(
tf.reshape(topk_hyps, [-1]), max_seq_length=max_seq_length)
# [num_beams, num_hyps_per_beam].
topk_scores = tf.reshape(topk_scores, tf.shape(topk_hyps))
return BeamSearchDecodeOutput(final_done_hyps, topk_hyps, topk_ids,
topk_lens, topk_scores, None,
final_other_states)
def testTopKEquivalent(self, length_normalization, coverage_penalty,
length_ratio, populate_hyps):
"""Tests that top_k_from_beam_search_outs is indeed equivalent."""
with self.session(use_gpu=False) as sess:
hyp_size = 32
num_beams = 8
num_hyps_per_beam = hyp_size // num_beams
seq_len = 10
hyps = np.random.randint(3, 100, size=[seq_len, hyp_size])
# We align all the hyps to make cumulative_score easy to compute.
prev_hyps = np.tile(np.arange(hyp_size), [seq_len, 1])
done_hyps = np.ones([seq_len, hyp_size], dtype=np.bool)
scores = np.random.uniform(-0.5, 1, size=[seq_len, hyp_size])
cumulative_scores = np.cumsum(scores, axis=0)
eos_scores = np.random.uniform(-0.5, 1, size=[seq_len, hyp_size])
atten_probs = np.random.uniform(0,
.05,
size=[seq_len, hyp_size, seq_len])
eos_atten_probs = np.random.uniform(
0, .05, size=[seq_len, hyp_size, seq_len])
cum_atten_probs = np.cumsum(atten_probs, axis=0)
cum_atten_probs = np.pad(cum_atten_probs, ((1, 0), (0, 0), (0, 0)),
'constant')[:seq_len, :, :]
cum_atten_probs = cum_atten_probs + eos_atten_probs
results = ops.top_k_from_beam_search_outs(
hyps,
prev_hyps,
done_hyps,
cumulative_scores,
eos_scores,
scores=scores if populate_hyps else 0,
atten_probs=atten_probs if populate_hyps else 0,
eos_atten_probs=eos_atten_probs if populate_hyps else 0,
cumulative_atten_probs=cum_atten_probs
if coverage_penalty > 0 else 0,
length_normalization=length_normalization,
coverage_penalty=coverage_penalty,
num_hyps_per_beam=num_hyps_per_beam,
max_seq_length=seq_len,
target_seq_length_ratio=length_ratio,
populate_topk_hyps=populate_hyps,
)
outs = sess.run(results)
final_done_hyps = ops.hyps_from_beam_search_outs(
hyps,
prev_hyps,
done_hyps,
scores,
atten_probs,
eos_scores,
eos_atten_probs,
eos_id=2,
num_hyps_per_beam=num_hyps_per_beam,
)
src_seq_lengths = np.ones([num_beams], dtype=np.int32) * seq_len
topk_hyps = ops.top_k_terminated_hyps(
final_done_hyps,
src_seq_lengths,
k=num_hyps_per_beam,
num_hyps_per_beam=num_hyps_per_beam,
length_normalization=length_normalization,
coverage_penalty=coverage_penalty,
target_seq_length_ratio=length_ratio)
topk_ids, topk_lens, topk_scores = ops.unpack_hyp(
topk_hyps, max_seq_length=seq_len)
topk_hyps, topk_ids, topk_lens, topk_scores = sess.run(
[topk_hyps, topk_ids, topk_lens, topk_scores])
self.assertAllEqual(outs[0], topk_ids)
self.assertAllEqual(outs[1], topk_lens)
self.assertAllClose(outs[2], topk_scores)
if populate_hyps:
self.assertAllEqual(outs[3].shape, topk_hyps.shape)
self.assertAllEqual(outs[3].shape, [num_beams, num_hyps_per_beam])
for i in range(num_beams):
for j in range(num_hyps_per_beam):
self._SameHyp(outs[3][i, j], topk_hyps[i, j])
