def _testDecoderFPropFloatHelper(self,
func_inline=False,
num_decoder_layers=1,
target_seq_len=5,
residual_start=0):
"""Computes decoder from params and computes loss with random inputs."""
cluster = cluster_factory.ForTestingWorker(add_summary=True)
config = tf.ConfigProto(graph_options=tf.GraphOptions(
optimizer_options=tf.OptimizerOptions(
do_function_inlining=func_inline)))
with cluster, self.session(use_gpu=False, config=config) as sess:
tf.set_random_seed(8372749040)
vn_config = py_utils.VariationalNoiseParams(None, False, False)
p = self._DecoderParams(vn_config)
p.rnn_layers = num_decoder_layers
p.residual_start = residual_start
p.target_seq_len = target_seq_len
dec = p.Instantiate()
src_seq_len = 5
src_enc = tf.random_normal([src_seq_len, 2, 8], seed=9283748)
src_enc_padding = tf.constant(
[[0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 1.0], [1.0, 1.0]],
dtype=tf.float32)
encoder_outputs = py_utils.NestedMap(encoded=src_enc,
padding=src_enc_padding)
target_ids = tf.transpose(
tf.constant([[0, 1, 2, 3], [1, 2, 3, 4], [10, 11, 12, 15],
[5, 6, 7, 8], [10, 5, 2, 5]],
dtype=tf.int32))
target_labels = tf.transpose(
tf.constant([[0, 1, 2, 3], [1, 2, 3, 4], [10, 11, 12, 13],
[5, 7, 8, 10], [10, 5, 2, 4]],
dtype=tf.int32))
target_paddings = tf.transpose(
tf.constant([[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 1, 0],
[0, 1, 0, 0], [1, 1, 1, 1]],
dtype=tf.float32))
target_transcripts = tf.constant(
['abcd', 'bcde', 'klmp', 'fghi', 'kfcf'])
target_weights = 1.0 - target_paddings
targets = py_utils.NestedMap({
'ids': target_ids,
'labels': target_labels,
'weights': target_weights,
'paddings': target_paddings,
'transcripts': target_transcripts,
})
metrics = dec.FPropDefaultTheta(encoder_outputs, targets).metrics
loss = metrics['loss'][0]
correct_predicts = metrics['fraction_of_correct_next_step_preds'][
0]
summaries = tf.summary.merge(
tf.get_collection(tf.GraphKeys.SUMMARIES))
tf.global_variables_initializer().run()
loss_v, _ = sess.run([loss, correct_predicts])
summaries.eval()
return loss_v
def testDecoderSampleTargetSequences(self):
p = self._DecoderParams(vn_config=py_utils.VariationalNoiseParams(
None, False, False),
num_classes=8)
p.target_seq_len = 5
p.random_seed = 1
config = tf.ConfigProto(graph_options=tf.GraphOptions(
optimizer_options=tf.OptimizerOptions(do_function_inlining=False)))
with self.session(use_gpu=False, config=config) as sess:
tf.set_random_seed(8372740)
np.random.seed(35315)
dec = p.Instantiate()
source_sequence_length = 5
batch_size = 4
source_encodings = tf.constant(np.random.normal(
size=[source_sequence_length, batch_size, p.source_dim]),
dtype=tf.float32)
source_encoding_padding = tf.constant(
[[0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 1.0],
[0.0, 1.0, 1.0, 1.0], [0.0, 1.0, 1.0, 1.0],
[0.0, 1.0, 1.0, 1.0]],
dtype=tf.float32)
encoder_outputs = py_utils.NestedMap(
encoded=source_encodings, padding=source_encoding_padding)
sampled_sequences = dec.SampleTargetSequences(
dec.theta, encoder_outputs, random_seed=tf.to_int32(123))
self.assertAllEqual([batch_size, p.target_seq_len],
sampled_sequences.ids.shape)
tf.global_variables_initializer().run()
decoder_output = sess.run(sampled_sequences)
print('ids=%s' % np.array_repr(decoder_output.ids))
lens = np.sum(1 - decoder_output.paddings, axis=1)
print('lens=%s' % lens)
# pyformat: disable
# pylint: disable=bad-whitespace,bad-continuation
expected_ids = [[6, 2, 2, 2, 2], [0, 0, 7, 5, 1], [6, 1, 5, 1, 5],
[6, 7, 7, 4, 4]]
# pylint: enable=bad-whitespace,bad-continuation
# pyformat: enable
expected_lens = [2, 5, 5, 5]
self.assertAllEqual(expected_lens, lens)
self.assertAllEqual(expected_ids, decoder_output.ids)
# Sample again with the same random seed.
decoder_output2 = sess.run(
dec.SampleTargetSequences(dec.theta,
encoder_outputs,
random_seed=tf.to_int32(123)))
# Get the same output.
self.assertAllEqual(decoder_output.ids, decoder_output2.ids)
self.assertAllEqual(decoder_output.paddings,
decoder_output2.paddings)
# Sample again with a different random seed.
decoder_output3 = sess.run(
dec.SampleTargetSequences(dec.theta,
encoder_outputs,
random_seed=tf.to_int32(123456)))
# Get different sequences.
self.assertNotAllClose(expected_ids, decoder_output3.ids)
def _no_opt_sess_cfg(self):
# Disable constant folding for convenience.
return tf.config_pb2.ConfigProto(graph_options=tf.GraphOptions(
optimizer_options=tf.OptimizerOptions(
opt_level=tf.OptimizerOptions.L0,
do_common_subexpression_elimination=False,
do_function_inlining=False,
do_constant_folding=False)),
cluster_def=self._cluster_def)
def _testDecoderBeamSearchDecodeHelperWithOutput(self,
params,
src_seq_len=None,
src_enc_padding=None):
config = tf.ConfigProto(graph_options=tf.GraphOptions(
optimizer_options=tf.OptimizerOptions(do_function_inlining=False)))
p = params
with self.session(use_gpu=False,
config=config) as sess, self.SetEval(True):
tf.set_random_seed(837274904)
np.random.seed(837575)
p.beam_search.num_hyps_per_beam = 4
p.dtype = tf.float32
p.target_seq_len = 5
dec = p.Instantiate()
if src_seq_len is None:
src_seq_len = 5
src_enc = tf.constant(np.random.uniform(size=(src_seq_len, 2, 8)),
tf.float32)
if src_enc_padding is None:
src_enc_padding = tf.constant(
[[0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 1.0],
[1.0, 1.0]],
dtype=tf.float32)
encoder_outputs = py_utils.NestedMap(encoded=src_enc,
padding=src_enc_padding)
done_hyps = dec.BeamSearchDecode(encoder_outputs).done_hyps
tf.global_variables_initializer().run()
softmax_wts = sess.run(dec.vars.softmax)
print('softmax wts = ', softmax_wts)
done_hyps_serialized = sess.run([done_hyps])[0]
hyp = Hypothesis()
print('done hyps shape = ', done_hyps_serialized.shape)
for i in range(5):
for j in range(8):
print(i, j, len(done_hyps_serialized[i, j]))
hyp.ParseFromString(done_hyps_serialized[2, 5])
print('hyp = ', hyp)
return hyp
def _testDecoderBeamSearchDecodeHelperWithOutput(self,
params,
src_seq_len=None,
src_enc_padding=None):
config = tf.config_pb2.ConfigProto(graph_options=tf.GraphOptions(
optimizer_options=tf.OptimizerOptions(do_function_inlining=False)))
p = params
with self.session(use_gpu=False, config=config), self.SetEval(True):
tf.random.set_seed(837274904)
np.random.seed(837575)
p.beam_search.num_hyps_per_beam = 4
p.beam_search.length_normalization = 10.
p.dtype = tf.float32
p.target_seq_len = 5
dec = p.Instantiate()
if src_seq_len is None:
src_seq_len = 5
src_enc = tf.constant(np.random.uniform(size=(src_seq_len, 2, 8)),
tf.float32)
if src_enc_padding is None:
src_enc_padding = tf.constant(
[[0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 1.0],
[1.0, 1.0]],
dtype=tf.float32)
encoder_outputs = py_utils.NestedMap(encoded=src_enc,
padding=src_enc_padding)
topk_hyps = dec.BeamSearchDecode(encoder_outputs).topk_hyps
self.evaluate(tf.global_variables_initializer())
softmax_wts = self.evaluate(dec.vars.softmax)
print('softmax wts = ', softmax_wts)
topk_hyps_serialized = self.evaluate([topk_hyps])[0]
hyp = Hypothesis()
hyp.ParseFromString(topk_hyps_serialized[1, 2])
return hyp
def _testDecoderFPropGradientCheckerHelper(self, func_inline=False):
config = tf.ConfigProto(graph_options=tf.GraphOptions(
optimizer_options=tf.OptimizerOptions(
do_function_inlining=func_inline)))
with self.session(use_gpu=False, config=config) as sess:
tf.set_random_seed(8372749040)
np.random.seed(274854)
vn_config = py_utils.VariationalNoiseParams(None, False, False)
p = self._DecoderParams(vn_config)
p.dtype = tf.float64
dec = p.Instantiate()
src_seq_len = 5
src_enc = tf.constant(np.random.uniform(size=(src_seq_len, 2, 8)),
tf.float64)
src_enc_padding = tf.constant(
[[0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 1.0], [1.0, 1.0]],
dtype=tf.float64)
encoder_outputs = py_utils.NestedMap(encoded=src_enc,
padding=src_enc_padding)
target_ids = tf.transpose(
tf.constant([[0, 1, 2, 3], [1, 2, 3, 4], [10, 11, 12, 15],
[5, 6, 7, 8], [10, 5, 2, 5]],
dtype=tf.int32))
target_labels = tf.transpose(
tf.constant([[0, 1, 2, 3], [1, 2, 3, 4], [10, 11, 12, 13],
[5, 7, 8, 10], [10, 5, 2, 4]],
dtype=tf.int32))
target_paddings = tf.transpose(
tf.constant([[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 1, 0],
[0, 1, 0, 0], [1, 1, 1, 1]],
dtype=tf.float64))
target_transcripts = tf.constant(
['abcd', 'bcde', 'klmp', 'fghi', 'kfcf'])
target_weights = 1.0 - target_paddings
targets = py_utils.NestedMap({
'ids': target_ids,
'labels': target_labels,
'weights': target_weights,
'paddings': target_paddings,
'transcripts': target_transcripts,
})
metrics = dec.FPropDefaultTheta(encoder_outputs, targets).metrics
loss = metrics['loss'][0]
all_vars = tf.trainable_variables()
grads = tf.gradients(loss, all_vars)
def DenseGrad(var, grad):
if isinstance(grad, tf.Tensor):
return grad
elif isinstance(grad, tf.IndexedSlices):
return tf.unsorted_segment_sum(grad.values, grad.indices,
tf.shape(var)[0])
dense_grads = [DenseGrad(x, y) for (x, y) in zip(all_vars, grads)]
tf.global_variables_initializer().run()
test_utils.CompareToGoldenSingleFloat(self, 3.458078, loss.eval())
# Second run to make sure the function is determistic.
test_utils.CompareToGoldenSingleFloat(self, 3.458078, loss.eval())
symbolic_grads = [x.eval() for x in dense_grads if x is not None]
numerical_grads = []
for v in all_vars:
numerical_grads.append(
test_utils.ComputeNumericGradient(sess, loss, v))
for x, y in zip(symbolic_grads, numerical_grads):
self.assertAllClose(x, y)
