def testGuidedAlignmentCostUnderDistributionStrategy(
self, cost_type, with_length):
strategy = tf.distribute.MirroredStrategy(devices=["/cpu:0"])
attention_probs = tf.random.uniform([2, 5, 6])
gold_alignment = tf.random.uniform([2, 5, 6])
if with_length:
sequence_length = tf.constant([4, 5], dtype=tf.int32)
else:
sequence_length = None
with strategy.scope():
losses.guided_alignment_cost(attention_probs,
gold_alignment,
sequence_length=sequence_length,
cost_type=cost_type)
def compute_loss(self, outputs, labels, training=True):
params = self.params
if not isinstance(outputs, dict):
outputs = dict(logits=outputs)
logits = outputs["logits"]
noisy_logits = outputs.get("noisy_logits")
attention = outputs.get("attention")
if noisy_logits is not None and params.get("contrastive_learning"):
return losses.max_margin_loss(
logits,
labels["ids_out"],
labels["length"],
noisy_logits,
labels["noisy_ids_out"],
labels["noisy_length"],
eta=params.get("max_margin_eta", 0.1),
)
(
loss,
loss_normalizer,
loss_token_normalizer,
) = losses.cross_entropy_sequence_loss(
logits,
labels["ids_out"],
sequence_length=labels["length"],
sequence_weight=labels.get("weight"),
label_smoothing=params.get("label_smoothing", 0.0),
average_in_time=params.get("average_loss_in_time", False),
training=training,
)
if training:
gold_alignments = labels.get("alignment")
guided_alignment_type = params.get("guided_alignment_type")
if gold_alignments is not None and guided_alignment_type is not None:
if attention is None:
tf.get_logger().warning(
"This model did not return attention vectors; "
"guided alignment will not be applied"
)
else:
loss += losses.guided_alignment_cost(
attention[:, :-1], # Do not constrain last timestep.
gold_alignments,
sequence_length=self.labels_inputter.get_length(
labels, ignore_special_tokens=True
),
cost_type=guided_alignment_type,
weight=params.get("guided_alignment_weight", 1),
)
return loss, loss_normalizer, loss_token_normalizer
