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none_encoded_decoded hint mode #126

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12 changes: 9 additions & 3 deletions clrs/_src/baselines.py
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Original file line number Diff line number Diff line change
Expand Up @@ -144,6 +144,7 @@ def __init__(
hidden_dim: int = 32,
encode_hints: bool = False,
decode_hints: bool = True,
compute_hint_loss: bool = True,
encoder_init: str = 'default',
use_lstm: bool = False,
learning_rate: float = 0.005,
Expand Down Expand Up @@ -175,6 +176,7 @@ def __init__(
message-passing vectors.
encode_hints: Whether to provide hints as model inputs.
decode_hints: Whether to provide hints as model outputs.
compute_hint_loss: Whether to add hint loss to the output loss.
encoder_init: The initialiser type to use for the encoders.
use_lstm: Whether to insert an LSTM after message passing.
learning_rate: Learning rate for training.
Expand Down Expand Up @@ -202,15 +204,19 @@ def __init__(

Raises:
ValueError: if `encode_hints=True` and `decode_hints=False`.
if `compute_hint_loss=True` and `decode_hints=False`.
"""
super(BaselineModel, self).__init__(spec=spec)

if encode_hints and not decode_hints:
raise ValueError('`encode_hints=True`, `decode_hints=False` is invalid.')
if compute_hint_loss and not decode_hints:
raise ValueError('`compute_hint_loss=True`, `decode_hints=False` is invalid.')

assert hint_repred_mode in ['soft', 'hard', 'hard_on_eval']

self.decode_hints = decode_hints
self.compute_hint_loss = compute_hint_loss
self.checkpoint_path = checkpoint_path
self.name = name
self._freeze_processor = freeze_processor
Expand Down Expand Up @@ -414,7 +420,7 @@ def _loss(self, params, rng_key, feedback, algorithm_index):
)

# Optionally accumulate hint losses.
if self.decode_hints:
if self.compute_hint_loss:
for truth in feedback.features.hints:
total_loss += losses.hint_loss(
truth=truth,
Expand Down Expand Up @@ -455,7 +461,7 @@ def verbose_loss(self, feedback: _Feedback, extra_info) -> Dict[str, _Array]:
losses_ = {}

# Optionally accumulate hint losses.
if self.decode_hints:
if self.compute_hint_loss:
for truth in feedback.features.hints:
losses_.update(
losses.hint_loss(
Expand Down Expand Up @@ -591,7 +597,7 @@ def _loss(self, params, rng_key, feedback, mp_state, algorithm_index):
)

# Optionally accumulate hint losses.
if self.decode_hints:
if self.compute_hint_loss:
for truth in feedback.features.hints:
loss = losses.hint_loss_chunked(
truth=truth,
Expand Down
16 changes: 13 additions & 3 deletions clrs/examples/run.py
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Original file line number Diff line number Diff line change
Expand Up @@ -73,7 +73,7 @@
'during training instead of predicted hints. Only '
'pertinent in encoded_decoded modes.')
flags.DEFINE_enum('hint_mode', 'encoded_decoded',
['encoded_decoded', 'decoded_only', 'none'],
['encoded_decoded', 'decoded_only', 'none_encoded_decoded', 'none'],
'How should hints be used? Note, each mode defines a '
'separate task, with various difficulties. `encoded_decoded` '
'requires the model to explicitly materialise hint sequences '
Expand All @@ -85,7 +85,9 @@
'note that we currently do not make any efforts to '
'counterbalance the various hint losses. Hence, for certain '
'tasks, the best performance will now be achievable with no '
'hint usage at all (`none`).')
'hint usage at all (`none`). `none_encoded_decoded` produces'
'similar computation graphs to `encoded_decoded` but without'
'hint usage.')
flags.DEFINE_enum('hint_repred_mode', 'soft', ['soft', 'hard', 'hard_on_eval'],
'How to process predicted hints when fed back as inputs.'
'In soft mode, we use softmaxes for categoricals, pointers '
Expand Down Expand Up @@ -365,14 +367,21 @@ def main(unused_argv):
if FLAGS.hint_mode == 'encoded_decoded':
encode_hints = True
decode_hints = True
compute_hint_loss = True
elif FLAGS.hint_mode == 'decoded_only':
encode_hints = False
decode_hints = True
compute_hint_loss = True
elif FLAGS.hint_mode == 'none_encoded_decoded':
encode_hints = True
decode_hints = True
compute_hint_loss = False
elif FLAGS.hint_mode == 'none':
encode_hints = False
decode_hints = False
compute_hint_loss = False
else:
raise ValueError('Hint mode not in {encoded_decoded, decoded_only, none}.')
raise ValueError('Hint mode not in {encoded_decoded, decoded_only, none_encoded_decoded, none}.')

train_lengths = [int(x) for x in FLAGS.train_lengths]

Expand All @@ -396,6 +405,7 @@ def main(unused_argv):
hidden_dim=FLAGS.hidden_size,
encode_hints=encode_hints,
decode_hints=decode_hints,
compute_hint_loss=compute_hint_loss,
encoder_init=FLAGS.encoder_init,
use_lstm=FLAGS.use_lstm,
learning_rate=FLAGS.learning_rate,
Expand Down