def testProofStateEmbedding(self, predictor_str): predictor = self._get_predictor(predictor_str) goal = 'a f bool v A y' state = predictions.ProofState(goal=goal) goal_emb = predictor.goal_embedding(goal) expected_state_emb = predictions.EmbProofState(goal_emb=goal_emb) actual_state_emb = predictor.proof_state_embedding(state) self.assertAllClose(expected_state_emb.goal_emb, actual_state_emb.goal_emb) self.assertAllEqual(expected_state_emb[1:], actual_state_emb[1:])
def proof_state_from_search(self, node) -> predictions.ProofState: """Convert from proof_search_tree.ProofSearchNode to ProofState.""" return predictions.ProofState(goal=str(node.goal.conclusion))
def proof_state_from_search(self, node):
return predictions.ProofState(goal='goal')
def step(self, node: proof_search_tree.ProofSearchNode,
premises: proof_assistant_pb2.PremiseSet) -> List[Suggestion]:
"""Generates a list of possible ApplyTactic argument strings from a goal.
Args:
node: state of the proof search, starting at current goal.
premises: Specification of the selection of premises that can be used for
tactic parameters. Currently we are supporting only a single
DatabaseSection.
Returns:
List of string arugments for HolLight.ApplyTactic function, along with
scores (Suggestion).
"""
assert not premises.reference_sets, ('Premise reference sets are not '
'supported.')
assert len(premises.sections) == 1, ('Premise set must have exactly one '
'section.')
# TODO(szegedy): If the premise is not specified, we want the whole
# database to be used. Not sure if -1 or len(database.theorems) would do
# that or not. Assertion will certainly fail before that.
# Also we don't have checks on this use case.
assert premises.sections[0].HasField('before_premise'), ('Premise is '
'required.')
fp = premises.sections[0].before_premise
thm_number = self.thm_index_by_fingerprint.get(fp)
assert thm_number is not None
assert theorem_fingerprint.Fingerprint(
self.theorem_database.theorems[thm_number]) == fp
thm_names = self.thm_names[:thm_number]
tf.logging.debug(thm_names)
# TODO(smloos): update predictor api to accept theorems directly
proof_state = predictions.ProofState(
goal=str(normalization_lib.normalize(node.goal).conclusion))
proof_state_emb = self.predictor.proof_state_embedding(proof_state)
proof_state_enc = self.predictor.proof_state_encoding(proof_state_emb)
tf.logging.debug(proof_state_enc)
tactic_scores = self._compute_tactic_scores(proof_state_enc)
empty_emb = self.predictor.thm_embedding('')
empty_emb_batch = np.reshape(empty_emb, [1, empty_emb.shape[0]])
enumerated_tactics = enumerate(self.tactics)
if self.options.asm_meson_only:
enumerated_tactics = [
v for v in enumerated_tactics if str(v[1].name) == 'ASM_MESON_TAC'
]
assert enumerated_tactics, (
'action generator option asm_meson_only requires ASM_MESON_TAC.')
ranked_closest = self.compute_closest(node.goal, thm_number)
if ranked_closest:
tf.logging.info(
'Cosine closest picked:\n%s', '\n'.join(
['%s: %.6f' % (name, score) for score, name in ranked_closest]))
ret = []
thm_scores = None
# TODO(smloos): This computes parameters for all tactics. It should cut off
# based on the prover BFS options.
for tactic_id, tactic in enumerated_tactics:
if (thm_scores is None or self.model_architecture ==
deephol_pb2.ProverOptions.PARAMETERS_CONDITIONED_ON_TAC):
thm_scores = self._get_theorem_scores(proof_state_enc, thm_number,
tactic_id)
tf.logging.debug(thm_scores)
no_params_score = self.predictor.batch_thm_scores(
proof_state_enc, empty_emb_batch, tactic_id)[0]
tf.logging.info('Theorem score for empty theorem: %f0.2',
no_params_score)
thm_ranked = sorted(
zip(thm_scores, self.thm_names),
reverse=True)[:self.options.max_theorem_parameters]
pass_no_arguments = thm_ranked[-1][0] < no_params_score
thm_ranked = self.add_similar(thm_ranked, ranked_closest)
tf.logging.info('thm_ranked: %s', str(thm_ranked))
tactic_str = str(tactic.name)
try:
tactic_params = _compute_parameter_string(
list(tactic.parameter_types), pass_no_arguments, thm_ranked)
for params_str in tactic_params:
ret.append(
Suggestion(
string=tactic_str + params_str,
score=tactic_scores[tactic_id]))
except ValueError as e:
tf.logging.warning('Failed to compute parameters for tactic %s: %s',
tactic.name, str(e))
return ret