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import operator
import networkx as nx
import attr
import torch
from seq2struct.beam_search import Hypothesis
from seq2struct.models.nl2code.decoder import TreeState, get_field_presence_info
from seq2struct.models.nl2code.tree_traversal import TreeTraversal
@attr.s
class Hypothesis4Filtering(Hypothesis):
column_history = attr.ib(factory=list)
table_history = attr.ib(factory=list)
key_column_history = attr.ib(factory=list)
def beam_search_with_heuristics(model, orig_item, preproc_item, beam_size, max_steps, from_cond=True):
"""
Find the valid FROM clasue with beam search
"""
inference_state, next_choices = model.begin_inference(orig_item, preproc_item)
beam = [Hypothesis4Filtering(inference_state, next_choices)]
cached_finished_seqs = [] # cache filtered trajectories
beam_prefix = beam
while True:
# search prefixes with beam search
prefixes2fill_from = []
for step in range(max_steps):
if len(prefixes2fill_from) >= beam_size:
break
candidates = []
for hyp in beam_prefix:
# print(hyp.inference_state.cur_item.state, hyp.inference_state.cur_item.node_type )
if hyp.inference_state.cur_item.state == TreeTraversal.State.CHILDREN_APPLY \
and hyp.inference_state.cur_item.node_type == "from":
prefixes2fill_from.append(hyp)
else:
candidates += [(hyp, choice, choice_score.item(),
hyp.score + choice_score.item())
for choice, choice_score in hyp.next_choices]
candidates.sort(key=operator.itemgetter(3), reverse=True)
candidates = candidates[:beam_size-len(prefixes2fill_from)]
# Create the new hypotheses from the expansions
beam_prefix = []
for hyp, choice, choice_score, cum_score in candidates:
inference_state = hyp.inference_state.clone()
# cache column choice
column_history = hyp.column_history[:]
if hyp.inference_state.cur_item.state == TreeTraversal.State.POINTER_APPLY and \
hyp.inference_state.cur_item.node_type == "column":
column_history = column_history + [choice]
next_choices = inference_state.step(choice)
assert next_choices is not None
beam_prefix.append(
Hypothesis4Filtering(inference_state, next_choices, cum_score,
hyp.choice_history + [choice],
hyp.score_history + [choice_score],
column_history))
prefixes2fill_from.sort(key=operator.attrgetter('score'), reverse=True)
# assert len(prefixes) == beam_size
# emuerating
beam_from = prefixes2fill_from
max_size = 6
unfiltered_finished = []
prefixes_unfinished = []
for step in range(max_steps):
if len(unfiltered_finished) + len(prefixes_unfinished) > max_size:
break
candidates = []
for hyp in beam_from:
if step > 0 and hyp.inference_state.cur_item.state == TreeTraversal.State.CHILDREN_APPLY \
and hyp.inference_state.cur_item.node_type == "from":
prefixes_unfinished.append(hyp)
else:
candidates += [(hyp, choice, choice_score.item(),
hyp.score + choice_score.item())
for choice, choice_score in hyp.next_choices]
candidates.sort(key=operator.itemgetter(3), reverse=True)
candidates = candidates[:max_size - len(prefixes_unfinished)]
beam_from = []
for hyp, choice, choice_score, cum_score in candidates:
inference_state = hyp.inference_state.clone()
# cache table choice
table_history = hyp.table_history[:]
key_column_history = hyp.key_column_history[:]
if hyp.inference_state.cur_item.state == TreeTraversal.State.POINTER_APPLY:
if hyp.inference_state.cur_item.node_type == "table":
table_history = table_history + [choice]
elif hyp.inference_state.cur_item.node_type == "column":
key_column_history = key_column_history + [choice]
next_choices = inference_state.step(choice)
if next_choices is None:
unfiltered_finished.append(Hypothesis4Filtering(
inference_state,
None,
cum_score,
hyp.choice_history + [choice],
hyp.score_history + [choice_score],
hyp.column_history, table_history,
key_column_history))
else:
beam_from.append(
Hypothesis4Filtering(inference_state, next_choices, cum_score,
hyp.choice_history + [choice],
hyp.score_history + [choice_score],
hyp.column_history, table_history,
key_column_history))
unfiltered_finished.sort(key=operator.attrgetter('score'), reverse=True)
# filtering
filtered_finished = []
for hyp in unfiltered_finished:
mentioned_column_ids = set(hyp.column_history)
mentioned_key_column_ids = set(hyp.key_column_history)
mentioned_table_ids = set(hyp.table_history)
# duplicate tables
if len(mentioned_table_ids) != len(hyp.table_history):
continue
# the foreign key should be correctly used
# NOTE: the new version does not predict conditions in FROM clause anymore
if from_cond:
covered_tables = set()
must_include_key_columns = set()
candidate_table_ids = sorted(mentioned_table_ids)
start_table_id = candidate_table_ids[0]
for table_id in candidate_table_ids[1:]:
if table_id in covered_tables:
continue
try:
path = nx.shortest_path(
orig_item.schema.foreign_key_graph, source=start_table_id, target=table_id)
except (nx.NetworkXNoPath, nx.NodeNotFound):
covered_tables.add(table_id)
continue
for source_table_id, target_table_id in zip(path, path[1:]):
if target_table_id in covered_tables:
continue
if target_table_id not in mentioned_table_ids:
continue
col1, col2 = orig_item.schema.foreign_key_graph[source_table_id][target_table_id]['columns']
must_include_key_columns.add(col1)
must_include_key_columns.add(col2)
if not must_include_key_columns == mentioned_key_column_ids:
continue
# tables whose columns are mentioned should also exist
must_table_ids = set()
for col in mentioned_column_ids:
tab_ = orig_item.schema.columns[col].table
if tab_ is not None:
must_table_ids.add(tab_.id)
if not must_table_ids.issubset(mentioned_table_ids):
continue
filtered_finished.append(hyp)
filtered_finished.sort(key=operator.attrgetter('score'), reverse=True)
# filtered.sort(key=lambda x: x.score / len(x.choice_history), reverse=True)
prefixes_unfinished.sort(key=operator.attrgetter('score'), reverse=True)
# new_prefixes.sort(key=lambda x: x.score / len(x.choice_history), reverse=True)
prefixes_, filtered_ = merge_beams(prefixes_unfinished, filtered_finished, beam_size)
if filtered_:
cached_finished_seqs = cached_finished_seqs + filtered_
cached_finished_seqs.sort(key=operator.attrgetter('score'), reverse=True)
if prefixes_ and len(prefixes_[0].choice_history) < 200:
beam_prefix = prefixes_
for hyp in beam_prefix:
hyp.table_history = []
hyp.column_history = []
hyp.key_column_history = []
elif cached_finished_seqs:
return cached_finished_seqs[:beam_size]
else:
return unfiltered_finished[:beam_size]
# merge sorted beam
def merge_beams(beam_1, beam_2, beam_size):
if len(beam_1) == 0 or len(beam_2) == 0:
return beam_1, beam_2
annoated_beam_1 = [("beam_1", b) for b in beam_1]
annoated_beam_2 = [("beam_2", b) for b in beam_2]
merged_beams = annoated_beam_1 + annoated_beam_2
merged_beams.sort(key=lambda x: x[1].score, reverse=True)
ret_beam_1 = []
ret_beam_2 = []
for label, beam in merged_beams[:beam_size]:
if label == "beam_1":
ret_beam_1.append(beam)
else:
assert label == "beam_2"
ret_beam_2.append(beam)
return ret_beam_1, ret_beam_2
def beam_search_with_oracle_column(model, orig_item, preproc_item, beam_size, max_steps, visualize_flag=False):
inference_state, next_choices = model.begin_inference(orig_item, preproc_item)
beam = [Hypothesis(inference_state, next_choices)]
finished = []
assert beam_size == 1
# identify all the cols mentioned in the gold sql
root_node = preproc_item[1].tree
col_queue = list(reversed([val for val in model.decoder.ast_wrapper.find_all_descendants_of_type(root_node, "column")]))
tab_queue = list(reversed([val for val in model.decoder.ast_wrapper.find_all_descendants_of_type(root_node, "table")]))
col_queue_copy = col_queue[:]
tab_queue_copy = tab_queue[:]
predict_counter = 0
for step in range(max_steps):
if visualize_flag:
print('step:')
print(step)
# Check if all beams are finished
if len(finished) == beam_size:
break
# hijack the next choice using the gold col
assert len(beam) == 1
hyp = beam[0]
if hyp.inference_state.cur_item.state == TreeTraversal.State.POINTER_APPLY:
if hyp.inference_state.cur_item.node_type == "column" \
and len(col_queue) > 0:
gold_col = col_queue[0]
flag = False
for _choice in hyp.next_choices:
if _choice[0] == gold_col:
flag = True
hyp.next_choices = [_choice]
col_queue = col_queue[1:]
break
assert flag
elif hyp.inference_state.cur_item.node_type == "table" \
and len(tab_queue) > 0:
gold_tab = tab_queue[0]
flag = False
for _choice in hyp.next_choices:
if _choice[0] == gold_tab:
flag = True
hyp.next_choices = [_choice]
tab_queue = tab_queue[1:]
break
assert flag
# for debug
if hyp.inference_state.cur_item.state == TreeTraversal.State.POINTER_APPLY:
predict_counter += 1
# For each hypothesis, get possible expansions
# Score each expansion
candidates = []
for hyp in beam:
candidates += [(hyp, choice, choice_score.item(),
hyp.score + choice_score.item())
for choice, choice_score in hyp.next_choices]
# Keep the top K expansions
candidates.sort(key=operator.itemgetter(3), reverse=True)
candidates = candidates[:beam_size - len(finished)]
# Create the new hypotheses from the expansions
beam = []
for hyp, choice, choice_score, cum_score in candidates:
inference_state = hyp.inference_state.clone()
next_choices = inference_state.step(choice)
if next_choices is None:
finished.append(Hypothesis(
inference_state,
None,
cum_score,
hyp.choice_history + [choice],
hyp.score_history + [choice_score]))
else:
beam.append(
Hypothesis(inference_state, next_choices, cum_score,
hyp.choice_history + [choice],
hyp.score_history + [choice_score]))
if (len(col_queue_copy) + len(tab_queue_copy)) != predict_counter:
# print("The number of column/tables are not matched")
pass
finished.sort(key=operator.attrgetter('score'), reverse=True)
return finished
def beam_search_with_oracle_sketch(model, orig_item, preproc_item, beam_size, max_steps, visualize_flag=False):
inference_state, next_choices = model.begin_inference(orig_item, preproc_item)
hyp = Hypothesis(inference_state, next_choices)
parsed = model.decoder.preproc.grammar.parse(orig_item.code, "val")
if not parsed:
return []
queue = [
TreeState(
node = preproc_item[1].tree,
parent_field_type=model.decoder.preproc.grammar.root_type,
)
]
while queue:
item = queue.pop()
node = item.node
parent_field_type = item.parent_field_type
if isinstance(node, (list, tuple)):
node_type = parent_field_type + '*'
rule = (node_type, len(node))
if rule not in model.decoder.rules_index:
return []
rule_idx = model.decoder.rules_index[rule]
assert inference_state.cur_item.state == TreeTraversal.State.LIST_LENGTH_APPLY
next_choices = inference_state.step(rule_idx)
if model.decoder.preproc.use_seq_elem_rules and \
parent_field_type in model.decoder.ast_wrapper.sum_types:
parent_field_type += '_seq_elem'
for i, elem in reversed(list(enumerate(node))):
queue.append(
TreeState(
node=elem,
parent_field_type=parent_field_type,
))
hyp = Hypothesis(
inference_state,
None,
0,
hyp.choice_history + [rule_idx],
hyp.score_history + [0])
continue
if parent_field_type in model.decoder.preproc.grammar.pointers:
assert inference_state.cur_item.state == TreeTraversal.State.POINTER_APPLY
# best_choice = max(next_choices, key=lambda x: x[1])
# node = best_choice[0] # override the node
assert isinstance(node, int)
next_choices = inference_state.step(node)
hyp = Hypothesis(
inference_state,
None,
0,
hyp.choice_history + [node],
hyp.score_history + [0])
continue
if parent_field_type in model.decoder.ast_wrapper.primitive_types:
field_value_split = model.decoder.preproc.grammar.tokenize_field_value(node) + [
'<EOS>']
for token in field_value_split:
next_choices = inference_state.step(token)
hyp = Hypothesis(
inference_state,
None,
0,
hyp.choice_history + field_value_split,
hyp.score_history + [0])
continue
type_info = model.decoder.ast_wrapper.singular_types[node['_type']]
if parent_field_type in model.decoder.preproc.sum_type_constructors:
# ApplyRule, like expr -> Call
rule = (parent_field_type, type_info.name)
rule_idx = model.decoder.rules_index[rule]
inference_state.cur_item.state == TreeTraversal.State.SUM_TYPE_APPLY
extra_rules = [
model.decoder.rules_index[parent_field_type, extra_type]
for extra_type in node.get('_extra_types', [])]
next_choices = inference_state.step(rule_idx, extra_rules)
hyp = Hypothesis(
inference_state,
None,
0,
hyp.choice_history + [rule_idx],
hyp.score_history + [0])
if type_info.fields:
# ApplyRule, like Call -> expr[func] expr*[args] keyword*[keywords]
# Figure out which rule needs to be applied
present = get_field_presence_info(model.decoder.ast_wrapper, node, type_info.fields)
rule = (node['_type'], tuple(present))
rule_idx = model.decoder.rules_index[rule]
next_choices = inference_state.step(rule_idx)
hyp = Hypothesis(
inference_state,
None,
0,
hyp.choice_history + [rule_idx],
hyp.score_history + [0])
# reversed so that we perform a DFS in left-to-right order
for field_info in reversed(type_info.fields):
if field_info.name not in node:
continue
queue.append(
TreeState(
node=node[field_info.name],
parent_field_type=field_info.type,
))
return [hyp] |