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import argparse
import ast
import itertools
import json
import os
import sys
import _jsonnet
import asdl
import astor
import torch
import tqdm
from seq2struct import beam_search
from seq2struct import datasets
from seq2struct import models
from seq2struct import optimizers
from seq2struct.utils import registry
from seq2struct.utils import saver as saver_mod
from seq2struct.models.spider import spider_beam_search
class Inferer:
def __init__(self, config):
self.config = config
if torch.cuda.is_available():
self.device = torch.device('cuda')
else:
self.device = torch.device('cpu')
torch.set_num_threads(1)
# 0. Construct preprocessors
self.model_preproc = registry.instantiate(
registry.lookup('model', config['model']).Preproc,
config['model'])
self.model_preproc.load()
def load_model(self, logdir, step):
'''Load a model (identified by the config used for construction) and return it'''
# 1. Construct model
model = registry.construct('model', self.config['model'], preproc=self.model_preproc, device=self.device)
model.to(self.device)
model.eval()
model.visualize_flag = False
# 2. Restore its parameters
saver = saver_mod.Saver({"model": model})
last_step = saver.restore(logdir, step=step, map_location=self.device, item_keys=["model"])
if not last_step:
raise Exception('Attempting to infer on untrained model')
return model
def infer(self, model, output_path, args):
output = open(output_path, 'w')
with torch.no_grad():
if args.mode == 'infer':
orig_data = registry.construct('dataset', self.config['data'][args.section])
preproc_data = self.model_preproc.dataset(args.section)
if args.limit:
sliced_orig_data = itertools.islice(orig_data, args.limit)
sliced_preproc_data = itertools.islice(preproc_data, args.limit)
else:
sliced_orig_data = orig_data
sliced_preproc_data = preproc_data
assert len(orig_data) == len(preproc_data)
self._inner_infer(model, args.beam_size, args.output_history, sliced_orig_data, sliced_preproc_data, output, args.use_heuristic)
elif args.mode == 'debug':
data = self.model_preproc.dataset(args.section)
if args.limit:
sliced_data = itertools.islice(data, args.limit)
else:
sliced_data = data
self._debug(model, sliced_data, output)
elif args.mode == 'visualize_attention':
model.visualize_flag = True
model.decoder.visualize_flag = True
data = registry.construct('dataset', self.config['data'][args.section])
if args.limit:
sliced_data = itertools.islice(data, args.limit)
else:
sliced_data = data
self._visualize_attention(model, args.beam_size, args.output_history, sliced_data, args.res1, args.res2, args.res3, output)
def _infer_one(self, model, data_item, preproc_item, beam_size, output_history=False, use_heuristic=True):
if use_heuristic:
# TODO: from_cond should be true from non-bert model
beams = spider_beam_search.beam_search_with_heuristics(
model, data_item, preproc_item, beam_size=beam_size, max_steps=1000, from_cond=False)
else:
beams = beam_search.beam_search(
model, data_item, preproc_item, beam_size=beam_size, max_steps=1000)
decoded = []
for beam in beams:
model_output, inferred_code = beam.inference_state.finalize()
decoded.append({
'orig_question': data_item.orig["question"],
'model_output': model_output,
'inferred_code': inferred_code,
'score': beam.score,
**({
'choice_history': beam.choice_history,
'score_history': beam.score_history,
} if output_history else {})})
return decoded
def _inner_infer(self, model, beam_size, output_history, sliced_orig_data, sliced_preproc_data, output, use_heuristic=False):
for i, (orig_item, preproc_item) in enumerate(
tqdm.tqdm(zip(sliced_orig_data, sliced_preproc_data),
total=len(sliced_orig_data))):
if use_heuristic:
#TODO: from_cond should be true from non-bert model
beams = spider_beam_search.beam_search_with_heuristics(
model, orig_item, preproc_item, beam_size=beam_size, max_steps=1000, from_cond=False)
else:
beams = beam_search.beam_search(
model, orig_item, preproc_item, beam_size=beam_size, max_steps=1000)
decoded = []
for beam in beams:
model_output, inferred_code = beam.inference_state.finalize()
decoded.append({
'orig_question': orig_item.orig["question"],
'model_output': model_output,
'inferred_code': inferred_code,
'score': beam.score,
**({
'choice_history': beam.choice_history,
'score_history': beam.score_history,
} if output_history else {})})
output.write(
json.dumps({
'index': i,
'beams': decoded,
}) + '\n')
output.flush()
def _debug(self, model, sliced_data, output):
for i, item in enumerate(tqdm.tqdm(sliced_data)):
(_, history), = model.compute_loss([item], debug=True)
output.write(
json.dumps({
'index': i,
'history': history,
}) + '\n')
output.flush()
def _visualize_attention(self, model, beam_size, output_history, sliced_data, res1file, res2file, res3file, output):
res1 = json.load(open(res1file, 'r'))
res1 = res1['per_item']
res2 = json.load(open(res2file, 'r'))
res2 = res2['per_item']
res3 = json.load(open(res3file, 'r'))
res3 = res3['per_item']
interest_cnt = 0
cnt = 0
for i, item in enumerate(tqdm.tqdm(sliced_data)):
if res1[i]['hardness'] != 'extra':
continue
cnt += 1
if (res1[i]['exact'] == 0) and (res2[i]['exact'] == 0) and (res3[i]['exact'] == 0):
continue
interest_cnt += 1
'''
print('sample index: ')
print(i)
beams = beam_search.beam_search(
model, item, beam_size=beam_size, max_steps=1000, visualize_flag=True)
entry = item.orig
print('ground truth SQL:')
print(entry['query_toks'])
print('prediction:')
print(res2[i])
decoded = []
for beam in beams:
model_output, inferred_code = beam.inference_state.finalize()
decoded.append({
'model_output': model_output,
'inferred_code': inferred_code,
'score': beam.score,
**({
'choice_history': beam.choice_history,
'score_history': beam.score_history,
} if output_history else {})})
output.write(
json.dumps({
'index': i,
'beams': decoded,
}) + '\n')
output.flush()
'''
print(interest_cnt * 1.0 / cnt)
def add_parser():
parser = argparse.ArgumentParser()
parser.add_argument('--logdir', required=True)
parser.add_argument('--config', required=True)
parser.add_argument('--config-args')
parser.add_argument('--step', type=int)
parser.add_argument('--section', required=True)
parser.add_argument('--output', required=True)
parser.add_argument('--beam-size', required=True, type=int)
parser.add_argument('--output-history', action='store_true')
parser.add_argument('--limit', type=int)
parser.add_argument('--mode', default='infer', choices=['infer', 'debug', 'visualize_attention'])
parser.add_argument('--use_heuristic', action='store_true')
parser.add_argument('--res1', default='outputs/glove-sup-att-1h-0/outputs.json')
parser.add_argument('--res2', default='outputs/glove-sup-att-1h-1/outputs.json')
parser.add_argument('--res3', default='outputs/glove-sup-att-1h-2/outputs.json')
args = parser.parse_args()
return args
def main(args):
if args.config_args:
config = json.loads(_jsonnet.evaluate_file(args.config, tla_codes={'args': args.config_args}))
else:
config = json.loads(_jsonnet.evaluate_file(args.config))
if 'model_name' in config:
args.logdir = os.path.join(args.logdir, config['model_name'])
output_path = args.output.replace('__LOGDIR__', args.logdir)
if os.path.exists(output_path):
print('Output file {} already exists'.format(output_path))
sys.exit(1)
inferer = Inferer(config)
model = inferer.load_model(args.logdir, args.step)
inferer.infer(model, output_path, args)
if __name__ == '__main__':
args = add_parser()
main(args)
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